$99.5\\%$, Chempur®, Piekary Slaskie, Poland), Folin–Ciocalteu reagent (analytical grade, Chempur®, Piekary Slaskie, Poland), gallic acid (Sigma-Aldrich®, Darmstadt, Germany). Reagents for the CUPRAC assay were as follows: ethanol $96\\%$ (LiChrosolv®, Darmstadt, Germany), 6-Hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid (Sigma-Aldrich®, Buchs, Switzerland), copper (II) chloride (Sigma-Aldrich®, St. Louis, MO, USA), ammonium acetate (Sigma-Aldrich®, Darmstadt, Germany), neocuproine (Sigma-Aldrich®, Darmstadt, Germany). Reagents for the DPPH assay were as follows: methanol (LiChrosolv®, Darmstadt, Germany), 2,2-diphenyl-1-picrylhydrazyl (Sigma-Aldrich®, Darmstadt, Germany).\n\nReagents for the nitrites and nitrates determination were as follows: hydrochloric acid 35–$38\\%$ (Chempur®, Piekary Slaskie, Poland), acetic acid min. $99.5\\%$ (Chempur®, Piekary Slaskie, Poland), sodium nitrite (Chempur®, Piekary Slaskie, Poland), sodium tetraborate (POCH, Gliwice, Poland), sulfanilamide (Sigma-Aldrich®, Darmstadt, Germany), N-naphthylethylenediamine dihydrochloride (Sigma-Aldrich®, St. Louis, MO, USA), zinc acetate dihydrate (POCH, Gliwice, Poland), Carrez solution I—Potassium hexacyanoferrate (II) 0.25 mol/L (aqueous solution) (VWR, Leuven, Belgium), ammonium buffer pH 9.6 (obtained from ammonia (POCH, Gliwice, Poland) and hydrochloric acid $37\\%$ (VWR, Leuven, Belgium), cadmium sulphate (VI) (Chempur®, Piekary Slaskie, Poland), zinc sticks ≥$99.99\\%$ (Merck Millipore, Darmstadt, Germany).\n\nUltrapure water (18.2 MΩ·cm, Millipore Simplicity System, Billerica, MA, USA) was used for all aqueous solutions.\n\n## 2.2. The Total Antioxidant Capacity (TAC) and Total Phenolic Content (TPC)\n\nThe assessment of the TAC was carried out using the CUPRAC and DPPH assays. Moreover, the Folin–Ciocalteu (FC) method was applied to determine TPC.\n\n## 2.2.1. Extract Preparation for TAC and TPC Determination\n\nBased on the conditions described by Capanoglu et al. [ 23] and other literature reports on the extraction of beetroot products [24,25,26,27,28], optimisation of the extraction was performed using seven combinations of solvents in three variants of extraction (Figure 2). Each variant was examined in triplicate with a threefold measurement. Variant C was chosen as the most optimal (based on results of ANOVA test): two-stage ultrasound-assisted extraction with $50\\%$ MeOH + $0.1\\%$ FA (marked in yellow in Figure 2; $p \\leq 0.05$). Table S3 summarises the obtained results. The extraction was carried out on the selected lyophilizate; therefore, the results are expressed in mg GAE/g of lyophilizate.\n\nFigure 3 depicts the extraction procedure. Each sample was examined in triplicate with a threefold measurement. Lyophilizates and DSs were homogenized in a mortar straight before analyses. The samples were kept in the freezer all of the time and were thawed at room temperature before starting the individual analyses.\n\n## 2.2.2. TPC Determination\n\nThe total phenolic content (TPC) in the extracted samples was determined using the Folin-Ciocalteau reagent (FCR), according to the optimised and validated method developed by the authors based on literature research [20,29,30,31,32,33,34]. The mutual ratio of the reagents used (FCR and Na2CO3) and the incubation time before the measurement were optimised according to the scheme shown in Figure 2. Each variant was examined in triplicate with a threefold measurement. The obtained results are summarised in Tables S4 and S5. Model 1 was found to be the most efficient (ANOVA; $p \\leq 0.05$), thus, 5 mL of FCR was mixed with 10 mL of Na2CO3, and 30 min of incubation was applied. It was assumed that the composition of supplements in the form of tablets may differ the most from pure lyophilizate due to the presence of auxiliary substances enabling the tabletting process; thus, the time of incubation was also optimised for the product in a tablet.\n\nSample extracts (x mL) and Mili-Q water ((1.0 − x) mL) were placed in a centrifuge tube to have a volume of 1 mL. Next, 5 mL of FCR was added, the sample was mixed, and it was left for 3 min. Then, 10 mL of saturated sodium carbonate solution (150 g/L) was added. The test tubes were carefully blended after the addition of each reagent using a vortex (Lab dancer, VWR®, Gdansk, Poland). The absorbance was measured threefold at 760 nm (Genesys 10S, Thermo Fisher Scientific, Waltham, MA, USA) after incubation (30 min at room temperature without light). Results were expressed in gallic acid equivalents (mg GAE/g of product and mg GAE/daily dose of product for DSs or mg GAE/g dry weight (d.w.) and mg GAE/100 g fresh weight (f.w.) of beetroot).\n\nAnalogously, a calibration curve was prepared to range from 0.1 to 10 µg/mL. The calibration curve was made in three independent replications with threefold measurements.\n\n## 2.2.3. CUPRAC\n\nThe CUPRAC assay was carried out as described by Apak et al. [ 35]. Analogously, a calibration curve was prepared in the range of 0.0005 to 0.07 uM/mL of Trolox. Three independent replications with threefold measurements were used to create the calibration curve. Results were expressed in Trolox acid equivalents (TE) (µmol TE/g of product and µmol TE/daily dose of the product, or µmol TE/g d.w. and µmol TE/100 g f.w. of beetroot). The incubation time of the samples was previously optimised. Results after 30 min and 60 min of incubation did not differ significantly, so the first one was applied.\n\n## 2.2.4. DPPH\n\nThe DPPH assay was carried out as described by Ravichandran et al. [ 36]. The absorbance of the sample was measured threefold at 515 nm (Genesys 10S, Thermo Fisher Scientific, Waltham, MA, USA) after incubation (30 min at room temperature). Results were expressed as a percentage of the antioxidant activity, which was calculated as follows:[1]Activity (%)=Ac − AsAc×$100\\%$ Ac—absorbance of control; As—absorbance of a sample.\n\n## 2.3. The Nitrite and Nitrate Determination\n\nQuantification of nitrites and nitrates in beetroot (*Beta vulgaris* L.) and beetroot products was carried out by spectrophotometry using Griess reagents I, II and III according to ISO 6635-1984 (E) [37].\n\n## 2.3.1. Extraction for Nitrites and Nitrates Determination\n\nA total of 1.0 to 10 g of the test sample were weighed, according to the expected nitrite content. Then, 3.0 g of activated carbon, 5 mL of disodium tetraborate solution, and 100 mL of hot, purified water were added to each sample. The flasks were shaken for 15 min at 80 °C. Next, 2 mL of potassium hexacyanoferrate (II) and 2 mL of zinc acetate solution were added to the samples. The solutions, after cooling to room temperature, were transferred to 200 mL volumetric flasks, made up to the mark, and shaken. Finally, solutions were filtered into conical flasks through paper filters.\n\n## 2.3.2. Nitrites Determination\n\nAt least 10 mL of solution was transferred to the 50 mL volumetric flask and diluted into 30 mL with purified water. Then, 5 mL of solution I (sulfanilamide dissolved in water with hydrochloric acid) and 3 mL of solution III (hydrochloric acid) were added. The content of the flask was thoroughly mixed and left for 1 min at ambient temperature, protected from light. Next, 1 mL of solution II ($0.1\\%$ solution of N-(1-naphthyl)ethylenediamine dihydrochloride) was added, mixed carefully, and left for 3 min at ambient temperature, protected from light. After making up to the mark with water, the solution was mixed. The absorbance at a wavelength of 538 nm was measured within 15 min using the spectrometer. Results were expressed as µg/g of NO2¯ and µg/daily dose of NO2¯ or µg/g d.w. of NO2¯and µg/100 g f.w. of NO2¯ of beetroot, which is calculated as follows:[2](NO2−)=m1×200V1×m0 m0—the mass, in grams, of the test portion; m1—the mass, in micrograms, of nitrite ion (NO2¯) contained in the aliquot portion (V1) of filtrate taken, read from the calibration graph; V1—the volume, in millilitres, of the aliquot portion of filtrate taken.\n\nAnalogously, a calibration curve was prepared to range from 0.0 to 0.06 µg/mL of nitrites. The calibration curve was made in three independent replications with threefold measurements.\n\n## 2.3.3. Nitrates Determination\n\nAbout 2 g of the cadmium and 5 mL of the buffer solution, and an aliquot portion of the filtrate (10 mL or less) were placed in a 25 mL conical flask. The flask was agitated for 5 min. Next, the solution was filtered into a 50 mL one-mark volumetric flask and made up to the mark. The determination proceeded analogously to total nitrites (Section 2.3.2) using 10 mL of the test solution. Results of nitrate determination were expressed as µg/g of NO3¯ and µg/daily dose of NO3¯ or µg/g d.w. of NO3¯and µg/100 g f.w. of NO3¯ of beetroot, which was calculated as follows:[3](NO3−)=1.348(m2×10 000V3×V2×m0−m1×200V1×m0) m2—the total mass of nitrite, in micrograms of nitrite ion (NO2¯), contained in the volume (V2) of test solution taken, read from the calibration graph; V2—the volume, in millilitres, of the test solution taken for the spectrometric measurement; V3—the volume, in millilitres, of the aliquot portion of the filtrate taken for the preparation of the test solution;\n\nm0, m1, V1—have the same meanings as in Equation [2].\n\nThe ratio between the relative molecular masses of the nitrate ion (NO3¯) and nitrite ion (NO2¯) is 1.348.\n\n## 2.4. Validation\n\nThe following validation parameters were determined for all methods: linearity range, precision, accuracy, the limit of determination (LOD), and the limit of quantification (LOQ). The LOD and LOQ were computed as described by Huber [38]: [4]LOD=3.3SDab SDa—standard deviation of the intercept for the calibration curve; b—slope for the calibration curve. [ 5]LOD=3×LOD Table 1 shows the results of the validation. Due to the lack of reference material corresponding to the analysed material, accuracy was determined using the method of standard addition (GA in the FC assay and Trolox in the CUPRAC assay) to the chosen DS and lyophilizate and was expressed as recovery. DPPH assay was validated based on gallic acid standard solutions. The average recovery for the selected methods was in the range of 80–$120\\%$ which was an acceptable level for such an analysis. The precision was computed as the coefficient of variation for all the results obtained in all the analysed samples during validation. The signal obtained for standards (Sexpected) and the signal calculated from the calibration equation (Scalculated) were applied for the calculation of recovery for calibration curves (Rcc): [6]Rcc=⌊Sexpected− Scalculated⌋Sexpected\n\n## 2.5. Labelling Assessment\n\nThirty-four packages of DSs and eight traditional food products were assessed. Before evaluating packaging and labelling, each product’s registration in the register of products subject to notification of first market placement was checked [39]. As a result, eight products could not be included in the “dietary supplement” category due to the lack of appropriate labelling on the packaging and registration with the Chief Sanitary Inspectorate (GIS). Requirements on food and DS labelling are specified in Regulation (EU) No $\\frac{1169}{2011}$ [40] and the Act on Food and Nutrition Safety of 25 August 2006 [41]. As food, DSs have been assessed because of the requirements specified in the Regulation of the Minister of Health of 9 October 2007 [42]. The correctness of the labelling was assessed according to the following criteria [39,40,41,42]: Labelling in Polish;The name of the food;The list of ingredients;The net amount of food;The date of minimum durability or best-before date;The presence of the term “dietary supplement”;Indication of the recommended daily portion of the product;The presence of a warning regarding not exceeding the recommended daily portion;A statement that dietary supplements cannot be used as a substitute for a varied diet;A statement that they should be kept out of the reach of small children.\n\nIn addition, the manufacturer should provide information on the content of active ingredients per recommended daily portion and information on the content of vitamins and minerals in percentages concerning the reference daily intake (RDI). Particular attention was paid to the health claims on the packaging of the tested products, which were compared with the list of permitted health claims defined in Regulation No. $\\frac{1924}{2006}$ [43,44] and with the statements contained in the register of the European Food Safety Authority [45]. The difference in the number of analysed packages versus the total number of analysed DSs is due to the fact that some products were purchased in multiple repetitions (different lot numbers), resulting in the same package design.\n\n## 2.6. Statistical Analyses\n\nThe data were reported as the mean ± standard deviation of three independent samples, each measured three times. Statistical analyses such as the ANOVA Kruskal–Wallis test, the U Mann–Whitney test, or Spearman’s rank correlation coefficient preceded by an analysis of the normality (the Shapiro–Wilk test) of the distribution were used to compare the treatments. They were performed by the Statistica for Windows (version 13, Statsoft, Cracow, Poland) software package. Differences at $p \\leq 0.05$ were deemed significant. The validation parameters for spectrophotometric assays, the overall mean, and the standard error values were calculated using the Microsoft Office Excel software (version 2007 12.0.6787.5000 SP3 MSO, Microsoft Corporation, Redmond, WA, USA).\n\n## 3.1. Total Phenolic Content and the TAC\n\nThe averaged values of TAC (CUPRAC, DPPH) and TPC (FC) in the analysed beetroot and DSs, divided into groups (tablets, capsules, powders), are shown in Table 2 and Table 3, respectively. For the FC, results are expressed in gallic acid equivalents (GAE), for CUPRAC in Trolox equivalents (TE), and for DPPH as a percentage reduction in DPPH. Tables S6 and S7 show the full characteristics of the beetroot and beetroot-based DSs studied due to their TAC, TPC, nitrate, and nitrite contents. Powders were characterised by significantly higher TPC (mg GAE/d. d.; Table S8) than tablets (U Mann–Whitney test, $$p \\leq 9.9$$ × 10−5) and capsules (U Mann–Whitney test, $$p \\leq 4.4$$ × 10−5). However, lyophilizates showed the highest TPC compared to any group of DSs. There was no statistically significant difference between tablets and capsules (U Mann–Whitney test, $$p \\leq 0.99$$). In the case of TPC per gram of a product, a statistically significant difference was found only between tablets and lyophilizates (U Mann–Whitney test, $$p \\leq 0.0049$$), which could be caused by the presence of excipients in tablets used in tabletting processes. In the FC assay, the product marked as T7 (41 mg GAE/d. d.) was characterised by the highest TPC among the tablets, C8A (42 mg GAE/d. d.) and C8B (41 mg GAE/d. d.) among the capsules, and product P9 (251 mg GAE/d. d.) among the powders.\n\nPowders showed higher TAC than tablets (U Mann–Whitney test, $$p \\leq 1.6$$ × 10−4) and capsules (U Mann–Whitney test, $$p \\leq 2.3$$ × 10−5). Lyophilizates provided a higher TAC than DSs. Considering TAC expressed as TE/g, lyophilizates showed significantly more antioxidants than all DS formulations. The highest TAC among the tablets was found in the T7 product (350 μmol TE/d. d.), the C13 (363 μmol TE/d. d.) among capsules, and the P9 product (3520 μmol TE/d. d.) among the powders. It is worth noting that product P9 exhibited a higher TPC (251 mg GAE/d. d.) and TAC (3520 μmol TE/d. d.) than average beetroots (Table 2). Different trends between the FC and CUPRAC methods may result from the variability of the conditions under which the tests were conducted and the reaction mechanisms. In the CUPRAC method, for example, the antioxidant potential is tested at pH = 7, which is close to the pH of human blood, as opposed to the FC method, which tests at pH 8–9. These changes in pH can influence the development of various antioxidant capacities of products, especially considering the complex matrix of DSs. In addition, the reaction with the DPPH radical is specific to individual antioxidants; they can react at different rates. Despite the concentrated DS formula as dried material, the daily portion of fresh beetroot (100 g f.w.) was richer in antioxidants.\n\nIn the DPPH method, the highest TAC was shown by the product T7 ($90\\%$) among the tablets and the products C6 and C8B among the capsules—the activities of which were both $90\\%$—while the activity of the product C8A was $74\\%$. Among the powders, the product P11 ($90\\%$) showed the highest TAC. The content of antioxidants in the dietary supplements T2A, T2B, and T2C was greatly varied, despite the use of the standard material standardisation procedure. Moreover, all three supplements were sold as the same commercial product. Three other products were characterised by a high RSD ($30\\%$ for T6; $41\\%$ for T10; and $33\\%$ for C2). In all cases, the analysis was repeated, but the analogous results were obtained, and the Q-Dixon test did not show a significant error. A high RSD could be caused by the heterogeneity of the supplement, as the method has been validated and the analysis conditions have not changed.\n\nIn a study conducted by Guldiken et al. [ 20], in which the content of antioxidants was measured using colorimetric methods in fresh beetroot, the following results were obtained: 255 mg GAE/100 g of fresh weight in the FC method and 15,538 μmol TE/100 g (3889 mg TE/100 g) in the CUPRAC method. The values obtained by the FC method are comparable to those obtained in this work for the majority of whole and peeled beetroot lyophilizates (Table S7). However, the values obtained by the CUPRAC method in this work are lower for most samples. Only the samples of skins 6Sk and 7Sk, which were from organic farming, can be considered comparable (9779 and 8932 μmol TE/100 g, respectively). This may be due to the differences in the profile of compounds and antioxidant capacity between different varieties and the freshness of the material analysed. In this study, the vegetables were processed and freeze-dried immediately after purchase. However, there is no way to trace the storage conditions of fresh material before purchase. There is a lack of reports in the literature regarding the TPC and TAC of DSs made from beetroot. Comparing the results obtained for supplements per gram in capsules (0.68 to 33 mg GAE/g), tablets (2.0 to 41 mg GAE/g) or powders (4–61 mg GAE/g) with the results obtained by Guldiken et al. [ 20] for dried beetroot 3.3 mg GAE/g (347 mg GAE/100 g), there can be observed a lower TPC in dried beetroot compared to our supplements calculated per g of d.w.\n\nMoreover, Spearman’s rank correlation coefficient was performed to test for a potential correlation between the antioxidant potential results obtained by different methods. A fairly strong relationship (0.7–0.9) or a very strong relationship (>0.9) was observed between the results obtained by the FC, CUPRAC, and DPPH methods in all the groups analysed (beetroots, capsules, tablets, and powders) (Table S9). In the study by Apak et al. [ 46], the correlation between the CUPRAC and FC methods was comparable and amounted to $r = 0.966.$ Another study by Güçlü et al. showed a high correlation ($r = 0.93$) between the FC and CUPRAC methods [47].\n\n## 3.2. Nitrate and Nitrite Content\n\n*In* general, significantly lower levels of nitrite ions (0.21–78 µg/d. d.) than nitrate ions (0.197–169 mg/d. d.) were found in DS samples. Due to the lack of a normal distribution (the Shapiro–Wilk test, $p \\leq 0.05$), the U Mann–Whitney test was applied to check for statistically significant differences. It was found that supplements in tablets ($$p \\leq 0.000295$$) and capsules ($$p \\leq 0.014038$$) contained significantly fewer nitrite ions, as well as supplements in tablets ($$p \\leq 0.262612$$) had statistically significantly fewer nitrate ions than lyophilized vegetables, considering their content in 1 g of product (dry weight for beetroot). The nitrite ion content of powders and beetroots did not differ significantly, nor did the nitrate ion content of beetroots, capsules, and powders. Moreover, individual parts of beetroot (peeled beetroot, skins) did not differ significantly in the content of nitrites ($$p \\leq 0.133615$$) and nitrates ($$p \\leq 0.830324$$) (the U Mann–Whitney test, $p \\leq 0.05$). All results of the U Mann–Whitney test were shown in Table S10. An average portion of conventional beetroots provided more nitrites (49 µg/100 g f.w.) and nitrates (90 mg/100 g f.w., Table 3) than most of the other products analysed. Only products P9 (169 mg/d.d.), P10 (99 mg/d.d.), and P13 (131 mg/d.d.) contained more nitrates than beetroots. The highest content of nitrite ions was found in supplement number C4 (8.4 µg/g) in the case of capsules, T3A (2.48 µg/g) for tablets, and P13 (6.4 µg/g) for powders. The highest level of nitrate ions was found in T11 (3746 mg/kg), C16 (15,186 mg/kg), C7B (11,924 mg/kg), P13 (13,110 mg/kg), and P9 (10,224 mg/kg) DSs.\n\nIn all tested vegetable samples, a significantly lower content of nitrite ions (0.702 µg/g–15 µg/g) than nitrate ions (423 mg/kg-8801 mg/kg) was determined. The highest content of nitrite ions among vegetables was found in skin samples, except for group 7, where the highest level of these ions was determined in a sample of peeled beetroot. In the case of nitrate ions, the situation was the opposite: the skin samples were characterised by the lowest content of these ions, except for sample no. 1, where their level was the highest in the batch. Moreover, individual subgroups of beetroot (peeled, unpeeled, skins) differed in terms of TAC and TPC, regardless of the method used to assess the potential (ANOVA Kruskal–Wallis test: FC $$p \\leq 0.0022$$, CUPRAC $$p \\leq 0.0016$$, DPPH $$p \\leq 0.006$$). The skins were the richest in antioxidants (Dunn’s test, $p \\leq 0.05$). There were no statistically significant differences in the content of nitrites and nitrates between the individual subgroups (ANOVA, $p \\leq 0.05$). For the comparison of vegetable supplements, reference was made to the average values for conventional and organic beetroots (Table 2). Manufacturers usually do not provide information on how the beetroot has been processed before preparing supplements from it. Several products contained information that whole beetroot was used, which is why we also included vegetables with skin in our analysis. The content of these compounds in beetroot depends primarily on the amount of nitrogen fertilization, agrotechnical treatments, and the plant growth phase [48]. Gościnna and Czapski [48] observed higher contents of nitrates in the middle parts of the root compared to its outer parts. Although they used a different division of the tuber (into 4 parts), it can be considered that the conclusions from our study and their research are consistent-beetroot skin is characterised by a lower content of nitrates.\n\nOnly four DS had a nitrate content declaration. Supplements C10 and C11 did not contain nitrates (<LOQ) despite their presence being declared by the manufacturer. Both products were produced by the same manufacturer but were available under different trade names and with different graphic designs. Product C1 contained a negligible amount of nitrates compared to the declaration ($4.2\\%$). Product P9 contained nitrates in amounts close to the declared one ($85\\%$). Simultaneously, it is the product that contains the most nitrates per daily portion of all the tested foods, as well as more than the average portion of fresh beetroot.\n\nIn the years 2003–2004, research on the content of nitrites and nitrates was carried out on certain vegetables purchased in random shops in Olsztyn [49]. Among these vegetables, beetroot was included, which was classified as a plant with a high content of nitrate—an average of 1408.17 mg/kg. A high level of nitrates (III) was determined in the analysed beetroots (on average 11.4 mg/kg), which differed from the average values for this vegetable. The content of nitrite and nitrate ions in the beetroot samples in this study was 0.120–2.935 mg/kg for nitrites and 102.30–1619.80 mg/kg for nitrates, respectively.\n\n## Health Risk Assessment\n\nIn terms of nitrite content ($2.1\\%$ ADI for NO2¯), none of the products tested posed a risk (Table 4). Fresh beetroot (100 g) provided more NO3¯ (15–$20.1\\%$ ADI for NO3¯, conventional and organic, respectively) than any of the analysed DSs in the form of tablets ($3.2\\%$ ADI for NO3¯) or capsules ($5.1\\%$ ADI for NO3¯). DSs in powders provided a similar amount of the substance as a serving of beetroot (based on the realisation of the ADI). Product P9, marketed by the manufacturer as having an “increased dose of nitrates”, had the highest nitrate dose ($48\\%$ of the ADI for NO3¯) and the lowest nitrite levels ($0.21\\%$ of the ADI for NO2¯). That is why the manufacturer advertised it as a product for athletes to be consumed before and after training “to increase the body’s efficiency, accelerate regeneration after training, and reduce accumulated lactic acid” (information on the packaging). In comparison, the recommended daily dose of products in capsules provided a maximum of $3.2\\%$ ADI for NO3¯ and $5.1\\%$ ADI for NO3¯ in tablets.\n\nKeller et al. [ 50] analysed eighteen DSs in terms of the content of nitrites and nitrate and determined the percentage of ADI to evaluate the exposure to these compounds through the intake of the recommended portion. The ADI for nitrate amounted to $22\\%$ in the case of the Neo40 supplement, described by the manufacturer as “improving the functioning of the cardiovascular system”, and $97\\%$ in BeetElite—advertised as “improving exercise endurance and increasing oxygen supply in the body. The ADI for nitrites amounted to $450\\%$ and $225\\%$, respectively. The rest of the DSs were characterised by values within the range of 0.01–$47.26\\%$ for nitrates and 0.00–$21.11\\%$ for nitrites [50]. The reason for such a high content of nitrites and nitrates in some supplements was most likely their composition—rich in nitrates dehydrated or concentrated forms of vegetables or concentrated vegetable juices. The supplements analysed for this work did not pose a risk to the consumer because of the ADI.\n\nStudies have shown [50] that consumption of beetroot products is more beneficial than supplementation with nitrate salts because the flavonoids and vitamin C present in beetroot reduce the risk of nitrosamine formation. Haem iron may increase the risk of the formation of these compounds [50]. However, beetroot supplements, if fortified with iron, are in the non-haem form (usually iron gluconate or fumarate; see T2, T5, and T6). Because of ADI limits, all sources of nitrates in the diet should be considered when estimating daily nitrate and nitrite consumption and performing a safety assessment. Green leafy vegetables and root vegetables such as beetroot constitute rich sources of these substances [51,52]. Moreover, nitrates are added to meat and meat products to prevent Clostridium botulinum, Listeria monocytogenes, Bacillus cereus, Clostridium perfringens, and *Staphylococcus aureus* growth, improve their colour, and develop their characteristic flavour [19,53,54]. According to some studies, their high concentrations in water (>50 mg/L) can cause methemoglobinemia and gastrointestinal carcinogenesis [55]. It should be mentioned that hypotensive effects were observed after the nitrate dose corresponding with the upper limit of the WHO ADI. Ashor et al. [ 56] described the hypotensive effect after using beetroot juice rich in NO3¯ (70 mL containing 400 mg). Similarly, Mills et al. [ 57] discovered a hypotensive effect after 6 months of drinking beetroot juice rich in NO3¯ (70 mL containing 694 mg of NO3¯). Furthermore, Kapil et al. [ 58] reported that 4 weeks of supplementation with beetroot juice containing 450 mg of NO3¯ had beneficial therapeutic effects on endothelium and arterial stiffness. However, no therapeutic effects were observed with additional daily administration of 300 mg of NO3¯ [59,60]. Considering these values, the tested supplements are probably not able to exert a hypotensive effect even with long-term use, as the highest content of nitrates found in product P9 amounted to $48\\%$ of ADI.\n\n## 3.3. The Correlation between the Antioxidant Potential and the Content of Nitrites and Nitrates in Beetroot and Beetroot–Based Products\n\nA statistically significant negative correlation (Spearman’s rank correlation analysis) was found only in beetroot samples, both between the results of the antioxidant potential obtained by the FC and CUPRAC methods and the content of nitrates (Table S11). The group of beetroot samples was more homogeneous in terms of composition than DSs. Some of the DSs were enriched in various substances such as nitrates, iron compounds, and vitamin C, which could have disturbed the existence of a potential correlation. The Spearman’s rank correlation coefficient equals −0.54 for the FC method and −0.62 for CUPRAC, which means that dependence is moderate.\n\n## 3.4. Labelling Assessment\n\nAn assessment of thirty-four packages of dietary supplements and eight traditional food products was carried out because of Polish and European food labelling regulations. It was found that $64\\%$ of packaging did not meet the legal requirements for food labelling, $12\\%$ were not reported to the Chief Sanitary Inspectorate, and $6\\%$ did not have the term “dietary supplement” on the packaging, despite having registration in the GIS in this category. Furthermore, $26\\%$ of products were not fully labelled in Polish, as a result of which the consumer is not able to get acquainted with the information presented on the packaging in detail and its content is not formulated understandably. It is worth emphasising that $21\\%$ of the tested products contained prohibited health or non-registered claims, which means that $15\\%$ of the products suggested that they had the properties of preventing or treating diseases. It is also worth noting that the product P9, which contained the highest amount of nitrates and was sold as a supplement, contained significant labelling deficiencies, including the lack of the wording “dietary supplement”. Moreover, part of the labelling was only in English, and the dosage was not precisely defined, which poses a risk of nitrate overdose.\n\nThe detailed results of the analysis are summarised in Table 5.\n\n## 4. Conclusions\n\nThe importance of this study was to determine and compare TAC, TPC, nitrite, and nitrate content in beetroot-based DSs and beetroot. Moreover, the safety of consumption of DSs because of nitrites, nitrates, and the correctness of labelling were assessed.\n\nThe research revealed that TAC, TPC, nitrate, and nitrite concentrations expressed per unit of product weight (g or kg), DSs in capsules, and DSs in powders were comparable to the average beetroot. Tablets contained notably fewer of these substances, which might result from the presence of auxiliary substances used for tabletting. However, the average portion (100 g) of conventional or organic beetroot provided significantly more nitrates, nitrites, and substances with antioxidant properties than most of the DSs in capsules, tablets, and powders dosed according to the manufacturer’s recommendations. Only P9 ($48\\%$ ADI for NO3¯), P10 ($28\\%$ ADI for NO3¯), and P13 ($37\\%$ ADI for NO3¯) delivered higher doses than beetroots. Most of the products did not have the declared content of nitrates. The antioxidant content in a serving of tablets or capsules was negligible, so their use has a low health value. In many of the samples studied, the nitrate content was not correlated to the antioxidant potential. A statistically significant negative correlation was found only in beetroot samples between the results of the FC and CUPRAC methods and the content of nitrates.\n\nThe labelling assessment has shown that $64\\%$ of packaging did not meet the legal requirements for food labelling. Some DSs contained illegal health claims that suggested healing properties or were misleading. This situation might result in reduced effectiveness or withdrawal from conventional therapies by consumers who would choose adulterated DSs. There were significant deficiencies in labelling, including a lack of full labelling in Polish, unclear dosage and others. Such deficiencies, combined with unknown nitrite and nitrate content, may result in consumers overdosing on these substances as a result of incorrect product intake. In addition, the unknown content of nitrates and nitrites may pose a threat to the consumer because the content of these compounds in vegetables varies depending on the place of origin and growing conditions. The conducted research indicated a strong need for more rigorous control when launching DSs, both in terms of composition and labelling. Although production and labelling guidelines are published, there is a lack of decisive action by national and European authorities related to the control and withdrawal of defective products.\n\n## References\n\n1. Grembecka M., Szefer P., Dybek K., Gurzynska A.. **Ocena zawartosci wybranych biopierwiastkow w warzywach**. *Rocz. 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DOI: 10.1093/ajcn/nqaa024"},"embed":{"kind":"list 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0.00025172936148010194,\n -0.010320903733372688,\n -0.00855879858136177,\n -0.015670152381062508,\n 0.017621055245399475,\n 0.009943310171365738,\n -0.012712333351373672,\n -0.0033354139886796474,\n -0.01963489130139351,\n 0.0037287413142621517,\n -0.0028476885054260492,\n -0.008370001800358295\n]","isConversational":false}}},{"rowIdx":61,"cells":{"text":{"kind":"string","value":"---\ntitle: 'Simplifying carb counting: A randomized controlled study – Feasibility and\n efficacy of an individualized, simple, patient‐centred carb counting tool'\nauthors:\n- Shulamit Witkow\n- Idit F. Liberty\n- Irina Goloub\n- Malka Kaminsky\n- Olga Otto\n- YonesAbu Rabia\n- Ilana Harman Boehm\n- Rachel Golan\njournal: Endocrinology, Diabetes & Metabolism\nyear: 2023\npmcid: PMC10000617\ndoi: 10.1002/edm2.411\nlicense: CC BY 4.0\n---\n\n# Simplifying carb counting: A randomized controlled study – Feasibility and efficacy of an individualized, simple, patient‐centred carb counting tool\n\n## Abstract\n\nThe Scc tool is a simple tool enabling accurate insulin dosing to all diabetes patients treated with basal and bolus insulin. The SCC tool has the potential to apply to all diabetes patients, particular to those who are uncomfortable with the use of advanced technology, or who do not have access to such technology due to age, education or language barriers.\n\n### Introduction\n\nThe purpose of this study was to introduce and test a simple, individualized carbohydrate counting tool designed for persons with Type 1 Diabetes Mellitus (T1DM) in order to determine whether the tool improved A1C levels for participants with age, education or language barriers.\n\n### Methods\n\nIn a randomized controlled trial, 85 participants were offered six diabetes instructional sessions free of charge over a six‐month period. Forty‐one received guidance using the regular carbohydrate counting (RCC) method. Forty‐four received guidance using an individualized ‘Simple Carb Counting’ (SCC), involving two customized tables prepared for participants.\n\n### Results\n\nThe simple, individualized SCC tool for carbohydrate counting was non‐inferior to the standard method of RCC. The SCC tool was more effective among participants aged 40 and older, while no differences were found when comparing participants by education level. Irrespective of intervention group, all participants improved their A1C level ($9.9\\%$ = 13.2 mmol/L vs $8.6\\%$ = 11.1 mmol/L, $$p \\leq .001$$). A greater improvement in A1C level was seen in newly diagnosed participants (−6.1 vs −0.7, $$p \\leq .005$$, −3.4 vs 0.9, $$p \\leq .032$$) in both the RCC and SCC groups. All participants expressed improved emotional level per their PAID5 questionnaires (Problem Areas in Diabetes Scale‐PAID), (10.6 (±5.7) vs 9.5 (±5.7), $$p \\leq .023$$), with women reporting greater improvement than men.\n\n### Conclusions\n\nSCC is a simple, individualized, feasible, low‐tech tool for carbohydrate counting, which promotes and enables accurate insulin dosing in people with T1DM. It was found more effective among participants aged 40 and older. Additional studies are needed to corroborate these findings.\n\n## INTRODUCTION\n\nAchieving glycaemic control in Type 1 diabetes mellitus (T1DM) is vital in order to reduce diabetes‐related complications. 1, 2 Treatment recommendations include multiple daily injections of prandial and basal insulin, or continuous insulin infusion (CSII). As carbohydrates are the major nutrient affecting the post‐prandial response, it is important to educate individuals on matching prandial insulin doses to carbohydrate intake by means of carbohydrate counting (CC). The checking of pre‐meal blood glucose levels and co‐ordination of anticipated physical activity 3 are also recommended for optimizing glucose control.\n\nCC takes into account the carbohydrate intake per meal, and enables adjustment of the prandial insulin dosage necessary to achieve individual post‐prandial glucose targets. 4 To manage CC, the patient has many variables to consider: (i) the personal insulin/carb ratio (I:C) – the amount of insulin the patient needs to overcome a 15‐gram portion of carbohydrates; (ii) the accurate assessment of the amount of carbohydrates consumed; (iii) the insulin sensitivity response (IS), that is, the decrease in blood glucose after injecting one unit of insulin to counter an elevated glucose level; (iv) the individual target glucose level pre‐ and post‐meal; and (v) the level of glucose prior to the meal. This means that in order to normalize post‐prandial glucose levels, individuals need to be highly motivated to maintain wellbeing through on‐going compliant conduct, and to be possessed of high mathematical and literacy skills. Studies 5 have shown the difficulties of successfully carrying out CC, especially concerning the over‐ and underestimation of carbohydrate content in various foods that can lead to post prandial hypo or hyperglycaemia respectively. In a systematic review 6 of CC efficacy in managing T1DM, Bell et al 6 found only partial benefit in achieving glycaemic control through CC, notwithstanding the difficulty of complying with CC instructions. These difficulties are added to other barriers patients with T1DM cope with following dietary recommendations such as, frequent glucose measurements and importantly their feelings about having diabetes.\n\nIn recent years, new technologies have been developed to simplify CC. 7 *While various* commercial applications and bolus calculators may lead to better glucose control, such options are not available to all populations including lower socioeconomic status, older people and those with lower technological skills. In an attempt to overcome disparities in access and other barriers, the Diabetes Clinic of Soroka University Medical Center (SUMC) developed a simple, easy‐to‐use tool – Simple Carb Counting ‘SCC.’ This tool includes adjustments for those of differing educational backgrounds, cultures and cognitive abilities. It affords persons with diabetes continued enjoyment of their personal eating habits and food preferences through the successful adoption of the SCC method into their daily routines with ease and accuracy.\n\nThe aim of this randomized controlled study was to test the feasibility and efficacy of SCC, compared to the regular CC method. Our *Hypothesis is* that this simple tool will enable all people with T1DM improve diabetes control.\n\n## METHODS\n\nWe performed an open‐label randomized controlled trial at the Diabetes Clinic of SUMC, a tertiary 1200‐bed hospital that treats a diverse population, including Bedouin and other Arabs, and Jews from the general, ultra‐Orthodox and Ethiopian sectors. Patients with T1DM were eligible for this study if they were (i) over 18 years old, (ii) treated either with an insulin pump or with multiple daily injections of insulin and (iii) had a hemoglobinA1c (A1C) level equal to, or >8.5. The study excluded pregnant or lactating women, patients with severe renal failure, heart failure or under active treatment for cancer. All participants signed an informed consent statement. The study was approved by the SUMC Helsinki Committee on 8 October 2015, approval number 0320‐15, and is registered at ClinicalTrials.gov, ID NCT04132128, and conducted from November 2015 to July 2017.\n\nUsing simple randomization, we assigned participants to one of the two groups, (RCC) Regular carbohydrate counting, or (SCC) simplified individualized tool for carbohydrate counting. The selection process was purely random for every assignment made by Diabetes Clinic staff.\n\nAll participants were allowed up to six instructional sessions with a registered dietitian who is also a diabetes care and education specialist during a period of about 6 months. All sessions were free of charge and lasted at least 60 min. During the first session, participants were introduced to the carbohydrate counting method to which they were assigned at randomization. Subsequent sessions were dedicated to reinforcing and practicing their method and changes in insulin dosage parameters were made if needed. All patients treated with insulin pumps were encouraged to use the bolus calculator. The primary end‐point was level of A1C 6 months after the intervention. Additional data parameters were collected including those of demographics, education and duration of diabetes. Weight was measured and blood studies were conducted before and after the intervention to determine baseline A1C and lipid levels. In order to identify depression and diabetes‐related distress, the participants were asked to complete the PAID5 questionnaire (Problem Areas in Diabetes Scale‐PAID) 8 at baseline and post‐intervention.\n\n## Regular carbohydrate counting (CC)\n\nDuring the instructional sessions, the rationale for carbohydrate counting was explained. Commercial booklets containing a list of the carbohydrate content of foods were provided, and participants were introduced to websites or cellular phone applications designed to assist the public with determining the carbohydrate content of various foods. The participants were taught to calculate the amount of insulin needed using their personal I:C ratio, IS, correction factor, and the glucose target goals prescribed by the Diabetes Clinic team. Participants were encouraged to keep a food diary to assist them with carbohydrate counting.\n\n## Simplified individualized carbohydrate counting (SCC)\n\nThe SCC tool consisted of two tables written in the participant's native language and adjusted to the participant's specific requirements. Insulin doses were calculated by professional staff using personalized I:C ratios and IS.\n\nFirst Table: The first table, derived from patients' personal IS, listed the number of units that participants needed to administer in order to correct every pre‐meal blood glucose level so as to reach their target glucose.\n\nSecond Table: The second table contained a list of food items derived from participants' personal eating habits, as recorded in their food diaries. The list consisted mainly of the most common foods they regularly consumed, the carbohydrate content of those foods and the number of insulin units needed, as calculated by the personal I:C ratio per usual portion of each food item. High carbohydrate content foods that participants included in their diet were listed, not for healthy nutrition education, but for purposes of facilitating carb counting. Foods that did not contain carbohydrates, such as protein or fat items, were also listed to ensure that the patient realized that these foods contained no carbs. Patients treated with insulin pumps received a personalized table containing the carbohydrates in their food list as grams to accurately calculate the amount of carbs entered to the calculator (Figures 1 and 2).\n\n**FIGURE 1:** *SCC carb counting chart for MDI users.* **FIGURE 2:** *SCC carb counting chart for insulin pump users.*\n\nAt each instructional session, participants' tables were reviewed, personal dosing was tested, food items were added to, or deleted from participants' lists as warranted, and the logic of the method was reiterated for the purpose of reinforcing participants' understanding and compliance.\n\n## Statistical analysis\n\nOn the basis of the expected A1C difference of $0.8\\%$ between groups with $90\\%$ power providing an SD of $0.4\\%$ and a significance level of $0.05\\%$ we estimated that the sample size should consist at least 37 participants in each group. We used descriptive statistics to summarize the data, reporting results as means and standard deviations. Categorical variables were summarized as counts and percentages. Paired t‐test was used to examine within changes in A1C between baseline and follow‐up, and Student's t‐test was used to examine between differences as to the two intervention groups. We further analysed the data after stratifying the study population by sex, education level (above and below 12 years of school), age (above and below age 40) and duration of diabetes (more or <5 years since diagnosis). p values are shown. All analyses were performed with IBM SPSS.\n\n## RESULTS\n\nIn total, 107 men and women were recruited for the study, of whom 22 were excluded, as shown in Figure 3. Of the 85 people who were deemed eligible, $48.2\\%$, $$n = 41$$, of participants (23 women, 18 men) were assigned to RCC method group, and $51.8\\%$, $$n = 44$$, of participants, (17 women, 27 men) to the SCC method group. The mean age of participants was 43.1 (18–74). About $43\\%$ of the RCC method group were treated with an insulin pump versus $36\\%$ of the SCC group ($$p \\leq .48$$). All patients monitored their glucose by self‐monitoring of blood glucose (SMBG) (Figure 1 and Table 1).\n\n**FIGURE 3:** *Flow diagram – Trial of standard carb counting versus simple individualized carb counting.* TABLE_PLACEHOLDER:TABLE 1 The participants were followed for a mean period of 6 months, during which all participants in the study improved their A1C level between baseline and follow‐up ($9.9\\%$ = 13.2 mmol/L vs $8.6\\%$ = 11.1 mmol/L, $$p \\leq .001$$). Other biomarkers did not change from baseline in either of the groups.\n\nWe stratified the study population by participant age (older and younger than age 40). Among older participants (mean age 55.2 (±9.4)), only those who used the SCC method exhibited significant improvement in A1C level, from baseline (9.6 (±1.3) = 12.7 mmol/L to 8.6 (±1.1) = 11.1 mmol/L, $$p \\leq .002$$). While those using the RCC method showed some improvement, it did not reach statistical significance (9.7 (±1.3) = 12.9 mmol/L to 9.2 (±1.5) = 12.1 mmol/L, $$p \\leq .09$$). ( Figure 4).\n\n**FIGURE 4:** *HbA1c results SCC vs RCC.*\n\nAmong younger participants (mean age 29.6 (±6.3)), a significant improvement was found in both the RCC group (10.2 (±2.3) = 13.7 mmol/L to 8.3 (±1.4) = 10.6 mmol/L, $$p \\leq .001$$) and SCC group (10.3 (±1.9) = 13.8 mmol/L to 8.3 (±1.1) = 10.6 mmol/L, $$p \\leq .002$$).\n\nWe stratified the study population by level of education (above and below 12 school years). Both higher and lower educated participants in the SCC group demonstrated a significant improvement in A1C level. The results for those with above and below 12 school years were −$1.4\\%$ (±2.0) vs −$1.3\\%$ (±1.9) respectively, $$p \\leq .7.$$\n\nWe found a greater improvement in A1C levels when we compared participants with more recently diagnosed diabetes (<5 years from diagnosis, $$n = 13$$) to those whose diabetes was of longer duration ($$n = 72$$). ( −6.1 vs −0.7, span >−3.4 vs 0.9, $$p \\leq .032$$) in both the RCC and SCC groups.\n\nWe measured the degree of patient compliance by the number of instructional sessions attended during the study period. Compliance was fairly good for all participants, with a mean of 4.8 visits (out of the six allowed). No differences were found in compliance between participants in the RCC group and those in the SCC group, but women were more compliant than men, with a mean of 5.3 (1.3) visits, compared to 4.4 (1.9) for the men ($$p \\leq .01$$). Compliance tended to be higher among participants with more than 12 years of education, compared to those with fewer years of education (4.5 visits (±1.9) vs 5.3 (±1.3), $$p \\leq .08$$). The degree of compliance correlated with decreased A1C level post‐intervention.\n\nSixty‐three participants (30 in RCC and 33 in SCC) completed the PAID5 questionnaire at baseline and post‐intervention. All participants reported increased satisfaction, as exhibited by a decreased PAID5 score (10.6 (±5.7) vs 9.5 (±5.7), $$p \\leq .023$$). Based on the questionnaire responses, there were no differences in diabetes‐related emotional distress between participants in the RCC group and those in the SCC group. Yet, when the investigators stratified the data by sex, they found a significant improvement among women, who reported a decrease in diabetes‐related emotional distress from 11.5 to 9.9, $$p \\leq .04$$, compared to men, 9.9 to 9.18, $$p \\leq .27$$ (Table 2).\n\n**TABLE 2**\n\n| Unnamed: 0 | Baseline PAID Score (±SD) | Post‐intervention PAID Score (±SD) | p‐value | p‐value (SCC vs RCC) |\n| --- | --- | --- | --- | --- |\n| All participants (N = 63) | 10.6 (5.7) | 9.5 (5.7) | 0.02 | |\n| All SCC (N = 33) | 12.5 (5.4) | 11.1 (5.5) | 0.03 | |\n| All RCC (N = 30) | 9.3 (5.4) | 8.42 (5.6) | 0.09 | 0.69 |\n| All women (N = 30) | 11.5 (5.8) | 9.9 (5.6) | 0.02 | |\n| SCC women (N = 18) | 13.11 (5.5) | 11.28 (5.6) | 0.04 | |\n| RCC women (N = 12) | 9 (5.6) | 7.75 (5.1) | 0.15 | 0.7 |\n| All men (N = 33) | 9.9 (5.7) | 9.18 (5.9) | 0.13 | |\n| SCC men (N = 12) | 10.67 (6.2) | 9.83 (6.1) | 0.23 | |\n| RCC men (N = 21) | 9.48 (5.5) | 8.81 (5.9) | 0.21 | 0.9 |\n| Education >12 y (N = 31) | 8.65 (4.4) | 7.87 (4.7) | 0.12 | |\n| SCC education >12 y (N = 15) | 9.5 (4.9) | 8.3 (5.2) | 0.11 | |\n| RCC education >12 y (N = 16) | 7.9 (3.9) | 7.4 (4.4) | 0.32 | 0.6 |\n| Education <12 y (N = 32) | 12.6 (6.3) | 11.1 (6.2) | 0.02 | |\n| SCC education <12 y (N = 15) | 14.8 (5.5) | 13.1 (5.5) | 0.08 | |\n| RCC education <12 y (N = 17) | 10.6 (6.4) | 9.3 (6.5) | 0.08 | 0.77 |\n| Age > 40 (N = 33) | 9.1 (6.0) | 8.1 (5.3) | 0.07 | |\n| SCC age > 40 (N = 13) | 11.3 (6.6) | 9.6 (5.4) | 0.08 | |\n| RCC age > 40 (N = 20) | 7.75 (5.2) | 7.2 (5.2) | 0.2 | 0.4 |\n| Age < 40 (N = 30) | 12.3 (5.1) | 11 (5.9) | 0.04 | |\n| SCC age < 40 (N = 17) | 12.8 (5.2) | 12 (6.1) | 0.11 | |\n| RCC age < 40 (N = 13) | 11.7 (5.6) | 10.3 (5.8) | 0.11 | 0.9 |\n\nDuring the 6 month follow‐up, two patients from the RCC group were hospitalized with diabetic ketoacidosis (DKA). There were no hospitalizations or ER visits due to hypoglycaemia in both groups.\n\n## DISCUSSION\n\nThe findings showed that the SCC simple, individualized tool for carbohydrate counting was non‐inferior to the standard method of RCC. The SCC tool was more effective among participants aged 40 and older, while no differences were found when comparing participants above and below 12 school years. However, significant improvement in A1C level was observed in all participants. Participants in both RCC and SCC groups who were diagnosed with diabetes within the previous 5 years exhibited significantly greater improvement in A1C level, compared to participants with diabetes of longer duration.\n\nThe SCC method presented in our study was developed in order to overcome difficulties and barriers that the diabetes clinic patients encountered in implementing CC, as described in several studies. Kawamura et al 5 tested the errors in carbohydrate content estimation among 37 paediatric patients, their parents, and their health care professionals, including physicians and dietitians. In all groups studied, they found overestimation of the carb content in foods with small amounts of carbs, and underestimation in foods with high carb content.\n\nWhile past experience in CC was important, some foods, such as rice, were hard to estimate even by experienced participants. In the qualitative study of Gürkan et al 9 investigators interviewed adolescents with diabetes, finding multiple barriers to effective treatment. Among the barriers were patients' negative feelings about having diabetes, as well as personal and environmental barriers. Personal barriers included lack of knowledge about the disease, trouble with glucose measurement, and difficulty following dietary recommendations. These findings were corroborated by Ahola et al 10 who found that many patients experienced difficulty managing their post‐prandial glucose, and were subject to a high percentage of time in a hyperglycaemic state.\n\nIn this study, we present an option to overcome some of the barriers described in the above studies. Through personalization, flexibility, and a departure from a restrictive diet paradigm, SCC affords persons with diabetes an opportunity to continue eating their usual diet, including the customary dishes of their culture, and to go on with their life‐long social dining habits.\n\nThe study showed superiority in reaching glycaemic control in participants older than age 40 who used the SCC method, compared to those in the RCC group. Treating older patients with T1DM is complicated by the combined challenges of insulin‐dependent diabetes, age‐related complications, and possible comorbidities, all of which negatively affect the older population's ability to self‐manage diabetes. 11 The SCC tool presented in the study simplifies the tasks needed for carbohydrate control, and consequently leads to better glycaemic control especially for older age group.\n\nContrary to these findings, the study demonstrates that SCC was non‐inferior in people with various levels of education. In a cross‐sectional multicentre study of 768 subjects under age 18 with T1DM, Gesuita et al 12 found that only $28.1\\%$ of participants reached target A1C values (<$7.5\\%$). A strong correlation was found between higher socio‐economic status (SES), higher level of education and higher ability to follow ordinary CC. Significantly, Gesuita et al. highlighted the need for an accessible tool for non‐privileged populations.\n\nRecently diagnosed participants in both RCC and SCC groups showed the greatest benefit in improving glycaemic control. This may be explained in two possible ways, one psychological and one physiological. When first diagnosed, many patients are highly motivated to do well. In addition, in the early period after diagnosis, sometimes called the ‘honeymoon period,’ the pancreas seems to do better and secretes more insulin, although this phenomenon decreases with time and differs with each patient.\n\nAll participants exhibited significant decreases in their PAID5 scores. Studies have shown 13, 14 that people with diabetes suffer from higher levels of psychological distress than does the healthy population. People with T1DM are three times more likely to develop depression than those without diabetes. 15 Moreover, psychological distress has been shown to be associated with hyperglycaemia, complications and higher mortality rate. 16, 17 Thus, there is a consensus that treating psychological stress and achieving psychological wellbeing ought to be one of the treatment goals of diabetes care. 18 A study by Zagarins et al 19 revealed a correlation between improvement of glycaemic control and alleviation of overall psychological stress, but not in depression. Our study corroborates these findings, and underscores the need for on‐going diabetes education, better understanding and treatment of diabetes and promoting a greater sense of self‐efficacy among patients in controlling the disease, as means of improving not only metabolic control, but also mental health.\n\n## Limitations\n\nThe intervention tool was introduced at a single diabetes clinic in a tertiary teaching hospital with one registered dietitian/diabetes care and education specialist. The method was not tested on paediatric patients, a population that has more difficulties with glycaemic control than others. A larger population of people from the lower socio‐economic and more culturally diverse backgrounds should be studied in order to corroborate the results and establish generalizability across populations.\n\n## CONCLUSIONS AND IMPLICATIONS\n\nIn large measure, the research into T1DM treatment is focused on advanced technologies including insulin pumps, continuous glucose monitoring, the artificial pancreas and various applications to support CC and diabetes management. Studies have shown 20 that although advanced applications are accessible and improve glycaemic control, only a small percentage of the population with T1DM chooses to use them. This may be explained by the human factor, that is, personal expectations, perceptions of the burden of new technologies, user‐friendliness and long‐term cost. The SCC tool tested in this study has the potential to apply to all diabetes patients, and in particular to those who are uncomfortable with the use of advanced technology, or who do not have access to such technology.\n\nIn conclusion this study presents a simple, feasible, low‐tech tool that simplifies carbohydrate counting and which promotes and enables accurate insulin dosing in people with T1DM. Additional studies are needed to corroborate these findings.\n\n## AUTHOR CONTRIBUTIONS\n\nShulamit Witkow: Conceptualization (lead); data curation (lead); investigation (equal); methodology (equal); resources (lead). Idit F Liberty: Data curation (equal); formal analysis (supporting); investigation (equal); methodology (equal); project administration (equal); supervision (lead); validation (equal); writing – original draft (lead); writing – review and editing (lead). Irina Goloub: Data curation (supporting); resources (supporting). Malka Kaminsky: Conceptualization (supporting); data curation (equal); investigation (supporting); resources (supporting). Olga Otto: Data curation (equal); investigation (supporting). Yones Abu Rabia: Data curation (supporting); investigation (supporting); resources (equal). Ilana Harman Boehm: Conceptualization (equal); data curation (equal); investigation (equal); methodology (equal); resources (equal). Rachel Golan: *Formal analysis* (lead); writing – review and editing (supporting).\n\n## CONFLICT OF INTEREST STATEMENT\n\nThe Authors declare that there are no conflicts of interest.\n\n## DATA AVAILABILITY STATEMENT\n\nRaw data were generated at the diabetes centre of SUMC. Derived data supporting the findings of this study are available from the corresponding author [IFL] on request\n\n## References\n\n1. 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PMID: 23062861\n\n16. van Dooren FE, Nefs G, Schram MT, Verhey FR, Denollet J, Pouwer F. **Depression and risk of mortality in people with diabetes mellitus: a systematic review and meta‐analysis**. *PloS One* (2013) **8**. PMID: 23472075\n\n17. Pouwer F, Nefs G, Nouwen A. **Adverse effects of depression on glycemic control and health outcomes in people with diabetes: a review**. *Endocrinol Metab Clin North Am* (2013) **42** 529-544. PMID: 24011885\n\n18. Jones A, Vallis M, Pouwer F. **If it does not significantly change HbA1c levels why should we waste time on it? A plea for the prioritization of psychological well‐being in people with diabetes**. *Diabet Med* (2015) **32** 155-163. PMID: 25354315\n\n19. Zagarins SE, Allen NA, Garb JL, Welch G. **Improvement in glycemic control following a diabetes education intervention is associated with change in diabetes distress but not change in depressive symptoms**. *J Behav Med* (2012) **35** 299-304. PMID: 21691844\n\n20. Liberman A, Barnard K. **Diabetes technology and the human factor**. *Diabetes Technol Ther* (2018) **20** 128-138"},"embed":{"kind":"list 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0.026771305128932\n]","isConversational":false}}},{"rowIdx":62,"cells":{"text":{"kind":"string","value":"---\ntitle: Blocking EREG/GPX4 Sensitizes Head and Neck Cancer to Cetuximab through Ferroptosis\n Induction\nauthors:\n- Aude Jehl\n- Ombline Conrad\n- Mickaël Burgy\n- Sophie Foppolo\n- Romain Vauchelles\n- Carole Ronzani\n- Nelly Etienne-Selloum\n- Marie-Pierre Chenard\n- Aurélien Danic\n- Thomas Dourlhes\n- Claire Thibault\n- Philippe Schultz\n- Monique Dontenwill\n- Sophie Martin\njournal: Cells\nyear: 2023\npmcid: PMC10000618\ndoi: 10.3390/cells12050733\nlicense: CC BY 4.0\n---\n\n# Blocking EREG/GPX4 Sensitizes Head and Neck Cancer to Cetuximab through Ferroptosis Induction\n\n## Abstract\n\n[1] Background: Epiregulin (EREG) is a ligand of EGFR and ErB4 involved in the development and the progression of various cancers including head and neck squamous cell carcinoma (HNSCC). Its overexpression in HNSCC is correlated with short overall survival and progression-free survival but predictive of tumors responding to anti-EGFR therapies. Besides tumor cells, macrophages and cancer-associated fibroblasts shed EREG in the tumor microenvironment to support tumor progression and to promote therapy resistance. Although EREG seems to be an interesting therapeutic target, no study has been conducted so far on the consequences of EREG invalidation regarding the behavior and response of HNSCC to anti-EGFR therapies and, more specifically, to cetuximab (CTX); [2] Methods: EREG was silenced in various HNSCC cell lines. The resulting phenotype (growth, clonogenic survival, apoptosis, metabolism, ferroptosis) was assessed in the absence or presence of CTX. The data were confirmed in patient-derived tumoroids; [3] Results: Here, we show that EREG invalidation sensitizes cells to CTX. This is illustrated by the reduction in cell survival, the alteration of cell metabolism associated with mitochondrial dysfunction and the initiation of ferroptosis characterized by lipid peroxidation, iron accumulation and the loss of GPX4. Combining ferroptosis inducers (RSL3 and metformin) with CTX drastically reduces the survival of HNSCC cells but also HNSCC patient-derived tumoroids; [4] Conclusions: The loss of EREG might be considered in clinical settings as a predictive biomarker for patients that might undergo ferroptosis in response to CTX and that might benefit the most from the combination of ferroptosis inducers and CTX.\n\n## 1. Introduction\n\nWith nearly 932,000 new cases and 450,000 deaths in 2018 worldwide, head and neck cancers, and, more particularly, head and neck squamous cell carcinoma (HNSCC), rank sixth among the most frequently observed cancers in the world [1]. Locally advanced HNSCC (LA-HNSCC, stage III/IV) represents $70\\%$ of patients at diagnosis. They require primary surgery followed by adjuvant (chemo)-radiotherapy or definitive chemoradiotherapy including cetuximab (CTX) [2,3]. The chimeric antibody CTX targets the epidermal growth factor receptor (EGFR), which happens to be overexpressed in more than $90\\%$ of HNSCC. CTX prevents ligand binding and dimerization with other HER family members [4,5]. Once bound, CTX blocks EGFR phosphorylation and signal transduction and promotes EGFR internalization, thus turning down the oncogenic EGFR signaling [6,7]. Unfortunately, some patients do not benefit from CTX treatment, and others show recurrences soon after the end of the treatment. Intrinsic or therapeutically acquired resistance to CTX is extensively studied to understand the mechanisms involved. We have recently shown the involvement of the caveolin-1/epiregulin/YAP axis in the resistance to CTX and irradiation therapy [8].\n\nEpiregulin (EREG), encoded by the EREG gene located on chromosome 4q13.3, belongs to the ErbB family of ligands. The 162 amino acids transmembrane proform of EREG is proteolytically cleaved by ADAM17 [9] to release a soluble form of 46 amino acids [10]. EREG shares 24–$50\\%$ of its sequence with those of other members of the EGF family [10]. EREG binds to EGFR (extracellular domain I and III that partially overlaps the EGF binding site [11]) and ErbB4. It stimulates homodimers of EGFR and ErbB4 in addition to heterodimers of ErbB2 and ErbB3, leading to the activation and the transduction of downstream signaling pathways [12]. In contrast to EGF, EREG leads to complete EGFR recycling and not to lysosomal degradation [13]. EREG induces less stable EGFR dimers than EGF, but unexpectedly, this weakened dimerization elicits more sustained EGFR signaling than EGF [14]. Low or non-existent in most human tissues, the elevation of EREG expression is observed in the early stages of cancer development, in which EREG induces epithelial-mesenchymal transition [15]. EREG is a transcriptional target of the oncogenic KRAS and is also overexpressed in cells with an oncogenic mutation of EGFR and BRAF [16]. EREG also promotes tumorigenicity, metastasis, drug resistance and cell plasticity and modulates the tumor microenvironment and metabolism [17]. The increased expression of EREG is associated with short overall survival in patients with HNSCC/OSCC [11,15,18,19,20]. Elevated levels of EREG appear to be a potential predictive biomarker of anti-EGFR therapies in several cancer types including HNSCC [21]. We recently reported that the overexpression of caveolin-1 in HNSCC is associated with the total loss of EREG as well as the oncogenic addiction to EGFR. Silencing EREG activates the YAP/TAZ pathway, which enables cells to resist CTX/radiotherapy. The resistance of HNSCC cells to therapy is linked to the protection of the mitochondria and drives the recurrence of caveolin-1-expressing HNSCC tumors [8].\n\nHow the loss of EREG affects the cellular metabolism and how it might influence the response to anti-EGFR therapies is only sparsely documented in HNSCC. Here, we aimed to clarify this point and highlighted that the loss of EREG sensitizes cells to CTX through the induction of ferroptosis. Our study reveals the glutathione metabolism as a targetable vulnerability of HNSCC that should be exploited in CTX-based therapies.\n\n## 2.1. Cell Culture, Transfection and Drugs\n\nThe CAL27, CAL33 and SCC9 cell lines were purchased from the ATCC® and DSMZ (authenticated by STR profiling). All cell lines tested negative for mycoplasma contamination. CAL27 and CAL33 were grown in DMEM (PAN Biotech, Aidenbach, Germany) supplemented with 2 mM ultra-glutamine, 0.5 mM sodium pyruvate and $10\\%$ heat-inactivated FBS (Gibco, Dutscher SAS, Brumath, France). SCC9 were grown in DMEM-F12 (PAN Biotech) supplemented with 2.5 mM ultra-glutamine, 15 mM HEPES, 400 ng/mL hydrocortisone (Sigma-Aldrich, St Quentin Fallavier, France) and $10\\%$ FBS (Gibco). EREG expression was downregulated by transfecting the cell lines with 25 nM siRNAEREG (Human EREG and the respective control siRNACtrl, SMARTPool from Dharmacon) using Lipofectamine 2000TM (Invitrogen, Thermo Fischer Scientific, Illkirch, France). Efficient EREG silencing was determined by Western blot. When indicated, cells were treated with solvent, 30 nM of CTX (Erbitux™, 5 mg/mL, Merck, ICANS, Strasbourg, France), 5 µM RSL3 (MedChemExpress, Clinisciences, Nanterre, France) or 1 mM metformin (MedChemExpress) alone or in combination with CTX.\n\n## 2.2. Sphere Evasion Assay\n\nAfter the treatments, 500,000 cells were resuspended in 1 mL of regular culture medium supplemented with $20\\%$ methylcellulose. Spheroids were formed using the hanging drop culture method. Drops of 20 µL cell suspension were placed onto the lids of 60 mm dishes, which were inverted over the dishes. The dishes were cultured in humidified chambers for 48 h to allow the formation of round aggregates. The spheroids were harvested and seeded in plastic 24-well plates (6 spheres/well) for an additional 24 h to allow for the evasion of cells from the attached spheres. Pictures were taken using the Evos XI Core microscope (AMG, Thermo Fischer Scientific, Illkirch, France), with 10× magnification. The results were expressed, in pixels, as the evasion area of the cells relative to the area of the attached sphere (total area − area of the sphere), determined using ImageJ (https://imagej.nih.gov, 1.53t).\n\n## 2.3. IncuCyte® Assay\n\nAfter transfection, the cells were seeded (4000 for CAL33 and 8000 for CAL27 and SCC9 cells/200 µL/well) in 96-well plates. The plates were placed at 37 °C, and the confluence, growth, cell health and morphology were monitored for 164 h/7 days. The percentage of confluence was determined using the IncuCyte® analysis software after normalization to day 0 (Essen BioScience, Sartorius, Goettingen, Germany).\n\n## 2.4. Clonogenic Survival Assay\n\nA total of 72 h after the transfection and/or an additional 48 h of treatment with the solvent, 30 nM CTX, 5 µM RSL3, 1 mM metformin or co-treatments, the cells were seeded (500 for CAL27 and SCC9 and 1000 for CAL33 cells/2 mL/well) in 6-well plates and allowed to grow for 10 days. The cells were colored with crystal violet at $0.1\\%$ (Sigma-Aldrich, St Quentin Fallavier, France). The colonies were counted to determine the plating efficiency (PE) and the surviving fraction (SF). PE = number of surviving cells/number of cells plated. SF = PE of the experimental group/PE of the control group.\n\n## 2.5. Metabolic Assays\n\nAfter the treatment, 20,000 cells were plated in a Seahorse XF Cell Culture microplate in XF growth medium (non-buffered DMEM containing 10 mM glucose, 4 mM L-glutamine and 2 mM sodium pyruvate). The OCR (oxygen consumption rate), ECAR (extracellular acidification rate) and ATP consumption were measured using the ATP rate assay procedure under basal conditions and in response to 1.5 μM oligomycin and 0.5 µM rotenone/antimycin A with the XFp Extracellular Flux Analyzer (Seahorse Bioscience, Agilent, Les Ulis, France). The metabolic profiles were analyzed using the online Seahorse analytics platform.\n\n## 2.6. Western Blot\n\nA total of 72 h after the transfection and/or an additional 48 h of treatment with the solvent, 30 nM CTX, 5 µM RSL3, 1 mM metformin or co-treatments, the cells were lysed with the lysis buffer ($1\\%$ Triton, 100 nM NaF, 10 mM Na4O7P2, 1 mM Na3VO4, protease inhibitor cocktail (Roche, Meylan, France) in PBS) for 30 min at 4 °C and then sonicated. The supernatant was recovered by centrifugation at 20,000× g for 10 min at 4 °C. In total, 5 to 20 μg of proteins were separated on a 4–$20\\%$ TGX-denaturing polyacrylamide gel (SDS-PAGE Bio-Rad Marnes-La-Coquette, France) and transferred to polyvinylidene difluoride (PVDF) membrane (Amersham, Sigma-Aldrich, St. Quentin Fallavier, France). After 1 h of blocking at room temperature, the membranes were probed with appropriate primary antibodies (see Supplementary Table S1) overnight at 4 °C. The membranes were subsequently incubated with anti-rabbit or anti-mouse antibodies conjugated to horseradish peroxidase (Promega, Charbonnieres les-Bains, France), developed using chemoluminescence (ECL, Bio-Rad, Marnes-La-Coquette, France) and visualized with an Las4000 image analyzer (GE Healthcare, Tremblay-en-France France). The quantification of non-saturated images was carried out using ImageJ software (National Institutes of Health, Bethesda, MD, USA). GAPDH was used as the loading control. The results were expressed as histograms representing the mean ± SEM of the ratios protein/GAPDH normalized against the controls.\n\n## 2.7. Quantification of Intracellular Fe2+ Accumulation\n\nA total of 72 h after the transfection and/or an additional 48 h of treatment with 30 nM CTX, the cells were seeded at 20,000 cells for CAL27 and CAL33 and at 15,000 cells for SCC9 for 24 h in 96-well plates with opaque walls. The intracellular accumulation of Fe2+ was determined using the intracellular probe FerroOrange at 1 µM for 30 min (Dojindo, TebuBio, LePerray en Yvelines, France). The fluorescence intensity was measured with a Varioskan LUX (Thermo Scientific, Illkirch, France) plate reader. In parallel, the cells were seeded at 30,000 cells for CAL27 and at 20,000 cells for CAL33 and SCC9 in an eight-well LabTek for imaging with an LEICA TCS SPE II confocal microscope (Leica Microsystems SA, Nanterre Cedex, France), with a ×20 magnification objective, and analyzed with ImageJ software.\n\n## 2.8. Detection of the Accumulation of Lipid Peroxides\n\nA total of 72 h after the transfection and/or an additional 48 h of treatment with 30 nM CTX, the cells were seeded for 24 h at 30,000 for CAL27 and at 20,000 for CAL33 and SCC9 in an eight-well LabTek. The accumulation of lipid peroxides was determined using the Liperfluo kit at 1 µM for 30 min (Dojindo, China). The cells were also seeded into LabTek wells for imaging with an LEICA TCS SPE II confocal microscope (Leica Microsystems SA, Nanterre Cedex, France), with a ×20 magnification objective, and analyzed with ImageJ software (https://imagej.nih.gov, 1.53t).\n\n## 2.9. Tumoroids Culture\n\nThe study was approved by the Scientific Committee of the tumor bank and the Department of Pathology of the CHU Strasbourg-Hautepierre (France). The patients have signed an informed consent form. Tumor extractions were carried out in the Department of Cervico-Facial Surgery of the CHU Strasbourg-Hautepierre (France). The resected pieces were histologically diagnosed. The tumoroids were extracted from head and neck cancer surgical resection following the protocol developed by Driehus et al. [ 22] and cultured in advanced DMEM/F12 supplemented with GlutaMax, Penicilin/streptomycin, 10 mM HEPES, 10 µM Y-27632 (Euromedex, Souffelweyersheim, France), 0.5 µg/mL Capsofungin (Sigma), 1× B27 supplement (Thermo Fisher Scientific), 1.25 mM N-acetyl-L-cysteine (Sigma-Aldrich), 10 mM Nicotinamide (Sigma-Aldrich), 500 nM A83-01 (Sigma-Aldrich), 0.3 µM CHIR99021 (Sigma-Aldrich), 50 ng/mL human EGF (PeproTech, Thermo Scientific, Illkirch, France), 10 ng/mL human FGF10 (PeproTech), 5 ng/mL human FGF2 (PeproTech), 1 µM Prostaglandin E2 (Bio-techne, R&D Systems, Noyal Châtillon sur Seiche, France) and 1 µM Forskolin (Bio-techne), $4\\%$ (col/col) RSPO3-Fc fusion protein conditioned medium (ImmunoPrecise, IPATherapeutics, Utrecht, Netherlands) and $4\\%$ (vol/vol) Noggin-Fc fusion protein conditioned medium (ImmunoPrecise). Quality control of the tumoroids was performed. The tumoroids were plated at 2500 cells/10 µL of $70\\%$ Cultrex UltiMatrix reduced growth factor basement membrane Extract (R&D Systems, Noyal Châtillon sur Seiche, France) in 24-well plates. The tumoroids were treated with the solvent, 30 nM CTX, 5 µM RSL3, 1 mM metformin or co-treatments 7 days after plating for an additional 10 days. The cell viability was assessed after the exposure of the cells to trypan blue (Bio-Rad) and reading via a TC20 Automated Cell Counter (Bio-Rad). Moreover, this culture was monitored by imaging at ×4 and ×20 magnification via an Evos XI Core microscope (AMG).\n\n## 2.10. Immunohistochemistry on Tumoroids\n\nFollowing the recovery, the tumoroids were fixed in PFA $4\\%$ for 20 min and washed in PBS. After a 15 min permeabilization step in PBS/$0.1\\%$ Tween-20 and a 60 min blocking step in PBS/$0.1\\%$ Triton X-100/$2\\%$ BSA/$5\\%$ NGS, the tumoroids were incubated overnight at 4 °C with primary antibodies (Rabbit anti-EREG, #CSB-PA007779NA01HU, Cusabio Technology, dilution $\\frac{1}{300}$; Mouse anti-Caveolin-1, #66067-1-lg, Proteintech, dilution $\\frac{1}{1000}$). After washing in PBS/$0.1\\%$ Triton X-100/$0.2\\%$ BSA, the cells were incubated for 3 h at room temperature with appropriate secondary antibodies (Life Technologies; dilution $\\frac{1}{500}$) and DAPI (#D9542; Sigma-Aldrich, St Quentin Fallavier, France; 1 µg/mL). After washing twice in PBS/$0.1\\%$ Triton X-100/$0.2\\%$ BSA and twice in PBS, the slides were mounted using FUnGI medium ($50\\%$ (v/v) glycerol, $9.4\\%$ (v/v) dH2O, 10.6 mM Tris base, 1.1 mM EDTA, 2.5 M fructose and 2.5 M urea). Images were acquired using an LEICA TCS SPE II confocal microscope (Leica Microsystems SA, Nanterre Cedex, France), with a 20× magnification objective, and analyzed with ImageJ software (https://imagej.nih.gov, version 1.53r, access on 3 May 2021) or Imaris software (Imaris x64 9.3.1—22 May 2019).\n\n## 2.11. Statistical Analysis\n\nQuantitative variables are presented as their mean and standard deviations and were compared to univariate analyses with a Student’s t-test if they followed a Gaussian distribution (Shapiro–Wilk tests were used to assess the Gaussian distribution) or with a Wilcoxon’s rank test if they followed a non-Gaussian distribution.\n\n## 3.1. Silencing EREG Prevents Survival and Growth and Sensitizes to CTX\n\nWe recently reported that decreased EREG expression conferred Cav1-overexpressing cells resistance to CTX/radiotherapy [8]. We postulated that it was the result of a decrease in EREG-driven oncogenic addiction to EGFR. To go further, EREG was silenced in a panel of three basal-like HNSCC cell lines using siRNA. The basal expression of Cav1, EREG and EGFR was not altered by the transfection of siRNActrl. Silencing EREG does not alter Cav1 expression and exhibits a significant reduction in EGFR, which is in contrast with the molecular alterations observed previously (Figure 1A). EREG-silenced cells (siRNAEREG) show reduced clonogenic survival (Figure 1B). Although CTX significantly reduces the survival of control (siRNActrl-transfected) cells, its effect is even more pronounced in EREG-silenced cells (Figure 1B; with 33 ± $10\\%$ and 51 ± $5\\%$ inhibition by CTX for siRNActrl-CAL27 and siRNAEREG-CAL27, 23 ± $6\\%$ and 49 ± $7\\%$ inhibition by CTX for siRNActrl-CAL33 and siRNAEREG-CAL33 and 27 ± $5\\%$ and 45 ± 5 inhibition by CTX for siRNActrl-SCC9 and siRNA EREG-SCC9, respectively).\n\nReduced clonogenic survival is associated with an altered growth capacity, reflected here by the inability of cells to reach confluency. Again, CTX is more prone to blocking the growth of cells silenced for EREG (Figure 1C; with 19 ± $1\\%$ and 28 ± $1\\%$ inhibition by CTX for siRNActrl-CAL27 and siRNAEREG-CAL27, 39 ± $1\\%$ and 46 ± $1\\%$ inhibition by CTX for siRNActrl-CAL33 and siRNAEREG-CAL33 and 17 ± $3\\%$ and 5 ± $2\\%$ inhibition by CTX for siRNActrl-SCC9 and siRNAEREG-SCC9, respectively).\n\nThe cleavage of PARP, reflecting apoptosis induction, could only barely be detected and only in cells exposed to CTX (Figure 1D). No additional cleavage could be observed in siRNAEREG-transfected cells when compared to the controls. Thus, apoptosis induction could not account for the reduction in survival and growth observed in siRNAEREG-cells remaining untreated or treated with CTX. Taken together, these data show that the concomitant silencing of EREG and EGFR targeting would be more effective in inhibiting tumor growth and survival.\n\n## 3.2. Silencing EREG Promotes Mitochondrial Dysfunction and Inhibits Autophagy in Reponse to CTX\n\nThe metabolic reprogramming of cancer cells has a beneficial effect not only on tumor growth and survival but also on metastasis and chemoresistance. We therefore investigated how EREG might affect the mitochondrial metabolism. The ATP Rate assays reveal that all three cell lines exhibit different basal oxygen consumption (OCR), extracellular acidification (ECAR) and ATP production. CAL33 cells are the most energetic (Figure 2A, left). EREG-silencing significantly reduces OCR and ECAR (Figure 2A, right) and ATP production in all three cell lines, with the highest efficiency in the most energetic cell line, CAL33 (Figure 2A, right, with a 22 ± $3\\%$, 41 ± $3\\%$ and 26 ± $1\\%$ inhibition in siRNAEREG-CAL27, siRNAEREG-CAL33 and siRNAEREG-SCC9, respectively). Although CTX significantly reduces the production of ATP in both siRNActrl- and siRNAEREG-transfected cells, SCC9 seem less sensitive to it (Figure 2A, right). EREG-silenced cells treated with CTX appear less metabolically active. Thus, silencing EREG and blocking EGFR with CTX cause mitochondria dysfunction, which is more important in highly metabolic cells.\n\nAutophagy is a critical protective mechanism against mitochondrial dysfunction. It maintains cellular homeostasis by removing damaged macromolecules and organelles, including mitochondria. The expression of ULK-1, a kinase regulating the early stages of the autophagosome formation, is only induced in cells exposed to CTX, and no differences are observed between siRNActrl- and siRNAEREG-transfected cells. In contrast, the silencing EREG reduces the expression of Beclin1 and LC3B in CAL33 and the expression of LC3B in SCC9 without affecting CAL27 cells (Figure 2B). Although CTX does not affect Beclin1 and LC3B by itself in any cell line tested, it reduces their expression even further in siRNAEREG-transfected CAL33 and SCC9 cells without affecting CAL27 cells (Figure 2B). The data show that silencing both EREG and EGFR signaling inhibits autophagy.\n\n## 3.3. Silencing EREG Promotes Ferroptosis in Response to CTX\n\nWe next focused on ferroptosis, a different class of cell death, characterized by the accumulation of ferrous ions (Fe2+) and the increase in the production of lipid reactive oxygen species (ROS). The accumulation of Fe2+ was monitored using the FerroOrange probe. Silencing EREG significantly reduces Fe2+ staining in CAL27 and CAL33 cells (Figure 3A,B, with a 33 ± $5\\%$, 43 ± $7\\%$ and 0 ± $5\\%$ inhibition in siRNAEREG-CAL27, siRNAEREG-CAL33 and siRNAEREG-SCC9, respectively). Although CTX does not affect Fe2+ accumulation in siRNActrl-cells, it significantly induces Fe2+ staining in siRNAEREG-transfected CAL27 and CAL33 without affecting SCC9 (Figure 3A,B). Lipid peroxides were monitored using the LiperFluo probe and revealed similar staining profiles to the ones observed in Figure 3B. Thus, silencing EREG reduces the accumulation of lipid peroxides in CAL27 and CAL33 cells (Figure 3C). Turning to CTX, it significantly induces lipid peroxides staining in siRNAEREG-transfected CAL27 and CAL33 without affecting SCC9 (Figure 3C). Altogether, EREG-silencing reprograms cells to induce ferroptosis in the presence of CTX.\n\n## 3.4. EREG-Silencing Uncovers the Vulnerability of Cells to GPX4 Inhibition\n\nIt has been shown that ferroptosis is initiated either by the loss of glutathione peroxidase 4 (GPX4, an enzyme involved in lipid repair) or the depletion of cystine. GPX4, together with its co-factor glutathione (GSH), catalyzes the inhibition of lipid peroxides. Its loss is concomitant with the accumulation of lipid peroxides in membranes, which leads to ferroptosis. Silencing EREG does not affect GPX4 expression in any of the cell lines tested. The exposure of siRNActrl-transfected cells to CTX does not affect it either (Figure 4A). However, the treatment of siRNAEREG cells with CTX results in a significant inhibition of GPX4 expression in all three cell lines (Figure 4A; with a 24 ± $9\\%$, 42 ± $6\\%$ and 26 ± $5\\%$ inhibition in siRNAEREG-CAL27, siRNAEREG-CAL33 and siRNAEREG-SCC9, respectively).\n\nThe cystine/glutamate antiporter system (also called x-c or xCT (coded by the genes SLC7A11 and SLC3A2)) imports extracellular cystine that will be further reduced into cysteine. Cysteine acts as a precursor for the synthesis of GSH, the cofactor of GPX4. GPX4 is also a direct transcriptional target of NRF2. SLC7A11 (as well as SLC1A5 and SLC7A5) and NRF2 are under the control of the oncogene c-Myc [23]; we investigated how EREG and/or CTX might affect c-Myc expression in our system. Silencing EREG or exposing siRNActrl-transfected cells to CTX does not affect c-Myc expression in any of the cell lines tested (Figure 4B). However, the treatment of siRNAEREG cells with CTX results in a significant inhibition of c-Myc expression in all three cell lines (Figure 4B; with a 33 ± $14\\%$, 23 ± $9\\%$ and 19 ± $6\\%$ inhibition in siRNAEREG-CAL27, siRNAEREG-CAL33 and siRNAEREG-SCC9, respectively).\n\nPharmacological inhibitors such as RSL3 have been reported to either degrade GPX4 or inhibit its function. RSL3 reduces the surviving fraction of siRNActrl- and siRNAEREG-transfected cells to similar levels as CTX (Figure 4C compared to 1B for untreated cells). The surviving fraction of both siRNActrl- and siRNAEREG-transfected cells is even further inhibited when RSL3 is combined with CTX (Figure 4C). Metformin, already used in the treatment of diabetes, was recently described as promoting ferroptosis in different ways, including by the inhibition of SLC7A11 [24,25]. We therefore studied the effects of this non-specific inducer of ferroptosis in our model. Metformin reduces the surviving fraction of siRNActrl- and siRNAEREG-transfected cells to similar levels as CTX in CAL33, but it was far more potent in CAL27 and SCC9 cells (Figure 4D, compared to 1B for untreated cells). The surviving fraction of both siRNActrl- and siRNAEREG-transfected cells is even further inhibited when metformin is combined with CTX (Figure 4D). The exposure of the cells to RSL3 or metformin alone or in combination with CTX results in a significant inhibition of GPX4 expression, which is even more pronounced in EREG-silenced cells (Figure 4E). Altogether, the data show that GPX4 is crucial for cell survival and that its disappearance sensitizes to CTX.\n\n## 3.5. GPX4 Inhibition Sensitizes the Patient-Derived Tumoroid to CTX\n\nIn order to validate our hypothesis, we exposed patient-derived tumoroids to CTX, RSL3, metformin and a combination of drugs for 7 days. Tumoroids are treated 7 days after plating in 3D drops of basement membrane extract (BME dotted circle, Figure 5A, left panel before treatment) to allow for formation. After 7 days of treatment, the tumoroids were photographed (pictures only shown for T1) at a low magnification (×4, Figure 5A middle panel) to follow the growth in the 3D BME drops (dotted circle) characterized by an increase in the size and at a high magnification (×10, Figure 5A right panel) to observe the variations in the morphology related to the different treatments. CTX and RSL3 alone do not affect the growth or the viability (Figure 5A,B) of tumoroids when compared to the untreated tumoroids. In contrast, the combination of CTX and RSL3 clearly reduces the size (Figure 5A) and the viability to 60 ± $4\\%$ and 59 ± $5\\%$ in tumoroids 1 and 2, respectively (Figure 5B). The non-specific inducer of ferroptosis, metformin by itself, reduces the size (Figure 5A) and the viability of tumoroids 1 and 2 to 48±$6\\%$ and 31 ± $5\\%$, respectively (Figure 5B). The combination of CTX and metformin reduces the viability even further in tumoroid 1 but not in tumoroid 2 (12 ± $2\\%$ and 25 ± $8\\%$ in tumoroids 1 and 2, respectively; Figure 5B) and is more efficient than CTX and RSL3. Reduced viability is associated with the appearance of debris in 3D BME drops (arrows in Figure 5A).\n\nFinally, the exposure of tumoroids to CTX does not affect GPX4 expression (Figure 5C). GPX4 is significantly reduced by RSL3 alone (30 ± $10\\%$) and even further when RSL3 is combined with CTX (80 ± $9\\%$). Similar results were obtained in T2 and T3 (data not shown). It is also significantly reduced by metformin alone (76 ± $14\\%$) and totally lost when metformin is combined with CTX (Figure 5C). Altogether, the data confirm the efficacy of targeting xCT and GPX4 in CTX-resistant tumors.\n\n## 4. Discussion\n\nThe dysregulation of EREG may contribute to the progression of various cancers including HNSCC and may be a putative mechanism of resistance to EGFR-targeted therapies. EREG is usually overexpressed in HNSCC and correlates with short overall survival and progression-free survival [11,15,18,19,20]. EREG conducts an even more potent mitogenic signal than EGF in HNSCC mimicking EGFR oncogenic mutations [11,20]. Job et al. recently described a subgroup of HPV-negative HNSCC named “basal” sharing molecular similarities such as the upregulation of genes involved in the EGFR signaling pathway including EREG and AREG [21]. Cells sharing these characteristics appear to be more sensitive to EGFR-targeted therapy, with CTX being the least efficient. Because the suppression of EREG expression reduces cell survival, the authors suggested that cells may be addicted to an EREG feedback loop and that EREG should be considered as a functional biomarker for HNSCC sensitivity to EGFR blockade [21]. In line with this study, we observed that HNSCC tumor cells expressing caveolin-1 could use EREG silencing, but not AREG silencing, to overcome oncogenic dependence on EGFR and develop resistance to CTX/irradiation combination therapy [8]. The resistance was due, at least in part, to the silencing of the HIPPO pathway, leading to the activation of YAP/TAZ [8]. The cross-suppression of both AREG and EREG has also been reported to lead to the emergence of CTX resistance, which is related to the loss of cell addiction to EGFR, compensated by the hyperactivation and addiction to FGFR3 in melanoma [26]. We show here that the direct suppression of EREG expression reduces both EGFR expression and HNSCC basal cell survival. Rather than driving resistance to CTX, the loss of EREG reduces survival even further. While it cannot be excluded that long-term EREG silencing may lead to the emergence of CTX resistance, the acute targeting of EREG combined with CTX is effective in reducing cell survival and could be a feasible antitumor strategy for HNSCC. Fepixnebart (LY3016859, developed by Eli Lilly and Co.) is a monoclonal antibody that binds epiregulin and TGF-α and is well tolerated and efficient in neutralizing both targets [27]. It is currently in phase II for back pain and neuropathic pain and in phase III for diabetic neuropathies. It would be interesting to determine its anti-tumor effect and adjuvant effect for EGFR-targeting therapies. Besides its autocrine feedback loop, EREG is also secreted by the component of the tumor microenvironment such as macrophages [15] and cancer-associated fibroblasts (CAF) [28]. Macrophages-derived EREG induces EGFR-TKI resistance in NSCLC, and CAF-derived EREG promotes OSCC invasion and metastasis through the induction of EMT. Thus, targeting EREG might not only prevent therapy resistance but also HNSCC progression.\n\nAberrant metabolism and metabolism reprogramming represent malignant tumor hallmarks that are required for cancer cells to proliferate and progress. The metabolism of cancer cells is mainly based on nonoxidative glycolysis, followed by the fermentation of lactic acid to produce ATP, a phenomenon known as the Warburg effect. EREG/EGFR signaling enhances glycolysis by increasing glucose consumption, lactate production, extracellular acidification (ECAR) and the intracellular levels of ATP as well as by activating several glycolytic genes [29,30]. However, HNSCC also depend on glutamine for producing energy [31], which is imported in cells by the Na+-glutamine/Na+-cysteine exchanger ASCT2. Besides serving as a source of carbon and nitrogen for macromolecule synthesis, glutamine provides α-ketoglutarate for the tricarboxylic acid (TCA) cycle and contributes to the production of the most powerful antioxidant, glutathione (GSH) (for a review, see [23]). The production of GSH also requires cysteine, which is imported into cells through the x-c or xCT cystine/glutamate antiporter. GSH serves as a cofactor of the glutathione peroxidase 4 (GPX4) to suppress destructive lipid reactive oxygen species (ROS). This pathway plays a key role in the regulation of ferroptosis, which is a regulated cell death triggered by an iron-dependent lipid peroxidation [32]. Both GPX4 and x-c antiporter are crucial regulators of ferroptosis. Here, we show that silencing EREG as well as blocking EGFR lead to mitochondrial defects characterized by a reduction in ATP production, oxygen consumption (OCR) and ECAR. Combining EREG silencing with an EGFR blockade shifts cells from an energetic state toward a less metabolic phenotype. If the dysfunction of the mitochondria could affect cell survival through energetic stress, death could neither be attributed to apoptosis, which was undetectable, or to autophagy, which was inhibited. Mitochondria play a major role in regulating oxidative metabolism, are the main source of reactive oxygen species (ROS) and are the primary site of Fe2+ iron storage and utilization. Therefore, the dysregulation of mitochondria induced by the silencing of EREG and the inhibition of EGFR might alter the iron metabolism and generate a redox imbalance strong enough to trigger ferroptosis. Here, we show that the silencing of EREG combined with the blockade of EGFR lead to the accumulation of Fe2+ and lipid peroxides associated with the downregulation of GPX4. No ferroptosis could be achieved by the loss of EREG alone or by the treatment with CTX. Accordingly, although CTX downregulates ASCT2 via a CTX-dependent EGFR endocytosis, it does not alter survival by itself. However, it decreases the intracellular uptake of glutamine and the levels of GSH, which sensitizes HNSCC to ROS-induced death [33,34]. In colorectal cancer cells, CTX neither affected proliferation or survival by itself, even though it inhibited NRF2 signaling (known to promote GPX4 and HO-1 transcription). By targeting NRF2/HO-1, CTX enhances RSL-3-induced ferroptosis [35]. Further studies will be needed to determine if those molecular targets are also altered by the loss of EREG. However, we did show the inhibition of c-Myc and GPX4 expression in EREG-silenced cells treated by CTX. We previously reported that the oncogene c-Myc, a known target of EREG/EGFR, is downregulated by CTX in EREG-/caveolin-1-expressing cells [8,11]. As c-Myc regulates ASCT2, LAT1, x-c antiporter as well as NRF2 [23,36], it deserves further investigations.\n\nInducing ferroptosis seems to be an attractive potential anti-cancer strategy with broad clinical implications. Several preclinical studies show that ferroptosis inducers can synergize with traditional chemotherapeutics [35,37,38]. They either target the depletion of the cellular antioxidant GSH through the x-c antiporter (Erastin) or directly target GPX4 (RSL3). Here, the loss of GPX4 is associated with the induction of ferroptosis in EREG-silenced cells exposed to CTX. Inhibiting GPX4 by using RSL3 reduces the survival of control cells exposed to CTX to levels equivalent to those observed in EREG-silenced cells exposed to CTX. However, RSL3 also sensitizes EREG-silenced cells to CTX to an even greater extent. The maximum decrease in cell viability corresponds to a steep decrease in GPX4. The data uncover the value of targeting GPX4 to effectively sensitize tumor cells to CTX. Accordingly, the overexpression of GPX4 was described in EGFR-TKI-resistant lung adenocarcinoma and colorectal cancers. RSL3 restores their sensitivity to EGFR-TKI [35,38]. Similar results could be obtained using metformin, which is widely used for the treatment of type 2 diabetes mellitus (T2DM). Its use in T2DM has been associated with cancer incidence and mortality decreases, including in HNSCC (for a review, see [39,40,41,42]). This effect seems to be due to the reduction in circulating insulin, since both the insulin–IGF system and hyperglycemia have been associated with cancer risk. However, metformin also has a direct anti-tumor effect via the induction of energetic stress. It inhibits the mitochondrial respiratory chain complex I, leading to mitochondrial dysfunctions, changes in the levels of ROS and the iron homeostasis (for a review, see [43]). Acting independently of GPX4, metformin also downregulates SLC7A11 (the catalytic unit of the x-c cytine/glutamate antiporter), protein stability and expression by inhibiting UFM1 expression and the subsequent UFMylation of SLC7A11 [25]. Metformin increases intracellular total ROS and lipid ROS levels and reduces intracellular GSH, which ultimately leads to ferroptosis [25]. Metformin was also recently reported to induce ferroptosis in breast cancer by inhibiting autophagy [44]. In our hands, metformin was more effective in reducing cell survival than RSL3, which is probably due to its multiple targets. Its co-administration with CTX in control cells demonstrated an adjuvant effect of metformin that is even more pronounced in EREG-silenced cells. Again, the decrease in cell viability is associated with the loss of GPX4. Although the targeting of the x-c antiporter or GPX4 sensitizes control cells to CTX treatment, the most striking effects are seen in cells where EREG is lost.\n\nTumoroids are 3D tumor-resembling cellular clusters generated from primary patient material. They closely recapitulate the 3D tissue architecture, cellular composition and characteristics (including genetic and cellular intratumor heterogeneity as well as resistance to therapy) of the tumor from which they were derived, offering useful benefits over conventional 2D cell culture and 3D multicellular spheroids. They can be grown long-term without genetic or functional changes [45,46]. The results obtained to date indicate that tumoroids respond in a largely consistent manner to the patients they were derived from [47], show heterogeneous sensitivities to standard treatments [48,49] and might predict a patient’s clinical outcome. Tumors hold promise for biomarker identification, drug discovery and aiding personalized therapy. For all these reasons, we have chosen to generate HNSCC tumoroids to validate our therapeutic approaches combining ferroptosis inducers with CTX. As a proof of concept, patient-derived HNSCC tumoroids showing resistance to CTX were co-treated with RSL3 or metformin. Tumoroids survival was drastically decreased with RSL3-CTX co-treatment and was almost completely abrogated in response to metformin-CTX. Metformin combined with CTX was therefore more effective in reducing viability than RSL3/CTX. As stated above, it might be related to the fact that RSL3 only targets GPX4, whereas metformin has a multitude of targets, some of which, such as SCL7A11, act further upstream in antioxidant signaling. The data also underline a heterogeneity in the response of tumoroids. Indeed, if metformin sensitizes tumoroid 1 to CTX, this is not the case for the second. An inhibition of EGFR expression by metformin could lead to this desensitization to CTX, as previously observed [50]. Further studies will be necessary to understand this heterogeneity. The maximum diminution of cell viability is associated with the strongest reduction in GPX4 levels.\n\n## 5. Conclusions\n\nTo our knowledge, this is the first study reporting that a loss of EREG might sensitize HNSCC to CTX through the induction of ferroptosis. To date, only a high expression of EREG was considered to predict the response of a patient to anti-EGFR therapies. However, care should be taken, since emerging studies report that secreted EREG in the microenvironment might support therapy resistance and tumor progression [15,28]. Thus, using EREG expression levels to identify patients likely to benefit from EGFR-TKI therapies could lead to the exclusion of some who would be better responders. Here, we propose combining ferroptosis inducers with CTX. 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-0.0027924003079533577,\n 0.02080737054347992,\n 0.007371936924755573,\n -0.006318802945315838,\n 0.03625333309173584\n]","isConversational":false}}},{"rowIdx":63,"cells":{"text":{"kind":"string","value":"---\ntitle: A clinical trial about effects of prebiotic and probiotic supplementation on\n weight loss, psychological profile and metabolic parameters in obese subjects\nauthors:\n- Rym Ben Othman\n- Nadia Ben Amor\n- Faten Mahjoub\n- Olfa Berriche\n- Chaima El Ghali\n- Amel Gamoudi\n- Henda Jamoussi\njournal: Endocrinology, Diabetes & Metabolism\nyear: 2023\npmcid: PMC10000630\ndoi: 10.1002/edm2.402\nlicense: CC BY 4.0\n---\n\n# A clinical trial about effects of prebiotic and probiotic supplementation on weight loss, psychological profile and metabolic parameters in obese subjects\n\n## Abstract\n\nThe supplementation with prebiotics and probiotics showed an improvement in lean mass, glycaemic profile, insulin resistance and uric acid more than diet alone.\n\n### Introduction\n\nThe management of obesity is difficult with many failures of lifestyle measures, hence the need to broaden the range of treatments prescribed. The aim of our work was to study the influence of pre and probiotics on weight loss psychological profile and metabolic parameters in obese patients.\n\n### Methods\n\nIt is a clinical trial involving 45 obese patients, recruited from the Obesity Unit of the National Institute of Nutrition between March and August 2022 divided into three groups: diet only (low‐carbohydrate and reduced energy diet), prebiotics (30 g of carob/day) and probiotics (one tablet containing Bifidobacterium longum, Lactobacillus helveticus, Lactococcus lactis, Streptococcus thermophilus/day). The three groups were matched for age, sex and BMI. Patients were seen after 1 month from the intervention. Anthropometric measures, biological parameters, dietary survey and psychological scores were performed.\n\n### Results\n\nThe average age of our population was 48.73 ± 7.7 years, with a female predominance. All three groups showed a significant decrease in weight, BMI and waist circumference with $p \\leq .05.$ Only the prebiotic and probiotic group showed a significant decrease in fat mass ($$p \\leq .001$$) and a significant increase in muscle strength with $$p \\leq .008$$ and.004, but the differences were not significant between the three groups. Our results showed also a significant decrease in insulinemia and HOMA‐IR in the prebiotic group compared to the diet‐alone group ($$p \\leq .03$$; $$p \\leq .012$$) and the probiotic group showed a significant decrease in fasting blood glucose compared to the diet alone group ($$p \\leq .02$$). A significant improvement in sleep quality was noted in the prebiotic group ($$p \\leq .02$$), with a significant decrease in depression, anxiety and stress in all three groups.\n\n### Conclusions\n\nThe prescription of prebiotics and probiotics with the lifestyle measures seems interesting for the management of obesity especially if it is sarcopenic, in addition to the improvement of metabolic parameters and obesity‐related psychiatric disorders.\n\n## INTRODUCTION\n\nToday, obesity is a global epidemy according to the World Health Organization, given the increase in its frequency in the world, and its responsibility in the appearance of several chronic pathologies, such as type 2 diabetes, hypertension, cardiovascular diseases, respiratory diseases, osteoarticular diseases, cancer and other pathologies.\n\nIn 2021, the WHO announced that more than $40\\%$ of men and women, or 2.2 billion people, are overweight and that an unbalanced diet was responsible for at least 8 million deaths per year. It is estimated that by 2025, 167 million people would be at risk of impaired health due to obesity. 1 In Tunisia, the prevalence of obesity was $26.2\\%$ in 2016 according to the results of the “Tunisian Health Examination Survey‐2016”. 2 This disease is multifactorial, among the contributing factors of obesity are: a high‐fat diet, a sedentary lifestyle, but also the imbalance of the intestinal flora, “the gut microbiota” 3 which today represents the focus of several publications.\n\nGut microbiota is defined by all the beneficial microorganisms that live and grow in the intestine. It is set up from birth and evolves according to different factors such as antibiotic treatments or diet (presence of fibres, richness of foods in pre and probiotics). Probiotics are living microorganisms, they are bacteria such as Lactobacilli, Bifidobacteria, Streptococci and many others or yeasts. They can be present naturally in our diet, especially in fermented foods such as certain yoghurts or fermented milks, whereas prebiotics represent substrates for these bacteria which allow them to ensure their growth and thus exercise their beneficial roles, they are also provided by our diet, from the dietary fibres present in vegetables and fruits, such as carob, chicory and others.\n\nToday, the microbiota is considered a therapeutic revolution, where researchers use its enrichment to prevent or treat certain diseases including obesity, 4 such as faecal transplantation, 5 but also the enrichment of the microbiota by prebiotics and probiotics to treat obesity. 6, 7 Hence, our interest in transposing these theoretical results to clinical practice.\n\nAim: The objective of this interventional clinical trial was to evaluate the effects of a probiotic supplement containing Bifidobacteruim, Lactobacillus strains and a prebiotic supplement by carob on the changes in body composition and metabolic biomarkers in subjects with obesity (main purpose), we also checked the psychological profile of the population (quality of sleep, stress, anxiety and depression) as secondary purpose.\n\n## MATERIALS AND METHODS\n\nWe conducted a prospective interventional study at the obesity unit at the Zouhair El Kallel National Institute of Nutrition and Food Technology of Tunis, from March 2022 to August 2022.\n\nWe included in our study obese patients (BMI ≥30 kg/m2) aged over 18 years. Patients with: renal failure, hypothyroidism, cancer, diabetic patients on insulin, on long‐term corticosteroid therapy, former patients of the obesity unit were not included. No participants dropped out of the study during the intervention period.\n\nForty‐five patients were recruited on their first visit to the obesity unit (T0) and were randomly assigned to three groups matched for age, sex and BMI. All participants were enrolled in the weight loss program at the beginning of the study and followed a low‐carbohydrate, reduced‐energy intake eating plan provided by the same dietician. First group called “diet only”: on low‐calorie diet alone without any intervention (15 patients).Second group: 15 patients on the same diet plan but additionally received prebiotic supplementation (2 carob beans/day about 30 g) called “prebiotic group”. Third group: same diet with probiotic supplementation ($$n = 15$$). The probiotic component used in the study was one tablet containing an association of four microbiological strains which are: Bifidobacteruim longum, Lactobacillus helveticus, Lactococcus lactis, *Streptococcus thermophilus* (1 tablet (10.109 UFC/capsule)/day) called “probiotic group”. The probiotic supplement was produced by Pileje Labs.\n\nPatients were reassessed after 1 month (T1) and we track adherence by regular phone calls.\n\nAll subjects gave their informed consent for participating in the study. The study was approved by the ethical committee of the national institute of nutrition of Tunis and the clinical trial was registered under number PACTR202210705998795 in the Pan African Clinical Trial Registry.\n\nBody mass index (BMI) was calculated using body weight and height measured with bare feet and in minimal clothing according to the World Health Organization definition and classification. 8 Body composition parameters (body fat mass and percentage and body lean mass) were acquired before and after 1 month of intervention by impedance meter TANITA BC418MA.\n\nWe took the waist circumference of the patients. Muscle strength was measured by the handgrip. Sarcopenia was defined by muscle strength lower than 27 kg for men and 16 for women.\n\nA biological assessment was carried out at T0 and T1 including: fasting glycaemia, HbA1C, Cholesterol, triglycerides, HDL, calculated LDL Friedwald formula, 9 insulinemia, calculated HOMA‐IR (HOMA‐IR = (insulin (mU/l) x glycaemia (mmol/l))/22.5), AST, ALT, GGT, creatinine and calculated eGFR.\n\nBlood glucose results were interpreted according to American diabetes association guidelines. 10 We looked at the physical examination for blood pressure and other complications of obesity such as hernia, sleep apnoea syndrome, osteoarthritis and NASH and if necessary we completed with the necessary radiological examinations.\n\nAll patients benefited from an interview including food survey, stress questionnaire (Cunji), sleep questionnaire (Epworth), symptoms of depression and anxiety (HADS).\n\nFor the evaluation of stress, we used the brief stress evaluation scale, this is the scale of Cungi 1997. 11 This scale is made up of 11 items, and for each the response is from 1 to 6.\n\nThe evaluation of the quality of sleep was carried out using the Epworth Sleepiness Scale, 12 this questionnaire assesses the level of daytime sleepiness of the patient. It is composed of eight items, and for each situation, the patient must select an answer from (0 to 3). The interpretation is as follows: A total of less than 10 suggests that there is no excessive daytime sleepiness. A total of 10 and above suggests excessive daytime sleepiness.\n\nTo assess the depressive state of the patients, we used the “HAD” scale (Hospital Anxiety and Depression Scale). 13 *This is* a structured questionnaire of 14 items. This questionnaire consists of two subscales, each having 7 items, one for anxiety, the other for depression. Each item is rated on a 4‐point scale, that is from 0 to 3, evaluating the intensity of symptoms over the past week. The scores therefore range from 0 to 21 and the highest scores correspond to the presence of more severe symptoms. The addition of the scores obtained for each item allows the following interpretation: Less than 7 points: no symptoms of depression. Eight to 10 points: doubtful symptomatology. Eleven and over: certain symptomatology.\n\n## Statistical analysis\n\nThe three‐variable ANOVA with Student's t test for paired series were used for group comparison of the body composition and metabolic parameters at T1 and T0 (SPSS Statistics, v. 25). The results were expressed as mean ± SD, and mean differences were considered significant at $p \\leq .05.$\n\n## RESULTS\n\nThe average age of our population was 48.73 ± 7.7 years with extremes ranging from 33 to 63 years. Half of the population ($51\\%$) was over 50 years old. The majority of participants were female $93.3\\%$ ($$n = 42$$) against $6.7\\%$ ($$n = 3$$) of men. Past medical history, complications and lab test results are present in Table 1.\n\n**TABLE 1**\n\n| Unnamed: 0 | Diet only (%) | Prebiotic (%) | Probiotic (%) | p |\n| --- | --- | --- | --- | --- |\n| Past medical history | Past medical history | Past medical history | Past medical history | Past medical history |\n| Diabetes | 6.7 | 26.7 | 13.3 | .3 |\n| Hypertension | 6.7 | 20 | 33.3 | .2 |\n| Dyslipidaemia | 6.7 | 26.7 | 13.3 | .3 |\n| Active smokers (%) | 6.7 | 13.3 | 6.7 | .7 |\n| Osteoarthritis (%) | 33.3 | 20 | 26.7 | .6 |\n| Sleep apnoea syndrome (%) | 26.7 | 66.7 | 33.3 | .07 |\n| Hernia (%) | 13.3 | 6.7 | 13.3 | .6 |\n| NASH (%) | 24 | 30 | 24 | .42 |\n| Diabetes (%) | 13.3 | 53.3 | 20 | .06 |\n| Prediabetes (%) | 13.3 | 6.7 | 33.3 | .06 |\n| Insulin resistance (%) | 6.7 | 0 | 7.1 | .65 |\n| High TG levels (%) | 46.7 | 66.7 | 26.7 | .18 |\n| Low HDL levels(%) | 46.7 | 46.7 | 33.3 | .27 |\n| High LDL levels(%) | 26.7 | 33.3 | 33.3 | .4 |\n\nBlood pressure values are comparable in the three groups.\n\nOur three groups were matched for BMI. There was no statistically significant difference for anthropometric measurements (weight, height, IMC, fat mass, muscle mass and waist circumference) between the three groups. In addition, the majority of patients in all three groups had normal muscle strength. Sarcopenia at T0 was noted in $20\\%$ in the diet‐only group, $6.7\\%$ in the prebiotic group and $13.3\\%$ in the probiotic group.\n\nIn each group, $93.3\\%$ of patients were sedentary.\n\nAt recruitment, we performed a frequency questionnaire consumption of foods rich in prebiotics and probiotics such as coffee, tea, garlic, onion, fermented foods, cacao, yoghurts and fruits. There were no differences between groups.\n\nNo patient reported alcohol consumption and none had a regular consumption of carob.\n\nMost of the patients of the three groups had a high level of anxiety, depression and stress but without statistically significant difference.\n\nThe result of the intervention after 1 month are in Table 2.\n\n**TABLE 2**\n\n| Unnamed: 0 | Diet only | Diet only.1 | Diet only.2 | Prebiotic | Prebiotic.1 | Prebiotic.2 | Probiotic | Probiotic.1 | Probiotic.2 |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| | T0 | T1 | p | T0 | T1 | p | T0 | T1 | p |\n| Weight (kg) | 103.7 | 101.2 | .001 | 103.5 | 101.6 | .003 | 106.09 | 104.4 | .02 |\n| Fat mass (kg) | 46.7 | 44.8 | .07 | 47.3 | 44.3 | .001 | 47.5 | 45.01 | .001 |\n| Lean mass (kg) | 54.06 | 53.5 | .3 | 55.1 | 55.6 | .2 | 55.5 | 56.4 | .08 |\n| Waist circumference (cm) | 119 | 117.3 | .01 | 124 | 120 | .03 | 122 | 119 | .001 |\n| Muscle strength (kg) | 24.4 | 24.3 | .8 | 27.4 | 28.8 | .008 | 24.8 | 26.5 | .004 |\n| Systolic blood pressure (mmHg) | 13 | 12.8 | .6 | 13 | 12.2 | .03 | 13.3 | 12.6 | .01 |\n| Fasting glucose (mmol/l) | 5.3 | 5.5 | .27 | 7.5 | 5.1 | .2 | 5.66 | 5.6 | .6 |\n| HbA1c (%) | 5.6 | 5.5 | .03 | 6.6 | 6.3 | .3 | 5.8 | 5.6 | .003 |\n| Insulin (μUI/l) | 18.4 | 15.2 | .07 | 23.8 | 14.5 | .002 | 17.5 | 13.7 | .005 |\n| HOMA‐IR | 4.3 | 3.8 | .2 | 9.1 | 3.8 | .009 | 4.5 | 3.4 | .009 |\n| Cholesterol (mmol/l) | 5.2 | 4.8 | .03 | 5.3 | 4.9 | .005 | 5.2 | 4.6 | .08 |\n| HDL (mmol/l) | 1.6 | 1.05 | .9 | 1.07 | 1.08 | .8 | 1.2 | 1.22 | .7 |\n| LDL (mmol/l) | 3.2 | 2.9 | .05 | 3.3 | 2.9 | .003 | 3.2 | 2.8 | .004 |\n| Triglycerides (mmol) | 1.7 | 1.6 | .4 | 1.9 | 1.4 | .001 | 1.6 | 1.4 | .03 |\n| ALAT (UI/l) | 21.2 | 18.4 | .03 | 21.3 | 20.8 | .7 | 21.1 | 17.8 | .01 |\n| Uric acid | 277.2 | 289.4 | .4 | 346.3 | 365.3 | .3 | 295.7 | 284.2 | .1 |\n| Epworth | 9.8 | 8.6 | .06 | 8.7 | 7 | .02 | 10.2 | 7.9 | .03 |\n| Anxiety | 13.3 | 11.2 | .02 | 11.4 | 9.4 | .01 | 13.3 | 11.6 | .06 |\n| depression | 12.4 | 9.9 | .001 | 11.2 | 8.06 | .01 | 11.5 | 8.9 | .001 |\n| Stress | 40.1 | 33.4 | .01 | 36.2 | 31.3 | .001 | 35.6 | 29.3 | .002 |\n\nThe results of anthropometric measurements after the intervention in the three groups showed a statistically significant decrease in weight, BMI and WC, but muscle strength has increase only with pre and probiotics.\n\nThe population has significantly decreased energy and macronutrient (protein, carbohydrate and lipid) intake, with a significant decrease in sugar and sodium intake.\n\nA significant increase in fibre intake was noted in the diet and prebiotic group but not in the probiotic group. The quality of sleep was not improved by the diet only and probiotics did not enhance anxiety.\n\nTaking probiotics was associated with the occurrence of diarrhoea in $20\\%$ of cases ($p \\leq .001$).\n\nThen we compared the diet alone versus prebiotics group for all the parameters listed in Table 3. The difference was not significant. Then it was the diet alone group versus probiotics and finally prebiotics versus probiotics.\n\n**TABLE 3**\n\n| Unnamed: 0 | Mean difference (T0–T1) | Mean difference (T0–T1).1 | p | Mean difference (T0–T1).2 | Mean difference (T0–T1).3 | p.1 | Mean difference (T0–T1).4 | Mean difference (T0–T1).5 | p.2 |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| | Diet only | Prebiotics | p | Diet only | Probiotics | p | Prebiotics | Probiotics | p |\n| Fasting glycaemia (mmol/l) | −0.18 | 1.6 | .016 | −0.18 | 0.06 | .02 | 1.6 | 0.06 | .3 |\n| HbA1c (%) | 0.12 a | 0.2 | .17 | 0.12 a | 0.18 a | .3 | 0.2 | 0.18 a | .4 |\n| Insulin (μUI/l) | 2.9 | 9.3 a | .03 | 2.9 | 3.8 a | .3 | 9.3 a | 3.8 a | .2 |\n| HOMA‐IR | 0.5 | 5.3 a | .012 | 0.5 | 1.04 a | .1 | 5.3 | 1.04 a | .2 |\n| Uric acid | −12.2 | −19.07 | .75 | −12.2 | 11.6 | .001 | −19.07 | 11.6 | .02 |\n\nOur conclusion is that the different therapeutic means are equal for the dietary survey, the different scores (stress, sleep, anxiety and depression).\n\nThe influence of the three means on weight loss is equivalent even if it is the diet alone group which reduced the weight more except for the lean mass which was clearly increased by probiotics compared to diet ($$p \\leq .05$$). On the other hand, significant differences between the three means were found in the results of the blood tests represented in Table 3.\n\nPrebiotics and probiotics were better than diet for the reduction of fasting glycemia and insulin resistance but probiotics did not lower uric acid as much as others.\n\n## DISCUSSION\n\nThis study was an interventional clinical trial designed to examine the effects of a combination of probiotic bacteria B. longum, L. helveticus, L. lactis, S. thermophilus and a prebiotic supplement by 30 g/day of carob on changes in body composition, metabolic biomarkers and psychological profile in obese human subjects enrolled on a weight loss program. The weight loss program was a low‐carbohydrate, energy‐restricted eating plan. The study has confirmed that a low‐carbohydrate, restricted‐energy diet can be effectively used for weight loss in obese individuals.\n\nOur work has some strength—to our knowledge in Tunisia no one studied the association between prebiotics or probiotics and obesity, the only Tunisian study that has worked on the microbiota has studied the imbalance of the microbiota in diabetic patients. 14 The use of carob as a prebiotic for weight loss is an innovation that fits into abandoned Tunisian habits. Carob is available at a nominal cost less than some fruits and vegetables. Our study focused on several parameters apart from anthropometry, such as biology and other assessment tests such as the Epworth score, the HAD and the Cungi stress score but it has some limitations like the small number of patients for each group and microbiological analysis for the gut microbia was not performed. In addition, the study was conducted over a month; perhaps a longer duration of intervention would show other results.\n\nMany studies have shown the effect of pre or probiotic on the weight loss. Sergeev et al., 15 compared the effect of symbiotic supplementation (prebiotic and probiotic) on the body composition of obese patients against a placebo group which received only a low‐calorie diet, they found a significant decrease in weight in both groups. However, the study of Hiel et al., 16 using inulin as prebiotic compared to placebo, found a significant reduction in weight in the prebiotic group. This difference may be due to the difference in the prescribed diet and also to the difference in the number of patients. In addition, the study by Stenman et al., 17 which is a study that compared the effect of prebiotic alone, probiotic alone and prebiotic+probiotic to a placebo group, found that only the probiotic alone group presented weight loss compared to the other groups. Some other studies did not found a difference between groups. 18, 19 This difference may be due to the difference in the diet given and also the type of prebiotic and probiotic used. Similarly, Rodriguez in their studies showed that there were responders and non‐responders in obese patients treated with prebiotics depending on the initial species of intestinal flora present in the host during the intervention. 20 Indeed, the microbiota intervenes in the regulation of energy expenditure by acting on specific hormones, thanks to a bidirectional signalling between the brain and the intestine, the gut microbiota regulates appetite and energy expenditure then follows a weight regulation. 21 Prebiotics act on the microbiota by increasing the production of short‐chain fatty acids, which in turn causes a cascade of modifications leading to weight reduction and improved metabolic parameters. 22 Our study showed a significant increase in muscle strength in both the prebiotic group and the probiotic group. As well as Zahao and Kang in their studies. 23, 24 Alteration of the gut microbiota has been shown to directly affect muscle strength. Probiotics, prebiotics and short‐chain fatty acids are potential new therapies to improve lean mass and physical performance. Strains of Lactobacillus and Bifidobacterium (present in Lactibiane*) can restore age‐related muscle loss. The pathways by which microbiota influence muscle are diverse and complex. 25 Our results showed a beneficial effect of prebiotics and probiotics on carbohydrate metabolism. These results were in agreement with the study conducted by Miller et al., 26 which found that the symbiotic yoghurt protected mice against diabetes by significantly improving fasting blood glucose levels versus unenriched control yoghurt. In addition, a preparation rich in fibre and lactulose as prebiotics used in an old clinical study, 27 showed a decrease in blood sugar in 10 obese patients.\n\nOral supplementation with prebiotics and probiotics acts on the regulation of glycaemia, the mechanism of action consists in reducing the secretion of inflammatory markers such as IFN‐γ and IL‐1β by increasing the production of IL‐10 anti‐inflammatory. In addition, probiotics stimulate the secretion of the neurotransmitter GABA which decreases the production of glucagon and stimulates the production of insulin. 28, 29 Our study showed a decrease in uric acid in the probiotic group with a significant difference compared to the diet‐alone group and the prebiotic group. To study the effect of probiotics on uric acid, there was first the pilot study of Garcia‐Arroyo carried out in 2018 on six rats which affirmed this hypothesis. 30 Then other studies followed with the same results. 31, 32 The decrease in energy intake found after prebiotic and probiotic supplementation is explained by the stimulation of leptin secretion and the decrease in ghrelin secretion, which increase satiety and consequently decrease in intake. In addition, the reduction of microbiome lipopolysaccharides by pre and probiotics promotes reduced appetite by increasing satiety. 33 A decrease in Epworth score was found in all three groups. Our study was consistent with others. 34, 35 However, the study by Buigues et al. 36 did not show conclusive results of prebiotics on sleep quality. Following the fermentation of fibres from prebiotics by microbiota, there will be production of butyrate which improves sleep quality 37 but the mechanisms involved are more complex than that. 38\n\nThe three means were comparable in their influence on depression and anxiety. Other studies proved a good improvement of these symptoms when patients took probiotic. 39, 40 It has been shown that probiotics stimulate the production of inhibitory neurotransmitters such as the neurotransmitter GABA, which causes a reduction in anxiety and depression. 41 On the other hand, the imbalance of the gut microbiota is responsible for the occurrence of depression by the decrease in the production of some lipid metabolites (endogenous cannabinoids). 42 As for the stress, prebiotics and probiotics increase the production of serotonin, which is a molecule involved in mood regulation, by stimulating the synthesis of tryptophan 43 which improves the symptoms of stress.\n\n## CONCLUSION\n\nThe imbalance in the functioning of the body is due on the one hand to the imbalance of the gut microbiota because of obesity which alters the beneficial microorganisms and on the other hand this alteration which further promotes obesity by several mechanisms and signalling pathways. 44 The intestinal microbiota, as it is called the second brain, intervenes in the regulation of the functioning of the organism, which has been demonstrated by several studies. Hence the importance of modulating the gut microbiota with prebiotics and probiotics to treat obesity and improve related metabolic parameters.\n\nIn the light of this study and other studies, it is advisable to take certain measures to treat obesity: *Follow a* diet balanced in energy intake to prevent the alteration of the gut microbiota. Enrich the diet with foods rich in prebiotics and probiotics, either to prevent the onset of obesity or to treat it. Treatment with pre and probiotics should be considered in case of sarcopenic obesity. Adopt treatment with prebiotics and probiotics, especially if obesity is linked to a glycaemic disorder. Prescription of prebiotics and probiotics can Improve the quality of sleep, anxiety and stress in some cases.\n\n## AUTHOR CONTRIBUTIONS\n\nNadia Ben Amor: Visualization (equal). Faten Mahjoub: Visualization (equal). Olfa Berriche: Visualization (equal). Chaima El Ghali: Investigation (equal). Amel Gamoudi: Project administration (equal). Henda Jamoussi: Writing – review and editing (equal).\n\n## FUNDING INFORMATION\n\nThis research received no funding.\n\n## CONFLICT OF INTEREST\n\nThe authors declare no conflict of interest.\n\n## DATA AVAILABILITY STATEMENT\n\nData sharing is not applicable to this article as no new data were created or analyzed in this study.\n\n## References\n\n1. **Obesity: preventing and managing the global epidemic. Report of a WHO consultation**. *World Health Organ Tech Rep Ser* (2000.0) **894** 1-253\n\n2. **Rapport de l'enquête national THES‐2016 ‐ Ministère de la santé publique [Internet]. [cité 6 sept 2022]. Disponible sur**\n\n3. Dao MC, Clément K. **Gut microbiota and obesity: concepts relevant to clinical care**. *Eur J Intern Med* (2018.0) **48** 18-24. PMID: 29110901\n\n4. Brahe LK, Astrup A, Larsen LH. **Can we prevent obesity‐related metabolic diseases by dietary modulation of the gut microbiota?**. *Adv Nutr* (2016.0) **7** 90-101. PMID: 26773017\n\n5. 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differences being more pronounced in diabetic individuals.\n\n### Introduction\n\nType 2 diabetes mellitus (T2DM) is among the world's top 10 leading causes of death. Additionally, prediabetes is a major risk factor for diabetes. Identifying diabetes co‐occurring disorders can aid in reducing adverse effects and facilitating early detection. In this study, we evaluated dyslipidaemia, metabolic syndrome (MetS), and liver enzyme levels in pre‐diabetic and T2DM patients in the Persian cohort compared to a control group.\n\n### Materials and Methods\n\nIn this cross‐sectional study, 2259 pre‐diabetes, 1664 T2DM and 5840 controls (35–70 years) who were selected from the Hoveyzeh cohort centre were examined. Body mass index, blood pressure, fasting blood glucose (FBG), total cholesterol (TC), high‐density lipoprotein cholesterol (HDL‐C), triglyceride (TG) and liver enzymes: γ‐glutamyltransferase (GGT), alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were determined using the standard protocols. MetS subjects were also identified based on the National Cholesterol Education Program guidelines.\n\n### Results\n\nPrediabetes and T2MD were closely correlated with the lipid profile, MetS, and liver enzymes (ALT, GGT, ALT/AST). MetS increases the risk of T2DM by 12.45 [$95\\%$ CI: 10.88–14.24] fold, while an increase in ALT/AST ratio increases the risk of T2DM by 3.68 [$95\\%$ CI: 3.159–4.154] fold. ROC curve analysis also revealed the diagnostic roles of GGT, ALT, AST and the ALT/AST ratio among pre‐diabetics, diabetics and the control group. The GGT level corresponds to the highest AUCs (0.685) with the highest sensitivity ($70.25\\%$).\n\n### Conclusions\n\nOur results indicated a significant increase in liver enzymes, lipid profile and MetS status in both pre‐diabetic and T2MD subjects, with the differences being more pronounced in diabetic individuals. Consequently, on the one hand, these variables may be considered predictive risk factors for diabetes, and on the other hand, they may be used as diagnostic factors. In order to confirm the clinical applications of these variables, additional research is required.\n\n## INTRODUCTION\n\nDiabetes mellitus, as a metabolic disorder, 1 is one of the most prevalent global public health issues 2 and contributes to a rise in morbidity and mortality. 3 According to estimates from the International Diabetes Federation (IDF), 1 in 11 individuals between the ages of 20 and 79 had type 2 diabetes mellitus (T2DM) in 2015, 4 which could reach 629 million by 2045. 2 *Diabetes is* hyperglycaemia resulting from insulin deficiency, insulin resistance or both. 1, 3 *Prediabetes is* a major diabetes risk factor. 2 *It is* a hyperglycaemic condition marked by impaired fasting glucose (IFG), impaired glucose tolerance (IGT) or glycated haemoglobin (A1C) of $6.0\\%$–$6.4\\%$, or a combination of these. 1, 2 *Both dyslipidaemia* and hypertension are significant risk factors for T2DM. According to the American Diabetes Association, patients with T2DM who have dysregulated levels of lipids such as total cholesterol, triglycerides, very‐low‐density lipoprotein (VLDL), low‐density lipoprotein (LDL) and high‐density lipoprotein (HDL) are diagnosed with diabetic dyslipidaemia. Alternatively, lipid markers may be a useful predictor of risk in diabetic patients. 5 In addition, prediabetes and T2DM are common metabolic syndrome (MetS) manifestations. 1 Some studies indicate that individuals with metabolic syndrome are four times more likely to develop T2DM. 6 MetS are characterized by hypertriglyceridemia, low HDL cholesterol, abdominal obesity or a high BMI ratio, glucose intolerance or insulin resistance, hypertension and microalbuminuria. 7\n\nInsulin resistance syndrome may result in hepatic dysfunction, resulting in T2DM. 6 Therefore, patients with advanced liver disease have a higher incidence of diabetes than the general population. 8 Conversely, releasing free fatty acids (FFAs) due to T2DM decreases hepatic mitochondrial function. In turn, this causes further triglyceride storage in the hepatocyte and, ultimately, liver damage. 8 Serum levels of liver enzymes, such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), and to a lesser extent γ‐glutamyltransferase (GGT), are frequently used as indicators of liver damage. 9 In the past decade, several studies have linked serum concentrations of these enzymes to multiple metabolic syndrome symptoms, including hepatic insulin resistance, T2DM and dyslipidaemia. 9, 10, 11 Since then, little research has been conducted on the relationship between dyslipidaemia, metabolic syndrome and liver enzyme levels in pre‐diabetic and T2DM patients. In order to determine the relationship between these risk factors and the development of prediabetes and diabetes in the adult population of Hoveyzeh cohort centre, this study was conducted on three groups: healthy, pre‐diabetic and T2DM.\n\n## MATERIALS AND METHODS\n\nWe conducted a cross‐sectional study in men and women aged 35–70 who underwent a comprehensive health screening exam at the Hoveyzeh cohort centre for Prospective Epidemiological Research Studies in Iran (PERSIAN), a region in Iran's Southwest Khuzestan province, between 1 May 2016 and 31 August 2018. Therefore, 10,009 people were recruited in Hoveyzeh cohort centre. Patients with any of the following conditions at baseline were excluded from the study: a history of cancer, renal failure, known liver disease, ALT>3 times normal, alcohol consumption, recent (1 year) MI, acute coronary syndrome, stroke and weight loss of more than 5 kg in a month as well as microvascular complications. Finally, 9763 of 10,009 cases had the criteria for this study. All participants then completed questionnaires, including demographic information, cigarette smoking, opium use, consumable drugs, disease history and physical activity.\n\nFirst, blood samples for analysis were obtained from the antecubital vein of patients and subjects who had fasted for 10 to 12 h. In the central laboratory of the Hoveyzeh cohort centre, all biochemical parameters were measured using standardized protocols on automated equipment.\n\nFasting serum glucose was assayed using the hexokinase/glucose‐6‐phosphate dehydrogenase method. Diabetes was defined as FBS levels ≥126 mg/dl or receiving anti‐diabetic drugs or self‐reported diagnosis of diabetes.\n\nStandard enzymatic colorimetric techniques were used to measure serum total cholesterol (TC), triacylglycerol (TG) and high‐density lipoprotein cholesterol (HDL‐C) levels. The level of low‐density lipoprotein cholesterol (LDL‐C) was determined using the Friedewald et al. formula (LDL‐C = TC ‐ HDL ‐ VLDL cholesterol). 9 The levels of AST, ALT and GGT were determined using the International Federation of Clinical Chemistry's method. All these analyses were done using commercial kits (Pars Azmon Inc.).\n\nMetS is defined by three or more of the following National Cholesterol Education Program criteria: high TG (≥150 mg/dl); low HDL‐C (≤40 mg/dl) for men and <50 for women; high fasting blood sugar (≥100 mg/dl) or known type 2 diabetes; hypertension (at least $\\frac{135}{85}$ mmHg or receiving antihypertensive medication); and a waist circumference greater than 102 cm for men and 88 for women. 6, 12, 13\n\n## Statistical analysis\n\nThe statistical analyses were conducted using SPSS (v. 15.0). For quantitative variables, data were presented as mean ± standard deviation; for qualitative variables, data are expressed as frequency (number (%)), The normality of data was determined using the Kolmogorov–Smirnov test, and the chi‐square test was used to determine the association between qualitative variables.\n\nDifferences between the two groups were calculated by Mann–Whitney tests for skewed data. In addition, the Kruskal–Wallis test was used to compare variables in three groups.\n\nMoreover, logistic regression analysis was employed to calculate studied risk factors for prediabetes and diabetes vs. control group. Then, multivariable model was performed for adjusting of age, gender and BMI. Receiver operating characteristic (ROC) curve analysis was used to determine the prognostic relationship of liver enzymes and lipid profile in prediabetes and diabetes. All p‐values were two‐tailed, and $p \\leq .05$ were considered statistically significant.\n\n## Characteristics of the study participants according to FBS tertiles\n\nThe final database contained 9763 subjects (3809 males and 5954 females); subjects were divided into three groups based on FBS levels. Table 1 illustrates the characteristics of three distinct groups. T2DM prevalence was $17.0\\%$ ($18.1\\%$ in males and $16.4\\%$ in females), prediabetes prevalence was $23.1\\%$ ($21.0\\%$ in males and $24.5\\%$ in females), and control prevalence was $59.8\\%$ ($60.9\\%$ in males, $59.1\\%$ in females). Participants with prediabetes and T2DM were older and had a higher BMI, waist circumference, diastolic blood pressure (DBP) and systolic blood pressure (SBP) than control subjects.\n\n**TABLE 1**\n\n| Variables | Fasting glucose (mg/dl) | Fasting glucose (mg/dl).1 | Fasting glucose (mg/dl).2 | Fasting glucose (mg/dl).3 |\n| --- | --- | --- | --- | --- |\n| Variables | FBS ≤100 5840 (59.82%) | FBS: 100–125 2259 (23.14%) | FBS≥126 1664 (17.04%) | p‐Value |\n| Anthropometrics | Anthropometrics | Anthropometrics | Anthropometrics | Anthropometrics |\n| Gender | Gender | Gender | Gender | Gender |\n| Male | 2321 (60.9%) A | 799 (21.0%) B | 689 (18.1%) C | <.0001** |\n| Female | 3519 (59.1%) A | 1460 (24.5%) B | 975 (16.4%) C | <.0001** |\n| Age (year) | 47.03 ± 8.79 A | 50.53 ± 9.27 B | 52.81 ± 8.89 C | <.0001* |\n| Waist Circumference (cm) | 97.62 ± 11.83 A | 102.45 ± 12.09 B | 103.15 ± 11.46 B | <.0001* |\n| BMI (kg/m2) | 28.14 ± 5.15 A | 30.03 ± 5.52 B | 29.63 ± 5.23 C | <.0001* |\n| Diastolic blood pressure (mmHg) | 70.32 ± 10.97 A | 72.60 ± 11.38 B | 73.16 ± 11.51 B | <.0001* |\n| Systolic blood pressure (mmHg) | 110.46 ± 16.95A | 115.49 ± 18.90 B | 118.27 ± 20.19 C | <.0001* |\n| Metabolic syndrome | Metabolic syndrome | Metabolic syndrome | Metabolic syndrome | Metabolic syndrome |\n| No | 4409 (75.5%) A | 630 (27.9%) B | 330 (19.8%) C | <.0001** |\n| Yes | 1413 (24.5%) A | 1629 (72.1) B | 1334 (80.2%) C | <.0001** |\n| Biochemicals | Biochemicals | Biochemicals | Biochemicals | Biochemicals |\n| FBS (mg/dl) | 88.97 ± 6.54 A | 108.25 ± 6.96 B | 201.48 ± 67.98 C | <.0001* |\n| LDL (mg/dl) | 105.62 ± 31.17A | 109.51 ± 33.83A | 106.86 ± 37.33B | <.0001* |\n| TG (mg/dl) | 147.6 ± 84.2 A | 170.06 ± 107.3 B | 202.02 ± 135.4 C | <.0001* |\n| Total Cholesterol (mg/dl) | 185.76 ± 37.1 A | 193.97 ± 40.8 B | 196.34 ± 47.9 B | <.0001* |\n| HDL (mg/dl) | 50.68 ± 12.24 A | 50.34 ± 11.75 A | 49.21 ± 11.61 B | <.0001* |\n| Hepatic enzymes | Hepatic enzymes | Hepatic enzymes | Hepatic enzymes | Hepatic enzymes |\n| AST (units/L) | 18.06 ± 7.62 A | 19.30 ± 9.19 A | 17.36 ± 9.05 B | <.0001* |\n| ALT (units/L) | 20.52 ± 13.72 A | 22.03 ± 14.88 B | 22.25 ± 13.35 C | <.0001* |\n| GGT (units/L) | 24.14 ± 16.61 A | 27.05 ± 17.68 B | 34.73 ± 34.01 C | <.0001* |\n| ALT/AST | 1.06 ± 0.384 A | 1.10 ± 0.380 B | 1.28 ± 0.514 C | <.0001* |\n\nIn prediabetes and T2DM, biochemical variables, including TG, were significantly higher than in the control group. Compared to the control group, prediabetes and diabetes had significantly higher mean total cholesterol levels, whereas there was no significant difference between prediabetes and diabetes. In addition, the mean LDL in diabetes and normal groups was significantly higher than in the prediabetes group, but there was no significant difference between diabetes and normal groups. In contrast, the HDL level was significantly lower in T2DM compared to prediabetes and the control group, whereas there was no significant difference between prediabetes and the control group.\n\nThose who developed prediabetes and T2DM had significantly higher levels of hepatic enzymes, including GTT and ALT, compared to the control group. In contrast, the mean AST was significantly lower in T2DM than in prediabetes and the control group, and there was no significant difference between prediabetes and the control group (Table 1).\n\n## ROC curve analysis\n\nReceiver operating characteristic curve analysis revealed the significance of GGT, ALT, AST and the ALT/AST ratio in identifying prediabetes or diabetes (Table 2, Figure 1). The ROC curve analysis is presented in Table 2.\n\nRoc curve analysis of GTT, ALT, AST and ALT/AST in diabetes vs. control group was as follows: AUC = 0.685; ($95\\%$ CI: 0.673–0.694; $p \\leq .0001$; Cut‐off value: >21.36; Sensitivity: $70.25\\%$; Specificity: $57.76\\%$), AUC = 0.564; ($95\\%$ CI: 0.553–0.575; $p \\leq .0001$; Cut‐off value: >14; Sensitivity: $70.85\\%$; Specificity: $39.88\\%$), AUC = 0.588; ($95\\%$ CI: 0.577–0.600; $p \\leq .0001$; Cut‐off value: <14; Sensitivity: $43.33\\%$; Specificity: $71.64\\%$), AUC = 0.669; ($95\\%$ CI: 0.658–0.679; $p \\leq .0001$; Cut‐off value: >1.06; Sensitivity: $68.87\\%$; Specificity: $57.29\\%$), respectively. Similar results were also observed in the case of prediabetes vs. control group, including: AUC = 0.573; ($95\\%$ CI: 0.562–0.583; $p \\leq .0001$; Cut‐off value: >20.33; Sensitivity: $57.10\\%$; Specificity: $53.89\\%$), AUC = 0.537; ($95\\%$ CI: 0.526–0.548; $p \\leq .0001$; Cut‐off value:>15; Sensitivity: $60.69\\%$; Specificity: $45.26\\%$), AUC = 0.513; ($95\\%$ CI: 0.526–0.548; $$p \\leq .082$$; Cut‐off value: >25; Sensitivity: $14.52\\%$; Specificity: $88.48\\%$), AUC = 0.542; ($95\\%$ CI: 0.531–0.553; $p \\leq .0001$; Cut‐off value: >0.95; Sensitivity: $61.66\\%$; Specificity: $46.25\\%$).\n\n## Logistic regression analysis\n\nAccording to logistic regression analysis, some liver enzymes, lipid profiles and metabolic syndrome were associated with an increased odds of developing prediabetes or diabetes (Table 3). The estimated ORs for metabolic syndrome in the prediabetes and diabetes groups were 7.966 ($95\\%$ CI: 7.139–8.889; $p \\leq .0001$) and 12.45 ($95\\%$ CI: 10.88–14.24; $p \\leq .0001$), respectively. In the case of AST, however, the odds ratio (0.976) indicated a reduction in diabetes odds ($95\\%$ CI: 0.968–0.984; $p \\leq .0001$). On the contrary, the ALT/AST ratio increases the odds of prediabetes and diabetes development by 1.347 ($95\\%$ CI: 0.968–0.984; $p \\leq .0001$) and 3.623 ($95\\%$ CI: 3.159–4.154; $p \\leq .0001$, respectively). After the adjustment for age, sex and BMI, there was almost no difference with the results obtained from univariate analysis. However, both analyses display significantly positive relationships between ALT/AST ratio and metabolic syndrome with prediabetes and diabetes.\n\n**TABLE 3**\n\n| Prediabetes | Prediabetes.1 | Prediabetes.2 | Prediabetes.3 | Diabetes | Diabetes.1 | Diabetes.2 |\n| --- | --- | --- | --- | --- | --- | --- |\n| Variable | Odds Ratios | 95% CI | p‐Value | Odds Ratios | 95% CI | p‐Value |\n| model 1 a | model 1 a | model 1 a | model 1 a | model 1 a | model 1 a | model 1 a |\n| GGT | 1.009 | 1.006–1.012 | <.0001 | 1.024 | 1.021–1.027 | <.0001 |\n| AST | 1.010 | 1.004–1.016 | <.0001 | 0.976 | 0.968–0.984 | <.0001 |\n| ALT | 1.007 | 1.004–1.010 | <.0001 | 1.008 | 1.004–1.012 | <.0001 |\n| ALT/AST | 1.347 | 1.190–1.525 | <.0001 | 3.623 | 3.159–4.154 | <.0001 |\n| LDL | 1.003 | 1.002–1.005 | <.0001 | 1.001 | 0.999–1.002 | <.0001 |\n| HDL | 0.997 | 0.993–1.001 | <.0001 | 0.989 | 0.984–0.993 | <.0001 |\n| TG | 1.002 | 1.001–1.002 | <.0001 | 1.005 | 1.004–1.005 | <.0001 |\n| TC | 1.005 | 1.004–1.006 | <.0001 | 1.006 | 1.005–1.007 | <.0001 |\n| MetS | 7.966 | 7.139–8.889 | <.0001 | 12.45 | 10.88–14.24 | <.0001 |\n| model 2 b | model 2 b | model 2 b | model 2 b | model 2 b | model 2 b | model 2 b |\n| GGT | 1.010 | 1.007–1.013 | <.0001 | 1.024 | 1.021–1.027 | <.0001 |\n| AST | 1.015 | 1.009–1.021 | <.0001 | 0.979 | 0.970–0.988 | <.0001 |\n| ALT | 1.012 | 1.009–1.016 | <.0001 | 1.014 | 1.010–1.018 | <.0001 |\n| ALT/AST | 1.595 | 1.382–1.842 | <.0001 | 5.632 | 4.776–6.640 | <.0001 |\n| LDL | 1.002 | 1.000–1.003 | .040 | 0.999 | 0.997–1.000 | .151 |\n| HDL | 0.996 | 0.992–1.001 | .087 | 0.988 | 0.983–0.993 | <.0001 |\n| TG | 1.002 | 1.002–1.003 | <.0001 | 1.005 | 1.004–1.006 | <.0001 |\n| TC | 1.004 | 1.002–1.005 | <.0001 | 1.005 | 1.003–1.006 | <.0001 |\n| MetS | 6.833 | 6.100–7.654 | <.0001 | 10.67 | 9.268–12.28 | <.0001 |\n\n## DISCUSSION\n\nIn the current study, we observed a significant increase in all metabolic risk factors and liver enzymes, except for HDL‐C and AST, in both prediabetic and T2MD subjects, with the differences being more pronounced in diabetic individuals.\n\nIn subjects with prediabetes and T2DM, the mean LDL, TG and TC levels were higher. Consistent with these findings, Dhoj et al. 14 demonstrated that diabetes is associated with a high prevalence of dyslipidaemia characterized by elevated levels of cholesterol, TG and LDL. Additionally, Jasim et al. 5 identified TG as one of the promising biomarkers for predicting prediabetes and T2DM. These findings support that diabetes patients are more susceptible to co‐occurring diseases such as hyperglycaemia, chronic renal failure, hypothyroidism and polypharmacy, with drugs known to have adverse effects on lipid profiles. Patients with diabetes must therefore be treated to prevent coronary artery disease. 15 Individual metabolic syndrome characteristics (such as higher BMI, waist circumference, DBP and SBP levels, among others) were associated with the prevalence of prediabetes and T2DM, according to the findings of this study. Thus, $80\\%$ of subjects with T2DM and $72\\%$ in the prediabetes group had MetS, whereas only $24\\%$ of the control group exhibited metabolic syndrome symptoms. In addition, Ogedengbe et al. 16 found that the prevalence of MetS among T2DM patients is extremely high.\n\nThis study revealed that liver enzymes, including ALT and GGT but not AST, and the ALT/AST ratio were significantly elevated in prediabetes and T2MD cases. However, some studies have found no correlation between elevated ALT and diabetes, possibly due to the ethnic diversity of the study populations. 6 Forlani et al. 17 reported a high prevalence of elevated ALT, AST and GGT levels in T2DM, which is consistent with our findings. Although there are no clear biological explanations for the relationships between liver indicators and glucose metabolism, one possible mechanism is that MetS and T2DM increase the risk of liver damage, increasing liver enzyme levels. 9 To reduce the risk of liver damage, prediabetics and diabetic patients may require a comprehensive clinical, laboratory and histological examination.\n\nIn addition, GGT, ALT and the ALT/AST ratio, but not AST, can be used to identify prediabetes and diabetes based on ROC results. Among prediabetic and diabetic subjects, the GGT level has the highest areas under the curve (AUC) and the highest sensitivity compared to the control group. In contrast, logistic regression analysis revealed that higher levels of ALT, GGT and ALT/AST were independent risk factors for prediabetics and diabetics and that an increase in the ALT/AST ratio increased the risk of T2MD by 3.68‐fold, whereas lower AST levels were associated with the risk of diabetes. Sun‐Hye et al. 18 observed that higher levels of GGT and ALT and a lower AST/ALT ratio were independent risk factors for diabetes and impaired fasting glucose (IFG). Additionally, Zhao et al. 19 evidenced that the ALT/AST ratio may be a useful indicator of insulin resistance (IR) in the Chinese population.\n\nAccording to several studies, elevated GGT and ALT levels are also beneficial for identifying early markers of dysregulated glucose metabolism, which strongly correlate with prediabetes and diabetes. 20 A second proposed mechanism for the relationship between hepatic indices and glucose metabolism is that elevated serum ALT and GGT levels indicate hepatic steatosis, resulting in hepatic insulin resistance (IR). 18 IR is a risk factor for T2DM. 19 Therefore, it is unknown whether T2DM increases liver enzyme levels or whether elevated liver enzyme levels increase the risk of developing T2DM. Therefore, additional research is required to clarify these theories.\n\nIn contrast to our findings, some studies have found that elevated GGT levels, but not ALT or AST, can be used to predict the onset of T2DM. 9 Sattar et al. 21 also demonstrated that elevated ALT levels within the ‘normal’ range predict diabetes independently of elevated AST levels. Although we did not examine the role of gender in transaminase levels in this study, a possible explanation for these contradictory findings may be that transaminase levels are gender‐specific, according to the findings of some studies. 22 Consequently, it appears that using the ratio of variables, such as ALT/AST, rather than each variable individually may be more effective in evaluating diabetes patients.\n\n## CONCLUSION\n\nOur results indicated a significant increase in liver enzymes except AST, lipid profile except HDL‐C, and MetS status in both prediabetic and T2MD subjects, with the differences being more pronounced in diabetic individuals. On the one hand, these variables or their ratio may be considered predictive risk factors for diabetes, and on the other hand, they may be utilized as diagnostic factors. However, it is unknown whether T2DM increases liver enzyme levels or whether elevated liver enzyme levels increase the incidence of T2DM, and the pathophysiologic pathways underlying this association are unclear. Therefore, additional research is required to clarify these theories and validate their clinical applications.\n\n## AUTHOR CONTRIBUTIONS\n\nN. M. designed and supervised the study. N. D. wrote the paper. S. SP., Z. R. and B. C. analysed data. All authors read and approved the final manuscript.\n\n## CONFLICT OF INTEREST\n\nThe authors declare no conflict of interest.\n\n## ETHICAL APPROVAL\n\nThis study was approved by the Ethics Committee of Ahvaz Jundishapur University of Medical Sciences (Ethical code: IR. AJUMS. REC.1398.455), and the informed consent was taken from all patients who participated in Hoveyzeh Cohort.\n\n## DATA AVAILABILITY STATEMENT\n\nData will be made available on request.\n\n## References\n\n1. 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Buday B, Pach PF, Literati‐Nagy B. **Sex influenced association of directly measured insulin sensitivity and serum transaminase levels: why alanine aminotransferase only predicts cardiovascular risk in men?**. *Cardiovasc Diabetol* (2015) **14** 1-13. 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0.0010965083492919803\n]","isConversational":false}}},{"rowIdx":65,"cells":{"text":{"kind":"string","value":"---\ntitle: Additional Active Movements Are Not Required for Strength Gains in the Untrained\n during Short-Term Whole-Body Electromyostimulation Training\nauthors:\n- Holger Stephan\n- Udo Frank Wehmeier\n- Tim Förster\n- Fabian Tomschi\n- Thomas Hilberg\njournal: Healthcare\nyear: 2023\npmcid: PMC10000632\ndoi: 10.3390/healthcare11050741\nlicense: CC BY 4.0\n---\n\n# Additional Active Movements Are Not Required for Strength Gains in the Untrained during Short-Term Whole-Body Electromyostimulation Training\n\n## Abstract\n\nRecommendations for conventional strength training are well described, and the volume of research on whole-body electromyostimulation training (WB-EMS) is growing. The aim of the present study was to investigate whether active exercise movements during stimulation have a positive effect on strength gains. A total of 30 inactive subjects (28 completed the study) were randomly allocated into two training groups, the upper body group (UBG) and the lower body group (LBG). In the UBG ($$n = 15$$; age: 32 (25–36); body mass: 78.3 kg (53.1–114.3 kg)), WB-EMS was accompanied by exercise movements of the upper body and in the LBG ($$n = 13$$; age: 26 (20–35); body mass: 67.2 kg (47.4–100.3 kg)) by exercise movements of the lower body. Therefore, UBG served as a control when lower body strength was considered, and LBG served as a control when upper body strength was considered. Trunk exercises were performed under the same conditions in both groups. During the 20-min sessions, 12 repetitions were performed per exercise. In both groups, stimulation was performed with 350 μs wide square pulses at 85 Hz in biphasic mode, and stimulation intensity was 6–8 (scale 1–10). Isometric maximum strength was measured before and after the training (6 weeks set; one session/week) on 6 exercises for the upper body and 4 for the lower body. Isometric maximum strength was significantly higher after the EMS training in both groups in most test positions (UBG $p \\leq 0.001$–0.031, $r = 0.88$–0.56; LBG $$p \\leq 0.001$$–0.039, $r = 0.88$–0.57). Only for the left leg extension in the UBG ($$p \\leq 0.100$$, $r = 0.43$) and for the biceps curl in the LBG ($$p \\leq 0.221$$, $r = 0.34$) no changes were observed. Both groups showed similar absolute strength changes after EMS training. Body mass adjusted strength for the left arm pull increased more in the LBG group ($$p \\leq 0.040$$, $r = 0.39$). Based on our results we conclude that concurring exercise movements during a short-term WB-EMS training period have no substantial influence on strength gains. People with health restrictions, beginners with no experience in strength training and people returning to training might be particularly suitable target groups, due to the low training effort. Supposedly, exercise movements become more relevant when initial adaptations to training are exhausted.\n\n## 1. Introduction\n\nWhole-body electromyostimulation (WB-EMS) is a training method that can complement or to some extent replace traditional resistance training, as it can be used alone, superimposed, or combined (different training time points). Since several electrodes are used [1], different muscles can be stimulated at the same time [2]. Strength improvements can be achieved with both high-intensity resistance training and WB-EMS [3]. Previous studies have shown that WB-EMS is applicable in healthy people [4] and also in patients, e.g., in people suffering from Parkinson [5] or sarcopenic obesity [6]. In conventional resistance training, the one repetition maximum (1-RM) is used to describe the training intensity [7]. Since it represents the maximal voluntary contraction, a comparison between electromyostimulation (EMS) and normal contraction is possible [8]. A low-cost way to determine the intensity of strength training is to capture the perceived exertion using the Borg scale [9], which is also used in WB-EMS training [10,11]. In contrast to the 1-RM, where voluntary force production under external load is recorded, the perceived exertion reflects the internal load. For beginners in conventional strength training, at least 2 training sessions per week are recommended. Both multi-joint and single-joint exercises can be performed, using a variety of equipment and the own body weight. Per set, 8 to 12 repetitions should be completed at 60–$70\\%$ of the repetition maximum [12]. To provide a safe and effective application of WB-EMS, guidelines recommend restricting the duration of one session to a maximum of 20 min. Moreover, the frequency should be limited to one session a week for at least the first eight weeks or a minimum interval of four days should be maintained thereafter [13]. Perceived exertion should be rated approximately as “hard” to “hard+” (lower during initial training) [13], corresponding to 5 to 6 on the Borg CR 10 scale [14]. Nevertheless, in some trials, the training frequencies were higher [2,15], and sometimes lower with one session a week [16,17] compared to the aforementioned recommendation after familiarization. The aggregated training stimulus consists of the number of sessions a week and the length of the training period. Usually, eight sessions or more have been conducted in strength related WB-EMS studies with healthy subjects [10,18]. Early strength improvements due to strength training can be attributed mainly to neural factors. From the third to fifth week on, strength development is mainly caused by hypertrophy [19]. Increases after very few sessions (as seen after three training sessions) are supposedly attributable to lower antagonist activity or motoric improvements of synergists [20]. Elgueta-Cancino and colleagues [21] elicited less inhibitory activity in the cortex, higher corticospinal excitability, and altered motor unit activation as assumed mechanisms of initial strength gain. Muscle growth and strength gain can also be achieved by compact training (eight weeks with three sessions a week) with neuromuscular electrical stimulation [22]. Similar to conventional strength training early strength gains owing to EMS-training are achieved without muscle growth [23].\n\nThe body of research on WB-EMS training is growing [24]. EMS can be superimposed on maximum or sub-maximum voluntary dynamic or isometric contractions or applied without any concomitant voluntary contraction. Nevertheless, little is known about the importance of active exercise movements during stimulation. Strength gains due to EMS with exercise movements were previously shown [25,26] and some authors addressed the impact of EMS superimposed on intense strength training [27,28]. To our knowledge, only Kemmler and colleagues [29] investigated the effects of smaller, WB-EMS accompanying movements. In this randomized controlled trial (RCT), participants trained once a week for 12 weeks. However, only older females with little muscle mass were included for the comparison between dynamic use (movements during stimulation) and passive use (only isometric contractions during stimulation) limiting the generalizability of the results obtained. Therefore, the present study aims to investigate whether active exercise movements during stimulation have a positive effect on strength gains of selected upper and lower body muscles in young healthy subjects of both sexes in training sessions using mobile, easily accessible fitness equipment, or the own body mass. We hypothesized that WB-EMS combined with concurrent exercise movements will result in higher strength gains than WB-EMS alone. Hence, this study was designed to clarify whether movement sequences are necessary for strength gains during WB-EMS or, whether the electrostimulation alone induces strength gains. The results might help fitness professionals and EMS-users to optimize recommendations for WB-EMS training depending on individual goals and requirements.\n\n## 2.1. Subjects\n\nThe number of subjects to be included in the study was determined using an a priori sample size calculation for statistical comparison of the means of two unpaired groups (using the program GPower 3.1) based on the mean of the effect sizes (Δ strength leg extension: $d = 1.67$; Δ strength leg flexion: $d = 0.79$) reported by Kemmler and colleagues [29]. This study is similar to the present study. A predefined lower limit of statistical power of $80\\%$ and anα error probability of 0.05 were assumed. A dropout rate of $20\\%$ was further added. Based on the results of this calculation, a total of 30 subjects were initially recruited for participation. Subjects were included when being aged between 20 and 40 years and having abstained from physical activity for at least six months prior to the start of the study. Access was possible for both sexes. Subjects were excluded when acute injuries or physical complaints were reported or when contraindications as listed by Kemmler and colleagues [30] or Stöllberger and Finsterer [31] were present (e.g., epilepsy, bleeding disorders). No other exclusion criteria were defined (e.g., BMI, VO2max). The study was conducted in accordance with the principles of the Declaration of Helsinki [32] and approved by the ethics committee of the University of Wuppertal (MS/BBL 200114 Wehmeier). All subjects signed a written consent to participate in the study.\n\n## 2.2. Experimental Design\n\nThe procedure was based on a randomized controlled trial design (Figure 1). Subjects were randomly assigned to two training groups (with the program RandList 1.2), with the number of subjects in both groups being equal. In the upper body group (UBG), WB-EMS was accompanied by exercise movements of the upper body only and in the lower body group (LBG) by exercise movements of the lower body only. Therefore, the UBG served as a control when lower body strength is considered, and the LBG served as a control when upper body strength is considered. With this design, WB-EMS without exercises and WB-EMS with exercises could be compared. Intervention duration was set to six weeks, training frequency to one session/week, and the duration of the training session to 20 min. Before and after the training period, maximum force was determined during various exercises. Blinding of subjects was not possible because the intervention is identifiable. Blinding of the investigator was not applicable because the training instructions and the test instructions were given by the same person, a professional EMS trainer with a bachelor’s degree in sports science. Subjects were asked to maintain their dietary habits and to keep their physical activity levels constant, which also meant avoiding additional physical activity. All interventions and measurements were conducted in an EMS studio (go!Orange—Studio für EMS, Remscheid, Germany).\n\n## 2.3. WB-EMS Procedure\n\nBoth the UBG and the LBG received the same WB-EMS application (miha bodytec II; miha bodytec GmbH, Gersthofen, Germany) once a week. Subjects wore thin tight-fitting underwear. The vest with wetted electrodes was placed on the upper body and the wetted electrode bands on the arms, buttocks, and legs (miha bodytec). During the 20-min training, the upper and lower back, abdominal muscles, buttocks, muscles around the thigh, chest, and muscles around the upper arm were stimulated with 85 Hz of 350 μs wide rectangular pulses in biphasic mode. Both the duration of the pulse interval (stimulation on) and the pulse pause (stimulation off) were set to 4 s. The pulses were ramped up to the targeted intensity without delay (full intensity directly available) and similarly ramped down to zero (direct interruption of the stimulation) at the end of the stimulation phase. To maintain the same conditions, the stimulation intensity was adjusted to 6–8 on a scale of 1 (hardly noticeable) to 10 (painful) [33]. Regardless of group affiliation, muscles were voluntarily tensed during the stimulation episode.\n\n## 2.4. Exercise Procedure\n\nBoth groups received WB-EMS and performed exercises meanwhile. ( Supplementary Figures S1–S3). The UBG used upper body exercise movements (chest and upper back including shoulders and arms) and the LBG used lower body exercise movements (buttocks and thigh muscles including abductors and adductors). The UBG training consisted of rowing, butterfly reverse, latissimus pulls, pushups, butterfly, biceps curls, and triceps pulldowns. The LBG training consisted of squats, lunges, adductions, abductions, hip lifts, and leg raises. Both groups exercised the trunk (abdomen and lower back) with back extensions, crunches, and oblique crunches. Selected exercises were performed with additional fitness equipment (fitness tubes and elastic bands, each with varying resistance, and a Swiss Ball). During the first 1 to 2 sessions (depending on the training level), subjects maintained the position over the period of stimulation that they had taken at the onset of the stimulus. One set of 12 repetitions was performed per exercise, with each repetition beginning with the onset of the pulse. To maintain the same physical load level, i.e.,16 to 17 on the Borg RPE scale [33], the number of movements during an impulse interval could be increased up to three. If the training stimulus was not sufficient after the aforementioned customization, the originally targeted static exercise position should be maintained during the interval break. However, overexertion led to a backward correction. Another way to increase the intensity to the desired level was to increase the resistance either by giving an additional fitness tube or rubber band, or by using a version that offered more resistance.\n\n## 2.5. Isometric Strength Testing Procedure\n\nIsometric maximum strength (N) was determined during 10 different exercises (arm adduction, arm pull, leg extension, and leg curl, each unilateral left and unilateral right, as well as during biceps curl and triceps pulldown, each bilateral) in standardized positions (Supplementary Figure S4) pre (initial measurement) and post (final measurement) intervention using a mobile device (KD 9363 including DMS measuring amplifier GVS-2; ME-measuring systems GmbH, Hennigsdorf, Germany), which was more practicable than the determination of the 1-RM. Reliability of the isometric maximum strength measurement method was verified by Runkel and colleagues for several test positions (triceps pulldown, biceps curl, arm pull, sit-up, leg curl, leg extension) in healthy subjects with a comparable body mass index [34] by a high interclass correlation coefficient ($r = 0.764$ to 0.934). At both time points, the tests were performed three times in each position. The pause was set to 10 seconds between individual tests. In each case, the maximum value was used for analysis. The whole testing procedure lasted approximately 20 min.\n\n## 2.6. Statistical Analysis\n\nDue to the presence of some discordant values (see box plots), skewed distribution in some cases (Shapiro–Wilk test), partial heterogeneity of error variances (Levene’s test), and partial heterogeneity of covariances (Box test), nonparametric statistical tests were employed. The differences between the initial and the final maximum isometric strength were determined separately for each group using the Wilcoxon test. The initial and the final values were compared between the groups using the Mann–Whitney U test. Absolute differences were calculated by subtracting the initial values from the final values, and relative differences were calculated by dividing the final values by the initial values (the initial value was set to $100\\%$). Group comparisons were performed using the Mann–Whitney U test for absolute and relative differences. The significance level was set to < 0.05. Two-tailed analyses were used. The results of the non-parametric tests were used to calculate the effect sizes [35]. A distinction was made between large effects (r ≥ 0.5), medium effects (< 0.5 to 0.3), and small effects (< 0.3 to 0.1) [36]. Statistics were calculated using SPSS (IBM SPSS Statistics for Windows, Version 28.0., IBM Corp., Armonk, NY, USA) and Excel (Microsoft Excel for Windows, 16.0., Microsoft Corp., Redmond, WA, USA). An intention-to-treat analysis was not possible due to dropouts occurring at baseline.\n\n## 3. Results\n\nOf the included subjects, 28 completed the study. The dropouts occurred due to personal reasons. The characteristics of the groups did not differ significantly from each other (Table 1) and the total training volume was similar in both groups. Most subjects ($$n = 9$$ in each group) completed five sessions and no adverse effects occurred. The body mass remained unchanged in both the UBG and the LBG (Table 1). Neither the initial nor the final values differed significantly between the two groups. Isometric maximum strength was significantly higher after EMS training in both groups, both in absolute terms (Table 2 UBG; Table 3 LBG) and body mass adjusted (N/kg), except for left leg extension in the UBG and biceps curl in the LBG. The changes in absolute strength were similar in both groups (Table 4). Body mass adjusted strength during left arm pull showed a higher increase in the LBG (Figure 2). In the other test positions, group affiliation made no difference (Figure 2, Figure 3 and Figure 4). Furthermore, the LBG achieved a higher percentage strength gain in left arm pull, both absolute (Table 4) and body mass adjusted (UBG median $114.25\\%$ vs. LBG median $137.05\\%$; $$p \\leq 0.020$$; $r = 0.44$).\n\n## 4.1. Overview\n\nSignificant strength changes were observed in both groups after about five weeks training (one session per week). The percentage differences between the initial and final tests were higher than those found in the reliability analysis of the test device by Runkel and colleagues [34]. Therefore, the changes could be attributed to training. LBG training improved left arm pull strength more than UBG training. However, there were no group differences in the other exercises. Initial values between the two groups were not significantly different, but possibly at clinically relevant levels. If the higher initial values had been due to differences in training history, a lower ability to further increase strength would have be needed to be considered [37]. However, subjects should have abstained from intense physical activity for at least six months before starting the study.\n\n## 4.2. Accompanying Voluntary Activity\n\nLittle is known about the effects of movements for strength gain during EMS. During local application, movements are usually avoided and isometric contractions are performed. Maffiuletti [38] summarized that there are no differences in strength increase between EMS and EMS superimposed on voluntary contractions. However, the conclusion is based on the results of isometric interventions. Although movements are thought to promote the activity of stimulated muscles [26], our results failed to show a consistent influence of active exercise movements on strength gains. Furthermore, strength gains from conventional resistance training depend, among others, on the range of motion used [39,40]. However, isometric contractions at multiple joint angles might cover at least in part the physiological range of motion. For EMS training, Maffiuletti [38] recommends changing the joint position and furthermore, changing the electrode positioning to increase recruitment. Admittedly, Kemmler and colleagues [29] demonstrated the benefit of movement during WB-EMS use, with participants exercising in supine position. In contrast, our participants performed exercises in different positions. Therefore, any movements of body parts that were not primarily intended for the exercises and possible differences in resistance to gravity might have influenced the results. Furthermore, it needs to be considered that additional fitness equipment (fitness tubes and elastic bands with different resistance as well as a Swiss Ball) was used for selected exercises. However, exercise movements using additional fitness equipment did not affect the results. In addition, both the UBG and LBG performed exercises for the trunk. Therefore, both groups received partially similar dynamic training stimuli (three exercises). Movements inevitably lead to changes in muscle length and shape (e.g., biceps muscle during curl). Hence, changes in the electrode contact were very likely to occur. Furthermore, training that aims to enhance endurance and strength at the same time, such as EMS superimposed on cycling [41,42], requires movements. However, stimulation intensity must be considered to ensure the range of motion [43].\n\n## 4.3. Training Models and Adaptations\n\nSupraspinal mechanisms appear to be responsible for the initial strength development through EMS training [23]. Bezerra and colleagues [44] showed increased strength after EMS superimposed onto maximum isometric quadriceps contractions, not only of the exercised leg but also of the unexercised leg, confirming neural contribution. The potential to use EMS for rapid strength gains was demonstrated by Deley and colleagues [45], who reported that maximum dynamic leg extension torque in prepubertal girls could be increased by up to $50.6\\%$ with three weekly isometric applications over a three-week period. According to Adams [46], atrophic patients as well as casualties are target groups for the use of EMS. After 5 to 6 weeks, a 10 to $15\\%$ enhancement of muscle function can be achieved, but three sessions a week are recommended. Several studies confirmed the impact of WB-EMS on strength [10,26]. However, to our knowledge, only Kemmler and colleagues [29] have studied the effects of exercise during WB-EMS to date. In most cases, the lower body was investigated. Von Stengel and Kemmler [25] showed that leg/hip strength can be improved with 1.5 WB-EMS training sessions (with unloaded, low effort exercises) per week over a 14 to 16 week period, regardless of age. Furthermore, strength gains due to unloaded WB-EMS were similar compared to a HIT training after 16 weeks with three sessions in two weeks [3]. An increase in strength was also observed after shorter training periods. For example, WB-EMS superimposed on jumps twice a week over seven weeks significantly improved leg strength in contrast to normal jump training [10,47,48]. In the study by Wirtz and colleagues [28], leg flexors strength increased only after combining stimulation of multiple body parts with loaded squats ($100\\%$ 10 RM) twice per week and it was higher three weeks after the six-week training compared to the same training without stimulation. Dörmann and colleagues [18] showed significant improvements in leg strength after a four-week, eight-session WB-EMS training program that were similar to those seen in the control group, which performed the same training that included strength exercises, without additional stimulation, and in which intensification was accomplished using other training tools. However, not only leg muscles but also upper body muscles could benefit from dynamic WB-EMS. Reljic and colleagues [26] observed improvements throughout the entire body after a 12-week WB-EMS program with slight motions, consisting of two sessions per week. Our results suggest that even fewer training sessions are beneficial than previously described, whether or not exercise movements are performed during stimulation, which appears to be due to neural factors. Therefore, not only locally applied EMS training regimens have the potential to increase strength, but also WB-EMS training regimens without additional exercise movements.\n\n## 4.4. Transferability\n\nBenefits from WB-EMS can also be expected, for example, for patients suffering from sarcopenia, sarcopenic obesity, and low back pain [14]. It might be useful especially for beginners to start WB-EMS training with a five-week training period without additional exercise movements to improve basic strength before starting a more challenging exercise program. WB-EMS without additional exercise movements can be a first access to training when health conditions do not allow conventional exercises or when a lack of compliance exists. Relative to WB-EMS, local application appears to be superior in gaining strength [49]. However, the lack of focus on selected zones owing to stimulation of the entire body is a suggested explanation for the difference [14]. Therefore, only target muscles could be stimulated and not all available electrodes could be used, even if an electrode suit is worn, or zones could be stimulated in an individual order.\n\n## 4.5. Limitations\n\nWe have shown that the effect of WB-EMS on strength gains is independent of the concomitant exercise movements. Nevertheless, some limitations need to be acknowledged. A test of core strength would have been useful, as both groups performed core strength exercises under the same conditions and a higher strength can be expected as observed in the study by Berger and colleagues [1], although they used a more extensive training program. Owing to two dropouts, the group sizes were slightly different, which affected the comparison. Furthermore, the strength gains of the dynamically trained muscles might have been underestimated, since only isometric strength was tested. It must also be mentioned that the increase in strength might have been influenced by deviations from the predefined number of training sessions. To evaluate the intensity of the movement sequences, an unstimulated group could have been used. Furthermore, an inactive group could have been used as a reference for the interventions. However, the study focused on the comparison between the EMS application without and the application with concurring exercise movements. When using WB-EMS training technology, the load parameters must be set with care to avoid unintended side effects, particularly during the first sessions of novices when adaptation to the load has not yet occurred in the form of the “repeated bout effect” [50].\n\n## 5. Conclusions\n\nWB-EMS training without accompanying movement exercises leads to substantial strength gains even during a short WB-EMS training period. At the beginning of WB-EMS training, electromyostimulation is more important for strength gains than active exercise movements. Therefore, future studies should examine the effects of exercise movements during long-term training periods, or consider individuals already adapted to WB-EMS training or strength training. The transferability of the results to a collective experienced with WB-EMS or strength training should be questioned, as movements (and maybe other approaches, e.g., additional mass or complicating tasks) may become more relevant when initial adaptations to training are exhausted. Since the training effort with WB-EMS is low, people with health restrictions, beginners without experience in strength training, and those returning to training might benefit from these results. These groups could refrain from exercise movements during the first WB-EMS training sessions and integrate them during the course of the subsequent training.\n\n## References\n\n1. 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0.014633513055741787,\n 0.010005820542573929,\n -0.004940373823046684,\n -0.0025170892477035522,\n -0.018010476604104042,\n 0.0006292723119258881\n]","isConversational":false}}},{"rowIdx":66,"cells":{"text":{"kind":"string","value":"---\ntitle: 'Two‐year trends from the LANDMARC study: A 3‐year, pan‐India, prospective,\n longitudinal study on the management and real‐world outcome in patients with type\n 2 diabetes mellitus'\nauthors:\n- Ashok K. Das\n- Sanjay Kalra\n- Shashank Joshi\n- Ambrish Mithal\n- Prasanna Kumar K. M.\n- Ambika G. Unnikrishnan\n- Hemant Thacker\n- Bipin Sethi\n- Subhankar Chowdhury\n- Amarnath Sugumaran\n- Senthilnathan Mohanasundaram\n- Shalini K. Menon\n- Vaibhav Salvi\n- Deepa Chodankar\n- Saket Thaker\n- Chirag Trivedi\n- Subhash K. Wangnoo\n- Abdul H. Zargar\n- Nadeem Rais\njournal: Endocrinology, Diabetes & Metabolism\nyear: 2023\npmcid: PMC10000633\ndoi: 10.1002/edm2.404\nlicense: CC BY 4.0\n---\n\n# Two‐year trends from the LANDMARC study: A 3‐year, pan‐India, prospective, longitudinal study on the management and real‐world outcome in patients with type 2 diabetes mellitus\n\n## Abstract\n\nThis 2‐year interim analysis of the 3‐year long LANDMARC study highlights the T2DM burden, management practices and related complications, across metropolitan and non‐metropolitan cities of India. It indicates that the burden of uncontrolled T2DM in *India is* high with $20.8\\%$ of participants achieving glycaemic control in 2 years (HbA1c < $7\\%$; 53 mmol/mol); and $17.6\\%$ having microvascular and $3.1\\%$ having macrovascular complications. There is need for effective diabetes management to meet glycaemic targets and prevent CV risk and vascular complications in a developing country like India with high prevalence of T2DM.\n\n### Introduction\n\nThere are limited data on the real‐world management of diabetes in the Indian population. In this 2‐year analysis of the LANDMARC study, the management of type 2 diabetes mellitus (T2DM) and related complications were assessed.\n\n### Method\n\nThis multicenter, observational, prospective study included adults aged ≥25 to ≤60 years diagnosed with T2DM (duration ≥2 years at enrollment) and controlled/uncontrolled on ≥2 anti‐diabetic agents. This interim analysis at 2 years reports the status of glycaemic control, diabetic complications, cardiovascular (CV) risks and therapy, pan‐India including metropolitan and non‐metropolitan cities.\n\n### Results\n\nOf the 6234 evaluable patients, 5318 patients completed 2 years in the study. Microvascular complications were observed in $17.6\\%$ of patients ($\\frac{1096}{6234}$); macrovascular complications were observed in $3.1\\%$ of patients ($\\frac{195}{6234}$). Higher number of microvascular complications were noted in patients from non‐metropolitan than in metropolitan cities ($p \\leq .0001$). In 2 years, an improvement of $0.6\\%$ from baseline ($8.1\\%$) in mean glycated haemoglobin (HbA1c) was noted; $20.8\\%$ of patients met optimum glycaemic control (HbA1c < $7\\%$). Hypertension ($\\frac{2679}{3438}$, $77.9\\%$) and dyslipidaemia ($\\frac{1776}{3438}$, $51.7\\%$) were the predominant CV risk factors in 2 years. The number of patients taking oral anti‐diabetic drugs in combination with insulin increased in 2 years (baseline: $\\frac{1498}{6234}$ [$24.0\\%$] vs. 2 years: $\\frac{1917}{5763}$ [$33.3\\%$]). While biguanides and sulfonylureas were the most commonly prescribed, there was an evident increase in the use of dipeptidyl peptidase‐IV inhibitors (baseline: $\\frac{3049}{6234}$, $48.9\\%$ vs. 2 years: $\\frac{3526}{5763}$, $61.2\\%$).\n\n### Conclusion\n\nThis longitudinal study represents the control of T2DM, its management and development of complications in Indian population.\n\n### Clinical Trial Registration Number\n\nCTRI/$\\frac{2017}{05}$/008452.\n\n## INTRODUCTION\n\nIndia has been severely affected by the global diabetes epidemic. As per the 10th edition of International Diabetes Federation's (IDF) diabetes atlas [2021], India has 74.2 million people living with diabetes currently, with an age‐adjusted prevalence of $9.6\\%$ among adults. India is expected to have 124.9 million people in the age range of 20–79 years living with diabetes by 2045. 1 The American Diabetes Association (ADA) recommends a combination of modified lifestyle and pharmacological treatment to achieve good metabolic control in diabetes and long‐term maintenance. 2, 3 Earlier studies have demonstrated that baseline glycated haemoglobin (HbA1c) and body mass index (BMI) can serve as important biomarkers to understand the disease aetiology and to identify suitable treatment options. 4, 5 Long‐term uncontrolled diabetes can cause cardiovascular (CV) diseases and damage kidneys, nerves and other vital organs. If optimum diabetes control is achieved, these serious complications can be delayed or prevented altogether. 1 This has also been substantiated by a study demonstrating that the occurrence of variations in glycaemic levels was associated with microvascular and macrovascular complications. 6 The INSPIRED study, which was conducted in India, included 19,084 individuals (aged 10–97 years) with type 2 diabetes mellitus (T2DM) and varying phenotypic characteristics. The findings of this study emphasized the association between increased hazards of retinopathy and nephropathy with a rise in blood glucose levels. 7 The Million death study also conducted in India showed that diabetes was associated with a significantly increased odds of stroke mortality (odds ratio, $95\\%$ confidence intervals [CI]: 1.6, 1.4–1.7, $p \\leq .0001$). 8 The American College of Cardiology (ACC)/American Heart Association (AHA)/Heart Failure Society of America (HFSA) guideline for the management of heart failure states that diabetes and heart failure often occur concomitantly, and each disease independently increases the risk of the other. 9 Additionally, a review of clinical evidence‐based research on the association between T2DM and myocardial infarction (MI) showed that not only T2DM is strongly associated with MI, but it also increases the risk of developing MI and related complications. 10 The review further discusses that in people with T2DM, MI is the primary cause of death and that T2DM leads to an increase in the risk of coronary events in individuals both with or without previous history of coronary events. 10 Hence, tight glycaemic control is essential in the early stages of diabetes.\n\nThe LANDMARC study (LongitudinAl Nationwide stuDy on Management And Real‐world outComes of diabetes in India) was a 3‐year comprehensive, robust, longitudinal and prospective study. It aimed to collect data on glycaemic therapy and diabetes complications in people with diabetes living in different regions of India (including metropolitan and non‐metropolitan cities). The study protocol, 11 baseline data 12 and 1‐year results 13 have been published earlier. The 1‐year results of the LANDMARC study indicate the progression of vascular complications and accumulation of CV risk among Indian patients with T2DM. Hypertension and dyslipidaemia, the most common CV risk factors reported, were pronounced in those who were overweight/had obesity. About one‐fifth of the patients had optimal glycaemic control (HbA1c < $7\\%$). Patients from both non‐metropolitan and metropolitan cities were comparable in terms of improvement in glycaemic status and having optimum control. 13 The aim of this 2‐year interim analysis was to further evaluate and understand the diabetic complications and T2DM management pattern in adult patients with T2DM across India (including a sub‐analysis of metropolitan and non‐metropolitan cities).\n\n## Study design\n\nThis was a multicenter, observational, prospective study conducted over 3 years (conducted between March 2017 and July 2021). The study was divided into seven visits with an interval of 6 months each. The present manuscript includes results from the second year (within a window period of ±90 days) of the 3‐year evaluation period.\n\n## Study patients\n\nAdults aged between ≥25 years and ≤60 years with T2DM for ≥2 years and who were controlled/uncontrolled on ≥2 anti‐diabetic agents at the time of enrollment were included in the study. The details of the study design, methodology, inclusion/exclusion criteria and statistical analysis have been published previously. 11, 12, 13\n\n## Study assessments\n\nAt the end of the second year (visit 5), data related to glycaemic control status (fasting plasma glucose [FPG], post‐prandial glucose [PPG] and HbA1c) were collected. The proportion of patients with macrovascular complications (non‐fatal MI, non‐fatal stroke, CV death and peripheral vascular disease [PVD]), microvascular complications (retinopathy, nephropathy and neuropathy) and CV risk factors (hypertension, dyslipidaemia and albuminuria) was assessed. The glycaemic parameters and complications in patients from metropolitan and non‐metropolitan cities were assessed. The proportion of patients taking oral anti‐diabetic drugs (OADs) and injectable glucose‐lowering drugs was also assessed. Data related to anthropometry (weight) and frequency and severity of hypoglycaemia episodes were collected.\n\n## Data collection\n\nThe data related to the study end‐points were collected prospectively every 6 months up to the end of the study at 36 months. The 450 sites that were selected for this study represent the four geographical regions (East, West, North and South), urban/rural practice across India. The study design was planned to mirror real‐life management of patients with T2DM; hence, none of the assessments were mandated. The available data were recorded in electronic‐Case Report Forms (e‐CRFs). Data quality control was performed by qualified designated personnel. An adverse drug reaction related to any Sanofi product (clinical signs, laboratory values or other) was reported and followed up until the clinical recovery was complete and laboratory results (if clinically significant) had returned to normal or until progression had been stabilized. This was a planned interim analysis to assess the changes in the disease characteristics from baseline and may require modification in the assessment parameters for subsequent interim and final analyses.\n\n## Statistics\n\nA minimum sample size of 4387 was decided assuming that the percentage of patients with composite incidence of non‐fatal MI, stroke and CV death after 3 years would have been $3\\%$. The study planned to evaluate 6300 patients to estimate the composite incidence percentage with a precision of at least $1\\%$, considering a $30\\%$ rate of patients dropping out from the study before the end of the 3 years.\n\n## Ethics\n\nThe protocol complies with the Declaration of Helsinki and this study was conducted in accordance with the principles laid by the 18th World Medical Assembly (Helsinki, 1964) and all subsequent amendments. The study was also in accordance with the guidelines for Good Epidemiology Practice (US & European) 14, 15 and aligned to the local regulations, ethics committee(s) (institutional review board/independent ethics committee) and competent authorities. The study was approved by the ethics committees of all participating sites (or a central ethics committee, where applicable). All the patients provided written informed consent before data collection/documentation.\n\n## Demographics and baseline characteristics\n\nAmong the 450 sites, data from 382 sites were analysed for the 2‐year visit. Of the 6279 patients recruited, 6234 patients were evaluated; of these, 5318 patients completed 2 years in the study (Figure 1). At baseline, the mean ± standard deviation (SD) age of the patients was 52.1 ± 9.2 years with $57.0\\%$ ($\\frac{3552}{6234}$) of the study population in the age range of 50 to 65 years; more than half of the patients ($56.6\\%$, $\\frac{3526}{6234}$) were men. The mean ± SD baseline BMI was 27.2 ± 4.6 kg/m2, and the majority of the patients were obese ($66.8\\%$, $\\frac{4149}{6215}$) (Table 1). Most of the patients ($74.4\\%$, $\\frac{4640}{6234}$) were taking only OADs. ( Table S1).\n\n**FIGURE 1:** *Patient disposition. *Reasons for CV death were sudden death ($$n = 19$$), myocardial infarction ($$n = 9$$), stroke ($$n = 1$$) and coronary artery procedure ($$n = 1$$). CV, cardiovascular; n, number of patients.* TABLE_PLACEHOLDER:TABLE 1\n\n## Glycaemic status\n\nIn 2 years, all the glycaemic parameters improved (decreased) significantly from baseline (mean change ± SD: HbA1c: −0.6 ± $1.7\\%$; FPG: −14.6 ± 54.5 mg/dl; and PPG: −22.0 ± 79.0 mg/dl; $p \\leq .0001$) (Figure 2). When patients were stratified by HbA1c levels, a significant ($p \\leq .0001$) reduction in the number of patients in the HbA1c $8\\%$–$8.9\\%$ and HbA1c ≥ $9\\%$ subgroups were noted at the 2‐year visit compared with baseline; while a significant ($p \\leq .0001$) increase in patient numbers in the HbA1c $7\\%$–$7.9\\%$ subgroup was noted. Overall, $20.8\\%$ ($\\frac{1297}{6234}$) of the patients met optimum glycaemic control (HbA1c < $7\\%$) in 2 years (Figure 3).\n\n**FIGURE 2:** *Change in glycaemic parameters at the end of 2 years. Values are presented as mean ± standard deviation. For change from baseline, HbA1c: n = 3020, FPG: n = 3668, PPG: n = 3454. FPG, fasting plasma glucose; HbA1c, glycated haemoglobin; n, number of patients analysed; PPG, postprandial glucose.* **FIGURE 3:** *Proportion of patients across HbA1c categories (N = 6234). Data presented as n (%) from baseline (N = 6234). HbA1c was not measured for all patients and hence the percentage may not add up to 100%.p‐values are reported using McNemar's test with the null hypothesis that the proportion of paired samples is equal. Patients who met each criterion and those who did not meet the criteria are considered as binary outcomes for the test. The p‐values reported are not adjusted for inflation in type I error. *p < .0001. HbA1c, glycated haemoglobin; N, number of patients analysed; n, number of patients with non‐missing results at the visit.*\n\n## Microvascular and macrovascular complications in 2 years\n\nMicrovascular complications were noted in $17.6\\%$ of patients ($\\frac{1096}{6234}$) (Table 2); while new macrovascular complications were noted in $3.1\\%$ of patients ($\\frac{195}{6234}$) (Table 3). The most frequently noted microvascular complication was neuropathy, which was reported in $0.6\\%$ of patients ($\\frac{32}{6234}$), followed by nephropathy in $0.3\\%$ of patients ($\\frac{19}{6234}$) and retinopathy in $0.1\\%$ of patients ($\\frac{6}{6234}$) (Table 2). Overall, 57 new events of microvascular complications were reported in 55 patients at the 2‐year visit. Nephropathy, neuropathy and retinopathy were significantly ($p \\leq .0001$) higher in patients with CV risk factors, while retinopathy was found to be significantly ($$p \\leq .0351$$) higher in the HbA1c ≥ $7\\%$ subgroup. ( Table S2).\n\nIn 2 years, the most reported new macrovascular complications included CV death ($0.1\\%$, $\\frac{9}{6234}$) and PVD ($0.1\\%$, $\\frac{4}{6234}$) (Table 3). A total of 41 deaths were reported; of which, 30 deaths were attributed to CV causes (sudden death [$$n = 19$$], MI [$$n = 9$$], stroke [$$n = 1$$] and coronary artery procedure [$$n = 1$$]) and remaining 11 deaths were due to other causes (Figure 1). Three patients were hospitalized between the 1‐year to 18‐month period due to MI (one patient) and acute coronary syndrome (ACS; two patients). One patient was hospitalized due to ACS, heart failure and unstable angina between the 18‐month to 2‐year period (Table S3).\n\n## Cardiovascular risk factors\n\nAn increasing trend in the CV risk profile of patients was observed (baseline: $52.6\\%$, $\\frac{3281}{6234}$ vs. 2‐year: $55.1\\%$, $\\frac{3438}{6234}$). Among the 63 new cases (69 events) of CV risk factors, dyslipidaemia and hypertension (33 cases, each) were the most commonly reported (Table 4). Hypertension was noted more in men than women ($$p \\leq .0019$$). Patients with hypertension and dyslipidaemia were greater in the subgroup having BMI ≥ 23 kg/m2 versus BMI < 23 kg/m2 ($p \\leq .0001$ and $$p \\leq .0525$$, respectively). Patients having hypertension and dyslipidaemia were higher in the subgroup having uncontrolled HbA1c levels (≥$7\\%$) versus controlled HbA1c levels (<$7\\%$) (Table 4).\n\n**TABLE 4**\n\n| CV risk factors | Total N = 6234 | Total N = 6234.1 | Total N = 6234.2 | Total N = 6234.3 |\n| --- | --- | --- | --- | --- |\n| CV risk factors | Baseline | 2 years | Patients at risk with new CV risk factors until 2 years, n | Patients with new CV risk factors at 2 years |\n| Total number of CV risk factors, Ne | 4419 | 4698 | | 69 |\n| Patients with CV risk factors | 3281 (52.6) | 3438 (55.1) | 6234 | 63 (1.0) |\n| Hypertension b | 2566 (78.2) | 2679 (77.9) | 3588 | 33 (0.9) |\n| Dyslipidaemia b | 1635 (49.8) | 1776 (51.7) | 4491 | 33 (0.7) |\n| Albuminuria b | 153 (4.7) | 169 (4.9) | 6234 | 3 (0.0) |\n| Family history of PCD b | 65 (2.0) | 65 (1.9) | ‐ | ‐ |\n| No complications | 2562 | ‐ | ‐ | ‐ |\n| Unknown c | 391 | ‐ | ‐ | ‐ |\n\n## Glycaemic trends and vascular complications in metropolitan and non‐metropolitan cities\n\nThe baseline age, disease duration and HbA1c parameters were comparable across patients of non‐metropolitan and metropolitan cities (Table S4A). In 2 years, an improvement (decrease) was noted in all the glycaemic parameters (HbA1c, FPG and PPG) in patients from both metropolitan and non‐metropolitan cities (Table S4A).\n\nThe microvascular complications (neuropathy, nephropathy and retinopathy) were significantly ($p \\leq .0001$) higher in patients from non‐metropolitan than in metropolitan cities (Table S4B). The number of CV deaths was higher in patients from non‐metropolitan than in metropolitan cities ($19.7\\%$, $\\frac{25}{135}$ vs. $7.4\\%$, $\\frac{5}{70}$). Of the newly reported cases of macrovascular complications in the second year, in non‐metropolitan cities, PVD was reported in four patients, MI in one patient, CV death in eight patients and stroke in one patient; while in metropolitan cities, one new case was reported (CV death) (Table S4C). Overall, the number of diabetes‐related complications in metropolitan and non‐metropolitan cities increased from baseline over 2 years (Table S4C).\n\n## Anti‐diabetic treatment therapies\n\nIn 2 years, the total proportion of patients taking OAD + insulin increased (baseline: $24.0\\%$, $\\frac{1498}{6234}$ vs. 2 years: $33.3\\%$, $\\frac{1917}{5763}$), while the proportion of those taking only OADs, decreased (baseline: $74.4\\%$, $\\frac{4640}{6234}$ vs. 2 years: $64.8\\%$, $\\frac{3735}{5763}$) (Table S1).\n\nBiguanides and sulfonylureas were the most prescribed OADs at baseline and 2 years (biguanides, baseline: $93.0\\%$, $\\frac{5798}{6234}$ and in 2 years: $92.7\\%$, $\\frac{5340}{5763}$; sulfonylureas, baseline: $76.3\\%$, $\\frac{4759}{6234}$ and in 2 years: $77.7\\%$, $\\frac{4480}{5763}$). The highest increase in OAD addition was seen for dipeptidyl peptidase 4 (DPP4) inhibitors (baseline: $48.9\\%$, $\\frac{3049}{6234}$ vs. 2‐years: $61.2\\%$, $\\frac{3526}{5763}$) followed by sodium‐glucose cotransporter‐2 inhibitors (baseline: $10.5\\%$, $\\frac{654}{6234}$ vs. 2 years: $21.3\\%$, $\\frac{1227}{5763}$) (Table S5). The mean ($95\\%$ CI) change in HbA1c from baseline was −0.5 (−0.5, −0.4) in ≤3 OAD subgroup and −0.4 (−0.6, −0.2) in >3 OAD subgroup. Improvement in the glycaemic parameters in 2 years was more in the ≤3 OAD versus >3 OAD subgroup (p values were 0.8193, 0.1139 and 0.5541 for HbA1c, FPG and PPG, respectively) (Table 5).\n\n**TABLE 5**\n\n| Unnamed: 0 | Change in HbA1c (%) a | Change in FPG (mg/dl) a | Change in PPG (mg/dl) a |\n| --- | --- | --- | --- |\n| | n; mean (95% CI) | n; mean (95% CI) | n; mean (95% CI) |\n| Insulin‐naïve | 1454; −0.4 (−0.5, −0.4) | 1821; −9.9 (−12.1, −7.8) | 1705; −17.2 (−20.6, −13.8) |\n| Insulin | 505; −1.0 (−1.2, −0.9) | 721; −25.8 (−30.5, −21.0) | 697; −38.7 (−45.2, −32.2) |\n| p‐value b | <.0001 | <.0001 | <.0001 |\n| Basal (with/without prandial) insulin | 234; −1.1 (−1.4, −0.9) | 323; −26.0 (−32.9, −19.2) | 323; −38.3 (−47.5, −29.0) |\n| Premix (with/without prandial) | 198; −1.0 (−1.3, −0.8) | 298; −27.9 (−35.5, −20.4) | 282; −37.3 (−47.9, −26.8) |\n| p‐value b | .5758 | .7125 | .8915 |\n| Basal long‐acting (without prandial) insulin | 174; −1.3 (−1.6, −1.0) | 238; −23.8 (−31.5, −16.0) | 230; −40.0 (−51.4, −28.7) |\n| Premix (without prandial) insulin | 205; −1.1 (−1.3, −0.8) | 304; −27.7 (−35.3, −20.2) | 289; −39.7 (−50.2, −29.3) |\n| p‐value b | .2520 | .4747 | .9696 |\n| ≤3 OAD | 843; −0.5 (−0.5, −0.4) | 1110; −10.9 (−13.5, −8.3) | 1024; −17.4 (−21.6, −13.3) |\n| >3 OAD | 218; −0.4 (−0.6, −0.2) | 262; −6.0 (−11.8, −0.1) | 247; −14.6 (−23.1, −6.0) |\n| p‐value b | .8193 | .1139 | .5541 |\n\nIn 2 years, the commonly prescribed injectables were basal and premix insulins (basal insulin, baseline: $13.5\\%$, $\\frac{839}{6234}$ and 2 years: $20.6\\%$, $\\frac{1188}{5763}$; premix insulin, baseline: $11.0\\%$, $\\frac{683}{6234}$ and 2 years: $14.7\\%$, $\\frac{849}{5763}$) (Table S5). The change from baseline in all three glycaemic parameters in 2 years was significantly more in the insulin‐receiving subgroup than in the insulin‐naïve subgroup ($p \\leq .0001$). The mean ($95\\%$ CI) change in HbA1c from baseline was −1.0 (−1.2, −0.9) in insulin subgroup and −0.4 (−0.5, −0.4) in insulin‐naïve subgroup ($p \\leq .0001$, both) (Table 5).\n\n## Adverse drug reactions\n\nA total of 19 events (18 patients) of hypoglycaemia were recorded between the 1‐year to 18‐month period. In the latter 6 months of the 2‐year visit, $0.3\\%$ of patients ($\\frac{17}{6234}$) reported hypoglycaemic events (documented symptomatic, $$n = 11$$; asymptomatic, $$n = 2$$; nocturnal, $$n = 4$$). ( Table S3). One adverse drug reaction (hypoglycaemia) by one patient was noted until the end of 2 years.\n\n## DISCUSSION\n\nThis pan‐India, real‐world, large‐scale, longitudinal study is designed to assess glycaemic control, treatment trends, CV risks and development of macro‐ and microvascular complications over 3 years in Indian adults with T2DM. Herewith, we report an interim analysis done at the 2‐year time point. Over 2 years, while there was an overall improvement in glycaemic status, only 1 in 5 patients achieved HbA1c < $7\\%$. Approximately one‐third of the patients in metropolitan ($30.6\\%$) and non‐metropolitan ($35.4\\%$) cities had HbA1c < $7\\%$ at the end of 2 years. While also highest at baseline among microvascular complications, the proportion of patients with neuropathy showed an increase at the end of 2 years. Hypertension and dyslipidaemia were the most reported CV risks. The 2‐year results show that the majority of the patients with T2DM are treated with OADs. Biguanides and sulfonylureas are the most commonly used OADs in Indian routine clinical practice.\n\nIn this study, most of the patients (~$90\\%$) were aged between 31 and 65 years and had obesity ($67\\%$). With age it is difficult to reduce weight as the deposition of central fat becomes more pronounced and obesity sets in. Obesity paves the way for lifestyle disorders, one of them being T2DM. 16 Previous reports demonstrate that obesity is a well‐established risk factor for chronic illnesses like T2DM. 17, 18 Worsening of T2DM leads to CV risks and vascular complications. 6, 7, 8, 9, 10 A possible mechanism linking T2DM and obesity with subsequent CV complications is inflammation and lipid accumulation due to overexpression of cytokines (tumour necrosis factor‐α, interleukin (IL)‐1, IL‐6, leptin, resist in monocyte chemoattractant protein (MCP)‐1, plasminogen activator inhibitor (PAI)‐1, fibrinogen and angiotensin) by adipose tissue, which have a deleterious effect on blood vessels and can lead to the development of MI and cardiomyopathy. 19 Current treatment recommendations instate close monitoring and control of glycaemic levels to improve cardiac outcomes. 2 However, a considerable gap exists between diabetes care followed in real practice versus that recommended by evidence‐based guidelines. 20 The results of this study shed light on the real‐world burden of uncontrolled diabetes in India. In this 2‐year analysis, $20.8\\%$ of the study population had optimal glycaemic control (HbA1c < $7\\%$). In the GOAL study, $29.7\\%$ of patients had glycaemic control after 12 months, and in the wave −7 of IDMPS study, $25.2\\%$ of patients had optimal glycaemic control. 21, 22 The proportion of patients having optimum glycaemic control worsened over 2 years despite an increase in the use of OADs, reiterating the need for early control.\n\nDespite improvement in the HbA1c levels in $0.6\\%$ of patients in 2 years, there was an overall increase in the number of patients with microvascular and macrovascular complications. Those patients who were overweight or had suboptimal glycaemic control or CV risk factors had more complications versus those without these comorbidities, thus, substantiating an established fact that high BMI and poor glycaemic control lead to vascular complications. 18, 19, 23 The UKPDS 38 study examined the effect of tight control of blood pressure on macrovascular and microvascular complications in patients with T2DM. After 9 years of follow‐up, the results showed a $34\\%$ reduction in macrovascular complications (MI, sudden death, stroke and PVD) and a $37\\%$ reduction in the risk of microvascular complications (retinopathy requiring photocoagulation, vitreous haemorrhage and fatal or non‐fatal renal failure) in tightly controlled blood pressure group compared with the less tightly controlled group. 24 *In this* study, neuropathy was the most reported complication in 2 years. These results are consistent with the observation of the 1‐year LANDMARC study 13 and A1chieve study. 25 *It is* a well‐known fact that in people with T2DM, uncontrolled high blood sugar for a long duration degenerates the neurons, leading to a loss of sensory function or diabetic neuropathy. 26 Previous reports have established the observation that hypertension and dyslipidaemia are generally prevalent in people with diabetes. 27, 28 The 2‐year data in the present study also showed that all three microvascular complications (neuropathy, nephropathy and retinopathy) and heart failure were reported in more patients with CV risks than without CV risks.\n\nAs anticipated with the progressive nature of the disease after 2 years in the study, half of the patients who had diabetes for >10 years at baseline were taking OAD + insulin and those who had diabetes for ≤10 years at baseline were predominantly on OADs. Biguanides and sulfonylureas continued to remain the most used OAD classes in 2 years. Similar to the 1‐year results, the highest addition was seen in patients on DPP4 inhibitors. 13 There was a shift from the use of OADs towards the introduction of insulin as the need for injectables is common in people with longer duration of diabetes. 2, 30 In 2 years, improvement in glycaemic parameters was significantly higher in the insulin receiving subgroup than in the insulin‐naïve subgroup ($p \\leq .0001$), which is in alignment to the 1‐year results of the LANDMARC study. 13 The ORIGIN study showed that the progression of diabetes was substantially reduced with timely insulin treatment in comparison with standard of care. 29 *There is* also evidence that in patients who are diagnosed with severe hyperglycaemia (HbA1c > $9\\%$–$10\\%$), insulin can control gluco‐ and lipo‐toxicity within a few days of therapy. 30 Therefore, timely initiation and intensification of insulin treatment can help achieve glycaemic control and improve the treatment outcomes.\n\nThere was an improvement in the HbA1c levels in patients from both metropolitan and non‐metropolitan cities. The microvascular complications were significantly more in patients from non‐metropolitan cities than in metropolitan cities ($p \\leq .0001$). The number of CV deaths and newly reported macrovascular complications were also higher in patients from non‐metropolitan cities than in metropolitan cities. Difference in the disease management between metropolitan and non‐metropolitan cities could result in variable outcomes. Further studies can help understand the diabetes management patterns in metropolitan and non‐metropolitan cities.\n\nLANDMARC is one of the first‐of‐its‐kind large‐scale longitudinal studies from India involving 6234 patients from 382 centers to investigate microvascular and macrovascular complications, glycaemic control and therapy pattern in patients with T2DM over 3 years across India. This real‐world data analysis provides a longitudinal course of the T2DM burden, management practices and related complications across the nation including a sub‐analysis across metropolitan and non‐metropolitan cities of India. This study is inherent to the limitations associated with a real‐world study. Moreover, being observational in nature, any study‐specific procedures or screening for complications or CV risks were not possible. Furthermore, this study does not capture data on factors such as financial status, educational qualification of the patients and access to treatment facilities that warranted a better understanding, if investigated.\n\n## CONCLUSIONS\n\nThe results of the second year of the LANDMARC study showed high burden of uncontrolled diabetes in patients with T2DM from India. In 2 years, $17.6\\%$ of the study population had microvascular complications, predominantly neuropathy. A higher number of complications were observed in patients from non‐metropolitan versus metropolitan cities. Hypertension was the most reported CV risk. In 2 years, an increase in number of injectables was also observed. These 2‐year trends are similar to those observed in the 1‐year results of the LANDMARC study. This pan‐India, real‐world study highlights the need for effective diabetes management including enhanced awareness among patients and providers to meet glycaemic targets and prevent CV risk and vascular complications in a developing country like India with high prevalence of T2DM.\n\n## AUTHOR CONTRIBUTIONS\n\nAshok Kumar Das: Conceptualization (equal); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (lead); methodology (equal); project administration (supporting); resources (supporting); software (supporting); supervision (lead); validation (lead); visualization (lead); writing – original draft (equal); writing – review and editing (equal). Sanjay Kalra: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Shashank R Joshi: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Ambrish Mithal: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Prasanna Kumar KM: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Ambika G Unnikrishnan: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Hemant Thacker: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Bipin Sethi: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Subhankar Chowdhury: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Amarnath Sugumaran: Conceptualization (equal); data curation (equal); formal analysis (supporting); funding acquisition (equal); investigation (equal); methodology (equal); project administration (equal); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Senthilnathan Mohanasundaram: Conceptualization (equal); data curation (equal); formal analysis (supporting); funding acquisition (equal); investigation (equal); methodology (equal); project administration (equal); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Shalini Kesav Menon: Conceptualization (equal); data curation (equal); formal analysis (supporting); funding acquisition (equal); investigation (equal); methodology (equal); project administration (equal); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Vaibhav Salvi: Conceptualization (supporting); data curation (equal); formal analysis (equal); funding acquisition (supporting); investigation (equal); methodology (equal); project administration (equal); resources (supporting); software (equal); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Deepa Chodankar: Conceptualization (equal); data curation (equal); formal analysis (lead); funding acquisition (supporting); investigation (equal); methodology (lead); project administration (equal); resources (lead); software (lead); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Saket Thaker: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Chirag Trivedi: Conceptualization (equal); data curation (equal); formal analysis (equal); funding acquisition (equal); investigation (equal); methodology (equal); project administration (equal); resources (equal); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Subhash Kumar Wangnoo: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Abdul Zargar: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Nadeem Rais: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal).\n\n## FUNDING INFORMATION\n\nThis study was funded by Sanofi.\n\n## CONFLICT OF INTEREST\n\nAKD, AM, AGU and NR received honoraria from Sanofi and other pharmaceutical companies. KMPK is on the advisory board of Sanofi and received an honorarium for his talks. SJ received speaker/advisory/research grants from Abbott, AstraZeneca, Biocon, Boehringer Ingelheim (BI), Eli Lilly, Franco Indian, Glenmark, Lupin, Marico, MSD, Novartis, Novo Nordisk, Roche, Sanofi, Serdia, Twinhealth and Zydus. SK received honoraria/speaker fees from Eli Lilly, Novo Nordisk and Sanofi. HT received honoraria from MSD, Novartis, Sanofi and other companies for advice and lectures. BS received an honorarium from Aventis, Novo Nordisk, Eli Lilly, BI and MSD. SC received honoraria/grants from Biocon, BI, Intas, Novartis, Sanofi and Serdia. SKW has nothing to declare. AHZ received honoraria from Novo Nordisk, Eli Lilly, Johnson & Johnson, AstraZeneca, BI and Sanofi. AS, SM, SKM, DC, VS, ST and CT are employees of Sanofi and may hold stock options.\n\n## PREVIOUS PRESENTATIONS AND PUBLICATIONS\n\nPart of the data from this paper was presented at the 82nd Scientific Sessions of American Diabetes Association (ADA) 2022, New Orleans, LA, and the 58th Annual Meeting of the European association for the study of Diabetes, Stockholm, 19–23 September 2022. The protocol of this study is published in Diabetic Medicine; DOI: 10.1111/dme.14171. The baseline data and 1‐year data of this study have been published in Endocrinology, Diabetes & Metabolism; their DOIs are 10.1002/edm2.231 and 10.1002/edm2.316, respectively.\n\n## DATA AVAILABILITY STATEMENT\n\nQualified researchers may request access to person‐level data and related study documents including the clinical study report, study protocol with any amendments, blank case report form, statistical analysis plan and data set specifications. Person‐level data will be anonymized, and study documents will be redacted to protect the privacy of study patients. Further details on Sanofi's data sharing criteria, eligible studies and the process for requesting access can be found at https://vivli.org/.\n\n## References\n\n1. 1\nInternational Diabetes Federation\n. IDF Diabetes Atlas. 10th ed.\nInternational Diabetes Federation; 2021 Available at: https://diabetesatlas.org/atlas/tenth‐edition/?dlmodal=active&dlsrc=https%3A%2F%2Fdiabetesatlas.org%2Fidfawp%2Fresource‐files%2F2021%2F07%2FIDF_Atlas_10th_Edition_2021.pdf. *IDF Diabetes Atlas* (2021)\n\n2. **Standards of medical care in diabetes – 2021**. *Diabetes Care* (2021) **44** S1-S232. PMID: 33298409\n\n3. **Standards of medical care in diabetes – 2022**. *Diabetes Care* (2022) **45** S1-S255. PMID: 34964812\n\n4. 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haematological parameters and metabolic\n syndrome: A population‐based study'\nauthors:\n- Mohammad Javad Najafzadeh\n- Amir Baniasad\n- Reza Shahabinejad\n- Mahdieh Mashrooteh\n- Hamid Najafipour\n- Mohammad Hossein Gozashti\njournal: Endocrinology, Diabetes & Metabolism\nyear: 2023\npmcid: PMC10000634\ndoi: 10.1002/edm2.407\nlicense: CC BY 4.0\n---\n\n# Investigating the relationship between haematological parameters and metabolic syndrome: A population‐based study\n\n## Abstract\n\nChronic inflammation plays a role in Metabolic syndrome (MetS); hematologic inflammatory parameters can be used as MetS predicting factors. Adults with high WBC count, RDW, MHR, and NHR without any associated underlying chronic disease must be screened because they are at high risk of developing MetS.\n\n### Background\n\nMetabolic syndrome (MetS) is a global public health concern. Chronic inflammation plays a role in MetS; haematological inflammatory parameters can be used as MetS predicting factors.\n\n### Objective\n\nHereditary and environmental factors play an important role in the development of MetS. This study aimed to determine the relationship between haematological parameters and MetS in the adult population of southeastern Iran, Kerman.\n\n### Methods\n\nThis cross‐sectional study was a sub‐analysis of 1033 subjects who participated in the second phase of the Kerman Coronary Artery Disease Risk Factor Study (KERCADRS). Metabolic syndrome was diagnosed according to Adult Treatment Panel III (ATP III) definition. Pearson correlation coefficient was used to investigate the relationship between haematological parameters with age and components of metabolic syndrome. The role of WBC, neutrophil, lymphocyte and monocyte in predicting metabolic syndrome was evaluated using the receiver operating characteristic (ROC) curve.\n\n### Results\n\nWhite blood cell (WBC) and its subcomponent cells count, red cell distribution width (RDW), monocyte to HDL ratio (MHR) and Neutrophil to HDL ratio (NHR) had a significant positive correlation with the severity of MetS. The cut‐off value of WBC was 6.1 (×103/μL), the sensitivity was $70\\%$, the specificity was $52.9\\%$ for females, the cut‐off value of WBC was 6.3 (×103/μL), the sensitivity was $68.2\\%$ and the specificity was $46.7\\%$, for males.\n\n### Conclusion\n\nWBC and its subcomponent count, RDW, MHR and NHR parameters are valuable biomarkers for further risk appraisal of MetS in adults. These markers are helpful in early diagnoses of individuals with MetS.\n\n## INTRODUCTION\n\nMetabolic syndrome (MetS) has had different definitions since 1988, which was first introduced by Reaven. 1 Based on the latest definition of this syndrome, MetS includes at least three factors from the following disorders: central obesity, hypertension, elevated fasting glucose and dyslipidemia (reduced high‐density lipoprotein (HDL) or elevated triglycerides (TG)), 2 which increases the risk of insulin resistance, diabetes mellitus, cerebrovascular disease, cardiovascular disease, common cancers, osteoporosis and total mortality. 3, 4, 5 Prevalence and incidence of MetS have increased significantly following the increase in urbanization, improper nutrition and lack of physical activity, and it has become a global health concern. 6, 7 Although the underlying mechanism of MetS has not been known yet, oxidative stress, chronic inflammation and insulin resistance seem to be the most likely mechanism. 8 A growing number of studies emphasize the association of MetS components and haematological parameters, including white blood cell (WBC), red blood cell (RBC), platelet (PLT) count and haematocrit (HCT) level as potential indicators markers of thrombotic and inflammatory states. 9, 10, 11, 12 Meng et al. demonstrated that leukocyte was a good marker for assessing the risk of MetS and cardiovascular disease. 13 Some studies reported that WBC and PLT counts were significantly correlated with the numbers of MetS components. 14, 15 Ahmadzadeh et al. pointed out that high haemoglobin (HB) levels and HCT can also indicate MetS development. 16\n\nSince inflammation plays a role in MetS, these haematological inflammatory parameters can be used as MetS predicting factors. Performing cost‐effective CBC tests can easily measure haematological parameters from peripheral blood. Hereditary and environmental factors play an important role in the development of MetS. Currently, no study has investigated the characteristics of MetS and its relationship with blood parameters in the population of southeastern Iran. Therefore, this study aimed to determine the relationship between haematological parameters and MetS in southeastern Iran, Kerman.\n\n## Study design and participants\n\nThis cross‐sectional study was a sub‐analysis of 1033 subjects who participated in the second phase of the Kerman Coronary Artery Disease Risk Factor Study (KERCADRS). 17 The sampling method was a cluster from the entire population of Kerman residents. In the first phase of the KERCADRS, according to the post‐office list of city residents, 250 postal codes were selected randomly. We invited people over 15 to participate in the study. In the first phase, 24 people were collected in each cluster. In the second phase (420 clusters including 24 participants), people were contacted again, and 1033 who met the inclusion criteria from February 2017 to October 2018 were included in our study (Figure 1). None of the included participants had a history of chronic infectious or inflammatory diseases or the use of any drugs known to affect haematological parameters or lipoprotein metabolism. More details about the data collection method have been published in the study of Najafipour et al. 17\n\n**FIGURE 1:** *The flowchart of included participants.*\n\n## Data collection\n\nAfter obtaining informed consent forms from the subjects, demographic data (age, gender) and anthropometric information were collected. A trained interviewer asked participants about cigarette smoking and opium use. People who routinely smoked cigarettes or consumed opium at the time of data collection were considered cigarette smokers and opium addicts, respectively. Height in the standing position without shoes, from heel to head, with an error of 0.5 cm error, weight without shoes and extra clothing with an error of 100 g on a digital scale, body mass index (BMI) which the weight (kg) of the patients was divided by the square of their height (m2), waist circumference (WC) in the standing position with 20–30 cm distance between the feet were measured. WC (cm) was measured at the umbilical level. Hip circumference (HC) (cm) was measured based on the largest circumference around the buttocks. Waist‐to‐hip ratio (WHR) was calculated by dividing WC by HC. After 10 min of rest, blood pressure (BP) was measured with a standard manometer from the right arm in the sitting position according to the World Health Organization (WHO) standards, and the blood samples were taken after 12–14 hours of fasting and kept at room temperature. CBC, fasting plasma glucose (FPG) and serum lipids (HDL cholesterol and TG) were tested by routine laboratory methods.\n\nAccording to Adult Treatment Panel III (ATP III) definition, the presence of at least two of the following five factors is required for the diagnosis of metabolic syndrome: blood pressure over $\\frac{130}{80}$ mm Hg or consumption of antihypertensive drugs, TG level over 150 mg/dl, FPG over 100 mg/dl or consumption of anti‐diabetic medication like insulin, HDL cholesterol level less than 40 mg/dL (men) or 50 mg/dl (women) and WC over 102 cm (men) or 88 cm (women).\n\n## Sample size estimation\n\nIn the study of Oda and Kawai, the mean WBC in women with three components of metabolic syndrome was 5416 ± 1163 and in women with only two components of metabolic syndrome was 5077 ± 1358. 18 *The minimum* sample size required based on the mentioned numbers and considering the power of 0.8 and alpha of 0.05 for each group was considered to be at least 275 people.\n\n## Statistical analysis\n\nStatistical analysis was performed using SPSS version 16 software (SPSS Inc.). Quantitative variables were reported as mean ± standard deviation, and qualitative variables were reported as numbers and percentages. Qualitative variables were compared between the two groups using the Pearson chi‐square or Fisher's exact test. Quantitative variables were compared separately between individuals with and without metabolic syndrome in male and female groups using the independent samples t test. Pearson correlation coefficient was used to investigate the relationship between haematological parameters with age and components of metabolic syndrome. The relationship between haematological parameters and the variable number of components of metabolic syndrome was investigated using Spearman's correlation coefficient. The role of WBC, neutrophil, lymphocyte and monocyte in predicting metabolic syndrome was evaluated using the receiver operating characteristic (ROC) curve and with MedCalc® Statistical Software version 20.013 (MedCalc Software Ltd). The optimal cut‐off point was determined using the Youden index.\n\n## RESULTS\n\nA total of 1033 individuals (660 women, 373 men) were included in this study, and the sociodemographic, laboratory parameters and clinical characteristics of the participants are summarized in Table 1.\n\n**TABLE 1**\n\n| Unnamed: 0 | Male | Male.1 | p value a | Female | Female.1 | p value a.1 |\n| --- | --- | --- | --- | --- | --- | --- |\n| | Normal | Syndrome Metabolic | p value a | Normal | Syndrome Metabolic | p value a |\n| | N = 291 | N = 82 | p value a | N = 463 | N = 197 | p value a |\n| Age (years) | 45.48 ± 16.21 | 51.90 ± 13.84 | <.001 | 40.29 ± 14.07 | 53.33 ± 11.72 | <.001 |\n| Smoking, n (%) | 69 (23.7) | 18 (22.0) | .739 | 3 (0.6) | 1 (0.5) | .655 |\n| Opium addiction, n (%) | 64 (22.0) | 17 (20.7) | .807 | 21 (4.5%) | 15 (7.6%) | .111 |\n| BMI (kg/m2) | 24.99 ± 4.22 | 29.18 ± 3.86 | <.001 | 26.38 ± 4.86 | 30.81 ± 5.17 | <.001 |\n| WC (cm) | 88.26 ± 11.94 | 101.29 ± 9.37 | <.001 | 83.30 ± 11.83 | 98.16 ± 10.47 | <.001 |\n| WHR | 0.89 ± 0.07 | 0.96 ± 0.05 | <.001 | 0.82 ± 0.08 | 0.93 ± 0.08 | <.001 |\n| SBP (mmHg) | 114.98 ± 16.01 | 127.99 ± 16.92 | <.001 | 108.98 ± 15.56 | 122.60 ± 17.43 | <.001 |\n| DBP (mmHg) | 74.98 ± 9.39 | 82.38 ± 11.28 | <.001 | 71.79 ± 10.46 | 78.11 ± 9.46 | <.001 |\n| FPG | 90.86 ± 24.05 | 116.20 ± 41.39 | <.001 | 86.67 ± 18.34 | 120.40 ± 47.38 | <.001 |\n| TG (mg/dl) | 127.03 ± 66.25 | 221.15 ± 163.26 | <.001 | 100.11 ± 43.90 | 184.71 ± 78.75 | <.001 |\n| HDL (mg/dl) | 46.43 ± 10.55 | 37.87 ± 7.27 | <.001 | 52.97 ± 12.41 | 44.31 ± 9.57 | <.001 |\n| LDL (mg/dl) | 109.31 ± 31.35 | 101.07 ± 37.57 | .077 | 109.29 ± 35.07 | 113.65 ± 40.42 | .188 |\n| Cholestrol (mg/dl) | 181.08 ± 37.04 | 181.66 ± 43.56 | .912 | 182.07 ± 43.53 | 194.74 ± 45.56 | .001 |\n| WBC (×103/μL) | 6.76 ± 1.68 | 7.08 ± 1.68 | .131 | 6.30 ± 1.66 | 6.92 ± 1.52 | <.001 |\n| Neutrophil (×103/μL) | 3.46 ± 1.25 | 3.72 ± 1.38 | .119 | 3.35 ± 1.21 | 3.70 ± 1.12 | .001 |\n| Lymphocyte (×103/μL) | 2.48 ± 0.73 | 2.51 ± 0.73 | .793 | 2.24 ± 0.66 | 2.46 ± 0.68 | <.001 |\n| Monocyte (×103/μL) | 0.58 ± 0.17 | 0.61 ± 0.16 | .174 | 0.51 ± 0.14 | 0.54 ± 0.15 | .005 |\n| RBC (×106/μL) | 5.33 ± 0.54 | 5.35 ± 0.60 | .745 | 4.78 ± 0.47 | 4.78 ± 0.49 | .951 |\n| HB (gr/dl) | 15.19 ± 1.26 | 15.33 ± 1.28 | .378 | 13.24 ± 1.18 | 13.40 ± 1.38 | .177 |\n| HCT (%) | 46.04 ± 3.51 | 45.87 ± 4.22 | .741 | 41.08 ± 3.57 | 41.31 ± 4.23 | .516 |\n| PLT(×103/μL) | 220.67 ± 49.95 | 215.24 ± 45.32 | .376 | 254.18 ± 62.34 | 255.26 ± 56.46 | .834 |\n| MPV (fL) | 10.27 ± 0.87 | 10.22 ± 0.77 | .640 | 10.54 ± 0.94 | 10.47 ± 0.90 | .355 |\n| RDW‐SD | 42.97 ± 3.14 | 42.76 ± 3.24 | .602 | 42.94 ± 2.98 | 43.56 ± 3.10 | .017 |\n| RDW‐CV | 13.89 ± 1.35 | 13.94 ± 1.26 | .787 | 14.02 ± 1.34 | 14.16 ± 1.39 | .242 |\n| NLR | 1.49 ± 0.68 | 1.59 ± 0.76 | .233 | 1.57 ± 0.62 | 1.61 ± 0.67 | .539 |\n| PLR | 94.85 ± 31.90 | 91.59 ± 28.99 | .405 | 120.76 ± 40.62 | 110.52 ± 37.39 | .003 |\n| PMR | 403.18 ± 128.29 | 367.38 ± 96.79 | .007 | 529.20 ± 169.01 | 499.46 ± 162.80 | .037 |\n| MHR | 0.013 ± 0.005 | 0.0176 ± 0.01 | <.001 | 0.010 ± 0.004 | 0.013 ± 0.004 | <.001 |\n| NHR | 0.08 ± 0.04 | 0.10 ± 0.04 | <.001 | 0.07 ± 0.03 | 0.09 ± 0.03 | <.001 |\n\nIn both males and females, in the participants with MetS, age, BMI, WC, WHR, systolic blood pressure (SBP), diastolic blood pressure (DBP), FPG, TG, monocyte to HDL ratio (MHR) and Neutrophil to HDL ratio (NHR) were significantly higher compared with the participants without MetS. In females with MetS, WBC, Red Cell distribution width‐standard deviation (RDW‐SD), Neutrophil, Lymphocyte and Monocyte were significantly higher than the females without MetS (Table 1).\n\nHDL and platelet to Monocyte ratio (PMR) were significantly lower in participants with MetS compared with those without MetS in males and females. In females with MetS, the platelet to Lymphocyte ratio (PLR) was significantly lower than those without MetS (Table 1).\n\nIn males with MetS, smoking, opium addiction, WC, WHR, SBP, DBP, monocyte, RBC, HB, HCT, MHR and NHR were significantly higher than these parameters in females with MetS. In females with MetS, BMI, HDL, LDL, cholesterol, PLT, MPV, PLR and PMR were significantly higher than these parameters in males with MetS (Table 2).\n\n**TABLE 2**\n\n| Unnamed: 0 | Syndrome Metabolic | Syndrome Metabolic.1 | p Value a |\n| --- | --- | --- | --- |\n| | Male | Female | |\n| | N = 82 | N = 197 | |\n| Age (years) | 51.90 ± 13.84 | 53.33 ± 11.72 | 0.381 |\n| Smoking, n (%) | 18 (22.0) | 1 (0.5) | <0.001 |\n| Opium addiction, n (%) | 17 (20.7) | 15 (7.6%) | 0.002 |\n| BMI (kg/m2) | 29.18 ± 3.86 | 30.81 ± 5.17 | 0.011 |\n| WC (cm) | 101.29 ± 9.37 | 98.16 ± 10.47 | 0.020 |\n| WHR | 0.96 ± 0.05 | 0.93 ± 0.08 | 0.001 |\n| SBP (mmHg) | 127.99 ± 16.92 | 122.60 ± 17.43 | 0.019 |\n| DBP (mmHg) | 82.38 ± 11.28 | 78.11 ± 9.46 | 0.001 |\n| FPG | 116.20 ± 41.39 | 120.40 ± 47.38 | 0.485 |\n| TG (mg/dl) | 221.15 ± 163.26 | 184.71 ± 78.75 | 0.057 |\n| HDL (mg/dl) | 37.87 ± 7.27 | 44.31 ± 9.57 | <0.001 |\n| LDL (mg/dl) | 101.07 ± 37.57 | 113.65 ± 40.42 | 0.018 |\n| Cholestrol (mg/dl) | 181.66 ± 43.56 | 194.74 ± 45.56 | 0.028 |\n| WBC (×103/μL) | 7.08 ± 1.68 | 6.92 ± 1.52 | 0.434 |\n| Neutrophil (×103/μL) | 3.72 ± 1.38 | 3.70 ± 1.12 | 0.935 |\n| Lymphocyte (×103/μL) | 2.51 ± 0.73 | 2.46 ± 0.68 | 0.640 |\n| Monocyte (×103/μL) | 0.61 ± 0.16 | 0.54 ± 0.15 | 0.001 |\n| RBC (×106/μL) | 5.35 ± 0.60 | 4.78 ± 0.49 | <0.001 |\n| HB (gr/dl) | 15.33 ± 1.28 | 13.40 ± 1.38 | <0.001 |\n| HCT (%) | 45.87 ± 4.22 | 41.31 ± 4.23 | <0.001 |\n| PLT(×103/μL) | 215.24 ± 45.32 | 255.26 ± 56.46 | <0.001 |\n| MPV (fL) | 10.22 ± 0.77 | 10.47 ± 0.90 | 0.030 |\n| RDW‐SD | 42.76 ± 3.24 | 43.56 ± 3.10 | 0.056 |\n| RDW‐CV | 13.94 ± 1.26 | 14.16 ± 1.39 | 0.212 |\n| NLR | 1.59 ± 0.76 | 1.61 ± 0.67 | 0.883 |\n| PLR | 91.59 ± 28.99 | 110.52 ± 37.39 | <0.001 |\n| PMR | 367.38 ± 96.79 | 499.46 ± 162.80 | <0.001 |\n| MHR | 0.0176 ± 0.01 | 0.013 ± 0.004 | <0.001 |\n| NHR | 0.10 ± 0.04 | 0.09 ± 0.03 | 0.002 |\n\nAs shown in Table 3, we have considered the number of metabolic components as a measure to determine the severity of MetS, WBC count, Neutrophil, Lymphocyte, Monocyte, RDW‐SD, Red cell distribution width‐coefficient of variation (RDW‐CV), PLR, MHR and NHR parameters that were significantly correlated with the severity of MetS. The correlation was positive in mentioned parameters except for PLR. WBC was significantly correlated with all metabolic components except age (Table 3).\n\n**TABLE 3**\n\n| Variables | Age (years) b | WC (cm) b | FPG (mg/dl) b | TG (mg/dl) b | HDL (mg/dl) b | SBP (mmHg) b | DBP (mmHg) b | Number of components a |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| WBC (×103/μL) | −0.001 | 0.139** | 0.095** | 0.165** | −0.161** | 0.080* | 0.069* | 0.191 |\n| Neutrophil (×103/μL) | −0.029 | 0.122** | 0.081** | 0.095** | −0.112** | 0.049 | 0.054 | 0.164** |\n| Lymphocyte (×103/μL) | 0.022 | 0.087** | 0.069* | 0.188** | −0.138** | 0.074* | 0.047 | 0.150** |\n| Monocyte (×103/μL) | 0.059 | 0.113** | 0.045 | 0.089** | −0.139** | 0.086** | 0.063* | 0.089** |\n| RBC (×106/μL) | −0.027 | 0.097** | −0.021 | 0.132** | −0.128** | 0.154** | 0.176** | −0.029 |\n| HB (gr/dl) | 0.074* | 0.130** | −0.001 | 0.178** | −0.142** | 0.150** | 0.161** | −0.026 |\n| HCT (%) | 0.123** | 0.136** | −0.044 | 0.142** | −0.053 | 0.173** | 0.188** | −0.038 |\n| PLT (×103/μL) | −0.099** | −0.035 | −0.026 | −0.005 | 0.117** | −0.015 | 0.025 | 0.034 |\n| MPV (fL) | −0.037 | −0.031 | 0.059 | −0.085** | −0.038 | −0.075* | −0.092** | 0.016 |\n| RDW‐SD* | 0.280** | 0.152** | −0.046 | −0.026 | 0.088** | 0.080** | 0.057 | 0.067* |\n| RDW‐CV | 0.037 | 0.074* | −0.017 | −0.025 | −0.033 | 0.060 | 0.051 | 0.106** |\n| NLR | −0.025 | 0.047 | 0.030 | −0.037 | 0.002 | −0.007 | 0.020 | 0.025 |\n| PLR | −0.077* | −0.106** | −0.073* | −0.158** | 0.195** | −0.074* | −0.022 | −0.107** |\n| PMR | −0.101** | −0.126** | −0.059 | −0.070* | 0.204** | −0.087** | −0.034 | −0.054 |\n| MHR | 0.022 | 0.231** | 0.077* | 0.300** | −0.649** | 0.080* | 0.053 | 0.335** |\n| NHR | −0.038 | 0.224** | 0.101** | 0.268** | −0.579** | 0.058 | 0.054 | 0.371** |\n\nGenders had different accuracy of WBC, Neutrophil, Lymphocyte and Monocyte in predicting MetS. The accuracy of WBC was higher for females (AUC = 0.632; $p \\leq .001$; $95\\%$ confidence interval [CI]: 0.594–0.669) than for males (AUC = 0.564; $$p \\leq .074$$; $95\\%$ confidence interval [CI]: 0.512–0.615) (Table 4).\n\n**TABLE 4**\n\n| Unnamed: 0 | WBC (×103/μL) | Neutrophil (×103/μL) | Lymphocyte (×103/μL) | Monocyte (×103/μL) |\n| --- | --- | --- | --- | --- |\n| Male | Male | Male | Male | Male |\n| AUC (95% CI) | 0.564 (0.512–0.615) | 0.566 (0.514–0.617) | 0.503 (0.451–0.555) | 0.549 (0.497–0.600) |\n| Optimal cut‐off point | 6.34 | 3.41 | 2.37 | 0.43 |\n| Sensitivity (%) | 68.29 | 57.32 | 56.10 | 93.9 |\n| Specificity (%) | 46.74 | 56.70 | 51.89 | 17.87 |\n| Youden index | 0.150 | 0.140 | 0.080 | 0.118 |\n| p Value | .074 | .065 | .943 | .154 |\n| Female | Female | Female | Female | Female |\n| AUC (95% CI) | 0.632 (0.594–0.669) | 0.608 (0.569–645) | 0.609 (0.566–0.642) | 0.570 (0.532–0.609) |\n| Optimal cut‐off point | 6.15 | 3.67 | 2.36 | 0.44 |\n| Sensitivity (%) | 70.05 | 50.25 | 52.28 | 77.16 |\n| Specificity (%) | 52.92 | 71.0 | 63.93 | 36.93 |\n| Youden index | 0.230 | 0.212 | 0.162 | 0.141 |\n| p Value | <.001 | <.001 | <.001 | .003 |\n\nThe cut‐off value of WBC was 6.1 (×103/μL), the sensitivity was $70\\%$, the specificity was $52.9\\%$ for females, the cut‐off value of WBC was 6.3 (×103/μL), the sensitivity was $68.2\\%$, the specificity was $46.7\\%$, for males. Neutrophil for males (AUC = 0.566) and WBC for females (AUC = 0.632) had better accuracy in predicting MetS compared to other parameters (Table 4) (Figures 2 and 3).\n\n**FIGURE 2:** *Areas Under the ROC curve (AUC) for WBC, Neutrophil, Lymphocyte and Monocyte in predicting metabolic syndrome for males.* **FIGURE 3:** *Areas Under the ROC curve (AUC) for WBC, Neutrophil, Lymphocyte and Monocyte in predicting metabolic syndrome for females.*\n\n## DISCUSSION\n\nWe found that MetS affected the haematological parameters of the patients, including WBC and its subcomponent cell count, RDW, PLR, MHR and NHR.\n\nIn our study, WBC and its subcomponent cells count had a significant positive correlation with the severity of MetS, especially in females. Our results were in parallel with previous studies, which had reported a significant difference in the WBC and its subcomponent cells, between participants with or without MetS. 11, 16, 19 Yang et al. reported that the number of total leukocyte‐related parameters were elevated in individuals aged 60 years or above. 20 Ahmadzadeh et al. demonstrated that MetS components were significantly correlated with WBC and its subcomponent cells count. 16 In Hedayati et al. study in western Iran, the means of WBC count in the MetS group were significantly higher than the control group. 21 Consistent with our data in a study by Chen et al., contrary to the platelet‐related parameters, the WBC‐related parameters had significant changes in patients with MetS. 22 *In a* study on a total of 100 healthy subjects and 200 patients with MetS, total leukocyte and neutrophil counts were significantly increased in all groups of MetS patients compared to the healthy group. 23 Insulin resistance and chronic inflammation are associated with metabolic syndrome by synthesizing some cytokines leading to an increase in WBC and its subcomponent cells count. 24 Lorenzo et al. observed an association between the increased risk of diabetes and elevated WBC, neutrophil and lymphocyte counts due to insulin resistance/sensitivity mechanism. 25 In addition, the relationship between higher levels of WBC count and higher BMI values has been observed in both sexes. 26 *In this* study, it was found that in the group of patients with MetS, women had greater BMI, higher cholesterol, PLT and platelet‐related ratios, and men had a history of more smoking and opium consumption, higher BP, HB, HCT, RBC, monocytes and monocytes‐related ratios. Consistent with our results in another study, it has been determined that the predominant feature of MetS in women was abdominal obesity and impaired lipid profile, and in men, it was high BP and impaired lipid profile. 27 In our study, RDW had a significant positive correlation with the severity of MetS, especially in females; however, this correlation was not observed in mean platelet volume (MPV). So far, minimal studies have been done in this field. Consistent with our data, Farah et al. indicated that both RDW and MPV markers increased as the severity of MetS increased. 28 Abdel‐Moneim et al. found higher levels of MPV in MetS patients. 23 Zhao et al. demonstrated that MPV was inversely related to MS in women. 29 In another study, no significant difference in the MPV between those with and without MetS was observed. 16\n\nIn our study, MHR and NHR had a significant positive correlation with the severity of MetS. A recent study demonstrated that NHR and Lymphocyte to HDL ratio (LHR) were significantly correlated with the prevalence of MetS; also, the correlation was more profound in females. 30 A recent study showed that both MHR and NHR were significantly increased in patients with nascent MetS. 31 Considering that monocyte is an indicating factor for inflammatory conditions and atherosclerosis, 32, 33 some studies revealed that the ratio of MHR is a suitable predictor to determine the development and severity of MetS and cardiovascular events. 34, 35 According to our result, Neutrophil to Lymphocyte ratio (NLR) was not recognized as a MetS predictive factor. Ryder et al. observed no association between NLR and obesity or insulin resistance. 36 Contrary to our results, it was found in two studies that patients with MetS had a higher NLR. 23, 37 Liu et al. relieved that the risk of MetS increased with increasing NLR, and NLR was mentioned as a factor for predicting the development of MetS. 38 In addition, this ratio has been mentioned as a predictive factor for diabetes in obese individuals. 39 PLR had a significant negative correlation with all metabolic components except DBP in this study. In another study, it was reported that the amount of PLR in patients with MetS was higher than in patients without MetS, and the amount of PLR had a significant positive correlation with C‐reactive protein (CRP) levels. 40 In Abdel‐Moneim et al. study, the PLR was significantly higher in all patients with MetS than in healthy subjects. 23 The cut‐off points for WBC and its subcomponent cell counts are used to determine the potential risk of developing MetS. The cut‐off value of WBC was 6.1 (×103/μL) and 6.3 (×103/μL) for females and males, respectively, in our study. Our results are confirmatory of previous study findings. Pei et al. reported a cut‐off point of 5.6 (×103/μL) for men and 5.8 (×103/μL) for women, 41 and De Oliveira et al. reported a cut‐off point of 7.5 (×103/μL) for men and 5.6 (×103/μL) for women. 42\n\n## Limitations\n\nOur study had multiple limitations. Firstly, this is a cross‐sectional study, and the analysis of the causative effects was not performed. Secondly, the study sample size was small, and the number of males was smaller than females. Further, our study population included people from southeastern Iran, Kerman; we cannot generalize our results to the whole Iran population.\n\n## Future directions\n\nThe measurement of haematological parameters is easily available in most parts of the world. However, unfortunately, in public health policies, these parameters do not have a place in the diagnosis and follow‐up of patients with MetS, which will cause these patients to be missed and impose a lot of financial and social costs on the global health system. The results of our study can b an incentive to conduct prospective studies that will lead to the inclusion of haematological parameters in the diagnostic criteria of MetS. Measuring these haematological parameters is cost‐effective and convenient and facilitates screening patients suspected of MetS and their follow‐up. Prospective studies are required to explain the causality effects between MetS and haematological parameters, confirm our data and evaluate the need to change the risk assessment criteria for MetS.\n\n## CONCLUSION\n\nThe higher levels of WBC and its subcomponent cell count, RDW, MHR and NHR could redict an increased chance of developing MetS, regardless of gender differences. WBC also correlated with MetS components, such as WC, FPG, TG, HDL, SBP and DBP; these parameters are easy to access in patients. Considering that no study has been done on this topic in the population of Southeast Iran, our findings provide additional evidence for using these markers for the early detection of MetS components, which ultimately improves the existing clinical practice in identifying and following MetS patients.\n\n## AUTHOR CONTRIBUTIONS\n\nMohammad Javad Najafzadeh: Conceptualization (equal); investigation (equal); writing – original draft (equal); writing – review and editing (equal). Amir Baniasad: Conceptualization (equal); formal analysis (equal); methodology (equal); writing – review and editing (equal). Reza Shahabinejad: Data curation (equal); investigation (equal); software (equal); writing – original draft (equal). Mahdieh Mashrooteh: *Formal analysis* (equal); investigation (equal); methodology (equal); software (equal). Dr Hamid Najafipour: Conceptualization (equal); investigation (equal); methodology (equal); project administration (equal); writing – review and editing (equal). Mohammad Hossein Gozashti: Conceptualization (equal); investigation (equal); methodology (equal); project administration (equal); writing – original draft (equal); writing – review and editing (equal).\n\n## FUNDING INFORMATION\n\nThe Kerman University of Medical Sciences funded this research project (Reg. No. 95000008).\n\n## CONFLICT OF INTEREST\n\nThe authors declare that they have no conflict of interest.\n\n## ETHICAL APPROVAL\n\nThe study protocol was reviewed and approved by the ethics committee of the Kerman University of Medical Sciences (ethic code: IR.KMU.REC.1395.775). Informed consent was obtained from all participants in the study.\n\n## DATA AVAILABILITY STATEMENT\n\nThe data supporting this study's findings are available from the corresponding author upon reasonable request.\n\n## References\n\n1. Alverti K. **Metabolic syndrome: a new world‐wide definition: a consensus statement from the international diabetes federation**. *Diabet Med* (2006) **23** 469-480. PMID: 16681555\n\n2. Butnoriene J, Bunevicius A, Saudargiene A. **Metabolic syndrome, major depression, generalized anxiety disorder, and ten‐year all‐cause and cardiovascular mortality in middle aged and elderly patients**. *Int J Cardiol* (2015) **190** 360-366. PMID: 25939128\n\n3. 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0.035533785820007324\n]","isConversational":false}}},{"rowIdx":68,"cells":{"text":{"kind":"string","value":"---\ntitle: 'A population‐based cohort of adult patients with diabetes mellitus in a Western\n District of Austria: The Diabetes Landeck cohort'\nauthors:\n- Veronika Haslwanter\n- Ursula Rochau\n- Uwe Siebert\n- Hans‐Robert Schönherr\n- Willi Oberaigner\njournal: Endocrinology, Diabetes & Metabolism\nyear: 2022\npmcid: PMC10000636\ndoi: 10.1002/edm2.395\nlicense: CC BY 4.0\n---\n\n# A population‐based cohort of adult patients with diabetes mellitus in a Western District of Austria: The Diabetes Landeck cohort\n\n## Abstract\n\nThe aims of this project and study are to establish a cohort including all adult DM patients in a district in western Austria, describe the demographic and clinical characteristics of these patients, and provide an estimation of diabetes prevalence, both at an overall level and stratified by gender and age groups.\n\n### Introduction\n\nDiabetes mellitus (DM) has become an important and exacerbating health epidemic, with severe consequences for both patients and health systems. The National Diabetes Strategy of Austria addresses the lack of high‐quality data on DM in Austria and the need for developing a national data network. The aims of our study are to establish a cohort including all adult diabetes patients in a district in western Austria, describe the demographic and clinical characteristics of this cohort, and provide an estimation of diabetes prevalence.\n\n### Methods\n\nWe recruited a population‐based cohort of adult patients with a diagnosis of DM in cooperation with a network of all caregivers. Data collection was based on a case report form, including patient characteristics, clinical parameters and long‐term complications.\n\n### Results\n\nIn total, 1845 patients with DM were recruited and analysed. We observed an overall prevalence of $5.3\\%$ [$95\\%$ CI: $5.0\\%$–$5.5\\%$]. For the subsequent main analysis, we included 1755 patients with DM after excluding 90 patients with gestational DM. There were significant differences between genders in the distribution of specific clinical parameters, patient characteristics, and the long‐term complications diabetic foot, amputation and cardiovascular disease.\n\n### Conclusion\n\nTo the best of our knowledge, we established the first diabetes cohort study in Austria. Prevalence and the proportion of specific long‐term complications were lower when compared to the international context. We assume that the Diabetes Landeck Cohort has reached a high degree of completeness; however, we were not able to identify independent data sources for a valid check of completeness.\n\n## INTRODUCTION\n\nDiabetes mellitus (DM) has become a considerable and exacerbating health epidemic that has severe consequences for both the patients and health systems. 1 According to the tenth edition of the International Diabetes Federation (IDF) Atlas, approximately 537 million people aged 20 to 79 years suffered from DM worldwide in 2021, representing a prevalence of approximately $10.5\\%$. 2 The global prevalence is expected to increase in the future, with 783 million people predicted to suffer from DM by 2045, corresponding to a prevalence of around $10.9\\%$. 2 In Europe, one in eleven adults has DM, which means that 61 million people suffer from DM, and the prevalence is estimated to increase from $9.2\\%$ in 2021 to $10.4\\%$ in 2045. Furthermore, the economic burden associated with DM is substantial: The IDF reports that the share of the global health expenditure for DM ranges from $3.4\\%$ in the Middle East and North African region to $19.6\\%$ in Europe and to $43\\%$ in North America and the Caribbean. 2 Patients with DM suffer from a lifelong burden caused by the treatment and disease complications. One of the main challenges is an increased risk of developing long‐term complications such as neuropathy, nephropathy and cardiovascular disease. 3, 4 In addition, patients with DM have an increased risk of early mortality. Worldwide, about 6.7 million people aged between 20 and 79 years are estimated to die from DM or its complications in 2021. 2 For Austria, the number of patients with DM aged 20–79 years in 2021 was estimated at about 450,000, corresponding to a prevalence of $6.6\\%$. In addition, the number of undiagnosed DM cases was estimated at approximately 150,000. 2 Due to the increasing number of patients with DM and the long‐term complications, considerable DM‐associated healthcare expenditures are also expected in Austria. The annual costs of DM and its comorbidities in Austria amount to an estimated 3 billion euros. 5 Therefore, the main targets of the diabetes strategy of the Austrian Federal Ministry of Labor, Social Affairs, Health and Consumer Protection from 2017 are to reduce the incidence of DM and to prevent long‐term complications. 6 This Austrian strategy document states that there is a lack and necessity of valid data on DM in Austria. Moreover, a report of the Austrian Court of Audition on DM prevention and DM care from 2019 points to the lack of high‐quality data. 7 To provide valid data on DM in Austria, a project called the Diabetes Landeck Cohort was initiated, with the aim to set up a population‐based cohort of patients with DM in a district in western Austria. This cohort should serve as a basis to collect and analyse valid and comprehensive data on DM.\n\nThe aims of this project and study are to establish a cohort including all adult DM patients in a district in western Austria, describe the demographic and clinical characteristics of these patients, and provide an estimation of diabetes prevalence, both at an overall level and stratified by gender and age groups.\n\n## Settings of the study and data collection\n\nThe recruitment of the cohort titled ‘Diabetes Landeck Cohort’ started in 2018 and ended in June 2021. Eligible participants were adults (aged ≥20 years) with type 1 DM (T1DM) or type 2 DM (T2DM) or other types of diabetes (e.g. gestational, latent autoimmune diabetes in adults) and with the main residence in the district of Landeck in the western part of Austria. The district of *Landeck is* a well‐defined region with clear geographical borders, either high mountains within Austria or national borders towards Italy and Switzerland in the south. The district of Landeck with a population of 35,148 persons aged 20 and older consists of the city of Landeck with a population of 6120 persons aged 20 and older and 30 municipalities. This rural area is rather typical for rural areas in Austria. The study was conducted by the Research Unit of Diabetes Epidemiology of the Department of Public Health, Health Services Research and Health Technology Assessment at UMIT TIROL. Before beginning data collection, a case report form (CRF) was designed by diabetes and public health experts. The CRF includes questions on patient characteristics, and questions on time‐varying data based on visits at care units. A network connecting all care units has been established including 22 general practitioners, four diabetes specialists in private practice, the hospital in Zams, the Medical University Innsbruck and retirement homes in the district of Landeck. Data collection was mainly performed by two qualified study nurses who visited the care units. Direct documentation by the treating physician was also technically possible but conducted only in individual cases due to the heavy workload of the physicians caused by the COVID‐19 pandemic. All cohort data are stored in the pseudonymized web‐based database software ASKIMED. 8 ASKIMED provides the possibility to store visits for each patient at different care units and allows the documentation by different persons and the mapping of the necessary access rights. A pseudonym was generated based on each patient's social security number (with a SHA‐2 procedure), which makes it impossible, due to the current computing performance, to identify the specific patient based on the pseudonym. For research purposes, data were transferred to the statistical software Stata (Version 17). 9 In order to address aspects of data privacy, all patients had to sign a written consent form. For data protection reasons, the data of the very few patients who did not agree to sign the consent have not been included into the database for analysis. In addition, every care unit signed a contract stating the rights and obligations between the care units and the research group. The ethics committee at the Medical University of Innsbruck approved the complete project and the project was carried out in accordance with the Helsinki Declaration of 1975, as revised in 2008. A scientific advisory board supervised the study and decided in favour of a minimal data set to build up a population‐based cohort, given restrictions by the limited budget and the COVID‐19 pandemic.\n\n## Patient characteristics\n\nWe collected the following patient characteristics: diagnosis, age, migration background, diagnosis site, year of diagnosis, diabetes duration, family history of coronary heart disease and family history of DM, participation in the Austrian disease management program, smoking status, participation in a diabetes education program and sufficient knowledge on diabetes according to the physician.\n\nAll diagnoses were clinically confirmed according to the criteria of the Austrian Diabetes Society (ADS) based on a fasting glucose or oral glucose tolerance test or haemoglobin A1c (HbA1c). 10 The study protocol did not include patients with prediabetes in the study population. Gestational diabetes (GDM) was documented separately. Diagnosis of GDM was based on the HAPO criteria. 11 Age at the last visit was used for the analysis and was cut into categories, namely 20–49, 50–64, 65–74 and ≥75 years, taking into account the definition of geriatrics of the elderly/old person. The migration background was classified based on Schenk's approach and adapted to the Austrian situation. 12 During documentation, participants were asked whether their DM diagnosis had been made in the hospital or at a physician's office (diagnosis site). For most patients, the year of diagnosis was recalled from patients' memory, and we decided to collect the exact year only for patients diagnosed during the past 10 years. The diabetes duration was defined as the difference between the year of DM diagnosis and the year of last contact with a care unit and was analysed in the following groups: 0–4 years, 5–9 years, and 10 years or longer. We documented family history of coronary heart disease and family history of DM. Family included children, parents and/or siblings. Participation in the Austrian Disease Management Program during the study period 13 was also documented. Additionally, the smoking status at the time of diagnosis was recorded retrospectively in the categories ‘active smoker’ or ‘ex‐smoker’ and ‘never smoker’. We assessed whether each patient had participated at least once in a diabetes education program and whether the patient had sufficient knowledge on DM according to the physician.\n\nEach patient's life status was verified by a study nurse who inspected hospital/private care records and local newspapers in case there were no up‐to‐date visit records. Pseudonymization prevented linking records with official mortality data. It was therefore impossible to systematically check the patients' life status.\n\n## Clinical parameters\n\nData collection included the following clinical parameters: body mass index (BMI), HbA1c, low‐density lipoprotein (LDL), systolic and diastolic blood pressure, microalbumin, physical activity, eye and foot inspection, hypoglycaemia (requiring external help for recovery), diabetes‐specific therapy and lipid therapy.\n\nMean values were computed for BMI, HbA1c, LDL, and systolic and diastolic blood pressure, which were collected at each visit. BMI was calculated according to the formula BMI = weight/height2 (kg/m2), and the classification was based on WHO recommendations. In addition, obesity was defined as a BMI ≥30 kg/m2. 14 The mean values of HbA1c were categorized into four groups (<$6.5\\%$, $6.5\\%$–$7.49\\%$, $7.5\\%$–$8.99\\%$ and >$9\\%$) based on the ADS guidelines. 10 Increased blood pressure was defined as systolic pressure ≥140 mmHg or diastolic pressure ≥90 mmHg according to WHO guidelines. 15 It should be noted that increased blood pressure is based on blood pressure measurements only. We did not collect information on the diagnosis of hypertension and/or medication.\n\nLDL is the primary therapeutic target for lipid control in patients with DM. The LDL classification was based on current recommendations of the ESC/EAS. 16 We did not collect information on the diagnosis of hyperlipidaemia, but we surveyed the proportion of patients with well‐controlled LDL levels. We recorded whether microalbumin was determined at least at one visit. Patients were asked if they were physically active (defined as at least moderate activity for less than two and a half hours per week). We also documented if an ophthalmologist had performed at least one eye inspection during visits. The inspections followed the recommendations of the ADS. 10 Furthermore, we recorded foot inspections during the study period. Foot inspection was defined as at least the removal of shoes and socks and the examination of the feet by the diabetes assistant or physician. Furthermore, we noted severe hypoglycaemia (requiring external help for recovery) during the study period and calculated a cumulative number of events. Finally, we recorded information on the diabetes‐specific therapy. DM treatment categories were lifestyle adaptation only, metformin only, oral antidiabetic drugs (OADs) without metformin, insulin only, insulin and oral antidiabetic drugs, or another form of therapy. Each form of therapy was documented if it was observed during at least one visit over the study period.\n\n## Long‐term complications\n\nWe documented the following long‐term complications: neuropathy, nephropathy, retinopathy, cardiovascular and cerebrovascular disease, diabetic foot ulcers and amputations based on the recommendations of the ADS. 10 *Neuropathy is* defined as nerve injuries due to DM and is confirmed with a positive monofilament test. Nephropathy requires positive albumin results at two subsequent visits. Retinopathy is diagnosed according to the guidelines provided by the Austrian Ophthalmologist Society. 17 Cardiovascular late complication was defined as myocardial infarction, bypass or percutaneous coronary intervention. Cerebrovascular late complication was defined as minor and major strokes, including transient ischaemic attacks. We used the strict definition of the long‐term complication diabetic foot according to the guidelines of the ADS 10 and therefore only collected information on the presence of diabetic foot ulcers. For amputations, we documented any non‐traumatic amputation due to diabetic foot ulcers. For all late complications, the year of the first occurrence was recorded if the diagnosis was made within the past 10 years. In all other cases, we only documented the diagnosis of the respective late complication without the year of occurrence. For the analysis we counted every long‐term complication, not only long‐term complications diagnosed in the study period.\n\nThe most frequent long‐term complication was cardiovascular disease ($$n = 332$$, $19.2\\%$), followed by nephropathy ($$n = 313$$, $18.1\\%$) and neuropathy ($$n = 223$$, $12.9\\%$). We observed significant differences between women and men for diabetic foot, amputation and cardiovascular disease. More men than women suffered from these three long‐term complications (Figure 1). Further details for the total population and genders are presented in Table 3.\n\n**FIGURE 1:** *Long‐term complications stratified by gender ($$n = 1728$$, ‘GDM only’ and missing values excluded)* TABLE_PLACEHOLDER:TABLE 3 In our study, we observed 90 cases with GDM that did not develop T2DM. A mean value of HbA1c < 6 was documented for $94\\%$ of patients with GDM. Only three patients with GDM showed a HbA1c ≥ 6.5. Lifestyle adaptation was sufficient in $82.5\\%$ of patients with GDM, and insulin therapy was necessary for $13.4\\%$.\n\nSee Tables S1–S2 for patient characteristics and clinical parameters according to the age groups 20–49, 50–64, 65–74 and ≥75 years. Briefly, we observed differences in the following patient characteristics: diabetes duration, migration background and smoking status. We also found a difference in the participation in education programmes and sufficient diabetes knowledge, but the percentage was still very high across all age groups. For the clinical parameters stratified by age group, we observed differences in the BMI, LDL, microalbumin, diabetes therapy, physical activity, foot inspection, and for most of the long‐term complications. Further details on patient characteristics and clinical parameters are described in Tables S1–S2.\n\n## Statistical analysis\n\nPatient characteristics are described as counts and percentages for categorical data. We present all results stratified by gender and age groups (Tables S1–S2 only). Fisher's exact test or the chi‐squared test were used to test differences across gender and age groups. Statistical significance was established as $p \\leq .05.$ Cases with GDM that did not result in a life‐long type of DM (called ‘GDM only’) were described in a separate part because they differ in many clinical aspects. The main analysis of demographic and clinical parameters does not include cases with ‘GDM only’. In case of missing data, we report data and percentages for non‐missing values in a first step and present the number of cases with missing values in a second step; this procedure was adopted for every variable reported in the result tables.\n\nFor the computation of prevalence figures, we included all patients with DM detected in our study in the region of Landeck. Population data for the region of Landeck per age group were obtained from Statistics Austria. 18 According to this population data, 35,148 individuals aged 20 and older were living in the district of Landeck at the time of the study, with slightly more women (17,839, $50.8\\%$) than men (17,309, $49.2\\%$). The population remained rather stable during the study period. Prevalence was computed for the entire district and for subregions, which are defined by the geographic structure of the following districts: Kaunertal, Stanzertal, Sonnenterasse, Oberes Gericht and Paznauntal. Prevalence and $95\\%$ confidence intervals ($95\\%$ CI) are provided for the overall cohort and also stratified by gender, subregions and age groups. We applied the concept of period prevalence, that is, we included patients with at least one visit at a care unit stay during the study period as a prevalent case.\n\nAll statistical analyses were performed using Stata Version 17. 9\n\n## Study population/diabetes Landeck cohort\n\nThe recruitment started in 2018 and ended in June 2021. In total, we recruited and analysed 1845 cases with DM including 90 cases with ‘GDM only’. For the main analysis, we excluded ‘GDM only’ cases resulting in a total 1755 cases with DM, $5.7\\%$ with T1DM, $92.4\\%$ with T2DM and $1.9\\%$ with another DM diagnosis. Of the 1755 patients, 25 ($1.4\\%$) died during the study and two subjects were lost in the follow‐up. At the last visit, $9.6\\%$ were aged 20–49 years, $27.2\\%$ were 50–64 years old, $26.6\\%$ were aged 65–74 years, and $36.6\\%$ were 75 years or older. We observed 812 ($46.3\\%$) women and 943 ($53.7\\%$) men. Patient characteristics of the cohort according to gender are presented in Table 1. We only describe results with significant differences between female and male patients. Age distributions differed between genders. For example, in the age group ≥75, the proportion of women with DM was higher than for men. Among patients with DM, women had a longer diabetes duration ($55.9\\%$ ≥10 years vs. $49.2\\%$ in men) and more often reported a family history of diabetes ($38\\%$ in women vs. $31.7\\%$ in men). In men, we observed a substantially higher percentage of active smokers ($14.1\\%$ in men vs. $7.2\\%$ in women) and ex‐smokers compared to women ($53.4\\%$ in men vs. $19.4\\%$ in women).\n\n**TABLE 1**\n\n| Unnamed: 0 | Female | Female.1 | Male | Male.1 | Total | Total.1 | p‐value a |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| | N | % b | N | % b | N | % b | p‐value a |\n| Age group | Age group | Age group | Age group | Age group | Age group | Age group | Age group |\n| 20–49 | 67 | 8.3 | 101 | 10.7 | 168 | 9.6 | <.001* |\n| 50–64 | 170 | 20.9 | 308 | 32.7 | 478 | 27.2 | |\n| 65–74 | 215 | 26.5 | 251 | 26.6 | 466 | 26.6 | |\n| ≥75 | 360 | 44.3 | 283 | 30.0 | 643 | 36.6 | |\n| Total | 812 | 100.0 | 943 | 100.0 | 1755 | 100.0 | |\n| Missing values c | 0 | 0 | 0 | 0 | 0 | 0 | |\n| Diagnosis | Diagnosis | Diagnosis | Diagnosis | Diagnosis | Diagnosis | Diagnosis | Diagnosis |\n| T1DM | 40 | 4.9 | 59 | 6.3 | 99 | 5.7 | .112 |\n| T2DM | 760 | 93.7 | 856 | 91.3 | 1616 | 92.4 | |\n| Other DM | 11 | 1.4 | 23 | 2.5 | 34 | 1.9 | |\n| Total | 811 | 100.0 | 938 | 100.0 | 1749 | 100.0 | |\n| Missing values | 1 | 0.1 | 5 | 0.5 | 6 | 0.3 | |\n| Diagnosis site | Diagnosis site | Diagnosis site | Diagnosis site | Diagnosis site | Diagnosis site | Diagnosis site | Diagnosis site |\n| Hospital | 243 | 32.1 | 325 | 36.8 | 568 | 34.6 | .048* |\n| Private practice | 514 | 67.9 | 559 | 63.2 | 1073 | 65.4 | |\n| Total | 757 | 100.0 | 884 | 100.0 | 1641 | 100.0 | |\n| Missing values | 55 | 6.8 | 59 | 6.3 | 114 | 6.5 | |\n| Diabetes duration | Diabetes duration | Diabetes duration | Diabetes duration | Diabetes duration | Diabetes duration | Diabetes duration | Diabetes duration |\n| 0–4 years | 185 | 22.8 | 258 | 27.4 | 443 | 25.2 | .016* |\n| 5–9 years | 173 | 21.3 | 221 | 23.4 | 394 | 22.5 | |\n| ≥10 years | 454 | 55.9 | 464 | 49.2 | 918 | 52.3 | |\n| Total | 812 | 100.0 | 943 | 100.0 | 1755 | 100.0 | |\n| Missing values | 0 | 0 | 0 | 0 | 0 | 0 | |\n| Family history of diabetes | Family history of diabetes | Family history of diabetes | Family history of diabetes | Family history of diabetes | Family history of diabetes | Family history of diabetes | Family history of diabetes |\n| No | 482 | 62.0 | 611 | 68.3 | 1093 | 65.4 | .007* |\n| Yes | 295 | 38.0 | 283 | 31.7 | 578 | 34.6 | |\n| Total | 777 | 100.0 | 894 | 100.0 | 1671 | 100.0 | |\n| Missing values | 35 | 4.3 | 49 | 5.2 | 84 | 4.8 | |\n| Family history of coronary heart disease | Family history of coronary heart disease | Family history of coronary heart disease | Family history of coronary heart disease | Family history of coronary heart disease | Family history of coronary heart disease | Family history of coronary heart disease | Family history of coronary heart disease |\n| No | 621 | 79.7 | 727 | 81.4 | 1348 | 80.6 | .386 |\n| Yes | 158 | 20.3 | 166 | 18.6 | 324 | 19.4 | |\n| Total | 779 | 100.0 | 893 | 100.0 | 1672 | 100.0 | |\n| Missing values | 33 | 4.1 | 50 | 5.3 | 83 | 4.7 | |\n| Participation in disease management program | Participation in disease management program | Participation in disease management program | Participation in disease management program | Participation in disease management program | Participation in disease management program | Participation in disease management program | Participation in disease management program |\n| No | 726 | 89.4 | 843 | 89.4 | 1569 | 89.4 | 1.000 |\n| Yes | 86 | 10.6 | 100 | 10.6 | 186 | 10.6 | |\n| Total | 812 | 100.0 | 943 | 100.0 | 1755 | 100.0 | |\n| Missing values | 0 | 0 | 0 | 0 | 0 | 0 | |\n| Life status | Life status | Life status | Life status | Life status | Life status | Life status | Life status |\n| Alive | 800 | 98.5 | 928 | 98.4 | 1728 | 98.5 | .416 |\n| Deceased | 12 | 1.5 | 13 | 1.4 | 25 | 1.4 | |\n| Lost/moved | 0 | 0.0 | 2 | 0.2 | 2 | 0.1 | |\n| Total | 812 | 100.0 | 943 | 100.0 | 1755 | 100.0 | |\n| Missing values | 0 | 0 | 0 | 0 | 0 | 0 | |\n| Migration background | Migration background | Migration background | Migration background | Migration background | Migration background | Migration background | Migration background |\n| No | 705 | 87.3 | 793 | 84.3 | 1498 | 85.6 | .087 |\n| Yes | 103 | 12.7 | 148 | 15.7 | 251 | 14.4 | |\n| Total | 808 | 100.0 | 941 | 100.0 | 1749 | 100.0 | |\n| Missing values | 4 | 0.5 | 2 | 0.2 | 6 | 0.3 | |\n| Smoking status | Smoking status | Smoking status | Smoking status | Smoking status | Smoking status | Smoking status | Smoking status |\n| Active smoker | 55 | 7.2 | 125 | 14.1 | 180 | 10.9 | .000* |\n| Ex‐smoker | 147 | 19.4 | 474 | 53.4 | 621 | 37.7 | |\n| Never smoker | 557 | 73.4 | 288 | 32.5 | 845 | 51.3 | |\n| Total | 759 | 100.0 | 887 | 100.0 | 1646 | 100.0 | |\n| Missing values | 53 | 6.5 | 56 | 5.9 | 109 | 6.2 | |\n| Participation in education program | Participation in education program | Participation in education program | Participation in education program | Participation in education program | Participation in education program | Participation in education program | Participation in education program |\n| No | 168 | 21.0 | 209 | 22.5 | 377 | 21.8 | .483 |\n| Yes | 632 | 79.0 | 720 | 77.5 | 1352 | 78.2 | |\n| Total | 800 | 100.0 | 929 | 100.0 | 1729 | 100.0 | |\n| Missing values | 12 | 1.5 | 14 | 1.5 | 26 | 1.5 | |\n| Sufficient diabetes knowledge (according to physician) | Sufficient diabetes knowledge (according to physician) | Sufficient diabetes knowledge (according to physician) | Sufficient diabetes knowledge (according to physician) | Sufficient diabetes knowledge (according to physician) | Sufficient diabetes knowledge (according to physician) | Sufficient diabetes knowledge (according to physician) | Sufficient diabetes knowledge (according to physician) |\n| No | 115 | 15.1 | 109 | 12.2 | 224 | 13.5 | .097 |\n| Yes | 648 | 84.9 | 785 | 87.8 | 1433 | 86.5 | |\n| Total | 763 | 100.0 | 894 | 100.0 | 1657 | 100.0 | |\n| Missing values | 49 | 6.0 | 49 | 5.2 | 98 | 5.6 | |\n\nGender differences were found for specific clinical parameters, such as BMI, HbA1c, LDL, determination of microalbumin, DM therapy and physical activity. Table 2 shows the comparison of these characteristics between genders. We observed more overweight subjects (i.e. BMI = 25.0–29.99) in men than women. The proportion of obesity (i.e. BMI≥30) was similar for both genders. Women had a significantly greater proportion of well‐controlled HbA1c levels (HbA1c < 6.5) than men. The distribution of LDL showed a shift to higher values in men compared to women. Microalbumin was identified more frequently in men than in women, and more men were physically active ($28.5\\%$ vs. $21.9\\%$). We also observed differences in DM therapy. More women than men only adapted their lifestyle ($22.3\\%$ vs. $16.2\\%$). In men, metformin was more frequently used than in women, for example, more men ($22.9\\%$) received oral antidiabetic drugs therapy AND insulin than women ($18.5\\%$). Further details on clinical parameters are shown in Table 2.\n\n**TABLE 2**\n\n| Unnamed: 0 | Female | Female.1 | Male | Male.1 | Total | Total.1 | p‐value a |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| | N | % b | N | % b | N | % b | p‐value a |\n| BMI | BMI | BMI | BMI | BMI | BMI | BMI | BMI |\n| <18.5 | 9 | 1.1 | 5 | 0.5 | 14 | 0.8 | .015* |\n| 18.5–24.99 | 174 | 22.0 | 165 | 18.0 | 339 | 19.8 | |\n| 25.0–29.99 | 275 | 34.7 | 379 | 41.4 | 654 | 38.3 | |\n| ≥30 | 334 | 42.2 | 367 | 40.1 | 701 | 41.0 | |\n| Total | 792 | 100.0 | 916 | 100.0 | 1708 | 100.0 | |\n| Missing values c | 20 | 2.5 | 27 | 2.9 | 47 | 2.7 | |\n| HbA1c | HbA1c | HbA1c | HbA1c | HbA1c | HbA1c | HbA1c | HbA1c |\n| 0–6.49 | 320 | 41.9 | 325 | 36.2 | 645 | 38.8 | .033* |\n| 6.5–7.49 | 254 | 33.3 | 303 | 33.7 | 557 | 33.5 | |\n| 7.5–8.99 | 158 | 20.7 | 235 | 26.2 | 393 | 23.7 | |\n| 9–99 | 31 | 4.1 | 35 | 3.9 | 66 | 4.0 | |\n| Total | 763 | 100.0 | 898 | 100.0 | 1661 | 100.0 | |\n| Missing values | 49 | 6.0 | 45 | 4.8 | 94 | 5.4 | |\n| LDL | LDL | LDL | LDL | LDL | LDL | LDL | LDL |\n| <55 | 77 | 10.2 | 110 | 12.6 | 187 | 11.5 | .017* |\n| 55–69 | 92 | 12.2 | 111 | 12.7 | 203 | 12.5 | |\n| 70–99 | 202 | 26.7 | 273 | 31.3 | 475 | 29.2 | |\n| ≥100 | 386 | 51.0 | 378 | 43.3 | 764 | 46.9 | |\n| Total | 757 | 100.0 | 872 | 100.0 | 1629 | 100.0 | |\n| Missing values | 55 | 6.8 | 71 | 7.5 | 126 | 7.2 | |\n| Blood pressure (measured) | Blood pressure (measured) | Blood pressure (measured) | Blood pressure (measured) | Blood pressure (measured) | Blood pressure (measured) | Blood pressure (measured) | Blood pressure (measured) |\n| Within normal range | 585 | 74.0 | 670 | 73.0 | 1255 | 73.4 | .661 |\n| Increased (≥140/90) | 206 | 26.0 | 248 | 27.0 | 454 | 26.6 | |\n| Total | 791 | 100.0 | 918 | 100.0 | 1709 | 100.0 | |\n| Missing values | 21 | 2.6 | 25 | 2.7 | 46 | 2.6 | |\n| Microalbumin | Microalbumin | Microalbumin | Microalbumin | Microalbumin | Microalbumin | Microalbumin | Microalbumin |\n| No | 361 | 47.8 | 357 | 40.7 | 718 | 43.9 | .004* |\n| Yes | 395 | 52.2 | 521 | 59.3 | 916 | 56.1 | |\n| Total | 756 | 100.0 | 878 | 100.0 | 1634 | 100.0 | |\n| Missing values | 56 | 6.9 | 65 | 6.9 | 121 | 6.9 | |\n| Diabetes therapy | Diabetes therapy | Diabetes therapy | Diabetes therapy | Diabetes therapy | Diabetes therapy | Diabetes therapy | Diabetes therapy |\n| Only metformin | 313 | 38.9 | 403 | 43.1 | 716 | 41.1 | .008* |\n| OAD without metformin | 51 | 6.3 | 50 | 5.3 | 101 | 5.8 | |\n| Only insulin | 106 | 13.2 | 112 | 12.0 | 218 | 12.5 | |\n| OAD + insulin | 149 | 18.5 | 214 | 22.9 | 363 | 20.9 | |\n| Another form of therapy | 6 | 0.7 | 5 | 0.5 | 11 | 0.6 | |\n| Only lifestyle adaptation | 179 | 22.3 | 152 | 16.2 | 331 | 19.0 | |\n| Total | 804 | 100.0 | 936 | 100.0 | 1740 | 100.0 | |\n| Missing values | 8 | 1.0 | 7 | 0.7 | 15 | 0.9 | |\n| Number of occurrences of hypoglycaemia | Number of occurrences of hypoglycaemia | Number of occurrences of hypoglycaemia | Number of occurrences of hypoglycaemia | Number of occurrences of hypoglycaemia | Number of occurrences of hypoglycaemia | Number of occurrences of hypoglycaemia | Number of occurrences of hypoglycaemia |\n| 0 | 790 | 97.5 | 914 | 97.0 | 1704 | 97.3 | .812 |\n| 1 | 12 | 1.5 | 17 | 1.8 | 29 | 1.7 | |\n| ≥2 | 8 | 1.0 | 11 | 1.2 | 19 | 1.1 | |\n| Total | 810 | 100.0 | 942 | 100.0 | 1752 | 100.0 | |\n| Missing values | 2 | 0.2 | 1 | 0.1 | 3 | 0.2 | |\n| Physical activity | Physical activity | Physical activity | Physical activity | Physical activity | Physical activity | Physical activity | Physical activity |\n| Inactive | 568 | 78.1 | 609 | 71.5 | 1177 | 74.5 | .003* |\n| Active | 159 | 21.9 | 243 | 28.5 | 402 | 25.5 | |\n| Total | 727 | 100.0 | 852 | 100.0 | 1579 | 100.0 | |\n| Missing values | 85 | 10.5 | 91 | 9.7 | 176 | 10.0 | |\n| Eye inspection | Eye inspection | Eye inspection | Eye inspection | Eye inspection | Eye inspection | Eye inspection | Eye inspection |\n| No | 303 | 37.4 | 355 | 37.7 | 658 | 37.6 | .921 |\n| Yes | 507 | 62.6 | 587 | 62.3 | 1094 | 62.4 | |\n| Total | 810 | 100.0 | 942 | 100.0 | 1752 | 100.0 | |\n| Missing values | 2 | 0.2 | 1 | 0.1 | 3 | 0.2 | |\n| Foot inspection | Foot inspection | Foot inspection | Foot inspection | Foot inspection | Foot inspection | Foot inspection | Foot inspection |\n| No | 170 | 21.0 | 185 | 19.6 | 355 | 20.3 | .512 |\n| Yes | 640 | 79.0 | 757 | 80.4 | 1397 | 79.7 | |\n| Total | 810 | 100.0 | 942 | 100.0 | 1752 | 100.0 | |\n| Missing values | 2 | 0.2 | 1 | 0.1 | 3 | 0.2 | |\n\n## Prevalence\n\nIn the age group ≥20 with a total population of 35,148, we observed an overall diabetes prevalence of $5.3\\%$ ($95\\%$ CI: 5.0–5.5). Prevalence was slightly higher in men ($5.5\\%$, $95\\%$ CI: 5.1–5.8) than in women ($5.1\\%$, $95\\%$ CI: 4.7–5.4, difference not statistically significant). Prevalence differed between subregions: The lowest prevalence was observed in the subregion Sonnenterasse with $3.1\\%$ ($95\\%$ CI: 2.4–3.9) and the highest in the central area Landeck and surroundings with $6.1\\%$ ($95\\%$ CI: 5.7–6.5). The prevalence was $3.4\\%$ ($95\\%$ CI: 2.9–3.9) in the region Paznauntal, $4.1\\%$ ($95\\%$ CI: 3.1–5.5) in Kaunertal, $4.6\\%$ ($95\\%$ CI: 4.1–5.3) in Stanzertal and $6.0\\%$ ($95\\%$ CI: 5.4–6.6) in the region Oberes Gericht. Regarding gender, there were no statistically significant differences in prevalence for the subregions. The results for the subregions, genders and age groups are shown in Table 4.\n\n**TABLE 4**\n\n| Unnamed: 0 | Females | Males | Total |\n| --- | --- | --- | --- |\n| | Prevalence in % (95% CI) | Prevalence in % (95% CI) | Prevalence in % (95% CI) |\n| Total | 5.1 (4.7–5.4) | 5.5 (5.1–5.8) | 5.3 (5.0–5.5) |\n| Regions | Regions | Regions | Regions |\n| Landeck/surroundings | 5.8 (5.3–6.4) | 6.5 (5.9–7.1) | 6.1 (5.7–6.5) |\n| Oberes Gericht | 5.9 (5.2–6.8) | 6.0 (5.2–6.9) | 6.0 (5.4–6.6) |\n| Sonnenterasse | 3.6 (2.4–4.9) | 2.6 (1.7–3.7) | 3.1 (2.4–3.9) |\n| Kaunertal | 4.6 (3.0–6.7) | 3.7 (2.3–5.5) | 4.1 (3.1–5.5) |\n| Paznauntal | 2.8 (2.2–3.6) | 3.9 (3.2–4.8) | 3.4 (2.9–3.9) |\n| Stanzer Tal | 4.4 (3.6–5.3) | 4.9 (4.1–5.8) | 4.6 (4.1–5.3) |\n| Age groups | Age groups | Age groups | Age groups |\n| 20–49 | 1.8 (1.5–2.1) | 1.1 (0.9–1.4) | 1.5 (1.3–1.6) |\n| 50–64 | 3.6 (3.0–4.1) | 6.3 (5.6–7.0) | 4.9 (4.5–5.4) |\n| 65–74 | 11.0 (9.6–12.6) | 14.3 (12.6–16.2) | 12.5 (11.4–13.7) |\n| ≥75 | 15.4 (13.8–17.0) | 18.1 (16.1–20.3) | 16.5 (15.2–17.8) |\n\n## DISCUSSION\n\nWe established, to the best of our knowledge, the first population‐based cohort of patients with DM in Austria following the recommendations of the National Diabetes Strategy. Our cohort should support data‐driven healthcare decision‐making and should contribute improving outcomes of patients with DM.\n\nIn total, we analysed 1845 adult cases with DM (including ‘GDM only’) and observed an overall prevalence of $5.3\\%$, with no statistically significant difference between genders but substantial differences between subregions. In addition, we identified differences between women and men in the following patient characteristics: diabetes duration, diagnosis site, family history of diabetes, smoking status, distribution of age groups, and for specific clinical parameters such as BMI, HbA1c, LDL, determination of microalbumin, DM therapy and physical activity. We also observed significant differences between female and male patients for the long‐term complications diabetic foot, amputation and cardiovascular disease.\n\nIn order to derive unmet needs and healthcare services quality gaps, it is important to set our results into the context of other countries. The prevalence of DM varies strongly across the globe and between European countries. The IDF Diabetes Atlas reports a prevalence ranging from $4.0\\%$ in Ireland to $15.9\\%$ in Turkey, and reports a prevalence for Austria of $6.6\\%$, which is clearly below the European average of $9.2\\%$. 2 The overall prevalence of $5.3\\%$ in the district of *Landeck is* even lower than the prevalence estimate of the IDF for Austria, which lies above the $95\\%$ CI of the district Landeck ($95\\%$ CI: $5.0\\%$–$5.5\\%$). One reason for this difference within Austria could be the so‐called east–west gap in Austria. A report by the Federal Ministry of Health showed that mortality due to DM in western *Austria is* lower than in eastern Austria. 19 *There is* also an evident east–west variation in cardiovascular mortality among persons over 64. Overweight/obesity, which is a major risk factor for DM, 20 is an even more significant problem in the population over the age of 64 in the east of Austria than in the west. 19 However, these estimates are not standardized, for example, for age, sociodemographic characteristics or proportion living in urban areas.\n\nOur prevalence estimation for the district of *Landeck is* in line with the reported prevalence of $4\\%$ in Switzerland, 21 which borders Landeck. Germany also shows a southwest‐to‐northeast gradient. The regional standardized prevalence was highest in the east, with $12.0\\%$ ($95\\%$ CI: $10.3\\%$–$13.7\\%$), and lowest in the south, with $5.8\\%$ ($95\\%$ CI: $4.9\\%$–$6.7\\%$). 22 However, we cannot exclude an underestimation of the prevalence in our study because we were unable to validate the completeness of the population‐based cohort with independent data sources.\n\nWhen comparing the frequencies with the literature, it should be noted that we included all DM patients treated in a region, not only those treated in a study using only a sample of the target population. Therefore, we predominantly compare our figures with results from population‐based diabetes registries. In addition, it is worth noting that our proportion of patients with high blood pressure should not be compared with hypertensive patients of study results from the published literature. The same is true for patients with higher LDL levels versus hyperlipidaemia.\n\nIn the following, we compare the parameters obesity, glycaemic control, smoking status, foot inspections and participation in a diabetes education program with data from the Scottish diabetes registry, 23 the Swedish registry, 24 and with results from the DAWN2 study. 25 We also compare diabetes therapies with *Austrian data* 26 and with data from a diabetes surveillance system for Germany at the Robert Koch Institute. 22 In our study, $42.2\\%$ of women and $40.1\\%$ of men were obese. In Scotland, the proportion of obese patients was at $55\\%$. 23 In Sweden, obesity was also more frequent, with a prevalence of $61\\%$ for women and $54\\%$ for men, although data from Sweden are only available for patients in the age group 30–60. 24 Overall, the proportion of obese patients in our study is lower compared to global numbers and the Austrian average, 27 which fits the east–west gradient in Austria mentioned above.\n\nConcerning glycaemic control, the proportion of patients with HbA1c < $7.5\\%$ was $55.4\\%$ in Scotland 23 and $72.3\\%$ in our study ($75.2\\%$ in women and $69.9\\%$ in men). One reason could be that the population in this western part of *Austria is* less obese and more physically active. Even overweight patients perform substantial physical activity. In addition, diabetic care is rather well structured.\n\nThe participation in a diabetes education program was $78.7\\%$ in the DAWN2 study, 25 which is nearly identical to our results of $78.2\\%$. The DAWN2 study reported $62.5\\%$ foot inspections (vs. $79.7\\%$ in the Diabetes Landeck Cohort); however, the DAWN2 data were based on self‐assessments. In the Diabetes Landeck Cohort, the proportion of active smokers was $10.9\\%$ (two times as many men than women), which is lower compared to $14.3\\%$ of the study of Panisch et al. 28 and to $17.7\\%$ in Scotland. 23 Our findings on diabetes therapy data do not describe the patients' current therapy (at the last contact) but rather summarize all therapy modalities that were documented during the study period. The proportion of patients with DM who did not need diabetes‐specific therapy (lifestyle adaptation only) is relatively high ($19\\%$) but is similar to the Swedish registry. 24 The percentage of DM patients treated with OADs corresponds to $72\\%$ in the publication of Engler et al., 26 which describes patients in the diabetes register in Tyrol. Among 45‐ to 79‐year‐old people with T2DM in Germany, $29.6\\%$ of women and $37.2\\%$ of men received metformin monotherapy in 2010. 22 This percentage is higher in our study ($38.9\\%$ in women and $42.1\\%$ in men). Also, the percentage for OAD+ insulin is higher in our cohort, which included all patients with DM compared to the study across Germany that only assessed patients with T2DM. 22 Comparisons of diabetes‐specific therapies with the literature should be carefully interpreted as many studies exclude elderly patients. In contrast, in our cohort, all patients with DM were registered, and the proportion of patients aged ≥75 was $36\\%$.\n\nWhen comparing our results with published data, it is essential to consider that the frequency of the respective long‐term complications depends on several factors, such as diabetes duration, age, gender and distribution of other risk factors. However, it also depends on the definition (and it is worthwhile to mention that definitions of each late complication differ in some respect), the extent of appropriate screening measures and the diagnostic methods used. Nonetheless, in the following paragraph, we attempt to compare our data on long‐term complications to the literature.\n\nIn Germany, the proportion of patients with DM with documented chronic kidney disease (as an indicator of nephropathy) was $15.1\\%$ (women: $14.9\\%$; men: $15.3\\%$) in 2013. 22 For the Diabetes Landeck Cohort, the percentage is higher ($18.1\\%$) compared to Germany, but differences in the documentation and diagnosis standards complicate a comparison. Desphande et al. 29 have provided an overview of the prevalence of the most common diabetes complications among individuals with T2M. 29 They reported a frequency of nephropathy of $28\\%$. As the disease DM progresses, the frequency of nephropathy is low in the first 10–15 years after the diagnosis and then increases significantly. In the Diabetes Landeck Cohort, the diabetes duration is less than 10 years in $50\\%$ of patients; this may explain the lower frequency of nephropathy, with $19.4\\%$ in women and $17.0\\%$ in men compared to the data of Desphande et al. 29 Another explanation may be the relatively high proportion of patients with an HbA1c below $7.5\\%$. The frequency of nephropathy correlates strongly with blood glucose control. 30 In 2013, $13.5\\%$ of adults with DM had documented diabetic polyneuropathy in Germany (women: $12.7\\%$; men: $14.4\\%$). 22 The proportion of patients with neuropathy is similar to that in our study (women: $12.6\\%$; men: $13.1\\%$). In Germany, $37.1\\%$ of adults with T2DM have cardiovascular disease, with a significantly lower prevalence in women ($30.6\\%$) than in men ($42.8\\%$), but this includes long‐term complications and comorbidities. 22 In our study, we observed lower frequencies but also a significant gender difference (women: $12.3\\%$; men: $25.2\\%$) for cardiovascular disease as a long‐term complication. Our study and the meta‐analysis of Einarson 31 show nearly identical results for cerebrovascular long‐term complications. The percentage of retinopathy in our cohort is very small ($2.2\\%$), but the prevalence of retinopathy increases progressively in patients with DM with increasing duration of the disease, 32 and we could be confronted with problems in communication diagnoses from ophthalmologists to care givers. In Germany, $5.7\\%$ of women and $6.6\\%$ of men with DM had documented diabetic foot syndrome in 2013. 22 For the Diabetes Landeck Cohort, the percentage is lower for women ($2.5\\%$) and similar for men ($5.3\\%$).\n\nAll patient data registered in this study were pseudonymized according to EU data protection laws. This means that the patient can no longer be identified in the research database, and implausible data can no longer be verified. The process of pseudonymization is based on the Austrian social security number, which means that patients cannot be registered without a social security number; however, the proportion of individuals without a social security number was fairly small in our study (about $0.3\\%$ of all patients). 33 One strength of our study is that it covers a well‐defined population and region, all care providers are clearly identified, and the area is served almost exclusively by one hospital. We established a network of general practitioners, diabetes specialists in private practices, retirement homes and the hospital in a defined district. Cooperation with all care units was excellent, with very few exceptions. Another strength was the use of a workable documentation system. Furthermore, by limiting the data to a minimum basic dataset, we achieved an acceptable data quality and thus gained a valid representation of the quality of care. Our study design could be a prototype for other countries and can contribute to assess a good overview of the quality of care of patients with DM, given a limited budget. We were also able to identify the problems in the data collection, which is important for future updates of the cohort or planning of diabetes registries in Austria.\n\nThis study has several limitations. First, most of the study period was dominated by the COVID‐19 pandemic. The documentation took place during one of the most difficult periods in the Austrian healthcare system in recent decades, and this could have affected our data quality. The care units could not be visited at the predefined time intervals for a more extended period due to the COVID‐19 lockdown restrictions, and caregivers had less time to maintain and update records. This may lead to an underestimation of the true prevalence. Second, some general practitioners retired during the study, which limited cooperation. A third limitation that became evident during the study was the lack of coding DM diagnoses in most practice systems. This means that patients with DM can currently only be identified by a free text search, which is usually very time‐consuming and can be associated with measurement bias. 34 This applies particularly to patients with DM who are not detected by the search criteria, which can lead to an underestimation of the true prevalence. It should also be considered that not all essential information is stored in the medical records and therefore was not accessible to the study nurse. Most physicians did not have the time to document or complete data themselves. Therefore, the majority of data documentation was completed by the study nurse who did not have direct contact with the patients. A fifth limitation of our study is that only a minimal data set was registered due to budget restrictions and the current conditions. For example, we were not able to collect data on triglycerides, ischaemic cardiomyopathy or other types of cardiomyopathy, or specific event types of cerebrovascular diseases or vascular events. As our results represent the population structure of the district Landeck and were not standardized to the Austrian population, crude comparisons with Austria should be interpreted with caution, see the discussion of the so‐called east–west gap in Austria. Another significant limitation, the need for a uniform definition of long‐term complications, applies to all published studies and was also a challenge we faced in our study. Finally, we were not able to verify the completeness of the prevalence estimate with independent data resources and could not systematically survey the patients' life statuses.\n\n## CONCLUSIONS\n\nAs explicitly stated in Austria's National Strategy Report on diabetes, there is a lack of high‐quality data on DM in the country. 6 There are, to the best of our knowledge, no systematic population‐based data on patients with DM in Austria. The Diabetes Landeck Cohort closes this gap for one region in Austria and provides, for the first time in Austria, a nearly complete set of patients with DM living in a well‐defined region. Some results, such as the diabetes prevalence or the frequency of some long‐term complications, are lower compared to international data. We succeeded in establishing a population‐based cohort and related database; however, we were not able to identify independent sources to verify our results. Therefore, for the future, we strongly suggest evaluating both completeness and comparability of data with well‐accepted methods. *In* general, documentation by study nurses who should ideally be located in the care units is recommended to obtain valid data, because many important data are not stored in the practice systems or cannot be accessed in a systematic way. To access patients with specific diagnoses, support for the coding of diagnoses by physicians in private practices should be developed and applied in the practice systems. To make the best use of already existing data in the Austrian healthcare system, we recommend developing and/or optimizing systems to link different databases (e.g. civil registration and death data). The Diabetes Landeck Cohort should allow to evaluate and improve the quality of care of patients with DM in the future. *In* general, the cohort should be optimized and updated because high‐quality data provide an essential basis to optimize the care of patients with DM. The data could also be used to supplement a biobank, for long‐term monitoring of diabetes patients, for questions in health services research and healthcare economics, and for the investigation of new electronic communication methods between physicians and patients. Further research is needed, and in a subsequent step, we will extend this study by carefully taking the limitations into account.\n\n## AUTHOR CONTRIBUTIONS\n\nVeronika Haslwanter: Conceptualization (lead); data curation (equal); formal analysis (equal); investigation (equal); methodology (equal); project administration (equal); validation (equal); visualization (lead); writing – original draft (lead). Ursula Rochau: Supervision (supporting); writing – original draft (supporting); writing – review and editing (supporting). Uwe Siebert: Supervision (supporting); writing – original draft (supporting); writing – review and editing (supporting). Hans Schoenherr: Conceptualization (supporting); data curation (equal); methodology (equal). Wilhelm Oberaigner: Conceptualization (supporting); formal analysis (equal); methodology (equal); supervision (lead); writing – review and editing (equal).\n\n## FUNDING INFORMATION\n\nWe thank the Tyrolean Health Fund (Tiroler Gesundheitsfond, TGF) for funding this project.\n\n## CONFLICT OF INTEREST\n\nThe authors have no competing interests.\n\n## ETHICS APPROVAL\n\nThe present study was approved by the Ethics Committee of the Medical University of Innsbruck and was carried out in accordance with the Helsinki Declaration of 1975, as revised in 2008.\n\n## DATA AVAILABILITY STATEMENT\n\nThe data that support the findings of this study are available from Tiroler Gesundheitsfond. Restrictions apply to the availability of these data, which were used under licence for this study.\n\n## References\n\n1. Cho NH, Shaw JE, Karuranga S. **IDF diabetes atlas: global estimates of diabetes prevalence for 2017 and projections for 2045**. *Diabetes Res Clin Pract* (2018.0) **138** 271-281. 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Hans‐Jürgen S. *Medizinische Informatik, Biometrie und Epidemiologie* (2019.0)"},"embed":{"kind":"list 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0.011304481886327267\n]","isConversational":false}}},{"rowIdx":69,"cells":{"text":{"kind":"string","value":"---\ntitle: Levothyroxine therapy, calculated deiodinases activity and basal metabolic\n rate in obese or nonobese patients after total thyroidectomy for differentiated\n thyroid cancer, results of a retrospective observational study\nauthors:\n- Rosario Le Moli\n- Pasqualino Malandrino\n- Marco Russo\n- Dario Tumino\n- Tommaso Piticchio\n- Adriano Naselli\n- Valentina Rapicavoli\n- Antonino Belfiore\n- Francesco Frasca\njournal: Endocrinology, Diabetes & Metabolism\nyear: 2023\npmcid: PMC10000637\ndoi: 10.1002/edm2.406\nlicense: CC BY 4.0\n---\n\n# Levothyroxine therapy, calculated deiodinases activity and basal metabolic rate in obese or nonobese patients after total thyroidectomy for differentiated thyroid cancer, results of a retrospective observational study\n\n## Abstract\n\nBecause of controversial issues about the optimal T4 replacement dose in obese hypothyroid subjects and the great importance of thyroid hormones in energy homeostasis, glucose and lipid metabolism, body composition and resting energy expenditure (REE), we compared the correlation between L‐T4 administered dose, thyroid hormone levels and TSH secretion with estimated basal metabolic rate (BMR) and total deiodinase activity (GD) in obese and nonobese athyreotic subjects. We aimed to define individualized set points that might provide appropriate therapeutic and biochemical targets to be clinically tested in obese and nonobese patients.\n\n### Introduction\n\nTherapy for hypothyroid obese patients is still under definition since the thyrotropin‐stimulating hormone (TSH) level is a less reliable marker of euthyroidism than nonobese patients. Indeed, TSH levels positively correlate with body mass index (BMI), and this increase may be a compensatory mechanism aimed at increasing energy expenditure in obese people. In contrast, the correlation of BMI with thyroid hormone levels is not completely clear, and conflicting results have been obtained by several studies. The L‐T4 replacement dose is more variable in obese hypothyroid patients than in nonobese patients, and a recent study indicated that the L‐T4 replacement dose is related to lean body mass in obese thyroidectomized patients. We aimed to study the correlations of L‐T4‐administered dose, thyroid hormone levels and TSH secretion with basal metabolic rate (BMR) and total calculated deiodinase activity (GD) in obese and nonobese athyreotic patients. We also looked for individualized L‐T4 replacement dose set points to be used in clinical practice.\n\n### Methods\n\nWe studied retrospectively 160 athyreotic patients, 120 nonobese and 40 obese. GD was calculated by SPINA Thyr 4.2, the responsiveness of the hypothalamic/pituitary thyrotrope by Jostel's thyrotropin (TSH) index and BMR by the Mifflin‐St. Jeor formula, the interplay of GD and BMR with L‐T4, thyroid hormones and TSH index (TSHI) was also evaluated.\n\n### Results\n\nIn our study, the L‐T4 dose was an independent predictor of GD, and approximately $30\\%$ of athyreotic patients under L‐T4 therapy had a reduced GD; FT4 levels were higher and negatively modulated by BMR in obese athyreotic patients respect to nonobese, in these patients a T4 to T3 shunt, in terms of TSHI suppression is observed suggesting a defective hypothalamic pituitary T4 to T3 conversion and a resistance to L‐T4 replacement therapy.\n\n### Conclusions\n\nL‐t4 dose is the most important predictor of GD, BMR modulates T4 levels in obese athyreotic patients that are resistant to L‐T4 replacement therapy.\n\n## INTRODUCTION\n\nLevothyroxine (L‐T4) therapy has a long record in clinical use, with a defined pharmacological profile and safety in hypothyroidism management. Obesity and thyroid disorders are common among the general population and may be associated with both clinical and molecular aspects. This relationship has become epidemiologically relevant in the context of the significantly increased prevalence of obesity worldwide. However, treatment for obese patients with subclinical or overt hypothyroidism is still under definition regarding both the threshold and modality (liquid L‐T4 vs. pills; L‐T4 monotherapy vs. liothyronine [L‐T3]/L‐T4 combinations). The prerequisite for treatment with L‐T4 is the presence of hypothyroidism, and the goal is restoration of euthyroidism. Achievement of a thyrotropin‐stimulating hormone (TSH) value within the age‐adjusted euthyroid range is the accepted therapeutic target, as several studies indicate improvement in symptoms, quality of life and cardiovascular risk. 1, 2, 3, 4 However, among euthyroid subjects, TSH levels usually correlate with body mass index (BMI), being higher in obese than in normal subjects. 5 TSH elevation in obese euthyroid people may be a compensatory mechanism in the pituitary‐thyroid axis aimed at increasing energy expenditure. 6, 7 At variance with TSH, the correlation between BMI and thyroid hormones (T4 and T3) is not clear, as several studies obtained conflicting results. Some studies indicate that BMI is negatively related to FT4 and positively related to FT3. Another study, by contrast, indicated hyperactivation of the pituitary‐thyroid axis with increased FT4 levels in obese patients. 8, 9 Other studies describe a decreased FT4/FT3 ratio in obese patients. 5, 10, 11, 12 This adaptation of thyroid hormone homeostasis in obese subjects has been attributed to leptin and insulin actions. 3 The observation of higher TSH and lower FT4 in obese euthyroid people is in accordance with increased L‐thyroxine replacement dose in hypothyroid obese patients. L‐T4 replacement therapy is approximately 1.6 μg/kg in hypothyroid patients with any functional thyroid tissue, while in obese patients, the correct T4 replacement dose is more variable. Recently, the American Thyroid Association (ATA) task force identified obesity as a morbid condition implying an increase in the L‐T4 replacement dose because of reduced thyroid hormone absorption. 2 This observation is reinforced by the evidence that in obese subjects, acute overload of L‐T4 administration takes longer to achieve a plasmatic concentration peak in comparison with nonobese people. 1 However, a recent study indicated that in obese thyroidectomized patients, the L‐T4 replacement dose is positively related to lean body mass. Indeed, the ideal body weight (IBW) should be preferred to real body weight (RBW) for L‐T4 dose titration because lean body mass results in a better predictor of T4 requirement than fat mass. 6, 7 Because of these controversial issues about the optimal T4 replacement dose in obese hypothyroid subjects and the great importance of thyroid hormones in energy homeostasis, glucose and lipid metabolism, body composition and resting energy expenditure (REE), 10, 12, 13, 14 we compared the correlation between L‐T4‐administered dose, thyroid hormone levels and TSH secretion with estimated basal metabolic rate (BMR) and total deiodinase activity (GD) in obese and nonobese athyreotic subjects. Moreover, we aimed to define individualized set points that might provide appropriate therapeutic and biochemical targets to be clinically tested in obese and nonobese patients.\n\n## Patients\n\nWe retrospectively evaluated 1150 thyroidectomized patients referred to our outpatient thyroid clinic between 2010 and 2015 who were also subjected to 131I ablation because of differentiated thyroid cancer (DTC). In all patients, thyroglobulin levels were between 0.01 and 0.5 ng/ml, and antithyroglobulin antibody (TgAb) was negative. In this cohort, devoid of functional thyroid tissue, all circulating T4 levels originated from levothyroxine replacement therapy. These patients obtain circulating T3 from the conversion of exogenous T4 and represent an ideal model to study peripheral tissue ability to generate biologically active hormones. We excluded from the analysis patients with hypothalamic/pituitary, gastric, intestinal or neurological diseases and pregnant women ($$n = 72$$) and those who were taking combined T3/T4 thyroid replacement therapy and/or other drugs interfering with thyroid hormone homeostasis ($$n = 198$$). Patients with variations in L‐T4 daily dose, body weight and thyroid hormone level fluctuations within 3 months before the start of the study were also excluded ($$n = 720$$). Finally, 160 athyreotic patients under L‐T4 therapy were included in the analysis (Figure 1). All patients were euthyroid on the basis of their TSH, FT4 and FT3 levels within the normal range.\n\n**FIGURE 1:** *Flow chart of study patients selection*\n\n## Phenotypic evaluation of the study patients\n\nClinical records included a detailed history, physical examination, standardized questionnaire documenting sex, age, height, weight and BMI. BMI was calculated as weight in kilograms divided by the square of height in metres (kg/m2) and considered a categorical variable according to the World Health Organization (WHO). Obesity was defined as BMI ≥ 30, which is an adequate indicator of obesity and is associated with increased body fat mass. In our study, 120 patients had a BMI ≥ 30, while 40 had a BMI < 30.\n\n## Basal metabolic rate (BMR) evaluation\n\nWe evaluated BMR by the Mifflin‐St. Jeor formula (MSTF). The MSTF equation is commonly used in the assessment of basal metabolism and is more particularly used in obese patients. The MSTF was also applied differently to female and male sex as follows: Females = 9.99 × weight (kg) + 6.25 × height (cm) − 4.92 × age (years) − 161.Males = 9.99 × weight (kg) + 6.25 × height (cm) − 4.92 × age (years) + 5.\n\nWe studied the effect of the L‐T4 replacement dose on thyroid hormone homeostasis, estimated BMR and total deiodinase activity (GD) in obese and nonobese patients. Data were collected from patients after thyroidectomy, 131I administration and a persistent euthyroid state under replacement therapy for approximately 3 months with any significant change in L‐T4 dose administration, daily caloric intake and body weight. A subgroup of 45 patients maintaining the same replacement dose over the last 6 months was also studied to better evaluate the interplay between the L‐T4 administered dose and total GD in the long term.\n\n## Evaluation of stimulated deiodination (GD)\n\nGD, which reflects the maximum stimulated activity of deiodination, was calculated by SPINA Thyr 4.2 (Structure Parameter Inference Approach by Johannes W. Dietrich, Lab XU44, Bergmannsheil University Hospitals, Ruhr University of Bochum, D‐44789 Bochum, NRW, Germany), which is a mathematical tool for the integrated interpretation of laboratory results. SPINA allows calculation of GD from TSH, FT4 and FT3 serum levels obtained from routine laboratory assays. The method is based upon mathematical/cybernetic modelling of processing structures. 15 In particular, the SPINA algorithm is based on equilibrium analysis of a compartmental nonlinear model: GD = β31Km1+FT41+K30TBGFT3α31FT4, where β31 is the clearance exponent for T3, Km1 is the dissociation constant of type 1 deiodinase, K30 is the dissociation constant of T3 at thyroxine‐binding globulin, and α31 is the dilution factor for triiodothyronine. On the basis of several studies, normal values of calculated GD vary between 21 and 26 nmol/s. 15, 16 Hence, a GD < 21 nmol/s is considered low.\n\n## Responsiveness of the hypothalamic/pituitary thyrotrope\n\nWe also assessed the responsiveness of the hypothalamic/pituitary thyrotrope by Jostel's thyrotropin (TSH) index: (JTSHI) = ln([TSH]) + β[FT4] and obtained a standardized TSH index (TSHI) = JTSHI − $\\frac{2.7}{0.676}$ for statistical comparison.\n\n## Laboratory measurements\n\nSerum TSH was assessed by an ultrasensitive enhanced chemiluminescence immunoassay (ECLIA) assay. Serum hormones were measured by microparticle enzyme immunoassay (Abbot AxSYM‐MEIA) with interassay coefficients of variation of less than $10\\%$ over the analytical ranges of 1.7–46.0 pmol/L for FT3, 5.15–77.0 pmol/L for FT4 and 0.03–10.0 mU/L for TSH. The within‐run and between‐run precisions for the FT3, FT4 and TSH assays showed coefficients of variation <$5\\%$. Measurement of antithyroglobulin antibodies (TgAbs) by an automated chemiluminescence assay system (AntiTg, Ready Pack). Thyroglobulin levels were measured with a second‐generation chemiluminescent Tg immunoassay (Tg Access; Beckman Coulter) with a functional sensitivity of 0.1 ng/ml.\n\n## Statistical analysis\n\nStatistical analysis was performed using the SPSS package (IBM SPSS Statistics for Windows, Version 26.0. IBM Corp). For the descriptive analysis, continuous variables were expressed as the mean ± standard deviation (SD) or median (with its 25th–75th percentile); categorical variables were expressed as numbers and percentages. Univariate analysis of variance (ANOVA) was performed to identify predictive variables significantly associated with the clinical outcome. The shapes of the distribution of each variable were evaluated by visual inspection of the population pyramid charts; for distributions of similar shapes, we reported the medians, and for distributions of different shapes, we reported average ranks. The Mann–Whitney U test was used to analyse the continuous variables without a normal distribution. Categorical variables were analysed by the Chi‐square test, if cells with fewer than five expected cell numbers were found, by Fisher's exact test. Complete and partial bivariate analysis was used to evaluate no categorical variables, and Pearson's coefficient was computed. Binary logistic regression analysis was performed for the outcome variables. Covariates were selected on the basis of the results of univariate analysis, and the final model was built using forced entry and a hierarchical method. Linearity of the continuous variables with respect to the logit of the dependent variable was assessed by the Box‐Tidwell procedure, and a Bonferroni correction was applied using all terms in the model to assess its statistical significance. Multicollinearity was excluded after checking tolerance and variance inflation factor statistics and the proportion of the variance of each predictor's b value attributed to each eigenvalue. The ability of the model to discriminate between outcome categories was investigated in more detail by elaborating the ROC curve. This analysis was performed for LT4 × week/BMR ratio vs deiodinase activity on the basis of the regression outputs. Youden's best cut‐off was also calculated, and the greater values were chosen to balance the better sensitivity and specificity for the studied variable.\n\n## Univariate analysis\n\nStructure parameter influence assay (SPINA) revealed that GD was reduced in $\\frac{50}{160}$ ($31.2\\%$) of the thyroidectomized patients (Table 1).\n\n**TABLE 1**\n\n| Age (years) | 44.6 (13.9) |\n| --- | --- |\n| Sex (F/M) | 117/43 |\n| Weight (kg) | 73.1 (18.3) |\n| Height (cm) | 163.6 (12.2) |\n| BMI (kg/Height2) | 27.1 (6.1) |\n| TSH (mU/L) | 1.6 (0.4–2.9) |\n| FT4 (pmol/L) | 14.1 (11.6–21.9) |\n| FT3 (pmol/L) | 4.1 (2.1–5.4) |\n| FT3/FT4ratio | 0.25 (0.05) |\n| BMR (Kcal/24 h) | 1419.1 (265.5) |\n| LT‐4 × week/BMRr (μg) | 0.6 (0.4–1.4) |\n| LT‐4 × week (μg) | 835.7 (238.7) |\n| GD (nmol/s) | 24.1 (12.0–40.0) |\n| GD < 21 nmol/s (n/%) | 50/31.2 |\n| TSHI | 2.0 (0.0–3.9) |\n\nPatients were divided into two groups according to normal (≥21 nmol/s) or low (<21 nmol/s) GD. Sex, age, BMI and BMR were not different between the two groups (Table 2). Univariate analysis revealed that FT3 and the FT3/FT4 ratio were significantly reduced in patients with low GD compared to patients with normal GD ($p \\leq .004$–.0001). However, in low GD TSHI, FT4, LT‐4 weekly cumulative dose (LT‐4 × week) and the ratio between LT‐4 weekly cumulative dose and basal metabolic rate (LT‐4 × week/BMR) were significantly increased ($p \\leq .0001$) (Table 2).\n\n**TABLE 2**\n\n| Unnamed: 0 | GD activity <21 nmol/s (n = 50) | GD activity ≥21 nmol/s (n = 110) | p |\n| --- | --- | --- | --- |\n| Age (years) | 42.9 (17.3) | 45.2 (12.1) | 0.6 |\n| Sex (F/M) | 34/16 | 83/27 | 0.2 |\n| Weight (kg) | 73.3 (20.2) | 73.1 (17.5) | 0.9 |\n| Height (cm) | 162.9 (16.9) | 163.9 (9.5) | 0.7 |\n| BMI (kg/Height2) | 26.8 (6.7) | 27.1 (5.6) | 0.6 |\n| TSH (mU/L) a | 0.8 (0.1) | 1.0 (0.1) | 0.4 |\n| FT4 (pmol/L) a | 18.1 (2.0) | 14.2 (2.6) | 0.0001 |\n| FT3 (pmol/L) a | 3.7 (0.1) | 4.3 (0.0) | 0.0001 |\n| FT3/FT4 ratio a | 0.20 (0.05) | 0.30 (0.05) | 0.0001 |\n| BMR (Kcal/24 h) a | 1435.1 (40.3) | 1409.8 (24.7) | 0.4 |\n| LT‐4 × week/BMRr (μg/BMR) | 0.6 (0.2) | 0.5 (0.1) | 0.01 |\n| LT‐4 × week (μg) | 910.2 (259.2) | 802.5 (222.3) | 0.006 |\n| GD (nmol/s) | 18.3 (0.3) | 26.6 (0.3) | 0.0001 |\n| TSHI a | 2.2 (0.1) | 1.7 (0.1) | 0.004 |\n\n## Binary logistic regression analysis and ROC curve\n\nVariables reaching statistical significance by univariate analysis were then analysed by binary logistic regression analysis models. LT‐4 × week/BMR was independently and inversely related to GD [B = −3.88, wald = 7.6, $R = 0.021$ (0.001–0.329; $95\\%$ confidence interval (CI)), $$p \\leq .006$$], FT3 levels were directly and independently related to GD [$B = 2.81$, wald = 25.1, $R = 17.4$ (5.6–53.4, $95\\%$ CI) $$p \\leq .0001$$]. In contrast, BMR, BMI, body weight, TSH and FT4 were not independently related to GD. To evaluate the effect of LT4 × week/BMR on GD, we used a classic receiver operating characteristic (ROC) model that was very well validated by the study of area under the curve (AUC) = 0.81 ± 0.073 (0.66–0.95, $95\\%$ CI, $$p \\leq .001$$). To better define the cut‐off of LT‐4 dose beyond which GD was reduced, we researched the best cut‐off of Youden's statistic (YS). YS = 60 indicates that LT‐4 × week/BMR > 0.56 mcg × week/kcal is a good predictor of suppressed GD with sensitivity = $83\\%$ and specificity = $77\\%$ (e.g. a total of 144 mcg of LT‐4 daily dose reduces GD in patients with 1800 kcal/die estimated BMR).\n\n## Linear regression, complete or partial bivariate analysis with calculation of Pearson coefficient\n\nFT3 and FT4 were increased in obese patients compared with nonobese patients ($$p \\leq .07$$ and $$p \\leq .01$$, respectively), while GD and LT‐4 × week/BMR were similar in the two groups (Table 3).\n\n**TABLE 3**\n\n| Unnamed: 0 | Non‐obese (n = 120) | BMI ≥30 < 35 (n = 20) | BMI ≥35 (n = 20) | p |\n| --- | --- | --- | --- | --- |\n| Sex m/f | 28/92 | 8/12 | 7/13 | |\n| Age (years) | 43.4 (14.5) | 46.7 (11.5) | 48.3 (11.1) | 0.1 |\n| Weight (kg) | 65.5 (12.2) | 89.3 (10.8) | 101.6 (16.4) | 0.0 |\n| Height (cm) | 163.1 (12.7) | 167.9 (10.5) | 160.9 (8.8) | 0.4 |\n| BMI (weight/[height]2) | 24.3 (3.2) | 31.6 (1.3) | 39.7 (4.9) | 0.0 |\n| TSH (mU/L) | 0.8 (0.05) | 1.0 (0.2) | 1.3 (0.2) | 0.6 |\n| TSHI | 1.7 (0.8) | 2.1 (0.7) | 2.2 (0.2) | 0.1 |\n| FT4 (pmol/L) | 15.6 (2.4) | 16.7 (3.2) | 17.7 (4.2) | 0.01 |\n| FT3 (pmol/L) | 3.9 (0.6) | 4.1 (0.6) | 4.3 (0.5) | 0.07 |\n| FT3/FT4 ratio | 0.25 (0.04) | 0.24 (0.05) | 0.24 (0.05) | 0.1 |\n| BMR | 1336.8 (216.6) | 1633.4 (254.8) | 1688.1 (258.4) | 0.0 |\n| GD (nmol/s) | 24.1 (4.8) | 23.7 (5.5) | 24.4 (6.1) | 0.2 |\n| GD < 21 (nmol/s) n/% | 36/30 | 7/35 | 7/35 | 0.3 |\n| Lt4 × week/BMR | 0.6 (0.1) | 0.6 (0.1) | 0.6 (0.1) | 0.3 |\n| Lt4 × week (μg) | 779.2 (214.1) | 1006.9 (270.3) | 1002.0 (173.1) | 0.0 |\n\nPartial bivariate analysis revealed that FT4 levels were positively related to BMI and negatively related to BMR after subtraction of the BMI effect: $$p \\leq .01$$ and $$p \\leq .02.$$ Pituitary thyreotropic activity, evaluated by TSHI, was positively related to BMI and LT‐4 × week/BMR: $R = 0.13$, $$p \\leq .05$$, $R = 0.14$, $$p \\leq .03$$ and inversely related to GD: $$p \\leq .0004$$ (Figure 2). FT4 levels were positively related to TSHI in both obese and nonobese patients. In obese patients, the FT4 to TSHI increment was 3.3 times greater than the increment in nonobese patients: $0.1\\%$ versus $0.03\\%$, R 2 =.24, $$p \\leq .001$$ versus R 2 =.04, $$p \\leq .02$$ (BMI ≥ 30 vs. BMI < 30) (Figure 3).\n\n**FIGURE 2:** *Linear correlation of SPINA GD (nmol/s) with TSHI* **FIGURE 3:** *(A and B) Correlation between TSHI and FT4 in obese and non‐obese athyreotic patients*\n\nIn obese patients ($$n = 40$$), FT3 levels were inversely related to TSHI; a TSHI increment of 1 unit was related to an FT3 decrement of $0.095\\%$: R 2 =.1; $$p \\leq .045.$$ In contrast, FT3 levels were not related to TSHI variations in nonobese patients: R 2 =.002, $$p \\leq .58$$ (Figure 4).\n\n**FIGURE 4:** *(A and B) Correlation between TSHI and FT3 in obese and non‐obese athyreotic patients*\n\nThese data confirm that the feedback sensitivity of thyroid hormones with the pituitary is significantly different in obese and nonobese patients.\n\n## DISCUSSION\n\nSeveral lines of evidence indicate that hypothyroid patients under levothyroxine replacement therapy may present impaired T3 production and a reduced T3/T4 ratio. 13 The T3 pool derived from intrathyroidal conversion is absent and fails to maintain normal FT3 levels. As a consequence, their peripheral tissues may be underexposed to circulating T3. Our previous data indicate that $29.6\\%$ of levothyroxine‐treated athyreotic patients have a reduced FT3/FT4 ratio, and this percentage may progressively increase with increasing replacement levothyroxine dose. 17 These changes may be due to an imbalance between central and peripheral deiodinase activity that may disrupt thyroid hormone homeostasis in this subset of hypothyroid patients. 12, 13, 14 In our study, we evaluated the total deiodinase activity (GD) by the SPINA cybernetic model. 15, 16 We found that our athyreotic patients with impaired GD received a larger dose of LT‐4 and had increased FT4 and TSHI levels, while the FT3/FT4 ratio and FT3 levels were reduced (all <0.0001). GD was reduced in $31.2\\%$ of study patients, confirming our previous report since GD is well correlated with the FT3/FT4 ratio 17 (Table 2). To better evaluate the interplay between GD, BMR and LT‐4 weekly cumulative dose (LT‐4 × week), we evaluated the ratio between LT‐4 × week and basal metabolic rate (LT‐4 × week/BMR) calculated by the formula of Mifflin St.‐Jeor. By this tool largely used to evaluate BMR in obese patients, 18, 19 we demonstrated that total GD activity was independently and inversely related to LT‐4 × week/BMR. According to this view, we analysed a subgroup of 45 patients with a stable LT‐4 dose, caloric intake and level of thyroid hormones for almost six months, and we found that a LT‐4 × week/BMR value of 0.56 mcg × week/Kcal can predict the impairment of GD (<21 nmol/s) with good sensitivity and specificity ($$p \\leq .01$$). To our knowledge, this is a new finding with a possible clinical implication in athyreotic patients receiving LT‐4 substitutive therapy. Interestingly, estimated BMR, BMI, age and sex were similar between the patients with normal or reduced GD, suggesting that LT‐4 dose and FT3 production are the two independent stronger predictors of GD. Cross‐sectional and longitudinal studies comparing post‐ and presurgical levels of L‐T4 prove that higher L‐T4 doses are associated with the suppression of deiodinase activity. 16 FT4 and FT3 were higher in our obese (BMI ≥ 30) than in nonobese patients (BMI < 30) ($$p \\leq .01$$, $$p \\leq .07$$), and TSHI was positively related to BMI and LT‐4 × week/BMR and inversely related to GD. However, GD and LT‐4 × week/BRM were not different between obese and nonobese patients, suggesting that BMI is not an independent determinant of GD. The pituitary thyrotropic activity, expressed by the relationship between TSHI and thyroid hormone levels, was different between nonobese and obese patients. TSHI suppression was constantly exerted by increasing levels of FT4 in nonobese patients, while this suppression was significantly attenuated at higher levels of FT4 in obese patients, suggesting increased hypothalamic–pituitary resistance in response to increased T4 levels. The increment of FT4 for each unit of TSHI increment was significantly higher in obese patients than in nonobese patients ($$p \\leq .04$$) (Figure 3). However, in accordance with the FT4 results, increasing levels of FT3 constantly suppressed TSHI in nonobese patients, while this suppression was increased at increasing levels of FT3 in obese patients (Figure 4). This T4 to T3 shunt, in terms of TSHI suppression observed in obese patients, suggests a defective hypothalamic pituitary T4 to T3 conversion. Moreover, FT4 levels were positively related to BMI as well as to T4 dose but only partially and inversely related to BMR when BMI effect was subtracted (Pearson, $$p \\leq .01$$). Considering that FT4 levels in athyreotic patients are entirely dependent on LT‐4 adsorbed dose and on the extent of T4 degradation, 17 this finding unravels a role of BMR on the modulation of FT4 bioavailability both in nonobese and in obese patients, those with greater lean body mass that leads to increased BMR. 6, 7, 8 Differently than some recent studies, 20 we did not evidence a statistically significant correlation of GD with BMR, however differently than the others studies we evaluated patients athyreotic by total thyroidectomy and 131I ablation, this might contribute to increase the severity of suppression of the feedback loop and the ability to relay type 1 and type 2 allostatic load to T3 production. Moreover, we did not evaluate separately free fat mass and lean body mass. Under normal conditions, thyroid hormones and TSH are inversely correlated, while in patients with resistance to thyroid hormone, higher thyroid hormone levels correspond to high TSH levels due to a possible condition of resistance to FT4, such as in obese patients. 9, 10, 21, 22 One study demonstrated that deiodinase ubiquitination was an important factor in restoring euthyroidism. Indeed, the ubiquitin proteasome system in the hypothalamus of obese mice fails to maintain adequate function. Hence, a defective function of the ubiquitin proteasome system, resulting in deiodinase imbalance, might play a major role in the regulation of the response to thyroid hormones in obese subjects. 9, 21, 22, 23 Thyroid hormone action is modulated by the hypothalamic pituitary thyroid axis, 6 and cell membrane transport, tissue deiodination and degradation and thyroid hormone metabolism in the liver may play an important role. 9, 22, 23, 24 Metabolism of exogenous substrates in the liver occurs by enzymes that either modify and/or conjugate the functional groups to endogenous substrates to increase their solubility to be readily eliminated. Approximately half of obese subjects display several abnormalities in liver enzymatic activity due to steatosis. 25, 26, 27 In particular, increasing BMI and thyroid hormone receptor β are inversely correlated with different stages of nonalcoholic fatty liver disease (NAFLD), 28, 29 which, in turn, is related to decreased multidrug resistance protein (MRP2) activity in the liver. This condition is associated with alterations in the expression and function of enzymes and transporters resulting in an altered glucuronoconjugation of thyroid hormones. 23 However, our study is descriptive and does not allow any direct evaluation of mechanistic insights related to T4 activation, degradation and stability.\n\n## CONCLUSIONS\n\nApproximately one‐third of athyreotic patients under LT‐4 replacement therapy have reduced GD. GD activity is inversely and independently related to LT‐4 dose and FT3 levels. We found that an LT‐4 weekly cumulative dose of 0.56 mcg/kcal was an independent predictor of reduced GD, while sex, age, BMI or BMR were not. FT4 levels are higher in athyreotic obese patients, who therefore appear more resistant to LT‐4 replacement therapy. Indeed, FT4 is positively related to BMI and inversely related to BMR, which, in turn, negatively modulates the FT4 increment, especially in patients with elevated body lean mass. Other metabolic pathways both centrally and perimetrically might be involved in FT4 and FT3 degradation.\n\n## AUTHOR CONTRIBUTIONS\n\nPasqualino Malandrino: Data curation (equal); validation (equal). Marco Russo: Data curation (equal); validation (equal). Dario Tumino: Data curation (equal); validation (equal); visualization (equal). Tommaso Piticchio: Data curation (equal); formal analysis (equal); visualization (equal). Adriano Naselli: Data curation (equal); formal analysis (equal); software (lead); supervision (equal); writing – review and editing (equal). Valentina Rapicavoli: Data curation (equal); resources (equal). Antonino Belfiore: Conceptualization (equal); funding acquisition (equal); methodology (equal); project administration (equal); resources (equal); supervision (equal); validation (equal); visualization (equal); writing – review and editing (equal). Francesco Frasca: Conceptualization (equal); funding acquisition (equal); investigation (lead); methodology (equal); project administration (equal); resources (equal); supervision (equal); validation (equal); visualization (equal); writing – review and editing (equal). Rosario Le Moli: Conceptualization (equal); data curation (equal); formal analysis (equal); investigation (equal); methodology (equal); project administration (equal); software (equal); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal).\n\n## CONFLICT OF INTEREST\n\nThe authors declare no conflict of interest.\n\n## INSTITUTIONAL REVIEW BOARD STATEMENT\n\nThe studies involving human participants were reviewed and approved by Ethics Committee Garibaldi Nesima Hospital ‐ Catania.\n\n## INFORMED CONSENT\n\nWritten informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.\n\n## DATA AVAILABILITY STATEMENT\n\nThe data presented in this study are available on request from the corresponding author.\n\n## References\n\n1. 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-0.00914539024233818,\n 0.02050040476024151\n]","isConversational":false}}},{"rowIdx":70,"cells":{"text":{"kind":"string","value":"---\ntitle: 'Association between Pro‐oxidant‐Antioxidant balance and high‐sensitivity C‐reactive\n protein in type 2 diabetes mellitus: A Study on Postmenopausal Women'\nauthors:\n- Hassan Ehteram\n- Sara Raji\n- Mina Rahmati\n- Hanieh Teymoori\n- Samaneh Safarpour\n- Nahid Poursharifi\n- Mona Hashem Zadeh\n- Reza Pakzad\n- Hossein Habibi\n- Naser Mobarra\njournal: Endocrinology, Diabetes & Metabolism\nyear: 2022\npmcid: PMC10000638\ndoi: 10.1002/edm2.400\nlicense: CC BY 4.0\n---\n\n# Association between Pro‐oxidant‐Antioxidant balance and high‐sensitivity C‐reactive protein in type 2 diabetes mellitus: A Study on Postmenopausal Women\n\n## Abstract\n\nSerum PAB, hs‐CRP concentration, and lipid profile were significantly different between postmenopausal women with and without diabetes mellitus. These differences may contribute to the development of coronary complications.\n\n### Introduction\n\nOxidative stress known as a predictive marker for cardiovascular and metabolic diseases could be measured through pro‐oxidant antioxidant balance (PAB). The present study aimed to evaluate PAB and its association with high‐sensitivity C‐reactive protein (hs‐CRP) in the serum of postmenopausal women with diabetes mellitus.\n\n### Methods\n\nIn this case–control study, 99 diabetic and 100 healthy postmenopausal women without diabetes mellitus were recruited. Serum PAB values, hs‐CRP, lipid profile, insulin, and vitamin D levels were measured. Moreover, insulin resistance (HOMA‐IR, HOMA‐β and QUICKI), waist circumference (WC), waist‐to‐hip ratio (WHR), waist‐to‐height ratio (WHtR), and body mass index (BMI) were calculated.\n\n### Results\n\nSerum PAB, hs‐CRP, insulin resistance, HOMA‐β, QUICKI, low‐density lipoprotein cholesterol (LDL‐C), high‐density lipoprotein cholesterol (HDL‐C), and triglycerides (TG) levels were significantly higher in the postmenopausal women with diabetes mellitus, while there was no significant difference in the total cholesterol (TC), serum insulin, WC, WHR, WHtR and vitamin D levels between the groups. Pearson correlation coefficient showed that HDL‐C and insulin levels were directly correlated with serum PAB. Also, there was a significant direct relationship between LDL‐C and insulin levels and hs‐CRP. There was no meaningful relationship between serum insulin and vitamin D levels and other assessed parameters. Backward logistic regression showed a positive relationship between diabetes mellitus and serum PAB and an inverse relationship with serum HDL levels.\n\n### Conclusions\n\nSerum PAB, hs‐CRP concentration, and lipid profile were significantly different between postmenopausal women with and without diabetes mellitus. These differences may contribute to the development of coronary complications.\n\n## INTRODUCTION\n\nAn imbalance between the production of oxidants and their scavengers leads to oxidative stress (OS). OS may also stimulate the production of inflammatory factors, such as high‐sensitivity C‐reactive protein (hs‐CRP). Hs‐CRP is an inflammatory marker induced via cytokines, especially interleukin‐6 (IL‐6). OS and hs‐CRP are predictive markers of cardiovascular disease (CVD) and metabolic diseases, including type II diabetes. 1, 2, 3 Type II diabetes mellitus (T2DM) is a severe, multifactorial and metabolic disease, which affects women more than men in many countries. T2DM increases CVD risk by 2–3 folds, which leads to a higher mortality rate than in non‐diabetic people. 4 Moreover, OS increases in menopausal women, which is associated with loss of ovarian follicular function and oestrogen (E2) production because E2 has antioxidant activity. 5 After menopause, the production of antioxidants is reduced, and OS increases. 6, 7 Thus, menopause may be a risk factor for OS, CVD, osteoporosis, and diabetes. Although OS and inflammation are well established in postmenopausal women, there are limited studies about pro‐oxidant‐antioxidant balance (PAB), CRP levels and their association with insulin resistance in diabetic postmenopausal women. 1, 8, 9 We sought to assess the serum PAB values using a modified PAB assay to measure the pro‐oxidant burden and antioxidant capacity. This study also evaluated hs‐CRP and whether serum PAB values are associated with hs‐CRP in diabetic postmenopausal women.\n\n## Study groups\n\nThis case–control study was carried out on 99 postmenopausal women who had recently been diagnosed with only diabetes type II and attended the Women's Health Research Center in Gorgan, Iran. The control group included 100 healthy participants age‐matched to the patient group recruited between January 2017 and June 2018 for routine check‐ups. This group consisted of postmenopausal women with no diabetes. Clinical history and other relevant data were collected from all participants. They were excluded if they had taken vitamin supplements, hormones, anti‐inflammatory drugs, and fish oil capsules. Moreover, smokers and pregnant subjects were excluded from the study. Those suffering a myocardial infarction (MI), acute infection or any acute illnesses were excluded. One hundred and 99 subjects met the inclusion/exclusion criteria. They were informed about the study protocol, written consent was obtained from each participant and the research was approved by the Mashhad University of Medical Sciences Ethics Committee (NO: IR.MUMS.REC.1399.533).\n\n## Anthropometric parameters and blood collection\n\nAfter overnight fasting, 5 ml of venous blood was drawn into EDTA and plain tubes, centrifuged at 2500 rpm for 15 min at room temperature, and serum was allocated to several microtubes and stored at −70°C until analysis. Furthermore, body weight, height, waist circumference (WC), and hip circumference (HC) were measured to calculate the waist‐to‐hip ratio (WHR), waist‐to‐height (WHtR), and body mass index (BMI) (kg/m2).\n\n## Biochemical analysis processing\n\nFasting glucose and lipid profile indices, including total cholesterol (TC), triglyceride (TG), and HDL‐C, were measured by enzymatic methods and commercial kits using the BT‐3000 Auto‐analyser (Biotechnica). Moreover, LDL‐C was indirectly evaluated in participants with the Friedewald formula.\n\nThe levels of insulin were assessed using commercial kits using a radioimmunoassay from the Immuno Nuclear Corporation (Stillwater). Insulin resistance was calculated using the HOMA equation: HOMA‐IR = [Fasting insulin (μIU/ml) fasting glucose (mM/L)]/22.5.\n\nAlso, homeostasis model assessment of β‐cell function (HOMA‐β) and quantitative insulin sensitivity check index (QUICKI) were used to assess β‐cell function and insulin sensitivity, respectively, as follows: HOMA‐β: (fasting plasma insulin [μU/ml] * 20)/(fasting blood glucose [mmol/l] – 3.5) and QUICKI: 1/(log fasting blood glucose [mmol/l] + log fasting plasma insulin [μU/ml]).\n\nFurthermore, serum 25‐hydroxyvitamin D [25(OH) D] levels were assessed using a commercial ELISA kit (25‐Hydroxyvitamin D ELISA kit; Immuno Diagnostic Systems).\n\n## Measurements of hs‐CRP\n\nThe PEG (polyethylene glycol)‐enhanced immuno‐turbidometry method and commercially available kits on an Alcyon® analyser (Abbott) were used to measure hs‐CRP levels.\n\n## Assessment of PAB\n\nSerum PAB values were measured in all subjects as previously described by Alamdari et al. 10 In the first step, we added horseradish peroxidase enzyme and chloramine‐T as oxidizing agents to TMB. Redox index resulted in the combined activity of a colour cation (by oxidants) or reduced to a colourless compound (by antioxidants). In standard solutions, various proportions ($0\\%$–$100\\%$) of 250 μM hydrogen peroxide (as an oxidizing substance) were mixed with 3 mM uric acid (in 10 mM NaOH) (as an antioxidant). The absorption of 10 μl samples was measured with an enzyme‐linked immunosorbent assay (ELISA) reader at 450 nm for the reference, 630 nm, and the values of PAB were expressed in arbitrary (Hamidi. Koliakos [H.K]) units).\n\n## Statistical methods\n\nThe normality of the data was assessed by the Kolmogorov–Smirnov test. The mean and SD (for normal distribution) and median and interquartile range (IQR) (for non‐normal distribution) were used to describe the study variables. The independent student t‐test (for variable normality distribution) was used to compare the mean of study variables between case and control groups. A logistic regression method was used to determine the variables related to diabetes, including age, BMI, PAB, systolic blood pressure (SYSp), diastolic blood pressure (DIAp), GLUCOSE (Glc), insulin, InsulinR, TC, LDL, HDL, TG, hs‐CRP and vitamin D. Based on the Hosmer–Lemeshow method, simple logistic regression was utilized to determine the relationship between study variables and diabetes. Then, the variables with $p \\leq .2$ were added to the final model and analysed using multiple logistic regressions. We used SPSS for Windows software (version 18 software package SPSS Inc). A p‐value less than.05 was considered statistically significant.\n\n## Participants' characteristics and demographic findings\n\nAll data showed a normal distribution. Demographic data, including age, BMI, SYSp and DIAp, were not significantly different between the two groups. Except for serum TC, insulin, vitamin D, WC, WHR and WHtR, other laboratory findings in diabetic subjects were significantly different from the non‐diabetic subjects ($p \\leq .05$). Table 1 shows the features of the two groups.\n\n**TABLE 1**\n\n| Variables | Diabetic (n = 99) | Control (n = 100) | p‐value |\n| --- | --- | --- | --- |\n| Age (y) | 65.33 ± 5.34 | 61.20 ± 5.990 | .283 |\n| BMI (kg/m2) | 26.6 ± 2.0 | 26.2 ± 2.4 | .253 |\n| SYSp (mmHg) | 13.03 ± 1.16 | 13.22 ± 1.20 | .951 |\n| DIAp (mmHg) | 7.49 ± 0.66 | 7.53 ± 0.75 | .729 |\n| Glc (mmol/L) | 198.72 ± 69.78 | 94.87 ± 5.68 | <.001* |\n| TC (mg/dl) | 152.56 ± 10.77 | 150.19 ± 10.68 | .122 |\n| LDL (mg/dl) | 144.72 ± 33.27 | 131.70 ± 31.43 | .005* |\n| HDL (mg/dl) | 46.03 ± 9.03 | 49.07 ± 10.10 | .026* |\n| TG (mg/dl) | 166.19 ± 37.01 | 155.59 ± 26.76 | .022* |\n| Vit D (ng/ml) | 19.29 ± 10.58 | 19.63 ± 8.12 | .798 |\n| PAB (H.K) | 0.40 ± 0.29 | 0.22 ± 0.13 | <.001* |\n| hs‐CRP (mg/dL) | 5.11 ± 6.03 | 2.96 ± 3.07 | .002* |\n| Insulin R** (μU/mL × mmol/L) | 4.10 ± 4.22 | 2.63 ± 2.52 | .003* |\n| HOMA‐ β (%) | 1.09 ± 0.89 | 1.85 ± 1.38 | <.001* |\n| QUICKI | 0.32 ± 0.03 | 0.36 ± 0.04 | <.001* |\n| Insulin (μU/ml) | 9.33 ± 6.31 | 8.45 ± 6.48 | .335 |\n| WC (cm) | 95.49 ± 7.19 | 94.31 ± 10.3 | .352 |\n| HC (cm) | 102.54 ± 7.18 | 101.64 ± 5.88 | .337 |\n| WHR | 0.93 ± 0.07 | 0.93 ± 0.11 | .767 |\n| WHtR | 0.56 ± 0.04 | 0.55 ± 0.06 | .717 |\n\n## PAB values, hs‐CRP concentration and insulin resistance among postmenopausal women\n\nSerum PAB levels in the diabetic subjects were significantly higher than in the control group ($p \\leq .001$) (Table 1). Also, serum hs‐CRP concentrations were statistically different in the two groups ($$p \\leq .002$$) (Table 1). Unsurprisingly, in diabetic women, there was a statistically significant difference in insulin resistance, HOMA‐β and QUICKI compared to non‐diabetic women (all $p \\leq .05$), whereas no considerable difference was demonstrated between diabetic patients and healthy participants in serum insulin concentrations ($$p \\leq .335$$).\n\n## The relationship between serum PAB values, BMI, and hs‐CRP concentrations and other laboratory parameters\n\nAs shown in Table 2, the Pearson correlation coefficient analysis was performed to evaluate the correlation between serum PAB values, BMI, hs‐CRP concentrations and other laboratory parameters. Scatter plots graphically showed a strong and positive uncorrected association between serum PAB values and hs‐CRP levels ($r = .258$ and $$p \\leq .010$$) (Figure 1). We did not find any significant correlation between PAB values and insulin resistance ($r = .095$ and $$p \\leq .347$$) (Figure 2). Moreover, serum PAB and hs‐CRP levels were positively correlated with serum insulin ($r = .212$, $$p \\leq .035$$; $r = .211$, $$p \\leq .037$$), respectively. Among the other study factors, a significant association was observed between serum PAB values and LDL‐C levels ($r = .209$, $$p \\leq .038$$) and a negative correlation with HDL‐C levels (r = −0.224 and $$p \\leq .026$$). Moreover, a comparison of the relationship between BMI and other values showed a significant correlation between BMI and TG levels ($r = .207$ and $$p \\leq .042$$). In addition, we did not find any association between vitamin D levels and other laboratory parameters listed in this study.\n\n## Multiple logistic regressions\n\nLogistic regression in the backward approach explained that InsulinR (OR: 1.16, p: 0.012), cholesterol (OR: 1.033; p: 0.047) and LDL‐C (OR: 1.017; p: 0.002) levels, and PAB values (OR: 174.89; $p \\leq .001$) had a positive association with diabetes mellitus in patients compared to non‐diabetic women (Table 3). Moreover, these results showed that diabetes had an inverse association with HDL‐C (OR: −0.932; $p \\leq .001$).\n\n**TABLE 3**\n\n| Unnamed: 0 | Variable | OR | 95% CI | p‐value |\n| --- | --- | --- | --- | --- |\n| Multiple logistic regression (entered approach, pseudo R 2 = .396) | InsulinR | 1.151 | 1.019–1.300 | .024 |\n| Multiple logistic regression (entered approach, pseudo R 2 = .396) | Cholesterol | 1.032 | 0.998–1.066 | .063 |\n| Multiple logistic regression (entered approach, pseudo R 2 = .396) | LDL‐C | 1.015 | 1.004–1.026 | .007 |\n| Multiple logistic regression (entered approach, pseudo R 2 = .396) | HDL‐C | 0.935 | 0.900–0.973 | .001 |\n| Multiple logistic regression (entered approach, pseudo R 2 = .396) | TG | 1.010 | 0.999–1.022 | .073 |\n| Multiple logistic regression (entered approach, pseudo R 2 = .396) | Vit D | 0.997 | 0.962–1.033 | .877 |\n| Multiple logistic regression (entered approach, pseudo R 2 = .396) | PAB | 140.451 | 16.426–1200.97 | <.001 |\n| Multiple logistic regression (entered approach, pseudo R 2 = .396) | hs‐CRP | 1.064 | 0.971–1.166 | .186 |\n| Multiple logistic regression (backward approach, pseudo R 2 = .373) | Insulin R | 1.165 | 1.034–1.313 | .012 |\n| Multiple logistic regression (backward approach, pseudo R 2 = .373) | Cholesterol | 1.033 | 1.01–1.067 | .047 |\n| Multiple logistic regression (backward approach, pseudo R 2 = .373) | LDL‐C | 1.017 | 1.006–1.028 | .002 |\n| Multiple logistic regression (backward approach, pseudo R 2 = .373) | HDL‐C | −0.932 | 0.897–0.968 | <.001 |\n| Multiple logistic regression (backward approach, pseudo R 2 = .373) | PAB | 174.893 | 21.563–1418.518 | <.001 |\n\n## DISCUSSION\n\nTo our knowledge, this is the first case–control study to report PAB values and investigate the relationship between hs‐CRP levels and PAB values in postmenopausal women with and without diabetes mellitus. The main finding of the present study was the serum PAB and hs‐CRP elevation in diabetic postmenopausal women compared to non‐diabetic cases.\n\nThis finding is in accordance with earlier studies demonstrating the presence of systemic inflammation in diabetes. The increased level of OS is significantly associated with metabolic parameters in diabetic patients. 11, 12 OS can be induced by inflammation 4, 13; for example, higher concentrations of interleukin‐6 are an important stimulant for the production of hs‐CRP 14 and inflammation can induce the production of free radicals. 15 The present study showed that serum hs‐CRP levels were positively associated with serum PAB values in diabetic women. Moreover, earlier reports support the presence of high OS and hs‐CRP levels in stroke, cardiovascular and beta‐thalassemia patients. 16, 17 *There is* strong evidence of the correlation between inflammation and OS because both factors contribute to the pathogenesis of diabetes. 18 Moreover, diabetic postmenopausal women also had higher levels of blood glucose and HOMA‐IR index. In correlation with previous studies, dysregulated lipid metabolism in diabetics has been reported, which could be attributed to increased lipolysis due to impaired insulin function in adipose tissue. In addition, the accumulation of free fatty acids in the liver leads to the high hepatic synthesis of TGs and results in hypertriglyceridemia. 11, 19 *In this* study, as shown by Barrett‐Connor et al., 20 no relationship was observed in total cholesterol between diabetic and non‐diabetic subjects. We did not find any significant difference between serum hs‐CRP, glucose, TG, LDL‐C levels, and BMI. These results were inconsistent with those of Yang et al. 21 The reason may be due to the menopause subjects and the changes in the oestrogen hormone and its function in the liver. Moreover, parallel to our report, earlier reports have suggested that OS plays a major role in developing insulin resistance. 22, 23 Consistent with many studies, 23, 24 we can suggest that diabetic women have significantly altered lipid profiles than healthy postmenopausal subjects. Contrary to our work, many studies have reported that increased BMI values were strongly associated with hs‐CRP and OS levels. 25 We suggest that independent of BMI, OS may also be an essential determinant of hs‐CRP levels in diabetic people. Therefore, the link between OS and hs‐CRP levels may involve pathways unrelated to BMI. In line with the study by Goodarzi et al., 7 there was no significant difference in BMI between the two groups. Moreover, consistent with Zaman et al., the patient and control groups were overweight but not obese. 26 Overweight women are not necessarily diabetic, and diabetes mellitus is not the only reason for the BMI increase in overweight type 2 diabetics; other factors may be involved. In addition, in line with our study, many studies have shown that people with diabetes also have a low BMI, and some have a very low BMI. 26, 27 On the contrary, unlike some studies, 28 our study found that diabetes mellitus in our diabetic patients was not necessarily dependent on insulin. Therefore, it can be concluded that in people with type 2 diabetes, other factors may have a role in the incidence of diabetes. Hence, it can alter insulin levels in people who have diabetes without a statistically considerable difference from healthy subjects.\n\nIn contrast to previous literature, 29, 30 our findings demonstrated a positive relationship between serum hs‐CRP and insulin levels because inflammatory markers decrease insulin secretion and signalling in peripheral tissues. Moreover, interleukin‐6 decreases insulin signalling in the liver. 31 In the present study, we found an irreversible correlation between PAB values and HDL‐C levels in line with A. Cagnacci et al. 32 because oxidants can be reduced by the antioxidant enzyme paraoxonase carried by HDL‐C lipoproteins. 33, 34 Moreover, we found a significant relationship between TG levels and BMI. This finding demonstrated that high TG can cause obesity and ultimately increase BMI in diabetic postmenopausal women. Besides, in contrast to the Cardiovascular Health Study and research by Mendall et al., surprisingly, no relationship was found between hs‐CRP levels and BMI in women. Due to this controversy with the prior investigation, we think that diabetes in postmenopausal women can cause these outcomes. Our finding was in agreement with that of Kahn et al., 35, 36 indicating that diabetic postmenopausal women were characterized by insulin resistance. Moreover, it has been noted that insulin has a significantly negative relationship with higher hs‐CRP levels and PAB values. However, in Table 3, PAB values showed a positive correlation with LDL‐C levels and an irreversible association with HDL‐C levels. Therefore, the evidence supporting these results is that HDL cholesterol is the major lipoprotein carrier of antioxidant enzymes, and LDL is the main factor correlated with oxidative markers.\n\nOur study had a few limitations. The present work focused only on PAB values. However, several other factors can affect these biochemical parameters in OS, including sex hormones. Another limitation was the small sample size.\n\n## CONCLUSIONS\n\nWe found significantly higher PAB values in diabetic postmenopausal women. Moreover, we demonstrated that increased hs‐CRP concentrations are strongly associated with PAB values, a reliable OS marker. This finding was independent of BMI and insulin resistance in diabetic postmenopausal women. Measurement of PAB hs‐CRP levels and other biochemical parameters may be a valuable marker for OS and inflammation and a helpful diagnostic factor to prevent injury and develop coronary artery disease. Future studies with larger sample sizes on PAB values and hs‐CRP may lead to the more practical use of these two markers in clinical diagnosis and follow‐up of diseases and better the quality of life for patients.\n\n## AUTHOR CONTRIBUTIONS\n\nHassan Ehteram: Conceptualization (supporting); writing – review and editing (equal). Sara Raji: Data curation (equal); writing – original draft (equal); writing – review and editing (equal). Mina Rahmati: Data curation (equal); writing – review and editing (equal). Hanieh Teymoori: Data curation (equal); writing – review and editing (equal). Samaneh Safarpour: Data curation (equal); writing – review and editing (equal). Nahid Poursharifi: Data curation (equal); writing – review and editing (equal). Mona Hashem Zadeh: Data curation (equal); writing – original draft (equal); writing – review and editing (equal). Reza Pakzad: *Formal analysis* (lead); writing – review and editing (equal). Hossein Habibi: Writing – review and editing (equal). Naser Mobarra: Conceptualization (lead); supervision (lead); writing – review and editing (equal).\n\n## FUNDING INFORMATION\n\nThis study is funded by Mashhad University of Medical Sciences (Grant No: 981826)\n\n## CONFLICT OF INTEREST\n\nThe authors declared no conflicts of interest.\n\n## ETHICAL APPROVAL\n\nThe Ethics Committee of Mashhad University of Medical Sciences approved the study (IR.MUMS.REC.1399.533).\n\n## DATA AVAILABILITY STATEMENT\n\nThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.\n\n## References\n\n1. Park S, Kim M, Paik JK, Jang YJ, Lee SH, Lee JH. **Oxidative stress is associated with C‐reactive protein in nondiabetic postmenopausal women, independent of obesity and insulin resistance**. *Clin Endocrinol (Oxf)* (2013) **79** 65-70. PMID: 22816656\n\n2. 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the Physicochemical Properties of Alginate-Based\n Edible Nanoparticle Coatings and Quality Maintenance of Shrimp (Penaeus monodon)\n during Refrigerated Storage\nauthors:\n- Narin Charoenphun\n- Bharathipriya Rajasekaran\n- Suguna Palanisamy\n- Karthikeyan Venkatachalam\njournal: Foods\nyear: 2023\npmcid: PMC10000639\ndoi: 10.3390/foods12051103\nlicense: CC BY 4.0\n---\n\n# Impact of Longkong Pericarp Extract on the Physicochemical Properties of Alginate-Based Edible Nanoparticle Coatings and Quality Maintenance of Shrimp (Penaeus monodon) during Refrigerated Storage\n\n## Abstract\n\nThe objective of this study was to evaluate the impact of varying concentrations of longkong pericarp extract (LPE) on the physicochemical properties of alginate-based edible nanoparticle coatings (NP-ALG) on shrimp. For developing the nanoparticles, the alginate coating emulsion with different LPE concentrations (0.5, 1.0, and $1.5\\%$) was ultrasonicated at 210 W with a frequency of 20 kHz for 10 min and a pulse duration of 1s on and 4 off. After that, the coating emulsion was separated into four treatments (T): T1: Coating solution containing basic ALG composition and without the addition of LPE or ultrasonication treatment; T2: ALG coating solution converted into nano-sized particles with ultrasonication and containing $0.5\\%$ LPE; T3: ALG coating solution converted into nano-sized particles with ultrasonication and containing $1.0\\%$ LPE; T4: ALG coating solution converted into nano-sized particles with ultrasonication and containing $1.5\\%$ LPE. A control (C) was also used, where distilled water was used instead of ALG coating. Before coating the shrimp, all the coating materials were tested for pH, viscosity, turbidity, whiteness index, particle size, and polydispersity index. The control samples had the highest pH and whiteness index and was followed by the lowest viscosity and turbidity ($p \\leq 0.05$). Among the T1–T4 coating materials, T4 coating had higher turbidity, particle size, polydispersity index, but lower pH, viscosity, and whiteness index ($p \\leq 0.05$). To study the quality and shelf-life of the shrimp, all coated shrimp samples were refrigerated at 4 °C for a period of 14 days. At 2-day intervals, physiochemical and microbial analyses were performed. The coated shrimp also had a lower increase in pH and weight loss over the storage period ($p \\leq 0.05$). Coatings containing $1.5\\%$ LPE significantly reduced the polyphenol oxidase activity in the shrimp ($p \\leq 0.05$). The addition of LPE to NP-ALG coatings demonstrated dose-dependent antioxidant activity against protein and lipid oxidation. The highest LPE concentration ($1.5\\%$) led to increased total and reactive sulfhydryl content, along with a significant decrease in carbonyl content, peroxide value, thiobarbituric acid reactive substances, p-anisidine, and totox values at the end of the storage period ($p \\leq 0.05$). Additionally, NP-ALG-LPE coated shrimp samples exhibited an excellent antimicrobial property and significantly inhibited the growth of total viable count, lactic acid bacteria, Enterobacteriaceae, and psychotropic bacteria during storage. These results suggested that NP-ALG-LPE $1.5\\%$ coatings effectively maintained the quality as well as extended the shelf-life of shrimp during 14 days of refrigerated storage. Therefore, the use of nanoparticle-based LPE edible coating could be a new and effective way to maintain the quality of shrimp during prolonged storage.\n\n## 1. Introduction\n\nShrimp has become increasingly popular among consumers for its distinctive taste, but it is not just a delicacy. It is also a rich source of protein, healthy PUFA, vitamins, and minerals [1]. The high moisture content and various nutrients in shrimp makes it more vulnerable to changes in physical, biochemical, and microbiological characteristics and such alterations can negatively impact the quality and shelf-life of shrimp, ultimately resulting in reduced market value [2]. Several chemicals including ethylenediaminetetraacetic acid, benzoic acid, polyphosphates, ascorbic acid, and sodium chloride have been tried as preservatives to maintain the quality and extend the shelf-life of shrimp during storage. However, sulfite-based formulation produces allergic reactions, thus synthetic preservatives exert an adverse effect on human health. Moreover, to preserve the quality of shrimp during prolonged storage, various advanced techniques have been studied, such as modified atmospheric packaging, vacuum packaging, high-pressure treatment, the use of plant extract, and applying edible coatings [3]. The edible coating is a promising and reliable method for extending the shelf-life of shrimp among the various techniques studied [4]. It creates a barrier against oxygen, slows down oxidation, prevents microbial contamination, reduces moisture loss, and preserves flavor [5]. Edible coatings also serve as a means of delivering food additives, such as antioxidants and antimicrobial agents. Using natural plant extracts in these coatings can extend the shelf-life of perishable foods, such as fruits, vegetables, and seafood [6]. Alginate (ALG), a polysaccharide derived from brown algae, is widely used as a coating material. It is considered a GRAS (generally recognized as safe) substance and is composed of D-mannuronic acid and L-guluronic acid. Alginate is popular as a coating material because it can form a strong gel and maintain its insolubility when reacting with multivalent cations [7]. Many studies have shown that incorporating preservatives into coatings can help prevent quality changes in perishable foods during prolonged storage [1,8]. However, consumers often prefer preservatives that come from natural sources to ensure the safety of their food. Phenolic compounds, which are widely found in plants, are one such example of these natural preservatives. Phenolic compounds are well known for their antimicrobial and antioxidant properties, making them a potent alternative to synthetic agents [9]. Longkong (Aglaia dookkoo Griff.) is an economically valuable, non-climacteric tropical fruit belonging to the Meliaceae family, primarily found in southern Thailand [4]. Longkong fruit is composed of three main parts—pericarp, flesh, and seeds—and the pericarp contains a high level of polyphenols. Studies indicate that longkong pericarp extract (LPE) exhibits multiple biological and pharmacological effects, including radical scavenging, germicidal, cytostatic, antimalarial, and depigmentation [10]. In addition, LPE contains a rich source of lansic acid, lansiosides, lansiolic acid, and iso-onoceratriene. These chemical compounds in LPE could control the hormonal imbalance in humans and promote anti-baldness, antipyretic and anti-feeding activities. Nevertheless, ellagic acid and corilagin in the LPE could promote anti-fibrosis and anti-glaucoma effects. Incorporating nanotechnology and natural plant extracts into the coating medium is an effective approach to preserve the stability of bioactive compounds and as well as food products against deterioration from oxidation and microorganism [11]. Additionally, the use of nanotechnology to produce particles of nano-dimension increases the surface area per unit weight, which results in better dispersion of the active substances in the coating medium, thereby enhancing its functionality and bioactivity [12]. Furthermore, the use of nano-sized particles allows for the controlled and gradual release of bioactive substances during prolonged storage [13]. Additionally, nanotechnology can incorporate active substances without altering the sensory characteristics of foods and increase their shelf-life [14]. Although, the antibacterial activity of LPE and alginate coating is well known, their combination, especially in the nanoparticles system, has not been studied. Therefore, the present study utilized this opportunity to examine the properties of LPE-added ALG-based edible nanoparticles coating and to investigate their preventive effect on the quality maintenance of shrimp during 14 days of storage at refrigerated conditions.\n\n## 2.1. Raw Materials, Chemicals, and Reagents\n\nLongkong fruits fully ripe were harvested from a local garden in Surat Thani province, Thailand. Black tiger shrimp (Penaeus monodon) measuring 6–8 cm in length were purchased from a nearby farm. The shrimp were placed on ice at a ratio of 1:2 (shrimp:ice) and transported to the lab within an hour. Upon arrival, the shrimp were rinsed with cold water and kept on ice until use, not exceeding 5 h. Food-grade sodium alginate (Keltone LV, ISP, San Diego, CA, USA) was used as a biopolymer for coating formulation. The analytical-grade solvents and chemical agents utilized in this study encompassed chloroform, ethanol, methanol, ethyl acetate, sodium hydroxide, Triton X-100, sodium chloride, 1-3,4-dihydroxyphenylalanine (DOPA), Tris, glycine, sodium ammonium sulfate, 5,5′-dithio-bis-(2-nitrobenzoic acid) (DNTB), ethylenediamine tetra acetic acid (EDTA), guanidine chlorate, dipotassium hydrogen phosphate, sulfosalicylic acid, potassium dihydrogen phosphate, hydrochloric acid, potassium iodide, sodium thiosulfate, urea, anhydrous sodium sulfate, trichloroacetic acid (TCA), ascorbic acid, thiobarbituric acid (TBA) (Merck, Darmstadt, Germany), Tween 80 (Labchem, Zelienople, PA, USA), 1,1,3,3-tetramethoxypropane (MDA) (Sigma-Aldrich, St. Louis, MO, USA), and acetic acid (Lab-Scan, Pathum Wan, Bangkok, Thailand). The media used for the microbiological analyses, namely plate count agar, peptone, deMan, Rogosa, Sharpe (MRS) agar, and violet red bile glucose agar (VRBG), were all analytical grade and purchased from Merck, Darmstadt, Germany.\n\n## 2.2. Preparation of Longkong Pericarp Extract (LPE)\n\nUpon the arrival of the longkong fruits, their pericarps were isolated from the flesh and washed using cold water with $2\\%$ ascorbic acid. The longkong pericarp extract (LPE) was obtained as guided by Nagarajan et al. [ 4] with some modifications. First, the pericarps were dried in a hot air oven at 40 °C until a consistent weight was reached, and the dried sample was grounded into fine powder. To prepare the LPE, 5 g of pericarp powder was mixed with 100 mL of absolute ethanol. The mixture was then heated with agitation in a water bath at 40 °C for 4 h. The resulting solution was then placed in the solvent evaporator to remove the solvent thoroughly at 40 °C. The final LPE was freeze-dried, stored in a sealed amber bottle, and kept at −20 °C until required for further experimentation.\n\n## 2.3. Preparation of Coating Solution\n\nThe ALG coating solution was prepared by following the method of Sharifimehr et al. [ 5] with some modifications, dissolving $1\\%$ ALG (w/v) and $4\\%$ Tween 80 (v/v) in distilled water, and then adding LPE at different concentrations while constantly stirring. The mixture was thoroughly stirred using a magnetic stirrer to obtain a homogenous solution. The particle size of the coating was reduced to the nanoscale using ultrasonication (Hielscher UP200Ht, Hielscher Ultrasonics GmbH, Teltow, Germany) at 210 W, a frequency of 20 kHz for 10 min with a pulse duration of 1 s on and 4 s off. The temperature was maintained at 25 °C during the ultrasonication process. Four treatments (T) were used: T1: Coating solution containing basic ALG composition and without the addition of LPE or ultrasonication treatment; T2: ALG coating solution converted into nano-sized particles with ultrasonication and containing $0.5\\%$ LPE; T3: ALG coating solution converted into nano-sized particles with ultrasonication and containing $1.0\\%$ LPE; T4: ALG coating solution converted into nano-sized particles with ultrasonication and containing $1.5\\%$ LPE. A control (C) was also used, where distilled water was used instead of ALG coating. All the tested coating solutions were measured for physicochemical properties as shown in Section 2.5.\n\n## 2.4.1. pH\n\nThe pH of the coating solution was measured using a digital pH meter (Mettler-Toledo GmbH, Giessen, Germany).\n\n## 2.4.2. Viscosity\n\nTo determine the viscosity of the coating solution, a digital tabletop Brookfield viscometer (Brookfield DVE viscometer, Middleborough, MA, USA) was utilized. First, 150 mL of the coating solution was placed in a beaker measuring 70 mm in diameter and 125 mm in height. A Viscometer equipped with a number 2 spindle, set to run at a speed of 12 rpm, was used to measure viscosity. The outcomes were expressed in centipoise (cP).\n\n## 2.4.3. Turbidity\n\nThe turbidity of the coating solution was measured using a turbidimeter (Hanna Instruments, model HI 93703, Woonsocket, RI, USA), and the results were expressed in percentages.\n\n## 2.4.4. Whiteness Index\n\nThe HunterLab colorimeter (MiniScan EZ 4000, Hunter Associates, Inc., Reston, VA, USA) was utilized to determine the color of the coating solution following the methodology outlined by Josewin et al. [ 15]. After calibrating the instrument using a white standard plate ($L = 91.83$, a = −0.73, $b = 1.52$), the lightness (L*), redness/greenness (a*), and yellowness/blueness (b*) were measured. The whiteness index (WI) was subsequently calculated using the following formula:[1]WI=100−(100−L*)2+(a*)2+(b*)2\n\n## 2.4.5. Particle Size and Polydispersity Index (PDI)\n\nThe particle size and polydispersity index (PDI) were assessed using a method adapted from Venkatachalam [16]. Backscatter detection at a 170° scattering angle was utilized in the process. The sample was stabilized within the device for 60 s before data collection at 25 °C. The particle size outcomes were reported in nanometers, while the PDI values were expressed as the polydispersity index.\n\n## 2.5. Coating and Storage of Shrimps\n\nThe schematic illustration of the ALG coating preparation and coating of the shrimp is shown in Figure 1. The shrimp at a refrigerated temperature (4–8 °C) were coated by immersing them fully in a respective coating solution, as described in Section 2.4, and subsequently, the coated shrimps were placed on a wire rack for 1 min to allow excess coating material to drip off. The drying process of the coating material on the surface of the shrimp was facilitated by exposing coated shrimps to air blown by an electric fan. Next, the shrimps were arranged in polystyrene trays with 12 shrimps per tray, then wrapped with polyolefin film, and kept at 4 °C for 14 days. At every two-day interval during the storage period, samples were chosen randomly for shelf-life analysis as per the protocol described in Section 2.6.\n\n## 2.6.1. Weight Loss\n\nPrior to weighing, the stored shrimps were first dried off from any surface moisture by using a clean paper towel. Then, they weighed on an electronic weighing balance (SECURA124-1CIT, Sartorius, Goettingen, Germany). The following formula was used to determine the weight loss (WL) in the samples [17]:[2]WL (%)=W1−W2W1×100 where W1 and W2 denote the weight of shrimp on the initial and final day of storage, respectively.\n\n## 2.6.2. pH\n\nThe pH of the shrimp samples was determined using a digital pH meter (Mettler-Toledo GmbH, Giessen, Germany). To prepare the samples, 5 g of shrimp meat was homogenized in 25 mL of distilled water for 1 min. The homogenate was then filtered through a muslin cloth and the filtrate was collected and measured according to Ebadi et al. [ 3].\n\n## 2.6.3. Extraction of Polyphenol Oxidase\n\nThe crude polyphenol oxidase (PPO) was extracted in accordance with the method of Basiri et al. [ 18]. PPO from the cephalothorax was pulverized in the presence of liquid nitrogen and mixed using a pestle and mortar with 0.05 mol/L sodium phosphate buffer (pH 7.2) containing 1.0 mol/L NaCl and 0.2 g/100 mL Triton X-100 (1:3 w/v ratio). The mixture was mixed continuously for 30 min, followed by refrigerated centrifugation for 30 min at 8000× g. The supernatant was subjected to $40\\%$ saturation with sodium ammonium sulfate and left to stand at 4 °C for 30 min and centrifuged (12,000× g for 30 min at 4 °C) to collect the pellet. After centrifugation and precipitation with sodium ammonium sulfate, the resulting pellet was dialyzed using buffer overnight with buffer changes (15 volumes with three changes), and the insoluble material was collected by centrifugation (3000× g at 4 °C for 30 min). The resulting supernatant served as the “crude PPO extract.” To determine PPO activity, the method described by Simpson et al. [ 19] was followed using DOPA as the substrate. Specifically, 100 µL of the crude PPO extract was mixed in 1100 µL of the buffer solution containing 400 µL of 0.05 mol/L phosphate buffer (pH 6.0), 600 µL of 15 mmol/L DOPA, and 100 µL of deionized water. The mixture was incubated at 45 °C and the increase in absorbance at 475 nm was monitored using a UV-160 spectrophotometer (Shimadzu, Kyoto, Japan) for 3 min at 30-s intervals to measure the formation of dopachrome. PPO activity was defined as an increase in absorbance of 0.001 at 475 nm and expressed as one unit of PPO activity. The control was run in the same manner, except that deionized water was used instead of the PPO extract. The relative PPO activity was determined as the residual activity compared to the control by using the following formula:[3]Relative activity (%)=BA×100 where A: PPO activity of control; B: PPO activity of the sample.\n\n## 2.6.4. Total Sulfhydryl Content\n\nThe shrimp protein isolate was prepared based on the procedure by Liu et al. [ 20]. The procedure involved dissolving the sample in a buffer (0.086 M Tris, 0.09 M glycine, 4 mM EDTA) at pH 8 and centrifuging at 10,000× g for 15 min to eliminate any insoluble protein. The total sulfhydryl content was then quantified via Ellman’s technique [21] by mixing 4.5 mL of the supernatant with 0.5 mL of Ellman’s reagent (10 mM DTNB) and the absorbance was read at 412 nm with a spectrophotometer (RF-15001, Shimadzu, Kyoto, Japan). The sulfhydryl content was quantified in µmol sulfhydryl/g protein using a molar extinction coefficient of 13,600 M−1 cm−1. In addition, the protein content of the isolate was determined using the Biuret method [22].\n\n## Reactive Sulfhydryl Content\n\nThe samples were placed in a solubilizing buffer (0.086 M Tris, 0.09 M glycine, 4 mM EDTA, 8 M urea) at pH 8 and then centrifuged at 10,000× g for 15 min to remove any insoluble protein. The reactive sulfhydryl content was determined using the DTNB assay [23]. Specifically, the supernatant (4.5 mL) was mixed with 10 mM DTNB (0.5 mL), then the absorbance was recorded at both 412 nm and 540 nm using a spectrofluorometer (RF-15001, Shimadzu, Kyoto, Japan). The reactive sulfhydryl value was calculated using the following equations:Reactive sulfhydryl content (µmol/g) = 73.53 × (A412 − 1.6934 × A532 + 0.009932)[4] where A412 and A532 were the absorbance 412 nm and 532 nm of the assay solution, respectively.\n\n## 2.6.5. Carbonyl Content\n\nCarbonyl groups were detected by reactivity with 2,4-dinitrophenylhydrazine (DNPH). The method of Parrilla-Taylor et al. [ 24] was used. The samples were initially homogenized with 1 mL of $5\\%$ sulfosalicylic acid and centrifuged at 15,000× g, then the supernatant was discarded and the pellet was mixed with 10 mM DNPH in 2 M HCl and allowed to incubate at 25 °C for 1 h. The protein was precipitated by adding 0.5 mL of $20\\%$ TCA and centrifuged for 5 min at 15,000× g. The HCl was used as blanks. The resulting pellets were washed three times with a solution of ethanol and ethyl acetate (1:1, v/v) and resuspended in 6 M guanidine chlorate, followed by incubation at 37 °C for 15 min. The samples were then centrifuged for 5 min at 15,000× g, and the supernatant was collected for spectrophotometric measurement of protein carbonyl content at the maximum absorbance within the range of 360–401 nm. The results were reported as nmol of carbonyl proteins per gram of sample.\n\n## 2.6.6. Peroxide Value (PV)\n\nThe method used for lipid extraction in the shrimp was based on the approach described in the study of Bligh and Dyer [25]. A 25 g sample was homogenized with a mixture of chloroform, methanol, and distilled water (50:100:50) at 9500 rpm for 2 min at 4 °C using an IKA labortechnik homogenizer (Model T18, Bangkok, Thailand). Then, 50 mL of chloroform was added and homogenized at 9500 rpm for 1 min and 25 mL of distilled water was added and homogenized again for 30 s. The homogenate was then centrifuged at 3000 rpm at 4 °C for 15 min using a refrigerated centrifuge (Beckman Coulter, Avanti J-E Centrifuge, Fullerton, CA, USA). Finally, the chloroform phase was collected. It was then transferred into a 125 mL Erlenmeyer flask that contained about 2–5 g of anhydrous sodium sulfate, shaken well, and decanted into a round-bottom flask through a Whatman No.4 filter paper. Subsequently, the solvent was evaporated at 25 °C using an EYELA N-100 rotary evaporator (Tokyo, Japan), and the remaining solvent was removed by nitrogen flush. To determine the peroxide value (PV), the Kim et al. [ 6] method was adopted. The lipid sample (1.0 g) was combined with 25 mL of a solvent mixture (chloroform: acetic acid) at a ratio of 2:3 (v/v) and vigorously shaken, and 1 mL of saturated potassium iodide was introduced. After incubating the mixture in the dark for 5 min, 75 mL of distilled water was added and shaken followed by the addition of 0.5 mL of starch solution ($1\\%$, w/v) as an indicator. The mixture was then titrated against 0.01 N sodium thiosulfate solution, and the peroxide value was expressed as milliequivalents per kilogram of lipid.\n\n## 2.6.7. Thiobarbituric Acid Reactive Substance (TBARS)\n\nThe TBARS analysis was performed in accordance with the method of Benjakul and Bauer [26]. To begin, 1 g of minced shrimp meat was combined with 9 mL of a $15\\%$ TCA solution that contained $0.375\\%$ TBA. The resulting mixture was then subjected to heating in boiling water for 10 min, followed by cooling with running water. After this, the mixture was centrifuged for 20 min at 4000× g, the supernatant was collected, and its absorbance was measured at 532 nm using a UV-160 spectrophotometer (RF-15001, Shimadzu, Kyoto, Japan). The TBARS value was then determined by calculating the standard curve of MDA (ranging from 0–2 ppm) and expressed as mg MDA per kilogram of shrimp meat.\n\n## 2.6.8. Anisidine Value (AnV)\n\nThe Anisidine value (AnV) was determined using the method developed by Okpala et al. [ 27]. First, 100 mg of the lipid sample was dissolved in 25 mL of isooctane. Then, 2.5 mL of the resulting solution was mixed with 0.5 mL of $0.5\\%$ AnV in acetic acid (w/v) and kept in dark for 10 min. After that, the reaction mixture was read using a UV-Vis spectrophotometer (RF-15001, Shimadzu, Kyoto, Japan) at 350 nm and the following formula was applied to calculate the AnV value in the samples:[5]AnV=25 × ((1.2×A2)−A1W) where A1 and A2 represent the absorbances measured at 350 nm before and after the addition of AnV, respectively. Furthermore, W stands for the weight of the sample (g).\n\n## 2.6.9. Total Oxidation Value\n\nThe totox oxidation (TOTOX) value (TV) was determined using the protocol outlined by de Abreu et al. [ 28]. The TV is determined by adding the peroxide value and Anisidine value (AnV), as follows:[6]TV=2PV+AnV\n\n## 2.7. Total Volatile Basic Nitrogen (TVB-N)\n\nThe Conway microdiffusion technique [13] was used to evaluate the TVB-N level in shrimp using 0.1 M KOH. The results were expressed in milligrams of nitrogen per 100 g of shrimp.\n\n## 2.8. Microbiological Analyses\n\nTo conduct the microbiological tests, randomly selected shrimp samples were taken from the same tray, and each test was performed in triplicate. For these experiments, 10-g samples were transferred into sterile zipper bags containing 90 mL of peptone water and were homogenized using a stomacher at 250 rpm for 2 min. Subsequently, serial dilutions were made in test tubes containing $0.1\\%$ peptone water, and the diluted samples (0.1 mL) were then spread on the surface of dry media for microbial enumeration. Total viable plate counts (TVC) and psychrotrophic bacteria counts were determined by the pour plate method using plate count agar, which was incubated at 37 °C for 48 h and 7 °C for 10 days, respectively [29]. Enterobacteriaceae was evaluated by the spread plate method using violet red bile glucose (VRBG, Merck, Darmstadt, Germany) agar incubated at 37 °C for 24 h. Lactic acid bacteria were enumerated by the spread plate method using MRS agar incubated at 30 °C for 72 h [11]. The results were expressed in log CFU/g.\n\n## 2.9. Statistical Analysis\n\nThe statistical analysis in this study was conducted using a completely randomized design, with all experiments performed in triplicate. Mean values were compared using analysis of variance (ANOVA) and Duncan’s multiple range test. The data were analyzed using the Statistical Package for Social Sciences (SPSS) version 6 for Windows (SPSS Inc., Chicago, IL, USA).\n\n## 3.1.1. pH, Viscosity, and Turbidity\n\nThe pH values of the tested coating samples are illustrated in Figure 2A. On average, the pH of the coatings varied between 6.1 and 7.1. The pH of the control and T1 coating was found to be in the neutral range ($p \\leq 0.05$), while the pH of the NP-ALG-LPE coating (T2–T4) was observed to be impacted by the LPE concentration, showing a decrease in pH as the LPE concentration increased from $0.5\\%$ to $1.5\\%$ ($p \\leq 0.05$). Of all the samples, T4 had the lowest pH (at 6.2) value, while the control had the highest pH (at 7.1) value. Generally, the pH of longkong pericarp is slightly acidic (at 4.71) in nature [16], which may have affected the pH of the NP-ALG-LPE coatings. A study by Chen et al. [ 30] found that the presence of organic acids in longkong pericarp, such as glycolic acid, malic acid, and citric acid, can contribute to the low pH. Fresh longkong pericarp typically has a relatively stable pH due to its high moisture content [31]. However, the extraction process may have concentrated the naturally occurring organic acids in the pericarp, leading to a decrease in the pH of the coatings [32]. In addition, the use of ascorbic acid during extraction could have also lowered the pH of the LPE. A lower pH in the NP-ALG-LPE coatings can be beneficial as it can inhibit the growth of microorganisms. Gokoglu [33] also noted that organic acids can reduce the pH and inhibit the growth of spoilage and pathogenic bacteria. Similarly, Baek et al. [ 13] found that the pH of the ALG coating decreased with the addition of grapefruit seed extract due to the presence of organic compounds. The viscosity of the different coatings is shown in Figure 2B. *In* general, T1 had the highest viscosity, while the control had the lowest viscosity ($p \\leq 0.05$). Among the NP-ALG-LPE coatings (T2–T4), no significant differences were observed, regardless of the LPE concentrations ($p \\leq 0.05$). This could be due to the reduction of molecular weight of ALG caused by the ultrasonication process, which decreased the thickening properties of the ALG [34]. *In* general, the viscosity of the coatings is primarily determined by the material used [8]. The turbulence and cavitation effect of ultrasound causes an irreversible ordered–disordered conformation transition of ALG molecules, resulting in reduced particle size [5]. However, a lower viscosity of the coating could provide a thinner film over the food samples. Pilon et al. [ 14] stated that a thin layer over the product has better barrier properties during prolonged storage. As noted by Rodriguez-Turienzo et al. [ 35], the viscosity of a whey protein isolate-based coating was found to be decreased when ultrasonication treatment was applied in the coating emulsion and thus attributed to the formation of nano-sized particles. The turbidity levels of the different coatings are shown in Figure 2C. The NP-ALG-LPE coatings (T2–T4) that were subjected to ultrasonication had lower turbidity compared to T1 ($p \\leq 0.05$). This can be attributed to the reduction of starch granules in the ALG caused by the ultrasonication process, which leads to a decrease in opacity [36]. The overall transparency of NP-ALG-LPE coatings was improved as a result of ultrasonication. The solubility of ALG increased in samples T2, T3, and T4 due to ultrasonication, while T1 without ultrasonication had more undissolved particles [37]. Jambrak et al. [ 38] postulated that ultrasonication destroyed the crystalline region of starch granules, resulting in higher solubility of corn starch. Among the NP-ALG-LPE coatings (T2–T4), turbidity increased with augmenting concentrations of LPE from $0.5\\%$ to $1.5\\%$, respectively ($p \\leq 0.05$). Generally, the higher turbidity of the coating negatively impacts the product’s appearance, thus reducing marketability [5]. Increasing the coating ingredients promoted more polymers to be present in the coating, giving rise to greater light scattering [39]. Moreover, the higher concentration of LPE ($1.5\\%$) in T4 had a larger particle size. Therefore, more light scattering increased the turbidity of the coating.\n\n## 3.1.2. Whiteness Index, Particle Size, and Polydispersity Index\n\nFigure 3A displays the whiteness index (WI) of the tested coating samples. Generally, coatings with higher WI have a more desirable commercial appeal. The T1 coating had a higher WI than the NP-ALG-LPE coatings (T2–T4) with a significant difference ($p \\leq 0.05$). The decrease in WI of NP-ALG-LPE coatings (T2–T4) was attributed to the yellowish-brownish color of LPE [4]. As the concentration of LPE increased from 0.5–$1.5\\%$, the WI significantly decreased ($p \\leq 0.05$). According to Lichanporn et al. [ 31], brown pigments are mainly present in the pericarp area of longkong fruits. Therefore, the higher amount of LPE ($1.5\\%$) used in the coating resulted in a reduction of whiteness. Figure 3B illustrates the particle size of various coatings. *In* general, the NP-ALG-LPE coatings (T2–T4) had smaller particle sizes than T1 with a significant difference ($p \\leq 0.05$). The shear force created by ultrasonic cavitation caused a reduction in the particle size of polymers in the coatings [40]. Among the NP-ALG-LPE coatings, the particle size increased as the concentration of LPE increased from $0.5\\%$ to $1.5\\%$ with a significant difference ($p \\leq 0.05$). T2 had the smallest particle size, followed by T3 and T4 ($p \\leq 0.05$). The particle size of NP-ALG-LPE coatings ranged from 218 to 260 nm. This is in line with the findings of Baek et al. [ 13] and Lin et al. [ 41], who reported particle sizes of 206 nm and 247 nm for ultrasonicated ALG and chitosan-based coatings, respectively. Figure 3C shows the polydispersity index (PDI) of the tested coating samples. The PDI measures the heterogeneity of a coating based on its particle size [42]. Typically, a lower PDI indicates a more uniform distribution and smaller size of polymers in the coating [43,44]. *In* general, NP-ALG-LPE coatings (T2–T4) had a lower PDI compared to T1 with a significant difference ($p \\leq 0.05$). This difference was likely due to the disruption of structural integrity, induction of dissociation, and degradation of ALG molecules caused by the cavitation effect of ultrasonication [5], resulting in a reduction in the size of ALG molecules. This implies that ultrasonicated NP-ALG-LPE coatings have a homogenous dispersion of particles in the medium. Among the NP-ALG-LPE coatings, T2 had the lowest PDI. The increase in LPE concentration from $0.5\\%$ to $1.5\\%$ resulted in larger particle size and uneven distribution, leading to a higher PDI in T4 coating, followed by T3, as compared to T2 ($p \\leq 0.05$). This is consistent with the particle size results shown in Figure 3B. Thus, the application of ultrasound decreases the particle size and PDI of the coating.\n\n## 3.2.1. Physicochemical Properties\n\nFigure 4A shows the weight loss of control and coated shrimps during storage at 4 °C. Generally, weight loss represents moisture loss of fresh food, impacting both economic benefits and product quality, making it a crucial factor for marketability [8,12]. Weight loss in all samples rose as storage days progressed ($p \\leq 0.05$). The probable cause of weight loss in the shrimp sample was due to the denaturation and degradation of protein through autolysis and microbial enzymes, resulting in structural breakdown during storage [32]. Consequently, the water-holding capacity of shrimp muscle significantly decreased with storage time. At the end of storage, the T4-coated sample had the lowest weight loss as compared to the other samples. No significant difference was noted between the control and T1-coated sample until day 6 of storage ($p \\leq 0.05$). Afterward, the T1-coated sample displayed a noticeable decrease in moisture loss compared to the control ($p \\leq 0.05$) due to the barrier properties of ALG, which reduced moisture loss during storage [13]. The semi-permeable coating layer acted as a barrier to the flow of O2, CO2, and H2O, thus reducing moisture loss [6]. Moreover, the protein–polysaccharide complex formed between ALG, and the muscle protein of shrimp could enhance the overall water-holding capacity of shrimp during storage [45]. In addition to this, dehydration needs to be considered because it is one of the important quality parameters during refrigerated storage. According to Song et al. [ 8], the primary reason for the decrease in dehydration of products coated with ALG is that the gel coating serves as a sacrificial agent. Thus, moisture in the gel evaporates before any substantial desiccation occurs in coated food. Additionally, no significant difference was observed among NP-ALG-LPE coated shrimp samples until the 10th day of storage. However, at the end of storage, the T4-coated sample had the lowest weight loss, followed by T3, when compared to T2 ($p \\leq 0.05$). This was due to the formation of an additional layer on the shrimps, providing further resistance to mass transfer and slowing the increase in water loss after a certain storage period [39]. As a result, the addition of LPE to the ALG coating on shrimp effectively reduced moisture loss during prolonged storage. pH is recognized to be one of the important indicators for identifying the changes in pH of control and coated shrimps during storage microbial spoilage in seafood or aquatic products [32]. On day 0, the samples’ pH was 6.6 to 6.9, consistent with previous reports [1,6]. The slightly lowered pH of the NP-ALG-LPE-coated samples (T2–T4) was due to the reduced pH of the coatings with the addition of LPE (0.5–$1.5\\%$). This was in agreement with the pH of the NP-ALG-LPE coatings (Figure 4B). Overall, pH values were gradually increased in all samples over the storage period. At the end of storage, the lowest pH was observed in the T4-coated shrimp, while the control sample had the highest pH ($p \\leq 0.05$). The rise in pH was associated with the accumulation of alkaline compounds, primarily produced due to microbial activity. Ebadi et al. [ 3] noted that endogenous enzymes and microorganisms breaking down protein in shrimp produce volatile bases like ammonia and trimethylamine, causing an increase in shrimp muscle pH during storage [46]. T4-coated shrimp had a smaller increase in pH, which matched the controlled growth of the microorganisms. *In* general, shrimp is considered unacceptable if the pH exceeds 7.6 [47]. The control sample reached this limit on day 6, while coated shrimp remained within acceptable limits throughout storage. Among NP-ALG-LPE-coated shrimp samples (T2–T4), the pH value of the samples was strongly decreased with increasing LPE concentration ($p \\leq 0.05$). This suggests that LPE may help to slow microbial growth, thus reducing spoilage and decomposition. Shrimp coated with T4, which contains $1.5\\%$ LPE, showed lower pH due to the inhibitory effect of LPE against microbial action. This is because LPE contains high levels of polyphenols [31], and their activity against microbes is dependent on concentration. Polyphenols interact with microbial membranes, altering their permeability and functions and leading to cell death [48,49].\n\n## 3.2.2. Brown Pigment Enzyme Activities\n\nFigure 5A displays the levels of PPO activities in both control and coated shrimp samples during storage at 4 °C. PPO is a major contributor to browning in the shrimp samples during prolonged storage. Enzymatic browning, also known as melanosis, affects the quality and acceptance of shrimps. PPO is the main cause of browning, converting colorless quinones into dark pigments [50]. PPO triggers enzymatic browning by catalyzing the reaction between substrates, oxygen, phenolic compounds, and reaction by-products, causing black spot formation [6]. Among the samples, the control exhibited the highest PPO activity. This was attributed to the basic mechanism of melanosis, in which PPO converts colorless monophenols into diphenols, which then react with oxygen to form highly colored quinones, leading to the formation of brown polymers through reaction with amino acids [51]. Shrimp coated with T4 had the lowest PPO activity during storage due to the higher concentration of LPE ($1.5\\%$) compared to T3 ($1.0\\%$) and T2 ($0.5\\%$) ($p \\leq 0.05$). The inhibition of PPO activity by phenolic compounds can occur through various mechanisms, including direct inhibition of PPO, scavenging of oxygen, and reduction of quinones back to diphenols to prevent melanin formation [46]. Balti et al. [ 52] found that shrimps coated with microalgal exopolysaccharides enriched with $1.5\\%$ red seaweed extract (contains rich sources of polyphenols) reduced PPO activity more effectively than those coated with $0.5\\%$ and $1.0\\%$ extract during cold storage. Hence, the results suggest that coating shrimp with $1.5\\%$ LPE effectively inhibits PPO activity during storage, making it a promising natural inhibitor.\n\n## 3.2.3. Protein Oxidation\n\nFigure 6A,B depicts the total and reactive sulfhydryl groups of the control and coated shrimps during storage at 4 °C. Sulfhydryl is a highly active group found in myofibrillar protein, which possesses weak secondary bonds [53]. Overall, sulfhydryl content decreased in all samples with increasing storage time ($p \\leq 0.05$). This indicated that prolonged storage of shrimp had a significant effect on protein oxidation. However, the decrease of sulfhydryl groups was found lower in coated samples (T1–T4) when compared to the control ($p \\leq 0.05$). Morachis-Valdez et al. also reported that carp fillets coated with chitosan showed less reduction in sulfhydryl content than uncoated ones during 5 months of storage at −18 °C [54]. *In* general, protein oxidation leads to a decrease in sulfhydryl groups, which become disulfides [55]. Additionally, muscle protein denaturation and aggregation are related to disulfide bonds [4]. Among the NP-ALG-LPE coatings (T2–T4), T2 showed the greatest reduction while T4 showed the least loss in the sulfhydryl groups. The loss of sulfhydryl groups affects the structural, functional, and nutritional properties of shrimp muscle protein [56]. This negative effect can be reduced with the use of LPE which showed a superior protective effect and showed greater stability during storage. The carbonyl content in control and coated shrimps during storage at 4 °C is shown in Figure 6C. Carbonyl is a marker of protein oxidation, measured using DNPH (2,4-Dinitrophenylhydrazine) [57]. Protein oxidation decreases shrimp quality and nutrition due to the loss of essential amino acids and reduced digestibility [58]. Carbonyl content increased in all samples during storage ($p \\leq 0.05$), indicating oxidative damage to amino acid side chains, such as lysine, proline, arginine, and histidine [59]. On day 4, the control sample had higher carbonyl content than all coated samples (T1–T4), which increased over time. Shrimp coated with NP-ALG-LPE (T2–T4) significantly prevented the level of protein oxidation as compared with the ALG coating alone (T1) and control ($p \\leq 0.05$). However, the addition of LPE in the NP-ALG coating (T2–T4) did not significantly affect the carbonyl content in the samples until day 8. At the end of storage, samples coated with T3 and T4 had lower carbonyl content than T2 ($p \\leq 0.05$). Secondary products of lipid oxidation, such as aldehydes (e.g., malondialdehyde and 4-hydroxy-2-nonenal) or ketones, can react with amino acid residues through covalent bonds, known as Michael addition reactions, leading to indirect oxidation of protein [60]. Hence, LPE could scavenge this oxidation process in the stored shrimps as it contains an abundant level of polyphenols which act as a primary scavenger for oxidation and oxidation-induced byproducts.\n\n## 3.2.4. Lipid Oxidation\n\nThe peroxide value (PV) is a measure of major lipid oxidation products (hydroperoxides) in a sample [61]. The abstraction of hydrogen from fatty acid double bonds produces fatty acid-free radicals, which further react with oxygen to form hydroperoxides [50]. All samples showed a gradual increase in PV (Figure 7A) level over storage ($p \\leq 0.05$), indicating oxidation of fatty acids in shrimp muscle, producing hydroperoxides or peroxides [52]. On day 6 of storage, a significant difference was observed between control and coated samples ($p \\leq 0.05$), with control samples having higher PV, indicating greater lipid oxidation. Coated samples, however, showed lower PV during storage ($p \\leq 0.05$), attributed to the oxygen barrier capacity of ALG reducing oxygen diffusion and preventing lipid oxidation [6]. Shrimp coated with T4 had the lowest PV compared to control and T1, T2, and T3 throughout storage ($p \\leq 0.05$). In addition, PV did not exceed the acceptable limit of 18–20 meq/kg [62]. This showed that the antioxidant activity of LPE in preventing lipid oxidation was concentration dependent. Lipid oxidation is generally accelerated during storage due to high levels of polyunsaturated fatty acids in crustacean cell membranes [11], but phenolic compounds in LPE might scavenge free radicals, reducing lipid oxidation by lowering lipid radicals [35]. Thus, shrimp coated with T4, with the lowest PV, showed the best protection against lipid oxidation. The increase in TBARS (a measure of lipid oxidation) was due to partial oxidation and dehydration of unsaturated fatty acids [32]. All samples showed a rise in TBARS over 14 days of storage ($p \\leq 0.05$) (Figure 7B). Shrimps are susceptible to lipid oxidation, which can occur through autoxidation, photosensitized oxidation, or enzymatic reactions, such as lipoxygenase, peroxidase, and microbial enzymes [17]. In the present study, shrimp coated with $1.5\\%$ LPE (T4) showed the lowest TBARS level compared to T1 and control samples at all storage times ($p \\leq 0.05$), followed by T3 and T2. The decrease in TBARS values was consistent with the decrease in peroxide values (PV), indicating the ability of LPE to scavenge free radicals and prevent the formation of secondary oxidation products. The NP-ALG coating acted as a barrier to oxygen permeation and a carrier for the antioxidants in LPE, thus reducing the production of secondary oxidation products in shrimp [13]. Notably, T4 had the highest amount of LPE ($1.5\\%$) in its coating, thus resulting in the lowest TBARS value. Polyphenols, which are abundant in longkong fruit, have a strong reducing capacity and are known to retard and inhibit lipid oxidation [63]. Souza et al. [ 64] demonstrated that a polyphenol-rich leaf extract from an Amazonian plant act as a powerful antioxidant in human LDL protein by reducing TBARS levels. Furthermore, the TBARS value of all groups was below the acceptable limit (1–2 mg MDA/kg) [11], consistent with the study by Dehghani et al. [ 65]. Thus, shrimp coated with T4 showed higher stability against lipid oxidation. The secondary oxidation products of shrimp were measured using AnV, which detects non-volatile oxidation products, such as aldehydes and ketones [27]. AnV reacts with oxidation products to produce a yellow product [66]. The trend of AnV was similar to other lipid oxidation product assays in this study. The control had higher AnV compared to coated samples, with T4 having the lowest AnV (Figure 7C). This suggests that LPE in ALG coating reduces secondary oxidation product formation, especially in non-volatile compounds. The results from AnV are consistent with PV and TBARS, confirming that LPE in ALG coating is an effective way to maintain shrimp quality. The totox value measures the total lipid degradation products and indicates the oxidative stage of a product [67,68]. The totox value combines primary and secondary oxidation products and is commonly used in the food industry. The totox value increased over time for all samples, with the highest value found in the control (Figure 7D). A lower totox value indicates better shrimp quality. Among the NP-ALG-LPE-coated shrimps, T4 showed the lowest totox value compared to T2 and T3. Generally, the coating application on the food surface can cause the migration of compounds from the coating into the food [69]. As a result, coating with a higher LPE content ($1.5\\%$) resulted in higher levels of migrated substances and a stronger antioxidant effect in the shrimp, providing greater stability against lipid oxidation.\n\n## 3.2.5. TVB-N\n\nThe results of TVB-N levels in control and coated shrimp during storage at 4 °C are shown in Figure 8. TVB-N indicates decomposition of protein by microorganisms or endogenous enzymes in foods, particularly seafoods [70]. An analytical technique called TVB-N (Total Volatile Basic Nitrogen) measures the levels of nitrogenous chemicals in shrimp or seafood products, which reveals the level of freshness. TVB-N provides the total base volatile nitrogen content that begins to accumulate in the tissues with degradation during shrimp storage [65]. TVB-N, the combination of ammonia (from amino acid degradation), di-methylamine (generated by self-degrading enzymes), and trimethylamine (result of spoilage bacteria), generally rises with bacterial growth, enzyme degradation, or a combination of both during storage [71]. At day 0, TVB-N levels of all samples were below 10 mg N/100 g, showing freshness of the raw material. The control’s TVB-N content rapidly rose with storage time ($p \\leq 0.05$), reaching 58.4 mg N/100 g on day 14. Shrimp coated with the NP-ALG-LPE coatings (T2–T4) showed a slower increase in TVB content compared to the control and T1-coated samples during storage ($p \\leq 0.05$). Additionally, samples coated with T4 had lower TVB content compared to those coated with T2 and T3 ($p \\leq 0.05$). The European Commission considers TVB-N levels of 30–35 mg N/100 g as the upper acceptable limit [72]. The amount of TVB-N increased as a sum of ammonia, dimethylamine, trimethylamine, and volatile amine compounds. A small amount of ammonia is generally found during the first weeks of storage, and the total volatile alkalinity is slowly increased during storage. This may be caused by the amine removal process (deamination) of amino acids [73]. This study found that the NP-ALG-LPE-coated samples (T2–T4) had remained within acceptable limits at the end of storage. The reduced TVB-N content in the NP-ALG-LPE-coated samples was due to either reduced degradation of non-protein nitrogen compounds or slowed bacterial growth, or both. This shows LPE’s antibacterial properties. Olatunde et al. [ 74] also found that coconut husk extract reduced the increase in TVB in Asian sea-bass slices during 12 days of storage at 4 °C. Moreover, the quality and shelf-life of shrimp coated in active edible coatings made of gelatin and orange peel essential oil were determined. The shelf-life of shrimp was evaluated over a 14-day storage period by TVB-N analysis. Compared to the control group, the addition of orange peel essential oil in the edible gelatin coating improved the quality and prolonged the shelf-life of the shrimp. The incorporation of orange peel essential oil helped in preserving the chemical and microbial quality of the shrimp [75].\n\n## 3.2.6. Microbial Analysis\n\nThe initial total viable count (TVC) of the shrimp was around 2.4–2.9 log CFU/g, similar to the result of 2.5–3 log CFU/g reported by Mohebi et al. [ 76]. During 14 days of storage, a general increase in TVC was observed in all samples ($p \\leq 0.05$), with the highest increase seen in the control (Figure 9A), reaching 23.15 log CFU/g on the 14th day ($p \\leq 0.05$). Dipping the shrimp in ALG reduced the increasing trend of TVC but adding LPE significantly enhanced the microbial inhibitory effect ($p \\leq 0.05$). There were no significant differences among NP-ALG-LPE-coated samples (T2–T4) until day 4 of storage ($p \\leq 0.05$), but the T4-coated sample had the lowest TVC among all tested samples at the end of storage ($p \\leq 0.05$). This indicates that the inhibitory effect increased with increasing LPE concentration (0.5–$1.5\\%$) ($p \\leq 0.05$). Phytochemical compounds can damage bacterial cells by disrupting cell membranes and precipitating cell protein, causing death [52]. Kim et al. [ 6] found that shrimp coated with chitosan-alginate containing grape seed extract reduced TVC during 15 days of storage at 4 °C. Liu et al. [ 77] reported that an alginate-calcium coating with methanol extract from citrus fruit effectively reduced the increasing trend of TVC compared to those coated with $1\\%$ chitosan. Lactic acid bacteria (LAB) are facultative anaerobes and form a significant part of the natural microbiota in seafood stored in anaerobic conditions [78]. On the initial day, the count of lactic acid bacteria (LAB) in all samples was 1.0 log CFU/g (Figure 9B). During storage, there was an observed increase in LAB count in all samples ($p \\leq 0.05$). However, samples coated with T1–T4 exhibited a reduced increment compared to the control ($p \\leq 0.05$). The NP-ALG coating had an inhibitory effect on LAB growth, which was intensified by the addition of LPE at a different concentration. T1, T2, T3, and T4 reduced the LAB count by 2, 2.16, 2.67, and 3.15 log CFU/g compared to the control, respectively. Khaledian et al. [ 11] found that shrimp coated with a tragacanth gum-based coating containing lime peel extract inhibited LAB growth during 10-day storage at 4 °C. Among the NP-ALG-LPE-coated shrimp samples, T4 had a lower LAB count due to higher LPE concentration ($1.5\\%$) and greater antimicrobial effect. This can be attributed to the presence of terpenoids and lansiosides, which are the two major antibacterial substances found in longkong fruit [79,80]. A smoked eel fillet coated with carboxy methylcellulose containing rosemary extract (200–800 ppm) also showed a decrease in LAB count [81]. Enterobacteriaceae are used as a measure of hygiene [11]. Their presence in seafood, especially in cases of contaminated water or delayed chilling after capture, increases the likelihood of spoilage [82]. The initial count of Enterobacteriaceae in all samples was 0.95 log CFU/g (Figure 9C). During storage, the count of this bacteria increased continuously in all samples ($p \\leq 0.05$). Among the NP-ALG-LPE-coated shrimp samples (T2–T4), T4 had the lowest count of Enterobacteriaceae as compared to the control ($p \\leq 0.05$). T4 was effective in slowing the growth of Enterobacteriaceae, followed by T3 and T2 ($p \\leq 0.05$). This suggests that the antimicrobial effect of LPE was dose dependent. Alsaggaf et al. [ 83] found that shrimp coated with chitosan enriched with pomegranate peel extract reduced the microbial count during 30-day storage at 4 °C, with higher PPE concentration (0.5–$2.0\\%$) improving the antimicrobial activity. At the beginning of shrimp storage, the psychrotrophic bacteria count (PBC) of all samples was recorded at 1.85 log CFU/g (Figure 9D). Over time, the PBC of the control sample continued to rise ($p \\leq 0.05$), but the count decreased in shrimp coated with ALG (T1) ($p \\leq 0.05$). Gram-negative psychrotrophic bacteria are a key cause of spoilage in iced and/or refrigerated seafood [84]. Shrimp coated with NP-ALG-LPE coatings had an even lower level of PBC as compared to T1 and the control ($p \\leq 0.05$), which confirms the antibacterial activity of LPE. Other studies have also found similar results in shrimp samples treated with green tea extract [46] and fish fillets treated with microalgal exopolysaccharides and red seaweed extract [52]. The use of NP-ALG enriched with LPE as an active edible coating is a promising solution for maintaining the quality of shrimp during refrigerated storage by limiting the growth of multiple bacteria.\n\n## 4. Conclusions\n\nThe study investigated the impact of adding LPE (0.5–$1.5\\%$) to the ALG coating, using the ultrasonication process to convert the ALG-LPE coating to become nano-sized, and tested various physiochemical properties in the coating emulsion and as well as on the shrimp samples to control the quality loss and prolong the shelf-life for up to 14 days at 4 °C. The results showed that LPE addition to a coating emulsion significantly increased the viscosity, turbidity, particle size, and polydispersity index of the coating material while lowering the pH and whiteness index. The best results were observed when $1.5\\%$ LPE was added to the NP-ALG-based coating and tested shrimp samples, which resulted in lower pH, weight loss, and polyphenol oxidase activity, as well as stronger antioxidant effects against protein and lipid oxidation in the shrimp samples. The study also found that the microbial count, including total viable count, lactic acid bacteria, Enterobacteriaceae, and psychrotrophic bacterial count, was lower in the shrimps coated with the ALG-LPE coating. Thus, the results suggest that the addition of $1.5\\%$ LPE to the ALG coating can effectively maintain the quality of shrimp for up to 14 days of storage and is a good alternative to synthetic preservatives. In summary, synergistic effect of an alginate-based nanoparticle coating and LPE can serve as promising approach for the quality maintenance of seafood during storage. This finding could be potentially beneficial for food industries to extend the shelf-life of food products without the addition of synthetic preservatives. Further research is anticipated to rationally designed edible coatings with precisely adjusted coating properties for the effective enhancement of food products’ quality and storability.\n\n## References\n\n1. 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-0.008373891934752464\n]","isConversational":false}}},{"rowIdx":72,"cells":{"text":{"kind":"string","value":"---\ntitle: 'Results of DUET: A Web-Based Weight Loss Randomized Controlled Feasibility\n Trial among Cancer Survivors and Their Chosen Partners'\nauthors:\n- Wendy Demark-Wahnefried\n- Robert A. Oster\n- Tracy E. Crane\n- Laura Q. Rogers\n- W. Walker Cole\n- Harleen Kaur\n- David Farrell\n- Kelsey B. Parrish\n- Hoda J. Badr\n- Kathleen Y. Wolin\n- Dori W. Pekmezi\njournal: Cancers\nyear: 2023\npmcid: PMC10000640\ndoi: 10.3390/cancers15051577\nlicense: CC BY 4.0\n---\n\n# Results of DUET: A Web-Based Weight Loss Randomized Controlled Feasibility Trial among Cancer Survivors and Their Chosen Partners\n\n## Abstract\n\n### Simple Summary\n\nEffective and scalable diet, exercise, and weight management interventions are needed for primary cancer prevention in the general public, as well as for cancer control and tertiary prevention among the growing population of cancer survivors. A 6-month online intervention, entitled “Daughters, dUdes, mothErs, and others Together” (DUET), was designed to promote weight loss, a healthful diet, and increased physical activity among cancer survivors and their chosen partners. Fifty-six cancer survivor-partner dyads ($$n = 112$$ participants in total) were recruited into a randomized controlled trial that compared DUET to a waitlist control. The trial surpassed all feasibility endpoints with regard to uptake, retention, and safety; the DUET intervention also resulted in significant weight loss and reductions in caloric intake, as well as having a promising impact on physical activity and performance, blood glucose, and indicators of inflammation.\n\n### Abstract\n\n[1] Background: A healthful diet, regular physical activity, and weight management are cornerstones for cancer prevention and control. Yet, adherence is low in cancer survivors and others, calling for innovative solutions. Daughters, dUdes, mothers, and othErs fighting cancer Together (DUET) is a 6-month, online, diet-and-exercise, weight-loss intervention to improve health behaviors and outcomes among cancer survivor-partner dyads. [ 2] Methods: DUET was tested in 56 dyads (survivors of obesity-related cancers and chosen partners) ($$n = 112$$), both with overweight/obesity, sedentary behavior, and suboptimal diets. After baseline assessment, dyads were randomized to DUET intervention or waitlist control arms; data were collected at 3- and 6-months and analyzed using chi-square, t-tests, and mixed linear models (α < 0.05). [ 3] Results: Retention was $89\\%$ and $100\\%$ in waitlisted and intervention arms, respectively. Dyad weight loss (primary outcome) averaged −1.1 (waitlist) vs. −2.8 kg (intervention) ($$p \\leq 0.044$$/time-by-arm interaction $$p \\leq 0.033$$). Caloric intake decreased significantly in DUET survivors versus controls ($$p \\leq 0.027$$). Evidence of benefit was observed for physical activity and function, blood glucose, and c-reactive protein. Dyadic terms were significant across outcomes, suggesting that the partner-based approach contributed to intervention-associated improvements. [ 4] Conclusions: DUET represents a pioneering effort in scalable, multi-behavior weight management interventions to promote cancer prevention and control, calling for studies that are larger in size, scope, and duration.\n\n## 1. Introduction\n\nOver the past four decades, the prevalence of obesity has tripled [1], as has the number of cancer survivors [2]. While these trends are not directly related, obesity is an acknowledged risk factor for 13 different cancers [3]. Obesity and weight gain after a cancer diagnosis also are linked to a poorer prognosis [4]. Thus, weight management has been suggested as a means of primary cancer prevention [5], as well as tertiary prevention of second cancers and other prevalent forms of comorbidity among cancer survivors [6].\n\nGiven that social support plays an integral role in achieving the lifestyle changes that underlie weight management [7], buddy systems have been implemented in weight loss programs to enhance efficacy [8]. With respect to cancer, buddy pairings that unite cancer survivors with individuals in their social networks and which emphasize weight management to prevent or control cancer may be one way to expand reach and fortify intervention uptake, especially those that are minimal touch.\n\nTo date, there have been two dyadic interventions that have promoted diet, physical activity, and weight loss as a means of cancer prevention and control among both cancer survivors and their family members. The Daughters and Mothers (DAMES) study included 68 breast cancer survivors (mothers) who had overweight or obesity and were insufficiently active, paired with their adult biological daughters who had similar lifestyle behaviors and increased adiposity. Randomized dyads received 12 months of bimonthly print materials that were either team-tailored, individually tailored, or standardized (not tailored at all) [9]. Although baseline-to-follow-up improvements in weight, waist circumference, and accelerometry-measured physical activity were observed across all arms, as hypothesized, the tailored interventions resulted in significantly greater improvements in waist circumference and physical activity than the standardized intervention. Surprisingly, only the individually or personally tailored intervention and not the team-tailored intervention (which provided tailored feedback for both the survivor and daughter simultaneously) resulted in significant reductions in body mass index ([BMI] among cancer survivors but not daughters. While DAMES was deemed feasible in terms of safety, retention ($90\\%$), and achievement of the accrual target, recruitment of mother-daughter dyads was difficult ($3\\%$ enrollment rate [#consented/#contacted]).\n\nSubsequently, “Healthy Moves” focused on survivor-spouse dyads ($$n = 22$$), which were randomized to intervention arms that targeted the survivor alone (using a modified version of the DAMES individually tailored intervention) or both the survivor and spouse (using a modified version of the DAMES team-tailored intervention, plus nine videoconferencing sessions with a marriage counselor to enhance spousal communication) [10]. Compared to DAMES, Healthy Moves had a higher enrollment rate ($12.7\\%$); however, despite intensive efforts to enhance communication among couples assigned to the spouse-survivor arm, improvements in weight and fruit and vegetable consumption (and related physical function outcomes) were similar across arms. Spouses receiving the intervention versus spouses of dyads randomized to the survivor-alone condition (i.e., who did not receive the intervention) experienced significant improvements in lifestyle behaviors and physical function outcomes.\n\nBuilding on this research, the current study, Daughters, dUdes, mothers, and othErs fighting cancer Together (DUET) trial, sought to expand eligibility by encouraging survivors to select any partner they felt could participate with them in the weight loss intervention [11]. Furthermore, instead of relying on a tailored, mailed print intervention that was informed by mailed surveys, DUET incorporated newer technology, i.e., Fitbits® and Aria® scales, for monitoring and used a more contemporary and scalable web-based platform to deliver the intervention. Herein, we report the main outcomes of the DUET trial that was aimed at promoting weight loss among cancer survivors and their chosen supportive partners. Hypotheses were that dyads assigned to the DUET intervention would lose significantly more weight (primary outcome) at 6-month follow-up than dyads assigned to the waitlisted control; moreover, the intervention also would result in more favorable changes in secondary outcomes, such as other measures of adiposity (e.g., waist circumference), diet quality, physical activity, quality-of-life, and physical performance, as well as related biomarkers (e.g., insulin, glucose, total and high-density lipoprotein (HDL) cholesterol, triglycerides, leptin, adiponectin, and c-reactive protein (CRP).\n\n## 2.1. Overview\n\nDUET was a single-blinded, 2-arm randomized controlled trial (RCT) that tested a 6-month web-based lifestyle intervention against a waitlist control among 56 dyads. Each dyad comprised a survivor of an obesity-related cancer, and their chosen partner, both of whom had obesity or overweight, were insufficiently active, and consumed suboptimal diets. This trial was approved by the University of Alabama at Birmingham (UAB) Institutional Review Board [300003882] and registered within ClinicalTrials.gov (NCT04132219). Detailed methods for DUET were published upon attainment of the accrual target of 56 dyads which ensued over a 9-month period ranging from October 2020 to July 2021 [11]; these methods are briefly summarized below.\n\n## 2.2. Participants: Recruitment, Screening, Consent, and Randomization\n\nStudy invitations were distributed to adult survivors of localized renal cancer and loco-regional ovarian, colorectal, prostatic, endometrial, and female breast cancers (obesity-related cancers with 5-year survival rates >$70\\%$) identified from the UAB cancer registry, and listings of individuals expressing previous interest in lifestyle RCTs. Additionally, the Love Research Army (https://drsusanloveresearch.org/love-research-army) (accessed on 2 March 2023 initiated a series of email “blasts” to its members, and a recruitment website was established. Study staff provided telephone follow-up on mailings and contacts by placing up to six calls at various days and times. The study was explained, and interested survivors were screened for eligibility. Inclusion criteria were: [1] BMI ≥25 kg/m2; [2] moderate-to-vigorous physical activity (MVPA) < 150 min/week; [3] English speaking and writing; [4] educational attainment ≥5th grade; and [5] daily internet use and mobile phone ownership. Exclusion criteria were: [1] adhering to modified diets or enrolled in structured diet or exercise programs; [2] recent physician’s advice to limit PA and/or health issues precluding unsupervised PA or weight loss; and [3] residence in an assisted-nursing facility. Once eligibility was established and cancer case status (type and date of diagnosis) was verified by treating physicians of self-referrals, the survivor was asked to identify a partner with whom they interacted in person on at least a biweekly basis. Partners had identical inclusion/exclusion criteria (cancer survivorship was optional).\n\nTelephone or Zoom calls were scheduled to review the study and acquire signed consent electronically (Adobe Sign®, San Jose, CA, USA). Participants completed baseline assessments, and their addresses were used to derive rural-urban commuting area codes (RUCA) as well as to estimate the distance between dyad members using Google Maps (https://www.google.com/maps) (accessed on 2 March 2023) since rural-urban status and proximity of dyad pairs could potentially affect access to healthy food procurement and exercise opportunities and support [9,12,13]. Dyads were randomly and evenly assigned to the DUET intervention or waitlist control using a permuted block design (block size = 4).\n\n## 2.3. DUET Intervention\n\nThe DUET web-based intervention was adapted from two previously established programs: [1] the tailored mail-based, dyadic DAMES intervention, which was expanded to meet the needs of cancer survivors beyond those with just post-menopausal breast cancer and for partners beyond just biological daughters [9]; and [2] SurvivorSHINE, a web-based diet and exercise program for cancer survivors [14,15]. Like both of these interventions, DUET was theoretically grounded on Social Cognitive Theory (SCT) and emphasized skills training, modeling, incremental goal setting (with reinforcement), overcoming barriers, and self-monitoring (through the incorporation of new technologies, i.e., Fitbits and Aria Scales) [7]. Concepts from Interdependence Theory [16] and the Theory of Communal Coping [17] also guided the dyads’ commitment to relationship quality and the development of mutual goals to promote the adoption and maintenance of health behaviors and the provision and request for social support.\n\nUpon randomization, one dyad member was mailed a box of supplies that included two sets of Portion Doctor ® tableware, two Fitbits (Inspire®), two Aria 2® digital scales, and two sets of instructions to connect to MyFitnessPal® to automate weight and exercise tracking and provide additional reinforcement and support. Fitbit accounts also were linked to the password-protected, interactive DUET website, which formed the central core of the DUET intervention. Here each dyad member received tailored guidance over 24 weeks based on World Cancer Research Fund—American Institute of Cancer Research (WCRF-AICR) guidelines [18]. Thus, each dyad member was encouraged to set incremental goals that would eventually lead over the course of the 6-month intervention to exercising (including aerobic, resistance, flexibility, and balance) at least 150 min a week and adhering to a plant-based diet that included ample amounts of whole grains, vegetables and fruit (V and F), and limited amounts of red and processed meats, sugar, and refined (fast) food, while promoting a loss of roughly 0.5 kg per week. The website was designed with the following key features: [1] My Profile; [2] Topical Content; [3] Tip of the Day; [4] Sessions; [5] Tools; [6] News You Can Use; and [7] Support. Participants initially logged in to “My Profile” to enter age, height, gender, and current data on night-time snacking and intakes of V and F, whole and processed grains, red and processed meats, added sugars, supplement use, and alcohol. Survivors were prompted for data on cancer type, treatment, and coping style (Fighting Spirit or Fatalist) [19], which were used to provide tailored feedback, e.g., graphical displays with motivational messaging on overcoming treatment-related barriers (such as intolerance of high fiber V and Fs among survivors of colorectal cancer treated with a colostomy or urinary incontinence among survivors of prostate cancer), and calorie budgets to promote a loss of 0.5 kg w−1 [20]. Additionally, discrete tabs were provided to facilely reference topical information on healthy weight, healthy eating, and exercise. Daily tips for weight management, diet, and exercise were continually refreshed over the 6-month intervention as a means to enhance engagement. Furthermore, 24 weekly interactive sessions averaging 15 min in length were created using Articulate Storyline software (Articulate Global, LLC, New York, NY, USA) to guide participants through topics such as portion control, grocery shopping and food preparation, and various forms of exercise (aerobic, resistance, balance, and flexibility). A variety of tools also were provided on goal setting, customized meal plans, recipes, grocery lists, exercise guides, etc., in formats that could be downloaded and printed off. A tab entitled “News You Can Use” provided “take-away” summaries of recently released news stories and research pertaining to diet and exercise for cancer control. Finally, the webpage offered tips, such as active listening, to enhance dyad-based support. To enhance engagement with the website, Short Message System (SMS) text messages were issued thrice weekly. On each Monday of the 24-week intervention, dyads received a “push” message with a direct website link to the newly-released weekly session. On Wednesdays, dyads received a support message to reinforce the weekly content, and on Fridays, a “call-to-action” inquired about progress towards incremental goals.\n\n## 2.4. Waitlist Control\n\nWaitlisted dyads received all DUET resources and programming once 6-month follow-up data were collected.\n\n## 2.5. Measures\n\nBecause DUET was implemented during COVID-19, several measures were adapted for remote assessment and were captured via Zoom® (San Jose, CA, USA); validation study results were published previously [21].\n\n## 2.5.2. Dietary Intake (Captured at Baseline and 6 Months)\n\nTwo 24-h dietary recalls of a non-consecutive weekday and weekend day were conducted via telephone by a registered dietitian using the Automated Self-Administered (ASA-24) dietary assessment tool (https://epi.grants.cancer.gov/asa24) (accessed on 2 March 2023) at baseline and 6-months. Averaged intakes were obtained for calories, and diet quality was assessed using the Healthy Eating Index (HEI)-2015 [23].\n\n## 2.5.3. Physical Activity and Sleep (Captured at Baseline and 6 Months)\n\nObjective PA data were captured using Actigraphs® (Fort Walton, FL, USA) with instructions to wear the device at waist level on a provided belt during waking hours and to move the device to a provided wristband upon retiring to sleep. This procedure was followed for 7 days and accompanied by a written log. Minutes of MVPA were then downloaded and processed with similar methods used previously [24]. The Godin Leisure Time Exercise Questionnaire (GLTEQ) was administered online, giving excellent reliability and validity among cancer survivors [25].\n\n## 2.5.4. Physical Performance (Captured at Baseline and 6 Months)\n\nSeveral physical performance measures were adapted for remote delivery and validated [21]. Dyads were mailed soccer cones, measuring tapes, 8′ lengths of cord, and stickers before assessments to perform measures. Trained assessors recorded images of testing via Zoom and then replayed them to capture accurate times and observations. Once data were entered into databases, videos were erased. Details of the remote assessment of the 30-s chair stand, 8′ get-up-and-go, sit-and-reach, back scratch, 2-min step test, and balance testing are reported by Pekmezi et al. [ 11] and Hoenemeyer et al. [ 21]\n\n## 2.5.5. Circulating Biomarkers (Captured at Baseline and 6 Months)\n\nParticipants received print and video instruction (https://youtu.be/lBPLS4PoHv4) (accessed on 2 March 2023) to self-collect 5 dried blood spots (DBS) on a designated card. These were dried for >4 h at room temperature, then inserted into a foil pouch with desiccant and frozen (0 F° or below) until analyzed. DBS eluents were batch-tested against known standards for insulin, glucose, leptin, adiponectin, high-density lipoprotein (HDL), and total cholesterol, triglycerides, and c-reactive protein (CRP) at the University of Washington as described previously [26]. To assure the validity of DBS assays in the current sample and data presented in this report, assays were performed using traditional multiplex methods on sera collected via phlebotomy from 36 participants at baseline and compared to a matched analysis of assays performed on DBS samples collected at the same time. Coefficients (R2) generated by ordinal logistic regression indicated strong correlations and were as follows: glucose = 0.981; CRP = 0.979; triglycerides = 0.979; total cholesterol = 0.963; leptin = 0.919; HDL = 0.899; adiponectin = 0.799; and insulin = 0.700. Values are expressed in plasma equivalent terms.\n\n## 2.5.6. Online Surveys\n\nOnline surveys were administered via REDCap® (https://projectredcap.org) (accessed on 2 March 2023) at baseline, 3- and 6-months, though demographic information was only collected at baseline.\n\n## 2.5.7. Safety\n\nAll participants were encouraged to call a toll-free study number to report any adverse events. In addition, changes in health status were systematically ascertained in both study arms at 3 and 6 months. Events considered permanently disabling, life-threatening, or resulting in overnight hospitalization were deemed “serious,” with attribution to the intervention explored further.\n\n## 2.6. Statistical Considerations\n\nWhile accrual, retention, and safety form the basis of this feasibility trial, between-arm differences (i.e., differences between the intervention group and the waitlist control group) in weight loss (primary outcome) from baseline to 6 months were formally tested. Power calculations were performed using nQuery (version 8.5; GraphPad Software DBA Statistical Solutions, San Diego, CA, USA). These calculations assumed a standard deviation of 4.6 kg for the mean weight loss, as presented in our DUET protocol paper [11]. Assuming a sample size of 25 dyads/arm, a common standard deviation of 4.6 kg, a two-sided two-group t-test, and a significance level of $5\\%$, there was >$80\\%$ power to detect between-arm differences in weight loss of −3.72 kg or greater.\n\nTo determine whether important demographic and clinical characteristics of the sample were evenly distributed between the two study arms, the chi-square test (or Fisher’s exact test if the assumptions for the chi-square were not valid) for categorical study variables and the two-group t-test for continuous study variables were used. Distributions of continuous study variables were examined using stem-and-leaf, box, and normal probability plots and the Kolmogorov–Smirnov test; variables deviating from normal distribution were log10 transformed prior to analysis. All analyses were performed using SAS software (version 9.4; SAS Institute, Inc., Cary, NC, USA).\n\nArm differences in weight loss were assessed using an intent-to-treat approach. General linear mixed models, in particular, mixed model repeated measures analyses, were used to test for between-arm differences (two study arms), within-arm differences (three time points), and the arm-by-time point interaction simultaneously. These analyses were performed using PROC MIXED of SAS. This method accounts for the repeated measurements as well as the covariance between survivors, partners, or dyad members. A compound symmetry covariance matrix was assumed. This method provides tests of statistical significance (Type 3 tests which produce an F value and a p-value) for the between-arm effect, within-arm effect, and the interaction effect. When any of these effects were statistically significant, the Tukey–Kramer multiple comparisons test (performed using PROC MIXED of SAS) was used to determine which specific pairs of means for that effect were significantly different and also identified the time points at which those differences occurred. Such testing was helpful in comparing the multiple groups of survivors, partners, and dyads over two time points (when most outcomes were assessed) as well as three points (for survey data). Analyses were performed separately for survivors, partners, and combined dyads (thus, three sets of analyses were performed). Post-randomization exclusions (i.e., the three participants who either received gastrointestinal surgery or developed a cancer recurrence within 2 weeks of randomization) were omitted from 3- and 6-month analyses. Otherwise, all available data were used, though if a dyad member dropped out, data from that dyad were excluded from the dyadic analysis.\n\nAnalyses of secondary continuous outcomes were performed using general linear mixed models, as described in the previous paragraph for the primary outcome. These analyses were again followed by the use of the Tukey–Kramer multiple comparisons tests (for post hoc testing).\n\n## 3.1. Study Sample, Retention, and Safety\n\nSample characteristics are shown in Table 1. Overall, participants were diverse in terms of race, age, and geography (Alabama, Illinois, Mississippi, North Carolina, and Tennessee). Most were female, urban dwellers, and non-smokers, and roughly half reported being college graduates and currently employed with annual incomes >USD 50,000. Mean levels of V and F intake and PA were low as compared to the guidelines [5,6,18], while the average BMI was in the obese range, and participants reported an average of three other health conditions in addition to their cancer diagnosis. Survivors tended to be “long-term” (i.e., having diagnoses more than 5 years out), with most reporting early-stage cancers, of which a high proportion were breast cancer. A small number of previous cancer diagnoses (four breast, two gynecologic, and one testicular) were reported among partners. Given the high proportion of breast cancer survivors with spousal partners, survivors were significantly more likely to gender identify as female, while partners were more likely to report as male (p-values < 0.05). There were no statistically significant differences detected between the intervention vs. the waitlist control arms for any of the characteristics collected.\n\nThe CONSORT diagram (Figure 1) shows an enrollment rate of ~$5.5\\%$ ($$n = 61$$/$$n = 1114$$). Of the 112 participants enrolled, three exclusions occurred within two weeks after randomization within the waitlisted arm (one survivor developed a cancer recurrence, another received emergency gastrointestinal surgery, and one partner received bariatric surgery), all of which were discontinued from the study and analysis, since all of these conditions affect the primary outcome (weight status). Additionally, three waitlisted partners were lost to follow-up (two of three dropped out when their survivor did so). Thus, the retention rate was $89\\%$ in the waitlisted arm and $100\\%$ in the intervention arm; the difference was statistically significant ($$p \\leq 0.027$$).\n\nAdverse events totaled 14 and 16 in waitlist and intervention arms, respectively. All events were non-attributable, and all except four were non-serious (two cancer recurrences, one myocardial infarction, and one acute cholecystitis), with no differences in events noted between the waitlist control and intervention and the intervention arms.\n\n## 3.2. Changes in Adiposity\n\nSignificant weight loss occurred in both study arms (Table 2), though the magnitude of weight loss was significantly larger in survivors, partners, and dyads randomized within the DUET intervention arm. Dyads assigned to the DUET intervention lost significantly more weight (an average of 2.8 kg or $3.2\\%$ of their body weight) as compared to dyads that were waitlisted (who lost an average of 1.1 kg or $1.2\\%$ of their body weight). Findings related to waist circumference paralleled the results for weight loss, but differences between the two study arms did not reach statistical significance.\n\n## 3.3. Changes in Dietary Intake and Physical Activity\n\nBoth study arms also significantly reduced their caloric intakes, with reductions being particularly notable among partners and dyads within the DUET intervention arm. However, calorie intake was significantly less among survivors assigned to the intervention arm than those randomized to the waitlist control (Table 2). While values for diet quality increased among intervention participants as compared to decreasing values among controls over the study period, these differences did not achieve statistical significance. Both study arms also showed significant increases in MVPA assessed either via self-report or accelerometry over the study period, though increases among survivors within the DUET study arm were of greater magnitude. That being said, differences between study arms did not reach statistical significance.\n\n## 3.4. Changes in Physical Performance\n\nAs shown in Table 3, both study arms experienced significant improvements in several indices of physical performance (i.e., 30-s chair stand, 8′ get-up-and-go, sit-and-reach, and 2-min step test) over the study period, with DUET intervention arm Survivors showed improvements of greater magnitude for all four tests and DUET dyads in 3-out-of-4 tests (i.e., all except the 2-min step test). DUET partners also showed notable improvements in the 30-s chair stand. However, in comparing improvements in the two study arms over time, significant differences were only detected for the flexibility measure, i.e., the sit-and-reach among survivors and dyads.\n\n## 3.5. Changes in Circulating Biomarkers\n\nAs shown in Table 4, both study arms experienced significant decreases in circulating glucose, and while decreases were particularly noteworthy among DUET-assigned partners and dyads and among waitlisted survivors, these beneficial effects did not differ in statistical significance between study arms. Significant decreases over time also were observed among partners and dyads in both study arms for total cholesterol, as well as HDL cholesterol among all three subgroups (i.e., survivors, partners, and dyads). The effects on HDL cholesterol were particularly notable among dyad members of both study arms, with DUET dyads experiencing significantly greater decreases in HDL cholesterol than waitlisted dyads. Similarly, levels of CRP also decreased significantly over time among survivors and dyads in both study arms, and while these differences were particularly notable among survivors within the DUET intervention arm, no statistically significant differences were noted when the waitlist vs. the intervention arm were compared. Data on the adipokines, leptin, and adiponectin were less consistent, though significantly higher increases in leptin were observed among partners in the DUET intervention than among the waitlist control. No differences were detected for circulating levels of insulin or triglycerides.\n\n## 3.6. Changes in Patient-Reported Outcomes\n\nSignificant improvements in physical QOL were observed over time among survivors of both study arms, though differences were not observed among other subgroups and also not for mental QOL. Further, no differences between the DUET intervention arm vs. the waitlist control were detected (Table S1). While social support and self-efficacy for both diet and exercise increased over the 6-month period for intervention participants compared to decreasing levels among controls, these differences did not achieve statistical significance. In contrast, barriers decreased significantly over time in both study groups, with survivors and dyads reporting significantly fewer barriers to pursuing a low-calorie diet and partners and dyads reporting significantly fewer barriers toward exercise. While no statistically significant differences were identified between arms, p-values for time-by-arm interactions approached significance (e.g., $$p \\leq 0.051$$).\n\n## 3.7. Model Dyadic Terms\n\nOf note, the models generated for DUET uncovered several significant dyadic terms, suggesting that the relationship established between the survivor and their partner appeared important for influencing effects on body weight ($$p \\leq 0.009$$), waist circumference ($$p \\leq 0.023$$), diet quality ($$p \\leq 0.009$$), subjectively- and objectively-assessed PA (p’s < 0.001), sleep efficiency ($p \\leq 0.001$), most physical performance tests (except the sit-and-reach) (p’s < 0.007), HDL cholesterol ($$p \\leq 0.038$$), CRP ($$p \\leq 0.002$$) and mental health (PROMIS; $p \\leq 0.001$). Trends also were noted for caloric intake ($$p \\leq 0.083$$), diet self-efficacy ($$p \\leq 0.089$$), sleep fragmentation index ($$p \\leq 0.0504$$), adiponectin ($$p \\leq 0.095$$), and total cholesterol ($$p \\leq 0.076$$).\n\n## 4. Discussion\n\nThe DUET diet and exercise intervention was found to be feasible and resulted in significant weight loss among cancer survivors and their chosen partners. The $3.2\\%$ loss in body weight was not only statistically significant as compared to the $1.2\\%$ weight loss among controls but also is considered clinically significant and of the magnitude shown to exert favorable effects on glucose control and blood lipids by the American Heart Association, American College of Cardiology and The Obesity Society guidelines panel for the management of obesity and overweight [36]. This intervention is one of a handful of dyadic-based lifestyle interventions among cancer survivors [9,10,38,39,40] and among the few that promote change in multiple behaviors. Additionally, it is the only one that has employed a web-based platform. Moreover, while evidence is less consistent across both dyad members and as compared to the waitlist control, the DUET intervention also was associated with favorable effects on waist circumference, caloric restriction, self-reported and objective PA, as well as physical performance and blood glucose and CRP. While the relatively modest sample size may have limited power to detect differences in self-efficacy and social support, the intervention appeared to decrease the number of barriers affecting adherence to a calorically-restricted diet or increased PA. Thus, the theoretical concepts of SCT on which the DUET intervention was framed appear supported by these data and should be preserved in future trials. [ 7,11] DUET achieved these favorable effects with minimal touch and excellent retention and safety; hence, results support future web-based interventions. Heretofore, variable success has been reported for multi-behavior, web-based interventions among cancer survivors. Bantum et al. [ 41] evaluated a comprehensive, 6-week, web-based symptom management program (including PA, weight management, and a healthful, plant-based diet) among 352 adult survivors of various cancers in Hawaii. The “Surviving and Thriving” intervention resulted in significant increases in moderate PA as compared to waitlisted controls but did not show concomitant increases in V and F intake (weight status was unreported). Kanera et al. [ 42] reported similar findings with a 6-month, web-based program (“Cancer Aftercare Guide”) among 462 Dutch survivors of mixed cancers and again found significant increases in self-reported moderate PA (+74.7 min/week) in the intervention arm but failed to detect significant increases in V and F intake in controlled analyses. By achieving improvements in calorie control and evidence of improvement in both self-reported and objectively-measured PA, DUET contributes to the unfolding science related to multi-behavior, web-based interventions and also demonstrates an impact on weight status. DUET is the first web-based intervention that reduced obesity and decreased caloric intake among cancer survivors, though, like the other studies also failed to detect significant differences in diet quality (albeit the sample size was six-to-nine-fold smaller and likely precluded our ability to detect between-arm differences).\n\nDUET also promoted improvements in levels of CRP and glucose that have been observed in other more intensive weight loss interventions among cancer survivors, [43,44] though few differences were detected in other biomarkers typically associated with weight loss, i.e., leptin, adiponectin, and insulin [45,46]. Curiously, some participant groups (e.g., partners assigned to the intervention) experienced increases in leptin rather than decreases [46] and decreases in HDL cholesterol despite increased PA. Seasonal variation in circulating lipids may explain this latter finding since most participants were accrued during the summer when HDL cholesterol peaks and then descends towards its winter time nadir (corresponding to a 6-month follow-up) [47].\n\nUnlike our previous Reach-out to ENhancE Wellness among older cancer survivors (RENEW) RCT that found significant improvements in both physical and mental QOL with a home-based diet and exercise weight management intervention among 641 breast, prostate, and colorectal cancer survivors [48], the current RCT only detected significant improvements in physical QOL, which it observed in both study arms. A probable explanation for this discrepancy was the smaller sample size, as the DAMES study [9], which also has a more modest sample size, likewise was unable to detect changes in this outcome.\n\nAn innovation of DUET was the expansion of dyadic composition beyond the family unit. This expansion increased the intervention reach and also likely enhanced DUET’s accrual (56 dyads in 9 months) versus Healthy Moves (22 dyads over 15 months) and DAMES (68 dyads over 2 years). While spouses and children still comprised roughly two-thirds of chosen partners, other family members, friends, and neighbors comprised the remainder. Furthermore, the significance of the dyadic term for most outcomes suggested that the synergy of the dyads was still strong, despite eligibility not being contingent on family relatedness. Yet, the inability to ascertain an eligible and willing partner served as an enrollment barrier. With social isolation and a lack of companionship reported at levels topping $20\\%$ in Western countries [49], and rates of living alone nearing $30\\%$ [50], there is an obvious need to explore effective “match-making” where cancer survivors could be paired with others based on factors that have the potential to create the synergy observed with naturally-occurring partnerships.\n\nThe DUET trial had several strengths. It formally tested a theoretically-grounded intervention using a randomized controlled design and validated, rigorous measures that were captured by trained staff blinded to study condition. Moreover, the study sample was diverse in geographic range and age, and the proportion of non-Hispanic Whites was representative of the U.S. population ($62.5\\%$ vs. $59.3\\%$) [50]. In addition, retention was excellent in both the intervention and waitlisted arms over the 6-month study period—exceeding the $70\\%$ benchmark that characterizes a tier-1 trial [51]; however, drop-out was significantly higher in the waitlisted arm. While differential drop-out can result in bias, this threat is considered minimal given the relatively small numbers ($$n = 6$$). Like many feasibility studies, DUET had a relatively modest sample size which may have undermined the statistical power to detect differences. Moreover, because of the focus on feasibility, statistical comparisons, of which there were many, were uncontrolled for multiple testing and may have uncovered spurious findings. The trial also did not assess whether weight loss and behavior changes were maintained long-term. Another weakness was the relatively low enrollment rate, i.e., uptake, especially among survivors of cancers other than breast, which may limit the generalizability of findings. However, the preponderance of breast cancer survivor participation in the current study is a phenomenon that has been reported commonly in traditionally delivered clinical interventions [52], as well as those that are digital [53].\n\n## 5. Conclusions\n\nDUET represents a pioneering effort in scalable, multi-behavior weight management interventions to promote cancer prevention and control. DUET not only demonstrated feasibility, safety, and excellent retention, it also promoted significant weight loss via caloric restriction and increased PA. Moreover, it exerted favorable effects on physical performance and markers of glucose metabolism and inflammation. Future studies are needed which are larger in size, scope, and duration and which assess longer-term effects of buddy-system interventions—interventions that include family members but that also extend cancer control to friends and neighbors.\n\n## References\n\n1. **Obesity and Overweight**\n\n2. 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0.010759513825178146\n]","isConversational":false}}},{"rowIdx":73,"cells":{"text":{"kind":"string","value":"---\ntitle: Assessment of Blood Microcirculation Changes after COVID-19 Using Wearable\n Laser Doppler Flowmetry\nauthors:\n- Elena V. Zharkikh\n- Yulia I. Loktionova\n- Andrey A. Fedorovich\n- Alexander Y. Gorshkov\n- Andrey V. Dunaev\njournal: Diagnostics\nyear: 2023\npmcid: PMC10000665\ndoi: 10.3390/diagnostics13050920\nlicense: CC BY 4.0\n---\n\n# Assessment of Blood Microcirculation Changes after COVID-19 Using Wearable Laser Doppler Flowmetry\n\n## Abstract\n\nThe present work is focused on the study of changes in microcirculation parameters in patients who have undergone COVID-19 by means of wearable laser Doppler flowmetry (LDF) devices. The microcirculatory system is known to play a key role in the pathogenesis of COVID-19, and its disorders manifest themselves long after the patient has recovered. In the present work, microcirculatory changes were studied in dynamics on one patient for 10 days before his disease and 26 days after his recovery, and data from the group of patients undergoing rehabilitation after COVID-19 were compared with the data from a control group. A system consisting of several wearable laser Doppler flowmetry analysers was used for the studies. The patients were found to have reduced cutaneous perfusion and changes in the amplitude–frequency pattern of the LDF signal. The obtained data confirm that microcirculatory bed dysfunction is present in patients for a long period after the recovery from COVID-19.\n\n## 1. Introduction\n\nThe propagation of coronavirus infection, also known as COVID-19, has caused a huge number of illnesses and deaths. To date, there have been more than 650 million confirmed cases of SARS-CoV-2 infection and more than 6 million deaths worldwide (according to the Johns Hopkins University Coronavirus Resource Center). Three years after the first reported cases of SARS-CoV-2 infection, the pandemic is still far from being over. Despite the development and widespread implementation of vaccines and containment measures, COVID-19 still has a significant impact on the lives of millions of people worldwide. Emerging evidence suggests a close link between severe clinical COVID-19 and an increased risk of its vascular complications, such as thromboembolism [1]. Approximately 40–$45\\%$ of cases are asymptomatic with SARS-CoV-2, but clinical observations suggest that complications may occur even in the asymptomatic course of the disease [2].\n\nAlthough COVID-19 was originally considered a respiratory disease, it has now been established that it affects multiple organs and systems, including the cardiovascular system, gastrointestinal system, brain, kidney, liver, skeletal muscle, and skin of infected patients [3,4]. Recently, there is increasing evidence of the negative impact of this disease on the microcirculatory system of the blood [5,6,7]. It is known that SARS-CoV-2 affects the microcirculatory bed, causing edema and damage to endothelial cells, affects the development of microthrombosis, and capillary blockage, and causes a variety of other negative effects [8]. The development of these disorders, in addition to the direct threat to the patient’s life and health, can also be a key factor in the development of long-term consequences of coronavirus infection, significantly reducing the quality of life of patients. Serious concerns are caused by the fact that proinflammatory status and procoagulation activity can remain in patients for a long time after the recovery [9].\n\nRecent observations show that a fairly large proportion of patients who have recovered from a coronavirus infection subsequently suffer long-term effects of the disease [10]. These include symptoms such as weakness, breathlessness, chest, and joint pain, confusion, memory and concentration problems (so-called “brain fog”), mood changes, etc. These and other symptoms can persist for months after the disease itself and significantly reduce patients’ quality of life [11]. These disorders are referred to as “long COVID” or post-COVID syndrome. Current research is largely focused on the acute stage of SARS-CoV-2, but ongoing monitoring of the long-term effects of the disease is also necessary. In this context, the need for research into the rehabilitation of patients after coronavirus infection is clear.\n\nThere is a significant body of evidence suggesting that cardiovascular complications of coronavirus can also occur in an asymptomatic course [2], making it even more difficult to detect such complications at an early stage. This means that there will be an urgent need for both diagnostic and rehabilitative measures in the next few years for patients who have suffered from this disease. In addition, there are risks of a similar clinical outcome not only with COVID-19 but also with possible future epidemics of respiratory infections. Existing diagnostic methods routinely used in clinical practice do not allow adequate assessment of blood flow at the microcirculatory level. Currently, there is a need to develop new approaches to the diagnosis of microcirculatory disorders occurring in coronavirus infection, as well as to develop strategies for individual therapy and rehabilitation of patients after COVID-19.\n\nDespite the widespread prevalence of the disease and the incidence of cardiovascular complications, as well as the proven extensive involvement of microvasculature in pathological processes, only very few papers have been published to date on the noninvasive assessment of blood microcirculation after COVID-19 [12,13,14].\n\nOne of the most common and applicable methods for diagnosing the state of the blood microcirculation system is laser Doppler flowmetry (LDF) [15,16]. This method is widely used in the diagnosis of complications of diabetes mellitus [17,18], rheumatic diseases [19], hypertension [20] and a number of other socially important diseases. Over the years, different modifications of the conventional laser Doppler technique had been introduced, including several attempts at developing wearable devices [21,22,23].\n\nIn the COVID-19 clinic, the main focus of research using LDF was on studying the dynamic characteristics of blood flow, including the application of functional tests. It has been shown that, during the acute phase of COVID-19, patients demonstrate a reduced vasodilatory response to local heating and reduced microvascular reactivity [24]. The correlations between microcirculatory parameters measured by LDF and laboratory test results of patients during the acute period of the disease were also analysed [25]. Another study using laser speckle contrast imaging technology demonstrated reduced vasodilation in patients with COVID-19 in response to acetylcholine and sodium nitroprusside, which persists for at least 3 months after the disease [26]. We did not find any studies in the English-language literature devoted to spectral analysis of LDF recordings in patients who underwent COVID-19. Since it is known that such analysis provides valuable diagnostic information about the state of systems regulating blood flow, including the nervous system and endothelial function, the present work aimed to fill the gaps in this area.\n\nIn this context, this work aimed to comprehensively examine the changes in blood microcirculation that occur both in the acute period of the COVID-19 disease and in the long term during rehabilitation procedures.\n\n## 2.1. Experimental Equipment\n\nA distributed system consisting of 4 wireless wearable microcirculatory blood flow analysers implementing LDF method “LAZMA PF” (LAZMA Ltd, Russia; in EU/UK this device made by Aston Medical Technology Ltd., UK as “FED-1b”) was used for data recording in this study [27,28,29]. These analysers use VCSEL die chips (850 nm, 1.4 mW/3.5 mA, Philips, The Netherlands) as a single-mode radiation source. The analysers are implemented without optical fibres with direct skin irradiation from a window at the back of the instrument. This allows for avoiding fibre coupling losses as well as decreasing the movement artefacts which are common in fibre-based LDF monitors. The devices operate autonomously on internal battery power and transfer the measured signal via Bluetooth and/or Wi-Fi. The devices also have built-in motion and temperature sensors to eliminate the possible influence of motion artefacts and temperature changes on the recorded signal. When processing motion sensor data, recordings simultaneous with the subject’s movements are identified as potential sources of distortion of the LDF gram and filtered using special software. The appearance of the analysers (left) as well as the options for mounting them on the volunteer’s hands (right) are shown in Figure 1.\n\n## 2.2. Experimental Protocol\n\nThe present study comprised 2 phases. The first stage involved a dynamic assessment of the processes occurring in the blood microcirculatory system during the acute period of coronavirus infection. During routine daily LDF measurements, an 18-year-old male patient was found to be accidentally infected with SARS-CoV-2 (confirmed by PCR analysis of nasopharyngeal swabs). The patient had not been vaccinated against COVID-19 prior to the study nor did he have previous experience with COVID-19. The measurements were carried out in the supine position, each lasting for 10 min. To record signals, analysers were attached to the pads of the third fingers and big toes, as well as on the dorsal surfaces of the wrists and the inner parts of the upper third of the shins. The positioning and attachment of wearable devices on the patient’s body during the study are shown in Figure 2. The measurements were taken 10 days before the onset of the disease and during 26 days after the recovery. No measurements were taken during the acute phase of the disease (7 days) because of the patient’s poor well-being. A total of more than 170 LDF signals were measured and processed over the entire study period for this patient.\n\nThe second phase of the study involved the comparison of blood microcirculation parameters measured by LDF in a group of patients undergoing rehabilitation procedures after COVID-19 and a group of conditionally healthy volunteers with no previous history of coronavirus infection. The main group consisted of 23 subjects who had long COVID symptoms for a prolonged period of time after the recovery from an acute coronavirus infection and were undergoing rehabilitation in a private healthcare facility. Three of them had had a severe COVID-19 infection; all the other patients experienced moderate symptoms of COVID-19. Patients in the main group were measured between 1 and 6 months after the recovery. The mean age of the main group was 58±9 years. The control group included 13 conventionally healthy volunteers of a matching age who were measured in 2019 before the pandemic spread, suggesting that the volunteers in the control group had never encountered COVID-19. Volunteers with any history of cardiovascular or other serious chronic diseases affecting the circulatory system were excluded from the study. The study was conducted with the subject in the supine position in a relaxed state and consisted of a 10-min measurement of microcirculation using a wearable LDF device (“LAZMA-PF”). The analysers were attached to the dorsal surface of the forearms at a point 2 cm above the styloid process and on the inside of the upper third of the shins (see Figure 2C,D) as these points proved to be the most informative from the previous stage of the study. Figure 3 shows a diagram of the experimental design of the study.\n\n## 2.3. Data Analysis\n\nIn the present study, the analysed parameters were the value of the index of blood microcirculation—Im and amplitudes of blood flow oscillations in the different frequency bands corresponding to different mechanisms of microcirculatory blood flow regulation, measured in relative perfusion units (p.u.) [ 30]. The endothelial (Ae) band (0.005–0.021 Hz) reflects the vascular tone regulation due to the endothelium activity, both NO-dependent and independent; the neurogenic (An) band (0.021–0.052 Hz) represents the influence of neural innervation on blood flow; the myogenic (Am) band (0.052–0.145 Hz) corresponds to vascular smooth muscle activity; and respiratory (Ar) and cardiac (Ac) bands (0.145–0.6 Hz and 0.6–2 Hz, respectively) carry information about the influence of heart rate and movement of the thorax on the peripheral blood flow [31,32]. To calculate the amplitude–frequency spectra of the LDF signal, we used a mathematical apparatus of wavelet transform implemented in the software of wireless wearable analysers “LAZMA-PF”. This software performs a continuous wavelet transform using the complex-valued Morlet wavelet as the analysing wavelet.\n\nIn addition, the parameter of nutritive blood flow (Imn), estimated by a well-known algorithm [33], was calculated. The use of this parameter makes it possible to estimate the distribution of blood flow along capillary and shunt vessels.\n\nThe statistical analysis of the data was performed in Origin Pro 2021 software. Due to the limited sample size, a non-parametric Mann–Whitney U test was used to check the statistical significance of differences. Values of $p \\leq 0.05$ were considered significant. The results are presented as the mean ± SD unless otherwise indicated.\n\n## 3. Results\n\nThe first phase of the study demonstrated that COVID-19 results in changes in microcirculatory blood flow regulation mechanisms, which can be measured by assessing the spectral characteristics of the LDF signal. The results of the measurements are shown in Table 1.\n\nNo significant changes were observed in fingers and toes in this measurement. However, there was a general trend towards a decrease in microcirculation after the disease, and also in the magnitude of the nutritive blood flow in the upper extremities. Figure 4 shows box plots of the amplitude of blood flow oscillations for the stages before and after the disease, measured in wrists and shins.\n\nA statistically significant decrease in the amplitude of myogenic oscillations was found in the arms after the disease. In the legs, a significant decrease in the amplitudes of respiratory and cardiac oscillations was observed. Similar changes can be traced in the upper extremities, but they do not reach statistically significant levels there. Figure 5 shows the dynamic changes in blood flow oscillations measured in wrists (a) and shins (b).\n\nThe figures show that COVID-19 causes high-amplitude changes in the magnitude of endothelial and neurogenic blood flow oscillations immediately after the recovery, which probably caused a high variability of these values at the “After” stage and failure to achieve a statistically significant difference in them when there is a trend for their increase after the disease. These changes are especially pronounced in the upper extremities. In the legs, there is a significant drop in the amplitude of the cardiac oscillations immediately after the disease and of the respiratory oscillations one week after the recovery, which also correlates with the results obtained in the upper extremities.\n\nThe results of the second stage of the experimental study were subsequently analysed. Table 2 presents the data obtained from the second stage of the study.\n\nBoth upper and lower extremities show significantly lower values of microcirculation and nutritive blood flow. Whisker boxes for these parameters are shown in Figure 6.\n\nAn increase in overall oscillatory blood flow activity was also noted in both upper and lower extremities, with statistically significant differences in the neurogenic, respiratory and cardiac ranges in wrists. Whisker boxes for the respiratory and cardiac oscillations measured in wrists are shown in Figure 7.\n\n## 4. Discussion\n\nIn the present work, we obtained experimental data, which confirm the presence of microcirculatory bed dysfunction for a long period after the recovery from COVID-19. The first part of the study, which included daily measurements of one volunteer for 10 days before his disease and almost a month after the recovery, showed that after a month the parameters did not recover to their original values.\n\nThis stage of the studies revealed a decrease in the myogenic activity of microcirculation in the upper extremities. It is worth noting that the changes in the patterns of peripheral blood flow oscillations in the post-COVID phase have not yet been studied in detail. Myogenic oscillations play an important role in the process of oxygen delivery to biological tissues [34]. A decrease in myogenic oscillations leads to an increase in the dynamic resistance of microvessels and, as a consequence, to a decrease in the nutritive blood flow. Combined with the observed decrease in neurogenic regulatory activity, this change may indicate the activation of blood flow shunt pathways. In addition, some studies show that high temperature can inhibit vasomotion [35,36], so the decrease in myogenic activity revealed in our study may be a consequence of the high body temperature of the patient during the period of the disease.\n\nThe period immediately after the recovery from COVID-19 in this study was also characterized by decreased values of respiratory and cardiac microcirculatory oscillations in both upper and lower extremities (with significant differences in legs). In this case, dynamic observations show that cardiac fluctuations are reduced immediately after the disease, and respiratory fluctuations change during the week after the recovery.\n\nAnother interesting observation of this study was the increased amplitude of endothelial oscillations in the post-COVID phase and the dynamics of these changes. Numerous studies demonstrate endothelial dysfunction as one of the main pathogenic mechanisms of COVID-19 [37,38], which can persist for more than 12 months after the recovery. Studies also show that long COVID-19 symptoms, especially nonrespiratory symptoms, are due to persistent endothelial dysfunction [39]. In our work, we observed increased amplitudes of these fluctuations both in the early stages of recovery from the disease and in the later stages (in the second phase of the study), although these differences did not reach a statistically significant level.\n\nIn a group of patients undergoing rehabilitation after COVID-19, the most interesting observation in the amplitude–frequency spectrum of the LDF signal, in our opinion, was an increase in the amplitude of neurogenic oscillations. A decrease in neurogenic tone leads to the dilation of the arterioles [40,41] and, consequently, the amplitude of cardiac oscillations significantly increases (which we can observe in our study).\n\nThe lumen size of skin arterio-venous anastomoses (AVA) is regulated exclusively by neurogenic mechanisms, so we can assume that they also expand amidst the decrease of neurogenic tone. The dilation of AVA leads to arterio-venous shunting of the blood bypassing the capillary channel, which explains the significant decrease of Imn, a decrease of the number of functioning capillaries [13,14], reduction of perfusion (Im) and venular overflow due to arterial blood discharge that in its turn leads to the dilation of venules [40,41] and a significant increase of the amplitude of respiratory-driven blood flow oscillations amplitude.\n\n## Study Limitations\n\nThe present study was conducted on a small group of patients, some of whom had comorbidities, so there is no certainty that the results will be true for the broader study population. The data obtained, however, should be taken into account for the development of new diagnostic criteria in assessing the degree of microcirculatory disturbances and rehabilitation processes in recently recovered patients. There is a need for additional studies with a larger group of patients, including patients with different courses of COVID-19 (mild, moderate, and severe disease).\n\nDespite the already three-year history of coronavirus infection and the undoubted advantages of the LDF method for diagnosing microcirculatory disorders, there are almost no studies devoted to spectral analysis of LDF signal in COVID-19 pathology. In this pilot study, we demonstrated the possibilities of laser Doppler flowmetry coupled with the wavelet analysis of the obtained signals to detect microcirculatory disorders in patients who have undergone COVID-19 that makes it a promising tool for future research and assessment of the dynamical changes in microcirculation during the recovery process.\n\n## 5. Conclusions\n\nThe present work demonstrates the use of laser Doppler flowmetry and peripheral blood flow oscillations analysis to diagnose vascular disorders in patients who have undergone COVID-19 in their early and advanced stages of recovery. Our work demonstrated a significant increase in the amplitude of neurogenic oscillations in the upper extremities of patients undergoing COVID-19, which, as we suggest, may be a factor preceding dilation of arterioles and venules and redirection of microcirculatory blood flow from the nutritive to the shunt pathways.\n\nThe obtained data show that optical noninvasive technologies have the potential for further application, but more research is needed to fully understand the changes in the mechanisms of blood flow regulation that occur after an infection.\n\n## References\n\n1. Levi M., Thachil J., Iba T., Levy J.H.. **Coagulation abnormalities and thrombosis in patients with COVID-19**. *Lancet Haematol.* (2020) **7** e438-e440. 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Jiaxiong Wu\n- Sihuan Shen\n- Qiang Gao\n- Chengxiao Yu\n- Huan Cheng\n- Haibo Pan\n- Shiguo Chen\n- Xingqian Ye\n- Jianle Chen\njournal: Foods\nyear: 2023\npmcid: PMC10000670\ndoi: 10.3390/foods12050943\nlicense: CC BY 4.0\n---\n\n# RG-I Domain Matters to the In Vitro Fermentation Characteristics of Pectic Polysaccharides Recycled from Citrus Canning Processing Water\n\n## Abstract\n\nCanned citrus is a major citrus product that is popular around the world. However, the canning process discharges large amounts of high-chemical oxygen demand wastewater, which contains many functional polysaccharides. Herein, we recovered three different pectic polysaccharides from citrus canning processing water and evaluated their prebiotic potential as well as the relationship between the RG-I domain and fermentation characteristics using an in vitro human fecal batch fermentation model. Structural analysis showed a large difference among the three pectic polysaccharides in the proportion of the rhamnogalacturonan-I (RG-I) domain. Additionally, the fermentation results showed that the RG-I domain was significantly related to pectic polysaccharides’ fermentation characteristics, especially in terms of short-chain fatty acid generation and modulation of gut microbiota. The pectins with a high proportion of the RG-I domain performed better in acetate, propionate, and butyrate production. It was also found that Bacteroides, Phascolarctobacterium, and Bifidobacterium are the main bacteria participating in their degradation. Furthermore, the relative abundance of Eubacterium_eligens_group and Monoglobus was positively correlated with the proportion of the RG-I domain. This study emphasizes the beneficial effects of pectic polysaccharides recovered from citrus processing and the roles of the RG-I domain in their fermentation characteristics. This study also provides a strategy for food factories to realize green production and value addition.\n\n## 1. Introduction\n\nCitrus fruits, including orange (Citrus sinensis), tangerine or mandarin (Citrus reticulata), grapefruit (Citrus vitis), lemon (Citrus limonum), and lime (Citrus aurantifulia), are among the most extensively cultivated and consumed fruits around the globe [1,2,3]. Some citrus fruits are consumed directly by people, and the rest are processed into products in different forms. Citrus canning has a dominant position in the citrus processing industry, and the total export of canned citrus in *China is* 265,800 tons, accounting for over $50\\%$ of the total global amount [4,5]. The most important step of citrus canning processing is eliminating segment membranes, and the traditional process is chemical hydrolysis with hydrochloric acid and sodium hydroxide. This process is more practical and economical for industrial manufacture. However, the chemical treatment method generates substantial amounts of processing water, which is rich in pectic polysaccharides and polyphenols [5,6]. The waste-processing effluents are disposed of via different methods, such as by releasing them into rivers directly and discharging them into the city sewage system, thereby leading to many environmental problems [7]. Therefore, processing industries need to efficiently extract or recycle these value-added compounds or phytochemicals.\n\nPectin, an acidic heteropolysaccharide, is abundant in the cell wall of higher plants and is extensively used in the food industry as a gelatinizer, stabilizer, emulsifier, and fat substitute [8,9,10]. Structurally, pectin comprises the homogalacturonan (HG), rhamnogalacturonan-I (RG-I), and rhamnogalacturonan-II (RG-II) domains [11]. As the “smooth region” of pectin, HG is a linear chain comprising α-1,4-D-galacturonic acid (GalA) with different methoxycarbonyl groups. RG-I is the “hairy region” of pectin and is formed by the backbone of repeating disaccharides of GalA and α-1,2-L-rhamnose (Rha) and the side chains of neutral sugars (such as α-L-arabinose and galactose). RG-II, which is also the “hairy region” of pectin, comprises the GalA chain and various sugars such as xylose, fucose, and apiose.\n\nPectin is gradually being considered as a prebiotic candidate because of its indigestibility and microbiota accessibility [12,13]. Pectin has been confirmed to enable improvements in inflammatory bowel disease, obesity, and cancer, probably due to its fermented metabolites [8,14]. Moreover, several studies have pointed out that slowly fermentable and highly polymerized carbohydrates may be more important than quickly fermentable carbohydrates. The reason may be that the lack of fermentable carbohydrates in the distal colon caused by carbohydrates’ fast fermentation will induce gut microbiota to degrade protein and produce harmful metabolites, but slowly fermentable carbohydrates can reach the distal colon and be fermented there [15,16,17]. Many studies have also reported the biological activities of RG-I-type pectic polysaccharides, which may be attributed to the function of neutral sugar chains. These studies have found that arabino-oligosaccharides and galactosan can regulate gut microbiota and be considered as potential prebiotics [18,19]. Therefore, studying the fermentation characteristic of pectins (especially RG-I-type pectic polysaccharides) is important and meaningful.\n\nThe relationship between the structure and function of pectin is attracting research attention, and many studies have focused on pectin obtained using different extraction methods (e.g., ultra high pressure technology and microwave irradiation-associated extraction technology) or different sources (such as citrus, Ganoderma atrum, and seaweed) to explore the influence of its structure on fermentation characteristics [20,21,22,23]. Our previous study found that the structure of pectic polysaccharides from three citrus canning processes was different, which might lead to differences in functions [4]. However, most studies did not consider the diversity of factors and the individual effects of different pectins’ molecular weight (Mw), degree of esterification (DE), or monosaccharide composition on their fermentation characteristics, which still remain unclear. Herein, we extracted three different RG-I-type pectic polysaccharides from citrus canning wastewater. Structurally, the Mw and DE of these three pectins were similar, and the most significant difference among them was found in the proportion of the RG-I domain. Then, we evaluated their fermentation characteristics using an in vitro human fecal batch fermentation model and found that the RG-I domain was crucial for pectin in short-chain fatty acid (SCFA) generation and modulation of gut microbiota. This study aims to explore the beneficial effects of reclaimed RG-I pectic polysaccharides and further clarify the structure–activity relationship.\n\n## 2.1. Materials and Chemicals\n\nSatsuma mandarin (Citrus unshiu Marc. Owari satsuma) and sweet orange (*Citrus sinensis* (L.) Osbeck) were obtained from the Citrus Research Institute of Zhejiang Province, China. Fucose (Fuc), rhamnose (Rha), arabinose (Ara), galactose (Gal), glucose (Glc), mannose (Man), glucuronic acid (GlcA), galacturonic acid (GalA), xylose (Xyl), SCFA standards, 1-phenyl-3-methyl-5-pyrazolone (PMP), and fructooligosaccharide (FOS) were all purchased from Sigma-Aldrich (Shanghai, China). All other chemicals used in this study were analytically pure or chromatographically pure.\n\n## 2.2. Extraction of RG-I Pectic Polysaccharides from Citrus Segment Membranes\n\nFrom our previous study published by Shen et al. [ 4], three different pectic polysaccharides rich in the RG-I domain from segment membranes of satsuma mandarin and sweet orange were extracted using a novel citrus canning processing technology (recycling wastewater for electrolyte beverage production). The monosaccharide composition of the pectic polysaccharides is presented in Table 1. The content of the RG-I domain was calculated using the formula RG-I (%) ≈ 2Rha(mol%) + Ara(mol%) + Gal(mol%). On the basis of the proportion of the RG-I domain, these pectic polysaccharides were named RG-46, RG-56, and RG-67.\n\nRG-46 was extracted from satsuma mandarin segment membranes using the acid–alkali sequential extraction method. In detail, mandarin segment membranes were first treated with $0.4\\%$ citric acid and $0.1\\%$ HCl for 40 min at 30 °C. After filtration, the membranes were treated with $0.2\\%$ NaOH and $0.1\\%$ KOH for 10 min. Then, the alkaline extracting solution was precipitated overnight with $95\\%$ ethanol at a volume ratio of 1:1. The sediments were redissolved in ultrapure water and then dialyzed with 500-mesh dialysis bags for 48 h. After vacuum freeze drying, RG-46 was obtained. RG-67 was extracted from sweet orange segment membranes under similar conditions (the only difference was that the sweet orange segment membranes were treated with $0.4\\%$ citric acid and $0.1\\%$ HCl for 50 min rather than 40 min). RG-56 was extracted by repeating the extraction method of RG-67 three times according to the circulating water system used in factory production.\n\n## 2.3. Monosaccharide Composition Analysis\n\nAccording to the method proposed by Yan and colleagues [6], monosaccharide composition analysis was carried out. About 1 mL of pectic polysaccharide solution (2–3 mg/mL) or fermented supernatant was mixed with 4 mol/L trifluoroacetic acid (TFA, 1 mL) into an ampoule and then sealed using an alcohol blast burner. The system was hydrolyzed at 110 °C for 8 h. After cooling, samples were dried with methanol and blown with nitrogen to remove redundant TFA. Then, 1mL of distilled water was added to redissolve monosaccharides and mixed with 1 mL of PMP solution (0.5 mol/L) at 70 °C for 30 min. After derivation, the system was leached with chloroform three times to remove excrescent PMP. The supernatant was filtered using 0.22 μm membranes and analyzed via HPLC (Waters, Milford, MA, USA). The HPLC system was equipped with a Zorbax Eclipse XDB-C18 column for monosaccharide analysis. Solvent A was a 0.05 mol/L KH2PO4 buffer (adjusted pH of 6.80) with $15\\%$ (V:V) acetonitrile, and solvent B was the same buffer with $40\\%$ (V:V) acetonitrile. The flow rate was 1 mL/min, and the column temperature was 25 ± 5 °C. The elution program was 0–$15\\%$ B (0–10 min), 15–$25\\%$ B (10–30 min), and 25–$0\\%$ B (30–45 min). All detections were carried out at 250 nm.\n\n## 2.4. Molecular Weight Analysis\n\nHigh-performance size-exclusion chromatography (HPSEC) was used to measure the molecular weight. Pectic polysaccharide samples or supernatants of fermented medium were filtered using 0.22 μm membranes and then detected with a multi-angle laser light scattering and refractive index detector (MALLS-RI) at 40 °C. A Shodex OHpak SB-G column, SB-804 HQ (10 μm, 8.0 × 300 mm, exclusion limit 1 × 106) column, and SB-806 HQ (13 μm, 8.0 × 300 mm, exclusion limit 2 × 107) column were used together for analysis. All samples were eluted with 0.15 mol/L NaCl solution containing $0.02\\%$ proclin at the flow rate of 0.5 mL/min for 60 min. The dn/dc value used here was 0.138. All data analyses were conducted using ASTRA software (Version7.1.8, Wyatt Technologies Co., Santa Barbara, CA, USA).\n\n## 2.5. Degree of Esterification Analysis\n\nThe degrees of methyl esterification (DM) and acetylation (DA) were determined together via HPLC (Waters, Milford, MA, USA) [24]. In brief, 5 mg of pectic polysaccharide was mixed with 0.5 mL of an aqueous solution containing 10 mmol/L CuSO4 and 10 mmol/L isopropanol. After mixing thoroughly, 0.5 mL of NaOH (1 mol/L) was added to the system. Then, the system was incubated at 4 °C for 30 min to initiate saponification and then centrifuged at 8000× g/min for 10 min. The supernatant was obtained, and the pH was adjusted to 3. After filtering with 0.22 μm filter membranes, the samples were used for analysis. The internal standard method was used for determination, and thus the response factors of methanol and acetic acid needed to be measured first. A chromatographic mixture of methanol, acetic acid, and isopropanol with a mass ratio of 3:1:1 was prepared and injected into the HPLC system. The HPLC system was equipped with a C18 column (SinoChrom ODS-BP, 5 μm, 250 × 4.6 mm) and refractive index detector (RID). The mobile phase used was 4 mmol H2SO4, and the flow rate was 0.8 mL/min. The column temperature was 30 °C, and elution time was 30 min. The calculation formulas were as follows: FR = (MMeOH or MHAc × AIPA)/(MIPA × AMeOH or AHAc) DM = (FR × AMeOH × MIPA × 1,760,000)/(MSample × AIPA × GalA% × 32) DA = (FR × AHAC × MIPA × 1,760,000)/(MSample × AIPA × GalA% × 60) where FR is the response factor, MeOH represents methanol, HAc represents acetic acid, IPA represents isopropanol, Sample represents different pectic polysaccharides, A represents the peak area, M represents mass, and GalA% represents the mass fraction of galacturonic acid in the samples.\n\n## 2.6. In Vitro Human Fecal Microbiota Fermentation\n\nThe fecal microbiota fermentation experiment was carried out according to the method described by Yu et al., with some amendments [25]. An amount of 35 mg of FOS (as a positive control group) and RG-46, RG-56, and RG-67 were added to 6.3 mL of carbon-free medium (10.0 g/L casein peptone, 2.5 g/L yeast extract, 1.5 g/L NaHCO3, 1.0 g/L cysteine-HCl, 0.9 g/L NaCl, 0.45 g/L KH2PO4, 0.45 g/L K2PO4, 0.09 g/L MgSO4·7H2O, 0.09 g/L CaCl2, 10.0 mg/L hemin, 10.0 mg/L vitamin B6, 5.0 mg/L vitamin B2, 5.0 mg/L p-aminobenzoic acid, 2.0 mg/L vitamin B7, 2.0 mg/L folic acid, 0.8 mg/L resazurin solution, and 0.1 mg/L vitamin B12) and then mixed thoroughly. Fresh human excrement was collected from six healthy donors (3 men and 3 women, 18 < BMI < 24) who did not have any gastrointestinal diseases and had not taken antibiotics in the last three months. The excrement was dissolved in PBS ($10\\%$, w/v) and then filtered with four layers of gauze after homogenization. An amount of 0.7 mL of the fecal slurry was immediately added to 6.3 mL of medium to make sure that the final concentration of polysaccharides was 5 mg/mL. All samples were incubated at 37 °C in an anaerobic chamber and sampled at 0 h, 4 h, 8 h, 12 h, and 24 h during the fermentation. After centrifugation (10,000× g, 5 min, 4 °C) and pH measurement, the supernatant and pellet were kept at −80 °C for further analysis. All samples were analyzed in triplicate in the anaerobic chamber.\n\n## 2.7. Measurement of pH, SCFAs, and Total Sugar\n\nThe fermentation supernatant of different fermentation periods was taken to determine the pH degree. The supernatant was diluted fourfold and then filtered using hyperfiltration membranes for the determination of SCFAs. The short-chain fatty acid concentrations of all groups were analyzed using an Agilent 6890 N GC-FID, which was equipped with an HP-INNOWAX column. Nitrogen was the carrier gas, and the flow rate was 20 mL/min. The inlet temperature and detector temperature were both 240 °C. The initial oven temperature was 100 °C, which was then heated to 180 °C at a rate of 4 °C/min. The injection volume of each sample was 1 μL, and the running time was 20.5 min. The SCFA concentrations were calculated according to the standard curve formed by the concentration peak area.\n\nThe total sugar content was determined using the phenol-sulfuric acid method. In brief, 1 mL of $6\\%$ phenol solution and 5 mL of concentrated sulfuric acid were added to 1 mL of fermented supernatant. The system was mixed evenly and heated in a boiling water bath for 10 min, and then the absorbance value was measured at 490 nm.\n\n## 2.8. Gut Microbiota Analysis\n\nThe gut microbiota composition was analyzed in samples collected from the 24 h fermentation. The samples were centrifuged at 10,000× g/min for 5 min to obtain the fecal pellets. Total bacterial DNA was extracted using a TIANamp Stool DNA Kit. All extracted DNA samples were sent to the analysis company for quality identification and gut microbiota analysis. The V4 region of 16S rDNA was selected for amplification, and the Illumina Miseq platform was used for high-throughput sequencing and bioinformatics analysis. All analyses were based on sequencing reads and operational classification units (OTUs).\n\n## 2.9. Statistical Analysis\n\nAll experiments were repeated three times in parallel. The data are expressed as the mean ± standard deviation. Statistical analysis was accomplished using SPSS Statistics 26 software (IBM Corp, Armonk, NY, USA), and the significance of the differences was evaluated using one-way ANOVA ($p \\leq 0.05$). Origin 8.0 software (OriginLab Corp., Northampton, MA, USA) and GraphPad Prism 8 software (Graphpad Corp., Boston, MA, USA) were used for mapping.\n\n## 3.1. Analysis of Monosaccharide Composition, Molecular Weight, and Degree of Esterification\n\nThe monosaccharide composition of the three pectic polysaccharides with different proportions of the RG-I domain (RG-46, 56, and 67) is shown in Table 1. Large differences existed in the monosaccharide composition among RG-46, RG-56, and RG-67. GalA, Ara, and Gal were the main monosaccharides, accounting for over $30\\%$, $20\\%$, and $10\\%$ of the total monosaccharides, respectively. Although the Rha amount was significantly lower than that of the above monosaccharides, it was indispensable to the pectic polysaccharides’ primary structure. The chain backbone of the RG-I domain was composed of repeating GalA and Rha [26]. Apparently, a higher content of Rha meant a longer chain in the RG-I domain. The side chains were composed of neutral sugars (Gal and Ara) intertwined with each other and absorbed into the backbone, forming the RG-I domain [27]. Clearly, the content of Ara in RG-67 was significantly higher than that in RG-46 and RG-56. According to the formulas, the molar percentages of the HG and RG-I domains were calculated. The proportion of the HG domain in RG-46 was significantly higher than that in the other samples, and the proportion of the RG-I domain in RG-67 was the highest.\n\nThe molecular weight of the three pectic polysaccharides is listed in Table 1. The molecular weight of three RG-I-type polysaccharides was very close, and the order was RG-67, RG-56, and RG-46 from high to low. This finding may be related to the primary structure and multistage structure of polysaccharides; for example, the manifold and long neutral sugar chains in RG-67 resulted in its relatively large Mw. It was found that the Mw and Mn were positively correlated with the proportion of the RG-I domain. Although the Mw of the three RG-I polysaccharides were close, the difference in the Mn among them was significant, leading to varying polydispersity (Mw/Mn). A larger polydispersity corresponds to a wider sample distribution [28]. Therefore, RG-46 was evenly distributed, while RG-56 and RG-67 were more widely distributed.\n\nThe degrees of methyl esterification (DM) and acetylation (DA) were also measured. It was obvious that the DM of these three pectic polysaccharides was small, accounting for only about $10\\%$. The DM was related to the proportion of the RG-I domain, and RG-67 had the highest degree. Meanwhile, the value of DA was extremely low and could not even be detected in RG-56 and RG-67. This indicates that a low esterification degree and low acetylation degree are distinguishing features of pectic polysaccharides extracted via the alkaline process, on account of the saponification reaction [29].\n\n## 3.2. pH Change and SCFA Generation during Fermentation\n\nThe initial pH of each group was within 6.8 to 7.2 (as shown in Figure 1a). The pH of FOS and all pectic polysaccharides decreased during fermentation, especially within 0 to 4 h. The results suggested that the pH of FOS gradually decreased during the whole in vitro fermentation. However, the pH changes of RG-67, RG56, and RG-46 did not reflect those of FOS; they tended to be constant or slightly increase within 12 to 24 h. The final pH decline degree of RG-67 was relatively greater than that of the other samples.\n\nDuring fermentation, the substrates were degraded by gut microbiota and transformed into a series of complex metabolites. SCFAs, as the main metabolites in carbohydrate metabolism, benefit human gut health by maintaining intestinal homeostasis, improving metabolic syndrome, and exerting anti-inflammation and antitumor activities [30,31,32]. The concentrations of SCFA accumulation during fermentation are shown in Figure 1b–e. Obviously, all substrates could significantly promote the production of SCFAs, and the concentration increased with fermentation. The total SCFA concentration in all samples at 24 h was significantly higher than that in the blank group. Among the four samples, the order of the total SCFA concentrations at 24 h was RG-67, RG-56, RG-46, and FOS from high to low. These results suggested that RG-I-type pectic polysaccharides could produce more SCFAs than the traditional prebiotic FOS, and that the proportion of the RG-I domain is positively correlated with SCFA production.\n\nAcetate was the dominant SCFA (as shown in Figure 1c), followed by propionate and butyrate (as shown in Figure 1d,e), whereas the contents of isobutyrate, valerate, and isovalerate were very low [33]. Compared to FOS and the other two RG-I pectins, RG-67 performed the best in SCFA production, regardless of whether it was acetate, propionate, or butyrate. The growth trends of acetate and butyrate among the groups were similar to the results of the total SCFAs, revealing that a high RG-I domain proportion could lead to more acetate and butyrate production. However, this correlation between the RG-I domain and SCFAs was not shown in propionate production. These results might be related to the different bacteria involved in the degradation of different RG-I pectic polysaccharides.\n\n## 3.3. Degradation of Pectic Polysaccharides during Fermentation\n\nThe results of the HPSEC in RG-46, RG-56, and RG-67 showed a backward retention time and decreased corresponding peak area with fermentation (Figure 2a–c). This indicates that the molecular weight and molar mass of the pectic polysaccharides gradually decreased during fermentation. During the whole fermentation, 0–4 h was the main period for the degradation of pectic polysaccharides, and nearly half of the substrates were utilized by microbiota. A low Mw fraction at 40–43 min also indicated remarkable degradation. Notably, RG-67 showed a slower degradation speed, although it was almost exhausted before the end of fermentation. Considering its SCFA accumulation, this could be explained by the slow fermentability of RG-67, which was associated with solubility.\n\nAccording to the analysis of the monosaccharide composition at different time points, the relative percentage of monosaccharides remaining from 4 h to 24 h was analyzed and compared with the amounts at 0 h. Microbiota manifested a similar preference to monosaccharides among RG-46, RG-56, and RG-67. Specifically, arabinose, xylose, galactose, and mannose in the pectic polysaccharides were more easily utilized, but galacturonic acid, rhamnose, fucose, glucose, and glucuronic acid were relatively more difficult to degrade. We noted that gut microbiota possessed distinct sugar preferences, and neutral sugars were more easily selected than acidic sugars, which is consistent with previous findings [34,35]. The total carbohydrate contents are also shown in the diagram, indicating that the sugar utilization rates differed among groups. It was noted that pectins with a high RG-I domain proportion showed a relatively slow degradation speed. These results were in accordance with the Mw degradation and utilization of monosaccharides, meaning that a high proportion of the RG-I domain prolonged the fermentation and metabolized into more SCFAs by microbiota after fermentation. The concentration and sites of SCFA generation were found to be vital for human gut health, and the lack of carbon sources could promote bacteria to degrade protein and nitrogen metabolism and generate numerous noxious gases to harm gut health [15]. Based on the above results, RG-I pectic polysaccharides (especially RG-67) might benefit humans by prolonging fermentation and promoting the level of fermentable carbohydrates in the distal colon.\n\n## 3.4. Diversity and Composition Analysis of Gut Microbiota\n\nIn response to the effect of gut microbiota, all four substrates were degraded to varying degrees and transformed into a series of metabolites. However, these substrates and metabolites modulated the composition of the gut microbiota in turn. The alpha diversity of gut microbiota among the five groups is shown in Table 2. Sobs, Chao, and Ace are the indices of community richness, reflecting the richness of the microbiota composition in the groups. Clearly, RG-46, RG-56, and RG-67 were better than FOS in facilitating microbiota richness. Shannon, Simpson, and Heip are the indices of community diversity. A higher value of the Shannon and Heip indices or a lower value of the Simpson index indicates the samples’ superiority in microbiota diversity. In summary, RG-46, RG-56, and RG-67 all had better performances than FOS in increasing bacteria diversity.\n\nThe composition analysis of gut microbiota among the blank, FOS, RG-46, RG-56, and RG-67 groups is shown in Figure 3. The Venn diagram (Figure 3a) among different groups was used to count the number of common and unique OTUs in multiple samples, showing the similarities and overlaps of OTUs more intuitively. A total of 258 OTUs were shared by the five groups, and each group had its own unique OTUs. Principal component analysis at the genus level (Figure 3b) reflected a large difference in the microbiota composition of the blank, FOS, and pectic polysaccharide groups. However, RG-46, RG-56, and RG-67 performed similarly in the PCA analysis, which may be due to a minor disparity in their structures.\n\nThe relative proportions of community abundance at the phylum, genus, and species levels are shown in Figure 3c–e. Bacteroidota, Firmicutes, Proteobacteria, and Actinobacteria were the main phyla in microbiota, accounting for over $98\\%$ [36]. After 24 h of fermentation, the abundances of Bacteroidota and Actinobacteriota increased in the FOS and RG-I pectic polysaccharide groups. As a strong competitor in the gut ecosystem, *Bacteroidota is* involved in many important metabolic activities in the human colon, including carbohydrate fermentation, nitrogen utilization, and bioconversion of bile acids and other steroids [37,38]. Apparently, RG-I pectic polysaccharides are comparable to traditional prebiotics such as FOS in promoting Bacteroidota growth. However, the four samples inhibited the development of Proteobacteria, including the pathogenic bacteria Salmonella, Escherichia coli, and Shigella.\n\nAccording to the result of community abundance at the genus level (Figure 3d), the composition of gut microbiota changed significantly compared with the blank group. FOS and the RG-I pectic polysaccharides markedly enhanced the relative abundances of Prevotella, Bifidobacterium, and Lactobacillus and substantially decreased the relative abundances of Escherichia-Shigella, Klebsiella, and Megamonas. As traditional probiotics, Bifidobacterium and Lactobacillus proliferated in the three RG-I pectic polysaccharides, and the high-RG-I domain pectic polysaccharides performed better. The dominant bacteria in RG-46, RG-56, and RG-67 were Bacteroides, Prevotella, Phascolarctobacterium, and Bifidobacterium, showing a similar microbiota composition to that of the FOS group. All RG-I pectic polysaccharides showed stronger effects in promoting Bacteroides and Phascolarctobacterium, indicating that these two bacteria may play a crucial role in the degradation and utilization of pectic polysaccharides. Meanwhile, they led to a reduction in the abundances of Escherichia-Shigella and Klebsiella, indicating that RG-I pectic polysaccharides possessed conspicuous abilities in hindering the growth and breeding of conditional pathogens.\n\nThe community abundance at the species level (Figure 3e) could provide more detailed information about the composition of gut microbiota. A huge discrepancy existed among the blank, FOS, and RG-I pectic polysaccharide groups, although the microbial community structures of RG-46, RG-56, and RG-67 were homogeneous. All RG-I pectic polysaccharides could significantly facilitate the growth of specific bacteria, such as Bacteroides_vulgatus, metagenome_g_Prevotella, Phascolarctobacterium_faecium, Bifidobacterium_pseudocatenulatum, and Bacteroides_thetaiotaomicron. These bacteria were the main microflora of the degraded pectic polysaccharides, and the high content of the RG-I domain affected their growth and propagation. The most obvious evidence for this is that the abundance of Bacteroides_vulgatus and Bifidobacterium_pseudocatenulatum in RG-67 and RG-56 was evidently higher than that in RG-46.\n\n## 3.5. Group Variation Analysis of Gut Microbiota\n\nFigure 4a–d show the results of the one-way ANOVA with Welch’s post hoc test based on the abundance of the top 20 genera among the five groups. Figure 4a shows the five genera with the highest abundance in the gut microenvironment, indicating that RG-I-type pectic polysaccharides had different abilities in modulating gut microbiota.\n\nThe traditional prebiotic FOS showed outstanding capacities in promoting Bifidobacterium and Prevotella. As a traditional probiotic, Bifidobacterium plays an important role in the biological barrier, antitumor effect, immune function, and gut health [39]. Prevotella is a key bacterium in host–microbiome interactions, particularly in relation to nutrition and complex carbohydrate metabolism [40].\n\nRG-46, RG-56, and RG-67 also showed the ability to stimulate these bacteria’s growth. Interestingly, Bacteroides and Phascolarctobacterium showed distinct preferences for RG-I-type pectic polysaccharides. In addition to the above bacteria, all RG-I pectic polysaccharides could selectively promote Blautia, Sutterella, Dorea, Collinsella, Monoglobus, and Eubacterium_eligens_group. Most of the dominant bacteria in RG-I polysaccharides are involved in the degradation of carbohydrates and the generation of SCFAs, benefiting gut health through multiple mechanisms. For example, Blautia has potential probiotic properties, such as preventing inflammation and promoting the production of SCFAs to maintain intestinal homeostasis [41].\n\nSutterella, Dorea, and Monoglobus are related to the fermentation of pectin, consistent with literature reports [42]. Among them, *Monoglobus pectinilyticus* was identified as a pectin-degrading specialist bacterium that participates in degrading various pectins, RG-I, and galactans to produce degradation products that are presumably shared with other bacteria [43]. Another bacterium with specificity for RG-I pectic polysaccharides is Eubacterium_eligens_group, which was significantly abundant in RG-46, RG-56, and RG-67 but hardly existed in the FOS and blank groups. We came to the same conclusion as other studies that the growth of Eubacterium_eligens_group and Blautia occurs only with pectic polysaccharides [44]. Notably, Eubacterium_eligens_group is related to butyrate production, but the effects and specific mechanisms in maintaining human health and pectin degradation remain unclear [45,46].\n\nLinear discriminant analysis effect size (LEfSe) multilevel discriminant analysis (Figure 4e) is a useful tool for discovering biomarkers among different groups. Each group had its specific biomarkers, and linear discriminant analysis (LDA) scores of those biomarkers were calculated. Only biomarkers with LDA scores greater than 3.5 are shown in Figure 4. Phascolarctobacterium, Sutterella, and Lachnospira were the main biomarkers in RG-46 at the genus level. RG-56 and RG-67 also possessed their typical genera as biomarkers. In RG-56, they were Bacteroides, Collinsella (butyrate producer), and Eubacterium_eligens_group [47]. However, in RG-67, the biomarkers were TM7x, Monoglobus, Granulicatella, and Lachnospiraceae_NK4A136_group. This finding indicates that the proportion of the RG-I domain and high neutral sugar contents affected the modulation of intestinal microbial communities, as shown by the changes in the dominant bacteria and biomarkers.\n\n## 3.6. Function Prediction and Pearson Correlation Analysis\n\nPICRUSt was used to standardize the OTU abundance table, and then the corresponding green gene ID of each OTU was used to annotate its clusters of orthologous groups (COG) functions. This method can offer annotation information on the function levels and their abundance in different groups. FOS served as the control group, and the functional abundances of RG-46, RG-56, and RG-67 are compared in Figure 5a. The abundance of the COG function in RG-67 was higher than in FOS and the other RG-I pectic polysaccharides, especially in carbohydrate transport and metabolism, cell wall/membrane biogenesis, and energy production and conversion. Combined with its abilities in SCFA production and microbiota modulation, RG-67 may have the best performance in promoting energy metabolism and maintaining intestinal health.\n\nThe correlation analysis (Figure 5b) of the intestinal microflora composition, monosaccharides, and SCFAs showed that monosaccharides played a crucial role in regulating bacteria and generating SCFAs. For the high-RG-I domain pectic polysaccharides, Ara, GalA, Gal, and Rha were the core components, conferring polysaccharides special functions and promoting the growth of probiotics. GalA, as the monosaccharide with the highest proportion among the polysaccharides, maintained a positive relationship with Bifidobacterium ($p \\leq 0.05$) and a negative relationship with Megamonas ($p \\leq 0.01$). The presence of Ara was highly positively correlated with the development of Bacteroides, Bifidobacterium, and Monoglobus, and was also connected with the accumulation of acetic acid, propionic acid, and butyric acid. Another neutral sugar, Gal, also had unique effects in affecting gut microbiota, such as restraining the reproduction of Escherichia-Shigella ($p \\leq 0.01$), Subdoligranulum, Megamonas, Lachnoclostridium, Klebsiella, and Lachnospira. In the results based on RG-46, RG-56, and RG-67, it was found that Rha, Ara, GalA, and Gal had better capacities than the other monosaccharides in this aspect. Ara and Rha were fundamental to the RG-I domain, and their contents determined the polysaccharides’ functional characteristics and the regulation of gut microbiota to some extent. The above might explain why RG-67 performed better in SCFA generation and modulation of gut microbiota. Furthermore, Bacteroides, Phascolarctobacterium, Prevotella, Bifidobacterium, and Monoglobus were positively correlated with the formation of SCFAs.\n\n## 4. Conclusions\n\nThree different pectic polysaccharides (RG-46, RG-56, and RG-67) with high RG-I domain proportions recovered from citrus canning processing wastewater were used in this study, and the relationship between the RG-I domain and in vitro fermentation characteristics was investigated. Structurally, the main difference among RG-46, RG-56, and RG-67 was the proportion of the RG-I domain. The fermentation results showed that the RG-I domain was significantly related to pectic polysaccharides’ fermentation characteristics, especially in SCFA generation and modulation of gut microbiota. RG-67 performed better than RG-56 and RG-46 in producing acetate, propionate, and butyrate. The main bacteria that participated in degrading the RG-I pectic polysaccharides were Bacteroides, Phascolarctobacterium, Bifidobacterium, and Blautia. Furthermore, Eubacterium_eligens_group and Monoglobus showed particular preferences for RG-I-type pectic polysaccharides, and their abundances were closely related to the proportion of the RG-I domain. Our study had limitations. For example, the structure–function relationship between the RG-I domain of pectic polysaccharides and its effect on health needs to be further determined in vivo. 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0.012319836765527725\n]","isConversational":false}}},{"rowIdx":75,"cells":{"text":{"kind":"string","value":"---\ntitle: Nucleolin Regulates Pulmonary Artery Smooth Muscle Cell Proliferation under\n Hypoxia by Modulating miRNA Expression\nauthors:\n- Jihui Lee\n- Hara Kang\njournal: Cells\nyear: 2023\npmcid: PMC10000680\ndoi: 10.3390/cells12050817\nlicense: CC BY 4.0\n---\n\n# Nucleolin Regulates Pulmonary Artery Smooth Muscle Cell Proliferation under Hypoxia by Modulating miRNA Expression\n\n## Abstract\n\nHypoxia induces the abnormal proliferation of vascular smooth muscle cells (VSMCs), resulting in the pathogenesis of various vascular diseases. RNA-binding proteins (RBPs) are involved in a wide range of biological processes, including cell proliferation and responses to hypoxia. In this study, we observed that the RBP nucleolin (NCL) was downregulated by histone deacetylation in response to hypoxia. We evaluated its regulatory effects on miRNA expression under hypoxic conditions in pulmonary artery smooth muscle cells (PASMCs). miRNAs associated with NCL were assessed using RNA immunoprecipitation in PASMCs and small RNA sequencing. The expression of a set of miRNAs was increased by NCL but reduced by hypoxia-induced downregulation of NCL. The downregulation of miR-24-3p and miR-409-3p promoted PASMC proliferation under hypoxic conditions. These results clearly demonstrate the significance of NCL–miRNA interactions in the regulation of hypoxia-induced PASMC proliferation and provide insight into the therapeutic value of RBPs for vascular diseases.\n\n## 1. Introduction\n\nHypoxia induces changes in gene expression which can trigger adaptive processes, such as cell proliferation or motility [1]. The alterations in numerous RNA-binding proteins (RBPs) and microRNAs (miRNAs) under hypoxic conditions have been investigated to better understand the mechanisms underlying adaptive cellular processes [2,3,4,5]. Hypoxia-regulated RBPs or miRNAs bind to specific target mRNAs for the selective regulation of expression in response to hypoxia [6,7].\n\nHypoxia induces structural changes in the medial compartment of the pulmonary arterial wall, including pulmonary artery smooth muscle cell (PASMC) proliferation, hypertrophy, matrix protein production, and recruitment of adventitial or circulating cells. These changes contribute to pulmonary vascular remodeling and hypertension [8,9]. Several miRNAs have been shown to modulate gene expression and PASMC function during the pathogenesis of vascular disorders under hypoxia [10,11]. However, the functions of RBPs in vascular cells under hypoxic conditions are not fully understood. Recent evidence suggests that RBPs play a role in vascular smooth muscle cells (VSMCs). For example, Hu Antigen R (HuR) contributes to the proliferation of human aortic smooth muscle cells in response to platelet-derived growth factor (PDGF) signaling [12]. In addition, the downregulation of heterogeneous nuclear ribonucleoprotein A2/B1 (HNRNPA2B1) protects against atherosclerosis by suppressing VSMC proliferation [13]. These findings suggest that RBPs contribute to the regulation of PASMC function under hypoxic conditions. Elucidating the molecular mechanism by which RBPs mediate PASMC functions under hypoxia is expected to provide a basis for the development of therapeutic strategies for pulmonary vascular diseases.\n\nNucleolin (NCL) is an RBP implicated in the response to hypoxia. Under hypoxia, NCL regulates the expression of matrix-metalloproteinase-9 (MMP-9) and collagen prolyl 4-hydroxylase-alpha(I) (C-P4H-alpha(I)), which are involved in ECM remodeling in human fibrosarcoma cells [14,15]. In addition to the hypoxic response, NCL is involved in a variety of biological processes, such as DNA transcription, ribosomal biogenesis, and the regulation of RNA stability [16,17,18,19,20,21]. Moreover, NCL regulates the expression of several miRNAs, including miR-15a/16, miR-21, miR-221, and miR-103. The abrogation of NCL expression affects the biogenesis of specific miRNAs, including miR-21, miR-221, and miR-103; however, the specific underlying mechanisms are unknown [22]. NCL is involved in the primary miRNA processing of miR-15a/16 through direct interactions with the microprocessor complex, DGCR8, and Drosha [23].\n\nInterestingly, functional interactions between RBPs and miRNAs have been reported in various cancer cells [24]. Some miRNAs regulate RBP expression, and, conversely, some RBPs can modulate miRNA expression in cancer [25,26]. However, little is known about the functional relationships between RBPs and miRNAs in vascular diseases. We have hypothesized that NCL regulates the expression of hypoxia-responsive miRNAs in PASMCs and is associated with hypoxic vascular disorders. In this study, we observed that NCL levels in PASMCs are altered in response to hypoxia. To investigate the role of NCL in PASMCs in response to hypoxia, we examined its interactions with miRNAs and the functional relevance of NCL–miRNA interactions in the responses of PASMCs to hypoxia, such as their proliferation.\n\n## 2.1. Hypoxia Downregulates NCL Expression by Histone Deacetylation in PASMCs\n\nTo identify RBPs involved in the regulation of the PASMC phenotypes under hypoxia, we searched for RBPs with mRNA expression changes in the PASMCs in response to hypoxia in the next-generation RNA sequencing results from our previous studies [27]. Among the thirteen RBPs with established roles in the hypoxia-induced responses of various cells (CIRBP, CPEB1, CPEB2, HNRNPA2B1, HNRNPL, IREB2, NCL, PTBP1, PTBP3, RBM3, TIA1, ZFP36, and ZFP36L1), the NCL mRNA levels showed the greatest difference between the hypoxia-exposed and control PASMCs (Figure 1A) [14,15,28,29,30,31,32,33,34,35,36,37,38,39,40]. These results were confirmed using qRT-PCR analysis of the transcript levels in hypoxia-exposed PASMCs after 24 h (Figure 1B). Hypoxia significantly reduced the NCL mRNA levels to $49\\%$ of those in the control, which is consistent with the RNA sequencing data. None of the other 12 genes investigated showed significant changes in PASMCs under hypoxic conditions. A reduction in NCL protein levels following hypoxia was validated by immunoblotting (Figure 1C). The level of hypoxia-inducible factor 1-alpha (HIF1α) protein was examined by immunoblotting to confirm the hypoxic conditions in the PASMCs (Figure 1D). As expected, significant induction of HIF1α upon hypoxia was observed.\n\nTo investigate whether the hypoxia-induced decrease in NCL is specific to PASMCs, a variety of cells, including pulmonary arterial endothelial cells (PAEC), HEK293, and HeLa, were exposed to hypoxia for 24 h, and the NCL mRNA and protein levels were examined using qRT-PCR and immunoblotting. Neither the mRNA nor protein levels of the NCL were changed by hypoxia (Figure 1E,F). The induction of HIF1α with hypoxia exposure was confirmed in the PAEC, HEK293, and HeLa cells (Figure 1F). These results indicate that NCL expression is downregulated by hypoxia in the PASMCs specifically, suggesting that it plays an important role in the regulation of PASMC functions in response to hypoxia. The unique responsiveness of PASMCs to hypoxia has been reported [41]. Hypoxia increases the proliferation of PASMCs, whereas it inhibits proliferation in many other cells [41]. PASMC-specific reduction of NCL expression may contribute to inducing the unique responsiveness of PASMCs to hypoxia.\n\nRepression of NCL can be mediated by histone deacetylation via histone deacetylase 1 (HDAC1) and HDAC2 [42]. We examined whether histone deacetylation is involved in the hypoxia-induced repression of NCL in PASMCs. PASMCs were treated with a histone deacetylase (HDAC) inhibitor, sodium butyrate (NaBu), and exposure to hypoxia. As determined by qRT-PCR (Figure 1G), the downregulation of NCL by hypoxia was abolished upon treatment with NaBu, suggesting that histone deacetylation is responsible for the repression of NCL under hypoxic conditions. To examine the role of HDAC1 or HDAC2 in the repression of NCL under hypoxia, endogenous HDAC1 and HDAC2 were reduced in PASMCs using small interfering RNAs (si-HDAC1 and si-HDAC2). The repression of NCL in response to hypoxia exposure was prevented in the PASMCs transfected with si-HDAC1 or si-HDAC2, suggesting that HDAC1 and HDAC2 are involved in the repression of NCL (Figure 1H). The knockdown of HDAC1 and HDAC2 was confirmed by qRT-PCR and immunoblotting (Figure 1I,J). According to previous studies, the HDAC1 expression levels were elevated in the lungs of patients with idiopathic pulmonary arterial hypertension and rats exposed to hypoxia, and HDAC inhibitors prevented hypoxia-induced pulmonary hypertension [43,44,45]. Therefore, hypoxia is likely to downregulate NCL expression specifically in PASMCs by histone deacetylation.\n\n## 2.2. NCL Inhibits PASMC Proliferation\n\nAs hypoxia stimulates the proliferation of PASMCs, the role of NCL in this process was investigated. First, NCL expression in PASMCs was downregulated using siRNAs. PASMCs transfected with siRNA against NCL (si-NCL) for 24 h were stained with a Ki-67 antibody to quantify the proliferating cells (Figure 2A). Hoechst dye was then used for nuclear staining. The percentage of Ki-67-positive cells among the si-NCL-transfected cells was approximately 1.85-fold higher than that in negative control siRNA-transfected cells, suggesting that the downregulation of NCL is sufficient to promote the proliferation of PASMCs. We then overexpressed exogenous NCL mRNAs in PASMCs using Nucleofector (Lonza) for 48 h and examined the changes in the number of Ki-67-positive proliferating cells by immunofluorescence staining (Figure 2B). The empty pEGFP-N1 vector (Addgene) was used as a control. The percentage of proliferating cells decreased significantly to $48\\%$ when exogenous NCL mRNAs were overexpressed, indicating that NCL inhibited the proliferation of PASMCs. These results suggest that NCL is involved in the regulation of PASMC proliferation.\n\nTo investigate the significance of the hypoxia-induced downregulation of NCL on PASMC proliferation, we examined whether the hypoxia-induced increase in proliferation was affected by NCL overexpression (Figure 2C). PASMCs transfected with exogenous NCL mRNA for 48 h were exposed to hypoxia for 24 h and then stained with a Ki-67 antibody. Approximately $5.1\\%$ of the control cells were Ki-67-positive under normoxia. The percentage of proliferating cells increased to $12\\%$ under hypoxia, and this increase in cell proliferation was not detected in NCL-overexpressing cells. The results suggest that the hypoxia-induced downregulation of NCL is essential for the promotion of PASMC proliferation under hypoxic conditions.\n\nCell proliferation was also examined by the cell counting assay. The number of viable cells increased 24 h after the transfection of PASMCs with si-NCL (Figure 2D), whereas the number of cells decreased 24 h after the transfection of PASMCs with an NCL-overexpressing vector (Figure 2E). The number of cells increased by hypoxia decreased when NCL was overexpressed (Figure 2F). These results corroborate the observation of Ki-67 immunofluorescence staining. The efficiency of the NCL knockdown or overexpression was confirmed by qRT-PCR and immunoblotting analyses (Figure 2G,H).\n\n## 2.3. NCL Binds to a Subset of miRNAs\n\nIn PASMCs, miRNAs play important roles in the cellular responses to hypoxia [10,11]. Interestingly, recent reports have suggested that NCL is involved in the biogenesis of several miRNAs [22,23]. Therefore, we hypothesized that hypoxia-induced changes in NCL expression result in the modulation of specific miRNAs, thereby promoting the proliferation of PASMCs. As NCL has RNA-binding properties, we searched for miRNAs associated with NCL in the PASMCs. RNAs were immunoprecipitated with an antibody against NCL or rabbit IgG as a negative control, followed by NGS-based small RNA sequencing (GSE184972). The small RNA sequencing library was made from the total RNAs from 1.5 × 106 PASMCs and two samples per condition were sequenced. We identified 39 miRNAs that were specifically pulled down by NCL antibodies (≥2-fold change in pull-down samples using NCL antibodies in comparison to those using IgG, $p \\leq 0.05$), supporting the potential role of miRNAs in NCL-induced changes in PASMCs (Table 1).\n\nThe binding of NCL to these miRNAs was validated by qRT-PCR after immunoprecipitation with an NCL antibody or rabbit IgG. The four miRNAs most highly enriched in NCL pull-down samples from the small RNA sequencing data (i.e., miR-423-3p, miR-744-5p, miR-24-3p, and miR-409-3p) showed approximately 5.5- to 33.5-fold higher levels in the pull-down samples using NCL antibodies than in those using IgG (Figure 3A). The level of miR-497-5p, which does not bind to NCL based on the results of sequencing data after immunoprecipitation, was also confirmed in the NCL pull-down sample. As expected, miR-497-5p was not enriched in either the NCL or IgG pull-down samples.\n\nTo determine whether a conserved motif exists within the sequences of the 39 potential target miRNAs of NCL, we analyzed the miRNA sequences using the motif-based sequence analysis tool MEME Suite 5.1.1 (Figure 3B). The most frequently observed motif was 5′-G/UGCUC-3′, and its position along the miRNA sequences was not identical. Since NCL is known to regulate mRNA stability by binding to a GC-rich element, it is likely that NCL binds to miRNAs with high GC contents [46].\n\nTo further confirm the interactions between the NCL and miRNAs, PASMCs were transfected with biotinylated miRNAs (bio-miR), such as bio-miR-24-3p or bio-miR-409-3p with known roles in the regulation of PASMC function, followed by affinity purification using streptavidin beads and immunoblotting with an NCL antibody (Figure 3C) [47,48,49,50]. Biotinylated Caenorhabditis elegans miR-67 (bio-cel-miR-67) was used as a negative control. An immunoblot analysis indicated that NCL binds to bio-miR-24-3p and bio-miR-409-3p but not to bio-cel-miR-67 (Figure 3C). The expression of exogenous biotinylated miRNAs in PASMCs transfected with bio-miR-24-3p or bio-miR-409-3p was confirmed by qRT-PCR (Figure 3D). Taken together, these results further support the hypothesis that NCL binds to specific miRNAs.\n\nWe subsequently examined whether the 5′-G/UGCUC-3′ motif is critical for the binding of NCL to miRNAs. Mutations were introduced in the motifs of bio-miR-24-3p and bio-miR-409-3p (bio-miR-24-3p mutant and bio-miR-409-3p mutant) (Figure 3B). PASMCs were transfected with these mutants or bio-cel-miR-67, followed by a pull-down assay (Figure 3C). Mutations in the motif abrogate the binding of NCL to miRNAs, suggesting that the 5′-G/UGCUC-3′ motif serves as an NCL-binding site. These results suggest that NCL binds to specific miRNAs via this binding site and selectively regulates miRNA expression.\n\n## 2.4. NCL Modulates miRNA Expression\n\nWe examined whether NCL affects the expression levels of miRNAs that bind to NCL. PASMCs were transfected with negative control siRNA (control) or si-NCL for 24 h and miRNA levels were measured by qRT-PCR. When NCL was downregulated by siRNAs, the expression levels of the miRNAs, including miR-423-3p, miR-744-5p, miR-24-3p, and miR-409-3p, were reduced by 51–$70\\%$ compared with levels in the control (Figure 4A). We subsequently overexpressed exogenous NCL mRNAs in PASMCs using Nucleofector (Lonza) for 24 h and examined the levels of these miRNAs (Figure 4B). The expression levels of four miRNAs were 1.3–2.8-fold higher in the NCL-overexpressed cells than in the control cells transfected with the empty pEGFP-N1 vector (control). The level of miR-497-5p, which does not bind to NCL, was not affected by the knockdown or overexpression of NCL. These results suggest that NCL binds to certain miRNAs and regulates their expression.\n\nPrevious studies have shown that NCL can affect the biogenesis of miRNAs or promote targeted mRNA degradation of miRNAs via interactions with miRNA-associated proteins, such as DGCR8, Drosha, or Ago2 [23,51,52]. Thus, we investigated their interactions in PASMCs. The cellular NCL from the PASMCs was immunoprecipitated with an NCL antibody or IgG control and analyzed by Western blotting with antibodies against DGCR8, Ago2, or NCL (Figure 4C,D). Conversely, lysates of PASMCs were immunoprecipitated with antibodies against DGCR8, Ago2, or IgG, and Western blotting was used to determine whether NCL was present in the pull-down (Figure 4E,F). We found that NCL binds to DGCR8 and Ago2 in PASMCs. These results further support the role of NCL in the regulation of miRNA expression and activity.\n\n## 2.5. NCL Mediates Hypoxia-Induced Regulation of miRNA Expression\n\nGiven that hypoxia downregulates NCL expression, miRNAs regulated by NCL are expected to show lower expression under hypoxic conditions. We examined the changes in the expression levels of miRNAs, including miR-423-3p, miR-744-5p, miR-24-3p, and miR-409-3p, under hypoxia using qRT-PCR. The expression levels of miR-423-3p, miR-744-5p, miR-24-3p, and miR-409-3p were all reduced by exposure to hypoxia for 24 h (Figure 5A). These results suggest that the hypoxia-induced downregulation of NCL is responsible for the reduced expression of certain miRNAs.\n\nTo determine whether the modulation of NCL expression influences the hypoxia-induced regulation of miRNA expression, we overexpressed exogenous NCL mRNAs in PASMCs using Nucleofector for 24 h before hypoxia exposure and examined the levels of miR-423-3p, miR-744-5p, miR-24-3p, and miR-409-3p using qRT-PCR. When NCL was overexpressed, the hypoxia-induced reduction in the miRNA levels was restored (Figure 5B). These results indicate that the hypoxia-induced modulation of NCL expression controls the expression of certain miRNAs.\n\n## 2.6. Downregulation of miR-24-3p and miR-409-3p Promotes PASMC Proliferation under Hypoxia\n\nWe determined the biological consequences of NCL-mediated miRNA regulation in hypoxic PASMCs. As NCL was observed to regulate PASMC proliferation (Figure 2), we further examined whether miRNAs regulated by NCL, such as miR-24-3p and miR-409-3p, would affect PASMC proliferation. PASMCs were transfected with control, miR-24-3p mimic, miR-409-3p mimic, miR-24-3p antisense inhibitor RNA (anti-miR-24-3p), or anti-miR-409-3p and stained with a Ki-67 antibody. miR-24-3p and miR-409-3p mimics significantly reduced the number of Ki-67-positive proliferating cells by $61\\%$ and $70\\%$, respectively, compared to cell counts in the control (Figure 6A). Conversely, cells transfected with anti-miR-24-3p or anti-miR-409-3p showed increased numbers of proliferating cells (i.e., 1.83-fold or 1.62-fold higher than counts in the control) (Figure 6B). These results demonstrate that the downregulation of miR-24-3p and miR-409-3p is required to promote PASMC proliferation.\n\nTo examine whether the modulation of miR-24-3p or miR-409-3p affects the hypoxia-induced proliferative response of PASMCs, PASMCs were transfected with control, miR-24-3p mimic, or miR-409-3p mimic prior to exposure to hypoxia and stained with a Ki-67 antibody. The hypoxia-induced increase in PASMC proliferation was inhibited in cells transfected with miR-24-3p mimic or miR-409-3p mimic (Figure 6C). Therefore, it is likely that the hypoxia-induced downregulation of NCL promotes PASMC proliferation by downregulating a subset of miRNAs, including miR-24-3p and miR-409-3p.\n\nWe also carried out a cell counting assay to determine cell proliferation. Consistent with the results of Ki-67 immunostaining, the rate of proliferation decreased in PASMCs transfected with miR-24-3p, or miR-409-3p mimics (Figure 6D). In contrast, the proliferation of PASMCs was promoted by anti-miR-24-3p, or anti-miR-409-3p (Figure 6E). The hypoxia-induced increase in cell proliferation was inhibited by miR-24-3p or miR-409-3p (Figure 6F). Therefore, the downregulation of miR-24-3p and miR-409-3p is essential for promoting PASMC proliferation under hypoxia. To confirm the overexpression or downregulation of miR-24-3p and miR-409-3p, their levels were measured in the PASMCs at 24 h after transfection with the control, miR-24-3p mimic, miR-409-3p mimic, anti-miR-24-3p, or anti-miR-409-3p (Figure 6G,H).\n\n## 3. Discussion\n\nRBPs are important regulators of gene expression via post-transcriptional regulation. Under hypoxia, RBPs regulate the expression of hypoxia-inducible genes. However, the role of RBPs in the functions of PASMCs under hypoxic conditions and the molecular mechanisms underlying their effects are not yet fully understood. In this study, we identified NCL as an essential regulator of PASMC proliferation under hypoxia and characterized its molecular function. miRNAs act as critical mediators of the response to hypoxia in PASMCs. As recent studies have revealed that NCL is involved in the biogenesis of specific miRNAs, we evaluated NCL–miRNA interactions in PASMCs by immunoprecipitation and small RNA sequencing. Thirty-nine miRNAs enriched in NCL pull-down were identified. We further found that the hypoxia-induced downregulation of NCL affects the expression of these miRNAs and demonstrated that NCL-mediated miRNA regulation induces the proliferation of PASMCs under hypoxic conditions.\n\nGiven that an RBP deficiency is associated with cardiovascular developmental defects, RBPs may play a critical role in maintaining cardiovascular health. Recently, RBPs have been implicated in systematic cardiovascular disease via the post-transcriptional regulation of target genes. For example, quaking (QKI) in VSMCs binds to myocardin and derives alternative splicing in response to vessel injury [53]. The identification of the modulation of NCL in PASMCs under hypoxia extends our understanding of the functions of RBPs in vascular conditions and provides new targets for the treatment of vascular diseases.\n\nNCL expression has been shown to be regulated by HuR and several miRNAs. HuR interacts with the 3′UTR of NCL and promotes its translation, whereas miR-494, miR-194, and miR-206 suppress NCL expression [54,55]. In this study, we observed that both the mRNA and protein levels of NCL were significantly reduced by hypoxia. We examined whether the levels of miR-494, miR-194, or miR-206 increased in hypoxia-exposed PASMCs to suppress NCL expression. Our previously generated small RNA sequencing data showed that miR-494 and miR-194 levels did not change in response to hypoxia, and the expression of miR-206 was not determined [27]. It is therefore unlikely that these three miRNAs are responsible for the decrease in NCL expression in PASMCs under hypoxia. Rather, we elucidated the role of HDAC in the transcriptional repression of NCL under hypoxia. NCL has been linked to a variety of pathologies, including carcinogenesis, and thus, elucidating the regulatory mechanisms underlying its expression should provide a basis for the development of new therapeutic strategies for a variety of diseases, including hypoxia-induced vascular diseases.\n\nThere is emerging evidence of the involvement of RBPs in the regulation of miRNA biogenesis. For example, HNRNPA1 promotes Drosha cleavage by restructuring pri-miR-18a [56]. NCL has also been reported to enhance the maturation of specific miRNAs, including miR-21, miR-221, and miR-222, and is consequently involved in the pathogenesis of cancer [22]. We have demonstrated that NCL controls the fate of miRNAs in response to hypoxia in PASMCs. NCL binds to and regulates certain miRNAs, particularly those that contain the 5′-G/UGCUC-3′ sequence. We have provided the first evidence to elucidate the biochemical interactions between NCL and miRNAs in PASMCs and their role in the proliferation of PASMCs. The regulation of miRNA expression by NCL is essential for PASMC responses to hypoxic conditions.\n\nThe proliferation of VSMCs is a hallmark of several vascular pathologies as well as hypoxia-induced remodeling [41,57]. Multiple miRNAs involved in the proliferation of VSMCs have also been explored [58,59,60]. For example, miR-24 inhibits high glucose-stimulated VSMC proliferation by targeting high mobility group box-1 (HMGB1) [49]. Overexpression of miR-24 reduced neointimal hyperplasia and VSMC proliferation by inhibiting the Wnt4 signaling pathway [47]. In addition, miR-24 suppressed the platelet-derived growth factor-BB (PDGF-BB) signaling pathway by decreasing the expression levels of activator protein 1 (AP-1) and the PDGF-receptor (PDGF-R), resulting in the inhibition of VSMC proliferation and vascular remodeling [50]. The results imply that miR-24 may also regulate VSMC proliferation under hypoxia. While few previous studies have explored the function of miR-409 in VSMCs, decreased miR-409 expression levels were observed during high phosphate-induced vascular calcification, triggering VSMC de-differentiation [48]. This finding suggests that miR-409 may be involved in the regulation of VSMC proliferation. We have demonstrated that the target miRNAs of NCL influence the proliferation of PASMCs. For example, miR-24-3p and miR-409-3p inhibit PASMC proliferation and their overexpression further prevents hypoxia-induced proliferation. These results add a layer of valuable information about a specific set of miRNAs that regulate the proliferation of PASMCs. In addition, as the target miRNA level is regulated by the level of NCL expression, it is clear that NCL–miRNA interactions are essential for the regulation of PASMC proliferation. As miRNAs are potent regulators of cellular function in pathophysiological conditions, our illustration of NCL–miRNA interactions and the role of NCL in PASMC functions via the regulation of miRNAs improves our general understanding of the mechanisms underlying the pathogenesis of vascular conditions related to hypoxia. To explore the potential therapeutic benefits of NCL or interacting miRNAs on pulmonary hypertension, it is necessary to investigate whether modulation of NCL or interacting miRNAs is effective in attenuating pulmonary vascular remodeling in animal models, such as a chronic hypoxia-induced rat model.\n\n## 4. Conclusions\n\nIn this study, we provide clear evidence for the role of the RBP nucleolin (NCL) in hypoxia-induced PASMC proliferation. NCL is downregulated by histone deacetylation under hypoxic conditions in PASMCs, which consequently promotes PASMC proliferation. Furthermore, we demonstrated that these effects of NCL are mediated by interactions with a subset of miRNAs using immunoprecipitation and NGS-based small RNA sequencing. Thirty-nine miRNAs were found to be enriched in NCL pull-down, and NCL regulates particular miRNA expressions via the 5′-G/UGCUC-3′ binding sites. Hypoxia-mediated regulation of NCL affects miRNA expression, and these miRNAs, such as miR-24-3p and miR-409-3p, are involved in the proliferation of PASMCs under hypoxia. Collectively, the identification of NCL-miRNA interactions in hypoxia-induced PASMC proliferation provides a basis for further studies of the molecular mechanisms underlying vascular diseases.\n\n## 5.1. Cell Culture and Hypoxia\n\nHuman primary pulmonary artery smooth muscle cells (PASMCs) were purchased from Lonza (CC-2581) and were maintained in Sm-GM2 medium (Lonza, Basel, Switzerland) containing $5\\%$ fetal bovine serum (FBS). For hypoxia, the cells were placed in fresh medium and incubated in a sealed modular incubator chamber (Billups-rothenberg Inc., San Diego, CA, USA) for 24 h at 37 °C after flushing with a mixture of $5\\%$ CO2, $1\\%$ O2 and $94\\%$ N2 for 4 min.\n\n## 5.2. Sodium Butyrate (NaBu) Treatment\n\nNaBu was purchased from Sigma-Aldrich (St. Louis, MO, USA, #B5887). The cells were treated with 10 mM NaBu for 24 h.\n\n## 5.3. Quantitative Reverse Transcriptase-PCR (qRT-PCR)\n\nQuantitative analysis of the change in expression levels was performed using real-time PCR. The mRNA levels were normalized to 18S rRNA. The primers used were as follows: 18S rRNA, 5′-GTAACCCGTTGAACCCCATT-3′ and 5′-CCATCCAATCGGTAGTAGCG-3; CIRBP, 5′-CTTTTTGTTGGAGGGCTGAG-3′ and 5′-CTTGCCTGCCTGGTCTACTC-3′; CPEB1, 5′-TCTGCCCTTCCTGTCTCTGT-3′ and 5′-TATGCTGAAGGGGTCTTTGG-3′; CPEB2, 5′-GCGAGTTGCTTTCTCCAATC-3′ and 5′-CCTGGCATTCATCACACATC-3′; HNRNPA2B1, 5′-GGCTACGGAGGTGGTTATGA-3′ and 5′- ATAACCCCCACTTCCTCCAC -3′; HNRNPL, 5′-AGATCACCCCGCAGAATATG -3′ and 5′-CAAGCCATAGACCATGAGCA -3′; IREB2, 5′-GCACCGGATTCAGTTTTGTT-3′ and 5′-CTTAGCGGCAGCACTATTCC-3′; NCL, 5′-GAAGGAAATGGCCAAACAGA-3′ and 5′-ACGCTTTCTCCAGGTCTTCCA-3′; PTBP1, 5′-ACGGACCGTTTATCATGAGC-3′ and 5′-GTTTTTCCCCTTCAGCATCA-3′; PTBP3, 5′-CATTCCTGGGGCTAGTGGTA-3′ and 5′-CCATCTGAACCAAGGCATTT-3′; RBM3, 5′-CAGGCACTGGAAGACCACTT-3′ and 5′-CTCTCATGGCAACTGAAGCA-3′; TIA1, 5′-TGCTATTGGGGCAAAGAAAC-3′ and 5′-GCGGTTGCACTCCATAATTT-3′; ZFP36, 5′-TCCACAACCCTAGCGAAGAC-3′ and 5′-GAGAAGGCAGAGGGTGACAG-3′; and ZFP36L1, 5′-GAGGAAAACGGTGCCTGTAA-3′ and 5′-CTCTTCAGCGTTGTGGATGA-3′. For the quantification of mature miRNAs, such as miR-423-3p (MS00004179), miR-744-5p (MS00010549), miR-24-3p (MS00006552), miR-409-3p (MS00006895), and miR-497-5p (MS00004361), the miScript PCR assay kit (#218073, Qiagen, Hilden, Germany) was used according to the manufacturer’s instructions. Data analysis was performed using a comparative CT method in the Bio-Rad software 3.1. The levels of the miRNAs were normalized to U6 small nuclear RNA or U61 small nucleolar RNA (SNORD61). Three experiments were performed in triplicate, and the mean results with standard errors are presented.\n\n## 5.4. miRNA Mimics and Anti-miRNA Oligonucleotides\n\nChemically modified double-stranded RNAs designed to mimic the endogenous mature miR-24-3p and miR-409-3p were purchased from Genolution Pharmaceuticals (Seoul, Republic of Korea). Antisense inhibitor RNAs (anti-miR-24-3p and anti-miR-409-3p) and negative control miRNA were purchased from Bioneer (Daejeon, Republic of Korea). The miRNA mimics and anti-miRNA oligonucleotides were transfected at 5 nM and 25 nM, respectively, using RNAi Max (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s protocol.\n\n## 5.5. RNA Interference\n\nSmall interfering RNA (siRNA) duplexes were synthesized by Genolution Pharmaceuticals (Seoul, Republic of Korea) and Integrated DNA Technologies (Coralville, IA, USA). NCL siRNA (si-NCL): 5′-GGAUAGUUACUGACCGGGA-3′, HDAC1 siRNA (si-HDAC1): 5′-AGUUUCCUUUUUGAGAUACUAUUTT-3′, and HDAC2 siRNA (si-HDAC2): 5′-GAAUUUCUAUUCGAGCAUCAGACAA-3′. Negative control siRNA (Genolution) was used as a control. The cells were transfected with 100 nM si-NCL, 25 nM si-HDAC1, or si-HDAC2 using RNAi Max (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s protocol.\n\n## 5.6. Immunoprecipitation\n\nPASMC lysates were prepared in lysis buffer (20 mM Tris–HCl [pH 7.5], 100 mM KCl, 5 mM MgCl2, and $0.5\\%$ NP-40) containing a protease inhibitor cocktail (#11697498001, Roche, Basel, Switzerland). The lysates were precleared with DynabeadsTM Protein G (#10007D, Invitrogen, Carlsbad, CA, USA) at 4 °C with gentle rotation for 1 h. The precleared lysates were incubated with DynabeadsTM Protein G coated with 2 μg each of rabbit anti-nucleolin antibody (#ab22758, Abcam, Cambridge, UK) or rabbit IgG (#2729, Cell Signaling Technology, Danvers, MA, USA) at 4°C for overnight. For DGCR8 IP and Ago2 IP, 5 μg of mouse anti-DGCR8 antibody (#60084-1-Ig, Proteintech, Rosemont, IL, USA) and mouse anti-Ago2 antibody (#ab57113, Abcam) were used, respectively. A reaction containing mouse IgG (#sc-2025, Santa Cruz Biotechnology, Dallas, TX, USA) served as a negative control. Unbound materials were washed off using NT2 buffer (50 mM Tris–HCl [pH 7.5], 150 mM NaCl, 1 mM MgCl2, and $0.05\\%$ NP-40). All collected protein complexes were eluted with 2X Laemmli sample buffer supplemented with β-mercaptoethanol and boiled. The boiled supernatants and input ($2\\%$) samples were resolved by SDS-PAGE and analyzed by immunoblotting with the anti-NCL antibody (#ab22758), anti-DGCR8 antibody (#60084-1-Ig), or anti-Ago2 antibody (#ab57113).\n\n## 5.7. RNA Immunoprecipitation\n\nPASMC lysates were prepared in lysis buffer (20 mM Tris–HCl (pH 7.5), 100 mM KCl, 5 mM MgCl2, and $0.5\\%$ NP-40) containing 40 U/μL RiboLock RNase Inhibitor (#EO0381, Thermo Fisher Scientific, Waltham, MA, USA) and a protease inhibitor cocktail (#11697498001, Roche, Basel, Switzerland). The lysates were incubated with DynabeadsTM Protein G (#10007D, Invitrogen, Carlsbad, CA, USA) coated with 2 μg each of rabbit anti-NCL antibody (#ab22758, Abcam, Cambridge, UK) or rabbit IgG (#2729, Cell Signaling Technology, Danvers, MA, USA) at 4 °C for 2 h. Unbound materials were washed off using NT2 buffer (50 mM Tris–HCl (pH 7.5), 150mM NaCl, 1 mM MgCl2, and $0.05\\%$ NP-40). The pellet was subsequently incubated with NT2 buffer containing RNase-free Dnase I (1 U/μL) (#EN0521, Thermo Fisher Scientific, Waltham, MA, USA) at 30 °C for 15 min and NT2 buffer containing $0.1\\%$ SDS and 0.1 mg/mL Proteinase K (#25530049, Thermo Fisher Scientific, Waltham, MA, USA) at 55 °C for 15 min. The RNA was extracted using Trizol in the presence of glycoblue (#AM9515, Thermo Fisher Scientific, Waltham, MA, USA) and analyzed by NGS-based small RNA sequencing or qRT-PCR.\n\n## 5.8. NGS-Based Small RNA Sequencing\n\nThe extracted RNA was qualified using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). cDNA libraries were constructed with the NEBNext Multiplex small RNA library prep kit (NEB, Ipswich, MA, USA) using the total RNA from RNA immunoprecipitation, according to the manufacturer’s instructions. Briefly, adapter ligation, reverse transcription, PCR amplification, and purification using a QIAquick PCR Purification Kit (Qiagen) and AMPure XP beads (Beckman Coulter, Brea, CA, USA) were conducted to generate a library product. The yield and size distribution of the small RNA libraries were assessed by high-sensitivity DNA analysis on an Agilent 2100 Bioanalyzer. High-throughput sequences were produced by single-end 75 sequencing using the NextSeq 500 system (Illumina, San Diego, CA, USA). Sequence reads were mapped using the Bowtie 2 software tool to obtain the BAM file (alignment file). A mature miRNA sequence was used as a reference for mapping. Read counts mapped onto the mature miRNA sequence were extracted from the alignment file using bedtools (v2.25.0) and Bioconductor, which uses R (version 3.2.2) statistical programming language (R development Core Team, 2011). The read counts were then used to determine the expression levels of miRNAs. The quantile normalization method was used to compare samples.\n\n## 5.9. Biotinylated miRNA Pull-Down Assay\n\nThe 3′-biotinylated miR-24-3p mimic (bio-miR-24-3p), 3′-biotinylated miR-409-3p mimic (bio-miR-409-3p), 3′-biotinylated miR-24-3p mutant (bio-miR-24-3p mutant), 3′-biotinylated miR-409-3p mutant (bio-miR-409-3p mutant), and 3′-biotinylated control Caenorhabditis elegans miR-67 mimic (bio-cel-miR-67) were synthesized by Integrated DNA Technologies (Coralville, IA, USA). PASMCs were transfected with 150 nM bio-miR-24-3p, bio-miR-409-3p, bio-miR-24-3p mutant, bio-miR-409-3p mutant, or bio-cel-miR-67 mimic using RNAi MAX (Invitrogen, Carlsbad, CA, USA). Twenty-four hours later, the cells were trypsinized and lysed in lysis buffer (20 mM Tris–HCl (pH 7.5), 100 mM KCl, 5 mM MgCl2, and $0.5\\%$ NP-40) containing 40 U/μL RiboLock RNase Inhibitor (#EO0381, Thermo Fisher Scientific, Waltham, MA, USA) and a protease inhibitor cocktail (#11697498001, Roche, Basel, Switzerland) on ice for 20 min and centrifuged at 12,000 rpm for 10 min at 4 °C. The lysates were incubated with Streptavidin Mag Sepharose (#GE28-9857-38, Sigma-Aldrich, St. Louis, MO, USA) at 4 °C for 4 h, and unbound materials were washed off using NT2 buffer. The pull-down sample was boiled in 2X Laemmli sample buffer supplemented with β-mercaptoethanol. The boiled pull-down and input ($1\\%$) samples were resolved by SDS-PAGE and analyzed by immunoblotting with the anti-NCL antibody (#ab22758, Abcam).\n\n## 5.10. NCL Expression Plasmid\n\nThe plasmid GFP-NCL was a gift from Michael Kastan (Addgene plasmid #28176; http://n2t.net/addgene:28176; RRID: Addgene_28176) [61]. PASMCs were transfected with 1 μg of the GFP-NCL or the empty pEGFP-N1 vector (Addgene, Watertown, MA, USA) using the P1 Primary Cell 4D-NucleofectorTM X kit (Lonza, Basel, Switzerland) according to the manufacturer’s protocol.\n\n## 5.11. Immunoblotting\n\nCells were lysed in TNE buffer (50 mM Tris–HCl (pH 7.4), 100 mM NaCl, and 0.1 mM EDTA) and total cell lysates were separated by SDS-PAGE, transferred to PVDF membranes, immunoblotted with antibodies, and visualized using an enhanced chemiluminescence detection system (Bio-Rad Laboratories, Hercules, CA, USA). Antibodies against NCL (#ab22758), HIF1α (#ab2185), and Ago2 (#ab57113) were purchased from Abcam (Cambridge, UK). An anti-β-actin antibody (#sc47778), anti-HDAC1 antibody (#sc81598), anti-HDAC2 antibody (#sc-81599), and anti-DGCR8 antibody (#60084-1-Ig) were purchased from Santa Cruz Biotechnology (Dallas, TX, USA) and Proteintech (Rosemont, IL, USA).\n\n## 5.12. Immunofluorescence Staining\n\nEqual amounts of PASMCs were seeded in chamber well slides and transfected with control mimic, miR-24-3p, miR-409-3p, anti-miR-24-3p, or anti-miR-409-3p. Cells were exposed to normoxia or hypoxia and then fixed in $2\\%$ paraformaldehyde, blocked in $3\\%$ BSA in PBS, and permeabilized in $0.1\\%$ Triton X-100 (Sigma-Aldrich, St. Louis, MO, USA) in PBS. The slides were sequentially probed with rabbit anti-human Ki-67 antibody (#ab16667, Abcam) and goat anti-rabbit IgG (H + L) cross-adsorbed secondary antibody Alexa flour 488 (#A-11008, Thermo Fisher Scientific). Nuclei were stained with Hoechst 33342 (#62249, Thermo Fisher Scientific). The slides were imaged by a Zeiss Axio Imager Z1 microscope (Oberkochen, Germany). At least 2000 cells were counted per condition, and the percentages of Ki-67-positive cells were presented. The results are the mean ± S.E. for triplicate assays.\n\n## 5.13. Cell Counting Assay\n\nEqual amounts of PASMCs were seeded in plates and transfected with negative control siRNA, si-NCL, pEGFP-N1 vector, GFP-NCL, control mimic, miR-24-3p mimic, miR-409-3p mimic, anti-miR-24-3p, or anti-miR-409-3p. The cells were trypsinized and manually counted using a hemocytometer. The total cell numbers were compared and presented as a fold change.\n\n## 5.14. Statistical Analysis\n\nAll experiments were performed with at least three independent repetitions. The results were presented as the mean with standard error. Statistical analyses were performed by an analysis of variance followed by Student’s t-test, multiple t-test, one-way ANOVA, or two-way ANOVA using Prism 8 software (GraphPad Software Inc., San Diego, CA, USA). p-values of <0.05 were considered significant and are indicated with asterisks. *, **, ***, and **** represent p-values less than 0.05, 0.005, 0.0005, and 0.0001, respectively.\n\n## 5.15. Accession Numbers\n\nThe RNA sequencing dataset generated during the current study is available from the corresponding author on reasonable request. The accession numbers for the data reported in this paper are GEO: GSE184972.\n\n## References\n\n1. Majmundar A.J., Wong W.J., Simon M.C.. **Hypoxia-inducible factors and the response to hypoxic stress**. *Mol. Cell* (2010) **40** 294-309. DOI: 10.1016/j.molcel.2010.09.022\n\n2. 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-0.001661794725805521\n]","isConversational":false}}},{"rowIdx":76,"cells":{"text":{"kind":"string","value":"---\ntitle: 'Combination of Muscle Quantity and Quality Is Useful to Assess the Necessity\n of Surveillance after a 5-Year Cancer-Free Period in Patients Who Undergo Radical\n Cystectomy: A Multi-Institutional Retrospective Study'\nauthors:\n- Naoki Fujita\n- Masaki Momota\n- Hirotaka Horiguchi\n- Itsuto Hamano\n- Jotaro Mikami\n- Shingo Hatakeyama\n- Hiroyuki Ito\n- Takahiro Yoneyama\n- Yasuhiro Hashimoto\n- Shoji Nishimura\n- Kazuaki Yoshikawa\n- Chikara Ohyama\njournal: Cancers\nyear: 2023\npmcid: PMC10000682\ndoi: 10.3390/cancers15051489\nlicense: CC BY 4.0\n---\n\n# Combination of Muscle Quantity and Quality Is Useful to Assess the Necessity of Surveillance after a 5-Year Cancer-Free Period in Patients Who Undergo Radical Cystectomy: A Multi-Institutional Retrospective Study\n\n## Abstract\n\n### Simple Summary\n\nAlthough continuous surveillance after a 5-year cancer-free period in patients with bladder cancer who undergo curative surgery is recommended, optimal candidates for continuous surveillance remain unclear. Sarcopenia is associated with an unfavorable prognosis in bladder cancer. We aimed to investigate the impact of low muscle quantity and quality (defined as severe sarcopenia) on prognosis after a 5-year cancer-free period in patients who underwent radical cystectomy. Our results showed that the 10-year recurrence rate after a 5-year cancer-free period was low (approximately $5\\%$), and severe sarcopenia was not associated with increased recurrence risk. Moreover, severe sarcopenia was selected as a significant risk factor for mortality unrelated to bladder cancer. Taken together, patients with severe sarcopenia might not need continuous surveillance after a 5-year cancer-free period, considering high mortality unrelated to bladder cancer.\n\n### Abstract\n\nBackground: *Although continuous* surveillance after a 5-year cancer-free period in patients with bladder cancer (BC) who undergo radical cystectomy (RC) is recommended, optimal candidates for continuous surveillance remain unclear. Sarcopenia is associated with unfavorable prognosis in various malignancies. We aimed to investigate the impact of low muscle quantity and quality (defined as severe sarcopenia) on prognosis after a 5-year cancer-free period in patients who underwent RC. Methods: We conducted a multi-institutional retrospective study assessing 166 patients who underwent RC and had five years or more of follow-up periods after a 5-year cancer-free period. Muscle quantity and quality were evaluated using the psoas muscle index (PMI) and intramuscular adipose tissue content (IMAC) using computed tomography images five years after RC. Patients with lower PMI and higher IMAC values than the cut-off values were diagnosed with severe sarcopenia. Univariable analyses were performed to assess the impact of severe sarcopenia on recurrence, adjusting for the competing risk of death using the Fine-Gray competing risk regression model. Moreover, the impact of severe sarcopenia on non-cancer-specific survival was evaluated using univariable and multivariable analyses. Results: The median age and follow-up period after the 5-year cancer-free period were 73 years and 94 months, respectively. Of 166 patients, 32 were diagnosed with severe sarcopenia. The 10-year RFS rate was $94.4\\%$. In the Fine-Gray competing risk regression model, severe sarcopenia did not show a significant higher probability of recurrence, with an adjusted subdistribution hazard ratio of 0.525 ($$p \\leq 0.540$$), whereas severe sarcopenia was significantly associated with non-cancer-specific survival (hazard ratio 1.909, $$p \\leq 0.047$$). These results indicate that patients with severe sarcopenia might not need continuous surveillance after a 5-year cancer-free period, considering the high non-cancer-specific mortality.\n\n## 1. Introduction\n\nBladder cancer (BC) is the tenth most common cancer worldwide [1]. Radical cystectomy (RC) with pelvic lymph node dissection and urinary diversion remains the gold standard treatment for muscle-invasive and high-risk non-muscle-invasive BC [2].\n\nContinuous surveillance after a 5-year cancer-free period in patients who undergo RC is recommended by the European Association of Urology guidelines [2]. However, late recurrence after RC has been reported to be infrequent [3,4]. Moreover, optimal candidates for continuous surveillance remain unclear. Identifying these candidates may be helpful in the development of individualized surveillance protocols.\n\nSarcopenia is represented by two dysregulation patterns of body composition: loss of skeletal muscle quantity (myopenia) and quality (myosteatosis) [5]. Although sarcopenia has been reported to be associated with unfavorable prognosis in patients who underwent surgical treatment for various malignancies [6,7], the prognostic value of sarcopenia in patients who undergo RC remains controversial [8,9,10,11]. Moreover, no study has investigated its impact on oncological outcomes and non-cancer-specific mortality after a 5-year cancer-free period. Considering the close relationship between sarcopenia and high mortality caused by non-malignant diseases [12,13,14], we hypothesized that patients with low muscle quantity and quality (defined here as severe sarcopenia) might have a high non-cancer-specific mortality; therefore, they might not need continuous surveillance after a 5-year cancer-free period.\n\nThe aim of the present study was to evaluate the impact of low muscle quantity and quality on recurrence-free survival (RFS) and non-cancer-specific survival after a 5-year cancer-free period in patients with BC who underwent RC.\n\n## 2.1. Ethics Statement\n\nThis study followed the principles of the Declaration of Helsinki and was approved by the ethics committees of the Hirosaki University Graduate School of Medicine (authorization number: 2019-099-1) and all hospitals included in this study. Written consent was not obtained due to the public disclosure of the study information (opt-out approach).\n\n## 2.2. Patient Selection\n\nTo include patients who had sufficient follow-up periods (five years or more) after a 5-year cancer-free period, we retrospectively evaluated 431 patients with BC who underwent RC between October 1995 and December 2012 at one academic center and five general hospitals. We excluded 193 patients who experienced local recurrence and/or distant metastasis, died from any cause, or were lost to follow-up within five years after RC and 72 patients who had no information on their heights or digital computed tomography (CT) scans available for body composition analysis. Ultimately, 166 patients were included in this study (Figure 1).\n\n## 2.3. Evaluation of Variables\n\nThe following variables were analyzed: age, sex, Eastern Cooperative Oncology Group performance status (ECOG PS), hypertension (HTN), diabetes mellitus, history of cardiovascular disease (CVD), chronic kidney disease (CKD), clinical stage, neoadjuvant chemotherapy (NAC), urinary diversion, pathological outcomes, and adjuvant chemotherapy. Age and comorbidities at five years after RC were used in the analyses. Renal function was evaluated by estimated glomerular filtration rate (eGFR) using a modified version of the abbreviated Modification of Diet in Renal Disease *Study formula* for Japanese patients [15] and CKD was defined as eGFR < 60 mL/min/1.73 m2. Tumor stage was assigned according to the 2009 TNM classification system recorded by the Union of International Cancer Control. Tumor grade was classified according to the 1973 World Health Organization classification system.\n\n## 2.4. NAC and Adjuvant Chemotherapy\n\nSince September 2004, patients have received two–four courses of NAC, composed of a platinum-based combination regimen of gemcitabine plus cisplatin, gemcitabine plus carboplatin, or methotrexate, vinblastine, adriamycin, and cisplatin. Regimens were selected based on guidelines regarding eligibility for the proper use of cisplatin, overall patient status, and the clinician’s discretion. The cycles were repeated every 21 days.\n\nAdjuvant chemotherapy was not routinely administered. Indications for adjuvant chemotherapy included pT4, pathological lymph node involvement, grade 3, lymphovascular invasion, or positive surgical margins in patients who were not treated with NAC. Patients were selected for adjuvant chemotherapy at the clinician’s discretion. We administered one–three courses of adjuvant chemotherapy to patients with a feasible postoperative status for toxic chemotherapy. Adjuvant chemotherapy comprises a platinum-based combination regimen of gemcitabine plus cisplatin, gemcitabine plus carboplatin, or methotrexate, vinblastine, doxorubicin, and cisplatin.\n\n## 2.5. Surgical Procedures\n\nRC was performed using a previously described basic technique [16]. Briefly, the patients underwent RC, standard pelvic lymph node dissection, and urinary diversion (orthotopic ileal neobladder construction, ileal conduit diversion, or cutaneous ureterostomy).\n\n## 2.6. Follow-Up Schedule\n\nThe follow-up schedule after the 5-year cancer-free period comprised annual urine analysis, urine cytology, blood chemistry, and lung, abdominal, and pelvic CT scans.\n\n## 2.7. Evaluation of Muscle Quantity and Quality\n\nMuscle quantity was evaluated using the psoas muscle index (PMI). We measured the cross-sectional areas of the right and left psoas muscles on plain CT images at the level of the third lumbar vertebra (L3) five years after RC. The muscles were identified based on their anatomical features, and the bilateral psoas muscle areas were evaluated using manual tracing. The PMI was calculated by normalizing these cross-sectional areas to their height (cm2/m2) [17].\n\nMuscle quality was evaluated based on intramuscular adipose tissue content (IMAC) using L3 level plain CT images five years after RC. We precisely traced the multifidus muscle and subcutaneous fat to measure their CT values (Hounsfield units). IMAC was calculated by dividing the CT value of the multifidus muscles by that of the subcutaneous fat. A higher IMAC indicates a greater amount of adipose tissue in the skeletal muscles and, therefore, lower skeletal muscle quality [18].\n\nSince the ranges of PMI and IMAC in men and women are quite different [19,20], and their optimal cut-off values for mortality in patients with BC have been to be established, their optimal cut-off values for non-cancer-specific mortality were calculated separately for men and women using receiver operating characteristic (ROC) curves. In the present study, we defined patients with both low muscle quantity and quality as patients with severe sarcopenia. Patients were divided into two groups: those with lower PMI and higher IMAC values than their cut-off values (severe sarcopenia group) and those with higher PMI and/or lower IMAC values (control group) (Figure 1).\n\n## 2.8. Statistical Analysis\n\nSPSS version 24.0 (SPSS Corp., Armonk, NY, USA), R 4.0.2 (The R Foundation for Statistical Computing, Vienna, Austria), and GraphPad Prism 5.03 (GraphPad Software, San Diego, CA, USA) were used for statistical analyses. Quantitative variables are expressed as medians with interquartile ranges. Differences in quantitative variables between the two groups were analyzed using the Mann–Whitney U test. Categorical variables were compared using Fisher’s exact test or the chi-squared test. RFS, overall survival (OS), and non-cancer-specific survival were evaluated using the Kaplan–Meier method and compared using the log-rank test. Moreover, the cumulative incidences of recurrence was estimated and death before recurrence was defined as a competing risk. The Gray test was performed to compare cumulative incidences between the control and severe sarcopenia groups. Subsequent univariable analyses were performed to assess the impact of severe sarcopenia on recurrence, adjusting for the competing risk of death using the Fine-Gray subdistribution hazards model. Univariable Cox proportional hazards regression analyses were performed to identify the significant factors associated with RFS. Univariable and multivariable Cox proportional hazards regression analyses were performed to evaluate the impact of severe sarcopenia on non-cancer-specific survival. These outcomes were calculated from five years after RC to the date of the first event or last follow-up. Recurrence was defined as local pelvic recurrence, remnant urothelial recurrence, or distant metastasis. Non-cancer-specific mortality was defined as death unrelated to BC. Statistical significance was set at $p \\leq 0.05.$\n\n## 3.1. Patients’ Backgrounds\n\nThe median age and follow-up period after the 5-year cancer-free period were 73 years and 94 months, respectively. Of the 166 patients, 85 ($51\\%$) and 19 ($11\\%$) received NAC and adjuvant chemotherapy, respectively. The patients’ backgrounds are summarized in Table 1.\n\n## 3.2. Evaluation of Muscle Quantity and Quality\n\nThe median PMI values in men and women were 6.18 cm2/m2 and 4.65 cm2/m2, respectively. The optimal cut-off values of PMI for non-cancer-specific mortality in men and women was 5.28 cm2/m2 and 6.35 cm2/m2, respectively. Of the 166 patients, 94 ($57\\%$) and 72 ($43\\%$) had PMI values higher and lower than the cut-off values, respectively.\n\nThe median IMAC values in men and women were −0.46 and −0.33, respectively. The optimal cut-off values of IMAC for non-cancer-specific mortality in men and women was −0.49 and −0.04, respectively. Of the 166 patients, 84 ($51\\%$) and 82 ($49\\%$) had IMAC values lower and higher than the cut-off values, respectively.\n\nPatients were divided into two groups: those with lower PMI and higher IMAC values (severe sarcopenia group, $$n = 32$$) and those with higher PMI and/or lower IMAC values (control group, $$n = 134$$) (Figure 1). No significant differences in patients’ background were observed between the two groups, except for age and ECOG PS (Table 1).\n\n## 3.3. BC Recurrence\n\nBy the end of the follow-up period after the 5-year cancer-free period, nine ($5.4\\%$) patients experienced BC recurrence, including recurrence in the upper urinary tract ($$n = 3$$), lymph nodes ($$n = 2$$), urethra ($$n = 1$$), local pelvis ($$n = 1$$), neobladder ($$n = 1$$), and distant metastasis ($$n = 1$$). Of the nine patients who experienced BC recurrence, eight ($6.0\\%$) and one ($3.1\\%$) patients were in the control and severe sarcopenia groups, respectively. The 5-year and 10-year RFS rates were $95.2\\%$ and $94.4\\%$, respectively (Figure 2A). Almost all recurrence detection rates ([number of patients with recurrence/number of patients with surveillance during a certain period] × 100) were under $1\\%$ throughout the entire follow-up period (Figure 2B).\n\nIn the Gray test, the cumulative incidence rate of recurrence was not significantly different between the control and severe sarcopenia groups (Figure 2C, $$p \\leq 0.528$$). In the univariable analyses, none of the patient factors, clinical stage, perioperative chemotherapy, or pathological outcomes were significantly associated with shorter RFS (Table S1). Similarly, none of lower PMI, higher IMAC, and severe sarcopenia showed significant higher probabilities of recurrence (Figure 2D; subdistribution hazard ratio [SHR] 0.664, $95\\%$ confidence interval [CI] 0.168–2.630, $$p \\leq 0.560$$; SHR 0.814, $95\\%$ CI 0.220–3.010, $$p \\leq 0.760$$; SHR 0.525, $95\\%$ CI 0.067–4.140, $$p \\leq 0.540$$; respectively).\n\n## 3.4. OS and Non-Cancer-Specific Survival\n\nBy the 5-year, 10-year, and end of the follow-up period after the 5-year cancer-free period, 29 ($18\\%$), 51 ($31\\%$), and 64 ($39\\%$) patients died from any cause, respectively. The main causes of death during the entire follow-up period were other malignancies ($25\\%$) and CVD ($22\\%$), followed by infectious diseases ($14\\%$) (Figure 3). Of the 64 patients who died from any cause, six ($9.4\\%$) died of BC (Figure 3).\n\nThe OS in patients with lower PMI values was significantly shorter than that in patients with higher PMI values (Figure 4A, $$p \\leq 0.003$$). The OS in patients with higher IMAC values was significantly shorter than that in patients with lower IMAC values (Figure 4B, $$p \\leq 0.025$$). The OS in the severe sarcopenia group was significantly shorter than that in the control group (Figure 4C, $p \\leq 0.001$).\n\nBy the end of the follow-up period after the 5-year cancer-free period, 37 ($28\\%$) and 21 ($66\\%$) patients in the control and severe sarcopenia group died from non-cancer-specific cause, respectively. The 5-year and 10-year non-cancer-specific mortality rates in the severe sarcopenia group were significantly higher than those in the control group (Figure 5A,B; $38\\%$ vs. $10\\%$, $p \\leq 0.001$; $56\\%$ vs. $21\\%$, $p \\leq 0.001$; respectively). The non-cancer-specific survival in patients with lower PMI values was significantly shorter than that in patients with higher PMI values (Figure 5C, $$p \\leq 0.001$$). The non-cancer-specific survival in patients with higher IMAC values was significantly shorter than that in patients with lower IMAC values (Figure 5D, $$p \\leq 0.010$$). The non-cancer-specific survival in the severe sarcopenia group was significantly shorter than that in the control group (Figure 5E, $p \\leq 0.001$). In univariable analyses, age, EGOS PS, HTN, CKD, lower PMI, higher IMAC, and severe sarcopenia were significantly associated with shorter non-cancer-specific survival (Table 2). In multivariable analyses, lower PMI and higher IMAC were not significantly associated with shorter non-cancer-specific survival (HR 1.267, $95\\%$ CI 0.696–2.308, $$p \\leq 0.439$$; HR 1.377, $95\\%$ CI 0.688–2.757, $$p \\leq 0.367$$; respectively), whereas severe sarcopenia was significantly associated with shorter non-cancer-specific survival (HR 1.909, $95\\%$ CI 1.007–3.619, $$p \\leq 0.047$$) (Table 3). Age and CKD were also associated with shorter non-cancer-specific survival (Table 3).\n\n## 4. Discussion\n\nTo the best of our knowledge, this is the first study to evaluate the impact of low muscle quantity and quality (defined here as severe sarcopenia) on oncological outcomes and non-cancer-specific mortality after a 5-year cancer-free period in patients with BC who underwent RC. The results of the present study showed that the 10-year recurrence rate after the 5-year cancer-free period was low (approximately $5\\%$), and severe sarcopenia was not associated with increased recurrence risk. In contrast, severe sarcopenia was identified as a significant risk factor for non-cancer-specific mortality. These results suggest that patients with severe sarcopenia may not need continuous surveillance after a 5-year cancer-free period. Although a prospective validation study with a larger sample size is warranted, these results might be helpful for clinicians to optimize individualized surveillance protocols after a 5-year cancer-free period.\n\nIn the present study, neither severe sarcopenia nor low muscle quantity or quality was associated with BC recurrence after a 5-year cancer-free period. Although several studies have investigated the impact of preoperative sarcopenia on oncological outcomes in patients who underwent RC [8,9,10,11], to our knowledge, there is no available evidence about its impact on oncological outcomes after a 5-year cancer-free period in both BC and other malignancies. However, our results are consistent with those of previous studies that have focused on preoperative sarcopenia. Smith et al. revealed that sarcopenia evaluated by total psoas area was not associated with worse 2-year survival in 200 patients with BC who underwent RC [10]. Likewise, Wang et al. demonstrated no significant association between sarcopenia and shorter disease-free survival in 112 patients with BC who underwent RC [21]. In contrast, Ornaghi et al. reported opposite results. They conducted a systematic literature review to investigate the impact of sarcopenia on long-term mortality rates in patients with BC treated with RC and revealed that sarcopenia was significantly associated with unfavorable 5-year cancer-specific survival (CSS) (HR 1.73, $p \\leq 0.05$) [22]. Similarly, a systematic review and meta-analysis conducted by Hu et al. demonstrated that sarcopenia was associated with poor CSS in patients with BC who underwent RC (HR 1.73, $p \\leq 0.001$) [23]. Although we do not have a clear answer about our negative results, these conflicting results might be caused by the varied definitions of sarcopenia between studies due to the lack of international consensus. Because the lack of available evidence and several limitations in the present study, especially the small number of recurrence events, prevent us from making definitive conclusions, further prospective studies with an appropriate sample size and recurrence events are warranted.\n\nIn the present study, severe sarcopenia (low muscle quantity and quality) was associated with increased non-cancer-specific mortality after a 5-year cancer-free period, whereas low muscle quantity or quality alone had marginal effects. Although many studies have investigated the impact of both low muscle quantity and quality on prognosis in a single study of several malignant and non-malignant diseases [5,24,25,26], the combined effects of these parameters have rarely been reported. Hopkins et al. assessed 968 patients with colorectal cancer who underwent curative resection and demonstrated that both low muscle quantity and low muscle radiodensity were independently predictive of worse OS (HR 1.45 and HR 1.53, respectively), but the presence of both increased the HR for OS (HR 2.23) [27]. Similarly, Caan et al. assessed 1628 female patients with colorectal cancer who underwent surgical resection and revealed that patients with both low muscle quantity and high total adipose tissue area had a higher risk of overall mortality (HR 1.64) than patients with low muscle quantity or high total adipose tissue area alone (HR 1.38 and HR 1.30, respectively) [28]. Although the included patient populations and evaluated outcomes in these studies were different from those in the present study, these results indicate the potential additive effects of low muscle quantity and quality on prognosis in patients with malignancies.\n\nIt is unclear how other diseases contribute to the mortality of BC survivors after RC. In the present study, only six ($3.6\\%$) patients died from BC after the 5-year cancer-free period, whereas 58 ($34.9\\%$) died from other causes (Figure 3). Kong et al. reported similar results. They assessed 81,843 patients with BC who survived 5–10 years after treatment ($93.9\\%$ were treated with surgery) and demonstrated that only $6.9\\%$ of them died from BC while $47.9\\%$ died from other causes, including CVD ($11.0\\%$), pulmonary disease ($7.7\\%$), and other cancers ($3.0\\%$) [29]. Moreover, late recurrence after RC has been reported to be infrequent [3,4]. These results indicate that the contribution of other diseases to mortality after a 5-year cancer-free period is much greater than that of BC. The association between sarcopenia and increased mortality caused by CVD and infectious diseases has been reported [12,13,14,30]. Moreover, our results showed a relationship between sarcopenia and increased non-cancer-specific mortality after a 5-year cancer-free period. Taken together, patients with sarcopenia might not need continuous surveillance after a 5-year cancer-free period in patients who undergo RC.\n\nThe present study had several limitations. First, we were unable to control for selection bias and other unquantifiable confounders in retrospective studies. Moreover, patients without available CT scans for muscle quantity and quality measurements were excluded, which might have caused a selection bias. In addition, skeletal muscle loss is associated with aging and patients in the severe sarcopenia group were significantly older than patients in the control group in the present study (Table 1). Thus, it might have caused an association bias regardless of the adjustment for age in the multivariable analyses. Second, a relatively small number of patients were enrolled, and the number of recurrence events was also small. Moreover, the small number of cancer-specific deaths prevented us from evaluating cancer-specific survival. Third, sarcopenia was assessed by manual tracing, which may have been subject to human error. Finally, given its retrospective nature, we had no information on other frailty metrics, such as walking speed, grip strength, and nutritional status.\n\n## 5. Conclusions\n\nRecurrence after a 5-year cancer-free period in patients with BC who underwent RC was infrequent. 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0.008810305036604404\n]","isConversational":false}}},{"rowIdx":77,"cells":{"text":{"kind":"string","value":"---\ntitle: Clustering of Health and Oral Health-Compromising Behaviours in Army Personnel\n in Central Peninsular Malaysia\nauthors:\n- Ahmad Asyraf Azil\n- Zamros Yuzadi Mohd Yusof\n- Jamaludin Marhazlinda\njournal: Healthcare\nyear: 2023\npmcid: PMC10000684\ndoi: 10.3390/healthcare11050640\nlicense: CC BY 4.0\n---\n\n# Clustering of Health and Oral Health-Compromising Behaviours in Army Personnel in Central Peninsular Malaysia\n\n## Abstract\n\nHealth- and oral health-compromising behaviours (HOHCBs) impact the health readiness of military personnel, resulting in decreased fitness performance, thus affecting combat readiness. This study aimed to identify the clustering patterns and number of HOHCBs in army personnel in Central Peninsular Malaysia. Thus, a cross-sectional study using a multistage sampling technique and a validated 42-item online questionnaire was conducted to assess ten health (medical screening, physical activity, sedentary lifestyle, smoking status, alcohol consumption, substance abuse, aggressive behaviours, sleep, and road safety habits) and five oral health behaviour domains (tooth brushing, fluoridated toothpaste use, flossing, dental visits, and bruxism). Each HOHCB was dichotomised into healthy and health-compromising behaviour and analysed using hierarchical agglomerative cluster analysis (HACA). With the majority being males ($92.5\\%$), of other ranks ($96.8\\%$), and healthy ($83.9\\%$), 2435 army members of a mean age of 30.3 years (SD = 5.9) participated, with a response rate of $100\\%$. HACA identified two clustering patterns: (i) ‘high-risk behaviours’ (30 HOHCBs) and (ii) ‘most common risk behaviours’ (12 HOHCBs) with a mean clustering number of 14.1 (SD = 4.1). In conclusion, army personnel in Central Peninsular Malaysia displayed 2 broad HOHCB clustering patterns, ‘high-risk’ and ‘most common risk’, with an average of 14 HOHCB clusters per person.\n\n## 1. Introduction\n\nThe health readiness of military personnel is one of the core sub-factors that directly contribute to ‘combat readiness’, which refers to the military’s preparedness and ability to perform during military operations [1,2]. One of the primary concerns and critical factors that may significantly impact the health readiness of military personnel is health-compromising behaviours [3,4].\n\nHealth-compromising behaviours, also referred to as health risk behaviours, are defined as detrimental actions that increase the likelihood of illness or impede recovery [5]. For example, tobacco use, excessive alcohol consumption, physical inactivity, and an unhealthy diet are health-compromising behaviours known to be significant risk factors for cardiovascular diseases, chronic respiratory diseases, cancer, and diabetes. These non-communicable diseases (NCDs), also known as chronic diseases, have become global health problems, as they account for increased health morbidity and mortality worldwide [6]. When coupled with poor oral hygiene practices, these health-compromising behaviours are also known as oral health-compromising behaviours. Evidence has shown that both health- and oral health-compromising behaviours (HOHCBs) negatively affect oral health and overall health, increasing the risk of NCDs [7,8]. More importantly, NCDs such as diabetes mellitus, cardiovascular diseases, chronic respiratory diseases, obesity, and oral diseases (e.g., dental caries, periodontal disease, and oral cancer) directly or indirectly share several behavioural risk factors and intermediary determinants [9,10].\n\nThese risk factors may not occur in isolation; instead, they commonly co-occur or cluster together [11,12]. For example, a systematic review only focusing on United Kingdom studies reported strong evidence of behavioural clustering of alcohol consumption, smoking, and unhealthy diet in general adult populations [11]. In Spain, three cluster groups, namely, ‘cluster 1: unhealthy lifestyle with moderate risk’, ‘cluster 2: unhealthy lifestyles with high-risk’, and ‘cluster 3: healthy lifestyles with low risk’, comprising seven health-related lifestyles (i.e., smoking, alcohol consumption, drug abuse, physical activity, sedentary habits, dietary habits, and diet quality) were identified in adults [13]. Meanwhile, a study conducted in military personnel assessing the clustering of lifestyle factors in the Hungarian Defence Forces discovered sixteen distinct profiles, including eating habits, smoking status, daily physical activity, sports habits, mental toughness, psychosomatic symptoms, and sleep apnoea symptoms [14]. Other studies related to the clustering of HOHCBs were mainly conducted in the adolescent population [12,15,16,17,18]. For example, a study performed in Saudi Arabian adolescents found two clusters of HOHCBs, namely, non-adherence to preventive behaviours and the undertaking of risk behaviours, clustering among nine health behaviours and three oral health behaviours (i.e., dietary intake, physical activity, sedentary behaviour, smoking status, alcohol consumption, drug use, physical fighting, bullying, use of electronic media communication, frequency of tooth brushing, use of fluoridated toothpaste, and flossing behaviour) [15,16]. Similarly, a study performed in Brazilian adolescents found that the first cluster reflected a combination of the lack of adherence to preventive behaviours and the undertaking of risky conduct, and the second cluster reflected an unhealthy lifestyle [17].\n\nMost importantly, HOHCBs may ultimately impact combat readiness, particularly the health readiness of military personnel. This may result in decreased performance and fitness, affecting military personnel’s physical and mental health, which may later contribute to non-combat injuries (e.g., NCDs) and combat casualties [19,20]. Therefore, determining whether HOHCBs cluster together in military personnel could help to further understand and facilitate prevention, which could benefit combat readiness. In addition, it helps identify the military populations at the greatest risk to plan targeted health promotion and intervention programmes. These initiatives should focus on multiple behaviours, which promises a more significant impact on public health than conventional interventions focusing on a single behaviour, for example, through the common risk factor approach [10]. As such, further evidence, insights, and understanding regarding the occurrence of HOHCBs and how these may cluster and influence the health readiness of army personnel is of paramount importance and very valuable.\n\nTo our knowledge, no local study has been conducted on the clustering of HOHCBs in Malaysian adults, and limited studies have been performed in military personnel. The only research study on the clustering of HOHCBs in Malaysia focused on Malaysian adolescents [21]. In addition, previous studies in this area focused on a limited number of behaviours, such as dietary habits, smoking, alcohol consumption, and physical activity. This research gap can limit the understanding of how the diverse health and oral health behaviours are connected and may influence the military population’s health readiness. Therefore, the objective of this study was to identify the clustering patterns and to assess the clustering number of HOHCBs in army personnel in Central Peninsular Malaysia.\n\n## 2.1. Study Design and Sample\n\nA cross-sectional study was conducted in army personnel in an army division of Central Peninsular Malaysia. Using the G*Power 3.0.10 programme, with the power of the study set to $80\\%$, the significance level of 0.050, the dropout rate of $10\\%$, and the design effect of 1.2 [16,21], the sample size required for this study was found to be 1768. Thus, the proposed minimum sample size for the study was 1800.\n\nA multistage sampling method involving stratified, proportionate, and simple random sampling was employed in different stages. First, the army division, which was divided into four main army troop/brigade groups, was further stratified into four subgroups: Headquarters, Combat Elements, Combat Support Elements, and Services Support Elements. Subsequently, the required sample was divided proportionately according to the ratio of army personnel between each group and subgroup. Each subgroup’s randomly chosen army units were further stratified into officer and other rank groups. After each unit gathered its army personnel, those who fulfilled the inclusion and exclusion criteria within each rank group were invited as the research respondents. The inclusion criteria were all regular (professional) army personnel in service. Those on the resettlement list and outside the unit, training/studying in military/non-military institutions, on active duty, on long vacation, and on sick leave during the data collection period, along with Regular Reserves and Volunteer Force army personnel, were excluded. Based on the ratio, the number of respondents who participate should achieve the minimum number required by the researcher. In this study, all randomly selected army units satisfied the number of respondents needed; thus, the selection of other units was unnecessary.\n\n## 2.2. Study Instrument\n\nThe validated online self-administered questionnaire (Google Forms) used in this study was developed and customised based on several existing validated questionnaires used in eleven studies (345 items) measuring health and oral health behaviours [21,22,23,24,25,26,27,28,29,30,31]. Only relevant questions were customised to address the objectives of the present study. The content was validated by the expert committee on the HOHCB questionnaire for Malaysian military personnel. Following a thorough review of the literature and several discussions among members of the expert committee, ten health behaviour domains (‘medical visit’, ‘physical activity’, ‘sedentary lifestyle’, ‘dietary intake’, ‘smoking’, ‘alcohol consumption’, ‘drug and substance abuse’, ‘sleep’, ‘aggressive behaviour’, and ‘road safety behaviour’) and five oral health behaviours (‘toothbrushing’, ‘fluoridated toothpaste’, ‘flossing’, ‘dental visit’, and ‘bruxism’) were identified to be of great importance to military personnel and Malaysian Armed Forces (MAF). Therefore, the HOHCB items were either adopted fully, developed as new items, or modified to fit the military circumstances by rewording, combining, and excluding irrelevant items.\n\nThe questionnaire was pre-tested on 20 army members to assess its face validity, including whether they understood the purpose of the questionnaire, its instructions, the meaning of the items, and the adequacy of the response options, as well as whether they had other related comments. A pilot study involving 248 military members assessing the validity and reliability of the questionnaire revealed a Cronbach’s alpha of acceptable reliability (α = 0.800), with the α-value of all items ranging from 0.300 to 0.980. The factor analysis revealed 16 factors with acceptable Kaiser–Meyer–Olkin (KMO) sampling adequacy values (KMO = 0.740) and factor loadings ranging from 0.303 to 0.968. The questionnaire redistributed to a group of respondents ($$n = 20$$) after ten days for test–retest reliability assessment reported a good weighted kappa value for health and oral health behaviours ($K = 0.72$, $p \\leq 0.010$) and excellent intraclass correlation coefficient (ICC = 0.900, $95\\%$ CI (0.880–0.910), $p \\leq 0.010$).\n\nAfter considering the pre-test and pilot study findings and items essential from military and public health viewpoints, the expert committee agreed on a total of 52 health behaviour items and 9 oral health behaviour items in the final questionnaire. Regarding the sociodemographic background, data included military profile (rank, type of service, and years of service) and general profile (date of birth, gender, ethnicity, marital status, number of children, education level, and medical history), and the respondents’ socioeconomic status was assessed based on their family monthly income [32].\n\nFinally, the self-administered questionnaire comprised ten health-compromising behaviours and five oral health-compromising behaviours. Several items were grouped under the same behaviour; thus, for determining the clustering of HOHCBs, the 42 most relevant items were selected to represent the domains of HOHCBs. Details of each domain are explained in the subsequent sub-sections.\n\n### 33 Selected Items)\n\nThe respondents were asked about the frequency of visiting a medical doctor or medical staff for health check-ups or screenings. The response options included ‘12 months or less’, ‘13 months to two years’, and ‘more than two years. Those with more than twelve-month intervals between medical visits were considered to engage in health-compromising behaviour [33].\n\nSix questions elicited the respondents’ frequency of performing three forms of physical activity in the past seven days: (i) vigorous physical activity, (ii) moderate physical activity, and (iii) walking activity. The response was assessed by day (from zero to seven days) and duration (minute).\n\nThe metabolic equivalent task (MET) minutes per week (MET-min week−1) were calculated as minutes of [activity/day] × [days per week] × [MET level] [34]. Respondents who had either (i) 75 min of vigorous physical activity, (ii) 150 min of moderate physical activity, or (iii) 600 min of MET of a combination of vigorous, moderate, and/or walking activities in the past seven days were considered to engage in healthy behaviour [28,35].\n\nThe respondents were prompted to recall the amount of time (hours) that they spent sitting or lying at work (e.g., using a computer), at home (e.g., watching television), during free time (e.g., chatting with friends), and while travelling (e.g., commuting to and from work), and the amount of screen time (hours) spent in both work and daily personal situations. The response options for both questions were ‘one hour or less’, ‘more than one hour to two hours’, ‘more than two hours to four hours’, ‘more than four hours to six hours’, ‘more than six hours to eight hours’, and ‘more than eight hours’.\n\nA sedentary lifestyle refers to individuals who spend more than eight hours per day sitting and lying [36,37,38], as well as more than two hours of screen time per day using electronic devices. Those with a sedentary lifestyle are considered to engage in health-compromising behaviour [39].\n\nRespondents were asked if they had smoked (i) a cigarette and (ii) other forms of tobacco products (i.e., electronic cigarette, shisha/hookah, snuff/chew tobacco, cigar, or other) in the past 30 days and the (iii) frequency of being exposed to tobacco product smoke in the previous week. The respondent who smoked any tobacco products on one or more days in the past 30 days was considered a ‘current smoker’ and thus to engage in health-compromising behaviour, while those who had been exposed to any tobacco product smoke for at least one day were considered to engage in health-compromising behaviour.\n\nThe question regarded the frequency of drinking at least one alcoholic beverage in the past 30 days. The respondent was considered a ‘current drinker’ if they had consumed alcoholic drinks on one or more days in the past 30 days, and they were categorised as displaying health-compromising behaviour [28].\n\nAny illegal use of substances such as opioids (e.g., marijuana, heroin, and morphine), amphetamine or methamphetamine (e.g., ecstasy, syabu, ice, and yaba pills), kratom (ketum), and inhalants (e.g., glue and petrol) was defined as drug and substance abuse [28]. It also included the abuse and misuse of prescription medicines, such as painkillers, cough syrup, or sleeping pills [27].\n\nThe respondents were asked whether they had been using any forms of drugs or engaged in substance abuse. The response options ranged between ‘Never’, ‘Yes, for the past 30 days’, and ‘Yes, in the entire life but not the past 30 days’. Respondents who had abused drugs or substances at least once in the past 30 days were categorised as current drug users practising health-compromising behaviour [28].\n\nThe respondents were asked about their sleeping hours (i) during the working week and (ii) on weekends or holidays, as well as the amount of time that they required to feel refreshed and to function normally (ranging from ‘four hours or less’ to ‘more than eight hours’), and (iii) the perceived quality of sleep in the past seven days (‘excellent’, ‘good’, ‘moderate’, and ‘poor’). Respondents whose daily sleeping hours were equal to or greater than their perceived hours to feel refreshed and function well engaged in healthy behaviour, while others were deemed to engage in health-compromising behaviour [27].\n\nAggressive behaviours included yelling or shouting, kicking or smashing objects, threatening with physical violence, and physically fighting or hitting an individual in the past 30 days. The choices of response ranged from ‘none’ to ‘five times or more’. Respondents who had been involved in any form of aggressive behaviour for one or more days in the past 30 days were considered to engage in health-compromising behaviour [27].\n\nRespondents were asked about their frequency of wearing (i) seat belt and (ii) helmet, as well as (iii) texting and (iv) calling others while driving or riding a vehicle in the past seven days. The questions on the seat belt and helmet concerned the driver/rider and passenger. The response options were ‘always’, ‘most of the time’, ‘seldom’, ‘never’, and ‘I did not drive or ride a vehicle in the past seven days’. Respondents who claimed that they ‘always’ wore a seatbelt and helmet and ‘never’ texted or called while driving or riding a vehicle in the past seven days were determined as practising healthy behaviour, and vice versa [27].\n\n### 9 Items)\n\nRespondents were asked about the frequency of brushing their teeth and the time spent tooth brushing daily. Answer options ranged from ‘rarely’, ‘less than three times a week’, ‘three to four times a week’, ‘once a day’, and ‘twice a day or more’, and ‘after waking up from sleep’, ‘before breakfast’, ‘after main meals’, ‘after eating sugary food or drink’, and ‘before sleep’. Those who brushed their teeth less than twice a day and did not brush before sleep were considered as displaying oral health-compromising behaviours.\n\nRespondents had to indicate whether their toothpaste was ‘fluoridated’, ‘not fluoridated’, ‘not sure’, or ‘I did not even know what fluoride was’ and subsequently state the brand of their toothpaste. Those who used non-fluoridated toothpaste were considered as practising oral health-compromising behaviours.\n\nRespondents were asked about the frequency of flossing in the past 30 days. The response options were ‘once daily’, ‘once every two days’, ‘once to twice a week’, ‘once to twice a month’, ‘rarely or never’, and ‘I do not know floss’. Those who flossed less than once every two days were considered as practising oral health-compromising behaviours.\n\nRespondents had to indicate the time interval since their (i) last dental visit, whether it was ‘6 months or less’, ‘more than six months to one year’, ‘more than one year to 2 years’, or ‘more than two years’. In addition, respondents had to state the main reason for their most recent dental visit, both on (ii) their initiative or (iii) as a part of service requirements. The response options were ‘dental check-up’, ‘preventive treatment’, ‘treatment of related oral health problems’, and ‘I did not visit the dentist in the past 12 months’.\n\nThose who had had their dental check-up more than 12 months before and whose last dental visit was for oral health problem treatment, or those who had not visited the dentist in the past 12 months were considered as practising oral health-compromising behaviours.\n\nThis question elicited the respondents’ awareness of clenching and grinding their teeth while asleep as told by their spouse or family members. The response options were either ‘Yes ‘or ‘No’, and those with bruxism had oral health-compromising behaviours.\n\n## 2.3. Conduct of the Study\n\nThe authors obtained ethical permission from relevant authorities, including Universiti Malaya and MAF, before conducting the study. Once approval was obtained, an online briefing was held for the person in charge (PIC) of the army units via Google Meet. In addition, face-to-face briefings were conducted with units that could not attend the online session.\n\nThe questionnaire was administered to army personnel of Central Peninsular Malaysia units. The briefing session (physical, online, or video briefing) and questionnaire distribution were conducted according to the availability of each unit. Those who agreed consented to participate in the study. The respondents then scanned a quick response (QR) code or visited a website link to a Google form via their smartphone or personal computer. While answering the survey questions, a researcher was present (or in a WhatsApp group/personal message/call) to help the respondents if they faced any questions or problems. After the respondents completed the questionnaire, the researchers checked each questionnaire to ensure all sections were completed and submitted. Any incomplete questionnaires were rectified by informing the respondents (or through PICs) and asking them to complete their attempts.\n\n## 2.4. Data Management\n\nIn order to protect the confidentiality of the online data, the Google documents were password-protected and deleted from the ’cloud‘ after they were downloaded. Only the researchers had access to the password-protected documents stored on the external hard drive. Each respondent was given an individual identifier, and their names and Army Identification Numbers were not utilised in any data analysis or presentation.\n\nThere were $6.4\\%$ of respondents who had at least one missing value, where the proportion of items with a missing value ranged from $1.4\\%$ to $5.4\\%$. No respondents were excluded from the study because the missing value was far less than the $20\\%$ cut-off point. Subsequently, in the data analysis, we used mean values (unit imputation) to replace the missing values. Therefore, the completion rate was $100\\%$ ($$n = 2435$$).\n\nAll data were entered into Statistical Package for Social Sciences (SPSS) software, version 25. They were initially checked, cleaned, and explored using descriptive statistics and graphs for each variable. Next, means and standard deviations (SDs) were used to describe the continuous variables, while frequencies and percentages were used for categorical data. The data on each HOHCB were then dichotomised into binary codes (0 = healthy behaviour; 1 = health-compromising behaviour) for the 42 HOHCB items (Table S1).\n\n## 2.5. Statistical Analysis\n\nHierarchical agglomerative cluster analysis (HACA) with the between-groups linkage clustering method and squared Euclidean distance measure without pre-determined cluster membership was used in the analysis. HACA was chosen because it allows researchers (i) to compare the clustering result with an increasing number of clusters, (ii) because there is no need to make an a priori decision about the final number of clusters, and (iii) because it is more stable than the non-hierarchical method [40].\n\nTwo-step HACA with the between-group linkage clustering method and squared Euclidean distance measure without pre-determined cluster membership were performed in this study. The first step was to generate a squared Euclidean proximity matrix of all 42 HOHCB items, while the second step involved the use of a dendrogram to visualise the distance and cluster combination. The number of clusters was counted and reported as frequency and percentage, including the mean number of clustering.\n\nIn addition, repeated HACA on different sub-samples and K-means cluster analysis were used to validate the findings compared to the two-step HACA findings.\n\n## 3.1. Sociodemographic Characteristics\n\nAs shown in Table 1, the mean age of all the 2435 army members (response rate = $100\\%$) was 30.3 years (SD = 5.9), with the majority being male ($92.5\\%$). In addition, most respondents were of Malay ethnicity ($77.0\\%$), married ($67.5\\%$), had received education up to the secondary school level ($87.4\\%$), and had no medical conditions ($83.9\\%$).\n\nMilitary-wise, almost all respondents were army personnel of other ranks ($96.8\\%$), with two-thirds of them being junior non-commissioned officers (NCOs) ($66.2\\%$). Nearly $60\\%$ of them had served for 12 years or less. According to the respondent’s Corps and Regiments, less than half of them were from the Combat Element ($45.3\\%$), and approximately one-fourth were from the Combat Support Element ($28.9\\%$), while the remaining quarter was from the Services Support Element ($25.7\\%$). Socioeconomically, the vast majority of respondents were in the bottom $40\\%$ (B40; MYR 4849 and below) family income group ($92.6\\%$).\n\n## 3.2.1. Hierarchical Agglomerative Cluster Analysis\n\nEuclidean distances were calculated between all pairs of cases, where a low coefficient indicated high similarity between the two variables, thus classified as one group. Conversely, a high coefficient indicated low similarity between the variables, and they became more heterogeneous than the previous combinations (Table S2).\n\nAs shown in Figure 1, the cluster began with the smallest squared Euclidean distance of high consumption of carbonated drinks and acidic foods (9.000). Subsequently, the cluster was combined with high sweet-food consumption, high sweet-drink consumption, and sitting and lying behaviours to form a small group, which merged with other behaviours and groups, forming Sub-cluster 1a (1001.348). This sub-cluster consisted of 25 behaviours (high consumption of carbonated, acidic, and sweet foods and drinks; extended sitting and lying; drug use; physical inactivity; non-fluoridated toothpaste use; not following tooth-brushing recommendations; alcohol consumption; not wearing a helmet and seat belt; aggressive behaviour; bruxism; symptomatic dental visits; high fatty-food consumption; and extended screen time). Meanwhile, another smaller group comprising two behaviours, namely, medical screenings and dental check-ups, formed Sub-cluster 1b (845.000). Next, inadequate sleep during work and holidays formed a cluster (584.000) combined with substandard sleep quality to form Sub-cluster 1c (886.000). Subsequently, Sub-cluster 1a was merged with Sub-cluster 1b (1043.580) and Sub-cluster 1c (1065.741). Finally, these three sub-clusters (Sub-clusters 1a + 1b + 1c) were combined to form a broad cluster, Cluster 1.\n\nAt the bottom of the dendrogram are Sub-clusters 2a, 2b, and 2c. Both Sub-cluster 2a (low plain-water consumption, and texting and calling while driving) (1072.000) and Sub-cluster 2b (exposure to smoke, and smoking cigarettes and other tobacco products) (744.500) had three behaviours, respectively. Sub-cluster 2c had six behaviours (unrecommended vegetable, fruit, cereal/cereal-product, milk/dairy-product, and poultry/fish/meat/legume consumption; and infrequent flossing) (1001.500). Sub-cluster 2a was subsequently merged with combined Sub-cluster 2b and Sub-cluster 2c (1111.944) to form a broad cluster, Cluster 2 (Sub-clusters 2a + 2b + 2c).\n\nCluster 1 was named ‘unhealthy lifestyles with high-risk behaviours’ and comprised 30 HOHCBs. Cluster 2 was named ‘most common risk behaviours’ and contained 12 HOHCBs. At the end of the analysis, Cluster 1 and Cluster 2 were merged into one large cluster (1399.217). The validity and stability of the cluster analysis were confirmed by performing repeated HACA on different sub-samples randomly drawn from the study sample (SPSS random sample of cases; approximately $25\\%$ of respondents for each sample) [12,16].\n\n## 3.2.2. K-Means Cluster Analysis\n\nK-means cluster analysis requires an a priori number of clusters to be extracted from the data before performing the analysis. Therefore, using information obtained with the HACA, K-means cluster analysis with two cluster memberships was used. As a result, Cluster 1 comprised $50.3\\%$ ($$n = 1224$$) of respondents. Regarding the average score (mean) for each HOHCB, the cluster consisted of 17 HOHCBs. These included infrequent medical screenings; extended screen time (work); extended screen time (personal); low fruit consumption; high consumption of fried food; inadequate sleep during working days; flossing infrequently/never flossing; infrequent dental check-ups; symptomatic dental visits upon self-initiative; low vegetable consumption; unrecommended rice/noodle/cereal/tuber, milk/dairy-product, and poultry/fish/meat/egg/legume consumption; cigarette smoking; exposure to cigarettes/tobacco products smoke; and texting and calling while driving. In contrast, Cluster 2 comprised $49.7\\%$ of respondents, with nine HOHCBs. Eight were the same as in Cluster 1, with one distinct behaviour, low plain-water consumption. In addition, there were 24 HOHCBs with a mean score equal to zero for both cluster memberships (Table S3).\n\n## 3.3. Clustering Number of HOHCB\n\nThe clustering HOHCB number for each respondent was calculated, and the total score was interpreted as the clustering HOHCB number for all respondents. The clustering number for the 42 behaviours ranged from 0 (no HOHCBs) to 29 HOHCBs, with the average number of clustering behaviours in army personnel being 14.1 (SD = 4.1). The most common clustering number among the respondents was 15 ($9.2\\%$), followed by 13 ($9.0\\%$), 16 ($8.7\\%$), and 14 ($8.7\\%$) HOHCBs (Table 2).\n\n## 4. Discussion\n\nAs this is the first study investigating HOHCB clustering in this population in Malaysia, this study investigated multiple important and relevant HOHCBs in the adult and MAF populations, consisting of 15 HOHCB domains (health behaviours: 10 domains, with 33 items; oral health behaviours: 5 domains, with 9 items), for a total of 42 items. In comparison with other studies with a lesser number of items, ranging from 2 [41] to 17 items [17], conducted in adolescents and adults in the United Kingdom, the United States, Brazil, Saudi Arabia, Malaysia, and Korea, the present study considered a large number of HOHCB items, including most HOHCB domains from previous studies. The reason was to allow us to conduct a complete and all-inclusive assessment of all relevant HOHCBs in the MAF population to reflect the actual situation and use the information to advocate for comprehensive health and oral health programmes.\n\nHACA and K-means cluster analysis revealed two broad clusters in respondents with a relatively similar pattern. Nevertheless, one cluster membership contained more HOHCBs than the other, reflecting a high-risk behaviour clustering group. The first cluster, ‘high-risk behaviours’, consisted of 30 HOHCBs. The cumulative and synergistic negative effects of health-compromising behaviours increase the risk of adverse effects on health [5,42]. Hence, the respondents in this group may have a higher chance of developing diseases, with those diseases having a higher chance of having greater severity. Physical inactivity and sedentary lifestyles, as well as fast food, fatty-food, and high sugar intake, contribute to increased risk of obesity and overweight, cardiovascular diseases, musculoskeletal disorders, and cancer [43,44]. Furthermore, both drug abuse and alcohol harm health and functional ability. They may also have medical (e.g., addiction, immunodepression, infectious diseases, and mortality), physical (e.g., physical abuse and injuries, agitation, and restlessness), social (e.g., crime, interpersonal and relationship problems, and loss of employment), and psychological (e.g., mental illness, violent and aggressive behaviour, and suicidal behaviour) implications. Meanwhile, sleep deprivation increases the risk of depression, suicide, post-traumatic stress disorder, accidents and injuries, cardiometabolic disorders, and even mortality [45]. The risk of major road traffic injuries, including thoracic, head, and neck injuries, and even mortality is increased when seatbelts and helmets are not used [46,47]. Moreover, unrecommended tooth-brushing frequency, non-fluoridated toothpaste use, and symptomatic dental visits are associated with oral health diseases such as dental caries, periodontal disease, and edentulousness, all of which negatively impact the quality of life [48,49].\n\nThe second cluster comprised a total of 12 HOHCBs, involving respondents who displayed (i) ‘unhealthy nutrition intake’ (unrecommended cereal and cereal-product consumption; unrecommended fish, poultry, meat, egg, and legume consumption; unrecommended milk and dairy-product consumption; low fruit intake; and low plain water intake), (ii) ‘tobacco product use’ (smoking cigarettes, use of other tobacco products, and exposure to tobacco product smoke), (iii) ‘risky driving’ (texting and calling while driving), and (iv) flossing infrequently or never flossing. Most of these HOHCBs were among the eleven most prevalent health-compromising behaviours in the army personnel in this study. At least two-fifths of respondents engaged in these HOHCBs ($44.2\\%$ (other tobacco product use)–$82.0\\%$ (unrecommended cereal and cereal-product consumption)). Thus, the second cluster may represent the most common HOHCBs in army personnel. The possibility of adverse health impacts also exists in the second cluster group. For instance, malnutrition, micronutrient deficiencies, anaemia, gastrointestinal diseases, and osteoporosis have all been linked to unhealthy dietary consumption [50,51]. Tobacco products (e.g., cigarettes and electronic cigarettes) and second-hand smoke are associated with several adverse health effects, such as respiratory diseases, cardiovascular diseases, and cancer, all of which contribute to premature mortality [52,53]. Texting and calling without a hands-free device while driving endanger drivers, passengers, and other road users, as these behaviours increase the likelihood of accidents due to the driver’s inability to focus on the road, respond to major traffic events, and maintain vehicle control within the lane [54,55]. Flossing infrequently or never flossing is associated with an increased risk of interproximal caries and periodontal disease [56].\n\nHowever, no direct comparison can be made between the present study and previous studies of clustering HOHCBs, given that the present study comprised a different number of clustering patterns with varying HOHCB composition. In previous studies, the number of clustering patterns ranged from two [12,16,17,21,57] to six clusters [58]. A study in the Hungarian Defence Forces personnel reported 16 cluster profiles, but the clusters were also classified according to military characteristics [14].\n\nNevertheless, the present study’s findings are comparable in terms of ‘risk categorisation’. In this study, the clustering patterns in army personnel were categorised into ‘high-risk behaviours’ and ‘most common risk behaviours’, a type of categorisation similar to that in studies in Europe, China, Australia, the United Kingdom, and the United States [13,38,57,59,60,61,62]. For instance, Hobbs et al. [ 38] identified three clusters, (i) ‘lower risk’, (ii) ‘moderate risk’, and (iii) ‘elevated risk’ in Australian adults. A United Kingdom study conducted in adults by Mawditt et al. [ 60] also performed similar categorisations: (i) ‘risky’, (ii) ‘moderate’, and (iii) ‘mainstream’ clustering pattern groups. These studies classified the clustering patterns as ‘high-risk’ and ‘low/moderate/mainstream’, where mainstream behaviour is similar to the most common risk behaviour. Additionally, a study performed in Hong Kong adults by Chan and Leung [57] identified two clusters: (i) ‘healthy’ and (ii) ‘less healthy’. This risk categorisation was based on the type and number of risk behaviours in the cluster. It determined the level of the clustering group in relation to the likelihood of developing diseases and the severity of diseases. A greater number of HOHCBs, for example, increase the risk of disease. Furthermore, categorisation may aid in determining which clusters to focus on, such as the targeted population group and high-risk behaviour approach in health promotion activities [10,12].\n\nAt the same time, some compromising behaviours clustered together within the same cluster group. Similar to Cluster 1 (‘high-risk behaviours’) in the present study, Ali [21] reported that alcohol consumption, aggressive behaviours (bullying and physical fighting) and drug users clustered in the same group in Malaysian adolescents. In addition, Jordao, Malta, and Freire [17], and Skalamera and Hummer [62] reported that alcohol consumption clustered with drug use in young adults in the United States and Brazil. In terms of sedentary lifestyles and unrecommended dietary intake, Hobbs et al. [ 38] revealed that physical inactivity, extended sitting time, and fast-food consumption clustered within the ‘elevated risk’ cluster. A similar pattern was reported by Skalamera and Hummer [62], where physical inactivity and fast-food consumption clustered together, but with the addition of no doctor or dentist visits. Regarding Cluster 2 (most common risk behaviours), the results align with the findings by Ali [21], in which inadequate consumption of vegetables and fruits, and low consumption of milk and dairy products clustered in a group.\n\nIdentifying the two HOHCB cluster patterns in army personnel, as conducted in the present study, is essential for oral health promotion activities. The findings assist in better understanding risk factor clustering and could facilitate prevention, which could benefit the combat readiness of military populations. Thus, health promotion and intervention programmes could be prioritized and target high-risk personnel. These initiatives should focus on multiple behaviours, which promises a more significant impact on public health than conventional interventions focusing on a single behaviour, for example, through the common risk factor approach. For instance, in the population-based approach, health promotion activities should focus on behaviours in the ‘Cluster 2: most common risk behaviours’ clustering pattern, since they involve several of the most common HOHCBs in army personnel. The goal is to empower army personnel regarding proper oral hygiene self-care (particularly, flossing), healthy nutrient consumption, smoking control programmes, and road safety from a health standpoint. Therefore, tackling this behaviour cluster may help reduce the prevalence of the most common risk behaviours in army personnel. Nevertheless, in a situation where resources are available, targeting the first cluster (high-risk behaviours) has its advantages. As the cluster consists of 30 health-compromising behaviours, addressing this behaviour cluster might eventually tackle many HOHCBs in the army.\n\nEqually important is the number of clustering HOHCBs that can occur in one army member. This study revealed that on average, army personnel could have 14.1 (SD = 4.1) clustering behaviours, with the most common clustering number in the respondents being 15 ($9.2\\%$), followed by 13 ($9.0\\%$), 16 ($8.7\\%$), and 14 ($8.7\\%$) HOHCBs. However, the results cannot be compared with those of other studies, because studies on clustering numbers are minimal, with different numbers of HOHCBs being investigated. Nonetheless, this could be one of the novelties of the present study in terms of clustering behaviour research, as the findings prove that an individual can simultaneously engage in multiple HOHCBs. For example, on average, one army member in this study engaged in 14 HOHCBs, which is alarming. Knowing the clustering number could also be the focus of prevention and promotion activities, prioritising those with a higher number of clustering risk behaviours. Additionally, identifying the clustering number could complement the use of information on clustering patterns. For example, during the planning and execution of an intervention, we could concentrate on one clustering pattern and target those with high clustering numbers within the cluster. Hence, it is highly recommended that future studies investigate the clustering patterns and clustering number of HOHCBs at the individual level.\n\nThis study has a few limitations. First, the results heavily depend on the respondents’ truthfulness in the use of the self-administered questionnaire. Some HOHCB items might have been under-reported or over-reported, e.g., substance abuse and alcohol consumption. Second, the present study only involved army personnel in Central Peninsular Malaysia. Similar data on the navy and air force personnel who are also part of the three main MAF branches were not included. They may have different HOHCB clustering patterns. This also includes other army formations such as other infantry divisions and army troops. However, the main strength of the study is that limited studies have looked into clustering patterns and clustering numbers of HOHCBs, especially in military personnel, and this study is one of the few that have done so. Although the findings are here mostly compared with studies in the general population, the results are still beneficial to providing evidence and assisting in reorienting the health promotion approaches in the army population. Thus, more studies on the clustering of HOHCBs in military personnel should be conducted in the future, since the HOHCBs are linked to the health readiness and, by extension, the combat readiness of the MAF.\n\n## 5. Conclusions\n\nThis study identified two broad HOHCB clustering patterns in army personnel in Central Peninsular Malaysia, ‘high-risk’ behaviours and ‘most common risk behaviours’, with an average of 14 HOHCB clusters for each army member. As HOHCBs significantly impact the health readiness and, eventually, the combat readiness of the army population, taking cognisance of these findings could assist policy makers or health managers in formulating strategies for effective health promotion and disease prevention programmes targeting the army personnel engaged in these two HOHCB clusters. The common risk factor approach becomes very relevant and could be applied. With limited resources, future health prevention and promotion policies and activities should focus on army personnel engaged in Cluster 2 ‘most common risk behaviours’, as the findings show that this cluster comprises the most prevalent health-compromising behaviours in army personnel, followed by those engaged in Cluster 1 behaviours. Furthermore, policymakers or health managers could further prioritise those with a high number of clustered risk behaviours within this targeted group. 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0.01797465980052948\n]","isConversational":false}}},{"rowIdx":78,"cells":{"text":{"kind":"string","value":"---\ntitle: Preparation and Characterization of Intracellular and Exopolysaccharides during\n Cycle Cultivation of Spirulina platensis\nauthors:\n- Yuhuan Liu\n- Yu Wang\n- Leipeng Cao\n- Zhenghua Huang\n- Yue Zhou\n- Ruijuan Fan\n- Congmiao Li\njournal: Foods\nyear: 2023\npmcid: PMC10000700\ndoi: 10.3390/foods12051067\nlicense: CC BY 4.0\n---\n\n# Preparation and Characterization of Intracellular and Exopolysaccharides during Cycle Cultivation of Spirulina platensis\n\n## Abstract\n\nThe dried cell weight (DCW) of *Spirulina platensis* gradually decreased from 1.52 g/L to 1.18 g/L after five cultivation cycles. Intracellular polysaccharide (IPS) and exopolysaccharide (EPS) content both increased with increased cycle number and duration. IPS content was higher than EPS content. Maximum IPS yield (60.61 mg/g) using thermal high-pressure homogenization was achieved after three homogenization cycles at 60 MPa and an S/I ratio of 1:30. IPS showed a more fibrous, porous, and looser structure, and had a higher glucose content and Mw (272.85 kDa) compared with EPS, which may be indicative of IPS’s higher viscosity and water holding capacity. Although both carbohydrates were acidic, EPS had stronger acidity and thermal stability than IPS; this was accompanied by differences in monosaccharide. IPS exhibited the highest DPPH (EC50 = 1.77 mg/mL) and ABTS (EC50 = 0.12 mg/mL) radical scavenging capacity, in line with IPS’s higher total phenol content, while simultaneously showing the lowest HO• scavenging and ferrous ion chelating capacities; thus characterizing IPS as a superior antioxidant and EPS as a stronger metal ion chelator.\n\n## 1. Introduction\n\nSpirulina platensis is a multicellular filamentous cyanobacterium and has been nicknamed the “Edible Queen” by the FAO and the FDA for its nutritional value [1,2]. S. platensis and its derivates have been widely used in dietary supplements and other food products targeted at the health-aware consumer, and are increasingly gaining recognition as functional ingredients [3,4]. Among its most promising derivatives, the polysaccharides of S. platensis (PSP) have attracted significant attention due to their antioxidant, antiaging, antiviral, anti-inflammatory, and immunomodulatory potential, as well as their physicochemical attributes [5,6]. Previous studies have shown that PSP is bioactive and comprises polymeric carbohydrates composed of long chains of monosaccharide units bound together by glycosidic linkages, and that its multiple biological activities are closely related to monosaccharide composition, diverse glycosidic linkages, molecular weight, and spatial configuration [7,8]. Moreover, PSP is interesting for its potential to improve intestinal function and health, and to prevent cancer cell proliferation. Therefore, with the increasing demand for PSP in trade markets, the cost-efficient preparation of PSP is currently a pressing research need.\n\nBroadly, PSP can be isolated from the cell bodies of S. platensis and from culture media to obtain intracellular polysaccharides (IPSs) and exopolysaccharides (EPSs), respectively. IPSs consist of complex acid sulphate polysaccharides, and account for 15–$20\\%$ of the cell mass of S. platensis cell mass [2,8]. Variations in IPS content are related to culture conditions, including carbon source, light, pH, salinity, and cultivation time. Large-scale extraction of IPS is usually performed by cell disruption, chemical maceration, and enzymatic treatment [9]. On the other hand, EPS is a water-soluble heteropolysaccharide that is secreted during growth and binds tightly to S. platensis cell walls, forming a protective capsule against dehydration and toxic agents [10,11,12]. EPS content in S. platensis cultures is potentiated by high salinity and low nutrient availability [13]. EPS is obtained by ultrafiltration with membranes intended for the appropriate molecular weight. Both IPS and EPS harbor a variety of functional groups, including -OH, -COOH, -SO3H, and -CH3, which, together with the structural diversity of these polysaccharides, are thought to give them bioactive properties such as antifungal and antioxidant activity, free-radical scavenging, and inhibition of lipid peroxidation [14,15,16]. However, little is known about how IPS and EPS content may be affected during cycle cultivation, and distinct structural-functional profiles of IPS vs. EPS are yet to be characterized.\n\nTherefore, the aims of the present work are to investigate the influence of S. platensis cycle cultivation on IPS and EPS content, and to characterize the structural-functional properties of IPS and EPS; in particular, their physicochemical properties, monosaccharide composition, molecular weight, functional groups, and antioxidant activity. Additionally, an optimized method for the preparation of IPS and EPS based on thermal water coupled with high-pressure homogenization (HPH) is offered. The data herein described thus provides theoretical and technical resources for the cost-efficient production and adequate application of IPS and EPS from S. platensis.\n\n## 2.1. Cultivation of S. platensis\n\nS. platensis strains (FACHB: GY-D18) were purchased from the Institute of Hydrobiology, Chinese Academy of Science, PR China. Batches of S. platensis were cultured in 500 mL Erlenmeyer flasks with 300 mL of modified Zarrouk’s medium for eight days at 25 °C under cycle illumination (5500 lx). After harvesting cells, culture medium was reused to culture a second batch of S. platensis, for a total of five cultivation cycles. Deionized water and analytical grade chemicals and solvents were used in all cases. Erlenmeyer flasks and culture medium were sterilized at 121 °C for 20 min before use.\n\n## 2.2. Evaluation of S. platensis Growth\n\nA 20 mL aliquot of cell suspension was filtered through a Whatman filter paper and dried at 105 °C for 24 h. Biomass was calculated according to the method of Zhou et al. [ 17] and expressed as dry cell weight (DCW; g), following the formula:DCW (g/L) = (Wn − W0)/0.02 Growth rate (g/L/d) = (DCWn+1 − DCWn)/1 where Wn corresponds to total dry weight (g) of the filter paper with algae, W0 represents the dry weight (g) of the filter paper alone, 0.02 is the aliquoted volume (L), and n is time (days).\n\n## 2.3. Experimental Design of RSM for Extraction of IPS from S. platensis\n\nIPSs were extracted from S. platensis cell bodies using hot water coupled with high-pressure homogenization (HPH; GJJ-$\\frac{0.06}{100}$, Shanghai Taichi Light Industry Equipment Co, LTD), according to the experimental design of RSM (Table 1). Extracts were centrifuged at 7104× g for 10 min, and supernatants were concentrated at 80 °C. Concentrated supernatants were treated five times with savage reagent (Chloroform: n-butanol = 4:1) to remove protein, then mixed (1:4) with $95\\%$ ethanol and allowed to precipitate for 12 h at 4 °C. Precipitates were suspended in a small aliquot of deionized water before dialysis, and then dialyzed in molecular weight cut-off bags (8–10 kDa) for 48 h to eliminate residual salts. Finally, samples were lyophilized, weighed, and kept for further experiments. The yield of IPS (mg/g) was calculated as follows:Yield (mg/g) = c × n × v / m where c is the concentration (mg/mL), n is the dilution factor, v is the sample volume (mL), and m is the dried sample weight (g).\n\n## 2.4. Extraction of Extracellular Polysaccharide (EPS)\n\nEPSs were obtained from S. platensis culture medium according to the method of Li et al. [ 13]. The medium was filtered through a 0.45 μm membrane, and the filtrate was concentrated at 50 °C and then precipitated with ice-cold ethanol (1:4 v/v) at 4 °C for 12 h. Precipitates were dialyzed, lyophilized, weighed, and stored in the same manner as IPS extracts.\n\n## 2.5. Chemical Composition Analysis of IPS and EPS\n\nTotal sugar and protein content were determined by the phenol-sulfuric acid and the Coomassie brilliant blue methods, respectively [18]. Total phenol content was estimated using the Folin–Ciocalteu reagent and measuring absorbance at 750 nm; gallic acid (20–100 µg/mL) served as standard, based on Chaiklahan et al. [ 8]. Phenol content was expressed in gallic acid equivalents. Ash content was determined based on weight loss after 4 h at 550 °C.\n\nThe monosaccharide composition of IPS and EPS was determined with a precolumn derivation HPLC method using 1–phenyl–3-methyl-5–pyrazalone (PMP) (Ma et al., 2019). Samples were thoroughly hydrolyzed to monosaccharides by treatment with 4 M trifluoroacetic acid for 8 h at 110 °C, and then mixed with the PMP solution and chloroform. Samples were then analyzed by HPLC (Agilent, Santa Clara, CA, USA) and UV detection at 245 nm. A 4:21 (v/v) mixture of acetonitrile and 0.125 mol/L KH2PO4 was used as the mobile phase at a flow rate of 0.8 mL/min, at 30 °C.\n\nThe molecular weight (Mw) of IPS and EPS was determined by gel permeation chromatography (GPC, ELEOS System, Wyatt Technology Co., Goleta, CA, USA), based on the method of Zhang et al. [ 10] with slightly modified chromatographic conditions: 0.2 mol/L NaNO3 served as mobile phase at a flow rate of 0.5 mL/min and a column temperature of 25 °C; injection volume was 20 μL.\n\n## 2.6. Fourier-Transform Infrared (FTIR) Spectroscopy and Scanning Electron Microscopy (SEM)\n\nInfrared spectra of IPS and EPS were obtained using a FTIR spectrometer (Thermo Scientific Nicolet IS50, MA, USA) according to the method of Sasaki et al. [ 19]. A dried 1.0 mg sample was ground and pressed into tablets mixed with 100 mg of KBr. Tablets were scanned at a wavelength range of 4000-400 cm−1.\n\nThe surface morphology of IPS and EPS was observed by SEM. The powdered sample was sprinkled on the surface of a piece of double-sided tape which was adhered to the microscope’s aluminum column, and then sputter-coated with platinum powder using an ion sputter coater for observation.\n\n## 2.7. Zeta Potentials and Thermal-Gravimetric (TG) Analysis\n\nSample solutions (1.0 mg/mL) were prepared in ultrapure water. Zeta potentials were determined at 25 °C in the pH range of 2.0–9.0 using a Zeta sizer Nano-ZS particle diameter and potentiometric analyzer (Malvern Instruments, MC, UK). All samples were measured in triplicate.\n\nThe thermodynamic characteristics of IPS and EPS samples were analyzed by differential scanning calorimetry (DSC) (Netzsch, DSC 214 Polyma, Selb, Germany). A 5.0 mg sample was weighed in an aluminum pan, using an empty pan as reference. Measurements were performed under nitrogen flow (40 mL/min), at a heating rate of 10 °C/min in a range of 30 °C to 800 °C.\n\n## 2.8.1. DPPH Radical Scavenging Assay\n\nThe scavenging activity of different concentrations of IPS and EPS on 2,2-diphenyl-1-picrylhydrazyl (DPPH) radicals was evaluated according to the method of Su et al. [ 20], with slight modifications. Briefly, 1.0 mL of polysaccharide extract (0–2.5 mg/mL) was thoroughly mixed with 1.0 mL of DPPH solution (0.2 mmol/L in $95\\%$ ethanol). The mixture was allowed to react for 30 min, protected from light, and absorbance was then measured at 517 nm with a UV/Vis spectrophotometer. In this step, $95\\%$ Ethanol and 0–2.5 mg/mL ascorbic acid were used as blank and positive control, respectively. DPPH radical scavenging activity was calculated with the formula:DPPH radical scavenging ability (%) = (A0 − A1 + A2) × 100/A0 where A0 represents the absorbance of the DPPH solution alone, A1 is the absorbance of the DPPH solution containing the sample, and A2 is the absorbance of the ethanol solution with the sample.\n\n## 2.8.2. ABTS Radical Scavenging Assay\n\nScavenging activity on 2,2′-Azino-bis (3-ethylbenzthiazoline-6-sulfonate) (ABTS) radicals was analyzed as described by Tian et al. [ 6], with some modifications. Equal volumes of an aqueous solution of 7.0 mmol/L ABTS and 2.45 mmol/L K2S2O8 were mixed and allowed to incubate at RT for 12 h, while protected from light, to acquire the ABTS+• solution. This ABTS radical solution was then diluted with phosphate buffer solution (pH 7.4) until reaching an absorbance of 0.70 ± 0.02 at 734 nm. A 100 μL aliquot of the diluted ABTS+• solution was mixed with 100 μL of sample (0.1–2.5 mg/mL), and absorbance was measured at 734 nm after 30 s oscillation. Deionized water and ascorbic acid (0–2.5 mg/mL) served as blank and positive control, respectively. ABTS radical scavenging activity was calculated as follows:ABTS radical scavenging ability (%) = (A0 − A1 + A2) × 100/A0 where A0 is the absorbance of the diluted ABTS+• solution alone, A1 is the absorbance of the diluted ABTS+• solution mixed with the sample, and A2 stands for the absorbance of the sample in deionized water.\n\n## 2.8.3. HO• Radical Scavenging Assay\n\nHO• scavenging activity was assayed according to the method described by Ji et al. [ 21] with some modifications as follows: A 1.0 mL aliquot of a sample solution (0–2.5 mg/mL) in $95\\%$ ethanol was thoroughly mixed with 1.0 mL of each of the following: 9 mmol/L H2O2, 9 mmol/L FeSO4, and 9 mmol/L salicylic acid. The solution was then incubated at 37 °C for 60 min with cycle-shaking, and absorbance was measured at 510 nm. Ascorbic acid (0–2.5 mg/mL) was used as a positive control. Hydroxyl radical scavenging activity was calculated as follows:HO• scavenging ability (%) = (A0 − A1 + A2) × 100/A0 where A0 is the absorbance of deionized water, A1 is the absorbance of the sample, and A2 is the absorbance of the solution without sample.\n\n## 2.8.4. Fe2+ Chelating Ability\n\nFe2+ chelating ability was determined as described by Chang et al. [ 22], with minor modifications. Briefly, 1.0 mL of sample (0–2.5 mg/mL) was mixed with 3.7 mL deionized water and 0.1 mL of 2.0 mmol/L FeCl2·6H2O solution, vigorously stirred for 30 s, and then 0.2 mL of 5 mmol/L ferrozine solution was added. The mixture was incubated for 10 min at 25 °C and absorbance was measured at 562 nm. Deionized water and sodium ethylenediamine tetra acetic acid (EDTA-Na2) (0–2.5 mg/mL) were used as blank and positive control, respectively. Chelating capacity (%) was calculated with the formula:Chelating ability (%) = (A0 − A1) × 100/A0 where A0 is the absorbance of the reaction solution without sample and A1 is the absorbance of the reaction solution with the sample.\n\n## 2.8.5. EC50 Calculation\n\nEC50 represents the mass concentration of the sample when clearance is $50\\%$. To calculate EC50 values for DPPH, ABTS, and HO• radical scavenging activity, and for Fe2+ chelating capacity, the clearance ratios of different sample concentrations were plotted and fitted linearly.\n\n## 2.9. Statistical Analysis\n\nAll the experiments were conducted in triplicate. Data plotting was performed with Design Expert 13, Origin 2021, and IBM SPSS Statistics 26. Analysis of variance (ANOVA) was carried out wherever applicable and $p \\leq 0.01$ was regarded as a significant difference. For all figures and tables, data were presented as mean ± std ($$n = 3$$) of the three independent replicates.\n\n## 3.1. Change of S. platensis and Polysaccharide Content during Cycle Cultivation\n\nFigure 1 shows the growth curve of S. platensis and the growth in polysaccharide content (IPS and EPS) during cycle cultivation. DCW of S. platensis and polysaccharide content showed a linear increase with prolonged cultivation time. The growth of S. platensis behaved as a parabola, reaching its maximum rate (0.24 g/L/day) on the fourth day of cultivation. After eight days in culture, the DCW of S. platensis reached 1.52 g/L, representing a $660\\%$ increase, and appeared as a regular spiral filament under the microscope (Figure 1a). Total polysaccharide content significantly increased with extended cultivation time, with IPS (80.08 mg/L) reaching a concentration three times higher than that of EPS (27.94 mg/L) by the end of cultivation (Figure 1b).\n\nAs shown in Figure 1c, the DCW of S. platensis gradually decreased from 1.52 g/L to 1.18 g/L with each cultivation cycle. This is likely explained by a decrease in the microalgae photosystem II, as a consequence of the accumulation of dissolved organic matter (DOM) and increased viscosity of the culture medium [13]. IPS content increased together with the number of cycles, which may also be due to DOM accumulation and reduced DCWP, reaching 203.34 mg/L (or a $136\\%$ increase) after five cultivation cycles. Meanwhile, EPS content increased to 52.62 mg/L by the second cycle and remained stable in subsequent cycles. Reusing culture media several times is likely to curb the availability of nitrogen and other nutrients, which can in turn contribute to increasing the C/N ratio and thus promote the incorporation of carbon into the EPS fraction [11].\n\n## 3.2. Single-Factor Test of IPS Extraction\n\nWith the gradual increase in the solid–liquid ratio, the extraction rate was the first to increase and then decrease (Figure 2a), and when the material–liquid ratio was 1:30 g/mL, the extraction rate could reach 54.30 ± 0.75 mg/g. With the increase in high-pressure homogenization pressure, the extraction rate of intracellular polysaccharide reached the maximum at 60 MPa (Figure 2b), and the extraction rate was 48.13 ± 0.90 mg/g. The highest extraction rate was achieved when the number of extractions was three (Figure 2c), and it was 48.40 ± 0.29 mg/g.\n\n## 3.3. Optimization of IPS Extraction\n\nTo optimize the extraction procedure of IPS from S. platensis, a total of 17 experiments with three independent variables (A = S/I ratio; B = pressure; C = number of homogenizations) were performed following a Box–Behnken design (BBD) (Table 1). IPS yield ranged from 39.87 to 60.33 mg/g (dry weight) across all 17 experiments. Based on multiple regression analysis on the experimental data, a second-order polynomial equation expressing the relationship between each variable was generated:Yield (%) = −161.98 + 4.97A + 3.17B + 33.46C − 0.11AB + 0.06AC − 0.08BC − 0.07A2 − 0.02B2 − 5.19C2 The results and RSM analysis are presented in Table 2. The F value for the model was 363.16 ($p \\leq 0.0001$), indicating that the model was statistically significant. The p value of the linear (A; B; C), interaction (AB; BC), and quadratic term coefficients (A2; B2; C2) were all lower than 0.01, which implied that these variables had significant effects on the extraction yield. The correlation coefficient (R2) was 0.9979, indicating that the predicted and observed values were similar and that the model was a good fit. In addition, the determination coefficient (R2adj) was 0.9951, which indicated that only $0.49\\%$ of the total variation could not be captured by the regression model. The p value for lack of fit was 0.1532, which means that lack of fit and pure error were not significantly different. These results thus indicated that the regression model could adequately predict IPS extraction yield.\n\nThe relationship between independent and response variables and response is visually represented as a 3D surface response (Figure 3a). For S/I ratio and pressure, the projection of 3D response surface at the bottom was elliptical, indicating that the mutual interaction between S/I ratio and pressure was significant. A similar trend was observed for S/I ratio and number of homogenizations, and for pressure and number of homogenizations (Figure 3b,c). The peak point at their response surfaces also simultaneously existed in their minimum elliptical, indicating that there was an extremum value in the chosen range.\n\nBased on multiple regression and 3D surface response analyses, the optimal conditions for IPS extraction were predicted as follows: S/I ratio = 1:30.79; pressure = 61.08 MPa; and three homogenizations, for an extraction yield of 60.20 mg/g. A verification experiment was performed under the optimal conditions predicted by the model (S/I ratio = 1:30; pressure = 60 MPa; and three homogenizations). The observed IPS extraction yield was 60.61 mg/g, which was not statistically different from the predicated value. Therefore, the regression model was suitable for the prediction of IPS extraction from S. platensis.\n\n## 3.4. IPS and EPS Composition\n\nTable 3 shows the chemical compositions of IPS and EPS. Both IPS and EPS contained more than $65\\%$ total sugars and less than $5\\%$ protein. The phenolic content in IPS ($7.3\\%$) was higher than in EPS, indicating a stronger antioxidant capacity. The carbohydrates present in both IPS and EPS but in different ratios (Figure 4) included mannitol, ribose, rhamnose, glucuronic acid, galacturonic acid, glucose, galactose, xylose, and fucose. However, the proportion of each monosaccharide content was statistically significantly different in IPS vs. EPS (Table 3). IPS’s main monosaccharides were glucose ($83.62\\%$), rhamnose ($4.42\\%$), fucose ($3.25\\%$), and glucuronic acid ($2.39\\%$). In comparison, EPS contained mainly fucose ($19.99\\%$), rhamnose ($15.61\\%$), glucose ($14.75\\%$), galacturonic acid ($11.13\\%$), and galactose ($10.78\\%$) content, and had a lower molecular weight (185.13 kDa). This indicated that IPS may have a higher viscosity than EPS [19]. These differences in monosaccharide content and Mw between PSP fractions point to remarkably distinct functional properties and potential when used as food additives e.g., as thickening stabilizers.\n\n## 3.5. FTIR Spectrum Analysis and SEM Imaging\n\nThe FTIR spectra of IPS and EPS indicated large similarities in the functional groups contained in both polysaccharide fractions (Figure 5a). The absorption peaks observed at around 3413 and 2925 cm−1 are typical of the O−H and C−H stretching vibrations in rhamnose and fucose, respectively [23]. The amide I band at 1650 cm−1 can be taken to represent the symmetrical and asymmetrical stretching vibration of C=O in COO− and −NHCOCH3, together with the bending vibration in the N-H bond [24]. Similarly, the amide II band with peak absorption at 1542 cm−1 can be mainly attributed to the symmetrical stretching vibration in the C−O bond. Next, absorption peaks at 1400–1200 cm−1 represent variation angle vibrations. The absorption peak at 1240 cm−1 can be attributed to the asymmetrical stretching vibration in −S=O, indicating that both IPS and EPS contained a small number of -SO3H groups [6]. The presence of the pyran ring and the carbohydrate skeleton (C−O−C) is indicated by their characteristic peaks at 1153 and 1064 cm−1, respectively [10]. Finally, the absorption peaks at 898 and 819 cm−1 correspond to the deformation mode of the β−D−pyranoside bond (C−H) and α−Mannitose, respectively [25].\n\nTo better understand their physical properties, the surface and microstructure of IPS and EPS was visualized by SEM. IPS and EPS were remarkably different in shape and size (Figure 5b). IPS presented a smooth surface with irregular thin stripes at 2000× magnifications. At 5000× magnifications, IPS exhibited a loose, finely lamellar, and porous web-like structure; these characteristics could imply an enhanced solubility exposure of active groups in IPS. In contrast, EPS had a relatively smoother and flatter surface, and a more coarsely lamellar, less porous structure. Because of its fibrous and porous structure, IPS has likely more versatile application in various foods, and may be especially superior for its water holding capacity compared with EPS [10].\n\n## 3.6. Zeta Potential and TG Analysis\n\nThe changes in zeta potential of IPS and EPS solutions in response to pH are shown in Figure 6a. As pH increased from 2.0 to 9.0, the zeta potentials of IPS and EPS decreased from −25.73 to −29.77, and from −26.43 to −37.5, respectively. The smaller differential between IPS and EPS in this pH range may be explained by the high glucose content in IPS. Although both extracts had negative zeta potentials, meaning both of them are acidic polysaccharides, EPS showed a more negative potential than IPS overall. This points to EPS’s stronger acidity which, in agreement with previous reports, is probably due to a higher abundance of −SO3H in EPS.\n\nThermal stability is a crucial physicochemical property for the commercial application of polysaccharides. The TG and derivative TG curves were experimentally determined for IPS and EPS (Figure 6b). Analysis of weight loss revealed three major stages: [1] 50–200 °C; [2] 200–500 °C; and [3] 500–800 °C. Weight loss during the first stage was $4.78\\%$ for IPS and $10.11\\%$ for EPS, and could be attributed to the evaporation and dehydration of adsorbed and surface water from the polysaccharide’s surface. During the second stage, weight loss was approximately $58.47\\%$ (IPS) and $53.03\\%$ (EPS), and was possibly due to the degradation of long carbohydrate chains and the depolymerization of fragments. By the third stage, weight loss slowed down, only decreasing by $29.94\\%$ (IPS) and $13.71\\%$ (EPS), which could also be due to the fact that the remaining compounds were further carbonized and some carbonates were converted into CO2. Maximal weight loss reached $93.84\\%$ (IPS) and $77.36\\%$ (EPS) at 800 °C. These results showed that IPS and EPS are thermally stable below 220 °C, and that EPS’s thermal stability is higher, possibly as a consequence of its higher fucose and rhamnose content.\n\n## 3.7. Antioxidant Capacity Analysis\n\nDPPH is a stable nitrogen−centered radical and is widely used for the in vitro evaluation of the antioxidant capacity of natural products [10]. IPS and EPS showed an overall strong scavenging activity on DPPH radicals (Figure 7a), and this was dependent on the concentration of polysaccharide, reaching its maximum value at 2.5 mg/mL ($65.9\\%$ and $44.7\\%$ for IPS and EPS, respectively). The EC50 value of IPS (1.77 mg/mL) was lower than that of EPS (4.67 mg/mL). The greater ability to scavenge DPPH radicals of IPS is consistent with its higher phenolic content. For comparison, the DPPH scavenging activities of S. platensis−derived polysaccharides are superior to those derived from other bacteria and microalgae, specifically *Pseudomonas fluorescens* (approximately $30\\%$ at 1.0 mg/mL EPS) [26] and *Sargassum carpophyllum* ($66.6\\%$ at 12 mg/mL IPS) [6].\n\nScavenging of ABTS radicals is another common indicator of the antioxidant potential of natural compounds. Both IPS and EPS were shown to be strongly capable of scavenging ABTS radicals in a concentration-dependent manner (Figure 7b). Scavenging activity reached $95.26\\%$ at 1.0 mg/mL and $94.47\\%$ at 2.5 mg/mL for IPS and EPS, respectively, and these values were not statistically different from the positive control at the same concentration. The EC50 values for ABTS radical scavenging were 0.12 mg/mL (IPS) and 0.60 mg/mL (EPS); this heightened scavenging activity for IPS may be explained by a lower sulphate/sugar content ($p \\leq 0.05$). Likewise, PSP extracts showed better ABTS radical scavenging performance when compared with polysaccharides sourced from *Oudemansiella radicata* mushroom (EC50 = at 0.2 mg/mL ORP) [25] and *Botryococcus braunii* (EC50 = 5.13 mg/mL EPS) [27].\n\nHO• is a highly reactive radical known for its deleterious biological effects, including red blood cell death, DNA damage, and cell membrane degradation, and is prominently implicated in ageing [28]. For this reason, scavenging HO• radicals constitutes an important antioxidant defense mechanism. Both IPS and EPS presented scavenging activity on HO• radicals and this also increased with concentration (Figure 7c). The scavenging capacity of PSPs is directly related to the function of electrons and hydrogen, as supported by previous reports [6]. The EC50 of IPS (1.72 mg/mL) was higher than that of EPS (0.75 mg/mL), $p \\leq 0.05$; this superior ability of EPS to scavenge HO• radicals may stem from its rich alcohol hydroxyl groups in the structure of fucose.\n\nThe chelating ability of IPS and EPS on ferrous ions also increased at higher concentrations (Figure 7d). EPS had the strongest chelating capacity ($85.91\\%$) at 1.0 mg/mL. Remarkably, this was higher than the positive control’s ($73.90\\%$) and IPS’s ($40.84\\%$) chelating abilities at same concentration. The EC50 of IPS and EPS were 1.54 mg/mL and 0.38 mg/mL, respectively. Thus, EPS showed a stronger chelating power on ferrous ions, which is probably due to the abundance of COO− and SO42- in EPS.\n\n## 4. Conclusions\n\nIn these results, the content and functional properties of IPS and EPS were investigated during cycle cultivation of S. platensis. The results showed that the DCW of S. platensis gradually decreased with the increase in number of cycles during cycle cultivation, and the IPS and EPS content gradually increased with the increase in number of cycles and extension of time during cycle cultivation, and IPS content was far higher than EPS. The maximum yield of IPS (60.61 mg/g) could be obtained under the condition of 1:30 S/I ratio and 60 MPa, three times, using thermal-HPH technology. The same carbohydrates were present in both IPS and EPS but in different ratios. IPS has more loose fibrous porous structures, higher glucose, and larger Mw than EPS, indicating higher water holding capacity and viscosity. Both IPS and EPS were shown to be acidic carbohydrates, but the acidity and thermal stability of EPS were stronger than those of IPS, which might be closely related to the monosaccharide content. IPS exhibited a better scavenging capacity on DPPH and ABTS radicals than EPS, possibly due to higher total phenol content, and far lower scavenging ability on OH• radicals and lower ferrous ion chelating ability than EPS, which indicated that IPS showed high antioxidant capacity, but EPS had strong chelating ability on metal ions. These results could provide theoretical direction for the cost-efficient production and adequate application of IPS and EPS from S. platensis as food additives or medicinal ingredients. In future studies, the extraction efficiency of IPS should be improved for large-scale production. Moreover, the rheology, water holding capacity and number of major functional groups of IPS and EPS should also be further analyzed to better understand their functional properties.\n\n## References\n\n1. 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0.009206721559166908\n]","isConversational":false}}},{"rowIdx":79,"cells":{"text":{"kind":"string","value":"---\ntitle: Exploring the Impact of α-Amylase Enzyme Activity and pH on Flavor Perception\n of Alcoholic Drinks\nauthors:\n- Maria João Santos\n- Elisete Correia\n- Alice Vilela\njournal: Foods\nyear: 2023\npmcid: PMC10000705\ndoi: 10.3390/foods12051018\nlicense: CC BY 4.0\n---\n\n# Exploring the Impact of α-Amylase Enzyme Activity and pH on Flavor Perception of Alcoholic Drinks\n\n## Abstract\n\nThe introduction of a drink in the mouth and the action of saliva and enzymes cause the perception of basic tastes and some aromas perceived in a retro-nasal way. Thus, this study aimed to evaluate the influence of the type of alcoholic beverage (beer, wine, and brandy) on lingual lipase and α-amylase activity and in-mouth pH. It was possible to see that the pH values (drink and saliva) differed significantly from the pH values of the initial drinks. Moreover, the α-amylase activity was significantly higher when the panel members tasted a colorless brandy, namely Grappa. Red wine and wood-aged brandy also induced greater α-amylase activity than white wine and blonde beer. Additionally, tawny port wine induced greater α-amylase activity than red wine. The flavor characteristics of red wines due to skin maceration and the contact of the brandy with the wood can cause a synergistic effect between beverages considered “tastier” and the activity of human α-amylase. We can conclude that saliva-beverage chemical interactions may depend on the saliva composition but also on the chemical composition of the beverage, namely its constitution in acids, alcohol concentration, and tannin content. This work is an important contribution to the e-flavor project, the development of a sensor system capable of mimicking the human perception of flavor. Furthermore, a better understanding of saliva–drink interactions allow us to comprehend which and how salivary parameters can contribute to taste and flavor perception.\n\n## 1. Introduction\n\nFrom an early age, taste and aroma have been known for walking together in a single direction [1]. It is the combination of that leads to the recognition of the most varied sensory experiences to which the human being’s palate is constantly subjected.\n\nTrigeminal sensations—or “mouthfeel”—are defined as “a group of sensations that is characterized by the tactile response in the mouth” [2] and described as “tactile properties (sensation) perceived from the moment that food or drink—solid, semi-solid or liquid—is placed in the mouth until swallowed” [3]. These sensations, together with the taste and olfactory sensations (ortho- and retronasal perception), contribute to the flavor of the food/drinks that are essential in the acceptance of these products by consumers [4]. Understanding the factors affecting flavor perception may provide clues to drive food consumption toward keeping a proper nutritional status [5].\n\nApart from food characteristics, flavor perception is strongly influenced by oral physiology, namely saliva [5]. Saliva’s constitution is approximately $99\\%$ water, and the rest is inorganic and organic compounds [6,7]. The pH of saliva is between 6.2 and 7.4 [8]. Being slightly acidic or basic, saliva is a highly viscoelastic fluid and has unique properties that facilitate chewing, digestion, homeostasis, and flavor perception, among many other things [9,10]. Moreover, in addition to dilution, the buffering capacity of saliva has also been reported to decrease the response to acid stimulation, although the concentration of organic acids in food and drinks and, therefore, titratable acidity also contributes to sourness [11].\n\nThe different constituents of saliva are responsible for the interaction and formation of new compounds within the oral cavity [6], and salivary proteins are of the utmost importance since they fulfill various roles, such as oral digestion (amylases and lipases), neutralization of toxic molecules (proline-rich proteins and histatins), defense against microorganisms (immunoglobulins and peroxidases), lubrication of the oral cavity (mucins), and the transport of flavor molecules (lipocalins) [12]. These enzymes are secreted by the salivary glands or originate from the lysis of desquamated epithelial cells [12]. Given the presence of microbiota in the mouth, some enzymes can be of bacterial origin [13]. The total protein concentration in saliva is around 0.2–2.0 mg/mL, and more than 4000 different proteins are present [14]. Among others, two enzymes may contribute to flavor-taste perception: lipase and α-amylase. Several researchers state that orally expressed lipases might hydrolyze triacylglycerols and, consequently, release non-esterified fatty acids (NEFA). However, fatty acids are poorly soluble in aqueous solvents, which could prevent their access to their sensory receptors. It has also been hypothesized that salivary proteins could play a role in the transport of NEFA in the mouth [5]. The α-amylase enzyme is the main protein in human saliva and is responsible for catalyzing the hydrolysis of 1,4-glycosidic bonds in starch and other polysaccharides, such as glucose and maltose, which are smaller sugars that can be detected by the sweet receptors in the mouth [15,16]. Moreover, the secretion of salivary proteins, such as α-amylase, has been reported to be dependent on the diet [17], and the salivary proteome and glucose levels have been related to sweet taste sensitivity in young adults [18] and α-amylase concentrations in healthy children [19].\n\nRegarding aroma perception, namely the retro-nasal pathway, volatile molecules, before being detected by olfactory receptors, are influenced by factors such as body temperature and saliva pH, allowing the identification of different and equally important aromas [20,21]. When volatiles are released from food into the saliva phase, chemical and biochemical reactions occur, which could affect the volatile concentration and retro-nasal aroma perception [22]. Several authors note that salivary proteins, such as mucins and α-amylase, can ‘trap’ aroma compounds depending on their structure [23]. Some aroma compounds can be individually metabolized in the oral cavity, leading to the formation of new metabolites with different odor thresholds [24,25]. Moreover, the persistence of an aroma compound also depends on its metabolism in the oral cavity, as referred to by Muñoz-Gonzalez et al. [ 26], who found that the perceived aroma intensity of the compounds that are metabolized in the oral cavity decreases faster than that of the nonmetabolized compounds. Furthermore, aroma compounds may adsorb onto the mucosal pellicle, while the aggregation of the mucosal pellicle by tannins may disturb these interactions [27].\n\nThe main objective of this work was to study the differences in pH and the variations that occur in saliva enzyme activity, lipase, and α-amylase, depending on the type of alcoholic beverage that is consumed. This work is an important contribution to the e-flavor project, the development of a sensor system capable of mimicking the human perception of flavor in alcoholic drinks, namely wine. Furthermore, a better understanding of the saliva–drink interactions allow us to comprehend which and how salivary parameters can contribute to taste and flavor perception. To this end, we analyzed the enzymatic activity of lipase and α-amylase and pH changes before and after the contact of alcoholic drinks with the tasters’ saliva.\n\n## 2.1. Ascending Method of Limits for Primary Taste Detection Thresholds in Aqueous and Hydroalcoholic Solutions\n\nAqueous and hydroalcoholic ($12\\%$, v/v) solutions associated with each basic taste (sweet, salty, acid, and bitter) were prepared to determine the sensory and perception threshold of the tasters. For each basic taste attribute, seven aqueous solutions with different concentrations were prepared (Table S1). For the acid taste attribute, acids were tested in separated solutions (Table S2). In each aqueous solution, 0.5 L of water (bottled water—Caldas of Penacova) was used. The procedure was similar to the preparation of the $12\\%$ hydroalcoholic solutions (Tables S3 and S4). Before preparing the solutions, a mother solution was prepared with water and ethanol, with a concentration of $12\\%$ (v/v), simulating the alcohol level of table wines.\n\nThe final pH of each of the solutions at different concentrations was also measured (Table S2).\n\nThe solutions were presented to the tasters in ascending order of concentration [28,29] at a temperature of 20 °C. After tasting, the tasters had to indicate on the tasting sheet if the solution caused any sensation—sensation threshold—and identify the taste in question—perception threshold.\n\n## 2.2. Tasting Panel Characterization and Laboratory Conditions\n\nFor this study, two different panels were used. The first panel (P1) of tasters was composed of 11 individuals, of which 10 were female and only 1 was male. The ages of the panel were between 39 and 59 years, and they had previously participated in assessments of food and beverages in several published works [30,31,32]. The second panel (P2) of tasters was composed of 19 individuals, of which 4 were female and 15 were male. The ages of the panel were between 20 and 50 years, and they were the untrained panel. In order not to impair the results of the sensory evaluation, the tasters were non-smokers and were told not to use any type of perfume or other cosmetics with a strong aroma and not to eat or drink anything, except water, for one hour before the start of the tests.\n\nAll evaluations were conducted from 4:00 to 6:00 p.m. and took place in individual tasting booths in a sensory laboratory [33] using the 21.5 cl transparent ISO wine-tasting glasses [34]. Sessions were carried out under controlled conditions at 20 °C (±2 °C) and relative humidity of $60\\%$ (±$20\\%$).\n\nA volume of 20 mL was used in all the tasting sessions. This volume was chosen to make it possible for the tasters to put all the liquid in their mouths for 10 s and then spit it back into the glass to be collected and stored.\n\n## 2.3.1. Solutions and Alcoholic Beverages\n\nEach basic taste was associated with an aqueous solution with a certain concentration of the taste in question. For bitter taste and astringency in the lipase and α-amylase tests, we choose to use a tannin solution and not quinine, as tannin is an alcoholic drink component important for bitter taste and astringency.\n\nThe selection of concentration was based on the concentration where most tasters perceived and identified the taste (Table 1). The procedure for the preparation of the hydroalcoholic solutions (ethanol $12\\%$, v/v) was identical to what was described above.\n\nRegarding alcoholic beverages, each taster from panel one (P1) was presented with one beer (blonde beer, with an alcohol content of $4.9\\%$ (v/v) and pH 4.34), two wines (white wine with an alcohol content of $13.0\\%$ (v/v) and pH 3.37, and red wine with an alcohol content of $13.9\\%$ (v/v) and pH 3.87) and two brandies (a colorless brandy and a wood-aged brandy with alcohol contents of $41.0\\%$ (v/v) and $40.0\\%$ (v/v), respectively, and pH 4.30 and 3.93, correspondingly). To P2 (panel two) were presented two wines, one red wine and one port wine, with alcohol contents of $13.5\\%$ and $19\\%$ (v/v), respectively.\n\nThe wines, beer, and brandies were stored in a climate-controlled dark cellar maintained at 11 °C ± 1 °C. The day before the sensory sessions, the white wine and beer were stored at 4 °C ± 2 °C in a fridge.\n\n## 2.3.2. Tasting Procedure and Saliva Collection\n\nIn the first part of the work, for P1, saliva was collected from the members of the panel during two tasting sessions, one for the aqueous and hydroalcoholic solutions and another for the alcoholic drinks (beer, wine, and brandy). In the second part of the work, for P2, saliva was collected after tasting two alcoholic beverages, namely red wine, and port wine. The procedure was similar in both sessions.\n\nThe solutions described in Table 1 and the beverages in both procedures were presented to the tasting panel in codded 21.5 cl transparent ISO wine-tasting glasses [34] with a quantity sufficient for the taster to place in the mouth (20 mL). The tasting procedure was explained, and it was requested that the tasters place the sample in their mouths, swirl the liquid through the mouth cavity, and wait for 10 s. After 10 s, they were asked to spit the solution into the respective glass and wait 5 min before proceeding to the next solution/beverage. Next, in the first part, the temperature and pH of each expectorated solution/beverage were measured using a glass thermometer and a potentiometer (Hanna Instruments Inc., Woodstock, GA, USA), respectively. Then, for both tests, the samples were pipetted into 5 mL glass vials in the case of aqueous and hydroalcoholic solutions and into plastic 1.5 mL Eppendorf microtubes in the case of alcoholic beverages. All samples were coded and stored at −18 °C until enzymatic activity determination.\n\n## 2.3.3. Lipase and α-Amylase Activity Determination\n\nLipase enzyme activity was only measured for the first part of the study for the alcoholic beverages beer, wine, and brandy. The lipase enzyme assay kit (Abnova, Taipé, Taiwan) is based on a method in which -SH groups (thiol groups), formed from tributyrate-dimercaptopropanol (BALB) lipase cleavage, interact with 5,5 dithiobis (2-nitrobenzoic acid)—DTNB—to form a yellow-colored product. Color intensity was measured with the aid of a microplate reader (SPECTROstarNano, BMG LABTECH, Offenburg, Germany) at 412 nm. The measured absorbance is proportional to the enzyme activity in the sample [35]. The enzyme kit contained the reagents described in Supplementary Table S5. The detection threshold of the enzyme kit is in the range of 40 to 1600 U/L, with minimum and maximum values.\n\nTo prepare the working reagent, the colored reagent—DTNB—was mixed with assay buffer, and the flask was shaken to obtain a better homogenization. Then, 0.8 mL of BALB reagent was added.\n\nDuring the experimental procedure, 150 µL of water (H2O) was transferred to one well, and 150 µL of the calibrator (a substance not defined in the enzymatic kit—Table S1) was transferred to another well of the microplate. In the remaining wells, 10 µL of sample and 140 µL of working reagent prepared as mentioned above were added. The plates were covered, and the reaction mixture was homogenized.\n\nThe OD412nm was read in the microplate reader at 10 min (OD10min) and 20 min (OD20min) after the start of the reaction. The activity of the lipase enzyme was calculated according to Equation [1]. [ 1]Lipase enzyme activity=OD (20min.)−OD (10min.)OD (calibrator)−OD (H2O)×735 U/L where OD20min/OD10min represents the OD412nm values of the sample at 20 min and 10 min, respectively, and ODcalibrator and ODH2O represent the OD412nm values of the calibrator and water at 20 min.\n\nThe number “735” is the equivalent activity (U/L) of the calibrator under the assay conditions.\n\nFor the determination of α-amylase, enzymatic kits from Biovision (Milpitas, CA, USA) were used. For each kit, a standard curve and respective equation of the straight line were drawn.\n\nThe assay uses ethylidene–pNP–G7 as a substrate. Ethylidene–pNP–G7 is specifically cleaved by α-amylase. The final chromophore release is measured with the help of a microplate reader (SPECTROstarNano, BMG LABTECH, Offenburg, Germany) at a wavelength of 405 nm. The enzyme kit contained the reagents described in Table S5.\n\nThe α-amylase enzyme kit was previously stored at a temperature of −18 °C.\n\nFor the use of the kit, the buffer was warmed to room temperature (±20 °C), and the samples and positive control were kept on ice during the assay. The nitrophenol standard curve (standard curve) was prepared with 0, 2, 4, 6, 8, and 10 µL of nitrophenol (2 nM) in duplicate, making a final volume of 50 µL with distilled water on the microplate.\n\nAn absorbance/concentration curve was plotted for 0, 4, 12, 16, and 20 nmol/well of nitrophenol. In one of the microplate wells, 5 µL of positive amylase control and 45 µL of distilled water were added.\n\nRegarding the samples, 50 µL of each sample plus 50 µL of assay buffer and 50 µL of substrate mixture were added to each well, and two repetitions were performed. In the end, each microplate well contained a total volume of 150 µL. The content of the microplate was homogenized before reading.\n\nOptical density (OD) was measured at a wavelength of 405 nm to obtain the OD T0. After eight minutes (T1), the OD was measured again.\n\nThe standard nitrophenol curves were drawn from the results, and the equations were defined. The variation of OD [ΔOD = OD (T1) − OD (T0)] was calculated for the standard curve of nitrophenol, namely for the “y” of the first-degree equation, and the “x” of nitrophenol generated by the amylase between T0 and T1.\n\nFinally, the α-amylase activity was calculated according to Equation [2]. [ 2]Amylase enzyme activity (mUmL)=BT × V Where B is the nitrophenol amount from the standard curve (in nmol), T is the time between T0 and T1 (in min), and V is the pretreated sample volume added to the reaction well (in mL).\n\n## 2.4. Beverage Sensory Evaluation through a Descriptive Analysis (DA) Sensory Test\n\nRegarding the alcoholic beverages, in addition to performing the procedure described under Section 2.3.2, the tasters carried out a descriptive analysis [36]. For P1 (panel one), a tasting sheet for each class of beverage (beer, wine, and brandy) was constructed based on a list of attributes previously defined. The same process was carried out for P2 (panel two) for red wine and port wine, with new tasting sheets with descriptors specifically chosen for the work (Supplementary Table S6). In both tasting sessions, with P1 and P2, the tasters were asked to check the attributes they considered adequate to describe the different drinks.\n\n## 2.5. Data Analysis\n\nData were presented as mean (M) and standard deviation (SD) when appropriate. The assumption of normality of distributions in each of the solutions/beverages studied was evaluated using the Shapiro-Wilk test. The assumption of homogeneity of variances was evaluated using the Levene test. To determine whether saliva influences the pH values of different alcoholic beverages, a one-sample t-test was performed.\n\nTo assess whether there were significant differences in α-amylase and lipase activity between aqueous and hydroalcoholic solutions and to verify whether alcohol influenced the substances representing the basic tastes, a Kruskal–Wallis test and the Wilcoxon–Mann–Whitney test were used, respectively, since the conditions of normality and homogeneity of variances were not verified. To identify which basic tastes were significantly different, non-parametric pairwise comparisons were performed.\n\nTo verify whether there were significant differences in alcoholic beverages (beer, wines, and brandies), in panel one (P1), in the enzymatic activity of lipase and α-amylase, a univariate analysis of variance (ANOVA) was executed, followed by a post hoc HSD Tukey test whenever possible.\n\nTo find out whether there were significant differences between red wine and port wine for panel two (P2) in the enzymatic activity of α-amylase, the Student’s t-test for independent samples was performed.\n\nThe statistical analyses were performed using SPSS Statistics (version 27.0), and in all analyses, a probability value of ≤0.05 was considered significant.\n\nThe sensory data obtained after the descriptive analysis (DA) test was graphically represented using the percentage of a citation for each descriptor.\n\n## 3.1. Primary Taste Detection Thresholds in Aqueous and Hydroalcoholic Solutions\n\nIn the determination of the primary taste detection threshold in aqueous and hydroalcoholic solutions, there were some divergences in the answers given by the tasters. Nevertheless, in aqueous solutions, for $78\\%$ of the tasters, the sensation threshold of malic acid occurred at the concentration of 0.2 g/L and for about $22\\%$ at the concentration of 0.0 g/L. The threshold of perception occurred for $44.5\\%$ of the tasters at concentrations of 0.2 g/L or 0.5 g/L, while the remaining $11\\%$ achieved it at the concentration of 1.0 g/L.\n\nRegarding the threshold of sensation of citric acid and lactic acid, $90\\%$ of the individuals reported the existence of some substance at a concentration of 0.2 g/L and $10\\%$ at a concentration of 0.0 g/L. As for the perception threshold, for citric acid, $70\\%$ of the tasters detected the basic acid taste at the concentration of 0.2 g/L and $30\\%$ at the concentration of 0.5 g/L, while for lactic acid, the tasters were divided into the following percentages: $50\\%$ at the concentration of 0.2 g/L; $40\\%$ at a concentration of 0.5 g/L, and $10\\%$ at a concentration of 0.8 g/L. For lactic acid, two tasters mentioned that they detected a bitter basic taste and a salty basic taste instead of the acidic/sour taste.\n\nFor succinic acid, it was possible to verify that the sensation threshold of $90\\%$ of the tasters was at a concentration of 0.2 g/L, and the other $10\\%$ detected it at a concentration of 0.0 g/L. For the threshold of perception, the responses varied between a concentration of 0.2 g/L for $30\\%$ of the tasters and a concentration of 0.5 g/L for $70\\%$ of the individuals.\n\nThe salty taste was recognized by the panel, with a visible divergence in concentrations. Regarding the threshold of sensation of salty taste identification, $11\\%$ of the tasters reported a change in the taste of the solution at concentrations of 0.0 g/L and 0.5 g/L, while $33\\%$ reported a change at 1.0 g/L and the remaining $45\\%$ at 0.25 g/L. As for the threshold of perception, this was divided into different concentrations, with $11\\%$ of the taster’s detecting saltiness, represented by NaCl, at concentrations of 0.25 g/L and 4.0 g/L, $22\\%$ at 0.5 g/L and 2.5 g/L, and finally, $34\\%$ at of 1.0 g/L.\n\nConcerning the basic sweet taste, $11\\%$ of the tasters noticed the existence of something in the solutions at 0.0 g/L, 7.5 g/L, and 10 g/L, and $33.5\\%$ in the solutions with 2.5 g/L and 5.0 g/L of sugar. As for the bitter taste, it was detected by $11\\%$ of the tasters at concentrations of 2.5 g/L, 7.5 g/L, and 13 g/L, and $33.5\\%$ of the tasters detected it at concentrations of 5.0 g/L and 10 g/L.\n\nConcerning the primary taste detection threshold in hydroalcoholic solutions, for $75\\%$ of the tasters, the sensation threshold of succinic acid occurred at the concentration of 0.2 g/L and for about $25\\%$ at the concentration of 0.5 g/L. Regarding the threshold of perception, $17\\%$ of the tasters reported this at the concentration of 0.2 g/L, $33\\%$ at the concentration of 0.5 g/L, $42\\%$ at a concentration of 0.8 g/L, and the remaining $8\\%$ at a concentration of 1.0 g/L.\n\nFor the threshold of sensation of lactic acid, $92\\%$ of the individuals reported the existence of some substance at the concentration of 0.2 g/L, while $8\\%$ perceived it at the concentration of 0.5 g/L. As for the perception threshold, for lactic acid, $25\\%$ of the tasters detected it at the concentrations of 0.2 g/L or 1.0 g/L, while for $42\\%$ of the tasters, it was detected at the concentration of 0.5 g/L, and $8\\%$ detected it at a concentration of 1.2 g/L.\n\nAs for the previous acids, for citric acid, the sensation threshold concentrations of 0.2 g/L and 0.5 g/L were perceived by $83\\%$ and $17\\%$ of the tasters, respectively. The perception threshold occurred for $42\\%$ of the tasters at 0.2 g/L, $33\\%$ at a concentration of 0.5 g/L, and $25\\%$ at a concentration of 0.8 g/L.\n\nFor the determination of the threshold of the sensation of bitter taste, $92\\%$ of the tasters perceived the taste at a concentration of 0.5 g/L and $8\\%$ at a concentration of 2.5 g/L. The perception threshold, for $75\\%$ of the tasters, occurred at a concentration of 0.5 g/L, while for $17\\%$, it occurred at 2.5 g/L, and for the remaining $8\\%$ at a concentration of 5.0 g/L. All tasters identified the basic bitter taste corresponding to tannins, and the sensation of astringency was also mentioned throughout the tasting.\n\nFor the sweet taste, $67\\%$ of the tasters had the sensation of sweetness at a concentration of 2.5 g/L, while $17\\%$ perceived it at a concentration of 5.0 g/L and $8\\%$ at concentrations of 7.5 g/L or 10 g/L. Regarding the sweet taste perception threshold, $33\\%$ of the panel detected it at concentrations of 2.5 g/L or 5.0 g/L, $17\\%$ at a concentration of 7.5 g/L, and $8\\%$ at concentrations of 10 g/L or 13 g/L.\n\nConsidering all these results, it was possible to choose average values of concentrations of compounds to prepare solutions for the next task: to determine the pH changes and the lipase and α-amylase activity after basic taste solutions, including the aqueous and hydroalcoholic solutions, came into contact with human saliva.\n\n## 3.2.1. Temperature and pH\n\nThe temperature of aqueous/hydroalcoholic solutions and alcoholic beverages was measured before and after contact with the tasters’ oral cavity. It was found that the temperature of the solutions/drinks during the 10 s they were in the mouth increased by approximately 3.5 ± 1.0 °C.\n\nTo determine whether saliva influences the pH values of different alcoholic beverages, a one-sample t-test was performed after verifying the assumptions of data normality ($p \\leq 0.05$). The pH values after contact with saliva are significantly higher than the initial values of the solutions ($p \\leq 0.05$), except for the hydroalcoholic lactic acid solution (Table 2).\n\nIn both the aqueous solutions and hydroalcoholic solutions, the acidic solutions showed high resistance to pH changes. For example, in the citric acid solution, the pH changed from 3.19 to 3.39 and from 3.36 to 3.53 in aqueous and hydroalcoholic solutions, respectively. On the contrary, the aqueous solutions that stood out for having a lower resistance to changes in pH were the tannin and sodium chloride solutions, where an increase in pH values from 3.72 to 4.34 and from 4.96 to 5.92, respectively, was observed. In hydroalcoholic solutions, a significant change occurred in the tannin solution, in which the pH varied from 3.88 to 4.36, and in the sucrose solution, in which the pH varied from 6.19 to 6.70 (Table 2).\n\nIn alcoholic beverages, there were also significant differences in pH variation (Table 2). In colorless brandy and colored brandy, pH values were significantly higher after contact with human saliva, ranging from 4.30 to 4.84 ($p \\leq 0.001$) in the case of colorless brandy and from 3.93 to 4.45 ($p \\leq 0.001$) in the colored brandy. In contrast, for red and white wines, pH values were lower. In red wine, the pH dropped from 3.87 to 3.74 ($p \\leq 0.05$); for white wine, the pH dropped from 3.37 to 3.22 ($p \\leq 0.05$). However, the pH of the blonde beer did not significantly change ($$p \\leq 0.941$$).\n\n## 3.2.2. Lipase and α-Amylase Enzymes Activity\n\nA Kruskal–Wallis test was performed to verify if there are differences in the enzymatic activity (lipase and α-amylase enzymes) in aqueous or hydroalcoholic solutions once the conditions of normality ($p \\leq 0.05$) and homogeneity of variances ($p \\leq 0.05$) were not verified.\n\nThe higher value of amylase activity was obtained in the sucrose and sodium chloride solutions (93.28 and 100.91 mU/mL, respectively), and the lower value was found in the tannin solution (0.49 mU/mL). The results obtained show significant differences in α-amylase activity in the presence of aqueous solutions (Table 3). According to the multiple pairwise comparisons of mean ranks, the differences per column are between tannin and sucrose solutions ($$p \\leq 0.008$$), tannin and NaCl ($$p \\leq 0.004$$), lactic acid and NaCl ($$p \\leq 0.025$$), lactic acid and sucrose ($$p \\leq 0.048$$), and citric acid and NaCl ($$p \\leq 0.048$$).\n\nIn hydroalcoholic solutions, the results also indicated statistically significant differences in α-amylase activity. According to the multiple pairwise comparisons of mean ranks, the differences are, per column, between the solutions of tannin and lactic acid ($$p \\leq 0.002$$), tannin and sucrose ($$p \\leq 0.001$$), succinic acid and sucrose ($$p \\leq 0.016$$), and tannin and citric acid ($$p \\leq 0.014$$). The lowest value of enzymatic activity was found in the tannin solution (0.82 mU/mL), and the highest value was found in the sucrose solution (85.21 mU/mL).\n\nTo verify whether alcohol influenced α-amylase activity when tasters tasted solutions representing basic tastes, a Wilcoxon–Mann–Whitney test was performed (Table 4) once the conditions of normality ($p \\leq 0.05$) and homogeneity of variances ($p \\leq 0.05$) were not verified. It was possible to confirm that the differences observed between aqueous and hydroalcoholic solutions concerning lactic acid were significant ($$p \\leq 0.025$$, Table 4). For this acid, aqueous solutions presented lower values of amylase activity than the hydroalcoholic solution.\n\nTo verify if there are differences in the enzymatic activity (lipase and α-amylase enzymes) in alcoholic beverages tasted by P1 (beer, wines, and brandies), an ANOVA was performed once the conditions of normality ($p \\leq 0.05$) and homogeneity of variances ($p \\leq 0.05$) were verified. The analysis of the results for alcoholic beverages for the α-amylase enzyme revealed that in at least one of the beverages, the mean value is significantly different from the others (F[3, 5] = 56.59; $p \\leq 0.001$) (Table 5). According to the post hoc test, the colorless brandy induced a significantly higher α-amylase enzyme activity (148.11 mU/mL) compared to the other beverages ($p \\leq 0.001$). Similarly, red wine elicited greater enzyme activity (13.84 mU/mL) than white wine (4.15 mU/mL, $$p \\leq 0.006$$) and blonde beer (2.03 mU/mL, $p \\leq 0.001$) (Table 5).\n\nRegarding the lipase enzyme activity, results from the aqueous and hydroalcoholic solutions were not considered for analysis because they were below the detection threshold of the lipase enzymatic kit (40 U/L). Only in the citric acid hydroalcoholic solution (Table 3) was it possible to observe a measurable lipase activity (57.95 U/L). However, results for lipase were obtained after saliva contact with alcoholic beverages, except for red wine. The higher enzymatic activity was observed for color brandy and white wine (400.80 and 270.65 U/L, respectively), and the lower enzymatic activity was observed for colorless brandy (41.49 U/L). Due to the considerable variations in the standard deviation, the differences were not significant (Table 5).\n\nTo verify if there are differences in the enzymatic activity (α-amylase) in alcoholic beverages (red wine and port wine, tasted by P2), a Student’s t-test for independent samples was performed. The results indicate that the differences found were significant ($$p \\leq 0.005$$), as shown in Table 6. Furthermore, the amylase values of port wine are significantly higher than those found in red wine ($$p \\leq 0.003$$).\n\n## 3.3. Sensory Profile of the Alcoholic Drinks Determined by Descriptive Analysis (DA) Sensory Test\n\nFigure 1 shows the percentage of citations of each descriptor obtained after data analysis of the DA test tasting report for beer, wines, and brandies obtained with the help of the first panel (P1) composed of 11 individuals. Descriptors with percentages inferior to or equal to $10\\%$ were discarded. Despite the reduced number of tasters, the DA test was chosen because it was only intended to verify which attributes, from the list of attributes presented for each type of beverage, were identified by the tasters. For the blonde beer (Figure 1a), the descriptors that present the higher percentage of citations (higher than $60\\%$) are “foaming” and “foaming color”, “malt aroma”, “acidity”, “bitterness”, “sparkling”, the mouthfeel sensation of bubbles, and “malt taste”.\n\nAs can be seen in Figure 1b, the tasters perceived the red wine as more “fruity” (red fruit, black fruit, dried fruit sensations) and “sweeter” than the white wine. In red wine, the descriptors “tannin/astringent”, “body”, and “spices” also presented a percentage of citation higher than $60\\%$, and the “spicy” sensation was only mentioned in the red wine. However, white wine was felt as having more “citrus” and “tropical fruits” and more “mineral”, “acidic”, and “floral” flavors.\n\nRegarding the brandies (Figure 1c), although the alcoholic degree is similar for both drinks (41–$40\\%$, v/v—Table 5), the colorless brandy (Grappa) was perceived as having more “alcohol” and with a “vegetable/herbaceous” aroma. The varnish aroma of the descriptor “ethyl acetate” was also mentioned by twice as many tasters in the colorless brandy. The colorful brandy (Aguardente Velha) was characterized by the aromas “spices/wood”, “roasted/burnt”, “smoke/ash”, and was also perceived to be more “sweet”, “persistent”, “fruity, and “spiced”.\n\nTo better understand what the tasters’ reaction was regarding the sensory analysis of alcoholic beverages, a more specific descriptive analysis was carried out for red wine (with more descriptors to be evaluated), as shown in Figure 2a, and another typical Portuguese beverage was taste-tested, port wine, as shown in Figure 2b. For this second sensory analysis, a second panel (P2) of 22 tasters was used.\n\nConcerning red wine, it can be seen, once again, that the most quoted characteristics were “astringent”, “tannin”, and “wood/spices”. “ Red berries” (>$60\\%$) and “acidity” (>$35\\%$) also stand out. Port wine is a fortified wine with very distinctive characteristics. The most mentioned attributes were “dried fruits”, “sweet taste”, and “wood”, with more than $55\\%$, and “caramel” and “alcohol”, with approximately $65\\%$.\n\n## 4.1. Sensation and Perception Threshold Determination\n\nIn some cases, the panelists felt the presence of a taste even in the absence of the substance in an aqueous solution. These results can be explained by considering several aspects: (i) it was possible that the concentration of the taste was too low for the assessor to correctly identify what taste it was; (ii) the latest meal that tasters ate (one to two hours before the tasting) may have influenced their taste perception. During the tasting session, the members of the panel described the last meal they made to find possible explanations for future results. It was possible to verify that tasters who felt the presence of a taste even in the absence of the substance ate foods with strong and persistent flavors, such as spicy sausages, hamburgers, and fries. The bibliography indicates that the taste of “meat” also evokes other mouth sensations such as “astringency” and “succulence” [37,38]. Regarding fried potatoes, more specifically French fries, they can bring quite complex flavors that come from frying [39]. A high lipolytic activity caused by the ingestion of these foods leads to an increase in the perception of fat and its aromatic compounds [11]. Studies also have shown that xenobiotic-metabolizing enzymes of saliva can be overexpressed after the consumption of certain foods rich in bioactive molecules [40], namely with salivary glutathione transferases, aldehyde dehydrogenase, and NADPH quinone oxidoreductases, which are overexpressed after the consumption of coffee or broccoli [41]. Thus, it is possible to infer that the food eaten two hours before the tasting can have a considerable impact on the results obtained, as all the taste sensations can last for more than an hour after the meal in the mouth.\n\nAnother explanation for these strange results may be related to the analytical composition of the water—Caldas de Penacova—used in the preparation of the solutions. This water has a pH of 5.2 ± 0.4 and can be felt as slightly acidic, eliciting the sensation of “acid taste” even if no acid is present in the water.\n\nMoreover, some confusion of perceptions was acknowledged. For instance, one taster, when detecting malic acid, classified it as having a “bitter taste”. The reason for the wrong classification may be related to the fact that the taster confuses the “sour taste” with the “bitter taste”, which reveals the importance of training the panelists and allowing them to familiarize themselves with the basic tastes. Furthermore, past a certain level of sourness, perception may be altered, and it has been known for many years that some people are extremely sensitive to the taste of bitter substances, while others perceive little or no bitter taste [42]. Another reason that can explain the confusion regarding the perception of the several tastes may be the fact that the acids studied, in addition to acidity, may confer characteristics such as bitterness and astringency to the solutions [43]. Furthermore, according to some authors, in addition to activating H+ ion receptors, acids also can stimulate nociceptive receptors that are connected to the nerve endings of the trigeminal nerve, which is responsible for sensations such as astringency [44].\n\nIn addition, tannin solutions were always accompanied by a feeling of astringency, a strong sensation characterized by roughness and dryness in the oral cavity. The feeling of astringency is produced by the binding and precipitation of salivary proteins and phenolic compounds, such as tannins [45]. Furthermore, we must not forget that the state of mind the tasters faced at the moment of the test, and the emotions they felt at that moment, could also be the cause of a change in sensation and perception [46].\n\nIn hydroalcoholic solutions, besides all the reasons mentioned above, ethanol becomes the most important factor for the difference in the responses obtained. Ethanol is an oral chemosensory stimulus and has complex sensorial attributes that are detected by multiple sensorial receptors and afferent fibers [2,47]. Neurophysiological studies have demonstrated a positive association between responses to alcohol and sweet stimuli in nerve fibers [48]. Several authors have described the sweet taste of ethanol in an aqueous solution containing low levels of ethanol (0–$4\\%$ alc. vol.) [ 49,50], while others show that increasing the ethanol content of red and white wines to cover a range generally observed in dry wines (from 12 to $14\\%$ v/v) revealed no modification of the perception of wine sweetness, suggesting that ethanol has no direct effect on the sweet taste of wine [51]. In our results, the percentage of panelists that detected sweetness in aqueous and hydroalcoholic solutions was similar for the same sugar concentration, so, indeed, ethanol did not influence the results.\n\nAccording to the literature, ethanol also has a strong effect on bitter taste sensitivity. A bitter taste and a burning sensation have been associated with higher levels of ethanol (10–$22\\%$ v/v) [49,50], while more recent studies [51] showed that ethanol was not directly responsible for the perceived bitterness in white wine. In our work, the bitter taste elicited by the tannin solutions was perceived at lower concentrations in hydroalcoholic solutions than in aqueous solutions. Furthermore, as was mentioned, all tasters identified the bitter taste corresponding to tannin and perceived the solution astringency, contrary to what was mentioned by McRae et al. [ 52], who referred to the interference of ethanol with hydrophobic interactions between proteins and tannins, which may lead to a reduction of tannin precipitation and a decreased astringent sensation.\n\n## 4.2. Influence of Saliva on the pH Variations\n\nSaliva, like other body fluids, has a buffering capacity that allows it to absorb or release hydrogen ions (H+) to minimize changes in their concentration, that is, in the pH value [11,53]. The differences obtained in the pH values before and after contact with the human saliva allowed us to conclude that saliva influences the change in pH in alcoholic beverages in aqueous and hydroalcoholic solutions. An exception was seen for the solution with lactic acid in a hydroalcoholic medium. This acidic solution was the only one that did not show a significant difference in terms of pH change after the expectoration, that is, after contact with human saliva. Possible explanations for what happened may be related to lactic acid being a milder acid [54], being naturally present in the oral microflora [55] and having a pKa of about 3.85 (in the range of 19–23 °C), which can lead to a lower saliva–hydroalcoholic solution complex pH value [56].\n\nWhen analyzing what occurred in the alcoholic beverages, we verified that there were divergences in the pH values that varied according to the beverage analyzed. In the case of brandies, the contact time of the drink with saliva and its buffering capacity was, once again, a limitation for the greater action of the pH of the saliva in these drinks. On the other hand, the slight decrease that occurred in the pH of red and white wine can be explained by considering a drink made up of different acids and in different concentrations. These acids, in particular, tartaric, malic, and citric acids, are responsible for limiting the pH of the wine as well as giving it a buffering capacity, which can be effective depending on the acidic components that are present [53], capable of causing an inhibition of the expected effect on the impact of saliva pH.\n\nRegarding the blonde beer, the pH remained unchanged. Interestingly, in the blonde beer, the descriptor “foaming” presented a high frequency of citation, showing that beer has a high foam formation. According to Dysvik et al. [ 57], pH has a very complex effect on the foaming properties of beer, and higher pH values may be associated with higher foam production. Indeed, the pH of beer changes during the brewing process. First, we can consider that water will have a pH of over 7; when combined with crushed malt, the pH of the grain and water mixture drops considerably compared to the initial pH of the water alone. This observed pH decrease is the result of the precipitation of phosphates and amino acids derived from the malt. Phosphates, such as phosphoric acid, will disassociate. The presence of other minerals within the brewing water can interfere with the pH decrease during the brewing process. Specifically, the carbonate and bicarbonate ions associated with temporary water hardness can act as buffers to pH decrease. Usually, the pH of an infusion mash is around 5.2–5.6 [58]. During fermentation, the pH continues to drop. Yeast cells take in ammonium ions (which are strongly basic) and excrete organic acids (including lactic acid). Therefore, the biggest drop in pH is caused by fermentation and the acids, namely lactic acid, that is formed [58]. The beer presented to the tasters showed a high and persistent foam, following its higher pH (4.34), when compared with white and red wine’s pH (3.37 and 3.87, respectively). As the pH is closer to the pH of saliva, it is natural that the pH change becomes more difficult within 10 s of contact with saliva. On the other hand, lactic acid is one of the acids more present in beer [57] and is also one of the most important components responsible for the perceptible acidity of this drink (Figure 1a—descriptor “acidity”). Additionally, lactic acid is a much milder acid than tartaric and malic acid. Furthermore, the pKa of lactic acid can lower the pH of the saliva-beer complex to values very close to the beer pH.\n\n## 4.3. Enzymatic Activity Elicited by Aqueous and Hydroalcoholic Solutions and Alcoholic Beverages\n\nThe salivary α-amylase is an endoglycohydrolase encoded by the gene Amy1. It hydrolyzes internal α-1,4-glucoside bonds of starch to the disaccharide maltose and moderate-length oligosaccharides called limit dextrins. These products adhere to chewed food and hold the bolus together for swallowing [59]. Human salivary amylase is inactivated in the acid pH of the gastric lumen [60] but more stable in the presence of the mouth saliva pH or in solutions where the pH is closer to the natural saliva pH, such as in the sucrose solutions tasted (pH between 5.67 and 6.19 in aqueous and hydroalcoholic solutions, respectively, Table 2). The high α-amylase activity in the sucrose solutions, both aqueous and hydroalcoholic (Table 3), reinforced that α-amylase is protected when in contact with these solutions and able to aid in the digestion of carbohydrates and starch [61]. Moreover, the acidic solutions (succinic, citric, lactic, and malic acids) in the two stock solutions (aqueous and hydroalcoholic) also elicited a relevant activity of the α-amylase enzyme (Table 3). The human body tends to increase saliva flow to dilute the acidity and thus protect itself [62], and the increase in saliva flow implicates an increase in the concentration of the α-amylase enzyme, although some acid inactivation may occur due to the lower pH of the acidic solutions, which explains why the activity of α-amylase is higher in sucrose solution than in acid solutions, despite the increase in saliva flow.\n\nRegarding the low enzyme activity observed in tannin solutions (0.49 and 0.82 mU/mL in aqueous and hydroalcoholic solutions, respectively), the attraction between tannin and the α-amylase enzyme is reflected in the precipitation of the tannin—α-amylase complex, which may be responsible for the inactivation of the enzyme [63,64,65,66].\n\nRegarding the salty taste, in aqueous solutions, the sodium chloride solution showed the greatest activity of the α-amylase enzyme (100.91 mU/mL, Table 2). These results are probably due to the properties of the salt, which, in addition to reducing the adsorption of proteins by the glass flasks where the saliva-solutions samples were stored, also has chloride ions, which are an essential cofactor for the enzyme’s activity [64].\n\nOverall, aqueous solutions elicited lower enzyme activity compared to the hydroalcoholic solutions (Table 3 and Table 4). These differences may be due to the ethanol present in the hydroalcoholic solutions. This compound causes stress to the taster due to the heat and burning felt when tasting, triggering an increase in the production of α-amylase [16]. In lactic acid, this variance is considerably high as, in the hydroalcoholic solution, a many-fold increase in α-amylase activity occurred when compared with the activity observed in the aqueous solution.\n\nThus far, ‘food–saliva interactions’ has been used as a general term to refer to all possible interactions between food ingredients and saliva compounds. An example is the work of Zhao et al. [ 67], which studied the interaction between human salivary α-amylase and sorghum procyanidin tetramer. Recently, food–saliva interactions have also been investigated in beer [68], wine [69], and other food systems [70].\n\nRegarding beer, Ramsey et al. [ 68] hypothesized that ethanol and saliva interactions, namely, ethanol and α-amylase interactions, directly impact the sensory and flavor properties of beer. These authors found that ethanol has a subtle inhibitory effect on the binding of hydrophobic compounds to α-amylase, thereby increasing their headspace concentration in the $5\\%$ (v/v) alcoholic beers as compared to the $0\\%$ beers. The sensory data did not show significant differences in orthonasal perception between these two kinds of beer, yet retronasal results showed that $0\\%$ lager was perceived as maltier with reduced fruitiness, sweetness, fullness/body, and alcohol-warming sensation ($p \\leq 0.05$). In our work, the blonde beer with $4.9\\%$ (v/v) alcohol content was characterized by the sensory descriptors of foaming, malt aroma, acidity, bitterness, and sparkling, the mouthfeel sensation of bubbles, and malt taste. This beer induced lower α-amylase enzyme activity when compared to red wine, probably due to the lower alcoholic concentration content when compared with the other beverages.\n\nRed wine is perceived to be more fruity and sweeter, and brandy aged in oak wood is considered sweet, persistent, fruity, and spiced; these drinks induced a greater activity of the α-amylase enzyme. This phenomenon probably occurred because the vinification process of red wine—which allows for an increase in reducing sugars—and of colored spirits—which makes it less aggressive and with more roasted and caramelized aromas—caused a positive synergistic effect between these drinks and the activity of the enzyme.\n\nSmall molecules, such as ethanol, influence the compound retention effect and the hydrolysis function by reacting with salivary amylase [71]. Interestingly, despite its alcoholic concentration ($41\\%$ (v/v)), the colorless brandy (bottle-aged) showed significantly higher α-amylase activity compared to the other analyzed beverages (Table 5), showing that something must have triggered a higher production of amylase once some of it reacted with ethanol. According to several authors [64,65,66], α-amylase activity increases in response to stress, whether physical or psychological. Thus, the high alcohol content (41.0 %, v/v) of the brandy and the fact that it caused an alcohol sensation in the mouth and sensations of varnish and vegetable/herbaceous aroma (DA test, Figure 1c) when it was tasted may indicate an increase in stress and anxiety on the part of the taster as a physiological response to tasting. The roasted and caramelized aromas and flavors presented in the colored brandy came from the contact it had with the wood during the manufacturing process. These changes produced by the wood make the burning sensation less intense, and consequently, the tasters preferred this brandy compared to the colorless one, meaning that it probably did not cause so much stress when tasting, leading to lower α-amylase activity when compared to the colorless brandy (Table 5).\n\nTo confirm the hypotheses and obtain more reliable data, a second panel (P2) with a larger number of tasters was used, and new tests and saliva samples were taken to assess amylase enzyme activity. The beverages in question were again a red wine and, this time, a port wine. It was possible to verify that the enzyme activity was higher in port wine (with $19\\%$ (v/v) alcohol content) compared to red wine ($13.5\\%$ (v/v)), as seen in Table 6.\n\nThe port wine used was a tawny wine. This style of wine presents a sugar content of 40g—65 g/L, so it is a very sweet wine. Its aroma ranges from jam to dried fruits, such as hazelnuts and walnuts [72]. The aging in wood gives the fortified wines some touches of caramel and wood resulting from the Maillard reactions [72,73]. All these descriptors were also mentioned by the tasters. These characteristics, together with the alcohol and acidity also detected by the tasters, as seen in Figure 2a, tend to reinforce the idea of increased α-amylase activity in this type of beverage.\n\nThe spectrophotometric methods for the determination of lipase activity make use of synthetic lipase substrates transformed upon enzyme-catalyzed hydrolysis into products able to be detected spectrophotometrically. However, this method was not efficient in determining the lipase activity after tasting aqueous and hydroalcoholic solutions, and the results were not considered for analysis because they were below the threshold for detection of the lipase enzyme kit (40 U/L). Moreover, DTNB (5,5 dithiobis (2-nitrobenzoic acid) can also react with free thiol from salivary proteins, leading to background variation from one saliva sample to another depending on the individual salivary protein concentration [74]. Only in the citric acid hydroalcoholic solution was it possible to observe a measurable activity, that is, a value within the detection threshold of the lipase enzyme kit. This situation may be related to the fact that the lipase enzyme is present in low concentrations in human saliva [62], and, therefore, the lipase enzyme activity kit used in this study was not the most suitable for measuring its activity.\n\nRegarding alcoholic beverages, positive results regarding the activity of this enzyme were obtained after saliva contact with these beverages, except for red wine. One possible explanation for the lipase activity being measurable in alcoholic beverages is the fact that the beverages are complex and fatty acids are part of their composition. These fatty acids come from raw material, the fermentative activity of yeasts, and from the yeast themselves once they release fatty acids into the medium (wine or beer) after finishing the fermentation process. Nonetheless, and as referred to by Stoytcheva et al. [ 75], a common disadvantage of the spectrophotometric methods is the low substrate specificity of the enzyme towards the synthetic substrate analogs, which can cause some discrepancies in the results.\n\n## 5. Conclusions\n\nThe composition and buffering capacity of saliva influence the perception of flavor. The pH of aqueous hydroalcoholic solutions and alcoholic beverages remained close to their initial values and not to the pH values of human saliva, that is, between 6.2 and 7.4. Thus, it is possible to conclude that the buffering capacity of saliva is not sufficient to maintain a constant pH after contact with the solutions/beverages.\n\nThe α-amylase activity significantly increased when solutions contained acids and/or ethanol and decreased in the presence of tannin, probably due to its precipitation caused by tannin–protein interactions.\n\nWhen comparing red wine with port wine, we also verified a higher α-amylase activity in port wine, probably due to its sweet taste and higher alcoholic degree.\n\nRegarding the activity of the lipase enzyme, it was observed that the existing concentration in human saliva may be below the detection thresholds of the enzyme kit used. 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-0.017009766772389412,\n 0.00035182072315365076,\n 0.016153160482645035,\n -0.000661575875710696,\n 0.018478235229849815,\n -0.011013518087565899,\n 0.00917793158441782\n]","isConversational":false}}},{"rowIdx":80,"cells":{"text":{"kind":"string","value":"---\ntitle: Prediction of Gastrointestinal Tract Cancers Using Longitudinal Electronic\n Health Record Data\nauthors:\n- Andrew J. Read\n- Wenjing Zhou\n- Sameer D. Saini\n- Ji Zhu\n- Akbar K. Waljee\njournal: Cancers\nyear: 2023\npmcid: PMC10000707\ndoi: 10.3390/cancers15051399\nlicense: CC BY 4.0\n---\n\n# Prediction of Gastrointestinal Tract Cancers Using Longitudinal Electronic Health Record Data\n\n## Abstract\n\n### Simple Summary\n\nCancers of the gastrointestinal tract—including the esophagus, stomach, and intestines—are often diagnosed at an advanced stage, when curative treatments are rare. These cancers can all cause gastrointestinal bleeding, but this often occurs gradually and may be unnoticed by patients. Changes in routine laboratory parameters such as the complete blood count may be able to show these subtle changes prior to clinical presentation or the development of iron deficiency anemia. The aim of our study was to develop models for the prediction of luminal gastrointestinal tract cancers (esophageal, gastric, small bowel, colorectal, anal) using data routinely available within an electronic health record, in a retrospective cohort from an academic medical center. The cohort included 148,158 individuals, with 1025 gastrointestinal tract cancers. We found that longitudinal prediction models using the complete blood count outperformed a single timepoint logistic model for 3-year cancer prediction.\n\n### Abstract\n\nBackground: Luminal gastrointestinal (GI) tract cancers, including esophageal, gastric, small bowel, colorectal, and anal cancers, are often diagnosed at late stages. These tumors can cause gradual GI bleeding, which may be unrecognized but detectable by subtle laboratory changes. Our aim was to develop models to predict luminal GI tract cancers using laboratory studies and patient characteristics using logistic regression and random forest machine learning methods. Methods: The study was a single-center, retrospective cohort at an academic medical center, with enrollment between 2004–2013 and with follow-up until 2018, who had at least two complete blood counts (CBCs). The primary outcome was the diagnosis of GI tract cancer. Prediction models were developed using multivariable single timepoint logistic regression, longitudinal logistic regression, and random forest machine learning. Results: The cohort included 148,158 individuals, with 1025 GI tract cancers. For 3-year prediction of GI tract cancers, the longitudinal random forest model performed the best, with an area under the receiver operator curve (AuROC) of 0.750 ($95\\%$ CI 0.729–0.771) and Brier score of 0.116, compared to the longitudinal logistic regression model, with an AuROC of 0.735 ($95\\%$ CI 0.713–0.757) and Brier score of 0.205. Conclusions: Prediction models incorporating longitudinal features of the CBC outperformed the single timepoint logistic regression models at 3-years, with a trend toward improved accuracy of prediction using a random forest machine learning model compared to a longitudinal logistic regression model.\n\n## 1. Introduction\n\nMalignancies of the gastrointestinal (GI) tract—including esophageal, gastric, small bowel, colorectal, and anal cancers—are a leading cause of morbidity and mortality, with over 200,000 new diagnoses and approximately 80,000 deaths per year in the United States [1]. While routine screening is recommended for colorectal cancer (CRC), many patients go unscreened, particularly in vulnerable and underserved populations [2]. Recent studies have also noted a rising incidence of CRC in younger patients for whom screening may be impractical or ineffective [3,4,5,6]. As a result, even with lowering the age for initiation of CRC screening to 45 [7,8], existing screening programs for GI tract cancers remain inadequate, and there is no routine screening recommended for GI tract cancers in average-risk adults other than for CRC (e.g., stool testing or colonoscopy).\n\nAs GI tract cancers often do not present clinically until they are at an advanced stage, early diagnosis is critical for improving outcomes [9,10,11,12,13]. Improved diagnosis could be achieved by leveraging a physiological common link in luminal GI tract cancers: gradual occult blood loss, ultimately resulting in iron deficiency anemia (IDA) [14]. Healthcare providers often obtain complete blood counts (CBCs) as part of routine clinical care [15,16], but clinicians do not always diagnose IDA accurately and may not obtain the recommended diagnostic evaluation of bidirectional endoscopy (esophagogastroduodenoscopy [EGD] and colonoscopy) for patients with new-onset IDA [15,17,18]. One approach that has been described is the use of electronic trigger tools based on concerning patterns in laboratory data such as new-onset IDA [19]; however, they have not been widely adopted in clinical practice. In addition, site-specific prediction models have examined the association between longitudinal changes in CBCs and the diagnosis of CRC [20,21,22], but new models for prediction of occult malignancy within the entire GI tract are needed. Such models could utilize existing longitudinal laboratory data combined with other patient characteristics stored within the electronic health record (EHR) and serve as automated tools to help improve diagnosis.\n\nIn this paper, we describe the development of models for the prediction of luminal GI tract cancers (esophageal, gastric, small bowel, colorectal, anal) using a single-center retrospective cohort. We developed and compared models using single timepoint logistic regression, longitudinal logistic regression, and longitudinal random forest machine learning.\n\n## 2. Materials and Methods\n\nThe study was conducted as a single-center, retrospective cohort study of patients receiving care at an academic medical center (Michigan Medicine, Ann Arbor, MI, USA) between 2004–2018. This study was approved with a waiver of informed consent by the University of Michigan Institutional Review Board (HUM00156237), due to the large retrospective nature of the study. Data analysis and model development were performed using SAS 9.4 (SAS Institute, Cary, NC, USA) and Python 3.8 (Python Software Foundation, Wilmington, DE, USA). Elements of the TRIPOD guidelines for transparent reporting of multivariable prediction models were used [23].\n\nSubjects were identified as individuals from the Michigan Medicine Clinical Data Warehouse who had at least 2 CBCs within a rolling 2-year time frame between 1 January 2004 and 31 December 2013. Michigan *Medicine is* a large referral center as well as a primary care system. We used the presence of 2 CBCs to identify patients seeking regular care at Michigan Medicine and to provide at least two data points for a longitudinal prediction model. Subjects were excluded if age < 18, given the low incidence of GI tract cancers and paucity of routine blood draws in a pediatric population. Data were collected from the date of subject’s inclusion until 31 December 2018 (or diagnosis of GI tract cancer), including laboratory values from complete blood counts (CBCs), basic metabolic panels (BMPs), age, sex, self-reported race (as documented in the EHR demographics field), and Body Mass Index (BMI, in kg/m²). Data pre-processing was performed in SAS 9.4 (SAS Institute, Cary, NC, USA), with merging of the demographic, laboratory, biometric, and cancer registry data into a unified file. Biologically implausible laboratory or BMI values were excluded.\n\n## 2.1. Predictor Variables\n\nEach model included patients’ demographic variables (age, sex, race), BMI, the individual component variables of the CBC, and the most recent BMP components. We included all the variables from the CBC since subtle changes within laboratory parameters other than hemoglobin or hematocrit may also reflect an iron-deficient state, e.g., elevated red cell distribution width (RDW), low mean cellular hemoglobin (MCH), and low mean cellular volume (MCV) [24,25]. We also included the BMP in these models, which may reflect comorbidities with associated potential links to GI tract cancers, e.g., reported associations between CRC and chronic kidney disease [26] (suggested by elevated blood urea nitrogen and creatinine) and gastric cancer and diabetes [27,28,29] (as might be suggested by hyperglycemia). As machine learning methods can identify patterns that are imperceptible to clinicians, we included all variables from the CBC and BMP, as these methods tend to perform better with additional data rather than making fixed assumptions about the importance of individual predictors.\n\n## 2.2. Primary Outcome\n\nThe primary outcome was the diagnosis of a GI tract cancer, as determined by linkage to the University of Michigan Cancer Center Registry, which contains confirmed pathologic diagnoses of all cancers diagnosed at Michigan Medicine. We chose this method due to the lack of specificity of International Classification of Diseases (ICD)-$\\frac{9}{10}$ codes at differentiating between the time of a diagnosis and the time of documentation in a chart (e.g., potential that a newly charted diagnostic code may reflect documentation of an existing GI tract cancer that occurred many years prior rather than a new diagnosis of cancer). In addition, during the study period, Michigan Medicine updated its EHR system (beginning in 2012), which resulted in overlapping usage of ICD9 and ICD10 codes beginning in 2012. As a result, we selected the cancer registry as it provided a consistent source of confirmed cancers.\n\nWe limited the outcomes to the diagnosis of luminal GI tract cancers, as defined for this study as cancers of the esophagus, stomach, small intestine, colon, rectum, or anus. Non-luminal GI tract cancers such as pancreaticobiliary cancers or liver cancers were excluded from this analysis as we were primarily interested in potential effects of occult GI tract bleeding, as might be reflected by changes in the CBC. For individuals with GI tract cancers within the cancer registry, we used the date of the diagnosis as the individual’s final observation. For individuals with no GI tract cancer within the registry, we used the date of the last recorded CBC to define the end of the observation period.\n\n## 2.3. Model Development\n\nWe used three techniques of prediction model development: [1] single timepoint multivariate logistic regression; [2] multivariate logistic regression incorporating summarized longitudinal features; and [3] random forest machine learning incorporating longitudinal features. For each prediction technique, we developed a prediction model for diagnosis of a GI tract cancer at 6-months, 1-year, 3-years, and 5-years. The eligible sub-population for each time interval was determined in SAS and exported to Python for model building. For each model prediction interval, subjects were included who had at least 2 CBCs prior to the prediction interval, and at least one CBC within the year prior to the beginning of the prediction interval. For example, for the 1-year prediction interval, subjects were included who had at least one CBC between 1 and 2 years prior to the final observation (diagnosis of GI tract cancer/no cancer). For the 6-month prediction, we included those subjects who had at least one CBC between 6 and 12 months prior to the final observation.\n\nFor the single timepoint multivariate logistic regression prediction models, we selected observations on the date of the CBC laboratory draw that was closest to the prediction window. Predictor variables included: age, sex, race, most recent BMI, values from the individual components of the CBC on that date (Hemoglobin [Hgb], platelets [Plt], White Blood Cell [WBC] count, etc.), and the values from the most recently available BMP (Sodium [SOD], Glucose [Gluc], blood urea nitrogen [UN], creatinine [Creat], etc.).\n\nTo incorporate longitudinal elements into the logistic regression and random forest machine learning models, we calculated summary statistics for each subject, summarizing the trends of the individual components of the CBC in the 3 years prior to the prediction window. For example, for each individual component of the CBC (Hgb, Plt, WBC, etc.), we summarized the values over the prior 3 years by the maximum and minimum; the maximum and minimum slope of each predictor variable (i.e., where the slope is the ratio of the change in value/difference in time between two consecutive observations); and the total variation (mean of the absolute value of the slopes). Because the laboratory data were obtained through routine clinical care (irregular intervals that varied between individuals), the use of slopes between observations helped to better describe changes in laboratory values over time. These summary statistics were then added to the predictor variables in the base single timepoint logistic regression models for the longitudinal logistic regression and longitudinal random forest machine learning models. Missing values for individual summary statistics or individual laboratory parameters were determined through imputation using the median value observed across the cohort.\n\n## 2.4. Statistical Analysis\n\nWe calculated descriptive statistics of the cohort at baseline inclusion. For each prediction interval (6-months, 1-year, 3-years, 5-years), we performed a random $\\frac{70}{30}$ split, with $70\\%$ of the individuals in a training set and $30\\%$ in a testing set. Within each prediction interval, we used a training set to fit single timepoint logistic regression, longitudinal logistic regression, and longitudinal random forest machine learning models and evaluated prediction performances using the same testing set. We repeated this procedure 10 times and reported the mean performance characteristics on the testing set over 10 random splits. We implemented logistic regression models with L2 regularization to minimize the potential effects of overfitting. To tune the optimal penalty coefficient for regularized logistic regression, we conducted 5-fold cross-validation, and then the model was fitted with the selected coefficient using the training set.\n\nFor the longitudinal machine learning model, we used the random forest technique. Random forest machine learning is an ensemble, tree-based machine learning algorithm used to classify individuals [30,31], which has been used in multiple prior models and described in detail [32,33,34]. Briefly, each tree classifies the individuals independently. Next, the random forest combines the decisions from each tree to generate a final classification for an individual, which can be understood as the majority vote from trees. We also used 5-fold cross-validation to tune the hyperparameters related to the number, size, and feature of trees in the random forest. For both logistic regression and random forest models, we adjusted the class weight using a built-in argument in the Python scikit-learn package to solve the problem of imbalanced classification (rare events of cancers relative to the population).\n\nFinally, for each model, we determined the area under the receiver operator curve (AuROC), Brier score (measurement of overall performance), and the optimal (maximal) sensitivity/specificity using the test dataset. To balance the sensitivity and specificity, we determined the optimal cut-point, defined here as the point closest to the perfect classification point [0, 1] on the receiver operator curve. We also determined the relative variable importance rankings for the predictor variables in these models. In addition, we performed additional analysis of the performance of the models at predicting cancers by age categories and by GI tract tumor.\n\n## 3.1. Baseline Cohort\n\nWe identified 148,158 individuals who met the inclusion criteria (Table 1). The mean age was 49.4 (SD = 17.3) and the majority were women ($62.1\\%$, $$n = 91$$,$\\frac{995}{148}$,158). Most of the subjects were Caucasian ($81.3\\%$, $$n = 120$$,$\\frac{385}{148}$,158), with $10.5\\%$ being African American ($$n = 15$$,$\\frac{510}{148}$,158), and $4.6\\%$ Asian ($$n = 6795$$/148,158). Within the cohort, we identified 1025 GI tract cancers during the study period: the majority were CRCs ($53.5\\%$, $$n = 548$$/1025), followed by gastric cancer ($16.6\\%$, $$n = 170$$/1025), esophageal cancers ($12.5\\%$, $$n = 128$$/1025), anal cancers ($8.6\\%$, $$n = 88$$/1025), small bowel cancers ($7.7\\%$, $$n = 79$$/1025), and not otherwise specified within the GI tract ($1.2\\%$, $$n = 12$$/1025).\n\n## 3.2. Single Timepoint Prediction Using Logistic Regression\n\nWe developed prediction models for the diagnosis of GI tract cancer at 6-months, 1-year, 3-years, and 5-years using multivariate logistic regression at a single timepoint (the last CBC prior to the prediction interval). We included patients’ age, sex, race, BMI, individual components of the CBC, and the most recent BMP (on or prior to the date of the CBC used for prediction). The results of the models’ performance are shown in Table 2. For 6-month prediction of GI tract cancer, the area under the receiver operator curve (AuROC) was 0.697 ($95\\%$ CI 0.679–0.715), corresponding to a sensitivity of 0.603 and specificity of 0.690 in this population, with a Brier score of 0.007. At increasing time periods of prediction, the AuROC increased; however, the Brier score also increased to above 0.2, indicating lower performing models (Table 3).\n\n## 3.3. Longitudinal Logistic Regression Model\n\nWe developed longitudinal logistic prediction models for the diagnosis of GI tract cancer (at 6-months, 1-year, 3-years, 5-years) using the predictor variables from the single timepoint logistic regression model with the addition of summary variables of the longitudinal CBCs (maximum/minimum, total variation, maximum/minimum slopes). Addition of these longitudinal features led to higher AuROCs for prediction at 6-months, 1-year, and 3-years as compared to the corresponding single timepoint logistic regression models (Table 2). For example, the 3-year AuROC was 0.735 ($95\\%$ CI 0.713–0.757) compared to 0.683 ($95\\%$ CI 0.665–0.701) for the single timepoint logistic regression prediction model (Figure 1). The 1-year longitudinal logistic regression AuROC was 0.705 ($95\\%$ CI 0.689–0.722) with a Brier score of 0.008, compared to the 1-year single timepoint logistic regression model of 0.683 ($95\\%$ CI 0.665–0.701) with a Brier score of 0.224 (indicating poor performance).\n\n## 3.4. Longitudinal Random Forest Machine Learning Model\n\nWe developed longitudinal random forest machine learning prediction models for diagnosis of GI tract cancer (at 6-months, 1-year, 3-years, 5-years) using the predictor variables from the single timepoint logistic regression model with the addition of summary variables of the longitudinal CBCs. The random forest model AuROCs were greater than both logistic regression models for 6-months, 1-year, and 3-year predictions (Figure 1), with an AuROC of 0.750 at 3 years ($95\\%$ CI 0.729–0.771) and a Brier score of 0.116. However, the confidence intervals of the AuROCs overlapped with the longitudinal logistic regression model for all three time periods (Table 2). The variable importance factors for the random forest machine learning models at 1-year and 3-years demonstrated that the most recent (last) mean platelet volume (MPV), minimum MPV, and age were the three most heavily influential variables in these models (Figure 2).\n\n## 3.5. Subanalysis by Age and Tumor Type\n\nWe analyzed the longitudinal logistic regression and longitudinal random forest machine learning prediction models for their prediction success at 1- and 3-years by age group and category of GI tract cancer. We selected three age categories: age less than 50, age 50 years or older and less than 75, and age greater than or equal to 75. These ages were selected as they corresponded with screening age groups for colorectal cancer screening during this study period: CRC screening was recommended starting at age 50, not recommended for those less than 50, and individualized screening decision was recommended between ages 75 and 85. There was a trend toward lower AuROCs for those older than 75, suggesting that the models performed less well in this age group, although some of the confidence intervals overlapped, suggesting this was not a statistically significant difference (Table A1).\n\nTo describe the imbalanced nature of these groups (overall cohort population was younger, with a median age of 49.4), we determined the imbalance ratio as the ratio of the number of negative samples (individuals without cancer) to the number of positive samples (individuals with cancer) in each category. These findings are consistent with established epidemiological trends of increasing GI tract cancers with age. Although the study was not powered to predict individual GI tract cancers, we calculated the performance of the models on the prediction of individual cancers (Table A2). In this setting, the imbalance ratios were more pronounced, e.g., there were only 30 small bowel cancers with sufficient longitudinal data to make 3-year predictions. The models performed less well in the setting of fewer events for this category.\n\n## 4. Discussion\n\nThe results of this retrospective single-center cohort study demonstrate that data within the electronic health record (including CBCs, BMPs, age, sex, race, BMI) can be used to help predict the diagnosis of luminal GI tract cancers (esophageal, gastric, small intestine, colorectal, and anal), with an AuROC of up to 0.750 for prediction of GI tract cancer at 3 years ($95\\%$ CI 0.729–0.771; Brier score = 0.116) with a random forest machine learning model, compared to the longitudinal logistic regression model with an AuROC of 0.735 ($95\\%$ CI 0.713–0.757) with a Brier score of 0.205. While there was a trend toward improvement with machine learning compared to longitudinal logistic regression, the overlapping confidence intervals mean the model is not definitively better. One possible explanation is the relative rarity of GI cancers compared to the cohort as a whole and the general need for more events to outperform logistic regression in machine learning techniques [35]. In addition, this lack of superiority of ML has been found in other clinical prediction domains as well, highlighting the strengths of multivariate logistic regression and the difficulties in outperforming these models with newer techniques [34,36]. At 5-years’ prediction, when longitudinal changes would be less likely to have immediate predictive power, the single timepoint logistic regression model had a higher point estimate AuROC than the longitudinal models at 0.703 ($95\\%$ CI 0.686–0.720), but with a Brier score of 0.213 (indicating overall lower performance). Nonetheless, this study demonstrates important signals that prediction models of luminal GI tract cancers may be useful adjunctive tools to existing clinical intuition and practice guidelines (e.g., that patients with overt GI bleeding or IDA should undergo endoscopic evaluation) [17].\n\nOne important aspect of the random forest machine learning method is that predictor variable associations can be identified that may not be otherwise intuitive. For example, mean platelet volume (MPV) was one of the most important variables in the longitudinal random forest machine learning model. There has been growing interest in the potential usefulness of MPV as a marker of systemic inflammation in GI tract cancers, with possible diagnostic implications for gastric and colorectal cancers [37,38,39,40,41] and possible prognostic implications for esophageal cancers [42]. Clinicians rarely use this feature in routine practice, with a prior clinician survey reporting that clinicians consider MPV to be the least useful component of the CBC [24]. Other predictor variables, such as age, are likely more intuitive to clinicians, consistent with established epidemiologic data of increasing incidence of GI tract cancers with increasing age [2,3].\n\nWhile these models would be inadequate to replace existing CRC [7,8] screening programs [43], they might still have adjunctive utility. For example, guidelines have already implicitly established a tolerance for the “number needed to scope,” or the number of colonoscopies needed to detect one cancer. For example, guidelines recommend bidirectional endoscopy (EGD and colonoscopy) for new-onset IDA [17], with a number needed to scope for a diagnosis of cancer of between to 10 and 100 (incidence ranging between 1 and $10\\%$) [44,45]. Similarly, the threshold for fecal immunochemical testing (FIT) for CRC screening has a PPV of 2.9–$7.8\\%$ for diagnosis of GI tract cancer [46], corresponding to a number needed to scope of approximately 13 to 35 to diagnose one cancer. Thus, using these reference points, a prediction model utilizing EHR data could be calibrated to achieve a higher specificity, while tolerating a lower sensitivity (as this would not be replacing routine screening), until the positive predictive value reached an acceptable threshold for recommending diagnostic endoscopy. This type of model would be complementary to existing screening programs.\n\nThere are several limitations to this study. First, this study was performed retrospectively at an academic medical center, using data collected through routine clinical care, and thus may not apply to other clinical practice settings. The eligible population included all patients receiving care at Michigan Medicine, which includes patients seen by Michigan Medicine primary care providers and specialists. We further narrowed our cohort to individuals with longitudinal follow-up within the Michigan Medicine health system by requiring at least two CBCs over two years. To maximize our eligible cohort, we did not exclude individuals based on the type of provider(s) seen at Michigan Medicine or other exclusions. For this exploratory study, we did not have an external validation cohort, so the accuracy of the models may decline in other settings, as there may be other unmeasured differences between populations. Second, because our inclusion criteria required the presence of at least two CBCs per patient (to determine longitudinal trends), there may be unknown, systematic differences between these patients and those with fewer CBCs (who were excluded from the cohort). An alternate model incorporating a single CBC could have advantages in a setting where primary care follow-up is limited or where CBCs are less commonly obtained. It is also reasonable to consider whether the increased performance of a longitudinal model is “worth” the added computational complexity that would be required for its deployment. Third, for purposes of this analysis, we considered the diagnosis of any GI tract cancer as a binary outcome, given the relatively rare incidence of GI tract cancers relative to the cohort size. However, the tumor biology of GI tract cancers is heterogenous. As a result, there may be different patterns specific for individual subtypes of GI tract cancer that this study was not powered to detect. Models focused solely on a single organ have the potential to have higher specificity but would not be designed to detect other GI tract tumors. Fourth, we were limited by predictor variable inclusion in our models due to a high degree of missingness of some suspected useful variables (CBC with differentials, ferritin), potentially limiting the predictive power relative to prior models for CRC that incorporated CBCs with differentials [20]. An additional limitation is that these models do not incorporate additional clinical history such as the findings of prior EGD or colonoscopy procedures, but we intentionally chose to focus on readily ascertainable parameters from existing EHR data for easier potential use in the future.\n\n## 5. Conclusions\n\nNonetheless, despite these limitations, this work offers some important contributions to the diagnostic evaluation of GI tract cancers, demonstrating that logistic regression or random forest machine learning models using EHR data can help predict the presence of GI tract cancers. Improved diagnosis in this domain is critical. First, given epidemiologic trends with an increasing incidence of CRC at younger ages, additional detection strategies are needed to identify diseases earlier in this younger cohort, who would not yet have undergone routine CRC screening. Second, given limited endoscopic access in some settings, methods to identify patients at greatest risk of GI tract cancer are increasingly important, as they could help prioritize GI diagnostic evaluations on those individuals at greatest risk. Third, prior work has demonstrated that IDA is not always diagnosed or evaluated fully, meaning that additional automated methods of helping clinicians identify patients at increased risk of GI tract cancers are needed. In summary, these models could help fill an important need and assist clinicians in the diagnosis of GI tract cancers. 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0.023542514070868492\n]","isConversational":false}}},{"rowIdx":81,"cells":{"text":{"kind":"string","value":"---\ntitle: Leveraging Tumor Microenvironment Infiltration in Pancreatic Cancer to Identify\n Gene Signatures Related to Prognosis and Immunotherapy Response\nauthors:\n- Jiabin Yang\n- Liangtang Zeng\n- Ruiwan Chen\n- Leyi Huang\n- Zhuo Wu\n- Min Yu\n- Yu Zhou\n- Rufu Chen\njournal: Cancers\nyear: 2023\npmcid: PMC10000708\ndoi: 10.3390/cancers15051442\nlicense: CC BY 4.0\n---\n\n# Leveraging Tumor Microenvironment Infiltration in Pancreatic Cancer to Identify Gene Signatures Related to Prognosis and Immunotherapy Response\n\n## Abstract\n\n### Simple Summary\n\nPancreatic ductal adenocarcinoma (PDAC) has an insidious onset and rapid progression, and its morbidity and mortality are increasing year by year. Currently, there are limited therapeutic methods and no effective therapeutic guidance. Tumor microenvironments (TME) of PDAC are highly specific and associated with the failure of chemotherapy, radiotherapy, and immunotherapy. Different TMEs have different sensitivities to treatment modalities. Therefore, constructing a prediction model based on TME classification and giving corresponding treatment measures according to the classification results will provide a new idea for clinical precision diagnosis and treatment. Further verification of gene function related to TME will greatly provide effective potential clinical treatment targets for personalized therapy.\n\n### Abstract\n\nThe hallmark of pancreatic ductal adenocarcinoma (PDAC) is an exuberant tumor microenvironment (TME) comprised of diverse cell types that play key roles in carcinogenesis, chemo-resistance, and immune evasion. Here, we propose a gene signature score through the characterization of cell components in TME for promoting personalized treatments and further identifying effective therapeutic targets. We identified three TME subtypes based on cell components quantified by single sample gene set enrichment analysis. A prognostic risk score model (TMEscore) was established based on TME-associated genes using a random forest algorithm and unsupervised clustering, followed by validation in immunotherapy cohorts from the GEO dataset for its performance in predicting prognosis. Importantly, TMEscore positively correlated with the expression of immunosuppressive checkpoints and negatively with the gene signature of T cells’ responses to IL2, IL15, and IL21. Subsequently, we further screened and verified F2R-like Trypsin Receptor1 (F2RL1) among the core genes related to TME, which promoted the malignant progression of PDAC and has been confirmed as a good biomarker with therapeutic potential in vitro and in vivo experiments. Taken together, we proposed a novel TMEscore for risk stratification and selection of PDAC patients in immunotherapy trials and validated effective pharmacological targets.\n\n## 1. Introduction\n\nPancreatic ductal adenocarcinoma (PDAC) is one of the most challenging cancers in alimentary malignancies [1,2]. For most patients with PDAC, cytotoxic chemotherapy remains the mainstay of treatment. However, despite recent improvements in chemotherapeutic regimens and treatment modalities, their survival benefits remain limited [3]. In addition, many efforts have been made to develop targeted therapies for PDAC, but there has been no substantial improvement [4,5]. Progress in strategies targeting homologous recombination defects, while substantial, currently shows applicability and efficacy in only a small proportion of patients [6]. Furthermore, PDAC is known to lack an effective immune response with low immunogenicity, which results in rapid cancer progression and a limited response to cancer immunotherapy [7,8]. Despite the aggressive molecular behavior driven by intrinsic oncogenic genetic alterations, the tumor microenvironment (TME) of pancreatic cancer has been deemed to be responsible for the above dilemma [9,10,11,12].\n\nPDAC is characterized by extensive deposition of desmoplastic stroma, which may comprise more than $80\\%$ of the whole tumor mass [13,14]. The extracellular matrix, vessels, and stromal cells comprise the TME of pancreatic cancer [15]. The cell component surrounding PDAC cells consists predominantly of cancer-associated fibroblasts, various immune cells, and endothelial cells. The complex interactions between TME cells and cancer cells contribute to tumor progression in a multifaceted way [16]. For example, cancer-associated fibroblasts, infiltrated inflammatory cells, and desmoplastic stroma enhance cancer growth, invasion, metastasis, and treatment resistance in direct or indirect ways [15,17]. The immune-suppressor cells in TME establish an immunosuppressive tumor microenvironment, which results in rapid cancer progression and a low immune response to immunotherapy [18]. Accumulating studies have revealed that the TME context correlates with clinical outcomes, therapy benefits, and immune response [19,20,21,22,23,24,25].\n\nBy now, although clinical decision-making based on molecular subtypes has been well established in some cancer types, subtypes of PDAC do not currently provide effective support for clinical decisions [26]. However, accumulating molecular subtypes have been defined in PDAC with the development of the genome project, which defined various PDAC subtypes with distinct tumor biological behaviors and clinical characteristics [27]. Although the several mechanisms associated with the role of TME have been highlighted in some previous subtypes [27,28,29,30,31], the comprehensive landscape of cells infiltrating the TME of PDAC has not yet been elucidated, as well as that there is a lack of a molecular subtype based on TME signatures to inform treatment decisions, including the applicability of immunotherapy. Therefore, in the present study, we evaluated the cell components of the TME in PDAC using computational algorithms and then established subtypes based on TME infiltration signatures. Finally, a robust TMEscore and an effective biomarker capable of risk stratification and informing treatment decisions were developed.\n\n## 2.1. Sample Data Collection\n\nThe expression data (RPKM) of 182 PDAC patients were downloaded from The Cancer Genome Atlas (TCGA) data portal (https://portal.gdc.cancer.gov/ (accessed on 1 January 2020)) by using TCGAbiolinks. The fragments per kilobase of exon model per million (FPKM) data from TCGA were transformed into transcripts per kilobase per million (TPM) values. Additionally, other public PDAC datasets were obtained through the retrieval of the GEO database (https://www.ncbi.nlm.nih.gov/geo/ (accessed on 1 January 2020)) with the following retrieval strategy: (“pancreatic neoplasms” [MeSH Terms] OR pancreatic cancer [All Fields]) AND “Homo sapiens” [porgn] AND (“Expression profiling by array” [Filter]). Samples with survival data were retained for further analysis. The detailed information on the retrieved datasets was summarized in Supplementary Table S1.\n\n## 2.2. Estimation of Cell Components in TME\n\nThe ESTIMATE algorithm was used to calculate the level of stromal cell and immune cell infiltration in each sample and further infer tumor purity [32]. Single-sample gene set enrichment analysis (ssGSEA) [33,34], a deconvolution algorithm based on gene set enrichment analysis (GSEA), was used to qualify the relative abundance of 29 cell types within the TME. The ssGSEA was run using the GSVA R package. *Signature* genes of each cell type were obtained from previous publications [35,36]. The ssGSEA score was normalized to a unity distribution, in which zero is the minimum score and one is the maximal score for each cell type. In some analyses, the immune infiltrations were also quantified by the GSVA algorithm [37], for which the normalized GSVA scores were obtained from a recent publication [38].\n\n## 2.3. Consensus Clustering for Infiltrating Cells of TME\n\nUnsupervised consensus clustering was performed on normalized ssGSEA scores of TME cell components by using the ConsensusClusterPlus R package (parameters: reps = 1000, pItem = 0.8, pFeature = 1). The complete method and Manhattan distance were used as the clustering algorithm and distance metric, respectively.\n\n## 2.4. Identification of TME Signature Genes\n\nTo identify the signature genes of TME, we first estimated the differentially expressed genes (DEGs) associated with TME subtypes determined by consensus clustering of TME infiltration. The DEGs among TME subtypes were obtained using the limma R package with the selection criteria of Log2FoldChange > 1 and an adjusted p-value < 0.05 (Benjamini–Hochberg correction). Next, the random forest method was used to evaluate the contribution of these DEGs to the cluster grouping of the TME cell population, and the genes that had less influence on the grouping were filtered out. Finally, 74 DEGs that influenced the prognosis were obtained. An unsupervised clustering method (K-means) with Ward. D and Euclidean distance was used to classify patients into subgroups based on the 74 DEGs. The ConsensusClusterPlus R package was adopted and used to annotate gene patterns and define gene clusters.\n\n## 2.5. Development of TMEscore\n\nAfter obtaining the two gene clusters in the above part, the genes in each cluster were extracted to serve as the TME gene signature sets, respectively: TME signature gene set A from cluster 1, and TME signature gene set B from cluster 2. The ssGSEA algorithm was used to calculate the enrichment score of each TME signature gene set for each sample. Thereafter, the TMEscore for each sample was obtained by using the following formula: TMEscore = TMEscoreB − TMEscoreA, where TMEscoreB stands for the ssGSEA score of TME signature gene set B, TMEscoreA stands for the ssGSEA score of TME signature gene set A. A schematic diagram illustrating the process of generation of the TMEscore was described in Supplementary Figure S1.\n\n## 2.6. Functional Annotation and Enrichment Analysis\n\nA functional annotation analysis was conducted based on the GO database (http://geneontology.org/page/go-database (accessed on 1 January 2020)) and the KEGG database (http://www.kegg.jp/kegg/ko.html (accessed on 1 January 2020)). KEGG and GO term gene set enrichment analysis (GSEA) was conducted using the clusterProfiler R package. We also estimated the enrichment of pathways among TME gene clusters or samples with different TMEscores by running gene set enrichment analysis (GSEA) [33,39]. For GSEA analysis, gene sets for certain pathways were collected via searching the MSigDB database (https://www.gsea-msigdb.org/gsea/msigdb/ (accessed on 1 January 2020)). GSEA was performed using the GSEA software and visualized by the ggplot R package.\n\n## 2.7. Mutation Analysis\n\nTCGA-PAAD mutation data were downloaded in January 2020 from the GDC data portal. The copy number events were filtered for those with at least 10 supporting probes and a segment mean >0.2 (amplifications) or <−0.2 (deletions), as recommended by a previous study [40,41]. The waterfall plots of mutational landscapes were drawn using the maftools Bioconductor package [42]. Mutation types were ordered by their potential impact, from most deleterious to least.\n\n## 2.8. Cell Lines and Cell Culture\n\nHuman cell lines BxPC-3, PANC-1, AsPC-1, Capan-2, MIA-PaCa2, SW1990, and hTERT-HPNE were purchased from the ATCC (American Type Culture Collection, Rockville, MD, USA). Cells were cultured in DMEM (Gibco, Billings, MT, USA) or RPMI 1640 medium (Gibco, USA). All media were supplemented with $10\\%$ fetal bovine serum (FBS, BI, Israel) and $1\\%$ penicillin/streptomycin. All cells were cultured in a humid environment containing $5\\%$ CO2 at 37 °C.\n\n## 2.9. Cell Transfection and Lentiviral Infection\n\nFor cell transfection, the siRNAs for knocking down F2R-like Trypsin Receptor1 (F2RL1) and the F2RL1 overexpression plasmid were purchased from IGE (Guangzhou, China). Transfection was performed using the Lipofectamine 3000 kit (Invitrogen, Cat# L3000015, Waltham, MA, USA) according to the manufacturer’s instructions.\n\nFor lentivirus infection, in order to construct stable knockdown cell lines, the shRNA sequence of F2RL1 was cloned into a pLKO.1-Puro vector by IGE, and then the lentivirus packaging plasmids containing psPAX2 and pMD2G were cotransfected into HEK-293T cells (ATCC, RRID: CVCL_0063). After transfection for 72 h, lentivirus was collected and concentrated. Subsequently, the cell lines were infected with lentivirus and selected by treatment with puromycin (Solarbio, Beijing, China) for 2 weeks. All the sequences of oligonucleotides are shown in Supplementary Table S9.\n\n## 2.10. RNA Extraction and qRT-PCR Analysis\n\nThe total RNA of PDAC cell lines was extracted using Trizol reagent (Takara Bio, Shiga, Japan) according to the instructions. Subsequently, the total RNA was reverse transcribed into cDNA using the Hiscript III Reverse Transcriptase Kit (Vazyme, Nanjing, China). Finally, qRT-PCR was used to detect the expression of RNA using the ChamQ Universal SYBR qPCR Master Mix kit (Vazyme, Nanjing, China). GAPDH was used as an internal control. The sequences of primers are listed in Supplementary Table S9.\n\n## 2.11. Colony Formation Assay\n\nA total of 500 cells transfected with siRNA or stably overexpressing F2RL1 were cultured in 6-well plates in a humidified atmosphere containing $5\\%$ CO2 at 37 °C for 2 weeks. After that, the colonies were fixed in $4\\%$ paraformaldehyde for 20 min, stained with $0.1\\%$ crystal violet for 15 min, and washed twice with phosphate-buffered saline (PBS). Count colonies manually. Each group repeated the experiment at least three different times.\n\n## 2.12. EdU Assay\n\nThe cells transfected with siRNA or stably overexpressed F2RL1 were pre-seeded in 24-well plates and cultured at 37 °C containing $5\\%$ CO2 for 24 h. Then, using BeyoClickTM EdU-555 detection kits (Beyotime, Shanghai, China) and according to the manufacturer’s instructions, the cells were stained with EdU for 2 h, fixed with $4\\%$ paraformaldehyde for 15 min, incubated with click reaction solution for 30 min and stained with Hoechst 33,342 for 10 min. The images were obtained by fluorescence microscope Nikon TI-S (Nikon, Tokyo, Japan). Each group repeated the experiment at least three different times.\n\n## 2.13. Wound Healing Assay\n\nThe cells transfected with siRNA or stably overexpressed F2RL1 were seeded in a 12-well plate according to 2 × 105 cells/well and cultured in a moist environment containing $5\\%$ CO2 at 37 °C until the cell density was about $90\\%$. Then adherent cells were scraped with the 10 μL sterile pipette tips in a straight line, and the images were obtained by an inverted microscope, the Nikon TI-S (Nikon, Tokyo, Japan), at 0 h and 24 h, respectively. The cell migration distance was measured and calculated. Each group repeated the experiment at least three different times.\n\n## 2.14. Transwell Assay\n\nThe cells transfected with siRNA or stably overexpressed F2RL1 were added to 200 μL serum-free medium with or without Matrigel (BD Biosciences, Franklin Lakes, NJ, USA), respectively, and seeded in a Transwell chamber, then placed in a 24-well plate. 700 μL complete medium was added to each well in the lower layer of the 24-well plate in advance. PANC1 was incubated for about 8 h, and BxPC-3 was incubated for about 48 h. Then the Transwell chamber was removed, fixed with $4\\%$ paraformaldehyde for 15 min, and stained with $0.1\\%$ crystal purple for 15 min. Images were obtained by an inverted microscope, the Nikon TI-S (Nikon, Tokyo, Japan), and the number of cells that migrated or invaded was counted. Each group repeated the experiment at least three different times.\n\n## 2.15. Animal Experiment\n\nTo construct a subcutaneous tumorigenicity animal model, 5 × 106 BxPC-3 cells in suspension, stably overexpressing F2RL1 and Vector, were injected subcutaneously into the left dorsal side of BALB/c nude mice ($$n = 5$$) aged 4 to 5 weeks, purchased from the Guangdong Medical Laboratory Animal Center. Tumor growth was measured every 4 days, and tumor volume was recorded. Volume = 0.5 × length × width2. Four weeks later, all the mice were sacrificed, and the tumor tissue was dissected, weighed, and fixed with $37\\%$ formalin and embedded in paraffin.\n\n## 2.16. Immunohistochemistry (IHC)\n\nImmunohistochemical staining was performed on paraffin sections, which were first treated at 60 °C for 2 h, then dewaxed with xylene, rehydrated with different grades of ethanol, repaired antigen with EDTA, and blocked with normal goat serum. The sections were incubated with primary antibodies at 4℃ overnight and secondary antibodies at room temperature for 2 h. Finally, DAB chromogenic reagent was used to label the antigen, followed by counterstaining with hematoxylin. The staining was judged by two independent observers. Images were obtained by a microscope, the Nikon 80i (Nikon, Tokyo, Japan). The antibodies used in this study are listed in Supplementary Table S10.\n\n## 2.17. Statistical Analysis\n\nThe distribution of two sets of continuous variables was compared using a t-test. If continuous variables did not follow a normal distribution, the Mann–Whitney U test was applied. Unless explicitly stated, the association between categorical variables was evaluated using Pearson’s chi-square test. To divide the samples assessed into groups according to high versus low TMEscore, the MaxStat R package was used. Survival curves were compared using the Kaplan–Meier method with a log-rank t-test. The influence of the TMEscore on survival was additionally evaluated through the Cox proportional hazard model. The independence of association was verified by a multivariate Cox regression model of survival. The resulting p-values of differently expressed genes between two groups were corrected for multiple testing by the Benjamini–Hochberg method. All reported p-values are two-sided. R (version 3.6.3) and SPSS (version 17.0; SPSS Inc., Chicago, IL, USA) were used to perform statistical analyses. Figures were generated with the ggplot R package and GraphPad Prism 8 (GraphPad Prism Software, San Diego, CA, USA). Two-sided $p \\leq 0.05$ was considered significant.\n\n## 3.1. Identification of Tumor Microenvironment Subtypes in PDAC Cases\n\nThe flow chart of the study is shown in Figure 1A. First, we defined the tumor microenvironment (TME) infiltration pattern of each tumor as the relative abundance of an array of twenty-eight cell populations of immune cells and fibroblasts. TME cell profiles were estimated via the ssGSEA algorithm (Supplementary Table S2). To select the optimal cluster number, we grouped the ssGSEA scores of the resectable PDAC tumors from the TCGA dataset using hierarchical clustering. As a result, we obtained three robust subtypes of PDAC (named TMEgroups1–3) (Figure 1B) (Supplementary Table S3). The TMEgroup3 is defined as the smallest group of cases ($\\frac{23}{177}$, $13.0\\%$), followed by TMEgroup1 ($\\frac{71}{177}$, $40.1\\%$), and TMEgroup2 ($\\frac{83}{177}$, $46.9\\%$). TMEgroup3 was associated with better overall survival in comparison with the other two TMEgroups (Log-rank test, $$p \\leq 0.017$$, Figure 1C). In many solid tumors, the degree of immune infiltration of the TME is highly correlated with immunotherapy efficacy [43]. Due to the high tumor heterogeneity and TME heterogeneity in PDAC, the specific biological characteristics are different from other solid tumors [44]. As previous studies have shown, the abundance of immune cells tends to predict a poor prognosis [45,46]. The overall survival rate of TMEgroup3, which had the fewest immune cells, was higher than that of the other two groups.\n\n## 3.2. Identification of the TME Signature Genes and Functional Annotation\n\nTo identify the signature genes associated with TMEgroups, differentially expressed genes (DEGs) between each TMEgroup and others were obtained using the limma R package, and the results are shown in Supplementary Figure S2 and Supplementary Table S4. As shown in the Venn diagram of Figure S2, there was no overlap between the DEGs from each TMEgroup, suggesting the high specificity of DEGs for each TMEgroup. Next, a random forest method was then used to estimate the contribution of these DEGs to the clustering of the TME cell population, and 74 genes with influence on the clustering were finally retained. By performing unsupervised hierarchical cluster analysis based on the 74 TME-related DEGs, we identified 2 robust groups for TCGA-PAAD samples: TMEgeneGroup1 and TMEgeneGroup2. *The* gene symbols for signature genes for each group were summarized in Supplementary Table S5. The patient-level annotation of the DEGs is visualized in Figure 2A. A significant decreased overall survival was found in TMEgeneGroup1 (Log-rank test, $$p \\leq 0.0034$$, Figure 2B). The differences in class assignments between the two clustering methods were visualized with an alluvial diagram (Figure 2C). Most deaths occurred in TMEgroup3 and all deaths that occurred in TMEgroup2 were assigned to TMEgeneGroup1, suggesting signature genes had better prognostic discrimination values, such as the identification of patients at a high risk of death from subgroups with better prognosis.\n\nIn addition, we analyzed the tumor purity of the TCGA dataset, and the results showed that there was a difference between TMEgeneGroup1 and TMEgeneGroup2 in the tumor purity, suggesting that the tumor may be related to immune infiltration of TME (Supplementary Figure S2G, Supplementary Table S7). Further, we found that tumor purity was negatively correlated with the immune ssGSEA (Supplementary Figure S2H). Moreover, we further analyzed the laser microdissected dataset from Maurer C et al. [ 47]. Through the comparison of stromal and epithelial cells, we obtained 6308 DEGs representing stromal regions, which were further intersected with our 74 DEGs. Interestingly, the results showed that there were 41 duplicate DEGs (Supplementary Figure S2E), suggesting that those 41 DEGs we analyzed had the characteristics of representing stromal cells in non-tumor regions. Subsequently, we also verified the effect of ssGSEA used in this project in reflecting TME characteristics (Supplementary Figure S2F). The above results show that the difference in TME can be better reflected by screening effective DEGs to distinguish cell components.\n\nTo further explore the biological function and mechanism behind the signature genes, DEGs between TMEgeneGroup1 and TMEgeneGroup2 were determined (Supplementary Table S6). Functional annotation analysis of these DEGs was conducted. Significantly enriched KEGG pathways and GO biological processes were summarized in Supplementary Figure S3. We found TMEgeneGroup1 and TMEgeneGroup2 had distinct differences in the enriched pathways and biological processes. Genes overexpressed in TMEgeneGroup1 were involved in several well-known carcinogenesis mechanisms, such as the PI3K/Akt signaling pathway and the p53 signaling pathway. In addition, mechanisms involved in extracellular matrix composition, cell-extracellular matrix interaction, and cell adhesion were enriched in TMEgeneGroup1. In contrast, genes overexpressed in TMEgeneGroup2 were mainly involved in signal molecule transduction-related mechanisms, such as the cAMP signaling pathway, ligand-receptor interaction, single release, chemical synaptic transmission, and regulation of membrane potential. Furthermore, using disease network enrichment analysis with all DEGs between TMEgeneGroup1 and TMEgeneGroup2 (Figure 2D), we found enriched disease gene sets related to chronic pancreatitis, cholangiocarcinoma, and breast carcinoma stage IV. In addition, we identified seven core DEGs: GSTP1, ERBB2, MUC1, F2RL1, PTGS2, CCND1, and CXCL8, which were involved in all three gene sets.\n\n## 3.3. Establishment of TMEscore Model Based on TME Signature Gene Sets\n\nAs mentioned above, the unsupervised hierarchical cluster analysis was based on the 74 most representative DEGs and separated the PDAC cohort into 2 distant patient clusters (Figure 2A). Subsequently, the 74 DEGs were divided into 2 distinct clusters, termed TME signature gene set A (enriched in TMEgeneGroup1) and TME signature gene set B (enriched in TMEgeneGroup2). Furthermore, based on the two TME signature gene sets, we estimated two TME-related scores using the ssGSEA algorithm as described in the “Methods” part: TMEscoreA from TME signature gene set A and TMEscoreB from TME signature gene set B, thus obtaining the final TMEscore through the following formula: TMEscore = TMEscoreB − TMEscoreA.\n\nAfter having identified the TMEscore for each patient in the TCGA-PAAD cohort, we sought to determine whether the TMEscore could effectively predict prognosis. As shown in Figure 3A–C, low TMEscoreA was correlated with improved survival (Log-rank test, $$p \\leq 0.0005$$) in TCGA-PAAD patients, and a low TMEscoreB was associated with poor survival (Log-rank test, $$p \\leq 0.00152$$). At last, survival analysis revealed that patients with a low TMEscore had a less favorable outcome (Log-rank test, $$p \\leq 0.00065$$). Multivariate Cox models revealed that the TMEscore was an independent prognostic variable for overall survival (HR = 1.72. $95\\%$CI 1.07–2.80, $$p \\leq 0.025$$) (Figure 3D). Next, we tried to validate the prognostic value of TMEscore with seven external data sets obtained from the GEO database. As shown in Supplementary Figure S4, upon stratification of the samples according to TMEscore, significant differences in overall survival were found between the TMEscore low and high groups for all datasets except GSE28735 ($$p \\leq 0.14$$, sample size = 43, the dataset with the lowest sample size), which confirmed the robust prognosis stratification ability of the TMEscore.\n\n## 3.4. Exploring the Biological Characteristics of Patients with Different TMEscore\n\nTo explore the underlying molecular mechanisms associated with the TMEscore, we first compared the profile of oncogenic mutations between patients with a low and high TMEscore (Figure 4A). We found significantly increased mutation rates in KRAS, TP53, and CDKN2A in the low TMEscore group. Afterward, the GSEA was performed to evaluate the pathways associated with the TMEscore (Figure 4B). The significantly enriched gene sets in samples with a low TMEscore were correlated with KRAS, NF-κβ, P53, MEK, AKT, and cell cycle signaling pathways, all directly associated with tumor development. On the other hand, genes up regulated in neurons and in response to overexpressing Src were enriched in samples with a high TMEscore. The GSEA results were consistent with the differences in mutation spectras between patients with a low and high TMEscore. Additionally, these results also indicated that the TME-based score could reflect tumor-intrinsic mechanisms at the level of driver gene profiles.\n\nNext, we analyzed the infiltration of cell populations between two groups. Figure 4C displays the differences in TME cell infiltration in the two groups with high and low TMEscores. Overall, most cell populations were increased in samples with a high TMEscore, especially since the infiltration of activated CD4/CD8 T cells was significantly increased. In light of the well-recognized close relationship between TME and immune status, we further compared the well-known biological mechanisms/pathways associated with TME and cancer-immune phenotypes between the two groups. The feature genes for each mechanism/pathway were summarized in Supplementary Table S8. As shown in Figure 4D, the ssGSEA analysis confirmed a significant enrichment of genes representing EMT, the TGF-β pathway, the Wnt pathway, homologous recombination, mismatch repair, and DNA damage repair in low TMEscore samples. This suggests that the TMEscore may reflect tumor environment infiltrations, tumor-intrinsic mechanisms, and immune status.\n\n## 3.5. Characterization of Immune-Phenotypes across Samples with Different TMEscore\n\nTo further delineate the link between TMEscore and immune status, we evaluated the relationship between a well-established immune phenotype and TMEscore. The immune phenotypes of 156 TCGA-PAAD samples were obtained from a previous publication, which classified tumors into three immune phenotypes: poor cytotoxicity, intermediate cytotoxicity, and high cytotoxicity on the basis of cytotoxic infiltration [38]. The abundance of cytotoxic cells was estimated by either ssGSEA (Figure 5A) or GSVA (Figure 5B). As shown in the heatmaps (Figure 5A,B) and the result of the chi-square test (Figure 5C), we observed that the cytotoxic level of the immune phenotype tended to increase with the elevated level of TMEscore. A previous study reported that tumors with a highly cytotoxic immune phenotype tend to show an increased abundance of cytotoxic infiltration with ectopic expression of negative immune checkpoints [38]. So, we next estimated the infraction of activated CD8 T cells, effector memory T cells, and γδ T cells by ssGSEA (Figure 5D) and GSVA (Figure 5E). Both algorithms showed increased infiltration of CD8 T cells and effector memory T cells in samples with high TMEscores. Additionally, we observed the same increase in the cytotoxic score (Figure 5F). These results collectively suggested that a high TMEscore indicates a TME with high cytotoxic activity. Therefore, we next estimated the enrichment of gene sets associated with the T cell–inflamed phenotype, which correlates with improved responsiveness to therapies dependent on T cell killing, such as checkpoint blockade and adoptive cell therapy [48]. We used three independent gene sets for each comparison, including gene sets that have been confirmed to be predictive of response to immunotherapy across different cancer types. Notably, all gene sets displayed significant enrichment in the high TMEscore group (Figure 5G). Meanwhile, we also observed the enrichment of immune checkpoints.\n\nAt last, we assessed the enrichment of gene programs defining PDAC subtypes and their association with the TMEscore (Figure 5H). The expression of genes defining the ADEX (aberrantly differentiated endocrine exocrine) subtype [30] was increased in TMEscore-high tumors, while the genes defining the pancreatic progenitor subtype tended to increase in TMEscore-low tumors. TMEscore-high tumors were statistically enriched for the immune gene programs (GP6 and GP8) from Bailey and colleagues [30]. These two gene programs contain signature genes for CD8+ T cells and B cells, supporting the finding that TMEscore-high tumors had high cytotoxic infiltration. Furthermore, TMEscore-high tumors were enriched for the normal stroma gene program. The normal stroma gene signature contains markers of pancreatic stellate cells, which have been linked to an immunosuppressive tumor microenvironment through blocking antigen presentation [29,49,50]. In contrast, TMEscore-low tumors were enriched for the activated stroma gene program. The activated stroma was characterized by a more diverse set of genes associated with activated fibroblasts and activated inflammatory stromal responses, both of which were responsible for a low antitumor immune response [29].\n\n## 3.6. The Predictive Value of TMEscore for Response to Immune Therapy\n\nThe above data suggested that the TMEscore can stratify PDAC patients into distinct clusters or subtypes not only with different tumor-intrinsic characteristics but also with different stromal statuses and immune environments. To further validate this finding, we performed GSEA and found enrichment of immune sensing pathways involved in T cell priming (STING and NLRP3 inflammasome signaling) in TMEscore-low tumors (Figure 6A). We found that both STING and NLRP3 inflammasome signaling were enriched in samples with low TMEscore. Consistent with this, the antigen presentation activity, which was measured by the ssGSEA score of two independent gene sets reflecting the antigen presentation mechanism (APM), was elevated in samples with a low TMEscore (Figure 6B,C). Interestingly, the expression of signature genes of Batf3-dendritic cells, a key antigen-presenting cell population for driving T cell immunity and response to immunotherapy in PDAC [51,52], was increased in TMEscore-high tumors (Figure 6D). These results support the above hypothesis that a low TMEscore identified tumors with normal stroma status, which was characterized by blocked antigen presentation. In addition, we found both the CD8/CD4 ratio and the CD8/Treg ratio were significantly increased in TMEscore-high tumors (Figure 6E,F). Both of them were biomarkers of elevated cytotoxic activity. We also found that downregulated genes after IL15, IL2, or IL21 stimulation were enriched in TMEscore-high tumors (Figure 6A). IL15, IL2, and IL21 were responsible for the expansion of cytotoxic T cells; therefore, the negative correlation between TMEscore and the expression of these gene sets supported a cytotoxic environment with exhaustion in TMEscore-high tumors. Collectively, the stratification of patients with TMEscore based on transcriptional profiling may differentiate between tumors with different immune evasion mechanisms and different immunotherapy responses.\n\nImmune checkpoint blockade therapy, such as inhibitors targeting the PD1–PDL1 axis, shows promising prospects for cancer treatment. We subsequently explored the prognostic value of the TMEscore in patients who received immune-checkpoint therapy. As there is currently no available cohort with both transcriptome and survival information for immune therapy in pancreatic cancer, we used two well-known solid tumor cohorts receiving immune checkpoint blockade therapy. As shown in Figure 6G–J, patients with high TMEscores had significantly longer overall survival than those with low TMEscores in both the GSE78220 [53] cohort (anti-PD1) and IMvigor210 [54] cohort (anti-PDL1). In line with survival analysis, patients with a high TMEscore also showed an increased response rate to both anti-PD-1 (GSE78220) and anti-PD-L1 (IMvigor210) antibody treatment. Taken together, our data suggest that TMEscore could predict the response to checkpoint inhibitor immunotherapy.\n\n## 3.7. F2RL1 Was Significantly Associated with the Malignant Progression of PDAC\n\nTo further explore potential drug therapeutic targets for PDAC, we screened the aforementioned core DEGs: GSTP1, ERBB2, MUC1, F2RL1, PTGS2, CCND1, and CXCL8, and verified the expression between BxPC-3 and PANC-1 cell lines. The results showed that the expression of F2RL1 was significantly high (Figure 7A,B). Currently, the mechanism of F2RL1 in the malignant progression of pancreatic cancer remains unclear, and more scientific evidence is needed. TCGA and Genotype-Tissue Expression (GTEx) database analysis showed that F2RL1 was highly expressed in PDAC tumor tissues compared with non-tumor tissues (NAT) (Figure 7C). Then, univariate and multivariate Cox analyses revealed that F2RL1 was an independent influence factor on the overall survival (OS) and disease-free survival (DFS) of PDAC patients (Figure 7D, Supplementary Tables S11 and S12). Importantly, Kaplan–*Meier analysis* showed that PDAC patients with high F2RL1 expression had shorter OS and DFS, suggesting that F2RL1 is associated with the malignant progression of PDAC (Figure 7E,F). Notably, further ssGSEA correlation analysis showed that the expression of F2RL1 was related to some immune cells in TME (Figure 7G). Further, we used the TISCH platform (http://tisch.comp-genomics.org (accessed on 2 January 2023)) to analyze the expression of F2RL1 at the cellular level. The results showed that F2RL1 was mainly distributed in malignant cell subsets (Figure 7H–J). Moreover, through functional annotation analysis of malignant cells, the enrichment results of the KEGG pathway and GO biological process showed that F2RL1 was mainly related to extracellular matrix composition, signaling pathways, cell adhesion, and other mechanisms (Figure 7K,L). The above results suggested that the upregulation of F2RL1 could promote the malignant progression of PDAC.\n\n## 3.8. As an Effective Therapeutic Target, F2RL1 Promotes the Proliferation and Invasion of PDAC In Vitro and Vivo\n\nGiven that F2RL1 was associated with a poor prognosis for PDAC, we further explored its biological function. We analyzed the expression of F2RL1 in PDAC cell lines, and the results showed that the expression of F2RL1 was significantly high in PANC-1 and BxPC-3 (Figure 8A). Further, we verified the transfection efficiency of F2RL1 in BxPC-3 and PANC-1 cell lines by knockdown and overexpression of F2RL1 (Figure 8B–E). The colony formation assay (Figure 8F–H, Supplementary Figure S5A–C) and EdU assay (Figure 8I–K, Supplementary Figure S5D–F) showed that the proliferation ability of cells, compared with the control group, was decreased after F2RL1 expression was down-regulated. The overexpression of F2RL1 showed the opposite effect. These results suggested that the upregulation of F2RL1 can promote the proliferation of PDAC cells. The transwell assay (Figure 8O–Q, Supplementary Figure S5J–L) and the wound healing assay (Figure 8L–N, Supplementary Figure S5G–I) showed that, compared with the control group, the cell invasion ability was weakened after F2RL1 was down-expressed, while the effect was opposite after F2RL1 was over-expressed. Therefore, these results indicated that the overexpression of F2RL1 could promote the proliferation and invasion of PDAC cells in vitro.\n\nFurther, we verified the carcinogenic function of F2RL1 in a subcutaneous tumorigenicity mouse model (Figure 9A,B). Animal experiments showed that, compared with the sh-NC group ($$n = 5$$), the tumor volume and weight in the sh-F2RL1#1 group were lower (Figure 9C,D). Remarkably, IHC staining showed lower levels of Ki-67 expression in stable knockdown F2RL1 tissues (Figure 9E,F). Therefore, the downregulation of F2RL1 can strikingly inhibit the proliferation of PDAC in vivo.\n\nCollectively, F2RL1 may be a potential biomarker for predicting survival outcomes in patients with PDAC.\n\n## 4. Discussion\n\nIt is well acknowledged that the TME is of vital importance in cancer progression and therapeutic responses [55]. In this context, we evaluated the infiltration pattern of TME cells through the computational integration of their signature genes and developed a TMEscore that robustly predicts the prognosis for PDAC patients. Through comparison with the established PDAC molecular subtypes, we found the TMEscore differed across multiple established PDAC subtypes. Overall, tumors with a high TMEscore tend to share transcriptional commonalities with ADEX/Exocrine-like subtypes, which were defined by the transcriptional expression of multiple genes associated with terminally differentiated pancreatic tissues [30]. Instead, tumors with a low TMEscore were enriched with squamous/classical subtypes which reflect the molecular characteristics of squamous tumors across multiple tissue types, such as hypoxia response, metabolic reprogramming, and TGF-β signaling [30]. The close relationship between the TMEscore and tumor essential character-based subtypes suggested a correlation between TME and tumor cell-intrinsic properties. On the other hand, as expected, TMEscore-low tumors showed a subtype of “activated stroma” [29], while tumors with a high TMEscore displayed a “normal stroma” subtype [29]. Beyond that, TME high-score tumors were enriched for the immune gene programs (GP6 and GP8) from Bailey and colleagues [30]. These two gene programs were associated with B cells and CD8+ T cell infiltration signatures and T cell co-inhibitory phenotypes, respectively [30]. Therefore, the TMEscore is a comprehensive index reflecting tumor intrinsic features, stromal states, and immunophenotype.\n\nPDAC is strikingly resistant to traditional treatment [56,57,58]. Immune checkpoint inhibitors, represented by PD-1/PD-L1 blockers, are widely believed to be a promising modality in pancreatic cancer, but the high prevalence of immunotherapy resistance of PDAC remains a main obstacle [59]. Effective identification in PDAC patients with potential benefits from immunotherapy could facilitate the translation of immunotherapy agents from preclinical research to clinical trials or applications. However, the biomarker for the efficiency of immunotherapy is thus far lacking [60]. A strong relationship between the tumor mutational burden (TMB) and the activity of immune checkpoint inhibitor (ICI) therapies has been identified across multiple cancers, but not pancreatic cancer [61]. MMR-D pancreatic cancer has been reported to respond to checkpoint inhibitor therapy, but it occurs in less than $1\\%$ of PDAC patients [62,63]. The MSI-H/dMMR phenotype is also very rare in PDAC [62]. The essential role of TME in immunosuppressants has been well known, and the impact of TME on the immune behavior of tumors has become a research focus in the immunotherapy of PDAC [9,64,65,66]. Therefore, personalized immunotherapy for solid tumors can be realized based on the characteristics of TME cell components. With the help of several computational algorithms, the TMEscore established in this study could represent the landscape of the whole infiltration. Notably, in the context of the lack of immunotherapy in the PDAC cohort, we also determined that a high TMEscore is associated with increased response to anti-PD-L1/anti-PD-1 agents and improved survival time in two cohorts of patients with other solid tumors. Therefore, our present TMEscore might help predict the response to immune checkpoint inhibitors and thus promote the precision immunotherapy of PDAC.\n\nThe predictive value of our TMEscore was supported by some facts. The finding that cytolytic activity in PDA did not correlate with TMB or neoantigen load reveals the distinct difference between pancreatic and others in antitumor immunity [50]. It seems that the immune privilege of pancreatic cancer depends much more on intrinsic oncogenic processes than that of other cancers [50]. Several intrinsic oncogenic mechanisms responsible for the immunosuppression in pancreatic cancer have been identified, including the Kras mutation [67,68], the CDKN2A mutation [69], the TGF-beta within the TME [70], the activation of WNT/beta-catenin signaling [71], PTEN loss, and PI3K–AKT pathway activation [72,73,74,75,76], which were all highly enriched in TMEscore-low tumors. The essential role of TME as an immunosuppressant has been well known, and the heterogeneous TME shaped by infiltrated cells might illustrate the underlying mechanisms of altered response to immunotherapy. Importantly, a low TMEscore may indicate the presence of stubborn intrinsic immunosuppression. Taken together, different TME cell components are reflected according to different immune infiltration subtypes, which has significant clinical value for further guiding the refinement of immunotherapy.\n\nDespite the tumor-intrinsic acquired resistance, tumor-extrinsic acquired resistance also contributes to immunosuppression and resistance to immunotherapy [77,78]. The tumor-extrinsic immune microenvironment can be mostly reflected by the evaluation of the degree of T cell-inflamed phenotype. A baseline T cell-inflamed TME phenotype has been demonstrated to correlate with responsiveness to checkpoint blockade therapy and adoptive cell therapy [48]. Multiple T cell-inflamed gene signatures have been established and proven to be correlated with clinical response to PD-1PD-/L1 blockade across a variety of tumor types. Through assessing the T cell-inflamed status of PDAC tumors via the well-established signatures, including T cell-inflamed signatures [79], IFN-related genes [79], effector T cell signatures [80], and cytotoxic genes [81], we found significant enrichment of all signatures in TMEscore-high tumors. In addition, ratios of CD8+ to CD4 and Treg were also elevated in TMEscore-high tumors. All these results support the TMEscore-high tumors have T cell-inflamed phenotypes and vice versa for the TMEscore-low tumors. Despite the T cell-inflamed phenotype, there is other evidence providing support for a negative correlation between resistance to immunotherapy and TMEscore. It is known that NLRP3 signaling drives resistance to anti-PD-1 immunotherapy and is responsible for adaptive immune suppression through promoting the production of IL-1β in pancreatic carcinoma [82]. Our GSEA results indicated an underlying enrichment of NLRP3-related genes in TMEscore-low tumors. Furthermore, the gene sets associated with down-regulated genes in response to the stimulation of IL-2, IL-15, or IL-21 in T cells were negatively enriched. The impaired response to the proliferation stimulus suggested a dysfunctional state of infiltrated cytotoxic cells. The enrichment of multiple inhibitory checkpoint molecules in the group with a high TMEscore was also observed. Collectively, the TMEscore-high tumor has a series of characteristics suitable for immunotherapy.\n\nThere are some interesting findings that should be noted in our present study. F2R-like Trypsin Receptor 1 (F2RL1), as a G-protein-coupled receptor, can be activated by serine proteases and plays a key role in tumor progression [83]. We have confirmed that the upregulation of F2RL1 in PDAC tumors can significantly promote tumor proliferation, invasion, and migration. Moreover, it has been reported that the enrichment of F2RL1 in the tumor matrix region is increased [84,85], which is also similar to our results, suggesting that it mediates TME matrix remodeling to further drive tumor malignant progression. Therefore, whether there are subsets of cells with F2RL1 as a marker in the TME of PDAC as a bridge or not, the interaction between the tumor and TME remains to be further explored. The exact site structure of receptors and ligands also needs to be clarified. This provides new insights into individualized treatment schemes based on risk stratification based on the TMEscore and targeted therapy for F2RL1. In addition, the STING pathway, a cytosolic DNA sensing pathway, is a proximal event required for optimal type I interferon production, dendritic cell activation, and priming of CD8+ T cells against tumor-associated antigens [86]. STING pathway activation with antigen-presenting cells in the tumor microenvironment leads to the spontaneous generation of antitumor CD8+ T-cell responses [86]. Interestingly, we found the STING pathway was enriched in low TMEscore samples, which was somewhat at odds with that. Low TMEscore was associated with low cytotoxic infiltration and low T cell-inflamed activity, but the high APM score of TMEscore-low samples was in coherence with the enrichment of the STING pathway. The different STING activity and antigen presentation scores between TMEscore high and low samples could potentially be explained by the difference within TME. For example, the normal stromal phenotype was considered associated with the immunosuppressive pancreatic stellate cells, which inhibit the function of dendritic cells [29]. A previous study has reported that combining STING-based agonists with checkpoint modulators could enhance antitumor immunity in murine pancreatic cancer [87]. Therefore, considering the character of the low STING activity, high TME group, the combination of STING pathway agonists with immune checkpoint inhibitors seems to be a promising strategy in these patients.\n\nTaken together, the present TMEscore was associated with a variety of molecular hallmarks of immunosuppression and antitumor immunity. The prognostic value of the TMEscore was validated in multiple PDAC cohorts and two cohorts of patients treated with immune checkpoint inhibitors, suggesting the TMEscore has the potential to improve precision immunotherapy. The distinct tumor cell-intrinsic and tumor-extrinsic characteristics among tumors with different TMEscores indicate different treatment strategies according to the TMEscore category. Due to the strong intrinsic immunosuppression mechanisms and low cytotoxic infiltration, patients with a low TMEscore are unlikely to benefit from treatment with checkpoint inhibitors alone, but their response to immunotherapy might be rescued through a combined blockade of other intrinsic suppressive molecules, such as TGF signaling or others. Patients with a high TMEscore might benefit more from the immune checkpoint blockage, and their sensitivity to immunotherapy might be enhanced through the association of chemotherapy and/or a STING agonist in order to promote antigen presentation.\n\n## 5. Conclusions\n\nIn summary, our study systematically analyzed TME-related genes and proposed a novel TMEscore for risk stratification and selection of PDAC patients in clinical practice. 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0.020330136641860008\n]","isConversational":false}}},{"rowIdx":82,"cells":{"text":{"kind":"string","value":"---\ntitle: Caregiving Self-Efficacy of the Caregivers of Family Members with Oral Cancer—A\n Descriptive Study\nauthors:\n- Ching-Hui Cheng\n- Shu-Yuan Liang\n- Ling Lin\n- Tzu-Ting Chang\n- Tsae-Jyy Wang\n- Ying Lin\njournal: Healthcare\nyear: 2023\npmcid: PMC10000709\ndoi: 10.3390/healthcare11050762\nlicense: CC BY 4.0\n---\n\n# Caregiving Self-Efficacy of the Caregivers of Family Members with Oral Cancer—A Descriptive Study\n\n## Abstract\n\nIn Taiwan, oral cancer is the fourth most common cause of cancer death in men. The complications and side effects of oral cancer treatment pose a considerable challenge to family caregivers. The purpose of this study was to analyze the self-efficacy of the primary family caregivers of patients with oral cancer at home. A cross-sectional descriptive research design and convenience recruiting were adopted to facilitate sampling, and 107 patients with oral cancer and their primary family caregivers were recruited. The Caregiver Caregiving Self-Efficacy Scale-Oral Cancer was selected as the main instrument to be used. The primary family caregivers’ mean overall self-efficacy score was 6.87 (SD = 1.65). Among all the dimensions, managing patient-related nutritional issues demonstrated the highest mean score (mean = 7.56, SD = 1.83), followed by exploring and making decisions about patient care (mean = 7.05, SD = 1.92), acquiring resources (mean = 6.89, SD = 1.80), and managing sudden and uncertain patient conditions (mean = 6.17, SD = 2.09). Our results may assist professional medical personnel to focus their educational strategies and caregiver self-efficacy enhancement strategies on the dimensions that scored relatively low.\n\n## 1. Introduction\n\nIn 2018, oral cancer incidence and death were the highest among men in Taiwan, and oral cancer was the fourth most common cause of cancer-induced death in men [1]. When patients with oral cancer undergo treatment and experience its side effects [2,3,4], patients themselves, their families, and medical caregivers encounter great challenges in caregiving.\n\nStage classification of oral cancer includes four stages according to the size of the primary tumor (T), involvement of locoregional lymph nodes (N), and distant metastases (M) [5,6,7,8]. Stage I is determined by T1–2 and N0–1, stage II by T1–2 and N2 or T3 and N0–2, and stage III by T4 or N3. Stage IV is for patients with metastatic disease [7]. This classification can aid in treatment planning, the estimation of recurrence risk, and the assessment of patient survival [5]. The overall 5-year survival rate for patients in a cohort study at Memorial Sloan Kettering Cancer Center was $63\\%$ [9]. In a multicenter retrospective analysis, an advanced T stage was significantly correlated with poor overall survival and disease-specific survival of patients [10]. Lymph node involvement is the most important prognostic factor in oral cancer. The survival rate is reduced by $50\\%$ when compared with those with similar primary tumors without neck lymph node involvement [11,12]. The impact of oral cancer at different stages on patients’ physical symptoms and impairments was supported, especially the impact of advanced oral cancer [13].\n\nOral cancer treatment may involve the combined use of surgery, chemotherapy, and radiotherapy, among which surgery is the most essential [14]. However, surgical treatment may change patients’ facial appearance and cause oral disabilities, such as impaired communication and eating functions [2]. In addition, patients with oral cancer encounter the side effects of chemotherapy or radiotherapy. Therefore, care for oral cancer is more challenging than that for other cancers [15].\n\nIn Taiwan, family members play a crucial role in the home care of patients with oral cancer, as exemplified by the trends during outpatient treatment. For instance, these family members handle patients’ nutritional problems, make care decisions, manage disease-related emergencies, and seek relevant resources [16]. However, the difficulties they encounter during home care [16] may discourage these family members from putting effort into patient care, particularly when they lack belief in their own capability, worsening the subsequent care results.\n\nSelf-efficacy refers to an individual’s capability belief or perceived capability to perform specific health care behavior [17]. During health care processes, self-efficacy is an essential ability that helps individuals overcome difficulties and strive for better health [18]. Self-efficacy is a key factor that affects health care behavior [19] because self-efficacy positively affects individuals’ behavioral motivation and persistence when they encounter care difficulties [18].\n\nIn the research literature, investigations that examined the difference in gender regarding self-efficacy produced inconsistent findings. Several researchers described self-efficacy as one factor that accounts for gender differences [20,21]. While some researchers suggested that men reported greater self-efficacy than women [20], others suggested that females reported greater efficacy than men [21]. In contrast, no gender differences regarding self-efficacy ratings were noted in some studies [22,23,24].\n\nBandura [25] also suggested that age may be a factor that contributes to personal efficacy due to the biological processes of aging resulting in declining ability. Research on the effects of age on self-efficacy has produced mixed results [20,22,23,24]. Several studies indicated no relationship between self-efficacy ratings and age [20,22,24].\n\nEducational and socio-economic levels may also be personal factors that are associated with self-efficacy since they lead to better access to resources. A researcher has suggested self-efficacy expectations as one factor that accounts for educational differences in responses to outcome measures [22]. However, several studies showed no relationship between self-efficacy and educational levels [23,24]. Most studies on the effects of economic levels on self-efficacy showed no significant difference [23,24].\n\nUnderstanding the self-efficacy of family caregivers can assist medical teams to understand their capability belief in taking care of patients with oral cancer at home, identify relevant influential factors, and provide countermeasures to enhance their capability belief in patient care. This may improve the home care quality for patients with oral cancer. Therefore, the purpose of this study was to assess the self-efficacy of the primary family caregivers of patients with oral cancer at home.\n\n## 2.1. Study Design\n\nThe current study adopted a cross-sectional descriptive research design and convenience recruiting to facilitate the sampling and discussion on the self-efficacy of the primary family caregivers of patients with oral cancer at home.\n\n## 2.2. Sample and Procedure\n\nIn total, 107 primary family caregivers of outpatients were recruited for a structured questionnaire survey. The participants were enrolled from the radiology outpatient department of a teaching hospital in northern Taiwan from May 2016 to May 2018. Only patients who [1] were aged ≥20 years; [2] were diagnosed as having oral cancer; and [3] received oral cancer-related surgery, chemotherapy, or radiotherapy were included. Moreover, the family caregivers of these patients were required to be [1] aged ≥20 years, [2] recognized as the primary family caregivers by the patients, and [3] living with the patients.\n\nAfter this study passed the ethical review and the family caregivers signed the informed consent form, a research assistant distributed our questionnaires to the family caregivers. The assistant checked whether the retrieved questionnaires were completely filled out immediately after the caregivers submitted them. The participants who missed items were asked to fill them out. Regarding patient medical characteristics, they were all collected from medical records by the research assistant.\n\n## 2.3. Ethical Considerations\n\nThis study was approved by the institutional review board of a teaching hospital in northern Taiwan (VGHIRB No.: 2014-04-001AC). The research assistant verbally explained the research objective, data protection principles, and research procedures to obtain the participants’ consent and asked them to sign the informed consent form. Codes were used in the questionnaire in place of personal information to protect participant privacy. For participants who were unwilling to proceed with the survey or were not physically suitable for further investigations, the research assistant acknowledged their withdrawal intention and stopped collecting their data.\n\n## 3.1. Sociodemographic Variables\n\nThe current study collected the sociodemographic variables of the family caregivers and patients’ medical characteristics. The collected sociodemographic variables were sex, age, marital status, education level, religious affiliation, employment status, and household income. The collected medical characteristics were the time of sickness, stage of cancer, current treatment status, and treatment side effects. Information related to the family caregivers, such as the family caregivers’ relationships with the patients, manner of care, and care time, were also collected.\n\n## 3.2. Caregiver Caregiving Self-Efficacy Scale-Oral Cancer\n\nThe current study applied the Caregiver Caregiving Self-Efficacy Scale-Oral Cancer (CSES-OC) [26] to estimate the self-efficacy of the family caregivers. The scale consisted of 18 items. According to factor analysis, the scale could be divided into four subscales: acquiring resources (AR; six items), managing sudden and uncertain patient conditions (MS; five items), managing patient-related nutritional issues (MN; four items), and exploring and making decisions on patient care (MD; three items). Some examples of the items for AR are “I am confident that I am able to acquire financial support”, “I am confident that I am able to seek consultation on the provision of sick family member care”, and “I am confident that I am able to acquire respite from caregiving”. Examples for MS are “I am confident that I am able to manage the sudden onset of conditions in the sick family member”, “I am confident that I am able to handle uncertainty about cancer progression”, and “I am confident that I am able to handle the sick family member’s uncertainty about death”. Examples for MN are “I am confident that I am able to prepare a suitable diet” and ”I am confident that I am able to improve the sick family member’s willingness to eat”. Examples of the items for MD are “I am confident that I am able to explore the most suitable care for the sick family member” and ” I am confident that I am able to make decisions on sick family member care”. The Cronbach’s alpha of each subscale ranged between 0.78 and 0.91, and that of the overall scale was 0.95. The test–retest reliability with a 2-week interval was $r = 0.83$ ($p \\leq 0.001$), and its criterion-related validity with the General Self-Efficacy Scale was $r = 0.59$ ($p \\leq 0.001$). Regarding the scale used, an 11-point Likert-type scale ranging from 0 (not at all confident) to 10 (completely confident) points was adopted, where the higher the total score, the higher the self-efficacy [26].\n\n## 3.3. Statistical Analysis\n\nThe current study used SPSS for Windows (version 22.0; SPSS, Chicago, IL, USA) for the data processing. Descriptive statistics, such as means, SDs, frequencies, and percentages, were obtained to examine the family caregivers’ sociodemographic variables, patients’ medical characteristics, caregiver–patient relationships, manner of care, care times, and caregiving self-efficacies. The differences in the variables in caregiving self-efficacy (e.g., family caregivers’ sociodemographic variables, patients’ medical characteristics, caregiver–patient relationships, and manner of care) were estimated using the independent sample t-test and analysis of variance (ANOVA). In addition, a Pearson product–moment correlation test was performed to verify the correlation between caregiver age, care time, patient time of sickness, and caregiving self-efficacy.\n\n## 4.1. Sociodemographic Variables of the Primary Family Caregivers and the Manner of Care\n\nThe current study recruited 107 primary family caregivers as participants, with a mean age of 51 years (SD = 10.8 years, range = 20–70 years). Among the participants, $91.6\\%$ were female, $72.9\\%$ were the patients’ spouses, $56.1\\%$ had an education level of senior high school and above, $87.9\\%$ were married, $26.2\\%$ were continuing their job, $47.7\\%$ had an annual household income of <TWD 500,000, $86.9\\%$ had a religious affiliation, and $26.2\\%$ had a chronic disease (Table 1). Moreover, $41.1\\%$ provided care with the assistance of other caregivers, $40.2\\%$ provided care without rest, $83.20\\%$ had no experience in patient care, and the mean care time was 36.4 months (SD = 40.3 months, range = 1–171 months; Table 1).\n\n## 4.2. Patients’ Medical Characteristics\n\nAmong the 107 patients with oral cancer, the mean time of sickness was 42.5 months (SD = 44.4 months, range = 1–171 months). Of all the patients, $36.4\\%$ had stage IV oral cancer, $78.5\\%$ had completed their treatment, and $36.4\\%$ were still experiencing the side effects of the treatment (Table 2).\n\n## 4.3. Caregiving Self-Efficacy of the Primary Family Caregivers\n\nThe CSES-OC was used to measure the self-efficacy of the primary family caregivers. The overall and subscale (i.e., AR, MS, MN, and MD) scores were considered. The mean overall self-efficacy score was 6.87 (SD = 1.65). Moreover, of all the subscales, MN demonstrated the highest mean score of 7.56 (SD = 1.83), followed by MD (7.05, SD = 1.92), AR (6.89, SD = 1.80), and MS (6.17, SD = 2.09) (Table 3).\n\n## 4.4. Differences in the Sociodemographic Variables of the Primary Family Caregivers and Manner of Care in Caregiving Self-Efficacy\n\nNo significant correlations were discovered between the overall self-efficacy score and age ($r = 0.06$, $p \\leq 0.05$) and between the overall self-efficacy score and care time ($r = 0.08$, $p \\leq 0.05$). Moreover, no significant differences were noted for the other sociodemographic variables and manner of care in caregiving self-efficacy (Table 1).\n\n## 4.5. Differences in Medical Characteristics in Caregiving Self-Efficacy\n\nNo significant correlations were discovered between the time of sickness and the overall self-efficacy score ($r = 0.11$, $p \\leq 0.05$). Moreover, the differences among patients’ other medical characteristics in the overall self-efficacy were nonsignificant (Table 2).\n\n## 5. Discussion\n\nIn this study, the researchers analyzed the caregiving self-efficacy of the primary family caregivers of patients with oral cancer. Results of the current study may aid professional caregivers in understanding the capability belief of primary family caregivers in facing challenges during the care process and the most challenging tasks they are likely to encounter.\n\nAccording to the self-efficacy classification proposed by Kobau and DiIorio [27], a self-efficacy score of 4–7 (range: 0–10) denotes a moderate level of self-efficacy. Here, the mean caregiving self-efficacy score was 6.87, indicating that the caregivers in this study had moderate self-efficacy. However, because the scoring methods used for measuring self-efficacy have varied between previous relevant studies [28,29,30], the researchers could not compare the results of the current study with those of other studies directly. The mean self-efficacy score of the current study was close to that of Liang, Yates, Edwards, and Tsay [22], where the opioid-taking self-efficacy of patients with cancer was estimated, and it was slightly lower than that of Kobau and DiIorio [27], where the self-efficacy of patients with epilepsy was assessed. The possible reason for this was that the care difficulty differed between diseases, which may have affected the participants’ perceived level of capability.\n\nHere, the caregiving self-efficacy in the MN dimension scored the highest, with a mean score of 7.56. Handling the nutritional issues of patients might not be the most challenging task for caregivers. Increasing their willingness to eat and preparing suitable food for them [26] were found to be essential behavior tasks to promote their physiological recovery.\n\nThe self-efficacy in the MD dimension scored the second highest, with a mean score of 7.05. In this dimension, the behavior tasks relevant to caregiving self-efficacy included managing the side effects due to cancer treatment and making treatment-related decisions [26]. These types of behavior tasks aim at providing home-based medical assistance.\n\nMoreover, the AR dimension scored the third highest, with a mean score of 6.89. Here, the caregiving self-efficacy-related behavior tasks encompass managing emotional issues, receiving care counseling, and being able to rest during the care process [26]. Emotional management was related to tasks such as dealing with the emotions of patients who were facing oral cancer treatment and prognosis, as well as the emotions of caregivers themselves [16,26]. According to the current results, this was the second most challenging set of behavioral tasks. It was a self-assistance behavior task related to the maintenance of the physical and mental health of the caregivers themselves.\n\nFinally, the MS dimension scored the lowest, with a mean score of 6.17. For caregivers, handling the safety and death issues of patients was the most challenging task. The caregiving self-efficacy-related behavior tasks include handling sudden situations, managing the uncertainty in the disease process, and managing poor prognosis [26]. These most difficult care tasks indicate the care priorities for patients and their family caregivers for health care professionals.\n\nFamily caregivers’ capability belief (i.e., self-efficacy) is a key factor that affects subsequent care behavior and care results [31,32]. Professional medical personnel can increase family caregivers’ capability belief according to the four sources of efficacy beliefs in the self-efficacy theory: family caregivers’ performance accomplishment, vicarious experience, professional caregivers’ verbal persuasion, and consideration of family caregivers’ physical and emotional arousal [17,32]. Furthermore, professional medical personnel could integrate relevant educational strategies, including diary logs [33], videos, and brochures [32], to improve family caregivers’ capability beliefs in taking care of patients with oral cancer.\n\nIn this study, the researchers adopted a cross-sectional descriptive research design. Therefore, the current study could not obtain the changes in family caregiving self-efficacy with respect to the patient’s condition or required care time. The present study involved all patients in the disease period. The timing of patient enrollment was not controlled. Some patients were still undergoing their course of treatment, some patients had finished their treatment. Different times or stages of treatment may affect the challenge of the care of family and, therefore, may affect their ability cognition. In addition, the sample size was small for all sociodemographic and medical variable groups. It is unlikely that statistical differences could be detected in this population. On the other hand, the current research used convenience sampling, which may have caused sampling deviation. Families with large care loads may have been eliminated naturally. The samples were collected from a teaching hospital in northern Taiwan alone, which might affect the inference of the current results.\n\n## 6. Conclusions\n\nOur current results indicated that family caregiving self-efficacy scores in the CSES-OC MS and AR items were the lowest and the second lowest, respectively. The current study recommends that professional medical teams focus their educational strategies and caregiver self-efficacy enhancement strategies on the dimensions that scored relatively low (i.e., handling patients’ safety and death issues and managing physical and mental health problems through self-assistance). For example, issues in these dimensions include managing the emotional distress of a sick family member and the caregiver themself, handling uncertainty about the sick family member’s cancer progression and death, and managing the sudden onset of conditions in the sick family member. Through family caregivers’ performance accomplishment, vicarious experience, professional caregivers’ verbal persuasion, consideration of caregivers’ physical and emotional arousal, and using educational media, the self-efficacy of family caregivers regarding taking care of a patient with cancer may be increased. The current results are from an exploratory study. 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Lipopolysaccharide-Activated Microglia\nauthors:\n- Rosa Calvello\n- Chiara Porro\n- Dario Domenico Lofrumento\n- Melania Ruggiero\n- Maria Antonietta Panaro\n- Antonia Cianciulli\njournal: Cells\nyear: 2023\npmcid: PMC10000713\ndoi: 10.3390/cells12050681\nlicense: CC BY 4.0\n---\n\n# Decoy Receptors Regulation by Resveratrol in Lipopolysaccharide-Activated Microglia\n\n## Abstract\n\nResveratrol is a polyphenol that acts as antioxidants do, protecting the body against diseases, such as diabetes, cancer, heart disease, and neurodegenerative disorders, such as Alzheimer’s (AD) and Parkinson’s diseases (PD). In the present study, we report that the treatment of activated microglia with resveratrol after prolonged exposure to lipopolysaccharide is not only able to modulate pro-inflammatory responses, but it also up-regulates the expression of decoy receptors, IL-1R2 and ACKR2 (atypical chemokine receptors), also known as negative regulatory receptors, which are able to reduce the functional responses promoting the resolution of inflammation. This result might constitute a hitherto unknown anti-inflammatory mechanism exerted by resveratrol on activated microglia.\n\n## 1. Introduction\n\nResveratrol (3,5,4′-trihydroxy-trans-stilbene), also called polyphenol, is a stilbenoid belonging to the phytoalexin superfamily, mostly found in red grapes, blueberries, raspberries, mulberries, and peanuts [1]. Resveratrol has two isomers with trans and cis configurations. In this regard, the trans-resveratrol is the non-toxic stereoisomer that has been widely described to have beneficial effects on health [2].\n\nResveratrol is part of a group of compounds that act as antioxidants do, protecting the body against diseases, such as diabetes, cancer, heart disease, ileitis, obesity, and neurodegenerative disorders, such as Alzheimer’s (AD) and Parkinson’s diseases (PD) [3,4,5,6]. In this respect, in experimental models of both AD and PD, it has been showed that resveratrol exerts neuroprotective actions; however, its application in therapeutic protocols is limited by its poor bioavailability due to quick metabolization in the intestine and liver [3,4,5,6,7].\n\nResveratrol is able to cross the blood–brain barrier (BBB) via tight junctions, thus carrying out a protective action in the brain tissue that could reduce the loss of neurons, which arises due to neurodegenerative diseases [4,5,6,7,8].\n\nMany studies carried out in recent years have focused on researching the therapeutic potential for the additional treatment of neurodegenerative diseases of many natural compounds, in particular those extracted from plants [5,6,7,8,9,10]. Among the vast diversity of natural compounds that have been studied for their neuroprotective effects, there are polyphenolic compounds, such as curcumin, capsaicin, epigallocatechin gallate, and resveratrol too [7,8,9,11,12,13]. Apart from having antioxidant and anti-inflammatory actions, resveratrol modulates the intracellular signals involved in neurons survival and inhibits beta-amyloid (Aβ) protein aggregation [10,11,12,13,14].\n\nConsistent with these data, it was reported in a mouse model of Parkinson’s-like disease that a resveratrol treatment protects the dopaminergic (DA) neurons of the Substantia Nigra pars compacta (SNpc) against neurotoxic insult by modulating inflammatory reactions through SOCS-1 activation [11,12,13,14,15].\n\nDecoy receptors are involved in mechanisms of immune evasion adopted by pathogens, including IL-1R2 and atypical chemokine receptors (ACKRs). In IL-1R2, the lack of the intracellular TIR domain makes this receptor unable to initiate signal transduction following binding with IL-1 [12,16]. The main types of ACKRs are ACKR1, ACKR2, ACKR3, and ACKR4 [13,17]. These molecules are able to recognize and bind specific growth factors or inflammatory cytokines efficiently; however, they are structurally incapable of initiating and transducing signals, acting as a molecular trap for the agonist and for signaling receptor components. All of these members, also referred as chemokine-binding proteins, scavengers, receptor antagonists, negative regulatory receptors, anti-inflammatory ligands, and decoys, act as brakes in the functional responses [14,18].\n\nIL-1R2 functions as a negative regulator of several IL-1 family members, as well as of TLRs, thus it is involved in several pathophysiological contexts in which inflammation and innate and adaptive immune responses play a significative role [19]. ACKR2, previously known as D6, through inhibiting inflammation, mediates the resolution of inflammation in various conditions such as infections, autoimmune diseases, cancer, and neurodegenerative conditions [14,15,16,17,18,19,20].\n\nWe reported in a previous work that in LPS-activated cells, the pre-treatment of microglia with resveratrol up-regulated the phosphorylation of JAK1 and STAT3, as well as the expression of the suppressor of cytokine signaling (SOCS)3, demonstrating that the JAK-STAT signaling pathway is involved in the anti-inflammatory effect exerted by resveratrol [15,16,17,18,19,20,21].\n\nThe aim of the present research was designed to determine the potential anti-inflammatory effects of resveratrol through the regulation of decoy receptor expression, IL-1R2 and ACKR2, on the activated microglia after prolonged exposure to LPS. The results obtained from this study provide, for the first time, evidence of a new anti-inflammatory mechanism exerted by resveratrol on the activated microglia.\n\n## 2.1. Cell Culture and Treatments\n\nThe murine microglial cell line N13 was grown in RPMI 1640 basal medium enriched with $10\\%$ heat-inactivated fetal bovine serum (FBS), $1\\%$ L-glutamine (2 mM), and $1\\%$ penicillin-streptomycin solution (100 U/mL penicillin; 100 μg/mL streptomycin) (Life Technologies-Invitrogen, Milan, Italy) in a CO2 incubator set to $5\\%$ CO2 at 37 °C in a humidified atmosphere until $70\\%$ confluence. For the treatments, we used 10 μM resveratrol (trans-3,40, 5-trihydroxystilbene; purity > $99\\%$ GC; Sigma Aldrich, St. Louis, MO, USA) and the cell wall component LPS of *Salmonella typhimurium* at a concentration of 100 ng/mL. N13 cells were submitted to a single treatment with LPS or resveratrol and to a combined treatment with resveratrol, followed up an hour later by LPS (Sigma Aldrich) for 72 h.\n\n## 2.2. Cytotoxicity Assay\n\nCell viability of N13 cells was evaluated by the MTT (3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide) assay. The cells were seeded in 96-well multi-well plates at a density of 8 × 103/well to be treated with LPS alone or in presence of resveratrol. The MTT was solubilized in PBS 1X to be added to the wells at a working concentration of 0.5 mg/mL starting from a stock solution of 5 mg/mL. After 4 h of incubation in a CO2 incubator at 37 °C in a humidified atmosphere, the formazan crystals were solubilized in Dimethyl sulfoxide (DMSO) keeping the plates in agitation for 20 min. Since the amount of formazan is directly proportional to the number of viable cells, it is quantified by measuring the optical density at 560 nm and subtracting the background at 670 nm by using a Victor Multiplate Reader (Wallac, Perkin Elmer, Milan, Italy).\n\n## 2.3. Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) and End-Point PCR\n\nCells were harvested, and the total RNA was extracted by using the TRIzol isolation reagent (Invitrogen, Milan, Italy) according to the manufacturer’s instructions. Once isolated, the RNA was reverse transcribed back into cDNA, causing a reaction between 3 μg of total RNA, 40 U of RNase Out (Invitrogen), 40 mU of oligo dT, 0.5 mM dNTP (PCR Nucleotide Mix, Roche Diagnostics, Milan, Italy), and 40 U of Moloney Murine Leukemia Virus Reverse Transcriptase (Roche Diagnostics). The cDNA synthesis was initiated at 37 °C for 59 min and terminated at 95 °C for 5 min to remain at 4 °C. The cDNA was amplified by performing a polymerase chain reaction (PCR) for 30 cycles using a thermal cycler (Eppendorf, Milan, Italy) together with the cDNA of GAPDH, which was used as a reference gene. At the completion of the PCR, TriTrack Loading Dye 6X (Thermo Fisher, Waltham, MA, USA) was added to the amplified samples prior to be loaded onto the agarose gel. The DNA bands were quantified by densitometry with the ImageJ software, and the results were normalized with GAPDH. Primer sequences for the tested genes are reported in Table 1.\n\n## 2.4. Electrophoresis\n\nAfter 72 h from the treatments, the cells were harvested and lysed with a lysis buffer ($1\\%$ Triton X-100, 20 mM Tris-HCl, 137 mM NaCl, $10\\%$ glycerol, 2 mM EDTA, 1 mM phenylmethylsulfonyl fluoride (PMSF), 20 μM leupeptin hemisulfate salt, and 0.2 U/mL aprotinin) and subjected to many cycles of freezing and thawing to facilitate the lysis. The lysates were obtained by centrifugation at 12,800× g for 20 min at 4 °C, and the proteins were quantified by the Bradford’s protein assay [22,23,24,25,26,27,28,29,30,31,32,33,34,35]. A quantity of 25 μg of proteins from each sample were diluted with a sample buffer (0.5 M Tris-HCl pH 6.8, $10\\%$ glycerol, $10\\%$ (w/v) SDS, $5\\%$ β2-mercaptoethanol, and $0.05\\%$ (w/v) bromophenol blue), and then boiled for 3 min. At the end, the samples were loaded onto $4\\%$–$12\\%$ SDS precast polyacrylamide gels (BioRad Laboratories, Tokyo, Japan) and fractionated in relation to the size by applying a voltage of 200 V.\n\n## 2.5. Western Blotting\n\nUpon completion of electrophoresis, the resolved proteins were transferred onto ni-trocellulose membranes and blocked with $5\\%$ fat-free milk diluted in a solution containing $0.1\\%$ (v/v) Tween $20\\%$ and PBS to avoid nonspecific binding. After three ten-minute washes with $0.1\\%$ Tween 20-PBS (T-PBS), the membranes were incubated overnight at 4 °C with mouse monoclonal antibody (mAb) anti-CD11b (1:200), anti-iNOS (1:200) mAb, anti-COX-1 (1:200) mAb, anti-COX-2 (1:200) mAb, anti-phospho-cPLA2 (1:200), anti-phospho-IkBα (1:200) mAb, anti-IL-1R2 (1:100) mAb, and anti-ACKR2 receptor (1:100) mAb, and mouse polyclonal Ab anti β-actin (all from Santa Cruz Biotechnology, Inc., Milan, Italy) according to the manufacturer’s protocol. Then, the membranes were washed with $0.1\\%$ Tween 20-PBS (for 20 min, 3 times) and incubated with specific horseradish peroxidase (HRP)-conjugated secondary antibody anti-mouse (Santa Cruz Biotechnology, Milan, Italy) diluted to 1:10,000 for 1 h in agitation and in the dark. At the end, the protein bands were highlighted by chemiluminescence, and images were acquired using a ChemiDoc Imaging System. The bands were normalized against β-actin, and the results, expressed as means ± SD, were provide as the relative optical density.\n\n## 2.6. Enzyme-Linked Immunosorbent Assay (ELISA)\n\nThe sandwich ELISA was performed following the kit manufacturer’s instructions to measure the levels of TNF-α (Cat. # BMS607-3 Thermo Fisher—Invitrogen Technology, Milan, Italy) and IL-1β (Cat. # BMS6002 Thermo Fisher—Invitrogen Technology, Milan, Italy) cytokines in the cell culture supernatants withdrawn after 72 h from the treatments. Since the intensity of the signal is directly proportional to the concentration of the antigen, the concentration was quantified and expressed in pg/mL. The determinations were performed in triplicate.\n\n## 2.7. NO Production\n\nNO, quantified as the NO2− concentration in the cell culture supernatants, was determined by the Griess assay. The supernatants were collected after 72 h from the treatments and centrifuged to remove possible cellular residues. After adding the Griess Reactive ($0.1\\%$ N-(1-naphthyl) ethylenediamine dihydrochloride and $1\\%$ sulfanilamide in $2.5\\%$ H3PO4) (1:1 v/v), the samples were incubated in the dark at room temperature for 10 min. At the end, the absorbance was spectrophotometrically measured at 540 nm by using the conditioned medium as a blank to clear the interference of nitrites. The NO2− concentration was calculated by interpolation on a standard curve of sodium nitrite (NaNO2) and is expressed as μmol/mL.\n\n## 2.8. PGE2 Assay\n\nTo measure the PGE2 in the cell culture supernatants, we performed the PGE2 assay. N13 cells (3 × 106/well) were seeded in 6-well plates, pre-treated with resveratrol for 1 h, and then stimulated with LPS at a concentration of 100 ng/mL. The cultures were maintained at 37 °C for 72 h in a humidified air containing $5\\%$ CO2. The PGE2 levels were determined in the supernatant using the competitive binding immunoassay (Cayman Chemical, Ann Arbor, MI, USA) according to the manufacturer’s instructions. Unstimulated cells were included as a control. The optical density was measured at λ = 405–420 nm using a precision microplate reader, and the PGE2 concentration, expressed in ng/mL, was determined by using a PGE2 standard curve.\n\n## 2.9. Statistical Analysis\n\nThe statistical analysis was carried out with the software package MINITAB Release 14.1 (Minitab Ltd., Coventry, UK). The results were analyzed by the ANOVA one-way followed by the Tukey test, assuming that the p-values ≤ 0.05 were significant.\n\n## 3.1. Effects of Resveratrol and Prolonged LPS Treatment on Cell Viability of N13 Microglial Cells\n\nThe effect of the pre-treatment with resveratrol on the N13 cells treated with LPS was verified by MTT cell viability test. We used an optimal concentration of LPS (100 ng/mL) and an optimal non-toxic resveratrol concentration (10 μM) selected on the basis of the experiments reported in our previous works [15,16,21,23]. Furthermore, in the experiments of this work, we used prolonged exposure to LPS by treating the N13 cells with LPS for 72 h. The viability of the cells exposed for 72 h to 100 ng/mL LPS was significantly reduced in comparison to that of the untreated cells; however, the pre-treatment with resveratrol was able to significantly increase the cell viability in the cells treated with LPS with respect to that of the cells treated with LPS alone (Figure 1).\n\n## 3.2. Resveratrol Modulates CD11b Expression Levels in LPS-Treated N13 Microglial Cells\n\nThe pre-treatment with resveratrol of the N13 cells treated with LPS determined the modulation of the expression of the microglial activation marker CD11b both at transcriptional and post-transcriptional levels (Figure 2). In particular, resveratrol is able to determine a significant decrease in the mRNA expression levels of CD11b in the N13 cells treated with LPS in comparison to those observed in cells treated with LPS alone (Figure 2A). The same results were observed for CD11b protein expression. In this context, the treatment with LPS induced a significantly higher increase in the CD11b protein expression levels in the cells treated with LPS in comparison to that of the control cells. In addition, resveratrol showed the ability to significantly decrease the CD11b protein expression levels in the N13 cells treated with LPS compared to that observed in the cells treated with LPS alone (Figure 2B). These results confirm the role of resveratrol as a modulator of microglial activation even in case of prolonged exposure to LPS.\n\n## 3.3. Effects of Resveratrol on Nitric Oxide Production and Inducible Nitric Oxide Synthase Protein Expression Levels in LPS-Treated N13 Microglial Cells\n\nTo evaluate the effect of resveratrol on NO production in the N13 cells subjected to prolonged exposure to LPS, the levels of NO produced by the microglia treated for 72 h with LPS in the absence and in the presence of resveratrol were tested. The levels of NO released by the untreated cells and those treated with resveratrol alone were low. The treatment of microglia with LPS for 72 h, on the other hand, resulted in a significant increase in NO release compared to that which was observed in the control cells. Conversely, the cells treated with LPS pre-treated with resveratrol showed a significant reduction of NO production in comparison to that of the cells treated with LPS alone (Figure 3A). In addition, to evaluate whether the inhibitory effect of resveratrol on NO production could derive from an action of resveratrol on the inducible isoform of NO synthase (iNOS), the protein expression of iNOS after the different treatments was determined by the Western blot. Again, significantly higher levels of iNOS protein expression were found in the cells treated with LPS alone in comparison to that which was shown by the untreated cells. Similarly, as observed for NO release, the pre-treatment with resveratrol was able to significantly inhibit the expression of iNOS in the microglia submitted to prolonged exposure to LPS (Figure 3B).\n\n## 3.4. Effects of Resveratrol on Pro-Inflammatory Cytokine Production in LPS-Treated N13 Microglial Cells\n\nPro-inflammatory cytokines production levels were assessed in culture supernatants by ELISA both in the presence and absence of resveratrol. As shown in Figure 4, there was a marked increase in TNF-a and IL-1β production in the microglial cells after 72 h of LPS stimulation. No effect by the treatment with resveratrol alone on the pro-inflammatory cytokine production was observed in the microglial cells. Moreover, we observed that the treatment with 10 μg/mL of resveratrol in the LPS-treated cells significantly down-regulated the production levels of pro-inflammatory cytokines in comparison to those of the N13 cells stimulated with LPS alone, suggesting that resveratrol was able to negatively modulate the production levels of pro-inflammatory cytokines in LPS-activated microglial cells.\n\n## 3.5. Effects of Resveratrol on Arachidonic Acid (AA) Pathway in LPS-Treated Microglia\n\nCyclooxygenase-2 (COX-2) and phospholipase A2 (cPLA2) participate in eicosanoid production, such as prostaglandin E2 (PGE2), which is implicated in the Arachidonic Acid (AA) pathway and is a key factor in neuroinflammatory and neurodegenerative diseases. Moreover, it is well known that COX-1 could be an important player in neuroinflammation by being predominantly localized in the microglia, and thus, being implicated in the secretion of prostaglandins (PGs) in response to microglia activation [17,24]. For this reason, in microglial cells exposed for a prolonged time to LPS, we have verified the anti-inflammatory ability of resveratrol in terms of the modulation of the of COX-1, COX-2, and p-cPLA2 protein expression. In addition, the evaluation of COX activity by the quantification of the PGE2 production by the enzymatic conversion of AA has been widely used and is well accepted as a method to evaluate potential COX inhibitors [18,25]. Therefore, we also verified the inhibitory action of resveratrol on the release of PGE2 in N13 cells treated with LPS for 72 h. From our results, it appears to be evident that resveratrol is able to determine a significant decrease in the expression levels of COX-1, COX-2, and p-cPLA2 in the cells treated for 72 h with LPS that had undergone a pre-treatment of 1 h with resveratrol in comparison to those of the cells treated with LPS alone (Figure 5A–C). We observed similar results in the PGE2 release assay. Resveratrol, in fact, determined a significant decrease in the release of this inflammatory mediator in the microglia exposed to the prolonged treatment with LPS and pre-treated with resveratrol compared to those subjected to the treatment with LPS alone (Figure 5D). From these results, it is, therefore, possible to highlight that resveratrol, in cases of prolonged inflammation, is able to show an anti-inflammatory effect by inhibiting the AA pathway.\n\n## 3.6. Effects of Resveratrol on NF-kB Pathway in LPS-Treated N13 Microglial Cells\n\nIn order to evaluate NF-kB activation, we measured the levels of the phosphorylated form of IkBα (p-IkBα), the inhibitory complex of NF-kB, since its phosphorylation is an essential step for NF-kB activation. In this regard, we determined the expression of p-IkB in cell lysates obtained from LPS-stimulated N13 microglial cells. In this context, we observed that the LPS treatment for 72 h significantly increased the expression level of phosphorylated IkB-α protein compared to that of the control cells, and the resveratrol pre-treatment significantly prevented this increase, as revealed by the densitometric analysis (Figure 6). These data indicate that resveratrol inhibited NF-kB activity in the LPS-treated N13 cells by suppressing the degradation of IkB-α, and consequentially, relieving the pro-inflammatory mediator’s expression.\n\n## 3.7. Effects of Resveratrol on IL1-R2 and ACKR2 Decoy Receptor Expression in LPS-Treated Microglia\n\nIL-1R2 is a decoy receptor that causes a block of signal transduction after IL-1 binding. By regulating IL-1R2 expression, cells can modulate inflammation in response to exogenous stimuli. It has been showed that the up-regulation of IL-1R2 in microglial cells and brain endothelial cells attenuates CNS inflammation [12,16].\n\nACKR2, also known as the D6 decoy receptor, scavenges various inflammatory chemokines, thus affecting the inflammatory microenvironment. In this regard it is thought that the D6 decoy receptor could be a resolving agent in the neuroinflammatory processes because of its capacity to scavenge chemokines, leading to the alleviation of inflammation in different situations, including neuroinflammatory-based neurological disorders [20]. Therefore, in our study, we verified the ability of resveratrol to modulate the expression of decoy receptor IL-1R2 and decoy receptor ACKR2 both in terms of mRNA and protein expression. The analysis of mRNA expression for both the decoy IL1-R2 receptor and the decoy ACKR2 receptor showed a significantly reduced expression of both these receptors in the microglial cells subjected to prolonged exposure to LPS in comparison to that of those cells treated with Resveratrol alone. Interestingly, in the cells exposed to LPS but pre-treated with resveratrol, there was a drastic and highly significant increase in mRNA expression for both of the decoy receptors studied in comparison to that of the cells treated with LPS alone (Figure 7A,B).\n\nThese results were confirmed by the Western blotting analysis on IL1-R2 and ACKR2 protein expression. Additionally, in this case, resveratrol was able to cause a significant increase in the protein expression of both IL1-R2 and ACKR2 decoy receptors in the cells treated for 72 h with LPS that received a pre-treatment of 1 h with resveratrol in comparison to that of those cells treated with LPS alone (Figure 7C,D).\n\nAll together, these results certainly confirm the already known anti-inflammatory effect that resveratrol elicits on microglial cells in case of neuroinflammation. At the same time, however, these experiments demonstrate, for the first time, the ability of resveratrol to modulate the expression of IL1-R2 and ACKR2 decoy receptors, which could represent a new potential therapeutic target especially in cases of the prolonged inflammation of the CNS.\n\nBased on the previous results evidencing that the resveratrol treatment on the LPS activated microglia responses exerts both an inhibition of pro-inflammatory mechanisms and an induction of anti-inflammatory responses [15,16,21,23], we aimed, in this study, to expand our knowledge regarding the other possible effects of this polyphenolic compound on the inflammatory responses of microglia submitted to a prolonged LPS treatment. Here, we demonstrated that resveratrol, without affecting the viability of these cells, is able to specifically interfere with the pro-inflammatory responses induced by LPS in terms of both the decreased production of IL-1β and the increased production of the IL-1β decoy receptor. IL-1β, a member of the IL-1 family, is a potent pro-inflammatory cytokine in the acute and chronic phases of inflammation, therefore, the reduced production of IL-1β after 72 h of incubation in resveratrol-treated cells demonstrates that this polyphenol could limit the amplification phase of inflammation.\n\nTo analyze whether the resveratrol-treated microglia display a reduced ability to react to pro-inflammatory stimuli, we also investigated the response of the cells to LPS in terms of NO and of TNF-a release. In this regard, we detected that after 72 h of treatment, resveratrol was able to significantly reduce the production of both of these mediators. Moreover, we also demonstrated that after a prolonged incubation of microglia cells to LPS, the resveratrol treatment was able to counteract the pro-inflammatory processes down-regulating the IkB degradation, which resulted significantly reduced in comparison to that of the cells treated with LPS alone.\n\nNF-kB is considered to be the most important transcription factor involved in the inflammatory responses, thereby in the regulation of NO, TNF-α, and IL-1β [19,20,26,27].\n\nPreviously published papers have reported in other cell types [16,23,28,29] that resveratrol significantly inhibited the degradation of IκBα in microglia stimulated with LPS, as well as the subsequent iNOS expression and production of TNF-α, suggesting that resveratrol can modulate the signaling pathways triggered by pro-inflammatory stimuli, such as LPS. However, in the present study, we observed that this action of resveratrol on the production of TNF-α and the degradation of IκB-α is also evident after a more prolonged incubation time, evidencing how this compound is effective at modulating the inflammatory responses protracted over time and not only in the acute ones.\n\nIn addition, we also demonstrated that the resveratrol treatment determined a significant reduction of COX-1, COX-2, and p-cPLA2, which are all mediators of pro-inflammatory responses. Cyclooxygenase exists as COX-1 and COX-2 distinct isoforms [23,24,30,31] and converts arachidonic acid (AA) released by PLA2 acting at the sn-2 position of membrane phospholipids into prostaglandins and other lipid mediators. Both isoforms are important pro-inflammatory enzyme, whose abnormal expression is a significant marker of neuroinflammation, as previously reported [24,31]. Moreover, AA plays also a key role in inflammation and neurodegenerative disorders [25,32]. In mammalians, there are the three major classes of PLA2s, secretory, calcium-independent, and calcium-dependent ones: among them, the calcium-dependent cytosolic PLA2α (cPLA2α) has received the most attention because the cPLA2-AA-COX-2 pathway is an important signaling pathway in different inflammatory paradigms and neurodegeneration [26,33]. In this regard, it has been demonstrated that the oxidative responses observed in many types of brain damage are associated with increased COX activity [27,34]. Moreover, it was reported that a treatment with COX inhibitors may significantly reduce in neuronal and microglial cell LPS- and IL-1β-induced oxidative damage [28,35]. The results of our study are in accordance with ones showing that in mouse microglial cells, the reduction of COX-2 expression observed after a resveratrol treatment could be determined by the inhibition of NF-κB activation [29,36].\n\nTherefore, our data evidence that NF-κB pathway inhibition through the targeting of IκB phosphorylation by resveratrol ultimately may reduce a pro-inflammatory phenotype, thereby down-regulating different mediators, including COX-1, COX-2, and p-cPLA2.\n\nOne aspect that is particularly important emerging from our study was the ability of resveratrol to modulate the expression of the so-called decoy receptors, such as IL-1R2 and ACKR2.\n\nIL-1R2, first identified on monocytes, neutrophils, dendritic and B cells, in both human and mice, has been reported to be largely involved in driving myeloid cells polarization, and consequently, orientating the immune response. In fact, anti-inflammatory M2 stimuli, such as IL-4, IL-13, IL-10, IL-27, and aspirin, lead to the up-regulation of IL-1R2 expression, whereas the M1 phenotype activated by pro-inflammatory molecules (such as LPS, IFNγ, and TNF-α) exhibits a down-regulation of IL-1R2 [12,16].\n\nThe modulation of IL-1R2 expression has been reported in many cell types as a way to counterbalance and limit sustained inflammation in response to exogenous stimuli. In this regard, IL-1R2 up-regulation in the microglia and brain endothelial cells reduced the brain inflammation in experimental models of IL-1β-induced neurotoxicity, as previously reported [30,31,32,37,38,39].\n\nACKRs are a group (four in humans) of proteins with a high degree of homology with chemokine receptors. ACKRs are chemotactic receptors; however, since they are devoid of the structural domains required to activate canonical G protein-dependent receptor signaling and chemotactic functions, they do not transduce signals through G proteins and lack chemotactic activity [33,40]. Consequently, ACKRs fail to initiate classical signaling pathways after ligand binding, playing a crucial role as regulatory components of chemokine networks in many physiological and pathological processes.\n\nInterestingly, the resveratrol treatment enhanced the expression of the anti-inflammatory IL-1β decoy receptor IL-1R2 and increased the expression of the other decoy receptor, ACKR2. IL-1R2 is the decoy receptor for IL-1; when IL-1R2 binds to IL-1β, signal transduction cannot be triggered, and consequently, the pro-inflammatory action of this cytokine is neutralized [34,41]. Therefore, the increased expression of IL-1R2 on the microglia surface indicates a reduced responsiveness of these cells to IL-1β stimulation, significantly dampening the pro-inflammatory profile. Moreover, IL-1R2 also exists in soluble form that can be rapidly shed, so the increased release of the soluble form by IL-1R2-overexpressing cells could neutralize the action of IL-1β on other cells, thus reducing the extent of the pro-inflammatory responses.\n\nThe results of our pioneering work describe, for the first time, that the resveratrol treatment of the microglia exposed to a prolonged pro-inflammatory stimulus is able to counterbalance inflammatory responses through the regulation of decoy receptors. These findings suggest that the naturally occurring polyphenol resveratrol ability to drive microglial activation, thus regulating the inflammatory response, may help to explain its neuroprotective effects in several in vivo models of neuroinflammation.\n\n## 4. Conclusions\n\nThe results of the present in vitro study suggest that polyphenolic compounds, such as resveratrol, may be useful in the treatment of inflammation associated with neurodegeneration and that clinical studies may evaluate the possibility of their use as a therapeutic support strategy. The results of this study highlight the direct effects of resveratrol in the regulation of functional in vitro responses by microglial cells, therefore it would be of considerable importance to investigate the effect of this polyphenol in vivo for future clinical use also in nano-formulations or intranasal spray applications, for example, in order to overcome the bioavailability problems linked to the BBB or metabolism of endothelial cells. In light of these results, we plan to carry out further studies to clarify the modulation mechanisms of the decoy receptors underlying the neuroprotective effects of polyphenols.\n\n## References\n\n1. 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Oat (Avena sativa L.) and Pea (Pisum sativum L.) Saponins\n in Plant-Based Food Products by Hydrophilic Interaction Liquid Chromatography Coupled\n with Mass Spectrometry\nauthors:\n- Anastassia Bljahhina\n- Dmitri Pismennõi\n- Tiina Kriščiunaite\n- Maria Kuhtinskaja\n- Eeva-Gerda Kobrin\njournal: Foods\nyear: 2023\npmcid: PMC10000715\ndoi: 10.3390/foods12050991\nlicense: CC BY 4.0\n---\n\n# Quantitative Analysis of Oat (Avena sativa L.) and Pea (Pisum sativum L.) Saponins in Plant-Based Food Products by Hydrophilic Interaction Liquid Chromatography Coupled with Mass Spectrometry\n\n## Abstract\n\nThis work presents the sample extraction methods for solid and liquid sample matrices for simultaneous quantification of oat (*Avena sativa* L.) and pea (*Pisum sativum* L.) saponins: avenacoside A, avenacoside B, 26-desglucoavenacoside A, and saponin B and 2,3-dihydro-2,5-dihydroxy-6-methyl-4H-pyran-4-one (DDMP) saponin, respectively. The targeted saponins were identified and quantified using a hydrophilic interaction liquid chromatography with mass spectrometric detection (HILIC-MS) method. The simple and high-throughput extraction procedure was developed for solid oat- and pea-based food samples. In addition, a very simple extraction procedure for liquid samples, without the need to use lyophilisation, was also implemented. Oat seed flour (U-13C-labelled) and soyasaponin Ba were used as internal standards for avenacoside A and saponin B, respectively. Other saponins were relatively quantified based on avenacoside A and saponin B standard responses. The developed method was tested and successfully validated using oat and pea flours, protein concentrates and isolates, as well as their mixtures, and plant-based drinks. With this method, the saponins from oat- and pea-based products were separated and quantified simultaneously within 6 min. The use of respective internal standards derived from U-13C-labelled oat and soyasaponin Ba ensured high accuracy and precision of the proposed method.\n\n## 1. Introduction\n\nThe demand for sustainable protein sources in food production is continuously growing [1,2]. Oat (*Avena sativa* L.) and pea (*Pisum sativum* L.) proteins in the form of concentrates or isolates can act as an alternative to animal proteins due to their potential ability to provide desirable technological properties in plant-based meat and dairy substitutes [3,4]. Pea protein is an insufficient source of methionine but, on the other hand, has a high content of the essential amino acid lysine and branched-chain amino acids-leucine, isoleucine, and valine [4]. In contrast to pea, oat contains enough methionine but a scarce amount of lysine [3]. Blending oat and pea proteins in products is one way to achieve a complete essential amino acid profile [5], and such products are already available on the market. However, one of the main obstacles in the application of plant-based proteins in food production is their bitter and astringent off-taste [6,7,8]. It has been suggested that saponins might be the main cause of this sensation [9,10,11,12,13,14] influencing consumer acceptance.\n\nSaponins are a diverse group of secondary defence metabolites widely spread in plant species [15]. Saponins investigated in this study are amphiphilic molecules, with polar water-soluble sugar moieties attached to a nonpolar, water-insoluble steroid or triterpene core [16]. Oats, as the only cereals capable of accumulating saponins, contain bisdesmosidic steroidal saponins avenacoside A and B, and monodesmosidic 26-desglucoavenacoside A in their leaves and grains (Figure 1) [9,10,12,13,17]. Saponin B and 2,3-dihydro-2,5-dihydroxy-6-methyl-4H-pyran-4-one (DDMP) saponin are monodesmosidic triterpenoid saponins found in peas [18,19]. Besides being taste-active bitter compounds, saponins have also been reported as antinutrients. As such, they may affect nutrient absorption by inhibiting metabolic and digestive enzymes [20] and by binding to minerals such as zinc and iron [21]. High concentrations of saponins in the diet may lead to hypocholesterolemic effect [22], hypoglycemia [23], inefficient protein digestion, vitamin and mineral uptake in the gut, and the development of a leaky gut [24]. Despite the reported negative nutritional impact, some studies have also shown positive cholesterol-lowering [25] and anticancerogenic [26] effects of saponins.\n\nThe analysis of saponins could be performed using a wide range of classical methods such as gravimetry [15,27], hemolysis [28], bioassays [29], and spectrophotometry [30]. In addition, different saponins could be separated and analysed using chromatographic methods, e.g., thin-layer chromatography [31,32], gas chromatography [33], and high-performance liquid chromatography (HPLC) [19]. The detection of the saponin class compounds could be carried out using the simplest optical detection methods [34,35], but these methods usually lack the selectivity and sensitivity of more advanced analytical techniques such as mass spectrometry [9,17,36,37,38]. The saponin extraction and the pre- and post-extraction sample clean-up before the LC analysis [9,17,19,39] are required to obtain a clean extract which would minimise matrix effect in mass spectrometric measurements. However, these sample preparation procedures are time-consuming and unsuitable for routine analysis of large amounts of samples. This creates the need for an improved, efficient, sensitive, more selective, and reproducible extraction method of saponins prior to the analysis. The use of liquid chromatography coupled with mass spectrometry (LC-MS) allows more precise and selective determination of the contents of different types of saponins in various plant species: oat [9,13,17], pea [19,32], and soya [39,40]. Although, the amounts of saponins have been quantified mainly from the seeds or husks of numerous oat and pea varieties [9,19,32], there is a lack of data concerning the concentrations of saponins in processed food ingredients, and the half- and end-products produced therefrom. To the best of our knowledge, there is no versatile method for the determination of saponins derived from different plant species in various food matrices.\n\nThe objective of this study was to develop simple sample extraction methods for solid and liquid plant-based food sample matrices for the selective and quantitative determination of five oat and pea saponins: avenacoside A, avenacoside B, 26-desglucoavenacoside A, saponin B, and DDMP saponin, using hydrophilic interaction liquid chromatography with mass spectrometric detection (HILIC-MS). To our knowledge, there are no reports on simultaneous HILIC analysis of the above-mentioned saponins in solid and liquid samples containing concurrent oat and pea ingredients.\n\n## 2.1. Chemicals and Materials\n\nHPLC-grade acetonitrile (MeCN), methanol (MeOH), ethanol (EtOH), hexane, propan-2-ol (IPA), and formic acid (FA) (for MS, $98\\%$) were purchased from Honeywell (Charlotte, NC, USA). The standard compounds avenacoside A, saponin B (soyasaponin I), and soyasaponin Ba phyproof® were purchased from Sigma-Aldrich (Darmstadt, Germany). Uniformly isotopically labelled oat seed flour (U-13C oat seeds, *Avena sativa* 97 atom%) was obtained from IsoLife BV (Wageningen, The Netherlands). Ultrapure water (18.2 MΩ·cm) was prepared with MilliQ® HX 7040SD equipped with MilliQ LC-Pak (Merck KGaA, Darmstadt, Germany). Biotage Isolute® PLD+ and C18 columns (100 mg/1 mL) were purchased from Biotage Sweden AB (Uppsala, Sweden). Amicon Ultra-0.5 centrifugal filter units (3, 10, 30, 50 kDa) and Millex-LCR filters (pore size 0.2 µm, filter dimension 13 mm) were obtained from Merck KgaA (Darmstadt, Germany).\n\n## 2.2. Food Samples\n\nYellow pea flour, whole-grain oat flour, and oat and pea drinks were purchased from a local supermarket. Pea protein isolate (Bang & Bonsomer Estonia OÜ, Tallinn, Estonia), pea protein concentrate (Aloja-Starkelsen Ltd., Limbažu novads, Latvia), and oat protein concentrate (Lantmännen, Stockholm, Sweden) were obtained from producers. The composition and nutritional information available on the product label of these products is available in Supplementary Table S1. Untreated and extruded blends of pea protein isolate, oat protein concentrate, and pea protein concentrate (52:28:20, w/w) were produced in-house by following a previously published protocol [41].\n\n## 2.3. Extraction Method for Solid Samples and for Liquid Samples\n\nSample extraction methods 1A, 1B, 2A, 2B, and 2C, which were tested during solid sample extraction method development, are described in the supplementary information.\n\nSolid sample extraction (method 2D) was performed according to Heng et al. [ 19] with some modifications. Powdered non-defatted solid sample (100 mg) was weighed into a 10 mL volumetric flask ($$n = 3$$), filled with aqueous EtOH ($70\\%$, v/v), mixed thoroughly, and ultrasonicated for 30 min (without additional heating). After ultrasonication, samples were centrifuged (14,000× g for 10 min at 10 °C) to remove insoluble matter. The supernatant (500 µL) was passed through PLD+ columns by applying positive pressure to remove proteins and phospholipids. The obtained filtrate was diluted to receive an aqueous MeCN ($50\\%$, v/v) solution. The diluted filtrate (100 µL) was transferred to the LC-MS vials, mixed with 50 µL soyasaponin Ba working solution and 50 µL U-13C-oat extract working solution, and injected into the LC-MS.\n\nA homogeneous liquid sample was weighed (0.25 g) into a 5 mL volumetric flask ($$n = 3$$), filled with ultrapure water, and mixed thoroughly. Diluted sample solutions were centrifuged (14,000× g for 15 min at 10 °C) to remove insoluble matter. Sample supernatant (200 μL) and 800 µL MeCN were transferred into the next tube, mixed thoroughly, and centrifuged (14,000× g for 15 min at 10 °C) to remove precipitated proteins. The supernatant (500 µL) was passed through PLD+ columns. The obtained filtrate (300 μL) was transferred into a clear tube and diluted with 180 µL ultrapure water to obtain an aqueous MeCN solution ($50\\%$, v/v). The diluted sample filtrate was combined with internal standard solutions as described for solid samples and injected into the LC-MS.\n\n## 2.4. Preparation of Standard Solutions\n\nThe stock solution of avenacoside A (500 mg/L) was prepared in ultrapure water and the aliquots were stored at −80 °C. The stock solution of saponin B (500 mg/L) was prepared in aqueous EtOH ($60\\%$, v/v) and aliquots were stored at −80 °C. The internal standard stock solution of soyasaponin Ba (100 mg/L) was prepared in MeOH.\n\nThe stock solution of U-13C-oat seed flour extract containing 13C51-avenacoside A was prepared using the previously described solid sample extraction method 2D with some modifications. U-13C-oat seed flour (150 mg) was weighed into a 50 mL volumetric flask, filled with EtOH ($70\\%$, v/v), and mixed thoroughly. The flask was ultrasonicated for 30 min (without additional heating) and the obtained solution was centrifuged (17,000× g for 10 min at 10 °C) to remove insoluble matter. The supernatant was passed through PLD+ columns using a vacuum manifold. The cleaned extract was aliquoted and stored at −80 °C.\n\nThe internal standard working solutions were prepared freshly before the analysis. The working solution of internal standard soyasaponin Ba was prepared by diluting stock solution in the aqueous MeCN ($50\\%$, v/v). The U-13C-oat extract working solution was prepared by diluting the stock solution two-fold with neat MeCN.\n\n## 2.5. Liquid Chromatography Mass Spectrometry\n\nSamples were analysed using a Waters UPLC® system (Waters Corporation, Milford, MA, USA) coupled with a Waters Quattro Premier XE Mass Spectrometer equipped with ZSpray™ Source and controlled by Waters MassLynx™ 4.1 (V4.1 SCN805, Waters Corporation, Milford, MA, USA). Mobile phases were as follows: (A) $0.1\\%$ FA in ultrapure water, (B) $0.1\\%$ FA in MeCN. Weak needle wash was composed of MeCN in ultrapure water ($90\\%$, v/v), and strong needle wash consisted of IPA in MeCN ($50\\%$, v/v). The seal wash solution was aqueous MeCN ($50\\%$, v/v). Samples were stored in an autosampler which was set at 8 °C. The injection volume was 2 µL. Saponins were separated using BEH Amide column (1.0 × 50 mm, 1.7 μm) coupled with BEH Amide VanGuard Pre-column (2.1 × 5 mm) from Waters Corporation (Milford, MA, USA). The final gradient was as follows: 0–0.17 min at $10\\%$ A, 0.17–3.5 min linear gradient 10–$70\\%$ A, 3.5–4.0 min at $70\\%$ A, 4.0–4.5 min linear gradient 70–$10\\%$ A, 4.5–6.0 min at $10\\%$ A. The column temperature was held at 50 °C during all experiments. The flow rate was set at 200 µL/min.\n\nThe analytes were ionised under negative electrospray ionisation (ESI-) and optimised source conditions. The source temperature was set to 120 °C, and high-purity nitrogen was fed into the source at 25 L/h (cone) and 600 L/h (desolvation) and desolvation gas was heated to 350 °C. The capillary voltage was set to −1.5 kV, cone voltage to 80 V, and extractor voltage to 3 V. For measurement of analytes, a set of m/z values for single-ion-recording (SIR) experiments was recorded simultaneously during one chromatographic run. For saponin quantification, deprotonated molecules [M-H]- were chosen based on a scan-type experiment. Mass-to-charge ratios (m/z ± 0.5 Da) for SIR channels were set as follows: avenacoside A—m/z 1061.5; avenacoside B—m/z 1223; 26-desglucoavenacoside A—m/z 899.5; 13C51-avenacoside A—m/z 1112.5 (internal standard); saponin B—m/z 941.5; DDMP saponin—m/z 1067; soyasaponin Ba—m/z 957.5 (internal standard). Data acquisition was performed in Waters MassLynx™ V4.1 (SCN805, Waters Corporation, Milford, MA, USA). Data analysis was performed in Waters QuanLynx™ V4.1 (SCN805, Waters Corporation, Milford, MA, USA) and Microsoft Excel® (Microsoft 365 Apps for enterprise).\n\n## 2.6. Calibration and Quantification\n\nThe working solution was prepared by diluting standard stock solutions 100 times with MeCN:H2O:EtOH solution (50:36:14, v/v). Internal standards, soyasaponin Ba and U-13C-oat extract, were added before injection to the autosampler vial, and their concentration in the vial was set at 0.75 mg/L and 0.3 mg/L, respectively. Calibration curve standard solutions (100 µL) were mixed with internal standards working solutions (50 µL U-13C-oat extract working solution and 50 µL soyasaponin Ba working solution). Calibration curves were built for avenacoside A (0.01–2.44 mg/L) and saponin B (0.01–2.48 mg/L) using eight-point measurements of serially diluted standards, which were run in triplicate. The regression was found by fitting points to the linear equation. The external standard calibration curves were built by correlating the concentrations of external standards to the response factors, which were calculated according to Equation [1]. response factor (RF) = (area of analyte)/(area of internal standard)[1] As only the avenacoside A standard was commercially available, other analytes of interest (avenacoside B and 26-desglucoavenacoside A) were quantified relatively using the avenacoside A calibration curve. Avenacoside B and 26-desglucoavenacoside A results are presented in avenacoside A equivalents. Avenacosides were quantified using isotopically labelled 13C-avenacoside A as an internal standard. As DDMP saponin could not be sourced commercially, its quantification was based on the saponin B standard curve, and the results are given in saponin B equivalents. Both were quantified using soyasaponin Ba as an internal standard.\n\n## 2.7. Validation of the Method\n\nThe following parameters were assessed during method validation: linearity, limit of detection (LOD), limit of quantification (LOQ), precision, specificity, sample extraction recoveries, and matrix effect (ME). Developed extraction methods for solid and liquid samples were validated separately. Oat protein concentrate and pea protein isolate were used to validate the solid sample extraction method. Saponin determination in liquid samples was validated using oat and pea drinks.\n\nThe linear range and linearity were evaluated via repeated measurements of standard solutions of avenacoside A and saponin B consisting of 8 individual points obtained from serial dilution of stock solutions. For the calculation of LOD and LOQ values for avenacoside A and saponin B compounds, the standard deviation (SD), obtained by analysing the peak areas of the lowest standard concentration point, was multiplied by three or ten, respectively [42].\n\nTo determine the intra-day precision of the instrumental method, oat protein concentrate and pea protein isolate extracts containing all analytes and internal standards were injected six times, and for inter-day precision, sample extracts were studied across three independent days to confirm the stability of the retention times and peak areas. The precision of the extraction methods was determined by repeatability (intra-day) and intermediate precision (inter-day). Repeatability was carried out by performing six repeated analyses of the samples on the same day, while the intermediate precision of the method was assessed using samples that were analysed on three different days over two months under the same experimental conditions.\n\nThe total recoveries for avenacoside A and saponin B were evaluated by spiking the solid and liquid samples with a known amount of avenacoside A and saponin B at three different concentration levels. For estimation of solid sample extraction method recovery, oat protein concentrate and pea protein isolate (100 mg) were weighed into a 10 mL volumetric flask ($$n = 3$$). Aliquots of avenacoside A and saponin B standard solutions (10 mL) at three different concentrations were prepared in aqueous EtOH ($70\\%$, v/v) separately. These solutions were added to oat protein concentrate and pea protein isolate, mixed thoroughly and subjected to the solid sample extraction method as described above. The recoveries of avenacoside A and saponin B in oat and pea liquid samples were determined by cross-matrix spiking both sample matrices. For estimation of liquid sample extraction method recovery, separate standard stock solutions of avenacoside A and saponin B were prepared (200 mg/L). These solutions were added in different volumes to 0.25 g of liquid sample (oat and pea drink) ($$n = 3$$) weighed into a 5 mL volumetric flask, mixed thoroughly, and subjected to the liquid sample extraction method as described above. The total recovery was calculated using Equation [2] [43], total recovery (%) = (Cspiked/(Cunspiked + Cspike)) × $100\\%$[2] where *Cspiked is* the amount of saponin determined in the spiked sample, *Cunspiked is* the amount of saponin in the unspiked sample, and *Cspike is* the amount of saponins at three different concentration levels.\n\nME as one of the most problematic issues in LC-MS was evaluated for all four sample matrices (oat protein concentrate and pea protein isolate and plant-based drinks) by post-extraction sample spiking with calibration curve standard solutions, then constructing a calibration curve based on response factors and spiked standard concentrations, and comparing the matrix-matched calibration curve slope with the calibration curve slope in solvent (Equation [3]) [42] ME (%) = slopematrix-matched/slopesolvent × $100\\%$.[3] *Statistical analysis* was carried out using Excel® (Microsoft® 365 for enterprise). The results are presented as mean ± SD or relative standard deviation (RSD).\n\n## 3.1. Development of Liquid Chromatography Method\n\nThe HPLC method was developed and assessed by analysing external standards and compounds available in oat and pea sample matrices. During development of the liquid chromatography method, two types of stationary phase chemistry were tested (C18 and HILIC) as well as different column dimensions. The best separation performance in terms of time of analysis, selectivity, and efficiency was achieved by the BEH Amide column (1.0 × 50 mm, 1.7 μm). Based on the literature [9,19] and scan-type experiments of oat flour and pea flour sample extracts, m/z values for SIR channels were chosen for the detection and relative quantification of targeted compounds without existing standard compounds in these sample matrices. Avenacoside B and 26-desglucoavenacoside A were found to be present in the oat sample matrix in addition to avenacoside A. DDMP saponin also occurred in the pea sample matrix besides saponin B. MRM experiments were conducted during development of a methodology but we have found that the MRM approach did not bring any more selectivity but significantly reduced sensitivity by not producing consistent fragments. The example of a chromatogram obtained by injecting the oat and pea flour extracts is shown in Supplementary Figure S1.\n\n## 3.2. Development of Sample Extraction Methods\n\nTwo previously published extraction methods (avenacosides in grain and husks of oats [9] and saponins in peas [19]) were the starting points for the development of a method for simultaneous saponin extraction from oat and pea matrices. As both extraction methods were time-consuming, a more efficient sample preparation was proposed for saponin quantification. All samples were analysed using LC-MS method described in the Materials and Methods section.\n\nTable 1 shows the main steps of extraction methods and saponin extraction yields obtained by reference methods (1A and 2A) and modified methods (1B, 2B, 2C, and 2D). To demonstrate the efficiency of the optimized methods, oat protein concentrate and pea protein isolate were analysed in duplicate.\n\nSince both reference methods [9,19] started by fat elimination, defatted oat protein concentrate (fat $18.9\\%$) and pea protein isolate (fat $4.7\\%$) were extracted using methods 1A, 1B, 2A, and 2B. The oat protein concentrate extracted using method 1B gave $37\\%$ higher avenacoside A concentration compared to method 1A, and method 2B resulted in $50\\%$ higher yield than method 2A. Overall, the highest avenacoside A content in oat protein concentrate was achieved using extraction method 2B. Using method 1B, the pea protein isolate gave two times higher saponin B yield than using extraction method 1A, and method 2B gave a $76\\%$ higher yield than method 2A. Thus, the highest saponin B amount from pea protein isolate was extracted using method 2B. Although both improved methods 1B and 2B gave similar saponin yields in analysed matrices, it was decided to proceed with more process-efficient method 2B, as method 1B utilizing two-step methanol reflux extraction is very time-consuming.\n\nThe necessity for fat removal before saponin extraction from the matrix was determined. For this, saponins from four samples (oat flour and protein concentrate and pea flour and protein isolate) were extracted using extraction methods 2B and 2C, and lastly, the extracts were filtered through different filtering devices (the molecular weight cut-off filters with different membrane pore sizes (3, 10, 30, and 50 kDa), 0.2 μm syringe filter, and ISOLUTE® PLD+ Protein and Phospholipid Removal columns) before the LC-MS analysis. The results of this experiment are shown in Supplementary Figure S2.\n\nNo significant differences in avenacoside A, avenacoside B, saponin B, and DDMP saponin content were determined in Soxhlet-defatted and non-defatted oat and pea matrices. On the other hand, different molecular cut-off sizes had a significant impact on the recovery of saponins. The 3 kDa and 10 kDa cut-off filters showed inferior performance irrespective of the sample matrix and saponin type determined. The maximum recovery of analytes in the samples was achieved using 50 kDa and in some cases 30 kDa cut-off devices. In all sample matrices except oat protein concentrate, the application of PLD+ columns and syringe filters gave even better results than 30 kDa or 50 kDa cut-off filters. Although the PLD+ and 0.2 μm syringe filters gave quite similar analyte recovery, the application of PLD+ columns resulted in clearer MS chromatograms with a minimum number of interfering peaks in the chromatogram baseline. Moreover, filtering through the PLD+ column enables an easy transition of the procedure to a high-throughput workflow in the case of using 96-well PLD+ plates. The ISOLUTE® PLD+ proprietary multifunctional sorbent phase is optimised to selectively retain proteins and phospholipids [44]. The results indicated that pre-extraction fat removal is not necessary before saponin extraction and could be omitted and the application of PLD+ columns is the best solution for post-extraction clean-up of sample extracts. This resulted in a modified method 2C (described in Table 1).\n\nThe influence of ultrasonic power on the saponin extraction yields was also investigated. Saponins from oat protein concentrate, pea protein isolate, and oat and pea flours were extracted using methods 2C and 2D (results are shown in Supplementary Table S2). The results showed that ultrasonication did not have a statistically significant effect on saponin yield but considering the extraction time the application of ultrasonication is preferable. It should be noted that heating taking place during sonication had no effect on the analytes. During this experiment, the ultrasonic bath heated itself from ambient temperature (23 °C) to 40 °C in 30 min. Previous research has shown that the exposure of DDMP saponin to a temperature higher than 40 °C has a profound effect on its degradation into saponin B [18]. However, in another study, it was reported that the pure DDMP saponin in methanolic solution started to decrease in concentration when heated at 65 °C [45].\n\nBased on the obtained results and considering the extraction time and yield, method 2D was utilized for analysis and validation of all solid samples.\n\nLiquid food samples were analysed without the need to use lyophilisation before the sample extraction. The sample preparation method was based only on the application of ISOLUTE® PLD+ cartridges for sample extract purification before LC-MS analysis, previously chosen as the most efficient for cleaning the extracts of the solid samples.\n\n## 3.3. Validation of the Method\n\nWhen the chromatographic methods and sample extraction methods were developed, validation was performed to evaluate the linear range, LODs and LOQs, precision, recoveries, and matrix effect of the proposed method. The linearity of response and other calibration parameters for avenacoside A and saponin B are presented in Table 2. Linearity for these two saponin standards was obtained in the concentration range of 0.01–2.5 mg/L. The LOQs were estimated from the lowest point of the calibration curve ranging from 0.015 mg/L for avenacoside A and 0.014 mg/L for saponin B. The obtained LOQ results were lower than or in accordance with previous research [9,13,17,39].\n\nAfter linearity was found to be acceptable for avenacoside A and saponin B, the repeatability of the method was appraised. Repeatability of retention times and peak areas were studied first with six replicate injections of oat protein concentrate and pea protein isolate extract. Table 3 shows the repeatability of retention times, peak areas, and the precision of solid and liquid sample extraction methods. RSDs of peak areas for all saponins did not exceed $6\\%$. Intra- and inter-day RSDs were at a similar level, indicating that the methods are reproducible to an acceptable extent for the routine analysis of oat and pea products. Intra-day and inter-day RSDs were determined by extracting oat protein concentrate, pea protein isolate, and plant-based drinks on different days. The RSD of the intra-day precision ranged from 6 to $13\\%$ and inter-day precision from 7 to $11\\%$ in powdered oat and pea samples. For oat and pea plant-based drinks, the intra-day precision ranged from 3 to $12\\%$ and inter-day precision from 7 to $16\\%$. The precisions for the DDMP saponin pea drink were not evaluable despite multiple measurements (DDMP saponin content in this sample was <LOQ).\n\nThe recoveries were determined in oat protein concentrate and pea protein isolate powder by spiking the oat matrix with avenacoside A and the pea matrix with saponin B. The recovery of analytes in the case of the liquid sample extraction method was investigated separately. Table 4 shows the recovery results of powdered and liquid samples. The recoveries of avenacoside A and saponin B ranged from 90 to $115\\%$ and from 82 to $100\\%$ in oat protein concentrate and pea protein isolate, respectively. In the oat drink, the recovery of avenacoside A ranged from 96 to $113\\%$ and saponin B from 98 to $113\\%$. In the pea drink matrix, the recoveries of avenacoside A and saponin B were from 94 to $106\\%$ and from 89 to $98\\%$, respectively. According to validation guidelines, the acceptable recovery range for this method should be in the range of 80 to $110\\%$ [46]. Thus, the mean values of obtained recoveries were acceptable for both matrices. The recovery results obtained with the current procedure were similar to ones reported for previously proposed methods [9,13].\n\nOat protein concentrate ME on avenacoside A was $100\\%$, and pea protein isolate ME on saponin B was $110\\%$. Avenacoside A and saponin B ME were $107\\%$ and $105\\%$ in the oat drink and $105\\%$ and $102\\%$ in the pea drink, respectively. All measured ME were in the optimal range between 90 and $110\\%$ [47].\n\nThe stock solution of U-13C-oat seed flour extract was analysed for purity. The unlabelled avenacosides were not detected; thus, isotopically labelled avenacoside A was regarded as fully labelled. The working solution of 13C-oat flour was added into the LC-MS vial before the analysis to assess the quantity of analytes and take into account ME. Moreover, recovery experiments confirmed that the method could be used even with internal standards added post-extraction.\n\nOverall, the method has demonstrated acceptable validation performance in terms of recovery, sensitivity, specificity, and precision, and could be characterised as robust and effective and could potentially be applied in a high-throughput environment. Thus, the developed sample extraction method and the LC-MS method are suitable tools for the analysis of oat and pea saponins in different matrices, e.g., flours, protein concentrates and isolates, mixed matrices, and liquid plant-based drinks.\n\n## 3.4. Determined Concentrations of Saponins in Food Ingredients, Half- and End-Products\n\nHigh sensitivity and reproducibility as well as very short analysis time make the developed method suitable for routine quality analysis of oat- and pea-based food ingredients and foods, as well as products containing oat and pea components. The results of saponin contents in various samples are shown in Table 5.\n\nIn whole-grain oat flour, the contents of avenacoside A, avenacoside B, and 26-desglucoavenacoside A were 23.4 ± 2.9 mg/100 g, 14.0 ± 1.5 mg/100 g, and below LOQ, respectively. According to previous research, the concentrations of avenacosides and their ratios are different and depend largely on the variety of oats [9]. According to the latter study, the average avenacoside A content in oat grain in 16 analysed varieties was 36 ± 8 mg/100 g, avenacoside B content was in the range of 30 ± 4 mg/100 g, and 26-desglucoavenacoside A was 2.4 ± 0.8 mg/100 g [9]. Indeed, the contents of avenacoside A differed up to two-fold depending on the variety, and the ratios of avenacoside A to avenacoside B varied from 0.9 to 1.7 [9]. According to Günther-Jordanland et al. [ 2020], avenacoside A and avenacoside B content in oat flour has been reported to be 24.6 mg/100 g and 21.9 mg/100 g, respectively [13]. Thus, the concentration of avenacosides in the whole-grain oat flour determined in the present study is in a good correspondence with the results reported before [9,13]. In oat protein concentrate ($53\\%$ protein; Table S1), avenacoside A content was 42.3 ± 3.0 mg/100 g, avenacoside B was 33.8 ± 0.7 mg/100 g, and 26-desglucoavenacoside A was 5.1 ± 0.2 mg/100 g. According to specification (Table S1), this product was manufactured from oat bran. Previous research has shown that the average content of avenacoside A and avenacoside B in three analysed oat bran products was 26 ± 7 mg/100 g and 8 ± 2 mg/100 g, respectively [17], which is similar to concentrations determined in the whole-grain flour in the current study. Thus, the increased content of avenacosides in oat protein concentrate should be ascribed to the partial concentration of the oat saponins together with the protein fraction during the production process of oat protein concentrate. In an oat drink, avenacoside A content was 4.6 ± 0.1 mg/100 g, avenacoside B was 2.7 ± 0.2 mg/100 g, and 26-desglucoavenacoside A was below LOQ. As it was a commercial liquid product with low dry matter content, it resulted in an apparently lower content of measured saponins. Nevertheless, according to specification (Table S1), the product contains only $1\\%$ of protein and the oat base is the only protein source in the oat drink. In this respect, considering the oat drink and, e.g., the whole-grain oat flour ($12.5\\%$ of protein), the ratio of avenacosides to protein is much higher in the oat drink. One can suppose the considerable migration of saponins into the liquid phase when soaking the oats during the initial step of oat drink manufacture.\n\nIn pea flour ($17.9\\%$ protein; Table S1), saponin B content was 6.2 ± 0.4 mg/100 g and relatively quantified DDMP saponin content was 61.1 ± 2.0 mg/100 g. In fact, our findings are inconsistent with the results of Reim and Rohn [2015], who analysed saponin B and DDMP saponin contents in hulls and peas in six different pea varieties using the HPTLC method [32]. They reported that saponin content in peeled peas was 10 to 40 mg/100 g of saponin B and 0 to 20 mg/100 g of DDMP saponin depending on pea variety [32]. Nonetheless, the present findings of high DDMP content in pea flour are comparable with the results of Heng et al. [ 2006]: the DDMP saponin content varied from 70 to 150 mg/100 g DM, whereas saponin B varied from 0 to 40 mg/100 g DM [19]. Our results confirm that the DDMP saponin is the predominant naturally occurring saponin present in pea. The high level of DDMP saponin in pea flour was observed in the current study most likely because it has not been thermally treated and DDMP saponin has not been converted into saponin B. In pea protein concentrate ($46.9\\%$ protein; Table S1), the saponin B content was 80.3 ± 1.6 mg/100 g and DDMP content was 107.6 ± 4.1 mg/100 g. Saponins are found in the cotyledons and are often associated with the protein bodies of legumes [4]. Therefore, saponin accumulation in pea concentrate produced by dry milling and air classification is evident [4], which is in accordance with at least twice higher levels of saponins in pea protein concentrate compared to pea flour determined in our study. In pea protein isolate ($75\\%$ protein; Table S1), saponin B content was 243.8 ± 6.2 mg/100 g and DDMP content was 10.8 ± 0.7 mg/100 g. These results show that protein wet extraction and isoelectric precipitation, likely performed to achieve protein isolate, degrade unstable DDMP saponin naturally occurring in peas into saponin B. In the pea drink, saponin B content was 3.5 ± 0.2 mg/100 g and DDMP saponin was below LOQ. According to the product specification (Table S1), it contains $2\\%$ of protein, and the only protein source is pea. Although the exact production process of the pea drink is unknown, taking into account the content of saponin B per 1 g of pea drink protein (1.75 mg), the probable pea protein source should contain at least 175 mg of saponins (sum of saponin B and DDMP saponin, as DDMP saponin is converted into saponin B during drink pasteurization) per 100 g of pure pea protein.\n\nTo test the applicability of the developed method for simultaneous determination of oat and pea saponins from one matrix, the blend of pea isolate, oat protein concentrate, and pea protein concentrate was used. In addition, the part of the mixture was extruded according to the previously published article [41]. Results show that avenacoside A, avenacoside B, 26-desglucoavenacoside A, saponin B, and DDMP saponin content in the blend were 13.5 ± 1.0 mg/100 g, 10.9 ± 0.3 mg/100 g, 1.3 ± 0.3 mg/100 g, 123.9 ± 6.2 mg/100 g, and 27.1 ± 3.5 mg/100 g, respectively. Considering that this blend was composed of $52\\%$ pea protein isolate, $28\\%$ oat protein concentrate, and $20\\%$ pea protein concentrate, which were also analysed separately, the recoveries of avenacoside A, avenacoside B, 26-desglucoavenacoside A, saponin B, and DDMP saponin were $114\\%$, $115\\%$, $90\\%$, $95\\%$, and $100\\%$, respectively. In the extruded blend, avenacoside B and 26-desglucoavenacoside A content did not change significantly, avenacoside A content decreased by $21\\%$, and saponin B content increased from 123.9 to 132.9 mg/100 g, which could potentially happen due to DDMP saponin conversion into saponin B during extrusion cooking.\n\n## 4. Conclusions\n\nIn conclusion, the HILIC-MS-based method for oat and pea matrices, with a relatively simple extraction procedure for solid and liquid samples, allowing the simultaneous quantification of avenacoside A and saponin B, and the relative quantification of avenacoside B, 26-desglucoavenacoside A, and DDMP saponin, was employed for analysis of saponins in various food ingredients and products. Oat protein concentrate, pea protein isolate, and oat- and pea-based drinks were chosen for development and validation of the sample extraction methods. The optimised HILIC-MS method was able to absolutely quantify avenacoside A and saponin B in the matrices; other compounds were quantified based on existing standard compounds. The validation of the improved methods for both sample types (solid and liquid) showed the acceptable linear range, LODs and LOQs, precisions, recoveries, and MEs. Generally, an inter-day precision was below $20\\%$. 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0.002044535242021084\n]","isConversational":false}}},{"rowIdx":85,"cells":{"text":{"kind":"string","value":"---\ntitle: 'Monitoring Moroccan Honeys: Physicochemical Properties and Contamination Pattern'\nauthors:\n- Abir Massous\n- Tarik Ouchbani\n- Vincenzo Lo Turco\n- Federica Litrenta\n- Vincenzo Nava\n- Ambrogina Albergamo\n- Angela Giorgia Potortì\n- Giuseppa Di Bella\njournal: Foods\nyear: 2023\npmcid: PMC10000722\ndoi: 10.3390/foods12050969\nlicense: CC BY 4.0\n---\n\n# Monitoring Moroccan Honeys: Physicochemical Properties and Contamination Pattern\n\n## Abstract\n\nThe physicochemical traits and an array of organic and inorganic contaminants were monitored in monofloral honeys (i.e., jujube [Ziziphus lotus], sweet orange [Citrus sinensis], PGI Euphorbia [Euphorbia resinifera] and Globularia alyphum) from the Moroccan Béni Mellal-Khénifra region (i.e., Khénifra, Beni Méllal, Azlal and Fquih Ben Salah provinces). Moroccan honeys were in line with the physicochemical standards set by the European Union. However, a critical contamination pattern has been outlined. In fact, jujube, sweet orange, and PGI Euphorbia honeys contained pesticides, such as acephate, dimethoate, diazinon, alachlor, carbofuran and fenthion sulfoxide, higher than the relative EU Maximum Residue Levels. The banned 2,3′,4,4′,5-pentachlorobiphenyl (PCB118) and 2,2′,3,4,4′,5,5′-heptachlorobiphenyl (PCB180) were detected in all samples and quantified in jujube, sweet orange and PGI Euphorbia honeys; while polycyclic aromatic hydrocarbons (PAHs), such as chrysene and fluorene, stood out for their higher contents in jujube and sweet orange honeys. Considering plasticizers, all honeys showed an excessive amount of dibutyl phthalate (DBP), when (improperly) considering the relative EU Specific Migration Limit. Furthermore, sweet orange, PGI Euphorbia and G. alypum honeys were characterized by Pb exceeding the EU Maximum Level. Overall, data from this study may encourage Moroccan governmental bodies to strengthen their monitoring activity in beekeeping and to find suitable solutions for implementing more sustainable agricultural practices.\n\n## 1. Introduction\n\nHoney is a sweet product of the bee *Apis mellifera* endowed with very specific physicochemical properties, which make it unique from other viscous solutions, as well as precious healthy and therapeutic properties, mainly related to the presence of enzymes, vitamins, phenolics and minerals, which make it a functional food [1]. However, despite its biological activities, honey is not free of contaminants, and the monitoring of its chemical safety is still crucial today not only for assuring product quality and consumer health protection, but also for preserving the environment with its landscapes and biodiversity.\n\nBees are exposed to numerous pollutants during their foraging activities: their hairy bodies can easily retain a variety of environmental contaminants, and they can be contaminated through food resources when collecting pollen and nectar from flowers or water [2,3,4]. As a result, xenobiotic residues are transferred into and accumulate in bee products, including honey [5,6], which can be regarded not only as food products but also as reliable indicators of environmental contamination [7,8].\n\nAmong organic pollutants, organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) represent substances with a high environmental persistence and detrimental effects on human health, whose production is severely banned or restricted all over the world according to the International Stockholm Convention [9]. Nevertheless, their residues are still found in the environment, in food—honey included—and in biological matrices [10,11,12,13]. While not listed under the Stockholm Convention, polycyclic aromatic hydrocarbons (PAHs) were, in June 1998, one of 16 groups of chemicals listed under the United Nations Economic Commission for Europe (UN-ECE) Convention on Long Range Transboundary Air Pollution (LRTAP), that was signed by 33 countries and the European Commission.\n\nThe surrounding environment, with its urban, industrial, and agricultural activities, and even beekeeping [14], is not the only source of contamination. Plastic additives, such as phthalates non-phthalate plasticizers (PAEs and NPPs) and bisphenols (BPs) may also occur in honey, and their presence may be explained not only by the chemical load of the same raw material, due to the ubiquitous presence of such contaminants, but also by the processing involving the direct contact of honey with plastic materials [15,16,17].\n\nTo protect the authenticity of honey from adulteration events, precise standards on its physicochemical quality have been provided by the Codex Alimentarius [18], and then, further adjusted by the EU Community [19]. However, the international legislative framework on the chemical safety of such bee products is still fragmentary. Indeed, the International Codex Standard for honey refers only to contaminants such as pesticides and heavy metals and suggests that the respective maximum residue limits (MRLs) and maximum levels (MLs) should comply with the standards set by the Codex Alimentarius Commission for contaminants in food [20]. In this scenario, the EU took the lead in regulating apicultural products in September 2018 by approving the “Technical guidelines for determining the magnitude of pesticide residues in honey and setting Maximum Residue Levels in honey” [21]. On the other hand, for heavy metals that may exhibit potential toxicity to consumers, and other organic contaminants, such as PAHs, the MLs set by Regulation (EC) No. $\\frac{1881}{2006}$ for a variety of foodstuffs may be (improperly) considered [22].\n\nSimilar issues are also experienced when considering process contaminants. Specific migration limits (SMLs) have been applied by the EU to those contact materials containing plastic additives with the potential of leaching into food. SMLs have been set up by the Commission Regulations No. $\\frac{10}{2011}$ for some PAEs and No. $\\frac{213}{2018}$ and No. $\\frac{1907}{2006}$ for bisphenol A and S [23,24,25], and, in the absence of legal limits of process contaminants in food, they are often used, albeit improperly, to obtain an idea of the degree of food contamination.\n\nSince the EU market is a major honey importer and greatly affects the agricultural practices of exporter countries, beekeepers all over the world tend to adopt the mentioned EU legislations as a reference for quality control of honey.\n\nDue to its important floristic, faunal and landscape diversity, *Morocco is* endowed with an important and unique beekeeping potential, resulting one of the most valuable territories for honey production in the *Mediterranean area* [26]. Here, beekeeping is a well-rooted tradition and one of the most profitable businesses, thanks to the conspicuous production not only of honey, but also of pollen, propolis, beeswax, and royal jelly. A market overview showed that the Moroccan honey production increased from 4.7 tons to almost 8 tons between 2010 and 2020, with a turnover of around 101 million [27]. Moreover, a vibrant modernization of the sector has started in the last decade, thanks to the leverage effect of the Green Morocco Plan (GMP), the National Initiative for Human Development (NIHD) and, not least, the Moroccan Ministry of Agriculture, setting a referential catalog for high-quality terroir products, including honey, with the final aim to label them under Geographical Indications, Designations of Origin or Agricultural Labels, thus, promoting their consumption [28]. The Béni Mellal-Khénifra region, placed between the High and Middle Atlas mountain ranges and the Tadla Plain, is well known for its rich and varied botanical diversity allowing for significant honey production [29]. Particularly, the region boasts the monofloral Euphorbia honey of Tadla-Azilal, labeled with Protected Geographical Origin (PGI) and produced from Euphorbia resinifera, an endemic Moroccan species mainly distributed in Azilal and Béni Mellal areas [30].\n\nA literature overview suggests that, over the past decade, major efforts have been devoted to the characterization of physicochemical, melissopalynological, antioxidant and microbiological traits of Moroccan honey [28,31,32,33,34,35,36,37,38,39,40,41], while very few and fragmentary data are available on contaminants [42,43]. However, the chemical safety of such a product showing a considerable ability to accumulate xenobiotics from the surrounding environment should not be overlooked, also in view of the patchy regulatory framework, as well as the ongoing modernization facing Moroccan beekeeping and its labelled products.\n\nWithin this background, the aim of the study was to explore the physicochemical traits and the contamination pattern of monofloral honeys from diverse areas of the Béni Mellal-Khénifra region, including the PGI Euphorbia honey of Tadla-Azilal. Data from the main quality indicators (i.e., moisture, sugars, pH, electrical conductivity, and acidity), as well as from an array of organic chemicals -including regulated (i.e., pesticides, PAEs, NPPs and BPs) and banned/restricted substances (i.e., OCPs, PCBs and PAHs)- and inorganic pollutants (i.e., potentially toxic elements) were employed to statistically evaluate the relation between the quality and safety of honey and the actual production scenarios of the Moroccan region. In addition, the dietary exposure to contaminants derived from the consumption of such honeys was evaluated.\n\n## 2.1. Study Area\n\nThe Béni Mellal-Khénifra region is a new Moroccan region created according to the new administrative subdivision of 2015; it represents $4\\%$ of Morocco’s national territory and covers 28 088 km2, of which more than $65\\%$ is mountainous. The region is located in central Morocco, between the High and Middle Atlas and the Tadla Plain, and comprises five provinces: Azilal, Béni Mellal, Fquih Ben Salah, Khénifra and Khouribga (Figure 1).\n\nThanks to diversified climates and landscapes, this region has a rich natural heritage and high biodiversity, with a significant potential for agricultural development. Indeed, the Béni Mellal-Khénifra region has an urbanization rate ($49\\%$) lower than the national average ($60.36\\%$), and more than half of the population lives in rural areas ($51\\%$), being strongly committed to the agricultural sector, which not only constitutes the major economic activity of the region, but it is also in the process of modernization as required by the GMP [44,45]. However, intense agricultural practices have been seen as a relevant factor for environmental pollution. In this respect, recent studies have reported in diverse areas cases of agricultural soils and groundwater/wastewater intended for irrigation being contaminated, especially in terms of heavy metals and pesticides [46,47,48,49].\n\n## 2.2. Honey Samples\n\nThe study was conducted on 12 honey samples produced in 2021 by beekeepers located in diverse provinces from the Béni Mellal-Khénifra region of Morocco. They included $$n = 3$$ honey samples from *Ziziphus lotus* (i.e., jujube honey) produced in Khénifra, $$n = 3$$ honeys from *Citrus sinensis* (i.e., sweet orange honey) collected in the Béni-Mellal province, $$n = 3$$ PGI honeys from *Euphorbia resinifera* (i.e., Euphorbia honey) produced in the Tadla-Azilal area, and $$n = 3$$ honeys from *Globularia alypum* obtained from the Fquih Ben Salah province. Honeys from this study derived from pooling a given type of honey from different hives of a given area and they were collected in glass jars of ~125 g and stored at room temperature in a dark place until analysis.\n\n## 2.3. Chemicals and Reagents\n\nAnalytical standards of $$n = 108$$ pesticides, $$n = 18$$ PCBs, and $$n = 13$$ PAHs were purchased from Sigma-Aldrich (Chicago, Il, USA), Fluka Analytical (Milan, Italy) and Dr. Ehrenstorfer (Augsburg, Germany). For pesticides, deuterated analogues intended as internal standards (ISs) were carbofuran-d3, dimethoate-d6, atrazine-d5, cyprodinil-d5, imazalil-d5, malathion-d6, methiocarb-d3, and trifloxystrobin-d6. They were all provided by Toronto Research Chemicals (Toronto, CA, USA). For the analysis of PCBs and PAHs, the deuterated analogues were naphtalene-d8, acenaphtene-d10 and phenanthrene-d10, from Cambridge Isotope Laboratories Inc. (Andover, MA, USA). Analytical standards of $$n = 10$$ PAEs and $$n = 8$$ NPPs (certified purity ≥$96\\%$) were provided by Supelco (Bellefonte, PA, USA). DBP-d4 and DEHP-d4 were the ISs purchased from Cambridge Isotope Laboratories Inc. Analytical standards of $$n = 9$$ BPs (certified purity ≥$97\\%$) were purchased from Sigma-Aldrich (Steinheim, Germany), while the IS 13C12-BPA was obtained from Cambridge Isotope Laboratories.\n\nSolvents (i.e., acetonitrile, water, and n-hexane, LiChrosolv and Parasol grade) were purchased from Merck (Darmstadt, Germany). The Q-sep QuEChERS extraction kit (4 g MgSO4 and 1 g NaCl) and QuEChERS d-SPE (750 mg MgSO4, 250 mg of primary and secondary amines PSA and 125 mg C18) were purchased from Agilent Technologies Italia S.p. A. (Milan, Italy).\n\nOverall, the contact of laboratory equipment and solvents with samples, the sample preparation time, and the solvent volumes were mandatorily minimized to significantly reduce the background contamination caused by solvents and laboratory materials. Glassware and stainless-steel instruments were washed with acetone, rinsed with hexane, dried at 400 °C for at least 4 h, and finally wrapped with aluminum foil until analysis. All solvents were tested before use, and due to the negligible levels of background contamination, they were employed throughout the analytical procedures with no further purification.\n\nFor the screening of inorganic elements, HNO3 ($65\\%$ v/v) and H2O2 ($30\\%$ v/v) were of Suprapur grade (Mallinckrodt Baker, Milan, Italy). Ultrapure water (<5 mg/L TOC) was obtained from a Barnstead Smart2Pure 12 water purification system (Thermo Scientific, Milan, Italy). A standard solution of Re (1000 mg/L in $2\\%$ HNO3) was provided by Fluka (Milan, Italy) and employed as IS. Single-element standard solutions of inorganic elements such as K, Na, K, Mg, Ca, Fe, Mn, Cr, Co, Ni, Cu, Zn, Al, Pb, Cd and As, at a concentration of 1000 mg/L in $2\\%$ HNO3 (Fluka, Milan, Italy) were used to prepare multielement stock standard solutions. To avoid undesirable cross-contamination, laboratory glassware and plastic instruments necessary for sample collection, handling, and storage, as well as polytetrafluoroethylene (PTFE) digestion vessels, were washed with $5\\%$ HNO3 before use.\n\n## 2.4. Physicochemical Parameters\n\nMoisture (%) and total soluble solids (TSS) represented by soluble sugars content and expressed as °Brix, were obtained from the tables of correspondence between a given water content/°Brix and the refractive index calculated for each sample at 20 °C. If the index was not determined at a temperature of 20 °C, the correction temperature was considered, and the result was reduced to a temperature of 20 °C [50].\n\nFree, combined, and total acidity were determined by the titrimetric method proposed by Bogdanov and colleagues [50]. Briefly, the titration of the honey sample (10 g diluted with 75 mL of distilled water) was carried out with 0.05 N NaOH to pH 8.5 (free acidity). Then, a 10 mL volume of NaOH was added and titrated again with 0.05 N HCl to pH 8.3 (combined acidity). Total acidity was calculated obtained by the sum of free and combined acidities.\n\nThe pH and electrical conductivity for every honey sample were determined by a pH/conductivity meter. Approximately 10 g honey was dissolved in 75 mL distilled water and the pH and electrical conductivity were measured. For electrical conductivity, the quantity of honey to be weighed was calculated using the following Equation [1]:[1]$M = 20$×100100−AM: mass of honey (g); 20: is the theoretical nominal mass of honey; A: water content in %.\n\nThe ashes were obtained by drying 5 g of every honey sample at 600 °C until constant weight, according to the AOAC protocol [51]. For the determination of minerals (K, Mg, Na, and Ca) and essential trace elements (Mn, Fe, Cu, Zn, Se, Cr, Co and Ni), see Section 2.7.\n\n## 2.5. Pesticide, PCB, and PAH Residues\n\nFor extraction of pesticides, PCBs, and PAHs from honey samples, the procedure of Saitta et al. [ 52], with some modifications, was used as described below. Briefly, 10 g of honey was weighed into a tube with 10 mL of water and 10 mL of acetonitrile, and vortexed for 5 min. Then, Q-sep QuEChERS kit and d-SPE (described in Section 2.2) was added and centrifuged for 5 min (5000 rpm). At the end, 5 mL of the organic phase was collected, reduced to 1 mL in a rotary evaporator at 30 °C and reduced to a volume of 0.5 mL volume under a stream of nitrogen. Before instrumental analysis, a known amount of every IS was added to every sample. The multiresidue screening was performed by a Thermo Scientific Trace GC Ultra coupled with a TSQ Quantum XLS triple quadrupole mass spectrometer equipped with a TrisPlus RSH automatic sampler. Separation conditions, and mass spectrometry (MS) details are available in our previous work [6]. Compound identification occurred by comparison of their retention times and mass spectra with those of corresponding commercial standards. The quantitative procedure was carried out in multiple reaction monitoring (MRM) mode, exploiting the IS normalization. The MRM transitions, as well as the main figures of merit of analytical validation are reported in Table S1. Every honey sample was monitored in triplicate, along with analytical blanks.\n\n## 2.6. PAEs and NPPs Residues\n\nThe extraction of plasticizers from the various honey samples was performed according to a method already reported in Liotta et. al. [ 6], with some modifications. Briefly, 5 g of honey was weighed into a tube and extracted with 10 mL of acetonitrile. Then Q-sep QuEChERS was added and centrifuged for 5 min (5000 rpm). Approximately 2 mL of the organic phase were collected, evaporated to 1 mL in a rotary evaporator at 30 °C and finally reduced to a volume of 0.5 mL volume under nitrogen stream. Before instrumental analysis, a known amount of DBP-d4 and DEHP-d4 was added to every sample. The multiresidue screening was carried out by a gas chromatography system (GC-2010, Shimadzu, Japan) equipped with an autosampler (HT300A, HTA, Italy) and coupled to a single quadrupole mass spectrometer (QP-2010 Plus, Shimadzu, Japan) according to the operating conditions already described in our previous work [53]. Identification of PAEs and NPPs occurred by comparison of their retention times and mass spectra with those of corresponding commercial standards, while the quantitative assay was performed in SIM mode, considering the base peak ion out of three characteristic mass fragments for each target analyte (Table S2) and using the IS normalization. The parameters of acquisition, as well as the main figures of merit of analytical validation are reported in Table S2. Measurements were conducted in triplicate for every sample, alternated with analytical blanks.\n\n## 2.7. BP Residues\n\nFor the extraction of the nine bisphenols, the method already proposed by Liotta et al. [ 6] with some modification, was applied. Briefly, 5 g of honey was placed in centrifuge tubes with 10 mL of ultrapure water and 10 mL of acetonitrile, and vortexed for 5 min. Then, 4 g of MgSO4 and 1.5 g of NaCl were added. The obtained mixture was vortexed for 5 min, and centrifuged at 4000 rpm for 10 min. Then, 5 mL of supernatant was added to the QuEChERS d-SPE cleaning tube, vortexed and placed in centrifuge at 4000 rpm for 10 min. Hence, 1 mL of supernatant was recovered and filtered by 0.22 µm nylon filter and analyzed by HPLC-MS/MS. Analysis was performed on an LC apparatus (Prominence UFLC XR system, Shimadzu, Kyoto, Japan) consisting of a controller (CBM-20 A), binary pumps (LC-20AD-XR), degasser (DGU-20A3R), column oven (CTO-20AC), and autosampler (SIL-20 A XR). An electrospray ionization (ESI) source interfaced the LC system to a triple quadrupole mass spectrometer (MS) (LCMS-8040, Shimadzu, Kyoto, Japan). Data were acquired in MRM mode and the resulting ion transitions were used for the identification and quantification (internal standard method) of BPs. MRM transitions and the main figures of merit of analytical validation for every target analyte are reported in Table S3. Every honey sample was monitored in triplicate along with analytical blanks.\n\n## 2.8. Inorganic Elements\n\nMineralization of honey samples was carried out following the method proposed by Di Bella and coworkers [54]. About 0.5 g of each honey sample was weighed, and 1 mL of IS at 0.5 mg/L was added. The samples were digested with 7 mL of HNO3 ($65\\%$, v/v) and 1 mL of H2O2 ($30\\%$, v/v) in a microwave ETHOS 1 digestion system (Milestone, Bergamo, Italy) using the following instrumental parameters: 15 min at 1000 W up to 200 °C, 15 min at 1000 W at 200 °C. The digested samples were conveniently diluted with ultrapure water and their analysis was carried out by means of a single quadrupole inductively coupled plasma-mass spectrometer (ICP-MS, iCAP-Q, Thermo Scientific, Waltham, MA, USA) according to the operating conditions already reported in our previous studies [5,55,56]. All samples were processed in triplicate along with analytical blanks. The analytical validation of the ICP-MS method is reported in Table S4.\n\n## 2.9. Statistical Analysis\n\nStatistical analysis was carried out using the SPSS 13.0 software package for Windows (SPSS Inc., Chicago, IL, USA). Initially the non-parametric Kruskal–Wallis test was applied on log-transformed data to assess differences between honey samples, with a statistical significance at $p \\leq 0.05.$ Subsequently, a Principal Component Analysis (PCA) was conducted on a starting data matrix where the cases [12] were the analyzed honey samples and the variables [54] were the values of physicochemical parameters, as well as pesticides, PCBs, PAHs, plasticizers, BPs residues and element concentrations that were higher than their respective LOQs. When concentrations were below the limit of quantification (LOQ), these were replaced with half the limit of detection (LOD/2). Then, the data set was normalized to achieve independence of the different variables scale factors and a PCA was performed to evaluate the differentiation of honey samples in relation to the different production context and/or floral origin according to the investigated variables.\n\n## 2.10. Assessment of the Dietary Exposure to Contaminants\n\nTo evaluate the health risks of organic and inorganic contaminants derived from the intake of Moroccan honey, the relative estimated daily intakes (EDI) were calculated by multiplying the mean contaminant concentration found in every sample (mg/Kg or µg/Kg) by the amount of honey consumed daily (g/day) and dividing the obtained result by the consumer’s body weight (Kgbw). Hazard quotient (HQ), which is the ratio between a given EDI and the corresponding oral reference dose (RfD) proposed by the U.S. Environmental Protection Agency (US EPA,) was also employed to assess the plausibility of risk. An HQ (dimensionless) >1 entails a high non-carcinogenic risk.\n\n## 3.1.1. Moisture, TSS, Acidity and pH\n\nThe values of moisture, TSS, free, combined, and total acidity, and pH of Moroccan honeys are shown in Table 1.\n\nThe moisture of honey is strictly related with the harvest time and practices performed by beekeepers, and, not least, the level of honey maturity reached in the hive [57]. This parameter influences honey flavor, color, density, and viscosity, and determines its stability and granulation during storage [58]. In the honeys investigated, moisture values were always below the maximum limit ($20\\%$) set by the Codex Alimentarius and EU standards [18,19]. This may be due not only to the correct time of extraction by Moroccan beekeepers, but also to the current use of modern hives with better moisture control. Specifically, moisture values ranged between 14.93–$16.57\\%$, thus indicating a good degree of maturity of all products. Despite the small variability, the upper and lower moisture values, represented respectively by the jujube honey from Khénifra and the sweet orange honey produced in Béni Mellal province, were significantly different ($p \\leq 0.05$), which may suggest a variation in such parameters in relation to the climatic conditions [59].\n\nFor TSS, minimum/maximum values of 82.67 °Brix/85.83 °Brix were found respectively in sweet orange and G. alypum honeys, fall within the acceptability range (78.8 and 85 °Brix). Indeed, TSS values are known to decrease with the increasing concentration of starch, molasses, glucose, and distilled water. As a result, this parameter is inversely related to the moisture content and useful in the detection of adulteration events [60].\n\nHoney acidity and pH are correlated with each other, being dependent on the level of organic acids and enzymatic activity in honey. As a result, their variation in honey samples could be attributed to the floral origin rather than the environmental context [57].\n\nThese physicochemical parameters are generally intended as a marker of honey freshness since the higher the acidity and the lower the pH, the better the environment that inhibits microorganism growth [61]. In the present study, all products showed acidity values below the EU standards 50 meq/kg [14], thus suggesting the absence of undesirable fermentation and/or bacterial spoilage. Specifically, free, and total acidity values ranged from 15.41 meq/kg and 16.39 meq/kg in sweet orange honeys (Khénifra) to 39.28 meq/kg and 39.88 meq/kg in G. alypum honeys (Fquih Ben Salah), thus yielding significantly different results in these types of honey ($p \\leq 0.05$).\n\nAll samples analyzed showed an acidic character. Although non significantly different ($p \\leq 0.05$), pH values were in accordance with the acidity values and varied probably due to the different floral origin. In fact, the lowest pH was observed in G. alypum honey (3.98), while the highest values were found in sweet orange and jujube honey (4.24).\n\nOverall, the physicochemical traits discussed in this study were in line with those reported for the recent production of Moroccan honey from Middle Atlas, also with the same floral origin [28,32,33,38,40,41,58].\n\n## 3.1.2. Electrical Conductivity, Ash, and Mineral Content\n\nThe electrical conductivity, ash, and mineral profile (K, Ca, Na, and Mg) and essential trace elements (Mn, Fe, Zn, Cu, Se, Cr, Co, and Ni) of investigated Moroccan honey are reported in Table 2.\n\nThe electrical conductivity of the honey is closely related to the concentration of minerals and organic acids and its assessment is useful in the discrimination between blossom and honeydew honeys. In fact, such a parameter tends to be higher in honeydew honeys and it varies in relation to the same honeydew content. The Codex Alimentarius and EU legislation require blossom honeys to have conductivity values not higher than 800 µS/cm [18,19]. As a result, honeydew honeys generally show higher values than 800 µS/cm. Additionally, in monofloral honeys, this parameter shows great variability according to the floral origin [62,63].\n\nIn Moroccan honeys, conductivity varied from 157.00 µS/cm to 633.67 µS/cm, respectively in sweet orange and G. alypum honey, which consequently gave significantly different results ($p \\leq 0.05$). On the other hand, jujube and Euphorbia honeys showed intermediate and non-significantly different conductivities (respectively, 381.33 µS/cm and 362.67 µS/cm, $p \\leq 0.05$). All values were below the maximum limit (800 µS/cm) set by the Codex Alimentarius and EU standards for such parameter [18,19].\n\nDifferently from conductivity, there is no specific legislation on maximum level of ash, minerals, and trace elements content in honey, which, consequently, are not yet considered as a quality parameter by either the Codex Alimentarius or the EU. However, they are very important quality markers of honey, reflecting both the floral source of honey as well as its environmental context of production [64].\n\nAshes followed the same trend of electrical conductivity in investigated honeys, ranging from 0.34 g/Kg in sweet orange honey from Béni Mellal to 1.17 g/Kg in jujube honey from Khénifra ($p \\leq 0.05$). In terms of concentrations, similar considerations could also be made for the element profile.\n\nConsidering minerals, K was the most abundant mineral in all honeys analyzed, followed by Ca, Na and Mg. The predominance of K over the other minerals was already highlighted in other honeys—Moroccan and not—being a peculiar characteristic of such a bee product [33,40,41,58,63,64].\n\nG. alypum honey from the Fquih Ben Salah province had the highest concentration of K (849.73 mg/Kg), while the lowest value of K was found in the sweet orange honey from Béni Mellal (102.80 mg/Kg). The highest Ca content was found in the PGI Euphorbia honey from Azilal (125.62 mg/Kg), and the lowest in sweet orange honey from Béni Mellal (81.70 mg/Kg). In jujube and G. alypum honeys, Na was the third mineral element with a concentration of 76.84 mg/Kg and 89.99 mg/Kg, respectively; while in sweet orange and PGI Euphorbia honeys, Mg was the third most abundant mineral, with a concentration of 65.46 mg/kg and 69.54 mg/Kg, respectively.\n\nThe mineral content of honeys under study agreed with the range of values reported for jujube and sweet orange honeys from the Béni Mellal-Khénifra region [40], as well as for the PGI Euphorbia honey [37,58].\n\nFor essential trace elements, the most significant contributions to the element profile came from Fe, Zn and Mn. Specifically, the highest concentration of Fe was found in G. alypum honey with a concentration of 16.51 mg/Kg, while the sweet orange honey had the lowest amount (6.89 mg/Kg). On the other hand, the PGI Euphorbia honey showed the most abundant concentrations of Mn and Zn (4.00 mg/Kg and 6.98 mg/Kg, respectively). Other essential trace elements (i.e., Cu, Se, Cr, Co, and Ni) were revealed at concentrations ≤1 mg/Kg.\n\nDifferently from major elements, no efforts have been devoted to the screening of trace elements in honey from the Béni Mellal-Kenifra region. However, Bettar et al. and Moujanni et al. recently revealed lower Fe and Mn contents for the PGI Euphorbia honey (respectively, 4.37–5.5 mg/Kg and <1 mg/Kg) [37,58].\n\nOverall, it could be argued that the elemental profiles of different honeys are greatly affected by the floral source. Indeed, elements are primarily introduced from the soil into the nectar via the root system of the plant. Additionally, bees are in contact with the surrounding environment during foraging and further amounts of inorganic elements can be accidentally transferred from soil, water and soil to the hive. As a result, the elemental profile of honey is a bio-accumulative picture of the geographical context as well as of the activity near the apiary site [65,66].\n\n## 3.2. Pesticide, PCB, and PAH Residues\n\nNo honey samples were shown to be free of pesticides. However, among the $$n = 108$$ pesticides investigated, no OCPs were revealed, and only $$n = 11$$ pesticides were found at levels higher than the respective LODs, mostly belonging to the organophosphate class (OPs) and its metabolites (Table 3). The jujube honey from the Khénifra province was among the samples with the highest number of quantifiable pesticides ($$n = 8$$), detected moreover at the highest levels. Such honey stood out for the highest level of carbaryl (1060.90 µg/Kg, $p \\leq 0.05$), acephate (1251.19 µg/Kg, $p \\leq 0.05$) and cyromazine (2060.99 µg/Kg, $p \\leq 0.05$). Additionally, it was the only honey to show quantifiable residues of quinalphos (5.92 µg/Kg) and fenthion sulfoxide (16.53 µg/Kg). Intermediate and similar levels of contamination were found in the sweet orange honey produced in Béni Mellal and the PGI Euphorbia honey collected in Azilal, in which the most abundant residues were carbaryl (146.30 µg/Kg and 277.41 µg/Kg, $p \\leq 0.05$) and cyromazine (223.72 µg/Kg and 113.60 µg/Kg, $p \\leq 0.05$). Finally, the G. alyphum honey from the Fquih Ben Salah province had the lowest number of quantifiable pesticides ($$n = 5$$). In such honey, the most abundant residues were confirmed to be carbaryl, acephate and cyromazine. However, they were found at very low levels when compared with the other honey samples ($p \\leq 0.05$). According to the Regulation (EC) No. $\\frac{396}{2005}$ and subsequent amendments [67], $75\\%$ of investigated samples (all samples from jujube, sweet orange, and the PGI Euphorbia honeys) widely exceeded the MRL of 0.05 mg/kg for carbaryl and cyromazine, as well as the MRL of 0.02 mg/Kg for acephate, while $50\\%$ of the samples (all samples from jujube and sweet orange honey) exceeded the MRLs of 0.01 mg/kg for dimethoate and diazinon. All samples of jujube honey greatly exceeded the MRLs of 0.01 mg/kg, 0.05 mg/kg, and 0.01 mg/kg respectively for alachlor, carbofuran and fenthion sulfoxide. As mentioned in the introduction section, very few efforts have been devoted to the assessment of the chemical safety of Moroccan honeys. A recent study conducted on the Euphorbia honey reported the identification and quantification of the 202 pesticides, including the ones detected in this study. However, contrasting results were obtained, since the detected residues were always within the set MRLs, thus indicating a good quality of the PGI product [42].\n\nThe pesticide fingerprint of different honeys clearly reflects the different agronomic practices of the different provinces of such Moroccan region and, more specifically, a more pronounced and prolonged use of OPs in the Khénifra province. The persistence of these pesticides on plants and soil can create shifts in the entire food chain. In fact, regardless of the type of honey, worker bees may transfer such contaminants from the pollen and nectar of plants to the hive, thus being inevitably incorporated into the different hive products [52].\n\nOf the $$n = 18$$ PCBs under analysis, $$n = 2$$ compounds were found. Specifically, PCB118 was detected in all honey samples and quantified only in jujube honey from Khénifra province (0.71 µg/Kg, $p \\leq 0.05$) and PCB180 was revealed in all types of honey and quantified in $75\\%$ of samples (0.42–0.73 µg/Kg, $p \\leq 0.05$), apart from G. alypum honey from Fquih Ben Salah province (Table 3). Of the $$n = 13$$ PAHs investigated, $$n = 6$$ congeners were present in all samples, but quantified in $50\\%$ of them (i.e., jujube and sweet orange honeys). In particular, the jujube honey from Khénifra was the most contaminated product, with $$n = 5$$ PAHs detected at a level >LOQ. Between these, chrysene (2.10 µg/Kg), anthracene (1.54 µg/Kg) and fluorene (1.14 µg/Kg) were the most abundant toxicants. To follow, $$n = 4$$ PAHs were quantified in the sweet orange honey from Béni Mellal, with the most abundant compounds represented by benzo[a]anthracene (1.71 µg/Kg) and chrysene (1.62 µg/Kg) (Table 3). As previously mentioned, there are still no regulatory limits for PCBs and PAHs in honey and no toxicological consideration can be made in reference to the Reg. ( EC) No. $\\frac{1881}{2006}$ [22], since it fixes the ML of just one PAH, namely the benzo[a]pyrene, and establishes a ML for the sum of PCBs, taking into account the share of fat in food.\n\nHowever, the monitoring of PCBs and PAHs in Moroccan honey is very scarce. To the best knowledge of the authors, only Chakir et al. investigated diverse honey samples from different South, Center–South and East Moroccan regions and from many floral origins, including C. sinensis and E. resiniphera [68]. The study reported that a small share of samples was contaminated with PCBs, with concentration levels between 0.06 and 5.1 μg/kg. Furthermore, PAHs were present in all investigated samples with levels in the same range or slightly higher than those observed in this study (0.26–7.58 μg/kg). However, congeners such as dibenzo(a,h)anthracene and acenaphthylene were revealed at the highest levels.\n\nA literature review pointed out that honey from the *Mediterranean area* produced during the last decade was poorly monitored with respect to pesticides, PCBs, and PAHs. In this respect, few recent works on Italian honey generally showed higher standards of chemical safety. In fact, organophosphorus pesticides were detected in the order of ng/g and not exceeding the relative MRLs; in addition, PAHs, such as acenaphthylene, fluorene, phenanthrene and pyrene, were found in the range >LOD-7.70 ng/g. PCBs were absent in all honeys investigated [52,67,69].\n\n## 3.3. Plasticizers and BPs\n\nPlasticizer and bisphenol residues revealed in the several honeys from the Béni Mellal-Khénifra region are shown in Table 4. Five PAEs (i.e., DEHP, DEP, DPrP, DiBP, and DBP) and five NPPs (i.e., DEA, DiBA, DBA, DEHA and DEHT) were determined at a concentration >LOQ in $100\\%$ samples. Among PAEs, DEP was the most abundant congener (0.94–3.17 mg/Kg $p \\leq 0.05$) in the various honey, except for the G. alypum honey (0.94 mg/Kg, $p \\leq 0.05$), followed by DBP (0.49–1.05 mg/Kg, $p \\leq 0.05$) and DiBP (0.45–0.79 mg/Kg, $p \\leq 0.05$) in all honey samples.\n\nAmong the NPPs, DBA was the most abundant compound (8.62–12.42 mg/Kg, $p \\leq 0.05$) in the Moroccan honeys, except for the G. alypum honey (0.50 mg/Kg, $p \\leq 0.05$), followed by DEA, with a concentration ranging between 1.30–5.65 mg/kg ($p \\leq 0.05$). Furthermore, three BPs were also determined, namely BPA, BPB and BPAF. BPA was detected in $100\\%$ samples but quantified only in sweet orange and PGI Euphorbia honey samples (respectively, 7.74 µg/kg and 8.07 µg/kg, $p \\leq 0.05$); BPAF was determined in all samples except the G. Alypum honey (1.48–158 µg/kg, $p \\leq 0.05$); and BPB was the most abundant BP in all Moroccan honeys (4.16–8.75, $p \\leq 0.05$). Among the determined plasticizers, Reg. ( EU) No. $\\frac{10}{2011}$ has established SMLs from food contact material for DBP (0.3 mg/Kg), DEHP (1.5 mg/Kg), as well as DEP, DiBP and DEHT (60 mg/Kg) [23]. On this basis, all Moroccan honeys had an excessive amount of DBP, this PAE being at a concentration level 1.5–3 times higher than the relative SML. On the other hand, the regulatory SML for BPA migration from food contact material is 600 ng/g [24]. Table 4 shows that none of the honey samples contained BPA at concentrations higher than the SML.\n\nSimilarly to pesticides, PAHs and PCBs in the jujube honey from Khénifra and the sweet orange honey from Béni-Mellal demonstrated the highest levels of plasticizers and BPs, while the G. alypum honey from the Fquih Ben Salah province was generally the least contaminated product. However, in such honey, one PAE, i.e., DEHP, and one NPP, i.e., DEHT, were found at the highest levels (1.06 mg/kg and 1.14 mg/Kg, $p \\leq 0.05$).\n\nTo the best of our knowledge, there is no literature regarding plasticizers and BPs in Moroccan honey. However, recent efforts can be observed in the determination of such chemicals in honey from the Mediterranean basin. In this respect, Lo Turco and coworkers [16] determined much lower levels of PAEs (e.g., DEP, 0.006 mg/Kg; DiBP, 0.042 mg/Kg; DBP, 0.039 mg/Kg; and DEHP, 0.191 mg/Kg) in Sicilian and Calabrian honeys than those determined in Moroccan honeys, and BPA was lower than analytical LOQ in all samples. More recently, Notardonato and colleagues [15] confirmed the higher purity of Italian honey, pointing out lower frequencies of PAE determination in honey samples, with only the DEHP found at concentrations (0.005–0.960) similar to those of the Moroccan honeys under study. Indeed, other PAEs, such as DEP (0.020–0.371 mg/Kg), DiBP (0.028–0.299 mg/Kg), DBP (0.019–0.550 mg/Kg) yielded much lower levels. However, Italian honeys revealed higher levels of BPA (18.8–996.8 µg/kg).\n\nSince plasticizers and BPs could be easily released from the plastic components of honey production equipment (e.g., honey extractor and uncorkers), it may be assumed that the Moroccan honey could be contaminated by such compounds during production steps, as already observed not only in honey [15,16] but also in a variety of processed food [70,71]. Additionally, prolonged periods of storage with given conditions in terms of temperature, humidity, and light, may affect not only the peculiar physicochemical properties of honey, but also cause gradual polymer degradation, and, consequently, the migration of plastic additives from the plastic packaging into the honey [72]. However, due to the ubiquitous presence of plasticizers and BPs in the environment, the contamination of honey from the nectar source should always be considered [17].\n\n## 3.4. Potentially Toxic Elements\n\nThe profile of potentially toxic trace elements of Moroccan honeys is reported in Table 5. Among investigated elements, Al was the most abundant metal and, differently from the trend of other contaminants, the highest and lowest levels of such metal were found respectively in the PGI Euphorbia honey from Azilal (5.56 mg/Kg, $p \\leq 0.05$) and in the jujube honey produced in Khénifra (1.69 mg/Kg, $p \\leq 0.05$).\n\nAluminum may be related to the soil acidification caused by elevated industrial emissions and poor mining practices occurring in such Moroccan regions, and it may be increasingly bioavailable to organisms through plant roots. Inevitably, the metal can then spread up the food chain through pollen, and nectar collection [73].\n\nTo follow, equal amounts of Pb and As (0.06–0.16 mg/Kg, $p \\leq 0.05$) were determined in all samples, while Cd was lower than LOQ (0.003 µg/Kg) in any case. Regulation (EU) No. $\\frac{2015}{1005}$, amending Regulation (EC) No. $\\frac{1881}{2006}$ for the ML of Pb in certain foods [74], has introduced a ML of 0.1 mg/Kg in honey. Accordingly, 3 out of the 4 types of Moroccan honey, namely the sweet orange, the PGI Euphorbia and the G. alypum honeys, were characterized by a Pb content higher than the regulatory limit.\n\nAnthropogenic activities, including industrial applications and agricultural chemicals, are responsible for the heavy metal contamination of surroundings. As with other contaminants, bees and bee products including honey are exposed to these contaminants via polluted pollen, water and air. The role of honeybees as “filters” of heavy metals and their protective function against honey contamination have been commonly accepted [75,76]. In contrast, honey is still considered as a typical indicator of heavy metal pollution related not only to anthropogenic activities (e.g., agriculture, industry etc.) but also to the entire production process, as poor beekeeping practices may be also source of heavy metal residues in honey [77,78].\n\nIn the last decade, the issue of potentially toxic trace elements in honey from Mediterranean countries has been approached and, as expected, great data variability was pointed out. For example, carob honey from different Moroccan areas and the PGI Euphorbia honey did not display heavy metals such as Cd and Pb, probably because of the high instrumental LOQ values [41,42]. Honey from several Libyan locations had very high levels of Pb (2.42–10.98 mg/Kg), Cd (0.125–0.150 mg/Kg), but low As contents (0.006–0.018 mg/Kg) [79]. Despite honeys from different Italian regions generally showing Cd and Al at higher levels than Moroccan honeys (respectively, 0.003–0.02 mg/Kg and 0.52–26 mg/Kg) [80,81,82,83,84], Pb was always under the regulatory limit (0.05–0.06 mg/Kg) [79,80] and As at lower levels than those detected in this study (0.01 mg/Kg) [81,84]. On the other hand, different varieties of Spain honeys were marked by lower levels of As (0.004–0.010 mg/Kg) and Pb (0.011–0.041 mg/Kg) [85].\n\n## 3.5. PCA Analysis\n\nIn the present study, PCA provided information on the most significant variables describing the whole data set, enabling data reduction at the same time with a minimum loss of original information.\n\nFour principal components (PCs) with eigenvalues exceeding one (325.627, 18.195, 7.575 and 1.257) were extracted according to the Kaiser Criterion, and they explained up to $97.51\\%$ of total variance (i.e., $47.457\\%$, $33.695\\%$, $14.029\\%$ and $2.327\\%$, respectively). Figure 2 illustrates the bidimensional score and loading plots. Defined by the first two PCs accounting for more than $81\\%$ of the variability of the system, the score plot (Figure 2left) showed four distinguished clusters of honey samples. Such clusters correspond to the four types of Moroccan honey investigated, which differed from each other not only in their botanical origin (i.e., jujube, sweet orange, Euphorbia and G. alypum honeys) but also for the production area (i.e., provinces of Khénifra, Béni Mellal, Azilal and Fquih Ben Salah). Due to such a sample arrangement in the study, it is somewhat troublesome to define whether the clustering of honeys occurred according the botanical or geographic origin. However, based on the array of variables investigated, the obtained data and the provided considerations, it may be argued that both factors noticeably contributed to such sample differentiation. Indeed, the loading plot (Figure 2right) shows that, in accordance with the results of the Kruskal–Wallis test, almost all investigated parameters (i.e., physicochemical indicators, minerals, organic and inorganic contaminants), each related to the floral and/or geographical origin, contributed significantly to the differentiation of honey samples, except for pH, DEHT, DEHA, BPAF, Pb and As.\n\nBy overlapping the loading and score plots, it becomes clear that variables such as pesticides, PAHs, plasticizers and BPs weighed more on the jujube honey from Khénifra and the sweet orange honey from Béni Mellal which, in fact, were the honey samples most affected by organic contamination.\n\nThe PGI Euphorbia honey was in general less contaminated than the above honeys, but it was still characterized by the highest content of pesticides, such as diazinon and metalaxyl-M, and potentially toxic metals, such as Al.\n\nOn the other hand, the G. alypum honey was the least contaminated Moroccan product and, moreover, it was marked by the most convenient physicochemical traits (i.e., TSS, acidity and conductivity), as well as precious contents of minerals and essential trace elements.\n\n## 3.6. Dietary Exposure to Contaminants\n\nThe quality of Moroccan honeys and the potential health risk to consumers were assessed by calculating the EDI and the non-carcinogenic risk (HQ) of organic and inorganic contaminants (Table 6).\n\nEDIs and HQs were calculated by considering the amount of honey consumed daily in the diet by an adult consumer (70 Kg) from Europe (1.8 g/day) and North Africa (0.3 g/day), according to FAO [86], as well as guideline values recommended by international organizations (Table S5).\n\nAs shown in Table 6, the EDIs calculated were well below the intake levels of relative pollutants recommended by international regulatory bodies, thus indicating that Moroccan honey can be safely consumed through the provided dietary amounts. For the non-carcinogenic risk assessment, HQ did not exceed the threshold value of 1 for each contaminant potentially ingested by adults through honey in both European and North-African diets, thus indicating that non-carcinogenic health effects derived from the consumption of these Moroccan honeys were not significant.\n\n## 4. Conclusions\n\nFor the first time, a comprehensive characterization of the physicochemical traits and contaminants of four monofloral honeys from different provinces of the Moroccan region Béni Mellal-Khénifra was carried out, thus corroborating the scarce literature on Moroccan honey. According to the physicochemical parameters, all honeys under analysis were in line with those EU standards established for assuring the authenticity of such bee products. However, a critical contamination pattern was outlined, with several toxicants often exceeding the EU regulatory limits available for honey. Specifically, the jujube honey from Khénifra and the sweet orange honey from the Béni Mellal province were the most contaminated products, as opposed to the G. alypum honey from the Fquih Ben Salah province, which was shown to be the least contaminated one. In this arrangement, the PGI Euphorbia honey from the *Azilal area* had an intermediate contamination degree. However, the dietary exposure assessment highlighted that small amounts of all honeys can be safely introduced both in European and North African diets on a daily basis.\n\nHopefully, findings from this study should not only encourage the enforcement and harmonization of the international regulatory framework on the chemical safety of honey, which is still suffering from evident flaws and shortcomings, but also serve as a “wake-up call” for Moroccan governmental bodies to strengthen monitoring activity in beekeeping and to find suitable solutions for accomplishing more sustainable agricultural practices and modernize the beekeeping sector.\n\n## References\n\n1. Christy E.M.L., Anna M.C.. **An overview of honey: Therapeutic properties and contribution in nutrition and human health**. *Afr. J. Microbiol. Res.* (2011.0) **5** 844-852\n\n2. 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Nilesh J. Washnik\n- Ishan Sunilkumar Bhatt\n- Alexander V. Sergeev\n- Prashanth Prabhu\n- Chandan Suresh\njournal: Diagnostics\nyear: 2023\npmcid: PMC10000734\ndoi: 10.3390/diagnostics13050934\nlicense: CC BY 4.0\n---\n\n# Auditory Electrophysiological and Perceptual Measures in Student Musicians with High Sound Exposure\n\n## Abstract\n\nThis study aimed to determine (a) the influence of noise exposure background (NEB) on the peripheral and central auditory system functioning and (b) the influence of NEB on speech recognition in noise abilities in student musicians. Twenty non-musician students with self-reported low NEB and 18 student musicians with self-reported high NEB completed a battery of tests that consisted of physiological measures, including auditory brainstem responses (ABRs) at three different stimulus rates (11.3 Hz, 51.3 Hz, and 81.3 Hz), and P300, and behavioral measures including conventional and extended high-frequency audiometry, consonant–vowel nucleus–consonant (CNC) word test and AzBio sentence test for assessing speech perception in noise abilities at −9, −6, −3, 0, and +3 dB signal to noise ratios (SNRs). The NEB was negatively associated with performance on the CNC test at all five SNRs. A negative association was found between NEB and performance on the AzBio test at 0 dB SNR. No effect of NEB was found on the amplitude and latency of P300 and the ABR wave I amplitude. More investigations of larger datasets with different NEB and longitudinal measurements are needed to investigate the influence of NEB on word recognition in noise and to understand the specific cognitive processes contributing to the impact of NEB on word recognition in noise.\n\n## 1. Introduction\n\nMusical training involves processing auditory features in challenging situations while performing complex cognitive tasks [1]. Structured musical experiences can systematically shape the auditory–cognitive processes of professional musicians. Recent studies suggest that professional musicians have an “advantage” while processing suprathreshold sounds and can outperform non-musicians in a wide range of auditory perceptual tasks [2,3,4]. It is suggested that musical training can improve the coding precision of the acoustic features (e.g., frequency, intensity, rhythm, and duration) in the classical auditory pathway [5,6]. Moreover, musicians exhibit enhanced cognitive abilities, such as better attention and extended auditory working memory, which are necessary for auditory processing in background noise [7,8]. The musicians’ advantage is also reflected in auditory coding precision at the brainstem and cortical levels, measured through auditory evoked potentials [4,9,10,11].\n\nA large body of literature indicates musicians’ advantage while processing speech in noise (SIN) [4,12,13]. Parbery-Clark et al. [ 4] studied the performance of age-matched young musicians and non-musicians with normal hearing sensitivity and similar non-verbal intelligence quotients. They reported that the performance of young musicians was significantly better than non-musicians in the QuickSIN and hearing in noise test (HINT), which are widely used clinical measures for assessing SIN perception. Musicians revealed better working memory (WM) capacity than non-musicians. The study further showed that WM was a significant predictor for QuickSIN scores, contributing to about one third of variability in the dependent variable. The number of years of musical training accounted for an additional $6\\%$ of the variability in the QuickSIN scores. Similar results were reported in older professional musicians when their speech-in-noise perception, WM, and auditory temporal acuity were compared to age-matched non-musicians [14]. Slater and Kraus [15] attributed the musicians’ advantage to heightened rhythm sensitivity, while other researchers attributed it to better pitch processing [12] and temporal resolution [16]. Taken together, recent evidence highlights that structural musical experiences can improve speech and music coding in the auditory pathway and improve auditory cognitive processes [17].\n\nProfessional musicians are routinely exposed to loud traumatic sound levels. A large body of literature suggests that hazardous sound levels encountered during musical training and performances can put them at higher risk of noise-induced hearing loss (NIHL) than non-musicians [18,19,20,21,22,23]. Collegiate students and music faculty members are exposed to sound levels that range from 80 dBA to 104.5 dBA during solo and group rehearsals and performances [24,25]. Gopal et al. [ 19] measured sound exposure levels among collegiate musicians during 50 min jazz ensemble activities and reported that the equivalent continuous noise level (Leq) during the ensemble ranged from 95 dBA to 105.8 dBA. Recent research indicates that about $40\\%$ of musicians report hearing difficulties due to high sound exposure [26]. Musicians have a $57\\%$ higher hazard ratio for tinnitus and an approximately four-fold higher hazard ratio for NIHL when compared to the general population [26]. In conclusion, recent evidence shows that professional musicians are at a higher risk of acquiring NIHL than their non-musical counterparts.\n\nNIHL is typically characterized by an audiometric notch at frequencies 3, 4, and 6 kHz with recovery at 8 kHz [27]. Phillips et al. [ 28] reported that $45\\%$ of young music students aged 18–25 showed a notched audiogram with 15 dB or greater notch depth in at least one ear. Recent studies suggest that conventional hearing thresholds are not sensitive enough to detect subtle hearing deficits induced by noise exposure, and a substantial amount of synaptic loss can remain “hidden” from behavioral audiograms [29]. Research on various animal models, including rodents [29], guinea pigs [30,31], and rhesus monkeys [32] have revealed that short-term exposure to medium to high-intensity noise can inflict irreversible damage to the synaptic connections between the inner hair cells (IHSs) of the cochlea and spiral ganglion neurons, even when hearing threshold recuperate and hair cell recovers. These studies examined the auditory functions before and after a complete recovery from temporary threshold shift (TTS) using distortion-product otoacoustic emissions (DPOAEs) and auditory brainstem responses (ABRs). The histopathological results of these studies showed abrupt and permanent loss of up to $50\\%$ of afferent nerve terminal connections between IHCs and auditory nerve fibers. The DPOAE amplitudes and ABR thresholds showed complete recovery to pre-noise exposure levels. However, ABR wave I amplitudes at high stimulus levels were significantly more reduced in noise-exposed animals than in the controls and the pre-exposure baseline. A similar loss of synaptic ribbons is observed in aging ears [33,34]. The loss of synaptic connections between inner hair cells and auditory nerve fibers without damaging hair cells or permanent threshold shift is referred to as noise-induced cochlear synaptopathy (NICS), also known as hidden hearing loss (HHL) [35]. Conventional hearing thresholds could substantially underestimate NICS [36]. There is evidence to suggest that the damage caused to auditory nerve fibers in NICS might also have functional consequences while processing suprathreshold stimuli, suggesting that suprathreshold measures might be more sensitive than conventional audiograms for detecting early staged NIHL [37,38,39].\n\nIn recent years, various attempts have been made to extend the findings of NICS in animal models to the normal-hearing human population. Recent studies on noise-induced HHL in humans showed a correlation between [1] auditory evoked potential measures and noise exposure history, [2] psychoacoustic measures and noise exposure, and [3] speech-in-noise measures and noise exposure history. Investigations in normal-hearing adults with high noise exposure have sometimes shown a correlation with ABR responses [40,41,42,43,44,45,46,47,48] and sometimes have not [49,50,51]. The studies showing an association between noise exposure and electrophysiological measures utilized different metrics of ABR. For example, Valderrama et al. [ 48] and Stamper and Johnson [46] showed a negative association between wave I amplitude and noise exposure; Grose et al. [ 41] showed reduced wave I/V amplitude ratio in the high noise exposure group; and Liberman et al. [ 43] reported an enhanced summating potential/action potential (SP/AP) ratio in their high-risk group.\n\nRecent studies examined the possible perceptual consequences of cochlear synaptopathy using psychoacoustic tests. Grose et al. [ 41] compared temporal and spectral modulation detection acuity and sensitivity to phase interaural phase differences between high- and low-risk groups of collegiate students. They found no significant differences between the groups on any psychoacoustic tests. Similarly, Fullgrabe et al. [ 52] investigated the influence of noise exposure on the ability to process temporal cues using a few psychoacoustic tests and found no significant difference between the high- and low-noise exposure groups on temporal acuity measures.\n\nIn addition to ABR and psychoacoustic measures, researchers have used various speech-in-noise perception measures to study the functional changes associated with suspected primary neural degeneration due to high noise exposure history in individuals with normal hearing [41,43,48,49,53]. Liberman et al. [ 43] found that collegiate students at high risk of NIHL due to frequent exposure to noisy events/activities had poorer speech recognition in noise scores compared to age-matched controls. On the contrary, some studies did not find any significant association between speech-in-noise performance and noise exposure history [41,49,53,54].\n\nMost investigations on cochlear synaptopathy in humans have focused on ABR measurements which provide information on the peripheral auditory system. In the present study, ABRs were recorded at low (11.3/s), medium (51.3/s), and high (81.3/s) rates, and at each rate, ABR wave amplitude and latency were obtained. ABRs obtained at low, medium, and high rates provide a way to look into the temporal dynamics of synaptic activity and neural conduction as the auditory system is strained [55]. High click rates are associated with prolonged ABR absolute and interpeak latencies and decreased ABR amplitudes [55]. The ABR protocol utilized in this study mainly targets the peripheral adaptive processes which capture the inefficiencies in synaptic processing associated with NICS. It is also important to consider that any damage to the peripheral auditory system may cause dysfunction in the central auditory system [56,57]. Hence, the influence of peripheral pathology such as NICS on the central auditory system could be examined by incorporating a test battery that includes tests sensitive enough to identify subtle electrophysiological changes in the peripheral and higher auditory centers and associated functional changes such as speech-in-noise perception.\n\nThe higher auditory centers can be analyzed using non-invasive long latency auditory evoked potentials such as P300. P300 is one of the auditory evoked late latency responses generated by the reticulothalamus, frontal cortex, and medial septal area and occurs approximately 250–350 ms after the stimulus onset. P300 is typically elicited by an instructed and infrequently presented target stimulus [58,59]. The P300 response is associated with stimulus assessment and allocation of attentional resources while updating working memory [60]. Thus, the inclusion of the P300 measure in the test battery for evaluating NICS can help in determining the influence of noise exposure on the central auditory system.\n\nThe goal of this study was to determine [1] the effects of noise exposure on the peripheral and central auditory nervous system (CANS) functioning using electrophysiological measures among young musicians and non-musicians, and [2] the effects of noise exposure history on speech recognition in noise at the word level and sentence level among young musicians and non-musicians. To address these objectives, we evaluated ABR waveforms (waves I and V obtained at low, medium, and high stimulus repetition rates), P300 measures, and speech-in-noise performance in young musicians and non-musicians.\n\n## 2.1. Participants\n\nThe study was approved (IRB number—18-X-247) by Ohio University’s Institutional Review Board (IRB). A total of 38 students aged 18–30 years were enrolled from Ohio University’s School of Music and non-music disciplines. Student musicians were selected because of their routine exposure to loud sounds during ensemble and solo rehearsals, and their noise exposure is expected to be higher than non-musicians. Participants’ inclusion criteria were (a) no history of hearing, tinnitus, balance, or language impairments and (b) no history of previous developmental, cognitive, neurological, and attention-related disorders. All the participants were recruited via emails and flyers posted across the Ohio University campus.\n\nThe enrolled participants were asked to complete an online noise exposure questionnaire [61], which would quantify their annual noise exposure background (NEB). The noise exposure questionnaire includes questions on the duration and frequency of noise exposure and provides a quantitative estimate of annual noise exposure. Based on the responses to the online questionnaire, 20 non-musicians (10 males and 10 females) and 18 musicians (11 males and 7 females) of European descent were shortlisted and recruited for the study. Participants of European descent were selected in this study, as previous investigations indicate that people of European ethnicity are more prone to NIHL than people of African ethnicity [62,63]. The recruited student musicians were percussion, brass, and saxophone majors. Participants were contacted via email to schedule appointments for the testing session.\n\nThe data were collected in two sessions. The first session includes a brief case history, a battery of hearing tests, a consonant–vowel nucleus–consonant (CNC) test, and an AzBio sentence test. The second session was composed of DPOAE and electrophysiological (ABR and P300) tests. Both data collection sessions occurred within 15 days of each other.\n\n## 2.2. Noise Exposure Questionnaire\n\nBefore the scheduling of appointments for the testing sessions, each participant’s noise exposure history was measured using an online noise exposure screening questionnaire. This noise exposure questionnaire was developed by Johnson et al. [ 61]. This questionnaire has been validated for estimating the overall annual acoustic exposure and used in previous investigations to quantify noise exposure in young adult populations [46,64,65].\n\nParticipants were required to submit their responses to the online questionnaire (Supplementary Material S1) at least a week before the first testing session. This questionnaire was used to estimate participants’ annual noise exposure background (NEB). The first part of the questionnaire has nine sections targeting different types of noise exposure such as aircraft, firearms, heavy equipment, power tools, music through speakers, and headphones. The second part of the questionnaire consisted of nine questions related to the musical instruments played by the participant. The questionnaire also includes questions on the duration and frequency of noise exposure. The participants’ responses were elicited using a forced-choice method and then rated by category to calculate the noise dose of last 12 months. The noise dose was calculated via these responses for each area of high noise exposure. Time spent in routine or mundane activities performed in quiet environments was calculated by subtracting overall time spent in noisy activities from 8760 h (365 days/year × 24 h/day). Questionnaire responses were further used to calculate the activity-related noise dose and overall annual noise dose, reported as LAeq8760h. Here, “L” represents the sound pressure level measured in decibels (dB), “A” indicates application of A-weighted frequency response; “eq” represents the sound pressure level (in dB) equivalent to the total acoustic energy over a given amount of time; and “8760 h” represents the overall duration of the noise exposure in hours over one year (365 days/year × 24 h/day). LAeq8760h was extracted from the questionnaire responses utilizing the 3-dB exchange rate for calculation of the time/intensity level relation. Details of the questionnaire are reported in Stamper and Johnson [46] [2015] and Johnson et al. [ 61].\n\nNon-musician participants who reported playing any instrument including voice on a daily basis were excluded from the non-musician group. For the purpose of this study, non-musician participants with LAeq8760h values of 76 or greater were not included in the study. All the student musician participants had LAeq8760h values higher than 76. Potential participants were contacted via email to schedule appointments for the testing sessions. Participants were also informed through email to avoid loud sound exposure for at least 12 h before the testing appointment time. Before administering the tests, the participants were asked to confirm that they had abstained from loud events or activities as requested. Participants who reported exposure to loud sounds in the last 12 h were rescheduled.\n\n## 2.3. First Session\n\nThe session I started with obtaining informed consent, followed by a brief case history, which comprised questions related to health, hearing, head trauma, and balance. After completing the brief case history, an otoscopy was performed on both ears of each participant, followed by a middle ear examination (tympanometry), pure tone audiometry and extended high-frequency audiometry. Tympanograms of both ears were obtained over a pressure range of +400 to −400 daPa using a 226 Hz probe tone presented through a GSI 39 (GSI, Eden Prairie, MN, USA) middle ear analyzer. All the participants had a normal type “A” tympanogram. Hearing sensitivity was measured in an audiometric testing booth meeting ANSI standards (ANSI S3.1e1999). Air conduction thresholds were obtained for both ears at 250, 500, 1000, 2000, 3000, 4000, 6000, and 8000 Hz using an audiometer (AVANT MedRx, Largo, FL, USA) with ER-3A insert earphones (Etymotic Research. Inc., Elk Grove Village, IL, USA). The pure tone average of the hearing thresholds at 3000, 4000, and 6000 Hz (PTA346) was also calculated because hearing sensitivity at these frequencies is typically affected in individuals with high noise exposure history. Normal hearing of participants was defined as audiometric thresholds of ≤15 dBHL for frequencies between 0.5 and 8 kHz, and this was one of the inclusion criteria for the study. Extended high-frequency audiometry was carried out using circumaural earphones (Sennheiser, HDA 200) at 10, 12.5, and 16 kHz. At these extended high frequencies, the hearing thresholds were averaged to obtain the extended high-frequency pure-tone average (EHFPTA).\n\nAfter high frequency audiometry, word and sentence recognition in noise was tested binaurally using the CNC and AzBio tests, respectively. The CNC test assesses open-set monosyllabic word recognition in quiet and noise. A customized MATLAB program for controlling the stimulus presentation was utilized to administer the CNC test, which consists of 10 lists. Each list includes 50 monosyllabic words. Each participant was seated at the center of the double-walled audiometric booth, meeting ANSI standards (ANSI S3.1e1999). Participants were asked to listen to the word through circumaural headphones (Sennheiser HD 280; Sennheiser, Wedemark, Hanover, Germany). Participants were instructed to type what they heard on the LCD monitor in front of them. If they were unsure of the word, they took their best guess or typed ellipses to indicate that they did not know. A practice test was performed for each participant before the actual test. The practice test consisted of three separate words that the participant could see on the LCD monitor after entering what they heard. The actual test included 250 words from five randomized lists. The CNC word lists were prerecorded by one male talker. These words were presented at 65 dB SPL in the presence of two-talker babble at five signal-to-noise ratios (SNRs) (−9, −6, −3, 0, and +3 dB); one list was administered per condition. Lists number 2, 3, 4, 7, and 10 were used. Responses were scored based on the entire word (% correct; CNC-Word) and the number of phonemes (% correct; CNC-Phoneme) repeated correctly. The AzBio test assesses sentence recognition in quiet and in noise. The AzBio test was also administered in the same acoustic environment as the CNC test, and a similar customized MATLAB program was used for conducting the AzBio test. The AzBio test consists of 33 different sentence lists. Each list has 20 different sentences. Participants were asked to listen to the word through circumaural headphones (Sennheiser HD280). A practice test was administered before the actual test. The practice test had three sentences wherein the participant could look at the actual sentence after typing in what they heard. The actual test consisted of 100 sentences from 5 randomized lists spoken by two male and two female talkers in a randomized manner. These sentences were presented at 65 dB SPL in the presence of two-talker babble at five SNRs (−9, −6, −3, 0, and +3 dB); one list was administered per condition. Lists number 2, 3, 4, 5, and 10 were used. Responses were scored based on the percentage of words repeated correctly for sentences at different SNRs. After the AzBio test, the session I was terminated. An appointment for the second session was scheduled at the lab. Both testing sessions occurred within one or two weeks of each other. Participants were also instructed to avoid loud sound exposure at least 12 h before their second testing session.\n\n## 2.4. Second Session\n\nThe second session started with the DPOAE test for evaluating the outer hair cell functioning of the cochlea (inner ear). DPOAEs of all participants were measured using a commercial system (Smart DPOAE—Intelligent Hearing Systems, Miami, FL, USA) connected to an ER- 10 D probe (Etymotic Research. Inc., Elk Grove Village, IL, USA) across the range of frequencies from 500 Hz to 6 kHz. DPOAEs at 2F1-F2 were obtained for F2 values ranging from 500 to 6000 Hz in two data points per octave. A stimulus-level combination of $\\frac{65}{55}$, sound pressure level (SPL), and stimulus frequency ratio of 1.22 were used. The DPOAE test was followed by the ABR test. The ABR test was conducted using a commercial system (Duet, Intelligent Hearing Systems, Miami, FL, USA) in the same environment as in session I. ABRs were obtained using a one-channel electrode montage with a mastoid-placed electrode from the left ear. The stimulus and acquisition parameters set to record ABRs are shown in Table 1. The left ear was selected for ABR because the noise-induced damage is more prevalent in the left ear than in the right ear [28,66,67].The non-inverting and ground electrodes were placed on the participant’s forehead (Fz) and low forehead (Fpz), while the inverting electrode was placed on the mastoid of the left ear. These areas were prepped using alcohol wipes and a Nuprep skin prep gel to effectively reduce the inter-electrode impedance values. Impedance values at each electrode site were monitored to remain below 3 kOhms with an inter-impedance value of less than 2 kOhms. These impedance values were monitored throughout the testing procedure. ABR stimuli were presented with alternating polarity at rates 11.3, 51.3, and 81.3/using insert earphones (ER-3A, St. Paul, MN, USA). ABR responses were obtained using 100 ms click stimuli presented at 80 dB nHL (85.7 ± 0.3 dB SPL, calibration in an IEC-711ear simulator). At each stimulus rate, two replications of 2000 sweeps were collected for analysis. Recording parameters included a gain of 100,000 and band-pass filtering from 100 Hz to 3000 Hz. The artifact rejection threshold was set at 31 mV. ABRs were collected with a pre-stimulus window of 12.5 ms, a post-stimulus window of 12.5 ms, and a sampling frequency of 40,000 Hz.\n\nThe ABR test was followed by the P300 test. P300 testing was done with the same system (Duet, Intelligent Hearing Systems, Miami, FL, USA). A two-channel montage was used, with channel A assigned for P300 measures and channel B for measuring and monitoring eye movements and eye blink artifacts. The stimulus and acquisition parameters set to record P300 are shown in Table 1. The prerecorded speech tokens stimuli used were the consonant–vowel /ba/as frequent stimuli ($80\\%$) and/ta/as infrequent stimuli ($20\\%$). The stimuli sequence was presented monoaurally to the left ear of each participant at 80 dB SPL. In total, 500 stimuli were used (100 rare and 400 frequent) to obtain the P300 responses.\n\n## 2.5. Electrophysiological Waveform Analysis\n\nAfter completing the ABR test, the two replications at each rate were averaged, and the averaged waveforms were utilized for evaluating the ABR I and V waveforms. The amplitude of ABR waves I and waves V was calculated from the voltage difference between the identified positive peak and the following trough. Similarly, the P300 amplitude was calculated from the voltage difference between the identified positive peak and the following trough. Two audiologists identified the waveforms separately. Any disagreement pertaining to the peak measurements between two audiologists was resolved by them reviewing the data together.\n\n## 2.6. Statistical Analysis\n\nBoth descriptive and inferential statistical analyses for this study were performed using IBM SPSS (version 26.0; IBM Corp.: Armonk, NY, USA). Multivariable linear regression modeling was used to estimate the relationship between NEB and peripheral auditory electrophysiological measures (wave I and V amplitude). To estimate the influence of NEB on ABR wave I and V amplitudes while controlling the effect of gender, the ABR wave I and V amplitudes were included as continuous dependent variables, and gender and NEB as independent variables. Similar analyses were performed to estimate the relationship between NEB and central auditory electrophysiological measures (P300 amplitude and latency). The relationship between NEB and speech-in-noise measures at the word level (CNC) and sentence level (AzBio test) at five SNRs was also estimated using linear regression modeling.\n\nMixed effect linear regression models were used to study the effects of non-musician/musician groups, gender, and SNR and interaction between these factors on CNC and AzBio measures. The subjects were considered a random variable in these analyses.\n\n## 3.1. Descriptive Statistics\n\nA total of 38 participants (17 females, 21 males) from 18–30 years were included in this study (mean age 21.9 years). These 38 participants were divided into two groups, non-musicians (10 females, 10 males) and musicians (7 females, 11 males). The group means audiograms of musicians and non-musicians are shown in Figure 1 (Panel A). As specified by the inclusion criteria, both musician and non-musician groups had thresholds within clinically normal limits (≤15 dB HL) for the octave frequencies 500 to 8000 Hz. There was increased variability at the extended high frequencies, particularly at 16 kHz. However, there were no significant group differences at any frequencies from 500 to 16 kHz (Supplementary Material S2). The average hearing thresholds at 3, 4, 6 kHz (PTA346) were calculated because the effect of noise exposure is higher on these frequencies [66]. Likewise, the average of EHF hearing thresholds (PTA 101216) was also calculated. PTA346 and PTA101216 were not statistically different between non-musicians and musician groups (See Supplementary Material S2). Outer hair cell functioning was evaluated by recording DPOAEs at frequencies 0.5 to 6 kHz. DPOAE amplitudes (Figure 1, panel B) were not statistically significant between the two groups (see Supplementary Material S2). An independent sample t-test revealed that the mean NEB between non-musicians and musicians groups was significantly different. The mean NEB was higher for musicians compared to the mean for non-musicians (mean (musician)-mean (non-musician) = 8.65 LAeq8760h, $p \\leq 0.001$). The mean NEB of non-musicians and musician groups was 70.46 and 79.11 LAeq8760h, respectively. Figure 2 shows NEB data as a function of experimental and control groups. The mean differences in NEB between non-musician and musician groups were attributed to our sampling scheme. The results of the linear regression revealed no statistically significant linear association between NEB and PTA 101216 (r[36] = −0.068, $$p \\leq 0.680$$). Similar analysis showed no linear association between NEB and PTA 346 kHz (r[36] = −0.045, $$p \\leq 0.780$$).\n\n## 3.2. Electrophysiological Measures\n\nTable 2 shows means and standard deviations for latencies and amplitude of ABR waves I and V obtained at rates of 11.3, 51.3, and 81.3 clicks per second according to gender. The means and standard deviations for latencies and amplitudes of P300 of 34 participants [18 non-musicians (9 females, 9 males); 16 musicians (6 females, 10 males)] are shown in Table 3. The P300 data of four participants were not included due to poor wave morphology and artifacts. The grand average ABR and P300 waveforms of musicians and non-musicians are shown in Figure 3 and Figure 4, respectively. The wave I amplitude is highest at rate 11.3/s, and decreases at higher stimulus rates. The results of the regression analyses for examining the relationship between NEB and ABR measures are shown in Table 4. The relationship between the NEB and the amplitude of wave I and between the NEB and wave V at three stimulus rates was investigated while controlling the effects of gender. The NEB revealed no significant association with wave I and wave V amplitudes at all three stimulus rates (See Figure S1 in Supplementary Material S3). Similar regression analyses were also performed to investigate the relationship between the group and ABR measures. The group revealed no significant association with wave I and wave V’s amplitude at all three stimulus rates.\n\nRegression analyses were also performed to study the relationship between NEB and P300 amplitude and latency while controlling the effect of gender. There was no significant association between NEB and P300 amplitude (R2 = −0.043, F[2,31] = 0.315, $$p \\leq 0.732$$), and latency measure (R2 = 0.034, F[2,31] =1.579, $$p \\leq 0.222$$), (See Figure S2 in Supplementary Material S3. The relationship between groups and P300 amplitude and latency were examined using regression analyses while controlling the effect of gender. There was no significant association between groups and P300 amplitude (R2 = −0.052, F[2,31] = 0.184, $$p \\leq 0.833$$) and latency measure (R2 = 0.015, F[2,31] =1.250, $$p \\leq 0.300$$).\n\n## 3.3. Word Recognition in Noise\n\nWord recognition in noise was examined in all the participants using the CNC test at +3, 0, −3, −6, and −9 dB SNRs. The relationship between NEB and the performance on CNC test at different SNRs was investigated while controlling the confounding effect of gender. NEB showed a significant association with performance on CNC measures at all SNRs. Table 5 shows the results of regression analyses of NEB and CNC measures. The adjusted R2 values for the models ranged from 0.079 to 0.245, suggesting that a small portion of the variance in the dependent variables was exclusively attributed to NEB. Figure 5 (left panel) reveals a significant negative relationship between NEB and performance in the CNC test at all five SNRs. In addition, the effect of groups, gender, SNRs and interaction between these variables on CNC measures were evaluated using mixed model linear regression. The results of this analysis are shown in Supplementary Material S4. The main effects of groups (F[1,34] = 8.630; $$p \\leq 0.006$$) and SNRs (F[4,140] = 526.737; $$p \\leq 0.000$$) were statistically significant, while the main effect of gender (F[1,34] = 1.623; $$p \\leq 0.211$$) was not statistically significant. Furthermore, there was not strong evidence of interaction between groups and gender (F[1,34] = 0.772; $$p \\leq 0.386$$), gender and SNRs (F[4,140] = 0.210; $$p \\leq 0.932$$), and between groups and SNRs (F[4,140] = 0.684; $$p \\leq 0.604$$). This finding shows that the overall performance of non-musicians on the CNC test was significantly better than that of musicians.\n\n## 3.4. Sentence Recognition in Noise\n\nTable 6 shows the results of regression analyses for examining the relationship between NEB and performance on the AzBio test at +3, 0, −3, −6, and −9 dB SNRs. The relationship between NEB and the performance on the AzBio test at different SNRs was investigated while controlling the confounding effect of gender. NEB showed a significant association with performance on the AzBio test at 0 dB SNR. At all other four SNR conditions, there was no significant association between NEB and performances on the AzBio test. Figure 5 (right panel) displays the scatter plots between the NEB and performance on the AzBio test of non-musicians and musicians at +3, 0, −3, −6, and −9 dB SNRs.\n\nThe results of a mixed model linear regression analysis examining the effects of group, gender, SNRs and interaction between these variables on AzBio measures are shown in Supplementary Material S4. The main effect of SNRs was significant (F[4,140] = 1002.669; $$p \\leq 0.000$$). The main effect of groups (F[1,34] = 0.302; $$p \\leq 0.586$$) and gender (F[1,34] = 0.082; $$p \\leq 0.776$$) were not statistically significant. Similarly, the interaction between groups and gender (F[1,34] = 0.310; $$p \\leq 0.581$$), gender and SNRs (F[4,140] = 0.763; $$p \\leq 0.551$$) and between groups and SNRs (F[4,140] = 1.268; $$p \\leq 0.285$$) were also not statistically significant. The results of this analysis indicates that the performance of non-musicians in the AzBio test was not significantly different from that of musicians.\n\n## 4. Discussion\n\nThe present study aimed to investigate the effect of noise exposure history on the peripheral and central auditory system, and on performance on speech-in-noise tests. It was hypothesized that the influence of high noise exposure on peripheral and central auditory systems would be manifested in the form of compromised electrophysiological and speech-in-noise measures in normal-hearing collegiate students with high NEB. We obtained supporting evidence for this hypothesis, suggesting that musicians with high NEB exhibit poorer speech-in-noise performance than their non-musicians counterparts.\n\n## 4.1. The Relationship between NEB and Performances on Speech-in-Noise Tasks\n\nWe recruited musicians with high NEB and non-musicians with low NEB. We obtained a significant main effect for groups in the CNC test and a negative relationship between NEB and CNC scores at –9, −6, −3, 0, and +3 dB SNRs. As shown in Figure 5, the relationship between NEB and performance on the CNC test is consistent at all five SNRs, indicating that high NEB might compromise suprathreshold speech perception abilities among young musicians. Similarly, the result of mixed linear regression indicates that musicians perform poorer compared to non-musicians. The difference between the groups does not reach the conventional $p \\leq 0.05$ level of statistical significance at any SNR, which could possibly be due to the smaller sample size of our study. Further research with a larger sample size is warranted to clarify more definitively the implications of these findings.\n\nWe obtained no association between NEB and sentence recognition in noise performance in the AzBio test at –9, −6, −3, and +3 dB SNRs. Similarly, the main effect for groups in the mixed model linear regression analysis was also not statistically significant. We found a significant negative relationship between NEB and AzBio test performance at 0 dB SNR, as shown in Figure 5. The discrepancy between the findings of the CNC and AzBio tests could be attributed to the stimuli used in these two tests. Cognitive and linguistic factors might influence the performance on AzBio tests, but they might exhibit a lower influence on CNC scores. Although a sentence may be a realistic stimulus with better face validity, the contextual cues contribute heavily to intelligibility and make basic auditory functions difficult to determine [68]. A few studies have reported an approximate difference of 6–7 dB SNR in the speech recognition performance of words and sentences among adults, with sentences always requiring lower SNR than words [69,70].\n\nThe observed negative trend between NEB and word recognition in noise performance on the CNC test might be influenced by the effect of noise exposure on central auditory structures. The results of the CNC tests are consistent with the findings of previous studies on normal-hearing adults with high noise exposure histories [43,44,51]. Some studies on adults with high noise exposure have found no association between speech-in-noise performance and noise exposure history [42,54,71,72]. Further research is required to quantify the influence of cochlear synaptopathy on suprathreshold speech perceptions.\n\n## 4.2. The Relationship between NEB and Electrophysiological Measures\n\nThe findings of the present study showed no relationship between NEB and ABR wave I amplitude obtained at low (11.3/s), medium (51.3/s), and high (81.3/s) stimulus repetition rates. We could not find any difference between ABR wave I between musicians and non-musicians. As is apparent in Figure 3, there is no significant association between NEB and ABR wave I amplitude and between NEB and ABR wave V amplitude. Our past study indicated a modest association between NEB and ABR wave I amplitude in young musicians and non-musicians [65]. The present study could not replicate these findings, possibly due to our smaller sample size and the high inter-subject variability in audiological measures. We observed that the standard error (SE) of a mean for ABR wave I amplitude obtained at rate 11.3 (SE11.3wave $I = 0.019$ µV) was higher than the SE of a mean for ABR wave I obtained at rate 51.3 (SE51.3wave $I = 0.013$ µV) and 81.3 (SE81.3wave $I = 0.012$ µV). A similar trend was observed for ABR wave V amplitude (SE11.3wave $I = 0.021$ µV, SE51.3wave $I = 0.19$ µV, SE81.3wave $I = 0.020$ µV). These findings correspond with the results of other studies investigating the association between noise exposure and electrophysiological measures [49,50,51,54,73]. This result is in accordance with some previous studies on different study populations [49,53,54,74]. The first possible explanation for this insignificant finding could be the higher variability of auditory evoked potentials, particularly ABR wave I in humans. In a study by Prendergast et al. [ 50], the coefficient of variation for ABR waves I amplitude was $25\\%$ in the low noise exposure group, and this may indicate a substantial degree of variability compared to the effect being measured. Washnik et al. [ 65] also reported higher variability in ABR wave I amplitude obtained at 90, 75, and 60 dB nHL. The differences in adult head size and geometry might also contribute to the inter-subject variability and reduced statistical power to identify differences in auditory electrophysiological measures in the human population [75,76]. In addition, there is another possibility that noise exposure induces cochlear synaptopathy only in selected portions of the cochlea [29,30,38], and therefore, the effect of cochlear synaptopathy is enshrouded when ABRs are evoked by transient click stimuli, which present energy in a broad frequency range.\n\nFurthermore, no significant influence of NEB on P300 amplitude and latency was found in the present study. The P300 measures are reflective of attentional capacity. Many studies have reported that musical training enhances neural coding to discriminate subtle differences, leading to enhanced discrimination abilities of the brain; this is manifested in the form of shorter P300 latencies and higher P300 amplitude among musicians when compared to non-musicians [77,78]. Our P300 amplitude and latency findings are consistent with other studies on the human population with high noise exposure history [79,80]. Thakur and Banerjee [79] studied the influence of high noise exposure on the central auditory pathway using P300 among ground crew members of an airport. They found no significant difference in P300 amplitude and latency between the experimental and control groups. One reason for the lack of significant association between NEB and P300 could be the sample size. Future research is needed to investigate the influence of noise exposure on auditory–cognitive responses such as P300.\n\n## 4.3. Speech-in-Noise and Electrophysiological Measures in Musicians\n\nSeveral studies have shown musicians’ advantage in speech-in-noise (SIN) perception [4,14,81,82]. In contrast, others reported no significant difference in SIN performances between musicians and non-musicians [13,83,84,85]. A possible factor influencing these mixed findings is inter-subject variability in noise exposure among musicians. Musicians are regularly exposed to high sound levels during large and small ensemble rehearsals, individual practice sessions, music performances, and listening to music pieces through speakers or headphones. Skoe et al. [ 86] found that noise exposure among musicians suppresses the musicians’ SIN perception advantage. The result of our study indicates that noise exposure is negatively associated with SIN performance at the word level among musicians. Though our speech-in-noise findings are in line with the investigation by Liberman et al. [ 43] and Hope et al. [ 87], other researchers found no significant relationship between noise exposure history and SIN performance [48,49,53,72]. The null results of the SIN measures in the above studies could be related to methodological factors, such as the complexity of stimuli and their difficulty levels. Valderrama et al. [ 48] and Yeend et al. [ 72] used sentences in the listening in spatialized noise—sentences high cue condition (LiSN-S) test, which may be influenced by cognitive factors. Other studies on humans with history of high noise exposure utilized SIN measures such as word-in noise (WIN) tests [49,53]; however, these studies administered WIN tests at SNRs ranging from 0 to 30 dB, which was comparatively higher than the SNRs used for CNC test in our study. Le Prell [88] suggested that studies incorporating the most difficult SIN tasks may show greater sensitivity to the detection of the relationship between noise exposure and SIN performance. In a recent systematic review, DiNino et al. [ 89] mentioned that speech-in-noise tests that use low SNRs and maximize minute sensory details by using stimuli that offer minimal lexical, syntactic, or semantic cues are more likely to show an interest in the relationship between human studies and HHL.\n\nSpeech-in-noise measures, particularly the CNC test in our study, have shown that NEB is negatively related to speech-in-noise performance, and that the overall performance of musicians as a group is significantly poorer than non-musicians. On the other hand, the outcomes of the electrophysiological measures, such as amplitude and latency of P300 and ABR wave I and V, showed no association with NEB. The insignificant findings in the ABR measures of this study could also be associated with the possibility that noise exposure induces synaptopathy only in certain regions of the cochlea [29,30,38]; hence, the effect of synaptopathy becomes obscured when ABR are evoked by broad-range frequency stimuli such as clicks.\n\nThe results of the current study show that despite similar peripheral processing (DPOAE responses and ABR wave I amplitude), speech-in-noise performance with CNC words was reduced in individuals with high NEB. Recent investigations have revealed the negative influence of noise exposure on human cognition [90,91,92,93]. Patel et al. [ 2022] suggested that high-level cognitive tasks and their corresponding brain regions are not equally susceptible to high noise exposure [93]. Thus, it can be hypothesized that the central processing (except the central processes involved in P300 generation) involved in understanding speech in highly demanding situations, such as CNC words in noise, might be more prone to the negative effects of noise exposure, and this might be manifested in the form of reduced performance in speech-in-noise tasks at the word level. Unfortunately, the current study did not measure specific cognitive domains such as working memory and other executive functions contributing to speech-in-noise performance. The specific central processes involved in the reduced speech-in-noise performance of individuals with high NEB need to be further explored in future studies.\n\n## 4.4. Study Limitations and Future Directions\n\nThere are a few methodological factors that may have influenced the outcomes of this study, and these should be considered when interpreting the results. Firstly, noise exposure was measured using a retrospective noise exposure questionnaire developed by Johnson et al. [ 61], and this questionnaire estimates the amount of noise exposure in one year. Although many studies have used this questionnaire [49,51,53,73], it does not include a comprehensive list of noise exposure areas and does not account for noise exposure beyond an individual’s last 12 months of noise exposure. On the other hand, other studies measured noise exposure across a lifetime [40,48,50], and performed noise dosimetry measurements [45,86] to obtain real and more accurate noise exposure data. As the effect of noise exposure is cumulative, an estimate of noise exposure will be more accurate if noise dosimetry measurements are performed along with the administration of a lifetime noise exposure questionnaire. Future studies should include noise dosimetry measurements and a lifetime noise exposure questionnaire to obtain more reliable and accurate noise exposure data which can be compared with auditory electrophysiological and behavioral measures.\n\nIn addition, there is no widely accepted standard protocol for evaluating cochlear synaptopathy in the human population, and it can be argued that the evoked potential metrics other than the one used by the present study might be more sensitive in detecting cochlear synaptopathy in humans [94]. The inclusion of non-musicians and musicians with different ranges of noise exposure is another critical factor. All the student musician participants had LAeq8760h values higher than 76, while all the non-musicians had LAeq8760h values below 76. Such division in the noise exposure range between musicians and non-musicians may influence the findings of this study.\n\nCognitive factors such as working memory and non-verbal IQ that have been linked to speech recognition in noise abilities were not measured in the current study. Previous investigations have revealed that greater WM capacity is associated with enhanced speech-in-noise perception abilities [4,84,95] Recent findings indicate that the cognitive abilities of the individual may be the crucial factor in their speech recognition in noise ability, rather than musicianship [83,84]. Hence, the reduced performance on the CNC test among student musicians cannot be solely attributed to noise exposure, because the effect of non-verbal IQ and WM cannot be ruled out.\n\nWith regard to the set of tests utilized in the current study, there are a few other sensitive auditory tests, such as the threshold equalizing nose (TEN) test and contralateral OAE suppression, whose inclusion into the test battery might have provided better insight into the association between noise exposure and speech-in-noise deficits. Lastly, the participants in the current study were of European ethnicity. Thus, the results of this study should not be generalized beyond individuals of European ethnicity. Future studies can address the influence of noise exposure on auditory electrophysiological and perceptual measures among other ethnic groups.\n\n## 5. Conclusions\n\nThe findings of several studies investigating noise-induced HHL in humans have been mixed [40,41,42,43,49,50,51,52,53,54,72]. The present study obtained a significant association between noise exposure and word-level speech-in-noise measures. However, we did not find any association between NEB and any electrophysiological measures used in the present study. These findings indicate that noise exposure may affect the central auditory structures. We found a negative relationship between NEB and speech recognition in noise at the word level. More interestingly, we found that musicians perform more poorly than non-musicians on word-level tasks, but not on sentence-level tasks. Collectively, these results suggest that musicians with high NEB could lose their perceptual advantage for processing words in background noise over non-musicians. The null findings in the AzBio test suggest that the deficit in speech processing at the word level was compensated at the sentence level. 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-0.010856997221708298\n]","isConversational":false}}},{"rowIdx":87,"cells":{"text":{"kind":"string","value":"---\ntitle: An Innovative Mei-Gin Formula Exerts Anti-Adipogenic and Anti-Obesity Effects\n in 3T3-L1 Adipocyte and High-Fat Diet-Induced Obese Rats\nauthors:\n- Hsin-Lin Cheng\n- Wei-Tang Chang\n- Jiun-Ling Lin\n- Ming-Ching Cheng\n- Shih-Chien Huang\n- Shiuan-Chih Chen\n- Yue-Ching Wong\n- Chin-Lin Hsu\njournal: Foods\nyear: 2023\npmcid: PMC10000739\ndoi: 10.3390/foods12050945\nlicense: CC BY 4.0\n---\n\n# An Innovative Mei-Gin Formula Exerts Anti-Adipogenic and Anti-Obesity Effects in 3T3-L1 Adipocyte and High-Fat Diet-Induced Obese Rats\n\n## Abstract\n\nBackground: To investigate the potential anti-obesity properties of an innovative functional formula (called the Mei-Gin formula: MGF) consisting of bainiku-ekisu, Prunus mume ($70\\%$ ethanol extract), black garlic (water extract), and Mesona procumbens Hemsl. ( $40\\%$ ethanol extract) for reducing lipid accumulation in 3T3-L1 adipocytes in vitro and obese rats in vivo. Material and Methods: The prevention and regression of high-fat diet (HFD)-induced obesity by the intervention of Japan Mei-Gin, MGF-3 and -7, and positive health supplement powder were investigated in male Wistar rats. The anti-obesity effects of MGF-3 and -7 in rats with HFD-induced obesity were examined by analyzing the role of visceral and subcutaneous adipose tissue in the development of obesity. Results: The results indicated that MGF-1-7 significantly suppressed lipid accumulation and cell differentiation through the down-regulation of GPDH activity, as a key regulator in the synthesis of triglycerides. Additionally, MGF-3 and MGF-7 exhibited a greater inhibitory effect on adipogenesis in 3T3-L1 adipocytes. The high-fat diet increased body weight, liver weight, and total body fat (visceral and subcutaneous fat) in obese rats, while these alterations were effectively improved by the administration of MGF-3 and -7, especially MGF-7. Conclusion: This study highlights the role of the Mei-Gin formula, particularly MGF-7, in anti-obesity action, which has the potential to be used as a therapeutic agent for the prevention or treatment of obesity.\n\n## 1. Introduction\n\nObesity is characterized by a defective body fat storage capacity caused by a chronic imbalance of energy due to excess dietary consumption and insufficient physical activity [1]. The prevalence of obesity is still rising globally and has become a pervasive public health threat. Obesity precedes type 2 diabetes mellitus, dyslipidemia, fatty liver injury, hypertension, and cancer, and is greatly associated with a higher premature disability and mortality rate [2]. Excess calorie intake is accompanied by less energy expenditure, leading to adipogenesis in both the liver and the adipose tissue, and subsequently promoting the development of metabolic disorders [3,4].\n\nWhite adipose tissue (WAT) represents a key reservoir for energy storage such as triglycerides (TG) in adipose tissue and expands via increasing individual size (hypertrophy) or number (hyperplasia) of differentiated mature adipocytes to allow adequate tissue expansion in response to high-fat dietary consumption or overnutrition [5,6,7]. The adipocyte differentiation process begins from adipocyte progenitors’ mitogenic expansion in the determination phase, and later gains the characteristics of mature adipocytes in the terminal differentiation phase. Anatomically, subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) are considered the two main types of WAT; the enlargement of the SAT and VAT enlargement are considered to mediate obesity development and its related metabolic complications [8,9,10,11]. In particular, rodents were fed a high-fat diet with the consequence of a significant imbalance in energy storage and expenditure capacity between WAT and brown adipose tissue (BAT) depots [12,13]. Evidence from animal and human studies indicates that the inability of SAT and VAT to expand when faced with dietary phytochemicals is pharmacologically beneficial to the metabolic health status of obesity [14,15,16]. Lipid metabolism is often considered a complex mechanism with the involvement of regulatory elements such as glycerol-3-phosphate dehydrogenase (GPDH), which mediate the rate-determining reaction in the synthesis of triglycerides and serve as a marker of adipogenesis [17]. Numerous studies indicated that inhibition of GPDH expression or activity can suppress lipid accumulation in 3T3-L1 adipocytes and may act as an anti-obesity target in adipocytes [17,18,19,20].\n\nBainiku-ekisu is the fruit juice concentrate of Prunus mume and has been proposed to have pharmacological properties that might benefit the treatment of dyspepsia and diarrhea. An in vitro study has shown that bainiku-ekisu exhibited immediate bacteriostatic activity on serious strains of H. pylori at a concentration of $0.3\\%$ in 15 min [21]. Furthermore, an in vivo pilot study reported that H. pylori-positive patients received $1\\%$ bainiku-ekisu solutions for 2 weeks, resulting in a slightly decreased in the urea breath test (UBT) values [22]. Yang et al. demonstrated that bainiku-ekisu has a higher total phenolic content (1.9-fold) and flavonoid content (1.4-fold) than fresh Japanese apricot juice, which may have direct effects on improving metabolic disorders, and, therefore, improve metabolic diseases such as type 2 diabetes and hyperlipidemia [23]. Black garlic, a fermented product of fresh garlic, is generated by the application of controlled high temperature under high humidity over 10 days [24,25]. A previous study suggested that fermented black garlic exhibited bioprotective properties against vascular disease through the downregulation of the MAPK pathway in a model of zebrafish vascular lesions [26]. Extracts from black garlic decreased body weight ($7.69\\%$) and lumbar subcutaneous fat mass ($16.88\\%$) in a high-fat diet-fed rodents model [27]. Treatment with black garlic extract was founded to increase cellular oxygen uptake and alter UCP-1-based thermogenesis in human adipose-derived stem cells [28]. Mesona procumbens Hemsl., also called Hsian-tsao, is consumed as folk medicine and is considered to have therapeutic potential for the treatment of liver disease, heat shock, and metabolic disorders. The anti-adipogenic activity of Mesona procumbens in 3T3-L1 cells was also reported to decrease lipid droplet accumulation by transcriptionally inhibiting peroxisome proliferator-activated receptor γ (PPARγ) and transcription factors CCAAT/enhancer-binding protein (C/EBP) β expressions [29]. We have previously shown that in vivo and in vitro, the Mesona procumbens Hemsl. extract could decrease xanthine oxidase activity and prevent the overproduction of serum uric acid, which is suggested to be a novel hypouricemic agent [30]. In addition, extracts rich in phenolic compounds from Hsian-tsao have been demonstrated to exhibit antioxidant properties and served as free radical scavengers [31].\n\nBased on the accumulated experimental evidence, we have developed an innovative bainiku-ekisu-based functional combination of ethanol extracts from Prunus mume, Mesona procumbens Hemsl., and water extract from black garlic, called the Mei-*Gin formula* (MGF). However, due to the bioavailability of individual substances in the complex mixture and differences in the pharmacokinetics of the active substance, a deeper understanding of the beneficial effect of the Mei-*Gin formula* in the prevention or treatment of metabolic parameters in obesity is now an urgent need. In the present study, our aim is to investigate the efficacy of the Mei-*Gin formula* in pre-adipose cell differentiation and a high-fat diet-fed rat model.\n\n## 2.1. Composition Analysis of the Mei-Gin Formula (MGF)\n\nMGF-1-7 capsules containing mixed extract of functional powder, consisting of bainiku-ekisu (decompress concentration process), Prunus mume ($70\\%$ ethanol extract), black garlic (water extract), and Hsian-tsao ($40\\%$ ethanol extract) were provided by Dr. Ming-Ching Cheng, Department of Healthy Food, Chung Chou University of Science and Technology. In brief, analysis of the phenolic content of the Mei-Gin formulas was carried out using a high-performance liquid chromatography system (L-2130 pump and L-2400 UV detector, Hitachi, Tokyo, Japan), and permeation data were recorded on a computer with the LC solution 1.25 sp1 software. Elution was carried out with two buffers: buffer A ($0.1\\%$ formic acid in water) and buffer B ($0.1\\%$ formic acid in acetonitrile); the flow rate was set at 1 mL/min. The absorption spectra of the samples were detected at a UV wavelength of 280 nm, and all phenolic acid identification was carried out by comparing their retention time with known reference standards [32].\n\n## 2.2. Cell Culture and Treatment\n\n3T3-L1 preadipocytes (BCRC No. 60159) obtained from the Bioresource Collection and Research Center (BCRC, Food Industry Research and Development Institute, Hsinchu, Taiwan) were cultured in high glucose Dulbecco’s modified *Eagle medium* (high glucose DMEM) supplemented with $9\\%$ newborn calf serum (NBCS), NaHCO3 (1.5 g/L) and $1\\%$ penicillin-streptomycin (PSN) in an incubator with an atmosphere of $5\\%$ CO2. For the differentiation of adipocytes, 3T3-L1 cells were cultured in the differentiation medium containing 0.5 mM 3-isobutyl-1-methylxanthine (IBMX), 1 μM dexamethasone (DEX), 1 μM insulin, 1.5 g/L NaHCO3, and $1\\%$ PSN. After 4 days of incubation, the medium was changed to high glucose DMEM containing $8\\%$ fetal bovine serum (FBS), 1 μM insulin, 1.5 g/L NaHCO3, and $1\\%$ PSN for 2 days. The mature 3T3-L1 adipocytes were treated with appropriate doses of MGF-1-7 solution (0, 10, 25, 50, 100, and 250 μg/mL) and then incubated for 48 h.\n\n## 2.3. Animals and Diet\n\nSix-week-old male Wistar rats were purchased from BioLASCO Taiwan Co., Ltd. (Taipei, Taiwan) and supplied with a pelletized commercial laboratory diet (Purina Lab Chow) and water ad libitum. The rats were maintained under an air-conditioned environment (23 ± 2 ℃ with $60\\%$ relative humidity) with a 12 h light/dark cycle at the Experimental Animal Center of Chung Shan Medical University. All the experimental manipulations involving animals were strictly implemented according to ethical guidelines for animal experiments and were approved by the Laboratory Animals Center of Chung Shan Medical University (IACUC No. 1664). After 1 week of acclimatization, the rats were randomly divided into nine groups of 12 rats (Figure 1). Rats in the control group were fed an AIN-93G control diet ($7\\%$ fat); rats in the experimental groups were fed an AIN-93G-based high-fat diet containing $32\\%$ lipids ($7\\%$ soybean oil and $25\\%$ lard). For the positive control treatment, a health supplement containing hydroxycitric acid (HCA) and chlorogenic acid (CGA) was obtained from Taiyen Biotech Co., Ltd. (Tainan, Taiwan), which has been proven to have antibody fat accumulation effects by the Taiwan Food and Drug Administration (TFDA, License NO. A00274). Furthermore, the experimental groups were distributed into eight subgroups: (a) continued received HFD throughout the period (HFD), (b) HFD supplemented with 100 mg/kg body weight Japan Mei-Gin (low-dose Japan Mei-Gin, HFD + JMG-LD), (c) HFD supplemented with 300 mg/kg body weight Japan Mei-Gin (high-dose Japan Mei-Gin, HFD + JMG-HD), (d) HFD supplemented with 100 mg/kg body weight Mei-Gin formula-3 (low-dose Mei-Gin formula-3, HFD + MGF-3-LD), (e) HFD supplemented with 300 mg/kg body weight Mei-Gin formula-3 (high-dose Mei-Gin formula-3, HFD + MGF-3-HD), (f) HFD supplemented with 100 mg/kg body weight Mei-Gin formula-7 (low-dose Mei-Gin formula-7, HFD + MGF-7-LD), (g) HFD supplemented with 300 mg/kg body weight Mei-Gin formula-7 (high-dose Mei-Gin formula-7, HFD + MGF-7-HD), and (h) HFD supplemented with 140.6 mg/kg body weight HCA + GCA powder capsules (HFD + PC). During the experiment period, the body weight, daily feed, and water were measured and used to calculate the feeding efficiency. After 8 weeks, animals were fasted overnight, euthanized by carbon dioxide, and whole blood was collected from the abdominal aorta. The heart, liver, spleen, lung, kidney, visceral adipose tissue (perirenal fat, epididymal fat, and mesenteric fat), and subcutaneous adipose tissue (retroperitoneal fat and inguinal fat) were dissected, rinsed, weighed, and stored at −80 °C.\n\n## 2.4. Oil Red O Lipid Staining\n\nFor measuring intracellular lipid accumulation, oil red O staining was performed to determine the effect of MGF-1-7 on lipid synthesis. Briefly, mature 3T3-L1 adipocytes were harvested and then washed by PBS and fixed in $10\\%$ neutral buffered formalin for 20 min at room temperature. Subsequently, cells were placed in $100\\%$ propylene glycol for 3 min and then stained with a mixture of oil red O working solution (oil red O solution/water 3:2, v/v) for 60 min. The lipid droplets were visualized and photographed by microscopy (Motic AE$\\frac{30}{31}$), and the oil red O was solubilized in isopropanol and measured spectrophotometrically at 510 nm.\n\n## 2.5. Determination of Glycerol-3-Phosphate Dehydrogenase (GPDH) Activity\n\nUsing a GPDH activity colorimetric assay kit (Cat No. K640-100, BioVision, Milpitas, CA, USA), mature 3T3-L1 adipocytes were harvested 48 h after MGF-1-7. Based on the manufacturer’s instructions, protein concentration was determined spectrophotometrically according to the reduced nicotinamide adenine dinucleotide (NADH) standard. GPDH activity (%) was pressed as a percentage change against control ($100\\%$).\n\n## 2.6. Statistical Analysis\n\nFor all experiments, the quantitative data are expressed as mean ± SEM. The analysis of variance was followed by one-way ANOVA with Duncan’s multiple range test by using the SPSS software program, version22.0 (APSS Inc., Chicago, IL, USA). A statistically significant difference was established only if the p-value < 0.05.\n\n## 3.1. Determination of Phenolic Contents of the Mei-Gin Formulas\n\nThe content of the main phenolic compounds in MGF-1-7 was identified using HPLC analysis and as follows: MGF-1 (chlorogenic acid: 0.46, caffeic acid: 0.19, and p-coumaric acid: 0.09 mg/g), MGF-2 (chlorogenic acid: 0.26, caffeic acid: 0.24, and p-coumaric acid: 0.07 mg/g), MGF-3 (chlorogenic acid: 0.34, caffeic acid: 0.18, and p-coumaric acid: 0.08 mg/g), MGF-4 (chlorogenic acid: 0.32, caffeic acid: 0.26, and p-coumaric acid: 0.08 mg/g), MGF-5 (chlorogenic acid: 0.54, caffeic acid: 0.11, and p-coumaric acid: 0.08 mg/g), MGF-6 (chlorogenic acid: 0.70, caffeic acid: 0.19, and p-coumaric acid: 0.07 mg/g), and MGF-7 (chlorogenic acid: 0.72, caffeic acid: 0.11, and p-coumaric acid: 0.08 mg/g). The data indicated that MGF-7 had higher chlorogenic acid, MGF-4 had higher contents of caffeic acid content, and the contents of p-coumaric acid content was similar in each group.\n\n## 3.2. Effects of Mei-Gin Formulas on Lipid Accumulation in 3T3-L1 Adipocytes\n\nTo determine the effects of the Mei-*Gin formula* on intracellular lipid accumulation in adipocyte cells, the effects of serially diluted MGF-1-7 on 3T3-L1 adipocytes were visualized using oil red O staining. MGF-1-7 effectively reduced lipid accumulation in mature 3T3-L1 adipocytes. To validate the observation of reduced lipid accumulation, a TG quantification assay was performed to confirm the changes. As shown in Figure 2A,B, similar to the reduction of lipid accumulation, MGF-1-7 significantly decreased the cellular level of TG in 3T3-L1 adipocytes. The 250 μg/mL MGF-3 and -7 treatments were observed to show a significant 31.4 and $35.9\\%$ reduction in TG content compared to untreated control cells, respectively (Figure 2B), indicating that in the presence of high-dose MGF-3 and -7, the intracellular level of TG levels was dramatically decreased. Additionally, a further detailed analysis of the effects of MGF-1-7 on GPDH activity is shown in Figure 2C. The alteration of intracellular GPDH activity was observed in 3T3-L1 adipocytes treated with MGF-1-7. In particular, GPDH activity was decreased in the same manner as previously observed in the intracellular TG level in cells treated with MGF-3 and -7. The results suggested that MGF-3 and -7 appeared to have the most potency in reducing 3T3-L1 adipocytes. Therefore, we used MGF-3 and -7 in subsequent experiments involving animal models.\n\n## 3.3. Effect of the Mei-Gin Formula on Body Weight, Feed Intake, Energy Intake, Feed Efficiency, and Mass of Selected Organs in HFD-Induced Obesity\n\nAs shown in Figure 3 and Table 1, the body weight during the experimental period progressively decreased among the JMG, MGF, and PC intervention groups, as compared to the HFD group. In detail, consumption of low-dose Japanese MG, high (300 mg/kg) MGF-3, and both low and high (100 mg/kg and 300 mg/kg) MGF-7 significantly decreased body weight change and weight gain compared to that of the HFD group ($p \\leq 0.05$). The feed intake and energy intake of the JMG-fed rats were significantly lower than those of the HFD-fed rats. Although the feed efficiency of rats that consumed MGF-3 (300 mg/kg), MGF-7 (300 mg/kg), and HCA + GCA powder capsules (140.6 mg/kg) was significantly lower than that of the HFD group (Table 2), no significant differences were observed in the weights of the heart, spleen, lungs, and kidneys among each group. A significant reversal in increased liver weight was observed in rats that ate low (100 mg/kg) JMG, high (300 mg/kg) MGF-3, low and high (100 mg/kg and 300 mg/kg) MGF-7, and powder capsules (140.6 mg/kg) (Table 3).\n\n## 3.4. Effect of the Mei-Gin Formula on Body Fat Mass and Adipose Tissue in Rates with HFD-Induced Obesity Rates\n\nAs shown in Table 4 and Table 5, the rats fed a high-fat diet exhibited persistent higher total body fat mass compared to the counterpart ND group, and this was significantly attenuated by consuming low (100 mg/kg) JMG, high (300 mg/kg) MGF-3, both low and high (100 mg/kg and 300 mg/kg) MGF-7, and HCA + GCA powder capsules (140.6 mg/kg). Similarly, a trend of reduction in subcutaneous adipose tissue was also observed among those five groups as the result of increased retroperitoneal and inguinal adipose tissue. Although the effect of low-dose JMG on visceral adipose tissue was not statistically different, it still showed potential to lower the mass of adipose tissue at week 8. Among visceral (perirenal, epididymal, and mesenteric) adipose tissue, a significant reduction was found in perirenal and mesenteric adipose tissue after administration of low-dose JMG, high-dose MGF-3, both high- and low-dose MGF-7, and powder capsules; while the weight of epididymal adipose tissue did not show a significant difference between each group after dietary intervention.\n\n## 4. Discussion\n\nObesity is characterized by defective excess body fat content, including the determination of size and body fat distribution. Recently, emerging evidence has focused on dietary phenolic compounds that provide a therapeutic strategy for people with obesity, as naturally occurring plant products reduced the potential for side effects [33,34,35,36]. Concerning the risk of adverse medication reactions, various options for obesity management and treatments have been constantly conducted in various biological properties of natural phenolic compounds that exhibited a preventive or therapeutic potential to improve lipid and glucose dysregulation [37,38,39]. Existing anti-obesity medications including orlistat and sibutramine, with very modest efficacy, can cause clinically adverse drug reactions. Therefore, there is a strong need to exploit and discover naturally occurring foods and substances as safe and acceptable alternatives to designer drugs. Based on Chinese herbology, we developed a novel Mei-*Gin formula* to exert a powerful synergistic effect to target the development of obesity [40,41]. In this study, our aim was to determine the anti-obesity effect of the Mei-*Gin formula* on the 3T3-L1 cells in vitro and in vivo HFD-induced rat model by monitoring the regulation of cell lipid accumulation and adipose tissue. In vitro cell models, particularly 3T3-L1 preadipocyte differentiation and adipogenesis of 3T3-L1 preadipocytes, with the main characteristic of intracellular triglyceride accumulation, are associated with the development of obesity [42,43]. In this regard, we demonstrated that the inhibitory effects of the Mei-*Gin formula* on 3T3-L1 adipocyte differentiation were due to the downregulation of GPDH activity and this subsequently led to a reduction in cellular triglyceride production. As shown in Figure 2, including the serial tested Mei-Gin formulas (MGF-1-7), all are capable of altering the differentiation capacity of the 3T3-L1 preadipocyte into the 3T3-L1 adipocyte. On the basis of the HPLC analysis, we determined the phenolic compounds in the Mei-*Gin formula* which were most abundant in p-coumaric acid, caffeic acid, and chlorogenic acid. Among food phenolic compounds, hydroxycinnamic acid represents a major class of phenolic acid available in fruits, seeds, and vegetables [44,45]. A diet supplement of hydroxycinnamic acid can easily reach levels of 0.5–1 g or even higher in humans. Previously, evidence indicated that hydroxycinnamic acid, including p-coumaric acid caffeic acid, ferulic acid, and chlorogenic acid, can serve as the primary antioxidant [46], have powerful anti-inflammatory [47] and anti-cancer activity [48,49], and are involved in improving insulin resistance [50]. The study reveals that caffeic acid phenethyl ester effectively prevents body weight gain and the gain of epididymal adipose tissue [51]. The result of oil red O staining indicated that caffeic acid phenethyl ester significantly reduced adipogenesis in 3T3-L1 preadipocytes, which is in accordance with our in vivo results. Dietary consumption of chlorogenic acid markedly altered the plasma lipid profile and attenuated the fatty liver by increasing hepatic PPAR-α expression in hypercholesterolemic rats [52]. Furthermore, p-coumaric acid-induced activation of the AMPK pathway subsequently leads to the inhibition of adipogenesis in 3T3-L1 adipocytes [53]. To understand the synergistic effects for each of the natural plants in our new formula, different ratios of each individual plant were used to form MGF-1-7 in the context. Interestingly, quantitative results from intracellular triglycerides in 3T3-L1 adipocytes indicated that MGF-3, -4, and -7 exhibited predominant inhibitory effects on lipid accumulation, while the inhibitory capacity among Mei-Gin formulas on GPDH activity was found mostly in MGF-3, -5, and -7. Taken together, MGF-3 and -7 appeared to be the most potent in regulating the development of obesity. Therefore, we examined the effectiveness of MGF-3 and -7 to ameliorate weight gain and further identified the contribution of adipose tissues to obesity in the HFD-induced rat model.\n\nPreviously, rats fed a high-fat/high-energy diet have been shown to have significantly increased body weight compared to normal diet-fed rats, and are widely used in diet-induced obesity studies [54,55]. In the rat model induced by a high-fat diet, excessive lipid accumulation in subcutaneous and visceral adipose tissue is a key feature of obesity. In the present study, we observed that the administration of the Mei-*Gin formula* in obese rats for 8 weeks significantly reduced final body weight and weight gain. Furthermore, both low- and high-dose MGF-7 effectively suppressed body weight compared to MGF-3. Importantly, diet-induced obesity is associated with lipid burden in adipose tissue, as well as in non-adipose tissue. The increased fat deposition was observed in the liver and eventually leads to weight gain in the obese animal model [36,56,57,58]. Our data reveal a similar trend of reduction of liver weight in HFD-induced rats after administration of the Mei-Gin formula. Interestingly, a similar result was shown in 3T3-L1 adipocytes in vitro and obese mice in vivo as a supplement of plant resin [59]. Recently, the complete analysis of Prunus mume extracts was reported to identify the phytochemical composition, including chlorogenic acid, lupeol, mumefural, and ursolic acid, which are proposed to have anti-cancer properties [60,61,62,63]. A pilot study conducted on 18 H. pylori-positive participants in the stomach proposed an anti-bacterial activity of bainiku-ekisu therapy [22]. Bainiku-ekisu is a Prunus mume juice concentrate and was previously reported to exhibit strong anti-bacterial activity in vitro [23]. Due to the concentration process, the phytochemical contents such as phenolic acid and flavonoids were higher in bainiku-ekisu than those in Prunus mume juice [64,65]. Herein, we are the first to verify the effects of the Mei-Gin-based plant mixture in regulating body weight gain of obese rats. High-performance liquid chromatography analysis confirmed the phenolic acid and flavonoid constituents in the thermal process and was found to increase markedly in black garlic as compared to that of fresh garlic [66,67,68]. The HPLC isolation and identification of black garlic showed variable quantities of phenolic acid, including garlic acid, vanillic acid, chlorogenic acid, caffeic acid, p-coumaric acid, and ferulic acid. Hung et al. have investigated the antioxidant activity and active components of Hsian-tsao [69]. Several phenolic acids were identified from the water extract of Hsian-tsao, including apigenin, caffeic acid, vanillic acid, and kaempferol [70]. The authors also determined that the amounts of caffeic acid and kaempferol were the highest among those phenolic acids and are considered the main functional components, and may contribute to the antioxidant properties of Hsian-tsao. The diet supplement of phenolic compounds from natural plants that have served as potent anti-obesity agents has been well documented. However, the effects of natural plants on obesity are not yet defined. The composition of the innovative plant formula was based on the hypothesis that combining the candidates of the plants with particular reference mentioned above can modulate the development of obesity in vivo and in vitro. Our data demonstrated that low-dose JMG, high-dose Mei-Gin formula, both high-and low-dose Mei-Gin formula, and HCA + GCA powder capsules effectively reduced total body fat in HFD-induced rats, which is consistent with the initial observation in body weight gain and liver weight. In addition, we identified the contribution of adipose tissue in obese rats. Along the same lines, it leads to a significant reduction in visceral (perirenal and mesenteric adipose tissue) and subcutaneous (retroperitoneal and inguinal adipose tissue) adipose compartments in HFD-induced rats compared to HFD controls. These data demonstrated that the administration of the Mei-*Gin formula* caused weight loss, which affected both visceral and subcutaneous adipose tissue. However, no differences were established regarding low-dose JMG and HFD controls.\n\nJMG is a condensed extract obtained using traditional thermal condensation methods. Accumulated studies have shown that JMG inhibits the proliferation of hepatocellular carcinoma cells [71] and improves hyperglycemia [72], which can serve as a dietary intervention as adjuvant therapy. However, phytochemical compounds such as phenolic acid have been reported to be destroyed after heat treatment at high temperatures for a long period of time [64,65]. In the present study, high-dose MGF-3 and high- and low-dose MGF-7 administration in HFD-induced rats results in a more efficient reduction in body weight gain, liver weight, and total body fat compared to JMG groups, which may be attributed to our decompression-processed Mei-Gin, which kept more bioactive components. Among each group, the optimal level of each MG formula group has been determined in the corresponding HFD-induced rat model. The results suggested that high-dose MGF-7 exerts the most potent anti-obesity activity.\n\n## 5. Conclusions\n\nIn conclusion, this is the first study to verify the effects of a Mei-Gin-based plant formula on obesity both in vivo and in vitro. MGF-1-7 reduced 3T3-L1 adipocyte differentiation and lipid accumulation by suppressing GPDH activity. 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Pericarp\nauthors:\n- Bianca R. Albuquerque\n- Maria Inês Dias\n- José Pinela\n- Ricardo C. Calhelha\n- Tânia C. S. P. Pires\n- Maria José Alves\n- Rúbia C. G. Corrêa\n- Isabel C. F. R. Ferreira\n- Maria Beatriz P. P. Oliveira\n- Lillian Barros\njournal: Foods\nyear: 2023\npmcid: PMC10000740\ndoi: 10.3390/foods12050994\nlicense: CC BY 4.0\n---\n\n# Insights into the Chemical Composition and In Vitro Bioactive Properties of Mangosteen (Garcinia mangostana L.) Pericarp\n\n## Abstract\n\nThe industrial processing of mangosteen (*Garcinia mangostana* L.) generates high amounts of waste, as ~$60\\%$ of the fruit is formed by an inedible pericarp. However, its pericarp has been explored as a source of xanthones; nevertheless, studies addressing the recovery of other chemical compounds from such biomass are still scarce. Hence, this study intended to elucidate the chemical composition of the mangosteen pericarp, including fat-soluble (tocopherols and fatty acids) and water-soluble (organic acids and phenolic compound non-xanthones) compounds present in the following extracts: hydroethanolic (MT80), ethanolic (MTE), and aqueous (MTW). In addition, the antioxidant, anti-inflammatory, antiproliferative and antibacterial potentials of the extracts were assessed. The mangosteen pericarp showed a composition with seven organic acids, three tocopherol isomers, four fatty acids and fifteen phenolic compounds. Regarding the extraction of phenolics, the MT80 was the most efficient (54 mg/g extract), followed by MTE (19.79 mg/g extract) and MTW (4.011 mg/g extract). All extracts showed antioxidant and antibacterial activities; however, MT80 and MTE extracts were more efficient than MTW. Only MTW did not show anti-inflammatory properties, whereas MTE and MT80 showed inhibitory activities towards tumor cell lines. Notwithstanding, MTE showed cytotoxicity towards normal cells. Our findings support the idea that the ripe mangosteen pericarp is a source of bioactive compounds, although their recovery is dependent on the extraction solvent.\n\n## 1. Introduction\n\nThe global agri-food sector generates significant volumes of food waste each year. For instance, the processing of fruit yields significant quantities of biowaste, which account for between 25 and $60\\%$ of the fruit’s weight. In turn, this biowaste is mainly composed of peels and seed that have a chemical composition rich in bioactive compounds, that can be recovered and used to create health supplements or added to food products to improve their nutritional content [1].\n\nGarcinia mangostana L. is a tropical shrub belonging to the family Clusiaceae, native to South Asia, which can also be found in other tropical territories such as South America [2]. Due to its unique form and flavor, mangosteen fruit is acknowledged as “the queen of fruit” (Figure 1). Mangosteen is a spherical fruit; when immature, it has a green color, and when entirely mature, its color is completely purple. This fruit holds many edible sweet petals (pulp), with delicious and widely appreciated unique flavor and aroma—so much that mangosteen is considered a delicacy worldwide [3].\n\nIndeed, the edible part of the mangosteen is small, as more than $60\\%$ of the whole fruit comprises an inedible tick dark purple or reddish pericarp, which leads to a high amount of residue production after consumption/fruit processing. It is estimated that approximately 30.8 million tons of mangosteen pericarp waste are produced per year around the planet [4,5]. On the other hand, in traditional medicine, the mangosteen fruit shell has been used for the treatment of several ailments, such as skin infections, diarrhea, and fever [5]; in addition, in some regions of South America, it is employed in the preparation of digestive and energetic teas [6]. In the past few years, mangosteen fruit, including its pericarp, has been exploited for the acquisition of manifold dietary supplements, essentially capsules and functional drinks. Such products normally claim to improve the immune system, protect against free radicals, reduce allergic reactions and weight loss, among other health-promoting properties [7,8,9]. Notwithstanding, most of these potential health effects are not scientifically supported [10] for the pharmaceutical use of mangosteen. Moreover, the greater part of existing scientific evidence on mangosteen fruit and its by-products, including original articles and reviews, only addresses its xanthones compounds and corresponding bioactivities [3,11,12]. In addition, the correlation between the mangosteen chemical composition and its bioactive profile has not been completely elucidated yet [7,13,14].\n\nConsidering all of the above, this study aimed to elucidate the phytochemical profile of the mangosteen pericarp, including lipophilic compounds such as tocopherols and fatty acids, and hydrophilic compounds, namely organic acids, and phenolic compounds, including anthocyanins. Furthermore, diverse in vitro assays were performed to evaluate the antioxidant, anti-inflammatory, antiproliferative, and antibacterial potential of the hydroethanolic (MT80), ethanolic (MTE) and aqueous (MTW) extracts obtained from this by-product.\n\n## 2.1. Fruit Material\n\nRipe mangosteen fruits with completely purple pericarp were acquired locally in Bragança, Portugal. Fruits were washed in current water and the pericarp and pulp were manually separated. Then, the pericarps were frozen (−20 °C), lyophilized, and ground into uniform particles, which were kept frozen until analysis.\n\n## 2.2.1. Organic Acids\n\nOrganic acids were recovered from the sample (1 g) by maceration (room temperature for 20 min) with 25 mL metaphosphoric acid ($4.5\\%$ (w/v)). The extract obtained was injected in a Shimadzu 20A series UFLC-PDA. A C18 column was used to separate the compounds, and sulphuric acid (3.6 mM) was used for the elution, with a flow rate of 0.8 mL/min. The preferred wavelengths for detection in a PDA were 215 and 245 nm (for ascorbic acid). Compounds were identified by comparing the area of extract peaks with calibration curves produced from commercial standards. Data were presented in mg per 100 g of dry pericarp (dw).\n\n## 2.2.2. Tocopherols\n\nTocopherols were extracted from the sample (500 mg) through successive homogenization and centrifugation (4000× g at 10 °C for 5 min, three times) with methanol and hexane, and the supernatant phase was gathered, dried in a flow of nitrogen, re-diluted in hexane (2 mL), and analyzed using a HPLC-FL [15]. For quantification, genuine standards of α-, β-, γ-, and δ-tocopherol, as well as tocol (internal standard) were used. The results were reported in mg per 100 g of dw.\n\n## 2.2.3. Fatty Acids\n\nFatty acids were obtained from the lipid fraction assisted by Soxhlet extraction, followed by methylation with 5 mL of methanol, sulphuric acid, and toluene 2:1:1 (v:v:v), for at least 12 h at 50 °C and 160 rpm. Next, 3 mL of deionized water were added to obtain phase separation. The FAME were recovered with 3 mL of diethyl ether by shaking in vortex. A gas–liquid chromatography with flame ionization detection was performed using a YOUNG IN Crhomass 6500 GC System instrument equipped with a split/splitless injector set at 250 °C with a split ratio of 1:50, a flame ionization detector (FID) set at 260 °C, and a Zebron-Fame column (30 m). It was set to the following oven temperature program: Initial temperature of 100 °C, maintained for 2 min, increase of 10 °C/min to 140 °C, then a ramp of 3 °C/min to 190 °C, and a final ramp of 30 °C/min to 260 °C. At 250 °C, the carrier gas (hydrogen) flow rate was 1.2 mL/min. The results were described as a relative percentage of each fatty acid.\n\n## 2.2.4. Phenolic Compounds\n\nThree distinct extracts were prepared by adding 1 g of dry sample (mangosteen lyophilized pericarp) to 30 mL of solvent, which contained [1] solution composed of $80\\%$ ethanol and $20\\%$ water for hydroethanolic extraction (MT80); [2] distilled water for aqueous extraction (MTW); and [3] ethanol ($100\\%$) for ethanolic extraction (MTE). For the extraction of anthocyanin compounds, the same solvents were acidified with citric acid ($0.1\\%$, 1 µM). The samples were extracted for 1 h under stirring at room temperature and filtrated (qualitative filter paper of 20–25 µm). After that, the residues were subsequently extracted a second time for 1 h under the same circumstances using additional 30 mL of the same solvent. The ethanol presented in the extracts was evaporated (at 40 °C) under vacuum condition, and aqueous phases of the hydroethanolic and aqueous extracts were lyophilized to produce dry extracts.\n\nThe freeze-dried extracts were resuspended (5 mg/mL) in of ethanol/water (20:80 v/v) and were analyzed using an HPLC-DAD-MSn, working under optimized conditions as in other studies [16,17]. The results were expressed as mg per g of extract (E) and mg per g of dw.\n\n## 2.3.1. Antioxidant Potential\n\nTwo cell-based assays that measure the capacity to [1] prevent the generation of thiobarbituric acid reactive substances (TBARS) [15] and [2] postpone the oxidative hemolysis (OxHLIA) [18] were used to examine the antioxidant potential of the extracts (3.12–400 µg/mL). The TBARS results were defined as EC50 values (µg/mL), which represents the extract concentration that suppresses TBARS by $50\\%$. The OxHLIA results were defined as IC50 values (µg/mL) for a Δt of 60 min, which is the amount of extract needed to maintain $50\\%$ of the sheep erythrocyte population for 60 min. In both experiments, Trolox acted as a positive control.\n\n## 2.3.2. Anti-Inflammatory Potential\n\nThe ability of the extracts (6.25–400 g/mL) to prevent lipopolysaccharide-stimulated murine macrophage RAW 264.7 cells from producing nitric oxide (NO) was used to assess their anti-inflammatory potential [19]. A positive control was applied, which was Dexamethasone (50 μM). The results were represented as IC50 values (µg/mL), which correspond to the amount of the extract that causes $50\\%$ of the NO generation to be inhibited.\n\n## 2.3.3. Antiproliferative Potential\n\nThe evaluation of the antiproliferative potential of the extracts (6.25–400 µg/mL) was performed following the protocol for the sulforhodamine B (SRB) assay [20]. Four human tumor cell lines, namely NCI-H460 (non-small lung carcinoma cells); Caco-2 (colon adenocarcinoma cells); MCF-7 (breast carcinoma cells); and AGS (gastric adenocarcinoma cells), besides one non-tumor cell line obtained from African green monkey kidney (Vero), were used. The positive control employed was Ellipticine. The results were expressed as GI50 values (µg/mL), which correspond to the extract’s concentration required to inhibit $50\\%$ of cell proliferation.\n\n## 2.3.4. Antibacterial Potential\n\nIn order to evaluate the antibacterial potential of the extracts on pathogenic bacteria commonly causing nosocomial infections, six Gram-negative (Escherichia coli, Klebsiella pneumoniae, Morganella morganni, Proteus mirabilis, and Pseudomonas aeruginosa) and three Gram-positive (Enterococcus faecalis, Listeria monocytogenes, and Methicillin-resistant *Staphylococcus aureus* (MRSA)) bacteria were selected. Following a protocol established [21], the extracts were re-dissolved in water (20 mg/mL) and successive dilutions were carried out in a 96-well plate until 0.15 mg/mL. The minimum inhibitory concentrations (MIC), were determined by the rapid p-iodonitrotetrazolium chloride (INT) colorimetric assay. The minimum bactericidal concentrations (MBC) were determined by transferring a portion (10 µL) of each well that showed no color change to a blood agar medium ($7\\%$ sheep blood) and incubated at 37 °C/24 h. MBC was determined as the lowest concentration capable of eradicating bacteria. Streptomycin and ampicillin were used as positive controls.\n\n## 2.4. Statistical Analysis\n\nThe results of the analysis were expressed as mean value ± standard deviation, which were all carried out in triplicate. Statistical analyses were carried out using the R software (version 11). Student’s t-test was applied to detect statistical differences ($p \\leq 0.05$) between two samples; for three samples, the analyses of variance (ANOVA) were used for detecting significant differences ($p \\leq 0.05$) between them. The Tukey’s honest significant difference (HSD) test at the $5\\%$ of significance was applied to discriminate the samples.\n\n## 3.1.1. Organic Acids\n\nThe organic acid profile of mangosteen pericarp is presented in Table 1. Citric acid ($56.72\\%$) was the major compound detected in the sample, followed by quinic acid ($17.99\\%$). A low concentration of ascorbic acid was detected, whereas only traces of shikimic and fumaric acids were observed. Other studies reported different organic acid profiles for mangosteen pericarp samples. For instance, Mamat et al. [ 22] detected five organic acids by chromatography-mass spectrometry (GC-MS), namely malic, L-(+)-tartaric, citraconic, malonic and succinic acids when investigating mangosteen ripened pericarp. More recently, the same research team assessed the mangosteen metabolites throughout all ripening process (from green fruit (stage 0) to purple dark fruit (stage 6)) and detected traces of aspartic acid until stage 4 (brownish red), and the presence of ascorbic acid-2-glucoside, 2-butynedioic acid, and quinic acid was detected only in stage 2. Indeed, organic acid composition can be associated with the stage of fruit ripening [23]. In our investigation, only purple dark pericarps were analysed.\n\n## 3.1.2. Tocopherols\n\nTocopherol isomers present in the mangosteen pericarp were identified by HPLC-FD, and the results are shown in Table 1. In total, three tocopherol isomers were detected and quantified. β-tocopherol was the most abundant isomer, γ-tocopherol was detected in the lowest concentration, and α-tocopherol was detected in a median amount. Mangosteen pericarp showed a considerable amount of tocopherol isomers (9.9 mg/100 g dw).\n\nIsabelle et al. [ 24] identified γ-, α-, and δ-tocopherol in samples of mangosteen flesh, α-tocopherol being the most abundant (5.74 mg/g fw) within a total tocopherol content of 9.9 mg/100 g dw. In another study concerning the G. mangostana pulp, the amount of vitamin E (α-tocopherol) quantified was 0.18 mg/100 g fw [25]. As expected, such composition of tocopherol is distinct from the one herein reported, which addresses only the mangosteen pericarp. Until the publication of this study, to the best of our knowledge, there was no previous report on the tocopherol profile of this part of the fruit.\n\n## 3.1.3. Fatty Acids\n\nMangosteen pericarp showed a low content of lipid (2.7 ± 0.6 g/100 g dw), and this lipid fraction was evaluated regarding its fatty acid composition, the results of which are presented in Table 1. Oleic acid was the most abundant fatty acid detected, followed by palmitic and linoleic acid, whereas stearic acid was the minor fatty acid constituent. In total, mangosteen pericarp showed $40.6\\%$ saturated, $34.1\\%$ monounsaturated, and $25.2\\%$ polyunsaturated fatty acids. The ratio between PUFA and SFA verified for the mangosteen shell of 0.62 can be considered appropriate for maintaining good health [26]. As far as we are aware, this is also the first research on the fatty acid profile of mangosteen pericarp. However, the fatty acid composition of the mangosteen seed has been described in the literature [27]. Similar to the pericarp, the mangosteen seed shows a higher amount of palmitic and oleic acid, besides linoleic acid in low concentrations [27].\n\n## Non-Anthocyanin Compounds\n\nThe non-anthocyanin compounds present in the different extracts of mangosteen pericarp were assessed by LS-MS and the identification of the compounds was conducted considering their main characteristics in the mass spectral (mass fragmentation (MS²), maximum UV absorption (λmax)) and based on information obtained from the literature. The identification and quantification of phenolic compounds detected in the extracts are presented in Table 2 and Table 3, respectively.\n\nCompound 1 ([M-H]− at m/z 353) showed four fragment ions at MS² and comparing its mass spectrum with commercial standard, this compound was tentatively identified as a 5-O-caffeoylquinic acid. No data in the literature was discovered related to the presence of such compound in mangosteen fruit samples. Compounds 2, 4, and 11 ([M-H]− at m/z 577) also showed the presence of ion fragment at m/z 289 after the loss of 288 Da, and based on its chromatographic characteristics and literature data, this compound was tentatively identified as procyanidin dimer [28]. Compounds 3, 6, and 10 ([M-H]− at m/z 865) released three fragment ions at MS², corresponding to the successive breaking of bonds between monomers of epicatechin/catechin molecule (m/z 289). According to the mass spectrum and previous studies regarding mangosteen, these compounds have been identified as procyanidin trimer isomers [28]. The mass characteristics of compound 5 ([M-H]− at m/z 289) were compared with commercial standards, and this compound was tentatively identified as a (+)-epicatechin. Regarding compounds 7 and 8 ([M-H]− at m/z 1153), the analysis of MS² allowed the detection of four epicatechin/catechin molecules; however, these compounds were identified as procyanidin tetramer isomers, compounds previously identified in mangosteen pericarp by Zhou et al. [ 28]. Compound 9 ([M-H]− at m/z 863) showed five fragment ions at MS²; according to the literature, this molecule has been discovered in mangosteen pericarp and identified as a procyanidin-A-like linkage [28]. Compound 12 ([M-H]− at m/z 609) released a unique fragment ion MS² at m/z 301; the mass spectrum of this compound allowed its identification as a quercetin-3-O-rutinoside through DAD-MSn data. Compound 13 ([M-H]− at m/z 449) showed two fragment ions at m/z 303 and m/z 285. Comparing its spectrum characteristics with those of standard compounds, this compound was identified as taxifolin-O-rhamnoside. These last two compounds have not been detected in mangosteen fruit before.\n\nMost studies on the chemical composition of mangosteen fruit focus on xanthones, a restricted polyphenol class present in a small group of plants and fungi, as mangosteen fruit has been considered one of the major sources of such phytochemicals [9,31]. However, the present study focused on other classes of biocompounds. As a result, among non-anthocyanin compounds, condensed tannins were the most abundant polyphenols present in all extracts of mangosteen pericarp. In addition, one phenolic acid, two flavonoids, one flavonol and one flavanonol, were detected in this by-product.\n\nAccording to Table 3, the hydroethanolic extract (MT80) showed the higher amount of phenolics among samples (MT80 > MTE > MTW). However, proanthocyanidins were the most abundant compound class present in all extracts, accounting for $95\\%$, $94\\%$ and $90\\%$ of the total phenolic compounds non-anthocyanin in MT80, MTE, and MTW, respectively. 5-O-caffeoylquinic and traces of quercetin-3-O-rutinoside (Compound 12) were detected only in MT80, whereas taxifolin-O-rhamnoside (Compound 13) was detected in low concentration in all extracts, which, as far as we know, is an unprecedented information regarding the phenolic profile of mangosteen fruit. MTW showed the lowest total phenolic compound non-anthocyanin, and the concentration of each compound detected in it is also lower than the concentration present in the other extracts. Such result can be associated with the poor solubility of proanthocyanidins in water [32].\n\nOnly few works have described in detail the phenolic composition of the mangosteen pericarp. For example, Zarena and Sankar [33] identified the presence of thirteen phenolic acids in the fractionated and hydrolysed extract of mangosteen fruit shell. These authors concluded that most of the phenolic acids naturally present in this bioresidue are bound to glucoside molecules [33]. Zhou et al. [ 28] identified proanthocyanidins from purified extracts of mangosteen pericarp that showed high antioxidant activity by chemimal methods, namely ferric reduncing antioxidant power (FRAP), Trolox equivalent antioxidant capacity (TEAC), and DPPH radiacal scavenging capacity.\n\n## Anthocyanin Compounds\n\nAccording to the anthocyanin composition data shown in Table 2, only two anthocyanin compounds were detected in the MT80 extract. The first one detected was compound 14 ([M]+ at m/z 611) that released MS² fragment at m/z 287 (−324 u, loss of two hexose units), suggesting the presence of a cyanidin-O-dihexoside. The same fragmentation behaviour was previously described in the identification of cyanidin-O-sophoroside in this part of mangosteen fruit [29,30]. The other anthocyanin compound (Compound 15 ([M]+ at m/z 435) was identified as delphinidin-O-pentoside due to the loss −132 u, which revealed the MS² fragmentation m/z 303, characteristic of an aglycone delphinidin. Following our knowledge, this is the first time that a delphinidin derivate is detected in the mangosteen fruit.\n\nZarena and Sankar [30] reported the identification of other two anthocyanins in mangosteen pericarp, namely pelargonidin-3-O-glucoside and cyanidin-3-O-glucoside. On the other hand, Yenrina et al. [ 34] did not detect such components in their mangosteen pericarp samples. Palapol et al. [ 29], which evaluated the anthocyanin composition of mangosteen fruit during repining, also reported the presence of cyanidin-3-O-glucoside in this bioresidue, besides other anthocyanins, such as cyanidin-glucoside-pentoxide and other three cyanidin derivates. In their studies, cyanidin-O-sophoroside was the most abundant anthocyanin compound detected, which corroborates our findings [29,30].\n\nAccording to Table 3, the hydroethanolic extract (MT80) showed a 3.66 ± 0.02 mg/g E value equivalent to 1.062 ± 0.007 mg/g dw. This result is slightly lower than the one reported by Cheok et al. [ 35] when analyzing extracts obtained by conventional extraction with ethanol $70\\%$ (1.62 mg/g dw); on the other hand, the same authors registered the amount of 2.92 mg/g dw of anthocyanin for the extract produced via ultrasound-assisted extraction with methanol $70\\%$. Another study performed by Muzykiewicz et al. [ 36] shows that anthocyanin extraction by ultrasound process from mangosteen epicarp is dependent on the solvent concentration, time of extraction, and initial condition of the pericarp (whether fresh or frozen (−20 °C)). According to the authors, the highest anthocyanin recovery yield (±24 mg cyanidin-3-O-glucoside/L) was obtained with extraction time of 60 min, ethanol $70\\%$ as solvent and fresh pericarp. Interestingly, the authors registered the lowest amounts of anthocyanin recovered when using the minimal and the maximum ethanol concentration ($20\\%$ and $96\\%$, respectively) [36], which is similar to what happened in our study, where no anthocyanin compounds were detected for minimal and maximum ethanol concentrations (Table 3). Therefore, selective methods and optimized conditions can improve the recovery of this specific class of color compounds present in mangosteen pericarp.\n\n## 3.2.1. Antioxidant Potential\n\nThe different extracts obtained from mangosteen pericarp were evaluated regarding their antioxidant potential. According to the results presented in Figure 2, all extracts have the capability to prevent lipid oxidation and oxidative hemolysis. However, the MT80 and MTE, which showed similar activities, were more efficient in inhibiting lipid oxidation than MTW, although no sample demonstrated a Trolox-like potential (IC50 value = 5.8 ± 0.6). Regarding the preservation of the blood erythrocytes, the MTE showed the best activity, being more protective than the positive control (Trolox, IC50 value = 19.6 ± 0.7 µg/mL), whereas MT80 had an antioxidant activity equivalent to that of the control, and a higher concentration of MTW was necessary to keep $50\\%$ of erythrocytes intact. In the study performed by Muzykiewicz et al. [ 36], extracts obtained by ultrasound showed better antioxidant activity when the solvent used had an ethanol concentration greater than $20\\%$. Some studies suggested that the mangosteen pericarp has more antioxidant activity than the corresponding edible part, which has been correlated with its higher amount of phenolic compounds [12]. Among the phenolic compound classes, tannin and phenolic acid fractions of mangosteen have shown scavenging free radical capacity and anti-lipid peroxidation [28,33]. Some studies show that the tannin fraction has more free radical scavenging activity than the xanthone fraction. However, young fruits (rich in tannins, IC50 = 5.56 µg/mL) are more antioxidant than mature fruit (rich in xanthones, IC50 > 150 mg/mL) due to the change in phenolic composition occurring throughout ripening. In another study, xanthones isolated from mangosteen pericarp have shown less antioxidant activity than a crude extract of this by-product. In the study by Ngawhirunpat et al. [ 32], isolated α-mangostin showed lower antioxidant potential than isolated epicatechin and tannin. For instance, α-mangostin, the major compound of this class present in mangosteen, did not show free radical scavenging ability in DPPH assay (EC50 > 150 µg/mL) [2]. According to previous reports, phenolic compounds are mainly responsible for the antioxidant activity of mangosteen, especially ellagitannin derivatives [32]. In our study, MT80 had the highest concentration of phenolic compounds, which could justify its potent lipid oxidation inhibition. However, MTE was the most antioxidant extract in the OxHLIA system, what indicates that other classes of bioactive compounds, not identified in this study (such as xanthones) may also contribute for its antioxidant potential.\n\nAs demonstrated herein, the antioxidant potential of the extracts depends on the ethanol concentration (Figure 2). Several studies on diverse vegetal matrices have demonstrated that the antioxidant potentials of extracts obtained with the binary solvent water + ethanol tends to increase with the ethanol concentration between 60 and $80\\%$, which is also correlated with the amounts of phenolic acids and flavanols recovered, as well as with the total phenolic content [37,38,39,40,41].\n\n## 3.2.2. Anti-Inflammatory Potential\n\nMangosteen pericarp extracts were evaluated regarding their ability to inhibit NO production on RAW 264.7 cells. Only MT80 and MTE had moderate anti-inflammatory potential, once their IC50 values, 85 ± 9 and 341 ± 2 μg/mL, respectively, were more than 5-fold higher than the concentration required for the positive control (Dexamethasone, IC50 value = 16 ± 1 μg/mL). On the other hand, the aqueous extract did not show inhibition of NO production at the highest concentration tested, which is likely related to the low concentration of bioactivity detected in this extract. Moreover, although the determination of xanthone compounds was not carried out in this study, it is known that this compound class has low solubility in water [11]. Hence, the combination of the factors cited above may justify the low anti-inflammatory potential verified for the MTW extract. Other studies have reported the potential of mangosteen extracts and their isolated compounds as anti-inflammatory agents. For instance, a low concentration (IC50 = 1 µg/mL) of an ethanolic extract obtained from exhaustive maceration of mangosteen pericarp was required to inhibit the NO production by RAW 264.7 cells [42]. In the same work, isolated xanthones, namely α- and γ-mangostin, showed IC50 values of 3.1 and 6.0 µM, respectively. Likewise, the proanthocyanidins present in the mangosteen pericarp showed the ability to bind LPS and neutralize its cytotoxicity [43]. Furthermore, the administration of silver nanoparticle biosynthesized with mangosteen pericarp extract to mice a dosage of 5 mg/mL/day for one week was able to inhibit the development of Listeria-induced infection [44]. The body of evidence mentioned above indicates that perhaps, after more specific studies, the pericarp of mangosteen and its isolated compounds may become a natural alternative to the traditional medicines used to control inflammation.\n\n## 3.2.3. Antiproliferative Potential\n\nNumerous studies have proven the anticancer properties of the xanthone extracts and isolated compounds from the mangosteen fruit. These compounds, mainly α-mangostin, have shown high antiproliferative activity on diverse tumor cell lines [10]. However, the present study focused on the determination of the antiproliferative activity of crude extracts poor in xanthone compounds. The results obtained are presented in Figure 3. All extracts showed cytotoxicity on NCI-H460, AGS and Caco-2 cell lines: for both lines, lower MT80 concentrations were required (GI50 values = 19–74 µg/mL), whereas higher MTW concentrations were needed (GI50 values = 93–141 µg/mL). MTE showed the highest antiproliferative activity on MCF-7 cells among samples, while MTW did not show antiproliferative action at the highest concentration tested. According to the literature, compounds from mangosteen fruit belonging to the xanthone class have shown anticancer proprieties against several malignant cell lines [45,46,47]. Moreover, mangosteen pericarp extracts displayed inhibitory activities against hepatocellular carcinoma in an animal model [47]. The action against the proliferation of HeLa cells was determined in a crude hydroethanolic extract (GI50 value of 18.087 μg/mL) of mangosteen pericarp, whereas the isolated α-mangosteen was highly cytotoxic on this cancer cell line (GI50 values of 6.5 μg/mL) [48]. MTW showed considerable antiproliferative potential against Caco-2 and AGS tumor-cell lines. Such bioactivity can be related, inter alia, to the presence of proanthocyanidin compounds detected in this extract [49].\n\nRegarding the antiproliferative potential of our extracts on the Vero cell line, the MT80 and MTW extracts did not show toxicity in the highest concentration tested. Although the MTE extract was harmful to the proliferation of VERO cells, it only happened in a concentration (GI50 value of 76 ± 7 µg/mL) higher than the one required to inhibit the proliferation of tumor cell lines (GI50 values 17–73 µg/mL). Similar results were reported in the study performed by Ngawhirunpat et al. [ 32] using water, methanol and hexane as solvents. According to the authors, the first extract did not show toxicity in human keratinocyte cells (HaCat) in the maximal level tested, whereas the other extracts and isolated compounds were harmful to cell viability (GI50 values were 72, 30 and 2.5 μg/mL, respectively). Their aqueous extract did not show α-mangostin in its composition, while this compound was quantified in high amounts in their other extracts (15.5 and $18.7\\%$ (w/w), in methanol and hexane extracts, respectively).\n\nFinally, it is worth noting that in the balance between the antiproliferative potential on tumor and non-tumor cell lines tested, the hydroethanolic extract could be considered safe for the development of anticancer drugs. In addition, previous in vitro studies have also verified the toxicity of different extracts obtained from mangosteen pericarp and their isolated compounds, namely xanthones and proanthocyanidins, towards diverse non-cancerous human cell lines.\n\n## 3.2.4. Antibacterial Potential\n\nThe minimal inhibitory concentration (MIC) of the extracts required for each bacteria tested is shown in Table 4. The pericarp samples exhibited antibacterial potential against all the bacteria tested, except against P. aeruginosa to which no inhibition was observed in the highest concentration of extract tested (20 mg/mL). However, no extract showed bactericidal effect at the highest concentration tested. Lower extract concentrations were required to inhibit the proliferation of Gram-positive bacteria (0.625–1.25 mg/mL) rather than of Gram-negative bacteria (2.5–10 mg/mL). The same tendency was reported by Taokaew et al. [ 50] when investigating the antibacterial potential of a multifunctional cellulosic nanofiber enhanced with mangosteen pericarp extract. They believe that this activity is related to the ability of α-mangostin to diffuse through the cell membrane of Gram-positive bacteria. In another study, aqueous extracts obtained from mangosteen by-products did not show antibacterial potential against Gram-positive bacteria, as their extracts had a low concentration of bioactive compounds, namely tartaric acid and flavonols, and likely less ability to cause damage to the cell membrane of the Gram-positive bacteria [51]. Furthermore, according to the extensive review performed by Lima et al. [ 52], fruit extracts in methanol and ethanol are more effective in inhibiting pathogenic bacteria than fruit extracts obtained with water as solvent.\n\nAccording to the literature, xanthones from mangosteen hold high antibacterial activity towards several bacterial strains, such as Propionibacterium acnes, Staphylococcus epidermidis, and *Streptococcus mutans* [2,53]. On the other hand, proanthocyanidins from mangosteen have been shown to contribute to the inhibition of L. monocytogenes growth [54]. Regarding potential applications, the antibacterial activity of mangosteen pericarp extract and its derivatives have been explored for the development of dye cotton with properties against S. aureus and E. coli [55], self-nanoemulsifying drug delivery with action against *Staphylococcus epidermidis* [56], and for the production of medical glove films with antimicrobial activity [57].\n\nCompared to other fruit by-products, mangosteen pericarp has greater antibacterial potential against E. coli, S. aureus, and L. monocytogenes than *Punica granatum* L. pericarp (MIC= 50–60 mg/mL), *Sambucus nigra* L. peels and seeds (MIC = 7.81–15.63 mg/mL), and *Prunus domestica* L. peels (MIC = 7.81–15.63 mg/mL) [52]. As a result, it might be a fascinating source of antimicrobial substances.\n\n## 4. Conclusions\n\nMangosteen is an exotic fruit with cultural and economic relevance in Asia countries. Its industrial exploitation generates huge amount of by-products, as the inedible pericarp can constitute more than $50\\%$ of the whole fruit. Some researchers have focused on the xanthone composition of the mangosteen residue; however, our study showed that other interesting bioactive compounds can be recovered from this biomass. Among them, proanthocyanidins are the most abundant, the successful recovery of which depends on solvent extraction. In the conditions tested in this work, the hydroethanolic solvent ($80\\%$) was the most efficient for proanthocyanidin recovery, as anthocyanin compounds and a quercetin derivative were only detected in this extract (MT80). Moreover, the MT80 extract showed good bioactivity, such as high antioxidant, antiproliferative, anti-inflammatory and antibacterial potential. However, it is worth nothing that a good bioactive potential was also verified for the ethanol extract, although this extract showed some cytotoxic at a low concentration. 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0.009066177532076836\n]","isConversational":false}}},{"rowIdx":89,"cells":{"text":{"kind":"string","value":"---\ntitle: On the Implementation of a Post-Pandemic Deep Learning Algorithm Based on a\n Hybrid CT-Scan/X-ray Images Classification Applied to Pneumonia Categories\nauthors:\n- Abdelghani Moussaid\n- Nabila Zrira\n- Ibtissam Benmiloud\n- Zineb Farahat\n- Youssef Karmoun\n- Yasmine Benzidia\n- Soumaya Mouline\n- Bahia El Abdi\n- Jamal Eddine Bourkadi\n- Nabil Ngote\njournal: Healthcare\nyear: 2023\npmcid: PMC10000749\ndoi: 10.3390/healthcare11050662\nlicense: CC BY 4.0\n---\n\n# On the Implementation of a Post-Pandemic Deep Learning Algorithm Based on a Hybrid CT-Scan/X-ray Images Classification Applied to Pneumonia Categories\n\n## Abstract\n\nThe identification and characterization of lung diseases is one of the most interesting research topics in recent years. They require accurate and rapid diagnosis. Although lung imaging techniques have many advantages for disease diagnosis, the interpretation of medial lung images has always been a major problem for physicians and radiologists due to diagnostic errors. This has encouraged the use of modern artificial intelligence techniques such as deep learning. In this paper, a deep learning architecture based on EfficientNetB7, known as the most advanced architecture among convolutional networks, has been constructed for classification of medical X-ray and CT images of lungs into three classes namely: common pneumonia, coronavirus pneumonia and normal cases. In terms of accuracy, the proposed model is compared with recent pneumonia detection techniques. The results provided robust and consistent features to this system for pneumonia detection with predictive accuracy according to the three classes mentioned above for both imaging modalities: radiography at $99.81\\%$ and CT at $99.88\\%$. This work implements an accurate computer-aided system for the analysis of radiographic and CT medical images. The results of the classification are promising and will certainly improve the diagnosis and decision making of lung diseases that keep appearing over time.\n\n## 1. Introduction\n\nThe world was gripped by the COVID-19 pandemic over the first half of 2020, it’s spread had severe and damaging sociological impacts and led to a significant slowdown in economic activities. In this context, it is mentioned that the respiratory virus SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2) became a major pandemic because of its easy spread in the air and contact with contaminated objects and people. In less than a year, it has plunged humanity into an unprecedented crisis that has spared no area of life [1]. So far, this pandemic has affected more than 200 countries, with more than 591 million coronavirus pneumonia cases and more than 6.4 million deaths directly linked to this coronavirus, officially counted on 19 August 2022 [2,3]. This enormous number of cases has led the medical community to take a greater interest in this kind of disease. Over time, the emergence of respiratory viruses has placed humanity in a context of uncertainty and fear, often accentuated by the urgency of the response. They affect all people of both sexes and all age groups, including smokers and non-smokers, making them one of the most widespread health problems worldwide [4]. Among the most common lung diseases are asthma, bronchitis, emphysema, lung cancer, common pneumonia, and coronavirus pneumonia. It should be noted that pneumonia caused by coronaviruses may be misdiagnosed as common pneumonia. It is an infection that affects one or both lungs. It can be caused by bacteria, viruses, or fungi. Symptoms can range from mild to severe and may include coughing either with or without mucus (a slimy substance), fever, chills, and breathing difficulty [5]. Since pneumonia caused by respiratory viruses causes the same symptoms, additional high-precision methods are needed to confirm this pneumonia’s etiology. One of the most reliable tests is the Polymerase Chain Reaction (PCR) which is recommended by the WHO [6,7]. Among the instrumental methods used, as an alternative or confirmation approach and recommended by the WHO to diagnose lung lesions, imaging can be done either by radiography, Computed Tomography (CT) or ultrasound [8]. Coronavirus pneumonia infection is not so rare in everyday life. Over time, several coronavirus cases have manifested as common pneumonia, since they both have very similar symptoms. Therefore, the main objective is to relieve the medical community and facilitate the differentiation between these diseases. Given the increasing number of cases, the use of such methods shows limitations. A computer science resource will be relevant. Besides, since the 1990s, the Artificial Intelligence (AI) industry has made significant progress. It was first described by John McCarthy in 1956 as “The science and engineering of making intelligent machines.” [ 9]. Nevertheless, early models’ flaws hindered widespread adoption and medical application. With the rise of Machine Learning (ML) in the 1980s and Deep Learning (DL) in 2010, many of these limitations were overcome. Thus, the ability of AI to exploit significant associations in a dataset has started to be used for diagnosis, therapy, and outcome prediction in many clinical contexts [10]. In 2013, Maoling Zhu et al. used the computer-aided diagnosis to differentiate Pancreatic Cancer (PC) from normal tissue [11]. In 2017, Gargeya et al. used DL for diabetic retinopathy screening, their model achieved $94\\%$ sensitivity and $98\\%$ specificity [12]. By the same year, AI was also applied to reliably predict cardiovascular risk, failing to recognize a large number of individuals who would benefit from preventative care while others underwent unneeded intervention. Machine Learning was used to improve accuracy by taking advantage of the complex interactions between risk factors [13]. Pneumology was no exception to this trend since many AI techniques were used both to classify and detect lung diseases. In this context, the emergence of pneumonia coronavirus around the world has challenged researchers to provide rapid and effective diagnostic tools to make healthcare systems intelligent in the fight against pandemics caused by respiratory viruses [14]. Thus, the use of AI remains mandatory, and the development of convolutional networks and DL algorithms that researchers, specialists, and companies around the world are deploying, has enabled a revolution in the rapid processing of hundreds of X-ray and CT scan images. Several algorithms to enhance, accelerate, and make an accurate diagnosis of different cases of pneumonia and aid in decision-making were developed [15]. For that, this work will present a Post-Pandemic Classification Algorithm (PPCA) in three classes: common pneumonia, coronavirus pneumonia, and healthy lungs; on two different modalities, chest X-rays and chest CT scans. This will be done using artificial intelligence tools such as the Convolution Neural Network (CNN) called EfficientNet and preprocessing techniques. The major contributions of this paper are:Using preprocessing techniques to enhance the quality of images;Training DL model to distinguish between common pneumonia, coronavirus pneumonia and normal cases;Performing classification on two different modalities including chest X-ray and chest CT scan.\n\nMany studies have been devoted to deep learning-based solutions for detecting lung diseases. Among the studies already mentioned in the related word section, we cite: (i) Nishio et al. [ 16] who used pre-trained models, (VGG16, Resnet-50, MobileNet, DenseNet-121 and EfficientNet) for X-ray image classification between COVID-19 pneumonia, non-COVID-19 pneumonia and healthy individuals with an accuracy of $83.6\\%$ and an average sensitivity of COVID-19 pneumonia of $90.9\\%$; (ii) Ucar and Korkmaz [17] proposed a Bayes-SqueezeNet based rapid diagnostic system; an accuracy performance was $98.3\\%$ (among normal, pneumonia and Covid cases), and $100\\%$ for unique COVID-19 recognition; (iii) Maftouni et al [18] presented a robust COVID-19 classifier on chest CT images by proposing a deep learning model of pre-trained Residual Attention-92 and DenseNet architectures ensemble. The results were $97.93\\%$ and $98.32\\%$ accuracy respectively with COVID-19 pneumonia sensitivity of $96.72\\%$ and $98.06\\%$. Regarding the present study, comparing to the above mentioned studies the proposed model based on EfficientNet B7 architecture showed its effectiveness in providing better results for hybrid CT Scan and X-ray classification between COVID-19 pneumonia, non-COVID-19 pneumonia and healthy lungs, with an accuracy successively of $99.81\\%$ and $99.88\\%$ and a sensitivity to COVID-19 pneumonia of $100\\%$ for both imaging modalities.\n\nThe following is the structure of the research paper. Section 2 summarizes previous work in the field of pneumonia classification. Section 3 covers all stages of the proposed approach, from image preprocessing to image classification. Section 4 discusses the research’s implementation specifics and the experimental findings. Section 5 depicts a discussion, and finally, Section 6 closes the paper with a conclusion.\n\n## 2. Related Work\n\nDeep Learning (DL) technology is currently being implemented in various subfields of medicine, including diagnostics, bioinformatics, and education. Since the onset of the pandemic, many researchers have shown the effectiveness of using the concept of transfer learning in DL frameworks, even is still limited. Part of the challenge deals with distinguishing between common pneumonia, coronavirus pneumonia, and normal cases. Several DL models can be implemented in medical image classification and analysis to support speed and correct decision-making.\n\n## 2.1. X-ray-Based Approaches\n\nTripathi et al. [ 19] proposed and evaluated a deep Convolutional Neural Network (CNN) designed to classify thoracic diseases. The proposed model consists of convolutional layers, ReLU activations, a pooling layer, and a fully connected layer. An open-source dataset called Chest X-ray 14 was used. It consists of fifteen categories called atelectasis, cardiomegaly, effusion, infiltration, mass, nodule, pneumonia, pneumothorax, consolidation, edema, emphysema, fibrosis, pleural thickening, and hernia. This model gives an average accuracy of $89.77\\%$ obtained for the classification of different diseases. The comparative analysis shows the effectiveness of the proposed model. Zhang et al. [ 20] developed a deep model to identify coronavirus pneumonia infection from radiological images. This model was trained on a dataset comprising radiological images from 1008 patients with common pneumonia and 70 patients with coronavirus pneumonia. It achieved a sensitivity of $96.0\\%$ and a specificity of $70.7\\%$ with an AUC of $95.2\\%$. Sarki et al. [ 21] have developed a CNN-based system trained from scratch for persuasive classification and reliable detection of coronavirus pneumonia using a public X-ray database for training and validation. The data collected consisted of 1341 healthy images, 296 images with positive and suspect coronavirus pneumonia, and 3875 images with positive viral and bacterial pneumonia. Therefore, an imbalance in the collected data can be observed, which can lead to misleading classification results. Therefore, they examined all images manually and removed overexposed and underexposed images. Finally, they selected 140 images from each category for their experiments. Tuncer et al. [ 22] used the novel fuzzy tree classification approach for X-ray images in three classes (normal cases, common pneumonia, and coronavirus pneumonia). They applied Multi-Kernel Local Binary Pattern (MKLBP) to generate features, which were selected using the interactive neighborhood component (INCA) feature selector. INCA selected 616 features, which were forwarded to 16 conventional classifiers in five groups: Decision Tree (DT), Linear Discriminant (LD), Support Vector Machine (SVM), Ensemble Learning (EL), and K-Nearest Neighbor (K-NN). The best classifier was the cubic SVM which achieved $97.01\\%$ classification accuracy. The application was applied using the MATLAB (2019b) software, with the MATLAB Classification Learner Toolbox (MCLT) for classification. Nishio et al. [ 16] aimed at developing and validating a computer-aided diagnostic system for the classification of a total of 1248 chest X-ray images, including 215 with coronavirus pneumonia, 533 with common pneumonia, and 500 healthy. They used 4 pre-trained models for transfer learning. VGG16 was the most accurate for category classification with a ratio of $83.6\\%$. As the study dataset was relatively small [1248], it was necessary to improve the robustness of the CNN models by building an accurate CNN model using both transfer learning with VGG16 and a combination of data augmentation methods. Asif et al. [ 5] aimed to automatically detect patients with coronavirus pneumonia using chest X-ray images while maximizing detection accuracy using deep convolutional neural networks (DCNN) on 864 cases of coronavirus pneumonia, 1345 cases of viral pneumonia, and 1341 cases of normal pneumonia. The authors used Inception-v3 with transfer learning. The classification accuracy reached more than $98\\%$, with a training accuracy of $97\\%$ and a validation accuracy of $93\\%$. Here, DCNN performed better with a larger dataset than with a smaller one. Shelke et al. [ 23] tended to apply a specific method. From an X-ray screening machine, the chest X-ray image was run through the VGG16 model, with the results bracketed as normal, pneumonia, and TB. The pneumonia images were then run through the DenseNet-161 model and classified as normal pneumonia and coronavirus pneumonia. These coronavirus pneumonia images were run through a ResNet-18 model and classified as severe, moderate, and mild Coronavirus pneumonia. The VGG16 achieved an accuracy rate of $96\\%$, denseNet-161 reached $98.9\\%$, and $76\\%$ for ResNet-18. Ucar and Korkmaz [17] handled the problem of imbalanced data of the public dataset by using a multiscale offline augmentation technique. After that, the authors trained the augmented data with SqueezeNet architecture. The approach achieved an accuracy of $98.3\\%$. Elaraby et al. [ 24] designed a new Gray-Scale Spatial Exploitation Net (GSEN) to classify patients with coronavirus pneumonia. This approach used web page crawling across cloud computing environments. The approach achieved an accuracy of $92.76\\%$ for three-class labels and $95.60\\%$ for two-class labels. Table 1 shows a summary of the comparison between the different studies that discussed X-ray-based approaches.\n\n## 2.2. CT-Scan Based Approaches\n\nSaba et al. [ 25] used six models, namely K-NN and RF based on traditional machine learning, VGG19, and Inception-v3 based on transfer learning. Then, they used CNN and iCNN based on personalized deep learning to address the classification between coronavirus pneumonia and common pneumonia from CT images. The dataset used was 2758 coronavirus pneumonia CT scans and 990 non-coronavirus pneumonia CT scans. The customized deep learning models of CNN and iCNN gave good results, with a ratio of $99.53\\%$ for CNN and $99.69\\%$ for iCNN. Shi et al. [ 26] used CT scan images of 1658 patients with coronavirus pneumonia and 1027 patients with common pneumonia. The authors proposed the infection-size-aware random forest (iSARF) method. Subjects were automatically classified with different ranges of infected lesion sizes, followed by random forests within each group for classification. The results were: acc: $87\\%$, AUC: $94\\%$, sensitivity: $90\\%$ and specificity: $83\\%$. The proposed method has been integrated into the Ucloud platform as an online service and is available to more than 20 clinical facilities in China. Maftouni et al. [ 18] presented a coronavirus pneumonia classifier on chest CT images with noisy labels by proposing an ensemble deep-learning model of pre-trained residual attention and DenseNet architectures. The novelty of this method is that the features extracted from the two deep networks (core learners) are stacked together and processed by a meta-learner to provide the final, robust prediction. The results in terms of precision were respectively $97.93\\%$ and $98.32\\%$ for the two proposed models. Nguyen et al. [ 27] conducted an evaluation of DL classification models trained to identify patients with positive coronavirus pneumonia on 3D computed tomography (CT) datasets from different countries: CC-CCII Dataset (China), COVID-CTset (Iran) and MosMedData (Russia) in addition to a dataset at UT Southwestern (UTSW). Models trained on a single dataset achieved receiver operating accuracy (AUC) values of $\\frac{0.87}{0.826}$ (UTSW), $\\frac{0.97}{0.988}$ (CC-CCCI) and $\\frac{0.86}{0.873}$ (COVID-CTset). Apart from this, models trained on multiple datasets and evaluated on a test set from one of the datasets used for training performed better. However, performance dropped by almost an AUC of 0.5 (random estimate) for all models when evaluated on a different dataset outside of its training datasets, including the MosMedData. Pathan et al. [ 28] proposed two models for *Coronavirus pneumonia* detection: (i) based on a transfer learning approach and (ii) using a novel strategy to optimize CNN hyperparameters using the BAT algorithm based on Whale optimization + AdaBoost classifier built using dynamic ensemble selection techniques. The proposed system achieved $96\\%$ classification accuracy in detecting *Coronavirus pneumonia* using chest CT scans. Table 2 shows a summary of the comparison between the different studies that discussed CT-Scan-based approaches.\n\nThrough our approach, we aim to improve the results obtained by other authors. The main objective is to create a lung disease classification model with higher accuracy that will be trained to recognize both X-ray images and CT scans.\n\n## 3. Materials and Methods\n\nTo carry out this work, image processing was done to improve the quality of the used images. Followed by the choice and enhancement of the classification network to automatically classify pneumonia.\n\n## 3.1. Image Preprocessing\n\nThe preprocessing pipeline is habitually used for preparing the input layer to satisfy the CNN requirements. In this work, image preprocessing was performed in four steps (Figure 1). The first step concerns only the CT scan dataset. The second step represents the image quality improvement, which is separated into two different methods: Contrast Limited Adaptive Histogram Equalization for X-ray modality and standard Histogram Equalization for CT scan modality. The third and the last steps are common for both modalities.\n\n## 3.1.1. Cropping the Region of Interest (RoI)\n\nCT scan images contain a high number of unwanted and insignificant pixels. For this reason, the RoI is cropped using some image processing techniques. First, Otsu’s *Method is* applied to perform unsupervised image thresholding. This step provides two different thresholds that are taken into consideration for the quantization of the images. The second step is quantizing the image using two quantization levels (i.e., two values of the threshold) to obtain only two regions. Third, binarizing the image to keep only the RoI that contains the lungs as well as the surrounding thoracic tissue. As shown in Figure 2, the RoI is cropped to extract only the efficient pulmonary regions.\n\n## 3.1.2. Improving the Image Quality\n\nHistogram *Equalization is* an image processing technique that adjusts image intensities to enhance the contrast. In this method, the probability density function of a given image is modified into a uniform probability density function which spreads out from the lowest pixel value to the highest one.\n\nContrast Limited Adaptive Histogram Equalization (CLAHE) aims to enhance the local contrast of an image [29]. CLAHE calculates the contrast transform function for each region individually. The contrast of each tile is enhanced so that the histogram of the output region approximately matches the histogram specified by the ’Distribution’ value. Neighboring regions are then combined using bilinear interpolation to remove artificially induced boundaries. The contrast, especially in homogeneous areas, can be limited so as not to amplify any noise present in the image. Figure 3 depicts the image quality improvement after applying the CLAHE technique, in which some lung details are more discriminant.\n\n## 3.1.3. Image Resizing\n\nDeep Learning models generally train more quickly on small images. For this reason, it is necessary to resize all the images into 256×256 dimensions, to provide the most adequate dataset for the used model which will allow obtaining the best results. The method takes the input image as input and a scaling factor and scales the input image with that factor.\n\n## 3.1.4. Data Augmentation\n\nTo get good performance, the used model should be trained on a proportional number of examples. To both increase the number of training images and avoid problems with unbalanced data, the Augmentor tool was used. Augmentor is a Python package designed to generate artificial images for machine and deep-learning problems. For this purpose, rotational, vertical, and horizontal mirror transformations were used to generate 600 images per class for the X-ray dataset. Since the CT scan dataset is large enough to train the proposed model, the data augmentation step was skipped.\n\n## 3.2. Classification Network Architecture\n\nA deep learning architecture based on EfficientNetB7 which is known as the most advanced architecture in convolutional networks was built [30]. EfficientNetB7 shows a particularity in using a scaling strategy that employed a compound coefficient to equitably scale all the architecture parameters including resolution, depth, and width. First, a convolutional network is used to learn feature maps, while the second is used to classify the input images. Each convolutional layer was followed by an activation layer (ReLU- Rectified Linear Unit). By measuring a weighted sum, activation determines whether a neuron needs to be activated or not. It is used to introduce nonlinearity into the output of a neuron. In the following steps, max-pooling is performed to downsample the input image, reduce dimensionality, and prepare it for processing. The pre-trained model was used on the ImageNet dataset and the last layers of each model (1000 classes). Afterward, batch normalization, fully connected, and dropout layers were applied. As shown in Figure 4, the dropout layers are used to prevent overfitting. Initialization of the weight kernel with orthogonal weights was also performed. During the forward pass through a CNN, this initialization prevents the layers’ activation outputs from exploding or disappearing. Lastly, a fully-connected layer with three neurons was added to represent the class scores (i.e., output layer).\n\n## 3.3.1. X-ray Dataset\n\nThe used dataset is composed of the publicly available datasets named MOMA Dataset and was published in June 2020 [31]. It is composed of 603 X-ray images in JPEG format, downloaded from Mendeley. 234 of these images were normal, 221 were positive for coronavirus pneumonia and 148 of them were positive for pneumonia. The pneumonia images were completed by 100 images recovered from the Cheikh Zaid International University Hospital.\n\n## 3.3.2. CT Scan Dataset\n\nMosMedData [32] was acquired from 1 March 2020 to 25 April 2020 at municipal hospitals in Moscow, Russia. It consists of anonymized lung CT scans with COVID-19 signs, as well as CT scans without such findings. It contains several CT scans for 1110 patients, of whom $42\\%$ were males, $56\\%$ were females and $2\\%$ were not identified. The patient’s age ranges from 18 to 97 years old. Every exam has been saved in NIFTI (Neuroimaging Informatics Technology Initiative) format and archived in GZIP format. During this process, only every 10th instance was maintained in the final file. To complete the missing pneumonia cases for classification on 03 classes, a database named the largest COVID-19 CT dataset was sollicted [18] from [(https://www.kaggle.com/datasets/maedemaftouni/large-covid19-ct-slice-dataset) (accessed on 17 February 2022)].\n\n## 3.3.3. Cheikh Zaid Data\n\nValidation images were obtained on one CT system (Somatom Def AS, Siemens Healthineers, Germany). The main scanning parameters were as follow: Tube voltage: 120 kV, pitch factor $\\frac{1}{4}$ 0.3–1.5 mm, recon matrix $\\frac{1}{4}$ 512×512, slice thickness $\\frac{1}{4}$ 1 mm. The patients were positioned toward the front of the imaging equipment where the face is in an upward direction (i.e., Head FirstSupine). The patient’s dataset is saved in DICOM (Digital Imaging and Communication in Medicine) 3.0 format. This study is a prospective analysis approved by the ethics committee of the Cheikh Zaid International University Hospital. 127 patients suspected of pneumonia including coronavirus pneumonia on a base of clinical symptomatology or an uncontrasted CT scan with suspicious images, were admitted to isolation departments at Cheikh Zaid International University Hospital in Rabat. Details of the clinical characteristics of these patients are summarized in Table 3.\n\n## 3.4. Experimental Settings\n\nThe network was trained on Google Colab. All training and testing phases were performed in the same environment, using Keras deep learning framework and Python 3.5 as the programming language. The network training is performed with the hyperparameters illustrated in Table 4. Both datasets are divided into a training set and a testing set with a ratio of 0.7:0.3, respectively.\n\n## 4. Experimental Results\n\nIn this section, experimental results on both public and private datasets are illustrated. The performance of the classification model was evaluated based on different metrics including, accuracy, confusion matrix, precision, recall and F1-score.\n\n## 4.1. Results on X-ray Modality\n\nThe confusion matrix is used to determine the performance of the classification models for a given set of test data. It can only be determined if the true values for test data are known. According to Figure 5, the outcomes of the proposed model demonstrated the highest performance of the EfficientNet model, in which only one image of pneumonia is misclassified as the normal lung. Also, Table 5 shows that this model can efficiently classify the three classes with the highest ratio of precision for common pneumonia and coronavirus pneumonia is $100\\%$ and $99\\%$ for normal cases. This result assures that the classification is performed correctly for the three classes on the X-ray modality.\n\nResNet125V2 [33], DenseNet121 [34], and EfficientNetB7 [30] are all deep neural network architectures that have been used for various computer vision tasks such as image classification, object detection, and semantic segmentation. To validate the use of EfficientNetB7 in pneumonia classification, we perform different experiments in terms of accuracy and execution time. Each of these architectures has its own strengths and weaknesses, and the choice of which one to use depends on the specific task and available computational resources. As shown in Table 6, EfficientNetB7 is the best architecture in terms of accuracy, but it takes more time to train and produce a classification. Training a deep learning model typically requires a significant amount of computational resources and can take a long time to complete, depending on the size and complexity of the model and the amount of training data. Although training time is important, we can save the model once the results are very significant. Then the model can be used for inference. The inference time of EfficientNetB7 is about 2.51 s, and it refers to the classification of 540 X-ray images. On average, each image can be classified in 4 ms.\n\n## 4.2. Results on CT Scan Modality\n\nFurthermore, training and testing of the suggested network were done on the CT scan dataset. The confusion matrix is an important metric for the performance evaluations of a classification model. As depicted in Figure 6, only three normal images were confused with coronavirus pneumonia classes. From the values of Table 7, a precision of $99\\%$ was achieved for coronavirus pneumonia and $100\\%$ for both normal and common pneumonia. Also, this model attained $100\\%$ of recall for coronavirus pneumonia and common pneumonia classes. In this work, one of the main findings is that the proposed model can distinguish correctly between the lungs infected with coronavirus pneumonia and all the patients infected with common pneumonia.\n\nTable 8 depicts comparison results by CT scan modality. We can assert that EfficienNetB7 is the best architecture for pneumonia classification. The inference time of EfficientNetB7 on CT scan images is about 41.50 s, and it refers to the classification of 2953 X-ray images. On average, each CT scan can be classified in 14 ms.\n\n## 4.3. Results on Cheikh Zaid Data\n\nAll validation DICOM images taken at the Cheikh Zaid International University Hospital were analyzed and reviewed by a radiologist, who overlooked epidemiological history and clinical symptoms. He classified chest CT as normal, positive for coronavirus pneumonia, or positive for common pneumonia.\n\nAll Cheikh Zaid images were treated following the preprocessing pipeline. As shown in Figure 7, cropping of the original image was performed to remove the unwanted regions, then to improve the image quality, the histogram equalization technique was used. After that, the image was resized to be suitable for the input model. To perform classification, the trained model was loaded and the class of each Cheikh Zaid image was predicted. As a result, shown in Table 9, the proposed model achieved an accuracy of $95\\%$ on Moroccan CT scan images. Thus, it will help the radiologist to classify pneumonia diseases for further diagnosis.\n\n## 4.4. Comparison with the State-of-the-Art\n\nTable 10 compares the proposed model based on the EfficientNet architecture as well as the preprocessing pipeline with the state-of-the-art. Previous approaches have used the X-ray dataset or CT scan dataset to attain coronavirus pneumonia classification with data augmentation techniques and transfer learning. A high accuracy Was achieved on both chest X-ray and CT scan modalities. These results are very encouraging and can be used by radiologists to diagnose pneumonia diseases early and correctly.\n\n## 5. Discussion\n\nThe proposed EfficientNet model showed higher classification performance on the three classes with the highest accuracy ratio on public datasets. The results of the present work only show that the proposed CADx system achieved high accuracy in public datasets for both X-ray and CT scans. For radiographic modality, Table 5 shows that the model efficiently and correctly classified the three classes with the highest accuracy ratio, which is $100\\%$ for common pneumonia and coronavirus pneumonia and $99\\%$ for normal cases. Regarding the CT scan modality and based on Table 6 values, an accuracy of $99\\%$ was obtained for coronavirus pneumonia and $100\\%$ for normal and common pneumonia. Similarly, this model achieved $100\\%$ recall for coronavirus pneumonia and common pneumonia classes. Therefore, the proposed model can correctly distinguish lungs infected by coronavirus pneumonia from those infected by common pneumonia. As a result, the proposed model achieved an accuracy of $95\\%$ on Moroccan CT images. This may occur in the presence of low-quality images that contain artifacts and noise similar to the opacity of patients’ lungs. Moreover, the characteristics of public datasets may be different from those of Moroccan clinical data. In such a case, an overfit may have occurred during external validation. In addition, the specificity of the proposed model can be summed up in the fact that it has achieved very satisfactory accuracy rates on two imaging modalities based on public data. This shows its robustness when compared with models cited in related works that have treated a single imaging modality. Therefore, its use will be of great help to radiologists in classifying pneumonia diseases for further diagnosis.\n\n## 6. Conclusions\n\nIn this work, a Post-Pandemic Classification Algorithm (PPCA) based on the EfficientNet architecture is proposed in the context of pneumonia virus classification. The proposed approach consists of two major phases. In the first phase, preprocessing is performed on both X-ray and CT scan images. Whereas, in the second phase, the used architecture is trained on two public datasets as well as the Cheikh Zaid dataset. In sum, this work was able to implement an accurate computer-aided system for the analysis of both radiographic and CT medical images that led to more promising classification results and higher accuracy. 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-0.010226420126855373\n]","isConversational":false}}},{"rowIdx":90,"cells":{"text":{"kind":"string","value":"---\ntitle: CD24 Gene Expression as a Risk Factor for Non-Alcoholic Fatty Liver Disease\nauthors:\n- Mona A. Amin\n- Halla M. Ragab\n- Nabila Abd El Maksoud\n- Wafaa Abd Elaziz\njournal: Diagnostics\nyear: 2023\npmcid: PMC10000766\ndoi: 10.3390/diagnostics13050984\nlicense: CC BY 4.0\n---\n\n# CD24 Gene Expression as a Risk Factor for Non-Alcoholic Fatty Liver Disease\n\n## Abstract\n\nIn light of increasing NAFLD prevalence, early detection and diagnosis are needed for decision-making in clinical practice and could be helpful in the management of patients with NAFLD. The goal of this study was to evaluate the diagnostic accuracy of CD24 gene expression as a non-invasive tool to detect hepatic steatosis for diagnosis of NAFLD at early stage. These findings will aid in the creation of a viable diagnostic approach. Methods: This study enrolled eighty individuals divided into two groups; a study group included forty cases with bright liver and a group of healthy subjects with normal liver. Steatosis was quantified by CAP. Fibrosis assessment was performed by FIB-4, NFS, Fast-score, and Fibroscan. Liver enzymes, lipid profile, and CBC were evaluated. Utilizing RNA extracted from whole blood, the CD24 gene expression was detected using real-time PCR technique. Results: It was detected that expression of CD24 was significantly higher in patients with NAFLD than healthy controls. The median fold change was 6.56 higher in NAFLD cases compared to control subjects. Additionally, CD24 expression was higher in cases with fibrosis stage F1 compared to those with fibrosis stage F0, as the mean expression level of CD24 was 7.19 in F0 cases as compared to 8.65 in F1 patients but without significant difference ($$p \\leq 0.588$$). ROC curve analysis showed that CD24 ∆CT had significant diagnostic accuracy in the diagnosis of NAFLD ($$p \\leq 0.034$$). The optimum cutoff for CD24 was 1.83 for distinguishing patients with NAFLD from healthy control with sensitivity $55\\%$ and specificity $74.4\\%$; and an area under the ROC curve (AUROC) of 0.638 ($95\\%$ CI: 0.514–0.763) was determined. Conclusion: In the present study, CD24 gene expression was up-regulated in fatty liver. Further studies are required to confer its diagnostic and prognostic value in the detection of NAFLD, clarify its role in the progression of hepatocyte steatosis, and to elucidate the mechanism of this biomarker in the progression of disease.\n\n## 1. Introduction\n\nNAFLD is a clinico-pathologic syndrome that encompasses various medical entities, including simple fatty liver or simple steatosis, nonalcoholic steatohepatitis (NASH), cirrhosis, and its complications [1]. NAFLD now affects up to $25\\%$ of people around the world. The highest prevalence rate is in the Middle East ($32\\%$), followed by South America ($30\\%$), while the lowest is in Africa ($13\\%$). It also accounts for $2\\%$ of total deaths [2]. The increase in NAFLD prevalence parallels the rise in obesity and is tightly associated with metabolic comorbidities (diabetes, hypertension, insulin resistance, and dyslipidemia). It also places patients at higher risk for progressive liver disease [3].\n\nIt became clear that, as with different complex multisystem disorders, NAFLD is triggered by a variety of underlying mechanisms; the most important one of them is the alterations in hepatic and extra-hepatic lipid metabolism [4]. The study of genetic factors in NAFLD is a rapidly growing field, as they determine not only the response of different individuals to excess caloric consumption, but also the resulting metabolic derangements [5].\n\nCluster of differentiation 24 (CD24) is a glycophosphatidylinositol (GPI)-anchored mucin-like cell surface glycoprotein, encoded by a gene located on chromosome 6. It is expressed on mature granulocytes and B cells and regulates growth and differentiation signals to these cells. Accumulating evidence showed that abnormal over-expression of this protein is a prognostic factor in many types of cancers, resulting in cancer cell growth, proliferation, and metastasis [6]. The expression of the cell surface molecule CD24 has previously been shown to identify a subset of adipocyte progenitor cells that is crucial for the reconstitution of white adipose tissue (WAT) function in vivo, as well as a particular regulator of adipogenesis in vitro [7]. Recently, CD24 has been identified as a possible biomarker for distinguishing NAFLD/NASH. It was concluded that the mRNA expression of CD24 is upregulated in the fatty liver [8]. Additionally, Feng et al., [ 2021] detected that CD24 was positively associated with NAFLD severity, and it could also differentiate mild NAFLD patients from severe NAFLD patients [9].\n\nTherefore, the present study aimed to identify the association between gene expression of CD24 and early stage of NAFLD.\n\n## 2. Subjects and Methods\n\nThe present study is a prospective study that was carried out on 80 subjects who attended outpatient clinics of the Internal Medicine Department of Kasr Al Ainy Hospital Cairo, Egypt during the period from May 2019 to December 2020 either for general health checks or to identify and treat the complications of other metabolic disorders such as diabetes or obesity.\n\nThe selected subjects were divided into two groups according to the sonographic findings of steatosis: 40 NAFLD patients with bright liver echogenicity and 40 healthy subjects with normal liver echogenicity. All cases have age ranging between 19 to 56 years old.\n\nThose with clinical, biochemical, or histological evidence of cirrhosis, those with known causes of liver disease [viral hepatitis B and C, autoimmune hepatitis, primary biliary cirrhosis, haemochromatosis or Wilson disease], those with history of current or past excessive alcohol drinking as defined by an average daily consumption of more than 20 g alcohol, drug-induced liver disease, pregnant women and patients on hormonal contraceptive drugs (oral, parenteral), hormone replacement therapy were excluded from the study. The study was approved by Medical Research Ethical Committee of the National Research Center, Cairo, Egypt (Approval No.19-001), and informed consent was obtained from all patients.\n\nAll patients were evaluated by history and clinical examination and measurement of anthropometric parameters, such as weight (kg), height (m), body mass index (BMI; kg/m2), waist circumference (cm), and mid-arm circumference (cm).\n\nBody mass index (BMI) was determined by dividing weight by square height (kg/m2). BMI is calculated as weight in kilograms divided by the height in metres squared. According to WHO, People with BMI = 18.5–24.9 have normal weight, people with BMI = 25.0–29.9 were classified overweight, while people with BMI ≥ 30 kg/m2 defines obese. BMI is calculated as weight in kilograms divided by the height in metres squared. According to WHO, in adults, overweight is defined as a BMI of 25–29.9, while a BMI ≥ 30 kg/m2 defines obesity.\n\nWaist circumference (WC) was obtained from each subject by measuring at the midpoint between the lower rib margin and the iliac crest using a conventional tape graduated in centimeters (cm). Mid-arm circumference was measured as the right upper arm measured at the midpoint between the tip of the shoulder and the tip of the elbow (olecranon process and the acromium).\n\nCases were divided according to their previous diagnosis or levels of fasting blood sugar: a fasting blood sugar level less than 115 mg/dL is considered normal or prediabetes. While, if the fasting blood sugar level is 126 mg/dL or higher, the patient was diagnosed diabetic.\n\nComplete blood count was determined using the automated hematology analyzer SF-300 (Sysmex Corporation, Japan). Additionally, liver enzymes (ALT, AST, ALP, GGT), serum albumin, prothrombin time, INR, serum creatinine, lipid profile, and fasting blood sugar were measured to all individuals according to the manufacture instructions. The reagents were purchased from Spectrum Company, Cairo, Egypt.\n\nNAFLD fibrosis score (NFS), FIB-4, and Fast score were calculated as mentioned previously by Angulo et al. [ 2007] and Calès et al. [ 2009] [10,11] to assess fibrosis of the NAFLD patients’ group.\n\nNFS score = −1.675 + 0.037 × age [y] + 0.094× BMI [kg/m2] + 1.13 × IFG/diabetes [yes = 1, no = 0] + 0.99 × AST/ALT ratio − 0.013 × platelet count [×109/L] − 0.66 × albumin [g/dL] FIB-4 score = Age [y] × AST [U/L]/platelet [×109/L] × ALT [U/L] FAST score was calculated according to Newsome et al., [ 2020] [12] as: FAST = {exp (–1.65 + 1.07 × ln (LSM) + 2.66 × 10–8 × CAP3 – 63.3 × AST–1)}/{1 + exp (–1.65 + 1.07 × ln (LSM) + 2.66 × 10–8 × CAP3 – 63.3 × AST–1)}[1] Abdominal ultrasonography was performed to all individuals using the 3.5 MHz probe of Logic 6 of a General Electric machine.\n\n## 2.1. Liver Stiffness Measurement (LSM) and Controlled Attenuation Parameter (CAP)\n\nFibroscan (M probe, Echosens, Paris) was carried out by an experienced examiner in all patients (with at least 6 h of fasting) in left lateral position and the median liver stiffness of the 10 successful measurements fulfilling the criteria (success rate of greater than $60\\%$ and interquartile range/median ratio of <$30\\%$) were noted (in kPa).\n\nThe final CAP value, which ranges from 100 to 400 (dB/m), is the median of individual measurements. As an indicator of variability, the ratio of the IQR of CAP values to the median (IQR/MCAP) was calculated. The operator was blinded to the patients’ clinical data. According to the manufacturer’s instructions, in addition to previous studies, the stages of fibrosis (F0: 1–6, F1: 6.1–7, F2: 7–9, F3: 9.1–10.3, and F4: ≥10.4) were defined in kPa [13]. Moreover, steatosis stages (S0: <215, S1: 216– 252, S2: 253–296, S3: >296) were defined in dB/m [13].\n\n## 2.2. Sample Collection\n\n10 mL venous blood were drawn from all study participants in the morning after a 12 h fast; a portion of the blood was collected on EDTA tube for the extraction of RNA and for the determination of routine blood pictures (CBC) by Sysmex, the automated hematology analyzer SF-300, which was produced by Sysmex Corporation, Japan. The other portion was left to clot at room temperature. Serum was separated by centrifuging for 10 min at 3000 rpm. Sera were used immediately for other biochemical investigations including aspartate aminotransferase (AST), alanine aminotransferase (ALT), bilirubin, serum albumin, fasting blood glucose, cholesterol, triglycerides, HDL-C, and LDL-C according to the manufacturer’s instructions. The reagents were purchased from Spectrum Company, Cairo, Egypt.\n\n## 2.3. CD24 Gene Expression by Quantitative Real Time-PCR (qRT-PCR):\n\nTotal RNA was isolated from whole blood using GeneJET Whole Blood RNA Purification Mini Kit (Thermo Scientific, Lithuania) following the manufacturer’s suggestions.\n\n## 2.4. Reverse Transcription for cDNA Synthesis and Quantitative Real-Time PCR (RTqPCR)\n\nReverse transcription (RT) was performed to obtain cDNA from 400 ng of purified RNA using the High-Capacity cDNA Reverse Transcription Kits (Applied Biosystem, Lithuania) with random hexamers according to the manufacturer’s suggestions. A value of 10 µL of the 2X-RT master mix was pipetted into each tube and then 10 µL of RNA sample was added to it and mixed well. The tubes were centrifuged to spin down the content and to eliminate any air bubbles. After that, the tubes were placed on the PCR machine (Cleaver Scientific, UK) programmed as follows: 25 °C, 10 min, 37 °C, 120 min, and 85 °C, 5 min. After detection of cDNA concentration and purity, they were stored in −20 °C until carryover quantitative real-time PCR (QRT-PCR).\n\nCD-24 gene expression for enrolled samples was quantified using PowerUp SYBR Green master mix (2×) (ThermoFisher Scientific, Lithuania). The sequences for used primers were as follows: PrimerPrimer SequenceCD24 Forward primer5′-ACC CAC GCA GAT TTA TTC CA-3′CD24 Reverse primer5′-ACC ACG AAG AGA CTG GCT GT-3′β-actin Forward primer5′-TGA GCG CGG CTA CAG CTT-3′β-actin Reverse primer5′-TCC TTA ATG TCA CGC ACG ATT T-3′ PCR amplification was carried out in 20 μL reaction volume containing 1 µL cDNA, 10 µL PowerUp SYBR Green master mix, 7 μL nuclease-free water, and 1 µL of gene-specific forward and reverse primers as mentioned in table. The reaction was run in the Rotor-Gene Q instrument, (QIAGEN).\n\nFluorescence measurements were made in every cycle, and the thermal profile was used as the follows: The amplification program included a UDG activation at 50 °C with a 2-min hold, and a dual-lock DNA polymerase at 94 °C with a 3-min hold, followed by 45 cycles with denaturation at 94 °C for 30-s, annealing at 55 °C for 30-s, and extension at 72 °C for 30-s. The expression levels of CD-24 in tested samples were expressed in the form of ∆∆CT (cycle threshold) value, which was calculated based on threshold cycle (Ct) values, corrected by β-actin expression, with the following equation.\n\nThe relative amount of CD-24 = 2–ΔΔCt; ΔΔCt = [ΔCt of cases − ΔCt of control]; [ΔCt = Ct (CD-24) − Ct (β-actin)]. The following primers were used in the quantitative real-time PCR analyses.\n\n## 2.5. Statistical Analysis\n\nSPSS version 16.0 (SPSS Inc., Chicago, IL, USA) was used for statistical analysis with a two-side significant criterion at $p \\leq 0.05.$ The clinical data were expressed as mean ± SD (continuous, normally distributed variables). Categorical data were summarized as percentages. The significance for the difference between groups was determined by using a two-tailed Student’s t-test. Additionally, qualitative variables were assessed by chi-squared χ2-test. Correlations between different parameters were performed using Pearson’s and spearman’s correlation coefficients. A receiver operating characteristic (ROC) curve was plotted to assess the diagnostic power of CD24 in NAFLD and controls, and the area under the curve (AUC) greater than 0.5 considered to be statistically significant. The probability (p) values of ≤0.05 were considered statistically significant and indicated, while $p \\leq 0.05$ was considered statistically not significant and indicated NS.\n\n## 3. Results\n\nThe present study is a case-control study recruited 80 adult subjects, (28 males and 52 females). Their age ranged from 19 to 56 years.\n\nThe demographic, anthropometric, clinical, and biochemical characteristics of both groups (NAFLD and controls) are summarized in Table 1. Patients with NAFLD were significantly older than controls (mean age 42.18 ± 11.1 4 y vs. 29.65 ± 6.63 y, $p \\leq 0.0001$). There were more males in the control group ($45\\%$), but the majority was females in the NAFLD group ($75\\%$). NAFLD patients exhibited a higher mean BMI (31.8 ± 2.9 kg/m2) than the control group (23.76 ± 1.4 kg/m2) ($p \\leq 0.001$).\n\nPatients with NAFLD had a higher prevalence of hypertension and diabetes mellitus in comparison to healthy controls ($p \\leq 0.001$) (Table 1).\n\nAmong studied NAFLD patients, $22.5\\%$ had a family history of diabetes, and $30\\%$ had family history of liver disease, and $62.5\\%$ of NAFLD cases ($$n = 25$$) have enlarged liver size on ultrasound.\n\nThe mean serum fasting blood glucose was significantly higher in NAFLD patients than that in controls (122.6 ± 40.97 vs. 96.03 ± 7.77); ($p \\leq 0.001$). In addition, hemoglobin levels were lower in NAFLD cases (11.56 ± 1.4 (g/dL) than in healthy controls (12.81 ± 1.06 (g/dL), ($p \\leq 0.001$). No significant difference was observed in total leucocytic count (TLC) and platelet count between the NAFLD and control groups ($p \\leq 0.05$).\n\nNAFLD patients had significantly higher serum levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), and gamma-glutamyl transferase (GGT) compared to healthy controls ($p \\leq 0.001$). On the other hand, the mean albumin level was almost normal (3.8 ± 0.38 g/dL) in the NAFLD group.\n\nThere was a significant elevation in total cholesterol, triglycerides, and LDL-cholesterol among NAFLD patients compared to controls, while there was significant decrement in HDL in the NAFLD group as opposed to controls ($p \\leq 0.05$).\n\nTable 2 shows clinical and biochemical characteristics of participants stratified by sex and presence/absence of NAFLD. In both sexes, participants with NAFLD were older, had a higher BMI, as well as a higher prevalence of diabetes. Levels of hemoglobin was significantly lower in female cases compared to male cases in NAFLD group ($$p \\leq 0.001$$). However, ALT and AST levels were significantly higher in male NAFLD cases compared to female NAFLD casess ($$p \\leq 0.009$$ and $$p \\leq 0.038$$; respectively) (Table 2).\n\nThe mean Fibroscan value in all NAFLD patients was 5.1 ± 0.99 (kPa), indicating that all patients had mild fibrosis with a stage less than 2. Thirty patients had fibrosis belonging to stage 0, while the rest had fibrosis stage 1. Mean Fibroscan values for cases with fibrosis stages 0 and 1 were 4.7 ± 0.67 and 6.5 ± 0.3 (kPa), respectively. There was a statistically significant difference in liver stiffness measurements in patients with stage 0 fibrosis as compared to stage 1 fibrosis ($p \\leq 0.001$). In addition, there was a stepwise increase in Cap score parallel to the increase in severity of liver fibrosis ($p \\leq 0.001$) (Table 3).\n\nThis study showed that both NFS and FIB-4 score were similar in patients with fibrosis stages 0 and those with fibrosis stages 1 ($p \\leq 0.05$). This may be due to that all cases included in our study have mild fibrosis. Additionally, performances of FIB-4 and NFS to rule in advanced fibrosis are rather inadequate, meaning that further assessment with another test is needed in case of positive results.\n\nAccording to the RT-PCR results, it was detected that expression of CD24 was significantly higher in patients with NAFLD than healthy controls. The median fold change in the expression of CD24 was 6.56 higher in NAFLD cases compared to control subjects (Figure 1).\n\nThe present study showed higher expression of CD24 in female cases with NAFLD compared to male cases (fold change was 6.9 in females vs 4.4 in males, but without significant difference; $$p \\leq 0.262$$) (Figure 2).\n\nAdditionally, CD24 expression was higher in cases with fibrosis stage F1 compared to those with fibrosis stage F0, as the mean expression level of CD24 was 7.19 in F0 cases as compared to 8.65 in F1 patients, but without significant difference ($$p \\leq 0.588$$). Furthermore, there was no difference in CD24 fold change between overweight patients (median fold change = 9) and obese cases (median fold change = 5.89) ($$p \\leq 0.447$$) (Figure 3). Additionally, the median fold change in CD24 in diabetic cases was seven compared to 5.13 in non-diabetic cases ($$p \\leq 0.609$$) (Figure 4).\n\n## 3.1. Evaluation of the Diagnostic Accuracy of CD24 Gene Expression for Distinguishing Patients with NAFLD from Healthy Controls\n\nFigure 5 illustrates the ROC plots to assess the diagnostic accuracy of CD24 ∆CT to distinguish patients with NAFLD from healthy controls.\n\nROC curve analysis showed that CD24 ∆CT had significant diagnostic accuracy in the diagnosis of NAFLD ($$p \\leq 0.034$$). ROC curve showed the optimum cutoff for CD24 was 1.83 for distinguishing patients with NAFLD from healthy control with sensitivity $55\\%$ and specificity $74.4\\%$; and an area under the ROC curve (AUROC) 0.638 ($95\\%$ CI: 0.514–0.763).\n\n## 3.2. Correlation between Different Non-Invasive Fibrosis Markers and CD24 Gene Expression\n\nTable 4 shows the correlation of Kpa, CAP, FAST, NFS, and FIB-4 with CD 24 gene expression. Pearson’s correlation test showed positive significant correlation between CD24 and NFS ($r = 0.356$, $$p \\leq 0.001$$).\n\nBy binary logistic regression analysis, none of the examined parameters found to be significant determinant of NAFLD after adjusting the effects of potential cofounders of age, gender, suffering of diabetes, and BMI, respectively (Table 5).\n\n## 4. Discussion\n\nNAFLD is known nowadays as the most common liver disorder in the 21st century. It is diagnosed by the presence of more than $5\\%$ fat accumulation in liver cells without excess alcohol consumption or secondary causes of fat accumulation in the background. Approximately $25\\%$ of the world’s adult’s population has NAFLD, and the prevalence is still increasing [13].\n\nNAFLD may eventually deteriorate to HCC as a result of excessive lipid accumulation, liver cell damage, immune system dysfunction, which leads to scarring, and permanent liver damage [14]. In light of increasing NAFLD prevalence, early detection and diagnosis are needed for decision-making in clinical practice and could be helpful in the management of patients with NAFLD.\n\nThe present study showed a significant trend of elder age with the progression of non-alcoholic fatty liver disease. This finding substantiates previous findings in the literature, which suggested that the prevalence of NAFLD increases with increasing age [15].\n\nThe present study showed that, regarding gender distribution, there were more males in the control group ($45\\%$) compared to the NAFLD group ($25\\%$), but the majority was females in the NAFLD group ($75\\%$). These results revealed that there was no statistically significant difference between both studied groups according to gender as $$p \\leq 0.061.$$ The explanation for the gender difference is different distributions of fat mass by gender, e.g., more abdominal visceral adipose tissue in male and more subcutaneous adipose tissue mass in female. Additionally, previous results showed that Hispanic women having a higher risk for NAFLD compared to men, whereas, for the non-Hispanic population, the prevalence of NAFLD is more frequent in males [16]. Additionally, Lonardo et al. mentioned that gender is one of the main cause of variation in NAFLD risk factors. They also detected that NAFLD is more common and more severe in men than women. However, it is more common in women after menopause, indicating that estrogen may be beneficial [17].\n\nIn the current study, the incidence of NAFLD has been increasing in concert with the presence of multiple metabolic disorders, such as dyslipidemia, diabetes, hypertension, and visceral obesity. As expected, the incidence of diabetes and hypertension was significantly higher in patients suffering from NAFLD. This is in good agreement with previous studies that mentioned impaired glucose tolerance as an independent risk factor for the progression of NAFLD [18,19].\n\nAccording to the International Diabetes Federation (IDF), the prevalence of DM among Egyptian adults is $15.2\\%$, which may be an underestimation [20]. Lonardo et al. reported that patients with T2DM had $80\\%$ higher liver fat contents compared to non-diabetic patients [21].\n\nAdditionally, Lee, et al., [ 2019], mentioned that compared to the general population (around $25\\%$), $50\\%$ to $70\\%$ of people with diabetes have NAFLD, and NAFLD severity (including fibrosis) tends to be worsened by the presence of diabetes [22].\n\nAdditionally, another study carried out on the Egyptian college students showed that around 1 in 3 had steatosis, and 1 in 20 had fibrosis. The prevalence of NAFLD in young adults was estimated to be $31.6\\%$, which is perfectly in line with the $31.8\\%$ prevalence rate found in a meta-analysis of numerous epidemiological studies across general Middle Eastern populations. It is known that the Middle East and North Africa region has one of the highest prevalence rates of NAFLD globally, and that Egypt ranked among the highest 10 nations with obesity prevalence. Combing both may explain our unexpected observation. In our cohort, 59 ($49.2\\%$) of participants were overweight or obese [23].\n\nNAFLD is caused by a variety of different molecular pathways and cellular alterations. The molecular pathways of NAFLD pathogenesis in the liver have been identified in several studies. The major genes linked to illness development and the underlying functional pathways are yet unknown, and whether the differentially expressed CD24 is involved in hepatic lipid metabolism is still unclear.\n\nMicroarray technologies have revealed a large number of new molecular markers (DNA, RNA, and protein) in recent years. Further research is needed to confirm the clinical utility of these impending novel indicators in relation to hepatic steatosis. CD24 is one of these markers, which was recently reported by Huang et al. as a possible biomarker in the course of hepatocyte steatosis [8].\n\nVarious studies have recently discovered that CD24 expression is relatively high in many human malignancies, including HCC [24,25,26,27,28]. Additionally, CD24 overexpression has been correlated with increased invasiveness, proliferation, and metastasis [29]. It was previously identified that a subpopulation of adipocyte progenitor cells with the expression of the cell surface molecule CD24 being necessary for reconstitution of white adipose tissue function in vivo as well as being a key regulator of adipogenesis in vitro [30].\n\nIn our study, we investigated the association between CD24 gene expression and the prevalence of NAFLD.\n\nThe current study found that CD24 gene expression was considerably greater in NAFLD cases compared to controls, and the normalized CD24 gene expression in NAFLD was up-regulated 6.56-fold. These findings suggest that the CD24 gene is important in the development of NAFLD. This could be related to CD24 gene expression’s impact on the immune/inflammatory response via T-cell activation [31]. Several immune cell-mediated inflammatory processes are involved in NAFLD and its progression to NASH. They also influence the generation of cytokines by necrotic liver cells [32].\n\nThis confirms the previous results detected by Feng et al., who observed the up-regulation of CD24 gene expression in the livers of HFD-induced NAFLD mice and in cultured HepG2 cells exposed to glucolipotoxicity (palmitic acid or advanced glycation end products) [9]. Up until now, the precise role and the underlying mechanisms of CD24 in NAFLD progression remain unclear. However, Huang and his colleague identified the prominent correlation between CD24 and NAFLD/NASH. They mentioned that CD24 could play a key role in one of the pathways that may cause IR and may induce NAFLD/NASH in humans including [“glycolysis/gluconeogenesis”, “p53 signaling pathway” and “glycine”, serine and threonine metabolism [8].\n\nAdditionally, CD24 expression was higher in cases with fibrosis stage F1 compared to those with fibrosis stage F0, as the mean expression level of CD24 was 7.19 in F0 cases as compared to 8.65 in F1 patients, but without significant difference ($$p \\leq 0.588$$). This may be because that all cases included in the present study have mild fibrosis. This results most be confirmed by other studies based on large number of samples and on patients with severe stage of fibrosis.\n\nThe changes in liver tissue-transcriptome in a subset of 25 mild-NAFLD and 20 NASH biopsies were examined in a cross-sectional study. CD24 was revealed to be one of five differentially expressed genes (DEGs) positively linked with disease severity and to be main classifiers of mild and severe NAFLD [33].\n\nAdditionally, CD24-positive cells isolated from hepatocellular carcinoma cell lines exhibited stemness properties, such as self-renewal, chemotherapy resistance, metastasis, and tumorigenicity [34]. These results indicate that CD24 may play a role in hepatocyte injury and promote regeneration during the development and progression of NAFLD.\n\nAnother Egyptian study detected that CD24 polymorphism 170 CT/TT may affect the incidence of infection with CHC, as well as HCC [35]. They revealed that the P170T allele, which is expressed at a higher level than P170C, encodes a certain protein, which is responsible for the progression of chronic HCV infection by affecting the efficiency of cleavage of posttranslational GPI. Additionally, Robert and Pelletier [2018] showed that the P170T allele affects the progression of chronic HCV infection through posttranslational mechanisms [36]. Another study by Kristiansen et al. [ 2010] also suggested that CD24 SNPs are prognostic markers for hepatic carcinoma [37].\n\nInterestingly, CD24 was also up-regulated in the NAFLD patients with type 2 diabetes than its expression in non-diabetic cases, but without significant difference.\n\nAnother study carried out by Shapira et al. [ 2021] reported that CD24 may negatively regulate peroxisome proliferator-activated receptor gamma (PPAR-γ) expression in male mice. *This* gene is a regulator of adipogenesis that plays a role in insulin sensitivity, lipid metabolism, and adipokine expression in adipocytes. Furthermore, they concluded the association between the CD24 and insulin sensitivity, suggesting its possible mechanism for diabetes [38].\n\n## 5. Conclusions\n\nThe current study found CD24 gene expression was considerably greater in NAFLD cases compared to controls. This could indicate that CD24 may contribute to hepatic steatosis, but a current study showed that it cannot be used as an independent predictor of NAFLD.\n\nFurther studies are required to confer its diagnostic and prognostic value in the detection of NAFLD, as well as to clarify its role in the progression of hepatocyte steatosis in patients with advanced stage of fibrosis and to elucidate the mechanism of this biomarker in the progression of disease. However, our study is limited because of the small sample size, because all participants in this study have early stage of NAFLD, and because accurate diagnosis of liver fibrosis or hepatocellular injury are invasive and very expensive. 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-0.007402228657156229,\n 0.004582331981509924,\n 0.010830966755747795,\n -0.020123807713389397,\n -0.01897021383047104,\n 0.003973490558564663\n]","isConversational":false}}},{"rowIdx":91,"cells":{"text":{"kind":"string","value":"---\ntitle: The Adaptive Force as a Potential Biomechanical Parameter in the Recovery Process\n of Patients with Long COVID\nauthors:\n- Laura V. Schaefer\n- Frank N. Bittmann\njournal: Diagnostics\nyear: 2023\npmcid: PMC10000769\ndoi: 10.3390/diagnostics13050882\nlicense: CC BY 4.0\n---\n\n# The Adaptive Force as a Potential Biomechanical Parameter in the Recovery Process of Patients with Long COVID\n\n## Abstract\n\nLong COVID patients show symptoms, such as fatigue, muscle weakness and pain. Adequate diagnostics are still lacking. Investigating muscle function might be a beneficial approach. The holding capacity (maximal isometric Adaptive Force; AFisomax) was previously suggested to be especially sensitive for impairments. This longitudinal, non-clinical study aimed to investigate the AF in long COVID patients and their recovery process. AF parameters of elbow and hip flexors were assessed in 17 patients at three time points (pre: long COVID state, post: immediately after first treatment, end: recovery) by an objectified manual muscle test. The tester applied an increasing force on the limb of the patient, who had to resist isometrically for as long as possible. The intensity of 13 common symptoms were queried. At pre, patients started to lengthen their muscles at ~$50\\%$ of the maximal AF (AFmax), which was then reached during eccentric motion, indicating unstable adaptation. At post and end, AFisomax increased significantly to ~$99\\%$ and $100\\%$ of AFmax, respectively, reflecting stable adaptation. AFmax was statistically similar for all three time points. Symptom intensity decreased significantly from pre to end. The findings revealed a substantially impaired maximal holding capacity in long COVID patients, which returned to normal function with substantial health improvement. AFisomax might be a suitable sensitive functional parameter to assess long COVID patients and to support therapy process.\n\n## 1. Introduction\n\nLong term sequelae of SARS-CoV-2 infections increasingly challenge medical, social and economic systems worldwide. Different terms are used to define persisting post-infectious symptoms, such as ‘long COVID’, ‘post-COVID syndrome’, ‘post-acute COVID’ or ‘persistent post-COVID’, mostly depending on the duration of symptoms after acute infection. For simplification, the term ‘long COVID’ will be used in the following for patients suffering from symptoms at least 4 weeks after acute infection. Reports on the amount of patients with at least one persistent symptom after SARS-CoV-2 infection range from $10\\%$ to $57\\%$, or even up to $87\\%$ in hospitalized patients, depending on the time span after acute infection or hospitalization vs. non-hospitalization [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]. Long COVID occurs in $10\\%$ to $35\\%$ of non-hospitalized patients [1,18], which is most important, since only $5\\%$ to $7\\%$ of patients are hospitalized [20]. Current data show a lower rate of long COVID after infection with omicron variants than with delta ($4.5\\%$ vs. $10.8\\%$) [21]. Infection severity is considered to not be a major factor for the development of long COVID [16,22]. According to ‘COVID-19 data Explorer’ from Johns Hopkins University, more than 570 million SARS-CoV-2 cases were confirmed worldwide (Europe, Asia, North America, South America, Africa, Australia) from 22 January 2020 to 28 July 2022. Assuming that $10\\%$ of them develop long term sequelae, more than 57 million people suffer or suffered from long COVID. The socioeconomic relevance becomes clear.\n\nThe medical community is mainly describing the characteristics of long COVID, but the pathomechanisms or causality are not sufficiently known [6,23]. Furthermore, there is a lack of diagnostic and therapeutic approaches, which are urgently needed to intercept the large amount of sick leave [1,24].\n\nPost-infectious syndromes have been studied for decades and they are known to emerge after different viral infections [25,26,27,28,29,30]. They partly result in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) [11,25,26,30,31,32,33], which is closely connected to long COVID. Symptoms of long COVID range from fatigue, tiredness, muscle weakness, joint/muscle pain, cognitive impairments (‘brain fog’), depression, anxiety, dyspnoea, chest pain/tightness, cough, loss of taste/smell, headache, cardiac symptoms, insomnia, diarrhoea and more [1,2,11,13,14,19,34,35,36]. As can be seen, different systems are involved, including the respiratory, cardiovascular, musculoskeletal, integumentary, gastrointestinal, endocrine and neurological systems [14]. A dysfunction of the autonomous nervous system (ANS) has been discussed as a cause for the symptoms [25,26,30,31,37,38,39]. However, the diagnosis of post-infectious syndromes is difficult and is usually based on a diagnosis of exclusion [24,40]. Patients frequently report that they are not taken seriously by their doctors [34], which even increases the helplessness and anxiety.\n\nA possible supportive diagnostic approach could be to investigate the neuromuscular system since muscle weakness and musculoskeletal pain occur frequently in long COVID patients. Some researchers examined the maximal voluntary isometric contraction (MVIC, e.g., hand grip force) in patients with post-infectious syndromes [41,42,43,44]. Two studies reported non-significant differences between patients and controls regarding the MVIC of the quadriceps femoris muscle (90° knee flexion) or of elbow flexors (90° elbow flexion, maximal supination forearm), respectively [41,42]. Two further studies revealed a significantly reduced hand grip force in ME/CFS [43,44]. However, in Meeus et al., gender effects were not considered [44]. Females were overrepresented in ME/CFS patients vs. controls ($96\\%$ vs. $62\\%$) [44], which might explain the lower strength. The findings are inconclusive and highlight that common maximal strength assessments might not be that appropriate to investigate muscle dysfunction in post-infectious states.\n\nThe Adaptive Force (AF) was inaugurated as a special neuromuscular function, which was found to be sensitive to stimuli [45,46,47,48,49,50,51,52]. The AF characterizes the capacity of the neuromuscular system to adapt to external varying forces in an isometric holding manner [45,46,47,48,49,50,51,52]. It can be assessed by a technical device using pneumatics [45,46,47] or by an objectified manual muscle test (MMT) using a handheld device which measures dynamics and kinematics during the MMT [49,50,51,52]. For the latter, it was shown that the maximal isometric AF (AFisomax; maximal holding capacity) was significantly reduced in reaction to negative stimuli, such as unpleasant emotional imagery or odors in healthy participants [50,51,52]. AFisomax immediately decreased by perceiving the negative stimulus and switched back instantaneously to baseline values by applying the positive stimulus. The peak value (maximal AF; AFmax) was reached during the subsequent eccentric action and was similarly high as for the baseline and positive stimuli. For baseline or under positive stimuli, the muscles remained stable during the whole force increase up to AFmax (AFisomax ≥ $99\\%$ of AFmax). Thereby, AFisomax was similar to AFmax of unstable muscles. Hence, the maximal force was not influenced by the stimuli but the isometric holding function. In other words, under disturbing influences, the isometric holding capacity broke down to a significantly low level but the maximal strength was not affected. This was interpreted as a high sensitivity of AFisomax with respect to possibly impairing stimuli [49,50,51,52]. Neurophysiological explanations were given previously.\n\nThis longitudinal study aimed to investigate the AF in patients with long COVID in a non-clinical setting. For that, AF parameters were assessed at three time points in the course of long COVID: [1] in the long COVID state (pre), [2] after the first treatment (post) and [3] with substantial health improvement (recovery; end). The individual treatments received were not part of the study. It was not a clinical trial; therefore, it was not aimed to measure treatment efficacy. However, the treatments were queried and described to gain an impression of potentially helpful approaches without any claim of evidence.\n\nBased on the current scientific knowledge of AF and of therapeutical experience, the main hypotheses were [1] the holding capacity would be significantly reduced in patients with long COVID and then it would stabilize, thus increase, during the recovery process. [ 2] AFmax would show no significant differences between the time points. [ 3] AF at onset of oscillations (AFosc) would be significantly higher in long COVID state compared to post and end.\n\nThe study provides early data on AF in long COVID patients. If the hypotheses are positively verified, AF might be used as a supportive biomechanical parameter to examine patients with long COVID. Furthermore, AF could help to find the appropriate treatment approach, which will be explained and discussed.\n\n## 2. Materials and Methods\n\nThis longitudinal non-clinical study investigated patients in a long COVID state and in the course of their recovery process. Patients were not approached directly. They consulted the practice for Integrative Medicine (Potsdam, Germany; complementary medicine) out of their own personal initiative. If they were diagnosed with post-COVID syndrome or long COVID, they were invited to participate in the study. Regardless of their response, AF data were measured anyway for diagnostic purposes in daily practice. The treatments were neither subject nor part of the investigation. We only aimed to investigate the AF in those patients and its behavior during the recovery process. Therefore, a control group was not included. The measurements took place at the practice of Integrative Medicine and were conducted by researchers from the University of Potsdam (Potsdam, Germany).\n\n## 2.1. Patients\n\nUntil July 2022, 37 patients diagnosed with long COVID attended the above-mentioned practice for consultation and the AF was measured initially using a handheld device. The only inclusion criterion was the medical diagnosis ‘post-COVID-syndrome’ or ‘long COVID’, which was received from medical doctors before the patients visited the practice. Exclusion criteria were pre-existing complaints of arm, shoulder, hip or knee of the measured side. Seventeen patients were included in this study since they reported a substantially improved or regained health state by July 2022. The remaining 20 patients were still in therapy or cancelled further therapy because of various reasons (long distance between home and the practice, difficulties in finding appointments, other ongoing treatments/rehabilitation or unknown reasons).\n\nOf the 17 included patients, 14 were female (age: 44.43 ± 14.78 yrs., body height: 168.75 ± 5.23 cm, body mass: 69.93 ± 13.18 kg) and three were male (49.00 ± 7.94 yrs., 187.5 ± 3.54 cm, 94.75 ± 0.35 kg). Further information is given in the Results section (intensity of acute infection, duration from acute SARS-CoV-2 infection to input measurement, symptoms and others).\n\nThe study was conducted according to the Declaration of Helsinki and permission from the local ethics committee of the University of Potsdam (Germany) was given (no. $\\frac{70}{2021}$, date: 16 February 2022). Each participant gave written informed consent.\n\n## 2.2. Questionnaires\n\nThe patients filled out two questionnaires. The first one assessed information with respect to acute SARS-CoV-2 infection: duration, medical diagnosis and examination, symptoms and degree of severity (0 = symptom free, 1 = mild, 2 = moderate, 3 = severe but without hospitalization, 4 = hospitalization without intensive care, 5 = hospitalization with intensive care without invasive ventilation, 6 = intensive care with invasive ventilation); as well as concerning long COVID state: period between acute infection and onset of long COVID, periods of improvement, symptoms, diagnosis, medical examinations, experiences with health care, treatments and their effects.\n\nThe second questionnaire queried the intensity of common symptoms during long COVID using a scale from 0 (no) to 10 (very strong). The assessed symptoms were fatigue, breathing difficulties, cough, chest pain, chest tightness, memory/concentration problems, headache, muscle pain, fast/strong heartbeat, loss of smell/taste, depression/anxiety, fever, dizziness and post-exertion malaise. Professional and personal stress levels were also queried. The questionnaire was filled out for the following time points: [1] retrospectively for the pre-COVID baseline (before acute SARS-CoV-2 infection), [2] in long COVID state (time of input measurement; pre), [3] 1 day after first treatment (post) and [4] after recovery/with substantial health improvement (output measurement; end).\n\n## 2.3. Handheld Device to Measure the Adaptive Force\n\nThe AF of the hip and elbow flexors of one side was assessed by the objectified MMT using the handheld device which was used in previous studies [49,50,51,52]. ( Figure 1a; development funded by the Federal Ministry of Economic Affairs and Energy; project no. ZF4526901TS7). It records force and position simultaneously and has proven to be reliable and valid [49]. Strain gauges (co. sourcing map, model: a14071900ux0076, precision: 1.0 ± $0.1\\%$, sensitivity: 0.3 mV/V) and kinematic sensor technology (Bosch BNO055, 9-axis absolute orientation sensor, sensitivity: ±$1\\%$) are implemented inside the device. The reaction force between tester and the patient’s limb, as well as the linear accelerations and angular velocity were captured during the MMT. The sampling rate was 180 Hz. The data were buffered, A/D converted and sent via Bluetooth 5.0 to a tablet. An app (Sticky Notes, comp.: StatConsult) saved the transmitted data [49,50,51,52].\n\n## 2.4. Manual Muscle Test to Assess the Adaptive Force: Procedure and Setting\n\nFor testing the AF, the MMT in the form of a ‘break test’ was performed [53], since it enables a flexible and time-saving approach. This is especially necessary in fatigued long COVID patients. The MMT aims to assess the patient’s neuromuscular capacity to adapt to an external force increase. It does not test the maximal strength of the patient in the sense of MVIC. MMT characteristics were described previously [49,50,51,52] (Figure 1b). The starting positions of MMT of elbow and hip flexors, including the application of the handheld device, are illustrated in Figure 1c,d (according to [50,51,52]). The patient laid supine. The starting position for the elbow flexor test was 90° elbow flexion and maximal supination of the forearm (Figure 1c). For the hip flexor test, hip and knee angles were ~90° (Figure 1d). The contact with the handheld device was at the distal forearm or thigh, respectively. The contact points were marked and the lever was measured from the lateral epicondyle of the humerus and trochanter major, respectively, to the respective contact point for the standardization of retests. The tester applied a smoothly increasing force (Figure 1b) on the participant’s limb in the direction of muscle lengthening until a considerably high force level was reached. The patient had the task to maintain the starting position in an isometric holding manner for as long as possible. The patient is supposed to react and adapt to the applied force, but the patient was not allowed to push against the tester (for explanation see [50,51,52]). The whole MMT lasted ~4 s.\n\nThe MMTs were rated subjectively by the tester: ‘unstable’: the muscle started to lengthen during the force increase, hence, the patient was not able to maintain the isometric position. In that case, the maximal holding capacity (AFisomax) was lower than AFmax, which was then reached during eccentric muscle action. ‘ Stable’: the patient was able to maintain the isometric position until an oscillating force equilibrium occurred at a considerably high force level; in that case, the maximal AF (AFmax) was reached under isometric conditions (AFmax = AFisomax). Healthy persons usually show such stable adaptation (AFisomaxAFmax ≥ $99\\%$) [50,51,52]. ‘ Unclear’: the muscle was neither completely stable nor unstable; slight suspensions were present.\n\nA reproducible force application is a necessary precondition for valid data. Experienced testers are able to perform reliable force profiles over time [49]. Both testers (female, 36 years, 168 cm, 55 kg, 9 yrs. of MMT experience; male, 65 years, 185 cm, 87 kg, 26 yrs. of MMT experience) who assessed the AF of the patients in the present study, had previously proven their ability to test reproducibly [49]. Moreover, the force profiles over time matched precisely between both testers [49].\n\n## 2.5. Procedure\n\nAt the first appointment, the patient was examined by means of the MMT by one of the two testers. This tester also conducted all subsequent MMTs of the same patient. Four muscle groups of the lower and upper extremities on both sides were assessed manually (without handheld device), respectively, to obtain an overall impression of the neuromuscular functionality. Then, the input measurements (pre) were performed: AF of hip and elbow flexors of one side was recorded utilizing the handheld device for objectification during the MMT. Patients chose the side to measure, in case of complaints, the complaint-free side was used. Both muscle groups were measured consecutively in alternating order three times, each starting with hip flexors (~1 min resting period between trials). The subjective assessment of the performed MMT by the tester was noted (0 = unstable; 1 = stable, 2 = unclear). Subsequently, the patients received their individual treatment which was not part of the study. Following this treatment (~1 h after input measurements), the AF of hip and elbow flexors was measured again (post) according to the procedure of the pre-measurements. A treatment period of varying duration and number of treatments were prescribed for each patient. During this phase the patients received their individual treatments, which they would have received anyway regardless of the study. The patients were prompted to contact the tester as soon as they felt substantially better or recovered. Then, a final appointment was scheduled for the end measurements (end), that followed the same measuring procedure as for the pre/post measurements. It should be emphasized that no treatment was given at the final appointment prior to the end measurements.\n\n## 2.6. Data Processing and Statistical Analyses\n\nData processing and evaluation were performed according to Schaefer et al. [ 50,51,52] in NI DIAdem 2020 (National Instruments, Austin, TX, USA). The recorded data (force and gyrometer signals) were transferred from the measuring app to NI DIAdem. They were interpolated (1 kHz) and filtered (Butterworth, filter degree 5, cut-off frequency 20 Hz). For visualization proposed, the angular velocity was additionally filtered (degree: 3, cut-off: 10 Hz) to smoothen the oscillations. The following AF parameters were captured for further evaluation:Maximal Adaptive Force (AFmax): AFmax (N) refers to the peak value of a trial. This could have been reached either during isometric or eccentric muscle action.\n\nAFisomax stands for the highest force value under isometric conditions. It was defined as the force at the moment in which the gyrometer signal increased above zero (breaking point). This indicated a yielding of the limb and, accordingly, muscle lengthening. If the gyrometer signal oscillated around zero during the entire trial, AFmax = AFisomax. Thus, the muscle length stayed stable during the whole MMT until the peak force value was reached (stable MMT). If the muscle started to lengthen in the course of MMT, AFisomax was reached during the force increase prior to the peak value. This points out that the position of the limb has to be considered to assess AFisomax. In 1 of 256 trials, AFisomax could not be determined because of peculiarities in the curve shape (excluded from evaluation). The ratio of AFisomax to AFmax (%) was additionally calculated per trial.\n\nAFosc (N) characterizes the force at the moment in which oscillations start to appear regularly (onset of oscillations). Previous studies [50,51,52] showed that both interacting partners develop an oscillating force equilibrium, especially during stable MMTs. This was indicated by oscillations which arose in the force signal mostly in phase 3 of MMT (linear increase) showing a clearly distinguishable regular oscillatory behavior. During unstable MMTs, this oscillatory up swing was missing or occurred attenuated on a considerably higher force level. To evaluate AFosc, the force signal was checked for oscillations (force maxima) appearing sequentially during the force increase. If four maxima with a time distance dx < 0.15 s appeared consecutively, the force value of the first maximum was defined as AFosc. Time delta dx < 0.15 s was chosen due to the knowledge that mechanical muscle oscillations occur ~10 Hz [54,55,56,57,58,59,60,61,62,63]. In case no such oscillatory onset occurred, AFosc = AFmax. In 2 of 256 trials, AFosc could not be clearly determined, hence, they were excluded from evaluation. Ratios of AFosc to AFmax (%), as well as AFosc to AFisomax (%) were calculated per trial. The latter is based on previous findings that for stable MMTs, AFosc arose on a lower level than AFisomax, and for unstable MMTs, oscillations occurred—if at all—after that breaking point.\n\nThe slope of force increase prior to the breaking point (AFisomax) of all trials was evaluated to control the force application by the tester. This has to be similar between the trials for a valid comparison. The difference quotient m = y2−y1x2−x1 was used to calculate the slope, whereby x refers to time and y to the respective force values. The reference points (time, force) were $70\\%$ and $100\\%$ of the averaged AFisomax of all of the assessed unstable MMTs of one patient. The decadic logarithm was taken from the slope values (lg(N/s)) since the force rise was exponential. In 11 of 256 trials, the slope could not be determined since oscillations occurred too intensively which would have distorted the slope value.\n\nArithmetic means (M), standard deviation (SD), coefficient of variation (CV) and $95\\%$ confidence intervals (CIs) were calculated per parameter, muscle and time point (pre, post, end) in Microsoft Excel (Microsoft 365, Redmond, WA, USA, Microsoft Corp).\n\nStatistical evaluation was performed with IBM SPSS Statistics 28 (Windows, Armonk, NY, USA, IBM Corp). All parameters (AFmax, AFisomax, AFosc, their ratios and slope) were checked for normal distribution by a Shapiro–Wilk test. In case of normal distribution, a repeated measures ANOVA (RM ANOVA) was used to compare the three time points (pre, post, end). In case the Mauchly test of sphericity was significant, the Greenhouse–Geisser correction was chosen (FG). For post-hoc tests, a Bonferroni correction was applied (adjusted p values are given by padj). Effect size eta squared (η2) was given for the RM ANOVA. For pairwise comparisons, the effect size Cohen’s dz was used, which was interpreted as small (0.2), moderate (0.5), large (0.80) or very large (1.3) [64,65]. Since the RM ANOVA is known to be robust against violation of the normal distribution [66,67], a Friedman test was only executed to compare the three time points if more than one dataset (pre, post or end) was not normally distributed (applied for the ratio of AFisomax to AFmax). The Bonferroni post-hoc test was used for pairwise comparisons (padj) and the effect size Pearson’s r was calculated by r = zn in Microsoft Excel. Significance level was α = 0.05.\n\nIn addition to the AF parameters, the intensities of the different queried symptoms were evaluated by calculating M and SD. Those values were also compared between the three time points using a Friedman test. Furthermore, the percentage of patients who stated their respective symptoms with an intensity of at least 2 was calculated for descriptive purposes.\n\n## 3.1. Number of Trials and Subjective MMT Ratings by the Testers\n\nThe hip flexors were measured in all 17 patients at the three time points (pre, post, end). The measurements of the elbow flexors were only completed in 14 patients due to reasons such as lack of time, shoulder complaints, too exhausted or similar. In total, 144 MMTs were performed using the handheld device for hip flexors and 118 for elbow flexors. One patient was only tested twice for both muscles because of a lack of time. In two other patients, hip and elbow flexors were only measured twice at pre and post because of tiredness. The hip flexors of another patient were assessed only once at end because of hip pain. In total, 141 valid trials were gathered for the evaluation of hip flexors and 115 for elbow flexors, since technical issues occurred in six trials (3× hip, 3× elbow). In one patient, four trials of hip flexors were performed at the end because the device indicated an error, but the data were nevertheless transferred and, therefore, used for evaluation.\n\nThe female tester assessed ten patients and the male tester assessed seven. The number of MMTs rated as ‘unstable’, ‘stable’ and ‘unclear’ by the testers is given in Table 1.\n\nAs can be seen, all MMTs ($100\\%$) were rated as ‘unstable’ at pre. This reflects that elbow and hip flexors started to lengthen during the force increase, thus, the patients were not able to adapt their muscle length and force adequately in an isometric position during the external force increase. At post and end, the majority of MMTs were rated as ‘stable’ for both muscles (elbow: $92.1\\%$ and $97.6\\%$, respectively; hip: $89.4\\%$ and $95.9\\%$, respectively). This indicates that the patients were able to adapt to the external force increase in an isometric holding position in the vast majority of MMTs, and the muscles did not yield during the force rise. In total, six MMTs were rated as ‘unclear’ (elbow: $2.6\\%$ at post; hip: $6.4\\%$ at post, $4.1\\%$ at end). This highlights that the MMTs could not be rated as completely stable. The testers mostly described that the muscle showed stronger suspensions than usual for stable MMTs or that the muscle started to yield at a very high force level (especially in comparison to the pre trials). Those subjective ratings should be verified using the data from the handheld device.\n\n## 3.2. Parameters of Adaptive Force in the Course of Long COVID\n\nFigure 2 exemplifies the force and gyrometer signals of three MMTs of elbow and hip flexors, respectively, of one female patient (24 years, 168 cm, 65 kg) tested by the male tester at the pre, post and end time points. As can be seen in the uppermost graphs of Figure 2, force rises at pre, post and end can be regarded as similar except for one curve at pre. The single values of each parameter and patient are provided in the Supplementary Materials. Table 2 displays the group averages and statistical results.\n\n## 3.2.1. Slope of Force Increase\n\nSlope was similar for elbow and hip flexors (Table 2, Figure 3) and did not differ significantly between the three time points, neither for elbow, nor for hip flexors. For the latter, the RM ANOVA was close to significant. The lowest slope was present for pre. Thus, at post and end, the challenge for patients to adapt to the external load can be assumed as even higher. The slope can be interpreted as statistically similar between the time points, which is the prerequisite for comparison of the AF parameters.\n\n## 3.2.2. Maximal Adaptive Force and Maximal Isometric Adaptive Force\n\nThe AFmax did not differ significantly between the three time points for both muscles (Table 2, Figure 4a,d). As can be seen in Table 2, AFmax was considerably high in the pre measurements. One female patient (outlier) showed an extremely low AFmax in the pre trials, with only 61.43 ± 4.86 N for elbow and 67.38 ± 8.66 N for hip flexors. At post, she could increase her AFmax immediately to 146.44 ± 22.05 N for elbow and to 162.58 ± 26.01 N for hip flexors. In her case, the AFmax at pre amounted to only $42\\%$ of AFmax at post for elbow and $41\\%$ for hip flexors, respectively. This is usually not expected and will be discussed later. The other patients showed AFmax values between 145.83 and 295.05 N for elbow and 124.10 and 257.27 N for hip flexors. For timepoints post and end the AFmax was considerably high for all patients (Table 2, Figure 4a,d). The AFmax at pre related to post amounted averagely 100 ± $14\\%$ for elbow and 106 ± $24\\%$ for hip flexors, respectively (excl. outlier). Similar for post vs. end with 100 ± $10\\%$ for elbow and 102 ± $15\\%$ for hip, respectively. Hence, AFmax seems to not be appropriate to reflect the testers’ MMT ratings adequately, which showed clear differences between pre and post and pre and end, as well as similar ratings between post and end (Table 1). It has to be mentioned that for all pre trials, AFmax was reached during muscle lengthening, whereby for the majority of the post and end trials, AFmax arose during isometric muscle action. Therefore, the suggested main parameter to quantify the manually found difference is the maximal force during the isometric muscle action (holding capacity; AFisomax).\n\nThe AFisomax showed clearly lower values at pre vs. post/end, with a significant main effect in the RM ANOVA (Table 2, Figure 4b,e). The pairwise comparisons revealed significantly lower values for pre vs. post (elbow: t[13] = −11.144, padj < 0.0001, $d = 2.978$; hip: t[16] = −10.228, padj < 0.0001, $d = 2.481$; one-tailed) and for pre vs. end (elbow: t[13] = −12.140, padj < 0.0001, $d = 3.245$; hip: t[16] = −10.007, padj < 0.0001, $d = 2.427$, one-tailed). Post vs. end did not differ significantly (elbow: padj = 1.000; hip: padj = 1.000).\n\nFor elbow flexors, four patients showed an AFisomax below 60 N at pre (range 20.56 to 58.66 N), which has to be considered as extremely low. Hereby, the outlier mentioned above, showed the lowest value. Another patient showed a very high AFisomax = 229.94 N. The others ranged from 62.21 to 156.30 N. All patients showed considerably high AFisomax values at post and end (Table 2, Figure 4).\n\nFor hip flexors, three patients showed AFisomax < 60 N at pre (range: 27.36 to 52.00 N), whereby the mentioned outlier again showed the lowest value. The highest AFisomax for pre was 166.73 N, which was reached by the same patient who showed the highest value for elbow flexors. At post and end, AFisomax was considerably high for all patients.\n\nThis is mirrored by the ratio of AFisomax to AFmax (Table 2, Figure 4c,f). For elbow flexors, it ranged from $23\\%$ to $78\\%$ at pre, $89\\%$ to $100\\%$ at post and $97\\%$ to $100\\%$ at end; for hip flexors, it ranged from $28\\%$ to $69\\%$ at pre, $87\\%$ to $100\\%$ at post and $98\\%$ to $100\\%$ at end. The patients started to lengthen their muscles already at 47 ± $16\\%$ of their maximal force (AFmax) for elbow flexors and at 49 ± $12\\%$ for hip flexors in the pre trials. Some patients showed an extremely low ratio in single MMTs. The lowest ratios were $14\\%$ for elbow and $15\\%$ for hip flexors. In 15 of 36 MMTs (elbow) and 13 of 46 MMTs (hip), the ratio amounted less than $40\\%$. In 11 and 13 MMTs, respectively, it was >$60\\%$. The others were in-between $40\\%$ and $60\\%$.\n\nAt post, already 12 of 14 patients were able to generate at least $98\\%$ of AFmax for elbow flexors, two patients reached lower values ($89\\%$ and $96\\%$). It was similar for hip flexors, whereby 14 of 17 patients were able to demand at least $98\\%$ of AFmax, three showed lower values ($87\\%$, $93\\%$ and $97\\%$). In all end trials, the patients were able to reach $100\\%$ of AFmax in an isometric holding position, except for two patients who showed values of ~$97\\%$ or ~$98\\%$ for elbow flexors and one who reached ~$98\\%$ for hip flexors.\n\n## 3.2.3. Onset of Oscillations during Force Increase\n\nThe AFosc revealed a significant main effect in the RM ANOVA for both muscles (Table 2). For elbow flexors, oscillations started at an $18\\%$ and $17\\%$ higher force level comparing pre vs. post and pre vs. end, respectively. The pairwise comparisons missed significance after the Bonferroni correction (Figure 5a,d). For hip flexors, oscillations occurred at a $43\\%$ and $52\\%$ higher force level comparing pre vs. post and pre vs. end, respectively. Pairwise comparisons were highly significant (pre vs. post: t[16] = 5.997, padj < 0.0001, $d = 1.454$; pre vs. end: t[16] = 5.892, padj < 0.0001, $d = 1.429$). Post vs. end did not differ significantly (padj = 1.000) (Figure 5a,d).\n\nThe above-mentioned outlier regarding AFmax showed an extremely low AFosc for both muscles at pre with 60.84 (elbow) and 66.48 N (hip). For elbow flexors, AFosc was 100.94 N at post and 100.90 N at end. For hip flexors AFosc was similarly low comparing pre (66.48 N), post (62.88 N) and end (69.89 N). The other patients showed AFosc of elbow flexors in the range of 145.69 to 272.70 N at pre, 62.98 to 217.69 N at post and 52.00 to 236.84 N at end. For hip flexors, it ranged from 116.90 to 242.17 N at pre, from 62.88 to 188.86 N at post and from 29.86 to 191.76 N at end. The between-patients CV for pre, post and end was large with ~31 ± $2\\%$ for elbow and ~32 ± $6\\%$ for hip flexors. The intraindividual CV was considerably lower with 5.8 ± $2.8\\%$ (pre), 12.1 ± $8.3\\%$ (post) and 12.8 ± $5.3\\%$ (end) for elbow flexors and 6.1 ± $4.1\\%$, 12.3 ± $7.03\\%$ and 16.5 ± $15.1\\%$, respectively, for hip flexors.\n\nThe ratio of AFosc to AFmax was clearly and significantly higher at pre vs. post and pre vs. end for both muscles (Table 2, Figure 5b,e) (elbow: pre vs. post: t[13] = 5.455, padj < 0.0001, $d = 1.458$; pre vs. end: t[13] = 5.863, padj < 0.0001, $d = 1.567$; hip: pre vs. post: t[16] = 8.306, padj < 0.0001, $d = 2.014$; pre vs. end: t[16] = 9.876, padj < 0.0001, $d = 2.395$). No significant differences were present comparing post vs. end.\n\nDisplayed by the ratio of AFosc to AFisomax, the oscillations arose consistently after the breaking point (AFisomax) at pre. At post and end, they occurred before AFisomax (Table 2, Figure 5c,f). Only in one MMT of elbow flexors at post, the oscillations arose just with AFisomax (AFosc/AFisomax = $100\\%$). The RM ANOVA revealed a significant main effect for both muscles (Table 2). Pairwise comparisons were highly significant for pre vs. post (elbow: t[13] = 5.918, padj < 0.0001, $d = 1.582$; hip: t[16] = 8.905, padj < 0.0001, $d = 2.160$) and pre vs. end (elbow: t[13] = 5.892, padj < 0.0001, $d = 1.575$; hip: t[17] = 8.979, padj < 0.0001, $d = 2.178$). Post vs. end showed no significant differences (padj = 1.000 for both muscles).\n\n## 3.3. Patients Characteristics Regarding Long COVID\n\nThe patients’ professions were teachers/educators [6] students/trainees [2], IT specialist [1], editor [1], lawyer [1], filmmaker [1], social insurance clerk [1], physiotherapist [1], business economist [1], manager of a coronavirus test center [1] and pensioner [1]. From the 16 employed patients, 14 were unable to work because of long COVID at the first appointment (pre), one had just started to work again and one had no sick leave at all. At timepoint end, eight of the 14 incapacitated patients were working again and six wanted to return to work again soon. One was still on sick leave.\n\nThe acute SARS-CoV-infection lasted on average 15.29 ± 9.40 days (range: 7 to 40, $$n = 17$$). The median of acute infection severity was 2.25 ($$n = 16$$). One patient was admitted to hospital due to vertigo, another because of a suspected heart attack (nevertheless, she rated the intensity with 1). Overall, infections could be interpreted as mild.\n\nThe duration from acute infection to input measurement (pre) was on average 274.88 ± 210.70 days (range: 32 to 688). From pre to end, the duration amounted to 71.06 ± 44.43 days (range: 26 to 166 days). The patients had on average 3.29 ± 1.79 (range: 1 to 7, $$n = 17$$) treatment appointments at the practice from pre to end. At end, four patients were completely recovered and required no further appointments. Thirteen patients reported to feel sustainably better but wanted to receive further treatments. For 10 of those 13 patients the therapy phase was completed after an average of 2.80 ± 1.99 further treatments. Three patients were still in therapy (July 2022), since they had not regained their full quality of life back or they wanted to stabilize their health further, especially with regard to mental stability.\n\nThe patients reported a large variety of symptoms in the long COVID state, which were not all regarded in the questionnaire. Beyond the queried symptoms, the patients reported recurrent ‘crashes’, (muscle) weakness, joint stiffness, limb heaviness, feeling of whole body paralysis, brain fog to black outs, aphasia, forgetfulness, slowed reaction, sensitivity to stimuli, such as light/noise, hypersomnia or sleeping problems, vertigo, nausea, diarrhea, sore throat, ague, strong sweating, impaired vision, olfactory hallucination, body aches (back/shoulder/neck/heart/lung/tooth/eyes), tingles in the nerves/limbs/head/tongue, cold hands/feet, increased convulsion tendency, internal vibrating, inner restlessness, being phlegmatic, high level of irritability, fast overload, mental imbalance, depression, tachycardia, extrasystoles, hair loss, eczema, herpes, reactivated Epstein–*Barr virus* infection and tinnitus.\n\nFigure 6a illustrates the percentage of patients who stated the respective symptoms with an intensity of at least 2. As can be seen, no patient reported to have chest pain/tightness, cough, dizziness, loss of smell/taste or fever with such an intensity before COVID. However, the majority of symptoms was already present in some patients—at least slightly—before COVID infection ($7.14\\%$ to $35.71\\%$, $$n = 14$$). Depression/anxiety showed the highest percentage before COVID. This is in line with the statements regarding job-related and personal stress before COVID (Table 3), which were rated with an intensity of ≥2 in $90.91\\%$ and $100\\%$, respectively ($$n = 11$$). Those values declined in long COVID state to $85.71\\%$ ($$n = 14$$, three patients did not check the boxes because of sick leave) and $88.24\\%$ ($$n = 17$$), respectively; at timepoint end, they amounted to $30.33\\%$ and $50.00\\%$ ($$n = 12$$), respectively.\n\nAbsolute symptom intensities are displayed in Table 3 and Figure 6b. The Friedman test was significant for each symptom. From ‘before COVID’ to ‘long COVID state’, the intensities increased significantly for each symptom ($p \\leq 0.001$ to 0.024), except for fever ($$p \\leq 0.202$$). In long COVID state, all patients reported to suffer from fatigue, post-exertion malaise and breathing difficulties with an intensity of at least 1. Most prominent were post-exertion-malaise and fatigue with an intensity of 8.1 and 7.8, respectively. The other symptoms did not occur in each patient, whereby fever was the rarest (four patients).\n\nComparing ‘long COVID state’ vs. end, the intensity of all symptoms declined, most of them significantly ($p \\leq 0.001$ to 0.031), except for fever ($$p \\leq 0.281$$) and loss of smell/taste ($$p \\leq 0.062$$) (Table 3, Figure 6).\n\nThe symptom intensities did not differ significantly between ‘before COVID’ and end ($$p \\leq 0.202$$ to 0.922), whereby for concentration/memory problems, significance was just missed with $$p \\leq 0.050.$$ The latter was still present in 11 of 13 patients (three of them stated an intensity of 1, one patient of 9). Another patient stated that an elevated temperature was partly still present at timepoint end when he was physically active (rated fever with intensity 3); after one further treatment this was resolved, too.\n\nAlthough the individual treatments were not part of the evaluation, they should be reported briefly. Fifteen of 17 patients filled out the questionnaire with respect to their diagnosis and therapies prior to the first appointment. At least seven patients had large diagnostic assessments in centers or rehabilitation facilities for long COVID, the others stated to have received assessments from medical specialists (pulmonologists, internists and similar). The initiated treatments ranged from rehabilitation measures, such as physiotherapy including lymph drainage, manual therapy/massage, reflective breathing therapy, hot role and exercise therapy to (hyperbaric) oxygen therapy, ergotherapy, psychotherapy or psychological guidance, behavioral therapy, speech therapy, concentration training, pharmacological approaches (antibiotics, cortisone, asthma inhaler) to dietary changes. The most common advice from medical specialists was ‘pacing’. According to the patients’ statements, this was very frustrating. Four patients stated that they received no advice or arranged therapy from medical specialists. They were told to rest or were not taken seriously. The majority of patients reported having the impression that conventional medicine had no treatment approach and some reported that medical specialists were ‘clueless’. Others stated that as soon as no somatic reason for their condition could be found, the patients were diagnosed with a psychosomatic disorder. Nevertheless, some patients reported positive effects regarding reflective breathing therapy and for psychological guidance to cope with the condition. Regarding physiotherapeutic measures, the effects varied widely. Some patients reported at least a supporting effect regarding manual therapy which helped to reduce some pain in the short-term. Others stated that lymph drainage worsened the condition. Some also stated that exercise therapy helped for their musculature and cardiovascular system, however, others reported a deterioration after low-intensity exercise therapy.\n\nThe mentioned helplessness and—if at all—mostly short-term supportive therapies led the patients to try interventions on their own initiative. Those included supplements (mostly vitamins, trace elements), walks, relaxation techniques or meditation, planning of daily routine, concentration training and rest. At least two patients went to naturopaths or specialists in traditional Chinese medicine. However, none of those measures led to the desirable improvement of their health condition. That is why the patients were seeking other approaches and made an appointment at the practice, where the AF measurements took place. Two of the 17 patients were transferred from a pulmonologist, the others came via other ways. The patients were still partly undergoing therapies elsewhere. The additive treatment at the practice for integrative medicine involved an individual approach based on the muscular holding capacity. Some regularities regarding the applied treatments were found. For each patient, an individualized pulsed electromagnetic field therapy (PEMF) was applied. Based on several studies [31,69,70,71,72], an influence on the ANS is assumed. For a single case, the PEMF therapy in long COVID was recently described [68]. Moreover, 11 of 17 patients were treated for mental stress (persisting or post traumatic situations) using an individualized treatment approach. The lymphatic system was treated in seven of 17 patients using manual methods, as well as individualized PEMF. In some cases, osteopathic and chiropractic techniques for the cranial and/or the musculoskeletal system were applied.\n\n## 4. Discussion\n\nThe present pilot study investigated the AF of elbow and hip flexors via an objectified MMT in patients with long COVID at three time points: during long COVID state (pre), after the first treatment (post) and after recovery/substantial health improvement (end). The additionally received individual treatments of the patients were not part of the study and were only included descriptively. The evaluation of the slope of applied force rise by the tester revealed a non-significant difference between the three time points. Therefore, the results are based on reproducible force increases and can be regarded as valid. The results supported the hypotheses and will be discussed with regard to different physiological and practical aspects.\n\n## 4.1. Comparison of the Subjective Ratings of the Manual Muscle Test and Measured AF\n\nThe MMT was comprehensibly criticized to be subjective. The applied force increase as well as the ratings of MMTs are based on the manual ability and ‘feeling’ of the tester. By measuring the dynamics and kinematics during the MTT, the force increase and breaking point can be objectified. The question remains whether the measured AF parameters support the subjective MMT ratings of the tester. Since the results of elbow and hip flexors showed similar characteristics, they will be considered together. All 84 MMTs at pre were assessed as ‘unstable’ by the testers. The MMTs at post and end were rated as ‘stable’ in the majority of trials (164 of 173), as ‘unstable’ in four of 173 cases and as ‘unclear’ in six of 173 cases. Regarding MMTs rated as either ‘unstable’, ‘stable’ or ‘unclear’, the ratio of AFisomax to AFmax amounted to 50.27 ± $13.15\\%$, 99.69 ± $0.64\\%$ or 97.95 ± $2.61\\%$, respectively. It can be concluded that the testers’ MMT ratings were in accordance with the measured AF. Under unstable conditions, AFisomax was only half as high as for the stable tests. The AF values rated as unclear in the MMTs were rather in accordance with the stable ones. Obviously, they showed a high AFisomax. However, during the MMT, the testers felt higher suspensions and the muscle resistance felt ‘softer’. The values of stable MMTs support the previous findings, in which the ratio of AFisomax to AFmax was ≥$99\\%$ [50,51,52]. Unstable MMTs previously revealed values of ~$56\\%$, which is slightly higher than the ~$50\\%$ found here. This might be attributable to the fact that the previous studies were performed on healthy participants who were affected temporarily by unpleasant odors or imagery. Unhealthy individuals with long COVID seem to show—at least in part—even stronger impairment of muscular adaptation. Some patients showed extremely low AFisomax values; the lowest was $15\\%$ for AFmax for hip flexors and $14\\%$ for elbow flexors. This is interpreted as a—partly extremely—impaired muscular adaptation, probably due to the long COVID state. However, it cannot be stated whether their muscular adaptation was already impaired before SARS-CoV-2 infection. Based on the findings, it can be concluded that the MMT ratings of both experienced testers were strongly in accordance with the measured AF values. Therefore, AF measured by the objectified MMT seems to be a suitable biomechanical parameter to evaluate the muscular function in adaptation to an external increasing force.\n\n## 4.2. Adaptive Force in the Recovery Process of Long COVID\n\nFatigue is considered as the main symptom of long COVID [73,74,75]. The link between fatigue and muscle weakness has already been raised previously [11,15,30,76,77,78,79]. That is why maximal strength is partly investigated in post-infectious syndromes or ME/CFS. As was mentioned in the introduction, the findings have been inconclusive until now [41,42,43,44]. In the presented study, AFmax did not differ significantly between the three time points, as was assumed. Since AFmax was previously found to be similar to MVIC [45,46,47,48,49,50,51,52], the assumption that maximal forces (eccentric/MVIC) might not be suitable parameters to investigate patients in post-infectious states is supported. However, one outlier existed here, who showed extremely low AFmax values at pre. This could be a hint that some individuals suffering from post-infectious syndromes or ME/CFS may also have significantly reduced common maximal strengths, as was found in [43,44]. Nevertheless, the results of AFmax can also explain, why other investigations did not find such differences [41,42].\n\nThe findings for AFisomax suggest that the holding capacity seems to be a more sensitive biomechanical parameter to assess muscle function. AFisomax was significantly lower with very large effect sizes for pre vs. post and pre vs. end, whereby post vs. end did not differ significantly, according to the hypothesis. In the long COVID state (pre), the patients were not able to maintain an isometric position while trying to adapt to the increasing applied force. Muscles gave way at less than half of the maximal AF. Hence, patients could not exert their maximal strengths at this stage. This was further supported by the ratio of AFisomax to AFmax, which was significantly reduced at pre.\n\nAs the main result of the study, AFisomax turned out to be a sensitive parameter for a long COVID state, because $100\\%$ of the patients showed initially clear instability (this was also the case for the other 20 patients measured in long COVID state, but who were not included in the study). To the authors’ knowledge, only one study assessed muscle strength in SARS-CoV-2 patients [80]. MVIC was measured directly at the discharge of elderly hospitalized patients. Thereby, $73\\%$ and $86\\%$ of patients showed a ‘weakness’ for biceps brachii and quadriceps femoris muscles, respectively. Muscle weakness was defined as strength which “was inferior to $80\\%$ of the predicted normal value” based on Andrews et al. [ 81]. However, those patients are not comparable with those included in the study, since measurements were executed at the end of acute infection following a period of hospitalization (averagely 20.7 days). More than $90\\%$ received oxygen supply and all of them were pharmacologically treated. Jäkel et al. reported a sensitivity of ~$70\\%$ and ~$86\\%$ for maximal hand grip strength in CFS/ME patients aged 20–39 years and 50–59 years, respectively (prior to the COVID-19 pandemic) [43].\n\nAFisomax of all long COVID patients responded immediately following the treatment at the first appointment with a clear and significant increase. This instant change leads to the assumption that AFisomax does not reflect the maximal strength capacity but a functional aspect of motor control that can be influenced by stimuli. It can switch immediately from instability to stability or vice versa. This was shown in previous studies involving healthy participants [50,51,52]. The health condition of the long COVID patients in this study was not improved directly after the first treatment (except for one patient), but the motor control already clearly responded. It is hypothesized that the motor reaction could have been a first hint at a helpful intervention, at least in a share of subjects. The actual causality remains unclear. There could have been helpful treatment methods, but also possible mental factors, such as an empathetic atmosphere or the like.\n\nThe significant differences between pre and end revealed that the holding capacity was not only substantially improved, but even fully normalized until recovery. This result has to be discussed independently of the possible causations of the improvement. Because the study was non-clinical, no control group was included. Therefore, the reasons for improvement of health conditions and AF parameters remain unclear.\n\nConsidering the queried symptoms, it was visible that they behave inversely proportional to the holding capacity: at pre, the symptom intensity was significantly higher in most items compared to timepoint end ($p \\leq 0.001$ to 0.031; except for fever ($$p \\leq 0.281$$) and loss of smell/taste ($$p \\leq 0.062$$)), whereby AFisomax and the ratio of AFisomax to AFmax was significantly lower at pre vs. end with large effect sizes of > 2.42. This indicates an inverse correlation of the health condition and holding capacity. The directionality and causation of this connection can only be assumed. Since the holding capacity was improved already directly after the first treatment (post), the holding capacity cannot be a direct indicator for the improvement in health. Moreover, it remains unclear whether that observed instant improvement was sustainable. It seems to be likely that the motor response was a more transitional phenomenon at the beginning. MMTs at following treatment appointments showed a fallback to muscular instability for the most patients; however, this was not verified by objective measures. Because the output-measurements (end) were not carried out after an immediately preceding treatment, the observed stability could be interpreted as a part of the improved health state. We assume that the holding capacity is regained prior to the decrease of symptom intensity. Hence, after suitable treatments, the functionality is first restored. A probable improvement in the health condition is time-delayed and might possibly depend on the sustainability of this regained functionality, mirrored by the stable muscle function.\n\n## 4.3. Neurophysiological Considerations with Respect to the Reaction of AF in Long COVID\n\nThe discussion on the etiology of long COVID should not be opened here in detail. Brain stem dysfunction [36], a reduced cerebral blood flow [31,82] and the involvement of the ANS [25,26,30,31,37,38,39] were discussed. Recently, preinfection psychological distress was reported as a risk factor for long COVID [75,83]. This is in line with the self-reported stress prior to acute infection regarding the patients in the present study. Central structures, such as the brain stem, thalamus, basal ganglia, cerebellum, inferior olivary nucleus, cingulate cortex and more are involved in processing and controlling nociception, emotions and motor control [84,85,86,87,88]. Hence, the influence of possibly interfering inputs in the complex control circuitries of motor function are conceivable. The adaptive holding capacity in reaction to an external increasing force was suggested to be especially vulnerable regarding such stimuli. The length-tension control with respect to an increasing external load challenges the regulation and control processes of motor control in a specific way (for detailed discussion see [49,50,51,52]). Therefore, it is conceivable that a health state, such as long COVID, can influence the holding capacity. Based on the findings of previous studies on the influence of emotions on AF in healthy participants [50,51,52] and on long-term practical experience that mental stress can reduce the holding capacity, we assume that the motor output in the sense of AF could have been impaired already prior to SARS-CoV-2 infection because of mental stress. This might have affected the functionality of the human system on different levels. Especially an impairment of the immune system is known to be associated with mental distress [83,89,90,91,92,93,94]. Hence, the individually perceived mental stress could have diminished the resilience of the individual with regard to the virus and, probably, could have impeded the recovery of the acute infection, resulting in long COVID. Wang et al. [ 83] highlighted that the findings that psychological distress is a risk factor for long COVID “should not be misinterpreted as supporting a hypothesis that post-COVID-19 conditions are psychosomatic”. We concur with this statement. From our point of view, mental stress might lead to disbalances of different bodily systems, e.g., the immune system [83,89,90] or the ANS [95]. This, in turn, could lower the resilience and might favor long COVID. We interpret the long COVID state rather as a sign of dysfunction. The found instability of the holding muscle function might be a part of the complex physiological functional disbalance in long COVID patients.\n\nThe onset of oscillation (AFosc) might also reflect an impaired functionality. The neuromuscular system is known to be characterized by oscillations. AFosc was significantly higher for pre vs. post and pre vs. end, as was hypothesized. Moreover, in all of the 84 MMTs of elbow and hip flexors at pre (rated as unstable), oscillations arose—if at all—after the breaking point, thus during muscle lengthening. For the remaining 173 MMTs at post and end (mostly rated as stable), the up-swing of oscillations arose regularly during isometric actions. Those findings support the previous ones that, in case of stability, oscillations occur during isometric muscle action; in case of instability, they do not arise. This indicates that oscillations might be a prerequisite for the stable adaptation in the sense of AF, as was suggested previously [50,51,52]. The evidence consolidates that oscillations are playing a major role in the neuromuscular adaptation with respect to external forces.\n\nBased on the connection of physiological disbalances and motor control, the AF might be a suitable biomechanical parameter to check for such functional impairments. Due to the immediate response of the holding capacity to supporting or disrupting inputs, the recovery process of long COVID could also be controlled, and a potentially supportive therapy approach might be ascertained by assessing the holding capacity.\n\n## 4.4. Limitations\n\nOne limitation was the non-standardized duration from post to end measurements. Due to the individual recovery process, this limitation is difficult to resolve. The duration depended on the self-reported health state of the patients. This self-report is another limitation. Further studies could include a more quantitative assessment of the health state. However, the individual feeling of health is the most important one, also for return to work. Furthermore, the study was not blinded. The testers were aware of the patients’ health state. However, the evaluation of the slope and AFmax revealed statistically similar values between the three time points. Only AFisomax, as well as AFosc, showed significant differences between pre vs. post and pre vs. end. This strongly indicates that the AF assessment was not influenced by lack of blinding.\n\n## 5. Conclusions\n\nThe investigation of the AF in patients with long COVID and in the course of their recovery process revealed that the holding capability was significantly reduced in long COVID state and was stabilized after the first treatment and with substantial health improvement. AFmax did not reflect this difference. The holding capacity seems to be sensitive but is assumed to be not specific for long COVID. Nonetheless, its assessment might support the diagnostics of long COVID and especially the choice of the individual helpful therapy approach, since the holding function can switch immediately from instability to stability. This should be used to identify a treatment tailored to the patient’s individual conditions and requirements. It is concluded that the assessment of AFisomax could be a supportive biomechanical parameter to assess the functional health state, follow up and recovery process in patients with long COVID. The next step should be to investigate the mentioned treatment approaches in a clinical design. Based on the present study, it cannot be judged whether the treatments were the reason for the recovery. Possibly, other received treatments or a spontaneous recovery over time could have led to the improved health state. In case the treatment approaches are verified positively, this would be a big step towards diagnostics and therapy with regard to long COVID. 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0.0007394345593638718\n]","isConversational":false}}},{"rowIdx":92,"cells":{"text":{"kind":"string","value":"---\ntitle: Multiparametric Profiling of Neutrophil Function via a High-Throughput Flow\n Cytometry-Based Assay\nauthors:\n- Kyle D. Timmer\n- Daniel J. Floyd\n- Allison K. Scherer\n- Arianne J. Crossen\n- Johnny Atallah\n- Adam L. Viens\n- David B. Sykes\n- Michael K. Mansour\njournal: Cells\nyear: 2023\npmcid: PMC10000770\ndoi: 10.3390/cells12050743\nlicense: CC BY 4.0\n---\n\n# Multiparametric Profiling of Neutrophil Function via a High-Throughput Flow Cytometry-Based Assay\n\n## Abstract\n\nNeutrophils are a vital component of the innate immune system and play an essential function in the recognition and clearance of bacterial and fungal pathogens. There is great interest in understanding mechanisms of neutrophil dysfunction in the setting of disease and deciphering potential side effects of immunomodulatory drugs on neutrophil function. We developed a high throughput flow cytometry-based assay for detecting changes to four canonical neutrophil functions following biological or chemical triggers. Our assay detects neutrophil phagocytosis, reactive oxygen species (ROS) generation, ectodomain shedding, and secondary granule release in a single reaction mixture. By selecting fluorescent markers with minimal spectral overlap, we merge four detection assays into one microtiter plate-based assay. We demonstrate the response to the fungal pathogen, Candida albicans and validate the assay’s dynamic range using the inflammatory cytokines G-CSF, GM-CSF, TNFα, and IFNγ. All four cytokines increased ectodomain shedding and phagocytosis to a similar degree while GM-CSF and TNFα were more active in degranulation when compared to IFNγ and G-CSF. We further demonstrated the impact of small molecule inhibitors such as kinase inhibition downstream of Dectin-1, a critical lectin receptor responsible for fungal cell wall recognition. Bruton’s tyrosine kinase (Btk), Spleen tyrosine kinase (Syk), and Src kinase inhibition suppressed all four measured neutrophil functions but all functions were restored with lipopolysaccharide co-stimulation. This new assay allows for multiple comparisons of effector functions and permits identification of distinct subpopulations of neutrophils with a spectrum of activity. Our assay also offers the potential for studying the intended and off-target effects of immunomodulatory drugs on neutrophil responses.\n\n## 1. Introduction\n\nNeutrophils possess a repertoire of functions as the first line of defense in controlling invading pathogens. While the absolute neutrophil count is of obvious importance, the functional capacity of neutrophils to properly execute these functions is also critical for the prevention of disease [1,2,3]. Despite having normal or greater than normal neutrophil counts, studies suggest select patient cohorts face an increased risk of infection due to inherited or acquired defects in neutrophil function, including those with diabetes [4,5], cirrhosis [6,7], chronic granulomatous disease [8,9], and recipients of organ transplants [10]. Interrogating neutrophil functions can help to explain why neutrophils may be ineffective in controlling pathogens in these patients. For example, neutrophils from patients with diabetes have decreased reactive oxygen species (ROS) production, chemotaxis, phagocytosis, and neutrophil recruitment. Shedding light on these impairments may explain patients’ increased vulnerability to infection [11].\n\nIn addition to genetic and metabolic causes, many approved therapeutics can have unintended side effects leading to neutrophil dysfunction. Bruton’s tyrosine kinase (Btk) inhibitors, such as ibrutinib, a backbone in treating chronic lymphocytic leukemia [12], impairs neutrophil function [13]. Patients on treatment with ibrutinib have neutrophils with diminished phagocytosis, ROS generation, and cytokine production, which may explain the increased incidence of invasive aspergillosis in this patient population [13]. Developing an assay that rapidly and efficiently analyzes a diverse array of neutrophil functions could improve our understanding of neutrophil dysfunction in various disease settings and highlight potential off-target effects of medications on neutrophil function.\n\nFlow cytometry is commonly used for the concurrent measurement of multiple parameters on large populations of cells. Reactive dyes, fluorescent pathogens, analyte capture beads, and antibody labeling of cell surface proteins allow one to quantify various neutrophil functions (Figure 1) rapidly and can be accomplished within hours of blood collection [14]. Flow cytometry-based multifunctional assays have identified aberrant neutrophil activity in specific subpopulations of patients such as those with community-acquired pneumonia [14], HIV [15], or severe-injury-related trauma [16]. By quickly identifying patients with neutrophil dysfunction, interventions can be promptly employed to manage at-risk patients. To date, the breadth of measurements in such assays has been limited to two functions: phagocytosis and oxidative burst [14,15,16]. Specific protocols increase the study dimensions with parallel assays or downstream secondary analyses of supernatant to measure the release of soluble factors [17,18]. A high throughput assay that measures multiple functions remains an unmet need for granulocyte phenotyping. We present a microtiter plate assay for simultaneously measuring four neutrophil functions, including phagocytosis, ROS generation, ectodomain shedding, and degranulation.\n\nThe selected functions are cardinal features of neutrophil response. Ectodomain shedding, an often-overlooked neutrophil function, refers to the cleavage of specific motif-bearing surface proteins by selective proteases, most often of the a disintegrin and metalloproteases (ADAMs) family [19]. These changes to the neutrophil surface (ectodomain) influence various neutrophil activities such as cell-cell adhesion and rolling through the cleavage of L-selectin (CD62L) [20], opsonin recognition through the cleavage of the Fc receptor CD16b [21], paracrine signaling through the cleavage of tumor necrosis factor alpha (TNFα) and the TNFα receptors [22,23], and triggering neutrophil activation through the cleavage of the receptor TREM-1 [24,25]. CD62L is rapidly shed within minutes of stimulation [26] and is a classic and early indicator of neutrophil activation [27]. Additionally, CD62L, CD16b, TNFα, and the TNFα receptor are cleaved by the same metalloprotease, ADAM17 [28]. Thus, CD62L is an ideal marker for measuring ectodomain shedding because it acts as a metric for other surface changes and is an early sign of neutrophil activation.\n\nDegranulation is another critical function in neutrophil pathogen response. Neutrophils possess granules that release preformed enzymes into the extracellular environment during degranulation [29]. These granules contain antimicrobials such as myeloperoxidase (MPO), lysozyme, lactoferrin, mixed metalloprotease 9, and neutrophil elastase [30]. Granule subsets are categorized based on their contents and are released in a regulated order: secretory vesicles, tertiary, secondary, and finally primary granules [31]. We chose to analyze secondary granule release, through expression of CD66b, since it requires greater activation than secretory vesicles and tertiary granules and because these granules contain multiple antimicrobial peptides such as lactoferrin, neutrophil gelatinase-associated lipocalin (NGAL), and prodefensin [30,32].\n\nHere, we describe a new high-throughput assay that quantifies four neutrophil functions simultaneously from a single sample. By selecting compatible fluorescence read-outs, we minimize spectral overlap and demonstrate the potential to detect increases and decreases in neutrophil activity following treatment with cytokines, kinase inhibitors, and common immunomodulatory drugs. Our assay provides a convenient workflow for multiple rounds of priming or inhibition, pathogen co-culture, antibody labeling, and data acquisition within the same reaction well.\n\n## 2.1. Reagents\n\nFlow cytometry staining buffer (FACS buffer) was prepared with $2\\%$ heat-inactivated fetal bovine serum (FBS) (Life Technologies, Carlsbad, CA, USA) and 1 mM EDTA (Life Technologies) in phosphate-buffered saline (PBS) without calcium and magnesium (Corning, Corning, New York, NY, USA). Common immunosuppressive drugs were used to demonstrate the ability to test off-target therapeutic effects on neutrophil functions including cyclosporine A (10 μg/mL, Selleckchem, Houston, TX, USA) and mycophenolic acid (30 μM, Selleckchem). Cytokines used included G-CSF (100 ng/mL, Peprotech, Cranbury, NJ, USA), IFNγ (100 ng/mL, Peprotech), TNFα (10 ng/mL, Peprotech), and GM-CSF (10 ng/mL, Peprotech). Chemical inhibitors included diphenyleneiodonium for ROS inhibition (DPI, 10 μM, Selleckchem), TMI-005 for inhibition of CD62L shedding (2.5 μM, Cayman Chemical, Ann Arbor, MI, USA), PP1 for Src inhibition (10 μM, Cayman Chemical), R406 for Syk inhibition (20 μM, Selleckchem), and IBT for Btk inhibition (1 μM, Cayman Chemical). Lipopolysaccharide (LPS) from E. coli strain K12 was purchased from InvivoGen (San Diego, CA, USA). Complete RPMI (cRPMI) was prepared from RPMI (Corning), $10\\%$ FBS, 2 mM L-glutamine (Life Technologies), and $1\\%$ penicillin-streptomycin (Thermo Fisher Scientific, Waltham, MA, USA).\n\n## 2.2. Preparation of Human Neutrophils\n\nHealthy blood donors were consented under the Massachusetts General Hospital Institutional Review Board-approved protocol (2019P002840). Whole blood was collected in EDTA-coated vacutainers (Beckton Dickinson, Franklin Lakes, NJ, USA) and subsequently centrifuged at 1500xg for 15 min. Buffy coat was collected, and neutrophil isolation was performed using the negative selection EasySep Direct Human Neutrophil Isolation Kit, according to the manufacturer’s instructions (STEMCELL Technologies, Seattle, WA, USA). Wright-Giemsa staining was performed after the isolation process to confirm neutrophil purity from the isolation kit. Flow cytometry was also used to verify a high neutrophil purity from the isolation procedure (≥$94\\%$ neutrophil purity). Cell concentration and viability were measured by staining the cells with a 1:10 dilution of acridine orange/propidium iodide followed by automatic cell counting using the LUNA fl Dual Fluorescence Cell Counter (Logos Biosystems, Annandale, VA, USA) (≥$99\\%$ live). Neutrophils were resuspended in cRPMI at a concentration of 2 × 106 cells/mL.\n\n## 2.3. Preparation of C. albicans\n\nWildtype strain SC5314 Candida albicans was purchased from the American Type Culture Collection (American Type Culture Collection, Manassas, VA, USA). SC5314 constitutively expressing far-red fluorescent protein (C. albicans iRFP) was kindly donated by Robert Wheeler (University of Maine, Orono, ME) [33]. C. albicans was grown in YPD liquid media (yeast extract, peptone, dextrose) containing $1\\%$ yeast extract (Acros Organics, Fair Lawn, NJ, USA), $2\\%$ peptone (BD Biosciences, San Jose, CA, USA), and $2\\%$ dextrose (Sigma-Aldrich). C. albicans was cultured overnight at 30 °C on a rotating culture wheel (Thermo Fisher Scientific). The following day, C. albicans was removed from the wheel, washed twice with PBS and resuspended in PBS. C. albicans was counted using the LUNA automatic cell counter and kept on ice until the time of the assay.\n\n## 2.4. Neutrophil-Candida Co-Incubation\n\nDrugs, cytokines, or appropriate vehicles were prepared at a 2X concentration in cRPMI, with a maximum DMSO concentration of $0.1\\%$ v/v. In a 96-well V bottom polypropylene plate (Corning) the perimeter and outer wells were moated with 200 μL sterile PBS. Next, 50 μL of cRPMI or 50 μL of 2X drug or cytokine was mixed with 50 μL of neutrophil stock in a reaction well. The plate was sealed with breathable film (VWR, Radnor, PA) and placed in an incubator at 37 °C, $5\\%$ CO2 for 30 min to one hour. A 30-min cytokine incubation was chosen based on previous time courses. Neutrophils incubated with immunomodulatory drugs for 1 h, as this was the midpoint seen in similar published studies [34,35,36]. After the neutrophils were incubated, 20 μL of C. albicans at the desired multiplicity of infection (MOI) was added to the appropriate wells and mixed by pipetting. Immediately following, 30 μL of dihydrorhodamine 123 (DHR123) (5 μM, Thermo Fisher Scientific) was added to the wells and mixed well by pipetting. The plate was sealed and returned to the incubator for 30 min for coincubation with C. albicans. After the 30 min, the plate was placed on ice for 10 min in the dark and prepared for flow cytometry. During rescue studies, neutrophils were first treated with kinase inhibitors for 30 min [37] in an incubator. LPS (400 ng/mL) was then added to the well and the plate was returned to the incubator for an additional 45 min [37] prior to co-culturing neutrophils with C. albicans for 30 min.\n\n## 2.5. Flow Cytometry\n\nCells were pelleted at 4 °C and stained in 50 μL of cold FACS buffer containing (BV605) anti-human CD62L antibody (1:200 dilution; clone DREG-56; BioLegend, San Diego, CA, USA) and (BV421) anti-CD66b (1:200 dilution; clone $\\frac{6}{40}$c; BioLegend). The cells were incubated for 30 min at 4 °C in the dark. Cells were rinsed with 150 μL cold FACS buffer, centrifuged at 4 °C and resuspended in 150 μL cold FACS buffer. The 96 well plate was left on ice until just prior to data acquisition on a BD FACSCelesta (BD Biosciences, San Jose, CA, USA) with a blue, violet, red (BVR) laser configuration with specific wavelengths at 488 nm, 405 nm, 640 nm respectively. Bandpass filters for the Celesta include $\\frac{450}{40}$, $\\frac{525}{50}$, $\\frac{610}{20}$, $\\frac{660}{20}$, $\\frac{780}{60}$ for the 405 nm laser; $\\frac{530}{30}$, $\\frac{575}{26}$, $\\frac{610}{20}$, $\\frac{695}{40}$ for the 488 nm laser; and $\\frac{670}{30}$, $\\frac{730}{45}$, $\\frac{780}{60}$ for the 640 nm laser. Before recording data, gates were prepared so that 7000 neutrophil events could be collected. Compensation was performed with single color controls and was calculated using BD FACSDiva Software (BD Biosciences). A compensation matrix demonstrating the spectral overlap values can be seen in Supplementary Table S1. FCS files were exported from BD FACSDiva Software in a 3.0 format. Analysis of FCS files was performed using FlowJo v.10 software (BD Biosciences). T-distributed stochastic neighbor embedding (tSNE) was performed in FlowJo on gated neutrophils and calculated with the four neutrophil function flow cytometer parameters. Heat map statistics for iRFP (phagocytosis), DHR123 (ROS), CD62L expression (shedding), and CD66b expression (degranulation) were then overlayed on tSNE plots. Fluorescence minus one (FMO) controls were performed to ensure minimal spectral overlap. For each FMO control, the multiparametric assay was run equivalently but the flow cytometry preparation excluded one fluorescent marker while retaining the others. Supplementary Table S2 provides further information on the fluorescence reagents and detection.\n\n## 2.6. Statistical Analysis\n\nAll statistical analyses for normality and significance were performed on GraphPad Prism 9 (San Diego, CA) using ordinary one-way ANOVA. A p-value greater than 0.05 was considered nonsignificant (ns).\n\n## 3. Results\n\nNeutrophils employ a variety of effector functions to combat pathogens including phagocytosis, oxidative burst, degranulation, cytokine release, ectodomain shedding, and NETosis as illustrated in Figure 1. Here, we demonstrate a simple multiparametric assay to examine four neutrophil functions to Candida albicans challenge. Our assay is performed in a 96 well plate format and requires 1 × 105 cells per well. Neutrophils isolated from healthy donors or patients can be pre-incubated with test biologics or small molecules to assess their influence on neutrophil functions. Following pre-incubation, fluorescent C. albicans are introduced along with DHR123 as an indicator for ROS production. Following co-incubation with C. albicans, neutrophils are immunostained for CD62L and CD66b. CD62L allows for the detection of ectodomain changes and elevation of CD66b serves as a metric for secondary granule release (Figure 2A).\n\nA stepwise flow cytometric gating strategy identified neutrophils. Total events were gated by forward and side scatter properties to target live cells with the correct size and granularity. This gate removed most free-floating C. albicans and non-granulocytes (Figure 2B). It is important to note that this initial gate was generous because activated neutrophils engaging in the various functions tend to increase in size (FSC) and granularity (SSC). Single cells were identified, and neutrophils were selected by their expression of the neutrophil-specific surface protein CD66b (Figure 2B). Gates were set by comparing fluorescence minus one control to unstained as well as unstimulated stained samples. Phagocytosing neutrophils were gated as iRFP(+) events, ROS producing neutrophils were gated as DHR123(hi) events, shedding neutrophils were gated as CD62L[-] events, and degranulating neutrophils were gated as CD66b(hi) events. Neutrophil functions were analyzed simultaneously (Figure 2A) or individually (Figure 2B).\n\nIncreasing the ratio of C. albicans to neutrophils in the assay format increased the frequency of cells responding in terms of ectodomain shedding, phagocytosis, degranulation, and ROS production (Figure 2C–F). The multiplicity of infection (MOI) of 1, 2, 4, and 8 were statistically different in the frequencies of neutrophils engaging in phagocytosis, ROS generation, and secondary granule release (Figure 2C,D,F), while CD62L shedding plateaued between MOI or 4 and MOI of 8 (Figure 2E). Vastly different levels of neutrophil activity were achieved simply by increasing the ratio of C. albicans to neutrophils. Using an MOI of 8 triggered robust neutrophil responses resulting in $77\\%$ phagocytosis and $79\\%$ secondary granule release compared to $33\\%$ and $29\\%$, respectively, at an MOI of 2 (Figure 2C,F).\n\nMany neutrophil functional studies have examined phagocytosis and ROS production [14,15], while our assay simultaneously measures four canonical neutrophil functions. The multiparametric nature of the assay reveals that not all neutrophils perform the exact coordination of functional responses (Figure 3A). For example, shedding and degranulation may occur regardless of whether a neutrophil is phagocytosing and generating ROS. Further, some neutrophils may be performing all four simultaneous functions (population b) while others have not begun any of the measured responses (population a). We additionally, determined the overlap of neutrophils that could participate in more than one function. Figure 3B summarizes the proportion of individual neutrophils engaging in multiple simultaneous functions after co-culture with C. albicans. This presentation of the data reveals relationships between neutrophil functions. For example, there was great overlap between phagocytosing and ROS-generating neutrophils. Most phagocytosing neutrophils also generated ROS; however, only about $60\\%$ of degranulating neutrophils were simultaneously phagocytosing C. albicans. Furthermore, our assay allows one to measure the frequency of neutrophils participating in four concurrent functions. At an MOI of 1, on average, $40\\%$ of phagocytosing neutrophils participate in all four functions simultaneously compared to $82\\%$ at MOI of 4.\n\nEach flow cytometer is equipped with multiple lasers and filters that allow different fluorescent panel possibilities. The cytometer configuration is carefully paired with fluorescent markers to minimize spectral overlap and the possibility of false positives. To ensure our multiparametric assay accurately detected individual functions, gates were set using fluorescence minus one (FMO) controls. In the degranulation (BV421), ROS (DHR123), and phagocytosis (iRFP) FMO controls, there were fewer than $0.4\\%$ positive events for each function (Figure 4A,B). In the ectodomain shedding (BV605) FMO control, $99.8\\%$ of neutrophil events were negative for CD62L expression (Figure 4A,B). Our assay reliably measures individual functions, and results are not confounded by spectral overlap.\n\nAn essential application of the multiparametric assay is to screen potential bioactive small molecule drugs for neutrophil-modulating effects. We selected a panel of compounds known to influence immune function. These chemicals are inhibitors of ROS production (DPI) [38], and ectodomain shedding (TMI-005) [20], as well as two clinically relevant compounds used for immune suppression in transplant patients, mycophenolate (MPA) [39] and cyclosporine A (CSA) [40]. Treatment with compounds for one hour before C. albicans co-culture did not significantly affect neutrophil phagocytic ability. DPI was the only chemical to profoundly affect ROS generation, decreasing the number of ROS-positive cells by $87\\%$ (Figure 5). Ectodomain shedding was influenced by many of the small molecule compounds tested. The ADAM17 inhibitor TMI-005, nearly abolished ectodomain shedding. Treatment with MPA did not cause significant impairments to neutrophil functions. CSA however did cause a striking $55\\%$ reduction in ectodomain shedding and $45\\%$ reduction in secondary granule release. The small molecules tested resulted in unique changes to specific neutrophil behaviors and highlight the value of determining the precise functional area of neutrophil impairment.\n\nTo demonstrate the ability to measure neutrophil augmentation, we pre-stimulated neutrophils with cytokines, including granulocyte colony-stimulating factor (G-CSF), interferon-gamma (IFNγ), granulocyte-macrophage colony-stimulating factor (GM-CSF), and tumor necrosis factor-alpha (TNFα). These four inflammatory cytokines significantly increased the frequency of neutrophils engaging in phagocytosis, ROS generation, ectodomain shedding, and secondary granule release relative to the vehicle (Figure 6A–D). The response was nonuniform and cytokine-specific; IFNγ elicited the least improvement in neutrophil function. While IFNγ increased secondary granule release, the response was less than that of G-CSF, GM-CSF, or TNFα stimulation. Additionally, some of these cytokines began to trigger neutrophil responses in the absence of C. albicans co-culture. TNFα and GM-CSF produced significantly more ectodomain shedding at rest (Figure 6C). Interestingly, GM-CSF and TNFα encouraged degranulation in approximately $80\\%$ of neutrophils cultured with C. albicans (Figure 6D), nearly a sevenfold increase over the vehicle control. TNFα stimulation was unique in that it induced significant secondary granule release in the absence of a pathogen, a result seen in other studies [41].\n\nWe also sought to determine how the assay might be used to study the recovery of functional responses in dysfunctional or attenuated neutrophils. Inhibition of downstream kinases (e.g., Btk, Syk, Src) within the Dectin-1 signal transduction pathway can render neutrophils unresponsive to fungal pathogens such as C. albicans or *Aspergillus fumigatus* [42,43]. Syk and Btk inhibition can completely block neutrophil functions, including swarming, phagocytosis, oxidative burst, and cytokine production, even when challenged with C. albicans or A. fumigatus [37,44,45]. In the context of our assay, the inhibition of Btk, Syk, and Src, prevented phagocytosis, ROS production, ectodomain shedding, and secondary granule release (Figure 7A–D, respectively).\n\nNeutrophil inhibition by ibrutinib (IBT) or R406 can be overcome by alternate pathway stimulation (e.g., TLR stimulation using LPS) before challenging with C. albicans [37]. LPS stimulation alone did not trigger ROS production in pre-treatment conditions when C. albicans was not present (Figure 7B). However, when inhibited neutrophils were stimulated with LPS and challenged with C. albicans, phagocytosis, and ROS production were significantly restored (Figure 7A,B). All LPS-treated neutrophil conditions had near unanimous shedding of CD62L (Figure 7C), and secondary granule release was improved with LPS stimulation (Figure 7D). These results suggest that the multiparametric assay can measure inhibition, augmentation, and subsequent pathogen challenge in the presence of crucial kinase inhibitors.\n\n## 4. Discussion\n\nWhile the absolute number of circulating neutrophils is critical for pathogen control, the neutrophil function is another crucial factor that is less easily quantified. Microbial killing assays provide information on the overall ability of neutrophils to recognize and eliminate pathogens, though they do not explain which specific functions contribute to pathogen detection and containment. In studying how specific disease states and small molecules may influence pathogen killing, we wished to quantify multiple neutrophil functions simultaneously. Here, we have demonstrated a simple high-throughput multiparametric flow cytometer-based assay that permits studying four canonical neutrophil functions: phagocytosis, ROS production, ectodomain shedding, and secondary granule release. Furthermore, we demonstrated our assay’s clinical relevance and sensitivity in detecting improvements and diminution of neutrophil activities by pre-stimulating neutrophils with biological and small molecule agents.\n\nOur study does have limitations. There is growing evidence of neutrophil heterogeneity in peripheral blood [46], suggesting there may be populations with distinct functional potentials [47]. Our multiparametric assay did not include markers, such as CD10, to identify left shift neutrophils, an immature subset known to behave differently than mature circulating neutrophils [48,49]. Our study focused on healthy individuals who tend to contain scarce populations of immature neutrophils in circulation [48]. While it is unlikely that a left-shift neutrophil population significantly influenced the results demonstrated here, future work with the multiparametric assay should incorporate heterogeneity markers to fractionate results into additional unique subsets.\n\nOur assay is not exhaustive of all neutrophil functions, and future iterations could be expanded to define additional neutrophil responses (Figure 1). The most versatile flow cytometers can be equipped with up to nine lasers and corresponding detectors, allowing for the theoretical recognition of ~40 different parameters. Our assay did not include a measurement of NETosis, a well-defined mechanism for pathogen control. During NETosis, neutrophils ensnare the microbe in a conglomerate of nuclear material laced with high concentrations of antimicrobial proteins [50,51]. NETosis can be quantified by flow cytometry using cell impermeable nucleic acid dyes such as SYTOX and fluorochrome-conjugated antibodies targeting MPO and/or citrullinated histone 3 [52,53] (Figure 1). In addition to pathogen elimination, measuring relative neutrophil extracellular trap formation is also essential for understanding potential tissue damage in the host. Excessive NET formation is implicated in ongoing endothelial tissue damage [54] and can contribute to microthromboses [55,56].\n\nFurther adaptations to the panel could also include readouts of other granule types. For example, primary (also known as azurophilic) granule release results in the extracellular expulsion of toxic proteins such as proteinase 3, neutrophil elastase, and MPO [30]. Primary granule release can be measured using fluorochrome conjugated antibodies against CD63 which is not traditionally expressed on the neutrophil surface but is deposited on the cellular membrane upon primary granule fusion (Figure 1) [57,58,59]. In developing future multiparametric assays to measure neutrophil function, primary granules are of key interest in bacterial pathogenesis. Some pathogens inhibit primary granule release as an immune evasion strategy [60]. Furthermore, the recognition of excessive primary degranulation of neutrophil elastase has been implicated in host tissue damage [61]. Alternatively, secretory granule release can be measured by similarly detecting increases in the surface level expression of CD11b or CD35, though these responses can be non-specifically provoked making them markers of general neutrophil activation [32,62].\n\nThe discarded reaction media from the neutrophil-pathogen co-culture contains informative metabolites and biomolecules that could shed light on specific neutrophil responses including the measurement of cytokines involved in cell-cell communication. Advancements in multiplex cytokine panels could allow for multiple soluble analyte measurements from microtiter volumes, making it compatible with the miniaturized design of our assay. Shed compounds such as CD62L, TREM-1 or the release of factors like G-CSF, or other pro and anti-inflammatory cytokines could be measured by bead capture and quantified by flow cytometry [63,64].\n\nIn our assay, we used live C. albicans marked by the constitutive expression of a fluorescent protein. Enforced expression of fluorescent proteins by pathogens are useful tools however, genetic manipulation may introduce additional changes from the parental strain [65]. In our FMO experiment, lower rates of all functions were observed, when the nonfluorescent parental strain of C. albicans was incubated with neutrophils. This result was obtained on multiple separate occasions and highlights a limitation in utilizing fluorescent protein expressing pathogen. Live fluorescent pathogen culture is not feasible in all lab settings and instead, uncolored pathogens may be conveniently FITC stained [66] or surface labeled using succinimidyl ester-based reactions [37]. Live pathogens may also not be feasible, or approved, for use in cytometer or within every flow cytometry core facility. If so, the quantification of phagocytosis can be adapted to measure the uptake of alternative targets such as fixed or heat-killed samples. Additionally, inert fluorescent bioparticles or fluorescent beads coated with microbial antigens could also substitute the use of live pathogens [17,67,68].\n\nThe multiparametric assay accurately measures augmented neutrophil functions. Cytokine therapies have been explored for multiple patient diagnoses, such as cirrhosis [10,69] and cases of neutrophil dysfunction [70]. Cytokine screens in our multiparametric assay could identify the most effective treatments for enhancing or attenuating neutrophil activity for specific diagnoses and therefore increase patient outcomes. Our assay design may also be applied to studies investigating the role of endogenous cytokine levels in tissue-specific microenvironments in the setting of diseases such as cystic fibrosis [71] or cancer [71,72].\n\nNewly evolving biologic and small molecules are constantly being developed for the treatment of a variety of autoimmune disorders and malignancies. Small molecule kinase inhibitors have revolutionized such treatments; however, they can have off-target effects and can leave the patient vulnerable to invasive fungal disease [73]. We showed that three kinase inhibitors (IBT, R406, and PP1) eliminate neutrophil responses to C. albicans but that function can be restored with cytokines and growth factors. In the development of novel drugs, our assay could be used to detect negative consequences of kinase inhibiting therapeutics on neutrophil function and potential ways to overcome function inhibition.\n\nIn addition to kinase inhibitors, we tested two FDA-approved drugs, CSA and MPA, that are frequently used to suppress T and B cells [74,75] in transplant patients. The calcineurin inhibitor, CSA, suppressed innate immune cell function including neutrophil ectodomain shedding and secondary granule release. Other studies have also observed inhibitory effects of CSA on neutrophil function in vitro and in vivo, including reductions in primary granule release, neutrophil migration, and ROS production [34,35]. Our assay did not detect a decrease in ROS production though it appears that CSA diminishes ROS production in response to some but not all neutrophil-stimulating molecules [34]. This distinction may also reflect the type of ROS detected, specifically those from external release versus internal signaling [76,77].\n\nAs our assay detects functions simultaneously, one can observe changes to coupled mechanisms. Figure 3 shows a substantial overlap between individual cells capable of phagocytosis and ROS production. The strong association between phagocytosis and ROS-producing cells can be explained by the fact that NOX2 is activated upon phagosome formation [78,79]. When neutrophils were treated with the ROS inhibitor DPI, phagocytosis was relatively unchanged, but ROS was nearly abolished, highlighting the ability to investigate uncoupled functional processes.\n\nOur assay yields reproducible results and possesses numerous applications for studying functional deficits. Larger donor enrollment could establish a healthy neutrophil function range from which aberrant neutrophil activity could be identified for clinical applications. We showed that our multiparametric assay could detect function improvement upon pre-incubation with inflammatory cytokines. 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0.014691025950014591\n]","isConversational":false}}},{"rowIdx":93,"cells":{"text":{"kind":"string","value":"---\ntitle: 'The Influence of Ethnicity on Survival from Malignant Primary Brain Tumours\n in England: A Population-Based Cohort Study'\nauthors:\n- Hiba A. Wanis\n- Henrik Møller\n- Keyoumars Ashkan\n- Elizabeth A. Davies\njournal: Cancers\nyear: 2023\npmcid: PMC10000771\ndoi: 10.3390/cancers15051464\nlicense: CC BY 4.0\n---\n\n# The Influence of Ethnicity on Survival from Malignant Primary Brain Tumours in England: A Population-Based Cohort Study\n\n## Abstract\n\n### Simple Summary\n\nPrevious reports using broad ethnic group classifications have suggested that patient outcomes may vary. This study examined survival differences in malignant primary brain tumours of various morphologies between well-recorded and detailed ethnic groups for the whole of England. An ethnic difference in brain tumour survival was found with patients of an Indian background, Any Other White, Other Ethnic Group, and Unknown/Not Stated Ethnicity Groups having better one-year survival than the White British Group, following adjustment for known prognostic factors. By investigating the ethnic variations associated with better brain tumour survival, we may begin to better understand any ethnic inequalities that exist and possibly identify subgroups of patients that could benefit from personalised medicine.\n\n### Abstract\n\nBackground: In recent years, the completeness of ethnicity data in the English cancer registration data has greatly improved. Using these data, this study aims to estimate the influence of ethnicity on survival from primary malignant brain tumours. Methods: Demographic and clinical data on adult patients diagnosed with malignant primary brain tumour from 2012 to 2017 were obtained ($$n = 24$$,319). Univariate and multivariate Cox proportional hazards regression analyses were used to estimate hazard ratios (HR) for the survival of the ethnic groups up to one year following diagnosis. Logistic regressions were then used to estimate odds ratios (OR) for different ethnic groups of [1] being diagnosed with pathologically confirmed glioblastoma, [2] being diagnosed through a hospital stay that included an emergency admission, and [3] receiving optimal treatment. Results: After an adjustment for known prognostic factors and factors potentially affecting access to healthcare, patients with an Indian background (HR 0.84, $95\\%$ CI 0.72–0.98), Any Other White (HR 0.83, $95\\%$ CI 0.76–0.91), Other Ethnic Group (HR 0.70, $95\\%$ CI 0.62–0.79), and Unknown/Not Stated Ethnicity (HR 0.81, $95\\%$ CI 0.75–0.88) had better one-year survivals than the White British Group. Individuals with Unknown ethnicity are less likely be diagnosed with glioblastoma (OR 0.70, $95\\%$ CI 0.58–0.84) and less likely to be diagnosed through a hospital stay that included an emergency admission (OR 0.61, $95\\%$ CI 0.53–0.69). Conclusion: The demonstrated ethnic variations associated with better brain tumour survival suggests the need to identify risk or protective factors that may underlie these differences in patient outcomes.\n\n## 1. Background\n\nEach year, over 5000 new cases of primary brain tumours are diagnosed in the United Kingdom (UK) [1]. In particular England has become both more multicultural in recent decades [2] and seen a steady increase in the incidence of malignant primary brain tumours [3]. One study considering broad ethnic groups found higher incidence rates of 4.8 per 100,000 population from 2001 to 2007 for people from White ethnic group compared to those from South Asian, Black, and Chinese ethnic groups (respective rates of 3.1, 2.8, and 2.7 per 100,000 population) [4].\n\nEnglish population-based studies have reported ethnic differences in the incidence of most cancers with individuals from non-White groups generally having a lower cancer risk than the White Group [5,6]. Survival for the four common cancers has been widely reported [6], but not examined in detail for brain tumours using the well-defined ethnicity information now available. A small study of high-grade gliomas in South-East England 2000–2009 [7], reported that patients of White and Not Known ethnicities had the worse survival for all tumour groups after adjusting for sex, age, morphology, socio-economic deprivation, and co-morbidity.\n\nThe improved and detailed National Health Service (NHS) data on ethnicity captured by the National Disease Registration Service, which is part of NHS England, provided an opportunity to explore the impact of ethnicity on brain tumour survival. This resulted from major efforts across the NHS to increase self-reporting of this variable. With data collected on over 300,000 cancer cases in England each year, this is also the first English study to consider the more detailed classifications for malignant primary brain tumours including all gliomas, primary central nervous system lymphoma (PCNSL), as well as unclassified malignant brain neoplasms. It aims to examine the possible effect of ethnicity on the route or pathway taken to diagnosis and of receiving optimal treatment. A better understanding of any ethnic inequalities in brain cancer could potentially lead to improved treatment or services for these patients.\n\n## 2.1. Study Population\n\nData on all adult patients diagnosed with a malignant primary brain tumour during 2012–2017, who are resident in England and registered with a general practitioner (GP), were extracted from the English cancer registration data.\n\n## 2.2. Selection of Cases\n\nCases for this study were identified using the International Classification of Diseases [version 10] (ICD-10) tumour site C71. For those with PCNSL, ICD-10 code site was used along with the morphology codes for lymphoma. Other inclusion criteria were cases having a complete tumour registration and known sex. The brain tumour morphological subtypes considered in this study were based on the 2016 WHO Classification of Tumours of the Central Nervous System [8]. WHO updated this classification in 2021; however, the changes have minimal effect on the analysis of this data. Due to sample sizes, histological tumour subtypes were grouped as follows: glioblastoma, anaplastic astrocytoma, astrocytoma NOS, oligodendroglioma, PCNSL, malignant glioma, and unclassified malignant. Data on all tumours were extracted from the English cancer registration irrespective of their pathological confirmation—gliomas without a specified classification or as unclassified malignant neoplasms were included. In addition, glioblastomas with a pathological confirmation were included but tumours of benign, uncertain, and metastatic nature were not included. Molecular data are not available for this study cohort.\n\nInpatient hospital episodes statistics (HES) data were linked to the cancer registration data from 2012. These records include ethnicity data that are almost always self-reported upon admission to NHS hospitals. The categories of ethnicities were as follows: White British, Bangladeshi, Indian, Pakistani, Chinese, Black African, Black Caribbean, and Unknown/Not Stated, and due to small numbers in these groups—White Irish and Any Other White were grouped together as Any Other White, and Mixed Ethnic Groups and Any Other Ethnic Group were grouped as Other Ethnic Group.\n\nSocio-economic deprivation was measured using the income domain of the index of multiple deprivation (IMD) 2015, divided into quintiles across England and Wales, and assigned to cases using postcode of residence at diagnosis. Charlson co-morbidity score was based on conditions occurring within one year of the cancer diagnosis date. The conditions were weighted according to their severity and scores were grouped as 0 (where none were recorded), 1, and 2 or more. Route to *Diagnosis is* defined as the sequence of interactions between the patient and the NHS, leading to a cancer diagnosis [9]. This is identified using an algorithm linking various sources based on the setting of diagnosis, and the pathway and referral route into secondary care.\n\nInformation on surgical resections, chemotherapy, and radiotherapy treatments received within the first 18 months following diagnosis were also extracted. Treatment options were categorised to reflect clinical practice as: radiotherapy only, chemotherapy only, surgical resection only, all three treatments given as surgical resection followed by radiotherapy and chemotherapy (optimal treatment), radiotherapy plus chemotherapy, surgical resection plus radiotherapy, surgical resection plus chemotherapy, and no treatment. Surgical resections did not include cases with biopsies only.\n\n## 2.3. Data Analysis\n\nWe first extracted 27,934 records, cleaning the dataset to exclude duplicated cases, those without the required brain tumour morphology, with unknown vital status, or who were registered by death certificate only (DCO) (Figure 1). Survival time was calculated from the date of diagnosis until date of death with a survival period of up to one year. To retain 145 patients who died on their date of diagnosis, we added half a day to their survival time. The final study population included 24,319 cases.\n\nInitially, we examined the distribution of patients by demographic factors (age, sex, area of residence, and socio-economic status), co-morbidity, tumour morphology, route to diagnosis, and treatment factors. Univariate and multivariate Cox proportional hazards regressions were then used to estimate hazard ratios (HR) and their $95\\%$ confidence intervals ($95\\%$ CI) for the survival of each ethnic group up to one year following diagnosis. The follow-up period ended on 31 December 2018. χ2 Tests estimated the p-values for trend and heterogeneity, excluding unknown categories. We then carried out a sensitivity analysis in which each variable was adjusted to identify how much variation it contributed to the model, and as a result we finally focused the analysis on age, sex, co-morbidity, socio-economic deprivation, tumour morphology, route to diagnosis, and treatment received. Due to the high fatality of malignant primary brain tumours, cancer-specific survival was not studied, as this is similar to overall survival.\n\nLogistic regression was used to generate odds ratios (OR) (and their $95\\%$ CI) for each ethnic group of [1] being diagnosed with pathologically confirmed glioblastoma, [2] being diagnosed during a hospital stay that included an emergency admission, and [3] receiving optimal treatment (surgical resection followed by radiotherapy and chemotherapy). ORs were adjusted for age, sex, socio-economic deprivation, co-morbidity, morphology, route to diagnosis (patient’s pathway to diagnosis), and treatment. All analyses were performed using Stata Software, version 16 (StataCorp, TX, USA).\n\n## 2.4. Ethical Approval\n\nData for this study were collected and analysed under the National Disease Registries Directions 2021, made in accordance with sections 254[1] and 254[6] of the 2012 Health and Social Care Act. Further ethical approval for this study was not required per the definition of research according to the UK Policy Framework for Health and Social Care Research.\n\n## 3. Results\n\nData from 24,319 patients with a malignant primary brain tumour diagnosed between 2012 and 2017 in England were included. Table 1 displays the distribution of patient, tumour and clinical characteristics, and univariate and mutually adjusted HRs. Brain tumour diagnosis increased with age, peaking at 65–74 years with most patients being men ($58.0\\%$ $$n = 14$$,094). In absolute numbers, it was more frequent in people living in Southeast England, an area that is highly populated and more ethnically diverse. Overall, the most aggressive morphology, glioblastoma, was the most common type ($60.7\\%$ $$n = 14$$,768). The Kaplan–*Meier analysis* for brain tumour morphology (Figure 2) demonstrates glioblastoma as having a very high mortality, followed by malignant glioma ($7.0\\%$ of all cases, $$n = 1709$$) and unclassified malignant tumours ($11.1\\%$ of all cases, $$n = 2707$$) (log-rank test, $p \\leq 0.001$). Over one half of cases ($53.2\\%$ $$n = 12$$,926) were diagnosed during a hospital stay that included an emergency admission, with most patients receiving either the optimal treatment ($23.0\\%$ $$n = 5585$$), or no treatment ($34.9\\%$ $$n = 8483$$). In the univariate analysis, each of the covariates was correlated with survival. The effects of age, sex, and co-morbidity were attenuated in the mutually adjusted analyses.\n\nAlmost all patients ($95.6\\%$) were recorded as having a known ethnicity. The most common ethnic group representing $85.5\\%$ ($$n = 20$$,795) of the patients was the White British Group, followed by $4.2\\%$ ($$n = 1018$$) from the Any Other White Group and $2.8\\%$ ($$n = 674$$) from Other Ethnic Group. The more specific ethnic groups were less common with $1.3\\%$ ($$n = 321$$) of patients defining themselves as Indian, $0.8\\%$ ($$n = 186$$) as Pakistani, and less than $0.4\\%$ as Bangladeshi ($$n = 30$$), Chinese ($$n = 37$$), Black African ($$n = 84$$), and Black Caribbean ($$n = 94$$) (Table 2). The univariate model for ethnicity showed a survival difference and the mutually adjusted model demonstrated that patients with Other Ethnic Group and Unknown/Not Stated Ethnicity had a $18\\%$ and $23\\%$ decreased risk of death from any cause, respectively, compared to the White British Group.\n\nIn a sensitivity analysis, the association of survival with age seemed to disappear in most non-white ethnic groups. This could be explained by the younger age of these groups leading to a lower median age at diagnosis than for the White British population (Table 2). The effect on survival in the Unknown/Not Stated Group was less sensitive to statistical adjustment by age, as the median age was older than for the White British Group. After fully adjusting for age, sex, co-morbidity, socio-economic deprivation, tumour morphology, route to diagnosis and treatment received, patients from the Indian Group (HR 0.84, $95\\%$ CI 0.72–0.98), Any Other White (HR 0.83, $95\\%$ CI 0.76–0.91), Other Ethnic Group (HR 0.70, $95\\%$ CI 0.62–0.79) and Unknown/Not Stated Ethnicity (HR 0.81, $95\\%$ CI 0.75–0.88), had better one-year survivals than the White British Group (Table 3). There was no difference between the White British Group and the remaining Bangladeshi, Pakistani, Chinese, Black Caribbean, and Black African Ethnic minority groups.\n\nThe ethnic difference in survival was further explored by investigating whether there was any interaction between ethnicity and glioblastoma diagnosis, route or pathway to diagnosis, and optimal treatment received (Table 4). The Any Other White Group were more likely to be diagnosed through a hospital stay that included an emergency admission (OR 1.16, $95\\%$ CI 1.02–1.33). The Other Ethnic Group were nearly a third more likely to receive the diagnosis of glioblastoma (OR 1.28, $95\\%$ CI 1.04–1.56) than the White British Group. However, individuals with Unknown/Not Stated Ethnicity had the most favourable prognosis and were less likely to be diagnosed with a glioblastoma (OR 0.70, $95\\%$ CI 0.58–0.84), less likely to be diagnosed through a hospital stay that included an emergency admission (OR 0.61, $95\\%$ CI 0.53–0.69), and more likely to receive the optimal treatment option for their other-than-glioblastoma diagnosis (OR 0.39, $95\\%$ CI 0.31–0.49).\n\n## 4.1. Main Findings\n\nThis study of 24,319 people residing in England and diagnosed with a brain tumour between 2012 and 2017 shows better one-year survival for patients from Indian, Any Other White, Other Ethnic Groups, and Unknown/Not Stated Ethnic Groups than for the White British Group (HR 0.84 ($95\\%$ CI 0.72–0.98), HR 0.83 ($95\\%$ CI 0.76–0.91), HR 0.70 ($95\\%$ CI 0.62–0.79), and HR 0.81 ($95\\%$ CI 0.75–0.88), respectively). The survival analysis was adjusted for age, sex, co-morbidity, socio-economic deprivation, tumour morphology, route to diagnosis, and treatment received. Individuals with Unknown/Not Stated Ethnicity had the best prognoses and as a group were less likely be diagnosed with glioblastoma or to be diagnosed through a hospital stay, including an emergency admission.\n\n## 4.2. Comparisons to Other Findings\n\nIn comparison to the smaller study by Ratneswaren et al. [ 2014], which was limited to high-grade glioma patients living in South East England [7], our current study was able to incorporate additional factors that may influence the impact of ethnicity on survival. In this larger national dataset, the heterogenous ethnicities were categorised into better defined groups for a precise analysis. We also found the Indian and Other Ethnic Group had a better survival than the White British Group. However, we identified the Unknown/Not Stated Ethnic Group having a better one-year survival than the White British Group, in contrast to the reverse finding in the earlier study. This could be due to a higher proportion of unknown ethnicity data, which was $21.7\\%$ compared to only $4.4\\%$ in the current study.\n\nUS population-based studies have also reported racial and ethnic variations in brain tumour incidence and survival. Most have shown that Caucasian people have poorer survival outcomes compared to Black/African Americans and Asian/Pacific Islander Americans [10,11,12,13,14,15]. Other work, however, has reported that African Americans have an increased risk of death from malignant brain tumour compared to Caucasians and other race and ethnicities [16], which was explained further by an interaction between race and surgery type.\n\n## 4.3. Interpretations and Implications\n\nIn this study, we have demonstrated that the White British Ethnic Group has a poorer survival compared to other ethnic groups. An English paper by Maile et al. [ 2016] has reported incidence data broadly similar to the US finding that patients from White Ethnic Groups were significantly more likely to develop glioblastoma than other racial/ethnic groups [4,17]. They did not evaluate survival; however, their results could help explain the association between White British ethnicity and a higher risk of mortality from high-grade glioma.\n\nIncreasing age is known to be a poor prognostic factor for patients with malignant brain tumour [17]. The demography of ethnic minorities in England reflects the fact that people from these groups are younger and congregate in major cities, such as London, compared to other areas of England. A cohort study from the US also identified that patients of Hispanic background were diagnosed at a younger age compared to non-Hispanic Whites and had an improved overall survival [18]. A report by The King’s Fund suggested that, overall, people from ethnic minorities have poorer access to UK healthcare compared to the White British Groups [19], and this could correlate with fewer individuals from these being registered with the NHS. As a result, the probability of White people being diagnosed with a glioma could be increased due to their greater use of diagnostic tools, even from a young age, and therefore, they have a greater risk of ionising radiation exposure [20]. Since our finding of better survival for patients from Any Other White and Unknown/Not Stated Ethnic *Group is* new, it needs further exploration. One explanation could be that these individuals travel to their countries of origin for better healthcare and social support following their diagnosis, which could mean that their deaths abroad were not formally registered in the English system [21].\n\nBrain tumours are considered difficult to diagnose [22], as these cancers tend to [1] involve 3 or more GP visits before diagnosis [23,24] and [2] are likely to present as an emergency [9,25]; both could lead to poorer outcomes [9]. The potential impact of family support on outcome might also differ between ethnic groups. People from minority ethnic groups, particularly those from Asian backgrounds, are more likely to be surrounded by extended families compared to the nuclear family structure, typical of the White British Group; it is possible that extended families may be more likely to recognise subtle signs of a brain tumour, including neuro-cognitive changes, as well as possible recurrences and encourage earlier diagnosis.\n\nThe current standard therapy for gliomas consists of surgical resection followed by adjuvant chemotherapy and radiation, prolonging median overall survival to 15 months for glioblastomas [26,27], and is represented in this study by the optimal treatment option. From our results, we demonstrated that the Unknown/Not Stated Ethnic Group are less likely to receive optimal treatment, but that could be due to the lower chance of being diagnosed with a glioblastoma.\n\nStudies investigating possible factors explaining brain tumour occurrence have identified genes that could be associated with glioma development and tumours carrying the worst prognosis [28,29]. The presence of such genes and their specific alterations could perhaps explain the differences in prognosis by ethnicity. Epigenetic age acceleration, which is the difference between age predicted by DNA methylation and chronological age, has been linked with many cancers [30]. A recent study by Crimmins et al. [ 2021], which examined epigenetic clocks to evaluate a linkage with race/ethnicity, found that the majority of clocks indicated slower epigenetic ageing among Hispanic and African American individuals compared to White individuals [31]. The reports of differing incidence and survival by racial/ethnic groups, make it important to explore possible genetic alterations and variation in signalling pathways to identify and compare polymorphisms between ethnic minority and White individuals [13]. For example, one study found a $42\\%$ reduction of risk of glioma in patients with a history of diabetes [32], and a recent meta-analysis confirmed this inverse relationship where elevated blood sugar, or a previous history of diabetes, are inversely associated with risk of glioma [33]. In England, people of Black (African and Caribbean) and South Asian (Bangladeshi, Indian, and Pakistani) backgrounds are at higher risk of developing type 2 diabetes from a younger age compared to those of a White background [34], and this could possibly be associated with the decreased glioma mortality observed here.\n\nThe better survival for Indian individuals after adjusting for other factors was of particular interest and may suggest that there are other more specific influencing factors. As there were no significant differences between Indian and White British Groups in terms of patient characteristics, tumour morphology, and route or pathway to diagnosis, further explanation is needed to justify the difference when treatment received is added to the Cox proportional regression model. Due to curcumin’s anti-tumoural effects on glioma cells in preclinical in vitro and in vivo [35], we speculate that an Indian diet that usually includes curcumin might play a role in their prolonged brain tumour survival, perhaps linked to a better response to treatment. A recent study in India evaluated the molecular biomarkers of brain tumours in Indian patients [36] and reported a high prevalence of isocitrate dehydrogenase 1 (IDH1) mutation in astrocytoma and glioblastoma in these patients [37]. The presence of IDH1 mutation correlates with a survival benefit and is more common among glioblastomas progressing from a lower grade glioma compared with 5–$10\\%$ of de novo glioblastomas [38,39]. Consequently, the association between individuals of an Indian background and improved survival could be related in some way to this mutation, in addition to, or perhaps independent of, the therapeutic potential of curcumin. Obtaining detailed information on molecular, genetics, and lifestyle or environmental factors could enable us to further compare outcomes with other populations.\n\n## 4.4. Strengths and Limitations\n\nThe extended period of time covered by this study, along with the greatly improved cancer registration data in recent years for England, have meant that more detailed ethnic groups can be analysed. The previously reported NCIN incidence data for the years 2002 to 2006, and other related studies, found a quarter of cancer patients had unknown (i.e., missing) ethnicity information [4,6,7]. While cancer registration acknowledges that ethnicity data may not be self-reported and may possibly have been derived from already held information, the very large increase in completeness means we can be confident in these analyses. In these new data, only $4\\%$ of patients had unknown/not stated ethnicity information and were analysed separately to get a better understanding of this individual group. Our study also considered the importance of analysing more defined ethnic groups, as significant heterogeneity of risk for many cancers can be seen particularly among those from Black and South Asian Groups [4]. Another strength of this study is the adjustment for many prognostic variables that could vary by ethnicity and prognosis. However, other and unknown factors could still be relevant including patient’s performance status, tumour location within the brain, extent of tumour excision, and more interestingly, molecular biomarkers and genetic information. Our study had some limitations. Reporting patterns of incidence for the histological subtypes by ethnicity would have strengthened the study; however, this was restricted by the small number of patients in each subgroup. In addition, limited numbers of patients in the mixed ethnic groups and those from a White Irish background meant they had to be combined within Any Other Ethnic and Any Other White Groups respectively. Additionally, cancer registration did not collate information that could possibly influence survival, such as recurrences, glioblastoma progressing from a low-grade glioma, or biopsy-only—and hence, we observed a high proportion of patients ($34.9\\%$) who had no surgical resection but could possibly just had a diagnostic biopsy.\n\n## 5. Conclusions\n\nTo obtain a better understanding of potential ethnic differences in malignant primary brain tumour survival, we carried out a detailed evaluation of factors, including age at diagnosis, sex, co-morbidity, socio-economic deprivation, histologic tumour subtype (which is correlated with tumour grade), route or pathway to diagnosis, and treatment options that might affect prognosis. 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Risk Factors'\nauthors:\n- Saleh H. Alwadei\n- Suliman Alsaeed\n- Ahmed Ibrahim Masoud\n- Farhan Alwadei\n- Khalid Gufran\n- Abdurahman Alwadei\njournal: Healthcare\nyear: 2023\npmcid: PMC10000777\ndoi: 10.3390/healthcare11050747\nlicense: CC BY 4.0\n---\n\n# Sleep-Disordered Breathing among Saudi Primary School Children: Incidence and Risk Factors\n\n## Abstract\n\nThis study aimed to identify the incidence and risk factors of sleep-disordered breathing (SDB) using an Arabic version of the pediatric sleep questionnaire (PSQ). A total of 2000 PSQs were circulated to children aged 6–12 years who were randomly selected from 20 schools in Al-Kharj city, Saudi Arabia. The questionnaires were filled out by the parents of participating children. The participants were further divided into two groups (younger group: 6–9 years and older group: 10–12 years). Out of 2000 questionnaires, 1866 were completed and analyzed ($93.3\\%$ response rate), of which $44.2\\%$ were from the younger group and $55.8\\%$ were from the older group. Among all the participants, a total of 1027 participants were female ($55\\%$) and 839 were male ($45\\%$) with a mean age of 9.67 ± 1.78 years. It showed that $13\\%$ of children were suffering from a high risk of SDB. Chi-square test and logistic regression analyses within this study cohort showed a significant association between SDB symptoms (habitual snoring; witnessed apnea; mouth breathing; being overweight; and bedwetting) and risk of developing SDB. In conclusion: habitual snoring; witnessed apnea; mouth breathing; being overweight; and bedwetting strongly contribute the to development of SDB.\n\n## 1. Introduction\n\nMany airway dysfunctions, including obstructive sleep apnea (OSA) and primary snoring, are caused by sleep-disordered breathing (SDB) [1]. OSA is a severe form of SDB characterized by upper airway obstruction, either partial or intermittent, that interrupts the normal sleeping pattern [2]. Among children, SDB is linked with growth retardation, behavioral problems, disturbances in cognitive development, failure to thrive, and attention deficit/hyperactivity disorder [2,3]. The myriad of SDB symptoms in children also include abnormal breathing, snoring, sweating, aggressiveness, irritability, hyperactivity, sleepiness, excessive fatigue, memory impairments, and poor school performance, among many others [1,2,3,4,5]. Furthermore, the craniofacial morphology associated with SDB in children includes retrognathic mandible, increased lower facial height, narrow maxillary arch with a high vault, posterior crossbite, anterior open bite, and restriction in the upper airway space [5,6].\n\nThe prevalence of pediatric OSA ranges from 1–$6\\%$, while habitual snoring varies by definition and ranges from 4–$17\\%$ [1,7,8,9,10]. A previous study in southern Italy found that $4.9\\%$ school children were possessed habitual snoring and among them only $1\\%$ had OSA [11]. Another Turkish study stated that the prevalence of 6–13 years children’s snoring habit is $7\\%$ [12]. Moreover, $11.4\\%$, $12\\%$ and $27.6\\%$ prevalence rates of snoring habit were observed in the Indian, Chinese and Brazilian populations, respectively [13,14,15].\n\nIn Saudi Arabia, the prevalence among adults and children leans toward the higher end of the range reported in the literature [16,17,18]. However, the majority of children with OSA remain undiagnosed [19]. In order to diagnose OSA in children, nocturnal sleep-based polysomnography (PSG) is widely used and is considered the gold standard to diagnose OSA [8]. Regardless of its diagnostic advantages, PSG presents challenges related to its cost, time, complexity, and availability/access which necessitates the use of an efficient screening test that may prompt early detection and treatment [9,19,20]. As dentists and selected dental specialists obtain radiographic x-rays regularly and examine children daily, they could effectively identify the children who are at risk of SDB [9,21]. This is especially true given the previously mentioned association between SDB and craniofacial growth and development, as well as the proven positive responses to various treatment modalities ranging from interceptive orthodontics to orthognathic surgery [22,23,24].\n\nAlthough thorough clinical history and physical examination are an integral part of best practice in any healthcare profession including assessment of SDB, they may not be sufficient to identify children suspected (or at high risk) of OSA [19]. It is important to consider efficient, reliable, and accurate screening tests for timely diagnosis and management (including referral) of children with SDB since it might prevent associated comorbidities [20]. Pediatric sleep questionnaires have been developed for the screening of SDB in children and adolescents, and epidemiological studies have shown them to be clinically significant and relevant [25,26]. As subjective parent report tools, sleep questionnaires are apprehensive about the risk factors and sign symptoms of SDB and OSA [27]. Among these questionnaires, the pediatric sleep questionnaire-22 (PSQ-22) has been validated in groups of referred snoring children and controls, showing excellent specificity and sensitivity for identifying children with OSA [2]. Therefore, this study aimed to determine the incidence of SDB and identify related risk factors among primary school children in Al-Kharj, Saudi Arabia.\n\n## 2. Materials and Methods\n\nThis was a cross-sectional study that was conducted from September 2018 to December 2018 in Al-Kharj city, Saudi Arabia. Al-Kharj city is situated in the Riyadh province of Saudi Arabia with about 376,325 residents (https://www.stats.gov.sa (accessed on 21 November 2017)). Ethical approval was obtained from the College of Dentistry Research Centre at Prince Sattam Bin Abdulaziz College of Dentistry (Registration No: 1439-03-003). In addition, permission to conduct this research was also obtained from the authority of the Ministry of Education. A stratified randomization technique was used to list the schools to be included in this study to ensure the proper sample representation in the Al-Kharj. A free online randomization software (http://www.randomization.com (accessed on 21 November 2017)) was used to select 20 schools (10 boys’ schools and 10 girls’ schools). A formal letter was sent to the principal of each school to obtain permission by explaining the purpose of the study. Once the school authorities agreed to participate in the current research, they were requested to provide a list of primary school students with an assigned number corresponding to each student. Then, a randomization table was used to select the students.\n\nA required sample size of 454 subjects was estimated based on a statistical power calculation described by Pourhoseingholi and colleagues [2013] [28], considering 0.20 non-responses, a confidence level of $95\\%$, a precision of 0.04, and $20\\%$ prevalence SBD as reported in Saudi-based literature using PSQ [17,18].\n\nEach child was given a folder in order to obtain permission from the parents which included: [1] a cover page explaining the aim of the study, the significance of the study, and the confidentiality measures taken to protect collected information, [2] a consent form, and [3] the PSQ. Parents/guardians were asked to observe their child’s sleep pattern for one week before filling out the PSQ to improve response accuracy. Children aged 6–12 years who presented a consent form signed by their parents were included in the current study. The participants were divided into two groups by age: the younger group (age 6–9 years) and the older group (age 10–12 years).\n\nA total of 2000 folders containing a cover letter, a consent form, and the PSQ were distributed to randomly selected children, and the folders were collected a week later.\n\nThe PSQ was previously validated by Chervin et al. [ 2000]; therefore, no validation was required for this study. Moreover, the Arabic version of PSQ was validated by Baidas et al. [ 2019]; therefore, the Arabic form of PSQ was used in this study in order to explain the PSQ properly by the parents [17]. The PSQ contains 22 items, and each item consists of three options to respond to with the following options: ‘yes’ = 1; ‘no’ = 0; and ‘don’t know’ = missing. If participants scored ≥8 items to ‘yes’, they would be considered at high risk of SDB, whilst if they scored <8 items to ‘yes’, they would be considered at a low risk of SDB.\n\n## Statistical Analysis\n\nStatistical analysis was performed using SPSS software version 28 (IBM Corp. Armonk, NY, USA). The demographic of the children and the prevalence rate of SDB was assessed with descriptive statistics. A Chi-square test was performed to identify the differences in demographic variables and SDB symptoms related to the risk factors. For the Chi-square test (2 × 2), demographic variables were dichotomized as (male/female) for gender and (older/younger) for age, when the test was performed to identify differences in being at high risk of developing SBD (yes/no). Moreover, possible risk factors for the SDB were assessed by binary logistic regression. The p-value was set to <0.05 as statistically significant.\n\n## 3. Results\n\nA total of 1866 parents out of 2000 agreed to participate and complete the questionnaire ($93.3\\%$). The mean age of the participants was 9.67 ± 1.78 years. Among all the participants, a total of 1027 children were female ($55\\%$) and 839 were male ($45\\%$). The younger group and older group consisted of a total of $44.2\\%$ and $55.8\\%$ of participants, respectively. The outcome of the PSQ scoring among all the participants was presented in Table 1. Based on the PSQ scores, a total of 243 children ($13\\%$) were categorized as high risk of SBD.\n\nTable 2 showed that the most prevalent symptom of SBD is mouth breathing ($14.4\\%$) and the least prevalent symptom is witnessed apnea ($6.6\\%$). The Chi-square test showed that there were significant differences between gender (females/males) and age (younger/males) in relation to being at high risk of developing SBD ($p \\leq 0.05$). Moreover, there was a significant difference between high and low-risk children concerning habitual snoring, mouth breathing, witnessed apnea, being overweight, and bedwetting (Table 2).\n\nTable 3 presented that binary logistic regression exhibited no significant association with gender in terms of developing SDB. However, the younger age group is significantly associated with the risk of developing SDB. The risks of developing SDB were 1.43 times higher in younger children compared to older children. In addition, children with habitual snoring, witnessed apnea, mouth breathing, being overweight, and bedwetting were at 8.9 times, 2.15 times, 6.6 times, 4.57 times, and 4.81 times higher risk of developing SDB, respectively (Table 3).\n\n## 4. Discussion\n\nThe current study determined the incidence of SDB and identified related risk factors among primary school children in Al-Kharj, Saudi Arabia with the Arabic version of PSQ, which is the most used pediatric sleep questionnaire [17,27]. The original PSQ was translated into the Arabic language by Baidas et al. [ 2018], which assessed 1350 Saudi children, with $91\\%$ of the questionnaire reporting good concordance [17].\n\nThe risk of undiagnosed and untreated pediatric OSA could result in significant medical comorbidities including, but not limited to, cardiovascular, cognitive, metabolic, and growth hormone dysfunction [29]. The lack of awareness of pediatric SDB is one of the main barriers for families to seeking proper care, which starts with a formal diagnosis by a sleep physician using PSG followed by proper treatment [1]. This is the first population-based study in Al-Kharj which determined the incidence of SDB among school-going children. The response rate of the current study is $93.3\\%$ from a total of 1866 participants which is relatively large compared to previous studies [17,30], and somewhat comparable to a recent Saudi study [13]. Such a high response rate ($93.3\\%$) might be attributed to the way the questionnaire was distributed through school principals who are figures of authority at their schools.\n\nThe prevalence of SDB varies by definition and ranges from 4–$17\\%$ [1,7,8,9,10]. The main finding of the study showed that $13\\%$ of participants with an age limit of 6–12 years were at a high risk of SDB. Additionally, $14.4\\%$ were considered mouth breathing, and $6.6\\%$ had witnessed OSA. In comparison to previous Saudi studies that used PSQ and included children with similar age ranges and comparable gender distribution exhibited $21\\%$ and $23\\%$ of high-risk SBD which is higher compared to the current study [17,18]. Moreover, samples from the previous studies reported participants with habitual snoring are more reported ($10.7\\%$ and $15.9\\%$) and witnessed apneas reported to a lesser extent ($3.4\\%$ and $4\\%$) compared to the present study [17,18]. The variation between our findings and Baidas et al. [ 2018] is due to differences in the operational definition of what constitutes habitual snoring. Moreover, Al Ehaideb et al. [ 2021] conducted a PSQ-based survey study of 285 Saudi children seeking orthodontic treatment and found that $47.7\\%$ were at high risk of developing SDB [31]. They also reported $11.3\\%$ and $11.6\\%$ of their sample to have habitual snoring and witnessed apnea, respectively [31]. These larger numbers could be because their sample was collected from an orthodontic clinic at a tertiary public hospital that receives referrals of cases with moderate or severe forms of malocclusions. Globally, studies that used the PSQ reported the prevalence of children at high risk of developing SDB to range from $7.9\\%$ to $12.8\\%$ [1,32,33].\n\nThe current study shows that there is a significant difference ($$p \\leq 0.009$$) in SBD risk factors between younger and older groups. Additionally, it also showed that younger children were 1.43 times more likely to be at high risk of developing SDB compared to older children. In children, enlarged adenoids are the main reason for developing SDB and adenoidectomy/tonsillectomy is considering the primary treatment of SDB in children [9,34]. Adenoids reach their maximum size between the ages of 5 and 7 and begin to shrink afterward [35]. Therefore, enlarged adenoids were more likely to be found in the younger age group in our sample aged 6 to 9 years, and this might be the reason why the younger age group exhibited increased risks of developing SDB. In addition to enlarged adenoids, obesity has also been considered a possible cause of OSA. This study showed that there was a significant association between the high-risk group and children being overweight as perceived by their parents, which agrees with other studies conducted in Saudi Arabia [17,18,31].\n\nIn terms of gender distribution, this study showed a significant association between females and OSA, which is opposite to the reported male predilection of pediatric OSA in other studies [1,11,12,14,17]. The reason why there are usually more males affected by OSA is suggested to be due to the differences in the puberty age between males and females, as females enter puberty first. This variation in OSA prevalence between males and females usually increases as they age [1]. This finding reinforced the information from the previous studies where a significant difference between gender and OSA was observed, including children older than 12–13 years [36,37], while studies that did not show a significant association between gender and OSA were mostly limited to the younger age group [1,10]. This might explain why the current study, which was limited to children younger than 12 years old, was not in line with other studies in terms of the association between males and OSA. Nonetheless, it is important to note that the unique contribution of gender in the regression model was not significant in relation to being at high risks of developing SBD, controlling for other variables (including age, habitual snoring, witnessed apnea, parent perception of the child being overweight, and bedwetting). Therefore, cautious interpretation is warranted regarding this study findings in terms of gender association with, and contribution to, being at high risk of developing SBD.\n\nIt is not surprising that this study showed a significant association between high risks of developing SBD and snoring as snoring is one of the main symptoms of SDB and OSA [1]. Similar findings were observed in other previous studies [6,17,31]. Hence, parents need to appreciate the importance of seeking medical care when their child snores during sleeping. In the future, it would be worthwhile to explore whether the parents who participated in the study have considered medical care for their children, especially those with a high risk of pediatric SDB.\n\nOne of the common misapplications of PSQ questionnaires in the literature is the interpretation of a “yes” response to question no. 6 in the first domain (Table 1), as many authors have interpreted this as the presence of OSA [17,29]. It is recommended that authors use the term “witnessed apnea” for “yes” responses to this question instead of the inaccurate assumption that the child has OSA or is considered at high risk to develop SDB. The current study also showed that the high-risk group ($19.3\\%$) is more associated with OSA compared to the low-risk group ($4.7\\%$). Additionally, children with witnessed apneas were 2.84 times more likely to be at high risk for SDB. However, not every child with witnessed apnea was at high risk for SDB. There were 77 children who had witnessed apneas yet were considered low risk for SDB based on the answers to the remaining questions.\n\nThis study consists of some risk of bias due to the nature of its methodology. Moreover, being overweight was assessed by the parent’s perception. Using the body mass index would have been more objective yet was much harder to do especially given the large sample used in the current study. Additionally, having a PSG would have given a definitive OSA diagnosis. A future study could include a subgroup of the total sample to assess the prevalence more accurately. Although it is not the aim of the study, public awareness programs regarding snoring for children and the risk of pediatric SDB and its symptoms need to be implemented in schools as many local studies have reported similar findings of high rates of snoring and witnessed apnea.\n\n## 5. Conclusions\n\nIn conclusion, $13\\%$ of the school-going children in Al-Kharj Saudi Arabia are at high risk of developing SDB at a younger age. Moreover, habitual snoring, mouth breathing, being overweight, bedwetting, and witnessed apnea were more prevalent in children with a high risk of SDB. Thus, the importance of further exploration of SDB among Saudi school-going children needs to be recognized, strategized, and materialized.\n\n## References\n\n1. 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-0.0094903614372015,\n -0.010931935161352158,\n -0.01327449269592762,\n -0.018980722874403,\n 0.03435751050710678\n]","isConversational":false}}},{"rowIdx":95,"cells":{"text":{"kind":"string","value":"---\ntitle: Comprehensive Analysis of Prognosis and Immune Landscapes Based on Lipid-Metabolism-\n and Ferroptosis-Associated Signature in Uterine Corpus Endometrial Carcinoma\nauthors:\n- Pusheng Yang\n- Jiawei Lu\n- Panpan Zhang\n- Shu Zhang\njournal: Diagnostics\nyear: 2023\npmcid: PMC10000778\ndoi: 10.3390/diagnostics13050870\nlicense: CC BY 4.0\n---\n\n# Comprehensive Analysis of Prognosis and Immune Landscapes Based on Lipid-Metabolism- and Ferroptosis-Associated Signature in Uterine Corpus Endometrial Carcinoma\n\n## Abstract\n\n[1] Background: The effect of tumor immunotherapy is influenced by the immune microenvironment, and it is unclear how lipid metabolism and ferroptosis regulate the immune microenvironment of uterine corpus endometrial carcinoma (UCEC). [ 2] Methods: Genes associated with lipid metabolism and ferroptosis (LMRGs-FARs) were extracted from the MSigDB and FerrDb databases, respectively. Five hundred and forty-four UCEC samples were obtained from the TCGA database. The risk prognostic signature was constructed by consensus clustering, univariate cox, and LASSO analyses. The accuracy of the risk modes was assessed through receiver operating characteristic (ROC) curve, nomogram, calibration,, and C-index analyses. The relationship between the risk signature and immune microenvironment was detected by the ESTIMATE, EPIC, TIMER, xCELL, quan-TIseq, and TCIA databases. The function of a potential gene, PSAT1, was measured by in vitro experiments. [ 3] Results: A six-gene (CDKN1A, ESR1, PGR, CDKN2A, PSAT1, and RSAD2) risk signature based on MRGs-FARs was constructed and evaluated with high accuracy in UCEC. The signature was identified as an independent prognostic parameter and it divided the samples into high- and low-risk groups. The low-risk group was positively associated with good prognosis, high mutational status, upregulated immune infiltration status, high expression of CTLA4, GZMA and PDCD1, anti-PD-1 treatment sensitivity, and chemoresistance. [ 4] Conclusions: We constructed a risk prognostic model based on both lipid metabolism and ferroptosis and evaluated the relationship between the risk score and tumor immune microenvironment in UCEC. Our study has provided new ideas and potential targets for UCEC individualized diagnosis and immunotherapy.\n\n## 1. Introduction\n\nUterine corpus endometrial carcinoma (UCEC) is one of the most common gynecologic malignancies, with an increasing incidence of about $1\\%$ per year [1]. Approximately $15\\%$ of UCEC patients are diagnosed at an advanced stage, and approximately 15–$20\\%$ of patients will experience relapse after primary surgical treatment [2,3]. Although surgery, carboplatin/paclitaxel systemic chemotherapy, and hormone therapy are effective treatments, patients with advanced disease, recurrence, or drug resistance still have poor prognoses [4,5]. In recent years, it has been reported that patients with advanced endometrial cancer may benefit from immunotherapy. The main immunotherapy approaches include immune checkpoint inhibitors (ICIs), adoptive cell transfer (ACT), cancer vaccines, and lymphocyte-promoting cytokines. For example, dostarlimab, a drug that inhibits the programmed cell death 1 and programmed cell death ligand 1 pathway, can improve the prognosis of patients receiving platinum chemotherapy or progressive mismatch repair deficiency endometrial cancer [6]. However, the effect of immunotherapy is not ideal due to the complexity of the immune microenvironment and differences in the response to immunotherapy [7,8]. Therefore, it is vital to identify potential diagnostic and prognostic targets or risk signatures and to tailor individualized immunotherapy strategies for improving the outcomes of UCEC patients.\n\nObesity is an independent risk factor for UCEC [9]. Almost all UCEC patients with obesity have altered lipid metabolism [10]. Tan et al. built an 11 lipid metabolism gene (LMG) signature to reflect the prognosis of UCEC patients [11]. Lipids are susceptible to oxidation by oxygen free radicals. Overproduction and elimination failure of lipid peroxidation are the main reasons for the novel iron-dependent cell death ferroptosis [12,13,14]. Liu et al., Wang et al., and Wei et al. constructed a ferroptosis-related gene signature to predict the prognosis of UCEC patients [15,16,17]. Lipid synthesis, storage, and degradation processes can be regulated by ferroptosis [18,19]. Iron depletion leads to a large amount of lipid accumulation in breast cancer cells [20]. Iron accumulation is due to altered lipid metabolism associated with increased oxidative stress in myelodysplastic syndromes [21]. Ferroptosis is closely associated with lipid metabolism pathways [22,23]. Inhibiting β-oxidation can restore tumor cell sensitivity to ferroptosis [24]. Upregulating stearoyl CoA desaturase 1 (SCD1), the rate-limiting enzyme in fatty acid synthesis, increases the resistance of tumor cells to ferroptosis. Increasing evidence suggests that lipid metabolism and ferroptosis closely affect each other [25,26]. However, the interaction and shared role of ferroptosis and lipid metabolism in UCEC remains unclear.\n\nIn the present study, we aimed to construct a prognostic risk signature based on both lipid metabolism and ferroptosis to comprehensively analyze their combined effects on UCEC. We screened six risk genes (CDKN1A, ESR1, PGR, CDKN2A, PSAT1, and RSAD2) as reliable diagnostic and prognostic biomarkers and divided UCEC patients into high- and low-risk groups based on their risk score. Then, we estimated differences in immune score, immune infiltration, immune checkpoint, immunotherapy, and chemotherapy response between the high- and low-risk groups. The findings provide a new idea for individualized therapy strategies to improve the prognosis of UCEC patients.\n\n## 2.1. Dataset Information\n\nSequencing RNA data (HTSeq-FPKM) and clinical information were obtained from The Cancer Genome Atlas (TCGA) database, and 579 cases were selected for study, including 544 UCEC samples and 35 normal samples. The detailed clinical information of the UCEC patients is shown in Table S1.\n\n## 2.2. Extraction of Lipid-Metabolism-Related and Ferroptosis-Associated Genes\n\nLipid-metabolism-related genes (LMRGs) were collected from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the Molecular Signatures Database (MSigDB), including the GSEA, HALLMARK, and REACTOME databases [27]. The detailed gene sets are shown in Table S2. A total of 1457 genes were selected for analyses after removing duplicate genes (Table S3). In addition, we downloaded 288 ferroptosis-associated genes (FAGs) from the FerrDb database (http://zhounan.org/ferrdb/legacy/index.html, accessed on 1 June 2022). After removing the replicates, 259 individual FAGs were used for further investigation.\n\n## 2.3. Construction of the LMRG and FAR Prognostic Signature\n\nThe evaluation of the differentially expressed LMRGs (DE-LMRGs) was performed using the default settings for the “lmFit”, “eBayes”, and “topTable” functions in the “limma” R package. The screening criteria were $p \\leq 0.05$, |Log2 Fold Change (FC)| > 1, and a false discovery rate (FDR) < 0.05. Then, univariate Cox regression analysis was applied to determine LMRGs with overall survival (OS) in UCEC by using the coxph function in the “survival” R package at $p \\leq 0.05.$ The molecular classification of DE-LMRGs in UCEC was analyzed by the “ConsensusClusterPlus” R package. Principal component analysis (PCA) was performed to identify the grouping ability of our model with the R package “stats”. Then, the FAGs interacted with the results of the consensus clustering approach, and the genes of interaction were selected for further study.\n\nWe performed univariate cox and least absolute shrinkage and selection operator (LASSO) analyses to identify significant prognostic genes based on both LMRGs and FARs with a threshold of $p \\leq 0.05.$ Then, a risk score signature was created by considering the estimated cox regression correlation coefficients and the expression values of the optimized LMRGs and FARs. The formula is risk score = Σi1expGenei*coeffi. According to the median value of the calculated risk scores from the TCGA-UCEC, the patients were divided into low- and high-risk groups. The prognostic ability and stability of the signature was measured by the Kaplan–Meier (K–M) analysis, multivariate Cox regression analysis, and receiver operating characteristic (ROC) curve with the “Survival” and “sevivalROC” R package ($p \\leq 0.05$).\n\n## 2.4. Functional Enrichment Analysis\n\nTo examine the distinction between the high- and low-risk group of our model, we further carried out gene set variation analysis (GSVA) using the “GSVA” function with method parameters (min.sz = 10, max.sz = 500, verbose = TRUE) of the “GSVA” R package, and conducted KEGG pathway analysis and Gene Ontology (GO) analysis via the “clusterProfiler (version 3.14.3)” R package ($p \\leq 0.05$).\n\n## 2.5. Tumor Mutational Burden (TMB) Analysis\n\nWe downloaded the somatic mutation data from TCGA. Using Perl, we calculated the TMB value of each sample and divided all samples into high- and low-TMB groups based on the median TMB [28]. Then, K–M analysis was used to assess survival differences between the groups. We also calculated the expression differences in TMB between the high- and low-risk groups and analyzed the relationship between TMB and the risk score ($p \\leq 0.05$)\n\n## 2.6. Immune Infiltration of the Prognostic Model\n\nThe CIBERSORT algorithm was utilized to evaluate the 22 types of immune fractions between the high- and low-risk groups, and the results were visualized with the “vioplot” R package. Then, we used the Tumor Immune Estimation Resource (TIMER) to evaluate correlations between expression of six model genes and the immune infiltration level of tumor-infiltrating immune cells. We also analyzed the relationship between innovative targeted therapy and risk prognostic models. The Wilcoxon test was used to detect expression of potential immune checkpoints between the high-risk and low-risk groups ($p \\leq 0.05$). Furthermore, we downloaded clinical data from The Cancer Immunome Atlas (TCIA) to predict the response to immune checkpoint blockade (CTLA-4 and PD-1) in patients in the high- and low-risk groups by the immunophenoscore. In addition, according to the Genomics of Drug Sensitivity in Cancer (GDSC) database, the R package “pRRophetic” was used to measure the half-maximal inhibitory concentration (IC50) of chemotherapeutic drugs.\n\n## 2.7. Cell Culture\n\nThe UCEC cell lines Ishikawa, HEC-1A, HEC-1B, and ECC-1 were obtained from the American Type Culture Collection (ATCC). The HEC-1A cell lines were cultured in McCoy’s 5A (Gibco, New York, NY, USA) supplemented with $10\\%$ fetal bovine serum (FBS, Biological Industries, Kibbutz Beit-Haemek, Israel) and $1\\%$ penicillin/streptomycin (P/S); the others were cultured in RPMI 1640 culture medium with $10\\%$ FBS and $1\\%$ P/S. All of the cells were cultured at 37 °C in a humidified incubator under $5\\%$ CO2.\n\n## 2.8. Small Interfering RNA (siRNA) Transfection\n\nThe siRNA PSAT1 and scrambled control sequences were obtained from Gene Pharma (Shanghai, China). The details of the sequences are as follows: si-PSAT1-1: forward 5′-CAGUGUUGUUAGAGAUACAdTdT-3′, reverse 5′-UGUAUCUCUAACAACACUGdTdT-3′; si-PSAT1-2: forward 5′-GCUGUUCCAGACAACUAUAdTdT-3′, reverse 5′-UAUAGUUGUCUGGAACAGCdTdT-3′. siRNA transfection was carried out using Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA).\n\n## 2.9. Quantitative Real-Time PCR (qRT–PCR)\n\nTotal RNA was extracted using TRIzol reagent (Sangon Biotech, Shanghai, China) after transfecting siRNA for 48 h, and reverse transcription was performed using PrimeScriptTM RT Reagent Kit (TAKARA, RR047A). QRT–PCR was conducted with the SYBR Green qPCR Supermix kit (Invitrogen). The primers used were purchased from Tsingke Biotechnology Co (Beijing, China), as follows: PSAT1 Forward 5′-ACTTCCTGTCCAAGCCAGTGGA-3′; PSAT1 Reverse 5′-CTGCACCTTGTATTCCAGGACC-3′; GAPDH Forward 5′-GGAGCGAGATCCCTCCAAAAT-3′; GAPDH Reverse 5′-GGCTGTTGTCATACTTCTCATGG-3′.\n\n## 2.10. Western Blot Analysis\n\nTotal proteins were obtained from cells using PIPA buffer (New Cell & Molecular Biotech, Suzhou, China) at 72 h after siRNA transfection, separated by sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE), and transferred to polyvinylidene fluoride (PVDF) membranes (Millipore, New York, NY, USA). The membranes were blocked using $5\\%$ BSA for at least 1 h at room temperature and incubated with PSAT1 (10501-1-AP, Proteintech, Wuhan, China) or GAPDH (10494-1-AP, Proteintech) at 4 °C overnight. The next day, the membranes were incubated with secondary antibody (GB23303, Servicebio, Shanghai, China) for 1 h at room temperature, and bands were detected by chemiluminescence.\n\n## 2.11. Cell Proliferation Assay\n\nCell proliferation was detected by the Cell Counting Kit-8 assay (CCK-8) and colony formation assay. For CCK-8, the cells were seeded into 96-well plates at a density of 2000 cells/well after 72 h of transfection. At the indicated time, CCK-8 solution (10 μL) was added to each well of the culture medium. Cell viability was measured using an automatic enzyme-linked immune detector after incubation for 1 h. For the colony formation assay, 1000 transfected cells were seeded into six-well plates for 10–14 days, and the culture medium was changed every three days. After staining with $0.1\\%$ crystal violet and photographing, cell colonies were statistically analyzed by the t-test.\n\n## 2.12. Cell Migration and Invasion Assay\n\nCell migration and invasion were assessed using 24-well transwell chambers (8 μm; Millipore). In brief, a sample of 4 × 104 cells suspended in 200 μL serum-free medium was seeded in the upper chamber, and the lower chamber contained 600 μL medium with $10\\%$ FBS. After 48 h, the chambers were fixed with $4\\%$ paraformaldehyde and stained with $0.1\\%$ crystal violet dye for 30 min. The upper chamber cells were wiped off and then photographed and counted under a microscope. For the invasion assays, Matrigel (BD, biocoat, #358248) was used to coat the upper chamber, after which the cells were seeded; the next step was the same as above.\n\n## 2.13. Statistical Analysis\n\nBioinformatic statistical analyses were performed using R (v.3.6.1) software. Pearson correlation analysis was employed for correlation analysis between TMB and the risk model. All of the in vitro experiments were independently performed in triplicate and analyzed by the t-test. Data were analyzed using the IBM SPSS Statistics 22 and visualized in GraphPad Prism 9. The values were presented as the mean ± standard deviation (SD). $p \\leq 0.05$ was considered statistically significant.\n\n## 3.1. Identification and Clustering of LMRGs\n\nA brief workflow of this research is presented in Figure S1. We screened 1457 LMRGs for differential expression analysis and identified 88 differentially expressed LMRGs (DE-LMRGs) with the “limma” R package based on 544 UCEC samples and 35 normal samples from TCGA. The boxplot of the expression patterns of the 88 DE-LMRGs is shown in Figure 1A. KEGG analysis and GO analysis showed that these significant genes mainly participate in lipid metabolic processes (Figure S2A,B). Then, univariate cox hazards regression and Kaplan–Meier (K–M) analyses were utilized to screen out prognostic LMRGs based on TCGA, and we obtained six risk genes and three protective genes for survival (Figure S2C,D). The consensus clustering approach was used to divide the UCEC samples with the non-negative matrix factorization (NMF) algorithm. Based on LMRGs expression, the optimal clustering stability was confirmed when $K = 3$ (Figure 1B and Figure S2E,F). We also performed principal component analysis (PCA), which showed the good grouping ability of our clustering (Figure 1C). Therefore, all of the UCEC samples were divided into three clusters, and the heatmap showed lower expression for the DE-LMRG genes in Cluster A (Figure S2G). Moreover, K–M analysis indicated a significant difference in OS among the three subgroups, with the patients in Cluster A having the best prognosis (Figure 1D, $p \\leq 0.05$). By further analyzing the clinical characteristics among the three clusters, we found that patients in Cluster C had an older age and a higher grade and stage (Figure S2H–J, $p \\leq 0.05$).\n\n## 3.2. Signature Construction Based on LMRGs and FAGs\n\nDifferentially expressed genes among the three clusters were obtained from consensus clustering analysis and intersected with FAGs. Then, we obtained both lipid metabolism-related and ferroptosis-associated genes (LMG-FAGs) (Figure 2A). We performed overall-survival-based univariate regression analysis on the lipid-metabolism-related and ferroptosis-associated genes (LMG-FAGs) obtained through consensus clustering analysis. This approach revealed 211 LMG-FAGs associated with the prognosis of endometrial cancer, and we classified them into 87 risk genes and 124 protective genes according to the hazard ratio (HR) and p value (Table S4, $p \\leq 0.05$). To avoid overfitting and bias, the results of univariate regression analysis were subjected to LASSO regression analysis using the “glmnet” R package, and the accuracy of the model was tested by cross-validation (Figure 2B,C). Hence, a six-gene prognostic risk model was established by the following formula: risk score = [CDKN1A expression × (−0.02353)] + [CDKN2A expression × (0.11554)] + [ESR1 expression × (−0.05874)] + [PGR expression × (−0.11493)] + [PSAT1 expression × (0.05505)] + [RSAD2 expression × (0.01431)]. We analyzed the relationship between different risk scores and patient follow-up times, events, and expression changes of individual genes, and it was observed that with an increase in the risk score, the survival rate of patients decreased significantly. CDKN1A, ESR1, and PGR were found to be protective factors that showed downregulated expression with increased risk scores; CDKN2A, PSAT1, and RSAD2 showed the opposite result (Figure 2D $p \\leq 0.05$). Furthermore, we detected expression levels and performed multivariate Cox regression and K-M survival analyses on the six independent prognostic genes. The results indicated that high expression of CDKN1A, ESR1, and PGR was related to better prognosis, whereas high expression of CDKN2A, PSAT1, and RSAD2 was not (Figure S3A–C, $p \\leq 0.05$). According to the median cut-off value of the risk score, the high- and low-risk groups were established to differentiate the UCEC patients in TCGA, and the high-risk patients had a worse prognosis than the low-risk patients (Figure 2E, $p \\leq 0.05$). Then, time-dependent ROC analysis was applied to evaluate the prediction capacity of the signature, with an area under the receiver operating characteristic curve (AUC) of 0.67, 0.70, and 0.70 at 365, 1905, and 1825 days, respectively (Figure 2F, $p \\leq 0.05$).\n\n## 3.3. Prognosis and Validation of the LMRG- and FAG-Based Signature\n\nTo assess the accuracy of the model, we evaluated the performance of this signature with regard to pathological features (age, grade, and stage). The results indicated that high risk was significantly associated with older age and higher grade and stage (Figure 3A–C, $p \\leq 0.05$). Then, the pathological features were added for univariate and multivariate cox regression, and the forest plot showed that age, grade, and stage were still independent prognostic factors, which means that the signature had high accuracy (Figure 3D, $p \\leq 0.05$). In addition, we built a nomogram to predict the 1-year, 3-year, and 5-year survival probability of UCEC patients based on all of the above prognostic elements (Figure 3E, $p \\leq 0.05$), and the calibration plot showed a C-index of 0.767 (0.741–0.793), indicating that the nomogram had good predictive ability (Figure 3F, $p \\leq 0.05$).\n\n## 3.4. DEG and Functional Enrichment Analyses of the Signature\n\nTo investigate the relationship between the six genes in the risk model, we constructed a protein–protein interaction (PPI) network (Figure S4A) and analyzed the correlations (Figure S4B). The results showed that PSAT1 and RSAD2 were more independent and less associated with other genes. Next, a volcano plot and heatmap showed the DEGs between the two risk groups; 81 genes were upregulated and 195 genes were downregulated (Figure 4A,B). The PPI network of the DEGs is depicted in Figure S4C. To reveal the underlying biological characteristics associated with the risk scores, KEGG and GO analyses were performed based on DEGs between the high- and low-risk groups. The results indicated that pathways such as kinase and peptidase regulation, apparatus morphogenesis, cell cycle regulation, viral infection, and antiviral innate immune response were highly enriched (Figure 4C,D, $p \\leq 0.05$). In addition, we performed GSVA to probe differences in pathways between the two risk groups. As illustrated in the heatmap in Figure 4E, pathways related to lipid metabolism and ferroptosis, such as “tyrosine metabolism”, “fatty acid metabolism”, “alpha linolenic acid metabolism”, and “DNA replication”, were significantly enriched ($p \\leq 0.05$).\n\n## 3.5. Relationship between the Tumor Mutational Burden (TMB) and the Risk Model\n\nTMB, the somatic coding errors, is generally considered high when >10 or >16 mutations/megabase DNA are present [28]. Recently, TMB is thought to be closely related to the survival prognosis of tumor patient [29]. To examine in more depth how well the risk-prognosis model predicts tumor development, we investigated its relationship with TMB. First, correlation analysis showed that the TMB level had a negative association with the LMRG-FAG risk score (Figure 5A, $p \\leq 0.05$), and the high-risk group showed lower TMB levels (Figure 5B, $p \\leq 0.05$). We also investigated the survival of patients with different TMB statuses by K-M analysis, and the results demonstrated that the patients in the low-TMB group had poor prognostic outcomes (Figure 5C, $p \\leq 0.05$). In addition, mutation information of the genes in the low- and high-TMB groups was explored using a waterfall chart, and PTEN ($58.2\\%$), PIK3CA ($48.7\\%$), TTN ($44.5\\%$), ARID1A ($43.5\\%$), and TP53 ($36.4\\%$) were the top five mutated genes (Figure 5D). We further studied and classified the mutation information, variant type, and SNV class, and the results demonstrated that missense mutations, single nucleotide polymorphism (SNP), and C > T accounted for the largest proportion (Figure S5A–C). The number of altered bases in each sample and the mutation types in different colors are shown in Figure S5D,E; mutation information for the six risk genes [PGR ($37\\%$), ESR1 ($33\\%$), RSAD2 ($27\\%$), PSAT1 ($18\\%$), CDKN1A ($14\\%$), and CDKN2A ($5\\%$)] is provided in Figure S5F. Recently, multiple pieces of research have illustrated that TMB is closely associated with tumor immune cell infiltration and affects the efficacy of immunotherapy [30,31]. Therefore, we evaluated the value of TBM in the complexity of the tumor immune microenvironment. We discovered that most immune cells had a positive correlation with the TMB level, especially T cells CD8+, T cells CD4+, and B cells (Figure S5G). In addition, T cells CD8+, T cells CD4+ memory activated, T cells CD4+ memory resting, and T cells regulatory had higher expression in the high-TMB group compared to the low-TMB group (Figure S5F, $p \\leq 0.05$), suggesting that TMB may have an effect on the immune response.\n\n## 3.6. Immune Infiltration Associated with the LMRG-FAG-Based Signature\n\nRecent studies have shown that lipid metabolism and ferroptosis are important components of the tumor microenvironment and are strongly associated with tumor immune activities [32,33,34,35]. We first used ESTIMATE to determine the relationship of tumor immune infiltration between the two risk groups. The stromal, immune score, and ESTIMATE score were significantly downregulated in the high-risk group (Figure 6A–C, Wilcoxon $p \\leq 0.05$). Then, the CIBERSORT algorithm was applied to detect the composition of the 22 immune cells in UCEC patients (Figure S6A). A boxplot demonstrated that the difference in the distribution of the 10 immune-infiltrating cells between the two risk groups was significant. The naive B cells, memory B cells, resting CD4 memory T cells, regulatory T cells (Tregs), and resting dendritic cells had low expression in the high-risk group compared to the low-risk group. Meanwhile, the follicular helper T cells, monocytes, M1 macrophages, activated dendritic cells, and M2 macrophages were significantly upregulated in the high-risk group compared to the low-risk group (Figure 6D, $p \\leq 0.05$). We also analyzed immune infiltration using the EPIC, TIMER, xCELL, and quanTIseq databases, which fully confirmed the six-gene prognostic risk signature to be closely related to immune activity (Figure S6B–E). In addition, the TIMER database was utilized to assess the relationship between the six risk genes and tumor-infiltrating immune cells. The results showed that only RSAD2 correlated positively with B cells (cor = 0.1858, $$p \\leq 0.0015$$); except for RSAD2, the other genes were significantly associated with CD8+ T cells (Figure S7, $p \\leq 0.05$).\n\n## 3.7. Immunotherapy and Chemotherapy in Different Risk Groups\n\nRecently, immune checkpoints have been identified as key targets of immunotherapy, and immune checkpoint inhibitors (ICIs) are regarded as an effective therapeutic strategy for patients with advanced disease [36,37]. Therefore, we identified potential relationships between the expression of immune checkpoint molecules and our risk model. The results showed that IDO1 and LAG3 expression was significantly increased in the high-risk group compared with the low-risk group, while the expression of CTLA4, GZMA and PDCD1 was obviously decreased in the high-risk group compared with the low-risk group (Figure 6E, $p \\leq 0.05$). Then, we conducted immunophenoscore (IPS) analysis to predict immunotherapy response. As shown in Figure 6F, low-risk patients were more sensitive to anti-PD-1 therapy ($p \\leq 0.05$), suggesting that immunotherapy of blocking CTLA-4 and PDCD1 may be more beneficial for patients in the low-risk group. Since chemotherapy is the main treatment for advanced and recurrent UCEC, we evaluated the response of chemotherapeutics to UCEC patients using the pRRophetic algorithm based on our signature and found that the estimated IC50 of typical chemotherapy drugs (cisplatin, paclitaxel, doxorubicin, and etoposide, etc.) were significantly higher in the low-risk group (Figure 6G, $p \\leq 0.05$). For the other 40 chemotherapy and small molecule drugs, such as lenalidomide, gefitinib, AMG.706, and JNK inhibitor VIII, patients in the high-risk group were identified as being more sensitive (Figure S8, $p \\leq 0.05$). Thus, we indicated that patients with low risk scores were more resistant to chemotherapy than those with high risk scores, but they were more sensitive to anti-PD-1 therapy. In addition, patients in the high-risk group were better suited for chemotherapy. These results may have important implications for individualized immunotherapy in patients with advanced UCEC.\n\n## 3.8. In Vitro Function of the Risk Gene PSAT1 in UCEC Cells\n\nTo further validate the ability of risk signatures to predict prognosis, we investigated protein expression of the six risk genes between normal and UCEC tissues with the CPTAC and HPA (Human Protein Atlas) databases (Figure S9A,B, $p \\leq 0.05$), and the results corresponded with previous analysis. Combined with prognostic analysis and literature searches, we selected PSAT1 for further in vitro functional assays. We identified the mRNA and protein expression of PSAT1 in four UCEC cell lines (Ishikawa, HEC-1A, HEC-1B, and ECC1), and Ishikawa and HEC-1B cells were selected for subsequent studies (Figure 7A,B). Next, we knocked down PSAT1 with siRNA, and the efficiency was verified by qPCR (Figure 7C, $p \\leq 0.05$) and Western blot analysis (Figure 7D). CCK-8 and colony formation assays showed that knockdown of PSAT1 significantly suppressed the proliferation of Ishikawa and HEC-1B cells (Figure 7E,F, $p \\leq 0.05$). In addition, the migration and invasion of the two cell lines were also apparently inhibited after PSAT1 knockdown, as determined by transwell assays (Figure 7G). These results demonstrate that the risk gene PSAT1 significantly promotes progression of UCEC and may affect the prognosis of UCEC patients.\n\n## 4. Discussion\n\nUCEC is one of the most lethal gynecological malignancies. Although many studies over the past decades have sought to improve treatment efficacy, patients with advanced and recurrent disease still have poor prognosis [38]. With the rise and application of immunotherapy, it is insufficient to estimate the prognosis of UCEC patients based on traditional clinicopathological stage [39]. Therefore, our study included the tumor immune microenvironment and immunotherapy in UCEC based on both lipid metabolism and ferroptosis to select more effective prognostic targets and guide individualized treatment of patients.\n\nPrevious studies have established prognostic models of lipid metabolism or ferroptosis in UCEC [11,15,16,17]. However, they only took a single influencing factor into account, and the complex tumor microenvironment was not considered. In our study, we comprehensively considered the interrelationship between lipid metabolism and ferroptosis, based on which a prognostic model of six genes was constructed. We deeply explored the relationship between the model risk score and the tumor immune microenvironment. We found that infiltration of B cells, T cells, and NK cells and expression of the immune checkpoints (CTLA4, GZMA, and PDCD1), as well as sensitivity and chemotherapy resistance to anti-PD-1 treatment in UCEC patients were closely related to the risk scores of the prognostic model. Moreover, in vitro experiments demonstrated that one of the potential targets, PSAT1, promoted the proliferation, migration, and invasion of UCEC cells. Our experiments provide new ideas and a basis for individualized immunotherapy for UCEC patients and provide a potential target for UCEC therapy.\n\nIn the present study, we obtained genes associated with both lipid metabolism and ferroptosis by consensus clustering analysis. After LASSO Cox regression, we constructed a prognostic signature containing six risk genes (CDKN1A, ESR1, PGR, CDKN2A, PSAT1, and RSAD2) based on LMG-FAGs. K-M survival analysis, ROC curves, a nomogram, and calibration identified that the signature had high predictive ability. Estrogen receptor 1 (ESR1) and a progesterone receptor (PGR) were reported to participate in lipid metabolism by encoding estrogen or steroid receptors to promote tumor progression [40,41,42]. Cyclin-dependent kinase inhibitors 1A and 2A (CDKN1A and CDKN2A) have been identified as ferroptosis-related genes in recent studies and can be regarded as biomarkers that influence the tumor microenvironment [43,44,45]. Radical s-adenosyl methionine domain containing 2 (RSAD2) is an interferon-stimulated gene that exerts antiviral effects by dysregulating cellular lipid metabolism [46,47]. Phosphoserine aminotransferase 1 (PSAT1) has been reported to affect the progression of various cancers by participating in lipid metabolism processes [48,49,50]. In conclusion, the six-gene prognostic model showed a significant correlation with lipid metabolism or ferroptosis. In our study, these six genes were used for risk scoring, and each UCEC patient was categorized into two risk groups according to the risk score. We then explored the pathological features of the risk signature, with the high-risk group being related to older age and higher grade and stage. We also found that knockdown of PSAT1 inhibited the proliferation, migration, and invasion of UCEC cells, enhancing the reliability of our model.\n\nSubsequently, we comprehensively analyzed the impact of the risk signature on UCEC. A total of 276 genes were identified to be closely related to the risk score. GO, KEGG, and GSVA analyses based on the signature demonstrated that pathways associated with lipid metabolism and ferroptosis were significantly enriched, which also confirmed the accuracy of our signature. TMB is reported to correlate highly with tumor progression; for example, gastrointestinal tumor patients with low TMB have lower objective response rates and shorter progression-free survival [51], and high TMB is a poor prognostic factor for non-small cell lung cancer [52]. We found that TMB levels had a negative relationship with the LMRG-FAG risk model, which means that patients with low risk and high a mutational burden have a better prognosis in UCEC.\n\nBecause surgery and chemoradiotherapy have limited effects in patients with advanced and recurrent UCEC and traditional pathological staging has an insufficient ability to estimate prognosis, we focused on the relationship of the LMRG-FAG-based risk model with immunotherapy. Stromal, immune, and ESTIMATE scores were significantly downregulated in the high-risk group, indicating that lipid metabolism and ferroptosis are significantly associated with the immune status of UCEC. CIBERSORT algorithm analysis showed that the distribution of 10 immune cells varied between the high- and low-risk groups, with antitumor cells (B cells, T cell CD8, and monocytes, etc.) present at higher abundance in the low-risk group. According to the results, we suggest that the risk score is associated with immune infiltration and immune status in UCEC.\n\nAdverse T cell regulatory pathways tend to be overactive when cancer occurs. CTLA-4 inhibits the immune response at the early stage of T cell induction, and PDCD1 prevents T cell function in peripheral tissues in the later stages [53,54]. Recently, immune checkpoint blockade, one of the major immunotherapy methods, has proven to be an effective strategy for enhancing the effector activity and clinical impact of anti-tumor T cells [55]. Among the ICIs, blocking CTLA-4 and PDCD1 are the two most eminent approaches. CTLA-4 and PDCD1 blockade could induce tumor immunity by improving effector T cell activity or consuming Treg [56]. In 2011, Ipilimumab, a CTLA-4 inhibitor, was approved for melanoma [57]. In 2017, the PDCD1 inhibitor pembrolizumab was approved for UCEC patients with microsatellite instability, and half of the patients benefited from it [58]. Since the predictive value of immune checkpoints has been demonstrated in a variety of human malignancies, we then explored immune checkpoint expression between the two risk groups to guide individualized immunotherapy for UCEC patients. The expression of CTLA4, GZMA and PDCD1 was significantly upregulated in patients with low risk scores, and IDO1 and LAG3 were increased in the high-risk group. Therefore, we indicated that specially blocking CTLA-4 and PDCD1 immunotherapy would be more effective for patients in the low-risk group. Meanwhile, we detected the difference in sensitivity to PD-1 and CTLA-4 inhibitors, and the results indicated that low-risk patients were more sensitive to anti-PD-1 therapy, meaning that immunotarget therapy was more effective in low-risk patients. Accordingly, our risk signature has a certain guiding role in the anti-PD-1 immunotherapy of UCEC patients. Interestingly, high-risk patients were more sensitive to traditional chemotherapeutic agents and small molecule inhibitors such as cisplatin, paclitaxel, AMG.706, and ABT.888. Hence, patients in the high-risk group were more likely to benefit from chemotherapy and our signature can be used to guide personalized treatment of UCEC patients.\n\nHowever, it is undeniable that our study also has some limitations. First, the study data were obtained from only TCGA, and we did not verify the accuracy of our model with more cohorts. Second, immunotherapy and chemosensitivity analyses were only derived from the transcriptome, and we still need to obtain more prospective experimental data to support the findings. Finally, as a potential therapeutic target, the molecular mechanism underlying the risk-related gene PSTA1 needs to be further clarified.\n\n## 5. 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0.020248856395483017\n]","isConversational":false}}},{"rowIdx":96,"cells":{"text":{"kind":"string","value":"---\ntitle: Dynamic NIR Fluorescence Imaging and Machine Learning Framework for Stratifying\n High vs. Low Notch-Dll4 Expressing Host Microenvironment in Triple-Negative Breast\n Cancer\nauthors:\n- Shayan Shafiee\n- Jaidip Jagtap\n- Mykhaylo Zayats\n- Jonathan Epperlein\n- Anjishnu Banerjee\n- Aron Geurts\n- Michael Flister\n- Sergiy Zhuk\n- Amit Joshi\njournal: Cancers\nyear: 2023\npmcid: PMC10000786\ndoi: 10.3390/cancers15051460\nlicense: CC BY 4.0\n---\n\n# Dynamic NIR Fluorescence Imaging and Machine Learning Framework for Stratifying High vs. Low Notch-Dll4 Expressing Host Microenvironment in Triple-Negative Breast Cancer\n\n## Abstract\n\n### Simple Summary\n\nBreast cancer is a disease that is affected by both the tumor cells and the host environment. It is well known that the tumor blood vessels are aberrant in structure and function due to rapid angiogenesis, and this aberrant vasculature plays a major role in drug delivery and therapy response of breast cancer. Dll4 is a protein that helps control the growth of blood vessels in tumors. This study used near-infrared optical imaging and a novel machine learning framework to determine if Dll4 levels can be predicted from simple noninvasive imaging assays. The eventual results of this study may help physicians decide if a given triple-negative breast cancer patient will benefit from a Dll4 targeted therapy.\n\n### Abstract\n\nDelta like canonical notch ligand 4 (Dll4) expression levels in tumors are known to affect the efficacy of cancer therapies. This study aimed to develop a model to predict Dll4 expression levels in tumors using dynamic enhanced near-infrared (NIR) imaging with indocyanine green (ICG). Two rat-based consomic xenograft (CXM) strains of breast cancer with different Dll4 expression levels and eight congenic xenograft strains were studied. Principal component analysis (PCA) was used to visualize and segment tumors, and modified PCA techniques identified and analyzed tumor and normal regions of interest (ROIs). The average NIR intensity for each ROI was calculated from pixel brightness at each time interval, yielding easily interpretable features including the slope of initial ICG uptake, time to peak perfusion, and rate of ICG intensity change after reaching half-maximum intensity. Machine learning algorithms were applied to select discriminative features for classification, and model performance was evaluated with a confusion matrix, receiver operating characteristic curve, and area under the curve. The selected machine learning methods accurately identified host Dll4 expression alterations with sensitivity and specificity above $90\\%$. This may enable stratification of patients for Dll4 targeted therapies. NIR imaging with ICG can noninvasively assess Dll4 expression levels in tumors and aid in effective decision making for cancer therapy.\n\n## 1. Introduction\n\nBreast cancer heterogeneity has been extensively studied and it has enabled the classification and categorization of tumors into molecular subtypes depending on the overexpression of antigens or hormone receptors on tumor cells [1]. Identification of tumor subtypes improves cancer patients’ care and prognosis by tailoring therapies to the subtypes [2,3,4]. Breast and many other cancers are highly heritable, yet most causative variants are unknown, and most of the known risk variants are considered tumor-cell-autonomous, with far less emphasis placed on identifying the role of germline variants impacting the tumor microenvironment (TME). The TME is a complex and dynamic system that includes cancer cells, stromal cells, blood vessels, and extracellular matrix [5] and plays a significant role both in tumor cell proliferation and in chemo- or radiotherapy delivery and response [5,6,7]. There is growing evidence that heritable modifiers of the tumor microenvironment can profoundly impact tumor behavior and response to diagnostic and therapeutic interventions [8,9,10,11,12].\n\nTumor blood vessels have abnormal structure and function, which leads to heterogeneity in blood perfusion both temporally and spatially [13]. This heterogeneity has multiple adverse consequences, including limiting the access of blood-borne drugs and effector immune cells to poorly perfused regions of tumors [14]. As a result, these areas become hypoxic and have low extracellular pH [15]. Hypoxia has been shown to play a significant role in tumor progression and metastasis by inducing genetic instability, angiogenesis, immunosuppression, inflammation, and resistance to cell death by apoptosis and autophagy [16,17]. Anti-angiogenic drugs are designed to target the vasculature in order to starve tumors and prevent them from growing. However, recent studies have shown that the efficacy of these drugs may be limited by specific biomarkers and pathways associated with resistance [15]. For example, it has been shown that some patients may not benefit from anti-VEGF therapies if they have elevated levels of plasma sVEGFR1 [18]. Similar outcomes have been observed with increased levels of SDF1α and anti-VEGF therapies [19]. Further the vascular TME and therapy response differs in primary tumor and metastasis sites [20] and the anatomic location [21,22,23]. Thus, characterizing angiogenesis in tumors holistically may have therapeutic implications.\n\nThe process of tumor angiogenesis is closely regulated by a balance between promoting and suppressing angiogenic factors [24,25]. Delta like canonical notch ligand 4 (Dll4) is a protein-coding gene that provides instructions for making a protein part of a signaling pathway known as the notch pathway, which is essential for the normal development of many tissues throughout the body, affecting cell functions [26,27], modulating tumor angiogenesis [28], promoting vessel maturation, and inhibiting vessel sprouting by inducing apoptosis of tip endothelial cells (TECs) [28,29,30]. Dll4 is overexpressed in various types of cancer, including breast, ovarian, and colorectal cancer, and has been shown to promote tumor angiogenesis, growth, and metastasis by interacting with receptors on endothelial cells (ECs) [31,32,33,34]. Blockade of Dll4 activity results in enhanced vessel sprouting and increased vascular permeability [29,30,35], but anti-Dll4 therapy has not been universally successful, as Dll4 has been shown to have both pro-tumorigenic and anti-tumorigenic effects depending on the context of its expression [34,36,37]. We recently reported that Dll4 expression on the host TME rather than on tumor cells determines the EPR or enhanced permeation and retention effect in breast tumor xenografts and thus governs nanomedicine delivery and therapy response [38].\n\nDespite the increasing evidence about the function of germline genetic modifiers, such as Dll4, in TME heterogeneity and enhanced permeability and retention (EPR) effects, the underlying influencers have mainly remained unexplored because of the lack of a systematic approach to studying them. Therefore, we developed the Consomic Xenograft Model (CXM) as a strategy for mapping heritable modifiers of TME heterogeneity. In the CXM, human breast cancer cells are orthotopically implanted into consomic xenograft host strains. These strains are derived from two parental strains with different susceptibilities to breast cancer. Salt-sensitive (SS) rats were employed as a tumor promoting strain, while Brown Norway (BN) rats were used as a tumor suppressing strain. A sequence of consomic strains were generated with chromosomes of SS rats replaced by those of BN rats one at a time and used for breast tumor xenograft studies [7,38,39]. Because the host backgrounds genetically differ by one chromosome, whereas the tumor cells are unvaried, any observed phenotypic differences are due to TME modifier(s) and can be linked to a single chromosome. These modifiers can be further localized by congenic mapping (inbred strains containing a given sub-chromosomal region in their genome). By combining CXM with dynamic epifluorescence near-infrared (DE-NIR) imaging, systemic injections of indocyanine green (ICG) through a tail vein in tumor-bearing rats, and multiparametric analysis of pharmacokinetic modeling, we localized and identified the function of the vascular-specific Dll4 allele on rat chromosome 3 (RNO3) as a heritable host TME modifier of EPR [38]. The SS.BN3IL2Rγ− CXM strain with low-level expression of Dll4 (referred to as Dll4−) had significant tumor growth inhibition compared with the parental SSIL2Rγ− strain with higher expression of Dll4 (Dll4+), despite a paradoxical increase in tumor blood vessel density in Dll4+. Further analysis revealed that the changes in the Dll4+ tumors were accompanied by altered expression of Dll4, which was previously linked with nonproductive angiogenesis. Additionally, Dll4 was found to be co-localized within a host TME modifier locus (Chr3: 95–131 Mb) identified by congenic mapping and correlated with the phenotypic differences observed at the consomic level [7,39,40].\n\nThe inheritance of functionally different Dll4 alleles can influence the efficacy of nanoparticle (NP) therapy, and previous results indicate that inherited microvascular distribution patterns, rather than overall NP uptake, ultimately determine the effectiveness of NP-mediated photothermal therapy (PTT). Consequently, patients with high endothelial Dll4 expression can be selected for treatment with anti-Dll4 targeted nanoparticles as opposed to patients with low Dll4 expression, where PEGylated nanoparticles will provide sufficient therapy response [38]. Recent advances in dynamic vascular imaging techniques, such as DCE-MRI and perfusion computed tomography, have facilitated the investigation of the time kinetics of a contrast agent to extract multiple vascular parameters and have been successfully applied in clinical trials of anti-angiogenic drugs [41,42]. However, these techniques have certain drawbacks, including a lack of high temporal resolution and the need for a heavy hardware system with sophisticated analysis software. Dynamic NIR fluorescence imaging, on the other hand, offers a sufficient and effective alternative to other dynamic vascular imaging techniques for characterizing germline-dependent vascular phenotypes [7,43]. This has led to the combination of these modalities, such as in the paired agent MRI-coupled fluorescence tomography approach for noninvasive quantification of paired-agent uptake in response to anti-angiogenesis therapy in vivo [44].\n\nAs the field of artificial intelligence continues to advance, researchers are increasingly utilizing AI techniques, particularly machine learning, to develop predictive models that can support effective decision making in various domains including cancer therapy selection [45,46]. Previous research has investigated the use of machine learning algorithms to analyze near-infrared (NIR) signal intensity and perfusion patterns to differentiate between healthy, benign, and malignant tissue [47]. This work demonstrated that the signal intensity time course of an FDA-cleared near-infrared dye ICG inflow during the wash-in phase and ICG outflow during the wash-out phase could serve as significant markers for tissue distinction. This finding offers a new method for noninvasive tissue distinction and has prognostic potential [48,49]. However, there remains a need for further exploration of the use of machine learning for classifying host genetic tumor microenvironment (TME) modifiers and predicting therapy responses based on dynamic contrast-enhanced imaging of tumors, particularly DE-NIR fluorescence imaging data [47].\n\n## 2. Hypothesis and Objective\n\nWe hypothesize that the observation of subtle differences in vasculature structure and perfusion patterns characterized by ICG inflow and outflow using DE-NIR imaging could be used to differentiate between inherited tumor vascular microenvironment differences, such as Dll4 expression levels. We propose an experimental framework to noninvasively assess Dll4 expression levels in tumors based on the NIR signal intensity time course of perfusion patterns characterized by ICG time kinetics to develop a predictive model to support effective decision making in cancer therapy. Herein, we used two rat-based CXM strains of breast cancer, SSIL2Rγ−(Dll4+) and SS.BN3IL2Rγ− (Dll4−) [7,38,50], as well as eight congenic xenograft strains, CG1–CG8 (Figure 1a,b), to assess the impact of germline TME vascular heterogeneity on the signal intensity of DE-NIR imaging with systemically delivered ICG. Principal component analysis (PCA)-based decomposition of time-dependent epifluorescence videos (image stacks) was used for visualization and anatomical segmentation of tumors, liver, lungs, and fat pads [7]. In addition, we utilized modified principal component analysis (PCA)-based anatomical segmentation techniques to identify and analyze regions of interest (ROIs) representing potential tumors within the current dataset. To gather further information, we calculated the average NIR intensity for each ROI by analyzing the brightness of individual pixels at each time interval, resulting in a series of intensity measurements for each ROI. From this analysis, several easily interpretable features were extracted, including the slope of the initial uptake of ICG, the time it takes to reach peak perfusion, and the rate of ICG intensity changes once the half-maximum intensity is reached (which, to the best of our knowledge, has not been previously reported in the literature). We then applied a subset of machine learning algorithms, including Support Vector Machines (SVMs), Naive Bayesian Classifiers (NBCs), Generalized Additive Models (GAMs), Decision Trees (DTs), Nearest Neighbors (NN), and Logistic Regression (LR) to select the most discriminative features for classification. The performance of the model was evaluated using confusion matrix, receiver operating characteristic curve (ROC), and the area under the curve. To further evaluate our hypothesis of detecting Dll4 expression levels from DE-NIR imaging and test the generalizability of our framework, we conducted a secondary performance evaluation method using congenic groups with high and low Dll4 expression levels. The classification models were trained based on the selected features, and the performance of the model was tested on the remaining congenic groups. We demonstrate that robust ML methods can identify the alterations in host Dll4 expression from the tumor dynamic imaging datasets, and thus these methods can potentially stratify patients for Dll4 targeted therapies.\n\n## 3. Materials and Methods\n\nAll methods have been carried out in accordance with relevant guidelines and regulations. Approved protocols by the Medical College of Wisconsin Institutional Biosafety Committee (IBC) and Institutional Animal Care and Use Committee (IACUC) were followed. All live animal experiments are reported per the ARRIVE guidelines’ recommendations [51]. All results were rigorously adjusted for multiple comparisons.\n\n## 3.1. Animals\n\nAll animal protocols employed in this study were approved by the Institutional Animal Care and Use Committee (IACUC), Medical College of Wisconsin (MCW). The MCW has an Animal Welfare Assurance (Assurance number D16-00064 (A3102-01)) on file with the Office of Laboratory Animal Welfare, National Institutes of Health (NIH). Animal experiments were performed according to the relevant guidelines and regulations and in compliance with the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health (NIH publication no.85–23, revised 1996). SS.BN3 rats were developed as part of the Consomic Xenograft Model at the Medical College of Wisconsin (MCW) [40]. SS and SS.BN3 rats were purchased from the Rat Research Models Service Center at the Medical College of Wisconsin [52]. All rats were provided reverse osmosis hyper-chlorinated water ad libitum. All animal experiments were performed on anesthetized animals. The animal was placed in a transparent induction chamber to induce anesthesia. Isoflurane was delivered through a precision vaporizer and compressed O2 to the chamber. For induction, the percentage of isoflurane was up to $5\\%$. Once the animal was unconscious, it was removed from the chamber. The unconscious animal was then placed on a warm surface and fitted with a nose cone attached to the vaporizer in the presence of a scavenging system and oxygen source. At this point, the concentration of isoflurane was reduced to this level that maintained the correct anesthesia plane, usually between 0.5 and $3\\%$. After the end of the experiment, or when other criteria for animal protocols were justified, rats were euthanized. Rats were placed in an approved euthanasia chamber and exposed to CO2 from a compressed gas cylinder until the animal was no longer breathing. To ensure death in rats, a pneumothorax was created via thoracotomy for rats weighing more than 200 g. For rats weighing less than 200 g, a pneumothorax was created, or a cervical dislocation was performed.\n\nAs previously described [39,40], consomic strains (SS and SS.BN3 rats) were generated by sequentially replacing SS chromosomes with the outbred wild-type and tumor-resistant strain of Brown Norway (BN) rats referred to as SS.BN#, which are reported for their tumorigenic potential, where # refers to the chromosome number. These parental SS and consomic SS.BN# strains were genetically ablated by knocking down the IL2Rγ gene to allow the grafting and growth of human cancer cell lines. Such immunocompromised strains are labeled as SSIL2Rγ−(Dll4+) and SS.BN#IL2Rγ−(Dll4−).\n\nPrevious research has localized inherited modifier(s) of TME vascular heterogeneity to RNO3 by CXM mapping and further narrowed by congenic mapping to a 36 *Mb locus* containing Dll4 alleles with distinct vascular expression patterns in the SS.BN3IL2Rγ− consomic (Dll4−) and SSIL2Rγ− (Dll4+) rat strains, and verified via species-specific RNA sequencing and immunohistochemistry that strains inheriting the SS Dll4 allele on chromosome 3 have higher Dll4 expression on tumor-associated endothelium and that the blood vessel tortuosity and dysfunction increased in Dll4− strains [38,39]. Since there are many other candidate alleles on chromosome 3 that could also potentially account for the observed differences in therapeutic efficacy between the SS.BN3IL2Rγ− and SSIL2Rγ− strains [38], to further investigate the potential contribution of Dll4 to inherited tumor vascular heterogeneity, eight novel SS.BN3IL2Rγ− congenic xenograft host strains (CG1 to CG8) were constructed by introgression of the F1 progeny and F2 generation to capture different regions of RNO3 by marker-assisted selection, as described previously [50,53]. The exclusion congenic mapping localized a 7.9 Mb candidate region (marked by the SSLP marker D3Mgh11) that was associated with inherited tumor vascular heterogeneity and contained the Dll4 locus. As a result, eight new congenic strains CGN(s-e) were generated, where N (1 to 8) refers to the congenic group, while s and e refer to the starting and ending of Simple Sequence Length Polymorphism (SSLP) marker regions, respectively. This resulted in generating CG1(D3Rat26-D3Mgh30), CG2(D3Rat222-D3Got42), CG3(D3Rat222-D3Mco33), CG4(D3Rat164-D3Rat218), CG5(D3Rat26-D3Mco218), CG6(D3Rat86-D3Rat218), CG7(D3Mgh13-D3Rat218), and CG8(D3Rat160-D3Rat218) congenic groups (Figure 1a,b).\n\n## 3.2. Cell Culture and Triple-Negative Breast Cancer Xenografts\n\nAs previously described [38], triple-negative breast cancer MDA-MB-231 cells were maintained in DMEM media (Sigma, Burlington, MA, USA) supplemented with $10\\%$ FBS (Gibco, New York, NY, USA) and $1\\%$ penicillin and streptomycin (Lonza, Cohasset, MN, USA) and incubated in $5\\%$ CO2 at 37 °C. These cells (6 × 106) in $50\\%$ Matrigel were orthotopically implanted into the mammary fat pads (MFP) of 4- to 6-week-old female Dll4+ ($$n = 8$$) and Dll4– ($$n = 17$$) rats and eight congenic strains CG1 ($$n = 19$$), CG2 ($$n = 2$$), CG3 ($$n = 26$$), CG4 ($$n = 12$$), CG5 ($$n = 28$$), CG6 ($$n = 12$$), CG7 ($$n = 5$$), and CG8 ($$n = 4$$) (Figure 1c) [54]. Tumors were treated after 10 days of implantation at an approximate size of 600 mm3, consistent across all rat strains.\n\n## 3.3. In Vivo NIR Fluorescence Imaging\n\nA customized NIR imaging system was assembled for imaging the rats. A bifurcated optical fiber bundle was used to deliver 785 nm excitation light (~5 mW/cm2 power at the surface, diode laser, ThorLabs Inc., Newton, NJ, USA) from two positions for uniform illumination of the entire rat body surface. A 16-bit deep-cooled intensified charge-coupled device camera (PIMAX4 ICCD, Princeton Instruments, Trenton, NJ, USA) equipped with 830 nm long-pass filter positioned following a holographic notch rejection filter in the optical path (ThorLabs Inc.) was used to image the rats through computer-controlled LightField® software (Teledyne Princeton Instruments, Trenton, NJ, USA) (Figure 1d).\n\nDynamic contrast-enhanced NIR fluorescence imaging was performed on anesthetized rats, as reported previously for 800 nm NIR imaging [7]. In this study, the setup was used for imaging the whole body. A total of 133 rats (Dll4+ ($$n = 8$$), Dll4– ($$n = 17$$)) and eight congenic strains CG1 ($$n = 19$$), CG2 ($$n = 2$$), CG3 ($$n = 26$$), CG4 ($$n = 12$$), CG5 ($$n = 28$$), CG6 ($$n = 12$$), CG7 ($$n = 5$$), and CG8 ($$n = 4$$) were imaged. NIR imaging was performed for approximately 6 min following ICG injection with the CCD array hardware binned to 256 × 256 with a frame rate of 10.6 fps. A total of 3000 frames were captured for each imaging case, including about 50 frames for background correction. ICG (MP Biomedicals) was delivered in an intravenous bolus of 1200 µM ICG/200 g body weight into the tail vein via a catheter with a 32-gauge needle tip connected to a syringe pump (Harvard Apparatus PHD 2000 syringe pump, Holliston, MA, USA) operated at a speed of 0.2 mL/s. The injected volume was calibrated to provide a body-weight-equilibrated dose to each rat.\n\n## 3.4. Denoising and Motion Correction\n\nImage processing and data analysis were performed in MATLAB (R2021b MATHWORKS Inc.) software. The time-dependent image frames were assembled as 3D arrays (two spatial and one temporal dimensions) for all animals. A custom-designed breathing correction method with a low-pass temporal filter combined with a 1D wavelet-based denoising was used to filter the high-frequency jitter generated by the animal’s respiratory motion from the fluorescence kinetic sequences of each pixel, as described previously [7]. An average of pre-ICG injection frames (acquired in the ~5 s before ICG injection) was used as background, incorporating contributions from CCD noise and excitation light leakage from emission filters and subtracted from all the frames. ( Refer to Video S1 for respiratory motion corrected time course images of ICG biodistribution).\n\n## 3.5. Principal Component Analysis for Extraction of Spatial Patterns of Internal Organs\n\nFirst, motion correction and background subtraction were performed on the imaging data described in the previous steps. This was carried out to remove any potential artifacts or noise that could affect the accuracy of the principal component analysis. Next, the data were decomposed by PCA along the time dimension using MATLAB software following the previously published methods [55,56]. This resulted in converting the imaging data to a k-component vector for each pixel, where k is the number of time-frames in the original dataset. The PCA on the dynamic fluorescence image was used to extract spatial patterns of internal organs linked to statistically similar kinetic behaviors. This was carried out by comparing the k-component vectors for each pixel and identifying those that displayed similar patterns over time. The contribution of the first six principal components on a time basis is illustrated in Figure S1.\n\n## 3.6. PCA Ranking Tumor Detection\n\nThe ROI detection module employed three steps in order to identify the tumor area in images (Figure 2a): [1] spatial alignment, [2] PCA ranking and selection, and [3] ROI selection and masking. First, images were registered to a reference image using a rigid body transformation in order to ensure consistent spatial alignment for the detection of the tumor area in subsequent steps. The variability in the visual appearance of internal organs or tumors within certain principal components necessitated the adaptation of existing methods for ranking normalized components based on their two-dimensional cross-correlation (2DCC) with a reference image containing the tumor. This was achieved by generating a stack of the first ten normalized principal components and applying a two-dimensional cross-correlation function (xcorr2 in MATLAB) to each component. The principal components were ranked according to the correlation scores obtained and a semi-automatic algorithm was then utilized to carry out the subsequent two steps for tumor ROI generation.\n\nTo select the appropriate principal component for tumor identification, the algorithm prioritized the PC with the highest likelihood of containing the tumor tissue based on the ranking from the previous step. Once the proper PC was selected, we used 2DCC to estimate the tumor’s location in the frame. This was performed by creating a stack of four reference images using PCs that clearly visualized the tumor and generated a bounding box around the tumor. Next, we applied basic morphological dilation and image arithmetic operations to emphasize the tumor’s boundaries. A threshold was then applied to separate the tumor from the background. Finally, we used Blob Analysis (Computer Vision Toolbox™, MATLAB, MathWorks, MA, USA) to extract the centroid and exact location of the tumor in the frame, generating a region of interest (ROI). The use of a graphic user-interface-enabled semi-automatic platform allowed for real-time evaluation and adjustment of the algorithm’s performance if necessary.\n\n## 3.7. Video Processing\n\nThe underlying assumption for the NIR fluorescence intensity video analysis is that intensity was proportional to the ICG concentration in the tissue, which has been shown in the literature [57].\n\nFrom each available video, p, between 4 and 5 fluorescence time series are extracted, one based on the tumor ROI generated from earlier steps, and between three and four from mammary fat pads (MFPs) (Figure 2b). For each ROI, r, and each included time step, t, the mean brightness of the pixels inside that ROI in the NIR video is stored as Ip,r(t). The result is a collection of time series Ip,r(t), where p ranges from 1 to N (the number of animals in each group in our dataset), r ranges from 4 to the number of ROIs generated in the specific animal (up to 5, 1 tumor and 3 or 4 fat pads), and t from 0 to 3000 (Figure 2b).\n\n## 3.8. Peak and Latency Estimation\n\nFirst, we started with smoothing the data to exclude any potential noise from motion artifacts. The smoothing was conducted using a Savitzky–Golay filter of order 3 with window length 31 [58]. The parameters for this filter were determined through manual annotation of peak and latency in a subset of the data, with the goal of minimizing average estimation error. Subsequently, we used a MATLAB function to identify the time point of maximum intensity, which corresponded to the peak of the fluorescence signal. We employed a custom latency detector script that analyzed the smoothed derivative of the curves and identified the first “robust” zero crossing as the latency point (Lp,r(t)) [48].\n\n## 3.9. Feature Design\n\nThe features were chosen in two steps as previously described (Figure 3) [47,48]: first, the following characteristic numbers were chosen for each normalized time series individually.\n\nThe time to peak (TTPp,r) is simply the time difference between the peak intensity time point and the latency Lp,r.\n\nThe upslope (Up,r) is computed as [1]Up,$r = 1$−Ip,r(Lp,r)TTPp,r, which is the average slope between initial ICG arrival and the peak.\n\nThe downslopes (DS) are the downslopes between the peak and S seconds further:[2]Dp,$r = 1$−Ip,r(Lp,r+TTPp,r+S)S,S∈{2,4,5,6,8,10,12,14,16,18,20,23,25,30,35,40,50,60,70,80}.\n\nThe time ratio (TR) is the ratio between TTPp and when Ip(t) reaches half the peak values.\n\nThe half intensity forward (HIF) is the average slopes between when Ip,r(t) reaches half the peak values (T$\\frac{1}{2}$p,r) and S seconds further, so [3]HIFp,$r = 1$−Ip,r(Lp,r+T$\\frac{1}{2}$p,r+S)I,S∈{2,4,5,6,8,10,12,14,16,18,20,23,25,30,35,40,50,60,70,80}.\n\nTo increase the robustness of estimated downslope-based and HIF-based features, we introduced a window around Lp,r+TTPp,r+S and Lp,r+T$\\frac{1}{2}$p,r+S time steps taking the median of those downslopes:[4]DSp,r,avg=median s∈Window D(S+s)p,r, [5]HIFp,r,avg=median s∈Window D(S+s)p,r,Window={−1.5,−1.3,…,1.3,1.5}seconds.\n\nThese features provide insight into the tumor’s ICG uptake and decay. U and TTP relate to the initial uptake of ICG, while DS relates to the decay of ICG fluorescence [48]. TR is a measure of the temporal inhomogeneity of the initial uptake, and HIF is a measure of the temporal inhomogeneity of both the initial uptake and decay of ICG fluorescence.\n\nTo address inter-animal variation, we propose a feature design that relates the features of tumor intensity (Fr=tumor) to the features of healthy tissue intensity (Fr=fatpad) in the same animal. The median value of a feature across the healthy tissue (fat pad) is chosen as a reference value, and each feature is defined as its percentage difference to that reference value. This results in a single normalized feature for each animal. By using the median as an average that is robust to outliers, our feature design allows for a more accurate comparison of tumor intensity across different animals. [ 6]Fn =Ftumor−median(Ffat pad)median(Ffat pad),\n\n## 3.10. Classification Algorithms and Feature Selection\n\nThe feature extraction and design required specialized knowledge of the tumor microenvironment (TME) and its impact on near-infrared intensity. However, the classification based on the inheritances of Dll4 can be considered a standard binary classification problem with feature selection as a sub-problem: given the features of an animal for which the Dll4 inheritance is unknown, we want to assign the label “Dll4high” or “Dll4low” to it. We also want to investigate which small subset of features performs best [44]. We restricted ourselves to a subset of available ML algorithms which were reported to perform best with intensity time series classification. [ 48], We excluded neural networks from consideration and evaluated Support Vector Machines (SVMs), Naive Bayesian Classifiers (NBs), Generalized Additive Models (GAMs), Decision Trees (DTs), Nearest Neighbors (NN), and Logistic Regression (LR).\n\n## 3.11. Primary Classification\n\nDTs and SVMs using the full set of 86 features were trained. Each ML model was tuned to the training set in an internal cross-validation procedure of 10-fold. This process was repeated 20 times, and the performance metrics are reported as classifier performance. One of the key metrics used to evaluate the performance of a classification algorithm is accuracy, which measures the proportion of correctly classified instances in the dataset. Other important metrics include sensitivity, which measures the proportion of positive instances that were correctly classified, and specificity, which measures the proportion of negative instances that were correctly classified. In our case, we are interested in classifying animals as either Dll4high or Dll4low, and we use the average metric (Ascore) for each pair of groups to evaluate the performance of the different classification algorithms.\n\nThe Ascore for a given pair of groups is calculated as the average of accuracy, sensitivity, and specificity, as follows:[7]Ascore (Dll4high| Dll4low)=Accuracy+Sensitivity+Specificity3,\n\n## 3.12. Congenic Pair Selection\n\nIn this study, we describe a two-step method for selecting congenic pairs with high and low Dll4 expression levels for feature selection and hypothesis testing. This method was used to identify well-behaved pairs for binary classification and to identify the pair with the highest classification performance: The primary selection of congenic pairs is based on their primary classification scores (Ascore) and their performance against parental strains using all available features. The secondary selection is based on the classification performance using the 2 best-performing features.\n\nThe congenic pair selection process involved the selection of all possible combinations of congenic pairs with high and low Dll4 expression levels with n > 5, resulting in a total of 12 pairs: Dll4+|Dll4−, Dll4+|CG3−, Dll4+|CG4−, Dll4−|CG1+, Dll4−|CG5+, Dll4−|CG6−, CG1+|CG3−, CG1+|CG4−, CG3−|CG5+, CG3−|CG6+, CG4−|CG5+, CG4−|CG6+. It should be noted that we only included subgroups with n > 5 in our analysis, to avoid introducing noise into the feature selection process. This is because smaller sample sizes can be more prone to variability and may not represent the larger population [59]. Therefore, we focused on more significant subgroups to ensure our feature selection process was robust and reliable. These 12 pairs have gone through our primary classification algorithm with a split of $75\\%$–$25\\%$ for testing and training with seeded randomization and proportional distribution of each group in training and testing datasets. Each ML model was tuned to the training set in an internal cross-validation procedure of 10-fold and evaluated by its performance on the test set. This process was repeated 20 times, and the best Ascore was reported as the metric of classifier performance. The result of this step was used to identify well-behaved congenic pairs for binary classification by calculating the separation score (Sscore) for each pair of congenic groups with CG#+ and CG#− as below, which is bound between 0 and 1 and reported in Table 1:[8]Sscore (CG#+| CG#−)=Ascore (Dll4+| CG#−)+Ascore (CG#+| Dll4−)+2∗ Ascore (CG#+| CG#−)4, The congenic pairs with Sscore above $80\\%$ were selected for secondary congenic pair selection.\n\nFor the secondary selection, we evaluated the classification performance (Ascore) of each pair determined this time by using the two most effective features as described in Section 3.13. We used a $75\\%$–$25\\%$ split for testing and training, with randomization to ensure a proportionate representation of each group in both datasets.\n\nThe pair with the highest classification performance, Ascore, based on the two most effective features was selected for the final classification model training. To ensure a high-quality classification model, we set a minimum threshold for the Ascore of 0.70 for inclusion in the feature selection process. Congenic groups with an Ascore below 0.70 were considered to have an insignificant contribution to the classification model and were excluded from further consideration in the feature selection process. This approach allowed us to focus on the most informative feature pairs, improving the overall classification performance of our machine learning model. Each machine learning model was tuned to the training set using a 10-fold cross-validation procedure and evaluated based on its performance on the test set. We repeated this process 20 times and reported the best Ascore as the classifier’s performance.\n\n## 3.13. Feature Selection and Secondary Classification\n\nWe selected the best pair of features in terms of achieved sensitivity, specificity, and accuracy by a two-step procedure as previously reported: DTs and SVMs using the full set of 86 features were trained, and recursive feature elimination (RFE) was performed to refine a much smaller set of best-performing features [60]. RFE is a widely used machine learning classification algorithm that helps in reducing the dimensionality of feature space and selecting a small subset of features that yield the best classification performance. This was achieved through an iterative procedure that uses a ranking criterion to eliminate features one or more at a time. The RFE algorithm started by selecting a subset of features and training a model on this subset. The features were then ranked based on their contribution to the model’s performance, and the least important feature was eliminated. The process was then repeated with the remaining features, and the best subset of features was selected based on a model selection criterion [61].\n\nOne of the main advantages of RFE is that it helps to reduce the risk of overfitting when the number of features is large, and the number of training patterns is comparatively small [62]. This is because the algorithm selects only a subset of features that are relevant to the classification task, and this helps to avoid the inclusion of irrelevant and redundant features. RFE can be used in conjunction with other techniques such as regularization and support vector machines (SVMs) to further improve the performance of the classification model. In addition, projection methods such as principal component analysis can reduce the feature space’s dimensionality before applying RFE [55].\n\nWe used a k-fold cross-validation strategy to assess the performance of our model. We also reserved a portion of the training data for primary testing of the model after hyperparameter optimization. Our experiments were conducted with 20 random splits of the training and test datasets, and the mean performance metrics were reported for sensitivity, specificity, and accuracy as Ascore. To facilitate the interpretation of our results, we limited the number of final features to two. Furthermore, given the small size of our dataset, there was no justification for using high-dimensional feature spaces.\n\n## 3.14. Data Augmentation\n\nThe use of data augmentation has become a popular technique in machine learning and deep learning, especially in the field of computer vision. Data augmentation involves applying random transformations to the training dataset to increase its diversity and improve the performance of a model. In this study, we used data augmentation on raw near-infrared (NIR) image stacks to evaluate the robustness of a classification model. We used a dataset of 3000 frames of the original raw 256 × 256 NIR images for this part of our study. These images were augmented using TensorFlow and the Keras API, which allowed us to apply random transformations to the dataset. The transformations included random rotation followed by a horizontal flipping, and up to $2\\%$ rescaling.\n\n## 3.15. Training and Testing Dataset\n\nThe final training and testing dataset for the machine learning models was determined by the outcome of the congenic pair selection and feature selection steps. This dataset included all congenic groups except for Dll4+ and Dll4−, as well as the selected CG#+ and CG#− groups in the previous step. This step was conducted separately for the original dataset and augmented dataset. The models were trained using 10-fold cross-validation and a portion of the training data was reserved for testing after hyperparameter optimization, with $25\\%$ for the original dataset and $20\\%$ for the augmented dataset. The performance of the models was assessed using a confusion matrix, receiver operating characteristic curve (ROC), and the area under the curve.\n\n## 3.16. Statistical Analysis\n\nRepeated measures models are a powerful tool in statistical analysis that allow researchers to study the effects of different factors on a given outcome while accounting for the inherent dependence of multiple measurements taken on the same subject. In this study, a mixed effects model with appropriate time varying covariates was used to analyze the average fluorescence intensity of indocyanine green (ICG) in the tumor with multiple measurements per subject, with the subject number serving as the repeated measure indicator and the rat strain serving as a covariate. This allows for flexible time-based modeling when using multiple measures, likely dependent from the same animal [63,64]. Customized scripts in MATLAB were used to generate the fitted coefficients, covariance parameters, design matrix, error degrees of freedom, and between- and within-subjects factor names for the repeated measures model. The output was then analyzed with a multiple comparison of the estimated marginal means based on the variable strain, using the Tukey–Kramer test statistic [65]. This allowed estimation of multiplicity-adjusted p-values for the post hoc comparisons, which indicate whether the groups significantly differed with respect to strain. The data were then visualized as a p-value matrix, providing a clear illustration of the significant differences between groups.\n\n## 3.17. Data Availability\n\nThis study employed the established consomic rat models SS and SS.BN3 as well as our congenic strains CG1 to CG8. The publicly accessible and NIH-supported Rat Genome Database (rgd.mcw.edu) catalogs has tools to explore the genotype and phenotype information for the SS (Dll4+) and SS.BN3 (Dll4−) and congenic strains under strain records: Dll4+ (RGDID:61499), Dll4− (RGDID:1358154), CG1 (RGDID:155782881), CG2 (RGDID:155782883), CG3 (RGDID:155782884), CG4 (RGDID:155791428), CG5 (RGDID:155791426), CG6 (RGDID:155791430), CG7 (RGDID:155791429), and CG8 (RGDID:155791427).\n\n## 4.1. Dynamic Contrast-Enhanced NIR Fluorescence Imaging and Tumor Detection\n\nDynamic contrast-enhanced NIR fluorescence imaging has been widely used for tumor detection in various studies [66,67,68]. The use of NIR imaging allows for the visualization of internal organs and tissues without the need for invasive procedures, which can be particularly useful in detecting tumors due to their vascular heterogeneity compared to surrounding healthy tissues.\n\nIn previous studies, the use of principal component analysis (PCA) on the time domain of dynamic fluorescence images was utilized to extract spatial patterns of internal organs linked to statistically similar kinetic behaviors, such as liver, kidneys, lungs, and various tumors [7,56]. However, this technique required manual inspection and selection of proper principal components, which was time consuming and prone to human error and bias.\n\nIn order to overcome the limitations present in the current dataset, we implemented a modified method that utilizes near-infrared imaging and principal component analysis to detect tumors with high accuracy and without the need for manual correction (Figure 2 and Figure S1). The use of principal component analysis in this context not only allows for dimensionality reduction and noise removal but also enhances the robustness and efficiency of the method.\n\nOur study also implemented a novel method of ranking PCA components based on the 2D cross-correlation of a reference image containing the tumor. This added to the simplicity and computational efficiency of the framework. However, it should be noted that this method may not be effective for detecting tumors with random locations. On the other hand, it could be useful for detecting tumors or tissues of interest with high localization, such as the lungs, liver, and kidney, and lesions in breast tissue. Overall, our method shows potential for improving the accuracy and efficiency of tumor detection using NIR imaging and PCA (Figure 4a). However, further experimentation is needed to expand the framework to a general tumor detection algorithm.\n\n## 4.2. Dll4 and Its Effect on the NIR Time Series\n\nThe analysis of the average fluorescence intensity of indocyanine green (ICG) in the tumor tissue of Dll4+ and Dll4− rats bearing triple-negative breast cancer (TNBC) tumors revealed that ICG uptake occurred more rapidly in Dll4− tissues and was retained for longer periods of time compared to Dll4+ hosts (Figure 4b). This indicates systemic differences in vascular function between the two rat strains. Our previous histological data showed that Dll4+ tumors have a higher vascular density and tortuosity, indicating a genetic microenvironment that promotes nonproductive angiogenesis [38]. This is further supported by the slower ICG wash-out observed in the Dll4+ tumors. These findings provide insight into the effects of host genetics on tumor angiogenesis and suggest potential therapeutic targets for TNBC.\n\nIn order to further investigate the role of Dll4 in vascular function in tumors, we divided chromosome 3 into regions with and without the *Dll4* gene in congenic rat strains (Figure 1b) and then examined the ICG fluorescence intensity of tumors in Dll4-high and Dll4-low rats (Video S2) (Figure 4c). Our findings reveal significant systemic differences in vascular function between tumors in Dll4+ and Dll4− rats (parental strains), indicating the critical role of the *Dll4* gene in tumor angiogenic response [38]. However, analysis of the ICG fluorescence intensity of tumors for individual strains (Figure 4d) reveals more complex behavior than the obvious differences in wash-in and wash-out patterns observed between Dll4+ and Dll4−. This supports the need for further investigation into the impact of Dll4 on NIR time series signatures and the potential use of Dll4-directed therapies for cancer treatment.\n\nIt is worth noting that although there are significant differences in Dll4-low vs. Dll4-high rat strains (when all the strains of Dll4 expression levels are combined), they are inconsistent with the observations made in Dll4+ and Dll4− rats. These results have significant implications for developing novel therapies that target Dll4 and other host TME modifiers involved in angiogenesis, as they demonstrate the critical role of these genes in tumor vascular function and angiogenic response.\n\nAdditionally, our research further highlights the capricious nature of the NIR signal, which is influenced by various heritable tumor microenvironments across different groups, as shown in Figure 4b,c. We aim to illustrate and categorize the impact of the Dll4 expression level on the NIR signal through this erratic behavior.\n\nWe used a repeated measures model to analyze the average fluorescence intensity of ICG in the tumor over time, with the rat strain serving as a covariate. Figure 5a,b show the estimated response covariances matrix, which is the covariance of the repeated measures. The higher values in this matrix indicate the time points at which groups experience the greatest differences. By projecting the diagonal of the covariance matrix onto the time axis (Figure 5c), we were able to visualize the amount of difference between groups over time.\n\nThis projection, when compared to the average fluorescence intensity of ICG in the tumor (Figure 4b,d), showed the strongest differences between groups at the points where the NIR signal regions from half of its peak value to the peak value and at the tail of the curve, which are measures of the temporal inhomogeneity of the initial uptake and the decay of ICG fluorescence, were found to be particularly useful in discriminating between groups with different levels of Dll4 expression. This projection of the diagonal of the estimated response covariances matrix on the time curve can be used in feature design to focus on regions with the maximum amount of useful information for discriminating between groups and, subsequently, between classes with different levels of Dll4 expression. This could potentially improve the accuracy of tumor classification and ultimately improve therapy outcomes.\n\nOur repeated measures model, which included responses as measurements and strains as predictor variables, allowed us to conduct multiple comparisons of estimated marginal means between groups. The resulting p-value matrix (Figure 5d) revealed significant differences in estimated marginal means between the Dll4+ and Dll4− groups, with a p-value of 4.71 × 10−7. In addition, we observed significant differences between Dll4+ and CG3, CG4, and CG8, with p-values of 1.67 × 10−5, 7.03 × 10−7, and 2.18 × 10−3, respectively.\n\nFor each group pair with high and low Dll4 expression levels, the separation score was calculated. First each of Dll4+|CG#−, CG#+|Dll4−, and CG#+|CG#− went through our classification algorithm with 10-fold cross-validation using Nearest Neighborhood, Linear SVM, RBF SVM, Decision Tree, Naive Bayes, and Logistic Regression models. The highest average classification metrics (Accuracy + Specificity + Sensitivity)/3 for Dll4+|CG#−, CG#+|Dll4− and CG#+|CG#−) was used to calculate the separation score (Score Dll4+|CG#− + Score CG#+|Dll4− + 2 × Score CG#+|CG#−)/4.\n\nFurthermore, our analysis showed significant differences between Dll4− and CG5 and CG6, with p-values of 8.70 × 10−3 and 2.58 × 10−4, respectively. This supports the hypothesis that Dll4 expression levels can act as a heritable TME modifier on NIR time series intensity. However, the smallest p-value between Dll4+ and Dll4− suggests that there are other factors on chromosome 3, in addition to Dll4, that contribute to the observed differences in the NIR time series signature between these groups.\n\nIn contrast, no significant differences were found between Dll4− and the congenic strains with low levels of Dll4 expression (CG2, CG3, CG4, CG7, and CG8). This further supports the notion that Dll4 plays a crucial role in determining tumor vascular function and NIR time series intensity.\n\nAmong the congenic groups, the most significant differences were observed between CG5, CG6, and CG3, CG4 from the Dll4-high and Dll4-low groups, respectively. Notably, the differences were most significant between CG4 and CG6, with a p-value of 0.0003. This suggests that very narrow regions of differences on chromosome 3 between these two groups, one containing Dll4 and the other lacking it, have a significant effect on the NIR time series signature.\n\n## 4.3. Primary Classification and Congenic Dissimilarity\n\nThe relationship between Dll4 expression and classification performance was analyzed using a total of 12 congenic pairs with n > 5 based on their levels of Dll4 expression (Dll4+|Dll4−, Dll4+|CG3, Dll4+|CG4, Dll4−|CG1, Dll4−|CG5, Dll4−|CG6, CG1|CG3, CG1|CG4, CG3|CG5, CG3|CG6, CG4|CG5, and CG4|CG6). The pairs were then subjected to a primary classification algorithm and their mean performance metrics, the Ascore, were calculated and reported in Table 1. The congenic pairs with low levels of Dll4 expression showed a mean Ascore of 0.91 +/− 0.01, indicating a high level of classification performance when compared to the Dll4+ parental strain. In contrast, the congenic pairs with high levels of Dll4 expression showed a mean Ascore of 0.8 +/− 0.05 when classified against the Dll4− consomic strain. Among the congenic pairs, the CG5|CG4 pair demonstrated the highest Ascore of 0.8, followed by the CG6|CG4 and CG6|CG3 pairs with Ascore values of 0.78 and 0.77, respectively. The results of the Ascore calculation are visualized in Figure 5e through a Sankey diagram.\n\nTo account for potential differences between the congenic pairs and the parental pairs, the Sscore was calculated. The CG5|CG4, CG6|CG3, and CG6|CG4 pairs showed the highest Sscore values of 0.84, 0.84, and 0.85, respectively, and were selected for the feature selection step. These results align with the multiple comparison of estimated marginal means between groups, indicating that CG5|CG4, CG6|CG3, and CG6|CG4 show the strongest differences in classification performance.\n\n## 4.4. Feature Selection\n\nRFE is a wrapper method that evaluates the entire classification algorithm and has shown improved classification accuracy and reduced overfitting compared to other feature selection methods [69]. However, RFE can be sensitive to noise and irrelevant features, leading to suboptimal feature subsets and reduced classification performance. Additionally, RFE is computationally intensive, which can pose a challenge for large datasets with a high number of features. Despite these limitations, RFE remains a valuable tool for selecting an optimal subset of features that maximizes classification performance [70,71].\n\nTo address these limitations, we performed feature selection in two steps to optimize the selection process and improve the performance of the classifier. First, we used RFE to select only two features out of the 86 available features for congenic pairs Dll4+|Dll4−, Dll4+|CG4, CG5|Dll4−, CG5|CG4, CG6|Dll4−, and CG6|CG4. The CG3 and its combinations (CG6|CG3, CG5|CG3, and Dll4+|CG3) were dropped from the feature selection process as the performances of the classifiers, the Ascore, using only two features were below 0.70, and lower than the other strains. The congenic pairs CG5|CG4 and CG6|CG4 as well as the parental and consomic group Dll4+|Dll4− went through our feature selection algorithm, and for each pair the two best-performing classification algorithms based on Ascore and associated feature pair were reported (Table 2).\n\nThe highest Ascore for the two best-performing models for CG5|CG4 was 0.78 ± 0.04 compared to CG6|CG4 with an Ascore of 0.72 ± 0.19 and 0.72 ± 0.22, resulting in the selection of CG5|CG4 for final congenic pair selection.\n\nFinally, from each pair of Dll4+|Dll4−, Dll4+|CG4, CG5|Dll4, and CG4|CG5, four of the best performing features regardless of the ML model were chosen and were used as a collection of features for the final feature selection (Table 2). A combination of parental and consomic groups and the final selected congenic pair (Dll4+, Dll4−, CG5, and CG4) was used to select the final feature pair out of the 16 selected features, resulting in the selection of HIF5_avg and HIF50_avg as the best-performing features.\n\n## 4.5. Performance of the Classification Models Based on the Selected Features\n\nTo evaluate the performance of the selected features, we trained datasets consisting of all the remaining congenic groups excluding the Dll4+, Dll4−, CG4, and CG5 (CG1 to CG3 and CG5 to CG8) using 10-fold cross-validation and keeping $25\\%$ of the dataset for testing the trained models. This allowed us to assess the generalizability of our model and test it on previously unseen datasets. The results of this step are reported as a confusion matrix, ROC curve, and AUC (Figure 6), as well as general classification metrics (Table 3). The best-performing models based on the selected features were SVM and KNN, with sensitivity and specificity of 1.00 and 0.81 and 1.00 and 0.75, respectively.\n\nIn order to further assess the effectiveness of our model, the selected features, and the generated congenic pair, we generated an augmented dataset consisting of all remaining congenic pairs excluding Dll4+, Dll4−, CG4 and CG5 (CG1 to CG3 and CG5 to CG8, with random variations in rotation, horizontal flip, and limited scaling (up to ±$2\\%$) to increase the diversity of the dataset. This resulted in a total of 606 data points. The performance of the models was evaluated using 10-fold cross-validation and a $20\\%$ hold out. The results of this step were reported as a confusion matrix, ROC curve, AUC (Figure 7), and overall classification metrics (Table 4). The best-performing models based on the selected features were SVM and KNN, with sensitivity and specificity of 0.97 and 0.91, and 0.97 and 0.92, respectively. These results align closely with the performance of the models over the original dataset, indicating the generalizability of our framework.\n\nIt is noteworthy that of the 16 most contributing features used to select the final feature pair, 12 were the newly proposed HIF features, and the other 4 were DS features, which we previously reported [48]. Additionally, the HIF features were amongst the best features for identifying genetic TME modifiers.\n\nThe relationship between covariance of the repeated measures and optimal feature design in machine learning classification algorithms is an essential factor in developing effective classification algorithms. Combined with our recent report [47], our analysis found that the DS and HIF features, which are generated in regions where the NIR signal varies from half of its peak value to the peak value and at the tail of the curve, were particularly effective in discriminating between benign/malignant tumors (DS features) and groups with different levels of Dll4 expression (HIF features). Furthermore, the projection of the covariance matrix onto the time axis revealed similar regions, indicating a relationship between this projection and optimal feature design. These findings have significant implications for feature design in machine learning classification algorithms. By focusing on the regions with the greatest amount of useful information for discrimination, we can design features specifically to capture these differences and improve the accuracy of tumor classification. This can ultimately lead to better therapy outcomes for patients. It is worth noting that this relationship between the covariance of the repeated measures and optimal feature design is not limited to HIF features and the specific context of our analysis. *In* general, considering the covariance of repeated measures can provide valuable information for identifying key regions and designing effective features for machine learning classification algorithms.\n\n## 5. Conclusions\n\nDynamic vascular imaging techniques such as DCE-MRI and perfusion CT are used to extract multiple vascular parameters and have been used in clinical trials of anti-angiogenic drugs. However, these techniques have limitations, such as low temporal resolution and the need for specialized hardware and software [41,42]. To overcome these limitations, dynamic near-infrared (NIR) fluorescence imaging can serve as an effective alternative for characterizing germline-dependent vascular phenotypes. It can be combined with other modalities, such as in a paired-agent or multimodal MRI and fluorescence tomography approaches for noninvasive quantification of response to anti-angiogenesis therapy and classifying in vivo vascular phenotypes [7,43,44,67]. Furthermore, DE-NIR imaging, as a potential alternative for characterizing germline-dependent vascular phenotypes in preclinical models, can be extended to clinical modalities upon validation with cross-sectional dynamic contrast enhanced imaging.\n\nThe present study proposes that by combining DE-NIR imaging and machine learning algorithms with consomic xenograft models with human tumors, the role of inherited notch protein Dll4 (rat variant of delta like canonical ligand 4) expression specifically in the host vascular microenvironment can be studied. Specifically, in the context of breast cancer, where different genetic subtypes can impact treatment outcomes, identifying patients with high or low DLL4 (human variant of delta like canonical ligand 4) expression levels through noninvasive imaging could assist in selecting personalized treatment options. Nonetheless, the study authors acknowledge notable differences between the rat model utilized in the study and the human system, which could affect the generalization of the findings to human cases, as in human tumors DLL4 expression maybe be both on tumor cells and host vasculature, whereas in our CXM model, we focused specifically on the inherited variation in rat-derived host vasculature Dll4 expression in human xenograft tumors. Such differences may result in amplification or suppression of vascular phenotype responses if both tumor cells and the host microenvironment express high levels or contrasting levels of DLL4. However, even in that case, dynamic imaging will be useful in identifying patients likely to respond better to DLL4 targeted therapies.\n\nFuture studies will be necessary to validate this study’s findings, to assess the reliability and validity of the developed imaging and machine learning algorithms in a large and diverse patient population to determine if contrast agent kinetic profiles observed in human DCE-MRI or dyna-CT imaging datasets for primary and/or metastatic disease differ in human patients with high vs. low DLL4 expression. In the metastatic setting, where surgery is no longer an option, a machine-learning-enabled analysis of dynamic contrast-enhanced imaging will be valuable to assess the expression levels of DLL4 and guide therapy selection, especially in cases where a biopsy is not taken or if biopsy results are inconclusive [72,73]. Human anti-DLL4 antibodies have been reported for cancer treatment [54,74,75,76]. In one study, immunotoxin DLL4Nb-PE was developed, potentially as a cell cytotoxic agent and angiogenesis maturation inhibitor [76]. Another study successfully developed a bispecific monoclonal antibody that targets both human DLL4 and VEGF and showed efficacy in inhibiting proliferation, migration, and tube formation of human umbilical vein endothelial cells (HUVEC) [74]. In a phase 1a trial, navicixizumab, a bispecific antibody that inhibits DLL4 and VEGF, was tested in refractory solid tumor patients and showed the potential to inhibit tumor growth [75]. While DLL4 blockade is an attractive therapy, long-term extended use of DLL4 mAbs has demonstrated concerning off-target effects [77,78]. Pharmacokinetic modulation of DLL4 mAbs may reduce off-target effects [77], such as via short-term administration or by focusing on patients where dynamic contrast imaging indicates a high DLL4 vascular phenotype. As we have shown in prior work, high vascular DLL4 expressing tumors may also be susceptible to DLL4 targeted nanomedicine [38] or as combination therapy with anti-DLL4 monoclonal antibodies with nanomedicine drugs such as nab-paclitaxel (Abraxane) or Liposomal Doxorubicin (DoxilTM).\n\nThe use of noninvasive DE-NIR imaging to detect heritable TME modifiers is significant for several reasons. First, this method allows for the identification of potential modifiers without the need for invasive procedures, reducing the potential for discomfort and complications for patients. Second, the use of machine learning and DE-NIR imaging to develop a predictive model for cancer nanomedicine therapy can support effective decision making in the treatment process. While data processing and preparation and algorithm training can be complex, the resulting algorithms are simple and allow for the prediction of heterogeneity in a single step using ROI brightness measurements. Interestingly, traditional features such as time-to-peak and upslope do not appear in our selection of the most discriminative features. However, two novel features derived from HIF (HIF5_avg and HIF50_avg), which is a measure of the temporal inhomogeneity of both the initial uptake and decay of ICG fluorescence, were identified.\n\nIt is important to note that the training and testing sets used in this study are minimal, and therefore the high accuracy rates obtained should be interpreted with caution. Further research with larger datasets will be necessary to assess the reliability and validity of these findings with confidence.\n\nWe have reported novel dynamic enhanced near-infrared (NIR) fluorescence imaging and machine learning algorithms to noninvasively assess Dll4 expression levels in tumors. Our results showed that observation of subtle differences in vasculature structure and perfusion patterns characterized by ICG time kinetics could be used to differentiate between inherited tumor vascular microenvironment differences, such as Dll4 expression levels. Additionally, our analysis demonstrated the importance of considering the covariance of the repeated measures in the design of features for machine learning classification algorithms. By utilizing this information, we can improve the accuracy of tumor classification and ultimately improve therapy outcomes for patients.\n\nTo summarize, based on our recent study, we investigated the impact of genetically heterogeneous notch-Dll4 inheritance on the contrast agent uptake and clearance in triple-negative breast cancer xenografts. The differences in Dll4 inheritance have been shown to impact nanomedicine biodistribution, pharmacokinetics, and therapy response in our prior work. 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0.0037233750335872173,\n 0.010142987594008446,\n -0.018231699243187904,\n -0.001733295270241797\n]","isConversational":false}}},{"rowIdx":97,"cells":{"text":{"kind":"string","value":"---\ntitle: Influence of Pig Genetic Line and Salt Reduction on Peptide Production and\n Bioactivity of Dry-Cured Hams\nauthors:\n- Beatriz Muñoz-Rosique\n- Noelia Hernández-Correas\n- Adela Abellán\n- Estefanía Bueno\n- Rafael Gómez\n- Luis Tejada\njournal: Foods\nyear: 2023\npmcid: PMC10000787\ndoi: 10.3390/foods12051022\nlicense: CC BY 4.0\n---\n\n# Influence of Pig Genetic Line and Salt Reduction on Peptide Production and Bioactivity of Dry-Cured Hams\n\n## Abstract\n\nHam (Jamón) is a product of great value in Spanish gastronomy, although experts have recommended reducing its consumption due to its high salt content and its relationship with cardio-vascular diseases due to the increase in blood pressure it may cause. Therefore, the objective of this study was to evaluate how the reduction of salt content and the pig genetic line influence bioactivity in boneless hams. For this purpose, 54 hams were studied, 18 boneless Iberian hams (RIB), 18 boneless white hams from commercial cross-bred pigs (RWC), and 18 salted and traditionally processed Iberian hams (TIB) to check if the pig genetic line (RIB vs. RWC) or the processing (RIB vs. TIB) affect the peptide production and bioactivity of the hams. The pig genetic line significantly affected the activity of ACE-I and DPPH, with RWC having the highest ACE-I activity and RIB having the highest antioxidative activity. This coincides with the results obtained in the identification of the peptides and the bioactivity analysis performed. Salt reduction positively affected the different hams, influencing their proteolysis and increasing their bioactivity in traditionally cured hams.\n\n## 1. Introduction\n\nIn recent years, due to the increasing concern of the population for health and nutrition, there is a tendency to investigate and demonstrate the added value and nutritional properties of certain foods. Therefore, the term “Functional Foods” is becoming increasingly prevalent and these foods could prevent the appearance or improve symptoms of certain chronic diseases [1].\n\nMeat products have also been studied for this bifunctionality [2], and numerous proposals have been reformulated to reduce or even eliminate certain components such as salt [3] or fat, seeking to improve the nutritional profile of the food. One of the most important challenges in the food industry is the development of functional foods that maintain the organoleptic characteristics necessary to meet consumer demands, especially regarding flavor and texture [4,5] Bioactive peptides are a group of biological molecules normally buried in the structure of parent proteins that become active after the cleavage of the proteins. During the ham curing process, the action of endopeptidases (capthesins and calpains) and exoproteases is crucial for the formation of peptides. Capthepsins and calpeins begin their action during the cold phase of the dryer (post-salting stage), but the increase in salt concentration, together with drying, causes their activity to decrease. However, exoprotease activity is highly favored when the temperature is increased in the dryer above 25 °C (temperature-dependent activity) and is maintained until the most advanced stages of the process. Intense proteolysis is thus triggered, leading to the hydrolysis of proteins and the release of non-protein nitrogenous compounds (NPNs). Due to this process, free amino acids and small peptides accumulate. These would comprise 2–20 amino acids (AA) with a molecular mass less than 6000 Da [6].\n\nThe natural generation of these bioactive peptides is a consequence of the intense proteolysis of muscle peptidases produced during the processing of cured ham [4,7]. However, there is still little information on the amount of these peptides in the final product. Due to the large number of bioactive peptides, the low abundance of each one and their presence within a complex matrix such as dry-cured ham makes their extraction and analysis difficult [7,8,9]. Peptides modify the texture of the cured meat and influence the aroma at the end of the processing [4,10,11].\n\nNumerous studies have described the beneficial properties that these peptides may have on health. The influence they may have on certain diseases of chronic evolution has been evaluated, exerting antioxidant activity [12,13,14], antihypertensive activity [15,16], immunomodulatory [17], appetite regulator activity [18], or antidiabetogenic activity [19]; with good results and evidence for the ability to inhibit the angiotensin-I converting enzyme (ACE-I) [15,20,21].\n\nThe direct or indirectly prescription of these peptides, which are naturally derived from food, could treat certain diseases and thus circumvent adverse effects secondary to the use of artificial drugs. Other authors described strategies to increase the consumption of meat products in meals, to add precursor proteins of these functional peptides to certain meals or to directly add the peptides, after studying their encapsulation [22].\n\nSalt plays a fundamental role during the salting period of cured ham. Water retention capacity, texture, flavor, pH, and proteolysis are parameters directly influenced by salt content [23,24]. Despite being one parameter that defines the quality of cured ham, and being physiologically necessary for the proper function of an organism [25], the current excessive consumption of high salt products, such us ultra-processed foods has caused current human populations to exceed the necessary daily intake of this mineral, causing hypertension, directly related to cardiovascular diseases and other diseases of risk [26,27]. Therefore, numerous studies are being conducted on various foods to reduce their salt content.\n\nIn the case of ham, salt reduction directly influences proteolysis, which is greaterwhen the salt content is lower, and which, together with water activity, will result in a softer texture of the ham, which, as a consequence, will alter the quality of the final product [28]. The increase in proteolysis leads to protein degradation, resulting in the generation of free AA and peptides through the proteolytic action of endopeptidases that can have biological activity, which can counteract and/or prevent diseases [29].\n\nScant research has been performed on meat-derived peptides, and the degree to which salt reduction has influenced the generation of peptides or the generation of bioactive peptides has not been investigated.\n\nNumerous authors have identified peptides with antihypertensive and antioxidant activity in different types of cured ham [29] that show in vitro ACE inhibitory activity, most important in the peptides Ala-Ala-Pro-Leu-Ala-Pro and Ile-Ala-Gly-Arg-Pro [30]. Some of the identified peptides showed multifunctional activity, i.e., some showed antioxidant or anti-inflammatory activity besides antihypertensive activity [29].\n\nSome trials have been conducted with peptides found in ham to test whether they had bioactivity. A study conducted in hypertensive rats showed a significant reduction in systolic pressure after administering a peptide with antihypertensive activity (ACE inhibitor) eight hours earlier [31]. Furthermore, this multifunctional activity had been recognized from bioactive peptides in other foods [32], most commonly generated from hydrophobic waste [33].\n\nDespite advances made in this field, the effect of salt reduction in ham on peptide generation and bioactivity has not been studied. Furthermore, no studies have been found comparing the peptides present in Iberian ham with those present in white ham, and no studies have attempted to identify peptides in boneless hams. Therefore, the objective of this study was to evaluate the influence of the reduction of salt content and pig genetic line on peptide production and bioactivity in boneless cured hams.\n\n## 2. Materials and Methods\n\nFor this study, 54 hams were selected from different genetic lines of Iberian pigs (thirty-six hams with a racial percentage of $50\\%$ Iberian and $50\\%$ Duroc) and white pigs (eighteen hams from crossbreeding Landrace x Large White or Hampshire).\n\nThe hams from the Iberian pork genetic line were distributed in six batches of six hams. Half were processed on bone traditionally (18 TIB hams) and were the control batches. The other half of the Iberian hams (18 RIB hams) and the three batches of white hams (18 RWC hams) were freshly deboned and subjected to the new process developed to achieve salt reduction (RIB and RWC, respectively). To evaluate the effect of processing (deboning and salt reduction), TIB and RIB hams were compared, so only salt reduction in the Iberian hams was compared. To study the effect of the genetic line, RIB and RWC hams were compared.\n\nTo compare hams from different genetic lines, the percentage of loss was taken as a reference. During the processing phases, each sample was weighed in triplicate to determine the percentage weight loss of each ham respect to the initial fresh weight of each piece. We considered the optimal curing moment, or the final product, when the ham reached $38\\%$ of weight loss. The experimental design is shown in Figure 1.\n\nFresh hams were deboned and salted using sea salt and nitrifying salts. These hams were kept in a cold room at 3 °C for an established period (0.8 days per kilogram of ham weight). The hams were then removed and washed with water, following the normal curing process [34]. The next stage (rest or post-salting) was conducted at 3 °C, gradually increasing this temperature to 6 °C until the percentage of weight loss of the hams rose to $18\\%$. When this phase ended, the temperature was increased to 28 °C, reaching its completion when the hams reached a loss of $38\\%$. Once the hams were cured, 18 hams were selected (six TIB hams, six RIB hams and six RWC hams). The samples were taken during the drying stage ($33\\%$ weight loss) and final product stage ($38\\%$ weight loss). All samples were taken when the ham reached the required percentage of weight loss. To avoid damage to the piece during the sampling process, for all the analysis all samples were taken from the femoral muscle (biceps) using a stainless steel cylinder with a diameter of 2 cm. After, samples were kept refrigerated until their analysis.\n\n## 2.1. Non-Protein Nitrogenous Compounds\n\nTo prepare the extracts, 2 g of sample were weighed in an Erlenmeyer flask, after which 30 mL of distilled water were added, and agitated for 15–20 min in a magnetic stirrer. Then, 15 mL of $20\\%$ trichloroacetic acid was added and shaken for 10 min. The content of the Erlenmeyer was filtered using a funnel and filter paper in a 50 mL volumetric flask. After filtration, the flask was filled with distilled water [35]. Finally, 10 mL of the extract were used for the Kjeldahl method [36].\n\n## 2.2. Antioxidant Activity\n\nThe determination of antioxidant activity was conducted following the method of Bersuder et al. [ 1998] [37] with minor variations. First, a standard was created with the TROLOX reagent (6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid), which is an analog of vitamin E with antioxidant capacity, used to react with the DPPH radical (2,2-diphenyl-1-picrylhydrazyl). The stock solution of TROLOX (2 Mm) was prepared by weighing 12.5 mg of TROLOX and diluting it in 25 mL of ethanol. A second 0.1 mM stock solution of 10 mL was then prepared from the first stock solution of 2 Mm. This second stock solution was prepared with 0.5 mL of the first stock solution and 9.5 mL of ethanol. The TROLOX concentrations of the straight standard were 5, 10, 15, 20, 40, and 50 µM. Two milliliters of each of the concentrations were prepared from the second 0.1 mM stock solution to prepare the standard line, obtaining the following linear equation: $y = 1.01$x + 10.224 (R2 = 0.9988).\n\nThe antioxidant activity of the samples was then determined using a $0.02\\%$ (w/v) solution of the DPPH radical in ethanol. In Eppendorf tubes, 500 µL of ethanol, 500 µL of sample, and 125 µL of $0.02\\%$ (w/v) DPPH solution were added. For the blank, 500 µL of ethanol, 500 µL of water, and 125 µL of DPPH were used. Samples were incubated for 1 h in the dark at room temperature. The samples were then centrifuged at 10,000× g for 2 min and their absorbance was measured at 517 nm. % DPPH RSA = (Abs Blank − Abs Sample)/Abs Blank × 100 where, % DPPH RSA is % DPPH radical scavenging activity. Abs blank is the absorbance of DPPH with water instead of hydrolysate and Abs sample is the absorbance of DPPH radical in the presence of hydrolysate. The analyses for the different hydrolysates were performed three times.\n\nTo calculate the IC50 (concentration of our sample that inhibits $50\\%$ of the DPPH radical) of our sample, we use the equation of the TROLOX standard we performed previously.\n\n## 2.3. Angiotensin-I-Converting Enzyme Inhibitory Activity\n\nThe ACE-I activity of the hydrolysates was conducted according to the spectro-photometric method of Cushman and Cheung [1971] [38], modified by Miguel et al. [ 2004] [39].\n\nFor this purpose, 40 µL of each hydrolysate was incubated at 37 °C with 100 µL of 5 mM HHL dissolved in 0.1 M borate buffer and 0.3 M NaCl (pH 8.3). Next, 2 mU of ECA was added to the substrate. Thirty minutes later, 150 µL of 1 M HCl was added. The formed hippuric acid was recovered with 1000 µL ethyl acetate and centrifuged 10 min at 4000× g and the organic phase (800 µL) was collected. The ethyl acetate was removed by bringing the temperature to 95 °C. The resulting hippuric acid was resuspended in 1000 µL of distilled water and the absorbance was measured at 228 nm. ACE inhibitory activity was determined by the following equation [1]ACE inhibitory activity (%)=(Acontrol−Ablank)−(Asample−Ablank)(Acontrol−Ablank)×100 where, *Acontrol is* the measure of hippuric acid produced by the action of uninhibited ACE, *Asample is* the measure of hippuric acid produced by the action of ACE with the sample, and *Ablank is* the measure of unreacted HHL.\n\nTo calculate the IC50, (the peptide concentration required for inhibit $50\\%$ of ACE activity), dilutions were prepared at different concentrations of the hydrolysate and the percentage inhibition was calculated for each concentration tested. Subsequently, the percentage of ACE-I activity versus the concentration of hydrolysate used (µg peptides/mL) was plotted. The equation of the line (y = ax + b) was obtained and the concentration of the hydrolysate giving an inhibition activity of $50\\%$, i.e., IC50 = (50 − b)/a, was calculated.\n\n## 2.4. Peptide Identification\n\nThe identification was determined using tandem mass spectroscopy (MS) analysis using non-liquid chromatography from the NPN fraction obtained in Section 2.1. The methodology used was described by Bueno-Gavilá et al. [ 2019]. The identification of the peptide sequences of the hydrolysates of the different hams was conducted at the Proteomics and Bioinformatics Unit of the University of Córdoba, Spain. MS2 spectra were found with SEQUEST HT against the UnitProtKB database.\n\nThe peptides of each hydrolysate were identified and quantified using the peptide spectral matches (PSM). Quantification values were normalized, focusing on the total PSM for all peptides in the sample. Thus, the quantification of a single peptide was comparable between those of the different samples. Additionally, we performed a search for each of the identified peptides in the BIOPEP-UWM database [40]. Two types of searches were performed: identification of activated biopeptides in the sample and identification of potential biopeptides containing fragments of bioactive sequences in their primary structure. Data analysis was performed with R (version 3.4.1; https://www.r-project.org, accessed on 17 December 2022).\n\n## 2.5. Statistical Analysis\n\nAll analyses of our samples were performed in triplicate. Statistical analysis of our samples was performed using SPSS software (version 21.0, IBM Corporation, Armonk, NY, USA). To evaluate whether salt reduction and deboning affected the different analyses, a one-way ANOVA between RIB and TIB hams was performed. The effect of breed was also evaluated using a one-way ANOVA between RIB and RWC hams. When the effect of transformation or breed was significant ($p \\leq 0.05$), the results were compared using a Fisher’s LSD test.\n\n## 3.1. Evaluation of Antihypertensive Activity (ACE-I) in Hams with Different Curing Losses (33% and 38%)\n\nAngiotensin-I-converting enzyme (ACE-I) is one of the key enzymes in the regulation of blood pressure, given its participation in the renin angiotensin–aldosterone system (RAAS) [41]. Figure 2 indicates the evolution of ACE inhibitory activity throughout the assay as a function of peptide concentration in hams with different curing loss.\n\nTable 1 and Table 2 indicate the effect of pig genetic line and processing on ACE inhibitory activity, represented as the concentration of peptides necessary (mg/mL) to inhibit $50\\%$ of this activity (IC50). All samples showed ACE inhibitory activity, which increased with increasing concentration of the peptides. This could be due to the presence of small peptides smaller than 3 kDa [15,42,43]. The ham that showed the highest ACE-I activity (IC50) was RIB33, having a greater potential to control diseases associated with the cardiovascular system [44].\n\nThe IC50 in the final product was lower (higher activity) in Iberian hams than in white hams (Table 1), probably due to the longer curing time used in Iberian hams, consistent with what has been observed in other studies [15]. However, when the weight loss is $33\\%$, no significant differences were observed between genetic lines (Table 2).\n\nThe processing method did not significantly influence ACE-I activity in Iberian hams (p ≥ 0.05), although it was slightly higher in salt-reduced hams (RIB38) (Table 1).\n\nA study conducted at the Catholic University of Murcia (UCAM) showed that the consumption of cured ham rich in bioactive peptides has a positive influence on the regulation of glycaemia and cholesterolemia in healthy patients, so that far from being a restricted food, its regular consumption has a positive effect on modifiable risk factors associated with premature cardiovascular disease [20].\n\nTable 3 presents the results of the effect of processing time on the production of ACE inhibitory activity. In RWC, hams with a weight loss of $33\\%$ have greater antihypertensive activity than those with a $38\\%$ weight loss (p ≤ 0.05). However, in Iberian hams, processing time does not imply greater ACE-I activity. In Serrano and Panxian hams, some have observed that this activity increases significantly in the last curing phase [30,42]. Furthermore, other authors have also observed this behavior for dipeptide AA, which increases its activity by $40\\%$ from 6 months to 12 months of ham curing [45]. Because ACE-I has been detected in the hams studied, it could counteract the harmful effects of sodium in the body [46].\n\n## 3.2. Antioxidant Activity\n\nThe DPPH radical study to evaluate the antioxidant activity of samples has been described as a suitable procedure for this purpose [47].\n\nCured ham has been identified as a source of peptides with antioxidant activity [48]. Despite this, no studies have evaluated antioxidant activity in salt-reduced hams. DPPH scavenging activity is also a commonly used technique to evaluate antioxidant capacity. This activity is directly associated with hydrophobic AA in peptides, so these AA will exist in antioxidant peptides [49,50].\n\nFigure 3 indicates the evolution of in vitro antioxidant activity as a function of the peptide concentration of the hams with different processing; all samples show higher antioxidant activity as the concentration of peptides increases. RWC38 has higher antioxidant activity, reaching $75\\%$ inhibition. In RIB38, we also observed an increase in antioxidant activity as the curing process progressed, higher than the healing process, higher than in RIB33. TIB38 shows lower antioxidant activity than RWC38 and is like RIB38. The ham with the lowest antioxidant activity was RIB33 in all the peptide concentrations we studied.\n\nTable 4 and Table 5 indicate the in vitro DPPH radical (antioxidant) scavenging activity of the ham in the drying and final phases, respectively. The concentration (mg/mL) of each NPN needed to inhibit $50\\%$ of the antioxidant activity (IC50) was evaluated. All the samples studied showed antioxidant activity both in the drying phase and in the final product.\n\nTable 4 indicates the IC50 values obtained for each sample and the effect of pig genetic line and processing on the antioxidant activity of the TIB, RIB, and RWC hams. Genetic line significantly influenced the uptake of the DPPH radical in these samples (p ≤ 0.05), as did RIB33 and RWC33 (Table 5). However, salt reduction and deboning did not influence the antioxidant activity of the samples, although it was higher in TIB38. The RWC38 hams have the highest antioxidant activity because they reached the IC50 with a lower peptide concentration (0.155 ± 0.013 mg/mL). These data coincide with the higher proteolysis index obtained in white hams in a previous study [3] due to the higher activity of cathepsins and calpains of this breed [4]. In Serrano hams, peptides have been identified with an IC50 at a concentration of 1.5 mg/mL [46]. Furthermore, Jinhua hams in eastern China, managed an IC50 at a lower concentration of 1 mg/mL [51]. However, in subsequent studies, Jinhua hams achieved an IC50 at 2.5 mg/mL, whereas Xuanwei hams required a concentration of 4.5 mg/mL [52]. In contrast to this study, others have shown that meat from purebred and Duroc-crossed Iberian pigs would be less predisposed to oxidation than those from white pig breeds [53]. Others claim that meat products such as Iberian ham have a greater antioxidant capacity than fresh ham products before being cured, or other foods such as red wine [54].\n\nTable 6 shows that, for both salt-reduced Iberian and white hams, the increase in curing time significantly affects the antioxidant capacity of the samples (p ≤ 0.05), being higher in RIB38 and RWC38. This could be due to the increase observed in proteolytic activity in the later stages of curing [3], often related to the increase in temperature [23].\n\nThe results show that the antioxidant capacity of the hams increases as the curing process progresses and is not affected by the reduction in the Iberian ham. Therefore, cured hams would be a good source of antioxidant activity despite containing pro-oxidant agents such as salt and heme and even reactive oxygen species (ROS), which can cause cell damage [55,56].\n\n## 3.3. Bioactive Peptide Sequencing\n\nThe peptides present in the samples of the hams from the five batches studied (RIB38, RIB33, RWC38, RWC33, and TIB38) were sequenced by LC-MS/MS analysis. Table 7 indicates the number of sequenced peptides per sample. The ham with the highest number of sequenced peptides was RWC38, RIB33 had the lowest number of peptides sequenced, and RIB38 presented a greater number of peptides than TIB38. This coincides with the values of non-protein nitrogen and the proteolysis index (PI) obtained in a previous study in hams with a loss of $38\\%$, where the highest and lowest NPN and PI were found in salt-reduced white hams (RWC38) and traditionally cured hams (TIB38), respectively [3].\n\nIn this study, no peptides already obtained from the database were found among the peptides obtained in the proteomic study. Therefore, their bioactivity has not been demonstrated in previous studies.\n\nIn other studies, identical sequences were found in cured ham, for example, KAAAAP, AAPLAP, and KPVAAP, with origin in different types of myosin protein, were identified as the peptides with the highest ACE-I activity in Teruel PDO ham [30], and are also present in Serrano ham [20].\n\nTheir stability and their retention of bioactivity during processing and after in vitro digestion were examined. In vivo studies showed that the AAATP peptide had the highest antihypertensive activity, lowering systolic blood pressure with a short-term effect [46]. Furthermore, other sequences with antihypertensive activity were identified, such as ASGPINFT and DVITGA (both also derived from myosin protein). In another study, AAATP with the KA dipeptide had DPP4 inhibitory activity that would contribute to improving the concentration of glucose in the blood [20].\n\nThe antioxidant power is another bioactivity studied in traditional Serrano ham [46]. The SAGNPN peptide has been identified to have the greatest capacity to donate electrons, neutralizing the oxidative capacity, even more than the peptides synthesized [46,57]; furthermore, the peptide GLAGA had the highest reducing power [58]. Moreover, SNAAC and AEEEYPDL, identified in the cured ham, had high antioxidant activity [59].\n\nNumerous bioactive peptides with a high antihypertensive power have been identified in Iberian ham, which are higher than those in Serrano ham. The sequences that are repeated most frequently, which coincide with the BIOPEP database, are PPK, PAP, and AAP [60]. However, the following dipeptides, such as EA, with ACE-I activity, or PP and VE, which showed ACE- and DPP4-inhibitory activity have also been sequenced [61].\n\nDipeptides with anti-inflammatory and cardiovascular protective activity (PA, GA, VG, EE, ES, DA, and DG) have been identified in hams with reduced salt content, besides contributing to the product aroma and flavor [62]. However, no studies have been found in fresh deboned and salt-reduced Iberian or white ham.\n\n## Study of Putative Activity Peptide Sequences\n\nA search has been conducted for peptide precursors that may contain biopeptides in their sequence and could theoretically be activated after digestion. This technique is useful for very small sequences (less than seven Amino Acids) and by using the proteomics procedure, it is impossible to detect them.\n\nTo contextualize the type of bioactivity of the samples, a Z-scoring was performed to plot the variation between samples regarding the mean of the different activities (heatmap). The results are shown in Figure 4; a higher intensity red color means that this activity will be over-represented regarding the mean of the five samples. Bioactivities are grouped according to the intensity of occurrence in each sample. In addition, the succession of rows and columns is rearranged to avoid intersection of the dendrogram lines. Blue lines represent the value of the coefficient. Individually, we have represented in which sample each group of activities stands out for each group or clusters (Figure 5), each corresponding to a group of bioactivities. In RWC33, the main activity is immunostimulatory. No studies have been found on the presentation of this bioactivity in cured ham.\n\nRWC38 showed the highest antioxidant activity. These results coincide with the activity observed in vitro using DPPH (Table 4). Other bioactivities that stand out in this sample are those of neuropeptide activation, hypolipemic, anti-inflammatory, anti-cancer, and hypotensive activities. Antioxidant and hypotensive activity have also been well studied in white pig hams.\n\nSeveral studies confirm the occurrence of these bioactivities in ham [48]. Recently, peptides with anti-inflammatory activity have been identified in Xuanwei hams, showing reduced symptoms of inflammatory bowel disease in mice, and it has been pro-posed that these peptides could be a functional drug in patients suffering from this disease [63].\n\nIn RIB33 hams, the predominant activities are stimulatory, immunomodulatory, a CaMPDE inhibitor, a DPP4 inhibitor, antithrombotic, and ACE-I, consistent with our results for antihypertensive activity (Table 2), where RIB33 had the highest ACE-I. Likewise, the Iberian ham showed greater ACE-I activity compared to the traditional Serrano hams [60].\n\nAntihypertensive activity is well studied in ham [20,21,60]. There are studies that would claim that Serrano ham would be a good source of DPP4 and that these peptides could be an adjunct in the treatment of type 2 diabetes [64].\n\nRIB38 hams have HMG-CoA reductase inhibitory, regulatory, and immunological activity. HMG-CoA reductase inhibitors play an important role in the control of hyper-cholesterolemia and, indirectly, in the control of the onset of cardiovascular disease. Other studies have found dipeptides such as DA, DD, EE, ES, and LL in cured ham, which have been identified as the main inhibitors of this coenzyme [65]. Furthermore, TIB38 hams stand out for their binding, ubiquitin mediator protein activator, renin inhibitor, dipeptidyl peptidase III inhibitor, and embryotoxic activity, bioactivities that have not yet been studied.\n\nEach group of bioactivities was represented by a color (Figure 4). In Figure 5, we can observe six clusters, one for each group of bioactivities, where the values of that group of bioactivities are quantified for each sample.\n\nBecause bioactive sequence fragments are found in the samples, a spider web plot with normalized quantification of the peptide precursors of the five hams is shown in Figure 6. This distribution allows differentiation between hams according to activity. The potential bioactivity of the peptides identified in each sample is reflected by using the same scale and amplitude and the same scale and width of the axis, allowing comparison between them.\n\nCured ham is considered a good source of different bioactive peptides that have important functional activities, such as the inhibition of the angiotensin converting enzyme, hypoglycemic, and anti-inflammatory activities [29].\n\n## 3.4. Bioactivity Analysis Based on Amino Acid Composition\n\nThe composition of Amino Acid (AA) used to analyze the bioactivity of the samples was conducted on $38\\%$ cured hams, as these had the best organoleptic characteristics and, therefore, would be destined for the end consumer.\n\nIn bioactivity studies, it is important to consider the structural properties of sequences [66]. Certain characteristics, such as size, hydrophobicity, and composition, may influence the stability or bioavailability of the peptides. Approximately 20 sequences were selected from those identified in each ham with less than 1.5 kDa and with a maximum of 12 AA in their chain. Processing time causes the size of the peptides to decrease and increases the antioxidant activity of the peptides [49], as short AA sequences are more likely to be bioactive [62,67]. In addition, over $50\\%$ of the AAs in the chain should be hydrophobic, as this contributes to antioxidant activity [68]. The presence of AAs A, D, E, G, L, P, and V confers antioxidant and antihypertensive activity on the peptide sequence [68,69,70], and this activity is directly related to the molecular weight of the peptide sequence [71]. However, the presence of H, Y, W, F, M, and C could inhibit free radicals by direct electron transfer [67]. The amino acid sequences of the peptides identified from salt-reduced Iberian hams (RIB) are shown in Table 7.\n\n## 3.5. Identification of Peptides Present in RIB Hams\n\nThe AA sequences of the peptides identified from the hydrolysates of salt-reduced Iberian hams (RIB) are shown in Table 8.\n\nAntioxidant activity is highly present among the selected sequences. Some have over $50\\%$ of the peptides that provide antioxidant activity. The LDLALEKD, AAFPPDVGGN, AGNPDLVLPV, and AFGPGLEGGL peptides stand out for having over $80\\%$ of AAs that would favor antioxidant activity, with AFGPGLEGGL having the highest antioxidant activity ($90\\%$ of its AAs).\n\nThe AAFPPDVGGN and AFPPDVGGN have been identified as present in pork [72] and six sequences containing them have been found (AFPPDVGGN, AAFPPDVGGN, AFPPDVGGNV, AAFPPDVGGGGNV, AFPPDVGGGGNVD, and AAFPPDVGGGGNVD). The peptides FPPDVGGN and FPPDVGGNVD originating from the protein could also be derived from these sequences, identified as myosin [46]. From the action of the enzyme, dipeptidyl peptidase [73] could be released from some sequences as the VD dipeptide, which would have DPP4 inhibitory activity and, therefore, anti-diabetic activity [64,74].\n\nThe most prominent sequence is CLFVCR, as it has $83\\%$ of hydrophobic AAs, $67\\%$ of AAs conferring ACE-I activity, and $50\\%$ of AAs scavenging free radicals. ACE-I activity would be more present in sequences containing hydrophobic AA residues in the three C-terminal positions [75]. For this sample, the sequence AGNPDLVLPV has three hydrophobic AAs at the C-terminus. The dipeptide WK could be extracted from longer peptides originating from β-enolase, such as DGADFAKW (Table 8). This dipeptide has been identified as an inhibitor of DPP4 [76,77]. Likewise, the sequences LIGIEVPH, IDLIEKPM, FDKIEDMA, WNDEIAPQ, and DLDISAPQ originate from the IE and SI dipeptides of the α-enolase protein; they have been described as ACE- and DPP4-inhibitory peptides, respectively [74,78]. These dipeptides could be responsible for the high antihypertensive activity observed in this study for sample RIB38 (Table 1).\n\nRecently, some dipeptides related to anti-inflammatory activity, which could confer cardiovascular protection, have been identified in salt-reduced cured hams [62]. These dipeptides are PA, GA, DA, and DG and could be derived from sequences found in RIB38 (ALQPALKF, WNDEIAPQ, MADTFLEH, DLDISAPQ, DGADFAKW, MADTFLEH, and AGNPDLVLPV), with GA being mainly identified in the study.\n\nTable 9 indicates the prominent peptide sequences detected in the ham samples of traditionally cured Iberian ham (TIB). In these samples, six of the selected sequences presented over $80\\%$ of the AAs that could provide antioxidant activity to the product (AFPPDVGGNV, AAFPPDVGGN, DVVLPGGNL, VAVGDKVPAD, DIAVDGEPLG AGNPDLVLPV, and AFGPGLEGGL). RIB38 has the highest antioxidant activity. However, the sequence that stands out for having the highest amount of hydrophobic peptides is ILPGPAPW. This peptide comprises the Pro-Ala-Pro sequence, one of the most repeated sequences among the bioactive peptides described in the literature [15], which would confer good antioxidant activity to the sample [68]. Furthermore, these sequences could contribute to the bioactivity described for TIB38 ham (Figure 4).\n\nFour sequences (ILPGPAPW, VMGAPGAPM, GDLGIEIPA, and AGNPDLVLPV) have three hydrophobic AAs at the C-terminus, and are therefore more likely to develop ACE-I activity [75]. The AGNPDLVLPV sequence matches that found in RIB38. In the GDLGIEIPA and IELIEKPM sequences, we can find the dipeptide IE dipeptide related to ACE-I bioactivity [74]. The same six sequences identified in RIB38 have also been found in TIB38 (AAFPPDVGGNV, AAFPPDVGGN, AFPPDVGGNVD, AAFPPDVGG-NVD, AFPPDVGGN, and AFPPDVGGNV), have been identified in pork, and could have inhibited DPP4 derived from the dipeptide DV [61].\n\nFrom a comparison study between traditional and salt-reduced cured hams, di-peptides such as DA, PA, and VG would be present in a higher proportion in traditional cured hams [62]. The last two sequences of Table 9 could derive from the peptides found in sample TIB38 and could contribute to its anti-inflammatory and antihypertensive activity. Other sequences, which were identified in this study, are GA (ACE and DPP4 inhibitory activities) and DG (ACE-I activity).\n\nThe selected AA sequences of salt-reduced white hams (RWC) are shown in Table 10. The sequences that stand out for having over $80\\%$ of AAs and confer antioxidant activity are DLAEDAPW and AEVIALPVE. The latter sequence is also present in RIB38, with the highest antioxidant activity.\n\nThe sequence that stands out for having the highest amount of hydrophobic AAs is ILPGPAPW, the same as TIB38, and has one of the most repeated sequences among bio-active peptides (PAP) [15]. Furthermore, this sequence has $75\\%$ of AAs that confer antioxidant activity, three hydrophobic AAs at the C-terminus that confer ACE-I activity, and $13\\%$ of AAs that could inhibit free radicals. However, six peptide sequences with three hydrophobic AAs at the C-terminus (ILPGPAPW, AVIGPSLPL, VMGAPGAPM, ISAPSADAPM, DLAEDAPW, and GDLGIEIPA) were found in the RWC38 samples, which conferred ACE-I activity. However, in RIB38, of the sequences selected, none had over two hydrophobic AAs at the C-terminus. The sequences ILPGPAPW, VMGAP-GAPM, and GDLGIEIPA match TIB38. Furthermore, the LKGADPEDVITGA and GADPEDVITGA would contain the bioactive peptide DVITGA in their chain, related to high ACE-I activity due to the presence of AA alanine at the C-terminus [39,46]. Despite this, the RWC38 ham showed the least antihypertensive activity (Figure 4); it would be necessary to study if these peptides confer ACE-I activity and in what quantity they are present. There are more sequences from which the dipeptide IE could be derived, already described as a precursor of this bioactivity [74].\n\nThe sequence identified in RWC38, FKAEEEYPDLS, once digested, could cause the peptide AEEEYPDL, derived from protein creatine kinase and identified as a potent antioxidant [59]. Using multiple reaction monitoring (MRM), it was quantified at a concentration of 0.148 fg/g in cured ham [79]. This could explain why the RWC38 hams showed the highest antioxidant activity (Figure 4) and the highest rate of proteolysis obtained in white hams [3].\n\nIn RWC38 hams, the same six sequences described in RIB38 and TIB38 (AAFPPDVGGNV, AAFPPDVGGNV, AFPPDVGGNVD, AAFPPDVGGNVD, AFPPDVGGNV, and AFPPDVGGNV) have been identified. However, this would explain the difference in proteolytic activity [3] between different pig genetic lines and influence of processing, because salt-reduced Iberian hams (RIB38 and RIB33) had the highest DPP4 inhibitory activity, lower than white hams (RWC38 and RWC33) and traditional Iberian ham (TIB38).\n\nIn a recent study of salt-reduced white ham, hydrophobic PA dipeptides (related to bitter taste and with ACE-I and anti-inflammatory activity) and VG (related to bitter and umami taste and with ACE-I activity) were identified that could be derived from the sequences identified in our sample [62].\n\nDifferent unique and common sequences that could act as peptide precursors and that have been identified in the samples would be responsible for the bioactivities found in the different types of ham. The results show that in RIB38 ham, the precursors found could be responsible for its high antihypertensive capacity, noting that the change in processing varies the sequences identified in both samples (RIB38 and TIB38). Furthermore, peptides already referenced in the literature have been found in the RWC38 ham, including a sequence that gives rise to a potent antioxidant peptide (AEEEYPDL) that would explain its increased bioactivity. However, none of these co-inciding peptides are found in Iberian hams.\n\n## 4. Conclusions\n\nSalt-reduced boneless hams presented a higher concentration of peptides and higher bioactivity compared to traditionally cured hams, due to higher proteolysis.\n\nThe salt-reduced white ham had the highest antioxidant activity and the salt-reduced Iberian ham the highest antihypertensive activity. Antioxidant activity was significantly influenced by pig genetic line and antihypertensive activity was modified by pig genetic line in the final product. 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-0.04005281254649162\n]","isConversational":false}}},{"rowIdx":98,"cells":{"text":{"kind":"string","value":"---\ntitle: A New Artificial Intelligence Approach Using Extreme Learning Machine as the\n Potentially Effective Model to Predict and Analyze the Diagnosis of Anemia\nauthors:\n- Dimas Chaerul Ekty Saputra\n- Khamron Sunat\n- Tri Ratnaningsih\njournal: Healthcare\nyear: 2023\npmcid: PMC10000789\ndoi: 10.3390/healthcare11050697\nlicense: CC BY 4.0\n---\n\n# A New Artificial Intelligence Approach Using Extreme Learning Machine as the Potentially Effective Model to Predict and Analyze the Diagnosis of Anemia\n\n## Abstract\n\nThe procedure to diagnose anemia is time-consuming and resource-intensive due to the existence of a multitude of symptoms that can be felt physically or seen visually. Anemia also has several forms, which can be distinguished based on several characteristics. It is possible to diagnose anemia through a quick, affordable, and easily accessible laboratory test known as the complete blood count (CBC), but the method cannot directly identify different kinds of anemia. Therefore, further tests are required to establish a gold standard for the type of anemia in a patient. These tests are uncommon in settings that offer healthcare on a smaller scale because they require expensive equipment. Moreover, it is also difficult to discern between beta thalassemia trait (BTT), iron deficiency anemia (IDA), hemoglobin E (HbE), and combination anemias despite the presence of multiple red blood cell (RBC) formulas and indices with differing optimal cutoff values. This is due to the existence of several varieties of anemia in individuals, making it difficult to distinguish between BTT, IDA, HbE, and combinations. Therefore, a more precise and automated prediction model is proposed to distinguish these four types to accelerate the identification process for doctors. Historical data were retrieved from the Laboratory of the Department of Clinical Pathology and Laboratory Medicine, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia for this purpose. Furthermore, the model was developed using the algorithm for the extreme learning machine (ELM). This was followed by the measurement of the performance using the confusion matrix and 190 data representing the four classes, and the results showed $99.21\\%$ accuracy, $98.44\\%$ sensitivity, $99.30\\%$ precision, and an F1 score of $98.84\\%$.\n\n## 1. Introduction\n\nThe main task of the circulatory system is to allow the flow of blood, oxygen, and nutrients to all cells and tissues in the body [1,2]. However, there are disorders of the circulatory system, better known as blood disorders, in which blood circulation is obstructed [3,4] Blood disorders commonly experienced by humans include anemia, hemophilia [5,6], and blood clots [7,8], as well as blood cancers such as leukemia [9,10], lymphoma [11,12], and myeloma [13,14]. A blood disorder is a condition that affects the ability of blood to function properly in humans [15], with most of these disorders having the capacity to reduce the number of cells, proteins, platelets, or nutrients in the blood, thereby impairing its function [16,17]. It is important to note that most of these problems are caused by abnormalities in certain genes and can be passed down via families [18]. Some medical issues such as drug use and lifestyle also lead to blood abnormalities [19]. It has been reported that anemia is the most common blood disorder seen in humans [20].\n\nAnemia has been defined as a decrease in red blood cells, hemoglobin, and the blood’s ability to carry oxygen throughout the body [21,22]. It is a serious and persistent issue affecting individuals worldwide [23,24]. The prevalence of anemia among Indonesian women of reproductive age is shown in Figure 1 to exceed the global incidence [25]. A previous study also noted that iron deficiency is the major cause of anemia in every part of the globe [26]. As previously stated, this disease is the substantial reduction in the number of red blood cells circulating inside the body [27], thereby leading to a great decrease in the ability of the blood to transport oxygen [28]. The diagnosis of anemia is usually confirmed by the concentration of hemoglobin in the blood or the hematocrit, which is the ratio of the number of red blood cells to the total volume of a blood sample [29]. A patient with hemoglobin or hematocrit values that are more than two standard deviations below the normal range is believed to have anemia [30]. Meanwhile, the blood’s hemoglobin and hematocrit levels may not adequately reflect the severity of the anemia in a patient with a low RBC mass who is also suffering from hypovolemia-caused dehydration-induced plasma volume loss because the values are likely to fall within the normal range [31].\n\nThere are many people suffering from anemia in Indonesia [33]. Iron is believed to be an essential component of several enzymes; it has a role in the formation of hemoglobin in the human body [34]. This means its deficiency can cause anemia [35]. A survey conducted showed that the frequency of anemia in the Indonesian population was anticipated to grow by $0.8\\%$ in 2019 reaching $31.20\\%$ of the population [32]. The prevalence of anemia sufferers worldwide increased by $0.3\\%$ in 2019 to $29.90\\%$ [32]. It has also been noted that abnormal production of alpha (α)- or beta (β)-globin chains is the root cause of thalassemia, which is a hematological disease that runs in families, usually passed down from one generation to another [36,37]. Serum ferritin levels [38], serum iron [39], total iron binding capacity, and transferrin saturation percentage are the tests most often used to confirm the presence of IDA, as presented in Table 1 [40,41]. The identification of BTT and HbE is normally performed through Hb tests using high-performance liquid chromatography, capillary/hemoglobin electrophoresis, or DNA analysis [42,43]. The application of DNA analysis is not accessible in normal labs due to the need for specialized equipment; in addition, it is time-consuming and expensive [44]. When a patient is assumed to be anemic, doctors often prescribe a hematology test cassette that includes the diagnosis [45]. The high cost of this laboratory test can exhaust patients’ resources and government funds, thereby precipitating a financial crisis in the national healthcare systems of low- and middle-income nations. Therefore, a web-based application is proposed in this study to assist doctors in prescribing cost-effective and sensible laboratory tests for the diagnosis of anemia to aid in the rational use of laboratories by clinicians.\n\nAnemia is a significant threat to public health on a global scale, and its incidence is disproportionately higher among young children and pregnant women. According to the World Health Organization (WHO), $42\\%$ of children under the age of five and $40\\%$ of pregnant women worldwide are affected by anemia [46]. Indonesia Family Life Surveys (IFLS) reported that the prevalence among Indonesian children, adolescents, women, and men continued to fall from 1997 to 2008 as indicated in Figure 2 [47]. The anticipated decline among non-pregnant women in Southeast Asia between 1995 and 2011 was predicted to be $8\\%$ less than the $9.4\\%$ decline among non-pregnant women between 1997 and 2008 as reported by IFLS [47]. It was also discovered that the reduction among pregnant women during the period was comparable to IFLS ($7.8\\%$) and Southeast Asian ($9\\%$) estimates [47]. However, the decline among children under the age of five was much greater in the IFLS ($14.6\\%$) than in Southeast Asia ($4\\%$) [47].\n\nThe Fick equation is normally used to determine the flow of oxygen to a certain bodily region [48] using three independent variables, including the blood flow [49], the concentration of red blood cells [50], and the portion of hemoglobin that has released oxygen on its journey from the arteries to the veins [51]. The oxygen-carrying capacity of anemic individuals’ blood was discovered to be diminishing while the remaining two variables underwent compensatory modifications as illustrated in Equation [1] and in the further discussion in [52]. [ 1] where DPG is diphosphoglycerate, *Asat is* arterial blood, and *Vsat is* venous blood.\n\nIn anemic individuals, the blood flow to crucial organs such as the heart, brain, liver, and kidneys is boosted, while the blood supply to less vital organs is diminished [53]. The anemic patient appears pale because blood is taken from the skin to ensure the vital organs continue to get sufficient oxygen [54]. Moreover, cardiac output is projected to be lower at rest and greater during exercise in individuals with mild or severe anemia compared to healthy individuals [55]. It has also been reported that severe anemia has the ability to increase resting cardiac output in individuals with coronary artery disease or other preexisting cardiovascular diseases, thereby increasing their risk of developing high-output heart failure [55].\n\nIt was discovered, as shown in Table 1, that indices and discriminant formulas had promising prediction outcomes in several investigations, but the forecast findings for different populations remain unsatisfactory, particularly when assessing the efficacy of the methodologies used. Some research was observed to be contentious due to gender, age, or ethnic variances [56,57,58]. Meanwhile, machine learning-based strategies are believed to have a short-term translational effect. This is indicated by their most significant applications in the field of biomedicine such as medical diagnostics, radiological diagnostics, and medication synthesis. Therefore, it is feasible to create discriminant models using machine learning software approaches to undertake large-scale assessments of laboratory data. It is pertinent to state that machine learning is one of the subfields under the umbrella of artificial intelligence [59].\n\nArtificial intelligence (AI) is defined as the effort of computers to simulate human cognitive processes [60]. This is observed in signal processing, voice recognition, expert systems, and natural language processing. The continuous development of interest in AI has enhanced competition among businesses to showcase the AI features of their goods and services [61]. It is important to note that the creation and training of machine learning and deep learning algorithms require the application of specialized hardware and software [62]. This is mainly due to the fact that the operation of AI systems entails the intake of massive quantities of labeled training data as well as the analysis of the data for correlations and patterns to predict future states [61]. AI programming focuses on three cognitive skills, which include learning [59], reasoning [63], and self-correction [64].\n\nThis aspect of AI programming involves the collection of data and the development of rules to translate the data into actionable knowledge. Algorithms provide computer systems with detailed instructions to perform a certain task [65]. It is important to state that AI technology is experiencing a time of rapid expansion, but few are aware it has several subfields, one of which is deep learning [65]. The subfields and branches were developed to reduce the very large scope of AI for the purpose of development or research [66]. It is anticipated that AI has the ability to expedite the process of discovering human issues. For example, Laengsri et al. [ 67] classified 6935 data obtained from the Medical Laboratory Service Centre, Faculty of Medical Technology, Mahidol University between July 2014 and September 2016 as either thalassemia trait or iron deficiency anemia using k-nearest neighbor, decision tree, random forest, artificial neural network, and support vector machine methods. It was discovered that the decision tree algorithm attained a maximum degree of accuracy of $98.03\\%$. The study used seven hematology analyzer-generated features to determine the existence of anemia in individuals. This is necessary because it is difficult to determine the kind of anemia present in a patient using only a blood sample. The findings are expected to allow medical personnel to conduct further diagnostic tests without difficulty and also to ensure a more precise and specific diagnosis [67].\n\nAnother study also applied extreme learning machines and regularized extreme learning machines to anemia cases [58]. A total of 342 patients, including 152 with beta-thalassemia-type anemia obtained from the Elazig Public Health Laboratory between 1 December 2016 and 23 May 2019 and 190 with iron deficiency anemia obtained from the Elazig City Hospital Biochemistry Laboratory between 1 March 2018 and 31 July 2018, were studied. The investigation considered a large number of other variables such as gender, in addition to the findings of the clinical pathology test. The regularized extreme learning machine approach produced a $95.59\\%$accuracy rate by combining the k-nearest neighbor, support vector machine, extreme learning machine, and regularized extreme learning machine methods [58].\n\nThis present research focuses on establishing an AI model to rapidly, precisely, and reliably diagnose anemia. The process involves classifying anemia into four types, which include beta thalassemia trait (BTT), hemoglobin E (HbE), iron deficiency anemia (IDA), and combination (BTT and IDA or HbE and IDA) using the extreme learning machine approach. Previous studies [23,52,57,66] have shown the relevance of data mining and increasing computing capacity in several biological applications. Therefore, this research seeks to develop a trustworthy and interpretable computational model through the following: (a) collection of clear and dependable laboratory datasets for training and validation, (b) demonstration of dataset characteristics or descriptors to predict the intrinsic properties, and (c) development of a simple and interpretable model.\n\n## 2. Related Work\n\nSupport vector machines (SVM), naive Bayes (NB), decision trees (DT), k-nearest neighbor (KNN), multilayer perceptron (MLP), hybrid classifier machine learning, average ensemble (AE), genetic algorithm convolutional neural network (GA-CNN), genetic algorithm stacked-encoder (GA-SAE), support vector machines (SVM), and random forest (RF) are different types of AI. Several studies have been published on the application of machine learning to categorize different kinds of anemia, as indicated by [68,69,70], which forecasted data in the form of a complete blood count (CBC) and constructed a model to identify anemia.\n\nHemoglobin level estimation is an important step in any task related to blood analysis [50], and it also determines whether a person is anemic. A study [68] used blood test characteristics and applied a machine learning model to calculate hemoglobin levels and identify anemia. The dataset used consists of 9004 data with $75\\%$, or 6753, for training and $25\\%$, or 2251, for testing. A total of three machine learning algorithms—including DT, NB, and NN, as well as a combination of all three approaches known as a hybrid classifier—were applied. Moreover, the MAE and RMSE methodologies were used to assess the performance of the approach, and the MAE results showed that the hybrid classifier had 0.083, the best RMSE value of 0.015, and an accuracy of $0.996\\%$ [68].\n\nTremendous advances in the healthcare industry have resulted in the production of significant data in everyday life [71]. There is a need to extract information for analysis, prediction, recommendation, and decision-making purposes. It was discovered in the realm of medical research that the prediction of disease is essential to design effective prevention and treatment methods. The presentation of wrong information occasionally leads to death. Therefore, a recent study applied 200 CBC data fields obtained from the Pathology Centre and Laboratory Test Centre, as well as RF, C4.5, and NB, which are considered three distinct types of machine learning. K-fold cross-validation and mean absolute error were both used at different stages of the model evaluation process. It was discovered that the C4.5 approach produced the most precise answers, with a precision percentage of 96.0909 and an absolute mean error of 0.0333 [70].\n\nAnemia was also found to be a severe public health problem, particularly for children, in Bangladesh [69]. Thus, the prediction of illness is essential to formulate community and healthcare policy as well as to forecast resource planning. The study used the common risk variables to determine the appropriate machine learning method to predict anemia status in children (under five years) [69]. The 2013 data containing all relevant characteristics for the children, obtained through a nationally representative cross-sectional study conducted by the Bangladesh Demographic and Health Survey (BDHS) in 2011, were used. The investigation employed six techniques, which included the LDA, CART, KNN, SVM, RF, and LR, and they were assessed using the confusion matrix, accuracy, sensitivity, and specificity. The findings showed that the CART approach yielded the greatest evaluation scores of $62.35\\%$, $71.54\\%$, and $53.52\\%$ [69].\n\nIt is important to note that “deep learning” and “machine learning” are interchangeable when discussing artificial intelligence (AI) [62]. Deep learning is established based on the concept of creating learning algorithms or models that can simulate the human brain [65]. Humans use neurons in their brains to process information, while deep learning algorithms utilize artificial neural networks to perform the same function [72]. Some recent studies [73,74,75] used deep learning to enhance the process of identifying anemia in patients. The single red blood cell count imaging data of 130 individuals with sickle cell anemia (SCA) were surveyed and discovered to exceed 9000 single red blood count image data of patients [73]. SCA is a severe hematological illness that often leads to lifelong hospitalization and, in some circumstances, death [73]. It is important to note that the manual location and classification of aberrant cells in the blood films of SCA patients is time-consuming, difficult, and error-prone, and it requires the skill of a hematologist. The study used the AlexNet deep learning model, and the accuracy was recorded to be $95.92\\%$, sensitivity was $77\\%$, specificity was $98.82\\%$, and precision was $90\\%$ based on the assessment conducted using the confusion matrix [73].\n\nDeep learning algorithms are gaining importance in the prognosis and prediction of diseases using patients’ data [76]. It is pertinent to state that the lack of prompt diagnosis and treatment of anemia can lead to a life-threatening illness [51]. Therefore, several artificial intelligence algorithms have been employed to forecast nutritional anemia cases, especially those related to iron deficiency [35,53]. Each algorithm was observed to be optimized for a certain subset of data, and this means there is a need to develop new processing techniques. The trend was identified in a previous study where the properties of each dataset are unique, and the size was governed by the number of records and variables specific to the dataset [74]. The strategy blends machine and deep learning to improve the identification process. These were observed to be in the form of genetic algorithm (GA), stacked autoencoder (SAE), and convolutional neural network (CNN) methods, which were used to predict the HGB, nutritional or iron deficiency, B12 deficiency, and folate deficiency anemia as well as to examine individuals without the illness [74]. Moreover, a confusion matrix was used to assess the model, and the greatest level of accuracy for the GA-CNN algorithm was recorded to be $98.5\\%$; the F1 score was $98.8\\%$, sensitivity was $98.7\\%$, and precision was $99.00\\%$ [74].\n\nHemoglobin, a protein contained in red blood cells, is important for the transport and storage of oxygen throughout the body [77]. It has been reported to have the ability to preserve its elasticity, spherical form, and stability in healthy individuals [78]. This is the reason it can float above the red blood cells, but its structure does not ameliorate the symptoms of sickle cell disease [22]. The phenomenon is associated with red blood cells that are twisted and blocked with fluid. It is also important to note that blood circulation is hindered by dysfunctional cells. This is dangerous and has the ability to lead to a range of symptoms, including excruciating pain, organ damage, and even heart attacks [49]. It also has the potential to reduce the average human lifespan. Sickle cell disease identified at an early stage can be treated with antibiotics, vitamins, blood transfusions, painkillers, and other medications. However, the manual grading, diagnosis, and cell counts are time-intensive, and this poses a risk of inaccurate data and misclassification because a single sample usually comprises millions of red blood cells. This is the reason the application of data mining techniques is considered effective and efficient in determining the status of sickle cells inside the human body [75]. An example of this is the adoption of a robust and rapid MLP (multilayer perceptron) classification algorithm to separate sickle cell anemia (SCA) patients into three groups, and the method was observed to surpass the constraints of the manual methods. It was discovered that there are three different types of red blood cells, which include normal, sickle, and thalassemic cells [75]; this discovery was followed by the application of the confusion matrix to analyze the performance of the MLP approach. The results obtained using the 1387 datasets gathered between August 2017 and August 2019 showed a correctness score of $96.04\\%$ [75] while the 100 most recent datasets obtained from the Thalassemia and Sickle Cell Society (TSCS) in Rajendra Nagar, Hyderabad, Telangana, India [75] from September 2019 to August 2020 showed $99\\%$ [75].\n\n## 3.1. Data Collection\n\nThis research was conducted using 165 females and 25 males between the ages of 15 and 41 diagnosed with different kinds of anemia. The data used were compiled by the Clinical Pathology Laboratory at Dr. Sardjito General Hospital in Yogyakarta, Indonesia, and the Department of Clinical Pathology and Laboratory Medicine of the Faculty of Medicine, Public Health, and Nursing at Universitas Gadjah Mada. Moreover, a hematological measure was generated from patients with BTT, IDA, HbE, and a combination of BTT and IDA or HbE and IDA. It is important to note that the Medical and Health Research Ethics Committee (MHREC) of the Faculty of Medicine, Public Health, and Nursing at Dr. Sardjito General Hospital, Universitas Gadjah Mada, issued an ethical letter for the conduct of this research, with the identifier KE/FK/1255/EC/2021. The parameters used include the RBC, Hb, HCT, MCV, MCH, and MCHC in addition to RDW. A total of 24 patients were diagnosed with BTT, 41 with HbE, 104 with IDA, and 21 with the combination method. The definitions of several acronyms used during the investigation are presented in Table 2.\n\n## 3.2. Research Flow\n\nThe data derived from the results of a full blood count performed in the laboratory using Advia and Sysmex hematology analyzers produced seven primary characteristics. Moreover, serum ferritin was applied to acquire the gold standard from IDA while hemoglobin electrophoresis was used for BTT and HbE, and the data obtained were examined further and placed in the database based on the flow presented in Figure 3. The seven characteristics previously identified were processed in the database, and the data were labeled by clinical pathology physicians. The data put into the database were preprocessed through cleansing, deletion, the MinMax scaler, and the LabelEncoder. The remaining data were divided into $67\\%$training and $33\\%$testing. Furthermore, the ELM algorithm was used to train the data, which were subsequently applied as the standard to grade the test data. The doctor was involved in the process to provide training courses based on the findings from the laboratory tests. This was followed by the application of the ELM algorithm to classify the data, and its performance was also evaluated. The performance results were further used by the clinical pathology doctor to analyze the data once more to ensure transparency and accountability of the categorization.\n\n## 3.3. Extreme Learning Machine\n\nThe research on the predictive capacities of feedforward neural networks has been mathematically centered on two aspects. The first is the simultaneous estimate of the number of inputs while the second is the estimation within a certain period. Thus, the focus of several studies has been on the feedforward neural networks, as indicated by those conducted on the extensive approximation capabilities of typical multilayer feedforward neural networks [79,80,81,82,83]. Due to their benefits, these networks have been extensively adopted across a variety of commercial sectors over the last few decades. These benefits include the capability to predict complex nonlinear mappings using the available input samples, as well as to provide models for an extraordinarily high number of natural and artificial occurrences, which are considered problematic for standard parametric techniques designed for such events [84]. The single hidden layer feedforward networks, also known as SLFNs, are among the most well-known feedforward neural networks, and their learning and fault tolerance properties have been the topic of discussion in both theoretical and practical studies [85,86,87,88]. The recent development of the extreme learning machine (ELM) neural algorithm for SLFNs [81,88] was used to improve their performance. It is a novel training method that is exceedingly efficient and effective as indicated in Figure 4. The SLFNs were used in this research to analyze anemia data. It is pertinent to note that the behavior of a linear function as a sum of all linear functions in the network is identical to that of a perceptron regardless of the number of layers comprising the neural network [89,90,91]. Thus, a linear function can be described, but there is a possibility of obtaining a nonlinear outcome when an attempt is made to imitate reality. Therefore, a nonlinear activation function was included in the model. It is also pertinent to note that when a network with several layers fails to provide the desired output, the weights and biases need to be modified. The absence of an activation function can cause a change, such as a switch in the neuron signal from 0 to 1, a huge shift, with each neuron feeding a few neurons in the next layer causing a few more neurons to flip. This means minute modifications to the weights and biases used can have a dramatic effect on the end conclusions. Therefore, an activation function was applied to the neuron’s output, and small changes in the function’s weights can lead to moderate changes in the output. Moreover, the sigmoid function receives any number between -infinity and +infinity, but its output is always between 0 and 1. The Adam optimization method is used. This method is the optimal method for these research data in order to obtain optimal performance results. Table 3 explains the mathematical notation used in the ELM formula.\n\nIn Equation [2], xi represents the input vector, oj is the output vector, βj=[βj1,βj2,…,βjm]T indicates the output layer’s density, wj=[wj1,wj2,…,wjn]T represents the difference in weight between the input and hidden layers, bj is the function’s threshold, and g(.) is the function of activation. Moreover, the output matrix of hidden layers H and output-hidden layer weights b for the given input-output sample pairs allows the ELM to obtain an output calculated as Hβ=O as indicated in Equation [3], [2]oj=∑$j = 1$Nβjg(∑$j = 1$Nwjxi+bj), where wj and bj are randomly generated learning parameters of hidden jth nodes, βj are the links connecting hidden jth nodes and output nodes, and g is the sigmoid activation function for ELM.\n\nThe wj. xi part becomes the product of the parts of wj and xi. Equation [3] is, therefore, presented as follows:[3]Hβ=O where [4]H=[g(w1x1+b1)⋯g(wNx1+bN)⋮⋱⋮g(w1xn+b1)⋯g(wNxn+bN)]n×N [5]β=[β1T⋮βNT]N×m, and [6]O=[O1T⋮ONT]N×m, and H is referred to here as the output matrix of the hidden layer, [7]β^=HTt where HT is the generalized Moore–Penrose inverse of H and t is the target class/data label. Therefore, the output weights were calculated using a mathematical transformation that eliminates the need for a lengthy training phase requiring repeated updates of the network’s parameters through suitable learning parameters such as learning rate and iteration.\n\nIt is possible to implement the ELM method in two simple steps, which include the training and testing steps.\n\n## 3.4. Blood\n\nAll blood cells in the body, as shown in Table 4, are derived from pluripotent stem cells located in bone marrow [52]. It is important to note that one of the basic activities of red blood cells is to transfer oxygen from the lungs to the tissues and also to move carbon dioxide in the opposite direction [78]. Moreover, the platelets, which are vital to hemostasis, circulate for just ten days, but red blood cells have a lifetime of four months [91,92]. It has also been stated that different kinds of phagocytes—including neutrophils, eosinophils, basophils, monocytes, and lymphocytes—comprise white blood cells [93]. The B cells are responsible for the creation of antibodies while the T cells are in charge of immunological responses and defending against viruses and other foreign cells [94]. The white blood cells are present in the blood’s white component to combat illnesses caused by bacteria and fungus. Furthermore, the lymphocytes are responsible for the generation of antibodies. Previous studies have also shown that white blood cells have a relatively lengthy lifetime [95,96].\n\nRed blood cells are the most numerous blood cells [98], and they appear as biconcave discs densely packed with cytoplasm rich in the oxygen-carrying protein hemoglobin on smears of human peripheral blood [68]. They have a clever structure that allows them to perform their primary functions of transporting oxygen from the lungs to the tissues in the body’s periphery and transporting carbon dioxide from the tissues in the body’s periphery to the lungs, where it can be expelled via respiration. This means red blood cells facilitate the exchange of oxygen and carbon dioxide between the lungs and peripheral tissues of the body [99]. They also have an average lifespan of 120 days [93]. Meanwhile, platelets which are also known as thrombocytes are microscopic, fully nucleated, and granular-colored cell fragments. They are usually released by the megakaryocytes in bone marrow [98] and play a key part in the control of hemostasis together with the clotting factors of plasma [99,100]. Platelets have a seven- to ten-day lifespan [101]. It has also been discovered that there are several varieties of white blood cells [94]. These include the granulocytes, which are bone marrow-derived, short-lived cells that look identical on a peripheral smear [101,102,103,104]. Neutrophils, sometimes referred to as polymorphonuclear leukocytes, are the most prevalent kind of white blood cell, which possess between three and five lobes on their nucleus and an abundance of light purple granules in their cytoplasm [105]. They are phagocytes that provide defense against a variety of acute pathogens [105].\n\nMonocytes are the biggest white blood cells, ranging from 12 to 20 μm in diameter [105]. They have a folded or kidney-shaped nucleus and an abundance of light blue cytoplasm with a modest number of extremely tiny granules [106]. Monocytes, like neutrophils, are extremely phagocytic, although they vary from neutrophils in a crucial aspect [107]. They primarily develop into relatively long-lived macrophages capable of recognizing “danger” signals created by infection or tissue damage upon emigration into tissues [108]. Meanwhile, eosinophils with a diameter of 12 to 15 have two nuclear lobes and an abundance of red cytoplasmic granules (as befits the cell named after Eos, goddess of the dawn) [109]. They have a crucial role in some chronic immunological responses, including those linked with worm infections, asthma, and certain forms of allergic reactions [110].\n\nThe rarest of the granulocytes is basophil, and its nucleus is enveloped by numerous dark blue cytoplasmic granules [111]. They have a diameter of 12 to 15 μm [110], and many of the circumstances linked with an increase in eosinophil counts are also related to a small rise in basophil numbers [112]. It was also discovered that the mononuclear cells, another kind of white blood cell, lack the segmented nucleus typical of granulocytes [113]. Furthermore, lymphocytes are an essential part of the adaptive immune system [114] and are found to be approximately the same size, 7 to 9 μm in diameter, as a typical red blood cell while at rest and feature a spherical, compact nucleus with minimal cytoplasm [115]. However, the active cells have the potential to grow to a maximum size of 20 μm and also have a small number of granules in addition to the expanded nucleus and copious cytoplasm [115]. Unless the cells are evaluated for the presence of certain lineage-specific markers, it is impossible to tell with absolute certainty whether circulating lymphocytes are B cells, T cells, or natural killer cells. This is due to the fact that the lymphocytes circulating in the blood can be any of these three types [116]. The immune system also has the ability to “remember” the pathogen exposures from many years ago since it has the necessary foundation due to its longevity [117].\n\n## 3.5. Anemia\n\nAnemia is usually defined through the blood hemoglobin level which is below what is considered normal for a person’s age and gender, as indicated in Table 5. The results can vary across labs, but the average values for adult men and women are fewer than 135 g/L and 115 g/L, respectively [118]. The existence of less than 110 g/L for children between the ages of 2 and puberty implies anemia [119], and because newborns have high hemoglobin levels, the minimum acceptable threshold at birth is 140 g/L [120,121]. The World Health Organization classifies individuals as having anemia when their hemoglobin levels fall below 130 g/L for males and 120 g/L for women [122]. This scenario shows that approximately $40\\%$ of the world’s population was expected to suffer from anemia in 2019. There is a higher prevalence in females than males of any age, and in children less than five years old. Moreover, the greatest occurrence throughout the globe has been reported in Sub-Saharan South Asia, and Central, West, and East Africa [23]. The primary causes were found to be iron deficiency (hookworms, schistosomiasis), sickle cell disease, thalassemia, malaria, and chronic diseases [123].\n\nPhysicians usually inquire about the patient’s medical and family history, conduct a physical examination, and perform some tests including a full blood count to diagnose anemia [124]. More concern is usually placed on the hematocrit and hemoglobin levels, as well as the total number of red blood cells present in the patient’s blood, as indicated in Figure 5a. The natural differences between the quantity of blood components present in males and females are presented in Table 4 [97]. It is important to note that the blood counts can possibly be lower in those engaging in or those who have engaged in significant physical activity, particularly in pregnant women or the elderly [125,126]. Smoking and being at higher altitudes can also increase the number [124,125]. The testing process usually requires analyzing the size and content of red blood cells [127,128] as well as the shape and color deviations. A doctor can also prescribe further tests to establish the underlying reason and occasionally examine a sample of bone marrow to determine the existence of anemia, as indicated in Figure 5b [129].\n\nSome patients can exhibit symptoms such as shortness of breath (particularly during physical exercise), weakness, tiredness, palpitations, and headaches [121,122]. Other symptoms—such as heart failure, angina pectoris, intermittent claudication, and disorientation—are more prevalent among the elderly [132,133,134]. Moreover, vision impairment due to retinal hemorrhages can be a serious consequence of anemia, particularly when it develops rapidly [135]. These signals can be classified as either generic or particular. It is also important to note that pallid mucous membranes and nail beds, as shown in Figure 6, are prominent indicators of a hemoglobin concentration below 90 g/L. It is pertinent to state that the color of a person’s skin is not a reliable indicator, but tachycardia, pulse rate, cardiomegaly, and a systolic flow murmur indicate hyperdynamic circulation, particularly at the apex. The symptoms of congestive heart failure can also manifest at any age, but they are more prevalent in older people. Furthermore, certain symptoms are linked to each subtype of anemia, such as koilonychia, sometimes referred to as “spoon nails”, with iron deficiency, jaundice with hemolytic or megaloblastic anemia, foot ulcers with sicklecell and other hemolytic anemias, and skeletal abnormalities with thalassemia major [136]. Koilonychia is usually caused by the deficiency of iron in the body and is classified as a disorder characterized by inwardly curled nails resembling spoons [137]. It is important to note that megaloblastic anemia is the most prevalent kind.\n\nThe conjunction of anemic symptoms with severe infection or spontaneous bruising shows the presence of neutropenia or thrombocytopenia, potentially due to bone marrow failure [137]. People with blood hemoglobin levels below the values considered normal for their age and gender are believed to have anemia. Moreover, individual cell size can be used to assess when red blood cells are macrocytic, normocytic, or microcytic. It is also pertinent to state that the cause of anemia can be diagnosed in part by examining the reticulocyte count, the red blood cell shape, and any changes to the white blood cell and/or platelet count [123]. The common clinical manifestations include exertional dyspnea, pale mucous membranes, and tachycardia [138], while the other symptoms associated with some forms of anemia include jaundice and leg ulcers [139]. The aspiration or trephine biopsy of bone marrow can also be used to investigate anemia and a variety of other hematological disorders [140]. It is also possible to conduct specialized examinations such as immunology and cytogenetics on the cells recovered [140].\n\n## 4. Experimental Results\n\nThe experiment involved using an ELM model to identify and categorize illness in a dataset of individuals with beta thalassemia trait, iron deficiency anemia, hemoglobin E, and the combinations previously defined. The model parameters utilized are listed in Table 2. The real anemic dataset was used and split into training and testing sets during the experiment, and the classification method applied was evaluated using a Python-written confusion matrix. The process was conducted on an Apple M1 machine with 512GB internal memory and 8GB RAM.\n\n## 4.1. Evaluation Model\n\nThe data used were classified into test and training data, and they were both evaluated using the confusion matrix model. This was necessary due to the feasibility of determining the accuracy of classification algorithms using an industry-standard technique. It was discovered that the dataset had five separate classifications, which included the BTT, IDA, HbE, and their combinations. The training data accounted for $67\\%$ of the entire data set for the inquiry while test data made up the remaining $33\\%$. The assessment conducted was based on the accuracy, precision, sensitivity, and F1 score of the classification algorithm as listed in Equations [12]–[15]. The method of value distribution is highlighted in Table 6.\n\nTable 6 shows that positive data correctly classified by the system are referred to as the “true positive” (TP), negative data correctly identified as negative are referred to as the “true negative” (TN), negative data incorrectly perceived as positive are known as “false negatives” (FN), and “positive” data incorrectly recognized as “positive” are “false positives” (FP).\n\nThese values were further used to determine the accuracy, precision, recall, and F1 scores through the following formulas. [ 12]Accuracy: TP+TNTP+TN+FP+FN [13]Precision: TPTP+FP [14]Sensitivity: TPTP+FN [15]F1-Score: 2×Recall × PrecisionRecall + Precision\n\n## 4.2. Experimental Results of Extreme Learning Machine\n\nThe ELM classification model applied to categorize the anemia dataset used a single feedforward network with a hidden layer implementation (SLFNs). This strategy reduced the processing time required for the concealed layer. The usefulness of the model was assessed based on accuracy, precision, sensitivity, and the F1 score in classifying anemic datasets. Table 7 shows the model used in the ELM. The findings of the ELM performance model are presented in Table 8, and it was discovered that it performed best on the four-class anemia dataset, with $99.21\\%$ accuracy, $99.30\\%$ precision, $98.44\\%$ sensitivity, and $98.84\\%$ F1 score.\n\nThe confusion matrix for the model is presented in Table 9, with each row representing an instance of the prediction class while each column indicates an instance of the actual class. The RF approach use n estimators = 400, max features = auto, and entropy. Although entropy is more sophisticated than the Gini index, entropy provides ideal results. In contrast, the KNN technique employs several experiments, including Euclidean distance to determine the distance between classes, and $K = 15$, which is derived from the K error rate calculation. Several tests were conducted by employing polynomial kernels, RBF kernels, and linear kernels in the SVM approach. In linear kernels, optimal outcomes were obtained. The ELM model, which is the approach described in this work, employed the sigmoid activation function, with the number of hidden layers [9] modified based on the number of inputs and outputs, followed by gradient descent to optimize the weights.\n\nELM was used to optimize the classification process for anemic datasets in order to improve the success rate of the approach as indicated in Table 9. The performance index of each class and the recommended strategy with the highest rate of success are, therefore, presented in Table 10. It was discovered that the random forest, k-nearest neighbor, support vector machine, and extreme learning machine techniques provided the most accurate predictions for the beta thalassemia trait and iron deficiency anemia classes. Moreover, the forecasts for the hemoglobin E and the combination classes were rather correct.\n\n## 5. Discussion\n\nIt is very dangerous in the field of medicine to erroneously identify healthy individuals with sickness and vice versa due to the possibility of severe repercussions. This has led to an increase in the usage of data mining technologies for a reliable diagnosis. Therefore, this research used a model of an extreme learning machine to reliably detect and diagnose anemia as well as construct a decision support system to aid clinicians in making decisions.\n\nA total of 127 training and 63 test data were employed, and it was discovered that the ELM approach performed much better than RF, KNN, and SVM as indicated by its $99.21\\%$ accuracy, $98.44\\%$ sensitivity, $99.30\\%$ precision, and $98.84\\%$ F1 score compared to RF’s $77.01\\%$ accuracy, $90.83\\%$ precision, $78.40\\%$ recall, and $80.99\\%$ F1 score as well as KNN’s $65.42\\%$, $59.40\\%$, $62.81\\%$, and $51.74\\%$, respectively. A previous study by [67] used 6935 data with 986 variables and applied two of the five techniques, KNN ($92.36\\%$) and RF ($94.16\\%$), to classify BTT and IDA into two groups. Another study by [69] predicted the risk of childhood anemia using several machine learning techniques including KNN and RF. The results showed that KNN had a classification accuracy of $61.95\\%$, a sensitivity of $65.85\\%$, and a specificity of $58.20\\%$while RF had $68.5\\%$, $70.7\\%$, and $66.4\\%$, respectively. This means the overall performance of RF was better than KNN in all three aspects. Another research conducted in 2020 [58] showed that RELM had an accuracy of $95.59\\%$ when applied to separate 342 patient records into two types of anemia, IDA and BTT. It is important to note that the ELM method was also applied in the research.\n\nThus, studies have been conducted on the ELM approach, and the concept has progressed to the point where it has shifted from a single hidden layer to a 100-node multilayer hidden layer. This is known as the enhanced improved multilayer extreme learning machine (IML-ELM) with the neural activity occurring both during and after training in the proposed network architecture. Moreover, each layer of the first IML-ELM (IML-ELM1) network was assigned an orthonormal random connection weight while only the very first layer of the second iteration of the IML-ELM contained the random orthonormal connection weights (IML-ELM2). The output weight matrix of the layer was used to calculate the connection weights. The application of the IML-ELM2 assignment method considerably reduced the amount of time required for calculations, and the root mean square error test was observed to have produced 0.627977, 0.104272 ($83\\%$), and 0.092685 ($85\\%$) [143].\n\nThe three studies conducted by [58,67,69] used several machine and deep learning techniques, and RELM was reported to have the highest level of performance with $95.59\\%$ in distinguishing two forms of anemia. The outcomes of this experiment conducted using the ELM method have been encouraging, with the anemia classified into four separate subtypes, thereby increasing the diagnostic accuracy to $99.21\\%$, precision to $99.30\\%$, sensitivity to $98.44\\%$, and F1 score to $98.84\\%$.\n\nTable 11 compares the findings of this research with those from previous studies based on the accuracy metric. It is important to note that this research divided the patients into four distinct groups, including BTT, IDA, HbE, and combinations, and the differences between these groups were categorized with a greater degree of precision than previous approaches, as indicated by the $99.21\\%$ recorded for each class. Future studies are expected to focus on analyzing the characteristics considered to be the most important components of anemia to ensure an easier diagnosis process for physicians using a more ideal system. There is also the need for a technique to identify and recommend appropriate anemia datasets using deep learning.\n\n## 6. Conclusions\n\nIt is difficult to distinguish between BTT, IDA, and HbE, as well as combinations of these three variables, due to the variability of the anemia-afflicted population. The introduction of computer models was observed to have ensured the rapid screening of anemia at a lower cost. This research provided a summary of the findings of the health system analysis as well as the challenges and barriers encountered throughout the globe in treating anemia patients by using a thorough analysis. Therefore, an ELM approach was applied to expedite the identification of different kinds of anemia. The method using 190 data and seven parameters was found to have an accuracy, sensitivity, and precision of $99.21\\%$, $98.44\\%$, and $99.30\\%$, respectively, as well as an F1 score of $98.84\\%$ using a confusion matrix. 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Blood–Brain Barrier Permeability to Fluorescent Dextran\n and Antisense Oligonucleotide in Mice\nauthors:\n- David Dubayle\n- Arnaud Vanden-Bossche\n- Tom Peixoto\n- Jean-Luc Morel\njournal: Cells\nyear: 2023\npmcid: PMC10000817\ndoi: 10.3390/cells12050734\nlicense: CC BY 4.0\n---\n\n# Hypergravity Increases Blood–Brain Barrier Permeability to Fluorescent Dextran and Antisense Oligonucleotide in Mice\n\n## Abstract\n\nThe earliest effect of spaceflight is an alteration in vestibular function due to microgravity. Hypergravity exposure induced by centrifugation is also able to provoke motion sickness. The blood–brain barrier (BBB) is the crucial interface between the vascular system and the brain to ensure efficient neuronal activity. We developed experimental protocols of hypergravity on C57Bl/6JRJ mice to induce motion sickness and reveal its effects on the BBB. Mice were centrifuged at 2× g for 24 h. Fluorescent dextrans with different sizes (40, 70 and 150 kDa) and fluorescent antisense oligonucleotides (AS) were injected into mice retro-orbitally. The presence of fluorescent molecules was revealed by epifluorescence and confocal microscopies in brain slices. Gene expression was evaluated by RT-qPCR from brain extracts. Only the 70 kDa dextran and AS were detected in the parenchyma of several brain regions, suggesting an alteration in the BBB. Moreover, Ctnnd1, Gja4 and Actn1 were upregulated, whereas Jup, Tjp2, Gja1, Actn2, Actn4, Cdh2 and *Ocln* genes were downregulated, specifically suggesting a dysregulation in the tight junctions of endothelial cells forming the BBB. Our results confirm the alteration in the BBB after a short period of hypergravity exposure.\n\n## 1. Introduction\n\nAstronauts are exposed to successive phases of hypergravity phases during the takeoff and landing of spaceflights and due to microgravity in space. The most important and earliest reported symptom, related to days 1–3 of the spaceflight, is space motion sickness due to vestibular dysfunction [1,2,3,4,5,6,7]. The fluid shift is the shift in the distribution of human body fluids due to microgravity exposure. It was proposed to be responsible for space motion sickness. Several ground devices and protocols were rapidly developed to reproduce this phenomenon, such as centrifugation and parabolic flights [8,9]. Moreover, the decreases in plasma volume and cardiac performance, and the increase in intracranial blood pressure participate in vascular deterioration, as recently reviewed [10,11,12]. Furthermore, the alterations in gravity induce cardiovascular adaptations via the modifications in endothelial and smooth muscle vascular cell functions [13,14,15,16,17]. It is noticeable that the effects of centrifugation are only partially described in humans [18,19,20,21,22]. Like during microgravity exposure, hypergravity exposure, from 1.5 to 5 g, affects the vestibular functions [23,24,25,26] and modifies gene expression in the brain [27,28,29] and cognitive performances [30,31,32]. The use of hypergravity by centrifugation is required to qualify the biological effects of space motion sickness.\n\nLikewise, centrifugation, close to 2× g, is also proposed as a countermeasure against the deleterious effects of microgravity seen in humans [33,34]. Therefore, before the exposure of humans to centrifugation, it is important to study its biological impacts.\n\nThe cerebral blood vessels are crucial in brain functions regarding oxygen supply and exchanges of nutrients and wastes. The endothelial cells of brain capillaries are organized to form the blood–brain barrier (BBB), assuming the fine-tuning of these exchanges to maintain brain homeostasis [35,36]. The efficacy of the BBB is regulated by the nychthemeral rhythms [37,38,39,40]. BBB alterations are clearly implicated in stroke and neurodegenerative disorders [41,42,43,44].\n\nGravity changes are able to modify endothelial cell functions [45]. Many in vitro models have been developed to reproduce the BBB [46], and experiments that exposed endothelial cells to gravity modifications revealed miscellaneous results, as reviewed [47]. Depending on hypergravity levels from 3 g to 20 g, endothelial cells modify their gene expression, angiogenesis, cytoskeleton architecture and tube formation [48,49,50,51]. Moreover, in devices that reproduce the barrier function, the effects of short-term exposure to hypergravity remain unclear. In fact, exposure (2 g and 4 g) increases the barrier efficacy, shown by resistance measurements of the endothelial cell culture [52], whereas a higher level (10 g) decreases it, as shown by the increase in fluorescent molecules passing through the culture monolayer [53].\n\nThe effects of hypergravity on the capacity of endothelial cells to form a barrier in vitro are insufficient to interpret the modifications in the BBB observed in vivo. More information should be collected in vivo.\n\nIn mice exposed to hypergravity at 2 g for 24 h, we measured the transit through the BBB of different fluorescent molecules with different sizes, such as dextrans and antisense oligonucleotides (AS). We also investigated the regulation of expression of genes involved in junctions between endothelial cells.\n\n## 2.1. Animals and Centrifugation\n\nIn accordance with the principles of the European community, the experimental protocols were validated by the local ethics committee (CEEA-Loire, APAFIS #38819), the animal welfare committee of PLEXAN (PLateforme d’EXpérimentations et d’ANalyses, Faculty of Medicine, Université Jean Monnet, Saint Etienne, France, agreement n°42-18-0801) and the French Ministry of Research. In this study, 86 male C57BL/6JRJ mice (8 weeks old, 22.5 ± 0.1 g, Janvier Labs, France) were used. The animals were housed (3 mice per cage) in standard conditions (22 °C, humidity $55\\%$; 12 h/12 h day/night cycle; unlimited access to food and water). They were familiarized with the centrifugation room the week before the experiments and monitored by video in the centrifuge. In order to expose all the animals to the same environmental conditions, the mice were centrifuged at 2× g for 24 h, and the control mice in normogravity at 1 g for 24 h were placed simultaneously in the experimental room. The centrifugation protocol was detailed in our previous publication.\n\n## 2.2. In Vivo Injection of Antisense Oligonucleotide and Dextrans\n\nAll the fluorescent molecules were diluted in saline solution (sodium chloride 9 g/L) and retro-orbitally injected in the blood, under isoflurane anesthesia ($5\\%$). In our hands, this route of administration is safer (more rapid, efficient and reproducible) than other routes of i.v. administration. Sham mice were injected with vehicle solution. Mice received only one injection with one fluorescent tracer. Phosphorothioate antisense oligonucleotide directed against angiopoietin-2 (Angpt2, named AS, GCG-TTA-GAC-ATG-TAG-GG, 6084.9 g/mol, Eurogentec) was coupled to 5-carboxyfluorescein (excitation: 492 nm, blue light; emission: 518 nm, green light) and injected (18 mg/Kg). Fluorescein isothiocyanate-dextrans D40, D70 and D150 (FD40-100MG, FD70S-100MG, FD150S-1G, respectively, Sigma-Aldrich, St. Louis, MI, USA) were solubilized in vehicle (2× g/100 mL to be injected retro-orbitally at 150 mg/Kg, near 200 µL/mouse). Fluorescein isothiocyanate-dextrans were maximally excited at 490 nm (blue) and maximally emitted at 525 nm (green).\n\n## 2.3. Collection of Biological Samples\n\nMice were randomly killed by lethal intraperitoneal injection of sodium pentobarbital (Euthasol, 175 mg/Kg, i.p.), within 2 h after stopping the centrifuge. Before intracardiac perfusion, a catheter was introduced in the right atrium, and blood samples were collected and placed in microtubes. Finally, mice were perfused intracardiacally (5 mL/min) with 30 mL of phosphate-buffered saline (0.01 M PBS, pH: 7.4) to discard blood cells and residual fluorescence of the injected tracers into vessel lumen. This step was followed by 30 mL formalin solution ($10\\%$, Merck, HT501128) to fix the tissues. Brains and the left lobe of livers were dissected and post-fixed for 24 h in a formalin solution at room temperature, placed for 48 h in a $30\\%$ sucrose–PBS solution at 4 °C and cryopreserved before being sliced.\n\n## 2.4. Corticosterone Assay\n\nThe microtubes containing blood samples were centrifuged (10 min at 2000× g) and 20 µL of serum was collected. Some serum samples were excluded due to hemolysis. The others ($$n = 60$$) were used for corticosterone assay (ELISA kit, K014, Arbor Assays, Ann Arbor, MI, USA), following the protocol of the supplier.\n\n## 2.5. Histology\n\nUsing a freezing microtome (frigomobil, Reichert-Jung), coronal sections of the brain (40 μm thick) were made. Olfactory bulbs were removed, and 3.2 mm after beginning the rostro-caudal slicing, the new slices were collected and placed individually in 48-well plates. To ensure reproducibility, we anatomically selected three similar brain slices for each mouse. Using a binocular device, the slices corresponding to interaural 1.98 mm; Begma −1.82 mm of the Atlas of the mouse brain in stereotaxic coordinates [54] were retained. Indeed, the medial habenular nuclei and mammillothalamic tract were anatomical landmarks, as well as the form and volume of the hippocampus. In the same manner, the left lobes of the liver were sliced (40 µm), and three slices per mouse were mounted. All the floating sections were incubated for 10 min in DAPI (4′,6-diamidino-2-phenylindole, 1:250,000, Interchim, Mannheim, Germany) and rinsed twice in PBS (10 and 20 min, respectively). Finally, they were mounted on glass slides (Superfrost) with a handmade medium based on Mowiol. All slices were DAPI-labeled and mounted on the same day. Slices presenting red blood cells in capillaries in ROI were excluded to reduce experimental bias [55].\n\n## 2.6. Image Acquisition\n\nThe fluorescence of labeled brain slices was observed by confocal microscopy (SP5, Leica Microsystems, Wetzlar, Germany) and the slide scanner Nanozoomer (2.0 HT, Hamamatsu Photonics, Shizuoka Prefecture, Japan). The Nanozoomer contains a fluorescence imaging module using objective UPS APO 20X NA 0.75 combined with an additional lens 1.75X. Virtual slides were acquired with a TDI-3 CCD camera. The fluorescent acquisitions were conducted with a mercury lamp (LX2000 200W—Hamamatsu Photonics, Massy, France), and the set of filters adapted for DAPI and FITC/FAM fluorescence were usable for both fluorescein isothiocyanate-dextrans and 5-carboxyfluorescein antisense oligonucleotide. The DAPI labeling, revealing the double strain of DNA in the cell nuclei, was used for the automated focus required for Nanozoomer imaging. To reduce bias, all images (slices from control and centrifuged mice) were performed randomly in one batch. To localize antisense oligonucleotides in the brain and liver tissues, some images were acquired with SP5 confocal microscope. In this case, fluorescent molecules were excited with the 488 nm line of Argon laser and all acquisition parameters were kept constant.\n\n## 2.7. Fluorescence Analyses\n\nSeveral types of fluorescence analyses were double-blindly performed on Nanozoomer images. To evaluate the intensity level of fluorescence, the ndpi files generated by Nanozoomer were converted into tiff images with NDPI software (version 2). The tiff files were opened with Fiji software 2.9.0, and the intensity levels were measured in regions of interest (ROI defined as red circle of 960 µm2 in Figures and placed on the hippocampus (HPC), dorsal thalamic nuclei (THAL) and the retrosplenial and ectorhinal cortices (DCx and LCx, respectively) on both hemispheres of the three slices. No filter settings were applied to the images and we checked that the images did not have any saturated dots. The mean of fluorescence was calculated for each mouse and reported in the statistical analysis. A similar analysis was performed in three liver slices. Five ROI were randomly placed on each slice. Moreover, the image analysis of fluorescent spots was performed with QuPath directly on the ndpi files. The software is able to identify and localize fluorescent spots. We empirically determined parameters to segregate fluorescent spots in brain slices from 5 mice (control and centrifuged mice) and we applied these parameters to the project containing the entire sample. The parameters were: pixel size 0.5 µm, background radius 30 µm, median filter radius 0, sigma 1, minimum area 5 µm2, maximal area 1000 µm2 and threshold 7. The collected data were attributed to experimental groups (2 g vs. 1 g) and compared statistically. The analyses, reported, were performed on the ROI anatomically defined as HPC (hippocampus), THAL (grouping all medio-dorsal and lateral thalamic nuclei), DCx (containing retrosplenial cortices), SoCx (containing somatosensorial cortices) and PirCx (containing piriform cortices). A similar analysis was performed on the left lobe liver slices.\n\n## 2.8. Gene Expression by RT-qPCR\n\nFor this experiment, 16 mice were used (8 were exposed to 2 g and 8 to 1 g, as described before). They were anesthetized with isoflurane $5\\%$ and decapitated, and the brains were directly frozen and stored at −80 °C. Hippocampus were dissected on ice and placed in 2 mL tubes containing 500 µL of Tri-reagent (MRCgene) and 10 ceramic beads (diameter 1.5 mm). Samples were mashed in a Beadbug6 shaker (Benchmark, 3 cycles, level of speed 4350 and 60 s time). RNA was isolated, following the instruction of the protocol elaborated by MRCgene. The concentration of RNA was measured with Nanodrop (Thermoscientific, Waltham, MA, USA) and adjusted close to 100 ng/µL. The cDNA was produced with the RT-i-script gDNA clear cDNA synthesis kit (Bio-Rad’s reference 1725035), using 100 ng of RNA and following the protocol from the supplier. The qPCR was performed using the endothelial cell contacts by junction M96 (predesigned for use with SYBR green; Bio-Rad’s plate reference 10029202) and the Sso-advanced universal SYBR green PCR kit (Bio-Rad’s reference 1725275). The qPCR was performed with CFX96 thermocycler (Bio-Rad). Samples were allocated randomly in plates, and some of them were tested twice to verify the quality of the experiment. The validation of Hprt and Gapdh as reference genes was evaluated with CFX Maestro software (Bio-Rad). The analysis of gene expression was performed on Actb, Actg1, Actn1, Actn2, Actn4, Cdh2, Cdh5, Cldn1, Cldn3, Cldn5, Ctnna1, Ctnnb1, Ctnnd1, Dsp, F11r, Gja1, Gja4, Gja5, Jam2, Jup, Ocln, Tjp1, Tjp2 and Vim. The threshold of the regulation by hypergravity on gene expression was chosen at 1.5. To discuss the RT-qPCR results, we checked the brain localization, cell types expressing genes and function of proteins encoded by these genes in endothelial cells using databases: https://www.proteinatlas.org; http://mousebrain.org; http://betsholtzlab.org and https://www.informatics.jax.org (accessed on 26 January 2023).\n\n## 2.9. Statistical Analysis\n\nThe data were statistically compared using paired t-tests, non-parametric Mann–Whitney test, or one- and two-way ANOVA with post hoc comparisons when applicable. The normogravity (1 g) is the control condition. The software used was GraphPad Prism V9, which calculated the p value as the probability of observing two identical conditions. If $p \\leq 0.05$, the two compared conditions were considered statistically different.\n\n## 3.1. Effects of Centrifugation on Mice\n\nThe body weight gain, expressed as the difference in weight in a 24 h period (Figure 1), is the difference in body weight measured before and after exposure to centrifugation (2× g) or control conditions (1 g). As expected, the exposure to centrifugation induced a decrease in body weight (Figure 1A, $p \\leq 0.0001$).\n\nMore precisely, the decrease in body weight was similar in mice injected with saline solution (Sham) and solution containing fluorescent antisense oligonucleotide directed against angiopoietin-2 (AS) (two-way ANOVA coupled with Sidak post hoc test, interaction $$p \\leq 0.0009$$; 1 g vs. 2 g: $p \\leq 0.0001$; sham vs. AS $$p \\leq 0.589$$; Figure 1B). The decrease in body weight gain due to centrifugation was similarly observed in mice injected with dextrans (D40, D70 and D150, one-way ANOVA coupled with Sidak post hoc test, 1 g vs. 2 g: $p \\leq 0.0001$; $p \\leq 0.05$ for comparison of 1 g groups as well as for 2 g groups, Figure 1C). The effects observed in mice injected with AS or dextrans (D40, D70 and D150) were similar (statistical analysis shown in Figure 1C). In conclusion, the injection of fluorescent tracers did not influence the effect of centrifugation on body weight gain. To explain the decrease in body weight, we also measured the food and water consumption. As shown in Figure 1D and 1E, the comparison of food and water consumption, respectively, during the day before the centrifugation with the consumption during the 24 h of centrifugation exposure showed that both food and water consumption specifically decreased in the group exposed to the centrifugation (two-way ANOVA, time x gravity $$p \\leq 0.0001$$ for both parameters).\n\nThe stress was evaluated by the concentration of corticosterone in plasma. The comparison between 1 g and 2 g conditions, including all the samples, did not reveal a variation in corticosterone concentration (Figure 2A, Mann–Whitney test, $$p \\leq 0.255$$). We also separately analyzed the corticosterone concentration in each experimental group. In mice injected with saline solution (Sham), AS, D40, D70 and D150, the centrifugation had no significant effect on the corticosterone concentration (Figure 2B, one-way ANOVA, $$p \\leq 0.278$$). In conclusion, centrifugation at 2× g did not modify the plasma concentration of corticosterone in mice injected with fluorescent tracers.\n\n## 3.2. Effects of Centrifugation on Extravasation of Fluorescent Dextrans in Brain\n\nThe extravasation of dextrans through the BBB were firstly evaluated by the analysis of fluorescence intensities of several brain areas. To minimize local variations, we performed all analyses on slices containing similar anatomical landmarks. The regions of interest were distributed in different cerebral areas (red ROI in thalamus, hippocampus and dorsal and lateral cortices, Figure 3).\n\nThe centrifugation was not able to modify the fluorescent levels in THAL (Figure 3A, Mann–Whitney tests comparing 1 g vs. 2 g conditions for D40, $$p \\leq 0.93$$; for D70 $$p \\leq 0.29$$ and for D150 $$p \\leq 0.069$$), HPC (Figure 3B, Mann–Whitney tests comparing 1 g vs. 2 g conditions for D40 $$p \\leq 0.53$$; for D70 $$p \\leq 0.76$$ and for D150 $$p \\leq 0.089$$) and LCx (Figure 3C, Mann–Whitney tests comparing 1 g vs. 2 g conditions for D40 $$p \\leq 0.50$$; for D70 $$p \\leq 0.08$$ and for D150 $$p \\leq 0.16$$). In DCx, the hypergravity can increase the level of fluorescence only in D70 (Mann–Whitney tests comparing 1 g vs. 2 g conditions for D40 $$p \\leq 0.051$$; for D70 $$p \\leq 0.040$$ and for D150 $$p \\leq 0.050$$; Figure 3D). The differences in D70 fluorescence across brain sections are illustrated in Figure 3E. More marked fluorescence diffusion is observed in the DCx of 2 g-exposed mice. In conclusion, these sets of data analysis suggested that centrifugation significantly increased the presence of D70 in DCx.\n\n## 3.3. Effects of Centrifugation on Extravasation of Fluorescent AS in Liver\n\nWe tested the ability of hypergravity to promote the passage of a molecule that can be captured by liver parenchyma cells. To test this hypothesis, we injected mice with fluorescent antisense oligonucleotides and compared the 1 g and 2 g conditions. The same quantification methods used for dextrans were applied on images obtained with Nanozoomer (Figure 4A).\n\nA significant increase in fluorescence in liver parenchyma was revealed in mice exposed to hypergravity (Figure 4B, Mann–Whitney tests, 1 g vs. 2 g $$p \\leq 0.0291$$). Moreover, the number of areas containing fluorescence evaluated with QuPath was higher in 2 g in comparison with 1 g (Figure 4C, Mann–Whitney tests, 1 g vs. 2 g $p \\leq 0.0001$). With confocal microscopy, the presence of AS was qualitatively revealed as spots of fluorescence close to vessel walls in the liver parenchyma. Taken together, these results strongly suggest that hypergravity increased the AS extravasation in the liver parenchyma.\n\n## 3.4. Effects of Centrifugation on Extravasation of Fluorescent AS in Brain\n\nThe qualitative analysis of images obtained with Nanozoomer and confocal SP5 showed fluorescent spots in the brain parenchyma only in slices from 2 g-exposed mice (Figure 5A and Figure 6A). The confocal images also revealed that these fluorescent spots were more localized in the brain parenchyma close to the vessel walls (Figure 5A, right panel).\n\nThe quantitative analyses of images from Nanozoomer showed an increase in fluorescence level in HPC and DCx due to hypergravity exposure (Mann–Whitney tests, 1 g vs. 2 g in THAL $$p \\leq 0.369$$, in HPC $$p \\leq 0.033$$, in DCx $$p \\leq 0.016$$ and in LCx $$p \\leq 0.265$$; Figure 5B).\n\nThe analysis with QuPath software was used to segregate fluorescent areas from the background in several brain regions (Figure 6A) using the same filtering parameters in both 1 g and 2 g conditions. The analyses confirmed that the exposure to hypergravity increased the number of fluorescent spots in HPC and DCx, but not in THAL (Mann–Whitney tests, 1 g vs. 2 g in THAL $$p \\leq 0.0536$$, in HPC $$p \\leq 0.0003$$ and in DCx $p \\leq 0.0001$; Figure 6B). Moreover, it also revealed an increase in the number of fluorescent spots in SoCx and PirCx (Mann–Whitney tests, 1 g vs. 2 g $p \\leq 0.0001$ and $$p \\leq 0.0024$$, respectively; Figure 6B).\n\nIn conclusion, our data suggest that hypergravity induced a BBB leakage able to increase the presence of AS in brain parenchyma.\n\n## 3.5. Effects of Centrifugation on Expression of Genes Involved in Endothelial Cells Interactions\n\nUsing Hprt and Gapdh as reference genes, the RT-qPCR analysis of the expression of genes involved in the regulation of endothelial cells interactions revealed that Gja4, Ctnnd1 and Actn1 were upregulated. Cdh2 was downregulated more than 2-fold and Ocln, Actn2, Jup, Actn4, Tjp2 and Gja1 were downregulated between 1.5- and 2-fold (Figure 7). The expressions of Actb, Actg1, Cdh5, Cldn1, Cldn5, Ctnna1, Ctnnd1, Dsp, F11r, Gja5, Jam2, Tjp1 and Vim were considered not altered (less than 1.5-fold modification), and Cldn3 appeared not expressed. The names, functions and cell types expressing these genes are summarized in Table 1 and supplementary Table S1.\n\n## 4. Discussion\n\nIn the present study, our results suggest that hypergravity induces an increase in BBB permeability, allowing the passage of antisense oligonucleotides as well as dextran from blood to brain parenchyma. Moreover, the RT-qPCR experiments suggested an alteration in the expression of genes involved in endothelial cell junctions.\n\nIn a ground model of hindlimb unloaded animals without [56] or in combination with radiation [57], as well as during spaceflight [58], the BBB was altered, suggesting that vestibular regulations were involved. As reviewed recently, the increase in gravity by centrifugation modifies vestibular function and induces motion sickness [59]. Our experiments confirm the decrease in body weight generated by centrifugation [18,60]. It is linked with the decrease in food intake [61], and probably linked to vestibular impairments [18,62].\n\nHypergravity exposure at 2 g increases the corticosterone concentration when it is measured during the first hour of exposure [63]. The increase in the hypergravity level can transiently increase the plasma corticosterone level [64]. Nevertheless, as our data showed, after 24 h of weak exposure at 2 g, the corticosterone levels were not altered in the first hour following the stop of the centrifuge [65]. The stress induced by the centrifugation is controversial and probably depends on the design of the centrifuge and experimental procedure with animals [18,27,63]. Moreover, our data showed a large spread of individual values of corticosterone concentration, confirming other studies [18,23,65].\n\nIn motion sickness, the relationship between brain and intestinal functions were known and clearly demonstrated, including microgravity and hypergravity models [66,67,68]. The most probable link is hypophagia. In mice and rats, the decrease in food intake was observed at the beginning of the 2 g exposure (first two days) and depended on the vestibular organ [18,69].\n\nThe hypophagia could have several causes, including: 1. modifications in microbiota [70] that can decrease the gastric acid synthesis [71], 2. metabolism dysregulation, such as decreases in leptin and insulin plasma concentrations [60] and 3. modifications in the expression of the starvation-induced genes [72]. Moreover, the serotonin pathways are involved in this phenomenon [69,73]. In conclusion, our results also confirm that the hypophagia induced a decrease in body weight. This is more related to the hypergravity and not related to an increase in corticosterone levels [30,60,62,65].\n\nFluorescent polysaccharides such as dextrans are safe at low concentrations, available in sizes from 3 to 2000 kDa. They can be used to study BBB permeability [74,75,76] and to determine the size of a BBB leak [77,78,79]. After 24 h exposure to 2 g hypergravity, our results demonstrated that 70 kDa dextran can be exported in cortex parenchyma, but not 40 or 150 kDa dextran. The lower molecular weight dextrans, the faster they are excreted. In fact, in less than one hour, dextrans between 30 and 40 kDa were excreted in urine, whereas the 62 kDa dextran was always present in the blood circulation and not highly present in urine [80]. This suggests that after 24 h, the 40 kDa dextran would be excreted. Thereby, the BBB leak required more than one hour of hypergravity exposure, confirming our previous data showing that short exposure (1–9 min at 5 g) was not efficient in destabilizing the BBB [65]. Because we cannot exclude an alteration in urine excretion of dextran in the hypergravity context, our data should be completed by the evaluation of the kinetics of dextran excretion in centrifuged mice.\n\nBecause of the molecule shape, 150 kDa dextran was unable to flow from the circulation to the tissues in physiological conditions [80]. Our results showed that the BBB leak is not sufficient for 150 kDa dextran extravasation, suggesting that this leak was not comparable to the BBB disruption induced by stroke or acute hypertension [81]. In our previous study [65], the extravasation of IgG (around 150 kDa) was measured, suggesting that the nature of the molecule is also a crucial parameter. Moreover, our data showing the extravasation of antisense oligonucleotide in the cortex and hippocampus confirm that the BBB properties depend on the brain areas and the chemical nature of the markers [82,83,84]. In conclusion, our results showed an increase in the transfer of fluorescent molecules from blood to tissues, suggesting a global modification in effluxes due to hypergravity.\n\nTo assess the alterations in the BBB in centrifuged mice, we focused the molecular investigation on gene expression using a set of primers targeting consensual genes involved in BBB efficacy. As reviewed recently [85,86], all of the proteins encoded by the genes studied here are involved in the scaffoldings required to maintain endothelial cell interactions to create the BBB, as well as in the initiation of angiogenesis and/or vascular repair. The database queries concerning the expression level in non-neuronal cells of the brain indicated that the proteins encoded by studied genes are also expressed in endothelial cells, but not exclusively (Supplementary Table S2). As expected, the modifications in gene expression are related to the durations of centrifugation and the levels of hypergravity, as suggested by the comparison between this current study and the RNAseq performed previously on the same device and the same mouse strains [29]. Moreover, the regulation of gene expression is not comparable to acute and chronic stress (Supplementary Table S3). Globally, the observed modifications could be interpreted as a specific dysregulation of gene expression that can alter the turnover and replacement of proteins involved in BBB efficacy as observed in BBB disruption models such as stroke, middle cerebral artery occlusion or hypoxia.\n\n## 5. Conclusions\n\nThis work suggests that the modification in gravity, which is accompanied by a modification in the vestibular functions, leads to an alteration in the BBB via a modification in the expression of genes which code the proteins in the junctions between the endothelial cells. As now studied, an alteration in the BBB, and not its destruction, allows the passage of molecules defined by their sizes and their chemical natures. Our work insists on this point; an alteration in the BBB is characterized according to the means of study, i.e., markers and measurement methods. This can be considered in two antagonistic ways, either as a minimally invasive physical means of crossing the BBB by molecules of therapeutic interest or, on the contrary, as something deleterious that can be found in the pathology of alterations in vestibular functions during spaceflight. The most important limit of this study is that the RT-qPCR was performed on RNA extracted from whole brain, and the query of hipposeq.janelia.org indicated that we cannot exclude the alteration in molecular scaffolding of synapses also implicating these genes. Finally, our study can be considered an extension of studies relating to the effectiveness of molecules to modulate the passage across the BBB. In a hypergravity context, but also in other models of alteration in vestibular functions, the transduction pathways involved in alterations in the BBB should also be investigated. For example, the angiopoietin-2 pathway is crucial for endothelial cell disassembly [87], and GPCR internalization in endothelial cells [88] should be considered in the context of centrifugation. The last topic that we can investigate is the effects of gravity modulation on angiogenesis, which is required to renovate the endothelium and form new brain capillaries. In fact, experiments on cultured endothelial cells have suggested that hypergravity reduces their capacity to form tubes and alters their responses to angiogenic factors [48,49,50,51]. In centrifugation as well as during parabolic flights, the in vivo responses to angiogenic factors have not yet been investigated. Moreover, it has been shown that during the takeoff and landing of a space module (BION-M 1), hypergravity induces cardiovascular changes [89]. More experiments should be conducted to precise how these cardiovascular changes can modify the structure of the BBB and neurovascular unit functions. To restore physiological functions after spaceflight or bed-rest in humans, a daily sequence of short exposure to centrifugation close to 2× g has been proposed. It is crucial to verify if this protocol has any effect on the BBB. Recently, biomarkers of BBB alteration have been listed [90], and their investigation in the spaceflight context should be performed. Finally, centrifugation can be considered to potentiate vectorization and should be used to investigate cell functions with antisense oligonucleotides in pathophysiological contexts.\n\n## References\n\n1. Graybiel A.. **Significance of vestibular organs in problems of weightlessness**. *Life Sci. Space Res.* (1963.0) **1** 19-32. 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title: 'Emergency department use and Artificial Intelligence in Pelotas: design and
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---
title: Systematic Identification and Comparison of the Expressed Profiles of Exosomal
MiRNAs in Pigs Infected with NADC30-like PRRSV Strain
authors:
- Feng Cheng
- Hui Wang
- Lei Zhou
- Ganqiu Lan
- Hanchun Yang
- Lixian Wang
- Ligang Wang
- Jing Liang
journal: 'Animals : an Open Access Journal from MDPI'
year: 2... | [
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---
title: Fecal Microbiota, Forage Nutrients, and Metabolic Responses of Horses Grazing
Warm- and Cool-Season Grass Pastures
authors:
- Jennifer R. Weinert-Nelson
- Amy S. Biddle
- Harini Sampath
- Carey A. Williams
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000167
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---
title: 'Two Worlds in One: What ‘Counts’ as Animal Advocacy for Veterinarians Working
in UK Animal Research?'
authors:
- Renelle McGlacken
- Alistair Anderson
- Pru Hobson-West
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000174
doi: 10.3390/ani13050776
license: CC BY 4.0
---
# Tw... | [
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---
title: Metacarpophalangeal Joint Pathology and Bone Mineral Density Increase with
Exercise but Not with Incidence of Proximal Sesamoid Bone Fracture in Thoroughbred
Racehorses
authors:
- Kira J. Noordwijk
- Leyi Chen
- Bianca D. Ruspi
- Sydney Schurer
- Brittany Papa
- Diana C. Fasanello
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---
title: Dietary Fermentation Product of Aspergillus Oryzae Prevents Increases in Gastrointestinal
Permeability (‘Leaky Gut’) in Horses Undergoing Combined Transport and Exercise
authors:
- Melissa McGilloway
- Shannon Manley
- Alyssa Aho
- Keisha N. Heeringa
- Lynsey Whitacre
- Yanping Lou
- E. James Squires
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---
title: 'Neuropathology of Central and Peripheral Nervous System Lymphoma in Dogs and
Cats: A Study of 92 Cases and Review of the Literature'
authors:
- Niccolò Fonti
- Francesca Parisi
- Çağla Aytaş
- Sara Degl’Innocenti
- Carlo Cantile
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
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---
title: The Gut Microbiota of Young Asian Elephants with Different Milk-Containing
Diets
authors:
- Chengbo Zhang
- Junmin Chen
- Qian Wu
- Bo Xu
- Zunxi Huang
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000238
doi: 10.3390/ani13050916
license: CC BY 4.0
---
# The Gut Microbiota o... | [
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---
title: Physicochemical and Biochemical Properties of Trypsin-like Enzyme from Two
Sturgeon Species
authors:
- Abbas Zamani
- Maryam Khajavi
- Abdolmohammad Abedian Kenari
- Masoumeh Haghbin Nazarpak
- Atefeh Solouk
- Mina Esmaeili
- Enric Gisbert
journal: 'Animals : an Open Access Journal from MDPI'
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---
title: Characteristics of circulating small noncoding RNAs in plasma and serum during
human aging
authors:
- Ping Xiao
- Zhangyue Shi
- Chenang Liu
- Darren E. Hagen
journal: Aging Medicine
year: 2023
pmcid: PMC10000275
doi: 10.1002/agm2.12241
license: CC BY 4.0
---
# Characteristics of circulating small noncodi... | [
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---
title: Bone health in ambulatory male patients with chronic obstructive airway disease
– A case control study from India
authors:
- Mohammad Sadiq Jeeyavudeen
- Samuel George Hansdek
- Nihal Thomas
- Thangakunam Balamugesh
- Mahasampath Gowri
- Thomas V. Paul
journal: Aging Medicine
year: 2023
pmcid: PMC10000280
... | [
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-0... |
---
title: Circadian dysfunction and Alzheimer's disease – An updated review
authors:
- Faizan Ahmad
- Punya Sachdeva
- Jasmine Sarkar
- Raafiah Izhaar
journal: Aging Medicine
year: 2022
pmcid: PMC10000289
doi: 10.1002/agm2.12221
license: CC BY 4.0
---
# Circadian dysfunction and Alzheimer's disease – An updated revie... | [
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---
title: 'Apocynum venetum, a medicinal, economical and ecological plant: a review update'
authors:
- Tian Xiang
- Longjiang Wu
- Murtala Bindawa Isah
- Chen Chen
- Xiaoying Zhang
journal: PeerJ
year: 2023
pmcid: PMC10000306
doi: 10.7717/peerj.14966
license: CC BY 4.0
---
# Apocynum venetum, a medicinal, economical ... | [
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... |
---
title: 'Efficacy of silymarin in patients with non-alcoholic fatty liver disease —
the Siliver trial: a study protocol for a randomized controlled clinical trial'
authors:
- Camila Ribeiro de Avelar
- Beatriz Vieira Coelho Nunes
- Betina da Silva Sassaki
- Mariana dos Santos Vasconcelos
- Lucivalda Pereira Magalh... | [
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... |
---
title: Factors Associated with Lack of Health Screening among People with Disabilities
Using Andersen’s Behavioral Model
authors:
- Ye-Soon Kim
- Seung Hee Ho
journal: Healthcare
year: 2023
pmcid: PMC10000362
doi: 10.3390/healthcare11050656
license: CC BY 4.0
---
# Factors Associated with Lack of Health Screenin... | [
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---
title: Micrandilactone C, a Nortriterpenoid Isolated from Roots of Schisandra chinensis,
Ameliorates Huntington’s Disease by Inhibiting Microglial STAT3 Pathways
authors:
- Minhee Jang
- Jong Hee Choi
- Dae Sik Jang
- Ik-Hyun Cho
journal: Cells
year: 2023
pmcid: PMC10000367
doi: 10.3390/cells12050786
license: CC ... | [
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---
title: Blood Count-Derived Inflammatory Markers Correlate with Lengthier Hospital
Stay and Are Predictors of Pneumothorax Risk in Thoracic Trauma Patients
authors:
- Vlad Vunvulea
- Răzvan Marian Melinte
- Klara Brinzaniuc
- Bogdan Andrei Suciu
- Adrian Dumitru Ivănescu
- Ioana Hălmaciu
- Zsuzsanna Incze-Bartha
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---
title: The Contribution of Scalded and Scalded-Fermented Rye Wholemeal Flour to Quality
Parameters and Acrylamide Formation in Semi-Wheat-Rye Bread
authors:
- Dovile Klupsaite
- Vytaute Starkute
- Egle Zokaityte
- Darius Cernauskas
- Ernestas Mockus
- Evaldas Kentra
- Rugilė Sliazaite
- Gabriele Abramaviciute
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---
title: Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ensemble Based
Convolutional Neural Networks
authors:
- Swaminathan Sundaram
- Meganathan Selvamani
- Sekar Kidambi Raju
- Seethalakshmi Ramaswamy
- Saiful Islam
- Jae-Hyuk Cha
- Nouf Abdullah Almujally
- Ahmed Elaraby
journal: Diagnostics
yea... | [
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---
title: Thyroid Profile in the First Three Months after Starting Treatment in Children
with Newly Diagnosed Cancer
authors:
- Chantal A. Lebbink
- Cor van den Bos
- Miranda P. Dierselhuis
- Marta Fiocco
- Annemarie A. Verrijn Stuart
- Eef G. W. M. Lentjes
- Sabine L. A. Plasschaert
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---
title: 'Zinc Supplementation Induced Transcriptional Changes in Primary Human Retinal
Pigment Epithelium: A Single-Cell RNA Sequencing Study to Understand Age-Related
Macular Degeneration'
authors:
- Eszter Emri
- Oisin Cappa
- Caoimhe Kelly
- Elod Kortvely
- John Paul SanGiovanni
- Brian S. McKay
- Arthur A. B... | [
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---
title: Blackcurrant Alleviates Dextran Sulfate Sodium (DSS)-Induced Colitis in Mice
authors:
- Hye-Jung Moon
- Youn-Soo Cha
- Kyung-Ah Kim
journal: Foods
year: 2023
pmcid: PMC10000425
doi: 10.3390/foods12051073
license: CC BY 4.0
---
# Blackcurrant Alleviates Dextran Sulfate Sodium (DSS)-Induced Colitis in Mice
#... | [
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---
title: The Effect and Mechanism of Corilagin from Euryale Ferox Salisb Shell on LPS-Induced
Inflammation in Raw264.7 Cells
authors:
- Minrui Wu
- Yuhan Jiang
- Junnan Wang
- Ting Luo
- Yang Yi
- Hongxun Wang
- Limei Wang
journal: Foods
year: 2023
pmcid: PMC10000429
doi: 10.3390/foods12050979
license: CC BY 4.0
--... | [
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---
title: Prevalence of Malnutrition in Hospitalized Patients in Lebanon Using Nutrition
Risk Screening (NRS-2002) and Global Leadership Initiative on Malnutrition (GLIM)
Criteria and Its Association with Length of Stay
authors:
- Krystel Ouaijan
- Nahla Hwalla
- Ngianga-Bakwin Kandala
- Emmanuel Kabengele Mpinga
... | [
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---
title: A Triphenylphosphonium-Functionalized Delivery System for an ATM Kinase Inhibitor
That Ameliorates Doxorubicin Resistance in Breast Carcinoma Mammospheres
authors:
- Venturina Stagni
- Archontia Kaminari
- Claudia Contadini
- Daniela Barilà
- Rosario Luigi Sessa
- Zili Sideratou
- Spiros A. Vlahopoulos
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---
title: 'Formation of Nε-Carboxymethyl-Lysine and Nε-Carboxyethyl-Lysine in Heated
Fish Myofibrillar Proteins with Glucose: Relationship with Its Protein Structural
Characterization'
authors:
- Siqi Zhang
- Pengcheng Zhou
- Peng Han
- Hao Zhang
- Shiyuan Dong
- Mingyong Zeng
journal: Foods
year: 2023
pmcid: PMC1... | [
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0.... |
---
title: 'Cretan Aging Cohort-Phase III: Methodology and Descriptive Characteristics
of a Long-Term Longitudinal Study on Predictors of Cognitive Decline in Non-Demented
Elderly from Crete, Greece'
authors:
- Maria Basta
- Eleni Skourti
- Christina Alexopoulou
- Alexandros Zampetakis
- Andronikos Ganiaris
- Marin... | [
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0.000087... |
---
title: Low Gut Microbial Diversity Augments Estrogen-Driven Pulmonary Fibrosis in
Female-Predominant Interstitial Lung Disease
authors:
- Ozioma S. Chioma
- Elizabeth Mallott
- Binal Shah-Gandhi
- ZaDarreyal Wiggins
- Madison Langford
- Andrew William Lancaster
- Alexander Gelbard
- Hongmei Wu
- Joyce E. Johnson
... | [
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-0.0... |
---
title: PGRMC1 Ablation Protects from Energy-Starved Heart Failure by Promoting Fatty
Acid/Pyruvate Oxidation
authors:
- Sang R. Lee
- Moeka Mukae
- Kang Joo Jeong
- Se Hee Park
- Hi Jo Shin
- Sang Woon Kim
- Young Suk Won
- Hyo-Jung Kwun
- In-Jeoung Baek
- Eui-Ju Hong
journal: Cells
year: 2023
pmcid: PMC10000468
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title: Sulforaphane Potentiates Gemcitabine-Mediated Anti-Cancer Effects against Intrahepatic
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authors:
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title: Fermented Soybean Paste Attenuates Biogenic Amine-Induced Liver Damage in Obese
Mice
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- Eun-Hye Byeon
- Dawon Kang
- Seong-Geun Hong
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title: 'Age Matters: The Moderating Effect of Age on Styles and Strategies of Coping
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title: 'Importance of Asprosin for Changes of M. Rectus Femoris Area during the Acute
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authors:
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---
title: Multiple Organic Contaminants Determination Including Multiclass of Pesticides,
Polychlorinated Biphenyls, and Brominated Flame Retardants in Portuguese Kiwano
Fruits by Gas Chromatography
authors:
- Virgínia Cruz Fernandes
- Martyna Podlasiak
- Elsa F. Vieira
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title: Identification of a Link between Suspected Metabolic Syndrome and Cognitive
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authors:
- Zuzana Macekova
- Tomas Fazekas
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---
title: 'Assessment of the Possibility of Using the Laryngoscopes Macintosh, McCoy,
Miller, Intubrite, VieScope and I-View for Intubation in Simulated Out-of-Hospital
Conditions by People without Clinical Experience: A Randomized Crossover Manikin
Study'
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---
title: 11,12-EET Regulates PPAR-γ Expression to Modulate TGF-β-Mediated Macrophage
Polarization
authors:
- Xiaoming Li
- Sebastian Kempf
- Stefan Günther
- Jiong Hu
- Ingrid Fleming
journal: Cells
year: 2023
pmcid: PMC10000544
doi: 10.3390/cells12050700
license: CC BY 4.0
---
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---
title: 'Rational Food Design Targeting Micronutrient Deficiencies in Adolescents:
Nutritional, Acoustic-Mechanical and Sensory Properties of Chickpea-Rice Biscuits'
authors:
- Clara Talens
- Laura Garcia-Fontanals
- Paula Fabregat
- Mónica Ibargüen
journal: Foods
year: 2023
pmcid: PMC10000554
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---
title: 'Examining Factors Associated with Dynapenia/Sarcopenia in Patients with Schizophrenia:
A Pilot Case-Control Study'
authors:
- Ryuichi Tanioka
- Kyoko Osaka
- Hirokazu Ito
- Yueren Zhao
- Masahito Tomotake
- Kensaku Takase
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journal: Healthcare
year: 2023
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---
title: Validation of an LC-MS/MS Method for the Determination of Abscisic Acid Concentration
in a Real-World Setting
authors:
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- Ilaria Neri
- Maria Maisto
- Ettore Novellino
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---
title: Blocking Store-Operated Ca2+ Entry to Protect HL-1 Cardiomyocytes from Epirubicin-Induced
Cardiotoxicity
authors:
- Xian Liu
- Yan Chang
- Sangyong Choi
- Chuanxi Cai
- Xiaoli Zhang
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journal: Cells
year: 2023
pmcid: PMC10000558
doi: 10.3390/cells12050723
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---
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title: CRP/Albumin Ratio and Glasgow Prognostic Score Provide Prognostic Information
in Myelofibrosis Independently of MIPSS70—A Retrospective Study
authors:
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title: HLA-B*57:01/Carbamazepine-10,11-Epoxide Association Triggers Upregulation of
the NFκB and JAK/STAT Pathways
authors:
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journal: Cells
year: 2023
pmcid: PMC10000580
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title: Pro-Apoptotic and Anti-Cancer Activity of the Vernonanthura Nudiflora Hydroethanolic
Extract
authors:
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- Anna Shteinfer-Kuzmine
- Swaroop Kumar Pandey
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journal: Cancers
year: 2023
pmcid: PMC10000589
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---
title: Heparan Sulfates Regulate Axonal Excitability and Context Generalization through
Ca2+/Calmodulin-Dependent Protein Kinase II
authors:
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title: Decreasing the Crystallinity and Degree of Polymerization of Cellulose Increases
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authors:
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title: Antioxidant Capacity, Nitrite and Nitrate Content in Beetroot-Based Dietary
Supplements
authors:
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- Paulina Malinowska
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journal: Foods
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pmcid: PMC10000616
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---
title: 'Simplifying carb counting: A randomized controlled study – Feasibility and
efficacy of an individualized, simple, patient‐centred carb counting tool'
authors:
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- Irina Goloub
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title: Blocking EREG/GPX4 Sensitizes Head and Neck Cancer to Cetuximab through Ferroptosis
Induction
authors:
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- Ombline Conrad
- Mickaël Burgy
- Sophie Foppolo
- Romain Vauchelles
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---
title: A clinical trial about effects of prebiotic and probiotic supplementation on
weight loss, psychological profile and metabolic parameters in obese subjects
authors:
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- Nadia Ben Amor
- Faten Mahjoub
- Olfa Berriche
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---
title: 'Examining dyslipidaemia, metabolic syndrome and liver enzyme levels in patients
with prediabetes and type 2 diabetes in population from Hoveyzeh cohort study: A
case–control study in Iran'
authors:
- Negar Dinarvand
- Bahman Cheraghian
- Zahra Rahimi
- Samaneh Salehipour Bavarsad
- Amirhooshang Bavarsad... | [
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---
title: Additional Active Movements Are Not Required for Strength Gains in the Untrained
during Short-Term Whole-Body Electromyostimulation Training
authors:
- Holger Stephan
- Udo Frank Wehmeier
- Tim Förster
- Fabian Tomschi
- Thomas Hilberg
journal: Healthcare
year: 2023
pmcid: PMC10000632
doi: 10.3390/healthca... | [
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---
title: 'Two‐year trends from the LANDMARC study: A 3‐year, pan‐India, prospective,
longitudinal study on the management and real‐world outcome in patients with type
2 diabetes mellitus'
authors:
- Ashok K. Das
- Sanjay Kalra
- Shashank Joshi
- Ambrish Mithal
- Prasanna Kumar K. M.
- Ambika G. Unnikrishnan
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---
title: 'Investigating the relationship between haematological parameters and metabolic
syndrome: A population‐based study'
authors:
- Mohammad Javad Najafzadeh
- Amir Baniasad
- Reza Shahabinejad
- Mahdieh Mashrooteh
- Hamid Najafipour
- Mohammad Hossein Gozashti
journal: Endocrinology, Diabetes & Metabolism
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---
title: 'A population‐based cohort of adult patients with diabetes mellitus in a Western
District of Austria: The Diabetes Landeck cohort'
authors:
- Veronika Haslwanter
- Ursula Rochau
- Uwe Siebert
- Hans‐Robert Schönherr
- Willi Oberaigner
journal: Endocrinology, Diabetes & Metabolism
year: 2022
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---
title: Levothyroxine therapy, calculated deiodinases activity and basal metabolic
rate in obese or nonobese patients after total thyroidectomy for differentiated
thyroid cancer, results of a retrospective observational study
authors:
- Rosario Le Moli
- Pasqualino Malandrino
- Marco Russo
- Dario Tumino
- Tomma... | [
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---
title: 'Association between Pro‐oxidant‐Antioxidant balance and high‐sensitivity C‐reactive
protein in type 2 diabetes mellitus: A Study on Postmenopausal Women'
authors:
- Hassan Ehteram
- Sara Raji
- Mina Rahmati
- Hanieh Teymoori
- Samaneh Safarpour
- Nahid Poursharifi
- Mona Hashem Zadeh
- Reza Pakzad
- Hosse... | [
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---
title: Impact of Longkong Pericarp Extract on the Physicochemical Properties of Alginate-Based
Edible Nanoparticle Coatings and Quality Maintenance of Shrimp (Penaeus monodon)
during Refrigerated Storage
authors:
- Narin Charoenphun
- Bharathipriya Rajasekaran
- Suguna Palanisamy
- Karthikeyan Venkatachalam
jou... | [
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---
title: 'Results of DUET: A Web-Based Weight Loss Randomized Controlled Feasibility
Trial among Cancer Survivors and Their Chosen Partners'
authors:
- Wendy Demark-Wahnefried
- Robert A. Oster
- Tracy E. Crane
- Laura Q. Rogers
- W. Walker Cole
- Harleen Kaur
- David Farrell
- Kelsey B. Parrish
- Hoda J. Badr
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... |
---
title: Assessment of Blood Microcirculation Changes after COVID-19 Using Wearable
Laser Doppler Flowmetry
authors:
- Elena V. Zharkikh
- Yulia I. Loktionova
- Andrey A. Fedorovich
- Alexander Y. Gorshkov
- Andrey V. Dunaev
journal: Diagnostics
year: 2023
pmcid: PMC10000665
doi: 10.3390/diagnostics13050920
license... | [
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---
title: RG-I Domain Matters to the In Vitro Fermentation Characteristics of Pectic
Polysaccharides Recycled from Citrus Canning Processing Water
authors:
- Jiaxiong Wu
- Sihuan Shen
- Qiang Gao
- Chengxiao Yu
- Huan Cheng
- Haibo Pan
- Shiguo Chen
- Xingqian Ye
- Jianle Chen
journal: Foods
year: 2023
pmcid: PMC100... | [
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---
title: Nucleolin Regulates Pulmonary Artery Smooth Muscle Cell Proliferation under
Hypoxia by Modulating miRNA Expression
authors:
- Jihui Lee
- Hara Kang
journal: Cells
year: 2023
pmcid: PMC10000680
doi: 10.3390/cells12050817
license: CC BY 4.0
---
# Nucleolin Regulates Pulmonary Artery Smooth Muscle Cell Proli... | [
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0.008379856124520302,
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0.01115213893353939,
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-0.02... |
---
title: 'Combination of Muscle Quantity and Quality Is Useful to Assess the Necessity
of Surveillance after a 5-Year Cancer-Free Period in Patients Who Undergo Radical
Cystectomy: A Multi-Institutional Retrospective Study'
authors:
- Naoki Fujita
- Masaki Momota
- Hirotaka Horiguchi
- Itsuto Hamano
- Jotaro Mika... | [
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... |
---
title: Clustering of Health and Oral Health-Compromising Behaviours in Army Personnel
in Central Peninsular Malaysia
authors:
- Ahmad Asyraf Azil
- Zamros Yuzadi Mohd Yusof
- Jamaludin Marhazlinda
journal: Healthcare
year: 2023
pmcid: PMC10000684
doi: 10.3390/healthcare11050640
license: CC BY 4.0
---
# Clusterin... | [
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-0.03347005695104599,
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0.0025257496163249016,
-... |
---
title: Preparation and Characterization of Intracellular and Exopolysaccharides during
Cycle Cultivation of Spirulina platensis
authors:
- Yuhuan Liu
- Yu Wang
- Leipeng Cao
- Zhenghua Huang
- Yue Zhou
- Ruijuan Fan
- Congmiao Li
journal: Foods
year: 2023
pmcid: PMC10000700
doi: 10.3390/foods12051067
license: CC ... | [
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---
title: Exploring the Impact of α-Amylase Enzyme Activity and pH on Flavor Perception
of Alcoholic Drinks
authors:
- Maria João Santos
- Elisete Correia
- Alice Vilela
journal: Foods
year: 2023
pmcid: PMC10000705
doi: 10.3390/foods12051018
license: CC BY 4.0
---
# Exploring the Impact of α-Amylase Enzyme Activity... | [
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---
title: Prediction of Gastrointestinal Tract Cancers Using Longitudinal Electronic
Health Record Data
authors:
- Andrew J. Read
- Wenjing Zhou
- Sameer D. Saini
- Ji Zhu
- Akbar K. Waljee
journal: Cancers
year: 2023
pmcid: PMC10000707
doi: 10.3390/cancers15051399
license: CC BY 4.0
---
# Prediction of Gastrointes... | [
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---
title: Leveraging Tumor Microenvironment Infiltration in Pancreatic Cancer to Identify
Gene Signatures Related to Prognosis and Immunotherapy Response
authors:
- Jiabin Yang
- Liangtang Zeng
- Ruiwan Chen
- Leyi Huang
- Zhuo Wu
- Min Yu
- Yu Zhou
- Rufu Chen
journal: Cancers
year: 2023
pmcid: PMC10000708
doi: 10.... | [
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---
title: Caregiving Self-Efficacy of the Caregivers of Family Members with Oral Cancer—A
Descriptive Study
authors:
- Ching-Hui Cheng
- Shu-Yuan Liang
- Ling Lin
- Tzu-Ting Chang
- Tsae-Jyy Wang
- Ying Lin
journal: Healthcare
year: 2023
pmcid: PMC10000709
doi: 10.3390/healthcare11050762
license: CC BY 4.0
---
# Ca... | [
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---
title: Decoy Receptors Regulation by Resveratrol in Lipopolysaccharide-Activated Microglia
authors:
- Rosa Calvello
- Chiara Porro
- Dario Domenico Lofrumento
- Melania Ruggiero
- Maria Antonietta Panaro
- Antonia Cianciulli
journal: Cells
year: 2023
pmcid: PMC10000713
doi: 10.3390/cells12050681
license: CC BY 4.0
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-0.035... |
---
title: Quantitative Analysis of Oat (Avena sativa L.) and Pea (Pisum sativum L.) Saponins
in Plant-Based Food Products by Hydrophilic Interaction Liquid Chromatography Coupled
with Mass Spectrometry
authors:
- Anastassia Bljahhina
- Dmitri Pismennõi
- Tiina Kriščiunaite
- Maria Kuhtinskaja
- Eeva-Gerda Kobrin
j... | [
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---
title: 'Monitoring Moroccan Honeys: Physicochemical Properties and Contamination Pattern'
authors:
- Abir Massous
- Tarik Ouchbani
- Vincenzo Lo Turco
- Federica Litrenta
- Vincenzo Nava
- Ambrogina Albergamo
- Angela Giorgia Potortì
- Giuseppa Di Bella
journal: Foods
year: 2023
pmcid: PMC10000722
doi: 10.3390/food... | [
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... |
---
title: Auditory Electrophysiological and Perceptual Measures in Student Musicians
with High Sound Exposure
authors:
- Nilesh J. Washnik
- Ishan Sunilkumar Bhatt
- Alexander V. Sergeev
- Prashanth Prabhu
- Chandan Suresh
journal: Diagnostics
year: 2023
pmcid: PMC10000734
doi: 10.3390/diagnostics13050934
license: C... | [
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... |
---
title: An Innovative Mei-Gin Formula Exerts Anti-Adipogenic and Anti-Obesity Effects
in 3T3-L1 Adipocyte and High-Fat Diet-Induced Obese Rats
authors:
- Hsin-Lin Cheng
- Wei-Tang Chang
- Jiun-Ling Lin
- Ming-Ching Cheng
- Shih-Chien Huang
- Shiuan-Chih Chen
- Yue-Ching Wong
- Chin-Lin Hsu
journal: Foods
year: 202... | [
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---
title: Insights into the Chemical Composition and In Vitro Bioactive Properties of
Mangosteen (Garcinia mangostana L.) Pericarp
authors:
- Bianca R. Albuquerque
- Maria Inês Dias
- José Pinela
- Ricardo C. Calhelha
- Tânia C. S. P. Pires
- Maria José Alves
- Rúbia C. G. Corrêa
- Isabel C. F. R. Ferreira
- Maria B... | [
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... |
---
title: On the Implementation of a Post-Pandemic Deep Learning Algorithm Based on a
Hybrid CT-Scan/X-ray Images Classification Applied to Pneumonia Categories
authors:
- Abdelghani Moussaid
- Nabila Zrira
- Ibtissam Benmiloud
- Zineb Farahat
- Youssef Karmoun
- Yasmine Benzidia
- Soumaya Mouline
- Bahia El Abdi
- ... | [
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0.0... |
---
title: CD24 Gene Expression as a Risk Factor for Non-Alcoholic Fatty Liver Disease
authors:
- Mona A. Amin
- Halla M. Ragab
- Nabila Abd El Maksoud
- Wafaa Abd Elaziz
journal: Diagnostics
year: 2023
pmcid: PMC10000766
doi: 10.3390/diagnostics13050984
license: CC BY 4.0
---
# CD24 Gene Expression as a Risk Factor f... | [
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0... |
---
title: The Adaptive Force as a Potential Biomechanical Parameter in the Recovery Process
of Patients with Long COVID
authors:
- Laura V. Schaefer
- Frank N. Bittmann
journal: Diagnostics
year: 2023
pmcid: PMC10000769
doi: 10.3390/diagnostics13050882
license: CC BY 4.0
---
# The Adaptive Force as a Potential Biom... | [
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---
title: Multiparametric Profiling of Neutrophil Function via a High-Throughput Flow
Cytometry-Based Assay
authors:
- Kyle D. Timmer
- Daniel J. Floyd
- Allison K. Scherer
- Arianne J. Crossen
- Johnny Atallah
- Adam L. Viens
- David B. Sykes
- Michael K. Mansour
journal: Cells
year: 2023
pmcid: PMC10000770
doi: 10... | [
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0.0023881152737885714,... |
---
title: 'The Influence of Ethnicity on Survival from Malignant Primary Brain Tumours
in England: A Population-Based Cohort Study'
authors:
- Hiba A. Wanis
- Henrik Møller
- Keyoumars Ashkan
- Elizabeth A. Davies
journal: Cancers
year: 2023
pmcid: PMC10000771
doi: 10.3390/cancers15051464
license: CC BY 4.0
---
# T... | [
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---
title: 'Sleep-Disordered Breathing among Saudi Primary School Children: Incidence
and Risk Factors'
authors:
- Saleh H. Alwadei
- Suliman Alsaeed
- Ahmed Ibrahim Masoud
- Farhan Alwadei
- Khalid Gufran
- Abdurahman Alwadei
journal: Healthcare
year: 2023
pmcid: PMC10000777
doi: 10.3390/healthcare11050747
license: ... | [
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-... |
---
title: Comprehensive Analysis of Prognosis and Immune Landscapes Based on Lipid-Metabolism-
and Ferroptosis-Associated Signature in Uterine Corpus Endometrial Carcinoma
authors:
- Pusheng Yang
- Jiawei Lu
- Panpan Zhang
- Shu Zhang
journal: Diagnostics
year: 2023
pmcid: PMC10000778
doi: 10.3390/diagnostics1305087... | [
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-0.0... |
---
title: Dynamic NIR Fluorescence Imaging and Machine Learning Framework for Stratifying
High vs. Low Notch-Dll4 Expressing Host Microenvironment in Triple-Negative Breast
Cancer
authors:
- Shayan Shafiee
- Jaidip Jagtap
- Mykhaylo Zayats
- Jonathan Epperlein
- Anjishnu Banerjee
- Aron Geurts
- Michael Flister
- ... | [
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... |
---
title: Influence of Pig Genetic Line and Salt Reduction on Peptide Production and
Bioactivity of Dry-Cured Hams
authors:
- Beatriz Muñoz-Rosique
- Noelia Hernández-Correas
- Adela Abellán
- Estefanía Bueno
- Rafael Gómez
- Luis Tejada
journal: Foods
year: 2023
pmcid: PMC10000787
doi: 10.3390/foods12051022
license... | [
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0.03229258209466934,
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0.03454555198550224,
0.003473330056294799,
-0.0... |
---
title: A New Artificial Intelligence Approach Using Extreme Learning Machine as the
Potentially Effective Model to Predict and Analyze the Diagnosis of Anemia
authors:
- Dimas Chaerul Ekty Saputra
- Khamron Sunat
- Tri Ratnaningsih
journal: Healthcare
year: 2023
pmcid: PMC10000789
doi: 10.3390/healthcare11050697
... | [
-0.0007260157726705074,
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0.005646789446473122,
0.05704870447516441,
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-0.014391246251761913,
0.04285106435418129,
0.01180985663086176,
0.0019521757494658232,
-0... |
---
title: Hypergravity Increases Blood–Brain Barrier Permeability to Fluorescent Dextran
and Antisense Oligonucleotide in Mice
authors:
- David Dubayle
- Arnaud Vanden-Bossche
- Tom Peixoto
- Jean-Luc Morel
journal: Cells
year: 2023
pmcid: PMC10000817
doi: 10.3390/cells12050734
license: CC BY 4.0
---
# Hypergravity... | [
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0.0034164811950176954,
... |