Interaction of Genetics Risk Score (GRS) and Plant-Based Diet on factors Atherogenic and body adiposity indexes among overweight and obese women: a cross-sectional study

Interaction of Genetics Risk Score (GRS) and Plant-Based Diet on factors Atherogenic and body adiposity indexes among overweight and obese women: a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Interaction of Genetics Risk Score (GRS) and Plant-Based Diet on factors Atherogenic and body adiposity indexes among overweight and obese women: a cross-sectional study Mahya Mehri Hajmir, Atieh Mirzababaei, Faezeh Abaj, Yasaman Aali, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4587951/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The association between plant-based foods with obesity, cardiovascular diseases (CVD), and their novel predictive biomarkers considering genetic predisposition remains uncertain. Given that diet is a significant and modifiable risk factor, we sought to investigate the interactions between plant-based diet and genetic susceptibility with atherogenic factors, and visceral and body adiposity indices in Iranian women. Methods: This cross-sectional study was conducted on 377 obese and overweight women, aged 18–48 from Iran. Using standard protocols, anthropometric indices, body composition, physical activity, and serum profiles were measured. A validated 147-item semi-quantitative food frequency questionnaire (FFQ) was used to create three plant-based diets including the overall plant-based diet index (PDI), healthy plant-based diet index (hPDI), and unhealthy plant-based diet index (uPDI). A genetic risk score (GRS) was calculated based on the risk alleles of the three BMI-related SNPs. The interaction between GRS and PDI was analyzed using a generalized linear model (GLM). Result There was a negative significant interaction between tertile 2 of PDI and moderate risk allele and high-risk alleles with AIP, TGy, LAP, and VAI compared to the low-risk allele (P-value < 0.05). There was a negative borderline significant interaction between tertile 2 of hPDI and moderate risk allele (P-value = 0.05) with ABSI compared to low-risk allele participants. There was a positive significant interaction between tertile 2 of uPDI and moderate risk allele with CRI.I (P-value = 0.03), CRI.II (P-value = 0.03) compared to low-risk allele participants. Also, there was a positive significant interaction between tertile 3 of uPDI and high-risk allele with ABSI (P-value = 0.02) compared to low-risk allele participants. Conclusion The present study provides evidence that interaction between PDI, hPDI, and uPDI with GSR is associated with atherogenic index and body composition. Prospective and interventional studies in different populations and ethnicities need to be conducted to further the knowledge about examining the interaction between PDI, hPDI, and uPDI with GSR are associated with atherogenic index and body composition. A body shape index body roundness index Lipid accumulation product Body adiposity index atherogenic index of plasma Plant Dietary Index Introduction Over the last few decades, obesity has become a global public health concern, affecting both developed and developing countries (1). According to a systematic review and meta-analysis in 2019, the prevalence of obesity among Iranian older adults was reported at 21.4% (2) and it is universally more common in women (3). It has been revealed that obesity and cardiovascular disease (CVD), dyslipidemia, and metabolic abnormalities have strong associations (4). Indeed, obesity and hyperlipidemia are recognized the most important risk factors for CVD (5, 6). In this regard, several obesity or atherogenic-related predictive indices were identified which might predispose a person to obesity or cardiovascular diseases. A body shape index (ABSI) and body roundness index (BRI), based on height, weight, and waist circumference (WC), are two new anthropometric indices that have been proposed recently. ABSI has proved to be associated with all-cause mortality (7, 8) and BRI provides comprehensive identification of visceral adiposity tissue and body fat percentage (9). Lipid accumulation product (LAP), which is focused on a combination of WC and TG, might be an accurate marker of central obesity (10). The Body adiposity index (BAI), a novel indicator of % fat, has been suggested as a more health outcome predictor than the BMI (11). Another novel biomarker developed as a robust biomarker to predict atherosclerosis and CVD events and closely related to abdominal obesity is the atherogenic index of plasma (AIP) (12–18). The result of a previous study has suggested that a higher AIP value is associated with a higher risk of chronic diseases (19). More so, constructed indices such as Castelli risk index-I (CRI-I), Castelli risk index-II (CRI-II), and triglyceride glucose (TyG) index, based on lipoprotein cholesterol concentrations, have been considered better predictors of atherosclerosis and CVD events (20–23). Among environmental factors, different dietary patterns have life-long effects on CVD and other metabolic-related risk factors (24). Plant-based diet indices which reflect the difference between plant-derived foods and their association with the risk of disease, as graded scoring systems, have been developed in three categories as follows: a plant-based diet index (PDI) which illustrates the whole consumption of plant food with lower intake of animal food, a healthy plant-based diet index (hPDI), and an unhealthy plant-based diet index (uPDI) (25). Recent studies reported that adherence to hPDI could decrease the risk of chronic diseases and improve CVDs, while diets focused on uPDI resulted in a higher risk of chronic diseases (25–29). According to the World Health Organization (WHO), genetic susceptibility has also been implicated as a risk factor in the onset and development of CVD (30). GRS is calculated by adding genetic risk alleles for each single nucleotide polymorphism (SNP) (31), could provide a better understanding in terms of trait variability of an individual and improve genetic risk prediction affecting the variables tested in this context compared to a single variant method (32). Caveolin-1 (CAV-1), abundant in adipocytes (33), was previously reported to be associated with obesity, dyslipidemia, and atherosclerosis (34–37). Furthermore, the risk allele C for melanocortin 4 receptor (MC4R) rs17782313 was considered a key factor in developing obesity and increased cardiovascular risk factors (38–42). Cryptochromes (Cry) 1 has also been shown to play critical roles in metabolism regulation, obesity, and elevated cardiometabolic traits (43–45). A large variety of studies have reported the contribution of healthy dietary patterns and PDI to decreasing genetic risk factors of obesity and CVD (46, 47). However, no study has been conducted on the association between PDI and the aforementioned genetic factors and its association with tested variables among women. Therefore, the present study sought to evaluate the interactions between BMI-GRS based on 3 SNPs, namely, MC4R (rs17782313), CAV-1 (rs3807992), and Cry-1 (rs2287161) with hPDI and uPDI on CVD and obesity-related risk factors in Iranian overweight and obese women. Methods and materials 2.1. Study population This cross-sectional study was conducted on 377 overweight and obese women aged 18–48, with BMI ranging from 25 to 40 kg/m 2 , referred to health centers in Tehran, Iran. The participants were chosen using a random cluster sampling method. Subjects with the following conditions were not included in this study; pregnancy, lactation, menopause, the history of diseases including type I and type II diabetes, non-alcoholic fatty liver disease (NAFLD), thyroid illness, kidney or liver diseases, polycystic ovary syndrome (PCOS), malignancies, and stroke. Taking any supplements or medications, weight loss program, or total calorie intakes not in the range of 800–4200 (kcal/day), were all exclusion factors. Before enrolling, all participants completed the informed consent form, which was reviewed and approved by Ethics Committee of the TUMS (NO: IR. TUMS.VCR.REC.1398.142). This literature was performed according to relevant guidelines and regulations. 2.2. General, anthropometric, and physical activity assessments General information including age, marital status, educational level, and history of recent weight loss was collected via a demographic questionnaire. The measurement of height and weight were recorded, with light clothes and in a standing position, using a digital scale (Seca, Germany) with precisions of 0.1 cm and 0.1 kg, respectively. BMI was calculated by dividing weight (kg) by the square of height (m 2 ). A trained expert measured waist circumference and hip circumference following standard protocols and the waist-to-hip ratio (WHR) was computed as the waist measurement divided by the hip measurement. Overweight and obesity were defined as BMI 25-29.9 kg/m 2 and BMI 30–40 kg/m 2 , respectively (48). Body composition was assessed using bioelectrical impedance analysis (BIA 770 (South Korea)), following manufacturer guidelines (49). In addition, physical activity (PA) was evaluated using the validated self-report International Physical Activity Questionnaire (IPAQ) short form (50), categorized as follows: intense activity, moderate activity, and inactive. 2.3. Laboratory tests All blood samples were obtained after 12–14 h of fasting and were centrifuged and stored at -80°C at the Nutrition and Biochemistry Laboratory of the School of Nutritional and Dietetics at TUMS. Fasting blood sugar (FBS) was measured using the Glucose Oxidase Phenol 4-Aminoantipyrine Peroxidase (GOD/PAP) method. Radio-immune assay was used to measure serum insulin values and all lipid biomarkers, including total cholesterol (T-Chol) (mg/dl), low-density lipoprotein (LDL) (mg/dl), high-density lipoprotein (HDL) (mg/dl), and triglyceride (TG) (mg/dl) serum levels, was determined by enzymatic methods (Pars Azmun Co., Tehran, Iran). 2.4. Atherogenic index of plasma (AIP) and lipid ratio assessment The atherogenic index of plasma (AIP) was calculated from the logarithmic ratio of TG to HDL-C. For lipid ratio, calculations occurred as follows: CRI - I = TC/HDL-C, CRI – II = LDL-C/HDL-C (51). TyG was estimated as: Ln [fasting triglycerides (mg/dl) × FPG (mg/dl)/2 ] (52) and LAP women = \(\left(WC-58\right)\times TG\) (53). 2.5. A body shape index (ABSI), body roundness index (BRI), and Body adiposity index (BAI) definitions The ABSI, BRI, and BAI were calculated using the following articles (7, 9, 11). ABSI was derived from WC which was adjusted for height and weight. ABSI = WC / BMI 2/3 Height 1/2 . BRI was calculated as follows: BRI = 364.2–365.5 Eccentricity. Eccentricity calculates the degree of circularity of an ellipse, which ranges between 0 and 1, with 0 characterizing a perfect circle, and 1, a vertical line. BAI was calculated as follows: BAI: Hip / Height x In this formula, the hip reflects hip circumference (in cm), height is measured in meters, and X is a unitless power term. 2.6. Dietary assessments and plant-based dietary pattern Dietary intakes were evaluated using a validated 147-item semi-quantitative FFQ whose validity and reliability have been previously approved (54). In the presence of expert dietitians, subjects were asked to report their consumption frequency during the past year, based on their usual diet, which was converted to grams per day. Utilizing the Iranian Food Composition Table (FCT) and N4 software, total energy and dietary nutrients were analyzed. According to the plant-based dietary intake, three indices including overall PDI, hPDI, and uPDI were calculated using the method proposed by Satija et al (55). In brief, all food intakes were divided into 18 groups, which were animal foods (dairy, animal fat, egg, fish and seafood, meat, miscellaneous animal-based foods), healthy (whole grains, fruits, vegetables, nuts, legumes, vegetable oils, tea, and coffee), and unhealthy plant-based diets (fruit juices, sugar-sweetened beverages, refined grains, potatoes, sweets, and desserts) (25, 56). A total of 18 energy-adjusted food groups were divided into quintiles with an assigned score between 1 and 5 for positive or reverse scores. Focusing on positive scores, a score of 5 was given to the highest quintiles, and a score of 1 was assigned to the lowest quintiles, whereas this pattern was inversed for reverse scores. For PDI, both healthy and unhealthy plant-based foods were given positive scores. For hPDI and uPDI, only healthy plant foods and unhealthy plant foods received positive scores, respectively. Animal food groups were given reverse ratings in all three indices. Finally, the observed scores for each plant-based diet index ranged from 18 to 90 and a higher total score was associated with higher adherence to that diet index (25). 2.7. Genotyping and GRS We extracted the DNA using salting out method (57) and then, we used 1% agarose gel to assess the DNA integrity. DNA concentration was assessed by a nanodrop 8000 Spectrophotometer (Thermo Scientific, Waltham, MA, USA). For genotyping of the SNPs, the PCR-allele technique performed by the TaqMan Open Array (Life Technologies Corporation, Carlsbad, CA, USA) was used (58). For CAV-1 (rs3807992), the forward primer is 3′AGTATTGACCTGATTTGCCATG 5′ and the reverse primer is 5′ GTCTTCTGGAAAAAGCACATGA 3′. The fragments containing three genotypes (GG, GA, and AA) were distinguished. Based on a previous study, the MC4R gene primer was selected (59). The forward and reverse primer of MC4R (rs17782313) are 5- AAGTTCTACCTACCATGTTCTTGG-3 and 5-TTCCCCCTGAAGCTTTTCTTGTCATTTTGAT-3, respectively. The fragments containing three genotypes (CC, CT, and TT) were distinguished. We used PCR with the following primers for Cry1 (rs2287161): forward primer is 5′-GGAACAGTGATTGGCTCTATCT − 3′ and the reverse primer is 5′-GGTCCTCGGTCTCAAGAAG-3′. The fragments containing three genotypes (CC, GC, and GG) were distinguished. We created GRS by summing up three single nucleotide polymorphisms [CAV-1 (rs3807992), Cry-1 (rs2287161), and MC4R (rs17782313)] that had been linked to obesity-related traits, based on genomic-associated studies like Large-scale genome-wide association studies GWAS (60–64). According to the number of risk alleles for higher BMI, genotypes were coded as 0, 1, or 2 for each SNP. In this method, the unweighted GRS was calculated using the risk alleles of the three SNPs. The GRS scale ranges from 0 to 6. Higher scores represent a greater genetic predisposition to higher BMI or body weight (65). 2.8. Statistical analysis The Kolmogorov-Smirnov test was used to assess the normality of distribution. The Hardy-Weinberg equilibrium and comparison of categorical variables were assessed with the chi-square test. Descriptive analysis was applied to evaluate demographic characteristics and all data were reported by the mean ± standard deviation. Analysis of variance (ANOVA) and analysis of covariance (ANCOVA) were performed to compare anthropometric measurements and metabolic profiles between subjects and remove confounding results, respectively. The adjustment was made for age, BMI, physical activity, and energy intake. To evaluate the interactions between GRS and PDI, a generalized linear model (GLM) was used. We used SPSS (version 25; SPSS Inc., IL) to analyze all data. Unilateral P-values were applied and a P-value < 0.05 was considered statistically significant and for interactions, P-value < 0.1 was considered significant. Result Study population characteristics based on GRS The present study included 377 Iranian women. The characteristics of individuals are presented in Table 1 . There was a significant difference between GRS with, body weight (P-value = 0.03), BMI (P-value = 0.02), WC (P-value = 0.03), and WHR (P-value = 0.03) in the crude model. After adjustment for confounders (BMI, age, kcal, IPAC) there was a significant mean difference for the body weight (P-value = 0.04), BMI (P-value = 0.01), WC (P-value = 0.03), WHR (P-value = 0.01), BRI (P-value = 0.02), ABSI (P-value = 0.03), and LAP (P-value = 0.02) in participants. Table 1 Characteristics of the study population among participants based on GSR. Quantitative variables No risk (< 3 risk allele) (N = 123) Moderate risk (3&4 risk allele) (N = 171) High risk (≥ 5 risk allele) (N = 42) P-value P-value* Age (year) 35.97 ± 8.99 36.39 ± 8.01 36.32 ± 8.51 0.94 0.81 Anthropometric indices Body weight (kg) 79.38 ± 9.59 78.002 ± 11.03 83.46 ± 10.63 0.03 0.04 Height (cm) 162.37 ± 5.29 160.78 ± 6.12 160.93 ± 4.35 0.11 0.15 BMI (kg/m 2 ) 30.11 ± 3.40 30.17 ± 3.70 31.30 ± 3.33 0.02 0. 01 WC (cm) 97.57 ± 8.66 96.60 ± 8.90 101.14 ± 8.96 0.03 0.03 WHR (cm) 0.92 ± 0.05 0.92 ± 0.04 0.95 ± 0.05 0.03 0.01 Body composition BRI 5.47 ± 1.18 5.58 ± 1.25 6.09 ± 1.21 0.08 0.02 ABSI 0.79 ± 0.02 0.78 ± 0.02 0.79 ± 0.03 0.33 0.03 LAP 54.49 ± 34.08 49.52 ± 28.93 64.66 ± 40.31 0.09 0.02 VAI 290.63 ± 184.80 286.32.196.71 379.24 ± 302.10 0.11 0.09 Biochemical parameter TC (mg/dl) 187.26 ± 33.37 183.01 ± 37.01 176.84 ± 37.80 0.41 0.73 TG (mg/dl) 120.67 ± 58.05 110.69 ± 55.20 133.72 ± 75.79 0.17 0.06 LDL-c (mg/dl) 97.37 ± 22.15 95.13 ± 25.06 88.32 ± 27.44 0.26 0.62 HDL-c (mg/dl) 47.38 ± 9.58 47.14 ± 11.61 45.92 ± 12.32 0.84 0.96 FBS (mg/dl) 87.17 ± 9.08 86.62 ± 10.14 88.56 ± 9.64 0.66 0.60 Atherogenic index AIP 0.36 ± 0.22 0.34 ± 0.25 0.42 ± 0.28 0.29 0.19 CRI.1 4.04 ± 0.94 4.14 ± 1.80 4.16 ± 1.38 0.88 0.96 CRI.II 2.08 ± 0.50 2.09 ± 0.61 2.02 ± 0.64 0.83 0.92 TGy 1.39 ± 0.71 1.28 ± 0.59 1.48 ± 0.82 0.31 0.09 Qualitative variables N (%) P-value P-value* Marital situation Single 29 (49.2) 25 (42.4) 5 (8.5) 0.16 0.18 Married 69 (35.9) 96 (50) 27 (14.1) Education Less diploma 15 (41.7) 14 (38.9) 7 (19.4) 0.30 0.32 Diploma 34 (35.1) 48 (49.5) 15 (15.5) Bachelor or higher 49 (42.2) 57 (49.1) 10 (8.6) Job Employed 45 (46.9) 40 (41.7) 11 (11.5) 0.16 0.20 Unemployed 52 (34.7) 77 (51.3) 21 (14) Economic status Poor 19 (34.5) 27 (49.1) 9 (16.4) 0.77 0.84 Moderate 46 (39.3) 56 (47.9) 15 (12.8) Good 28 (43.8) 30 (46.9) 6 (9.4) BMI: body mass index, WC: waist circumference, WHR: waist hip ratio, AIP: atherogenic index of plasma, AC: atherogenic coefficient, CRI−I: Castelli risk index 1, CRI−II: Castelli risk index II, TyG: Triglyceride−glucose index, BRI: body roundness index, ABSI: a body shape index, LAP: lipid accumulation product, VAI: visceral adiposity index, TC: total cholesterol, TG: triglyceride, LDL−c: low−density lipoprotein, HDL−c: high−density lipoprotein, FBS: fasting blood sugar, CAV−1: Caveolin, CRY: Cryptochrome Circadian Regulator, MC4R: Melanocortin 4 Receptor . Quantitative variables as means ± SD obtained from the ANOVA test . Qualitative variables N (%) obtained from the chi−square analysis . P−value* obtained from ANCOVA test. P−values < 0.05 were considered significant . P−value* for adjustment model, based on age, energy intake, BMI, and IPAQ Table 2 Characteristics of study population in among tertile of PDI, hPDI, uPDI. PDI hPDI uPDI Variables T1 (N = 111) (< 51 gr) T2 (N = 91) (51–57 gr) T3 (N = 88) (≥ 57) P-value P-value* T1 (N = 97) (< 51 gr) T2 (N = 106) (51–57 gr) T3 (N = 87) ((≥ 57) P-value P-value* T1 (N = 104) (< 45 gr) T2 (N = 100) (45–51 gr) T3 (N = 86) (≥ 51) P-value P-value* Age (year) 36.29 ± 8.93 36.00 ± 8.23 36.57 ± 8.11 0.92 0.91 33.46 ± 7.94 37.02 ± 8.77 38.07 ± 7.98 0.002 0.003 37.36 ± 8.62 35.87 ± 8.44 35.40 ± 8.22 0.31 0.22 Anthropometric indices and Body weight (kg) 79.09 ± 10.51 79.82 ± 10.25 80.08 ± 11.15 0.79 0.82 79.21 ± 10.26 80.08 ± 10.83 79.48 ± 10.75 0.84 0.91 79.04 ± 10.11 81.75 ± 11.30 77.89 ± 10.05 0.04 0.03 Height (cm) 160.72 ± 5.90 162.30 ± 6.26 160.69 ± 5.38 0.11 0.18 162.21 ± 6.12 160.88 ± 5.48 160.54 ± 6.07 0.13 0.23 161.58 ± 5.36 160.43 ± 5.74 161.64 ± 6.61 0.28 0.31 BMI (kg/m 2 ) 30.18 ± 3.83 30.13 ± 3.32 31.04 ± 3.84 0.25 0.24 29.60 ± 3.27 30.77 ± 4.07 30.80 ± 3.55 0.07 0.08 30.25 ± 3.48 31.28 ± 3.95 29.62 ± 3.48 0.02 0.03 WC (cm) 96.60 ± 8.73 98.24 ± 9.35 98.51 ± 9.57 0.34 0.76 96.95 ± 8.50 97.91 ± 9.69 98.07 ± 9.29 0.72 0.45 97.35 ± 9.01 99.47 ± 9.44 95.94 ± 8.82 0.06 0.07 WHR (cm) 0.92 ± 0.04 0.93 ± 0.05 0.92 ± 0.04 0.25 0.53 0.93 ± 0.04 0.92 ± 0.04 0.93 ± 0.05 0.68 0.77 0.92 ± 0.05 0.93 ± 0.04 0.92 ± 0.04 0.24 0.26 Body composition BRI 5.55 ± 1.28 5.59 ± 1.25 5.78 ± 1.31 0.50 0.42 5.47 ± 1.14 5.72 ± 1.44 5.68 ± 1.22 0.45 0.52 5.56 ± 1.20 5.91 ± 1.34 5.39 ± 1.27 0.03 0.04 ABSI 0.78 ± 0.02 0.79 ± 0.02 0.78 ± 0.02 0.16 0.29 0.79 ± 0.02 0.78 ± 0.02 0.78 ± 0.02 0.09 0.19 0.79 ± 0.02 0.79 ± 0.02 0.78 ± 0.02 0.93 0.88 LAP 56.47 ± 37.76 49.66 ± 28.56 53.28 ± 29.52 0.42 0.45 53.29 ± 29.07 49.97 ± 30.58 57.50 ± 37.96 0.35 0.27 50.86 ± 28.71 58.33 ± 37.69 50.91 ± 31.08 0.26 0.30 VAI 318.20 ± 230.52 270.06 ± 184.37 301.60 ± 197.44 0.34 0.37 305.21 ± 203.12 269.42 ± 165.55 325.41 ± 249.12 0.22 0.20 274.08 ± 173.59 313.02 ± 210.49 312.81 ± 241.89 0.39 0.33 Biochemical parameter TC (mg/dl) 185.21 ± 39.06 182.70 ± 33.47 185.83 ± 35.04 0.85 0.93 183.97 ± 38.22 182.18 ± 37.71 188.07 ± 32.33 0.56 0.45 184.97 ± 34.89 185.51 ± 37.15 183.24 ± 36.97 0.92 0.90 TG (mg/dl) 122.54 ± 64.54 109.02 ± 52.13 120.54 ± 59.66 0.31 0.27 123.67 ± 64.98 106.90 ± 51.73 124.65 ± 61.46 0.09 0.04 112.92 ± 52.52 122.66 ± 65.90 118.75 ± 60.74 0.55 0.60 LDL-c (mg/dl) 96.20 ± 24.87 93.85 ± 23.96 94.62 ± 23.86 0.81 0.87 91.06 ± 24.31 96.37 ± 24.90 97.36 ± 23.18 0.21 0.53 96.86 ± 24.02 96.17 ± 25.24 91.27 ± 23.12 0.30 0.21 HDL-c (mg/dl) 47.32 ± 10.81 47.50 ± 10.83 46.10 ± 10.50 0.68 0.74 46.13 ± 10.009 47.95 ± 11.12 46.81 ± 10.90 0.54 0.67 48.43 ± 11.21 46.57 ± 9.69 45.69 ± 11.11 0.25 0.07 FBS (mg/dl) 86.81 ± 10.07 87.16 ± 8.17 88.36 ± 10.29 0.56 0.62 87.18 ± 8.88 87.97 ± 10.80 86.94 ± 8.94 0.77 0.57 86.41 ± 7.86 87.89 ± 10.42 88.04 ± 10.61 0.48 0.37 Atherogenic index AIP 0.38 ± 0.25 0.32 ± 0.23 0.36 ± 0.23 0.34 0.34 0.37 ± 0.26 0.32 ± 0.21 0.38 ± 0.25 0.28 0.22 0.34 ± 0.21 0.36 ± 0.25 0.37 ± 0.27 0.75 0.53 CRI.1 4.16 ± 1.84 3.95 ± 0.95 4.12 ± 1.23 0.65 0.70 4.24 ± 2.16 3.94 ± 1.01 4.10 ± 0.91 0.43 0.15 3.93 ± 0.83 4.06 ± 1.06 4.31 ± 2.23 0.27 0.22 CRI.II 2.09 ± 0.55 2.03 ± 0.56 2.08 ± 0.59 0.75 0.84 2.003 ± 0.56 2.09 ± 0.63 2.10 ± 0.49 0.48 0.80 2.05 ± 0.50 2.10 ± 0.60 2.06 ± 0.62 0.86 0.73 TGy 1.42 ± 0.71 1.25 ± 0.62 1.31 ± 0.60 0.26 0.30 1.37 ± 0.68 1.21 ± 0.50 1.43 ± 0.75 0.08 0.05 1.32 ± 0.63 1.37 ± 0.70 1.32 ± 0.62 0.86 0.77 Qualitative variables N (%) P-value P-value* N (%) P-value P-value* N (%) P-value P-value* Frequency Low-risk allele 31 (31.6) 32 (32.7) 35 (35.7) 0.58 0.42 35 (35.7) 36 (36.7) 27 (26.6) 0.91 0.88 40 (40.8) 31 (31.6) 27 (27.6) 0.75 0.91 Moderate risk allele 49 (42.2) 33 (28.4) 34 (29.3) 36 (31) 42 (36.2) 38 (32.8) 43 (37.1) 35 (30.2) 38 (32.8) High risk allele 10 (33.3) 9 (30) 11 (36.7) 9 (30) 11 (36.7) 10 (33.3) 11 (36.7) 12 (40) 7 (23.3) Marital situation Single 26 (39.4) 16 (24.2) 24 (36.4) 0.29 0.33 27 (40.9) 22 (33.3) 17 (25.8) 0.33 0.28 22 (33.3) 21 (31.8) 23 (34.8) 0.57 0.63 Married 85 (37.9) 75 (33.5) 64 (28.6) 70 (31.3) 84 (37.5) 70 (31.3) 82 (36.6) 79 (35.3) 63 (28.1) Education Less diploma 10 (23.3) 13 (30.2) 20 (46.5) 0.02 0.01 10 (23.3) 12 (27.9) 21 (48.8) 0.06 0.07 10 (23.3) 16 (37.2) 17 (39.5) 0.10 0.23 Diploma 39 (35.1) 39 (35.1) 33 (29.7) 36 (32.4) 44 (39.6) 31 (27.9) 36 (32.4) 43 (38.7) 32 (28.8) Bachelor or higher 62 (46.3) 38 (25.4) 34 (25.4) 50 (37.3) 49 (36.6) 35 (26.1) 58 (43.3) 39 (29.1) 37 (27.6) Job Employed 56 (50) 31 (27.7) 25 (22.3) 0.002 0.001 34 (30.4) 45 (40.2) 33 (29.5) 0.46 0.51 40 (35.7) 36 (32.1) 36 (32.1) 0.80 0.83 Unemployed 51 (29.7) 59 (34.3) 62 (36) 61 (35.5) 57 (33.1) 54 (31.4) 61 (35.5) 61 (35.5) 50 (29.1) Economic status Poor 21 (31.3) 25 (37.3) 21 (31.3) 0.58 0.64 15 (22.4) 17 (25.4) 35 (52.2) < 0.001 < 0.001 27 (40.3) 23 (34.3) 17 (25.4) 0.21 0.22 Moderate 55 (39.9) 40 (29) 43 (31.2) 50 (36.2) 58 (42) 30 (21.7) 45 (32.6) 42 (30.4) 51 (37) Good 31 (43.7) 21 (29.6) 19 (26.8) 27 (38) 25 (35.2) 19 (26.8) 29 (40.8) 26 (36.6) 16 (22.5) BMI: body mass index, WC: waist circumference, WHR: waist−hip ratio, AIP: atherogenic index of plasma, AC: atherogenic coefficient, CRI−I: Castelli risk index 1, CRI−II: Castelli risk index II, TyG: Triglyceride−glucose index, BRI: body roundness index, ABSI: a body shape index, LAP: lipid accumulation product, VAI: visceral adiposity index, TC: total cholesterol, TG: triglyceride, LDL−c: low−density lipoprotein, HDL−c: high−density lipoprotein, FBS: fasting blood sugar . Quantitative variables as means ± SD were obtained from the ANOVA test . Qualitative variables N (%) were obtained from the chi−square analysis . P−value* obtained from ANCOVA test. P−values < 0.05 were considered significant . P−value* for adjustment model, based on age, energy intake, BMI, and IPAQ Table 3 Dietary intakes according to GRS in participants. Variables No risk (N = 123) Moderate risk (N = 171) High risk (N = 42) P-value P-value* Macronutrients Energy (Kcal) 2745.61 ± 729.83 2561.11 ± 705.13 2632.58 ± 781.46 0.18 0.23 Carbohydrates (gr/ day) 394.97 ± 122.13 366.21 ± 111.29 366.74 ± 127.05 0.17 0.51 Protein (gr/day) 93.31 ± 28.70 86.85 ± 26.84 90.23 ± 34.62 0.26 0.92 Total fat (gr/day) 97.60 ± 28.77 91.59 ± 31.57 96.66 ± 33.69 0.33 0.80 Subgroup types of fat CHOL (gr/day) 264.53 ± 107.65 245.32 ± 100.88 252.27 ± 115.31 0.41 0.94 Saturated fat (gr/day) 29.05 ± 11.42 27.35 ± 10.21 28.09 ± 12.21 0.52 0.85 Trans fat (gr/day) 0.0008 ± 0.002 0.0007 ± 0.001 0.001 ± 0.002 0.28 0.30 MUFA (gr/day) 32.05 ± 9.78 30.53 ± 10.75 31.99 ± 13.14 0.55 0.85 PUFA (gr/day) 20.54 ± 7.30 19.27 ± 8.31 20.80 ± 8.30 0.42 0.98 Micronutrients Vitamins B1 (mg/day) 2.18 ± 0.66 2.02 ± 0.59 2.15 ± 0.80 0.19 0.45 B2 (mg/day) 2.32 ± 0.82 2.13 ± 0.72 2.23 ± 1.02 0.23 0.44 B3 (mg/day) 26.89 ± 10.001 24.30 ± 7.52 26.77 ± 12.64 0.10 0.28 B6 (mg/day) 2.31 ± 0.71 2.12 ± 0.68 2.15 ± 0.79 0.15 0.64 B9 (mg/day) 645.44 ± 170.50 588.25 ± 171.45 624.19 ± 188.19 0.05 0.32 B12 (mg/day) 4.36 ± 2.005 4.45 ± 2.37 4.38 ± 2.86 0.95 0.35 Vitamin D (µ/day) 1.99 ± 1.78 2.09 ± 1.52 1.87 ± 1.64 0.77 0.40 Vitamin E (mg/day) 18.03 ± 8.60 16.41 ± 7.83 18.49 ± 11.26 0.28 0.66 Vitamin C (mg/day) 218.25 ± 120.82 184.05 ± 107.09 191.73 ± 203.72 0.14 0.03 Minerals Calcium (mg/day) 1220.89 ± 416.63 1154.14 ± 416.84 1106.32 ± 350.03 0.30 0.58 Iron (mg/day) 19.86 ± 5.95 18.07 ± 5.41 18.95 ± 6.92 0.08 0.40 Phosphor (mg/day) 1716.34 ± 524.59 1617.40 ± 507.05 1614.48 ± 491.42 0.33 0.72 Magnesium (mg/day) 485.88 ± 148.74 452.92 ± 143.96 445.74 ± 145.94 0.19 0.19 Zinc (mg/day) 13.54 ± 4.18 12.75 ± 4.05 12.97 ± 4.34 0.37 0.65 Selenium 125.76 ± 45.19 117.41 ± 40.64 119.56 ± 43.38 0.35 0.90 Other Total fiber (gr/day) 48.48 ± 19.67 43.51 ± 16.46 45.004 ± 22.50 0.14 0.35 Total sugar 152.22 ± 60.67 142.23 ± 59.75 126.34 ± 51.69 0.10 0.05 Glucose 22.99 ± 13.39 20.69 ± 10.94 16.71 ± 7.99 0.03 0.02 Galactose 2.77 ± 1.98 2.69 ± 1.80 2.59 ± 1.55 0.88 0.94 Fructose 27.33 ± 14.92 25.22 ± 13.31 20.54 ± 10.60 0.05 0.06 Caffeine 158.36 ± 210.20 145.54 ± 98.47 156.46 ± 137.65 0.82 0.77 CHOL: cholesterol, MUFA: monounsaturated fatty acid, PUFA: polyunsaturated fatty acid . All data are presented as mean ± SD . P−value* for adjustment model, based on age, energy intake, BMI and IPAQ Study population characteristics according to tertile of PDI, hPDI, uPDI There was no significant difference between the tertile of PDI with characteristics of the study population in crude and adjusted model (P-value > 0.05), except education (P-value = 0.01) and job status (P-value = 0.001). According to the findings, with the increasing age of the participants, the adherence to the hPDI food pattern increases, and this difference was statistically significant (0.002), and after controlling for confounders, this consistency remained (0.003). After adjusting for potential confounder, there was a borderline significant difference between the tertile of hPDI and TGy (P-value = 0.05) and a significant difference for TG (P-value = 0.04) and economic status (P-value < 0.001) in participants. In crude model, there was a significant difference between the tertile of the uPDI with BMI (P-value = 0.02) and BRI (P-value = 0.03). This significant relation remained even after adjustment for confounding variables. Dietary intakes according to GSR in participants The crude model showed a significant difference between GRS and glucose (P-value = 0.03). Also, The crude model showed a significant difference between GRS and glucose (P-value = 0.03). Also, there was a borderline significance between, GRS and vitamin B9 (P-value = 0.05) and fructose (P-value = 0.05). After adjustment for confounding variables (BMI, age, kcal, IPAC) there was a significant difference between GRS and Vitamin C (P-value = 0.03) and glucose (P-value = 0.02). Interaction between GSR and PDI with atherogenic index and body composition. The findings revealed a negative significant interaction between tertile 2 of PDI and moderate risk alleles (β =-0.34, 95% CI= -0.61 to -0.07, p = 0.01) and high-risk alleles (β =-0.38, 95% CI= -0.64 to -0.12, P-value = 0.004) with AIP compare to low-risk alleles. This significant interaction remained even after adjustment for confounding variables. There was a negative significant interaction between tertile 2 of PDI and high-risk alleles (β =-0.63, 95% CI= -1.28 to 0.01, P-value = 0.05) with CRI.II compare to low-risk allele participants, in the adjusted model. There was a negative significant interaction between tertile 2 of PDI and moderate risk allele (β =-0.83, 95% CI= -1.57 to -0.09, P-value = 0.02) and high-risk allele (β =-1.004, 95% CI= -1.71 to -0.28, P-value = 0.006) with TGy compare to low-risk allele participants. This significant interaction remained even after adjustment for confounding variables. There was a negative significant interaction between tertile 2 of PDI and moderate risk allele (β =-56.53, 95% CI= -97.12 to -15.94, P-value = 0.006) and high-risk alleles (β =-66.72, 95% CI= -105.95 to -27.49, P-value = 0.001) with LAP compare to low-risk allele participants. After adjusting for potential confounder there was a negative significant interaction between tertile 2 of PDI and moderate risk allele (β = -78.06, 95% CI= -120.23 to -35.90, P-value = < 0.001) and high-risk alleles (β =-83.85, 95% CI= -124.45 to -43.25, P-value < 0.001) with LAP compare to low-risk allele participants. There was a negative significant interaction between tertile 2 of PDI and moderate risk allele (β =-273.40, 95% CI= -499.34 to -47.45, P-value = 0.01) and high-risk alleles (β =-311.03, 95% CI= -529.61 to -92.45, P-value = 0.005) with VAI compare to low-risk allele participants, in the crude model. There was a negative significant interaction between tertile 2 of PDI and moderate risk allele (β =-362.91, 95% CI= -611.44 to -114.38, P-value = 0.004) and high-risk alleles (β =-393.001, 95% CI= -632.49 to -153.50, P-value = 0.001) with VAI compare to low-risk allele participants in adjusted model (Table 4 ). Interaction between GSR and hPDI with atherogenic index and body composition. There was a negative significant interaction between tertile 2 of hPDI and moderate risk allele (β =-0.03, 95% CI= -0.06 to -0.01, P-value = 0.008) with ABSI compared to low-risk allele participants. After adjusting for potential confounder this significant interaction remained (β =-0.02, 95% CI= -0.05 to 0.001, P-value = 0.05) (Table 4 ). Interaction between GSR and uPDI with atherogenic index and body composition. In the crude model, there was a positive significant interaction between tertile 2 (β = 2.11, 95% CI = 0.39 to 3.83, P-value = 0.01) and tertile 3 (β = 1.90, 95% CI = 0.18 to 3.61, P-value = 0.03) of uPDI and moderate risk allele with CRI. I compare to low-risk allele participants. In the adjusted model, there was a positive significant interaction between tertile 2 (β = 2.03, 95% CI = 0.17 to 3.89, P-value = 0.03) of uPDI and moderate risk allele with CRI. I compare to low-risk allele participants. After adjusting for potential confounder, there was a positive significant interaction between tertile 2 (β = 0.78, 95% CI = 0.07 to 1.48, P-value = 0.03) of uPDI and moderate risk allele with CRI. II compared to low-risk allele participants. In the adjusted model, there was a positive significant interaction between tertile 2 (β = 0.03, 95% CI = 0.009 to 0.06, P-value = 0.01) of uPDI and moderate risk allele with ABSI. Also, there was a positive significant interaction between tertile 3 (β = 0.03, 95% CI = 0.005 to 0.06, P-value = 0.02) of uPDI and high-risk allele with ABSI compared to low-risk allele participants ( Table 4 ). Discussion The current cross-sectional study indicates a pioneering investigation aiming to establish an interaction of genetics risk score (GRS) and plant-based diet on factors atherogenic and body adiposity indexes among overweight and obese women. We showed that plant-based dietary patterns regarding genetic susceptibility can have effects on the predictable risk factors of cardiometabolic disease in our population. Our results emphasize the importance of following plant-based dietary patterns, individuals at higher or moderate genetic risk may benefit from the positive effects of these dietary patterns to modify cardiometabolic risk factors. Our results unveil a significant negative interaction between tertile 2 of PDI and moderate risk allele and high-risk alleles with AIP, TGy, LAP, and VAI compared to low-risk allele. Additionally, we detected a negative significant interaction between tertile 2 of PDI and high-risk allele with CRI. II compare to low-risk allele participants. Furthermore, there was a negative significant interaction between tertile 2 of hPDI and moderate risk allele with ABSI compared to low-risk allele participants. There was a positive significant interaction between tertile 2 of uPDI and moderate risk allele with CRI. I and ABSI compare to low-risk allele participants. The present study found a significant mean difference between GRS and the participant's body weight, BMI, WC, WHR, BRI, ABSI, and LAP. Our results are in line with previous investigations regarding the genetic effects of GRS on BMI and waist circumference (66–68). A cross-sectional study illustrated the merged effect of some genetic variants on obesity in Pakistanis and showed by a GRS for obesity, the possibility of the prognosis of anthropometric traits (69), and several investigations indicate the positive relationship between obesity risk factors such as BMI, WC, and WHR with cardiometabolic diseases (70–72). CAV-1 as one of the considered genes in this study, has some functions such as the main regulator for fat distribution and genetic lipodystrophy in humans and it can be relatively higher in obese women compared to thin women (73–76). CAV-1 is also associated with oxidative stress, which can therefore play a role in many metabolic diseases. According to a study, following the PDI diet can reduce metabolic diseases among those who carry a risk allele in the CAV-1 gene (77). Other roles, such as abnormalities in the binding of cholesterol and fatty acids, disturbance in the path of differentiation of fat cells, dysfunction of fat droplets, and increase in insulin signaling, are attributed to this gene (73, 78). In addition, several studies showed the association of the MC4R gene and its role in energy balance, food intake regulation, total fat, total obesity, peripheral obesity, abdominal obesity, and higher BMI (79, 80). The results of the PREDIMED trial, show that following plant-based dietary patterns can significantly reduce the risk of CVD (81), and the Adventist Health Study also showed that vegetarian diets reduce CVD mortality by exerting their protective effect (82). Comparing other diet indices (such as Alternate Healthy Eating Index, Dietary Approaches to Stop Hypertension [DASH], and Mediterranean diet scores), PDI dietary patterns are derived from some food items, including healthy and less healthy plant-derived foods, to capture the combined and varying intakes of different food items. Moreover, some components in the uPDI, such as coffee, sugar-sweetened beverages, and saturated fat, could be involved in genetic predisposition to obesity (83–88). Interactions between GRS and PDI food patterns can be due to less adherence to less healthy plant-based foods and animal foods, low energy density, the increased involvement of plant bioactives through the regulation of thermogenesis without energy consumption and shivering, as well as the improvement of the balance of intestinal microbiota due to the increase in dietary fiber consumption (88–91). According to a study conducted in 2023 by Fatemeh Gholami et al. on 377 obese and overweight women, significant interactions between GRS and h-PDI were observed on body fat mass index, body mass index (BMI), and waist circumference. In this study, the interaction between GRS and PDI was performed on some predictive factors of cardiovascular diseases such as C-reactive protein, plasminogen activator inhibitor 1, and insulin. According to the findings of this study, following a plant-based food pattern despite the genetic differences in people seems to be a protective factor against the risks of cardiometabolic abnormalities (92). Interestingly, according to past results, following more healthy plant-based diets in people who are at a higher genetic risk of obesity (regardless of the presence of basic obesity), causes more benefits in these people (93, 94). Our findings revealed a negative significant interaction between the second tertile of PDI and moderate risk allele and high-risk allele with AIP compared to the low-risk allele. In a cross-sectional study conducted in 2023 by Farnaz Shahdadian et al., it was found that participants with the highest quartile of PDI, as well as the third quartile of hPDI, were associated with reduced odds of having high-risk AIP compared to the first quartile (95). In this regard, other studies reported the relationship between PDI, especially hPDI, and their role in the management and prevention of high-risk AIP (96–98). One of the reasons for the effect of a PDI on reducing high-risk AIP can be attributed to its role in reducing TG because AIP is a logarithm of the ratio of triglycerides and HDL (95). Mahdieh Khodarahmi et al. in a cross-sectional study observed that the interactions between DASH score and MC4R rs17782313 genotypes on AIP among the female group were statistically significant (99). In an observational study, a significant interaction of GRS and hPDI on lipid factors was observed, and in this regard, other studies also reveal the beneficial effects of healthier plant-based diets in reducing TC levels and controlling HDL (93, 100–102). However, the results for TG have been controversial (101). Findings indicate a positive significant interaction between tertile 2 of uPDI and moderate risk allele with CRI.I compare low-risk allele participants and positive significant interaction between tertile 2 of uPDI and moderate risk allele with CRI.II compared to low-risk allele participants. In a study conducted in 2019 on 96 participants, CRI.I was considered as an index to predict cardiovascular risk among people in two diet groups, vegetarians and omnivores. In this study, CRI-I was significantly greater in omnivores than in vegetarians, and people who have higher adherence to a vegetarian diet elevate by 17 times the probability of having a normal CRI-I (103, 104). Earlier studies have found the potential role of diet in body composition; however, little attention has been paid to LAP and TyG, which are strong indicators of cardiovascular disease (105, 106). We found a negative significant interaction between the second tertile of PDI and moderate risk allele and high-risk allele with TGy compared to low-risk allele participants. Also, there was a negative significant interaction between the second tertile of PDI and moderate risk allele and high-risk allele with LAP compared to low-risk allele participants. In a cross-sectional study conducted in 2020 by Mahshid Shahavandi and his colleagues on 270 adults, the results indicated that more adherence to hPDI was associated with a decrease in BMI, WC, WHR, and LAP. However, in this study, there was no correlation between following PDIs and TyG (107). In another study conducted on Iranian adults in 2017, no significant relationship was found between healthy dietary patterns with the TyG index and visceral fat level (108). However, in a prospective cohort study conducted in 2020, a negative association between an anti-inflammatory diet and the TyG index was found (109). In our findings, there was a negative significant interaction between tertile 2 of PDI and moderate risk allele and high-risk allele with VAI compared to low-risk allele participants. Results show a negative significant interaction between tertile 2 of hPDI and moderate risk allele with ABSI compared to low-risk allele participants. Moreover, a positive significant interaction between tertile 2 of uPDI and moderate risk allele with ABSI was shown. Also, there was a positive significant interaction between tertile 3 of uPDI and high-risk allele with ABSI compared to low-risk allele participants. Based on a cross-sectional study a significant positive association between fat intake and visceral adipose tissue (VAT) was observed among overweight young adults. In this investigation, participants who followed a diet rich in carbohydrates, sugar, and total fat, as well as saturated fat, showed an increased mean in VAI and LAP. However, following a diet rich in vitamins, and minerals, with a high amount of fiber was associated with reduced levels of VAI and LAP (108). In another study conducted in 2018 on older Americans, a negative association was indicated between the DASH diet index and VAI (110). Strength and limitations: Our investigation is a novel study to evaluate the interaction of genetics risk score (GRS) and Plant-based diet on atherogenic, visceral, and body adiposity, which examines certain factors that have not been considered in previous studies. The current study's major strengths include appropriate sample size and adjusting for potential confounders. However, there are some limitations such as the design of the study which cannot explain the causal relation between confounders. As our study was conducted on overweight women, our obtained findings cannot be generalized to other groups of society. Conclusion The involvement of PDI, h-PDI, and uPDI appears to be a protective factor against cardiovascular risk factors in overweight and obese women with increased GRS. Prospective and interventional studies with greater sample sizes in different populations and ethnicities need to be conducted to further the knowledge about examine interaction between PDI, hPDI, and uPDI with GSR are associated with atherogenic index and body composition. Abbreviations GRS: Interaction of Genetics Risk Score, CVD: cardiovascular diseases, FFQ: food frequency questionnaire, PDI: plant-based diet index, hPDI: healthy plant-based diet index, uPDI: unhealthy plant-based diet index, GLM: generalized linear model, ABSI: A body shape index, BRI: body roundness index, WC: waist circumference, LAP: Lipid accumulation product, BAI: Body adiposity index, AIP: atherogenic index of plasma, CRI-I: Castelli risk index-I, CRI-II: Castelli risk index-II, TyG: triglyceride glucose, CAV-1: Caveolin-1, MC4R: melanocortin 4 receptor, Cry: Cryptochromes, NAFLD: non-alcoholic fatty liver disease, PCOS: polycystic ovary syndrome, FBS: Fasting blood sugar, GOD/PAP: Glucose Oxidase Phenol 4-Aminoantipyrine Peroxidase, T-Chol: total cholesterol, LDL: low-density lipoprotein, HDL: high-density lipoprotein, TG: triglyceride, FCT: Food Composition Table, ANOVA: Analysis of variance. Declarations Acknowledgments We are grateful to all participants for their contribution to this research. Grants from the Tehran University of Medical Sciences, Tehran, Iran, supported this study. This study is funded by the Tehran University of Medical Sciences (TUMS) grants. (NO: IR. TUMS.VCR.REC.1398.142) We state that all methods are based on the relevant guidelines and regulations. Authors’ contributions MM wrote the paper, FA performed the statistical analyses, KhM and AM had full access to all of the data in the study, and took responsibility for the integrity and accuracy of the data. All authors read and approved the final manuscript. Funding This study was supported by the Tehran University of Medical Sciences (NO: IR. TUMS.VCR.REC.1398.142). Availability of data and materials Participants of this study disagreed on their data to be shared publicly, so supporting data is not available. Ethics approval and consent to participate The protocol of the study was approved by the ethics committee of Tehran University of medical sciences (NO: IR. TUMS.VCR.REC.1398.142) and is acknowledged by the authors. A signed written informed consent was collected from all participants. Consent for publication All authors performed editing and approving the final version of this paper for submission, also participated in the finalization of the manuscript and approved the final draft. Competing interests The authors declare that there is no conflict of interest in this study References Azadbakht L HF, Esmaillzadeh A. White rice consumption, body mass index, and waist circumference among Iranian female adolescents. J Am Coll Nutr. 2016;35(6):491–499. A. Vaisi-Raygani MM, R. Jalali, A. Ghobadi, and N. Salari, . “*e prevalence of obesity in older adults in Iran: a systematic review and meta-analysis,” BMC Geriatrics. 2019;vol. 19, no. 1:1-9. Maskarinec Gea. 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Sugar-sweetened beverages and genetic risk of obesity. New England Journal of Medicine. 2012;367(15):1387-96. Tyrrell J, Wood AR, Ames RM, Yaghootkar H, Beaumont RN, Jones SE, et al. Gene–obesogenic environment interactions in the UK Biobank study. International journal of epidemiology. 2017;46(2):559-75. Olsen NJ, Ängquist L, Larsen SC, Linneberg A, Skaaby T, Husemoen LLN, et al. Interactions between genetic variants associated with adiposity traits and soft drinks in relation to longitudinal changes in body weight and waist circumference. The American journal of clinical nutrition. 2016;104(3):816-26. Wang T, Huang T, Kang JH, Zheng Y, Jensen MK, Wiggs JL, et al. Habitual coffee consumption and genetic predisposition to obesity: gene-diet interaction analyses in three US prospective studies. BMC medicine. 2017;15:1-9. Casas-Agustench P, Arnett DK, Smith CE, Lai C-Q, Parnell LD, Borecki IB, et al. 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Gholami F, Samadi M, Rasaei N, Yekaninejad MS, Keshavarz SA, Javdan G, et al. Interactions Between Genetic Risk Score and Healthy Plant Diet Index on Cardiometabolic Risk Factors Among Obese and Overweight Women. Clinical Nutrition Research. 2023;12(3):199. Heianza Y, Zhou T, Sun D, Hu FB, Qi L. Healthful plant-based dietary patterns, genetic risk of obesity, and cardiovascular risk in the UK biobank study. Clinical Nutrition. 2021;40(7):4694-701. Lyall DM, Celis-Morales C, Ward J, Iliodromiti S, Anderson JJ, Gill JM, et al. Association of body mass index with cardiometabolic disease in the UK Biobank: a Mendelian randomization study. JAMA cardiology. 2017;2(8):882-9. Shahdadian F, Saneei P, Lotfi K, Feizi A, Askari G, Safavi SM. Association of plant-based diets with adropin, atherogenic index of plasma, and metabolic syndrome and its components: A cross-sectional study on adults. Frontiers in Nutrition. 2023;10:1077709. Gómez-Donoso C, Martínez-González MÁ, Martínez JA, Gea A, Sanz-Serrano J, Perez-Cueto FJ, et al. A provegetarian food pattern emphasizing preference for healthy plant-derived foods reduces the risk of overweight/obesity in the SUN cohort. Nutrients. 2019;11(7):1553. Satija A, Malik V, Rimm EB, Sacks F, Willett W, Hu FB. Changes in intake of plant-based diets and weight change: results from 3 prospective cohort studies. The American journal of clinical nutrition. 2019;110(3):574-82. Di Renzo L, Cinelli G, Dri M, Gualtieri P, Attinà A, Leggeri C, et al. Mediterranean personalized diet combined with physical activity therapy for the prevention of cardiovascular diseases in Italian women. Nutrients. 2020;12(11):3456. Khodarahmi M, Jafarabadi MA, Farhangi MA. Melanocortin-4 receptor (MC4R) rs17782313 polymorphism interacts with Dietary Approach to Stop Hypertension (DASH) and Mediterranean Dietary Score (MDS) to affect hypothalamic hormones and cardio-metabolic risk factors among obese individuals. Genes & Nutrition. 2020;15:1-12. Abaj F, Koohdani F, Rafiee M, Alvandi E, Yekaninejad MS, Mirzaei K. Interactions between Caveolin-1 (rs3807992) polymorphism and major dietary patterns on cardio-metabolic risk factors among obese and overweight women. BMC Endocrine Disorders. 2021;21(1):138. Wang F, Zheng J, Yang B, Jiang J, Fu Y, Li D. Effects of vegetarian diets on blood lipids: a systematic review and meta‐analysis of randomized controlled trials. Journal of the American Heart Association. 2015;4(10):e002408. Schwingshackl L, Hoffmann G, Iqbal K, Schwedhelm C, Boeing H. Food groups and intermediate disease markers: a systematic review and network meta-analysis of randomized trials. The American journal of clinical nutrition. 2018;108(3):576-86. Pimentel CVdMB, Teodorov E, Simomura VL, Rogero MM, Philippi ST. Cardiovascular risk and BDNF concentration in vegetarians in the city of São Paulo–SP. Journal of Cardiology & Current Research. 2019;12(6):142-9. Rizzo NS, Sabaté J, Jaceldo-Siegl K, Fraser GE. Vegetarian dietary patterns are associated with a lower risk of metabolic syndrome: the adventist health study 2. Diabetes care. 2011;34(5):1225-7. Unger G, Benozzi SF, Perruzza F, Pennacchiotti GL. Triglycerides and glucose index: a useful indicator of insulin resistance. Endocrinología y Nutrición (English Edition). 2014;61(10):533-40. Xia C, Li R, Zhang S, Gong L, Ren W, Wang Z, et al. Lipid accumulation product is a powerful index for recognizing insulin resistance in non-diabetic individuals. European journal of clinical nutrition. 2012;66(9):1035-8. Shahavandi M, Djafari F, Shahinfar H, Davarzani S, Babaei N, Ebaditabar M, et al. The association of plant-based dietary patterns with visceral adiposity, lipid accumulation product, and triglyceride-glucose index in Iranian adults. Complementary Therapies in Medicine. 2020;53:102531. Mazidi M, Kengne AP, Mikhailidis DP, Toth PP, Ray KK, Banach M. Dietary food patterns and glucose/insulin homeostasis: a cross-sectional study involving 24,182 adult Americans. Lipids in health and disease. 2017;16:1-9. Amini MR, Shahinfar H, Babaei N, Davarzani S, Ebaditabar M, Djafarian K, et al. Association of dietary patterns with visceral adiposity, lipid accumulation product, and triglyceride-glucose index in Iranian adults. Clinical Nutrition Research. 2020;9(2):145. Silveira BKS, Novaes JFd, Reis NdA, Lourenço LP, Capobiango AHM, Vieira SA, et al. “Traditional” and “healthy” dietary patterns are associated with low cardiometabolic risk in Brazilian subjects. Cardiology Research and Practice. 2018;2018. Table 4 Table 4 is available in the Supplementary Files section. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4587951","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":319829164,"identity":"bb0567bb-6c2a-448c-be44-acc5e15fac17","order_by":0,"name":"Mahya Mehri Hajmir","email":"","orcid":"","institution":"George Washington University","correspondingAuthor":false,"prefix":"","firstName":"Mahya","middleName":"Mehri","lastName":"Hajmir","suffix":""},{"id":319829165,"identity":"a60636f2-28ac-49ca-b49a-059e6cf3c871","order_by":1,"name":"Atieh Mirzababaei","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Atieh","middleName":"","lastName":"Mirzababaei","suffix":""},{"id":319829166,"identity":"decc6450-77ef-4c5d-831c-503004d22d80","order_by":2,"name":"Faezeh Abaj","email":"","orcid":"","institution":"Monash University","correspondingAuthor":false,"prefix":"","firstName":"Faezeh","middleName":"","lastName":"Abaj","suffix":""},{"id":319829167,"identity":"28cf2877-3535-4fde-8703-797e8d83991a","order_by":3,"name":"Yasaman Aali","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yasaman","middleName":"","lastName":"Aali","suffix":""},{"id":319829168,"identity":"bba061c5-bfdd-418b-9396-b9da58578c38","order_by":4,"name":"Mahsa Samadi","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mahsa","middleName":"","lastName":"Samadi","suffix":""},{"id":319829169,"identity":"e41177a6-204a-4e0f-9177-8eea1a335d4b","order_by":5,"name":"Khadijeh Mirzaei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYDACZjApwSAhwXyAIQFZho2wFrYEsBYeglpgQEKCxwDM4CGgkEG3nffxhx8VFgySs3s+f3i4w47BXiI7+QNDjR0Dn/QBrFrMDrMbGPackWCQljm7TSLxTDIDj0TuNgmGY8kMbHwJOLSwMSTwtkkwyAFVMiS2MYO1AD1ygIENhxNBWg7+/QfSkvP4Q2JbPUjL5g8M//BqYWzmbQA6TCKHQSKx7TBIywYJxja8WpiZZY5JMEjOSDMDajnOw3PmLdBTfck8OLWcP8b88U1NHYPEjeTHH3+2VcuxtwMd9uGbnZx8D3YtMFDfAGVAjE4gIn5GwSgYBaNgFOAGALEtTI2NU8DRAAAAAElFTkSuQmCC","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Khadijeh","middleName":"","lastName":"Mirzaei","suffix":""}],"badges":[],"createdAt":"2024-06-15 23:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4587951/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4587951/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62212402,"identity":"ccf98db9-603b-4848-813c-829f2e72f929","added_by":"auto","created_at":"2024-08-11 09:34:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1208389,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4587951/v1/51f15e13-2f38-46e1-ae13-302a44b5d3e8.pdf"},{"id":59473596,"identity":"a8c56475-ce7f-40aa-b842-9e5497e57d41","added_by":"auto","created_at":"2024-07-02 08:17:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":36241,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-4587951/v1/f6c30dda75634f8b6bef9683.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Interaction of Genetics Risk Score (GRS) and Plant-Based Diet on factors Atherogenic and body adiposity indexes among overweight and obese women: a cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOver the last few decades, obesity has become a global public health concern, affecting both developed and developing countries (1). According to a systematic review and meta-analysis in 2019, the prevalence of obesity among Iranian older adults was reported at 21.4% (2) and it is universally more common in women (3). It has been revealed that obesity and cardiovascular disease (CVD), dyslipidemia, and metabolic abnormalities have strong associations (4). Indeed, obesity and hyperlipidemia are recognized the most important risk factors for CVD (5, 6). In this regard, several obesity or atherogenic-related predictive indices were identified which might predispose a person to obesity or cardiovascular diseases. A body shape index (ABSI) and body roundness index (BRI), based on height, weight, and waist circumference (WC), are two new anthropometric indices that have been proposed recently. ABSI has proved to be associated with all-cause mortality (7, 8) and BRI provides comprehensive identification of visceral adiposity tissue and body fat percentage (9). Lipid accumulation product (LAP), which is focused on a combination of WC and TG, might be an accurate marker of central obesity (10). The Body adiposity index (BAI), a novel indicator of % fat, has been suggested as a more health outcome predictor than the BMI (11). Another novel biomarker developed as a robust biomarker to predict atherosclerosis and CVD events and closely related to abdominal obesity is the atherogenic index of plasma (AIP) (12\u0026ndash;18). The result of a previous study has suggested that a higher AIP value is associated with a higher risk of chronic diseases (19). More so, constructed indices such as Castelli risk index-I (CRI-I), Castelli risk index-II (CRI-II), and triglyceride glucose (TyG) index, based on lipoprotein cholesterol concentrations, have been considered better predictors of atherosclerosis and CVD events (20\u0026ndash;23).\u003c/p\u003e \u003cp\u003eAmong environmental factors, different dietary patterns have life-long effects on CVD and other metabolic-related risk factors (24). Plant-based diet indices which reflect the difference between plant-derived foods and their association with the risk of disease, as graded scoring systems, have been developed in three categories as follows: a plant-based diet index (PDI) which illustrates the whole consumption of plant food with lower intake of animal food, a healthy plant-based diet index (hPDI), and an unhealthy plant-based diet index (uPDI) (25). Recent studies reported that adherence to hPDI could decrease the risk of chronic diseases and improve CVDs, while diets focused on uPDI resulted in a higher risk of chronic diseases (25\u0026ndash;29).\u003c/p\u003e \u003cp\u003eAccording to the World Health Organization (WHO), genetic susceptibility has also been implicated as a risk factor in the onset and development of CVD (30). GRS is calculated by adding genetic risk alleles for each single nucleotide polymorphism (SNP) (31), could provide a better understanding in terms of trait variability of an individual and improve genetic risk prediction affecting the variables tested in this context compared to a single variant method (32). Caveolin-1 (CAV-1), abundant in adipocytes (33), was previously reported to be associated with obesity, dyslipidemia, and atherosclerosis (34\u0026ndash;37). Furthermore, the risk allele C for melanocortin 4 receptor (MC4R) rs17782313 was considered a key factor in developing obesity and increased cardiovascular risk factors (38\u0026ndash;42). Cryptochromes (Cry) 1 has also been shown to play critical roles in metabolism regulation, obesity, and elevated cardiometabolic traits (43\u0026ndash;45). A large variety of studies have reported the contribution of healthy dietary patterns and PDI to decreasing genetic risk factors of obesity and CVD (46, 47). However, no study has been conducted on the association between PDI and the aforementioned genetic factors and its association with tested variables among women. Therefore, the present study sought to evaluate the interactions between BMI-GRS based on 3 SNPs, namely, MC4R (rs17782313), CAV-1 (rs3807992), and Cry-1 (rs2287161) with hPDI and uPDI on CVD and obesity-related risk factors in Iranian overweight and obese women.\u003c/p\u003e"},{"header":"Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study population\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was conducted on 377 overweight and obese women aged 18–48, with BMI ranging from 25 to 40 kg/m\u003csup\u003e2\u003c/sup\u003e, referred to health centers in Tehran, Iran. The participants were chosen using a random cluster sampling method. Subjects with the following conditions were not included in this study; pregnancy, lactation, menopause, the history of diseases including type I and type II diabetes, non-alcoholic fatty liver disease (NAFLD), thyroid illness, kidney or liver diseases, polycystic ovary syndrome (PCOS), malignancies, and stroke. Taking any supplements or medications, weight loss program, or total calorie intakes not in the range of 800–4200 (kcal/day), were all exclusion factors. Before enrolling, all participants completed the informed consent form, which was reviewed and approved by Ethics Committee of the TUMS (NO: IR. TUMS.VCR.REC.1398.142). This literature was performed according to relevant guidelines and regulations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. General, anthropometric, and physical activity assessments\u003c/h2\u003e \u003cp\u003eGeneral information including age, marital status, educational level, and history of recent weight loss was collected via a demographic questionnaire. The measurement of height and weight were recorded, with light clothes and in a standing position, using a digital scale (Seca, Germany) with precisions of 0.1 cm and 0.1 kg, respectively. BMI was calculated by dividing weight (kg) by the square of height (m\u003csup\u003e2\u003c/sup\u003e). A trained expert measured waist circumference and hip circumference following standard protocols and the waist-to-hip ratio (WHR) was computed as the waist measurement divided by the hip measurement. Overweight and obesity were defined as BMI 25-29.9 kg/m\u003csup\u003e2\u003c/sup\u003e and BMI 30–40 kg/m\u003csup\u003e2\u003c/sup\u003e, respectively (48).\u003c/p\u003e \u003cp\u003e Body composition was assessed using bioelectrical impedance analysis (BIA 770 (South Korea)), following manufacturer guidelines (49). In addition, physical activity (PA) was evaluated using the validated self-report International Physical Activity Questionnaire (IPAQ) short form (50), categorized as follows: intense activity, moderate activity, and inactive.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Laboratory tests\u003c/h2\u003e \u003cp\u003eAll blood samples were obtained after 12–14 h of fasting and were centrifuged and stored at -80°C at the Nutrition and Biochemistry Laboratory of the School of Nutritional and Dietetics at TUMS. Fasting blood sugar (FBS) was measured using the Glucose Oxidase Phenol 4-Aminoantipyrine Peroxidase (GOD/PAP) method. Radio-immune assay was used to measure serum insulin values and all lipid biomarkers, including total cholesterol (T-Chol) (mg/dl), low-density lipoprotein (LDL) (mg/dl), high-density lipoprotein (HDL) (mg/dl), and triglyceride (TG) (mg/dl) serum levels, was determined by enzymatic methods (Pars Azmun Co., Tehran, Iran).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Atherogenic index of plasma (AIP) and lipid ratio assessment\u003c/h2\u003e \u003cp\u003eThe atherogenic index of plasma (AIP) was calculated from the logarithmic ratio of TG to HDL-C. For lipid ratio, calculations occurred as follows: CRI - I = TC/HDL-C, CRI – II = LDL-C/HDL-C (51). TyG was estimated as: Ln [fasting triglycerides (mg/dl) × FPG (mg/dl)/2 ] (52) and LAP \u003csub\u003ewomen\u003c/sub\u003e =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(WC-58\\right)\\times TG\\)\u003c/span\u003e\u003c/span\u003e (53).\u003c/p\u003e \u003cp\u003e \u003cem\u003e2.5. A body shape index (ABSI), body roundness index (BRI), and Body adiposity index (BAI) definitions\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe ABSI, BRI, and BAI were calculated using the following articles (7, 9, 11). ABSI was derived from WC which was adjusted for height and weight.\u003c/p\u003e \u003cp\u003eABSI = WC / BMI\u003csup\u003e2/3\u003c/sup\u003e Height\u003csup\u003e1/2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBRI was calculated as follows:\u003c/p\u003e \u003cp\u003eBRI = 364.2–365.5 Eccentricity.\u003c/p\u003e \u003cp\u003eEccentricity calculates the degree of circularity of an ellipse, which ranges between 0 and 1, with 0 characterizing a perfect circle, and 1, a vertical line.\u003c/p\u003e \u003cp\u003eBAI was calculated as follows:\u003c/p\u003e \u003cp\u003eBAI: Hip / Height\u003csup\u003ex\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn this formula, the hip reflects hip circumference (in cm), height is measured in meters, and X is a unitless power term.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Dietary assessments and plant-based dietary pattern\u003c/h2\u003e \u003cp\u003eDietary intakes were evaluated using a validated 147-item semi-quantitative FFQ whose validity and reliability have been previously approved (54). In the presence of expert dietitians, subjects were asked to report their consumption frequency during the past year, based on their usual diet, which was converted to grams per day. Utilizing the Iranian Food Composition Table (FCT) and N4 software, total energy and dietary nutrients were analyzed.\u003c/p\u003e \u003cp\u003eAccording to the plant-based dietary intake, three indices including overall PDI, hPDI, and uPDI were calculated using the method proposed by Satija \u003cem\u003eet\u003c/em\u003e al (55). In brief, all food intakes were divided into 18 groups, which were animal foods (dairy, animal fat, egg, fish and seafood, meat, miscellaneous animal-based foods), healthy (whole grains, fruits, vegetables, nuts, legumes, vegetable oils, tea, and coffee), and unhealthy plant-based diets (fruit juices, sugar-sweetened beverages, refined grains, potatoes, sweets, and desserts) (25, 56). A total of 18 energy-adjusted food groups were divided into quintiles with an assigned score between 1 and 5 for positive or reverse scores. Focusing on positive scores, a score of 5 was given to the highest quintiles, and a score of 1 was assigned to the lowest quintiles, whereas this pattern was inversed for reverse scores. For PDI, both healthy and unhealthy plant-based foods were given positive scores. For hPDI and uPDI, only healthy plant foods and unhealthy plant foods received positive scores, respectively. Animal food groups were given reverse ratings in all three indices. Finally, the observed scores for each plant-based diet index ranged from 18 to 90 and a higher total score was associated with higher adherence to that diet index (25).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Genotyping and GRS\u003c/h2\u003e \u003cp\u003eWe extracted the DNA using salting out method (57) and then, we used 1% agarose gel to assess the DNA integrity. DNA concentration was assessed by a nanodrop 8000 Spectrophotometer (Thermo Scientific, Waltham, MA, USA). For genotyping of the SNPs, the PCR-allele technique performed by the TaqMan Open Array (Life Technologies Corporation, Carlsbad, CA, USA) was used (58). For CAV-1 (rs3807992), the forward primer is 3′AGTATTGACCTGATTTGCCATG 5′ and the reverse primer is 5′ GTCTTCTGGAAAAAGCACATGA 3′. The fragments containing three genotypes (GG, GA, and AA) were distinguished. Based on a previous study, the MC4R gene primer was selected (59). The forward and reverse primer of MC4R (rs17782313) are 5- AAGTTCTACCTACCATGTTCTTGG-3 and 5-TTCCCCCTGAAGCTTTTCTTGTCATTTTGAT-3, respectively. The fragments containing three genotypes (CC, CT, and TT) were distinguished. We used PCR with the following primers for Cry1 (rs2287161): forward primer is 5′-GGAACAGTGATTGGCTCTATCT − 3′ and the reverse primer is 5′-GGTCCTCGGTCTCAAGAAG-3′. The fragments containing three genotypes (CC, GC, and GG) were distinguished.\u003c/p\u003e \u003cp\u003eWe created GRS by summing up three single nucleotide polymorphisms [CAV-1 (rs3807992), Cry-1 (rs2287161), and MC4R (rs17782313)] that had been linked to obesity-related traits, based on genomic-associated studies like Large-scale genome-wide association studies GWAS (60–64). According to the number of risk alleles for higher BMI, genotypes were coded as 0, 1, or 2 for each SNP. In this method, the unweighted GRS was calculated using the risk alleles of the three SNPs. The GRS scale ranges from 0 to 6. Higher scores represent a greater genetic predisposition to higher BMI or body weight (65).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Statistical analysis\u003c/h2\u003e \u003cp\u003eThe Kolmogorov-Smirnov test was used to assess the normality of distribution. The Hardy-Weinberg equilibrium and comparison of categorical variables were assessed with the chi-square test. Descriptive analysis was applied to evaluate demographic characteristics and all data were reported by the mean ± standard deviation. Analysis of variance (ANOVA) and analysis of covariance (ANCOVA) were performed to compare anthropometric measurements and metabolic profiles between subjects and remove confounding results, respectively. The adjustment was made for age, BMI, physical activity, and energy intake. To evaluate the interactions between GRS and PDI, a generalized linear model (GLM) was used. We used SPSS (version 25; SPSS Inc., IL) to analyze all data. Unilateral P-values were applied and a P-value \u0026lt; 0.05 was considered statistically significant and for interactions, P-value \u0026lt; 0.1 was considered significant.\u003c/p\u003e "},{"header":"Result","content":"\u003ch2\u003eStudy population characteristics based on GRS\u003c/h2\u003e\n\u003cp\u003eThe present study included 377 Iranian women. The characteristics of individuals are presented in Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e. There was a significant difference between GRS with, body weight (P-value\u0026thinsp;=\u0026thinsp;0.03), BMI (P-value\u0026thinsp;=\u0026thinsp;0.02), WC (P-value\u0026thinsp;=\u0026thinsp;0.03), and WHR (P-value\u0026thinsp;=\u0026thinsp;0.03) in the crude model. After adjustment for confounders (BMI, age, kcal, IPAC) there was a significant mean difference for the body weight (P-value\u0026thinsp;=\u0026thinsp;0.04), BMI (P-value\u0026thinsp;=\u0026thinsp;0.01), WC (P-value\u0026thinsp;=\u0026thinsp;0.03), WHR (P-value\u0026thinsp;=\u0026thinsp;0.01), BRI (P-value\u0026thinsp;=\u0026thinsp;0.02), ABSI (P-value\u0026thinsp;=\u0026thinsp;0.03), and LAP (P-value\u0026thinsp;=\u0026thinsp;0.02) in participants.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCharacteristics of the study population among participants based on GSR.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQuantitative variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo risk\u003c/p\u003e\n \u003cp\u003e(\u0026lt;\u0026thinsp;3 risk allele)\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;123)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModerate risk\u003c/p\u003e\n \u003cp\u003e(3\u0026amp;4 risk allele)\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;171)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh risk\u003c/p\u003e\n \u003cp\u003e(\u0026ge;\u0026thinsp;5 risk allele)\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.97\u0026thinsp;\u0026plusmn;\u0026thinsp;8.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.39\u0026thinsp;\u0026plusmn;\u0026thinsp;8.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.32\u0026thinsp;\u0026plusmn;\u0026thinsp;8.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnthropometric indices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody weight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.38\u0026thinsp;\u0026plusmn;\u0026thinsp;9.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.002\u0026thinsp;\u0026plusmn;\u0026thinsp;11.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.46\u0026thinsp;\u0026plusmn;\u0026thinsp;10.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162.37\u0026thinsp;\u0026plusmn;\u0026thinsp;5.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160.78\u0026thinsp;\u0026plusmn;\u0026thinsp;6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160.93\u0026thinsp;\u0026plusmn;\u0026thinsp;4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.30\u0026thinsp;\u0026plusmn;\u0026thinsp;3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0. 01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.57\u0026thinsp;\u0026plusmn;\u0026thinsp;8.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.60\u0026thinsp;\u0026plusmn;\u0026thinsp;8.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101.14\u0026thinsp;\u0026plusmn;\u0026thinsp;8.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWHR (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody composition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eABSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.49\u0026thinsp;\u0026plusmn;\u0026thinsp;34.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.52\u0026thinsp;\u0026plusmn;\u0026thinsp;28.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.66\u0026thinsp;\u0026plusmn;\u0026thinsp;40.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e290.63\u0026thinsp;\u0026plusmn;\u0026thinsp;184.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e286.32.196.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e379.24\u0026thinsp;\u0026plusmn;\u0026thinsp;302.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBiochemical parameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187.26\u0026thinsp;\u0026plusmn;\u0026thinsp;33.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183.01\u0026thinsp;\u0026plusmn;\u0026thinsp;37.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176.84\u0026thinsp;\u0026plusmn;\u0026thinsp;37.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120.67\u0026thinsp;\u0026plusmn;\u0026thinsp;58.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110.69\u0026thinsp;\u0026plusmn;\u0026thinsp;55.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133.72\u0026thinsp;\u0026plusmn;\u0026thinsp;75.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL-c (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.37\u0026thinsp;\u0026plusmn;\u0026thinsp;22.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.13\u0026thinsp;\u0026plusmn;\u0026thinsp;25.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.32\u0026thinsp;\u0026plusmn;\u0026thinsp;27.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL-c (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.38\u0026thinsp;\u0026plusmn;\u0026thinsp;9.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.14\u0026thinsp;\u0026plusmn;\u0026thinsp;11.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.92\u0026thinsp;\u0026plusmn;\u0026thinsp;12.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFBS (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.17\u0026thinsp;\u0026plusmn;\u0026thinsp;9.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.62\u0026thinsp;\u0026plusmn;\u0026thinsp;10.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.56\u0026thinsp;\u0026plusmn;\u0026thinsp;9.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAtherogenic index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCRI.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCRI.II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTGy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQualitative variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital situation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (49.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (42.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess diploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (38.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (49.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBachelor or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (42.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (49.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJob\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (46.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77 (51.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEconomic status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (49.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (39.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (47.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (46.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003csub\u003eBMI: body mass index, WC: waist circumference, WHR: waist hip ratio, AIP: atherogenic index of plasma, AC: atherogenic coefficient, CRI\u0026minus;I: Castelli risk index 1, CRI\u0026minus;II: Castelli risk index II, TyG: Triglyceride\u0026minus;glucose index, BRI: body roundness index, ABSI: a body shape index, LAP: lipid accumulation product, VAI: visceral adiposity index, TC: total cholesterol, TG: triglyceride, LDL\u0026minus;c: low\u0026minus;density lipoprotein, HDL\u0026minus;c: high\u0026minus;density lipoprotein, FBS: fasting blood sugar, CAV\u0026minus;1: Caveolin, CRY: Cryptochrome Circadian Regulator, MC4R: Melanocortin 4 Receptor\u003c/sub\u003e.\u003c/p\u003e\n \u003cp\u003e\u003csub\u003eQuantitative variables as means \u0026plusmn; SD obtained from the ANOVA test\u003c/sub\u003e.\u003c/p\u003e\n \u003cp\u003e\u003csub\u003eQualitative variables N (%) obtained from the chi\u0026minus;square analysis\u003c/sub\u003e.\u003c/p\u003e\n \u003cp\u003e\u003csub\u003eP\u0026minus;value* obtained from ANCOVA test. P\u0026minus;values \u0026lt; 0.05 were considered significant\u003c/sub\u003e.\u003c/p\u003e\n \u003cp\u003e\u003csub\u003eP\u0026minus;value* for adjustment model, based on age, energy intake, BMI, and IPAQ\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCharacteristics of study population in among tertile of PDI, hPDI, uPDI.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003ePDI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003ehPDI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003euPDI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;111) (\u0026lt;\u0026thinsp;51 gr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;91) (51\u0026ndash;57 gr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;88) (\u0026ge;\u0026thinsp;57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;97) (\u0026lt;\u0026thinsp;51 gr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;106) (51\u0026ndash;57 gr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;87) ((\u0026ge;\u0026thinsp;57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;104) (\u0026lt;\u0026thinsp;45 gr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;100) (45\u0026ndash;51 gr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;86) (\u0026ge;\u0026thinsp;51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.29\u0026thinsp;\u0026plusmn;\u0026thinsp;8.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.00\u0026thinsp;\u0026plusmn;\u0026thinsp;8.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.57\u0026thinsp;\u0026plusmn;\u0026thinsp;8.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.46\u0026thinsp;\u0026plusmn;\u0026thinsp;7.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.02\u0026thinsp;\u0026plusmn;\u0026thinsp;8.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.07\u0026thinsp;\u0026plusmn;\u0026thinsp;7.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.36\u0026thinsp;\u0026plusmn;\u0026thinsp;8.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.87\u0026thinsp;\u0026plusmn;\u0026thinsp;8.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.40\u0026thinsp;\u0026plusmn;\u0026thinsp;8.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnthropometric indices and\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody weight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.09\u0026thinsp;\u0026plusmn;\u0026thinsp;10.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.82\u0026thinsp;\u0026plusmn;\u0026thinsp;10.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.08\u0026thinsp;\u0026plusmn;\u0026thinsp;11.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.21\u0026thinsp;\u0026plusmn;\u0026thinsp;10.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.08\u0026thinsp;\u0026plusmn;\u0026thinsp;10.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.48\u0026thinsp;\u0026plusmn;\u0026thinsp;10.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.04\u0026thinsp;\u0026plusmn;\u0026thinsp;10.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.75\u0026thinsp;\u0026plusmn;\u0026thinsp;11.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.89\u0026thinsp;\u0026plusmn;\u0026thinsp;10.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160.72\u0026thinsp;\u0026plusmn;\u0026thinsp;5.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162.30\u0026thinsp;\u0026plusmn;\u0026thinsp;6.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160.69\u0026thinsp;\u0026plusmn;\u0026thinsp;5.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162.21\u0026thinsp;\u0026plusmn;\u0026thinsp;6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160.88\u0026thinsp;\u0026plusmn;\u0026thinsp;5.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160.54\u0026thinsp;\u0026plusmn;\u0026thinsp;6.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161.58\u0026thinsp;\u0026plusmn;\u0026thinsp;5.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160.43\u0026thinsp;\u0026plusmn;\u0026thinsp;5.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161.64\u0026thinsp;\u0026plusmn;\u0026thinsp;6.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.18\u0026thinsp;\u0026plusmn;\u0026thinsp;3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.13\u0026thinsp;\u0026plusmn;\u0026thinsp;3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.04\u0026thinsp;\u0026plusmn;\u0026thinsp;3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.60\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.77\u0026thinsp;\u0026plusmn;\u0026thinsp;4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.80\u0026thinsp;\u0026plusmn;\u0026thinsp;3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.28\u0026thinsp;\u0026plusmn;\u0026thinsp;3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.62\u0026thinsp;\u0026plusmn;\u0026thinsp;3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.60\u0026thinsp;\u0026plusmn;\u0026thinsp;8.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.24\u0026thinsp;\u0026plusmn;\u0026thinsp;9.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.51\u0026thinsp;\u0026plusmn;\u0026thinsp;9.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.95\u0026thinsp;\u0026plusmn;\u0026thinsp;8.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.91\u0026thinsp;\u0026plusmn;\u0026thinsp;9.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.07\u0026thinsp;\u0026plusmn;\u0026thinsp;9.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.35\u0026thinsp;\u0026plusmn;\u0026thinsp;9.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.47\u0026thinsp;\u0026plusmn;\u0026thinsp;9.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.94\u0026thinsp;\u0026plusmn;\u0026thinsp;8.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWHR (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody composition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eABSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.47\u0026thinsp;\u0026plusmn;\u0026thinsp;37.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.66\u0026thinsp;\u0026plusmn;\u0026thinsp;28.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.28\u0026thinsp;\u0026plusmn;\u0026thinsp;29.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.29\u0026thinsp;\u0026plusmn;\u0026thinsp;29.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.97\u0026thinsp;\u0026plusmn;\u0026thinsp;30.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.50\u0026thinsp;\u0026plusmn;\u0026thinsp;37.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.86\u0026thinsp;\u0026plusmn;\u0026thinsp;28.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.33\u0026thinsp;\u0026plusmn;\u0026thinsp;37.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.91\u0026thinsp;\u0026plusmn;\u0026thinsp;31.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e318.20\u0026thinsp;\u0026plusmn;\u0026thinsp;230.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e270.06\u0026thinsp;\u0026plusmn;\u0026thinsp;184.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e301.60\u0026thinsp;\u0026plusmn;\u0026thinsp;197.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e305.21\u0026thinsp;\u0026plusmn;\u0026thinsp;203.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e269.42\u0026thinsp;\u0026plusmn;\u0026thinsp;165.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e325.41\u0026thinsp;\u0026plusmn;\u0026thinsp;249.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e274.08\u0026thinsp;\u0026plusmn;\u0026thinsp;173.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e313.02\u0026thinsp;\u0026plusmn;\u0026thinsp;210.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e312.81\u0026thinsp;\u0026plusmn;\u0026thinsp;241.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBiochemical parameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e185.21\u0026thinsp;\u0026plusmn;\u0026thinsp;39.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182.70\u0026thinsp;\u0026plusmn;\u0026thinsp;33.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e185.83\u0026thinsp;\u0026plusmn;\u0026thinsp;35.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183.97\u0026thinsp;\u0026plusmn;\u0026thinsp;38.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182.18\u0026thinsp;\u0026plusmn;\u0026thinsp;37.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188.07\u0026thinsp;\u0026plusmn;\u0026thinsp;32.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184.97\u0026thinsp;\u0026plusmn;\u0026thinsp;34.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e185.51\u0026thinsp;\u0026plusmn;\u0026thinsp;37.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183.24\u0026thinsp;\u0026plusmn;\u0026thinsp;36.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122.54\u0026thinsp;\u0026plusmn;\u0026thinsp;64.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109.02\u0026thinsp;\u0026plusmn;\u0026thinsp;52.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120.54\u0026thinsp;\u0026plusmn;\u0026thinsp;59.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123.67\u0026thinsp;\u0026plusmn;\u0026thinsp;64.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106.90\u0026thinsp;\u0026plusmn;\u0026thinsp;51.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124.65\u0026thinsp;\u0026plusmn;\u0026thinsp;61.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112.92\u0026thinsp;\u0026plusmn;\u0026thinsp;52.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122.66\u0026thinsp;\u0026plusmn;\u0026thinsp;65.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118.75\u0026thinsp;\u0026plusmn;\u0026thinsp;60.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL-c (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.20\u0026thinsp;\u0026plusmn;\u0026thinsp;24.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.85\u0026thinsp;\u0026plusmn;\u0026thinsp;23.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.62\u0026thinsp;\u0026plusmn;\u0026thinsp;23.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.06\u0026thinsp;\u0026plusmn;\u0026thinsp;24.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.37\u0026thinsp;\u0026plusmn;\u0026thinsp;24.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.36\u0026thinsp;\u0026plusmn;\u0026thinsp;23.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.86\u0026thinsp;\u0026plusmn;\u0026thinsp;24.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.17\u0026thinsp;\u0026plusmn;\u0026thinsp;25.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.27\u0026thinsp;\u0026plusmn;\u0026thinsp;23.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL-c (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.32\u0026thinsp;\u0026plusmn;\u0026thinsp;10.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.50\u0026thinsp;\u0026plusmn;\u0026thinsp;10.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.10\u0026thinsp;\u0026plusmn;\u0026thinsp;10.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.13\u0026thinsp;\u0026plusmn;\u0026thinsp;10.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.95\u0026thinsp;\u0026plusmn;\u0026thinsp;11.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.81\u0026thinsp;\u0026plusmn;\u0026thinsp;10.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.43\u0026thinsp;\u0026plusmn;\u0026thinsp;11.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.57\u0026thinsp;\u0026plusmn;\u0026thinsp;9.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.69\u0026thinsp;\u0026plusmn;\u0026thinsp;11.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFBS (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.81\u0026thinsp;\u0026plusmn;\u0026thinsp;10.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.16\u0026thinsp;\u0026plusmn;\u0026thinsp;8.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.36\u0026thinsp;\u0026plusmn;\u0026thinsp;10.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.18\u0026thinsp;\u0026plusmn;\u0026thinsp;8.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.97\u0026thinsp;\u0026plusmn;\u0026thinsp;10.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.94\u0026thinsp;\u0026plusmn;\u0026thinsp;8.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.41\u0026thinsp;\u0026plusmn;\u0026thinsp;7.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.89\u0026thinsp;\u0026plusmn;\u0026thinsp;10.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.04\u0026thinsp;\u0026plusmn;\u0026thinsp;10.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAtherogenic index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCRI.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.24\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.31\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCRI.II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.003\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTGy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQualitative variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow-risk allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (31.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (40.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (31.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate risk allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (42.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (29.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (37.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh risk allele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital situation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (39.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (40.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85 (37.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75 (33.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82 (36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79 (35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63 (28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"15\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess diploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (46.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (48.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (29.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (39.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBachelor or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 (46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58 (43.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJob\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"12\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (40.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 (29.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 (34.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61 (35.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (33.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61 (35.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61 (35.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEconomic status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (40.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (34.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55 (39.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58 (42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 (37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (43.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (40.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"16\"\u003e\n \u003cp\u003e\u003csub\u003eBMI: body mass index, WC: waist circumference, WHR: waist\u0026minus;hip ratio, AIP: atherogenic index of plasma, AC: atherogenic coefficient, CRI\u0026minus;I: Castelli risk index 1, CRI\u0026minus;II: Castelli risk index II, TyG: Triglyceride\u0026minus;glucose index, BRI: body roundness index, ABSI: a body shape index, LAP: lipid accumulation product, VAI: visceral adiposity index, TC: total cholesterol, TG: triglyceride, LDL\u0026minus;c: low\u0026minus;density lipoprotein, HDL\u0026minus;c: high\u0026minus;density lipoprotein, FBS: fasting blood sugar\u003c/sub\u003e.\u003c/p\u003e\n \u003cp\u003e\u003csub\u003eQuantitative variables as means \u0026plusmn; SD were obtained from the ANOVA test\u003c/sub\u003e.\u003c/p\u003e\n \u003cp\u003e\u003csub\u003eQualitative variables N (%) were obtained from the chi\u0026minus;square analysis\u003c/sub\u003e.\u003c/p\u003e\n \u003cp\u003e\u003csub\u003eP\u0026minus;value* obtained from ANCOVA test. P\u0026minus;values \u0026lt; 0.05 were considered significant\u003c/sub\u003e.\u003c/p\u003e\n \u003cp\u003e\u003csub\u003eP\u0026minus;value* for adjustment model, based on age, energy intake, BMI, and IPAQ\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDietary intakes according to GRS in participants.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo risk (N\u0026thinsp;=\u0026thinsp;123)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModerate risk (N\u0026thinsp;=\u0026thinsp;171)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh risk (N\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMacronutrients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnergy (Kcal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2745.61\u0026thinsp;\u0026plusmn;\u0026thinsp;729.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2561.11\u0026thinsp;\u0026plusmn;\u0026thinsp;705.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2632.58\u0026thinsp;\u0026plusmn;\u0026thinsp;781.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarbohydrates (gr/ day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e394.97\u0026thinsp;\u0026plusmn;\u0026thinsp;122.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e366.21\u0026thinsp;\u0026plusmn;\u0026thinsp;111.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e366.74\u0026thinsp;\u0026plusmn;\u0026thinsp;127.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProtein (gr/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.31\u0026thinsp;\u0026plusmn;\u0026thinsp;28.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.85\u0026thinsp;\u0026plusmn;\u0026thinsp;26.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.23\u0026thinsp;\u0026plusmn;\u0026thinsp;34.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal fat (gr/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.60\u0026thinsp;\u0026plusmn;\u0026thinsp;28.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.59\u0026thinsp;\u0026plusmn;\u0026thinsp;31.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.66\u0026thinsp;\u0026plusmn;\u0026thinsp;33.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubgroup types of fat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHOL (gr/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e264.53\u0026thinsp;\u0026plusmn;\u0026thinsp;107.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e245.32\u0026thinsp;\u0026plusmn;\u0026thinsp;100.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e252.27\u0026thinsp;\u0026plusmn;\u0026thinsp;115.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSaturated fat (gr/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.05\u0026thinsp;\u0026plusmn;\u0026thinsp;11.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.35\u0026thinsp;\u0026plusmn;\u0026thinsp;10.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.09\u0026thinsp;\u0026plusmn;\u0026thinsp;12.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrans fat (gr/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0008\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0007\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMUFA (gr/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.05\u0026thinsp;\u0026plusmn;\u0026thinsp;9.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.53\u0026thinsp;\u0026plusmn;\u0026thinsp;10.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.99\u0026thinsp;\u0026plusmn;\u0026thinsp;13.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePUFA (gr/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.54\u0026thinsp;\u0026plusmn;\u0026thinsp;7.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.27\u0026thinsp;\u0026plusmn;\u0026thinsp;8.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.80\u0026thinsp;\u0026plusmn;\u0026thinsp;8.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMicronutrients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVitamins\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB1 (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB2 (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB3 (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.89\u0026thinsp;\u0026plusmn;\u0026thinsp;10.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.30\u0026thinsp;\u0026plusmn;\u0026thinsp;7.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.77\u0026thinsp;\u0026plusmn;\u0026thinsp;12.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB6 (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB9 (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e645.44\u0026thinsp;\u0026plusmn;\u0026thinsp;170.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e588.25\u0026thinsp;\u0026plusmn;\u0026thinsp;171.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e624.19\u0026thinsp;\u0026plusmn;\u0026thinsp;188.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB12 (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.36\u0026thinsp;\u0026plusmn;\u0026thinsp;2.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.45\u0026thinsp;\u0026plusmn;\u0026thinsp;2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVitamin D (\u0026micro;/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVitamin E (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.03\u0026thinsp;\u0026plusmn;\u0026thinsp;8.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.41\u0026thinsp;\u0026plusmn;\u0026thinsp;7.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.49\u0026thinsp;\u0026plusmn;\u0026thinsp;11.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVitamin C (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218.25\u0026thinsp;\u0026plusmn;\u0026thinsp;120.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184.05\u0026thinsp;\u0026plusmn;\u0026thinsp;107.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e191.73\u0026thinsp;\u0026plusmn;\u0026thinsp;203.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinerals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcium (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1220.89\u0026thinsp;\u0026plusmn;\u0026thinsp;416.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1154.14\u0026thinsp;\u0026plusmn;\u0026thinsp;416.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1106.32\u0026thinsp;\u0026plusmn;\u0026thinsp;350.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIron (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.86\u0026thinsp;\u0026plusmn;\u0026thinsp;5.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.07\u0026thinsp;\u0026plusmn;\u0026thinsp;5.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.95\u0026thinsp;\u0026plusmn;\u0026thinsp;6.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhosphor (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1716.34\u0026thinsp;\u0026plusmn;\u0026thinsp;524.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1617.40\u0026thinsp;\u0026plusmn;\u0026thinsp;507.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1614.48\u0026thinsp;\u0026plusmn;\u0026thinsp;491.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMagnesium (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e485.88\u0026thinsp;\u0026plusmn;\u0026thinsp;148.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e452.92\u0026thinsp;\u0026plusmn;\u0026thinsp;143.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e445.74\u0026thinsp;\u0026plusmn;\u0026thinsp;145.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZinc (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.54\u0026thinsp;\u0026plusmn;\u0026thinsp;4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.75\u0026thinsp;\u0026plusmn;\u0026thinsp;4.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.97\u0026thinsp;\u0026plusmn;\u0026thinsp;4.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelenium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125.76\u0026thinsp;\u0026plusmn;\u0026thinsp;45.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117.41\u0026thinsp;\u0026plusmn;\u0026thinsp;40.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119.56\u0026thinsp;\u0026plusmn;\u0026thinsp;43.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal fiber (gr/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.48\u0026thinsp;\u0026plusmn;\u0026thinsp;19.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.51\u0026thinsp;\u0026plusmn;\u0026thinsp;16.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.004\u0026thinsp;\u0026plusmn;\u0026thinsp;22.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal sugar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152.22\u0026thinsp;\u0026plusmn;\u0026thinsp;60.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142.23\u0026thinsp;\u0026plusmn;\u0026thinsp;59.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126.34\u0026thinsp;\u0026plusmn;\u0026thinsp;51.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.99\u0026thinsp;\u0026plusmn;\u0026thinsp;13.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.69\u0026thinsp;\u0026plusmn;\u0026thinsp;10.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.71\u0026thinsp;\u0026plusmn;\u0026thinsp;7.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGalactose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFructose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.33\u0026thinsp;\u0026plusmn;\u0026thinsp;14.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.22\u0026thinsp;\u0026plusmn;\u0026thinsp;13.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.54\u0026thinsp;\u0026plusmn;\u0026thinsp;10.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaffeine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158.36\u0026thinsp;\u0026plusmn;\u0026thinsp;210.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145.54\u0026thinsp;\u0026plusmn;\u0026thinsp;98.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156.46\u0026thinsp;\u0026plusmn;\u0026thinsp;137.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003csub\u003eCHOL: cholesterol, MUFA: monounsaturated fatty acid, PUFA: polyunsaturated fatty acid\u003c/sub\u003e.\u003c/p\u003e\n \u003cp\u003e\u003csub\u003eAll data are presented as mean \u0026plusmn; SD\u003c/sub\u003e.\u003c/p\u003e\n \u003cp\u003e\u003csub\u003eP\u0026minus;value* for adjustment model, based on age, energy intake, BMI and IPAQ\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch2\u003eStudy population characteristics according to tertile of PDI, hPDI, uPDI\u003c/h2\u003e\n\u003cp\u003eThere was no significant difference between the tertile of PDI with characteristics of the study population in crude and adjusted model (P-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05), except education (P-value\u0026thinsp;=\u0026thinsp;0.01) and job status (P-value\u0026thinsp;=\u0026thinsp;0.001). According to the findings, with the increasing age of the participants, the adherence to the hPDI food pattern increases, and this difference was statistically significant (0.002), and after controlling for confounders, this consistency remained (0.003). After adjusting for potential confounder, there was a borderline significant difference between the tertile of hPDI and TGy (P-value\u0026thinsp;=\u0026thinsp;0.05) and a significant difference for TG (P-value\u0026thinsp;=\u0026thinsp;0.04) and economic status (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in participants. In crude model, there was a significant difference between the tertile of the uPDI with BMI (P-value\u0026thinsp;=\u0026thinsp;0.02) and BRI (P-value\u0026thinsp;=\u0026thinsp;0.03). This significant relation remained even after adjustment for confounding variables.\u003c/p\u003e\n\u003ch2\u003eDietary intakes according to GSR in participants\u003c/h2\u003e\n\u003cp\u003eThe crude model showed a significant difference between GRS and glucose (P-value\u0026thinsp;=\u0026thinsp;0.03). Also, The crude model showed a significant difference between GRS and glucose (P-value\u0026thinsp;=\u0026thinsp;0.03). Also, there was a borderline significance between, GRS and vitamin B9 (P-value\u0026thinsp;=\u0026thinsp;0.05) and fructose (P-value\u0026thinsp;=\u0026thinsp;0.05). After adjustment for confounding variables (BMI, age, kcal, IPAC) there was a significant difference between GRS and Vitamin C (P-value\u0026thinsp;=\u0026thinsp;0.03) and glucose (P-value\u0026thinsp;=\u0026thinsp;0.02).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInteraction between GSR and PDI with atherogenic index and body composition.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings revealed a negative significant interaction between tertile 2 of PDI and moderate risk alleles (\u0026beta; =-0.34, 95% CI= -0.61 to -0.07, p\u0026thinsp;=\u0026thinsp;0.01) and high-risk alleles (\u0026beta; =-0.38, 95% CI= -0.64 to -0.12, P-value\u0026thinsp;=\u0026thinsp;0.004) with AIP compare to low-risk alleles. This significant interaction remained even after adjustment for confounding variables. There was a negative significant interaction between tertile 2 of PDI and high-risk alleles (\u0026beta; =-0.63, 95% CI= -1.28 to 0.01, P-value\u0026thinsp;=\u0026thinsp;0.05) with CRI.II compare to low-risk allele participants, in the adjusted model.\u003c/p\u003e\n\u003cp\u003eThere was a negative significant interaction between tertile 2 of PDI and moderate risk allele (\u0026beta; =-0.83, 95% CI= -1.57 to -0.09, P-value\u0026thinsp;=\u0026thinsp;0.02) and high-risk allele (\u0026beta; =-1.004, 95% CI= -1.71 to -0.28, P-value\u0026thinsp;=\u0026thinsp;0.006) with TGy compare to low-risk allele participants. This significant interaction remained even after adjustment for confounding variables.\u003c/p\u003e\n\u003cp\u003eThere was a negative significant interaction between tertile 2 of PDI and moderate risk allele (\u0026beta; =-56.53, 95% CI= -97.12 to -15.94, P-value\u0026thinsp;=\u0026thinsp;0.006) and high-risk alleles (\u0026beta; =-66.72, 95% CI= -105.95 to -27.49, P-value\u0026thinsp;=\u0026thinsp;0.001) with LAP compare to low-risk allele participants. After adjusting for potential confounder there was a negative significant interaction between tertile 2 of PDI and moderate risk allele (\u0026beta; = -78.06, 95% CI= -120.23 to -35.90, P-value\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and high-risk alleles (\u0026beta; =-83.85, 95% CI= -124.45 to -43.25, P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with LAP compare to low-risk allele participants.\u003c/p\u003e\n\u003cp\u003eThere was a negative significant interaction between tertile 2 of PDI and moderate risk allele (\u0026beta; =-273.40, 95% CI= -499.34 to -47.45, P-value\u0026thinsp;=\u0026thinsp;0.01) and high-risk alleles (\u0026beta; =-311.03, 95% CI= -529.61 to -92.45, P-value\u0026thinsp;=\u0026thinsp;0.005) with VAI compare to low-risk allele participants, in the crude model. There was a negative significant interaction between tertile 2 of PDI and moderate risk allele (\u0026beta; =-362.91, 95% CI= -611.44 to -114.38, P-value\u0026thinsp;=\u0026thinsp;0.004) and high-risk alleles (\u0026beta; =-393.001, 95% CI= -632.49 to -153.50, P-value\u0026thinsp;=\u0026thinsp;0.001) with VAI compare to low-risk allele participants in adjusted model (Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eInteraction between GSR and hPDI with atherogenic index and body composition.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was a negative significant interaction between tertile 2 of hPDI and moderate risk allele (\u0026beta; =-0.03, 95% CI= -0.06 to -0.01, P-value\u0026thinsp;=\u0026thinsp;0.008) with ABSI compared to low-risk allele participants. After adjusting for potential confounder this significant interaction remained (\u0026beta; =-0.02, 95% CI= -0.05 to 0.001, P-value\u0026thinsp;=\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInteraction between GSR and uPDI with atherogenic index and body composition.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the crude model, there was a positive significant interaction between tertile 2 (\u0026beta;\u0026thinsp;=\u0026thinsp;2.11, 95% CI\u0026thinsp;=\u0026thinsp;0.39 to 3.83, P-value\u0026thinsp;=\u0026thinsp;0.01) and tertile 3 (\u0026beta;\u0026thinsp;=\u0026thinsp;1.90, 95% CI\u0026thinsp;=\u0026thinsp;0.18 to 3.61, P-value\u0026thinsp;=\u0026thinsp;0.03) of uPDI and moderate risk allele with CRI. I compare to low-risk allele participants. In the adjusted model, there was a positive significant interaction between tertile 2 (\u0026beta;\u0026thinsp;=\u0026thinsp;2.03, 95% CI\u0026thinsp;=\u0026thinsp;0.17 to 3.89, P-value\u0026thinsp;=\u0026thinsp;0.03) of uPDI and moderate risk allele with CRI. I compare to low-risk allele participants. After adjusting for potential confounder, there was a positive significant interaction between tertile 2 (\u0026beta;\u0026thinsp;=\u0026thinsp;0.78, 95% CI\u0026thinsp;=\u0026thinsp;0.07 to 1.48, P-value\u0026thinsp;=\u0026thinsp;0.03) of uPDI and moderate risk allele with CRI. II compared to low-risk allele participants.\u003c/p\u003e\n\u003cp\u003eIn the adjusted model, there was a positive significant interaction between tertile 2 (\u0026beta;\u0026thinsp;=\u0026thinsp;0.03, 95% CI\u0026thinsp;=\u0026thinsp;0.009 to 0.06, P-value\u0026thinsp;=\u0026thinsp;0.01) of uPDI and moderate risk allele with ABSI. Also, there was a positive significant interaction between tertile 3 (\u0026beta;\u0026thinsp;=\u0026thinsp;0.03, 95% CI\u0026thinsp;=\u0026thinsp;0.005 to 0.06, P-value\u0026thinsp;=\u0026thinsp;0.02) of uPDI and high-risk allele with ABSI compared to low-risk allele participants \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current cross-sectional study indicates a pioneering investigation aiming to establish an interaction of genetics risk score (GRS) and plant-based diet on factors atherogenic and body adiposity indexes among overweight and obese women. We showed that plant-based dietary patterns regarding genetic susceptibility can have effects on the predictable risk factors of cardiometabolic disease in our population. Our results emphasize the importance of following plant-based dietary patterns, individuals at higher or moderate genetic risk may benefit from the positive effects of these dietary patterns to modify cardiometabolic risk factors.\u003c/p\u003e \u003cp\u003eOur results unveil a significant negative interaction between tertile 2 of PDI and moderate risk allele and high-risk alleles with AIP, TGy, LAP, and VAI compared to low-risk allele. Additionally, we detected a negative significant interaction between tertile 2 of PDI and high-risk allele with CRI. II compare to low-risk allele participants. Furthermore, there was a negative significant interaction between tertile 2 of hPDI and moderate risk allele with ABSI compared to low-risk allele participants. There was a positive significant interaction between tertile 2 of uPDI and moderate risk allele with CRI. I and ABSI compare to low-risk allele participants. The present study found a significant mean difference between GRS and the participant's body weight, BMI, WC, WHR, BRI, ABSI, and LAP. Our results are in line with previous investigations regarding the genetic effects of GRS on BMI and waist circumference (66\u0026ndash;68). A cross-sectional study illustrated the merged effect of some genetic variants on obesity in Pakistanis and showed by a GRS for obesity, the possibility of the prognosis of anthropometric traits (69), and several investigations indicate the positive relationship between obesity risk factors such as BMI, WC, and WHR with cardiometabolic diseases (70\u0026ndash;72). CAV-1 as one of the considered genes in this study, has some functions such as the main regulator for fat distribution and genetic lipodystrophy in humans and it can be relatively higher in obese women compared to thin women (73\u0026ndash;76). CAV-1 is also associated with oxidative stress, which can therefore play a role in many metabolic diseases. According to a study, following the PDI diet can reduce metabolic diseases among those who carry a risk allele in the CAV-1 gene (77). Other roles, such as abnormalities in the binding of cholesterol and fatty acids, disturbance in the path of differentiation of fat cells, dysfunction of fat droplets, and increase in insulin signaling, are attributed to this gene (73, 78). In addition, several studies showed the association of the MC4R gene and its role in energy balance, food intake regulation, total fat, total obesity, peripheral obesity, abdominal obesity, and higher BMI (79, 80).\u003c/p\u003e \u003cp\u003eThe results of the PREDIMED trial, show that following plant-based dietary patterns can significantly reduce the risk of CVD (81), and the Adventist Health Study also showed that vegetarian diets reduce CVD mortality by exerting their protective effect (82). Comparing other diet indices (such as Alternate Healthy Eating Index, Dietary Approaches to Stop Hypertension [DASH], and Mediterranean diet scores), PDI dietary patterns are derived from some food items, including healthy and less healthy plant-derived foods, to capture the combined and varying intakes of different food items. Moreover, some components in the uPDI, such as coffee, sugar-sweetened beverages, and saturated fat, could be involved in genetic predisposition to obesity (83\u0026ndash;88). Interactions between GRS and PDI food patterns can be due to less adherence to less healthy plant-based foods and animal foods, low energy density, the increased involvement of plant bioactives through the regulation of thermogenesis without energy consumption and shivering, as well as the improvement of the balance of intestinal microbiota due to the increase in dietary fiber consumption (88\u0026ndash;91). According to a study conducted in 2023 by Fatemeh Gholami et al. on 377 obese and overweight women, significant interactions between GRS and h-PDI were observed on body fat mass index, body mass index (BMI), and waist circumference. In this study, the interaction between GRS and PDI was performed on some predictive factors of cardiovascular diseases such as C-reactive protein, plasminogen activator inhibitor 1, and insulin. According to the findings of this study, following a plant-based food pattern despite the genetic differences in people seems to be a protective factor against the risks of cardiometabolic abnormalities (92). Interestingly, according to past results, following more healthy plant-based diets in people who are at a higher genetic risk of obesity (regardless of the presence of basic obesity), causes more benefits in these people (93, 94). Our findings revealed a negative significant interaction between the second tertile of PDI and moderate risk allele and high-risk allele with AIP compared to the low-risk allele. In a cross-sectional study conducted in 2023 by Farnaz Shahdadian et al., it was found that participants with the highest quartile of PDI, as well as the third quartile of hPDI, were associated with reduced odds of having high-risk AIP compared to the first quartile (95). In this regard, other studies reported the relationship between PDI, especially hPDI, and their role in the management and prevention of high-risk AIP (96\u0026ndash;98). One of the reasons for the effect of a PDI on reducing high-risk AIP can be attributed to its role in reducing TG because AIP is a logarithm of the ratio of triglycerides and HDL (95). Mahdieh Khodarahmi et al. in a cross-sectional study observed that the interactions between DASH score and MC4R rs17782313 genotypes on AIP among the female group were statistically significant (99). In an observational study, a significant interaction of GRS and hPDI on lipid factors was observed, and in this regard, other studies also reveal the beneficial effects of healthier plant-based diets in reducing TC levels and controlling HDL (93, 100\u0026ndash;102). However, the results for TG have been controversial (101).\u003c/p\u003e \u003cp\u003eFindings indicate a positive significant interaction between tertile 2 of uPDI and moderate risk allele with CRI.I compare low-risk allele participants and positive significant interaction between tertile 2 of uPDI and moderate risk allele with CRI.II compared to low-risk allele participants. In a study conducted in 2019 on 96 participants, CRI.I was considered as an index to predict cardiovascular risk among people in two diet groups, vegetarians and omnivores. In this study, CRI-I was significantly greater in omnivores than in vegetarians, and people who have higher adherence to a vegetarian diet elevate by 17 times the probability of having a normal CRI-I (103, 104).\u003c/p\u003e \u003cp\u003eEarlier studies have found the potential role of diet in body composition; however, little attention has been paid to LAP and TyG, which are strong indicators of cardiovascular disease (105, 106). We found a negative significant interaction between the second tertile of PDI and moderate risk allele and high-risk allele with TGy compared to low-risk allele participants. Also, there was a negative significant interaction between the second tertile of PDI and moderate risk allele and high-risk allele with LAP compared to low-risk allele participants. In a cross-sectional study conducted in 2020 by Mahshid Shahavandi and his colleagues on 270 adults, the results indicated that more adherence to hPDI was associated with a decrease in BMI, WC, WHR, and LAP. However, in this study, there was no correlation between following PDIs and TyG (107). In another study conducted on Iranian adults in 2017, no significant relationship was found between healthy dietary patterns with the TyG index and visceral fat level (108). However, in a prospective cohort study conducted in 2020, a negative association between an anti-inflammatory diet and the TyG index was found (109).\u003c/p\u003e \u003cp\u003eIn our findings, there was a negative significant interaction between tertile 2 of PDI and moderate risk allele and high-risk allele with VAI compared to low-risk allele participants. Results show a negative significant interaction between tertile 2 of hPDI and moderate risk allele with ABSI compared to low-risk allele participants. Moreover, a positive significant interaction between tertile 2 of uPDI and moderate risk allele with ABSI was shown. Also, there was a positive significant interaction between tertile 3 of uPDI and high-risk allele with ABSI compared to low-risk allele participants. Based on a cross-sectional study a significant positive association between fat intake and visceral adipose tissue (VAT) was observed among overweight young adults. In this investigation, participants who followed a diet rich in carbohydrates, sugar, and total fat, as well as saturated fat, showed an increased mean in VAI and LAP. However, following a diet rich in vitamins, and minerals, with a high amount of fiber was associated with reduced levels of VAI and LAP (108). In another study conducted in 2018 on older Americans, a negative association was indicated between the DASH diet index and VAI (110).\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStrength and limitations:\u003c/h2\u003e \u003cp\u003eOur investigation is a novel study to evaluate the interaction of genetics risk score (GRS) and Plant-based diet on atherogenic, visceral, and body adiposity, which examines certain factors that have not been considered in previous studies. The current study's major strengths include appropriate sample size and adjusting for potential confounders. However, there are some limitations such as the design of the study which cannot explain the causal relation between confounders. As our study was conducted on overweight women, our obtained findings cannot be generalized to other groups of society.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":" \u003cp\u003eThe involvement of PDI, h-PDI, and uPDI appears to be a protective factor against cardiovascular risk factors in overweight and obese women with increased GRS. Prospective and interventional studies with greater sample sizes in different populations and ethnicities need to be conducted to further the knowledge about examine interaction between PDI, hPDI, and uPDI with GSR are associated with atherogenic index and body composition.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGRS: Interaction of Genetics Risk Score, CVD: cardiovascular diseases, FFQ: food frequency questionnaire, PDI: plant-based diet index, hPDI: healthy plant-based diet index, uPDI: unhealthy plant-based diet index, GLM: generalized linear model, ABSI: A body shape index, BRI: body roundness index, WC: waist circumference, LAP: Lipid accumulation product, BAI: Body adiposity index, AIP: atherogenic index of plasma, CRI-I: Castelli risk index-I, CRI-II: Castelli risk index-II, TyG: triglyceride glucose, CAV-1: Caveolin-1, MC4R: melanocortin 4 receptor, Cry: Cryptochromes, NAFLD: non-alcoholic fatty liver disease, PCOS: polycystic ovary syndrome, FBS: Fasting blood sugar, GOD/PAP: Glucose Oxidase Phenol 4-Aminoantipyrine Peroxidase, T-Chol: total cholesterol, LDL: low-density lipoprotein, HDL: high-density lipoprotein, TG: triglyceride, FCT: Food Composition Table, ANOVA: Analysis of variance.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all participants for their contribution to this research. Grants from the Tehran University of Medical Sciences, Tehran, Iran, supported this study. This study is funded by the Tehran University of Medical Sciences (TUMS) grants. (NO: IR. TUMS.VCR.REC.1398.142)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; We state that all methods are based on the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMM wrote the paper, FA performed the statistical analyses, KhM and AM had full access to all of the data in the study, and took responsibility for the integrity and accuracy of the data. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This study was supported by the Tehran University of Medical Sciences (NO: IR. TUMS.VCR.REC.1398.142).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipants of this study disagreed on their data to be shared publicly, so supporting data is not available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protocol of the study was approved by the ethics committee of Tehran University of medical sciences (NO: IR. TUMS.VCR.REC.1398.142) and is acknowledged by the authors. A signed written informed consent was collected from all participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors performed editing and approving the final version of this paper for submission, also participated in the finalization of the manuscript and approved the final draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest in this study\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAzadbakht L HF, Esmaillzadeh A. White rice consumption, body mass index, and waist circumference among Iranian female adolescents. J Am Coll Nutr. 2016;35(6):491\u0026ndash;499.\u003c/li\u003e\n\u003cli\u003eA. Vaisi-Raygani MM, R. Jalali, A. Ghobadi, and N. 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Interactions between Caveolin-1 (rs3807992) polymorphism and major dietary patterns on cardio-metabolic risk factors among obese and overweight women. BMC endocrine disorders. 2021;21:138.\u003c/li\u003e\n\u003cli\u003eKhatibi N MA, Shiraseb F, Abaj F, Koohdani F, Mirzaei Kh. Interactions between caveolin 1 polymorphism and the Mediterranean and Mediterranean-DASH Intervention for Neurodegenerative Delay diet (MIND) diet on metabolic dyslipidemia in overweight and obese adult women: a cross-sectional study. BMC Res Notes. 2021;14:364.\u003c/li\u003e\n\u003cli\u003eGrant SF BJ, Zhang H, Wang K, Kim CE, Annaiah K, et al. Investigation of the locus near MC4R with childhood obesity in Americans of European and African ancestry Obesity. 2009;17:1461e5.\u003c/li\u003e\n\u003cli\u003eChambers JC EP, Zabaneh D, Zhang W, Li Y, Froguel P, et al. Common genetic variation near MC4R is associated with waist circumference and insulin resistance. 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Cardiology Research and Practice. 2018;2018.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 4","content":"\u003cp\u003eTable 4 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"A body shape index, body roundness index, Lipid accumulation product, Body adiposity index, atherogenic index of plasma, Plant Dietary Index","lastPublishedDoi":"10.21203/rs.3.rs-4587951/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4587951/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eThe association between plant-based foods with obesity, cardiovascular diseases (CVD), and their novel predictive biomarkers considering genetic predisposition remains uncertain. Given that diet is a significant and modifiable risk factor, we sought to investigate the interactions between plant-based diet and genetic susceptibility with atherogenic factors, and visceral and body adiposity indices in Iranian women.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was conducted on 377 obese and overweight women, aged 18\u0026ndash;48 from Iran. Using standard protocols, anthropometric indices, body composition, physical activity, and serum profiles were measured. A validated 147-item semi-quantitative food frequency questionnaire (FFQ) was used to create three plant-based diets including the overall plant-based diet index (PDI), healthy plant-based diet index (hPDI), and unhealthy plant-based diet index (uPDI). A genetic risk score (GRS) was calculated based on the risk alleles of the three BMI-related SNPs. The interaction between GRS and PDI was analyzed using a generalized linear model (GLM).\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eThere was a negative significant interaction between tertile 2 of PDI and moderate risk allele and high-risk alleles with AIP, TGy, LAP, and VAI compared to the low-risk allele (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There was a negative borderline significant interaction between tertile 2 of hPDI and moderate risk allele (P-value\u0026thinsp;=\u0026thinsp;0.05) with ABSI compared to low-risk allele participants. There was a positive significant interaction between tertile 2 of uPDI and moderate risk allele with CRI.I (P-value\u0026thinsp;=\u0026thinsp;0.03), CRI.II (P-value\u0026thinsp;=\u0026thinsp;0.03) compared to low-risk allele participants. Also, there was a positive significant interaction between tertile 3 of uPDI and high-risk allele with ABSI (P-value\u0026thinsp;=\u0026thinsp;0.02) compared to low-risk allele participants.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe present study provides evidence that interaction between PDI, hPDI, and uPDI with GSR is associated with atherogenic index and body composition. Prospective and interventional studies in different populations and ethnicities need to be conducted to further the knowledge about examining the interaction between PDI, hPDI, and uPDI with GSR are associated with atherogenic index and body composition.\u003c/p\u003e","manuscriptTitle":"Interaction of Genetics Risk Score (GRS) and Plant-Based Diet on factors Atherogenic and body adiposity indexes among overweight and obese women: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-02 08:17:04","doi":"10.21203/rs.3.rs-4587951/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1f36e01b-5e2a-4d7e-b03d-a8c7896276f0","owner":[],"postedDate":"July 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-11T09:26:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-02 08:17:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4587951","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4587951","identity":"rs-4587951","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}