Association between weight-adjusted-waist index and cardiovascular diseases: a cross-sectional study based on RaNCD cohort data

Association between weight-adjusted-waist index and cardiovascular diseases: a cross-sectional study based on RaNCD cohort data | 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 Association between weight-adjusted-waist index and cardiovascular diseases: a cross-sectional study based on RaNCD cohort data Sepehr Sadafi, Ali Azizi, Ebrahim Shakiba, Yahya Pasdar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4331367/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 weight-adjusted-waist index (WWI) is a relatively new index to measurment obesity. The present study was conducted with the aim of investigating the relationship between WWI and cardiovascular disease (CVD). Methods: This cross-sectional study included 8,899 participants from the Ravansar non-communicable diseases study (RaNCD) cohort study. The WWI was calculated by dividing waist circumference (WC) by the square root of weight. CVD is described as having a history of stroke, ischemic heart disease (IHD), angina, heart failure, myocardial infarction (MI), or using medication for CVD. The study utilized multiple logistic regression to assess the association between WWI and CVD. Results: The average age of the participants was 47.52± 8.29 years, 45.30% were men and 41.13% were rural residents. The prevalence of CVD was 17.36%. A positive association between WWI and CVD was observed. Participants in the highest quartile of WWI had a 36% (OR= 1.36, 95%CI:1.11, 1.78) higher odds of CVD than those in the lowest quartile (OR= 1.03, 95%CI: 0.79, 1.33) (Ptrend= 0.010) . The subgroup analyses revealed stronger associations between WWI and CVD in participants older than 50 years of age, male, urban residents, high SES, and passive smokers (P<0.001). The receiver operating characteristic (ROC) analysis indicated that WWI has a greater ability to predict CVD (AUC: 0.64, 95%CI: 0.61, 0.64) compared to body mass index (BMI) (AUC: 0.60, 95%CI: 0.58, 0.61) and WC (AUC: 0.61, 95%CI: 0.59, 0.62) . Conclussion: The association between higher WWI and an increased risk of CVD suggests that WWI management is crucial for preventing CVD. Cardiovascular diseases obesity weight-adjusted-waist index Persian Figures Figure 1 Figure 2 Introduction Obesity represents a significant risk factor for numerous chronic metabolic conditions that have been considered in the health system [ 1 – 3 ]. It has been estimated that by 2030, around 38% of the world's adult population will be obese [ 4 ]. In a meta-analysis study by Wong et al. (2020), the overall prevalence of central obesity in the world was reported to be 41.5% [ 5 ]. Previous studies have indicated that obesity increases the risk of CVD [ 6 , 7 ]. However, onflicting findings from some studies suggest that overweight and obese individuals may have a lower risk of CVD and hypertension compared to those with normal weight, and being overweight or obese may play a protective role [ 8 – 10 ]. Among various anthropometric indices, body mass index (BMI) and waist circumference (WC) are widely used as the primary indices to assess both general and central obesity [ 11 ]. Nevertheless, these markers are not able to distinguish between fat and muscle mass [ 12 ]. Park and et al. (2018) introduced the weight-adjusted-waist index (WWI) as a new measure of obesity [ 13 ]. This index assesses central obesity by taking into account both muscle and fat mass, and it is calculated by dividing WC by the square root of body weight [ 14 , 15 ]. Several studies have reported a positive association between WWI and chronic diseases such as CVD, stroke, non-alcoholic fatty liver disease (NAFLD) and chronic kidney disease [ 16 – 19 ]. However, a comprehensive study in this field has not been conducted in Iran. CVD is a multifactorial chronic condition, with obesity identified as one of its risk factors. Consequently, reducing obesity can be effective in the prevention and management of CVD. A robust definition of obesity highlights the strength of the connection between this risk factor and CVD. Hence, the current study aims to explore the association between WWI and CVD among adults in western Iran. Methods Data sources and participants This study is a cross-sectional analysis of data from the baseline phase of the Ravansar non-communicable diseases study (RaNCD) cohort study [ 20 ]. The RaNCD cohort is one of the Prospective Epidemiological Research Studies in Iran (PERSIAN) [ 21 ], which started with a 15-year design since 2014 in Ravansar city located in Kermanshah province in the west of Iran. The study population of RaNCD cohort is urban and rural adult men and women of Ravansar. All participants from the initial phase of the RaNCD cohort were involved in this research. Following the application of exclusion criteria, 8,899 participants were examined ( Fig. 1 ). Study variables The data was collected in compliance with the cohort study protocol, and trained professionals used digital questionnaires to gather information. The socio-economic status (SES) was established by considering factors such as education level, place of residence, welfare amenities and wealth through the principal component analysis (PCA) method. After this analysis, the SES was divided into three categories (low, moderate, and high) [ 22 ]. Physical activity was evaluated by using 22 questions regarding sports, work, and leisure activities over a 24-hour period, measured in MET/hour per day [ 23 ]. Participants who stated that they had smoked more than 100 cigarettes in their lifetime were grouped as current smokers. Exposure to cigarette smoke at home, in the workplace, etc., is defined as passive smoking in people who are not smokers themselves [ 24 ]. The anthropometric measurements such as BMI, percent body fat (PBF), fat mass index (FMI), waist-hip ratio (WHR), WC, visceral fat area (VFA), skeletal muscle mass (SMM) and total body weight (TBW) were determined using an Impedance Analyzer BIA (Inbody 770, Inbody Co, Seoul, Korea). The dietary intake of participants was assessed using the food frequency questionnaire (FFQ) [ 25 ]. The lipid profile, consisting of triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and fasting blood sugar (FBS), was assessed by drawing 25 cc of blood from the participants. As per the protocol, participants were instructed to fast for 8–12 hours before the blood collection. The WWI was computed by dividing the waist circumference (cm) by the square root of the body weight (kg) [ 13 ]. A patient with cardiovascular disease (CVD) was someone who had experienced at least one of the following conditions: a history of ischemic heart disease (IHD), heart failure, angina, stroke, myocardial infarction (MI), and/or was currently taking medication for CVD. The participants' systolic and diastolic blood pressure (SBP and DBP) were assessed using the standard method while seated on a chair, following a 10-minute rest period, with measurements taken from both the right and left arms [ 26 ]. Subsequently, the average was computed. Participants with SBP ≥ 140mmHg and/or DBP ≥ 90mmHg, or those taking antihypertensive medications, were classified as hypertensive [ 26 ]. Statistical analysis In this study, Stata software version 14.2 (Stata Corp, College Station, TX, USA) was used to perform all analyses. The study was presented the basic characteristics of the participants as mean ± standard deviation and number (percentage) across WWI quartiles. We used the one-way ANOVA test for continuous variables and the chi-square test for qualitative variables to compare the differences among WWI quartiles. Logistic regression analysis was conducted to explore the association between CVD and WWI. The multiple regression model was adjusted for age, sex, residence, SES, smoking, physical activity, hypertension, energy intake, FBS, LDL, HDL, TG and TC variables. Throughout the analyses, a p-value of less than 0.05 with 95% confidence intervals (CIs) was deemed significant. Results The average age of the participants was 47.52 ± 8.29 years, 45.30% were men and 41.13% were rural residents. Among the 8,899 participants, 1445 (17.36%) had a CVD. Participants in the highest WWI quartile had significantly higher age compared to those in the lowest quartile (43.90 ± 7.16 vs. 51.10 ± 8.38, Ptrend < 0.001). The prevalence of hypertension and CVD has increased significantly across WWI quartiles (Ptrend < 0.001). The level of vigorous physical activity in the fourth WWI quartile is lower than that in the first quartile (Ptrend < 0.001). The average FBS, LDL-C, HDL-C and TC across WWI quartiles have increased significantly (Ptrend < 0.001) ( Table 1 ). Table 1 Baseline characteristics of the study participants according to weight-adjusted-waist index quartiles Variables Weight-adjusted-waist index P value trend* Q1 (n = 2114) Q2 (n = 2195) Q3 (n = 2281) Q4 (n = 2309) Age, (year) 43.90 ± 7.16 46.39 ± 7.76 48.34 ± 8.04 51.10 ± 8.38 < 0.001 Sex Men 1578 (39.15) 1283 (31.83) 884 (21.93) 286 (7.10) < 0.001 Women 536 (11.01) 912 (18.73) 1397 (28.70) 2023 (41.56) Residence Urban 1535 (29.30) 1378 (26.30) 1222 (23.33) 1104 (21.07) 0.001 Rural 579 (15.82) 817 (22.32) 1059 (28.93) 1205 (32.92) Socioeconomic status Low 458 (21.67) 617 (28.12) 840 (36.83) 1079 (46.77) < 0.001 Moderate 683 (32.31) 714 (32.54) 779 (34.15) 778 (33.72) High 973 (46.03) 863 (39.33) 662 (29.02) 450 (19.51) Smoking Never 764 (36.33) 906 (41.56) 963 (42.37) 1079 (46.91) < 0.001 Current 360 (17.12) 290 (13.30) 221 (9.72) 95 (4.13) Former 191 (9.08) 195 (8.94) 192 (8.45) 162 (7.04) Passive 788 (37.47) 789 (36.19) 897 (39.46) 964 (41.91) Physical activity (MET/h per day) Low 677 (32.02) 717 (32.67) 639 (28.01) 703 (30.45) < 0.001 Moderate 850 (40.21) 970 (44.19) 1184 (51.91) 1294 (56.04) Vigorous 587 (27.77) 508 (23.14) 458 (20.08) 312 (13.51) Hypertension 243 (11.49) 321 (14.62) 364 (15.96) 488 (21.13) < 0.001 CVD 207 (9.79) 307 (13.99) 413 (18.11) 618 (26.76) < 0.001 FBS (mg/dL) 92.39 ± 24.34 96.24 ± 29.30 98.73 ± 32.16 100.60 ± 31.18 < 0.001 TG (mg/dL) 139.42 ± 87.48 135.65 ± 81.74 134.66 ± 78.23 137.47 ± 78.98 0.551 LDL-C (mg/dL) 108.11 ± 30.03 109.31 ± 29.08 112.33 ± 30.90 116.49 ± 33.64 < 0.001 HLD-C (mg/dL) 43.44 ± 10.58 45.14 ± 10.76 47.66 ± 11.35 49.83 ± 11.62 0.001 TC (mg/dL) 179.44 ± 36.12 181.57 ± 35.93 186.91 ± 36.79 193.82 ± 40.51 < 0.001 * P- value was obtained one-way ANOVA and Chi square tests. Abbreviation: HDL-C: High-density lipoprotein cholesterol, LDL-C: Low-density lipoprotein cholesterol, TG: Triglycerides, TC: Total cholesterol, FBS: Fasting blood sugar, CVD: cardiovascular diseases; Q: quartile Table 2 presents the status of anthropometric indicators and nutritional intake of the participants based on WWI quartile. The participants in the highest WWI quartile had higher values of BMI, WC, WHR, FMI, VFA and PBF compared to those in the lowest quartile of WWI (Ptrend < 0.001). The average SMM in the first quartile was significantly higher than that in the fourth quartile of WWI (29.61 ± 5.70 vs. 22.32 ± 3.62, Ptrend < 0.001). Additionally, the average TBW was lower in the highest WWI quartile (Q1: 39.34 ± 6.92 vs. Q4: 30.25 ± 4.37, Ptrend < 0.001). The fourth quartile of WWI showed the highest percentage of energy intake from carbohydrates, while the first quartile of WWI showed the highest percentage of energy intake from protein. Table 2 Anthropometric indices and dietary intake of the study participants according to weight-adjusted-waist index quartiles Variables Weight-adjusted-waist index P value trend* Q1 (n = 2114) Q2 (n = 2195) Q3 (n = 2281) Q4 (n = 2309) Anthropometric indices BMI (kg/m 2 ) 26.58 ± 4.36 27.07 ± 4.50 27.31 ± 4.48 28.76 ± 4.90 < 0.001 WHR 0.93 ± 0.06 0.94 ± 0.06 0.94 ± 0.06 0.95 ± 0.06 < 0.001 WC (cm) 90.13 ± 9.20 95.58 ± 8.83 98.56 ± 8.80 104.10 ± 10.10 < 0.001 SMM (kg) 29.61 ± 5.70 27.21 ± 5.36 25.10 ± 4.54 22.32 ± 3.62 < 0.001 FMI 7.94 ± 3.51 8.97 ± 3.77 9.85 ± 3.77 11.87 ± 3.87 < 0.001 VFA (cm 2 ) 102.73 ± 45.80 114.64 ± 50.22 123.81 ± 49.97± 147.77 ± 48.94 < 0.001 PBF 28.80 ± 8.58 32.01 ± 9.11 34.95 ± 8.74 40.25 ± 7.48 < 0.001 TBW (liter) 39.34 ± 6.92 36.53 ± 6.50 33.58 ± 5.55 30.25 ± 4.37 < 0.001 Dietary intake Energy (kcal/d) 2792.39 ± 718.69 2622.03 ± 720.97 2476.39 ± 719.08 2224.99 ± 690.78 < 0.001 Carbohydrate (%E) 61.10 ± 6.10 61.46 ± 6.10 61.56 ± 6.21 61.70 ± 6.35 0.002 Fat (%E) 26.88 ± 5.94 26.62 ± 5.98 26.79 ± 5.89 26.85 ± 6.09 0.613 Protein (%E) 14.03 ± 2.26 13.83 ± 2.11 13.60 ± 2.10 13.48 ± 2.09 < 0.001 Bread and cereals (g/d) 520.49 ± 3.47 523.99 ± 3.38 508.03 ± 3.27 506.45 ± 3.31 0.001 Fruits (g/d) 264.70 ± 4.43 259.83 ± 4.31 269.18 ± 4.18 266.51 ± 4.23 0.397 Vegetables (g/d) 270.31 ± 3.51 268.68 ± 3.55 266.84 ± 3.45 269.07 ± 3.48 0.910 Dairy (g/d) 371.58 ± 8.01 427.60 ± 7.78 472.84 ± 7.54 482.39 ± 7.63 0.001 Legumes (g/d) 36.10 ± 0.66 32.17 ± 0.64 31.98 ± 0.62 31.24 ± 0.63 0.001 Red meat (g/d) 22.22 ± 0.56 20.50 ± 0.57 20.30 ± 0.56 18.81 ± 0.56 0.004 White meat (g/d) 53.31 ± 0.86 51.03 ± 0.84 48.24 ± 0.80 48.11 ± 0.82 < 0.001 *P- value was obtained one-way ANOVA test The mean ± SE of dietary intake is adjusted for daily energy intake Abbreviation: BMI: Body mass index; TBW: Total body water; VFA: Visceral fat area; PBF: Percent body fat; WC: Waist circumference; WHR: Waist hip ratio; SMM: Skeletal muscle mass; FMI: Fat mass index; Q: quartile The unadjusted model shows that CVD increases significantly with increasing WWI in men and women (Ptrend < 0.001). After adjusting for age, sex and residence, it was observed that the risk of CVD has increased 1.07 times (OR: 1.07, 95%CI: 0.88, 1.31) in the second quartile, 1.13 times (OR: 1.13, 95%CI: 0.93, 1.38) in the third quartile, and 1.26 times (OR: 1.26, 95%CI: 1.04, 1.54) in the fourth quartile of WWI (Ptrend = 0.025). After adjusting for age, sex, residence, SES, smoking, physical activity, hypertension, energy intake, FBS, LDL, HDL, TG, TC variables, we observed that the odds of developing CVD in the second, third, and fourth quartiles of WWI increased by 1.03, 1.25, and 1.36 times, respectively, compared to the first quartile (Ptrend = 0.010) ( Table 3 ). Table 3 The association between weight-adjusted-waist index and cardiovascular diseases Weight-adjusted-waist index quartiles Model 1 Model 2 Model 3 OR (95% CI) Q1 Ref (1.00) Ref (1.00) Ref (1.00) Q2 1.50 (1.24, 1.81) 1.07 (0.88, 1.31) 1.03 (0.79, 1.33) Q3 2.03 (1.70, 2.43) 1.13 (0.93, 1.38) 1.25 (0.98, 1.61) Q4 3.37 (2.83, 3.99) 1.26 (1.04, 1.54) 1.36 (1.11, 1.78) P trend < 0.001 0.025 0.010 Model 1 : Unadjusted; Model 2 : Adjusted for age, sex and residence; Model 3 : Adjusted for age, sex, residence, SES, smoking, physical activity, hypertension, energy intake, FBS, LDL, HDL, TG, TC Table 4 presents subgroup analyses stratified by age, sex, place of residence, SES, smoking, hypertension, physical activity, and obesity to assess the association between WWI and CVD. Table 4 Subgroup analysis of the association between weight-adjusted-waist index and cardiovascular diseases Subgroup OR (95% CI) * P value Age 35–50 years 1.22 (1.04, 1.45) 0.022 51–65 years 1.26 (1.10, 1.46) 0.002 Sex Men 1.24 (1.02, 1.59) 0.036 Women 1.14 (1.02, 1.10) 0.048 Residence Urban 1.17 (1.03, 1.33) 0.025 Rural 1.21 (0.98, 1.49) 0.069 Socioeconomic status Low 1.10 (0.92, 1.31) 0.312 Moderate 1.19 (0.98, 1.45) 0.072 High 1.30 (1.04, 1.63) 0.020 Smoking Never 1.13 (0.95, 1.35) 0.164 Current 0.90 (0.60, 1.34) 0.614 Former 1.26 (0.89, 1.78) 0.184 Passive 1.28 (1.10, 1.53) 0.007 Physical activity (MET/h per day) Low 1.15 (0.93, 1.40) 0.180 Moderate 1.23 (1.06, 1.44) 0.008 Vigorous 1.10 (0.82, 1.45) 0.530 Hypertension No 1.18 (1.03, 1.36) 0.017 Yes 1.14 (0.94, 1.36) 0.172 Body mass index < 25 kg/m 2 1.18 (1.02, 1.40) 0.023 ≥ 25 kg/m 2 1.10 (0.92, 1.32) 0.286 *Adjusted for age, sex, residence, SES, smoking, physical activity, hypertension, energy intake, FBS, LDL, HDL,TG, TC A positive relationship between WWI and CVD was observed in two age groups, with a stronger association found in the 50 to 65-year-old age group (OR: 1.26, 95% CI: 1.10, 1.46). Moreover, with every unit increase in the WWI, the odds of CVD increased by 24% in men and 14% in women (P < 0.05). In urban and rural, the odds of CVD have increased with the rise in the WWI, although this increase was not statistically significant in the rural. The findings of the ROC analysis revealed that the WWI exhibited superior predictive capability (AUC: 0.64, 95% CI: 0.61, 0.64) compared to BMI (AUC: 0.60, 95% CI: 0.58, 0.61) and WC (AUC: 0.61, 95% CI: 0.59, 0.62) in predicting CVD within the studied population ( Fig. 2 ). Discussion In this cross-sectional study involving 8,899 Iranian adults was found a positive association between WWI and CVD. The odds of developing CVD in the second, third, and fourth quartiles of WWI were 1.03, 1.25, and 1.36 times higher than in the first quartile, respectively. Furthermore, subgroup analyses revealed a stronger association between WWI and CVD in participants older than 50 years, male, urban residents, high SES, and among passive smokers. The findings of the ROC analysis revealed that the WWI exhibited superior predictive capability compared to BMI and WC in predicting CVD within the studied population. A cross-sectional study of US adults indicates a positive association between WWI and CVD [ 16 ]. In a prospective study involving a Chinese population, Ding et al. discovered a non-linear positive relationship between CVD mortality and WWI [ 27 ]. A cross-sectional study on 23,389 participants of the National Health and Nutrition Examination Survey (NHANES) has also shown that the prevalence of stroke increases significantly with increasing WWI [ 17 ]. Our study was also consistent with previous studies and indicated a positive association between WWI and the odds of CVD. Given that weight is adjusted in the calculation of the WWI Index, this metric primarily reflects central obesity independently of weight. WWI has demonstrated superior accuracy compared to BMI [ 14 , 28 , 29 ]. Traditionally, changes in body weight were frequently utilized in the BMI formula to indicate fat accumulation and obesity in the adult population due to height stability. However, in recent years, researchers have raised doubts about this concept [ 8 , 30 ]. Furthermore, with the introduction of the muscle-fat axis concept, researchers have noted that weight loss can also be associated with a reduction in muscle mass, often accompanied by an increase in visceral fat. This may suggest a more precise assessment of obesity [ 31 ]. Several non-traditional obesity indices have been found to accurately predict metabolic diseases including CVD, such as Visceral adiposity index (VAI), body roundness index (BRI), and a body shape index (ABSI) and atherogenic index of plasma (AIP) [ 32 , 33 ]. Hamzeh et al.'s research involving 7,362 individuals demonstrated that VAI and AIP levels are reliable markers for predicting CVD [ 33 ]. Nonetheless, the calculation of these indicators can be complex and necessitate invasive methods. Recent studies have indicated that WWI is a better predictor of the risk of metabolic diseases compared to BMI and WC [ 18 , 34 , 35 ]. In the present study, ROC curve analysis revealed that WWI exhibits greater predictive power for CVD compared to BMI and WC. In general, computing WWI is straightforward, cost-effective, and feasible. The WWI's superior efficacy in forecasting CVD risk is evident and calls for the consideration of healthcare providers within health systems. The subgroup analyses identified a more robust link between WWI and CVD among individuals aged over 50, while two prior studies suggested that the association between WWI and CVD was stronger in younger individuals (under 50 years), they attributed this to the higher obesity rates in younger age groups [ 16 , 17 ]. This discrepancy could be explained by the nearly equal prevalence of obesity in both age groups in the current study. The association between WWI and CVD can be explained by various mechanisms. Central obesity can elevate oxidative stress in the body, closely linked to atherosclerosis development [ 36 , 37 ]. An increased WWI signifies an accumulation of excessive body fat and a reduction in muscle mass. This imbalance between muscle and fat leads to disturbances in adipocytokine release, inflammatory responses, endothelial dysfunction, and diminished physical function, culminating in the development of CVD [ 38 – 40 ]. Additionally, metabolic conditions associated with obesity like glucose intolerance, hypertriglyceridemia, and hypertension also heighten the risk of CVD, particularly stroke [ 41 ]. This study has several strong points. Firstly, we utilized a national sample of the general adult population in Western Iran from the RaNCD study, which implemented strict study protocols and quality controls. Secondly, to enhance result accuracy, we adjusted for most potential confounding variables. Nevertheless, it cannot be claimed that we eliminated the impact of all potential confounding variables. However, there were limitations in this study. The cross-sectional nature of the research prevented us from establishing a direct cause-and-effect relationship between WWI and CVD. Therefore, additional longitudinal studies are required to validate these results. Conclusion The results of the current study indicate that high levels of WWI are strongly associated to a higher risk of CVD in Iranian adults. A more robust association was observed between WWI and CVD in participants older than 50 years, male, urban residents, with high SES, and among passive smokers. Furthermore, the WWI was identified as the best obesity index for predicting CVD compared to BMI and WC in the study population. Therefore, WWI management is recommended for preventing CVD in Iranian adults. However, further confirmation of this finding is needed through longitudinal and prospective studies. Declarations Authors’ contributions S.S and A.Aconceived the idea of the study. Y.P and E.SH contributed to the interpretation of the results. S.S and A.Adrafted the original manuscript. All authors reviewed the manuscript draft and revised it critically on intellectual content. All authors approved the final version of the manuscript to be published. Ethics approval and consent to participate The study was approved by the ethics committee of Kermanshah University of Medical Sciences (KUMS.REC.1394.318). All methods were carried out in accordance with relevant guidelines and regulations. All the participants were provided oral and written informed consent. This study was conducted by the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials The data analyzed in the study are available from the corresponding author upon reasonable request. Competing interests The authors declare no conflicts of interest. Funding This research was supported by Kermanshah University of Medical Sciences (grant number: 92472). The Iranian Ministry of Health and Medical Education has also contributed to the funding used in the PERSIAN Cohort through Grant no 700/534. Acknowledgements The authors thank the PERSIAN cohort Study collaborators and of Kermanshah University of Medical Sciences. References Vusirikala A, Thomas T, Bhala N, Tahrani A, Thomas G, Nirantharakumar K. 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Adult current smoking: differences in definitions and prevalence estimates—NHIS and NSDUH, 2008. Journal of environmental and public health. 2012;2012. Eghtesad S, Hekmatdoost A, Faramarzi E, Homayounfar R, Sharafkhah M, Hakimi H, et al. Validity and reproducibility of a food frequency questionnaire assessing food group intake in the PERSIAN Cohort Study. Frontiers in Nutrition. 2023;10. Chobanian AV. National heart, lung, and blood institute joint national committee on prevention, detection, evaluation, and treatment of high blood pressure; national high blood pressure education program coordinating committee. The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC 7 report. Jama. 2003;289:2560-72. Ding C, Shi Y, Li J, Li M, Hu L, Rao J, et al. Association of weight-adjusted-waist index with all-cause and cardiovascular mortality in China: A prospective cohort study. Nutrition, Metabolism and Cardiovascular Diseases. 2022;32(5):1210-7. Li Q, Qie R, Qin P, Zhang D, Guo C, Zhou Q, et al. Association of weight-adjusted-waist index with incident hypertension: The Rural Chinese Cohort Study. Nutrition, Metabolism and Cardiovascular Diseases. 2020;30(10):1732-41. Cai S, Zhou L, Zhang Y, Cheng B, Zhang A, Sun J, et al. Association of the weight-adjusted-waist index with risk of all-cause mortality: a 10-year follow-up study. Frontiers in Nutrition. 2022;9:894686. Heymsfield SB, Cefalu WT. Does body mass index adequately convey a patient's mortality risk? Jama. 2013;309(1):87-8. Zambon Azevedo V, Silaghi CA, Maurel T, Silaghi H, Ratziu V, Pais R. Impact of sarcopenia on the severity of the liver damage in patients with non-alcoholic fatty liver disease. Frontiers in nutrition. 2022;8:774030. Baveicy K, Mostafaei S, Darbandi M, Hamzeh B, Najafi F, Pasdar Y. Predicting metabolic syndrome by visceral adiposity index, body roundness index and a body shape index in adults: a cross-sectional study from the Iranian RaNCD cohort data. Diabetes, Metabolic Syndrome and Obesity. 2020:879-87. Hamzeh B, Pasdar Y, Mirzaei N, Faramani RS, Najafi F, Shakiba E, et al. Visceral adiposity index and atherogenic index of plasma as useful predictors of risk of cardiovascular diseases: evidence from a cohort study in Iran. Lipids in health and disease. 2021;20(1):1-10. Cao S, Hu X, Shao Y, Wang Y, Tang Y, Ren S, et al. Relationship between weight-adjusted-waist index and erectile dysfunction in the United State: results from NHANES 2001-2004. Frontiers in Endocrinology. 2023;14:1128076. Xie F, Xiao Y, Li X, Wu Y. Association between the weight-adjusted-waist index and abdominal aortic calcification in United States adults: Results from the national health and nutrition examination survey 2013–2014. Frontiers in Cardiovascular Medicine. 2022;9:948194. Čolak E, Pap D. The role of oxidative stress in the development of obesity and obesity-related metabolic disorders. Journal of Medical Biochemistry. 2021;40(1):1. Park JS, Park SB. Association between abdominal obesity and oxidative stress in Korean adults. Korean Journal of Family Medicine. 2019;40(6):395. Hamjane N, Benyahya F, Nourouti NG, Mechita MB, Barakat A. Cardiovascular diseases and metabolic abnormalities associated with obesity: What is the role of inflammatory responses? A systematic review. Microvascular Research. 2020;131:104023. Evans K, Abdelhafiz D, Abdelhafiz AH. Sarcopenic obesity as a determinant of cardiovascular disease risk in older people: a systematic review. Postgraduate medicine. 2021;133(8):831-42. Singhal A. Endothelial dysfunction: role in obesity-related disorders and the early origins of CVD. Proceedings of the Nutrition Society. 2005;64(1):15-22. Wiklund P, Toss F, Weinehall L, Hallmans G, Franks PW, Nordstrom A, et al. Abdominal and gynoid fat mass are associated with cardiovascular risk factors in men and women. The Journal of Clinical Endocrinology & Metabolism. 2008;93(11):4360-6. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4331367","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":296590473,"identity":"ec26a806-dd17-4c1d-9367-c640ce93f525","order_by":0,"name":"Sepehr Sadafi","email":"","orcid":"","institution":"Kermanshah University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sepehr","middleName":"","lastName":"Sadafi","suffix":""},{"id":296590474,"identity":"077afa83-991f-445b-a970-18c7cecbcb36","order_by":1,"name":"Ali Azizi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIie3OsQqCQBzH8b8c6PKPWy+IfIW/SAct9SqCL9AUQWCGYIvQs7Q0B4IuRmviEvQEbTc05GBLg+fYcN/l4ODD7w9gMv1jjhUDECDH7w/TEdaRcTacdC9VQ+/iCdu/1Cqa+FU1OylYuOCMHr1E5FYiBNkor5lsEEIvZg71z+RWLIgQ5Q1l094ZALP7hduuqIAE+keUtYKdnlBupeLSztAok3eEXE+8lsxjClBUxbpBKr1UR6bl4Vmrd7TkWXiu1Wbrcl70k987ATQbJpPJZBrSBwy1Ni6Ccpc9AAAAAElFTkSuQmCC","orcid":"","institution":"Kermanshah University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Ali","middleName":"","lastName":"Azizi","suffix":""},{"id":296590475,"identity":"adbd1548-8d45-4279-b2d5-0157b32ac96f","order_by":2,"name":"Ebrahim Shakiba","email":"","orcid":"","institution":"Kermanshah University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ebrahim","middleName":"","lastName":"Shakiba","suffix":""},{"id":296590476,"identity":"bfc2dcb3-19a6-413e-bbf2-5efd7c945588","order_by":3,"name":"Yahya Pasdar","email":"","orcid":"","institution":"Kermanshah University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yahya","middleName":"","lastName":"Pasdar","suffix":""}],"badges":[],"createdAt":"2024-04-26 19:26:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4331367/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4331367/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56021093,"identity":"905b0ec9-bbe4-4e84-bfc0-30f52f9ed9f5","added_by":"auto","created_at":"2024-05-07 16:11:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49675,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study participants\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4331367/v1/40ccd720c4947b1d73338461.jpg"},{"id":56021096,"identity":"aa644c64-f20d-41b2-a190-72a492fc4b79","added_by":"auto","created_at":"2024-05-07 16:11:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67009,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis of weight-adjusted-waist index for predicting of cardiovascular diseases risk\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4331367/v1/b2ddab7d24f6c88ca281b269.jpg"},{"id":56052968,"identity":"d1fa098a-dde1-4987-887d-d163e3e84bfb","added_by":"auto","created_at":"2024-05-08 02:24:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":806234,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4331367/v1/6b1c95bc-06d6-4b9f-8bff-216f8ca5ca29.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between weight-adjusted-waist index and cardiovascular diseases: a cross-sectional study based on RaNCD cohort data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity represents a significant risk factor for numerous chronic metabolic conditions that have been considered in the health system [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It has been estimated that by 2030, around 38% of the world's adult population will be obese [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In a meta-analysis study by Wong et al. (2020), the overall prevalence of central obesity in the world was reported to be 41.5% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Previous studies have indicated that obesity increases the risk of CVD [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, onflicting findings from some studies suggest that overweight and obese individuals may have a lower risk of CVD and hypertension compared to those with normal weight, and being overweight or obese may play a protective role [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong various anthropometric indices, body mass index (BMI) and waist circumference (WC) are widely used as the primary indices to assess both general and central obesity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Nevertheless, these markers are not able to distinguish between fat and muscle mass [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Park and et al. (2018) introduced the weight-adjusted-waist index (WWI) as a new measure of obesity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This index assesses central obesity by taking into account both muscle and fat mass, and it is calculated by dividing WC by the square root of body weight [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Several studies have reported a positive association between WWI and chronic diseases such as CVD, stroke, non-alcoholic fatty liver disease (NAFLD) and chronic kidney disease [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, a comprehensive study in this field has not been conducted in Iran.\u003c/p\u003e \u003cp\u003eCVD is a multifactorial chronic condition, with obesity identified as one of its risk factors. Consequently, reducing obesity can be effective in the prevention and management of CVD. A robust definition of obesity highlights the strength of the connection between this risk factor and CVD. Hence, the current study aims to explore the association between WWI and CVD among adults in western Iran.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources and participants\u003c/h2\u003e \u003cp\u003eThis study is a cross-sectional analysis of data from the baseline phase of the Ravansar non-communicable diseases study (RaNCD) cohort study [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The RaNCD cohort is one of the Prospective Epidemiological Research Studies in Iran (PERSIAN) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], which started with a 15-year design since 2014 in Ravansar city located in Kermanshah province in the west of Iran. The study population of RaNCD cohort is urban and rural adult men and women of Ravansar. All participants from the initial phase of the RaNCD cohort were involved in this research. Following the application of exclusion criteria, 8,899 participants were examined \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy variables\u003c/h2\u003e \u003cp\u003eThe data was collected in compliance with the cohort study protocol, and trained professionals used digital questionnaires to gather information. The socio-economic status (SES) was established by considering factors such as education level, place of residence, welfare amenities and wealth through the principal component analysis (PCA) method. After this analysis, the SES was divided into three categories (low, moderate, and high) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Physical activity was evaluated by using 22 questions regarding sports, work, and leisure activities over a 24-hour period, measured in MET/hour per day [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Participants who stated that they had smoked more than 100 cigarettes in their lifetime were grouped as current smokers. Exposure to cigarette smoke at home, in the workplace, etc., is defined as passive smoking in people who are not smokers themselves [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe anthropometric measurements such as BMI, percent body fat (PBF), fat mass index (FMI), waist-hip ratio (WHR), WC, visceral fat area (VFA), skeletal muscle mass (SMM) and total body weight (TBW) were determined using an Impedance Analyzer BIA (Inbody 770, Inbody Co, Seoul, Korea). The dietary intake of participants was assessed using the food frequency questionnaire (FFQ) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe lipid profile, consisting of triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and fasting blood sugar (FBS), was assessed by drawing 25 cc of blood from the participants. As per the protocol, participants were instructed to fast for 8\u0026ndash;12 hours before the blood collection.\u003c/p\u003e \u003cp\u003eThe WWI was computed by dividing the waist circumference (cm) by the square root of the body weight (kg) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A patient with cardiovascular disease (CVD) was someone who had experienced at least one of the following conditions: a history of ischemic heart disease (IHD), heart failure, angina, stroke, myocardial infarction (MI), and/or was currently taking medication for CVD. The participants' systolic and diastolic blood pressure (SBP and DBP) were assessed using the standard method while seated on a chair, following a 10-minute rest period, with measurements taken from both the right and left arms [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Subsequently, the average was computed. Participants with SBP\u0026thinsp;\u0026ge;\u0026thinsp;140mmHg and/or DBP\u0026thinsp;\u0026ge;\u0026thinsp;90mmHg, or those taking antihypertensive medications, were classified as hypertensive [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn this study, Stata software version 14.2 (Stata Corp, College Station, TX, USA) was used to perform all analyses. The study was presented the basic characteristics of the participants as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and number (percentage) across WWI quartiles. We used the one-way ANOVA test for continuous variables and the chi-square test for qualitative variables to compare the differences among WWI quartiles. Logistic regression analysis was conducted to explore the association between CVD and WWI. The multiple regression model was adjusted for age, sex, residence, SES, smoking, physical activity, hypertension, energy intake, FBS, LDL, HDL, TG and TC variables. Throughout the analyses, a p-value of less than 0.05 with 95% confidence intervals (CIs) was deemed significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe average age of the participants was 47.52\u0026thinsp;\u0026plusmn;\u0026thinsp;8.29 years, 45.30% were men and 41.13% were rural residents. Among the 8,899 participants, 1445 (17.36%) had a CVD. Participants in the highest WWI quartile had significantly higher age compared to those in the lowest quartile (43.90\u0026thinsp;\u0026plusmn;\u0026thinsp;7.16 vs. 51.10\u0026thinsp;\u0026plusmn;\u0026thinsp;8.38, Ptrend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The prevalence of hypertension and CVD has increased significantly across WWI quartiles (Ptrend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The level of vigorous physical activity in the fourth WWI quartile is lower than that in the first quartile (Ptrend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The average FBS, LDL-C, HDL-C and TC across WWI quartiles have increased significantly (Ptrend\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the study participants according to weight-adjusted-waist index quartiles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eWeight-adjusted-waist index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value trend*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2114)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2195)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2281)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2309)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.90\u0026thinsp;\u0026plusmn;\u0026thinsp;7.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.39\u0026thinsp;\u0026plusmn;\u0026thinsp;7.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.34\u0026thinsp;\u0026plusmn;\u0026thinsp;8.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.10\u0026thinsp;\u0026plusmn;\u0026thinsp;8.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1578 (39.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1283 (31.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e884 (21.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e286 (7.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e536 (11.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e912 (18.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1397 (28.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2023 (41.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1535 (29.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1378 (26.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1222 (23.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1104 (21.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e579 (15.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e817 (22.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1059 (28.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1205 (32.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eSocioeconomic status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e458 (21.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e617 (28.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e840 (36.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1079 (46.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e683 (32.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e714 (32.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e779 (34.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e778 (33.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e973 (46.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e863 (39.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e662 (29.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e450 (19.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e764 (36.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e906 (41.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e963 (42.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1079 (46.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e360 (17.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e290 (13.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e221 (9.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95 (4.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e191 (9.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195 (8.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e192 (8.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e162 (7.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePassive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e788 (37.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e789 (36.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e897 (39.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e964 (41.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003ePhysical activity (MET/h per day)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e677 (32.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e717 (32.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e639 (28.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e703 (30.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e850 (40.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e970 (44.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1184 (51.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1294 (56.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVigorous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e587 (27.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e508 (23.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e458 (20.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e312 (13.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e243 (11.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e321 (14.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e364 (15.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e488 (21.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e207 (9.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e307 (13.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e413 (18.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e618 (26.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBS (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.39\u0026thinsp;\u0026plusmn;\u0026thinsp;24.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.24\u0026thinsp;\u0026plusmn;\u0026thinsp;29.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.73\u0026thinsp;\u0026plusmn;\u0026thinsp;32.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100.60\u0026thinsp;\u0026plusmn;\u0026thinsp;31.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139.42\u0026thinsp;\u0026plusmn;\u0026thinsp;87.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.65\u0026thinsp;\u0026plusmn;\u0026thinsp;81.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e134.66\u0026thinsp;\u0026plusmn;\u0026thinsp;78.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e137.47\u0026thinsp;\u0026plusmn;\u0026thinsp;78.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108.11\u0026thinsp;\u0026plusmn;\u0026thinsp;30.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109.31\u0026thinsp;\u0026plusmn;\u0026thinsp;29.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112.33\u0026thinsp;\u0026plusmn;\u0026thinsp;30.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116.49\u0026thinsp;\u0026plusmn;\u0026thinsp;33.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLD-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.44\u0026thinsp;\u0026plusmn;\u0026thinsp;10.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.14\u0026thinsp;\u0026plusmn;\u0026thinsp;10.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.66\u0026thinsp;\u0026plusmn;\u0026thinsp;11.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.83\u0026thinsp;\u0026plusmn;\u0026thinsp;11.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179.44\u0026thinsp;\u0026plusmn;\u0026thinsp;36.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181.57\u0026thinsp;\u0026plusmn;\u0026thinsp;35.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e186.91\u0026thinsp;\u0026plusmn;\u0026thinsp;36.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e193.82\u0026thinsp;\u0026plusmn;\u0026thinsp;40.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e* P- value was obtained one-way ANOVA and Chi square tests.\u003c/p\u003e \u003cp\u003eAbbreviation: HDL-C: High-density lipoprotein cholesterol, LDL-C: Low-density lipoprotein cholesterol, TG: Triglycerides, TC: Total cholesterol, FBS: Fasting blood sugar, CVD: cardiovascular diseases; Q: quartile\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the status of anthropometric indicators and nutritional intake of the participants based on WWI quartile. The participants in the highest WWI quartile had higher values of BMI, WC, WHR, FMI, VFA and PBF compared to those in the lowest quartile of WWI (Ptrend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The average SMM in the first quartile was significantly higher than that in the fourth quartile of WWI (29.61\u0026thinsp;\u0026plusmn;\u0026thinsp;5.70 vs. 22.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.62, Ptrend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, the average TBW was lower in the highest WWI quartile (Q1: 39.34\u0026thinsp;\u0026plusmn;\u0026thinsp;6.92 vs. Q4: 30.25\u0026thinsp;\u0026plusmn;\u0026thinsp;4.37, Ptrend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The fourth quartile of WWI showed the highest percentage of energy intake from carbohydrates, while the first quartile of WWI showed the highest percentage of energy intake from protein.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnthropometric indices and dietary intake of the study participants according to weight-adjusted-waist index quartiles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eWeight-adjusted-waist index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value trend*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2114)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2195)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2281)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2309)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eAnthropometric indices\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.58\u0026thinsp;\u0026plusmn;\u0026thinsp;4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.07\u0026thinsp;\u0026plusmn;\u0026thinsp;4.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.31\u0026thinsp;\u0026plusmn;\u0026thinsp;4.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.76\u0026thinsp;\u0026plusmn;\u0026thinsp;4.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.13\u0026thinsp;\u0026plusmn;\u0026thinsp;9.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.58\u0026thinsp;\u0026plusmn;\u0026thinsp;8.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.56\u0026thinsp;\u0026plusmn;\u0026thinsp;8.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104.10\u0026thinsp;\u0026plusmn;\u0026thinsp;10.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMM (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.61\u0026thinsp;\u0026plusmn;\u0026thinsp;5.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.21\u0026thinsp;\u0026plusmn;\u0026thinsp;5.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.10\u0026thinsp;\u0026plusmn;\u0026thinsp;4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.94\u0026thinsp;\u0026plusmn;\u0026thinsp;3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.97\u0026thinsp;\u0026plusmn;\u0026thinsp;3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.85\u0026thinsp;\u0026plusmn;\u0026thinsp;3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.87\u0026thinsp;\u0026plusmn;\u0026thinsp;3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVFA (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.73\u0026thinsp;\u0026plusmn;\u0026thinsp;45.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114.64\u0026thinsp;\u0026plusmn;\u0026thinsp;50.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123.81\u0026thinsp;\u0026plusmn;\u0026thinsp;49.97\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e147.77\u0026thinsp;\u0026plusmn;\u0026thinsp;48.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.80\u0026thinsp;\u0026plusmn;\u0026thinsp;8.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.01\u0026thinsp;\u0026plusmn;\u0026thinsp;9.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.95\u0026thinsp;\u0026plusmn;\u0026thinsp;8.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.25\u0026thinsp;\u0026plusmn;\u0026thinsp;7.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBW (liter)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.34\u0026thinsp;\u0026plusmn;\u0026thinsp;6.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.53\u0026thinsp;\u0026plusmn;\u0026thinsp;6.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.58\u0026thinsp;\u0026plusmn;\u0026thinsp;5.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.25\u0026thinsp;\u0026plusmn;\u0026thinsp;4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDietary intake\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy (kcal/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2792.39\u0026thinsp;\u0026plusmn;\u0026thinsp;718.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2622.03\u0026thinsp;\u0026plusmn;\u0026thinsp;720.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2476.39\u0026thinsp;\u0026plusmn;\u0026thinsp;719.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2224.99\u0026thinsp;\u0026plusmn;\u0026thinsp;690.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbohydrate (%E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.10\u0026thinsp;\u0026plusmn;\u0026thinsp;6.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.46\u0026thinsp;\u0026plusmn;\u0026thinsp;6.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.56\u0026thinsp;\u0026plusmn;\u0026thinsp;6.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.70\u0026thinsp;\u0026plusmn;\u0026thinsp;6.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat (%E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.88\u0026thinsp;\u0026plusmn;\u0026thinsp;5.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.62\u0026thinsp;\u0026plusmn;\u0026thinsp;5.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.79\u0026thinsp;\u0026plusmn;\u0026thinsp;5.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.85\u0026thinsp;\u0026plusmn;\u0026thinsp;6.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein (%E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.83\u0026thinsp;\u0026plusmn;\u0026thinsp;2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.60\u0026thinsp;\u0026plusmn;\u0026thinsp;2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBread and cereals (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e520.49\u0026thinsp;\u0026plusmn;\u0026thinsp;3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e523.99\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e508.03\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e506.45\u0026thinsp;\u0026plusmn;\u0026thinsp;3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFruits (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e264.70\u0026thinsp;\u0026plusmn;\u0026thinsp;4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e259.83\u0026thinsp;\u0026plusmn;\u0026thinsp;4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e269.18\u0026thinsp;\u0026plusmn;\u0026thinsp;4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e266.51\u0026thinsp;\u0026plusmn;\u0026thinsp;4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetables (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e270.31\u0026thinsp;\u0026plusmn;\u0026thinsp;3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e268.68\u0026thinsp;\u0026plusmn;\u0026thinsp;3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e266.84\u0026thinsp;\u0026plusmn;\u0026thinsp;3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e269.07\u0026thinsp;\u0026plusmn;\u0026thinsp;3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDairy (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e371.58\u0026thinsp;\u0026plusmn;\u0026thinsp;8.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e427.60\u0026thinsp;\u0026plusmn;\u0026thinsp;7.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e472.84\u0026thinsp;\u0026plusmn;\u0026thinsp;7.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e482.39\u0026thinsp;\u0026plusmn;\u0026thinsp;7.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLegumes (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed meat (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite meat (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e*P- value was obtained one-way ANOVA test\u003c/p\u003e \u003cp\u003eThe mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE of dietary intake is adjusted for daily energy intake\u003c/p\u003e \u003cp\u003eAbbreviation: BMI: Body mass index; TBW: Total body water; VFA: Visceral fat area; PBF: Percent body fat; WC: Waist circumference; WHR: Waist hip ratio; SMM: Skeletal muscle mass; FMI: Fat mass index; Q: quartile\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe unadjusted model shows that CVD increases significantly with increasing WWI in men and women (Ptrend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for age, sex and residence, it was observed that the risk of CVD has increased 1.07 times (OR: 1.07, 95%CI: 0.88, 1.31) in the second quartile, 1.13 times (OR: 1.13, 95%CI: 0.93, 1.38) in the third quartile, and 1.26 times (OR: 1.26, 95%CI: 1.04, 1.54) in the fourth quartile of WWI (Ptrend\u0026thinsp;=\u0026thinsp;0.025). After adjusting for age, sex, residence, SES, smoking, physical activity, hypertension, energy intake, FBS, LDL, HDL, TG, TC variables, we observed that the odds of developing CVD in the second, third, and fourth quartiles of WWI increased by 1.03, 1.25, and 1.36 times, respectively, compared to the first quartile (Ptrend\u0026thinsp;=\u0026thinsp;0.010) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe association between weight-adjusted-waist index and cardiovascular diseases\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWeight-adjusted-waist index quartiles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef (1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef (1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef (1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.50 (1.24, 1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07 (0.88, 1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.79, 1.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.03 (1.70, 2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13 (0.93, 1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25 (0.98, 1.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.37 (2.83, 3.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26 (1.04, 1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.36 (1.11, 1.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP trend\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 1\u003c/b\u003e: Unadjusted; \u003cb\u003eModel 2\u003c/b\u003e: Adjusted for age, sex and residence; \u003cb\u003eModel 3\u003c/b\u003e: Adjusted for age, sex, residence, SES, smoking, physical activity, hypertension, energy intake, FBS, LDL, HDL, TG, TC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents subgroup analyses stratified by age, sex, place of residence, SES, smoking, hypertension, physical activity, and obesity to assess the association between WWI and CVD.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubgroup analysis of the association between weight-adjusted-waist index and cardiovascular diseases\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI) *\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;50 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22 (1.04, 1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u0026ndash;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26 (1.10, 1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24 (1.02, 1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14 (1.02, 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17 (1.03, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21 (0.98, 1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocioeconomic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10 (0.92, 1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19 (0.98, 1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30 (1.04, 1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13 (0.95, 1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90 (0.60, 1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26 (0.89, 1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePassive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28 (1.10, 1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity (MET/h per day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15 (0.93, 1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23 (1.06, 1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVigorous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10 (0.82, 1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18 (1.03, 1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14 (0.94, 1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18 (1.02, 1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10 (0.92, 1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e*Adjusted for age, sex, residence, SES, smoking, physical activity, hypertension, energy intake, FBS, LDL, HDL,TG, TC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA positive relationship between WWI and CVD was observed in two age groups, with a stronger association found in the 50 to 65-year-old age group (OR: 1.26, 95% CI: 1.10, 1.46). Moreover, with every unit increase in the WWI, the odds of CVD increased by 24% in men and 14% in women (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In urban and rural, the odds of CVD have increased with the rise in the WWI, although this increase was not statistically significant in the rural.\u003c/p\u003e \u003cp\u003eThe findings of the ROC analysis revealed that the WWI exhibited superior predictive capability (AUC: 0.64, 95% CI: 0.61, 0.64) compared to BMI (AUC: 0.60, 95% CI: 0.58, 0.61) and WC (AUC: 0.61, 95% CI: 0.59, 0.62) in predicting CVD within the studied population \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this cross-sectional study involving 8,899 Iranian adults was found a positive association between WWI and CVD. The odds of developing CVD in the second, third, and fourth quartiles of WWI were 1.03, 1.25, and 1.36 times higher than in the first quartile, respectively. Furthermore, subgroup analyses revealed a stronger association between WWI and CVD in participants older than 50 years, male, urban residents, high SES, and among passive smokers. The findings of the ROC analysis revealed that the WWI exhibited superior predictive capability compared to BMI and WC in predicting CVD within the studied population.\u003c/p\u003e \u003cp\u003eA cross-sectional study of US adults indicates a positive association between WWI and CVD [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In a prospective study involving a Chinese population, Ding et al. discovered a non-linear positive relationship between CVD mortality and WWI [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A cross-sectional study on 23,389 participants of the National Health and Nutrition Examination Survey (NHANES) has also shown that the prevalence of stroke increases significantly with increasing WWI [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Our study was also consistent with previous studies and indicated a positive association between WWI and the odds of CVD.\u003c/p\u003e \u003cp\u003eGiven that weight is adjusted in the calculation of the WWI Index, this metric primarily reflects central obesity independently of weight. WWI has demonstrated superior accuracy compared to BMI [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Traditionally, changes in body weight were frequently utilized in the BMI formula to indicate fat accumulation and obesity in the adult population due to height stability. However, in recent years, researchers have raised doubts about this concept [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Furthermore, with the introduction of the muscle-fat axis concept, researchers have noted that weight loss can also be associated with a reduction in muscle mass, often accompanied by an increase in visceral fat. This may suggest a more precise assessment of obesity [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Several non-traditional obesity indices have been found to accurately predict metabolic diseases including CVD, such as Visceral adiposity index (VAI), body roundness index (BRI), and a body shape index (ABSI) and atherogenic index of plasma (AIP) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Hamzeh et al.'s research involving 7,362 individuals demonstrated that VAI and AIP levels are reliable markers for predicting CVD [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Nonetheless, the calculation of these indicators can be complex and necessitate invasive methods. Recent studies have indicated that WWI is a better predictor of the risk of metabolic diseases compared to BMI and WC [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In the present study, ROC curve analysis revealed that WWI exhibits greater predictive power for CVD compared to BMI and WC. In general, computing WWI is straightforward, cost-effective, and feasible. The WWI's superior efficacy in forecasting CVD risk is evident and calls for the consideration of healthcare providers within health systems.\u003c/p\u003e \u003cp\u003eThe subgroup analyses identified a more robust link between WWI and CVD among individuals aged over 50, while two prior studies suggested that the association between WWI and CVD was stronger in younger individuals (under 50 years), they attributed this to the higher obesity rates in younger age groups [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This discrepancy could be explained by the nearly equal prevalence of obesity in both age groups in the current study.\u003c/p\u003e \u003cp\u003eThe association between WWI and CVD can be explained by various mechanisms. Central obesity can elevate oxidative stress in the body, closely linked to atherosclerosis development [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. An increased WWI signifies an accumulation of excessive body fat and a reduction in muscle mass. This imbalance between muscle and fat leads to disturbances in adipocytokine release, inflammatory responses, endothelial dysfunction, and diminished physical function, culminating in the development of CVD [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Additionally, metabolic conditions associated with obesity like glucose intolerance, hypertriglyceridemia, and hypertension also heighten the risk of CVD, particularly stroke [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study has several strong points. Firstly, we utilized a national sample of the general adult population in Western Iran from the RaNCD study, which implemented strict study protocols and quality controls. Secondly, to enhance result accuracy, we adjusted for most potential confounding variables. Nevertheless, it cannot be claimed that we eliminated the impact of all potential confounding variables. However, there were limitations in this study. The cross-sectional nature of the research prevented us from establishing a direct cause-and-effect relationship between WWI and CVD. Therefore, additional longitudinal studies are required to validate these results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results of the current study indicate that high levels of WWI are strongly associated to a higher risk of CVD in Iranian adults. A more robust association was observed between WWI and CVD in participants older than 50 years, male, urban residents, with high SES, and among passive smokers. Furthermore, the WWI was identified as the best obesity index for predicting CVD compared to BMI and WC in the study population. Therefore, WWI management is recommended for preventing CVD in Iranian adults. However, further confirmation of this finding is needed through longitudinal and prospective studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.S\u0026nbsp;and\u0026nbsp;A.Aconceived the idea of the study.\u0026nbsp;Y.P\u0026nbsp;and\u0026nbsp;E.SH\u0026nbsp;contributed to the interpretation of the results.\u0026nbsp;S.S\u0026nbsp;and\u0026nbsp;A.Adrafted the original manuscript. All authors reviewed the manuscript draft and revised it critically on intellectual content. All authors approved the final version of the manuscript to be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the ethics committee of Kermanshah University of Medical Sciences\u0026nbsp;(KUMS.REC.1394.318).\u0026nbsp;All methods were carried out in accordance with relevant guidelines and regulations. All the participants were provided oral and written informed consent. This study was conducted by the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data analyzed in the study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by\u0026nbsp;Kermanshah University of Medical Sciences (grant number: 92472).\u0026nbsp;The Iranian Ministry of Health and Medical Education has also contributed to the funding used in the PERSIAN Cohort through Grant no 700/534.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the PERSIAN cohort Study collaborators and of Kermanshah University of Medical Sciences.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVusirikala A, Thomas T, Bhala N, Tahrani A, Thomas G, Nirantharakumar K. 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The Journal of Clinical Endocrinology \u0026amp; Metabolism. 2008;93(11):4360-6.\u003c/li\u003e\n\u003c/ol\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":"Cardiovascular diseases, obesity, weight-adjusted-waist index, Persian","lastPublishedDoi":"10.21203/rs.3.rs-4331367/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4331367/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe weight-adjusted-waist index (WWI) is a relatively new index to measurment obesity. The present study was conducted with the aim of investigating the relationship between WWI and cardiovascular disease (CVD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis cross-sectional study included 8,899 participants from the Ravansar non-communicable diseases study (RaNCD) cohort study. The WWI was calculated by dividing waist circumference (WC) by the square root of weight. CVD is described as having a history of stroke, ischemic heart disease (IHD), angina, heart failure, myocardial infarction (MI), or using medication for CVD. The study utilized multiple logistic regression to assess the association between WWI and CVD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe average age of the participants was 47.52± 8.29 years, 45.30% were men and 41.13% were rural residents. The prevalence of CVD was 17.36%. A positive association between WWI and CVD was observed. Participants in the highest quartile of WWI had a 36% (OR= 1.36, 95%CI:1.11, 1.78) higher odds of CVD than those in the lowest quartile (OR= 1.03, 95%CI: 0.79, 1.33) (Ptrend= 0.010)\u003cstrong\u003e. \u003c/strong\u003eThe subgroup analyses revealed stronger associations between WWI and CVD in participants older than 50 years of age, male, urban residents, high SES, and passive smokers (P\u0026lt;0.001).\u003cstrong\u003e \u003c/strong\u003eThe receiver operating characteristic (ROC) analysis indicated that WWI has a greater ability to predict CVD (AUC: 0.64, 95%CI: 0.61, 0.64) compared to body mass index (BMI) (AUC: 0.60, 95%CI: 0.58, 0.61) and WC (AUC: 0.61, 95%CI: 0.59, 0.62)\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclussion: \u003c/strong\u003eThe association between higher WWI and an increased risk of CVD suggests that WWI management is crucial for preventing CVD.\u003c/p\u003e","manuscriptTitle":"Association between weight-adjusted-waist index and cardiovascular diseases: a cross-sectional study based on RaNCD cohort data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-07 16:11:50","doi":"10.21203/rs.3.rs-4331367/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":"089b659a-6646-4bc3-872b-c8e15cd81b86","owner":[],"postedDate":"May 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-08T02:16:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-07 16:11:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4331367","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4331367","identity":"rs-4331367","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}