Association between the weight-adjusted-waist index and cardiovascular disease among US adults: Results from the National Health and Nutrition Examination Survey 2009-2016

Association between the weight-adjusted-waist index and cardiovascular disease among US adults: Results from the National Health and Nutrition Examination Survey 2009-2016 | 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 the weight-adjusted-waist index and cardiovascular disease among US adults: Results from the National Health and Nutrition Examination Survey 2009-2016 Feng Xie, Meng Li, Kai Li, Yanqing Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2021929/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 As a new obesity-related index, the weight-adjusted-waist index (WWI) appears to be a good predictor of cardiovascular disease (CVD) in East Asian populations. This study aimed to evaluate the association between WWI and the risk of CVD in United States (US) adults. Methods The data were obtained from the 2009–2016 National Health and Nutrition Examination Survey (NHANES). WWI was calculated as waist circumference divided by the square root of weight, and CVD was ascertained based on self-reported physician diagnoses. Multivariable regression analysis and subgroup analysis were performed to evaluate the association between WWI and CVD. Results A total of 21,040 participants were included, with the mean age being 47.11 ± 16.79 years. There was a positive linear relationship between WWI and the odds of CVD ( P = 0.310). After adjusting for all covariates, each unit of increased WWI was associated with a 48% increased risk of CVD (odds ratio [OR]: 1.48, 95% confidence interval [CI]: 1.25–1.74). Moreover, compared with the lowest quintile (< 10.3 cm/√kg), the multivariable-adjusted OR was 3.18 (95% CI: 1.81–5.60) in the highest quintile (≥ 11.8 cm/√kg). Besides, subgroup analyses showed that stronger associations between WWI and CVD were detected in participants younger than 50 years of age ( P for interaction < 0.001). Conclusions High levels of WWI were significantly associated with an increased risk of CVD in US adults, particularly in people under 50 years of age. These findings indicate that WWI may be an intervention indicator to reduce the risk of CVD in the general adult population. weight-adjusted-waist index obesity cardiovascular disease NHANES cross-sectional study Figures Figure 1 Figure 2 Figure 3 Introduction Obesity is a major public health problem worldwide. The global prevalence of obesity has doubled since 1980, and it is expected to reach 18% in men and surpass 21% in women by 2025 [ 1 , 2 ]. According to statistics from the National Health and Nutrition Examinations Survey (NHANES) 2015–2016, approximately 39.6% of adults and 18.5% of youths were obese in the United States [ 3 ]. Furthermore, large-scale and long-term studies have consistently demonstrated that obesity is associated with a significantly increased risk of cardiovascular disease (CVD) morbidity and mortality [ 4 , 5 ]. Therefore, early identification of obese individuals with high risk is crucial to preventing CVD. Body mass index (BMI) and waist circumference (WC) are the most commonly used obesity-related indices. Furthermore, accumulating data has indicated that they are strongly associated with a rise in the prevalence of hypertension, stroke, and other CVDs [ 6 , 7 ]. Despite that, these anthropometric indicators can not clearly distinguish between muscle mass and fat mass [ 8 , 9 ]. This may partly contribute to the “obesity paradox” between BMI or WC and some health outcomes [ 10 , 11 ]. Although the waist-to-height ratio (WHtR) appears to be superior in assessing obesity, it remains controversial in predicting obesity-related CVD risk and mortality [ 12 , 13 ]. As a result, these traditional indices may not accurately reflect the association between obesity and CVD. In this context, a new anthropometric index called the weight-adjusted-waist index (WWI) was proposed, which standardized WC for body weight. Subsequently, Park et al. found that in the Korean National Cohort study, WWI was a better predictor of CVD mortality than BMI, WC, and WHtR. Also, only WWI demonstrated a linear positive correlation between adiposity indices and cardiovascular mortality, but not BMI, WC, or WHtR [ 14 ]. Moreover, several prospective studies in China also discovered that higher WWI levels were associated with an increased risk of all-cause and cardiovascular mortality [ 15 , 16 ]. However, the associations were only validated in East Asian populations. Given the substantial difference in body composition by race, it is unclear whether WWI was also suitable for predicting the risk of CVD in the US population. Therefore, our study was designed to investigate the association between WWI and the risk of CVD in US adults based on data obtained from the NHANES. Methods Study design and population NHANES is an ongoing cross-sectional survey administered by the National Center for Health Statistics (NCHS), which is part of the Centers for Disease Control and Prevention (CDC), to assess the health and nutritional status of adults and children in the United States. It documents a repeated two-year cycle with five major parts, including demographic data, dietary data, examination data, laboratory data, and questionnaire data. Due to the multi-stage, stratified, and probability sampling design, the included participants showed relatively great representativeness. The details of NHANES study design and methods have been previously described [ 17 ]. The NHANES study protocol was approved by the NCHS Research Ethics Review Board, and written informed consent was obtained from each participant. We used the continuous NHANES data from 2009 to 2016 (N = 40,439). Participants younger than 18 years of age (N = 15,943) and those with incomplete CVD evaluation data (N = 1,231) were excluded. Then, we further excluded participants with missing data on weight and WC value (N = 2,225). Finally, a total of 21,040 participants were included for further analysis. The detailed flow chart of participant selection is shown in Supplementary Fig. 1. All data included in this manuscript are publicly available at https://www.cdc.gov/nchs/nhanes/ . Covariates Potential covariates, including demographic data (age, gender, educational level, and race/ethnicity), lifestyle variables (smoking status and alcohol drinking), anthropometric measurements (height, weight, WC, and blood pressure [BP]), and laboratory results (hemoglobin A1c [HbA1c], total bilirubin [TBIL], low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol [HDL-C], total cholesterol [TC], triglycerides [TG], serum creatinine [Scr], serum uric acid [SUA], and urinary albumin/creatinine ratio [UACR]) were selected based on clinical relevance and statistical significance. Demographics and lifestyle data were derived from the household interview questionnaires administered by highly trained medical personnel. Anthropometric indicators and biochemical parameters were obtained through medical examinations and subsequent laboratory assessments in the Mobile Examination Centre (MEC). The educational level was further categorized as less than 9th grade, 9-11th grade, high school graduate, some college or AA degree, and college graduate or above. Race/ethnicity was classified as Mexican American, other Hispanic, non-Hispanic black, non-Hispanic white, and other races. The smoking status was determined by “Smoked at least 100 cigarettes in life”, and the alcohol drinking status was evaluated by “Had at least 12 alcohol drinks per year”. The laboratory results, including HbA1c, TBIL, LDL-C, HDL-C, TC, TG, Scr, SUA and UACR levels were determined using standardized methods. The detailed measurement processes of these variables are publicly available at https://www.cdc.gov/nchs/nhanes/ . Additionally, the BMI in kg/m 2 was calculated by dividing weight (kg) by the square of height (m 2 ), and the WHtR was determined by WC (cm) divided by height (cm). The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration creatinine equation [ 18 ]. Exposure variable and outcomes In this study, WWI (cm/√kg) was designed as exposure variable. WWI was calculated as WC (cm) divided by the square root of weight (kg) .Weight was measured to the nearest 0.1 kg using a digital weight scale, and WC was measured by a retractable steel measuring tape, positioning the measuring tape around the waist at the uppermost lateral border of the ilium at the midaxillary line. The full procedure, including the protocols, equipment, and quality control, was described at https://wwwn.cdc.gov/nchs/nhanes/ContinuousNhanes/Manuals.aspx?BeginYear=2009 . The outcome variable was CVD. According to previous studies, CVD was defined as a composite of 5 self-reported cardiovascular outcomes, which included congestive heart failure (CHF), coronary heart disease (CHD), angina/angina pectoris, heart attack, and stroke [ 19 ]. All participants were asked the following questions: “Has a doctor or other health professional ever told you that you have congestive heart failure/coronary heart disease/angina pectoris/heart attack/stroke? ” Participants who answered “yes” to any of the questions were considered to have CVD. Additionally, we also collected data for each CVD subtype to further analyze the association with WWI. Statistical analysis All statistical analyses were performed in accordance with CDC guidelines. Four waves of continuous survey data (NHANES 2009–2010, 2011–2012, 2013–2014, and 2015–2016) were combined, and an 8-year sampling weight was calculated by using a quarter of the 2-year sampling weight (WTMEC2YR). Data are presented as weighted mean ± standard deviation (SD) or median (quartile range, IQR) for continuous variables, and frequency (weighted percentage) for categorical variables. Comparisons between the CVD and non-CVD groups were performed using either the weighted Chi-square test (categorical variables) or the weighted linear regression model (continuous variables). WWI was divided into quintiles for further analysis, and the reference category was considered to be the lowest quintile. Multivariate logistic regression model was used to calculate odds ratios (ORs) and corresponding 95% confidence intervals (CIs) to determine the prevalence of CVD related to WWI. We developed three models to adjust for potential confounders: model 1 was a crude model; model 2 was adjusted for age, gender, and race; model 3 was the same as model 2 with additional adjustment for education level, smoking, alcohol drinking, eGFR, systolic and diastolic BP, HbA1c, TBIL, LDL-C, HDL-C, TC, TG, Scr, SUA and UACR. The restricted cubic spline model was used for the dose-response analysis between WWI and total CVD. Subgroup analysis stratified by gender, age, race, BMI, WC, eGFR, smoking, and alcohol drinking was conducted by stratified multivariate regression analysis. A receiver operating characteristic (ROC) curve was used to analyze the predictive value of WWI and traditional obesity-related indices (BMI, WC, and WHtR) for CVD. Besides, we used multiple imputation (MI), based on 5 replications and the Markov-chain Monte Carlo method in the SAS MI procedure, to maximize statistical power and minimize bias that might occur if covariates with missing data were excluded from data analyses. All analyses were performed using R version 4.0.3 ( www.R-project.org ) and EmpowerStates ( www.empowerstats.com ). A two-sided P value of < 0.05 was considered statistically significant. Results Baseline characteristics The characteristics of the study population are presented in Table 1 . A total of 21,040 participants were included in this study, 51.47% of whom were females, with an average age of 47.11 ± 16.79 years. The weighted mean WWI was 10.98 ± 0.83 cm/√kg overall, and the weighted prevalence of total CVD, CHF, CHD, angina, heart attack, and stroke were 7.91% (N = 2063), 2.20% (N = 599), 3.21% (N = 794), 1.96% (N = 481), 3.12% (N = 810) and 2.47% (N = 691), respectively. Compared with the non-CVD group, the CVD group was older and more likely to be male; to have higher BMI, WC, WHtR, WWI, SBP, HbA1c, TG, Scr, SUA, and UACR levels; to have a higher proportion of smoking and non-Hispanic White individuals; to have a lower rate of alcohol drinking; and to have lower eGFR, LDL-C, and HDL-C levels (all P < 0.05). Table 1 Baseline characteristics of the study participants. Variables # Total (n = 21040) Non-CVD (n = 18977) CVD (n = 2063) P value Age (years) 47.11 ± 16.79 45.65 ± 16.25 64.15 ± 13.32 < 0.001 Gender (%) < 0.001 Male 10243 (48.53) 9059 (47.93) 1184 (55.46) Female 10797 (51.47) 9918 (52.07) 879 (44.54) Race (%) < 0.001 Mexican American 3153 (8.65) 2941 (8.96) 212 (5.04) Other Hispanic 2263 (5.97) 2078 (6.14) 185 (4.08) Non-Hispanic White 8505 (66.26) 7449 (65.73) 1056 (72.50) Non-Hispanic Black 4424 (11.12) 3973 (11.06) 451 (11.70) Other Race 2695 (8.01) 2536 (8.12) 159 (6.68) Educational level (%) < 0.001 < 9th grade 2135 (5.45) 1832 (5.16) 303 (8.83) 9−11th grade 2915 (10.56) 2551 (10.22) 364 (14.57) High school 4630 (21.28) 4114 (20.86) 516 (26.18) College 6273 (32.09) 5715 (32.18) 558 (30.98) Graduate or above 5070 (30.57) 4750 (31.53) 320 (19.41) BMI (kg/m 2 ) 28.94 ± 6.74 28.79 ± 6.68 30.65 ± 7.25 < 0.001 WC (cm) 99.20 ± 16.38 98.57 ± 16.21 106.57 ± 16.57 < 0.001 WHtR 0.59 ± 0.10 0.59 ± 0.10 0.64 ± 0.10 < 0.001 WWI (cm/√kg) 10.98 ± 0.83 10.93 ± 0.82 11.55 ± 0.75 < 0.001 Smoking (%) 9183 (44.00) 7933 (42.46) 1250 (61.91) < 0.001 Alcohol drinking (%) 13935 (77.98) 12579 (78.38) 1356 (73.57) < 0.001 SBP (mmHg) 122.11 ± 17.17 121.49 ± 16.75 129.32 ± 20.04 < 0.001 DBP (mmHg) 70.52 ± 12.17 70.76 ± 11.96 67.66 ± 14.11 < 0.001 eGFR (ml/min/1.73m 2 ) 118.05 ± 46.03 120.32 ± 45.50 91.72 ± 43.90 < 0.001 HbA1c (%) 5.63 ± 0.93 5.59 ± 0.88 6.17 ± 1.25 < 0.001 TBIL (umol/L) 11.49 ± 5.24 11.48 ± 5.22 11.71 ± 5.44 0.082 HDL-C (mmol/L) 1.39 ± 0.44 1.39 ± 0.44 1.29 ± 0.43 < 0.001 LDL-C (mmol/L) 2.95 ± 0.91 2.99 ± 0.90 2.55 ± 0.88 < 0.001 TC (mmol/L) 5.01 ± 1.08 5.04 ± 1.07 4.63 ± 1.09 < 0.001 TG (mmol/L) 1.40 ± 1.18 1.38 ± 1.17 1.57 ± 1.19 < 0.001 Scr (umol/L) 77.91 ± 30.25 76.56 ± 27.31 93.57 ± 51.11 < 0.001 SUA (umol/L) 321.45 ± 83.23 318.95 ± 82.03 350.49 ± 91.20 < 0.001 UACR (mg/g) 7.05 (4.55, 13.33) 6.78 (4.46, 12.22) 11.82 (6.16, 33.19) < 0.001 # Values are presented as weighted mean ± standard deviation, medians (interquartile range), or frequency (weighted percentages) when appropriate. Abbreviations: CVD, cardiovascular disease; BMI, body mass index; WC, waist circumference; WHtR, waist-to-height ratio; WWI, weight-adjusted-waist index; SBP, systolic blood pressure; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HbA1c, hemoglobin A1c; TBIL, total bilirubin; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; TG, triglycerides; Scr, serum creatinine; SUA, serum uric acid; UACR, urinary albumin/creatinine ratio. Association of WWI with CVD Multivariable logistic regression models were performed to explore the association between CVD and the WWI as continuous and categorical variables (Table 2 ). When WWI was analyzed as a continuous variable, we found that increased WWI was associated with a higher risk of CVD (Model 1: OR = 2.50, 95% CI: 2.33–2.69; Model 2: OR = 1.74, 95% CI: 1.58–1.92; all P < 0.001). In the fully adjusted Model 3, the results indicated that each unit of increased WWI was associated with 48% increased risk of CVD (OR = 1.48, 95% CI: 1.25–1.74, P < 0.001). In sensitivity analysis, the multivariable-adjusted ORs (reference to Quintile 1) was 1.74 (95% CI: 1.00-3.03; P = 0.048) for Quintile 2, 2.38 (95% CI: 1.38–4.12; P = 0.002) for Quintile 3, 2.56 (95% CI: 1.47–4.45; P < 0.001) for Quintile 4, and 3.18 (95% CI: 1.81–5.60; P < 0.001) for Quintile 5, indicating a stable positive association between higher WWI and increased risk of CVD ( P for trend < 0.001). Moreover, we reanalyzed the association between WWI and CVD using imputation data and the results did not change qualitatively (Supplementary Table 1). In addition, the restricted cubic spline with a multivariate logistic regression model revealed that there was a positive linear relationship between WWI and the odds of CVD ( P for nonlinear = 0.310) (Fig. 1 ). Table 2 Adjusted odds ratios (95% CI) for association between WWI and the prevalence of CVD. WWI (cm/√kg) Events (%) OR (95% CI), P value Model 1 Model 2 Model 3 Continuous 2063 (7.91) 2.50 (2.33, 2.69) < 0.001 1.74 (1.58, 1.92) < 0.001 1.48 (1.25, 1.74) < 0.001 Categorical Quintile 1 (< 10.3) 108 (1.75) Reference Reference Reference Quintile 2 (10.3–10.8) 215 (3.95) 2.31 (1.69, 3.14) < 0.001 1.42 (1.04, 1.96) 0.027 1.74 (1.00, 3.03) 0.048 Quintile 3 (10.8–11.3) 391 (7.96) 4.85 (3.64, 6.45) < 0.001 2.25 (1.67, 3.04) < 0.001 2.38 (1.38, 4.12) 0.002 Quintile 4 (11.3–11.8) 560 (11.19) 7.06 (5.36, 9.30) < 0.001 2.65 (1.96, 3.58) < 0.001 2.56 (1.47, 4.45) < 0.001 Quintile 5 (≥ 11.8) 789 (17.57) 11.94 (9.12, 15.63) < 0.001 3.87 (2.86, 5.22) < 0.001 3.18 (1.81, 5.60) < 0.001 P for trend < 0.001 < 0.001 < 0.001 Model 1: crude model; Model 2: adjusted for age, gender and race; Model 3: adjusted for age, gender, race, education level, smoking, alcohol drinking, systolic blood pressure, diastolic blood pressure, estimated glomerular filtration rate, hemoglobin A1c, total bilirubin, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, triglycerides, serum creatinine, serum uric acid, and urinary albumin/creatinine ratio; Abbreviations: WWI, weight-adjusted-waist index; CVD, cardiovascular disease; OR, odds ratio; CI, confidence interval. Association between WWI and specific CVDs We further analyzed the associations between WWI and the prevalence of five specific CVDs (CHF, CHD, angina, heart attack, and stroke) (Supplementary Table 2). After adjusting for all covariates (Model 3), the fifth quintile of WWI was positively associated with the increased prevalence of CHF (quintile 5: OR = 2.67, 95% CI: 1.12-6.33, P = 0.026), angina (quintile 5: OR = 3.16, 95% CI: 1.05-9.54, P = 0.041) and heart attack (quintile 5: OR = 13.68, 95% CI: 4.93-38.03, P < 0.001), and the fourth quintile of WWI was associated with the increased prevalence of CHD (quintile 4: OR = 2.57, 95% CI: 1.06-6.27, P = 0.037). However, no quintile level of WWI was found to be associated with stroke. Subgroup analysis We performed a further stratified analysis to assess the effect of WWI on CVD in various subgroups (Figure 2). None of the variables, including gender (female or male), race (Mexican American, other Hispanic, non-Hispanic white, non-Hispanic black and other race), BMI (< 25 or ≥ 25 kg/m 2 ), WC (normal and abnormal), eGFR ( 0.05). Nevertheless, there was a significant interaction between WWI and age (< 50 or ≥ 50 years) on CVD. A stronger positive association between WWI and CVD was found in participants younger than 50 years of age (OR = 2.80, 95% CI: 2.17-3.61) compared with their counterparts (OR = 1.62; 95% CI: 1.42-1.85) ( P for interaction < 0.001). ROC curves between WWI and traditional obesity indices The ROC curves of the different indices for CVD are shown in Table 3 and Figure 3. For both males and females, WWI had the highest area under the curve (AUC) of 0.736 (95% CI: 0.722-0.751, P < 0.05) and 0.677 (95% CI: 0.659-0.694, P < 0.05), with a cut-off value of 11.11 cm/√kg and 11.47 cm/√kg, respectively. Table 3 Comparison of the ability of different obesity-related indices to predict CVD. Test AUC (95% CI) Cut-off Specificity Sensitivity Youden index P value Males BMI 0.567 (0.549, 0.584) 27.86 0.523 0.579 0.102 < 0.001 WC 0.638 (0.622, 0.653) 97.15 0.463 0.752 0.215 < 0.001 WHtR 0.659 (0.643, 0.674) 0.58 0.542 0.705 0.247 < 0.001 WWI 0.736 (0.722, 0.751) 11.11 0.654 0.709 0.363 < 0.001 Females BMI 0.569 (0.550, 0.589) 29.62 0.579 0.533 0.112 < 0.001 WC 0.612 (0.593, 0.631) 104.45 0.699 0.464 0.163 < 0.001 WHtR 0.629 (0.610, 0.647) 0.59 0.504 0.694 0.198 < 0.001 WWI 0.677 (0.659, 0.694) 11.47 0.619 0.639 0.258 < 0.001 Abbreviations: WWI, weight-adjusted-waist index; BMI, body mass index; WHtR, waist-to-height ratio; WC, waist circumference; AUC, area under the curve; CI, confidence interval. Discussion To our knowledge, this cross-sectional study was the first to demonstrate the relationship between WWI and CVD in a national representative sample of the US adult population. The results indicated that WWI was significantly associated with an increased risk of CVD, presenting a nearly linear dose-response relationship. The highest WWI category (≥11.8 cm/√kg) was 3.18-fold associated with CVD as compared with the lowest WWI category (<10.3 cm/√kg). Moreover, the subgroup analyses showed that a stronger association between WWI and CVD was detected in participants younger than 50 years of age. Besides, the ROC curve analysis showed that WWI was the best screening tool for CVD compared to BMI, WC, and WHtR. Obesity, a condition of excessive body fat accumulation caused by long-term energy intake exceeding energy expenditure, is a well-established risk factor for CVD and mortality in adult populations [5]. Accordingly, measurement of body fat has been a critical issue in clinical practice for assessing obesity and then identifying individuals at risk of CVD. However, most anthropometric indices, such as BMI and WC, cannot distinguish between differences in muscle and fat mass or fat distribution. This is one of the reasons for the seemingly paradoxical relationship between obesity and some health outcomes [8, 11]. WWI is a newly proposed anthropometric index that has the ability to assess fat and muscle mass reciprocally. Indeed, a recent study involving 602 participants showed that WWI was positively correlated with total and abdominal fat measures but negatively correlated with appendicular skeletal muscle mass in older adults [20]. Furthermore, several studies have demonstrated that WWI is positively correlated with CVD mortality in East Asian populations [14-16]. Our study further verified the association between WWI and CVD in the US adult population. Moreover, the WWI showed a better predictive value for CVD than BMI, WC, and WHtR. These findings suggest that WWI may be a superior indicator of obesity, which is not limited to East Asians but generally applicable to diverse populations. Due to the substantial differences in body composition by race, anthropometric indicators such as BMI have different cut-points for identifying obesity according to race [21]. For instance, the risk of diabetes in Chinese populations with a BMI of 26.9 kg/m² was the same as that of White populations with a BMI of 30 kg/m² [22]. Nevertheless, in the case of WWI, there were no statistically significant differences in the mean and distribution between Whites, Asians, and African Americans, supporting our current findings [23]. This may be related to the fact that WWI measures the ratio of fat and muscle mass rather than the absolute fat amount. Moreover, the Korean Frailty and Aging Cohort study showed that WWI was strongly associated with sarcopenic obesity, which is defined as the presence of high fat mass and low muscle mass combined with low physical function [24]. In this context, elevated WWI reflects a state of excessive body fat accumulation and increased muscle mass loss. The muscle-fat imbalance results in dysregulation of adipocytokine release, inflammatory responses, endothelial dysfunction, and declined physical function, ultimately leading to the development of CVD [25-27]. The subgroup analysis demonstrated that there was no dependence of gender, race, BMI, WC, eGFR, smoking, and alcohol drinking on this positive association between WWI with CVD. Nevertheless, the WWI-CVD association was more significant in participants younger than 50 years of age ( P for interaction < 0.001). Similar to our results, a study by Cai showed that the association between WWI and all-cause mortality was not significant at age ≥75 years [16]. In the Rural Chinese Cohort Study, Li et al. found an association between WWI and hypertension in people under 60 years of age, but it disappeared in older adults (age ≥ 60 years) [28]. These results might be attributed to the different body fat distribution between older and younger individuals [29]. Despite that, further research is needed to investigate the relationship between age, WWI and CVD. Our study has important implications for clinical practice. First, we found a positive association between WWI and CVD in populations outside East Asia, suggesting that WWI could be a universal health index that applies to various races or ethnic groups. Besides, WWI based on weight and WC was considered to be a better predictor for CVD in our study. Therefore, for people with high WWI levels, early assessment of target organ damage and timely intervention could reduce the risk of CVD and improve outcomes. Moreover, due to its simple calculation and economic nature, WWI can be used by medical and health institutions at all levels, especially in areas where medical resources are limited. Strengths And Limitation This study has several strengths. We used a national representative sample of the general adult population of the US from NHANES, which applied rigorous study protocols and quality controls. Furthermore, we adjusted for most confounding covariates to ensure that our findings were reliable. Additionally, we used MI to maximize statistical power and minimize bias that might occur if covariates with missing data were excluded from data analyses. However, the limitations should also be noted. Firstly, due to the cross-sectional nature of NHANES, we could not obtain a causal relationship between WWI and CVD. Therefore, further longitudinal studies are needed to verify these findings. Secondly, although we have adjusted for many confounding covariates, we could not rule out any potential residual confounding, such as genetic factors and drug use. Thirdly, CVD was ascertained based on participants’ self-reported data, which may lead to some recall bias. However, previous studies have demonstrated that NHANES self-reported outcomes are a valid method for determining prevalence [30]. Conclusions This study demonstrated that high levels of WWI were significantly associated with an increased risk of CVD in US adults, particularly in people under 50 years of age. These findings indicate that WWI may be an intervention indicator to reduce the risk of CVD in the general adult population. However, further longitudinal studies are still needed to clarify the precise causality of this relationship. Abbreviations NHANES: National Health and Nutrition Examinations Survey; NCHS: National Center for Health Statistics; CDC: Centers for Disease Control and Prevention; MEC: Mobile Examination Centre; CVD: Cardiovascular disease; BMI: Body mass index; WC: Waist circumference; WHtR: Waist-to-height ratio; WWI: Weight-adjusted-waist index; BP: Blood pressure; HbA1c: Hemoglobin A1c; TBIL: Total bilirubin; LDL-C: Low-density lipoprotein cholesterol; HDL-C: High-density lipoprotein cholesterol; TC: Total cholesterol; TG: Triglycerides; Scr: Serum creatinine; SUA: Serum uric acid; UACR: Urinary albumin/creatinine ratio; eGFR: Estimated glomerular filtration rate; CHF: Congestive heart failure; CHD: Coronary heart disease; ROC: Receiver operating characteristic; AUC: Area under the curve; MI: Multiple imputation. Declarations Ethics approval and consent to participate The NHANES was reviewed and approved by the NCHS Research Ethics Review Board, and each participant provided informed consent. All NHANES data released by the NCHS is de-identified, and remained anonymous during data analysis. Consent for publication Not applicable. Availability of data and materials The data that support the findings of this study are available from the corresponding author upon reasonable request. Competing interests There are no competing interests to declare. Funding This work was supported by the National Key Research and Development Plan of China (No. 20212BBG71004), the National Natural Science Foundation of China (No. 82160085), the Beijing Health Promotion Association (No. 20181BCB24013), and the Natural science funding (No. 20202BAB206005). Author contributions FX and YQW conceived and designed the study. FX, ML, and KL contributed to data collection and statistical analysis. FX drafted the manuscript. YQW had primary responsibility for the final content of the manuscript. All authors interpreted the results, and reviewed and approved the manuscript. Acknowledgements We thank all NHANES participants and staff for their valuable efforts and contributions. References Chooi YC, Ding C, Magkos F. The epidemiology of obesity . Metabolism. 2019; 92 :6–10. NCD Risk Factor Collaboration NCD-RisC . Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants . Lancet. 2016; 387 ( 10026 ):1377–96. Hales CM, Carroll MD, Fryar CD, Ogden CL. Prevalence of Obesity Among Adults and Youth: United States, 2015–2016 . NCHS Data Brief. 2017;( 288 ):1–8. van Dis I, Kromhout D, Geleijnse JM, Boer JM, Verschuren WM. Body mass index and waist circumference predict both 10-year nonfatal and fatal cardiovascular disease risk: study conducted in 20,000 Dutch men and women aged 20–65 years . Eur J Cardiovasc Prev Rehabil 2009; 16 ( 6 ):729–34. Khan SS, Ning H, Wilkins JT, Allen N, Carnethon M, Berry JD, Sweis RN, Lloyd-Jones DM. Association of body mass index with lifetime risk of cardiovascular disease and compression of morbidity . JAMA Cardiol 2018; 3 ( 4 ):280–7. Zhang M, Zhao Y, Wang G, Zhang H, Ren Y, Wang B, et al. Body mass index and waist circumference combined predicts obesity-related hypertension better than either alone in a rural Chinese population . Sci Rep 2016; 6 : 31935. Campbell DJ, Gong FF, Jelinek MV, Castro JM, Coller JM, McGrady M, et al. Threshold body mass index and sex-specific waist circumference for increased risk of heart failure with preserved ejection fraction . Eur J Prev Cardiol 2019; 26 ( 15 ): 1594–602. Romero-Corral A, Somers VK, Sierra-Johnson J, Thomas RJ, Collazo-Clavell ML, Korinek J, Allison TG, Batsis JA, Sert-Kuniyoshi FH, Lopez-Jimenez F. Accuracy of body mass index in diagnosing obesity in the adult general population . Int J Obes (Lond) 2008; 32 ( 6 ):959–66. Antonopoulos AS, Oikonomou EK, Antoniades C, Tousoulis D. From the BMI paradox to the obesity paradox: the obesity-mortality association in coronary heart disease . Obes Rev 2016; 17 ( 10 ): 989–1000. Clark AL, Fonarow GC, Horwich TB. Waist circumference, body mass index, and survival in systolic heart failure: the obesity paradox revisited . J Card Fail 2011; 17 ( 5 ):374–80. Kim NH, Lee J, Kim TJ, Kim NH, Choi KM, Baik SH, Choi DS, Pop-Busui R, Park Y, Kim SG. Body mass index and mortality in the general population and in subjects with chronic disease in Korea: a nationwide cohort study (2002–2010) . PLoS One 2015; 10 ( 10 ): e139924. Pasdar Y, Moradi S, Moludi J, Saiedi S, Moradinazar M, Hamzeh B, Jafarabadi MA, Najafi F. Waist-to-height ratio is a better discriminator of cardiovascular disease than other anthropometric indicators in Kurdish adults . Sci Rep 2020; 10 ( 1 ): 16228. Kammar-García A, Hernández-Hernández ME, López-Moreno P, Ortíz-Bueno AM, Martínez-Montaño ML. Relation of body composition indexes to cardiovascular disease risk factors in young adults . Semergen 2019; 45 ( 3 ): 147–55. Park Y, Kim NH, Kwon TY, Kim SG. A novel adiposity index as an integrated predictor of cardiometabolic disease morbidity and mortality . Sci Rep 2018; 8 ( 1 ): 16753. 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 . Nutr Metab Cardiovasc Dis 2022; 32 ( 5 ): 1210–7. 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 . Front Nutr 2022; 9 : 894686. Zipf G, Chiappa M, Porter KS, Ostchega Y, Lewis BG, Dostal J. National health and nutrition examination survey: plan and operations, 1999–2010 . Vital Health Stat 2013; ( 56 ) 1 : 1–37. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI, et al. A new equation to estimate glomerular filtration rate . Ann Intern Med 2009; 150 ( 9 ):604–12. Xu C, Weng Z, Zhang L, Xu J, Dahal M, Basnet TB, Gu A. HDL cholesterol: A potential mediator of the association between urinary cadmium concentration and cardiovascular disease risk . Ecotoxicol Environ Saf 2021; 208 : 111433. Kim NH, Park Y, Kim NH, Kim SG. Weight-adjusted waist index reflects fat and muscle mass in the opposite direction in older adults . Age Ageing 2021; 50 ( 3 ): 780–6. Deurenberg P, Deurenberg-Yap M, Guricci S. Asians are different from Caucasians and from each other in their body mass index/body fat per cent relationship . Obes Rev 2002; 3 ( 3 ): 141–6. Caleyachetty R, Barber TM, Mohammed NI, Cappuccio FP, Hardy R, Mathur R, Banerjee A, Gill P. Ethnicity-specific BMI cutoffs for obesity based on type 2 diabetes risk in England: a population-based cohort study . Lancet Diabetes Endocrinol 2021; 9 ( 7 ): 419–26. Kim JY, Choi J, Vella CA, Criqui MH, Allison MA, Kim NH. Associations between Weight-Adjusted Waist Index and Abdominal Fat and Muscle Mass: Multi-Ethnic Study of Atherosclerosis . Diabetes Metab J 2022. Kim JE, Choi J, Kim M, Won CW. Assessment of existing anthropometric indices for screening sarcopenic obesity in older adults . Br J Nutr 2022 :1–13. 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 . Microvasc Res 2020; 131 : 104023. Evans K, Abdelhafiz D, Abdelhafiz AH. Sarcopenic obesity as a determinant of cardiovascular disease risk in older people: a systematic review . Postgrad Med 2021; 133 ( 8 ): 831–42. Singhal A. Endothelial dysfunction: role in obesity-related disorders and the early origins of CVD . Proc Nutr Soc 2005; 64 ( 1 ): 15–22. 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 . Nutr Metab Cardiovasc Dis 2020; 30 ( 10 ): 1732–41. Szulc P, Duboeuf F, Chapurlat R. Age-related changes in fat mass and distribution in men-the cross-sectional STRAMBO study . J Clin Densitom 2017; 20 ( 4 ): 472–9. Lopez-Jimenez F, Batsis JA, Roger VL, Brekke L, Ting HH, Somers VK. Trends in 10-year predicted risk of cardiovascular disease in the United States, 1976 to 2004 . Circ Cardiovasc Qual Outcomes 2019; 2 ( 5 ): 443–50. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.doc 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2021929","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":134125735,"identity":"d657b198-9386-4194-b36a-439e0365c3e5","order_by":0,"name":"Feng Xie","email":"","orcid":"","institution":"Nanchang University Second Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Xie","suffix":""},{"id":134125736,"identity":"e11b5b86-2696-44fc-81b3-3dea0dd3fd67","order_by":1,"name":"Meng Li","email":"","orcid":"","institution":"Nanchang University Second Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Li","suffix":""},{"id":134125737,"identity":"c413c6f8-1648-45d6-90f0-c8cdce65f22e","order_by":2,"name":"Kai Li","email":"","orcid":"","institution":"Nanchang University Second Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Li","suffix":""},{"id":134125738,"identity":"b08cdaa0-4c0f-4c0f-b6b6-43863cc7b71f","order_by":3,"name":"Yanqing Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYPACCQYGZsbGBwwGJGlhb242IEULEPAcb5MgSqHBjRzDzwVlFnnyEYltlT8K7sgzsB8+uoGAFmPpGeckig1vJLbd5jF4ZtjAk5Z2A58Wsxs5BtK8bRKJG2cAtTAYHGZskOAxI6TF+DdMS+EPg8P2xGgxA9syn+dgGwOPweFEglrszzwrs+Y5J5G4gb2xWRqoJbmNkF8k25M33+Ypq0uc38z+8OOPP4dt+9kPH8OrhYGBAxh9bMCgOwDls+FXDgLsD8DK5BsIKx0Fo2AUjIIRCgCoIUzL/9CUQwAAAABJRU5ErkJggg==","orcid":"","institution":"Nanchang University Second Affiliated Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yanqing","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2022-09-01 12:59:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2021929/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2021929/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":26220748,"identity":"aff2c5f0-8007-4b35-b261-3f3782b01ad0","added_by":"auto","created_at":"2022-09-08 17:34:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":12471,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDose-response relationship between WWI and the risk of CVD.\u003c/strong\u003e Data are ORs (solid line) and 95% CIs (dashed lines) from multiple logistic regression analysis with restricted cubic splines, with WWI 10.8 cm/√kg as the reference. Abbreviations: WWI, weight-adjusted-waist index; CVD, cardiovascular disease; OR, odds ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2021929/v1/47fc305b5e19a3d00bd58600.png"},{"id":26220746,"identity":"e0e4d5fd-6a06-45dc-9e8d-69aba9de1aab","added_by":"auto","created_at":"2022-09-08 17:34:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53840,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analyses of the association between WWI and CVD in US adults.\u003c/strong\u003e All presented covariates were adjusted (as Model 3) except the corresponding stratification variable. Abbreviations: WWI, weight-adjusted-waist index; CVD, cardiovascular disease; US, the United States; BMI, body mass index; WC, waist circumference; eGFR, estimated glomerular filtration rate; OR, odds ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2021929/v1/55d20b4dfe4c11966bea6a74.png"},{"id":26221151,"identity":"b7c0b3d3-c921-49db-a22d-b24726468ed1","added_by":"auto","created_at":"2022-09-08 17:39:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33071,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves of each obesity index by sex.\u003c/strong\u003e WWI, weight-adjusted-waist index; BMI, body mass index; WHtR, waist-to-height ratio; WC, waist circumference; ROC, receiver operating characteristic; AUC, area under the curve.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2021929/v1/026e66161f17957567284ca1.png"},{"id":26627637,"identity":"2697dd1e-8159-487f-8390-26b230546133","added_by":"auto","created_at":"2022-09-19 02:29:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":500488,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2021929/v1/56a17687-5507-4e42-ab2f-2f0b0b893d1c.pdf"},{"id":26220749,"identity":"dd64ccab-37e8-4bbd-b9db-2e0e815d9984","added_by":"auto","created_at":"2022-09-08 17:34:38","extension":"doc","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":158208,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.doc","url":"https://assets-eu.researchsquare.com/files/rs-2021929/v1/c7e040c13afe5c074f924fb9.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between the weight-adjusted-waist index and cardiovascular disease among US adults: Results from the National Health and Nutrition Examination Survey 2009-2016","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity is a major public health problem worldwide. The global prevalence of obesity has doubled since 1980, and it is expected to reach 18% in men and surpass 21% in women by 2025 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to statistics from the National Health and Nutrition Examinations Survey (NHANES) 2015\u0026ndash;2016, approximately 39.6% of adults and 18.5% of youths were obese in the United States [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Furthermore, large-scale and long-term studies have consistently demonstrated that obesity is associated with a significantly increased risk of cardiovascular disease (CVD) morbidity and mortality [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, early identification of obese individuals with high risk is crucial to preventing CVD.\u003c/p\u003e \u003cp\u003eBody mass index (BMI) and waist circumference (WC) are the most commonly used obesity-related indices. Furthermore, accumulating data has indicated that they are strongly associated with a rise in the prevalence of hypertension, stroke, and other CVDs [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Despite that, these anthropometric indicators can not clearly distinguish between muscle mass and fat mass [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This may partly contribute to the \u0026ldquo;obesity paradox\u0026rdquo; between BMI or WC and some health outcomes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Although the waist-to-height ratio (WHtR) appears to be superior in assessing obesity, it remains controversial in predicting obesity-related CVD risk and mortality [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. As a result, these traditional indices may not accurately reflect the association between obesity and CVD.\u003c/p\u003e \u003cp\u003eIn this context, a new anthropometric index called the weight-adjusted-waist index (WWI) was proposed, which standardized WC for body weight. Subsequently, Park et al. found that in the Korean National Cohort study, WWI was a better predictor of CVD mortality than BMI, WC, and WHtR. Also, only WWI demonstrated a linear positive correlation between adiposity indices and cardiovascular mortality, but not BMI, WC, or WHtR [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Moreover, several prospective studies in China also discovered that higher WWI levels were associated with an increased risk of all-cause and cardiovascular mortality [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, the associations were only validated in East Asian populations.\u003c/p\u003e \u003cp\u003eGiven the substantial difference in body composition by race, it is unclear whether WWI was also suitable for predicting the risk of CVD in the US population. Therefore, our study was designed to investigate the association between WWI and the risk of CVD in US adults based on data obtained from the NHANES.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eNHANES is an ongoing cross-sectional survey administered by the National Center for Health Statistics (NCHS), which is part of the Centers for Disease Control and Prevention (CDC), to assess the health and nutritional status of adults and children in the United States. It documents a repeated two-year cycle with five major parts, including demographic data, dietary data, examination data, laboratory data, and questionnaire data. Due to the multi-stage, stratified, and probability sampling design, the included participants showed relatively great representativeness. The details of NHANES study design and methods have been previously described [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The NHANES study protocol was approved by the NCHS Research Ethics Review Board, and written informed consent was obtained from each participant.\u003c/p\u003e \u003cp\u003eWe used the continuous NHANES data from 2009 to 2016 (N\u0026thinsp;=\u0026thinsp;40,439). Participants younger than 18 years of age (N\u0026thinsp;=\u0026thinsp;15,943) and those with incomplete CVD evaluation data (N\u0026thinsp;=\u0026thinsp;1,231) were excluded. Then, we further excluded participants with missing data on weight and WC value (N\u0026thinsp;=\u0026thinsp;2,225). Finally, a total of 21,040 participants were included for further analysis. The detailed flow chart of participant selection is shown in Supplementary Fig.\u0026nbsp;1. All data included in this manuscript are publicly available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003ePotential covariates, including demographic data (age, gender, educational level, and race/ethnicity), lifestyle variables (smoking status and alcohol drinking), anthropometric measurements (height, weight, WC, and blood pressure [BP]), and laboratory results (hemoglobin A1c [HbA1c], total bilirubin [TBIL], low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol [HDL-C], total cholesterol [TC], triglycerides [TG], serum creatinine [Scr], serum uric acid [SUA], and urinary albumin/creatinine ratio [UACR]) were selected based on clinical relevance and statistical significance. Demographics and lifestyle data were derived from the household interview questionnaires administered by highly trained medical personnel. Anthropometric indicators and biochemical parameters were obtained through medical examinations and subsequent laboratory assessments in the Mobile Examination Centre (MEC).\u003c/p\u003e \u003cp\u003eThe educational level was further categorized as less than 9th grade, 9-11th grade, high school graduate, some college or AA degree, and college graduate or above. Race/ethnicity was classified as Mexican American, other Hispanic, non-Hispanic black, non-Hispanic white, and other races. The smoking status was determined by \u0026ldquo;Smoked at least 100 cigarettes in life\u0026rdquo;, and the alcohol drinking status was evaluated by \u0026ldquo;Had at least 12 alcohol drinks per year\u0026rdquo;. The laboratory results, including HbA1c, TBIL, LDL-C, HDL-C, TC, TG, Scr, SUA and UACR levels were determined using standardized methods. The detailed measurement processes of these variables are publicly available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Additionally, the BMI in kg/m\u003csup\u003e2\u003c/sup\u003e was calculated by dividing weight (kg) by the square of height (m\u003csup\u003e2\u003c/sup\u003e), and the WHtR was determined by WC (cm) divided by height (cm). The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration creatinine equation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eExposure variable and outcomes\u003c/h2\u003e \u003cp\u003eIn this study, WWI (cm/\u0026radic;kg) was designed as exposure variable. WWI was calculated as WC (cm) divided by the square root of weight (kg) .Weight was measured to the nearest 0.1 kg using a digital weight scale, and WC was measured by a retractable steel measuring tape, positioning the measuring tape around the waist at the uppermost lateral border of the ilium at the midaxillary line. The full procedure, including the protocols, equipment, and quality control, was described at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/nchs/nhanes/ContinuousNhanes/Manuals.aspx?BeginYear=2009\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/nchs/nhanes/ContinuousNhanes/Manuals.aspx?BeginYear=2009\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe outcome variable was CVD. According to previous studies, CVD was defined as a composite of 5 self-reported cardiovascular outcomes, which included congestive heart failure (CHF), coronary heart disease (CHD), angina/angina pectoris, heart attack, and stroke [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. All participants were asked the following questions: \u0026ldquo;Has a doctor or other health professional ever told you that you have congestive heart failure/coronary heart disease/angina pectoris/heart attack/stroke? \u0026rdquo; Participants who answered \u0026ldquo;yes\u0026rdquo; to any of the questions were considered to have CVD. Additionally, we also collected data for each CVD subtype to further analyze the association with WWI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e All statistical analyses were performed in accordance with CDC guidelines. Four waves of continuous survey data (NHANES 2009\u0026ndash;2010, 2011\u0026ndash;2012, 2013\u0026ndash;2014, and 2015\u0026ndash;2016) were combined, and an 8-year sampling weight was calculated by using a quarter of the 2-year sampling weight (WTMEC2YR).\u003c/p\u003e \u003cp\u003eData are presented as weighted mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median (quartile range, IQR) for continuous variables, and frequency (weighted percentage) for categorical variables. Comparisons between the CVD and non-CVD groups were performed using either the weighted Chi-square test (categorical variables) or the weighted linear regression model (continuous variables). WWI was divided into quintiles for further analysis, and the reference category was considered to be the lowest quintile. Multivariate logistic regression model was used to calculate odds ratios (ORs) and corresponding 95% confidence intervals (CIs) to determine the prevalence of CVD related to WWI. We developed three models to adjust for potential confounders: model 1 was a crude model; model 2 was adjusted for age, gender, and race; model 3 was the same as model 2 with additional adjustment for education level, smoking, alcohol drinking, eGFR, systolic and diastolic BP, HbA1c, TBIL, LDL-C, HDL-C, TC, TG, Scr, SUA and UACR. The restricted cubic spline model was used for the dose-response analysis between WWI and total CVD. Subgroup analysis stratified by gender, age, race, BMI, WC, eGFR, smoking, and alcohol drinking was conducted by stratified multivariate regression analysis. A receiver operating characteristic (ROC) curve was used to analyze the predictive value of WWI and traditional obesity-related indices (BMI, WC, and WHtR) for CVD. Besides, we used multiple imputation (MI), based on 5 replications and the Markov-chain Monte Carlo method in the SAS MI procedure, to maximize statistical power and minimize bias that might occur if covariates with missing data were excluded from data analyses.\u003c/p\u003e \u003cp\u003eAll analyses were performed using R version 4.0.3 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://www.cdc.gov/nchs/nhanes/\" target=\"_blank\"\u003ewww.R-project.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and EmpowerStates (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://www.cdc.gov/nchs/nhanes/\" target=\"_blank\"\u003ewww.empowerstats.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.empowerstats.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A two-sided \u003cem\u003eP\u003c/em\u003e value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003ch2\u003eBaseline characteristics\u003c/h2\u003e\n \u003cp\u003eThe characteristics of the study population are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 21,040 participants were included in this study, 51.47% of whom were females, with an average age of 47.11\u0026thinsp;\u0026plusmn;\u0026thinsp;16.79 years. The weighted mean WWI was 10.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83 cm/\u0026radic;kg overall, and the weighted prevalence of total CVD, CHF, CHD, angina, heart attack, and stroke were 7.91% (N\u0026thinsp;=\u0026thinsp;2063), 2.20% (N\u0026thinsp;=\u0026thinsp;599), 3.21% (N\u0026thinsp;=\u0026thinsp;794), 1.96% (N\u0026thinsp;=\u0026thinsp;481), 3.12% (N\u0026thinsp;=\u0026thinsp;810) and 2.47% (N\u0026thinsp;=\u0026thinsp;691), respectively. Compared with the non-CVD group, the CVD group was older and more likely to be male; to have higher BMI, WC, WHtR, WWI, SBP, HbA1c, TG, Scr, SUA, and UACR levels; to have a higher proportion of smoking and non-Hispanic White individuals; to have a lower rate of alcohol drinking; and to have lower eGFR, LDL-C, and HDL-C levels (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics of the study participants.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;21040)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-CVD\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;18977)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2063)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.11\u0026thinsp;\u0026plusmn;\u0026thinsp;16.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.65\u0026thinsp;\u0026plusmn;\u0026thinsp;16.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.15\u0026thinsp;\u0026plusmn;\u0026thinsp;13.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10243 (48.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9059 (47.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1184 (55.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10797 (51.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9918 (52.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e879 (44.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3153 (8.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2941 (8.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e212 (5.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2263 (5.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2078 (6.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e185 (4.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8505 (66.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7449 (65.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1056 (72.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4424 (11.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3973 (11.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e451 (11.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2695 (8.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2536 (8.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159 (6.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational level (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;9th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2135 (5.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1832 (5.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e303 (8.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026minus;11th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2915 (10.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2551 (10.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e364 (14.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4630 (21.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4114 (20.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e516 (26.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollege\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6273 (32.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5715 (32.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e558 (30.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGraduate or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5070 (30.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4750 (31.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e320 (19.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.94\u0026thinsp;\u0026plusmn;\u0026thinsp;6.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.79\u0026thinsp;\u0026plusmn;\u0026thinsp;6.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.65\u0026thinsp;\u0026plusmn;\u0026thinsp;7.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.20\u0026thinsp;\u0026plusmn;\u0026thinsp;16.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.57\u0026thinsp;\u0026plusmn;\u0026thinsp;16.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106.57\u0026thinsp;\u0026plusmn;\u0026thinsp;16.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWHtR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWWI (cm/\u0026radic;kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9183 (44.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7933 (42.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1250 (61.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol drinking (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13935 (77.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12579 (78.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1356 (73.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122.11\u0026thinsp;\u0026plusmn;\u0026thinsp;17.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121.49\u0026thinsp;\u0026plusmn;\u0026thinsp;16.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129.32\u0026thinsp;\u0026plusmn;\u0026thinsp;20.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.52\u0026thinsp;\u0026plusmn;\u0026thinsp;12.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.76\u0026thinsp;\u0026plusmn;\u0026thinsp;11.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.66\u0026thinsp;\u0026plusmn;\u0026thinsp;14.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeGFR (ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118.05\u0026thinsp;\u0026plusmn;\u0026thinsp;46.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120.32\u0026thinsp;\u0026plusmn;\u0026thinsp;45.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.72\u0026thinsp;\u0026plusmn;\u0026thinsp;43.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTBIL (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.49\u0026thinsp;\u0026plusmn;\u0026thinsp;5.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.48\u0026thinsp;\u0026plusmn;\u0026thinsp;5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.71\u0026thinsp;\u0026plusmn;\u0026thinsp;5.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScr (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.91\u0026thinsp;\u0026plusmn;\u0026thinsp;30.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.56\u0026thinsp;\u0026plusmn;\u0026thinsp;27.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.57\u0026thinsp;\u0026plusmn;\u0026thinsp;51.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSUA (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e321.45\u0026thinsp;\u0026plusmn;\u0026thinsp;83.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e318.95\u0026thinsp;\u0026plusmn;\u0026thinsp;82.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e350.49\u0026thinsp;\u0026plusmn;\u0026thinsp;91.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUACR (mg/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.05 (4.55, 13.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.78 (4.46, 12.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.82 (6.16, 33.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003csup\u003e#\u003c/sup\u003e Values are presented as weighted mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, medians (interquartile range), or frequency (weighted percentages) when appropriate. Abbreviations: CVD, cardiovascular disease; BMI, body mass index; WC, waist circumference; WHtR, waist-to-height ratio; WWI, weight-adjusted-waist index; SBP, systolic blood pressure; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HbA1c, hemoglobin A1c; TBIL, total bilirubin; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; TG, triglycerides; Scr, serum creatinine; SUA, serum uric acid; UACR, urinary albumin/creatinine ratio.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003eAssociation of WWI with CVD\u003c/h2\u003e\n \u003cp\u003eMultivariable logistic regression models were performed to explore the association between CVD and the WWI as continuous and categorical variables (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). When WWI was analyzed as a continuous variable, we found that increased WWI was associated with a higher risk of CVD (Model 1: OR\u0026thinsp;=\u0026thinsp;2.50, 95% CI: 2.33\u0026ndash;2.69; Model 2: OR\u0026thinsp;=\u0026thinsp;1.74, 95% CI: 1.58\u0026ndash;1.92; all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the fully adjusted Model 3, the results indicated that each unit of increased WWI was associated with 48% increased risk of CVD (OR\u0026thinsp;=\u0026thinsp;1.48, 95% CI: 1.25\u0026ndash;1.74, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In sensitivity analysis, the multivariable-adjusted ORs (reference to Quintile 1) was 1.74 (95% CI: 1.00-3.03; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048) for Quintile 2, 2.38 (95% CI: 1.38\u0026ndash;4.12; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) for Quintile 3, 2.56 (95% CI: 1.47\u0026ndash;4.45; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for Quintile 4, and 3.18 (95% CI: 1.81\u0026ndash;5.60; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for Quintile 5, indicating a stable positive association between higher WWI and increased risk of CVD (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Moreover, we reanalyzed the association between WWI and CVD using imputation data and the results did not change qualitatively (Supplementary Table 1). In addition, the restricted cubic spline with a multivariate logistic regression model revealed that there was a positive linear relationship between WWI and the odds of CVD (\u003cem\u003eP\u003c/em\u003e for nonlinear\u0026thinsp;=\u0026thinsp;0.310) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAdjusted odds ratios (95% CI) for association between WWI and the prevalence of CVD.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eWWI (cm/\u0026radic;kg)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eEvents\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eOR (95% CI), \u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2063 (7.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.50 (2.33, 2.69)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.74 (1.58, 1.92)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48 (1.25, 1.74)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCategorical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuintile 1 (\u0026lt;\u0026thinsp;10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e108 (1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuintile 2 (10.3\u0026ndash;10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e215 (3.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.31 (1.69, 3.14)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42 (1.04, 1.96) 0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.74 (1.00, 3.03) 0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuintile 3 (10.8\u0026ndash;11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e391 (7.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.85 (3.64, 6.45)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.25 (1.67, 3.04)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.38 (1.38, 4.12) 0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuintile 4 (11.3\u0026ndash;11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e560 (11.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.06 (5.36, 9.30)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.65 (1.96, 3.58)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.56 (1.47, 4.45)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuintile 5 (\u0026ge;\u0026thinsp;11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e789 (17.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.94 (9.12, 15.63)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.87 (2.86, 5.22)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.18 (1.81, 5.60)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003eModel 1: crude model;\u003c/p\u003e\n \u003cp\u003eModel 2: adjusted for age, gender and race;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eModel 3: adjusted for age, gender, race, education level, smoking, alcohol drinking, systolic blood pressure, diastolic blood pressure, estimated glomerular filtration rate, hemoglobin A1c, total bilirubin, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, triglycerides, serum creatinine, serum uric acid, and urinary albumin/creatinine ratio;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAbbreviations: WWI, weight-adjusted-waist index; CVD, cardiovascular disease; OR, odds ratio; CI, confidence interval.\u003c/p\u003e\u003cbr\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/br\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between WWI and specific CVDs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe further analyzed the associations between WWI and the prevalence of five specific CVDs (CHF, CHD, angina, heart attack, and stroke) (Supplementary Table 2). After adjusting for all covariates (Model 3), the fifth quintile of WWI was positively associated with the increased prevalence of CHF (quintile 5: OR = 2.67, 95% CI: 1.12-6.33, \u003cem\u003eP\u003c/em\u003e = 0.026), angina (quintile 5: OR = 3.16, 95% CI: 1.05-9.54, \u003cem\u003eP\u003c/em\u003e = 0.041) and heart attack (quintile 5: OR = 13.68, 95% CI: 4.93-38.03, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and the fourth quintile of WWI was associated with the increased prevalence of CHD (quintile 4: OR = 2.57, 95% CI: 1.06-6.27, \u003cem\u003eP\u003c/em\u003e = 0.037). However, no quintile level of WWI was found to be associated with stroke.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed a further stratified analysis to assess the effect of WWI on CVD in various subgroups (Figure 2). None of the variables, including gender (female or male), race (Mexican American, other Hispanic, non-Hispanic white, non-Hispanic black and other race), BMI (\u0026lt; 25 or \u0026ge; 25 kg/m\u003csup\u003e2\u003c/sup\u003e), WC (normal and abnormal), eGFR (\u0026lt; 90 or \u0026ge; 90\u0026nbsp;ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e), smoking (yes or no), and alcohol drinking (yes or no) significantly modified the association between WWI and CVD (all \u003cem\u003eP\u003c/em\u003e for interaction \u0026gt; 0.05). Nevertheless, there was a significant interaction between WWI and age (\u0026lt; 50 or \u0026ge; 50 years) on CVD. A stronger positive association between WWI and CVD was found in participants younger than 50 years of age (OR = 2.80, 95% CI: 2.17-3.61) compared with their counterparts (OR = 1.62; 95% CI: 1.42-1.85) (\u003cem\u003eP\u0026nbsp;\u003c/em\u003efor interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROC curves between WWI and traditional obesity indices\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ROC curves of the different indices for CVD are shown in Table 3 and Figure 3. For both males and females, WWI had the highest area under the curve (AUC) of 0.736 (95% CI: 0.722-0.751, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) and 0.677 (95% CI: 0.659-0.694, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), with a cut-off value of 11.11 cm/\u0026radic;kg and 11.47 cm/\u0026radic;kg, respectively.\u003c/p\u003e\n\u003c/br\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab3\" style=\"margin-left: calc(0%); width: 100%;\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of the ability of different obesity-related indices to predict CVD.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 12.0219%;\"\u003e\n \u003cp\u003eTest\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003eAUC (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCut-off\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYouden index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 12.0219%;\"\u003e\n \u003cp\u003eMales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 12.0219%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.567 (0.549, 0.584)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 12.0219%;\"\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.638 (0.622, 0.653)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 12.0219%;\"\u003e\n \u003cp\u003eWHtR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.659 (0.643, 0.674)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 12.0219%;\"\u003e\n \u003cp\u003eWWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.736 (0.722, 0.751)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 12.0219%;\"\u003e\n \u003cp\u003eFemales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 19.6721%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 12.0219%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.569 (0.550, 0.589)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 12.0219%;\"\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.612 (0.593, 0.631)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 12.0219%;\"\u003e\n \u003cp\u003eWHtR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.629 (0.610, 0.647)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 12.0219%;\"\u003e\n \u003cp\u003eWWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 19.6721%;\"\u003e\n \u003cp\u003e0.677 (0.659, 0.694)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eAbbreviations: WWI, weight-adjusted-waist index; BMI, body mass index; WHtR, waist-to-height ratio; WC, waist circumference; AUC, area under the curve; CI, confidence interval.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this cross-sectional study was the first to demonstrate the relationship between WWI and CVD in a national representative sample of the US adult population. The results indicated that WWI was significantly associated with an increased risk of CVD, presenting a nearly linear dose-response relationship. The highest WWI category (\u0026ge;11.8 cm/\u0026radic;kg) was 3.18-fold associated with CVD as compared with the lowest WWI category (\u0026lt;10.3 cm/\u0026radic;kg). Moreover, the subgroup analyses showed that a stronger association between WWI and CVD was detected in participants younger than 50 years of age. Besides, the ROC curve analysis showed that WWI was the best screening tool for CVD compared to BMI, WC, and WHtR.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eObesity, a condition of\u0026nbsp;excessive body fat accumulation\u0026nbsp;caused by long-term energy intake exceeding energy expenditure,\u0026nbsp;is a well-established risk factor for CVD and mortality in adult populations\u0026nbsp;[5]. Accordingly, measurement of body fat has been a critical issue in clinical practice for assessing obesity and then identifying individuals at risk of CVD. However, most anthropometric indices, such as BMI and WC, cannot distinguish between differences in muscle and fat mass or fat distribution. This is one of the reasons for the seemingly paradoxical relationship between obesity and some health outcomes\u0026nbsp;[8, 11]. WWI is a newly proposed anthropometric index that has the ability to assess fat and muscle mass reciprocally. Indeed, a recent study involving 602 participants showed that WWI was positively correlated with total and abdominal fat measures but negatively correlated with appendicular skeletal muscle mass in older adults\u0026nbsp;[20]. Furthermore, several studies have demonstrated that WWI is positively correlated with CVD mortality in East Asian populations\u0026nbsp;[14-16]. Our study further verified the association between WWI and CVD in the US adult population. Moreover, the WWI showed a better predictive value for CVD than BMI, WC, and WHtR. These findings suggest that WWI may be a superior indicator of obesity, which is not limited to East Asians but generally applicable to diverse populations.\u003c/p\u003e\n\u003cp\u003eDue to the substantial differences in body composition by race, anthropometric indicators such as BMI have different cut-points for identifying obesity according to race\u0026nbsp;[21]. For instance, the risk of diabetes in Chinese populations with a BMI of 26.9 kg/m\u0026sup2; was the same as that of White populations with a BMI of 30 kg/m\u0026sup2;\u0026nbsp;[22]. Nevertheless, in the case of WWI, there were no statistically significant differences in the mean and distribution between Whites, Asians, and African Americans, supporting our current findings\u0026nbsp;[23]. This may be related to the fact that WWI measures the ratio of fat and muscle mass rather than the absolute fat amount. Moreover, the Korean Frailty and Aging Cohort study showed that WWI was strongly associated with sarcopenic obesity, which is defined as the presence of high fat mass and low muscle mass combined with low physical function\u0026nbsp;[24]. In this context, elevated WWI reflects a state of excessive body fat accumulation and increased muscle mass loss. The muscle-fat imbalance results in dysregulation of adipocytokine release, inflammatory responses, endothelial dysfunction, and declined physical function, ultimately leading to the development of CVD\u0026nbsp;[25-27].\u003c/p\u003e\n\u003cp\u003eThe subgroup analysis demonstrated that there was no dependence of gender, race, BMI, WC, eGFR, smoking, and alcohol drinking on this positive association between WWI with CVD. Nevertheless, the WWI-CVD association was more significant in participants younger than 50 years of age (\u003cem\u003eP\u003c/em\u003e for interaction \u0026lt; 0.001). Similar to our results, a study by Cai showed that the association between WWI and all-cause mortality was not significant at age \u0026ge;75 years\u0026nbsp;[16]. In the Rural Chinese Cohort Study, Li et al. found an association between WWI and hypertension in people under 60 years of age, but it disappeared in older adults (age \u0026ge; 60 years)\u0026nbsp;[28]. These results might be attributed to the different body fat distribution between older and younger individuals\u0026nbsp;[29]. Despite that, further research is needed to investigate the relationship between age, WWI and CVD.\u003c/p\u003e\n\u003cp\u003eOur study has important implications for clinical practice. First, we found a positive association between WWI and CVD in populations outside East Asia, suggesting that WWI could be a universal health index that applies to various races or ethnic groups. Besides, WWI based on weight and WC was considered to be a better predictor for CVD in our study. Therefore, for people with high WWI levels, early assessment of target organ damage and timely intervention could reduce the risk of CVD and improve outcomes. Moreover, due to its simple calculation and economic nature, WWI can be used by medical and health institutions at all levels, especially in areas where medical resources are limited.\u0026nbsp;\u003c/p\u003e"},{"header":"Strengths And Limitation","content":"\u003cp\u003eThis study has several strengths. We used a national representative sample of the general adult population of the US from NHANES, which applied rigorous study protocols and quality controls. Furthermore, we adjusted for most confounding covariates to ensure that our findings were reliable. Additionally,\u0026nbsp;we used MI to maximize statistical power and minimize bias that might occur if covariates with missing data were excluded from data analyses. However, the limitations should also be noted. Firstly, due to the cross-sectional nature of NHANES, we could not obtain a causal relationship between WWI and CVD. Therefore, further longitudinal studies are needed to verify these findings. Secondly, although we have adjusted for many confounding covariates, we could not rule out any potential residual confounding, such as genetic factors and drug use. Thirdly, CVD was ascertained based on participants\u0026rsquo; self-reported data, which may lead to some recall bias. However, previous studies have demonstrated that NHANES self-reported outcomes are a valid method for determining prevalence\u0026nbsp;[30].\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrated that high levels of WWI were significantly associated with an increased risk of CVD in US adults, particularly in people under 50 years of age. These findings indicate that WWI may be an intervention indicator to reduce the risk of CVD in the general adult population. However, further longitudinal studies are still needed to clarify the precise causality of this relationship.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNHANES: National Health and Nutrition Examinations Survey; NCHS: National Center for Health Statistics; CDC: Centers for Disease Control and Prevention; MEC: Mobile Examination Centre; CVD: Cardiovascular disease; BMI: Body mass index; WC: Waist circumference; WHtR: Waist-to-height ratio; WWI: Weight-adjusted-waist index; BP: Blood pressure; HbA1c: Hemoglobin A1c; TBIL: Total bilirubin; LDL-C: Low-density lipoprotein cholesterol; HDL-C: High-density lipoprotein cholesterol; TC: Total cholesterol; TG: Triglycerides; Scr: Serum creatinine; SUA: Serum uric acid; UACR: Urinary albumin/creatinine ratio; eGFR: Estimated glomerular filtration rate; CHF: Congestive heart failure; CHD: Coronary heart disease; ROC: Receiver operating characteristic; AUC: Area under the curve; MI: Multiple imputation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe NHANES was reviewed and approved by the NCHS Research Ethics Review Board, and each participant provided informed consent. All NHANES data released by the NCHS is de-identified, and remained anonymous during data analysis.\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 that support the findings of this 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\u003eThere are no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key Research and Development Plan of China (No. 20212BBG71004), the National Natural Science Foundation of China (No. 82160085), the Beijing Health Promotion Association (No. 20181BCB24013), and the Natural science funding (No. 20202BAB206005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFX and YQW conceived and designed the study. FX, ML, and KL contributed to data collection and statistical analysis. FX drafted the manuscript. YQW had primary responsibility for the final content of the manuscript. All authors interpreted the results, and reviewed and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all NHANES participants and staff for their valuable efforts and contributions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChooi YC, Ding C, Magkos F. \u003cem\u003eThe epidemiology of obesity\u003c/em\u003e. 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Lancet Diabetes Endocrinol 2021; \u003cem\u003e9\u003c/em\u003e (\u003cem\u003e7\u003c/em\u003e): 419\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim JY, Choi J, Vella CA, Criqui MH, Allison MA, Kim NH. \u003cem\u003eAssociations between Weight-Adjusted Waist Index and Abdominal Fat and Muscle Mass: Multi-Ethnic Study of Atherosclerosis\u003c/em\u003e. Diabetes Metab J 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim JE, Choi J, Kim M, Won CW. \u003cem\u003eAssessment of existing anthropometric indices for screening sarcopenic obesity in older adults\u003c/em\u003e. Br J Nutr \u003cem\u003e2022\u003c/em\u003e:1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamjane N, Benyahya F, Nourouti NG, Mechita MB, Barakat A. \u003cem\u003eCardiovascular diseases and metabolic abnormalities associated with obesity: what is the role of inflammatory responses? A systematic review\u003c/em\u003e. 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Circ Cardiovasc Qual Outcomes 2019; \u003cem\u003e2\u003c/em\u003e (\u003cem\u003e5\u003c/em\u003e): 443\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\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":"weight-adjusted-waist index, obesity, cardiovascular disease, NHANES, cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-2021929/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2021929/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAs a new obesity-related index, the weight-adjusted-waist index (WWI) appears to be a good predictor of cardiovascular disease (CVD) in East Asian populations. This study aimed to evaluate the association between WWI and the risk of CVD in United States (US) adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe data were obtained from the 2009\u0026ndash;2016 National Health and Nutrition Examination Survey (NHANES). WWI was calculated as waist circumference divided by the square root of weight, and CVD was ascertained based on self-reported physician diagnoses. Multivariable regression analysis and subgroup analysis were performed to evaluate the association between WWI and CVD.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 21,040 participants were included, with the mean age being 47.11\u0026thinsp;\u0026plusmn;\u0026thinsp;16.79 years. There was a positive linear relationship between WWI and the odds of CVD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.310). After adjusting for all covariates, each unit of increased WWI was associated with a 48% increased risk of CVD (odds ratio [OR]: 1.48, 95% confidence interval [CI]: 1.25\u0026ndash;1.74). Moreover, compared with the lowest quintile (\u0026lt;\u0026thinsp;10.3 cm/\u0026radic;kg), the multivariable-adjusted OR was 3.18 (95% CI: 1.81\u0026ndash;5.60) in the highest quintile (\u0026ge;\u0026thinsp;11.8 cm/\u0026radic;kg). Besides, subgroup analyses showed that stronger associations between WWI and CVD were detected in participants younger than 50 years of age (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eHigh levels of WWI were significantly associated with an increased risk of CVD in US adults, particularly in people under 50 years of age. These findings indicate that WWI may be an intervention indicator to reduce the risk of CVD in the general adult population.\u003c/p\u003e","manuscriptTitle":"Association between the weight-adjusted-waist index and cardiovascular disease among US adults: Results from the National Health and Nutrition Examination Survey 2009-2016","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-09-08 17:34:36","doi":"10.21203/rs.3.rs-2021929/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":"b2ece04c-db0a-49af-8a23-7667fe832154","owner":[],"postedDate":"September 8th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-09-19T02:29:25+00:00","versionOfRecord":[],"versionCreatedAt":"2022-09-08 17:34:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2021929","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2021929","identity":"rs-2021929","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}