Obesity Distribution Indices and Cardio-Cerebrovascular Metabolic Multi-morbidity: Insights from a National Longitudinal Cohort Study

Obesity Distribution Indices and Cardio-Cerebrovascular Metabolic Multi-morbidity: Insights from a National Longitudinal Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Obesity Distribution Indices and Cardio-Cerebrovascular Metabolic Multi-morbidity: Insights from a National Longitudinal Cohort Study Tingting Yang, Yanfeng Gong, Huanbing Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6954915/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 Objective This study examines the associations between body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), visceral adiposity index (VAI), and Chinese visceral adiposity index (CVAI) with the risk of cardiovascular, cerebrovascular, and metabolic multimorbidity, collectively referred to as cardio-cerebrovascular-metabolic multimorbidity (CCMM). Methods We analyzed data from 6,472 individuals aged 45 and older from the CHARLS cohort. Logistic regression was used to evaluate the impact of obesity indices on CCMM, while restricted cubic spline analysis examined dose-response relationships. Subgroup analyses accounted for age, sex, baseline cardio-cerebrovascular-metabolic disease count, and hypertension status. Receiver operating characteristic (ROC) curves assessed predictive efficacy, with the net reclassification index (NRI) and integrated discriminant improvement (IDI) measuring incremental predictive value. Additionally, logistic regression was applied to investigate the influence of obesity indices on the three most prevalent CCMM patterns. Results CVAI showed the strongest association with CCMM compared to BMI, WC, WHtR, and VAI. All obesity indices displayed a nonlinear relationship with CCMM risk. Among them, CVAI had the highest AUC value and contributed the most significant incremental risk when added to the fully adjusted model. Overall, the analysis indicated that obesity indices primarily impact metabolic disease patterns within multimorbidity. Conclusions BMI, WC, WHtR, VAI, and CVAI independently predict CCMM, with CVAI emerging as the strongest predictor, especially in middle-aged individuals and those without pre-existing cardio-cerebrovascular-metabolic conditions. Additionally, these obesity indices significantly influence multi-morbidity patterns, particularly those linked to metabolic diseases. Body mass index waist-to-height ratio waist circumference visceral adiposity index Chinese visceral adiposity index cardio-cerebrovascular-metabolic multi-morbidity Figures Figure 1 Figure 2 Introduction As society advances, the prevalence of chronic non-communicable diseases (NCDs) and the accompanying multimorbidity defined as the coexistence of two or more chronic conditions—are increasingly posing significant clinical and public health challenges[ 1 ]. Multimorbidity is associated with a heightened risk of adverse drug events and increased dependency on care[ 2 ], correlates with higher mortality rates[ 3 ], and contributes to decreased functional status[ 4 ] and higher instances of hospital and outpatient healthcare use[ 5 ]. Among all NCDs, cardiovascular and cerebrovascular diseases (CCVD) still stand as the leading cause of death globally[ 6 , 7 ].From 2009 to 2019, cardiovascular and cerebrovascular diseases persisted as the primary mortality factors among the Chinese population aged over 65[ 8 ]. Moreover, Dyslipidemia, hyperglycemia, and hypertension are currently recognized as the major metabolic risk factors leading to CCVD[ 9 ]. Notably, the health impact of Cardiovascular, cerebrovascular, and metabolic multimorbidity (CCMM) is significantly more severe than that of any single Cardiovascular, cerebrovascular, and metabolic disease. For instance, patients with any two cardiometabolic diseases have their average life expectancy reduced by five years compared to those with a single cardiometabolic condition[ 10 ]. However, traditionally focused on specific diseases, clinical work, medical research, and education often lack a unified and comprehensive strategy, leading to redundant efforts that reduce clinical efficiency and exacerbate patient disease burdens and the risk of adverse events[ 11 ]. As a result, individuals with multiple health conditions necessitate integrated and overarching preventive and treatment strategies. Our study is centered on the middle-aged and elderly Chinese demographic, aiming to examine the prevalence of CCMM, which involves the coexistence of two or more conditions such as hypertension, dyslipidemia, hyperglycemia, heart disease, and stroke. Obesity is linked to an extensive spectrum of diseases, contributing significantly to the risk of conditions like type 2 diabetes mellitus, fatty liver, hypertension, myocardial infarction, stroke, dementia, osteoarthritis, obstructive sleep apnea, and various cancers, making it a primary risk factor for cardiovascular, cerebrovascular, and metabolic diseases[ 12 ]. Obesity-related indicators, commonly utilized in clinical practice and epidemiological studies due to their convenience and efficacy, are pivotal in assessing obesity status. Body mass index (BMI) has become the most prevalent indicator for assessing obesity in clinical settings, indicating the overall level of body fatness. However, BMI fails to capture age-related changes in height or body composition, potentially introducing significant biases in obesity assessments[ 13 ]. Furthermore, BMI-based diagnoses of obesity exhibit heterogeneity: the presence of metabolic dysfunctions and chronic comorbidities is not universal among individuals classified as obese, and conversely, metabolic abnormalities can occur in persons of normal weight[ 14 ]. Waist circumference (WC) and waist-to-height ratio (WHtR) are recognized as significant anthropometric markers for abdominal obesity[ 15 ]. While WC and WHtR offer advantages over BMI in indicating visceral fat, they encounter challenges in differentiating subcutaneous from visceral fat deposits[ 16 ]. Fortunately, the visceral adiposity index (VAI) and the Chinese visceral adiposity index (CVAI), novel indicators of obesity, have emerged, distinguishing between subcutaneous and visceral fat in relation to the latter's function[ 17 , 18 ]. An elevated BMI is linked to a spectrum of 21 diseases, as demonstrated in a multicohort observational study, with a BMI ≥ 30.00 kg/m^2 being implicated in cardiovascular, metabolic, digestive, respiratory, neurological, musculoskeletal, and infectious pathologies, which indicates a dose-response relationship with comorbidity complexity[ 19 ]. Furthermore, a study has shown that WHtR, WC, and BMI are independent predictors of cardiometabolic multimorbidity among middle-aged and elderly Chinese populations, with WHtR and WC surpassing BMI in predictive accuracy[ 20 ]. Additionally, VAI has been identified as the most sensitive and specific predictor of metabolic syndrome[ 21 ] and exhibits a positive correlation with stroke prevalence[ 22 ]. Notably, CVAI is considered the most appropriate indicator for predicting cardiometabolic multimorbidity in Chinese middle-aged and elderly populations, when compared to other indicators of insulin resistance[ 23 ]. The above studies have demonstrated that BMI, WC, WHtR, VAI, and CVAI are closely correlated with the development of cardio-cerebrovascular-metabolic multimorbidity disorders; however, the relationship between these indicators and CCMM, as well as their impact on multimorbidity patterns, remains unevaluated. In this research, we explored how BMI, WC, WHtR, VAI, and CVAI were longitudinally associated with CCMM among Chinese middle-aged and older adults for the first time. Additionally, we evaluated their predictive power for the risk of cardio-metabolic morbidities. Moreover, to enhance our understanding of how these indices relate to CCMM, we investigated their impact on multimorbidity patterns. Methods Study design The study population originated from the China Health and Retirement Longitudinal Study (CHARLS), a national cohort study [ 24 ]. CHARLS gathers high-quality data representative of Chinese individuals aged 45 and older, encompassing demographic background, health status, economic factors, and more. Blood samples were analyzed in 2011 and 2015 as part of CHARLS. Conducted in 2011, the CHARLS national baseline survey covered 28 provinces and has been updated biennially up to 2020. We used baseline data from 2011, with follow-up extending through 2018. Approval was obtained from the Biomedical Ethics Review Committee of Peking University, and informed consent was secured from all participants. Study population All participants meeting the following criteria in the national baseline survey were selected: (1) aged 45 years or older; (2) absence of CCMM history at baseline, defined as not having been diagnosed with two or more of the following conditions: hypertension, diabetes, dyslipidemia, heart disorders, or stroke; (3) complete collection of anthropometric measurements and laboratory indicators; and (4) comprehensive follow-up. We excluded subjects with incomplete information, including birth year or age, gender, urban/rural residency, smoking and drinking habits, and baseline disease status, as well as those with any manifestly incorrect data. Ultimately, the study comprised 6472 participants. Baseline data collection and definitions A standardized questionnaire administered by trained clinical staff captured patients' demographic details (age, gender, place of residence) and lifestyle factors (smoking, alcohol consumption). Trained clinicians also measured anthropometric parameters (weight, height, and WC) following a uniform protocol. In addition, we incorporated select blood parameters, including triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C), from CHRALS for VAI and CVAI calculations. Further details on these data are provided in the Supplementary Material. BMI, WHtR, VAI [ 17 ],and CVAI [ 18 ] were formulated as: Body mass index: $$\:\text{B}\text{M}\text{I}=\frac{\text{w}\text{e}\text{i}\text{g}\text{h}\text{t}\left(\text{k}\text{g}\right)}{{\text{h}\text{e}\text{i}\text{g}\text{h}\text{t}}^{2}\left({\text{m}}^{2}\right)}$$ Waist-to-height ratio: $$\:\text{W}\text{H}\text{t}\text{R}=\frac{\text{W}\text{C}\left(\text{c}\text{m}\right)}{\text{h}\text{e}\text{i}\text{g}\text{h}\text{t}\left(\text{c}\text{m}\right)}$$ Visceral adiposity index: $$\:\text{V}\text{A}\text{I}\text{­}\text{m}\text{a}\text{l}\text{e}=\left[\frac{\text{W}\text{C}\left(\text{c}\text{m}\right)}{39.68+1.88\times\:\text{B}\text{M}\text{I}(\text{k}\text{g}/\text{m}²)}\right]\text{*}\left[\frac{\text{T}\text{G}(\text{m}\text{m}\text{o}\text{l}/\text{L})}{1.03}\right]\text{*}\left[\frac{1.31}{\text{H}\text{D}\text{L}(\text{m}\text{m}\text{o}\text{l}/\text{L})}\right]$$ $$\:\text{V}\text{A}\text{I}\text{­}\text{f}\text{e}\text{m}\text{a}\text{l}\text{e}=\left[\frac{\text{W}\text{C}\left(\text{c}\text{m}\right)}{36.58+1.89\times\:\text{B}\text{M}\text{I}(\text{k}\text{g}/\text{m}²)}\right]\text{*}\left[\frac{\text{T}\text{G}(\text{m}\text{m}\text{o}\text{l}/\text{L})}{0.81}\right]\text{*}\left[\frac{1.52}{\text{H}\text{D}\text{L}(\text{m}\text{m}\text{o}\text{l}/\text{L})}\right]$$ Chinese visceral adiposity index: $$\:\text{C}\text{V}\text{A}\text{I}\text{­}\text{m}\text{a}\text{l}\text{e}=-267.93+0.68\text{*}\text{a}\text{g}\text{e}\left(\text{y}\text{e}\text{a}\text{r}\text{s}\right)+0.03\text{*}\text{B}\text{M}\text{I}\left(\text{k}\text{g}/\text{m}²\right)+4.00\text{*}\text{W}\text{C}\left(\text{c}\text{m}\right)+22.00\text{*}\text{L}\text{g}\left(\text{T}\text{G}\right)\left(\text{m}\text{m}\text{o}\text{l}/\text{L}\right)-16.32\text{*}\text{H}\text{D}\text{L}\text{­}\text{C}(\text{m}\text{m}\text{o}\text{l}/\text{L})$$ $$\:\text{C}\text{V}\text{A}\text{I}\text{­}\text{f}\text{e}\text{m}\text{a}\text{l}\text{e}=-187.32+1.71\text{*}\text{a}\text{g}\text{e}\left(\text{y}\text{e}\text{a}\text{r}\text{s}\right)+4.23\text{*}\text{B}\text{M}\text{I}\left(\text{k}\text{g}/\text{m}²\right)+1.12\text{*}\text{W}\text{C}\left(\text{c}\text{m}\right)+39.76\text{*}\text{L}\text{g}\left(\text{T}\text{G}\right)(\text{m}\text{m}\text{o}\text{l}/\text{L})-11.66\text{*}\text{H}\text{D}\text{L}\text{­}\text{C}(\text{m}\text{m}\text{o}\text{l}/\text{L})$$ Covariates Covariates included sociodemographic characteristics, lifestyle factors, and current disease status. Sociodemographic characteristics comprised age (years), sex (male/female), and residence (rural/urban). Lifestyle factors consisted of current smoking and drinking status (yes/no). Present disease conditions (yes/no) covered hypertension, dyslipidemia, diabetes, heart problem, stroke, cancer, chronic lung disease, liver disease, kidney disease, digestive system disease, psychological problem, memory-related disease, arthritis or rheumatism, and asthma. Additional details are described in the Supplementary Material. Outcome CCMM entailed having at least two of the following conditions: hypertension, dyslipidemia, diabetes, heart problem, and stroke. Diagnosis relied on participants' self-reported information. Statistical analysis Categorical variables were represented as frequencies (percentages) and compared using the chi-square test. Continuous data were presented as medians (interquartile ranges) and compared using the Wilcoxon rank sum test. In subsequent analyses, BMI was divided into four categories following the Chinese adult standard: underweight (< 18.5 kg/m2), healthy weight (18.5–23.9 kg/m2), overweight (24-27.9 kg/m2), and obesity (≥ 28 kg/m2)[ 25 ]. Additionally, WC, WHtR, VAI, and CVAI were grouped into quartiles: WCQ1-WCQ4, WHtRQ1-WHtRQ4, VAIQ1-VAIQ4, and CVAIQ1-CVAIQ4. A binary logistic regression model assessed the association between BMI, WC, WHtR, VAI, and CVAI and CCMM, estimating the odds ratios (ORs) and 95% confidence intervals (CIs). Subsequent models were established to investigate the effects of progressively adjusting covariates. Model 1 included adjustments for age, sex, residence, current smoking, and current drinking. Model 2 added adjustments for cancer, chronic lung disease, liver disease, kidney disease, digestive system disease, psychological problem, memory-related disease, arthritis or rheumatism, and asthma to those in Model 1. Model 3 incorporated Model 2’s parameters with additional adjustments for hypertension, dyslipidemia, diabetes, heart problem, and stroke. To investigate the dose-response relationships between BMI, WC, WHtR, VAI, CVAI, and the incidence of CCMM, we utilized restricted cubic spline analysis. Considering the prevalence of baseline hypertension, subgroup analyses were conducted focusing on age, sex, the number of baseline cardio-cerebrovascular-metabolic diseases, and hypertension status, with a detailed examination of interactions between subgroups. Subsequently, we calculated the AUC values to assess the predictive potential of BMI, WC, WHtR, VAI, and CVAI for CCMM. Following this, differences in AUC values for WC, WHtR, VAI, CVAI, and BMI were compared separately using the DeLong test[ 26 ]. Additionally, the AUC values, along with the net reclassification index (NRI) and the integrated discriminant improvement (IDI), were computed to assess the predictive efficacy and additive predictive value of BMI, WC, WHtR, VAI, and CVAI, over and above the established covariates in various models. In the final stage of our analysis, we turned our attention to the three most prevalent CCMM patterns and examined their association with BMI, WC, WHtR, VAI, and CVAI using logistic regression. Analyses were conducted using IBM SPSS Statistics 27 and R version 4.3.1, with the threshold for significance set at a two-sided P-value of less than 0.05. Results Baseline characteristics 6742 cases (2946 males and 3526 females) were included in this study, featuring a median age of 57 (51–64) years. Following a 7-year period (2011–2018), 1502 participants (23.21%) developed CCMM. The risk of developing CCMM was higher among women and individuals residing in urban areas, compared to their male and rural counterparts. Compared to individuals without CCMM, those affected were generally older and had higher measures of BMI, WC, WHtR, VAI, CVAI; they also exhibited significantly increased risks for hypertension, diabetes, dyslipidemia, heart problem, chronic lung disease, liver disease, kidney disease, digestive system disease, memory-related disease, arthritis or rheumatism, and asthma (all p < 0.05). It is noteworthy that patients with CCMM exhibited lower rates of smoking and drinking compared to those without the condition. (shown in Table 1 ). Table 1 Baseline characteristics of the study population. Characteristics Total (n = 6472) Without multimorbidity (n = 4970) With multimorbidity (n = 1502) p- value Age, years 57(51–64) 57(51–64) 59(53–65) < 0.001 Male (%) 2946(45.52) 2330(46.88) 616(41.01) < 0.001 Rural (%) 4399(67.97) 3418(68.77) 981(65.31) 0.012 Current smoking (%) 2011(31.07) 1618(32.56) 393(26.17) < 0.001 Current drinking (%) 1665(25.73) 1336(26.88) 329(21.90) < 0.001 BMI, kg/m² 22.96(20.78–25.46) 22.60(20.54–24.86) 24.48(22.09–27.05) < 0.001 Underweight (< 18.5) (%) 428(6.61) 371(7.46) 57(3.79) Healthy weight (18.5–23.9) (%) 3568(55.13) 2945(59.26) 623(41.48) Overweight (24.0–27.9) (%) 1846(28.52) 1295(26.06) 551(36.68) Obesity (≥ 28.0) (%) 630(9.73) 359(7.22) 271(18.04) WC, cm 84.00(77.20–91.00) 82.80(76.30–89.40) 88.20(81.00-95.40) 91.00) (%) 1537(23.75) 965(19.42) 572(38.08) WHtR 0.53(0.49–0.58) 0.52(0.48–0.57) 0.56(0.51–0.61) 0.58) (%) 1567(24.21) 999(20.10) 568(37.82) VAI 1.42(0.87–2.47) 1.32(0.81–2.26) 1.89(1.12–3.27) 2.47) (%) 1615(24.95) 1081(21.75) 534(35.55) CVAI 90.67(65.37-118.81) 85.01(61.85-112.15) 110.41(85.26-137.59) 118.81) (%) 1618(25.00) 999(20.10) 619(41.21) Hypertension (%) 1006(15.54) 545(10.97) 461(30.69) < 0.001 Dyslipidemia (%) 193(2.98) 119(2.39) 74(4.93) < 0.001 Diabetes (%) 121(1.87) 65(1.31) 56(3.73) < 0.001 Heart problem (%) 324(5.01) 177(3.56) 147(9.79) < 0.001 Stroke (%) 36(0.56) 24(0.48) 12(0.80) 0.149 Cancer (%) 52(0.80) 38(0.76) 14(0.93) 0.524 Chronic lung disease (%) 568(8.78) 402(8.09) 166(11.05) < 0.001 Liver disease (%) 221(3.41) 157(3.16) 64(4.26) 0.039 Kidney disease (%) 384(5.93) 275(5.53) 109(7.26) 0.013 Digestive system disease (%) 1498(23.15) 1117(22.47) 381(25.37) 0.020 Psychological problem (%) 73(1.13) 50(1.01) 23(1.53) 0.091 Memory-related disease (%) 42(0.65) 20(0.40) 22(1.46) < 0.001 Arthritis or rheumatism (%) 2225(34.38) 1669(33.58) 556(37.02) 0.014 Asthma (%) 203(3.14) 138(2.78) 65(4.33) 0.003 Note: BMI, body mass index; WC, waist circumference; WHtR, waist-to-height ratio; VAI, visceral adiposity index; CVAI, Chinese visceral adiposity index. Data are summarized as n (%) or median (25th-75th percentile). Relationship between BMI, WC, WHtR, VAI, CVAI and CCMM When adjusted for age, sex, residence, current smoking and current drinking alone (model 1), higher levels of the aforementioned obesity indices were all significantly associated with an increased CCMM risk. The five obesity indices remained significantly associated with CCMM when adjusted for covariates excluding the diseases that comprise CCMM (model 2). Moreover, after adjusting for all covariates (model 3), the association between these obesity indicators and the risk of CCMM persisted. The obesity group continued to show a strong association with CCMM occurrence (OR: 3.09, 95% CI: 2.54–3.77). Similarly, the highest quartile groups for WC, WHtR, VAI, and CVAI had ORs (95% CI) of 3.34 (2.77–4.03), 3.25 (2.67–3.95), 2.70 (2.23–3.27), and 3.42 (2.82–4.17), respectively, when compared to the lowest quartiles. Notably, of all the indices, the highest quartile of CVAI demonstrated the greatest risk for CCMM in all three models. (shown in Table 2 ). A significant non-linear association was observed between BMI, WC, WHtR, VAI, CVAI, and CCMM (all P and P for nonlinear < 0.001). BMI exhibited an S-shaped curve in relation to CCMM. WC, WHtR, and CVAI demonstrated J-shaped curves with CCMM, with inflection points at 74.10, 0.47, and 50.29, respectively. Conversely, VAI presented a logarithmic relationship with CCMM. (shown in Fig. S1). In subgroup analyses, positive associations between BMI, WC, WHtR, VAI, CVAI, and CCMM incidence remained consistent across various subgroups. Notably, significant interaction was noted between sex and BMI in CCMM risk (P for interaction < 0.05), with a stronger association observed in males than in females. However, a significant interaction between hypertension status and WC was observed in CCMM risk, with higher WC levels correlating more strongly with CCMM incidence in individuals without hypertension. Additionally, CVAI's association with CCMM risk was amplified in individuals under 65, without hypertension, and free from baseline cardio-cerebrovascular-metabolic diseases (P for interaction < 0.05). (shown in Fig. 1 ). Table 2 Association between BMI, WC, WHtR, VAI, CVAI and CCMM. Indices Groups Model 1 Model 2 Model 3 OR(95%CI) P -value OR(95%CI) P -value OR(95%CI) P -value BMI Healthy weight 1.00 1.00 1.00 Underweight 0.63(0.46–0.84) 0.002 0.59(0.43–0.79) < 0.001 0.62(0.45–0.84) 0.003 Overweight 2.11(1.85–2.42) < 0.001 2.15(1.87–2.46) < 0.001 1.91(1.66–2.20) < 0.001 Obesity 3.82(3.17–4.61) < 0.001 3.97(3.29–4.79) < 0.001 3.09(2.54–3.77) < 0.001 WC Q1 (≤ 77.20) 1.00 1.00 1.00 Q2 (77.30–84.00) 1.37(1.13–1.66) 0.001 1.41(1.16–1.70) < 0.001 1.35(1.11–1.65) 0.003 Q3 (84.10–91.00) 2.29(1.91–2.75) < 0.001 2.35(1.96–2.83) < 0.001 2.16(1.79–2.62) 91.00) 3.83(3.21–4.58) < 0.001 4.01(3.36–4.81) < 0.001 3.34(2.77–4.03) < 0.001 WHtR Q1 (≤ 0.49) 1.00 1.00 1.00 Q2 (0.50–0.53) 1.49(1.23–1.80) < 0.001 1.50(1.24–1.82) < 0.001 1.46(1.19–1.78) < 0.001 Q3 (0.54–0.58) 2.25(1.88–2.71) < 0.001 2.28(1.90–2.75) < 0.001 2.00(1.65–2.42) 0.58) 3.68(3.06–4.44) < 0.001 3.79(3.15–4.58) < 0.001 3.25(2.67–3.95) < 0.001 VAI Q1 (≤ 0.87) 1.00 1.00 1.00 Q2 (0.88–1.42) 1.48(1.23–1.79) < 0.001 1.50(1.24–1.81) < 0.001 1.42(1.17–1.72) < 0.001 Q3 (1.43–2.47) 2.11(1.76–2.54) < 0.001 2.18(1.82–2.63) < 0.001 2.02(1.67–2.45) 2.47) 3.00(2.51–3.61) < 0.001 3.12(2.60–3.75) < 0.001 2.70(2.23–3.27) < 0.001 CVAI Q1 (≤ 65.37) 1.00 1.00 1.00 Q2 (65.38–90.67) 1.24(1.01–1.51) 0.041 1.26(1.03–1.55) 0.025 1.19(0.97–1.47) 0.104 Q3(90.68-118.81) 2.48(2.06-3.00) < 0.001 2.56(2.12–3.10) < 0.001 2.37(1.95–2.88) 118.81) 4.11(3.42–4.95) < 0.001 4.37(3.63–5.28) < 0.001 3.42(2.82–4.17) < 0.001 Note: CCMM, cardio-cerebrovascular-metabolic multimorbidity; BMI, body mass index; WC, waist circumference; WHtR, waist-to-height ratio; VAI, visceral adiposity index; CVAI, Chinese visceral adiposity index; CI, confidence interval; OR, odds ratio. Model 1 included age, sex, residence, current smoking and current drinking. Model 2 included age, sex, residence, current smoking, current drinking, cancer, chronic lung disease, liver disease, kidney disease, digestive system disease, psychological problem, memory-related disease, arthritis or rheumatism, and asthma. Model 3 included age, sex, residence, current smoking, current drinking, cancer, chronic lung disease, liver disease, kidney disease, digestive system disease, psychological problem, memory-related disease, arthritis or rheumatism, asthma, hypertension, dyslipidemia, diabetes, heart problem, and stroke. Predictive performance of BMI, WC, WHtR, VAI, and CVAI for CCMM The AUC values for BMI, WC, WHtR, VAI, and CVAI as determined by our analysis were 0.641, 0.645, 0.643, 0.622, and 0.670, with CVAI manifesting the highest metric when contrasted with the other obesity indicators. Additionally, a significant difference was noted in the AUC values among CVAI, VAI, and the other indicators (P < 0.05). (shown in Fig. S2 and table S1). Incremental predictive value of BMI, WC, WHtR, VAI, and CVAI indices in the risk assessment of CCMM Adding BMI, WC, WHtR, VAI, and CVAI along with age, sex, residence, current smoking, and current drinking (model 1) significantly enhanced discriminatory power. Similarly, incorporating these five obesity indices into a model with age, sex, residence, current smoking, current drinking, cancer, chronic lung disease, liver disease, kidney disease, digestive system disease, psychological problem, memory-related disease, arthritis or rheumatism, and asthma, the NRI and IDI is also significantly increased. Furthermore, the inclusion of BMI, WC, WHtR, VAI, and CVAI within all covariates (model 3) significantly improved CCMM predictive utility, reflected in NRI and IDI estimates (95% CI) of 0.359(0.295–0.412) and 0.026(0.022–0.031) for BMI, 0.376(0.315–0.440) and 0.022(0.018–0.026) for WC, 0.343(0.291–0.405) and 0.018(0.014–0.022) for WHtR, 0.247(0.189–0.307) and 0.005(0.003–0.007) for VAI, and 0.444(0.385–0.507) and 0.032(0.027–0.037) for CVAI, respectively. Additionally, the AUC values of the three models were significantly enhanced by adding BMI, WC, WHtR, VAI or CVAI (all P < 0.001). In all three models mentioned above, CVAI showed the best incremental predictive value among these five obesity indicators. (shown in Table 3 ). Table 3 Improvement in discrimination and risk reclassification for CCMM after adding obesity indices. Model AUC NRI IDI Estimate(95%CI) P value Estimate(95%CI) P value Estimate(95%CI) P value Model1 0.576(0.560–0.593) Ref. Ref. Ref. Model1 + BMI 0.663(0.647–0.678) < 0.001 0.420(0.358–0.473) < 0.001 0.043(0.038–0.049) < 0.001 Model1 + WC 0.656(0.640–0.672) < 0.001 0.453(0.395–0.512) < 0.001 0.035(0.031–0.040) < 0.001 Model1 + WHtR 0.647(0.631–0.663) < 0.001 0.388(0.335–0.446) < 0.001 0.029(0.024–0.033) < 0.001 Model1 + VAI 0.607(0.591–0.624) < 0.001 0.284(0.229–0.343) < 0.001 0.008(0.006–0.011) < 0.001 Model1 + CVAI 0.672(0.657–0.688) < 0.001 0.511(0.449–0.566) < 0.001 0.051(0.045–0.057) < 0.001 Model2 0.589(0.572–0.605) Ref. Ref. Ref. Model2 + BMI 0.672(0.657–0.687) < 0.001 0.424(0.364–0.485) < 0.001 0.046(0.040–0.052) < 0.001 Model2 + WC 0.664(0.648–0.680) < 0.001 0.448(0.387–0.505) < 0.001 0.037(0.033–0.042) < 0.001 Model2 + WHtR 0.655(0.639–0.671) < 0.001 0.378(0.327–0.442) < 0.001 0.030(0.025–0.034) < 0.001 Model2 + VAI 0.617(0.601–0.634) < 0.001 0.303(0.240–0.356) < 0.001 0.008(0.006–0.011) < 0.001 Model2 + CVAI 0.681(0.666–0.697) < 0.001 0.515(0.453–0.569) < 0.001 0.054(0.048–0.060) < 0.001 Model3 0.694(0.678–0.710) Ref. Ref. Ref. Model3 + BMI 0.731(0.716–0.745) < 0.001 0.359(0.295–0.412) < 0.001 0.026(0.022–0.031) < 0.001 Model3 + WC 0.724(0.709–0.739) < 0.001 0.376(0.315–0.440) < 0.001 0.022(0.018–0.026) < 0.001 Model3 + WHtR 0.720(0.705–0.735) < 0.001 0.343(0.291–0.405) < 0.001 0.018(0.014–0.022) < 0.001 Model3 + VAI 0.706(0.690–0.721) < 0.001 0.247(0.189–0.307) < 0.001 0.005(0.003–0.007) < 0.001 Model3 + CVAI 0.733(0.718–0.747) < 0.001 0.444(0.385–0.507) < 0.001 0.032(0.027–0.037) < 0.001 Note: CCMM, cardio-cerebrovascular-metabolic multimorbidity; BMI, body mass index; WC, waist circumference; WHtR, waist-to-height ratio; VAI, visceral adiposity index; CVAI, Chinese visceral adiposity index; CI, confidence interval; AUC, area under curve; NRI, net reclassification improvement; IDI, integrated discrimination improvement. Model 1 included age, sex, residence, current smoking and current drinking. Model 2 included age, sex, residence, current smoking, current drinking, cancer, chronic lung disease, liver disease, kidney disease, digestive system disease, psychological problem, memory-related disease, arthritis or rheumatism, and asthma. Model 3 included age, sex, residence, current smoking, current drinking, cancer, chronic lung disease, liver disease, kidney disease, digestive system disease, psychological problem, memory-related disease, arthritis or rheumatism, asthma, hypertension, dyslipidemia, diabetes, heart problem, and stroke. Relationship between BMI, WC, WHtR, VAI, CVAI and the patterns of multimorbidity of CCMM Multimorbidity patterns, which consist of two and three diseases and recorded as the top three occurrences in 2018, were selected for analysis in relation to BMI, WC, WHtR, VAI, and CVAI. The most frequent dyadic disease patterns were "hypertension + diabetes" (n = 137), "hypertension + heart problem" (n = 256), and "hypertension + dyslipidemia" (n = 238). After adjusting for all covariates, the highest odds ratios (ORs) for the "hypertension + diabetes" pattern were observed in the obesity group and the upper quartiles of WC, WHtR, and CVAI. The ORs (95% CI) for the obese group was 3.66 (95% CI 2.11–6.40) versus the healthy weight group. When compared to the lowest quartile for WC, WHtR, and CVAI, the adjusted ORs (95% CI) for the highest quartile were 3.78 (2.17–6.60), 3.38 (1.90–6.02), 3.08 (1.76–5.40), respectively. However, the highest quartile of VAI exhibited the largest ORs for the "hypertension + dyslipidemia" pattern, at 3.63 (95% CI: 2.34–5.63). Furthermore, the three most frequent multimorbidity patterns involving three conditions were “hypertension + dyslipidemia + heart problem” (n = 122), “hypertension + dyslipidemia + diabetes” (n = 97), and “hypertension + heart problem + diabetes” (n = 40). The highest ORs were observed in the highest WC, VAI, and CVAI quartile groups for the multimorbidity pattern of “hypertension + dyslipidemia + diabetes”. Compared with the lowest quartile group, the ORs (95% CI) for WCQ4, VAIQ4, and CVAIQ4 were 6.65 (95% CI 3.09–14.30), 11.02(95% CI 4.62–26.28) and 7.78(95% CI 3.58–16.92), respectively. Whereas, the ORs for the “hypertension + heart problem + diabetes” multimorbidity pattern were highest in the obesity group and in the highest quartile of WHtR after adjusting for all covariates, with 6.66 (95% CI 2.71–16.35) in the obese group, and 5.88 (95% CI 1.90-18.24) in the WHtRQ4 group, respectively. Notably, the difference in ORs between the two multimorbidity patterns were minimal in both the obesity and WHtRQ4 groups (shown in Fig. 2 ). Discussion In this national cohort study, we assessed the association between BMI, WC, WHtR, VAI, CVAI, and CCMM. Our analyses demonstrated that all five obesity indicators were positively correlated with the risk of CCMM. Subsequent subgroup analysis confirmed that these associations persisted across various subgroups, including age, sex, cardio-cerebrovascular-metabolic diseases count and hypertension status at baseline. Additionally, the CVAI index emerged as the strongest predictor of CCMM compared with BMI, WC, WHtR, and VAI, exhibiting a J-shaped association with CCMM. When integrated into various models, CVAI demonstrated the most substantial incremental predictive value for CCMM risk. Furthermore, CVAI, along with other obesity indices, primarily influences the pattern of metabolic disorders within CCMM. Obesity is closely linked to the risk of cardiovascular and cerebrovascular metabolic diseases, and the metabolic implications of obesity differ with the distribution of adipose tissue. Emerging evidence suggests that visceral fat accumulation has a stronger association with metabolic abnormalities than subcutaneous fat[ 27 – 31 ]. Despite the lack of direct comparative studies on the correlation of BMI, WC, WHtR, VAI, and CVAI with CCMM, numerous studies have demonstrated that emerging obesity indicators, which reflect visceral fat function, offer superior predictive value for cardio-cerebrovascular-metabolic diseases, metabolic syndrome (MS), and cardiometabolic multimorbidity compared to traditional measures. And CVAI shows a stronger correlation with these conditions in Asian populations. A large-sample cross-sectional study demonstrated that CVAI was the strongest predictor of hypertension and a reliable indicator for its early identification, outperforming other obesity indices like BMI, WC, VAI, lipid accumulation product index (LAP), body roundness index (BRI), body shape index (ABSI), conicity index (CI), triglyceride glucose index (TyG-index) and its correlation index (TyG-BMI, TyG-WC, TyG-WHtR)[ 32 , 33 ]. Moreover, CVAI proves superior as a clinical indicator for the development of diabetes mellitus compared to BMI, WC, VAI in the Chinese elderly population, particularly among women[ 34 ], with low initial levels significantly associated with prediabetes regression to normoglycemia[ 35 ]. The risk of incident cardiovascular disease is influenced by both long-term cumulative high CVAI exposure and its duration among hypertensive patients, with early accumulation posing a greater risk than later, highlighting the critical importance of early optimal CVAI management[ 36 ]. In a cohort study of 14,595 rural Chinese patients, CVAI was identified as the most suitable predictor of stroke, compared to VAI, LAP, TyG, TyG-BMI, and TyG-WC[ 37 ]. Among middle-aged and older adults, VAI, CVAI, and other obesity-related indices can predict metabolic syndrome (MS), with CVAI deemed the most effective indicator for MS, especially in women[ 38 , 39 ]. Furthermore, CVAI emerges as the most appropriate indicator for predicting cardiometabolic multimorbidity in middle-aged and older Chinese populations, surpassing other surrogate insulin resistance indexes[ 23 ]. Similarly, our study identified a positive correlation between CCMM and CVAI, offering scientific evidence for employing CVAI in diagnosing CCMM. Given CVAI's superior predictive capacity over BMI, WC, WHtR, and VAI for CCMM, its application in future screening practices appears viable. Our study noted a higher CCMM prevalence in urban populations, which may be associated with better healthcare access and increased health awareness among urbanites, resulting in more diagnoses. Notably, CCMM prevalence is higher among current smokers and drinkers compared to their non-smoking and non-drinking counterparts. This observation could stem from our study's methodology, which categorized individuals who had previously smoked or consumed alcohol but currently abstain as non-smokers and non-drinkers, possibly affecting the results. Furthermore, the influence of additional health-related lifestyle factors must also be considered. However, the difference in stroke prevalence, as a component of CCMM, was not statistically significant between groups with and without this condition, likely due to the limited number of stroke cases at baseline, a mere 36, potentially impacting the findings. In subgroup analyses, we found that CVAI was linked to a reduced risk of CCMM among individuals aged 65 or older compared to those under 65, with similar trends found in BMI, WC, and WHtR. Furthermore, prior research indicates that visceral fat is not linked to atherosclerotic cardiovascular events in elderly males[ 40 ]. This phenomenon may be attributed to the diminished impact of obesity on health in older adults. More studies in different age groups in China are needed for further evidence. Moreover, significant sex-based differences were observed in the impact of BMI on CCMM incidence, with males demonstrating a higher risk of developing CCMM following an elevation in BMI compared to females. A comparable trend was observed in research examining the effects of BMI, WC, and waist-to-hip ratio (WHR) on chronic diseases in elderly Indian cohorts[ 41 ]. This discrepancy may be ascribed to sex-differential adiposity distribution patterns, typically manifesting as apple-shaped physiques in males and pear-shaped physiques in females[ 42 ]. In addition, our findings indicated that WC and CVAI had a stronger association with the prevalence of CCMM in individuals without hypertension. Moreover, CVAI was associated with an elevated risk of CCMM in individuals without baseline cardio-cerebrovascular-metabolic diseases, relative to those with such a condition. Similar trends were observed in BMI, WHtR, VAI. Analogous studies have demonstrated that a heightened CVAI correlated more significantly with the risk of carotid plaque in non-diabetic patients than in diabetic ones[ 43 ]. Zenglei Zhang et al.[ 44 ] also discovered that a higher CVAI significantly correlated with an increased incidence of stroke, particularly in individuals without hypertension, diabetes, or heart disease. Potential reasons include: firstly, patients diagnosed with cardio-cerebrovascular-metabolic diseases might have received medication (e.g., antihypertensive and hypoglycemic drugs) and altered their lifestyle and diet during the follow-up, potentially impacting the correlation between CVAI, WC, and CCMM. Secondly, the relatively small sample sizes for individuals with hypertension (n = 1006) and those with a single cardio-cerebrovascular-metabolic disease (n = 1680) at baseline might also explain the variability. Thirdly, due to disease interactions, having a chronic disease itself can lead to an increased risk of CCMM, potentially diminishing the relevance of obesity indicators for predicting CCMM. In the concluding multimorbidity pattern analysis, it was demonstrated that obesity-related metrics primarily influence a pattern comprising multiple metabolic disorders, highlighting the critical need for early intervention in these diseases. There may be reasons for this: Firstly, both VAI and CVAI are predicated upon insulin resistance, a key mechanism underlying metabolic disorders. Secondly, given that metabolic disorders like hypertension, diabetes mellitus, and dyslipidemia pose risks for cardiovascular and cerebrovascular diseases, the latter’s extended durations compared to metabolic diseases might have skewed the study’s outcomes. Thirdly, the small sample size utilized for analyzing the multimorbidity pattern could have biased the outcomes, necessitating a larger cohort to validate the findings. The possible mechanisms of the association between BMI, WC, WHtR, VAI, CVAI and CCMM may include: First, excess adipose tissue, especially visceral adipose tissue, produces excess inflammatory cytokines and adipokines[ 45 ]. And adipokines are further involved in microvascular injury by mediating endothelial dysfunction, induction of oxidative stress, inflammation, activation of the renin-angiotensin-aldosterone system and endoplasmic reticulum stress[ 46 , 47 ]. Secondly, insulin resistance due to obesity impedes normal cardiac function by inhibiting metabolic pathways and overstimulating growth factors[ 48 ]. And insulin resistance affects apolipoprotein A1 production or hepatic high-density lipoprotein secretion, which further contributes to metabolic diseases[ 49 ]. Third, excess free fatty acids are produced outside of fat-storing tissues and transferred to ectopic sites, including viscera, heart and vascular system, ultimately leading to cardiovascular and cerebrovascular metabolic diseases[ 50 ].Furthermore, monitoring diets in older adults is critical, with processed foods and fried foods with high fat having been shown to exacerbate morbidity and mortility. On the other hand, Lactobacillus acidophilus and Bifidobacterium bifidum-fortified cheese is a source of increased nutritional value and safe for intake and therefore a beneficial food source[ 51 , 52 ]. limitations Our study is the inaugural investigation into the association of BMI, WC, WHtR, VAI, and CVAI with CCMM risk. Additionally, we further assessed the effects of these indices on CCMM's multimorbidity patterns. However, this study still has some limitations: Firstly, although it was a cohort study, the binary logistic regression model failed to account for temporal effects and excluded deceased patients; Secondly, the follow-up period of 7 years is relatively short for chronic disease studies, limiting our ability to assess long-term associations; Thirdly, significant factors like physical activity weren't included as covariates due to only 40% of CHARLS participants reporting on it, despite its crucial role in chronic disease development; Fourthly, the small sample size constrained our evaluation of cardiovascular-metabolic multimorbidity patterns; Fifthly, chronic diseases were identified using standardized questionnaires, introducing potential information bias, despite their high reliability in epidemiological research[ 53 ]. Future research should focus on larger sample sizes and extended follow-up periods to validate these findings. Conclusions BMI, WC, WHtR, VAI, and CVAI exhibited a nonlinear positive correlation with the risk of CCMM among the middle-aged and elderly Chinese cohort. Moreover, CVAI outperformed BMI, WC, WHtR, and VAI as a predictor of CCMM, especially in the middle-aged individuals and those without any cardiovascular, cerebrovascular and metabolic diseases. The analysis of multimorbidity patterns indicates that BMI, WC, WHtR, VAI, and CVAI may primarily influence the pattern consisting of metabolic diseases. Abbreviations Body Mass Index - BMI Waist Circumference - WC Waist-to-Height Ratio - WHtR Visceral Adiposity Index - VAI Chinese Visceral Adiposity Index - CVAI Cardio-Cerebrovascular-Metabolic Multimorbidity - CCMM Receiver Operating Curve - ROC Net Reclassification Index –NRI Integrated Discriminant Improvement - IDI Declarations Availability of data and material All data generated or analyzed during this study are included in this published article. Trial Registration: Not applicable- This is an observational cohort study using secondary data. Conflict Of Interest The authors declare no conflict of interest in this study. Funding This study is funded by the Foundation of Traditional Chinese Medicine of Jiangxi Province(Grant Number 2019B122 ) and a partnership grant from the central government guides local funds for scientific and technological development (Grant Number 20221ZDG020070 ) Ethical Approval and informed consent of Participants This study was approved by the Biomedical Ethics Review Committee of Peking University. Written informed consent was obtained from all participants prior to their inclusion in the study. All procedures involving human participants and/or data were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards (https://www.wma.net/policies-post/wma-declaration-of-helsinki/). Author contribution 1- Tingting Yang, Conceptualization and methodology ,Writing—original draft preparation 2- Yanfeng Gong, Software and visualization, writing—review and editing ,Project administration and funding acquisition 3- Huanbing Liu, Validation, Formal analysis, Investigation, Resources and data curation, Supervision Acknowledgments We gratefully acknowledge the Department of General Medical Section, Nanchang University, and the funding organization for their support in the successful conduct of this study. References Barnett K, et al. 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Jassim FH, Mulakhudair AR, Shati ZRK. Improving Nutritional and Microbiological Properties of Monterey Cheese using Bifidobacterium bifidum. in IOP Conference Series: Earth and Environmental Science. 2023. IOP Publishing. Barr EL, et al. Validity of self-reported cardiovascular disease events in comparison to medical record adjudication and a statewide hospital morbidity database: the AusDiab study. Intern Med J. 2009;39(1):49–53. Additional Declarations No competing interests reported. Supplementary Files floatimage1.jpeg Graphical Abstract Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6954915","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495731203,"identity":"2b9ec39a-ef92-4994-8173-5a90cb19e8ab","order_by":0,"name":"Tingting Yang","email":"","orcid":"","institution":"Sichuan Provincial People's Hospital East Sichuan Hospital \u0026 Dazhou First People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Yang","suffix":""},{"id":495731205,"identity":"7077fcb6-1dd1-4d27-adab-872cbf2c2b0f","order_by":1,"name":"Yanfeng Gong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsUlEQVRIiWNgGAWjYDACCR42hgQDCTk29vYDJGj5UGBhzMdzJoF4LYwzPlQkzpNwMCBOh8Ht3mOPeQwk0tskGBIYflRsI6xFcs65dGOgltw26cYDjD1nbhPWwi+RYyYN1iJzIIGZsY0ILWxQLelsEgkGxGkB2SI5w0AigXgtkjPy0iQ+GEgYtgED+SBRfjG4kXtMIuFPnbx8e/vBBz8qiNCCAg6QqH4UjIJRMApGAS4AAMpkNc1uBeRFAAAAAElFTkSuQmCC","orcid":"","institution":"Nanchang University","correspondingAuthor":true,"prefix":"","firstName":"Yanfeng","middleName":"","lastName":"Gong","suffix":""},{"id":495731206,"identity":"3aec9f61-adbe-4c94-be3e-62c03e8ad390","order_by":2,"name":"Huanbing Liu","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Huanbing","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-06-23 09:08:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6954915/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6954915/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88533007,"identity":"6f129282-7fc0-4571-b2ad-825234af49eb","added_by":"auto","created_at":"2025-08-07 12:01:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":143498,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between obesity indices and CCMM in different subgroups. Each subgroup was adjusted for age, sex, residence, current smoking, current drinking, cancer, chronic lung disease, liver disease, kidney disease, digestive system disease, psychological problem, memory-related disease, arthritis or rheumatism, asthma, hypertension, dyslipidemia, diabetes, heart problem, and stroke. All groups use healthy weight/Q1 as a reference. CCMM, cardio-cerebrovascular-metabolic multimorbidity; BMI, body mass index; WC, waist circumference; WHtR, waist-to-height ratio; VAI, visceral adiposity index; CVAI, Chinese visceral adiposity index; CI, confidence interval; OR, odds ratio. *Number of cardio-cerebrovascular-metabolic diseases at baseline.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6954915/v1/bbf555f18e11e4ea49a30350.png"},{"id":88533009,"identity":"661109d5-353c-4365-b32b-4da3f8f41064","added_by":"auto","created_at":"2025-08-07 12:01:04","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":987538,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between BMI, WC, WHtR, VAI, CVAI and the patterns of multimorbidity of CCMM. Adjusted for age, sex, residence, current smoking, current drinking, cancer, chronic lung disease, liver disease, kidney disease, digestive system disease, psychological problem, memory-related disease, arthritis or rheumatism, asthma, hypertension, dyslipidemia, diabetes, heart problem, and stroke. CCMM, cardio-cerebrovascular-metabolic multimorbidity; BMI, body mass index; WC, waist circumference; WHtR, waist-to-height ratio; VAI, visceral adiposity index; CVAI, Chinese visceral adiposity index. Significant at p\u0026lt;0.05 are shown in bold. \u003cstrong\u003eA \u003c/strong\u003eCCMM patterns consisting of two diseases. \u003cstrong\u003eB \u003c/strong\u003eCCMM patterns consisting of three diseases.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6954915/v1/8ba02965469041ed764b6615.jpeg"},{"id":95223988,"identity":"1e521fcc-5ca9-4192-810b-8f211814cb40","added_by":"auto","created_at":"2025-11-05 16:23:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2201973,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6954915/v1/e67509c4-4db9-471d-8666-fa2933f60180.pdf"},{"id":88533011,"identity":"4fb15193-3848-4d70-bc73-c35def7c853e","added_by":"auto","created_at":"2025-08-07 12:01:06","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":627952,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6954915/v1/27c6eeb2b2617c48877274b9.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Obesity Distribution Indices and Cardio-Cerebrovascular Metabolic Multi-morbidity: Insights from a National Longitudinal Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs society advances, the prevalence of chronic non-communicable diseases (NCDs) and the accompanying multimorbidity defined as the coexistence of two or more chronic conditions\u0026mdash;are increasingly posing significant clinical and public health challenges[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Multimorbidity is associated with a heightened risk of adverse drug events and increased dependency on care[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], correlates with higher mortality rates[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and contributes to decreased functional status[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and higher instances of hospital and outpatient healthcare use[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Among all NCDs, cardiovascular and cerebrovascular diseases (CCVD) still stand as the leading cause of death globally[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].From 2009 to 2019, cardiovascular and cerebrovascular diseases persisted as the primary mortality factors among the Chinese population aged over 65[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Moreover, Dyslipidemia, hyperglycemia, and hypertension are currently recognized as the major metabolic risk factors leading to CCVD[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Notably, the health impact of Cardiovascular, cerebrovascular, and metabolic multimorbidity (CCMM) is significantly more severe than that of any single Cardiovascular, cerebrovascular, and metabolic disease. For instance, patients with any two cardiometabolic diseases have their average life expectancy reduced by five years compared to those with a single cardiometabolic condition[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, traditionally focused on specific diseases, clinical work, medical research, and education often lack a unified and comprehensive strategy, leading to redundant efforts that reduce clinical efficiency and exacerbate patient disease burdens and the risk of adverse events[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. As a result, individuals with multiple health conditions necessitate integrated and overarching preventive and treatment strategies. Our study is centered on the middle-aged and elderly Chinese demographic, aiming to examine the prevalence of CCMM, which involves the coexistence of two or more conditions such as hypertension, dyslipidemia, hyperglycemia, heart disease, and stroke.\u003c/p\u003e\u003cp\u003eObesity is linked to an extensive spectrum of diseases, contributing significantly to the risk of conditions like type 2 diabetes mellitus, fatty liver, hypertension, myocardial infarction, stroke, dementia, osteoarthritis, obstructive sleep apnea, and various cancers, making it a primary risk factor for cardiovascular, cerebrovascular, and metabolic diseases[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Obesity-related indicators, commonly utilized in clinical practice and epidemiological studies due to their convenience and efficacy, are pivotal in assessing obesity status. Body mass index (BMI) has become the most prevalent indicator for assessing obesity in clinical settings, indicating the overall level of body fatness. However, BMI fails to capture age-related changes in height or body composition, potentially introducing significant biases in obesity assessments[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Furthermore, BMI-based diagnoses of obesity exhibit heterogeneity: the presence of metabolic dysfunctions and chronic comorbidities is not universal among individuals classified as obese, and conversely, metabolic abnormalities can occur in persons of normal weight[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Waist circumference (WC) and waist-to-height ratio (WHtR) are recognized as significant anthropometric markers for abdominal obesity[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. While WC and WHtR offer advantages over BMI in indicating visceral fat, they encounter challenges in differentiating subcutaneous from visceral fat deposits[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Fortunately, the visceral adiposity index (VAI) and the Chinese visceral adiposity index (CVAI), novel indicators of obesity, have emerged, distinguishing between subcutaneous and visceral fat in relation to the latter's function[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. An elevated BMI is linked to a spectrum of 21 diseases, as demonstrated in a multicohort observational study, with a BMI\u0026thinsp;\u0026ge;\u0026thinsp;30.00 kg/m^2 being implicated in cardiovascular, metabolic, digestive, respiratory, neurological, musculoskeletal, and infectious pathologies, which indicates a dose-response relationship with comorbidity complexity[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, a study has shown that WHtR, WC, and BMI are independent predictors of cardiometabolic multimorbidity among middle-aged and elderly Chinese populations, with WHtR and WC surpassing BMI in predictive accuracy[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, VAI has been identified as the most sensitive and specific predictor of metabolic syndrome[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and exhibits a positive correlation with stroke prevalence[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Notably, CVAI is considered the most appropriate indicator for predicting cardiometabolic multimorbidity in Chinese middle-aged and elderly populations, when compared to other indicators of insulin resistance[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The above studies have demonstrated that BMI, WC, WHtR, VAI, and CVAI are closely correlated with the development of cardio-cerebrovascular-metabolic multimorbidity disorders; however, the relationship between these indicators and CCMM, as well as their impact on multimorbidity patterns, remains unevaluated.\u003c/p\u003e\u003cp\u003eIn this research, we explored how BMI, WC, WHtR, VAI, and CVAI were longitudinally associated with CCMM among Chinese middle-aged and older adults for the first time. Additionally, we evaluated their predictive power for the risk of cardio-metabolic morbidities. Moreover, to enhance our understanding of how these indices relate to CCMM, we investigated their impact on multimorbidity patterns.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy design\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study population originated from the China Health and Retirement Longitudinal Study (CHARLS), a national cohort study [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. CHARLS gathers high-quality data representative of Chinese individuals aged 45 and older, encompassing demographic background, health status, economic factors, and more. Blood samples were analyzed in 2011 and 2015 as part of CHARLS. Conducted in 2011, the CHARLS national baseline survey covered 28 provinces and has been updated biennially up to 2020. We used baseline data from 2011, with follow-up extending through 2018. Approval was obtained from the Biomedical Ethics Review Committee of Peking University, and informed consent was secured from all participants.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll participants meeting the following criteria in the national baseline survey were selected: (1) aged 45 years or older; (2) absence of CCMM history at baseline, defined as not having been diagnosed with two or more of the following conditions: hypertension, diabetes, dyslipidemia, heart disorders, or stroke; (3) complete collection of anthropometric measurements and laboratory indicators; and (4) comprehensive follow-up. We excluded subjects with incomplete information, including birth year or age, gender, urban/rural residency, smoking and drinking habits, and baseline disease status, as well as those with any manifestly incorrect data. Ultimately, the study comprised 6472 participants.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBaseline data collection and definitions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA standardized questionnaire administered by trained clinical staff captured patients' demographic details (age, gender, place of residence) and lifestyle factors (smoking, alcohol consumption). Trained clinicians also measured anthropometric parameters (weight, height, and WC) following a uniform protocol. In addition, we incorporated select blood parameters, including triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C), from CHRALS for VAI and CVAI calculations. Further details on these data are provided in the Supplementary Material.\u003c/p\u003e\u003cp\u003eBMI, WHtR, VAI [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e],and CVAI [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] were formulated as:\u003c/p\u003e\u003cp\u003eBody mass index:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{B}\\text{M}\\text{I}=\\frac{\\text{w}\\text{e}\\text{i}\\text{g}\\text{h}\\text{t}\\left(\\text{k}\\text{g}\\right)}{{\\text{h}\\text{e}\\text{i}\\text{g}\\text{h}\\text{t}}^{2}\\left({\\text{m}}^{2}\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWaist-to-height ratio:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\text{W}\\text{H}\\text{t}\\text{R}=\\frac{\\text{W}\\text{C}\\left(\\text{c}\\text{m}\\right)}{\\text{h}\\text{e}\\text{i}\\text{g}\\text{h}\\text{t}\\left(\\text{c}\\text{m}\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eVisceral adiposity index:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\text{V}\\text{A}\\text{I}\\text{\u0026shy;}\\text{m}\\text{a}\\text{l}\\text{e}=\\left[\\frac{\\text{W}\\text{C}\\left(\\text{c}\\text{m}\\right)}{39.68+1.88\\times\\:\\text{B}\\text{M}\\text{I}(\\text{k}\\text{g}/\\text{m}\u0026sup2;)}\\right]\\text{*}\\left[\\frac{\\text{T}\\text{G}(\\text{m}\\text{m}\\text{o}\\text{l}/\\text{L})}{1.03}\\right]\\text{*}\\left[\\frac{1.31}{\\text{H}\\text{D}\\text{L}(\\text{m}\\text{m}\\text{o}\\text{l}/\\text{L})}\\right]$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\text{V}\\text{A}\\text{I}\\text{\u0026shy;}\\text{f}\\text{e}\\text{m}\\text{a}\\text{l}\\text{e}=\\left[\\frac{\\text{W}\\text{C}\\left(\\text{c}\\text{m}\\right)}{36.58+1.89\\times\\:\\text{B}\\text{M}\\text{I}(\\text{k}\\text{g}/\\text{m}\u0026sup2;)}\\right]\\text{*}\\left[\\frac{\\text{T}\\text{G}(\\text{m}\\text{m}\\text{o}\\text{l}/\\text{L})}{0.81}\\right]\\text{*}\\left[\\frac{1.52}{\\text{H}\\text{D}\\text{L}(\\text{m}\\text{m}\\text{o}\\text{l}/\\text{L})}\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eChinese visceral adiposity index:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:\\text{C}\\text{V}\\text{A}\\text{I}\\text{\u0026shy;}\\text{m}\\text{a}\\text{l}\\text{e}=-267.93+0.68\\text{*}\\text{a}\\text{g}\\text{e}\\left(\\text{y}\\text{e}\\text{a}\\text{r}\\text{s}\\right)+0.03\\text{*}\\text{B}\\text{M}\\text{I}\\left(\\text{k}\\text{g}/\\text{m}\u0026sup2;\\right)+4.00\\text{*}\\text{W}\\text{C}\\left(\\text{c}\\text{m}\\right)+22.00\\text{*}\\text{L}\\text{g}\\left(\\text{T}\\text{G}\\right)\\left(\\text{m}\\text{m}\\text{o}\\text{l}/\\text{L}\\right)-16.32\\text{*}\\text{H}\\text{D}\\text{L}\\text{\u0026shy;}\\text{C}(\\text{m}\\text{m}\\text{o}\\text{l}/\\text{L})$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:\\text{C}\\text{V}\\text{A}\\text{I}\\text{\u0026shy;}\\text{f}\\text{e}\\text{m}\\text{a}\\text{l}\\text{e}=-187.32+1.71\\text{*}\\text{a}\\text{g}\\text{e}\\left(\\text{y}\\text{e}\\text{a}\\text{r}\\text{s}\\right)+4.23\\text{*}\\text{B}\\text{M}\\text{I}\\left(\\text{k}\\text{g}/\\text{m}\u0026sup2;\\right)+1.12\\text{*}\\text{W}\\text{C}\\left(\\text{c}\\text{m}\\right)+39.76\\text{*}\\text{L}\\text{g}\\left(\\text{T}\\text{G}\\right)(\\text{m}\\text{m}\\text{o}\\text{l}/\\text{L})-11.66\\text{*}\\text{H}\\text{D}\\text{L}\\text{\u0026shy;}\\text{C}(\\text{m}\\text{m}\\text{o}\\text{l}/\\text{L})$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCovariates\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCovariates included sociodemographic characteristics, lifestyle factors, and current disease status. Sociodemographic characteristics comprised age (years), sex (male/female), and residence (rural/urban). Lifestyle factors consisted of current smoking and drinking status (yes/no). Present disease conditions (yes/no) covered hypertension, dyslipidemia, diabetes, heart problem, stroke, cancer, chronic lung disease, liver disease, kidney disease, digestive system disease, psychological problem, memory-related disease, arthritis or rheumatism, and asthma. Additional details are described in the Supplementary Material.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutcome\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCCMM entailed having at least two of the following conditions: hypertension, dyslipidemia, diabetes, heart problem, and stroke. Diagnosis relied on participants' self-reported information.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eCategorical variables were represented as frequencies (percentages) and compared using the chi-square test. Continuous data were presented as medians (interquartile ranges) and compared using the Wilcoxon rank sum test. In subsequent analyses, BMI was divided into four categories following the Chinese adult standard: underweight (\u0026lt;\u0026thinsp;18.5 kg/m2), healthy weight (18.5\u0026ndash;23.9 kg/m2), overweight (24-27.9 kg/m2), and obesity (\u0026ge;\u0026thinsp;28 kg/m2)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additionally, WC, WHtR, VAI, and CVAI were grouped into quartiles: WCQ1-WCQ4, WHtRQ1-WHtRQ4, VAIQ1-VAIQ4, and CVAIQ1-CVAIQ4. A binary logistic regression model assessed the association between BMI, WC, WHtR, VAI, and CVAI and CCMM, estimating the odds ratios (ORs) and 95% confidence intervals (CIs). Subsequent models were established to investigate the effects of progressively adjusting covariates. Model 1 included adjustments for age, sex, residence, current smoking, and current drinking. Model 2 added adjustments for cancer, chronic lung disease, liver disease, kidney disease, digestive system disease, psychological problem, memory-related disease, arthritis or rheumatism, and asthma to those in Model 1. Model 3 incorporated Model 2\u0026rsquo;s parameters with additional adjustments for hypertension, dyslipidemia, diabetes, heart problem, and stroke. To investigate the dose-response relationships between BMI, WC, WHtR, VAI, CVAI, and the incidence of CCMM, we utilized restricted cubic spline analysis. Considering the prevalence of baseline hypertension, subgroup analyses were conducted focusing on age, sex, the number of baseline cardio-cerebrovascular-metabolic diseases, and hypertension status, with a detailed examination of interactions between subgroups. Subsequently, we calculated the AUC values to assess the predictive potential of BMI, WC, WHtR, VAI, and CVAI for CCMM. Following this, differences in AUC values for WC, WHtR, VAI, CVAI, and BMI were compared separately using the DeLong test[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, the AUC values, along with the net reclassification index (NRI) and the integrated discriminant improvement (IDI), were computed to assess the predictive efficacy and additive predictive value of BMI, WC, WHtR, VAI, and CVAI, over and above the established covariates in various models. In the final stage of our analysis, we turned our attention to the three most prevalent CCMM patterns and examined their association with BMI, WC, WHtR, VAI, and CVAI using logistic regression. Analyses were conducted using IBM SPSS Statistics 27 and R version 4.3.1, with the threshold for significance set at a two-sided P-value of less than 0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eBaseline characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003e6742 cases (2946 males and 3526 females) were included in this study, featuring a median age of 57 (51\u0026ndash;64) years. Following a 7-year period (2011\u0026ndash;2018), 1502 participants (23.21%) developed CCMM. The risk of developing CCMM was higher among women and individuals residing in urban areas, compared to their male and rural counterparts. Compared to individuals without CCMM, those affected were generally older and had higher measures of BMI, WC, WHtR, VAI, CVAI; they also exhibited significantly increased risks for hypertension, diabetes, dyslipidemia, heart problem, chronic lung disease, liver disease, kidney disease, digestive system disease, memory-related disease, arthritis or rheumatism, and asthma (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). It is noteworthy that patients with CCMM exhibited lower rates of smoking and drinking compared to those without the condition. (shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of the study population.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;6472)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithout multimorbidity\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;4970)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWith multimorbidity\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1502)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57(51\u0026ndash;64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57(51\u0026ndash;64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59(53\u0026ndash;65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2946(45.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2330(46.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e616(41.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4399(67.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3418(68.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e981(65.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoking (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2011(31.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1618(32.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e393(26.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent drinking (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1665(25.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1336(26.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e329(21.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.96(20.78\u0026ndash;25.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.60(20.54\u0026ndash;24.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.48(22.09\u0026ndash;27.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight (\u0026lt;\u0026thinsp;18.5) (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e428(6.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e371(7.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57(3.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealthy weight (18.5\u0026ndash;23.9) (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3568(55.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2945(59.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e623(41.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight (24.0\u0026ndash;27.9) (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1846(28.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1295(26.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e551(36.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity (\u0026ge;\u0026thinsp;28.0) (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e630(9.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e359(7.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e271(18.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWC, cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84.00(77.20\u0026ndash;91.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82.80(76.30\u0026ndash;89.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.20(81.00-95.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1 (\u0026le;\u0026thinsp;77.20) (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1640(25.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1420(28.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e220(14.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2 (77.30\u0026ndash;84.00) (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1697(26.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1404(28.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e293(19.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3 (84.10\u0026ndash;91.00) (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1598(26.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1181(23.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e417(27.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4 (\u0026gt;\u0026thinsp;91.00) (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1537(23.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e965(19.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e572(38.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHtR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.53(0.49\u0026ndash;0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.52(0.48\u0026ndash;0.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.56(0.51\u0026ndash;0.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1 (\u0026le;\u0026thinsp;0.49) (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1654(25.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1436(28.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e218(14.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2 (0.50\u0026ndash;0.53) (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1576(24.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1286(25.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e290(19.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3 (0.54\u0026ndash;0.58) (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1675(25.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1249(25.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e426(28.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4 (\u0026gt;\u0026thinsp;0.58) (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1567(24.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e999(20.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e568(37.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.42(0.87\u0026ndash;2.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.32(0.81\u0026ndash;2.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.89(1.12\u0026ndash;3.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1 (\u0026le;\u0026thinsp;0.87) (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1632(25.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1400(28.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e232(15.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2 (0.88\u0026ndash;1.42) (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1607(24.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1291(25.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e316(21.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3 (1.43\u0026ndash;2.47) (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1618(25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1198(24.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e420(27.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4 (\u0026gt;\u0026thinsp;2.47) (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1615(24.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1081(21.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e534(35.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90.67(65.37-118.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85.01(61.85-112.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110.41(85.26-137.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1 (\u0026le;\u0026thinsp;65.37) (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1615(24.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1417(28.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e198(13.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2 (65.38\u0026ndash;90.67) (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1621(25.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1373(27.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e248(16.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3(90.68-118.81) (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1618(25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1181(23.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e437(29.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4 (\u0026gt;\u0026thinsp;118.81) (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1618(25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e999(20.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e619(41.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1006(15.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e545(10.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e461(30.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyslipidemia (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e193(2.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119(2.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74(4.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e121(1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65(1.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56(3.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart problem (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e324(5.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e177(3.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e147(9.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStroke (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36(0.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24(0.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12(0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52(0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38(0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14(0.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.524\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic lung disease (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e568(8.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e402(8.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e166(11.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver disease (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e221(3.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e157(3.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64(4.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKidney disease (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e384(5.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e275(5.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e109(7.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigestive system disease (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1498(23.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1117(22.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e381(25.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychological problem (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73(1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50(1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23(1.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMemory-related disease (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42(0.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20(0.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22(1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArthritis or rheumatism (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2225(34.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1669(33.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e556(37.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsthma (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e203(3.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e138(2.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65(4.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: BMI, body mass index; WC, waist circumference; WHtR, waist-to-height ratio; VAI, visceral adiposity index; CVAI, Chinese visceral adiposity index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eData are summarized as n (%) or median (25th-75th percentile).\u003c/p\u003e\u003cp\u003e\u003cb\u003eRelationship between BMI, WC, WHtR, VAI, CVAI and CCMM\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhen adjusted for age, sex, residence, current smoking and current drinking alone (model 1), higher levels of the aforementioned obesity indices were all significantly associated with an increased CCMM risk. The five obesity indices remained significantly associated with CCMM when adjusted for covariates excluding the diseases that comprise CCMM (model 2). Moreover, after adjusting for all covariates (model 3), the association between these obesity indicators and the risk of CCMM persisted. The obesity group continued to show a strong association with CCMM occurrence (OR: 3.09, 95% CI: 2.54\u0026ndash;3.77). Similarly, the highest quartile groups for WC, WHtR, VAI, and CVAI had ORs (95% CI) of 3.34 (2.77\u0026ndash;4.03), 3.25 (2.67\u0026ndash;3.95), 2.70 (2.23\u0026ndash;3.27), and 3.42 (2.82\u0026ndash;4.17), respectively, when compared to the lowest quartiles. Notably, of all the indices, the highest quartile of CVAI demonstrated the greatest risk for CCMM in all three models. (shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA significant non-linear association was observed between BMI, WC, WHtR, VAI, CVAI, and CCMM (all P and P for nonlinear\u0026thinsp;\u0026lt;\u0026thinsp;0.001). BMI exhibited an S-shaped curve in relation to CCMM. WC, WHtR, and CVAI demonstrated J-shaped curves with CCMM, with inflection points at 74.10, 0.47, and 50.29, respectively. Conversely, VAI presented a logarithmic relationship with CCMM. (shown in Fig. S1).\u003c/p\u003e\u003cp\u003eIn subgroup analyses, positive associations between BMI, WC, WHtR, VAI, CVAI, and CCMM incidence remained consistent across various subgroups. Notably, significant interaction was noted between sex and BMI in CCMM risk (P for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with a stronger association observed in males than in females. However, a significant interaction between hypertension status and WC was observed in CCMM risk, with higher WC levels correlating more strongly with CCMM incidence in individuals without hypertension. Additionally, CVAI's association with CCMM risk was amplified in individuals under 65, without hypertension, and free from baseline cardio-cerebrovascular-metabolic diseases (P for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05). (shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between BMI, WC, WHtR, VAI, CVAI and CCMM.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIndices\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGroups\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eOR(95%CI)\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eOR(95%CI)\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eOR(95%CI)\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHealthy weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.63(0.46\u0026ndash;0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.59(0.43\u0026ndash;0.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.62(0.45\u0026ndash;0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.11(1.85\u0026ndash;2.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.15(1.87\u0026ndash;2.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.91(1.66\u0026ndash;2.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.82(3.17\u0026ndash;4.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.97(3.29\u0026ndash;4.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.09(2.54\u0026ndash;3.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1 (\u0026le;\u0026thinsp;77.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2 (77.30\u0026ndash;84.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.37(1.13\u0026ndash;1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.41(1.16\u0026ndash;1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.35(1.11\u0026ndash;1.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3 (84.10\u0026ndash;91.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.29(1.91\u0026ndash;2.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.35(1.96\u0026ndash;2.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.16(1.79\u0026ndash;2.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4 (\u0026gt;\u0026thinsp;91.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.83(3.21\u0026ndash;4.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.01(3.36\u0026ndash;4.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.34(2.77\u0026ndash;4.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHtR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1 (\u0026le;\u0026thinsp;0.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2 (0.50\u0026ndash;0.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.49(1.23\u0026ndash;1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.50(1.24\u0026ndash;1.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.46(1.19\u0026ndash;1.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3 (0.54\u0026ndash;0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.25(1.88\u0026ndash;2.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.28(1.90\u0026ndash;2.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.00(1.65\u0026ndash;2.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4 (\u0026gt;\u0026thinsp;0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.68(3.06\u0026ndash;4.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.79(3.15\u0026ndash;4.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.25(2.67\u0026ndash;3.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1 (\u0026le;\u0026thinsp;0.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2 (0.88\u0026ndash;1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.48(1.23\u0026ndash;1.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.50(1.24\u0026ndash;1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.42(1.17\u0026ndash;1.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3 (1.43\u0026ndash;2.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.11(1.76\u0026ndash;2.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.18(1.82\u0026ndash;2.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.02(1.67\u0026ndash;2.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4 (\u0026gt;\u0026thinsp;2.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.00(2.51\u0026ndash;3.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.12(2.60\u0026ndash;3.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.70(2.23\u0026ndash;3.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1 (\u0026le;\u0026thinsp;65.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2 (65.38\u0026ndash;90.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.24(1.01\u0026ndash;1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.26(1.03\u0026ndash;1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.19(0.97\u0026ndash;1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3(90.68-118.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.48(2.06-3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.56(2.12\u0026ndash;3.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.37(1.95\u0026ndash;2.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4 (\u0026gt;\u0026thinsp;118.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.11(3.42\u0026ndash;4.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.37(3.63\u0026ndash;5.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.42(2.82\u0026ndash;4.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: CCMM, cardio-cerebrovascular-metabolic multimorbidity; BMI, body mass index; WC, waist circumference; WHtR, waist-to-height ratio; VAI, visceral adiposity index; CVAI, Chinese visceral adiposity index; CI, confidence interval; OR, odds ratio.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eModel 1 included age, sex, residence, current smoking and current drinking.\u003c/p\u003e\u003cp\u003eModel 2 included age, sex, residence, current smoking, current drinking, cancer, chronic lung disease, liver disease, kidney disease, digestive system disease, psychological problem, memory-related disease, arthritis or rheumatism, and asthma.\u003c/p\u003e\u003cp\u003eModel 3 included age, sex, residence, current smoking, current drinking, cancer, chronic lung disease, liver disease, kidney disease, digestive system disease, psychological problem, memory-related disease, arthritis or rheumatism, asthma, hypertension, dyslipidemia, diabetes, heart problem, and stroke.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePredictive performance of BMI, WC, WHtR, VAI, and CVAI for CCMM\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe AUC values for BMI, WC, WHtR, VAI, and CVAI as determined by our analysis were 0.641, 0.645, 0.643, 0.622, and 0.670, with CVAI manifesting the highest metric when contrasted with the other obesity indicators. Additionally, a significant difference was noted in the AUC values among CVAI, VAI, and the other indicators (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). (shown in Fig. S2 and table S1).\u003c/p\u003e\u003cp\u003e\u003cb\u003eIncremental predictive value of BMI, WC, WHtR, VAI, and CVAI indices in the risk assessment of CCMM\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAdding BMI, WC, WHtR, VAI, and CVAI along with age, sex, residence, current smoking, and current drinking (model 1) significantly enhanced discriminatory power. Similarly, incorporating these five obesity indices into a model with age, sex, residence, current smoking, current drinking, cancer, chronic lung disease, liver disease, kidney disease, digestive system disease, psychological problem, memory-related disease, arthritis or rheumatism, and asthma, the NRI and IDI is also significantly increased. Furthermore, the inclusion of BMI, WC, WHtR, VAI, and CVAI within all covariates (model 3) significantly improved CCMM predictive utility, reflected in NRI and IDI estimates (95% CI) of 0.359(0.295\u0026ndash;0.412) and 0.026(0.022\u0026ndash;0.031) for BMI, 0.376(0.315\u0026ndash;0.440) and 0.022(0.018\u0026ndash;0.026) for WC, 0.343(0.291\u0026ndash;0.405) and 0.018(0.014\u0026ndash;0.022) for WHtR, 0.247(0.189\u0026ndash;0.307) and 0.005(0.003\u0026ndash;0.007) for VAI, and 0.444(0.385\u0026ndash;0.507) and 0.032(0.027\u0026ndash;0.037) for CVAI, respectively. Additionally, the AUC values of the three models were significantly enhanced by adding BMI, WC, WHtR, VAI or CVAI (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In all three models mentioned above, CVAI showed the best incremental predictive value among these five obesity indicators. (shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eImprovement in discrimination and risk reclassification for CCMM after adding obesity indices.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eNRI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eIDI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEstimate(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEstimate(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.576(0.560\u0026ndash;0.593)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel1\u0026thinsp;+\u0026thinsp;BMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.663(0.647\u0026ndash;0.678)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.420(0.358\u0026ndash;0.473)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.043(0.038\u0026ndash;0.049)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel1\u0026thinsp;+\u0026thinsp;WC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.656(0.640\u0026ndash;0.672)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.453(0.395\u0026ndash;0.512)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.035(0.031\u0026ndash;0.040)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel1\u0026thinsp;+\u0026thinsp;WHtR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.647(0.631\u0026ndash;0.663)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.388(0.335\u0026ndash;0.446)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.029(0.024\u0026ndash;0.033)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel1\u0026thinsp;+\u0026thinsp;VAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.607(0.591\u0026ndash;0.624)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.284(0.229\u0026ndash;0.343)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008(0.006\u0026ndash;0.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel1\u0026thinsp;+\u0026thinsp;CVAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.672(0.657\u0026ndash;0.688)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.511(0.449\u0026ndash;0.566)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.051(0.045\u0026ndash;0.057)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.589(0.572\u0026ndash;0.605)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel2\u0026thinsp;+\u0026thinsp;BMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.672(0.657\u0026ndash;0.687)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.424(0.364\u0026ndash;0.485)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.046(0.040\u0026ndash;0.052)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel2\u0026thinsp;+\u0026thinsp;WC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.664(0.648\u0026ndash;0.680)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.448(0.387\u0026ndash;0.505)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.037(0.033\u0026ndash;0.042)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel2\u0026thinsp;+\u0026thinsp;WHtR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.655(0.639\u0026ndash;0.671)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.378(0.327\u0026ndash;0.442)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.030(0.025\u0026ndash;0.034)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel2\u0026thinsp;+\u0026thinsp;VAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.617(0.601\u0026ndash;0.634)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.303(0.240\u0026ndash;0.356)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008(0.006\u0026ndash;0.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel2\u0026thinsp;+\u0026thinsp;CVAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.681(0.666\u0026ndash;0.697)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.515(0.453\u0026ndash;0.569)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.054(0.048\u0026ndash;0.060)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.694(0.678\u0026ndash;0.710)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel3\u0026thinsp;+\u0026thinsp;BMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.731(0.716\u0026ndash;0.745)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.359(0.295\u0026ndash;0.412)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.026(0.022\u0026ndash;0.031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel3\u0026thinsp;+\u0026thinsp;WC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.724(0.709\u0026ndash;0.739)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.376(0.315\u0026ndash;0.440)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.022(0.018\u0026ndash;0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel3\u0026thinsp;+\u0026thinsp;WHtR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.720(0.705\u0026ndash;0.735)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.343(0.291\u0026ndash;0.405)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.018(0.014\u0026ndash;0.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel3\u0026thinsp;+\u0026thinsp;VAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.706(0.690\u0026ndash;0.721)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.247(0.189\u0026ndash;0.307)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.005(0.003\u0026ndash;0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel3\u0026thinsp;+\u0026thinsp;CVAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.733(0.718\u0026ndash;0.747)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.444(0.385\u0026ndash;0.507)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.032(0.027\u0026ndash;0.037)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: CCMM, cardio-cerebrovascular-metabolic multimorbidity; BMI, body mass index; WC, waist circumference; WHtR, waist-to-height ratio; VAI, visceral adiposity index; CVAI, Chinese visceral adiposity index; CI, confidence interval; AUC, area under curve; NRI, net reclassification improvement; IDI, integrated discrimination improvement.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eModel 1 included age, sex, residence, current smoking and current drinking.\u003c/p\u003e\u003cp\u003eModel 2 included age, sex, residence, current smoking, current drinking, cancer, chronic lung disease, liver disease, kidney disease, digestive system disease, psychological problem, memory-related disease, arthritis or rheumatism, and asthma.\u003c/p\u003e\u003cp\u003eModel 3 included age, sex, residence, current smoking, current drinking, cancer, chronic lung disease, liver disease, kidney disease, digestive system disease, psychological problem, memory-related disease, arthritis or rheumatism, asthma, hypertension, dyslipidemia, diabetes, heart problem, and stroke.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRelationship between BMI, WC, WHtR, VAI, CVAI and the patterns of multimorbidity of CCMM\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMultimorbidity patterns, which consist of two and three diseases and recorded as the top three occurrences in 2018, were selected for analysis in relation to BMI, WC, WHtR, VAI, and CVAI. The most frequent dyadic disease patterns were \"hypertension\u0026thinsp;+\u0026thinsp;diabetes\" (n\u0026thinsp;=\u0026thinsp;137), \"hypertension\u0026thinsp;+\u0026thinsp;heart problem\" (n\u0026thinsp;=\u0026thinsp;256), and \"hypertension\u0026thinsp;+\u0026thinsp;dyslipidemia\" (n\u0026thinsp;=\u0026thinsp;238). After adjusting for all covariates, the highest odds ratios (ORs) for the \"hypertension\u0026thinsp;+\u0026thinsp;diabetes\" pattern were observed in the obesity group and the upper quartiles of WC, WHtR, and CVAI. The ORs (95% CI) for the obese group was 3.66 (95% CI 2.11\u0026ndash;6.40) versus the healthy weight group. When compared to the lowest quartile for WC, WHtR, and CVAI, the adjusted ORs (95% CI) for the highest quartile were 3.78 (2.17\u0026ndash;6.60), 3.38 (1.90\u0026ndash;6.02), 3.08 (1.76\u0026ndash;5.40), respectively. However, the highest quartile of VAI exhibited the largest ORs for the \"hypertension\u0026thinsp;+\u0026thinsp;dyslipidemia\" pattern, at 3.63 (95% CI: 2.34\u0026ndash;5.63).\u003c/p\u003e\u003cp\u003eFurthermore, the three most frequent multimorbidity patterns involving three conditions were \u0026ldquo;hypertension\u0026thinsp;+\u0026thinsp;dyslipidemia\u0026thinsp;+\u0026thinsp;heart problem\u0026rdquo; (n\u0026thinsp;=\u0026thinsp;122), \u0026ldquo;hypertension\u0026thinsp;+\u0026thinsp;dyslipidemia\u0026thinsp;+\u0026thinsp;diabetes\u0026rdquo; (n\u0026thinsp;=\u0026thinsp;97), and \u0026ldquo;hypertension\u0026thinsp;+\u0026thinsp;heart problem\u0026thinsp;+\u0026thinsp;diabetes\u0026rdquo; (n\u0026thinsp;=\u0026thinsp;40). The highest ORs were observed in the highest WC, VAI, and CVAI quartile groups for the multimorbidity pattern of \u0026ldquo;hypertension\u0026thinsp;+\u0026thinsp;dyslipidemia\u0026thinsp;+\u0026thinsp;diabetes\u0026rdquo;. Compared with the lowest quartile group, the ORs (95% CI) for WCQ4, VAIQ4, and CVAIQ4 were 6.65 (95% CI 3.09\u0026ndash;14.30), 11.02(95% CI 4.62\u0026ndash;26.28) and 7.78(95% CI 3.58\u0026ndash;16.92), respectively. Whereas, the ORs for the \u0026ldquo;hypertension\u0026thinsp;+\u0026thinsp;heart problem\u0026thinsp;+\u0026thinsp;diabetes\u0026rdquo; multimorbidity pattern were highest in the obesity group and in the highest quartile of WHtR after adjusting for all covariates, with 6.66 (95% CI 2.71\u0026ndash;16.35) in the obese group, and 5.88 (95% CI 1.90-18.24) in the WHtRQ4 group, respectively. Notably, the difference in ORs between the two multimorbidity patterns were minimal in both the obesity and WHtRQ4 groups (shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this national cohort study, we assessed the association between BMI, WC, WHtR, VAI, CVAI, and CCMM. Our analyses demonstrated that all five obesity indicators were positively correlated with the risk of CCMM. Subsequent subgroup analysis confirmed that these associations persisted across various subgroups, including age, sex, cardio-cerebrovascular-metabolic diseases count and hypertension status at baseline. Additionally, the CVAI index emerged as the strongest predictor of CCMM compared with BMI, WC, WHtR, and VAI, exhibiting a J-shaped association with CCMM. When integrated into various models, CVAI demonstrated the most substantial incremental predictive value for CCMM risk. Furthermore, CVAI, along with other obesity indices, primarily influences the pattern of metabolic disorders within CCMM.\u003c/p\u003e\u003cp\u003eObesity is closely linked to the risk of cardiovascular and cerebrovascular metabolic diseases, and the metabolic implications of obesity differ with the distribution of adipose tissue. Emerging evidence suggests that visceral fat accumulation has a stronger association with metabolic abnormalities than subcutaneous fat[\u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Despite the lack of direct comparative studies on the correlation of BMI, WC, WHtR, VAI, and CVAI with CCMM, numerous studies have demonstrated that emerging obesity indicators, which reflect visceral fat function, offer superior predictive value for cardio-cerebrovascular-metabolic diseases, metabolic syndrome (MS), and cardiometabolic multimorbidity compared to traditional measures. And CVAI shows a stronger correlation with these conditions in Asian populations. A large-sample cross-sectional study demonstrated that CVAI was the strongest predictor of hypertension and a reliable indicator for its early identification, outperforming other obesity indices like BMI, WC, VAI, lipid accumulation product index (LAP), body roundness index (BRI), body shape index (ABSI), conicity index (CI), triglyceride glucose index (TyG-index) and its correlation index (TyG-BMI, TyG-WC, TyG-WHtR)[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Moreover, CVAI proves superior as a clinical indicator for the development of diabetes mellitus compared to BMI, WC, VAI in the Chinese elderly population, particularly among women[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], with low initial levels significantly associated with prediabetes regression to normoglycemia[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The risk of incident cardiovascular disease is influenced by both long-term cumulative high CVAI exposure and its duration among hypertensive patients, with early accumulation posing a greater risk than later, highlighting the critical importance of early optimal CVAI management[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In a cohort study of 14,595 rural Chinese patients, CVAI was identified as the most suitable predictor of stroke, compared to VAI, LAP, TyG, TyG-BMI, and TyG-WC[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Among middle-aged and older adults, VAI, CVAI, and other obesity-related indices can predict metabolic syndrome (MS), with CVAI deemed the most effective indicator for MS, especially in women[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Furthermore, CVAI emerges as the most appropriate indicator for predicting cardiometabolic multimorbidity in middle-aged and older Chinese populations, surpassing other surrogate insulin resistance indexes[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Similarly, our study identified a positive correlation between CCMM and CVAI, offering scientific evidence for employing CVAI in diagnosing CCMM. Given CVAI's superior predictive capacity over BMI, WC, WHtR, and VAI for CCMM, its application in future screening practices appears viable.\u003c/p\u003e\u003cp\u003eOur study noted a higher CCMM prevalence in urban populations, which may be associated with better healthcare access and increased health awareness among urbanites, resulting in more diagnoses. Notably, CCMM prevalence is higher among current smokers and drinkers compared to their non-smoking and non-drinking counterparts. This observation could stem from our study's methodology, which categorized individuals who had previously smoked or consumed alcohol but currently abstain as non-smokers and non-drinkers, possibly affecting the results. Furthermore, the influence of additional health-related lifestyle factors must also be considered. However, the difference in stroke prevalence, as a component of CCMM, was not statistically significant between groups with and without this condition, likely due to the limited number of stroke cases at baseline, a mere 36, potentially impacting the findings.\u003c/p\u003e\u003cp\u003eIn subgroup analyses, we found that CVAI was linked to a reduced risk of CCMM among individuals aged 65 or older compared to those under 65, with similar trends found in BMI, WC, and WHtR. Furthermore, prior research indicates that visceral fat is not linked to atherosclerotic cardiovascular events in elderly males[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This phenomenon may be attributed to the diminished impact of obesity on health in older adults. More studies in different age groups in China are needed for further evidence. Moreover, significant sex-based differences were observed in the impact of BMI on CCMM incidence, with males demonstrating a higher risk of developing CCMM following an elevation in BMI compared to females. A comparable trend was observed in research examining the effects of BMI, WC, and waist-to-hip ratio (WHR) on chronic diseases in elderly Indian cohorts[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This discrepancy may be ascribed to sex-differential adiposity distribution patterns, typically manifesting as apple-shaped physiques in males and pear-shaped physiques in females[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In addition, our findings indicated that WC and CVAI had a stronger association with the prevalence of CCMM in individuals without hypertension. Moreover, CVAI was associated with an elevated risk of CCMM in individuals without baseline cardio-cerebrovascular-metabolic diseases, relative to those with such a condition. Similar trends were observed in BMI, WHtR, VAI. Analogous studies have demonstrated that a heightened CVAI correlated more significantly with the risk of carotid plaque in non-diabetic patients than in diabetic ones[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Zenglei Zhang et al.[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] also discovered that a higher CVAI significantly correlated with an increased incidence of stroke, particularly in individuals without hypertension, diabetes, or heart disease. Potential reasons include: firstly, patients diagnosed with cardio-cerebrovascular-metabolic diseases might have received medication (e.g., antihypertensive and hypoglycemic drugs) and altered their lifestyle and diet during the follow-up, potentially impacting the correlation between CVAI, WC, and CCMM. Secondly, the relatively small sample sizes for individuals with hypertension (n\u0026thinsp;=\u0026thinsp;1006) and those with a single cardio-cerebrovascular-metabolic disease (n\u0026thinsp;=\u0026thinsp;1680) at baseline might also explain the variability. Thirdly, due to disease interactions, having a chronic disease itself can lead to an increased risk of CCMM, potentially diminishing the relevance of obesity indicators for predicting CCMM.\u003c/p\u003e\u003cp\u003eIn the concluding multimorbidity pattern analysis, it was demonstrated that obesity-related metrics primarily influence a pattern comprising multiple metabolic disorders, highlighting the critical need for early intervention in these diseases. There may be reasons for this: Firstly, both VAI and CVAI are predicated upon insulin resistance, a key mechanism underlying metabolic disorders. Secondly, given that metabolic disorders like hypertension, diabetes mellitus, and dyslipidemia pose risks for cardiovascular and cerebrovascular diseases, the latter\u0026rsquo;s extended durations compared to metabolic diseases might have skewed the study\u0026rsquo;s outcomes. Thirdly, the small sample size utilized for analyzing the multimorbidity pattern could have biased the outcomes, necessitating a larger cohort to validate the findings.\u003c/p\u003e\u003cp\u003eThe possible mechanisms of the association between BMI, WC, WHtR, VAI, CVAI and CCMM may include: First, excess adipose tissue, especially visceral adipose tissue, produces excess inflammatory cytokines and adipokines[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. And adipokines are further involved in microvascular injury by mediating endothelial dysfunction, induction of oxidative stress, inflammation, activation of the renin-angiotensin-aldosterone system and endoplasmic reticulum stress[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Secondly, insulin resistance due to obesity impedes normal cardiac function by inhibiting metabolic pathways and overstimulating growth factors[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. And insulin resistance affects apolipoprotein A1 production or hepatic high-density lipoprotein secretion, which further contributes to metabolic diseases[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Third, excess free fatty acids are produced outside of fat-storing tissues and transferred to ectopic sites, including viscera, heart and vascular system, ultimately leading to cardiovascular and cerebrovascular metabolic diseases[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].Furthermore, monitoring diets in older adults is critical, with processed foods and fried foods with high fat having been shown to exacerbate morbidity and mortility. On the other hand, Lactobacillus acidophilus and Bifidobacterium bifidum-fortified cheese is a source of increased nutritional value and safe for intake and therefore a beneficial food source[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003elimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur study is the inaugural investigation into the association of BMI, WC, WHtR, VAI, and CVAI with CCMM risk. Additionally, we further assessed the effects of these indices on CCMM's multimorbidity patterns. However, this study still has some limitations: Firstly, although it was a cohort study, the binary logistic regression model failed to account for temporal effects and excluded deceased patients; Secondly, the follow-up period of 7 years is relatively short for chronic disease studies, limiting our ability to assess long-term associations; Thirdly, significant factors like physical activity weren't included as covariates due to only 40% of CHARLS participants reporting on it, despite its crucial role in chronic disease development; Fourthly, the small sample size constrained our evaluation of cardiovascular-metabolic multimorbidity patterns; Fifthly, chronic diseases were identified using standardized questionnaires, introducing potential information bias, despite their high reliability in epidemiological research[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Future research should focus on larger sample sizes and extended follow-up periods to validate these findings.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBMI, WC, WHtR, VAI, and CVAI exhibited a nonlinear positive correlation with the risk of CCMM among the middle-aged and elderly Chinese cohort. Moreover, CVAI outperformed BMI, WC, WHtR, and VAI as a predictor of CCMM, especially in the middle-aged individuals and those without any cardiovascular, cerebrovascular and metabolic diseases. The analysis of multimorbidity patterns indicates that BMI, WC, WHtR, VAI, and CVAI may primarily influence the pattern consisting of metabolic diseases.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBody Mass Index\u003cstrong\u003e\u0026nbsp;- BMI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Waist Circumference\u003cstrong\u003e\u0026nbsp;- WC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWaist-to-Height Ratio\u003cstrong\u003e\u0026nbsp;- WHtR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVisceral Adiposity Index\u003cstrong\u003e\u0026nbsp;- VAI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Chinese Visceral Adiposity Index\u003cstrong\u003e- CVAI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCardio-Cerebrovascular-Metabolic Multimorbidity\u003cstrong\u003e- CCMM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReceiver Operating Curve\u003cstrong\u003e- ROC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNet Reclassification Index\u003cstrong\u003e\u0026nbsp;–NRI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIntegrated Discriminant Improvement\u003cstrong\u003e- IDI\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial Registration:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNot applicable-\u0026nbsp;\u003c/strong\u003eThis is an observational cohort study using secondary data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict Of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is funded by the Foundation of Traditional Chinese Medicine of Jiangxi Province(Grant Number 2019B122 ) and a partnership grant from the central government guides local funds for scientific and technological development (Grant Number 20221ZDG020070 )\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and informed consent of Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the \u003cstrong\u003eBiomedical Ethics Review Committee\u003c/strong\u003e of Peking University. Written informed consent was obtained from all participants prior to their inclusion in the study. All procedures involving human participants and/or data were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards (https://www.wma.net/policies-post/wma-declaration-of-helsinki/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1-\u003cstrong\u003eTingting Yang,\u0026nbsp;\u003c/strong\u003eConceptualization and \u0026nbsp;methodology ,Writing\u0026mdash;original draft preparation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2-\u003c/strong\u003e\u003cstrong\u003eYanfeng Gong,\u0026nbsp;\u003c/strong\u003e\u0026nbsp; Software \u0026nbsp;and visualization, writing\u0026mdash;review and editing ,Project administration and funding acquisition\u003c/p\u003e\n\u003cp\u003e3- \u003cstrong\u003eHuanbing Liu,\u0026nbsp;\u003c/strong\u003eValidation, Formal analysis, Investigation, Resources and \u0026nbsp;data \u0026nbsp; curation, Supervision\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the Department of General Medical Section, Nanchang University, and the funding organization for their support in the successful conduct of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBarnett K, et al. 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IOP Publishing.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarr EL, et al. Validity of self-reported cardiovascular disease events in comparison to medical record adjudication and a statewide hospital morbidity database: the AusDiab study. Intern Med J. 2009;39(1):49\u0026ndash;53.\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":"Body mass index, waist-to-height ratio, waist circumference, visceral adiposity index, Chinese visceral adiposity index, cardio-cerebrovascular-metabolic multi-morbidity","lastPublishedDoi":"10.21203/rs.3.rs-6954915/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6954915/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study examines the associations between body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), visceral adiposity index (VAI), and Chinese visceral adiposity index (CVAI) with the risk of cardiovascular, cerebrovascular, and metabolic multimorbidity, collectively referred to as cardio-cerebrovascular-metabolic multimorbidity (CCMM).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed data from 6,472 individuals aged 45 and older from the CHARLS cohort. Logistic regression was used to evaluate the impact of obesity indices on CCMM, while restricted cubic spline analysis examined dose-response relationships. Subgroup analyses accounted for age, sex, baseline cardio-cerebrovascular-metabolic disease count, and hypertension status. Receiver operating characteristic (ROC) curves assessed predictive efficacy, with the net reclassification index (NRI) and integrated discriminant improvement (IDI) measuring incremental predictive value. Additionally, logistic regression was applied to investigate the influence of obesity indices on the three most prevalent CCMM patterns.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCVAI showed the strongest association with CCMM compared to BMI, WC, WHtR, and VAI. All obesity indices displayed a nonlinear relationship with CCMM risk. Among them, CVAI had the highest AUC value and contributed the most significant incremental risk when added to the fully adjusted model. Overall, the analysis indicated that obesity indices primarily impact metabolic disease patterns within multimorbidity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBMI, WC, WHtR, VAI, and CVAI independently predict CCMM, with CVAI emerging as the strongest predictor, especially in middle-aged individuals and those without pre-existing cardio-cerebrovascular-metabolic conditions. Additionally, these obesity indices significantly influence multi-morbidity patterns, particularly those linked to metabolic diseases.\u003c/p\u003e","manuscriptTitle":"Obesity Distribution Indices and Cardio-Cerebrovascular Metabolic Multi-morbidity: Insights from a National Longitudinal Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-07 11:52:59","doi":"10.21203/rs.3.rs-6954915/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":"bcd7b5cd-dfc7-45c8-83f1-e8f30c497a7a","owner":[],"postedDate":"August 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-04T10:54:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-07 11:52:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6954915","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6954915","identity":"rs-6954915","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}