The joint effect between body mass index and waist circumference in the risk of heart disease: A national longitudinal cohort study

The joint effect between body mass index and waist circumference in the risk of heart disease: 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 The joint effect between body mass index and waist circumference in the risk of heart disease: A national longitudinal cohort study Xiaodi Tang, Hong Chen, Xingming Zhong, Yijiang Zhao, Kexi Zhang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8180481/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background: The association between body mass index (BMI), waist circumference (WC), and heart disease is a pivotal issue in the fields of public health, but the evidence in Chinese people is insufficient. Objective: The primary aim of this study is to investigate the association and joint effects of BMI and WC in the risk of heart disease. Methods: A retrospective cohort study was conducted involving 11,700 participants from the China Health and Retirement Longitudinal Study. The incidence of heart disease was collected by following up 9 years. For all analyses, we imputed missing data of the covariates by using multiple imputations. The Cox proportional hazards model and propensity score matching (PSM) were used to exclude confounding factors. The additive interaction test was to explore the joint effect of BMI and WC. Sensitive analysis was to confirm the robust association. Results: During a median follow-up of 8.49 years, 2055 (17.6%) people were diagnosed with heart disease. Upon full adjustment model and after PSM, a positive association was observed between BMI (HR: 1.41, 95% CI: 1.21–1.63), WC (HR: 1.22, 95% CI: 1.09–1.36) and the risk of heart disease. The joint effect (HR: 1.54, 95% CI: 1.34–1.77) of BMI and WC was higher than the simple effect. Subsequent subgroup analysis and sensitivity analyses further confirmed the robustness of the findings, suggesting minimal impact from unmeasured confounders. Conclusions: This cohort study demonstrated a significant association and joint effect between both high levels of BMI as well as high levels of WC and the risk of heart disease among individuals with Chinese people. body mass index waist circumference heart disease association joint effect Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Despite the introduction of new guidelines, treatments, and technologies, the morbidity and mortality rates of heart disease in China continue to rise each year[ 1 ]. According to the Annual Report on Cardiovascular Health and Disease in China (2022), the prevalence of heart disease in the country has significantly increased, now affecting around 330 million people, including many individuals suffering from conditions such as stroke, coronary artery disease, heart failure, and other related diseases[ 2 , 3 ]. Furthermore, nearly half of the adult population in China—approximately 50.7%—is classified as overweight or obese, a factor that may further intensify the overall burden of heart disease[ 4 ]. Nevertheless, body mass index (BMI), which is widely applied to evaluate obesity status, has limitations in accurately capturing individual cardiometabolic risk[ 5 ]. As a derived indicator based solely on height and weight, BMI neither directly quantifies body fat nor reflects its regional distribution. Evidence suggests that individuals with a normal BMI but excessive abdominal fat face a significantly higher risk of mortality and cardiovascular events[ 6 ]. Such risk-related information cannot be adequately captured by BMI alone. In this context, waist circumference (WC) and other anthropometric indicators may serve as complementary measures, allowing for a more accurate evaluation of abdominal adiposity and its related cardiovascular risk[ 7 ]. For instance, assessing WC can help identify individuals who, despite having a normal BMI, remain at an elevated risk of developing cardiovascular disease[ 8 , 9 ]. The objective of this study is to investigate the relationship between BMI, WC, and heart disease, as well as their combined effects, in a large Chinese population. Methods Study design We utilized data from the China Health and Retirement Longitudinal Study (CHARLS)[10], a nationally representative prospective cohort that surveys Chinese residents aged 45 years and older[11]. CHARLS collects comprehensive information encompassing socioeconomic conditions, demographic characteristics, physical and mental health status, as well as social relationships[12]. In addition, the study includes systematic measurements of anthropometric parameters and the presence of cardiometabolic conditions. The baseline investigation was initiated in 2011 and enrolled 17,385 participants from 150 counties or districts and 450 urban and rural communities across China, with follow-up assessments conducted biennially thereafter. This study was approved by the Biomedical Ethics Review Committee of Peking University, and all participants provided written informed consent. All procedures adhered to the ethical principles outlined in the Declaration of Helsinki. [1, 13]. Study population Participants drawn from the national baseline cohort were eligible for inclusion if they satisfied the following conditions: (1) absence of diagnosed heart disease at study entry, (2)availability of valid measurements for both body mass index (BMI) and waist circumference (WC), and (3) completion of follow-up assessments. Based on these criteria, a total of 11,700 individuals who were free of heart disease at baseline in 2011 were retained for the final analysis (Figure 1). BMI and WC measurements BMI is calculated as weight (kg) divided by the square of height (m). Height and weight are measured using a stadiometer and a weighing scale, with participants barefoot and in light clothing. When measuring waist circumference, trained measurers use a flexible tape measure to encircle the waist at the level of the navel. Outcome and follow-up Heart disease was identified using self-reported information on medical diagnosis. Specifically, participants were classified as having heart disease if they answered affirmatively to the CHARLS questionnaire item asking whether a physician had ever diagnosed them with any cardiac condition, including myocardial infarction, coronary heart disease, angina, heart failure, or other heart-related disorders. The primary endpoint of the present study was the development of new-onset heart disease, ascertained based on participants’ self-reported physician diagnoses. Incident cases were defined by an affirmative response to the questionnaire item inquiring whether a doctor had diagnosed any cardiac condition, such as angina, myocardial infarction, coronary heart disease, or other heart-related disorders. Follow-up began at baseline in 2011 and continued until the first occurrence of heart disease or the most recent survey wave conducted in 2020, whichever came earlier. Covariates Covariates included Social demographic characteristics, lifestyle factors, laboratory examination and current disease status [20]. Social demographic characteristics included age, sex, marital status and residence (rural/urban). Lifestyle factors included smoking status (ever smoking/never smoking) and drinking status (ever drinking/never drinking).Laboratory examination at baseline 2011 include white blood cell count (WBC), mean corpuscular volume (MCV), platelet count, blood urea nitrogen (BUN), glucose levels, serum creatinine (Scr), total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, C-reactive protein (CRP), glycosylated hemoglobin (HbA1c), uric acid (UA), hematocrit (HCT), hemoglobin (HGB). Current diseases (yes/no) included hypertension, diabetes, cancer, lung disease, stroke, liver disease, renal disease, digestive disease, asthma[10]. Statistical analysis Continuous variables following a normal distribution were summarized as means with standard deviations, whereas non-normally distributed continuous data were expressed as medians with corresponding interquartile ranges (IQRs). Categorical characteristics were reported in terms of counts and proportions. Between-group differences in continuous variables were assessed using either independent-samples Student’s t tests or Mann–Whitney U tests, depending on the underlying data distribution, while comparisons of categorical variables were performed using the chi-square test as appropriate. To evaluate the association between anthropometric measures and the risk of heart disease, multivariable Cox proportional hazards regression analyses were conducted, yielding hazard ratios (HRs) along with 95% confidence intervals (CIs). Body mass index (BMI) and waist circumference (WC) were incorporated into the models both as continuous measures and as categorical variables, based on their clinical relevance. Confounding variables were chosen based on clinical relevance, existing literature, or their association with the outcomes, particularly if they altered the effect estimate by over 10%. Three models were used: Model 1, an unadjusted analysis; Model 2, adjusted for age, sex, marital status, and rural residence; and Model 3, the primary model, which further adjusted for smoking, alcohol use, white blood cell count, platelet count, hemoglobin, and several health conditions, including hypertension, diabetes, cancer, lung disease, stroke, liver disease, renal disease, digestive issues, and asthma. Restricted cubic spline analyses were applied to flexibly model the associations between BMI, WC, and the risk of heart disease and to assess potential nonlinear dose–response patterns. In these analyses, BMI and WC were entered as continuous variables using four knots placed at the 5th, 35th, 65th, and 95th percentiles, following Harrell’s recommendations. Evidence of nonlinearity was evaluated by likelihood ratio tests comparing models with spline terms to corresponding models including only linear components. Missing values were addressed using multiple imputation with five iterations based on the chained equations framework, implemented through the mice package in R, in accordance with the method proposed by Van Buuren and Groothuis-Oudshoorn (2011). This strategy was adopted to improve analytical efficiency and reduce potential bias. For validation, all analyses were also conducted in the complete-case dataset. In addition, several sensitivity analyses were undertaken to examine the stability of the results and to evaluate the influence of different modeling assumptions on the observed associations. As part of the sensitivity analyses, multiple causal inference approaches were implemented, including propensity score adjustment (PSA), propensity score matching (PSM), inverse probability of treatment weighting (IPTW), standardized mortality ratio weighting (SMRW), pairwise algorithmic (PA), and overlap weighting (OW). Effect estimates and corresponding p values derived from each method were computed and systematically compared to assess the consistency of the findings across analytical strategies. R software (version 4.2.1; R Foundation for Statistical Computing; http:// www.Rproject.org), the R survey package (version 4.1-1), and Free Statistics software (version 1.7.1; Beijing Free Clinical Medical Technology Co., Ltd.) were used for analyses. In all analyses, a two-sided p-value < 0.05 was taken to indicate statistical significance. Results Flow chart of the study population inclusion Among the 17,385 individuals initially enrolled, 11,700 participants were retained for the final analysis. A total of 2,075 participants were excluded due to a documented diagnosis of heart disease at baseline in 2011, while an additional 3,610 individuals were removed from the analysis because of incomplete information on body mass index or waist circumference (Fig. 1 ). Baseline characteristic of participants Baseline demographic and clinical features of the study population are presented in Table 1 . Participants were categorized based on sex-specific waist circumference thresholds, defined as ≥ 85 cm for men and ≥ 80 cm for women. The overall mean age was 58.6 years with a standard deviation of 9.9, and men accounted for 52.3% of the cohort. Individuals in the elevated WC category exhibited less favorable metabolic profiles, including higher lipid levels, increased blood glucose, and greater inflammatory markers, as well as a higher prevalence of hypertension, diabetes, stroke, and digestive disorders. Over a median follow-up period of 8.49 years, 2,055 participants (17.6%) developed heart disease. Table 1 Baseline characteristic of participants stratified by waist circumference. Variables Total (n = 11700) WC Group 1 (n = 4973) WC Group 2 (n = 6727) p Male<85cm, Female<80cm Male ≥ 85cm, Female ≥ 80cm Age, years 58.6 ± 9.9 58.6 ± 10.2 58.7 ± 9.7 0.68 Male, n (%) 6119 (52.3) 3046 (61.3) 3073 (45.7) < 0.001 Marry, n (%) 10203 (87.2) 4234 (85.2) 5969 (88.7) < 0.001 Rural, n (%) 4108 (35.1) 1407 (28.3) 2701 (40.2) < 0.001 Smoke, n (%) 3642 (31.3) 1475 (29.9) 2167 (32.3) 0.005 Drink, n (%) 3911 (33.5) 1463 (29.5) 2448 (36.4) < 0.001 WBC, 10^9/L 6.3 ± 2.2 6.1 ± 1.9 6.4 ± 2.4 < 0.001 MCV, fl 90.7 ± 8.6 90.6 ± 9.1 90.7 ± 8.3 0.598 Platelet, 10^9/L 212.9 ± 76.4 212.5 ± 72.8 213.2 ± 78.9 0.679 BUN, mg/dl 15.7 ± 4.6 15.7 ± 4.7 15.7 ± 4.5 0.641 Glucose, mg/dl 109.7 ± 36.7 105.3 ± 33.2 113.0 ± 38.8 < 0.001 Scr, mg/dl 0.8 ± 0.2 0.8 ± 0.2 0.8 ± 0.3 < 0.001 TC, mg/dl 193.3 ± 38.7 190.1 ± 37.4 195.6 ± 39.5 < 0.001 TG, mg/dl 103.5 (74.3, 150.4) 89.4 (66.4, 128.3) 115.9 (81.4, 169.9) < 0.001 HDL, mg/dl 51.4 ± 15.3 56.1 ± 15.7 48.0 ± 14.1 < 0.001 LDL, mg/dl 116.2 ± 35.0 113.4 ± 33.2 118.2 ± 36.0 < 0.001 CRP, Median 1.0 (0.5, 2.1) 0.8 (0.4, 1.7) 1.2 (0.6, 2.4) < 0.001 HbA1c, % 5.2 ± 0.8 5.2 ± 0.7 5.3 ± 0.9 < 0.001 UA, mg/dl 4.4 ± 1.3 4.2 ± 1.2 4.6 ± 1.3 < 0.001 HCT, % 41.4 ± 6.3 40.3 ± 6.1 42.2 ± 6.4 < 0.001 HGB, g/dl 14.3 ± 2.2 13.9 ± 2.1 14.7 ± 2.3 < 0.001 Hypertension, n (%) 2582 (22.2) 685 (13.9) 1897 (28.3) < 0.001 Diabetes, n (%) 581 (5.0) 119 (2.4) 462 (6.9) < 0.001 Cancer, n (%) 102 (0.9) 42 (0.9) 60 (0.9) 0.795 Lung disease, n(%) 1055 (9.1) 474 (9.6) 581 (8.7) 0.09 Stroke, n (%) 258 (2.2) 81 (1.6) 177 (2.6) < 0.001 Liver disease, n (%) 344 (3.0) 135 (2.7) 209 (3.1) 0.225 Renal disease, n (%) 551 (4.7) 232 (4.7) 319 (4.8) 0.877 Digestive disease, n (%) 2431 (20.9) 1194 (24.2) 1237 (18.4) < 0.001 Asthma, n (%) 461 (4.0) 203 (4.1) 258 (3.9) 0.484 BMI, kg/m² 23.3 ± 3.8 20.8 ± 2.7 25.1 ± 3.5 < 0.001 Heart disease, n (%) 2055 (17.6) 754 (15.2) 1301 (19.3) < 0.001 WBC: white blood cell count, MCV: mean corpuscular volume, BUN: blood urea nitrogen, Scr: Creatinine, TC: Total Cholesterol, TG: Triglycerides, HDL: high density lipoprotein cholesterol, LDL: low density lipoprotein cholesterol, CRP: C-Reactive Protein, HbA1c : glycated hemoglobin, UA: Uric Acid, HCT: Hematocrit, HGB: Hemoglobin, BMI: body mass index, WC: waist circumference. Association of BMI and WC with heart disease In the fully adjusted Cox proportional hazards analysis (Table 2 , Model 3 ), higher levels of BMI and WC were independently associated with an elevated risk of heart disease. Each one-unit increase corresponded to a 4% higher risk for BMI (HR = 1.04, 95% CI: 1.02–1.05, P < 0.001) and a 2.0% increase for WC (HR = 1.020, 95% CI: 1.010–1.021, P < 0.001). Consistently, when analyzed as categorical variables, participants in group 2 showed a significantly greater risk compared with group 1 for both BMI and WC (BMI group 2 vs. group 1: HR = 1.41, 95% CI: 1.21–1.63, P < 0.001; WC group 2 vs. group 1: HR = 1.22, 95% CI: 1.09–1.36, P < 0.001). In addition, Figure S1 illustrates a linear association between BMI, WC, and heart disease risk. Table 2 Association of BMI and WC with cardiovascular disease after multiple imputations of missing covariates. Variable Model 1 Model 2 Model 3 HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value Continues BMI 1.05 (1.04–1.06) < 0.001 1.05 (1.04–1.06) < 0.001 1.04 (1.02–1.05) < 0.001 BMI. group Group.1 1(Ref) 1(Ref) 1(Ref) Group.2 1.67 (1.48–1.88) < 0.001 1.65 (1.46–1.86) < 0.001 1.41 (1.21–1.63) < 0.001 Continues WC 1.02 (1.02–1.03) < 0.001 1.02 (1.02–1.03) < 0.001 1.020 (1.010–1.021) < 0.001 WC. group Group.1 1(Ref) 1(Ref) 1(Ref) Group.2 1.31 (1.2–1.43) < 0.001 1.39 (1.27–1.53) < 0.001 1.22 (1.09–1.36) 0.001 Model 1 unadjusted model. Model 2 adjusted for age, sex, marry state, rural. Model 3 adjusted for age, sex, marry state, rural, smoke, drink, WBC, Platelet, HGB, hypertension, diabetes, cancer, lung disease, stroke, liver disease, renal disease, digestive disease, asthma. BMI. group 1: <28kg/m 2 , BMI. group 2: ≥28kg/m 2 WC.group1: male<85cm, female<80cm, WC.group2: male ≥ 85cm, female ≥ 80cm. Joint effect of BMI and WC with the risk of heart disease Figure 2 illustrates the combined effects of BMI and WC on the incidence of heart disease. After adjustment for relevant covariates (Table 3 ), individuals with both elevated BMI (≥ 28.0 kg/m²) and increased WC (≥ 85 cm for men and ≥ 80 cm for women) had a substantially higher risk of heart disease compared with those with low BMI and low WC (HR = 1.54, 95% CI: 1.34–1.77, P < 0.001). This risk exceeded that observed in participants with high BMI but normal WC or normal BMI with elevated WC. Table 3 Multivariate adjusted HRs of heart disease risk factors according to BMI status and waist circumference groups. BMI WC HR (95%CI) P value Measures of additive interaction RERI (95%CI) AP (95%CI) SI (95%CI) Group. 1 Group. 1 1 (Ref) Group. 2 Group. 1 1.14 (0.51–2.54) 0.75 Group. 1 Group. 2 1.18 (1.07–1.3) < 0.001 Group. 2 Group. 2 1.54 (1.34–1.77) < 0.001 0.22 (-0.71-1.15) 0.14 (-0.46-0.75) 1.69 (0.09–30.76) BMI. group 1: <28kg/m 2 , BMI. group 2: ≥28kg/m 2 WC. group1: male<85cm, female<80cm, WC.group2: male ≥ 85cm, female ≥ 80cm. However, no statistically significant additive interaction between BMI and WC was detected in relation to heart disease risk (Additive: RERI = 0.14, 95% CI: −0.71–1.15; AP = 0.14, 95% CI: −0.46–0.75), indicating that further studies are needed to clarify their joint effects (Table 3 ). Subgroup analysis, PSM analysis and other Sensitivity analyses To further assess the robustness of the results, subgroup analyses, PSM analysis, and additional Sensitivity analyses were performed. The subgroup analyses showed no significant effect modification by common cardiovascular risk factors, with hazard ratios consistently greater than 1 across all strata (Fig. 3 ). In addition, PSM was applied to balance potential baseline differences between BMI and WC categories, thereby reducing confounding bias. The associations observed after matching remained stable and were comparable to those of the primary analysis (Fig. 4 ). Moreover, sensitivity analyses restricted to participants with complete data were conducted using multivariable regression models, and the findings were in line with the main results ( Table S1 ). Discussion To the best of our knowledge, this extensive retrospective cohort study on heart disease has demonstrated a significant independent association between BMI and WC and an increase in the risk of heart disease. It is noteworthy that the joint effect of BMI and WC on the incidence of heart disease requires further investigation. The consistency of the associations was further supported by subgroup analyses, PSM, and multivariable Cox proportional hazards modeling. Collectively, these results underscore the important clinical implications of assessing both general and central obesity in cardiovascular risk evaluation[ 14 – 16 ]. Our research investigated the positive association and joint effect of BMI and WC with heart disease in the China Health and Retirement Longitudinal Study. Our findings are consistent with those of previous observational studies[ 17 , 18 ]. For example, Chaofu Ke et al. found a significant association between obesity and increased risk of cardiometabolic multimorbidity in the Chinese population (BMI: HR: 1.48, 95% CI: 0.98–2.24; WC: HR: 2.06, 95% CI: 1.29–3.27)[ 19 ]. A recent longitudinal cohort study initiated by Zahra Raisi-Estabragh et al. showed a remarkable association between obesity and cardiovascular outcomes, while Sarah Lewington et al. found the same result[ 17 ]. In this large Chinese cohort, both BMI and WC were identified as independent predictors of heart disease, and the combined impact of these two measures on cardiovascular risk was demonstrated for the first time[ 20 – 22 ]. These results suggest that incorporating WC alongside BMI offers a more comprehensive assessment of obesity, reducing misclassification and enhancing the ability to predict cardiovascular outcomes[ 23 – 25 ]. This study has several notable strengths, including its large-scale cohort, population-based framework, and the simultaneous evaluation of BMI and WC in relation to heart disease across multiple subgroups[ 26 – 28 ]. Nevertheless, as an observational investigation, residual confounding cannot be entirely excluded, even though extensive adjustments were made for known risk factors[ 29 ]. While additional sensitivity analyses that excluded certain covariates yielded results consistent with the primary findings, the potential influence of unmeasured or incompletely measured confounders should still be considered when interpreting the associations between BMI, WC, and cardiovascular outcomes[ 30 ]. Conclusions In Chinese individuals, high BMI or WC was significantly associated with an increased risk of incident heart disease. What's more, the combined use of BMI and WC to characterize obesity may better discriminate "pathological" obesity and provide better estimates of cardiovascular risk than either measure alone. Abbreviations BMI =body mass index WC =waist circumference CHARLS =the China Health and Retirement Longitudinal Study PSM =propensity score matching WBC =white blood cell count MCV =mean corpuscular volume BUN =platelet count, blood urea nitrogen Scr =glucose levels, serum creatinine TC=total cholesterol TG =triglycerides HDL =high-density lipoprotein LDL =cholesterol, low-density lipoprotein CRP =C-reactive protein HbA1c =glycosylated hemoglobin UA =uric acid HCT =hematocrit HGB= hemoglobin HRs =hazard ratios 95% CI =95% confidence intervals PSA =propensity score adjusted PSM =propensity score matching IPTW =inverse probability of treatment weighting SMRW =standardized mortality ratio weighting PA =pairwise algorithmic OW =overlap weight Declarations Clinical trial number Not applicable Ethics approval and consent to participate This study was approved by the Biomedical Ethics Review Committee of Peking University (IRB No. IRB00001052-11015), and all participants provided written informed consent. All procedures adhered to the ethical principles outlined in the Declaration of Helsinki. Clinical trial number: not applicable. Consent for publication Not applicable Data availability statement The data that support the findings of this study are available from the China Health and Retirement Longitudinal Study (CHARLS) database, but restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the China Health and Retirement Longitudinal Study (CHARLS) database. CHARLS web-site (https://charls.pku.edu.cn/). Disclosure statement The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies. Author Contributions Xiaodi Tang conceived and designed the study, performed the data analysis, and drafted the manuscript. Hong Chen and Xingming Zhong provided overall supervision, contributed to the study design, and critically revised the manuscript for important intellectual content. Yijiang Zhao, Kexi Zhang, Ying Xie and Yosen Yang contributed to data acquisition, data cleaning and interpretation of the results. Rong He contributed to the statistical analysis and interpretation of the findings. Ping Zhang supervised the project administration and provided critical revision of the manuscript. All authors read and approved the final version of the manuscript and agree to be accountable for all aspects of the work. Acknowledgments We thank Dr. Jie Liu(People’s Liberation Army of China General Hospital, Beijing, China) , Dr. Haibo Li(Peking Union Medical College Hospital)and Dr. Qilin Yang (The Second Affiliated Hospital of Guangzhou Medical University,Guangzhou, Guangdong, China) for helping in this revision. Supplementary material Supplementary material to this article can be found online Appendix A. 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Kim K, Di Giovanna E, Jung H, Bethineedi LD, Jun TJ, Kim YH: Association of metabolic health and obesity with coronary heart disease in adult cancer survivors . Eur J Clin Invest 2024, 54 (5):e14161. Huang R, Kong X, Geng R, Wu J, Chen T, Li J, Li C, Wu Y, You D, Zhao Y et al : Joint and interactive associations of body mass index and genetic factors with cardiovascular disease: a prospective study in UK Biobank . BMC public health 2024, 24 (1):2371. Poorthuis MHF, Sherliker P, de Borst GJ, Carter JL, Lam KBH, Jones NR, Halliday A, Lewington S, Bulbulia R: Joint Associations Between Body Mass Index and Waist Circumference With Atrial Fibrillation in Men and Women . Journal of the American Heart Association 2021, 10 (8):e019025. Chen F, Shi Y, Yu M, Hu Y, Li T, Cheng Y, Xu T, Liu J: Joint effect of BMI and metabolic status on mortality among adults: a population-based longitudinal study in United States . Sci Rep 2024, 14 (1):2775. Keramat SA, Alam K, Rana RH, Chowdhury R, Farjana F, Hashmi R, Gow J, Biddle SJH: Obesity and the risk of developing chronic diseases in middle-aged and older adults: Findings from an Australian longitudinal population survey, 2009-2017 . PLoS One 2021, 16 (11):e0260158. Armas Rojas NB, Lacey B, Soni M, Charles S, Carter J, Varona-Pérez P, Burrett JA, Martínez MC, Lorenzo-Vázquez E, Constantén SB et al : Body-mass index, blood pressure, diabetes and cardiovascular mortality in Cuba: prospective study of 146,556 participants . BMC public health 2021, 21 (1):963. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Additionalfile2.docx Additionalfile3.pdf Fig. S1 A multivariable-adjusted restricted cubic spline for the association of BMI (A) and WC (B) with heart disease. Adjusted for age, sex, marry state, rural, smoke, drink, WBC, Platelet, HGB, hypertension, diabetes, cancer, lung disease, stroke, liver disease, renal disease, digestive disease, asthma. BMI: body mass index, WC: waist circumference. Additionalfile4.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 07 Jan, 2026 Reviewers invited by journal 07 Jan, 2026 Editor assigned by journal 05 Jan, 2026 Editor invited by journal 18 Dec, 2025 Submission checks completed at journal 17 Dec, 2025 First submitted to journal 17 Dec, 2025 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-8180481","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":570937425,"identity":"66fcb1e7-6955-4f52-89d4-c41bfb79ce3f","order_by":0,"name":"Xiaodi Tang","email":"","orcid":"","institution":"Tsinghua University Affiliated Beijing Tsinghua Changgung Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaodi","middleName":"","lastName":"Tang","suffix":""},{"id":570937429,"identity":"e01d0a6f-4cae-42be-88a8-22ef67ec63a6","order_by":1,"name":"Hong Chen","email":"","orcid":"","institution":"Tsinghua University Affiliated 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1","display":"","copyAsset":false,"role":"figure","size":24427,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study population inclusion.\u003c/p\u003e","description":"","filename":"Binder21.png","url":"https://assets-eu.researchsquare.com/files/rs-8180481/v1/3679f2b918fc92c54b93c024.png"},{"id":100011415,"identity":"f52921dc-ba89-4852-bde8-93e9200a8ca7","added_by":"auto","created_at":"2026-01-12 06:10:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48307,"visible":true,"origin":"","legend":"\u003cp\u003eBMI, associations with high risk of heart disease and interaction with WC. BMI: body mass index, WC: waist circumference.\u003c/p\u003e","description":"","filename":"Binder22.png","url":"https://assets-eu.researchsquare.com/files/rs-8180481/v1/d8082747fed06288ae6b1b0e.png"},{"id":100011445,"identity":"9fed93fa-e376-4f05-9e37-b30fbc5f561a","added_by":"auto","created_at":"2026-01-12 06:10:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":142777,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses for the association of BMI (A) and WC (B) with risk of heart disease. HR hazard ratio, CI confidence interval, BMI: body mass index, WC: waist circumference.\u003c/p\u003e","description":"","filename":"Binder23.png","url":"https://assets-eu.researchsquare.com/files/rs-8180481/v1/5749e423b74df4ede463782b.png"},{"id":100011446,"identity":"e1159b95-1682-4a6a-8623-166bd690f6dd","added_by":"auto","created_at":"2026-01-12 06:10:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":94440,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot shows HRs of heart disease in BMI group (A) and WC (group) using propensity score matching (PSM).\u003c/p\u003e","description":"","filename":"Binder24.png","url":"https://assets-eu.researchsquare.com/files/rs-8180481/v1/09de3818bfb437651cc6cfde.png"},{"id":100380779,"identity":"acbf5f78-0224-445e-b2f8-11f133138b3d","added_by":"auto","created_at":"2026-01-16 10:33:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2967471,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8180481/v1/4e92dd08-3ddd-406f-8692-006a3a86b7ef.pdf"},{"id":100011432,"identity":"b6040bad-3a73-4994-bd46-0bdc2a9d3ac1","added_by":"auto","created_at":"2026-01-12 06:10:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32582,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8180481/v1/86ac2aef7f95be93ad309923.docx"},{"id":100011422,"identity":"46153067-1bf3-4d70-8e39-4a478582a9c5","added_by":"auto","created_at":"2026-01-12 06:10:12","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":93381,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8180481/v1/f706202cf4a156b505b74ba9.docx"},{"id":100011416,"identity":"d1d6b664-2bf7-487a-afec-99cf19103617","added_by":"auto","created_at":"2026-01-12 06:10:12","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":470014,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S1 \u003c/strong\u003eA multivariable-adjusted restricted cubic spline for the association of BMI (A) and WC (B) with heart disease. Adjusted for age, sex, marry state, rural, smoke, drink, WBC, Platelet, HGB, hypertension, diabetes, cancer, lung disease, stroke, liver disease, renal disease, digestive disease, asthma. BMI: body mass index, WC: waist circumference.\u003c/p\u003e","description":"","filename":"Additionalfile3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8180481/v1/f7368de4189252ad414e8532.pdf"},{"id":100011429,"identity":"58f6a597-4a22-4626-8edd-d8e8dae0957c","added_by":"auto","created_at":"2026-01-12 06:10:13","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":14860,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile4.docx","url":"https://assets-eu.researchsquare.com/files/rs-8180481/v1/00c9440f3619f456e0e3c525.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The joint effect between body mass index and waist circumference in the risk of heart disease: A national longitudinal cohort study","fulltext":[{"header":"Background","content":"\u003cp\u003eDespite the introduction of new guidelines, treatments, and technologies, the morbidity and mortality rates of heart disease in China continue to rise each year[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to the Annual Report on Cardiovascular Health and Disease in China (2022), the prevalence of heart disease in the country has significantly increased, now affecting around 330\u0026nbsp;million people, including many individuals suffering from conditions such as stroke, coronary artery disease, heart failure, and other related diseases[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, nearly half of the adult population in China\u0026mdash;approximately 50.7%\u0026mdash;is classified as overweight or obese, a factor that may further intensify the overall burden of heart disease[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Nevertheless, body mass index (BMI), which is widely applied to evaluate obesity status, has limitations in accurately capturing individual cardiometabolic risk[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. As a derived indicator based solely on height and weight, BMI neither directly quantifies body fat nor reflects its regional distribution. Evidence suggests that individuals with a normal BMI but excessive abdominal fat face a significantly higher risk of mortality and cardiovascular events[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Such risk-related information cannot be adequately captured by BMI alone. In this context, waist circumference (WC) and other anthropometric indicators may serve as complementary measures, allowing for a more accurate evaluation of abdominal adiposity and its related cardiovascular risk[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For instance, assessing WC can help identify individuals who, despite having a normal BMI, remain at an elevated risk of developing cardiovascular disease[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe objective of this study is to investigate the relationship between BMI, WC, and heart disease, as well as their combined effects, in a large Chinese population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe utilized data from the China Health and Retirement Longitudinal Study (CHARLS)[10],\u0026nbsp;a nationally representative prospective cohort that surveys Chinese residents aged 45 years and older[11]. CHARLS collects comprehensive information encompassing socioeconomic conditions, demographic characteristics, physical and mental health status, as well as social relationships[12]. In addition, the study includes systematic measurements of anthropometric parameters and the presence of cardiometabolic conditions. The baseline investigation was initiated in 2011 and enrolled 17,385 participants from 150 counties or districts and 450 urban and rural communities across China, with follow-up assessments conducted biennially thereafter. This study was approved by the Biomedical Ethics Review Committee of Peking University, and all participants provided written informed consent. All procedures adhered to the ethical principles outlined in the Declaration of Helsinki.\u0026nbsp;[1, 13].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants drawn from the national baseline cohort were eligible for inclusion if they satisfied the following conditions: (1) absence of diagnosed heart disease at study entry, (2)availability of valid measurements for both body mass index (BMI) and waist circumference (WC), and \u0026nbsp; (3) completion of follow-up assessments. Based on these criteria, a total of 11,700 individuals who were free of heart disease at baseline in 2011 were retained for the final analysis (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBMI and WC measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBMI is calculated as weight (kg) divided by the square of height (m). Height and weight are measured using a stadiometer and a weighing scale, with participants barefoot and in light clothing. When measuring waist circumference, trained measurers use a flexible tape measure to encircle the waist at the level of the navel.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome and follow-up\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeart disease was identified using self-reported information on medical diagnosis. Specifically, participants were classified as having heart disease if they answered affirmatively to the CHARLS questionnaire item asking whether a physician had ever diagnosed them with any cardiac condition, including myocardial infarction, coronary heart disease, angina, heart failure, or other heart-related disorders.\u003c/p\u003e\n\u003cp\u003eThe primary endpoint of the present study was the development of new-onset heart disease, ascertained based on participants\u0026rsquo; self-reported physician diagnoses. Incident cases were defined by an affirmative response to the questionnaire item inquiring whether a doctor had diagnosed any cardiac condition, such as angina, myocardial infarction, coronary heart disease, or other heart-related disorders. Follow-up began at baseline in 2011 and continued until the first occurrence of heart disease or the most recent survey wave conducted in 2020, whichever came earlier.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCovariates included Social demographic characteristics, lifestyle factors, laboratory examination and current disease status [20]. Social demographic characteristics included age, sex, marital status and residence (rural/urban). Lifestyle factors included smoking status (ever smoking/never smoking) and drinking status (ever drinking/never drinking).Laboratory examination at baseline 2011 include white blood cell count (WBC), mean corpuscular volume (MCV), platelet count, blood urea nitrogen (BUN), glucose levels, serum creatinine (Scr), total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, C-reactive protein (CRP), glycosylated hemoglobin (HbA1c), uric acid (UA), hematocrit (HCT), hemoglobin (HGB). Current diseases (yes/no) included hypertension, diabetes, cancer, lung disease, stroke, liver disease, renal disease, digestive disease, asthma[10].\u003c/p\u003e\n\u003ch3\u003eStatistical analysis\u003c/h3\u003e\n\u003cp\u003eContinuous variables following a normal distribution were summarized as means with standard deviations, whereas non-normally distributed continuous data were expressed as medians with corresponding interquartile ranges (IQRs). Categorical characteristics were reported in terms of counts and proportions. Between-group differences in continuous variables were assessed using either independent-samples Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e tests or Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e tests, depending on the underlying data distribution, while comparisons of categorical variables were performed using the chi-square test as appropriate.\u003c/p\u003e\n\u003cp\u003eTo evaluate the association between anthropometric measures and the risk of heart disease, multivariable Cox proportional hazards regression analyses were conducted, yielding hazard ratios (HRs) along with 95% confidence intervals (CIs). Body mass index (BMI) and waist circumference (WC) were incorporated into the models both as continuous measures and as categorical variables, based on their clinical relevance.\u003c/p\u003e\n\u003cp\u003eConfounding variables were chosen based on clinical relevance, existing literature, or their association with the outcomes, particularly if they altered the effect estimate by over 10%. Three models were used: Model 1, an unadjusted analysis; Model 2, adjusted for age, sex, marital status, and rural residence; and Model 3, the primary model, which further adjusted for smoking, alcohol use, white blood cell count, platelet count, hemoglobin, and several health conditions, including hypertension, diabetes, cancer, lung disease, stroke, liver disease, renal disease, digestive issues, and asthma.\u003c/p\u003e\n\u003cp\u003eRestricted cubic spline analyses were applied to flexibly model the associations between BMI, WC, and the risk of heart disease and to assess potential nonlinear dose\u0026ndash;response patterns. In these analyses, BMI and WC were entered as continuous variables using four knots placed at the 5th, 35th, 65th, and 95th percentiles, following Harrell\u0026rsquo;s recommendations. Evidence of nonlinearity was evaluated by likelihood ratio tests comparing models with spline terms to corresponding models including only linear components.\u003c/p\u003e\n\u003cp\u003eMissing values were addressed using multiple imputation with five iterations based on the chained equations framework, implemented through the \u003cem\u003emice\u003c/em\u003e package in R, in accordance with the method proposed by Van Buuren and Groothuis-Oudshoorn (2011). This strategy was adopted to improve analytical efficiency and reduce potential bias. For validation, all analyses were also conducted in the complete-case dataset. In addition, several sensitivity analyses were undertaken to examine the stability of the results and to evaluate the influence of different modeling assumptions on the observed associations.\u003c/p\u003e\n\u003cp\u003eAs part of the sensitivity analyses, multiple causal inference approaches were implemented, including propensity score adjustment (PSA), propensity score matching (PSM), inverse probability of treatment weighting (IPTW), standardized mortality ratio weighting (SMRW), pairwise algorithmic (PA), and overlap weighting (OW). Effect estimates and corresponding \u003cem\u003ep\u003c/em\u003e values derived from each method were computed and systematically compared to assess the consistency of the findings across analytical strategies.\u003c/p\u003e\n\u003cp\u003eR software (version 4.2.1; R Foundation for Statistical Computing; http:// www.Rproject.org), the R survey package (version 4.1-1), and Free Statistics software (version 1.7.1; Beijing Free Clinical Medical Technology Co., Ltd.) were used for analyses. In all analyses, a two-sided p-value \u0026lt; 0.05 was taken to indicate statistical significance.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eFlow chart of the study population inclusion\u003c/h2\u003e \u003cp\u003eAmong the 17,385 individuals initially enrolled, 11,700 participants were retained for the final analysis. A total of 2,075 participants were excluded due to a documented diagnosis of heart disease at baseline in 2011, while an additional 3,610 individuals were removed from the analysis because of incomplete information on body mass index or waist circumference (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristic of participants\u003c/h2\u003e \u003cp\u003eBaseline demographic and clinical features of the study population are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Participants were categorized based on sex-specific waist circumference thresholds, defined as \u0026ge;\u0026thinsp;85 cm for men and \u0026ge;\u0026thinsp;80 cm for women. The overall mean age was 58.6 years with a standard deviation of 9.9, and men accounted for 52.3% of the cohort. Individuals in the elevated WC category exhibited less favorable metabolic profiles, including higher lipid levels, increased blood glucose, and greater inflammatory markers, as well as a higher prevalence of hypertension, diabetes, stroke, and digestive disorders. Over a median follow-up period of 8.49 years, 2,055 participants (17.6%) developed heart disease.\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 characteristic of participants stratified by waist circumference.\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;11700)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWC Group 1 (n\u0026thinsp;=\u0026thinsp;4973)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWC Group 2 (n\u0026thinsp;=\u0026thinsp;6727)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u0026lt;85cm, Female\u0026lt;80cm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMale\u0026thinsp;\u0026ge;\u0026thinsp;85cm, Female\u0026thinsp;\u0026ge;\u0026thinsp;80cm\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\u003e58.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6119 (52.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3046 (61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3073 (45.7)\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\u003eMarry, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10203 (87.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4234 (85.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5969 (88.7)\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, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4108 (35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1407 (28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2701 (40.2)\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\u003eSmoke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3642 (31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1475 (29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2167 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3911 (33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1463 (29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2448 (36.4)\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\u003eWBC, 10^9/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\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\u003eMCV, fl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet, 10^9/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212.9\u0026thinsp;\u0026plusmn;\u0026thinsp;76.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e212.5\u0026thinsp;\u0026plusmn;\u0026thinsp;72.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e213.2\u0026thinsp;\u0026plusmn;\u0026thinsp;78.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109.7\u0026thinsp;\u0026plusmn;\u0026thinsp;36.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105.3\u0026thinsp;\u0026plusmn;\u0026thinsp;33.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113.0\u0026thinsp;\u0026plusmn;\u0026thinsp;38.8\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\u003eScr, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\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\u003eTC, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193.3\u0026thinsp;\u0026plusmn;\u0026thinsp;38.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190.1\u0026thinsp;\u0026plusmn;\u0026thinsp;37.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e195.6\u0026thinsp;\u0026plusmn;\u0026thinsp;39.5\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\u003eTG, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103.5 (74.3, 150.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.4 (66.4, 128.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115.9 (81.4, 169.9)\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\u003eHDL, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.4\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.1\u0026thinsp;\u0026plusmn;\u0026thinsp;15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.0\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1\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\u003eLDL, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116.2\u0026thinsp;\u0026plusmn;\u0026thinsp;35.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113.4\u0026thinsp;\u0026plusmn;\u0026thinsp;33.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e118.2\u0026thinsp;\u0026plusmn;\u0026thinsp;36.0\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\u003eCRP, Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0 (0.5, 2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8 (0.4, 1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2 (0.6, 2.4)\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\u003eHbA1c, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\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\u003eUA, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\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\u003eHCT, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\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\u003eHGB, g/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\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\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2582 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e685 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1897 (28.3)\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, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e581 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e462 (6.9)\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\u003eCancer, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung disease, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1055 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e474 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e581 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e258 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e177 (2.6)\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, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e344 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e209 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e551 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e232 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e319 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigestive disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2431 (20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1194 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1237 (18.4)\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\u003eAsthma, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e461 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e203 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e258 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.484\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\u003e23.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\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 disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2055 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e754 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1301 (19.3)\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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eWBC: white blood cell count, MCV: mean corpuscular volume, BUN: blood urea nitrogen, Scr: Creatinine, TC: Total Cholesterol, TG: Triglycerides, HDL: high density lipoprotein cholesterol, LDL: low density lipoprotein cholesterol, CRP: C-Reactive Protein, HbA1c : glycated hemoglobin, UA: Uric Acid, HCT: Hematocrit, HGB: Hemoglobin, BMI: body mass index, WC: waist circumference.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of BMI and WC with heart disease\u003c/h2\u003e \u003cp\u003eIn the fully adjusted Cox proportional hazards analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003eModel 3\u003c/b\u003e), higher levels of BMI and WC were independently associated with an elevated risk of heart disease. Each one-unit increase corresponded to a 4% higher risk for BMI (HR\u0026thinsp;=\u0026thinsp;1.04, 95% CI: 1.02\u0026ndash;1.05, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a 2.0% increase for WC (HR\u0026thinsp;=\u0026thinsp;1.020, 95% CI: 1.010\u0026ndash;1.021, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Consistently, when analyzed as categorical variables, participants in group 2 showed a significantly greater risk compared with group 1 for both BMI and WC (BMI group 2 vs. group 1: HR\u0026thinsp;=\u0026thinsp;1.41, 95% CI: 1.21\u0026ndash;1.63, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; WC group 2 vs. group 1: HR\u0026thinsp;=\u0026thinsp;1.22, 95% CI: 1.09\u0026ndash;1.36, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e illustrates a linear association between BMI, WC, and heart disease risk.\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 of BMI and WC with cardiovascular disease after multiple imputations of missing covariates.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05 (1.04\u0026ndash;1.06)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05 (1.04\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.04 (1.02\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\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. group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.67 (1.48\u0026ndash;1.88)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.65 (1.46\u0026ndash;1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.41 (1.21\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\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\u003eContinues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\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\u003e1.02 (1.02\u0026ndash;1.03)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02 (1.02\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.020 (1.010\u0026ndash;1.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\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. group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.31 (1.2\u0026ndash;1.43)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.39 (1.27\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.22 (1.09\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eModel 1 unadjusted model.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eModel 2 adjusted for age, sex, marry state, rural.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eModel 3 adjusted for age, sex, marry state, rural, smoke, drink, WBC, Platelet, HGB, hypertension, diabetes, cancer, lung disease, stroke, liver disease, renal disease, digestive disease, asthma.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eBMI. group 1: \u0026lt;28kg/m\u003csup\u003e2\u003c/sup\u003e, BMI. group 2: \u0026ge;28kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eWC.group1: male\u0026lt;85cm, female\u0026lt;80cm, WC.group2: male\u0026thinsp;\u0026ge;\u0026thinsp;85cm, female\u0026thinsp;\u0026ge;\u0026thinsp;80cm.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eJoint effect of BMI and WC with the risk of heart disease\u003c/h2\u003e \u003cp\u003e \u003cb\u003eFigure 2\u003c/b\u003e illustrates the combined effects of BMI and WC on the incidence of heart disease. After adjustment for relevant covariates (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), individuals with both elevated BMI (\u0026ge;\u0026thinsp;28.0 kg/m\u0026sup2;) and increased WC (\u0026ge;\u0026thinsp;85 cm for men and \u0026ge;\u0026thinsp;80 cm for women) had a substantially higher risk of heart disease compared with those with low BMI and low WC (HR\u0026thinsp;=\u0026thinsp;1.54, 95% CI: 1.34\u0026ndash;1.77, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This risk exceeded that observed in participants with high BMI but normal WC or normal BMI with elevated WC.\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\u003eMultivariate adjusted HRs of heart disease risk factors according to BMI status and waist circumference groups.\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMeasures of additive interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRERI (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAP (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSI (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup. 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup. 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup. 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup. 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14 (0.51\u0026ndash;2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003eGroup. 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup. 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18 (1.07\u0026ndash;1.3)\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003eGroup. 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup. 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.54 (1.34\u0026ndash;1.77)\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=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22 (-0.71-1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14 (-0.46-0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.69 (0.09\u0026ndash;30.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eBMI. group 1: \u0026lt;28kg/m\u003csup\u003e2\u003c/sup\u003e, BMI. group 2: \u0026ge;28kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eWC. group1: male\u0026lt;85cm, female\u0026lt;80cm, WC.group2: male\u0026thinsp;\u0026ge;\u0026thinsp;85cm, female\u0026thinsp;\u0026ge;\u0026thinsp;80cm.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHowever, no statistically significant additive interaction between BMI and WC was detected in relation to heart disease risk (Additive: RERI\u0026thinsp;=\u0026thinsp;0.14, 95% CI: \u0026minus;0.71\u0026ndash;1.15; AP\u0026thinsp;=\u0026thinsp;0.14, 95% CI: \u0026minus;0.46\u0026ndash;0.75), indicating that further studies are needed to clarify their joint effects (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis, PSM analysis and other Sensitivity analyses\u003c/h2\u003e \u003cp\u003eTo further assess the robustness of the results, subgroup analyses, PSM analysis, and additional Sensitivity analyses were performed. The subgroup analyses showed no significant effect modification by common cardiovascular risk factors, with hazard ratios consistently greater than 1 across all strata (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, PSM was applied to balance potential baseline differences between BMI and WC categories, thereby reducing confounding bias. The associations observed after matching remained stable and were comparable to those of the primary analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMoreover, sensitivity analyses restricted to participants with complete data were conducted using multivariable regression models, and the findings were in line with the main results (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this extensive retrospective cohort study on heart disease has demonstrated a significant independent association between BMI and WC and an increase in the risk of heart disease. It is noteworthy that the joint effect of BMI and WC on the incidence of heart disease requires further investigation. The consistency of the associations was further supported by subgroup analyses, PSM, and multivariable Cox proportional hazards modeling. Collectively, these results underscore the important clinical implications of assessing both general and central obesity in cardiovascular risk evaluation[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur research investigated the positive association and joint effect of BMI and WC with heart disease in the China Health and Retirement Longitudinal Study. Our findings are consistent with those of previous observational studies[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. For example, Chaofu Ke et al. found a significant association between obesity and increased risk of cardiometabolic multimorbidity in the Chinese population (BMI: HR: 1.48, 95% CI: 0.98\u0026ndash;2.24; WC: HR: 2.06, 95% CI: 1.29\u0026ndash;3.27)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A recent longitudinal cohort study initiated by Zahra Raisi-Estabragh et al. showed a remarkable association between obesity and cardiovascular outcomes, while Sarah Lewington et al. found the same result[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this large Chinese cohort, both BMI and WC were identified as independent predictors of heart disease, and the combined impact of these two measures on cardiovascular risk was demonstrated for the first time[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These results suggest that incorporating WC alongside BMI offers a more comprehensive assessment of obesity, reducing misclassification and enhancing the ability to predict cardiovascular outcomes[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study has several notable strengths, including its large-scale cohort, population-based framework, and the simultaneous evaluation of BMI and WC in relation to heart disease across multiple subgroups[\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Nevertheless, as an observational investigation, residual confounding cannot be entirely excluded, even though extensive adjustments were made for known risk factors[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. While additional sensitivity analyses that excluded certain covariates yielded results consistent with the primary findings, the potential influence of unmeasured or incompletely measured confounders should still be considered when interpreting the associations between BMI, WC, and cardiovascular outcomes[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn Chinese individuals, high BMI or WC was significantly associated with an increased risk of incident heart disease. What's more, the combined use of BMI and WC to characterize obesity may better discriminate \"pathological\" obesity and provide better estimates of cardiovascular risk than either measure alone.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI =body mass index\u003c/p\u003e\n\u003cp\u003eWC =waist circumference\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCHARLS =the China Health and Retirement Longitudinal Study\u003c/p\u003e\n\u003cp\u003ePSM =propensity score matching\u003c/p\u003e\n\u003cp\u003eWBC =white blood cell count\u003c/p\u003e\n\u003cp\u003eMCV =mean corpuscular volume\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBUN =platelet count, blood urea nitrogen\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eScr =glucose levels, serum creatinine\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTC=total cholesterol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTG =triglycerides\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHDL =high-density lipoprotein\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLDL =cholesterol, low-density lipoprotein\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCRP =C-reactive protein\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHbA1c =glycosylated hemoglobin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUA =uric acid\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHCT =hematocrit\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHGB= hemoglobin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHRs =hazard ratios\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e95% CI =95% confidence intervals\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePSA =propensity score adjusted\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePSM =propensity score matching\u003c/p\u003e\n\u003cp\u003eIPTW =inverse probability of treatment weighting\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSMRW =standardized mortality ratio weighting\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePA =pairwise algorithmic\u003c/p\u003e\n\u003cp\u003eOW =overlap weight\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Biomedical Ethics Review Committee of Peking University\u0026nbsp;(IRB No. IRB00001052-11015), and all participants provided written informed consent. All procedures adhered to the ethical principles outlined in the Declaration of Helsinki. Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the China Health and Retirement Longitudinal Study (CHARLS) database, but restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the China Health and Retirement Longitudinal Study (CHARLS) database. CHARLS web-site (https://charls.pku.edu.cn/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiaodi Tang conceived and designed the study, performed the data analysis, and drafted the manuscript. Hong Chen and Xingming Zhong provided overall supervision, contributed to the study design, and critically revised the manuscript for important intellectual content. Yijiang Zhao, Kexi Zhang, Ying Xie and Yosen Yang contributed to data acquisition, data cleaning and interpretation of the results. Rong He contributed to the statistical analysis and interpretation of the findings. Ping Zhang supervised the project administration and provided critical revision of the manuscript. All authors read and approved the final version of the manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Dr. Jie Liu(People\u0026rsquo;s Liberation Army of China General Hospital, Beijing, China) , Dr. Haibo Li(Peking Union Medical College Hospital)and Dr. Qilin Yang (The Second Affiliated Hospital of Guangzhou Medical University,Guangzhou, Guangdong, China) for helping in this revision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary material to this article can be found online Appendix A.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLin H, Xiao N, Lin S, Liu M, Liu GG: \u003cstrong\u003eAssociations of hypertension, diabetes and heart disease risk with body mass index in older Chinese adults: a population-based cohort 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prospective study of 146,556 participants\u003c/strong\u003e. \u003cem\u003eBMC public health \u003c/em\u003e2021, \u003cstrong\u003e21\u003c/strong\u003e(1):963. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"body mass index, waist circumference, heart disease, association, joint effect","lastPublishedDoi":"10.21203/rs.3.rs-8180481/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8180481/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eThe association between body mass index (BMI), waist circumference (WC), and heart disease is a pivotal issue in the fields of public health, but the evidence in Chinese people is insufficient.\u003c/p\u003e\u003ch2\u003eObjective:\u003c/h2\u003e \u003cp\u003eThe primary aim of this study is to investigate the association and joint effects of BMI and WC in the risk of heart disease.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eA retrospective cohort study was conducted involving 11,700 participants from the China Health and Retirement Longitudinal Study. The incidence of heart disease was collected by following up 9 years. For all analyses, we imputed missing data of the covariates by using multiple imputations. The Cox proportional hazards model and propensity score matching (PSM) were used to exclude confounding factors. The additive interaction test was to explore the joint effect of BMI and WC. Sensitive analysis was to confirm the robust association.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eDuring a median follow-up of 8.49 years, 2055 (17.6%) people were diagnosed with heart disease. Upon full adjustment model and after PSM, a positive association was observed between BMI (HR: 1.41, 95% CI: 1.21\u0026ndash;1.63), WC (HR: 1.22, 95% CI: 1.09\u0026ndash;1.36) and the risk of heart disease. The joint effect (HR: 1.54, 95% CI: 1.34\u0026ndash;1.77) of BMI and WC was higher than the simple effect. Subsequent subgroup analysis and sensitivity analyses further confirmed the robustness of the findings, suggesting minimal impact from unmeasured confounders.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eThis cohort study demonstrated a significant association and joint effect between both high levels of BMI as well as high levels of WC and the risk of heart disease among individuals with Chinese people.\u003c/p\u003e","manuscriptTitle":"The joint effect between body mass index and waist circumference in the risk of heart disease: A national longitudinal cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 06:08:20","doi":"10.21203/rs.3.rs-8180481/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"159821968316894438005574349878303055769","date":"2026-01-07T13:14:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-07T09:47:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-05T08:19:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-18T05:40:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-17T13:12:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-12-17T13:00:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b4406f52-a357-4c06-8d7c-89b185df8886","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T06:08:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 06:08:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8180481","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8180481","identity":"rs-8180481","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}