Waist-to-Height Ratio, Waist Circumference, Body Mass Index and Risk of Cardiometabolic Multimorbidity: A National Longitudinal Cohort Study

Waist-to-Height Ratio, Waist Circumference, Body Mass Index and Risk of Cardiometabolic Multimorbidity: 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 Original investigation Waist-to-Height Ratio, Waist Circumference, Body Mass Index and Risk of Cardiometabolic Multimorbidity: A National Longitudinal Cohort Study Yanqiang Lu, Siyuan Liu, Yanan Qiao, Guochen Li, Ying Wu, Chaofu Ke This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-137388/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Cardiometabolic multimorbidity is an increasing public health burden. This study aimed to evaluate the association of waist-to-height ratio (WHtR), waist circumference (WC) and body mass index (BMI) with risk of cardiometabolic multimorbidity. Methods: We used data from the China Health and Retirement Longitudinal Study (CHARLS). 10521 participants aged 45 and over were recruited, including 8807 individuals with 0 cardiometabolic disease at baseline (stage Ⅰ) and 1714 individuals with 1 cardiometabolic disease at baseline (stage Ⅱ). Logistic regression was conducted to estimate the odds ratios (ORs) and confidence intervals (CIs). Net reclassification index (NRI) and integrated discrimination improvement (IDI) were used to evaluate the incremental predictive value beyond conventional factors. Results: In stage Ⅰ, increased risk of cardiometabolic multimorbidity was observed among participants with WHtR ≥ 0.5 (OR: 1.76, 95% CI: 1.05–2.97), WC ≥ 85 (men) + WC ≥ 75 (women) (OR: 1.77, 95% CI: 1.05–2.97) or BMI ≥ 24 (OR: 1.48, 95% CI: 0.98–2.24). Furthermore, both NRI and IDI of WHtR and WC were higher than those of BMI. In stage Ⅱ, the adjusted ORs (95% CIs) of WHtR ≥ 0.5, WC ≥ 85 (men) + WC ≥ 75 (women), and BMI ≥ 24 were 2.04 (1.24–3.35), 2.07 (1.28–3.34), and 1.47 (1.06–2.04), respectively. In addition, WC exhibited the highest NRI and IDI. Conclusions: WHtR, WC and BMI are independent predictors of cardiometabolic multimorbidity in middle-aged and elderly Chinese population. WHtR and WC show better abilities in predicting cardiometabolic multimorbidity than BMI. Cardiac & Cardiovascular Systems waist-to-height ratio waist circumference body mass index cardiometabolic multimorbidity CHARLS Figures Figure 1 Figure 2 Introduction Cardiometabolic diseases, including stroke, diabetes, and heart disease, are the leading cause of deaths worldwide[ 1 , 2 ]. With the rapid population aging, cardiometabolic multimorbidity, defined as the co-occurrence of 2 or more cardiometabolic diseases, has become a prominent public health concern. Remarkably, the health damages attributed to cardiometabolic multimorbidity are far more significant than a single cardiometabolic disease. It is reported that, when compared with elderly people without cardiometabolic diseases, individuals with any of cardiometabolic diseases or with any two of cardiometabolic diseases were estimated to have reduced life expectancies of 7 years and 12 years, respectively[ 3 ]. Furthermore, individuals with one cardiometabolic disease or with cardiometabolic multimorbidity also had 1.41 and 1.89 times the odds of higher mental stress than those without cardiometabolic disease, respectively[ 4 ]. However, as a major and persistent problem[ 5 , 6 ], cardiometabolic multimorbidity has been quite inadequately studied, and early prevention measures need to be taken urgently. Obesity has been acknowledged as an important risk factor for cardiometabolic diseases. Large amounts of evidence also showed that body mass index (BMI), a common measure of obesity, could predict single cardiometabolic diseases. In contrast, only a few studies reported the prospective association of BMI with cardiometabolic multimorbidity in Europe and America[ 7 , 8 ]. However, the evidence for this association remains to be further validated in Asian populations. Recently, waist circumference (WC) and waist-to-height ratio (WHtR) have been proposed to be important anthropometric indicators of abdominal obesity[ 9 , 10 ], and many studies demonstrated that these two indicators were superior to BMI in predicting single cardiometabolic diseases[ 9 – 15 ]. Nevertheless, the relationship between WHtR, WC and cardiometabolic multimorbidity has yet to be assessed. In this study, we investigated the longitudinal relationship between BMI, WHtR, WC, and cardiometabolic multimorbidity in the middle-aged and elderly Chinese population. Our aims are two-fold: (1) to assess the association of BMI, WHtR and WC with cardiometabolic multimorbidity; (2) to compare the predictive ability of BMI, WHtR, and WC in risk of cardiometabolic multimorbidity. Methods Study Design We used data from the China Health and Retirement Longitudinal Study (CHARLS)[16]. CHARLS is a nationally representative longitudinal survey, which collects data from individuals aged 45 and above in China. A wide range of information concerning the economic standing, physical and psychological health, demographics and social networks has been collected. Anthropometric indicators and cardiometabolic diseases have also been assessed in CHARLS. The first national baseline survey of CHARLS was conducted in 2011-2012, which included 10,257 households and 17,708 individuals from 150 counties/districts and 450 villages/resident communities[17]. Participants were then followed up every 2 years. Details of the study design of CHARLS can be found in previous literature[18]. This study was approved by Biomedical Ethics Review Committee of Peking University. All participants signed informed consents. Study Population All participants recruited in the national baseline survey were included if they met the following criteria: (1) aged at least 45 years; (2) no history of cardiometabolic multimorbidity at baseline; (3) collected anthropometric indicators successfully; (4) successfully followed up. Finally, we included 8807 individuals without any cardiometabolic disease at baseline and 1714 individuals with one of cardiometabolic diseases at baseline (Figure 1). WHtR, WC and BMI Measurements BMI was calculated as weight (kg) divided by the square of height (m). WHtR was defined as WC (cm) divided by height (cm). Height and weight were measured by vertical height measuring instrument and weighing scale with bare feet and light clothes. When measuring WC, the measured personnel used a soft ruler to circle the waist horizontally at the level of the navel. Cardiometabolic multimorbidity and outcomes Cardiometabolic multimorbidity is defined as having two or more of the following three diseases: diabetes, stroke, and heart problems[3]. The diagnosis of cardiometabolic diseases was determined by participants’ self-reported information. This study evaluated the development of cardiometabolic multimorbidity from two situations: 1) From 0 cardiometabolic disease at baseline to cardiometabolic multimorbidity (stage Ⅰ); 2) From 1 cardiometabolic disease at baseline to cardiometabolic multimorbidity (Stage Ⅱ). For the first situation, subjects who did not suffer from any of the three diseases in 2011 were included. Subsequently, people who eventually developed two or more cardiometabolic diseases during follow-up were deemed as individuals with cardiometabolic multimorbidity. For the second situation, participants having only one of the three diseases in 2011 were included, and then developing one or more new-onset cardiometabolic diseases during follow-up was defined as cardiometabolic multimorbidity. Covariates Covariates included sociodemographic characteristics, lifestyle factors and current disease status[19]. Sociodemographic characteristics included age (years), gender (male/female), education level (less than lower secondary education/upper secondary & vocational training/tertiary education), and residence (rural/urban). Lifestyle factors included smoking status (ever smoking/never smoking) and drinking status (ever drinking/never drinking). Current diseases (yes/no) included hypertension, cancer, lung disease, psychological problems, arthritis, dyslipidemia, liver disease, kidney disease, stomach/digestive system diseases, and asthma. Statistical Analysis Categorical variables were presented by frequency (percentage) and compared by the Chi-square test. Continuous data were described as median (inter-quartile range) and compared by the Wilcoxon rank sum test[20, 21]. For subsequent analyses, we converted the continuous variables into binary variables, using the cut-off values of 24kg/m 2 for BMI[20], 0.5 for WHtR[13], and 75 cm (women) and 85cm (men) for WC[22], respectively. Binary logistic regression model was used to access the associations of BMI, WHtR and WC with cardiometabolic multimorbidity, and the odds ratios (ORs) and 95% confidence intervals (CIs) were estimated. Net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated to compare the predictive utilities of BMI, WHtR and WC for cardiometabolic multimorbidity beyond other conventional factors[17, 23]. All analyses were performed using SAS 9.4 (SAS Institute, Cary, NC, USA). A two-sided P -value less than 0.05 was deemed statistically significant. Results Stage Ⅰ: From 0 cardiometabolic disease to cardiometabolic multimorbidity A total of 8807 participants without any cardiometabolic disease at baseline were included in this stage. During a four-year follow-up, 112 (1.27%) participants were reported to have cardiometabolic multimorbidity. Participants with cardiometabolic multimorbidity were older and more likely to have higher anthropometric indicators (BMI, WHtR and WC) than those without cardiometabolic multimorbidity (all P < 0.05). Compared with people without cardiometabolic multimorbidity, individuals with cardiometabolic multimorbidity exhibited significantly higher prevalence of hypertension, lung disease, dyslipidemia, liver disease, kidney disease, and asthma (All P < 0.05) (Table 1 ). Table 1 Baseline characteristics of the study population Characteristics Stage I (n = 8807) Stage Ⅱ (n = 1714) Cardiometabolic multimorbidity Cardiometabolic multimorbidity No Yes P value No Yes P value N (%) 8695 (98.73) 112 (1.27) 1501 (87.57) 213 (12.43) Age (year) 57 (51–64) 60.5 (54–66.5) 0.0069 60 (54–67) 61 (55–67) 0.3303 Male (%) 4219 (48.52) 46 (41.07) 0.1169 628 (41.84) 83 (38.97) 0.4260 Rural (%) 5867 (67.48) 78 (69.64) 0.6265 899 (59.89) 109 (51.17) 0.0155 Education (%) Level Ⅰ 7839 (90.16) 99 (88.39) 0.2977 1333 (88.81) 191 (89.67) 0.1188 Level Ⅱ 755 (8.68) 13 (11.61) 144 (9.59) 15 (7.04) Level Ⅲ 101 (1.16) 0 (0) 24 (1.60) 7 (3.29) BMI (kg/m 2 ) 22.81 (20.64–25.36) 24.35 (21.75–27.47) < .0001 24.01 (21.52–26.78) 25.62 (23.21–28.35) < .0001 WHtR 0.53 (0.47–0.58) 0.57 (0.52–0.63) < .0001 0.55 (0.51–0.60) 0.58 (0.54–0.62) < .0001 WC (cm) 83.90 (77.20–91.00) 90.20 (80.50–98.85) < .0001 87.40 (80.0–94.8) 92.8 (85.0–100.0) < .0001 Current Drinking (%) 3465 (39.88) 34 (30.36) 0.0408 508 (33.87) 69 (32.39) 0.6705 Current Smoking (%) 2832 (32.67) 34 (30.36) 0.6043 365 (24.43) 45 (21.13) 0.2909 Hypertension (%) 1711 (19.75) 51 (45.54) < .0001 653 (43.59) 133 (62.44) < .0001 Cancer (%) 64 (0.74) 2 (1.79) 0.2052 28 (1.87) 5 (2.36) 0.8274 Lung disease (%) 800 (9.21) 18 (16.07) 0.0130 232 (15.50) 42 (19.72) 0.1161 Psychological problems (%) 100 (1.15) 2 (1.79) 0.8598 26 (1.73) 2 (0.94) 0.5751 Arthritis (%) 2890 (33.28) 38 (33.93) 0.8842 670 (44.76) 94 (44.13) 0.8638 Dyslipidemia (%) 503 (5.87) 24 (21.43) < .0001 296 (20.04) 66 (31.43) 0.0002 Liver disease (%) 274 (3.16) 8 (7.21) 0.0330 81 (5.42) 12 (5.63) 0.8967 Kidney disease (%) 432 (4.98) 11 (9.91) 0.0184 140 (9.38) 23 (10.80) 0.5093 Stomach/digestive system diseases (%) 1868 (21.51) 28 (25.00) 0.3723 464 (30.95) 61 (28.64) 0.4929 Asthma (%) 356 (4.11) 11 (9.82) 0.0099 111 (7.42) 20 (9.39) 0.3120 Note: WHtR, waist-to-height ratio; WC, waist circumference; BMI, body mass index. Values were presented as n (%) or median (25th -75th percentile). Level Ⅰ— Less than lower secondary education; level Ⅱ— Upper secondary or vocational training; level Ⅲ— Tertiary education. During the follow-up period, 58 (1.78%) participants with BMI ≥ 24 developed cardiometabolic multimorbidity, while 54 (0.97%) participants with BMI < 24 developed cardiometabolic multimorbidity. The number (percentage) of participants with WHtR ≥ 0.5 that developed cardiometabolic multimorbidity was 92 (1.54%), while it was only 20 (0.71%) in people with WHtR < 0.5. In addition, there were 87 (1.55%) new cardiometabolic multimorbidity cases in the group of WC ≥ 85 (men) + WC ≥ 75 (women), and 25 (0.79%) new cardiometabolic multimorbidity cases in the group of WC < 85 (men) + WC < 75 (women). In logistic regression models (Fig. 2 ), BMI ≥ 24 was significantly associated with increased risk of cardiometabolic multimorbidity (OR: 1.85, 95% CI: 1.27–2.68). After multivariate adjustments, the odds ratio was decreased with marginal significance (OR: 1.48, 95% CI: 0.98–2.24). In contrast, WHtR was closely related to risk of cardiometabolic multimorbidity (OR: 2.19, 95% CI: 1.35–3.55). Moreover, the association between WHtR and risk of cardiometabolic multimorbidity persisted after multivariate adjustments (OR: 1.76, 95% CI: 1.05–2.97). Likewise, the odds of developing cardiometabolic multimorbidity in the high WC group was about twice as high as that in the lower WC group (OR: 1.98, 95% CI: 1.27–3.10). After multivariate adjustments, participants with high WC was 1.77 times (OR: 1.77, 95% CI: 1.05–2.97) more likely to have cardiometabolic multimorbidity. The capacities of BMI, WHtR and WC for predicting cardiometabolic multimorbidity were further evaluated. The addition of BMI, WHtR or WC to model 1 (adjusted for age, smoking, gender and hypertension) significantly improved the discriminatory power, except for the IDI value for BMI. The estimates (95% CI) of NRI and IDI were 0.3077 (0.0945–0.4929) and 0.0009 (-0.0001–0.0019) for BMI, 0.3248 (0.1399–0.5097) and 0.0029 (0.0029–0.0048) for WHtR, and 0.3524 (0.1685–0.5364) and 0.0028 (0.0009–0.0047) for WC, respectively. Likewise, after adding BMI to model 2, the IDI value of BMI was still not statistically significant (IDI: 0.0014, 95% CI: -0.0004–0.0032). By contrast, adding WHtR and WC to model 2 brought about a significant improvement of the predictive utility in cardiometabolic multimorbidity, with the estimates (95% CI) of NRI and IDI being 0.3211 (0.1346–0.5076) and 0.0052 (0.0014–0.009) for WHtR, and 0.3364 (0.1504–0.5225) and 0.005 (0.0013–0.0086) for WC (Table 2 ). Table 2 NRI and IDI of BMI, WHtR and WC for prediction of cardiometabolic multimorbidity Model NRI IDI Estimate(95% CI) P value Estimate(95% CI) P value Stage I Model 1 a Reference – Reference – Model 1 + BMI 0.3077 (0.1224–0.4929) 0.0012 0.0009 (-0.0001–0.0019) 0.0787 Model 1 + WHtR 0.3248 (0.1399–0.5097) < .0001 0.0029 (0.0009–0.0048) 0.0035 Model 1 + WC 0.3524 (0.1685–0.5364) 0.0002 0.0028 (0.0009–0.0047) 0.0043 Model 2 b Reference – Reference – Model 2 + BMI 0.2746 (0.0871–0.4622) 0.0042 0.0014 (-0.0004–0.0032) 0.1254 Model 2 + WHtR 0.3211 (0.1346–0.5076) 0.0008 0.0052 (0.0014–0.009) 0.0074 Model 2 + WC 0.3364 (0.1504–0.5225) 0.0005 0.0050 (0.0013–0.0086) 0.0074 Stage 2 Model 1 a Reference – Reference – Model 1 + BMI 0.2643 (0.1215–0.4070) 0.0003 0.0092 (0.0042–0.0142) 0.0003 Model 1 + WHtR 0.2522 (0.1094–0.3950) 0.0006 0.0126 (0.0068–0.0184) < .0001 Model 1 + WC 0.3152 (0.1733–0.4572) < .0001 0.0187 (0.0116–0.0259) < .0001 Model 2 b Reference – Reference – Model 2 + BMI 0.2261 (0.0813–0.3708) 0.0023 0.0060 (0.0017–0.0102) 0.0058 Model 2 + WHtR 0.1875 (0.0426–0.3324) 0.0115 0.0089 (0.0039–0.0138) 0.0005 Model 2 + WC 0.2893 (0.1454–0.4333) < .0001 0.0140 (0.0078–0.0202) < .0001 Note: BMI, body mass index; WHtR, waist-to-height ratio; WC, waist circumference; CI, confidence interval. a Model 1 included age, smoking, gender and hypertension. b Model 2 included age, gender, education level, residence, smoking, drinking, hypertension, cancer, lung disease, psychological problems, arthritis, dyslipidemia, liver disease, kidney disease, Stomach/digestive system diseases, and asthma. Stage Ⅱ: From 1 Cardiometabolic Disease To Cardiometabolic Multimorbidity Among 1714 participants in stage Ⅱ, 213 (12.43%) participants were classed as having cardiometabolic multimorbidity and 1501 (87.57%) participants were defined as non-cardiometabolic multimorbidity. Likewise, people with cardiometabolic multimorbidity had higher BMI, WHtR and WC, and possessed higher prevalence of hypertension and dyslipidemia (all P < 0.05) (Table 1 ). During follow-up, incidence rates of cardiometabolic multimorbidity in the high BMI, WHtR and WC groups were 15.41% (137), 14.08% (193) and 14.21% (189), respectively, while incidence rates in the low BMI, WHtR and WC groups were 9.21% (76), 5.83% (20) and 6.25% (24), respectively. Furthermore, the risk of having cardiometabolic multimorbidity was most pronounced for WC (OR: 2.07, 95% CI: 1.28–3.34), followed by WHtR (OR: 2.04, 95% CI: 1.24–3.35) and BMI (OR: 1.47, 95% CI: 1.06–2.04) (Fig. 2 ). NRI and IDI values were applied to compare the predictive abilities of BMI, WHtR and WC on risk of cardiometabolic multimorbidity. The NRI and IDI values showed that WC was advantageous over BMI and WHtR in predicting cardiometabolic multimorbidity among individulas with only one cardiometabolic disease at baseline. Adding BMI, WHtR and WC to model 1 improved patient classification by 26.43% (12.15–40.70%), 25.22% (10.94–39.59%) and 31.52% (17.33–45.72%), respectively, and probabilitic difference of suffering cardiometabolic multimorbidity between cases and controls by 0.92% (0.42–1.42%), 1.26% (0.68–1.84%), and 1.87% (1.16–2.59%), respectively. Likewise, in model 2, the NRI and IDI (95% CI) were 0.2261 (0.0813–0.3708) and 0.006 (0.0017–0.0102) for BMI, 0.1875 (0.0426–0.3324) and 0.0089 (0.0039–0.0138) for WHtR, 0.2893 (0.1454–0.4333) and 0.014 (0.0078–0.0202), respectively (Table 2 ). Discussion The present study investigated the prospective association of BMI, WHtR and WC with cardiometabolic multimorbidity in a nationally representative cohort. Our results showed that BMI, WHtR and WC were all independently associated with increased risk of cardiometabolic multimorbidity among the middle-aged and elderly Chinese population. Moreover, compared with BMI, WHtR and WC exhibited better predictive utilities in future cardiometabolic multimorbidity. Cardiometabolic multimorbidity, as a growing problem, poses a major challenge to health care systems throughout the world. Previous studies have suggested that cardiometabolic multimorbidity is much more harmful than a single cardiometabolic disease. For example, compared to the absence of any of the three cardiometabolic diseases, the hazard ratio (HR) for all-cause mortality was about twice in any one of these diseases, 4 times in any two of these diseases, and 7 times in the presence of all three diseases[ 3 ]. In view of the serious harm of cardiometabolic multimorbidity, early predictive indicators need to be discovered urgently. However, quite limited studies have investigated the associations of easy-to access anthropometric indicators, such as BMI, WHtR and WC, with risk of cardiometabolic multimorbidity. To the best of our knowledge, there are only two studies exploring the link between BMI and cardiometabolic multimorbidity[ 7 , 8 ]. The study by Kivimäki et al. involving 16 longitudinal research databases and 120,813 subjects suggested that the risk of cardiometabolic multimorbidity increased as BMI increased[ 8 ]. This comprehensive analysis indicated that compared with healthy-weight individuals, overweight and obesity individuals (BMI ≥ 24) had twice the risk of developing cardiometabolic multimorbidity. The OR values between this study and ours are similar, indicating the predictive power of BMI. However, the Asian population has not been included in this study, and our study can be a supplement in this regard. Another study recruiting 8270 subjects showed that the hazard ratio for overweight/obesity was 1.19 times (95% CI: 1.00–1.43) higher for developing cardiometabolic multimorbidity from 1 baseline cardiometabolic disease than individuals with healthy weight. WC, as an abdominal obesity measurement indicator, has been supported as an obesity-related health risk indicator for both Western and Asian populations[ 24 – 26 ]. Previous studies have suggested that WC has greater predictive power for risk of cardiometabolic diseases than BMI[ 9 , 27 ]. Furthermore, a large amount of evidence has also supported that WHtR was more effective than BMI in predicting coronary heart disease, stroke and diabetes[ 9 , 12 – 14 , 21 , 28 , 29 ]. Remarkably, our study also found that WHtR and WC were independent predictors of cardiometabolic multimorbidity. Moreover, WHtR and WC were demonstrated to possess higher predictive abilities on risk of cardiometabolic multimorbidity than BMI in the current study. This phenomenon may be explained by the following reasons. First of all, BMI can only be used to measure the total body fat and cannot represent the body fat distribution[ 30 ]. The susceptibility of cardiometabolic diseases may depend on the difference of regional body fat distribution and the ability of subcutaneous adipose tissue[ 31 ]. Moreover, WC reflects body fat ratio more accurately than BMI, and it may play an important role in the early development of metabolic syndrome[ 32 , 33 ]. A recent systematic review demonstrated that compared with BMI, WC increased the ability to discriminate adverse cardiometabolic risk outcomes by 3%[ 29 ]. In addition, WHtR has been suggested to be less affected by race, age and gender and be relatively more stable[ 9 , 11 , 34 ]. People with the same BMI might have different risks of cardiometabolic diseases[ 31 ]. Even among people with normal BMI, those with high WHtR are more likely to suffer from cardiometabolic diseases. Notably, about 35% of men and 14% of women with high WHtR would be missed if screened by BMI only, which could bring serious consequences for cardiometabolic disease prevention[ 35 ]. Considering that WHtR and WC have better predictive power in cardiometabolic multimorbidity than BMI, screening by WHtR and WC might be applicable in future practice. The exact mechanisms underlying the association between BMI, WHtR, WC, and cardiometabolic multimorbidity remain to be illuminated, but insulin resistance and ectopic fat deposition may be the main contributors. Adipose tissue produces a large amount of bioactive mediators, which leads to insulin resistance. Insulin resistance may cause cardiometabolic diseases in the following ways. First, in a state of insulin resistance, inflammation occurs in the body, which eventually leads to atherosclerosis[ 36 ]. Second, insulin resistance affects the production of apolipoprotein A1 (apoA-Ⅰ) or the liver secretion of high-density lipoprotein (HDL), which could be a trigger for metabolic syndrome[ 37 ]. Third, insulin resistance would obstruct normal heart function through inhibiting metabolic pathways and over-stimulating growth factors[ 38 ]. In addition, ectopic fat deposition triggers a pathological metabolic response, increasing the risk of metabolic diseases[ 31 ]. Excess free fatty acids are produced outside the fat storage tissue, and transferred to ectopic sites, including the viscera, heart, and vasculature, ultimately leading to cardiometabolic diseases[ 39 ]. There are several merits in this study. Our study is the first to investigate the association of WHtR and WC with risk of cardiometabolic multimorbidity. Moreover, WHtR and WC were shown to be advantageous in predicting cardiometabolic multimorbidity than BMI. In addition, the two-stage strategy to assess cardiometabolic multimorbidity could provide mutual validation and increase the reliability of the associations. Limitations should be noted as well. First, our research was based on the four-year follow-up data, preventing the assessments of a long-term association. Second, the cardiometabolic diseases included in this study were self-reported. However, self-reported cardiometabolic diseases have been proved to be highly reliable in large-scale epidemiological studies[ 40 ]. Conclusions In the middle-aged and elderly Chinese population, WHtR, WC and BMI were found to be independent predictors of cardiometabolic multimorbidity. In addition, WHtR and WC exhibited better predictive power in future cardiometabolic multimorbidity than BMI. Our findings highlight the significance of screening and interventions of high-risk individuals through easy-to-access and cost-effective tools, such as WHtR and WC, for the prevention of cardiometabolic multimorbidity. Abbreviations WHtR, waist-to-height ratio; WC, waist circumference; BMI, body mass index; CHARLS, China Health and Retirement Longitudinal Study; ORs, odds ratios; CIs, confidence intervals; NRI, net reclassification index; IDI, integrated discrimination improvement. Declarations Ethics approval and consent to participate CHARLS was approved by Biomedical Ethics Review Committee of Peking University, and all participants signed informed consents. Consent for publication Not applicable. Availability of data and materials The data used and analyzed in this study are publicly available from the China Health and Retirement Longitudinal Study (http://charls.pku.edu.cn/zh-CN). Competing interests The authors declare that they have no competing interests. Funding This work was supported by National Natural Science Foundation of China (81703316, 81703322); Natural Science Foundation of Jiangsu Province (project number BK20170350) and A Project Funded by Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). Authors’ contributions CK conceived and designed the research; YL and SL wrote the manuscript; and GL, YQ and YW performed the data analysis. All authors contributed to the interpretations of the findings. All authors reviewed the manuscript. Acknowledgments This analysis uses data or information from the Harmonized CHARLS dataset and Codebook, Version C as of April 2018 developed by the Gateway to Global Aging Data. The development of the Harmonized CHARLS was funded by the National Institute on Ageing (R01 AG030153, RC2 AG036619, R03 AG043052). For more information, please refer to www.g2aging.org. References Ralston J, Nugent R. Toward a broader response to cardiometabolic disease. 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Predictive Values of Anthropometric Measurements for Cardiometabolic Risk Factors and Cardiovascular Diseases Among 44 048 Chinese. Journal of the American Heart Association. 2019;8(16):e010870. Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol. 2014;43(1):61–8. Shen Y, Zhang Y, Xiong S, Zhu X, Ke C. High-sensitivity C-reactive protein and cystatin C independently and jointly predict all-cause mortality among the middle-aged and elderly Chinese population. Clinical biochemistry. 2019;65:7–14. Li H, Qian F, Hou C, Li X, Gao Q, Luo Y, et al. Longitudinal Changes in Depressive Symptoms and Risks of Cardiovascular Disease and All-Cause Mortality: A Nationwide Population-Based Cohort Study. The journals of gerontology Series A, Biological sciences and medical sciences. 2020;75(11):2200–6. Li W, Taskin T, Gautam P, Gamber M, Sun W. Is there an association among sleep duration, nap, and stroke? Findings from the China Health and Retirement Longitudinal Study. Sleep & breathing = Schlaf & Atmung. 2020. Li Q, Zhang D, Guo C, Zhou Q, Tian G, Liu D, et al. Association of hypertriglyceridemic waist-to-height ratio and its dynamic status with incident hypertension: the Rural Chinese Cohort Study. Journal of hypertension. 2019;37(12):2354–60. Rådholm K, Chalmers J, Ohkuma T, Peters S, Poulter N, Hamet P, et al. Use of the waist-to-height ratio to predict cardiovascular risk in patients with diabetes: Results from the ADVANCE-ON study. Diabetes Obes Metab. 2018;20(8):1903–10. Zeng Q, He Y, Dong S, Zhao X, Chen Z, Song Z, et al. Optimal cut-off values of BMI, waist circumference and waist:height ratio for defining obesity in Chinese adults. Br J Nutr. 2014;112(10):1735–44. Martens FK, Tonk ECM, Janssens A. Evaluation of polygenic risk models using multiple performance measures: a critical assessment of discordant results. Genetics in medicine: official journal of the American College of Medical Genetics. 2019;21(2):391–7. Janssen I, Katzmarzyk PT, Ross R. Waist circumference and not body mass index explains obesity-related health risk. Am J Clin Nutr. 2004;79(3):379–84. Leitzmann MF, Moore SC, Koster A, Harris TB, Park Y, Hollenbeck A, et al. Waist circumference as compared with body-mass index in predicting mortality from specific causes. PloS one. 2011;6(4):e18582. Huxley R, Mendis S, Zheleznyakov E, Reddy S, Chan J. Body mass index, waist circumference and waist:hip ratio as predictors of cardiovascular risk–a review of the literature. Eur J Clin Nutr. 2010;64(1):16–22. Staiano AE, Reeder BA, Elliott S, Joffres MR, Pahwa P, Kirkland SA, et al Body mass index versus waist circumference as predictors of mortality in Canadian adults. International journal of obesity (2005). 2012;36(11):1450-4. Liu PJ, Ma F, Lou HP, Zhu YN. Comparison of the ability to identify cardiometabolic risk factors between two new body indices and waist-to-height ratio among Chinese adults with normal BMI and waist circumference. Public Health Nutr. 2017;20(6):984–91. Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obesity reviews: an official journal of the International Association for the Study of Obesity. 2012;13(3):275–86. Shen S, Lu Y, Qi H, Li F, Shen Z, Wu L, et al. Waist-to-height ratio is an effective indicator for comprehensive cardiovascular health. Scientific reports. 2017;7:43046. Piché ME, Poirier P, Lemieux I, Després JP. Overview of Epidemiology and Contribution of Obesity and Body Fat Distribution to Cardiovascular Disease: An Update. Prog Cardiovasc Dis. 2018;61(2):103–13. Cibičková Ľ, Langová K, Vaverková H, Lukeš J, Cibiček N, Karásek D. Superior Role of Waist Circumference to Body-Mass Index in the Prediction of Cardiometabolic Risk in Dyslipidemic Patients. Physiological research. 2019;68(6):931–8. Alberti KG, Zimmet P, Shaw J. The metabolic syndrome–a new worldwide definition. Lancet. 2005;366(9491):1059–62. Kazlauskaite R, Avery-Mamer EF, Li H, Chataut CP, Janssen I, Powell LH, et al. Race/ethnic comparisons of waist-to-height ratio for cardiometabolic screening: The study of women's health across the nation. American journal of human biology: the official journal of the Human Biology Council. 2017;29(1). Ashwell M, Gibson S. Waist to height ratio is a simple and effective obesity screening tool for cardiovascular risk factors: Analysis of data from the British National Diet And Nutrition Survey of adults aged 19–64 years. Obes Facts. 2009;2(2):97–103. Kwiterovich PO Jr. Clinical relevance of the biochemical, metabolic, and genetic factors that influence low-density lipoprotein heterogeneity. The American journal of cardiology. 2002;90(8a):30i–47i. Mertens I, Van Gaal LF. New International Diabetes Federation (IDF) and National Cholesterol Education Program Adult Treatment panel III (NCEP-ATPIII) criteria and the involvement of hemostasis and fibrinolysis in the metabolic syndrome. Journal of thrombosis haemostasis: JTH. 2006;4(5):1164–6. Van Gaal LF, Mertens IL, De Block CE. Mechanisms linking obesity with cardiovascular disease. Nature. 2006;444(7121):875–80. Britton KA, Fox CS. Ectopic fat depots and cardiovascular disease. Circulation. 2011;124(24):e837-41. Barr EL, Tonkin AM, Welborn TA, Shaw JE. Validity of self-reported cardiovascular disease events in comparison to medical record adjudication and a statewide hospital morbidity database: the AusDiab study. Intern Med J. 2009;39(1):49–53. 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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-137388","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Original investigation","associatedPublications":[],"authors":[{"id":7194773,"identity":"08f86d2d-bb74-412a-bd0c-e54f1841facd","order_by":0,"name":"Yanqiang Lu","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-4300-8654","institution":"Soochow University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yanqiang","middleName":"","lastName":"Lu","suffix":""},{"id":7194774,"identity":"18782c47-6f47-4cd6-80c5-0f4746455195","order_by":1,"name":"Siyuan Liu","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Siyuan","middleName":"","lastName":"Liu","suffix":""},{"id":7194775,"identity":"cfd1c0c0-827c-4c1e-8d46-a893df1e7efb","order_by":2,"name":"Yanan Qiao","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yanan","middleName":"","lastName":"Qiao","suffix":""},{"id":7194776,"identity":"5b9f4ff3-08dc-46af-af0b-b269dd563fb3","order_by":3,"name":"Guochen Li","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guochen","middleName":"","lastName":"Li","suffix":""},{"id":7194777,"identity":"fc110879-6ded-42c4-a21c-77e98def8533","order_by":4,"name":"Ying Wu","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Wu","suffix":""},{"id":7194778,"identity":"e6b9b8ec-c6d5-4207-99eb-c0267ffa47d7","order_by":5,"name":"Chaofu Ke","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chaofu","middleName":"","lastName":"Ke","suffix":""}],"badges":[],"createdAt":"2020-12-28 21:35:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-137388/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-137388/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":4615973,"identity":"b51d271c-a010-446a-a849-cb59fb12f3ce","added_by":"auto","created_at":"2020-12-30 23:58:01","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1644213,"visible":true,"origin":"","legend":"Flowchart of participant selection","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-137388/v1/816b80a3903993e290f5d41e.jpg"},{"id":4616122,"identity":"6b7ff3a4-c69f-41d5-b9b9-c5702f183477","added_by":"auto","created_at":"2020-12-31 00:04:01","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132251,"visible":true,"origin":"","legend":"Association of BMI, WHtR and WC with risk of cardiometabolic multimorbidity","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-137388/v1/d4fdd5bcccdc1cd7e4504dec.jpg"},{"id":13641081,"identity":"6d2d25e8-c412-4cbe-a0f3-71035d6fe061","added_by":"auto","created_at":"2021-09-17 09:02:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":518574,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-137388/v1/0d5be694-e501-4547-8b7e-f2ff2940e5e0.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eWaist-to-Height Ratio, Waist Circumference, Body Mass Index and Risk of Cardiometabolic Multimorbidity: A National Longitudinal Cohort Study\u003c/p\u003e","fulltext":[{"header":"Introduction","content":" \u003cp\u003eCardiometabolic diseases, including stroke, diabetes, and heart disease, are the leading cause of deaths worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. With the rapid population aging, cardiometabolic multimorbidity, defined as the co-occurrence of 2 or more cardiometabolic diseases, has become a prominent public health concern. Remarkably, the health damages attributed to cardiometabolic multimorbidity are far more significant than a single cardiometabolic disease. It is reported that, when compared with elderly people without cardiometabolic diseases, individuals with any of cardiometabolic diseases or with any two of cardiometabolic diseases were estimated to have reduced life expectancies of 7\u0026nbsp;years and 12 years, respectively[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Furthermore, individuals with one cardiometabolic disease or with cardiometabolic multimorbidity also had 1.41 and 1.89 times the odds of higher mental stress than those without cardiometabolic disease, respectively[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, as a major and persistent problem[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], cardiometabolic multimorbidity has been quite inadequately studied, and early prevention measures need to be taken urgently.\u003c/p\u003e \u003cp\u003eObesity has been acknowledged as an important risk factor for cardiometabolic diseases. Large amounts of evidence also showed that body mass index (BMI), a common measure of obesity, could predict single cardiometabolic diseases. In contrast, only a few studies reported the prospective association of BMI with cardiometabolic multimorbidity in Europe and America[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, the evidence for this association remains to be further validated in Asian populations. Recently, waist circumference (WC) and waist-to-height ratio (WHtR) have been proposed to be important anthropometric indicators of abdominal obesity[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and many studies demonstrated that these two indicators were superior to BMI in predicting single cardiometabolic diseases[\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Nevertheless, the relationship between WHtR, WC and cardiometabolic multimorbidity has yet to be assessed.\u003c/p\u003e \u003cp\u003eIn this study, we investigated the longitudinal relationship between BMI, WHtR, WC, and cardiometabolic multimorbidity in the middle-aged and elderly Chinese population. Our aims are two-fold: (1) to assess the association of BMI, WHtR and WC with cardiometabolic multimorbidity; (2) to compare the predictive ability of BMI, WHtR, and WC in risk of cardiometabolic multimorbidity.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003eStudy Design\u003c/h3\u003e\n\u003cp\u003eWe used data from the China Health and Retirement Longitudinal Study (CHARLS)[16]. CHARLS is a nationally representative longitudinal survey, which collects data from individuals aged 45 and above in China. A wide range of information concerning the economic standing, physical and psychological health, demographics and social networks has been collected. Anthropometric indicators and cardiometabolic diseases have also been assessed in CHARLS. The first national baseline survey of CHARLS was conducted in 2011-2012, which included 10,257 households and 17,708 individuals from 150 counties/districts and 450 villages/resident communities[17]. Participants were then followed up every 2 years. Details of the study design of CHARLS can be found in previous literature[18]. This study was approved by Biomedical Ethics Review Committee of Peking University. All participants signed informed consents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants recruited in the national baseline survey were included if they met the following criteria: (1) aged at least 45 years; (2) no history of cardiometabolic multimorbidity at baseline; (3) collected anthropometric indicators successfully; (4) successfully followed up. Finally, we included 8807 individuals without any cardiometabolic disease at baseline and 1714 individuals with one of cardiometabolic diseases at baseline (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWHtR, WC and BMI Measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBMI was calculated as weight (kg) divided by the square of height (m). WHtR was defined as WC (cm) divided by height (cm). Height and weight were measured by vertical height measuring instrument and weighing scale with bare feet and light clothes. When measuring WC, the measured personnel used a soft ruler to circle the waist horizontally at the level of the navel.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCardiometabolic multimorbidity and outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCardiometabolic multimorbidity is defined as having two or more of the following three diseases: diabetes, stroke, and heart problems[3]. The diagnosis of cardiometabolic diseases was determined by participants\u0026rsquo; self-reported information. This study evaluated the development of cardiometabolic multimorbidity from two situations: 1) From 0 cardiometabolic disease at baseline to cardiometabolic multimorbidity (stage Ⅰ); 2) From 1 cardiometabolic disease at baseline to cardiometabolic multimorbidity (Stage Ⅱ). For the first situation, subjects who did not suffer from any of the three diseases in 2011 were included. Subsequently, people who eventually developed two or more cardiometabolic diseases during follow-up were deemed as individuals with cardiometabolic multimorbidity. For the second situation, participants having only one of the three diseases in 2011 were included, and then developing one or more new-onset cardiometabolic diseases during follow-up was defined as cardiometabolic multimorbidity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCovariates included sociodemographic characteristics, lifestyle factors and current disease status[19]. Sociodemographic characteristics included age (years), gender (male/female), education level (less than lower secondary education/upper secondary \u0026amp; vocational training/tertiary education), and residence (rural/urban). Lifestyle factors included smoking status (ever smoking/never smoking) and drinking status (ever drinking/never drinking). Current diseases (yes/no) included hypertension, cancer, lung disease, psychological problems, arthritis, dyslipidemia, liver disease, kidney disease, stomach/digestive system diseases, and asthma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCategorical variables were presented by frequency (percentage) and compared by the Chi-square test. Continuous data were described as median (inter-quartile range) and compared by the Wilcoxon rank sum test[20, 21].\u003c/p\u003e\n\u003cp\u003eFor subsequent analyses, we converted the continuous variables into binary variables, using the cut-off values of 24kg/m\u003csup\u003e2\u003c/sup\u003e for BMI[20], 0.5 for WHtR[13], and 75 cm (women) and 85cm (men) for WC[22], respectively. Binary logistic regression model was used to access the associations of BMI, WHtR and WC with cardiometabolic multimorbidity, and the odds ratios (ORs) and 95% confidence intervals (CIs) were estimated. Net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated to compare the predictive utilities of BMI, WHtR and WC for cardiometabolic multimorbidity beyond other conventional factors[17, 23].\u003c/p\u003e\n\u003cp\u003eAll analyses were performed using SAS 9.4 (SAS Institute, Cary, NC, USA). A\u0026nbsp;two-sided \u003cem\u003eP\u003c/em\u003e-value less than 0.05 was deemed statistically significant.\u003c/p\u003e"},{"header":"Results","content":" \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStage Ⅰ: From 0 cardiometabolic disease to cardiometabolic multimorbidity\u003c/h2\u003e \u003cp\u003eA total of 8807 participants without any cardiometabolic disease at baseline were included in this stage. During a four-year follow-up, 112 (1.27%) participants were reported to have cardiometabolic multimorbidity. Participants with cardiometabolic multimorbidity were older and more likely to have higher anthropometric indicators (BMI, WHtR and WC) than those without cardiometabolic multimorbidity (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared with people without cardiometabolic multimorbidity, individuals with cardiometabolic multimorbidity exhibited significantly higher prevalence of hypertension, lung disease, dyslipidemia, liver disease, kidney disease, and asthma (All \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the study population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eStage I (n\u0026thinsp;=\u0026thinsp;8807)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eStage Ⅱ (n\u0026thinsp;=\u0026thinsp;1714)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCardiometabolic multimorbidity\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\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eCardiometabolic multimorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8695 (98.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112 (1.27)\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 \u003cp\u003e1501 (87.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e213 (12.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (51\u0026ndash;64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.5 (54\u0026ndash;66.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60 (54\u0026ndash;67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61 (55\u0026ndash;67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4219 (48.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (41.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e628 (41.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83 (38.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4260\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5867 (67.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (69.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e899 (59.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e109 (51.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel Ⅰ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7839 (90.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (88.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1333 (88.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e191 (89.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel Ⅱ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e755 (8.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (11.61)\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 \u003cp\u003e144 (9.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15 (7.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel Ⅲ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101 (1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\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 \u003cp\u003e24 (1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7 (3.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.81 (20.64\u0026ndash;25.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.35 (21.75\u0026ndash;27.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.01 (21.52\u0026ndash;26.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.62 (23.21\u0026ndash;28.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.53 (0.47\u0026ndash;0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57 (0.52\u0026ndash;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.55 (0.51\u0026ndash;0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.58 (0.54\u0026ndash;0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.90 (77.20\u0026ndash;91.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.20 (80.50\u0026ndash;98.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87.40 (80.0\u0026ndash;94.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e92.8 (85.0\u0026ndash;100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Drinking (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3465 (39.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (30.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e508 (33.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69 (32.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.6705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Smoking (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2832 (32.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (30.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e365 (24.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45 (21.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1711 (19.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (45.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e653 (43.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e133 (62.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28 (1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5 (2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.8274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung disease (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e800 (9.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (16.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e232 (15.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42 (19.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological problems (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26 (1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 (0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArthritis (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2890 (33.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (33.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e670 (44.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94 (44.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.8638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e503 (5.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (21.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e296 (20.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66 (31.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver disease (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274 (3.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (7.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81 (5.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12 (5.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.8967\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney disease (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e432 (4.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (9.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140 (9.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23 (10.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStomach/digestive system diseases (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1868 (21.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e464 (30.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61 (28.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsthma (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e356 (4.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (9.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e111 (7.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20 (9.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: WHtR, waist-to-height ratio; WC, waist circumference; BMI, body mass index. Values were presented as n (%) or median (25th -75th percentile).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eLevel Ⅰ\u0026mdash; Less than lower secondary education; level Ⅱ\u0026mdash; Upper secondary or vocational training; level Ⅲ\u0026mdash; Tertiary education.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDuring the follow-up period, 58 (1.78%) participants with BMI\u0026thinsp;\u0026ge;\u0026thinsp;24 developed cardiometabolic multimorbidity, while 54 (0.97%) participants with BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 developed cardiometabolic multimorbidity. The number (percentage) of participants with WHtR\u0026thinsp;\u0026ge;\u0026thinsp;0.5 that developed cardiometabolic multimorbidity was 92 (1.54%), while it was only 20 (0.71%) in people with WHtR\u0026thinsp;\u0026lt;\u0026thinsp;0.5. In addition, there were 87 (1.55%) new cardiometabolic multimorbidity cases in the group of WC\u0026thinsp;\u0026ge;\u0026thinsp;85 (men)\u0026thinsp;+\u0026thinsp;WC\u0026thinsp;\u0026ge;\u0026thinsp;75 (women), and 25 (0.79%) new cardiometabolic multimorbidity cases in the group of WC\u0026thinsp;\u0026lt;\u0026thinsp;85 (men)\u0026thinsp;+\u0026thinsp;WC\u0026thinsp;\u0026lt;\u0026thinsp;75 (women). In logistic regression models (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), BMI\u0026thinsp;\u0026ge;\u0026thinsp;24 was significantly associated with increased risk of cardiometabolic multimorbidity (OR: 1.85, 95% CI: 1.27\u0026ndash;2.68). After multivariate adjustments, the odds ratio was decreased with marginal significance (OR: 1.48, 95% CI: 0.98\u0026ndash;2.24). In contrast, WHtR was closely related to risk of cardiometabolic multimorbidity (OR: 2.19, 95% CI: 1.35\u0026ndash;3.55). Moreover, the association between WHtR and risk of cardiometabolic multimorbidity persisted after multivariate adjustments (OR: 1.76, 95% CI: 1.05\u0026ndash;2.97). Likewise, the odds of developing cardiometabolic multimorbidity in the high WC group was about twice as high as that in the lower WC group (OR: 1.98, 95% CI: 1.27\u0026ndash;3.10). After multivariate adjustments, participants with high WC was 1.77 times (OR: 1.77, 95% CI: 1.05\u0026ndash;2.97) more likely to have cardiometabolic multimorbidity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe capacities of BMI, WHtR and WC for predicting cardiometabolic multimorbidity were further evaluated. The addition of BMI, WHtR or WC to model 1 (adjusted for age, smoking, gender and hypertension) significantly improved the discriminatory power, except for the IDI value for BMI. The estimates (95% CI) of NRI and IDI were 0.3077 (0.0945\u0026ndash;0.4929) and 0.0009 (-0.0001\u0026ndash;0.0019) for BMI, 0.3248 (0.1399\u0026ndash;0.5097) and 0.0029 (0.0029\u0026ndash;0.0048) for WHtR, and 0.3524 (0.1685\u0026ndash;0.5364) and 0.0028 (0.0009\u0026ndash;0.0047) for WC, respectively. Likewise, after adding BMI to model 2, the IDI value of BMI was still not statistically significant (IDI: 0.0014, 95% CI: -0.0004\u0026ndash;0.0032). By contrast, adding WHtR and WC to model 2 brought about a significant improvement of the predictive utility in cardiometabolic multimorbidity, with the estimates (95% CI) of NRI and IDI being 0.3211 (0.1346\u0026ndash;0.5076) and 0.0052 (0.0014\u0026ndash;0.009) for WHtR, and 0.3364 (0.1504\u0026ndash;0.5225) and 0.005 (0.0013\u0026ndash;0.0086) for WC (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNRI and IDI of BMI, WHtR and WC for prediction of cardiometabolic multimorbidity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNRI\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\u003eIDI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEstimate(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u0026thinsp;+\u0026thinsp;BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3077 (0.1224\u0026ndash;0.4929)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0009 (-0.0001\u0026ndash;0.0019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u0026thinsp;+\u0026thinsp;WHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3248 (0.1399\u0026ndash;0.5097)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0029 (0.0009\u0026ndash;0.0048)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u0026thinsp;+\u0026thinsp;WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3524 (0.1685\u0026ndash;0.5364)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0028 (0.0009\u0026ndash;0.0047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u0026thinsp;+\u0026thinsp;BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2746 (0.0871\u0026ndash;0.4622)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0014 (-0.0004\u0026ndash;0.0032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u0026thinsp;+\u0026thinsp;WHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3211 (0.1346\u0026ndash;0.5076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0052 (0.0014\u0026ndash;0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u0026thinsp;+\u0026thinsp;WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3364 (0.1504\u0026ndash;0.5225)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0050 (0.0013\u0026ndash;0.0086)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u0026thinsp;+\u0026thinsp;BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2643 (0.1215\u0026ndash;0.4070)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0092 (0.0042\u0026ndash;0.0142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u0026thinsp;+\u0026thinsp;WHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2522 (0.1094\u0026ndash;0.3950)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0126 (0.0068\u0026ndash;0.0184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u0026thinsp;+\u0026thinsp;WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3152 (0.1733\u0026ndash;0.4572)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0187 (0.0116\u0026ndash;0.0259)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u0026thinsp;+\u0026thinsp;BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2261 (0.0813\u0026ndash;0.3708)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0060 (0.0017\u0026ndash;0.0102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u0026thinsp;+\u0026thinsp;WHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1875 (0.0426\u0026ndash;0.3324)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0089 (0.0039\u0026ndash;0.0138)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u0026thinsp;+\u0026thinsp;WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2893 (0.1454\u0026ndash;0.4333)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0140 (0.0078\u0026ndash;0.0202)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: BMI, body mass index; WHtR, waist-to-height ratio; WC, waist circumference; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e Model 1 included age, smoking, gender and hypertension.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003eb\u003c/sup\u003e Model 2 included age, gender, education level, residence, smoking, drinking, hypertension, cancer, lung disease, psychological problems, arthritis, dyslipidemia, liver disease, kidney disease, Stomach/digestive system diseases, and asthma.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \n\u003ch2\u003eStage Ⅱ: From 1 Cardiometabolic Disease To Cardiometabolic Multimorbidity\u003c/h2\u003e\n \u003cp\u003eAmong 1714 participants in stage Ⅱ, 213 (12.43%) participants were classed as having cardiometabolic multimorbidity and 1501 (87.57%) participants were defined as non-cardiometabolic multimorbidity. Likewise, people with cardiometabolic multimorbidity had higher BMI, WHtR and WC, and possessed higher prevalence of hypertension and dyslipidemia (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDuring follow-up, incidence rates of cardiometabolic multimorbidity in the high BMI, WHtR and WC groups were 15.41% (137), 14.08% (193) and 14.21% (189), respectively, while incidence rates in the low BMI, WHtR and WC groups were 9.21% (76), 5.83% (20) and 6.25% (24), respectively. Furthermore, the risk of having cardiometabolic multimorbidity was most pronounced for WC (OR: 2.07, 95% CI: 1.28\u0026ndash;3.34), followed by WHtR (OR: 2.04, 95% CI: 1.24\u0026ndash;3.35) and BMI (OR: 1.47, 95% CI: 1.06\u0026ndash;2.04) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNRI and IDI values were applied to compare the predictive abilities of BMI, WHtR and WC on risk of cardiometabolic multimorbidity. The NRI and IDI values showed that WC was advantageous over BMI and WHtR in predicting cardiometabolic multimorbidity among individulas with only one cardiometabolic disease at baseline. Adding BMI, WHtR and WC to model 1 improved patient classification by 26.43% (12.15\u0026ndash;40.70%), 25.22% (10.94\u0026ndash;39.59%) and 31.52% (17.33\u0026ndash;45.72%), respectively, and probabilitic difference of suffering cardiometabolic multimorbidity between cases and controls by 0.92% (0.42\u0026ndash;1.42%), 1.26% (0.68\u0026ndash;1.84%), and 1.87% (1.16\u0026ndash;2.59%), respectively. Likewise, in model 2, the NRI and IDI (95% CI) were 0.2261 (0.0813\u0026ndash;0.3708) and 0.006 (0.0017\u0026ndash;0.0102) for BMI, 0.1875 (0.0426\u0026ndash;0.3324) and 0.0089 (0.0039\u0026ndash;0.0138) for WHtR, 0.2893 (0.1454\u0026ndash;0.4333) and 0.014 (0.0078\u0026ndash;0.0202), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e "},{"header":"Discussion","content":" \u003cp\u003eThe present study investigated the prospective association of BMI, WHtR and WC with cardiometabolic multimorbidity in a nationally representative cohort. Our results showed that BMI, WHtR and WC were all independently associated with increased risk of cardiometabolic multimorbidity among the middle-aged and elderly Chinese population. Moreover, compared with BMI, WHtR and WC exhibited better predictive utilities in future cardiometabolic multimorbidity.\u003c/p\u003e \u003cp\u003eCardiometabolic multimorbidity, as a growing problem, poses a major challenge to health care systems throughout the world. Previous studies have suggested that cardiometabolic multimorbidity is much more harmful than a single cardiometabolic disease. For example, compared to the absence of any of the three cardiometabolic diseases, the hazard ratio (HR) for all-cause mortality was about twice in any one of these diseases, 4 times in any two of these diseases, and 7 times in the presence of all three diseases[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In view of the serious harm of cardiometabolic multimorbidity, early predictive indicators need to be discovered urgently. However, quite limited studies have investigated the associations of easy-to access anthropometric indicators, such as BMI, WHtR and WC, with risk of cardiometabolic multimorbidity. To the best of our knowledge, there are only two studies exploring the link between BMI and cardiometabolic multimorbidity[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The study by Kivim\u0026auml;ki et al. involving 16 longitudinal research databases and 120,813 subjects suggested that the risk of cardiometabolic multimorbidity increased as BMI increased[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This comprehensive analysis indicated that compared with healthy-weight individuals, overweight and obesity individuals (BMI\u0026thinsp;\u0026ge;\u0026thinsp;24) had twice the risk of developing cardiometabolic multimorbidity. The OR values between this study and ours are similar, indicating the predictive power of BMI. However, the Asian population has not been included in this study, and our study can be a supplement in this regard. Another study recruiting 8270 subjects showed that the hazard ratio for overweight/obesity was 1.19 times (95% CI: 1.00\u0026ndash;1.43) higher for developing cardiometabolic multimorbidity from 1 baseline cardiometabolic disease than individuals with healthy weight.\u003c/p\u003e \u003cp\u003eWC, as an abdominal obesity measurement indicator, has been supported as an obesity-related health risk indicator for both Western and Asian populations[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Previous studies have suggested that WC has greater predictive power for risk of cardiometabolic diseases than BMI[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Furthermore, a large amount of evidence has also supported that WHtR was more effective than BMI in predicting coronary heart disease, stroke and diabetes[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Remarkably, our study also found that WHtR and WC were independent predictors of cardiometabolic multimorbidity. Moreover, WHtR and WC were demonstrated to possess higher predictive abilities on risk of cardiometabolic multimorbidity than BMI in the current study. This phenomenon may be explained by the following reasons. First of all, BMI can only be used to measure the total body fat and cannot represent the body fat distribution[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The susceptibility of cardiometabolic diseases may depend on the difference of regional body fat distribution and the ability of subcutaneous adipose tissue[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Moreover, WC reflects body fat ratio more accurately than BMI, and it may play an important role in the early development of metabolic syndrome[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. A recent systematic review demonstrated that compared with BMI, WC increased the ability to discriminate adverse cardiometabolic risk outcomes by 3%[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In addition, WHtR has been suggested to be less affected by race, age and gender and be relatively more stable[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. People with the same BMI might have different risks of cardiometabolic diseases[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Even among people with normal BMI, those with high WHtR are more likely to suffer from cardiometabolic diseases. Notably, about 35% of men and 14% of women with high WHtR would be missed if screened by BMI only, which could bring serious consequences for cardiometabolic disease prevention[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Considering that WHtR and WC have better predictive power in cardiometabolic multimorbidity than BMI, screening by WHtR and WC might be applicable in future practice.\u003c/p\u003e \u003cp\u003eThe exact mechanisms underlying the association between BMI, WHtR, WC, and cardiometabolic multimorbidity remain to be illuminated, but insulin resistance and ectopic fat deposition may be the main contributors. Adipose tissue produces a large amount of bioactive mediators, which leads to insulin resistance. Insulin resistance may cause cardiometabolic diseases in the following ways. First, in a state of insulin resistance, inflammation occurs in the body, which eventually leads to atherosclerosis[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Second, insulin resistance affects the production of apolipoprotein A1 (apoA-Ⅰ) or the liver secretion of high-density lipoprotein (HDL), which could be a trigger for metabolic syndrome[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Third, insulin resistance would obstruct normal heart function through inhibiting metabolic pathways and over-stimulating growth factors[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In addition, ectopic fat deposition triggers a pathological metabolic response, increasing the risk of metabolic diseases[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Excess free fatty acids are produced outside the fat storage tissue, and transferred to ectopic sites, including the viscera, heart, and vasculature, ultimately leading to cardiometabolic diseases[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are several merits in this study. Our study is the first to investigate the association of WHtR and WC with risk of cardiometabolic multimorbidity. Moreover, WHtR and WC were shown to be advantageous in predicting cardiometabolic multimorbidity than BMI. In addition, the two-stage strategy to assess cardiometabolic multimorbidity could provide mutual validation and increase the reliability of the associations. Limitations should be noted as well. First, our research was based on the four-year follow-up data, preventing the assessments of a long-term association. Second, the cardiometabolic diseases included in this study were self-reported. However, self-reported cardiometabolic diseases have been proved to be highly reliable in large-scale epidemiological studies[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eIn the middle-aged and elderly Chinese population, WHtR, WC and BMI were found to be independent predictors of cardiometabolic multimorbidity. In addition, WHtR and WC exhibited better predictive power in future cardiometabolic multimorbidity than BMI. Our findings highlight the significance of screening and interventions of high-risk individuals through easy-to-access and cost-effective tools, such as WHtR and WC, for the prevention of cardiometabolic multimorbidity.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eWHtR, waist-to-height ratio; WC, waist circumference; BMI, body mass index; CHARLS, China Health and Retirement Longitudinal Study; ORs, odds ratios; CIs, confidence intervals; NRI, net reclassification index; IDI, integrated discrimination improvement.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCHARLS was approved by Biomedical Ethics Review Committee of Peking University, and all participants signed informed consents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used and analyzed in this study are publicly available from the China Health and Retirement Longitudinal Study (http://charls.pku.edu.cn/zh-CN).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\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 work was supported by National Natural Science Foundation of China (81703316, 81703322); Natural Science Foundation of Jiangsu Province (project number BK20170350) and A Project Funded by Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCK conceived and designed the research; YL and SL wrote the manuscript; and GL, YQ and YW performed the data analysis. All authors contributed to the interpretations of the findings. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis analysis uses data or information from the Harmonized CHARLS dataset and Codebook, Version C as of April 2018 developed by the Gateway to Global Aging Data. The development of the Harmonized CHARLS was funded by the National Institute on Ageing (R01 AG030153, RC2 AG036619, R03 AG043052). For more information, please refer to \u003ca href=\"http://www.g2aging.org.\"\u003ewww.g2aging.org.\u003c/a\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRalston J, Nugent R. Toward a broader response to cardiometabolic disease. Nature medicine. 2019;25(11):1644\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan S, Li X, Jin Y, Lu J. 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New International Diabetes Federation (IDF) and National Cholesterol Education Program Adult Treatment panel III (NCEP-ATPIII) criteria and the involvement of hemostasis and fibrinolysis in the metabolic syndrome. Journal of thrombosis haemostasis: JTH. 2006;4(5):1164\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Gaal LF, Mertens IL, De Block CE. Mechanisms linking obesity with cardiovascular disease. Nature. 2006;444(7121):875\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBritton KA, Fox CS. Ectopic fat depots and cardiovascular disease. Circulation. 2011;124(24):e837-41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarr EL, Tonkin AM, Welborn TA, Shaw JE. Validity of self-reported cardiovascular disease events in comparison to medical record adjudication and a statewide hospital morbidity database: the AusDiab study. Intern Med J. 2009;39(1):49\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"waist-to-height ratio, waist circumference, body mass index, cardiometabolic multimorbidity, CHARLS","lastPublishedDoi":"10.21203/rs.3.rs-137388/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-137388/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Cardiometabolic multimorbidity is an increasing public health burden. This study aimed to evaluate the association of waist-to-height ratio (WHtR), waist circumference (WC) and body mass index (BMI) with risk of cardiometabolic multimorbidity.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We used data from the China Health and Retirement Longitudinal Study (CHARLS). 10521 participants aged 45 and over were recruited, including 8807 individuals with 0 cardiometabolic disease at baseline (stage Ⅰ) and 1714 individuals with 1 cardiometabolic disease at baseline (stage Ⅱ). Logistic regression was conducted to estimate the odds ratios (ORs) and confidence intervals (CIs). Net reclassification index (NRI) and integrated discrimination improvement (IDI) were used to evaluate the incremental predictive value beyond conventional factors.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIn stage Ⅰ,\u003cstrong\u003e \u003c/strong\u003eincreased risk of cardiometabolic multimorbidity was observed among participants with WHtR ≥ 0.5 (OR: 1.76, 95% CI: 1.05–2.97), WC ≥ 85 (men) + WC ≥ 75 (women) (OR: 1.77, 95% CI: 1.05–2.97) or BMI ≥ 24 (OR: 1.48, 95% CI: 0.98–2.24). Furthermore, both NRI and IDI of WHtR and WC were higher than those of BMI. In stage Ⅱ, the adjusted ORs (95% CIs) of WHtR ≥ 0.5, WC ≥ 85 (men) + WC ≥ 75 (women), and BMI ≥ 24 were 2.04 (1.24–3.35), 2.07 (1.28–3.34), and 1.47 (1.06–2.04), respectively. In addition, WC exhibited the highest NRI and IDI.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eWHtR, WC and BMI are independent predictors of cardiometabolic multimorbidity in middle-aged and elderly Chinese population. WHtR and WC show better abilities in predicting cardiometabolic multimorbidity than BMI.\u003c/p\u003e","manuscriptTitle":"Waist-to-Height Ratio, Waist Circumference, Body Mass Index and Risk of Cardiometabolic Multimorbidity: A National Longitudinal Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-12-30 23:57:59","doi":"10.21203/rs.3.rs-137388/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e154c0e7-816c-4f07-9222-592a6958c723","owner":[],"postedDate":"December 30th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":1665764,"name":"Cardiac \u0026 Cardiovascular Systems"}],"tags":[],"updatedAt":"2020-12-30T23:58:01+00:00","versionOfRecord":[],"versionCreatedAt":"2020-12-30 23:57:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-137388","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-137388","identity":"rs-137388","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}