Predictive Values of Anthropometric Measurements for Cardiometabolic Risk Factors and Cardiovascular Diseases Among 44 048 Chinese.

Background The predictive value of adiposity indices and the newly developed index for cardiometabolic risk factors and cardiovascular diseases (CVDs) remains unclear in the Chinese population. This study aimed to compare the predictive value of A Body Shape Index with other 5 conventional obesity-related anthropometric indices (body mass index, waist circumference, hip circumference, waist-to-hip ratio, waist-to-height ratio) in Chinese population. Methods and Results A total of 44 048 participants in the study were derived from the baseline data of the PURE-China (Prospective Urban and Rural Epidemiology) study in China. All participants' anthropometric parameters, CVDs, and risk factors (dyslipidemia, abnormal blood pressure, and hyperglycemia) were collected by standard procedures. Multivariable logistic regression models and receiver operator characteristic curve analysis were used to evaluate the predictive values of obesity-related anthropometric indices to the cardiometabolic risk factors and CVDs. A positive association was observed between each anthropometric index and cardiometabolic risk factors and CVDs in all models (P<0.001). Compared with other anthropometric indices (body mass index, waist circumference, hip circumference, waist-to-hip ratio, and A Body Shape Index), waist-to-height ratio had significantly higher areas under the curve (AUCs) for predicting dyslipidemia (AUCs: 0.646, sensitivity: 65%, specificity: 44%), hyperglycemia (AUCs: 0.595, sensitivity: 60%, specificity: 45%), and CVDs (AUCs: 0.619, sensitivity: 59%, specificity: 41%). Waist circumference showed the best prediction for abnormal blood pressure (AUCs: 0.671, sensitivity: 66%, specificity: 40%) compared with other anthropometric indices. However, the new body shape index did not show a better prediction to either cardiometabolic risk factors or CVDs than that of any other traditional obesity-related indices. Conclusions Waist-to-height ratio appeared to be the best indicator for dyslipidemia, hyperglycemia, and CVDs, while waist circumference had a better prediction for abnormal blood pressure.

Clinical Perspective

What Is New?

We compared different anthropometric measurements of both traditional and a newly built index in a large Chinese population.

We found that waist‐to‐height ratio outperformed body mass index, waist circumference, hip circumference, waist‐to‐hip ratio, and A Body Shape Index in predicting the presence of cardiovascular diseases and most cardiometabolic risk factors.

The exception was that waist circumference was the best predictor of abnormal blood pressure.

What Are the Clinical Implications?

Waist‐to‐height ratio may be a more convenient and effective primary index to predict cardiovascular diseases and its risk factors.

A growing body of evidence supports that avoidance of abdominal obesity is the prioritized primary prevention strategies for cardiovascular diseases, along with control of other major cardiometabolic risk factors.

Introduction

Worldwide, obesity is one of the leading risk factors for cardiovascular diseases (CVDs) driven by the elevated level of cardiometabolic risk factors that are highly influenced by increased adiposity.1, 2, 3, 4, 5 In 2015, ≈107 million children and 603 million adults were obese, with a global prevalence of 5% and 12%, respectively. The World Health Organization (WHO) reported that obesity (ie, body mass index [BMI] ≥30 kg/m2) was linked to 4 million excess deaths worldwide and the loss of 120 million disability‐adjusted life‐years, equivalent to 7.1% of all causes of death and 4.9% of all adults’ disability‐adjusted life‐years.6, 7 In China, the prevalence of overweight and obesity was 30.1% and 11.9%, respectively, in 2002, and increased to 32.0% and 67.6%, respectively, in 2012.8 In every 100,000 people, it was estimated that about 49 men and 34 women died from obesity.7

BMI ≥30 kg/m2 is a widely used indictor for defining general obesity in a non‐Chinese population, and waist circumference (WC) >90 cm for men (80 cm for women) is a cutoff measurement of abdominal obesity recommended by the WHO.9, 10 Many epidemiological studies investigated the predictive value of BMI for cardiometabolic risk factors and cardiovascular events and consistently showed that BMI had a lower discriminatory power than WC and waist‐to‐height ratio (WHtR) to distinguish individuals with high muscle mass from those with excess fat or abdominal obesity.11, 12, 13, 14, 15 Two systematic reviews indicated that WHtR was a better predictor for cardiometabolic risk factors and CVDs than BMI and WC,16, 17 but the results had significant heterogeneities as they identified more than 30 studies covering 15 ethnic populations.

A Body Shape Index (ABSI) is a new comprehensive adiposity index combining BMI and WC, which was developed by Krakauer in 2012, showing a better predictive value for mortality and CVDs than that of BMI or WC in whites.18, 19, 20, 21 ABSI has also been adopted by several small Asian studies (Indonesia, Iran, and China) to investigate the association with hypertension, mortality, and metabolic syndrome,22, 23, 24 but none of these previous studies examined the association with CVDs and there is a lack of knowledge on the predictive value of ABSI for cardiovascular risk factors among the Chinese population. In addition, there has been no research comparing the discriminatory power between ABSI and other anthropometric measurements for cardiometabolic risk factors and CVD risk in a single large Chinese population.

This study aimed to compare the predictive value of a newly developed ABSI in a large Chinese population with other 5 conventional obesity‐related anthropometric indices (BMI, WC, hip circumference [HC], waist‐to‐hip ratio [WHR], and WHtR) for predicting cardiometabolic risk factors and CVDs. The optimal thresholds of these anthropometric indices were also evaluated.

Methods

The data, analytic methods, and study materials will not be made available to other researchers for purposes of reproducing the results or replicating the procedure.

Enrollment of Participants

Participants in this study were a subset of Chinese subjects who were enrolled from the baseline survey of the PURE (Prospective Urban and Rural Epidemiology) study during 2005–2009. PURE is a large prospective cohort of 153 996 individuals enrolled from 628 urban and rural communities in 17 low‐, middle‐, and high‐income countries including China.25, 26, 27

Being a major partnership of PURE, PURE‐China recruited 46 285 subjects (aged 35–70 years) from 115 communities (70 urban and 45 rural) in 12 provinces in China, representing various levels of development and encompassing a large sociocultural diversity.28, 29 The PURE‐China cohort study was approved by an institutional review committee and all participants gave informed consent. For the current analyses, we excluded participants without anthropometric indices (n=715; 1.5%) or blood sample data (n=1522; 3.3%). Thus, 44 048 participants were included in this study.

Data Collection

Trained research staff interviewed each participant using standardized questionnaires and collected information on demographic data, lifestyle behaviors (such as physical activity level and energy intake), and medical history.30 Physical activity was obtained using the International Physical Activity Questionnaire and evaluated in metabolic equivalents.31 Daily energy intake was assessed using a validated Food Frequency Questionnaire.32, 33

Anthropometric Measurements

Anthropometric measurements were collected by trained staff following standard procedures. Height was measured to the nearest 0.1 cm without shoes using a standard stadiometer. Weight was measured in subjects wearing light indoor clothing to the nearest 0.1 kg using a rigid measurement device. WC was measured to the nearest 0.1 cm at the midpoint between the lowest rib margin and the level of the anterior superior iliac crest by a flexible anthropometric tape. HC was measured to the nearest 0.1 cm at the greatest protrusion of the gluteal muscles. WHR and WHtR were calculated as WC/HC and WC/height, respectively. ABSI was calculated using the following formula: .18 Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured 2 times after resting for 5 minutes in the sitting position using an OMRON blood pressure (BP) monitor. If there was a difference >5 mm Hg, measurements were repeated.

Definitions of Outcomes

Dyslipidemia, abnormal BP, and hyperglycemia were considered cardiometabolic risk factors. Dyslipidemia was defined by criteria of the 2016 Chinese guidelines for the management of dyslipidemia in adults.34 Dyslipidemia was defined if any of the following blood lipid measurements were abnormal: total cholesterol ≥5.2 mmol/L; low‐density lipoprotein cholesterol ≥3.4 mmol/L; high‐density lipoprotein cholesterol ≤1.0 mmol/L; or triglycerides ≥1.7 mmol/L. Abnormal BP was defined as self‐reported hypertension or SBP ≥120 mm Hg and/or DBP ≥80 mm Hg.35 Participants were regarded as being hyperglycemic if they had self‐reported diabetes or a recent laboratory result of fasting blood glucose level ≥5.6 mmol/L.36 Self‐reported physician‐diagnosed CVDs included coronary heart disease, heart failure, and other CVDs (arrhythmia, valvular heart disease, cardiomyopathy, and myocarditis).

Statistical Analyses

Continuous data were reported as means and SDs and categorical data as frequencies with proportions. Means and proportions were compared using 2‐sided t tests and χ2, respectively. Three different logistic regression models were used to evaluate standardized (per 1‐SD increase) associations of obesity‐related anthropometric indices with cardiometabolic risk factors and CVDs. Odds ratios (ORs) were standardized by using transformed observations [(observation−mean)/SD] in the models. Potential confounding factors to be adjusted in the multiple regression models were sociodemographic variables (age, sex, regions, location, and education levels), lifestyle characteristics (physical activity and daily energy intake), self‐reported disease history, and family history of chronic diseases. Missing adjusted factors were imputed by maximum frequency for statistical analysis. We also performed sensitivity analysis for original data (Tables S1 through S5) and found that the results remained consistent.

For each index, receiver operator characteristic (ROC) curve analysis was used to identify the value with the maximum sum of sensitivity and specificity as a predictor of cardiometabolic risk factors and CVDs. The optimal cut point for each measure of adiposity in detecting cardiometabolic risk factors was chosen as the point on the curve with the highest Youden Index (sensitivity+specificity−1). Furthermore, we performed 2 subgroup analyses based on sex and age group. All reported P values were 2‐sided and <0.05 was considered significant. All analyses were performed in SAS version 9.4 (SAS Institute Inc).

Results

Demographic data of 44 048 eligible participants included in the current study are presented in Table 1. There was a statistically significant difference between participants with and without CVDs with respect to age, sex, living area, education, lifestyle characteristics, and family disease history. People with CVDs had significantly higher anthropometric indices (P<0.001) and laboratory indicators than those without CVDs.

Table 1

Participants Sociodemographic and Lifestyle Characteristics

Characteristica	Total Sample (N=44 048)	CVDsb (n=2755)	No CVDs (n=41 293)	P Valuec
Age	51.2±9.8	51.3±8.8	50.8±9.7	<0.001
Sex (Male)	18 139 (41.2)	954 (34.6)	17 185 (41.6)	<0.001
Education				<0.001
Low (none, primary, or unknown)	14 941 (34.0)	1129 (41.1)	13 812 (33.5)
Middle (secondary, high, higher secondary school)	25 142 (57.2)	1342 (48.9)	23 800 (57.8)
High (trade, college, or university)	3857 (8.78)	276 (10.0)	3581 (8.7)
Regions				<0.001
Eastern	23 313 (52.9)	1372 (49.8)	21 941 (53.1)
Middle	10 014 (22.7)	598 (21.7)	9416 (22.8)
Western	10 721 (24.3)	785 (28.5)	9936 (24.1)
Current smokingd	9806 (22.5)	420 (15.4)	9386 (23.0)	<0.001
Current drinkinge	9258 (21.1)	402 (14.7)	8856 (21.6)	<0.001
Physical activity (MET score level)				0.0182
Low (<600 MET min/wk)	5354 (12.7)	290 (11.1)	5064 (12.8)
Middle (600–3000 MET min/wk)	5354 (43.7)	1189 (45.5)	17 217 (43.6)
High (>3000 MET min/wk)	5354 (43.5)	1132 (43.4)	17 197 (43.6)
SBP, mm Hg	133.5±22.4	140.3±23.4	133.1±22.3	<0.001
DBP, mm Hg	82.8±13.2	85.3±12.6	82.6±13.3	<0.001
Glucose, mmol/L	5.6±1.6	5.9±2.1	5.56±1.5	<0.001
Cholesterol, mmol/L	4.7±1.0	4.9±1.1	4.67±1.0	<0.001
Triglyceride, mmol/L	1.6±1.2	1.8±1.3	1.6±1.2	<0.001
HDL‐C, mmol/L	1.4±0.4	1.4±0.5	1.4±0.3	<0.001
LDL‐C, mmol/L	2.6±0.8	2.8±0.9	2.6±0.8	<0.001
Weight, kg	63.8±12.1	65.4±12.7	63.7±12.1	<0.001
Height, m	160.8±8.2	159.8±8.3	160.9±8.3	<0.001
BMI, kg/m2	24.6±4.0	25.6±4.3	24.6±4.0	<0.001
WC, cm	81.1±10.5	84.6±10.9	80.9±10.5	<0.001
HC, cm	94.3±8.1	96.7±8.5	94.1±8.0	<0.001
WHR	0.86±0.07	0.87±0.08	0.86±0.07	<0.001
WHtR	0.50±0.06	0.53±0.07	0.50±0.06	<0.001
ABSI, m7/6/kg2/3	0.08±0.01	0.08±0.01	0.08±0.01	0.0657
Dyslipidemiaf	22 959 (52.12)	1785 (64.8)	21 174 (51.3)	<0.001
Abnormal BPg	33 278 (75.55)	2345 (85.1)	30 933 (74.9)	<0.001
Hyperglycemiah	16 977 (38.54)	1294 (47.0)	15 683 (38.0)	<0.001

ABSI indicates A Body Shape Index; BMI, body mass index; HC, hip circumference; MET, metabolic equivalent; WC, waist circumference; WHR, waist‐to‐hip ratio; WHtR, waist‐to‐height ratio.

Data are presented as mean±SD or number (percentage). The missing values are as follows: education: 108; smoking status: 464; drinking status: 255; and physical activity level: 1959.

Coronary heart disease or heart failure or other cardiovascular diseases (CVDs; arrhythmia, valvular heart disease, cardiomyopathy, and myocarditis).

CVDs vs no CVDs.

Current smoking: consuming at least 1 tobacco product per day.

Current drinking: consuming alcohol at least once a month.

Dyslipidemia: total cholesterol ≥5.2 mmol/L or low‐density lipoprotein cholesterol (LDL‐C) ≥3.4 mmol/L or high‐density lipoprotein cholesterol (HDL‐C) ≤1.0 mmol/L or triglycerides ≥1.7 mmol/L.

Abnormal blood pressure (BP): self‐reported hypertension or systolic BP (SBP) ≥120 mm Hg and/or diastolic BP (DBP) ≥80 mm Hg.

Hyperglycemia: self‐reported diabetes or fasting blood glucose level ≥5.6 mmol/L.

The associations between anthropometric measurements and cardiometabolic risk factors and CVDs are illustrated in Table 2. Higher levels of adiposity were associated with greater risks of cardiometabolic risk factors and CVDs in both the univariable and multivariable models (P<0.001). The standardized adjusted ORs for dyslipidemia, abnormal BP, hyperglycemia, and CVDs ranged from 1.284 to 1.674, from 1.150 to 1.879, from 1.064 to 1.343, and from 1.176 to 1.353, respectively. WHtR showed a higher OR than BMI, WC, HC, WHR, and ABSI for all cardiometabolic risk factors and CVDs.

Table 2

Crude and Adjusted ORs (Per 1‐SD Increase) for Cardiometabolic Risk Factors and CVDs in Relation to Anthropometric Indices (N=44 048)

Outcomes	Model 1 OR (95% CI)a	Model 2 OR (95% CI)b	Model 3 OR (95% CI)c
Dyslipidemiad
BMI	1.556 (1.522–1.591)	1.528 (1.494–1.563)	1.524 (1.490–1.559)
WC	1.671 (1.637–1.705)	1.651 (1.616–1.687)	1.647 (1.612–1.683)
HC	1.454 (1.425–1.483)	1.434 (1.404–1.464)	1.431 (1.401–1.461)
WHR	1.513 (1.482–1.546)	1.551 (1.516–1.588)	1.547 (1.511–1.583)
WHtR	1.699 (1.665–1.735)	1.679 (1.643–1.716)	1.674 (1.638–1.711)
ABSI	1.333 (1.307–1.360)	1.285 (1.259–1.313)	1.284 (1.257–1.312)
Abnormal BPe
BMI	1.842 (1.791–1.894)	1.738 (1.693–1.785)	1.734 (1.689–1.780)
WC	1.893 (1.846–1.940)	1.810 (1.762–1.860)	1.806 (1.758–1.855)
HC	1.523 (1.488–1.559)	1.553 (1.514–1.593)	1.547 (1.508–1.587)
WHR	1.721 (1.678–1.765)	1.552 (1.510–1.596)	1.551 (1.509–1.595)
WHtR	1.842 (1.797–1.888)	1.886 (1.832–1.943)	1.879 (1.824–1.935)
ABSI	1.340 (1.310–1.371)	1.150 (1.122–1.178)	1.150 (1.122–1.178)
Hyperglycemiaf
BMI	1.321 (1.293–1.348)	1.321 (1.293–1.350)	1.320 (1.292–1.348)
WC	1.356 (1.330–1.383)	1.335 (1.308–1.364)	1.333 (1.306–1.362)
HC	1.206 (1.183–1.230)	1.211 (1.186–1.237)	1.209 (1.184–1.235)
WHR	1.317 (1.290–1.344)	1.277 (1.250–1.306)	1.276 (1.248–1.304)
WHtR	1.384 (1.357–1.412)	1.346 (1.318–1.374)	1.343 (1.315–1.371)
ABSI	1.136 (1.114–1.158)	1.066 (1.044–1.088)	1.064 (1.042–1.087)
CVDs
BMI	1.215 (1.178–1.253)	1.185 (1.148–1.225)	1.182 (1.143–1.221)
WC	1.412 (1.360–1.467)	1.315 (1.265–1.367)	1.307 (1.256–1.359)
HC	1.369 (1.317–1.422)	1.323 (1.271–1.377)	1.313 (1.261–1.367)
WHR	1.204 (1.164–1.245)	1.179 (1.137–1.223)	1.176 (1.134–1.220)
WHtR	1.495 (1.441–1.552)	1.362 (1.308–1.418)	1.353 (1.299–1.409)
ABSI	1.329 (1.280–1.380)	1.182 (1.135–1.230)	1.176 (1.130–1.224)

ABSI indicates A Body Shape Index; BMI, body mass index; HC, hip circumference; OR, odds ratio; WC, waist circumference; WHR, waist‐to‐hip ratio; WHtR, waist‐to‐height ratio.

Model 1: crude model.

Model 2: adjusted for sociodemographic characteristics (age, sex, regions, location, and education levels).

Model 3: adjusted for sociodemographic (age, sex, regions, location, and education levels), lifestyle (physical activity and daily energy intake), and medical history characteristics (family history of coronary heart disease or heart failure or other cardiovascular diseases [CVDs]).

Dyslipidemia: total cholesterol ≥5.2 mmol/L or low‐density lipoprotein cholesterol ≥3.4 mmol/L or high‐density lipoprotein cholesterol ≤1.0 mmol/L or triglycerides ≥1.7 mmol/L.

Abnormal blood pressure (BP): self‐reported hypertension or systolic BP ≥120 mm Hg and/or diastolic BP ≥80 mm Hg.

Hyperglycemia: self‐reported diabetes or fasting blood glucose level ≥5.6 mmol/L.

Table 3 presents the AUCs (95% CIs) and optimal cut points for anthropometric measurements in relation to cardiometabolic risk factors and CVDs. The discriminatory ability of the anthropometric indices in identifying cardiometabolic risk factors and CVDs was not high. Compared with other cardiometabolic parameters, WHtR tended to be the best predictor for dyslipidemia (AUCs: 0.646, sensitivity 65%, specificity 44%, cutoff point: 0.49), hyperglycemia (AUCs: 0.595, sensitivity 60%, specificity 45%, cutoff point: 0.50), and CVDs (AUCs: 0.619, sensitivity 59%, specificity 41%, cutoff point: 0.52). WC showed the best prediction for abnormal BP (AUCs: 0.671, sensitivity: 66%, specificity: 40%, cutoff point: 78.20 cm). In this study, because of its higher specificity (from 39% to 57%) than other indices, the ABSI did not show better prediction (AUCs: from 0.540 to 0.597, cutoff point: from 0.75 to 0.77 m7/6/kg2/3), even if its sensitivity was similar to that of the other indices (from 53% to 64%).

Table 3

AUCs and Optimal Cut Points for Anthropometric Measurements in Relation to Cardiometabolic Risk Factors and CVDs (N=44 048)

Outcomes	AUC (95% CI)a	Cutoff Point	Sensitivity	Specificity	Youden Index
Dyslipidemiab
BMI	0.627 (0.622–0.633)	23.55	0.68	0.49	0.19
WC	0.642 (0.636–0.647)	79.50	0.65	0.44	0.21
HC	0.607 (0.602–0.612)	94.35	0.56	0.41	0.16
WHR	0.618 (0.612–0.623)	0.85	0.62	0.45	0.18
WHtR	0.646 (0.641–0.651)	0.49	0.65	0.44	0.22
ABSI	0.589 (0.583–0.594)	0.75	0.63	0.49	0.14
Abnormal BPc
BMI	0.653 (0.647–0.659)	23.58	0.64	0.41	0.23
WC	0.671 (0.665–0.677)	78.20	0.66	0.40	0.26
HC	0.624 (0.618–0.629)	94.60	0.52	0.33	0.19
WHR	0.649 (0.643–0.656)	0.84	0.67	0.44	0.23
WHtR	0.664 (0.658–0.669)	0.50	0.60	0.36	0.24
ABSI	0.597 (0.591–0.603)	0.75	0.59	0.44	0.15
Hyperglycemiad
BMI	0.591 (0.586–0.596)	23.87	0.64	0.50	0.14
WC	0.588 (0.582–0.593)	80.40	0.60	0.46	0.13
HC	0.555 (0.549–0.560)	92.05	0.66	0.58	0.08
WHR	0.582 (0.577–0.588)	0.90	0.35	0.22	0.12
WHtR	0.595 (0.590–0.600)	0.50	0.60	0.45	0.15
ABSI	0.540 (0.535–0.546)	0.75	0.64	0.57	0.07
CVDs
BMI	0.576 (0.565–0.587)	25.56	0.47	0.36	0.11
WC	0.601 (0.590–0.612)	82.50	0.59	0.43	0.15
HC	0.590 (0.579–0.602)	95.05	0.58	0.44	0.13
WHR	0.570 (0.559–0.581)	0.87	0.57	0.47	0.10
WHtR	0.619 (0.608–0.630)	0.52	0.59	0.41	0.18
ABSI	0.591 (0.580–0.602)	0.77	0.53	0.39	0.14

ABSI indicates A Body Shape Index; AUC, area under the receiver operating characteristic curve; BMI, body mass index; CVDs, coronary heart disease or heart failure or other cardiovascular diseases (arrhythmia, valvular heart disease, cardiomyopathy, and myocarditis); HC, hip circumference; WC, waist circumference; WHR, waist‐to‐hip ratio; WHtR, waist‐to‐height ratio.

Models adjusted for sociodemographic (age, sex, regions, location, and education levels), lifestyle (physical activity and daily energy intake), and medical history characteristics (family history of coronary heart disease or heart failure or other CVDs).

Dyslipidemia: total cholesterol ≥5.2 mmol/L or low‐density lipoprotein cholesterol ≥3.4 mmol/L or high‐density lipoprotein cholesterol ≤1.0 mmol/L or triglycerides ≥1.7 mmol/L.

Abnormal blood pressure (BP): self‐reported hypertension or systolic BP ≥120 mm Hg and/or diastolic BP ≥80 mm Hg.

Hyperglycemia: self‐reported diabetes or fasting blood glucose level ≥5.6 mmol/L.

Table 4 compares the standardized ORs for each anthropometric measurement stratified by sex and age groups. For each index, the standardized OR for men was higher than for women. WHtR still showed a higher standardized OR than that of BMI, WC, HC, WHR, and ABSI for each cardiometabolic risk factor and CVDs for both men and women. For each anthropometric index, the ORs for middle‐aged people were higher than for the elderly except for HC in the association with abnormal BP. WHtR showed the highest OR for each cardiometabolic risk factor and CVDs in both middle‐aged and elderly people.

Table 4

Adjusted ORs (Per 1‐SD Increase) for Cardiometabolic Risk Factors in Relation to Anthropometric Measurements, Stratified by Sex and Age Groups (N=44 048)

Outcomes	Sexa		Age Groupsa
	Male (n=18 139)	Female (n=25 909)	35≤ Age <65 y (n=39 632)	Age ≥65 y (n=4416)
Dyslipidemiab
BMI	1.723 (1.659–1.790)	1.374 (1.335–1.413)	1.558 (1.522–1.596)	1.321 (1.232–1.416)
WC	1.836 (1.771–1.903)	1.480 (1.441–1.521)	1.714 (1.675–1.753)	1.411 (1.323–1.505)
HC	1.593 (1.539–1.650)	1.307 (1.273–1.343)	1.453 (1.421–1.486)	1.348 (1.261–1.441)
WHR	1.690 (1.626–1.756)	1.392 (1.351–1.433)	1.603 (1.564–1.642)	1.335 (1.245–1.432)
WHtR	1.839 (1.775–1.904)	1.504 (1.462–1.548)	1.729 (1.690–1.770)	1.451 (1.358–1.552)
ABSI	1.361 (1.314–1.410)	1.192 (1.160–1.224)	1.338 (1.309–1.369)	1.176 (1.106–1.250)
Abnormal BPc
BMI	1.837 (1.756–1.923)	1.635 (1.582–1.690)	1.829 (1.781–1.879)	1.819 (1.628–2.033)
WC	1.863 (1.782–1.947)	1.721 (1.662–1.782)	1.870 (1.819–1.922)	1.805 (1.611–2.022)
HC	1.617 (1.549–1.688)	1.477 (1.430–1.525)	1.560 (1.520–1.601)	1.689 (1.508–1.892)
WHR	1.621 (1.547–1.699)	1.463 (1.413–1.515)	1.630 (1.585–1.676)	1.480 (1.319–1.661)
WHtR	2.047 (1.945–2.154)	1.758 (1.695–1.823)	2.296 (2.010–2.622)	1.892 (1.836–1.949)
ABSI	1.179 (1.131–1.230)	1.101 (1.068–1.134)	1.235 (1.205–1.265)	1.014 (0.919–1.118)
Hyperglycemiad
BMI	1.382 (1.336–1.430)	1.253 (1.220–1.287)	1.336 (1.306–1.366)	1.206 (1.136–1.281)
WC	1.350 (1.306–1.395)	1.287 (1.253–1.322)	1.388 (1.358–1.420)	1.194 (1.122–1.271)
HC	1.255 (1.213–1.297)	1.161 (1.131–1.193)	1.235 (1.208–1.262)	1.107 (1.040–1.178)
WHR	1.265 (1.222–1.310)	1.248 (1.213–1.284)	1.328 (1.298–1.360)	1.201 (1.125–1.283)
WHtR	1.399 (1.350–1.450)	1.288 (1.253–1.325)	1.419 (1.388–1.450)	1.247 (1.168–1.331)
ABSI	1.050 (1.015–1.087)	1.045 (1.018–1.073)	1.137 (1.112–1.163)	0.978 (0.923–1.035)
CVDs
BMI	1.237 (1.164–1.315)	1.150 (1.105–1.196)	1.206 (1.115–1.305)	1.189 (1.148–1.232)
WC	1.416 (1.317–1.523)	1.247 (1.189–1.308)	1.436 (1.375–1.500)	1.224 (1.124–1.332)
HC	1.371 (1.275–1.474)	1.274 (1.213–1.338)	1.342 (1.283–1.404)	1.264 (1.157–1.382)
WHR	1.246 (1.171–1.326)	1.128 (1.077–1.182)	1.239 (1.193–1.288)	1.107 (1.008–1.214)
WHtR	1.441 (1.343–1.546)	1.292 (1.229–1.359)	1.451 (1.387–1.517)	1.297 (1.187–1.417)
ABSI	1.254 (1.166–1.349)	1.131 (1.077–1.187)	1.330 (1.271–1.391)	1.055 (0.971–1.147)

ABSI indicates A Body Shape Index; BMI, body mass index; HC, hip circumference; OR, odds ratio; WC, waist circumference; WHR, waist‐to‐hip ratio; WHtR, waist‐to‐height ratio.

Models adjusted for sociodemographic (age, sex, regions, location, and education levels), lifestyle (physical activity and daily energy intake), and medical history characteristics (family history of coronary heart disease or heart failure or other cardiovascular diseases [CVDs]).

Dyslipidemia: total cholesterol ≥5.2 mmol/L or low‐density lipoprotein cholesterol ≥3.4 mmol/L or high‐density lipoprotein cholesterol ≤1.0 mmol/L or triglycerides ≥1.7 mmol/L.

Abnormal blood pressure (BP): self‐reported hypertension or systolic BP ≥120 mm Hg and/or diastolic BP ≥80 mm Hg.

Hyperglycemia: self‐reported diabetes or fasting blood glucose level ≥5.6 mmol/L.

AUCs for men and women and cutoff points for each parameter are demonstrated in Table 5. For men, WHtR is likely the best predictor for dyslipidemia (AUCs: 0.636, sensitivity: 70%, specificity: 45%, cutoff point: 0.49) and CVDs (AUCs: 0.622, sensitivity: 58%, specificity: 39%, cutoff point: 0.52) and BMI for abnormal BP (AUCs: 0.656, sensitivity: 61%, specificity: 38%, cutoff point: 23.71 kg/m2) and hyperglycemia (AUCs: 0.595, sensitivity: 59%, specificity: 44%, cutoff point: 24.28 kg/m2). However, in women, WC was superior for dyslipidemia (AUCs: 0.669, sensitivity: 63%, specificity: 43%, cutoff point:78.0 cm) and WHtR for abnormal BP (AUCs: 0.674, sensitivity: 62%, specificity: 36%, cutoff point: 0.50), hyperglycemia (AUCs: 0.601, sensitivity: 61%, specificity: 46%, cutoff point: 0.50), and CVDs (AUCs: 0.614, sensitivity: 56%, specificity: 38%, cutoff point: 0.52).

Table 5

AUCs and Optimal Cut Points for Anthropometric Measurements in Relation to Cardiometabolic Risk Factors and CVDs, Stratified by Sex

Outcomes	Men (n=18 139)					Women (n=25 909)
	AUC (95% CI)a	Cutoff Point	Sensitivity	Specificity	Youden Index	AUC (95% CI)a	Cutoff Point	Sensitivity	Specificity	Youden Index
Dyslipidemiab
BMI	0.607 (0.600–0.614)	23.51	0.71	0.47	0.24	0.657 (0.649–0.665)	23.87	0.63	0.47	0.16
WC	0.628 (0.622–0.635)	82.45	0.68	0.43	0.25	0.669 (0.661–0.677)	78.0	0.63	0.43	0.20
HC	0.585 (0.579–0.592)	94.15	0.61	0.41	0.20	0.638 (0.630–0.646)	94.3	0.54	0.41	0.13
WHR	0.614 (0.607–0.621)	0.87	0.71	0.49	0.22	0.643 (0.63 5–0.651)	0.84	0.59	0.42	0.17
WHtR	0.636 (0.629–0.642)	0.49	0.70	0.45	0.25	0.662 (0.654–0.670)	0.51	0.54	0.34	0.20
ABSI	0.590 (0.583–0.597)	0.76	0.70	0.55	0.14	0.592 (0.583–0.600)	0.74	0.63	0.50	0.14
Abnormal BPc
BMI	0.656 (0.646–0.666)	23.71	0.61	0.38	0.24	0.655 (0.648–0.663)	23.64	0.64	0.41	0.23
WC	0.653 (0.643–0.663)	81.95	0.63	0.39	0.23	0.668 (0.661–0.675)	77.85	0.61	0.35	0.26
HC	0.630 (0.620–0.640)	93.15	0.61	0.42	0.19	0.619 (0.612–0.626)	94.60	0.51	0.33	0.18
WHR	0.623 (0.613–0.634)	0.88	0.63	0.43	0.20	0.646 (0.638–0.653)	0.83	0.62	0.40	0.23
WHtR	0.654 (0.644–0.664)	0.48	0.70	0.47	0.23	0.674 (0.667–0.681)	0.50	0.62	0.36	0.26
ABSI	0.570 (0.560–0.580)	0.76	0.65	0.54	0.11	0.595 (0.587–0.603)	0.75	0.54	0.39	0.15
Hyperglycemiad
BMI	0.595 (0.587–0.604)	24.28	0.59	0.44	0.15	0.589 (0.582–0.596)	23.87	0.64	0.50	0.14
WC	0.581 (0.573–0.590)	83.05	0.61	0.49	0.12	0.590 (0.583–0.597)	79.05	0.57	0.44	0.14
HC	0.557 (0.549–0.566)	92.05	0.68	0.59	0.09	0.552 (0.545–0.559)	91.25	0.69	0.62	0.07
WHR	0.579 (0.570–0.587)	0.90	0.47	0.32	0.14	0.585 (0.578–0.592)	0.87	0.40	0.27	0.13
WHtR	0.590 (0.581–0.598)	0.50	0.58	0.44	0.14	0.601 (0.593–0.608)	0.50	0.61	0.46	0.15
ABSI	0.522 (0.514–0.531)	0.76	0.63	0.58	0.04	0.548 (0.541–0.555)	0.75	0.57	0.49	0.08
CVDs
BMI	0.586 (0.568–0.604)	25.92	0.44	0.31	0.13	0.569 (0.555–0.584)	25.57	0.47	0.36	0.11
WC	0.620 (0.602–0.638)	88.05	0.51	0.33	0.18	0.608 (0.594–0.621)	82.25	0.52	0.36	0.16
HC	0.604 (0.586–0.622)	95.05	0.61	0.46	0.15	0.584 (0.571–0.598)	95.15	0.55	0.42	0.13
WHR	0.595 (0.590–0.600)	0.90	0.58	0.44	0.14	0.586 (0.572–0.600)	0.85	0.53	0.40	0.13
WHtR	0.622 (0.604–0.640)	0.52	0.58	0.39	0.19	0.614 (0.601–0.628)	0.52	0.56	0.38	0.18
ABSI	0.603 (0.585–0.622)	0.78	0.59	0.43	0.16	0.600 (0.587–0.614)	0.76	0.54	0.38	0.16

ABSI indicates A Body Shape Index; AUC, area under the receiver operating characteristic curve; BMI, body mass index; CVDs, coronary heart disease or heart failure or other cardiovascular diseases (arrhythmia, valvular heart disease, cardiomyopathy, and myocarditis); HC, hip circumference; WC, waist circumference; WHR, waist‐to‐hip ratio; WHtR, waist‐to‐height ratio.

Models adjusted for sociodemographic (age, sex, regions, location, and education levels), lifestyle (physical activity and daily energy intake), and medical history characteristics (family history of coronary heart disease or heart failure or other CVDs).

Dyslipidemia: total cholesterol ≥5.2 mmol/L or low‐density lipoprotein cholesterol ≥3.4 mmol/L or high‐density lipoprotein cholesterol ≤1.0 mmol/L or triglycerides ≥1.7 mmol/L.

Abnormal blood pressure (BP): self‐reported hypertension or systolic BP ≥120 mm Hg and/or diastolic BP ≥80 mm Hg.

Hyperglycemia: self‐reported diabetes or fasting blood glucose level ≥5.6 mmol/L.

Table 6 shows the predictive values for each parameter stratified by age group. WHtR had the highest predictive value in middle‐aged people for dyslipidemia (AUCs: 0.647, sensitivity 64%, specificity 43%, cutoff point: 0.49), hyperglycemia (AUCs: 0.594, sensitivity 58%, specificity 44%, cutoff point: 0.50), and CVDs (AUCs: 0.614, sensitivity 57%, specificity 39%, cutoff point: 0.52). As for the elderly, BMI was superior for abnormal BP (AUCs: 0.696, sensitivity 64%, specificity 41%, cutoff point: 23.60 kg/m2) and hyperglycemia (AUCs: 0.584, sensitivity 70%, specificity 56%, cutoff point: 23.22 kg/m2).

Table 6

AUCs and Optimal Cut Points for Anthropometric Measurements in Relation to Cardiometabolic Risk Factors and CVDs, Stratified by Age Groups

Outcomes	35≤ Age <65 y (n=39 632)					Age ≥65 y (n=4416)
	AUC (95% CI)a	Cutoff Point	Sensitivity	Specificity	Youden Index	AUC (95% CI)a	Cutoff Point	Sensitivity	Specificity	Youden Index
Dyslipidemiab
BMI	0.630 (0.624–0.635)	24.34	0.59	0.40	0.20	0.609 (0.592–0.626)	23.43	0.68	0.50	0.17
WC	0.646 (0.640–0.651)	79.50	0.65	0.43	0.22	0.590 (0.573–0.607)	77.70	0.76	0.61	0.15
HC	0.609 (0.603–0.614)	94.10	0.58	0.42	0.16	0.588 (0.571–0.605)	96.65	0.45	0.32	0.13
WHR	0.622 (0.617–0.628)	0.85	0.62	0.43	0.18	0.553 (0.537–0.571)	0.86	0.66	0.57	0.08
WHtR	0.647 (0.642–0.653)	0.49	0.64	0.43	0.22	0.617 (0.600–0.634)	0.51	0.68	0.50	0.18
ABSI	0.591 (0.585–0.596)	0.74	0.68	0.54	0.14	0.543 (0.525–0.560)	0.75	0.77	0.70	0.07
Abnormal BPc
BMI	0.653 (0.647–0.659)	23.60	0.64	0.41	0.23	0.696 (0.670–0.721)	23.60	0.64	0.41	0.23
WC	0.668 (0.662–0.674)	78.20	0.65	0.40	0.25	0.665 (0.638–0.693)	77.70	0.72	0.46	0.26
HC	0.622 (0.616–0.628)	94.20	0.54	0.36	0.19	0.654 (0.627–0.680)	93.0	0.62	0.39	0.23
WHR	0.647 (0.641–0.653)	0.84	0.66	0.43	0.23	0.608 (0.578–0.638)	0.83	0.78	0.57	0.21
WHtR	0.658 (0.652–0.664)	0.49	0.62	0.38	0.23	0.674 (0.647–0.702)	0.49	0.72	0.44	0.28
ABSI	0.590 (0.583–0.596)	0.75	0.61	0.47	0.14	0.546 (0.516–0.576)	0.78	0.52	0.42	0.10
Hyperglycemiad
BMI	0.592 (0.587–0.598)	23.87	0.64	0.50	0.14	0.584 (0.567–0.601)	23.22	0.70	0.56	0.15
WC	0.589 (0.583–0.595)	80.80	0.57	0.44	0.13	0.549 (0.532–0.566)	80.10	0.67	0.58	0.09
HC	0.556 (0.550–0.562)	92.05	0.66	0.58	0.08	0.532 (0.515–0.549)	90.05	0.76	0.70	0.07
WHR	0.583 (0.577–0.589)	0.90	0.34	0.21	0.12	0.545 (0.528–0.562)	0.87	0.60	0.52	0.08
WHtR	0.594 (0.589–0.600)	0.50	0.58	0.44	0.15	0.561 (0.544–0.578)	0.50	0.67	0.57	0.10
ABSI	0.539 (0.533–0.545)	0.75	0.62	0.56	0.07	0.515 (0.498–0.532)	0.76	0.63	0.56	0.07
CVDs
BMI	0.574 (0.561–0.587)	25.65	0.46	0.35	0.12	0.585 (0.562–0.609)	23.08	0.76	0.63	0.14
WC	0.597 (0.584–0.610)	82.05	0.59	0.44	0.15	0.578 (0.554–0.602)	85.60	0.52	0.34	0.13
HC	0.585 (0.572–0.597)	95.05	0.57	0.44	0.12	0.600 (0.576–0.623)	95.10	0.61	0.46	0.16
WHR	0.571 (0.558–0.583)	0.88	0.49	0.38	0.11	0.519 (0.494–0.543)	0.85	0.69	0.63	0.06
WHtR	0.614 (0.601–0.626)	0.52	0.57	0.39	0.18	0.580 (0.557–0.604)	0.52	0.63	0.50	0.13
ABSI	0.587 (0.575–0.599)	0.77	0.50	0.37	0.13	0.525 (0.500–0.549)	0.78	0.58	0.51	0.07

ABSI indicates A Body Shape Index; AUC, area under the receiver operating characteristic curve; BMI, body mass index; CVDs, coronary heart disease or heart failure or other cardiovascular diseases (arrhythmia, valvular heart disease, cardiomyopathy, and myocarditis); HC, hip circumference; WC, waist circumference; WHR, waist‐to‐hip ratio; WHtR, waist‐to‐height ratio.

Models adjusted for sociodemographic (age, sex, regions, location, and education levels), lifestyle (physical activity and daily energy intake), and medical history characteristics (family history of coronary heart disease or heart failure or other CVDs).

Dyslipidemia: total cholesterol ≥5.2 mmol/L or low‐density lipoprotein cholesterol ≥3.4 mmol/L or high‐density lipoprotein cholesterol ≤1.0 mmol/L or triglycerides ≥1.7 mmol/L.

Abnormal blood pressure (BP): self‐reported hypertension or systolic BP ≥120 mm Hg and/or diastolic BP ≥80 mm Hg.

Hyperglycemia: self‐reported diabetes or fasting blood glucose level ≥5.6 mmol/L.

Discussion

Using baseline data from a large prospective cohort study among Chinese participants, we found strong evidence for a positive association between obesity‐related anthropometric indices and cardiometabolic risk factors and CVDs, but these measures had only moderate discriminatory ability for predicting these cardiometabolic risk factors. The results of this study indicated that markers of central obesity measured by WHtR were more strongly related to dyslipidemia, hyperglycemia, and CVDs risk than BMI.

The strong associations shown in this study between obesity‐related indices and cardiometabolic risk factors and CVDs were consistent with previous studies from both the developed and developing countries.11, 12, 13, 15, 37, 38, 39 In this study, when calculating ORs that allow adjusting for further confounding factors, WHtR showed a slightly stronger association with all cardiometabolic risk factors and CVD risk than that of any other indices. Possibly, WHtR takes height into account, which is more sensitive to muscle mass distribution and has a better interpretation on the whole body shape. Men showed higher ORs than women in this study, which is similar to that found in previous studies21, 40 on the association between obesity‐related indices and cardiometabolic risk factors and CVDs. This could be partially explained by other modified risk factors (ie, smoking or drinking) and the diversity of fat and muscle distributions. We found that in middle‐aged people, anthropometric indices had stronger associations with cardiometabolic risk factors and CVDs. The gain of fat mass and loss of lean mass with increasing age may interpret the differences.41, 42

Two previous reviews of meta‐analyses combining 30 and 31 studies indicated that WHtR was a better predictor for cardiometabolic risk factors and CVD risk than BMI and WC.16, 17 Our results also indicate that WHtR may be the best index to predict dyslipidemia, hyperglycemia, and CVD risk compared with all other anthropometric parameters. This may be explained by the fact that WHtR does not misclassify muscular but low body fat individuals as BMI does. However, we found WC was a slightly better predictor for abnormal BP. WC is regarded as a visceral fat indicator and reflects both lean and fat mass, which has been shown to be more closely related to high BP than BMI. ABSI as a new body shape index has been used to predict CVD mortality in 4 European populations with HRs (95% CIs) per SD increase of 1.34 (1.26–1.44).20 In this study, a positive association was also observed between ABSI and all cardiometabolic risk factors and CVDs both in univariate and multivariable models (P<0.001). However, in this study, ABSI did show this superiority over any other traditional obesity‐related indices, which may be related to the distinct difference in body figures between Chinese and white people. Based on previous studies, the cutoff value of ≈0.5 for WHtR was considered properly for the Asian population.40, 43, 44 Our study suggests a similar cutoff value (range, 0.49–0.52).

Study Strengths

The main strengths of the present study are the large size of the study population and standardized measures and high quality of data. We measured and compared detailed information of different anthropometric measurements of both the traditional and new‐built indices in the same population, which presented a whole picture of the predictive values of all anthropometric parameters in a single Chinese population.

Study Limitations

Several limitations of the present study should be considered. First, our study used only baseline data from the PURE‐China cohort, and cause and effect may not be inferred from this study alone. Our results only assessed the predictive abilities of anthropometric indices on prevalence and did not directly predict the prospective risk of cardiovascular events. Therefore, the results of the current study should be verified by further follow‐up studies. In addition, we recognize that misclassification of disease diagnoses is a concern, as they were self‐report and not further verified by medical record or available laboratory tests. This misclassification, however, was likely to be nondifferential, which led to an underestimation of reported associations.

Conclusions

This study suggests that there is a positive association of obesity‐related anthropometric indices with cardiometabolic risk factors and CVDs. WHtR appeared to be a better indicator of dyslipidemia, hyperglycemia, and CVD risk than BMI, WC, HC, WHR, and ABSI. A growing body of evidence supports that avoidance of abdominal obesity is the prioritized primary prevention strategy for CVDs, along with control of other major CVD risk factors.

Appendix

PURE‐China Investigators (*Regional PI, **National PI)

China Coordination Center Beijing Office: Lisheng Liu**, Wei Li**, Bing Liu, Bo Hu, Chunming Chen, Guo Jin, Hongye Zhang, Hui Chen, Jian Bo, Jian Li, Juan Li, Jun Yang, Kean Wang, Li Zhang, Qing Deng, Ren Bing, Tao Chen, Tao Xu, Wei Wang, Wenhua Zhao, Xiaohong Chang, Xiaoru Cheng, Xinye He, Xixin Hou, Xingyu Wang, Xiulin Bai, Xiuwen Zhao, Xu Liu, Xuan Jia, Yang Wang, Yi Sun, Yi Zhai; Beijing Jishuitan Hospital: Dong Li*, Di Chen*, Hui Jin, Jiwen Tian, Yumin Ma; Center for Disease Control & Prevention of Beijing Shunyi District: Yindong Li*, Chao He, Kai You, Songjian Zhang; Hospital of Traditional Chinese Medicine of Beijing Shijingshan District: Xiuzhen Tian*, Xu Xu*, Jinling Di, Jianquan Wu, Mei Wang, Qiang Zhou; Center for Disease Control & Prevention of Bayannaoer, Inner Mongolia: Shiying Zhang*, Aiying Han, Minzhi Cao; Center for Disease Control & Prevention of Changzhou Wujin District, Jiangsu Province: Jianfang Wu*, Weiping Jiang*, Deren Qiang, Jing Qin, Shan Qian, Suyi Shi, Yihong Zhou; Jiangxinzhou community health service center of Nanjing, Jiangsu Province: Zhenzhen Qian*, Zhengrong Liu; Nanjing Jianye Hospital: Changlin Dong*, Ming Wan; Nanjing Xiaohang Hospital, Jiangsu Province: Jun Li*, Jinhua Tang; Institute of Geriatrics, Jiangsu Province: Jun Li*, Yongzhen Mo*, Rongwen Bian, Qinglin Lou; Center for Disease Control & Prevention of Nanchang County, Jiangxi Province: Rensheng Lei*, Lihua Hu, Shuwei Xiong, Yan Zhong; Qingshan lake community health service station of Nanchang: Ning Li*, Xincheng Tang*, Shuli Ye; The 242 Hospital of Shenyang, Liaoning Province: Yu Liu*, Chunyi Li, Yujin Li; Daxing District Health Center of Shenyang, Liaoning Province: Minfan Fu*, Qiuyang Wang, Xiaoli Fu; The Red Cross Hospital of Shenyang, Liaoning Province: Xiaojie Xing*, Baoxia Guo*, Huilian Feng, Lihui Xu; Center for Disease Control & Prevention of Xining, Qinghai Province: Yuqing Yang*, Haibin Ma, Ruiqi Wu, Yali Wang; Huizu Hospital of Xining, Qinghai Province: Xiaolan Ma*, Hongze Liu, Yurong Ma; West‐China Hospital of Sichuan University, Sichuan Province: Xiaoyang Liao*, Bo Yuan, Qian Zhao; Jianshe Road Health Center of Chengdu, Sichuan Province: Guofan Xu*, Hui He, Jiankang Liu, Xin Wang; Dayi caichang Town Health Center of Sichuan Province: Ming Chen*, Wenqing Deng*; Shandong Cardiovascular Research Institute, Shandong Province: Fanghong Lu*, Zhendong Liu*, Hua Zhang, Shangwen Sun, Shujian Wang, Yingkin Zhao, Yutao Diao; Jinan Qiluhuayuan Hospital, Shandong Province: Mei Wang*, Xuezheng Shi; Department of Public Health, Zhangqiu City, Shandong Province: Debin Ren*, Chuanrui Wei; Shanxi Cardiovascular Hospital, Shanxi Province: Liangqing Zhang*, Jufang Wang; Peoples Hospital of Jingle County, Shanxi Province: Lianghou Fan*, Guoqin Liu; Balingqiao Community Healthcare Center of Taiyuan, Shanxi Province: Yan Hou*, Cuiying Wu, Guilan Ma, Hua Wei, Junying Wang, Xiongfei Bao, Yue Tang; Hospital of Xidian University, Xi'an City, Shanxi Province: Tianlu Liu*, Yahong Zhi; Guanshan Hospital of Yanliang District, Shaanxi Province: Peng Zhang*, Ailing Wang, Huijuan Wang, Jianna Liu, Qinzhou Liu, Rong Wang; Center for Disease Control & Prevention of Hetian, Xinjiang Province: Jianguo Wu*, Aideer Aili*, Ayoufumiti Wula, Aibi Bula, Dongmei Yang, Qian Wen, Reshalaiti; Centers for Disease Control & Prevention of Yunnan Province: Yize Xiao*, Qingping Shi, Ying Shao; Damenglong Health Center of Xishuangbanna, Yunnan Province: Jing He*, Kehua Li, Wuba Bai, Jinkui Yang; Center for Disease Control & Prevention of Mengla County, Yunnan Province: Yunchun Jiang*, Huaxing Liu*, Shunyun Yang.

Sources of Funding

The PURE study in China is supported through a grant from the Population Health Research Institute, Hamilton, Ontario, Canada, and a grant from CAMS Innovation Fund for Medical Sciences (project number: 2016‐I2M‐2‐004).

Disclosures

None.

Table S1. Crude and Adjusted ORs (Per 1‐SD Increase) for Cardiometabolic Risk Factors and CVDs in Relation to Anthropometric Indices

Table S2. AUCs* and Optimal Cut Points for Anthropometric Measurements in Relation to Cardiometabolic Risk Factors and CVDs (N=40 543)

Table S3. Adjusted ORs (Per 1‐SD Increase) for Cardiometabolic Risk Factors in Relation to Anthropometric Measurements, Stratified by Sex and Age Groups (N=40 543)

Table S4. AUCs* and Optimal Cut Points for Anthropometric Measurements in Relation to Cardiometabolic Risk Factors and CVDs, Stratified by Sex

Table S5. AUCs* and Optimal Cut Points for Anthropometric Measurements in Relation to Cardiometabolic Risk Factors and CVDs, Stratified by Age Groups

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