key: cord-0018231-m6kbw88l
authors: Mu, Lin; Liu, Jiamin; Zhou, Guohai; Wu, Chaoqun; Chen, Bowang; Lu, Yuan; Lu, Jiapeng; Yan, Xiaofang; Zhu, Zhihong; Nasir, Khurram; Spatz, Erica S.; Krumholz, Harlan M.; Zheng, Xin
title: Obesity Prevalence and Risks Among Chinese Adults: Findings From the China PEACE Million Persons Project, 2014–2018
date: 2021-06-10
journal: Circ Cardiovasc Qual Outcomes
DOI: 10.1161/circoutcomes.120.007292
sha: 79ad88fd8521f7673628cb1cba26a7f917fb7135
doc_id: 18231
cord_uid: m6kbw88l

China has seen a burgeoning epidemic of obesity in recent decades, but few studies reported nationally on obesity across socio-demographic subgroups. We sought to assess the prevalence and socio-demographic associations of obesity nationwide. METHODS: We assessed the prevalence of overall obesity (body mass index ≥28 kg/m(2)) and abdominal obesity (waist circumference ≥85/90 cm for women/men) among 2.7 million community-dwelling adults aged 35 to 75 years in the China PEACE Million Persons Project from 2014 to 2018 and quantified the socio-demographic associations of obesity using multivariable mixed models. RESULTS: Age-standardized rates of overall and abdominal obesity were 14.4% (95% CI, 14.3%–14.4%) and 32.7% (32.6%–32.8%) in women and 16.0% (15.9%–16.1%) and 36.6% (36.5%–36.8%) in men. Obesity varied considerably across socio-demographic subgroups. Older women were at higher risk for obesity (eg, adjusted relative risk [95% CI] of women aged 65–75 versus 35–44 years: 1.29 [1.27–1.31] for overall obesity, 1.76 [1.74–1.77] for abdominal obesity) while older men were not. Higher education was associated with lower risk in women (eg, adjusted relative risk [95% CI] of those with college or university education versus below primary school: 0.47 [0.46–0.48] for overall obesity, 0.61 [0.60–0.62] for abdominal obesity) but higher risk in men (1.07 [1.05–1.10], 1.17 [1.16–1.19]). CONCLUSIONS: In China, over 1 in 7 individuals meet criteria for overall obesity, and 1 in 3 for abdominal obesity. Wide variation exists across socio-demographic subgroups. The associations of age and education with obesity are significant and differ by sex. Understanding obesity in contemporary China has broad domestic policy implications and provides a valuable international reference.

O besity is a global epidemic. More than 2 billion people are overweight or obese worldwide, and 62% of the population with obesity live in developing countries. 1 Over the past decades, Asia has seen substantial increases in obesity. 2 In 2014, China became home to the greatest number of obese women and men, totaling 90 million obese individuals and accounting for 14% of the world's obesity based on body mass index (BMI). 3 Furthermore, as central or visceral adiposity becomes increasingly recognized as a key cardiometabolic risk indicator beyond BMI, 4 research suggests that the prevalence of abdominal obesity in China reached 32% in 2013 to 2014. 5 As China's rising obesity epidemic occurs in the context of its rapid economic and demographic shifts, 6 understanding its current state of obesity helps inform health care policies in China and potentially other countries that undergo similar socioeconomic and epidemiological transitions. 7 In addition, while culprits of obesity at the individual level are known to include unhealthy dietary changes (eg, increased consumption of energy-dense food) and reduced physical activities (eg, associated with occupational shift), 8, 9 interventions at the population level require understanding the heterogeneity within 1.4-billion Chinese people and warrant large-scale research to assess obesity across broad socio-demographic subgroups. For example, by 2014, across Chinese provinces, overall obesity rates had differed by about 20% points and abdominal obesity rates by over 30% points. 5 However, current literature has limited reports on obesity across socio-demographic subgroups at the national level (Supplement I in the Data Supplement). This knowledge gap may partly result from a limitation of nationally representative studies, whereby potential sampling variability becomes a concern for analyses of subgroups that have very small sample sizes. 10 In this study, we report obesity prevalence and risks in China from 2014 to 2018, using one of its largest and most recent health surveys-China Patient-centered Evaluative Assessment of Cardiac Events Million Persons Project. 11 By analyzing 2.7 million participants in Million Persons Project, we aim to describe the prevalence of obesity and associated characteristics in women and men across broad socio-demographic subgroups.

The data are not available to other researchers at this time.

The China Patient-centered Evaluative Assessment of Cardiac Events Million Persons Project protocol has been described previously. 11 From September 2014 to November 2018, we sampled 189 regions (114 rural counties, 75 urban districts) in all 31 provinces of Mainland China to reflect geographic, ethnic, and economic diversity across the country. Within each region, about 5 rural towns or urban sub-districts were sampled based on their size, population stability, and local capacity to execute the study protocol. Participants were recruited by local staff via extensive publicity campaigns on television and in newspapers and enrolled if they were aged 35 to 75 years and currently registered in the selected region's Hukou (an official record identifying residents in the region). Participants were then screened in person for health risks with collection of past medical, medication, family, and social histories and measurement of height, weight, and waist circumference (WC) among other anthropometric and biochemical assessments. Specifically, height and weight were measured to the nearest 0.1 kg or cm, while participants wore light clothes but no shoes or caps. WC was measured midway on skin between the iliac crest and the lower rib margin using a soft tape. Of note, because WC was not collected during the first year of study, we excluded all participants during that period in all WC-related analyses. This project complied with the Declaration of Helsinki and was approved by the central ethics committee at the China National Center for Cardiovascular Disease and the Institutional Review Board at Yale University. All enrolled participants provided informed consent.

Based on Chinese expert consensus on obesity-related hypertension management, 12 we categorized BMI (calculated as weight in kilograms divided by height in meters squared) into underweight (BMI <18.5 kg/m 2 ), normal weight (18.5-23.9 kg/m 2 ), overweight (24.0-27.9 kg/m 2 ), and obese (≥28.0 kg/m 2 ) and defined (1) overall obesity as BMI ≥28.0 kg/m 2 for both women and men and (2) abdominal obesity as WC ≥85 cm for women or WC ≥90 cm for men. We assessed

• Obesity is a global epidemic and has accelerated in China over the past decades, leaving the country with the largest number of obese individuals in the world. • With rapid socio-demographic transformations, China sees immense heterogeneity within its population of 2.7 million.

• This study researched 2.7 million participants in China and found that more than 1 in 7 individuals meet criteria for overall obesity and 1 in 3 meet criteria for abdominal obesity. • It also showed that wide variation in obesity prevalence exists across socio-demographic subgroups and that the associations of age and education with obesity are significant and differ by sex. • These findings provide a sobering update on the current state of obesity in China, fill the knowledge gaps of obesity across socio-demographic subgroups, and provide a valuable international reference on obesity epidemiology. 

We calculated the prevalence of overall and abdominal obesity by sex and as a sensitivity analysis determined the agestandardized prevalence at the national level by adjusting observation weights to match the age distributions in the 2010 Chinese Census. 13 In addition, we reported descriptive statistics of obesity by socio-demographic factors and comorbidities and developed multivariable mixed models (with random effects to account for geographic autocorrelation) to quantify the association of socio-demographic factors with obesity. Lastly, we examined the prevalence of obesity in population subgroups that were defined by combinations of the 12 studied socio-demographic factors (details in Methods in the Data Supplement) and illustrated the probability densities of obesity prevalence in the subgroups with ≥500 people. Analyses were conducted with SAS 9.3 (SAS Institute Inc, Cary, NC) and R 3.02 (The R Foundation for Statistical Computing, Vienna, Austria). Compared with individuals with normal weight, women with overall obesity had higher prevalence of hypertension, dyslipidemia, diabetes, stroke, and myocardial infarction with percent differences in prevalence of 30.4, 16.1, 6.0, 1.4, and 0.4, respectively; men with overall obesity had higher prevalence by 29.9, 31.2, 5.8, 1.4, and 0.7 (Table 1 and Table 2 ). Higher prevalence of these comorbidities was also observed in women and men with abdominal obesity compared with those without (Table 3 and Table 4 ).

Distributions of BMI in socio-demographic subgroups of women and men were shown in Table 1 and Table 2 ; distributions of WC were shown in Table 3 and Table 4 . In multivariable-adjusted models, older women were at substantially higher risks for overall and abdominal obesity while older men were not (Figure 2 ). For example, compared with women aged 35 to 44 years, those aged 65 to 75 years were 29% and 76% more likely to meet criteria for overall and abdominal obesity, whereas men aged 65 to 75 years were 26% less likely to meet criteria for overall obesity compared with those aged 35 to 44 years. Interestingly, the abdominal obesity rates in subpopulations of women aged 65 to 75 years were evidently higher than those in subpopulations of women aged 35 to 45 years, while the overall and abdominal obesity rates in subpopulations of men aged 35 to 44 and 65 to 75 years overlapped considerably ( Figure 3 ).

Other factors associated with obesity were notable for ethnicity, education, annual household income, and current smoking and drinking status. Overall, non-Han people were at higher adjusted risks for obesity ( Figure 2 ). For both overall and abdominal obesity, higher education was associated with lower adjusted risks in women but higher risks in men ( Figure 2 ). For example, women with college or university education were associated with 3.46 (3.37-3.54) cm smaller WC than those with below primary school education, while men with college or university education were associated with 1.36 (1.26-1.46) cm larger WC than those with below primary school education (Table in the Data  Supplement) . For both overall and abdominal obesity, among all annual household income categories, women with annual household income of 10 000 to <50 000 RMB were at the highest adjusted risk, while men with annual household income ≥50 000 RMB were at the highest adjusted risk ( Figure 2 ). Lastly, for both women and men, current smoking was associated with lower adjusted risks for obesity, while current drinking was associated with higher adjusted risks for obesity (Figure 2 ), although the associations were smaller in magnitude among women than men (Table  in the Data Supplement) .

In this study of 2.7 million Chinese adults from 2014 to 2018, we report >15% women and men with overall obesity and over 35% with abdominal obesity. We further show that the significant associations of age and education with obesity differ by sex and that considerable heterogeneity in obesity prevalence exists across socio-demographic subgroups. Our results unequivocally document abdominal obesity as a highly prevalent phenotype in China and demonstrate sex differences in age and education as potential risk factors for obesity.

Studying obesity epidemiology is crucial to population health. BMI is strongly associated with higher incidence of ischemic stroke and diabetes in China, 14, 15 and for a given BMI, mainland Chinese have higher odds of comorbidities than White people in the US. 16 In our study, the prevalence of hypertension, dyslipidemia, diabetes, stroke, and myocardial infarction in the overall obese population is about twice that in the normal weight population. In addition, WC is important for obesity research in China, because many high-WC Asians at what is considered a normal BMI (<25 kg/m 2 ) in Western studies are at higher risks for cardiometabolic diseases. 4, 17 Our study finds an alarmingly high prevalence of abdominal obesity at over 35%, more than twice the prevalence of overall obesity based on BMI alone.

For domestic comparison, our results among adults aged 35 to 75 years are similar to estimates based on the most recently published, nationally 

Less than 0.1% of data (N=1816) represented BMI >50 kg/m 2 , BMI <10 kg/m 2 , WC >140 cm, or WC <40 cm and were not shown on the histograms above. 

All variables are mutually adjusted in log-binomial models with community/village-specific random effects. For analysis of abdominal obesity in men, Poisson-binomial models were used to achieve convergence. 7 RMB was ≈1 USD during the study period. representative data. For example, the China Chronic Disease and Risk Factor Surveillance data suggest that among adults aged 18 years or older in 2013 to 2014, prevalence of overall and abdominal obesity were 14% and 31% in men and 14% and 32% in women, 5 

Center data suggest that among Chinese adults aged 35 to 59 years in 2014, prevalence of overall and abdominal obesity were 14% to 15% and 25% to 37%, respectively. 18 For international comparison, take the United States as an example: in 2015 to 2016, rates of overall and abdominal obesity were 41% and 69% among females and 38% and 48% among males 19 ; before that, overall obesity had appeared stabilized from 2003 to 2012, 20 while abdominal obesity had continued climbing. 21 Given the prevalent burden of abdominal obesity in China and perhaps globally, WC should be considered as a routine measure in clinical services, a meaningful target in preventive health care, and an important metric in public health policy. One other important finding of this study is the strong, positive association of age groups with BMI and WC in women but the strong, negative association of age groups with BMI in men. This pattern is not widely reported or characterized in the literature. One possible biological explanation is that postmenopausal women tend to accumulate more abdominal fat due to the deficiency of estrogen, which normally reduces visceral fat by modulating adipose tissue metabolism and leptin responsiveness. 22 Our study also finds that the association of education with obesity differs by sex. This interesting pattern has been noted in the literature, and a recent study of Chinese monozygotic twins suggests that genetic and shared environmental factors confound the (negative) association between education and BMI in females and that unique environmental correlations between education and BMI were only significant in males. 23 While future research should examine how education may affect obesity differently in different demographics, increasing educational attainment and reducing inequality remains a top public health priority as lower educational level has been shown to be the leading modifiable risk factor for death worldwide and the second for cardiovascular disease in middle-income countries. 24 One notable complex finding of our study is on the relationship between household income and obesity for several reasons. First, data from 1991 to 2011 in China suggest that socioeconomic status positively affected BMI trajectories in males but not females, a reflection of social disparity. 25 Our study similarly found positive associations between household income and obesity in men but not women and may add to this literature. Second, increasing household income could exhibit both deleterious effects of nutritional excess and protective effects of improved dietary quality. For example, the negative income elasticity of demand for inferior goods supports that increasing household income may reduce demand for staple food like rice and potato in favor of substitutes with higher dietary quality and diversity. Third, annual household income may misrepresent long-term household resource, which more reliably determines nutritional status. For example, when Russia experienced rapid economic decline followed by a drastic rebound in 1996 to 2000, population nutritional status was affected by long-term household resource but resilient to short-term fluctuation. 26 Therefore, favorable dietary changes and an imperfect household resource measure may obscure the link between income and obesity in our study.

Understanding the state of obesity in China has implications for the world. Current global trends predict obesity prevalence of 18% in men and 21% in women by 2025. 3 In health trajectory forecasts of 195 countries and territories for 2016 to 2040, rising BMI will slow down the global progress in extending life expectancy and by 2040 become the leading risk factor for years of life lost in 76 countries, followed by tobacco in 48 and high blood pressure in 25; because rising BMI is amenable to population-level interventions, ample room exists for both improvement and deterioration of future health trajectories. 27 In highincome countries, abdominal obesity is the third leading modifiable risk factor for death with a population attributable fraction of 11%. 24 In low-income and middle-income countries, nutritional transition has followed similar patterns that high-income countries experienced in the past, but it has occurred faster and earlier relative to their levels of socioeconomic development. 28 In addition, socioeconomic advancement in low-income populations may increase cardiometabolic disease risk even at levels that do not substantially affect high-income populations. 29 In fact, over the past 3 decades, over half of the global rise in BMI was due to increases in BMI in rural areas in low-and middle-income countries. 30 Interpreting our results in this context highlights the urgency to address the rising obesity epidemic globally and particularly in emerging economies via early, proactive, multi-level interventions.

Several limitations apply to our study. First, timely recruitment of 2.7 million participants across China came with the trade-off of fully representative sampling. For example, compared with the underlying population, rural participants could be healthier (eg, healthy worker effect) or less healthy (eg, participation in research to fulfill unmet healthcare needs). However, our main results closely resemble agestandardized prevalence estimates using the 2010 Chinese Census data and 2 most recently published, independent reports based on nationally representative data from 2014. 5, 18 Second, we do not currently have diet and physical activity data on all participants to interrogate their associations with obesity at the individual level; instead, we focus on understanding socio-demographic factors in China to address obesity at the population level. Third, given its cross-sectional design, this study lacks data to assess BMI and WC patterns over time or establish causal determinants of obesity, although a longitudinal study on a similar scale is unprecedented.

In summary, we estimated the prevalence of obesity and its socio-demographic associations in China and report more than one in 7 individuals with overall obesity and one in 3 with abdominal obesity. We show that wide variation in obesity prevalence exists across sociodemographic subgroups and that the associations of age and education with obesity are significant and differ by sex. Understanding the current state of obesity in China has broad implications on public health policy in China and in other emerging economies.

members of the provincial coordinating offices of the China Patient-centered Evaluative Assessment of Cardiac Events Million Persons Project (Supplement II in the Data Supplement). We also appreciate the project teams at the National Center for Cardiovascular Diseases for their contributions to project design and operations, particularly Yang Yang, Jingwei Yang, Jianlan Cui, Wei Xu, Bo Gu, Xi Li, and Hui Zhong for data collection. We also appreciate Yale New Haven Hospital Center for Outcomes Research and Evaluation for their contributions to project design. 

Dr Mu reports personal fees from HealthCare Royalty Partners outside the submitted work. Dr Y. Lu is supported by the National Heart, Lung, and Blood Institute (K12HL138037) and the Yale Center for Implementation Science. Dr Spatz receives support from the Centers for Medicare and Medicaid Services to develop and maintain performance measures used in public reporting programs and from the Food and Drug Administration to support projects within the Yale-Mayo Clinic Center of Excellence in Regulatory Science and Innovation (CERSI); receives support from the National Institute on Minority Health and Health Disparities (U54MD010711-01) to study precision-based approaches to diagnosing and preventing hypertension and the National Institute of Biomedical Imaging and Bioengineering (R01EB028106-01) to study a cuff-less blood pressure device. In the past three years, Harlan Krumholz received expenses and/or personal fees from UnitedHealth, IBM Watson Health, Element Science, Aetna, Facebook, the Siegfried and Jensen Law Firm, Arnold and Porter Law Firm, Martin/BaughmanLaw Firm, F-Prime, and the National Center for Cardiovascular Diseases in Beijing. He is a co-founder of Refactor Health and Hu-goHealth, and had grants and/or contracts from the Centers for Medicare & Medicaid Services, Medtronic, the U.S. Food and Drug Administration,Johnson & Johnson, and the Shenzhen Center for Health Information. The other authors report no conflicts. Figures I and II  Table 

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We thank all study participants for their participation. We appreciate all provincial and regional officers and research staff for their collection of data and list