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The Lancet Regional Health: Western Pacific logoLink to The Lancet Regional Health: Western Pacific
. 2026 Mar 19;68:101833. doi: 10.1016/j.lanwpc.2026.101833

Major risk factors of obesity in China and recommendations for future prevention and control efforts: a systematic review and meta-analysis

Jiajin Hu a,b,c,u, Jinchen Xie d,e,u, Borui Liu a,b,u, Wen Peng f, Buyun Liu g, Yinkun Yan h, Ling Zhang i,j, Xue Wang k, Yuandi Xi l, Yanan Ma m, Yanhui Lu n, Yanfen Jiang o,p, Fengyan Chen o,p, Izzuddin M Aris q, Jianduan Zhang o,p,∗, Deliang Wen a,b,r,∗∗, Youfa Wang a,e,s,t,∗∗∗
PMCID: PMC13015754  PMID: 41889380

Summary

Background

Despite increasing national efforts, the prevalence of overweight and obesity among Chinese adults reached 57%, suggesting a disconnect between the drivers and current intervention. This study aims to identify the multilevel risk factors and evaluate existing policy strategies, to inform more effective, evidence-based interventions.

Methods

We projected national overweight and obesity trends for 2030 and 2035 using surveillance data. We systematically searched China National Knowledge Infrastructure, Wanfang Data, SinoMed, PubMed, Web of Science, and Scopus, for cohort, case–control, and/or cross-sectional studies published from Jan 1, 2000, to Oct 1, 2024. Studies addressing determinants of overweight, obesity, and central obesity in the Chinese population were included; meta-analyses were performed where appropriate. Expert consultations supplemented evidence on meso- and macro-level determinants. National policies on obesity prevention and control were reviewed.

Findings

The prevalence of overweight and obesity in China was projected to reach 72.2% (95% confidence interval [CI] 69.7%–74.7%) for adults and 41.9% (37.4%–46.1%) for children/adolescents by 2035. In total 1512 studies identified, with 873 eligible for meta-analysis, including 322 studies on adults (21,566,496 individuals). We identified 17 risk factors for adult overweight and obesity, with individual-level behavioral factors being most prevalent. These included higher red meat intake (odds ratio 2.28, 95% CI 1.07–4.82), rapid eating speed (2.04, 1.35–3.08), higher sugar-sweetened beverages intake (1.90, 1.10–3.29), higher red and white meat intake (1.54, 1.11–2.14), meat preference (1.51, 1.03–2.22), alcohol consumption (1.23, 1.15–1.31), higher percentage of energy from fat (1.22, 1.14–1.31), lower physical activity (1.24, 1.02–1.51) and shorter nocturnal sleep duration (1.09, 1.03–1.15). Additional risk factors for central obesity included lower adherence to a “traditional Chinese dietary pattern” (1.57, 1.08–2.27), higher adherence to a “modern dietary pattern” (1.34, 1.05–1.71), higher spicy food intake (1.19, 1.05–1.36), higher staple food intake (1.18, 1.05–1.33), lower fruit intake (1.12, 1.02–1.23), longer sitting time (1.24, 1.09–1.41) and screen time (1.09, 1.05–1.14). For children and adolescents, 26 risk factors for overweight and obesity, and three for central obesity were identified, and 18 for macrosomia in neonates. Expert consultation emphasized importance of environmental and policy-level factors, especially food environment. Policy analysis revealed critical gaps in existing framework, including limited multisectoral collaboration, weak regulatory frameworks, lack of life-course-specific interventions, inadequate focus on high-risk populations (e.g., postmenopausal women), and limited evidence-based policymaking.

Interpretation

The obesity epidemic in China is driven by a complex interplay of individual, environmental, and systemic factors. Effective, comprehensive, government-led strategies are urgently needed to address both individual risk factors and broader obesogenic environments through coordinated and evidence-based actions.

Funding

This work was supported by the Chinese National Science and Technology Innovation 2030, Noncommunicable Chronic Diseases-National Science and Technology Major Project (Grant No. 2023ZD0508500, 2023ZD0508504).

Keywords: China, Obesity, Risk factors, Policy, Meta-analysis, Expert consultations

Research in context.

Evidence before this study

  • •

    Prevalence of overweight and obesity in China has been increased substantially over the past a few decades, making China home to the largest number of individuals with overweight and obesity worldwide; this constitutes a major public health crisis.

  • •

    Existing research emphasizes individual-level factors such as dietary habits and physical inactivity, whereas broader meso- and macro-level determinants remain underexplored.

  • •

    Despite extensive national initiatives, efforts to curb obesity in China has achieved only limited success, indicating a mismatch between the drivers of the epidemic and existing prevention and control strategies.

Added value of this study

  • •

    Provided a comprehensive examination of the risk factors and drivers of the obesity epidemic in China, and assessed their relative importance.

  • •

    Identified multilevel determinants of overweight and obesity across all age groups in China at individual, meso-, and macro-level, using an integrated approach that combines systematic review, meta-analyses, and expert consultations.

  • •

    Quantifies and ranks the effect sizes of individual risk factors (including distinct and unique risk factors differ from those in Western contexts), highlights under-addressed role of systemic drivers, including policy and environmental influences.

  • •

    Provided actionable recommendations to inform China's future efforts, including strengthening policy-driven interventions, enhancing multi-sector collaboration, and addressing key drivers of obesity.

Implications of all the available evidence

  • •

    This study highlights the urgent need of a comprehensive, government-led strategy to prevent obesity epidemic in China, considering both individual and systemic factors. It provides an evidence-based roadmap for policymakers to enhance future interventions to combat the obesity epidemic in China. The study offers novel insights and timely guidance, particularly considering China's ongoing 2024–2027 three-year Weight Management Year Campaign; and are helpful for other countries as well.

Introduction

Obesity has become a global public health crisis, with its prevalence plateauing at high levels in developed countries while accelerating at an alarming rate in developing nations.1,2 High body mass index (BMI) is a key driver of non-communicable-diseases globally, contributing to 1.6 million (15%) of premature deaths and 44.3 million (27%) person-years of ill health.3 Over the past four decades, China has experienced a surge in overweight and obesity prevalence, and now hosting the largest population living with these conditions worldwide.4 Currently, more than half of Chinese adults and about one-fifth of school-age children are classified as overweight or obesity.5, 6, 7, 8 Without effective prevention, China faces the risk of experiencing a health and economic burden related to obesity comparable to that of developed countries.9

Despite the implementation of national policies and intervention programs, the burden of overweight and obesity keeps increasing. This persistent trend highlights the misalignment between the drivers of the epidemic and prevention strategies. China's unique sociocultural, economic and environment contexts, including food culture, urbanization patterns, among others,10, 11, 12 substantially shape both individual and meso-level risk factors. However, existing epidemiological studies have tended to examine a narrow set of obesity determinants in isolation, primarily on individual behavioral factors.13, 14, 15

There is a pressing need for a comprehensive examination of the determinants of obesity in China, including individual- (e.g., eating behaviors, physical activity), meso-level (e.g., community food environments), and macro-level (e.g., food policy and regulation) factors, in order to direct future interventions.16 A comprehensively review of existing national policies is also essential to identify gaps, mismatches, and opportunities for strengthening obesity prevention and control in China.

Recently we have systematically reviewed major determinants of obesity in Chinese children and adolescents.17 In the current study, we systematically examined the risk factors of obesity in China covering adults and children, with a primary focus on adults. We also conducted expert consultations to capture broader structural influences, evaluated national obesity related policies; and provided recommendations for future work. Moreover, we projected future overweight and obesity prevalence among Chinese adults.

Methods

Data resources of prevalence and projection of overweight and obesity

We collated relevant information from diverse sources, including national survey reports and academic studies. Specifically, we analyzed the prevalence of overweight and obesity among Chinese adults, children, and adolescents using nationally representative data from two key surveys: the Chronic Disease Risk Factor Surveillance in China (CCDRFS)18 and the Chinese National Survey on Students’ Constitution and Health (CNSSCH),19 respectively.

The CCDRFS is a series of cross-sectional surveys targeting adults aged 18–69 years. Launched in 2004, it is conducted every 3 years based on China's National Disease Surveillance Points System, covering rural and urban areas across all provinces, autonomous regions, and municipalities in mainland China.18 Trained health workers collected physical measurements (including standing height and body weight) for all participants. Overweight was defined as a body mass index (BMI) ≥24 kg/m2, and obesity as a BMI ≥28 kg/m2, using the standards set by the Working Group on Obesity in China.20

The CNSSCH is a large-scale, nationwide school-based cross-sectional survey implemented in 30 mainland Chinese provinces via a multistage, stratified cluster sampling method. Initiated in 1985, it is conducted every 5 years among children and adolescents aged 7–18 years.19 During each survey, trained technicians performed medical examinations, including measurements of standing height and weight. Sex-specific BMI-for-age Z-scores were calculated for each participant; overweight was defined as a Z-score > median + 1SD and ≤ median + 2SD, and obesity as a Z-score > median + 2SD, following the 2007 WHO growth references.21

In the current analysis, we used polynomial regression models combined with a parametric bootstrap approach to predict the prevalence of overweight and obesity, along with the corresponding 95% confidence intervals, among Chinese adults, and children and adolescents in 2030 and 2035. These projections were derived from historical prevalence data from two national surveys: the CCDRFS (with data points in 2004, 2007, 2010, 2013, 2018, and 2023) and the CNSSCH (with data points in 1985, 1995, 2000, 2005, 2010, 2014, and 2019). The historical prevalence data used for model fitting were available only as point estimates without corresponding 95% confidence intervals, which limited the direct assessment of sampling variability in the original data.

Systematic review and meta-analysis

Search strategy and selection criteria

This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Chinese and international databases, including China National Knowledge Infrastructure database, Wanfang Data, SinoMed, PubMed, Web of Science, and Scopus, from Jan 1, 2000, to Oct 1, 2024. The search strategy used combinations of keywords related to obesity (e.g., Adiposity OR Obesity OR Overweight), study design (Cohort Studies OR Case-control studies OR Cross-Sectional Studies) and population (China). The full search strategy is detailed in Table S1.

Record screening used a two-stage screening method, with two reviewers, to ensure screening reliability and reproducibility, and discrepancies were resolved by a third reviewer. Inclusion criteria includes (1) cohort, case–control, and/or cross-sectional studies on (2) Chinese population (adults, children/adolescents, neonates); (3) involved healthy individuals without special illness; and (4) reported the associations between risk factors and categorical outcomes for overweight and/or obesity. Due to variations in the definition of obesity across studies, we included obesity outcomes reported using any internationally recognized criteria (e.g., those from the International Obesity Task Force, World Health Organization, Centers for Disease Control and Prevention 2000, Working Group on Obesity in China, and other national guidelines), with detailed definitions provided in Table S2. Central obesity was defined by waist circumference or waist-to-height ratio. For neonates, macrosomia (≥4000 g) and large-for-gestational age (LGA; birth weight for gestational age ≥90th percentile) were used as proxies for fetal growth and later-life obesity risk.22 Age-specific analyses were conducted separately for adults, children/adolescents, and neonates. The selection process is illustrated in Fig. 1.

Fig. 1.

Fig. 1

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Flow chart of the search: PRISMA diagram.

Data were independently extracted by two reviewers using a standard data extraction form capturing publication details, study design, participant characteristics, sample sizes, exposures/outcomes, covariates, and effect estimates (Table S2). We harmonized exposure categories across studies, opting for the most prevalent classification and amalgamating disparate categories when feasible. For example, instances of sleep duration exceeding 10 h/day were integrated into the more common category of over 7 h/day. Where precise cutoffs were unavailable, comparisons were categorized by relative degrees (e.g., higher vs. lower red meat intake). The original cutoff point values of determinants of included studies were illustrated in Table S3. Two predominant dietary patterns were identified from the included studies: a “traditional Chinese dietary pattern”, characterized by high intake of whole grains, vegetables, fruits, soy products, and moderate consumption of meat23; and a “modern dietary pattern”, characterized by excessive intake of red meat, refined grains, ultra-processed foods, and sugar-sweetened beverages (SSBs). For tea beverage consumption, most included studies failed to specify the beverage composition (e.g., pure unsweetened tea vs. sugar-added tea-based drinks), therefore we defined the exposure as “drinking tea-based beverage”, a category that encompasses both pure tea and sugar-sweetened tea-flavored beverages.

We extracted reported odds ratios (ORs) or analogous effect measures (relative risks, hazard ratios, or prevalence ratios) for the health outcome of interest, or relevant data to compute these statistics, among participants with at least one of the categorical assessments of overweight and/or obesity. In studies investigating multiple outcomes simultaneously (e.g., obesity and central obesity), we extracted association data for each outcome and conducted separate meta-analyses. For studies that conducted both the main analysis and sensitivity analyses using different obesity definition criteria (e.g., the World Health Organization standard and the Working Group on Obesity in China standard), we included estimates from the main analysis. When multiple studies reported data for the same outcome and sample, we included the study with largest sample size. Two reviewers examined study quality using the Newcastle–Ottawa Scale (NOS). Discrepancies were resolved by consensus with a third reviewer. We classified studies into low (0–3 points), moderate (4–6 points), and high quality (7–9 points) (Table S4).24,25 All studies were included in primary analysis.

Data analysis

We separately performed random-effects meta-analyses for adults, children/adolescents, and neonates. Risk factors assessed in ≥3 independent studies were included in the meta-analyses. We calculated pooled odds ratios (ORs) and 95% confidence intervals (CI) for each risk factor, using adjusted effect estimates when available; if no adjusted estimate was reported, we used the crude estimate. When ORs were reported for subgroups within the same sample, we pooled subgroup-specific estimates before analysis. Different outcomes (e.g., overweight, obesity, overweight and obesity, and central obesity) were analyzed separately. Among these, we used overweight and obesity, and central obesity as the primary outcomes for adults because overweight and obesity are the most common outcomes in the literatures with the largest number of different risk factors, while central obesity is a more sensitive predictor of cardiovascular disease risk.26 In the sensitivity analyses, we repeated the primary analyses after restricting to three subsets of eligible studies: those reporting adjusted effect estimates, studies of moderate-to-high methodological quality, and prospective cohort studies.

We assessed study heterogeneity using the I2 statistic and the Cochrane Q test (P < 0.05 suggesting significant heterogeneity).27 Publication bias and small study effects were evaluated using Egger's tests and funnel plots28 (Table S5). We further explored potential sources of heterogeneity via meta-regression when feasible (for outcomes with ≥10 studies and high between-estimate heterogeneity). Univariate analyses were performed to examine the study-level characteristics with pooled estimates, including study design (case–control study and cohort study vs. cross-sectional study), OR data (adjusted vs. unadjusted), study quality (score ≥4 vs. <4), and sample size (>4629 vs. ≤4629, based on the median sample size of included studies). Statistical analyses were performed with Stata 16 and R version 4.0.0, with P < 0.05 considered statistically significant.

Expert consultation

Noting the limited empirical evidence on broader societal and policy-level determinants of obesity in China, we conducted expert consultations to complement the meta-analytic findings. Based on the Social Ecological Model (SEM), we invited 10 senior experts in the obesity field to complete a questionnaire. The SEM includes individual-, meso- (family and companions, organization and institution, community and built environment), and macro-level (natural and social environment, and policy) factors. For individual-level risk factors, the experts were requested to identify the top 10 determinants contributing to the obesity epidemic in China. A scoring system was used to quantify consensus, with each determinant received a score based on the frequency of expert selection. Determinants with score ≥3 were considered consensus priority factors.

Policy identification

National policies on obesity prevention and control were retrieved from the official websites of the Chinese Government, the National Health Commission, and the National Bureau of Disease Prevention and Control. Eligible policies were issued at national level and applied to the general population from 2000 onward. Keyword search strings included (Adiposity OR Obesity OR Overweight) and (Strategy OR Plan OR Approach OR Guideline OR Protocol OR Framework OR Principle OR Rule OR Standard OR Procedure) and (China OR Procedure) and (China OR China mainland OR National OR Nationwide). Two reviewers independently screened records using a two-stage method to ensure reliability, and discrepancies were resolved through discussion by involving a third reviewer. We categorized the policies according to their action emphasis and extracted the key content of each policy. We adopted Schneider & Ingram's public policy evaluation framework, assessing relevant policies across goals, targets, and implementation structures.29

Role of the funding source

The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

Results

Projection of prevalence of overweight and obesity among Chinese adults and children/adolescents

Between 2004 and 2023, the prevalence of overweight and obesity among Chinese adults nearly doubled, increasing from 30.1% to 56.9%. In the absence of effective interventions, this rate is projected to rise further to 65.5% (95% confidence interval [CI] 63.7%–67.3%) by 2030 and 72.2% (95% CI 69.7%–74.7%) by 2035 (model fit: R2 = 0.98) (Fig. 2). Similarly, the prevalence of overweight and obesity among Chinese children and adolescents has surged dramatically, climbing from 1.2% in 1985 to 23.4% in 2019. Without targeted interventions, it is expected to continue rising, reaching 35.7% (95% CI 32.2%–39.2%) by 2030 and 41.9% (95% CI 37.4%–46.4%) by 2035 (model fit: R2 = 0.74).

Fig. 2.

Fig. 2

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Projections of overweight and obesity prevalence in Chinese adults(a)and children/adolescents(b). For adults (a), observed prevalence data spanning 2004–2023 were extracted from the Chronic Disease Risk Factor Surveillance in China (CCDRFS). Projections for 2030 and 2035 were generated using polynomial regression models. Overweight and obesity were defined in line with the standards of the Working Group on Obesity in China: overweight as a body mass index (BMI) ≥ 24 kg/m2, and obesity as a BMI ≥28 kg/m2. For children and adolescents (b), observed prevalence data spanning 1985–2019 were extracted from the Chinese National Survey on Students' Constitution and Health (CNSSCH). Projections for 2030 and 2035 were generated using polynomial regression models. Overweight and obesity were defined in accordance with the 2007 WHO growth references: overweight as sex-specific BMI-for-age Z-scores > median + 1SD and ≤ median + 2SD, and obesity as Z-scores > median + 2SD.

Meta-analysis results on overweight and obesity determinants

We identified 62,878 references across six databases, after removing duplicates and screening titles and abstracts, 3241 articles were selected for full-text evaluation. Of these, 1514 studies were eligible for inclusion. A total of 639 references were excluded due to challenges in classifying exposures into specific groups, or less than three studies for certain risk factors. Ultimately, 873 studies retained for meta-analysis, representing 21,566,496 adults (322 studies), 8,949,485 children and adolescents (336 studies), and 10,634,696 neonates (215 studies) (Table S2).

Risk factors in adults

We identified 17 risk factors significantly associated with overweight and obesity (Fig. 3) and 13 with central obesity (Fig. 4), each derived from meta-analysis of multiple studies. Specifically, for overweight and obesity, diet and eating behaviors are the most common determinants, which including higher red meat intake (odds ratio [OR] 2.28, 95% CI 1.07–4.82), rapid eating speed (OR 2.04, 95% CI 1.35–3.08), higher SSB intake (1.90, 1.10–3.29), higher red and white meat intake (1.54, 1.11–2.14), meat preference (1.51, 1.03–2.22), drinking tea-based beverage (1.38, 1.10–1.74), alcohol consumption (1.23, 1.15–1.31), and higher percentage of energy from fat (1.22, 1.14–1.31). We additionally observed lower adherence to a “traditional Chinese dietary pattern” (1.57, 1.08–2.27), and higher adherence to a “modern dietary pattern” (1.34, 1.05–1.71), higher spicy food intake (1.19, 1.05–1.36), higher staple food intake (1.18, 1.05–1.33), alcohol consumption (1.14, 1.08–1.21) and lower fruit intake (1.12, 1.02–1.23) were associated with increased central obesity risks. Other behavioral factors such as low physical activity (1.24, 1.02–1.51), shorter nocturnal sleep duration (1.09, 1.03–1.15) were associated with higher risk of overweight and obesity, and longer sitting time (1.24, 1.09–1.41) and screen time (1.09, 1.05–1.14) were associated with central obesity.

Fig. 3.

Fig. 3

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Forest plot of determinants of overweight and obesity among Chinese adults (aged ≥18 years).

Fig. 4.

Fig. 4

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Forest plot of determinants of central obesity among Chinese adults (aged ≥18 years).

Males have higher risk of being overweight and obesity (1.28, 1.20–1.37), but lower risks of being central obesity (0.74, 0.66–0.83) compared with females. Individuals who were older than 60 years old, have been married, had higher household income had higher risks of overweight and obesity and central obesity. Individuals live in urban area, had non-manual work or being macrosomia at birth had higher risks of overweight and obesity. Menopausal women had higher risk of central obesity compared with premenopausal women. We identified a total of six determinants for overweight (Figure S1) and 13 determinants for obesity, respectively (Figure S2).

In sensitivity analyses restricted to studies of moderate-to-high quality, the overall results remained unchanged (Figures S3–S6). However, limiting the analysis to studies that reported adjusted effect estimates substantially reduced the number of cases included in the analysis (Figures S7–S10), resulting in a small subset of risk factors that remained statistically significant associated with overweight and obesity, including alcohol consumption, a higher percentage of energy derived from fat, low physical activity, being married, holding non-manual occupations, and being macrosomia at birth (Figure S7). We observed similar trends in the sensitivity analyses to central obesity, that fewer determinants remained in the analyses, and a small subset of risk factors remained statistically significant, including menopausal status (among women), aged 60 years and above, urban residence, being married, holding non-manual occupations, lower adherence to a “traditional Chinese dietary pattern”, higher adherence to a “modern dietary pattern”, alcohol consumption, drinking tea-based beverage, higher spicy food intake, and exposure to prenatal famine (Figure S8). Similarly, sensitivity analyses of prospective cohort studies (Figures S11–S14) revealed that only male gender, age ≥60 years, and higher dietary fat intake remained statistically significantly associated with overweight and obesity (Figure S11). In contrast, no factors were found to show such a significant association with central obesity, which could be ascribed to the limited number of included studies (Figure S14). We identified considerable heterogeneity (I2 of >75%) between estimates for more than half of the outcomes (Table S5). In meta-regression analyses, no significant associations were observed between study-level covariates and pooled ORs for most outcomes. However, regarding the determinant of low education level, studies with a high quality score reported higher odds of central obesity than those with a low quality score (β = 1.20; SE[β] = 0.07; P = 0.01) (Table S6).

Risk factors in children/adolescents and neonates

For children and adolescents, we identified 26 risk factors for overweight and obesity (Figure S15), three for central obesity (Figure S16), nine for overweight (Figure S17), and 26 for obesity (Figure S18). For neonates, we identified 18 risk factors for macrosomia (Figure S19) and 10 for LGA (Figure S20).

In sensitivity analyses restricting to moderate-high quality studies, the overall results remained unchanged, while some risk factors were excluded from meta-analysis due to limited number of studies (Figures S21–S24). In sensitivity analyses restricting to studies reporting adjusted effect estimates, several risk factors remained significantly associated with an increased risk of overweight and obesity among children and adolescents, such as male sex, urban residence, parental overweight and obesity (at least one parent), being an only child and higher sugar-sweetened beverage intake (Figures S25–S28). For macrosomia and LGA infants, 11 and 6 risk factors remained significantly, respectively (Figures S29 and S30). In sensitivity analyses restricting to prospective cohort studies focusing on children and adolescents (Figures S31–S33), only a few risk factors remained statistically significantly associated with overweight and obesity, including male gender, formula feeding during infancy, maternal pre-pregnancy overweight and obesity, excessive gestational weight gain in mothers, gestational diabetes mellitus, and macrosomia at birth (Figures S31). Additionally, for macrosomia and LGA infants, 10 and 9 risk factors remained statistically significant, respectively (Figures S34 and S35).

We identified considerable heterogeneity (I2 of >75%) across studies for most of the outcomes (Table S5). In meta-regression analyses, no significant associations were observed between study-level covariates and pooled ORs for most outcomes. However, for sugar-sweetened beverages intake, studies with adjusted estimates reported higher odds than those with unadjusted estimates (β = 0.72; SE [β] = 0.09; P = 0.03) (Table S6).

Expert consultation results on determinants of overweight and obesity

Expert consultation identified additional individual-level determinants of adult overweight and obesity that have not been commonly examined in quantitative literature, including prenatal exposures (e.g., maternal overweight and obesity before pregnancy, excessive gestational weight gain, gestational diabetes mellitus [GDM]), childhood overweight and obesity, and other adult–stage exposures, such as excessive total energy intake, added sugar intake, night eating, higher psychological stress, negative body image, and exposure to environmental pollutants (Figure S36). The differences between the determinants derived from expert consultation and those from the meta–analysis were compared within a SEM framework, with the results presented in Fig. 5.

Fig. 5.

Fig. 5

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Determinants of overweight and obesity in China based on social ecological model according to meta-analyses and expert consultation. †: significant risk factors for overweight and obesity identified by the meta-analysis. ‡: significant risk factors for central obesity identified by the meta-analysis. ⁋: significant risk factors for overweight identified by the meta-analysis. §: significant risk factors for obesity identified by the meta-analysis. #: risk factors identified by both meta-analysis and expert consultation.

Moreover, expert consultation findings highlighted key macro- and meso–level determinants that are underrepresented in existing literature, including policy–related factors (e.g., tax on unhealthy foods), natural and social environments (e.g., environmental pollution), community and built environments, organization and institute factors, as well as family and companion determinants. Among these, environmental and policy determinants associated with the availability of healthy vs. unhealthy foods (e.g., taxes on unhealthy foods, availability of healthy food in community) were identified as the most prevalent driving factors across multiple levels.

Existing policies related to obesity prevention and control in China

Since 2000, 70 national-level policies and programs have been implemented in China (Table S7). These can be classified into six categories: (1) comprehensive national plan, such as the Implementation Plan for Weight Management Year Campaign (2024)30; (2) medical and health service actions, China's Medium- and Long-Term Plan for the Prevention and Treatment of Chronic Diseases (2017–2025)31; (3) national nutrition guidelines, such as Dietary Guidelines for Chinese Residents (2022)23; (4) physical activity promotion strategies, such as the “14th Five Year Plan” for Sports Development32; (5) child health-focused initiatives, such as Action Plan to Improve Child Health (2021–2025),33 and (6) other policies. We benchmarked these existing policies against three global frameworks: WHO Best Buys For NCDs, the Ending Childhood Obesity Framework, and the Acceleration Plan to STOP Obesity34, 35, 36 (Table S8). We also evaluated the existing policies based on Schneider & Ingram's public policy evaluation framework, and the characteristics and shortcomings of the 70 relevant policies have been summarized in Table S9. Among these, 40 policies (57.1%) predominantly focused on downstream individual behavioral interventions, with insufficient attention to upstream social, economic, and environmental determinants of obesity. Only six policies (8.6%) targeted individuals with obesity, reflecting a clear gap in the provision of tailored interventions for this group. Furthermore, merely 26 policies (37.1%) adopted an executive-driven approach, indicating a lack of detailed implementation guidelines and well-defined accountability mechanisms in China's current obesity prevention policies.

Recommendations for improving future work

Guided by the SEM framework, we developed a set of evidence-based recommendations integrating findings from the meta-analysis and expert opinions, and drawing on dietary guidelines and obesity prevention strategies endorsed by Chinese and international organizations (e.g., Chinese Nutrition Society, World Health Organization). To ensure effective implementation, multisectoral collaboration is needed to foster an enabling environment that facilitates the healthy lifestyle adoption and support sustainable weight management. Detailed recommendations were presented in Fig. 6.

Fig. 6.

Fig. 6

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Key points on recommendation for China's actions on obesity prevention and control.

Discussion

The study provided a comprehensive examination of the risk factors and drivers of the obesity epidemic in China, assessing relative importance of individual determinants through meta-analyses and highlighting systemic drivers that remain under-addressed as derived from expert consultations. We identified 17 risk factors for adult overweight and obesity and ranked their effect size. Individual-level behavioral factors were most prevalent, including higher red meat intake, rapid eating speed, higher SSB intake, higher red and white meat intake, meat preference, alcohol consumption, higher percentage of energy from fat, lower physical activity and shorter nocturnal sleep duration. Additional risk factors for central obesity included lower adherence to a “traditional Chinese dietary pattern”, higher adherence to a “modern dietary pattern”, higher spicy food intake, higher staple food intake, lower fruit intake, prolonged time and screen time. In children and adolescents, 26 risk factors for overweight and obesity were identified, along with additional three for central obesity and 18 for macrosomia.

Expert consultation emphasized the importance of environmental and policy-level factors, especially food environment. Our policy review revealed persistent deficiencies in national efforts despite considerable interventions. These gaps highlight a critical need for enhanced multi-stakeholder collaboration, stricter regulatory measures, life-course-specific interventions, targeted approaches for vulnerable subgroups, and stronger evidence-based policymaking.

Individual determinants

Our study systematically identified multi-level determinants of adult overweight and obesity through a dual approach of expert consultation and meta-analysis. Among individual risk factors, meta-analysis findings highlighted the importance of individual behavioral lifestyle factors among adults (e.g., unhealthy dietary, sedentary lifestyle), while expert consultation findings further extended the landscape of risk factors to psychological factors (e.g., high psychological stress) and early-life risk factors (e.g., maternal pre-pregnancy weight status, gestational weight again, and GDM).

The dietary shift from the plant-dominated low-calorie “traditional Chinese dietary pattern” to a high-calorie “modern dietary pattern”, characterized by higher intakes of fat, red meat, SSBs, ultra-processed food, and lower fruit and vegetable intake, has emerged as a major contributing factor for the current obesity epidemic in China. The growing popularity of app-based food delivery and dining out, particularly the youth,37,38 further exacerbates these risks. These lifestyle factors were reinforced by expert opinion, mirrors global trends but are particularly pronounced in China.39 Previous meta-analyses have reported that unsweetened green tea benefits obesity prevention40,41; however, our meta-analysis finding revealed that drinking tea-based beverages was associated with an increased risk of obesity. A limitation here is that most of the included studies failed to explicitly define “tea intake” or specify whether the consumed products were unsweetened pure tea, sugar-added tea, or tea-flavored beverages (e.g., sweet milk tea, a highly popular drink among young people in China). These findings emphasize the need to strictly distinguish between pure tea and commercial tea beverages when formulating public health recommendations. In addition to dietary factors, sedentary lifestyles which further disrupt the energy equation, were identified as a risk factor for obesity development by both meta-analyses and expert consultations. Meta-analysis findings indicated physical inactive was associated with 24% increased risk of overweight and obesity and non-manual workers are 20% more likely to experience excessive weight. The excessive screen time and prolonger sitting are linked with increased risk of central obesity–a strong predictor for cardiovascular diseases.42

China's unique social environment (e.g., food culture and family structure) shapes distinct obesity risk factors. For example, higher spicy food intake, a characteristic of the Chinese diet rarely observed in Western populations, was associated with an increased risk of obesity. Another social factor linked to obesity risk is the status of being an only child in a family, a common outcome of China's One-child Policy, which was associated with higher risks of both general and central obesity among children and adolescents. Males consistently exhibited a higher risk of obesity than females across both adults and children, highlighting a persistent gender difference in China. This discrepancy may stem from traditional Chinese patriarchal values that prioritize sons over daughters, where males are often provided with more nutrient-dense (and calorie-rich) foods, less restrictive dietary controls, and relatively fewer expectations for physical activity compared to females in family settings. Chinese pregnant women face higher rate of excessive gestational weight gain,43 GDM44 and cesarean delivery45 than their western counterparts, factors that are well-documented as key predictors of offspring obesity. These adverse maternal pregnancy outcomes are likely driven by prevailing cultural norms that favor overnutrition and physical inactivity during pregnancy for the benefits for mothers and babies.46 These China-specific sociocultural, dietary, and demographic features underscore the critical importance of developing targeted prevention strategies tailored to local contexts to effectively mitigate the country's obesity epidemic.

A notable characteristic of our meta-analysis is the predominance of cross-sectional studies in the included literature. While cross-sectional studies provide a broad overview of the associations between lifestyle, demographic, and early-life factors and obesity risk in the Chinese population, they cannot establish definitive causal relationships. By contrast, findings supported by longitudinal evidence in the sensitivity analyses, such as dietary risk factors for adults (e.g., higher fat intake), and early-life risk factors for children and adolescents (e.g., maternal pre-pregnancy overweight and obesity, formula feeding in infancy), carry greater weight for causal inference. Public health interventions should prioritize factors with robust longitudinal evidence to maximize effectiveness and avoid unintended consequences. Future research should leverage large-scale prospective birth cohorts and randomized controlled trials to clarify causal pathways and identify modifiable targets for obesity prevention.

Systemic drivers of obesity epidemic in China

Beyond individual determinants, our expert consultation highlighted the role of obesogenic environments, filling the gap left by insufficient research on China's obesity macro-determinants that precludes a meta-analysis. China's urbanization rate increased from 28.5% in 1994 to 66.2% in 2023, with over 930 million people now living in cities.47 This rapid transition leads to many changes in living and working environment,48, 49, 50, 51, 52, 53 for example, the development of food processing industry and fast food retailing business exposed people to marketing for mass-produced, ultra-processed foods and beverages; the inactive design of major Chinese cities has limited opportunities for active living, influenced by large-scale urbanization patterns, decentralized land use planning, gated neighborhoods, and policies and practices regarding motor vehicle travel, transit, and bicycle and pedestrian infrastructure; fast-paced and stressful lifestyles for adults and children that is commonly associated with unhealthy behaviors such as skipping breakfast, eating outside and ordering take-out, night eating, inactive leisure activities, reduced household chores and motorized transportation.

This link between broader socio-economic factors and individual lifestyle behaviors reflects how societal factors aggregate health disparities.54,55 Our such findings call for immediate action to develop interventions that are sensitive to the unique needs of diverse populations (e.g., populations with different sex, age, social economic status, residence and geographic areas), so as to mitigate obesity-related health disparities.

Current policies for obesity prevention and control

Our investigation of related national policies shows considerable gaps and challenges, and the growing prevalence of obesity further reflects a clear disconnect between the number of policies and their real-world impact. Over the past four decades, China's obesity prevention frameworks have been disproportionately emphasized individual behavioral changes, overlooking the role of structural conditions like legislation and laws that shape the environment in which individual choices are made. Although laws like the Advertising Law aims to mitigate the false claims of weight-loss products, their enforcement remains weak, particularly concerning the unhealthy-food marketing.56 Benchmarking with global frameworks also suggests that certain upstream structural interventions recommended by WHO, including fiscal police and regulatory restrictions, have not yet been fully adopted in China. Target population coverage is also imbalanced: only 8.6% of policies are tailored for individuals with obesity, reflecting a focus on universal prevention and a notable gap in customized interventions for affected groups. This “prevention-over-intervention” tendency may preclude obese individuals from accessing precise dietary guidance, exercise interventions, and health management services, which not only impairs the control of individual obesity progression and comorbidity risks but also undermines the closed-loop efficacy of overall prevention initiatives. Furthermore, only 37.1% of policies are executive-oriented, most lacking detailed implementation plans and clear accountability mechanisms, thus directly limiting their effectiveness. Obesity control requires cross-departmental collaboration (health, education, market supervision, sports, etc.) to clarify responsibilities, refine processes, and implement assessments. However, the “soft constraint” of current policies leads to weak implementation and fragmented resources, failing to consolidate a cohesive prevention and control force.

In 2024, China launched a national three-year Weight Management Year Campaign, which was further endorsed by the 2025 National People's Congress and Chinese People's Political Consultative Conference.57 This initiative has attracted great attention and responses from the public. The campaign aims to build a supportive environment for weight management, improve awareness and skills of weight management and promote healthy lifestyles across the population. The plan also emphasized the importance of involvement of multiple stakeholders, including government agencies, healthcare institutions, industries, workplaces, families and individuals. It marks a shift from individual-centered approaches towards systemic, government-led national interventions.

Recommendations for future work

China has a moderate prevalence of overweight and obesity (57% in 2023) compared with developed economies (e.g., USA and UK).58 Our projections suggest that without decisive action, China is on track to match comparable levels within the next decade. Building on multilevel risk factors identified through meta-analysis, system-level gaps highlighted by expert consultation, and critical shortcomings revealed by policy analysis, multifaceted, life-course and population-sensitive approaches are necessary to fight obesity in China. To support the national three-year Weight Management Year Campaign, the recommendations below are explicitly linked to key findings from our quantitative synthesis and expert-derived evidence:

  • (1)

    Strengthen multisectoral governance to address systemic drivers of obesity. Expert consultation consistently identified fragmented regulatory authority across food systems, urban planning, and health promotion, along with the absence of a single body accountable for population-level obesity outcomes, as major barriers to effective prevention. Establishing a multi-stakeholder governance framework, anchored by a designated national coordinating body (e.g., under the State Council or National Health Commission), is essential to ensure cross-sectional accountability, resource alignment, and coordinated actions across health, food systems, urban planning, education, media, and legislation.

  • (2)

    Implement legal and fiscal measures targeting high-risk dietary factors. Our meta-analysis identified sugar-sweetened beverage (SSB) intake and red meat consumption as significant risk factors for adult obesity, providing empirical justification for fiscal policies such as SSB taxation. Stronger enforcement of existing regulations is also required. In particular, improved implementation of the Advertising Law is needed to curb misleading food marketing practices, which may reinforce features of the existing “modern dietary pattern”. This dietary pattern has been associated with increased central obesity risk.

  • (3)

    Prioritize targeted interventions across high-risk life stages and population subgroups. Life-course and population-specific risk stratification is critical. Macrosomia, identified as an early-life marker associated with later obesity, supports integrating maternal weight management, gestational weight gain monitoring, and gestational diabetes control into routine prenatal care. Targeted strategies are also warranted for high-risk subgroups, including menopausal women, as well as older adults and individuals living in urban areas, who showed higher risks of overweight and obesity. Existing maternal and child health service and primary health care systems provide feasible platforms for delivering screening, counseling, and tailored lifestyle interventions.

  • (4)

    Reform urban and built environments to counter obesogenic behaviors. Prolonged sitting time and excessive screen time were significantly associated with central obesity, highlighting the interplay between individual behavioral choices and structural constraints imposed by the built environment. Expert consultation further emphasized the role of rapid urbanization in shaping these behaviors. Urban planning policies should therefore mandate “active design” principles, such as pedestrian-friendly infrastructure, mixed land use, and transit-oriented development, to promote population-level physical activity.

  • (5)

    Advance implementation-oriented research to support evidence-based policymaking. Our policy review and expert consultation revealed a persistent implementation-design mismatch, with many interventions lacking rigorous evaluation or real-world scalability. Greater investment is needed in randomized trials, high-quality prospective studies, and policy-driven implementation research to evaluate effectiveness, equity impact, and scalability, alongside the strategic use of digital technologies to support adaptive, data-driven policing.

Strengthens and limitations

This study has several strengths. It is the first comprehensive, large-scale meta-analysis to identify the risk factors for obesity and quantify their relative importance in China, providing an evidence-based foundation for designing more targeted, feasible and cost-effective interventions. By integrating these meta-analysis quantitative results with expert consultation and applying SEM, we mapped obesity determinants both individual and systemic-levels, supporting the development of more comprehensive strategies aligned with real-world complexity. We also systematically reviewed and benchmarked 70 national-level obesity-related policies against global WHO frameworks. This revealed critical gaps in upstream interventions, such as fiscal policies and regulatory restrictions. Importantly, our study draws attention to the disconnect between the existence of national policies and their limited real-world impact, highlighting a policy-implementation gaps that is rarely addressed in the current literature. As a rapidly developing economy, China is well positioned to pioneer an early-stage, evidence-based obesity interventions that could offer valuable insights for other counties facing similar challenges.

This study has several limitations. While we conducted a comprehensive meta-analysis, the reliance on observational studies with cross-sectional designs, may introduce residual confounding and limit causal inferences. The 95% confidence intervals for our long-term projections of overweight and obesity prevalence in 2030 and 2035 were derived from a parametric bootstrap approach based on polynomial regression models; the validity of these intervals relies on the assumption that historical prevalence trends and residual distributions will remain stable through 2030 and 2035, and they may not account for unforeseen shifts in public health policies, socioeconomic factors, or population health behaviors that could alter the trajectory of overweight and obesity prevalence. Heterogeneity exposure definitions necessitated categorizing risk factors based on relative comparisons (e.g., high-vs. low intake), which may reduce precision, introduces classification bias and prevention accurate estimation of population-attributable for each determinant. Limited availability of studies on meso- and macro-level determinants restricted our ability to conduct quantitative analysis of these broader influences. Despite our extensive efforts to retrieve relevant policy documents, the review may still be constrained by the incomplete public availability and accessibility of certain policy documents. The subjective nature of SEM-based categorization may also limit the consistency and generalizability of policy interpretation.

Conclusion

For four decades, China has been experiencing a steady increase in overweight and obesity rates, driven by multilevel and systemic determinants. The meta-analysis provides robust evidence on key individual risk factors, particularly dietary behaviors, physical activity, sleep, and early-life exposures, while experts consultations highlight the importance of broader environmental and policy influences. Despite increasing national efforts, current obesity prevention strategies in China remain largely ineffective, indicating a misalignment with the root causes of the epidemic, and/or poor implementation of existing policies. There is an urgent need for a comprehensive, government-led strategy supported by robust evidence. This strategy should incorporate regulatory measures, engage multiple stakeholders, adopt a life-course approach, and prioritize vulnerable subgroups. Strengthening supply-side reforms, such as promoting access to healthy food and fostering a health-friendly environment, is essential to achieve long-term public health goals.

Contributors

YF W, JJ H and JD Z conceptualized the study and developed the protocol. JJ H, JD Z and JC X have contributed to data collection and drafting the manuscript. DL W and YF W provided supervision and admiration support for the study. JJ H, L Z, and X W developed the search strategy and did the literature search. BR L, JD Z, YD X, JC X, BY L, YF J and FY C did data curation. BR L, JD Z, YD X, and BY L assessed data quality. BR L and JC X analyzed the data. All authors revised the manuscript and provided input for data interpretation. All authors had contributed to the study and had final responsibility for the decision to submit for publication.

Data sharing statement

Most data generated or analyzed in this article are included in the appendix, and are available from the first author.

Declaration of interests

None.

Acknowledgements

We want thank the fundamental work of the student group in China Medical University, Huazhong University of Science and Technology University, Xi'an Jiaotong University, University of Science and Technology of China and Capital Medical University. We are grateful to Professor Qijun Wu and Dr. Heli Xu, who contributed in conducting the literature search and data extraction.

Funding: This work was supported in part by the Chinese National Science and Technology Innovation 2030, Noncommunicable Chronic Diseases-National Science and Technology Major Project (Grant No. 2023ZD0508500, 2023ZD0508504). The funders had no role in the design of this study, analyses, interpretation of the data, writing of the paper, or decision to publish.

Footnotes

Appendix A

Supplementary data related to this article can be found at https://doi.org/10.1016/j.lanwpc.2026.101833.

Contributor Information

Jianduan Zhang, Email: jd_zh@hust.edu.cn.

Deliang Wen, Email: dlwen@cmu.edu.cn.

Youfa Wang, Email: youfawang@gmail.com, youfawang@xjtu.edu.cn.

Appendix A. Supplementary data

Supplementary Materials
mmc1.docx (12.5MB, docx)
Referencce 47
mmc2.pdf (718KB, pdf)
Referencce 56
mmc3.pdf (204.1KB, pdf)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Materials
mmc1.docx (12.5MB, docx)
Referencce 47
mmc2.pdf (718KB, pdf)
Referencce 56
mmc3.pdf (204.1KB, pdf)

Articles from The Lancet Regional Health: Western Pacific are provided here courtesy of Elsevier

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