Evidence map›Paper›PMID 41889380›Full record

ArticleThe Lancet regional health. Western Pacific2026

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

Jiajin Hu, Jinchen Xie, Borui Liu, Wen Peng, Buyun Liu, Yinkun Yan, Ling Zhang, Xue Wang, Yuandi Xi, Yanan Ma and 7 more

Abstract read
In one paragraph

Article in The Lancet regional health. Western Pacific, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Jiajin HuHealth Sciences Institute, China Medical University, Shenyang, Liaoning, China.
Jinchen XieDepartment of Sociology, Northwest Agriculture and Forestry University, Xian Yang, Shaanxi, China.
Borui LiuHealth Sciences Institute, China Medical University, Shenyang, Liaoning, China.
Wen PengDepartment of Public Health, Medical College, Qinghai University, Xining, Qinghai, China.
Buyun LiuInstitute of Public Health Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
Yinkun YanCenter for Noncommunicable Disease Management, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
Ling ZhangDepartment of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.
Xue WangMedical Information Research Department/Library, Xuanwu Hospital, Capital Medical University, Beijing, China.
Yuandi XiSchool of Public Health, Capital Medical University, Beijing, China.
Yanan MaSchool of Public Health, China Medical University, Shenyang, Liaoning, China.
Yanhui LuSchool of Nursing, Peking University, Beijing, China.
Yanfen JiangDepartment of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Fengyan ChenDepartment of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Izzuddin M ArisDivision of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA.
Jianduan ZhangDepartment of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Deliang WenHealth Sciences Institute, China Medical University, Shenyang, Liaoning, China.
Youfa WangHealth Sciences Institute, China Medical University, Shenyang, Liaoning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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).

Indexed as

ChinaExpert consultationsMeta-analysisObesityPolicyRisk factors

Identifiers

PMID41889380
PMCPMC13015754

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.