BMI-discrepant phenotypes and transitions reveal sex-specific heterogeneity and reshape cardiometabolic risk in Chinese adults

Body mass index (BMI) alone inadequately captures the heterogeneous cardiometabolic risk associated with obesity. In four Chinese cohorts( N = 175,642), BMI–biomarker phenotypic profiles were identified using residual-based clustering of seven routine biomarkers, revealing sex-specific metabolic patterns associated with type 2 diabetes(T2D), chronic kidney disease (CKD) and cardiovascular disease (CVD). The high triglycerides (HTG) profile increased T2D risk and the high blood pressure (HBP) profile increased CVD risk across sexes. Showing distinct sex differences, the high fasting glucose (HFG) profile elevated T2D risk in women (hazard ratio [HR] 2.38[2.03–2.78]) while increasing CVD risk in men (HR 1.12, 95% CI 1.05–1.20). During a median follow-up of 3.8 years, certain phenotype transitions were associated with lower subsequent cardiovascular risk in women, with HFG reversion linked to a 68% lower risk of CVD (HR 0.32, 95% CI 0.11–0.90) and HBP reversion to a 50% lower risk of stroke (HR 0.50, 95% CI 0.30–0.86). These findings provide a phenotype-driven framework for characterizing metabolic heterogeneity and exploring temporal changes in cardiometabolic risk.

Authors

Institutions

Publication Details

Journal
Cardiovascular Diabetology
Published
2026-10-29
DOI
https://doi.org/10.1186/s12933-026-03425-4
Primary Topic
Diabetes, Cardiovascular Risks, and Lipoproteins
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

BMI-discrepant phenotypes and transitions reveal sex-specific heterogeneity and reshape cardiometabolic risk in Chinese adults

Yufang Bi, Ruying Hu, 罗佐杰, Qiuyu Cao et al.
5 links
Cardiovascular Diabetology
Diabetes, Cardiovascular Risks, and Lipoproteins
article

BMI-discrepant phenotypes and transitions reveal sex-specific heterogeneity and reshape cardiometabolic risk in Chinese adults

Yufang Bi, Ruying Hu, 罗佐杰, Qiuyu Cao, Yanan Huo, Xiaojing Jia, Tianshu Zeng, Guang Ning, Feixia Shen, Xingkun Xu, Guijun Qin, Guixia Wang, Qin Wan, Yuchen Xu, Mu Yiming, Hong Qiao, Yifang Zhang, Weiqing Wang, Jinfeng Wang, Yingfen Qin, Hong Lin, Yan Li, Zhengnan Gao, Yonghua Yu, Xuefeng Yu, Shengli Wu, Yinfei Zhang, Tiange Wang, Xuejiang Gu, Shuangyuan Wang, Lulu Chen, Lixin Shi, for the 4C Study Group, Jiajun Zhao, Yu Xu, Li Chen, Gang Chen, Youmin Wang, Xinguo Hou, Huacong Deng, Qing Su, Qiang Li, Xulei Tang, Jieli Lu, Chao Liu, Min Xu, Feiyue Huang, Tao Yang, Zhen Ye, Mian Li
article en

Abstract

Body mass index (BMI) alone inadequately captures the heterogeneous cardiometabolic risk associated with obesity. In four Chinese cohorts( N = 175,642), BMI–biomarker phenotypic profiles were identified using residual-based clustering of seven routine biomarkers, revealing sex-specific metabolic patterns associated with type 2 diabetes(T2D), chronic kidney disease (CKD) and cardiovascular disease (CVD). The high triglycerides (HTG) profile increased T2D risk and the high blood pressure (HBP) profile increased CVD risk across sexes. Showing distinct sex differences, the high fasting glucose (HFG) profile elevated T2D risk in women (hazard ratio [HR] 2.38[2.03–2.78]) while increasing CVD risk in men (HR 1.12, 95% CI 1.05–1.20). During a median follow-up of 3.8 years, certain phenotype transitions were associated with lower subsequent cardiovascular risk in women, with HFG reversion linked to a 68% lower risk of CVD (HR 0.32, 95% CI 0.11–0.90) and HBP reversion to a 50% lower risk of stroke (HR 0.50, 95% CI 0.30–0.86). These findings provide a phenotype-driven framework for characterizing metabolic heterogeneity and exploring temporal changes in cardiometabolic risk.

Cardiovascular Diabetology
Fujian Medical University (CN), Nanchang University (CN), Shandong University (CN), Sun Yat-sen University (CN), Shanghai Jiao Tong University (CN), National Health and Family Planning Commission (CN), Dalian University of Technology (CN), XinHua Hospital (CN), Ruijin Hospital (CN), First Affiliated Hospital of Wenzhou Medical University (CN), Southwest Medical University (CN), Affiliated Hospital of Southwest Medical University (CN), Jiading District Central Hospital (CN), Sun Yat-sen Memorial Hospital (CN), Jiangxi Provincial People's Hospital (CN), Shandong Provincial Hospital (CN), First Hospital of Jilin University (CN), First Affiliated Hospital of GuangXi Medical University (CN), Wuhan Union Hospital (CN), The Affiliated Yongchuan Hospital of Chongqing Medical University (CN), Second Affiliated Hospital of Harbin Medical University (CN), Zhejiang Center for Disease Control and Prevention (CN), First Affiliated Hospital of Anhui Medical University (CN), Fujian Provincial Hospital (CN), Karamay Central Hospital (CN), Jiangsu Province Hospital (CN), First Affiliated Hospital of Zhengzhou University (CN), Tongji Hospital (CN), Chinese People's Liberation Army (CN), Qilu Hospital of Shandong University (CN), First Hospital of Lanzhou University (CN), State Key Laboratory of Medical Genomics (CN), Huazhong University of Science and Technology (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, Science and Technology Commission of Shanghai Municipality
Good health and well-being
Openalex Percentile: Top 11%
Diabetes, Cardiovascular Risks, and Lipoproteins
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon