China CDC weekly · 2026
Development and Application of a Metabolic Health Index for the Chinese Population Aged 18 Years and Above.
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Zhang Yujing, Fan Xiaoyan, Zhang Tianhao, Wang Junjing, Wang Wei, Kan Juntao, Luo Rong, Zhu Yizhun, Zhao Zhenping
Аннотация
INTRODUCTION: Metabolic health status plays a central role in the development of a broad spectrum of chronic diseases. Yet existing assessment approaches rely on comparatively complex measurement techniques, which constrain their widespread adoption and limits their utility in population-based metabolic health management.
METHODS: The research employed a two-phase approach to construct a metabolic health index. We conducted a Delphi consultation to define and prioritize key metabolic health indicators, then drew on data from the China Chronic Disease and Risk Factor Surveillance, linked to the national mortality surveillance system, and built Cox proportional hazards models to estimate indicator-specific weights. Using these weights, we developed the Metabolic Health Index (MHI) and established a classification system to stratify metabolic health subtypes. We subsequently analyzed MHI score distributions and subtype prevalence across population subgroups.
RESULTS: The Delphi process identified 11 indicators for inclusion in the model: age, smoking, alcohol consumption, body-mass index, waist-to-height ratio, fasting blood glucose, blood pressure, blood lipids, serum uric acid, metabolic-associated fatty liver disease, and family history of metabolic diseases. Applying the classification system, we identified 13 metabolic health subtypes across 7 categories. MHI scores declined with age, and females consistently outscored males across all age groups. Over half of the participants (54.5%) had relatively high MHI scores, and the proportion with higher scores decreased with age.
CONCLUSION: The MHI converts population health data into a quantitative metric for assessing metabolic health in adults and provides a practical approach for public health practice across diverse regions and settings.
METHODS: The research employed a two-phase approach to construct a metabolic health index. We conducted a Delphi consultation to define and prioritize key metabolic health indicators, then drew on data from the China Chronic Disease and Risk Factor Surveillance, linked to the national mortality surveillance system, and built Cox proportional hazards models to estimate indicator-specific weights. Using these weights, we developed the Metabolic Health Index (MHI) and established a classification system to stratify metabolic health subtypes. We subsequently analyzed MHI score distributions and subtype prevalence across population subgroups.
RESULTS: The Delphi process identified 11 indicators for inclusion in the model: age, smoking, alcohol consumption, body-mass index, waist-to-height ratio, fasting blood glucose, blood pressure, blood lipids, serum uric acid, metabolic-associated fatty liver disease, and family history of metabolic diseases. Applying the classification system, we identified 13 metabolic health subtypes across 7 categories. MHI scores declined with age, and females consistently outscored males across all age groups. Over half of the participants (54.5%) had relatively high MHI scores, and the proportion with higher scores decreased with age.
CONCLUSION: The MHI converts population health data into a quantitative metric for assessing metabolic health in adults and provides a practical approach for public health practice across diverse regions and settings.