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Background <p>Cardiometabolic multimorbidity (CMM) burdens aging populations. Obesity drives CMM via insulin resistance and inflammation, but their nonlinear and combined effects remain unclear. We elucidated how these factors contribute to CMM incidence.</p> Methods <p>From CHARLS 2011 to 2018, 6,510 participants were enrolled. CMM was defined as ≥ 2 of diabetes, heart disease, and stroke. Cox regression, Kaplan-Meier, and Fine-Gray models were used. Restricted cubic splines (RCS) evaluated nonlinear relationships. Multiplicative and additive interactions were assessed, and mediation analysis with 1,000 bootstraps estimated indirect effects. K-means clustering based on eight standardized variables, including age, body mass index (BMI), waist circumference (WC), triglyceride-glucose (TyG), high-sensitivity C-reactive protein (hs-CRP), systolic blood pressure (SBP), high-density lipoprotein cholesterol (HDL-C), and fasting plasma glucose (FPG), identified metabolic phenotypes carried high CMM risk.</p> Results <p>Over 7 years, 212 (3.26%) developed CMM. RCS revealed a J-shaped association between WC and CMM. Optimal cut-offs were 60 years for age, 25.6&#xa0;kg/m² for BMI, 90.6&#xa0;cm for WC, 8.7 for the TyG index, 154.3&#xa0;mg/dL for LDL-C, and 0.86&#xa0;mg/L for hs-CRP. All six parameters independently predicted CMM. No significant additive interactions were found, but dual elevation markedly increased risk. The TyG index mediated 14.6% of the BMI effect and 10.0% of the WC effect on CMM. Clustering identified insulin-resistant and obese-insulin-resistant phenotypes.</p> Conclusion <p>Optimal cut-offs offer practical screening tools. Dual elevation markedly increases CMM risk and insulin resistance mediates adiposity effects. Clustering identified insulin-resistant and obese-insulin-resistant phenotypes, supporting phenotype-based prevention.</p> Graphical Abstract <p></p>

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Effects of adiposity, insulin resistance, and inflammation on cardiometabolic multimorbidity: insights from interaction analyses and metabolic phenotyping in the China health and retirement longitudinal study 2011–2018

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月3日

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  • Lili You,
  • Wenbo Zhao,
  • Meiguang Zheng,
  • Xiaosi Hong,
  • Meng Ren,
  • Wenpeng Li

摘要

Background

Cardiometabolic multimorbidity (CMM) burdens aging populations. Obesity drives CMM via insulin resistance and inflammation, but their nonlinear and combined effects remain unclear. We elucidated how these factors contribute to CMM incidence.

Methods

From CHARLS 2011 to 2018, 6,510 participants were enrolled. CMM was defined as ≥ 2 of diabetes, heart disease, and stroke. Cox regression, Kaplan-Meier, and Fine-Gray models were used. Restricted cubic splines (RCS) evaluated nonlinear relationships. Multiplicative and additive interactions were assessed, and mediation analysis with 1,000 bootstraps estimated indirect effects. K-means clustering based on eight standardized variables, including age, body mass index (BMI), waist circumference (WC), triglyceride-glucose (TyG), high-sensitivity C-reactive protein (hs-CRP), systolic blood pressure (SBP), high-density lipoprotein cholesterol (HDL-C), and fasting plasma glucose (FPG), identified metabolic phenotypes carried high CMM risk.

Results

Over 7 years, 212 (3.26%) developed CMM. RCS revealed a J-shaped association between WC and CMM. Optimal cut-offs were 60 years for age, 25.6 kg/m² for BMI, 90.6 cm for WC, 8.7 for the TyG index, 154.3 mg/dL for LDL-C, and 0.86 mg/L for hs-CRP. All six parameters independently predicted CMM. No significant additive interactions were found, but dual elevation markedly increased risk. The TyG index mediated 14.6% of the BMI effect and 10.0% of the WC effect on CMM. Clustering identified insulin-resistant and obese-insulin-resistant phenotypes.

Conclusion

Optimal cut-offs offer practical screening tools. Dual elevation markedly increases CMM risk and insulin resistance mediates adiposity effects. Clustering identified insulin-resistant and obese-insulin-resistant phenotypes, supporting phenotype-based prevention.

Graphical Abstract