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June 5, 2026Cardiovascular Diabetology0 citationsOpen Access

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

LYLili YouWZWenbo ZhaoMZMeiguang Zheng

Key Result

Adiposity, insulin resistance, and inflammation independently predicted cardiometabolic multimorbidity, with the TyG index mediating 14.6% of the BMI effect and 10.0% of the waist circumference effect.

Key Points

  • The study aims to clarify how adiposity, insulin resistance, and inflammation contribute to cardiometabolic multimorbidity incidence.
  • Analyzed data from 6,510 participants in the CHARLS from 2011 to 2018.
  • Utilized Cox regression and mediation analysis with 1,000 bootstraps for indirect effect estimation.
  • Identified metabolic phenotypes through K-means clustering based on eight variables.
  • Over 7 years, 212 (3.26%) participants developed cardiometabolic multimorbidity.
  • A J-shaped association was found between waist circumference and cardiometabolic multimorbidity.
  • The TyG index mediated 14.6% of the BMI effect and 10.0% of the WC effect on cardiometabolic multimorbidity.

Study Design

Type

Cohort (n=6,510)

Structured PICO

P
Population
6,510 participants from the China Health and Retirement Longitudinal Study followed for 7 years to assess the incidence of cardiometabolic multimorbidity.
O
Outcome
Incidence of cardiometabolic multimorbidity (CMM), defined as ≥ 2 of diabetes, heart disease, and strokecomposite

Insulin resistance significantly mediates the effect of adiposity on the development of cardiometabolic multimorbidity, highlighting the importance of phenotype-based prevention strategies.

Abstract

Abstract 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.

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Cite This Study

You et al. (2026) conducted a cohort in Cardiometabolic multimorbidity (CMM) (n=6,510). Adiposity, insulin resistance, and inflammation was evaluated on Incidence of cardiometabolic multimorbidity (≥ 2 of diabetes, heart disease, and stroke). Adiposity, insulin resistance, and inflammation independently predicted cardiometabolic multimorbidity, with the TyG index mediating 14.6% of the BMI effect and 10.0% of the waist circumference effect.

synapsesocial.com/papers/6a2267c3763171746d54673fhttps://doi.org/10.1186/s12933-026-03234-9
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