Cardiovascular diabetology · 2026
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.
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You Lili, Zhao Wenbo, Zheng Meiguang, Hong Xiaosi, Ren Meng, Li Wenpeng
Аннотация
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.
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.
Предметные рубрики MeSH
HumansFemaleAdiposityInsulin ResistanceMiddle AgedMalePhenotypeChinaObesityLongitudinal StudiesAgedTime FactorsInflammationInflammation MediatorsRisk AssessmentMultimorbidityIncidenceBiomarkersCardiometabolic Risk FactorsMetabolic SyndromeDiabetes MellitusCardiovascular DiseasesBlood GlucoseAge FactorsRisk Factors