Comparative analysis of 16 baseline obesity and lipid-related indices for cardiovascular disease risk prediction in adults with cardiovascular-kidney-metabolic syndrome stages 0–3: a nationwide prospective cohort study
摘要
In the context of the newly defined cardiovascular-kidney-metabolic (CKM) syndrome, this study aimed to systematically compare the predictive value of 16 different obesity- and lipid-related indices for new-onset cardiovascular disease (CVD) in a population with CKM stages 0–3.
MethodsThis prospective cohort study utilized data from the China Health and Retirement Longitudinal Study (CHARLS). A total of 5,782 participants aged 45 and older, free of CVD at baseline and classified within CKM stages 0–3, were included. We evaluated 16 indices, including traditional markers (e.g., BMI, WHtR) and novel composite markers (e.g., TyG-WC, CVAI, CTI). The primary outcome was incident CVD over a 6-year follow-up. Cox proportional hazards models were used to assess associations. Predictive performance was compared using the C-index, Akaike Information Criterion (AIC), Integrated Discrimination Improvement (IDI), and Decision Curve Analysis (DCA).
ResultsDuring the 6-year follow-up, 1,134 incident CVD events occurred. In the fully adjusted Cox model, the triglyceride-glucose waist circumference index (TyG-WC) demonstrated the strongest association with CVD risk, with each 1-standard deviation increase corresponding to a 15% higher risk (HR = 1.15, 95% CI: 1.08–1.22, P < 0.001). A prediction model incorporating TyG-WC showed the best performance, with a higher C-index (0.6434), a significant improvement in discrimination (IDI = 0.0035, P < 0.001), and the greatest net benefit in Decision Curve Analysis. The findings remained robust in both landmark and sensitivity analyses.
ConclusionThis study, based on nationally representative CHARLS cohort data, systematically compared the CVD risk prediction ability of 16 obesity and lipid-related indices in adults with CKM stages 0–3. The study found that the TyG-WC index demonstrated the strongest CVD risk prediction ability. These indices provide effective assessment tools for CVD risk stratification in CKM stages 0–3 populations through different pathophysiological mechanisms.