Predictive value of the triglyceride glucose index-a body shape index (TyG-ABSI) for cardiovascular disease and its comparison with other TyG-related obesity indices: a study based on the China health and retirement longitudinal study (CHARLS) cohort

BACKGROUND: Insulin resistance (IR) and obesity are recognized as major drivers of cardiovascular disease (CVD). The triglyceride-glucose index-a body shape index (TyG-ABSI), a novel metric integrating lipid metabolism with body morphology, may enhance risk stratification. Although this index has been verified in Western cohorts, its long-term prognostic value and prospective incremental benefit over conventional indices remain uncharacterized in the Chinese population, who present a distinct East Asian adiposity phenotype. METHODS: Utilizing follow-up data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2020, we enrolled participants free of CVD at baseline with complete essential information. Cox proportional hazards models were employed to estimate the associations between TyG-ABSI and incident CVD, while restricted cubic splines (RCS) were utilized to characterize dose-response relationships. The discriminative power of TyG-ABSI was evaluated using time-dependent receiver operating characteristic (ROC) curves and C-indices, with comparisons against other related indices, including ABSI, triglyceride-glucose (TyG), TyG-body mass index (TyG-BMI), TyG-Chinese visceral adiposity index (TyG-CVAI), TyG-waist circumference (TyG-WC) and TyG-waist-to-height ratio (TyG-WHtR). Predictive increments were quantified via net reclassification improvement (NRI) and integrated discrimination improvement (IDI), and clinical utility was assessed through decision curve analysis (DCA). Cross-parameter correlation testing was executed to evaluate index collinearity. RESULTS: Among 7197 participants, 1267 incident CVD cases occurred during the follow-up. In the fully adjusted model, baseline TyG-ABSI was independently associated with an increased risk of composite CVD (HR = 1.08, 95% CI 1.01-1.14, P = 0.016) and incident stroke (HR = 1.16, 95% CI 1.05-1.28, P = 0.003), whereas no independent correlation was identified for heart conditions. RCS analysis revealed no significant non-linear association for either CVD (P = 0.855) or stroke (P = 0.728). TyG-ABSI improved prediction over ABSI alone, but it offered no advantage over traditional indices. DCA confirmed that traditional indices had better net benefit than TyG-ABSI or its components. The independent predictive value remained highly consistent across sensitivity analyses. Correlation analysis revealed that while traditional indices exhibited severe multicollinearity clustering (r: 0.751-0.890), ABSI achieved near-perfect orthogonal independence from BMI (r = - 0.056). CONCLUSIONS: TyG-ABSI was significantly and positively associated with an increased risk of incident CVD. Although TyG-ABSI as an isolated screening tool did not surpass traditional parameters like TyG-BMI in overall prospective accuracy, it exhibited only a negligible correlation with BMI, demonstrating that this metric is not redundant. Consequently, TyG-ABSI retains the potential to capture specific pathogenic signals independent of gross body mass. Future risk stratification toolkits should consider transitioning toward an integrated Anthropometric Risk Indicator (ARI) framework that couples the metabolic sensitivity of TyG-BMI with the specific body shape risks indicated by TyG-ABSI.