Abstract
BACKGROUND: Metabolic Syndrome (MetS) significantly increases the risk of cardiovascular disease (CVD), with central obesity and insulin resistance as major contributors. The TyG-ABSI index is a newly proposed composite measure that combines the TyG index and ABSI, aiming to assess both insulin resistance and central obesity simultaneously. Previous studies have shown that TyG-ABSI has potential in predicting cardiovascular mortality, but its applicability in MetS populations remains unclear. This study aims to explore the association between TyG-ABSI and cardiovascular events in individuals with MetS and compare its predictive value with the traditional TyG index in this specific population. METHODS: Participants from the National Health and Nutrition Examination Survey (NHANES) between 2001 and 2018 were selected, with all data weighted for sample design, clustering, and stratification to ensure national representativeness. Associations between TyG-ABSI and other TyG indices with cardiovascular mortality and all-cause mortality were assessed using weighted Cox proportional hazards models; CVD prevalence was analyzed using weighted logistic regression models. Additional analyses included Kaplan-Meier survival curves and restricted cubic spline regression. Model performance was compared between TyG-ABSI, TyG, and its derived indices using ROC curves, NRI, IDI, and DCA. E-value, subgroup analyses, and competing risks models were conducted to assess robustness. RESULTS: This study analyzed data from 12,813 individuals with metabolic syndrome in the NHANES cohort to systematically compare the performance of TyG-ABSI and other TyG-related indices in assessing CVD and mortality. The results revealed significant associations between TyG-ABSI and CVD, cardiovascular mortality, and all-cause mortality. Specifically, for each 1-unit increase in TyG-ABSI, the risk of CVD increased by 28%, cardiovascular mortality by 25%, and all-cause mortality by 28%. These associations showed a dose-response relationship in stratified analyses based on tertiles, and TyG-ABSI outperformed the traditional TyG index in overall analysis. Compared to other TyG-related indices, TyG-ABSI demonstrated superior predictive performance in metrics such as the ROC curve, NRI, and DCA. Further analyses, including competing risks models, E-value estimation, and RCS modeling, confirmed the robustness of these associations. Subgroup analyses also supported the stability of TyG-ABSI, with limited interaction effects. CONCLUSION: Our study highlights the value of TyG-ABSI in assessing cardiovascular disease and mortality risk in populations with MetS, providing new evidence for medical practice and public health interventions.