Association between triglyceride-glucose derived indices with cardiometabolic multimorbidity: Findings from the Atherosclerosis Risk in Communities study

Background Cardiometabolic multimorbidity (CMM), characterized by the co-occurrence of diabetes mellitus (DM), stroke, and coronary heart disease (CHD), imposes substantial global health burden owing to its association with elevated mortality risk, reduced functional capacity, and increased healthcare costs. Despite its clinical importance, the value of insulin resistance (IR) and its surrogates, particularly triglyceride-glucose (TyG) indices combined with anthropometric measures, in predicting CMM remains underexplored. Methods In this study, we aimed to quantify the association between TyG-derived indices and incident CMM. For this purpose, we conducted a multivariate logistic regression analysis of data of the Atherosclerosis Risk in Communities (ARIC) study, deriving adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Nonlinear associations were investigated using restricted cubic spline modeling and diagnostic accuracy was evaluated using area under the curve (AUC) values from receiver operating characteristic (ROC) analyses. Results Nonlinear relationships were observed between TyG-body mass index (TyG-BMI) and TyG-waist-to-height ratio, whereas TyG and TyG-waist circumference exhibited linear trends. TyG-BMI demonstrated the strongest association with CMM risk, showing a 1.61-fold increase per standard deviation (adjusted OR: 1.61; 95% CI: 1.48–1.73) and a 5.67-fold higher risk in the highest versus the lowest quartiles. Predictive performance analysis revealed that TyG-BMI was the most discriminative marker (AUC: 0.684; 95% CI: 0.664–0.705). Conclusions TyG-BMI emerged as a robust predictor of CMM risk, highlighting the synergistic effects of IR and adiposity. The nonlinear risk escalation suggests threshold-dependent mechanisms, emphasizing its utility in early risk stratification.