Sex-Specific Associations of Triglyceride-Glucose Index and a Body Shape Index with Cardiometabolic Multimorbidity...

Background: Cardiometabolic multimorbidity (CMM) has emerged as a significant global health challenge. The triglyceride-glucose (TyG) index and a body shape index (ABSI), which are markers of insulin resistance and central obesity, respectively, have each been associated with CMM, but their combined utility remains unclear. This study analyzed the association of cumulative and single-point TyG-ABSI with CMM. Methods: This study included 5334 participants from the China Health and Retirement Longitudinal Study (CHARLS), covering the period from 2011 to 2020. Cumulative TyG-ABSI was derived from data collected in 2011 and 2015. Incident CMM was defined as having at least two of the following: heart disease, stroke, and diabetes. Cox regression models were used to estimate hazard ratios, and restricted cubic splines (RCS) were employed to examine nonlinear associations. Time-dependent receiver operating characteristic (ROC) curves were constructed to compare predictive performance. Subgroup and sensitivity analyses were also performed. Results: Over 9 years of follow-up, 424 participants (7.95%) developed CMM. Those in the highest TyG-ABSI quartile had a 2.46-fold higher risk than those in the lowest quartile. The association varied by sex: cumulative TyG-ABSI showed a nonlinear relationship with a threshold effect in females, whereas in males, only cumulative TyG-ABSI (not the single-point measure) was significant. Cumulative TyG-ABSI demonstrated better 5-year predictive performance than single-point measures. Conclusions: Cumulative TyG-ABSI independently predicts CMM risk across sexes, whereas single-point TyG-ABSI is only predictive in females. Quantifying long-term metabolic burden using cumulative TyG-ABSI offers a practical, noninvasive tool for chronic risk stratification in aging populations. Routine assessment of cumulative TyG-ABSI in clinical and community settings could facilitate early identification of high-risk individuals, enabling targeted preventive interventions to reduce the growing burden of CMM.