Joint contributions of metabolic dysfunction and biological aging to cardiometabolic multimorbidity and disease progression: a prospective cohort study

Background: Cardiometabolic multimorbidity (CMM), which refers to having two or more cardiometabolic conditions like type 2 diabetes, stroke, and coronary heart disease, is becoming an increasing global health challenge. Although metabolic dysfunction and biological aging may jointly contribute to CMM development, most previous studies have examined these dimensions separately. Whether their combined assessment improves risk stratification and prediction across the cardiometabolic disease continuum remains unclear. Methods: This prospective cohort study involved 8,767 participants aged 45 and older who did not have CMM at the start, as part of the China Health and Retirement Longitudinal Study (CHARLS). Baseline evaluations included the triglyceride-glucose (TyG) index and two biological age algorithms, Light BA and KDM BA. The residual from regressing biological age on chronological age was used to derive BAA. Continuous TyG BA composite indices were constructed as the products of TyG and biological age. Cumulative exposure and two wave trajectory analyses used repeated measurements from 2011 and 2015. Multistate models examined associations across the cardiometabolic disease continuum. Cox proportional hazards models, along with restricted cubic splines and time dependent discrimination analyses, were utilized to examine associations, dose response relationships, and incremental predictive performance. Results: During a median follow-up span of 108 months, 873 participants were newly diagnosed with CMM. TyG and biological age were independently associated with CMM, with mutually adjusted hazard ratios of 1.23 to 1.27 and 1.39 to 1.44 per standard deviation increase, respectively. Individuals with elevated TyG and rapid biological aging faced the greatest CMM risk, showing hazard ratios of 2.37 for Light BA and 2.26 for KDM BA, despite the absence of a significant multiplicative interaction. Continuous TyG BA composites were associated with 49% to 62% higher CMM risk per standard-deviation increase, with more than threefold higher risk in the highest versus lowest quartile and nonlinear dose response relationships. Significantly increased CMM risk was linked to higher cumulative exposure and elevated two wave trajectory levels, with hazard ratios ranging from 3.93 to 5.17 when comparing the highest and lowest exposure groups. Multistate analyses demonstrated consistent associations of the composites with transitions across the cardiometabolic disease continuum and with mortality. Adding TyG BA composites to the prespecified clinical model increased the Cindex by 0.015 to 0.024 and improved net clinical benefit, but did not improve discrimination beyond models containing TyG and biological age as separate covariates. Associations were stronger in younger and non frail participants in exploratory subgroup analyses. Conclusions: Metabolic dysfunction and biological aging represent complementary dimensions of CMM susceptibility and progression. TyG BA composites provide a parsimonious summary of combined metabolic-aging burden and improve risk discrimination beyond conventional clinical factors, but should not be interpreted as superior to models retaining TyG and biological age separately. These findings support the potential utility of a metabolic aging framework for risk stratification and warrant external validation, particularly for its application in earlier stages of cardiometabolic disease development. Keywords: cardiometabolic multimorbidity; TyG index; biological age; metabolic aging composite; risk stratification; prospective cohort study