Combined association of triglyceride-glucose index and the atherogenic index of plasma with the incidence of cardiovascular diseases among middle-aged and older population

BACKGROUND: Cardiovascular diseases (CVDs) are the leading cause of death worldwide. The atherogenic index of plasma (AIP) reflects atherogenic dyslipidemia and triglyceride-glucose (TyG) index is a surrogate of insulin resistance (IR). Evidence on their combined value for CVDs risk stratification remain limited. In this study, the associations between baseline levels and longitudinal changes of the composite TyG-AIP index and the incidence of CVDs were evaluated among middle-aged and older adults. METHODS: Data were obtained from the China Health and Retirement Longitudinal Study (CHARLS). A total of 6,986 participants were included in the baseline analysis(2012-2020), and 4,134 participants with repeated biomarker measurements in 2012 and 2015 were included in the longitudinal trajectory analysis. Multivariable cox proportional hazards models and restricted cubic spline(RCS) were applied to evaluate the associations of TyG, AIP, TyG 0.631 for stroke; 0.605 for heart disease). NRI and IDI analyses demonstrated that adding TyG-AIP significantly improved risk reclassification for CVDs (NRI, 0.036-0.054, P < 0.001) and stroke (NRI, 0.096-0.114, P < 0.001). In longitudinal analyses (N = 4,134), participants in the cluster with persistently high and rising TyG-AIP values exhibited the highest risks of CVDs (adjusted HR 1.25, 95% CI: 1.04-1.51), stroke (HR 1.43, 95% CI: 1.05-1.95), and heart disease (HR 1.25, 95% CI: 1.01-1.54). CONCLUSION: Both baseline and longitudinal changes of TyG-AIP were independently associated with the risk of developing CVDs, especially stroke, in middle-aged and older Chinese adults. Repeated assessment of TyG-AIP captured cardiometabolic deterioration over time and improved identification of individuals at elevated cardiovascular risk. Incorporating long-term monitoring of TyG-AIP into routine health evaluations may enhance population-level CVDs risk prediction and support more effective prevention strategies.