Assessment of cardiovascular disease risk prediction using combination of TyG and ACEF in an elderly population

PURPOSE: To explore the predictive value of TyG index and ACEF score for the risk of cardiovascular disease (CVD) in an elderly population. METHODS: A total of 2047 participants without history of CVD were enrolled in the follow-up. The endpoint was CVD incidence which was defined as stroke or coronary heart disease (CHD) during the follow-up period. Cox regression analyses was used to calculate hazard ratios. Kaplan-Meier curve was used to show the probability of CVD in different quartiles of TyG and ACEF. Restricted cubic spline further explored whether the relationship was linear. Finally, we assessed the discriminatory ability of TyG and ACEF for CVD using C-statistics, net reclassification index (NRI), and integrated discrimination improvement (IDI). RESULTS: During a median follow-up time of 4.66 years, 100 participants had CVD. Kaplan-Meier curve showed that TyG and ACEF were associated with CVD and participants with high TyG and ACEF were significantly more likely to have CVD. In the multivariate Cox regression analysis, the adjusted hazard ratios (HR) for TyG and ACEF were 1.21 (1.02-1.43) in TyG and 1.63 (1.25-2.17) in ACEF. In addition, multivariable adjusted HRs of TyG and ACEF still increased for CVD when stratified by various factors in subgroup analysis. Moreover, after adding TyG and ACEF to original risk prediction model, new model has higher C statistics of CVD (C-statistics = 0.733 than the original model (C-statistics = 0.656). Meanwhile, the results of NRI = 0.450 and IDI = 0.009 indicate that TyG and ACEF had enhancing effect on the prediction of CVD. CONCLUSIONS: Our study showed that TyG and ACEF were associated with CVD in elderly populations in eastern China. Furthermore, it suggests that TyG could be a new tool for identifying potential patients at high risk of primary CVD in elderly population.