The association between triglyceride-glucose index and related parameters and risk of cardiovascular disease in American adults under different glucose metabolic states

Abstract

Background Cardiovascular disease (CVD) encompasses an array of cardiac and vascular disorders, posing a significant threat to global health. It remains unclear whether there exists an association between triglyceride-glucose index (TyG) and its derived indices and the incidence of cardiovascular disease, and in particular, the strength of the association in populations with different glucose metabolisms is not known. Methods Data extracted from the National Health and Nutrition Examination Survey (NHANES) covering the period from 1999 to 2020, involving a cohort of 14,545 participants, were leveraged for the analysis. Statistical assessments were executed utilizing R software, employing multivariable logistic regression models to scrutinize the correlation between TyG and its associated parameters with the incidence of cardiovascular disease across diverse glucose metabolism categories. Interaction analyses and restricted cubic splines were applied to evaluate potential heterogeneity in associations and investigate the link between TyG and its derivatives with the occurrence of cardiovascular disease. Furthermore, receiver operating characteristic curves were constructed to evaluate the extent of variability in the predictive performance of TyG and its derived parameters for cardiovascular disease across distinct glucose metabolic statuses. Results This study found that TyG and its related parameters were differentially associated with the occurrence of cardiovascular disease in different glucose metabolic states. Curvilinear correlations were found between TyG in the IFG population and TyG-WC, TyG-BMI, and TyG-WHtR in the impaired glucose tolerance (IGT) population with the occurrence of cardiovascular disease. In addition, the introduction of TyG and its derived parameters into the classical Framingham cardiovascular risk model improved the predictive performance in different glucose metabolism populations. Among them, the introduction of TyG-WHtR in the normal glucose tolerance (NGT), impaired fasting glucose (IFG), IFG & IGT and diabetes groups and TyG in the IGT group maximized the predictive power. Conclusions The findings provide new insights into the relationship between the TyG index and its derived parameters in different glucose metabolic states and the risk of cardiovascular disease, offering important reference value for future clinical practice and research. The study highlights the potential for improved risk stratification and prevention strategies based on TyG and its derived parameters.