The association between triglyceride-glucose index and its combination with obesity indicators and cardiovascular disease: NHANES 2003–2018

Abstract Background In the American population, the relationship between the triglyceride-glucose (TyG) index and TYG combined with indicators of obesity and cardiovascular disease (CVD) and its mortality has been less well studied. Methods This cross-sectional study included 11,937 adults from the National Health and Nutrition Examination Survey (NHANES) 2003–2018. Cox proportional hazards model, binary logistic regression analyses, restricted cubic spline (RCS), and receiver operating characteristic (ROC) were used to analyze the relationship between TyG and its combined obesity-related indicators and CVD and its mortality. Mediation analysis explored the mediating role of glycated hemoglobin and insulin in the above relationships. Results In this study, except for no significant association between TyG and CVD mortality, TyG, TyG-WC, TyG-WHtR, and TyG-BMI were significantly and positively associated with CVD and CVD mortality. TyG-WHtR is the strongest predictor of CVD mortality (HR 1.66, 95% CI 1.21–2.29). The TyG index correlated better with the risk of coronary heart disease (OR 2.52, 95% CI 1.66–3.83). TyG-WC correlated best with total CVD (OR 2.37, 95% CI 1.77–3.17), congestive heart failure (OR 2.14, 95% CI 1.31–3.51), and angina pectoris (OR 2.38, 95% CI 1.43–3.97). TyG-WHtR correlated best with myocardial infarction (OR 2.24, 95% CI 1.45–3.44). RCS analyses showed that most of the above relationships were linear (P-overall 0.05). Otherwise, ROC curves showed that TyG-WHtR and TyG-WC had more robust diagnostic efficacy than TyG. In mediation analyses, glycated hemoglobin mediated in all the above relationships and insulin-mediated in partial relationships. Conclusions TyG-WC and TyG-WtHR enhance CVD mortality prediction, diagnostic efficacy of CVD and its mortality, and correlation with some CVD over and above the current hottest TyG. TyG-WC and TyG-WtHR are expected to become more effective metrics for identifying populations at early risk of cardiovascular disease and improve risk stratification.