Abstract
BACKGROUND: Insulin resistance (IR) is a central pathophysiological factor in metabolic syndrome (MetS) and an essential driver of cardiovascular disease (CVD) and mortality. The estimated glucose disposal rate (eGDR) is a reliable marker of IR and has been associated with CVD prognosis. This study aims to examine the relationship between eGDR, MetS, and their predictive roles in clinical outcomes. METHODS: Data from the NHANES (2001-2018) were utilized, with a cross-sectional design applied to evaluate the association between eGDR and MetS prevalence, and a cohort design employed for mortality follow-up. Weighted logistic regression models were used to examine the association between eGDR and MetS. Weighted Cox proportional hazard models were applied to assess the link between eGDR and both all-cause and CVD mortality. To examine the non-linear associations between the eGDR, MetS, and mortality outcomes, restricted cubic spline (RCS) analysis was applied. Additionally, the predictive performance of eGDR, and other IR indices (TyG, HOMA-IR), for mortality was assessed using the C-statistic. RESULTS: A robust negative association between eGDR and MetS prevalence was found, following full covariate adjustment (p < 0.001). The core findings were consistent across subgroups (all p < 0.001). Cox regression analysis indicated that in individuals with MetS, each standard deviation (SD) increment in eGDR was associated with an 11% and 18% decrement in the risk of all-cause and CVD mortality, respectively. RCS analysis displayed a non-linear association between eGDR and MetS prevalence, while a linear association between eGDR and mortality. The C-statistic showed that eGDR, compared to the TyG index and HOMA-IR, significantly improved predictive power for all-cause mortality (p = 0.007). CONCLUSION: eGDR is strongly associated with MetS and predicts all-cause and CVD mortality in individuals with MetS. Compared to TyG and HOMA-IR, eGDR offers superior predictive value for all-cause mortality, highlighting its potential as a useful tool in clinical risk assessment.