Key result
eGDR outperforms TyG and HOMA-IR for predicting all-cause mortality in metabolic syndrome.
Why the study?
Insulin resistance is a key factor in metabolic syndrome and cardiovascular mortality, but the relationship between estimated glucose disposal rate and these outcomes requires clarification.
Is estimated glucose disposal rate associated with metabolic syndrome prevalence and predictive of all-cause and cardiovascular mortality?
Observational
Is estimated glucose disposal rate associated with metabolic syndrome prevalence and predictive of all-cause and cardiovascular mortality?
Effect estimate: 11% decrement in all-cause mortality and 18% decrement in CVD mortality per SD increment
eGDR is strongly associated with metabolic syndrome and offers superior predictive value for all-cause mortality compared to TyG and HOMA-IR, highlighting its utility in clinical risk assessment.
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.
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Chen et al. (2025) conducted an observational in Metabolic syndrome. Estimated glucose disposal rate (eGDR) vs. Lower eGDR / TyG index / HOMA-IR was evaluated on All-cause and CVD mortality (11% decrement in all-cause mortality and 18% decrement in CVD mortality per SD increment). In individuals with metabolic syndrome, each standard deviation increment in estimated glucose disposal rate was associated with an 11% and 18% decrement in the risk of all-cause and CVD mortality.
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