Evidence map›Paper›PMID 39844166›Full record

ArticleCardiovascular diabetology2025

Association of estimated glucose disposal rate with metabolic syndrome prevalence and mortality risks: a population-based study.

Xiaoli Chen, Aihua Li, Qilin Ma

Abstract read
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Article in Cardiovascular diabetology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

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36citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

36 citing papers in PubMed.

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  19. Estimated glucose disposal rate is associated with brain aging and dementia among diabetes-free older adults.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Xiaoli ChenDepartment of Cardiovascular Medicine, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Kaifu District, Changsha, Hunan, China.
Aihua LiDepartment of Cardiovascular Medicine, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Kaifu District, Changsha, Hunan, China.
Qilin MaDepartment of Cardiovascular Medicine, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Kaifu District, Changsha, Hunan, China. mqilin2013@csu.edu.cn.ORCID 0000-0001-8608-2219

Funding

National Natural Science Foundation of China 8197021705
6 · The paper itself

Abstract

backgroundInsulin 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.

methodsData 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.

resultsA 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).

conclusioneGDR 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.

Indexed as

Blood GlucoseCardiovascular DiseasesInsulin ResistanceMetabolic SyndromeAdultAgedBiomarkersCause of DeathCross-Sectional StudiesFemaleHumansMaleMiddle AgedNutrition SurveysPredictive Value of TestsPrevalenceBiomarkersBlood GlucoseEstimated glucose disposal rateInsulin resistanceMetabolic syndromeMortalityNHANES

Identifiers

PMID39844166
PMCPMC11756087

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.