| Title: | Association between estimated glucose disposal rate and incident heart failure in patients with coronary heart disease: a prospective cohort study |
| Journal: | Cardiovascular Diabetology |
| Published: | 28 Mar 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41904572/ |
| DOI: | https://doi.org/10.1186/s12933-026-03153-9 |
Publication 18169
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Abstract
BackgroundEstimated glucose disposal rate (eGDR) is a composite index of insulin resistance (IR). However, its association with incident heart failure (HF) in coronary heart disease (CHD) patients remains unknown.MethodsThis prospective cohort study included 20,329 CHD patients from the UK Biobank. Baseline eGDR and several IR indices were calculated. Associations between eGDR, IR indices and incident HF were assessed separately based on Cox regression models. Also, receiver operating characteristic (ROC) analyses were conducted to evaluate the performance of the eGDR and other IR index in predicting the 10-year risk of HF. Finally, we specifically analyzed the temporal trajectory of eGDR levels in the 15 years leading up to HF onset based on time trajectory curves.Results3364 participants developed incident HF over the follow-up. eGDR demonstrated superior risk stratification ability compared to other IR indices. Specifically, each 1-unit increase was associated with a 51% reduction in risk (HR = 0.49, 95% CI 0.38-0.64). Furthermore, eGDR exhibited the highest discriminative ability for participants at high risk of HF among all insulin resistance indices (AUC = 0.612, 95% CI 0.601-0.623). Trajectory analysis revealed that eGDR levels in individuals who eventually developed HF remained at a low level starting 15 years prior to the onset of HF.ConclusioneGDR is closely linked to HF risk in the CHD population and exhibits stronger predictive power than other IR indices. Notably, eGDR levels change up to 15 years before the occurrence of HF, suggesting that eGDR could serve as an early predictor for HF among CHD patients.Graphical abstract</p>
8 Authors
- Jiatang Xu
- Jiachen Niu
- Huayang Li
- Qiushi Ren
- Yi Zhang
- Zhilin Miao
- Kai Huang
- Zhongkai Wu
1 Application
| Application ID | Title |
|---|---|
| 100739 | Artificial intelligence for using multi-modal data to improve the identification and prediction of diseases in the UK Biobank |
Enabling scientific discoveries that improve human health