ArticleScientific reports2025
The relationship between estimated glucose disposal rate and sarcopenia among middle-aged and older adults.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Uric acid to high-density lipoprotein cholesterol ratio as a novel biomarker for sarcopenia: a national study with machine learning insights.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026Article
- Association between estimated glucose disposal rate and metabolic syndrome in older adults with sarcopenia.Frontiers in nutrition · 2026Article
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Authors and funding
9 authors.
Funding
Abstract
Association between estimated glucose disposal rate (eGDR) and sarcopenia among middle-aged and older adults remains unclear. To explore the relationship between eGDR and sarcopenia, and its predictive ability for sarcopenia risk among middle-aged and older adults. Data in this study were obtained from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2015. Logistic regression and Cox regression models were used to estimate the association of eGDR with sarcopenia. Restricted cubic splines (RCS) were employed to describe the nonlinear link between them, and subgroup analysis was conducted to ensure the stability of the results. Receiver operating characteristic curves were used to assess the capabilities of eGDR and the other six indices of insulin resistance for predicting sarcopenia risk. Four machine learning models were introduced, and the contribution of variables in the best predictive model was applied. A total of 1,627 participants participated in the cohort study. Compared with participants with eGDR ˃10.89, those with eGDR < 6.99 were at higher risk of sarcopenia, especially in individuals who are aged over 64 years and had diabetes. RCS analysis indicated a significant negative nonlinear relationship between eGDR and the risk of sarcopenia when eGDR < 7.319. A random forest model exhibited the best performance; eGDR was the key predictive factor among all variables. eGDR exhibits good capability for predicting sarcopenia risk.
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Registered trials
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