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<p>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 &lt; 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 &lt; 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.</p>

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The relationship between estimated glucose disposal rate and sarcopenia among middle-aged and older adults

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月3日

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  • Yan Chen,
  • Lingli Gao,
  • Xiaolei Song,
  • Mingli Wu,
  • Ruiqing Li,
  • Kaiqi Su,
  • Zhuan Lv,
  • Jing Gao,
  • Xiaodong Feng

摘要

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.