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Objectives <p>The estimated glucose disposal rate (eGDR) and the metabolic score for insulin resistance (METS-IR) are widely used and reliable clinical indicators of IR. However, how their combined effect influences the risk of cardio-renal-metabolic multimorbidity (CRMM) has not been well characterized. This study sought to evaluate both the individual and synergistic associations of eGDR and METS-IR with CRMM risk.</p> Methods <p>Data analyzed in the present study were sourced from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2020). We employed multivariable logistic regression models to examine the relationships of eGDR and METS-IR, both individually and jointly, with the risk of CRMM. Predictive performance was measured via the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), as well as integrated discrimination improvement (IDI). For validation, the main findings were reproduced in an independent population from the National Health and Nutrition Examination Survey (NHANES, 2007–2018).</p> Results <p>Among 7,149 participants in the final analytical sample, eGDR was inversely associated with the risk of incident CRMM (hazard ratio [HR] = 0.82, 95% confidence interval [CI]: 0.78-0.84), whereas METS-IR was positively associated with CRMM risk (HR = 1.05, 95% CI: 1.04-1.05). In the joint classification analysis, participants with low eGDR and high METS-IR had a higher risk of incident CRMM than those with high eGDR and low METS-IR (HR = 2.94, 95% CI: 2.47-3.51). The addition of eGDR and METS-IR to the baseline model was associated with statistically significant improvements in predictive performance for CRMM (AUC = 0.662, NRI = 0.427, IDI = 0.037; all P-value &lt; 0.001). Sensitivity analyses yielded generally consistent results.</p> Conclusions <p>Higher METS-IR and lower eGDR were associated with an increased risk of incident CRMM, and their joint assessment provided additional risk-related information. Although the combined model showed statistically significant incremental predictive value, its clinical applicability requires further validation.</p>

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Combined associations of the metabolic score for insulin resistance and estimated glucose disposal rate with cardio-renal-metabolic multimorbidity: evidence from two large population studies

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  • 发表日期：

    2026年7月6日

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  • Fa-Ming Yang,
  • Zhi-Ping Song,
  • Feng-Ying Qiao

摘要

Objectives

The estimated glucose disposal rate (eGDR) and the metabolic score for insulin resistance (METS-IR) are widely used and reliable clinical indicators of IR. However, how their combined effect influences the risk of cardio-renal-metabolic multimorbidity (CRMM) has not been well characterized. This study sought to evaluate both the individual and synergistic associations of eGDR and METS-IR with CRMM risk.

Methods

Data analyzed in the present study were sourced from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2020). We employed multivariable logistic regression models to examine the relationships of eGDR and METS-IR, both individually and jointly, with the risk of CRMM. Predictive performance was measured via the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), as well as integrated discrimination improvement (IDI). For validation, the main findings were reproduced in an independent population from the National Health and Nutrition Examination Survey (NHANES, 2007–2018).

Results

Among 7,149 participants in the final analytical sample, eGDR was inversely associated with the risk of incident CRMM (hazard ratio [HR] = 0.82, 95% confidence interval [CI]: 0.78-0.84), whereas METS-IR was positively associated with CRMM risk (HR = 1.05, 95% CI: 1.04-1.05). In the joint classification analysis, participants with low eGDR and high METS-IR had a higher risk of incident CRMM than those with high eGDR and low METS-IR (HR = 2.94, 95% CI: 2.47-3.51). The addition of eGDR and METS-IR to the baseline model was associated with statistically significant improvements in predictive performance for CRMM (AUC = 0.662, NRI = 0.427, IDI = 0.037; all P-value < 0.001). Sensitivity analyses yielded generally consistent results.

Conclusions

Higher METS-IR and lower eGDR were associated with an increased risk of incident CRMM, and their joint assessment provided additional risk-related information. Although the combined model showed statistically significant incremental predictive value, its clinical applicability requires further validation.