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Background <p>Sarcopenia is a syndrome that occurs in older adults, marked by progressive deterioration in muscle mass, strength, and/or functional capacity. Abnormal lipid metabolism has been associated with a higher prevalence of sarcopenia, but evidence regarding the association between the Castelli Risk Index-I (CRI-I) and sarcopenia is still insufficient. The research was designed to assess the association between CRI-I and sarcopenia status among the Chinese population and to determine its incremental discriminative value within a machine learning model.</p> Methods <p>This research utilized information from the 2011 CHARLS survey wave. CRI-I was categorized into quartiles and its association with sarcopenia was evaluated through logistic regression and restricted cubic splines (RCS). Seven candidate models were developed using the 2011 data, and the optimal model was identified, followed by temporal external validation with the 2015 CHARLS wave. To assess the additional discriminative value of CRI-I, we evaluated model performance using receiver operating characteristic (ROC) curves, precision-recall curves (PRC), calibration plots, and decision curve analysis (DCA). Finally, the SHapley Additive exPlanations (SHAP) algorithm was used to show the importance of each feature.</p> Results <p>A total of 1,332 individuals (15.1%) met the diagnostic criteria for sarcopenia. After full adjustment, higher CRI-I levels were associated with progressively lower odds of sarcopenia. RCS analysis further demonstrated that the association exhibited a non-linear pattern. Incorporating CRI-I into the optimal model improved discriminative performance. The model also demonstrated good calibration and clinical utility. Additionally, the SHAP algorithm was applied to calculate feature importance for the model’s estimated probability of having sarcopenia, which identified age as the most important feature, followed by CRI-I.</p> Conclusions <p>CRI-I showed superior discriminative performance for sarcopenia compared with six other non-traditional lipid indices. Elevated CRI-I levels correlated with substantially reduced sarcopenia likelihood. Adding CRI-I to the model improved probability stratification and may help identify individuals more likely to have sarcopenia.</p>

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Machine learning evaluation of the discriminative ability of Castelli Risk Index-I and other non-traditional lipid indices for sarcopenia: a cross-sectional study based on CHARLS

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月10日

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  • Baidi Xu,
  • Huailong Sun,
  • Zimeng Zhang,
  • Guoqiang Wu,
  • Wenming Hong,
  • Fang Zhang

摘要

Background

Sarcopenia is a syndrome that occurs in older adults, marked by progressive deterioration in muscle mass, strength, and/or functional capacity. Abnormal lipid metabolism has been associated with a higher prevalence of sarcopenia, but evidence regarding the association between the Castelli Risk Index-I (CRI-I) and sarcopenia is still insufficient. The research was designed to assess the association between CRI-I and sarcopenia status among the Chinese population and to determine its incremental discriminative value within a machine learning model.

Methods

This research utilized information from the 2011 CHARLS survey wave. CRI-I was categorized into quartiles and its association with sarcopenia was evaluated through logistic regression and restricted cubic splines (RCS). Seven candidate models were developed using the 2011 data, and the optimal model was identified, followed by temporal external validation with the 2015 CHARLS wave. To assess the additional discriminative value of CRI-I, we evaluated model performance using receiver operating characteristic (ROC) curves, precision-recall curves (PRC), calibration plots, and decision curve analysis (DCA). Finally, the SHapley Additive exPlanations (SHAP) algorithm was used to show the importance of each feature.

Results

A total of 1,332 individuals (15.1%) met the diagnostic criteria for sarcopenia. After full adjustment, higher CRI-I levels were associated with progressively lower odds of sarcopenia. RCS analysis further demonstrated that the association exhibited a non-linear pattern. Incorporating CRI-I into the optimal model improved discriminative performance. The model also demonstrated good calibration and clinical utility. Additionally, the SHAP algorithm was applied to calculate feature importance for the model’s estimated probability of having sarcopenia, which identified age as the most important feature, followed by CRI-I.

Conclusions

CRI-I showed superior discriminative performance for sarcopenia compared with six other non-traditional lipid indices. Elevated CRI-I levels correlated with substantially reduced sarcopenia likelihood. Adding CRI-I to the model improved probability stratification and may help identify individuals more likely to have sarcopenia.