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Background <p>Sarcopenia is a progressive skeletal muscle disorder characterized by declining muscle mass and function in older adults. This study aimed to develop and validate a four-year predictive model for sarcopenia using data from the China Health and Retirement Longitudinal Study (CHARLS) to facilitate early identification and targeted interventions.</p> Methods <p>We analyzed data from 2,173 participants enrolled in the 2011 CHARLS baseline survey after applying exclusion criteria. Predictors including anthropometric measurements, laboratory biomarkers, and cognitive function were assessed. Data were split 7:3 for training and testing, with oversampling applied to address class imbalance in the training set. LASSO regression with 10-fold cross-validation was used for feature selection, followed by multivariable logistic regression to identify significant predictors. Model performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA).</p> Results <p>Among 2,173 participants, 160 (7.36%) developed sarcopenia over four years. Univariate analyses showed area under the curve (AUC) values ranging from 0.461 to 0.818. Ten significant predictors were identified: age, body mass index (BMI), cognition, waist circumference, mean corpuscular volume (MCV), platelet count, glucose, creatinine, high-density lipoprotein cholesterol (HDL), and hematocrit. Advancing age and higher MCV were positively associated with sarcopenia, whereas higher BMI, larger waist circumference, and better cognitive function were protective. The model showed excellent discrimination with AUCs of 0.859 (training) and 0.842 (testing), good calibration, and favorable clinical utility across a range of thresholds. An online nomogram was developed for clinical application.</p> Conclusion <p>This study developed and validated a four-year predictive model for sarcopenia in older adults, integrating key risk factors into a user-friendly nomogram. The model enables early identification of high-risk individuals, supporting targeted interventions and enhancing sarcopenia management strategies. External validation is recommended to confirm generalizability.</p>

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Development and validation of a Four-Year predictive model for sarcopenia in older adults: insights from the CHARLS cohort

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月12日

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  • Jingjing Chu,
  • Miaomiao Wang,
  • Luxi Weng,
  • Ruiyin Dong,
  • Luming Liu,
  • Zherong Xu

摘要

Background

Sarcopenia is a progressive skeletal muscle disorder characterized by declining muscle mass and function in older adults. This study aimed to develop and validate a four-year predictive model for sarcopenia using data from the China Health and Retirement Longitudinal Study (CHARLS) to facilitate early identification and targeted interventions.

Methods

We analyzed data from 2,173 participants enrolled in the 2011 CHARLS baseline survey after applying exclusion criteria. Predictors including anthropometric measurements, laboratory biomarkers, and cognitive function were assessed. Data were split 7:3 for training and testing, with oversampling applied to address class imbalance in the training set. LASSO regression with 10-fold cross-validation was used for feature selection, followed by multivariable logistic regression to identify significant predictors. Model performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA).

Results

Among 2,173 participants, 160 (7.36%) developed sarcopenia over four years. Univariate analyses showed area under the curve (AUC) values ranging from 0.461 to 0.818. Ten significant predictors were identified: age, body mass index (BMI), cognition, waist circumference, mean corpuscular volume (MCV), platelet count, glucose, creatinine, high-density lipoprotein cholesterol (HDL), and hematocrit. Advancing age and higher MCV were positively associated with sarcopenia, whereas higher BMI, larger waist circumference, and better cognitive function were protective. The model showed excellent discrimination with AUCs of 0.859 (training) and 0.842 (testing), good calibration, and favorable clinical utility across a range of thresholds. An online nomogram was developed for clinical application.

Conclusion

This study developed and validated a four-year predictive model for sarcopenia in older adults, integrating key risk factors into a user-friendly nomogram. The model enables early identification of high-risk individuals, supporting targeted interventions and enhancing sarcopenia management strategies. External validation is recommended to confirm generalizability.