Chinese Journal of Tissue Engineering Research ›› 2026, Vol. 30 ›› Issue (4): 849-857.doi: 10.12307/2025.986

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A prediction model for sarcopenia in postmenopausal women: information analysis based on the China Health and Retirement Longitudinal Study database

Li Guangzheng1, Li Wei1, Zhang Bochun1, Ding Haoqin1, Zhou Zhongqi2, Li Gang3, Liang Xuezhen1, 3   

  1. 1First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan 250355, Shandong Province, China; 2School of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250355, Shandong Province, China; 3Department of Microscopic Orthopedics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250014, Shandong Province, China
  • Received:2024-10-10 Accepted:2024-12-31 Online:2026-02-08 Published:2025-05-16
  • Contact: Liang Xuezhen, PhD, Associate professor, Master’s supervisor, First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan 250355, Shandong Province, China; Department of Microscopic Orthopedics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250014, Shandong Province, China Co-corresponding author: Li Gang, PhD, Professor, Chief physician, Doctoral supervisor, Department of Microscopic Orthopedics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250014, Shandong Province, China
  • About author:Li Guangzheng, Master, First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan 250355, Shandong Province, China
  • Supported by:
    the National Natural Science Foundation of China (General Program), No. 82074453 (to LG); the National Natural Science Foundation of China (Youth Program), No. 82205154 (to LXZ); the Shandong Provincial Natural Science Foundation of China (Youth Program), Nos. ZR2021QH004 and ZR2024MH156 (both to LXZ)

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Abstract: BACKGROUND: Sarcopenia is an age-related systemic skeletal muscle disease, which is associated with a variety of adverse outcomes such as falls, functional decline, frailty, and death. Postmenopausal women are one of the high-risk groups for sarcopenia. 
OBJECTIVE: To develop a predictive model for assessing the risk of sarcopenia in Chinese postmenopausal women based on high-quality database.
METHODS: Data for this study were derived from 2 370 postmenopausal women from the China Health and Retirement Longitudinal Study (CHARLS), and sarcopenia was assessed using the Asian Working Group on Sarcopenia 2019 (AWGS2019) recommended metrics. The study cohort was randomized into a training set (70%) and a validation set (30%). Risk factors for sarcopenia in postmenopausal women were screened using the least absolute shrinkage and selection operator, ten-fold cross-validation, and logistic regression. Nomogram predicting the risk of sarcopenia in postmenopausal women was constructed based on the risk factors, and the model efficacy was evaluated by the receiver operating characteristic curve and area under the curve (AUC), calibration curve, and decision curve analysis.
RESULTS AND CONCLUSION: The prevalence of sarcopenia in this study was 23.50% and age, place of residence, sleep quality, cognitive function, depression, and the number of chronic diseases were selected as predictors of sarcopenia in postmenopausal women. The nomogram model showed good discrimination between the training and validation sets, with an AUC value of 0.751 (95% confidence interval=0.724-0.778, P < 0.001), a specificity of 72.2%, and a sensitivity of 63.2% in the training set, and an AUC value of 0.763 (95% confidence interval=0.721-0.805, P < 0.001), with a specificity of 69.6% and a sensitivity of 70.8%. The calibration curve showed a relatively significant agreement between the nomogram model and the actual observations, and the decision curve analysis demonstrated broad and good clinical utility. To conclude, the nomogram to assess the risk of sarcopenia constructed based on age, place of residence, sleep quality, cognitive function, depression, and number of chronic diseases, provides an effective tool for identifying and eliminating risk factors for sarcopenia in Chinese postmenopausal women, and helps to reduce the incidence of sarcopenia.

Key words: sarcopenia, postmenopausal women, CHARLS, predictive modeling, nomogram, engineered tissue construction,

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