中国组织工程研究

中国组织工程研究 ›› 2026, Vol. 30 ›› Issue (4): 849-857.doi: 10.12307/2025.986

• 肌肉肌腱韧带组织构建 tissue construction of the muscle, tendon and ligament • 上一篇    下一篇

绝经后女性肌肉减少症预测模型：中国健康与养老全国追踪调查数据库信息分析

李广政1，李  威1，张博淳1，丁浩秦1，周忠起2，李  刚3，梁学振1，3   

  1. 1山东中医药大学第一临床医学院，山东省济南市  250355；2山东中医药大学中医学院，山东省济南市  250355；3山东中医药大学附属医院显微骨科，山东省济南市  250014

  • 收稿日期:2024-10-10 接受日期:2024-12-31 出版日期:2026-02-08 发布日期:2025-05-16
  • 通讯作者: 梁学振，博士，副教授，硕士生导师，山东中医药大学第一临床医学院，山东省济南市 250355；山东中医药大学附属医院显微骨科，山东省济南市 250014 通讯作者：李刚，博士，教授，主任医师，博士生导师，山东中医药大学附属医院显微骨科，山东省济南市 250014
  • 作者简介:李广政，男，2002年生，山东省泰安市人，汉族，硕士，主要从事中医药防治骨坏死、代谢性骨病的临床与基础研究。
  • 基金资助:
    国家自然科学基金面上项目(82074453)，项目负责人：李刚；国家自然科学基金青年项目(82205154)，项目负责人：梁学振；山东省自然科学基金青年项目(ZR2021QH004，ZR2024MH156)，项目负责人：梁学振

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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摘要：


文题释义：
肌肉减少症：也称为肌少症，是一种与年龄增长相关的全身性骨骼肌疾病，主要表现为肌肉质量和功能的逐渐丧失，该病症与跌倒、功能衰退、虚弱和死亡等不良结局密切相关。绝经后女性是肌肉减少症的高危人群，因为激素变化和生活方式的改变可能加速肌肉质量的减少。
列线图：可用于直观展示多个变量对某一结果的预测概率，将复杂的统计分析结果转化为图形，使医生和患者能够快速、直观地计算出个体的预后风险。列线图通常由一系列的线段组成，每个线段代表一个预测变量，通过在这些线段上标记相应的分数，然后将分数相加，最终得出总分，对应于特定的预后概率。

背景：肌肉减少症是一种与年龄相关的全身性骨骼肌疾病，与跌倒、功能衰退、虚弱和死亡等多种不良结局有关，而绝经后女性是肌肉减少症的高危人群之一。
目的：旨在为中国绝经后女性开发一个基于高质量数据库评估肌肉减少症风险的预测模型。
方法：研究数据源自中国健康与养老追踪调查(CHARLS)数据库中的2 370名绝经后女性，使用2019年亚洲肌肉减少症工作组(AWGS2019)推荐指标评估肌肉减少症。研究队列随机分为训练集(70%)和验证集(30%)。使用LASSO、十折交叉验证、逻辑回归筛选绝经后女性肌肉减少症的危险因素。基于危险因素构建预测绝经后女性肌肉减少症风险的列线图，通过受试者工作特征曲线及曲线下面积(AUC)、校准曲线和决策曲线分析来评价模型效能。
结果与结论：2 370名绝经后女性中肌肉减少症患病率为23.50%，年龄、居住地、睡眠质量、认知功能、抑郁、慢性疾病数量被选为绝经后女性肌肉减少症的预测因素。列线图模型在训练集和验证集中表现出较好的区分度，在训练集的AUC值为0.751(95%CI=0.724-0.778，P < 0.001)，特异性为72.2%，敏感性为63.2%；在验证集的AUC值为0.763(95%CI=0.721-0.805，P < 0.001)，特异性为69.6%，敏感性为70.8%。校准曲线显示列线图模型与实际观测值之间具有较为显著的一致性，决策曲线分析显示了广泛且良好的临床实用性。结果表明，基于年龄、居住地、睡眠质量、认知功能、抑郁、慢性疾病数量构建的预测绝经后女性肌肉减少症风险列线图模型，有助于中国绝经后女性识别并规避肌肉减少症的风险因素，减少绝经后女性肌肉减少症的患病率。
https://orcid.org/0009-0000-0083-1590(李广政)

中国组织工程研究杂志出版内容重点：干细胞；骨髓干细胞；造血干细胞；脂肪干细胞；肿瘤干细胞；胚胎干细胞；脐带脐血干细胞；干细胞诱导；干细胞分化；组织工程

关键词: 肌肉减少症, 绝经后女性, CHARLS, 预测模型, 列线图, 工程化组织构建

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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