摘要
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2370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:2370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。
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 2370 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.
作者
李广政
李威
张博淳
丁浩秦
周忠起
李刚
梁学振
Li Guangzheng;Li Wei;Zhang Bochun;Ding Haoqin;Zhou Zhongqi;Li Gang;Liang Xuezhen(First Clinical Medical College,Shandong University of Traditional Chinese Medicine,Jinan 250355,Shandong Province,China;School of Traditional Chinese Medicine,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)
出处
《中国组织工程研究》
北大核心
2026年第4期849-857,共9页
Chinese Journal of Tissue Engineering Research
基金
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。
