糖尿病患者肌肉减少症风险预测模型的构建与评估：一项基于中国健康与养老追踪调查（CHARLS）的研究
Construction and evaluation of sarcopenia risk prediction model for patients with diabetes: a study based on the China health and retirement longitudinal study (CHARLS).
作者信息
Zou Mingrui, Shao Zhenxing
机构信息
Department of Sports Medicine, Peking University Third Hospital, Institute of Sports Medicine of Peking University, Beijing, 100191, China.
Beijing Key Laboratory of Sports Injuries, Engineering Research Center of Sports Trauma Treatment Technology and Devices, Ministry of Education, Beijing, 100191, China.
出版信息
Diabetol Metab Syndr. 2024 Sep 16;16(1):230. doi: 10.1186/s13098-024-01467-w.
PURPOSE
Sarcopenia is a common complication of diabetes. Nevertheless, precise evaluation of sarcopenia risk among patients with diabetes is still a big challenge. The objective of this study was to develop a nomogram model which could serve as a practical tool to diagnose sarcopenia in patients with diabetes.
METHODS
A total of 783 participants with diabetes from China Health and Retirement Longitudinal Study (CHARLS) 2015 were included in this study. After oversampling process, 1,000 samples were randomly divided into the training set and internal validation set. To mitigate the overfitting effect caused by oversampling, data of CHARLS 2011 were utilized as the external validation set. Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate logistic regression analysis were employed to explore predictors. Subsequently, a nomogram was developed based on the 9 selected predictors. The model was assessed by area under receiver operating characteristic (ROC) curves (AUC) for discrimination, calibration curves for calibration, and decision curve analysis (DCA) for clinical efficacy. In addition, machine learning models were constructed to enhance the robustness of our findings and evaluate the importance of the predictors.
RESULTS
9 factors were selected as predictors of sarcopenia for patients with diabetes. The nomogram model exhibited good discrimination in training, internal validation and external validation sets, with AUC of 0.808, 0.811 and 0.794. machine learning models revealed that age and hemoglobin were the most significant predictors. Calibration curves and DCA illustrated excellent calibration and clinical applicability of this model.
CONCLUSION
This comprehensive nomogram presented high clinical predictability, which was a promising tool to evaluate the risk of sarcopenia in patients with diabetes.
目的
肌肉减少症是糖尿病的常见并发症。然而，精确评估糖尿病患者的肌肉减少症风险仍是一项巨大挑战。本研究的目的是开发一种列线图模型，作为诊断糖尿病患者肌肉减少症的实用工具。
方法
本研究纳入了中国健康与养老追踪调查（CHARLS）2015年的783名糖尿病参与者。经过过采样过程后，将1000个样本随机分为训练集和内部验证集。为减轻过采样导致的过拟合效应，将CHARLS 2011年的数据用作外部验证集。采用最小绝对收缩和选择算子（LASSO）回归分析和多变量逻辑回归分析来探索预测因素。随后，基于9个选定的预测因素开发了列线图。通过受试者操作特征（ROC）曲线下面积（AUC）评估模型的辨别力，通过校准曲线评估校准情况，通过决策曲线分析（DCA）评估临床疗效。此外，构建机器学习模型以增强研究结果的稳健性，并评估预测因素的重要性。
结果
9个因素被选为糖尿病患者肌肉减少症的预测因素。列线图模型在训练集、内部验证集和外部验证集中均表现出良好的辨别力，AUC分别为0.808、0.811和0.794。机器学习模型显示年龄和血红蛋白是最显著的预测因素。校准曲线和DCA表明该模型具有出色的校准和临床适用性。
结论
这种综合列线图具有较高的临床预测性，是评估糖尿病患者肌肉减少症风险的有前景的工具。
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