Qiao Mengyuan, Wang Haiyan, Qin Mengzhen, Xing Taohong, Li Yingyang
School of Nursing, Henan University of Science and Technology, Luoyang, China.
Xinjiang Emergency Center, People's Hospital of Xinjiang Uygur Autonomous Region, Ürümqi, China.
Front Public Health. 2025 Apr 3;13:1521736. doi: 10.3389/fpubh.2025.1521736. eCollection 2025.
People with diabetes mellitus (DM) have a significantly increased risk of sarcopenia. A cross-sectional analysis was performed using nationally representative data to evaluate possible sarcopenia in middle-aged and older adults with diabetes mellitus, and to develop and validate a prediction model suitable for possible sarcopenia in middle-aged and older adults with diabetes mellitus in the Chinese community.
Data from the China Health and Retirement Longitudinal Study (CHARLS), which focuses on people 45 years of age or older, served as the basis for the prediction model. CHARLS 2015 participants were used in the study, which examined 53 factors. In order to guarantee model reliability, the study participants were split into two groups at random: 70% for training and 30% for validation. Ten-fold cross-validation and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were used to determine the best predictors for the model. The factors associated with sarcopenia in DM were researched using logistic regression models. Nomogram were constructed to develop the predictive model. The performance of the model was assessed using area under the curve (), calibration curves and decision curve analysis ().
A total of 2,131 participants from the CHARLS database collected in 2015 passed the final analysis, and the prevalence of sarcopenia was 28.9% (616/2131). Eight factors were subsequently chosen as predictive models by LASSO logistic regression: age, residence, body mass index, diastolic blood pressure, cognitive function, activities of daily living, peak expiratory flow and hemoglobin. These factors were used in the nomogram predictive model, which showed good accuracy and agreement. The values for the training and validation sets were 0.867 (95%CI: 0.8470.887) and 0.849 (95%CI: 0.8160.883). Calibration curves and DCA indicated that the nomogram model exhibited good predictive performance.
The nomogram predictive model constructed in this study can be used to evaluate the probability of sarcopenia in middle-aged and older adult DM, which is helpful for early identification and intervention of high-risk groups.
糖尿病患者患少肌症的风险显著增加。利用全国代表性数据进行横断面分析，以评估中老年糖尿病患者可能存在的少肌症，并建立和验证适合中国社区中老年糖尿病患者少肌症可能性的预测模型。
以关注45岁及以上人群的中国健康与养老追踪调查（CHARLS）数据作为预测模型的基础。本研究使用了CHARLS 2015的参与者，共检测了53个因素。为确保模型可靠性，研究参与者被随机分为两组：70%用于训练，30%用于验证。采用十折交叉验证和最小绝对收缩与选择算子（LASSO）回归分析来确定模型的最佳预测因子。使用逻辑回归模型研究糖尿病患者少肌症的相关因素。构建列线图以建立预测模型。使用曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）评估模型性能。
2015年CHARLS数据库中共有2131名参与者通过最终分析，少肌症患病率为28.9%（616/2131）。随后，LASSO逻辑回归选择了8个因素作为预测模型：年龄、居住地、体重指数、舒张压、认知功能、日常生活活动能力、呼气峰值流速和血红蛋白。这些因素被用于列线图预测模型，该模型显示出良好的准确性和一致性。训练集和验证集的AUC值分别为0.867（95%CI：0.8470.887）和0.849（95%CI：0.8160.883）。校准曲线和DCA表明列线图模型具有良好的预测性能。
本研究构建的列线图预测模型可用于评估中老年糖尿病患者少肌症的发生概率，有助于高危人群的早期识别和干预。
