Zhang Guoyang, Huang Lidan, Liao Liangzhong
Department of Radiology, Xiamen Hospital of Traditional Chinese Medicine, Xiamen, China.
Front Med (Lausanne). 2025 Aug 12;12:1655263. doi: 10.3389/fmed.2025.1655263. eCollection 2025.
To explore the relationship between sarcopenia and bone mineral density (BMD) in middle-aged and elderly male patients with type 2 diabetes mellitus (T2DM), construct a prediction model for sarcopenia based on dual-energy X-ray absorptiometry (DXA), and evaluate its clinical value.
A total of 523 middle-aged and elderly male patients with T2DM in the hospital from January 2021 to December 2024 were selected and divided into the training set (366 cases) and the validation set (157 cases) at a ratio of 7:3. The BMD -value was measured by DXA, and clinical data were collected. A prediction model was constructed using multivariate logistic regression in the training set, and the model efficacy was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).
The incidence of sarcopenia was 27.05% (99/366) in the training set and 28.02% (44/157) in the validation set. Multivariate analysis showed that age, HbA1c, and HOMA-IR were independent risk factors for sarcopenia, while the lumbar L1-L4 -value, and femoral neck -value were independent protective factors for sarcopenia ( < 0.05). The C-index of the nomogram model were 0.773 (in the training set) and 0.750 (in the validation set) respectively. The calibration curve showed good agreement between predicted and actual values, and the Hosmer-Lemeshow test were significant (all > 0.05). The ROC curve showed the area under the curve (AUC) of the nomogram model for predicting the risk of sarcopenia was 0.773 (95% CI: 0.652-0.895) and 0.750 (95% CI, 0.686-0.814) in the training set and the validation set, respectively. The sensitivity and specificity were 0.714, 0.887 and 0.688, 0.796, respectively.
The prediction model constructed based on DXA can effectively predict the risk of sarcopenia in middle-aged and elderly male patients with T2DM, providing a basis for clinical early screening and intervention.
探讨中老年男性2型糖尿病（T2DM）患者肌肉减少症与骨密度（BMD）的关系，构建基于双能X线吸收法（DXA）的肌肉减少症预测模型，并评估其临床价值。
选取2021年1月至2024年12月在我院就诊的523例中老年男性T2DM患者，按7:3的比例分为训练集（366例）和验证集（157例）。采用DXA测量骨密度值，并收集临床资料。在训练集中采用多因素logistic回归构建预测模型，并通过受试者工作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）评估模型效能。
训练集肌肉减少症发生率为27.05%（99/366），验证集为28.02%（44/157）。多因素分析显示，年龄、糖化血红蛋白（HbA1c）和稳态模型胰岛素抵抗指数（HOMA-IR）是肌肉减少症的独立危险因素，而腰椎L1-L4骨密度值和股骨颈骨密度值是肌肉减少症的独立保护因素（P<0.05）。列线图模型的C指数在训练集和验证集分别为0.773和0.750。校准曲线显示预测值与实际值吻合良好，Hosmer-Lemeshow检验差异有统计学意义（均P>0.05）。ROC曲线显示，训练集和验证集中列线图模型预测肌肉减少症风险的曲线下面积（AUC）分别为0.773（95%CI：0.652-0.895）和0.750（95%CI，0.686-0.814）。敏感性和特异性分别为0.714、0.887和0.688、0.796。
基于DXA构建的预测模型可有效预测中老年男性T2DM患者肌肉减少症的风险，为临床早期筛查和干预提供依据。
