School of Nursing, Jinan University, Guangzhou, Guangdong, China.
Department of Neurosurgery, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.
Eur J Med Res. 2024 May 9;29(1):278. doi: 10.1186/s40001-024-01873-w.
Sarcopenia is a progressive age-related disease that can cause a range of adverse health outcomes in older adults, and older adults with severe sarcopenia are also at increased short-term mortality risk. The aim of this study was to construct and validate a risk prediction model for sarcopenia in Chinese older adults.
This study used data from the 2015 China Health and Retirement Longitudinal Study (CHARLS), a high-quality micro-level data representative of households and individuals aged 45 years and older adults in China. The study analyzed 65 indicators, including sociodemographic indicators, health-related indicators, and biochemical indicators.
3454 older adults enrolled in the CHARLS database in 2015 were included in the final analysis. A total of 997 (28.8%) had phenotypes of sarcopenia. Multivariate logistic regression analysis showed that sex, Body Mass Index (BMI), Mean Systolic Blood Pressure (MSBP), Mean Diastolic Blood Pressure (MDBP) and pain were predictive factors for sarcopenia in older adults. These factors were used to construct a nomogram model, which showed good consistency and accuracy. The AUC value of the prediction model in the training set was 0.77 (95% CI = 0.75-0.79); the AUC value in the validation set was 0.76 (95% CI = 0.73-0.79). Hosmer-Lemeshow test values were P = 0.5041 and P = 0.2668 (both P > 0.05). Calibration curves showed significant agreement between the nomogram model and actual observations. ROC and DCA showed that the nomograms had good predictive properties.
The constructed sarcopenia risk prediction model, incorporating factors such as sex, BMI, MSBP, MDBP, and pain, demonstrates promising predictive capabilities. This model offers valuable insights for clinical practitioners, aiding in early screening and targeted interventions for sarcopenia in Chinese older adults.
肌少症是一种与年龄相关的进行性疾病，可导致老年人出现一系列不良健康后果，严重肌少症的老年人也有更高的短期死亡风险。本研究旨在构建和验证中国老年人肌少症的风险预测模型。
本研究使用了 2015 年中国健康与退休纵向研究（CHARLS）的数据，这是一项代表中国 45 岁及以上家庭和个人的高质量微观水平数据。研究分析了 65 个指标，包括社会人口统计学指标、健康相关指标和生化指标。
纳入最终分析的 CHARLS 数据库 2015 年有 3454 名老年人，其中 997 名（28.8%）有肌少症表型。多变量逻辑回归分析显示，性别、体重指数（BMI）、平均收缩压（MSBP）、平均舒张压（MDBP）和疼痛是老年人肌少症的预测因素。这些因素被用来构建一个列线图模型，该模型显示了良好的一致性和准确性。在训练集中，预测模型的 AUC 值为 0.77（95%CI=0.75-0.79）；在验证集中，AUC 值为 0.76（95%CI=0.73-0.79）。Hosmer-Lemeshow 检验值分别为 P=0.5041 和 P=0.2668（均 P>0.05）。校准曲线显示列线图模型与实际观察结果有显著一致性。ROC 和 DCA 显示列线图具有良好的预测性能。
本研究构建的肌少症风险预测模型，纳入了性别、BMI、MSBP、MDBP 和疼痛等因素，具有良好的预测能力。该模型为临床医生提供了有价值的信息，有助于对中国老年人肌少症进行早期筛查和有针对性的干预。
