Qin Xu, Liu Huan, Tao Xiubin, Zhou Zhiqing, Mei Guangliang, Zhang Ming, Zou Shengqiang
School of Medicine, Jiangsu University, Zhenjiang, Jiangsu, China.
Department of the Interventional, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, Anhui, China.
Front Nutr. 2025 Jul 17;12:1611914. doi: 10.3389/fnut.2025.1611914. eCollection 2025.
In the context of aging in China, frailty has become a major public health challenge, placing an enormous economic burden on both society and families. Frailty can trigger serious adverse effects on the physical and mental health of older adults. It highlights the urgent requirement for addressing the issue of frailty among older adults. Accordingly, the present study was conducted to identify potential risk factors and develop a validated risk predictive model for frailty in older Chinese adults.
Following a cross-sectional design, the present study selected participants from Anhui Province, China, using convenience sampling. Eligible data were collected using a demographic questionnaire, the Fatigue, Resistance, Ambulation, Illnesses, & Loss of Weight (FRAIL) scale, the strength, assistance walking, rise from a chair, climb stairs, and falls (SARC-F) scale, the social FRAIL scale, and the short-form mini-nutritional assessment (MNA-SF). Furthermore, a one-way analysis of variance and a multivariate analysis were utilized to identify the optimal predictive factors of the model. The logistic regression model was used to explore frailty-associated factors in older Chinese adults. Finally, a nomogram was constructed to establish the predictive model, with the application of calibration curves to evaluate the accuracy of the nomogram. The area under the receiver operating characteristic (ROC) curve (AUC) and decision curve analysis (DCA) were used to evaluate the performance of prediction.
Our final analysis incorporated 1,611 older Chinese adults who completed the questionnaire, with the incidence of frailty found in 491 (30.5%) cases. Multivariate logistic regression analysis showed that age, sarcopenia, malnutrition, social frailty, and hospitalization within the past 6 months were predictors of frailty. Consequently, the resultant nomogram demonstrated good consistency and accuracy. The AUC values of the model and the internal validation set were 0.86 (95%CI: 0.84-0.89) and 0.89 (95%CI: 0.85-0.92), respectively (both > 0.05 via the Hosmer-Lemeshow test). In addition, the calibration curve showed significant agreement between the nomogram predictions and the observed values. ROC and DCA analyses revealed good predictive performance of the nomogram.
This study constructs a frailty risk predictive model with good consistency and predictive performance, facilitating an effective prediction of the onset of frailty among older Chinese adults. It may benefit the screening of high-risk populations and the implementation of early interventions clinically.
在中国老龄化的背景下，衰弱已成为一项重大的公共卫生挑战，给社会和家庭带来了巨大的经济负担。衰弱会对老年人的身心健康引发严重的不良影响。这凸显了应对老年人衰弱问题的迫切需求。因此，本研究旨在确定潜在风险因素，并为中国老年人群体的衰弱建立一个经过验证的风险预测模型。
本研究采用横断面设计，通过便利抽样从中国安徽省选取参与者。使用人口统计学问卷、疲劳、抵抗力、活动能力、疾病及体重减轻（FRAIL）量表、力量、辅助行走、从椅子上起身、爬楼梯及跌倒（SARC - F）量表、社会衰弱量表和简短微型营养评定量表（MNA - SF）收集符合条件的数据。此外，采用单因素方差分析和多因素分析来确定模型的最佳预测因素。运用逻辑回归模型探索中国老年人衰弱相关因素。最后，构建列线图以建立预测模型，并应用校准曲线评估列线图的准确性。采用受试者工作特征（ROC）曲线下面积（AUC）和决策曲线分析（DCA）来评估预测性能。
我们的最终分析纳入了1611名完成问卷的中国老年人，其中发现491例（30.5%）存在衰弱情况。多因素逻辑回归分析表明，年龄、肌肉减少症、营养不良、社会衰弱以及过去6个月内住院是衰弱的预测因素。因此，所得列线图显示出良好的一致性和准确性。模型和内部验证集的AUC值分别为0.86（95%CI：0.84 - 0.89）和0.89（95%CI：0.85 - 0.92）（通过Hosmer - Lemeshow检验，两者均>0.05）。此外，校准曲线显示列线图预测值与观察值之间具有显著一致性。ROC和DCA分析显示列线图具有良好的预测性能。
本研究构建了一个具有良好一致性和预测性能的衰弱风险预测模型，有助于有效预测中国老年人衰弱的发生。它可能有益于临床上高危人群的筛查和早期干预的实施。
