Zeng Mengying, Li Yuanyuan, Zhu Yuchen, Sun Ying
Department of Geriatrics, Beijing Friendship Hospital, Capital Medical University, No. 95, Yong'an Road, Xicheng District, Beijing, 100050, China.
School of Information Engineering, China University of Geosciences, Beijing, China.
BMC Geriatr. 2025 Jun 4;25(1):404. doi: 10.1186/s12877-025-06033-1.
Frailty in older adults leads to falls, disability, hospitalization, and death. Identifying frail individuals is a crucial means to delay the onset of adverse results. Chronic inflammation plays a key role in the onset and progression of frailty. Our study aims to explore the relationship between inflammatory markers and frailty in older adults, thereby contributing to more accurate assessments of frailty.
We included 4,097 cases aged ≥ 60 years admitted to the Geriatrics Department of Beijing Friendship Hospital between July 17, 2018 and February 27, 2024, 800 cases were ultimately included. Patients were divided into non-frail, pre-frail, and frail groups based on the Fried frailty phenotype. Logistic regression analyses were performed using "Python's statsmodels library" to identify risk factors. "The Sklearn library" was used to assess the predictive power of these factors.
Two hundred five individuals were identified as frail. Independent risk factors for frailty included age, coronary artery disease (CAD), old cerebral infarction (OCI), neutrophil, neutrophil to lymphocyte rate (NLR), high-sensitivity C-reactive protein (hs-CRP), albumin, fibrinogen to albumin ratio (FAR) and erythrocyte sedimentation rate (ESR). Receiver operating characteristic curve analysis of age, CAD, OCI, neutrophils, NLR, hs-CRP, albumin, FAR, and ESR showed AUCs of 0.851 and 0.841 for logistic regression and random forest models.
Inflammatory markers such as NLR, hs-CRP, FAR, and ESR, along with age, OCI, and CAD, were key independent risk factors for frailty. Incorporating these factors into predictive models could enhance frailty prediction.
老年人衰弱会导致跌倒、残疾、住院和死亡。识别衰弱个体是延缓不良后果发生的关键手段。慢性炎症在衰弱的发生和发展中起关键作用。本研究旨在探讨炎症标志物与老年人衰弱之间的关系,从而有助于更准确地评估衰弱。
我们纳入了2018年7月17日至2024年2月27日在北京友谊医院老年医学科住院的4097例年龄≥60岁的患者,最终纳入800例。根据弗里德衰弱表型将患者分为非衰弱、衰弱前期和衰弱组。使用“Python的statsmodels库”进行逻辑回归分析以识别危险因素。使用“Sklearn库”评估这些因素的预测能力。
确定205人为衰弱个体。衰弱的独立危险因素包括年龄、冠状动脉疾病(CAD)、陈旧性脑梗死(OCI)、中性粒细胞、中性粒细胞与淋巴细胞比率(NLR)、高敏C反应蛋白(hs-CRP)、白蛋白、纤维蛋白原与白蛋白比率(FAR)和红细胞沉降率(ESR)。对年龄、CAD、OCI、中性粒细胞、NLR、hs-CRP、白蛋白、FAR和ESR进行的受试者工作特征曲线分析显示,逻辑回归模型和随机森林模型的曲线下面积(AUC)分别为0.851和0.841。
NLR、hs-CRP、FAR和ESR等炎症标志物,以及年龄、OCI和CAD是衰弱的关键独立危险因素。将这些因素纳入预测模型可增强衰弱预测。