Inflammatory markers and clinical factors as key independent risk factors for frailty: a retrospective study
Abstract Background and objective 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 e...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-06-01
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| Series: | BMC Geriatrics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12877-025-06033-1 |
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| Summary: | Abstract Background and objective 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. Methods 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. Results 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. Conclusion 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. |
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| ISSN: | 1471-2318 |