{"title":"炎症标志物和临床因素是虚弱的关键独立危险因素:一项回顾性研究。","authors":"Mengying Zeng, Yuanyuan Li, Yuchen Zhu, Ying Sun","doi":"10.1186/s12877-025-06033-1","DOIUrl":null,"url":null,"abstract":"<p><strong>Background and objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":9056,"journal":{"name":"BMC Geriatrics","volume":"25 1","pages":"404"},"PeriodicalIF":3.4000,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12135400/pdf/","citationCount":"0","resultStr":"{\"title\":\"Inflammatory markers and clinical factors as key independent risk factors for frailty: a retrospective study.\",\"authors\":\"Mengying Zeng, Yuanyuan Li, Yuchen Zhu, Ying Sun\",\"doi\":\"10.1186/s12877-025-06033-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background and objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>\",\"PeriodicalId\":9056,\"journal\":{\"name\":\"BMC Geriatrics\",\"volume\":\"25 1\",\"pages\":\"404\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2025-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12135400/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Geriatrics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12877-025-06033-1\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GERIATRICS & GERONTOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Geriatrics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12877-025-06033-1","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GERIATRICS & GERONTOLOGY","Score":null,"Total":0}
Inflammatory markers and clinical factors as key independent risk factors for frailty: a retrospective study.
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.
期刊介绍:
BMC Geriatrics is an open access journal publishing original peer-reviewed research articles in all aspects of the health and healthcare of older people, including the effects of healthcare systems and policies. The journal also welcomes research focused on the aging process, including cellular, genetic, and physiological processes and cognitive modifications.