Moving towards the detection of frailty with biomarkers: A population health study.
biomarkers
frailty
machine learning
Journal
Aging cell
ISSN: 1474-9726
Titre abrégé: Aging Cell
Pays: England
ID NLM: 101130839
Informations de publication
Date de publication:
08 Dec 2023
08 Dec 2023
Historique:
revised:
18
10
2023
received:
28
08
2023
accepted:
22
10
2023
medline:
9
12
2023
pubmed:
9
12
2023
entrez:
9
12
2023
Statut:
aheadofprint
Résumé
Aging adults experience increased health vulnerability and compromised abilities to cope with stressors, which are the clinical manifestations of frailty. Frailty is complex, and efforts to identify biomarkers to detect frailty and pre-frailty in the clinical setting are rarely reproduced across cohorts. We developed a predictive model incorporating biological and clinical frailty measures to identify robust biomarkers across data sets. Data were from two large cohorts of older adults: "Invecchiare in Chianti (Aging in Chianti, InCHIANTI Study") (n = 1453) from two small towns in Tuscany, Italy, and replicated in the Atherosclerosis Risk in Communities Study (ARIC) (n = 6508) from four U.S. communities. A complex systems approach to biomarker selection with a tree-boosting machine learning (ML) technique for supervised learning analysis was used to examine biomarker population differences across both datasets. Our approach compared predictors with robust, pre-frail, and frail participants and examined the ability to detect frailty status by race. Unique biomarker features identified in the InCHIANTI study allowed us to predict frailty with a model accuracy of 0.72 (95% confidence interval (CI) 0.66-0.80). Replication models in ARIC maintained a model accuracy of 0.64 (95% CI 0.66-0.72). Frail and pre-frail Black participant models maintained a lower model accuracy. The predictive panel of biomarkers identified in this study may improve the ability to detect frailty as a complex aging syndrome in the clinical setting. We propose several concrete next steps to keep research moving toward detecting frailty with biomarker-based detection methods.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e14030Subventions
Organisme : NCATS NIH HHS
ID : UL1TR002649
Pays : United States
Organisme : NHLBI NIH HHS
ID : 75N92022D00005
Pays : United States
Organisme : NHLBI NIH HHS
ID : 75N92022D00004
Pays : United States
Organisme : NHLBI NIH HHS
ID : 75N92022D00003
Pays : United States
Organisme : NHLBI NIH HHS
ID : 75N92022D00002
Pays : United States
Organisme : NHLBI NIH HHS
ID : 75N92022D00001
Pays : United States
Informations de copyright
© 2023 The Authors. Aging Cell published by John Wiley & Sons Ltd and Anatomical Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
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