MPC Member Publications

This database contains a listing of population studies publications written by MPC Members. Anyone can add a publication by an MPC student, faculty, or staff member to this database; new citations will be reviewed and approved by MPC administrators.

Full Citation

Title: Moving towards the detection of frailty with biomarkers: A population health study

Citation Type: Journal Article

Publication Year: 2023

ISSN: 1474-9726

DOI: 10.1111/ACEL.14030

PMID: 38066663

Abstract: <p> 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: “ <italic>Invecchiare in Chianti</italic> (Aging in Chianti, InCHIANTI Study”) ( <italic>n</italic>  = 1453) from two small towns in Tuscany, Italy, and replicated in the Atherosclerosis Risk in Communities Study (ARIC) ( <italic>n</italic>  = 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. </p>

Url: https://pubmed.ncbi.nlm.nih.gov/38066663/

User Submitted?: No

Authors: Sargent, Lana; Nalls, Mike; Singleton, Andrew; Palta, Priya; Kucharska‐Newton, Anna; Pankow, Jim; Young, Hunter; Tang, Weihong; Lutsey, Pamela; Olex, Amy; Wendte, Jered M.; Li, Danni; Alonso, Alvaro; Griswold, Michael; Windham, B. Gwen; Baninelli, Stefania; Ferrucci, Luigi

Periodical (Full): Aging cell

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