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    A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts

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    Type
    Article
    Authors
    Gomez-Cabrero, David
    on behalf of the FRAILOMIC initiative
    Walter, Stefan
    Abugessaisa, Imad
    Miñambres-Herraiz, Rebeca
    Palomares, Lucia Bernad
    Butcher, Lee
    Erusalimsky, Jorge D.
    Garcia-Garcia, Francisco Jose
    Carnicero, José
    Hardman, Timothy C.
    Mischak, Harald
    Zürbig, Petra
    Hackl, Matthias
    Grillari, Johannes
    Fiorillo, Edoardo
    Cucca, Francesco
    Cesari, Matteo
    Carrie, Isabelle
    Colpo, Marco
    Bandinelli, Stefania
    Feart, Catherine
    Peres, Karine
    Dartigues, Jean-François
    Helmer, Catherine
    Viña, José
    Olaso, Gloria
    García-Palmero, Irene
    Martínez, Jorge García
    Jansen-Dürr, Pidder
    Grune, Tilman
    Weber, Daniela
    Lippi, Giuseppe
    Bonaguri, Chiara
    Sinclair, Alan J
    Tegner, Jesper cc
    Rodriguez-Mañas, Leocadio
    KAUST Department
    Biological and Environmental Science and Engineering (BESE) Division
    Bioscience Program
    Date
    2021-02-18
    Online Publication Date
    2021-02-18
    Print Publication Date
    2021-06
    Embargo End Date
    2022-02-18
    Submitted Date
    2020-10-06
    Permanent link to this record
    http://hdl.handle.net/10754/667522
    
    Metadata
    Show full item record
    Abstract
    Phenotype-specific omic expression patterns in people with frailty could provide invaluable insight into the underlying multi-systemic pathological processes and targets for intervention. Classical approaches to frailty have not considered the potential for different frailty phenotypes. We characterized associations between frailty (with/without disability) and sets of omic factors (genomic, proteomic, and metabolomic) plus markers measured in routine geriatric care. This study was a prevalent case control using stored biospecimens (urine, whole blood, cells, plasma, and serum) from 1522 individuals (identified as robust (R), pre-frail (P), or frail (F)] from the Toledo Study of Healthy Aging (R=178/P=184/F=109), 3 City Bordeaux (111/269/100), Aging Multidisciplinary Investigation (157/79/54) and InCHIANTI (106/98/77) cohorts. The analysis included over 35,000 omic and routine laboratory variables from robust and frail or pre-frail (with/without disability) individuals using a machine learning framework. We identified three protective biomarkers, vitamin D3 (OR: 0.81 [95% CI: 0.68-0.98]), lutein zeaxanthin (OR: 0.82 [95% CI: 0.70-0.97]), and miRNA125b-5p (OR: 0.73, [95% CI: 0.56-0.97]) and one risk biomarker, cardiac troponin T (OR: 1.25 [95% CI: 1.23-1.27]). Excluding individuals with a disability, one protective biomarker was identified, miR125b-5p (OR: 0.85, [95% CI: 0.81-0.88]). Three risks of frailty biomarkers were detected: pro-BNP (OR: 1.47 [95% CI: 1.27-1.7]), cardiac troponin T (OR: 1.29 [95% CI: 1.21-1.38]), and sRAGE (OR: 1.26 [95% CI: 1.01-1.57]). Three key frailty biomarkers demonstrated a statistical association with frailty (oxidative stress, vitamin D, and cardiovascular system) with relationship patterns differing depending on the presence or absence of a disability.
    Citation
    Gomez-Cabrero, D., Walter, S., Abugessaisa, I., Miñambres-Herraiz, R., Palomares, L. B., … Rodriguez-Mañas, L. (2021). A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts. GeroScience. doi:10.1007/s11357-021-00334-0
    Sponsors
    This work was supported by the European Union’s Seventh Framework Programme (FP7/2007-2013) FRAILOMIC Project (grant number 305483). The Three-City Study was conducted under a partnership agreement between the Institut National de la Santé et de la Recherche Médicale, Victor Segalen – Bordeaux2 University and the Sanofi-Synthélabo company. The Fondation pour la Recherche Médicale funded the preparation and beginning of the study. The 3C-Study was also sponsored by the Caisse Nationale Maladie des Travailleurs Salariés, Direction Générale de la Santé, Conseils Régionaux of Aquitaine and Bourgogne, Fondation de France, Ministry of Research-INSERM Program Cohortes et collections de données biologiques, the Fondation Plan Alzheimer (FCS 2009-2012), and the Caisse Nationale pour la Solidarité et l’Autonomie. The InCHIANTI study baseline (1998–2000) was supported as a ‘targeted project’ (ICS110.1/RF97.71) by the Italian Ministry of Health and in part by the U.S. National Institute on Aging (Contracts: 263 MD 9164 and 263 MD 821336) and by the Intramural Research Program of the National Institute on Aging, National Institutes of Health, Baltimore, Maryland.Data AvailabilityData will be made freely available
    Publisher
    Springer Nature
    Journal
    GeroScience
    DOI
    10.1007/s11357-021-00334-0
    PubMed ID
    33599920
    Additional Links
    http://link.springer.com/10.1007/s11357-021-00334-0
    ae974a485f413a2113503eed53cd6c53
    10.1007/s11357-021-00334-0
    Scopus Count
    Collections
    Articles; Biological and Environmental Science and Engineering (BESE) Division; Bioscience Program

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