A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts
Type
ArticleAuthors
Gomez-Cabrero, Davidon 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

Rodriguez-Mañas, Leocadio
KAUST Department
Biological and Environmental Sciences and Engineering (BESE) DivisionBioscience Program
Date
2021-02-18Embargo End Date
2022-02-18Submitted Date
2020-10-06Permanent link to this record
http://hdl.handle.net/10754/667522
Metadata
Show full item recordAbstract
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/s11357-021-00334-0Sponsors
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 availablePublisher
Springer Science and Business Media LLCJournal
GeroSciencePubMed ID
33599920Additional Links
http://link.springer.com/10.1007/s11357-021-00334-0ae974a485f413a2113503eed53cd6c53
10.1007/s11357-021-00334-0
Scopus Count
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