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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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Author
Gomez-Cabrero, David
Walter, S.
Abugessaisa, I.
Miñambres-Herraiz, R.
Palomares, L.B.
Butcher, Lee
Erusalimsky, Jorge
Garcia-Garcia, F.J.
Carnicero, J.
Hardman, T.C.
Mischak, H.
Zürbig, P.
Hackl, M.
Grillari, J.
Fiorillo, E.
Cucca, F.
Cesari, M.
Carrie, I.
Colpo, M.
Bandinelli, S.
Feart, C.
Peres, K.
Dartigues, J-F
Helmer, C.
Viña, J.
Olaso, G.
García-Palmero, I.
Martínez, J. G.
Jansen-Dürr, P.
Grune, T.
Weber, D.
Lippi, G.
Bonaguri, C.
Sinclair, A. J.
Tegner, J.
Rodriguez-Mañas, L.
on behalf of the FRAILOMIC initiative
Date
2021-02-18
Acceptance date
2021-02-02
Type
Article
acceptedVersion
Publisher
Springer
ISSN
2509-2715
2509-2723 (electronic)
Metadata
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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.
Journal/conference proceeding
GeroScience;
Citation
Gomez-Cabrero, D., Walter, S., Abugessaisa, I., Miñambres-Herraiz, R., Palomares, L.B., Butcher, L., Erusalimsky, J.D., Garcia-Garcia, F.J., Carnicero, J., Hardman, T.C., Mischak, H. et al (2021) 'A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts', GeroScience, pp.1-13. https://doi.org/10.1007/s11357-021-00334-0
URI
http://hdl.handle.net/10369/11321
DOI
https://doi.org/10.1007/s11357-021-00334-0
Description
Article published in GeroScience available at https://doi.org/10.1007/s11357-021-00334-0
Rights
http://www.rioxx.net/licenses/under-embargo-all-rights-reserved
Sponsorship
Cardiff Metropolitan University (Grant ID: Cardiff Metropolian (Internal))
Collections
  • Cardiovascular Health and Ageing [100]

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