dc.contributor.author | Gomez-Cabrero, David | |
dc.contributor.author | Walter, S. | |
dc.contributor.author | Abugessaisa, I. | |
dc.contributor.author | Miñambres-Herraiz, R. | |
dc.contributor.author | Palomares, L.B. | |
dc.contributor.author | Butcher, Lee | |
dc.contributor.author | Erusalimsky, Jorge | |
dc.contributor.author | Garcia-Garcia, F.J. | |
dc.contributor.author | Carnicero, J. | |
dc.contributor.author | Hardman, T.C. | |
dc.contributor.author | Mischak, H. | |
dc.contributor.author | Zürbig, P. | |
dc.contributor.author | Hackl, M. | |
dc.contributor.author | Grillari, J. | |
dc.contributor.author | Fiorillo, E. | |
dc.contributor.author | Cucca, F. | |
dc.contributor.author | Cesari, M. | |
dc.contributor.author | Carrie, I. | |
dc.contributor.author | Colpo, M. | |
dc.contributor.author | Bandinelli, S. | |
dc.contributor.author | Feart, C. | |
dc.contributor.author | Peres, K. | |
dc.contributor.author | Dartigues, J-F | |
dc.contributor.author | Helmer, C. | |
dc.contributor.author | Viña, J. | |
dc.contributor.author | Olaso, G. | |
dc.contributor.author | García-Palmero, I. | |
dc.contributor.author | Martínez, J. G. | |
dc.contributor.author | Jansen-Dürr, P. | |
dc.contributor.author | Grune, T. | |
dc.contributor.author | Weber, D. | |
dc.contributor.author | Lippi, G. | |
dc.contributor.author | Bonaguri, C. | |
dc.contributor.author | Sinclair, A. J. | |
dc.contributor.author | Tegner, J. | |
dc.contributor.author | Rodriguez-Mañas, L. | |
dc.contributor.author | on behalf of the FRAILOMIC initiative | |
dc.date.accessioned | 2021-02-23T10:53:27Z | |
dc.date.available | 2021-02-23T10:53:27Z | |
dc.date.issued | 2021-02-18 | |
dc.identifier.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 | en_US |
dc.identifier.issn | 2509-2715 | |
dc.identifier.issn | 2509-2723 (electronic) | |
dc.identifier.uri | http://hdl.handle.net/10369/11321 | |
dc.description | Article published in GeroScience available at https://doi.org/10.1007/s11357-021-00334-0 | en_US |
dc.description.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. | en_US |
dc.language.iso | en | |
dc.publisher | Springer | en_US |
dc.relation.ispartofseries | GeroScience; | |
dc.subject | frailty | en_US |
dc.subject | biomarkers | en_US |
dc.subject | omics | en_US |
dc.subject | clinical phenotype | en_US |
dc.subject | disability | en_US |
dc.title | A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts | en_US |
dc.type | Article | en_US |
dc.type | acceptedVersion | |
dc.identifier.doi | https://doi.org/10.1007/s11357-021-00334-0 | |
dcterms.dateAccepted | 2021-02-02 | |
rioxxterms.funder | Cardiff Metropolitan University | en_US |
rioxxterms.identifier.project | Cardiff Metropolian (Internal) | en_US |
rioxxterms.version | AM | en_US |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/under-embargo-all-rights-reserved | en_US |
rioxxterms.licenseref.startdate | 2022-02-18 | |
rioxxterms.freetoread.startdate | 2022-02-18 | |
rioxxterms.funder.project | 37baf166-7129-4cd4-b6a1-507454d1372e | en_US |