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

dc.rights.licenseopenen_US
dc.contributor.authorGOMEZ-CABRERO, David
dc.contributor.authorWALTER, Stefan
dc.contributor.authorABUGESSAISA, Imad
dc.contributor.authorMINAMBRES-HERRAIZ, Rebeca
dc.contributor.authorPALOMARES, Lucia Bernad
dc.contributor.authorBUTCHER, Lee
dc.contributor.authorERUSALIMSKY, Jorge D.
dc.contributor.authorGARCIA-GARCIA, Francisco Jose
dc.contributor.authorCARNICERO, Jose
dc.contributor.authorHARDMAN, Timothy C.
dc.contributor.authorMISCHAK, Harald
dc.contributor.authorZURBIG, Petra
dc.contributor.authorHACKL, Matthias
dc.contributor.authorGRILLARI, Johannes
dc.contributor.authorFIORILLO, Edoardo
dc.contributor.authorCUCCA, Francesco
dc.contributor.authorCESARI, Matteo
dc.contributor.authorCARRIE, Isabelle
dc.contributor.authorCOLPO, Marco
dc.contributor.authorBANDINELLI, Stefania
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorFEART-COURET, Catherine
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorPERES, Karine
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorDARTIGUES, Jean-Francois
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorHELMER, Catherine
dc.contributor.authorVINA, Jose
dc.contributor.authorOLASO, Gloria
dc.contributor.authorGARCIA-PALMERO, Irene
dc.contributor.authorMARTINEZ, Jorge Garcia
dc.contributor.authorJANSEN-DURR, Pidder
dc.contributor.authorGRUNE, Tilman
dc.contributor.authorWEBER, Daniela
dc.contributor.authorLIPPI, Giuseppe
dc.contributor.authorBONAGURI, Chiara
dc.contributor.authorSINCLAIR, Alan J.
dc.contributor.authorTEGNER, Jesper
dc.contributor.authorRODRIGUEZ-MANAS, Leocadio
dc.date.accessioned2021-04-02T09:21:07Z
dc.date.available2021-04-02T09:21:07Z
dc.date.issued2021-02-18
dc.identifier.issn2509-2723en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/26856
dc.description.abstractEnPhenotype-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.
dc.language.isoENen_US
dc.subject.enFrailty
dc.subject.enBiomarkers
dc.subject.enOmics
dc.subject.enClinical phenotype
dc.subject.enDisability
dc.title.enA robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts
dc.title.alternativeGeroscienceen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1007/s11357-021-00334-0en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed33599920en_US
dc.description.sponsorshipEuropeUtility of omic-based biomarkers in characterizing older individuals at risk for frailty, its progression to disability and general consequences to health and well-being - The FRAILOMIC Initiativeen_US
bordeaux.journalGeroScienceen_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.teamSEPIAen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.identifier.funderIDInstitut National de la Santé et de la Recherche Médicaleen_US
bordeaux.identifier.funderIDFondation pour la Recherche Médicaleen_US
bordeaux.identifier.funderIDConseil Régional Aquitaineen_US
bordeaux.identifier.funderIDConseil régional de Bourgogne-Franche-Comtéen_US
bordeaux.identifier.funderIDFondation de Franceen_US
bordeaux.identifier.funderIDFondation Plan Alzheimeren_US
bordeaux.identifier.funderIDCaisse nationale de solidarité pour l'autonomieen_US
hal.identifierhal-03188599
hal.version1
hal.date.transferred2021-04-02T09:21:12Z
hal.audienceInternationale
hal.exporttrue
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