Show simple item record

dc.contributor.authorGomez-Cabrero, David
dc.contributor.authorWalter, S.
dc.contributor.authorAbugessaisa, I.
dc.contributor.authorMiñambres-Herraiz, R.
dc.contributor.authorPalomares, L.B.
dc.contributor.authorButcher, Lee
dc.contributor.authorErusalimsky, Jorge
dc.contributor.authorGarcia-Garcia, F.J.
dc.contributor.authorCarnicero, J.
dc.contributor.authorHardman, T.C.
dc.contributor.authorMischak, H.
dc.contributor.authorZürbig, P.
dc.contributor.authorHackl, M.
dc.contributor.authorGrillari, J.
dc.contributor.authorFiorillo, E.
dc.contributor.authorCucca, F.
dc.contributor.authorCesari, M.
dc.contributor.authorCarrie, I.
dc.contributor.authorColpo, M.
dc.contributor.authorBandinelli, S.
dc.contributor.authorFeart, C.
dc.contributor.authorPeres, K.
dc.contributor.authorDartigues, J-F
dc.contributor.authorHelmer, C.
dc.contributor.authorViña, J.
dc.contributor.authorOlaso, G.
dc.contributor.authorGarcía-Palmero, I.
dc.contributor.authorMartínez, J. G.
dc.contributor.authorJansen-Dürr, P.
dc.contributor.authorGrune, T.
dc.contributor.authorWeber, D.
dc.contributor.authorLippi, G.
dc.contributor.authorBonaguri, C.
dc.contributor.authorSinclair, A. J.
dc.contributor.authorTegner, J.
dc.contributor.authorRodriguez-Mañas, L.
dc.contributor.authoron behalf of the FRAILOMIC initiative
dc.date.accessioned2021-02-23T10:53:27Z
dc.date.available2021-02-23T10:53:27Z
dc.date.issued2021-02-18
dc.identifier.citationGomez-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/s11357-021-00334-0en_US
dc.identifier.issn2509-2715
dc.identifier.issn2509-2723 (electronic)
dc.identifier.urihttp://hdl.handle.net/10369/11321
dc.descriptionArticle published in GeroScience available at https://doi.org/10.1007/s11357-021-00334-0en_US
dc.description.abstractPhenotype-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.isoen
dc.publisherSpringeren_US
dc.relation.ispartofseriesGeroScience;
dc.subjectfrailtyen_US
dc.subjectbiomarkersen_US
dc.subjectomicsen_US
dc.subjectclinical phenotypeen_US
dc.subjectdisabilityen_US
dc.titleA robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohortsen_US
dc.typeArticleen_US
dc.typeacceptedVersion
dc.identifier.doihttps://doi.org/10.1007/s11357-021-00334-0
dcterms.dateAccepted2021-02-02
rioxxterms.funderCardiff Metropolitan Universityen_US
rioxxterms.identifier.projectCardiff Metropolian (Internal)en_US
rioxxterms.versionAMen_US
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/under-embargo-all-rights-reserveden_US
rioxxterms.licenseref.startdate2022-02-18
rioxxterms.freetoread.startdate2022-02-18
rioxxterms.funder.project37baf166-7129-4cd4-b6a1-507454d1372een_US


Files in this item

Thumbnail

This item appears in the following collection(s)

Show simple item record