A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts
Martínez, J. G.
Sinclair, A. J.
on behalf of the FRAILOMIC initiative
Wedi’i wahardd nes
MetadataDangos cofnod eitem llawn
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.
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
Dynodwr Gwrthrych Digidol (DOI)https://doi.org/10.1007/s11357-021-00334-0
Article published in GeroScience available at https://doi.org/10.1007/s11357-021-00334-0
Cardiff Metropolitan University (Grant ID: Cardiff Metropolian (Internal))
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'In search of ‘Omics'-based biomarkers to predict risk of frailty and its consequences in older individuals: the FRAILOMIC initiative Erusalimsky, Jorge; Grillari, Johannes; Grune, Tilman; Jansen-Duerr, Pidder; Lippi, Giuseppe; Sinclair, Alan; Tegner, Jesper; Vina, Jose; Durrance-Bagale, Anna; Minambres, Rebecca; Viegas, Marcelo; Rodriguez-Manas, Leocadio (Karger, 2016)An increase in the number of older people experiencing disability and dependence is a critical aspect of the demographic change that will emerge within Europe due to the rise in life expectancy. In this scenario, prevention ...
Lippi, Giuseppe; Jansen-Duerr, Pidder; Viña, Jose; Durrance-Bagale, Anna; Abugessaisa, Imad; Gomez-Cabrero, David; Tegnér, Jesper; Grillari, Johannes; Erusalimsky, Jorge; Sinclair, Alan; Rodriguez-Mañaa, Leocadio (Pub Med, 2015-09)The FRAILOMIC consortium (available at: http://www.frailomic.org/) was created and funded under the European FP7 framework in order to overcome these limitations. The consortium comprises seven small and medium-sized ...
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