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 ...
Butcher, Lee; Carnicero, Jose Antonio; Peres, Karine; Colpo, Marco; Gomez-Cabrero, David; Dartigues, Jean-François; Bandinelli, Stefania; Garcia-Garcia, Francisco Jose; Manas, Leocadio Rodriguez; Erusalimsky, Jorge (Karger, 2021-01-21)Introduction: The evidence that blood levels of the soluble receptor for advanced glycation end products (sRAGE) predict mortality in people with cardiovascular diseases (CVD) is inconsistent. To clarify this matter, we ...