1. Geroscience
- Author
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Jorge García Martínez, Jorge D. Erusalimsky, Jose Viña, Jesper Tegnér, Catherine Féart, Timothy C. Hardman, Isabelle Carrié, Stefan Walter, Lucia Bernad Palomares, Matteo Cesari, Lee Butcher, Harald Mischak, Tilman Grune, Chiara Bonaguri, Giuseppe Lippi, David Gomez-Cabrero, Catherine Helmer, Imad Abugessaisa, Leocadio Rodríguez-Mañas, Stefania Bandinelli, Gloria Olaso, Alan J Sinclair, Francisco José García-García, Rebeca Miñambres-Herraiz, Irene García-Palmero, Daniela Weber, Edoardo Fiorillo, Marco Colpo, Matthias Hackl, Francesco Cucca, Pidder Jansen-Dürr, Petra Zürbig, José A. Carnicero, Jean-François Dartigues, Karine Pérès, Johannes Grillari, Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Santé et de la Recherche Médicale, Fondation pour la Recherche Médicale, Conseil Régional Aquitaine, Conseil régional de Bourgogne-Franche-Comté, Fondation de France, Fondation Plan Alzheimer, Caisse nationale de solidarité pour l'autonomie, and European Project: 305483,EC:FP7:HEALTH,FP7-HEALTH-2012-INNOVATION-1,FRAILOMIC(2013)
- Subjects
Proteomics ,Aging ,Geriatric care ,Frail Elderly ,Lutein / zeaxanthin ,Omics ,Machine learning ,computer.software_genre ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,Clinical phenotype ,Vitamin D and neurology ,Medicine ,Humans ,030212 general & internal medicine ,Healthy aging ,Pathological ,030304 developmental biology ,Biomarkers, Clinical phenotype, Disability, Frailty, Omics ,Aged ,0303 health sciences ,Disability ,Frailty ,business.industry ,3. Good health ,Case-Control Studies ,Nested case-control study ,Biomarker (medicine) ,[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie ,Artificial intelligence ,Geriatrics and Gerontology ,business ,computer ,Biomarkers - 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.
- Published
- 2020
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