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

on behalf of the FRAILOMIC initiative, David Gomez-Cabrero, Stefan Walter, Imad Abugessaisa, Rebeca Miñambres-Herraiz, Lucia Bernad Palomares, Lee Butcher, Jorge D. Erusalimsky, Francisco Jose Garcia-Garcia, José Carnicero, Timothy C. Hardman, Harald Mischak, Petra Zürbig, Matthias Hackl, Johannes Grillari, Edoardo Fiorillo, Francesco Cucca, Matteo Cesari, Isabelle Carrie, Marco ColpoStefania Bandinelli, Catherine Feart, Karine Peres, Jean François Dartigues, Catherine Helmer, José Viña, Gloria Olaso, Irene García-Palmero, Jorge García Martínez, Pidder Jansen-Dürr, Tilman Grune, Daniela Weber, Giuseppe Lippi, Chiara Bonaguri, Alan J. Sinclair, Jesper Tegner, Leocadio Rodriguez-Mañas

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)1317-1329
JournalGeroScience
Volume43
Issue number3
DOIs
Publication statusPublished - 2021

Keywords

  • Biomarkers
  • Clinical phenotype
  • Disability
  • Frailty
  • Omics

ASJC Scopus subject areas

  • Ageing
  • veterinary (miscalleneous)
  • Complementary and alternative medicine
  • Geriatrics and Gerontology
  • Cardiology and Cardiovascular Medicine

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