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

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.

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Acknowledgments

On behalf of the FRAILOMIC Initiative the authors would like to thank Perrine André MSc and Hermine Pellay MSc (Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, F-33000 Bordeaux, France), Mariam El Assar MSc and Betty Davies MD (Fundación de Investigación Biomedica Hospital Unversitario de Getafe), Eleonora Talluri PhD(USL Centro Toscana, Firenze, Italy), Valeria Orrù MSc and Michele Marongiu MSc (Institute for Genetic and Biomedical Research, Caligari, Italy), Esther García-Esquinas PhD and Esther Lopez-Garcia PhD and Pilar Guallar MD PhD and Fernando Rodriguez Artalejo MD PhD (Faculty of Medicine, Universidad Autonoma de Madrid, Madrid, Spain), Ignacio Ara PhD (GENUD Toledo Research Group, Universidad Castilla-La Mancha, Toledo, Spain), José-María Sánchez-Puelles PhD (Molecular Pharmacology Lab, Centre of Biological Sciences, CSIC, Madrid, Spain), Paloma Moraga MSc (Sistemas Genomicos, Valencia, Spain).

Funding

This work was supported by the European Union’s Seventh Framework Programme (FP7/2007-2013) FRAILOMIC Project (grant number 305483). The Three-City Study was conducted under a partnership agreement between the Institut National de la Santé et de la Recherche Médicale, Victor Segalen – Bordeaux2 University and the Sanofi-Synthélabo company. The Fondation pour la Recherche Médicale funded the preparation and beginning of the study. The 3C-Study was also sponsored by the Caisse Nationale Maladie des Travailleurs Salariés, Direction Générale de la Santé, Conseils Régionaux of Aquitaine and Bourgogne, Fondation de France, Ministry of Research-INSERM Program Cohortes et collections de données biologiques, the Fondation Plan Alzheimer (FCS 2009-2012), and the Caisse Nationale pour la Solidarité et l’Autonomie. The InCHIANTI study baseline (1998–2000) was supported as a ‘targeted project’ (ICS110.1/RF97.71) by the Italian Ministry of Health and in part by the U.S. National Institute on Aging (Contracts: 263 MD 9164 and 263 MD 821336) and by the Intramural Research Program of the National Institute on Aging, National Institutes of Health, Baltimore, Maryland.

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Correspondence to David Gomez-Cabrero or Stefan Walter or Jesper Tegner or Leocadio Rodriguez-Mañas.

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Conflicts of interest

Stefan Walter, Rebeca Miñambres, Lucía Bernard, Lee Butcher, Jorge Erusalimsky, Francisco José García-García, José Antonio Carnicero, Tim Hardman, Mattias Hacki, Johannes Grillari, Edoardo Fiorillo, Francesco Cucca, Matteo Cesari, Isabelle Carrie, Marco Colpo, Stefania Bandinelli, Karine Peres, Jean Francois Dartigues, Catherine Helmer,José Viña, Gloria Olaso, Irene Garcia, Jorge Garcia, Pidder Janssen-Dürr, Tilman Grune, Daniela Weber, Giuseppe Lippi, Chiara Bonaguri, and Alan Sinclair declare no conflicts of interest. David Gomez-Cabrero, Imad Abugesaissa and Jesper Tegner have been paid as consultants by YouHealth SB. David Gomez-Cabrero has received a speaker honorarium from Sanofi Aventis. Harald Mischak is the co-founder and co-owner of Mosaiques Diagnostics. Petra Zürbig is employed by Mosaiques Diagnostics. Catherine Féart received fees for conferences from Danone Institute and Nutricia, and served as consultant for Laboratoire Lescuyer and Cholé'Doc. Leocadio Rodriguez-Mañas has received fees for conferences from Abbott Laboratories and Novartis.

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David Gómez-Cabrero and Stefan Walter contributed equally to this work

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Gomez-Cabrero, D., Walter, S., Abugessaisa, I. et al. A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts. GeroScience (2021). https://doi.org/10.1007/s11357-021-00334-0

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Keywords

  • Frailty
  • Biomarkers
  • Omics
  • Clinical phenotype
  • Disability