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

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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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Authors and Affiliations

  1. Translational Bioinformatics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Spain

    David Gomez-Cabrero

  2. Dept. of Medicine and Public Health, Rey Juan Carlos University, Alcorcon, Spain

    Stefan Walter

  3. Dept. of Medicine, Karolinska Institute, Stockholm, Sweden

    Imad Abugessaisa & Jesper Tegner

  4. Sistemas Genomicos, Valencia, Spain

    Rebeca Miñambres-Herraiz & Lucia Bernad Palomares

  5. Department of Biomedical Sciences, Cardiff Metropolitan University, Cardiff, UK

    Lee Butcher & Jorge D. Erusalimsky

  6. Dept. of Geriatric Medicine, Complejo Hospitalario Universitario de Toledo (CHUT), Toledo, Spain

    Francisco Jose Garcia-Garcia & José Carnicero

  7. Niche Science & Technology Ltd., Richmond-upon-Thames, London, UK

    Timothy C. Hardman

  8. Mosaiques Diagnostics GmbH, Rotenburger Str. 20, 30659, Hannover, Germany

    Harald Mischak & Petra Zürbig

  9. Evercyte GmbH; BOKU-University of Natural Resources and Life Sciences Vienna, Department of Biotechnology, Ludwig Boltzmann Institute of Experimental and Clinical Traumatology, Vienna, Austria

    Matthias Hackl & Johannes Grillari

  10. Instituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Monserrato, Cagliari, Italy

    Edoardo Fiorillo & Francesco Cucca

  11. Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy

    Matteo Cesari

  12. Gérontopôle, Toulouse, France

    Isabelle Carrie

  13. USL Centro Toscana, Firenze, Italy

    Marco Colpo & Stefania Bandinelli

  14. Univ. Bordeaux, Bordeaux Population Health Research Center, UMR 1219, F-33000, Bordeaux, France

    Catherine Feart, Karine Peres, Jean-François Dartigues & Catherine Helmer

  15. Freshage, University of Valencia, Valencia, Spain

    José Viña & Gloria Olaso

  16. Lifelength, Madrid, Spain

    Irene García-Palmero & Jorge García Martínez

  17. Research Institute for Biomedical Aging Research, University of Innsbruck, Innsbruck, Austria

    Pidder Jansen-Dürr

  18. German Institute for Human Nutrition, Potsdam, Germany

    Tilman Grune & Daniela Weber

  19. Clinical Biochemistry and Molecular Biology, Universita di Verona, Verona, Italy

    Giuseppe Lippi

  20. Laboratoy Medicine Technical Sciences, Parma University, Parma, Italy

    Chiara Bonaguri

  21. Diabetes Frail and King’s College, London, Se1 8WA, UK

    Alan J Sinclair

  22. Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Jesper Tegner

  23. CIBER of Frailty and Healthy Aging, Madrid, Spain

    Leocadio Rodriguez-Mañas

  24. Dept. of Geriatric Medicine, Getafe University Hospital, Getafe, Spain

    Leocadio Rodriguez-Mañas

Authors
  1. David Gomez-Cabrero
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  2. Stefan Walter
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  3. Imad Abugessaisa
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  4. Rebeca Miñambres-Herraiz
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  5. Lucia Bernad Palomares
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  6. Lee Butcher
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  7. Jorge D. Erusalimsky
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  8. Francisco Jose Garcia-Garcia
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  9. José Carnicero
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  10. Timothy C. Hardman
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  11. Harald Mischak
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  12. Petra Zürbig
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  13. Matthias Hackl
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  14. Johannes Grillari
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  15. Edoardo Fiorillo
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  16. Francesco Cucca
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  17. Matteo Cesari
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  18. Isabelle Carrie
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  19. Marco Colpo
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  20. Stefania Bandinelli
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  21. Catherine Feart
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  22. Karine Peres
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  23. Jean-François Dartigues
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  24. Catherine Helmer
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  25. José Viña
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  26. Gloria Olaso
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  27. Irene García-Palmero
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  28. Jorge García Martínez
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  29. Pidder Jansen-Dürr
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  30. Tilman Grune
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  31. Daniela Weber
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  32. Giuseppe Lippi
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  33. Chiara Bonaguri
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  34. Alan J Sinclair
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  35. Jesper Tegner
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  36. Leocadio Rodriguez-Mañas
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on behalf of the FRAILOMIC initiative

Contributions

The authors of this manuscript certify that they comply with the ethical guidelines for authorship and publishing in the Journal.

Corresponding author

Correspondence to Leocadio Rodriguez-Mañas.

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Ethics approval

The original study protocols were approved by ethical committee according to the principles of the Declaration of Helsinki and all participants signed a written consent with participants agree to sample retention/analysis and data publication.

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 43, 1317–1329 (2021). https://doi.org/10.1007/s11357-021-00334-0

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