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.

Keywords: Biomarkers; Clinical phenotype; Disability; Frailty; Omics.

Conflict of interest statement

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.

Figures

Fig. 1

Graphical description of the data analysis pipeline. The figure depicts the flow initiated at the cohorts to the generation of the FRAILOMIC database. From the cohorts, samples are sent (single-blind) to the experimental labs. A raw database was generated by combining the laboratory and the clinical data for all patients from all cohorts. Harmonization is conducted in order to have the values of all individuals on the same scale for all variables (see eMethods: 1.2 Harmonization)

Fig. 2

Robust selection of frailty relevant omic features. a Summary description of the machine learning selection framework (see “Statistics and the machine learning framework”): after harmonization (Step 0), a non-parametric and meta-analysis study selects individual features to be selected (Step 1). Then, a framework is established to uncover all variables that can be combined to provide additional information on frailty using statistically significant signature variables (SESv) (Step 2). Finally, all possible combinations of two to six variables are considered and evaluated (Step 3) while also comparing models with only clinical variables or those, including also omic features. b, c Graphical representation of SESv-derived network. Node size represents the number of times (imputations) a node has been selected in a SESv subset. Edges identify whether nodes have been co-selected in a SESv, where darker shades depict larger number of times the two nodes were connected. F1 and F2 are depicted, respectively, in b and c. In c, gray nodes denote variables associated with disability and excluded in the analysis. d Each feature is shown in one of the x-axis windows depending on the percentage of imputations it was selected in a SESv from the total of 1000 imputations; the y-axis denotes how many features are in each window. Numbers are provided separately per cohort. 3-C 3-City Study; AMI: aging multidisciplinary investigation; TSHA Toledo Study for Healthy Aging; InCHIANTI Invecchiare in Chianti Study