Research Paper|Volume 12, Issue 13|pp 12517—12533

Untargeted metabolomics for uncovering biological markers of human skeletal muscle ageing

Daniel J. Wilkinson^1,^2,³, Giovanny Rodriguez-Blanco^1,^5,⁷, Warwick B. Dunn^1,⁵, Bethan E. Phillips^1,^2,³, John P. Williams³, Paul L. Greenhaff^1,^2,⁴, Kenneth Smith^1,^2,³, Iain J. Gallagher⁶, Philip J. Atherton^1,^2,³

¹MRC-Versus Arthritis Centre for Musculoskeletal Ageing Research, University of Nottingham, Nottingham, UK
²National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Nottingham, UK
³School of Medicine, University of Nottingham, Royal Derby Hospital Centre, Derby, UK
⁴School of Life Sciences, University of Nottingham, Queens Medical Centre, Nottingham, UK
⁵School of Biosciences and Phenome Centre Birmingham, University of Birmingham, Birmingham, Birmingham, UK
⁶University of Stirling, Faculty of Health Sciences and Sport, Stirling, UK
⁷Beatson Institute for Cancer Research, Glasgow, UK

Received: March 18, 2020Accepted: June 4, 2020Published: June 24, 2020

https://doi.org/10.18632/aging.103513

Copyright © 2020 Wilkinson et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Ageing compromises skeletal muscle mass and function through poorly defined molecular aetiology. Here we have used untargeted metabolomics using UHPLC-MS to profile muscle tissue from young (n=10, 25±4y), middle aged (n=18, 50±4y) and older (n=18, 70±3y) men and women (50:50). Random Forest was used to prioritise metabolite features most informative in stratifying older age, with potential biological context examined using the prize-collecting Steiner forest algorithm embedded in the PIUMet software, to identify metabolic pathways likely perturbed in ageing. This approach was able to filter a large dataset of several thousand metabolites down to subnetworks of age important metabolites. Identified networks included the common age-associated metabolites such as androgens, (poly)amines/amino acids and lipid metabolites, in addition to some potentially novel ageing related markers such as dihydrothymine and imidazolone-5-proprionic acid. The present study reveals that this approach is a potentially useful tool to identify processes underlying human tissue ageing, and could therefore be utilised in future studies to investigate the links between age predictive metabolites and common biomarkers linked to health and disease across age.