Research Paper Volume 12, Issue 13 pp 12517—12533

Untargeted metabolomics for uncovering biological markers of human skeletal muscle ageing

Daniel J. Wilkinson1,2,3, , Giovanny Rodriguez-Blanco1,5,7, , Warwick B. Dunn1,5, , Bethan E. Phillips1,2,3, , John P. Williams3, , Paul L. Greenhaff1,2,4, , Kenneth Smith1,2,3, , Iain J. Gallagher6, , Philip J. Atherton1,2,3, ,

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

Received: March 18, 2020       Accepted: June 4, 2020       Published: June 24, 2020
How to Cite

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.


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.


ANOVA: analysis of variance; BMI: Body Mass Index; COPD: Chronic Obstructive Pulmonary Disease; ECG: Electrocardiogram; HILIC-MS: Hydrophilic interaction liquid chromatography mass spectrometry; HMDB: Human metabolome database; HPLC: High performance liquid chromatography; MAP: Mean arterial pressure; RF: Random Forest; RHR: Resting heart rate; UHPLC: Ultra high performance liquid chromatography.