Brain Communications · 2021 · 23 citations · 30 references
Life SciencesNeurobiologyNeurologyInflammationMetabolomicsNeuroimmunologyClinical MetabolomicsMass Spectrometry-based MetabolomicsMetabolite ProfilingMultiple SclerosisNuclear Magnetic Resonance MetabolomicsUntargeted MetabolomicsBiofluid Biomarker DiscoveryBiofluid BiomarkersBiofluid MetabolomicsBiomarkersDisease Biomarker DiscoveryDisease Metabolite ProfilingMetabolite BiomarkersMetabolomic Biomarkers
Abstract Accurate determination of relapses in multiple sclerosis is important for diagnosis, classification of clinical course and therapeutic decision making. The identification of biofluid markers for multiple sclerosis relapses would add to our current diagnostic armamentarium and increase our understanding of the biology underlying the clinical expression of inflammation in multiple sclerosis. However, there is presently no biofluid marker capable of objectively determining multiple sclerosis relapses although some, in particular neurofilament-light chain, have shown promise. In this study, we sought to determine if metabolic perturbations are present during multiple sclerosis relapses, and, if so, identify candidate metabolite biomarkers and evaluate their discriminatory abilities at both group and individual levels, in comparison with neurofilament-light chain. High-resolution global and targeted 1H nuclear magnetic resonance metabolomics as well as neurofilament-light chain measurements were performed on the serum in four groups of relapsing-remitting multiple sclerosis patients, stratified by time since relapse onset: (i) in relapse (R); (ii) last relapse (LR) ≥ 1 month (M) to < 6 M ago; (iii) LR ≥ 6 M to < 24 M ago; and (iv) LR ≥ 24 M ago. Two hundred and one relapsing-remitting multiple sclerosis patients were recruited: R (n = 38), LR 1–6 M (n = 28), LR 6–24 M (n = 34), LR ≥ 24 M (n = 101). Using supervised multivariate analysis, we found that the global metabolomics profile of R patients was significantly perturbed compared to LR ≥ 24 M patients. Identified discriminatory metabolites were then quantified using targeted metabolomics. Lysine and asparagine (higher in R), as well as, isoleucine and leucine (lower in R), were shortlisted as potential metabolite biomarkers. ANOVA of these metabolites revealed significant differences across the four patient groups, with a clear trend with time since relapse onset. Multivariable receiver operating characteristics analysis of these four metabolites in discriminating R versus LR ≥ 24 M showed an area under the curve of 0.758, while the area under the curve for serum neurofilament-light chain was 0.575. Within individual patients with paired relapse–remission samples, all four metabolites were significantly different in relapse versus remission, with the direction of change consistent with that observed at group level, while neurofilament-light chain was not discriminatory. The perturbations in the identified metabolites point towards energy deficiency and immune activation in multiple sclerosis relapses, and the measurement of these metabolites, either singly or in combination, are useful as biomarkers to differentiate relapse from remission at both group and individual levels.
30
R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
HMDB 4.0: the human metabolome database for 2018
David S. Wishart, Yannick Djoumbou-Feunang, Ana Marcu et al. · Nucleic Acids Research · 2017 · 3.6K citations · Full text
Etienne Thévenot, Aurélie Roux, Ying Xu et al. · Journal of Proteome Research · 2015 · 1.7K citations
Serum Neurofilament light: A biomarker of neuronal damage in multiple sclerosis
Giulio Disanto, Christian Barro, Pascal Benkert et al. · Annals of Neurology · 2017 · 1.2K citations · Full text
Monitoring disease activity in multiple sclerosis using serum neurofilament light protein
Lenka Novakova, Henrik Zetterberg, Peter Sundström et al. · Neurology · 2017 · 428 citations · Full text