Metabolomics in multiple sclerosis disease course and progression. (April 2020)

• Record Type: Journal Article
• Authors: Bhargava, Pavan; Anthony, Daniel C.
• Other Names: Chitnis Tanuja guest-editor.; Prat Alexandre guest-editor.
• Is Part Of: Multiple sclerosis. Volume 26:Number 5(2020)
• Page Start: 591
• Page End: 598
• Publication Date: 2020-04
• Subjects: Metabolomics -- multiple sclerosis -- tryptophan -- mass spectrometry -- nuclear magnetic resonance; Central nervous system -- Diseases -- Periodicals; Myelin sheath -- Diseases -- Periodicals; Inflammation -- Periodicals; Multiple sclerosis -- Periodicals; Central Nervous System Diseases -- Periodicals; Demyelinating Diseases -- Periodicals; Inflammation -- Periodicals; Multiple Sclerosis -- Periodicals; Système nerveux central -- Maladies -- Périodiques; Gaine de myéline -- Maladies -- Périodiques; Inflammation (Pathologie) -- Périodiques; Sclérose en plaques -- Périodiques; Electronic journals; 616.834005
• DOI: 10.1177/1352458519876020
• Languages: English
• ISSNs: 1352-4585

Abstract

Multiple sclerosis (MS) is associated with changes in the metabolome. Numerous studies employing varying metabolomics platforms have examined a range of biological material ranging from brain tissue to urine and demonstrated consistently alterations in multiple metabolic pathways in MS. We review not only the studies that describe the ability of metabolomics to differentiate MS patients from healthy controls and other neurological disease but also discuss the potential of metabolomics-based methods to build predictive models that are able to stage disease, monitor progression, and select the most appropriate therapy. The increasing number of impressive claims for the capacity of metabolomics to distinguish between different types of demyelinating disease suggests that the provision of such tests may be close at hand. Besides the ability to provide potential diagnostic and prognostic biomarkers, metabolomics also provides us with unique insights into the pathophysiology of the disease and helps identify metabolic pathways that may be potential therapeutic targets. Future studies will integrate metabolomics data with other omics techniques to provide further insight into the source of these metabolic abnormalities and help with identification of the most promising targets for therapeutic intervention.