A blood-based, six metabolite signature for relapsing-remitting multiple sclerosis
Recommended Citation
Cerghet M, Poisson L, Dutta I, Suhil H, Elias SB, Giri S, and Mangalam A. A blood-based, six metabolite signature for relapsing-remitting multiple sclerosis. Mult Scler J 2017; 23(Suppl 1):14.
Document Type
Conference Proceeding
Publication Date
2017
Publication Title
Mult Scler J
Abstract
Background: Multiple sclerosis (MS) is a serious debilitating health problem. Monitoring of the disease more closely with a non-invasive marker in the clinic will be of immense benefit. Metabolomics provides a new powerful approach to discover diagnostic and therapeutic biomarkers by analyzing global changes in an individual's metabolic profile. Objectives: The aim of this study was to identify serum metabolites as disease biomarker(s) for relapsing-remitting multiple sclerosis (RRMS) using untargeted metabolomics approach. Methods: Using ultra-performance liquid chromatography linked to gas chromatography and tandem mass spectrometry (Metabolon, Durham, NC), we measured serum metabolites from 35 RRMS subjects without any drug treatment (mean age: 45 years and mean duration of disease 18.2 years; 64% female) and 14 healthy subjects with no disease (mean age: 40 years; 64% female). Results: A total of 621 known metabolites were detected with significance changes observed in 60 metabolites (53 up-regulated and 7 down-regulated) in the serum of RRMS compared to HS. Partial least-squares discriminant analysis of the metabolites reveals a separation of these groups. Bioinformatics analysis revealed 4 metabolic pathways being impacted and altered in RRMS including glycerophospholipid metabolism, citrate cycle (TCA), taurine and hypotaurine and pyruvate metabolism. Casual Network Analysis in IPA identified sphingosine and transforming growth factor beta as a master regulator of altered metabolites in RRMS. Further to identify the potential biomarker specific for RRMS, we identified 14 metabolites, which were selected for prediction model creation. Six out of 14 metabolites were validated in an independent cohort (HS=34, RRMS=40), which could be predicted as potential biomarker for RRMS. Conclusion: Identified and validated 6 metabolites signature have potential to be developed into a clinically useful diagnostic or biomarker tool, that could also contribute to further understanding of disease mechanisms.
Volume
23
Issue
Suppl 1
First Page
14
