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Updated: Jun 12, 2025

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Utility of an Untargeted Metabolomics Approach Using a 2D GC-GC-MS Platform to Distinguish Relapsing and Progressive

Indrani Datta1,2, Insha Zahoor3, Nasar Ata3

  • 1Department of Public Health Sciences, Henry Ford Health, Detroit, MI 48202, USA.

Metabolites
|September 27, 2024
PubMed
Summary

This study identified distinct serum metabolite profiles in multiple sclerosis (MS) patients, differentiating relapsing-remitting MS (RRMS) and primary progressive MS (PPMS) from healthy subjects (HS). These metabolic fingerprints could aid in diagnosing MS and other progressive autoimmune diseases.

Keywords:
GC-GC-MSPPMSRRMSmetabolomicsmultiple sclerosis

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Area of Science:

  • Neuroimmunology
  • Metabolomics
  • Biochemistry

Background:

  • Multiple sclerosis (MS) is a common neurodegenerative disease affecting young adults, characterized by distinct disease courses like relapsing-remitting MS (RRMS) and primary progressive MS (PPMS).
  • A significant knowledge gap exists in differentiating these MS phenotypes and healthy subjects (HS) based on serum metabolite profiles.

Purpose of the Study:

  • To investigate whether distinct serum metabolite profiles can differentiate between RRMS, PPMS, and HS.
  • To identify specific metabolic pathways and biomarkers associated with different MS disease courses.

Main Methods:

  • Global untargeted metabolomics using a 2D GC-GC-MS platform was employed.
  • Serum samples from 41 RRMS patients, 31 PPMS patients, and 91 HS were analyzed for 235 metabolites.
  • Statistical analyses and pathway enrichment analyses (MetaboAnalyst, Qiagen IPA) were performed to identify significant differences and associated biological pathways.

Main Results:

  • Significant alterations in metabolite profiles were observed between RRMS and HS (22 metabolites) and between PPMS and HS (28 metabolites).
  • Four metabolic pathways, including galactose metabolism and amino sugar metabolism, were commonly enriched in both RRMS and PPMS compared to HS.
  • Specific metabolites like valine, heptadecanoic acid, alpha-ketoisocaproic acid, and glycerol were linked to neurodegeneration and CNS inflammation in PPMS.

Conclusions:

  • Altered serum metabolite profiles can serve as potential biomarkers for distinguishing MS disease courses (RRMS, PPMS) from healthy individuals.
  • The identified metabolic fingerprints offer potential for developing diagnostic tools and metabolite panels for progressive autoimmune diseases like MS.
  • Further research into specific upstream regulators like SULF2 and ITGB1BP1 may elucidate MS pathogenesis.