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Multiple Sclerosis l: Introduction01:19

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Multiple sclerosis is a chronic autoimmune disease of the central nervous system (CNS) that affects the brain, spinal cord, and optic nerves. It is an inflammatory demyelinating disorder and a leading cause of neurological disability in young adults.EpidemiologyMS commonly begins between 20 and 40 years of age and is twice as common in women. Its exact cause remains unclear, but genetic susceptibility contributes, with higher risk in first-degree relatives and identical twins. A greater...

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Serum biomarker gMS-Classifier2: predicting conversion to clinically definite multiple sclerosis.

Georgina Arrambide1, Carmen Espejo, Jennifer Yarden

  • 1Department of Neurology-Neuroimmunology, Multiple Sclerosis Centre of Catalonia (Cemcat), Vall d'Hebron University Hospital and Research Institute, Universitat Autònoma de Barcelona, Barcelona, Spain.

Plos One
|April 5, 2013
PubMed
Summary
This summary is machine-generated.

The gMS-Classifier2 algorithm, which detects IgM antibodies against glycan P63, effectively predicts conversion to clinically definite multiple sclerosis (CDMS) in patients with clinically isolated syndromes (CIS). This tool offers valuable early prognostic information for MS progression.

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Area of Science:

  • Neuroimmunology
  • Biomarker Discovery
  • Multiple Sclerosis Pathogenesis

Background:

  • Anti-glycan antibodies are implicated in autoimmune diseases.
  • IgM against glycan P63 is detected in clinically isolated syndromes (CIS).
  • gMS-Classifier2 algorithm aims to identify patients at risk of a second demyelinating attack.

Purpose of the Study:

  • To evaluate gMS-Classifier2 as an early and independent predictor of conversion to clinically definite multiple sclerosis (CDMS).

Main Methods:

  • Prospective data collection from a CIS cohort (N=249).
  • gMS-Classifier2 determination in serum samples.
  • Primary endpoint: time to CDMS conversion at two years; secondary endpoints at five years and total follow-up.

Main Results:

  • gMS-Classifier2 positive patients showed a higher conversion rate to CDMS (41.3% vs 25.9% at 2 years, p=0.017).
  • gMS-Classifier2 status and units independently predicted CDMS conversion within two and five years.
  • Adding gMS-Classifier2 improved prediction models incorporating Barkhof criteria or T2 lesions.

Conclusions:

  • gMS-Classifier2 serves as an independent predictor of early CDMS conversion.
  • The classifier holds clinical relevance, especially when other biomarkers like OCB are unavailable.
  • This tool aids in early risk stratification for multiple sclerosis progression.