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Multimarker risk stratification approach at multiple sclerosis onset.

Lidia Fernández-Paredes1, Armanda Casrouge2, Jérémie Decalf2

  • 1Dept. of Clinical Immunology and IdISSC, Hospital Clínico San Carlos, Madrid, Spain; Dept. of Microbiology I, Complutense University School of Medicine, Madrid, Spain.

Clinical Immunology (Orlando, Fla.)
|June 5, 2017
PubMed
Summary

A new decision-tree model using 12 biomarkers improves early diagnosis of multiple sclerosis (MS) and its progressive forms. This analysis of serum and CSF samples aids in distinguishing MS subtypes and reveals potential immune response defects.

Keywords:
BiomarkersDiagnosticInnate immunity deregulationPrognostic

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Area of Science:

  • Neuroimmunology
  • Biomarker Discovery
  • Clinical Neurology

Background:

  • Diagnosing multiple sclerosis (MS) early and predicting its progression is challenging due to a lack of specific clinical and analytical markers.
  • Accurate diagnostic tools are crucial for timely intervention and management of MS.
  • Current diagnostic approaches may not sufficiently differentiate between various MS clinical subtypes at onset.

Purpose of the Study:

  • To develop and validate a decision-tree model for improved early diagnosis of MS and its progressive forms.
  • To identify a panel of 12 biomarkers in serum and cerebrospinal fluid (CSF) for classifying MS patients.
  • To investigate potential differences in immune response biomarkers between MS subtypes and other neurological diseases (OND).

Main Methods:

  • Analysis of a 12-biomarker panel in serum and CSF samples from patients with MS, OND, and healthy controls.
  • Development of a decision-tree model to classify patients based on biomarker levels.
  • Statistical analysis including odds ratios (OR) and p-values to determine biomarker significance and risk association.

Main Results:

  • Serum IL-7 levels <141 pg/ml were significant in classifying patients at neurological disease onset (OR=6.51, p<0.001).
  • A combination of IL-7 and CXCL10 levels identified high risk for primary progressive MS (PP-MS), with IL-7<141 and CXCL10<570 pg/ml associated with the highest risk (OR=22, p=0.01).
  • Both PP-MS and relapsing-remitting MS (RR-MS) patients showed decreased CSF biomarkers of inflammation and regeneration compared to OND, suggesting a defective intrinsic immune response.

Conclusions:

  • The proposed decision-tree model effectively utilizes a 12-biomarker panel for enhanced early diagnosis and subtype classification in MS.
  • Serum IL-7 and CXCL10 levels are promising predictors for MS onset and specific clinical forms like PP-MS.
  • Reduced inflammatory and regenerative biomarkers in CSF of MS patients suggest an intrinsic immune defect contributing to disease initiation.