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

PLoS One. 2013;8(3):e59953. doi: 10.1371/journal.pone.0059953. Epub 2013 Mar 28.

Abstract

Background: Anti-glycan antibodies can be found in autoimmune diseases. IgM against glycan P63 was identified in clinically isolated syndromes (CIS) and included in gMS-Classifier2, an algorithm designed with the aim of identifying patients at risk of a second demyelinating attack.

Objective: To determine the value of gMS-Classifier2 as an early and independent predictor of conversion to clinically definite multiple sclerosis (CDMS).

Methods: Data were prospectively acquired from a CIS cohort. gMS-Classifier2 was determined in patients first seen between 1995 and 2007 with ≥ two 200 µL serum aliquots (N = 249). The primary endpoint was time to conversion to CDMS at two years, the factor tested was gMS-Classifier2 status (positive/negative) or units; other exploratory time points were 5 years and total time of follow-up.

Results: Seventy-five patients (30.1%) were gMS-Classifier2 positive. Conversion to CDMS occurred in 31/75 (41.3%) of positive and 45/174 (25.9%) of negative patients (p = 0.017) at two years. Median time to CDMS was 37.8 months (95% CI 10.4-65.3) for positive and 83.9 months (95% CI 57.5-110.5) for negative patients. gMS-Classifier2 status predicted conversion to CDMS within two years of follow-up (HR = 1.8, 95% CI 1.1-2.8; p = 0.014). gMS-Classifier2 units were also independent predictors when tested with either Barkhof criteria and OCB (HR = 1.2, CI 1.0-1.5, p = 0.020) or with T2 lesions and OCB (HR = 1.3, CI 1.1-1.5, p = 0.008). Similar results were obtained at 5 years of follow-up. Discrimination measures showed a significant change in the area under the curve (ΔAUC) when adding gMS-Classifier2 to a model with either Barkhof criteria (ΔAUC 0.0415, p = 0.012) or number of T2 lesions (ΔAUC 0.0467, p = 0.009), but not when OCB were added to these models.

Conclusions: gMS-Classifier2 is an independent predictor of early conversion to CDMS and could be of clinical relevance, particularly in cases in which OCB are not available.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Algorithms
  • Area Under Curve
  • Biomarkers / blood*
  • Blood Glucose / analysis*
  • Demyelinating Diseases / genetics
  • Demyelinating Diseases / pathology
  • Disease Progression
  • Female
  • Gene Expression Regulation*
  • Humans
  • Immunoglobulin M / blood*
  • Longitudinal Studies
  • Male
  • Multiple Sclerosis / blood*
  • Multiple Sclerosis / physiopathology*
  • Prognosis
  • Proportional Hazards Models
  • Prospective Studies
  • ROC Curve
  • Time Factors

Substances

  • Biomarkers
  • Blood Glucose
  • Immunoglobulin M

Grants and funding

The “Red Española de Esclerosis Múltiple (REEM, www.reem.es)” (RD07/0060) sponsored by the Fondo de Investigación Sanitaria (FIS), Ministry of Economy and Competitively, Spain; the “Ajuts per donar Suport als Grups de Recerca de Catalunya” (2009 SGR 0793, http://www10.gencat.cat/agaur_web/AppJava/catala/a_beca.jsp?categoria=recerca&id_beca=13601), sponsored by the “Agència de Gestió d’Ajuts Universitaris i de Recerca” (AGAUR, www.gencat.cat/agaur/), Generalitat de Catalunya, Spain. The biomarker study was funded by the Fondo de Investigación Sanitaria (FIS, www.isciii.es); grant number PI08/0788; Ministry of Science and Innovation, Spain. Georgina Arrambide’s research is made possible by the McDonald Fellowship, awarded by the Multiple Sclerosis International Federation (www.msif.org). Carmen Espejo is partially supported by the “Miguel Servet” program (CP07/00146) from the FIS, Ministry of Economy and Competitively, Spain (www.isciii.es). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials