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Serum Biomarker gMS-Classifier2: Predicting Conversion to Clinically Definite Multiple Sclerosis
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 det...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3610690/ https://www.ncbi.nlm.nih.gov/pubmed/23555846 http://dx.doi.org/10.1371/journal.pone.0059953 |
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author | Arrambide, Georgina Espejo, Carmen Yarden, Jennifer Fire, Ella Spector, Larissa Dotan, Nir Dukler, Avinoam Rovira, Alex Montalban, Xavier Tintore, Mar |
author_facet | Arrambide, Georgina Espejo, Carmen Yarden, Jennifer Fire, Ella Spector, Larissa Dotan, Nir Dukler, Avinoam Rovira, Alex Montalban, Xavier Tintore, Mar |
author_sort | Arrambide, Georgina |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3610690 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-36106902013-04-03 Serum Biomarker gMS-Classifier2: Predicting Conversion to Clinically Definite Multiple Sclerosis Arrambide, Georgina Espejo, Carmen Yarden, Jennifer Fire, Ella Spector, Larissa Dotan, Nir Dukler, Avinoam Rovira, Alex Montalban, Xavier Tintore, Mar PLoS One Research Article 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. Public Library of Science 2013-03-28 /pmc/articles/PMC3610690/ /pubmed/23555846 http://dx.doi.org/10.1371/journal.pone.0059953 Text en © 2013 Arrambide et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Arrambide, Georgina Espejo, Carmen Yarden, Jennifer Fire, Ella Spector, Larissa Dotan, Nir Dukler, Avinoam Rovira, Alex Montalban, Xavier Tintore, Mar Serum Biomarker gMS-Classifier2: Predicting Conversion to Clinically Definite Multiple Sclerosis |
title | Serum Biomarker gMS-Classifier2: Predicting Conversion to Clinically Definite Multiple Sclerosis |
title_full | Serum Biomarker gMS-Classifier2: Predicting Conversion to Clinically Definite Multiple Sclerosis |
title_fullStr | Serum Biomarker gMS-Classifier2: Predicting Conversion to Clinically Definite Multiple Sclerosis |
title_full_unstemmed | Serum Biomarker gMS-Classifier2: Predicting Conversion to Clinically Definite Multiple Sclerosis |
title_short | Serum Biomarker gMS-Classifier2: Predicting Conversion to Clinically Definite Multiple Sclerosis |
title_sort | serum biomarker gms-classifier2: predicting conversion to clinically definite multiple sclerosis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3610690/ https://www.ncbi.nlm.nih.gov/pubmed/23555846 http://dx.doi.org/10.1371/journal.pone.0059953 |
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