Protein-based classifier to predict conversion from clinically isolated syndrome to multiple sclerosis

Multiple sclerosis is an inflammatory, demyelinating, and neurodegenerative disease of the central nervous system. In most patients, the disease initiates with an episode of neurological disturbance referred to as clinically isolated syndrome, but not all patients with this syndrome develop multiple...

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Detalhes bibliográficos
Autores: Borrás, Eva, Cantó, Ester, Choi, Meena, Villar, Luisa Maria, Álvarez Cermeño, Jose Carlos, Chiva, Cristina, Montalbán Gairín, Xavier, Vitek, Olga, Comabella López, Manuel, Sabidó Aguadé, Eduard, 1981-
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2016
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositório:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/26919
Acesso em linha:http://hdl.handle.net/10230/26919
http://dx.doi.org/10.1074/mcp.M115.053256
Access Level:Acceso aberto
Palavra-chave:Esclerosi múltiple
Descrição
Resumo:Multiple sclerosis is an inflammatory, demyelinating, and neurodegenerative disease of the central nervous system. In most patients, the disease initiates with an episode of neurological disturbance referred to as clinically isolated syndrome, but not all patients with this syndrome develop multiple sclerosis over time, and currently, there is no clinical test that can conclusively establish whether a patient with a clinically isolated syndrome will eventually develop clinically defined multiple sclerosis. Here, we took advantage of the capabilities of targeted mass spectrometry to establish a diagnostic molecular classifier with high sensitivity and specificity able to differentiate between clinically isolated syndrome patients with a high and a low risk of developing multiple sclerosis. Based on the combination of abundances of proteins chitinase 3-like 1 and ala-β-his-dipeptidase in cerebrospinal fluid, we built a statistical model able to assign to each patient a precise probability of conversion to clinically defined multiple sclerosis. Our results are of special relevance for patients affected by multiple sclerosis as early treatment can prevent brain damage and slow down the disease progression.