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Villoslada P, Alonso C, Agirrezabal I, et al. Metabolomic signatures associated with disease severity in multiple sclerosis. Neurol Neuroimmunol Neuroinflamm. 2017;4(2):e321doi: 10.1212/NXI.0000000000000321.
Villoslada, P., Alonso, C., Agirrezabal, I., Kotelnikova, E., Zubizarreta, I., Pulido-Valdeolivas, I., Saiz, A., Comabella, M., Montalban, X., Villar, L., Alvarez-Cermeño, J. C., Fernández, O., Alvarez-Lafuente, R., Arroyo, R., & Castro, A. (2017). Metabolomic signatures associated with disease severity in multiple sclerosis. Neurology(R) neuroimmunology & neuroinflammation, 4(2), e321. https://doi.org/10.1212/NXI.0000000000000321
Villoslada, Pablo, et al. "Metabolomic signatures associated with disease severity in multiple sclerosis." Neurology(R) neuroimmunology & neuroinflammation vol. 4,2 (2017): e321. doi: https://doi.org/10.1212/NXI.0000000000000321
Villoslada P, Alonso C, Agirrezabal I, Kotelnikova E, Zubizarreta I, Pulido-Valdeolivas I, Saiz A, Comabella M, Montalban X, Villar L, Alvarez-Cermeño JC, Fernández O, Alvarez-Lafuente R, Arroyo R, Castro A. Metabolomic signatures associated with disease severity in multiple sclerosis. Neurol Neuroimmunol Neuroinflamm. 2017 Jan 27;4(2):e321. doi: 10.1212/NXI.0000000000000321. eCollection 2017 Mar. PMID: 28180139; PMCID: PMC5278923.
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Neurol Neuroimmunol Neuroinflamm. 2017 Jan 27;4(2):e321. doi: 10.1212/NXI.0000000000000321. eCollection 2017 Mar.

Metabolomic signatures associated with disease severity in multiple sclerosis.

Neurology(R) neuroimmunology & neuroinflammation

Pablo Villoslada, Cristina Alonso, Ion Agirrezabal, Ekaterina Kotelnikova, Irati Zubizarreta, Irene Pulido-Valdeolivas, Albert Saiz, Manuel Comabella, Xavier Montalban, Luisa Villar, Jose Carlos Alvarez-Cermeño, Oscar Fernández, Roberto Alvarez-Lafuente, Rafael Arroyo, Azucena Castro

Affiliations

  1. Center of Neuroimmunology (P.V., I.A., E.K., I.Z., I.P.-V., A.S.), Institute d'Investigaciones Biomediques August Pi Sunyer (IDIBAPS)-Hospital Clinic, Barcelona, Spain; University of California (P.V.), San Francisco; OWL (C.A., A.C.), Parque Tecnológico de Bizkaia, Derio; Cemcat (M.C., X.M.), Hospital Vall d'Hebron, Barcelona; Hospital Ramon y Cajal (L.V., J.C.A.-C.), Madrid; Hospital Universitario Regional (O.F.), Instituto de Investigación Biomédica (IBIMA), Malaga; and Hospital Clinico San Carlos (R.A.-L., R.A.), Madrid, Spain.

PMID: 28180139 PMCID: PMC5278923 DOI: 10.1212/NXI.0000000000000321

Abstract

OBJECTIVE: To identify differences in the metabolomic profile in the serum of patients with multiple sclerosis (MS) compared to controls and to identify biomarkers of disease severity.

METHODS: We studied 2 cohorts of patients with MS: a retrospective longitudinal cohort of 238 patients and 74 controls and a prospective cohort of 61 patients and 41 controls with serial serum samples. Patients were stratified into active or stable disease based on 2 years of prospective assessment accounting for presence of clinical relapses or changes in disability measured with the Expanded Disability Status Scale (EDSS). Metabolomic profiling (lipids and amino acids) was performed by ultra-high-performance liquid chromatography coupled to mass spectrometry in serum samples. Data analysis was performed using parametric methods, principal component analysis, and partial least square discriminant analysis for assessing the differences between cases and controls and for subgroups based on disease severity.

RESULTS: We identified metabolomics signatures with high accuracy for classifying patients vs controls as well as for classifying patients with medium to high disability (EDSS >3.0). Among them, sphingomyelin and lysophosphatidylethanolamine were the metabolites that showed a more robust pattern in the time series analysis for discriminating between patients and controls. Moreover, levels of hydrocortisone, glutamic acid, tryptophan, eicosapentaenoic acid, 13S-hydroxyoctadecadienoic acid, lysophosphatidylcholines, and lysophosphatidylethanolamines were associated with more severe disease (non-relapse-free or increase in EDSS).

CONCLUSIONS: We identified metabolomic signatures composed of hormones, lipids, and amino acids associated with MS and with a more severe course.

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