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dc.contributor.authorJové Font, Mariona
dc.contributor.authorMauri-Capdevila, Gerard
dc.contributor.authorSuárez-Luis, Idalmis
dc.contributor.authorCambray Carner, Serafí
dc.contributor.authorSanahuja Montesinos, Jordi
dc.contributor.authorQuilez Martínez, Alejandro
dc.contributor.authorFarré, Joan
dc.contributor.authorBenabdelhak Abbou, Ikram
dc.contributor.authorPamplona Gras, Reinald
dc.contributor.authorPortero Otín, Manuel
dc.contributor.authorPurroy Garcia, Francisco
dc.date.accessioned2016-03-30T07:26:13Z
dc.date.available2016-03-30T07:26:13Z
dc.date.issued2015
dc.identifier.issn0028-3878
dc.identifier.urihttp://hdl.handle.net/10459.1/56762
dc.description.abstractObjective: To discover, by using metabolomics, novel candidate biomarkers for stroke recurrence (SR) with a higher prediction power than present ones. Methods: Metabolomic analysis was performed by liquid chromatography coupled to mass spectrometry in plasma samples from an initial cohort of 131 TIA patients recruited ,24 hours after the onset of symptoms. Pattern analysis and metabolomic profiling, performed by multivariate statistics, disclosed specific SR and large-artery atherosclerosis (LAA) biomarkers. The use of these methods in an independent cohort (162 subjects) confirmed the results obtained in the first cohort. Results: Metabolomics analyses could predict SR using pattern recognition methods. Low concentrations of a specific lysophosphatidylcholine (LysoPC[16:0]) were significantly associated with SR. Moreover, LysoPC(20:4) also arose as a potential SR biomarker, increasing the prediction power of age, blood pressure, clinical features, duration of symptoms, and diabetes scale (ABCD2) and LAA. Individuals who present early (,3 months) recurrence have a specific metabolomic pattern, differing from non-SR and late SR subjects. Finally, a potential LAA biomarker, LysoPC(22:6), was also described. Conclusions: The use of metabolomics in SR biomarker research improves the predictive power of conventional predictors such as ABCD2 and LAA. Moreover, pattern recognition methods allow us to discriminate not only SR patients but also early and late SR cases.ca_ES
dc.description.sponsorshipSupported by the Autonomous Government of Catalunya (2009SGR- 735), the Spanish Ministry of Health (FIS 11-02033), and the Marató of TV3 Foundation (95/C/2011). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Samples were obtained with the support of IRBLleida biobank and RETICS BIOBANCOS (RD09/0076/00059)ca_ES
dc.language.isoengca_ES
dc.publisherAmerican Academy of Neurologyca_ES
dc.relationMICINN/PN2008-2011/PI11/02033ca_ES
dc.relation.isformatofReproducció del document publicat a https://doi.org/10.1212/WNL.0000000000001093ca_ES
dc.relation.ispartofNeurology, 2015, vol. 84, núm. 1, p. 36-45ca_ES
dc.rights(c) American Academy of Neurology, 2015ca_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.titleMetabolomics predicts stroke recurrence after transient ischemic attackca_ES
dc.typearticleca_ES
dc.identifier.idgrec022340
dc.type.versionpublishedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.identifier.doihttps://doi.org/10.1212/WNL.0000000000001093


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(c) American Academy of Neurology, 2015
Except where otherwise noted, this item's license is described as (c) American Academy of Neurology, 2015