Neurology · 2014
Metabolomics predicts stroke recurrence after transient ischemic attack.
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Jové Mariona, Mauri-Capdevila Gerard, Suárez Idalmis, Cambray Serafi, Sanahuja Jordi, Quílez Alejandro, Farré Joan, Benabdelhak Ikram, Pamplona Reinald, Portero-Otín Manuel, Purroy Francisco
Аннотация
OBJECTIVE: 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.
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.
Предметные рубрики MeSH
Age FactorsAgedAged, 80 and overBiomarkersBlood PressureCerebral Small Vessel DiseasesCohort StudiesDiffusion Magnetic Resonance ImagingFemaleHumansIntracranial ArteriosclerosisIschemic Attack, TransientLysophosphatidylcholinesMaleMetabolomicsMiddle AgedMultivariate AnalysisProspective StudiesRecurrenceRisk AssessmentStrokeTime Factors