Predictive genomics of cardioembolic stroke.
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ABSTRACT:
SUBMITTER: Ramoni RB
PROVIDER: S-EPMC2752697 | biostudies-literature | 2009 Mar
REPOSITORIES: biostudies-literature
Publications
Predictive genomics of cardioembolic stroke.
Ramoni Rachel Badovinac RB Himes Blanca E BE Sale Michele M MM Furie Karen L KL Ramoni Marco F MF
Stroke 20081208 3 Suppl
Cardioembolic stroke is a complex disease resulting from the interaction of numerous factors. Using data from Genes Affecting Stroke Risk and Outcome Study (GASROS), we show that a multivariate predictive model built using Bayesian networks is able to achieve a predictive accuracy of 86% on the fitted values as computed by the area under the receiver operating characteristic curve relative to that of the individual single nucleotide polymorphism with the highest prognostic performance (area unde ...[more]
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Project description:Blood from subjects with cardioembolic stroke and controls was collected, and the RNA extracted was interrogated and whole genome U133 Affymetrix Arrays. Twenty-three control samples and sixty-nine cardioembolic stroke samples were assayed.
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Project description:Blood from subjects with cardioembolic stroke and controls was collected, and the RNA extracted was interrogated and whole genome U133 Affymetrix Arrays. Twenty-three control samples and sixty-nine cardioembolic stroke samples were assayed. Blood from subjects with cardioembolic stroke and controls was collected, and the RNA extracted was interrogated and whole genome U133 Affymetrix Arrays. Twenty-three control samples and sixty-nine cardioembolic stroke samples were assayed. Cardioembolic stroke subjects were analyzed at three time points: less than 3 hours, 5 hours, and 24 hours following the event.
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Project description:ObjectiveTo determine whether altered metabolic profiles represent a link between atrial dysfunction and cardioembolic (CE) stroke, and thus whether underlying dysfunctional atrial substrate may contribute to thromboembolism risk in CE stroke.MethodsA total of 144 metabolites were measured using liquid chromatography-tandem mass spectrometry in plasma samples collected within 9 hours of stroke onset in 367 acute stroke patients. Stroke subtype was assigned using the Causative Classification of Stroke System, and CE stroke (n = 181) was compared to non-CE stroke (n = 186). Markers of left atrial dysfunction included abnormal atrial function (P-wave terminal force in lead V1, PTFV1 >4,000 μV·ms), left atrial enlargement on echocardiography, and frank atrial fibrillation on ECG. Stroke recurrence risk was assessed using CHADS2 and CHA2DS2-VASc scores. Associations between metabolites and CE stroke, atrial dysfunction, and stroke recurrence risk were evaluated using logistic regression models.ResultsThree tricarboxylic acid metabolites-succinate (odds ratio [OR] 1.71, 95% confidence interval [CI] 1.36-2.15, p = 1.37 × 10-6), α-ketoglutarate (OR 1.62, 95% CI 1.29-2.04, p = 1.62 × 10-5), and malate (OR 1.58, 95% CI 1.26-1.97, p = 2.57 × 10-5)-were associated with CE stroke. Succinate (OR 1.36, 95% CI 1.31-1.98, p = 1.22 × 10-6), α-ketoglutarate (OR 2.14, 95% CI 1.60-2.87, p = 2.08 × 10-8), and malate (OR 2.02, 95% CI 1.53-2.66, p = 1.60 × 10-7) were among metabolites also associated with subclinical atrial dysfunction. Of these, succinate was also associated with left atrial enlargement (OR 1.54, 95% CI 1.23-1.94, p = 1.06 × 10-4) and stroke recurrence based on dichotomized CHADS2 (OR 2.63, 95% CI 1.68-4.13, p = 3.00 × 10-6) and CHA2DS2-VASc (OR 2.43, 95% CI 1.60-3.68, p = 4.25 × 10-6) scores.ConclusionsMetabolite profiling identified changes in succinate associated with CE stroke, atrial dysfunction, and stroke recurrence, revealing a putative underlying link between CE stroke and energy metabolism.
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