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Mol Med. 2012 May 9;18:669-75. doi: 10.2119/molmed.2011.00479.

Prediction of ischemic events on the basis of transcriptomic and genomic profiling in patients undergoing carotid endarterectomy.

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1
Center for Molecular Medicine, Department of Medicine, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden. lasse.folkersen@ki.se

Abstract

Classic risk factors, including age, smoking, serum cholesterol, diabetes and blood pressure, constitute the basis of present risk prediction models but fail to identify all individuals at risk. The objective of this study was to investigate if genomic and transcriptional patterns improve prediction of ischemic events in patients with established carotid artery disease. Genotype and gene expression profiles were obtained from carotid plaque tissue (n = 126) and peripheral blood mononuclear cells (n = 97) of patients undergoing carotid endarterectomy. Patients were followed for an average of 44 months, and 25 ischemic events occurred (18 ischemic strokes and 7 myocardial infarctions). Blinded leave-one-out cross-validation on Cox regression coefficients was used to assign gene expression-based risk scores to each patient. When compared with classic risk factors, addition of carotid plaque gene expression-based risk score improved the prediction of future ischemic events from an area under the curve (AUC) of 0.66 to an AUC of 0.79. The inclusion of gene expression risk score from peripheral blood mononuclear cells or from 25 established myocardial infarction risk single nucleotide polymorphisms only exhibited marginal effects on the prediction of ischemic events. Prediction of ischemic events is improved by inclusion of gene expression profiling from carotid endarterectomy tissue compared with prediction on the basis of classic risk markers alone in patients with atherosclerosis. The method may be developed to identify subjects at very high risk of ischemic events.

PMID:
22371308
PMCID:
PMC3388132
DOI:
10.2119/molmed.2011.00479
[Indexed for MEDLINE]
Free PMC Article
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