Circulating microRNAs strongly predict cardiovascular death in patients with coronary artery disease-results from the large AtheroGene study

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Abstract

Introduction: Stratification for subsequent coronary events among patients with coronary artery disease (CAD) is of considerable interest because of the potential to guide secondary preventive therapies. Recently, we identified eight microRNAs (miRNAs), which facilitated acute coronary syndrome (ACS) diagnosis. In this study, we aimed to evaluate their potential role as prognostic biomarkers for cardiovascular disease.

Methods: The serum concentrations of eight candidate miRNAs -miR-19a, miR-19b, miR-132, miR-140-3p, miR-142-5p, miR-150, miR-186, and miR-210 were measured in a cohort of 1112 patients with documented CAD-including 430 patients with ACS and 682 patients with stable angina pectoris. Cardiovascular death was the main outcome measure.

Results: During a median follow-up of 4.0 years, most miRNAs reliably predicted cardiovascular death in ACS patients. Cox regression analyses indicated that in particular miR-132 (HR 2.85 per 1 SD increase, P = 0.022), miR-140-3p (HR 2.88 per 1 SD increase, P = 0.022), and miR-210 (HR 3.10 per 1 SD increase, P = 0.039) were able to precisely predict cardiovascular death. Circulating miR-132, miR-140-3p, and miR-210 clearly improved various model performance measures, including C-statistics (AUC [area under the receiver-operating characteristic curve] for miR-132: 0.737; AUC for miR-140-3p: 0.756; AUC for miR-210: 0.754).

Conclusions: This is the largest study so far evaluating the prognostic value of circulating miRNAs in cardiovascular disease. Our study shows that single miRNAs derived from peripheral blood predict mortality in secondary prevention settings, and thereby represent valuable biomarkers for risk estimation in CAD.

Bibliographical data

Original languageEnglish
ISSN0195-668X
DOIs
Publication statusPublished - 14.02.2017

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Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2016. For permissions please email: journals.permissions@oup.com.

PubMed 27357355