Circulation Journal
Online ISSN : 1347-4820
Print ISSN : 1346-9843
ISSN-L : 1346-9843
Preventive Medicine
Circulating MicroRNA-423-3p Improves the Prediction of Coronary Artery Disease in a General Population ― Six-Year Follow-up Results From the China-Cardiovascular Disease Study ―
Xin WangYing DongTian FangXiaoxia WangLu ChenCongyi ZhengYuting KangLinlin JiangXin YouShujie GaiZengwu WangHuiqing Cao
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Supplementary material

2020 Volume 84 Issue 7 Pages 1155-1162

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Abstract

Background:Circulating microRNAs (miRNA) are potential prognostic biomarkers for cardiovascular disease. We aimed to identify serum miRNA as an effective predictor for coronary artery disease (CAD) events in a general population cohort.

Methods and Results:Serum miRNAs associated with CAD were determined by small RNA sequencing and quantitative RT-PCR. Further, the predictive ability of identified serum miRNAs was measured in a general population of 2,812 people. As a main outcome measure, CAD events were collected for 6 years and included acute myocardial infarction and subsequent myocardial infarction. Out of the 48 miRNA candidates, 5 miRNAs (miR-10a-5p, miR-126-3p, miR-210-3p, miR-423-3p and miR-92a-3p) showed better reliability and repeatability in serum. Then, the association of serum levels of the 5 miRNAs with CAD was validated. Furthermore, miR-10a-5p and miR-423-3p, which showed better performance, were tested in the large cohort, with a median follow up of 6.0 years. In multivariable Cox regression analysis, only miR-423-3p (P for trend<0.001) was able to precisely predict CAD events. Moreover, the addition of circulating miR-423-3p with the traditional risk factors together markedly improved the various model performance measures, including the area under the operating characteristics curve (0.782 vs. 0.806), Akaike Information Criterion (965.845 vs. 943.113) and net reclassification improvement (19.18%).

Conclusions:Circulating miR-423-3p can improve the prediction of primary CAD outcomes on the basis of a traditional risk factor model in general population.

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© 2020 THE JAPANESE CIRCULATION SOCIETY
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