John F O Sullivan1, Antoinette Neylon2, Catherine McGorrian3, Gavin J Blake3. 1. Cardiovascular Research Center, Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.; Heart Research Institute, Newtown, NSW 2042, Australia.; Charles Perkins Centre, Johns Hopkins Drive, The University of Sydney, NSW 2006, Australia; Department of Cardiology, Mater Misericordiae University Hospital, Eccles Street, Dublin 7, Ireland.. Electronic address: john.jos.osullivan@gmail.com. 2. Department of Cardiology, Mater Misericordiae University Hospital, Eccles Street, Dublin 7, Ireland. 3. Department of Cardiology, Mater Misericordiae University Hospital, Eccles Street, Dublin 7, Ireland.; A/Prof, University College Dublin, Dublin, Ireland.
Abstract
BACKGROUND: MicroRNAs (miRNAs), small non-coding RNAs, have been implicated as regulators of all mediators of atherosclerosis, and some reports have suggested increased levels in coronary artery disease (CAD) and acute myocardial infarction (AMI). However, the potential of miRNAs as biomarkers or predictors of disease remains to be established. METHODS: We designed a study comprising 150 patients (50 Control, 50 Stable CAD, and 50 ST Elevation Myocardial Infarction (STEMI)), and measured plasma miRNAs in each. We then determined the ability of differential miRNAs, adjusting for Framingham Heart Study (FHS) risk factors, to discriminate between CAD vs Control, and STEMI vs Control. RESULTS: Three miRNAs (miR15a-5p, miR16-5p, and miR93-5p) were significantly increased in Stable CAD vs Control groups and one (miR146a-5p) was significantly decreased in Stable CAD vs Control. One miRNA - miR499a-5p - was significantly increased in the STEMI group compared to Controls. After adjustment for FHS risk factors, miR93-5p levels remained an independent predictor of the presence of CAD (Odds Ratio [OR]=8.76, P=0.002). All 4 miRNAs improved discriminatory power for CAD over FHS alone in ROC analysis. Similarly, after adjustment for risk factors miR499-5p remained an independent predictor of STEMI (OR=3.03, P=0.001) and improved discriminatory power for STEMI in ROC analyses. CONCLUSION: We identified 4 miRNAs that were differentially expressed among stable CAD and control patients, and 1 miRNA that was elevated in STEMI patients vs controls. MiR93-5p was the strongest predictor of CAD after adjustment for traditional risk factors, suggesting potential diagnostic utility.
BACKGROUND: MicroRNAs (miRNAs), small non-coding RNAs, have been implicated as regulators of all mediators of atherosclerosis, and some reports have suggested increased levels in coronary artery disease (CAD) and acute myocardial infarction (AMI). However, the potential of miRNAs as biomarkers or predictors of disease remains to be established. METHODS: We designed a study comprising 150 patients (50 Control, 50 Stable CAD, and 50 ST Elevation Myocardial Infarction (STEMI)), and measured plasma miRNAs in each. We then determined the ability of differential miRNAs, adjusting for Framingham Heart Study (FHS) risk factors, to discriminate between CAD vs Control, and STEMI vs Control. RESULTS: Three miRNAs (miR15a-5p, miR16-5p, and miR93-5p) were significantly increased in Stable CAD vs Control groups and one (miR146a-5p) was significantly decreased in Stable CAD vs Control. One miRNA - miR499a-5p - was significantly increased in the STEMI group compared to Controls. After adjustment for FHS risk factors, miR93-5p levels remained an independent predictor of the presence of CAD (Odds Ratio [OR]=8.76, P=0.002). All 4 miRNAs improved discriminatory power for CAD over FHS alone in ROC analysis. Similarly, after adjustment for risk factors miR499-5p remained an independent predictor of STEMI (OR=3.03, P=0.001) and improved discriminatory power for STEMI in ROC analyses. CONCLUSION: We identified 4 miRNAs that were differentially expressed among stable CAD and control patients, and 1 miRNA that was elevated in STEMI patients vs controls. MiR93-5p was the strongest predictor of CAD after adjustment for traditional risk factors, suggesting potential diagnostic utility.
Authors: Kind-Leng Tong; Ahmad Syadi Mahmood Zuhdi; Wan Azman Wan Ahmad; Paul M Vanhoutte; Joao Pedro de Magalhaes; Mohd Rais Mustafa; Pooi-Fong Wong Journal: Int J Mol Sci Date: 2018-05-15 Impact factor: 5.923