Coronary artery disease is among the leading causes of death worldwide. Nevertheless, available cardiovascular risk prediction algorithms still miss a significant portion of individuals at-risk. Thus, the search for novel non-invasive biomarkers to refine cardiovascular risk assessment is both an urgent need and an attractive topic, which may lead to a more accurate risk stratification and/or prognostic score definition for coronary artery disease. A new class of such non-invasive biomarkers is represented by extracellular microRNAs (miRNAs) circulating in the blood. MiRNAs are non-coding RNA of 22–25 nucleotides in length that play a significant role in both cardiovascular physiology and pathophysiology. Given their high stability and conservation, resistance to degradative enzymes, and detectability in body fluids, circulating miRNAs are promising emerging biomarkers, and specific expression patterns have already been associated with a wide range of cardiovascular conditions. In this review, an overview of the role of blood miRNAs in risk assessment and prognosis of coronary artery disease is given.

Existing tools to estimate cardiovascular (CV) risk have sub-optimal predictive capacities. In this setting, non-invasive imaging techniques and omics biomarkers could improve risk-prediction models for CV events. This study aimed to identify gene expression patterns in whole blood that could differentiate patients with severe coronary atherosclerosis from subjects with a complete absence of detectable coronary artery disease and to assess associations of gene expression patterns with plaque features in coronary CT angiography (CCTA). Patients undergoing CCTA for suspected coronary artery disease (CAD) were enrolled. Coronary stenosis was quantified and CCTA plaque features were assessed. The whole-blood transcriptome was analyzed with RNA sequencing. We detected highly significant differences in the circulating transcriptome between patients with high-degree coronary stenosis (≥70%) in the CCTA and subjects with an absence of coronary plaque. Notably, regression analysis revealed expression signatures associated with the Leaman score, the segment involved score, the segment stenosis score, and plaque volume with density <150 HU at CCTA. This pilot study shows that patients with significant coronary stenosis are characterized by whole-blood transcriptome profiles that may discriminate them from patients without CAD. Furthermore, our results suggest that whole-blood transcriptional profiles may predict plaque characteristics.