Plasma microRNA Profiling Reveals Novel Biomarkers of Epicardial Adipose Tissue: A Multidetector Computed Tomography Study

• Main Authors: David de Gonzalo-Calvo, David Vilades, Pablo Martínez-Camblor, Àngela Vea, Andreu Ferrero-Gregori, Laura Nasarre, Olga Bornachea, Jesus Sanchez Vega, Rubén Leta, Núria Puig, Sonia Benítez, Jose Luis Sanchez-Quesada, Francesc Carreras, Vicenta Llorente-Cortés
• Format: Article
• Language: English
• Published: MDPI AG 2019-06-01
• Series: Journal of Clinical Medicine
• Subjects: biomarker; cardiometabolic disease; epicardial adipose tissue; epicardial fat; epicardial fat volume; microRNA
• Online Access: https://www.mdpi.com/2077-0383/8/6/780

Epicardial adipose tissue (EAT) constitutes a novel parameter for cardiometabolic risk assessment and a target for therapy. Here, we evaluated for the first time the plasma microRNA (miRNA) profile as a source of biomarkers for epicardial fat volume (EFV). miRNAs were profiled in plasma samples from 180 patients whose EFV was quantified using multidetector computed tomography. In the screening study, 54 deregulated miRNAs were identified in patients with high EFV levels (highest tertile) compared with matched patients with low EFV levels (lowest tertile). After filtering, 12 miRNAs were selected for subsequent validation. In the validation study, miR-15b-3p, miR-22-3p, miR-148a-3p miR-148b-3p and miR-590-5p were directly associated with EFV, even after adjustment for confounding factors (<i>p</i> value &lt; 0.05 for all models). The addition of miRNA combinations to a model based on clinical variables improved the discrimination (area under the receiver-operating-characteristic curve (AUC) from 0.721 to 0.787). miRNAs correctly reclassified a significant proportion of patients with an integrated discrimination improvement (IDI) index of 0.101 and a net reclassification improvement (NRI) index of 0.650. Decision tree models used miRNA combinations to improve their classification accuracy. These results were reproduced using two proposed clinical cutoffs for epicardial fat burden. Internal validation corroborated the robustness of the models. In conclusion, plasma miRNAs constitute novel biomarkers of epicardial fat burden.