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

Journal of Clinical Medicine

Laura Nasarre¹  Àngela Vea¹  Andreu Ferrero-Gregori²  Francesc Carreras²  Jesus Sanchez Vega³  Rubén Leta³  David Vilades³  JoseLuis Sanchez-Quesada⁴  Sonia Benítez⁴  Núria Puig⁴  Pablo Martínez-Camblor⁵  Olga Bornachea⁶  Vicenta Llorente-Cortés⁶  David de Gonzalo-Calvo⁶ 

[1] Biomedical Research Institute Sant Pau (IIB Sant Pau), 08041 Barcelona, Spain;CIBERCV, Institute of Health Carlos III, 28029 Madrid, Spain;Cardiac Imaging Unit, Cardiology Department, Hospital de la Santa Creu i Sant Pau, 08041 Barcelona, Spain;Cardiovascular Biochemistry, Biomedical Research Institute Sant Pau (IIB Sant Pau), 08041 Barcelona, Spain;Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, USA;Institute of Biomedical Research of Barcelona (IIBB) - Spanish National Research Council (CSIC), 08036 Barcelona, Spain;

关键词: biomarker;    cardiometabolic disease;    epicardial adipose tissue;    epicardial fat;    epicardial fat volume;    microRNA;   

DOI  :  10.3390/jcm8060780

来源: DOAJ

【 摘 要 】

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 (p value < 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.

【 授权许可】

Unknown