scholarly journals Evaluation of the relation between epicardial adipose tissue and hepatosteatosis with coronary atherosclerosis using multidetector computed tomography

2021 ◽  
Vol 2 (4) ◽  
pp. 143-148
Author(s):  
Elif UZUN ATA ◽  
Nilgün IŞIKSALAN ÖZBÜLBÜL
Keyword(s):  
Computed Tomography ◽  
Adipose Tissue ◽  
Multidetector Computed Tomography ◽  
Coronary Atherosclerosis ◽  
Epicardial Adipose Tissue

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

2019 ◽  
Vol 8 (6) ◽  
pp. 780 ◽  
Author(s):  
David de Gonzalo-Calvo ◽  
David Vilades ◽  
Pablo Martínez-Camblor ◽  
Àngela Vea ◽  
Andreu Ferrero-Gregori ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Adipose Tissue ◽  
Multidetector Computed Tomography ◽  
Epicardial Adipose Tissue ◽  
Significant Proportion ◽  
Epicardial Fat ◽  
P Value ◽  
Net Reclassification Improvement ◽  
Novel Biomarkers ◽  
Plasma Microrna

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.

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