Plasma microRNA Profiling Reveals Novel Biomarkers of Epicardial Adipose Tissue: A Multidetector Computed Tomography Study
de Gonzalo-Calvo D., Vilades D., Martínez-Camblor P., Vea À., Ferrero-Gregori A., Nasarre L.
Prospective Study with a reported sample of 180, published in J Clin Med (2019) — summary generated from the PubMed abstract.
Early human evidence such as case series or small samples is exploring possible benefits.
- Level A · Stronger Clinical Evidence
- Level B · Emerging clinical evidence with positive signals
- Level C · Early human research exploring benefits
- Level D · Scientific groundwork from lab and animal studies
- Emerging · Emerging topic under active research
This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.
- Study type
- Prospective Study
- Journal
- J Clin Med (2019)
- Reported sample size
- 180
- Source database
- Europe PMC
- PMID
- 31159404
- PMCID
- PMC6616954
- DOI
- 10.3390/jcm8060780
- Citations
- 9
Abstract (original English)
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. Interna
What this study does not prove
- • This study does not prove SVF is an approved treatment or a replacement for standard care.
Evidence level
Early human evidence such as case series or small samples is exploring possible benefits.
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