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

机译:血浆microRNA分析揭示了心外膜脂肪组织的新型生物标志物:多探测器计算机断层扫描研究。

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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.
机译:心外膜脂肪组织(EAT)构成了心脏代谢风险评估的新参数和治疗目标。在这里,我们首次评估了血浆microRNA(miRNA)谱作为心外膜脂肪量(EFV)生物标志物的来源。在来自180名患者的血浆样品中对miRNA进行了分析,这些患者的EFV使用多探测器计算机断层扫描技术进行了定量。在筛选研究中,与匹配的EFV水平低(三分位数最低)的患者相比,在EFV水平高(三分位数最高)的患者中鉴定出54个失控的miRNA。过滤后,选择了12个miRNA用于后续验证。在验证研究中,即使在调整混杂因素之后,miR-15b-3p,miR-22-3p,miR-148a-3p miR-148b-3p和miR-590-5p也直接与EFV相关(p值<0.05对于所有型号)。将miRNA组合添加到基于临床变量的模型中可改善区分度(接收者操作特征曲线(AUC)下的区域从0.721到0.787)。 miRNA正确地对很大一部分患者进行了重新分类,其综合辨别改善(IDI)指数为0.101,净重新分类改善(NRI)指数为0.650。决策树模型使用miRNA组合来提高其分类准确性。使用两个建议的心外膜脂肪负担的临床临界值来再现这些结果。内部验证证实了模型的稳健性。总之,血浆miRNA构成心外膜脂肪负担的新型生物标志物。

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