Department of Internal Medicine III, Cardiology, University Hospital, Goethe University Frankfurt, Frankfurt, Germany.
German Centre for Cardiovascular Research (DZHK), Partner site RheinMain, Frankfurt, Germany.
Sci Rep. 2017 Jul 3;7(1):4511. doi: 10.1038/s41598-017-04040-w.
Risk stratification is crucial in prevention. Circulating microRNAs have been proposed as biomarkers in cardiovascular disease. Here a miR panel consisting of miRs related to different cardiovascular pathophysiologies, was evaluated to predict outcome in the context of prevention. MiR-34a, miR-223, miR-378, miR-499 and miR-133 were determined from peripheral blood by qPCR and combined to a risk panel. As derivation cohort, 178 individuals of the DETECT study, and as validation cohort, 129 individuals of the SHIP study were used in a case-control approach. Overall mortality and cardiovascular events were outcome measures. The Framingham Risk Score(FRS) and the SCORE system were applied as risk classification systems. The identified miR panel was significantly associated with mortality given by a hazard ratio(HR) of 3.0 (95% (CI): 1.09-8.43; p = 0.034) and of 2.9 (95% CI: 1.32-6.33; p = 0.008) after adjusting for the FRS in the derivation cohort. In a validation cohort the miR-panel had a HR of 1.31 (95% CI: 1.03-1.66; p = 0.03) and of 1.29 (95% CI: 1.02-1.64; p = 0.03) in a FRS/SCORE adjusted-model. A FRS/SCORE risk model was significantly improved to predict mortality by the miR panel with continuous net reclassification index of 0.42/0.49 (p = 0.014/0.005). The present miR panel of 5 circulating miRs is able to improve risk stratification in prevention with respect to mortality beyond the FRS or SCORE.
风险分层在预防中至关重要。循环 microRNAs 已被提议作为心血管疾病的生物标志物。在这里,评估了由与不同心血管病理生理学相关的 mirs 组成的 mir 面板,以预测预防背景下的结果。通过 qPCR 从外周血中测定 miR-34a、miR-223、miR-378、miR-499 和 miR-133,并将其组合成风险面板。在病例对照研究中,使用 DETECT 研究的 178 名个体作为推导队列,SHIP 研究的 129 名个体作为验证队列。总死亡率和心血管事件是结果测量指标。Framingham 风险评分(FRS)和 SCORE 系统被用作风险分类系统。确定的 mir 面板与死亡率显著相关,风险比(HR)为 3.0(95%置信区间:1.09-8.43;p=0.034),在推导队列中调整 FRS 后为 2.9(95%置信区间:1.32-6.33;p=0.008)。在验证队列中,mir 面板的 HR 为 1.31(95%置信区间:1.03-1.66;p=0.03),在 FRS/SCORE 调整模型中为 1.29(95%置信区间:1.02-1.64;p=0.03)。FRS/SCORE 风险模型通过 mir 面板显著改善了死亡率的预测,连续净重新分类指数为 0.42/0.49(p=0.014/0.005)。目前的 5 种循环 mir 面板能够改善预防死亡率的风险分层,超过了 FRS 或 SCORE。