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首页> 外文期刊>EBioMedicine >Predictive value of targeted proteomics for coronary plaque morphology in patients with suspected coronary artery disease
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Predictive value of targeted proteomics for coronary plaque morphology in patients with suspected coronary artery disease

机译：靶向蛋白质组学对疑似冠心病患者冠状动脉斑块形态的预测价值

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Background Risk stratification is crucial to improve tailored therapy in patients with suspected coronary artery disease (CAD). This study investigated the ability of targeted proteomics to predict presence of high-risk plaque or absence of coronary atherosclerosis in patients with suspected CAD, defined by coronary computed tomography angiography (CCTA). Methods Patients with suspected CAD ( n ?=?203) underwent CCTA. Plasma levels of 358 proteins were used to generate machine learning models for the presence of CCTA-defined high-risk plaques or complete absence of coronary atherosclerosis. Performance was tested against a clinical model containing generally available clinical characteristics and conventional biomarkers. Findings A total of 196 patients with analyzable protein levels ( n ?=?332) was included for analysis. A subset of 35 proteins was identified predicting the presence of high-risk plaques. The developed machine learning model had fair diagnostic performance with an area under the curve (AUC) of 0·79?±?0·01, outperforming prediction with generally available clinical characteristics (AUC?=?0·65?±?0·04, p ??0·05). Conversely, a different subset of 34 proteins was predictive for the absence of CAD (AUC?=?0·85?±?0·05), again outperforming prediction with generally available characteristics (AUC?=?0·70?±?0·04, p??0·05). Interpretation Using machine learning models, trained on targeted proteomics, we defined two complementary protein signatures: one for identification of patients with high-risk plaques and one for identification of patients with absence of CAD. Both biomarker subsets were superior to generally available clinical characteristics and conventional biomarkers in predicting presence of high-risk plaque or absence of coronary atherosclerosis. These promising findings warrant external validation of the value of targeted proteomics to identify cardiovascular risk in outcome studies. Fund This study was supported by an unrestricted research grant from HeartFlow Inc. and partly supported by a European Research Area Network on Cardiovascular Diseases (ERA-CVD) grant (ERA CVD JTC2017, OPERATION). Funders had no influence on trial design, data evaluation, and interpretation.
机译：背景风险分层对于改善可疑冠心病（CAD）患者的定制治疗至关重要。这项研究调查了靶向蛋白质组学预测可疑CAD患者中高风险斑块的存在或不存在冠状动脉粥样硬化的能力，这是由冠状动脉计算机断层血管造影术（CCTA）定义的。方法疑似CAD（n =？203）的患者接受CCTA检查。血浆358种蛋白质的水平被用于生成CCTA定义的高危斑块的存在或完全没有冠状动脉粥样硬化的机器学习模型。针对包含普遍可用的临床特征和常规生物标志物的临床模型测试了性能。结果共纳入196名可分析蛋白质水平的患者（n = 32）。鉴定出35种蛋白质的子集，可预测高风险斑块的存在。所开发的机器学习模型具有良好的诊断性能，曲线下面积（AUC）为0·79？±？0·01，优于具有一般临床特征的预测（AUC？=？0·65？±？0·04） ，p≤＜0·05）。相反，由34种蛋白质组成的不同子集可预测是否不存在CAD（AUCα=？0·85？±？0·05），再次优于具有普遍可用特征的预测（AUCα=？0·70？±？0 ·04，p≤<0·05）。解释我们使用针对目标蛋白质组学进行了训练的机器学习模型，定义了两个互补的蛋白质标记：一个用于鉴定高危斑块的患者，另一个用于鉴定无CAD的患者。在预测是否存在高风险斑块或不存在冠状动脉粥样硬化方面，两种生物标志物亚组均优于一般可用的临床特征和常规生物标志物。这些有前途的发现需要对目标蛋白质组学的价值进行外部验证，以鉴定结局研究中的心血管风险。基金这项研究得到了HeartFlow Inc.的无限制研究资助，部分得到了欧洲心血管疾病研究区域网络（ERA-CVD）资助（ERA CVD JTC2017，OPERATION）的部分支持。资助者对试验设计，数据评估和解释没有影响。

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