Wei Dongmei, Melgarejo Jesus D, Van Aelst Lucas, Vanassche Thomas, Verhamme Peter, Janssens Stefan, Peter Karlheinz, Zhang Zhen-Yu
Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Campus Sint Rafaël, Kapucijnenvoer 7, Box 7001, BE-3000 Leuven, Belgium.
Division of Cardiology, University Hospitals Leuven, University of Leuven, Herestraat 49, 3000 Leuven, Belgium.
Eur J Prev Cardiol. 2023 Oct 10;30(14):1537-1546. doi: 10.1093/eurjpc/zwad087.
Coronary artery disease (CAD) is multifactorial, caused by complex pathophysiology, and contributes to a high burden of mortality worldwide. Urinary proteomic analyses may help to identify predictive biomarkers and provide insights into the pathogenesis of CAD.
Urinary proteome was analysed in 965 participants using capillary electrophoresis coupled with mass spectrometry. A proteomic classifier was developed in a discovery cohort with 36 individuals with CAD and 36 matched controls using the support vector machine. The classifier was tested in a validation cohort with 115 individuals who progressed to CAD and 778 controls and compared with two previously developed CAD-associated classifiers, CAD238 and ACSP75. The Framingham and SCORE2 risk scores were available in 737 participants. Bioinformatic analysis was performed based on the CAD-associated peptides. The novel proteomic classifier was comprised of 160 urinary peptides, mainly related to collagen turnover, lipid metabolism, and inflammation. In the validation cohort, the classifier provided an area under the receiver operating characteristic curve (AUC) of 0.82 [95% confidence interval (CI): 0.78-0.87] for the CAD prediction in 8 years, superior to CAD238 (AUC: 0.71, 95% CI: 0.66-0.77) and ACSP75 (AUC: 0.53 and 95% CI: 0.47-0.60). On top of CAD238 and ACSP75, the addition of the novel classifier improved the AUC to 0.84 (95% CI: 0.80-0.89). In a multivariable Cox model, a 1-SD increment in the novel classifier was associated with a higher risk of CAD (HR: 1.54, 95% CI: 1.26-1.89, P < 0.0001). The new classifier further improved the risk reclassification of CAD on top of the Framingham or SCORE2 risk scores (net reclassification index: 0.61, 95% CI: 0.25-0.95, P = 0.001; 0.64, 95% CI: 0.28-0.98, P = 0.001, correspondingly).
A novel urinary proteomic classifier related to collagen metabolism, lipids, and inflammation showed potential for the risk prediction of CAD. Urinary proteome provides an alternative approach to personalized prevention.
冠状动脉疾病（CAD）是多因素导致的，由复杂的病理生理学引起，在全球范围内造成了很高的死亡率负担。尿液蛋白质组分析可能有助于识别预测性生物标志物，并深入了解CAD的发病机制。
使用毛细管电泳结合质谱法对965名参与者的尿液蛋白质组进行了分析。在一个发现队列中，使用支持向量机对36名CAD患者和36名匹配对照进行分析，开发了一种蛋白质组分类器。该分类器在一个验证队列中进行了测试，该队列包括115名进展为CAD的个体和778名对照，并与之前开发的两个CAD相关分类器CAD238和ACSP75进行了比较。737名参与者可获得弗雷明汉和SCORE2风险评分。基于与CAD相关的肽段进行了生物信息学分析。这种新型蛋白质组分类器由160种尿液肽段组成，主要与胶原蛋白周转、脂质代谢和炎症相关。在验证队列中，该分类器在8年的CAD预测中，受试者工作特征曲线下面积（AUC）为0.82 [95%置信区间（CI）：0.78 - 0.87]，优于CAD238（AUC：0.71，95% CI：0.66 - 0.77）和ACSP75（AUC：0.53，95% CI：0.47 - 0.60）。在CAD238和ACSP75的基础上，加入新型分类器可将AUC提高到0.84（95% CI：0.80 - 0.89）。在多变量Cox模型中，新型分类器增加1个标准差与CAD风险更高相关（风险比：1.54，95% CI：1.26 - 1.89，P < 0.0001）。新型分类器在弗雷明汉或SCORE2风险评分的基础上进一步改善了CAD的风险重新分类（净重新分类指数：0.61，95% CI：0.25 - 0.95，P = 0.001；0.64，95% CI：0.28 - 0.98，P = 0.001，相应地）。
一种与胶原蛋白代谢、脂质和炎症相关的新型尿液蛋白质组分类器显示出对CAD风险预测的潜力。尿液蛋白质组为个性化预防提供了一种替代方法。
