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, Leuven, BE-3000, Belgium.
Department of Cardiology, Sint-Jan Hospital Bruges, Bruges, Belgium.
ESC Heart Fail. 2022 Apr;9(2):1216-1227. doi: 10.1002/ehf2.13796. Epub 2022 Jan 10.
Cardiac allograft vasculopathy (CAV) is the major long-term complication after heart transplantation, leading to mortality and re-transplantation. As available non-invasive biomarkers are scarce for CAV screening, we aimed to identify a proteomic signature for CAV.
We measured urinary proteome by capillary electrophoresis coupled with mass spectrometry in 217 heart transplantation recipients (mean age: 55.0 ± 14.4 years; women: 23.5%), including 76 (35.0%) patients with CAV diagnosed by coronary angiography. We randomly and evenly grouped participants into the derivation cohort (n = 108, mean age: 56.4 ± 13.8 years; women: 22.2%; CAV: n = 38) and the validation cohort (n = 109, mean age: 56.4 ± 13.8 years; women: 24.8%, CAV: n = 38), stratified by CAV. Using the decision tree-based machine learning methods (extreme gradient boost), we constructed a proteomic signature for CAV discrimination in the derivation cohort and verified its performance in the validation cohort. The proteomic signature that consisted of 27 peptides yielded areas under the curve of 0.83 [95% confidence interval (CI): 0.75-0.91, P < 0.001] and 0.71 (95% CI: 0.60-0.81, P = 0.001) for CAV discrimination in the derivation and validation cohort, respectively. With the optimized threshold of 0.484, the sensitivity, specificity, and accuracy for CAV differentiation in the validation cohort were 68.4%, 73.2%, and 71.6%, respectively. With adjustment of potential clinical confounders, the signature was significantly associated with CAV [adjusted odds ratio: 1.31 (95% CI: 1.07-1.64) for per 0.1% increment in the predicted probability, P = 0.012]. Diagnostic accuracy significantly improved by adding the signature to the logistic model that already included multiple clinical risk factors, suggested by the integrated discrimination improvement of 9.1% (95% CI: 2.5-15.3, P = 0.005) and net reclassification improvement of 83.3% (95% CI: 46.7-119.5, P < 0.001). Of the 27 peptides, the majority were the fragments of collagen I (44.4%), collagen III (18.5%), collagen II (3.7%), collagen XI (3.7%), mucin-1 (3.7%), xylosyltransferase 1 (3.7%), and protocadherin-12 (3.7%). Pathway analysis performed in Reactome Pathway Database revealed that the multiple pathways involved by the signature were related to the pathogenesis of CAV, such as collagen turnover, platelet aggregation and coagulation, cell adhesion, and motility.
This pilot study identified and validated a urinary proteomic signature that provided a potential approach for the surveillance of CAV. These proteins might provide insights into CAV pathological processes and call for further investigation into personalized treatment targets.
心脏同种异体移植血管病（CAV）是心脏移植后导致死亡和再次移植的主要长期并发症。由于目前缺乏用于 CAV 筛查的非侵入性生物标志物，我们旨在确定 CAV 的蛋白质组学特征。
我们通过毛细管电泳与质谱联用技术测量了 217 例心脏移植受者（平均年龄：55.0±14.4 岁；女性：23.5%）的尿蛋白质组，其中 76 例（35.0%）患者通过冠状动脉造影诊断为 CAV。我们将参与者随机、均匀地分为推导队列（n=108，平均年龄：56.4±13.8 岁；女性：22.2%；CAV：n=38）和验证队列（n=109，平均年龄：56.4±13.8 岁；女性：24.8%，CAV：n=38），按 CAV 分层。使用基于决策树的机器学习方法（极端梯度提升），我们构建了一个用于推导队列中 CAV 鉴别诊断的蛋白质组学特征，并在验证队列中验证了其性能。该蛋白质组学特征由 27 个肽组成，在推导队列中的曲线下面积为 0.83[95%置信区间（CI）：0.75-0.91，P<0.001]和 0.71（95%CI：0.60-0.81，P=0.001），用于验证队列中的 CAV 鉴别诊断。在优化的阈值为 0.484 的情况下，验证队列中 CAV 鉴别诊断的灵敏度、特异性和准确性分别为 68.4%、73.2%和 71.6%。在调整潜在临床混杂因素后，该特征与 CAV 显著相关[每增加 0.1%预测概率的优势比：1.31（95%CI：1.07-1.64），P=0.012]。加入该特征后，逻辑模型的综合鉴别改善了 9.1%（95%CI：2.5-15.3，P=0.005），净重新分类改善了 83.3%（95%CI：46.7-119.5，P<0.001），诊断准确性显著提高。在 27 个肽中，大部分是 I 型胶原（44.4%）、III 型胶原（18.5%）、II 型胶原（3.7%）、XI 型胶原（3.7%）、黏蛋白-1（3.7%）、木糖基转移酶 1（3.7%）和原钙黏蛋白-12（3.7%）的片段。在 Reactome 途径数据库中进行的途径分析显示，该特征所涉及的多个途径与 CAV 的发病机制有关，如胶原转化、血小板聚集和凝血、细胞黏附和运动。
这项初步研究确定并验证了一种尿液蛋白质组学特征，该特征为 CAV 的监测提供了一种潜在方法。这些蛋白质可能为 CAV 的病理过程提供了新的见解，并呼吁进一步研究个性化的治疗靶点。
