AIMS Fibromuscular dysplasia (FMD) is a poorly understood disease that predominantly affects women during middle-life, with features that include stenosis, aneurysm, and dissection of medium-large arteries. Recently, plasma proteomics has emerged as an important means to understand cardiovascular diseases. Our objectives were: (i) to characterize plasma proteins and determine if any exhibit differential abundance in FMD subjects vs. matched healthy controls and (ii) to leverage these protein data to conduct systems analyses to provide biologic insights on FMD, and explore if this could be developed into a blood-based FMD test. METHODS AND RESULTS Females with 'multifocal' FMD and matched healthy controls underwent clinical phenotyping, dermal biopsy, and blood draw. Using dual-capture proximity extension assay and nuclear magnetic resonance-spectroscopy, we evaluated plasma levels of 981 proteins and 31 lipid sub-classes, respectively. In a discovery cohort (Ncases = 90, Ncontrols = 100), we identified 105 proteins and 16 lipid sub-classes (predominantly triglycerides and fatty acids) with differential plasma abundance in FMD cases vs. controls. In an independent cohort (Ncases = 23, Ncontrols = 28), we successfully validated 37 plasma proteins and 10 lipid sub-classes with differential abundance. Among these, 5/37 proteins exhibited genetic control and Bayesian analyses identified 3 of these as potential upstream drivers of FMD. In a 3rd cohort (Ncases = 506, Ncontrols = 876) the genetic locus of one of these upstream disease drivers, CD2-associated protein (CD2AP), was independently validated as being associated with risk of having FMD (odds ratios  = 1.36; P = 0.0003). Immune-fluorescence staining identified that CD2AP is expressed by the endothelium of medium-large arteries. Finally, machine learning trained on the discovery cohort was used to develop a test for FMD. When independently applied to the validation cohort, the test showed a c-statistic of 0.73 and sensitivity of 78.3%. CONCLUSION FMD exhibits a plasma proteogenomic and lipid signature that includes potential causative disease drivers, and which holds promise for developing a blood-based test for this disease.

中文翻译：

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血浆纤维蛋白异型增生的蛋白质组学特征。

AIMS纤维肌发育不良（FMD）是一种鲜为人知的疾病，主要影响女性在中年生活，其特征包括狭窄，动脉瘤和中型大动脉解剖。最近，血浆蛋白质组学已成为理解心血管疾病的重要手段。我们的目标是：（i）表征血浆蛋白并确定FMD受试者与相匹配的健康对照相比是否存在差异丰度；（ii）利用这些蛋白数据进行系统分析以提供有关FMD的生物学见解，并探讨是否可以发展成基于血液的口蹄疫测试。方法和结果患有“多灶性” FMD并与健康对照匹配的女性进行了临床表型，皮肤活检和抽血。使用双捕获邻近扩展测定法和核磁共振波谱法，我们分别评估了981种蛋白质和31个脂质亚类的血浆水平。在一个发现队列中（Ncases = 90，Ncontrols = 100），我们确定了105种蛋白质和16种脂类亚类（主要是甘油三酸酯和脂肪酸），FMD病例与对照组的血浆丰度不同。在一个独立的队列中（Ncases = 23，Ncontrols = 28），我们成功地验证了37种血浆蛋白和10种脂类亚类，其丰度不同。在这些蛋白中，有5/37个蛋白表现出遗传控制作用，贝叶斯分析确定其中3个蛋白是FMD的潜在上游驱动因子。在第三个队列中（Ncases = 506，Ncontrols = 876），这些上游疾病驱动因素之一的遗传基因位点CD2相关蛋白（CD2AP），被独立验证为与患有口蹄疫的风险有关（比值= 1.36； P = 0.003）。免疫荧光染色确定CD2AP由中型大动脉的内皮表达。最后，使用在发现队列中训练的机器学习来开发FMD测试。当独立应用于验证队列时，该测试的c统计量为0.73，灵敏度为78.3％。结论FMD具有血浆蛋白基因组学和脂质特征，包括潜在的致病性疾病驱动因素，有望为该疾病开发基于血液的检测方法。在发现队列中接受训练的机器学习用于开发FMD测试。当独立应用于验证队列时，该测试的c统计量为0.73，灵敏度为78.3％。结论FMD具有血浆蛋白基因组学和脂质特征，包括潜在的致病性疾病驱动因素，有望为该疾病开发基于血液的检测方法。在发现队列中接受训练的机器学习用于开发FMD测试。当独立应用于验证队列时，该测试的c统计量为0.73，灵敏度为78.3％。结论FMD表现出血浆蛋白基因组学和脂质特征，包括潜在的致病性疾病驱动因素，并有望为该疾病开发基于血液的检测方法。