Tancin Lambert Anna, Kong Xiang Y, Ratajczak-Tretel Barbara, Atar Dan, Russell David, Skjelland Mona, Bjerkeli Vigdis, Skagen Karolina, Coq Matthieu, Schordan Eric, Firat Huseyin, Halvorsen Bente, Aamodt Anne H
Department of Neurology, Østfold Hospital Trust, Grålum, Norway,
Institute of Clinical Medicine, University of Oslo, Oslo, Norway,
Cerebrovasc Dis Extra. 2020;10(1):11-20. doi: 10.1159/000504529. Epub 2020 Feb 6.
Cardioembolic stroke due to paroxysmal atrial fibrillation (AF) may account for 1 out of 4 cryptogenic strokes (CS) and transient ischemic attacks (TIAs). The purpose of this pilot study was to search for biomarkers potentially predicting incident AF in patients with ischemic stroke or TIA.
Plasma samples were collected from patients aged 18 years and older with ischemic stroke or TIA due to AF (n = 9) and large artery atherosclerosis (LAA) with ipsilateral carotid stenosis (n = 8) and age- and sex-matched controls (n = 10). Analyses were performed with the Olink technology simultaneously measuring 184 biomarkers of cardiovascular disease. For bioinformatics, acquired data were analyzed using gene set enrichment analysis (GSEA). Selected proteins were validated using ELISA. Individual receiver operating characteristic (ROC) curves and odds ratios from logistic regression were calculated. A randomForest (RF) model with out-of-bag estimate was applied for predictive modeling.
GSEA indicated enrichment of proteins related to inflammatory response in the AF group. Interleukin (IL)-6, growth differentiation factor (GDF)-15, and pentraxin-related protein PTX3 were the top biomarkers on the ranked list for the AF group compared to the LAA group and the control group. ELISA validated increased expression of all tested proteins (GDF-15, PTX3, and urokinase plasminogen activator surface receptor [U-PAR]), except for IL-6. 19 proteins had the area under the ROC curve (AUC) over 0.85 including all of the proteins with significant evolution in the logistic regression. AUCs were very discriminant in distinguishing patients with and without AF (LAA and control group together). GDF-15 alone reached AUC of 0.95. Based on RF model, all selected participants in the tested group were classified correctly, and the most important protein in the model was GDF-15.
Our results demonstrate an association between inflammation and AF and that multiple proteins alone and in combination may potentially be used as indicators of AF in CS and TIA patients. However, further studies including larger samples sizes are needed to support these findings. In the ongoing NOR-FIB study, we plan further biomarker assessments in patients with CS and TIA undergoing long-term cardiac rhythm monitoring with insertable cardiac monitors.
阵发性心房颤动(AF)所致的心源性栓塞性卒中可能占四分之一的不明原因卒中(CS)和短暂性脑缺血发作(TIA)。本初步研究的目的是寻找可能预测缺血性卒中或TIA患者发生AF的生物标志物。
收集年龄在18岁及以上因AF导致缺血性卒中或TIA的患者(n = 9)、伴有同侧颈动脉狭窄的大动脉粥样硬化(LAA)患者(n = 8)以及年龄和性别匹配的对照组(n = 10)的血浆样本。使用Olink技术进行分析,同时测量184种心血管疾病生物标志物。对于生物信息学,使用基因集富集分析(GSEA)对获取的数据进行分析。使用酶联免疫吸附测定(ELISA)对选定的蛋白质进行验证。计算个体受试者工作特征(ROC)曲线和逻辑回归的比值比。应用具有袋外估计的随机森林(RF)模型进行预测建模。
GSEA表明AF组中与炎症反应相关的蛋白质富集。与LAA组和对照组相比,白细胞介素(IL)-6、生长分化因子(GDF)-15和五聚体相关蛋白PTX3是AF组排名列表上的前几位生物标志物。ELISA验证了除IL-6外所有测试蛋白质(GDF-15、PTX3和尿激酶型纤溶酶原激活剂表面受体[U-PAR])的表达增加。19种蛋白质的ROC曲线下面积(AUC)超过0.85,包括逻辑回归中所有有显著变化的蛋白质。AUC在区分有无AF的患者(LAA组和对照组合并)方面具有很好的鉴别力。单独的GDF-15的AUC达到0.95。基于RF模型,测试组中的所有选定参与者都被正确分类,模型中最重要的蛋白质是GDF-15。
我们的结果表明炎症与AF之间存在关联,多种蛋白质单独或联合使用可能潜在地用作CS和TIA患者中AF的指标。然而,需要包括更大样本量的进一步研究来支持这些发现。在正在进行的NOR-FIB研究中,我们计划对使用可插入式心脏监测器进行长期心律监测的CS和TIA患者进行进一步的生物标志物评估。