Background and Purpose: 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. Methods: 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. Results: 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. Conclusions: 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.
Cerebrovasc Dis Extra 2020;10:11–20

中文翻译：

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缺血性卒中患者与房颤相关的生物标志物：NOR-FIB研究的一项先导研究。

背景与目的：阵发性心房颤动（AF）引起的心脏栓塞性卒中可能占4种隐源性卒中（CS）和短暂性脑缺血发作（TIA）的1分。这项初步研究的目的是寻找可能预测缺血性卒中或TIA患者房颤事件发生的生物标志物。方法：从18岁及以上因房颤（ n = 9），伴同侧颈动脉狭窄（ n = 8）的大动脉粥样硬化（LAA）以及年龄和性别匹配的对照组的缺血性卒中或TIA患者中收集血浆样本（ n= 10）。使用Olink技术进行的分析同时测量了184种心血管疾病的生物标志物。对于生物信息学，使用基因集富集分析（GSEA）分析获得的数据。使用ELISA验证选择的蛋白质。计算了个体接收者的工作特征（ROC）曲线和逻辑回归的比值比。具有袋外估计的randomForest（RF）模型用于预测建模。结果：GSEA表明AF组中与炎症反应有关的蛋白质富集。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Pentraxin相关蛋白PTX3是AF组排名中的顶级生物标志物。ELISA验证了除IL-6外，所有测试蛋白（GDF-15，PTX3和尿激酶纤溶酶原激活物表面受体[U-PAR]）的表达均增加。19种蛋白质的ROC曲线下面积（AUC）超过0.85，包括所有在逻辑回归中具有显着进化的蛋白质。AUC在区分是否患有房颤（LAA和对照组）方面非常有区别。仅GDF-15的AUC达到0.95。根据RF模型，对测试组中所有选定的参与者进行了正确分类，结论：我们的结果表明炎症与房颤之间存在关联，并且多种蛋白质单独或组合可能潜在地用作CS和TIA患者房颤的指标。但是，需要进一步的研究，包括更大的样本量，以支持这些发现。在正在进行的NOR-FIB研究中，我们计划对正在通过可插入式心脏监护仪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。
Cerebrovasc Dis Extra 2020; 10：11–20