From the Section of Neurosurgery, Department of Surgery, University of Chicago Medicine and Biological Sciences, IL (R.G., H.A.Z., J.K., M.D.F., Y.C., C.S., T.M., R.L., A.S., K.C., S.P., R.S., I.A.A.).
Cytometry and Antibody Technology, Biological Sciences Division, Office of Shared Research Facilities, University of Chicago, IL (R.D., D.L.).
Circ Res. 2018 Jun 8;122(12):1716-1721. doi: 10.1161/CIRCRESAHA.118.312680. Epub 2018 May 2.
The clinical course of cerebral cavernous malformations is highly unpredictable, with few cross-sectional studies correlating proinflammatory genotypes and plasma biomarkers with prior disease severity.
We hypothesize that a panel of 24 candidate plasma biomarkers, with a reported role in the physiopathology of cerebral cavernous malformations, may predict subsequent clinically relevant disease activity.
Plasma biomarkers were assessed in nonfasting peripheral venous blood collected from consecutive cerebral cavernous malformation subjects followed for 1 year after initial sample collection. A first cohort (N=49) was used to define the best model of biomarker level combinations to predict a subsequent symptomatic lesional hemorrhagic expansion within a year after the blood sample. We generated the receiver operating characteristic curves and area under the curve for each biomarker individually and each weighted linear combination of relevant biomarkers. The best model to predict lesional activity was selected as that minimizing the Akaike information criterion. In this cohort, 11 subjects experienced symptomatic lesional hemorrhagic expansion (5 bleeds and 10 lesional growths) within a year after the blood draw. Subjects had lower soluble CD14 (cluster of differentiation 14; =0.05), IL (interleukin)-6 (=0.04), and VEGF (vascular endothelial growth factor; =0.0003) levels along with higher plasma levels of IL-1β (=0.008) and soluble ROBO4 (roundabout guidance receptor 4; =0.03). Among the 31 weighted linear combinations of these 5 biomarkers, the best model (with the lowest Akaike information criterion value, 25.3) was the weighted linear combination including soluble CD14, IL-1β, VEGF, and soluble ROBO4, predicting a symptomatic hemorrhagic expansion with a sensitivity of 86% and specificity of 88% (area under the curve, 0.90; <0.0001). We then validated our best model in the second sequential independent cohort (N=28).
This is the first study reporting a predictive association between plasma biomarkers and subsequent cerebral cavernous malformation disease clinical activity. This may be applied in clinical prognostication and stratification of cases in clinical trials.
脑动静脉畸形的临床病程极难预测,仅有少数横断面研究将促炎基因型和血浆生物标志物与既往疾病严重程度相关联。
我们假设,一组 24 种候选血浆生物标志物与脑动静脉畸形的病理生理学有关,可能预测随后的临床相关疾病活动。
对连续接受脑动静脉畸形治疗的患者在初次样本采集后 1 年内采集的非禁食外周静脉血中的血浆生物标志物进行评估。第一队列(N=49)用于确定预测血液样本后 1 年内症状性病变出血性扩大的最佳生物标志物水平组合模型。我们为每个生物标志物及其相关生物标志物的每个加权线性组合生成了接收者操作特征曲线和曲线下面积。选择最佳模型以预测病变活性的方法是最小化 Akaike 信息准则。在该队列中,11 例患者在采血后 1 年内出现症状性病变出血性扩大(5 例出血和 10 例病变生长)。与 IL-6(白细胞介素-6)(=0.04)和 VEGF(血管内皮生长因子)(=0.0003)相比,患者的可溶性 CD14(分化簇 14;=0.05)和 IL-1β(=0.008)水平较低,而血浆中 IL-1β(=0.008)和可溶性 ROBO4(圆斑 Guidance 受体 4)的水平较高(=0.03)。在这些 5 种生物标志物的 31 种加权线性组合中,最佳模型(Akaike 信息准则值最低,为 25.3)是包含可溶性 CD14、IL-1β、VEGF 和可溶性 ROBO4 的加权线性组合,预测症状性出血扩大的敏感性为 86%,特异性为 88%(曲线下面积,0.90;<0.0001)。然后,我们在第二个连续的独立队列(N=28)中验证了我们的最佳模型。
这是第一项报道血浆生物标志物与随后的脑动静脉畸形疾病临床活动之间存在预测关联的研究。这可应用于临床预后和临床试验中的病例分层。