Plasma Biomarkers of Inflammation and Angiogenesis Predict Cerebral Cavernous Malformation Symptomatic Hemorrhage or Lesional Growth.
Plasma Biomarkers of Inflammation and Angiogenesis Predict Cerebral Cavernous Malformation Symptomatic Hemorrhage or Lesional Growth.
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DOI：
10.1161/circresaha.118.312680
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发表时间：
2018-06-08
影响因子：
20.1
通讯作者：
Awad IA
中科院分区：
文献类型：
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作者：
Girard R;Zeineddine HA;Koskimäki J;Fam MD;Cao Y;Shi C;Moore T;Lightle R;Stadnik A;Chaudagar K;Polster S;Shenkar R;Duggan R;Leclerc D;Whitehead KJ;Li DY;Awad IA
脑海绵状血管瘤（CCM）的临床病程高度不可预测，很少有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（已报道在 CCM 的病理生理学中发挥作用）可以预测随后的临床相关疾病活动。在从连续 CCM 受试者收集的非空腹外周静脉血中评估血浆生物标志物，并在初始样本收集后跟踪一年。第一个队列（N = 49）用于定义生物标志物水平组合的最佳模型，以预测血液样本后一年内随后出现的症状性病变出血扩张。我们分别为每个生物标志物以及相关生物标志物的每个加权线性组合生成了受试者工作特征曲线和曲线下面积 (AUC)。选择预测病变活动的最佳模型作为最小化赤池信息准则（AIC）的模型。在该队列中，11 名受试者在抽血后一年内经历了有症状的病灶出血性扩张（5 例出血和 10 例病灶生长）。受试者的可溶性分化簇 14 (sCD14；p=0.05)、白细胞介素 6 (IL-6；p=0.04)、血管内皮生长因子 (VEGF；p=0.0003) 水平较低，而血浆白细胞介素 1β (IL-1β；p=0.008) 和迂回引导受体 4 (sROBO4；p=0.03) 水平较高。在这5种生物标志物的31个加权线性组合中，最佳模型（最低AIC值=25.3）是包括sCD14、IL-1β、VEGF和sROBO4的加权线性组合，预测症状性出血性扩张的敏感性为86%，特异性为88%（AUC=0.90，p<0.0001）。然后，我们在第二个连续独立队列 (N=28) 中验证了我们的最佳模型。这是第一项报告血浆生物标志物与随后的 CCM 疾病临床活动之间的预测关联的研究。这可以应用于临床预测和临床试验中病例的分层。
The clinical course of cerebral cavernous malformations (CCMs) is highly unpredictable, with few cross-sectional studies correlating pro-inflammatory 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 CCMs, may predict subsequent clinically relevant disease activity. Plasma biomarkers were assessed in non-fasting peripheral venous blood collected from consecutive CCM subjects followed for one 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 following the blood sample. We generated the receiver operating characteristic curves and area under curves (AUC) 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 (AIC). 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 cluster of differentiation 14 (sCD14; p=0.05), interleukin-6 (IL-6; p=0.04), vascular endothelial growth factor (VEGF; p=0.0003) levels along with higher plasma levels of interleukin-1 beta (IL-1β; p=0.008) and roundabout guidance receptor 4 (sROBO4; p=0.03). Among the 31 weighted linear combinations of these 5 biomarkers, the best model (with the lowest AIC value=25.3) was the weighted linear combination including sCD14, IL-1β, VEGF and sROBO4, predicting a symptomatic hemorrhagic expansion with a sensitivity of 86% and specificity of 88% (AUC=0.90, p<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 CCM disease clinical activity. This may be applied in clinical prognostication, and stratification of cases in clinical trials.