Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Department of Neurosurgery, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, United States of America.
PLoS One. 2018 Oct 31;13(10):e0205501. doi: 10.1371/journal.pone.0205501. eCollection 2018.
Current laboratory testing of cerebrospinal fluid (CSF) does not consistently discriminate between different central nervous system (CNS) disease states. Rapidly distinguishing CNS infections from other brain and spinal cord disorders that share a similar clinical presentation is critical. New approaches focusing on aspects of disease biology, such as immune response profiles that can have stimulus-specific attributes, may be helpful. We undertook this preliminary proof-of-concept study using multiplex ELISA to measure CSF cytokine levels in various CNS disorders (infections, autoimmune/demyelinating diseases, lymphomas, and gliomas) to determine the potential utility of cytokine patterns in differentiating CNS infections from other CNS diseases. Both agglomerative hierarchical clustering and mixture discriminant analyses revealed grouping of CNS disease types based on cytokine expression. To further investigate the ability of CSF cytokine levels to distinguish various CNS disease states, non-parametric statistical analysis was performed. Mann-Whitney test analysis demonstrated that CNS infections are characterized by significantly higher CSF lP-10/CXCL10 levels than the pooled non-infectious CNS disorders (p = 0.0001). Within the infection group, elevated levels of MDC/CCL22 distinguished non-viral from viral infections (p = 0.0048). Each disease group of the non-infectious CNS disorders independently showed IP-10/CXCL10 levels that are significantly lower than the infection group [(autoimmune /demyelinating disorders (p = 0.0005), lymphomas (p = 0.0487), gliomas (p = 0.0294), and controls (p = 0.0001)]. Additionally, of the non-infectious diseases, gliomas can be distinguished from lymphomas by higher levels of GRO/CXCL1 (p = 0.0476), IL-7 (p = 0.0119), and IL-8 (p = 0.0460). Gliomas can also be distinguished from autoimmune/demyelinating disorders by higher levels of GRO/CXCL1 (p = 0.0044), IL-7 (p = 0.0035) and IL-8 (p = 0.0176). Elevated CSF levels of PDGF-AA distinguish lymphomas from autoimmune/demyelinating cases (p = 0.0130). Interrogation of the above comparisons using receiver operator characteristic analysis demonstrated area under the curve (AUC) values (ranging from 0.8636-1.0) that signify good to excellent utility as potential diagnostic discriminators. In conclusion, our work indicates that upon formal validation, measurement of CSF cytokine levels may have clinical utility in both identifying a CNS disorder as infectious in etiology and, furthermore, in distinguishing viral from non-viral CNS infections.
目前,对脑脊液(CSF)的实验室检测并不能始终区分不同的中枢神经系统(CNS)疾病状态。快速区分中枢神经系统感染与具有相似临床表现的其他脑和脊髓疾病至关重要。关注疾病生物学方面的新方法,例如具有刺激特异性属性的免疫反应谱,可能会有所帮助。我们使用多重 ELISA 测量了各种中枢神经系统疾病(感染、自身免疫/脱髓鞘疾病、淋巴瘤和神经胶质瘤)中的 CSF 细胞因子水平,进行了这项初步的概念验证研究,以确定细胞因子模式在区分中枢神经系统感染与其他中枢神经系统疾病方面的潜在效用。凝聚层次聚类和混合判别分析都揭示了基于细胞因子表达的中枢神经系统疾病类型的分组。为了进一步研究 CSF 细胞因子水平区分各种中枢神经系统疾病状态的能力,我们进行了非参数统计分析。曼-惠特尼检验分析表明,中枢神经系统感染的 CSF lP-10/CXCL10 水平明显高于非感染性中枢神经系统疾病的聚集(p=0.0001)。在感染组中,MDC/CCL22 的升高水平将非病毒感染与病毒感染区分开来(p=0.0048)。非感染性中枢神经系统疾病的每个疾病组的 IP-10/CXCL10 水平均明显低于感染组[(自身免疫/脱髓鞘疾病(p=0.0005),淋巴瘤(p=0.0487),神经胶质瘤(p=0.0294)和对照组(p=0.0001)]。此外,在非感染性疾病中,GRO/CXCL1(p=0.0476)、IL-7(p=0.0119)和 IL-8(p=0.0460)水平较高可将神经胶质瘤与淋巴瘤区分开来。GRO/CXCL1(p=0.0044)、IL-7(p=0.0035)和 IL-8(p=0.0176)水平较高可将神经胶质瘤与自身免疫/脱髓鞘疾病区分开来。CSF 中 PDGF-AA 水平升高可将淋巴瘤与自身免疫/脱髓鞘病例区分开(p=0.0130)。使用接收者操作特征分析对上述比较进行询问,显示出曲线下面积(AUC)值(范围为 0.8636-1.0),表明作为潜在诊断鉴别器具有良好到极好的效用。总之,我们的工作表明,在正式验证后,测量 CSF 细胞因子水平可能具有临床效用,不仅可以确定中枢神经系统疾病的病因是否具有感染性,而且可以进一步区分病毒与非病毒中枢神经系统感染。