Kumar Raj G, Rubin Jonathan E, Berger Rachel P, Kochanek Patrick M, Wagner Amy K
Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA.
Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA.
Brain Behav Immun. 2016 Mar;53:183-193. doi: 10.1016/j.bbi.2015.12.008. Epub 2015 Dec 17.
Studies have characterized absolute levels of multiple inflammatory markers as significant risk factors for poor outcomes after traumatic brain injury (TBI). However, inflammatory marker concentrations are highly inter-related, and production of one may result in the production or regulation of another. Therefore, a more comprehensive characterization of the inflammatory response post-TBI should consider relative levels of markers in the inflammatory pathway. We used principal component analysis (PCA) as a dimension-reduction technique to characterize the sets of markers that contribute independently to variability in cerebrospinal (CSF) inflammatory profiles after TBI. Using PCA results, we defined groups (or clusters) of individuals (n=111) with similar patterns of acute CSF inflammation that were then evaluated in the context of outcome and other relevant CSF and serum biomarkers collected days 0-3 and 4-5 post-injury. We identified four significant principal components (PC1-PC4) for CSF inflammation from days 0-3, and PC1 accounted for the greatest (31%) percentage of variance. PC1 was characterized by relatively higher CSF sICAM-1, sFAS, IL-10, IL-6, sVCAM-1, IL-5, and IL-8 levels. Cluster analysis then defined two distinct clusters, such that individuals in cluster 1 had highly positive PC1 scores and relatively higher levels of CSF cortisol, progesterone, estradiol, testosterone, brain derived neurotrophic factor (BDNF), and S100b; this group also had higher serum cortisol and lower serum BDNF. Multinomial logistic regression analyses showed that individuals in cluster 1 had a 10.9 times increased likelihood of GOS scores of 2/3 vs. 4/5 at 6 months compared to cluster 2, after controlling for covariates. Cluster group did not discriminate between mortality compared to GOS scores of 4/5 after controlling for age and other covariates. Cluster groupings also did not discriminate mortality or 12 month outcomes in multivariate models. PCA and cluster analysis establish that a subset of CSF inflammatory markers measured in days 0-3 post-TBI may distinguish individuals with poor 6-month outcome, and future studies should prospectively validate these findings. PCA of inflammatory mediators after TBI could aid in prognostication and in identifying patient subgroups for therapeutic interventions.
研究已将多种炎症标志物的绝对水平确定为创伤性脑损伤(TBI)后不良预后的重要危险因素。然而,炎症标志物浓度高度相关,一种标志物的产生可能导致另一种标志物的产生或调节。因此,对TBI后炎症反应进行更全面的特征描述应考虑炎症途径中标志物的相对水平。我们使用主成分分析(PCA)作为降维技术,以表征对TBI后脑脊液(CSF)炎症谱变异性有独立贡献的标志物集。利用PCA结果,我们定义了具有相似急性CSF炎症模式的个体组(或聚类)(n = 111),然后在损伤后第0 - 3天和第4 - 5天收集的结局及其他相关CSF和血清生物标志物的背景下对其进行评估。我们从第0 - 3天的CSF炎症中确定了四个显著的主成分(PC1 - PC4),PC1占方差的最大百分比(31%)。PC1的特征是CSF中sICAM - 1、sFAS、IL - 10、IL - 6、sVCAM - 1、IL - 5和IL - 8水平相对较高。聚类分析随后定义了两个不同的聚类,使得聚类1中的个体具有高度正的PC1分数以及相对较高水平的CSF皮质醇、孕酮、雌二醇、睾酮、脑源性神经营养因子(BDNF)和S100b;该组血清皮质醇水平也较高,血清BDNF水平较低。多项逻辑回归分析表明,在控制协变量后,与聚类2相比,聚类1中的个体在6个月时GOS评分为2/3而非4/5的可能性增加了10.9倍。在控制年龄和其他协变量后,聚类组在死亡率方面与GOS评分为4/5的情况之间没有差异。在多变量模型中,聚类分组也不能区分死亡率或12个月的结局。PCA和聚类分析表明,在TBI后第0 - 3天测量的一部分CSF炎症标志物可能区分出6个月结局较差的个体,未来的研究应前瞻性地验证这些发现。TBI后炎症介质的PCA有助于预后评估和识别适合治疗干预的患者亚组。