Principal components derived from CSF inflammatory profiles predict outcome in survivors after severe traumatic brain injury.
Principal components derived from CSF inflammatory profiles predict outcome in survivors after severe traumatic brain injury.
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DOI：
10.1016/j.bbi.2015.12.008
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发表时间：
2016-03
期刊：
影响因子：
--
通讯作者：
Wagner AK
中科院分区：
文献类型：
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作者：
Kumar RG;Rubin JE;Berger RP;Kochanek PM;Wagner AK
研究表明，多种炎症因子的绝对水平是创伤性脑损伤（TBI）后不良预后的重要危险因素。然而，炎症标志物浓度是高度相关的，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，对脑外伤后炎症反应的更全面表征应该考虑炎症通路中标志物的相对水平。我们使用主成分分析（PCA）作为降维技术来表征脑外伤后脑脊液（CSF）炎症谱独立变异的标记集。使用PCA结果，我们定义了具有相似急性脑脊液炎症模式的个体（n=111）组（或集群），然后在损伤后0-3天和4-5天收集的结果和其他相关脑脊液和血清生物标志物的背景下进行评估。我们确定了4个显著的主成分（PC1- pc4）在0-3天的脑脊液炎症中，PC1占最大的方差百分比（31%）。PC1的特点是CSF sICAM-1、sFAS、IL-10、IL-6、sVCAM-1、IL-5和IL-8水平相对较高。聚类分析定义了两个不同的聚类，即聚类1中的个体PC1评分高度阳性，CSF皮质醇、孕酮、雌二醇、睾酮、脑源性神经营养因子（BDNF）和S100b水平相对较高；该组血清皮质醇升高，血清BDNF降低。多项逻辑回归分析显示，在控制协变量后，第1类个体在6个月时GOS评分的可能性为2/3，比第2类个体的4/5高10.9倍。在控制年龄和其他协变量后，聚类组没有区分死亡率与GOS评分的4/5。在多变量模型中，聚类分组也没有区分死亡率或12个月的结果。PCA和聚类分析证实，脑损伤后0-3天测量的脑脊液炎症标志物子集可以区分6个月预后差的个体，未来的研究应前瞻性地验证这些发现。脑外伤后炎症介质的PCA可以帮助预测和确定治疗干预的患者亚组。
Studies have characterized absolute levels of multiple inflammatory agents 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 versus 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.