Treelet transform analysis to identify clusters of systemic inflammatory variance in a population with moderate-to-severe traumatic brain injury.

BACKGROUND: Inflammatory cascades following traumatic brain injury (TBI) can have both beneficial and detrimental effects on recovery. Single biomarker studies do not adequately reflect the major arms of immunity and their relationships to long-term outcomes. Thus, we applied treelet transform (TT) analysis to identify clusters of interrelated inflammatory markers reflecting major components of systemic immune function for which substantial variation exists among individuals with moderate-to-severe TBI. METHODS: Serial blood samples from 221 adults with moderate-to-severe TBI were collected over 1-6 months post-injury (n = 607 samples). Samples were assayed for 33 inflammatory markers using Millipore multiplex technology. TT was applied to standardized mean biomarker values generated to identify latent patterns of correlated markers. Treelet clusters (TC) were characterized by biomarkers related to adaptive immunity (TC1), innate immunity (TC2), soluble molecules (TC3), allergy immunity (TC4), and chemokines (TC5). For each TC, a score was generated as the linear combination of standardized biomarker concentrations and cluster load for each individual in the cohort. Ordinal logistic or linear regression was used to test associations between TC scores and 6- and 12-month Glasgow Outcome Scale (GOS), Disability Rating Scale (DRS), and covariates. RESULTS: When adjusting for clinical covariates, TC5 was significantly associated with 6-month GOS (odds ratio, OR = 1.44; p-value, p = 0.025) and 6-month DRS scores (OR = 1.46; p = 0.013). TC5 relationships were attenuated when including all TC scores in the model (GOS: OR = 1.29, p = 0.163; DRS: OR = 1.33, p = 0.100). When adjusting for all TC scores and covariates, only TC3 was associated with 6- and 12-month GOS (OR = 1.32, p = 0.041; OR = 1.39, p = 0.002) and also 6- and 12-month DRS (OR = 1.38, p = 0.016; OR = 1.58, p = 0.0002). When applying TT to inflammation markers significantly associated with 6-month GOS, multivariate modeling confirmed that TC3 remained significantly associated with GOS. Biomarker cluster membership remained consistent between the GOS-specific dendrogram and overall dendrogram. CONCLUSIONS: TT effectively characterized chronic, systemic immunity among a cohort of individuals with moderate-to-severe TBI. We posit that chronic chemokine levels are effector molecules propagating cellular immune dysfunction, while chronic soluble receptors are inflammatory damage readouts perpetuated, in part, by persistent dysfunctional cellular immunity to impact neuro-recovery.

A mathematical model of neuroinflammation in severe clinical traumatic brain injury.

BACKGROUND: Understanding the interdependencies among inflammatory mediators of tissue damage following traumatic brain injury (TBI) is essential in providing effective, patient-specific care. Activated microglia and elevated concentrations of inflammatory signaling molecules reflect the complex cascades associated with acute neuroinflammation and are predictive of recovery after TBI. However, clinical TBI studies to date have not focused on modeling the dynamic temporal patterns of simultaneously evolving inflammatory mediators, which has potential in guiding the design of future immunomodulation intervention studies. METHODS: We derived a mathematical model consisting of ordinary differential equations (ODE) to represent interactions between pro- and anti-inflammatory cytokines, M1- and M2-like microglia, and central nervous system (CNS) tissue damage. We incorporated variables for several cytokines, interleukin (IL)-1ß, IL-4, IL-10, and IL-12, known to have roles in microglial activation and phenotype differentiation. The model was fit to cerebrospinal fluid (CSF) cytokine data, collected during the first 5 days post-injury in n = 89 adults with severe TBI. Ensembles of model fits were produced for three patient subgroups: (1) a favorable outcome group (GOS = 4,5) and (2) an unfavorable outcome group (GOS = 1,2,3) both with lower pro-inflammatory load, and (3) an unfavorable outcome group (GOS = 1,2,3) with higher pro-inflammatory load. Differences in parameter distributions between subgroups were ranked using Bhattacharyya metrics to identify mechanistic differences underlying the neuroinflammatory patterns of patient groups with different TBI outcomes. RESULTS: Optimal model fits to data showed different microglial and damage responses by patient subgroup. Upon comparison of model parameter distributions, unfavorable outcome groups were characterized by either a prolonged, pathophysiological or a transient, sub-physiological course of neuroinflammation. CONCLUSION: By developing a mathematical characterization of inflammatory processes informed by clinical data, we have created a system for exploring links between acute neuroinflammatory components and patient outcome in severe TBI.

Temporal Acute Serum Estradiol and Tumor Necrosis Factor-α Associations and Risk of Death after Severe Traumatic Brain Injury.

Severe traumatic brain injury (TBI) activates a robust systemic response that involves inflammatory and other factors, including estradiol (E2), associated with increased deaths. Tumor necrosis factor-alpha (TNFα) is a significant mediator of systemic shock, and it is an extra-gonadal transcription factor for E2 production. The study objectives were to test the hypotheses: (1) a positive feedback relationship exists between acute serum TNFα and E2; and (2) acute concentrations of E2 and TNFα are prognostic indicators of death after severe TBI. This prospective cohort study included N = 157 adults with severe TBI. Serum samples were collected for the first five days post-injury. The TNFα and E2 levels were averaged into two time epochs: first 72 h (T1) and second 72 h post-injury (T2). A cross-lag panel analysis conducted between T1 and T2 TNFα and E2 levels showed significant cross-lag effects: T1 TNFα and T1 E2 were related to T2 E2 and T2 TNFα, respectively. Cox proportional hazards multi variable regression models determined that increases in T1 E2 (hazard ratio [HR] = 1.79, 95% confidence interval [CI]: 1.15, 2.81), but not T2 E2 (HR = 0.91, 95% CI: 0.56, 1.47), were associated with increased risk of death. Increased T2 TNFα (HR = 2.47, 95% CI: 1.35, 4.53), and T1 TNFα (HR = 1.47, 95% CI: 0.99, 2.19), to a lesser degree, were associated with increased risk of death. Relationships of death with T2 TNFα and T1 E2 were mediated partially by cardiovascular, hepatic, and renal dysfunction. Both E2 and TNFα are systemic, reciprocally related biomarkers that may be indicative of systemic compromise and increased risk of death after severe TBI.