The University of Pittsburgh Institutional Review Board (IRB) approved this study. Informed consent was provided by next-of-kin for participants with moderate-to-severe TBI who survived their acute injury. Participants whose cognitive status improved sufficiently over the course of the study were given the opportunity to self-consent. Uninjured, control volunteers self-consented to provide blood samples to derive reference values for biomarkers measured. This prospective cohort study included 221 individuals, recruited from either acute admission or inpatient rehabilitation at the University of Pittsburgh Medical Center (UPMC) as a part of a larger study evaluating biomarkers after TBI. Fig. 1 summarizes data availability within this study cohort. We included men and women with moderate-to-severe TBI, aged 17–78 years, from whom blood samples (n = 607) were collected monthly over the first six months post-injury. Samples were collected on a monthly schedule (within a 2-week window) to the extent possible. Our clinical coordinators conducted home follow-up visits within a 2-week window of each monthly timepoint for each participant to collect a blood sample. These samples were drawn at each monthly visit unless participants refused blood draw, were unable to provide a sample, were lost to follow-up, or died. A portion of patients were also enrolled after the first month blood draw window (n = 61), thus missing that data point. After blood collection, samples were rested for ~ 30 min, stored at 4 degrees C for transport and then centrifuged at 2500 RPM for ten minutes at room temperature. Supernatant was aliquoted into 0.5 mL tubes, and immediately stored at −80 degreesC until batch analysis. On average, 3.23 sample timepoints were collected per study participant.

Relevant demographic and clinical variables were collected through personal interview and medical record review. Variables collected include: age, sex, body mass index (BMI), Glasgow Coma Scale (GCS) score, Injury Severity Scale (ISS) score (both head and non-head regions), mechanism of injury, hospital length-of-stay, and acute care computed tomography (CT) findings. Injury types from acute care CT reports were obtained via medical chart review for multiple, pre-specified lesion types. Intra-axial lesions included intra-ventricular hemorrhage (IVH), intra-parenchymal hemorrhage (IPH), and contusions. Extra-axial lesions included subdural hemorrhage (SDH), subarachnoid hemorrhage (SAH), and epidural hemorrhage (EDH). Data was also abstracted on diffuse axonal injury (DAI) and midline shift. The number of lesions identified on CT scan for the three sub-categories of intra-axial hemorrhages was not mutually exclusive, as an individual could have more than one lesion type. ISS assesses overall anatomical trauma severity (Baker et al., 1974) and is calculated based on the Abbreviated Injury Scale (AIS), an established scoring system sub-divided into six body regions: head/neck, face, chest, abdomen, extremities/pelvis, and external. The ISS is the sum of the squared, highest AIS score of the three most severely injured regions—ranging from 0 to 75 (Baker et al., 1974). We report ISS non-head scores, which are calculated from the top three most severely injured regions, excluding the head/neck (Baker et al., 1974; Copes et al., 1988). The GCS is a commonly used neurological injury severity scale in the clinical setting and is routinely used as an inclusion criterion for TBI clinical trials (Robertson et al., 2014; Wright et al., 2014; Meythaler et al., 2002) and for clinical cohort biomarker and genomics (Santarsieri et al.., 2014; Munoz et al., 2017; Garringer et al., 2013; Diamond et al., 2015; Kumar et al., 2018) studies inclusion criterion. The measure objectively assesses consciousness post-TBI via verbal, eye opening and motor response (Teasdale et al., 1979). The best GCS score collected within the first 24 h of admission was used for analysis.

Thirty-three inflammatory biomarkers were measured in (n = 607 samples) using a Luminex^™ bead array assay (Millipore, Billerica, Massachusetts), requiring a total of ~ 35 μl serum to run. These multiplex assays used microsphere technology where assay beads were tagged with various fluorescent-labeled markers. The protein binding onto the multiplex bead was analyzed with a fluorescence detection laser optic system. The Human High Sensitivity T-cell Magnetic Bead Panel included IL-10, IL-12p70, IL-13, IL17A, IL-1β, IL-2, IL-21, IL-4, IL-23, IL-5, IL-6, IL-7, IL-8, Macrophage Inflammatory Protein (MIP)-1α, MIP-1β, TNF-α, Fractalkine, Granulocyte Macrophage Colony Stimulating Factor (GM-CSF), Interferon-inducible T-cell alpha chemoattractant (ITAC), and Interferon (IFN)-γ. The intra-assay coefficient of variance (CV) was < 5%. The inter-assay CV was < 20%. The Human Neurodegenerative Disease Magnetic Bead included soluble Intracellular Adhesion Molecule (sICAM)-1, Regulated upon Activation, Normal T-cell Expressed and Secreted (RANTES), Neural Cell Adhesion Molecule (NCAM), and soluble Vascular Adhesion Molecule (sVCAM)-1. The intra-assay CV was < 6%. The inter-assay CV was < 13%. The Human Soluble Cytokine Receptor Magnetic Bead Panel included soluble (s)CD30, soluble glycoprotein (sgp)130, soluble interleukin-1 receptor (sIL-1R)-I, sIL-1RII, sIL-2α, sIL-4R, sIL-6R, soluble tumor necrosis factor receptor 1 (sTNFRI), and sTNFRII. The intra-assay CV was < 10% while inter-assay CV was < 15%. Of note, sIL-1RI assayed poorly for 68% of samples. Thus, this marker was omitted from analysis. All other inflammatory markers assayed were quantifiable in n = 185 individuals for ~ 3.23 sample timepoints per individual over the first 6 months post-injury.

Individuals in this cohort were interviewed at 6 and 12 months post-injury to assess global recovery using the Glasgow Outcome Scale (GOS) and Disability Rating Scale (DRS). GOS and DRS scores are commonly used study endpoints for both TBI clinical trials (Giacino et al., 2012; Meythaler et al., 2002; Robertson et al., 2014; Wright et al., 2014) and biomarker and genomics cohort (Wagner et al., 2011; Goyal et al., 2013; Myrga et al., 2016; Barton et al., 2016) research. GOS measures global, neurological recovery using a semi-structured interview and ranges from 1 to 5. The scores on this scale are defined as: 1) dead, 2) vegetative state, 3) severe disability, 4) moderate disability, and 5) good recovery (Jennett and Bond, 1975). For analyses, we grouped individuals into two categories: 1) unfavorable outcome (GOS = 2–3) and 2) favorable outcome (GOS = 4–5). The DRS similarly measures global recovery, via items assessing basic neurological function, cognitive capacity to direct activities of daily living, and capacity for more complex independent activities of daily living (e.g. employment). DRS scores range from 0 to 30, with lower scores corresponding to little/no disability, higher scores corresponding to more disability, and a score of 30 indicating death (Rappaport et al., 1982). DRS scores were categorized for analyses by severity: 0–4 (partial to no disability), 4–14 (moderate or severe disability), 15–29 (extreme severe disability, vegetative state). One participant, deceased at 6 months, was excluded from analysis.

Descriptive statistics, including median, inter-quartile range (IQR), mean, and standard error of the mean (SE), were computed for continuous variables. Univariate binary logistic, ordinal logistic, or linear regression models were used, as applicable, to assess associations to demographic and clinical factors. Pearson correlations measured strength and direction of associations between two continuous variables. Frequencies and percentages were calculated for categorical variables, and the Chi-square test (or Fisher’s exact test as appropriate) was used to identify group differences between these variables. Average values were computed for each biomarker assessed in samples collected 1–6 months post-injury; these values were then standardized to a mean of 0 and standard deviation of 1 prior to TT analysis. Biomarker patterns were analyzed in two stages: 1) TT and 2) bivariate comparisons between the TC scores derived from stage 1 and other demographic, clinical, injury, and outcome variables. Multivariable binary or ordinal logistic regressions were then performed, as applicable, to assess the association of TC scores with outcome. Statistical analyses were performed using SAS 9.4 (Cary, North Carolina) and Stata 15.0 (College Station, Texas).

Table 1 presents demographic information describing our cohort. The median age was 31 years, and the majority of the population included Caucasian men. Motor vehicle and motorcycle accidents and falls accounted for the majority of TBI causes. Of the 185 participants with inflammatory data, CT data was also available for 165 patients. One hundred thirty-two individuals had CT intra-axial hemorrhages, including IVH, IPH, and contusions; and, 150 individuals had extra-axial hemorrhages, including SDH, SAH, and EDH.

The TT analysis was applied to 33 inflammatory biomarkers from n = 185 individuals. The cut-level was set by examining cross-validation scores, which compared multiple possible cut-levels that could be adopted (Fig. 2 and Fig. 3).

Table 2 indicates the proportion of variance explained by each cluster and loadings, which signify contribution by each inflammatory marker to the given component. The five clusters capture a cumulative 43.64% of total variability with inflammation levels among individuals. The variance captured by each cluster are as follows: TC1 (14.66%), TC2 (10.45%), TC3 (7.84%), TC4 (5.92%), and TC5 (4.77%). The greatest contributors to cluster formation were: IL-2 and Fractalkine (TC1), IL-1β and TNF-α (TC2), sIL-2Rα and sTNFRII (TC3), while the markers of TC4 (IL-5, IL-13) and TC5 (ITAC, RANTES) had equal load contribution. Fig. 3 provides a correlation matrix dendrogram for the biologically justifiable cut-level of 16 established with five hierarchical components of the markers ranked by the proportion of variance contributed. The clusters represent the highest branching nodes to the left of the optimal cut-level.

Fig. 4 depicts all significant relationships based on correlations between the TC scores. We visually depicted how the different arms of immunity rendered by the TT are linked or related. TC1 had a moderately large association and significant relationship with TC2 (r = 0.33, p < 0.001) and TC4 (r = 0.37, p < 0.001). TC1 also had a small but significant association with TC3 (p = 0.029) and TC5 (p = 0.036). Further, TC2 had a moderately strong correlation coefficient that was associated with TC5 (r = 0.29, p < 0.001). TC2 also had a small but significant association with TC3 (p = 0.037). Lastly, TC3 had a moderately strong correlation with TC5 (r = 0.39, p < 0.001). These results indicate how certain components of immunity post-injury are biologically interrelated, and that they may have at least some degree of statistical collinearity.

Univariate linear regression models illustrate associations between TC scores and demographic variables (Table 5).

The results indicate that older age was associated with higher TC3 scores (β = 0.029, p < 0.001). Lower GCS scores were associated with higher TC1 (β = −0.096, p = 0.05), TC2 (β = −0.101, p = 0.018), and TC5 (β = −0.056, p = 0.049) scores. These findings indicate that the worse (lower) GCS score, the higher the adaptive, innate, and chemokine burden. Higher ISS scores were also associated with higher TC2 (innate) (β = 0.031, p = 0.037) and TC5 (chemokine) (β = 0.038, p < 0.001), while ISS non-head scores were only associated with higher TC5 (chemokine) scores (β = 0.052, p = <0.001).

We performed binary logistic regressions to model the relationships between each TC score and GOS scores, at 6 and 12 months post-injury (Table 6). Results indicate that for every unit increase in TC5 score, there were 56.9% higher odds for worse 6-month outcomes (p = 0.006). For each unit increase in TC2 (p = 0.051) and TC3 (p = 0.062), results indicate trend level associations wherein there were 23% and 22% increased odds of unfavorable GOS outcomes respectively. At 12 months post-injury, each unit increase in TC2 and TC3 was associated with 22.7% and 24.3% higher TC2 (p = 0.034) and TC3 (p = 0.046) scores respectively.

Similarly, individual ordinal regressions were performed to model the associations of TC scores and categorized DRS at 6 and 12 months post-TBI (Table 7). One unit increase in the TC3 (p = 0.034) and TC5 (p = 0.002) scores was associated with a 25% and 60% increase, respectively, in the odds of being in the higher DRS category (worse outcome) at 6 months post-TBI. Higher TC2 scores showed trend level associations (p = 0.072) with higher (worse) DRS categories at 6 months. Proportional odds assumptions were met for all 6-month models. At month 12, only TC3 was associated (p = 0.024) with worse DRS; indicating that for every unit increase in TC3 scores, there is a 27% increased odds of a higher DRS score. Proportional odds assumptions were also met for all 12-month models.

TC score correlations (Fig. 4), and the association of these scores with different demographic and clinical characteristics (Table 5), indicate the potential for confounding and interaction effects among variables. Thus 6- and 12-month binary GOS and 6- and 12-month categorized DRS regressions were developed using age, sex, and GCS with one TC score taken at a time. All TC scores were considered together in a full model (Tables 8–11). We tested for all possible 2-way interactions (data not shown) for each outcome, and none were significant in the full model. Proportional odds assumptions were met for all multivariable models.

Cluster scores provide a robust approach from which to investigate aggregated inflammatory relationships to outcome metrics. However, markers that do not cluster may still individually track to the outcome of interest and are therefore not captured in this analysis. Also, markers that cluster but do not individually track to the outcome of interest, may attenuate the strength of outcome relationships at the cluster level. To this end, we produced a GOS-specific treelet exemplar to address these caveats.

The treelet dendrogram presented in Section 3.2 represents the correlation structure of all 33 inflammatory markers studied and the associated contribution to overall variance by each significant cluster. However, 12 inflammatory markers are associated (p < 0.1) with 6-month GOS scores (Table 13).

We used this subset of variables (IL-7, IL-21, IL-1β, MIP-3α, MIP-1β, ITAC, RANTES, sIL-2Rα, sTNFRII, MIP-1α, sICAM-1, sIL-4R) to generate a GOS-specific treelet dendrogram (Fig. 5).

As described in Section 3.2, a 100-iteration cross validation process was used to determine the chosen cut point by assessing the frequency ofoptimal cut point ranges. The cut-level chosen was 4, and this TT yielded the following clusters and their respective contribution to percent variance captured: TC1_GOS: IL-7 and IL-21 (14.69%), TC2_GOS: sIL-2Rα and sTNFRII (14.05%), TC3_GOS: IL-1β and MIP-1β (13.85%), and TC4_GOS: ITAC and RANTES (13.13%). Together these clusters explained a total 55.73% of the variability among individuals’ GOS-related inflammatory marker profiles. All markers contribute equally to their respective clusters with loading values of 0.7071. TC1_GOS was associated with GCS and ISS (extracranial), TC2_GOS with age, TC3_GOS with GCS, and TC4_GOS with GCS, ISS (extracranial), and ISS (non-head) (Table 14). Individual logistic regressions showed a significant association of TC1_GOS (OR = 1.36, p = 0.0129), TC2_GOS (OR = 1.42, p = 0.0153), TC3_GOS (OR = 1.35, p = 0.0390), and TC4_GOS (OR = 1.57, p = 0.0058) with 6-month GOS (Table 15).

In the adjusted models (Table 16), the main effect of TC4_GOS (OR = 1.44, p = 0.0245), and the interaction effects TC2_GOS × GCS and TC3_GOS × GCS, were significant (both p = 0.046) (Model 2, 3, and 4). That is, the effects on global outcome of TC2_GOS (soluble molecules) and TC3_GOS (innate) differ in the context of different GCS scores demonstrating how injury severity contributes to inflammatory pathophysiology post-TBI. In the fully adjusted model with all covariates and TC_GOS scores (Model 6, Table 16), only the interaction TC2_GOS × GCS was significant (p = 0.048); one unit increase in TC2_GOS was associated with 4.41 × 0.89^GCS times higher odds of 6-month unfavorable GOS. The VIFs were within the normal range (Age: 1.17, GCS: 1.18, TC1_GOS: 1.62, TC2_GOS: 1.21, TC3_GOS: 1.5, TC4_GOS: 1.25). GCS was associated with 6-month GOS in all models.

Performing TT on a subset of the marker panel (GOS-associated markers) allowed us to test reproducibility and stability of marker clusters from the original treelet dendrogram. We observed a similar correlation structure and an understanding about adjacent cluster pairs (i.e. adaptive and innate immunity related markers) from the larger treelet, which may have commonalities that jointly influence outcome.

TT has been used to explore dietary patterns and myocardial infarction risk, and more recently, metabolite profiles and prostate cancer risk (Gorst-Rasmussen et al., 2011; Schmidt et al., 2020). We hypothesized that TT would be useful in characterizing distinct inflammatory patterns associated with demographic and clinical factors, as well as TBI outcomes. TT incorporates both PCA and cluster analysis to identify underlying data patterns. TT also streamlines the interpretation of clustered biomarkers by assigning a numeric biomarker loading based on their overall contribution to a given cluster (Gorst-Rasmussen, 2012). One consideration with TT is the discretion of choosing a cut point to produce the viable clusters used in later analyses. However, Fig. 2 shows the utility of cross validation in choosing cut levels that maximize variance captured by the clusters, while retaining interpretability of clustering markers.

Among the significant TCs that emerged, they ranked in contribution to overall variance capture as follows: adaptive immunity, innate immunity, soluble molecules, allergy immunity, and chemokines. These findings highlight the importance of systemic inflammation after neurotrauma and identify biomarker groups that differ most among the TBI population and contribute to heterogeneous injury responses. Notably, the adaptive immunity cluster contributes most to the population variance.

We characterized peripheral inflammatory patterns as a reflective indicator of chronic TBI-induced neuroinflammation due to supporting evidence in other clinical models for systemic immune marker readouts reflecting CNS pathology and degree of functional and emotional impairment. For example, Frodl and Amico review work in major depression disorder, elderly cognitive impairment, and Alzheimer’s disease (AD) that link magnetic resonance imaging (MRI)-detected and region-specific structural brain changes, and the subsequent emotional and cognitive deficits, to peripheral markers of brain inflammation (Frodl and Amico, 2014). Both clinical and experimental models of aging and AD suggest that neural activation and functional brain connectivity are vulnerable to peripheral inflammatory-induced abnormalities (Labrenz et al., 2016; Walker et al., 2020).

There is increasing attention on the CNS-systemic interface and joint involvement in mounting and coordinating an immune response to brain injury. Recent literature suggests that modulation of the CNS and systemic inflammatory systems is regulated by the autonomic nervous system (Kenney and Ganta, 2014); however, in a state of neurological physiological disruption like brain injury, homeostasis becomes difficult to maintain. This is evidenced by the chronic manifestation of serum inflammatory derangements for months after injury (Kokiko-Cochran and Godbout, 2018; Kumar et al., 2015) and TBI-induced inflammatory burden associations with long-term sequelae like cognitive dysfunction and depression (Bodnar et al., 2018; Walker and Tesco, 2013).

We hypothesized that TCs captured groups of interrelated inflammatory molecules that reflect different arms of immunity. Our results indicated that the generated TCs were biologically relevant and mapped well to the domains of adaptive immunity, innate immunity, allergy immunity, soluble signaling molecules/receptors, and chemokines. We leveraged TC scores to inform imbalanced inflammatory states after TBI, rather than narrowly assess perturbations of a single inflammatory marker. While TC score associations with demographic and injury factors (Table 5) were diverse, variation in global outcome and disability long-term were consistently attributed to elevated innate immunity, soluble molecule, and chemokine burden (Tables 6 and 7).

The innate immune response is dominated by chronic expression of IL-1β, TNF-α, and IL-6 and macrophage inducible proteins that act to propagate inflammatory activity well after the injury has occurred. We show that worse anatomical and neurological injury (i.e. higher ISS and lower GCS scores) sustains innate immunity elevations and, in turn, increases odds of unfavorable global outcome over the first-year post-injury (Table 6). This finding suggests that initial injury severity impacts acute pathophysiology and exacerbates maladaptive chronic innate immune response patterns, providing evidence that systemic inflammatory dysfunction is a common secondary condition post-TBI. Prolonged inflammation post-injury can activate the HPA axis, which when unregulated, may contribute to poor recovery, chronic stress, and depression (Bodnar et al., 2018). The innate immunity cluster was strongly correlated with the adaptive and chemokine clusters (Fig. 4), demonstrating how this non-specific component of the immune response can elicit a more specific, targeted injury repair response over time and a continued cellular immune response. The correlative nature between innate immunity and chemokines suggests early innate pathway propagation of chemokine production and unfavorable outcomes post-TBI (Ziebell and Morganti-Kossmann, 2010). The correlation between the innate and soluble molecules supports the idea that the innate immune cluster has an active role in perpetuating the overall immune response, including cellular immunity.

Once initiated, the immune response is intricately controlled by expression of cell signaling molecules that guide cell migration, proliferation, and communication via other cellular molecules. This biology was captured across two chronic inflammation TCs consisting of ITAC and RANTES (chemokines) and sTNFRI, sTNFRII, sIL-2Rα, and sVCAM-1 (soluble receptors/adhesion molecules). The correlational results show the innate cluster as associated with the chemokine cluster, which itself is associated with the soluble molecule cluster (Fig. 4). These relationships suggest a biologically relevant chain of events related to cell trafficking, adhesion, and activation that support the perpetuation of proinflammatory cascades.

Soluble receptors have a unique capacity for outcome discrimination with respect to age-related pathophysiology (Table 5 and 6), as expression increases with age and increases odds of unfavorable outcome and greater disability post-injury. This finding suggests that inflammatory marker families be studied in aggregate to assess degree to which circulating cytokines signal via membrane-bound versus soluble receptors. The study of soluble molecule biology, particularly the conditions that facilitate proteolytic cleavage, may further delineate potential mechanistic underpinnings for parent cytokine contributions to adverse outcomes (Levine, 2008).

The soluble receptor cluster was specifically associated with aging post-TBI. TNF molecules, including sTNFRI and sTNFRII, contribute to and are a product of early lymphopenia (Calcagno et al., 2018; Wagner et al., 2019), a phenomenon that may underlie impaired adaptive immune function mediated tissue repair (Suvannavejh et al., 2000). The literature suggests that harmful versus beneficial aspects of systemic immunity depend on timing post-injury and underlying demographics pre-injury (Chodobski et al., 2011; Timaru-Kast et al., 2012). Here, age-related associations with chronic soluble receptor burden pose another unique contributor to age-related risk for poor post-TBI recovery (Timaru-Kast et al., 2012).

As a post-hoc assessment of shared variance between age, GCS score, and soluble molecule expression, an interaction term between age and GCS score was included in the multivariable models (Section 3.7.3). The degree of TC3 changes with respect to age (slope: δTC3/δAge) increases with milder neurological injury severity (higher GCS score). That is, in the context of milder injuries associated with a tempered TNFα response (Woodcock and Morganti-Kossmann, 2013), age is a greater contributor to TNF soluble receptor activity (Patel and Brewer, 2008) and contributes to detrimental outcomes.

Interestingly, chronic chemokine production was associated with both initial injury severity and long-term global outcome and disability (Tables 5–7). Chemokines were the only cluster to track with non-head ISS, which may implicate their role in peripheral injury severity and repair, healing, and/or infection more so than clusters that track with neurological severity (Table 5). RANTES supports chemotaxis and T-cell expression (Mikolajczyk et al., 2016), while ITAC has potent chemoattractant properties regulated by IFNγ to recruit IL-2 activated T-cells (Cole et al., 1998). Together, this persistent chemo-attractant influence may render the chemokine TC particularly valuable in outcome discrimination capacity.

The adaptive immunity cluster is important for prolonged immune responses involved in neuro-repair. In this context, some individuals may be better equipped to immunologically generate a lympho-reparative response after TBI than those with reduced adaptive immune function due to factors like aging and stress (Gregory et al., 2007; Lundström et al., 2012). The adaptive cluster (TC1) explains the most variance captured in the TT analysis. Yet no significant associations were observed between TC1, DRS, and GOS. This finding may be due to other inflammatory responses having a larger (and perhaps overlapping) role in terms of pathophysiology and discriminating outcome. Alternatively, adaptive immune impacts may be more demonstrable with the development of specific secondary conditions and less sensitive to broad multidimensional outcomes like GOS and DRS. Notably, the adaptive immune cluster was correlated with the innate and allergy clusters (Fig. 4).

The literature describes links between CNS-derived autoantibody production in traumatic SCI (Arevalo-Martin et al., 2018; Hergenroeder et al., 2016), which occurs, in part, due to the adaptive immune system. In particular, immunoglobulin M (IgM) antibodies exhibit protective roles: recognize self-antigen, enhance phagocytosis of damaged cells, promote tissue homeostasis, and initiate adaptive immune responses by moderating humoral cell activity and autoantibody production (Grönwall et al., 2012). Adaptive immune molecules like IL-7 have been linked to lymphoproliferative mechanisms, specifically in producing IgM autoantibodies. Also, IL-21 enhances innate immunity features like apoptosis and phagocytosis, and it influences adaptive elements like auto-regulation and differentiation of T-cells and B-cells as well as Ig production (Schluns et al., 2000; Spolski and Leonard, 2014). Our recent work shows systemic IgM autoantibody production specific to the pituitary and hypothalamus as potentially reparative, associated with multiple adaptive immune markers, and associated with reduced neuroendocrine dysfunction in men with moderate-to-severe TBI (Vijapur et al., 2020). Together, these data support the need to further study adaptive immunity and how it may facilitate neurorecovery and repair via IgM related autoimmune mechanisms.

Based on our data, we hypothesized that failure to resolve immune system homeostasis contributes significantly to susceptibility to complications and secondary conditions associated with TBI. We proposed that these systemic inflammatory profiles provide biomarker readouts reflective of ongoing neuro-dysfunction after TBI and persistent lack of inflammatory regulation over the first 6 months post-injury. Perturbations in inflammatory signaling networks likely also impact neurotrophic and endocrine functioning in their contributions to TBI deficits (Wagner and Kumar, 2019).

Our results add to the discussion regarding the continued state of systemic inflammatory dysregulation after TBI. The literature shows ongoing microglial activation years after TBI (Norden et al., 2015) and examples of individual inflammatory marker elevations in both civilian and military populations with repetitive and moderate/severe TBI (Devoto et al., 2017; Rodney et al., 2020; Rusiecki et al., 2020). Chemokines are linked to both initial injury severity and 6-month long-term outcomes suggesting a key role for these markers on the longitudinal impacts of immune dysfunction. Soluble receptors, however, map to both 6- and 12-month recovery scores (Tables 8–11). These results may indicate that chemokines are effector molecules centered on propagating and regulating immune function, while soluble receptors are inflammatory readouts of damage that has occurred via persistent dysfunctional cellular immunity pathways.

The 33-marker treelet dendrogram provided a comprehensive characterization of post-TBI chronic inflammation and identified significant clusters contributing to variance in our TBI population irrespective of outcome. A post-hoc exemplar treelet was then generated for the subset of 12 inflammatory markers individually related to 6-month GOS. Similar cluster membership and associations between all TCs and global outcome were retained. However, covariate-adjusted multivariable models suggest that the chemokine cluster has an independent capacity for outcome discrimination that holds after demographic and injury variable adjustment (Table 16). After adjusting for all GOS-specific TC scores, the interaction term between soluble molecule load and GCS score (TC2xGCS) remained the only cluster-related variable significantly associated with unfavorable outcome. This suggests that initial neurological injury is a persistent contributor to systemic inflammation, particularly soluble molecule expression (sIL-2Rα, sTNFRII).

Assessing TC score profiles characteristic of both 1) demographic and injury groups and 2) longitudinal outcome groups provides information related to immune domains in dysfunction across the TBI recovery continuum. In addition to their descriptive and mechanistic value in characterizing early chronic immunity patterns after TBI, TC scores may support the selection of immunomodulatory agents to utilize as post-acute treatment strategies. Aside from direct therapeutic immune-agents, rehabilitation modalities like exercise could be assessed for their impact on these immune domains (Griesbach, 2011; Piao et al., 2013). When understanding how markers within these TCs covary after injury, the manipulation of one marker can be contextualized for its impact on the expression of another, an approach that may outperform single biomarkers to guide therapeutic intervention.

This study is not without limitations. The main inflammatory kit utilized was a 21-plex T-Cell High Sensitivity array. The clusters presented here were limited to the markers present in our three multiplex assays. Therefore, T-cell related markers were well characterized and incorporated into this treelet dendrogram while other immune domains may benefit from additional marker inclusion. Furthermore, a future direction might include creating a treelet capturing immune function beyond the first 6 months post-injury and characterizing their associations with outcome. We can validate our findings by replicating this treelet methodology in an independent cohort. Utilizing this treelet methodology in another population would allow us to evaluate if/how the cluster memberships might vary and be impacted by the underlying population.

Our data suggest that neurological injury severity, as measured by the GCS in this cohort with moderate-to-severe TBI, has a strong influence on inflammatory levels across TC domains for all multivariate analyses. Our descriptive analyses suggest that those with a “best in 24-hour” GCS in the 13–15 (or milder injury range) have lower adaptive, innate, and chemokine TC scores, suggesting that future directions should explore inflammatory patterns among cohorts with true mild TBI or concussion. Among those particularly interesting to study would be cohorts of individuals who go on to have persistent chronic symptoms versus those who recover without ongoing protracted symptomatic sequelae. Also, larger cohorts with TBI of all severities may be useful for sex or age stratified treelet transformation analyses evaluating unique inflammatory patterns and subsequent associations with TBI outcomes. Survivors with moderate-to-severe TBI, individuals often have multiple injury types, making inflammatory attributions by specific injury or lesion type challenging. However, larger cohorts may have a subset of individuals with isolated injury types for which such subanalyses might be possible.

Future work should also focus on markers that did not cluster because their expression patterns are not correlated with other serum inflammatory markers in our panel. They still hold capacity for outcome discrimination (Table 13) as the markers MIP-1α, sICAM-1, and sIL-4R did not associate with GOS score at the cluster level but did associate in a bivariate comparison. There is a potential masking effect within identified clusters for dominant markers (higher cluster load) related to a particular outcome. For example, IL-7 is related to GOS score in a bivariate comparison, but the adaptive immunity cluster itself is not. Perhaps when evaluating other survivor-based outcomes (cognition, depression, etc.), we may further our understanding of cluster and non-clustered marker relationships to recovery.

Another future direction would be to examine how inflammatory clusters can help understand the underlying pathophysiology associated with secondary conditions, including persistent hypogonadotropic hypogonadism (PHH) (Vijapur et al., 2020), cognition (Milleville et al., 2020), depression (Schulz et al., 2018), epilepsy (Diamond et al., 2014), and others. Also, it would be beneficial to consider how personal biology, injury factors (GCS in particular), and acute care hospitalization characteristics (lymphopenia, acute infection status) impact these immune domains chronically.

As noted in the methods, TT analysis does not lend itself well to studying temporal biomarker dynamics, and future work may consider how to employ latent growth curve analyses (Felt et al., 2017) or group-based trajectory analyses (Kumar et al., 2015; Niyonkuru et al., 2013) to evaluate important latent individual and subgroup temporal biomarker dynamics not appreciated when graphing markers at the cohort level. Future work may include a neural network-based approach for extracting models from dynamic (temporal) data using ordinary and partial differential equations (Sun et al., 2020). In particular, given the spatio-temporal nature of the data (biomarkers sampled over different time intervals), we can identify a model that takes into account the intrinsic differential structure. Resulting models may be parameterized by using both (shallow) multilayer perceptrons and nonlinear differential terms, in order to incorporate relevant correlations between biomarkers collected at varying timepoints. Finally, methods like agent-based modeling (ABM) may also be an appropriate extension to this work to attribute inflammatory marker profiles to activated cellular immune patterns during the chronic phases of TBI recovery. ABM would utilize the temporal cytokine signaling data and logical rules grounded in scientific literature to simulate interactive networks between agents (immune cells) generating and receiving inflammatory signals and inform states changes of healing, damage, and overall recovery (Anderson and Vadigepalli, 2016; Folcik et al., 2011).

There is an emerging body of literature regarding CNS-derived exosomes that cross the BBB, serve as a source of systemic biomarkers, and may reflect a CNS environment facilitative of neural repair and/or complications after neurological injury (Manek et al., 2018; Mondello et al., 2020). In addition to inflammatory biomarker levels themselves, blood exosomes originating in the CNS may provide a more direct readout of CNS recovery state as they are expressed by brain cells directly and communicate with the periphery.