Testing a Multivariate Proteomic Panel for Traumatic Brain Injury Biomarker Discovery: A TRACK-TBI Pilot Study

Interaction of obesity and proteins associated with the NLRP3 inflammasome following mild traumatic brain injury

The NOD-like receptor pyrin domain-containing protein 3 (NLRP3) inflammasome has been associated with worse outcomes from severe traumatic brain injury (TBI). The NLRP3 inflammasome is also strongly associated with other pro-inflammatory conditions, such as obesity. Little is known about the potential effect of mild TBI (mTBI) on the NLRP3 inflammasome and the extent to which modifying factors, such as obesity, may augment the inflammatory response to mTBI. The purpose of this study was to evaluate the association of NLRP3 inflammasome proteins with obese body mass index (BMI ≥ 30) within 24 h of mTBI after presenting to a level 1 trauma center emergency department. This is a secondary analysis of prospectively enrolled patients with mTBI who presented to the emergency department of one U.S. Level 1 trauma center from 2013 to 2018 (n = 243). A series of regression models were built to evaluate the association of NLRP3 proteins obtained from blood plasma within 24 h of injury and BMI as well as the potential interaction effect of higher BMI with NLRP3 proteins (n = 243). A logistic regression model revealed a significant association between IL-18 (p < 0.001) in mTBI patients with obese BMI compared to mTBI patients with non-obese BMI (< 30). Moderation analyses revealed statistically significant interaction effects between apoptotic speck-like protein (ASC), caspase-1, IL-18, IL-1β and obese BMI which worsened symptom burden, quality of life, and physical function at 2 weeks and 6 months post-injury. Higher acute concentrations of IL-1β in the overall cohort predicted higher symptoms at 6-months and worse physical function at 2-weeks and 6-months. Higher acute concentrations of IL-18 in the overall cohort predicted worse physical function at 6-months. In this single center mTBI cohort, obese BMI interacted with higher acute concentrations of NLRP3 inflammasome proteins and worsened short- and long-term clinical outcomes.

Chronic immunosuppression across 12 months and high ability of acute and subacute CNS-injury biomarker concentrations to identify individuals with complicated mTBI on acute CT and MRI

BACKGROUND

Identifying individuals with intracranial injuries following mild traumatic brain injury (mTBI), i.e. complicated mTBI cases, is important for follow-up and prognostication. The main aims of our study were (1) to assess the temporal evolution of blood biomarkers of CNS injury and inflammation in individuals with complicated mTBI determined on computer tomography (CT) and magnetic resonance imaging (MRI); (2) to assess the corresponding discriminability of both single- and multi-biomarker panels, from acute to chronic phases after injury.

METHODS

Patients with mTBI (n = 207), defined as Glasgow Coma Scale score between 13 and 15, loss of consciousness < 30 min and post-traumatic amnesia < 24 h, were included. Complicated mTBI - i.e., presence of any traumatic intracranial injury on neuroimaging - was present in 8% (n = 16) on CT (CT+) and 12% (n = 25) on MRI (MRI+). Blood biomarkers were sampled at four timepoints following injury: admission (within 72 h), 2 weeks (± 3 days), 3 months (± 2 weeks) and 12 months (± 1 month). CNS biomarkers included were glial fibrillary acidic protein (GFAP), neurofilament light (NFL) and tau, along with 12 inflammation markers.

RESULTS

The most discriminative single biomarkers of traumatic intracranial injury were GFAP at admission (CT+: AUC = 0.78; MRI+: AUC = 0.82), and NFL at 2 weeks (CT+: AUC = 0.81; MRI+: AUC = 0.89) and 3 months (MRI+: AUC = 0.86). MIP-1β and IP-10 concentrations were significantly lower across follow-up period in individuals who were CT+ and MRI+. Eotaxin and IL-9 were significantly lower in individuals who were MRI+ only. FGF-basic concentrations increased over time in MRI- individuals and were significantly higher than MRI+ individuals at 3 and 12 months. Multi-biomarker panels improved discriminability over single biomarkers at all timepoints (AUCs > 0.85 for admission and 2-week models classifying CT+ and AUC ≈ 0.90 for admission, 2-week and 3-month models classifying MRI+).

CONCLUSIONS

The CNS biomarkers GFAP and NFL were useful single diagnostic biomarkers of complicated mTBI, especially in acute and subacute phases after mTBI. Several inflammation markers were suppressed in patients with complicated versus uncomplicated mTBI and remained so even after 12 months. Multi-biomarker panels improved diagnostic accuracy at all timepoints, though at acute and 2-week timepoints, the single biomarkers GFAP and NFL, respectively, displayed similar accuracy compared to multi-biomarker panels.

Longitudinal Associations Between Persistent Post-Concussion Symptoms and Blood Biomarkers of Inflammation and CNS-Injury After Mild Traumatic Brain Injury

The aim of our study was to investigate the biological underpinnings of persistent post-concussion symptoms (PPCS) at 3 months following mild traumatic brain injury (mTBI). Patients (n = 192, age 16-60 years) with mTBI, defined as Glasgow Coma Scale (GCS) score between 13 and 15, loss of consciousness (LOC) <30 min, and post-traumatic amnesia (PTA) <24 h were included. Blood samples were collected at admission (within 72 h), 2 weeks, and 3 months. Concentrations of blood biomarkers associated with central nervous system (CNS) damage (glial fibrillary acidic protein [GFAP], neurofilament light [NFL], and tau) and inflammation (interferon gamma [IFNγ], interleukin [IL]-8, eotaxin, macrophage inflammatory protein-1-beta [MIP]-1β, monocyte chemoattractant protein [MCP]-1, interferon-gamma-inducible protein [IP]-10, IL-17A, IL-9, tumor necrosis factor [TNF], basic fibroblast growth factor [FGF]-basic platelet-derived growth factor [PDGF], and IL-1 receptor antagonist [IL-1ra]) were obtained. Demographic and injury-related factors investigated were age, sex, GCS score, LOC, PTA duration, traumatic intracranial finding on magnetic resonance imaging (MRI; within 72 h), and extracranial injuries. Delta values, that is, time-point differences in biomarker concentrations between 2 weeks minus admission and 3 months minus admission, were also calculated. PPCS was assessed with the British Columbia Post-Concussion Symptom Inventory (BC-PSI). In single variable analyses, longer PTA duration and a higher proportion of intracranial findings on MRI were found in the PPCS group, but no single biomarker differentiated those with PPCS from those without. In multi-variable models, female sex, longer PTA duration, MRI findings, and lower GCS scores were associated with increased risk of PPCS. Inflammation markers, but not GFAP, NFL, or tau, were associated with PPCS. At admission, higher concentrations of IL-8 and IL-9 and lower concentrations of TNF, IL-17a, and MCP-1 were associated with greater likelihood of PPCS; at 2 weeks, higher IL-8 and lower IFNγ were associated with PPCS; at 3 months, higher PDGF was associated with PPCS. Higher delta values of PDGF, IL-17A, and FGF-basic at 2 weeks compared with admission, MCP-1 at 3 months compared with admission, and TNF at 2 weeks and 3 months compared with admission were associated with greater likelihood of PPCS. Higher IL-9 delta values at both time-point comparisons were negatively associated with PPCS. Discriminability of individual CNS-injury and inflammation biomarkers for PPCS was around chance level, whereas the optimal combination of biomarkers yielded areas under the curve (AUCs) between 0.62 and 0.73. We demonstrate a role of biological factors on PPCS, including both positive and negative effects of inflammation biomarkers that differed based on sampling time-point after mTBI. PPCS was associated more with acute inflammatory processes, rather than ongoing inflammation or CNS-injury biomarkers. However, the modest discriminative ability of the models suggests other factors are more important in the development of PPCS.

The Australian Traumatic Brain Injury Initiative: Systematic Review of Predictive Value of Biological Markers for People With Moderate-Severe Traumatic Brain Injury

The Australian Traumatic Brain Injury Initiative (AUS-TBI) aims to co-design a data resource to predict outcomes for people with moderate-severe traumatic brain injury (TBI) across Australia. Fundamental to this resource is the data dictionary, which is an ontology of data items. Here, we report the systematic review and consensus process for inclusion of biological markers in the data dictionary. Standardized database searches were implemented from inception through April 2022. English-language studies evaluating association between a fluid, tissue, or imaging marker and any clinical outcome in at least 10 patients with moderate-severe TBI were included. Records were screened using a prioritization algorithm and saturation threshold in Research Screener. Full-length records were then screened in Covidence. A pre-defined algorithm was used to assign a judgement of predictive value to each observed association, and high-value predictors were discussed in a consensus process. Searches retrieved 106,593 records; 1,417 full-length records were screened, resulting in 546 included records. Two hundred thirty-nine individual markers were extracted, evaluated against 101 outcomes. Forty-one markers were judged to be high-value predictors of 15 outcomes. Fluid markers retained following the consensus process included ubiquitin C-terminal hydrolase L1 (UCH-L1), S100, and glial fibrillary acidic protein (GFAP). Imaging markers included computed tomography (CT) scores (e.g., Marshall scores), pathological observations (e.g., hemorrhage, midline shift), and magnetic resonance imaging (MRI) classification (e.g., diffuse axonal injury). Clinical context and time of sampling of potential predictive indicators are important considerations for utility. This systematic review and consensus process has identified fluid and imaging biomarkers with high predictive value of clinical and long-term outcomes following moderate-severe TBI.

Association of obesity with mild traumatic brain injury symptoms, inflammatory profile, quality of life and functional outcomes: a TRACK-TBI Study

OBJECTIVES

Obesity is associated with chronic inflammation, which may impact recovery from mild traumatic brain injury (mTBI). The objective was to assess the role of obesity in recovery of symptoms, functional outcome and inflammatory blood biomarkers after mTBI.

METHODS

TRACK-TBI is a prospective study of patients with acute mTBI (Glasgow Coma Scale=13-15) who were enrolled ≤24 hours of injury at an emergency department of level 1 trauma centres and followed for 12 months. A total of 770 hospitalised patients who were either obese (body mass index (BMI) >30.0) or healthy mass (BMI=18.5-24.9) were enrolled. Blood concentrations of high-sensitivity C reactive protein (hsCRP), interleukin (IL) 6, IL-10, tumour necrosis factor alpha; Rivermead Post-Concussion Symptoms Questionnaire (RPQ), Quality of Life After Brain Injury and Glasgow Outcome Score-Extended reflecting injury-related functional limitations at 6 and 12 months were collected.

RESULTS

After adjusting for age and gender, obese participants had higher concentrations of hsCRP 1 day after injury (mean difference (MD)=0.65; 95% CI: 0.44 to 0.87, p<0.001), at 2 weeks (MD=0.99; 95% CI: 0.74 to 1.25, p<0.001) and at 6 months (MD=1.08; 95% CI: 0.79 to 1.37, p<0.001) compared with healthy mass participants. Obese participants had higher concentrations of IL-6 at 2 weeks (MD=0.37; 95% CI: 0.11 to 0.64, p=0.006) and 6 months (MD=0.42; 95% CI: 0.12 to 0.72, p=0.006). Obese participants had higher RPQ total score at 6 months (MD=2.79; p=0.02) and 12 months (MD=2.37; p=0.049).

CONCLUSIONS

Obesity is associated with higher symptomatology at 6 and 12 months and higher concentrations of blood inflammatory markers throughout recovery following mTBI.

Interleukin-6 in Traumatic Brain Injury: A Janus-Faced Player in Damage and Repair

Traumatic brain injury (TBI) is a common and often devastating illness, with wide-ranging public health implications. In addition to the primary injury, victims of TBI are at risk for secondary neurological injury by numerous mechanisms. Current treatments are limited and do not target the profound immune response associated with injury. This immune response reflects a convergence of peripheral and central nervous system-resident immune cells whose interaction is mediated in part by a disruption in the blood-brain barrier (BBB). The diverse family of cytokines helps to govern this communication and among these, Interleukin (IL)-6 is a notable player in the immune response to acute neurological injury. It is also a well-established pharmacological target in a variety of other disease contexts. In TBI, elevated IL-6 levels are associated with worse outcomes, but the role of IL-6 in response to injury is double-edged. IL-6 promotes neurogenesis and wound healing in animal models of TBI, but it may also contribute to disruptions in the BBB and the progression of cerebral edema. Here, we review IL-6 biology in the context of TBI, with an eye to clarifying its controversial role and understanding its potential as a target for modulating the immune response in this disease.

Effect of Voluntary Exercise on Endogenous Pain Control Systems and Post-traumatic Headache in Mice

Traumatic brain injury (TBI) can cause acute and chronic pain along with motor, cognitive, and emotional problems. Although the mechanisms are poorly understood, previous studies suggest disruptions in endogenous pain modulation may be involved. Voluntary exercise after a TBI has been shown to reduce some consequences of injury including cognitive impairment. We hypothesized, therefore, that voluntary exercise could augment endogenous pain control systems in a rodent model of TBI. For these studies, we used a closed-head impact procedure in male mice modeling mild TBI. We investigated the effect of voluntary exercise on TBI-induced hindpaw nociceptive sensitization, diffuse noxious inhibitory control failure, and periorbital sensitization after bright light stress, a model of post-traumatic headache. Furthermore, we investigated the effects of exercise on memory, circulating markers of brain injury, neuroinflammation, and spinal cord gene expression. We observed that exercise significantly reduced TBI-induced hindpaw allodynia and periorbital allodynia in the first week following TBI. We also showed that exercise improved the deficits associated with diffuse noxious inhibitory control and reduced bright light stress-induced allodynia up to 2 months after TBI. In addition, exercise preserved memory and reduced TBI-induced increases in spinal BDNF, CXCL1, CXCL2, and prodynorphin expression, all genes previously linked to TBI-induced nociceptive sensitization. Taken together, our observations suggest that voluntary exercise may reduce pain after TBI by reducing TBI-induced changes in nociceptive signaling and preserving endogenous pain control systems. PERSPECTIVE: This article evaluates the effects of exercise on pain-related behaviors in a preclinical model of traumatic brain injury (TBI). The findings show that exercise reduces nociceptive sensitization, loss of diffuse noxious inhibitory control, memory deficits, and spinal nociception-related gene expression after TBI. Exercise may reduce or prevent pain after TBI.

Cerebrospinal fluid levels of neuroinflammatory biomarkers are increased in athletes with persistent post-concussive symptoms following sports-related concussion

A sports-related concussion (SRC) is often caused by rapid head rotation at impact, leading to shearing and stretching of axons in the white matter and initiation of secondary inflammatory processes that may exacerbate the initial injury. We hypothesized that athletes with persistent post-concussive symptoms (PPCS) display signs of ongoing neuroinflammation, as reflected by altered profiles of cerebrospinal fluid (CSF) biomarkers, in turn relating to symptom severity. We recruited athletes with PPCS preventing sports participation as well as limiting work, school and/or social activities for ≥ 6 months for symptom rating using the Sport Concussion Assessment Tool, version 5 (SCAT-5) and for cognitive assessment using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Following a spinal tap, we analysed 27 CSF inflammatory biomarkers (pro-inflammatory chemokines and cytokine panels) by a multiplex immunoassay using antibodies as electrochemiluminescent labels to quantify concentrations in PPCS athletes, and in healthy age- and sex-matched controls exercising ≤ 2 times/week at low-to-moderate intensity. Thirty-six subjects were included, 24 athletes with PPCS and 12 controls. The SRC athletes had sustained a median of five concussions, the most recent at a median of 17 months prior to the investigation. CSF cytokines and chemokines levels were significantly increased in eight (IL-2, TNF-α, IL-15, TNF-β, VEGF, Eotaxin, IP-10, and TARC), significantly decreased in one (Eotaxin-3), and unaltered in 16 in SRC athletes when compared to controls, and two were un-detectable. The SRC athletes reported many and severe post-concussive symptoms on SCAT5, and 10 out of 24 athletes performed in the impaired range (Z < - 1.5) on cognitive testing. Individual biomarker concentrations did not strongly correlate with symptom rating or cognitive function. Limitations include evaluation at a single post-injury time point in relatively small cohorts, and no control group of concussed athletes without persisting symptoms was included. Based on CSF inflammatory marker profiling we find signs of ongoing neuroinflammation persisting months to years after the last SRC in athletes with persistent post-concussive symptoms. Since an ongoing inflammatory response may exacerbate the brain injury these results encourage studies of treatments targeting the post-injury inflammatory response in sports-related concussion.

Associations of Microvascular Injury-Related Biomarkers With Traumatic Brain Injury Severity and Outcomes: A Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot Study

Traumatic brain injury (TBI) is characterized by heterogeneity in terms of injury severity, mechanism, outcome, and pathophysiology. A single biomarker alone is unlikely to capture the heterogeneity of even one injury subtype, necessitating the use of panels of biomarkers. Herein, we focus on traumatic cerebrovascular injury and investigate associations of a panel of 16 vascular injury-related biomarkers with indices of TBI severity and outcomes using data from 159 participants in the Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) Pilot Study. Associations of individual biomarkers and clusters of biomarkers identified using non-linear principal components analysis with TBI severity and outcomes were assessed using logistic regression models and Spearman's correlations. As individual biomarkers, higher levels of thrombomodulin, angiopoietin (Ang)-2, von Willebrand factor, and P-selectin were associated with more severe injury; higher levels of Ang-1, Tie2, vascular endothelial growth factor (VEGF)-C, and basic fibroblast growth factor (bFGF) were associated with less severe injury (all p < 0.05 in age-adjusted models). After false discovery rate correction for multiple comparisons, higher levels of Ang-2 remained associated with more severe injury and higher levels of Ang-1, Tie2, and bFGF remained associated with less severe injury at a p < 0.05 level. In principal components analysis, principal component (PC)1, comprised of Ang1, bFGF, P-selectin, VEGF-C, VEGF-A, and Tie2, was associated with less severe injury (age-adjusted odds ratio [OR]: 0.63, 95% confidence interval [CI]: 0.44-0.88 for head computer tomography [CT] positive vs. negative) and PC2 (Ang-2, E-selectin, Flt-1, placental growth factor, thrombomodulin, and vascular cell adhesion protein 1) was associated with greater injury severity (age-adjusted OR: 2.29, 95% CI: 1.49-3.69 for Glasgow Coma Scale [GCS] 3-12 vs. 13-15 and age-adjusted OR 1.59, 95% CI: 1.11-2.32 for head CT positive vs. negative). Neither individual biomarkers nor PCs were associated with outcomes in adjusted models (all p > 0.05). In conclusion, in this trauma-center based population of acute TBI patients, biomarkers of microvascular injury were associated with TBI severity.

Traumatic brain injury: progress and challenges in prevention, clinical care, and research

Traumatic brain injury (TBI) has the highest incidence of all common neurological disorders, and poses a substantial public health burden. TBI is increasingly documented not only as an acute condition but also as a chronic disease with long-term consequences, including an increased risk of late-onset neurodegeneration. The first Lancet Neurology Commission on TBI, published in 2017, called for a concerted effort to tackle the global health problem posed by TBI. Since then, funding agencies have supported research both in high-income countries (HICs) and in low-income and middle-income countries (LMICs). In November 2020, the World Health Assembly, the decision-making body of WHO, passed resolution WHA73.10 for global actions on epilepsy and other neurological disorders, and WHO launched the Decade for Action on Road Safety plan in 2021. New knowledge has been generated by large observational studies, including those conducted under the umbrella of the International Traumatic Brain Injury Research (InTBIR) initiative, established as a collaboration of funding agencies in 2011. InTBIR has also provided a huge stimulus to collaborative research in TBI and has facilitated participation of global partners. The return on investment has been high, but many needs of patients with TBI remain unaddressed. This update to the 2017 Commission presents advances and discusses persisting and new challenges in prevention, clinical care, and research.

In LMICs, the occurrence of TBI is driven by road traffic incidents, often involving vulnerable road users such as motorcyclists and pedestrians. In HICs, most TBI is caused by falls, particularly in older people (aged ≥65 years), who often have comorbidities. Risk factors such as frailty and alcohol misuse provide opportunities for targeted prevention actions. Little evidence exists to inform treatment of older patients, who have been commonly excluded from past clinical trials—consequently, appropriate evidence is urgently required. Although increasing age is associated with worse outcomes from TBI, age should not dictate limitations in therapy. However, patients injured by low-energy falls (who are mostly older people) are about 50% less likely to receive critical care or emergency interventions, compared with those injured by high-energy mechanisms, such as road traffic incidents.

Mild TBI, defined as a Glasgow Coma sum score of 13–15, comprises most of the TBI cases (over 90%) presenting to hospital. Around 50% of adult patients with mild TBI presenting to hospital do not recover to pre-TBI levels of health by 6 months after their injury. Fewer than 10% of patients discharged after presenting to an emergency department for TBI in Europe currently receive follow-up. Structured follow-up after mild TBI should be considered good practice, and urgent research is needed to identify which patients with mild TBI are at risk for incomplete recovery.

The selection of patients for CT is an important triage decision in mild TBI since it allows early identification of lesions that can trigger hospital admission or life-saving surgery. Current decision making for deciding on CT is inefficient, with 90–95% of scanned patients showing no intracranial injury but being subjected to radiation risks. InTBIR studies have shown that measurement of blood-based biomarkers adds value to previously proposed clinical decision rules, holding the potential to improve efficiency while reducing radiation exposure. Increased concentrations of biomarkers in the blood of patients with a normal presentation CT scan suggest structural brain damage, which is seen on MR scanning in up to 30% of patients with mild TBI. Advanced MRI, including diffusion tensor imaging and volumetric analyses, can identify additional injuries not detectable by visual inspection of standard clinical MR images. Thus, the absence of CT abnormalities does not exclude structural damage—an observation relevant to litigation procedures, to management of mild TBI, and when CT scans are insufficient to explain the severity of the clinical condition.

Although blood-based protein biomarkers have been shown to have important roles in the evaluation of TBI, most available assays are for research use only. To date, there is only one vendor of such assays with regulatory clearance in Europe and the USA with an indication to rule out the need for CT imaging for patients with suspected TBI. Regulatory clearance is provided for a combination of biomarkers, although evidence is accumulating that a single biomarker can perform as well as a combination. Additional biomarkers and more clinical-use platforms are on the horizon, but cross-platform harmonisation of results is needed. Health-care efficiency would benefit from diversity in providers.

In the intensive care setting, automated analysis of blood pressure and intracranial pressure with calculation of derived parameters can help individualise management of TBI. Interest in the identification of subgroups of patients who might benefit more from some specific therapeutic approaches than others represents a welcome shift towards precision medicine. Comparative-effectiveness research to identify best practice has delivered on expectations for providing evidence in support of best practices, both in adult and paediatric patients with TBI.

Progress has also been made in improving outcome assessment after TBI. Key instruments have been translated into up to 20 languages and linguistically validated, and are now internationally available for clinical and research use. TBI affects multiple domains of functioning, and outcomes are affected by personal characteristics and life-course events, consistent with a multifactorial bio-psycho-socio-ecological model of TBI, as presented in the US National Academies of Sciences, Engineering, and Medicine (NASEM) 2022 report. Multidimensional assessment is desirable and might be best based on measurement of global functional impairment. More work is required to develop and implement recommendations for multidimensional assessment. Prediction of outcome is relevant to patients and their families, and can facilitate the benchmarking of quality of care. InTBIR studies have identified new building blocks (eg, blood biomarkers and quantitative CT analysis) to refine existing prognostic models. Further improvement in prognostication could come from MRI, genetics, and the integration of dynamic changes in patient status after presentation.

Neurotrauma researchers traditionally seek translation of their research findings through publications, clinical guidelines, and industry collaborations. However, to effectively impact clinical care and outcome, interactions are also needed with research funders, regulators, and policy makers, and partnership with patient organisations. Such interactions are increasingly taking place, with exemplars including interactions with the All Party Parliamentary Group on Acquired Brain Injury in the UK, the production of the NASEM report in the USA, and interactions with the US Food and Drug Administration. More interactions should be encouraged, and future discussions with regulators should include debates around consent from patients with acute mental incapacity and data sharing. Data sharing is strongly advocated by funding agencies. From January 2023, the US National Institutes of Health will require upload of research data into public repositories, but the EU requires data controllers to safeguard data security and privacy regulation. The tension between open data-sharing and adherence to privacy regulation could be resolved by cross-dataset analyses on federated platforms, with the data remaining at their original safe location. Tools already exist for conventional statistical analyses on federated platforms, however federated machine learning requires further development. Support for further development of federated platforms, and neuroinformatics more generally, should be a priority.

This update to the 2017 Commission presents new insights and challenges across a range of topics around TBI: epidemiology and prevention (section 1 ); system of care (section 2 ); clinical management (section 3 ); characterisation of TBI (section 4 ); outcome assessment (section 5 ); prognosis (Section 6 ); and new directions for acquiring and implementing evidence (section 7 ). Table 1 summarises key messages from this Commission and proposes recommendations for the way forward to advance research and clinical management of TBI.

Acute serum free thiols: a potentially modifiable biomarker of oxidative stress following traumatic brain injury

Serum concentrations of free thiols (key components of the extracellular antioxidant machinery) reflect the overall redox status of the human body. The objective of this exploratory study was to determine the concentrations of serum free thiols in the acute phase after traumatic brain injury (TBI) and their association with long-term outcome. In this observational cohort study, patients with TBI of various severity were included from a biobank of prospectively enrolled TBI patients. Further eligibility criteria included an available blood sample and head computed tomography data, obtained within 24 h of injury, as well as a functional outcome assessment (Glasgow Outcome Scale Extended (GOSE)) at 6 months post-injury. Serum free thiol concentrations were markedly lower in patients with TBI (n = 77) compared to healthy controls (n = 55) (mean ± standard deviation; 210.3 ± 63.3 vs. 301.8 ± 23.9 μM, P < 0.001) indicating increased oxidative stress. Concentrations of serum free thiols were higher in patients with complete functional recovery (GOSE = 8) than in patients with incomplete recovery (GOSE < 8) (median [interquartile range]; 235.7 [205.1-271.9] vs. 205.2 [173-226.7] μM, P = 0.016), suggesting that patients with good recovery experience less oxidative stress in the acute phase after TBI or have better redox function. Acute TBI is accompanied by a markedly lower concentration of serum free thiols compared to healthy controls indicating that serum free thiols may be a novel biomarker of TBI. Future studies are warranted to validate our findings and explore the clinical applicability and prognostic capability of this candidate-biomarker.

Long-Term Stability of Blood Serum Biomarkers in Traumatic Brain Injury: A Feasibility Study

Few studies on traumatic brain injury (TBI) have investigated the stability of blood serum biomarkers after long-term storage at low temperatures. In the current feasibility study we analyzed acute phase serum samples from patients with mild TBI as well as patients with moderate and severe TBI that were collected more than 10 years ago (old samples). We were particularly interested in mild TBI, because injury effects are more subtle in this category as compared to moderate-severe TBI. Therefore, the primary objective was to find out whether several biomarkers were still detectable for these patients. Additionally, we examined whether biomarker levels varied as a function of injury severity. For comparison, we also analyzed samples from an ongoing mTBI cohort (new samples) and healthy controls. Samples were treated with care and were not being subjected to freeze-thaw cycles. We measured concentrations of interleukins (IL6 and 10) and brain specific markers (total tau, UCH-L1, GFAP, and NF-L). No significant differences in biomarker concentrations were found between old and new mild TBI samples. For IL6, IL10, and UCH-L1 higher concentrations were found in moderate and severe TBI as compared to mild TBI. In conclusion, our study shows that long-term storage does not rule out the detection of meaningful biomarker concentrations in patients with TBI, although further research by other laboratories is warranted.

Empowering Data Sharing and Analytics through the Open Data Commons for Traumatic Brain Injury Research

Traumatic brain injury (TBI) is a major public health problem. Despite considerable research deciphering injury pathophysiology, precision therapies remain elusive. Here, we present large-scale data sharing and machine intelligence approaches to leverage TBI complexity. The Open Data Commons for TBI (ODC-TBI) is a community-centered repository emphasizing Findable, Accessible, Interoperable, and Reusable data sharing and publication with persistent identifiers. Importantly, the ODC-TBI implements data sharing of individual subject data, enabling pooling for high-sample-size, feature-rich data sets for machine learning analytics. We demonstrate pooled ODC-TBI data analyses, starting with descriptive analytics of subject-level data from 11 previously published articles (N = 1250 subjects) representing six distinct pre-clinical TBI models. Second, we perform unsupervised machine learning on multi-cohort data to identify persistent inflammatory patterns across different studies, improving experimental sensitivity for pro- versus anti-inflammation effects. As funders and journals increasingly mandate open data practices, ODC-TBI will create new scientific opportunities for researchers and facilitate multi-data-set, multi-dimensional analytics toward effective translation.

Mixture Model Framework for Traumatic Brain Injury Prognosis Using Heterogeneous Clinical and Outcome Data

Prognoses of Traumatic Brain Injury (TBI) outcomes are neither easily nor accurately determined from clinical indicators. This is due in part to the heterogeneity of damage inflicted to the brain, ultimately resulting in diverse and complex outcomes. Using a data-driven approach on many distinct data elements may be necessary to describe this large set of outcomes and thereby robustly depict the nuanced differences among TBI patients' recovery. In this work, we develop a method for modeling large heterogeneous data types relevant to TBI. Our approach is geared toward the probabilistic representation of mixed continuous and discrete variables with missing values. The model is trained on a dataset encompassing a variety of data types, including demographics, blood-based biomarkers, and imaging findings. In addition, it includes a set of clinical outcome assessments at 3, 6, and 12 months post-injury. The model is used to stratify patients into distinct groups in an unsupervised learning setting. We use the model to infer outcomes using input data, and show that the collection of input data reduces uncertainty of outcomes over a baseline approach. In addition, we quantify the performance of a likelihood scoring technique that can be used to self-evaluate the extrapolation risk of prognosis on unseen patients.

Developing Biomarkers of Mild Traumatic Brain Injury: Promise and Progress of CNS-Derived Exosomes

Mild traumatic brain injuries (mTBI) are common injuries across civilian and military populations. Although most individuals recover after mTBI, some individuals continue to show long-term symptoms as well as increased risk for neurodegenerative and neuropsychiatric disorders. Currently, diagnosing TBI severity relies primarily on self-report and subjective symptoms, with limited tools for diagnosis or prognosis. Brain-derived exosomes, a form of extracellular vesicle, may offer a solution for interpreting injury states by aiding in diagnosis as well as outcome prediction with relatively low patient burden. Exosomes, which are released into circulation, contain both protein and RNA cargo that can be isolated and quantified, providing a molecular window into molecular status of the exosome source. Here we examined the current literature studying the utility of exosomes, in particular neuronal- and astrocyte-derived exosomes, to identify protein and miRNA biomarkers of injury severity, trajectory, and functional outcome. Current evidence supports the potential for these emerging new tools to capture an accessible molecular window into the brain as it responds to a traumatic injury, however a number of limitations must be addressed in future studies. Most current studies are relatively small and cross sectional; prospective, longitudinal studies across injury severity, and populations are needed to track exosome cargo changes after injury. Standardized exosome isolation as well as advancement in identifying/isolating exosomes from CNS-specific tissue sources will improve mechanistic understanding of cargo changes as well as reliability of findings. Exosomes are also just beginning to be used in model systems to understand functional effects of TBI-associated cargo such as toxicity. Finally linking exosome cargo changes to objective markers of neuronal pathology and cognitive changes will be critical in validating these tools to provide insights into injury and recovery states after TBI.

Quantitative Proteomics Reveals the Dynamic Pathophysiology Across Different Stages in a Rat Model of Severe Traumatic Brain Injury

Background

Severe traumatic brain injury (TBI) has become a global health problem and causes a vast worldwide societal burden. However, distinct mechanisms between acute and subacute stages have not been systemically revealed. The present study aimed to identify differentially expressed proteins in severe TBI from the acute to subacute phase.

Methods

Sixty Sprague Dawley (SD) rats were randomly divided into sham surgery and model groups. The severe TBI models were induced by the controlled cortical impact (CCI) method. We evaluated the neurological deficits through the modified neurological severity score (NSS). Meanwhile, H&E staining and immunofluorescence were performed to assess the injured brain tissues. The protein expressions of the hippocampus on the wounded side of CCI groups and the same side of Sham groups were analyzed by the tandem mass tag-based (TMT) quantitative proteomics on the third and fourteenth days. Then, using the gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and protein–protein interaction (PPI), the shared and stage-specific differentially expressed proteins (DEPs) were screened, analyzed, and visualized. Eventually, target proteins were further verified by Western blotting (WB).

Results

In the severe TBI, the neurological deficits always exist from the acute stage to the subacute stage, and brain parenchyma was dramatically impaired in either period. Of the significant DEPs identified, 312 were unique to the acute phase, 76 were specific to the subacute phase, and 63 were shared in both. Of the 375 DEPs between Sham-a and CCI-a, 240 and 135 proteins were up-regulated and down-regulated, respectively. Of 139 DEPs, 84 proteins were upregulated, and 55 were downregulated in the Sham-s and CCI-s. Bioinformatics analysis revealed that the differential pathophysiology across both stages. One of the most critical shared pathways is the complement and coagulation cascades. Notably, three pathways associated with gastric acid secretion, insulin secretion, and thyroid hormone synthesis were only enriched in the acute phase. Amyotrophic lateral sclerosis (ALS) was significantly enriched in the subacute stage. WB experiments confirmed the reliability of the TMT quantitative proteomics results.

Conclusion

Our findings highlight the same and different pathological processes in the acute and subacute phases of severe TBI at the proteomic level. The results of potential protein biomarkers might facilitate the design of novel strategies to treat TBI.

Behavioral and Cognitive Aspects of Concussion

PURPOSE OF REVIEW

This review provides the reader with an overview of concussion and mild traumatic brain injury (TBI). Key aspects of the pathophysiology, signs, and symptoms, treatment and rehabilitation, and recovery from concussion/mild TBI are reviewed with an emphasis on the variety of factors that may contribute to cognitive concerns following injury.

RECENT FINDINGS

Concussion remains a clinical diagnosis based on symptoms that occur in the immediate aftermath of an applied force and in the hours, days, and weeks thereafter. Although advances have been made in advanced diagnostics, including neuroimaging and fluid biomarkers in hopes of developing objective indicators of injury, such markers currently lack sufficient specificity to be used in clinical diagnostics. The symptoms of concussion are heterogeneous and may be seen to form subtypes, each of which suggests a targeted rehabilitation by the interdisciplinary team. Although the majority of patients with concussion recover within the first 30 to 90 days after injury, some have persistent disabling symptoms. The concept of postconcussion syndrome, implying a chronic syndrome of injury-specific symptoms, is replaced by a broader concept of persistent symptoms after concussion. This concept emphasizes the fact that most persistent symptoms have their basis in complex somatic, cognitive, psychiatric, and psychosocial factors related to risk and resilience. This framework leads to the important conclusion that concussion is a treatable injury from which nearly all patients can be expected to recover.

SUMMARY

Concussion/mild TBI is a significant public health problem in civilian, military, and organized athletic settings. Recent advances have led to a better understanding of underlying pathophysiology and symptom presentation and efficacious treatment and rehabilitation of the resulting symptoms. An interdisciplinary team is well-positioned to provide problem-oriented, integrated care to facilitate recovery and to advance the evidence base supporting effective practice in diagnosis, treatment, and prevention.

Blood-based traumatic brain injury biomarkers - Clinical utilities and regulatory pathways in the United States, Europe and Canada

INTRODUCTION

Traumatic brain injury (TBI) is a major global health issue, resulting in debilitating consequences to families, communities, and health-care systems. Prior research has found that biomarkers aid in the pathophysiological characterization and diagnosis of TBI. Significantly, the FDA has recently cleared both a bench-top assay and a rapid point-of-care assays of tandem biomarker (UCH-L1/GFAP)-based blood test to aid in the diagnosis mTBI patients. With the global necessity of TBI biomarkers research, several major consortium multicenter observational studies with biosample collection and biomarker analysis have been created in the USA, Europe, and Canada. As each geographical region regulates its data and findings, the International Initiative for Traumatic Brain Injury Research (InTBIR) was formed to facilitate data integration and dissemination across these consortia.

AREAS COVERED

This paper covers heavily investigated TBI biomarkers and emerging non-protein markers. Finally, we analyze the regulatory pathways for converting promising TBI biomarkers into approved in-vitro diagnostic tests in the United States, European Union, and Canada.

EXPERT OPINION

TBI biomarker research has significantly advanced in the last decade. The recent approval of an iSTAT point of care test to detect mild TBI has paved the way for future biomarker clearance and appropriate clinical use across the globe.

Blood-based biomarkers of inflammation in mild traumatic brain injury: A systematic review

VISSER, K., M. Koggel, J. Blaauw, H.J.v.d. Horn, B. Jacobs, and J.v.d. Naalt. Blood based biomarkers of inflammation in mild traumatic brain injury: A systematic review. NEUROSCI BIOBEHAV REV XX(X) XXX-XXX, 2021. - Inflammation is an important secondary physiological response to traumatic brain injury (TBI). Most of the current knowledge on this response is derived from research in moderate and severe TBI. In this systematic review we summarize the literature on clinical studies measuring blood based inflammatory markers following mild traumatic brain injury (mTBI) and identify the value of inflammatory markers as biomarkers. Twenty-three studies were included. This review suggests a distinct systemic inflammatory response following mTBI, quantifiable within 6 h up to 12 months post-injury. Interleukin-6 is the most promising biomarker for the clinical diagnosis of brain injury while interleukin-10 is a potential candidate for triaging CT scans. The diagnostic and prognostic utility of inflammatory markers may be more fully appreciated as a component of a panel of biomarkers. However, discrepancies in study design, analysis and reporting make it difficult to draw any definite conclusions. For the same reasons, a meta-analysis was not possible. We provide recommendations to follow standardized methodologies to allow for reproducibility of results in future studies.

Current Trends in Mild Traumatic Brain Injury

In this review, we provide an overview of the current research and treatment of all types of traumatic brain injury (TBI) before illustrating the need for improved care specific to mild TBI patients. Contemporary issues pertaining to acute care of mild TBI including prognostication, neurosurgical intervention, repeat radiographic imaging, reversal of antiplatelet and anticoagulation medications, and cost savings initiatives are reviewed. Lastly, the effect of COVID-19 on TBI is addressed.

FAIR Data Reuse in Traumatic Brain Injury: Exploring Inflammation and Age as Moderators of Recovery in the TRACK-TBI Pilot

The guiding principle for data stewardship dictates that data be FAIR: findable, accessible, interoperable, and reusable. Data reuse allows researchers to probe data that may have been originally collected for other scientific purposes in order to gain novel insights. The current study reuses the Transforming Research and Clinical Knowledge for Traumatic Brain Injury (TRACK-TBI) Pilot dataset to build upon prior findings and ask new scientific questions. Specifically, we have previously used a multivariate analytics approach to multianalyte serum protein data from the TRACK-TBI Pilot dataset to show that an inflammatory ensemble of biomarkers can predict functional outcome at 3 and 6 months post-TBI. We and others have shown that there are quantitative and qualitative changes in inflammation that come with age, but little is known about how this interaction affects recovery from TBI. Here we replicate the prior proteomics findings with improved missing value analyses and non-linear principal component analysis and then expand upon this work to determine whether age moderates the effect of inflammation on recovery. We show that increased age correlates with worse functional recovery on the Glasgow Outcome Scale-Extended (GOS-E) as well as increased inflammatory signature. We then explore the interaction between age and inflammation on recovery, which suggests that inflammation has a more detrimental effect on recovery for older TBI patients.

Prognostic Value of a Combination of Circulating Biomarkers in Critically Ill Patients with Traumatic Brain Injury: Results from the European CREACTIVE Study

Individualized patient care is essential to reduce the global burden of traumatic brain injury (TBI). This pilot study focused on TBI patients admitted to intensive care units (ICUs) and aimed at identifying patterns of circulating biomarkers associated with the disability level at 6 months from injury, measured by the extended Glasgow Outcome Scale (GOS-E). The concentration of 107 biomarkers, including proteins related to inflammation, innate immunity, TBI, and central nervous system, were quantified in blood samples collected on ICU admission from 80 patients. Patients were randomly selected among those prospectively enrolled in the Collaborative Research on Acute Traumatic Brain Injury in Intensive Care Medicine in Europe (CREACTIVE) observational study. Six biomarkers were selected to be associated with indicators of primary or secondary brain injury: three glial proteins (glial cell-derived neurotrophic factor, glial fibrillary acidic protein, and S100 calcium-binding protein B) and three cytokines (stem cell factor, fibroblast growth factor [FGF] 23 and FGF19). The subjects were grouped into three clusters according to the expression of these proteins. The distribution of the 6-month GOS-E was significantly different across clusters (p < 0.001). In two clusters, the number of 6-month deaths or vegetative states was significantly lower than expected, as calculated according to a customization of the corticosteroid randomization after significant head injury (CRASH) scores (observed/expected [O/E] events = 0.00, 95% confidence interval [CI]: 0.00-0.90 and 0.00, 95% CI: 0.00-0.94). In one cluster, less-than-expected unfavorable outcomes (O/E = 0.50, 95% CI: 0.05-0.95) and more-than-expected good recoveries (O/E = 1.55, 95% CI: 1.05-2.06) were observed. The improved prognostic accuracy of the pattern of these six circulating biomarkers at ICU admission upon established clinical parameters and computed tomography results needs validation in larger, independent cohorts. Nonetheless, the results of this pilot study are promising and will prompt further research in personalized medicine for TBI patients.

Biomarkers for Traumatic Brain Injury: Data Standards and Statistical Considerations

Recent biomarker innovations hold potential for transforming diagnosis, prognostic modeling, and precision therapeutic targeting of traumatic brain injury (TBI). However, many biomarkers, including brain imaging, genomics, and proteomics, involve vast quantities of high-throughput and high-content data. Management, curation, analysis, and evidence synthesis of these data are not trivial tasks. In this review, we discuss data management concepts and statistical and data sharing strategies when dealing with biomarker data in the context of TBI research. We propose that application of biomarkers involves three distinct steps-discovery, evaluation, and evidence synthesis. First, complex/big data has to be reduced to useful data elements at the stage of biomarker discovery. Second, inferential statistical approaches must be applied to these biomarker data elements for assessment of biomarker clinical utility and validity. Last, synthesis of relevant research is required to support practice guidelines and enable health decisions informed by the highest quality, up-to-date evidence available. We focus our discussion around recent experiences from the International Traumatic Brain Injury Research (InTBIR) initiative, with a specific focus on four major clinical projects (Transforming Research and Clinical Knowledge in TBI, Collaborative European NeuroTrauma Effectiveness Research in TBI, Collaborative Research on Acute Traumatic Brain Injury in Intensive Care Medicine in Europe, and Approaches and Decisions in Acute Pediatric TBI Trial), which are currently enrolling subjects in North America and Europe. We discuss common data elements, data collection efforts, data-sharing opportunities, and challenges, as well as examine the statistical techniques required to realize successful adoption and use of biomarkers in the clinic as a foundation for precision medicine in TBI.

Systematic review and cumulative meta-analysis of the diagnostic accuracy of glial fibrillary acidic protein vs. S100 calcium binding protein B as blood biomarkers in observational studies of patients with mild or moderate acute traumatic brain injury

Traumatic brain injuries (TBIs) and sports-related concussions (SRCs) are the leading causes of hospitalization and death in subjects <45 years old in the USA and Europe. Some biomarkers (BMs) have been used to reduce unnecessary cranial computed tomography (CCT). In recent years, the astroglial S100 calcium-binding B protein (S100B) has prevented approximately 30% of unnecessary CCTs. Glial fibrillary acidic protein (GFAP) has also been studied in direct comparison with S100B. The aim of our cumulative meta-analysis (cMA) is to compare - in the context of hospital emergency departments or SRC conditions - the differences in diagnostic accuracy (DA), sensitivity (Se) and specificity (Sp) of GFAP and S100B. The main cMA inclusion criterion was the assessment of both BMs in the included subjects since 2010, with blood samples drawn 1-30 h from the suspected TBI or SRC. The risk-of-bias (RoB) score was determined, and both the publication bias (with the Begg, Egger and Duval trim-and-fill tests) and sensitivity (with the box-and-whiskers plot) were analyzed for outliers. Seven studies with 899 subjects and nine observations (samples) were included. The diagnostic odds ratios (dORs) with their prediction intervals (PIs), Se and Sp (analyzed with a hierarchical model to respect the binomial data structure) were assessed, and a random-effects MA and a cMA of the difference in the BMs dOR natural logarithms (logOR(G-S)) between the BMs were performed. The cMA of dOR(G-S) was significant (5.78 (CI 2-16.6)) probably preventing approximately 50% of unnecessary CCTs. Further work is needed to standardize and harmonize GFAP laboratory methods.

Tau aggregation and increased neuroinflammation in athletes after sports-related concussions and in traumatic brain injury patients - A PET/MR study

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Traumatic brain injury (TBI) leads to axonal injury and an inflammatory response.

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Repeated sports-related concussions (rSRC) are linked to neurodegeneration.

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We studied tau aggregation and neuroinflammation in rSRC and TBI using PET/MRI.

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In young rSRC and TBI patients, tau aggregation and neuroinflammation was increased.

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PET useful when studying the long-term consequences of rSRC and TBI.

Traumatic brain injury (TBI) and repeated sports-related concussions (rSRCs) are associated with an increased risk for neurodegeneration. Autopsy findings of selected cohorts of long-term TBI survivors and rSRC athletes reveal increased tau aggregation and a persistent neuroinflammation. To assess in vivo tau aggregation and neuroinflammation in young adult TBI and rSRC cohorts, we evaluated 9 healthy controls (mean age 26 ± 5 years; 4 males, 5 females), 12 symptomatic athletes (26 ± 7 years; 6 males, 6 females) attaining ≥3 previous SRCs, and 6 moderate-to severe TBI patients (27 ± 7 years; 4 males, 2 females) in a combined positron emission tomography (PET)/magnetic resonance (MR) scanner ≥6 months post-injury. Dual PET tracers, [¹⁸F]THK5317 for tau aggregation and [¹¹C]PK11195 for neuroinflammation/microglial activation, were investigated on the same day. The Repeated Battery Assessment of Neurological Status (RBANS) scores, used for cognitive evaluation, were lower in both the rSRC and TBI groups (p < 0.05). Neurofilament-light (NF-L) levels were increased in plasma and cerebrospinal fluid (CSF; p < 0.05), and serum tau levels lower, in TBI although not in rSRC. In rSRC athletes, PET imaging showed increased neuroinflammation in the hippocampus and tau aggregation in the corpus callosum. In TBI patients, tau aggregation was observed in thalami, temporal white matter and midbrain; widespread neuroinflammation was found e.g. in temporal white matter, hippocampus and corpus callosum. In mixed-sex cohorts of young adult athletes with persistent post-concussion symptoms and in TBI patients, increased tau aggregation and neuroinflammation are observed at ≥6 months post-injury using PET. Studies with extended clinical follow-up, biomarker examinations and renewed PET imaging are needed to evaluate whether these findings progress to a neurodegenerative disorder or if spontaneous resolution is possible.

Modeling and Bioinformatics Identify Responders to G-CSF in Patients With Amyotrophic Lateral Sclerosis

Objective: Developing an integrative approach to early treatment response classification using survival modeling and bioinformatics with various biomarkers for early assessment of filgrastim (granulocyte colony stimulating factor) treatment effects in amyotrophic lateral sclerosis (ALS) patients. Filgrastim, a hematopoietic growth factor with excellent safety, routinely applied in oncology and stem cell mobilization, had shown preliminary efficacy in ALS. Methods: We conducted individualized long-term filgrastim treatment in 36 ALS patients. The PRO-ACT database, with outcome data from 23 international clinical ALS trials, served as historical control and mathematical reference for survival modeling. Imaging data as well as cytokine and cellular data from stem cell analysis were processed as biomarkers in a non-linear principal component analysis (NLPCA) to identify individual response. Results: Cox proportional hazard and matched-pair analyses revealed a significant survival benefit for filgrastim-treated patients over PRO-ACT comparators. We generated a model for survival estimation based on patients in the PRO-ACT database and then applied the model to filgrastim-treated patients. Model-identified filgrastim responders displayed less functional decline and impressively longer survival than non-responders. Multimodal biomarkers were then analyzed by PCA in the context of model-defined treatment response, allowing identification of subsequent treatment response as early as within 3 months of therapy. Strong treatment response with a median survival of 3.8 years after start of therapy was associated with younger age, increased hematopoietic stem cell mobilization, less aggressive inflammatory cytokine plasma profiles, and preserved pattern of fractional anisotropy as determined by magnetic resonance diffusion tensor imaging (DTI-MRI). Conclusion: Long-term filgrastim is safe, is well-tolerated, and has significant positive effects on disease progression and survival in a small cohort of ALS patients. Developing and applying a model-based biomarker response classification allows use of multimodal biomarker patterns in full potential. This can identify strong individual treatment responders (here: filgrastim) at a very early stage of therapy and may pave the way to an effective individualized treatment option.

iTRAQ-based proteomic profiling reveals protein alterations after traumatic brain injury and supports thyroxine as a potential treatment

Traumatic brain injury (TBI) is a primary cause of disability and death across the world. Previously, RNA analysis was widely used to study the pathophysiological mechanisms underlying TBI; however, the relatively low correlation between the transcriptome and proteome revealed that RNA transcription abundance does not reliably predict protein abundance, which led to the emergence of proteomic research. In this study, an iTRAQ proteomics approach was applied to detect protein alterations after TBI on a large scale. A total of 3937 proteins were identified, and 146 proteins were significantly changed after TBI. Moreover, 23 upregulated proteins were verified by parallel reaction monitoring (PRM), and fold changes in 16 proteins were consistent with iTRAQ outcomes. Transthyretin (Ttr) upregulation has been demonstrated at the transcriptional level, and this study further confirmed this at the protein level. After treatment with thyroxine (T4), which is transported by Ttr, the effects of T4 on neuronal histopathology and behavioral performance were determined in vivo (TBI + T4 group). Brain edema was alleviated, and the integrity of the blood brain barrier (BBB) improved. Escape latency in the Morris water maze (MWM) declined significantly compared with the group without T4 treatment. Modified neurological severity scores (mNSS) of the TBI + T4 group decreased from day 1 to day 7 post-TBI compared with the TBI + saline group. These results indicate that T4 treatment has potential to alleviate pathologic and behavioral abnormalities post-TBI. Protein alterations after T4 treatment were also detected by iTRAQ proteomics. Upregulation of proteins like Lgals3, Gfap and Apoe after TBI were reversed by T4 treatment. GO enrichment showed T4 mainly affected intermediate filament organization, cholesterol transportation and axonal regeneration. In summary, iTRAQ proteomics provides information about the impact of TBI on protein alterations and yields insight into underlying mechanisms and pathways involved in TBI and T4 treatment. Finally, Ttr and other proteins identified by iTRAQ may become potential novel treatment targets post-TBI.

"Omics" in traumatic brain injury: novel approaches to a complex disease

BACKGROUND

To date, there is neither any pharmacological treatment with efficacy in traumatic brain injury (TBI) nor any method to halt the disease progress. This is due to an incomplete understanding of the vast complexity of the biological cascades and failure to appreciate the diversity of secondary injury mechanisms in TBI. In recent years, techniques for high-throughput characterization and quantification of biological molecules that include genomics, proteomics, and metabolomics have evolved and referred to as omics.

METHODS

In this narrative review, we highlight how omics technology can be applied to potentiate diagnostics and prognostication as well as to advance our understanding of injury mechanisms in TBI.

RESULTS

The omics platforms provide possibilities to study function, dynamics, and alterations of molecular pathways of normal and TBI disease states. Through advanced bioinformatics, large datasets of molecular information from small biological samples can be analyzed in detail and provide valuable knowledge of pathophysiological mechanisms, to include in prognostic modeling when connected to clinically relevant data. In such a complex disease as TBI, omics enables broad categories of studies from gene compositions associated with susceptibility to secondary injury or poor outcome, to potential alterations in metabolites following TBI.

CONCLUSION

The field of omics in TBI research is rapidly evolving. The recent data and novel methods reviewed herein may form the basis for improved precision medicine approaches, development of pharmacological approaches, and individualization of therapeutic efforts by implementing mathematical "big data" predictive modeling in the near future.

Prediction of Neurocognitive Outcome after Moderate-Severe Traumatic Brain Injury Using Serum Neuron-Specific Enolase and S100 biomarkers

Seric biomarkers have been tested in a large number of studies on traumatic brain injuries (TBI) patients in order to predict severity, especially related to the short-term outcome. However, TBI patients have a high risk of developing long-term complications such as physical disability, cognitive impairment, psychiatric pathology, epilepsy, and others. The aim of this study was to assess the correlation between protein biomarkers S100 and neuron-specific enolase (NSE) and neurocognitive status at 10- and 90-days post-injury. Both biomarkers were tested in the first 4h and after 72h post-injury in 62 patients with moderate-severe TBI. The patients were evaluated by a series of neurocognitive tests: Early Rehabilitation Barthel Index (ERBI), Glasgow Outcome Scale-Extended (GOSE), The Mini-Mental State Examination (MMSE), Processing Speed Index (PSI), and Stroop Test, at 10 and 90 days post-injury and supplementary by the Hospital Anxiety and Depression Scale at 90 days. For evaluating the whole neurocognitive status instead of every scale separately, we used Structural Equation Modeling (SEM), while for anxiety and depressive symptoms, we used multiple regression analyses. SEM showed that NSE values at 4 hours were significant predictors of the cognitive status at 10 (p=0.034) and 90 days (p= 0.023). Also, there were found significant correlations between NSE at 4h and the anxiety level. This study demonstrated a significant correlation between NSE at 4h and short and medium-term neuropsychological outcomes, which recommends using this biomarker for selecting patients with a higher risk of cognitive dysfunction.

Unsupervised Machine Learning Reveals Novel Traumatic Brain Injury Patient Phenotypes with Distinct Acute Injury Profiles and Long-Term Outcomes

The heterogeneity of traumatic brain injury (TBI) remains a core challenge for the success of interventional clinical trials. Data-driven approaches for patient stratification may help to identify TBI patient phenotypes during the acute injury period as well as facilitate targeted trial patient enrollment and analysis of treatment efficacy. In this study, we implemented an unsupervised machine learning approach to identify TBI subpopulations at injury baseline using data from 1213 TBI patients who participated in the Citicoline Brain Injury Treatment Trial (COBRIT) Trial. A wrapper framework utilizing generalized low-rank models automatically selected relevant clinical features that were subsequently used to cluster patients using a partitioning around medoids clustering algorithm. Using this approach, we identified three patient phenotypes with unique clinical injury profiles based on a subset of acute injury features. Phenotype-specific differences in long-term functional outcome trajectories were respectively observed at 3 and 6 months after injury. In comparison, when patients were grouped by baseline Glasgow Coma Scale (GCS), no differences in baseline clinical feature profiles or long-term outcomes were observed. To test phenotype reproducibility in an external validation data set, we used a K-nearest neighbors algorithm to classify subjects in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot data set into corresponding phenotypes, then measured the Gower's dissimilarities between TRACK-TBI and COBRIT subjects in each phenotype. No significant differences were found between trial subjects within two phenotypes, suggesting that these phenotypes may be generalizable within a broad range of TBI severity. Further, Extended Glasgow Outcome Scale (GOS-E) outcomes in the TRACK-TBI data set similarly demonstrated phenotype-specific differences in long-term outcomes. Our results suggest that unsupervised machine learning is a promising and effective approach for discovery of novel injury subpopulations over the conventional GCS-based method, and may improve patient selection in future TBI clinical trials.

A Prospective Study of Acute Blood-Based Biomarkers for Sport-Related Concussion

OBJECTIVE

Prospectively characterize changes in serum proteins following sport-related concussion and determine whether candidate biomarkers discriminate concussed athletes from controls and are associated with duration of symptoms following concussion.

METHODS

High school and collegiate athletes were enrolled between 2015 and 2018. Blood was collected at preinjury baseline and within 6 hours (early acute) and at 24 to 48 hours (late acute) following concussion in football players (n = 106), matched uninjured football players (n = 84), and non-contact-sport athletes (n = 50). Glial fibrillary acidic protein, ubiquitin c-terminal hydrolase-L1, S100 calcium binding protein B, alpha-II-spectrin breakdown product 150, interleukin 6, interleukin 1 receptor antagonist, and c-reactive protein were measured in serum. Linear models assessed changes in protein concentrations over time. Receiver operating curves quantified the discrimination of concussed athletes from controls. A Cox proportional hazard model determined whether proteins were associated with symptom recovery.

RESULTS

All proteins except glial fibrillary acidic protein and c-reactive protein were significantly elevated at the early acute phase postinjury relative to baseline and both control groups and discriminated concussed athletes from controls with areas under the curve of 0.68 to 0.84. The candidate biomarkers also significantly improved the discrimination of concussed athletes from noncontact controls compared to symptom severity alone. Glial fibrillary acidic protein was elevated postinjury relative to baseline in concussed athletes with a loss of consciousness or amnesia. Finally, early acute levels of interleukin 1 receptor antagonist were associated with the number of days to symptom recovery.

INTERPRETATION

Brain injury and inflammatory proteins show promise as objective diagnostic biomarkers for sport-related concussion, and inflammatory markers may provide prognostic value. ANN NEUROL 2020;87:907-920.

Paediatric traumatic brain injury

PURPOSE OF REVIEW

To provide a summary of recent developments in the field of paediatric traumatic brain injury (TBI).

RECENT FINDINGS

The epidemiology of paediatric TBI with falling rates of severe TBI, and increasing presentations of apparently minor TBI. There is growing interest in the pathophysiology and outcomes of concussion in children, and detection of 'significant' injury, arising from concern about risks of long-term chronic traumatic encephalopathy. The role of decompressive craniectomy in children is still clarifying.

SUMMARY

Paediatric TBI remains a major public health issue.

An integrated perspective linking physiological and psychological consequences of mild traumatic brain injury

Despite the often seemingly innocuous nature of a mild traumatic brain injury (mTBI), its consequences can be devastating, comprising debilitating symptoms that interfere with daily functioning. Currently, it is still difficult to pinpoint the exact cause of adverse outcome after mTBI. In fact, extensive research suggests that the underlying etiology is multifactorial. In the acute and early sub-acute stages, the pathophysiology of mTBI is likely to be dominated by complex physiological alterations including cellular injury, inflammation, and the acute stress response, which could lead to neural network dysfunction. In this stage, patients often report symptoms such as fatigue, headache, unstable mood and poor concentration. When time passes, psychological processes, such as coping styles, personality and emotion regulation, become increasingly influential. Disadvantageous, maladaptive, psychological mechanisms likely result in chronic stress which facilitates the development of long-lasting symptoms, possibly via persistent neural network dysfunction. So far, a systemic understanding of the coupling between these physiological and psychological factors that in concert define outcome after mTBI is lacking. The purpose of this narrative review article is to address how psychophysiological interactions may lead to poor outcome after mTBI. In addition, a framework is presented that may serve as a template for future studies on this subject.

Data Dissemination: Shortening the Long Tail of Traumatic Brain Injury Dark Data

Translation of traumatic brain injury (TBI) research findings from bench to bedside involves aligning multi-species data across diverse data types including imaging and molecular biomarkers, histopathology, behavior, and functional outcomes. In this review we argue that TBI translation should be acknowledged for what it is: a problem of big data that can be addressed using modern data science approaches. We review the history of the term big data, tracing its origins in Internet technology as data that are "big" according to the "4Vs" of volume, velocity, variety, veracity and discuss how the term has transitioned into the mainstream of biomedical research. We argue that the problem of TBI translation fundamentally centers around data variety and that solutions to this problem can be found in modern machine learning and other cutting-edge analytical approaches. Throughout our discussion we highlight the need to pull data from diverse sources including unpublished data ("dark data") and "long-tail data" (small, specialty TBI datasets undergirding the published literature). We review a few early examples of published articles in both the pre-clinical and clinical TBI research literature to demonstrate how data reuse can drive new discoveries leading into translational therapies. Making TBI data resources more Findable, Accessible, Interoperable, and Reusable (FAIR) through better data stewardship has great potential to accelerate discovery and translation for the silent epidemic of TBI.

The Importance of Therapeutic Time Window in the Treatment of Traumatic Brain Injury

Traumatic brain injury (TBI) is a major cause of death and disability. Despite its importance in public health, there are presently no drugs to treat TBI. Many reasons underlie why drugs have failed clinical trials, one reason is that most drugs to treat TBI lose much of their efficacy before patients are first treated. This review discusses the importance of therapeutic time window; the time interval between TBI onset and the initiation of treatment. Therapeutic time window is complex, as brain injury is both acute and chronic, resulting in multiple drug targets that appear and disappear with differing kinetics. The speed and increasing complexity of TBI pathophysiology is a major reason why drugs lose efficacy as time to first dose increases. Recent Phase III clinical trials treated moderate to severe TBI patients within 4–8 h after injury, yet they turned away many potential patients who could not be treated within these time windows. Additionally, most head trauma is mild TBI. Unlike moderate to severe TBI, patients with mild TBI often delay treatment until their symptoms do not abate. Thus, drugs to treat moderate to severe TBI likely will need to retain high efficacy for up to 12 h after injury; drugs for mild TBI, however, will likely need even longer windows. Early pathological events following TBI progress with similar kinetics in humans and animal TBI models suggesting that preclinical testing of time windows assists the design of clinical trials. We reviewed preclinical studies of drugs first dosed later than 4 h after injury. This review showed that therapeutic time window can differ depending upon the animal TBI model and the outcome measure. We identify the few drugs (methamphetamine, melanocortin, minocycline plus N-acetylcysteine, and cycloserine) that demonstrated good therapeutic windows with multiple outcome measures. On the basis of their therapeutic window, these drugs appear to be excellent candidates for clinical trials. In addition to further testing of these drugs, we recommend that the assessment of therapeutic time window with multiple outcome measures becomes a standard component of preclinical drug testing.

Neurotrauma as a big-data problem

PURPOSE OF REVIEW

The field of neurotrauma research faces a reproducibility crisis. In response, research leaders in traumatic brain injury (TBI) and spinal cord injury (SCI) are leveraging data curation and analytics methods to encourage transparency, and improve the rigor and reproducibility. Here we review the current challenges and opportunities that come from efforts to transform neurotrauma's big data to knowledge.

RECENT FINDINGS

Three parallel movements are driving data-driven-discovery in neurotrauma. First, large multicenter consortia are collecting large quantities of neurotrauma data, refining common data elements (CDEs) that can be used across studies. Investigators are now testing the validity of CDEs in diverse research settings. Second, data sharing initiatives are working to make neurotrauma data findable, accessible, interoperable, and reusable (FAIR). These efforts are reflected by recent open data repository projects for preclinical and clinical neurotrauma. Third, machine learning analytics are allowing researchers to uncover novel data-driven-hypotheses and test new therapeutics in multidimensional outcome space.

SUMMARY

We are on the threshold of a new era in data collection, curation, and analysis. The next phase of big data in neurotrauma research will require responsible data stewardship, a culture of data-sharing, and the illumination of 'dark data'.