Association of Brain Injury Biomarkers and Circulatory Shock Following Moderate-Severe Traumatic Brain Injury: A TRACK-TBI Study

INTRODUCTION

Early circulatory shock following traumatic brain injury (TBI) is a multifactorial process; however, the impact of brain injury biomarkers on the risk of shock has not been evaluated. We examined the association between neuronal injury biomarker levels and the development of circulatory shock following moderate-severe TBI.

METHODS

In this retrospective cohort study, we examined adults with moderate-severe TBI (Glasgow Coma Scale score <13) enrolled in the TRACK-TBI study, an 18-center prospective TBI cohort study. The exposures were day-1 levels of neuronal injury biomarkers (glial fibrillary acidic protein, ubiquitin C-terminal hydrolase-L1 [UCH-L1], S100 calcium-binding protein B [S100B], neuron-specific enolase), and of an inflammatory biomarker (high-sensitivity C-reactive protein). The primary outcome was the development of circulatory shock, defined as cardiovascular Sequential Organ Failure Assessment Score ≥2 within 72 hours of admission. Association between day-1 biomarker levels and the development of circulatory shock was assessed with regression analysis.

RESULTS

The study included 392 subjects, with a mean age of 40 years; 314 (80%) were male and 165 (42%) developed circulatory shock. Median (interquartile range) day-1 levels of UCH-L1 (994.8 [518.7 to 1988.2] pg/mL vs. 548.1 [280.2 to 1151.9] pg/mL; P <0.0001) and S100B (0.47 μg/mL [0.25 to 0.88] vs. 0.27 [0.16 to 0.46] μg/mL; P <0.0001) were elevated in those who developed early circulatory shock compared with those who did not. In multivariable regression, there were associations between levels of both UCH-L1 (odds ratio, 1.63 [95% confidence interval, 1.25-2.12]; P <0.0005) and S100B (odds ratio, 1.73 [95% confidence interval 1.27-2.36]; P <0.0005) with the development of circulatory shock.

CONCLUSION

Neuronal injury biomarkers may provide the improved mechanistic understanding and possibly early identification of patients at risk for early circulatory shock following moderate-severe TBI.

Polygenic risk for mental disorders as predictors of posttraumatic stress disorder after mild traumatic brain injury

Many patients with mild traumatic brain injury (mTBI) are at risk for mental health problems such as posttraumatic stress disorder (PTSD). The objective of this study was to determine whether the polygenic risk for PTSD (or for related mental health disorders or traits including major depressive disorder [MDD] and neuroticism [NEU]) was associated with an increased likelihood of PTSD in the aftermath of mTBI. We used data from individuals of European ancestry with mTBI enrolled in TRACK-TBI (n = 714), a prospective longitudinal study of level 1 trauma center patients. One hundred and sixteen mTBI patients (16.3%) had probable PTSD (PCL-5 score ≥33) at 6 months post-injury. We used summary statistics from recent GWAS studies of PTSD, MDD, and NEU to generate polygenic risk scores (PRS) for individuals in our sample. A multivariable model that included age, sex, pre-injury history of mental disorder, and cause of injury explained 7% of the variance in the PTSD outcome; the addition of the PTSD-PRS (and five ancestral principal components) significantly increased the variance explained to 11%. The adjusted odds of PTSD in the uppermost PTSD-PRS quintile was nearly four times higher (aOR = 3.71, 95% CI 1.80-7.65) than in the lowest PTSD-PRS quintile. There was no evidence of a statistically significant interaction between PTSD-PRS and prior history of mental disorder, indicating that PTSD-PRS had similar predictive utility among those with and without pre-injury psychiatric illness. When added to the model, neither MDD-PRS nor NEU-PRS were significantly associated with the PTSD outcome. These findings show that the risk for PTSD in the context of mTBI is, in part, genetically influenced. They also raise the possibility that an individual's PRS could be clinically actionable if used-possibly with other non-genetic predictors-to signal the need for enhanced follow-up and early intervention; this precision medicine approach needs to be prospectively studied.

The evolution of white matter microstructural changes after mild traumatic brain injury: A longitudinal DTI and NODDI study

Neuroimaging biomarkers that can detect white matter (WM) pathology after mild traumatic brain injury (mTBI) and predict long-term outcome are needed to improve care and develop therapies. We used diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) to investigate WM microstructure cross-sectionally and longitudinally after mTBI and correlate these with neuropsychological performance. Cross-sectionally, early decreases of fractional anisotropy and increases of mean diffusivity corresponded to WM regions with elevated free water fraction on NODDI. This elevated free water was more extensive in the patient subgroup reporting more early postconcussive symptoms. The longer-term longitudinal WM changes consisted of declining neurite density on NODDI, suggesting axonal degeneration from diffuse axonal injury for which NODDI is more sensitive than DTI. Therefore, NODDI is a more sensitive and specific biomarker than DTI for WM microstructural changes due to mTBI that merits further study for mTBI diagnosis, prognosis, and treatment monitoring.

The Functional Status Examination in Mild Traumatic Brain Injury: A TRACK-TBI Sub-Study

OBJECTIVE

The Functional Status Examination (FSE) is a comprehensive measure of functional status post-traumatic brain injury (TBI) that has primarily been used in studies of moderate-to-severe TBI. The present observational study examines functional status using the FSE among patients who sustained mild TBIs (mTBIs; defined as Glasgow Coma Scale [GCS] = 13-15 at admission) seen in a Level 1 trauma center. Study aims included examining the course of functional status following mTBI, as well as exploring relationships of the FSE and other relevant constructs among those with GCS = 13-15.

METHOD

Participants were assessed at 2 weeks (n = 112), 3 months (n = 113), 6 months (n = 106), and 12 months (n = 88) post-injury for changes in functional status resulting both (a) from all injuries and (b) from TBI only.

RESULTS

Among seven domains of day-to-day functioning, participants generally experienced the greatest disruption in their primary activity (work or school) and in leisure and recreation. Subjects' overall functional status tended to improve over time, with sharpest increases in functionality occurring in the first 3 months post-injury. However, some subjects continued to report functional limitations even at 12 months post-injury. Functional status was largely unrelated to neurocognitive functioning, but related strongly to post-traumatic symptoms, life satisfaction, and emotional well-being, particularly at 3 months post-injury and beyond.

CONCLUSION

Findings indicate that functional impairments related to mTBI may be more likely to persist than widely believed, with those who experience lingering functional deficits at particular risk for emotional health difficulties.

Sleep disturbances precede depressive symptomatology following traumatic brain injury

The purpose of the present study was to evaluate the impact of sleep disturbances on subsequent depressive symptomatology among a representative sample of patients following traumatic brain injury (TBI). Within a retrospective cohort design, our sample included 305 individuals from the Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot (TRACK-TBI Pilot; NINDS-OD09-004) database. At 3-months post-TBI, symptoms of insomnia were reported by 34% of patients, and symptoms of hypersomnia were reported by 39% of patients. For the vast majority of individuals, sleep complaints were likely to persist through 6-month follow-up. Symptoms of hypersomnia but not insomnia at three months were associated with worsened depressive symptomatology at six months. These results highlight the importance of sleep disturbances in recovery from TBI and suggest targeted sleep treatments as a pathway to improve outcomes and quality of life following TBI.

Uncovering precision phenotype-biomarker associations in traumatic brain injury using topological data analysis

Background

Traumatic brain injury (TBI) is a complex disorder that is traditionally stratified based on clinical signs and symptoms. Recent imaging and molecular biomarker innovations provide unprecedented opportunities for improved TBI precision medicine, incorporating patho-anatomical and molecular mechanisms. Complete integration of these diverse data for TBI diagnosis and patient stratification remains an unmet challenge.

Methods and findings

The Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot multicenter study enrolled 586 acute TBI patients and collected diverse common data elements (TBI-CDEs) across the study population, including imaging, genetics, and clinical outcomes. We then applied topology-based data-driven discovery to identify natural subgroups of patients, based on the TBI-CDEs collected. Our hypothesis was two-fold: 1) A machine learning tool known as topological data analysis (TDA) would reveal data-driven patterns in patient outcomes to identify candidate biomarkers of recovery, and 2) TDA-identified biomarkers would significantly predict patient outcome recovery after TBI using more traditional methods of univariate statistical tests. TDA algorithms organized and mapped the data of TBI patients in multidimensional space, identifying a subset of mild TBI patients with a specific multivariate phenotype associated with unfavorable outcome at 3 and 6 months after injury. Further analyses revealed that this patient subset had high rates of post-traumatic stress disorder (PTSD), and enrichment in several distinct genetic polymorphisms associated with cellular responses to stress and DNA damage (PARP1), and in striatal dopamine processing (ANKK1, COMT, DRD2).

Conclusions

TDA identified a unique diagnostic subgroup of patients with unfavorable outcome after mild TBI that were significantly predicted by the presence of specific genetic polymorphisms. Machine learning methods such as TDA may provide a robust method for patient stratification and treatment planning targeting identified biomarkers in future clinical trials in TBI patients.

Trial Registration

ClinicalTrials.gov Identifier NCT01565551