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

The complex and heterogeneous nature of traumatic brain injury (TBI) has rendered the identification of diagnostic and prognostic biomarkers elusive. A single acute biomarker may not be sufficient to categorize injury severity and/or predict outcome. Using multivariate dimension reduction analyses, we tested the sensitivity and specificity of a multi-analyte panel of proteins as an ensemble biomarker for TBI. Serum was collected within 24 h of injury in a cohort of 130 patients enrolled in the multi-center prospective Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot (TRACK-TBI Pilot) study and run on an array that measured 72 proteins. Using unsupervised principal components analysis, we first identified the subset of protein changes accounting for the most variance across patients. This yielded a group of 21 proteins that reflected an inverse relationship between inflammatory cytokines and regulators of anti-inflammation, and generated an individual inflammatory profile score for each patient. We then tested the association between these scores and computed tomography (CT) findings at hospital admission, as well as their prognostic association with functional recovery at 3 and 6 months (Glasgow Outcome Scale-Extended), and cognitive recovery at 6 months (California Verbal Learning Test, Second Edition) after injury. Inflammatory signatures were significantly increased in patients with positive CT findings, as well as in those who showed poor or incomplete recovery. Inflammation biomarker scores also showed significant sensitivity and specificity as a discriminator of these outcome measures (all areas under the curve [AUCs] >0.62). This proof of concept for the feasibility of multivariate biomarker identification demonstrates the prognostic validity of using a proteomic panel as a potential biomarker for TBI.

Apolipoprotein E epsilon 4 (APOE-ε4) genotype is associated with decreased 6-month verbal memory performance after mild traumatic brain injury.

INTRODUCTION: The apolipoprotein E (APOE) ε4 allele associates with memory impairment in neurodegenerative diseases. Its association with memory after mild traumatic brain injury (mTBI) is unclear. METHODS: mTBI patients (Glasgow Coma Scale score 13-15, no neurosurgical intervention, extracranial Abbreviated Injury Scale score ≤1) aged ≥18 years with APOE genotyping results were extracted from the Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot (TRACK-TBI Pilot) study. Cohorts determined by APOE-ε4(+/-) were assessed for associations with 6-month verbal memory, measured by California Verbal Learning Test, Second Edition (CVLT-II) subscales: Immediate Recall Trials 1-5 (IRT), Short-Delay Free Recall (SDFR), Short-Delay Cued Recall (SDCR), Long-Delay Free Recall (LDFR), and Long-Delay Cued Recall (LDCR). Multivariable regression controlled for demographic factors, seizure history, loss of consciousness, posttraumatic amnesia, and acute intracranial pathology on computed tomography (CT). RESULTS: In 114 mTBI patients (APOE-ε4(-)=79; APOE-ε4(+)=35), ApoE-ε4(+) was associated with long-delay verbal memory deficits (LDFR: B = -1.17 points, 95% CI [-2.33, -0.01], p = .049; LDCR: B = -1.58 [-2.63, -0.52], p = .004), and a marginal decrease on SDCR (B = -1.02 [-2.05, 0.00], p = .050). CT pathology was the strongest predictor of decreased verbal memory (IRT: B = -8.49, SDFR: B = -2.50, SDCR: B = -1.85, LDFR: B = -2.61, LDCR: B = -2.60; p < .001). Seizure history was associated with decreased short-term memory (SDFR: B = -1.32, p = .037; SDCR: B = -1.44, p = .038). CONCLUSION: The APOE-ε4 allele may confer an increased risk of impairment of 6-month verbal memory for patients suffering mTBI, with implications for heightened surveillance and targeted therapies. Acute intracranial pathology remains the driver of decreased verbal memory performance at 6 months after mTBI.

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.

COMT Val158Met polymorphism is associated with post-traumatic stress disorder and functional outcome following mild traumatic brain injury.

Mild traumatic brain injury (mTBI) results in variable clinical trajectories and outcomes. The source of variability remains unclear, but may involve genetic variations, such as single nucleotide polymorphisms (SNPs). A SNP in catechol-o-methyltransferase (COMT) is suggested to influence development of post-traumatic stress disorder (PTSD), but its role in TBI remains unclear. Here, we utilize the Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot (TRACK-TBI Pilot) study to investigate whether the COMT Val158Met polymorphism is associated with PTSD and global functional outcome as measured by the PTSD Checklist - Civilian Version and Glasgow Outcome Scale Extended (GOSE), respectively. Results in 93 predominately Caucasian subjects with mTBI show that the COMT Met158 allele is associated with lower incidence of PTSD (univariate odds ratio (OR) of 0.25, 95% CI [0.09-0.69]) and higher GOSE scores (univariate OR 2.87, 95% CI [1.20-6.86]) 6-months following injury. The COMT Val158Met genotype and PTSD association persists after controlling for race (multivariable OR of 0.29, 95% CI [0.10-0.83]) and pre-existing psychiatric disorders/substance abuse (multivariable OR of 0.32, 95% CI [0.11-0.97]). PTSD emerged as a strong predictor of poorer outcome on GOSE (multivariable OR 0.09, 95% CI [0.03-0.26]), which persists after controlling for age, GCS, and race. When accounting for PTSD in multivariable analysis, the association of COMT genotype and GOSE did not remain significant (multivariable OR 1.73, 95% CI [0.69-4.35]). Whether COMT genotype indirectly influences global functional outcome through PTSD remains to be determined and larger studies in more diverse populations are needed to confirm these findings.

DRD2 C957T polymorphism is associated with improved 6-month verbal learning following traumatic brain injury.

Traumatic brain injury (TBI) often leads to heterogeneous clinical outcomes, which may be influenced by genetic variation. A single-nucleotide polymorphism (SNP) in the dopamine D2 receptor (DRD2) may influence cognitive deficits following TBI. However, part of the association with DRD2 has been attributed to genetic variability within the adjacent ankyrin repeat and kinase domain containing 1 protein (ANKK1). Here, we utilize the Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot (TRACK-TBI Pilot) study to investigate whether a novel DRD2 C957T polymorphism (rs6277) influences outcome on a cognitive battery at 6 months following TBI-California Verbal Learning Test (CVLT-II), Wechsler Adult Intelligence Test Processing Speed Index Composite Score (WAIS-PSI), and Trail Making Test (TMT). Results in 128 Caucasian subjects show that the rs6277 T-allele associates with better verbal learning and recall on CVLT-II Trials 1-5 (T-allele carrier 52.8 ± 1.3 points, C/C 47.9 ± 1.7 points; mean increase 4.9 points, 95% confidence interval [0.9 to 8.8]; p = 0.018), Short-Delay Free Recall (T-carrier 10.9 ± 0.4 points, C/C 9.7 ± 0.5 points; mean increase 1.2 points [0.1 to 2.5]; p = 0.046), and Long-Delay Free Recall (T-carrier 11.5 ± 0.4 points, C/C 10.2 ± 0.5 points; mean increase 1.3 points [0.1 to 2.5]; p = 0.041) after adjusting for age, education years, Glasgow Coma Scale, presence of acute intracranial pathology on head computed tomography scan, and genotype of the ANKK1 SNP rs1800497 using multivariable regression. No association was found between DRD2 C947T and non-verbal processing speed (WAIS-PSI) or mental flexibility (TMT) at 6 months. Hence, DRD2 C947T (rs6277) may be associated with better performance on select cognitive domains independent of ANKK1 following TBI.

COMT Val 158 Met polymorphism is associated with nonverbal cognition following mild traumatic brain injury.

Mild traumatic brain injury (mTBI) results in variable clinical outcomes, which may be influenced by genetic variation. A single-nucleotide polymorphism in catechol-o-methyltransferase (COMT), an enzyme which degrades catecholamine neurotransmitters, may influence cognitive deficits following moderate and/or severe head trauma. However, this has been disputed, and its role in mTBI has not been studied. Here, we utilize the Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot (TRACK-TBI Pilot) study to investigate whether the COMT Val (158) Met polymorphism influences outcome on a cognitive battery 6 months following mTBI--Wechsler Adult Intelligence Test Processing Speed Index Composite Score (WAIS-PSI), Trail Making Test (TMT) Trail B minus Trail A time, and California Verbal Learning Test, Second Edition Trial 1-5 Standard Score (CVLT-II). All patients had an emergency department Glasgow Coma Scale (GCS) of 13-15, no acute intracranial pathology on head CT, and no polytrauma as defined by an Abbreviated Injury Scale (AIS) score of ≥3 in any extracranial region. Results in 100 subjects aged 40.9 (SD 15.2) years (COMT Met (158) /Met (158) 29 %, Met (158) /Val (158) 47 %, Val (158) /Val (158) 24 %) show that the COMT Met (158) allele (mean 101.6 ± SE 2.1) associates with higher nonverbal processing speed on the WAIS-PSI when compared to Val (158) /Val (158) homozygotes (93.8 ± SE 3.0) after controlling for demographics and injury severity (mean increase 7.9 points, 95 % CI [1.4 to 14.3], p = 0.017). The COMT Val (158) Met polymorphism did not associate with mental flexibility on the TMT or with verbal learning on the CVLT-II. Hence, COMT Val (158) Met may preferentially modulate nonverbal cognition following uncomplicated mTBI.Registry: ClinicalTrials.gov Identifier NCT01565551.

Association of a common genetic variant within ANKK1 with six-month cognitive performance after traumatic brain injury.

Genetic association analyses suggest that certain common single nucleotide polymorphisms (SNPs) may adversely impact recovery from traumatic brain injury (TBI). Delineating their causal relationship may aid in development of novel interventions and in identifying patients likely to respond to targeted therapies. We examined the influence of the (C/T) SNP rs1800497 of ANKK1 on post-TBI outcome using data from two prospective multicenter studies: the Citicoline Brain Injury Treatment (COBRIT) trial and Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot (TRACK-TBI Pilot). We included patients with ANKK1 genotyping results and cognitive outcomes at six months post-TBI (n = 492: COBRIT n = 272, TRACK-TBI Pilot n = 220). Using the California Verbal Learning Test Second Edition (CVLT-II) Trial 1-5 Standard Score, we found a dose-dependent effect for the T allele, with T/T homozygotes scoring lowest on the CVLT-II Trial 1-5 Standard Score (T/T 45.1, C/T 51.1, C/C 52.1, ANOVA, p = 0.008). Post hoc testing with multiple comparison-correction indicated that T/T patients performed significantly worse than C/T and C/C patients. Similar effects were observed in a test of non-verbal processing (Wechsler Adult Intelligence Scale, Processing Speed Index). Our findings extend those of previous studies reporting a negative relationship of the ANKK1 T allele with cognitive performance after TBI. In this study, we demonstrate the value of pooling shared clinical, biomarker, and outcome variables from two large datasets applying the NIH TBI Common Data Elements. The results have implications for future multicenter investigations to further elucidate the role of ANKK1 in post-TBI outcome.

Measurement of the glial fibrillary acidic protein and its breakdown products GFAP-BDP biomarker for the detection of traumatic brain injury compared to computed tomography and magnetic resonance imaging.

Glial fibrillary acidic protein and its breakdown products (GFAP-BDP) are brain-specific proteins released into serum as part of the pathophysiological response after traumatic brain injury (TBI). We performed a multi-center trial to validate and characterize the use of GFAP-BDP levels in the diagnosis of intracranial injury in a broad population of patients with a positive clinical screen for head injury. This multi-center, prospective, cohort study included patients 16-93 years of age presenting to three level 1 trauma centers with suspected TBI (loss of consciousness, post-trauma amnesia, and so on). Serum GFAP-BDP levels were drawn within 24 h and analyzed, in a blinded fashion, using sandwich enzyme-linked immunosorbent assay. The ability of GFAP-BDP to predict intracranial injury on admission computed tomography (CT) as well as delayed magnetic resonance imaging was analyzed by multiple regression and assessed by the area under the receiver operating characteristic curve (AUC). Utility of GFAP-BDP to predict injury and reduce unnecessary CT scans was assessed utilizing decision curve analysis. A total of 215 patients were included, of which 83% suffered mild TBI, 4% moderate, and 12% severe; mean age was 42.1±18 years. Evidence of intracranial injury was present in 51% of the sample (median Rotterdam Score, 2; interquartile range, 2). GFAP-BDP demonstrated very good predictive ability (AUC=0.87) and demonstrated significant discrimination of injury severity (odds ratio, 1.45; 95% confidence interval, 1.29-1.64). Use of GFAP-BDP yielded a net benefit above clinical screening alone and a net reduction in unnecessary scans by 12-30%. Used in conjunction with other clinical information, rapid measurement of GFAP-BDP is useful in establishing or excluding the diagnosis of radiographically apparent intracranial injury throughout the spectrum of TBI. As an adjunct to current screening practices, GFAP-BDP may help avoid unnecessary CT scans without sacrificing sensitivity (Registry: ClinicalTrials.gov Identifier: NCT01565551).

Acute biomarkers of traumatic brain injury: relationship between plasma levels of ubiquitin C-terminal hydrolase-L1 and glial fibrillary acidic protein.

Biomarkers are important for accurate diagnosis of complex disorders such as traumatic brain injury (TBI). For a complex and multifaceted condition such as TBI, it is likely that a single biomarker will not reflect the full spectrum of the response of brain tissue to injury. Ubiquitin C-terminal hydrolase L1 (UCH-L1) and glial fibrillary acidic protein (GFAP) are among of the most widely studied biomarkers for TBI. Because UCH-L1 and GFAP measure distinct molecular events, we hypothesized that analysis of both biomarkers would be superior to analysis of each alone for the diagnosis and prognosis of TBI. Serum levels of UCH-L1 and GFAP were measured in a cohort of 206 patients with TBI enrolled in a multicenter observational study (Transforming Research and Clinical Knowledge in Traumatic Brain Injury [TRACK-TBI]). Levels of the two biomarkers were weakly correlated to each other (r=0.364). Each biomarker in isolation had good sensitivity and sensitivity for discriminating between TBI patients and healthy controls (area under the curve [AUC] 0.87 and 0.91 for UCH-L1 and GFAP, respectively). When biomarkers were combined, superior sensitivity and specificity for diagnosing TBI was obtained (AUC 0.94). Both biomarkers discriminated between TBI patients with intracranial lesions on CT scan and those without such lesions, but GFAP measures were significantly more sensitive and specific (AUC 0.88 vs. 0.71 for UCH-L1). For association with outcome 3 months after injury, neither biomarker had adequate sensitivity and specificity (AUC 0.65-0.74, for GFAP, and 0.59-0.80 for UCH-L1, depending upon Glasgow Outcome Scale Extended [GOS-E] threshold used). Our results support a role for multiple biomarker measurements in TBI research. ( ClinicalTrials.gov Identifier NCT01565551).

GFAP-BDP as an acute diagnostic marker in traumatic brain injury: results from the prospective transforming research and clinical knowledge in traumatic brain injury study.

Reliable diagnosis of traumatic brain injury (TBI) is a major public health need. Glial fibrillary acidic protein (GFAP) is expressed in the central nervous system, and breakdown products (GFAP-BDP) are released following parenchymal brain injury. Here, we evaluate the diagnostic accuracy of elevated levels of plasma GFAP-BDP in TBI. Participants were identified as part of the prospective Transforming Research And Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study. Acute plasma samples (<24 h post-injury) were collected from patients presenting with brain injury who had CT imaging. The ability of GFAP-BDP level to discriminate patients with demonstrable traumatic lesions on CT, and with failure to return to pre-injury baseline at 6 months, was evaluated by the area under the receiver operating characteristic curve (AUC). Of the 215 patients included for analysis, 83% had mild, 4% had moderate, and 13% had severe TBI; 54% had acute traumatic lesions on CT. The ability of GFAP-BDP level to discriminate patients with traumatic lesions on CT as evaluated by AUC was 0.88 (95% confidence interval [CI], 0.84-0.93). The optimal cutoff of 0.68 ng/mL for plasma GFAP-BDP level was associated with a 21.61 odds ratio for traumatic findings on head CT. Discriminatory ability of unfavorable 6 month outcome was lower, AUC 0.65 (95% CI, 0.55-0.74), with a 2.07 odds ratio. GFAP-BDP levels reliably distinguish the presence and severity of CT scan findings in TBI patients. Although these findings confirm and extend prior studies, a larger prospective trial is still needed to validate the use of GFAP-BDP as a routine diagnostic biomarker for patient care and clinical research. The term "mild" continues to be a misnomer for this patient population, and underscores the need for evolving classification strategies for TBI targeted therapy. (ClinicalTrials.gov number NCT01565551; NIH Grant 1RC2 NS069409).

A quantitative analysis of clinical trial designs in spinal cord injury based on ICCP guidelines.

Clinical studies of spinal cord injury (SCI) have evolved into multidisciplinary programs that investigate multiple types of neurological deficits and sequelae. In 2007, the International Campaign for Cures of SCI Paralysis (ICCP) proposed best practices for interventional trial designs, end-points, and inclusion criteria. Here we quantitatively assessed the extent to which SCI trials follow ICCP guidelines and reflect the overall patient population. We obtained data for all 288 SCI trials in ClinicalTrials.gov. We calculated summary statistics and observed trends pre-2007 versus 2007 onward. To compare the trial population to the overall SCI population, we obtained statistics from the National SCI Statistical Center. We generated tag clouds to describe heterogeneous trial outcomes. Most interventional studies were randomized (147, 73.1%), and utilized active (55, 36.7%) or placebo controls (49, 32.7%), both increasing trends (p=0.09). Most trials were open label (116, 53.5%), rather than double- (62, 28.6%) or single-blinded (39, 18.0%), but blinding has increased (p=0.01). Tag clouds of outcomes suggest an emphasis on assessment using scores and scales. Inclusion criteria related to American Spinal Injury Association (ASIA) status and neurological level allowed inclusion of most SCI patients. Age inclusion criteria were most commonly 18-65 or older. Consistent with ICCP recommendations, most trials were randomized and controlled, and blinding has increased. Age inclusion criteria skew older than the overall population. ASIA status criteria reflect the population, but neurological lesion criteria could be broadened. Investigators should make trial designs and results available in a complete manner to enable comparisons of populations and outcomes.

Informatics technology mimics ecology: dense, mutualistic collaboration networks are associated with higher publication rates.

Information technology (IT) adoption enables biomedical research. Publications are an accepted measure of research output, and network models can describe the collaborative nature of publication. In particular, ecological networks can serve as analogies for publication and technology adoption. We constructed network models of adoption of bioinformatics programming languages and health IT (HIT) from the literature.We selected seven programming languages and four types of HIT. We performed PubMed searches to identify publications since 2001. We calculated summary statistics and analyzed spatiotemporal relationships. Then, we assessed ecological models of specialization, cooperativity, competition, evolution, biodiversity, and stability associated with publications.Adoption of HIT has been variable, while scripting languages have experienced rapid adoption. Hospital systems had the largest HIT research corpus, while Perl had the largest language corpus. Scripting languages represented the largest connected network components. The relationship between edges and nodes was linear, though Bioconductor had more edges than expected and Perl had fewer. Spatiotemporal relationships were weak. Most languages shared a bioinformatics specialization and appeared mutualistic or competitive. HIT specializations varied. Specialization was highest for Bioconductor and radiology systems. Specialization and cooperativity were positively correlated among languages but negatively correlated among HIT. Rates of language evolution were similar. Biodiversity among languages grew in the first half of the decade and stabilized, while diversity among HIT was variable but flat. Compared with publications in 2001, correlation with publications one year later was positive while correlation after ten years was weak and negative.Adoption of new technologies can be unpredictable. Spatiotemporal relationships facilitate adoption but are not sufficient. As with ecosystems, dense, mutualistic, specialized co-habitation is associated with faster growth. There are rapidly changing trends in external technological and macroeconomic influences. We propose that a better understanding of how technologies are adopted can facilitate their development.

Dynamic, multi-level network models of clinical trials.

While networks models have often been applied to complex biological systems, they are increasingly being implemented to investigate clinical questions. Clinical trials have been studied extensively by traditional statistical methods but never, to our knowledge, using networks. We obtained data for 6,847 clinical trials from five "Nervous System Diseases" (NSD) and five "Behaviors and Mental Disorders" (BMD) from the clinicaltrials.gov registry. We constructed networks of diseases and interventions for visualization and analysis using Cytoscape software. To standardize nomenclature and enable multi-level annotation, we used MeSH and UMLS terms. We then constructed separate BMD and NSD networks to study dynamics over time. To assess how topology features related to clinical significance, we constructed a sub-network of Multiple Sclerosis and Alzheimer's trials and identified which trials had been published in high-profile medical journals. We found that the BMD network has evolved into a large, decentralized topology and does not distinctly reflect the five diseases by which it was defined, while the NSD network does, though other diseases and sub-phenotypes have emerged as areas of research. We also found that high-profile trials have distinctive network characteristics. Future work is needed to address mathematical questions such as scale-dependence of network features, clinical questions such as trial design optimization, and methodological questions such as data quality improvement.

Clinical and biological data integration for biomarker discovery.

Biomarkers hold promise for increasing success rates of clinical trials. Biomarker discovery requires searching for associations across a spectrum of data. The field of biomedical data integration has made strides in developing management and analysis tools for structured biological data, but best practices are still evolving for the integration of high-throughput data with less structured clinical data. Integrated repositories are needed to support data analysis, storage and access. We describe a data integration strategy that implements a clinical and biological database and a wiki interface. We integrated parameters across clinical trials and associated genetic, gene expression and protein data. We provide examples to illustrate the utility of data integration to explore disease heterogeneity and develop predictive biomarkers.

Race\ethnicity and outcome after traumatic brain injury at a single, diverse center.

BACKGROUND: Many factors may predict mortality and disability after traumatic brain injury (TBI), including age and injury severity. However, the role of race\ethnicity has typically been studied tangentially or in homogeneous settings. We investigated whether race\ethnicity was associated with medical outcomes at a single, diverse center. METHODS: We retrospectively identified patients with TBI older than 17 years with blunt injuries admitted to a Level I trauma center from 2001 to 2004. Glasgow Outcome Scale (GOS) was used to determine outcome at discharge. We performed multivariable logistic regression on two measures of outcome by dichotomizing Glasgow Outcome Scale scores. RESULTS: We identified 357 patients with TBI from five categories: whites (46.2%), Asians (19.9%), Hispanics (17.9%), blacks (10.9%), and other\unknown (5.0%). Without adjusting for other factors, Asians experienced higher mortality (odds ratio [OR] = 2.25, p = 0.01) compared with whites but not degree of disability. After adjusting for age and Injury Severity Score, a weaker trend remained for higher mortality in Asians (OR = 1.38, p = 0.35), and after excluding cases of assault, the finding was again significant (OR = 2.00, p = 0.04). We also confirmed the recently reported OR of higher mortality among blacks (OR = 1.30). Hispanics seemed to do slightly better at discharge. CONCLUSIONS: The question of whether and how race plays a role in TBI is controversial. At a single, diverse center, we found that mortality is associated with race, age, and Injury Severity Score. Future clinical studies will benefit from detailed genotypic and phenotypic data and should balance larger sample sizes with ethnic diversity.

Novel variants in human Aquaporin-4 reduce cellular water permeability.

Cerebral edema contributes significantly to morbidity and mortality after brain injury and stroke. Aquaporin-4 (AQP4), a water channel expressed in astrocytes, plays a key role in brain water homeostasis. Genetic variants in other aquaporin family members have been associated with disease phenotypes. However, in human AQP4, only one non-synonymous single-nucleotide polymorphism (nsSNP) has been reported, with no characterization of protein function or disease phenotype. We analyzed DNA from an ethnically diverse cohort of 188 individuals to identify novel AQP4 variants. AQP4 variants were constructed by site-directed mutagenesis and expressed in cells. Water permeability assays in the cells were used to measure protein function. We identified 24 variants in AQP4 including four novel nsSNPs (I128T, D184E, I205L and M224T). We did not observe the previously documented M278T in our sample. The nsSNPs found were rare ( approximately 1-2% allele frequency) and heterozygous. Computational analysis predicted reduced function mutations. Protein expression and membrane localization were similar for reference AQP4 and the five AQP4 mutants. Cellular assays confirmed that four variant AQP4 channels reduced normalized water permeability to between 26 and 48% of the reference (P < 0.001), while the M278T mutation increased normalized water permeability (P < 0.001). We identified multiple novel AQP4 SNPs and showed that four nsSNPs reduced water permeability. The previously reported M278T mutation resulted in gain of function. Our experiments provide insight into the function of the AQP4 protein. These nsSNPs may have clinical implications for patients with cerebral edema and related disorders.