Genome-Wide Assocation Study in Patients With Brain Injury Associated Fatigue and Altered Cognition (BIAFAC)

Study Overview
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Brief Summary
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The aim of this study is elucidate genetic susceptibility of patients with traumatic brain injury (TBI) and symptoms of Brain Injury Associated Fatigue and Altered Cognition (BIAFAC) using genome-wide association study (GWAS).

Detailed Description
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Annually 1.5 million children and adults experience trauma to the head and brain that results in a TBI. Our research suggests that in a subset of patients, TBI induces pituitary dysfunction and abnormal growth hormone (GH) secretion. The clinical syndrome associated with abnormal GH secretion is characterized by profound fatigue and cognitive dysfunction related to executive function, short-term memory, and processing speed index. Fatigue in these patients is profound and debilitating leaving them unable to maintain their usual activity levels. We have termed this syndrome Brain Injury Associated Fatigue and Altered Cognition (BIAFAC). Our recent work has shown that cognitive and physical dysfunction are significantly improved with recombinant human growth hormone replacement in patients with BIAFAC. Improvements in fatigue often precede ( 3 months) improvements in cognition ( 4-5 months) following rhGH treatment. Although rhGH replacement relieves BIAFAC symptoms, it does not cure the underlying cause, as symptoms reoccur with rhGH withdrawal. Although the mechanisms causing BIAFAC have not been determined, our previous research demonstrated that a year of GH treatment resulted in symptom relief which was associated with changes in brain morphometry and connectivity. These associated brain changes include increased frontal cortical thickness and gray matter volume as well as resting state connectivity changes in regions associated with somatosensory networks The next step to understanding BIAFAC is to develop a biomarker that identifies individuals that are susceptible to developing this syndrome. The University of Michigan maintains a searchable DataDirect database of over 4 million individual patient medical records linked via the Michigan Genomics Initiative (MGI) to genomic data collected from over 70,000 patients. By collaborating with the University of Michigan, we have a unique opportunity to combine their extensive genomic database with the more than 100 UTMB patients we are currently treating for BIAFAC to search for common genetic markers associated with BIAFAC. In order to identify patients in the UM genomic database with BIAFAC, we will develop a risk stratified machine-learning algorithm based on BIAFAC symptoms. Initial use of the algorithm will begin with approximately 9,000 patients in the UM database that have already been identified with a diagnosis code of fatigue and malaise. Once these patients are identified, a select cohort will be contacted to confirm the accuracy of the algorithm in identifying BIAFAC patients. Once we complete the genotyping of UTMB patients with BIAFAC and have identified the patients with BIAFAC in the UM genomic database, a genome-wide association study (GWAS) will be executed to look for common genetic markers Aims: Specific Aim 1: Identify patients in the UM MGI cohort who show positive traits associated with BIAFAC. Patients in the UM Michigan Genomic Initiative (MGI) cohort will be filtered through ICD-9, ICD-10, and CPT codes associated with fatigue, malaise, and other related diagnoses. Natural language processing (NLP) approaches will be developed to parse clinical notes from candidate patients, recognize relevant medical concepts, and combine features to identify candidates. These will be evaluated for algorithmic accuracy using manual review. Specific Aim 2: Develop medical concept mapping of EHR systems across UTMB and UM. Semantic representations of medical concepts in UTMB and UM will be generated based on co-occurrence patterns of these concepts summarized from each site. Statistical methods will be developed to generate a mapping of the medical concepts between UTMB and UM and harmonize the data across institutions leveraging the trained representations. The learned mapping can facilitate the transfer of trained algorithms from one system to another. Specific aim 3: Develop a computable phenotype to identify TBI patients with BIAFAC, combining the concept mapping identified in Aim 2 with clinical note-based features identified in Aim 1. Specific Aim 4: Conduct genetic analysis of the UTMB cohort. The MGI cohort individuals are genotyped on an Infinium Global Screening Array and imputed to contain >10M genetic markers. We will use this data to perform a genome-wide association study (GWAS) of the phenotypes identified in Aim 3 by testing each variant for association while accounting for confounders such as population stratification. Experimental Protocol. The investigators will study subjects (aged 18-70 years) with a history of mild TBI (n=100). All patients presenting with TBI and BIAFAC symptoms will be invited to participate. TBI subjects will have saliva and possibly blood taken for DNA extraction and genotyping, which will be used for the GWAS.

Official Title
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Machine Learning Tools to Identify and Associate Genetic Variants in Patients With Phenotypic Traits of Brain Injury Associated Fatigue and Altered Cognition (BIAFAC)

Conditions
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Traumatic Brain Injury

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: History of TBI History of BIAFAC symptoms Ages 18 to 70 years Exclusion Criteria: 1. Unable or unwilling to give written consent.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 70 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| TBI BIAFAC<br>100 TBI subjects with BIAFAC will be enrolled. No intervention | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Finding of single nucleotide polymorphisms (SNPs) associated with the traumatic brain injury BIAFAC [ Time Frame: Baseline ] | To identify SNPs related to TBI with BIAFAC using logistic regression after controlling for confounders (GWAS statistical significance threshold, P < 5.00*E-08) | Baseline |