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

The University of Texas Medical Branch, Galveston (Other)
Overall Status
Enrolling by invitation ID
University of Michigan (Other)

Study Details

Study Description

Brief Summary

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).

Condition or Disease Intervention/Treatment Phase

    Detailed Description

    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


    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.

    Study Design

    Study Type:
    Anticipated Enrollment :
    100 participants
    Observational Model:
    Time Perspective:
    Official Title:
    Machine Learning Tools to Identify and Associate Genetic Variants in Patients With Phenotypic Traits of Brain Injury Associated Fatigue and Altered Cognition (BIAFAC)
    Actual Study Start Date :
    Jun 21, 2021
    Anticipated Primary Completion Date :
    Dec 31, 2022
    Anticipated Study Completion Date :
    Dec 31, 2022

    Arms and Interventions

    Arm Intervention/Treatment

    100 TBI subjects with BIAFAC will be enrolled. No intervention

    Outcome Measures

    Primary Outcome Measures

    1. Finding of single nucleotide polymorphisms (SNPs) associated with the traumatic brain injury BIAFAC [ Time Frame: Baseline ] [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)

    Eligibility Criteria


    Ages Eligible for Study:
    18 Years to 70 Years
    Sexes Eligible for Study:
    Inclusion Criteria:
    1. History of TBI

    2. History of BIAFAC symptoms

    3. Ages 18 to 70 years

    Exclusion Criteria:
    1. Unable or unwilling to give written consent.

    Contacts and Locations


    Site City State Country Postal Code
    1 University of Texas Medical Branch Galveston Texas United States 77555

    Sponsors and Collaborators

    • The University of Texas Medical Branch, Galveston
    • University of Michigan


    • Principal Investigator: Randall J Urban, MD, University of Texas

    Study Documents (Full-Text)

    None provided.

    More Information


    None provided.
    Responsible Party:
    The University of Texas Medical Branch, Galveston Identifier:
    Other Study ID Numbers:
    • 20-0110
    First Posted:
    Sep 14, 2020
    Last Update Posted:
    Aug 17, 2022
    Last Verified:
    Aug 1, 2022
    Individual Participant Data (IPD) Sharing Statement:
    Plan to Share IPD:
    Studies a U.S. FDA-regulated Drug Product:
    Studies a U.S. FDA-regulated Device Product:
    Additional relevant MeSH terms:

    Study Results

    No Results Posted as of Aug 17, 2022