Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT04548180 |
| Other study ID # |
20-0110 |
| Secondary ID |
|
| Status |
Completed |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
June 21, 2021 |
| Est. completion date |
January 18, 2024 |
Study information
| Verified date |
January 2024 |
| Source |
The University of Texas Medical Branch, Galveston |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial 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).
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
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.
