# VA-DoD Long-Term Impact of Military-Relevant Brain Injury Consortium (LIMBIC): Phenotypes of Persistent Comorbidity in Post‐9/11 Era Veterans with mTBI

> **NIH VA I01** · VA SALT LAKE CITY HEALTHCARE SYSTEM · 2024 · —

## Abstract

The Chronic Effects of Neurotrauma Warfighter Epidemiology Cohort was developed to identify
phenotypes of comorbidity among deployed Post-9/11 Veterans in order to compare emergence
of neurosensory, neurodegenerative, pain, and mental health comorbidity in Veterans TBI. The
LIMBIC extension of the Warfighter Epidemiology Cohort will extend the work begun by CENC
in which we identified a cohort of Post-9/11 Veterans and identified comorbidity phenotypes. We
also obtained DoD trauma registry (DODTR) data, where available, and Military Health System
(MHS) inpatient, outpatient, and pharmacy data that was included in the DoD Mental Health
Data Cube. We now propose to expand upon this important data source for over 600,000
deployed SM’s to include a broader cohort of Post-9/11 era (deployed and nondeployed
Veterans and additional data sources that provide unique opportunities to examine long-term
comorbidity phenotypes and develop risk models for comorbidities of interest such as
neurodegenerative disease, SUD, psychological comorbidities, and self-harm behaviors.
These data will allow us to accomplish the following specific aims:
 Aim 1: Using “all sources” TBI severity algorithm and NLP/text embedding methods, identify
 phenotypes of mTBI in DoD and DoD+VA data that incorporate acute injury, mechanism of
 injury, and blast exposure.
 Aim 2: Identify prevalence of key comorbidities and outcomes at baseline, before and after
 mTBI exposure, and in VA (where relevant) and compare those rates by TBI severity and
 study group.
 Aim 3: Use deep learning models that incorporate mTBI phenotype, acute and chronic
 treatment approaches, and emergence of diverse comorbidities to develop risk scores for
 poor military outcomes and developing key comorbidities.
 Aim 4: Use deep learning models to identify optimal processes of care for mTBI.
 We will use data in DaVINCI to identify a cohort of Veterans who receive longitudinal VA care
(at least once a year for three or more years between FY2002 and FY19 (at least one of which is
after 2007 when TBI screening was mandated. We will also identify individuals who did not
receive VA care. We will then categorize those with and without VA care as deployed and not
deployed, creating four study groups: a) deployed with VA care; b) deployed without VA care; c)
not deployed with VA care; d) not deployed without VA care. We will compile VA and DoD data
sources and identify key comorbidities (Neuroendocrine dysfunction, substance use disorder,
mental health conditions, pain conditions, sleep conditions, self-harm behaviors) and TBI
characteristics. Those data will be used for machine/deep learning models that will develop TBI
phenotypes, comorbidity phenotypes, and model risk scores for developing key comorbidities,
and optimal processes of care for mTBI.
 Conducting these analyses for these four study groups will inform TBI pathways of care and
illuminate specific target areas to improve acute TBI care and subsequent suppor...

## Key facts

- **NIH application ID:** 10690550
- **Project number:** 5I01RX003443-05
- **Recipient organization:** VA SALT LAKE CITY HEALTHCARE SYSTEM
- **Principal Investigator:** Mary Jo Pugh
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2024
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2019-10-01 → 2025-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10690550

## Citation

> US National Institutes of Health, RePORTER application 10690550, VA-DoD Long-Term Impact of Military-Relevant Brain Injury Consortium (LIMBIC): Phenotypes of Persistent Comorbidity in Post‐9/11 Era Veterans with mTBI (5I01RX003443-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10690550. Licensed CC0.

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