# NIH grant: Associations of Inflammatory Activity and Disorders with Suicide- Related Outcomes in Veterans (1I01RD002186-01) — retained record

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[original source ↗](https://reporter.nih.gov/project-details/1I01RD002186-01)

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NIH grant 1I01RD002186-01: Associations of Inflammatory Activity and Disorders with Suicide- Related Outcomes in Veterans
Institute: {'code': 'VA', 'abbreviation': 'VA', 'name': 'Veterans Affairs', 'admin_org_id': '12500964', 'admin_funding_url': ''}. PI: Aoife  O'Donovan. Award: $.
Start: 2026-10-01T00:00:00.
Significance to VA: Preventing suicide is a leading priority for the Department of Veterans Affairs (VA).
Veterans have 57-66% higher risk for death by suicide compared to the general population, with an average of
17.6 US Veterans dying by suicide each day. Despite an accumulating literature implicating elevated
inflammation as both a biomarker and mechanism of suicide risk, we know little about the relationship of
inflammatory markers or chronic inflammatory autoimmune (AI) diseases with suicide-related thoughts,
behaviors, and deaths in Veterans. Moreover, we know little about the predictive utility of widely used clinical
inflammatory markers for suicide-related outcomes in any group, including those with AI diseases. Our goal is
to advance understanding of the role of elevated inflammation in suicide risk in order to enhance suicide
prevention efforts for Veterans. The objective of this proposal is to determine if clinical inflammatory markers
and AI disease diagnoses in VA electronic health records (EHR) are associated with increased risk of suicide
ideation, attempts, and deaths in Veterans, and to determine if inflammatory markers specifically predict
suicide among Veterans with AI diseases. Innovation and Impact: A better understanding of the links of
clinical inflammatory markers and AI disease with suicide-related behavior has the potential to: 1) enhance
suicide prediction algorithms such as the groundbreaking Recovery Engagement and Coordination for
Health—Veterans Enhanced Treatment (REACH VET) suicide prediction algorithm currently deployed in VA;
and 2) point us in the direction of new treatment targets and intervention approaches to prevent suicide. Our
proposal is innovative in being the first in suicide-inflammation research to: a) leverage VA administrative
databases that include clinical inflammatory markers, AI disease diagnoses, and uniquely well characterized
suicide-related outcomes; b) use data from routine tests of clinical inflammatory markers; c) conduct focused
analyses in Veterans with chronic immunological diseases; and d) employ survival models. This work will be
particularly impactful because it uses data from existing VA administrative databases and can immediately
inform REACH VET and guide efforts to identify existing interventions that can reduce inflammation and
suicide risk. Specific Aims: Our aims are to determine associations of inflammatory markers (Aim 1) and the
presence of any of 36 AI diseases (Aim 2) with suicide-related ideation, attempts, and deaths, and to
determine if inflammatory markers predict suicide-related outcomes in patients with AI diseases (Aim 3).
Methodology: We propose to leverage VA EHR and administrative databases to determine associations of
clinical inflammatory markers from standard laboratory tests and physician diagnoses of AI diseases with
suicide-related outcomes in Veterans. Inflammatory markers will include high sensitivity C-reactive protein
(hsCRP; primary) and six other inflammatory indices (secondary) captured from laboratory test databases. AI
diseases will include any of 36 physician-diagnosed AI diseases. Based on our preliminary analyses, we
conservatively estimate sample sizes of 712,000 Veterans for Aim 1; 1,080,000 for Aim 2; and 145,750 for
Aim 3. We will apply rigorous Cox proportional hazards survival models with competing risks to determine
associations between time-updated exposures (i.e., inflammatory markers, AI disease diagnoses) and time-to-
event suicide-related outcomes. Covariates will include sociodemographic factors, psychiatric and medical
diseases, medications, and healthcare utilization, as well as variables from the REACH VET algorithm. Path to
Translation/Implementation: This project will bridge our basic understanding of the role of inflammation in
suicide risk with the existing ecosystem of suicide prevention efforts and clinical practice in VA. Next steps will
include informing the REACH VET program about the potential of laboratory test data to improve suicide
prediction, extension of our methods to other inflammatory risk factors for suicide, and drug repurposing
studies in VA databases to identify interventions to prevent suicide.
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