# Suicide risk modification by statin prescriptions in US Veterans with common inflammation-mediated clinical conditions- a controlled, quasi-randomized epidemiological approach

> **NIH VA I01** · BALTIMORE VA MEDICAL CENTER · 2024 · —

## Abstract

In addition to their metabolic and cardiovascular protective effects, statins reproducibly engage multiple
pathophysiological factors implicated in suicidal behavior - neuroinflammation, increased oxidative
stress, excitotoxicity, and endothelial dysfunction. Add-on statins have been also reported to improve
therapeutic control in physical and mental health. The Veterans’ persistent higher rates of suicide have
remained unabated challenges and, and thus, demanding new ways of understanding and engaging in
preventative efforts. The long-term objective of our group is to uncovering new modifiable targets, novel
and repurposed treatments in suicide prevention, and identifying individuals at risk who are likely to most
benefit from specific interventions. Macro-epidemiological approaches using electronic medical records
in suicide research are irreplaceable for their capability to account for multiple interactive risk factors,
moderators and confounders, and potential for immediate impact. The primary aims of the proposed
research project are to: 1) Estimate potentiating interactions between traumatic brain injury (TBI), a very
common condition in US Veterans, and inflammation-mediated medical conditions (IMCs: allergies,
infection, and autoimmune conditions), in predicting suicide in US Veterans. Our preliminary data
support hypothesizing synergistic interactions. 2) Estimate the suicide protective effect of sustained vs.
unsustained statin treatment 3) Identify demographic and clinical Veteran characteristics and
pharmacological statin features (dose, lipophilia, potency, duration) conducive to stronger attenuating
effects of statins on suicidal behavior. We will test these hypotheses on a Veterans Health Administration
(VHA) retrospective cohort (individuals with clinical encounters in VA Medical Centers nationwide
beginning in 2004 and followed for 13 years) including 5,446,318 Veterans with 28,749 suicides. The
Cox proportional hazard model will be applied to evaluate the interactions between TBI immune
mediated conditions , with Relative Excess Risk due to Interaction (RERI), the Attributable Proportion
(AP) due to interaction, and the Synergy Index (SI) to test synergism on an additive scale (Aim 1). A Cox
proportional hazard model will also be applied to testing risk attenuation with statins, with propensity
scoring for time-independent confounding and marginal structural Cox proportional hazards (Aim 2).
Finally, we will identify the demographic, clinical (diagnostic codes, medications, laboratory markers of
inflammation (e.g., white blood count) and pharmacological characteristic of Veterans expected to benefit
the most from sustained statin treatment using an aggregate machine learning approach (the
SuperLearner integrative methodology). Considering the high prevalence of TBI history and its ongoing
sequelae,( “a silent epidemic”) , especially in the VA, and confirming their synergistic interaction with
IMCs may contribute to developing su...

## Key facts

- **NIH application ID:** 10811570
- **Project number:** 5I01CX002259-02
- **Recipient organization:** BALTIMORE VA MEDICAL CENTER
- **Principal Investigator:** TEODOR T POSTOLACHE
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2024
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2023-01-01 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10811570

## Citation

> US National Institutes of Health, RePORTER application 10811570, Suicide risk modification by statin prescriptions in US Veterans with common inflammation-mediated clinical conditions- a controlled, quasi-randomized epidemiological approach (5I01CX002259-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10811570. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*