# Identification of Demographic, Clinical, and Genetic profiles for Suicidal Behavior in US Veterans

> **NIH VA I01** · IOWA CITY VA MEDICAL CENTER · 2024 · —

## Abstract

Suicidal behavior, which includes both suicide attempts and death by suicide, has become an ever-increasing
public health concern. Approximately 17 Veterans die by suicide every day (1), with rates highest among
younger Veterans (age 18-34). While relatively little is known about its biological basis, epidemiological
studies make it clear that suicidality has a substantial heritable component, with heritability estimates of 30-
50% (2). While there is evidence that this heritability is moderated in part by a liability to psychiatric disorders,
such as mood disorders, other evidence suggests heritable factors independent of psychiatric disorders (3). It
has been observed that the rates of suicidal behavior are particularly high in Veterans with a Serious Mental
Illness (SMI) when assessed using the Columbia Suicide Severity Rating Scale (CSSRS) (4). In the
Cooperative Studies Program (CSP) #572 study, Veterans with bipolar disorder (BPI) have a suicide attempt
rate of 55%, with women Veterans having the highest rate (5). This is compared to the rate of suicide attempt
in the Million Veteran Program (MVP) as a whole (3.4%) (6). Recently, the International Suicide Genetics
Consortium (ISGC) identified a genome-wide association signal on chromosome 7, which was independently
replicated by MVP investigators (7). We are now proposing to conduct a machine learning analysis of the
attempted suicide phenotype in BPI using the CSP#572 and MVP datasets. As with all machine learning
studies based on electronic health record (EHR) data, one critical issue is that the information on rates of BPI
diagnosis and suicidal behavior in the EHR dataset will be incomplete. To circumvent this and make our
subsequent machine learning algorithm more effective, we plan to incorporate and leverage the information
gained through the CSP#572 (5). Importantly, the ~5400 CSP#572 veterans with BPI were genotyped in
parallel with the MVP dataset (5,8) and thoroughly phenotyped using the Structured Clinical Interview for DSM
Disorders (SCID; 9) clinical assessment and the gold standard Columbia Suicide Severity Rating Scale
(CSSRS; 4), generating a rich collection of relevant phenotypic data. This is a clear advantage for the machine
learning project as it gives us more reliable information on the presence or absence of bipolar disorder and
suicidal behavior on which to base our machine learning algorithm. In Aim 1, we propose an analysis utilizing
demographic variables and clinical comorbidities to screen BPI subjects from the CSP#572 and a matched set
of MVP controls for phenotypes relevant to suicidal behavior. Next, we will employ the Polygenic Risk Score
(PRS) approach, which can be used to identify individuals with increased genetic loading for various diseases.
Finally, we propose to generate predictive models of suicidal behavior using machine learning-based
approaches and the Aim 1 sample set. We will then test these results using an independent MVP cohort and in
the Uta...

## Key facts

- **NIH application ID:** 10696662
- **Project number:** 1I01BX006007-01A1
- **Recipient organization:** IOWA CITY VA MEDICAL CENTER
- **Principal Investigator:** VIRGINIA L WILLOUR
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2024
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2024-01-01 → 2027-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10696662

## Citation

> US National Institutes of Health, RePORTER application 10696662, Identification of Demographic, Clinical, and Genetic profiles for Suicidal Behavior in US Veterans (1I01BX006007-01A1). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10696662. Licensed CC0.

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