# Identifying suicidal subtypes and dynamic indicators of increasing and decreasing suicide risk

> **NIH NIH R01** · OHIO STATE UNIVERSITY · 2020 · $372,987

## Abstract

ABSTRACT
The U.S. general population suicide rate has increased steadily over the past 20 years. Those who have
served in the U.S. Armed Forces are a high risk subgroup among which rates have increased at a faster rate
as compared to those who have never served in the military. Emerging research suggests the existence of
several subtypes of suicidal states. Individuals in different subtypes may follow different pathways to high risk
states and may respond to treatment interventions in different ways. To date, studies have not examined
typologies using integrated datasets that include genetic, environmental, medical, and psychological variables.
To address this knowledge gap, we propose to leverage an archived dataset from the South Texas Region
Organization Network Guiding Studies of Trauma and Resilience (STRONG STAR) Repository, which contains
genetic, environmental, medical, and psychological variables from over 4000 military personnel who were
assessed before and after deployment. Using this dataset, we will (a) identify subgroups of suicidal military
personnel and (b) identify different patterns of increasing, decreasing, and static suicide risk. Results of this
analysis will enable us to identify discrete genotype-phenotype expressions of suicide risk, thereby enabling us
to identify multiple risk models that can be used to improve risk detection and refine suicide prevention
interventions.
Emerging research further indicates the process of suicide risk over time is nonlinear in nature. Unfortunately,
the majority of studies examining the emergence of suicide risk over time have employed research and data
analytic methods that are unable to accurately capture nonlinear change processes. To address this
knowledge gap, we propose to leverage archived datasets from six clinical trials included in the STRONG
STAR Repository (N>800), each of which includes repeated assessments (up to 13 total) of depression,
PTSD, and suicide ideation. Multivariate latent change score models, informed by dynamical systems theory,
will be used to model nonlinear change processes associated with low risk and high risk states. Results of this
analysis will yield posterior probabilities that can estimate the likelihood of a given patient transitioning to a high
risk state at a given point in time, which could lead to the development of “warning systems” that identify who
will experience increased risk over time, and when.
Although the proposed study uses archived data collected from military personnel, the proposed methods can
be translated to other populations and settings, thereby leading to significant advances in the detection and
individuals with elevated risk for suicide.

## Key facts

- **NIH application ID:** 10246660
- **Project number:** 7R01MH117600-04
- **Recipient organization:** OHIO STATE UNIVERSITY
- **Principal Investigator:** Craig J. Bryan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $372,987
- **Award type:** 7
- **Project period:** 2020-07-03 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10246660

## Citation

> US National Institutes of Health, RePORTER application 10246660, Identifying suicidal subtypes and dynamic indicators of increasing and decreasing suicide risk (7R01MH117600-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10246660. Licensed CC0.

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