Identifying Suicidal Subtypes and Dynamic Indicators of Increasing and Decreasing Suicide Risk

Sep 01, 2021

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 group whose rates have increased more than those who have never served in the military.

Emerging research suggests that there are several subtypes of suicidal states, and that individuals who fall under these subtypes may follow different pathways to becoming high risk for suicide. They also may respond to treatment interventions in different ways. However, no studies have examined these subtypes using integrated data that includes genetic, environmental, medical, and psychological variables to better understand suicide risk and treatment response.

Identifying risk types, patterns, and expressions to improve detection and prevention

To address that knowledge gap, a STRONG STAR-affiliated research team will analyze data stored in the STRONG STAR Repository, an unparalleled resource that contains data from the consortium’s array of studies with military service members and veterans. That includes genetic, environmental, medical, and psychological variables from over 4,000 military personnel who were assessed before and after deployment.

Using that repository data, the team will conduct analyses (a) to identify subgroups of suicidal military personnel and (b) to identify different patterns of increasing, decreasing, and static suicide risk. Through these analyses, researchers expect to identify genetic and physiological expressions of suicide risk. Importantly, they also hope to provide ways to identify multiple risk models that can be used to improve risk detection and refine suicide prevention interventions.

Understanding risk fluctuations over time and developing “warning systems”

Emerging research also suggests that suicide risk can wax and wane indiscriminately without a clear progression of symptoms. To better understand the process of suicide risk over time and the way that risk typically fluctuates between higher and lower states, the team also will analyze STRONG STAR Repository data from more than 800 individuals who participated in six clinical trials. Those archived datasets each include repeated assessments of depression, posttraumatic stress disorder, and suicide ideation.

The investigators believe that analysis of the clinical trial data will make it possible to estimate the likelihood of a given patient transitioning to a high-risk state at a given point in time. They also hope to develop “warning systems” that identify who will experience increased risk over time, and when.

They expect these data analyses to yield significant advances in detection and monitoring of suicide risk and methods for intervening to prevent suicide in military-affiliated individuals as well as civilians.