The soldiers that face combat are at high risk for the potentially significant repercussions of combat stress. Combat stress can lead to a number of impactful emotional and cognitive conditions, most notably Posttraumatic Stress Disorder (PTSD), Major Depressive Disorder (MDD), Generalized Anxiety Disorder (GAD), and mild Traumatic Brain Injury (mTBI). While there have been attempts to match a specific neurobiological pattern to a specific DSM identified disorder, the pursuit has met limited success. Clinically, different DSM diagnoses are often approached with similar treatments with similar response rates (~33-50%). Clinically groups are identified my sets behaviors (DSM disorders) and research has aimed to find the neurobiological underpinnings of these behavior defined groups.
Our aim i s to instead identify neurobiological groups in the context of underlying neurobiological and with the long term goal of improving response rates to medical trials be clustering of relevant features. However, due to the complex relationship between the neurobiological variables a simple linear relationship or risk score is not appropriate. Here we present a novel approach in which we define neurobiologically distinct subgroups ? based on the most feasible, most robust, and most likely to relate to treatment outcomes ? in these Veterans with combat related psychiatric distress. We have selected a set of brain imaging, molecular biology, and physiological markers such that measures will not be influenced by current clinical models. We will then seek to determine robust subgroups from this model-based hierarchical clustering approach. Next, we contrast our neurobiologically defined groups with traditional groups or general response. Finally, to help best understand the available data and feed forward for future studies, we will run a supervised machine learning (random forest) to determine the optimal variables and groups to predict treatment response. We have opted to solely test sertraline, as this is the most commonly prescribed medication in this population at the San Diego VA mental health clinics (FDA approved for MDD and PTSD). The model-based clustering approach allows us to look at the non-linear relationship between variables of interest and foster an attempt to better link clinical research and clinical practice to best benefit our Veteran population.

Public Health Relevance

In the proposed Merit Award, we aim to collect brain imaging, molecular biological, and physiological data on Veterans about to initiate a trial of SSRI. This proposal has the specific aims of identifying neurobiologically defined subgroups ? rather than DSM behavior based groups ? with the goal of better predicting medication response based on neurobiology. All Veterans treated in clinics will have had combat exposure and are anticipated to reach diagnostic criteria for PTSD, MDD, and/or GAD. The neurobiologically defined subgroups will be contrasted with the DSM identified groups (PTSD, GAD, MDD, or mTBI) with respect to medication efficacy. We have selected a set of variables based on prior literature and preliminary data. As a rich set of variables will be collected, a final analysis will be performed to determine which variables best predicted treatment response. The primary purpose of this grant is to link the advances in research measures and understanding of psychiatric disease following combat stress with actual clinical outcomes.

National Institute of Health (NIH)
Veterans Affairs (VA)
Non-HHS Research Projects (I01)
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Mental Health and Behavioral Science B (MHBB)
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VA San Diego Healthcare System
San Diego
United States
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