The current direction of our study is to develop a screening tool for early identification of signatures related to features of PTSD. Many of the early aspects of PTSD are sleep disorders, Circadian rhythm disruption, uncontrolled anger, somatization, a tendency for substance abuse, and other self-destructive actions. These aspects of PTSD contribute to disruption of family, work and social networks. An objective screening tool, applied early, is aimed to get vulnerable persons into appropriate treatment program and avert those disruptions and legal issues related to substance abuse.
Since PTSD is officially defined by the Psychiatric Medical Community in the “Diagnostic and Statistical Manual of Mental Disorders (DSM–5)” the list of features ‘required’ for a diagnosis of PTSD is divided into 4 categories of symptoms (B-E) and 4 essential characteristics (A, F-H). People with PTSD have different combinations of those characteristics (B-E), a fact which made finding objective molecular signatures difficult. Using a multi-omic study approach applied to specific cohorts for discovery, confirmation, and validation, we obtained sufficient data to apply machine learning techniques in an effort to gain a molecular understanding of various aspects of the unresolved stressors. As a result, we found that we could readily see 2 subgroups and they related to features of the illness. This has enabled us to obtain a better signal for the screening tool and has the potential to aid in determining appropriate therapeutic approaches for the varied conditions seen in PTSD subgroups.