Utilization of machine learning for identifying symptom severity military-related PTSD subtypes and their biological correlates.

We sought to find clinical subtypes of posttraumatic stress disorder (PTSD) in veterans 6-10 years post-trauma exposure based on current symptom assessments and to examine whether blood biomarkers could differentiate them. Samples were males deployed to Iraq and Afghanistan studied by the PTSD Systems Biology Consortium: a discovery sample of 74 PTSD cases and 71 healthy controls (HC), and a validation sample of 26 PTSD cases and 36 HC. A machine learning method, random forests (RF), in conjunction with a clustering method, partitioning around medoids, were used to identify subtypes derived from 16 self-report and clinician assessment scales, including the clinician-administered PTSD scale for DSM-IV (CAPS). Two subtypes were identified, designated S1 and S2, differing on mean current CAPS total scores: S2 = 75.6 (sd 14.6) and S1 = 54.3 (sd 6.6). S2 had greater symptom severity scores than both S1 and HC on all scale items. The mean first principal component score derived from clinical summary scales was three times higher in S2 than in S1. Distinct RFs were grown to classify S1 and S2 vs. HCs and vs. each other on multi-omic blood markers feature classes of current medical comorbidities, neurocognitive functioning, demographics, pre-military trauma, and psychiatric history. Among these classes, in each RF intergroup comparison of S1, S2, and HC, multi-omic biomarkers yielded the highest AUC-ROCs (0.819-0.922); other classes added little to further discrimination of the subtypes. Among the top five biomarkers in each of these RFs were methylation, micro RNA, and lactate markers, suggesting their biological role in symptom severity.

Epigenetic biotypes of post-traumatic stress disorder in war-zone exposed veteran and active duty males.

Post-traumatic stress disorder (PTSD) is a heterogeneous condition evidenced by the absence of objective physiological measurements applicable to all who meet the criteria for the disorder as well as divergent responses to treatments. This study capitalized on biological diversity observed within the PTSD group observed following epigenome-wide analysis of a well-characterized Discovery cohort (N = 166) consisting of 83 male combat exposed veterans with PTSD, and 83 combat veterans without PTSD in order to identify patterns that might distinguish subtypes. Computational analysis of DNA methylation (DNAm) profiles identified two PTSD biotypes within the PTSD+ group, G1 and G2, associated with 34 clinical features that are associated with PTSD and PTSD comorbidities. The G2 biotype was associated with an increased PTSD risk and had higher polygenic risk scores and a greater methylation compared to the G1 biotype and healthy controls. The findings were validated at a 3-year follow-up (N = 59) of the same individuals as well as in two independent, veteran cohorts (N = 54 and N = 38), and an active duty cohort (N = 133). In some cases, for example Dopamine-PKA-CREB and GABA-PKC-CREB signaling pathways, the biotypes were oppositely dysregulated, suggesting that the biotypes were not simply a function of a dimensional relationship with symptom severity, but may represent distinct biological risk profiles underpinning PTSD. The identification of two novel distinct epigenetic biotypes for PTSD may have future utility in understanding biological and clinical heterogeneity in PTSD and potential applications in risk assessment for active duty military personnel under non-clinician-administered settings, and improvement of PTSD diagnostic markers.

Multi-omic biomarker identification and validation for diagnosing warzone-related post-traumatic stress disorder.

Post-traumatic stress disorder (PTSD) impacts many veterans and active duty soldiers, but diagnosis can be problematic due to biases in self-disclosure of symptoms, stigma within military populations, and limitations identifying those at risk. Prior studies suggest that PTSD may be a systemic illness, affecting not just the brain, but the entire body. Therefore, disease signals likely span multiple biological domains, including genes, proteins, cells, tissues, and organism-level physiological changes. Identification of these signals could aid in diagnostics, treatment decision-making, and risk evaluation. In the search for PTSD diagnostic biomarkers, we ascertained over one million molecular, cellular, physiological, and clinical features from three cohorts of male veterans. In a discovery cohort of 83 warzone-related PTSD cases and 82 warzone-exposed controls, we identified a set of 343 candidate biomarkers. These candidate biomarkers were selected from an integrated approach using (1) data-driven methods, including Support Vector Machine with Recursive Feature Elimination and other standard or published methodologies, and (2) hypothesis-driven approaches, using previous genetic studies for polygenic risk, or other PTSD-related literature. After reassessment of ~30% of these participants, we refined this set of markers from 343 to 28, based on their performance and ability to track changes in phenotype over time. The final diagnostic panel of 28 features was validated in an independent cohort (26 cases, 26 controls) with good performance (AUC = 0.80, 81% accuracy, 85% sensitivity, and 77% specificity). The identification and validation of this diverse diagnostic panel represents a powerful and novel approach to improve accuracy and reduce bias in diagnosing combat-related PTSD.

Systems biology approach to understanding post-traumatic stress disorder.

Post-traumatic stress disorder (PTSD) is a psychological disorder affecting individuals that have experienced life-changing traumatic events. The symptoms of PTSD experienced by these subjects-including acute anxiety, flashbacks, and hyper-arousal-disrupt their normal functioning. Although PTSD is still categorized as a psychological disorder, recent years have witnessed a multi-directional research effort attempting to understand the biomolecular origins of the disorder. This review begins by providing a brief overview of the known biological underpinnings of the disorder resulting from studies using structural and functional neuroimaging, endocrinology, and genetic and epigenetic assays. Next, we discuss the systems biology approach, which is often used to gain mechanistic insights from the wealth of available high-throughput experimental data. Finally, we provide an overview of the current computational tools used to decipher the heterogeneous types of molecular data collected in the study of PTSD.

Ion-water clusters, bulk medium effects, and ion hydration.

Thermochemistry of gas-phase ion-water clusters together with estimates of the hydration free energy of the clusters and the water ligands are used to calculate the hydration free energy of the ion. Often the hydration calculations use a continuum model of the solvent. The primitive quasichemical approximation to the quasichemical theory provides a transparent framework to anchor such efforts. Here we evaluate the approximations inherent in the primitive quasichemical approach and elucidate the different roles of the bulk medium. We find that the bulk medium can stabilize configurations of the cluster that are usually not observed in the gas phase, while also simultaneously lowering the excess chemical potential of the ion. This effect is more pronounced for soft ions. Since the coordination number that minimizes the excess chemical potential of the ion is identified as the optimal or most probable coordination number, for such soft ions the optimum cluster size and the hydration thermodynamics obtained with and without account of the bulk medium on the ion-water clustering reaction can be different. The ideas presented in this work are expected to be relevant to experimental studies that translate thermochemistry of ion-water clusters to the thermodynamics of the hydrated ion and to evolving theoretical approaches that combine high-level calculations on clusters with coarse-grained models of the medium.