Faculty Bibliography

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.

BACKGROUND:We reanalyzed a multisite 26-week randomized double-blind placebo-controlled clinical trial of 600 mg twice-a-day Gabapentin Enacarbil Extended-Release (GE-XR), a gabapentin prodrug, designed to evaluate safety and efficacy for treating alcohol use disorder. In the original analysis (n = 338), published in 2019, GE-XR did not differ from placebo. Our aim is to advance precision medicine by identifying likely responders to GE-XR from the trial data and to determine for likely responders if GE-XR is causally superior to placebo. METHODS:The primary outcome measure in the reanalysis is the reduction from baseline of the number of heavy drinking days (ΔHDD). Baseline features including measures of alcohol use, anxiety, depression, mood states, sleep, and impulsivity were used in a random forest (RF) model to predict ΔHDD to treatment with GE-XR based on those assigned to GE-XR. The resulting RF model was used to obtain predicted outcomes for those randomized to GE-XR and counterfactually to those randomized to placebo. Likely responders to GE-XR were defined as those predicted to have a reduction of 14 days or more. Tests of causal superiority of GE-XR to placebo were obtained for likely responders and for the whole sample. RESULTS:For likely responders, GE-XR was causally superior to placebo (p < 0.0033), while for the whole sample, there was no difference. Likely responders exhibited improved outcomes for the related outcomes of percent HDD and drinks per week. Compared with unlikely responders, at baseline likely responders had higher HDDs; lower levels of anxiety, depression, and general mood disturbances; and higher levels of cognitive and motor impulsivity. CONCLUSIONS:There are substantial causal benefits of treatment with GE-XR for a subset of patients predicted to be likely responders. The likely responder statistical paradigm is a promising approach for analyzing randomized clinical trials to advance personalized treatment.

This article reports on a study aimed to elucidate the complex etiology of post-traumatic stress (PTS) in a longitudinal cohort of police officers, by applying rigorous computational causal discovery (CCD) methods with observational data. An existing observational data set was used, which comprised a sample of 207 police officers who were recruited upon entry to police academy training. Participants were evaluated on a comprehensive set of clinical, self-report, genetic, neuroendocrine and physiological measures at baseline during academy training and then were re-evaluated at 12 months after training was completed. A data-processing pipeline-the Protocol for Computational Causal Discovery in Psychiatry (PCCDP)-was applied to this data set to determine a causal model for PTS severity. A causal model of 146 variables and 345 bivariate relations was discovered. This model revealed 5 direct causes and 83 causal pathways (of four steps or less) to PTS at 12 months of police service. Direct causes included single-nucleotide polymorphisms (SNPs) for the Histidine Decarboxylase (HDC) and Mineralocorticoid Receptor (MR) genes, acoustic startle in the context of low perceived threat during training, peritraumatic distress to incident exposure during first year of service, and general symptom severity during training at 1 year of service. The application of CCD methods can determine variables and pathways related to the complex etiology of PTS in a cohort of police officers. This knowledge may inform new approaches to treatment and prevention of critical incident related PTS.

Annually, approximately 30 million patients are discharged from the emergency department (ED) after a traumatic event¹. These patients are at substantial psychiatric risk, with approximately 10-20% developing one or more disorders, including anxiety, depression or post-traumatic stress disorder (PTSD)^2-4. At present, no accurate method exists to predict the development of PTSD symptoms upon ED admission after trauma⁵. Accurate risk identification at the point of treatment by ED services is necessary to inform the targeted deployment of existing treatment^6-9 to mitigate subsequent psychopathology in high-risk populations^10,11. This work reports the development and validation of an algorithm for prediction of post-traumatic stress course over 12 months using two independently collected prospective cohorts of trauma survivors from two level 1 emergency trauma centers, which uses routinely collectible data from electronic medical records, along with brief clinical assessments of the patient's immediate stress reaction. Results demonstrate externally validated accuracy to discriminate PTSD risk with high precision. While the predictive algorithm yields useful reproducible results on two independent prospective cohorts of ED patients, future research should extend the generalizability to the broad, clinically heterogeneous ED population under conditions of routine medical care.

BACKGROUND:Traumatic stress can adversely affect physical and mental health through neurobiological stress response systems. We examined the effects of trauma exposure and posttraumatic stress disorder (PTSD) on telomere length, a biomarker of cellular aging, and volume of the amygdala, a key structure of stress regulation, in combat-exposed veterans. In addition, the relationships of psychopathological symptoms and autonomic function with telomere length and amygdala volume were examined. METHODS:Male combat veterans were categorized as having PTSD diagnosis (n = 102) or no lifetime PTSD diagnosis (n = 111) based on the Clinician-Administered PTSD Scale. Subjects were assessed for stress-related psychopathology, trauma severity, autonomic function, and amygdala volumes by magnetic resonance imaging. RESULTS:A significant interaction was found between trauma severity and PTSD status for telomere length and amygdala volume after adjusting for multiple confounders. Subjects with PTSD showed shorter telomere length and larger amygdala volume than those without PTSD among veterans exposed to high trauma, while there was no significant group difference in these parameters among those exposed to low trauma. Among veterans exposed to high trauma, greater telomere shortening was significantly correlated with greater norepinephrine, and larger amygdala volume was correlated with more severe psychological symptoms and higher heart rates. CONCLUSIONS:These data suggest that the intensity of the index trauma event plays an important role in interacting with PTSD symptomatology and autonomic activity in predicting telomere length and amygdala volume. These results highlight the importance of trauma severity and PTSD status in predicting certain biological outcomes.

Background: That PTSD is a heterogeneous condition is supported by both the failure to identify objective physiological measurements applicable to all who meet criteria for the disorder, and divergent responses associated with PTSD treatments.
Method(s): This study attempted to capitalize on biological diversity observed following epigenome-wide analysis in a well-characterized male veteran Discovery cohort (N=166) consisting of 83 PTSD+ and 83 PTSD- participants, to identify biologically relevant PTSD subtypes (biotypes) that might further improve molecular diagnosis and personalized treatment. Initial analysis revealed associations between DNA methylation (DNAm) profiles and 34 clinical features from which two epigenetically distinct biotypes - G1 and G2 - were derived.
Result(s): The findings were validated by examining participants (N=59) at a 3-year follow-up. Two other independent, cross-sectional veteran cohorts (N=54, and N=38, respectively), and a longitudinal active duty cohort (N = 133) were also used for validation of the initial biotypes. Interestingly, the biological pathways associated with the biotypes appeared to be regulated in opposite directions in comparison to controls. The impact of biotype-specific signal were evaluated in published DNAm markers, including an independent multi-omics biomarker developed using the same veteran cohort. Finally, we demonstrated filtering biotype-specific signal from a prior marker would result in a high-performance marker (AUC of 0.85).
Conclusion(s): 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 treatment-matching and monitoring of clinical outcome. Supported By: This work was supported by funding from the U.S. Army Research Office, through award numbersW911NF-13-1-0376, W911NF-17-2-0086, W911NF-18-2-0056, by the Army Research Laboratory under grant number W911NF-17-1-0069, and from the U.S. Department of Defense under W81XWH-10-1-0021, W81XWH-09-2-0044, and W81XWH-14-1-0043. Keywords: Biotypes, Epigenetic Biomarkers, Subtype Diagnosis, DNA Methylation, PT

Veterans Health Administration (VA) Medical Centers provide excellent care for many veterans. However, there are a number of veterans who are ineligible or choose not to access mental health treatment at the VA. To meet the needs of those veterans and of military family members, private centers have emerged to fill in gaps where care is unavailable or scarce. This paper describes how one such center, the Steven A. Cohen Military Family Center at NYU Langone Health, partnered with the local VA hospital to give one veteran ineligible for free mental health services the care he desperately needed. The case demonstrates the transformative work that can take place when public-private partnerships are forged and evidence-based treatments can be provided in a flexible way. It also illustrates the complexity of many veterans' presentations, which in this case required the therapist to continually challenge her conceptualization as she and the patient navigated different phases of his treatment.

Military families face specific challenges related to military service, deployments, separations, and coming together. The process of reintegration back to civilian life can be challenged by posttraumatic stress and other readjustment difficulties that can affect not only the veteran but the family as a whole. Strengthening bonds and relationships is an important step in recovery. In this paper, the authors review the application of emotionally focused therapy to couples therapy with military couples and identify factors that can facilitate the therapeutic process with this unique population.