Molecular signatures of post-traumatic stress disorder in war-zone-exposed veteran and active-duty soldiers
Post-traumatic stress disorder (PTSD) is a multisystem syndrome. Integration of systems-level multi-modal datasets can provide a molecular understanding of PTSD. Proteomic, metabolomic, and epigenomic assays are conducted on blood samples of two cohorts of well-characterized PTSD cases and controls: 340 veterans and 180 active-duty soldiers. All participants had been deployed to Iraq and/or Afghanistan and exposed to military-service-related criterion A trauma. Molecular signatures are identified from a discovery cohort of 218 veterans (109/109 PTSD+/-). Identified molecular signatures are tested in 122 separate veterans (62/60 PTSD+/-) and in 180 active-duty soldiers (PTSD+/-). Molecular profiles are computationally integrated with upstream regulators (genetic/methylation/microRNAs) and functional units (mRNAs/proteins/metabolites). Reproducible molecular features of PTSD are identified, including activated inflammation, oxidative stress, metabolic dysregulation, and impaired angiogenesis. These processes may play a role in psychiatric and physical comorbidities, including impaired repair/wound healing mechanisms and cardiovascular, metabolic, and psychiatric diseases.
Screening for PTSD and TBI in Veterans using Routine Clinical Laboratory Blood Tests
Post-traumatic stress disorder (PTSD) is a mental disorder diagnosed by clinical interviews, self-report measures and neuropsychological testing. Traumatic brain injury (TBI) can have neuropsychiatric symptoms similar to PTSD. Diagnosing PTSD and TBI is challenging and more so for providers lacking specialized training facing time pressures in primary care and other general medical settings. Diagnosis relies heavily on patient self-report and patients frequently under-report or over-report their symptoms due to stigma or seeking compensation. We aimed to create objective diagnostic screening tests utilizing Clinical Laboratory Improvement Amendments (CLIA) blood tests available in most clinical settings. CLIA blood test results were ascertained in 475 male veterans with and without PTSD and TBI following warzone exposure in Iraq or Afghanistan. Using random forest (RF) methods, four classification models were derived to predict PTSD and TBI status. CLIA features were selected utilizing a stepwise forward variable selection RF procedure. The AUC, accuracy, sensitivity, and specificity were 0.730, 0.706, 0.659, and 0.715, respectively for differentiating PTSD and healthy controls (HC), 0.704, 0.677, 0.671, and 0.681 for TBI vs. HC, 0.739, 0.742, 0.635, and 0.766 for PTSD comorbid with TBI vs HC, and 0.726, 0.723, 0.636, and 0.747 for PTSD vs. TBI. Comorbid alcohol abuse, major depressive disorder, and BMI are not confounders in these RF models. Markers of glucose metabolism and inflammation are among the most significant CLIA features in our models. Routine CLIA blood tests have the potential for discriminating PTSD and TBI cases from healthy controls and from each other. These findings hold promise for the development of accessible and low-cost biomarker tests as screening measures for PTSD and TBI in primary care and specialty settings.
Traumatic stress symptoms in family caregivers of patients with acute leukaemia: protocol for a multisite mixed methods, longitudinal, observational study
INTRODUCTION:The diagnosis, progression or recurrence of cancer is often highly traumatic for family caregivers (FCs), but systematic assessments of distress and approaches for its prevention and treatment are lacking. Acute leukaemia (AL) is a life-threatening cancer of the blood, which most often presents acutely, requires intensive treatment and is associated with severe physical symptoms. Consequently, traumatic stress may be common in the FCs of patients with AL. We aim to determine the prevalence, severity, longitudinal course and predictors of traumatic stress symptoms in FCs of patients with AL in the first year after diagnosis, and to understand their lived experience of traumatic stress and perceived support needs. METHODS AND ANALYSIS:This two-site longitudinal, observational, mixed methods study will recruit 223 adult FCs of paediatric or adult patients newly diagnosed with AL from two tertiary care centres. Quantitative data will be collected from self-report questionnaires at enrolment, and 1, 3, 6, 9 and 12 months after admission to hospital for initial treatment. Quantitative data will be analysed using descriptive and machine learning approaches and a multilevel modelling (MLM) approach will be used to confirm machine learning findings. Semi-structured qualitative interviews will be conducted at 3, 6 and 12 months and analysed using a grounded theory approach. ETHICS AND DISSEMINATION:This study is funded by the Canadian Institutes of Health Research (CIHR number PJT 173255) and has received ethical approval from the Ontario Cancer Research Ethics Board (CTO Project ID: 2104). The data generated have the potential to inform the development of targeted psychosocial interventions for traumatic stress, which is a public health priority for high-risk populations such as FCs of patients with haematological malignancies. An integrated and end-of-study knowledge translation strategy that involves FCs and other stakeholders will be used to interpret and disseminate study results.
The Genetic Basis for the Increased Prevalence of Metabolic Syndrome among Post-Traumatic Stress Disorder Patients
Post-traumatic stress disorder (PTSD) is a highly debilitating psychiatric disorder that can be triggered by exposure to extreme trauma. Even if PTSD is primarily a psychiatric condition, it is also characterized by adverse somatic comorbidities. One illness commonly co-occurring with PTSD is Metabolic syndrome (MetS), which is defined by a set of health risk/resilience factors including obesity, elevated blood pressure, lower high-density lipoprotein cholesterol, higher low-density lipoprotein cholesterol, higher triglycerides, higher fasting blood glucose and insulin resistance. Here, phenotypic association between PTSD and components of MetS are tested on a military veteran cohort comprising chronic PTSD presentation (n = 310, 47% cases, 83% male). Consistent with previous observations, we found significant phenotypic correlation between the various components of MetS and PTSD severity scores. To examine if this observed symptom correlations stem from a shared genetic background, we conducted genetic correlation analysis using summary statistics data from large-scale genetic studies. Our results show robust positive genetic correlation between PTSD and MetS (rg[SE] = 0.33 [0.056], p = 4.74E-09), and obesity-related components of MetS (rg = 0.25, SE = 0.05, p = 6.4E-08). Prioritizing genomic regions with larger local genetic correlation implicate three significant loci. Overall, these findings show significant genetic overlap between PTSD and MetS, which may in part account for the markedly increased occurrence of MetS among PTSD patients.
Randomized controlled experimental study of hydrocortisone and D-cycloserine effects on fear extinction in PTSD
Fear extinction underlies prolonged exposure, one of the most well-studied treatments for posttraumatic stress disorder (PTSD). There has been increased interest in exploring pharmacological agents to enhance fear extinction learning in humans and their potential as adjuncts to PE. The objective of such adjuncts is to augment the clinical impact of PE on the durability and magnitude of symptom reduction. In this study, we examined whether hydrocortisone (HC), a corticosteroid, and D-Cycloserine (DCS), an N-methyl-D-aspartate receptor partial agonist, enhance fear extinction learning and consolidation in individuals with PTSD. In a double-blind placebo-controlled 3-group experimental design, 90 individuals with full or subsyndromal PTSD underwent fear conditioning with stimuli that were paired (CS+) or unpaired (CS-) with shock. Extinction learning occurred 72â€‰h later and extinction retention was tested one week after extinction. HC 25â€‰mg, DCS 50â€‰mg or placebo was administered one hour prior to extinction learning. During extinction learning, the DCS and HC groups showed a reduced differential CS+/CS- skin conductance response (SCR) compared to placebo (bâ€‰=â€‰-0.19, CIâ€‰=â€‰-0.01 to -37, pâ€‰=â€‰0.042 and bâ€‰=â€‰-0.25, CIâ€‰=â€‰-08 to -0.43, pâ€‰=â€‰0.005, respectively). A nonsignificant trend for a lower differential CS+/CS- SCR in the DCS group, compared to placebo, (bâ€‰=â€‰-0.25, CIâ€‰=â€‰0.04 to -0.55, pâ€‰=â€‰0.089) was observed at retention testing, one week later. A single dose of HC and DCS facilitated fear extinction learning in participants with PTSD symptoms. While clinical implications have yet to be determined, our findings suggest that glucocorticoids and NMDA agonists hold promise for facilitating extinction learning in PTSD.
Enhancing Discovery of Genetic Variants for Posttraumatic Stress Disorder Through Integration of Quantitative Phenotypes and Trauma Exposure Information
BACKGROUND:Posttraumatic stress disorder (PTSD) is heritable and a potential consequence of exposure to traumatic stress. Evidence suggests that a quantitative approach to PTSD phenotype measurement and incorporation of lifetime trauma exposure (LTE) information could enhance the discovery power of PTSD genome-wide association studies (GWASs). METHODS:A GWAS on PTSD symptoms was performed in 51 cohorts followed by a fixed-effects meta-analysis (NÂ = 182,199 European ancestry participants). A GWAS of LTE burden was performed in the UK Biobank cohort (NÂ = 132,988). Genetic correlations were evaluated with linkage disequilibrium score regression. Multivariate analysis was performed using Multi-Trait Analysis of GWAS. Functional mapping and annotation of leading loci was performed with FUMA. Replication was evaluated using the Million Veteran Program GWAS of PTSD total symptoms. RESULTS:GWASs of PTSD symptoms and LTE burden identified 5 and 6 independent genome-wide significant loci, respectively. There was a 72% genetic correlation between PTSD and LTE. PTSD and LTE showed largely similar patterns of genetic correlation with other traits, albeit with some distinctions. Adjusting PTSD for LTE reduced PTSD heritability by 31%. Multivariate analysis of PTSD and LTE increased the effective sample size of the PTSD GWAS by 20% and identified 4 additional loci. Four of these 9 PTSD loci were independently replicated in the Million Veteran Program. CONCLUSIONS:Through using a quantitative trait measure of PTSD, we identified novel risk loci not previously identified using prior case-control analyses. PTSD and LTE have a high genetic overlap that can be leveraged to increase discovery power through multivariate methods.
A DNA methylation clock associated with age-related illnesses and mortality is accelerated in men with combat PTSD
DNA methylation patterns at specific cytosine-phosphate-guanine (CpG) sites predictably change with age and can be used to derive "epigenetic age", an indicator of biological age, as opposed to merely chronological age. A relatively new estimator, called "DNAm GrimAge", is notable for its superior predictive ability in older populations regarding numerous age-related metrics like time-to-death, time-to-coronary heart disease, and time-to-cancer. PTSD is associated with premature mortality and frequently has comorbid physical illnesses suggestive of accelerated biological aging. This is the first study to assess DNAm GrimAge in PTSD patients. We investigated the acceleration of GrimAge relative to chronological age, denoted "AgeAccelGrim" in combat trauma-exposed male veterans with and without PTSD using cross-sectional and longitudinal data from two independent well-characterized veteran cohorts. In both cohorts, AgeAccelGrim was significantly higher in the PTSD group compared to the control group (Nâ€‰=â€‰162, 1.26 vs -0.57, pâ€‰=â€‰0.001 and Nâ€‰=â€‰53, 0.93 vs -1.60 Years, pâ€‰=â€‰0.008), suggesting accelerated biological aging in both cohorts with PTSD. In 3-year follow-up study of individuals initially diagnosed with PTSD (Nâ€‰=â€‰26), changes in PTSD symptom severity were correlated with AgeAccelGrim changes (râ€‰=â€‰0.39, pâ€‰=â€‰0.049). In addition, the loss of CD28 cell surface markers on CD8â€‰+â€‰T cells, an indicator of T-cell senescence/exhaustion that is associated with biological aging, was positively correlated with AgeAccelGrim, suggesting an immunological contribution to the accelerated biological aging. Overall, our findings delineate cellular correlates of biological aging in combat-related PTSD, which may help explain the increased medical morbidity and mortality seen in this disease.
Pre-deployment risk factors for PTSD in active-duty personnelÂ deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors
Active-duty Army personnel can be exposed to traumatic warzone events and are at increased risk for developing post-traumatic stress disorder (PTSD) compared with the general population. PTSD is associated with high individual and societal costs, but identification of predictive markers to determine deployment readiness and risk mitigation strategies is not well understood. This prospective longitudinal naturalistic cohort study-the Fort Campbell Cohort study-examined the value of using a large multidimensional dataset collected from soldiers prior to deployment to Afghanistan for predicting post-deployment PTSD status. The dataset consisted of polygenic, epigenetic, metabolomic, endocrine, inflammatory and routine clinical lab markers, computerized neurocognitive testing, and symptom self-reports. The analysis was computed on active-duty Army personnel (Nâ€‰=â€‰473) of the 101st Airborne at Fort Campbell, Kentucky. Machine-learning models predicted provisional PTSD diagnosis 90-180 days post deployment (random forest: AUCâ€‰=â€‰0.78, 95% CIâ€‰=â€‰0.67-0.89, sensitivityâ€‰=â€‰0.78, specificityâ€‰=â€‰0.71; SVM: AUCâ€‰=â€‰0.88, 95% CIâ€‰=â€‰0.78-0.98, sensitivityâ€‰=â€‰0.89, specificityâ€‰=â€‰0.79) and longitudinal PTSD symptom trajectories identified with latent growth mixture modeling (random forest: AUCâ€‰=â€‰0.85, 95% CIâ€‰=â€‰0.75-0.96, sensitivityâ€‰=â€‰0.88, specificityâ€‰=â€‰0.69; SVM: AUCâ€‰=â€‰0.87, 95% CIâ€‰=â€‰0.79-0.96, sensitivityâ€‰=â€‰0.80, specificityâ€‰=â€‰0.85). Among the highest-ranked predictive features were pre-deployment sleep quality, anxiety, depression, sustained attention, and cognitive flexibility. Blood-based biomarkers including metabolites, epigenomic, immune, inflammatory, and liver function markers complemented the most important predictors. The clinical prediction of post-deployment symptom trajectories and provisional PTSD diagnosis based on pre-deployment data achieved high discriminatory power. The predictive models may be used to determine deployment readiness and to determine novel pre-deployment interventions to mitigate the risk for deployment-related PTSD.
Correction: A DNA methylation clock associated with age-related illnesses and mortality is accelerated in men with combat PTSD
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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 (Nâ€‰=â€‰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 (Nâ€‰=â€‰59) of the same individuals as well as in two independent, veteran cohorts (Nâ€‰=â€‰54 and Nâ€‰=â€‰38), and an active duty cohort (Nâ€‰=â€‰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.