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.
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.
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.
Neural correlates of anger expression in patients with PTSD
Anger is a common and debilitating symptom of post-traumatic stress disorder (PTSD). Although studies have identified brain circuits underlying anger experience and expression in healthy individuals, how these circuits interact with trauma remains unclear. Here, we performed the first study examining the neural correlates of anger in patients with PTSD. Using a data-driven approach with resting-state fMRI, we identified two prefrontal regions whose overall functional connectivity was inversely associated with anger: the left anterior middle frontal gyrus (aMFG) and the right orbitofrontal cortex (OFC). We then used concurrent TMS-EEG to target the left aMFG parcel previously identified through fMRI, measuring its cortical excitability and causal connectivity to downstream areas. We found that low-anger PTSD patients exhibited enhanced excitability in the left aMFG and enhanced causal connectivity between this region and visual areas. Together, our results suggest that left aMFG activity may confer protection against the development of anger, and therefore may be an intriguing target for circuit-based interventions for anger in PTSD.
Serum brain-derived neurotrophic factor remains elevated after long term follow-up of combat veterans with chronic post-traumatic stress disorder
Attempts to correlate blood levels of brain-derived neurotrophic factor (BDNF) with post-traumatic stress disorder (PTSD) have provided conflicting results. Some studies found a positive association between BDNF and PTSD diagnosis and symptom severity, while others found the association to be negative. The present study investigated whether serum levels of BDNF are different cross-sectionally between combat trauma-exposed veterans with and without PTSD, as well as whether longitudinal changes in serum BDNF differ as a function of PTSD diagnosis over time. We analyzed data of 270 combat trauma-exposed veterans (230 males, 40 females, average age: 33.29Â Â±Â 8.28 years) and found that, at the initial cross-sectional assessment (T0), which averaged 6 years after the initial exposure to combat trauma (SD=2.83 years), the PTSD positive group had significantly higher serum BDNF levels than the PTSD negative controls [31.03 vs. 26.95Â ng/mL, t(268)Â =Â 3.921, pÂ <Â 0.001]. This difference remained significant after excluding individuals with comorbid major depressive disorder, antidepressant users and controlling for age, gender, race, BMI, and time since trauma. Fifty-nine of the male veterans who participated at the first timepoint (T0) were re-assessed at follow-up evaluation (T1), approximately 3 years (SD=0.88 years) after T0. A one-way ANOVA comparing PTSD positive, "subthreshold PTSD" and control groups revealed that serum BDNF remained significantly higher in the PTSD positive group than the control group at T1 [30.05 vs 24.66Â ng/mL, F(2, 56)=Â 3.420, pÂ =Â 0.040]. Serum BDNF levels did not correlate with PTSD symptom severity at either time point within the PTSD group [r(128)Â =Â 0.062, pÂ =Â 0.481 and r(28)Â =Â 0.157, pÂ =Â 0.407]. Serum BDNF did not significantly change over time within subjects [t(56)Â =Â 1.269, pÂ =Â 0.210] nor did the change of serum BDNF from T0 to T1 correlate with change in PTSD symptom severity within those who were diagnosed with PTSD at T0 [r(27)Â =Â -0.250, pÂ =Â 0.192]. Our longitudinal data are the first to be reported in combat PTSD and suggest that higher serum BDNF levels may be a stable biological characteristic of chronic combat PTSD independent of symptom severity.
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: S2â€‰=â€‰75.6 (sd 14.6) and S1â€‰=â€‰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.
Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography
The understanding and treatment of psychiatric disorders, which are known to be neurobiologically and clinically heterogeneous, could benefit from the data-driven identification of disease subtypes. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the default mode network. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets of patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis.