Distinguishing vulnerability and resilience to posttraumatic stress disorder evaluating traumatic experiences, genetic risk and electronic health records.

What distinguishes vulnerability and resilience to posttraumatic stress disorder (PTSD) remains unclear. Levering traumatic experiences reporting, genetic data, and electronic health records (EHR), we investigated and predicted the clinical comorbidities (co-phenome) of PTSD vulnerability and resilience in the UK Biobank (UKB) and All of Us Research Program (AoU), respectively. In 60,354 trauma-exposed UKB participants, we defined PTSD vulnerability and resilience considering PTSD symptoms, trauma burden, and polygenic risk scores. EHR-based phenome-wide association studies (PheWAS) were conducted to dissect the co-phenomes of PTSD vulnerability and resilience. Significant diagnostic endpoints were applied as weights, yielding a phenotypic risk score (PheRS) to conduct PheWAS of PTSD vulnerability and resilience PheRS in up to 95,761 AoU participants. EHR-based PheWAS revealed three significant phenotypes positively associated with PTSD vulnerability (top association "Sleep disorders") and five outcomes inversely associated with PTSD resilience (top association "Irritable Bowel Syndrome"). In the AoU cohort, PheRS analysis showed a partial inverse relationship between vulnerability and resilience with distinct comorbid associations. While PheRSvulnerability associations were linked to multiple phenotypes, PheRSresilience showed inverse relationships with eye conditions. Our study unveils phenotypic differences in PTSD vulnerability and resilience, highlighting that these concepts are not simply the absence and presence of PTSD.

Prior resilience to trauma & coping during the COVID-19 pandemic.

BACKGROUND AND OBJECTIVE: This study examined the potential influence of pre-pandemic psychological resilience on use of approach or avoidant coping styles and strategies to manage stress during the COVID-19 pandemic. We hypothesized that higher resilience would be associated with more approach coping and less avoidant coping. DESIGN AND METHODS: Longitudinal cohort data were from the Nurses' Health Study II, including 13,143 female current and former healthcare professionals with pre-pandemic lifetime trauma. Pre-pandemic resilience was assessed between 2018-2019 and current coping during the outbreak of the pandemic in the United States (May-August 2020). Multiple linear regression model results identified associations between continuous pre-pandemic resilience scores and use of approach and avoidant coping styles, as well as individual coping strategies, adjusting for relevant covariates. RESULTS: Greater resilience was associated with higher use of approach coping (ß = 0.06, 95% CI 0.05, 0.08) and lower use of avoidant coping styles (ß = -0.39, 95% CI -0.41, -0.38). Higher pre-pandemic resilience was also associated with use of eight (distraction [ß = -0.18, 95% CI -0.20, -0.16], substance use [ß = -0.15, 95% CI -0.17, -0.13], behavioral disengagement [ß = -0.29, 95% CI -0.30, -0.27], self-blame [ß = -0.44, 95% CI -0.45, -0.42], emotional support (ß = 0.03, 95% CI 0.01, 0.05), positive reframing [ß = 0.13, 95% CI 0.12, 0.15], humor [ß = 0.03, 95% CI 0.01, 0.05] and religion [ß = 0.06, 95% CI 0.04, 0.08]) of the nine coping strategies in expected directions. CONCLUSION: Findings have important implications for intervention or even prevention efforts to support vulnerable groups, such as women with prior trauma histories, during this and other immensely stressful times. Supporting or building psychological resilience following trauma may promote effective coping in times of future stress.

Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder.

Post-traumatic stress disorder (PTSD) genetics are characterized by lower discoverability than most other psychiatric disorders. The contribution to biological understanding from previous genetic studies has thus been limited. We performed a multi-ancestry meta-analysis of genome-wide association studies across 1,222,882 individuals of European ancestry (137,136 cases) and 58,051 admixed individuals with African and Native American ancestry (13,624 cases). We identified 95 genome-wide significant loci (80 new). Convergent multi-omic approaches identified 43 potential causal genes, broadly classified as neurotransmitter and ion channel synaptic modulators (for example, GRIA1, GRM8 and CACNA1E), developmental, axon guidance and transcription factors (for example, FOXP2, EFNA5 and DCC), synaptic structure and function genes (for example, PCLO, NCAM1 and PDE4B) and endocrine or immune regulators (for example, ESR1, TRAF3 and TANK). Additional top genes influence stress, immune, fear and threat-related processes, previously hypothesized to underlie PTSD neurobiology. These findings strengthen our understanding of neurobiological systems relevant to PTSD pathophysiology, while also opening new areas for investigation.

Pre-Pandemic Resilience to Trauma & COVID-19 Infection in Older Women.

OBJECTIVE: Prior work suggests psychological resilience to trauma may protect not only mental but also physical health. This study examined the relationship of pre-pandemic psychological resilience to lifetime trauma with self-reported COVID-19 infection and symptoms during the early years of the COVID-19 pandemic. METHODS: Data are from 18,670 longitudinal cohort participants in the Nurses' Health Study II. Based on prior evidence that trauma and subsequent distress can increase infection risk and severity, and that psychological assets may offset this risk, we hypothesized higher versus lower psychological resilience to prior trauma would be associated with lower risk for COVID-19 infection. Pre-pandemic resilience was assessed via self-report between 2017-2019 based on self-reported lifetime trauma exposure and psychological health. COVID-19 infection and symptoms were self-reported on 7 questionnaires administered between May 2020 - October 2021, from which we derived a composite outcome measure of probable COVID-19 infection, defined as having 3+ COVID-19 symptoms (out of 9) and/or a positive COVID-19 test result at any single assessment. RESULTS: Multivariable regression revealed significant associations between higher pre-pandemic resilience scores and lower risk for probable COVID-19 infection, adjusting for socio-demographic and COVID-19-related risk factors (RR = 0.90 [95% CI 0.87, 0.93]). Considering subcomponents of the composite COVID-19 infection measure separately, pre-pandemic resilience was significantly associated with lower risk of reported symptoms (RR = 0.83 [95% CI 0.79, 0.88]), but not with a positive test result alone (RR = 0.96 (95% CI 0.91, 1.01]). CONCLUSION: Identifying protective factors for infection risk may help inform psychosocial interventions to improve health outcomes.

Integrating Electronic Health Records and Polygenic Risk to Identify Genetically Unrelated Comorbidities of Schizophrenia That May Be Modifiable.

Background: Patients with schizophrenia have substantial comorbidity that contributes to reduced life expectancy of 10 to 20 years. Identifying modifiable comorbidities could improve rates of premature mortality. Conditions that frequently co-occur but lack shared genetic risk with schizophrenia are more likely to be products of treatment, behavior, or environmental factors and therefore are enriched for potentially modifiable associations. Methods: Phenome-wide comorbidity was calculated from electronic health records of 250,000 patients across 2 independent health care institutions (Vanderbilt University Medical Center and Mass General Brigham); associations with schizophrenia polygenic risk scores were calculated across the same phenotypes in linked biobanks. Results: Schizophrenia comorbidity was significantly correlated across institutions (r = 0.85), and the 77 identified comorbidities were consistent with prior literature. Overall, comorbidity and polygenic risk score associations were significantly correlated (r = 0.55, p = 1.29 × 10-118). However, directly testing for the absence of genetic effects identified 36 comorbidities that had significantly equivalent schizophrenia polygenic risk score distributions between cases and controls. This set included phenotypes known to be consequences of antipsychotic medications (e.g., movement disorders) or of the disease such as reduced hygiene (e.g., diseases of the nail), thereby validating the approach. It also highlighted phenotypes with less clear causal relationships and minimal genetic effects such as tobacco use disorder and diabetes. Conclusions: This work demonstrates the consistency and robustness of electronic health record-based schizophrenia comorbidities across independent institutions and with the existing literature. It identifies known and novel comorbidities with an absence of shared genetic risk, indicating other causes that may be modifiable and where further study of causal pathways could improve outcomes for patients.

Patients with schizophrenia have many co-occurring diseases that contribute substantially to premature mortality of 10 to 20 years. Conditions that are comorbid but lack shared genetic risk with schizophrenia are likely to have causes that are more modifiable. Here, we calculated comorbidity from electronic health records from 2 independent health care institutions and associations with schizophrenia polygenic risk scores across the same phenotypes in linked biobanks. We identified known and novel diseases comorbid with schizophrenia, thereby validating our approach.

Effect of Stress-Related Neural Pathways on the Cardiovascular Benefit of Physical Activity.

BACKGROUND: The mechanisms underlying the psychological and cardiovascular disease (CVD) benefits of physical activity (PA) are not fully understood. OBJECTIVES: This study tested whether PA: 1) attenuates stress-related neural activity, which is known to potentiate CVD and for its role in anxiety/depression; 2) decreases CVD in part through this neural effect; and 3) has a greater impact on CVD risk among individuals with depression. METHODS: Participants from the Mass General Brigham Biobank who completed a PA survey were studied. A subset underwent 18F-fluorodeoxyglucose positron emission tomography/computed tomographic imaging. Stress-related neural activity was measured as the ratio of resting amygdalar-to-cortical activity (AmygAC). CVD events were ascertained from electronic health records. RESULTS: A total of 50,359 adults were included (median age 60 years [Q1-Q3: 45-70 years]; 40.1% male). Greater PA was associated with both lower AmygAC (standardized ß: -0.245; 95% CI: -0.444 to -0.046; P = 0.016) and CVD events (HR: 0.802; 95% CI: 0.719-0.896; P < 0.001) in multivariable models. AmygAC reductions partially mediated PA's CVD benefit (OR: 0.96; 95% CI: 0.92-0.99; P < 0.05). Moreover, PA's benefit on incident CVD events was greater among those with (vs without) preexisting depression (HR: 0.860; 95% CI: 0.810-0.915; vs HR: 0.929; 95% CI: 0.910-0.949; P interaction = 0.011). Additionally, PA above guideline recommendations further reduced CVD events, but only among those with preexisting depression (P interaction = 0.023). CONCLUSIONS: PA appears to reduce CVD risk in part by acting through the brain's stress-related activity; this may explain the novel observation that PA reduces CVD risk to a greater extent among individuals with depression.

Polygenic risk for suicide attempt is associated with lifetime suicide attempt in US soldiers independent of parental risk.

BACKGROUND: Suicide is a leading cause of death worldwide. Whereas some studies have suggested that a direct measure of common genetic liability for suicide attempts (SA), captured by a polygenic risk score for SA (SA-PRS), explains risk independent of parental history, further confirmation would be useful. Even more unsettled is the extent to which SA-PRS is associated with lifetime non-suicidal self-injury (NSSI). METHODS: We used summary statistics from the largest available GWAS study of SA to generate SA-PRS for two non-overlapping cohorts of soldiers of European ancestry. These were tested in multivariable models that included parental major depressive disorder (MDD) and parental SA. RESULTS: In the first cohort, 417 (6.3 %) of 6573 soldiers reported lifetime SA and 1195 (18.2 %) reported lifetime NSSI. In a multivariable model that included parental history of MDD and parental history of SA, SA-PRS remained significantly associated with lifetime SA [aOR = 1.26, 95%CI:1.13-1.39, p < 0.001] per standardized unit SA-PRS]. In the second cohort, 204 (4.2 %) of 4900 soldiers reported lifetime SA, and 299 (6.1 %) reported lifetime NSSI. In a multivariable model that included parental history of MDD and parental history of SA, SA-PRS remained significantly associated with lifetime SA [aOR = 1.20, 95%CI:1.04-1.38, p = 0.014]. A combined analysis of both cohorts yielded similar results. In neither cohort or in the combined analysis was SA-PRS significantly associated with NSSI. CONCLUSIONS: PRS for SA conveys information about likelihood of lifetime SA (but not NSSI, demonstrating specificity), independent of self-reported parental history of MDD and parental history of SA. LIMITATIONS: At present, the magnitude of effects is small and would not be immediately useful for clinical decision-making or risk-stratified prevention initiatives, but this may be expected to improve with further iterations. Also critical will be the extension of these findings to more diverse populations.

Development and multi-site external validation of a generalizable risk prediction model for bipolar disorder.

Bipolar disorder is a leading contributor to disability, premature mortality, and suicide. Early identification of risk for bipolar disorder using generalizable predictive models trained on diverse cohorts around the United States could improve targeted assessment of high risk individuals, reduce misdiagnosis, and improve the allocation of limited mental health resources. This observational case-control study intended to develop and validate generalizable predictive models of bipolar disorder as part of the multisite, multinational PsycheMERGE Network across diverse and large biobanks with linked electronic health records (EHRs) from three academic medical centers: in the Northeast (Massachusetts General Brigham), the Mid-Atlantic (Geisinger) and the Mid-South (Vanderbilt University Medical Center). Predictive models were developed and valid with multiple algorithms at each study site: random forests, gradient boosting machines, penalized regression, including stacked ensemble learning algorithms combining them. Predictors were limited to widely available EHR-based features agnostic to a common data model including demographics, diagnostic codes, and medications. The main study outcome was bipolar disorder diagnosis as defined by the International Cohort Collection for Bipolar Disorder, 2015. In total, the study included records for 3,529,569 patients including 12,533 cases (0.3%) of bipolar disorder. After internal and external validation, algorithms demonstrated optimal performance in their respective development sites. The stacked ensemble achieved the best combination of overall discrimination (AUC = 0.82-0.87) and calibration performance with positive predictive values above 5% in the highest risk quantiles at all three study sites. In conclusion, generalizable predictive models of risk for bipolar disorder can be feasibly developed across diverse sites to enable precision medicine. Comparison of a range of machine learning methods indicated that an ensemble approach provides the best performance overall but required local retraining. These models will be disseminated via the PsycheMERGE Network website.

Genetic Liability to Posttraumatic Stress Disorder Symptoms and Its Association With Cardiometabolic and Respiratory Outcomes.

Importance: Posttraumatic stress disorder (PTSD) has been reported to be a risk factor for several physical and somatic symptoms. However, the genetics of PTSD and its potential association with medical outcomes remain unclear. Objective: To examine disease categories and laboratory tests from electronic health records (EHRs) that are associated with PTSD polygenic scores. Design, Setting, and Participants: This genetic association study was conducted from July 15, 2021, to January 24, 2023, using EHR data from participants across 4 biobanks. The polygenic scores of PTSD symptom severity (PGS-PTSD) were tested with all available phecodes in Vanderbilt University Medical Center's biobank (BioVU), Mass General Brigham (MGB), Michigan Genomics Initiative (MGI), and UK Biobank (UKBB). The significant medical outcomes were tested for overrepresented disease categories and subsequently tested for genetic correlation and 2-sample mendelian randomization (MR) to determine genetically informed associations. Multivariable MR was conducted to assess whether PTSD associations with health outcomes were independent of the genetic effect of body mass index and tobacco smoking. Exposures: Polygenic score of PTSD symptom severity. Main Outcomes and Measures: A total of 1680 phecodes (ie, International Classification of Diseases, Ninth Revision- and Tenth Revision-based phenotypic definitions of health outcomes) across 4 biobanks and 490 laboratory tests across 2 biobanks (BioVU and MGB). Results: In this study including a total of 496 317 individuals (mean [SD] age, 56.8 [8.0] years; 263 048 female [53%]) across the 4 EHR sites, meta-analyzing associations of PGS-PTSD with 1680 phecodes from 496 317 individuals showed significant associations to be overrepresented from mental health disorders (fold enrichment = 3.15; P = 5.81 × 10-6), circulatory system (fold enrichment = 3.32; P = 6.39 × 10-12), digestive (fold enrichment = 2.42; P = 2.16 × 10-7), and respiratory outcomes (fold enrichment = 2.51; P = 8.28 × 10-5). The laboratory measures scan with PGS-PTSD in BioVU and MGB biobanks revealed top associations in metabolic and immune domains. MR identified genetic liability to PTSD symptom severity as an associated risk factor for 12 health outcomes, including alcoholism (ß = 0.023; P = 1.49 × 10-4), tachycardia (ß = 0.045; P = 8.30 × 10-5), cardiac dysrhythmias (ß = 0.016, P = 3.09 × 10-5), and acute pancreatitis (ß = 0.049, P = 4.48 × 10-4). Several of these associations were robust to genetic effects of body mass index and smoking. We observed a bidirectional association between PTSD symptoms and nonspecific chest pain and C-reactive protein. Conclusions and Relevance: Results of this study suggest the broad health repercussions associated with the genetic liability to PTSD across 4 biobanks. The circulatory and respiratory systems association was observed to be overrepresented in all 4 biobanks.

The genetic contribution to the comorbidity of depression and anxiety: a multi-site electronic health records study of almost 178 000 people.

BACKGROUND: Depression and anxiety are common and highly comorbid, and their comorbidity is associated with poorer outcomes posing clinical and public health concerns. We evaluated the polygenic contribution to comorbid depression and anxiety, and to each in isolation. METHODS: Diagnostic codes were extracted from electronic health records for four biobanks [N = 177 865 including 138 632 European (77.9%), 25 612 African (14.4%), and 13 621 Hispanic (7.7%) ancestry participants]. The outcome was a four-level variable representing the depression/anxiety diagnosis group: neither, depression-only, anxiety-only, and comorbid. Multinomial regression was used to test for association of depression and anxiety polygenic risk scores (PRSs) with the outcome while adjusting for principal components of ancestry. RESULTS: In total, 132 960 patients had neither diagnosis (74.8%), 16 092 depression-only (9.0%), 13 098 anxiety-only (7.4%), and 16 584 comorbid (9.3%). In the European meta-analysis across biobanks, both PRSs were higher in each diagnosis group compared to controls. Notably, depression-PRS (OR 1.20 per s.d. increase in PRS; 95% CI 1.18-1.23) and anxiety-PRS (OR 1.07; 95% CI 1.05-1.09) had the largest effect when the comorbid group was compared with controls. Furthermore, the depression-PRS was significantly higher in the comorbid group than the depression-only group (OR 1.09; 95% CI 1.06-1.12) and the anxiety-only group (OR 1.15; 95% CI 1.11-1.19) and was significantly higher in the depression-only group than the anxiety-only group (OR 1.06; 95% CI 1.02-1.09), showing a genetic risk gradient across the conditions and the comorbidity. CONCLUSIONS: This study suggests that depression and anxiety have partially independent genetic liabilities and the genetic vulnerabilities to depression and anxiety make distinct contributions to comorbid depression and anxiety.

Evaluating the impact of modeling choices on the performance of integrated genetic and clinical models.

The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results across studies. Here, we performed multiple modeling experiments integrating clinical and demographic data from electronic health records (EHR) and genetic data to understand which decision points may affect performance. Clinical data in the form of structured diagnostic codes, medications, procedural codes, and demographics were extracted from two large independent health systems and polygenic risk scores (PRS) were generated across all patients with genetic data in the corresponding biobanks. Crohn's disease was used as the model phenotype based on its substantial genetic component, established EHR-based definition, and sufficient prevalence for model training and testing. We investigated the impact of PRS integration method, as well as choices regarding training sample, model complexity, and performance metrics. Overall, our results show that including PRS resulted in higher performance by some metrics but the gain in performance was only robust when combined with demographic data alone. Improvements were inconsistent or negligible after including additional clinical information. The impact of genetic information on performance also varied by PRS integration method, with a small improvement in some cases from combining PRS with the output of a clinical model (late-fusion) compared to its inclusion an additional feature (early-fusion). The effects of other modeling decisions varied between institutions though performance increased with more compute-intensive models such as random forest. This work highlights the importance of considering methodological decision points in interpreting the impact on prediction performance when including PRS information in clinical models.

Shared Genetic Risk in the Association of Screen Time With Psychiatric Problems in Children.

Importance: Children's exposure to screen time has been associated with poor mental health outcomes, yet the role of genetic factors remains largely unknown. Objective: To assess the extent of genetic confounding in the associations between screen time and attention problems or internalizing problems in preadolescent children. Design, Setting, and Participants: This cohort study analyzed data obtained between 2016 and 2019 from the Adolescent Brain Cognitive Development Study at 21 sites in the US. The sample included children aged 9 to 11 years of genetically assigned European ancestry with self-reported screen time. Data were analyzed between November 2021 and September 2023. Exposure: Child-reported daily screen time (in hours) was ascertained from questionnaires completed by the children at baseline. Main Outcomes and Measures: Child psychiatric problems, specifically attention and internalizing problems, were measured with the parent-completed Achenbach Child Behavior Checklist at the 1-year follow-up. Genetic sensitivity analyses model (Gsens) was used, which incorporated polygenic risk scores (PRSs) of both exposure and outcomes as well as either single-nucleotide variant (SNV; formerly single-nucleotide polymorphism)-based heritability or twin-based heritability to estimate genetic confounding. Results: The 4262 children in the sample included 2269 males (53.2%) with a mean (SD) age of 9.9 (0.6) years. Child screen time was associated with attention problems (ß = 0.10 SD; 95% CI, 0.07-0.13 SD) and internalizing problems (ß = 0.03 SD; 95% CI, 0.003-0.06 SD). The television time PRS was associated with child screen time (ß = 0.18 SD; 95% CI, 0.14-0.23 SD), the attention-deficit/hyperactivity disorder PRS was associated with attention problems (ß = 0.13 SD; 95% CI, 0.10-0.16 SD), and the depression PRS was associated with internalizing problems (ß = 0.10 SD; 95% CI, 0.07-0.13 SD). These PRSs were associated with cross-traits, suggesting genetic confounding. Estimates using PRSs and SNV-based heritability showed that genetic confounding accounted for most of the association between child screen time and attention problems and for 42.7% of the association between child screen time and internalizing problems. When PRSs and twin-based heritability estimates were used, genetic confounding fully explained both associations. Conclusions and Relevance: Results of this study suggest that genetic confounding may explain a substantial part of the associations between child screen time and psychiatric problems. Genetic confounding should be considered in sociobehavioral studies of modifiable factors for youth mental health.

Meta-Analyses of Genome-Wide Association Studies for Postpartum Depression.

OBJECTIVE: Postpartum depression (PPD) is a common subtype of major depressive disorder (MDD) that is more heritable, yet is understudied in psychiatric genetics. The authors conducted meta-analyses of genome-wide association studies (GWASs) to investigate the genetic architecture of PPD. METHOD: Meta-analyses were conducted on 18 cohorts of European ancestry (17,339 PPD cases and 53,426 controls), one cohort of East Asian ancestry (975 cases and 3,780 controls), and one cohort of African ancestry (456 cases and 1,255 controls), totaling 18,770 PPD cases and 58,461 controls. Post-GWAS analyses included 1) single-nucleotide polymorphism (SNP)-based heritability ([Formula: see text]), 2) genetic correlations between PPD and other phenotypes, and 3) enrichment of the PPD GWAS findings in 27 human tissues and 265 cell types from the mouse central and peripheral nervous system. RESULTS: No SNP achieved genome-wide significance in the European or the trans-ancestry meta-analyses. The [Formula: see text] of PPD was 0.14 (SE=0.02). Significant genetic correlations were estimated for PPD with MDD, bipolar disorder, anxiety disorders, posttraumatic stress disorder, insomnia, age at menarche, and polycystic ovary syndrome. Cell-type enrichment analyses implicate inhibitory neurons in the thalamus and cholinergic neurons within septal nuclei of the hypothalamus, a pattern that differs from MDD. CONCLUSIONS: While more samples are needed to reach genome-wide levels of significance, the results presented confirm PPD as a polygenic and heritable phenotype. There is also evidence that despite a high correlation with MDD, PPD may have unique genetic components. Cell enrichment results suggest GABAergic neurons, which converge on a common mechanism with the only medication approved by the U.S. Food and Drug Administration for PPD (brexanolone).

Comorbidity Profiles of Posttraumatic Stress Disorder Across the Medical Phenome.

Background: Prior epidemiological research has linked PTSD with specific physical health problems, but the comprehensive landscape of medical conditions associated with PTSD remains uncharacterized. Electronic health records (EHR) provide an opportunity to overcome prior clinical knowledge gaps and uncover associations with biological relevance that potentially vary by sex. Methods: PTSD was defined among biobank participants (total N=123,365) in a major healthcare system using two ICD code-based definitions: broad (1+ PTSD or acute stress codes versus 0; NCase=14,899) and narrow (2+ PTSD codes versus 0; NCase=3,026). Using a phenome-wide association (PheWAS) design, we tested associations between each PTSD definition and all prevalent disease umbrella categories, i.e., phecodes. We also conducted sex-stratified PheWAS analyses including a sex-by-diagnosis interaction term in each logistic regression. Results: A substantial number of phecodes were significantly associated with PTSDNarrow (61%) and PTSDBroad (83%). While top associations were shared between the two definitions, PTSDBroad captured 334 additional phecodes not significantly associated with PTSDNarrow and exhibited a wider range of significantly associated phecodes across various categories, including respiratory, genitourinary, and circulatory conditions. Sex differences were observed, in that PTSDBroad was more strongly associated with osteoporosis, respiratory failure, hemorrhage, and pulmonary heart disease among male patients, and with urinary tract infection, acute pharyngitis, respiratory infections, and overweight among female patients. Conclusions: This study provides valuable insights into a diverse range of comorbidities associated with PTSD, including both known and novel associations, while highlighting the influence of sex differences and the impact of defining PTSD using EHR.

Discovery of 95 PTSD loci provides insight into genetic architecture and neurobiology of trauma and stress-related disorders.

Posttraumatic stress disorder (PTSD) genetics are characterized by lower discoverability than most other psychiatric disorders. The contribution to biological understanding from previous genetic studies has thus been limited. We performed a multi-ancestry meta-analysis of genome-wide association studies across 1,222,882 individuals of European ancestry (137,136 cases) and 58,051 admixed individuals with African and Native American ancestry (13,624 cases). We identified 95 genome-wide significant loci (80 novel). Convergent multi-omic approaches identified 43 potential causal genes, broadly classified as neurotransmitter and ion channel synaptic modulators (e.g., GRIA1, GRM8, CACNA1E ), developmental, axon guidance, and transcription factors (e.g., FOXP2, EFNA5, DCC ), synaptic structure and function genes (e.g., PCLO, NCAM1, PDE4B ), and endocrine or immune regulators (e.g., ESR1, TRAF3, TANK ). Additional top genes influence stress, immune, fear, and threat-related processes, previously hypothesized to underlie PTSD neurobiology. These findings strengthen our understanding of neurobiological systems relevant to PTSD pathophysiology, while also opening new areas for investigation.

Loneliness and depression: bidirectional mendelian randomization analyses using data from three large genome-wide association studies.

Major depression (MD) is a serious psychiatric illness afflicting nearly 5% of the world's population. A large correlational literature suggests that loneliness is a prospective risk factor for MD; correlational assocations of this nature may be confounded for a variety of reasons. This report uses Mendelian Randomization (MR) to examine potentially causal associations between loneliness and MD. We report on analyses using summary statistics from three large genome wide association studies (GWAS). MR analyses were conducted using three independent sources of GWAS summary statistics. In the first set of analyses, we used available summary statistics from an extant GWAS of loneliness to predict MD risk. We used two sources of outcome data: the Psychiatric Genomics Consortium (PGC) meta-analysis of MD (PGC-MD; N = 142,646) and the Million Veteran Program (MVP-MD; N = 250,215). Finally, we reversed analyses using data from the MVP and PGC samples to identify risk variants for MD and used loneliness outcome data from UK Biobank. We find robust evidence for a bidirectional causal relationship between loneliness and MD, including between loneliness, depression cases status, and a continuous measure of depressive symptoms. The estimates remained significant across several sensitivity analyses, including models that account for horizontal pleiotropy. This paper provides the first genetically-informed evidence that reducing loneliness may play a causal role in decreasing risk for depressive illness, and these findings support efforts to reduce loneliness in order to prevent or ameliorate MD. Discussion focuses on the public health significance of these findings, especially in light of the SARS-CoV-2 pandemic.

Associations of active-duty mental health trajectories with post-military adjustment: Results from the STARRS Longitudinal Study.

BACKGROUND: Many servicemembers experience difficulties transitioning from military to civilian life. We examined whether changes in mental health observed during active duty were associated with indices of post-military adjustment. METHODS: Survey data from the multi-wave Army STARRS Pre/Post Deployment Study (PPDS; conducted 2012-2014) were linked to follow-up data from wave 1 of the STARRS Longitudinal Study (STARRS-LS1; conducted 2016-2018). Empirical Bayes estimates of intercepts and slopes of posttraumatic stress, problematic anger, and depressive symptoms during the PPDS were extracted from mixed-effects growth models and evaluated as predictors of life stress among 1080 participants who had separated or retired from the Army at STARRS-LS1; and of job satisfaction among 586 veterans who were employed at STARRS-LS1. RESULTS: Higher average levels and larger increases in posttraumatic stress, anger, and depression over the deployment period were each associated with increased stress and (in the case of anger and depression) reduced job satisfaction. Posttraumatic stress and anger slopes were associated with overall stress (b = 5.60, p < 0.01 and b = 15.64, p = 0.04, respectively) and relationship stress (b = 5.50, p = 0.01 and b = 22.86, p = 0.01, respectively) beyond the average levels of those symptoms. LIMITATIONS: Some transition-related difficulties may have resolved before outcome assessment; some measures were not previously validated. CONCLUSIONS: Larger increases in posttraumatic stress and anger over a deployment period were associated with increased stress after leaving the Army, even after controlling for average symptom levels during the same period. Monitoring changes in mental health during active duty may help identify personnel who need additional support to facilitate the military-to-civilian transition.

Posttraumatic Stress Disorder, Depression, and Accelerated Aging: Leukocyte Telomere Length in the Nurses' Health Study II.

Background: Exposure to trauma, posttraumatic stress disorder (PTSD), and depression have been independently associated with leukocyte telomere length (LTL), a cellular marker of aging associated with mortality and age-related diseases. However, the joint contributions of trauma and its psychological sequelae on LTL have not been examined. Methods: We conducted an analysis of LTL in a subset of women from the Nurses' Health Study II (N = 1868). Lifetime exposure to traumatic events, PTSD, and depression was assessed with validated measures. DNA was extracted from peripheral blood leukocytes and telomere repeat copy number to single gene copy number was determined by quantitative real-time polymerase chain reaction telomere assay. Linear regression models assessed the association of trauma, PTSD, and depression with LTL after adjustment for health behaviors and medical conditions. Results: Trauma, PTSD, and depression were not independently associated with LTL in mutually adjusted models. However, individuals with severe psychological distress-characterized by comorbid PTSD and depression-had shorter LTL equivalent to being 7.62 years older (95% CI, 0.02 to 17.97) than participants who had never experienced a traumatic event and were not depressed. Further examination found only an association among individuals with the highest number of PTSD symptoms and comorbid depression equivalent to 9.71 additional years of aging (95% CI, 1.36 to 20.49). No effect was found among individuals meeting the minimum threshold for probable PTSD with comorbid depression. Conclusions: Severe psychological distress, as indicated by the presence of comorbid PTSD and depression, may be associated with shorter LTL.

Identifying modifiable comorbidities of schizophrenia by integrating electronic health records and polygenic risk.

Patients with schizophrenia have substantial comorbidity contributing to reduced life expectancy of 10-20 years. Identifying which comorbidities might be modifiable could improve rates of premature mortality in this population. We hypothesize that conditions that frequently co-occur but lack shared genetic risk with schizophrenia are more likely to be products of treatment, behavior, or environmental factors and therefore potentially modifiable. To test this hypothesis, we calculated phenome-wide comorbidity from electronic health records (EHR) in 250,000 patients in each of two independent health care institutions (Vanderbilt University Medical Center and Mass General Brigham) and association with schizophrenia polygenic risk scores (PRS) across the same phenotypes (phecodes) in linked biobanks. Comorbidity with schizophrenia was significantly correlated across institutions (r = 0.85) and consistent with prior literature. After multiple test correction, there were 77 significant phecodes comorbid with schizophrenia. Overall, comorbidity and PRS association were highly correlated (r = 0.55, p = 1.29×10-118), however, 36 of the EHR identified comorbidities had significantly equivalent schizophrenia PRS distributions between cases and controls. Fifteen of these lacked any PRS association and were enriched for phenotypes known to be side effects of antipsychotic medications (e.g., "movement disorders", "convulsions", "tachycardia") or other schizophrenia related factors such as from smoking ("bronchitis") or reduced hygiene (e.g., "diseases of the nail") highlighting the validity of this approach. Other phenotypes implicated by this approach where the contribution from shared common genetic risk with schizophrenia was minimal included tobacco use disorder, diabetes, and dementia. This work demonstrates the consistency and robustness of EHR-based schizophrenia comorbidities across independent institutions and with the existing literature. It identifies comorbidities with an absence of shared genetic risk indicating other causes that might be more modifiable and where further study of causal pathways could improve outcomes for patients.

Reduced Stress-Related Neural Network Activity Mediates the Effect of Alcohol on Cardiovascular Risk.

BACKGROUND: Chronic stress associates with major adverse cardiovascular events (MACE) via increased stress-related neural network activity (SNA). Light/moderate alcohol consumption (ACl/m) has been linked to lower MACE risk, but the mechanisms are unclear. OBJECTIVES: The purpose of this study was to evaluate whether the association between ACl/m and MACE is mediated by decreased SNA. METHODS: Individuals enrolled in the Mass General Brigham Biobank who completed a health behavior survey were studied. A subset underwent 18F-fluorodeoxyglucose positron emission tomography, enabling assessment of SNA. Alcohol consumption was classified as none/minimal, light/moderate, or high (<1, 1-14, or >14 drinks/week, respectively). RESULTS: Of 53,064 participants (median age 60 years, 60% women), 23,920 had no/minimal alcohol consumption and 27,053 ACl/m. Over a median follow-up of 3.4 years, 1,914 experienced MACE. ACl/m (vs none/minimal) associated with lower MACE risk (HR: 0.786; 95% CI: 0.717-0.862; P < 0.0001) after adjusting for cardiovascular risk factors. In 713 participants with brain imaging, ACl/m (vs none/minimal) associated with decreased SNA (standardized beta -0.192; 95% CI: -0.338 to -0.046; P = 0.01). Lower SNA partially mediated the beneficial effect of ACl/m on MACE (log OR: -0.040; 95% CI: -0.097 to -0.003; P < 0.05). Further, ACl/m associated with larger decreases in MACE risk among individuals with (vs without) prior anxiety (HR: 0.60 [95% CI: 0.50-0.72] vs 0.78 [95% CI: 0.73-0.80]; P interaction = 0.003). CONCLUSIONS: ACl/m associates with reduced MACE risk, in part, by lowering activity of a stress-related brain network known for its association with cardiovascular disease. Given alcohol's potential health detriments, new interventions with similar effects on SNA are needed.