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

Integration of Metabolomic and Brain Imaging Data Highlights Pleiotropy Among Posttraumatic Stress Disorder, Glycoprotein Acetyls, and Pallidum Structure

Background

The development of posttraumatic stress disorder (PTSD) is attributable to the interplay between exposure to severe traumatic events, environmental factors, and biological characteristics. Blood and brain imaging markers have been associated with PTSD. However, to our knowledge, no study has systematically investigated the genetic relationship between PTSD, metabolic biomarkers, and brainwide imaging.

Methods

We integrated genome-wide data informative of PTSD, 233 metabolic biomarkers, and 3935 brain imaging-derived phenotypes (IDPs). Pleiotropy was assessed by applying global and local genetic correlation, colocalization, and genetically inferred causality.

Results

We observed significant genetic overlap between PTSD and glycoprotein acetyls (GlycA) (a stable inflammatory biomarker) in 2 independent cohorts (discovery r g = 0.26, p = 1.00 × 10-4; replication r g = 0.23, p = 5.99 × 10-19). Interestingly, there was no genetic correlation between anxiety and GlycA (p = .33). PTSD and GlycA were both genetically correlated with median T2∗ in the left pallidum (IDP-1444: r g = 0.14, p = 1.39 × 10-5; r g = -0.38, p = 2.50 × 10-3, respectively). Local genetic correlation between PTSD and GlycA was observed in 7 genetic regions (p < 2.0 × 10-5), mapping genes related to immune and stress response, inflammation, and metabolic processes. Furthermore, we identified 1 variant, rs12048743, with evidence of horizontal pleiotropy linking GlycA and IDP-1444 (z IDP-1444 = 17.14, z GlycA = -6.07, theta p = 2.06 × 10-8). Regional colocalization was observed among GlycA, IDP-1444, and tissue-specific transcriptomic regulation for brain frontal cortex and testis (rs12048743-chr1q32.1; posterior probability > 0.8). While we also tested causality between PTSD, metabolomic biomarkers, and brain IDPs, these were not consistent across different genetically informed causal inference methods.

Conclusions

Our findings highlight a new putative pleiotropic mechanism that links systemic inflammation and pallidum structure to PTSD.

Association of Polygenic Risk for Psychiatric Disorders with Cardiometabolic Disease

IMPORTANCE

Clinical diagnoses of psychiatric disorders are associated with cardiometabolic diseases (CMDs) such as type 2 diabetes and ischemic heart diseases. Studying how genetic liability for psychiatric disorders relate to CMD risk will offer novel insight into the relationship between psychiatric disorders and CMDs.

OBJECTIVE

To evaluate the associations between psychiatric polygenic risk scores (PRSs) and clinically diagnosed CMDs while accounting for cross-disorder pleiotropy.

DESIGN SETTING AND PARTICIPANTS

This study computed PRSs for attention deficit-hyperactivity disorder (ADHD), major depressive disorder (MDD), anxiety disorder, post-traumatic stress disorder (PTSD), bipolar disorder, and schizophrenia. The analysis was conducted in three population-based Northern European cohorts: the Swedish Twin Registry (STR, N=17,378 genotyped samples), the Estonian Biobank (EstBB, N=208,383), and the Norwegian Mother, Father and Child Cohort Study (MoBa, N=129,398). Associations between psychiatric PRSs and clinical diagnoses of 10 major CMDs (including metabolic diseases such as hyperlipidemia, obesity, and type 2 diabetes, and cardiovascular diseases such as hypertensive disease, arteriosclerosis, ischemic heart disease, heart failure, thromboembolic disease, cerebrovascular disease, and arrhythmias) were estimated using models that mutually adjusted for all psychiatric PRSs. Supplementary analyses were performed by additionally controlling for self-reported body mass index (BMI). A discordant twin-pair analysis was conducted in the STR (N=70,619) to assess the association between self-reported lifetime MDD and subsequent CMD risk while adjusting for familial factors shared between monozygotic and dizygotic co-twins.

MAIN OUTCOMES AND MEASURES

Psychiatric PRSs were constructed based on both all available genetic risk variants and genome-wide significant risk variants from large-scale GWASs. Clinical diagnoses of psychiatric disorders and CMDs were ascertained through electronic health records (with primary care records used exclusively in the EstBB). Lifetime self-reported MDD in the STR was assessed via the Composite International Diagnostic Interview Short Form.

RESULTS

PRSs for ADHD and MDD were associated with increased risk of all CMDs. The ADHD PRS showed stronger associations with metabolic disease, whereas the MDD PRS showed stronger associations with cardiovascular diseases. PRSs for anxiety disorder, PTSD, and bipolar disorder showed only limited associations with CMDs, while increased levels of schizophrenia PRSs were associated with decreased risk of CMDs. These associations remained after adjustment for BMI. Finally, twins endorsing lifetime MDD were found to have an increased risk of subsequent CMD diagnoses compared to their unexposed co-twins.

CONCLUSIONS AND RELEVANCE

PRSs for ADHD and MDD showed robust associations with risk of CMDs and self-reported MDD was associated with subsequent CMD risk even after adjusting for familial factors shared between co-twins. These findings provide robust evidence for genetic overlap between ADHD and MDD with CMDs.

Towards Scalable Biomarker Discovery in Posttraumatic Stress Disorder: Triangulating Genomic and Phenotypic Evidence from a Health System Biobank

Importance

Biomarkers can potentially improve the diagnosis, monitoring, and treatment of posttraumatic stress disorder (PTSD). However, PTSD biomarkers that are scalable and easily integrated into real-world clinical settings have not been identified.

Objective

To triangulate phenotypic and genomic evidence from a health system biobank with a goal of identifying scalable and clinically relevant biomarkers for PTSD.

Design setting and participants

The analysis was conducted between June to November 2024 using genomic samples and laboratory test results recorded in the Mass General Brigham (MGB) Health System. The analysis included 23,743 European ancestry participants from the nested MGB Biobank study.

Exposures

The first exposure was polygenic risk score (PRS) for PTSD, calculated using the largest available European ancestry genome-wide association study (GWAS), employing a Bayesian polygenic scoring method. The second exposure was a clinical diagnosis of PTSD, determined by the presence of two or more qualifying PTSD phecodes in the longitudinal electronic health records (EHR).

Main outcomes and measures

The primary outcomes were the inverse normal quantile transformed, median lab values of 241 laboratory traits with non-zero h 2 SNP estimates.

Results

Sixteen unique laboratory traits across the cardiometabolic, hematologic, hepatic, and immune systems were implicated in both genomic and phenotypic lab-wide association scans (LabWAS). Two-sample Mendelian randomization analyses provided evidence of potential unidirectional causal effects of PTSD liability on five laboratory traits.

Conclusion and relevance

These findings demonstrate the potential of a triangulation approach to uncover scalable and clinically relevant biomarkers for PTSD.

Shared genetic architecture and bidirectional clinical risks within the psycho-metabolic nexus

BACKGROUND

Increasing evidence suggests a complex interplay between psychiatric disorders and metabolic dysregulations. However, most research has been limited to specific disorder pairs, leaving a significant gap in our understanding of the broader psycho-metabolic nexus.

METHODS

This study leveraged large-scale cohort data and genome-wide association study (GWAS) summary statistics, covering 8 common psychiatric disorders and 43 metabolic traits. We introduced a comprehensive analytical strategy to identify shared genetic bases sequentially, from key genetic correlation regions to local pleiotropy and pleiotropic genes. Finally, we developed polygenic risk score (PRS) models to translate these findings into clinical applications.

FINDINGS

We identified significant bidirectional clinical risks between psychiatric disorders and metabolic dysregulations among 310,848 participants from the UK Biobank. Genetic correlation analysis confirmed 104 robust trait pairs, revealing 1088 key genomic regions, including critical hotspots such as chr3: 47588462-50387742. Cross-trait meta-analysis uncovered 388 pleiotropic single nucleotide variants (SNVs) and 126 shared causal variants. Among variants, 45 novel SNVs were associated with psychiatric disorders and 75 novel SNVs were associated with metabolic traits, shedding light on new targets to unravel the mechanism of comorbidity. Notably, RBM6, a gene involved in alternative splicing and cellular stress response regulation, emerged as a key pleiotropic gene. When psychiatric and metabolic genetic information were integrated, PRS models demonstrated enhanced predictive power.

INTERPRETATION

The study highlights the intertwined genetic and clinical relationships between psychiatric disorders and metabolic dysregulations, emphasising the need for integrated approaches in diagnosis and treatment.

FUNDING

The National Key Research and Development Program of China (2023YFC2506200, SHH). The National Natural Science Foundation of China (82273741, SY).

[A study on post-traumatic stress disorder classification based on multi-atlas multi-kernel graph convolutional network]

Post-traumatic stress disorder (PTSD) presents with complex and diverse clinical manifestations, making accurate and objective diagnosis challenging when relying solely on clinical assessments. Therefore, there is an urgent need to develop reliable and objective auxiliary diagnostic models to provide effective diagnosis for PTSD patients. Currently, the application of graph neural networks for representing PTSD is limited by the expressiveness of existing models, which does not yield optimal classification results. To address this, we proposed a multi-graph multi-kernel graph convolutional network (MK-GCN) model for classifying PTSD data. First, we constructed functional connectivity matrices at different scales for the same subjects using different atlases, followed by employing the k-nearest neighbors algorithm to build the graphs. Second, we introduced the MK-GCN methodology to enhance the feature extraction capability of brain structures at different scales for the same subjects. Finally, we classified the extracted features from multiple scales and utilized graph class activation mapping to identify the top 10 brain regions contributing to classification. Experimental results on seismic-induced PTSD data demonstrated that our model achieved an accuracy of 84.75%, a specificity of 84.02%, and an AUC of 85% in the classification task distinguishing between PTSD patients and non-affected subjects. The findings provide robust evidence for the auxiliary diagnosis of PTSD following earthquakes and hold promise for reliably identifying specific brain regions in other PTSD diagnostic contexts, offering valuable references for clinicians.

Application of polygenic scores to a deeply phenotyped sample enriched for substance use disorders reveals extensive pleiotropy with psychiatric and somatic traits

Co-occurring psychiatric, medical, and substance use disorders (SUDs) are common, but the complex pathways leading to such comorbidities are poorly understood. A greater understanding of genetic influences on this phenomenon could inform precision medicine efforts. We used the Yale-Penn dataset, a cross-sectional sample enriched for individuals with SUDs, to examine pleiotropic effects of genetic liability for psychiatric and somatic traits. Participants completed an in-depth interview that provides information on demographics, environment, medical illnesses, and psychiatric and SUDs. Polygenic scores (PGS) for psychiatric disorders and somatic traits were calculated in European-ancestry (EUR; n = 5691) participants and, when discovery datasets were available, for African-ancestry (AFR; n = 4918) participants. Phenome-wide association studies (PheWAS) were then conducted. In AFR participants, the only PGS with significant associations was bipolar disorder (BD), all of which were with substance use phenotypes. In EUR participants, PGS for major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), schizophrenia (SCZ), body mass index (BMI), coronary artery disease (CAD), and type 2 diabetes (T2D) all showed significant associations, the majority of which were with phenotypes in the substance use categories. For instance, PGSMDD was associated with over 200 phenotypes, 15 of which were depression-related (e.g., depression criterion count), 55 of which were other psychiatric phenotypes, and 126 of which were substance use phenotypes; and PGSBMI was associated with 138 phenotypes, 105 of which were substance related. Genetic liability for psychiatric and somatic traits is associated with numerous phenotypes across multiple categories, indicative of the broad genetic liability of these traits.

Stress-induced NLRP3 inflammasome activation and myelin alterations in the hippocampus of PTSD rats

Inflammatory and myelin changes may contribute to the pathophysiology of post-traumatic stress disorder (PTSD). The NOD-like receptor (NLR) family, pyrin domain-containing protein 3 (NLRP3), a brain inflammasome, is activated in the hippocampus of mice with PTSD. In other psychiatric disorders, NLRP3 expression has been associated with axonal myelination and demyelination. However, the association between NLRP3 and myelin in rats with PTSD remains unclear. Therefore, this study aims to investigate the relationship between the NLRP3 inflammasome and myelin in the hippocampus of rats with PTSD. A rat model of post-traumatic stress disorder was established using the single-prolonged stress (SPS) approach. Hippocampal tissues were collected for the detection of NLRP3 inflammasome-associated proteins and myelin basic protein at 3, 7, and 14 days after SPS. To further explore the relationship between NLRP3 and myelin, the NLRP3-specific inhibitor MCC950 was administered intraperitoneally to rats starting 72 h before SPS, and then alterations in NLRP3 inflammasome-associated proteins and myelin were observed in the PTSD and control groups. We found that NLRP3 and downstream related proteins were activated in the hippocampus of rats 3 days after SPS, and the myelin content in the hippocampus increased after SPS stress. MCC950 reduced the expression of NLRP3-related pathway proteins, improved anxiety behaviour and spatial learning memory impairment, and inhibited the increase in myelin content in the hippocampal region of rats after SPS. In conclusion the study indicates that NLRP3 has a significant role in the hippocampal region of rats with PTSD. Inhibition of the NLRP3 inflammasome could be a potential target for treating PTSD.

Genetics of posttraumatic stress disorder and cardiovascular conditions using Life's Essential 8, Electronic Health Records, and Heart Imaging

BACKGROUND

Patients with post-traumatic stress disorder (PTSD) experience higher risk of adverse cardiovascular (CV) outcomes. This study explores shared loci, and genes between PTSD and CV conditions from three major domains: CV diagnoses from electronic health records (CV-EHR), cardiac and aortic imaging, and CV health behaviors defined in Life's Essential 8 (LE8).

METHODS

We used genome-wide association study (GWAS) of PTSD (N=1,222,882), 246 CV diagnoses based on EHR data from Million Veteran Program (MVP; N=458,061), UK Biobank (UKBB; N=420,531), 82 cardiac and aortic imaging traits (N=26,893), and GWAS of traits defined in the LE8 (N = 282,271 ~ 1,320,016). Shared loci between PTSD and CV conditions were identified using local genetic correlations (rg), and colocalization (shared causal variants). Overlapping genes between PTSD and CV conditions were identified from genetically regulated proteome expression in brain and blood tissues, and subsequently tested to identify functional pathways and gene-drug targets. Epidemiological replication of EHR-CV diagnoses was performed in AllofUS cohort (AoU; N=249,906).

RESULTS

Among the 76 PTSD-susceptibility risk loci, 33 loci exhibited local rg with 45 CV-EHR traits (|rg|≥0.4), four loci with eight heart imaging traits(|rg|≥0.5), and 44 loci with LE8 factors (|rg|≥0.36) in MVP. Among significantly correlated loci, we found shared causal variants (colocalization probability > 80%) between PTSD and 17 CV-EHR (in MVP) at 11 loci in MVP, that also replicated in UKBB and/or other cohorts. Of the 17 traits, the observational analysis in the AoU showed PTSD was associated with 13 CV-EHR traits after accounting for socioeconomic factors and depression diagnosis. PTSD colocalized with eight heart imaging traits on 2 loci and with LE8 factors on 31 loci. Leveraging blood and brain proteome expression, we found 33 and 122 genes, respectively, shared between PTSD and CVD. Blood proteome genes were related to neuronal and immune processes, while the brain proteome genes converged on metabolic and calcium-modulating pathways (FDR p <0.05). Drug repurposing analysis highlighted DRD2, NOS1, GFAP, and POR as common targets of psychiatric and CV drugs.

CONCLUSION

PTSD-CV comorbidities exhibit shared risk loci, and genes involved in tissue-specific regulatory mechanisms.

Estimating the direct effects of the genetic liabilities to bipolar disorder, schizophrenia, and behavioral traits on suicide attempt using a multivariable Mendelian randomization approach

Bipolar disorder (BD) and schizophrenia (SZ) are associated with higher odds of suicide attempt (SA). In this study, we aimed to explore the effect of BD and SZ genetic liabilities on SA, also considering the contribution of behavioral traits, socioeconomic factors, and substance use disorders. Leveraging large-scale genome-wide association data from the Psychiatric Genomics Consortium (PGC) and the UK Biobank (UKB), we conducted a two-sample Mendelian randomization (MR) analysis to evaluate the putative causal effect of BD (41,917 cases, 371,549 controls) and SZ (53,386 cases, 77,258 controls) on SA (26,590 cases, 492,022 controls). Then, we assessed the putative causal effect of BD and SZ on behavioral traits, socioeconomic factors, and substance use disorders. Considering the associations identified, we evaluated the direct causal effect of behavioral traits, socioeconomic factors, and substance use disorders on SA using a multivariable MR approach. The genetic liabilities to BD and SZ were associated with higher odds of SA (BD odds ratio (OR) = 1.24, p = 3.88 × 10-12; SZ OR = 1.09, p = 2.44 × 10-20). However, while the effect of mental distress (OR = 1.17, p = 1.02 × 10-4) and risk-taking (OR = 1.52, p = 0.028) on SA was independent of SZ genetic liability, the BD-SA relationship appeared to account for the effect of these risk factors. Similarly, the association with loneliness on SA was null after accounting for the effect of SZ genetic liability. These findings highlight the complex interplay between genetic risk of psychiatric disorders and behavioral traits in the context of SA, suggesting the need for a comprehensive mental health assessment for high-risk individuals.

Application of polygenic scores to a deeply phenotyped sample enriched for substance use disorders reveals extensive pleiotropy with psychiatric and medical traits

Co-occurring psychiatric, medical, and substance use disorders (SUDs) are common, but the complex pathways leading to such comorbidities are poorly understood. A greater understanding of genetic influences on this phenomenon could inform precision medicine efforts. We used the Yale-Penn dataset, a cross-sectional sample enriched for individuals with SUDs, to examine pleiotropic effects of genetic liability for psychiatric and medical traits. Participants completed an in-depth interview that provides information on demographics, environment, medical illnesses, and psychiatric and SUDs. Polygenic scores (PGS) for psychiatric disorders and medical traits were calculated in European-ancestry (EUR; n=5,691) participants and, when discovery datasets were available, for African-ancestry (AFR; n=4,918) participants. Phenome-wide association studies (PheWAS) were then conducted. In AFR participants, the only PGS with significant associations was bipolar disorder (BD), all of which were with substance use phenotypes. In EUR participants, PGS for major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), schizophrenia (SCZ), body mass index (BMI), coronary artery disease (CAD), and type 2 diabetes (T2D) all showed significant associations, the majority of which were with phenotypes in the substance use categories. For instance, PGS MDD was associated with over 200 phenotypes, 15 of which were depression-related (e.g., depression criterion count), 55 of which were other psychiatric phenotypes, and 126 of which were substance use phenotypes; and PGS BMI was associated with 138 phenotypes, 105 of which were substance related. Genetic liability for psychiatric and medical traits is associated with numerous phenotypes across multiple categories, indicative of the broad genetic liability of these traits.