Genetic Correlates of Treatment-Resistant Depression: Insights from Polygenic Scores Across Cognitive, Temperamental, and Sleep Traits in the All of US cohort.

Background: Treatment-resistant depression (TRD) is a major challenge in mental health, affecting a significant number of patients and leading to considerable economic and social burdens. The etiological factors contributing to TRD are complex and not fully understood. Objective: To investigate the genetic factors associated with TRD using polygenic scores (PGS) across various traits, and to explore their potential role in the etiology of TRD using large-scale genomic data from the All of Us Research Program (AoU). Methods: Data from 292,663 participants in the AoU were analyzed using a case-cohort design. Treatment resistant depression (TRD), treatment responsive Major Depressive Disorder (trMDD), and all others who have no formal diagnosis of Major Depressive Disorder (non-MDD) were identified through diagnostic codes and prescription patterns. Polygenic scores (PGS) for 61 unique traits from seven domains were used and logistic regressions were conducted to assess associations between PGS and TRD. Finally, Cox proportional hazard models were used to explore the predictive value of PGS for progression rate from the diagnostic event of Major Depressive Disorder (MDD) to TRD. Results: In the discovery set (104128 non-MDD, 16640 trMDD, and 4177 TRD), 44 of 61 selected PGS were found to be significantly associated with MDD, regardless of treatment responsiveness. Eleven of them were found to have stronger associations with TRD than with trMDD, encompassing PGS from domains in education, cognition, personality, sleep, and temperament. Genetic predisposition for insomnia and specific neuroticism traits were associated with increased TRD risk (OR range from 1.05 to 1.15), while higher education and intelligence scores were protective (ORs 0.88 and 0.91, respectively). These associations are consistent across two other independent sets within AoU (n = 104,388 and 63,330). Among 28,964 individuals tracked over time, 3,854 developed TRD within an average of 944 days (95% CI: 883 ~ 992 days) after MDD diagnosis. All eleven previously identified and replicated PGS were found to be modulating the conversion rate from MDD to TRD. Thus, those having higher education PGS would experiencing slower conversion rates than those who have lower education PGS with hazard ratios in 0.79 (80th versus 20th percentile, 95% CI: 0.74 ~ 0.85). Those who had higher insomnia PGS experience faster conversion rates than those who had lower insomnia PGS, with hazard ratios in 1.21 (80th versus 20th percentile, 95% CI: 1.13 ~ 1.30). Conclusions: Our results indicate that genetic predisposition related to neuroticism, cognitive function, and sleep patterns play a significant role in the development of TRD. These findings underscore the importance of considering genetic and psychosocial factors in managing and treating TRD. Future research should focus on integrating genetic data with clinical outcomes to enhance our understanding of pathways leading to treatment resistance.

Early Life Adversity, Microbiome, and Inflammatory Responses.

Early life adversity has a profound impact on physical and mental health. Because the central nervous and immune systems are not fully mature at birth and continue to mature during the postnatal period, a bidirectional interaction between the central nervous system and the immune system has been hypothesized, with traumatic stressors during childhood being pivotal in priming individuals for later adult psychopathology. Similarly, the microbiome, which regulates both neurodevelopment and immune function, also matures during childhood, rendering this interaction between the brain and the immune system even more complex. In this review, we provide evidence for the role of the immune response and the microbiome in the deleterious effects of early life adversity, both in humans and rodent models.

The role of recurrent trauma on post-traumatic stress disorder symptoms and substance use among trauma exposed youth.

Comorbidity between post-traumatic stress disorder (PTSD) and substance use disorder may be explained by a prospective trauma risk conferred by both conditions. The current study modeled concurrent and prospective associations of trauma, PTSD symptoms, and substance use (SU) behavior among trauma exposed youth (ages 8-20). Clinical interviews assessed trauma exposure, PTSD symptom severity, and SU behavior at baseline and at six- and 12-month follow up study visits (N = 2,069). Structural equation models assessed the associations of trauma, PTSD symptoms, and SU behavior. Lifetime trauma was associated with more severe PTSD symptoms and SU behaviors, whereas trauma exposure during the study was only associated with PTSD symptoms. PTSD symptom severity was prospectively associated with trauma exposure. PTSD symptom severity and SU behavior at follow-up study visits were prospectively associated. These results highlight the dynamic interplay between trauma, PTSD symptoms, and SU behavior during youth, a developmental period during which complex psychiatric presentations can have longstanding consequences for health.

Systems biology dissection of PTSD and MDD across brain regions, cell types, and blood.

The molecular pathology of stress-related disorders remains elusive. Our brain multiregion, multiomic study of posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) included the central nucleus of the amygdala, hippocampal dentate gyrus, and medial prefrontal cortex (mPFC). Genes and exons within the mPFC carried most disease signals replicated across two independent cohorts. Pathways pointed to immune function, neuronal and synaptic regulation, and stress hormones. Multiomic factor and gene network analyses provided the underlying genomic structure. Single nucleus RNA sequencing in dorsolateral PFC revealed dysregulated (stress-related) signals in neuronal and non-neuronal cell types. Analyses of brain-blood intersections in >50,000 UK Biobank participants were conducted along with fine-mapping of the results of PTSD and MDD genome-wide association studies to distinguish risk from disease processes. Our data suggest shared and distinct molecular pathology in both disorders and propose potential therapeutic targets and biomarkers.

Post-Roe v Wade psychiatry: legal, clinical, and ethical challenges in psychiatry under abortion bans.

In recent history, the world has witnessed a trend towards liberalization of abortion laws driven by an increasing understanding of the negative personal and public health consequences of criminalizing abortion. By contrast, several countries have recently implemented restrictive reproductive laws, joining the 112 countries where access to abortion care is banned completely or with narrow exceptions. On June 24, 2022, the US Supreme Court ruling in Dobbs v Jackson Women's Health Organization overturned its landmark decisions in Roe v Wade that established abortion until the point of viability of the fetus as a constitutional right. After Roe v Wade having been overturned, it is projected that many women in the USA will be prevented from accessing safe abortion care. Importantly, abortion bans not only impose constraints on patient autonomy, they also restrict physicians' ability to practice evidence-based medicine, which will negatively impact psychiatric care. It is therefore crucial for the practicing psychiatrist to be familiar with this new legal landscape. In this Personal View, we aim to provide a topical overview to help clinicians gain a clear understanding of legal, clinical, and ethical responsibilities, focusing on the USA. We also discuss the reality that psychiatrists might be called upon to determine medical necessity for an abortion on psychiatric grounds, which is new for most US psychiatrists. We predict that psychiatrists will be confronted with very difficult situations in which lawful and ethical conduct might be incongruent, and that abortion bans will result in greater numbers of patients needing psychiatric care from a system that is ill-prepared for additional demands.

The utility of measuring daily hassles and uplifts in understanding outcomes to treatments for major depressive disorder.

Little is known about the effects of common daily experiences in patients with major depressive disorder (MDD). The Daily Hassles and Uplifts Scale (HUPS) was assessed in 142 treatment-naïve adult MDD outpatients randomized to 12 weeks of treatment with either antidepressant medication (ADM) or Cognitive Behavior Therapy (CBT). Three HUPS measures were analyzed: hassle frequency (HF), uplift frequency (UF), and the mean hassle intensity to mean uplift intensity ratio (MHI:MUI). Remission after treatment was not predicted by these baseline HUPS measures and did not moderate outcomes by treatment type. In contrast, HUPS measures significantly changed with treatment and were impacted by remission status. Specifically, HF and MHI:MUI decreased and UF increased from baseline to week 12, with remission leading to significantly greater decreases in HF and MHI:MUI compared to non-remission. ADM-treated patients demonstrated significant improvements on all three HUPS measures regardless of remission status. In contrast, remitters to CBT demonstrated significant improvements in HF and MHI:MUI but not UF; among CBT non-remitters the only significant change was a reduction in HF. The changes in HUPS measures are consistent with how affective biases are impacted by treatments and support the potential value of increasing attention to positive events in CBT.

Pharmacogenomic Clinical Support Tools for the Treatment of Depression.

OBJECTIVE: In this review, the authors update the 2018 position statement of the American Psychiatric Association Council of Research Workgroup on Biomarkers and Novel Treatments on pharmacogenomic (PGx) tools for treatment selection in depression. METHODS: The literature was reviewed for new clinical trials and meta-analyses, published from 2017 to 2022, of studies using PGx tools for treatment selection in depression. The blinding and control conditions, as well as primary and secondary outcomes and post hoc analyses, were summarized. RESULTS: Eleven new clinical trials and five meta-analyses were identified; all studies had primary outcome measures related to speed or efficacy of treatment response. Three trials (27%) demonstrated efficacy on the primary outcome measure with statistical significance; the three studies used different PGx tools; one study was open-label and the other two were small single-blind trials. Five trials (45%) did not detect efficacy with statistical significance on either primary or secondary outcome measures. Only one trial (9%) used adverse events as a primary outcome measure. All studies had significant limitations; for example, none adopted a fully blinded study design, only two studies attempted to blind the treating clinician, and none incorporated measures to estimate the effectiveness of the blinds or the influence of lack of blinding on the study results. CONCLUSIONS: The addition of these new data do not alter the recommendations of the 2018 report, or the advice of the U.S. Food and Drug Administration, that the evidence does not support the use of currently available combinatorial PGx tools for treatment selection in major depressive disorder. Priority efforts for future studies and the development and testing of effective tools include fully blinded study designs, inclusion of promising genetic variants not currently included in any commercially available tests, and investigation of other uses of pharmacogenomics, such as estimating the likelihood of rare adverse drug effects, rather than increasing the speed or magnitude of drug response.

Optimizing Antidepressant Efficacy: Multimodal Neuroimaging Biomarkers for Prediction of Treatment Response.

Major depressive disorder (MDD) is a common and often severe condition that profoundly diminishes quality of life for individuals across ages and demographic groups. Unfortunately, current antidepressant and psychotherapeutic treatments exhibit limited efficacy and unsatisfactory response rates in a substantial number of patients. The development of effective therapies for MDD is hindered by the insufficiently understood heterogeneity within the disorder and its elusive underlying mechanisms. To address these challenges, we present a target-oriented multimodal fusion framework that robustly predicts antidepressant response by integrating structural and functional connectivity data (sertraline: R2 = 0.31; placebo: R2 = 0.22). Through the model, we identify multimodal neuroimaging biomarkers of antidepressant response and observe that sertraline and placebo show distinct predictive patterns. We further decompose the overall predictive patterns into constitutive network constellations with generalizable structural-functional co-variation, which exhibit treatment-specific association with personality traits and behavioral/cognitive task performance. Our innovative and interpretable multimodal framework provides novel insights into the intricate neuropsychopharmacology of antidepressant treatment and paves the way for advances in precision medicine and development of more targeted antidepressant therapeutics.

Comparing diagnostic criteria for posttraumatic stress disorder in a diverse sample of trauma-exposed youth.

Divergent conceptualization of posttraumatic stress disorder (PTSD) within the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5) and International Statistical Classification of Diseases and Related Health Problems (11th ed..; ICD-11) significantly confounds both research and practice. Using a diverse sample of trauma-exposed youth (N = 1,542, age range: 8-20 years), we compared these two diagnostic approaches along with an expanded version of the ICD-11 PTSD criteria that included three additional reexperiencing symptoms (ICD-11+). Within the sample, PTSD was more prevalent using the DSM-5 criteria (25.7%) compared to the ICD-11 criteria (16.0%), with moderate agreement between these diagnostic systems, κ = .57. The inclusion of additional reexperiencing symptoms (i.e., ICD-11+) reduced this discrepancy in prevalence (24.7%) and increased concordance with DSM-5 criteria, κ = .73. All three PTSD classification systems exhibited similar comorbidity rates with major depressive episode (MDE) or generalized anxiety disorder (GAD; 78.0%-83.6%). Most youths who met the DSM-5 PTSD criteria also met the criteria for ICD-11 PTSD, MDE, or GAD (88.4%), and this proportion increased when applying the ICD-11+ criteria (95.5%). Symptom-level analyses identified reexperiencing/intrusions and negative alterations in cognition and mood symptoms as primary sources of discrepancy between the DSM-5 and ICD-11 PTSD diagnostic systems. Overall, these results challenge assertions that nonspecific distress and diagnostically overlapping symptoms within DSM-5 PTSD inflate comorbidity with depressive and anxiety disorders. Further, they support the argument that the DSM-5 PTSD criteria can be refined and simplified without reducing the overall prevalence of psychiatric diagnoses in youth.

The role of resilience in the development of depression, anxiety, and post-traumatic stress disorder after trauma in children and adolescents.

This investigation, conducted within the Texas Childhood Trauma Research Network, investigated the prospective relationships between resiliency and emergent internalizing symptoms among trauma-exposed youth. The cohort encompassed 1262 youth, aged 8-20, from twelve health-related institutions across Texas, who completed assessments at baseline and one- and six-month follow-ups for resiliency, symptoms of depression, generalized anxiety, posttraumatic stress disorder (PTSD), and other demographic and clinical characteristics. At baseline, greater resilience was positively associated with older age, male (vs female) sex assigned at birth, and history of mental health treatment. Unadjusted for covariates, higher baseline resilience was associated with greater prospective depression and PTSD symptoms but not anxiety symptoms. Upon adjusting for demographic and clinical factors, higher baseline resilience was no longer associated with depression, PTSD, or anxiety symptoms. Our analyses demonstrate that the predictive value of resilience on psychopathology is relatively small compared to more readily observable clinical and demographic factors. These data suggest a relatively minor prospective role of resilience in protecting against internalizing symptoms among trauma-exposed youth and highlight the importance of controlling for relevant youth characteristics when investigating a protective effect of resilience on internalizing symptoms.

The mechanistic divide in psychedelic neuroscience: An unbridgeable gap?

In recent years, psychedelics have generated considerable excitement and interest as potential novel therapeutics for an array of conditions, with the most advanced evidence base in the treatment of certain severe and/or treatment-resistant psychiatric disorders. An array of clinical and pre-clinical evidence has informed our current understanding of how psychedelics produce profound alterations in consciousness. Mechanisms of psychedelic action include receptor binding and downstream cellular and transcriptional pathways, with long-term impacts on brain structure and function-from the level of single neurons to large-scale circuits. In this perspective, we first briefly review and synthesize separate lines of research on potential mechanistic processes underlying the acute and long-term effects of psychedelic compounds, with a particular emphasis on highlighting current theoretical models of psychedelic drug action and their relationships to therapeutic benefits for psychiatric and brain-based disorders. We then highlight an existing area of ongoing controversy we argue is directly informed by theoretical models originating from disparate levels of inquiry, and we ultimately converge on the notion that bridging the current chasm in explanatory models of psychedelic drug action across levels of inquiry (molecular, cellular, circuit, and psychological/behavioral) through innovative methods and collaborative efforts will ultimately yield the comprehensive understanding needed to fully capitalize on the potential therapeutic properties of these compounds.

Latent-state and model-based learning in PTSD.

Post-traumatic stress disorder (PTSD) is characterized by altered emotional and behavioral responding following a traumatic event. In this article, we review the concepts of latent-state and model-based learning (i.e., learning and inferring abstract task representations) and discuss their relevance for clinical and neuroscience models of PTSD. Recent data demonstrate evidence for brain and behavioral biases in these learning processes in PTSD. These new data potentially recast excessive fear towards trauma cues as a problem in learning and updating abstract task representations, as opposed to traditional conceptualizations focused on stimulus-specific learning. Biases in latent-state and model-based learning may also be a common mechanism targeted in common therapies for PTSD. We highlight key knowledge gaps that need to be addressed to further elaborate how latent-state learning and its associated neurocircuitry mechanisms function in PTSD and how to optimize treatments to target these processes.

Features of immunometabolic depression as predictors of antidepressant treatment outcomes: pooled analysis of four clinical trials.

BACKGROUND: Profiling patients on a proposed 'immunometabolic depression' (IMD) dimension, described as a cluster of atypical depressive symptoms related to energy regulation and immunometabolic dysregulations, may optimise personalised treatment. AIMS: To test the hypothesis that baseline IMD features predict poorer treatment outcomes with antidepressants. METHOD: Data on 2551 individuals with depression across the iSPOT-D (n = 967), CO-MED (n = 665), GENDEP (n = 773) and EMBARC (n = 146) clinical trials were used. Predictors included baseline severity of atypical energy-related symptoms (AES), body mass index (BMI) and C-reactive protein levels (CRP, three trials only) separately and aggregated into an IMD index. Mixed models on the primary outcome (change in depressive symptom severity) and logistic regressions on secondary outcomes (response and remission) were conducted for the individual trial data-sets and pooled using random-effects meta-analyses. RESULTS: Although AES severity and BMI did not predict changes in depressive symptom severity, higher baseline CRP predicted smaller reductions in depressive symptoms (n = 376, ßpooled = 0.06, P = 0.049, 95% CI 0.0001-0.12, I2 = 3.61%); this was also found for an IMD index combining these features (n = 372, ßpooled = 0.12, s.e. = 0.12, P = 0.031, 95% CI 0.01-0.22, I2= 23.91%), with a higher - but still small - effect size compared with CRP. Confining analyses to selective serotonin reuptake inhibitor users indicated larger effects of CRP (ßpooled = 0.16) and the IMD index (ßpooled = 0.20). Baseline IMD features, both separately and combined, did not predict response or remission. CONCLUSIONS: Depressive symptoms of people with more IMD features improved less when treated with antidepressants. However, clinical relevance is limited owing to small effect sizes in inconsistent associations. Whether these patients would benefit more from treatments targeting immunometabolic pathways remains to be investigated.

Pharmacogenomic testing for antidepressant treatment selection: lessons learned and roadmap forward.

Pharmacogenomic technology is a developing field with enthusiastic interest and broad application potential. Three large, controlled studies have been published exploring the benefit of pharmacogenomically guided antidepressant treatment selection. Though all three studies did not show significant benefit of using this technology, these studies laid the foundation for further research that should address the limitations of this previous research and currently available commercial platforms. Future research needs to include large scale pharmacogenomic trials with GWAS analytics across diverse groups with attention to cost-effectiveness models, particularly for cases of treatment resistance and polypharmacy. The application of results from these large scale pharmacogenomic trials must also include exploring optimal EHR user interface design.