European Neuropsychopharmacology最新文献

{"title":"PREDICTING SUICIDE ATTEMPT IN THOSE WITH IDEATION USING CLINICAL, GENETIC, AND SOCIAL FACTORS","authors":"Hyunjoon Lee, Michael Ripperger, Ketan Jadhav, Samuel Palmer, Peyton Coleman, Cosmin Bejan, Douglas Rudefer, Colin Walsh","doi":"10.1016/j.euroneuro.2025.08.532","DOIUrl":"10.1016/j.euroneuro.2025.08.532","url":null,"abstract":"<div><div>Understanding and identifying which patients with suicidal ideation (SI) will attempt suicide (SA) is a significant clinical challenge. According to the ideation-to-action framework, SI and SA share many common risk factors but are distinct behaviors. Only a small subset of individuals with SI progress to SA, and this progression depends on an individual’s social context and capability for suicide. Current statistical suicide risk models reliant on clinical factors may be inadequate here. We investigated whether incorporating genetic data and social and behavioral factors (SBFs) into existing suicide risk models might improve recognition of individual risk and improve their predictive performance.</div><div>We examined integrating individual-level genetic data and SBFs with clinical data for patients presenting to the emergency department with SI (ED-SI visit) using de-identified electronic health record (EHR) data from Vanderbilt University Medical Center (VUMC). To simulate a real-time clinical scenario, the ED-SI visit was the prediction point. Regularized Cox regression and random survival forests (RSF) were used to predict SA within 90 days. We assessed performance first using a validated risk score alone. Then, six SBFs (adverse childhood experiences (ACE), chronic stress, financial insecurity, homelessness, social isolation, loneliness) were extracted from clinical notes using a natural language processing (NLP)-based phenotype retrieval algorithm, appended to the risk score, and performance was reassessed. Finally, 16 polygenic risk scores including anxiety, bipolar disorder, schizophrenia, and risk-taking behavior were added. Model performance was assessed with area under the precision-recall curve (AUPRC) and positive predictive value (PPV). Feature importance was analyzed using Shapley Additive Explanations (SHAP).</div><div>We identified 8,841 ED-SI visits; 328 (3.7%) resulted in SA within 90 days. Regularized Cox regression and RSF showed integrating SBFs achieved nearly a three-fold increase in AUPRCs and two-fold increase in PPVs compared to clinical risk scores alone. The Cox model with SBFs achieved an AUPRC of 0.138 and a PPV of 0.101, while the RSF model with SBFs achieved an AUPRC of 0.113 and a PPV of 0.091. The clinical risk score-only models had significantly lower performance metrics (Cox: AUPRC = 0.052, PPV = 0.053; RSF: AUPRC = 0.046, PPV = 0.049). Genetic data inclusion did not improve predictive performance. SHAP analysis of the regularized Cox regression model further highlighted the impact of SBFs, identifying homelessness, chronic stress, and ACE as the strongest predictors of SA, while the clinical risk score was identified as the weakest predictor. Social isolation, loneliness, and financial insecurity exhibited variability in risk impact, leading to lower overall feature importance.</div><div>The study demonstrated that integrating NLP-extracted individual-level SBFs into suicide risk prediction models ","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Page 37"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ACCELERATING THE GENOMICS OF SUICIDE AND SUICIDAL BEHAVIORS: UPDATES FROM THE SUICIDE WORKING GROUP OF THE PSYCHIATRIC GENOMICS CONSORTIUM","authors":"Anna Docherty Chair ,&nbsp;Melek Chaouch Co-chair ,&nbsp;Na Cai Discussant","doi":"10.1016/j.euroneuro.2025.08.528","DOIUrl":"10.1016/j.euroneuro.2025.08.528","url":null,"abstract":"<div><div>Suicide spans psychiatric and medical diagnoses and reflects a global health crisis, accounting for more than 700,000 preventable deaths worldwide per year. Suicide attempts, defined as non-fatal self-injurious behaviors with intent to die, are up to 25 times more common, and are associated with disability, poor quality of life, and social and economic burden. Suicidality phenotypes, specifically suicidal ideation (SI), suicide attempt (SA) and suicide death (SD), are all substantially heritable, with biometrical heritability estimates in the range of 30-55%. Importantly, these phenotypes show strong, yet incomplete, genetic correlations with each other, and there are considerable epidemiological differences between SI, SA, and SD (e.g., sex differences) that further contribute to the genetic heterogeneity of these phenotypes.</div><div>In this session, the Suicide Working Group of the Psychiatric Genomics Consortium will present four complimentary, key studies in 2025 that 1) increase GWAS meta-analysis sample sizes, and include cohorts of multiple ancestral admixtures across SI, SA and SD, 2) leverage increased sample sizes of males and females to conduct the first sex-specific GWAS meta-analyses of SI, SA, and SD, 3) meta-analyze array-based genetic copy number variations (CNVs), in collaboration with the PGC CNV Working Group, and 4) leverage GWAS data to demonstrate that models including multimodal and genetic data outperform single modalities and social/behavioral factors in predicting risk in the clinic.</div><div>Sarah Colbert, our workgroup analyst from the Mount Sinai School of Medicine, will present the latest multi-ancestry GWAS meta-analyses of SI, SA and SD, comprising 37 SI cohorts, 46 SA cohorts, and 7 SD cohorts. Importantly, global cohorts reflect African, Central South Asian, East Asian, European, and Latino ancestral admixtures. Andrey Shabalin, from the University of Utah, will present our first sex-specific GWAS analyses and meta-analyses of suicidal behaviors and suicide death. Lucas Ito, from the University of Sao Paolo, will then present the first CNV meta-analyses of suicidal behaviors in these cohorts. And Colin Walsh, from Vanderbilt University, will present new translational results demonstrating the utility of integrating genetic data into our evolving models of suicide risk prediction in the clinic. Discussant Na Cai from ETH Zurich (PGC MDD), expert in cross-disorder statistical genomics methods, will provide insights and facilitate discussion about strategies to 1) reduce genetic heterogeneity while maximizing statistical power, and 2) leverage clinically meaningful secondary phenotypes to translate findings to clinical prediction, treatment, and prevention.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Pages 34-35"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MASSIVELY PARALLEL REPORTER ASSAYS REVEAL PLEIOTROPIC MECHANISMS IN PSYCHIATRIC DISORDERS","authors":"","doi":"10.1016/j.euroneuro.2025.08.492","DOIUrl":"10.1016/j.euroneuro.2025.08.492","url":null,"abstract":"","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Page 19"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GENOMIC MODERATION OF ANXIETY AND DEPRESSION BY CHILDHOOD MALTREATMENT AND INTIMATE PARTNER VIOLENCE","authors":"Brad Verhulst,&nbsp;Sarah Benstock,&nbsp;John Hettema","doi":"10.1016/j.euroneuro.2025.08.465","DOIUrl":"10.1016/j.euroneuro.2025.08.465","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Anxiety and depression are common, debilitating, and often comorbid mental health disorders that focus emotional negativity inward. While the specific etiological mechanisms underpinning anxiety and depression remain uncertain, both genetic factors and adverse life events play key roles in both disorders. To date, genomic research has separated the etiological factors into independent effects of genetic and environmental components. However, we expect people with higher genetic risk will be more sensitive to adverse life events because these stressors will activate or amplify their latent genetic predispositions.&lt;/div&gt;&lt;div&gt;Building on the diathesis-stress model, we performed a set of proof-of-concept genome-wide interaction studies (GxE GWAS) for both anxiety and depression in the European-like ancestry subsamples of the UK Biobank (N ∼ 120,000) and the National Epidemiologic Survey on Alcohol and Related Conditions-III (NESARC-III; N ∼ 15,000) using novel statistical methods. First, we conducted four genome-wide gene-environment interaction studies, two each for anxiety and depression treating childhood maltreatment and intimate partner violence as moderators in separate analyses. We then calculated marginal genetic effects for differing levels of the moderators that integrate the main effect and the interaction coefficients into a single parameter. Because these marginal genetic effects can be interpreted in the same ways as standard GWAS summary statistics, we were able to use standard post-GWAS methods to test whether genetic associations with, and the heritability of, anxiety and depression were amplified at higher levels of adversity.&lt;/div&gt;&lt;div&gt;Broadly speaking, our results suggest that adversity (i.e., childhood maltreatment and intimate partner violence) amplifies an individual’s genetic predisposition for both depression and anxiety. Specifically, we identified a few single nucleotide polymorphisms (SNPs) that have larger genetic associations with depression and anxiety when individuals experience higher levels of adversity. Notably, most SNPs are relatively robust to moderation, meaning that they are associated with depression and/or anxiety at approximately the same level regardless of an individual’s circumstances. Consistent with moderation at the level of individual variants, the SNP-based heritability (h2SNP) of depression and anxiety increases at higher levels of adversity. While moderation of each individual SNP accounts for a small proportion of phenotypic variation, differences in h2SNP at different levels of adversity are relatively large and potentially clinically meaningful. As such, we conclude that the genetic and environmental factors that culminate in anxiety and depression work in concert. While existing research has shown that individuals with either a higher diathesis or adverse contexts are more likely to experience psychological distress, our results suggest that adversity amplifies one’s genetic predispos","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Pages 5-6"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ACCELERATING GENE-ENVIRONMENT DISCOVERY FOR ACTION IN AUTISM THROUGH STAKEHOLDER ENGAGEMENT, BRAIN ORGANOID EXPERIMENTAL MODELS, AND HUMAN OBSERVATIONAL STUDIES","authors":"Christine Ladd-Acosta Chair ,&nbsp;Jakob Grove Co-chair ,&nbsp;Erin Dunn Discussant","doi":"10.1016/j.euroneuro.2025.08.466","DOIUrl":"10.1016/j.euroneuro.2025.08.466","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Autism is a complex neurodevelopmental condition with a substantial genetic basis. Recent advancements in genomic technologies have facilitated the discovery of multiple risk genes and variants through large-scale studies. Also, increasing evidence points to the important role of environmental exposures in shaping neurodevelopmental outcomes, including autism. Although gene-environment (GxE) interactions have long been thought to impact autism and its related outcomes, this area of research remains underdeveloped in the field with only a handful of studies to date. Improving our understanding of how environmental factors can influence genetic effects on autism-relevant outcomes can facilitate future precision public health and medicine efforts.&lt;/div&gt;&lt;div&gt;Despite a need to study gene- environment interplay in autism and related conditions, research has been limited due to a lack of sufficient sample sizes with unified human data and suitable experimental model systems to test gene-environment impacts on neurobiology under controlled conditions. This led us to establish the unique multi-site collaborative Network called GEARS – Genomics and Environmental science to accelerate Actionable Research and practice in Autism. GEARS aims to conduct gene-environment interaction testing at scale in human samples; GEARS brings together 18 sites across the US, Canada, and Denmark including nearly 175,000 participants to conduct this work. Additionally, GEARS aims to establish a brain organoid experimental resource for GxE and seeks to maximize the significance and impact of GEARS results through stakeholder engagement activities.&lt;/div&gt;&lt;div&gt;In this symposium, we present findings on gene-environment interplay in neurodevelopmental outcomes including autism, spanning from bench science to stakeholder perspectives, through a set of 4 talks:&lt;ul&gt;&lt;li&gt;&lt;span&gt;1)&lt;/span&gt;&lt;span&gt;&lt;div&gt;Ms. Trice will provide an overview of the symposium gene-environment session and present qualitative results from stakeholder focus groups on key themes they have identified as research priorities and how to best disseminate findings back to stakeholders.&lt;/div&gt;&lt;/span&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;2)&lt;/span&gt;&lt;span&gt;&lt;div&gt;Dr. Benke will present the results from our human GxE meta-analysis conducted across 6 GEARS sites (n= 70,647 participants) for the interaction of maternal infection or fever during pregnancy and a polygenic score for ASD (ASD-PGS) on the risk for ASD diagnosis. The results suggest independent effects of prenatal infection exposure and autism polygenic variation on diagnostic outcome.&lt;/div&gt;&lt;/span&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;3)&lt;/span&gt;&lt;span&gt;&lt;div&gt;Dr. Shil will present findings on the interactions between different types of maternal (during pregnancy) and child infections/fever and polygenic scores on the risk of diagnosis of Autism (n=20,067) and its related disorders including attention deficit hyperactivity disorder (n=23,593), schizophrenia (n=6218), bipolar disorder (n=3094), and anorexia (n=6541) ba","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Pages 6-7"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blood-based biomarkers of neuroaxonal and astroglial injury identify ultra-treatment-resistant schizophrenia.","authors":"Caio Andrade de Oliveira, Joel Porfirio Pinto, Carolina Saraiva Nunes de Pinho, Michelle Verde Ramo Soares, Maria Francilene Souza Silva, David Freitas Lucena, Pedro Braga-Neto, Eugênio de Moura Campos, Lia Lira O Sanders, Annyta Fernandes Frota, Danielle S Macedo","doi":"10.1016/j.euroneuro.2025.07.004","DOIUrl":"10.1016/j.euroneuro.2025.07.004","url":null,"abstract":"<p><strong>Background: </strong>Ultra-treatment-resistant schizophrenia (UTRS) is the most severe subtype of treatment refractoriness, with persistent symptoms despite clozapine use. Although immunoinflammatory mechanisms have been implicated in schizophrenia, the biological basis of UTRS remains unclear. We hypothesized that blood-based biomarkers of astroglial and neuroaxonal injury could distinguish UTRS from other schizophrenia subtypes and healthy controls.</p><p><strong>Methods: </strong>Plasma levels of glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), ubiquitin C-terminal hydrolase L1 (UCH-L1), and total tau were measured using the Quanterix Neurology 4-Plex A assay. Participants included individuals with treatment-sensitive schizophrenia (TSS, n = 11), treatment-resistant schizophrenia (TRS, n = 13), UTRS (n = 10), and healthy controls (n = 15), recruited from an outpatient psychiatric service in Brazil. Group differences were assessed using univariate analyses. ROC curves evaluated the discriminative performance of each biomarker.</p><p><strong>Results: </strong>Patients with UTRS exhibited significantly higher levels of GFAP, NfL, and UCH-L1 compared to controls (p < 0.05). TSS and TRS showed intermediate values. Total tau was elevated only in TRS. ROC analysis revealed excellent accuracy for GFAP (AUC = 0.913) and good performance for NfL and UCH-L1 (AUCs > 0.85) in distinguishing UTRS from controls.</p><p><strong>Conclusions: </strong>UTRS is associated with a distinct peripheral biomarker profile reflecting astroglial and neuroaxonal injury. Plasma levels of GFAP and NfL, measured via ultrasensitive assays, may serve as promising biomarkers for identifying ultra-resistant schizophrenia and guiding precision psychiatry strategies.</p>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"16-18"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144798544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SLEEP INERTIA DRIVES THE ASSOCIATION OF EVENING CHRONOTYPE WITH PSYCHIATRIC DISORDERS: GENETIC AND EPIDEMIOLOGICAL EVIDENCE","authors":"","doi":"10.1016/j.euroneuro.2025.08.458","DOIUrl":"10.1016/j.euroneuro.2025.08.458","url":null,"abstract":"","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Page 3"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"INTEGRATIVE GENOMIC APPROACHES TO PTSD: INSIGHTS FROM GENETIC, EPIGENETIC, AND MULTIVARIATE ANALYSES","authors":"Elizabeth Atkinson Chair ,&nbsp;Estela Bruxel Co-chair ,&nbsp;Janitza Montalvo-Ortiz Discussant","doi":"10.1016/j.euroneuro.2025.08.496","DOIUrl":"10.1016/j.euroneuro.2025.08.496","url":null,"abstract":"<div><div>Posttraumatic stress disorder (PTSD) is a highly heterogeneous psychiatric condition with complex biological underpinnings and substantial variability in symptom course, comorbidity patterns, and treatment response. Recent advances in genomics offer promising avenues for identifying the molecular architecture of PTSD, particularly through the integration of statistical genetics, neuroimaging, epigenetics, and polygenic prediction. This symposium brings together researchers leveraging cutting-edge analytic methods and large-scale datasets to uncover novel insights into the etiology and biology of PTSD.</div><div>Peter Barr will present recent results from a multivariate GWAS investigating the shared and distinct genetic influences on PTSD, alcohol consumption, and problematic alcohol use. Using GenomicSEM and data from multiple cohorts including COGA, his work elucidates how PTSD is genetically linked to alcohol-related problems, rather than alcohol use more broadly, and explores downstream associations with psychiatric and neurodevelopmental outcomes.</div><div>Sheila Nagamatsu will present findings from a 10-year longitudinal study of U.S. military veterans, showing that increases in PTSD symptoms over time are associated with accelerated epigenetic aging. This work provides compelling evidence of the long-term biological impact of chronic PTSD and underscores the importance of sustained intervention and monitoring.</div><div>Mary Mufford will discuss the shared genetic architecture between PTSD and brain morphology using data from the UK Biobank, PGC, and ENIGMA. Her talk will highlight findings from polygenic analyses and overlap detection methods, demonstrating how genetic variation contributes to brain structural changes associated with PTSD, and the potential for these findings to inform future biomarker development.</div><div>Elizabeth Atkinson will present several recent projects from her group that develop and apply ancestry-informed methods for psychiatric genomics. Her talk will include a new benchmarking study of polygenic risk score (PRS) accuracy across genotyping and sequencing platforms, the introduction of Tractor-Mix—a novel method for conducting local ancestry-informed GWAS in admixed cohorts with relatedness—and recent work in gnomAD producing ancestry-specific allele frequencies to improve variant interpretation for psychiatric and neurodevelopmental traits.</div><div>Together, these talks showcase complementary perspectives on PTSD etiology, spanning genetic overlap with comorbid conditions, neurobiological correlates, epigenetic trajectories, and analytic tools to improve discovery and interpretability across complex genetic backgrounds.</div><div><strong>Disclosure:</strong> Nothing to disclose. (Atkinson, Bruxel, Montalvo-Ortiz)</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Pages 20-21"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EMPIRICAL FINE-MAPPING OF GWAS VARIANTS ASSOCIATED WITH ASD AND ADHD USING MASSIVELY PARALLEL REPORTER ASSAYS","authors":"Hyejung Won","doi":"10.1016/j.euroneuro.2025.08.526","DOIUrl":"10.1016/j.euroneuro.2025.08.526","url":null,"abstract":"<div><div>Neurodevelopmental disorders (NDD), including autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), represent a complex and heterogeneous group of conditions that significantly impact cognitive and social functioning. These disorders are characterized by early-onset developmental deficits and constitute a major public health concern with substantial societal and economic implications. Genome-wide association studies (GWAS) have begun to identify genomic regions associated with NDD, but the majority of GWAS loci contain dozens of risk variants located in noncoding regions, making it challenging to pinpoint causal variants and understand their functional consequences.</div><div>To address this, we employed Massively Parallel Reporter Assays (MPRA) to investigate the genetic basis of NDD. We compiled 18 genome-wide significant (GWS) loci from NDD GWAS, encompassing 729 variants with association p &lt; 1e-5. Given that genetic risk for NDD is enriched in regulatory elements active during brain development, we conducted the MPRA in human neural progenitors.</div><div>Out of the 729 variants tested, 36 were classified as expression-modulating variants (emVars), demonstrating both allelic regulatory activity and enhancer activity. Characterization of these NDD emVars will offer important insights into the genetic etiology of neurodevelopmental disorders.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Pages 33-34"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LEVERAGING REAL-WORLD DATA FROM HEALTH REGISTRIES TO MEASURE ANTIDEPRESSANT TREATMENT OUTCOMES FOR LARGE-SCALE GENOME-WIDE ASSOCIATION ANALYSES","authors":"Elise Koch ,&nbsp;Alexey Shadrin ,&nbsp;Kevin S. O'Connell ,&nbsp;Maris Alver ,&nbsp;Hanna Sõnajalg ,&nbsp;Guðmundur Einarsson ,&nbsp;Chris Wai Hang Lo ,&nbsp;Anders Kämpe ,&nbsp;Brittany Mitchell ,&nbsp;Alfonso Buil ,&nbsp;Kári Stefánsson ,&nbsp;Yi Lu ,&nbsp;Lili Milani ,&nbsp;Cathryn Lewis ,&nbsp;Ole Andreassen","doi":"10.1016/j.euroneuro.2025.08.534","DOIUrl":"10.1016/j.euroneuro.2025.08.534","url":null,"abstract":"<div><div>There are extensive real-world healthcare data with treatment outcomes that can be applied in data-driven research to improve pharmacotherapy in psychiatry. Given that no standards for estimating treatment outcomes from health registries, hospital records, and self-reports exist, there is high variability, and findings are difficult to compare or meta-analyze. With the growing interest in AI-tools, there is a need for large training and test data from health care with better quality. Standardization of treatment outcome definitions is important for developing prediction and stratification tools for future precision psychiatry.</div><div>Within a group of experts across several EU and Wellcome Trust projects, as well as ECNP, we have made recommendations for how to leverage real-world data to measure pharmacological treatment outcomes in major psychiatric disorders. Proxy measures of treatment outcomes can be improved by integrating real-world data from various registries and healthcare systems that can be linked to self-reports on medication response and adverse effects. We have made recommendations for different scenarios and available data and highlight the importance of validating proxy phenotypes using other sources of information (Koch et al. 2025, Lancet Psychiatry).</div><div>We apply these recommendations in large-scale genome-wide association studies (GWAS) on antidepressant treatment outcomes utilizing data from prescription registries and other health registries across nine countries. In all cohorts, non-response to selective serotonin reuptake inhibitors (SSRIs) was defined as switching from an SSRI to another antidepressant between 6-14 weeks or augmentation or combination with another antidepressant or antipsychotic, while response to SSRIs was defined as treatment for ≥6 month with the same SSRI without switching or augmentation. Treatment-resistant depression was defined as ≥2 switches between antidepressants between 6-14 weeks, and the control group was defined by history of treatment with the same antidepressant for ≥6 month and less than 2 switches. Using these phenotypes, we performed GWAS including a total of ∼400,000 individuals. Questionnaire data on self-reported antidepressant response were used to validate the proxy phenotypes and GWAS findings.</div><div>Leveraging real-world data for well-defined and validated treatment outcome measures can increase sample sizes to improve the discovery of variants associated with non-response to antidepressants, with the potential to form the basis for precision psychiatry approaches.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Pages 38-39"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}