Genome-wide association study identifies 30 obsessive-compulsive disorder associated loci.

Obsessive-compulsive disorder (OCD) affects ~1% of the population and exhibits a high SNP-heritability, yet previous genome-wide association studies (GWAS) have provided limited information on the genetic etiology and underlying biological mechanisms of the disorder. We conducted a GWAS meta-analysis combining 53,660 OCD cases and 2,044,417 controls from 28 European-ancestry cohorts revealing 30 independent genome-wide significant SNPs and a SNP-based heritability of 6.7%. Separate GWAS for clinical, biobank, comorbid, and self-report sub-groups found no evidence of sample ascertainment impacting our results. Functional and positional QTL gene-based approaches identified 249 significant candidate risk genes for OCD, of which 25 were identified as putatively causal, highlighting WDR6, DALRD3, CTNND1 and genes in the MHC region. Tissue and single-cell enrichment analyses highlighted hippocampal and cortical excitatory neurons, along with D1- and D2-type dopamine receptor-containing medium spiny neurons, as playing a role in OCD risk. OCD displayed significant genetic correlations with 65 out of 112 examined phenotypes. Notably, it showed positive genetic correlations with all included psychiatric phenotypes, in particular anxiety, depression, anorexia nervosa, and Tourette syndrome, and negative correlations with a subset of the included autoimmune disorders, educational attainment, and body mass index.. This study marks a significant step toward unraveling its genetic landscape and advances understanding of OCD genetics, providing a foundation for future interventions to address this debilitating disorder.

A review on side effect management of second-generation antipsychotics to treat schizophrenia: a drug safety perspective.

INTRODUCTION: Effective side effects management present a challenge in antipsychotic treatment with second-generation antipsychotics (SGAs). In recent years, most of the commonly used SGAs, except for clozapine, have been shown to differ only slightly in their effectiveness, but considerably regarding perceived side effects, safety profiles, and compatibility to preexisting medical conditions. AREAS COVERED: The current state of available evidence on side-effect management in SGA treatment of patients with schizophrenia spectrum disorders (SSD) is reviewed. In addition, current guideline recommendations are summarized, highlighting evidence gaps. EXPERT OPINION: SGA safety and side effects needs to be considered in treatment planning. Shared decision-making assistants (SDMA) can support patients, practitioners and relatives to orient their decisions toward avoiding side effects relevant to patients' adherence. Alongside general measures like psychosocial and psychotherapeutic care, switching to better tolerated SGAs can be considered a relatively safe strategy. By contrast, novel meta-analytical evidence emphasizes that dose reduction of SGAs can statistically increase the risk of relapse and other unfavorable outcomes. Further, depending on the type and severity of SGA-related side effects, specific treatments can be used to alleviate induced side effects (e.g. add-on metformin to reduce weight-gain). Finally, discontinuation should be reserved for acute emergencies.

Discontinuation of psychotropic medication: a synthesis of evidence across medication classes.

Pharmacotherapy is an effective treatment modality across psychiatric disorders. Nevertheless, many patients discontinue their medication at some point. Evidence-based guidance for patients, clinicians, and policymakers on rational discontinuation strategies is vital to enable the best, personalized treatment for any given patient. Nonetheless, there is a scarcity of guidelines on discontinuation strategies. In this perspective, we therefore summarize and critically appraise the evidence on discontinuation of six major psychotropic medication classes: antidepressants, antipsychotics, benzodiazepines, mood stabilizers, opioids, and stimulants. For each medication class, a wide range of topics pertaining to each of the following questions are discussed: (1) Who can discontinue (e.g., what are risk factors for relapse?); (2) When to discontinue (e.g., after 1 year or several years of antidepressant use?); and (3) How to discontinue (e.g., what's the efficacy of dose reduction compared to full cessation and interventions to mitigate relapse risk?). We thus highlight how comparing the evidence across medication classes can identify knowledge gaps, which may pave the way for more integrated research on discontinuation.

The ethics of polygenic scores in psychiatry: minefield or opportunity for patient-centered psychiatry?

Recent advancements in psychiatric genetics have sparked a lively debate on the opportunities and pitfalls of incorporating polygenic scores into clinical practice. Yet, several ethical concerns have been raised, casting doubt on whether further development and implementation of polygenic scores would be compatible with providing ethically responsible care. While these ethical issues warrant thoughtful consideration, it is equally important to recognize the unresolved need for guidance on heritability among patients and their families. Increasing the availability of genetic counseling services in psychiatry should be regarded as a first step toward meeting these needs. As a next step, future integration of novel genetic tools such as polygenic scores into genetic counseling may be a promising way to improve psychiatric counseling practice. By embedding the exploration of polygenic psychiatry into the supporting environment of genetic counseling, some of the previously identified ethical pitfalls may be prevented, and opportunities to bolster patient empowerment can be seized upon. To ensure an ethically responsible approach to psychiatric genetics, active collaboration with patients and their relatives is essential, accompanied by educational efforts to facilitate informed discussions between psychiatrists and patients.

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts.

Background: Incorporating genomic data into risk prediction has become an increasingly useful approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not. Methods: Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts. Results: The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p-0.003), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD. Conclusion: Results, especially those from the eMRS, reinforce earlier findings that methylation and trauma are interconnected and can be leveraged to increase the correct classification of those with vs. without PTSD. Moreover, our models can potentially be a valuable tool in predicting the future risk of developing PTSD. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting the condition and, relatedly, improve their performance in independent cohorts.

Efficacy and effectiveness of antipsychotics in schizophrenia: network meta-analyses combining evidence from randomised controlled trials and real-world data.

BACKGROUND: There is debate about the generalisability of results from randomised clinical trials (RCTs) to real-world settings. Studying outcomes of treatments for schizophrenia can shed light on this issue and inform treatment guidelines. We therefore compared the efficacy and effectiveness of antipsychotics for relapse prevention in schizophrenia and estimated overall treatment effects using all available RCT and real-world evidence. METHODS: We conducted network meta-analyses using individual participant data from Swedish and Finnish national registries and aggregate data from RCTs. The target population was adults (age >18 and <65 years) with schizophrenia and schizoaffective disorder with stabilised symptoms. We analysed each registry separately to obtain hazard ratios (HRs) and 95% CIs for relapse within 6 months post-antipsychotic initiation as our main outcome. Interventions studied were antipsychotics, no antipsychotic use, and placebo. We compared HRs versus a reference drug (oral haloperidol) between registries, and between registry individuals who would be eligible and ineligible for RCTs, using the ratio of HRs. We synthesised evidence using network meta-analysis and compared results from our network meta-analysis of real-world data with our network meta-analysis of RCT data, including oral versus long-acting injectable (LAI) formulations. Finally, we conducted a joint real-world and RCT network meta-analysis. FINDINGS: We included 90 469 individuals from the Swedish and Finnish registries (mean age 45·9 [SD 14·6] years; 43 025 [47·5%] women and 47 467 [52·5%] men, ethnicity data unavailable) and 10 091 individuals from 30 RCTs (mean age 39·6 years [SD 11·7]; 3724 [36·9%] women and 6367 [63·1%] men, 6022 White [59·7%]). We found good agreement in effectiveness of antipsychotics between Swedish and Finnish registries (HR ratio 0·97, 95% CI 0·88-1·08). Drug effectiveness versus no antipsychotic was larger in RCT-eligible than RCT-ineligible individuals (HR ratio 1·40 [1·24-1·59]). Efficacy versus placebo in RCTs was larger than effectiveness versus no antipsychotic in real-world (HR ratio 2·58 [2·02-3·30]). We found no evidence of differences between effectiveness and efficacy for between-drug comparisons (HR ratio vs oral haloperidol 1·17 [0·83-1·65], where HR ratio >1 means superior effectiveness in real-world to RCTs), except for LAI versus oral comparisons (HR ratio 0·73 [0·53-0·99], indicating superior effectiveness in real-world data relative to RCTs). The real-world network meta-analysis showed clozapine was most effective, followed by olanzapine LAI. The RCT network meta-analysis exhibited heterogeneity and inconsistency. The joint real-world and RCT network meta-analysis identified olanzapine as the most efficacious antipsychotic amongst those present in both RCTs and the real world registries. INTERPRETATION: LAI antipsychotics perform slightly better in the real world than according to RCTs. Otherwise, RCT evidence was in line with real-world evidence for most between-drug comparisons, but RCTs might overestimate effectiveness of antipsychotics observed in routine care settings. Our results further the understanding of the generalisability of RCT findings to clinical practice and can inform preferential prescribing guidelines. FUNDING: None.

Vaccination and clozapine use: a systematic review and an analysis of the VAERS database.

In the context of COVID-19 concerns related to the potential interactions between clozapine and vaccination arose. With the ultimate goal of deriving recommendations for clinical practice, we systematically reviewed the current evidence regarding altered vaccine effectiveness in clozapine-treated patients and safety aspects of vaccination, such as haematological changes and the impact of vaccines on clozapine blood levels, in clozapine-treated patients. A systematic PRISMA-conform literature search of four databases (PubMed, PsycINFO, EMBASE and Cochrane Library) complemented by a case-by-case analysis of the Vaccine Adverse Event Reporting System (VAERS) database was performed. We then systematically appraised the joint evidence and tried to derive recommendations for clinical practice. 14 records were included in this analysis. These records consisted of 5 original articles and 9 case reports. Among the original articles, two studies provided data on the association between clozapine use and antibody responses to vaccination, both indicating that clozapine use in schizophrenia may be associated with reduced levels of immunoglobulins. Additionally, three studies examined vaccine safety in clozapine-treated patients, with no clinically significant adverse effects directly attributable to the interplay between vaccinations and clozapine. VAERS Analysis encompassed 137 reports and showed no consistent evidence of an increased risk for clozapine blood level increases or adverse events. We found no evidence indicating that clozapine impairs the effectiveness of vaccines. Moreover, no serious safety concerns seem to apply when patients on clozapine are receiving vaccines. However, it is crucial to acknowledge that data on the interaction between clozapine and vaccines remain limited.

Polygenetic risk scores and phenotypic constellations of obsessive-compulsive disorder in clozapine-treated schizophrenia.

Obsessive-compulsive symptoms (OCS) are frequently observed in individuals with schizophrenia (SCZ) treated with clozapine (CLZ). This study aimed to analyze prevalence of OCS and obsessive-compulsive disorder (OCD) in this subgroup and find possible correlations with different phenotypes. Additionally, this is the first study to examine polygenetic risk scores (PRS) in individuals with SCZ and OCS. A multicenter cohort of 91 individuals with SCZ who were treated with CLZ was recruited and clinically and genetically assessed. Symptom severity was examined using the Positive and Negative Symptom Scale (PANSS), Clinical Global Impression Scale (CGI), the Calgary Depression Scale for Schizophrenia (CDSS), Global Assessment of Functioning Scale (GAF) and Yale-Brown Obsessive-Compulsive Scale (Y-BOCS). Participants were divided into subgroups based on phenotypic OCS or OCD using Y-BOCS scores. Genomic-wide data were generated, and PRS analyses were performed to evaluate the association between either phenotypic OCD or OCS severity and genotype-predicted predisposition for OCD, SCZ, cross-disorder, and CLZ/norclozapine (NorCLZ) ratio, CLZ metabolism and NorCLZ metabolism. OCS and OCD were frequent comorbidities in our sample of CLZ-treated SCZ individuals, with a prevalence of 39.6% and 27.5%, respectively. Furthermore, the Y-BOCS total score correlated positively with the duration of CLZ treatment in years (r = 0.28; p = 0.008) and the PANSS general psychopathology subscale score (r = 0.23; p = 0.028). A significant correlation was found between OCD occurrence and PRS for CLZ metabolism. We found no correlation between OCS severity and PRS for CLZ metabolism. We found no correlation for either OCD or OCS and PRS for OCD, cross-disorder, SCZ, CLZ/NorCLZ ratio or NorCLZ metabolism. Our study was able to replicate previous findings on clinical characteristics of CLZ-treated SCZ individuals. OCS is a frequent comorbidity in this cohort and is correlated with CLZ treatment duration in years and PANSS general psychopathology subscale score. We found a correlation between OCD and PRS for CLZ metabolism, which should be interpreted as incidental for now. Future research is necessary to replicate significant findings and to assess possible genetic predisposition of CLZ-treated individuals with SCZ to OCS/OCD. Limitations attributed to the small sample size or the inclusion of subjects on co-medication must be considered. If the association between OCD and PRS for CLZ metabolism can be replicated, it should be further evaluated if CYP1A2 alteration, respectively lower CLZ plasma level, is relevant for OCD development.

Challenges, unmet needs and future directions - a critical evaluation of the clinical trial landscape in schizophrenia research.

INTRODUCTION: Developing novel antipsychotic mechanisms of action and repurposing established compounds for the treatment of schizophrenia is of utmost importance to improve relevant symptom domains and to improve the risk/benefit ratio of antipsychotic compounds. Novel trial design concepts, pathophysiology-based targeted treatment approaches, or even the return to old values may improve schizophrenia outcomes in the future. AREAS COVERED: In this review of the clinical trial landscape in schizophrenia, we present an overview of the challenges and gaps in current clinical trials and elaborate on potential solutions to improve the outcomes of people with schizophrenia. EXPERT OPINION: The classic parallel group design may limit substantial advantages in drug approval or repurposing. Collaborative approaches between regulatory authorities, industry, academia, and funding agencies are needed to overcome barriers in clinical schizophrenia research to allow for meaningful outcome improvements for the patients.

A posterior-alpha ageing network is differentially associated with antidepressant effects of venlafaxine and rTMS.

Major depressive disorder (MDD) is a highly prevalent psychiatric disorder, but chances for remission largely decrease with each failed treatment attempt. It is therefore desirable to assign a given patient to the most promising individual treatment option as early as possible. We used a polygenic score (PGS) informed electroencephalography (EEG) data-driven approach to identify potential predictors for MDD treatment outcome. Post-hoc we conducted exploratory analyses in order to understand the results in depth. First, an EEG independent component analysis produced 54 functional brain networks in a large heterogeneous cohort of psychiatric patients (n = 4,045; 5-84 yrs.). Next, the network that was associated to PGS for antidepressant-response (PRS-AR) in an independent sample (n = 722) was selected: an age-related posterior alpha network that explained >60 % of EEG variance, and was highly stable over recording time. Translational analyses were performed in two other independent datasets to examine if the network was predictive of psychopharmacotherapy (n = 535) and/or repetitive transcranial magnetic stimulation (rTMS) and concomitant psychotherapy (PT; n = 186) outcome. The network predicted remission to venlafaxine (p = 0.015), resulting in a normalized positive predicted value (nPPV) of 138 %, and rTMS + PT - but in opposite direction for women (p = 0.002) relative to men (p = 0.018) - yielding a nPPV of 131 %. Blinded out-of-sample validations for venlafaxine (n = 29) and rTMS + PT (n = 36) confirmed the findings for venlafaxine, while results for rTMS + PT could not be replicated. These data suggest the existence of a relatively stable EEG posterior alpha aging network related to PGS-AR that has potential as MDD treatment predictor.

Gene-environment interaction study on the polygenic risk score for neuroticism, childhood adversity, and parental bonding.

The present study examines whether neuroticism is predicted by genetic vulnerability, summarized as polygenic risk score for neuroticism (PRSN), in interaction with bullying, parental bonding, and childhood adversity. Data were derived from a general population adolescent and young adult twin cohort. The final sample consisted of 202 monozygotic and 436 dizygotic twins and 319 twin pairs. The Short Eysenck Personality questionnaire was used to measure neuroticism. PRSN was trained on the results from the Genetics of Personality Consortium (GPC) and United Kingdom Biobank (UKB) cohorts, yielding two different PRSN. Multilevel mixed-effects models were used to analyze the main and interacting associations of PRSN, childhood adversity, bullying, and parental bonding style with neuroticism. We found no evidence of gene-environment correlation. PRSN thresholds of .005 and .2 were chosen, based on GPC and UKB datasets, respectively. After correction for confounders, all the individual variables were associated with the expression of neuroticism: both PRSN from GPC and UKB, childhood adversity, maternal bonding, paternal bonding, and bullying in primary school and secondary school. However, the results indicated no evidence for gene-environment interaction in this cohort. These results suggest that genetic vulnerability on the one hand and negative life events (childhood adversity and bullying) and positive life events (optimal parental bonding) on the other represent noninteracting pathways to neuroticism.

Comprehensive dissection of prevalence rates, sex differences, and blood level-dependencies of clozapine-associated adverse drug reactions.

Clozapine is often underused due to concerns about adverse drug reactions (ADRs) but studies into their prevalences are inconclusive. We therefore comprehensively examined prevalences of clozapine-associated ADRs in individuals with schizophrenia and demographic and clinical factors associated with their occurrence. Data from a multi-center study (n = 698 participants) were collected. The mean number of ADRs during clozapine treatment was 4.8, with 2.4 % of participants reporting no ADRs. The most common ADRs were hypersalivation (74.6 %), weight gain (69.3 %), and increased sleep necessity (65.9 %), all of which were more common in younger participants. Participants with lower BMI prior to treatment were more likely to experience significant weight gain (>10 %). Constipation occurred more frequently with higher clozapine blood levels and doses. There were no differences in ADR prevalence rates between participants receiving clozapine monotherapy and polytherapy. These findings emphasize the high prevalence of clozapine-associated ADRs and highlight several demographic and clinical factors contributing to their occurrence. By understanding these factors, clinicians can better anticipate and manage clozapine-associated ADRs, leading to improved treatment outcomes and patient well-being.

Associations between psychotropic drugs and rsEEG connectivity and network characteristics: a cross-sectional study in hospital-admitted psychiatric patients.

Introduction: Resting-state EEG (rsEEG) characteristics, such as functional connectivity and network topology, are studied as potential biomarkers in psychiatric research. However, the presence of psychopharmacological treatment in study participants poses a potential confounding factor in biomarker research. To address this concern, our study aims to explore the impact of both single and multi-class psychotropic treatments on aforementioned rsEEG characteristics in a psychiatric population. Methods: RsEEG was analyzed in a real-world cross-sectional sample of 900 hospital-admitted psychiatric patients. Patients were clustered into eight psychopharmacological groups: unmedicated, single-class treatment with antipsychotics (AP), antidepressants (AD) or benzodiazepines (BDZ), and multi-class combinations of these treatments. To assess the associations between psychotropic treatments and the macroscale rsEEG characteristics mentioned above, we employed a general linear model with post-hoc tests. Additionally, Spearman's rank correlation analyses were performed to explore potential dosage effects. Results: Compared to unmedicated patients, single-class use of AD was associated with lower functional connectivity in the delta band, while AP was associated with lower functional connectivity in both the delta and alpha bands. Single-class use of BDZ was associated with widespread rsEEG differences, including lower functional connectivity across frequency bands and a different network topology within the beta band relative to unmedicated patients. All of the multi-class groups showed associations with functional connectivity or topology measures, but effects were most pronounced for concomitant use of all three classes of psychotropics. Differences were not only observed in comparison with unmedicated patients, but were also evident in comparisons between single-class, multi-class, and single/multi-class groups. Importantly, multi-class associations with rsEEG characteristics were found even in the absence of single-class associations, suggesting potential cumulative or interaction effects of different classes of psychotropics. Dosage correlations were only found for antipsychotics. Conclusion: Our exploratory, cross-sectional study suggests small but significant associations between single and multi-class use of antidepressants, antipsychotics and benzodiazepines and macroscale rsEEG functional connectivity and network topology characteristics. These findings highlight the importance of considering the effects of specific psychotropics, as well as their interactions, when investigating rsEEG biomarkers in a medicated psychiatric population.

Psychosis Endophenotypes: A Gene-Set-Specific Polygenic Risk Score Analysis.

BACKGROUND AND HYPOTHESIS: Endophenotypes can help to bridge the gap between psychosis and its genetic predispositions, but their underlying mechanisms remain largely unknown. This study aims to identify biological mechanisms that are relevant to the endophenotypes for psychosis, by partitioning polygenic risk scores into specific gene sets and testing their associations with endophenotypes. STUDY DESIGN: We computed polygenic risk scores for schizophrenia and bipolar disorder restricted to brain-related gene sets retrieved from public databases and previous publications. Three hundred and seventy-eight gene-set-specific polygenic risk scores were generated for 4506 participants. Seven endophenotypes were also measured in the sample. Linear mixed-effects models were fitted to test associations between each endophenotype and each gene-set-specific polygenic risk score. STUDY RESULTS: After correction for multiple testing, we found that a reduced P300 amplitude was associated with a higher schizophrenia polygenic risk score of the forebrain regionalization gene set (mean difference per SD increase in the polygenic risk score: -1.15 µV; 95% CI: -1.70 to -0.59 µV; P = 6 × 10-5). The schizophrenia polygenic risk score of forebrain regionalization also explained more variance of the P300 amplitude (R2 = 0.032) than other polygenic risk scores, including the genome-wide polygenic risk scores. CONCLUSIONS: Our finding on reduced P300 amplitudes suggests that certain genetic variants alter early brain development thereby increasing schizophrenia risk years later. Gene-set-specific polygenic risk scores are a useful tool to elucidate biological mechanisms of psychosis and endophenotypes, offering leads for experimental validation in cellular and animal models.

[Mental health and climate change: reconceptualizing eco-anxiety]. / Bestaat er zoiets als eco-angst?

Climate change may bring about anxiety, which may be referred to as eco-anxiety. Commonly accepted conceptual or diagnostic criteria for eco-anxiety are currently lacking. Here, we briefly summarize the current literature on climate change and mental illness. We suggest dividing the concept of eco-anxiety into adaptive eco-anxiety and an anxiety disorder where climate change plays a major role. This distinction may be helpful in clinical practice to discern relatively common and potentially healthy eco-anxiety from a disorder causing impairment in daily functioning. Benefits of adaptive eco-anxiety include the development of active coping strategies (increasing resilience) as well as behavioural changes to mitigate climate change. When debilitating anxiety comes with avoidance and centers around climate change, a specific phobia called eco-anxiety disorder may be considered. Importantly, as validated diagnostic criteria for this disorder are currently lacking, further conceptualization is highly needed. Future clinical research may help fill these current knowledge gaps.

Context v. algorithm: evidence that a transdiagnostic framework of contextual clinical characterization is of more clinical value than categorical diagnosis.

BACKGROUND: A transdiagnostic and contextual framework of 'clinical characterization', combining clinical, psychopathological, sociodemographic, etiological, and other personal contextual data, may add clinical value over and above categorical algorithm-based diagnosis. METHODS: Prediction of need for care and health care outcomes was examined prospectively as a function of the contextual clinical characterization diagnostic framework in a prospective general population cohort (n = 6646 at baseline), interviewed four times between 2007 and 2018 (NEMESIS-2). Measures of need, service use, and use of medication were predicted as a function of any of 13 DSM-IV diagnoses, both separately and in combination with clinical characterization across multiple domains: social circumstances/demographics, symptom dimensions, physical health, clinical/etiological factors, staging, and polygenic risk scores (PRS). Effect sizes were expressed as population attributable fractions. RESULTS: Any prediction of DSM-diagnosis in relation to need and outcome in separate models was entirely reducible to components of contextual clinical characterization in joint models, particularly the component of transdiagnostic symptom dimensions (a simple score of the number of anxiety, depression, mania, and psychosis symptoms) and staging (subthreshold, incidence, persistence), and to a lesser degree clinical factors (early adversity, family history, suicidality, slowness at interview, neuroticism, and extraversion), and sociodemographic factors. Clinical characterization components in combination predicted more than any component in isolation. PRS did not meaningfully contribute to any clinical characterization model. CONCLUSION: A transdiagnostic framework of contextual clinical characterization is of more value to patients than a categorical system of algorithmic ordering of psychopathology.

Independent contribution of polygenic risk for schizophrenia and cannabis use in predicting psychotic-like experiences in young adulthood: testing gene × environment moderation and mediation.

BACKGROUND: It has not yet been determined if the commonly reported cannabis-psychosis association is limited to individuals with pre-existing genetic risk for psychotic disorders. METHODS: We examined whether the relationship between polygenic risk score for schizophrenia (PRS-Sz) and psychotic-like experiences (PLEs), as measured by the Community Assessment of Psychic Experiences-42 (CAPE-42) questionnaire, is mediated or moderated by lifetime cannabis use at 16 years of age in 1740 of the individuals of the European IMAGEN cohort. Secondary analysis examined the relationships between lifetime cannabis use, PRS-Sz and the various sub-scales of the CAPE-42. Sensitivity analyses including covariates, including a PRS for cannabis use, were conducted and results were replicated using data from 1223 individuals in the Dutch Utrecht cannabis cohort. RESULTS: PRS-Sz significantly predicted cannabis use (p = 0.027) and PLE (p = 0.004) in the IMAGEN cohort. In the full model, considering PRS-Sz and covariates, cannabis use was also significantly associated with PLE in IMAGEN (p = 0.007). Results remained consistent in the Utrecht cohort and through sensitivity analyses. Nevertheless, there was no evidence of a mediation or moderation effects. CONCLUSIONS: These results suggest that cannabis use remains a risk factor for PLEs, over and above genetic vulnerability for schizophrenia. This research does not support the notion that the cannabis-psychosis link is limited to individuals who are genetically predisposed to psychosis and suggests a need for research focusing on cannabis-related processes in psychosis that cannot be explained by genetic vulnerability.

An international research agenda for clozapine-resistant schizophrenia.

Treatment-resistant symptoms occur in about a third of patients with schizophrenia and are associated with a substantial reduction in their quality of life. The development of new treatment options for clozapine-resistant schizophrenia constitutes a crucial, unmet need in psychiatry. Additionally, an overview of past and possible future research avenues to optimise the early detection, diagnosis, and management of clozapine-resistant schizophrenia is unavailable. In this Health Policy, we discuss the ongoing challenges associated with clozapine-resistant schizophrenia faced by patients and health-care providers worldwide to improve the understanding of this condition. We then revisit several clozapine guidelines, the diagnostic tests and treatment options for clozapine-resistant schizophrenia, and currently applied research approaches in clozapine-resistant schizophrenia. We also suggest methodologies and targets for future research, divided into innovative nosology-oriented field trials (eg, examining dimensional symptom staging), translational approaches (eg, genetics), epidemiological research (eg, real-world studies), and interventional studies (eg, non-traditional trial designs incorporating lived experiences and caregivers' perspectives). Finally, we note that low-income and middle-income countries are under-represented in studies on clozapine-resistant schizophrenia and propose an agenda to guide multinational research on the cause and treatment of clozapine-resistant schizophrenia. We hope that this research agenda will empower better global representation of patients living with clozapine-resistant schizophrenia and ultimately improve their functional outcomes and quality of life.

Adjusting for population stratification in polygenic risk score analyses: a guide for model specifications in the UK Biobank.

The current study was conducted to provide a general guidance for model specifications in polygenic risk score (PRS) analyses of the UK Biobank, such as adjusting for covariates (i.e. age, sex, recruitment centers, and genetic batch) and the number of principal components (PCs) that need to be included. To cover behavioral, physical and mental health outcomes, we evaluated three continuous outcomes (BMI, smoking, drinking) and two binary outcomes (Major Depressive Disorder and educational attainment). We applied 3280 (656 per phenotype) different models including different sets of covariates. We evaluated these different model specifications by comparing regression parameters such as R2, coefficients, and P values, as well as ANOVA tests. Findings suggest that only up to three PCs appears to be sufficient for controlling population stratification for most outcomes, whereas including other covariates (particularly age and sex) appears to be more essential for model performance.