Interrogating Associations Between Polygenic Liabilities and Electroconvulsive Therapy Effectiveness

Shared polygenic susceptibility to treatment response in severe affective and psychotic disorders: Evidence from GWAS data sets

While schizophrenia (SCZ), bipolar disorder (BD) and major depressive disorder (MDD) genetically correlate, the pleiotropy underlying response/resistance to drugs used in these disorders has not been investigated. The aim of this study is to analyze the genetic relationship between treatment-resistant schizophrenia (TRS), response to lithium in BD (respLi) and response to antidepressants in MDD (respAD) using the conditional/conjunctional false discovery rate (cond/conjFDR) methodology, based on the hypothesis that shared mechanisms related to a common psychopathology factor underlie these phenotypes. A cross-trait polygenic enrichment for TRS conditioned on associations with respLi was observed. The conjFDR analysis identified rs11631065 (chr15:66654304) as a shared locus between them. One of the genes at this locus is MAP2K1, previously reported as associated with TRS after conditioning on body mass index genome-wide association study (GWAS). The set of genes at TRS-respLi conjFDR < 0.95 showed enrichment in response to psychotropic drugs in severe mental disorders from GWAS Catalog as well as in neurodevelopment and synaptic pathways. In conclusion, our study constitutes the first evidence of a transdiagnostic genetic signal associated with response to different pharmacological treatments in psychotic and affective disorders. It is necessary to confirm these results when larger GWAS of these phenotypes are available.

A prediction model for electroconvulsive therapy effectiveness in patients with major depressive disorder from the Dutch ECT Consortium (DEC)

Reliable predictors for electroconvulsive therapy (ECT) effectiveness would allow a more precise and personalized approach for the treatment of major depressive disorder (MDD). Prediction models were created using a priori selected clinical variables based on previous meta-analyses. Multivariable linear regression analysis was used, applying backwards selection to determine predictor variables while allowing non-linear relations, to develop a prediction model for depression outcome post-ECT (and logistic regression for remission and response as secondary outcome measures). Internal validation and internal-external cross-validation were used to examine overfitting and generalizability of the model's predictive performance. In total, 1892 adult patients with MDD were included from 22 clinical and research cohorts of the twelve sites within the Dutch ECT Consortium. The final primary prediction model showed several factors that significantly predicted a lower depression score post-ECT: higher age, shorter duration of the current depressive episode, severe MDD with psychotic features, lower level of previous antidepressant resistance in the current episode, higher pre-ECT global cognitive functioning, absence of a comorbid personality disorder, and a lower level of failed psychotherapy in the current episode. The optimism-adjusted R² of the final model was 19%. This prediction model based on readily available clinical information can reduce uncertainty of ECT outcomes and hereby inform clinical decision-making, as prompt referral for ECT may be particularly beneficial for individuals with the above-mentioned characteristics. However, despite including a large number of pretreatment factors, a large proportion of the variance in depression outcome post-ECT remained unpredictable.

Smoking affects symptom improvement in schizophrenia: a prospective longitudinal study of male patients with first-episode schizophrenia

Patients with schizophrenia (SCZ) smoke up to three times more than general people. However, there are conflicting results regarding the relationship between tobacco smoke and clinical symptom severity in SCZ. The aim of this study was to assess the impact of smoking on clinical symptoms after antipsychotic treatment in a 12-week cohort study after controlling for confounding factors. One hundred and forty-five male patients with drug-naïve first-episode (DNFE) SCZ received antipsychotic monotherapy for 12 weeks. Symptom severity was assessed at baseline and at week 12 by the Positive and Negative Syndrome Scale (PANSS). We found no differences in clinical symptoms among male smokers with SCZ compared with male nonsmokers. However, male smokers showed greater improvement in negative symptoms after 12 weeks of treatment, controlling for age, years of education, onset age, and baseline body mass index (BMI). Our study showed that after 12 weeks of treatment with antipsychotics, male smokers showed greater improvement in negative symptoms than male nonsmokers.

Multi-polygenic scores in psychiatry: From disorder specific to transdiagnostic perspectives

The dense co-occurrence of psychiatric disorders questions the categorical classification tradition and motivates efforts to establish dimensional constructs with neurobiological foundations that transcend diagnostic boundaries. In this study, we examined the genetic liability for eight major psychiatric disorder phenotypes under both a disorder-specific and a transdiagnostic framework. The study sample (n = 513) was deeply phenotyped, consisting of 452 patients from tertiary care with mood disorders, anxiety disorders (ANX), attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorders, and/or substance use disorders (SUD) and 61 unaffected comparison individuals. We computed subject-specific polygenic risk score (PRS) profiles and assessed their associations with psychiatric diagnoses, comorbidity status, as well as cross-disorder behavioral dimensions derived from a rich battery of psychopathology assessments. High PRSs for depression were unselectively associated with the diagnosis of SUD, ADHD, ANX, and mood disorders (p < 1e-4). In the dimensional approach, four distinct functional domains were uncovered, namely the negative valence, social, cognitive, and regulatory systems, closely matching the major functional domains proposed by the Research Domain Criteria (RDoC) framework. Critically, the genetic predisposition for depression was selectively reflected in the functional aspect of negative valence systems (R2  = 0.041, p = 5e-4) but not others. This study adds evidence to the ongoing discussion about the misalignment between current psychiatric nosology and the underlying psychiatric genetic etiology and underscores the effectiveness of the dimensional approach in both the functional characterization of psychiatric patients and the delineation of the genetic liability for psychiatric disorders.

Applying polygenic risk score methods to pharmacogenomics GWAS: challenges and opportunities

Polygenic risk scores (PRSs) have emerged as promising tools for the prediction of human diseases and complex traits in disease genome-wide association studies (GWAS). Applying PRSs to pharmacogenomics (PGx) studies has begun to show great potential for improving patient stratification and drug response prediction. However, there are unique challenges that arise when applying PRSs to PGx GWAS beyond those typically encountered in disease GWAS (e.g. Eurocentric or trans-ethnic bias). These challenges include: (i) the lack of knowledge about whether PGx or disease GWAS/variants should be used in the base cohort (BC); (ii) the small sample sizes in PGx GWAS with corresponding low power and (iii) the more complex PRS statistical modeling required for handling both prognostic and predictive effects simultaneously. To gain insights in this landscape about the general trends, challenges and possible solutions, we first conduct a systematic review of both PRS applications and PRS method development in PGx GWAS. To further address the challenges, we propose (i) a novel PRS application strategy by leveraging both PGx and disease GWAS summary statistics in the BC for PRS construction and (ii) a new Bayesian method (PRS-PGx-Bayesx) to reduce Eurocentric or cross-population PRS prediction bias. Extensive simulations are conducted to demonstrate their advantages over existing PRS methods applied in PGx GWAS. Our systematic review and methodology research work not only highlights current gaps and key considerations while applying PRS methods to PGx GWAS, but also provides possible solutions for better PGx PRS applications and future research.

Outcome prediction of electroconvulsive therapy for depression

INTRODUCTION

We developed and tested a Bayesian network(BN) model to predict ECT remission for depression, with non-response as a secondary outcome.

METHODS

We performed a systematic literature search on clinically available predictors. We combined these predictors with variables from a dataset of clinical ECT trajectories (performed in the University Medical Center Utrecht) to create priors and train the BN. Temporal validation was performed in an independent sample.

RESULTS

The systematic literature search yielded three meta-analyses, which provided prior knowledge on outcome predictors. The clinical dataset consisted of 248 treatment trajectories in the training set and 44 trajectories in the test set at the same medical center. The AUC for the primary outcome remission estimated on an independent validation set was 0.686 (95%CI 0.513-0.859) (AUC values of 0.505 - 0.763 observed in 5-fold cross validation of the model within the train set). Accuracy 0.73 (balanced accuracy 0.67), sensitivity 0.55, specificity 0.79, after temporal validation in the independent sample. Prior literature information marginally reduced CI width.

DISCUSSION

A BN model comprised of prior knowledge and clinical data can predict remission of depression after ECT with reasonable performance. This approach can be used to make outcome predictions in psychiatry, and offers a methodological framework to weigh additional information, such as patient characteristics, symptoms and biomarkers. In time, it may be used to improve shared decision-making in clinical practice.

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.

Polygenic risk scores for predicting outcomes and treatment response in psychiatry: hope or hype?

Over the last years, the decreased costs and enhanced accessibility to large genome-wide association studies datasets have laid the foundations for the development of polygenic risk scores (PRSs). A PRS is calculated on the weighted sum of single nucleotide polymorphisms and measures the individual genetic predisposition to develop a certain phenotype. An increasing number of studies have attempted to utilize the PRSs for risk stratification and prognostic evaluation. The present narrative review aims to discuss the potential clinical utility of PRSs in predicting outcomes and treatment response in psychiatry. After summarizing the evidence on major mental disorders, we have discussed the advantages and limitations of currently available PRSs. Although PRSs represent stable trait features with a normal distribution in the general population and can be relatively easily calculated in terms of time and costs, their real-world applicability is reduced by several limitations, such as low predictive power and lack of population diversity. Even with the rapid expansion of the psychiatric genetic knowledge base, pure genetic prediction in clinical psychiatry appears to be out of reach in the near future. Therefore, combining genomic and exposomic vulnerabilities for mental disorders with a detailed clinical characterization is needed to personalize care.

Association Between Polygenic Risk Scores and Outcome of ECT

OBJECTIVE

Identifying biomarkers associated with response to electroconvulsive therapy (ECT) may aid clinical decisions. The authors examined whether greater polygenic liabilities for major depressive disorder, bipolar disorder, and schizophrenia are associated with improvement following ECT for a major depressive episode.

METHODS

Between 2013 and 2017, patients who had at least one treatment series recorded in the Swedish National Quality Register for ECT were invited to provide a blood sample for genotyping. The present study included 2,320 participants (median age, 51 years; 62.8% women) who had received an ECT series for a major depressive episode (77.1% unipolar depression), who had a registered treatment outcome, and whose polygenic risk scores (PRSs) could be calculated. Ordinal logistic regression was used to estimate the effect of PRS on Clinical Global Impressions improvement scale (CGI-I) score after each ECT series.

RESULTS

Greater PRS for major depressive disorder was significantly associated with less improvement on the CGI-I (odds ratio per standard deviation, 0.89, 95% CI=0.82, 0.96; R²=0.004), and greater PRS for bipolar disorder was associated with greater improvement on the CGI-I (odds ratio per standard deviation, 1.14, 95% CI=1.05, 1.23; R²=0.005) after ECT. PRS for schizophrenia was not associated with improvement. In an overlapping sample (N=1,207) with data on response and remission derived from the self-rated version of the Montgomery-Åsberg Depression Rating Scale, results were similar except that schizophrenia PRS was also associated with remission.

CONCLUSIONS

Improvement after ECT is associated with polygenic liability for major depressive disorder and bipolar disorder, providing evidence of a genetic component for ECT clinical response. These liabilities may be considered along with clinical predictors in future prediction models of ECT outcomes.