Interaction Testing and Polygenic Risk Scoring to Estimate the Association of Common Genetic Variants With Treatment Resistance in Schizophrenia

How Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry

Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification and holds great potential for the treatment of mental disorders. However, several important factors are needed to transform current practice into a precision psychiatry framework. Most important are 1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, 2) the development and validation of advanced analytical tools for stratification and prediction, and 3) the development of clinically useful management platforms for patient monitoring that can be integrated into health care systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements-well-powered samples from large biobanks integrated with electronic health records and health registry data using novel artificial intelligence algorithms-to predict outcomes in severe mental disorders and translate these models into clinical management and treatment approaches. Key elements are massive mental health data and novel artificial intelligence algorithms. For the clinical translation of these strategies, we discuss a precision medicine platform for improved management of mental disorders. We use cases to illustrate how precision medicine interventions could be brought into psychiatry to improve the clinical outcomes of mental disorders.

Optimisation of pharmacotherapy in psychiatry through therapeutic drug monitoring, molecular brain imaging and pharmacogenetic tests: Focus on antipsychotics

BACKGROUND

For psychotic disorders (i.e. schizophrenia), pharmacotherapy plays a key role in controlling acute and long-term symptoms. To find the optimal individual dose and dosage strategy, specialised tools are used. Three tools have been proven useful to personalise drug treatments: therapeutic drug monitoring (TDM) of drug levels, pharmacogenetic testing (PG), and molecular neuroimaging.

METHODS

In these Guidelines, we provide an in-depth review of pharmacokinetics, pharmacodynamics, and pharmacogenetics for 45 antipsychotics. Over 30 international experts in psychiatry selected studies that have measured drug concentrations in the blood (TDM), gene polymorphisms of enzymes involved in drug metabolism, or receptor/transporter occupancies in the brain (positron emission tomography (PET)).

RESULTS

Study results strongly support the use of TDM and the cytochrome P450 (CYP) genotyping and/or phenotyping to guide drug therapies. Evidence-based target ranges are available for titrating drug doses that are often supported by PET findings.

CONCLUSION

All three tools discussed in these Guidelines are essential for drug treatment. TDM goes well beyond typical indications such as unclear compliance and polypharmacy. Despite its enormous potential to optimise treatment effects, minimise side effects and ultimately reduce the global burden of diseases, personalised drug treatment has not yet become the standard of care in psychiatry.

Pharmacogenomic scores in psychiatry: systematic review of current evidence

In the past two decades, significant progress has been made in the development of polygenic scores (PGSs). One specific application of PGSs is the development and potential use of pharmacogenomic- scores (PGx-scores) to identify patients who can benefit from a specific medication or are likely to experience side effects. This systematic review comprehensively evaluates published PGx-score studies in psychiatry and provides insights into their potential clinical use and avenues for future development. A systematic literature search was conducted across PubMed, EMBASE, and Web of Science databases until 22 August 2023. This review included fifty-three primary studies, of which the majority (69.8%) were conducted using samples of European ancestry. We found that over 90% of PGx-scores in psychiatry have been developed based on psychiatric and medical diagnoses or trait variants, rather than pharmacogenomic variants. Among these PGx-scores, the polygenic score for schizophrenia (PGSSCZ) has been most extensively studied in relation to its impact on treatment outcomes (32 publications). Twenty (62.5%) of these studies suggest that individuals with higher PGSSCZ have negative outcomes from psychotropic treatment - poorer treatment response, higher rates of treatment resistance, more antipsychotic-induced side effects, or more psychiatric hospitalizations, while the remaining studies did not find significant associations. Although PGx-scores alone accounted for at best 5.6% of the variance in treatment outcomes (in schizophrenia treatment resistance), together with clinical variables they explained up to 13.7% (in bipolar lithium response), suggesting that clinical translation might be achieved by including PGx-scores in multivariable models. In conclusion, our literature review found that there are still very few studies developing PGx-scores using pharmacogenomic variants. Research with larger and diverse populations is required to develop clinically relevant PGx-scores, using biology-informed and multi-phenotypic polygenic scoring approaches, as well as by integrating clinical variables with these scores to facilitate their translation to psychiatric practice.

Pharmacogenomics polygenic risk score: Ready or not for prime time?

Pharmacogenomic Polygenic Risk Scores (PRS) have emerged as a tool to address the polygenic nature of pharmacogenetic phenotypes, increasing the potential to predict drug response. Most pharmacogenomic PRS have been extrapolated from disease-associated variants identified by genome wide association studies (GWAS), although some have begun to utilize genetic variants from pharmacogenomic GWAS. As pharmacogenomic PRS hold the promise of enabling precision medicine, including stratified treatment approaches, it is important to assess the opportunities and challenges presented by the current data. This assessment will help determine how pharmacogenomic PRS can be advanced and transitioned into clinical use. In this review, we present a summary of recent evidence, evaluate the current status, and identify several challenges that have impeded the progress of pharmacogenomic PRS. These challenges include the reliance on extrapolations from disease genetics and limitations inherent to pharmacogenomics research such as low sample sizes, phenotyping inconsistencies, among others. We finally propose recommendations to overcome the challenges and facilitate the clinical implementation. These recommendations include standardizing methodologies for phenotyping, enhancing collaborative efforts, developing new statistical methods to capitalize on drug-specific genetic associations for PRS construction. Additional recommendations include enhancing the infrastructure that can integrate genomic data with clinical predictors, along with implementing user-friendly clinical decision tools, and patient education. Ethical and regulatory considerations should address issues related to patient privacy, informed consent and safe use of PRS. Despite these challenges, ongoing research and large-scale collaboration is likely to advance the field and realize the potential of pharmacogenomic PRS.

Overcoming the barriers to identifying and managing treatment-resistant schizophrenia and to improving access to clozapine: A narrative review and recommendation for clinical practice

Clozapine is the only approved antipsychotic for treatment-resistant schizophrenia (TRS). Although a large body of evidence supports its efficacy and favorable risk-benefit ratio in individuals who have failed two or more antipsychotics, clozapine remains underused. However, variations in clozapine utilization across geographic and clinical settings suggest that it could be possible to improve its use. In this narrative review and expert opinion, we summarized information available in the literature on the mechanisms of action, effectiveness, and potential adverse events of clozapine. We identified barriers leading to discouragement in clozapine prescription internationally, and we proposed practical solutions to overcome each barrier. One of the main obstacles identified to the use of clozapine is the lack of appropriate training for physicians: we highlighted the need to develop specific professional programs to train clinicians, both practicing and in residency, on the relevance and efficacy of clozapine in TRS treatment, initiation, maintenance, and management of potential adverse events. This approach would facilitate physicians to identify eligible patients and offer clozapine as a treatment option in the early stage of the disease. We also noted that increasing awareness of the benefits of clozapine among healthcare professionals, people with TRS, and their caregivers can help promote the use of clozapine. Educational material, such as leaflets or videos, could be developed and distributed to achieve this goal. The information provided in this article may be useful to improve disease burden and support healthcare professionals, patients, and caregivers navigating the complex pathways to TRS management.

Distinct genetic liability profiles define clinically relevant patient strata across common diseases

Stratified medicine holds great promise to tailor treatment to the needs of individual patients. While genetics holds great potential to aid patient stratification, it remains a major challenge to operationalize complex genetic risk factor profiles to deconstruct clinical heterogeneity. Contemporary approaches to this problem rely on polygenic risk scores (PRS), which provide only limited clinical utility and lack a clear biological foundation. To overcome these limitations, we develop the CASTom-iGEx approach to stratify individuals based on the aggregated impact of their genetic risk factor profiles on tissue specific gene expression levels. The paradigmatic application of this approach to coronary artery disease or schizophrenia patient cohorts identified diverse strata or biotypes. These biotypes are characterized by distinct endophenotype profiles as well as clinical parameters and are fundamentally distinct from PRS based groupings. In stark contrast to the latter, the CASTom-iGEx strategy discovers biologically meaningful and clinically actionable patient subgroups, where complex genetic liabilities are not randomly distributed across individuals but rather converge onto distinct disease relevant biological processes. These results support the notion of different patient biotypes characterized by partially distinct pathomechanisms. Thus, the universally applicable approach presented here has the potential to constitute an important component of future personalized medicine paradigms.

Long-term persistence of the risk of agranulocytosis with clozapine compared with other antipsychotics: a nationwide cohort and case-control study in Finland

BACKGROUND

Agranulocytosis is a life-threatening side-effect of clozapine, the only approved drug for treatment-resistant schizophrenia. The long-term profile of this complication has not yet been well established. Here we aim to describe the risk of clozapine-induced agranulocytosis over the long term.

METHODS

We used the entire population of Finland to identify people diagnosed with schizophrenia or schizoaffective disorder between 1972 and 2014 and developed a Kaplan-Meier model of time to diagnosis of agranulocytosis during clozapine versus non-clozapine treatment over a 22-year observation period (1996 to 2017). Next, we developed a nested case-control model for agranulocytosis matching by sex, age, time since diagnosis, and being in the incident cohort on a 1 to 5 ratio. Various durations of use for clozapine and non-clozapine antipsychotic treatment were compared to the modal antipsychotic use duration, deriving adjusted odds ratios (aORs) in a multivariable regression model. Recurrence and lethality rates for clozapine-induced agranulocytosis were described. These data reflect on all individuals with lived experience of schizophrenia in Finland during the study time, although individuals with lived experience were not included in the design of the study.

FINDINGS

We identified 61 769 people with schizophrenia or schizoaffective disorder (14 037 individuals treated with clozapine and 47 732 individuals treated with non-clozapine antipsychotics), with a mean age of 46·67 years (IQR 34·44-57·61), of whom 30 721 (49·7%) were female and 31 048 (50·3%) were male (data on ethnicity not available). Among those, 398 individuals were diagnosed with agranulocytosis (231 individuals treated with clozapine and 167 individuals treated with non-clozapine antipsychotics), representing a cumulative incidence of agranulocytosis for 1·37% (95% CI 0·58-3·16) on clozapine and 0·13% (0·04-0·23) on non-clozapine antipsychotics. In the case (n=398) versus control (n=1987) model, the risk of clozapine-induced agranulocytosis decreased steeply over time from an aOR of 36·01 (95% CI 16·79-77·22) for less than 6 months on clozapine to 4·38 (1·86-10·34) for clozapine use of 54 months or more. Only one of 3559 individuals starting clozapine died because of clozapine-induced agranulocytosis.

INTERPRETATION

The risk of clozapine-induced agranulocytosis decreases steeply over time but might be persistently greater than that of non-clozapine antipsychotics. This long-term risk excess seems small in absolute terms compared with the known magnitude of the advantages of clozapine in relevant outcomes, including life expectancy. Given the widespread underuse of clozapine, relaxing the long-term neutrophil monitoring could favour the advantages of long-term clozapine use, including greater life expectancy, without incurring the intolerable risk of clozapine-induced agranulocytosis.

FUNDING

Northwell Health and Sigrid Jusèlius Foundation.

Polygenic liability for antipsychotic dosage and polypharmacy - a real-world registry and biobank study

Genomic prediction of antipsychotic dose and polypharmacy has been difficult, mainly due to limited access to large cohorts with genetic and drug prescription data. In this proof of principle study, we investigated if genetic liability for schizophrenia is associated with high dose requirements of antipsychotics and antipsychotic polypharmacy, using real-world registry and biobank data from five independent Nordic cohorts of a total of N = 21,572 individuals with psychotic disorders (schizophrenia, bipolar disorder, and other psychosis). Within regression models, a polygenic risk score (PRS) for schizophrenia was studied in relation to standardized antipsychotic dose as well as antipsychotic polypharmacy, defined based on longitudinal prescription registry data as well as health records and self-reported data. Meta-analyses across the five cohorts showed that PRS for schizophrenia was significantly positively associated with prescribed (standardized) antipsychotic dose (beta(SE) = 0.0435(0.009), p = 0.0006) and antipsychotic polypharmacy defined as taking ≥2 antipsychotics (OR = 1.10, CI = 1.05-1.21, p = 0.0073). The direction of effect was similar in all five independent cohorts. These findings indicate that genotypes may aid clinically relevant decisions on individual patients´ antipsychotic treatment. Further, the findings illustrate how real-world data have the potential to generate results needed for future precision medicine approaches in psychiatry.

Exome functional risk score and brain connectivity can predict social adaptability outcome of children with autism spectrum disorder in 4 years' follow up

Introduction

Autism Spectrum Disorder (ASD) is a common neurodevelopmental disorder emerging in early childhood, with heterogeneous clinical outcomes across individuals. This study aims to recognize neuroimaging genetic factors associated with outcomes of ASD after a 4-year follow-up.

Methods

A total of 104 ASD children were included in this study; they underwent clinical assessments, MRI data acquisition, and the whole exome sequencing (WES). Exome functional risk score (EFRS) was calculated based on WES; and two modalities of brain connectivity were constructed based on MRI data, that is functional connectivity (FC) for functional MRI (fMRI), and individual differential structural covariance network (IDSCN) for structural MRI (sMRI), to explore the neuroimaging genetic biomarker of outcomes of ASD children.

Results

Regression analysis found EFRS predicts social adaptability at the 4-year follow-up (Y = -0.013X + 9.29, p = 0.003). We identified 19 pairs of FC associated with autism symptoms severity at follow-up, 10 pairs of FC and 4 pairs of IDSCN associated with social adaptability at follow-up, and 10 pairs of FC associated with ASD EFRS by support vector regression (SVR). Related brain regions with prognostic predictive effects are mainly distributed in superior frontal gyrus, occipital cortex, temporal cortex, parietal cortex, paracentral lobule, pallidum, and amygdala for FC, and temporal cortex, thalamus, and hippocampus for IDSCN. Mediation model showed that ASD EFRS affects the social communication of ASD children through the mediation of FC between left middle occipital gyrus and left pallidum (RMSEA=0.126, CMIN=80.66, DF=42, p< 0.001, CFI=0.867, AIC=152).

Discussion

Our findings underscore that both EFRS and brain connectivity can predict social adaptability, and that brain connectivity serving as mediator in the relationship of EFRS and behaviors of ASD, suggesting the intervention targets in the future clinical application.

Genetic contribution to disease-course severity and progression in the SUPER-Finland study, a cohort of 10,403 individuals with psychotic disorders

Genetic factors contribute to the susceptibility of psychotic disorders, but less is known how they affect psychotic disease-course development. Utilizing polygenic scores (PGSs) in combination with longitudinal healthcare data with decades of follow-up we investigated the contributing genetics to psychotic disease-course severity and diagnostic shifts in the SUPER-Finland study, encompassing 10 403 genotyped individuals with a psychotic disorder. To longitudinally track the study participants' past disease-course severity, we created a psychiatric hospitalization burden metric using the full-coverage and nation-wide Finnish in-hospital registry (data from 1969 and onwards). Using a hierarchical model, ranking the psychotic diagnoses according to clinical severity, we show that high schizophrenia PGS (SZ-PGS) was associated with progression from lower ranked psychotic disorders to schizophrenia (OR = 1.32 [1.23-1.43], p = 1.26e-12). This development manifested already at psychotic illness onset as a higher psychiatric hospitalization burden, the proxy for disease-course severity. In schizophrenia (n = 5 479), both a high SZ-PGS and a low educational attainment PGS (EA-PGS) were associated with increased psychiatric hospitalization burden (p = 1.00e-04 and p = 4.53e-10). The SZ-PGS and the EA-PGS associated with distinct patterns of hospital usage. In individuals with high SZ-PGS, the increased hospitalization burden was composed of longer individual hospital stays, while low EA-PGS associated with shorter but more frequent hospital visits. The negative effect of a low EA-PGS was found to be partly mediated via substance use disorder, a major risk factor for hospitalizations. In conclusion, we show that high SZ-PGS and low EA-PGS both impacted psychotic disease-course development negatively but resulted in different disease-course trajectories.

Mechanism of electroconvulsive therapy in schizophrenia: a bioinformatics analysis study of RNA-seq data

OBJECTIVE

The molecular mechanism of electroconvulsive therapy (ECT) for schizophrenia remains unclear. The aim of this study was to uncover the underlying biological mechanisms of ECT in the treatment of schizophrenia using a transcriptional dataset.

METHODS

The peripheral blood mRNA sequencing data of eight patients (before and after ECT) and eight healthy controls were analyzed by integrated co-expression network analysis and the differentially expressed genes were analyzed by cluster analysis. Gene set overlap analysis was performed using the hypergeometric distribution of phypfunction in R. Associations of these gene sets with psychiatric disorders were explored. Tissue-specific enrichment analysis, gene ontology enrichment analysis, and protein-protein interaction enrichment analysis were used for gene set organization localization and pathway analysis.

RESULTS

We found the genes of the green-yellow module were significantly associated with the effect of ECT treatment and the common gene variants of schizophrenia ( P  = 0.0061; family-wise error correction). The genes of the green-yellow module are mainly enriched in brain tissue and mainly involved in the pathways of neurotrophin, mitogen-activated protein kinase and long-term potentiation.

CONCLUSION

Genes associated with the efficacy of ECT were predominantly enriched in neurotrophin, mitogen-activated protein kinase and long-term potentiation signaling pathways.

Association between psychiatric admissions in patients with schizophrenia and IL-6 plasma levels polygenic score

Schizophrenia diagnosis and admission history were associated with a polygenic score (PGS) for schizophrenia based on a subset of variants that act by modifying the expression of genes whose expression is also modified by antipsychotics. This gene set was enriched in cytokine production. Interleukin-6 (IL-6) is the only cytokine whose plasma levels were associated both with schizophrenia diagnosis and with acute decompensations in the largest meta-analysis. Therefore, we hypothesized that an IL-6 PGS, but not other cytokines PGSs, would be associated with schizophrenia chronicity/psychiatric admissions. Using the IL-6 PGS model from The PGS Catalog, IL-6 PGS was calculated in 427 patients with schizophrenia and data regarding admission history. Association between IL-6 PGS and chronicity, measured as number and duration of psychiatric admissions, or ever readmission was analyzed by multivariate ordinal and logistic regression, respectively. Specificity of results was assessed by analysis of PGSs from the other cytokines at The PGS Catalog with meta-analytic evidence of association with schizophrenia diagnosis or acute decompensations, IL-1RA, IL-4, IL-8, and IL-12. IL-6 PGS was associated with schizophrenia chronicity, explaining 1.51% of variability (OR = 1.29, 95% CI 1.07-1.55, P = 0.007). There was no association with ever readmission. Other cytokines PGSs were not associated with chronicity. Association with IL-6 PGS was independent of association with schizophrenia PGS. Our results provide evidence that genetically regulated higher levels of IL-6 are involved in schizophrenia chronicity, highlighting the relevance of immunity processes for a subgroup of patients.

Prediction of antipsychotics efficacy based on a polygenic risk score: a real-world cohort study

Background: Response to antipsychotics is subject to a wide interindividual variability, due to genetic and non-genetic factors. Several single nucleotide polymorphisms (SNPs) have been associated with response to antipsychotics in genome-wide association studies (GWAS). Polygenic risk scores (PRS) are a powerful tool to aggregate into a single measure the small effects of multiple risk alleles. Materials and methods: We studied the association between a PRS composed of SNPs associated with response to antipsychotics in GWAS studies (PRSresponse) in a real-world sample of patients (N = 460) with different diagnoses (schizophrenia spectrum, bipolar, depressive, neurocognitive, substance use disorders and miscellaneous). Two other PRSs composed of SNPs previously associated with risk of schizophrenia (PRSschizophrenia1 and PRSschizophrenia2) were also tested for their association with response to treatment. Results: PRSresponse was significantly associated with response to antipsychotics considering the whole cohort (OR = 1.14, CI = 1.03-1.26, p = 0.010), the subgroup of patients with schizophrenia, schizoaffective disorder or bipolar disorder (OR = 1.18, CI = 1.02-1.37, p = 0.022, N = 235), with schizophrenia or schizoaffective disorder (OR = 1.24, CI = 1.04-1.47, p = 0.01, N = 176) and with schizophrenia (OR = 1.27, CI = 1.04-1.55, p = 0.01, N = 149). Sensitivity and specificity were sub-optimal (schizophrenia 62%, 61%; schizophrenia spectrum 56%, 55%; schizophrenia spectrum plus bipolar disorder 60%, 56%; all patients 63%, 58%, respectively). PRSschizophrenia1 and PRSschizophrenia2 were not significantly associated with response to treatment. Conclusion: PRSresponse defined from GWAS studies is significantly associated with response to antipsychotics in a real-world cohort; however, the results of the sensitivity-specificity analysis preclude its use as a predictive tool in clinical practice.

Mendelian randomization study of the relationship between blood and urine biomarkers and schizophrenia in the UK Biobank cohort

BACKGROUND

The identification of suitable biomarkers is of crucial clinical importance for the early diagnosis of treatment-resistant schizophrenia (TRS). This study aims to comprehensively analyze the association between TRS and blood and urine biomarkers.

METHODS

Candidate TRS-related single nucleotide polymorphisms (SNPs) were obtained from a recent genome-wide association study. The UK Biobank cohort, comprising 376,807 subjects with blood and urine biomarker testing data, was used to calculate the polygenic risk score (PRS) for TRS. Pearson correlation analyses were performed to evaluate the correlation between TRS PRS and each of the biomarkers, using calculated TRS PRS as the instrumental variables. Bidirectional two-sample Mendelian randomization (MR) was used to assess potential causal associations between candidate biomarkers with TRS.

RESULTS

Here we identify a significant association between TRS PRS and phosphate (r = 0.007, P = 1.96 × 10-4). Sex subgroup analyses identify seven and three candidate biomarkers associated with TRS PRS in male and female participants, respectively. For example, total protein and phosphate for males, creatinine and phosphate for females. Bidirectional two-sample MR analyses indicate that TRS is negatively associated with cholesterol (estimate = -0.363, P  = 0.008). Conversely, TRS is positively associated with total protein (estimate = 0.137, P  = 0.027), mean corpuscular volume (estimate = 0.032, P  = 2.25 × 10-5), and mean corpuscular hemoglobin (estimate = 0.018, P  = 0.007).

CONCLUSIONS

Our findings provide insights into the roles of blood and urine biomarkers in the early detection and treatment of TRS.

Association of SLC6A3 variants with treatment-resistant schizophrenia: a genetic association study of dopamine-related genes in schizophrenia

Background

Most genetic analyses that have attempted to identify a locus or loci that can distinguish patients with treatment-resistant schizophrenia (TRS) from those who respond to treatment (non-TRS) have failed. However, evidence from multiple studies suggests that patients with schizophrenia who respond well to antipsychotic medication have a higher dopamine (DA) state in brain synaptic clefts whereas patients with TRS do not show enhanced DA synthesis/release pathways.

Patients and methods

To examine the contribution (if any) of genetics to TRS, we conducted a genetic association analysis of DA-related genes in schizophrenia patients (TRS, n = 435; non-TRS, n = 539) and healthy controls (HC: n = 489).

Results

The distributions of the genotypes of rs3756450 and the 40-bp variable number tandem repeat on SLC6A3 differed between the TRS and non-TRS groups. Regarding rs3756450, the TRS group showed a significantly higher ratio of the A allele, whereas the non-TRS group predominantly had the G allele. The analysis of the combination of COMT and SLC6A3 yielded a significantly higher ratio of the putative low-DA type (i.e., high COMT activity + high SLC6A3 activity) in the TRS group compared to the two other groups. Patients with the low-DA type accounted for the minority of the non-TRS group and exhibited milder psychopathology.

Conclusion

The overall results suggest that (i) SLC6A3 could be involved in responsiveness to antipsychotic medication and (ii) genetic variants modulating brain DA levels may be related to the classification of TRS and non-TRS.

Gene expression imputation provides clinical and biological insights into treatment-resistant schizophrenia polygenic risk

Genome-wide association studies (GWAS) have revealed the polygenic nature of treatment-resistant schizophrenia TRS. Gene expression imputation allowed the translation of GWAS results into regulatory mechanisms and the construction of gene expression (GReX) risk scores (GReX-RS).  In the present study we computed GReX-RS from the largest GWAS of TRS to assess its association with clinical features. We perform transcriptome imputation in the largest GWAS of TRS to find GReX associated with TRS using brain tissues. Then, for each tissue, we constructed a GReX-RS of the identified genes in a sample of 254 genotyped first episode of psychosis (FEP) patients to test its association with clinical phenotypes, including clinical symptomatology, global functioning and cognitive performance. Our analysis provides evidence that the polygenic basis of TRS includes genetic variants that modulate the expression of certain genes in certain brain areas (substantia nigra, hippocampus, amygdala and frontal cortex), which at the same time are related to clinical features in FEP patients, mainly persistence of negative symptoms and cognitive alterations in sustained attention, which have also been suggested as clinical predictors of TRS. Our results provide a clinical explanation of the polygenic architecture of TRS and give more insight into the biological mechanisms underlying TRS.

Deconstructing the genetic architecture of treatment-resistant schizophrenia in East Asian ancestry

BACKGROUND

Treatment-resistant schizophrenia (TRS) affects a substantial proportion of patients who do not respond adequately to antipsychotic medications, yet the underlying biological mechanism remains poorly understood. This study investigates the link between the genetic predisposition to schizophrenia and TRS.

METHODS

857 individuals diagnosed with schizophrenia were divided into TRS (n = 142) and non-TRS (n = 715) based on well-defined TRS criteria. Polygenic risk scores (PRS) were calculated using schizophrenia genome-wide association summary statistics from East-Asian and European ancestry populations. PRS was estimated using both P-value thresholding and Bayesian framework methods. Logistic regression analyses were performed to differentiate between TRS and non-TRS individuals.

RESULTS

The schizophrenia PRS derived from the East-Asian training dataset effectively distinguished between TRS and non-TRS individuals (R2 = 0.029, p = 4.86 ×10-5, pT = 0.1, OR = 1.52, 95% CI = 1.242-1.861), with higher PRS values observed in the TRS group. Similar PRS analysis was conducted based on the European ancestry GWAS summary statistics, but we found superior prediction based on the East-Asian ancestry discovery data.

CONCLUSION

This study reveals an association between common risk variants for schizophrenia and TRS status, suggesting that the genetic burden of schizophrenia may partly contribute to treatment resistance in individuals with schizophrenia. These findings propose the potential use of genetic risk factors for early TRS identification and timely access to clozapine. However, the ancestral background of the discovery sample is crucial for successfully implementing PRS in clinical settings.

Dimensional and transdiagnostic phenotypes in psychiatric genome-wide association studies

Genome-wide association studies (GWAS) provide biological insights into disease onset and progression and have potential to produce clinically useful biomarkers. A growing body of GWAS focuses on quantitative and transdiagnostic phenotypic targets, such as symptom severity or biological markers, to enhance gene discovery and the translational utility of genetic findings. The current review discusses such phenotypic approaches in GWAS across major psychiatric disorders. We identify themes and recommendations that emerge from the literature to date, including issues of sample size, reliability, convergent validity, sources of phenotypic information, phenotypes based on biological and behavioral markers such as neuroimaging and chronotype, and longitudinal phenotypes. We also discuss insights from multi-trait methods such as genomic structural equation modelling. These provide insight into how hierarchical 'splitting' and 'lumping' approaches can be applied to both diagnostic and dimensional phenotypes to model clinical heterogeneity and comorbidity. Overall, dimensional and transdiagnostic phenotypes have enhanced gene discovery in many psychiatric conditions and promises to yield fruitful GWAS targets in the years to come.

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.

Precision medicine in complex diseases-Molecular subgrouping for improved prediction and treatment stratification

Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease-both with regards to symptoms and underlying causal mechanisms-and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic-based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases-including psychiatric disorders and allergies-available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high-throughput molecular technologies, together with large collections of health data and novel data-driven approaches, offers promise toward improved individual health through precision medicine.

Polygenic risk scores of lithium response and treatment resistance in major depressive disorder

Treatment response and resistance in major depressive disorder (MDD) are suggested to be heritable. Due to significant challenges in defining treatment-related phenotypes, our understanding of their genetic bases is limited. This study aimed to derive a stringent definition of treatment resistance and to investigate the genetic overlap between treatment response and resistance in MDD. Using electronic medical records on the use of antidepressants and electroconvulsive therapy (ECT) from Swedish registers, we derived the phenotype of treatment-resistant depression (TRD) and non-TRD within ~4500 individuals with MDD in three Swedish cohorts. Considering antidepressants and lithium are first-line treatment and augmentation used for MDD, respectively, we generated polygenic risk scores (PRS) of antidepressants and lithium response for individuals with MDD and evaluated their associations with treatment resistance by comparing TRD with non-TRD. Among 1778 ECT-treated MDD cases, nearly all (94%) used antidepressants before their first ECT and the vast majority had at least one (84%) or two (61%) antidepressants of adequate duration, suggesting these MDD cases receiving ECT were resistant to antidepressants. We did not observe a significant difference in the mean PRS of antidepressant response between TRD and non-TRD; however, we found that TRD cases had a significantly higher PRS of lithium response compared to non-TRD cases (OR = 1.10-1.12 under various definitions). The results support the evidence of heritable components in treatment-related phenotypes and highlight the overall genetic profile of lithium-sensitivity in TRD. This finding further provides a genetic explanation for lithium efficacy in treating TRD.

Genomic findings in schizophrenia and their implications

There has been substantial progress in understanding the genetics of schizophrenia over the past 15 years. This has revealed a highly polygenic condition with the majority of the currently explained heritability coming from common alleles of small effect but with additional contributions from rare copy number and coding variants. Many specific genes and loci have been implicated that provide a firm basis upon which mechanistic research can proceed. These point to disturbances in neuronal, and particularly synaptic, functions that are not confined to a small number of brain regions and circuits. Genetic findings have also revealed the nature of schizophrenia's close relationship to other conditions, particularly bipolar disorder and childhood neurodevelopmental disorders, and provided an explanation for how common risk alleles persist in the population in the face of reduced fecundity. Current genomic approaches only potentially explain around 40% of heritability, but only a small proportion of this is attributable to robustly identified loci. The extreme polygenicity poses challenges for understanding biological mechanisms. The high degree of pleiotropy points to the need for more transdiagnostic research and the shortcomings of current diagnostic criteria as means of delineating biologically distinct strata. It also poses challenges for inferring causality in observational and experimental studies in both humans and model systems. Finally, the Eurocentric bias of genomic studies needs to be rectified to maximise benefits and ensure these are felt across diverse communities. Further advances are likely to come through the application of new and emerging technologies, such as whole-genome and long-read sequencing, to large and diverse samples. Substantive progress in biological understanding will require parallel advances in functional genomics and proteomics applied to the brain across developmental stages. For these efforts to succeed in identifying disease mechanisms and defining novel strata they will need to be combined with sufficiently granular phenotypic data.

Therapeutic outcomes wide association scan of different antipsychotics in patients with schizophrenia: Randomized clinical trials and multi-ancestry validation

AIM

This study identified discrepant therapeutic outcomes of antipsychotics.

METHODS

A total of 5191 patients with schizophrenia were enrolled, 3030 as discovery cohort, 1395 as validation cohort, and 766 as multi-ancestry validation cohort. Therapeutic Outcomes Wide Association Scan was conducted. Types of antipsychotics (one antipsychotic vs other antipsychotics) were dependent variables, therapeutic outcomes including efficacy and safety were independent variables.

RESULTS

In discovery cohort, olanzapine related to higher risk of weight gain (AIWG, OR: 2.21-2.86), liver dysfunction (OR: 1.75-2.33), sedation (OR: 1.76-2.86), increased lipid level (OR: 2.04-2.12), and lower risk of extrapyramidal syndrome (EPS, OR: 0.14-0.46); risperidone related to higher risk of hyperprolactinemia (OR: 12.45-20.53); quetiapine related to higher risk of sedation (OR = 1.73), palpitation (OR = 2.87), increased lipid level (OR = 1.69), lower risk of hyperprolactinemia (OR: 0.09-0.11), and EPS (OR: 0.15-0.44); aripiprazole related to lower risk of hyperprolactinemia (OR: 0.09-0.14), AIWG (OR = 0.44), sedation (OR: 0.33-0.47), and QTc prolongation (β = -2.17); ziprasidone related to higher risk of increased QT interval (β range: 3.11-3.22), nausea (OR: 3.22-3.91), lower risk of AIWG (OR: 0.27-0.46), liver dysfunction (OR: 0.41-0.38), and increased lipid level (OR: 0.41-0.55); haloperidol related to higher risk of EPS (OR: 2.64-6.29), hyperprolactinemia (OR: 5.45-9.44), and increased salivation (OR: 3.50-3.68). Perphenazine related to higher risk of EPS (OR: 1.89-2.54). Higher risk of liver dysfunction in olanzapine and lower risk of hyperprolactinemia in aripiprazole were confirmed in validation cohort, and higher risk of AIWG in olanzapine and hyperprolactinemia in risperidone were confirmed in multi-ancestry validation cohort.

CONCLUSION

Future precision medicine should focus on personalized side-effects.

Association of non-high-density lipoprotein cholesterol trajectories with the development of non-alcoholic fatty liver disease: an epidemiological and genome-wide association study

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) shares common risk factors with cardiovascular diseases. Effects of longitudinal trends in non-high-density lipoprotein (non-HDL) cholesterol on NAFLD development are not understood. This study aimed to assess the relationship between non-HDL cholesterol trajectories and the incidence of NAFLD and to identify genetic differences contributing to NAFLD development between non-HDL cholesterol trajectory groups.

METHODS

We analyzed data from 2203 adults (aged 40-69 years) who participated in the Korean Genome and Epidemiology Study. During the 6-year exposure periods, participants were classified into an increasing non-HDL cholesterol trajectory group (n = 934) or a stable group (n = 1269). NAFLD was defined using a NAFLD-liver fat score > -0.640. Multiple Cox proportional hazard regression analysis estimated the hazard ratio (HR) and the 95% confidence interval (CI) for the incidence of NAFLD in the increasing group compared with the stable group.

RESULTS

A genome-wide association study identified significant single-nucleotide polymorphisms (SNPs) associated with NAFLD. During the median 7.8-year of event accrual period, 666 (30.2%) newly developed NAFLD cases were collected. Compared with the stable non-HDL group, the adjusted HR (95% CI) for the incidence of NAFLD in the increasing non-HDL cholesterol group was 1.46 (1.25-1.71). Although there were no significant SNPs, the polygenic risk score was highest in the increasing group, followed by the stable and control groups.

CONCLUSION

Our study indicates that lifestyle or environmental factors have a greater effect size than genetic factors in NAFLD progression risk. Lifestyle modification could be an effective prevention strategy for NAFLD for people with elevated non-HDL cholesterol.

Increased Prevalence of Rare Copy Number Variants in Treatment-Resistant Psychosis

BACKGROUND

It remains unknown why ~30% of patients with psychotic disorders fail to respond to treatment. Previous genomic investigations of treatment-resistant psychosis have been inconclusive, but some evidence suggests a possible link between rare disease-associated copy number variants (CNVs) and worse clinical outcomes in schizophrenia. Here, we identified schizophrenia-associated CNVs in patients with treatment-resistant psychotic symptoms and then compared the prevalence of these CNVs to previously published schizophrenia cases not selected for treatment resistance.

METHODS

CNVs were identified using chromosomal microarray (CMA) and whole exome sequencing (WES) in 509 patients with treatment-resistant psychosis (a lack of clinical response to ≥3 adequate antipsychotic medication trials over at least 5 years of psychiatric hospitalization). Prevalence of schizophrenia-associated CNVs in this sample was compared to that in a previously published large schizophrenia cohort study.

RESULTS

Integrating CMA and WES data, we identified 47 cases (9.2%) with at least one CNV of known or possible neuropsychiatric risk. 4.7% (n = 24) carried a known neurodevelopmental risk CNV. The prevalence of well-replicated schizophrenia-associated CNVs was 4.1%, with duplications of the 16p11.2 and 15q11.2-q13.1 regions, and deletions of the 22q11.2 chromosomal region as the most frequent CNVs. Pairwise loci-based analysis identified duplications of 15q11.2-q13.1 to be independently associated with treatment resistance.

CONCLUSIONS

These findings suggest that CNVs may uniquely impact clinical phenotypes beyond increasing risk for schizophrenia and may potentially serve as biological entry points for studying treatment resistance. Further investigation will be necessary to elucidate the spectrum of phenotypic characteristics observed in adult psychiatric patients with disease-associated CNVs.

Polygenic overlap with body-mass index improves prediction of treatment-resistant schizophrenia

Treatment resistant schizophrenia (TRS) is characterized by repeated treatment failure with antipsychotics. A recent genome-wide association study (GWAS) of TRS showed a polygenic architecture, but no significant loci were identified. Clozapine is shown to be the superior drug in terms of clinical effect in TRS; at the same time it has a serious side effect profile, including weight gain. Here, we sought to increase power for genetic discovery and improve polygenic prediction of TRS, by leveraging genetic overlap with Body Mass Index (BMI). We analysed GWAS summary statistics for TRS and BMI applying the conditional false discovery rate (cFDR) framework. We observed cross-trait polygenic enrichment for TRS conditioned on associations with BMI. Leveraging this cross-trait enrichment, we identified 2 novel loci for TRS at cFDR <0.01, suggesting a role of MAP2K1 and ZDBF2. Further, polygenic prediction based on the cFDR analysis explained more variance in TRS when compared to the standard TRS GWAS. These findings highlight putative molecular pathways which may distinguish TRS patients from treatment responsive patients. Moreover, these findings confirm that shared genetic mechanisms influence both TRS and BMI and provide new insights into the biological underpinnings of metabolic dysfunction and antipsychotic treatment.

Distinct genetic liability profiles define clinically relevant patient strata across common diseases

Genome-wide association studies have unearthed a wealth of genetic associations across many complex diseases. However, translating these associations into biological mechanisms contributing to disease etiology and heterogeneity has been challenging. Here, we hypothesize that the effects of disease-associated genetic variants converge onto distinct cell type specific molecular pathways within distinct subgroups of patients. In order to test this hypothesis, we develop the CASTom-iGEx pipeline to operationalize individual level genotype data to interpret personal polygenic risk and identify the genetic basis of clinical heterogeneity. The paradigmatic application of this approach to coronary artery disease and schizophrenia reveals a convergence of disease associated variant effects onto known and novel genes, pathways, and biological processes. The biological process specific genetic liabilities are not equally distributed across patients. Instead, they defined genetically distinct groups of patients, characterized by different profiles across pathways, endophenotypes, and disease severity. These results provide further evidence for a genetic contribution to clinical heterogeneity and point to the existence of partially distinct pathomechanisms across patient subgroups. Thus, the universally applicable approach presented here has the potential to constitute an important component of future personalized medicine concepts.

Lost in Translation: Challenges in the Diagnosis and Treatment of Early-Onset Schizophrenia

Early-onset schizophrenia (EOS) is a heterogeneous condition that has a serious, insidious clinical course and poor long-term mental health outcomes. The clinical presentations are highly complex due to the overlapping symptomatology with other illnesses, which contributes to a delay in the diagnosis. The objective of the review is to study if an earlier age of onset (AAO) of EOS has poor clinical outcomes, the diagnostic challenges of EOS, and effective treatment strategies. The review provides a comprehensive literature search of 5966 articles and summarizes 126 selected for empirical evidence to methodically consider challenges in diagnosing and treating EOS for practicing clinicians. The risk factors of EOS are unique but have been shared with many other neuropsychiatric illnesses. Most of the risk factors, including genetics and obstetric complications, are nonmodifiable. The role of early diagnosis in reducing the duration of untreated psychosis (DUP) remains critical to reducing overall morbidity. Many specific issues contribute to the risk and clinical outcomes. Therefore, issues around diagnostic ambiguity, treatment resistance, nonadherence, and rehospitalizations further extend the DUP. There is hesitancy to initiate clozapine early, even though the empirical evidence strongly supports its use. There is a growing body of research that suggests the use of long-acting injectables to address nonadherence, and these measures are largely underutilized in acute settings. The clinical presentations of EOS are complex. In addition to the presence of specific risk factors, patients with an early onset of illness are also at a higher risk for treatment resistance. While there is a need to develop tools for early diagnosis, established evidence-based measures to address nonadherence, psychoeducation, and resistance must be incorporated into the treatment planning.

Insight into mitochondrial dysfunction mediated by clozapine-induced inhibition of PGRMC1 in PC12 cells

Clozapine is usually considered as the last resort for treatment-resistant schizophrenia (TRS). However, it shows limited efficacy in cognition improvement. Moreover, the metabolic side effects induced by clozapine can aggravate cognitive impairment, which is closely related to its neurotoxicity. Nevertheless, the mechanisms underlying clozapine's neurotoxicity remain largely elusive. In this study, PC12 cells were simultaneously treated with different concentrations (0μM, 10μM, 20μM, 40μM and 80μM) of clozapine and AG205 which functions as a blocking reagent of progesterone receptor membrane component 1 (PGRMC1). In addition, we examined the effect of PGRMC1 in clozapine-induced neurotoxicity through overexpressing or downregulating PGRMC1. Molecular docking and surface plasmon resonance (SPR) analysis indicated that clozapine and AG205 inhibited the binding of endogenous progesterone to PGRMC1. The results showed that high concentration of clozapine and AG205 induced a significant increase in cytotoxicity, reactive oxygen species (ROS) accumulation and mitochondrial membrane potential (MMP) collapse, all of which were worsened as concentration increases, while overexpression of PGRMC1 reverted the above toxic effect of clozapine on PC12 cells. Furthermore, clozapine and AG205 also downregulated the expression of PGRMC1, glucagon-like peptide-1 receptor (GLP-1R) and mitofusin2 (Mfn2). Interestingly, overexpression of PGRMC1 could revert these effects. Our data suggest that overexpression of PGRMC1 in PC12 cells prevents and restores clozapine-induced oxidative and mitochondrial damage. We propose PGRMC1 activation as a promising therapeutic strategy for clozapine-induced neurotoxicity to facilitate the relief of neuronal damage.

The genetic architecture of schizophrenia: review of large-scale genetic studies

Schizophrenia is a complex and often chronic psychiatric disorder with high heritability. Diagnosis of schizophrenia is still made clinically based on psychiatric symptoms; no diagnostic tests or biomarkers are available. Pathophysiology-based diagnostic scheme and treatments are also not available. Elucidation of the pathogenesis is needed for development of pathology-based diagnostics and treatments. In the past few decades, genetic research has made substantial advances in our understanding of the genetic architecture of schizophrenia. Rare copy number variations (CNVs) and rare single-nucleotide variants (SNVs) detected by whole-genome CNV analysis and whole-genome/-exome sequencing analysis have provided the great advances. Common single-nucleotide polymorphisms (SNPs) detected by large-scale genome-wide association studies have also provided important information. Large-scale genetic studies have been revealed that both rare and common genetic variants play crucial roles in this disorder. In this review, we focused on CNVs, SNVs, and SNPs, and discuss the latest research findings on the pathogenesis of schizophrenia based on these genetic variants. Rare variants with large effect sizes can provide mechanistic hypotheses. CRISPR-based genetics approaches and induced pluripotent stem cell technology can facilitate the functional analysis of these variants detected in patients with schizophrenia. Recent advances in long-read sequence technology are expected to detect variants that cannot be detected by short-read sequence technology. Various studies that bring together data from common variant and transcriptomic datasets provide biological insight. These new approaches will provide additional insight into the pathophysiology of schizophrenia and facilitate the development of pathology-based therapeutics.

Investigating genetic overlap between antidepressant and lithium response and treatment resistance in major depressive disorder

Treatment response and resistance in major depressive disorder (MDD) are suggested to be heritable. Due to significant challenges in defining treatment-related phenotypes, our understanding of their genetic bases is limited. This study aimed to derive a stringent definition of treatment resistance and to investigate genetic overlap between treatment response and resistance in MDD. Using electronic medical records on the use of antidepressants and electroconvulsive therapy (ECT) from Swedish registers, we derived the phenotype of treatment-resistant depression (TRD) within ~ 4 500 individuals with MDD in three Swedish cohorts. Considering antidepressants and lithium are first-line treatment and augmentation used for MDD, respectively, we generated polygenic risk scores of antidepressant and lithium response for individuals with MDD, and evaluated their associations with treatment resistance by comparing TRD with non-TRD. Among 1 778 ECT-treated MDD cases, nearly all (94%) used antidepressants before first ECT, and the vast majority had at least one (84%) or two (61%) antidepressants of adequate duration, suggesting these MDD cases receiving ECT were resistant to antidepressants. We found that TRD cases tend to have lower genetic load of antidepressant response than non-TRD, although the difference was not significant; furthermore, TRD cases had significantly higher genetic load of lithium response (OR = 1.10-1.12 under different definitions). The results support evidence of heritable components in treatment-related phenotypes and highlight the overall genetic profile of lithium-sensitivity in TRD. This finding further provides a genetic explanation for lithium efficacy in treating TRD.

New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications

Psychiatric genetics has made substantial progress in the last decade, providing new insights into the genetic etiology of psychiatric disorders, and paving the way for precision psychiatry, in which individual genetic profiles may be used to personalize risk assessment and inform clinical decision-making. Long recognized to be heritable, recent evidence shows that psychiatric disorders are influenced by thousands of genetic variants acting together. Most of these variants are commonly occurring, meaning that every individual has a genetic risk to each psychiatric disorder, from low to high. A series of large-scale genetic studies have discovered an increasing number of common and rare genetic variants robustly associated with major psychiatric disorders. The most convincing biological interpretation of the genetic findings implicates altered synaptic function in autism spectrum disorder and schizophrenia. However, the mechanistic understanding is still incomplete. In line with their extensive clinical and epidemiological overlap, psychiatric disorders appear to exist on genetic continua and share a large degree of genetic risk with one another. This provides further support to the notion that current psychiatric diagnoses do not represent distinct pathogenic entities, which may inform ongoing attempts to reconceptualize psychiatric nosology. Psychiatric disorders also share genetic influences with a range of behavioral and somatic traits and diseases, including brain structures, cognitive function, immunological phenotypes and cardiovascular disease, suggesting shared genetic etiology of potential clinical importance. Current polygenic risk score tools, which predict individual genetic susceptibility to illness, do not yet provide clinically actionable information. However, their precision is likely to improve in the coming years, and they may eventually become part of clinical practice, stressing the need to educate clinicians and patients about their potential use and misuse. This review discusses key recent insights from psychiatric genetics and their possible clinical applications, and suggests future directions.

Associations Between Polygenic Risk Score Loading, Psychosis Liability, and Clozapine Use Among Individuals With Schizophrenia

Importance

Predictors consistently associated with psychosis liability and course of illness in schizophrenia (SCZ) spectrum disorders (SSD), including the need for clozapine treatment, are lacking. Longitudinally ascertained medication use may empower studies examining associations between polygenic risk scores (PRSs) and pharmacotherapy choices.

Objective

To examine associations between PRS-SCZ loading and groups with different liabilities to SSD (individuals with SSD taking clozapine, individuals with SSD taking other antipsychotics, their parents and siblings, and unrelated healthy controls) and between PRS-SCZ and the likelihood of receiving a prescription of clozapine relative to other antipsychotics.

Design, Setting, and Participants

This genetic association study was a multicenter, observational cohort study with 6 years of follow-up. Included were individuals diagnosed with SSD who were taking clozapine or other antipsychotics, their parents and siblings, and unrelated healthy controls. Data were collected from 2004 until 2021 and analyzed between October 2021 and September 2022.

Exposures

Polygenic risk scores for SCZ.

Main Outcomes and Measures

Multinomial logistic regression was used to examine possible differences between groups by computing risk ratios (RRs), ie, ratios of the probability of pertaining to a particular group divided by the probability of healthy control status. We also computed PRS-informed odd ratios (ORs) for clozapine use relative to other antipsychotics.

Results

Polygenic risk scores for SCZ were generated for 2344 participants (mean [SD] age, 36.95 years [14.38]; 994 female individuals [42.4%]) who remained after quality control screening (557 individuals with SSD taking clozapine, 350 individuals with SSD taking other antipsychotics during the 6-year follow-up, 542 parents and 574 siblings of individuals with SSD, and 321 unrelated healthy controls). All RRs were significantly different from 1; RRs were highest for individuals with SSD taking clozapine (RR, 3.24; 95% CI, 2.76-3.81; P = 2.47 × 10-46), followed by individuals with SSD taking other antipsychotics (RR, 2.30; 95% CI, 1.95-2.72; P = 3.77 × 10-22), parents (RR, 1.44; 95% CI, 1.25-1.68; P = 1.76 × 10-6), and siblings (RR, 1.40; 95% CI, 1.21-1.63; P = 8.22 × 10-6). Polygenic risk scores for SCZ were positively associated with clozapine vs other antipsychotic use (OR, 1.41; 95% CI, 1.22-1.63; P = 2.98 × 10-6), suggesting a higher likelihood of clozapine prescriptions among individuals with higher PRS-SCZ.

Conclusions and Relevance

In this study, PRS-SCZ loading differed between groups of individuals with SSD, their relatives, and unrelated healthy controls, with patients taking clozapine at the far end of PRS-SCZ loading. Additionally, PRS-SCZ was associated with a higher likelihood of clozapine prescribing. Our findings may inform early intervention and prognostic studies of the value of using PRS-SCZ to personalize antipsychotic treatment.

Predicting treatment resistance from first-episode psychosis using routinely collected clinical information

Around a quarter of people who experience a first episode of psychosis (FEP) will develop treatment-resistant schizophrenia (TRS), but there are currently no established clinically useful methods to predict this from baseline. We aimed to explore the predictive potential for clozapine use as a proxy for TRS of routinely collected, objective biomedical predictors at FEP onset, and to externally validate the model in a separate clinical sample of people with FEP. We developed and externally validated a forced-entry logistic regression risk prediction Model fOr cloZApine tReaTment, or MOZART, to predict up to 8-year risk of clozapine use from FEP using routinely recorded information including age, sex, ethnicity, triglycerides, alkaline phosphatase levels, and lymphocyte counts. We also produced a least-absolute shrinkage and selection operator (LASSO) based model, additionally including neutrophil count, smoking status, body mass index, and random glucose levels. The models were developed using data from two UK psychosis early intervention services (EIS) and externally validated in another UK EIS. Model performance was assessed via discrimination and calibration. We developed the models in 785 patients, and validated externally in 1,110 patients. Both models predicted clozapine use well at internal validation (MOZART: C 0.70; 95%CI 0.63,0.76; LASSO: 0.69; 95%CI 0.63,0.77). At external validation, discrimination performance reduced (MOZART: 0.63; 0.58,0.69; LASSO: 0.64; 0.58,0.69) but recovered after re-estimation of the lymphocyte predictor (C: 0.67; 0.62,0.73). Calibration plots showed good agreement between observed and predicted risk in the forced-entry model. We also present a decision-curve analysis and an online data visualisation tool. The use of routinely collected clinical information including blood-based biomarkers taken at FEP onset can help to predict the individual risk of clozapine use, and should be considered equally alongside other potentially useful information such as symptom scores in large-scale efforts to predict psychiatric outcomes.

Decreased cortical gyrification and surface area in the left medial parietal cortex in patients with treatment-resistant and ultratreatment-resistant schizophrenia

AIM

Validating the vulnerabilities and pathologies underlying treatment-resistant schizophrenia (TRS) is an important challenge in optimizing treatment. Gyrification and surface area (SA), reflecting neurodevelopmental features, have been linked to genetic vulnerability to schizophrenia. The aim of this study was to identify gyrification and SA abnormalities specific to TRS.

METHODS

We analyzed 3T magnetic resonance imaging findings of 24 healthy controls (HCs), 20 responders to first-line antipsychotics (FL-Resp), and 41 patients with TRS, including 19 clozapine responders (CLZ-Resp) and 22 FL- and clozapine-resistant patients (patients with ultratreatment-resistant schizophrenia [URS]). The local gyrification index (LGI) and associated SA were analyzed across groups. Diagnostic accuracy was verified by receiver operating characteristic curve analysis.

RESULTS

Both CLZ-Resp and URS had lower LGI values than HCs (P = 0.041, Hedges g [g_H ] = 0.75; P = 0.013, g_H  = 0.96) and FL-Resp (P = 0.007, g_H  = 1.00; P = 0.002, g_H  = 1.31) in the left medial parietal cortex (Lt-MPC). In addition, both CLZ-Resp and URS had lower SA in the Lt-MPC than FL-Resp (P < 0.001, g_H  = 1.22; P < 0.001, g_H  = 1.75). LGI and SA were positively correlated in non-TRS (FL-Resp) (ρ = 0.64, P = 0.008) and TRS (CLZ-Resp + URS) (ρ = 0.60, P < 0.001). The areas under the receiver operating characteristic curve for non-TRS versus TRS with LGI and SA in the Lt-MPC were 0.79 and 0.85, respectively. SA in the Lt-MPC was inversely correlated with negative symptoms (ρ = -0.40, P = 0.018) and clozapine plasma levels (ρ = -0.35, P = 0.042) in TRS.

CONCLUSION

LGI and SA in the Lt-MPC, a functional hub in the default-mode network, were abnormally reduced in TRS compared with non-TRS. Thus, altered LGI and SA in the Lt-MPC might be structural features associated with genetic vulnerability to TRS.

Catalase and interleukin-6 serum elevation in a prediction of treatment-resistance in male schizophrenia patients

BACKGROUND

Oxidative stress (OS) and neuroinflammatory pathways play an important role in the pathophysiology of schizophrenia. The present study investigated the relationship between OS, inflammatory cytokines, and clinical features in male patients with treatment-resistant schizophrenia (TRS).

METHOD

We measured plasma OS parameters, including manganese-superoxide dismutase (Mn-SOD), copper/zinc-containing SOD (CuZn-SOD), total-SOD (T-SOD), malondialdehyde (MDA), catalase (CAT), and glutathione peroxidase (GSH-Px); and serum inflammatory cytokines, including interleukin (IL)- 1α, IL-6, tumor necrosis factor-alpha (TNF-α), and interferon (IFN)-γ, from 80 male patients with chronic schizophrenia (31 had TRS and 49 had chronic stable schizophrenia (CSS)), and 42 healthy controls. The severity of psychotic symptoms was evaluated using the Positive and Negative Syndrome Scale (PANSS).

RESULTS

Compared with healthy controls, plasma Mn-SOD, CuZn-SOD, T-SOD, GSH-Px, and MDA levels were significantly lower, while CAT and serum IL-6 levels were higher in both TRS and CSS male patients (all P < 0.05). Significant differences in the activities of CAT (F = 6.068, P = 0.016) and IL-6 levels (F = 6.876, P = 0.011) were observed between TRS and CSS male patients after analysis of covariance. Moreover, a significant positive correlation was found between IL-6 levels and PANSS general psychopathology subscores (r = 0.485, P = 0.006) and between CAT activity and PANSS total scores (r = 0.409, P = 0.022) in TRS male patients. CAT and IL-6 levels were predictors for TRS. Additionally, in chronic schizophrenia patients, a significant positive correlation was observed between IL-6 and GSH-Px (r = 0.292, P = 0.012), and the interaction effect of IL-6 and GSH-Px was positively associated with PANSS general psychopathology scores (r = 0.287, P = 0.014).

CONCLUSION

This preliminary study indicated that variations in OS and inflammatory cytokines may be involved in psychopathology for patients with chronic schizophrenia, especially in male patients with TRS.

Gene set enrichment analysis of pathophysiological pathways highlights oxidative stress in psychosis

Polygenic risk prediction remains an important aim of genetic association studies. Currently, the predictive power of schizophrenia polygenic risk scores (PRSs) is not large enough to allow highly accurate discrimination between cases and controls and thus is not adequate for clinical integration. Since PRSs are rarely used to reveal biological functions or to validate candidate pathways, to fill this gap, we investigated whether their predictive ability could be improved by building genome-wide (GW-PRSs) and pathway-specific PRSs, using distance- or expression quantitative trait loci (eQTLs)- based mapping between genetic variants and genes. We focused on five pathways (glutamate, oxidative stress, GABA/interneurons, neuroimmune/neuroinflammation and myelin) which belong to a critical hub of schizophrenia pathophysiology, centred on redox dysregulation/oxidative stress. Analyses were first performed in the Lausanne Treatment and Early Intervention in Psychosis Program (TIPP) study (n = 340, cases/controls: 208/132), a sample of first-episode of psychosis patients and matched controls, and then validated in an independent study, the epidemiological and longitudinal intervention program of First-Episode Psychosis in Cantabria (PAFIP) (n = 352, 224/128). Our results highlighted two main findings. First, GW-PRSs for schizophrenia were significantly associated with early psychosis status. Second, oxidative stress was the only significantly associated pathway that showed an enrichment in both the TIPP (p = 0.03) and PAFIP samples (p = 0.002), and exclusively when gene-variant linking was done using eQTLs. The results suggest that the predictive accuracy of polygenic risk scores could be improved with the inclusion of information from functional annotations, and through a focus on specific pathways, emphasizing the need to build and study functionally informed risk scores.

Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium

INTRODUCTION

Our aim was to, firstly, identify characteristics at first-episode of psychosis that are associated with later antipsychotic treatment resistance (TR) and, secondly, to develop a parsimonious prediction model for TR.

METHODS

We combined data from ten prospective, first-episode psychosis cohorts from across Europe and categorised patients as TR or non-treatment resistant (NTR) after a mean follow up of 4.18 years (s.d. = 3.20) for secondary data analysis. We identified a list of potential predictors from clinical and demographic data recorded at first-episode. These potential predictors were entered in two models: a multivariable logistic regression to identify which were independently associated with TR and a penalised logistic regression, which performed variable selection, to produce a parsimonious prediction model. This model was internally validated using a 5-fold, 50-repeat cross-validation optimism-correction.

RESULTS

Our sample consisted of N = 2216 participants of which 385 (17 %) developed TR. Younger age of psychosis onset and fewer years in education were independently associated with increased odds of developing TR. The prediction model selected 7 out of 17 variables that, when combined, could quantify the risk of being TR better than chance. These included age of onset, years in education, gender, BMI, relationship status, alcohol use, and positive symptoms. The optimism-corrected area under the curve was 0.59 (accuracy = 64 %, sensitivity = 48 %, and specificity = 76 %).

IMPLICATIONS

Our findings show that treatment resistance can be predicted, at first-episode of psychosis. Pending a model update and external validation, we demonstrate the potential value of prediction models for TR.

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.

Peripheral biomarkers of treatment-resistant schizophrenia: Genetic, inflammation and stress perspectives

Treatment-resistant schizophrenia (TRS) often results in severe disability and functional impairment. Currently, the diagnosis of TRS is largely exclusionary and emphasizes the improvement of symptoms that may not be detected early and treated according to TRS guideline. As the gold standard, clozapine is the most prescribed selection for TRS. Therefore, how to predict TRS in advance is critical for forming subsequent treatment strategy especially clozapine is used during the early stage of TRS. Although mounting studies have identified certain clinical factors and neuroimaging characteristics associated with treatment response in schizophrenia, the predictors for TRS remain to be explored. Biomarkers, particularly for peripheral biomarkers, show great potential in predicting TRS in view of their predictive validity, noninvasiveness, ease of testing and low cost that would enable their widespread use. Recent evidence supports that the pathogenesis of TRS may be involved in abnormal neurotransmitter systems, inflammation and stress. Due to the heterogeneity of TRS and the lack of consensus in diagnostic criteria, it is difficult to compare extensive results among different studies. Based on the reported neurobiological mechanisms that may be associated with TRS, this paper narratively reviews the updates of peripheral biomarkers of TRS, from genetic and other related perspectives. Although current evidence regarding biomarkers in TRS remains fragmentary, when taken together, it can help to better understand the neurobiological interface of clinical phenotypes and psychiatric symptoms, which will enable individualized prediction and therapy for TRS in the long run.

Treatment-resistant schizophrenia: How far have we traveled?

Treatment-resistant schizophrenia is a lack of adequate response to antipsychotic medications resulting in incomplete functional and social recovery from the illness. Different definitions have been proposed for clinical practice and research work. Antipsychotics that are used in the management of schizophrenia mainly act on multiple dopaminergic pathways which are implicated in the development of symptoms of schizophrenia. Newer antipsychotics also are implicated to affect the serotonergic pathways. Clozapine is the only evidence-based treatment available for the management of treatment-resistant cases. Neurobiologically, there is a considerable overlap between treatment-resistant and treatment-responsive cases. The factors that are implicated in the evolution of treatment resistance are still not conclusive. These make the management of such patients a challenge. However, certain peculiarities of treatment-resistant schizophrenia have been identified which can guide us in the early identification and precise treatment of the treatment-resistant cases.