Clinical indicators of treatment-resistant psychosis

Long-term clinical recovery and treatment resistance in first-episode psychosis: a 10-year follow-up study

Illness trajectories in people with first-episode psychosis (FEP) vary significantly over time. Identifying early-course parameters predicting outcomes is essential, but long-term data still needs to be provided. We conducted a 10-year follow-up study of a comprehensive first-episode psychosis (FEP) cohort investigating the prevalence of clinical recovery (CR) and treatment resistance (TR) after ten years, as well as clinical, demographic, and pre-illness predictors of long-term outcomes. 102 participants with FEP DSM-IV Schizophrenia spectrum disorders were recruited within their first year of treatment. The Treatment Response and Resistance in Psychosis Working Group (TRRIP) and the Remission in Schizophrenia Working Group (RSWG) criteria were used to define TR and CR, respectively. At 10-year follow-up, 29 (29%) of the participants were classified as in CR, while 32 (31%) were classified as TR. We also identified a larger middle group (n = 41, 40%) consisting of participants in partial recovery. 7% of all participants had tried Clozapine at the 10-year follow-up. Logistic regression analyses identified insidious onset (OR = 4.16) and baseline disorganized symptoms (OR = 2.96) as significantly associated with an increased risk of developing TR. Good premorbid academic adjustment (OR = 1.60) and acute onset (OR = 3.40) were associated with an increased chance of CR. We identified three long-term outcome groups by using recent consensus definitions. We also identified the potential importance of assessing baseline disorganized symptoms and monitoring patients with insidious onset more closely. Further, the findings suggest that clinicians should pay close attention to early-course parameters and provide adequate treatment to improve long-term outcomes of FEP.

Childhood trauma and treatment resistance in first-episode psychosis: Investigating the role of premorbid adjustment and duration of untreated psychosis

BACKGROUND

Early identification of treatment non-response in first-episode psychosis (FEP) is essential to outcome. Despite indications that exposure to childhood trauma (CT) can have adverse effects on illness severity, its impact on treatment non-response and the interplay with other pre-treatment characteristics is sparsely investigated. We use a lack of clinical recovery as an early indicator of treatment resistance to investigate the relationship between CT and treatment resistance status at one-year follow-up and the potential mediation of this effect by other pre-treatment characteristics.

METHODS

This prospective one-year follow-up study involved 141 participants recruited in their first year of treatment for a schizophrenia-spectrum disorder. We investigated clinical status, childhood trauma (CT), premorbid adjustment (PA), and duration of untreated psychosis (DUP) at baseline and clinical status at one-year follow-up. Ordinal regression analyses were conducted to investigate how PA and DUP affected the relationship between CT and one-year outcome in FEP.

RESULTS

45 % of the FEP sample reported moderate to severe CT, with significantly higher levels of CT in the early treatment resistant group compared to participants with full or partial early recovery. Ordinal regression analysis showed that CT was a significant predictor of being in a more severe outcome group (OR = 4.59). There was a partial mediation effect of PA and a full mediation effect of DUP on the effect of CT on outcome group membership.

DISCUSSION

Our findings indicate that reducing treatment delays may mitigate the adverse effects of CT on clinical outcomes and support the inclusion of broad trauma assessment in FEP services.

Contribution of copy number variants on antipsychotic treatment response in Han Chinese patients with schizophrenia

BACKGROUND

Response to antipsychotic drugs (APD) varies greatly among individuals and is affected by genetic factors. This study aims to demonstrate genome-wide associations between copy number variants (CNVs) and response to APD in patients with schizophrenia.

METHODS

A total of 3030 patients of Han Chinese ethnicity randomly received APD (aripiprazole, olanzapine, quetiapine, risperidone, ziprasidone, haloperidol and perphenazine) treatment for six weeks. This study is a secondary data analysis. Percentage change on the Positive and Negative Syndrome Scale (PANSS) reduction was used to assess APD efficacy, and more than 50% change was considered as APD response. Associations between CNV burden, gene set, CNV loci and CNV break-point and APD efficacy were analysed.

FINDINGS

Higher CNV losses burden decreased the odds of 6-week APD response (OR = 0.66 [0.44, 0.98]). CNV losses in synaptic pathway involved in neurotransmitters were associated with 2-week PANSS reduction rate. CNV involved in sialylation (1p31.1 losses) and cellular metabolism (19q13.32 gains) associated with 6-week PANSS reduction rate at genome-wide significant level. Additional 36 CNVs associated with PANSS factors improvement. The OR of protective CNVs for 6-week APD response was 3.10 (95% CI: 1.33-7.19) and risk CNVs was 8.47 (95% CI: 1.92-37.43). CNV interacted with genetic risk score on APD efficacy (Beta = -1.53, SE = 0.66, P = 0.021). The area under curve to differ 6-week APD response attained 80.45% (95% CI: 78.07%-82.82%).

INTERPRETATION

Copy number variants contributed to poor APD efficacy and synaptic pathway involved in neurotransmitter was highlighted.

FUNDING

National Natural Science Foundation of China, National Key R&D Program of China, China Postdoctoral Science Foundation.

Antipsychotic Use and Psychiatric Hospitalization in First-Episode Non-affective Psychosis and Cannabis Use Disorder: A Swedish Nationwide Cohort Study

BACKGROUND AND HYPOTHESIS

There is a paucity of research on treatment outcomes of patients with psychosis and cannabis use disorder (CUD). We aimed to compare the effectiveness of antipsychotics in reducing the risk of hospitalization in patients with first-episode psychosis (FEP) and co-occurring CUD.

STUDY DESIGN

We utilized a nationwide Swedish cohort of patients with longitudinal register data from the year 2006 to 2021. Participants were patients with FEP and co-occurring CUD (n = 1820, 84.73% men, mean age 26.80 years, SD 8.25 years). The main outcome was hospitalization due to psychotic relapse. Hospitalization due to any psychiatric disorder or substance use disorder (SUD) were examined as secondary outcomes. Within-individual Cox regression models were used to study these associations.

STUDY RESULTS

Use of any antipsychotic was associated with a 33% risk reduction of psychotic relapse (aHR = 0.67; 95% CI 0.60-0.75). Clozapine (0.43; 0.29-0.64), long-acting injectable (LAI) formulations of risperidone (0.40; 0.22-0.71), aripiprazole (0.42; 0.27-0.65), and paliperidone (0.46; 0.30-0.69) were associated with the lowest risk of relapse. The association between the LAI formulation of olanzapine and hospitalization due to psychosis was statistically non-significant (0.61; 0.35-1.05). Clozapine was associated with an 86% risk reduction of hospitalization due to SUD (0.14; 0.05-0.44). Of oral non-clozapine antipsychotics, aripiprazole was associated with the lowest risk of hospitalization due to psychotic relapse (0.61; 0.45-0.83).

CONCLUSIONS

These findings support the use of clozapine, LAI formulations of second-generation antipsychotics other than olanzapine, or oral aripiprazole to prevent hospitalization in FEP and co-occurring CUD.

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.

Development of an individualized risk calculator of treatment resistance in patients with first-episode psychosis (TRipCal) using automated machine learning: a 12-year follow-up study with clozapine prescription as a proxy indicator

About 15-40% of patients with schizophrenia are treatment resistance (TR) and require clozapine. Identifying individuals who have higher risk of development of TR early in the course of illness is important to provide personalized intervention. A total of 1400 patients with FEP enrolled in the early intervention for psychosis service or receiving the standard psychiatric service between July 1, 1998, and June 30, 2003, for the first time were included. Clozapine prescriptions until June 2015, as a proxy of TR, were obtained. Premorbid information, baseline characteristics, and monthly clinical information were retrieved systematically from the electronic clinical management system (CMS). Training and testing samples were established with random subsampling. An automated machine learning (autoML) approach was used to optimize the ML algorithm and hyperparameters selection to establish four probabilistic classification models (baseline, 12-month, 24-month, and 36-month information) of TR development. This study found 191 FEP patients (13.7%) who had ever been prescribed clozapine over the follow-up periods. The ML pipelines identified with autoML had an area under the receiver operating characteristic curve ranging from 0.676 (baseline information) to 0.774 (36-month information) in predicting future TR. Features of baseline information, including schizophrenia diagnosis and age of onset, and longitudinal clinical information including symptoms variability, relapse, and use of antipsychotics and anticholinergic medications were important predictors and were included in the risk calculator. The risk calculator for future TR development in FEP patients (TRipCal) developed in this study could support the continuous development of data-driven clinical tools to assist personalized interventions to prevent or postpone TR development in the early course of illness and reduce delay in clozapine initiation.

Assessing the validity of a self-reported clinical diagnosis of schizophrenia

Background

Diagnoses in psychiatric research can be derived from various sources. This study assesses the validity of a self-reported clinical diagnosis of schizophrenia.

Methods

The study included 3,029 clinically ascertained participants with schizophrenia or psychotic disorders diagnosed by self-report and/or research interview and 1,453 UK Biobank participants with self-report and/or medical record diagnosis of schizophrenia or schizoaffective disorder depressed-type (SA-D). We assessed positive predictive values (PPV) of self-reported clinical diagnoses against research interview and medical record diagnoses. We compared polygenic risk scores (PRS) and phenotypes across diagnostic groups, and compared the variance explained by schizophrenia PRS to samples in the Psychiatric Genomics Consortium (PGC).

Results

In the clinically ascertained sample, the PPV of self-reported schizophrenia to a research diagnosis of schizophrenia was 0.70, which increased to 0.81 when benchmarked against schizophrenia or SA-D. In UK Biobank, the PPV of self-reported schizophrenia to a medical record diagnosis was 0.74. Compared to self-report participants, those with a research diagnosis were younger and more likely to have a high school qualification (clinically ascertained sample) and those with a medical record diagnosis were less likely to be employed or have a high school qualification (UK Biobank). Schizophrenia PRS did not differ between participants that had a diagnosis from self-report, research diagnosis or medical record diagnosis. Polygenic liability r2, for all diagnosis definitions, fell within the distribution of PGC schizophrenia cohorts.

Conclusions

Self-report measures of schizophrenia are justified in research to maximise sample size and representativeness, although within sample validation of diagnoses is recommended.

The association between clinical, sociodemographic, familial, and environmental factors and treatment resistance in schizophrenia: A machine-learning-based approach

BACKGROUND

Prediction of treatment resistance in schizophrenia (TRS) would be helpful to reduce the duration of ineffective treatment and avoid delays in clozapine initiation. We applied machine learning to identify clinical, sociodemographic, familial, and environmental variables that are associated with TRS and could potentially predict TRS in the future.

STUDY DESIGN

Baseline and follow-up data on trait(-like) variables from the Genetic Risk and Outcome of Psychosis (GROUP) study were used. For the main analysis, we selected patients with non-affective psychotic disorders who met TRS (n = 200) or antipsychotic-responsive criteria (n = 423) throughout the study. For a sensitivity analysis, we only selected patients who met TRS (n = 76) or antipsychotic-responsive criteria (n = 123) at follow-up but not at baseline. Random forest models were trained to predict TRS in both datasets. SHapley Additive exPlanation values were used to examine the variables' contributions to the prediction.

STUDY RESULTS

Premorbid functioning, age at onset, and educational degree were most consistently associated with TRS across both analyses. Marital status, current household, intelligence quotient, number of moves, and family loading score for substance abuse also consistently contributed to the prediction of TRS in the main or sensitivity analysis. The diagnostic performance of our models was modest (area under the curve: 0.66-0.69).

CONCLUSIONS

We demonstrate that various clinical, sociodemographic, familial, and environmental variables are associated with TRS. Our models only showed modest performance in predicting TRS. Prospective large multi-centre studies are needed to validate our findings and investigate whether the model's performance can be improved by adding data from different modalities.

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.

Verbal and visual learning ability in patients with treatment-resistant schizophrenia: A 1-year follow-up study

Objective

In the general population, repeated cognitive testing produces learning effects with potential for improved test performance. It is currently unclear whether the same effect of repeated cognitive testing on cognition pertains to people living with schizophrenia, a condition often associated with significant cognitive impairments. This study aims to evaluate learning ability in people with schizophrenia and-considering the evidence that antipsychotic medication can additionally impair cognitive performance-explore the potential impact of anticholinergic burden on verbal and visual learning.

Method

The study included 86 patients with schizophrenia, treated with clozapine, who had persisting negative symptoms. They were assessed at baseline, weeks 8, 24 and 52 using Positive and Negative Syndrome Scale, Hopkins Verbal Learning Test-Revised (HVLT-R) and Brief Visuospatial Memory Test-R (BVMT-R).

Results

There were no significant improvements in verbal or visual learning across all measurements. Neither the clozapine/norclozapine ratio nor anticholinergic cognitive burden significantly predicted participants' total learning. Premorbid IQ was significantly associated with verbal learning on the HVLT-R.

Conclusions

These findings advance our understanding of cognitive performance in people with schizophrenia and demonstrate limited learning performance in individuals with treatment-refractory schizophrenia.

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.

Inflammatory disequilibrium and lateral ventricular enlargement in treatment-resistant schizophrenia

Treatment-resistant schizophrenia (TRS) patient respond poorly to antipsychotics. Inflammatory imbalance involving pro- and anti-inflammatory cytokines may play an important role in the mechanism of antipsychotic-medication response. This study aimed to investigate immune imbalance and how the latter relates to clinical manifestations in patients with TRS. The level of net inflammation was estimated by evaluating the immune-inflammatory response system and compensatory immune-regulatory reflex system (IRS/CIRS) in 52 patients with TRS, 47 with non-TRS, and 56 sex and age matched healthy controls. The immune biomarkers mainly included macrophagic M1, T helper, Th-1, Th-2, Th-17, and T regulatory cytokines and receptors. Plasma cytokine levels were measured using enzyme-linked immunosorbent assay. Psychopathology was assessed using the Positive and Negative Syndrome Scale (PANSS). Subcortical volumes were quantified using a 3-T Prisma Magnetic Resonance Imaging scanner. The results showed that (1) patients with TRS were characterized by activated pro-inflammatory cytokines and relatively insufficient anti-inflammatory cytokines, with an elevated IRS/CIRS ratio indicating a new homeostatic immune setpoint; (2) IRS/CIRS ratio was positively correlated with larger lateral ventricle volume and higher PANSS score in patients with TRS. Our findings highlighted the inflammatory disequilibrium as a potential pathophysiological process of TRS.

Short-term antipsychotic treatment response in early-onset, typical-onset, and late-onset first episode schizophrenia

In schizophrenia, the age at illness onset may reflect genetic loading and predict prognosis. We aimed to compare the pre-treatment symptom profiles and clinical symptom responses to antipsychotic treatment of individuals with late-onset schizophrenia (LOS; onset age: 40-59 years) with individuals with early-onset schizophrenia (EOS; onset age < 18 years) or typical-onset schizophrenia (TOS; onset age: 18-39 years). We conducted an 8-week cohort study in inpatient departments of five mental health hospitals in five cities in China. We included 106 individuals with LOS, 80 with EOS, and 214 with TOS. Their onset of schizophrenia was within three years and the disorders were minimally treated. The Positive and Negative Syndrome Scale (PANSS) was used to evaluate clinical symptoms at baseline and after 8 weeks of antipsychotic treatment. Mixed effect models were used to compare symptom improvement within eight weeks. Antipsychotic therapy reduced all PANSS factor scores in all three groups. LOS had significantly better improvement in PANSS positive factor scores than EOS at week 8 after adjusting for sex, duration of illness, dose equivalents of antipsychotics at baseline, sites as fixed effects, and individuals as random effects. LOS was associated with reduced positive factor scores at week 8 when receiving 1 mg olanzapine dose equivalent per 1 kg body weight compared with EOS or TOS. In conclusion, LOS had better early improvement of positive symptoms than EOS and TOS. Thus, personalized treatment for schizophrenia should consider the age of onset.

Cognitive performance at first episode of psychosis and the relationship with future treatment resistance: Evidence from an international prospective cohort study

BACKGROUND

Antipsychotic treatment resistance affects up to a third of individuals with schizophrenia, with recent research finding systematic biological differences between antipsychotic resistant and responsive patients. Our aim was to determine whether cognitive impairment at first episode significantly differs between future antipsychotic responders and resistant cases.

METHODS

Analysis of data from seven international cohorts of first-episode psychosis (FEP) with cognitive data at baseline (N = 683) and follow-up data on antipsychotic treatment response: 605 treatment responsive and 78 treatment resistant cases. Cognitive measures were grouped into seven cognitive domains based on the pre-existing literature. We ran multiple imputation for missing data and used logistic regression to test for associations between cognitive performance at FEP and treatment resistant status at follow-up.

RESULTS

On average patients who were future classified as treatment resistant reported poorer performance across most cognitive domains at baseline. Univariate logistic regressions showed that antipsychotic treatment resistance cases had significantly poorer IQ/general cognitive functioning at FEP (OR = 0.70, p = .003). These findings remained significant after adjusting for additional variables in multivariable analyses (OR = 0.76, p = .049).

CONCLUSIONS

Although replication in larger studies is required, it appears that deficits in IQ/general cognitive functioning at first episode are associated with future treatment resistance. Cognitive variables may be able to provide further insight into neurodevelopmental factors associated with treatment resistance or act as early predictors of treatment resistance, which could allow prompt identification of refractory illness and timely interventions.

Psychotic disorders as a framework for precision psychiatry

People with psychotic disorders can show marked interindividual variations in the onset of illness, responses to treatment and relapse, but they receive broadly similar clinical care. Precision psychiatry is an approach that aims to stratify people with a given disorder according to different clinical outcomes and tailor treatment to their individual needs. At present, interindividual differences in outcomes of psychotic disorders are difficult to predict on the basis of clinical assessment alone. Therefore, current research in psychosis seeks to build models that predict outcomes by integrating clinical information with a range of biological measures. Here, we review recent progress in the application of precision psychiatry to psychotic disorders and consider the challenges associated with implementing this approach in clinical practice.

Machine learning methods to predict outcomes of pharmacological treatment in psychosis

In recent years, machine learning (ML) has been a promising approach in the research of treatment outcome prediction in psychosis. In this study, we reviewed ML studies using different neuroimaging, neurophysiological, genetic, and clinical features to predict antipsychotic treatment outcomes in patients at different stages of schizophrenia. Literature available on PubMed until March 2022 was reviewed. Overall, 28 studies were included, among them 23 using a single-modality approach and 5 combining data from multiple modalities. The majority of included studies considered structural and functional neuroimaging biomarkers as predictive features used in ML models. Specifically, functional magnetic resonance imaging (fMRI) features contributed to antipsychotic treatment response prediction of psychosis with good accuracies. Additionally, several studies found that ML models based on clinical features might present adequate predictive ability. Importantly, by examining the additive effects of combining features, the predictive value might be improved by applying multimodal ML approaches. However, most of the included studies presented several limitations, such as small sample sizes and a lack of replication tests. Moreover, considerable clinical and analytical heterogeneity among included studies posed a challenge in synthesizing findings and generating robust overall conclusions. Despite the complexity and heterogeneity of methodology, prognostic features, clinical presentation, and treatment approaches, studies included in this review suggest that ML tools may have the potential to predict treatment outcomes of psychosis accurately. Future studies need to focus on refining feature characterization, validating prediction models, and evaluate their translation in real-world clinical practice.

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.

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.

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.

Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment

PURPOSE OF REVIEW

This review will cover the most relevant findings on the use of machine learning (ML) techniques in the field of non-affective psychosis, by summarizing the studies published in the last three years focusing on illness detection and treatment.

RECENT FINDINGS

Multiple ML tools that include mostly supervised approaches such as support vector machine, gradient boosting, and random forest showed promising results by applying these algorithms to various sources of data: socio-demographic information, EEG, language, digital content, blood biomarkers, neuroimaging, and electronic health records. However, the overall performance, in the binary classification case, varied from 0.49, which is to be considered very low (i.e., noise), to over 0.90. These results are fully justified by different factors, some of which may be attributable to the preprocessing of the data, the wide variety of the data, and the a-priori setting of hyperparameters. One of the main limitations of the field is the lack of stratification of results based on biological sex, given that psychosis presents differently in men and women; hence, the necessity to tailor identification tools and data analytic strategies. Timely identification and appropriate treatment are key factors in reducing the consequences of psychotic disorders. In recent years, the emergence of new analytical tools based on artificial intelligence such as supervised ML approaches showed promises as a potential breakthrough in this field. However, ML applications in everyday practice are still in its infancy.

Association between psychiatric hospitalizations of patients with schizophrenia and polygenic risk scores based on genes with altered expression by antipsychotics

OBJECTIVE

To test whether a schizophrenia polygenic risk score (PRS) based on the subset of polymorphisms that affect brain expression of genes with altered expression by antipsychotics (exprAP PRS) is associated with psychiatric readmission of patients with schizophrenia.

METHODS

The study involved 427 patients with schizophrenia. Genes with altered expression by antipsychotics were extracted from the Comparative Toxigenomics Database. ExprAP PRS was estimated using the clumping and thresholding (p < 0.05) method. Two additional PRS were tested based on subsets of exprAP polymorphisms whose schizophrenia risk allele has the same (unrestored PRS) or opposite (restored PRS) direction of effect on gene expression than antipsychotics. A general SCZ PRS was tested for comparison. Logistic and ordinal regression were used to test for association of each PRS with ever readmission and admission history, an outcome based on length and number of admissions, respectively. Webgestalt was used for Gene Ontology enrichment analysis.

RESULTS

ExprAP PRS was associated with ever readmission (OR = 1.48, 95%CI:1.10-1.97) and admission history (OR = 1.30, 95%CI 1.07-1.57). SCZ PRS (OR = 1.22, 95%CI: 1.01-1.48) and unrestored PRS (OR = 1.26, 95%CI 1.04-1.53) were only associated with admission history. Genes at exprAP PRS were enriched in regulation of cytokine production.

CONCLUSION

Our findings suggest that PRS based on genes with altered expression by antipsychotics may be better predictors of readmission than SCZ PRS, warranting further investigation in larger cohorts of patients. The action of antipsychotics may be related to brain gene expression, mainly in genes involved in immunity.

Latent Profiles of Premorbid Adjustment in Schizophrenia and Their Correlation with Measures of Recovery

Premorbid adjustment (PA) has classically been defined as psychosocial functioning in the areas of education, occupation, social and interpersonal relationships prior to evidence of characteristic positive symptomatology. It is a concept which possesses ample evidence regarding its predictive nature for the course of Schizophrenia. The study aimed to analyze the latent profiles of premorbid adjustment and their relationship with symptomatology, functionality, subjective recovery, stigma resistance and years of untreated psychosis. Latent class analysis (LCA) was used to elaborate a solution of three premorbid adjustment profiles in a sample of 217 patients diagnosed with Schizophrenia from Public Mental Health Centers in the city of Arica, Chile. The results show that premorbid adjustment was significantly correlated with recovery indicators and that latent profiles of better premorbid adjustment predict better outcomes in subjective recovery and stigma resistance. The results show that premorbid adjustment not only has implications for the severity of the disorder, but that psychosocial functioning prior to psychosis affects the patient's subjectivity, the representation of the disorder and the recovery process.

Dopaminergic dysfunction and excitatory/inhibitory imbalance in treatment-resistant schizophrenia and novel neuromodulatory treatment

Antipsychotic drugs are the mainstay in the treatment of schizophrenia. However, one-third of patients do not show adequate improvement in positive symptoms with non-clozapine antipsychotics. Additionally, approximately half of them show poor response to clozapine, electroconvulsive therapy, or other augmentation strategies. However, the development of novel treatment for these conditions is difficult due to the complex and heterogenous pathophysiology of treatment-resistant schizophrenia (TRS). Therefore, this review provides key findings, potential treatments, and a roadmap for future research in this area. First, we review the neurobiological pathophysiology of TRS, particularly the dopaminergic, glutamatergic, and GABAergic pathways. Next, the limitations of existing and promising treatments are presented. Specifically, this article focuses on the therapeutic potential of neuromodulation, including electroconvulsive therapy, repetitive transcranial magnetic stimulation, transcranial direct current stimulation, and deep brain stimulation. Finally, we propose multivariate analyses that integrate various perspectives of the pathogenesis, such as dopaminergic dysfunction and excitatory/inhibitory imbalance, thereby elucidating the heterogeneity of TRS that could not be obtained by conventional statistics. These analyses can in turn lead to a precision medicine approach with closed-loop neuromodulation targeting the detected pathophysiology of TRS.

Genome-wide association analyses of symptom severity among clozapine-treated patients with schizophrenia spectrum disorders

Clozapine is the most effective antipsychotic for patients with treatment-resistant schizophrenia. However, response is highly variable and possible genetic underpinnings of this variability remain unknown. Here, we performed polygenic risk score (PRS) analyses to estimate the amount of variance in symptom severity among clozapine-treated patients explained by PRSs (R2) and examined the association between symptom severity and genotype-predicted CYP1A2, CYP2D6, and CYP2C19 enzyme activity. Genome-wide association (GWA) analyses were performed to explore loci associated with symptom severity. A multicenter cohort of 804 patients (after quality control N = 684) with schizophrenia spectrum disorder treated with clozapine were cross-sectionally assessed using the Positive and Negative Syndrome Scale and/or the Clinical Global Impression-Severity (CGI-S) scale. GWA and PRS regression analyses were conducted. Genotype-predicted CYP1A2, CYP2D6, and CYP2C19 enzyme activities were calculated. Schizophrenia-PRS was most significantly and positively associated with low symptom severity (p = 1.03 × 10-3; R2 = 1.85). Cross-disorder-PRS was also positively associated with lower CGI-S score (p = 0.01; R2 = 0.81). Compared to the lowest tertile, patients in the highest schizophrenia-PRS tertile had 1.94 times (p = 6.84×10-4) increased probability of low symptom severity. Higher genotype-predicted CYP2C19 enzyme activity was independently associated with lower symptom severity (p = 8.44×10-3). While no locus surpassed the genome-wide significance threshold, rs1923778 within NFIB showed a suggestive association (p = 3.78×10-7) with symptom severity. We show that high schizophrenia-PRS and genotype-predicted CYP2C19 enzyme activity are independently associated with lower symptom severity among individuals treated with clozapine. Our findings open avenues for future pharmacogenomic projects investigating the potential of PRS and genotype-predicted CYP-activity in schizophrenia.

The potential of precision psychiatry: what is in reach?

Progress in developing personalised care for mental disorders is supported by numerous proof-of-concept machine learning studies in the area of risk assessment, diagnostics and precision prescribing. Most of these studies primarily use clinical data, but models might benefit from additional neuroimaging, blood and genetic data to improve accuracy. Combined, multimodal models might offer potential for stratification of patients for treatment. Clinical implementation of machine learning is impeded by a lack of wider generalisability, with efforts primarily focused on psychosis and dementia. Studies across all diagnostic groups should work to test the robustness of machine learning models, which is an essential first step to clinical implementation, and then move to prospective clinical validation. Models need to exceed clinicians' heuristics to be useful, and safe, in routine decision-making. Engagement of clinicians, researchers and patients in digitalisation and 'big data' approaches are vital to allow the generation and accessibility of large, longitudinal, prospective data needed for precision psychiatry to be applied into real-world psychiatric care.

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

IMPORTANCE

About 20% to 30% of people with schizophrenia have psychotic symptoms that do not respond adequately to first-line antipsychotic treatment. This clinical presentation, chronic and highly disabling, is known as treatment-resistant schizophrenia (TRS). The causes of treatment resistance and their relationships with causes underlying schizophrenia are largely unknown. Adequately powered genetic studies of TRS are scarce because of the difficulty in collecting data from well-characterized TRS cohorts.

OBJECTIVE

To examine the genetic architecture of TRS through the reassessment of genetic data from schizophrenia studies and its validation in carefully ascertained clinical samples.

DESIGN, SETTING, AND PARTICIPANTS

Two case-control genome-wide association studies (GWASs) of schizophrenia were performed in which the case samples were defined as individuals with TRS (n = 10 501) and individuals with non-TRS (n = 20 325). The differences in effect sizes for allelic associations were then determined between both studies, the reasoning being such differences reflect treatment resistance instead of schizophrenia. Genotype data were retrieved from the CLOZUK and Psychiatric Genomics Consortium (PGC) schizophrenia studies. The output was validated using polygenic risk score (PRS) profiling of 2 independent schizophrenia cohorts with TRS and non-TRS: a prevalence sample with 817 individuals (Cardiff Cognition in Schizophrenia [CardiffCOGS]) and an incidence sample with 563 individuals (Genetics Workstream of the Schizophrenia Treatment Resistance and Therapeutic Advances [STRATA-G]).

MAIN OUTCOMES AND MEASURES

GWAS of treatment resistance in schizophrenia. The results of the GWAS were compared with complex polygenic traits through a genetic correlation approach and were used for PRS analysis on the independent validation cohorts using the same TRS definition.

RESULTS

The study included a total of 85 490 participants (48 635 [56.9%] male) in its GWAS stage and 1380 participants (859 [62.2%] male) in its PRS validation stage. Treatment resistance in schizophrenia emerged as a polygenic trait with detectable heritability (1% to 4%), and several traits related to intelligence and cognition were found to be genetically correlated with it (genetic correlation, 0.41-0.69). PRS analysis in the CardiffCOGS prevalence sample showed a positive association between TRS and a history of taking clozapine (r2 = 2.03%; P = .001), which was replicated in the STRATA-G incidence sample (r2 = 1.09%; P = .04).

CONCLUSIONS AND RELEVANCE

In this GWAS, common genetic variants were differentially associated with TRS, and these associations may have been obscured through the amalgamation of large GWAS samples in previous studies of broadly defined schizophrenia. Findings of this study suggest the validity of meta-analytic approaches for studies on patient outcomes, including treatment resistance.

Predicting patients who will drop out of out-patient psychotherapy using machine learning algorithms

BACKGROUND

About 30% of patients drop out of cognitive-behavioural therapy (CBT), which has implications for psychiatric and psychological treatment. Findings concerning drop out remain heterogeneous.

AIMS

This paper aims to compare different machine-learning algorithms using nested cross-validation, evaluate their benefit in naturalistic settings, and identify the best model as well as the most important variables.

METHOD

The data-set consisted of 2543 out-patients treated with CBT. Assessment took place before session one. Twenty-one algorithms and ensembles were compared. Two parameters (Brier score, area under the curve (AUC)) were used for evaluation.

RESULTS

The best model was an ensemble that used Random Forest and nearest-neighbour modelling. During the training process, it was significantly better than generalised linear modelling (GLM) (Brier score: d = -2.93, 95% CI (-3.95, -1.90)); AUC: d = 0.59, 95% CI (0.11 to 1.06)). In the holdout sample, the ensemble was able to correctly identify 63.4% of cases of patients, whereas the GLM only identified 46.2% correctly. The most important predictors were lower education, lower scores on the Personality Style and Disorder Inventory (PSSI) compulsive scale, younger age, higher scores on the PSSI negativistic and PSSI antisocial scale as well as on the Brief Symptom Inventory (BSI) additional scale (mean of the four additional items) and BSI overall scale.

CONCLUSIONS

Machine learning improves drop-out predictions. However, not all algorithms are suited to naturalistic data-sets and binary events. Tree-based and boosted algorithms including a variable selection process seem well-suited, whereas more advanced algorithms such as neural networks do not.

What Do We Know About the Genetic Architecture of Psychopathology?

In the second half of the twentieth century, twin and family studies established beyond a reasonable doubt that all forms of psychopathology are substantially heritable and highly polygenic. These conclusions were simultaneously an important theoretical advance and a difficult methodological obstacle, as it became clear that heritability is universal and undifferentiated across forms of psychopathology, and the radical polygenicity of genetic effects limits the biological insight provided by genetically informed studies at the phenotypic level. The paradigm-shifting revolution brought on by the Human Genome Project has recapitulated the great methodological promise and the profound theoretical difficulties of the twin study era. We review these issues using the rubric of genetic architecture, which we define as a search for specific genetic insight that adds to the general conclusion that psychopathology is heritable and polygenic. Although significant problems remain, we see many promising avenues for progress.

Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Neuropsychological differences between treatment-resistant and treatment-responsive schizophrenia: a meta-analysis

Antipsychotic treatment resistance affects up to a third of individuals with schizophrenia. Of those affected, 70-84% are reported to be treatment resistant from the outset. This raises the possibility that the neurobiological mechanisms of treatment resistance emerge before the onset of psychosis and have a neurodevelopmental origin. Neuropsychological investigations can offer important insights into the nature, origin and pathophysiology of treatment-resistant schizophrenia (TRS), but methodological limitations in a still emergent field of research have obscured the neuropsychological discriminability of TRS. We report on the first systematic review and meta-analysis to investigate neuropsychological differences between TRS patients and treatment-responsive controls across 17 published studies (1864 participants). Five meta-analyses were performed in relation to (1) executive function, (2) general cognitive function, (3) attention, working memory and processing speed, (4) verbal memory and learning, and (5) visual-spatial memory and learning. Small-to-moderate effect sizes emerged for all domains. Similarly to previous comparisons between unselected, drug-naïve and first-episode schizophrenia samples v. healthy controls in the literature, the largest effect size was observed in verbal memory and learning [dl = -0.53; 95% confidence interval (CI) -0.29 to -0.76; z = 4.42; p < 0.001]. A sub-analysis of language-related functions, extracted from across the primary domains, yielded a comparable effect size (dl = -0.53, 95% CI -0.82 to -0.23; z = 3.45; p < 0.001). Manipulating our sampling strategy to include or exclude samples selected for clozapine response did not affect the pattern of findings. Our findings are discussed in relation to possible aetiological contributions to TRS.

Cross-sectional study comparing cognitive function in treatment responsive versus treatment non-responsive schizophrenia: evidence from the STRATA study

BACKGROUND

70%-84% of individuals with antipsychotic treatment resistance show non-response from the first episode. Emerging cross-sectional evidence comparing cognitive profiles in treatment resistant schizophrenia to treatment-responsive schizophrenia has indicated that verbal memory and language functions may be more impaired in treatment resistance. We sought to confirm this finding by comparing cognitive performance between antipsychotic non-responders (NR) and responders (R) using a brief cognitive battery for schizophrenia, with a primary focus on verbal tasks compared against other measures of cognition.

DESIGN

Cross-sectional.

SETTING

This cross-sectional study recruited antipsychotic treatment R and antipsychotic NR across four UK sites. Cognitive performance was assessed using the Brief Assessment of Cognition in Schizophrenia (BACS).

PARTICIPANTS

One hundred and six participants aged 18-65 years with a diagnosis of schizophrenia or schizophreniform disorder were recruited according to their treatment response, with 52 NR and 54 R cases.

OUTCOMES

Composite and subscale scores of cognitive performance on the BACS. Group (R vs NR) differences in cognitive scores were investigated using univariable and multivariable linear regressions adjusted for age, gender and illness duration.

RESULTS

Univariable regression models observed no significant differences between R and NR groups on any measure of the BACS, including verbal memory (ß=-1.99, 95% CI -6.63 to 2.66, p=0.398) and verbal fluency (ß=1.23, 95% CI -2.46 to 4.91, p=0.510). This pattern of findings was consistent in multivariable models.

CONCLUSIONS

The lack of group difference in cognition in our sample is likely due to a lack of clinical distinction between our groups. Future investigations should aim to use machine learning methods using longitudinal first episode samples to identify responder subtypes within schizophrenia, and how cognitive factors may interact within this.

TRAIL REGISTRATION NUMBER

REC: 15/LO/0038.

The impact of manic symptoms in first-episode psychosis: Findings from the UK National EDEN study

OBJECTIVE

The extant literature is inconsistent over whether manic symptoms in first-episode psychosis (FEP) impact on its development and trajectory. This study addressed the following: (1) Does Duration of Untreated Illness (DUI) and Duration of Untreated Psychosis (DUP) differ between FEP patients with and without manic symptoms? (2) Do manic symptoms in FEP have an impact on time to remission over 1 year?

METHODS

We used data from the National EDEN study, a longitudinal cohort of patients with FEP accessing early intervention services (EIS) in England, which measured manic, positive and negative psychotic symptoms, depression and functioning at service entry and 1 year. Data from 913 patients with FEP (639 without manic symptoms, 237 with manic symptoms) were analysed using both general linear modelling and survival analysis.

RESULTS

Compared to FEP patients without manic symptoms, those with manic symptoms had a significantly longer DUI, though no difference in DUP. At baseline, people with manic symptoms had higher levels of positive and negative psychotic symptoms, depression and worse functioning. At 12 months, people with manic symptoms had significantly poorer functioning and more positive psychotic symptoms. The presence of manic symptoms delayed time to remission over 1 year. There was a 19% reduced rate of remission for people with manic symptoms compared to those without.

CONCLUSIONS

Manic symptoms in FEP are associated with delays to treatment. This poorer trajectory persists over 1 year. They appear to be a vulnerable and under-recognised group for poor outcome and need more focussed early intervention treatment.

Genetic architecture of schizophrenia: a review of major advancements

Schizophrenia is a severe psychiatric disorder with high heritability. Consortia efforts and technological advancements have led to a substantial increase in knowledge of the genetic architecture of schizophrenia over the past decade. In this article, we provide an overview of the current understanding of the genetics of schizophrenia, outline remaining challenges, and summarise future directions of research. World-wide collaborations have resulted in genome-wide association studies (GWAS) in over 56 000 schizophrenia cases and 78 000 controls, which identified 176 distinct genetic loci. The latest GWAS from the Psychiatric Genetics Consortium, available as a pre-print, indicates that 270 distinct common genetic loci have now been associated with schizophrenia. Polygenic risk scores can currently explain around 7.7% of the variance in schizophrenia case-control status. Rare variant studies have implicated eight rare copy-number variants, and an increased burden of loss-of-function variants in SETD1A, as increasing the risk of schizophrenia. The latest exome sequencing study, available as a pre-print, implicates a burden of rare coding variants in a further nine genes. Gene-set analyses have demonstrated significant enrichment of both common and rare genetic variants associated with schizophrenia in synaptic pathways. To address current challenges, future genetic studies of schizophrenia need increased sample sizes from more diverse populations. Continued expansion of international collaboration will likely identify new genetic regions, improve fine-mapping to identify causal variants, and increase our understanding of the biology and mechanisms of schizophrenia.

Associations Between Schizophrenia Polygenic Liability, Symptom Dimensions, and Cognitive Ability in Schizophrenia

IMPORTANCE

Schizophrenia is a clinically heterogeneous disorder. It is currently unclear how variability in symptom dimensions and cognitive ability is associated with genetic liability for schizophrenia.

OBJECTIVE

To determine whether phenotypic dimensions within schizophrenia are associated with genetic liability to schizophrenia, other neuropsychiatric disorders, and intelligence.

DESIGN, SETTING, AND PARTICIPANTS

In a genetic association study, 3 cross-sectional samples of 1220 individuals with a diagnosis of schizophrenia were recruited from community, inpatient, and voluntary sector mental health services across the UK. Confirmatory factor analysis was used to create phenotypic dimensions from lifetime ratings of the Scale for the Assessment of Positive Symptoms, Scale for the Assessment of Negative Symptoms, and the MATRICS Consensus Cognitive Battery. Analyses of polygenic risk scores (PRSs) were used to assess whether genetic liability to schizophrenia, other neuropsychiatric disorders, and intelligence were associated with these phenotypic dimensions. Data collection for the cross-sectional studies occurred between 1993 and 2016. Data analysis for this study occurred between January 2019 and March 2021.

MAIN OUTCOMES AND MEASURES

Outcome measures included phenotypic dimensions defined from confirmatory factor analysis relating to positive symptoms, negative symptoms of diminished expressivity, negative symptoms of motivation and pleasure, disorganized symptoms, and current cognitive ability. Exposure measures included PRSs for schizophrenia, bipolar disorder, major depression, attention-deficit/hyperactivity disorder, autism spectrum disorder, and intelligence.

RESULTS

Of the 1220 study participants, 817 were men (67.0%). Participants' mean (SD) age at interview was 43.10 (12.74) years. Schizophrenia PRS was associated with increased disorganized symptom dimension scores in both a 5-factor model (β = 0.14; 95% CI, 0.07-0.22; P = 2.80 × 10-4) and a 3-factor model across all samples (β = 0.10; 95% CI, 0.05-0.15; P = 2.80 × 10-4). Current cognitive ability was associated with genetic liability to schizophrenia (β = -0.11; 95% CI, -0.19 to -0.04; P = 1.63 × 10-3) and intelligence (β = 0.23; 95% CI, 0.16-0.30; P = 1.52 × 10-10). After controlling for estimated premorbid IQ, current cognitive performance was associated with schizophrenia PRS (β = -0.08; 95% CI, -0.14 to -0.02; P = 8.50 × 10-3) but not intelligence PRS.

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that genetic liability for schizophrenia is associated with higher disorganized dimension scores but not other symptom dimensions. Cognitive performance in schizophrenia appears to reflect distinct contributions from genetic liabilities to both intelligence and schizophrenia.

Prevalence and predictors of treatment-resistant schizophrenia in a tertiary hospital in Northeast Brazil

OBJECTIVE

To investigate epidemiological factors related to treatment-resistant schizophrenia (TRS) in Northeast Brazil, a region where data about mental health are still scarce.

METHODS

This retrospective cross-sectional study included all patients with schizophrenia currently receiving treatment at the outpatient psychiatric clinic of a tertiary hospital in Northeast Brazil. They were divided into TRS and treatment-responsive groups, and epidemiological characteristics of both groups were compared. A logistic regression model investigated factors related to treatment resistance.

RESULTS

Two hundred and five patients were included, 155 treatment-resistant and 50 treatment-responsive. The TRS group had higher use of benzodiazepines (36.1 vs. 18%, p = 0.017) and antiepileptics (36.8 vs. 8.0%, p < 0.001), antipsychotic polypharmacy (28.6 vs. 8%, p = 0.003) and suicide attempts (35.6 vs. 20%, p = 0.04). Age at onset was younger (19.7±7.3 vs. 24.6±8.6 years, p = 0.001) and CGI was higher in TRS (3.72±1.00 vs. 3.16±1.00, p = 0.001). In logistic regression, being married was a protector (odds ratio [OR] = 0.248, 95% confidence interval [95%CI] 0.091-0.679, p = 0.007) and younger age at onset was a predictor (OR = 1.076, 95%CI 1.034-1.120, p < 0.001) of treatment resistance.

CONCLUSION

Early onset of disease was associated with more treatment resistance, while being married with less resistance. Clinicians should identify early predictors of resistance in order to reduce unfavorable outcomes.

Oxytocin system dysfunction in patients with treatment-resistant schizophrenia: Alterations of blood oxytocin levels and effect of a genetic variant of OXTR

Treatment-resistant schizophrenia (TRS) has a quite complex pathophysiology that includes not only severe positive symptoms but also other symptom domains. Much attention has been devoted to the overlapping psychological and biological profiles of schizophrenia and autistic spectrum disorder (ASD). We compared TRS patients (n = 30) with schizophrenia patients in remission (RemSZ, n = 28) and ASD patients (n = 28), focusing on general cognitive and social cognitive impairment and oxytocin system dysfunction. Our analyses revealed that there was no difference in oxytocin concentration among the three groups. The TRS patients' oxytocin blood concentrations were positively correlated with their processing speed and theory-of-mind scores, whereas the RemSZ and ASD groups had no significant relation with any measures. Rs53576, a single nucleotide polymorphism on the oxytocin receptor gene, affected social cognition abilities in the schizophrenia group. Although the overall findings are preliminary, they indicate that oxytocin system dysfunction could be involved in the serious cognitive deficits in TRS patients. Further, these results suggest that patients with TRS might have early neurodevelopmental abnormalities based on their shared biological features with ASD patients.

Schizophrenia polygenic risk scores in youth mental health: preliminary associations with diagnosis, clinical stage and functioning

BACKGROUND

The schizophrenia polygenic risk score (SCZ-PRS) is an emerging tool in psychiatry.

AIMS

We aimed to evaluate the utility of SCZ-PRS in a young, transdiagnostic, clinical cohort.

METHOD

SCZ-PRSs were calculated for young people who presented to early-intervention youth mental health clinics, including 158 patients of European ancestry, 113 of whom had longitudinal outcome data. We examined associations between SCZ-PRS and diagnosis, clinical stage and functioning at initial assessment, and new-onset psychotic disorder, clinical stage transition and functional course over time in contact with services.

RESULTS

Compared with a control group, patients had elevated PRSs for schizophrenia, bipolar disorder and depression, but not for any non-psychiatric phenotype (for example cardiovascular disease). Higher SCZ-PRSs were elevated in participants with psychotic, bipolar, depressive, anxiety and other disorders. At initial assessment, overall SCZ-PRSs were associated with psychotic disorder (odds ratio (OR) per s.d. increase in SCZ-PRS was 1.68, 95% CI 1.08-2.59, P = 0.020), but not assignment as clinical stage 2+ (i.e. discrete, persistent or recurrent disorder) (OR = 0.90, 95% CI 0.64-1.26, P = 0.53) or functioning (R = 0.03, P = 0.76). Longitudinally, overall SCZ-PRSs were not significantly associated with new-onset psychotic disorder (OR = 0.84, 95% CI 0.34-2.03, P = 0.69), clinical stage transition (OR = 1.02, 95% CI 0.70-1.48, P = 0.92) or persistent functional impairment (OR = 0.84, 95% CI 0.52-1.38, P = 0.50).

CONCLUSIONS

In this preliminary study, SCZ-PRSs were associated with psychotic disorder at initial assessment in a young, transdiagnostic, clinical cohort accessing early-intervention services. Larger clinical studies are needed to further evaluate the clinical utility of SCZ-PRSs, especially among individuals with high SCZ-PRS burden.

Towards personalized pharmacology: Antipsychotics and schizophrenia

Since the discovery of the first antipsychotic in 1952, many antipsychotic drugs have been developed, each with different pharmacokinetic and pharmacodynamic properties. The pharmacological heterogeneity of antipsychotic drugs should allow a personalized drug prescription adapted to the different clinical picture of schizophrenia. Schizophrenia is a chronic disease, during which 3 stages of pharmacological intervention can be identified: the first episode psychotic (FEP), the phase of therapeutic stabilization that can progress to situations of resistance, and the question of long-term prescription. During FEP, the choice of the first antipsychotic treatment seems to be underpinned by its safety profile in relation to the patient for whom it is prescribed, according to the adage start low and go-slow. The therapeutic stabilization phase is based on treatment optimization through a rigorous evaluation of the benefits-harm balance, with the use of tools such as personalized therapeutic drug monitoring and pharmacogenetics. Generally speaking, while some antipsychotic drugs seem to present a more favorable efficacy profile in certain situations, the differences are small, whereas the differences in safety are more important and should be considered in the first line. Individual factors such as the presence of co-morbidities, as well as previously experienced treatments must also be taken into account. Finally, the question of maintaining the prescription of antipsychotic drugs over the long term arises in view of the iatrogenic risk with controversial current data. Overall, the personalized prescription of antipsychotic drugs in schizophrenia remains limited by a lack of data in the literature, justifying the development of clinical studies in this field. But at present, the dogma remains that of primum non nocere.

A Synthetic Literature Review on the Management of Emerging Treatment Resistance in First Episode Psychosis: Can We Move towards Precision Intervention and Individualised Care?

Treatment resistance is prevalent in early intervention in psychosis services, and causes a significant burden for the individual. A wide range of variables are shown to contribute to treatment resistance in first episode psychosis (FEP). Heterogeneity in illness course and the complex, multidimensional nature of the concept of recovery calls for an evidence base to better inform practice at an individual level. Current gold standard treatments, adopting a ‘one-size fits all’ approach, may not be addressing the needs of many individuals. This following review will provide an update and critical appraisal of current clinical practices and methodological approaches for understanding, identifying, and managing early treatment resistance in early psychosis. Potential new treatments along with new avenues for research will be discussed. Finally, we will discuss and critique the application and translation of machine learning approaches to aid progression in this area. The move towards ‘big data’ and machine learning holds some prospect for stratifying intervention-based subgroups of individuals. Moving forward, better recognition of early treatment resistance is needed, along with greater sophistication and precision in predicting outcomes, so that effective evidence-based treatments can be appropriately tailored to the individual. Understanding the antecedents and the early trajectory of one’s illness may also be key to understanding the factors that drive illness course.

Predictors of Treatment-Resistant and Clozapine-Resistant Schizophrenia: A 12-Year Follow-up Study of First-Episode Schizophrenia-Spectrum Disorders

Studies on the long-term development and early predictors of treatment-resistant schizophrenia (TRS) and clozapine-resistant TRS (CR-TRS) in patients with first-episode schizophrenia-spectrum disorders (FES) are limited and have not considered the impact of early intervention services (EIS). This study aimed to explore the development of TRS and CR-TRS among patients with FES over 12 years of follow-up. Of the 1234 patients with FES, 15% developed TRS. A total of 450 patients with schizophrenia or schizoaffective disorder were included in a nested case-control study (157 TRS and 293 non-TRS). Younger age of onset, poorer premorbid social adjustment during adulthood, longer duration of first episode, a greater number of relapses, and a higher antipsychotic dose in the first 24 months were associated with earlier TRS. CR-TRS patients, constituting 25% of TRS patients, had a poorer premorbid social adjustment in late adolescence and longer delay before clozapine initiation compared with non-CR-TRS. CR-TRS had poorer clinical and functional outcomes at 12-year follow-up. However, TRS patients on clozapine had a lower mortality rate compared with non-TRS patients. EIS did not have a significant impact on the development of TRS, but patients in the EIS group had a shorter delay of clozapine initiation. Results suggested that neurodevelopmental factors, early clinical characteristics, and requirement for higher antipsychotic dose may be associated with TRS development, highlighting multiple pathways leading to this form of illness. Specific interventions including relapse prevention and early initiation of clozapine during the early course of illness may reduce the rate of TRS and improve patient outcomes.

Update on the genetic architecture of schizophrenia

A long-established hypothesis is that schizophrenia has a strong genetic component. In the early 1990s, the first genetic variant that substantially increases risk for psychosis was identified. Since this initial reporting of deletions in the chromosomal region 22q11.2, nearly two decades passed until substantial insights into schizophrenia’s genetic architecture were gained. Schizophrenia is a polygenic disorder and genetic risk is conferred by both common and rare alleles distributed across the genome. A small number of rare, deleterious copy number variants (CNVs) are associated with moderate to substantial increases in individual risk to schizophrenia. These deletions and duplications are also associated with a range of neurodevelopmental disorders. The diagnostic investigation of CNVs in patients with schizophrenia is likely to represent one of the first examples of genetic testing in clinical psychiatry. The prerequisites for this are currently being defined.

Familial and socioeconomic contributions to premorbid functioning in psychosis: Impact on age at onset and treatment response

Background.

Premorbid adjustment (PA) abnormalities in psychotic disorders are associated with an earlier age at onset (AAO) and unfavorable clinical outcomes, including treatment resistance. Prior family studies suggest that familial liability, likely reflecting increased genetic risk, and socioeconomic status (SES) contribute to premorbid maladjustment. However, their joint effect possibly indicating gene–environment interaction has not been evaluated.

Methods.

We examined whether family history of psychosis (FHP) and parental SES may predict PA and AAO in unrelated cases with first-episode psychosis (n = 108) and schizophrenia (n = 104). Premorbid academic and social functioning domains during childhood and early adolescence were retrospectively assessed. Regression analyses were performed to investigate main effects of FHP and parental SES, as well as their interaction. The relationships between PA, AAO, and response to antipsychotic medication were also explored.

Results.

Positive FHP associated with academic PA difficulties and importantly interacted with parental SES to moderate social PA during childhood (interaction p = 0.024). Positive FHP and parental SES did not predict differences in AAO. Nevertheless, an earlier AAO was observed among cases with worse social PA in childhood (β = −0.20; p = 0.005) and early adolescence (β = −0.19; p = 0.007). Further, confirming evidence emerged for an association between deficient childhood social PA and poor treatment response (p = 0.04).

Conclusions.

Familial risk for psychosis may interact with parental socioeconomic position influencing social PA in childhood. In addition, this study supports the link between social PA deviations, early psychosis onset, and treatment resistance, which highlights premorbid social functioning as a promising clinical indicator.

Translating insights from neuropsychiatric genetics and genomics for precision psychiatry

The primary aim of precision medicine is to tailor healthcare more closely to the needs of individual patients. This requires progress in two areas: the development of more precise treatments and the ability to identify patients or groups of patients in the clinic for whom such treatments are likely to be the most effective. There is widespread optimism that advances in genomics will facilitate both of these endeavors. It can be argued that of all medical specialties psychiatry has most to gain in these respects, given its current reliance on syndromic diagnoses, the minimal foundation of existing mechanistic knowledge, and the substantial heritability of psychiatric phenotypes. Here, we review recent advances in psychiatric genomics and assess the likely impact of these findings on attempts to develop precision psychiatry. Emerging findings indicate a high degree of polygenicity and that genetic risk maps poorly onto the diagnostic categories used in the clinic. The highly polygenic and pleiotropic nature of psychiatric genetics will impact attempts to use genomic data for prediction and risk stratification, and also poses substantial challenges for conventional approaches to gaining biological insights from genetic findings. While there are many challenges to overcome, genomics is building an empirical platform upon which psychiatry can now progress towards better understanding of disease mechanisms, better treatments, and better ways of targeting treatments to the patients most likely to benefit, thus paving the way for precision psychiatry.

Interaction of Cannabis Use Disorder and Striatal Connectivity in Antipsychotic Treatment Response

Antipsychotic (AP) medications are the mainstay for the treatment of schizophrenia spectrum disorders (SSD), but their efficacy is unpredictable and widely variable. Substantial efforts have been made to identify prognostic biomarkers that can be used to guide optimal prescription strategies for individual patients. Striatal regions involved in salience and reward processing are disrupted as a result of both SSD and cannabis use, and research demonstrates that striatal circuitry may be integral to response to AP drugs. In the present study, we used functional magnetic resonance imaging (fMRI) to investigate the relationship between a history of cannabis use disorder (CUD) and a striatal connectivity index (SCI), a previously developed neural biomarker for AP treatment response in SSD. Patients were part of a 12-week randomized, double-blind controlled treatment study of AP drugs. A sample of 48 first-episode SSD patients with no more than 2 weeks of lifetime exposure to AP medications, underwent a resting-state fMRI scan pretreatment. Treatment response was defined a priori as a binary (response/nonresponse) variable, and a SCI was calculated in each patient. We examined whether there was an interaction between lifetime CUD history and the SCI in relation to treatment response. We found that CUD history moderated the relationship between SCI and treatment response, such that it had little predictive value in SSD patients with a CUD history. In sum, our findings highlight that biomarker development can be critically impacted by patient behaviors that influence neurobiology, such as a history of CUD.