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Previous studies identified clusters of first-episode psychosis (FEP) patients based on cognition and premorbid adjustment. This study examined a range of socio-environmental risk factors associated with clusters of FEP, aiming a) to compare clusters of FEP and community controls using the Maudsley Environmental Risk Score for psychosis (ERS), a weighted sum of the following risks: paternal age, childhood adversities, cannabis use, and ethnic minority membership; b) to explore the putative differences in specific environmental risk factors in distinguishing within patient clusters and from controls.
Methods
A univariable general linear model (GLS) compared the ERS between 1,263 community controls and clusters derived from 802 FEP patients, namely, low (n = 223) and high-cognitive-functioning (n = 205), intermediate (n = 224) and deteriorating (n = 150), from the EU-GEI study. A multivariable GLS compared clusters and controls by different exposures included in the ERS.
Results
The ERS was higher in all clusters compared to controls, mostly in the deteriorating (β=2.8, 95% CI 2.3 3.4, η2 = 0.049) and the low-cognitive-functioning cluster (β=2.4, 95% CI 1.9 2.8, η2 = 0.049) and distinguished them from the cluster with high-cognitive-functioning. The deteriorating cluster had higher cannabis exposure (meandifference = 0.48, 95% CI 0.49 0.91) than the intermediate having identical IQ, and more people from an ethnic minority (meandifference = 0.77, 95% CI 0.24 1.29) compared to the high-cognitive-functioning cluster.
Conclusions
High exposure to environmental risk factors might result in cognitive impairment and lower-than-expected functioning in individuals at the onset of psychosis. Some patients’ trajectories involved risk factors that could be modified by tailored interventions.
Cannabis use severely affects the outcome of people with psychotic disorders, yet there is a lack of treatments. To address this, in 2019 the National Health Service (NHS) Cannabis Clinic for Psychosis (CCP) was developed to support adults suffering from psychosis to reduce and/or stop their cannabis use.
Aims
Examine outcome data from the first 46 individuals to complete the CCP's intervention.
Method
The sample (N = 46) consisted of adults (aged ≥ 18) with psychosis under the care of the South London and Maudsley NHS Foundation Trust, referred to the CCP between January 2020 and February 2023, who completed their intervention by September 2023. Clinical and functional measures were collected before (T0) and after (T1) the CCP intervention (one-to-one sessions and peer group attendance). Primary outcomes were changes in the Cannabis Use Disorders Identification Test-Revised (CUDIT-R) score and pattern of cannabis use. Secondary outcomes included T0–T1 changes in measures of delusions, paranoia, depression, anxiety and functioning.
Results
A reduction in the mean CUDIT-R score was observed between T0 (mean difference = 17.10, 95% CI = 15.54–18.67) and T1, with 73.91% of participants achieving abstinence and 26.09% reducing the frequency and potency of their use. Significant improvements in all clinical and functional outcomes were observed, with 90.70% being in work or education at T1 compared with 8.70% at T0. The variance in CUDIT-R scores explained between 34 and 64% of the variance in our secondary measures.
Conclusions
The CCP intervention is a feasible strategy to support cannabis use cessation/reduction and improve clinical and functional outcomes of people with psychotic disorders.
The association between cannabis and psychosis is established, but the role of underlying genetics is unclear. We used data from the EU-GEI case-control study and UK Biobank to examine the independent and combined effect of heavy cannabis use and schizophrenia polygenic risk score (PRS) on risk for psychosis.
Methods
Genome-wide association study summary statistics from the Psychiatric Genomics Consortium and the Genomic Psychiatry Cohort were used to calculate schizophrenia and cannabis use disorder (CUD) PRS for 1098 participants from the EU-GEI study and 143600 from the UK Biobank. Both datasets had information on cannabis use.
Results
In both samples, schizophrenia PRS and cannabis use independently increased risk of psychosis. Schizophrenia PRS was not associated with patterns of cannabis use in the EU-GEI cases or controls or UK Biobank cases. It was associated with lifetime and daily cannabis use among UK Biobank participants without psychosis, but the effect was substantially reduced when CUD PRS was included in the model. In the EU-GEI sample, regular users of high-potency cannabis had the highest odds of being a case independently of schizophrenia PRS (OR daily use high-potency cannabis adjusted for PRS = 5.09, 95% CI 3.08–8.43, p = 3.21 × 10−10). We found no evidence of interaction between schizophrenia PRS and patterns of cannabis use.
Conclusions
Regular use of high-potency cannabis remains a strong predictor of psychotic disorder independently of schizophrenia PRS, which does not seem to be associated with heavy cannabis use. These are important findings at a time of increasing use and potency of cannabis worldwide.
Positive, negative and disorganised psychotic symptom dimensions are associated with clinical and developmental variables, but differing definitions complicate interpretation. Additionally, some variables have had little investigation.
Aims
To investigate associations of psychotic symptom dimensions with clinical and developmental variables, and familial aggregation of symptom dimensions, in multiple samples employing the same definitions.
Method
We investigated associations between lifetime symptom dimensions and clinical and developmental variables in two twin and two general psychosis samples. Dimension symptom scores and most other variables were from the Operational Criteria Checklist. We used logistic regression in generalised linear mixed models for combined sample analysis (n = 875 probands). We also investigated correlations of dimensions within monozygotic (MZ) twin pairs concordant for psychosis (n = 96 pairs).
Results
Higher symptom scores on all three dimensions were associated with poor premorbid social adjustment, never marrying/cohabiting and earlier age at onset, and with a chronic course, most strongly for the negative dimension. The positive dimension was also associated with Black and minority ethnicity and lifetime cannabis use; the negative dimension with male gender; and the disorganised dimension with gradual onset, lower premorbid IQ and substantial within twin-pair correlation. In secondary analysis, disorganised symptoms in MZ twin probands were associated with lower premorbid IQ in their co-twins.
Conclusions
These results confirm associations that dimensions share in common and strengthen the evidence for distinct associations of co-occurring positive symptoms with ethnic minority status, negative symptoms with male gender and disorganised symptoms with substantial familial influences, which may overlap with influences on premorbid IQ.
A clinical tool to estimate the risk of treatment-resistant schizophrenia (TRS) in people with first-episode psychosis (FEP) would inform early detection of TRS and overcome the delay of up to 5 years in starting TRS medication.
Aims
To develop and evaluate a model that could predict the risk of TRS in routine clinical practice.
Method
We used data from two UK-based FEP cohorts (GAP and AESOP-10) to develop and internally validate a prognostic model that supports identification of patients at high-risk of TRS soon after FEP diagnosis. Using sociodemographic and clinical predictors, a model for predicting risk of TRS was developed based on penalised logistic regression, with missing data handled using multiple imputation. Internal validation was undertaken via bootstrapping, obtaining optimism-adjusted estimates of the model's performance. Interviews and focus groups with clinicians were conducted to establish clinically relevant risk thresholds and understand the acceptability and perceived utility of the model.
Results
We included seven factors in the prediction model that are predominantly assessed in clinical practice in patients with FEP. The model predicted treatment resistance among the 1081 patients with reasonable accuracy; the model's C-statistic was 0.727 (95% CI 0.723–0.732) prior to shrinkage and 0.687 after adjustment for optimism. Calibration was good (expected/observed ratio: 0.999; calibration-in-the-large: 0.000584) after adjustment for optimism.
Conclusions
We developed and internally validated a prediction model with reasonably good predictive metrics. Clinicians, patients and carers were involved in the development process. External validation of the tool is needed followed by co-design methodology to support implementation in early intervention services.
Psychosis is the generic name given to a range of illnesses that can affect the mind and interfere with how a person thinks, feels and behaves. The term psychosis covers several different conditions, for example, drug-induced psychosis, psychotic depression, schizoaffective disorder and schizophrenia spectrum disorders. The precise name used can change over time and will depend upon the pattern and length of difficulties that an individual has. A diagnosis of schizophrenia is considered the most severe type of psychotic illness and almost one person in every hundred people will be diagnosed at some point in their life. It used to be thought that schizophrenia was a discrete illness that was quite separate from other psychotic illnesses such as depressive psychosis.
Edited by
David Kingdon, University of Southampton,Paul Rowlands, Derbyshire Healthcare NHS foundation Trust,George Stein, Emeritus of the Princess Royal University Hospital
Psychosis is characterized by distortions in thinking (e.g. fixed, false beliefs), in perception (e.g. hearing voices or less commonly seeing things that are not there), emotions, language, sense of self and behaviour. Although it used to be thought that schizophrenia was a discrete entity, much recent evidence has shown that this is not so. Schizophrenia does not have clear boundaries; rather, it merges into schizoaffective disorder and bipolar disorder on the one hand and into schizotypal and paranoid personality on the other. It is best considered as the severe form of psychosis. The different psychotic disorders share some of the same risk factors and are sometimes associated with cognitive impairments, co-existing mental health conditions, substance misuse and physical health problems; the latter often develop over the course of the illness.
In this chapter, we review genetic and then environmental risk factors for psychosis. Much knowledge has accumulated regarding both in the last two decades. We now know that the aetiology of psychosis is multifactorial. Genetic and environmental factors occasionally act alone but usually in combination as well as operate at a number of levels and over time to influence an individual’s likelihood of developing psychotic symptoms.
Boduch-Grabka and Lev-Ari (2021) showed that so-called “native” British-English speakers judged statements produced by Polish-accented English speakers as less likely to be true than statements produced by “native” speakers and that prior exposure to Polish-accented English speech modulates this effect. Given the real-world consequences of this study, as well as our commitment to assessing and mitigating linguistic biases, we conducted a close replication, extending the work by collecting additional information about participants’ explicit biases towards Polish migrants in the UK. We did not reproduce the original pattern of results, observing no effect of speaker accent or exposure on comprehension or veracity. In addition, the measure of explicit bias did not predict differential veracity ratings for Polish- and British-accented speech. Although the current pattern of results differs from that of the original study, our finding that neither comprehension nor veracity were impacted by accent or exposure condition is not inconsistent with the Boduch-Grabka and Lev-Ari (2021) processing difficulty account of the accent-based veracity judgment effect. We explore possible explanations for the lack of replication and future directions for this work.
Incidence of first-episode psychosis (FEP) varies substantially across geographic regions. Phenotypes of subclinical psychosis (SP), such as psychotic-like experiences (PLEs) and schizotypy, present several similarities with psychosis. We aimed to examine whether SP measures varied across different sites and whether this variation was comparable with FEP incidence within the same areas. We further examined contribution of environmental and genetic factors to SP.
Methods
We used data from 1497 controls recruited in 16 different sites across 6 countries. Factor scores for several psychopathological dimensions of schizotypy and PLEs were obtained using multidimensional item response theory models. Variation of these scores was assessed using multi-level regression analysis to estimate individual and between-sites variance adjusting for age, sex, education, migrant, employment and relational status, childhood adversity, and cannabis use. In the final model we added local FEP incidence as a second-level variable. Association with genetic liability was examined separately.
Results
Schizotypy showed a large between-sites variation with up to 15% of variance attributable to site-level characteristics. Adding local FEP incidence to the model considerably reduced the between-sites unexplained schizotypy variance. PLEs did not show as much variation. Overall, SP was associated with younger age, migrant, unmarried, unemployed and less educated individuals, cannabis use, and childhood adversity. Both phenotypes were associated with genetic liability to schizophrenia.
Conclusions
Schizotypy showed substantial between-sites variation, being more represented in areas where FEP incidence is higher. This supports the hypothesis that shared contextual factors shape the between-sites variation of psychosis across the spectrum.
Edited by
Deepak Cyril D'Souza, Staff Psychiatrist, VA Connecticut Healthcare System; Professor of Psychiatry, Yale University School of Medicine,David Castle, University of Tasmania, Australia,Sir Robin Murray, Honorary Consultant Psychiatrist, Psychosis Service at the South London and Maudsley NHS Trust; Professor of Psychiatric Research at the Institute of Psychiatry
Edited by
Deepak Cyril D'Souza, Staff Psychiatrist, VA Connecticut Healthcare System; Professor of Psychiatry, Yale University School of Medicine,David Castle, University of Tasmania, Australia,Sir Robin Murray, Honorary Consultant Psychiatrist, Psychosis Service at the South London and Maudsley NHS Trust; Professor of Psychiatric Research at the Institute of Psychiatry
Edited by
Deepak Cyril D'Souza, Staff Psychiatrist, VA Connecticut Healthcare System; Professor of Psychiatry, Yale University School of Medicine,David Castle, University of Tasmania, Australia,Sir Robin Murray, Honorary Consultant Psychiatrist, Psychosis Service at the South London and Maudsley NHS Trust; Professor of Psychiatric Research at the Institute of Psychiatry
Edited by
Deepak Cyril D'Souza, Staff Psychiatrist, VA Connecticut Healthcare System; Professor of Psychiatry, Yale University School of Medicine,David Castle, University of Tasmania, Australia,Sir Robin Murray, Honorary Consultant Psychiatrist, Psychosis Service at the South London and Maudsley NHS Trust; Professor of Psychiatric Research at the Institute of Psychiatry
Edited by
Deepak Cyril D'Souza, Staff Psychiatrist, VA Connecticut Healthcare System; Professor of Psychiatry, Yale University School of Medicine,David Castle, University of Tasmania, Australia,Sir Robin Murray, Honorary Consultant Psychiatrist, Psychosis Service at the South London and Maudsley NHS Trust; Professor of Psychiatric Research at the Institute of Psychiatry
Edited by
Deepak Cyril D'Souza, Staff Psychiatrist, VA Connecticut Healthcare System; Professor of Psychiatry, Yale University School of Medicine,David Castle, University of Tasmania, Australia,Sir Robin Murray, Honorary Consultant Psychiatrist, Psychosis Service at the South London and Maudsley NHS Trust; Professor of Psychiatric Research at the Institute of Psychiatry
Edited by
Deepak Cyril D'Souza, Staff Psychiatrist, VA Connecticut Healthcare System; Professor of Psychiatry, Yale University School of Medicine,David Castle, University of Tasmania, Australia,Sir Robin Murray, Honorary Consultant Psychiatrist, Psychosis Service at the South London and Maudsley NHS Trust; Professor of Psychiatric Research at the Institute of Psychiatry
Edited by
Deepak Cyril D'Souza, Staff Psychiatrist, VA Connecticut Healthcare System; Professor of Psychiatry, Yale University School of Medicine,David Castle, University of Tasmania, Australia,Sir Robin Murray, Honorary Consultant Psychiatrist, Psychosis Service at the South London and Maudsley NHS Trust; Professor of Psychiatric Research at the Institute of Psychiatry
Edited by
Deepak Cyril D'Souza, Staff Psychiatrist, VA Connecticut Healthcare System; Professor of Psychiatry, Yale University School of Medicine,David Castle, University of Tasmania, Australia,Sir Robin Murray, Honorary Consultant Psychiatrist, Psychosis Service at the South London and Maudsley NHS Trust; Professor of Psychiatric Research at the Institute of Psychiatry
Edited by
Deepak Cyril D'Souza, Staff Psychiatrist, VA Connecticut Healthcare System; Professor of Psychiatry, Yale University School of Medicine,David Castle, University of Tasmania, Australia,Sir Robin Murray, Honorary Consultant Psychiatrist, Psychosis Service at the South London and Maudsley NHS Trust; Professor of Psychiatric Research at the Institute of Psychiatry
Edited by
Deepak Cyril D'Souza, Staff Psychiatrist, VA Connecticut Healthcare System; Professor of Psychiatry, Yale University School of Medicine,David Castle, University of Tasmania, Australia,Sir Robin Murray, Honorary Consultant Psychiatrist, Psychosis Service at the South London and Maudsley NHS Trust; Professor of Psychiatric Research at the Institute of Psychiatry