Drug-Induced Psychosis: How to Avoid Star Gazing in Schizophrenia Research by Looking at More Obvious Sources of Light

The prevalent view today is that schizophrenia is a syndrome rather than a specific disease. Liability to schizophrenia is highly heritable. It appears that multiple genetic and environmental factors operate together to push individuals over a threshold into expressing the characteristic clinical picture. One environmental factor which has been curiously neglected is the evidence that certain drugs can induce schizophrenia-like psychosis. In the last 60 years, improved understanding of the relationship between drug abuse and psychosis has contributed substantially to our modern view of the disorder suggesting that liability to psychosis in general, and to schizophrenia in particular, is distributed trough the general population in a similar continuous way to liability to medical disorders such as hypertension and diabetes. In this review we examine the main hypotheses resulting from the link observed between the most common psychotomimetic drugs (lysergic acid diethylamide, amphetamines, cannabis, phencyclidine) and schizophrenia

Complement system biomarkers in first episode psychosis

Several lines of evidence implicate immunological/inflammatory factors in development of schizophrenia. Complement is a key driver of inflammation, and complement dysregulation causes pathology in many diseases. Here we exploredwhether complement dysregulation occurred in first episode psychosis (FEP) andwhether this provides a source of biomarkers. Eleven complement analytes (C1q, C3, C4, C5, factor B [FB], terminal complement complex [TCC], factor H [FH], FH-related proteins [FHR125], Properdin, C1 inhibitor [C1inh], soluble complement receptor 1 [CR1]) plus C-reactive protein (CRP) were measured in serum from 136 first episode psychosis (FEP) cases and 42 mentally healthy controls using established in-house or commercial ELISA. The relationship between caseness and variables (analytes measured, sex, age, ethnicity, tobacco/cannabis smoking) was tested by multivariate logistic regression. Whenmeasured individually, only TCC was significantly different between FEP and controls (p=0.01). Stepwise selection demonstrated interdependence between some variables and revealed other variables that significantly and independently contributed to distinguishing cases and controls. The finalmodel included demographics (sex, ethnicity, age, tobacco smoking) and a subset of analytes (C3, C4, C5, TCC, C1inh, FHR125, CR1). A receiver operating curve analysis combining these variables yielded an area under the curve of 0.79 for differentiating FEP from controls. This model was confirmed by multiple replications using randomly selected sample subsets. The data suggest that complement dysregulation occurs in FEP, supporting an underlying immune/inflammatory component to the disorder. Classification of FEP cases according to biological variables rather than symptoms would help stratify cases to identify those that might most benefit from therapeuticmodification of the inflammatory response

Baseline high levels of complement component 4 predict worse clinical outcome at 1-year follow-up in first-episode psychosis

Background Recent evidence has highlighted the potential role of complement component 4 (C4) in the development of schizophrenia. However, it remains unclear whether C4 is also relevant for clinical outcome and if it could be considered a possible therapeutic target. The aim of this naturalistic longitudinal study was to investigate whether baseline levels of C4 predict worse clinical outcome at 1-year follow-up in patients with first episode psychosis. Methods Twenty-five patients with first episode psychosis were assessed at baseline and followed-up prospectively for their clinical outcome at 1 year from baseline assessment. Concentrations of complement component 4 (C4) were measured using ELISA methods from baseline serum samples. Twelve patients were classified as non-responders and 13 as responders. ANCOVA analyses were conducted to investigate differences in baseline C4 levels between responders and non-responders at 1-year covarying for baseline severity of symptoms and for levels of C reactive protein. Results Non-responders show significantly higher baseline C4 levels compared with responders when controlling for baseline psychopathology and baseline levels of C reactive protein (552.5 ± 31.3 vs 437.6 ± 25.5 mcg/ml; p = 0.008). When investigating the ability of C4 levels to distinguish responders from non-responders, we found that the area under the ROC curve was 0.795 and the threshold point for C4 to distinguish between responders and non-responders appear to be around 490 mcg/ml. Conclusions Our preliminary findings show that baseline C4 levels predict clinical outcome at 1-year follow-up in patients with first episode psychosis

COVID-19 UK family carers and policy implications

Informal (unpaid) carers are an integral part of all societies and the health and social care systems in the UK depend on them. Despite the valuable contributions and key worker status of informal carers, their lived experiences, wellbeing, and needs have been neglected during the COVID-19 pandemic. In this Health Policy, we bring together a broad range of clinicians, researchers, and people with lived experience as informal carers to share their thoughts on the impact of the COVID-19 pandemic on UK carers, many of whom have felt abandoned as services closed. We focus on the carers of children and young people and adults and older adults with mental health diagnoses, and carers of people with intellectual disability or neurodevelopmental conditions across different care settings over the lifespan. We provide policy recommendations with the aim of improving outcomes for all carers

Different types of childhood adversity and 5-year outcomes in a longitudinal cohort of first-episode psychosis patients

Little is known about the impact of different forms of childhood adversity on outcomes in first-episode psychosis (FEP) patients beyond the first year of treatment. We investigated associations between different types of childhood adversity and outcomes of FEP patients over the 5 years following their first contact with mental health services for psychosis. 237 FEP cases aged 18–65 years were followed on average for 5 years after first presentation to psychiatric services in South London, UK. Childhood adversity prior to 17 years of age was assessed at baseline using the Childhood Experience of Care and Abuse Questionnaire (CECA.Q). The results showed that exposure to at least one type of childhood adversity was significantly associated with a lower likelihood of achieving symptomatic remission, longer inpatient stays, and compulsory admission over the 5-year follow-up. There was no evidence though of a dose-response effect. Some specificity was evident. Childhood parental separation was associated with significantly greater likelihood of non-compliance with antipsychotic medications, compulsory admission, and substance dependence. Institutional care was significantly associated with longer total length of inpatient stays; and parental death was significantly associated with compulsory admissions. Clinicians should screen FEP patients for childhood adversity and tailor interventions accordingly to improve outcomes

A Prediction Modelling and Pattern Detection Approach for the First-Episode Psychosis Associated to Cannabis Use

Over the last two decades, a significant body of research has established a link between cannabis use and psychotic outcomes. In this study, we aim to propose a novel symbiotic machine learning and statistical approach to pattern detection and to developing predictive models for the onset of first-episode psychosis. The data used has been gathered from real cases in cooperation with a medical research institution, and comprises a wide set of variables including demographic, drug-related, as well as several variables specifically related to the cannabis use. Our approach is built upon several machine learning techniques whose predictive models have been optimised in a computationally intensive framework. The ability of these models to predict first-episode psychosis has been extensively tested through large scale Monte Carlo simulations. Our results show that Boosted Classification Trees outperform other models in this context, and have significant predictive ability despite a large number of missing values in the data. Furthermore, we extended our approach by further investigating how different patterns of cannabis use relate to new cases of psychosis, via association analysis and Bayesian techniques