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

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

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

The Maudsley environmental risk score for psychosis

Background Risk prediction algorithms have long been used in health research and practice (e.g. prediction of cardiovascular disease and diabetes). However, similar tools have not been developed for mental health. For example, for psychotic disorders, attempts to sum environmental risk are rare, unsystematic and dictated by available data. In light of this, we sought to develop a valid, easy to use measure of the aggregate environmental risk score (ERS) for psychotic disorders. Methods We reviewed the literature to identify well-replicated and validated environmental risk factors for psychosis that combine a significant effect and large-enough prevalence. Pooled estimates of relative risks were taken from the largest available meta-analyses. We devised a method of scoring the level of exposure to each risk factor to estimate ERS. Relative risks were rounded as, due to the heterogeneity of the original studies, risk effects are imprecisely measured. Results Six risk factors (ethnic minority status, urbanicity, high paternal age, obstetric complications, cannabis use and childhood adversity) were used to generate the ERS. A distribution for different levels of risk based on simulated data showed that most of the population would be at low/moderate risk with a small minority at increased environmental risk for psychosis. Conclusions This is the first systematic approach to develop an aggregate measure of environmental risk for psychoses in asymptomatic individuals. This can be used as a continuous measure of liability to disease; mostly relevant to areas where the original studies took place. Its predictive ability will improve with the collection of additional, population-specific data

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

Interaction Between Functional Genetic Variation of DRD2 and Cannabis Use on Risk of Psychosis

Both cannabis use and the dopamine receptor (DRD2) gene have been associated with schizophrenia, psychosis-like experiences, and cognition. However, there are no published data investigating whether genetically determined variation in DRD2 dopaminergic signaling might play a role in individual susceptibility to cannabis-associated psychosis. We genotyped (1) a case-control study of 272 patients with their first episode of psychosis and 234 controls, and also from (2) a sample of 252 healthy subjects, for functional variation in DRD2, rs1076560. Data on history of cannabis use were collected on all the studied subjects by administering the Cannabis Experience Questionnaire. In the healthy subjects' sample, we also collected data on schizotypy and cognitive performance using the Schizotypal Personality Questionnaire and the N-back working memory task. In the case-control study, we found a significant interaction between the rs1076560 DRD2 genotype and cannabis use in influencing the likelihood of a psychotic disorder. Among cannabis users, carriers of the DRD2, rs1076560, T allele showed a 3-fold increased probability to suffer a psychotic disorder compared with GG carriers (OR = 3.07; 95% confidence interval [CI]: 1.22-7.63). Among daily users, T carrying subjects showed a 5-fold increase in the odds of psychosis compared to GG carriers (OR = 4.82; 95% CI: 1.39-16.71). Among the healthy subjects, T carrying cannabis users had increased schizotypy compared with T carrying cannabis-naïve subjects, GG cannabis users, and GG cannabis-naïve subjects (all P ≤ .025). T carrying cannabis users had reduced working memory accuracy compared with the other groups (all P ≤ .008). Thus, variation of the DRD2, rs1076560, genotype may modulate the psychosis-inducing effect of cannabis us

An examination of polygenic score risk prediction in individuals with first-episode psychosis

Background Polygenic risk scores (PRSs) have successfully summarized genome-wide effects of genetic variants in schizophrenia with significant predictive power. In a clinical sample of first-episode psychosis (FEP) patients, we estimated the ability of PRSs to discriminate case-control status and to predict the development of schizophrenia as opposed to other psychoses. Methods The sample (445 case and 265 control subjects) was genotyped on the Illumina HumanCore Exome BeadChip with an additional 828 control subjects of African ancestry genotyped on the Illumina Multi-Ethnic Genotyping Array. To calculate PRSs, we used the results from the latest Psychiatric Genomics Consortium schizophrenia meta-analysis. We examined the association of PRSs with case-control status and with schizophrenia versus other psychoses in European and African ancestry FEP patients and in a second sample of 248 case subjects with chronic psychosis. Results PRS had good discriminative ability of case-control status in FEP European ancestry individuals (9.4% of the variance explained, p < 10−6), but lower in individuals of African ancestry (R2 = 1.1%, p = .004). Furthermore, PRS distinguished European ancestry case subjects who went on to acquire a schizophrenia diagnosis from those who developed other psychotic disorders (R2 = 9.2%, p = .002). Conclusions PRS was a powerful predictor of case-control status in a European sample of patients with FEP, even though a large proportion did not have an established diagnosis of schizophrenia at the time of assessment. PRS was significantly different between those case subjects who developed schizophrenia from those who did not, although the discriminative accuracy may not yet be sufficient for clinical utility in FEP