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Schizophrenia (SZ), bipolar disorder (BD) and depression (D) run in families. This susceptibility is partly due to hundreds or thousands of common genetic variants, each conferring a fractional risk. The cumulative effects of the associated variants can be summarised as a polygenic risk score (PRS). Using data from the EUropean Network of national schizophrenia networks studying Gene-Environment Interactions (EU-GEI) first episode case–control study, we aimed to test whether PRSs for three major psychiatric disorders (SZ, BD, D) and for intelligent quotient (IQ) as a neurodevelopmental proxy, can discriminate affective psychosis (AP) from schizophrenia-spectrum disorder (SSD).
Participants (842 cases, 1284 controls) from 16 European EU-GEI sites were successfully genotyped following standard quality control procedures. The sample was stratified based on genomic ancestry and analyses were done only on the subsample representing the European population (573 cases, 1005 controls). Using PRS for SZ, BD, D, and IQ built from the latest available summary statistics, we performed simple or multinomial logistic regression models adjusted for 10 principal components for the different clinical comparisons.
In case–control comparisons PRS-SZ, PRS-BD and PRS-D distributed differentially across psychotic subcategories. In case–case comparisons, both PRS-SZ [odds ratio (OR) = 0.7, 95% confidence interval (CI) 0.54–0.92] and PRS-D (OR = 1.31, 95% CI 1.06–1.61) differentiated AP from SSD; and within AP categories, only PRS-SZ differentiated BD from psychotic depression (OR = 2.14, 95% CI 1.23–3.74).
Combining PRS for severe psychiatric disorders in prediction models for psychosis phenotypes can increase discriminative ability and improve our understanding of these phenotypes. Our results point towards the potential usefulness of PRSs in specific populations such as high-risk or early psychosis phases.
Psychosis rates are higher among some migrant groups. We hypothesized that psychosis in migrants is associated with cumulative social disadvantage during different phases of migration.
We used data from the EUropean Network of National Schizophrenia Networks studying Gene-Environment Interactions (EU-GEI) case–control study. We defined a set of three indicators of social disadvantage for each phase: pre-migration, migration and post-migration. We examined whether social disadvantage in the pre- and post-migration phases, migration adversities, and mismatch between achievements and expectations differed between first-generation migrants with first-episode psychosis and healthy first-generation migrants, and tested whether this accounted for differences in odds of psychosis in multivariable logistic regression models.
In total, 249 cases and 219 controls were assessed. Pre-migration (OR 1.61, 95% CI 1.06–2.44, p = 0.027) and post-migration social disadvantages (OR 1.89, 95% CI 1.02–3.51, p = 0.044), along with expectations/achievements mismatch (OR 1.14, 95% CI 1.03–1.26, p = 0.014) were all significantly associated with psychosis. Migration adversities (OR 1.18, 95% CI 0.672–2.06, p = 0.568) were not significantly related to the outcome. Finally, we found a dose–response effect between the number of adversities across all phases and odds of psychosis (⩾6: OR 14.09, 95% CI 2.06–96.47, p = 0.007).
The cumulative effect of social disadvantages before, during and after migration was associated with increased odds of psychosis in migrants, independently of ethnicity or length of stay in the country of arrival. Public health initiatives that address the social disadvantages that many migrants face during the whole migration process and post-migration psychological support may reduce the excess of psychosis in migrants.
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.
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.
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.
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.
Jumping to conclusions (JTC), which is the proneness to require less information before forming beliefs or making a decision, has been related to formation and maintenance of delusions. Using data from the National Institute of Health Research Biomedical Research Centre Genetics and Psychosis (GAP) case–control study of first-episode psychosis (FEP), we set out to test whether the presence of JTC would predict poor clinical outcome at 4 years.
One-hundred and twenty-three FEP patients were assessed with the Positive and Negative Syndrome Scale (PANSS), Global Assessment of Functioning (GAF) and the probabilistic reasoning ‘Beads’ Task at the time of recruitment. The sample was split into two groups based on the presence of JTC bias. Follow-up data over an average of 4 years were obtained concerning clinical course and outcomes (remission, intervention of police, use of involuntary treatment – the Mental Health Act (MHA) – and inpatient days).
FEP who presented JTC at baseline were more likely during the follow-up period to be detained under the MHA [adjusted OR 15.62, 95% confidence interval (CI) 2.92–83.54, p = 0.001], require intervention by the police (adjusted OR 14.95, 95% CI 2.68–83.34, p = 0.002) and have longer admissions (adjusted IRR = 5.03, 95% CI 1.91–13.24, p = 0.001). These associations were not accounted for by socio-demographic variables, IQ and symptom dimensions.
JTC in FEP is associated with poorer outcome as indicated and defined by more compulsion police intervention and longer periods of admission. Our findings raise the question of whether the implementation of specific interventions to reduce JTC, such as Metacognition Training, may be a useful addition in early psychosis intervention programmes.
People who use cannabis have an increased risk of psychosis an effect attributed to the active ingredient δ9-tetrahydrocannabinol (Δ9-THC). There has recently been concern over an increase in the concentration of Δ9-THC in the cannabis available in many countries.
To investigate whether people with a first episode of psychosis were particularly likely to use high-potency cannabis.
We collected information on cannabis use from 280 cases presenting with a first episode of psychosis to the South London & Maudsley National Health Service (NHS) Foundation Trust, and from 174 healthy controls recruited from the local population.
There was no significant difference between cases and controls in whether they had ever taken cannabis, or age at first use. However, those in the cases group were more likely to be current daily users (OR = 6.4) and to have smoked cannabis for more than 5 years (OR = 2.1). Among those who used cannabis, 78% of the cases group used high-potency cannabis (sinsemilla, ‘skunk’) compared with 37% of the control group (OR 6.8).
The finding that people with a first episode of psychosis had smoked higher-potency cannabis, for longer and with greater frequency, than a healthy control group is consistent with the hypothesis that Δ9-THC is the active ingredient increasing risk of psychosis. This has important public health implications, given the increased availability and use of high-potency cannabis.
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