Individuals with borderline intellectual functioning and intellectual disabilities (intellectual impairment) may be at increased risk of psychosis. However, studies have been limited by small and selected samples. Moreover, the role of early life trauma, a key risk factor for psychosis, in the associations is unknown.

Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort, we investigated the associations between intellectual impairment, psychotic disorders, and psychotic experiences, and assessed the mediating role of trauma in childhood. Individuals with intellectual impairment were identified using a multisource measure utilizing indicators from ALSPAC combined with health and administrative records. Psychotic disorder diagnoses were extracted through linkage to primary care records. Psychotic experiences were assessed at ages 18 and 24 using the semi-structured Psychosis-Like Symptoms interview (PLIKSi). Trauma between ages 5 and 11 was assessed with questionnaires and interviews administered to children and parents at multiple ages. Multiple imputation was performed to mitigate bias due to missing data.

The maximum sample after multiple imputation was 9,407. We found associations between intellectual impairment and psychotic disorders (OR = 4.57; 95%CI: 1.56–13.39). Evidence was weaker in the case of psychotic experiences (OR = 1.63; 95%CI: 0.93–2.84). There was some evidence suggesting a mediating role of trauma in the associations between intellectual impairment and psychotic experiences (OR = 1.09; 95%CI: 1.03–1.15). Complete records analyses yielded comparable estimates.

Intellectual impairment is associated with psychotic disorders and experiences in adulthood. Research into the contribution of trauma could shape intervention strategies for psychotic disorders in this population.

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.

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.

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.

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.

Accurate diagnosis of bipolar disorder (BPD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A depressive episode often precedes the first manic episode, making it difficult to distinguish BPD from unipolar major depressive disorder (MDD).

We use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores (PRS) that may aid early differential diagnosis.

Based on individual genotypes from case–control cohorts of BPD and MDD shared through the Psychiatric Genomics Consortium, we compile case–case–control cohorts, applying a careful quality control procedure. In a resulting cohort of 51 149 individuals (15 532 BPD patients, 12 920 MDD patients and 22 697 controls), we perform a variety of GWAS and PRS analyses.

Although our GWAS is not well powered to identify genome-wide significant loci, we find significant chip heritability and demonstrate the ability of the resulting PRS to distinguish BPD from MDD, including BPD cases with depressive onset (BPD-D). We replicate our PRS findings in an independent Danish cohort (iPSYCH 2015, N = 25 966). We observe strong genetic correlation between our case–case GWAS and that of case–control BPD.

We find that MDD and BPD, including BPD-D are genetically distinct. Our findings support that controls, MDD and BPD patients primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BPD and, importantly, BPD-D from MDD.

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.

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.

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.

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.

To assess associations between self-reported advertising exposure to foods high in fats, salt and sugar and household purchases of energy, nutrients and specific product categories.

A cross-sectional design was used. Advertising exposure data were gathered using a questionnaire administered to the main shopper of each household, and purchase data from supermarkets and other stores for these households were accessed for a 4-week period during February 2019.

Households in London and the North of England.

Representative households (N 1289) from the Kantar Fast Moving Consumer Goods Panel. Main shoppers were predominantly female (71 %), with a mean age of 54 years (±13).

Linear regression models identified that exposure to foods high in fats, salt and sugar advertising through traditional mediums (including broadcast and print), but not digital, transport, recreational or functional mediums, was associated with greater purchases of energy (9779 kcal; 95 % CI 3515, 16 043), protein (416 g; 95 % CI 161, 671), carbohydrate (1164 g; 95 % CI 368, 1886) and sugar (514 g; 95 % CI 187, 841). Generalised linear models showed that individuals who reported exposure to sugary drink advertising were more likely to purchase sugary drinks (1·16; 95 % CI 2·94, 4·99) but did not purchase more energy or nutrients from sugary drinks. There was no evidence of associations between exposure to advertising for sugary cereals or sweet snacks and purchases from these categories.

There was a strong influence of traditional advertising and sugar-sweetened beverage advertising on household food and drink purchases, thus supporting the need for advertising restrictions across traditional formats and for sugary drinks specifically.

Autistic women are at high risk of developing restrictive eating disorders (REDs), such as anorexia nervosa.

This study provides an overview of the clinical characteristics of autistic women with REDs to (i) enhance understanding of increased risk, and (ii) support the identification of autistic women in eating disorder services.

We compared self-reported autistic and disordered eating characteristics of: autistic participants with REDs (Autism + REDs; n = 57); autistic participants without REDs (Autism; n = 69); and women with REDs who are not autistic (REDs; n = 80). We also included a group of women with high autistic traits (HATs) and REDs, but no formal autism diagnosis (HATs + REDs; n = 38).

Autism + REDs participants scored similarly to Autism participants in terms of autistic characteristics and to REDs participants in terms of experiencing traditional disordered eating symptoms. Autism + REDs participants were distinguished from both groups by having more restricted and repetitive behaviours and autism-specific eating behaviours related to sensory processing, flexibility and social differences. HATs + REDs participants showed a similar pattern of scores to Autism + REDs participants, and both also presented with high levels of co-occurring mental health difficulties, particularly social anxiety.

The presentation of autistic women with REDs is complex, including both traditional disordered eating symptoms and autism-related needs, as well as high levels of co-occurring mental health difficulties. In eating disorder services, the REDs presentation of autistic women and those with HATs should be formulated with reference to autism-specific eating behaviours and co-occurring difficulties. Treatment adaptations should be offered to accommodate autistic characteristics and related needs.

Knowledge of sex differences in risk factors for posttraumatic stress disorder (PTSD) can contribute to the development of refined preventive interventions. Therefore, the aim of this study was to examine if women and men differ in their vulnerability to risk factors for PTSD.

As part of the longitudinal AURORA study, 2924 patients seeking emergency department (ED) treatment in the acute aftermath of trauma provided self-report assessments of pre- peri- and post-traumatic risk factors, as well as 3-month PTSD severity. We systematically examined sex-dependent effects of 16 risk factors that have previously been hypothesized to show different associations with PTSD severity in women and men.

Women reported higher PTSD severity at 3-months post-trauma. Z-score comparisons indicated that for five of the 16 examined risk factors the association with 3-month PTSD severity was stronger in men than in women. In multivariable models, interaction effects with sex were observed for pre-traumatic anxiety symptoms, and acute dissociative symptoms; both showed stronger associations with PTSD in men than in women. Subgroup analyses suggested trauma type-conditional effects.

Our findings indicate mechanisms to which men might be particularly vulnerable, demonstrating that known PTSD risk factors might behave differently in women and men. Analyses did not identify any risk factors to which women were more vulnerable than men, pointing toward further mechanisms to explain women's higher PTSD risk. Our study illustrates the need for a more systematic examination of sex differences in contributors to PTSD severity after trauma, which may inform refined preventive interventions.

Disparities in CHD outcomes exist across the lifespan. However, less is known about disparities for patients with CHD admitted to neonatal ICU. We sought to identify sociodemographic disparities in neonatal ICU admissions among neonates born with cyanotic CHD.

Annual natality files from the US National Center for Health Statistics for years 2009–2018 were obtained. For each neonate, we identified sex, birthweight, pre-term birth, presence of cyanotic CHD, and neonatal ICU admission at time of birth, as well as maternal age, race, ethnicity, comorbidities/risk factors, trimester at start of prenatal care, educational attainment, and two measures of socio-economic status (Special Supplemental Nutrition Program for Women, Infants, and Children [WIC] status and insurance type). Multivariable logistic regression models were fit to determine the association of maternal socio-economic status with neonatal ICU admission. A covariate for race/ethnicity was then added to each model to determine if race/ethnicity attenuate the relationship between socio-economic status and neonatal ICU admission.

Of 22,373 neonates born with cyanotic CHD, 77.2% had a neonatal ICU admission. Receipt of WIC benefits was associated with higher odds of neonatal ICU admission (adjusted odds ratio [aOR] 1.20, 95% CI 1.1–1.29, p < 0.01). Neonates born to non-Hispanic Black mothers had increased odds of neonatal ICU admission (aOR 1.20, 95% CI 1.07–1.35, p < 0.01), whereas neonates born to Hispanic mothers were at lower odds of neonatal ICU admission (aOR 0.84, 95% CI 0.76–0.93, p < 0.01).

Maternal Black race and low socio-economic status are associated with increased risk of neonatal ICU admission for neonates born with cyanotic CHD. Further work is needed to identify the underlying causes of these disparities.