This study investigated the relationship between common genetic variation and co-occurring mental health conditions among autistic individuals.

The study was conducted with the Simons Foundation Powering Autism Research (SPARK) dataset, V9 release, and included probands [n = 17,582] with confirmed diagnosis of autism, who were also in the SPARK iWES1 array genotyping dataset. Six co-occurring mental health conditions (attention deficit hyperactivity disorder or ADHD, bipolar disorder, depression, schizophrenia, anxiety disorder and disruptive behaviour disorders) were analysed. Polygenic scores (PRS) were generated with PRScs software, using summary statistics from the most recent genome wide association studies (GWAS) for autism, ADHD, schizophrenia, bipolar disorder, depression, anxiety, neuroticism, p-factor, intelligence, educational attainment and hair colour (negative control). General linear models (GLM) and Cox proportional hazards models were computed, with age at registration, sex, cognitive impairment and genetic principal components included in both sets of models. Multiple testing correction was done using the Benjamini-Yekutieli method. Results were calculated using odds ratios (OR), 95% Confidence Intervals (CI) and corrected p values (p).

There were similar patterns of association and interaction for both GLMs and Cox models. Polygenic scores for educational attainment were significantly lower for those with co-occurring ADHD (GLM: OR=8.85E-01, 95% CI=8.48e-01–9.23e-01, p = 2.91E-07; Cox: OR=8.94E-01, 95% CI=8.66e-01–9.22e-01, p = 4.76E-11), bipolar disorder (GLM: OR=7.45E-01, 95% CI=6.54e-01–8.49e-01, p = 2.40E-04; Cox: OR=7.25E-01, 95% CI=6.39e-01–8.23e-01, p = 3.96E-05), depression (GLM: OR=8.63E-01, 95% CI=8.04e-01–9.26e-01, p = 5.13E-04; Cox: OR=8.56E-01, 95% CI=8.03e-01–9.12e-01, p = 2.80E-05), schizophrenia (GLM: OR=6.94E-01, 95% CI=5.71e-01–8.42e-01, p = 3.99E-03; Cox: OR=6.67E-01, 95% CI=5.52e-01–8.05e-01, p = 1.41E-03), anxiety disorder (GLM: OR=8.77E-01, 95% CI=8.37e-01–9.20e-01, p = 9.88E-07; Cox: OR=8.81E-01, 95% CI=8.49e-01–9.15e-01, p = 1.46E-09) and disruptive behaviour disorders (GLM: OR=7.10E-01, 95% CI=6.63e-01–7.60e-01, p = 3.22E-21; Cox: OR=7.10E-01, 95% CI=6.67e-01–7.57e-01, p = 1.35E-24).

Polygenic scores for educational attainment were associated with the co-occurrence of several mental health conditions among autistic individuals.

Reward processing has been proposed to underpin the atypical social feature of autism spectrum disorder (ASD). However, previous neuroimaging studies have yielded inconsistent results regarding the specificity of atypicalities for social reward processing in ASD.

Utilising a large sample, we aimed to assess reward processing in response to reward type (social, monetary) and reward phase (anticipation, delivery) in ASD.

Functional magnetic resonance imaging during social and monetary reward anticipation and delivery was performed in 212 individuals with ASD (7.6–30.6 years of age) and 181 typically developing participants (7.6–30.8 years of age).

Across social and monetary reward anticipation, whole-brain analyses showed hypoactivation of the right ventral striatum in participants with ASD compared with typically developing participants. Further, region of interest analysis across both reward types yielded ASD-related hypoactivation in both the left and right ventral striatum. Across delivery of social and monetary reward, hyperactivation of the ventral striatum in individuals with ASD did not survive correction for multiple comparisons. Dimensional analyses of autism and attention-deficit hyperactivity disorder (ADHD) scores were not significant. In categorical analyses, post hoc comparisons showed that ASD effects were most pronounced in participants with ASD without co-occurring ADHD.

Our results do not support current theories linking atypical social interaction in ASD to specific alterations in social reward processing. Instead, they point towards a generalised hypoactivity of ventral striatum in ASD during anticipation of both social and monetary rewards. We suggest this indicates attenuated reward seeking in ASD independent of social content and that elevated ADHD symptoms may attenuate altered reward seeking in ASD.

Autism and autistic traits are risk factors for suicidal behaviour.

To explore the prevalence of autism (diagnosed and undiagnosed) in those who died by suicide, and identify risk factors for suicide in this group.

Stage 1: 372 coroners’ inquest records, covering the period 1 January 2014 to 31 December 2017 from two regions of England, were analysed for evidence that the person who died had diagnosed autism or undiagnosed possible autism (elevated autistic traits), and identified risk markers. Stage 2: 29 follow-up interviews with the next of kin of those who died gathered further evidence of autism and autistic traits using validated autism screening and diagnostic tools.

Stage 1: evidence of autism (10.8%) was significantly higher in those who died by suicide than the 1.1% prevalence expected in the UK general alive population (odds ratio (OR) = 11.08, 95% CI 3.92–31.31). Stage 2: 5 (17.2%) of the follow-up sample had evidence of autism identified from the coroners’ records in stage 1. We identified evidence of undiagnosed possible autism in an additional 7 (24.1%) individuals, giving a total of 12 (41.4%); significantly higher than expected in the general alive population (1.1%) (OR = 19.76, 95% CI 2.36–165.84). Characteristics of those who died were largely similar regardless of evidence of autism, with groups experiencing a comparably high number of multiple risk markers before they died.

Elevated autistic traits are significantly over-represented in those who die by suicide.

The literature has demonstrated how the relationship between cognitive or emotional intelligence and age exhibits an inverted-U-shape and that this decline can be mitigated by an individual’s cognitive reserve (CR). Rather less is known, however, about the pattern of changes in cognitive empathy or the ability to recognize the thoughts or feelings of others.

The aim of the present study was firstly to analyze the effect of age, gender, and CR (measured through educational level), on the capacity to show cognitive empathy. Secondly, we aimed to evaluate what type of relationship—linear or quadratic—exists between age and cognitive empathy. We finally aimed to analyze the moderator role of educational level on the relationship between age and cognitive empathy.

Totally, 902 Spanish adults aged between 18 and 79 years (M = 43.53, SD = 11.86; 57% women).

Participants were asked to indicate their educational level (primary, high school, or college education) and their cognitive empathy was assessed using the Eyes test.

Women scored higher than men on cognitive empathy. Participants with a college education had higher scores on cognitive empathy than those with a lower educational level. Additionally, the relationship between age and cognitive empathy fit an inverted-U-shaped curve, consistent with the data found for cognitive and emotional intelligence. Finally, the age-related decrease in cognitive empathy appeared to be mitigated by a higher educational level, but only in those individuals aged 35 years and above. Limitations and clinical implications are discussed.

There is limited information on the presentation and characteristics of psychotic illness experienced by people with autism spectrum disorder (ASD).

To describe autistic and psychotic phenomenology in a group of individuals with comorbid ASD and psychosis (ASD–P) and compare this group with populations affected by either, alone.

We studied 116 individuals with ASD–P. We compared features of their ASD with people with ASD and no comorbid psychosis (ASD–NP), and clinical characteristics of psychosis in ASD–P with people with psychosis only.

Individuals with ASD–P had more diagnoses of atypical psychosis and fewer of schizophrenia compared with individuals with psychosis only. People with ASD–P had fewer stereotyped interests/behaviours compared with those with ASD–NP.

Our data show there may be a specific subtype of ASD linked to comorbid psychosis. The results support findings that psychosis in people with ASD is often atypical, particularly regarding affective disturbance.

Autism research has previously focused on either identifying a latent dimension or searching for subgroups. Research assessing the concurrently categorical and dimensional nature of autism is needed.

To investigate the latent structure of autism and identify meaningful subgroups in a sample spanning the full spectrum of genetic vulnerability.

Factor mixture models were applied to data on empathy, systemising and autistic traits from individuals on the autism spectrum, parents and general population controls.

A two-factor three-class model was identified, with two factors measuring empathy and systemising. Class one had high systemising and low empathy scores and primarily consisted of individuals with autism. Mainly comprising controls and parents, class three displayed high empathy scores and lower systemising scores, and class two showed balanced scores on both measures of systemising and empathy.

Autism is best understood as a dimensional construct, but meaningful subgroups can be identified based on empathy, systemising and autistic traits.

Recent reports estimate the prevalence of autism-spectrum conditions in the UK to be 1%.

To use different methods to estimate the prevalence of autism-spectrum conditions, including previously undiagnosed cases, in Cambridgeshire.

We carried out a survey of autism-spectrum conditions using the Special Educational Needs (SEN) register. A diagnosis survey was distributed to participating schools to be handed out to parents of all children aged 5–9 years. The mainstream primary school population was screened for unknown cases.

The prevalence estimates generated from the SEN register and diagnosis survey were 94 per 10 000 and 99 per 10 000 respectively. A total of 11 children received a research diagnosis of an autism-spectrum condition following screening and assessment. The ratio of known:unknown cases is about 3:2 (following statistical weighting procedures). Taken together, we estimate the prevalence to be 157 per 10 000, including previously undiagnosed cases.

This study has implications for planning diagnostic, social and health services.