Epidemiological studies have previously shown a link between cardiometabolic disease and severe mental illness. The extent and mechanisms behind this link are poorly understood currently but links to impairments in the stress response and cortisol regulation have been thought to play a significant role. BMAL1 is a circadian rhythm regulation gene found on chromosome 11 which has been associated with a variety of pro-inflammatory states as well as conditions such as depression, schizophrenia, type 2 diabetes mellitus and myocardial infarction. Our study aimed to investigate the genetic structure of the BMAL1 gene locus and its associations with both cardiometabolic and psychiatric traits and conditions.

We used genetic data from the UK Biobank which recruited ~500,000 participants. Of these we used a population of ~430,000 self-reported white British participants and data from a variety of questionnaires and investigations looking at severe mental illness and cardiometabolic traits. We performed association analyses using Plink 1.07 with Bonferroni correction being performed for multiple testing using a number of genetic variants. Our threshold for significance was defined as a p-value < 5.35 × 10−5. Conditional analysis was then performed to identify if there were multiple independent signals for each phenotype.

BMAL1 variants were associated with BMI, diastolic, systolic blood pressure, waist-hip ratio and neuroticism score, and risk of anhedonia, major depressive disorder and risk-taking behaviour. Multiple significant independent signals were identified for BMI and waist-hip ratio. Linkage disequilibrium (LD) analysis showed significant coinheritance of specific traits which could suggest a role for BMAL1 and the encoded protein as a link between cardiometabolic and mental health traits.

This is the first study that systematically investigated associations between the BMAL1 locus across a variety of different mental and cardiometabolic phenotypes in a population-level cohort. Our study has shown that there is a link between the BMAL1 locus and both cardiometabolic and mental health phenotypes. Further research is required to investigate the exact biological mechanism by which BMAL1 connects severe mental illness and cardiometabolic disease.

Systemic inflammation has been linked with mood disorder and cognitive impairment. The extent of this relationship remains uncertain, with the effects of serum inflammatory biomarkers compared to genetic predisposition toward inflammation yet to be clearly established.

We investigated the magnitude of associations between C-reactive protein (CRP) measures, lifetime history of bipolar disorder or major depression, and cognitive function (reaction time and visuospatial memory) in 84,268 UK Biobank participants. CRP was measured in serum and a polygenic risk score for CRP was calculated, based on a published genome-wide association study. Multiple regression models adjusted for sociodemographic and clinical confounders.

Increased serum CRP was significantly associated with mood disorder history (Kruskal–Wallis H = 196.06, p < 0.001, η2 = 0.002) but increased polygenic risk for CRP was not (F = 0.668, p = 0.648, η2 < 0.001). Compared to the lowest quintile, the highest serum CRP quintile was significantly associated with both negative and positive differences in cognitive performance (fully adjusted models: reaction time B = −0.030, 95% CI = −0.052, −0.008; visuospatial memory B = 0.066, 95% CI = 0.042, 0.089). More severe mood disorder categories were significantly associated with worse cognitive performance and this was not moderated by serum or genetic CRP level.

In this large cohort study, we found that measured inflammation was associated with mood disorder history, but genetic predisposition to inflammation was not. The association between mood disorder and worse cognitive performance was very small and did not vary by CRP level. The inconsistent relationship between CRP measures and cognitive performance warrants further study.

UK Biobank is a well-characterised cohort of over 500 000 participants including genetics, environmental data and imaging. An online mental health questionnaire was designed for UK Biobank participants to expand its potential.

Describe the development, implementation and results of this questionnaire.

An expert working group designed the questionnaire, using established measures where possible, and consulting a patient group. Operational criteria were agreed for defining likely disorder and risk states, including lifetime depression, mania/hypomania, generalised anxiety disorder, unusual experiences and self-harm, and current post-traumatic stress and hazardous/harmful alcohol use.

A total of 157 366 completed online questionnaires were available by August 2017. Participants were aged 45–82 (53% were ≥65 years) and 57% women. Comparison of self-reported diagnosed mental disorder with a contemporary study shows a similar prevalence, despite respondents being of higher average socioeconomic status. Lifetime depression was a common finding, with 24% (37 434) of participants meeting criteria and current hazardous/harmful alcohol use criteria were met by 21% (32 602), whereas other criteria were met by less than 8% of the participants. There was extensive comorbidity among the syndromes. Mental disorders were associated with a high neuroticism score, adverse life events and long-term illness; addiction and bipolar affective disorder in particular were associated with measures of deprivation.

The UK Biobank questionnaire represents a very large mental health survey in itself, and the results presented here show high face validity, although caution is needed because of selection bias. Built into UK Biobank, these data intersect with other health data to offer unparalleled potential for crosscutting biomedical research involving mental health.

UK Biobank is a well-characterised cohort of over 500 000 participants that offers unique opportunities to investigate multiple diseases and risk factors.

An online mental health questionnaire completed by UK Biobank participants was expected to expand the potential for research into mental disorders.

An expert working group designed the questionnaire, using established measures where possible, and consulting with a patient group regarding acceptability. Case definitions were defined using operational criteria for lifetime depression, mania, anxiety disorder, psychotic-like experiences and self-harm, as well as current post-traumatic stress and alcohol use disorders.

157 366 completed online questionnaires were available by August 2017. Comparison of self-reported diagnosed mental disorder with a contemporary study shows a similar prevalence, despite respondents being of higher average socioeconomic status than the general population across a range of indicators. Thirty-five per cent (55 750) of participants had at least one defined syndrome, of which lifetime depression was the most common at 24% (37 434). There was extensive comorbidity among the syndromes. Mental disorders were associated with high neuroticism score, adverse life events and long-term illness; addiction and bipolar affective disorder in particular were associated with measures of deprivation.

The questionnaire represents a very large mental health survey in itself, and the results presented here show high face validity, although caution is needed owing to selection bias. Built into UK Biobank, these data intersect with other health data to offer unparalleled potential for crosscutting biomedical research involving mental health.

G.B. received grants from the National Institute for Health Research during the study; and support from Illumina Ltd. and the European Commission outside the submitted work. B.C. received grants from the Scottish Executive Chief Scientist Office and from The Dr Mortimer and Theresa Sackler Foundation during the study. C.S. received grants from the Medical Research Council and Wellcome Trust during the study, and is the Chief Scientist for UK Biobank. M.H. received grants from the Innovative Medicines Initiative via the RADAR-CNS programme and personal fees as an expert witness outside the submitted work.

There is strong evidence that people born in winter and in spring have a small increased risk of schizophrenia. As this ‘season of birth’ effect underpins some of the most influential hypotheses concerning potentially modifiable risk exposures, it is important to exclude other possible explanations for the phenomenon.

Here we sought to determine whether the season of birth effect reflects gene-environment confounding rather than a pathogenic process indexing environmental exposure. We directly measured, in 136 538 participants from the UK Biobank (UKBB), the burdens of common schizophrenia risk alleles and of copy number variants known to increase the risk for the disorder, and tested whether these were correlated with a season of birth.

Neither genetic measure was associated with season or month of birth within the UKBB sample.

As our study was highly powered to detect small effects, we conclude that the season of birth effect in schizophrenia reflects a true pathogenic effect of environmental exposure.