The nosological status of unipolar mania and hypomania within UK Biobank according to objective and subjective measures of diurnal rest and activity.

BACKGROUND: There is uncertainty whether unipolar mania is a discrete sub-type of bipolar disorder. Disrupted rest/activity rhythms are a key feature of bipolar disorder (BD) but have not been well characterised in unipolar mania/hypomania (UM). We compared subjective and objective rest/activity patterns, demographic and mental health outcomes across BD, UM and control groups. METHODS: UK residents aged 37-73 years were recruited into UK Biobank from 2006 to 2010. BD, UM and control groups were identified via a mental health questionnaire. Demographic, mental health and subjective sleep outcomes were self-reported. Accelerometery data were available for a subset of participants, and objective measures of sleep and activity were derived. RESULTS: A greater proportion of males met UM criteria, and more females were in the BD group. Both BD and UM groups had poor mental health outcomes vs. controls. Objectively measured activity differed between all three groups: UM had highest levels of activity and BD lowest. The UM group had shorter sleep duration compared to controls. Subjective rest/activity measures showed that both mood disorder groups (compared to controls) had later chronotype preference, more disturbed sleep and increased difficulty getting up in the morning. However, the UM group were more likely to report an early chronotype compared to BD and control groups. CONCLUSIONS: BD and UM share features in common, but key differences support the proposition that UM may be a distinct and more clinically homogenous disorder. UM was characterised by a higher proportion of males, early chronotype, increased activity and shorter sleep duration.

Correction: The genomic basis of mood instability: identification of 46 loci in 363,705 UK Biobank participants, genetic correlation with psychiatric disorders, and association with gene expression and function.

A correction to this paper has been published and can be accessed via a link at the top of the paper.

The genomic basis of mood instability: identification of 46 loci in 363,705 UK Biobank participants, genetic correlation with psychiatric disorders, and association with gene expression and function.

Genome-wide association studies (GWAS) of psychiatric phenotypes have tended to focus on categorical diagnoses, but to understand the biology of mental illness it may be more useful to study traits which cut across traditional boundaries. Here, we report the results of a GWAS of mood instability as a trait in a large population cohort (UK Biobank, n = 363,705). We also assess the clinical and biological relevance of the findings, including whether genetic associations show enrichment for nervous system pathways. Forty six unique loci associated with mood instability were identified with a SNP heritability estimate of 9%. Linkage Disequilibrium Score Regression (LDSR) analyses identified genetic correlations with Major Depressive Disorder (MDD), Bipolar Disorder (BD), Schizophrenia, anxiety, and Post Traumatic Stress Disorder (PTSD). Gene-level and gene set analyses identified 244 significant genes and 6 enriched gene sets. Tissue expression analysis of the SNP-level data found enrichment in multiple brain regions, and eQTL analyses highlighted an inversion on chromosome 17 plus two brain-specific eQTLs. In addition, we used a Phenotype Linkage Network (PLN) analysis and community analysis to assess for enrichment of nervous system gene sets using mouse orthologue databases. The PLN analysis found enrichment in nervous system PLNs for a community containing serotonin and melatonin receptors. In summary, this work has identified novel loci, tissues and gene sets contributing to mood instability. These findings may be relevant for the identification of novel trans-diagnostic drug targets and could help to inform future stratified medicine innovations in mental health.

Carotid Intima-Media Thickness: Novel Loci, Sex-Specific Effects, and Genetic Correlations With Obesity and Glucometabolic Traits in UK Biobank.

OBJECTIVE: Atherosclerosis is the underlying cause of most cardiovascular disease, but mechanisms underlying atherosclerosis are incompletely understood. Ultrasound measurement of the carotid intima-media thickness (cIMT) can be used to measure vascular remodeling, which is indicative of atherosclerosis. Genome-wide association studies have identified many genetic loci associated with cIMT, but heterogeneity of measurements collected by many small cohorts have been a major limitation in these efforts. Here, we conducted genome-wide association analyses in UKB (UK Biobank; N=22 179), the largest single study with consistent cIMT measurements. Approach and Results: We used BOLT-LMM software to run linear regression of cIMT in UKB, adjusted for age, sex, and genotyping chip. In white British participants, we identified 5 novel loci associated with cIMT and replicated most previously reported loci. In the first sex-specific analyses of cIMT, we identified a locus on chromosome 5, associated with cIMT in women only and highlight VCAN as a good candidate gene at this locus. Genetic correlations with body mass index and glucometabolic traits were also observed. Two loci influenced risk of ischemic heart disease. CONCLUSIONS: These findings replicate previously reported associations, highlight novel biology, and provide new directions for investigating the sex differences observed in cardiovascular disease presentation and progression.

Changes in prescribing for bipolar disorder between 2009 and 2016: national-level data linkage study in Scotland.

BACKGROUND: People with bipolar disorder typically require long-term pharmacological treatment to prevent episodes of depression or mania. However, evidence-based guidelines are often not followed by prescribers and, in some countries, prescribing of lithium is in decline. Polypharmacy is also common in bipolar disorder.AimsTo employ a data linkage approach to describe and evaluate prescribing patterns in bipolar disorder in Scotland between 2009 and 2016. METHOD: By linking prescribing data to the electronic Scottish Morbidity Records, we identified a cohort of 23 135 patients with bipolar disorder who were prescribed psychotropic medication between 2009 and 2016. We examined trends in proportions of patients prescribed each of six drug categories. Random effects logistic models examined change in prescribing over years of interest. RESULTS: The most common form of treatment was antidepressant monotherapy (24.96%), with only 5.90% of patients receiving lithium monotherapy. Prescribing of antipsychotics and anti-epileptics increased from 2009 to 2016 (antipsychotics: odds ratio 1.16, 95% CI 1.15-1.18; anti-epileptics: odds ratio 1.34, 95% CI 1.32-1.36), whereas prescribing of lithium decreased (odds ratio 0.83, 95% CI 0.82-0.85). Prescribing of valproate decreased from 2009-2016 in women, but increased in men (women: odds ratio 0.93, 95% CI 0.90-0.97; men: odds ratio 1.11, 95% CI 1.04-1.18). CONCLUSIONS: Antidepressant monotherapy was the most common form of treatment for bipolar disorder in Scotland and prescribing of lithium has declined between 2009 and 2016. The findings are concerning and represent a gap between treatment guidelines and clinical practice.Declaration of interestNone.

Comprehensive assessment of sleep duration, insomnia, and brain structure within the UK Biobank cohort.

STUDY OBJECTIVES: To assess for associations between sleeping more than or less than recommended by the National Sleep Foundation (NSF), and self-reported insomnia, with brain structure. METHODS: Data from the UK Biobank cohort were analyzed (N between 9K and 32K, dependent on availability, aged 44 to 82 years). Sleep measures included self-reported adherence to NSF guidelines on sleep duration (sleeping between 7 and 9 hours per night), and self-reported difficulty falling or staying asleep (insomnia). Brain structural measures included global and regional cortical or subcortical morphometry (thickness, surface area, volume), global and tract-related white matter microstructure, brain age gap (difference between chronological age and age estimated from brain scan), and total volume of white matter lesions. RESULTS: Longer-than-recommended sleep duration was associated with lower overall grey and white matter volumes, lower global and regional cortical thickness and volume measures, higher brain age gap, higher volume of white matter lesions, higher mean diffusivity globally and in thalamic and association fibers, and lower volume of the hippocampus. Shorter-than-recommended sleep duration was related to higher global and cerebellar white matter volumes, lower global and regional cortical surface areas, and lower fractional anisotropy in projection fibers. Self-reported insomnia was associated with higher global gray and white matter volumes, and with higher volumes of the amygdala, hippocampus, and putamen. CONCLUSIONS: Sleeping longer than recommended by the NSF is associated with a wide range of differences in brain structure, potentially indicative of poorer brain health. Sleeping less than recommended is distinctly associated with lower cortical surface areas. Future studies should assess the potential mechanisms of these differences and investigate long sleep duration as a putative marker of brain health.

Head motion in the UK Biobank imaging subsample: longitudinal stability, associations with psychological and physical health, and risk of incomplete data.

Participant motion in brain magnetic resonance imaging is associated with processing problems including potentially non-useable/incomplete data. This has implications for representativeness in research. Few large studies have investigated predictors of increased motion in the first instance. We exploratively tested for association between multiple psychological and physical health traits with concurrent motion during T1 structural, diffusion, average resting-state and task functional magnetic resonance imaging in N = 52 951 UK Biobank imaging subsample participants. These traits included history of cardiometabolic, inflammatory, neurological and psychiatric conditions, as well as concurrent cognitive test scores and anthropometric traits. We tested for stability in motion in participants with longitudinal imaging data (n = 5305, average 2.64 years later). All functional and T1 structural motion variables were significantly intercorrelated (Pearson r range 0.3-0.8, all P < 0.001). Diffusion motion variables showed weaker correlations around r = 0.1. Most physical and psychological phenotypes showed significant association with at least one measure of increased motion including specifically in participants with complete useable data (highest ß = 0.66 for diabetes versus resting-state functional magnetic resonance imaging motion). Poorer values in most health traits predicted lower odds of complete imaging data, with the largest association for history of traumatic brain injury (odds ratio = 0.720, 95% confidence interval = 0.562 to 0.923, P = 0.009). Worse psychological and physical health are consistent predictors of increased average functional and structural motion during brain imaging and associated with lower odds of complete data. Average motion levels were largely consistent across modalities and longitudinally in participants with repeat data. Together, these findings have implications for representativeness and bias in imaging studies of generally healthy population samples.

Subjective and objective sleep and circadian parameters as predictors of depression-related outcomes: A machine learning approach in UK Biobank.

BACKGROUND: Sleep and circadian disruption are associated with depression onset and severity, but it is unclear which features (e.g., sleep duration, chronotype) are important and whether they can identify individuals showing poorer outcomes. METHODS: Within a subset of the UK Biobank with actigraphy and mental health data (n = 64,353), penalised regression identified the most useful of 51 sleep/rest-activity predictors of depression-related outcomes; including case-control (Major Depression (MD) vs. controls; postnatal depression vs. controls) and within-case comparisons (severe vs. moderate MD; early vs. later onset, atypical vs. typical symptoms; comorbid anxiety; suicidality). Best models (of lasso, ridge, and elastic net) were selected based on Area Under the Curve (AUC). RESULTS: For MD vs. controls (n(MD) = 24,229; n(control) = 40,124), lasso AUC was 0.68, 95 % confidence interval (CI) 0.67-0.69. Discrimination was reasonable for atypical vs. typical symptoms (n(atypical) = 958; n(typical) = 18,722; ridge: AUC 0.74, 95 % CI 0.71-0.77) but poor for remaining models (AUCs 0.59-0.67). Key predictors across most models included: difficulty getting up, insomnia symptoms, snoring, actigraphy-measured daytime inactivity and lower morning activity (~8 am). In a distinct subset (n = 310,718), the number of these factors shown was associated with all depression outcomes. LIMITATIONS: Analyses were cross-sectional and in middle-/older aged adults: comparison with longitudinal investigations and younger cohorts is necessary. DISCUSSION: Sleep and circadian measures alone provided poor to moderate discrimination of depression outcomes, but several characteristics were identified that may be clinically useful. Future work should assess these features alongside broader sociodemographic, lifestyle and genetic features.

Genetic architecture of DCC and influence on psychological, psychiatric and cardiometabolic traits in multiple ancestry groups in UK Biobank.

BACKGROUND: People with severe mental illness have a higher risk of cardiometabolic disease than the general population. Traditionally attributed to sociodemographic, behavioural factors and medication effects, recent genetic studies have provided evidence of shared biological mechanisms underlying mental illness and cardiometabolic disease. We aimed to determine whether signals in the DCC locus, implicated in psychiatric and cardiometabolic traits, were shared or distinct. METHODS: In UK Biobank, we systematically assessed genetic variation in the DCC locus for association with metabolic, cardiovascular and psychiatric-related traits in unrelated "white British" participants (N = 402,837). Logistic or linear regression were applied assuming an additive genetic model and adjusting for age, sex, genotyping chip and population structure. Bonferroni correction for the number of independent variants was applied. Conditional analyses (including lead variants as covariates) and trans-ancestry analyses were used to investigate linkage disequilibrium between signals. RESULTS: Significant associations were observed between DCC variants and smoking, anhedonia, body mass index (BMI), neuroticism and mood instability. Conditional analyses and linkage disequilibrium structure suggested signals for smoking and BMI were distinct from each other and the mood traits, whilst individual mood traits were inter-related in a complex manner. LIMITATIONS: Restricting analyses in non-"white British" individuals to the phenotypes significant in the "white British" sample is not ideal, but the smaller samples sizes restricted the phenotypes possible to analyse. CONCLUSIONS: Genetic variation in the DCC locus had distinct effects on BMI, smoking and mood traits, and therefore is unlikely to contribute to shared mechanisms underpinning mental and cardiometabolic traits.