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.

England's primary care service for psychological therapy (Improving Access to Psychological Therapies [IAPT]) treats anxiety and depression, with a target recovery rate of 50%. Identifying the characteristics of patients who achieve recovery may assist in optimizing future treatment. This naturalistic cohort study investigated pre-therapy characteristics as predictors of recovery and improvement after IAPT therapy.

In a cohort of patients attending an IAPT service in South London, we recruited 263 participants and conducted a baseline interview to gather extensive pre-therapy characteristics. Bayesian prediction models and variable selection were used to identify baseline variables prognostic of good clinical outcomes. Recovery (primary outcome) was defined using (IAPT) service-defined score thresholds for both depression (Patient Health Questionnaire [PHQ-9]) and anxiety (Generalized Anxiety Disorder [GAD-7]). Depression and anxiety outcomes were also evaluated as standalone (PHQ-9/GAD-7) scores after therapy. Prediction model performance metrics were estimated using cross-validation.

Predictor variables explained 26% (recovery), 37% (depression), and 31% (anxiety) of the variance in outcomes, respectively. Variables prognostic of recovery were lower pre-treatment depression severity and not meeting criteria for obsessive compulsive disorder. Post-therapy depression and anxiety severity scores were predicted by lower symptom severity and higher ratings of health-related quality of life (EuroQol questionnaire [EQ5D]) at baseline.

Almost a third of the variance in clinical outcomes was explained by pre-treatment symptom severity scores. These constructs benefit from being rapidly accessible in healthcare services. If replicated in external samples, the early identification of patients who are less likely to recover may facilitate earlier triage to alternative interventions.

Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and etiological subtypes. There are several challenges to integrating symptom data from genetically informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data.

We conducted genome-wide association studies of major depressive symptoms in three cohorts that were enriched for participants with a diagnosis of depression (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts who were not recruited on the basis of diagnosis (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors.

The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for the skip-structure in community cohorts (use of Depression and Anhedonia as gating symptoms).

The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analyzing genetic association data.

There is substantial variation in patient symptoms following psychological therapy for depression and anxiety. However, reliance on endpoint outcomes ignores additional interindividual variation during therapy. Knowing a patient's likely symptom trajectories could guide clinical decisions. We aimed to identify latent classes of patients with similar symptom trajectories over the course of psychological therapy and explore associations between baseline variables and trajectory class.

Patients received high-intensity psychological treatment for common mental health problems at National Health Service Improving Access to Psychological Therapies services in South London (N = 16 258). To identify trajectories, we performed growth mixture modelling of depression and anxiety symptoms over 11 sessions. We then ran multinomial regressions to identify baseline variables associated with trajectory class membership.

Trajectories of depression and anxiety symptoms were highly similar and best modelled by four classes. Three classes started with moderate-severe symptoms and showed (1) no change, (2) gradual improvement, and (3) fast improvement. A final class (4) showed initially mild symptoms and minimal improvement. Within the moderate-severe baseline symptom classes, patients in the two showing improvement as opposed to no change tended not to be prescribed psychotropic medication or report a disability and were in employment. Patients showing fast improvement additionally reported lower baseline functional impairment on average.

Multiple trajectory classes of depression and anxiety symptoms were associated with baseline characteristics. Identifying the most likely trajectory for a patient at the start of treatment could inform decisions about the suitability and continuation of therapy, ultimately improving patient outcomes.

While studies from the start of the COVID-19 pandemic have described initial negative effects on mental health and exacerbating mental health inequalities, longer-term studies are only now emerging.

In total, 34 465 individuals in the UK completed online questionnaires and were re-contacted over the first 12 months of the pandemic. We used growth mixture modelling to identify trajectories of depression, anxiety and anhedonia symptoms using the 12-month data. We identified sociodemographic predictors of trajectory class membership using multinomial regression models.

Most participants had consistently low symptoms of depression or anxiety over the year of assessments (60%, 69% respectively), and a minority had consistently high symptoms (10%, 15%). We also identified participants who appeared to show improvements in symptoms as the pandemic progressed, and others who showed the opposite pattern, marked symptom worsening, until the second national lockdown. Unexpectedly, most participants showed stable low positive affect, indicating anhedonia, throughout the 12-month period. From regression analyses, younger age, reporting a previous mental health diagnosis, non-binary, or self-defined gender, and an unemployed or a student status were significantly associated with membership of the stable high symptom groups for depression and anxiety.

While most participants showed little change in their depression and anxiety symptoms across the first year of the pandemic, we highlight the divergent responses of subgroups of participants, who fared both better and worse around national lockdowns. We confirm that previously identified predictors of negative outcomes in the first months of the pandemic also predict negative outcomes over a 12-month period.

Clinical, epidemiological, and genetic findings support an overlap between eating disorders, obsessive-compulsive disorder (OCD), and anxiety symptoms. However, little research has examined the role of genetics in the expression of underlying phenotypes. We investigated whether the anorexia nervosa (AN), OCD, or AN/OCD transdiagnostic polygenic scores (PGS) predict eating disorder, OCD, and anxiety symptoms in a large developmental cohort in a sex-specific manner.

Using summary statistics from Psychiatric Genomics Consortium AN and OCD genome-wide association studies, we conducted an AN/OCD transdiagnostic genome-wide association meta-analysis. We then calculated AN, OCD, and AN/OCD PGS in participants from the Avon Longitudinal Study of Parents and Children to predict eating disorder, OCD, and anxiety symptoms, stratified by sex (combined N = 3212–5369 per phenotype).

The PGS prediction of eating disorder, OCD, and anxiety phenotypes differed between sexes, although effect sizes were small. AN and AN/OCD PGS played a more prominent role in predicting eating disorder and anxiety risk than OCD PGS, especially in girls. AN/OCD PGS provided a small boost over AN PGS in the prediction of some anxiety symptoms. All three PGS predicted higher compulsive exercise across different developmental timepoints [β = 0.03 (s.e. = 0.01) for AN and AN/OCD PGS at age 14; β = 0.05 (s.e. = 0.02) for OCD PGS at age 16] in girls.

Compulsive exercise may have a transdiagnostic genetic etiology, and AN genetic risk may play a role in the presence of anxiety symptoms. Converging with prior twin literature, our results also suggest that some of the contribution of genetic risk may be sex-specific.

Retrospective self-reports of childhood trauma are associated with a greater risk of psychopathology in adulthood than prospective measures of trauma. Heritable reporter characteristics are anticipated to account for part of this association, whereby genetic predisposition to certain traits influences both the likelihood of self-reporting trauma and of developing psychopathology. However, previous research has not considered how gene–environment correlation influences these associations.

To investigate reporter characteristics associated with retrospective self-reports of childhood trauma and whether these associations are accounted for by gene–environment correlation.

In 3963 unrelated individuals from the Twins Early Development Study, we tested whether polygenic scores for 21 psychiatric, cognitive, anthropometric and personality traits were associated with retrospectively self-reported childhood emotional and physical abuse. To assess the presence of gene–environment correlation, we investigated whether these associations remained after controlling for composite scores of environmental adversity across development.

Retrospectively self-reported childhood trauma was associated with polygenic scores for autism spectrum disorder (ASD), body mass index (BMI), post-traumatic stress disorder (PTSD) and risky behaviours. When composite scores of environmental adversity were controlled for, only associations with the polygenic scores for ASD and PTSD remained significant.

Genetic predisposition to ASD and PTSD may increase liability to experiencing or interpreting events as traumatic. Associations between genetic predisposition for risky behaviour and BMI with self-reported childhood trauma may reflect gene–environment correlation. Studies of the association between retrospectively self-reported childhood trauma and later-life outcomes should consider that genetically influenced reporter characteristics may confound associations, both directly and through gene–environment correlation.

Anxiety and depressive disorders can be chronic and disabling. Although there are effective treatments, only a fraction of those impaired receive treatment. Predictors of treatment-seeking and treatment receipt could be informative for initiatives aiming to tackle the burden of untreated anxiety and depression.

To investigate sociodemographic characteristics associated with treatment-seeking and treatment receipt.

Two binary retrospective reports of lifetime treatment-seeking (n = 44 810) and treatment receipt (n = 37 346) were regressed on sociodemographic factors (age, gender, UK ethnic minority background, educational attainment, household income, neighbourhood deprivation and social isolation) and alternative coping strategies (self-medication with alcohol/drugs and self-help) in UK Biobank participants with lifetime generalised anxiety or major depressive disorder. Analyses were also stratified by gender.

Treatment access was more likely in those who reported use of self-help strategies, with university-level education and those from less economically advantaged circumstances (household income <£30 000 and greater neighbourhood deprivation). Treatment access was less likely in those who were male, from a UK ethnic minority background and with high household incomes (>£100 000). Men who self-medicated and/or had a vocational qualification were also less likely to seek treatment.

This work on retrospective reports of treatment-seeking and treatment receipt at any time of life replicates known associations with treatment-seeking and treatment receipt during time of treatment need. More work is required to understand whether improving rates of treatment-seeking improves prognostic outcomes for individuals with anxiety or depression.

The COVID-19 pandemic has disrupted lives and livelihoods, and people already experiencing mental ill health may have been especially vulnerable.

Quantify mental health inequalities in disruptions to healthcare, economic activity and housing.

We examined data from 59 482 participants in 12 UK longitudinal studies with data collected before and during the COVID-19 pandemic. Within each study, we estimated the association between psychological distress assessed pre-pandemic and disruptions since the start of the pandemic to healthcare (medication access, procedures or appointments), economic activity (employment, income or working hours) and housing (change of address or household composition). Estimates were pooled across studies.

Across the analysed data-sets, 28% to 77% of participants experienced at least one disruption, with 2.3–33.2% experiencing disruptions in two or more domains. We found 1 s.d. higher pre-pandemic psychological distress was associated with (a) increased odds of any healthcare disruptions (odds ratio (OR) 1.30, 95% CI 1.20–1.40), with fully adjusted odds ratios ranging from 1.24 (95% CI 1.09–1.41) for disruption to procedures to 1.33 (95% CI 1.20–1.49) for disruptions to prescriptions or medication access; (b) loss of employment (odds ratio 1.13, 95% CI 1.06–1.21) and income (OR 1.12, 95% CI 1.06 –1.19), and reductions in working hours/furlough (odds ratio 1.05, 95% CI 1.00–1.09) and (c) increased likelihood of experiencing a disruption in at least two domains (OR 1.25, 95% CI 1.18–1.32) or in one domain (OR 1.11, 95% CI 1.07–1.16), relative to no disruption. There were no associations with housing disruptions (OR 1.00, 95% CI 0.97–1.03).

People experiencing psychological distress pre-pandemic were more likely to experience healthcare and economic disruptions, and clusters of disruptions across multiple domains during the pandemic. Failing to address these disruptions risks further widening mental health inequalities.

Posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) are commonly reported co-occurring mental health consequences of psychological trauma exposure. The disorders have high genetic overlap. Trauma is a complex phenotype but research suggests that trauma sensitivity has a heritable basis. We investigated whether sensitivity to trauma in those with MDD reflects a similar genetic component in those with PTSD.

Genetic correlations between PTSD and MDD in individuals reporting trauma and MDD in individuals not reporting trauma were estimated, as well as with recurrent MDD and single-episode MDD, using genome-wide association study (GWAS) summary statistics. Genetic correlations were replicated using PTSD data from the Psychiatric Genomics Consortium and the Million Veteran Program. Polygenic risk scores were generated in UK Biobank participants who met the criteria for lifetime MDD (N = 29 471). We investigated whether genetic loading for PTSD was associated with reporting trauma in these individuals.

Genetic loading for PTSD was significantly associated with reporting trauma in individuals with MDD [OR 1.04 (95% CI 1.01–1.07), Empirical-p = 0.02]. PTSD was significantly more genetically correlated with recurrent MDD than with MDD in individuals not reporting trauma (rg differences = ~0.2, p < 0.008). Participants who had experienced recurrent MDD reported significantly higher rates of trauma than participants who had experienced single-episode MDD (χ2 > 166, p < 0.001)

Our findings point towards the existence of genetic variants associated with trauma sensitivity that might be shared between PTSD and MDD, although replication with better powered GWAS is needed. Our findings corroborate previous research highlighting trauma exposure as a key risk factor for recurrent MDD.

The UK Biobank contains data with varying degrees of reliability and completeness for assessing depression. A third of participants completed a Mental Health Questionnaire (MHQ) containing the gold-standard Composite International Diagnostic Interview (CIDI) criteria for assessing mental health disorders.

To investigate whether multiple observations of depression from sources other than the MHQ can enhance the validity of major depressive disorder (MDD).

In participants who did not complete the MHQ, we calculated the number of other depression measures endorsed, for example from hospital episode statistics and interview data. We compared cases defined this way with CIDI-defined cases for several estimates: the variance explained by polygenic risk scores (PRS), area under the curve attributable to PRS, single nucleotide polymorphisms (SNPs)-based heritability and genetic correlations with summary statistics from the Psychiatric Genomics Consortium MDD genome-wide association study.

The strength of the genetic contribution increased with the number of measures endorsed. For example, SNP-based heritability increased from 7% in participants who endorsed only one measure of depression, to 21% in those who endorsed four or five measures of depression. The strength of the genetic contribution to cases defined by at least two measures approximated that for CIDI-defined cases. Most genetic correlations between UK Biobank and the Psychiatric Genomics Consortium MDD study exceeded 0.7, but there was variability between pairwise comparisons.

Multiple measures of depression can serve as a reliable approximation for case status where the CIDI measure is not available, indicating sample size can be optimised using the entire suite of UK Biobank data.

Depression is a highly prevalent and heterogeneous disorder. This study aims to determine whether depression with atypical features shows different heritability and different degree of overlap with polygenic risk for psychiatric and immuno-metabolic traits than other depression subgroups.

Data included 30 069 European ancestry individuals from the UK Biobank who met criteria for lifetime major depression. Participants reporting both weight gain and hypersomnia were classified as ↑WS depression (N = 1854) and the others as non-↑WS depression (N = 28 215). Cases with non-↑WS depression were further classified as ↓WS depression (i.e. weight loss and insomnia; N = 10 142). Polygenic risk scores (PRS) for 22 traits were generated using genome-wide summary statistics (Bonferroni corrected p = 2.1 × 10−4). Single-nucleotide polymorphism (SNP)-based heritability of depression subgroups was estimated.

↑WS depression had a higher polygenic risk for BMI [OR = 1.20 (1.15–1.26), p = 2.37 × 10−14] and C-reactive protein [OR = 1.11 (1.06–1.17), p = 8.86 × 10−06] v. non-↑WS depression and ↓WS depression. Leptin PRS was close to the significance threshold (p = 2.99 × 10−04), but the effect disappeared when considering GWAS summary statistics of leptin adjusted for BMI. PRS for daily alcohol use was inversely associated with ↑WS depression [OR = 0.88 (0.83–0.93), p = 1.04 × 10−05] v. non-↑WS depression. SNP-based heritability was not significantly different between ↑WS depression and ↓WS depression (14.3% and 12.2%, respectively).

↑WS depression shows evidence of distinct genetic predisposition to immune-metabolic traits and alcohol consumption. These genetic signals suggest that biological targets including immune-cardio-metabolic pathways may be relevant to therapies in individuals with ↑WS depression.

Major depression (MD) is often characterised as a categorical disorder; however, observational studies comparing sub-threshold and clinical depression suggest MD is continuous. Many of these studies do not explore the full continuum and are yet to consider genetics as a risk factor. This study sought to understand if polygenic risk for MD could provide insight into the continuous nature of depression.

Factor analysis on symptom-level data from the UK Biobank (N = 148 957) was used to derive continuous depression phenotypes which were tested for association with polygenic risk scores (PRS) for a categorical definition of MD (N = 119 692).

Confirmatory factor analysis showed a five-factor hierarchical model, incorporating 15 of the original 18 items taken from the PHQ-9, GAD-7 and subjective well-being questionnaires, produced good fit to the observed covariance matrix (CFI = 0.992, TLI = 0.99, RMSEA = 0.038, SRMR = 0.031). MD PRS associated with each factor score (standardised β range: 0.057–0.064) and the association remained when the sample was stratified into case- and control-only subsets. The case-only subset had an increased association compared to controls for all factors, shown via a significant interaction between lifetime MD diagnosis and MD PRS (p value range: 2.23 × 10−3–3.94 × 10−7).

An association between MD PRS and a continuous phenotype of depressive symptoms in case- and control-only subsets provides support against a purely categorical phenotype; indicating further insights into MD can be obtained when this within-group variation is considered. The stronger association within cases suggests this variation may be of particular importance.

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.

Childhood maltreatment is one of the strongest predictors of adulthood depression and alterations to circulating levels of inflammatory markers is one putative mechanism mediating risk or resilience.

To determine the effects of childhood maltreatment on circulating levels of 41 inflammatory markers in healthy individuals and those with a major depressive disorder (MDD) diagnosis.

We investigated the association of childhood maltreatment with levels of 41 inflammatory markers in two groups, 164 patients with MDD and 301 controls, using multiplex electrochemiluminescence methods applied to blood serum.

Childhood maltreatment was not associated with altered inflammatory markers in either group after multiple testing correction. Body mass index (BMI) exerted strong effects on interleukin-6 and C-reactive protein levels in those with MDD.

Childhood maltreatment did not exert effects on inflammatory marker levels in either the participants with MDD or the control group in our study. Our results instead highlight the more pertinent influence of BMI.

D.A.C. and H.W. work for Eli Lilly Inc. R.N. has received speaker fees from Sunovion, Jansen and Lundbeck. G.B. has received consultancy fees and funding from Eli Lilly. R.H.M.-W. has received consultancy fees or has a financial relationship with AstraZeneca, Bristol-Myers Squibb, Cyberonics, Eli Lilly, Ferrer, Janssen-Cilag, Lundbeck, MyTomorrows, Otsuka, Pfizer, Pulse, Roche, Servier, SPIMACO and Sunovian. I.M.A. has received consultancy fees or has a financial relationship with Alkermes, Lundbeck, Lundbeck/Otsuka, and Servier. S.W. has sat on an advisory board for Sunovion, Allergan and has received speaker fees from Astra Zeneca. A.H.Y. has received honoraria for speaking from Astra Zeneca, Lundbeck, Eli Lilly, Sunovion; honoraria for consulting from Allergan, Livanova and Lundbeck, Sunovion, Janssen; and research grant support from Janssen. A.J.C. has received honoraria for speaking from Astra Zeneca, honoraria for consulting with Allergan, Livanova and Lundbeck and research grant support from Lundbeck.