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.

Anxiety disorders and treatment-resistant major depressive disorder (TRD) are often comorbid. Studies suggest ketamine has anxiolytic and antidepressant properties.

To investigate if subcutaneous racemic ketamine, delivered twice weekly for 4 weeks, reduces anxiety in people with TRD.

The Ketamine for Adult Depression Study was a multisite 4-week randomised, double-blind, active (midazolam)-controlled trial. The study initially used fixed low dose ketamine (0.5 mg/kg, cohort 1), before protocol revision to flexible, response-guided dosing (0.5–0.9 mg/kg, cohort 2). This secondary analysis assessed anxiety using the Hamilton Anxiety (HAM-A) scale (primary measure) and ‘inner tension’ item 3 of the Montgomery–Åsberg Depression Rating Scale (MADRS), at baseline, 4 weeks (end treatment) and 4 weeks after treatment end. Analyses of change in anxiety between ketamine and midazolam groups included all participants who received at least one treatment (n = 174), with a mixed effects repeated measures model used to assess the primary anxiety measure. The trial was registered at www.anzctr.org.au (ACTRN12616001096448).

In cohort 1 (n = 68) the reduction in HAM-A score was not statistically significant: −1.4 (95% CI [−8.6, 3.2], P = 0.37), whereas a significant reduction was seen for cohort 2 (n = 106) of −4.0 (95% CI [−10.6, −1.9], P = 0.0058), favouring ketamine over midazolam. These effects were mediated by total MADRS and were not maintained at 4 weeks after treatment end. MADRS item 3 was also significantly reduced in cohort 2 (P = 0.026) but not cohort 1 (P = 0.96).

Ketamine reduces anxiety in people with TRD when administered subcutaneously in adequate doses.

Exploring the neural basis related to different mood states is a critical issue for understanding the pathophysiology underlying mood switching in bipolar disorder (BD), but research has been scarce and inconsistent.

Resting-state functional magnetic resonance imaging data were acquired from 162 patients with BD: 33 (hypo)manic, 64 euthymic, and 65 depressive, and 80 healthy controls (HCs). The differences of large-scale brain network functional connectivity (FC) between the four groups were compared and correlated with clinical characteristics. To validate the generalizability of our findings, we recruited a small longitudinal independent sample of BD patients (n = 11). In addition, we examined topological nodal properties across four groups as exploratory analysis.

A specific strengthened pattern of network FC, predominantly involving the default mode network (DMN), was observed in (hypo)manic patients when compared with HCs and bipolar patients in other mood states. Longitudinal observation revealed an increase in several network FCs in patients during (hypo)manic episode. Both samples evidenced an increase in the FC between the DMN and ventral attention network, and between the DMN and limbic network (LN) related to (hypo)mania. The altered network connections were correlated with mania severity and positive affect. Bipolar depressive patients exhibited decreased FC within the LN compared with HCs. The exploratory analysis also revealed an increase in degree in (hypo)manic patients.

Our findings identify a distributed pattern of large-scale network disturbances in the unique context of (hypo)mania and thus provide new evidence for our understanding of the neural mechanism of BD.

Prior trials suggest that intravenous racemic ketamine is a highly effective for treatment-resistant depression (TRD), but phase 3 trials of racemic ketamine are needed.

To assess the acute efficacy and safety of a 4-week course of subcutaneous racemic ketamine in participants with TRD. Trial registration: ACTRN12616001096448 at www.anzctr.org.au.

This phase 3, double-blind, randomised, active-controlled multicentre trial was conducted at seven mood disorders centres in Australia and New Zealand. Participants received twice-weekly subcutaneous racemic ketamine or midazolam for 4 weeks. Initially, the trial tested fixed-dose ketamine 0.5 mg/kg versus midazolam 0.025 mg/kg (cohort 1). Dosing was revised, after a Data Safety Monitoring Board recommendation, to flexible-dose ketamine 0.5–0.9 mg/kg or midazolam 0.025–0.045 mg/kg, with response-guided dosing increments (cohort 2). The primary outcome was remission (Montgomery-Åsberg Rating Scale for Depression score ≤10) at the end of week 4.

The final analysis (those who received at least one treatment) comprised 68 in cohort 1 (fixed-dose), 106 in cohort 2 (flexible-dose). Ketamine was more efficacious than midazolam in cohort 2 (remission rate 19.6% v. 2.0%; OR = 12.1, 95% CI 2.1–69.2, P = 0.005), but not different in cohort 1 (remission rate 6.3% v. 8.8%; OR = 1.3, 95% CI 0.2–8.2, P = 0.76). Ketamine was well tolerated. Acute adverse effects (psychotomimetic, blood pressure increases) resolved within 2 h.

Adequately dosed subcutaneous racemic ketamine was efficacious and safe in treating TRD over a 4-week treatment period. The subcutaneous route is practical and feasible.

Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact.

We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations.

BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI.

We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

Identifying clinical features that predict conversion to bipolar disorder (BD) in those at high familial risk (HR) would assist in identifying a more focused population for early intervention.

In total 287 participants aged 12–30 (163 HR with a first-degree relative with BD and 124 controls (CONs)) were followed annually for a median of 5 years. We used the baseline presence of DSM-IV depressive, anxiety, behavioural and substance use disorders, as well as a constellation of specific depressive symptoms (as identified by the Probabilistic Approach to Bipolar Depression) to predict the subsequent development of hypo/manic episodes.

At baseline, HR participants were significantly more likely to report ⩾4 Probabilistic features (40.4%) when depressed than CONs (6.7%; p < .05). Nineteen HR subjects later developed either threshold (n = 8; 4.9%) or subthreshold (n = 11; 6.7%) hypo/mania. The presence of ⩾4 Probabilistic features was associated with a seven-fold increase in the risk of ‘conversion’ to threshold BD (hazard ratio = 6.9, p < .05) above and beyond the fourteen-fold increase in risk related to major depressive episodes (MDEs) per se (hazard ratio = 13.9, p < .05). Individual depressive features predicting conversion were psychomotor retardation and ⩾5 MDEs. Behavioural disorders only predicted conversion to subthreshold BD (hazard ratio = 5.23, p < .01), while anxiety and substance disorders did not predict either threshold or subthreshold hypo/mania.

This study suggests that specific depressive characteristics substantially increase the risk of young people at familial risk of BD going on to develop future hypo/manic episodes and may identify a more targeted HR population for the development of early intervention programs.

Bipolar disorder is a highly heritable polygenic disorder. Recent enrichment analyses suggest that there may be true risk variants for bipolar disorder in the expression quantitative trait loci (eQTL) in the brain.

We sought to assess the impact of eQTL variants on bipolar disorder risk by combining data from both bipolar disorder genome-wide association studies (GWAS) and brain eQTL.

To detect single nucleotide polymorphisms (SNPs) that influence expression levels of genes associated with bipolar disorder, we jointly analysed data from a bipolar disorder GWAS (7481 cases and 9250 controls) and a genome-wide brain (cortical) eQTL (193 healthy controls) using a Bayesian statistical method, with independent follow-up replications. The identified risk SNP was then further tested for association with hippocampal volume (n = 5775) and cognitive performance (n = 342) among healthy individuals.

Integrative analysis revealed a significant association between a brain eQTL rs6088662 on chromosome 20q11.22 and bipolar disorder (log Bayes factor = 5.48; bipolar disorder P = 5.85×10–5). Follow-up studies across multiple independent samples confirmed the association of the risk SNP (rs6088662) with gene expression and bipolar disorder susceptibility (P = 3.54×10–8). Further exploratory analysis revealed that rs6088662 is also associated with hippocampal volume and cognitive performance in healthy individuals.

Our findings suggest that 20q11.22 is likely a risk region for bipolar disorder; they also highlight the informative value of integrating functional annotation of genetic variants for gene expression in advancing our understanding of the biological basis underlying complex disorders, such as bipolar disorder.

There have been no previous factor analytic studies of the Hamilton Depression Rating Scale (HDRS) in samples with bipolar I depression, and no investigations of the utility of any derived factors in determining treatment response in this condition. This study aimed to identify and compare factors of a 31-item version of the HDRS (HDRS-31) in large samples of patients with bipolar depression and Major Depressive Disorder (MDD), then examine the responsiveness of such factors to lamotrigine compared with placebo in the bipolar depressed sample.

This multivariate analytical study was performed on 2 large depressed samples (one bipolar and the other MDD) that had been recruited for separate, contemporaneous, double-blind placebo-controlled trials of lamotrigine. The 2 studies had similar designs and assessment tools, the major measures being the Montgomery–Asberg Depression Rating Scale (MADRS) and HDRS-31. To identify the constructs underlying the scale, exploratory factor analyses were conducted using HDRS-31 baseline scores. Treatment responsiveness in the bipolar depressed sample—as indicated by improvement in the total MADRS and HDRS-31, as well as HDRS factors—were examined using both a mixed-effects analysis and individual time-point t-tests.

Seven factors of the HDRS-31 were identified: I—“depressive cognitions,” II—“psychomotor retardation,” III—“insomnia,” IV—“hypersomnia,” V—“appetite and weight change,” VI—“anxiety,” and VII—“anergia.” A significant therapeutic effect of lamotrigine in bipolar depression was found for the “depressive cognitions” factor (from week 3) and “psychomotor retardation” (from week 4).

This study has identified 7 factors of the HDRS in a large sample of patients with bipolar depression. The results suggest that that the clinical benefits of lamotrigine in acute bipolar depression are primarily upon depressive cognitions and psychomotor slowing.