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.

To minimise infection during COVID-19, the clozapine haematological monitoring interval was extended from 4-weekly to 12-weekly intervals in South London and Maudsley NHS Foundation Trust.

To investigate the impact of this temporary policy change on clinical and safety outcomes.

All patients who received clozapine treatment with extended (12-weekly) monitoring in a large London National Health Service trust were included in a 1-year mirror-image study. A comparison group was selected with standard monitoring. The proportion of participants with mild to severe neutropenia and the proportion of participants attending the emergency department for clozapine-induced severe neutropenia treatment during the follow-up period were compared. Psychiatric hospital admission rates, clozapine dose and concomitant psychotropic medication in the 1 year before and the 1 year after extended monitoring were compared. All-cause clozapine discontinuation at 1-year follow-up was examined.

Of 569 participants, 459 received clozapine with extended monitoring and 110 controls continued as normal. The total person-years were 458 in the intervention group and 109 in the control group, with a median follow-up time of 1 year in both groups. During follow-up, two participants (0.4%) recorded mild to moderate neutropenia in the intervention group and one (0.9%) in the control group. There was no difference in the incidence of haematological events between the two groups (IRR = 0.48, 95% CI 0.02–28.15, P = 0.29). All neutropenia cases in the intervention group were mild, co-occurring during COVID-19 infection. The median number of admissions per patient during the pre-mirror period remained unchanged (0, IQR = 0) during the post-mirror period. There was one death in the control group, secondary to COVID-19 infection.

There was no evidence that the incidence of severe neutropenia was increased in those receiving extended monitoring.

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.

Co-occurrence of common mental disorders (CMD) with psychotic experiences is well-known. There is little research on the public mental health relevance of concurrent psychotic experiences for service use, suicidality, and poor physical health. We aim to: (1) describe the distribution of psychotic experiences co-occurring with a range of non-psychotic psychiatric disorders [CMD, depressive episode, anxiety disorder, probable post-traumatic stress disorder (PTSD), and personality dysfunction], and (2) examine associations of concurrent psychotic experiences with secondary mental healthcare use, psychological treatment use for CMD, lifetime suicide attempts, and poor self-rated health.

We linked a prospective cross-sectional community health survey with a mental healthcare provider database. For each non-psychotic psychiatric disorder, patients with concurrent psychotic experiences were compared to those without psychotic experiences on use of secondary mental healthcare, psychological treatment for CMD, suicide attempt, physical functioning, and a composite multimorbidity score, using logistic regression and Cox regressions.

In all disorders except for anxiety disorder, concurrent psychotic experiences were accompanied by a greater odds of all outcomes (odds ratios) for a unit change in composite multimorbidity score ranged between 2.21 [95% confidence interval (CI) 1.49–3.27] and 3.46 (95% CI 1.52–7.85). Hazard ratios for secondary mental health service use for non-psychotic disorders with concurrent psychotic experiences, ranged from 0.53 (95% CI 0.15–1.86) for anxiety disorders with psychotic experiences to 4.99 (95% CI 1.22–20.44) among those with PTSD with psychotic experiences.

Co-occurring psychotic experiences indicate greater public mental health burden, suggesting psychotic experiences could be a marker for future preventive strategies improving public mental health.

Although psychotic experiences in people without diagnosed mental health problems are associated with mental health service use, few studies have assessed this prospectively or measured service use by real-world clinical data.

To describe and investigate the association between psychotic experiences and later mental health service use, and to assess the role of symptoms of common mental health disorders in this association.

We linked a representative survey of south-east London (SELCoH-1, n=1698) with health records from the local mental healthcare provider. Cox regression estimated the association of PEs with rate of mental health service use.

After adjustments, psychotic experiences were associated with a 1.75-fold increase in the rate of subsequent mental health service use (hazard ratio (HR) 1.75, 95% CI 1.03–2.97) compared with those without PEs. Participants with PEs experienced longer care episodes compared with those without.

Psychotic experiences in the general population are important predictors of public mental health need, aside from their relevance for psychoses. We found psychotic experiences to be associated with later mental health service use, after accounting for sociodemographic confounders and concurrent psychopathology.

Influenza A (H1N1) pdm09 became the predominant circulating strain in the United States during the 2013–2014 influenza season. Little is known about the epidemiology of severe influenza during this season.

A retrospective cohort study of severely ill patients with influenza infection in intensive care units in 33 US hospitals from September 1, 2013, through April 1, 2014, was conducted to determine risk factors for mortality present on intensive care unit admission and to describe patient characteristics, spectrum of disease, management, and outcomes.

A total of 444 adults and 63 children were admitted to an intensive care unit in a study hospital; 93 adults (20.9%) and 4 children (6.3%) died. By logistic regression analysis, the following factors were significantly associated with mortality among adult patients: older age (>65 years, odds ratio, 3.1 [95% CI, 1.4–6.9], P=.006 and 50–64 years, 2.5 [1.3–4.9], P=.007; reference age 18–49 years), male sex (1.9 [1.1–3.3], P=.031), history of malignant tumor with chemotherapy administered within the prior 6 months (12.1 [3.9–37.0], P<.001), and a higher Sequential Organ Failure Assessment score (for each increase by 1 in score, 1.3 [1.2–1.4], P<.001).

Risk factors for death among US patients with severe influenza during the 2013–2014 season, when influenza A (H1N1) pdm09 was the predominant circulating strain type, shifted in the first postpandemic season in which it predominated toward those of a more typical epidemic influenza season.

Infect. Control Hosp. Epidemiol. 2015;36(11):1251–1260