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.

Mental health policy makers require evidence-based information to optimise effective care provision based on local need, but tools are unavailable.

To develop and validate a population-level prediction model for need for early intervention in psychosis (EIP) care for first-episode psychosis (FEP) in England up to 2025, based on epidemiological evidence and demographic projections.

We used Bayesian Poisson regression to model small-area-level variation in FEP incidence for people aged 16–64 years. We compared six candidate models, validated against observed National Health Service FEP data in 2017. Our best-fitting model predicted annual incidence case-loads for EIP services in England up to 2025, for probable FEP, treatment in EIP services, initial assessment by EIP services and referral to EIP services for ‘suspected psychosis’. Forecasts were stratified by gender, age and ethnicity, at national and Clinical Commissioning Group levels.

A model with age, gender, ethnicity, small-area-level deprivation, social fragmentation and regional cannabis use provided best fit to observed new FEP cases at national and Clinical Commissioning Group levels in 2017 (predicted 8112, 95% CI 7623–8597; observed 8038, difference of 74 [0.92%]). By 2025, the model forecasted 11 067 new treated cases per annum (95% CI 10 383–11 740). For every 10 new treated cases, 21 and 23 people would be assessed by and referred to EIP services for suspected psychosis, respectively.

Our evidence-based methodology provides an accurate, validated tool to inform clinical provision of EIP services about future population need for care, based on local variation of major social determinants of psychosis.

Lewy body dementia, consisting of both dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD), is considerably under-recognised clinically compared with its frequency in autopsy series.

This study investigated the clinical diagnostic pathways of patients with Lewy body dementia to assess if difficulties in diagnosis may be contributing to these differences.

We reviewed the medical notes of 74 people with DLB and 72 with non-DLB dementia matched for age, gender and cognitive performance, together with 38 people with PDD and 35 with Parkinson's disease, matched for age and gender, from two geographically distinct UK regions.

The cases of individuals with DLB took longer to reach a final diagnosis (1.2 v. 0.6 years, P = 0.017), underwent more scans (1.7 v. 1.2, P = 0.002) and had more alternative prior diagnoses (0.8 v. 0.4, P = 0.002), than the cases of those with non-DLB dementia. Individuals diagnosed in one region of the UK had significantly more core features (2.1 v. 1.5, P = 0.007) than those in the other region, and were less likely to have dopamine transporter imaging (P < 0.001). For patients with PDD, more than 1.4 years prior to receiving a dementia diagnosis: 46% (12 of 26) had documented impaired activities of daily living because of cognitive impairment, 57% (16 of 28) had cognitive impairment in multiple domains, with 38% (6 of 16) having both, and 39% (9 of 23) already receiving anti-dementia drugs.

Our results show the pathway to diagnosis of DLB is longer and more complex than for non-DLB dementia. There were also marked differences between regions in the thresholds clinicians adopt for diagnosing DLB and also in the use of dopamine transporter imaging. For PDD, a diagnosis of dementia was delayed well beyond symptom onset and even treatment.

Music therapy has been shown to be effective for reducing anxiety and pain in people with a serious illness. Few studies have investigated the feasibility of integrating music therapy into general inpatient care of the seriously ill, including the care of diverse, multiethnic patients. This leaves a deficit in knowledge for intervention planning. This study investigated the feasibility and effectiveness of introducing music therapy for patients on 4 inpatient units in a large urban medical center. Capacitated and incapacitated patients on palliative care, transplantation, medical intensive care, and general medicine units received a single bedside session led by a music therapist.

A mixed-methods, pre-post design was used to assess clinical indicators and the acceptability and feasibility of the intervention. Multiple regression modeling was used to evaluate the effect of music therapy on anxiety, pain, pulse, and respiratory rate. Process evaluation data and qualitative analysis of observational data recorded by the music therapists were used to assess the feasibility of providing music therapy on the units and patients’ interest, receptivity, and satisfaction.

Music therapy was delivered to 150 patients over a 6-month period. Controlling for gender, age, and session length, regression modeling showed that patients reported reduced anxiety post-session. Music therapy was found to be an accessible and adaptable intervention, with patients expressing high interest, receptivity, and satisfaction.

This study found it feasible and effective to introduce bedside music therapy for seriously ill patients in a large urban medical center. Lessons learned and recommendations for future investigation are discussed.

Antineuronal antibodies are associated with psychosis, although their clinical significance in first episode of psychosis (FEP) is undetermined.

To examine all patients admitted for treatment of FEP for antineuronal antibodies and describe clinical presentations and treatment outcomes in those who were antibody positive.

Individuals admitted for FEP to six mental health units in Queensland, Australia, were prospectively tested for serum antineuronal antibodies. Antibody-positive patients were referred for neurological and immunological assessment and therapy.

Of 113 consenting participants, six had antineuronal antibodies (anti-N-methyl-D-aspartate receptor antibodies [n = 4], voltage-gated potassium channel antibodies [n = 1] and antibodies against uncharacterised antigen [n = 1]). Five received immunotherapy, which prompted resolution of psychosis in four.

A small subgroup of patients admitted to hospital with FEP have antineuronal antibodies detectable in serum and are responsive to immunotherapy. Early diagnosis and treatment is critical to optimise recovery.

None.

Depression and obesity are highly prevalent, and major impacts on public health frequently co-occur. Recently, we reported that having depression moderates the effect of the FTO gene, suggesting its implication in the association between depression and obesity.

To confirm these findings by investigating the FTO polymorphism rs9939609 in new cohorts, and subsequently in a meta-analysis.

The sample consists of 6902 individuals with depression and 6799 controls from three replication cohorts and two original discovery cohorts. Linear regression models were performed to test for association between rs9939609 and body mass index (BMI), and for the interaction between rs9939609 and depression status for an effect on BMI. Fixed and random effects meta-analyses were performed using METASOFT.

In the replication cohorts, we observed a significant interaction between FTO, BMI and depression with fixed effects meta-analysis (β=0.12, P = 2.7 × 10−4) and with the Han/Eskin random effects method (P = 1.4 × 10−7) but not with traditional random effects (β = 0.1, P = 0.35). When combined with the discovery cohorts, random effects meta-analysis also supports the interaction (β = 0.12, P = 0.027) being highly significant based on the Han/Eskin model (P = 6.9 × 10−8). On average, carriers of the risk allele who have depression have a 2.2% higher BMI for each risk allele, over and above the main effect of FTO.

This meta-analysis provides additional support for a significant interaction between FTO, depression and BMI, indicating that depression increases the effect of FTO on BMI. The findings provide a useful starting point in understanding the biological mechanism involved in the association between obesity and depression.