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.

Immune system abnormalities exist across a range of psychiatric disorders. Autoimmunity, characterized by the production of antibodies against the body’s own antigens, is a feature of immune system dysfunction and could play a role in mental disorder pathophysiology. Better understanding of the associations of auto-immunoglobulin G (IgG) repertoires with clinical features of mental illness could yield novel models of psychosis pathophysiology and markers for biological patient stratification.

To undertake global screening for auto-IgG expression in a large cohort of people with psychotic disorders; to determine whether associations exist between autoantibody expression and clinical features.

Cross-sectional quantification of auto-IgGs in blood plasma of 461 people with established psychotic disorder diagnoses. For global screening, pooled samples of phenotypically representative patient groups were exposed to planar protein microarrays containing 42,000 human antigens. For targeted profiling, expression levels of 380 autoantibodies were quantified by suspension bead array (SBA) in each patient’s plasma.

We identified highly individual autoantibody profiles with no evidence for co-expression patterns. We found 6 autoantibodies robustly associated with specific psychopathology: anti-AP3B2, detected in 5% of the cohort of whom 100% had persecutory delusions; anti-TDO2 (5% of the cohort, 100% hallucinations); anti-CRYGN (4%, 86% initial insomnia); anti-APMAP (3%, 86% poor appetite); anti-OLFM1 (2.5%, 100% above median cognitive function); and anti-WHAMMP3 (2%, 90% anhedonia and dysphoria). Examination of the auto-IgG binding site on the TDO2 protein revealed a putative pathophysiological mechanism involving the kynurenine pathway.

We identified 6 frequently occurring autoantibodies that were associated with specific clinical features in people with psychotic disorders.

No significant relationships.

The concept of indicated prevention has proliferated in psychiatry. Accumulating evidence suggests that it may indeed be possible to prevent or delay the onset of a First Episode of Psychosis (FEP) though adequate interventions in individuals deemed at Clinical High Risk (CHR) for such an event. However, a challenge undermining these efforts is the relatively poor predictive accuracy of clinical assessments used in practice for CHR individuals. to improve prediction by combining different types of assessments.

To improve prediction of clinical course of disease by combining biological and clinical types of assessment.

To present a probabilistic prediction model containing clinical and biological data for the transition to first episode psychosis.

Using data from published studies, and employing predictive models based on the odds ratio form of Bayes’ rule, we simulated scenarios where clinical interview, neurocognitive testing, structural magnetic resonance imaging (MRI) and electrophysiology are part of the initial assessment process of a CHR individual (Extended Diagnostic Approach).

Our findings indicate that for most at-risk patients, at least three types of assessments are necessary to arrive at a clinically meaningful differentiation into high- intermediate-, and low-risk groups. In particular, patients with equivocal results in the initial assessments require additional diagnostic testing to produce an accurate risk profile forming part of the comprehensive initial assessment.

The findings may inform future research into reliable identification and personalized therapeutic targeting of CHR patients, to prevent transition to full-blown psychosis and to inform decision in intervention.

Predicting transition from clinical high risk (CHR) to first episode psychosis has proven difficult. Assessment of oxidative stress biomarkers and the niacin skin flush response (NSFR) may improve prediction accuracy.

To predict transition to psychosis based on combined clinical and blood biomarker.

To analyse data from patients in placebo group of a 12-week trial of omega-3 fatty acid supplementation in CHR. Transition likelihood ratios (LRs) for baseline historical risks, clinical assessments (PANSS subscales and total, GAF), NSFR and blood markers (nervonic acid, superoxide dismutase, glutathione) were calculated. Variables with the highest positive and lowest negative LRs were included in an odds ratio form of Bayes’ rule transition prediction models. Model accuracy was calculated by area under the receiver operating curves (AUROC) of each model.

1-year transition to psychosis was 28% (n=40). Historical data showed no predictability (sensitivity 30%, specificity 100% (AUROC)=0.688, p=0.085). Clinical assessments alone produced a sensitivity of 30% at a specificity of 95% (AUROC=0.83, p<0.0001). The biomarker panel alone predicted transition with 40% sensitivity and 100% specificity (AUROC=0.73, p=0.03). Combining history and clinical assessment provided no improvement above clinical data alone (sensitivity = 30%, specificity = 100%, AUROC=0.85, p< 0.0001). The combination of history, clinical assessment and biomarkers identified transition with a sensitivity of 60% and specificity of 100% (AUROC=0.87, p< 0.0001).

Probabilistic models combining biomarkers and clinical data are able to target high-risk subgroups within CHR and may help to personalise treatment.

The use of Alzheimer disease medication for the treatment of dementia symptoms has shown significant benefits with regards to functional and cognitive outcomes as well as nursing home placement (NHP) and mortality. Hospitalisations in these patient groups are characterised by extended length of stays (LOS), frequent readmissions, frequent NHP and high-mortality rates. The impact of Alzheimer disease medication on the aforementioned outcomes remains still unknown. This study assessed the association of Alzheimer disease medication with outcomes of hospitalisation among patients with Alzheimer disease and other forms of dementia.

A dynamic retrospective cohort study from 2004 to 2015 was conducted which claims data from a German health insurance company. People with dementia (PWD) were identified using ICD-10 codes and diagnostic measures. The main predictor of interest was the use of Alzheimer disease medication. Hospitalisation outcomes included LOS, readmissions, NHP and mortality during and after hospitalisation across four hospitalisations. Confounding was addressed using a propensity score throughout all analyses.

A total of 1380 users of Alzheimer disease medication and 6730 non-users were identified. The use of Alzheimer disease medication was associated with significantly shorter LOS during the first hospitalisations with estimates for the second, third and fourth showed a tendency towards shorter hospital stays. In addition, current users of Alzheimer disease medication had a lower risk of hospital readmission after the first two hospitalisations. These associations were not significant for the third and fourth hospitalisations. Post-hospitalisation NHP and mortality rates also tended to be lower among current users than among non-users but differences did not reach statistical significance.

Our results indicate that Alzheimer disease medication might contribute to a reduction of the LOS and the number of readmissions in PWD.