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.

To evaluate the performance of the French version of the Mood Disorder Questionnaire (MDQ) in patients attending a general psychiatric outpatient service as well as whether MDQ scores are independent of patient mood state at time of completion.

183 patients completed the MDQ and were assessed with the MADRS and YMRS scales, before being interviewed with the SCID (time 1). MDQ, MADRS and YMRS assessment was repeated four to six weeks later (time 2).

According to the SCID, 44 patients were suffering from bipolar spectrum disorder and 102 from unipolar disorder (37 patients dropped out). The MDQ provided high specificity (83.3%). Sensitivity was 63.6%, with better identification of bipolar I (85.0%) than bipolar II patients (45.8%). In the whole sample, test-retest reliability was satisfactory (kappa = 0.64). Modest correlations were observed between the number of endorsed MDQ items and YMRS scores at time 1 (Spearman r = 0.19; p = 0.021) and time 2 (r = 0.26; p = 0.002).

Despite some fluctuations over time and a discrete influence of symptom severity, the screening algorithm can be used reliably, whether in the acute or remission phase of a depressive episode.

Early-life adversities represent risk factors for the development of bipolaraffective disorder and are associated with higher severity of the disorder.This may be the consequence of a sustained alteration of thehypothalamic–pituitary–adrenal (HPA) axis resulting fromepigenetic modifications of the gene coding for the glucocorticoid receptor(NR3C1).

To investigate whether severity of childhood maltreatment is associated withincreased methylation of the exon? 1FNR3C1 promoter in bipolar disorder.

A sample of people with bipolar disorder (n = 99) wereassessed for childhood traumatic experiences. The percentage ofNR3C1 methylation was measured for eachparticipant.

The higher the number of trauma events, the higher was the percentage ofNR3C1 methylation (β = 0.52, 95% CI0.46–0.59, P<<0.0001). The severity ofeach type of maltreatment (sexual, physical and emotional) was alsoassociated with NR3C1 methylation status.

Early-life adversities have a sustained effect on the HPA axis throughepigenetic processes and this effect may be measured in peripheral blood.This enduring biological impact of early trauma may alter the development ofthe brain and lead to adult psychopathological disorder.