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.

Hypomanic symptoms may be a useful predictor of mood disorder among young people at high risk for bipolar disorder.

To determine whether hypomanic symptoms differentiate offspring of parents with bipolar disorder (high risk) and offspring of well parents (control) and predict the development of mood episodes.

High-risk and control offspring were prospectively assessed using semi-structured clinical interviews annually and completed the Hypomania Checklist-32 Revised (HCL-32). Clinically significant sub-threshold hypomanic symptoms (CSHS) were coded.

HCL-32 total and active or elated scores were higher in control compared with high-risk offspring, whereas 14% of high-risk and 0% of control offspring had CSHS. High-risk offspring with CSHS had a fivefold increased risk of developing recurrent major depression (P=0.0002). The median onset of CSHS in high-risk offspring was 16.4 (6–31) years and was before the onset of major mood episodes.

CSHS are precursors to major mood episodes in high-risk offspring and could identify individuals at ultra-high risk for developing bipolar disorder.

Bipolar disorder is highly heritable and therefore longitudinal observation of children of affected parents is important to mapping the early natural history.

To model the developmental trajectory of bipolar disorder based on the latest findings from an ongoing prospective study of the offspring of parents with well-characterised bipolar disorder.

A total of 229 offspring from families in which 1 parent had confirmed bipolar disorder and 86 control offspring were prospectively studied for up to 16 years. High-risk offspring were divided into subgroups based on the parental long-term response to lithium. Offspring were clinically assessed and DSM-IV diagnoses determined on masked consensus review using best estimate procedure. Adjusted survival analysis and generalised estimating equations were used to calculate differences in lifetime psychopathology. Multistate models were used to examine the progression through proposed clinical stages.

High-risk offspring had an increased lifetime risk of a broad spectrum of disorders including bipolar disorder (hazard ratio (HR) = 20.89; P = 0.04), major depressive disorder (HR = 17.16; P = 0.004), anxiety (HR = 2.20; P = 0.03), sleep (HR = 28.21; P = 0.02) and substance use disorders (HR = 2.60; P = 0.05) compared with controls. However, only offspring from lithium non-responsive parents developed psychotic disorders. Childhood anxiety disorder predicted an increased risk of major mood disorder and evidence supported a progressive transition through clinical stages, from non-specific psychopathology to depressive and then manic or psychotic episodes.

Findings underscore the importance of a developmental approach in conjunction with an appreciation of familial risk to facilitate earlier accurate diagnosis in symptomatic youth.