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.

Lacunar stroke is a small (<2 cm) infarction that accounts for approximately 20% of all strokes. While a third of all stroke patients experience depressive symptoms, the prevalence of depression in the lacunar stroke patient population is unclear. This meta-analysis aimed to synthesize the evidence on the effect of lacunar stroke and deep white matter disease on depressive symptoms.

A systematic search of electronic databases was conducted, resulting in the inclusion of 12 studies. Analyses were performed on the effects of lacunar stroke, volume and location of lacunes on depression prevalence, and the effect on depression severity. The effects estimates were calculated in random-effects models.

None of the analyses produced statistically significant results. Lacunar stroke patients had a non-significantly higher prevalence of depression compared to patients with non-lacunar cerebrovascular diseases (OR = 1.46, 95% CI: 0.88–2.43, p = 0.15). Neither thalamic (OR = 1.37 (0.85–2.20), p = 0.19), deep white matter (RR = 1.16 (0.85–1.57), p = 0.35), multiple lacunes (OR = 1.34 (0.81–2.22), p = 0.25), or the volume of lacunes (MD = −4.71 (−351.59–342.18), p = 0.98) had an effect on depression prevalence. Lastly, lacunar stroke did not influence depressive symptom severity (MD = 0.96 (−1.57–3.48), p = 0.46).

The pooled group of patients with lacunar stroke and deep white matter disease appear to have a similar prevalence of depression compared to those with other types of cerebrovascular diseases. However, the small number of studies, heterogeneous comparison groups, and high statistical heterogeneity between studies posed an obstacle to the meta-analysis. To determine appropriate screening and treatment approaches, future research will need to separate lacunar stroke and deep white matter disease patients, and include larger sample sizes and healthy control groups to determine their distinct contributions to depression.