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 examine 5 years trajectories of secondary-treated late-life major depressive disorder (MDD), and evaluate whether pre-existing cerebrovascular disease and related risk factors are associated with more severe trajectories of late-life MDD.

Data were obtained from Danish registers. The sample included 11,184 adults ≥ 60 at index MDD diagnosis. Trajectories of in or outpatient contact at psychiatric hospitals for MDD over the 5 years period following index MDD diagnosis were modeled using latent class growth analysis. Risk factors included cerebrovascular disease, cardiovascular disease, hypertension, diabetes, and vascular dementia defined based on hospital diagnoses and prescription medications, demographic characteristics and characteristics of the index MDD diagnosis.

The final model included classes with consistently low (66%), high decreasing (19%), consistently high (9%) and moderate fluctuating (6%) probabilities of contact at a psychiatric hospital for MDD during the 5 year period following the index MDD diagnosis (Fig. 1). Older age, greater severity, inpatient treatment and > 12 antidepressant prescriptions within 5 years of the index MDD diagnosis predicted membership in more severe trajectory classes. Cerebrovascular disease and related risk factors were not associated with trajectory class membership.

A substantial proportion (34%) of individuals diagnosed with MDD in late-life require specialized psychiatric treatment for extended periods of time. We found no evidence that cerebrovascular disease or related risk factors predicted course trajectories in secondary-treated late-life MDD.

The authors have not supplied their declaration of competing interest.

Functional neurological disorders (FNDs), also known as conversion disorder, are unexplained neurological symptoms unrelated to a neurological cause. The disorder is common, yet poorly understood. The symptoms are experienced as involuntary but have similarities to voluntary processes. Here we studied intention awareness in FND.

A total of 26 FND patients and 25 healthy volunteers participated in this functional magnetic resonance study using Libet's clock.

FND is characterized by delayed awareness of the intention to move relative to the movement itself. The reporting of intention was more precise, suggesting that these findings are reliable and unrelated to non-specific attentional deficits. That these findings were more prominent with aberrant positive functional movement symptoms rather than negative symptoms may be relevant to impairments in timing for an inhibitory veto process. Attention towards intention relative to movement was associated with lower right inferior parietal cortex activity in FND, a region early in the processing of intention. During rest, aberrant functional connectivity was observed with the right inferior parietal cortex and other motor intention regions.

The results converge with observations of low inferior parietal activity comparing involuntary with voluntary movement in FND, emphasizing core deficiencies in intention. Heightened precision of this impaired intention is consistent with Bayesian theories of impaired top-down priors that might influence the sense of involuntariness. A primary impairment in voluntary motor intention at an early processing stage might explain clinical observations of slowed effortful voluntary movement, heightened self-directed attention and underlie functional movements. These findings further suggest novel therapeutic targets.

Depression is known to run in families, but the effects of parental history of other psychiatric diagnoses on depression rates are less well studied. Few studies have examined the impact of parental psychopathology on depression rates in older age groups.

We established a population-based cohort including all individuals born in Denmark after 1954 and alive on their 10th birthday (N = 29 76 264). Exposure variables were maternal and paternal history of schizophrenia, bipolar disorder, depression, anxiety or ‘other’ psychiatric diagnoses. Incidence rate ratios (IRRs) were estimated using Poisson regressions.

Parental history of any psychiatric diagnosis increased incidence rates of outpatient (maternal: IRR 1.88, p < 0.0001; paternal: IRR 1.68, p < 0.0001) and inpatient (maternal: IRR 1.99, p < 0.0001; paternal: IRR 1.83, p < 0.0001) depression relative to no parental history. IRRs for parental history of non-affective disorders remained relatively stable across age groups, while IRRs for parental affective disorders (unipolar or bipolar) decreased with age from 2.29–3.96 in the youngest age group to 1.53–1.90 in the oldest group. IRR estimates for all parental diagnoses were similar among individuals aged ⩾41 years (IRR range 1.51–1.90).

Parental history of any psychiatric diagnosis is associated with increased incidence rates of unipolar depression. In younger age groups, parental history of affective diagnoses is more strongly associated with rates of unipolar depression than non-affective diagnoses; however, this distinction disappears after age 40, suggesting that parental psychopathology in general, rather than any one disorder, confers risk for depression in middle life.

Distinguishing bipolar disorder (BP) from major depressive disorder (MDD) has important relevance for prognosis and treatment. Prior studies have identified clinical features that differ between these two diseases but have been limited by heterogeneity and lack of replication. We sought to identify depression-related features that distinguish BP from MDD in large samples with replication.

Using a large, opportunistically ascertained collection of subjects with BP and MDD we selected 34 depression-related clinical features to test across the diagnostic categories in an initial discovery dataset consisting of 1228 subjects (386 BPI, 158 BPII and 684 MDD). Features significantly associated with BP were tested in an independent sample of 1000 BPI cases and 1000 MDD cases for classifying ability in receiver operating characteristic (ROC) analysis.

Seven clinical features showed significant association with BPI compared with MDD: delusions, psychomotor retardation, incapacitation, greater number of mixed symptoms, greater number of episodes, shorter episode length, and a history of experiencing a high after depression treatment. ROC analyses of a model including these seven factors showed significant evidence for discrimination between BPI and MDD in an independent dataset (area under the curve = 0.83). Only two features (number of mixed symptoms, and feeling high after an antidepressant) showed an association with BPII versus MDD.

Our study suggests that clinical features distinguishing depression in BPI versus MDD have important classification potential for clinical practice, and should also be incorporated as ‘baseline’ features in the evaluation of novel diagnostic biomarkers.

Previous studies suggest that the relationship between genetic risk and depression may be moderated by stressful life events (SLEs). The goal of this study was to assess whether SLEs moderate the association between polygenic risk of major depressive disorder (MDD) and depressive symptoms in older adults.

We used logistic and negative binomial regressions to assess the associations between polygenic risk, SLEs and depressive symptoms in a sample of 8761 participants from the Health and Retirement Study. Polygenic scores were derived from the Psychiatric Genomics Consortium genome-wide association study of MDD. SLEs were operationalized as a dichotomous variable indicating whether participants had experienced at least one stressful event during the previous 2 years. Depressive symptoms were measured using an eight-item Center for Epidemiologic Studies Depression Scale subscale and operationalized as both a dichotomous and a count variable.

The odds of reporting four or more depressive symptoms were over twice as high among individuals who experienced at least one SLE (odds ratio 2.19, 95% confidence interval 1.86–2.58). Polygenic scores were significantly associated with depressive symptoms (β = 0.21, p ⩽ 0.0001), although the variance explained was modest (pseudo r2 = 0.0095). None of the interaction terms for polygenic scores and SLEs was statistically significant.

Polygenic risk and SLEs are robust, independent predictors of depressive symptoms in older adults. Consistent with an additive model, we found no evidence that SLEs moderated the association between common variant polygenic risk and depressive symptoms.