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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.
Childhood maltreatment (CM) is recognized as a non-specific risk factor for Eating Disorders (EDs), but the mechanisms explaining this association have been insufficiently assessed. We aim to explore the psychological pathways through which CM experiences promote ED core symptoms.
Two-hundred-twenty-eight people with EDs, 94 with anorexia nervosa restricting (ANR) type and 134 with binge-purging (BP) symptoms (including 23 with AN purging type and 111 with bulimia nervosa), completed the Eating Disorder Inventory-2, the State-Trait Anxiety Inventory and the Childhood Trauma Questionnaire. The variables provided by these questionnaires were included in a network analysis to identify the shortest pathways between CM nodes and ED core symptoms. Then mediation analysis was performed in order to confirm the mediation role of the nodes included in the shortest pathways from CM to ED core symptoms.
All types of CM experiences were connected to the ED psychopathology through emotional abuse. In the ANR group, interoceptive awareness was included in the shortest path between emotional abuse and drive to thinness and mediated this relationship. In the BP group, the shortest routes between CM and ED core symptoms included both ineffectiveness and interoceptive awareness.
Combining the network analysis approach with the mediation analyses provides for the first time a putative hybrid model, which reveals that all CM types converge towards ED symptoms through emotional abuse and that interoceptive awareness and ineffectiveness mediate these connections in people with ANR and BP symptoms, respectively. These findings may have possible implications for both research and treatment of EDs.
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