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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.
Patients with affective disorders have often been reported to experience subjective changes in how they perceive the flow of time. Time reproduction tasks provide information about the memory component of time perception and are thought to remain unaffected by pulse rate disturbances in the pacemaker of the internal clock.
In our study, 30 patients with acute depression, 30 patients with acute mania, and 30 healthy subjects of all age groups were presented with a time reproduction task. Participants were asked to observe a stimulus presented on a computer screen for a certain length of time and, subsequently, to reproduce the stimulus for a similar length of time by pressing the space bar on the computer keyboard. Stimuli were presented to each subject for 1, 6, and 37 s.
On average, the time intervals reproduced by manic patients were shorter than those reproduced by depressed patients. Manic patients reproduced the short time interval (6 s) correctly, but under-reproduced the long time interval (37 s, P < 0.001). Depressed patients correctly reproduced the long time interval, but over-reproduced the short time interval (P < 0.001).
Remembering time intervals as having been longer than they actually were may lead to a slowed experience of time, as has been described in depressed patients; precisely the converse seems to apply to manic patients.