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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.
Early-life adversities represent risk factors for the development of bipolar
affective disorder and are associated with higher severity of the disorder.
This may be the consequence of a sustained alteration of the
hypothalamic–pituitary–adrenal (HPA) axis resulting from
epigenetic modifications of the gene coding for the glucocorticoid receptor
To investigate whether severity of childhood maltreatment is associated with
increased methylation of the exon? 1FNR3C1 promoter in bipolar disorder.
A sample of people with bipolar disorder (n = 99) were
assessed for childhood traumatic experiences. The percentage of
NR3C1 methylation was measured for each
The higher the number of trauma events, the higher was the percentage of
NR3C1 methylation (β = 0.52, 95% CI
0.46–0.59, P<<0.0001). The severity of
each type of maltreatment (sexual, physical and emotional) was also
associated with NR3C1 methylation status.
Early-life adversities have a sustained effect on the HPA axis through
epigenetic processes and this effect may be measured in peripheral blood.
This enduring biological impact of early trauma may alter the development of
the brain and lead to adult psychopathological disorder.
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