Search

Summary

Attention deficit hyperactivity disorder (ADHD) is a childhood-onset disorder with a prevalence of 5%. It is clinically defined by symptoms of inattention, motor hyperactivity and impulsivity. In addition to the core symptoms, it has also been associated with impairment in cognitive domains. ADHD is highly heritable and multifactorial in origin; multiple genes and non-inherited factors contribute to the aetiology of the disorder. ADHD has been associated with slower maturation of long white matter tracts and decreased volume in brain regions relevant for executive function, sustained attention, impulse suppression and regulation of motor activity. In addition to structural brain changes, a range of brain networks and dysregulation of neurotransmitter signalling have also been associated with ADHD. However, no diagnostic neurobiological markers are currently available for clinical use.

Background

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.

Aims

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

Method

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.

Results

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.

Conclusions

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.

Search Results

Refine search

Refine search

Actions for selected content:

3 results

9.2 - Attention Deficit Hyperactivity Disorder

Summary

Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach – CORRIGENDUM

Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach

Search Results

Refine search

Refine search

Actions for selected content:

Save Search

3 results

9.2 - Attention Deficit Hyperactivity Disorder

Summary

Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach – CORRIGENDUM

Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach