Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach.
Mood stabilisers
bipolar affective disorders
depressive disorders
genetics
outcome studies
Journal
The British journal of psychiatry : the journal of mental science
ISSN: 1472-1465
Titre abrégé: Br J Psychiatry
Pays: England
ID NLM: 0342367
Informations de publication
Date de publication:
28 Feb 2022
28 Feb 2022
Historique:
entrez:
28
2
2022
pubmed:
1
3
2022
medline:
1
3
2022
Statut:
aheadofprint
Résumé
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.
Sections du résumé
BACKGROUND
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
OBJECTIVE
To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.
METHOD
METHODS
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
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
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.
Identifiants
pubmed: 35225756
doi: 10.1192/bjp.2022.28
pii: S0007125022000289
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1-10Subventions
Organisme : BLRD VA
ID : I01 BX003431
Pays : United States
Organisme : CSRD VA
ID : I01 CX000363
Pays : United States
Organisme : Centro de Investigación en Red de Salud Mental, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Centres de Recerca de Catalunya Programme/Generalitat de Catalunya, Miguel Servet II, Instituto de Salud Carlos III Intramural Research Program of the National Institute of Mental Health Intramural Research Program of the National Institute of Mental Health Investissements d'Avenir National Institute of Drug Abuse Swiss National Foundation NPU I Australian National Health and Medical Research Council INSERM (Institut National de la Santé et de la Recherche Médicale), AP-HP (Assistance Publique des Hôpitaux de Paris), Fondation FondaMental (RTRS Santé Mentale
Commentaires et corrections
Type : ErratumIn