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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F22%3A43920873" target="_blank" >RIV/00023752:_____/22:43920873 - isvavai.cz</a>

Result on the web

<a href="https://www.cambridge.org/core/journals/the-british-journal-of-psychiatry/article/using-polygenic-scores-and-clinical-data-for-bipolar-disorder-patient-stratification-and-lithium-response-prediction-machine-learning-approach/6AFF9E8BD7D3E7E086898EDB9522AAC1" target="_blank" >https://www.cambridge.org/core/journals/the-british-journal-of-psychiatry/article/using-polygenic-scores-and-clinical-data-for-bipolar-disorder-patient-stratification-and-lithium-response-prediction-machine-learning-approach/6AFF9E8BD7D3E7E086898EDB9522AAC1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1192/bjp.2022.28" target="_blank" >10.1192/bjp.2022.28</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30215 - Psychiatry

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

British Journal of Psychiatry

ISSN

0007-1250

e-ISSN

—

Volume of the periodical

220

Issue of the periodical within the volume

4

Country of publishing house

GB - UNITED KINGDOM

Number of pages

10

Pages from-to

219-228

UT code for WoS article

000762249500001

EID of the result in the Scopus database

2-s2.0-85126276148