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

Cearns M, Amare AT, Schubert KO, Thalamuthu A, Frank J, Streit F, Adli M, Akula N, Akiyama K, Ardau R, Arias B, Aubry JM, Backlund L, Bhattacharjee AK, Bellivier F, Benabarre A, Bengesser S, Biernacka JM, Birner A, Brichant-Petitjean C, Cervantes P, Chen HC, Chillotti C, Cichon S, Cruceanu C, Czerski PM, Dalkner N, Dayer A, Degenhardt F, Zompo MD, Depaulo JR, Étain B, Falkai P, Forstner AJ, Frisen L, Frye MA, Fullerton JM, Gard S, Garnham JS, Goes FS, Grigoroiu-Serbanescu M, Grof P, Hashimoto R, Hauser J, Heilbronner U, Herms S, Hoffmann P, Hofmann A, Hou L, Hsu YH, Jamain S, Jiménez E, Kahn JP, Kassem L, Kuo PH, Kato T, Kelsoe J, Kittel-Schneider S, Kliwicki S, König B, Kusumi I, Laje G, Landén M, Lavebratt C, Leboyer M, Leckband SG, Maj M, Manchia M, Martinsson L, McCarthy MJ, McElroy S, Colom F, Mitjans M, Mondimore FM, Monteleone P, Nievergelt CM, Nöthen MM, Novák T, O'Donovan C, Ozaki N, Millischer V, Papiol S, Pfennig A, Pisanu C, Potash JB, Reif A, Reininghaus E, Rouleau GA, Rybakowski JK, Schalling M, Schofield PR, Schweizer BW, Severino G, Shekhtman T, Shilling PD, Shimoda K, Simhandl C, Slaney CM, Squassina A, Stamm T, Stopkova P, Tekola-Ayele F, Tortorella A, Turecki G, Veeh J, Vieta E, Witt SH, Roberts G, Zandi PP, Alda M, Bauer M, McMahon FJ, Mitchell PB, Schulze TG, Rietschel M, Clark SR, Baune BT (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Book Volume: 220

Pages Range: 219-228

Journal Issue: 4

DOI: 10.1192/bjp.2022.28

Abstract

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.

Involved external institutions

University of Adelaide Australia (AU) Osaka University / 大阪大学 Japan (JP) Poznan University of Medical Sciences / Uniwersytet Medyczny im. Karola Marcinkowskiego w Poznaniu Poland (PL) Klinikum der Universität München (LMU Klinikum) Germany (DE) University of New South Wales (UNSW) Australia (AU) Universitätsklinikum Mannheim / University Medical Centre Mannheim (Universitätsmedizin Mannheim) Germany (DE) Rheinische Friedrich-Wilhelms-Universität Bonn Germany (DE) Charité - Universitätsmedizin Berlin Germany (DE) Medizinische Universität Graz Austria (AT) Università degli Studi di Perugia Italy (IT) Douglas Mental Health University Institute Canada (CA) Universitätsklinikum Frankfurt am Main (KGU) Germany (DE) Université Paris Cité France (FR) McGill University Health Centre (MUHC) / Centre universitaire de santé McGill Canada (CA) National Taiwan University Hospital (NTUH) / 國立台灣大學醫學院附設醫院 Taiwan (TW) Universitat de Barcelona (UB) / University of Barcelona Spain (ES) Hospital University Agency of Cagliari Italy (IT) National Taiwan University (NTU) Taiwan (TW) Juntendo University Japan (JP) University of California, San Diego (UC San Diego, UCSD) United States (USA) (US) Geneva University Hospitals / Hôpitaux universitaires de Genève (HUG) Switzerland (CH) Städtisches Klinikum Neunkirchen Germany (DE) National Institute of Mental Health Information Resource Center United States (USA) (US) Sahlgrenska University Hospital / Sahlgrenska Universitetssjukhuset Sweden (SE) Karolinska Institute Sweden (SE) National Institute for Health and Medical Research / Institut national de la santé et de la recherche médicale (INSERM) France (FR) VA San Diego Healthcare System United States (USA) (US) Università degli studi della Campania Luigi Vanvitelli Italy (IT) University of Cincinnati United States (USA) (US) Hospital del Mar Spain (ES) Johns Hopkins University (JHU) United States (USA) (US) Università degli Studi di Salerno Italy (IT) Universitätsklinikum Carl Gustav Carus Dresden Germany (DE) Università degli Studi di Cagliari Italy (IT) Montreal Neurological Institute and Hospital (MNI, The Neuro) Canada (CA) Neuroscience Research Australia NeuRA Australia (AU) Dokkyo Medical University Japan (JP) Sigmund-Freud-Privatuniversität Wien (SFU) / Sigmund Freud University Austria (AT) Dalhousie University Canada (CA) Westfälische Wilhelms-Universität (WWU) Münster Germany (DE) "Prof. Dr. Alexandru Obregia" Clinical Hospital of Psychiatry / Spitalul Clinic de Psihiatrie Prof. Dr. Alexandru Obregia Romania (RO) Mood Disorders Ottawa Canada (CA) Harvard T.H. Chan School of Public Health United States (USA) (US) Université de Lorraine France (FR) Ludwig-Maximilians-Universität (LMU) Germany (DE) Mayo Clinic United States (USA) (US) Centre Hospitalier Charles Perrens (CHCP) France (FR) South Australian Health and Medical Research Institute (SAHMRI) Australia (AU) US National Institutes of Health (NIH) United States (USA) (US) Nagoya University / 名古屋大学 Japan (JP)

How to cite

APA:

Cearns, M., Amare, A.T., Schubert, K.O., Thalamuthu, A., Frank, J., Streit, F.,... Baune, B.T. (2022). Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach. British Journal of Psychiatry, 220(4), 219-228. https://doi.org/10.1192/bjp.2022.28

MLA:

Cearns, Micah, et al. "Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach." British Journal of Psychiatry 220.4 (2022): 219-228.

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