Using multi-trait polygenic scores to predict lithium responsiveness in patients with bipolar disorder

Background The predictive power of polygenic scores (PGSs) for lithium treatment response in bipolar disorder remains limited. Aims To enhance the prediction of lithium responsiveness by developing a multi-trait PGS (mt-PGS) combining genetic information from multiple phenotypes implicated in lithium response and/or bipolar disorder aetiology. Method We analysed data collected from bipolar disorder patients who had received lithium treatment for at least 6 months and participated in the International Consortium on Lithium Genetics study ( N = 2367). The Alda scale was used to assess lithium responsiveness, and treatment outcome was defined as continuous total Alda score (0–10) and categorical outcome (favourable ≥7 versus unfavourable response). PGSs were calculated for 60 phenotypes grouped into 5 clinical–biological clusters: psychiatric and behavioural (#23 phenotypes), cardiometabolic (#17), autoimmune/inflammatory (#5), neurocognitive (#8) and renal function (#7). We applied cross-validated machine learning regression approaches in both outcomes within each cluster, with the selected features from each cluster subsequently combined to construct the final mt-PGS models. Model performance was assessed using explained variance ( R 2 ) for the continuous outcome, and both McFadden’s pseudo- R 2 and standard classification model parameters for the categorical outcome. Results mt-PGS explained between 5.70% (continuous outcome) and 9.11% (categorical outcome) of the interindividual variability in lithium responsiveness. Classification accuracy (area under the curve) for the categorical outcome was 68.24% (95% CI: 64.98–71.66), with a Brier score of 0.257. Of the 5 clusters, the PGSs for psychiatric and behavioural phenotypes were most strongly associated with lithium responsiveness, accounting for 3.67–6.52% of its variability. Conclusions By integrating PGSs for multiple relevant phenotypes, predictive accuracy for lithium response improved substantially compared with single-trait methods. Future research incorporating larger, more diverse populations and combining genetic scores with clinical data holds promise for further enhancing prediction and advancing clinical implementation.

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Journal
The British Journal of Psychiatry
Published
2026-10-07
DOI
https://doi.org/10.1192/bjp.2026.10776
Primary Topic
Bipolar Disorder and Treatment
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article
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article

Using multi-trait polygenic scores to predict lithium responsiveness in patients with bipolar disorder

Caterina Chillotti, Roland Hasler, Janos L. Kalman, Stéphane Jamain et al.
The British Journal of Psychiatry
Bipolar Disorder and Treatment
article

Using multi-trait polygenic scores to predict lithium responsiveness in patients with bipolar disorder

Caterina Chillotti, Roland Hasler, Janos L. Kalman, Stéphane Jamain, Per Hoffman, Paul Grof, Sabrina K. Schaupp, Piotr M. Czerski, Bárbara Arias, Urs Heilbronner, Fasil Tekola‐Ayele, Hélène Richard-Lepouriel, Georg Juckel, Maria Grigoroiu‐Serbânescu, Kazufumi Akiyama, Mojtaba Oraki Kohshour, Nina Dalkner, Carsten Konrad, Liping Hou, Stefan Herms, Scott Richard Clark, Sergi Papiol, Nirmala Akula, Fabian U. Lang, André Fischer, Antonio Benabarre, Anja Christine Rohenkohl, Armin Birner, Maria Heilbronner, Cüneyt Yildiz, Andreas J. Forstner, Hsi‐Chung Chen, Joanna M. Biernacka, Eva Christina Schulte, J Hauser, Sophia Stegmaier, Raffaella Ardau, M. Koch, Udo Dannlowski, Tatyana Shekhtman, Bruno Étain, Felix Bermpohl, Mazda Adli, Till F. M. Andlauer, Simon Hartmann, Detlef E. Dietrich, Nigussie Tadesse Sharew, Carla Gallo, Irina Falkenberg, Silvia Biere, Ceylan Schuster, Daniela Reich‐Erkelenz, Vivien Kraft, Kristina Adorjan, Jens Reimer, Carsten Spitzer, Lena Backlund, Kristiyana Petrova, Katrin Gade, Pablo Cervantes, Christian Figge, Abesh Kumar Bhattacharjee, Fanny Senner, Thomas Vogl, Markus Jäger, Franziska Degenhardt, Sébastien Gard, Sven Cichon, Martin Lambert, Monika Budde, Nader Perroud, Julia Schmidt, Jens Wiltfang, Jörg Zimmermann, Susanne Bengesser, Sarah Trost, Anghelescu Ion-George, Andrea Schmitt, Max Schmauß, Philipp Ritter, Ida S. Haussleiter, Cristiana Cruceana, Silke Matura, J. Raymond DePaulo, Thomas Ethofer, Maria Del Zompo, Fernando S. Goes, Peter Falkai, Mark A. Frye, Louise Frisen, Ryota Hashimoto, Janice M. Fullerton, Volker Arolt, Frank Bellivier, Anna Gryaznova, Tilo Kircher, Julie S. Garnham, Martin Von Hagen, Cynthia Marie-Claire, Andreas J. Fallgatter
article en

Abstract

Background The predictive power of polygenic scores (PGSs) for lithium treatment response in bipolar disorder remains limited. Aims To enhance the prediction of lithium responsiveness by developing a multi-trait PGS (mt-PGS) combining genetic information from multiple phenotypes implicated in lithium response and/or bipolar disorder aetiology. Method We analysed data collected from bipolar disorder patients who had received lithium treatment for at least 6 months and participated in the International Consortium on Lithium Genetics study ( N = 2367). The Alda scale was used to assess lithium responsiveness, and treatment outcome was defined as continuous total Alda score (0–10) and categorical outcome (favourable ≥7 versus unfavourable response). PGSs were calculated for 60 phenotypes grouped into 5 clinical–biological clusters: psychiatric and behavioural (#23 phenotypes), cardiometabolic (#17), autoimmune/inflammatory (#5), neurocognitive (#8) and renal function (#7). We applied cross-validated machine learning regression approaches in both outcomes within each cluster, with the selected features from each cluster subsequently combined to construct the final mt-PGS models. Model performance was assessed using explained variance ( R 2 ) for the continuous outcome, and both McFadden’s pseudo- R 2 and standard classification model parameters for the categorical outcome. Results mt-PGS explained between 5.70% (continuous outcome) and 9.11% (categorical outcome) of the interindividual variability in lithium responsiveness. Classification accuracy (area under the curve) for the categorical outcome was 68.24% (95% CI: 64.98–71.66), with a Brier score of 0.257. Of the 5 clusters, the PGSs for psychiatric and behavioural phenotypes were most strongly associated with lithium responsiveness, accounting for 3.67–6.52% of its variability. Conclusions By integrating PGSs for multiple relevant phenotypes, predictive accuracy for lithium response improved substantially compared with single-trait methods. Future research incorporating larger, more diverse populations and combining genetic scores with clinical data holds promise for further enhancing prediction and advancing clinical implementation.

The British Journal of Psychiatry
Goethe University Frankfurt (DE), University of Bern (CH), Ahvaz Jundishapur University of Medical Sciences (IR), Poznan University of Medical Sciences (PL), Debre Berhan University (ET), Dalhousie University (CA), National Institutes of Health (US), Mayo Clinic (US), Karolinska University Hospital (SE), University of Bonn (DE), Johns Hopkins University (US), University of Hildesheim (DE), Philipps University of Marburg (DE), The University of Melbourne (AU), National Taiwan University (TW), Forschungszentrum Jülich (DE), University of Cagliari (IT), Universidade de São Paulo (BR), Universität Ulm (DE), Universidad Peruana Cayetano Heredia (PE), Medical University of Graz (AT), University of Münster (DE), University Hospital Bonn (DE), Instituto de Salud Carlos III (ES), McGill University Health Centre (CA), University Hospital of Basel (CH), Karolinska Institutet (SE), UNSW Sydney (AU), University of California San Diego (US), Centro de Investigación Biomédica en Red de Salud Mental (ES), Fondation FondaMental (FR), Assistance Publique – Hôpitaux de Paris (FR), European Science Communication Institute (DE), National Center of Neurology and Psychiatry (JP), University Medical Center Hamburg-Eppendorf (DE), Orygen (AU), Universitätsmedizin Göttingen (DE), Hôpital Lariboisière (FR), Azienda Ospedaliero-Universitaria Cagliari (IT), University Hospital Frankfurt (DE), Universitätsmedizin Rostock (DE), Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE), Eunice Kennedy Shriver National Institute of Child Health and Human Development (US), Max Planck Institute of Psychiatry (DE), Spitalul Clinic de Psihiatrie Alexandru Obregia (RO), LWL-Universitätsklinikum Bochum (DE), Centre Hospitalier Charles Perrens (FR), Institute of Mental Health (RS), Consorci Institut D'Investigacions Biomediques August Pi I Sunyer (ES), Department of Health and Human Services (US), Agaplesion Diakonieklinikum Rotenburg (DE), Bezirkskrankenhaus Augsburg (DE), Optimisation Thérapeutique en Neuropsychopharmacologie (FR), Adelaide University (AU), McGill University (CA), The University of Adelaide (AU), University of Duisburg-Essen (DE), Technical University of Munich (DE), Geneva College (US), Universitat de Barcelona (ES), University of Göttingen (DE), Dokkyo Medical University (JP), Technische Universität Dresden (DE), Charité - Universitätsmedizin Berlin (DE), University of Tübingen (DE), Ludwig-Maximilians-Universität München (DE), Neuroscience Research Australia (AU), Ruhr University Bochum (DE)
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Bipolar Disorder and Treatment
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