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

Sharew N.T.; Clark S.R.; Hartmann S.; Papiol S.; Heilbronner U.; Degenhardt F.; Fullerton J.M.; Hou L.; Shekhtman T.; Adli M.; Akula N.; Akiyama K.; Ardau R.; Arias B.; Tekola-Ayele F.; Gallo C.; Hasler R.; Richard-Lepouriel H.; Perroud N.; Backlund L.; Bhattacharjee A.K.; Bellivier F.; Benabarre A.; Bengesser S.; Biernacka J.M.; Birner A.; Marie-Claire C.; Cervantes P.; Chen H.-C.; Chillotti C.; Cichon S.; Cruceana C.; Czerski P.M.; Dalkner N.; Zompo M.D.; DePaulo J.R.; Étain B.; Jamain S.; Falkai P.; Forstner A.J.; Frisen L.; Frye M.A.; Gard S.; Garnham J.S.; Goes F.S.; Grigoroiu-Serbanescu M.; Fallgatter A.J.; Stegmaier S.; Ethofer T.; Biere S.; Petrova K.; Schuster C.; Adorjan K.; Budde M.; Heilbronner M.; Kalman J.L.; Kohshour M.O.; Reich-Erkelenz D.; Schaupp S.K.; Schulte E.C.; Senner F.; Vogl T.; Anghelescu I.-G.; Arolt V.; Dannlowski U.; Dietrich D.E.; Figge C.; Jäger M.; Lang F.U.; Juckel G.; Konrad C.; Reimer J.; Schmauß M.; Schmitt A.; Spitzer C.; Von Hagen M.; Wiltfang J.; Zimmermann J.; Andlauer T.F.M.; Fischer A.; Bermpohl F.; Ritter P.; Matura S.; Gryaznova A.; Falkenberg I.; Yildiz C.; Kircher T.; Schmidt J.; Koch M.; Gade K.; Trost S.; Haussleiter I.S.; Lambert M.; Rohenkohl A.C.; Kraft V.; Grof P.; Hashimoto R.; Hauser J.; Herms S.; Hoffman P.; Jiménez E.; Kahn J.-P.; Kassem L.; Kuo P.-H.; Kato T.; Kelsoe J.; Kittel-Schneider S.; Ferensztajn-Rochowiak E.; König B.; Kusumi I.; Laje G.; Landén M.; Lavebratt C.; Leboyer M.; Leckband S.G.; Tortorella A.; Manchia M.; Martinsson L.; McCarthy M.J.; McElroy S.L.; Colom F.; Millischer V.; Mitjans M.; Mondimore F.M.; Monteleone P.; Nievergelt C.M.; Nöthen M.M.; Novak T.; O’Donovan C.; Ozaki N.; Pfennig A.; Pisanu C.; Potash J.B.; Reif A.; Reininghaus E.; Rouleau G.A.; Rybakowski J.K.; Schalling M.; Schofield P.R.; Schweizer B.W.; Severino G.; Shilling P.D.; Shimoda K.; Simhandl C.; Slaney C.M.; Squassina A.; Stamm T.; Stopkova P.; Maj M.; Turecki G.; Vieta E.; Veeh J.; Viswanath B.; Witt S.H.; Wright A.; Zandi P.P.; Mitchell P.B.; Bauer M.; Alda M.; Rietschel M.; McMahon F.J.; Schulze T.G.; Baune B.T.; Schubert K.O.; Amare A.T. · British Journal of Psychiatry (2026) · DOI: 10.1192/bjp.2026.10776

Resumen

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 Author(s), 2026. Published by Cambridge University Press on behalf of Royal College of Psychiatrists

Instituciones: Universidad Peruana Cayetano Heredia