{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 168,
  "authors": [
    {
      "name": "Nigussie Sharew"
    },
    {
      "name": "Scott Clark"
    },
    {
      "name": "Simon Hartmann"
    },
    {
      "name": "Sergi Papiol"
    },
    {
      "name": "Urs Heilbronner"
    },
    {
      "name": "Franziska Degenhardt"
    },
    {
      "name": "Janice Fullerton"
    },
    {
      "name": "Liping Hou"
    },
    {
      "name": "Tatyana Shekhtman"
    },
    {
      "name": "Mazda Adli"
    },
    {
      "name": "Nirmala Akula"
    },
    {
      "name": "Kazufumi Akiyama"
    },
    {
      "name": "Raffaella Ardau"
    },
    {
      "name": "Bárbara Arias"
    },
    {
      "name": "Fasil Tekola-Ayele"
    },
    {
      "name": "Carla Gallo"
    },
    {
      "name": "Roland Hasler"
    },
    {
      "name": "Hélène Richard-Lepouriel"
    },
    {
      "name": "Nader Perroud"
    },
    {
      "name": "Lena Backlund"
    },
    {
      "name": "Abesh Bhattacharjee"
    },
    {
      "name": "Frank Bellivier"
    },
    {
      "name": "Antonio Benabarre"
    },
    {
      "name": "Susanne Bengesser"
    },
    {
      "name": "Joanna Biernacka"
    },
    {
      "name": "Armin Birner"
    },
    {
      "name": "Cynthia Marie-Claire"
    },
    {
      "name": "Pablo Cervantes"
    },
    {
      "name": "Hsi-Chung Chen"
    },
    {
      "name": "Caterina Chillotti"
    },
    {
      "name": "Sven Cichon"
    },
    {
      "name": "Cristiana Cruceana"
    },
    {
      "name": "Piotr Czerski"
    },
    {
      "name": "Nina Dalkner"
    },
    {
      "name": "Maria Del Zompo"
    },
    {
      "name": "J Raymond Depaulo"
    },
    {
      "name": "Bruno Étain"
    },
    {
      "name": "Stéphane Jamain"
    },
    {
      "name": "Peter Falkai"
    },
    {
      "name": "Andreas Forstner"
    },
    {
      "name": "Louise Frisen"
    },
    {
      "name": "Mark Frye"
    },
    {
      "name": "Sebastien Gard"
    },
    {
      "name": "Julie Garnham"
    },
    {
      "name": "Fernando Goes"
    },
    {
      "name": "Maria Grigoroiu-Serbanescu"
    },
    {
      "name": "Andreas Fallgatter"
    },
    {
      "name": "Sophia Stegmaier"
    },
    {
      "name": "Thomas Ethofer"
    },
    {
      "name": "Silvia Biere"
    },
    {
      "name": "Kristiyana Petrova"
    },
    {
      "name": "Ceylan Schuster"
    },
    {
      "name": "Kristina Adorjan"
    },
    {
      "name": "Monika Budde"
    },
    {
      "name": "Maria Heilbronner"
    },
    {
      "name": "Janos Kalman"
    },
    {
      "name": "Mojtaba Kohshour"
    },
    {
      "name": "Daniela Reich-Erkelenz"
    },
    {
      "name": "Sabrina Schaupp"
    },
    {
      "name": "Eva Schulte"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c68my40b0948e.bib",
  "cited_by": 0,
  "doi": "10.1192/bjp.2026.10776",
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  "issns": [
    "0007-1250",
    "1472-1465"
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  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
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  "pdf_url": "https://www.cambridge.org/core/services/aop-cambridge-core/content/view/A3D29D4E2C7517AD4EF2201548A93BC5/S0007125026107764a.pdf/div-class-title-using-multi-trait-polygenic-scores-to-predict-lithium-responsiveness-in-patients-with-bipolar-disorder-div.pdf",
  "pmcid": null,
  "pmid": "42841250",
  "published": "2026-10-07",
  "publisher": "Royal College of Psychiatrists",
  "ris": "https://research.draconitic.com/paper/c68my40b0948e.ris",
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  "subjects": [
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      "field": "medicine",
      "origin": "openalex:2738|Bipolar Disorder and Treatment",
      "subfield": "psychiatry"
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  "title": "Using multi-trait polygenic scores to predict lithium responsiveness in patients with bipolar disorder",
  "title_text": "Using multi-trait polygenic scores to predict lithium responsiveness in patients with bipolar disorder",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:56Z",
  "url": "https://research.draconitic.com/paper/c68my40b0948e",
  "venue": "British Journal of Psychiatry",
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