Using multi-trait polygenic scores to predict lithium responsiveness in patients with bipolar disorder.
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Sharew Nigussie T, Clark Scott R, Hartmann Simon, Papiol Sergi, Heilbronner Urs, Degenhardt Franziska, Fullerton Janice M, Hou Liping, Shekhtman Tatyana, Adli Mazda, Akula Nirmala, Akiyama Kazufumi, Ardau Raffaella, Arias Bárbara, Tekola-Ayele Fasil, Gallo Carla, Hasler Roland, Richard-Lepouriel Hélène, Perroud Nader, Backlund Lena, Bhattacharjee Abesh Kumar, Bellivier Frank, Benabarre Antonio, Bengesser Susanne, Biernacka Joanna M, Birner Armin, Marie-Claire Cynthia, Cervantes Pablo, Chen Hsi-Chung, Chillotti Caterina, Cichon Sven, Cruceanu Cristiana, Czerski Piotr M, Dalkner Nina, Del Zompo Maria, DePaulo J Raymond, Étain Bruno, Jamain Stephane, Falkai Peter, Forstner Andreas J, Frisen Louise, Frye Mark A, Gard Sébastien, Garnham Julie S, Goes Fernando S, Grigoroiu-Serbanescu Maria, Fallgatter Andreas J, Stegmaier Sophia, Ethofer Thomas, Biere Silvia, Petrova Kristiyana, Schuster Ceylan, Adorjan Kristina, Budde Monika, Heilbronner Maria, Kalman Janos L, Kohshour Mojtaba Oraki, Reich-Erkelenz Daniela, Schaupp Sabrina K, Schulte Eva C, Senner Fanny, Vogl Thomas, Anghelescu Ion-George, Arolt Volker, Dannlowski Udo, Dietrich Detlef E, Figge Christian, Jäger Markus, Lang Fabian U, Juckel Georg, Konrad Carsten, Reimer Jens, Schmauß Max, Schmitt Andrea, Spitzer Carsten, von Hagen Martin, Wiltfang Jens, Zimmermann Jörg, Andlauer Till F M, Fischer Andre, Bermpohl Felix, Ritter Philipp, Matura Silke, Gryaznova Anna, Falkenberg Irina, Yildiz Cüneyt, Kircher Tilo, Schmidt Julia, Koch Marius, Gade Kathrin, Trost Sarah, Haussleiter Ida S, Lambert Martin, Rohenkohl Anja C, Kraft Vivien, Grof Paul, Hashimoto Ryota, Hauser Joanna, Herms Stefan, Hoffmann Per, Jiménez Esther, Kahn Jean-Pierre, Kassem Layla, Kuo Po-Hsiu, Kato Tadafumi, Kelsoe John, Kittel-Schneider Sarah, Ferensztajn-Rochowiak Ewa, König Barbara, Kusumi Ichiro, Laje Gonzalo, Landén Mikael, Lavebratt Catharina, Leboyer Marion, Leckband Susan G, Tortorella Alfonso, Manchia Mirko, Martinsson Lina, McCarthy Michael J, McElroy Susan L, Colom Francesc, Millischer Vincent, Mitjans Marina, Mondimore Francis M, Monteleone Palmiero, Nievergelt Caroline M, Nöthen Markus M, Novák Tomas, O'Donovan Claire, Ozaki Norio, Pfennig Andrea, Pisanu Claudia, Potash James B, Reif Andreas, Reininghaus Eva, Rouleau Guy A, Rybakowski Janusz K, Schalling Martin, Schofield Peter R, Schweizer Barbara W, Severino Giovanni, Shilling Paul D, Shimoda Katzutaka, Simhandl Christian, Slaney Claire M, Squassina Alessio, Stamm Thomas, Stopkova Pavla, Maj Mario, Turecki Gustavo, Vieta Eduard, Veeh Julia, Viswanath Biju, Witt Stephanie H, Wright Adam, Zandi Peter P, Mitchell Philip B, Bauer Michael, Alda Martin, Rietschel Marcella, McMahon Francis J, Schulze Thomas G, Baune Bernhard T, Schubert Klaus Oliver, Amare Azmeraw T
Аннотация
AIM: To enhance prediction of lithium responsiveness by developing a multi-trait PGS (mt-PGS) combining genetic information from multiple phenotypes implicated in lithium response and/or BD aetiology.
METHODS: We analysed data collected from BD patients who had received lithium treatment for at least six months and participated in the International Consortium on Lithium Genetics (ConLi+Gen, N=2,367) study. The ALDA scale was used to assess lithium responsiveness, and treatment outcome was defined as continuous ALDA score (0-10) and categorical outcome (favourable ≥7 vs unfavourable response). PGSs were calculated for 59 phenotypes grouped into five clinical-biological clusters: clinical lithium exemplar (#22 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, and the selected features from each cluster were subsequently combined to construct the final mt-PGS models. Model performance was assessed using explained variance (R2) for the continuous outcome, and McFadden's pseudo-R2 as well as standard classification model parameters for the categorical outcomes.
RESULTS: The mt-PGS explained 5.07% (continuous outcome) to 9.02% (categorical outcome) of the interindividual variability in lithium responsiveness. Classification accuracy (AUC) for the categorical outcome was 68.13% (95% CI: 64.86, 71.77). Of the five clusters, the PGSs for clinical lithium exemplar phenotypes were most strongly associated with lithium responsiveness, accounting for 2.97%-6.20% of its variability.
CONCLUSIONS: By integrating polygenic scores for multiple relevant phenotypes, predictive accuracy for lithium response improved up to nine-fold compared to 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.