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Integration of polygenic and gut metagenomic risk prediction for common diseases

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posted on 2024-05-27, 05:17 authored by Yang Liu, Scott C Ritchie, Shu Mei Teo, Matti O Ruuskanen, Oleg Kambur, Qiyun Zhu, Jon Sanders, Yoshiki Vázquez-Baeza, Karin Verspoor, Pekka Jousilahti, Leo Lahti, Teemu Niiranen, Veikko Salomaa, Aki S Havulinna, Rob Knight, Guillaume MericGuillaume Meric, Michael Inouye
Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases.


Y.L. was supported by funding from the Cambridge Baker Centre for Systems Genomics. S.C.R. was supported by a British Heart Foundation program grant (no. RG/18/13/33946). M.O.R. was funded by the Research Council of Finland (grant no. 338818). L.L. was supported by the European Union’s Horizon 2020 research and innovation program (grant no. 952914). T.N. was supported by the Finnish Foundation for Cardiovascular Research, the Sigrid Jusélius Foundation, the Southwestern Finland Hospital District and the Research Council of Finland (grant nos. 321351 and 354447). V.S. was supported by the Finnish Foundation for Cardiovascular Research and the Juho Vainio Foundation. A.S.H. was supported by the Research Council of Finland (grant no. 321356). M.I. was supported by the Munz Chair of Cardiovascular Prediction and Prevention and the NIHR Cambridge Biomedical Research Centre (grant nos. BRC-1215-20014 and NIHR203312). M.I. was also supported by the UK Economic and Social Research 878 Council (grant no. ES/T013192/1). The present study was supported by the Victorian Government’s Operational Infrastructure Support program and by core funding from the British Heart Foundation (grant no. RG/18/13/33946) and the NIHR Cambridge Biomedical Research Centre (grant nos. BRC-1215-20014 and NIHR203312). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Ofice of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome. Author contributions Y.L. and M.I. conceived and designed the study. Y.L.,


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Nature Aging








Springer Nature



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