Integration of polygenic and gut metagenomic risk prediction for common diseases.

Journal: Nature aging

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Affiliated Institutions:  Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. yl@medschl.cam.ac.uk. Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland. School of Life Sciences, Arizona State University, Tempe, AZ, USA. Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA. Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA. School of Computing Technologies, RMIT University, Melbourne, Victoria, Australia. Department of Computing, University of Turku, Turku, Finland. Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia. Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. mi@cam.ac.uk.

Abstract summary 

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.

Authors & Co-authors:  Liu Ritchie Teo Ruuskanen Kambur Zhu Sanders Vázquez-Baeza Verspoor Jousilahti Lahti Niiranen Salomaa Havulinna Knight Méric Inouye

Study Outcome 

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Citations :  Joshi, A. et al. Systems biology in cardiovascular disease: a multiomics approach. Nat. Rev. Cardiol. 18, 313–330 (2021).
Authors :  17
Identifiers
Doi : 10.1038/s43587-024-00590-7
SSN : 2662-8465
Study Population
Male,Female
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Study Design
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Country of Study
Publication Country
United States