A1 Refereed original research article in a scientific journal
Early prediction of incident liver disease using conventional risk factors and gut-microbiome-augmented gradient boosting
Authors: Liu Yang, Méric Guillaume, Havulinna Aki S, Teo Shu Mei, Åberg Fredrik, Ruuskanen Matti, Sanders Jon, Zhu Qiyun, Tripathi Anupriya, Verspoor Karin, Cheng Susan, Jain Mohit, Jousilahti Pekka, Vázquez-Baeza Yoshiki, Loomba Rohit, Lahti Leo, Niiranen Teemu, Salomaa Veikko, Knight Rob, Inouye Michael
Publisher: CELL PRESS
Publication year: 2022
Journal: Cell Metabolism
Journal name in source: CELL METABOLISM
Journal acronym: CELL METAB
Article number: 5
Volume: 34
Issue: 5
First page : 719
Last page: 730
Number of pages: 17
ISSN: 1550-4131
eISSN: 1932-7420
DOI: https://doi.org/10.1016/j.cmet.2022.03.002
Web address : https://doi.org/10.1016/j.cmet.2022.03.002
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/175481511
The gut microbiome has shown promise as a predictive biomarker for various diseases. However, the potential of gut microbiota for prospective risk prediction of liver disease has not been assessed. Here, we utilized shallow shotgun metagenomic sequencing of a large population-based cohort (N > 7,000) with -15 years of follow-up in combination with machine learning to investigate the predictive capacity of gut microbial predictors individually and in conjunction with conventional risk factors for incident liver disease. Separately, conventional and microbial factors showed comparable predictive capacity. However, microbiome augmentation of conventional risk factors using machine learning significantly improved the performance. Similarly, disease free survival analysis showed significantly improved stratification using microbiome-augmented models. Investigation of predictive microbial signatures revealed previously unknown taxa for liver disease, as well as those previously associated with hepatic function and disease. This study supports the potential clinical validity of gut metagenomic sequencing to complement conventional risk factors for prediction of liver diseases.
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