Early prediction of incident liver disease using conventional risk factors and gut-microbiome-augmented gradient boosting



: 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

: 2022

: Cell Metabolism

: CELL METABOLISM

: CELL METAB

: 5

: 34

: 5

: 719

: 730

: 17

: 1550-4131

: 1932-7420

DOI: https://doi.org/10.1016/j.cmet.2022.03.002

: https://doi.org/10.1016/j.cmet.2022.03.002

: 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.

Last updated on 2024-26-11 at 19:12