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

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Liu , Y , Meric , G , Havulinna , A S , Teo , S M , Åberg , F , Ruuskanen , M , Sanders , J , Zhu , Q , Tripathi , A , Verspoor , K , Cheng , S , Jain , M , Jousilahti , P , Vazquez-Baeza , Y , Loomba , R , Lahti , L , Niiranen , T , Salomaa , V , Knight , R & Inouye , M 2022 , ' Early prediction of incident liver disease using conventional risk factors and gut-microbiome-augmented gradient boosting ' , Cell Metabolism , vol. 34 , no. 5 , pp. 719-+ . https://doi.org/10.1016/j.cmet.2022.03.002

Title: Early prediction of incident liver disease using conventional risk factors and gut-microbiome-augmented gradient boosting
Author: Liu, Yang; Meric, 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; Vazquez-Baeza, Yoshiki; Loomba, Rohit; Lahti, Leo; Niiranen, Teemu; Salomaa, Veikko; Knight, Rob; Inouye, Michael
Contributor organization: Medicum
Institute for Molecular Medicine Finland
Complex Disease Genetics
University of Helsinki
Clinicum
HUS Abdominal Center
IV kirurgian klinikka
Date: 2022-05-03
Language: eng
Number of pages: 17
Belongs to series: Cell Metabolism
ISSN: 1550-4131
DOI: https://doi.org/10.1016/j.cmet.2022.03.002
URI: http://hdl.handle.net/10138/345818
Abstract: 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.
Subject: THERAPEUTIC TARGET
DYSBIOSIS
DIAGNOSIS
EPIDEMIOLOGY
ACTINOMYCES
PREVALENCE
VALIDATION
FIBROSIS
OBESITY
1182 Biochemistry, cell and molecular biology
Peer reviewed: Yes
Rights: cc_by_nc_nd
Usage restriction: openAccess
Self-archived version: publishedVersion


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