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

Verspoor Karin; Havulinna Aki S; Åberg Fredrik; Salomaa Veikko; Méric Guillaume; Zhu Qiyun; Knight Rob; Inouye Michael; Niiranen Teemu; Ruuskanen Matti; Jousilahti Pekka; Liu Yang; Loomba Rohit; Lahti Leo; Sanders Jon; Vázquez-Baeza Yoshiki; Jain Mohit; Teo Shu Mei; Tripathi Anupriya; Cheng Susan

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

Verspoor Karin
Havulinna Aki S
Åberg Fredrik
Salomaa Veikko
Méric Guillaume
Zhu Qiyun
Knight Rob
Inouye Michael
Niiranen Teemu
Ruuskanen Matti
Jousilahti Pekka
Liu Yang
Loomba Rohit
Lahti Leo
Sanders Jon
Vázquez-Baeza Yoshiki
Jain Mohit
Teo Shu Mei
Tripathi Anupriya
Cheng Susan
Katso/Avaa
mmc3.pdf (15.14Mb)
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CELL PRESS
doi:10.1016/j.cmet.2022.03.002
URI
https://doi.org/10.1016/j.cmet.2022.03.002
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Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2022081154052
Tiivistelmä
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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