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

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

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)719-730.e4
JournalCell Metabolism
Issue number5
StatePublished - May 3 2022
Externally publishedYes


  • disease
  • gut
  • liver disease
  • metagenomics
  • microbiome
  • microbiota
  • prediction

ASJC Scopus subject areas

  • Physiology
  • Molecular Biology
  • Cell Biology


Dive into the research topics of 'Early prediction of incident liver disease using conventional risk factors and gut-microbiome-augmented gradient boosting'. Together they form a unique fingerprint.

Cite this