Harnessing human microbiomes for disease prediction

Yang Liu; Muhamad Fachrul; Michael Inouye; Guillaume Méric

doi:10.1016/j.tim.2023.12.004

Harnessing human microbiomes for disease prediction

Trends Microbiol. 2024 Jul;32(7):707-719. doi: 10.1016/j.tim.2023.12.004. Epub 2024 Jan 20.

Authors

Yang Liu¹, Muhamad Fachrul², Michael Inouye³, Guillaume Méric⁴

Affiliations

¹ Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
² Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia; Human Genomics and Evolution Unit, St Vincent's Institute of Medical Research, Victoria, Australia; Melbourne Integrative Genomics, University of Melbourne, Parkville, Victoria, Australia; School of BioSciences, University of Melbourne, Parkville, Victoria, Australia.
³ Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK; British Heart Foundation Cambridge Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
⁴ Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Department of Cardiometabolic Health, University of Melbourne, Melbourne, Victoria, Australia; Central Clinical School, Monash University, Melbourne, Victoria, Australia; Department of Medical Science, Molecular Epidemiology, Uppsala University, Uppsala, Sweden; Department of Cardiovascular Research, Translation, and Implementation, La Trobe University, Melbourne, Victoria, Australia. Electronic address: [email protected].

PMID: 38246848
DOI: 10.1016/j.tim.2023.12.004

Abstract

The human microbiome has been increasingly recognized as having potential use for disease prediction. Predicting the risk, progression, and severity of diseases holds promise to transform clinical practice, empower patient decisions, and reduce the burden of various common diseases, as has been demonstrated for cardiovascular disease or breast cancer. Combining multiple modifiable and non-modifiable risk factors, including high-dimensional genomic data, has been traditionally favored, but few studies have incorporated the human microbiome into models for predicting the prospective risk of disease. Here, we review research into the use of the human microbiome for disease prediction with a particular focus on prospective studies as well as the modulation and engineering of the microbiome as a therapeutic strategy.

Keywords: disease prediction; gut microbiota; machine learning; metagenomics; microbiome.

Publication types

Review

MeSH terms

Cardiovascular Diseases / microbiology
Humans
Microbiota*
Risk Factors

Grants and funding

RG/18/13/33946/BHF_/British Heart Foundation/United Kingdom