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.
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.
Trends Microbiol. 2024 Jul;32(7):707-719. doi: 10.1016/j.tim.2023.12.004. Epub 2024 Jan 20.
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.
人类微生物组越来越被认为具有用于疾病预测的潜力。预测疾病的风险、进展和严重程度有望改变临床实践,增强患者的决策能力,并减轻各种常见疾病的负担,如心血管疾病或乳腺癌。传统上,人们倾向于结合多种可改变和不可改变的风险因素,包括高维基因组数据,但很少有研究将人类微生物组纳入预测疾病前瞻性风险的模型中。在这里,我们综述了利用人类微生物组进行疾病预测的研究,特别关注前瞻性研究以及作为治疗策略的微生物组的调节和工程。