Faecal microbiome-based machine learning for multi-class disease diagnosis.

Autor: Su Q; Microbiota I-Center (MagIC), Hong Kong SAR, China.; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.; Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China.; Center for Gut Microbiota Research, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China., Liu Q; Microbiota I-Center (MagIC), Hong Kong SAR, China.; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.; Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China.; Center for Gut Microbiota Research, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China., Lau RI; Microbiota I-Center (MagIC), Hong Kong SAR, China.; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.; Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China., Zhang J; Microbiota I-Center (MagIC), Hong Kong SAR, China.; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.; Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China.; Center for Gut Microbiota Research, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China., Xu Z; Microbiota I-Center (MagIC), Hong Kong SAR, China.; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.; Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China.; Center for Gut Microbiota Research, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China., Yeoh YK; Microbiota I-Center (MagIC), Hong Kong SAR, China., Leung TWH; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China., Tang W; Microbiota I-Center (MagIC), Hong Kong SAR, China.; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.; Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China., Zhang L; Microbiota I-Center (MagIC), Hong Kong SAR, China.; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.; Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China.; Center for Gut Microbiota Research, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China., Liang JQY; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.; Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China.; Center for Gut Microbiota Research, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China., Yau YK; Microbiota I-Center (MagIC), Hong Kong SAR, China.; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.; Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China., Zheng J; Microbiota I-Center (MagIC), Hong Kong SAR, China.; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.; Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China., Liu C; Microbiota I-Center (MagIC), Hong Kong SAR, China.; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.; Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China., Zhang M; Microbiota I-Center (MagIC), Hong Kong SAR, China.; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.; Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China., Cheung CP; Microbiota I-Center (MagIC), Hong Kong SAR, China.; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.; Center for Gut Microbiota Research, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China., Ching JYL; Microbiota I-Center (MagIC), Hong Kong SAR, China.; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.; Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China., Tun HM; Microbiota I-Center (MagIC), Hong Kong SAR, China.; Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China.; JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China., Yu J; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.; Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China.; Center for Gut Microbiota Research, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China., Chan FKL; Microbiota I-Center (MagIC), Hong Kong SAR, China.; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.; Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China.; Center for Gut Microbiota Research, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China., Ng SC; Microbiota I-Center (MagIC), Hong Kong SAR, China. siewchienng@cuhk.edu.hk.; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China. siewchienng@cuhk.edu.hk.; Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China. siewchienng@cuhk.edu.hk.; Center for Gut Microbiota Research, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China. siewchienng@cuhk.edu.hk.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2022 Nov 10; Vol. 13 (1), pp. 6818. Date of Electronic Publication: 2022 Nov 10.
DOI: 10.1038/s41467-022-34405-3
Abstrakt: Systemic characterisation of the human faecal microbiome provides the opportunity to develop non-invasive approaches in the diagnosis of a major human disease. However, shared microbial signatures across different diseases make accurate diagnosis challenging in single-disease models. Herein, we present a machine-learning multi-class model using faecal metagenomic dataset of 2,320 individuals with nine well-characterised phenotypes, including colorectal cancer, colorectal adenomas, Crohn's disease, ulcerative colitis, irritable bowel syndrome, obesity, cardiovascular disease, post-acute COVID-19 syndrome and healthy individuals. Our processed data covers 325 microbial species derived from 14.3 terabytes of sequence. The trained model achieves an area under the receiver operating characteristic curve (AUROC) of 0.90 to 0.99 (Interquartile range, IQR, 0.91-0.94) in predicting different diseases in the independent test set, with a sensitivity of 0.81 to 0.95 (IQR, 0.87-0.93) at a specificity of 0.76 to 0.98 (IQR 0.83-0.95). Metagenomic analysis from public datasets of 1,597 samples across different populations observes comparable predictions with AUROC of 0.69 to 0.91 (IQR 0.79-0.87). Correlation of the top 50 microbial species with disease phenotypes identifies 363 significant associations (FDR < 0.05). This microbiome-based multi-disease model has potential clinical application in disease diagnostics and treatment response monitoring and warrants further exploration.
(© 2022. The Author(s).)
Databáze: MEDLINE
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