Faecal microbiome-based machine learning for multi-class disease diagnosis.
Nat Commun
; 13(1): 6818, 2022 Nov 10.
Article
in English
| MEDLINE | ID: covidwho-2117855
ABSTRACT
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Microbiota
/
COVID-19
Type of study:
Diagnostic study
/
Prognostic study
Topics:
Long Covid
Limits:
Humans
Language:
English
Journal:
Nat Commun
Journal subject:
Biology
/
Science
Year:
2022
Document Type:
Article
Affiliation country:
S41467-022-34405-3
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