Related Experiment Video
Updated: Jul 31, 2025
Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
Published on: October 15, 2019
Performance of Gut Microbiome as an Independent Diagnostic Tool for 20 Diseases: Cross-Cohort Validation of
Min Li1, Jinxin Liu2, Jiaying Zhu1
1Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
Abstract:
Cross-cohort validation is essential for gut-microbiome-based disease stratification but was only performed for limited diseases. Here, we systematically evaluated the cross-cohort performance of gut microbiome-based machine-learning classifiers for 20 diseases. Using single-cohort classifiers, we obtained high predictive accuracies in intra-cohort validation (~0.77 AUC), but low accuracies in cross-cohort validation, except the intestinal diseases (~0.73 AUC). We then built combined-cohort classifiers trained on samples combined from multiple cohorts to improve the validation of non-intestinal diseases, and estimated the required sample size to achieve validation accuracies of >0.7. In addition, we observed higher validation performance for classifiers using metagenomic data than 16S amplicon data in intestinal diseases. We further quantified the cross-cohort marker consistency using a Marker Similarity Index and observed similar trends. Together, our results supported the gut microbiome as an independent diagnostic tool for intestinal diseases and revealed strategies to improve cross-cohort performance based on identified determinants of consistent cross-cohort gut microbiome alterations.
