PDF(Pubmed)
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.
摘要：
跨队列验证对于基于肠道微生物组的疾病分层至关重要，但仅适用于有限的疾病。这里,我们系统评估了基于肠道微生物组的机器学习分类器对20种疾病的跨队列性能.使用单队列分类器，我们在队列内验证中获得了很高的预测准确性(~0.77AUC)，但是交叉队列验证的准确性很低，除了肠道疾病（~0.73AUC）。然后，我们在来自多个队列的样本上构建了组合队列分类器，以提高非肠道疾病的有效性。并估计达到>0.7的验证精度所需的样本量。此外,在肠道疾病中,我们观察到使用宏基因组数据的分类器的验证性能高于16S扩增子数据.我们使用标记相似性指数进一步量化了跨队列标记的一致性，并观察到了相似的趋势。一起,我们的结果支持将肠道微生物组作为肠道疾病的独立诊断工具,并揭示了基于已确定的一致跨队列肠道微生物组改变的决定因素改善跨队列表现的策略.
