Simultaneous fecal microbial and metabolite profiling enables accurate classification of pediatric irritable bowel syndrome.
Simultaneous fecal microbial and metabolite profiling enables accurate classification of pediatric irritable bowel syndrome.
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DOI：
10.1186/s40168-015-0139-9
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发表时间：
2015-12-09
期刊：
影响因子：
15.5
通讯作者：
Paliy O
中科院分区：
文献类型：
--
作者：
Shankar V;Reo NV;Paliy O
我们之前发现，与年龄匹配的健康对照组相比，被诊断为腹泻型IBS(IBS-D)的青春期前和青春期美国儿童的粪便样本具有不同的微生物区系和代谢物组成。在这里，我们探讨了观察到的这两个青少年群体之间的粪便微生物区系和代谢物差异是否可以用来区分IBS和健康。我们在偏最小二乘多元分析的基础上构建了基于个体微生物群和代谢物的样本分类模型，然后应用贝叶斯方法将个体模型集成到单个分类器中。由此产生的组合分类在交叉验证测试中获得了84%的正确样本组分配和86%的IBS-D预测准确率。累积分类模型的性能通过对来自一个小型独立IBS-D队列的粪便样本的从头分析进一步验证。受试者粪便样本的高通量微生物和代谢物分析可用于促进IBS的诊断。本文的在线版本(doi：10.1186/s401680150139-9)包含补充材料，授权用户可以使用。
We previously showed that stool samples of pre-adolescent and adolescent US children diagnosed with diarrhea-predominant IBS (IBS-D) had different compositions of microbiota and metabolites compared to healthy age-matched controls. Here we explored whether observed fecal microbiota and metabolite differences between these two adolescent populations can be used to discriminate between IBS and health. We constructed individual microbiota- and metabolite-based sample classification models based on the partial least squares multivariate analysis and then applied a Bayesian approach to integrate individual models into a single classifier. The resulting combined classification achieved 84 % accuracy of correct sample group assignment and 86 % prediction for IBS-D in cross-validation tests. The performance of the cumulative classification model was further validated by the de novo analysis of stool samples from a small independent IBS-D cohort. High-throughput microbial and metabolite profiling of subject stool samples can be used to facilitate IBS diagnosis. The online version of this article (doi:10.1186/s40168-015-0139-9) contains supplementary material, which is available to authorized users.