[A machine learning model based on initial gut microbiome data for predicting changes of Bifidobacterium after prebiotics consumption].

作者：Yuemei Luo, Fei-Tong Liu, Mu-Xuan Chen, Wenli Tang, Yue Yang, Xi-lan Tan, Hongwei Zhou · 发表于：PubMed · 年份：2018 · DOI：10.3969/j.issn.1673-4254.2018.03.03 · 被引用次数：7 · 研究领域：Gut microbiota and health、Microbial Metabolites in Food Biotechnology、Clostridium difficile and Clostridium perfringens research

OBJECTIVE: To investigate the effects of prebiotics supplementation for 9 days on gut microbiota structure and function and establish a machine learning model based on the initial gut microbiota data for predicting the variation of Bifidobacterium after prebiotic intake. METHODS: With a randomized double-blind self-controlled design, 35 healthy volunteers were asked to consume fructo-oligosaccharides (FOS) or galacto-oligosaccharides (GOS) for 9 days (16 g per day). 16S rRNA gene high-throughput sequencing was performed to investigate the changes of gut microbiota after prebiotics intake. PICRUSt was used to infer the differences between the functional modules of the bacterial communities. Random forest model based on the initial gut microbiota data was used to identify the changes in Bifidobacterium after 5 days of prebiotic intake and then to build a continuous index to predict the changes of Bifidobacterium. The data of fecal samples collected after 9 days of GOS intervention were used to validate the model. RESULTS: Fecal samples analysis with QIIME revealed that FOS intervention for 5 days reduced the intestinal flora alpha diversity, which rebounded on day 9; in GOS group, gut microbiota alpha diversity decreased progressively during the intervention. Neither FOS nor GOS supplement caused significant changes in β diversity of gut microbiota. The area under the curve (AUC) of the prediction model was 89.6%. The continuous index could successfully predict the changes in B...