南方医科大学学报2017，Vol.37Issue(3)：290-295,6.DOI:10.3969/j.issn.1673-4254.2017.03.02
基于肠道菌群预测摄入胆碱后血氧化三甲胺的变化
A machine learning model using gut microbiome data for predicting changes of trimethylamine-N-oxide in healthy volunteers after choline consumption
摘要
Abstract
Objective To establish a machine learning model based on gut microbiota for predicting the level of trimethylamine N-oxide (TMAO) metabolism in vivo after choline intake to provide guidance of individualized precision diet and evidence for screening population at high risks of cardiovascular disease.Methods We quantified plasma levels of TMAO in 18 healthy volunteers before and 8 h after a choline challenge (ingestion of two boiled eggs).The volunteers were divided into two groups with increased or decreased TMAO level following choline challenge.Fresh fecal samples were collected before taking fasting blood samples for amplifying 16S rRNA V4 tags,and the PCR products were sequenced using the platform of Illumina HiSeq 2000.The differences in gut microbiata between subjects with increased and decreased plasma TMAO were analyzed using QIIME.Based on the gut microbiota data and TMAO levels in the two groups,the prediction model was established using the machine learning random forest algorithm,and the validity of the model was tested using a verified dataset.Results An obvious difference was found in beta diversity of the gut microbota between the subjects with increased and decreased plasma TMAO level following choline challenge.The area under the curve (AUC) of the model was 86.39％ (95％ CI:72.7％-100％).Using the verified dataset,the model showed a much higher probability f0r correctly predicting TMAO variation following choline challenge.Conclusion The model is feasible and reliable for predicting the level of TMAO metabolism in vivo based on gut microbiota.关键词
肠道菌群/氧化三甲胺/机器学习/模型Key words
gut microbiota/trimethylamine-N-oxide/machine learning/model引用本文复制引用
路浚齐,王珊,尹恝,吴珊,何彦,郑慧敏,盛华芳,周宏伟..基于肠道菌群预测摄入胆碱后血氧化三甲胺的变化[J].南方医科大学学报,2017,37(3):290-295,6.基金项目
国家自然科学基金(81671171) （81671171）
广东省自然科学基金(2014A030313353) （2014A030313353）
广州市科技计划项目(201510010078)Supported by National Natural Science Foundation of China (81671171). （201510010078）
