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  • Title: [A machine learning model using gut microbiome data for predicting changes of trimethylamine-N-oxide in healthy volunteers after choline consumption].
    Author: Lu JQ, Wang S, Yin J, Wu S, He Y, Zheng HM, Sheng HF, Zhou HW.
    Journal: Nan Fang Yi Ke Da Xue Xue Bao; 2017 Mar 20; 37(3):290-295. PubMed ID: 28377341.
    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 for 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. 目的: 建立基于肠道菌群预测摄入胆碱食物后体内氧化三甲胺（TMAO）代谢水平变化趋势的模型，为个体精准膳食提供指导，同时为潜在的心脑血管疾病高危人群筛选提供参考。 方法: 采集18位健康志愿者的空腹血清样本及食用等量胆碱食品8 h后的血清样本，根据前后血清TMAO水平升降趋势，将志愿者分为升高组和降低组。采集粪便样品，对粪便样本DNA的16 SrRNA基因V4可变区进行测序分析，比较两组志愿者的肠道菌群数据；根据肠道菌群及TMAO的数据，采用机器学习随机森林方法建立预测模型，并对模型进行检验。 结果: 升高组与降低组在反映肠道菌群β多样性的主坐标分析（PCOA）图展示明显的区分，两组肠道菌群物种多样性存在差异；建立的预测模型曲线下面积为86.39%，95%置信区间为（72.7%，100%）；验证集合代入模型显示降低组志愿者的TMAO变化水平被模型判定为降低的可能性明显高于升高组志愿者。 结论: 根据肠道菌群可以建立预测TMAO水平变化模型，模型预测效果良好。
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