[1]罗月梅,刘斐童,陈慕璇,等.基于肠道菌群结构预测摄入益生元后双歧杆菌的变化趋势[J].南方医科大学学报,2018,(03):251.
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基于肠道菌群结构预测摄入益生元后双歧杆菌的变化趋势()
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《南方医科大学学报》[ISSN:1673-4254/CN:44-1627/R]

卷:
期数:
2018年03期
页码:
251
栏目:
出版日期:
2018-04-03

文章信息/Info

Title:
A machine learning model based on initial gut microbiome data for predicting changes of Bifidobacterium after prebiotics consumption
作者:
罗月梅刘斐童陈慕璇唐文丽杨月莲谭细兰周宏伟
关键词:
益生元低聚果糖低聚半乳糖肠道菌群精准膳食双歧杆菌
Keywords:
prebiotics fructo-oligosaccharides galacto-oligosaccharides microbiota personalized diet Bifidobacterium
摘要:
目的研究益生元干预对肠道菌群的影响,基于肠道菌群数据建立模型预测摄入益生元后双歧杆菌的变化。方法将35名 健康志愿者随机分为2组,分别接受16 g/d低聚果糖(FOS)或低聚半乳糖(GOS)干预,干预时间9 d,采集第0、5、9天粪便标本进 行16s rRNA基因高通量测序分析,分析两种益生元干预对肠道菌群结构、多样性与功能的影响。用随机森林方法区分益生元 干预5 d后双歧杆菌变化,并用初始的菌群构建一个连续型指数(index),实现指数与双歧杆菌实际变化量相关联,而后,对模型 进行验证。结果FOS干预5 d后降低肠道菌群α多样性,9 d时回升,GOS在干预5、9 d时α多样性均下降。两种益生元对β多样 性影响均无统计学意义(P>0.05)。通过随机森林方法建立的预测模型,ROC曲线下面积为89.6%;建立的指数与双歧杆菌变化 量关联r值为0.45(P<0.01),验证模型r值为0.62(P<0.01)。结论益生元干预可迅速降低肠道菌群α多样性。根据干预前的肠 道菌群预测服用益生元后双歧杆菌的变化,预测效果良好,此方法为精准膳食的实现奠定基础。
Abstract:
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 Bifidobacterium (R=0.45, P=0.01), and the prediction accuracy was verified by the validation model (R= 0.62, P=0.01). Conclusion Short-term prebiotics intervention can significantly decrease α-diversity of the intestinal flora. The machine learning model based on initial gut microbiota data can accurately predict the changes in Bifidobacterium, which sheds light on personalized nutrition intervention and precise modulation of the intestinal flora.
更新日期/Last Update: 1900-01-01