赵敏, 吴竞, 李丛勇, 赵义名, 薛万国, 孙刚, 石金龙. 健康人群不同年龄组肠道菌群特征预测模型的研究[J]. 解放军医学院学报, 2019, 40(8): 734-739. DOI: 10.3969/j.issn.2095-5227.2019.08.007
引用本文: 赵敏, 吴竞, 李丛勇, 赵义名, 薛万国, 孙刚, 石金龙. 健康人群不同年龄组肠道菌群特征预测模型的研究[J]. 解放军医学院学报, 2019, 40(8): 734-739. DOI: 10.3969/j.issn.2095-5227.2019.08.007
ZHAO Min, WU Jing, LI Congyong, ZHAO Yiming, XUE Wanguo, SUN Gang, SHI Jinlong. Predictive modelling for analyzing characteristics of gut microbiota in different age groups of healthy population[J]. ACADEMIC JOURNAL OF CHINESE PLA MEDICAL SCHOOL, 2019, 40(8): 734-739. DOI: 10.3969/j.issn.2095-5227.2019.08.007
Citation: ZHAO Min, WU Jing, LI Congyong, ZHAO Yiming, XUE Wanguo, SUN Gang, SHI Jinlong. Predictive modelling for analyzing characteristics of gut microbiota in different age groups of healthy population[J]. ACADEMIC JOURNAL OF CHINESE PLA MEDICAL SCHOOL, 2019, 40(8): 734-739. DOI: 10.3969/j.issn.2095-5227.2019.08.007

健康人群不同年龄组肠道菌群特征预测模型的研究

Predictive modelling for analyzing characteristics of gut microbiota in different age groups of healthy population

  • 摘要:
      目的  利用16S测序数据建立健康人年龄段预测模型,探索不同年龄组肠道菌群特征。
      方法  基于SRA数据库中2016年923例16S测序数据筛选特征操作分类单元(operational taxonomic units,OTUs),应用随机森林算法建立幼儿园学生、小学生、中学生、青年人、中年人、老年人和长寿老人7个年龄段预测模型,分析组间菌群差异特征及功能,计算特征OTUs与组间差异通路的相关性。
      结果  基于278个与年龄变化相关的OTUs建立随机森林年龄段预测模型,准确率达到72.9%;不同年龄组之间存在249个显著差异通路,主要是异生素生物降解和代谢、脂质代谢和氨基酸代谢通路;在菌群组成和功能层面聚类分析,学生组和中年人组聚为一类,青年人组、老年人组和长寿老人组聚为一类。
      结论  预测模型以及组间聚类结果均说明学生组和中年人组肠道菌群相似,老年人、长寿老人与青年人之间的菌群特征更相近。

     

    Abstract:
      Objective  To explore the characteristics of gut microbiota in different age groups by establishing a predictive model based on 16S rRNA gene sequencing data in healthy population.
      Methods  The featured OTUs were screened based on 923 samples in 2016 from SRA database, and a predictive model of different groups was established by random forest model to analyze the differential characteristics and function of gut microbiota among different age groups and calculate the correlations between the featured OTUs and the differential pathways.
      Results  The accuracy of random forest model was 72.9% based on 278 featured OTUs associated with age. There were 249 differential pathways among different age groups, mainly in xenobiotics biodegradation, metabolism, lipid metabolism and amino acid metabolism. According to coliform group and function, pupil and mid-age were grouped together, and youth, elderly and centenarian were classified into the same cluster.
      Conclusion  The predictive model and clustering results show that the characteristics of gut microbiota in the elderly, centenarian and youth are similar.

     

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