文章摘要
蒋露芳,王莹莹,彭慧,李锐,张芬,王娜,邵月琴,姜庆五.学龄儿童肥胖与肠道菌群多样性及菌属丰度的关联研究[J].中华流行病学杂志,2022,43(2):260-268
学龄儿童肥胖与肠道菌群多样性及菌属丰度的关联研究
Association between obesity with the diversity and genus of gut microbiota in school-aged children
收稿日期:2021-06-17  出版日期:2022-02-16
DOI:10.3760/cma.j.cn112338-20210617-00478
中文关键词: 学龄儿童  肥胖  肠道菌群  多样性
英文关键词: School-aged children  Obesity  Gut microbiota  Diversity
基金项目:国家自然科学基金(81803304)
作者单位E-mail
蒋露芳 复旦大学公共卫生学院流行病学教研室, 上海 200032  
王莹莹 复旦大学公共卫生学院公共卫生安全教育部重点实验室, 上海 200032  
彭慧 上海市嘉定区疾病预防控制中心, 上海 201800  
李锐 复旦大学公共卫生学院公共卫生安全教育部重点实验室, 上海 200032  
张芬 上海市嘉定区疾病预防控制中心, 上海 201800  
王娜 复旦大学公共卫生学院流行病学教研室, 上海 200032
复旦大学公共卫生学院公共卫生安全教育部重点实验室, 上海 200032 
na.wang@fudan.edu.cn 
邵月琴 上海市嘉定区疾病预防控制中心, 上海 201800  
姜庆五 复旦大学公共卫生学院流行病学教研室, 上海 200032
复旦大学公共卫生学院公共卫生安全教育部重点实验室, 上海 200032 
 
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中文摘要:
      目的 分析学龄肥胖儿童和正常体重儿童肠道菌群多样性差异,识别肥胖儿童肠道特征菌属,为后续相关机制研究和学龄儿童肥胖的防治提供依据。方法 基于2016年在上海市嘉定区某小学建立的研究队列人群,将2016-2018年3年均处肥胖状态的儿童共63名纳入肥胖组,其中男生43名,女生20名。在3年均为正常体重的儿童中,根据年龄、性别和所在班级,将其与肥胖组儿童进行1∶1匹配,共选择63名纳入对照组。采用问卷收集儿童基本信息、饮食状况、母乳喂养等情况,收集两组儿童的粪便样本并进行16S rDNA测序。对质量优化后的测序序列按照97%相似性进行可操作分类单元聚类及物种注释。分析肥胖组和对照组儿童肠道菌群多样性及菌属丰度差异。计算肠道菌群的Ace、Chao1、Shannon、Simpson 4种α多样性指数,并利用主坐标分析,在非加权Unifrac距离和加权Unifrac距离的基础上表示β多样性。使用相似性分析(ANOSIM)比较两组β多样性的差异。利用STAMP软件挑选出两组儿童共有菌属中的差异细菌,并利用广义线性模型(GLM)分析肥胖与α多样性以及显著差异菌属的关联。结果 肥胖组的Ace和Chao1指数低于对照组,差异有统计学意义(P值分别为0.026和0.039),Shannon和Simpson指数差异无统计学意义(P值分别为0.879和0.922)。ANOSIM分析结果显示,两组儿童肠道菌群差异有统计学意义(R>0),但是组别贡献不显著(非加权Unifrac距离:R=0.006,P=0.223;加权Unifrac距离:R=0.010,P=0.134)。在肥胖组的特有菌属中,普雷沃菌属(Prevotella)和八叠球菌属(Sarcina)的相对丰度较高。STAMP分析结果显示,共有菌属中,15个菌属的相对丰度在两组儿童中差异有统计学意义(P<0.05)。GLM分析结果显示,在调整年龄、性别、SBP、饮食频次、近一周内抗生素使用情况、分娩方式、母乳喂养时间后,与对照组儿童相比,肥胖组的Ace指数和阿克曼菌属(Akkermansia muciniphila)的相对丰度显著较低,粪球菌属(Coprococcus_3)、瘤胃球菌属(Ruminococcus)、不动杆菌属(Agathobacter)、柯林斯菌属(Collinsella)的相对丰度较高。根据性别分层之后,在肥胖男生中,发现Chao1指数低于正常体重男生。但是,在肥胖女生中,仅发现粪球菌属高于正常体重女生。结论 与正常体重儿童相比,肥胖儿童肠道菌群α多样性较低、优势益生菌属相对丰度较低,但是与代谢紊乱、炎症促进、胆固醇降低有关的菌属相对丰度较高。
英文摘要:
      Objective To analyze the diversity differences of gut microbiota between school-aged obese children and normal-weight children and identify the characteristic flora in obese children to provide some evidence for the subsequent study of the following study-related mechanisms and the prevention and treatment of obesity in school-aged children. Methods This study was based on a cohort established in a primary school in Jiading district, Shanghai, 2016. Sixty-three children, including 43 boys and 20 girls, who were obese during the three years from 2016 to 2018, were enrolled in the obesity group. Among children who were average weight for three years, a total of 63 were selected as the control group, with matching with the obese ones in a 1 to 1 way according to age, sex and class. The primary characteristics, diet status, breastfeeding, and other information of children were collected by questionnaire. Fecal samples of participants in both groups were collected, and 16S rDNA sequencing was performed. Operable taxon units clustering according to 97% similarity and species annotation were based on the quality-optimized sequences. The difference in the diversity and genius of gut microbiota among children in the obesity and control groups were analyzed. The α diversity indices, including Ace, Chao1, Shannon, and Simpson index, were calculated. The β diversity was presented based on unweighted Unifrac distance and weighted Unifrac space by principal coordinate analysis. The differences in β diversity between the two groups were compared by similarity analysis (ANOSIM).The differences in genus distribution between the two groups were selected by STAMP software. The association of obesity with the α diversity and genus with significant differences were analyzed by the generalized linear model (GLM). Results The Ace and Chao1 index in the obesity group was significantly lower than those in the control group (The P values were 0.026 and 0.039, respectively). There was no significant difference in Shannon and Simpson index between the two groups (The P values were 0.879 and 0.922, respectively). The results of ANOSIM showed differences in gut microbiota between the two groups (R>0), but the group contribution was not significant (unweighted Unifrac distance: R=0.006, P=0.223; weighted Unifrac distance: R=0.010, P=0.134). Among the obese group, the relative abundance of Prevotella and Sarcina was highest in the endemic genus. The STAMP results showed that the relative lots of 15 genera were significantly different between the two groups (P<0.05). The results of GLM showed that, compared with the control group, the obesity group had a lower level of the relative abundance in Akkermansia muciniphila, while a higher level in Coprococcus_3, Ruminococcus, Agathobacter and Collinsella. After stratification by sex, the Chao1 index was also lower in the obese boys than that in the normal-weight boys. However, the obese girls only had a higher level in Coprococcus_3 than the ordinary weight girls. Conclusions Compared with children with average weight, obese children had lower α diversity of gut microbiota and lower relative abundance of dominant probiotics but had a higher relative lot of genus associated with metabolic disorders, inflammation promotion, and metabolism rate reduction.
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