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Chen X, Sun H, Jiang F, Shen Y, Li X, Hu X, Shen X, Wei P. Alteration of the gut microbiota associated with childhood obesity by 16S rRNA gene sequencing. PeerJ 2020; 8:e8317. [PMID: 31976177 PMCID: PMC6968493 DOI: 10.7717/peerj.8317] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 11/29/2019] [Indexed: 12/13/2022] Open
Abstract
Background Obesity is a global epidemic in the industrialized and developing world, and many children suffer from obesity-related complications. Gut microbiota dysbiosis might have significant effect on the development of obesity. The microbiota continues to develop through childhood and thus childhood may be the prime time for microbiota interventions to realize health promotion or disease prevention. Therefore, it is crucial to understand the structure and function of pediatric gut microbiota. Methods According to the inclusion criteria and exclusion criteria, twenty-three normal weight and twenty-eight obese children were recruited from Nanjing, China. Genomic DNA was extracted from fecal samples. The V4 region of the bacterial 16S rDNA was amplified by PCR, and sequencing was applied to analyze the gut microbiota diversity and composition using the Illumina HiSeq 2500 platform. Results The number of operational taxonomic units (OTUs) showed a decrease in the diversity of gut microbiota with increasing body weight. The alpha diversity indices showed that the normal weight group had higher abundance and observed species than the obese group (Chao1: P < 0.001; observed species: P < 0.001; PD whole tree: P < 0.001; Shannon index: P = 0.008). Principal coordinate analysis (PCoA) and Nonmetric multidimensional scaling (NMDS) revealed significant differences in gut microbial community structure between the normal weight group and the obese group. The liner discriminant analysis (LDA) effect size (LEfSe) analysis showed that fifty-five species of bacteria were abundant in the fecal samples of the normal weight group and forty-five species of bacteria were abundant in the obese group. In regard to phyla, the gut microbiota in the obese group had lower proportions of Bacteroidetes (51.35%) compared to the normal weight group (55.48%) (P = 0.030). There was no statistical difference in Firmicutes between the two groups (P = 0.436), and the Firmicutes/Bacteroidetes between the two groups had no statistical difference (P = 0.983). At the genus level, Faecalibacterium, Phascolarctobacterium, Lachnospira, Megamonas, and Haemophilus were significantly more abundant in the obese group than in the normal weight group (P = 0.048, P = 0.018, P < 0.001, P = 0.040, and P = 0.003, respectively). The fecal microbiota of children in the obese group had lower proportions of Oscillospira and Dialister compared to the normal weight group (P = 0.002 and P = 0.002, respectively). Conclusions Our results showed a decrease in gut microbiota abundance and diversity as the BMI increased. Variations in the bacterial community structure were associated with obesity. Gut microbiota dysbiosis might play a crucial part in the development of obesity in Chinese children.
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Affiliation(s)
- Xiaowei Chen
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.,Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
| | - Haixiang Sun
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China
| | - Fei Jiang
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.,Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
| | - Yan Shen
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.,Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
| | - Xin Li
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China
| | - Xueju Hu
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China
| | - Xiaobing Shen
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.,Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
| | - Pingmin Wei
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.,Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
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