Published online Apr 16, 2018. doi: 10.12998/wjcc.v6.i4.54
Peer-review started: January 2, 2018
First decision: January 18, 2018
Revised: February 2, 2018
Accepted: March 7, 2018
Article in press: March 7, 2018
Published online: April 16, 2018
The prevalence of metabolic syndrome has been one of the most pressing global health problems. The WHO defined that glucose intolerance, insulin resistance, obesity, hypertension and dyslipidaemia are essential component of metabolic syndrome. Metagenomic studies had identified various specific gut microbiota relate to metabolic syndrome. Based on the Illumina Miseq sequencing platform, 16s rDNA was widely studied on the distribution and diversity of microbial communities, however the analysis on the clinical indicators was not enough. Recently, the microbial community, based on 16s rDNA sequencing, has attracted substantial attention. Metagenomic studies had identified that various specific gut microbiota were relate to metabolic syndrome, such as Akkermansia municiphila. The changes of microbes in the community and the relationship between microbial community and the clinical indicators of metabolic syndrome can be used as an indicator of metabolic syndrome detection.
Except for the distribution and diversity of microbial communities, we aimed to find out a relationship between these special bacteria and metabolic diseases through the analysis of clinical data.
The main objectives were twenty patients with metabolic syndrome which were recruited from the hospital outpatient and inpatient department according to the international Diabetes Federation (IDF) criteria. The patient’s faecal samples were collected and analyzed by 16S rDNA sequencing.
16S rDNA gene sequencing is a non-culture method based on the high-throughput sequencing platforms. At present, 16S rDNA gene sequencing has been widely utilized for metagenomic analysis of the environment, including analysis of the composition of the human and animal guts and fecal microbiota. In this study, we analyzed the bacterial community structure, and found out a relationship between these special bacteria and metabolic diseases. The microbial flora could be used to guide the detection of metabolic syndrome and the changes of microbes in the community can be used as an indicator. Furthermore, Prevotella might be a target microorganism in patients with metabolic syndrome.
Firstly, Bacteroidetes, Firmicutes, Actinobacteria, Proteobacteria, Fusobacteria were the dominant phyla, and Prevotella, Bacteroides and Faecalibacterium was the top three genera in faecal samples. Secondly, compared with the health people (group C), patients with metabolic syndrome (group D) had much more species richness in faecal samples. However, the microbial diversity of group C was greater than that of group D. Thirdly, clinical data had correlation with the distribution and diversity of microbial communities. For example, the alkaline phosphatase and low-density lipoprotein was negatively correlated with the abundance of Prevotella and Ruminococcus respectively (P < 0.05). In contrast, there was a positive correlation between the high-density lipoprotein and the abundance of Ruminococcus (P < 0.05), additionally, another positive correlation were detected among the total protein, the alanine aminotransferase and Peptostreptococcus (P < 0.05).
In this study, the data on the composition of microbial communities of normal and metabolic syndrome patients were combined with the clinical indicators of metabolic syndrome. The species richness of metabolic syndrome samples (group D) was significantly higher than the healthy people (group C) (P < 0.05), and the microbial diversity of group C was greater than that of group D.
The changes microbial communities can be used as an indicator of metabolic syndrome, and Prevotella may be a target microorganism in patients with metabolic syndrome.