Gut microbiota and diet in patients with different glucose tolerance

Abstract

Type 2 diabetes (T2D) is a serious disease. The gut microbiota (GM) has recently been identified as a new potential risk factor in addition to well-known diabetes risk factors. To investigate the GM composition in association with the dietary patterns in patients with different glucose tolerance, we analyzed 92 patients: with normal glucose tolerance (n=48), prediabetes (preD, n=24), and T2D (n=20). Metagenomic analysis was performed using 16S rRNA sequencing. The diet has been studied by a frequency method with a quantitative evaluation of food intake using a computer program. Microbiota in the samples was predominantly represented by Firmicutes, in a less degree by Bacteroidetes. Blautia was a dominant genus in all samples. The representation of Blautia, Serratia was lower in preD than in T2D patients, and even lower in those with normal glucose tolerance. After the clustering of the samples into groups according to the percentage of protein, fat, carbohydrates in the diet, the representation of the Bacteroides turned to be lower and Prevotella abundance turned to be higher in carbohydrate cluster. There were more patients with insulin resistance, T2D in the fat–protein cluster. Using the Calinski–Harabasz index identified the samples with more similar diets. It was discovered that half of the patients with a high-fat diet had normal tolerance, the others had T2D. The regression analysis showed that these T2D patients also had a higher representation of Blautia. Our study provides the further evidence concerning the structural modulation of the GM in the T2DM pathogenesis depending on the dietary patterns.

Guidelines to Statistical Analysis of Microbial Composition Data Inferred from Metagenomic Sequencing

“…The black line denotes significance level, the area of the blue region is the probability of a type I error (α) and the area of the red region is the probability of a type II error (β). The illustration is based on the analysis of log-transformed data for healthy subjects and patients with type 2 diabetes (Egshatyan et al, 2016).…”

“…Common solutions include the square-root transformation, arcsine-square-root-and log-transformation paired with adding a pseudocount (in order to avoid infinite values) to all taxa ( Jonsson et al, 2016; normalized by the total sample sum log-transformed data (with adding one 'pseudocount'). The scatter plots are constructed for the published data on gut microbiota of healthy people and patients with diabetes (Egshatyan et al, 2016); the points correspond to 100 randomly selected OTUs. Wang et al, 2016).…”

“…On the other hand, the methods For the data normalized to 100% for one of the major OTUs, (b) for the data log-transformed and then normalized to 100% for one of the major OTUs, (c) for the data normalized to 100% for one of the minor OTUs, and (d) for the data log-transformed and then normalized to 100% for one of the minor OTUs. The plots are constructed using 82 gut metagenomic datasets of healthy individuals (Egshatyan et al, 2016).…”

Comparison study of gut microbiota in case of Parkinson’s disease and other neurological disorders

“…Выделение ДНК проводилось в соответствии с описанной методикой [9]. Подготовка библи-отек и ампликонное секвенирование маркерно-го вариабельного участка V3-V4 бактериальных генов 16S рРНК проводилось на приборе MiSeq (Illumina, США) согласно стандартному протоко-лу производителя.…”

Development of qPCR platform with probes for quantifying prevalent and biomedically relevant human gut microbial taxa

“…The gut is colonized with 10 13 of microbes belonging to thousands of species. Alterations of microbiome composition are linked to lifestyle changes, short-and long-term diet [1], probiotics intake [1,2], medications including antibiotics [3] and diseases including obesity, type 2 diabetes [4], colorectal cancer [5], Crohn's disease [6] and others. Based on the discovered associations, a number of microbial taxa have been proposed as potential biomarkers.…”