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. 2015 Jun 15;5(6):e159.
doi: 10.1038/nutd.2015.9.

Specific gut microbiota features and metabolic markers in postmenopausal women with obesity

L K Brahe  1 , E Le Chatelier  2 , E Prifti  2 , N Pons  2 , S Kennedy  2 , T Hansen  3 , O Pedersen  3 , A Astrup  1 , S D Ehrlich  2 , L H Larsen  1
Affiliations

Specific gut microbiota features and metabolic markers in postmenopausal women with obesity

L K Brahe et al. Nutr Diabetes. .

Abstract

Background: Gut microbial gene richness and specific bacterial species are associated with metabolic risk markers in humans, but the impact of host physiology and dietary habits on the link between the gut microbiota and metabolic markers remain unclear VSports手机版. The objective of this study was to identify gut metagenomic markers associated with estimates of insulin resistance, lipid metabolism and inflammation in obesity, and to explore whether the associations between metagenomic and metabolic markers persisted after adjustment for body fat, age and habitual dietary intake. .

Methods: Faecal DNA from 53 women with obesity was analysed through quantitative metagenomic sequencing and analysis, and a systematic search was performed for bacterial genes associated with estimates of insulin resistance, inflammation and lipid metabolism. Subsequently, the correlations between metagenomic species and metabolic markers were tested by linear regression models, with and without covariate adjustment. V体育安卓版.

Results: One hundred and fourteen metagenomic species correlated with metabolic markers (P<0. 001) including Akkermansia muciniphila, Bilophila wadsworthia, Bifidobacterium longum and Faecalibacterium prausnitzii, but also species not previously associated with metabolic markers including Bacteroides faecis and Dorea longicatena. The majority of the identified correlations between bacterial species and metabolic markers persisted after adjustment for differences in body fat, age and dietary macronutrient composition; however, the negative correlation with insulin resistance observed for B. longum and F V体育ios版. prausnitzii appeared to be modified by the intake of dietary fibre and fat, respectively. .

Conclusions: This study shows that several gut bacterial species are linked to metabolic risk markers in obesity, also after adjustment for potential confounders, such as long-term diet composition. The study supports the use of gut metagenomic markers for metabolic disease prediction and warrants further investigation of causality. VSports最新版本.

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Figures

Figure 1
Figure 1
Heatmap of correlations between MGS and markers for insulin resistance, lipid metabolism, inflammation, body composition, dietary intake and bacterial gene count. The heatmap reports significant Spearman's rank correlations (P<0.001) observed between 114 MGS signals (rows) and 29 different markers (columns) using Euclidean distance metric and complete linkage method. Markers are grouped in five categories given in the top of the figure: insulin resistance, lipid metabolism, inflammation, diet and others. MGS names are shown on the left of the figure and their taxonomical annotation at the species or genus level is given on the right, coloured according to the phylum: Firmicutes in black, Bacteroidetes in red, Actinobacteria in purple, Proteobacteria in blue, Verrucomicrobia in gold and Tenericutes in green. The strength of the correlation is coloured according to the colour key in the upper left corner of the figure. ALT, alanine aminotransferase; AST, aspartate aminotransferase; AUC, area-under-the-curve; BMI, body mass index; CD14, cluster of differentiation 14; F, fasting; FFAs, free fatty acids; HDL-C, high-density lipoprotein cholesterol; hsCRP, high sensitive C-reactive protein; HOMA-IR, homeostatic model assessment of insulin resistance; IAAT, intra-abdominal adipose tissue; LBP, lipopolysaccharide-binding protein; LDL-C, low-density lipoprotein cholesterol; TAG, triglycerides; Waist cir.; waist circumference; WBC, white blood cells.
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