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Alterations in gut microbiota composition and metabolic parameters after dietary intervention with barley beta glucans in patients with high risk for metabolic syndrome development.

Anaerobe | 2019

Metabolic syndrome is a complex disease that is exponentially increasing in the western world, and diet is one of the possible ways to improve the metabolic status of patients. Barley beta glucans are dietary fibres that show promise for improvement cholesterol levels and postprandial glucose response, but they have been rarely investigated in human trials with concurrent focus on gut microbiota. A double-blind, placebo-controlled, randomised clinical trial was conducted with 43 volunteers with high risk for metabolic syndrome development or with diagnosed metabolic syndrome. During a four-week intervention study, participants consumed experimental bread containing 6 g of barley beta glucans or equal bread but without beta glucans. After dietary intervention, total plasma cholesterol decreased in the test group (-0.26 ± 0.54, p = 0.019), but not in the control group. Short chain fatty acids (SCFA) composition in faeces significantly changed with increase of propionic acid in test group (43.2%, p = 0.045) and with decrease of acetic acid in control group (41.8%, p = 0.011). The microbiome analysis based on Illumina paired end sequencing of 16S rRNA genes showed a decrease in microbial diversity and richness in the test group. The pre-intervention gut microbiota composition showed higher abundance of health associated Bifidobacterium spp. and Akkermansia municiphila within cholesterol-responsive group, showing that diet-induced metabolic response is possibly dependent on individual gut microbiota composition.

Pubmed ID: 30396006 RIS Download

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phyloseq (tool)

RRID:SCR_013080

Software for handling and analysis of high-throughput microbiome census data.

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DESeq2 (tool)

RRID:SCR_015687

Software package for differential gene expression analysis based on the negative binomial distribution. Used for analyzing RNA-seq data for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates.

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