This study evaluated the possible prebiotic effect of a moderate intake of red wine polyphenols on the modulation of the gut microbiota composition and the improvement in the risk factors for the metabolic syndrome in obese patients. Ten metabolic syndrome patients and ten healthy subjects were included in a randomized, crossover, controlled intervention study. After a washout period, the subjects consumed red wine and de-alcoholized red wine over a 30 day period for each. The dominant bacterial composition did not differ significantly between the study groups after the two red wine intake periods. In the metabolic syndrome patients, red wine polyphenols significantly increased the number of fecal bifidobacteria and Lactobacillus (intestinal barrier protectors) and butyrate-producing bacteria (Faecalibacterium prausnitzii and Roseburia) at the expense of less desirable groups of bacteria such as LPS producers (Escherichia coli and Enterobacter cloacae). The changes in gut microbiota in these patients could be responsible for the improvement in the metabolic syndrome markers. Modulation of the gut microbiota by using red wine could be an effective strategy for managing metabolic diseases associated with obesity.

Obesity and its associated disorders are a major public health concern. Although obesity has been mainly related with perturbations of the balance between food intake and energy expenditure, other factors must nevertheless be considered. Recent insight suggests that an altered composition and diversity of gut microbiota could play an important role in the development of metabolic disorders. This review discusses research aimed at understanding the role of gut microbiota in the pathogenesis of obesity and type 2 diabetes mellitus. The establishment of gut microbiota is dependent on the type of birth. With effect from this point, gut microbiota remain quite stable, although changes take place between birth and adulthood due to external influences, such as diet, disease and environment. Understand these changes is important to predict diseases and develop therapies. A new theory suggests that gut microbiota contribute to the regulation of energy homeostasis, provoking the development of an impairment in energy homeostasis and causing metabolic diseases, such as insulin resistance or type 2 diabetes mellitus. The metabolic endotoxemia, modifications in the secretion of incretins and butyrate production might explain the influence of the microbiota in these diseases.

We assessed whether intermittent hypoxia, which emulates one of the hallmarks of obstructive sleep apnoea (OSA), leads to altered faecal microbiome in a murine model.In vivopartial pressure of oxygen was measured in colonic faeces during intermittent hypoxia in four anesthetised mice. 10 mice were subjected to a pattern of chronic intermittent hypoxia (20 s at 5% O₂and 40 s at room air for 6 h·day⁻¹) for 6 weeks and 10 mice served as normoxic controls. Faecal samples were obtained and microbiome composition was determined by 16S rRNA pyrosequencing and bioinformatic analysis by Quantitative Insights into Microbial Ecology.Intermittent hypoxia exposures translated into hypoxia/re-oxygenation patterns in the faeces proximal to the bowel epithelium (<200 μm). A significant effect of intermittent hypoxia on global microbial community structure was found. Intermittent hypoxia increased the α-diversity (Shannon index, p<0.05) and induced a change in the gut microbiota (ANOSIM analysis of β-diversity, p<0.05). Specifically, intermittent hypoxia-exposed mice showed a higher abundance of Firmicutes and a smaller abundance of Bacteroidetes and Proteobacteria phyla than controls.Faecal microbiota composition and diversity are altered as a result of intermittent hypoxia realistically mimicking OSA, suggesting the possibility that physiological interplays between host and gut microbiota could be deregulated in OSA.

OBJECTIVE Type 1 diabetes is associated with compositional differences in gut microbiota. To date, no microbiome studies have been performed in maturity-onset diabetes of the young 2 (MODY2), a monogenic cause of diabetes. Gut microbiota of type 1 diabetes, MODY2, and healthy control subjects was compared. RESEARCH DESIGN AND METHODS This was a case-control study in 15 children with type 1 diabetes, 15 children with MODY2, and 13 healthy children. Metabolic control and potential factors modifying gut microbiota were controlled. Microbiome composition was determined by 16S rRNA pyrosequencing. RESULTS Compared with healthy control subjects, type 1 diabetes was associated with a significantly lower microbiota diversity, a significantly higher relative abundance of Bacteroides, Ruminococcus, Veillonella, Blautia, and Streptococcus genera, and a lower relative abundance of Bifidobacterium, Roseburia, Faecalibacterium, and Lachnospira. Children with MODY2 showed a significantly higher Prevotella abundance and a lower Ruminococcus and Bacteroides abundance. Proinflammatory cytokines and lipopolysaccharides were increased in type 1 diabetes, and gut permeability (determined by zonulin levels) was significantly increased in type 1 diabetes and MODY2. The PICRUSt analysis found an increment of genes related to lipid and amino acid metabolism, ABC transport, lipopolysaccharide biosynthesis, arachidonic acid metabolism, antigen processing and presentation, and chemokine signaling pathways in type 1 diabetes. CONCLUSIONS Gut microbiota in type 1 diabetes differs at taxonomic and functional levels not only in comparison with healthy subjects but fundamentally with regard to a model of nonautoimmune diabetes. Future longitudinal studies should be aimed at evaluating if the modulation of gut microbiota in patients with a high risk of type 1 diabetes could modify the natural history of this autoimmune disease.

The Mediterranean diet is a well-recognized healthy diet that has shown to induce positive changes in gut microbiota. Lifestyle changes such as diet along with physical activity could aid in weight loss and improve cardiovascular risk factors. To investigate the effect of an intensive lifestyle weight loss intervention on gut microbiota. This is a substudy of the PREDIMED-Plus (Prevención con Dieta Mediterránea-Plus), a randomized controlled trial conducted in overweight/obese men and women (aged 55–75 y) with metabolic syndrome. The intervention group (IG) underwent an intensive weight loss lifestyle intervention based on an energy-restricted Mediterranean diet (MedDiet) and physical activity promotion, and the control group (CG) underwent a non-energy-restricted MedDiet for 1 y. Anthropometric, biochemical, and gut microbial 16S rRNA sequencing data were analyzed at baseline (n = 362) and 1-y follow-up (n = 343). IG participants had a weight loss of 4.2 (IQR, –6.8, –2.5) kg compared with 0.2 (IQR, –2.1, 1.4) kg in the CG (P < 0.001). Reductions in BMI, fasting glucose, glycated hemoglobin, and triglycerides and an increase in HDL cholesterol were greater in IG than in CG participants (P < 0.05). We observed a decrease in Butyricicoccus, Haemophilus, Ruminiclostridium 5, and Eubacterium hallii in the IG compared with the CG. Many genera shifted in the same direction within both intervention groups, indicating an overall effect of the MedDiet. Decreases in Haemophilus, Coprococcus 3, and few other genera were associated with a decrease in adiposity parameters in both intervention groups. Changes in Lachnospiraceae NK4A136 were positively associated with changes in MedDiet adherence. Weight loss induced by an energy-restricted MedDiet and physical activity induce changes in gut microbiota. The role of MedDiet-induced changes on the host might be via short-chain fatty acid producing bacteria, whereas with energy restriction, these changes might be modulated with other mechanisms, which need to be explored in future studies. This trial was registered at http://www.isrctn.com/ISRCTN89898870 as ISRCT 89898870.

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.