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Elevated postprandial plasma glucose is a risk factor for development of type 2 diabetes and cardiovascular disease. We hypothesized that the inter-individual postprandial plasma glucose response varies partly depending on the intestinal microbiome composition and function. We analyzed data from Danish adults (n = 106), who were self-reported healthy and attended the baseline visit of two previously reported randomized controlled cross-over trials within the Gut, Grain and Greens project. Plasma glucose concentrations at five time points were measured before and during three hours after a standardized breakfast. Based on these data, we devised machine learning algorithms integrating bio-clinical, as well as shotgun-sequencing-derived taxa and functional potentials of the intestinal microbiome to predict individual postprandial glucose excursions. In this post hoc study, we found microbial and clinical features, which predicted up to 48% of the inter-individual variance of postprandial plasma glucose responses (Pearson correlation coefficient of measured vs. predicted values, R = 0.69, 95% CI: 0.45 to 0.84, p<0.001). The features were age, fasting serum triglycerides, systolic blood pressure, BMI, fasting total serum cholesterol, abundance of Bifidobacterium genus, richness of metagenomics species and abundance of a metagenomic species annotated to Clostridiales at order level. A model based only on microbial features predicted up to 14% of the variance in postprandial plasma glucose excursions (R = 0.37, 95% CI: 0.02 to 0.64, p = 0.04). Adding fasting glycaemic measures to the model including microbial and bio-clinical features increased the predictive power to R = 0.78 (95% CI: 0.59 to 0.89, p<0.001), explaining more than 60% of the inter-individual variance of postprandial plasma glucose concentrations. The outcome of the study points to a potential role of the taxa and functional potentials of the intestinal microbiome. If validated in larger studies our findings may be included in future algorithms attempting to develop personalized nutrition, especially for prediction of individual blood glucose excursions in dys-glycaemic individuals. [ABSTRACT FROM AUTHOR]

Background: Many patients with celiac disease experience difficulties in adherence to a gluten-free diet. Methods for testing compliance to a gluten-free diet are costly and cumbersome. Thus, a simple biomarker of gluten intake is needed in a clinical setting and will be useful for epidemiologic studies investigating wider effects of gluten intake.Objective: The aim was to evaluate plasma total alkylresorcinol concentrations as a measure of gluten intake.Methods: In this randomized, controlled, crossover intervention study in 52 Danish adults with features of the metabolic syndrome, we compared 8 wk of a gluten-rich and gluten-poor diet separated by a washout period of ≥6 wk. We measured fasting plasma concentrations of alkylresorcinols to determine if they reflected differences in gluten intake as a secondary outcome of the original study. In addition, we investigated in 118 Danish adults the cross-sectional association between self-reported gluten intake and plasma alkylresorcinols in the same and a similar study at baseline. We used mixed-model ANCOVA for examining treatment effects, a classification tree to determine compliance to the gluten-poor diet, and linear regression models for examining baseline correlation between plasma alkylresorcinol concentrations and gluten intake.Results: Plasma total alkylresorcinols decreased more during the gluten-poor period (geometric mean: -124.8 nmol/L; 95% CI: -156.5, -93.0 nmol/L) than in the gluten-rich period (geometric mean: -31.8 nmol/L; 95% CI: -63.1, -0.4 nmol/L) (P < 0.001). On the basis of the plasma alkylresorcinol profile, we built a classification tree to objectively determine compliance and found an overall participant misclassification error of 3.9%. In the cross-sectional study we found a 5.6% (95% CI: 2.4%, 8.9%) increase in plasma total alkylresorcinols per 1-g increase in reported gluten intake (P < 0.001).Conclusion: We propose the use of plasma alkylresorcinols to monitor compliance to a gluten-free diet as well as to help investigations into the possible effects of gluten in the wider population. This trial was registered at www.clinicaltrials.gov as NCT017119913 and NCT01731366. [ABSTRACT FROM AUTHOR]

Background: Since information about macro- and micronutrient intake among vegans is limited we aimed to determine and evaluate their dietary and supplementary intake.Methods: Seventy 18-61 years old Danish vegans completed a four-day weighed food record from which their daily intake of macro- and micronutrients was assessed and subsequently compared to an age-range-matched group of 1,257 omnivorous individuals from the general Danish population. Moreover, the vegan dietary and supplementary intake was compared to the 2012 Nordic Nutrition Recommendations (NNR).Results: Dietary intake differed significantly between vegans and the general Danish population in all measured macro- and micronutrients (p < 0.05), except for energy intake among women and intake of carbohydrates among men. For vegans the intake of macro- and micronutrients (including supplements) did not reach the NNR for protein, vitamin D, iodine and selenium. Among vegan women vitamin A intake also failed to reach the recommendations. With reference to the NNR, the dietary content of added sugar, sodium and fatty acids, including the ratio of PUFA to SFA, was more favorable among vegans.Conclusions: At the macronutrient level, the diet of Danish vegans is in better accordance with the NNR than the diet of the general Danish population. At the micronutrient level, considering both diet and supplements, the vegan diet falls short in certain nutrients, suggesting a need for greater attention toward ensuring recommended daily intake of specific vitamins and minerals. [ABSTRACT FROM AUTHOR]

With the prevalence of cardio-metabolic disorders reaching pandemic proportions, the search for modifiable causative factors has intensified. One such potential factor is the vast microbial community inhabiting the human gastrointestinal tract, the gut microbiota. For the past decade evidence has accumulated showing the association of distinct changes in gut microbiota composition and function with obesity, type 2 diabetes and cardiovascular disease. Although causality in humans and the pathophysiological mechanisms involved have yet to be decisively established, several studies have demonstrated that the gut microbiota, as an environmental factor influencing the metabolic state of the host, is readily modifiable through a variety of interventions. In this review we provide an overview of the development of the gut microbiome and its compositional and functional changes in relation to cardio-metabolic disorders, and give an update on recent progress in how this could be exploited in microbiota-based therapeutics. [ABSTRACT FROM AUTHOR]

Corrections to the article "Intake of Macro- and Micronutrients in Danish Vegans," by Nadja B. Kristensen and colleagues, published online on February 9, 2016 are presented.

Little is known about how colonic transit time relates to human colonic metabolism and its importance for host health, although a firm stool consistency, a proxy for a long colonic transit time, has recently been positively associated with gut microbial richness. Here, we show that colonic transit time in humans, assessed using radio-opaque markers, is associated with overall gut microbial composition, diversity and metabolism. We find that a long colonic transit time associates with high microbial richness and is accompanied by a shift in colonic metabolism from carbohydrate fermentation to protein catabolism as reflected by higher urinary levels of potentially deleterious protein-derived metabolites. Additionally, shorter colonic transit time correlates with metabolites possibly reflecting increased renewal of the colonic mucosa. Together, this suggests that a high gut microbial richness does not per se imply a healthy gut microbial ecosystem and points at colonic transit time as a highly important factor to consider in microbiome and metabolomics studies.