“Normal” for the gut microbiota: For the benefit of future clinical studies, it is critical to establish what constitutes a “normal” gut microbiome, if it exists at all. Through fecal samples and questionnaires, Falony et al. and Zhernakova et al. targeted general populations in Belgium and the Netherlands, respectively. Gut microbiota composition correlated with a range of factors including diet, use of medication, red blood cell counts, fecal chromogranin A, and stool consistency. The data give some hints for possible biomarkers of normal gut communities. Science , this issue pp. 560 and 565

Background and aims: Proton pump inhibitors (PPIs) are among the top 10 most widely used drugs in the world. PPI use has been associated with an increased risk of enteric infections, most notably Clostridium difficile. The gut microbiome plays an important role in enteric infections, by resisting or promoting colonisation by pathogens. In this study, we investigated the influence of PPI use on the gut microbiome. Methods: The gut microbiome composition of 1815 individuals, spanning three cohorts, was assessed by tag sequencing of the 16S rRNA gene. The difference in microbiota composition in PPI users versus non-users was analysed separately in each cohort, followed by a meta-analysis. Results: 211 of the participants were using PPIs at the moment of stool sampling. PPI use is associated with a significant decrease in Shannon's diversity and with changes in 20% of the bacterial taxa (false discovery rate <0.05). Multiple oral bacteria were over-represented in the faecal microbiome of PPI-users, including the genus Rothia (p=9.8×10 ⁻³⁸ ). In PPI users we observed a significant increase in bacteria: genera Enterococcus , Streptococcus , Staphylococcus and the potentially pathogenic species Escherichia coli . Conclusions: The differences between PPI users and non-users observed in this study are consistently associated with changes towards a less healthy gut microbiome. These differences are in line with known changes that predispose to C. difficile infections and can potentially explain the increased risk of enteric infections in PPI users. On a population level, the effects of PPI are more prominent than the effects of antibiotics or other commonly used drugs.

Rationale:: Evidence suggests that the gut microbiome is involved in the development of cardiovascular disease, with the host–microbe interaction regulating immune and metabolic pathways. However, there was no firm evidence for associations between microbiota and metabolic risk factors for cardiovascular disease from large-scale studies in humans. In particular, there was no strong evidence for association between cardiovascular disease and aberrant blood lipid levels. Objectives:: To identify intestinal bacteria taxa, whose proportions correlate with body mass index and lipid levels, and to determine whether lipid variance can be explained by microbiota relative to age, sex, and host genetics. Methods and Results:: We studied 893 subjects from the LifeLines-DEEP population cohort. After correcting for age and sex, we identified 34 bacterial taxa associated with body mass index and blood lipids; most are novel associations. Cross-validation analysis revealed that microbiota explain 4.5% of the variance in body mass index, 6% in triglycerides, and 4% in high-density lipoproteins, independent of age, sex, and genetic risk factors. A novel risk model, including the gut microbiome explained ≤25.9% of high-density lipoprotein variance, significantly outperforming the risk model without microbiome. Strikingly, the microbiome had little effect on low-density lipoproteins or total cholesterol. Conclusions:: Our studies suggest that the gut microbiome may play an important role in the variation in body mass index and blood lipid levels, independent of age, sex, and host genetics. Our findings support the potential of therapies altering the gut microbiome to control body mass, triglycerides, and high-density lipoproteins.

A gluten-free diet (GFD) is the most commonly adopted special diet worldwide. It is an effective treatment for coeliac disease and is also often followed by individuals to alleviate gastrointestinal complaints. It is known there is an important link between diet and the gut microbiome, but it is largely unknown how a switch to a GFD affects the human gut microbiome. We studied changes in the gut microbiomes of 21 healthy volunteers who followed a GFD for four weeks. We collected nine stool samples from each participant: one at baseline, four during the GFD period, and four when they returned to their habitual diet (HD), making a total of 189 samples. We determined microbiome profiles using 16S rRNA sequencing and then processed the samples for taxonomic and imputed functional composition. Additionally, in all 189 samples, six gut health-related biomarkers were measured. Inter-individual variation in the gut microbiota remained stable during this short-term GFD intervention. A number of taxon-specific differences were seen during the GFD: the most striking shift was seen for the family Veillonellaceae (class Clostridia), which was significantly reduced during the intervention (p = 2.81 × 10−05). Seven other taxa also showed significant changes; the majority of them are known to play a role in starch metabolism. We saw stronger differences in pathway activities: 21 predicted pathway activity scores showed significant association to the change in diet. We observed strong relations between the predicted activity of pathways and biomarker measurements. A GFD changes the gut microbiome composition and alters the activity of microbial pathways.

Purpose There is a critical need for population-based prospective cohort studies because they follow individuals before the onset of disease, allowing for studies that can identify biomarkers and disease-modifying effects, and thereby contributing to systems epidemiology. Participants This paper describes the design and baseline characteristics of an intensively examined subpopulation of the LifeLines cohort in the Netherlands. In this unique subcohort, LifeLines DEEP, we included 1539 participants aged 18 years and older. Findings to date We collected additional blood (n=1387), exhaled air (n=1425) and faecal samples (n=1248), and elicited responses to gastrointestinal health questionnaires (n=1176) for analysis of the genome, epigenome, transcriptome, microbiome, metabolome and other biological levels. Here, we provide an overview of the different data layers in LifeLines DEEP and present baseline characteristics of the study population including food intake and quality of life. We also describe how the LifeLines DEEP cohort allows for the detailed investigation of genetic, genomic and metabolic variation for a wide range of phenotypic outcomes. Finally, we examine the determinants of gastrointestinal health, an area of particular interest to us that can be addressed by LifeLines DEEP. Future plans We have established a cohort of which multiple data levels allow for the integrative analysis of populations for translation of this information into biomarkers for disease, and which will offer new insights into disease mechanisms and prevention.