OBJECTIVE: Nonalcoholic fatty liver disease (i.e., increased intrahepatic triglyceride [IHTG] content), predisposes to type 2 diabetes and cardiovascular disease. Adipose tissue lipolysis and hepatic de novo lipogenesis (DNL) are the main pathways contributing to IHTG. We hypothesized that dietary macronutrient composition influences the pathways, mediators, and magnitude of weight gain-induced changes in IHTG. RESEARCH DESIGN AND METHODS: We overfed 38 overweight subjects (age 48 ± 2 years, BMI 31 ± 1 kg/m2, liver fat 4.7 ± 0.9%) 1,000 extra kcal/day of saturated (SAT) or unsaturated (UNSAT) fat or simple sugars (CARB) for 3 weeks. We measured IHTG (1H-MRS), pathways contributing to IHTG (lipolysis ([2H5]glycerol) and DNL (2H2O) basally and during euglycemic hyperinsulinemia), insulin resistance, endotoxemia, plasma ceramides, and adipose tissue gene expression at 0 and 3 weeks. RESULTS: Overfeeding SAT increased IHTG more (+55%) than UNSAT (+15%, P < 0.05). CARB increased IHTG (+33%) by stimulating DNL (+98%). SAT significantly increased while UNSAT decreased lipolysis. SAT induced insulin resistance and endotoxemia and significantly increased multiple plasma ceramides. The diets had distinct effects on adipose tissue gene expression. CONCLUSIONS: Macronutrient composition of excess energy influences pathways of IHTG: CARB increases DNL, while SAT increases and UNSAT decreases lipolysis. SAT induced the greatest increase in IHTG, insulin resistance, and harmful ceramides. Decreased intakes of SAT could be beneficial in reducing IHTG and the associated risk of diabetes.

The use of relative abundance data from next generation sequencing (NGS) can lead to misinterpretations of microbial community structures, as the increase of one taxon leads to the concurrent decrease of the other(s) in compositional data. Although different DNA- and cell-based methods as well as statistical approaches have been developed to overcome the compositionality problem, and the biological relevance of absolute bacterial abundances has been demonstrated, the human microbiome research has not yet adopted these methods, likely due to feasibility issues. Here, we describe how quantitative PCR (qPCR) done in parallel to NGS library preparation provides an accurate estimation of absolute taxon abundances from NGS data and hence provides an attainable solution to compositionality in high-throughput microbiome analyses. The advantages and potential challenges of the method are also discussed.

Bread as a staple food has been predominantly prepared from refined wheat flour. The world’s demand for food is rising with increased bread consumption in developing countries where climate conditions are unsuitable for wheat cultivation. This reliance on wheat increases the vulnerability to wheat supply shocks caused by force majeure or man-made events, in addition to negative environmental and health consequences. In this review, we discuss the contribution to the sustainability of food systems by partially replacing wheat flour with various types of plant ingredients in bread making, also known as composite bread. The sustainable sources of non-wheat flours, their example use in bread making and potential health and nutritional benefits are summarized. Non-wheat flours pose techno-functional challenges due to significantly different properties of their proteins compared to wheat gluten, and they often contain off-favor compounds that altogether limit the consumer acceptability of final bread products. Therefore, we detail recent advances in processing strategies to improve the sensory and nutritional profiles of composite bread. A special focus is laid on fermentation, for its accessibility and versatility to apply to different ingredients and scenarios. Finally, we outline research needs that require the synergism between sustainability science, human nutrition, microbiomics and food science.

Obesity is a major risk factor for the development of type 2 diabetes and cardiovascular diseases, and gut microbiota plays a key role in influencing the host energy homeostasis. Moreover, obese mice have a different gut microbiota composition, associated with an alteration of the intestinal mucus layer, which represents the interface between the bacteria and the host. We previously demonstrated that prebiotic treatment with oligofructose (FOS) counteracted the effects of diet-induced obesity, together with changes in the gut microbiota composition, but it is not known if the intestinal mucus layer could be involved. In this study, we found that, in addition to preventing high-fat diet (HFD) induced obesity in mice, the treatment with FOS increased the expression of numerous genes involved in mucus production, glycosylation and secretion, the expression of both secreted and transmembrane mucins, and the differentiation and number of goblet cells. These results were associated with significant changes in the gut microbiota composition, with FOS significantly increasing the relative and absolute abundance of the bacterial genera Odoribacter, Akkermansia, two unknown Muribaculaceae and an unknown Ruminococcaceae. Interestingly, all these bacterial genera had a negative association with metabolic parameters and a positive association with markers of the mucus layer. Our study shows that FOS treatment is able to prevent HFD-induced metabolic disorders, at least in part, by acting on all the processes of the mucus production. These data suggest that targeting the mucus and the gut microbiota by using prebiotics could help to prevent or mitigate obesity and related disorders.

The colonisation and development of the gut microbiota has been implicated in paediatric metabolic disorders via its powerful effect on host metabolic and immune homeostasis. Here we summarise the evidence from human studies on the early gut microbiota and paediatric overweight and obesity. Manipulation of the early gut microbiota may represent a promising target for countering the burgeoning metabolic disorders in the paediatric population, provided the assembly patterns of microbiota and their health consequences can be decoded. Therefore, in this review, we pay particular attention to the important ecological drivers affecting the community dynamics of the early gut microbiota. We then discuss the knowledge gaps in commonly studied exposures linking the gut microbiota to metabolic disorders, especially regarding maternal factors and antibiotic use. This review also attempts to give directions for future studies aiming to identify predictive and corrective measures for paediatric metabolic disorders based on the gut microbiota.

Background: Low-energy diets (LEDs) comprise commercially formulated food products that provide between 800 and 1200 kcal/day (3.3–5 MJ/day) to aid body weight loss. Recent small-scale studies suggest that LEDs are associated with marked changes in the gut microbiota that may modify the effect of the LED on host metabolism and weight loss. We investigated how the gut microbiota changed during 8 weeks of total meal replacement LED and determined their associations with host response in a sub-analysis of 211 overweight adults with pre-diabetes participating in the large multicentre PREVIEW (PREVention of diabetes through lifestyle intervention and population studies In Europe and around the World) clinical trial.Methods: Microbial community composition was analysed by Illumina sequencing of the hypervariable V3-V4 regions of the 16S ribosomal RNA (rRNA) gene. Butyrate production capacity was estimated by qPCR targeting the butyryl-CoA:acetate CoA-transferase gene. Bioinformatics and statistical analyses, such as comparison of alpha and beta diversity measures, correlative and differential abundances analysis, were undertaken on the 16S rRNA gene sequences of 211 paired (pre- and post-LED) samples as well as their integration with the clinical, biomedical and dietary datasets for predictive modelling.Results: The overall composition of the gut microbiota changed markedly and consistently from pre- to post-LED (P= 0.001), along with increased richness and diversity (bothP< 0.001). Following the intervention, the relative abundance of several genera previously associated with metabolic improvements (e.g.,AkkermansiaandChristensenellaceaeR-7 group) was significantly increased (P< 0.001), while flagellatedPseudobutyrivibrio, acetogenicBlautiaandBifidobacteriumspp. were decreased (allP< 0.001). Butyrate production capacity was reduced (P< 0.001). The changes in microbiota composition and predicted functions were significantly associated with body weight loss (P< 0.05). Baseline gut microbiota features were able to explain ~25% of variation in total body fat change (post–pre-LED).Conclusions: The gut microbiota and individual taxa were significantly influenced by the LED intervention and correlated with changes in total body fat and body weight in individuals with overweight and pre-diabetes. Despite inter-individual variation, the baseline gut microbiota was a strong predictor of total body fat change during the energy restriction period.Trial registration: The PREVIEW trial was prospectively registered at ClinicalTrials.gov (NCT01777893) on January 29, 2013.