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The gut microbiome has recently emerged as a critical modulator of brain function, with the so-called gut-brain axis having multiple links with a variety of neurodegenerative and mental health conditions, including Alzheimer’s Disease (AD). Various approaches for modulating the gut microbiome toward compositional and functional states that are consistent with improved cognitive health outcomes have been documented, including probiotics and prebiotics. While probiotics are live microorganisms that directly confer beneficial health effects, prebiotics are oligosaccharide and polysaccharide structures that can beneficially modulate the gut microbiome by enhancing the growth, survival, and/or function of gut microbes that in turn have beneficial effects on the human host. In this review, we discuss evidence showing the potential link between gut microbiome composition and AD onset or development, provide an overview of prebiotic types and their roles in altering gut microbial composition, discuss the effectiveness of prebiotics in regulating gut microbiome composition and microbially derived metabolites, and discuss the current evidence linking prebiotics with health outcomes related to AD in both animal models and human trials. Though there is a paucity of human clinical trials demonstrating the effectiveness of prebiotics in altering gut microbiome-mediated health outcomes in AD, current evidence highlights the potential of various prebiotic approaches for beneficially altering the gut microbiota or gut physiology by promoting the production of butyrate, indoles, and secondary bile acid profiles that further regulate gut immunity and mucosal homeostasis, which are associated with beneficial effects on the central immune system and brain functionality.

OBJECTIVES: The objective of this study was to determine whether a novel fiber supplement consumed by healthy individuals with a habitual diet low in fiber (

Intense recent interest in understanding how the human gut microbiome influences health has kindled a concomitant interest in linking dietary choices to microbiome variation. Diet is known to be a driver of microbiome variation, and yet the precise mechanisms by which certain dietary components modulate the microbiome, and by which the microbiome produces byproducts and secondary metabolites from dietary components, are not well-understood. Interestingly, despite the influence of diet on the gut microbiome, the majority of microbiome studies published to date contain little or no analysis of dietary intake. Although an increasing number of microbiome studies are now collecting some form of dietary data or even performing diet interventions, there are no clear standards in the microbiome field for how to collect diet data or how to design a diet-microbiome study. In this article, we review the current practices in diet-microbiome analysis and study design and make several recommendations for best practices to provoke broader discussion in the field. We recommend that microbiome studies include multiple consecutive microbiome samples per study timepoint or phase and multiple days of dietary history prior to each microbiome sample whenever feasible. We find evidence that direct effects of diet on the microbiome are likely to be observable within days, while the length of an intervention required for observing microbiome-mediated effects on the host phenotype or host biomarkers, depending on the outcome, may be much longer, on the order of weeks or months. Finally, recent studies demonstrating that diet-microbiome interactions are personalized suggest that diet-microbiome studies should either include longitudinal sampling within individuals to identify personalized responses, or should include an adequate number of participants spanning a range of microbiome types to identify generalized responses.

An improved analytical method compared with conventional ones was developed for simultaneous determination of 13 mycotoxins (deoxynivalenol, nivalenol, 3-acetylnivalenol, aflatoxin B1, aflatoxin B2, aflatoxin G1, aflatoxin G2, fumonisin B1, fumonisin B2, T-2, HT-2, zearalenone, and ochratoxin A) in cereal grains by liquid chromatography-tandem mass spectrometry (LC/MS/MS) after a single immunoaffinity column clean-up. The method showed a good linearity, sensitivity, specificity, and accuracy in mycotoxin determination by LC/MS/MS. The levels of 13 mycotoxins in 5 types of commercial grains (brown rice, maize, millet, sorghum, and mixed cereal) from South Korea were determined in a total of 507 cereal grains. Mycotoxins produced from Fusarium sp. (fumonisins, deoxynivalenol, nivalenol, and zearalenone) were more frequently (more than 5%) and concurrently detected in all cereal grains along with higher mean levels (4.3–161.0 ng/g) in positive samples than other toxins such as aflatoxins and ochratoxin A (less than 9% and below 5.2 ng/g in positive samples) from other fungal species.