Jotham Suez

TL;DR: A machine-learning algorithm is devised that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in an 800-person cohort and shows that it accurately predicts personalized postprandial glycemic response to real-life meals, and a blinded randomized controlled dietary intervention based on this algorithm resulted in significantly lower postpr andial responses and consistent alterations to gut microbiota configuration.

TL;DR: It is demonstrated that consumption of commonly used NAS formulations drives the development of glucose intolerance through induction of compositional and functional alterations to the intestinal microbiota, thereby calling for a reassessment of massive NAS usage.

TL;DR: The metagenomically characterized the murine and human mucosal-associated gastrointestinal microbiome and found it to only partially correlate with stool microbiome, indicating that empiric probiotics supplementation may be limited in universally and persistently impacting the gut mucosa.

TL;DR: Evidence of coordinated metaorganism diurnal rhythmicity is provided and jet-lag-induced dysbiosis in both mice and humans promotes glucose intolerance and obesity that are transferrable to germ-free mice upon fecal transplantation.

TL;DR: The major principles underlying effects of dietary constituents on the gut microbiota are reviewed, resolving aspects of the diet–microbiota–host crosstalk, and the promises and challenges of incorporating microbiome data into dietary planning are presented.