Personalized Nutrition by Prediction of Glycemic Responses.

By D. Zeevi, T. Korem, N. Zmora, D. Israeli, Daphna Rothschild, A. Weinberger, Orly Ben-Yacov, Dar Lador, T. Avnit-Sagi, M. Lotan-Pompan and 13 more. Cell, 2015

2,441 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology, Physiology

View Paper: https://www.semanticscholar.org/paper/d10ca0071dea7f012693b3015a73fcd4119e97d7

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Citations in the corpus

The microbiota of the respiratory tract: gatekeeper to respiratory health (2017, background): “Microbiota-based interventions are likely to be most beneficial in young children, as a window of opportunity within which the local microbiota primes specific features of the immune system seems to exist.”
Dietary lipids, gut microbiota and lipid metabolism (2019, background): “Even though efforts have been made to understand how dietary pattern affect the gut microbiota[138, 139], the importance of specific foods and combinations of nutrients in shaping microbial profile remains elusive.”
Antibiotic-Induced Changes in the Intestinal Microbiota and Disease. (2016, background): “An important role for the microbiome in modulating glycemic responses has been recently demonstrated with the development of an effective machine-learning algorithm to predict postprandial glucose levels in humans based on several personal as well as biochemical parameters, including the composition”
Personalized Gut Mucosal Colonization Resistance to Empiric Probiotics Is Associated with Unique Host and Microbiome Features. (2018, positive): “Similarly, we have recently demonstrated that person-specific variations in microbiome composition and function may contribute to the variability in glycemic responses to a variety of foods (Zeevi et al., 2015) and synthetic food supplements (Suez et al.”
Machine Learning and Data Mining Methods in Diabetes Research (2017, background): “Zeevi et al. developed a machine learning algorithm that takes into account blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota to predict personalized postprandial glycemic response to real-life meals [126].”
Interaction between microbiota and immunity in health and disease (2020, background): “It may be intriguing to determine the efficacy of personalized diets, selective diets or manipulation of dietary timing in treating immunological disease, and to investigate how these diets influence host immune responses.(266) Additionally, the large wealth of microbiomederived metabolites found in”
Nutrition Therapy for Adults With Diabetes or Prediabetes: A Consensus Report (2019, background): “wide interpersonal variability in blood glucose response to standardized meals that could be predicted by clinical and microbiome profiles (332).”
Effect of Diet on the Gut Microbiota: Rethinking Intervention Duration (2019, methodology): “One 2015 study of 800 healthy subjects observed for 46,898 meals found large and at times conflicting inter-personal post-prandial responses to the dietary interventions [63].”
Improvement of Insulin Sensitivity after Lean Donor Feces in Metabolic Syndrome Is Driven by Baseline Intestinal Microbiota Composition. (2017, methodology): “First, we chose to closely monitor caloric intake while keeping the regular diet of each study subject, as the introduction of a standardized diet influences gut microbiota composition (Zeevi et al., 2015).”
Gut Microbiota in Cardiovascular Health and Disease (2017, background): “correlated with individual gut microbiota composition.(151) Although the composition of the microbiota is resilient over an individual’s life span,(152,153) dietary interventions that induce rapid changes in certain nutrients can modify the microbiota composition.”