Clinical Research
Gut Microbiome Modulates the Effects of a Personalized Postprandial-Targeting (PPT) Diet on Cardiometabolic Markers: a Diet Intervention in Prediabetes
Ben-Yacov,et. al., Gut 2023 Aug; 72 (8): 1486–1496
https://doi.org/10.1136/gutjnl-2022-329201
The DayTwo diet induced more prominent changes to the gut microbiome composition when compared to the Mediterranean diet.
Summary: To explore the interplay between dietary modifications, microbiome composition and host metabolic responses in a dietary intervention setting of a personalized postprandial-targeting (PPT) diet versus a Mediterranean (MED) diet in pre-diabetes.
Findings support the role of gut microbiome in modulating the effects of dietary modifications on cardiometabolic outcomes, and advance the concept of precision nutrition strategies for reducing comorbidities in pre-diabetes.
Effects of Personalized Diets by Prediction of Glycemic Responses on Glycemic Control and Metabolic Health in Newly Diagnosed T2DM: A Randomized Dietary Intervention Pilot Trial
Rein, et. al., BMC Med 20, 56 (2022)
https://doi.org/10.1186/s12916-022-02254-y
The Daytwo program outperformed the traditional approach of a Mediterranean diet in multiple metabolic health parameters.
Summary: To evaluate the clinical effects of a personalized postprandial-targeting (PPT) diet on glycemic control and metabolic health in individuals with newly diagnosed T2DM as compared to the commonly recommended Mediterranean-style (MED) diet. In this crossover trial in subjects with newly diagnosed T2DM, a PPT diet improved CGM-based glycemic measures significantly more than a Mediterranean-style MED diet. Additional 6-month PPT intervention further improved glycemic control and metabolic health parameters, supporting the clinical efficacy of this approach.
An Atlas of Robust Microbiome Associations with Phenotypic Traits Based on Large-Scale Cohorts from Two Continents
Rothschild, et. al., PLoS ONE 17(3): e0265756.
https://doi.org/10.1371/journal.pone.0265756
By collecting and studying diverse phenotypes and gut microbiota from over 34,000 people in Israel and the U.S., an atlas of robust and numerous unreported associations between bacteria and physiological human traits was derived. Importantly, these associations were found to replicate consistently in cohorts from both continents.
Summary: Numerous human conditions are associated with the microbiome, yet studies are inconsistent as to the magnitude of the associations and the bacteria involved, likely reflecting insufficiently employed sample sizes. Here, we collected diverse phenotypes and gut microbiota from 34,057 individuals from Israel and the U.S.. Analyzing these data using a much-expanded microbial genomes set, we derive an atlas of robust and numerous unreported associations between bacteria and physiological human traits, which we show to replicate in cohorts from both continents. Using machine learning models trained on microbiome data, we show prediction accuracy of human traits across two continents. Subsampling our cohort to smaller cohort sizes yielded highly variable models and thus sensitivity to the selected cohort, underscoring the utility of large cohorts and possibly explaining the source of discrepancies across studies. Finally, many of our prediction models saturate at these numbers of individuals, suggesting that similar analyses on larger cohorts may not further improve these predictions.
Artificial Sweeteners Induce Glucose Intolerance by Altering the Gut Microbiota
Suez, et al., Nature 514, 181–186 (2014).
https://doi.org/10.1038/nature13793
We identify non-caloric artificial sweeteners (NAS) -altered microbial metabolic pathways that are linked to host susceptibility to metabolic disease, and demonstrate similar NAS-induced dysbiosis and glucose intolerance in healthy human subjects.
Summary: Non-caloric artificial sweeteners (NAS) are among the most widely used food additives worldwide, regularly consumed by lean and obese individuals alike. NAS consumption is considered safe and beneficial owing to their low caloric content, yet supporting scientific data remain sparse and controversial. Here we demonstrate that consumption of commonly used NAS formulations drives the development of glucose intolerance through induction of compositional and functional alterations to the intestinal microbiota. These NAS-mediated deleterious metabolic effects are abrogated by antibiotic treatment, and are fully transferable to germ-free mice upon fecal transplantation of microbiota configurations from NAS-consuming mice, or of microbiota anaerobically incubated in the presence of NAS. We identify NAS-altered microbial metabolic pathways that are linked to host susceptibility to metabolic disease, and demonstrate similar NAS-induced dysbiosis and glucose intolerance in healthy human subjects.
An Expanded Reference Map of the Human Gut Microbiome Reveals Hundreds of Previously Unknown Species
Leviatan, S. et al., Nat Commun 13, 3863 (2022).
https://doi.org/10.1038/s41467-022-31502-1
This study provides a gut microbial genome reference set that can serve as a valuable resource for further research.
Summary: The gut is the richest ecosystem of microbes in the human body and has great influence on our health. Despite many efforts, the set of microbes inhabiting this environment is not fully known, limiting our ability to identify microbial content and to research it. In this work, we combine new microbial metagenomic assembled genomes from 51,052 samples, with previously published genomes to produce a curated set of 241,118 genomes. Based on this set, we procure a new and improved human gut microbiome reference set of 3594 high quality species genomes, which successfully matches 83.65% validation samples’ reads. This improved reference set contains 310 novel species, including one that exists in 19% of validation samples.
Assessment of a Personalized Approach to Predicting Postprandial Glycemic Responses to Food Among Individuals Without Diabetes
JAMA Network Open. 2019;2(2):e188102.
https://doi.org/10.1001/jamanetworkopen.2018.8102
The Mayo Clinic study validated that a personalized, predictive model that considers unique features of the individual (such as clinical characteristics, physiological variables, and the microbiome, in addition to nutrient content) is more predictive than current dietary approaches that focus only on the calorie or carbohydrate content of foods.
Summary: Across the cohort of adults without diabetes who were examined, a personalized predictive model that considers unique features of the individual, such as clinical characteristics, physiological variables, and the microbiome, in addition to nutrient content was more predictive than current dietary approaches that focus only on the calorie or carbohydrate content of foods. Providing individuals with tools to manage their glycemic responses to food based on personalized predictions of their PPGRs may allow them to maintain their blood glucose levels within limits associated with good health.
Model of personalized postprandial glycemic response to food developed for an Israeli cohort predicts responses in Midwestern American individuals
Am J Clin Nutr 2019;110:63–75.
The algorithm (AI model) trained with an Israeli cohort predicts blood glucose response for a cohort of Midwestern individuals despite differences between the two populations, and outperforms common approaches used to inform dietary interventions to regulate blood sugar control.
Summary: We show that the modeling framework described in Zeevi et al. for an Israeli cohort is applicable to a Midwestern population, and outperforms commonly used approaches for the control of blood glucose responses. The adaptation of the model to the Midwestern cohort further enhances performance and is a promising means for designing effective nutritional interventions to control glycemic responses to foods. This trial was registered at clinicaltrials.gov as NCT02945514.
Personalized Nutrition by Prediction of Glycemic Responses
Zeevi et al., 2015, Cell 163, 1079–1094 November 19, 2015 ª2015 Elsevier Inc.
https://dx.doi.org/10.1016/j.cell.2015.11.001
Our results suggest that AI-personalized diets may successfully modify blood glucose response and its metabolic consequences.
Summary: We devised a machine-learning algorithm that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in this cohort and showed that it accurately predicts personalized postprandial glycemic response to real-life meals. We validated these predictions in an independent 100-person cohort. Finally, a blinded randomized controlled dietary intervention based on this algorithm resulted in significantly lower postprandial responses and consistent alterations to gut microbiota configuration. Together, our results suggest that personalized diets may successfully modify elevated postprandial blood glucose and its metabolic consequences.
Bread Affects Clinical Parameters and Induces Gut Microbiome-Associated Personal Glycemic Responses
Korem et al., 2017, Cell Metabolism 25, 1243–1253 June 6, 2017 ª 2017 Elsevier Inc.
https://dx.doi.org/10.1016/j.cmet.2017.05.002
We found no significant differential effects of bread type on multiple clinical parameters. The gut microbiota composition remained person specific throughout this trial and was generally not affected by the intervention.
Summary: Bread is consumed daily by billions of people, yet evidence regarding its clinical effects is contradicting. Here, we performed a randomized crossover trial of two 1-week-long dietary interventions comprising consumption of either traditionally made sourdough leavened whole-grain bread or industrially made white bread. We found no significant differential effects of bread type on multiple clinical parameters. The gut microbiota composition remained person specific throughout this trial and was generally resilient to the intervention. We demonstrate statistically significant interpersonal variability in the glycemic response to different bread types, suggesting that the lack of phenotypic difference between the bread types stems from a person-specific effect. We further show that the type of bread that induces the lower glycemic response in each person can be predicted based solely on microbiome data prior to the intervention. Together, we present marked personalization in both bread metabolism and the gut microbiome, suggesting that understanding dietary effects requires integration of person-specific factors.
Personalized Postprandial Glucose Response–Targeting Diet Versus Mediterranean Diet for Glycemic Control in Prediabetes
Diabetes Care 2021;44(9):1980–1991
https://doi.org/10.2337/dc21-0162
The Daytwo program outperformed the traditional approach of an ADA-backed Mediterranean diet.
Summary: To compare the clinical effects of a personalized postprandial-targeting (PPT) diet versus a Mediterranean (MED) diet on glycemic control and metabolic health in prediabetes.
In this clinical trial in prediabetes, a PPT diet improved glycemic control significantly more than a MED diet as measured by daily time of glucose levels >140 mg/dL (7.8 mmol/L) and HbA1c. These findings may have implications for dietary advice in clinical practice.
Environment dominates over host genetics in shaping human gut microbiota
Nature volume 555, pages 210–215 (2018)
Microbiome data significantly improve the prediction accuracy for many human traits, such as glucose and obesity measures, compared to models that use only host genetic and environmental data.
Summary: Human gut microbiome composition is shaped by multiple factors but the relative contribution of host genetics remains elusive. Here we examine genotype and microbiome data from 1,046 healthy individuals with several distinct ancestral origins who share a relatively common environment, and demonstrate that the gut microbiome is not significantly associated with genetic ancestry, and that host genetics have a minor role in determining microbiome composition. We show that, by contrast, there are significant similarities in the compositions of the microbiomes of genetically unrelated individuals who share a household, and that over 20% of the inter-person microbiome variability is associated with factors related to diet, drugs and anthropometric measurements. We further demonstrate that microbiome data significantly improve the prediction accuracy for many human traits, such as glucose and obesity measures, compared to models that use only host genetic and environmental data. These results suggest that microbiome alterations aimed at improving clinical outcomes may be carried out across diverse genetic backgrounds.