Model of personalized postprandial glycemic response to food developed for an Israeli cohort predicts responses in Midwestern American individuals

Abstract

Background Controlled glycemic concentrations are associated with a lower risk of conditions such as cardiovascular disease and diabetes. Models commonly used to guide interventions to control the glycemic response to food have low efficacy, with recent clinical guidelines arguing for the use of personalized approaches. Objective We tested the efficacy of a predictive model of personalized postprandial glycemic response to foods that was developed with an Israeli cohort and that takes into consideration food components and specific features, including the microbiome, when applied to individuals from the Midwestern US. Design We recruited 327 individuals for this study. Participants provided information regarding lifestyle, dietary habits, and health, as well as a stool sample for characterization of their gut microbiome. Participants were connected to continuous glucose monitors for 6 d, and the glycemic response to meals logged during this time was computed. The ability of a model trained using meals logged by the Israeli cohort to correctly predict glycemic responses in the Midwestern cohort was assessed and compared with that of a model trained using meals logged by both cohorts. Results When trained on the Israeli cohort meals only, model performance for predicting responses of individuals in the Midwestern cohort was better (R = 0.596) than that observed for models taking into consideration the carbohydrate (R = 0.395) or calorie content of the meals alone (R = 0.336). Performance increased (R = 0.618) when the model was trained on meals from both cohorts, likely because of the observed differences in age distribution, diet, and microbiome. Conclusions 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.

The Gut Microbiota and Its Implication in the Development of Atherosclerosis and Related Cardiovascular Diseases

“…), personalized nutrition is a new therapeutic possibility for prevention and control of disease [230]. Recent studies of cohorts have revealed great differences in post-meal glucose levels between individuals eating the same mealtimes [229,231].…”

Gut Microbiome and Colon Cancer: A Plausible Explanation for Dietary Contributions to Cancer

“…Complex algorithms based on clinical, phenotypic, and microbial data can now predict glycemic response in ways that outperform traditional dietary approaches that historically relied on information specific to the caloric and carbohydrate content of food. [20][21][22] The success of this work provides proof of principle in support of the future benefits of personalized nutrition. 23 Lastly, it should be clearly stated that we do not expect hydrogen sulfide to be the only story relevant to colon neoplasia formation.…”

Different continuous glucose monitors provide different personalized meal recommendations to minimize postprandial glucose in subjects without diabetes

“…Precision nutrition approaches are being developed to personalize an individual's diet to minimize CGM measured glycemic responses to meals (Mendes-Soares et al, 2019;Zeevi et al, 2015). An implicit assumption that has not previously been investigated is that the ranking of an individual's meals by glycemic response is relatively independent of the CGM used to make the measurements.…”

“…Postprandial glucose is believed to play an important role in health and disease (Blaak et al, 2012;Hall et al, 2018) and precision nutrition approaches are being developed to personalize an individual's diet to minimize postprandial glycemic responses measured using continuous glucose monitors (CGMs) (Mendes-Soares et al, 2019;Zeevi et al, 2015). However, it is unclear to what extent different CGMs provide discordant meal rankings in terms of glycemic response and therefore whether personalized diets might be CGM dependent.…”