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.

Keywords: carbohydrate content; continuous glucose monitors; diabetes; glycemic response; microbiome; personalized nutrition.

FIGURE 1

Flow diagram of the subjects included in the study. Israeli cohort used in this study represents a subset of the full cohort in Zeevi et al. (13). CGM, continuous glucose monitor; MN, Minnesota; FL, Florida.

FIGURE 2

Carbohydrate sensitivity in individuals of the cohorts of the study. (A) Histogram of per person Pearson correlation R values between carbohydrates and postprandial glycemic response (PPGR) for individuals in the Israeli (IL, green; mean 0.53, std 0.22) and Midwestern (MW, blue; mean 0.46, std 0.26) cohorts. Note the high variability across individuals in sensitivity to carbohydrates. (B) An example for an individual with a high carbohydrate to PPGR correlation (left) and an individual with a low carbohydrate to PPGR correlation (right). For each reported meal, the carbohydrate values (x-axis) and the PPGR values (y-axis) are shown.

FIGURE 3

Reproducibility of postprandial glycemic responses to a standardized meal, as given by the correlation between the glycemic responses to replicate bagel and cream cheese meals consumed by individuals in the Midwestern cohort (n = 111; R = 0.66; see Methods).

FIGURE 4

Schematic of the performance of the models trained with data exclusively from the Israeli (IL) cohort, exclusively from the Midwestern (MW) cohort, or both.

FIGURE 5

The Israeli and Midwestern cohorts showed substantial differences in terms of their nutritional behavior. (A), (B), (C), and (D) show the amounts of each nutrient consumed by the female and male participants of the Midwestern (MW) and Israeli (IL) cohorts [(A) fiber, (B) fat, (C) carbohydrates, (D) protein]. Note significant differences in dietary fiber and fat consumption in females (P values = 8e-24 and 0.0005, respectively), as well as significant differences in carbohydrate and protein consumption for both genders (males – P values = 0.008 and 0.004, respectively; females – P values = 0.0007 and 8e-10, respectively). All P values were computed using Welch's t-test. Boxes extend from the lower to upper quartile of the data, with a line at the median. Whiskers show the range of the data and flier points [>Q3 + 1.5(Q3–Q1) or <Q1 – 1.5(Q3–Q1)] are considered outliers. Pct stands for percentage.

FIGURE 6

Box plots showing Species Richness (A) and Shannon's species diversity index (B) for the Midwestern (MW) and Israeli (IL) cohorts. Note the significantly lower diversity and richness of the Midwestern cohort (P value <0.05 for both measures). (C) Average phyla abundances in the different cohorts. Abundances for phyla with abundance <1% were aggregated and labeled as “other” for visualization purposes. (D) same as (C) but for genera. (E) Box plots showing the relative abundance (RA) of Actinobacteria in the different cohorts (P value = 1e-17). (F) Box plots showing the Firmicutes to Bacteroidetes log-abundance in the different cohorts (P value = 7e-6). Abundances were capped from below by 10⁻⁶% and log abundances were clipped to the range of [-5, 5]. (G) Same as (F) but for the Prevotella to Bacteroides genera ratio (P value = 1e-18). In this case, due to the different distribution of abundances, log-abundance values were clipped to the range of [-10, 6]. (H) Same as (F) but for Alistipes genus abundance (P value = 0.002). Boxes extend from the lower to upper quartile of the data, with a line at the median. Whiskers show the range of the data and flier points [>Q3 + 1.5(Q3–Q1) or <Q1–.5(Q3–Q1)] are considered outliers. All P values were computed using Welch's t-test.

FIGURE 7

Partial dependence plots showing the marginal contribution of various features (x-axis) to the predicted postprandial glycemic response (y-axis). Red and green indicate above and below zero contributions, respectively. Boxplots (bottom) indicate the feature distribution across the cohort. Boxes extend from the lower to upper quartile of the data, with a line at the median. Whiskers show the range of the data and flier points [>Q3 + 1.5(Q3–Q1) or < Q1–1.5(Q3–Q1)] are considered outliers. Partial dependence plots for all features can be seen in Online Supporting Material, Supplemental Figure 2. r.a., relative abundance.