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Assessment of a Personalized Approach to Predicting Postprandial Glycemic Responses to Food Among Individuals Without Diabetes.

ABSTRACT: Importance:Emerging evidence suggests that postprandial glycemic responses (PPGRs) to food may be influenced by and predicted according to characteristics unique to each individual, including anthropometric and microbiome variables. Interindividual diversity in PPGRs to food requires a personalized approach for the maintenance of healthy glycemic levels. Objectives:To describe and predict the glycemic responses of individuals to a diverse array of foods using a model that considers the physiology and microbiome of the individual in addition to the characteristics of the foods consumed. Design, Setting, and Participants:This cohort study using a personalized predictive model enrolled 327 individuals without diabetes from October 11, 2016, to December 13, 2017, in Minnesota and Florida to be part of a study lasting 6 days. The study measured anthropometric variables, described the gut microbial composition, and assessed blood glucose levels every 5 minutes using a continuous glucose monitor. Participants logged their food and activity information for the duration of the study. A predictive model of individualized PPGRs to a diverse array of foods was trained and applied. Main Outcomes and Measures:Glycemic responses to food consumed over 6 days for each participant. The predictive model of personalized PPGRs considered individual features, including the microbiome, in addition to the features of the foods consumed. Results:Postprandial response to the same foods varied across 327 individuals (mean [SD] age, 45 [12] years; 78.0% female). A model predicting each individual's responses to food that considers several individual factors in addition to food features had better overall performance (R?=?0.62) than current standard-of-care approaches using nutritional content alone (R?=?0.34 for calories and R?=?0.40 for carbohydrates) to control postprandial glycemic levels. Conclusions and Relevance: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.

SUBMITTER: Mendes-Soares H

PROVIDER: S-EPMC6484621 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Assessment of a Personalized Approach to Predicting Postprandial Glycemic Responses to Food Among Individuals Without Diabetes.

Mendes-Soares Helena H Raveh-Sadka Tali T Azulay Shahar S Edens Kim K Ben-Shlomo Yatir Y Cohen Yossi Y Ofek Tal T Bachrach Davidi D Stevens Josh J Colibaseanu Dorin D Segal Lihi L Kashyap Purna P Nelson Heidi H

JAMA network open 20190201 2

<h4>Importance</h4>Emerging evidence suggests that postprandial glycemic responses (PPGRs) to food may be influenced by and predicted according to characteristics unique to each individual, including anthropometric and microbiome variables. Interindividual diversity in PPGRs to food requires a personalized approach for the maintenance of healthy glycemic levels.<h4>Objectives</h4>To describe and predict the glycemic responses of individuals to a diverse array of foods using a model that consider ...[more]

PMID: 30735238

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Project description:BackgroundControlled 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.ObjectiveWe 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.DesignWe 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.ResultsWhen 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.ConclusionsWe 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.

Project description:BACKGROUND/OBJECTIVE:There is substantial interest in dietary approaches to reducing postprandial glucose (PPG) responses, but the quantitative contribution of PPG to longer-term glycemic control (reflected in glycated hemoglobin, HbA1c) in the general population is not known. This study quantified the associations of preprandial glucose exposure, PPG exposure, and glycemic variability with HbA1c and estimated the explained variance in HbA1c in individuals with and without type 2 diabetes (T2D). SUBJECTS/METHODS:Participants in the A1c-Derived Average Glucose (ADAG) study without T2D (n = 77) or with non-insulin-treated T2D and HbA1c<6.5% (T2DHbA1c?<?6.5%, n = 63) or HbA1c???6.5% (T2DHbA1c???6.5%, n = 34) were included in this analysis. Indices of preprandial glucose, PPG, and glycemic variability were calculated from continuous glucose monitoring during four periods over 12 weeks prior to HbA1c measurement. In linear regression models, we estimated the associations of the glycemic exposures with HbA1c and calculated the proportion of variance in HbA1c explained by glycemic and non-glycemic factors (age, sex, body mass index, and ethnicity). RESULTS:The factors in the analysis explained 35% of the variance in HbA1c in non-diabetic individuals, 49% in T2DHbA1c?<?6.5%, and 78% in T2DHbA1c???6.5%. In non-diabetic individuals PPG exposure was associated with HbA1c in confounder-adjusted analyses (P?<?0.05). In the T2DHbA1c?<?6.5% group, all glycemic measures were associated with HbA1c (P?<?0.05); preprandial glucose and PPG accounted for 14 and 18%, respectively, of the explained variation. In T2DHbA1c???6.5%, these glycemic exposures accounted for more than 50% of the variation in HbA1c and with equal relative contributions. CONCLUSIONS:Among the glycemic exposures, PPG exposure was most strongly predictive of HbA1c in non-diabetic individuals, suggesting that interventions targeting lowering of the PPG response may be beneficial for long-term glycemic maintenance. In T2D, preprandial glucose and PPG exposure contributed equally to HbA1c.

Project description:Background: The potential confounding effect of different amounts and proportions of macronutrients across eating patterns on meal or dietary glycemic index (GI) and glycemic load (GL) value determinations has remained partially unaddressed.Objective: The study aimed to determine the effects of different amounts of macronutrients and fiber on measured meal GI and GL values.Design: Four studies were conducted during which participants [n = 20-22; women: 50%; age: 50-80 y; body mass index (in kg/m2): 25-30)] received food challenges containing different amounts of the variable nutrient in a random order. Added to the standard 50 g available carbohydrate from white bread was 12.5, 25, or 50 g carbohydrate; 12.5, 25, or 50 g protein; and 5.6, 11.1, or 22.2 g fat from rice cereal, tuna, and unsalted butter, respectively, and 4.8 or 9.6 g fiber from oat cereal. Arterialized venous blood was sampled for 2 h, and measured meal GI and GL and insulin index (II) values were calculated by using the incremental area under the curve (AUCi) method.Results: Adding carbohydrate to the standard white-bread challenge increased glucose AUCi (P < 0.0001), measured meal GI (P = 0.0066), and mean GL (P < 0.0001). Adding protein (50 g only) decreased glucose AUCi (P = 0.0026), measured meal GI (P = 0.0139), and meal GL (P = 0.0140). Adding fat or fiber had no significant effect on these variables. Adding carbohydrate (50 g), protein (50 g), and fat (11.1 g) increased the insulin AUCi or II; fiber had no effect.Conclusions: These data indicate that uncertainty in the determination of meal GI and GL values is introduced when carbohydrate-containing foods are consumed concurrently with protein (equal amount of carbohydrate challenge) but not with carbohydrate-, fat-, or fiber-containing foods. Future studies are needed to evaluate whether this uncertainty also influences the prediction of average dietary GI and GL values for eating patterns. This trial was registered at clinicaltrials.gov as NCT01023646.

Project description:Background: The potential impact of prior meal composition on the postprandial glycemic response and glycemic index (GI) and glycemic load (GL) value determinations remains unclear.Objective: We determined the effect of meals that varied in macronutrient composition on the glycemic response and determination of GI and GL values of a subsequent standard test food.Design: Twenty healthy participants underwent 6 test sessions within 12 wk. The subjects received each of 3 isocaloric breakfast meals (i.e., high carbohydrate, high fat, or high protein) on separate days in a random order, which was followed by a standard set of challenges (i.e., white bread and a glucose drink) that were tested on separate days in a random order 4 h thereafter. Each challenge provided 50 g available carbohydrate. Arterialized venous blood was sampled throughout the 2-h postchallenge period. GI, GL, and insulin index (II) values were calculated with the use of the incremental area under the curve (AUCi) method, and serum lipids were determined with the use of standard assays.Results: The consumption of the high-protein breakfast before the white-bread challenge attenuated the rise in the postprandial serum glucose response (P < 0.0001) and resulted in lower glucose AUCi (P < 0.0001), GI (P = 0.0096), and GL (P = 0.0101) values than did the high-carbohydrate and high-fat breakfasts. The high-protein breakfast resulted in a lower insulin AUCi (P = 0.0146) for white bread than did the high-fat breakfast and a lower II value (P = 0.0285) than did the high-carbohydrate breakfast. The 3 breakfasts resulted in similar serum lipid responses to the white-bread challenge.Conclusions: These data indicate that the macronutrient composition of the prior meal influences the glycemic response and the determination of GI and GL values for white bread. Future studies are needed to determine whether the background food macronutrient composition influences mean dietary GI and GL values that are calculated for eating patterns, which may alter the interpretation of the associations between these values and chronic disease risk. This trial was registered at clinicaltrials.gov as NCT01023646.

Project description:Several studies have demonstrated food texture manipulation on oral processing behaviour (OPB). We explored the effect of texture-differences of equivalent carbohydrate load on OPB, bolus properties and postprandial glycaemic responses (PPG). In a randomised cross-over, within-subjects, non-blinded design, healthy male participants (N = 39) consumed fixed portions of white rice (WR) and rice cake (RC) while being video recorded to measure microstructural eating behaviours. PPG was compared between test foods over a period of 120-min, and the bolus properties and saliva uptake at swallow were measured for both test foods. RC displayed higher instrumental hardness, chewiness and Young's modulus than WR (p = 0.01), and participants perceived RC as more springy and sticky than WR (p < 0.001). The RC meal was chewed more per bite (p < 0.001) and consumed at a faster eating rate (p = 0.033) than WR. WR bolus particles were smaller at swallow (p < 0.001) with a larger total surface area (p < 0.001), compared to RC. The glucose response for RC was significantly higher during the first 30-min postprandial period (p = 0.010), and lower in the later (30-120 min) postprandial period (p = 0.031) compared to WR. Total blood glucose iAUC did not differ significantly between WR and RC meals despite their large differences in texture, OPB and bolus properties. Oro-sensory exposure time was a significant predictor of glucose iAUC30min for both test meals (RC, p = 0.003; WR, p = 0.029). Saliva uptake in the bolus was significantly positively associated with blood glucose during the first 30-min postprandial period for the RC meal (p = 0.008), but not for WR. We conclude that food texture modifications can influence OPB and bolus properties which are key contributors to the dynamic evolution of the glycaemic response. Total blood glucose responses were the same for both test foods, though differences in oral processing and bolus properties influenced temporal changes in PPG.

Dataset Information

Assessment of a Personalized Approach to Predicting Postprandial Glycemic Responses to Food Among Individuals Without Diabetes.

Publications

Assessment of a Personalized Approach to Predicting Postprandial Glycemic Responses to Food Among Individuals Without Diabetes.

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