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.