A Personalized Approach to Predicting Postprandial Glycemic Responses to Food

A personalized predictive model that takes into consideration unique features of an individual in addition to food characteristics was more predictive than current dietary approaches for glycemic responses to food consumed. The study enrolled 327 individuals without diabetes from October 11, 2016, to December 13, 2017 in the US. The response to a standardized meal of bagel and cream cheese varied substantially across participants, with glycemic excursions (ie, the maximum glycemic elevation from baseline over time after eating a meal, considered to be a good predictor of overall blood glucose sensitivity) ranging from 6 to 94 mg/dL (mean, 30.7 mg/dL). A model predicting each individual’s responses to food that considers several individual factors , such as clinical characteristics, physiological variables, and the microbiome, 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. The study may be a critical step in defining and proving the value of a personalized diet. Source: https://jamanetwork.com/