A Metric Learning Approach for Personalized Meal Macronutrient Estimation from Postprandial Glucose Response Signals

Managing diabetes requires following a healthy lifestyle, including monitoring dietary intake. Prior work has shown that meals with different macronutrient composition can have distinct postprandial glucose responses (PPGR), therefore suggesting that PPGRs may be used to monitor diet automatically. Yet, PPGRs shown large variability across individuals. This paper proposes a metric-learning approach to achieve personalized meal macronutrient estimation from PPGRs. The metric learning approach utilizes a Siamese neural network (SNN) architecture, which learns a PPGR embedding via a contrastive loss function adapted to the task of interest. Specifically, the proposed contrastive loss is designed so that it maximizes the distance between meals of similar macronutrient composition and minimizes the distance between meals with different macronutrients. This loss is further computed within each individual, therefore reducing individual differences in PPGRs. Our results show that the proposed metric learning approach outperforms a feedforward neural network when estimating the amount of protein, carbohydrate, and fat in a meal. These suggest the feasibility of using PPGRs to track meal macronutrient composition, supporting dietary informatics applications for precision health and nutrition.