Macronutrient Constraints and Priors Improve Carbohydrate Predictions from Continuous Glucose Monitors

We propose an approach to estimate the macronu-trients in a meal automatically by analyzing the meal's glucose response using off-the-shelf wearable sensors (continuous glucose monitors). We rely on the fact that the shape of the glucose response to a meal depends on all the macronutrients in the meal, not just its carbohydrates (carbs). However, protein, fat, and fiber tend to affect the glucose response in similar ways, so recovering their individual amounts is numerically ill-conditioned. To address this problem, our approach compresses macronutrients into a latent variable that captures their correlated effects on glucose. Then, we train a machine learning model to predict the latent variable from the glucose response of a meal. Finally, we recover the amount of the original macronutrients by incorporating prior knowledge of how they co-occur in conventional meals. Using experimental data from 45 participants, we show that predicting carbs indirectly (through the latent variable) reduces the prediction error when compared to predicting carbs directly, i.e., without considering the protein and fats in the meal.