Learning Absorption Rates in Glucose-Insulin Dynamics from Meal Covariates

TL;DR

This paper introduces a neural network-based method to learn personalized glucose absorption rates from meal covariates, improving glucose dynamics modeling beyond traditional heuristic approaches.

Contribution

It proposes a novel neural differential equation framework that incorporates meal covariates to accurately model individual glucose absorption in daily life.

Findings

Achieves close approximation of true absorption rates on simulated data.

Provides better glucose forecast than heuristic models.

Generalizes to high-dimensional meal covariates like images.

Abstract

Traditional models of glucose-insulin dynamics rely on heuristic parameterizations chosen to fit observations within a laboratory setting. However, these models cannot describe glucose dynamics in daily life. One source of failure is in their descriptions of glucose absorption rates after meal events. A meal's macronutritional content has nuanced effects on the absorption profile, which is difficult to model mechanistically. In this paper, we propose to learn the effects of macronutrition content from glucose-insulin data and meal covariates. Given macronutrition information and meal times, we use a neural network to predict an individual's glucose absorption rate. We use this neural rate function as the control function in a differential equation of glucose dynamics, enabling end-to-end training. On simulated data, our approach is able to closely approximate true absorption rates, resulting in better forecast than heuristic parameterizations, despite only observing glucose, insulin, and macronutritional information. Our work readily generalizes to meal events with higher-dimensional covariates, such as images, setting the stage for glucose dynamics models that are personalized to each individual's daily life.