Predicting the meal macronutrient composition from continuous glucose monitors

TL;DR

This study demonstrates that a neural network can estimate the macronutrient composition of meals from continuous glucose monitor signals, potentially enabling automatic dietary monitoring for diabetes management.

Contribution

The paper introduces a neural network approach to predict meal macronutrients from CGM data, advancing automatic food intake monitoring techniques.

Findings

Neural network outperforms linear regression in prediction accuracy.

Subject-dependent models yield the best results.

Feasibility shown for automatic dietary monitoring using CGMs.

Abstract

Sustained high levels of blood glucose in type 2 diabetes (T2DM) can have disastrous long-term health consequences. An essential component of clinical interventions for T2DM is monitoring dietary intake to keep plasma glucose levels within an acceptable range. Yet, current techniques to monitor food intake are time intensive and error prone. To address this issue, we are developing techniques to automatically monitor food intake and the composition of those foods using continuous glucose monitors (CGMs). This article presents the results of a clinical study in which participants consumed nine standardized meals with known macronutrients amounts (carbohydrate, protein, and fat) while wearing a CGM. We built a multitask neural network to estimate the macronutrient composition from the CGM signal, and compared it against a baseline linear regression. The best prediction result comes from our proposed neural network, trained with subject-dependent data, as measured by root mean squared relative error and correlation coefficient. These findings suggest that it is possible to estimate macronutrient composition from CGM signals, opening the possibility to develop automatic techniques to track food intake.