Continuous Glucose Measurements for Diet Monitoring in Healthy Adults

Area of Science:

• Metabolic Health
• Nutritional Science
• Biomedical Engineering

Background:

• Dietary monitoring for diabetes prevention often relies on self-reported intake, which is subject to under-reporting.
• Lifestyle interventions and low glycemic diets are key in diabetes prevention strategies.
• Objective dietary assessment methods are needed to improve the accuracy of nutritional monitoring.

Purpose of the Study:

• To investigate the correlation between continuous glucose monitoring (CGM) metrics and glycemic load (GL) or daily macronutrient consumption.
• To explore the potential of CGM data for more accurate dietary intake assessment.
• To establish predictive models for GL and energy intake using CGM metrics.

Main Methods:

• Observational study utilizing one week of CGM data, actigraphy, and food diaries.
• Systematic literature review identified 19 relevant CGM metrics for correlation analysis.
• Linear mixed models were generated to predict GL and macronutrient intake from CGM metrics.

Main Results:

• Significant positive moderate correlations were found between GL and several CGM metrics (AUC, relative amplitude, SD, variance) within 2-4 hours post-meal.
• Carbohydrate intake showed moderate correlations with 24-hour CGM metrics including SD, variance, and MAGE.
• Valid linear mixed models were developed to predict GL and energy intake using CGM metrics, body composition, sleep, and physical activity.

Conclusions:

• Moderate correlations exist between GL and CGM metrics in healthy individuals.
• CGM metrics can be effectively utilized to predict GL and energy intake.
• This study highlights the potential of CGM as an objective tool for dietary assessment.