Continuous Glucose Measurements for Diet Monitoring in Healthy Adults
Authors' conclusion
We demonstrated moderate correlations between GL and CGM metrics in healthy populations. These CGM metrics were successfully used to predict GL or energy intake.
Study context
- Population
- Healthy adults (27 women, average age 28.2 years, average body mass index (BMI) 23.4 kg/m²); 1 week of continuous glucose monitoring (CGM), actigraphy and food diaries
- Participants
- 48 adults
- Outcome
- Correlation between CGM metrics and glycemic load (GL) or daily macronutrient consumption
Main finding
As reported in the abstract
- Glycemic load (GL) vs area under the curve (two-hour window), ρ
- 0.40
- GL vs relative amplitude (three hours), ρ
- 0.40
- GL vs relative amplitude (four hours), ρ
- 0.42
- GL vs SD (four hours), ρ
- 0.41
- GL vs variance (four hours), ρ
- 0.43
Observational data: associations, not effects.
Further findings
Correlations between carbohydrate and CGM metrics over 24 hours
- Carbohydrate vs SD, ρ
- 0.45
- Carbohydrate vs variance, ρ
- 0.44
- Carbohydrate vs mean amplitude of glycemic excursions (MAGE), ρ
- 0.40
- Significant positive moderate correlations (P < .0004)
Prediction models
- One valid mixed linear model predicted GL from CGM metrics obtained two hours after food intake
- A second model predicted energy intake using moderately correlated CGM metrics, body composition, sleep duration and physical activity
Original publication
Continuous Glucose Measurements for Diet Monitoring in Healthy Adults. J Diabetes Sci Technol. Published 12 August 2025. doi:10.
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