Portfolio Details

Nutritional & Clinical 2022

Postprandial glucose response analysis using CGM and dietary data

Project demonstrating integration and analysis of continuous glucose monitoring (CGM) and dietary intake data, including visualization of postprandial glucose responses, iAUC calculation, and statistical comparison of glycemic responses to different meals and food products.

R Studio Continuous Glucose Measurement iAUC

Project overview

This analysis was conducted as part of the broader GLYSIMI study, which investigates the role of the human small intestinal microbiota in shaping postprandial glucose responses (PPGR) to standardized meals. For the purpose of portfolio data from 10 participants are presented.

Aim

to assess

the consistency of glycaemic responses within individuals to repeated consumption of the same meal, and
difference in responses to different food products across participants.

                    Key insights
                    Although postprandial glycemic curves over the 2-hour period showed minor visual differences, most derived glycemic parameters (peak glucose, minimum glucose, and iAUC) were not significantly different between breakfast meals; an exception was observed for rice (nasi)-based meals.
Gingerbread with yoghurt elicited a significantly higher glycemic response compared to fruit with yoghurt, as reflected by both iAUC and peak glucose.
Nasi consumption resulted in a significantly higher peak glucose compared to pasta, indicating a stronger acute glycemic excursion despite similar overall exposure.
The glucose drink produced the highest peak glucose concentration; however, bread-based meals showed a higher overall glycemic exposure (iAUC), indicating a more prolonged postprandial response despite lower peak values and faster return toward baseline.
Considerable inter-individual variability in glycemic responses was observed, which may be influenced by factors such as metabolic health, age, genetic background, and gut microbiome composition.

                

Data

CGM recorded by FreeStyle Libre device, an ecological momentary dietary assesment by Traqq application

Methods

iAUC calculation, assesment of the normality of data distribution, linear mixed effects models with random effect

Output

View full HTML report →
View code on GitHub →

Analytical workflow

Data integration

Loading continuous glucose monitoring (CGM) measurements and dietary intake data recorded in the Traqq application, followed by merging both datasets.

Preprocessing

Extraction of baseline glucose measurements (last CGM measurement before meal consumption) and construction of 2-hour postprandial glucose response windows.

Quality control

Assessment of CGM data completeness, including the number of available measurements, timing of the first and last postprandial measurements, and overall coverage of the 2-hour response window.

Downstream analysis

Exploratory and statistical analysis of postprandial glycemic responses, including visualization of glucose curves, calculation of iAUC, peak glucose, and minimum glucose concentration, as well as comparison of glycemic responses between: