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. 2020 Jan 23;12(2):302.
doi: 10.3390/nu12020302.

The Role of Glycemic Index and Glycemic Load in the Development of Real-Time Postprandial Glycemic Response Prediction Models for Patients With Gestational Diabetes

Evgenii Pustozerov  1   2 , Aleksandra Tkachuk  2 , Elena Vasukova  2 , Aleksandra Dronova  2 , Ekaterina Shilova  2   3 , Anna Anopova  2 , Faina Piven  2 , Tatiana Pervunina  4 , Elena Vasilieva  2 , Elena Grineva  2 , Polina Popova  2   5
Affiliations

The Role of Glycemic Index and Glycemic Load in the Development of Real-Time Postprandial Glycemic Response Prediction Models for Patients With Gestational Diabetes

Evgenii Pustozerov et al. Nutrients. .

Abstract

The incorporation of glycemic index (GI) and glycemic load (GL) is a promising way to improve the accuracy of postprandial glycemic response (PPGR) prediction for personalized treatment of gestational diabetes (GDM). Our aim was to assess the prediction accuracy for PPGR prediction models with and without GI data in women with GDM and healthy pregnant women. The GI values were sourced from University of Sydney's database and assigned to a food database used in the mobile app DiaCompanion. Weekly continuous glucose monitoring (CGM) data for 124 pregnant women (90 GDM and 34 control) were analyzed together with records of 1489 food intakes. Pearson correlation (R) was used to quantify the accuracy of predicted PPGRs from the model relative to those obtained from CGM. The final model for incremental area under glucose curve (iAUC120) prediction chosen by stepwise multiple linear regression had an R of 0.705 when GI/GL was included among input variables and an R of 0.700 when GI/GL was not included. In linear regression with coefficients acquired using regularization methods, which was tested on the data of new patients, R was 0.584 for both models (with and without inclusion of GI/GL). In conclusion, the incorporation of GI and GL only slightly improved the accuracy of PPGR prediction models when used in remote monitoring.

Keywords: blood glucose prediction; gestational diabetes mellitus; glycemic index; postprandial glycemic response.

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Conflict of interest statement

The authors declare that there is no conflict of interest.

Figures

Figure 1
Figure 1
Density plot showing paired distribution of glycemic index (GI) and glycemic load (GL)/carbo in all meals included in the study (n = 1489).
Figure 2
Figure 2
Continuous glucose monitoring (CGM) and food diary matching strategy. On the top: black vertical lines: meal starts written in a paper protocol; red line: meal starts as written in the electronic diary; on the bottom: red lines: meal starts chosen for the final sets; upper marks: postprandial glycemic response (PPGR) features; lower marks: meal features. Green dots represent point estimations of blood glucose (BG) levels made with a glucometer used for sensor calibration, and red points correspond to point estimations of BG 1 h after the meal. It can be seen that meals coming as close as 60 min to each other were ignored, as well as records from the electronic diary, which did not have an exact time specified in the protocol.
Figure 3
Figure 3
Correlation coefficients between PPGR characteristics (iAUC120 on the left, BGRise on the right) and carbohydrates/glycemic load. The number next to each point depicts a patient’s individual identifier. In figure (a): cor_gi_iAUC120: correlation between glycemic load and incremental area under glucose curve 2 h after meal start; cor_carbo_iAUC120: correlation between consumed carbohydrates and incremental area under glucose curve. In figure (b): cor_gi_BGRise: correlation between glycemic load and blood glucose rise from meal start to peak value; cor_carbo_BGRise: correlation between consumed carbohydrates and blood glucose rise from meal start to peak value. Orange: GDM group; brown: healthy pregnant participants.
Figure 4
Figure 4
Prediction of iAUC120 on new patients with the regularized regression model (R = 0.584). Orange dots depict PPGRs whose errors are equal or below 1.0 mmol/L·h (92.3%), while brown dots depict those whose errors are above 1.0 mmol/L·h (7.7%).
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