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Article

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

1
Department of Biomedical Engineering, Saint Petersburg State Electrotechnical University, 197341 Saint Petersburg, Russia
2
Institute of Endocrinology, Almazov National Medical Research Centre, 194156 Saint Petersburg, Russia
3
Department of Gynecology and Endocrinology, The Research Institute of Obstetrics, Gynecology and Reproductology Named after D.O.Ott, 199034 Saint Petersburg, Russia
4
Institute of Perinatology and Pediatrics, Almazov National Medical Research Centre, 197341 Saint Petersburg, Russia
5
Department of Internal Diseases and Endocrinology, Pavlov First Saint Petersburg State Medical University, 197022 Saint Petersburg, Russia
*
Author to whom correspondence should be addressed.
Nutrients 2020, 12(2), 302; https://doi.org/10.3390/nu12020302
Received: 31 December 2019 / Revised: 18 January 2020 / Accepted: 20 January 2020 / Published: 23 January 2020
(This article belongs to the Special Issue Nutrition for Gestational Diabetes)
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. View Full-Text
Keywords: glycemic index; gestational diabetes mellitus; postprandial glycemic response; blood glucose prediction glycemic index; gestational diabetes mellitus; postprandial glycemic response; blood glucose prediction
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MDPI and ACS Style

Pustozerov, E.; Tkachuk, A.; Vasukova, E.; Dronova, A.; Shilova, E.; Anopova, A.; Piven, F.; Pervunina, T.; Vasilieva, E.; Grineva, E.; Popova, P. The Role of Glycemic Index and Glycemic Load in the Development of Real-Time Postprandial Glycemic Response Prediction Models for Patients with Gestational Diabetes. Nutrients 2020, 12, 302. https://doi.org/10.3390/nu12020302

AMA Style

Pustozerov E, Tkachuk A, Vasukova E, Dronova A, Shilova E, Anopova A, Piven F, Pervunina T, Vasilieva E, Grineva E, Popova P. The Role of Glycemic Index and Glycemic Load in the Development of Real-Time Postprandial Glycemic Response Prediction Models for Patients with Gestational Diabetes. Nutrients. 2020; 12(2):302. https://doi.org/10.3390/nu12020302

Chicago/Turabian Style

Pustozerov, Evgenii, Aleksandra Tkachuk, Elena Vasukova, Aleksandra Dronova, Ekaterina Shilova, Anna Anopova, Faina Piven, Tatiana Pervunina, Elena Vasilieva, Elena Grineva, and Polina Popova. 2020. "The Role of Glycemic Index and Glycemic Load in the Development of Real-Time Postprandial Glycemic Response Prediction Models for Patients with Gestational Diabetes" Nutrients 12, no. 2: 302. https://doi.org/10.3390/nu12020302

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