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

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Autores principales: Pustozerov, Evgenii, Tkachuk, Aleksandra, Vasukova, Elena, Dronova, Aleksandra, Shilova, Ekaterina, Anopova, Anna, Piven, Faina, Pervunina, Tatiana, Vasilieva, Elena, Grineva, Elena, Popova, Polina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7071209/
https://www.ncbi.nlm.nih.gov/pubmed/31979294
http://dx.doi.org/10.3390/nu12020302
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author Pustozerov, Evgenii
Tkachuk, Aleksandra
Vasukova, Elena
Dronova, Aleksandra
Shilova, Ekaterina
Anopova, Anna
Piven, Faina
Pervunina, Tatiana
Vasilieva, Elena
Grineva, Elena
Popova, Polina
author_facet Pustozerov, Evgenii
Tkachuk, Aleksandra
Vasukova, Elena
Dronova, Aleksandra
Shilova, Ekaterina
Anopova, Anna
Piven, Faina
Pervunina, Tatiana
Vasilieva, Elena
Grineva, Elena
Popova, Polina
author_sort Pustozerov, Evgenii
collection PubMed
description 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.
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spelling pubmed-70712092020-03-19 The Role of Glycemic Index and Glycemic Load in the Development of Real-Time Postprandial Glycemic Response Prediction Models for Patients with Gestational Diabetes Pustozerov, Evgenii Tkachuk, Aleksandra Vasukova, Elena Dronova, Aleksandra Shilova, Ekaterina Anopova, Anna Piven, Faina Pervunina, Tatiana Vasilieva, Elena Grineva, Elena Popova, Polina Nutrients Article 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. MDPI 2020-01-23 /pmc/articles/PMC7071209/ /pubmed/31979294 http://dx.doi.org/10.3390/nu12020302 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pustozerov, Evgenii
Tkachuk, Aleksandra
Vasukova, Elena
Dronova, Aleksandra
Shilova, Ekaterina
Anopova, Anna
Piven, Faina
Pervunina, Tatiana
Vasilieva, Elena
Grineva, Elena
Popova, Polina
The Role of Glycemic Index and Glycemic Load in the Development of Real-Time Postprandial Glycemic Response Prediction Models for Patients with Gestational Diabetes
title The Role of Glycemic Index and Glycemic Load in the Development of Real-Time Postprandial Glycemic Response Prediction Models for Patients with Gestational Diabetes
title_full The Role of Glycemic Index and Glycemic Load in the Development of Real-Time Postprandial Glycemic Response Prediction Models for Patients with Gestational Diabetes
title_fullStr The Role of Glycemic Index and Glycemic Load in the Development of Real-Time Postprandial Glycemic Response Prediction Models for Patients with Gestational Diabetes
title_full_unstemmed The Role of Glycemic Index and Glycemic Load in the Development of Real-Time Postprandial Glycemic Response Prediction Models for Patients with Gestational Diabetes
title_short The Role of Glycemic Index and Glycemic Load in the Development of Real-Time Postprandial Glycemic Response Prediction Models for Patients with Gestational Diabetes
title_sort role of glycemic index and glycemic load in the development of real-time postprandial glycemic response prediction models for patients with gestational diabetes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7071209/
https://www.ncbi.nlm.nih.gov/pubmed/31979294
http://dx.doi.org/10.3390/nu12020302
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