Literature DB >> 25526760

A physiology-based model describing heterogeneity in glucose metabolism: the core of the Eindhoven Diabetes Education Simulator (E-DES).

Anne H Maas1, Yvonne J W Rozendaal2, Carola van Pul3, Peter A J Hilbers2, Ward J Cottaar4, Harm R Haak5, Natal A W van Riel6.   

Abstract

Current diabetes education methods are costly, time-consuming, and do not actively engage the patient. Here, we describe the development and verification of the physiological model for healthy subjects that forms the basis of the Eindhoven Diabetes Education Simulator (E-DES). E-DES shall provide diabetes patients with an individualized virtual practice environment incorporating the main factors that influence glycemic control: food, exercise, and medication. The physiological model consists of 4 compartments for which the inflow and outflow of glucose and insulin are calculated using 6 nonlinear coupled differential equations and 14 parameters. These parameters are estimated on 12 sets of oral glucose tolerance test (OGTT) data (226 healthy subjects) obtained from literature. The resulting parameter set is verified on 8 separate literature OGTT data sets (229 subjects). The model is considered verified if 95% of the glucose data points lie within an acceptance range of ±20% of the corresponding model value. All glucose data points of the verification data sets lie within the predefined acceptance range. Physiological processes represented in the model include insulin resistance and β-cell function. Adjusting the corresponding parameters allows to describe heterogeneity in the data and shows the capabilities of this model for individualization. We have verified the physiological model of the E-DES for healthy subjects. Heterogeneity of the data has successfully been modeled by adjusting the 4 parameters describing insulin resistance and β-cell function. Our model will form the basis of a simulator providing individualized education on glucose control.
© 2014 Diabetes Technology Society.

Entities:  

Keywords:  diabetes; education; heterogeneity; model; parameter estimation; simulator

Mesh:

Substances:

Year:  2014        PMID: 25526760      PMCID: PMC4604593          DOI: 10.1177/1932296814562607

Source DB:  PubMed          Journal:  J Diabetes Sci Technol        ISSN: 1932-2968


  53 in total

1.  Short-term low carbohydrate/high-fat diet intake increases postprandial plasma glucose and glucagon-like peptide-1 levels during an oral glucose tolerance test in healthy men.

Authors:  S Numao; H Kawano; N Endo; Y Yamada; M Konishi; M Takahashi; S Sakamoto
Journal:  Eur J Clin Nutr       Date:  2012-06-06       Impact factor: 4.016

2.  Standards of medical care in diabetes--2013.

Authors: 
Journal:  Diabetes Care       Date:  2013-01       Impact factor: 19.112

3.  Joining forces of Bayesian and frequentist methodology: a study for inference in the presence of non-identifiability.

Authors:  Andreas Raue; Clemens Kreutz; Fabian Joachim Theis; Jens Timmer
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2012-12-31       Impact factor: 4.226

4.  The effects of postprandial glucose and insulin levels on postprandial endothelial function in subjects with normal glucose tolerance.

Authors:  Kazunari Suzuki; Kentaro Watanabe; Shoko Futami-Suda; Hiroyuki Yano; Masayuki Motoyama; Noriaki Matsumura; Yoshimasa Igari; Tatsuya Suzuki; Hiroshi Nakano; Kenzo Oba
Journal:  Cardiovasc Diabetol       Date:  2012-08-14       Impact factor: 9.951

5.  The Karlsburg Diabetes Management System: translation from research to eHealth application.

Authors:  Eckhard Salzsieder; Petra Augstein
Journal:  J Diabetes Sci Technol       Date:  2011-01-01

6.  Serum high-molecular weight adiponectin decreases abruptly after an oral glucose load in subjects with normal glucose tolerance or impaired fasting glucose, but not those with impaired glucose tolerance or diabetes mellitus.

Authors:  Noriyuki Ozeki; Kenji Hara; Chikako Yatsuka; Tomoki Nakano; Sachiko Matsumoto; Mariko Suetsugu; Takafumi Nakamachi; Kohzo Takebayashi; Toshihiko Inukai; Kohsuke Haruki; Yoshimasa Aso
Journal:  Metabolism       Date:  2009-07-09       Impact factor: 8.694

7.  Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure.

Authors:  Aram V Chobanian; George L Bakris; Henry R Black; William C Cushman; Lee A Green; Joseph L Izzo; Daniel W Jones; Barry J Materson; Suzanne Oparil; Jackson T Wright; Edward J Roccella
Journal:  Hypertension       Date:  2003-12-01       Impact factor: 10.190

Review 8.  The clinical effectiveness of diabetes education models for Type 2 diabetes: a systematic review.

Authors:  E Loveman; G K Frampton; A J Clegg
Journal:  Health Technol Assess       Date:  2008-04       Impact factor: 4.014

9.  Clinical validation of a new control-oriented model of insulin and glucose dynamics in subjects with type 1 diabetes.

Authors:  Pier Giorgio Fabietti; Valentina Canonico; Marco Orsini-Federici; Eugenio Sarti; Massimo Massi-Benedetti
Journal:  Diabetes Technol Ther       Date:  2007-08       Impact factor: 6.118

10.  Incretin responses to oral glucose load in Japanese non-obese healthy subjects.

Authors:  Etsuko Nagai; Tomoyuki Katsuno; Jun-Ichiro Miyagawa; Kosuke Konishi; Masayuki Miuchi; Fumihiro Ochi; Yoshiki Kusunoki; Masaru Tokuda; Kazuki Murai; Tomoya Hamaguchi; Mitsuyoshi Namba
Journal:  Diabetes Ther       Date:  2011-02-10       Impact factor: 2.945

View more
  3 in total

1.  Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge.

Authors:  Balázs Erdős; Bart van Sloun; Michiel E Adriaens; Shauna D O'Donovan; Dominique Langin; Arne Astrup; Ellen E Blaak; Ilja C W Arts; Natal A W van Riel
Journal:  PLoS Comput Biol       Date:  2021-03-31       Impact factor: 4.475

2.  Digital twin predicting diet response before and after long-term fasting.

Authors:  Oscar Silfvergren; Christian Simonsson; Mattias Ekstedt; Peter Lundberg; Peter Gennemark; Gunnar Cedersund
Journal:  PLoS Comput Biol       Date:  2022-09-12       Impact factor: 4.779

3.  An Updated Organ-Based Multi-Level Model for Glucose Homeostasis: Organ Distributions, Timing, and Impact of Blood Flow.

Authors:  Tilda Herrgårdh; Hao Li; Elin Nyman; Gunnar Cedersund
Journal:  Front Physiol       Date:  2021-06-01       Impact factor: 4.566

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.