Literature DB >> 20046650

Physical activity into the meal glucose-insulin model of type 1 diabetes: in silico studies.

Chiara Dalla Man1, Marc D Breton, Claudio Cobelli.   

Abstract

INTRODUCTION: A simulation model of a glucose-insulin system accounting for physical activity is needed to reliably simulate normal life conditions, thus accelerating the development of an artificial pancreas. In fact, exercise causes a transient increase of insulin action and may lead to hypoglycemia. However, physical activity is difficult to model. In the past, it was described indirectly as a rise in insulin. Recently, a new parsimonious model of exercise effect on glucose homeostasis has been proposed that links the change in insulin action and glucose effectiveness to heart rate (HR). The aim of this study was to plug this exercise model into our recently proposed large-scale simulation model of glucose metabolism in type 1 diabetes to better describe normal life conditions.
METHODS: The exercise model describes changes in glucose-insulin dynamics in two phases: a rapid on-and-off change in insulin-independent glucose clearance and a rapid-on/slow-off change in insulin sensitivity. Three candidate models of glucose effectiveness and insulin sensitivity as a function of HR have been considered, both during exercise and recovery after exercise. By incorporating these three models into the type 1 diabetes model, we simulated different levels (from mild to moderate) and duration of exercise (15 and 30 minutes), both in steady-state (e.g., during euglycemic-hyperinsulinemic clamp) and in nonsteady state (e.g., after a meal) conditions.
RESULTS: One candidate exercise model was selected as the most reliable.
CONCLUSIONS: A type 1 diabetes model also describing physical activity is proposed. The model represents a step forward to accurately describe glucose homeostasis in normal life conditions; however, further studies are needed to validate it against data. © Diabetes Technology Society

Entities:  

Keywords:  exercise; metabolism; simulation

Mesh:

Substances:

Year:  2009        PMID: 20046650      PMCID: PMC2769836          DOI: 10.1177/193229680900300107

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


  23 in total

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  24 in total

1.  Hypoglycemia prevention via pump attenuation and red-yellow-green "traffic" lights using continuous glucose monitoring and insulin pump data.

Authors:  Colleen S Hughes; Stephen D Patek; Marc D Breton; Boris P Kovatchev
Journal:  J Diabetes Sci Technol       Date:  2010-09-01

2.  A simplification of Cobelli's glucose-insulin model for type 1 diabetes mellitus and its FPGA implementation.

Authors:  Peng Li; Lei Yu; Qiang Fang; Shuenn-Yuh Lee
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3.  Physical activity measured by physical activity monitoring system correlates with glucose trends reconstructed from continuous glucose monitoring.

Authors:  Chiara Zecchin; Andrea Facchinetti; Giovanni Sparacino; Chiara Dalla Man; Chinmay Manohar; James A Levine; Ananda Basu; Yogish C Kudva; Claudio Cobelli
Journal:  Diabetes Technol Ther       Date:  2013-08-14       Impact factor: 6.118

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Authors:  Garry M Steil; Jaques Reifman
Journal:  J Diabetes Sci Technol       Date:  2009-03-01

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Authors:  Shah Atiqur Rahman; Yuxiao Huang; Jan Claassen; Nathaniel Heintzman; Samantha Kleinberg
Journal:  J Biomed Inform       Date:  2015-10-21       Impact factor: 6.317

6.  Historical data enhances safety supervision system performance in T1DM insulin therapy risk management.

Authors:  Colleen Hughes-Karvetski; Stephen D Patek; Marc D Breton; Boris P Kovatchev
Journal:  Comput Methods Programs Biomed       Date:  2012-02-17       Impact factor: 5.428

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Authors:  Roman Hovorka
Journal:  Nat Rev Endocrinol       Date:  2011-02-22       Impact factor: 43.330

8.  Empirical Dynamic Model Identification for Blood-Glucose Dynamics in Response to Physical Activity.

Authors:  Isuru S Dasanayake; Dale E Seborg; Jordan E Pinsker; Francis J Doyle; Eyal Dassau
Journal:  Proc IEEE Conf Decis Control       Date:  2015-12

9.  Reducing Glucose Variability Due to Meals and Postprandial Exercise in T1DM Using Switched LPV Control: In Silico Studies.

Authors:  Patricio H Colmegna; Ricardo S Sánchez-Peña; Ravi Gondhalekar; Eyal Dassau; Francis J Doyle
Journal:  J Diabetes Sci Technol       Date:  2016-05-03

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Authors:  Chinmay Manohar; Derek T O'Keeffe; Ling Hinshaw; Ravi Lingineni; Shelly K McCrady-Spitzer; James A Levine; Rickey E Carter; Ananda Basu; Yogish C Kudva
Journal:  Diabetes Technol Ther       Date:  2013-08-12       Impact factor: 6.118

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