Literature DB >> 23911174

Dynamic insulin on board: incorporation of circadian insulin sensitivity variation.

Chiara Toffanin1, Howard Zisser, Francis J Doyle, Eyal Dassau.   

Abstract

BACKGROUND: Insulin-on-board (IOB) estimation is used in modern insulin therapy with continuous subcutaneous insulin infusion (CSII) as well as different automatic glucose-regulating strategies (i.e., artificial pancreas products) to prevent insulin stacking that may lead to hypoglycemia. However, most of the IOB calculations are static IOB (sIOB): they are based only on approximated insulin decay and do not take into account diurnal changes in insulin sensitivity.
METHODS: A dynamic IOB (dIOB) that takes into account diurnal insulin sensitivity variation is suggested in this work and used to adjust the sIOB estimations. The dIOB function is used to correct the dosage of insulin boluses in light of this circadian variation.
RESULTS: Basal-bolus as applied by pump users and model predictive control therapy with and without dIOB were evaluated using the University of Virginia/Padova metabolic simulator. Three protocols with four meals of 1 g carbohydrate/kg body weight were evaluated: a nominal scenario and two robustness scenarios, one in which insulin sensitivity was 15% greater than estimated and the other where the lunch is 30% less than announced. In the nominal and robustness scenarios, respectively, the dIOB led to 6% and 24% and 40% less hypoglycemia episodes than approaches without IOB. The new approach was also compared with the sIOB to evaluate the improvements with respect to the previous approach.
CONCLUSIONS: Improved glucose regulation was demonstrated using the dIOB where circadian insulin sensitivity is used to adjust IOB estimation. Use of diurnal variations of insulin sensitivity appears to promote effective and safe insulin therapy using CSII or artificial pancreas. Clinical trials are warranted to determine whether nocturnal hypoglycemia can be reduced using the dIOB approach.
© 2013 Diabetes Technology Society.

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Year:  2013        PMID: 23911174      PMCID: PMC3879757          DOI: 10.1177/193229681300700415

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


  26 in total

Review 1.  Roles of circadian rhythmicity and sleep in human glucose regulation.

Authors:  E Van Cauter; K S Polonsky; A J Scheen
Journal:  Endocr Rev       Date:  1997-10       Impact factor: 19.871

2.  Diurnal variation in oral glucose tolerance: blood sugar and plasma insulin levels morning, afternoon, and evening.

Authors:  R J Jarrett; I A Baker; H Keen; N W Oakley
Journal:  Br Med J       Date:  1972-01-22

Review 3.  The dawn phenomenon: nocturnal blood glucose homeostasis in insulin-dependent diabetes mellitus.

Authors:  G Perriello; P De Feo; G B Bolli
Journal:  Diabet Med       Date:  1988-01       Impact factor: 4.359

4.  Circadian and ultradian rhythms in blood glucose and plasma insulin of healthy adults.

Authors:  L Mejean; A Bicakova-Rocher; M Kolopp; C Villaume; F Levi; G Debry; A Reinberg; P Drouin
Journal:  Chronobiol Int       Date:  1988       Impact factor: 2.877

5.  Early morning hyperglycaemia "dawn phenomenon" in non-insulin dependent diabetes mellitus (NIDDM): effects of cortisol suppression by metyrapone.

Authors:  J A Atiea; S M Aslan; D R Owens; S Luzio
Journal:  Diabetes Res       Date:  1990-08

6.  Modular closed-loop control of diabetes.

Authors:  S D Patek; L Magni; E Dassau; C Karvetski; C Toffanin; G De Nicolao; S Del Favero; M Breton; C Dalla Man; E Renard; H Zisser; F J Doyle; C Cobelli; B P Kovatchev
Journal:  IEEE Trans Biomed Eng       Date:  2012-04-03       Impact factor: 4.538

7.  Control of blood sugar in insulin-dependent diabetes: comparison of an artificial endocrine pancreas, continuous subcutaneous insulin infusion, and intensified conventional insulin therapy.

Authors:  R A Rizza; J E Gerich; M W Haymond; R E Westland; L D Hall; A H Clemens; F J Service
Journal:  N Engl J Med       Date:  1980-12-04       Impact factor: 91.245

8.  Daily glucose and insulin rhythms in diabetic dogs on the artificial beta cell.

Authors:  U Fischer; E J Freyse; G Albrecht; G Gebel; M Heil; W Unger
Journal:  Exp Clin Endocrinol       Date:  1985-02

9.  Circadian variation of insulin requirement in insulin dependent diabetes mellitus the relationship between circadian change in insulin demand and diurnal patterns of growth hormone, cortisol and glucagon during euglycemia.

Authors:  B G Trümper; K Reschke; J Molling
Journal:  Horm Metab Res       Date:  1995-03       Impact factor: 2.936

10.  Diurnal pattern of insulin action in type 1 diabetes: implications for a closed-loop system.

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Journal:  Diabetes       Date:  2013-02-27       Impact factor: 9.461

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9.  Automatic Adaptation of Basal Insulin Using Sensor-Augmented Pump Therapy.

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