Literature DB >> 10780934

Dose-response characteristics of insulin action on glucose metabolism: a non-steady-state approach.

A Natali1, A Gastaldelli, S Camastra, A M Sironi, E Toschi, A Masoni, E Ferrannini, A Mari.   

Abstract

The traditional methods for the assessment of insulin sensitivity yield only a single index, not the whole dose-response curve information. This curve is typically characterized by a maximally insulin-stimulated glucose clearance (Cl(max)) and an insulin concentration at half-maximal response (EC(50)). We developed an approach for estimating the whole dose-response curve with a single in vivo test, based on the use of tracer glucose and exogenous insulin administration (two steps of 20 and 200 mU x min(-1) x m(-2), 100 min each). The effect of insulin on plasma glucose clearance was calculated from non-steady-state data by use of a circulatory model of glucose kinetics and a model of insulin action in which glucose clearance is represented as a Michaelis-Menten function of insulin concentration with a delay (t(1/2)). In seven nondiabetic subjects, the model predicted adequately the tracer concentration: the model residuals were unbiased, and their coefficient of variation was similar to the expected measurement error (approximately 3%), indicating that the model did not introduce significant systematic errors. Lean (n = 4) and obese (n = 3) subjects had similar half-times for insulin action (t(1/2) = 25 +/- 9 vs. 25 +/- 8 min) and maximal responses (Cl(max) = 705 +/- 46 vs. 668 +/- 259 ml x min(-1) x m(-2), respectively), whereas EC(50) was 240 +/- 84 microU/ml in the lean vs. 364 +/- 229 microU/ml in the obese (P < 0.04). EC(50) and the insulin sensitivity index (ISI, initial slope of the dose-response curve), but not Cl(max), were related to body adiposity and fat distribution with r of 0.6-0.8 (P < 0.05). Thus, despite the small number of study subjects, we were able to reproduce information consistent with the literature. In addition, among the lean individuals, t(1/2) was positively related to the ISI (r = 0.72, P < 0.02). We conclude that the test here presented, based on a more elaborate representation of glucose kinetics and insulin action, allows a reliable quantitation of the insulin dose-response curve for whole body glucose utilization in a single session of relatively short duration.

Entities:  

Mesh:

Substances:

Year:  2000        PMID: 10780934     DOI: 10.1152/ajpendo.2000.278.5.E794

Source DB:  PubMed          Journal:  Am J Physiol Endocrinol Metab        ISSN: 0193-1849            Impact factor:   4.310


  23 in total

1.  Stochastic targeted (STAR) glycemic control: design, safety, and performance.

Authors:  Alicia Evans; Aaron Le Compte; Chia-Siong Tan; Logan Ward; James Steel; Christopher G Pretty; Sophie Penning; Fatanah Suhaimi; Geoffrey M Shaw; Thomas Desaive; J Geoffrey Chase
Journal:  J Diabetes Sci Technol       Date:  2012-01-01

2.  Intermediary variables and algorithm parameters for an electronic algorithm for intravenous insulin infusion.

Authors:  Susan S Braithwaite; Hemant Godara; Julie Song; Bruce A Cairns; Samuel W Jones; Guillermo E Umpierrez
Journal:  J Diabetes Sci Technol       Date:  2009-07-01

3.  Nasal insulin changes peripheral insulin sensitivity simultaneously with altered activity in homeostatic and reward-related human brain regions.

Authors:  M Heni; S Kullmann; C Ketterer; M Guthoff; K Linder; R Wagner; K T Stingl; R Veit; H Staiger; H-U Häring; H Preissl; A Fritsche
Journal:  Diabetologia       Date:  2012-03-21       Impact factor: 10.122

4.  Safety constraints in an artificial pancreatic beta cell: an implementation of model predictive control with insulin on board.

Authors:  Christian Ellingsen; Eyal Dassau; Howard Zisser; Benyamin Grosman; Matthew W Percival; Lois Jovanovic; Francis J Doyle
Journal:  J Diabetes Sci Technol       Date:  2009-05-01

5.  Roux-en-Y gastric bypass compared with equivalent diet restriction: Mechanistic insights into diabetes remission.

Authors:  Laurentiu M Pop; Andrea Mari; Tong-Jin Zhao; Lori Mitchell; Shawn Burgess; Xilong Li; Beverley Adams-Huet; Ildiko Lingvay
Journal:  Diabetes Obes Metab       Date:  2018-04-10       Impact factor: 6.577

6.  What makes tight glycemic control tight? The impact of variability and nutrition in two clinical studies.

Authors:  Fatanah Suhaimi; Aaron Le Compte; Jean-Charles Preiser; Geoffrey M Shaw; Paul Massion; Regis Radermecker; Christopher G Pretty; Jessica Lin; Thomas Desaive; J Geoffrey Chase
Journal:  J Diabetes Sci Technol       Date:  2010-03-01

7.  Metabolic response to sodium-glucose cotransporter 2 inhibition in type 2 diabetic patients.

Authors:  Ele Ferrannini; Elza Muscelli; Silvia Frascerra; Simona Baldi; Andrea Mari; Tim Heise; Uli C Broedl; Hans-Juergen Woerle
Journal:  J Clin Invest       Date:  2014-01-27       Impact factor: 14.808

8.  Organ failure and tight glycemic control in the SPRINT study.

Authors:  J Geoffrey Chase; Christopher G Pretty; Leesa Pfeifer; Geoffrey M Shaw; Jean-Charles Preiser; Aaron J Le Compte; Jessica Lin; Darren Hewett; Katherine T Moorhead; Thomas Desaive
Journal:  Crit Care       Date:  2010-08-12       Impact factor: 9.097

9.  Overview of glycemic control in critical care: relating performance and clinical results.

Authors:  J Geoffrey Chase; Christopher E Hann; Geoffrey M Shaw; Jason Wong; Jessica Lin; Thomas Lotz; Aaron Lecompte; Timothy Lonergan
Journal:  J Diabetes Sci Technol       Date:  2007-01

10.  Increased insulin resistance in intensive care: longitudinal retrospective analysis of glycaemic control patients in a New Zealand ICU.

Authors:  Jennifer L Knopp; J Geoffrey Chase; Geoffrey M Shaw
Journal:  Ther Adv Endocrinol Metab       Date:  2021-05-31       Impact factor: 3.565

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.