Literature DB >> 21796500

Hemoglobin glycation rate constant in non-diabetic Individuals.

Piotr Ladyżyński1, Jan M Wójcicki, Marianna I Bąk, Stanisława Sabalińska, Jerzy Kawiak, Piotr Foltyński, Janusz Krzymień, Waldemar Karnafel.   

Abstract

The objectives were as follows: (1) estimating mean value of the overall hemoglobin glycation rate constant (k); (2) analyzing inter-individual variability of k; (3) verifying ability of the hemoglobin A1c (HbA1c) formation model to predict changes of HbA1c during red blood cells cultivation in vitro and to reproduce the clinical data. The mean k estimated in a group of 10 non-diabetic subjects was equal to 1.257 ± 0.114 × 10(-9) L mmol(-1) s(-1). The mean k was not affected by a way of estimation of glycemia. The mean k differed less than 20% from values reported earlier and it was almost identical to the mean values calculated on basis of the selected published data. Analysis of variability of k suggests that inter-individual heterogeneity of HbA1c formation is limited or rare. The HbA1c mathematical model was able to predict changes of HbA1c in vitro resulting from different glucose levels and to reproduce a linear relationship of HbA1c and average glucose obtained in the A1C-Derived Average Glucose Study. This study demonstrates that the glycation model with the same k value might be used in majority of individuals as a tool supporting interpretation of HbA1c in different clinical situations.

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Year:  2011        PMID: 21796500     DOI: 10.1007/s10439-011-0366-6

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  7 in total

1.  Mechanistic modeling of hemoglobin glycation and red blood cell kinetics enables personalized diabetes monitoring.

Authors:  Roy Malka; David M Nathan; John M Higgins
Journal:  Sci Transl Med       Date:  2016-10-05       Impact factor: 17.956

2.  Modulation of red blood cell population dynamics is a fundamental homeostatic response to disease.

Authors:  Harsh H Patel; Hasmukh R Patel; John M Higgins
Journal:  Am J Hematol       Date:  2015-04-02       Impact factor: 10.047

3.  Diabetes Technology Meeting 2021.

Authors:  Nicole Y Xu; Kevin T Nguyen; Ashley Y DuBord; John Pickup; Jennifer L Sherr; Hazhir Teymourian; Eda Cengiz; Barry H Ginsberg; Claudio Cobelli; David Ahn; Riccardo Bellazzi; B Wayne Bequette; Laura Gandrud Pickett; Linda Parks; Elias K Spanakis; Umesh Masharani; Halis K Akturk; John S Melish; Sarah Kim; Gu Eon Kang; David C Klonoff
Journal:  J Diabetes Sci Technol       Date:  2022-05-02

4.  A Kinetic Model for Glucose Levels and Hemoglobin A1c Provides a Novel Tool for Individualized Diabetes Management.

Authors:  Yongjin Xu; Timothy C Dunn; Ramzi A Ajjan
Journal:  J Diabetes Sci Technol       Date:  2020-01-08

5.  Interindividual variability in average glucose-glycated haemoglobin relationship in type 1 diabetes and implications for clinical practice.

Authors:  Yongjin Xu; Richard M Bergenstal; Timothy C Dunn; Yashesvini Ram; Ramzi A Ajjan
Journal:  Diabetes Obes Metab       Date:  2022-06-02       Impact factor: 6.408

6.  Validation of a hemoglobin A1c model in patients with type 1 and type 2 diabetes and its use to go beyond the averaged relationship of hemoglobin A1c and mean glucose level.

Authors:  Piotr Ladyzynski; Piotr Foltynski; Marianna I Bak; Stanislawa Sabalinska; Janusz Krzymien; Jerzy Kawiak
Journal:  J Transl Med       Date:  2014-12-10       Impact factor: 5.531

7.  Simulation of a computed HbA1c using a weighted average glucose.

Authors:  W Boutayeb; A Boutayeb; M Lamlili; S Ben El Mostafa; N Zitouni
Journal:  Springerplus       Date:  2016-02-29
  7 in total

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