Literature DB >> 23307170

A semi-mechanistic model of the relationship between average glucose and HbA1c in healthy and diabetic subjects.

Rocío Lledó-García1, Norman A Mazer, Mats O Karlsson.   

Abstract

HbA1c is the most commonly used biomarker for the adequacy of glycemic management in diabetic patients and a surrogate endpoint for anti-diabetic drug approval. In spite of an empirical description for the relationship between average glucose (AG) and HbA1c concentrations, obtained from the A1c-derived average glucose (ADAG) study by Nathan et al., a model for the non-steady-state relationship is still lacking. Using data from the ADAG study, we here develop such models that utilize literature information on (patho)physiological processes and assay characteristics. The model incorporates the red blood cell (RBC) aging description, and uses prior values of the glycosylation rate constant (KG), mean RBC life-span (LS) and mean RBC precursor LS obtained from the literature. Different hypothesis were tested to explain the observed non-proportional relationship between AG and HbA1c. Both an inverse dependence of LS on AG and a non-specificity of the National Glycohemoglobin Standardization Program assay used could well describe the data. Both explanations have mechanistic support and could be incorporated, alone or in combination, in models allowing prediction of the time-course of HbA1c changes associated with changes in AG from, for example dietary or therapeutic interventions, and vice versa, to infer changes in AG from observed changes in HbA1c. The selection between the alternative mechanistic models require gathering of new information.

Entities:  

Mesh:

Substances:

Year:  2013        PMID: 23307170     DOI: 10.1007/s10928-012-9289-6

Source DB:  PubMed          Journal:  J Pharmacokinet Pharmacodyn        ISSN: 1567-567X            Impact factor:   2.745


  44 in total

1.  Tests of glycemia in diabetes.

Authors:  David E Goldstein; Randie R Little; Rodney A Lorenz; John I Malone; David M Nathan; Charles M Peterson
Journal:  Diabetes Care       Date:  2004-01       Impact factor: 19.112

2.  Hemoglobin loss from erythrocytes in vivo results from spleen-facilitated vesiculation.

Authors:  Frans L A Willekens; Bregt Roerdinkholder-Stoelwinder; Yvonne A M Groenen-Döpp; Harry J Bos; Giel J C G M Bosman; Annegeet G van den Bos; Arie J Verkleij; Jan M Werre
Journal:  Blood       Date:  2002-08-01       Impact factor: 22.113

3.  A mechanism-based disease progression model for comparison of long-term effects of pioglitazone, metformin and gliclazide on disease processes underlying Type 2 Diabetes Mellitus.

Authors:  Willem de Winter; Joost DeJongh; Teun Post; Bart Ploeger; Richard Urquhart; Ian Moules; David Eckland; Meindert Danhof
Journal:  J Pharmacokinet Pharmacodyn       Date:  2006-03-22       Impact factor: 2.745

4.  Use of prior information to stabilize a population data analysis.

Authors:  Per O Gisleskog; Mats O Karlsson; Stuart L Beal
Journal:  J Pharmacokinet Pharmacodyn       Date:  2002-12       Impact factor: 2.745

5.  Global standardization of glycated hemoglobin measurement: the position of the IFCC Working Group.

Authors:  Andrea Mosca; Ian Goodall; Tadao Hoshino; Jan O Jeppsson; W Garry John; Randie R Little; Kor Miedema; Gary L Myers; Hans Reinauer; David B Sacks; Cas W Weykamp
Journal:  Clin Chem Lab Med       Date:  2007       Impact factor: 3.694

6.  The clinical information value of the glycosylated hemoglobin assay.

Authors:  D M Nathan; D E Singer; K Hurxthal; J D Goodson
Journal:  N Engl J Med       Date:  1984-02-09       Impact factor: 91.245

7.  High and low hemoglobin glycation phenotypes in type 1 diabetes: a challenge for interpretation of glycemic control.

Authors:  James M Hempe; Ricardo Gomez; Robert J McCarter; Stuart A Chalew
Journal:  J Diabetes Complications       Date:  2002 Sep-Oct       Impact factor: 2.852

8.  Relationship between GHb concentration and erythrocyte survival determined from breath carbon monoxide concentration.

Authors:  Mark A Virtue; Julie K Furne; Frank Q Nuttall; Michael D Levitt
Journal:  Diabetes Care       Date:  2004-04       Impact factor: 19.112

9.  Relationship of A1C to glucose concentrations in children with type 1 diabetes: assessments by high-frequency glucose determinations by sensors.

Authors:  Darrell M Wilson
Journal:  Diabetes Care       Date:  2007-12-04       Impact factor: 19.112

10.  Relationship between glycated haemoglobin levels and mean glucose levels over time.

Authors:  D M Nathan; H Turgeon; S Regan
Journal:  Diabetologia       Date:  2007-09-13       Impact factor: 10.122

View more
  13 in total

Review 1.  A Comprehensive Review of Novel Drug-Disease Models in Diabetes Drug Development.

Authors:  Puneet Gaitonde; Parag Garhyan; Catharina Link; Jenny Y Chien; Mirjam N Trame; Stephan Schmidt
Journal:  Clin Pharmacokinet       Date:  2016-07       Impact factor: 6.447

2.  Efficacy of DPP-4 inhibitors, GLP-1 analogues, and SGLT2 inhibitors as add-ons to metformin monotherapy in T2DM patients: a model-based meta-analysis.

Authors:  Hiroyuki Inoue; Yoko Tamaki; Yushi Kashihara; Shota Muraki; Makoto Kakara; Takeshi Hirota; Ichiro Ieiri
Journal:  Br J Clin Pharmacol       Date:  2018-12-06       Impact factor: 4.335

3.  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

4.  Evaluation of the long-term durability and glycemic control of fasting plasma glucose and glycosylated hemoglobin for pioglitazone in Japanese patients with type 2 diabetes.

Authors:  Frances Stringer; Joost DeJongh; Kazuaki Enya; Emiko Koumura; Meindert Danhof; Kohei Kaku
Journal:  Diabetes Technol Ther       Date:  2014-12-22       Impact factor: 6.118

5.  Glycated Hemoglobin, Plasma Glucose, and Erythrocyte Aging.

Authors:  Manuel Beltran Del Rio; Mukesh Tiwari; Leo I Amodu; Joaquin Cagliani; Horacio Luis Rodriguez Rilo
Journal:  J Diabetes Sci Technol       Date:  2016-11-01

6.  Methods for Predicting Diabetes Phase III Efficacy Outcome From Early Data: Superior Performance Obtained Using Longitudinal Approaches.

Authors:  J B Møller; N R Kristensen; S Klim; M O Karlsson; S H Ingwersen; M C Kjellsson
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2014-07-02

7.  Comparison of Power, Prognosis, and Extrapolation Properties of Four Population Pharmacodynamic Models of HbA1c for Type 2 Diabetes.

Authors:  Gustaf J Wellhagen; Mats O Karlsson; Maria C Kjellsson
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2018-03-25

8.  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

9.  Weight-HbA1c-insulin-glucose model for describing disease progression of type 2 diabetes.

Authors:  S Choy; M C Kjellsson; M O Karlsson; W de Winter
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2015-12-16

10.  Urinary glucose excretion after dapagliflozin treatment: An exposure-response modelling comparison between Japanese and non-Japanese patients diagnosed with type 1 diabetes mellitus.

Authors:  Victor Sokolov; Tatiana Yakovleva; Shinya Ueda; Joanna Parkinson; David W Boulton; Robert C Penland; Weifeng Tang
Journal:  Diabetes Obes Metab       Date:  2018-12-16       Impact factor: 6.577

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.