The variability in beta-cell function in placebo-treated subjects with type 2 diabetes: application of the weight-HbA1c-insulin-glucose (WHIG) model.
The weight-glycosylated haemoglobin (HbA1C)-insulin-glucose (WHIG) model describes the effects of changes in weight on insulin sensitivity (IS) in newly diagnosed, obese subjects receiving placebo treatment. This model was applied to a wider population of placebo-treated subjects, to investigate factors influencing the variability in IS and β-cell function.



The WHIG model was applied to the WHIG dataset (Study 1) and two other placebo datasets (Studies 2 and 3). Studies 2 and 3 consisted of nonobese subjects and subjects with advanced type 2 diabetes mellitus (T2DM). Body weight, fasting serum insulin (FSI), fasting plasma glucose (FPG) and HbA1c were used for nonlinear mixed-effects modelling (using NONMEM v7.2 software). Sources of interstudy variability (ISV) and potential covariates (age, gender, diabetes duration, ethnicity, compliance) were investigated.



An ISV for baseline parameters (body weight and β-cell function) was required. The baseline β-cell function was significantly lower in subjects with advanced T2DM (median difference: Study 2: 15.6%, P < 0.001; Study 3: 22.7%, P < 0.001) than in subjects with newly diagnosed T2DM (Study 1). A reduction in the estimated insulin secretory response in subjects with advanced T2DM was observed but diabetes duration was not a significant covariate.



The WHIG model can be used to describe the changes in weight, IS and β-cell function in the diabetic population. IS remained relatively stable between subjects but a large ISV in β-cell function was observed. There was a trend towards decreasing β-cell responsiveness with diabetes duration, and further studies, incorporating subjects with a longer history of diabetes, are required.
Department of Medical Informatics, Erasmus Medical Centre, Rotterdam, the Netherlands.|Leiden Academic Centre for Drug Research (LACDR), Division of Pharmacology, Leiden University, Leiden, the Netherlands.|Faculty of Pharmacy, The University of Sydney, Sydney, NSW, Australia.
Janna K
JK
Duong
Janssen Prevention Center, Leiden, the Netherlands.
Willem
W
de Winter
Department of Pharmaceutical Biosciences, Pharmacometrics Research Group, Uppsala University, Uppsala, Sweden.
Steve
S
Choy
Global Pharmacometrics, Takeda Pharmaceuticals International, Zurich and Deerfield, Switzerland and USA.
Nele
N
Plock
Global Pharmacometrics, Takeda Pharmaceuticals International, Zurich and Deerfield, Switzerland and USA.|Quantitative Pharmacology, Biogen, Cambridge, MA, USA.
Himanshu
H
Naik
Global Clinical Pharmacology and Exploratory Development, Astellas Pharma Europe BV, Leiden, the Netherlands.
Walter
W
Krauwinkel
Early Stage Quantitative Pharmacology & Pharmacometrics, Merck, Upper Gwynedd, PA, USA.
Sandra A G
SA
Visser
Department of Medical Informatics, Erasmus Medical Centre, Rotterdam, the Netherlands.
Katia M
KM
Verhamme
Department of Medical Informatics, Erasmus Medical Centre, Rotterdam, the Netherlands.
Miriam C
MC
Sturkenboom
Department of Epidemiology, Erasmus Medical Centre, Rotterdam, the Netherlands.
B H
BH
Stricker
Leiden Academic Centre for Drug Research (LACDR), Division of Pharmacology, Leiden University, Leiden, the Netherlands.
Meindert
M
Danhof
D001786:Blood Glucose; D001835:Body Weight / Q000502:physiology*; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000503:physiopathology*; D018450:Disease Progression; D005215:Fasting; D005260:Female; D006442:Glycated Hemoglobin; D006801:Humans; D007328:Insulin / Q000097:blood*; D007333:Insulin Resistance / Q000502:physiology*; D050417:Insulin-Secreting Cells / Q000502:physiology*; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D009765:Obesity / Q000097:blood / Q000503:physiopathology; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data
D001786:Blood Glucose; D006442:Glycated Hemoglobin; D007328:Insulin
beta-cell function; disease progression; placebo treatment; semi-mechanistic modelling; type 2 diabetes mellitus
CPT Pharmacometrics Syst Pharmacol. 2013 Apr 17;2:e38
23887688
Diabetes Res Clin Pract. 1995 Aug;28 Suppl:S151-7
8529508
J Clin Endocrinol Metab. 2001 Sep;86(9):4047-58
11549624
Lancet Diabetes Endocrinol. 2016 Apr;4(4):350-9
26809680
J Pharmacokinet Pharmacodyn. 2007 Oct;34(5):575-93
17620001
Diabetologia. 2004 Jan;47(1):31-9
14666364
Comput Methods Programs Biomed. 2011 Jan;101(1):72-9
20627442
Clin Pharmacokinet. 2008;47(7):417-48
18563953
Diabetes Care. 2004 Jun;27(6):1487-95
15161807
Diabetes Care. 2009 Feb;32(2):335-41
18957530
CPT Pharmacometrics Syst Pharmacol. 2016 Jan;5(1):11-9
26844011
Clin Pharmacokinet. 2016 Jul;55(7):769-788
26798033
Pharm Res. 2009 Sep;26(9):2174-85
19568693
Br J Clin Pharmacol. 2017 Mar;83(3):487-497
27679422
Comput Methods Programs Biomed. 2005 Sep;79(3):241-57
16023764
Lancet. 1998 Sep 12;352(9131):837-53
9742976
Clin Pharmacol Ther. 2015 Aug;98(2):162-9
25974616
Diabetes Care. 2014 Jun;37(6):1668-74
24705614
Eur J Intern Med. 2016 Jan;27:37-47
26655787
Diabetes Metab J. 2014 Dec;38(6):426-36
25541605
QJM. 2003 Apr;96(4):281-8
12651972
Diabet Med. 1998 Apr;15(4):290-6
9585393
J Pharmacokinet Pharmacodyn. 2006 Jun;33(3):313-43
16552630
Diabetologia. 1985 Jul;28(7):412-9
3899825
Nucleic Acids Res. 2016 Jan 4;44(D1):D1054-68
26464438