Janna K Duong1,2,3, Willem de Winter4, Steve Choy5, Nele Plock6, Himanshu Naik6,7, Walter Krauwinkel8, Sandra A G Visser9, Katia M Verhamme1, Miriam C Sturkenboom1, B H Stricker10, Meindert Danhof2. 1. Department of Medical Informatics, Erasmus Medical Centre, Rotterdam, the Netherlands. 2. Leiden Academic Centre for Drug Research (LACDR), Division of Pharmacology, Leiden University, Leiden, the Netherlands. 3. Faculty of Pharmacy, The University of Sydney, Sydney, NSW, Australia. 4. Janssen Prevention Center, Leiden, the Netherlands. 5. Department of Pharmaceutical Biosciences, Pharmacometrics Research Group, Uppsala University, Uppsala, Sweden. 6. Global Pharmacometrics, Takeda Pharmaceuticals International, Zurich and Deerfield, Switzerland and USA. 7. Quantitative Pharmacology, Biogen, Cambridge, MA, USA. 8. Global Clinical Pharmacology and Exploratory Development, Astellas Pharma Europe BV, Leiden, the Netherlands. 9. Early Stage Quantitative Pharmacology & Pharmacometrics, Merck, Upper Gwynedd, PA, USA. 10. Department of Epidemiology, Erasmus Medical Centre, Rotterdam, the Netherlands.
Abstract
AIM: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
RCT Entities:
AIM: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
Authors: Ron J Keizer; Michel van Benten; Jos H Beijnen; Jan H M Schellens; Alwin D R Huitema Journal: Comput Methods Programs Biomed Date: 2010-06-02 Impact factor: 5.428
Authors: Kristina M Utzschneider; Ronald L Prigeon; Mirjam V Faulenbach; Jenny Tong; Darcy B Carr; Edward J Boyko; Donna L Leonetti; Marguerite J McNeely; Wilfred Y Fujimoto; Steven E Kahn Journal: Diabetes Care Date: 2008-10-28 Impact factor: 19.112
Authors: Janna K Duong; Willem de Winter; Steve Choy; Nele Plock; Himanshu Naik; Walter Krauwinkel; Sandra A G Visser; Katia M Verhamme; Miriam C Sturkenboom; B H Stricker; Meindert Danhof Journal: Br J Clin Pharmacol Date: 2016-11-17 Impact factor: 4.335