A Comprehensive Model-Based Meta-Analysis (MBMA) of Diabetes Studies in Type 2 Diabetes Mellitus Patients to Quantify the Relationship between HbA1c and Fasting Plasma Glucose
Pavan Vaddady (1) Mark Lovern (2) Jaap Mandema (2) Leon Bax (2) Thomas Kerbusch (2) Lokesh Jain (1) Ferdous Gheyas (1) Sandra A.G. Visser (1)
(1) Merck & Co., Inc., Kenilworth, NJ, USA (2) Quantitative Solutions, Certara Strategic Consultancy
Objectives: Describe the relationship between fasting plasma glucose (FPG) and HbA1c to predict long term efficacy from early clinical outcome by linking their MBMA based dose-time-response, to identify clinically meaningful covariates, and to evaluate the consistency of FPG-HbA1c relationship within and across mechanisms of anti-diabetic drugs studied.
Methods: A comprehensive Type 2 Diabetes Mellitus clinical outcomes database was developed to document clinical safety and efficacy information from trials investigating sulfonylureas, metformin, dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 agonists, thiazolidinedione, sodium–glucose cotransporter-2 inhibitors, glucagon receptor antagonists and glucokinase activators. For HbA1c and FPG, longitudinal MBMAs were developed using a non-parametric placebo response with Emax dose-response models incorporating covariate effects.
Results: These two MBMAs resulted in robust models from clinical trial data for HbA1c (464 trials) and FPG (477 trials). For HbA1c, dose and time dependencies were successfully characterized for most drugs and HbA1c reduction was greater in patients with higher baseline HbA1c, patients on a background of diet only compared to insulin or oral anti-diabetic drugs supporting less-than-additive efficacy commonly seen with combination treatments [1], patients with higher estimated glomerular filtration rate (for SGLT2s only), and in Japanese patients. The model developed for FPG was structurally similar to the HbA1c model. The covariates identified were identical and in the same direction of impact as in the HbA1c model. For the link between HbA1c and FPG, the drug effects for each of the endpoints at the primary time point were highly correlated. The correlation, however, changed over time until becoming stable after approximately 12 weeks allowing for predicting long term glycemic control based on early glycemic markers. The ratio of HbA1c (%)/FPG (10 mg/dl) followed similar trends over time for all drug classes, but the magnitudes were different.
Conclusion: This analysis provided a quantitative framework for comparison of treatment effects of existing diabetes drugs and linking the short term effects (FPG) to long term outcomes (HbA1c). It also enabled projections of HbA1c for specific subset of covariates which can be helpful in designing clinical trials and assessment of differentiation for novel treatments in the discovery and development pipeline.
References:
[1] Polidori D, Capuano G and Qiu R. Apparent subadditivity of the efficacy of initial combination treatments for type 2 diabetes is largely explained by the impact of baseline HbA1c on efficacy. Diabetes, Obesity and Metabolism (2016) 18: 348–354.