Mechanism-Based Modeling of Nutritional and Leptin Influences on Growth in Normal and Type 2 Diabetic Rats

Abstract

Influences of genetic and nutritional factors on body weight, fat mass, and leptin production and effects of leptin were assessed in normal [Wistar-Kyoto (WKY)] and diabetic [GotoKakizaki (GK)] rats by mechanism-based modeling. The study included 60 WKY and 60 GK rats; half received high-fat diet (HF), and the others received normal rat chow (N). Body weights and food consumption were measured twice weekly. Six rats per group were sacrificed at 4, 8, 12, 16, and 20 weeks. Abdominal fat was weighed, and plasma leptin was measured by enzyme-linked immunosorbent assay. All data were comodeled using NONMEM version VI level 1.1 (first-order conditional estimation with interaction) (Beal SL, Boeckmann AJ, Sheiner LB, and NONMEM Project Group, NONMEM Users Guides, University of California, San Francisco, CA, 2007). Weight gain was modeled as differences between energy intake and metabolic rate based on allometrically scaled lean body mass (LBM).The GK had higher metabolic rates (1.15 kcal/day/g LBM 0.75 ) than WKY-N (0.92) and WKY-HF (1.02) rats and higher efficiency in transforming energy into body weight. Leptin effect was modeled as inhibition of food consumption. Total body fat was estimated from abdominal fat. Leptin production from fat was 4.7-fold higher for GK (3.03 ng/ml/day/g) than WKY (0.66 ng/ml/day/g). Leptin production rate from LBM was 0.53 ng/ ml/day/g for all groups. The IC 50 for inhibition of food intake by leptin was approximately 3-fold higher in GK versus WKY, indicating leptin resistance for the effect on food consumption in GK. The GK had similar intake of kilocalories but lower body weights and fat mass than WKY, possibly because of higher metabolic rates. Our mechanism-based model explains intrinsic reasons for differences in growth, food intake, and leptin concentrations among these two strains of rats.Type 2 diabetes (T2DM) is a major health risk in many countries and the number of diabetic patients and associated costs for health systems are increasing. More than 180 million people are diabetic worldwide, and this number was estimated to more than double by 2030 (Centers for Disease Control and Prevention, http://www.cdc.gov/diabetes/pubs/ factsheets.htm). In 2005, an estimated 1.1 million people died from diabetes, and such deaths were predicted to increase by more than 50% over the next 10 years (Centers for Disease Control and Prevention, http://www.cdc.gov/ diabetes/pubs/factsheets.htm).

Modeling Disease Progression and Rosiglitazone Intervention in Type 2 Diabetic Goto-Kakizaki Rats

“…The rats started at 236 Ϯ 15 g, with no difference among the three groups. Compared with the previous study (Landersdorfer et al, 2009), the placebo rats had significantly lower (p Ͻ 0.05) body weights at matching ages. One possible reason could be stress from animal handling.…”

“…Rosiglitazone has been modeled as stimulation of glucose utilization, with a lag time describing its delayed effect in T2D subjects (Benincosa and Jusko, 1999;Landersdorfer et al, 2009). In the current study, rosiglitazone actually showed a hypoglycemic effect as early as the first 2 to 3 days.…”

“…Therefore, the current model was applied for the characterization of body weights. The WT ss was fixed from previous observations (Landersdorfer et al, 2009), because the rats had comparable body weights at the end of this study.…”

Performance and Robustness of the Monte Carlo Importance Sampling Algorithm Using Parallelized S-ADAPT for Basic and Complex Mechanistic Models

“…The availability of more powerful estimation algorithms, software packages, and computers enabled scientists to incorporate more sophisticated mechanisms in PK/PD models for clinical and experimental studies (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38). Whenever true biological variability between individuals is present and significant, population PK/PD modeling is a powerful concept to describe and predict the behavior of such systems.…”

Development of a New Pre- and Post-Processing Tool (SADAPT-TRAN) for Nonlinear Mixed-Effects Modeling in S-ADAPT

“…This benefit is greatest for mechanistic models that may contain 20 and more random model parameters (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27), as the likelihood for model specification errors increases with model complexity. The bi-directional and hierarchical error checks of the SADAPT-TRAN pre-processor (Fig.…”