Adapting Protocols or Models for Use in Insulin-Requiring Diabetes and Islet Transplant Recipients

The authors’ perspective is described regarding modifications made in their clinic to glucose challenge protocols and mathematical models in order to estimate insulin secretion, insulin sensitivity and glucose effectiveness in patients living with Insulin-Requiring Diabetes and patients who received Pancreatic Islet Transplants to treat Type I diabetes (T1D) with Impaired Awareness of Hypoglycemia. The evolutions are described of protocols and models for use in T1D, and Insulin-Requiring Type 2 Diabetes (T2D) that were the basis for studies in the Islet Recipients. In each group, the need for modifications, and how the protocols and models were adapted is discussed. How the ongoing application of the adaptations is clarifying the Islet pathophysiology in the Islet Transplant Recipients is outlined.

An Analysis of Glucose Effectiveness in Subjects With or Without Type 2 Diabetes via Hierarchical Modeling

Glucose effectiveness, defined as the ability of glucose itself to increase glucose utilization and inhibit hepatic glucose production, is an important mechanism maintaining normoglycemia. We conducted a minimal modeling analysis of glucose effectiveness at zero insulin (GEZI) using intravenous glucose tolerance test data from subjects with type 2 diabetes (T2D, n=154) and non-diabetic (ND) subjects (n=343). A hierarchical statistical analysis was performed, which provided a formal mechanism for pooling the data from all study subjects, to yield a single composite population model that quantifies the role of subject specific characteristics such as weight, height, age, sex, and glucose tolerance. Based on the resulting composite population model, GEZI was reduced from 0.021 min–1 (standard error – 0.00078 min–1) in the ND population to 0.011 min–1 (standard error – 0.00045 min–1) in T2D. The resulting model was also employed to calculate the proportion of the non–insulin-dependent net glucose uptake in each subject receiving an intravenous glucose load. Based on individual parameter estimates, the fraction of total glucose disposal independent of insulin was 72.8% ± 12.0% in the 238 ND subjects over the course of the experiment, indicating the major contribution to the whole-body glucose clearance under non-diabetic conditions. This fraction was significantly reduced to 48.8% ± 16.9% in the 30 T2D subjects, although still accounting for approximately half of the total in the T2D population based on our modeling analysis. Given the potential application of glucose effectiveness as a predictor of glucose intolerance and as a potential therapeutic target for treating diabetes, more investigations of glucose effectiveness in other disease conditions can be conducted using the hierarchical modeling framework reported herein.

The Roles of Insulin Secretion, Insulin Resistance and Glucose Effectiveness in Developing Diabetes in Non-Obese Old Women

Background: The impairment of glucose homeostasis are known to be attributed to the alterations of the four factors: first, second insulin secretion (FPIS, SPIS, respectively), glucose effectiveness (GE) and insulin resistance (IR). Objective: Older women were enrolled to investigate the relationships of the four factors with T2DM. Designs: A cross-sectional study. Settings: MJ Health Screening Center in Taiwan Patients and Methods: They were divided into normal glucose tolerance (NGT) and T2DM groups. Receiver operating characteristic (ROC) curve was performed and two models were built: Model 1: FPIS + GE and, Model 2: FPIS + GE + SPIS. Main Outcome Measures: The area under ROC curve (AUC) was used to predict type 2 DM. Sample Size: 644 non-obese women. Results: The AUC of SPIS was significantly higher than the diagonal line followed by GE and FPIS. Model 2 had the greatest AUC (0.968). An equation was built (-0.012-FPIS – 1003.9-GE – 119.4-SPIS + 20.7). It could predict the chance of having T2D with a sensitivity = 94.2% and specificity = 86.4%. Conclusions: SPIS is the most important contributor for T2DM in older women. The equation built from this model composed of FPIS, SPIS and GE could predict T2DM accurately.