Effect of exenatide on postprandial glucose fluxes, lipolysis, and ß-cell function in nondiabetic, morbidly obese patients

Aims: To investigate the effect of exenatide on glucose disposal, insulin secretion, ß-cell function, lipolysis, and hormone concentrations in non-diabetic, morbidly obese subjects under physiological conditions. Materials and methods: Patients were assigned to exenatide 10 µg twice daily (EXE, n=15) or control (CT, n=15) for 3 months. Patients received a meal test/tracer study (MTT) to measure endogenous glucose production (EGP), rate of oral glucose appearance (RaO), insulin secretion rate (ISR), ß-cell function, hepatic (HIR) and adipose tissue insulin resistance (AT-IR) and insulin sensitivity (IS). Results: Post-treatment the EXE group showed a significant reduction in body weight (p<0.001). The postmeal time-course of glucose, insulin, and ISR showed a lower peak between 60-180 min in phase with a reduction in RaO (p<0.01). After an initial similar suppression, EGP resumed at higher rates between 60-180 min (p=0.02) in EXE vs CT, while total RaO and EGP throughout the MTT were similar. In EXE, the postmeal glucagon, GLP1, and GIP responses were reduced (p<0.05). Fasting and postprandial lipolysis and ß-cell function were unaltered by active treatment. HIR, AT-IR, and IS were all improved after exenatide treatment (p<0.05). Conclusions: In morbidly obese non-diabetic subjects, exenatide causes weight loss, decreased postprandial glycaemia and glucagon response without changes in ß-cell function. These effects are consequent upon delayed oral glucose appearance in the circulation. Exenatide treatment is also associated with an improvement of hepatic, adipose tissue, and whole body insulin sensitivity with no influence on post-prandial lipolysis

Design and implementation of GRIP: a computerized glucose control system at a surgical intensive care unit

BACKGROUND: Tight glucose control by intensive insulin therapy has become a key part of critical care and is an important field of study in acute coronary care. A balance has to be found between frequency of measurements and the risk of hypoglycemia. Current nurse-driven protocols are paper-based and, therefore, rely on simple rules. For safety and efficiency a computer decision support system that employs complex logic may be superior to paper protocols. METHODS: We designed and implemented GRIP, a stand-alone Java computer program. Our implementation of GRIP will be released as free software. Blood glucose values measured by a point-of-care analyzer were automatically retrieved from the central laboratory database. Additional clinical information was asked from the nurse and the program subsequently advised a new insulin pump rate and glucose sampling interval. RESULTS: Implementation of the computer program was uneventful and successful. GRIP treated 179 patients for a total of 957 patient-days. Severe hypoglycemia (< 2.2 mmol/L) only occurred once due to human error. With a median (IQR) of 4.9 (4.2 – 6.2) glucose measurements per day the median percentage of time in which glucose fell in the target range was 78%. Nurses rated the program as easy to work with and as an improvement over the preceding paper protocol. They reported no increase in time spent on glucose control. CONCLUSION: A computer driven protocol is a safe and effective means of glucose control at a surgical ICU. Future improvements in the recommendation algorithm may further improve safety and efficiency

Oral Disposition Index Predicts the Development of Future Diabetes Above and Beyond Fasting and 2-h Glucose Levels

OBJECTIVE—We sought to determine whether an oral disposition index (DIO) predicts the development of diabetes over a 10-year period. First, we assessed the validity of the DIO by demonstrating that a hyperbolic relationship exists between oral indexes of insulin sensitivity and β-cell function

Oral Disposition Index Predicts the Development of Future Diabetes Above and Beyond Fasting and 2-h Glucose Levels

OBJECTIVE—We sought to determine whether an oral disposition index (DIO) predicts the development of diabetes over a 10-year period. First, we assessed the validity of the DIO by demonstrating that a hyperbolic relationship exists between oral indexes of insulin sensitivity and β-cell function

Computer Service to Life Insurance Investment Functions

About ten years ago, when the advent of the computer was dawning, writers, pundits and prophets were giving readers a picture of life during the age of the computer. Some writers envisioned the inevitable takeover of mankind by malevolent gray boxes intent on world domination. | Time has dulled the sharp edge of our recollection of these wild statements which were based on securing readership rather than on provable fact. Yet, it is now true that computers are doing more jobs well than any of the starry-eyed visionaries ever predicted. Daily new applications are conceived for and executed by third generation computers. One generation of man is usually pegged at 33 years. In an industry yet to celebrate its twentieth birthday, the father has been replaced by a more efficient son, and a still more efficient grandson. This is a real phenomenon, even in this century of fast technological change. | What the hindsight of several years experience has given us is the distinct assurance that the little black box, (or six other decorator colors) crammed full of electronic components, can consistently accomplish something of value as a management tool for its users and owners. | This paper is limited to a discussion of just one application for the computer: The use of computers to maintain an investment portfolio for life insurance companies.ProQuest Traditional Publishing Optio

A Glucose-Insulin Pharmacodynamic Surface Modeling Validation and Comparison of Metabolic System Models

invited special editionMetabolic systemmodeling for model-based glycaemic control is becoming increasingly important. Few metabolic system models are clinically validated for both fit to the data and prediction ability. This research introduces a new additional form of pharmaco-dynamic (PD) surface comparison for model analysis and validation. These 3D surfaces are developed for 3 clinically validated models and 1 model with an added saturation dynamic. The models include the well-known Minimal Model. They are fit to two different data sets of clinical PD data from hyperinsulinaemic clamp studies at euglycaemia and/or hyperglycaemia. The models are fit to the first data set to determine an optimal set of population parameters. The second data set is used to test trend prediction of the surface modeling as it represents a lower insulin sensitivity cohort and should thus require only scaling in these (or related) parameters to match this data set. This particular approach clearly highlights differences in modelingmethods, and the model dynamics utilized that may not appear as clearly in other fitting or prediction validation methods. Across all models saturation of insulin action is seen to be an important determinant of prediction and fit quality. In particular, the well-reported under-modeling of insulin sensitivity in the Minimal Model can be seen in this context to be a result of a lack of saturation dynamics, which in turn affects its ability to detect differences between cohorts. The overall approach of examining PD surfaces is seen to be an effective means of analyzing and thus validating a metabolic model’s inherent dynamics and basic trend prediction on a population level, but is not a replacement for data driven, patient-specific fit and prediction validation for clinical use. The overall method presented could be readily generalized to similar PD systems and therapeutics