A simulation model of the glucose-insulin system in the postprandial state can be useful in several circumstances, including testing of glucose sensors, insulin infusion algorithms and decision support systems for diabetes. Here, we present a new simulation model in normal humans that describes the physiological events that occur after a meal, by employing the quantitative knowledge that has become available in recent years. Model parameters were set to fit the mean data of a large normal subject database that underwent a triple tracer meal protocol which provided quasi-model-independent estimates of major glucose and insulin fluxes, e.g., meal rate of appearance, endogenous glucose production, utilization of glucose, insulin secretion. By decomposing the system into subsystems, we have developed parametric models of each subsystem by using a forcing function strategy. Model results are shown in describing both a single meal and normal daily life (breakfast, lunch, dinner) in normal. The same strategy is also applied on a smaller database for extending the model to type 2 diabetes.

Objective: Recent studies have provided new insights into nonlinearities of insulin action in the hypoglycemic range and into glucagon kinetics as it relates to response to hypoglycemia. Based on these data, we developed a new version of the UVA/PADOVA Type 1 Diabetes Simulator, which was submitted to FDA in 2013 (S2013). Methods: The model of glucose kinetics in hypoglycemia has been improved, implementing the notion that insulin-dependent utilization increases nonlinearly when glucose decreases below a certain threshold. In addition, glucagon kinetics and secretion and action models have been incorporated into the simulator: glucagon kinetics is a single compartment; glucagon secretion is controlled by plasma insulin, plasma glucose below a certain threshold, and glucose rate of change; and plasma glucagon stimulates with some delay endogenous glucose production. A refined statistical strategy for virtual patient generation has been adopted as well. Finally, new rules for determining insulin to carbs ratio (CR) and correction factor (CF) of the virtual patients have been implemented to better comply with clinical definitions. Results: S2013 shows a better performance in describing hypoglycemic events. In addition, the new virtual subjects span well the real type 1 diabetes mellitus population as demonstrated by good agreement between real and simulated distribution of patient-specific parameters, such as CR and CF. Conclusions: S2013 provides a more reliable framework for in silico trials, for testing glucose sensors and insulin augmented pump prediction methods, and for closed-loop single/dual hormone controller design, testing, and validation.

Arguably, a minimally invasive system using subcutaneous (s.c.) continuous glucose monitoring (CGM) and s.c. insulin delivery via insulin pump would be a most feasible step to closed-loop control in type 1 diabetes mellitus (T1DM). Consequently, diabetes technology is focusing on developing an artificial pancreas using control algorithms to link CGM with s.c. insulin delivery. The future development of the artificial pancreas will be greatly accelerated by employing mathematical modeling and computer simulation. Realistic computer simulation is capable of providing invaluable information about the safety and the limitations of closed-loop control algorithms, guiding clinical studies, and out-ruling ineffective control scenarios in a cost-effective manner. Thus computer simulation testing of closed-loop control algorithms is regarded as a prerequisite to clinical trials of the artificial pancreas. In this paper, we present a system for in silico testing of control algorithms that has three principal components: (1) a large cohort of n = 300 simulated “subjects” (n = 100 adults, 100 adolescents, and 100 children) based on real individuals' data and spanning the observed variability of key metabolic parameters in the general population of people with T1DM; (2) a simulator of CGM sensor errors representative of Freestyle Navigator™, Guardian RT, or Dexcom™ STS™, 7-day sensor; and (3) a simulator of discrete s.c. insulin delivery via OmniPod Insulin Management System or Deltec Cozmo® insulin pump. The system has been shown to represent adequate glucose fluctuations in T1DM observed during meal challenges, and has been accepted by the Food and Drug Administration as a substitute to animal trials in the preclinical testing of closed-loop control strategies.

Dehydroepiandrosterone (DHEA) and testosterone are widely promoted as antiaging supplements, but the long-term benefits, as compared with potential harm, are unknown. We performed a 2-year, placebo-controlled, randomized, double-blind study involving 87 elderly men with low levels of the sulfated form of DHEA and bioavailable testosterone and 57 elderly women with low levels of sulfated DHEA. Among the men, 29 received DHEA, 27 received testosterone, and 31 received placebo. Among the women, 27 received DHEA and 30 received placebo. Outcome measures included physical performance, body composition, bone mineral density (BMD), glucose tolerance, and quality of life. As compared with the change from baseline to 24 months in the placebo group, subjects who received DHEA for 2 years had an increase in plasma levels of sulfated DHEA by a median of 3.4 μg per milliliter (9.2 μmol per liter) in men and by 3.8 μg per milliliter (10.3 μmol per liter) in women. Among men who received testosterone, the level of bioavailable testosterone increased by a median of 30.4 ng per deciliter (1.1 nmol per liter), as compared with the change in the placebo group. A separate analysis of men and women showed no significant effect of DHEA on body-composition measurements. Neither hormone altered the peak volume of oxygen consumed per minute, muscle strength, or insulin sensitivity. Men who received testosterone had a slight increase in fat-free mass, and men in both treatment groups had an increase in BMD at the femoral neck. Women who received DHEA had an increase in BMD at the ultradistal radius. Neither treatment improved the quality of life or had major adverse effects. Neither DHEA nor low-dose testosterone replacement in elderly people has physiologically relevant beneficial effects on body composition, physical performance, insulin sensitivity, or quality of life. (ClinicalTrials.gov number, NCT00254371.)

The control of diabetes is an interdisciplinary endeavor, which includes a significant biomedical engineering component, with traditions of success beginning in the early 1960s. It began with modeling of the insulin-glucose system, and progressed to large-scale in silico experiments, and automated closed-loop control (artificial pancreas). Here, we follow these engineering efforts through the last, almost 50 years. We begin with the now classic minimal modeling approach and discuss a number of subsequent models, which have recently resulted in the first in silico simulation model accepted as substitute to animal trials in the quest for optimal diabetes control. We then review metabolic monitoring, with a particular emphasis on the new continuous glucose sensors, on the analyses of their time-series signals, and on the opportunities that they present for automation of diabetes control. Finally, we review control strategies that have been successfully employed in vivo or in silico, presenting a promise for the development of a future artificial pancreas and, in particular, discuss a modular architecture for building closed-loop control systems, including insulin delivery and patient safety supervision layers. We conclude with a brief discussion of the unique interactions between human physiology, behavioral events, engineering modeling and control relevant to diabetes.

Glucose tolerance decreases with age. For determining the cause of this decrease, 67 elderly and 21 young (70.1 ± 0.7 vs. 23.7 ± 0.8 years) participants ingested a mixed meal and received an intravenous injection of glucose. Fasting glucose and the glycemic response above basal were higher in the elderly than in the young participants after either meal ingestion (P < 0.001) or glucose injection (P < 0.01). Insulin action (Si), measured with the meal and intravenous glucose tolerance test models, was highly correlated (r = 0.72; P < 0.001) and lower (P ≤ 0.002) in the elderly than in the young participants. However, when adjusted for differences in percentage body fat and visceral fat, Si no longer differed between groups. When considered in light of the degree of insulin resistance, all indexes of insulin secretion were lower (P < 0.01) in the elderly participants, indicating impaired β-cell function. Hepatic insulin clearance was increased (P < 0.002), whereas total insulin clearance was decreased (P < 0.002) in the elderly subjects. Multivariate analysis (r = 0.70; P < 0.001) indicated that indexes of insulin action (Si) and secretion (Phi_total) but not age, peak oxygen uptake, fasting glucose, degree of fatness, or hepatic insulin clearance predicted the postprandial glycemic response. We conclude that the deterioration in glucose tolerance that occurs in healthy elderly subjects is due to a decrease in both insulin secretion and action with the severity of the defect in insulin action being explained by the degree of fatness rather than age per se.