Kinetic Modeling of the Glucoregulatory System to Improve Insulin Therapy
01 Oct 1985-IEEE Transactions on Biomedical Engineering (IEEE Trans Biomed Eng)-Vol. 32, Iss: 10, pp 846-855
TL;DR: For simulating glucose-insulin control systems with respect to application in an optimized therapy of insulin-dependent diabetes, a global transfer model was generated and supplemented with a structural model of the opened control circuit to determine those control constants of glucose-controlled insulin provision that are optimally adapted to a given set of state variables.
Abstract: For simulating glucose-insulin control systems with respect to application in an optimized therapy of insulin-dependent diabetes, a global transfer model was generated and supplemented with a structural model of the opened control circuit. The transfer characteristics within the established linear range of the intact system, which is identical with the range of euglycaemia, can be described by a 4th-order differential equation. A test strategy was developed to identify the state variables of the opened, i. e., of the diabetic, system and to determine by pole assignment those control constants of glucose-controlled insulin provision that are optimally adapted to a given set of state variables. The model system was verified by the prediction of plasma insulin and blood glucose reponses to various loads of glucose and/or insulin, by isotopic studies (U-14C-glucose) of glucose turnover, and by predicting the rates of absorption (i. e., of appearance within the distribution space) of orally administered glucose. The latter situations provide fields of application for this model strategy in addition to the main goal, namely, the individual optimization of control parameters for open-loop or closed-loop insulin therapy.
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TL;DR: Adapt nonlinear model predictive control is promising for the control of glucose concentration during fasting conditions in subjects with type 1 diabetes.
Abstract: A nonlinear model predictive controller has been developed to maintain normoglycemia in subjects with type 1 diabetes during fasting conditions such as during overnight fast. The controller employs a compartment model, which represents the glucoregulatory system and includes submodels representing absorption of subcutaneously administered short-acting insulin Lispro and gut absorption. The controller uses Bayesian parameter estimation to determine time-varying model parameters. Moving target trajectory facilitates slow, controlled normalization of elevated glucose levels and faster normalization of low glucose values. The predictive capabilities of the model have been evaluated using data from 15 clinical experiments in subjects with type 1 diabetes. The experiments employed intravenous glucose sampling (every 15 min) and subcutaneous infusion of insulin Lispro by insulin pump (modified also every 15 min). The model gave glucose predictions with a mean square error proportionally related to the prediction horizon with the value of 0.2 mmol L(-1) per 15 min. The assessment of clinical utility of model-based glucose predictions using Clarke error grid analysis gave 95% of values in zone A and the remaining 5% of values in zone B for glucose predictions up to 60 min (n = 1674). In conclusion, adaptive nonlinear model predictive control is promising for the control of glucose concentration during fasting conditions in subjects with type 1 diabetes.
1,164 citations
Cites methods from "Kinetic Modeling of the Glucoregula..."
...The development of advanced algorithms followed (Fischer et al 1990, Fisher and Teo 1989, Kienitz and Yoneyama 1993, Ollerton 1989, Salzsieder et al 1985, Shichiri et al 1983, Swan 1982)....
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TL;DR: 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, is presented.
Abstract: 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.
856 citations
TL;DR: A system for in silico testing of control algorithms that 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.
Abstract: 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.
712 citations
TL;DR: The artificial pancreas (AP), known as closed-loop control of blood glucose in diabetes, is a system combining a glucose sensor, a control algorithm, and an insulin infusion device that has proved the feasibility of external glucose control and stimulated further technology development.
Abstract: The artificial pancreas (AP), known as closed-loop control of blood glucose in diabetes, is a system combining a glucose sensor, a control algorithm, and an insulin infusion device. AP developments can be traced back 50 years to when the possibility for external blood glucose regulation was established by studies in individuals with type 1 diabetes using intravenous glucose measurement and infusion of insulin and glucose. After the pioneering work by Kadish (1) in 1964, expectations for effectively closing the loop were inspired by the nearly simultaneous work of five teams reporting closed-loop control results between 1974 and 1978: Albisser et al. (2), Pfeiffer et al. (3), Mirouze et al. (4), Kraegen et al. (5), and Shichiri et al. (6). In 1977, one of these realizations (3) resulted in the first commercial device—the Biostator (7; Fig. 1), followed by another inpatient system, the Nikkiso STG-22 Blood Glucose Controller, now in use in Japan (8).
FIG. 1.
The Biostator (courtesy of William Clarke, University of Virginia).
Although the intravenous route of glucose sensing and insulin infusion is unsuitable for outpatient use, these devices proved the feasibility of external glucose control and stimulated further technology development. Figure 2 presents key milestones in the timeline of AP progress.
FIG. 2.
Key milestones in the timeline of AP progress. EU, Europe; IP, intraperitoneal; NIH, National Institutes of Health; SC, subcutaneous.
In 1979, landmark studies by Pickup et al. (9) and Tamborlane et al. (10) showed that the subcutaneous route was feasible for continuous insulin delivery. Three years later, Shichiri et al. (11) tested a prototype of a wearable AP, which was further developed in subsequent studies (12,13). In the late 1980s, an implantable system was introduced using intravenous glucose sensing and intraperitoneal insulin infusion (14). This technology was further developed, leading to clinical trials and …
532 citations
TL;DR: A model-based-predictive control algorithm is developed to maintain normoglycemia in the Type I diabetic patient using a closed-loop insulin infusion pump and outperforms an internal model controller from literature under noise-free conditions.
Abstract: A model-based-predictive control algorithm is developed to maintain normoglycemia in the Type I diabetic patient using a closed-loop insulin infusion pump. Utilizing compartmental modeling techniques, a fundamental model of the diabetic patient is constructed. The resulting nineteenth-order nonlinear pharmacokinetic-pharmacodynamic representation is used in controller synthesis. Linear identification of an input-output model from noisy patient data is performed by filtering the impulse-response coefficients via projection onto the Laguerre basis. A linear model predictive controller is developed using the identified step response model. Controller performance for unmeasured disturbance rejection (50 g oral glucose tolerance test) is examined. Glucose setpoint tracking performance is improved by designing a second controller which substitutes a more detailed internal model including state-estimation and a Kalman filter for the input-output representation The state-estimating controller maintains glucose within 15 mg/dl of the setpoint in the presence of measurement noise. Under noise-free conditions, the model based predictive controller using state estimation outperforms an internal model controller from literature (49.4% reduction in undershoot and 45.7% reduction in settling time). These results demonstrate the potential use of predictive algorithms for blood glucose control in an insulin infusion pump.
528 citations
References
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TL;DR: A previously formulated glucose-insulin feed-back theory was simplified with appropriate assumptions for the purpose of determining which physiological sensitivity coefficients dominate the mathema.
Abstract: A previously formulated glucose-insulin feed-back theory was simplified with appropriate assumptions for the purpose of determining which physiological sensitivity coefficients dominate the mathema...
322 citations
TL;DR: It was concluded that the tracer infusion method can reliably measure Ra of glucose when it is changing rapidly, and the system is out of steady state.
Abstract: The aim of the present experiments is to validate, in conscious dogs, the tracer infusion methods of measuring nonsteady turnover rates. This was done in nine experiments performed in four normal dogs by infusing isotopically labeled glucose (2-3H, 6-3H, 1-14C) and monitoring the concentrations of both the labeled and unlabeled substances. The validation is based on the observation that a high exogenous infusion of glucose will suppress endogenous glucose production and become the sole source of glucose in the body. By infusing glucose at a high, time-varying rate, calculating its rate of appearance, (Ra) and comparing it to the infused rate, the method can be verified. The calculations were based on: a) a single-compartment model with a modified volume of distribution; b) a two-compartment model; and c) a generalized dispersion model. The absolute values of the areas of the deviations of the calculated from the infused curves were found to be, respectively, 9.5, 8.4, and 7.8 percent of the total area under the infused curve. It was concluded that the tracer infusion method can reliably measure Ra of glucose when it is changing rapidly, and the system is out of steady state.
300 citations
TL;DR: The integrated whole-organism model is of the comprehensive type, is nonlinear, and evidences the major crucial processes of glucose, insulin, and glucagon dynamics per se and their interrelationships.
Abstract: An integrated whole-organism model of the short-term blood glucose regulation system is presented. The model is of the comprehensive type, is nonlinear, and evidences the major crucial processes of glucose, insulin, and glucagon dynamics per se and their interrelationships. Validation of the model has been performed by dealing simultaneously with different kinds of test inputs in a variety of normal and pathological states and by looking not only at plasma accessible variables, but also at the behavior of unit processes. Current practical uses of the model in the area of carbohydrate metabolism regulation are briefly outlined.
183 citations
TL;DR: It is concluded that in the presence of basal insulin levels hyperglycemia inhibits glucose output independent of a rise in insulin or a fall in anti-insulin hormones.
Abstract: To evaluate the influence of hyperglycemia on hepatic glucose output in the absence of a rise in insulin, glucose was infused for 2 hours into six juvenile-onset diabetics receiving a constant infusion of insulin at a rate of 0.05–0.15 mU kg-1min-1. Prior to the infusion of glucose, insulin administration resulted in stable levels of plasma glucose (76±8 mg/dl) and glucose output (1.9±0.1 mg kg-1min-1). The addition of glucose produced a 2–3 fold rise in plasma glucose and a prompt fall in glucose output to 0.2–0.4 mg kg-1min-1, despite the unchanged rate of insulin infusion and the absence of a reduction in plasma glucagon or catecholamines. A similar decline in glucose output was observed when exogenous glucagon (1 ng kg-1min-1) was added to the glucose infusion. We conclude that in the presence of basal insulin levels hyperglycemia inhibits glucose output independent of a rise in insulin or a fall in anti-insulin hormones.
177 citations
TL;DR: It is demonstrated here that the response of blood-glucose concentration as a function of time (t) can be represented adequately by an equation involving only four constants in the equation: G=G0+A e−αt sin ωt.
Abstract: A complete description of the response of man to large doses of glucose involves the use of more than sixteen rate constants, each varying from one person to the next. It is demonstrated here that the response of blood-glucose concentration (G) as a function of time (t) can be represented adequately by an equation involving only four constants in the equation: G=G0+A e−αt sin ωt. The values of these four constants are defined by the four measurements usually made in an ordinary glucose-tolerance test. The natural frequency ω0=r(ω2 + α2) is shown to represent the product of the rate constants for insulin production due to added glucose and for glucose utilization due to insulin action. On the basis of measurements on over 750 persons, it is suggested that the value of ω0 can be used to distinguish normal from diabetic persons more closely than any other parameter.
160 citations