scholarly journals A comparison of the artificial pancreas (glucose controlled insulin infusion system) and a manual technique for assessing insulin sensitivity during euglycaemic clamping

Diabetologia ◽  
1984 ◽  
Vol 26 (6) ◽  
Author(s):  
M. Ponchner ◽  
R.J. Heine ◽  
A. Pernet ◽  
I. Manning ◽  
A.J. Francis ◽  
...  
Keyword(s):  
Insulin Sensitivity ◽  
Insulin Infusion ◽  
Artificial Pancreas ◽  
Infusion System

Development of the Biostator Glucose clamping algorithm.

1982 ◽  
Vol 28 (9) ◽  
pp. 1899-1904 ◽  
Author(s):  
A H Clemens ◽  
D L Hough ◽  
P A D'Orazio
Keyword(s):  
Animal Studies ◽  
Insulin Infusion ◽  
Blood Glucose Concentration ◽  
Human Subjects ◽  
Pancreatic Beta Cell ◽  
Model System ◽  
Exogenous Insulin ◽  
Maturity Onset Diabetes ◽  
Beta Cell Response ◽  
Infusion System

Abstract The "glucose clamping" technique has been proposed as a method for the early detection of a beginning derangement of glucose homeostasis and thus for the possible prevention of maturity-onset diabetes. This technique interrupts the physiological glucose-insulin relationship by placing a patient's blood glucose concentration under an investigator's control, for quantification of the pancreatic beta-cell response during hyperglycemic clamps and of sensitivity of body tissue to exogenous insulin during normoglycemic clamps. We report the development of a glucose clamping algorithm for use with the Biostator glucose-controlled insulin-infusion system (Horm. Metab. Res., Suppl. 8: 23-33, 1977). This algorithm adds simplicity and precision to the glucose clamping procedure and reduces operator effort to a minimum. We describe the early development of the algorithm with a model system and report evaluations made during animal studies and preliminary investigations with human subjects.

A secure insulin infusion system using verification monitors

2021 ◽  
Author(s):  
Abhinandan panda ◽  
Srinivas Pinisetty ◽  
Partha Roop
Keyword(s):  
Insulin Infusion ◽  
Infusion System

An Automatic Insulin Infusion System Based on the Genetic Algorithm FOPID Control

2020 ◽  
pp. 355-366
Author(s):  
Akshaya Kumar Patra ◽  
Anuja Nanda ◽  
Bidyadhar Rout ◽  
Dillip Kumar Subudhi ◽  
Sanjeeb Kumar Kar
Keyword(s):  
Genetic Algorithm ◽  
Insulin Infusion ◽  
Infusion System

scholarly journals Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Arthur Bertachi ◽  
Lyvia Biagi ◽  
Aleix Beneyto ◽  
Josep Vehí
Keyword(s):  
Type 1 Diabetes ◽  
Insulin Infusion ◽  
Artificial Pancreas ◽  
The Body ◽  
Control Algorithms ◽  
Rule Based ◽  
Control Blood Glucose ◽  
Glucose Levels ◽  
Blood Glucose Levels

The artificial pancreas (AP) is a system intended to control blood glucose levels through automated insulin infusion, reducing the burden of subjects with type 1 diabetes to manage their condition. To increase patients’ safety, some systems limit the allowed amount of insulin active in the body, known as insulin-on-board (IOB). The safety auxiliary feedback element (SAFE) layer has been designed previously to avoid overreaction of the controller and thus avoiding hypoglycemia. In this work, a new method, so-called “dynamic rule-based algorithm,” is presented in order to adjust the limits of IOB in real time. The algorithm is an extension of a previously designed method which aimed to adjust the limits of IOB for a meal with 60 grams of carbohydrates (CHO). The proposed method is intended to be applied on hybrid AP systems during 24 h operation. It has been designed by combining two different strategies to set IOB limits for different situations: (1) fasting periods and (2) postprandial periods, regardless of the size of the meal. The UVa/Padova simulator is considered to assess the performance of the method, considering challenging scenarios. In silico results showed that the method is able to reduce the time spent in hypoglycemic range, improving patients’ safety, which reveals the feasibility of the approach to be included in different control algorithms.

scholarly journals Plasma cathepsin D activity is negatively associated with hepatic insulin sensitivity in overweight and obese humans

Diabetologia ◽  
2019 ◽  
Vol 63 (2) ◽  
pp. 374-384 ◽  
Author(s):  
Lingling Ding ◽  
Gijs H. Goossens ◽  
Yvonne Oligschlaeger ◽  
Tom Houben ◽  
Ellen E. Blaak ◽  
...  
Keyword(s):  
Insulin Sensitivity ◽  
Cathepsin D ◽  
Insulin Infusion ◽  
Hepatic Insulin Sensitivity ◽  
Euglycaemic Clamp ◽  
Negatively Associated ◽  
Peripheral Insulin Sensitivity ◽  
Hyperinsulinaemic Euglycaemic Clamp ◽  
Tnf Α ◽  
Glucose Output

Abstract Aims/hypothesis Insulin resistance in skeletal muscle and liver plays a major role in the pathophysiology of type 2 diabetes. The hyperinsulinaemic–euglycaemic clamp is considered the gold standard for assessing peripheral and hepatic insulin sensitivity, yet it is a costly and labour-intensive procedure. Therefore, easy-to-measure, cost-effective approaches to determine insulin sensitivity are needed to enable organ-specific interventions. Recently, evidence emerged that plasma cathepsin D (CTSD) is associated with insulin sensitivity and hepatic inflammation. Here, we aimed to investigate whether plasma CTSD is associated with hepatic and/or peripheral insulin sensitivity in humans. Methods As part of two large clinical trials (one designed to investigate the effects of antibiotics, and the other to investigate polyphenol supplementation, on insulin sensitivity), 94 overweight and obese adults (BMI 25–35 kg/m2) previously underwent a two-step hyperinsulinaemic–euglycaemic clamp (using [6,6-2H2]glucose) to assess hepatic and peripheral insulin sensitivity (per cent suppression of endogenous glucose output during the low-insulin-infusion step, and the rate of glucose disappearance during high-insulin infusion [40 mU/(m2 × min)], respectively). In this secondary analysis, plasma CTSD levels, CTSD activity and plasma inflammatory cytokines were measured. Results Plasma CTSD levels were positively associated with the proinflammatory cytokines IL-8 and TNF-α (IL-8: standardised β = 0.495, p < 0.001; TNF-α: standardised β = 0.264, p = 0.012). Plasma CTSD activity was negatively associated with hepatic insulin sensitivity (standardised β = −0.206, p = 0.043), independent of age, sex, BMI and waist circumference, but it was not associated with peripheral insulin sensitivity. However, plasma IL-8 and TNF-α were not significantly correlated with hepatic insulin sensitivity. Conclusions/interpretation We demonstrate that plasma CTSD activity, but not systemic inflammation, is inversely related to hepatic insulin sensitivity, suggesting that plasma CTSD activity may be used as a non-invasive marker for hepatic insulin sensitivity in humans.

Development and evaluation of a glucose analyzer for a glucose controlled insulin infusion system ((Biostator).

1978 ◽  
Vol 24 (8) ◽  
pp. 1366-1372 ◽  
Author(s):  
E J Fogt ◽  
L M Dodd ◽  
E M Jenning ◽  
A H Clemens
Keyword(s):  
Blood Glucose ◽  
Whole Blood ◽  
Insulin Infusion ◽  
Full Range ◽  
Dynamic Control ◽  
On Line ◽  
The Moment ◽  
Glucose 6 Phosphate Dehydrogenase ◽  
Blood Glucose Concentrations ◽  
Infusion System

Abstract The Glucose-Controlled Insulin Infusion System (Biostator) is a modular, computerized, feedback control system for dynamic control of blood glucose concentrations in diabetics. This on-line glucose analyzer for use with whole blood utilizes a novel enzyme (glucose oxidase)-membrane configuration and an electrochemical cell to measure the H202 generated. The analyzer exhibits both short- and long-range stability, and instrument response and analyte concentration are linearly related over the full range of clinical interest. The response is fast, accurate, and precise, and permits determination of blood glucose within 2 min from the moment the blood leaves the patient. Correlation studies were completed to show the agreement between the Biostator Glucose Analyzer and the FDA's recommended hexokinase/glucose-6-phosphate dehydrogenase procedure on whole blood (e.g., average per cent recovered for 11 concentrations between 250 and 900 mg/liter was: hexokinase, 95.6%, Biostator Analyzer, 95.9%; bias and SDd, respectively, at low, normal, and high glucose values were: 12 and 41 mg/liter at the 500 mg/liter level; 4 and 52 mg/liter at the 1000 mg/liter level, and 4 and 128 mg/liter at the 4000 mg/liter level). No appreciable interference is observed with above-normal concentrations of bilirubin, uric acid, creatinine, sodium salicylate, or dextran. Platelet adhesion, which tends to decrease the useful life of the membrane, has been significantly decreased.

scholarly journals Modelling the effects of glucagon during glucose tolerance testing

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Ross A. Kelly ◽  
Molly J. Fitches ◽  
Steven D. Webb ◽  
S. R. Pop ◽  
Stewart J. Chidlow
Keyword(s):  
Insulin Sensitivity ◽  
Glucose Tolerance ◽  
Mathematical Models ◽  
Linear Relationship ◽  
Insulin Infusion ◽  
Glucose Tolerance Test ◽  
Tolerance Test ◽  
Glucose Kinetics ◽  
Glucose Effectiveness ◽  
Tolerance Testing

Abstract Background Glucose tolerance testing is a tool used to estimate glucose effectiveness and insulin sensitivity in diabetic patients. The importance of such tests has prompted the development and utilisation of mathematical models that describe glucose kinetics as a function of insulin activity. The hormone glucagon, also plays a fundamental role in systemic plasma glucose regulation and is secreted reciprocally to insulin, stimulating catabolic glucose utilisation. However, regulation of glucagon secretion by α-cells is impaired in type-1 and type-2 diabetes through pancreatic islet dysfunction. Despite this, inclusion of glucagon activity when modelling the glucose kinetics during glucose tolerance testing is often overlooked. This study presents two mathematical models of a glucose tolerance test that incorporate glucose-insulin-glucagon dynamics. The first model describes a non-linear relationship between glucagon and glucose, whereas the second model assumes a linear relationship. Results Both models are validated against insulin-modified and glucose infusion intravenous glucose tolerance test (IVGTT) data, as well as insulin infusion data, and are capable of estimating patient glucose effectiveness (sG) and insulin sensitivity (sI). Inclusion of glucagon dynamics proves to provide a more detailed representation of the metabolic portrait, enabling estimation of two new diagnostic parameters: glucagon effectiveness (sE) and glucagon sensitivity (δ). Conclusions The models are used to investigate how different degrees of pax‘tient glucagon sensitivity and effectiveness affect the concentration of blood glucose and plasma glucagon during IVGTT and insulin infusion tests, providing a platform from which the role of glucagon dynamics during a glucose tolerance test may be investigated and predicted.

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