Glucose-insulin System Research Articles

Computational model of insulin-glucose regulatory system to represent type 1 diabetes mellitus, hypoglycemia and hyperinsulinemia

Diabetes mellitus (DM) is probably one of the most popular diseases among people. In this disorder, a malfunction occurs in the insulin-glucose regulatory system. To model the DM pathophysiology, we propose a computational model for the insulin-glucose regulatory system. In this differential equation model, the complex behavior of this biological system has been considered. This model shows chaos and bifurcating properties which have been seen in dynamical diseases. We have analyzed static and dynamical properties of the proposed model to show its strength and capability to represent different types of diabetes and other dysfunction in the insulin-glucose system.

Control-Oriented Model With Intra-Patient Variations for an Artificial Pancreas.

In this work, a low-order model designed for glucose regulation in Type 1 Diabetes Mellitus (T1DM) is obtained from the UVA/Padova metabolic simulator. It captures not only the nonlinear behavior of the glucose-insulin system, but also intra-patient variations related to daily insulin sensitivity ( SI) changes. To overcome the large inter-subject variability, the model can also be personalized based on a priori patient information. The structure is amenable for linear parameter varying (LPV) controller design, and represents the dynamics from the subcutaneous insulin input to the subcutaneous glucose output. The efficacy of this model is evaluated in comparison with a previous control-oriented model which in turn is an improvement of previous models. Both models are compared in terms of their open- and closed-loop differences with respect to the UVA/Padova model. The proposed model outperforms previous T1DM control-oriented models, which could potentially lead to more robust and reliable controllers for glycemia regulation.

Semiglobal Sampled-Data Dynamic Output Feedback Controller for the Glucose–Insulin System

In this paper, we deal with the problem of tracking a desired plasma glucose concentration by means of intravenous insulin administration, for Type 2 diabetic patients. A nonlinear time-delay model is used to describe the glucose–insulin regulatory system, according to which a model-based approach is exploited to design a semiglobal sampled-data dynamic output feedback controller. It is shown that emulation, by Euler approximation, of a proposed continuous-time control law yields stabilization in the sample-and-hold sense of the glucose–insulin system. The glucose regulator makes use of only sampled glucose measurements. Theoretical results are preclinically validated through a virtual environment broadly accepted as a substitute to animal trials for the preclinical testing of control strategies in plasma glucose regulation. Numerical results are encouraging and pave the way to further clinical verifications.

Application of optimal control strategies for physiological model of type 1 diabetes - T1D

Type 1 diabetes is a serious disease that affects many children and adolescents. The disease causes the pancreas to stop producing insulin, a hormone that regulates blood sugar level Insulin is a hormone that lowers the blood glucose concentration by catalyzing storage of glucose. In this work, the mathematical model describing the whole blood glucose-insulin system was considered where it was derived both based upon the two minimal models of Bergman’s minimal model, which is primarily used to interpret an IVGTT. Our objective is to propose a therapeutic scheme adapted to the needs of the diabetic patient and this through a mathematical model describing type 1. To illustrate the obtained results using Matlab/Simulink TM, various examples are presented.

Anticipating Meals with Behavioral Profiles in an Artificial Pancreas System - An Informed Multistage Model Predictive Control Approach

This contribution presents an individualized multistage model predictive control (MS-MPC) algorithm for blood glucose (BG) stabilization and improved postprandial BG control for people with type 1 diabetes (T1D) with consistent meal patterns. The multistage formulation utilizes different meal patterns as disturbance realizations entering the glucose-insulin system, then assesses the best possible control input among all of the probable scenarios. The disturbance realizations, in the form of glucose rate of appearance traces, are estimated by using meal records (time and carbohydrate amount) as the input into an individualized oral model. Meal signatures are then clustered with the k-medoids algorithm to obtain meal patterns. Two approaches, a hybrid closed-loop (HCL) and fully closed-loop (FCL) MS-MPC were tested and compared with their respective control treatments (hybrid and fully automated MPC, respectively) using the complete in silico adult cohort of the FDA-accepted UVA/Padova metabolic simulator. Results confirm an improvement in both postprandial and overall percent time in 70-180 mg/dL 85.2 ± 15.5 v. 89.6 ± 12.2 and 94.1 ± 6.3 v. 95.7 ±5.0, respectively, using the HCL approach, and 37.8 ± 15.7 v. 63.4± 16.6 and 65.8 ± 12.7 v. 82.2± 9.2, using the FCL approach.P

The Effects of Insulin Infusion Protocol on the Glycemic Level of the Intensive Care Patients

Insulin infusion protocol is the standard protocol that has been practiced in Malaysia’s intensive care unit (ICU) for controlling the hyperglycemia. Multiple sliding scale method of the insulin infusion protocol may drive conflict in selecting an appropriate scale to be applied to the patient. The objective of this paper is to analyse the blood glucose outcome of eight sliding scales insulin infusion protocol adopted in the Universiti Sains Malaysia Hospital (HUSM). A retrospective data of 78 ICU patients of HUSM were fitted using a validated glucose-insulin system to identify insulin sensitivity profiles of the patients. Then, these SI profiles were simulated on various scale protocols. The results obtained from this study showed that among eight scales, Scale 4 had the highest percentage of BG within the HUSM’s target of 6.0–10.0 mmol/L. Scale 1 had the highest percentage of BG for the BG measurement more than 10.0 mmol/L while Scale 8 had the highest percentage of BG measurement of less than 6.0 mmol/L. However, none of the scale shown better performance than the current clinical practice. Furthermore, all of the eight scales had a more substantial number of BG measurement compared to the clinical. This study shows that Scale 2 and Scale 3 result in a similar outcome. Similarly, Scale 5 is almost the same as Scale 6. Thus, at least two sets of scale can be combined to reduce the number of scales. The reduction of scales consequently avoid confusion and helps the clinician in selecting the appropriate scale to be applied to the patients. From this study, it can be concluded that the HUSM protocol is a combination of scales. The scales may be shifted from one to another scale depending on patient condition and clinician judgement. A proper guideline for the scale shifting seems necessary to allow optimum glycemic management in the ICU.

Chaotic dynamics of a fractional order glucose-insulin regulatory system

The fractional order model of a glucose-insulin regulatory system is derived and presented. It has been extensively proved in the literature that fractional order analysis of complex systems can reveal interesting and unexplored features of the system. In our investigations we have revealed that the glucose-insulin regulatory system shows multistability and antimonotonicity in its fractional order form. To show the effectiveness of fractional order analysis, all numerical investigations like stability of the equilibrium points, Lyapunov exponents, and bifurcation plots are derived. Various biological disorders caused by an unregulated glucose-insulin system are studied in detail. This may help better understand the regulatory system.

P.032 A phase-2, double-blind, placebo-controlled, sequential parallel comparison design study of efficacy and safety of adjunctive pimavanserin in major depressive disorder

The controllability and observability of a glucose insulin system is checked for the Sturis–Tolic model and its modified form for a type 1 diabetic patient. This technique plays a vital role in the development of a fully automatic artificial pancreas and stabilizes the control loop system for the glucose insulin pump. It would be important for helping type 1 diabetic patients to control their disease. We treated the Sturis–Tolic model for a healthy person and modified this model for a type 1 diabetic patient to check the linear controllability and observability. The Lyapunov equation is used to check the stability analysis of the model, which yields that the modified model is stable. We show the numerical simulation of the model for type 1 diabetes mellitus for the controllability and observability matrix according to different initial conditions on state vector. These models can be used to simulate a glucose insulin system for the treatment of type 1 diabetes.

Fuzzy functional observer for the control of the glucose-insulin system

Fuzzy functional observer for the control of the glucose-insulin system

Revised convexity, normality and stability properties of the dynamical feedback fuzzy state space model (FFSSM) of insulin–glucose regulatory system in humans

This research tries to explore more important structural properties of the insulin–glucose regulatory system in humans. Consequently, an important theorem, namely “revised modified optimized defuzzified value theorem” for feedback systems is derived and then proved. Moreover, the properties concerning the convexity, normality and the bounded-input bounded-output stability of the induced solution of FFSSM are researched. The proposed theorems and lemmas are successfully implemented and verified for the insulin–glucose system in humans. The successful and promising results and proofs of the theorems of the relevant properties improve the credibility and reliability of the FFSSM model of the insulin–glucose regulatory system in humans.

Toward Better Understanding of Insulin Therapy by Translation of a PK-PD Model to Visualize Insulin and Glucose Action Profiles.

Insulin replacement therapy is a fundamental treatment for glycemic control for managing diabetes. The engineering of insulin analogues has focused on providing formulations with action profiles that mimic as closely as possible the pattern of physiological insulin secretion that normally occurs in healthy individuals without diabetes. Hence, it may be helpful to practitioners to visualize insulin concentration profiles and associated glucose action profiles. Expanding on a previous analysis that established a pharmacokinetic (PK) model to describe typical profiles of insulin concentration over time following subcutaneous administration of various insulin formulations, the goal of the current analysis was to link the PK model to an integrated glucose‐insulin (IGI) systems pharmacology model. After the pharmacokinetic‐pharmacodynamic (PK‐PD) model was qualified by comparing model predictions with clinical observations, it was used to project insulin (PK) and glucose (PD) profiles of common insulin regimens and dosing scenarios. The application of the PK‐PD model to clinical scenarios was further explored by incorporating the impact of several hypothetical factors together, such as changing the timing or frequency of administration in a multiple‐dosing regimen over the course of a day, administration of more than 1 insulin formulation, or insulin dosing adjusted for carbohydrates in meals. Visualizations of insulin and glucose profiles for commonly prescribed regimens could be rapidly generated by implementing the linked subcutaneous insulin PK‐IGI model using the R statistical program (version 3.4.4) and a contemporary web‐based interface, which could enhance clinical education on glycemic control with insulin therapy.

A Comparison of Minimal Pharmacokinetic Models for an Anti-diabetic Agent

In diabetes studies, pharmacologists rely on statistical techniques tied with experimental results to describe the plasma concentration of an anti-diabetic agent. However, these sets of statistical information only provide minimal inference to the drug’s kinetics. To understand the effect of an anti-diabetic agent to a glucose-insulin system, it is necessary to predict its movement in the system over a specific time interval. In this study, a set of simple pharmacokinetic models was formulated to describe the dynamics of the plasma concentration of an anti-diabetic agent known as metformin. The models were fitted to empirical data via nonlinear regression analysis and were compared using Akaike information criterion to determine the most reasonable model and parameter estimates. The results reveal that models considering varying absorption rate have a promising fit. These models can be extended to multiple drug dosage cases and can be used to estimate rate constants associated to other anti-diabetic agents.
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Role of Automated Insulin Delivery (Artificial Pancreas) in Islet Transplantation: An In Silico Assessment

Human pancreatic islet transplantation is a minimally-invasive procedure that is gaining recognition for the treatment of type 1 diabetes (T1D). Selected patients with unstable T1D, hypoglycemia unawareness, history of severe hypoglycemia, and glycemic lability, not successfully stabilized with intensive insulin treatment, can be offered this alternative therapy that has been shown to provide long-term glycemic control with near-normalization of hemoglobin A1c in the absence of severe hypoglycemia. Today, downsides of pancreatic islet transplantation include the need for chronic recipient immunosuppression and the limited supply of pancreatic islets. In addition, attaining long-term insulin independence remains a challenge. In this context, stabilization of a patient’s metabolic system with islet transplantation that is augmented by automated insulin delivery (AID) technology could be of significant interest. In this chapter, we want to frame and illustrate the problem of developing a combined bio-artificial system that includes an islet graft and mechanical AID. Our discussion will propose modeling approaches potentially deployable in describing glucose homeostasis after islet transplantation and will be supported by a series of in silico studies simulating post-transplant glycemic patterns and the impact of AID control strategies. To run our analyses, we used the UVA/Padova T1D Simulator – a simulation platform accepted by FDA as a substitute to animal trials in the pre-clinical evaluation of insulin treatment strategies, appropriately modified to describe the glucose-insulin regulation system in transplanted individuals. The results presented here are a very preliminary in silico assessment of the benefits of combining islet transplantation and AID. Further research, which relies on glucose and insulin data collected from transplanted patients, will be needed to optimize modeling and control strategies.

Control of Augmented Minimal Model for Glucose Insulin Pump

A control system can only be used in the form of closed-loop control to stabilize the system. The concept of controllability and observability for the linearized control system of human glucose insulin systems is used so that we can have a feedback control. We have treated an existing model i.e. Augmented minimal model of glucose kinetics for type 1 diabetes.

An adaptive backstepping based non-linear controller for artificial pancreas in type 1 diabetes patients

Abstract Artificial pancreas enables closed loop automated control for blood glucose regulation in type 1 diabetic patients. Simple backstepping controller for regulation of blood glucose level has recently been proposed in the literature. In this research work, we have proposed backstepping based adaptive controller for integration in artificial pancreas. Controller design has been based on Bergman's minimal model which represents the dynamics of blood glucose-insulin system of human body. Glucose effectiveness factor has been treated as an unknown parameter and its value has been adapted using Lyapunov based adaptive backstepping control approach. Effects of meal disturbance, physical non-linearities and sensor noise have also been considered in the controller design. A complete mathematical derivation of the proposed nonlinear controller has been described and simulation results have been presented using Matlab/Simulink environment. Results indicate improvement in tracking response and overshoot/undershoot characteristics as compared to some recently developed techniques in literature. Proposed controller assures dynamic stability against disturbances and deviations in human body parameters. Practical implementation of the proposed controller can result in better artificial pancreas.

Effects of dietary ferulic acid on reproductive function and metabolism of pre-pubertal hairbreed ewes during the anestrous season

This study aimed to evaluate the effects of dietary supplementation of ferulic acid (FA) on reproductive tract weight (RTW), ovarian activity and serum concentrations of some metabolites and thyroid hormones of pre-pubertal hairbreed ewe lambs during the natural anestrous season. Twenty Dorper x Pelibuey crossbred ewe lambs (initial body weight = 28.5 ± 0.5 kg and age = 5.0 mo) were fed a basal diet and assigned to two treatments (n = 10) under randomized complete block design: FA supplementation to dosages of 0 (control) or 300 mg of FA/d animal−1. Females were treated with FA during 34 d and then slaughtered to evaluate RTW and ovary. Blood samples were collected at days 1 and 34 to determine serum concentrations of metabolites (i.e. glucose, cholesterol, triglycerides, urea and total protein) and thyroid hormones. Final weight, dry matter intake, and serum concentrations of metabolites (except glucose) and thyroid hormones were unaltered by FA. Compared to control, FA-fed ewe lambs had lower (P = 0.05) serum glucose level but higher (P ≤ 0.05) RTW, ovarian mass, number of larger follicles and corpus luteum (CL). While percentage of ewe lambs with small follicles and number of small follicles per female did not vary, percentage of ewe lambs with large follicles and CL increased (P = 0.02) with FA. In conclusion, FA supplementation improves the reproductive tract development and ovarian activity of pre-pubertal hairbreed ewe lambs during the natural anestrous season. An improvement in functionality of the glucose-insulin system could be related with this benefic effect of FA.

Black-box Model Identification of Physical Activity in Type-l Diabetes Patients.

In this paper we consider the problem of predicting future values of glucose in type-1 diabetes. In particular, we investigate the benefit of including physical activity, measured by an off-the-shelf wearable device, to other physiologic signals frequently used to predict blood-glucose concentration, namely injected insulin, carbohydrates intake, and past glucose samples measured by a Continuous Glucose Monitoring (CGM) sensor. Derivation of individualized predictors is crucial to cope with the wide inter- and intra-subject variability: learning and updating patient-specific models of the glucose-insulin system and using them to design personalized control actions has the potential to improve substantially patients' quality oflife. On data collected by 6 subjects for 5 days, we identify a black-box liner model that uses insulin and meal as inputs and glucose as output. Prediction Error Method (PEM) is used for parameter estimation. The personalized model is employed to derive patient-tailored predictors. This procedure is then repeated using a further physiological input, accounting for physical activity. The prediction accuracy of the two models, including or not physical activity, was compared on the basis of two metrics commonly used in system identification, namely Coefficient of Determination (COD) and Root Mean Squared Error. The models identified with physical activity have better performance, increasing the 3-hr prediction COD by mean ± standard deviation of 18.5% ± 30.1%.

Biometric Analysis of <i>Hunteria umbellata</i> (K.Schum.) Hallier f and Metformin in the treatment of diabetes

Diabetes is a disease of genetic and environmental origin. It affects the glucose-insulin endocrine metabolic regulatory system due to the malfunction of the insulin producing cells of the pancreas. The purpose of this study is to carry out a comparative analysis of the potencies of hunteria umbellata and metformin on Type 1 diabetes mellitus using bio-statistical analysis. The period of attaining normal blood glucose level across times was taken into consideration as a basis for comparing the efficacy of the oral antidiabetic herbal drug to that of the orthodox antidiabetic drug. The biometric analysis of data from five diabetic patients that were placed on herbal treatment were taken daily for a period of 3 weeks during which they attained normal fasting blood glucose level of between 70mg and 100mg as compared with a recovery period of 8 weeks for the 15 patients treated with metformin 500mg. These results showed that hunteria umbellata is more efficacious in the treatment of diabetes mellitus than metformin 500mg.

Simplified Mathematical Model of Glucose-Insulin System

Mathematical modelling of glucose-insulin system is very important in medicine as a necessary tool to understand the homeostatic control of human body. It can also be used to design clinical trials and in the evaluation of the diabetes prevention. In the last three decades so much work has been done in this direction. One of the most notable models is the global six compartment-mathematical model with 22 ordinary differential equations due to John Thomas Sorensen. This paper proposes a more simplified three compartment-mathematical model with only 6 ordinary differential equations by introducing a tissue compartment comprising kidney, gut, brain and periphery. For model parameter identification, we use inverse problems technique to solve a specific optimal control problem where data are obtained by solving the global model of John Thomas Sorensen. Numerical results show that the proposed model is adaptable to data and can be used to adjust diabetes mellitus type I or type II for diabetic patients.

Modelling of glucose-insulin dynamics from low sampled data

In this paper we focus on modelling the glucose-insulin dynamics in the human body for the purpose of controlling the glucose level. Due to the fast dynamics in the glucose-insulin system compared to the natural sampling period (24 h) in a clinical situation, the model structure has to be adapted adequately. This results in a reduced order model with a nonlinear output relation. The development of the estimation methodology is based on a simulation study with a continuous time model. The resulting model structure is used for estimating the parameters of the non-linear system, representing the slow dynamics observed from the slow and sparse sampled clinical data.