Glucose Regulation In Type Research Articles

Pediatric glucose regulation without pre-meal insulin boluses: an approach based on switched control and time-varying IOB constraints

Artificial pancreas (AP) systems have shown to improve glucose regulation in type 1 diabetes (T1D) patients. However, full closed-loop performance remains a challenge particularly in children and adolescents, since these age groups often present the worst glycemic control. In this work, a new algorithm based on switched control and time-varying insulin-on-board (IOB) constraints is presented (ARGAE). This method is a combination of ideas from the previously introduced Automatic Regulation of Glucose (ARG) algorithm, which features no pre-meal insulin boluses, and the Amplitude Enable (AE) mode, which allows the controller to act more aggressively at the beginning of meal intake without risking postprandial hypoglycemia. The proposed control strategy is evaluated in silico and its performance contrasted with the ARG algorithm in the pediatric population. Results show that the ARGAE presents improved performance compared to the ARG algorithm even in presence of misclassified meals. Thus, future in vivo testing will involve the AE configuration.

Back-stepping sliding mode control Design for glucose regulation in type 1 diabetic patients

This article presents a robust scheme for regulating blood glucose in the patients suffering from type I diabetes based on BMM. A pump is used to inject glucose/insulin under the patient’s skin to control its concentration. Previous studies have presented a variety of such controllers. But, they mainly suffered from long settling time. This study employs back-stepping sliding control to reduce the settling time. Since the controller presented in this study employs sliding mode control, it is robust against external disturbances. Since the back-stepping scheme is used in this design, it guarantees that the state variables of the system converge to the desired value. The results obtained from simulation verify performance of the presented scheme.

Impact of free-living pattern of sedentary behaviour on intra-day glucose regulation in type 2 diabetes

PurposeTo investigate how the pattern of sedentary behaviour affects intra-day glucose regulation in type 2 diabetes.MethodsThis intensive longitudinal study was conducted in 37 participants with type 2 diabetes (age, 62.8 ± 10.5 years). Glucose and sedentary behaviour/physical activity were assessed with a continuous glucose monitoring (Abbott FreeStyle Libre) and an activity monitor (activPAL3) for 14 days. Multiple regression models with generalised estimating equations (GEEs) approach were used to assess the associations of sedentary time and breaks in sedentary time with pre-breakfast glucose, pre-lunch glucose, pre-dinner glucose, post-breakfast glucose, post-lunch glucose, post-dinner glucose, bedtime glucose, the dawn phenomenon, time in target glucose range (TIR, glucose 3.9–10 mmol/L) and time above target glucose range (TAR, glucose > 10 mmol/L).ResultsSedentary time was associated with higher pre-breakfast glucose (p = 0.001), pre-dinner glucose (p < 0.001), post-lunch glucose (p = 0.005), post-dinner glucose (p = 0.013) and the dawn phenomenon (p < 0.001). Breaks in sedentary time were associated with lower pre-breakfast glucose (p = 0.023), pre-dinner glucose (p = 0.023), post-breakfast glucose (p < 0.001) and the dawn phenomenon (p = 0.004). The association between sedentary time and less TIR (p = 0.022) and the association between breaks in sedentary time and more TIR (p = 0.001) were also observed.ConclusionsReducing sedentary time and promoting breaks in sedentary time could be clinically relevant to improve intra-day glucose regulation in type 2 diabetes.

High drug-loading gold nanoclusters for responsive glucose control in type 1 diabetes

BackgroundDiabetes is one of the biggest medical challenges worldwide. The key to efficiently treat type 1 diabetes is to accurately inject insulin according to the blood glucose levels. In this study, we aimed to develop an intelligent insulin-releasing gold nanocluster system that responds to environmental glucose concentrations.ResultsWe employed gold nanoclusters (AuNCs) as a novel carrier nanomaterial by taking advantage of their high drug-loading capacity. We prepared AuNCs in the protection of bovine serum albumin, and we decorated AuNCs with 3-aminophenylboronic acid (PBA) as a glucose-responsive factor. Then we grafted insulin onto the surface to obtain the glucose-responsive insulin-releasing system, AuNC-PBA-Ins complex. The AuNC-PBA-Ins complex exhibited high sensitivity to glucose concentration, and rapidly released insulin in high glucose concentration in vitro. In the type 1 diabetic mouse model in vivo, the AuNC-PBA-Ins complex effectively released insulin and regulated blood glucose level in the normoglycemic state for up to 3 days.ConclusionsWe successfully developed a phenylboronic acid-functionalized gold nanocluster system (AuNC-PBA-Ins) for responsive insulin release and glucose regulation in type 1 diabetes. This nanocluster system mimics the function of blood glucose regulation of pancreas in the body and may have potential applications in the theranostics of diabetes.

Robust observer based control for plasma glucose regulation in type 1 diabetes patient using attractive ellipsoid method.

This paper deals with the design of robust observer based output feedback control law for the stabilisation of an uncertain nonlinear system and subsequently apply the developed method for the regulation of plasma glucose concentration in Type 1 diabetes (T1D) patients. The principal objective behind the proposed design is to deal with the issues of intra‐patient parametric variation and non‐availability of all state variables for measurement. The proposed control technique for the T1D patient model is based on the attractive ellipsoid method (AEM). The observer and controller conditions are obtained in terms of linear matrix inequality (LMI), thus allowing to compute easily both the observer and controller gains. The closed‐loop response obtained using the designed controller avoids adverse situations of hypoglycemia and post‐prandial hyperglycemia under uncertain conditions. Further to validate the robustness of the design, closed‐loop simulations of random 200 virtual T1D patients considering parameters within the considered ranges are presented. The results indicate that hypoglycemia and post‐prandial hyperglycemia are significantly reduced in the presence of bounded (±30% ) parametric variability and uncertain exogenous meal disturbance.

Blood glucose regulation in type 1 diabetic patients: an adaptive parametric compensation control-based approach.

Here, a direct adaptive control strategy with parametric compensation is adopted for an uncertain non‐linear model representing blood glucose regulation in type 1 diabetes mellitus patients. The uncertain parameters of the model are updated by appropriate design of adaptation laws using the Lyapunov method. The closed‐loop response of the plasma glucose concentration as well as external insulin infusion rate is analysed for a wide range of variation of the model parameters through extensive simulation studies. The result indicates that the proposed adaptive control scheme avoids severe hypoglycaemia and gives satisfactory performance under parametric uncertainty highlighting its ability to address the issue of inter‐patient variability.

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.

Observer based nonlinear control design for glucose regulation in type 1 diabetic patients: An LMI approach

Abstract This paper deals with the design of observer-based nonlinear control of blood glucose concentration (BGC) of Type 1 diabetes mellitus (T1DM) patients in a Linear Matrix Inequality (LMI) framework. The controller design relies on the information of the states obtained from a nonlinear observer. The control law is derived using feedback linearisation and regional pole placement technique. Further, a numerical optimisation method is proposed for the computation of the controller gains by capturing the relationship between transformed domain and original domain dynamics by iteratively tuning the circular LMI region parameters (‘q’ and ‘r’) such that the locations of closed-loop poles of the original nonlinear system are attained. The proposed controller can deliver robust closed-loop response of BGC within a specified range of parametric uncertainty and meal disturbances owing to the appropriately tuned bound of LMI region parameters. The performance of the proposed controller is tested for 100 virtual T1DM patients in the presence of parametric uncertainty and uncertain meal disturbance. Both severe hypoglycemia (

Relationship between NK Cell Activity and Glucose Regulation in Type 2 Diabetes Patients

Introduction: NK cells are cytotoxic lymphocytes critical to innate immunity. The purpose of this study is to find out the difference in NK cell activity between type 2 diabetes patients and controls, and to investigate the association between NK cell activity and glucose control. Methods: 49 subjects were enrolled in this study, with 23 type 2 diabetes patients and 26 normal glucose tolerant controls. Anthropometric and biochemical parameters were assessed. Homeostatic model assessment (HOMA) was calculated for insulin resistance. NK cell activity was measured using a newly developed NK Vueⓡ Kit (ATgen, Seoul), which is a method of detecting and comparing interferon-gamma level from NK cells. Results: NK cell activity was lower in type 2 diabetes patients compared to control subjects. There was a significant inverse linear relationship between NK cell activity and fasting plasma glucose after adjusting for age and gender (r = -0.346, P = 0.045). Postprandial glucose and HbA1c showed a similar tendency with NK cell activity (r = -0.313 and -0.241, and P = 0.072 and 0.17, respectively). Regression analysis showed HbA1c, fasting glucose and postprandial glucose to be independent predictors of NK cell activity (beta = -0.303, -0.360, -0.356, and P = 0.034, 0.011, 0.033). There was no relationship between NK cell activity and HOMA-IR. Conclusions: NK cell activity was lower in type 2 diabetes compared to controls, and it was significantly related to degree of hyperglycemia. A further, larger population study is warranted to confirm the possible correlation. Disclosure J. Kim: None. K. Park: None. S. Lee: None. M. Kim: None. J. Nam: None. S. Kang: None. J. Park: None. C. Ahn: None. Y. Kim: None.

Fuzzy type-2 fractional Backstepping blood glucose control based on sliding mode observer

A new combination of fractional order (FO) nonlinear control and sliding mode observer (SMO) for blood glucose regulation in type 1 diabetes mellitus is proposed in this paper. An observer estimates the difficult-to-measure process variables that are significant to control law design and to prevent failures. A SMO is proposed to estimate non-measurable states variables of Bergman minimal model by data acquired from a continuous glucose monitoring sensor. At first based on Backstepping method with FO sliding surface a control signal is proposed, and then interval type 2 fuzzy logic is utilized to reduce the chattering phenomenon in control signal. The FO sliding mode control provides robust performance and the Backstepping algorithm protects the controller in front of mismatched and matched uncertainties. Simulation results of the proposed controller are compared with its integer counterpart.

Association between NK cell activity and glucose regulation in type 2 diabetes patients

Association between NK cell activity and glucose regulation in type 2 diabetes patients

The Artificial Pancreas and Meal Control: An Overview of Postprandial Glucose Regulation in Type 1 Diabetes

In healthy individuals, plasma glucose concentration is tightly regulated by the action of the hormones secreted by the endocrine pancreas, principally, insulin and glucagon. Insulin is secreted by the pancreatic beta cells to signal organs to absorb glucose, and glucagon is secreted by the pancreatic alpha cells to signal the liver to produce glucose [1]. In type 1 diabetes (T1D), insulin secretion is lost due to the autoimmune destruction of the beta cells [2]. T1D accounts for 5-15% of the 366 million people with diabetes worldwide [3].

Simultaneous model predictive control and moving horizon estimation for blood glucose regulation in Type 1 diabetes

SummaryA new estimation and control approach for the feedback control of an artificial pancreas to treat Type 1 diabetes mellitus is proposed. In particular, we present a new output‐feedback predictive control approach that simultaneously solves the state estimation and control objectives by means of a single min‐max optimization problem. This involves optimizing a cost function with both finite forward and backward horizons with respect to the unknown initial state, unmeasured disturbances and noise, and future control inputs and is similar to simultaneously solving a model predictive control (MPC) problem and a moving horizon estimation (MHE) problem. We incorporate a novel asymmetric output cost to penalize dangerous low blood glucose values more severely than less harmful high blood glucose values. We compare this combined MPC/MHE approach to a control strategy that uses state‐feedback MPC preceded by a Luenberger observer for state estimation. In‐silico results showcase several advantages of this new simultaneous MPC/MHE approach, including fewer hypoglycemic events without increasing the number of hyperglycemic events, faster insulin delivery in response to a meal consumption, and shorter insulin pump suspensions, resulting in smoother blood glucose trajectories.

Improved glucose regulation in type 2 diabetic patients with DPP-4 inhibitors: focus on alpha and beta cell function and lipid metabolism.

Inhibition of dipeptidyl peptidase-4 (DPP-4) is an established glucose-lowering strategy for the management of type 2 diabetes mellitus. DPP-4 inhibitors reduce both fasting and postprandial plasma glucose levels, resulting in reduced HbA1c with low risk for hypoglycaemia and weight gain. They act primarily by preventing inactivation of the incretin hormones glucose-dependent insulinotropic polypeptide and glucagon-like peptide-1, thereby prolonging the enhanced endogenous levels of these hormones after meal ingestion. This in turn causes islet and extrapancreatic effects, including increased glucose sensing in islet alpha and beta cells. These effects result in increased insulin secretion and decreased glucagon secretion being more effective in hyperglycaemic states and reduced insulin secretion and increased glucagon secretion being more effective during hypoglycaemia. Other secondary pharmacological actions of DPP-4 inhibitors include mobilisation and burning of fat during meals, decrease in fat extraction from the gut, reduction of fasting lipolysis and liver fat and increase in LDL particle size. These actions contribute to the clinical effects of DPP-4 inhibition, and the reduced demand for insulin could also lead to a durability benefit. This review summarises the current knowledge of the secondary pharmacological actions of DPP-4 inhibitors that lead to improved glucose regulation in patients with type 2 diabetes, focusing on alpha and beta cell function and lipid metabolism.

Beneficial Effect of Naringin on Glucose Regulation in Type 1 Diabetic Mice

The present study investigated the effect of naringin on glucose metabolism in non-obese diabetic (NOD) mice, a good animal model for studying human autoimmune type 1 diabetes or insulin dependent diabetes. Female NOD mice were divided into control and naringin (0.02%, w/w) and fed experimental diet for 11 weeks. There was no significant difference in food intake between groups. However, naringin inhibited weight loss and hyperglycemia and increased survival rate. In addition, naringin improved glucose tolerance, increased plasma insulin and C-peptide levels and decreased plasma glucagon level. Similar to plasma insulin and glucagon levels, pancreatic insulin protein expression was increased by naringin, whereas pancreatic glucagon protein expression was decreased. Moreover, naringin decreased activity of gluconeogenic enzymes, glucose-6-phosphatase and phosphoenolpyruvate carboxykinase, in liver, along with increased hepatic glucokinase activity and glycogen content. These results suggest that naringin may maintain plasma insulin and glucagon by protecting pancreatic α-cells and β-cells, thereby reducing gluconeogenesis and increasing glucose utilization in liver and ultimately preventing diabetes onset in type 1 diabetic NOD mice. Therefore, naringin may be useful phytochemical compound for improving and preventing type 1 diabetes.

Investigation of the Potential of Hemp, Pea, Rice and Soy Protein Hydrolysates as a Source of Dipeptidyl Peptidase IV (DPP-IV) Inhibitory Peptides

Food protein hydrolysates contain peptide sequences with dipeptidyl peptidase IV (DPP-IV) inhibitory properties which may find use to improve serum glucose regulation in type 2 diabetics. Four plant protein isolates from hemp (H), pea (P), rice (R) and soy (S) were hydrolysed with three enzyme preparations: Corolase L10 (Cor), Promod 144MG (Prom) and Protamex (Prot). From the 12 hydrolysates generated after 4-h incubation, 7 yielded DPP-IV inhibitory activity <2.0 mg protein equivalent mL−1. Their IC50 values ranged from 0.73 ± 0.11 to 3.54 ± 0.24 mg dry weight (dw) hydrolysate mL−1 for the 4 h P_Prot and 4 h H_Cor hydrolysates, respectively. Simulated gastrointestinal digestion (SGID) of the intact proteins yielded DPP-IV IC50 values between 1.85 ± 0.34 and 4.50 ± 0.55 mg dw hydrolysate mL−1 for the R and H, respectively. The DPP-IV inhibitory potency of the 4 h hydrolysates subjected to SGID (DPP-IV IC50 ranging from 1.00 ± 0.42 to 3.83 ± 0.36 mg dw hydrolysate mL−1 for the SGID of the 4 h S_Prom and 4 h H_Prot, respectively) was generally higher than that of the corresponding intact protein subjected to SGID, with the exception of the SGID of R and that of the 4 h R_Prot hydrolysate which had similar IC50 values (P > 0.05). To our knowledge, this is the first study reporting that H and P protein hydrolysates inhibit DPP-IV in vitro. This study also demonstrates the potential benefit, in some instances, of hydrolysing plant protein substrates prior to oral ingestion with the view of releasing DPP-IV inhibitory peptides.

A learning automata-based blood glucose regulation mechanism in type 2 diabetes

This paper proposes a learning automata-based mechanism for blood glucose regulation in type 2 diabetics. The proposed mechanism takes into account the past history of the blood glucose level to determine the correct dosage of the insulin. This method uses the learning automata theory to predict the required dosage of insulin and records the patient history in parameters of a Gaussian probability distribution function. The parameters of the distribution function are updated based on the difference between the actual glucose level regulated by the learning automata and the normal range in such a way that the gap between the actual glucose level and the normal one is minimized. As the proposed algorithm proceeds, it can be seen that it converges to the optimal insulin dosage that keeps the glucose level in normal range for a long time. Convergence of the proposed algorithm to the optimal insulin dosage is theoretically proven. A clinical study is conducted to show the performance of the proposed insulin therapy system for regulation of the blood glucose level of type 2 diabetics.

Developments of Glucagon Like Peptide-1 (GLP-1) and Long-acting Analogs in Clinical and Preclinical Studies for Treatment of Type 2 Diabetes

The outstanding physiological functions of glucagon-like peptide-1 make it a promising drug candidate for blood glucose regulation in type 2 diabetes. However, the short half-life of GLP-1 limited its widely clinical utility due to the rapid degradation by dipeptidyl peptidase IV (DPP-IV) and renal clearance. Therefore, stabilisation of glucagon-like peptide-1 is critical for the use of this peptide in drug development. Scientists in pharmaceutical companies have contributed in years to obtain long-acting or sustained GLP-1 derivatives which are reviewed in this report.

Hypoglycemic and antioxidant effects of jaceosidin in streptozotocin-induced diabetic mice

Purpose: In this study, we investigated the effects of jaceosidin on blood glucose regulation in type 1 diabetic mice. Methods: C57BL/6 mice were divided into four groups; normal control (Normal), diabetes control (D-Control), diabetes lowjaceosidin (D-0.005%), and diabetes high-jaceosidin (D-0.02%). Type 1 diabetes was induced by streptozotocin and mice were then fed a diet containing jaceosidin for eight weeks. Fasting blood glucose, oral glucose tolerance test, insulin tolerance test, lipid peroxidation, and antioxidant enzyme activities were assessed. Results: Jaceosidin supplementation for eight weeks had no effect on body weight, organ weight, and blood lipid profiles. However, jaceosidin supplementation significantly lowered fasting blood glucose level and reduced insulin resistance. We also found that jaceosidin supplementation increased antioxidant capacity by enhancement of catalase and GSH-px activities. Conclusion: These results suggest that jaceosidin could be a therapeutic candidate to ameliorate hyperglycemia through increase of antioxidant enzyme activity.

Extended adaptive predictive controller with robust filter to enhance blood glucose regulation in type I diabetic subjects

In this paper, an improved adaptive predictive control with robust filter is developed to be applied in an artificial pancreas. Several problems inherent to endocrine systems for diabetic persons have to be tackled such as nonlinearities, long time delays or daily variations of parameters. Three Finite Impulse Response models for insulin input and the same for meal intake (perturbations) corresponding to normal, hyper-hypoglycaemia levels to implement three zones control are taken into account. The glycaemia reference trajectory is shaped from a healthy person response. A variable weighting factor in the cost function is included to prevent dangerous glycaemia excursions out of the allowed limits. Additionally, a noisy blood glucose subcutaneous sensor model is used. This control strategy is tested on 30 virtual subjects from the UVa – Padova Simulator. Simultaneous meals and physiological disturbances are taken into account and the main conclusions are drawn from Control Variability Grid Analysis.