Glucose-insulin System Research Articles

The Internet of Bio-Nano Things with Insulin-Glucose, Security and Research Challenges: A Survey

The Internet of Bio-Nano Things (IoBNT) is collaborative cell biology and nanodevice technology interacting through Molecular Communication (MC). The IoBNT can be accomplished by using the Information and Communication Theory (ICT) study of biological networks. Various technologies such as the Internet of Nano Things (IoNT), the Internet of Bio-degradable Things (IoBDT) and the Internet of Ingestible Things (IoIT) contribute to the development of IoBNT. Our survey discussed the Bio-Nano network and the role of IoT-based technologies along with a comparative study from various literature. We surveyed the various applications of IoNT in which the drug delivery for Insulin-Glucose system is prominent. Our survey aims to provide information about the Insulin-Glucose system involving the IoBNT and MC. We described the details of various factors for a diabetes analysis. We surveyed the diffusion coefficients of Insulin, Glucose and the various parameters that influence insulin production in the body. Our survey identifies the security aspects of IoBNT such as attacks in nanonetworks, bio-cyber interface, Insulin-Glucose system and their possible mitigation techniques. Our survey also provides a hierarchical model of the Bio-Nano network collaborating all the related technologies. Finally, our survey includes the research challenges involved in the proper handling of the IoBNT.

Computational techniques to monitoring fractional order type-1 diabetes mellitus model for feedback design of artificial pancreas

Background and objectives:In this paper, we developed a significant class of control issues regulated by nonlinear fractal order systems with input and output signals, our goal is to design a direct transcription method with impulsive instant order. Recent advances in the artificial pancreas system provide an emerging treatment option for type 1 diabetes. The performance of the blood glucose regulation directly relies on the accuracy of the glucose-insulin modeling. This work leads to the monitoring and assessment of comprehensive type-1 diabetes mellitus for controller design of artificial panaceas for the precision of the glucose-insulin glucagon in finite time with Caputo fractional approach for three primary subsystems. Methods:For the proposed model, we admire the qualitative analysis with equilibrium points lying in the feasible region. Model satisfied the biological feasibility with the Lipschitz criteria and linear growth is examined, considering positive solutions, boundedness and uniqueness at equilibrium points with Leray–Schauder results under time scale ideas. Within each subsystem, the virtual control input laws are derived by the application of input to state theorems and Ulam Hyers Rassias. Results:Chaotic Relation of Glucose insulin glucagon compartmental in the feasible region and stable in finite time interval monitoring is derived through simulations that are stable and bounded in the feasible regions. Additionally, as blood glucose is the only measurable state variable, the unscented power-law kernel estimator appropriately takes into account the significant problem of estimating inaccessible state variables that are bound to significant values for the glucose-insulin system. The comparative results on the simulated patients suggest that the suggested controller strategy performs remarkably better than the compared methods. Conclusion:In the model under investigation, parametric uncertainties are identified since the glucose, insulin, and glucagon system’s parameters are accurately measured numerically at different fractional order values. In terms of algorithm resilience and Caputo tracking in the presence of glucagon and insulin intake disturbance to maintain the glucose level. A comprehensive analysis of numerous difficult test issues is conducted in order to offer a thorough justification of the planned strategy to control the type 1 diabetes mellitus with designed the artificial pancreas.

Comparing the Numerical Solution of Fractional Glucose–Insulin Systems Using Generalized Euler Method in Sense of Caputo, Caputo–Fabrizio and Atangana–Baleanu

Comparing the Numerical Solution of Fractional Glucose–Insulin Systems Using Generalized Euler Method in Sense of Caputo, Caputo–Fabrizio and Atangana–Baleanu

Dynamic modeling of the glucose–insulin system with inhibitors impulsive control

Pulse injection of insulin analogues is an important strategy to control glucose concentrations and can be combined with ‐glucosidase inhibitors to reduce adverse effects to improve glucose control. To elucidate this combination therapy strategy, we assumed dietary intake in the form of pulse glucose injection and proposed a novel mathematical model incorporating pulse injection insulin and ‐glucosidase inhibitors. In type 1 diabetes, the existence and uniqueness of the positive periodic solution is confirmed utilizing the Lambert W function. The global asymptotic stability of the positive periodic solution is achieved through the application of Floquet multiplier theory and the comparison principle. Furthermore, in type 2 diabetes, the permanence of the system is also confirmed through the comparison theorem. Numerical analysis validated the theoretical calculations, highlighting the significance of insulin injection dosage and frequency, as well as ‐glucosidase inhibitor therapy. Additionally, we systematically assessed a rational approach for diabetes treatment combined with ‐glucosidase inhibitors, providing more practical clinical strategies. This facilitates the extension of our model to encompass other drugs that may be utilized in future clinical interventions.

Generative deep learning for the development of a type 1 diabetes simulator

BackgroundType 1 diabetes (T1D) simulators, crucial for advancing diabetes treatments, often fall short of capturing the entire complexity of the glucose-insulin system due to the imprecise approximation of the physiological models. This study introduces a simulation approach employing a conditional deep generative model. The aim is to overcome the limitations of existing T1D simulators by synthesizing virtual patients that more accurately represent the entire glucose-insulin system physiology.MethodsOur methodology utilizes a sequence-to-sequence generative adversarial network to simulate virtual T1D patients causally. Causality is embedded in the model by introducing shifted input-output pairs during training, with a 90-min shift capturing the impact of input insulin and carbohydrates on blood glucose. To validate our approach, we train and evaluate the model using three distinct datasets, each consisting of 27, 12, and 10 T1D patients, respectively. In addition, we subject the trained model to further validation for closed-loop therapy, employing a state-of-the-art controller.ResultsThe generated patients display statistical similarity to real patients when evaluated on the time-in-range results for each of the standard blood glucose ranges in T1D management along with means and variability outcomes. When tested for causality, authentic causal links are identified between the insulin, carbohydrates, and blood glucose levels of the virtual patients. The trained generative model demonstrates behaviours that are closer to reality compared to conventional T1D simulators when subjected to closed-loop insulin therapy using a state-of-the-art controller.ConclusionsThese results highlight our approach’s capability to accurately capture physiological dynamics and establish genuine causal relationships, holding promise for enhancing the development and evaluation of therapies in diabetes.

Effects of bioavailability of macronutrients on overall control of plasma glucose: a review

Macronutrients play a crucial role in management of type 2 diabetes. This is seen in their ability to modulate plasma glucose concentrations. However, the ideal proportions of macronutrients to be consumed in order to maintain ideal plasma glucose concentrations remains elusive. Therefore, this paper set out to conduct a review to investigate the relationship between macronutrients and plasma glucose concentrations from a physiological perspective. The review was conducted using papers obtained from various databases such as MEDLINE (Pubmed), Open Access Journals Elsevier, Free Medical Journals and Google Scholar. The research papers included general reviews, systemic reviews, meta-analyses, and randomized control trials that examined the effect of macronutrients on plasma glucose concentration as well as papers on mathematical models describing the relationship between macronutrient bioavailability and plasma glucose concentration. The review assessed the effect of various macronutrients on post-prandial plasma glucose concentration, post-prandial plasma insulin, post-prandial glucose-dependent insulinotropic peptide plasma concentration, and post-prandial glucose-like peptide-1 plasma concentration. The results of the review showed that carbohydrates influence plasma glucose concentration in a dose dependant manner but this is dependent on their bioavailability. This bioavailability was shown to be subject to fluctuations determined by food processing techniques, food structure, and food matrix. The results also showed that some specific types of fats and proteins indirectly influence plasma glucose concentration through their effect on incretin hormones. The effect of fats and proteins on incretin hormones was through different mechanisms and pathways. In-lieu of the findings, the review concludes that the macronutrient composition of diets designed for type 2 diabetic patients should take into consideration the physiological relationship between the macronutrients and plasma glucose concentrations. In this way, diet proportions can be made in such a manner as to determine the exact amounts that will realize near normal plasma glucose concentrations for a type 2 diabetic patient. Key words: bioavailability, glucose-insulin system, macronutrients, incretin hormones, type 2 diabetes, glucose absorption models

Analysis and controllability of diabetes model for experimental data by using fractional operator

Diabetes is a silent illness that is endangering public health in society. Diabetes is a chronic disease affecting millions of people worldwide, and understanding the underlying mechanisms of glucose homeostasis is crucial for managing this condition. Diabetes is a significant public health issue due to the early morbidity, mortality, shortened life expectancy, and financial and other expenses to the patient, their careers, and the health care system. In this study, we propose a mathematical model consisting of β−cells, insulin, glucose, and growth hormone that incorporates the fractional operator. Using the Lyapunov function, we treated a global stability analysis and investigated the impact of a new wave of dynamical transmission on the equilibrium points of the second derivative. With the Lipschitz criteria and linear growth, the exact singular solution for the proposed model has been determined. Furthermore, we present a detailed analysis of infections, and numerical simulations are conducted using the Mittag-Leffler Kernel mathematical framework to illustrate the theoretical conclusions for various orders of the fractional derivative. Controllability and observability of the linear system are treated for close loop design to check the relation between the glucose and insulin systems. Overall, our results provide a comprehensive understanding of glucose homeostasis and its underlying mechanisms, contributing to the development of effective diabetes management strategies. The proposed model and mathematical framework offer a valuable tool for investigating complex systems and phenomena, with applications beyond the field of diabetes research and helpful to designing the closed loop for the glucose–insulin system.

A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation

Objective:Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR). Methods:A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation. Results:The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83%±27%. Conclusion:The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.

A simple modeling framework for prediction in the human glucose-insulin system.

Forecasting blood glucose (BG) levels with routinely collected data is useful for glycemic management. BG dynamics are nonlinear, complex, and nonstationary, which can be represented by nonlinear models. However, the sparsity of routinely collected data creates parameter identifiability issues when high-fidelity complex models are used, thereby resulting in inaccurate forecasts. One can use models with reduced physiological fidelity for robust and accurate parameter estimation and forecasting with sparse data. For this purpose, we approximate the nonlinear dynamics of BG regulation by a linear stochastic differential equation: we develop a linear stochastic model, which can be specialized to different settings: type 2 diabetes mellitus (T2DM) and intensive care unit (ICU), with different choices of appropriate model functions. The model includes deterministic terms quantifying glucose removal from the bloodstream through the glycemic regulation system and representing the effect of nutrition and externally delivered insulin. The stochastic term encapsulates the BG oscillations. The model output is in the form of an expected value accompanied by a band around this value. The model parameters are estimated patient-specifically, leading to personalized models. The forecasts consist of values for BG mean and variation, quantifying possible high and low BG levels. Such predictions have potential use for glycemic management as part of control systems. We present experimental results on parameter estimation and forecasting in T2DM and ICU settings. We compare the model's predictive capability with two different nonlinear models built for T2DM and ICU contexts to have a sense of the level of prediction achieved by this model.

Crosstalk between genetic variability of adiponectin and leptin, glucose-insulin system and subclinical atherosclerosis in patients with newly diagnosed type 2 diabetes. The Verona Newly Diagnosed Type 2 Diabetes Study 14.

To evaluate the relationship of genetic variability of adiponectin (ADIPOQ), leptin (LEP) and leptin receptor (LEPR) genes with glucose-insulin system and markers of subclinical atherosclerosis (ATS) in patients with newly diagnosed type 2 diabetes. In 794 subjects we performed: 1) euglycemic hyperinsulinemic clamp to assess insulin sensitivity; 2) mathematical modelling of a 5h-OGTT to estimate β-cell function; 3) resting ECG; 4) carotid artery and lower limb artery eco-doppler sonography to identify ATS; 5) genotyping of tag-SNPs within ADIPOQ, LEP and LEPR gene. Regression analyses showed: 1) adiponectin levels were negatively associated with BMI, waist-to-hip ratio and triglycerides and positively with HDL and insulin sensitivity (p-all<0.03); 2) leptin levels were positively associated with BMI, HDL-cholesterol and plasma triglycerides and negatively with insulin sensitivity (p-all<0.001). Two SNPs (rs1501299 and rs2241767) within ADIPOQ gene were associated with circulating levels of adiponectin. The ADIPOQ-GAACA haplotype was associated with plasma adiponectin (p=0.034; β=-0.24), ECG abnormalities (p=0.012; OR=2.76), carotid ATS (p=0.025; OR=2.00) and peripheral limb artery ATS (p=0.032; OR=1.90). The LEP-CTA haplotype showed an association with ischemic ECG abnormalities (p=0.017; OR=2.24). Finally, LEPR-GAACGG was associated with circulating leptin (p=0.005; β=-0.31) and worst β-cell function (p=0.023; β=-15.10). Omnibus haplotype analysis showed that ADIPOQ haplotypes were associated with levels of adiponectin and common carotid artery ATS, LEP with peripheral limb artery ATS, whereas LEPR haplotypes influenced circulating levels of leptin. Results of this study reinforce knowledge on adipokines' role in regulating glucose metabolism; in particular highlighted the potential atherogenic role of leptin and the anti atherogenic role of adiponectin.

On the Solution of a Nonlinear Fractional-Order Glucose-Insulin System Incorporating β -cells Compartment

In this work, we are interested in studying variations in plasma glucose and insulin levels over time using a fractional-order version of a mathematical model. Applying the fractional-order Caputo derivative, we can investigate different concentration rates among insulin, glucose, and healthy β-cells. The main aim is to obtain the numerical solution of the proposed model in order to show variations in plasma glucose and insulin levels over time, by applying the generalized Euler method. The local stability analysis of the proposed (discretization) Caputo fractional-order model was discussed. To check the feasibility of our analysis, we have investigated some numerical simulations for various fractional orders by varying values of the parameters with help of Mathematica. Numerical simulations were in good agreement with the theoretical findings. Three specific numerical examples are given as applications of the main results.

On Semi-Global Exponential Stability Under Sampling for Locally Lipschitz Time-Delay Systems

In this article, the stability preservation problem for locally Lipschitz nonlinear time-delay systems, by emulation of continuous-time dynamic output feedback controllers, is studied. Sufficient Lyapunov-like conditions are provided such that, by suitably fast sampling, the Euler emulation of continuous-time dynamic output feedback controllers ensures the semiglobal exponential stability of the related sampled-data closed-loop system. If other emulation schemes than the Euler one are used, practical semiglobal exponential stability is guaranteed, with arbitrarily small final target ball, by suitably fast sampling. The intersampling system behavior, as well as time-varying sampling intervals, is taken into account. In the provided results, the delay-free case is included as a special case. The practical applicability of the provided results is shown through the study of a nonlinear chemical reactor system with recycle and a glucose–insulin system.

E-DES-PROT: A novel computational model to describe the effects of amino acids and protein on postprandial glucose and insulin dynamics in humans

Current computational models of whole-body glucose homeostasis describe physiological processes by which insulin regulates circulating glucose concentrations. While these models perform well in response to oral glucose challenges, interaction with other nutrients that impact postprandial glucose metabolism, such as amino acids (AAs), is not considered. Here, we developed a computational model of the human glucose-insulin system, which incorporates the effects of AAs on insulin secretion and hepatic glucose production. This model was applied to postprandial glucose and insulin time-series data following different AA challenges (with and without co-ingestion of glucose), dried milk protein ingredients, and dairy products. Our findings demonstrate that this model allows accurate description of postprandial glucose and insulin dynamics and provides insight into the physiological processes underlying meal responses. This model may facilitate the development of computational models that describe glucose homeostasis following the intake of multiple macronutrients, while capturing relevant features of an individual's metabolic health.

New model of glucose-insulin regulation characterizes effects of physical activity and facilitates personalized treatment evaluation in children and adults with type 1 diabetes.

Accurate treatment adjustment to physical activity (PA) remains a challenging problem in type 1 diabetes (T1D) management. Exercise-driven effects on glucose metabolism depend strongly on duration and intensity of the activity, and are highly variable between patients. In-silico evaluation can support the development of improved treatment strategies, and can facilitate personalized treatment optimization. This requires models of the glucose-insulin system that capture relevant exercise-related processes. We developed a model of glucose-insulin regulation that describes changes in glucose metabolism for aerobic moderate- to high-intensity PA of short and prolonged duration. In particular, we incorporated the insulin-independent increase in glucose uptake and production, including glycogen depletion, and the prolonged rise in insulin sensitivity. The model further includes meal absorption and insulin kinetics, allowing simulation of everyday scenarios. The model accurately predicts glucose dynamics for varying PA scenarios in a range of independent validation data sets, and full-day simulations with PA of different timing, duration and intensity agree with clinical observations. We personalized the model on data from a multi-day free-living study of children with T1D by adjusting a small number of model parameters to each child. To assess the use of the personalized models for individual treatment evaluation, we compared subject-specific treatment options for PA management in replay simulations of the recorded data with altered meal, insulin and PA inputs.

Robust Optimal Impulsive Blood Glucose Control Exploiting a Direct Searching Algorithm

In spite of numerous developments in blood glucose control based on the artificial pancreas, multiple daily injections (MDIs) are still the most applicable method in insulin-dependent diabetes mellitus (IDDM) subjects. These patients measure their blood glucose level (BGL) regularly and inject insulin in multiple dosages. In the present study, an efficient, robust optimal impulsive control approach is introduced to attain a safe range of blood glucose considering everyday life factors, including exercise, food intake, and circadian rhythms. This article assumes the dosages of the MDIs as control design parameters and addresses an appropriate approach for optimal dosage determination. The BGL regulation problem by the MDI program is characterized as a robust optimal impulsive control problem with uncertainties, and it is required to minimize the BGL error as the objective function. To achieve this, the polytopic form of the nonlinear glucose-insulin system is derived, and the feasible region for MDI is determined using a direct searching algorithm. Then, the optimal MDI is found by a sequential quadratic programming (SQP) approach. Regarding several uncertainties and the impulsive nature of insulin injection dosages, the proposed approach efficiently solves the BGL regulation problem. The proposed approach is evaluated by a simulated model of the blood-glucose insulin system in two regular and overfeeding scenarios with (mild, moderate) and without physical activity. The results show that the proposed method reduces the amount of injected insulin from 14% to 20% compared to a state-of-the-art approach and, simultaneously, reduces the severe hyperglycemia risk from 15% to 25% for overeating in three injection scenarios.

A MATHEMATICAL MODEL OF GLUCOSE HOMEOSTASIS IN CHAD CONTEXT

During these decades, mathematical modeling has become a key domain in science, especially in biomedical sciences. It allows for an experimental and rigorous approach. Thanks to mathematical modeling, the glucose-insulin system could be materialized, which is also theoretical, in order to analyze and interpret it and to predict the results. Many of the mathematical models of the glucose-insulin system have emerged in recent years. In literature, there are models that show the role of physical activity and response of mathematical model to glucose-insulin system dynamics. We propose the mathematical model of ordinary differential equations to investigate simple homeostasis generated by the dynamics of physiological parameters of the glucose-insulin system during physical activity for a healthy subject. Model parameters are estimated using a nonlinear optimization method generally based on inverse problems. The numerical simulations show that the proposed model is adaptable to the data collected in Chad and can be used to test glucose homeostasis for glucose-insulin system.

Mathematical modelling of a glucose-insulin system for type 2 diabetic patients in Chad

In this paper, we focus on modelling the glucose-insulin system for the purpose of estimating glucagon, insulin, and glucose in the liver in the internal organs of the human body. A three-compartmental mathematical model is proposed. The model parameters are estimated using a nonlinear inverse optimization problem and data collected in Chad. In order to identify insulin and glucose in the liver for type 2 diabetic patients, the Sampling Importance Resampling (SIR) particle filtering algorithm is used and implemented through discretization of the developed mathematical model. The proposed mathematical model allows further investigation of the dynamic behavior of hepatic glucose, insulin, and glucagon in internal organs for type 2 diabetic patients. During periods of hyperglycemia (i.e., after meal ingestion), whereas insulin secretion is increased, glucagon secretion is reduced. The results are in agreement with empirical and clinical data and they are clinically consistent with physiological responses.

Sliding Mode Control Design for a Class of Nonlinear Fractional Systems with Application to Glucose-Insulin Systems

Sliding Mode Control Design for a Class of Nonlinear Fractional Systems with Application to Glucose-Insulin Systems

Robust stability control for nonlinear time varying delay fractional order practical systems and application in Glucose-Insulin system

Diabetes Mellitus is a long-term and prevalent disease that 10% of the world population suffer ‎from. This study uses a state feedback controller to control blood glucose level in the delayed ‎fractional order model of this disease. Thus, first, a proper state feedback controller for ‎stabilizing the fractional order model with time-variant delays is presented. Then, stability and ‎stabilization of the fractional order models in the fractional order insulin-glucose model with ‎time-variant delays is presented. The results show that the insulin-glucose system with the ‎proposed controller is stabilized and the blood glucose concentration is controlled in a proper ‎time. Further studies might provide the opportunity for novel technologies in controlling and ‎treating diabetic patients.

Conditional Synthesis of Blood Glucose Profiles for T1D Patients Using Deep Generative Models

Mathematical modeling of the glucose–insulin system forms the core of simulators in the field of glucose metabolism. The complexity of human biological systems makes it a challenging task for the physiological models to encompass the entirety of such systems. Even though modern diabetes simulators perform a respectable task of simulating the glucose–insulin action, they are unable to estimate various phenomena affecting the glycemic profile of an individual such as glycemic disturbances and patient behavior. This research work presents a potential solution to this problem by proposing a method for the generation of blood glucose values conditioned on plasma insulin approximation of type 1 diabetes patients using a pixel-to-pixel generative adversarial network. Two type-1 diabetes cohorts comprising 29 and 6 patients, respectively, are used to train the generative model. This study shows that the generated blood glucose values are statistically similar to the real blood glucose values, mimicking the time-in-range results for each of the standard blood glucose ranges in type 1 diabetes management and obtaining similar means and variability outcomes. Furthermore, the causal relationship between the plasma insulin values and the generated blood glucose conforms to the same relationship observed in real patients. These results herald the aptness of deep generative models for the generation of virtual patients with diabetes.