Optimal Regulation of Blood Glucose Level in Type I Diabetes using Insulin and Glucagon

The Glucose-Insulin-Glucagon nonlinear model [1-4] accurately describes how the body responds to exogenously supplied insulin and glucagon in patients affected by Type I diabetes. Based on this model, we design infusion rates of either insulin (monotherapy) or insulin and glucagon (dual therapy) that can optimally maintain the blood glucose level within desired limits after consumption of a meal and prevent the onset of both hypoglycemia and hyperglycemia. This problem is formulated as a nonlinear optimal control problem, which we solve using the numerical optimal control package PSOPT. Interestingly, in the case of monotherapy, we find the optimal solution is close to the standard method of insulin based glucose regulation, which is to assume a variable amount of insulin half an hour before each meal. We also find that the optimal dual therapy (that uses both insulin and glucagon) is better able to regulate glucose as compared to using insulin alone. We also propose an ad-hoc rule for both the dosage and the time of delivery of insulin and glucagon.Comment: Accepted for publication in PLOS ON

Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation

People with Type 1 diabetes (T1D) require regular exogenous infusion of insulin to maintain their blood glucose concentration in a therapeutically adequate target range. Although the artificial pancreas and continuous glucose monitoring have been proven to be effective in achieving closed-loop control, significant challenges still remain due to the high complexity of glucose dynamics and limitations in the technology. In this work, we propose a novel deep reinforcement learning model for single-hormone (insulin) and dual-hormone (insulin and glucagon) delivery. In particular, the delivery strategies are developed by double Q-learning with dilated recurrent neural networks. For designing and testing purposes, the FDA-accepted UVA/Padova Type 1 simulator was employed. First, we performed long-term generalized training to obtain a population model. Then, this model was personalized with a small data-set of subject-specific data. In silico results show that the single and dual-hormone delivery strategies achieve good glucose control when compared to a standard basal-bolus therapy with low-glucose insulin suspension. Specifically, in the adult cohort (n=10), percentage time in target range [70, 180] mg/dL improved from 77.6% to 80.9% with single-hormone control, and to $85.6\%$ with dual-hormone control. In the adolescent cohort (n=10), percentage time in target range improved from 55.5% to 65.9% with single-hormone control, and to 78.8% with dual-hormone control. In all scenarios, a significant decrease in hypoglycemia was observed. These results show that the use of deep reinforcement learning is a viable approach for closed-loop glucose control in T1D

Animal Models of GWAS-Identified Type 2 Diabetes Genes

More than 65 loci, encoding up to 500 different genes, have been implicated by genome-wide association studies (GWAS) as conferring an increased risk of developing type 2 diabetes (T2D). Whilst mouse models have in the past been central to understanding the mechanisms through which more penetrant risk genes for T2D, for example, those responsible for neonatal or maturity-onset diabetes of the young, only a few of those identified by GWAS, notably TCF7L2 and ZnT8/SLC30A8, have to date been examined in mouse models. We discuss here the animal models available for the latter genes and provide perspectives for future, higher throughput approaches towards efficiently mining the information provided by human genetics

Cross-Validation of a Glucose-Insulin-Glucagon Pharmacodynamics Model for Simulation using Data from Patients with Type 1 Diabetes

Background: Currently, no consensus exists on a model describing endogenous glucose production (EGP) as a function of glucagon concentrations. Reliable simulations to determine the glucagon dose preventing or treating hypoglycemia or to tune a dual-hormone artificial pancreas control algorithm need a validated glucoregulatory model including the effect of glucagon. Methods: Eight type 1 diabetes (T1D) patients each received a subcutaneous (SC) bolus of insulin on four study days to induce mild hypoglycemia followed by a SC bolus of saline or 100, 200, or 300 µg of glucagon. Blood samples were analyzed for concentrations of glucagon, insulin, and glucose. We fitted pharmacokinetic (PK) models to insulin and glucagon data using maximum likelihood and maximum a posteriori estimation methods. Similarly, we fitted a pharmacodynamic (PD) model to glucose data. The PD model included multiplicative effects of insulin and glucagon on EGP. Bias and precision of PD model test fits were assessed by mean predictive error (MPE) and mean absolute predictive error (MAPE). Results: Assuming constant variables in a subject across nonoutlier visits and using thresholds of ±15% MPE and 20% MAPE, we accepted at least one and at most three PD model test fits in each of the seven subjects. Thus, we successfully validated the PD model by leave-one-out cross-validation in seven out of eight T1D patients. Conclusions: The PD model accurately simulates glucose excursions based on plasma insulin and glucagon concentrations. The reported PK/PD model including equations and fitted parameters allows for in silico experiments that may help improve diabetes treatment involving glucagon for prevention of hypoglycemia. </jats:sec

Interstitial Glucose and Physical Exercise in Type 1 Diabetes: Integrative Physiology, Technology, and the Gap In-Between

Continuous and flash glucose monitoring systems measure interstitial fluid glucose concentrations within a body compartment that is dramatically altered by posture and is responsive to the physiological and metabolic changes that enable exercise performance in individuals with type 1 diabetes. Body fluid redistribution within the interstitial compartment, alterations in interstitial fluid volume, changes in rate and direction of fluid flow between the vasculature, interstitium and lymphatics, as well as alterations in the rate of glucose production and uptake by exercising tissues, make for caution when interpreting device read-outs in a rapidly changing internal environment during acute exercise. We present an understanding of the physiological and metabolic changes taking place with acute exercise and detail the blood and interstitial glucose responses with different forms of exercise, namely sustained endurance, high-intensity, and strength exercises in individuals with type 1 diabetes. Further, we detail novel technical information on currently available patient devices. As more health services and insurance companies advocate their use, understanding continuous and flash glucose monitoring for its strengths and limitations may offer more confidence for patients aiming to manage glycemia around exercise

Therapeutics for type-2 diabetes mellitus: a glance at the recent inclusions and novel agents under development for use in clinical practice

Diabetes mellitus (DM) is a chronic, progressive, and multifaceted illness resulting in significant physical and psychological detriment to patients. As of 2019, 463 million people are estimated to be living with DM worldwide, out of which 90% have type-2 diabetes mellitus (T2DM). Over the years, significant progress has been made in identifying the risk factors for developing T2DM, understanding its pathophysiology and uncovering various metabolic pathways implicated in the disease process. This has culminated in the implementation of robust prevention programmes and the development of effective pharmacological agents, which have had a favourable impact on the management of T2DM in recent times. Despite these advances, the incidence and prevalence of T2DM continue to rise. Continuing research in improving efficacy, potency, delivery and reducing the adverse effect profile of currently available formulations is required to keep pace with this growing health challenge. Moreover, new metabolic pathways need to be targeted to produce novel pharmacotherapy to restore glucose homeostasis and address metabolic sequelae in patients with T2DM. We searched PubMed, MEDLINE, and Google Scholar databases for recently included agents and novel medication under development for treatment of T2DM. We discuss the pathophysiology of T2DM and review how the emerging anti-diabetic agents target the metabolic pathways involved. We also look at some of the limiting factors to developing new medication and the introduction of unique methods, including facilitating drug delivery to bypass some of these obstacles. However, despite the advances in the therapeutic options for the treatment of T2DM in recent years, the industry still lacks a curative agent

CONTROLLABILITY AND OBSERVABILITY OF BLOOD GLUCOSE LEVELS AND THE IMPACT OF COVID-19 ON DIABETIC PATIENTS

Diabetes is a metabolic disorder that is characterized by high blood glucose concentrations resulting from insulin deficiency in case of type 1 or insulin inefficiency in case of type 2. While no cure for diabetes exists, the artificial pancreas is a possible way to manage diabetes, especially for type 1 diabetics. Where an artificial pancreas is a closed-loop control system with an integrated mathematical model. This control system imitates the function of a healthy pancreas. The first part of this thesis is concerned with the control system of an artificial pancreas that is based on Bergman’s minimal model of glucose-insulin dynamics. The aim of the first part of this thesis is to prove both the controllability and the observability of the minimal model which is a fundamental step in the design of an optimal control system. These proofs are based on several mathematical tools such as the insertion of time delays, and theorems such as the Banach contraction mapping theorem in addition to the results of previous related works. On a different note, COVID-19 is a highly infectious global pandemic that targets the respiratory system. The symptoms of this disease were found to be more severe towards patients with comorbidities including diabetes, and so, the second part of this thesis is concerned with the relation of COVID-19 with comorbidities, where a COVID-19 disease transmission model that focuses on comorbidity populations is presented. This model aims at determining the major factors that contribute to the transmission of this disease. The results of this model can aid in implementing strategies that can help in controlling the spread of this pandemic. Parameter estimations of the model are presented in addition to several related calculations including the basic reproduction number and the sensitivity indices of the model’s parameters