An equity-oriented analysis on using diabetes-related technology in children and adolescents with type 1 diabetes mellitus

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Medicina, Departamento de Medicina Preventiva y Salud Pública y Microbiología. Fecha de Lectura: 04-11-2021El manejo óptimo de la diabetes mellitus tipo 1 requiere un tratamiento intensivo de insulina de por vida, que puede ser empleado mediante múltiples dosis de insulina o mediante infusiones subcutánea continuas de insulina (ISCI). Aunque ambas terapias han demostrado ser efectivas en el manejo de la diabetes tipo 1 en niños y adolescentes, últimamente la ISCI ha ganado terreno frente al tratamiento convencional con jeringas y bolígrafos. Sin embargo, se sabe poco sobre la equidad y la imparcialidad con respecto al acceso a las nuevas tecnologías relacionadas con la diabetes, y si la decisión de comenzar con estas tecnologías es influenciada por la experiencia previa de los profesionales de salud en lugar de las recomendaciones de las guías clínicas. Además, la adopción de estas tecnologías puede verse afectada por diferencias considerables en la cobertura del sistema de salud entre los países y las preferencias de los individuos y de las familias. Por lo tanto, esta tesis tiene como objetivo abordar cuestiones sobre (i) los beneficios de los nuevos dispositivos para la diabetes en la mejora de los resultados glucémicos, (ii) la equidad de iniciar la ISCI entre aquellos que se beneficiarían más y (iii) la adopción de estas tecnologías entre proveedores por su toma de decisiones en recomendarlas a las personas con diabetes tipo

Contributions to modelling and control for improved hypoglycaemia and variability mitigation by dual-hormone artificial pancreas systems

[ES] Las personas con diabetes tipo 1 carecen de la capacidad de secretar insulina y, por lo tanto, necesitan regular su glucosa en sangre con la administración de insulina exógena. El páncreas artificial se presenta como la solución tecnológica ideal para alcanzar los objetivos terapéuticos de la normoglucemia, liberando al paciente de la carga actual de autocontrol y manejo. Sin embargo, el riesgo de hipoglucemia y la variabilidad glucémica siguen siendo factores limitantes en los algoritmos de control actuales integrados en el páncreas artificial. El propósito de la presente tesis es profundizar en el conocimiento de la hipoglucemia y avanzar los algoritmos de control del páncreas artificial para minimizar la incidencia de hipoglucemia y reducir la variabilidad glucémica. Después de proporcionar una visión general del estado del arte del control de la glucosa y el páncreas artificial, esta tesis aborda temas relacionados con el modelado y el control, con las siguientes contribuciones: Se presenta una extensión del modelo de Bergman Minimal que tiene en cuenta la respuesta contrarreguladora a la hipoglucemia. Este modelo explica la relación entre los diversos cambios fisiológicos producidos durante la hipoglucemia, con la adrenalina y los ácidos grasos libres como actores principales. Como resultado, se obtiene una mejor comprensión de la hipoglucemia, lo que permite explicar una auto-potenciación paradójica de la hipoglucemia como se modela a través de enfoques funcionales en el ampliamente utilizado simulador de diabetes tipo 1 UVA-Padova, que se utilizará en esta tesis para la validación in silico de los controladores desarrollados. Se realiza una evaluación de las métricas de variabilidad de la glucosa y los índices de calidad de control. La evaluación de la variabilidad glucémica en el desempeño de los controladores es necesaria; pero todavía no hay un conjunto de métricas de variabilidad glucémica que sea considerado como el "gold estándar". Por tanto, se lleva a cabo un análisis de las métricas de variabilidad disponibles en la literatura para definir un conjunto de indicadores recomendables. Debido a las limitaciones de los sistemas de páncreas artificiales unihormonales para mitigar la hipoglucemia en escenarios difíciles como el ejercicio, esta tesis se centra en el desarrollo de nuevos algoritmos de control bihormonales, con infusión simultanea de insulina y glucagón. Se propone un controlador coordinado bihormonal con estructuras de control paralelas como un algoritmo de control factible para la mitigación de la hipoglucemia y la reducción de la variabilidad glucémica, demostrando un rendimiento superior al de las estructuras de control utilizadas actualmente con lazos de control independientes de insulina y glucagón. Los controladores están diseñados y evaluados in silico en escenarios desafiantes y su rendimiento se evalúa principalmente con el conjunto de métricas definidas previamente como las recomendables.[CA] Les persones amb diabetis tipus 1 no tenen la capacitat de secretar insulina secreta i per tant, necessiten regular la seva glucosa en sang amb l'administració d'insulina exògena. El Pàncrees Artificial es presenta com la solució tecnològica ideal per assolir els objectius terapèutics de la normoglucèmia, alliberant al pacient de la càrrega actual d'autocontrol. No obstant, el risc d'hipoglucèmia i l'alta variabilitat glucèmica continuen sent un factor limitant en els algoritmes de control actuals integrats en el Pàncrees Artificials. El propòsit de la present tesi és aprofundir en el coneixement de la hipoglucèmia i millorar els algoritmes de control per corregir amb antelació la dosi excessiva d'insulina, minimitzant la incidència d'hipoglucèmia i reduint la variabilitat glucèmica. Després de donar una visió general de l'estat de l'art del control de la glucosa i el pàncrees artificial, aquesta tesi aborda aspectes de modelització i control, amb les següents contribucions: Es presenta una extensió del model Minimal de Bergman amb la contrarregulació. Aquest model explica la relació entre els diversos canvis siològics produïts durant la hipoglucèmia. Així, permet comprendre millor la hipoglucèmia i comparar els resultats amb els proporcionats per l'enfocament funcional del simulador de diabetis tipus 1 més utilitzat a la comunitat científica. Es realitza una avaluació de les mètriques de variabilitat glucèmica i dels índexs de qualitat de control. Es necessària l'avaluació de la variabilitat glucèmica en el rendiment dels controladors; però encara no hi ha un conjunt de mètriques considerades com les "gold standard". Per tant, es realitza una anàlisi de les mètriques de variabilitat disponibles a la literatura per definir un conjunt d'indicadors recomanables. Es proposa un controlador bi-hormonal coordinat amb estructures de control paral.leles com un algoritme de control viable per a la mitigació d'hipoglucèmia i la reducció de la variabilitat glucèmica. Els controladors estan dissenyats i avaluats in-silico en escenaris desafiadors i el seu rendiment es valora principalment amb el conjunt de mètriques definides prèviament com les mètriques recomanables.[EN] People with Type 1 Diabetes lack the ability to secrete insulin and therefore need to regulate their blood glucose with exogenous insulin delivery. The Artificial Pancreas is presented as the ideal technological solution to reach the therapeutic goals of normoglycaemia, freeing the patient from the current burden of self-control and management. Nevertheless, the risk of hypoglycaemia and the high glycaemic variability are still a limiting factors in the current control algorithms integrated in the Artificial Pancreas. The purpose of the present thesis is to delve into knowledge of hypoglycaemia and to advance in the artificial pancreas control algorithms in order to minimise hypoglycaemia incidence and reduce glycaemic variability. After providing an overview of the state of the art in the eld of glucose control and articial pancreas, this thesis addresses issues on modelling and control, with the following contributions: An extension of the Bergman Minimal model accounting for counterregulatory response to hypoglycaemia is presented. This model explains the relationship between the several physiological changes produced during hypoglycaemia, with adrenaline and free fatty acids as main players. As a result, a better understanding of hypoglycaemia is gained, allowing to explain a paradoxical auto-potentiation of hypoglycaemia as modeled through functional approaches in the widespread used UVA-Padova Type 1 Diabetes simulator, which will be used in this thesis for in silico validation of the developed controllers. An assessment of glucose variability metrics and control quality indices is carried out. The evaluation of the glycaemic variability on the controllers performance is necessary; but there is not a gold standard variability metrics yet. Therefore, an analysis of the variability metrics available in literature is conducted in order to define a recommendable set of indicators. Due to the limitations of single-hormone artificial pancreas systems in mitigating hypoglycaemia in challenging scenarios such as exercise, this thesis focuses on the developement of new dual-hormone control algorithms, with concomitant infusion of insulin and glucagon. A coordinated dual-hormone controller with parallel control structures is proposed as a feasible control algorithm for hypoglycaemia mitigation and glycaemic variability reduction, demonstrating superior performance as currently used control structures with independent insulin and glucagon control loops. The controllers are designed and evaluated in-silico under challenging scenarios and their performance are assessed mainly with the set of metrics defined previously as the recommendable ones.Moscardó García, V. (2019). Contributions to modelling and control for improved hypoglycaemia and variability mitigation by dual-hormone artificial pancreas systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/120456TESI

Dynamic Modeling of Free Fatty Acid, Glucose, and Insulin During Rest and Exercise in Insulin Dependent Diabetes Mellitus Patients

Malfunctioning of the beta-cells of the pancreas leads to the metabolic disease known as diabetes mellitus (DM), which is characterized by significant glucose variation due to lack of insulin secretion, lack of insulin action, or both. DM can be broadly classified into two types: type 1 diabetes mellitus (T1DM) - which is caused mainly due to lack of insulin secretion; and type 2 diabetes mellitus (T2DM) - which is caused due to lack of insulin action. The most common intensive insulin treatment for T1DM requires administration of insulin subcutaneously 3 - 4 times daily in order to maintain normoglycemia (blood glucose concentration at 70 to 120 mg/dl). Although the effectiveness of this technique is adequate, wide glucose fluctuations persist depending upon individual daily activity, such as meal intake, exercise, etc. For tighter glucose control, the current focus is on the development of automated closed-loop insulin delivery systems. In a model-based control algorithm, model quality plays a vital role in controller performance. In order to have a reliable model-based automatic insulin delivery system operating under various physiological conditions, a model must be synthesized that has glucose-predicting ability and includes all the major energy-providing substrates at rest, as well as during physical activity. Since the 1960s, mathematical models of metabolism have been proposed in the literature. The majority of these models are glucose-based and have ignored the contribution of free fatty acid (FFA) metabolism, which is an important source of energy for the body. Also, significant interactions exist among FFA, glucose, and insulin. It is important to consider these metabolic interactions in order to characterize the endogenous energy production of a healthy or diabetic patient. In addition, physiological exercise induces fundamental metabolic changes in the body; this topic has also been largely overlooked by the diabetes modeling community.This dissertation takes a more lipocentric (lipid-based) approach in metabolic modeling for diabetes by combining FFA dynamics with glucose and insulin dynamics in the existing glucocentric models. A minimal modeling technique was used to synthesize a FFA model, and this was coupled with the Bergman minimal model to yield an extended minimal model. The model predictions of FFA, glucose, and insulin were validated with experimental data obtained from the literature. A mixed meal model was developed to capture the absorption of carbohydrates (CHO), proteins, and FFA from the gut into the circulatory system. The mixed meal model served as a disturbance to the extended minimal model. In a separate study, an exercise minimal model was developed to incorporate the effects of exercise on glucose and insulin dynamics. Here, the Bergman minimal model was modified by adding equations and terms to capture the changes in glucose and insulin dynamics during and after mild-to-moderate exercise.A single composite model for predicting FFA-glucose-insulin dynamics during rest and exercise was developed by combining the extended and exercise minimal models. To make the composite model more biologically relevant, modifications were made to the original model structures. The dynamical effects of insulin on glucose and FFA were divided into three parts: (i) insulin-mediated glucose uptake by the tissues, (ii) insulin-mediated suppression of endogenous glucose production, and (iii) anti-lipolytic effects of insulin. Labeled and unlabeled intra-venous glucose tolerance test data were used to estimate the parameters of the glucose model which facilitated separation of insulin action on glucose utilization and production. The model successfully captured the FFA-glucose interactions at the systemic level. The model also successfully predicted mild-to-moderate exercise effects on glucose and FFA dynamics. A detailed physiologically-based compartmental model of FFA was synthesized and integrated with the existing physiologically-based glucose-insulin model developed by Sorensen. Distribution of FFA in the circulatory system was evaluated by developing mass balance equations across the major FFA-utilizing tissues/organs. Rates of FFA production or consumption were added to each of the physiologic compartments. In order to incorporate the FFA effects on glucose, modifications were made to the existing mass balance equations in the Sorensen model. The model successfully captured the FFA-glucose-insulin interactions at the organ/tissue levels.Finally, the loop was closed by synthesizing model predictive controllers (MPC) based on the extended minimal model and the composite model. Both linear and nonlinear MPC algorithms were formulated to maintain glucose homeostasis by rejecting disturbances from mixed meal ingestion. For comparison purposes, MPC algorithms were also synthesized based on the Bergman minimal model which does not account for the FFA dynamics. The closed-loop simulation results indicated a tighter blood glucose control in the post-prandial period with the MPC formulations based on the lipocentric (extended minimal and composite) models

Usability Challenges with Insulin Pump Devices in Diabetes Care: What Trainers Observe with First-Time Pump Users

Insulin pumps are designed for the self-management of diabetes mellitus in patients and are known for their complexity of use. Pump manufacturers engage trainers to teach patients how to use the devices correctly to control the symptoms of their disease. Usability research related to insulin pumps and other infusion pumps with first-time users as participants has centered on the relationship between user interface design and the effectiveness of task completion. According to prior research, the characteristics of system behavior in a real life environment remain elusive. A suitable approach to acquire information about potential usability problems encountered by first-time users is to obtain this information from the health care professionals who train them. The purpose of the study was to discover the lived experiences and shared impressions of insulin pump trainers during training sessions with first-time users. Interpretative Phenomenological Analysis (IPA) was used to uncover the phenomena associated with usability challenges that first-time users of insulin pumps face when learning to use the device. Six participants representing a homogeneous sample were recruited from a wide geographic area in the United States, and semi-structured interviews containing open-ended questions were conducted with the respondents. The data from the lived experiences and shared impressions of the participants were used to develop the following five super-ordinate themes: Emotion-charged Environment, Personalized Training, Safety Issues and Disaster Planning, Professional Dedication, and The Voice. The essence of participants’ experience was described around the pivotal moment when the training sessions are successfully completed and insulin pump therapy becomes alive. The findings of this study have implications for information systems professionals who conduct research on the safe design and usability of safety critical medical devices. In addition, the findings from this study create opportunities for practice to improve the initiation of insulin pump therapy in patients with diabetes

Development of in vivo flux analysis of hepatic glucose production in Type 2 Diabetes

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Vita.Includes bibliographical references (p. 289-300).Metabolic diseases are an increasing health concern in the developed world. Type 2 Diabetes, (T2D) affects over 100 million people worldwide and significantly contributes to chronic diseases such as atherosclerosis and kidney failure. This condition is characterized by deregulation of glucose homeostasis through the development of insulin resistance, manifested as increased glucose production in the liver. Hepatic gluconeogenesis provides de novo formation of glucose from three carbon precursors such as glycerol, lactate, pyruvate and alanine. The upregulation of this pathway underlies the persistent hyperglycemia observed in diabetic patients. We have developed stable isotope tracer methods to reconstruct hepatic glucose production fluxes by infusion of [13C, 2H]-glycerol and mass spectrometry analysis of plasma metabolites. Using this methodology we observe physiologic changes in liver cell lines and primary hepatocyte cultures in the presence of hormones insulin/glucagon and in response to gluconeogenic precursor availability. We put forth the hypothesis that in the presence of glycerol as a gluconeogenic substrate, glucose-6-phosphatase has an important role in modulating metabolic flux through upper gluconeogenesis. Infusion of simultaneous tracer combinations in vivo including a novel [U-13C,2H5]-glycerol allow detailed net flux and reversibility reconstruction of upper gluconeogenesis to an unprecedented degree in a single experiment. We deployed the developed methods to probe glucose overproduction in the liver insulin receptor knockout (LIRKO) transgenic model of Type 2 Diabetes, and found unexpected similarities in the metabolic flux profile not observed by genomic, protein or metabolite measurements.(cont.) Our results underscore the importance of flux measurement as a physiologic parameter akin to gene and protein expression in revealing the metabolic phenotype of cells, tissues and organisms. These methods have the potential to contribute as clinical assays to characterize excess glucose production as well as in drug development for new targets to control hepatic glucose production.by José O. Alemán.Ph.D

The Design and Validation of a Discrete-Event Simulator for Carbohydrate Metabolism in Humans

CarbMetSim is a discrete event simulator that tracks the changes of blood glucose level of a human subject after a timed series of diet and exercises activities. CarbMetSim implements wider aspects of carbohydrate metabolism in individuals to capture the average effect of various diet/exercise routines on the blood glucose level of diabetic patients. The simulator is implemented in an object-oriented paradigm, where key organs are represented as classes in the CarbMetSim. Key organs (stomach, intestine, portal vein, liver, kidney, muscles, adipose tissue, brain and heart) are implemented to the extent necessary to simulate their impact on the production and consumption of glucose. Metabolic pathways (glucose oxidation, glycolysis and gluconeogenesis) have been taken in account in the operation of various organs. In accordance with published research, the impact of insulin and insulin resistance on the operation of various organs/pathways is captured. CarbMetSim offers broad versatility to configure the insulin production ability, the average flux along various metabolic pathways and the impact of insulin resistance on different aspects of carbohydrate metabolism. However, the CarbMetSim project has not yet been finished. There are many aspects and metabolic pathways that have not been implemented or have been implemented in a simple manner. Also, additional validation is required before the simulator can be considered ready for use by people with Diabetes

Modelling, optimisation and model predictive control of insulin delivery systems in Type 1 Diabetes Mellitus

Type 1 Diabetes Mellitus is a metabolic disease requiring lifelong treatment with exogenous insulin which significantly affects patient’s lifestyle. Therefore, it is of paramount importance to develop novel drug delivery techniques that achieve therapeutic efficacy and ensure patient safety with a minimum impact on their quality of life. Motivated by the challenge to improve the living standard of a diabetic patient, the idea of an artificial pancreas that mimics the endocrine function of a healthy pancreas has been developed in the scientific society. Towards this direction, model predictive control has been established as a very promising control strategy for blood glucose regulation in a system that is dominated by high intra- and inter-patient variability, long time delays, and presence of unknown disturbances such as diet, physical activity and stress levels. This thesis presents a framework for blood glucose regulation with optimal insulin infusion which consists of the following steps: 1. Development of a novel physiologically based compartmental model analysed up to organ level that describes glucose-insulin interactions in type 1 diabetes, 2. Derivation of an approximate model suitable for control applications, 3. Design of an appropriate control strategy and 4. In-silico validation of the closed loop control performance. The developed model’s accuracy and prediction ability is evaluated with data obtained from the literature and the UVa/Padova Simulator model, the model parameters are individually estimated and their effect on the model’s measured output, the blood glucose concentration, is identified. The model is then linearised and reduced to derive low-order linear approximations of the underlying system suitable for control applications. The proposed control design aims towards an individualised optimal insulin delivery that consists of a patient-specific model predictive controller, a state estimator, a personalised scheduling level and an open loop optimisation problem subjected to patient specific process model and constraints. This control design is modifiable to address the case of limited patient data availability resulting in an “approximation” control strategy. Both designs are validated in-silico in the presence of predefined, measured and unknown meal disturbances using both the proposed model and the UVa/Padova Simulator model as a virtual patient. The robustness of the control performance is evaluated in several conditions such as skipped meals, variability in the meal content, time and metabolic uncertainty. The simulation results of the closed loop validation studies indicate that the proposed control strategies can achieve promising glycaemic control as demonstrated by the study data. However, further prospective validation of the closed loop control strategy with real patient data is required.Open Acces

A Discrete-Event Simulation Approach for Modeling Human Body Glucose Metabolism

This dissertation describes CarbMetSim (Carbohydrate Metabolism Simulator), a discrete-event simulator that tracks the blood glucose level of a person in response to a timed sequence of diet and exercise activities. CarbMetSim implements broader aspects of carbohydrate metabolism in human beings with the objective of capturing the average impact of various diet/exercise activities on the blood glucose level. Key organs (stomach, intestine, portal vein, liver, kidney, muscles, adipose tissue, brain and heart) are implemented to the extent necessary to capture their impact on the production and consumption of glucose. Key metabolic pathways (glucose oxidation, glycolysis and gluconeogenesis) are accounted for by using the published values of the average flux along these pathways in the operation of different organs. CarbMetSim has the ability to model different levels of insulin resistance and insulin production ability. The impact of insulin and insulin resistance on the operation of various organs and pathways is captured in accordance with published research. The protein and lipid metabolism are implemented only to the extent that they affect carbohydrate metabolism