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

STOCHASTIC SEASONAL MODELS FOR GLUCOSE PREDICTION IN TYPE 1 DIABETES

[ES] La diabetes es un importante problema de salud mundial, siendo una de las enfermedades no transmisibles más graves después de las enfermedades cardiovasculares, el cáncer y las enfermedades respiratorias crónicas. La prevalencia de la diabetes ha aumentado constantemente en las últimas décadas, especialmente en países de ingresos bajos y medios. Se estima que 425 millones de personas en todo el mundo tenían diabetes en 2017, y para 2045 este número puede aumentar a 629 millones. Alrededor del 10% de las personas con diabetes padecen diabetes tipo 1, caracterizada por una destrucción autoinmune de las células beta en el páncreas, responsables de la secreción de la hormona insulina. Sin insulina, la glucosa plasmática aumenta a niveles nocivos, provocando complicaciones vasculares a largo plazo. Hasta que se encuentre una cura, el manejo de la diabetes depende de los avances tecnológicos para terapias de reemplazo de insulina. Con la llegada de los monitores continuos de glucosa, la tecnología ha evolucionado hacia sistemas automatizados. Acuñados como "páncreas artificial", los dispositivos de control de glucosa en lazo cerrado suponen hoy en día un cambio de juego en el manejo de la diabetes. La investigación en las últimas décadas ha sido intensa, dando lugar al primer sistema comercial a fines de 2017, y muchos más están siendo desarrollados por las principales industrias de dispositivos médicos. Sin embargo, como dispositivo de primera generación, muchos problemas aún permanecen abiertos y nuevos avances tecnológicos conducirán a mejoras del sistema para obtener mejores resultados de control glucémico y reducir la carga del paciente, mejorando significativamente la calidad de vida de las personas con diabetes tipo 1. En el centro de cualquier sistema de páncreas artificial se encuentra la predicción de glucosa, tema abordado en esta tesis. La capacidad de predecir la glucosa a lo largo de un horizonte de predicción dado, y la estimación de las tendencias futuras de glucosa, es la característica más importante de cualquier sistema de páncreas artificial, para poder tomar medidas preventivas que eviten por completo el riesgo para el paciente. La predicción de glucosa puede aparecer como parte del algoritmo de control en sí, como en sistemas basados en técnicas de control predictivo basado en modelo (MPC), o como parte de un sistema de supervisión para evitar episodios de hipoglucemia. Sin embargo, predecir la glucosa es un problema muy desafiante debido a la gran variabilidad inter e intra-sujeto que sufren los pacientes, cuyas fuentes solo se entienden parcialmente. Esto limita las prestaciones predictivas de los modelos, imponiendo horizontes de predicción relativamente cortos, independientemente de la técnica de modelado utilizada (modelos fisiológicos, basados en datos o híbridos). La hipótesis de partida de esta tesis es que la complejidad de la dinámica de la glucosa requiere la capacidad de caracterizar grupos de comportamientos en los datos históricos del paciente que llevan naturalmente al concepto de modelado local. Además, la similitud de las respuestas en un grupo puede aprovecharse aún más para introducir el concepto clásico de estacionalidad en la predicción de glucosa. Como resultado, los modelos locales estacionales están en el centro de esta tesis. Se utilizan varias bases de datos clínicas que incluyen comidas mixtas y ejercicio para demostrar la viabilidad y superioridad de las prestaciones de este enfoque.[CA] La diabetisés un important problema de salut mundial, sent una de les malalties no transmissibles més greus després de les malalties cardiovasculars, el càncer i les malalties respiratòries cròniques. La prevalença de la diabetis ha augmentat constantment en les últimes dècades, especialment en països d'ingressos baixos i mitjans. S'estima que 425 milions de persones a tot el món tenien diabetis en 2017, i per 2045 aquest nombre pot augmentar a 629 milions. Al voltant del 10% de les persones amb diabetis pateixen diabetis tipus 1, caracteritzada per una destrucció autoimmune de les cèl·lules beta en el pàncrees, responsables de la secreció de l'hormona insulina. Sense insulina, la glucosa plasmàtica augmenta a nivells nocius, provocant complicacions vasculars a llarg termini. Fins que es trobi una cura, el maneig de la diabetis depén dels avenços tecnològics per a teràpies de reemplaçament d'insulina. Amb l'arribada dels monitors continus de glucosa, la tecnologia ha evolucionat cap a sistemes automatitzats. Encunyats com "pàncrees artificial", els dispositius de control de glucosa en llaç tancat suposen avui dia un canvi de joc en el maneig de la diabetis. La investigació en les últimes dècades ha estat intensa, donant lloc al primer sistema comercial a finals de 2017, i molts més estan sent desenvolupats per les principals indústries de dispositius mèdics. No obstant això, com a dispositiu de primera generació, molts problemes encara romanen oberts i nous avenços tecnològics conduiran a millores del sistema per obtenir millors resultats de control glucèmic i reduir la càrrega del pacient, millorant significativament la qualitat de vida de les persones amb diabetis tipus 1. Al centre de qualsevol sistema de pàncrees artificial es troba la predicció de glucosa, tema abordat en aquesta tesi. La capacitat de predir la glucosa al llarg d'un horitzó de predicció donat, i l'estimació de les tendències futures de glucosa, és la característica més important de qualsevol sistema de pàncrees artificial, per poder prendre mesures preventives que evitin completament el risc per el pacient. La predicció de glucosa pot aparèixer com a part de l'algoritme de control en si, com en sistemes basats en técniques de control predictiu basat en model (MPC), o com a part d'un sistema de supervisió per evitar episodis d'hipoglucèmia. No obstant això, predir la glucosa és un problema molt desafiant degut a la gran variabilitat inter i intra-subjecte que pateixen els pacients, les fonts només s'entenen parcialment. Això limita les prestacions predictives dels models, imposant horitzons de predicció relativament curts, independentment de la tècnica de modelatge utilitzada (models fisiològics, basats en dades o híbrids). La hipòtesi de partida d'aquesta tesi és que la complexitat de la dinàmica de la glucosa requereix la capacitat de caracteritzar grups de comportaments en les dades històriques del pacient que porten naturalment al concepte de modelatge local. A més, la similitud de les respostes en un grup pot aprofitar-se encara més per introduir el concepte clàssic d'estacionalitat en la predicció de glucosa. Com a resultat, els models locals estacionals estan al centre d'aquesta tesi. S'utilitzen diverses bases de dades clíniques que inclouen menjars mixtes i exercici per demostrar la viabilitat i superioritat de les prestacions d'aquest enfocament.[EN] Diabetes is a significant global health problem, one of the most serious noncommunicable diseases after cardiovascular diseases, cancer and chronic respiratory diseases. Diabetes prevalence has been steadily increasing over the past decades, especially in low- and middle-income countries. It is estimated that 425 million people worldwide had diabetes in 2017, and by 2045 this number may rise to 629 million. About 10% of people with diabetes suffer from type 1 diabetes, characterized by autoimmune destruction of the beta-cells in the pancreas, responsible for the secretion of the hormone insulin. Without insulin, plasma glucose rises to deleterious levels, provoking long-term vascular complications. Until a cure is found, the management of diabetes relies on technological developments for insulin replacement therapies. With the advent of continuous glucose monitors, technology has been evolving towards automated systems. Coined as "artificial pancreas", closed-loop glucose control devices are nowadays a game-changer in diabetes management. Research in the last decades has been intense, yielding a first commercial system in late 2017 and many more are in the pipeline of the main medical devices industry. However, as a first-generation device, many issues still remain open and new technological advancements will lead to system improvements for better glycemic control outputs and reduced patient's burden, improving significantly the quality of life of people with type 1 diabetes. At the core of any artificial pancreas system is glucose prediction, the topic addressed in this thesis. The ability to predict glucose along a given prediction horizon, and estimation of future glucose trends, is the most important feature of any artificial pancreas system, in order to be able to take preventive actions to entirely avoid risk to the patient. Glucose prediction can appear as part of the control algorithm itself, such as in systems based on model predictive control (MPC) techniques, or as part of a monitoring system to avoid hypoglycemic episodes. However, predicting glucose is a very challenging problem due to the large inter- and intra-subject variability that patients suffer, whose sources are only partially understood. These limits models forecasting performance, imposing relatively short prediction horizons, despite the modeling technique used (physiological, data-driven or hybrid approaches). The starting hypothesis of this thesis is that the complexity of glucose dynamics requires the ability to characterize clusters of behaviors in the patient's historical data naturally yielding to the concept of local modeling. Besides, the similarity of responses in a cluster can be further exploited to introduce the classical concept of seasonality into glucose prediction. As a result, seasonal local models are at the core of this thesis. Several clinical databases including mixed meals and exercise are used to demonstrate the feasibility and superiority of the performance of this approach.This work has been supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under the FPI grant BES-2014-069253 and projects DPI2013-46982-C2-1-R and DPI2016-78831-C2-1-R. Moreover, with relation to this grant, a short stay was done at the end of 2017 at the Illinois Institute of Technology, Chicago, United States of America, under the supervision of Prof. Ali Cinar, for four months from 01/09/2017 to 29/12/2017.Montaser Roushdi Ali, E. (2020). STOCHASTIC SEASONAL MODELS FOR GLUCOSE PREDICTION IN TYPE 1 DIABETES [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/136574TESI

A modular safety system for an insulin dose recommender: A feasibility study

Background: Delivering insulin in type 1 diabetes is a challenging, and potentially risky, activity; hence the importance of including safety measures as part of any insulin dosing or recommender system. This work presents and clinically evaluates a modular safety system that is part of an intelligent insulin dose recommender platform developed within the EU-funded PEPPER project. Methods: The proposed safety system is composed of four modules which use a novel glucose forecasting algorithm. These modules are: predictive glucose alerts and alarms; a predictive low-glucose basal insulin suspension module; an advanced rescue carbohydrate recommender for resolving hypoglycaemia; and a personalised safety constraint applied to insulin recommendations. The technical feasibility of the proposed safety system was evaluated in a pilot study including eight adult subjects with type 1 diabetes on multiple daily injections over a duration of six weeks. Glycaemic control and safety system functioning were compared between the two-weeks run-in period and the end-point at eight weeks. A standard insulin bolus calculator was employed to recommend insulin doses. Results: Overall, glycaemic control improved over the evaluated period. In particular, percentage time in the hypoglycaemia range (<3.0mmol/l) significantly decreased from 0.82 (0.05-4.79) % at run-in to 0.33 (0.00-0.93) % at endpoint (p=0.02). This was associated with a significant increase in percentage time in target range (3.9-10.0mmol/l) from 52.8 (38.3-61.5) % to 61.3 (47.5-71.7) % (p=0.03). There was also a reduction in number of carbohydrate recommendations. Conclusion: A safety system for an insulin dose recommender has been proven to be a viable solution to reduce the number of adverse events associated to glucose control in type 1 diabetes

Modeling and Prediction in Diabetes Physiology

Diabetes is a group of metabolic diseases characterized by the inability of the organism to autonomously regulate the blood glucose levels. It requires continuing medical care to prevent acute complications and to reduce the risk of long-term complications. Inadequate glucose control is associated with damage, dysfunction and failure of various organs. The management of the disease is non trivial and demanding. With today’s standards of current diabetes care, good glucose regulation needs constant attention and decision-making by the individuals with diabetes. Empowering the patients with a decision support system would, therefore, improve their quality of life without additional burdens nor replacing human expertise. This thesis investigates the use of data-driven techniques to the purpose of glucose metabolism modeling and short-term blood-glucose predictions in Type I Diabetes Mellitus (T1DM). The goal was to use models and predictors in an advisory tool able to produce personalized short-term blood glucose predictions and on-the-spot decision making concerning the most adequate choice of insulin delivery, meal intake and exercise, to help diabetic subjects maintaining glycemia as close to normal as possible. The approaches taken to describe the glucose metabolism were discrete-time and continuous-time models on input-output form and statespace form, while the blood glucose short-term predictors, i.e., up to 120 minutes ahead, used ARX-, ARMAX- and subspace-based prediction

EFFECT OF POTATO-BASED AND PREPACKAGED SPORTS SUPPLEMENT PRODUCTS ON MUSCLE GLYCOGEN RECOVERY AND EXERCISE PERFORMANCE IN TRAINED MALES AND FEMALES

Purpose Research has elucidated the impact of post exercise carbohydrate nutrition and environmental conditions on muscle glycogen re-synthesis. However, research has minimally considered the implications of glycogen recovery in females and has focused on commercial sport nutrition products. The purpose of this study was to determine the effects of varied mixed macronutrient feedings on glycogen recovery and subsequent exercise performance in both sexes. Methods 8 males and 8 females participated in a crossover study. Subjects completed a 90-minute cycling glycogen depletion trial then rested for 4 hours. Two carbohydrate feedings (1.6 g . kg-1) of either sport supplements or potato-based products were delivered at 0 and 2 hours post exercise. Muscle biopsies and blood samples (glucose, insulin) were collected during the recovery. Afterwards, subjects completed a 20km cycling time trial. Results There was no difference between sexes or trials for glycogen recovery rates (male: 7.9 ± 2.7, female: 8.2 ± 2.7, potato-based: 8.0 ± 2.5, sport supplement: 8.1 ± 3.1 mM . kg wet wt-1 hr-1, p \u3e 0.05). Time trial performance was not different between diets (38.3 ± 4.4 and 37.8 ± 3.9 minutes for potato and sport supplement, respectively, p \u3e 0.05). Conclusions These results indicate that food items, such as potato-based products, can be as effective as commercially marketed sports supplements when developing glycogen recovery oriented menus and that carbohydrate dose feedings (g . kg- 1) can be applied to both males and females

Hybrid Deep Learning Algorithm for Insulin Dosage Prediction Using Blockchain and IOT

This paper addresses the problem of predicting insulin dosage in diabetes patients using the PSO-LSTM, COA-LSTM, and LOA-LSTM algorithms. Accurate insulin dosage prediction is crucial in effectively managing Diabetes and maintaining blood glucose levels within the desired range. The study proposes a novel approach that combines particle swarm optimization (PSO) with the long short-term memory (LSTM) model. PSO is used to optimize the LSTM's parameters, enhancing its prediction capabilities specifically for insulin dosage. Additionally, two other techniques, COA-LSTM and LOA-LSTM, are introduced for comparison purposes. The algorithms utilize a dataset comprising relevant features such as past insulin dosages, blood glucose levels, carbohydrate intake, and physical activity. These features are fed into the PSO-LSTM, COA-LSTM, and LOA-LSTM models to predict the appropriate insulin dosage for future time points. The results demonstrate the effectiveness of the proposed PSO-LSTM algorithm in accurately predicting insulin dosage, surpassing the performance of COA-LSTM and LOA-LSTM. The PSO-LSTM model achieves a high level of accuracy, aiding in personalized and precise insulin administration for diabetes patients. By leveraging the power of PSO optimization and LSTM modeling, this research improves the accuracy and reliability of insulin dosage prediction. The findings highlight the potential of the PSO-LSTM algorithm as a valuable tool for healthcare professionals in optimizing diabetes management and enhancing patient outcomes

Applications of the Internet of Medical Things to Type 1 Diabetes Mellitus

Type 1 Diabetes Mellitus (DM1) is a condition of the metabolism typified by persistent hyperglycemia as a result of insufficient pancreatic insulin synthesis. This requires patients to be aware of their blood glucose level oscillations every day to deduce a pattern and anticipate future glycemia, and hence, decide the amount of insulin that must be exogenously injected to maintain glycemia within the target range. This approach often suffers from a relatively high imprecision, which can be dangerous. Nevertheless, current developments in Information and Communication Technologies (ICT) and innovative sensors for biological signals that might enable a continuous, complete assessment of the patient’s health provide a fresh viewpoint on treating DM1. With this, we observe that current biomonitoring devices and Continuous Glucose Monitoring (CGM) units can easily obtain data that allow us to know at all times the state of glycemia and other variables that influence its oscillations. A complete review has been made of the variables that influence glycemia in a T1DM patient and that can be measured by the above means. The communications systems necessary to transfer the information collected to a more powerful computational environment, which can adequately handle the amounts of data collected, have also been described. From this point, intelligent data analysis extracts knowledge from the data and allows predictions to be made in order to anticipate risk situations. With all of the above, it is necessary to build a holistic proposal that allows the complete and smart management of T1DM. This approach evaluates a potential shortage of such suggestions and the obstacles that future intelligent IoMT-DM1 management systems must surmount. Lastly, we provide an outline of a comprehensive IoMT-based proposal for DM1 management that aims to address the limits of prior studies while also using the disruptive technologies highlighted beforePartial funding for open access charge: Universidad de Málag

Deep learning methods for improving diabetes management tools

Diabetes is a chronic disease that is characterised by a lack of regulation of blood glucose concentration in the body, and thus elevated blood glucose levels. Consequently, affected individuals can experience extreme variations in their blood glucose levels with exogenous insulin treatment. This has associated debilitating short-term and long-term complications that affect quality of life and can result in death in the worst instance. The development of technologies such as glucose meters and, more recently, continuous glucose monitors have offered the opportunity to develop systems towards improving clinical outcomes for individuals with diabetes through better glucose control. Data-driven methods can enable the development of the next generation of diabetes management tools focused on i) informativeness ii) safety and iii) easing the burden of management. This thesis aims to propose deep learning methods for improving the functionality of the variety of diabetes technology tools available for self-management. In the pursuit of the aforementioned goals, a number of deep learning methods are developed and geared towards improving the functionality of the existing diabetes technology tools, generally classified as i) self-monitoring of blood glucose ii) decision support systems and iii) artificial pancreas. These frameworks are primarily based on the prediction of glucose concentration levels. The first deep learning framework we propose is geared towards improving the artificial pancreas and decision support systems that rely on continuous glucose monitors. We first propose a convolutional recurrent neural network (CRNN) in order to forecast the glucose concentration levels over both short-term and long-term horizons. The predictive accuracy of this model outperforms those of traditional data-driven approaches. The feasibility of this proposed approach for ambulatory use is then demonstrated with the implementation of a decision support system on a smartphone application. We further extend CRNNs to the multitask setting to explore the effectiveness of leveraging population data for developing personalised models with limited individual data. We show that this enables earlier deployment of applications without significantly compromising performance and safety. The next challenge focuses on easing the burden of management by proposing a deep learning framework for automatic meal detection and estimation. The deep learning framework presented employs multitask learning and quantile regression to safely detect and estimate the size of unannounced meals with high precision. We also demonstrate that this facilitates automated insulin delivery for the artificial pancreas system, improving glycaemic control without significantly increasing the risk or incidence of hypoglycaemia. Finally, the focus shifts to improving self-monitoring of blood glucose (SMBG) with glucose meters. We propose an uncertainty-aware deep learning model based on a joint Gaussian Process and deep learning framework to provide end users with more dynamic and continuous information similar to continuous glucose sensors. Consequently, we show significant improvement in hyperglycaemia detection compared to the standard SMBG. We hope that through these methods, we can achieve a more equitable improvement in usability and clinical outcomes for individuals with diabetes.Open Acces