EPIC (Entorno de Páncreas Artificial con Interfaz Clínica)

La Diabetes Mellitus se define como el trastorno del metabolismo de los carbohidratos, resultante de una producción insuficiente o nula de insulina en las células beta del páncreas, o la manifestación de una sensibilidad reducida a la insulina por parte del sistema metabólico. La diabetes tipo 1 se caracteriza por la nula producción de insulina por la destrucción de las células beta del páncreas. Si no hay insulina en el torrente sanguíneo, la glucosa no puede ser absorbida por las células, produciéndose un estado de hiperglucemia en el paciente, que a medio y largo plazo si no es tratado puede ocasionar severas enfermedades, conocidos como síndromes de la diabetes. La diabetes tipo 1 es una enfermedad incurable pero controlable. La terapia para esta enfermedad consiste en la aplicación exógena de insulina con el objetivo de mantener el nivel de glucosa en sangre dentro de los límites normales. Dentro de las múltiples formas de aplicación de la insulina, en este proyecto se usará una bomba de infusión, que unida a un sensor subcutáneo de glucosa permitirá crear un lazo de control autónomo que regule la cantidad optima de insulina aplicada en cada momento. Cuando el algoritmo de control se utiliza en un sistema digital, junto con el sensor subcutáneo y bomba de infusión subcutánea, se conoce como páncreas artificial endocrino (PAE) de uso ambulatorio, hoy día todavía en fase de investigación. Estos algoritmos de control metabólico deben de ser evaluados en simulación para asegurar la integridad física de los pacientes, por lo que es necesario diseñar un sistema de simulación mediante el cual asegure la fiabilidad del PAE. Este sistema de simulación conecta los algoritmos con modelos metabólicos matemáticos para obtener una visión previa de su funcionamiento. En este escenario se diseñó DIABSIM, una herramienta desarrollada en LabViewTM, que posteriormente se trasladó a MATLABTM, y basada en el modelo matemático compartimental propuesto por Hovorka, con la que poder simular y evaluar distintos tipos de terapias y reguladores en lazo cerrado. Para comprobar que estas terapias y reguladores funcionan, una vez simulados y evaluados, se tiene que pasar a la experimentación real a través de un protocolo de ensayo clínico real, como paso previo al PEA ambulatorio. Para poder gestionar este protocolo de ensayo clínico real para la verificación de los algoritmos de control, se creó una interfaz de usuario a través de una serie de funciones de simulación y evaluación de terapias con insulina realizadas con MATLABTM (GUI: Graphics User Interface), conocido como Entorno de Páncreas artificial con Interfaz Clínica (EPIC). EPIC ha sido ya utilizada en 10 ensayos clínicos de los que se han ido proponiendo posibles mejoras, ampliaciones y/o cambios. Este proyecto propone una versión mejorada de la interfaz de usuario EPIC propuesta en un proyecto anterior para gestionar un protocolo de ensayo clínico real para la verificación de algoritmos de control en un ambiente hospitalario muy controlado, además de estudiar la viabilidad de conectar el GUI con SimulinkTM (entorno gráfico de Matlab de simulación de sistemas) para su conexión con un nuevo simulador de pacientes aprobado por la JDRF (Juvenil Diabetes Research Foundation). SUMMARY The diabetes mellitus is a metabolic disorder of carbohydrates, as result of an insufficient or null production of insulin in the beta cellules of pancreas, or the manifestation of a reduced sensibility to the insulin from the metabolic system. The type 1 diabetes is characterized for a null production of insulin due to destruction of the beta cellules. Without insulin in the bloodstream, glucose can’t be absorbed by the cellules, producing a hyperglycemia state in the patient and if pass a medium or long time and is not treated can cause severe disease like diabetes syndrome. The type 1 diabetes is an incurable disease but controllable one. The therapy for this disease consists on the exogenous insulin administration with the objective to maintain the glucose level in blood within the normal limits. For the insulin administration, in this project is used an infusion pump, that permit with a subcutaneous glucose sensor, create an autonomous control loop that regulate the optimal insulin amount apply in each moment. When the control algorithm is used in a digital system, with the subcutaneous senor and infusion subcutaneous pump, is named as “Artificial Endocrine Pancreas” for ambulatory use, currently under investigate. These metabolic control algorithms should be evaluates in simulation for assure patients’ physical integrity, for this reason is necessary to design a simulation system that assure the reliability of PAE. This simulation system connects algorithms with metabolic mathematics models for get a previous vision of its performance. In this scenario was created DIABSIMTM, a tool developed in LabView, that later was converted to MATLABTM, and based in the compartmental mathematic model proposed by Hovorka that could simulate and evaluate several different types of therapy and regulators in closed loop. To check the performance of these therapies and regulators, when have been simulated and evaluated, will be necessary to pass to real experimentation through a protocol of real clinical test like previous step to ambulatory PEA. To manage this protocol was created an user interface through the simulation and evaluation functions od therapies with insulin realized with MATLABTM (GUI: Graphics User Interface), known as “Entorno de Páncreas artificial con Interfaz Clínica” (EPIC).EPIC have been used in 10 clinical tests which have been proposed improvements, adds and changes. This project proposes a best version of user interface EPIC proposed in another project for manage a real test clinical protocol for checking control algorithms in a controlled hospital environment and besides studying viability to connect the GUI with SimulinkTM (Matlab graphical environment in systems simulation) for its connection with a new patients simulator approved for the JDRF (Juvenil Diabetes Research Foundation).

Propuesta de algoritmos de predicción de glucosa en pacientes diabéticos

La diabetes mellitus es el conjunto de alteraciones provocadas por un defecto en la cantidad de insulina secretada o por un aprovechamiento deficiente de la misma. Es causa directa de complicaciones a corto, medio y largo plazo que disminuyen la calidad y las expectativas de vida de las personas con diabetes. La diabetes mellitus es en la actualidad uno de los problemas más importantes de salud. Ha triplicado su prevalencia en los últimos 20 anos y para el año 2025 se espera que existan casi 300 millones de personas con diabetes. Este aumento de la prevalencia junto con la morbi-mortalidad asociada a sus complicaciones micro y macro-vasculares convierten la diabetes en una carga para los sistemas sanitarios, sus recursos económicos y sus profesionales, haciendo de la enfermedad un problema individual y de salud pública de enormes proporciones. De momento no existe cura a esta enfermedad, de modo que el objetivo terapéutico del tratamiento de la diabetes se centra en la normalización de la glucemia intentando minimizar los eventos de hiper e hipoglucemia y evitando la aparición o al menos retrasando la evolución de las complicaciones vasculares, que constituyen la principal causa de morbi-mortalidad de las personas con diabetes. Un adecuado control diabetológico implica un tratamiento individualizado que considere multitud de factores para cada paciente (edad, actividad física, hábitos alimentarios, presencia de complicaciones asociadas o no a la diabetes, factores culturales, etc.). Sin embargo, a corto plazo, las dos variables más influyentes que el paciente ha de manejar para intervenir sobre su nivel glucémico son la insulina administrada y la dieta. Ambas presentan un retardo entre el momento de su aplicación y el comienzo de su acción, asociado a la absorción de los mismos. Por este motivo la capacidad de predecir la evolución del perfil glucémico en un futuro cercano, ayudara al paciente a tomar las decisiones adecuadas para mantener un buen control de su enfermedad y evitar situaciones de riesgo. Este es el objetivo de la predicción en diabetes: adelantar la evolución del perfil glucémico en un futuro cercano para ayudar al paciente a adaptar su estilo de vida y sus acciones correctoras, con el propósito de que sus niveles de glucemia se aproximen a los de una persona sana, evitando así los síntomas y complicaciones de un mal control. La aparición reciente de los sistemas de monitorización continua de glucosa ha proporcionado nuevas alternativas. La disponibilidad de un registro exhaustivo de las variaciones del perfil glucémico, con un periodo de muestreo de entre uno y cinco minutos, ha favorecido el planteamiento de nuevos modelos que tratan de predecir la glucemia utilizando tan solo las medidas anteriores de glucemia o al menos reduciendo significativamente la información de entrada a los algoritmos. El hecho de requerir menor intervención por parte del paciente, abre nuevas posibilidades de aplicación de los predictores de glucemia, haciéndose viable su uso en tiempo real, como sistemas de ayuda a la decisión, como detectores de situaciones de riesgo o integrados en algoritmos automáticos de control. En esta tesis doctoral se proponen diferentes algoritmos de predicción de glucemia para pacientes con diabetes, basados en la información registrada por un sistema de monitorización continua de glucosa así como incorporando la información de la insulina administrada y la ingesta de carbohidratos. Los algoritmos propuestos han sido evaluados en simulación y utilizando datos de pacientes registrados en diferentes estudios clínicos. Para ello se ha desarrollado una amplia metodología, que trata de caracterizar las prestaciones de los modelos de predicción desde todos los puntos de vista: precisión, retardo, ruido y capacidad de detección de situaciones de riesgo. Se han desarrollado las herramientas de simulación necesarias y se han analizado y preparado las bases de datos de pacientes. También se ha probado uno de los algoritmos propuestos para comprobar la validez de la predicción en tiempo real en un escenario clínico. Se han desarrollado las herramientas que han permitido llevar a cabo el protocolo experimental definido, en el que el paciente consulta la predicción bajo demanda y tiene el control sobre las variables metabólicas. Este experimento ha permitido valorar el impacto sobre el control glucémico del uso de la predicción de glucosa. ABSTRACT Diabetes mellitus is the set of alterations caused by a defect in the amount of secreted insulin or a suboptimal use of insulin. It causes complications in the short, medium and long term that affect the quality of life and reduce the life expectancy of people with diabetes. Diabetes mellitus is currently one of the most important health problems. Prevalence has tripled in the past 20 years and estimations point out that it will affect almost 300 million people by 2025. Due to this increased prevalence, as well as to morbidity and mortality associated with micro- and macrovascular complications, diabetes has become a burden on health systems, their financial resources and their professionals, thus making the disease a major individual and a public health problem. There is currently no cure for this disease, so that the therapeutic goal of diabetes treatment focuses on normalizing blood glucose events. The aim is to minimize hyper- and hypoglycemia and to avoid, or at least to delay, the appearance and development of vascular complications, which are the main cause of morbidity and mortality among people with diabetes. A suitable, individualized and controlled treatment for diabetes involves many factors that need to be considered for each patient: age, physical activity, eating habits, presence of complications related or unrelated to diabetes, cultural factors, etc. However, in the short term, the two most influential variables that the patient has available in order to manage his/her glycemic levels are administered insulin doses and diet. Both suffer from a delay between their time of application and the onset of the action associated with their absorption. Therefore, the ability to predict the evolution of the glycemic profile in the near future could help the patient to make appropriate decisions on how to maintain good control of his/her disease and to avoid risky situations. Hence, the main goal of glucose prediction in diabetes consists of advancing the evolution of glycemic profiles in the near future. This would assist the patient in adapting his/her lifestyle and in taking corrective actions in a way that blood glucose levels approach those of a healthy person, consequently avoiding the symptoms and complications of a poor glucose control. The recent emergence of continuous glucose monitoring systems has provided new alternatives in this field. The availability of continuous records of changes in glycemic profiles (with a sampling period of one or five minutes) has enabled the design of new models which seek to predict blood glucose by using automatically read glucose measurements only (or at least, reducing significantly the data input manually to the algorithms). By requiring less intervention by the patient, new possibilities are open for the application of glucose predictors, making its use feasible in real-time applications, such as: decision support systems, hypo- and hyperglycemia detectors, integration into automated control algorithms, etc. In this thesis, different glucose prediction algorithms are proposed for patients with diabetes. These are based on information recorded by a continuous glucose monitoring system and incorporate information of the administered insulin and carbohydrate intakes. The proposed algorithms have been evaluated in-silico and using patients’ data recorded in different clinical trials. A complete methodology has been developed to characterize the performance of predictive models from all points of view: accuracy, delay, noise and ability to detect hypo- and hyperglycemia. In addition, simulation tools and patient databases have been deployed. One of the proposed algorithms has additionally been evaluated in terms of real-time prediction performance in a clinical scenario in which the patient checked his/her glucose predictions on demand and he/she had control on his/her metabolic variables. This has allowed assessing the impact of using glucose prediction on glycemic control. The tools to carry out the defined experimental protocols were also developed in this thesis.

Extracción de conocimientos de la diabetes tipo 1 utilizando la metodología de "Data Mining"

La diabetes mellitus es un trastorno en la metabolización de los carbohidratos, caracterizado por la nula o insuficiente segregación de insulina (hormona producida por el páncreas), como resultado del mal funcionamiento de la parte endocrina del páncreas, o de una creciente resistencia del organismo a esta hormona. Esto implica, que tras el proceso digestivo, los alimentos que ingerimos se transforman en otros compuestos químicos más pequeños mediante los tejidos exocrinos. La ausencia o poca efectividad de esta hormona polipéptida, no permite metabolizar los carbohidratos ingeridos provocando dos consecuencias: Aumento de la concentración de glucosa en sangre, ya que las células no pueden metabolizarla; consumo de ácidos grasos mediante el hígado, liberando cuerpos cetónicos para aportar la energía a las células. Esta situación expone al enfermo crónico, a una concentración de glucosa en sangre muy elevada, denominado hiperglucemia, la cual puede producir a medio o largo múltiples problemas médicos: oftalmológicos, renales, cardiovasculares, cerebrovasculares, neurológicos… La diabetes representa un gran problema de salud pública y es la enfermedad más común en los países desarrollados por varios factores como la obesidad, la vida sedentaria, que facilitan la aparición de esta enfermedad. Mediante el presente proyecto trabajaremos con los datos de experimentación clínica de pacientes con diabetes de tipo 1, enfermedad autoinmune en la que son destruidas las células beta del páncreas (productoras de insulina) resultando necesaria la administración de insulina exógena. Dicho esto, el paciente con diabetes tipo 1 deberá seguir un tratamiento con insulina administrada por la vía subcutánea, adaptado a sus necesidades metabólicas y a sus hábitos de vida. Para abordar esta situación de regulación del control metabólico del enfermo, mediante una terapia de insulina, no serviremos del proyecto “Páncreas Endocrino Artificial” (PEA), el cual consta de una bomba de infusión de insulina, un sensor continuo de glucosa, y un algoritmo de control en lazo cerrado. El objetivo principal del PEA es aportar al paciente precisión, eficacia y seguridad en cuanto a la normalización del control glucémico y reducción del riesgo de hipoglucemias. El PEA se instala mediante vía subcutánea, por lo que, el retardo introducido por la acción de la insulina, el retardo de la medida de glucosa, así como los errores introducidos por los sensores continuos de glucosa cuando, se descalibran dificultando el empleo de un algoritmo de control. Llegados a este punto debemos modelar la glucosa del paciente mediante sistemas predictivos. Un modelo, es todo aquel elemento que nos permita predecir el comportamiento de un sistema mediante la introducción de variables de entrada. De este modo lo que conseguimos, es una predicción de los estados futuros en los que se puede encontrar la glucosa del paciente, sirviéndonos de variables de entrada de insulina, ingesta y glucosa ya conocidas, por ser las sucedidas con anterioridad en el tiempo. Cuando empleamos el predictor de glucosa, utilizando parámetros obtenidos en tiempo real, el controlador es capaz de indicar el nivel futuro de la glucosa para la toma de decisones del controlador CL. Los predictores que se están empleando actualmente en el PEA no están funcionando correctamente por la cantidad de información y variables que debe de manejar. Data Mining, también referenciado como Descubrimiento del Conocimiento en Bases de Datos (Knowledge Discovery in Databases o KDD), ha sido definida como el proceso de extracción no trivial de información implícita, previamente desconocida y potencialmente útil. Todo ello, sirviéndonos las siguientes fases del proceso de extracción del conocimiento: selección de datos, pre-procesado, transformación, minería de datos, interpretación de los resultados, evaluación y obtención del conocimiento. Con todo este proceso buscamos generar un único modelo insulina glucosa que se ajuste de forma individual a cada paciente y sea capaz, al mismo tiempo, de predecir los estados futuros glucosa con cálculos en tiempo real, a través de unos parámetros introducidos. Este trabajo busca extraer la información contenida en una base de datos de pacientes diabéticos tipo 1 obtenidos a partir de la experimentación clínica. Para ello emplearemos técnicas de Data Mining. Para la consecución del objetivo implícito a este proyecto hemos procedido a implementar una interfaz gráfica que nos guía a través del proceso del KDD (con información gráfica y estadística) de cada punto del proceso. En lo que respecta a la parte de la minería de datos, nos hemos servido de la denominada herramienta de WEKA, en la que a través de Java controlamos todas sus funciones, para implementarlas por medio del programa creado. Otorgando finalmente, una mayor potencialidad al proyecto con la posibilidad de implementar el servicio de los dispositivos Android por la potencial capacidad de portar el código. Mediante estos dispositivos y lo expuesto en el proyecto se podrían implementar o incluso crear nuevas aplicaciones novedosas y muy útiles para este campo. Como conclusión del proyecto, y tras un exhaustivo análisis de los resultados obtenidos, podemos apreciar como logramos obtener el modelo insulina-glucosa de cada paciente. ABSTRACT. The diabetes mellitus is a metabolic disorder, characterized by the low or none insulin production (a hormone produced by the pancreas), as a result of the malfunctioning of the endocrine pancreas part or by an increasing resistance of the organism to this hormone. This implies that, after the digestive process, the food we consume is transformed into smaller chemical compounds, through the exocrine tissues. The absence or limited effectiveness of this polypeptide hormone, does not allow to metabolize the ingested carbohydrates provoking two consequences: Increase of the glucose concentration in blood, as the cells are unable to metabolize it; fatty acid intake through the liver, releasing ketone bodies to provide energy to the cells. This situation exposes the chronic patient to high blood glucose levels, named hyperglycemia, which may cause in the medium or long term multiple medical problems: ophthalmological, renal, cardiovascular, cerebrum-vascular, neurological … The diabetes represents a great public health problem and is the most common disease in the developed countries, by several factors such as the obesity or sedentary life, which facilitate the appearance of this disease. Through this project we will work with clinical experimentation data of patients with diabetes of type 1, autoimmune disease in which beta cells of the pancreas (producers of insulin) are destroyed resulting necessary the exogenous insulin administration. That said, the patient with diabetes type 1 will have to follow a treatment with insulin, administered by the subcutaneous route, adapted to his metabolic needs and to his life habits. To deal with this situation of metabolic control regulation of the patient, through an insulin therapy, we shall be using the “Endocrine Artificial Pancreas " (PEA), which consists of a bomb of insulin infusion, a constant glucose sensor, and a control algorithm in closed bow. The principal aim of the PEA is providing the patient precision, efficiency and safety regarding the normalization of the glycemic control and hypoglycemia risk reduction". The PEA establishes through subcutaneous route, consequently, the delay introduced by the insulin action, the delay of the glucose measure, as well as the mistakes introduced by the constant glucose sensors when, decalibrate, impede the employment of an algorithm of control. At this stage we must shape the patient glucose levels through predictive systems. A model is all that element or set of elements which will allow us to predict the behavior of a system by introducing input variables. Thus what we obtain, is a prediction of the future stages in which it is possible to find the patient glucose level, being served of input insulin, ingestion and glucose variables already known, for being the ones happened previously in the time. When we use the glucose predictor, using obtained real time parameters, the controller is capable of indicating the future level of the glucose for the decision capture CL controller. The predictors that are being used nowadays in the PEA are not working correctly for the amount of information and variables that it need to handle. Data Mining, also indexed as Knowledge Discovery in Databases or KDD, has been defined as the not trivial extraction process of implicit information, previously unknown and potentially useful. All this, using the following phases of the knowledge extraction process: selection of information, pre- processing, transformation, data mining, results interpretation, evaluation and knowledge acquisition. With all this process we seek to generate the unique insulin glucose model that adjusts individually and in a personalized way for each patient form and being capable, at the same time, of predicting the future conditions with real time calculations, across few input parameters. This project of end of grade seeks to extract the information contained in a database of type 1 diabetics patients, obtained from clinical experimentation. For it, we will use technologies of Data Mining. For the attainment of the aim implicit to this project we have proceeded to implement a graphical interface that will guide us across the process of the KDD (with graphical and statistical information) of every point of the process. Regarding the data mining part, we have been served by a tool called WEKA's tool called, in which across Java, we control all of its functions to implement them by means of the created program. Finally granting a higher potential to the project with the possibility of implementing the service for Android devices, porting the code. Through these devices and what has been exposed in the project they might help or even create new and very useful applications for this field. As a conclusion of the project, and after an exhaustive analysis of the obtained results, we can show how we achieve to obtain the insulin–glucose model for each patient.

Mejora postpradial de un controlador basado en reglas

La Diabetes mellitus es una enfermedad caracterizada por la insuficiente o nula producción de insulina por parte del páncreas o la reducida sensibilidad del organismo a esta hormona, que ayuda a que la glucosa llegue a los tejidos y al sistema nervioso para suministrar energía. La Diabetes tiene una mayor prevalencia en los países desarrollados debido a múltiples factores, entre ellos la obesidad, la vida sedentaria, y disfunciones en el sistema endocrino relacionadas con el páncreas. La Diabetes Tipo 1 es una enfermedad crónica e incurable, en la que son destruidas las células beta del páncreas, que producen la insulina, haciéndose necesaria la administración de insulina de forma exógena para controlar los niveles de glucosa en sangre. El paciente debe seguir una terapia con insulina administrada por vía subcutánea, que debe estar adaptada a sus necesidades metabólicas y a sus hábitos de vida. Esta terapia intenta imitar el perfil insulínico de un páncreas sano. La tecnología actual permite abordar el desarrollo del denominado “páncreas endocrino artificial” (PEA), que aportaría precisión, eficacia y seguridad en la aplicación de las terapias con insulina y permitiría una mayor independencia de los pacientes frente a su enfermedad, que en la actualidad están sujetos a una constante toma de decisiones. El PEA consta de un sensor continuo de glucosa, una bomba de infusión de insulina y un algoritmo de control, que calcula la insulina a infusionar utilizando los niveles de glucosa del paciente como información principal. Este trabajo presenta una modificación en el método de control en lazo cerrado propuesto en un proyecto previo. El controlador del que se parte está compuesto por un controlador basal booleano y un controlador borroso postprandial basado en reglas borrosas heredadas del controlador basal. El controlador postprandial administra el 50% del bolo manual (calculado a partir de la cantidad de carbohidratos que el paciente va a consumir) en el instante del aviso de la ingesta y reparte el resto en instantes posteriores. El objetivo es conseguir una regulación óptima del nivel de glucosa en el periodo postprandial. Con el objetivo de reducir las hiperglucemias que se producen en el periodo postprandial se realiza un transporte de insulina, que es un adelanto de la insulina basal del periodo postprandial que se suministrará junto con un porcentaje variable del bolo manual. Este porcentaje estará relacionado con el estado metabólico del paciente previo a la ingesta. Además se modificará la base de conocimiento para adecuar el comportamiento del controlador al periodo postprandial. Este proyecto está enfocado en la mejora del controlador borroso postprandial previo, modificando dos aspectos: la inferencia del controlador postprandial y añadiendo una toma de decisiones automática sobre el % del bolo manual y el transporte. Se ha propuesto un controlador borroso con una nueva inferencia, que no hereda las características del controlado basal, y ha sido adaptado al periodo postprandial. Se ha añadido una inferencia borrosa que modifica la cantidad de insulina a administrar en el momento del aviso de ingesta y la cantidad de insulina basal a transportar del periodo postprandial al bolo manual. La validación del algoritmo se ha realizado mediante experimentos en simulación utilizando una población de diez pacientes sintéticos pertenecientes al Simulador de Padua/Virginia, evaluando los resultados con estadísticos para después compararlos con los obtenidos con el método de control anterior. Tras la evaluación de los resultados se puede concluir que el nuevo controlador postprandial, acompañado de la toma de decisiones automática, realiza un mejor control glucémico en el periodo postprandial, disminuyendo los niveles de las hiperglucemias. ABSTRACT. Diabetes mellitus is a disease characterized by the insufficient or null production of insulin from the pancreas or by a reduced sensitivity to this hormone, which helps glucose get to the tissues and the nervous system to provide energy. Diabetes has more prevalence in developed countries due to multiple factors, including obesity, sedentary lifestyle and endocrine dysfunctions related to the pancreas. Type 1 Diabetes is a chronic, incurable disease in which beta cells in the pancreas that produce insulin are destroyed, and exogenous insulin delivery is required to control blood glucose levels. The patient must follow a therapy with insulin administered by the subcutaneous route that should be adjusted to the metabolic needs and lifestyle of the patient. This therapy tries to imitate the insulin profile of a non-pathological pancreas. Current technology can adress the development of the so-called “endocrine artificial pancreas” (EAP) that would provide accuracy, efficacy and safety in the application of insulin therapies and will allow patients a higher level of independence from their disease. Patients are currently tied to constant decision making. The EAP consists of a continuous glucose sensor, an insulin infusion pump and a control algorithm that computes the insulin amount that has to be infused using the glucose as the main source of information. This work shows modifications to the control method in closed loop proposed in a previous project. The reference controller is composed by a boolean basal controller and a postprandial rule-based fuzzy controller which inherits the rules from the basal controller. The postprandial controller administrates 50% of the bolus (calculated from the amount of carbohydrates that the patient is going to ingest) in the moment of the intake warning, and distributes the remaining in later instants. The goal is to achieve an optimum regulation of the glucose level in the postprandial period. In order to reduce hyperglycemia in the postprandial period an insulin transport is carried out. It consists on a feedforward of the basal insulin from the postprandial period, which will be administered with a variable percentage of the manual bolus. This percentage would be linked with the metabolic state of the patient in moments previous to the intake. Furthermore, the knowledge base is going to be modified in order to fit the controller performance to the postprandial period. This project is focused on the improvement of the previous controller, modifying two aspects: the postprandial controller inference, and the automatic decision making on the percentage of the manual bolus and the transport. A fuzzy controller with a new inference has been proposed and has been adapted to the postprandial period. A fuzzy inference has been added, which modifies both the amount of manual bolus to administrate at the intake warning and the amount of basal insulin to transport to the prandial bolus. The algorithm assessment has been done through simulation experiments using a synthetic population of 10 patients in the UVA/PADOVA simulator, evaluating the results with statistical parameters for further comparison with those obtained with the previous control method. After comparing results it can be concluded that the new postprandial controller, combined with the automatic decision making, carries out a better glycemic control in the postprandial period, decreasing levels of hyperglycemia.

Targeted overexpression of protein kinase C β2 isoform in myocardium causes cardiomyopathy

Increased cardiovascular mortality occurs in diabetic patients with or without coronary artery disease and is attributed to the presence of diabetic cardiomyopathy. One potential mechanism is hyperglycemia that has been reported to activate protein kinase C (PKC), preferentially the β isoform, which has been associated with the development of micro- and macrovascular pathologies in diabetes mellitus. To establish that the activation of the PKCβ isoform can cause cardiac dysfunctions, we have established lines of transgenic mice with the specific overexpression of PKCβ2 isoform in the myocardium. These mice overexpressed the PKCβ2 isoform transgene by 2- to 10-fold as measured by mRNA, and proteins exhibited left ventricular hypertrophy, cardiac myocyte necrosis, multifocal fibrosis, and decreased left ventricular performance without vascular lesions. The severity of the phenotypes exhibited gene dose-dependence. Up-regulation of mRNAs for fetal type myosin heavy chain, atrial natriuretic factor, c-fos, transforming growth factor, and collagens was also observed. Moreover, treatment with a PKCβ-specific inhibitor resulted in functional and histological improvement. These findings have firmly established that the activation of the PKCβ2 isoform can cause specific cardiac cellular and functional changes leading to cardiomyopathy of diabetic or nondiabetic etiology.

Abnormalities of pancreatic islets by targeted expression of a dominant-negative KATP channel

ATP-sensitive K+ (KATP) channels are known to play important roles in various cellular functions, but the direct consequences of disruption of KATP channel function are largely unknown. We have generated transgenic mice expressing a dominant-negative form of the KATP channel subunit Kir6.2 (Kir6.2G132S, substitution of glycine with serine at position 132) in pancreatic beta cells. Kir6.2G132S transgenic mice develop hypoglycemia with hyperinsulinemia in neonates and hyperglycemia with hypoinsulinemia and decreased beta cell population in adults. KATP channel function is found to be impaired in the beta cells of transgenic mice with hyperglycemia. In addition, both resting membrane potential and basal calcium concentrations are shown to be significantly elevated in the beta cells of transgenic mice. We also found a high frequency of apoptotic beta cells before the appearance of hyperglycemia in the transgenic mice, suggesting that the KATP channel might play a significant role in beta cell survival in addition to its role in the regulation of insulin secretion.

Reversing adipocyte differentiation: Implications for treatment of obesity

Conventional treatment of obesity reduces fat in mature adipocytes but leaves them with lipogenic enzymes capable of rapid resynthesis of fat, a likely factor in treatment failure. Adenovirus-induced hyperleptinemia in normal rats results in rapid nonketotic fat loss that persists after hyperleptinemia disappears, whereas pair-fed controls regain their weight in 2 weeks. We report here that the hyperleptinemia depletes adipocyte fat while profoundly down-regulating lipogenic enzymes and their transcription factor, peroxisome proliferator-activated receptor (PPAR)γ in epididymal fat; enzymes of fatty acid oxidation and their transcription factor, PPARα, normally low in adipocytes, are up-regulated, as are uncoupling proteins 1 and 2. This transformation of adipocytes from cells that store triglycerides to fatty acid-oxidizing cells is accompanied by loss of the adipocyte markers, adipocyte fatty acid-binding protein 2, tumor necrosis factor α, and leptin, and by the appearance of the preadipocyte marker Pref-1. These findings suggest a strategy for the treatment of obesity by alteration of the adipocyte phenotype.

Defective insulin secretion and enhanced insulin action in KATP channel-deficient mice

ATP-sensitive K+ (KATP) channels regulate many cellular functions by linking cell metabolism to membrane potential. We have generated KATP channel-deficient mice by genetic disruption of Kir6.2, which forms the K+ ion-selective pore of the channel. The homozygous mice (Kir6.2−/−) lack KATP channel activity. Although the resting membrane potential and basal intracellular calcium concentrations ([Ca2+]i) of pancreatic beta cells in Kir6.2−/− are significantly higher than those in control mice (Kir6.2+/+), neither glucose at high concentrations nor the sulfonylurea tolbutamide elicits a rise in [Ca2+]i, and no significant insulin secretion in response to either glucose or tolbutamide is found in Kir6.2−/−, as assessed by perifusion and batch incubation of pancreatic islets. Despite the defect in glucose-induced insulin secretion, Kir6.2−/− show only mild impairment in glucose tolerance. The glucose-lowering effect of insulin, as assessed by an insulin tolerance test, is increased significantly in Kir6.2−/−, which could protect Kir6.2−/− from developing hyperglycemia. Our data indicate that the KATP channel in pancreatic beta cells is a key regulator of both glucose- and sulfonylurea-induced insulin secretion and suggest also that the KATP channel in skeletal muscle might be involved in insulin action.

Targeted disruption of mouse long-chain acyl-CoA dehydrogenase gene reveals crucial roles for fatty acid oxidation

Abnormalities of fatty acid metabolism are recognized to play a significant role in human disease, but the mechanisms remain poorly understood. Long-chain acyl-CoA dehydrogenase (LCAD) catalyzes the initial step in mitochondrial fatty acid oxidation (FAO). We produced a mouse model of LCAD deficiency with severely impaired FAO. Matings between LCAD +/− mice yielded an abnormally low number of LCAD +/− and −/− offspring, indicating frequent gestational loss. LCAD −/− mice that reached birth appeared normal, but had severely reduced fasting tolerance with hepatic and cardiac lipidosis, hypoglycemia, elevated serum free fatty acids, and nonketotic dicarboxylic aciduria. Approximately 10% of adult LCAD −/− males developed cardiomyopathy, and sudden death was observed in 4 of 75 LCAD −/− mice. These results demonstrate the crucial roles of mitochondrial FAO and LCAD in vivo.

Long-term correction of obesity and diabetes in genetically obese mice by a single intramuscular injection of recombinant adeno-associated virus encoding mouse leptin

The ob/ob mouse is genetically deficient in leptin and exhibits a phenotype that includes obesity and non-insulin-dependent diabetes melitus. This phenotype closely resembles the morbid obesity seen in humans. In this study, we demonstrate that a single intramuscular injection of a recombinant adeno-associated virus (AAV) vector encoding mouse leptin (rAAV-leptin) in ob/ob mice leads to prevention of obesity and diabetes. The treated animals show normalization of metabolic abnormalities including hyperglycemia, insulin resistance, impaired glucose tolerance, and lethargy. The effects of a single injection have lasted through the 6-month course of the study. At all time points measured the circulating levels of leptin in the serum were similar to age-matched control C57 mice. These results demonstrate that maintenance of normal levels of leptin (2–5 ng/ml) in the circulation can prevent both the onset of obesity and associated non-insulin-dependent diabetes. Thus a single injection of a rAAV vector expressing a therapeutic gene can lead to complete and long-term correction of a genetic disorder. Our study demonstrates the long-term correction of a disease caused by a genetic defect and proves the feasibility of using rAAV-based vectors for the treatment of chronic disorders like obesity.

Molecular identity and cellular distribution of advanced glycation endproduct receptors: relationship of p60 to OST-48 and p90 to 80K-H membrane proteins.

Advanced glycation endproducts (AGEs) are derivatives of nonenzymatic reactions between sugars and protein or lipids, and together with AGE-specific receptors are involved in numerous pathogenic processes associated with aging and hyperglycemia. Two of the known AGE-binding proteins isolated from rat liver membranes, p60 and p90, have been partially sequenced. We now report that the N-terminal sequence of p60 exhibits 95% identity to OST-48, a 48-kDa member of the oligosaccharyltransferase complex found in microsomal membranes, while sequence analysis of p90 revealed 73% and 85% identity to the N-terminal and internal sequences, respectively, of human 80K-H, a 80- to 87-kDa protein substrate for protein kinase C. AGE-ligand and Western analyses of purified oligosaccharyltransferase complex, enriched rough endoplasmic reticulum, smooth endoplasmic reticulum, and plasma membranes from rat liver or RAW 264.7 macrophages yielded a single protein of approximately 50 kDa recognized by both anti-p60 and anti-OST-48 antibodies, and also exhibited AGE-specific binding. Immunoprecipitated OST-48 from rat rough endoplasmic reticulum fractions exhibited both AGE binding and immunoreactivity to an anti-p60 antibody. Immune IgG raised to recombinant OST-48 and 80K-H inhibited binding of AGE-bovine serum albumin to cell membranes in a dose-dependent manner. Immunostaining and flow cytometry demonstrated the surface expression of OST-48 and 80K-H on numerous cell types and tissues, including mononuclear, endothelial, renal, and brain neuronal and glial cells. We conclude that the AGE receptor components p60 and p90 are identical to OST-48, and 80K-H, respectively, and that they together contribute to the processing of AGEs from extra- and intracellular compartments and in the cellular responses associated with these pathogenic substances.

Characterization of the osmotic response element of the human aldose reductase gene promoter.

Aldose reductase (EC 1.1.1.21) catalyzes the NADPH-mediated conversion of glucose to sorbitol. The hyperglycemia of diabetes increases sorbitol production primarily through substrate availability and is thought to contribute to the pathogenesis of many diabetic complications. Increased sorbitol production can also occur at normoglycemic levels via rapid increases in aldose reductase transcription and expression, which have been shown to occur upon exposure of many cell types to hyperosmotic conditions. The induction of aldose reductase transcription and the accumulation of sorbitol, an organic osmolyte, have been shown to be part of the physiological osmoregulatory mechanism whereby renal tubular cells adjust to the intraluminal hyperosmolality during urinary concentration. Previously, to explore the mechanism regulating aldose reductase levels, we partially characterized the human aldose reductase gene promoter present in a 4.2-kb fragment upstream of the transcription initiation start site. A fragment (-192 to +31 bp) was shown to contain several elements that control the basal expression of the enzyme. In this study, we examined the entire 4.2-kb human AR gene promoter fragment by deletion mutagenesis and transfection studies for the presence of osmotic response enhancer elements. An 11-bp nucleotide sequence (TGGAAAATTAC) was located 3.7 kb upstream of the transcription initiation site that mediates hypertonicity-responsive enhancer activity. This osmotic response element (ORE) increased the expression of the chloramphenicol acetyltransferase reporter gene product 2-fold in transfected HepG2 cells exposed to hypertonic NaCl media as compared with isoosmotic media. A more distal homologous sequence is also described; however, this sequence has no osmotic enhancer activity in transfected cells. Specific ORE mutant constructs, gel shift, and DNA fragment competition studies confirm the nature of the element and identify specific nucleotides essential for enhancer activity. A plasmid construct containing three repeat OREs and a heterologous promoter increased expression 8-fold in isoosmotic media and an additional 4-fold when the transfected cells are subjected to hyperosmotic stress (total approximately 30-fold). These findings will permit future studies to identify the transcription factors involved in the normal regulatory response mechanism to hypertonicity and to identify whether and how this response is altered in a variety of pathologic states, including diabetes.

Correction of diabetic alterations by glucokinase.

Hyperglycemia is a common feature of diabetes mellitus. It results from a decrease in glucose utilization by the liver and peripheral tissues and an increase in hepatic glucose production. Glucose phosphorylation by glucokinase is an initial event in glucose metabolism by the liver. However, glucokinase gene expression is very low in diabetic animals. Transgenic mice expressing the P-enolpyruvate carboxykinase/glucokinase chimeric gene were generated to study whether the return of the expression of glucokinase in the liver of diabetic mice might prevent metabolic alterations. In contrast to nontransgenic mice treated with streptozotocin, mice with the transgene previously treated with streptozotocin showed high levels of both glucokinase mRNA and its enzyme activity in the liver, which were associated with an increase in intracellular levels of glucose 6-phosphate and glycogen. The liver of these mice also showed an increase in pyruvate kinase activity and lactate production. Furthermore, normalization of both the expression of genes involved in gluconeogenesis and ketogenesis in the liver and the production of glucose and ketone body by hepatocytes in primary culture were observed in streptozotocin-treated transgenic mice. Thus, glycolysis was induced while gluconeogenesis and ketogenesis were blocked in the liver of diabetic mice expressing glucokinase. This was associated with normalization of blood glucose, ketone bodies, triglycerides, and free fatty acids even in the absence of insulin. These results suggest that the expression of glucokinase during diabetes might be a new approach to the normalization of hyperglycemia.

Prevention of diabetic alterations in transgenic mice overexpressing Myc in the liver.

Recent studies have demonstrated that the overexpression of the c-myc gene in the liver of transgenic mice leads to an increase in both utilization and accumulation of glucose in the liver, suggesting that c-Myc transcription factor is involved in the control of liver carbohydrate metabolism in vivo. To determine whether the increase in c-Myc might control glucose homeostasis, an intraperitoneal glucose tolerance test was performed. Transgenic mice showed lower levels of blood glucose than control animals, indicating that the overexpression of c-Myc led to an increase of blood glucose disposal by the liver. Thus, the increase in c-Myc might counteract diabetic hyperglycemia. In contrast to control mice, transgenic mice treated with streptozotocin showed normalization of concentrations of blood glucose, ketone bodies, triacylglycerols and free fatty acids in the absence of insulin. These findings resulted from the normalization of liver metabolism in these animals. While low glucokinase activity was detected in the liver of diabetic control mice, high levels of both glucokinase mRNA and enzyme activity were noted in the liver of streptozotocin-treated transgenic mice, which led to an increase in intracellular levels of glucose 6-phosphate and glycogen. The liver of these mice also showed an increase in pyruvate kinase activity and lactate production. Furthermore, normalization of both the expression of genes involved in the control of gluconeogenesis and ketogenesis and the production of glucose and ketone bodies was observed in streptozotocin-treated transgenic mice. Thus, these results suggested that c-Myc counteracted diabetic alterations through its ability to induce hepatic glucose uptake and utilization and to block the activation of gluconeogenesis and ketogenesis.

A role of Hsp60 in autoimmune diabetes: analysis in a transgenic model.

A pathogenic role for self-reactive cells against the stress protein Hsp60 has been proposed as one of the events leading to autoimmune destruction of pancreatic beta cells in the diabetes of nonobese diabetic (NOD) mice. To examine this hypothesis, we generated transgenic NOD mice carrying a murine Hsp60 transgene driven by the H-2E alpha class II promoter. This would be expected to direct expression of the transgene to antigen-presenting cells including those in the thymus and so induce immunological tolerance by deletion. Detailed analysis of Hsp60 expression revealed that the endogenous gene is itself expressed strongly in thymic medullary epithelium (and weakly in cortex) yet fails to induce tolerance. Transgenic mice with retargeted Hsp60 showed overexpression of the gene in thymic cortical epithelium and in bone marrow-derived cells. Analysis of spontaneous T-cell responses to a panel of self and heterologous Hsp60 antigens showed that tolerance to the protein had not been induced, although responses to an immunodominant 437-460 epitope implicated in disease were suppressed, probably indicating an epitope shift. This correlated with changes in disease susceptibility: insulitis in transgenic mice was substantially reduced so that pathology rarely progressed beyond periislet infiltration. This was reflected in a substantial reduction in hyperglycemia and disease. These data indicate that T cells specific for some epitopes of murine Hsp60 are likely to be involved in the islet-cell destruction that occurs in NOD mice.