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.

Genetic analysis of type 1 diabetes using whole genome approaches.

Whole genome linkage analysis of type 1 diabetes using affected sib pair families and semi-automated genotyping and data capture procedures has shown how type 1 diabetes is inherited. A major proportion of clustering of the disease in families can be accounted for by sharing of alleles at susceptibility loci in the major histocompatibility complex on chromosome 6 (IDDM1) and at a minimum of 11 other loci on nine chromosomes. Primary etiological components of IDDM1, the HLA-DQB1 and -DRB1 class II immune response genes, and of IDDM2, the minisatellite repeat sequence in the 5' regulatory region of the insulin gene on chromosome 11p15, have been identified. Identification of the other loci will involve linkage disequilibrium mapping and sequencing of candidate genes in regions of linkage.

Gene therapy for diabetes mellitus in rats by hepatic expression of insulin.

Type 1 diabetes mellitus is caused by severe insulin deficiency secondary to the autoimmune destruction of pancreatic beta cells. Patients need to be controlled by periodic insulin injections to prevent the development of ketoacidosis, which can be fatal. Sustained, low-level expression of the rat insulin 1 gene from the liver of severely diabetic rats was achieved by in vivo administration of a recombinant retroviral vector. Ketoacidosis was prevented and the treated animals exhibited normoglycemia during a 24-hr fast, with no evidence of hypoglycemia. Histopathological examination of the liver in the treated animals showed no apparent abnormalities. Thus, the liver is an excellent target organ for ectopic expression of the insulin gene as a potential treatment modality for type 1 diabetes mellitus by gene therapy.

Contribuição da consulta de enfermagem na adesão ao tratamento do diabetes mellitus na estratégia saúde da família do município de Ribeirão Preto, SP

Trata-se de estudo de intervenção tipo antes e depois, no qual o sujeito é seu próprio controle, fator que permite identificar os efeitos na adesão ao tratamento e controle dos níveis glicêmicos. Teve como objetivo avaliar a contribuição da consulta de enfermagem na adesão ao tratamento do diabetes mellitus tipo 2, em uma Unidade Saúde da Família, de acordo com o \"Protocolo de atendimento as pessoas com diabetes mellitus,\" em Ribeirão Preto, SP. A coleta de dados foi realizada no período de setembro de 2014 a janeiro de 2015. O trabalho foi aprovado pelo Comitê de Ética em Pesquisa da Escola de Enfermagem de Ribeirão Preto, SP, sob Parecer nº 648.970. Participaram 31 pessoas com diabetes mellitus, por meio de três consultas de enfermagem, na unidade de saúde e no domicílio, com intervalo de um mês entre as três consultas de todos os participantes. Foi utilizado um roteiro contendo variáveis sociodemográficos e clínicas e o teste de Medida de Adesão ao Tratamento. Para a análise da adesão, durante e após a intervenção, utilizou-se a estatística descritiva e o teste de Mann- Whitney; para a comparação do antes e após a intervenção, utilizou-se o teste de Wilcoxon; para análise de correlação com as variáveis numéricas, o coeficiente de correlação de Spearman e o teste Q de Cochran, para a comparação dos exames nos momentos anterior, durante e posterior à intervenção. Os resultados mostraram que os participantes tinham entre 33 e 79 anos, sendo 58,1% do sexo feminino; 71% tinham companheiro; renda familiar de 1 a 3 salários-mínimos (83,9%); 80,6% referiram ser profissionalmente inativos (aposentados, pensionistas ou do lar); média de 5,68 anos de estudo e predomínio de menos de 8 anos de estudo (67,7%). Em relação aos valores da pressão arterial sistêmica constatou hipertensão arterial sistêmica grau I em 25,8% das pessoas com diabetes mellitus, 90,3% com índice de massa corporal apresentando excesso de peso, quanto à circunferência abdominal, 32,2% dos homens estavam com valores maiores que 102 cm e 45,2% das mulheres com valores acima de 88 cm. A avaliação dos pés, com uso do monofilamento Semmes-Weinstein de 10g, apresentou 9,7% das pessoas com diabetes mellitus com pé em risco para ulceração e diminuição ou ausência de sensibilidade tátil pressórica protetora dos pés. O tempo de diagnóstico do diabetes mellitus tipo 2 variou entre 1 a 39 anos, predominando as comorbidades hipertensão arterial (83,9%), dislipidemia (58,1%) e obesidade (41,8%). Quanto aos exames laboratoriais, observa-se que, em 64,5% da população estudada, os níveis da glicemia de jejum estavam acima de 100 mg/dL , ocorrendo pequena redução para 61,3% nos casos de pessoas com diabetes mellitus durante a intervenção e se manteve após. No que se refere à glicemia pós-prandial, os casos das pessoas com diabetes mellitus com valores iguais ou acima de 160 mg/dL, antes da intervenção era de 45,2% e durante e após a intervenção caiu para 38,7%. Em contrapartida, aumentou o número de pessoas com diabetes mellitus durante e após a intervenção, com valores da glicemia pós-prandial abaixo de 160 mg/dL, de 54,8% para 61,3%. E, em relação à hemoglobina glicada, foi observado que em 61,3% das pessoas com diabetes mellitus os valores antes da intervenção eram iguais ou acima de 7%. Durante a intervenção, caiu para 19,3% e após a intervenção o número de pessoas com diabetes mellitus, com a hemoglobina glicada igual ou superior a 7%, chegou a 38,7%. Quanto aos valores abaixo de 7%, observou-se aumento de 38,7% antes da intervenção para 80,6 e 61,3% respectivamente, durante e após a intervenção, com diferença estatisticamente significante (p< 0,001). As pessoas com diabetes mellitus desse estudo, apresentaram 83,87% de adesão ao tratamento antes da intervenção, e esses escores subiram para 96,78% após a intervenção, fato corroborado pelo teste de Wilcoxon que mostrou escores estatisticamente significantes (p<0,001), entre antes e após a intervenção. Esse estudo contribui para ressaltar a importância do enfermeiro, enquanto integrante da equipe multiprofissional, seguindo as orientações do \"Protocolo de atendimento ao indivíduo com diabetes\", tanto no atendimento individual quanto em grupo, reorganizando o processo de trabalho, contribuindo para maior adesão ao tratamento e controle dos níveis glicêmicos, ao minimizar a fragmentação e assegurar a continuidade na assistência, por meio de abordagem integral ao diabético

Canine and Feline Diabetes Mellitus: Nature or Nurture?

There is evidence for the role of genetic and environmental factors in feline and canine diabetes. Type 2 diabetes is the most common form of diabetes in cats. Evidence for genetic factors in feline diabetes includes the overrepresentation of Burmese cats with diabetes. Environmental risk factors in domestic or Burmese cats include advancing age, obesity, male gender, neutering, drug treatment, physical inactivity, and indoor confinement. High-carbohydrate diets increase blood glucose and insulin levels and may predispose cats to obesity and diabetes. Low-carbohydrate, high-protein diets may help prevent diabetes in cats at risk such as obese cats or lean cats with underlying low insulin sensitivity. Evidence exists for a genetic basis and altered immune response in the pathogenesis of canine diabetes. Seasonal effects on the incidence of diagnosis indicate that there are environmental influences on disease progression. At least 50% of diabetic dogs have type 1 diabetes based on present evidence of immune destruction of P-cells. Epidemiological factors closely match those of the latent autoimmune diabetes of adults form of human type 1 diabetes. Extensive pancreatic damage, likely from chronic pancreatitis, causes similar to28% of canine diabetes cases. Environmental factors such as feeding of high-fat diets are potentially associated with pancreatitis and likely play a role in the development of pancreatitis in diabetic dogs. There are no published data showing that overt type 2 diabetes occurs in dogs or that obesity is a risk factor for canine diabetes. Diabetes diagnosed in a bitch during either pregnancy or diestrus is comparable to human gestational diabetes.

Mini-dose glucagon rescue for mild hypoglycaemia in children with type 1 diabetes: The Brisbane experience

Objectives: To evaluate the use of small doses of glucagon using an insulin syringe in mild or impending hypoglycaemia in children with type 1 diabetes. Methods: Data were collected from patients attending the Paediatric Diabetes Clinic at the Queensland Diabetes Centre at the Mater Hospital, Brisbane in 2002-2004 following the institution of a new protocol for home management of mild or impending hypoglycaemia associated with inability or refusal to take oral carbohydrate. The protocol recommended the use of subcutaneous injections of glucagon using insulin syringes at a dose of two ' units ' (20 mu g) in children 2 years of age or younger, and for older children one unit per year of age up to a maximum of 15 units (150 mu g), with an additional doubled dose given if the blood glucose had not increased in 20 min. Results: Over a 2-year period, 25 children were treated with mini-dose glucagon on a total of 38 occasions. Additional doses were required for recurring hypoglycaemia on 20 (53%) occasions. The child could be managed at home on 32 (84%) of these 38 occasions, with only 6 (16%) children needing hospital treatment. Conclusions: Our study confirmed that small doses of glucagon given subcutaneously with an insulin syringe is a simple, practical and effective home treatment of mild or impending hypoglycaemia due to gastroenteritis or food refusal in children with type 1 diabetes.

Diastolic dysfunction is a manifestation of ventricular-vascular interaction in patients with type 2 diabetes mellitus

Vascular disease is accelerated in patients with Type 2 diabetes mellitus (T2DM). Since the systemic vasculature plays a pivotal role in myocardial loading, this study aimed to determine the effect of arterial characteristics on left ventricular (LV) morphology and function in patients with T2DM. Conventional echocardiography and tissue Doppler imaging were performed in 172 T2DM patients (95 men; aged 55±11y) with preserved ejection fraction (62±5%). Patients were stratified into groups based on LV geometric pattern (normal [n = 79], concentric remodeling [n = 33], concentric hypertrophy [n = 29], eccentric hypertrophy [n = 31]). Total arterial compliance (TAC) was recorded by simultaneous radial tonometry and aortic outflow pulsed wave Doppler. Arterial (brachial and carotid) structure and function were determined by standard ultrasound methods. There were no significant differences between the LV geometric groups in demographic or clinical parameters. The concentric hypertrophy group had significantly increased carotid artery diameter (6.0±0.7mm versus 6.5±0.7mm; p < 0.05) and stiffness (1912±1203 dynes/cm2mm versus 2976±2695 dynes/cm2mm×10−6; p < 0.05) compared to those with normal geometry. However, TAC did not differ between groups. LV diastolic function, as determined by the ratio of diastolic mitral inflow velocity to mitral annulus tissue velocity (E/E_), was significantly associated with carotid artery relative wall thickness and intima media thickness (p < 0.05). Moreover, E/E_ was independently predicted by carotid artery relative wall thickness (β = 22.9; p = 0.007). We conclude that structural characteristics of the carotid artery are associated with abnormal LV structure and function in patients with T2DM. The LV functional irregularities may be a downstream consequence of amplified pressure wave reflections effecting sub-optimal ventricular-vascular interaction.