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.

Mice mutant for glucokinase regulatory protein exhibit decreased liver glucokinase: A sequestration mechanism in metabolic regulation

The importance of glucokinase (GK; EC 2.7.1.12) in glucose homeostasis has been demonstrated by the association of GK mutations with diabetes mellitus in humans and by alterations in glucose metabolism in transgenic and gene knockout mice. Liver GK activity in humans and rodents is allosterically inhibited by GK regulatory protein (GKRP). To further understand the role of GKRP in GK regulation, the mouse GKRP gene was inactivated. With the knockout of the GKRP gene, there was a parallel loss of GK protein and activity in mutant mouse liver. The loss was primarily because of posttranscriptional regulation of GK, indicating a positive regulatory role for GKRP in maintaining GK levels and activity. As in rat hepatocytes, both GK and GKRP were localized in the nuclei of mouse hepatocytes cultured in low-glucose-containing medium. In the presence of fructose or high concentrations of glucose, conditions known to relieve GK inhibition by GKRP in vitro, only GK was translocated into the cytoplasm. In the GKRP-mutant hepatocytes, GK was not found in the nucleus under any tested conditions. We propose that GKRP functions as an anchor to sequester and inhibit GK in the hepatocyte nucleus, where it is protected from degradation. This ensures that glucose phosphorylation is minimal when the liver is in the fasting, glucose-producing phase. This also enables the hepatocytes to rapidly mobilize GK into the cytoplasm to phosphorylate and store or metabolize glucose after the ingestion of dietary glucose. In GKRP-mutant mice, the disruption of this regulation and the subsequent decrease in GK activity leads to altered glucose metabolism and impaired glycemic control.

High fat diet-induced hyperglycemia: prevention by low level expression of a glucose transporter (GLUT4) minigene in transgenic mice.

High-fat intake leading to obesity contributes to the development of non-insulin-dependent diabetes mellitus (NIDDM, type 2). Similarly, mice fed a high-fat (safflower oil) diet develop defective glycemic control, hyperglycemia, and obesity. To assess the effect of a modest increase in the expression of GLUT4 (the insulin-responsive glucose transporter) on impaired glycemic control caused by fat feeding, transgenic mice harboring a GLUT4 minigene were fed a high-fat diet. Low-level tissue-specific (heart, skeletal muscle, and adipose tissue) expression of the GLUT4 minigene in transgenic mice prevented the impairment of glycemic control and accompanying hyperglycemia, but not obesity, caused by fat feeding. Thus, a small increase (< or = 2-fold) in the tissue level of GLUT4 prevents a primary symptom of the diabetic state in a mouse model, suggesting a possible target for intervention in the treatment of NIDDM.

Herdabilidade da velocidade de onda de pulso e associação do controle glicêmico e perfil lipídico com a rigidez arterial em uma população brasileira: \"Projeto Corações de Baependi\"

INTRODUÇÃO:A rigidez arterial aumentada é um importante determinante do risco cardiovascular e um forte preditor de morbimortalidade. Além disso, estudos demonstram que o enrijecimento vascular pode estar associado a fatores genéticos e metabólicos. Portanto,os objetivos do presente estudo são determinar a herdabilidade da velocidade de onda de pulso (VOP) e avaliar a associação do perfil lipídico e do controle glicêmico com o fenótipo de rigidez arterial em uma população brasileira.MÉTODOS:Foram selecionados 1675 indivíduos (ambos os gêneros com idade entre 18 e 102 anos) distribuídos em 109 famílias residentes no município de Baependi-MG. A VOP carótida-femoral foi avaliada de forma não invasiva através de um dispositivo automático.As variáveis lipídicas e a glicemia de jejum foram determinadas pelo método enzimático colorimétrico. Os níveis de hemoglobina glicada (HbA1c) foram determinados pelo método de cromatografia líquida de alta eficiência. As estimativas da herdabilidade da VOP foram calculadas utilizando-se a metodologia de componentes de variância implementadas no software SOLAR. RESULTADOS: A herdabilidade estimada para a VOP foi de 26%, sendo ajustada para idade, gênero, HbA1c e pressão arterial média. Os níveis de HbA1c foram associados a rigidez arterial, onde a elevação de uma unidade percentual da HbA1c representou um incremento de 54% na chance de risco para rigidez arterial aumentada. As variáveis lipídicas (LDL-c, HDL-c, colesterol não- HDL-c, colesterol total e triglicérides) apresentaram fraca correlação com a VOP. Além disso, uma análise de regressão linear estratificada para idade (ponto de corte >= 45 anos) demonstrou uma relação inversa entre LDL-c e VOP em mulheres com idade >= 45 anos. CONCLUSÃO: Os resultados indicam que a VOP apresenta herdabilidade intermediária (26%); a HbA1c esta fortemente associada a rigidez arterial aumentada; o LDL-c é inversamente relacionado com a VOP em mulheres com idade >= 45 anos, possivelmente devido às alterações metabólicas associadas à falência ovariana

Evaluation of a Post Discharge Intervention on 30-Day Re-admission and Emergency Room Use in Patients with Diabetes A Pilot Program

Thesis (Master's)--University of Washington, 2016-06

Tissue Doppler imaging for evaluation of myocardial function in patients with diabetes mellitus

Purpose of review Heart failure and diabetes mellitus are frequently associated, and diabetes appears to potentiate the clinical presentation of heart failure related to other causes. The purpose of this review is to examine recent advances in the application of tissue Doppler imaging for the assessment of diabetic heart disease. Recent findings Recent studies have documented that both myocardial systolic and diastolic abnormalities can be identified in apparently healthy patients with diabetes and no overt cardiac dysfunction. Interestingly, these are disturbances of longitudinal function, with compensatory increases of radial function-suggesting primary involvement of the subendocardium, which is a hallmark of myocardial ischemia. Despite this, there is limited evidence that diabetic microangiopathy is responsible-with reduced myocardial blood volume rather than reduced resting flow, and at least some evidence suggesting a normal increment of tissue velocity with stress. Finally, a few correlative studies have shown association of diabetic myocardial disease with poor glycemic control, while angiotensin converting enzyme inhibition may be protective. Summary Tissue Doppler imaging (and the related technique of strain rate imaging) appears to be extremely effective for the identification of subclinical LV dysfunction in diabetic patients It is hoped that the recognition of this condition will prompt specific therapy to prevent the development of overt LV dysfunction.

Telepaediatrics and diabetic retinopathy screening of young people with diabetes in Queensland

We have examined the feasibility of a telemedicine-enabled screening service for children and adolescents with diabetes in Queensland. There are approximately 1400 young people with diabetes in Queensland and only about two-thirds of them are screened in accordance with international guidelines. A regional retinal screening service was established using a non-mydriatic digital retinal camera. Seven centres volunteered to participate in the study. During a five-month pilot trial, 83 of the young people with diabetes who attend these centres underwent digital retinal screening (3.7%). Retinal images were sent via email to a paediatric ophthalmologist for review and results were returned via email. A copy of each participant's results was forwarded by mail to the referring diabetes doctor and the participant and family. The majority of the image files (96%) were rated as excellent or good. Only one participant was identified as having an abnormal result. Participants and their families expressed satisfaction with the digital retinal screening process.

Left ventricular mass in patients with type 2 diabetes is independently associated with central but not peripheral pulse pressure

An increase in left ventricular mass (LVM) occurs in the presence of type 2 diabetes, apparently independent of hypertension (1), but the determinants of this process are unknown. Brachial blood pressure is not representative of that at the ascending aorta (2) because the pressure wave is amplified from central to peripheral arteries. Central blood pressure is probably more clinically important since local pulsatile pressure determines adverse arterial and myocardial remodeling (3,4). Thus, an inaccurate assessment of the contribution of arterial blood pressure to LVM may occur if only brachial blood pressure is taken into consideration. In this study we sought the contribution of central blood pressure (and other interactive factors known to affect wave reflection, e.g., glycemic control and total arterial compliance) to LVM in patients with type 2 diabetes.

The relative benefits of endurance and strength training on the metabolic factors and muscle function of people with type 2 diabetes mellitus

Objective: To compare the effects of a 4-month strength training (ST) versus aerobic endurance training (ET) program on metabolic control, muscle strength, and cardiovascular endurance in subjects with type 2 diabetes mellitus (T2D). Design: Randomized controlled trial. Setting: Large public tertiary hospital. Participants: Twenty-two T21) participants (I I men, I I women; mean age +/- standard error, 56.2 +/- 1.1 y; diabetes duration, 8.8 +/- 3.5y) were randomized into a 4-month ST program and 17 T2D participants (9 men, 8 women; mean age, 57.9 +/- 1.4y; diabetes duration, 9.2 +/- 1.7y) into a 4-month ET program. Interventions: ST (up to 6 sets per muscle group per week) and ET (with an intensity of maximal oxygen consumption of 60% and a volume beginning at 15min and advancing to a maximum of 30min 3X/wk) for 4 months. Main Outcome Measures: Laboratory tests included determinations of blood glucose, glycosylated hemoglobin (Hb A(1c)), insulin, and lipid assays. Results: A significant decline in Hb A, was only observed in the ST group (8.3% +/- 1.7% to 7.1% +/- 0.2%, P=.001). Blood glucose (204 +/- 16mg/dL to 147 +/- 8mg/dL, P <.001) and insulin resistance (9.11 +/- 1.51 to 7.15 +/- 1.15, P=.04) improved significantly in the ST group, whereas no significant changes were observed in the ET group. Baseline levels of total cholesterol (207 +/- 8mg/dL to 184 +/- 7mg/dL, P <.001), low-density lipoprotein cholesterol (120 +/- 8mg/dL to 106 +/- 8mg/dL, P=.001), and triglyceride levels (229 +/- 25mg/dL to 150 +/- 15mg/dL, P=.001) were significantly reduced and high-density lipoprotein cholesterol (43 +/- 3mg/dL to 48 +/- 2mg/dL, P=.004) was significantly increased in the ST group; in contrast, no such changes were seen in the ET group. Conclusions: ST was more effective than ET in improving glycemic control. With the added advantage of an improved lipid profile, we conclude that ST may play an important role in the treatment of T2D.

Strength and endurance training lead to different post exercise glucose profiles in diabetic participants using a continuous subcutaneous glucose monitoring system

Background Although both strength training (ST) and endurance training (ET) seem to be beneficial in type 2 diabetes mellitus (T2D), little is known about post-exercise glucose profiles. The objective of the study was to report changes in blood glucose (BG) values after a 4-month ET and ST programme now that a device for continuous glucose monitoring has become available. Materials and methods Fifteen participants, comprising four men age 56.5 +/- 0.9 years and 11 women age 57.4 +/- 0.9 years with T2D, were monitored with the MiniMed (Northridge, CA, USA) continuous glucose monitoring system (CGMS) for 48 h before and after 4 months of ET or ST. The ST consisted of three sets at the beginning, increasing to six sets per week at the end of the training period, including all major muscle groups and ET performed with an intensity of maximal oxygen uptake of 60% and a volume beginning at 15 min and advancing to a maximum of 30 min three times a week. Results A total of 17 549 single BG measurements pretraining (619.7 +/- 39.8) and post-training (550.3 +/- 30.1) were recorded, correlating to an average of 585 +/- 25.3 potential measurements per participant at the beginning and at the end of the study. The change in BG-value between the beginning (132 mg dL(-1)) and the end (118 mg dL(-1)) for all participants was significant (P = 0.028). The improvement in BG-value for the ST programme was significant (P = 0.02) but for the ET no significant change was measured (P = 0.48). Glycaemic control improved in the ST group and the mean BG was reduced by 15.6% (Cl 3-25%). Conclusion In conclusion, the CGMS may be a useful tool in monitoring improvements in glycaemic control after different exercise programmes. Additionally, the CGMS may help to identify asymptomatic hypoglycaemia or hyperglycaemia after training programmes.

Continuous glucose monitoring in diabetic long distance runners

Marathon running is growing in popularity, and many diabetic patients are participating in various marathon races all over the world each year. This study aimed to investigate the prevalence and extent of glycemic excursions (hypo- and hyperglycemic) during a marathon run in patients with well-controlled diabetes mellitus using a continuous glucose monitoring system (CGMS). Five subjects with type 1 and one patient with type 2 diabetes mellitus were monitored with the Medtronic MiniMed CGMS during the 2002 Vienna City Marathon (n = 3) or the Fernwarme run (n = 3) long distance runs of 42.19/15.8 km. All six patients finished their course. The CGSM system was well tolerated in all patients over an average duration of 34 +/- 4.0 hours and it did not limit the patients' activities. The mean running time for the Vienna city marathon was 257 +/- 8 min (247 to 274 min) and for the Fernwarme run 134 +/- 118 min (113 to 150 min). A total of 1470 blood glucose measurements (mean 245 readings per subject) were performed. During and after the marathons frequent hypo and hyperglycemic episodes with and without clinical symptoms were measured. Our data confirm that the CGMS may help to identify asymptomatic hypoglycemia or hyperglycemia during and after a long distance run. The system may also be helpful to improve our understanding about the individual changes of glucose during and after a marathon and may protect hypoglycemic or hyperglycemic periods in future races.

Does high intensity resistance training maintain bone mass during moderate weight loss in older overweight adults with type 2 diabetes

The aim was to investigate whether the addition of supervised high intensity progressive resistance training to a moderate weight loss program (RT+WLoss) could maintain bone mineral density (BMD) and lean mass compared to moderate weight loss (WLoss) alone in older overweight adults with type 2 diabetes. We also investigated whether any benefits derived from a supervised RT program could be sustained through an additional home-based program. This was a 12-month trial in which 36 sedentary, overweight adults aged 60 to 80 years with type 2 diabetes were randomized to either a supervised gymnasium-based RT+WLoss or WLoss program for 6 months (phase 1). Thereafter, all participants completed an additional 6-month home-based training without further dietary modification (phase 2). Total body and regional BMD and bone mineral content (BMC), fat mass (FM) and lean mass (LM) were assessed by DXA every 6 months. Diet, muscle strength (1-RM) and serum total testosterone, estradiol, SHBG, insulin and IGF-1 were measured every 3 months. No between group differences were detected for changes in any of the hormonal parameters at any measurement point. In phase 1, after 6 months of gymnasium-based training, weight and FM decreased similarly in both groups (P < 0.01), but LM tended to increase in the RT+WLoss (n=16) relative to the WLoss (n = 13) group [net difference (95% CI), 1.8% (0.2, 3.5), P < 0.05]. Total body BMD and BMC remained unchanged in the RT+WLoss group, but decreased by 0.9 and 1.5%, respectively, in the WLoss group (interaction, P < 0.05). Similar, though non-significant, changes were detected at the femoral neck and lumbar spine (L2-L4). In phase 2, after a further 6 months of home-based training, weight and FM increased significantly in both the RT+WLoss (n = 14) and WLoss (n = 12) group, but there were no significant changes in LM or total body or regional BMD or BMC in either group from 6 to 12 months. These results indicate that in older, overweight adults with type 2 diabetes, dietary modification should be combined with progressive resistance training to optimize the effects on body composition without having a negative effect on bone health.

QUALIDADE DE VIDA, SUPORTE SOCIAL E CONTROLE GLICÊMICO EM PORTADORES DE DIABETES MELLITUS TIPO2

Diabetes Mellitus é uma doença crônica degenerativa que impõe uma série de limitações em função da necessidade de tratamento constante. Por isso, considerou-se que o estudo da qualidade de vida e suporte social poderia trazer conhecimento para melhorar a qualidade das intervenções para estes pacientes. O objetivo deste trabalho foi avaliar a qualidade de vida, suporte social e controle glicêmico de portadores de Diabetes Mellitus tipo2. Trata-se de um estudo descritivo e transversal, desenvolvido com 120 pacientes de ambos os gêneros, atendidos no ambulatório de endocrinologia de um hospital situado na cidade de São Bernardo do Campo. Para coleta dos dados foram utilizados os seguintes instrumentos: questionário para caracterização da população, questionário de qualidade de vida (WHOQOL-BREF) e a Escala de Percepção de Suporte Social (EPSS). Os dados referentes ao controle glicêmico foram coletados nos prontuários dos pacientes. Para análise dos dados utilizou-se à estatística descritiva e provas estatísticas (Pearson, QuiQuadrado, Exato de Fisher, Anova e Pos-hoc). Os resultados indicaram que o domínio das relações sociais foi o que mais contribuiu a qualidade de vida. E as variáveis tempo de diagnóstico, insulinoterapia, número de dependentes, escolaridade, dieta e medicação interferiram na qualidade de vida e na qualidade do tratamento. Estes resultados chamam a atenção para que as avaliações médicas devam ser atreladas a avaliações da qualidade de vida, suporte social e também variáveis que interferem na qualidade do tratamento para que, desta forma possam redimensionar ou melhorar fazeres ligados às intervenções com estes pacientes.

Type 2 diabetes:assessing the relative risks and benefits of glucose-lowering medications

The selection of appropriate pharmacologic therapy for any disease requires a careful assessment of benefit and risk. In the case of type 2 diabetes, this decision typically balances the benefits accrued from improved glycemic control with the risks inherent in glucose-lowering medications. This review is intended to assist therapeutic decision-making by carefully assessing the potential benefit from improved metabolic control relative to the potential risks of a wide array of currently prescribed glucose-lowering agents. Wherever possible, risks and benefits have been expressed in terms of absolute rates (events per 1000 patient-years) to facilitate cross-study comparisons. The review incorporates data from new studies (Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation, Action to Control Cardiovascular Risk in Diabetes, and the Veterans Affairs Diabetes Trial), as well as safety issues associated with newer glucose-lowering medications. © 2010 Elsevier Inc. All rights reserved.