Causas referidas para o desenvolvimento de úlceras em pés de pessoas com diabetes mellitus

OBJETIVO: Analisar as causas referidas na etiologia das úlceras em pés de pessoas com Diabetes mellitus (DM). MÉTODOS: Estudo seccional, quantitativo, realizado no Ambulatório de Diabetes de um Hospital Universitário em Ribeirão Preto - SP. Os dados foram coletados com instrumento estruturado e exame físico dos pés de amostra de 30 pacientes diabéticos. RESULTADOS: Amostra com idade média de 57,5 anos, predominância do sexo masculino e baixa escolaridade; 90% possuíam DM tipo 2, de longa duração e mal controlado; obesidade/sobrepeso em 77% e insensibilidade plantar em 93,3%. A região metatarsiana foi o local de úlcera referido com maior frequência, e a causa foi a calosidade. CONCLUSÃO: as causas referidas envolvidas na etiologia das úlceras correspondem, de forma direta ou indireta, a fatores extrínsecos que podem ser prevenidos com cuidados básicos e de baixo custo. A insensibilidade plantar, fator fundamental desencadeador das úlceras, no entanto não foi reconhecida pelas pessoas.

Relação entre conhecimento, atitude, escolaridade e tempo de doença em indivíduos com diabetes mellitus

OBJETIVOS: Relacionar o conhecimento e a atitude de usuários com Diabetes mellitus tipo 2 (DM2), conforme a escolaridade e o tempo da doença. MÉTODOS: Estudo de abordagem quantitativa, descritivo transversal realizado em uma Unidade Básica Distrital de Saúde do município de Ribeirão Preto, SP, em 2010. Foram entrevistados 123 usuários com DM2, que atenderam aos critérios de inclusão. Para coleta de dados, foram utilizados: Questionário de Conhecimento (DKN-A) e Questionário de Atitudes Psicológicas do Diabetes (ATT-19). Os dados foram obtidos por meio de entrevista dirigida. Para a análise, utilizou-se o teste Exato de Fisher. RESULTADOS: a média de idade foi de 63,87±9,09 anos, 4,54±3,66 anos de estudo, tempo médio de doença 11,18±8,64 anos. A escolaridade e o tempo de doença mostraram-se estatisticamente significantes (p<0,01 e 0,02, respectivamente) para a aquisição do conhecimento e prontidão para o autocuidado em Diabetes. CONCLUSÕES: escolaridade e tempo de doença são variáveis que influenciam o conhecimento e atitude do paciente com DM2.

Expression of adipokines and estrogen receptors in adipose tissue and placenta of patients with gestational diabetes mellitus

The purpose of this study was to assess the expression profile of genes with potential role in the development of insulin resistance (adipokines, cytokines/chemokines, estrogen receptors) in subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT) and placenta of pregnant women with gestational diabetes mellitus (GDM) and age-matched women with physiological pregnancy at the time of Caesarean section. qRT-PCR was used for expression analysis of the studied genes. Leptin gene expression in VAT of GDM group was significantly higher relative to control group. Gene expressions of interleukin-6 and interleukin-8 were significantly increased, whereas the expressions of genes for estrogen receptors alpha and beta were significantly reduced in SAT of GDM group relative to controls, respectively. We found no significant differences in the expression of any genes of interest (LEP, RETN, ADIPOR1, ADIPOR2, TNF-alpha, CD68, IL-6, IL-8, ER alpha, ER beta) in placentas of women with GDM relative to controls. We conclude that increased expression of leptin in visceral adipose depot together with increased expressions of proinflammatory cytokines and reduced expressions of estrogen receptors in subcutaneous fat may play a role in the etiopathogenesis of GDM.

Health risk appraisal for older people 4: case finding for hypertension, hyperlipidaemia and diabetes mellitus in older people in English general practice before the introduction of the Quality and Outcomes Framework

Early intervention can help to reduce the burden of disability in the older population, but many do not access preventive care. There is uncertainty over what factors influence case finding in older patients in general practice.

Microalbuminuria in diabetes mellitus: efficacy of a new screening method in comparison with timed overnight urine collection

Diabetic nephropathy and end-stage renal failure are still a major cause of mortality amongst patients with diabetes mellitus (DM). In this study, we evaluated the Clinitek-Microalbumin (CM) screening test strip for the detection of microalbuminuria (MA) in a random morning spot urine in comparison with the quantitative assessment of albuminuria in the timed overnight urine collection ("gold standard"). One hundred thirty-four children, adolescents, and young adults with insulin-dependent DM Type 1 were studied at 222 outpatient visits. Because of urinary tract infection and/or haematuria, the data of 13 visits were excluded. Finally, 165 timed overnight urine were collected in the remaining 209 visits (79% sample per visit rate). Ten (6.1%) patients presented MA of > or =15 microg/min. In comparison however, 200 spot urine could be screened (96% sample/visit rate) yielding a significant increase in compliance and screening rate (P<.001, McNemar test). Furthermore, at 156 occasions, the gold standard and CM could be directly compared. The sensitivity and the specificity for CM in the spot urine (cut-off > or =30 mg albumin/l) were 0.89 [95% confidence interval (CI) 0.56-0.99] and 0.73 (CI 0.66-0.80), respectively. The positive and negative predictive value were 0.17 (CI 0.08-0.30) and 0.99 (CI 0.95-1.00), respectively. Considering CM albumin-to-creatinine ratio, the results were poorer than with the albumin concentration alone. Using CM instead of quantitative assessment of albuminuria is not cost-effective (35 US dollars versus 60 US dollars/patient/year). In conclusion, to exclude MA, the CM used in the random spot urine is reliable and easy to handle, but positive screening results of > or =30 mg albumin/l must be confirmed by analyses in the timed overnight collected urine. Although the screening compliance is improved, in terms of analysing random morning spot urine for MA, we cannot recommend CM in a paediatric diabetic outpatient setting because the specificity is far too low.

[Will type I diabetes mellitus be treated with immunosuppressive drugs in the future?]

Even if the pathogenesis of type-I (insulin-dependent) diabetes mellitus is still not clarified in every detail, there is general agreement that this form of diabetes is induced by autoimmune mechanisms leading to beta-cell destruction. Therefore, it should theoretically be feasible to suppress the mechanism leading to type-I diabetes with appropriate and early immunotherapy. The current clinical data clearly document that the rate and duration of remissions in patients with newly diagnosed type-I diabetes can be increased significantly using appropriate immunosuppressive regimens. However, before these therapies can become standard therapy of type-I diabetes, the following important clinical requirements have to be fulfilled: the toxicity (especially to kidneys and beta-cells) has to be reduced, the patients should be diagnosed and treated in 'pre-diabetic' states, more selective immunosuppressive regimens have to be available in order to reduce the occurrence of treatment-associated lymphomas and neoplasias. Since accurate detection of 'pre-diabetic' patients is difficult and presents an immense logistic problem, it may take a long time before large-scale immunosuppressive therapies of type-I diabetes are feasible.

[Pancreas transplantation as treatment of diabetes mellitus]

Pancreatic transplantation is able to normalize blood glucose metabolism and achieve normoglycemia in a majority of patients with insulin-dependent diabetes mellitus. Hoping that normoglycemia will favorably influence development of late complications of diabetes, an increasing number of pancreas transplantations has been performed over the last years. However, the need for immunosuppressive therapy with its problems and possible complications confines pancreatic transplantation mainly to three groups of patients: patients who undergo kidney transplantation for diabetic nephropathy, patients who have already undergone kidney transplantation for diabetic nephropathy and, rarely, patients with extreme difficulties with metabolic control. The results of pancreatic transplantation have continuously improved over the last decade, and a limited number of controlled studies is providing some evidence of a favorable effect on late complications.

[Diabetes update: preventing type 2 diabetes. Individualized stepwise therapy (oral antidiabetic agents). Multifactorial intervention]

Preclinical disorders of glucose metabolism should be systematically included in the high-risk group for diabetes mellitus and affected individuals provided with preventive measures. Their underlying insulin resistance is determined with the help of a checklist and a method called homeostasis model assessment (HOMA). Patients with impaired fasting glucose (IFG) must change their lifestyles. If this does not lead to a response or the patient is unable to modify behavior, medication is required. In the case of manifest type 2 diabetes mellitus, a graded schedule is used for differential management, which should be based on nutritional and exercise therapy. Oral medication with metformin is probably the drug of choice in both obese and non-obese patients. It is crucial not to delay raising the level of treatment until HbA1c has fallen to within an unsatisfactory range (wait-and-see strategy). Rather, the level should be intensified when persistent exacerbation starts to become apparent (proactive therapy). In diabetes mellitus, the same guidelines for secondary prevention apply to the associated cardiovascular risk factors as with coronary heart disease. An intensified and, especially, early treatment is to be preferred over a conservative, wait-and-see approach, in this case as well.

Angiotensin I-converting enzyme-gene-polymorphism: relationship to albumin excretion and blood pressure in pediatric patients with type-I-diabetes mellitus

The purpose of this study was the evaluation of a predictive genetic marker for nephropathy and hypertension in patients with type-I-diabetes mellitus (IDDM). The study was performed on 247 pediatric patients with IDDM. The mean age was 15.5 years (range 3.1-29.3), the mean duration of diabetes was 7.6 years (range 0.1-25.7). Age-related blood pressure and nocturnal albumin excretion rate were compared with the insertion/deletion-(I/D) polymorphism of the angiotensin-I converting enzyme gene. The genotype distribution did not differ significantly between IDDM patients (ID 48%, D 28%, I 24%) and the control group (ID 44%, D 37%, I 19%). Neither in the entire group, nor in patients with IDDM for more than 5 years, was a correlation found bet-ween allele distribution and albumin excretion rate. No correlation was found between genotype and blood pressure. When patients with a chronological age above 12 years were analysed separately, the genotype distribution between the groups with normal and elevated blood pressure showed no significant difference. The previously reported association of the I/D-polymorphism with nephropathy could not be confirmed in this study. The development of microalbuminuria, nephropathy and hypertension will be followed in our pediatric patients.

Racial and ethnic disparities in diabetes mellitus and hypertension: Influence of income and insurance status in a cross-sectional study of the United States

Racial/ethnic disparities in diabetes mellitus (DM) and hypertension (HTN) have been observed and explained by socioeconomic status (education level, income level, etc.), screening, early diagnosis, treatment, prognostic factors, and adherence to treatment regimens. To the author's knowledge, there are no studies addressing disparities in hypertension and diabetes mellitus utilizing Hispanics as the reference racial/ethnic group and adjusting for sociodemographics and prognostic factors. This present study examined racial/ethnic disparities in HTN and DM and assessed whether this disparity is explained by sociodemographics. To assess these associations, the study utilized a cross-sectional design and examined the distribution of the covariates for racial/ethnic group differences, using the Pearson Chi Square statistic. The study focused on Non-Hispanic Blacks since this ethnic group is associated with the worst health outcomes. Logistic regression was used to estimate the prevalence odds ratio (POR) and to adjust for the confounding effects of the covariates. Results indicated that except for insurance coverage, there were statistically significant differences between Non-Hispanic Blacks and Non-Hispanic Whites, as well as Hispanics with respect to study covariates. In the unadjusted logistic regression model, there was a statistically significant increased prevalence of hypertension among Non-Hispanic Blacks compared to Hispanics, POR 1.36, 95% CI 1.02-1.80. Low income was statistically significantly associated with increased prevalence of hypertension, POR 0.38, 95% CI 0.32-0.46. Insurance coverage, though not statistically significant, was associated with an increase in the prevalence of hypertension, p>0.05. Concerning DM, Non-Hispanic Blacks were more likely to be diabetic, POR 1.10, 95% CI 0.85-1.47. High income was statistically significantly associated with decreased prevalence of DM, POR 0.47, 95% CI 0.39-0.57. After adjustment for the relevant covariates, the racial disparities between Hispanics and Non-Hispanic Blacks in HTN was removed, adjusted prevalence odds (APOR) 1.21, 95% CI 0.88-1.67. In this sample, there was racial/ethnic disparity in hypertension but not in diabetes mellitus between Hispanics and Non-Hispanic Blacks, with disparities in hypertension associated with socioeconomic status (family income, education, marital status) and also by alcohol, physical activity and age. However, race, education and BMI as class variables were statistically significantly associated with hypertension and diabetes mellitus p<0.0001. ^

Does diabetes mellitus contribute to the disease burden of vascular dementia?

Background. Vascular dementia (VaD) is the second most common of dementia. Multiple risk factors are associated with VaD, but the individual contribution of each to disease onset and progression is unclear. We examined the relationship between diabetes mellitus type 2 (DM) and the clinical variables of VaD.^ Methods. Data from 593 patients evaluated between June, 2003 and June, 2008 for cognitive impairment were prospectively entered into a database. We retrospectively reviewed the charts of 63 patients who fit the NINDS-AIREN criteria of VaD. The patients were divided into those with DM (VaD-DM, n=29) and those without DM (VaD, n=34). The groups were compared with regard to multiple variables.^ Results. Patients with DM had a significantly earlier onset of VaD (71.9±6.54 vs. 77.2±6.03, p<0.001), a faster rate of decline per year on the mini mental state examination (MMSE; 3.60±1.82 vs. 2.54±1.60 points, p=0.02), and a greater prevalence of neuropsychiatric symptoms (62% vs. 21%, p=0.02) at the time of diagnosis.^ Conclusions. This study shows that a history of pre-morbid DM is associated with an early onset and faster cognitive deterioration in VaD. Moreover, the presence of DM predicts the presence of neuropsychiatric symptoms in patients with VaD. A larger study is needed to verify these associations. It will be important to investigate whether better glycemic control will mitigate the potential effects of DM on VaD.^

Parallel workflows to personalize clinical guidelines recommendations: application to gestational diabetes mellitus

The MobiGuide system provides patients with personalized decision support tools, based on computerized clinical guidelines, in a mobile environment. The generic capabilities of the system will be demonstrated applied to the clinical domain of Gestational Diabetes (GD). This paper presents a methodology to identify personalized recommendations, obtained from the analysis of the GD guideline. We added a conceptual parallel part to the formalization of the GD guideline called "parallel workflow" that allows considering patient?s personal context and preferences. As a result of analysing the GD guideline and eliciting medical knowledge, we identified three different types of personalized advices (therapy, measurements and upcoming events) that will be implemented to perform patients? guiding at home, supported by the MobiGuide system. These results will be essential to determine the distribution of functionalities between mobile and server decision support capabilities.

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.

Risk factors for coronary artery disease in non-insulin dependent diabetes mellitus: United Kingdom prospective diabetes study (UKPDS: 23)

Objective: To evaluate baseline risk factors for coronary artery disease in patients with type 2 diabetes mellitus.