Treatment intensification with insulin glargine in patients with inadequately controlled type 2 diabetes improves glycaemic control with a high treatment satisfaction and no weight gain

PRINCIPLES We aimed to evaluate the efficacy of, and treatment satisfaction with, insulin glargine administered with SoloSTAR® or ClikSTAR® pens in patients with type 2 diabetes mellitus managed by primary care physicians in Switzerland. METHODS A total of 327 patients with inadequately controlled type 2 diabetes were enrolled by 72 physicians in this prospective observational study, which aimed to evaluate the efficacy of a 6-month course of insulin glargine therapy measured as development of glycaemic control (glycosylated haemoglobin [HbA1c] and fasting plasma glucose [FPG]) and weight change. We also assessed preference for reusable or disposable pens, and treatment satisfaction. RESULTS After 6 months, the mean daily dose of insulin glargine was 27.7±14.3 U, and dose titration was completed in 228 (72.4%) patients. Mean HbA1c decreased from 8.9%±1.6% (n=327) to 7.3%±1.0% (n=315) (p<0.0001), and 138 (43.8%) patients achieved an HbA1c≤7.0%. Mean FPG decreased from 10.9±4.5 to 7.3±1.8 mmol/l (p<0.0001). Mean body weight did not change (85.4±17.2 kg vs 85.0±16.5 kg; p=0.11). Patients' preference was in favour of the disposable SoloStar® pen (80%), as compared with the reusable ClickStar® pen (20%). Overall, 92.6% of physicians and 96.3% of patients were satisfied or very satisfied with the insulin glargine therapy. CONCLUSIONS In patients with type 2 diabetes insulin glargine administered by SoloSTAR® or ClikSTAR® pens, education on insulin injection and on self-management of diabetes was associated with clinically meaningful improvements in HbA1c and FPG without a mean collective weight gain. The vast majority of both patients and primary care physicians were satisfied with the treatment intensification.

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.

Methodological and physiological test-retest reliability of (13) C-MRS glycogen measurements in liver and in skeletal muscle of patients with type 1 diabetes and matched healthy controls.

Glycogen is a major substrate in energy metabolism and particularly important to prevent hypoglycemia in pathologies of glucose homeostasis such as type 1 diabetes mellitus (T1DM). (13) C-MRS is increasingly used to determine glycogen in skeletal muscle and liver non-invasively; however, the low signal-to-noise ratio leads to long acquisition times, particularly when glycogen levels are determined before and after interventions. In order to ease the requirements for the subjects and to avoid systematic effects of the lengthy examination, we evaluated if a standardized preparation period would allow us to shift the baseline (pre-intervention) experiments to a preceding day. Based on natural abundance (13) C-MRS on a clinical 3 T MR system the present study investigated the test-retest reliability of glycogen measurements in patients with T1DM and matched controls (n = 10 each group) in quadriceps muscle and liver. Prior to the MR examination, participants followed a standardized diet and avoided strenuous exercise for two days. The average coefficient of variation (CV) of myocellular glycogen levels was 9.7% in patients with T1DM compared with 6.6% in controls after a 2 week period, while hepatic glycogen variability was 13.3% in patients with T1DM and 14.6% in controls. For comparison, a single-session test-retest variability in four healthy volunteers resulted in 9.5% for skeletal muscle and 14.3% for liver. Glycogen levels in muscle and liver were not statistically different between test and retest, except for hepatic glycogen, which decreased in T1DM patients in the retest examination, but without an increase of the group distribution. Since the CVs of glycogen levels determined in a "single session" versus "within weeks" are comparable, we conclude that the major source of uncertainty is the methodological error and that physiological variations can be minimized by a pre-study standardization. For hepatic glycogen examinations, familiarization sessions (MR and potentially strenuous interventions) are recommended. Copyright © 2016 John Wiley & Sons, Ltd.

Outcomes and cost effectiveness of treating type 2 diabetes with a nurse case manager following treatment algorithms versus primary care physicians

Background. Diabetes places a significant burden on the health care system. Reduction in blood glucose levels (HbA1c) reduces the risk of complications; however, little is known about the impact of disease management programs on medical costs for patients with diabetes. In 2001, economic costs associated with diabetes totaled $100 billion, and indirect costs totaled $54 billion. ^ Objective. To compare outcomes of nurse case management by treatment algorithms with conventional primary care for glycemic control and cardiovascular risk factors in type 2 diabetic patients in a low-income Mexican American community-based setting, and to compare the cost effectiveness of the two programs. Patient compliance was also assessed. ^ Research design and methods. An observational group-comparison to evaluate a treatment intervention for type 2 diabetes management was implemented at three out-patient health facilities in San Antonio, Texas. All eligible type 2 diabetic patients attending the clinics during 1994–1996 became part of the study. Data were obtained from the study database, medical records, hospital accounting, and pharmacy cost lists, and entered into a computerized database. Three groups were compared: a Community Clinic Nurse Case Manager (CC-TA) following treatment algorithms, a University Clinic Nurse Case Manager (UC-TA) following treatment algorithms, and Primary Care Physicians (PCP) following conventional care practices at a Family Practice Clinic. The algorithms provided a disease management model specifically for hyperglycemia, dyslipidemia, hypertension, and microalbuminuria that progressively moved the patient toward ideal goals through adjustments in medication, self-monitoring of blood glucose, meal planning, and reinforcement of diet and exercise. Cost effectiveness of hemoglobin AI, final endpoints was compared. ^ Results. There were 358 patients analyzed: 106 patients in CC-TA, 170 patients in UC-TA, and 82 patients in PCP groups. Change in hemoglobin A1c (HbA1c) was the primary outcome measured. HbA1c results were presented at baseline, 6 and 12 months for CC-TA (10.4%, 7.1%, 7.3%), UC-TA (10.5%, 7.1%, 7.2%), and PCP (10.0%, 8.5%, 8.7%). Mean patient compliance was 81%. Levels of cost effectiveness were significantly different between clinics. ^ Conclusion. Nurse case management with treatment algorithms significantly improved glycemic control in patients with type 2 diabetes, and was more cost effective. ^

The impact of nutrition intervention on glycemic control in a pediatric type 1 diabetes mellitus population

Aim: The goal of this study was to evaluate the change in hemoglobin A1C and glycemic control after nutrition intervention among a population of type 1 diabetic pediatric patients. Methods: Data was collected from all type 1 diabetic patients who were scheduled for a consultation with the diabetes/endocrine RD from January 2006 through December 2006. Two groups were compared, those who kept their RD appointment and those who did not keep their appointment. The main outcome measure was HgbA1C. An independent samples t-test compared the two groups with respect to change in HbgA1C before and after the most recent scheduled appointment with the RD. Baseline characteristics were used as covariates and analyzed and controlled for using analysis of covariance (ANCOVA). Results: There was no difference in HgbA1c after either attending an RD appointment or not having attended an RD appointment. Those who arrived for and attended their RD appointment and those who did not arrive for and attend their RD appointment, had statistically different HgbA1C's before their scheduled appointment as well as after the RD appointment. However, the two groups were not equal at the beginning of the study period. Discussion: A study design with inclusion criteria of a specified range of HgbA1C values within which the study subjects needed to fall, would have potentially eliminated the difference between the two groups at the beginning of the study period. Conducting either another retrospective study that controlled for the initial HgbA1C value or conducting a prospective study that designated a range of HgbA1C values would be worth investigating to evaluate the impact of medical nutrition therapy intervention and the role of the RD in diabetes management. It is an interesting finding that there was a significant difference in the initial HgbA1c for those who came to the RD appointment compared to those who did not come. The fact that in this study those who did not arrive for their RD appointment had worse control of their diabetes suggests that this is a high-risk group. Targeting diabetes education toward this group of patients may prove to be beneficial. ^

Association between glycemic index and glycemic load and the risk of incident coronary heart disease among whites and African Americans with and without type 2 diabetes: The Atherosclerosis Risk in Communities study

Several studies have examined the association between high glycemic index (GI) and glycemic load (GL) diets and the risk for coronary heart disease (CHD). However, most of these studies were conducted primarily on white populations. The primary aim of this study was to examine whether high GI and GL diets are associated with increased risk for developing CHD in whites and African Americans, non-diabetics and diabetics, and within stratifications of body mass index (BMI) and hypertension (HTN). Baseline and 17-year follow-up data from ARIC (Atherosclerosis Risk in Communities) study was used. The study population (13,051) consisted of 74% whites, 26% African Americans, 89% non-diabetics, 11% diabetics, 43% male, 57% female aged 44 to 66 years at baseline. Data from the ARIC food frequency questionnaire at baseline were analyzed to provide GI and GL indices for each subject. Increases of 25 and 30 units for GI and GL respectively were used to describe relationships on incident CHD risk. Adjusted hazard ratios for propensity score with 95% confidence intervals (CI) were used to assess associations. During 17 years of follow-up (1987 to 2004), 1,683 cases of CHD was recorded. Glycemic index was associated with 2.12 fold (95% CI: 1.05, 4.30) increased incident CHD risk for all African Americans and GL was associated with 1.14 fold (95% CI: 1.04, 1.25) increased CHD risk for all whites. In addition, GL was also an important CHD risk factor for white non-diabetics (HR=1.59; 95% CI: 1.33, 1.90). Furthermore, within stratum of BMI 23.0 to 29.9 in non-diabetics, GI was associated with an increased hazard ratio of 11.99 (95% CI: 2.31, 62.18) for CHD in African Americans, and GL was associated with 1.23 fold (1.08, 1.39) increased CHD risk in whites. Body mass index modified the effect of GI and GL on CHD risk in all whites and white non-diabetics. For HTN, both systolic blood pressure and diastolic blood pressure modified the effect on GI and GL on CHD risk in all whites and African Americans, white and African American non-diabetics, and white diabetics. Further studies should examine other factors that could influence the effects of GI and GL on CHD risk, including dietary factors, physical activity, and diet-gene interactions. ^

Concordance between medical records and personal interview in ascertaining breast cancer and diabetes status: Analysis of the south Texas women's health project

Accurate ascertainment of risk factors and disease status is vital in public health research for proper classification of research subjects. The two most common ways of obtaining this data is by self-report and review of medical records (MRs). South Texas Women’s Health Project was a case-control study looking at interrelationships between hormones, diet, and body size and breast cancer among Hispanic women 30-79 years of age. History of breast cancer, diabetes mellitus (DM) and use of DM medications was ascertained from a personal interview. At the time of interview, the subject identified her major health care providers and signed the medical records release form, which was sent to the designated providers. The MRs were reviewed to confirm information obtained from the interview.^ Aim of this study was to determine the sensitivity and specificity between MRs and personal interview in diagnosis of breast cancer, DM and DM treatment. We also wanted to assess how successful our low-cost approach was in obtaining pertinent MRs and what factors influenced the quality of MR or interview data. Study sample was 721 women with both self-report and MR data available by June 2007. Overall response rate for MR requests was 74.5%. MRs were 80.9% sensitive and 100% specific in confirming breast cancer status. Prevalence of DM was 22.7% from the interviews and 16% from MRs. MRs did not provide definite information about DM status of 53.6% subjects. Sensitivity and specificity of MRs for DM status was 88.9% and 90.4% respectively. Disagreement on DM status from the two sources was seen in 15.9% subjects. This discordance was more common among older subjects, those who were married and were predominantly Spanish speaking. Income and level of education did not have a statistically significantly association with this disagreement.^ Both self-report and MRs underestimate the prevalence of DM. Relying solely on MRs leads to greater misclassification than relying on self-report data. MRs have good to excellent specificity and thus serve as a good tool to confirm information obtained from self-report. Self-report and MRs should be used in a complementary manner for accurate assessment of DM and breast cancer status.^

Thrombolytic therapy for acute myocardial infarction: A cohort study to evaluate a selective approach to invasive diagnosis and therapy. The Corpus Christi Heart Project

Coronary perfusion with thrombolytic therapy and selective reperfusion by percutaneous transluminal coronary angioplasty (PTCA) were examined in the Corpus Christi Heart Project, a population-based surveillance program for hospitalized acute myocardial infarction (MI) patients in a biethnic community of Mexican-Americans (MAs) and non-Hispanic whites (NHWs). Results were based on 250 (12.4%) patients who received thromobolytic therapy in a cohort of 2011 acute MI cases. Out of these 107 (42.8%) underwent PTCA with a mean follow-up of 25 months. There were 186 (74.4%) men and 64 (25.6%) women; 148 (59.2%) were NHWs, 86 (34.4%) were MAs. Thrombolysis and PTCA were performed less frequently in women than in men, and less frequently in MAs than in NHWs.^ According to the coronary reperfusion interventions used, patients were divided in two groups, those that received no-PTCA (57.2%) and the other that underwent PTCA (42.8%) after thrombolysis. The case-fatality rate was higher in no-PTCA patients than in the PTCA (7.7% versus 5.6%), as was mortality at one year (16.2% versus 10.5%). Reperfusion was successful in 48.0% in the entire cohort and (51.4% versus 45.6%) in the PTCA and no-PTCA groups. Mortality in the successful reperfusion patients was 5.0% compared to 22.3% in the unsuccessful reperfusion group (p = 0.00016, 95% CI: 1.98-11.6).^ Cardiac catheterization was performed in 86.4% thrombolytic patients. Severe stenosis ($>$75%) obstruction was present most commonly in the left descending artery (52.8%) and in the right coronary artery (52.8%). The occurrence of adverse in-hospital clinical events was higher in the no-PTCA as compared to the PTCA and catheterized patients with the exception of reperfusion arrythmias (p = 0.140; Fisher's exact test p = 0.129).^ Cox regression analysis was used to study the relationship between selected variables and mortality. Apart from successful reperfusion, age group (p = 0.028, 95% CI: 2.1-12.42), site of acute MI index (p = 0.050) and ejection-fraction (p = 0.052) were predictors of long-term survival. The ejection-fraction in the PTCA group was higher than (median 78% versus 53%) in the no-PTCA group. Assessed by logistic regression analysis history of high cholesterol ($>$200mg/dl) and diabetes mellites did have significant prognostic value (p = 0.0233; p = 0.0318) in long-term survival irrespective of treatment status.^ In conclusion, the results of this study support the idea that the use of PTCA as a selective intervention following thrombolysis improves survival of patients with acute MI. The use of PTCA in this setting appears to be safe. However, we can not exclude the possibility that some of these results may have occurred due to the exclusion from PTCA of high risk patients (selection bias). ^

DIABETIC RETINOPATHY IN NON-INSULIN-DEPENDENT DIABETES MELLITUS IN BLACK WOMEN: A DESCRIPTIVE STUDY OF RISK FACTORS

This cross-sectional study examines the prevalence of selected potential risk factors by stage of diabetic retinopathy (DR) among Black American women with non-insulin-dependent diabetes mellitus (NIDDM) followed at a university diabetes clinic. DR was assessed by ophthalmoscopy and five-field retinography, and graded on counts of microaneurysms, hemorrhages and/or exudates, and presence of proliferative DR. Prevalence of other vascular diseases was assessed from medical records. Potential risk factors included age, known duration of diabetes, type of hypoglycemic treatment, concentrations of random capillary blood glucose, glycosylated hemoglobin, urine protein and fibrinogen, body mass index, and blood pressure. Prevalence of these risk factors is reported for three categories: No DR, mild background DR, severe background or proliferative DR (including surgically treated DR). Duration, age at diagnosis and treatment of diabetes, concentration of urine protein and average blood glucose, hypertension and cardiovascular disease were significantly associated with DR in univariate analysis. The covariance analysis employed stratification on duration, age at diagnosis and therapy of diabetes. The highest DR scores were calculated for those diagnosed before age 45, regardless of duration, therapy, or average blood glucose. Only individuals diagnosed before age 45 had high blood glucose concentrations in all categories of duration. These findings suggest that in this clinic population of Black women, those diagnosed with NIDDm before age 45 who eventually required insulin treatment were at the greatest risk of developing DR and that longterm poor glucose control is a contributing factor. These results suggest that greater emphasis be placed on this subgroup in allocating the limited resources available to improve the quality of glucose regulation, particularly through measures affecting compliance behavior.^ Findings concerning the association of DR with concentration of blood glucose and urine protein, blood pressure/hypertension and weight were compared with those reported from American Indian and Mexican American populations of the Southwestern United States where prevalence of NIDDM, hypertension and obesity is also high. Additional comparative analyses are outlined to substantiate the preliminary finding that there are systematic differences between these ethnic populations. ^

SOCIAL, ATTITUDINAL AND BEHAVIORAL CORRELATES OF WEIGHT CHANGE AMONG MEXICAN AMERICAN WOMEN (OBESITY, DIET, TEXAS)

In industrialized countries the prevalence of obesity among women decreases with increasing socioeconomic status. While this relation has been amply documented, its explanation and implications for other causal factors of obesity has received much less attention. Differences in childbearing patterns, norms and attitudes about fatness, dietary behaviors and physical activity are some of the factors that have been proposed to explain the inverse relation.^ The objectives of this investigation were to (1) examine the associations among social characteristics and weight-related attitudes and behaviors, and (2) examine the relations of these factors to weight change and obesity. Information on social characteristics, weight-related attitudes, dietary behaviors, physical activity and childbearing were collected from 304 Mexican American women aged 19 to 50 living in Starr County, Texas, who were at high risk for developing diabetes. Their weights were recorded both at an initial physical examination and at a follow-up interview one to two and one-half years later, permitting the computation of current Body Mass Index (weight/height('2)) and weight change during the interval for each subject. Path analysis was used to examine direct and indirect relations among the variables.^ The major findings were: (1) After controlling for age, childbearing was not an independent predictor of weight change or Body Mass Index. (2) Neither planned exercise nor total daily physical activity were independent predictors of weight change. (3) Women with higher social characteristics scores reported less frequent meals and less use of calorically dense foods, factors associated with lower risk for weight gain. (4) Dietary intake measures were not significantly related to Body Mass Index. However, dietary behaviors (frequency of meals and snacks, use of high and low caloric density foods, eating restraint and disinhibition of restraint) did explain a significant portion (17.4 percent) of the variance in weight change, indicating the importance of using dynamic measures of weight status in studies of the development of obesity. This study highlights factors amenable to intervention to reverse or to prevent weight gain in this population, and thereby reduce the prevalence of diabetes and its sequelae. ^

GENETIC PREDICTORS OF HYPERGLYCEMIA DUE TO HYDROCHLOROTHIAZIDE THERAPY

Response to pharmacological treatment is variable among individuals. Some patients respond favorably to a drug while others develop adverse reactions. Early investigations showed evidence of variation in genes that code for drug receptors, drug transporters, and drug metabolizing enzymes; and pharmacogenetics appeared as the science that studies the relationship between drug response and genetic variation. Thiazide diuretics are the recommended first-line monotherapy for hypertension (i.e. SBP>140 or DBP>90). Even so, diuretics are associated with adverse metabolic side effects, such as hyperglycemia, which increase the risk of developing type 2 diabetes. Published approaches testing variation in candidate genes (e.g. the renin-angiotensin-aldosteron system (RAAS) and salt–sensitivity genes) have met with only limited success. We conducted the first genome wide association study to identify genes influencing hyperglycemia as an adverse effect of thiazide diuretics in non-Hispanic White hypertensive patients participating in the Genetic Epidemiology of Responses to Antihypertensives (GERA) and Pharmacogenomic Evaluation of Antihypertensive Responses (PEAR) clinical trials. No SNP reached the a priori defined threshold of statistical significance (p<5x10-8). We detected 50 SNPs in 9 genomic regions with suggestive p-values (p<1x10-5). Two of them, rs6870564 (p-value=3.28 X 10-6) and rs7702121 (p-value=5.09 X 10-6), were located close to biologic candidate genes, MYO and MGAT1, and one SNP in a genomic region in chromosome 6, rs7762018 (p-value=4.59 X 10-6) has been previously related to Insulin-Dependent Diabetes Mellitus (IDDM8). I conclude that 1) there are unlikely to be common SNPs with large effects on the adverse metabolic effects to hydrochlorothiazide treatment and 2) larger sample sizes are needed for pharmacogenetic studies of inter-individual variation in response to commonly prescribed medication.

PREDIRCAM eHealth platform for individualized telemedical assistance for lifestyle modification in the treatment of obesity, diabetes, and cardiometabolic risk prevention: a pilot study (PREDIRCAM 1)

Background: Healthy diet and regular physical activity are powerful tools in reducing diabetes and cardiometabolic risk. Various international scientific and health organizations have advocated the use of new technologies to solve these problems. The PREDIRCAM project explores the contribution that a technological system could offer for the continuous monitoring of lifestyle habits and individualized treatment of obesity as well as cardiometabolic risk prevention. Methods: PREDIRCAM is a technological platform for patients and professionals designed to improve the effectiveness of lifestyle behavior modifications through the intensive use of the latest information and communication technologies. The platform consists of a web-based application providing communication interface with monitoring devices of physiological variables, application for monitoring dietary intake, ad hoc electronic medical records, different communication channels, and an intelligent notification system. A 2-week feasibility study was conducted in 15 volunteers to assess the viability of the platform. Results: The website received 244 visits (average time/session: 17 min 45 s). A total of 435 dietary intakes were recorded (average time for each intake registration, 4 min 42 s ± 2 min 30 s), 59 exercises were recorded in 20 heart rate monitor downloads, 43 topics were discussed through a forum, and 11 of the 15 volunteers expressed a favorable opinion toward the platform. Food intake recording was reported as the most laborious task. Ten of the volunteers considered long-term use of the platform to be feasible. Conclusions: The PREDIRCAM platform is technically ready for clinical evaluation. Training is required to use the platform and, in particular, for registration of dietary food intake.

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.

Patient-oriented computerized clinical guidelines for mobile decision support in gestational diabetes

The risks associated with gestational diabetes (GD) can be reduced with an active treatment able to improve glycemic control. Advances in mobile health can provide new patient-centric models for GD to create personalized health care services, increase patient independence and improve patients’ self-management capabilities, and potentially improve their treatment compliance. In these models, decision-support functions play an essential role. The telemedicine system MobiGuide provides personalized medical decision support for GD patients that is based on computerized clinical guidelines and adapted to a mobile environment. The patient’s access to the system is supported by a smartphone-based application that enhances the efficiency and ease of use of the system. We formalized the GD guideline into a computer-interpretable guideline (CIG). We identified several workflows that provide decision-support functionalities to patients and 4 types of personalized advice to be delivered through a mobile application at home, which is a preliminary step to providing decision-support tools in a telemedicine system: (1) therapy, to help patients to comply with medical prescriptions; (2) monitoring, to help patients to comply with monitoring instructions; (3) clinical assessment, to inform patients about their health conditions; and (4) upcoming events, to deal with patients’ personal context or special events. The whole process to specify patient-oriented decision support functionalities ensures that it is based on the knowledge contained in the GD clinical guideline and thus follows evidence-based recommendations but at the same time is patient-oriented, which could enhance clinical outcomes and patients’ acceptance of the whole system.

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.