[Insulin and type 2 diabetes mellitus]

Worldwide an increasing number of persons suffers from type 2 diabetes. Often treatment with oral hypoglycemic agents is not sufficient for adequate glycemic control and additional insulin treatment is necessary. Treatment with insulin is recommended if HbA1c levels below 7% cannot be achieved despite lifestyle measures and the proper use of oral hypoglycemic agents. In addition, pregnancy, periods pre and post major operations, treatment in intensive care units, glucocorticoid medication, severe peripheral neuropathy as well as contraindications of oral hypoglycaemic agents may be indications for insulin therapy irrespective of the actual glycemic control. The choice of the appropriate insulin regimen depends on the daily blood glucose profiles and patient needs.

A reduction in severe hypoglycaemia in type 1 diabetes in a randomized crossover study of continuous intraperitoneal compared with subcutaneous insulin infusion

Continuous intraperitoneal insulin infusion (CIPII) with the DiaPort system using regular insulin was compared to continuous subcutaneous insulin infusion (CSII) using insulin Lispro, to investigate the frequency of hypoglycemia, blood glucose control, quality of life, and safety.

IGF-I treatment in adults with type 1 diabetes: effects on glucose and protein metabolism in the fasting state and during a hyperinsulinemic-euglycemic amino acid clamp

Type 1 diabetes is associated with abnormalities of the growth hormone (GH)-IGF-I axis. Such abnormalities include decreased circulating levels of IGF-I. We studied the effects of IGF-I therapy (40 microg x kg(-1) x day(-1)) on protein and glucose metabolism in adults with type 1 diabetes in a randomized placebo-controlled trial. A total of 12 subjects participated, and each subject was studied at baseline and after 7 days of treatment, both in the fasting state and during a hyperinsulinemic-euglycemic amino acid clamp. Protein and glucose metabolism were assessed using infusions of [1-13C]leucine and [6-6-2H2]glucose. IGF-I administration resulted in a 51% rise in circulating IGF-I levels (P < 0.005) and a 56% decrease in the mean overnight GH concentration (P < 0.05). After IGF-I treatment, a decrease in the overnight insulin requirement (0.26+/-0.07 vs. 0.17+/-0.06 U/kg, P < 0.05) and an increase in the glucose infusion requirement were observed during the hyperinsulinemic clamp (approximately 67%, P < 0.05). Basal glucose kinetics were unchanged, but an increase in insulin-stimulated peripheral glucose disposal was observed after IGF-I therapy (37+/-6 vs. 52+/-10 micromol x kg(-1) x min(-1), P < 0.05). IGF-I administration increased the basal metabolic clearance rate for leucine (approximately 28%, P < 0.05) and resulted in a net increase in leucine balance, both in the basal state and during the hyperinsulinemic amino acid clamp (-0.17+/-0.03 vs. -0.10+/-0.02, P < 0.01, and 0.25+/-0.08 vs. 0.40+/-0.06, P < 0.05, respectively). No changes in these variables were recorded in the subjects after administration of placebo. These findings demonstrated that IGF-I replacement resulted in significant alterations in glucose and protein metabolism in the basal and insulin-stimulated states. These effects were associated with increased insulin sensitivity, and they underline the major role of IGF-I in protein and glucose metabolism in type 1 diabetes.

An insulin infusion advisory system for type 1 diabetes patients based on non-linear model predictive control methods

In this paper, an Insulin Infusion Advisory System (IIAS) for Type 1 diabetes patients, which use insulin pumps for the Continuous Subcutaneous Insulin Infusion (CSII) is presented. The purpose of the system is to estimate the appropriate insulin infusion rates. The system is based on a Non-Linear Model Predictive Controller (NMPC) which uses a hybrid model. The model comprises a Compartmental Model (CM), which simulates the absorption of the glucose to the blood due to meal intakes, and a Neural Network (NN), which simulates the glucose-insulin kinetics. The NN is a Recurrent NN (RNN) trained with the Real Time Recurrent Learning (RTRL) algorithm. The output of the model consists of short term glucose predictions and provides input to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. For the development and the evaluation of the IIAS, data generated from a Mathematical Model (MM) of a Type 1 diabetes patient have been used. The proposed control strategy is evaluated at multiple meal disturbances, various noise levels and additional time delays. The results indicate that the implemented IIAS is capable of handling multiple meals, which correspond to realistic meal profiles, large noise levels and time delays.

A communication and information technology infrastructure for real time monitoring and management of type 1 diabetes patients

This paper is focused on the integration of state-of-the-art technologies in the fields of telecommunications, simulation algorithms, and data mining in order to develop a Type 1 diabetes patient's semi to fully-automated monitoring and management system. The main components of the system are a glucose measurement device, an insulin delivery system (insulin injection or insulin pumps), a mobile phone for the GPRS network, and a PDA or laptop for the Internet. In the medical environment, appropriate infrastructure for storage, analysis and visualizing of patients' data has been implemented to facilitate treatment design by health care experts.

Neural network based glucose - insulin metabolism models for children with Type 1 diabetes

In this paper two models for the simulation of glucose-insulin metabolism of children with Type 1 diabetes are presented. The models are based on the combined use of Compartmental Models (CMs) and artificial Neural Networks (NNs). Data from children with Type 1 diabetes, stored in a database, have been used as input to the models. The data are taken from four children with Type 1 diabetes and contain information about glucose levels taken from continuous glucose monitoring system, insulin intake and food intake, along with corresponding time. The influences of taken insulin on plasma insulin concentration, as well as the effect of food intake on glucose input into the blood from the gut, are estimated from the CMs. The outputs of CMs, along with previous glucose measurements, are fed to a NN, which provides short-term prediction of glucose values. For comparative reasons two different NN architectures have been tested: a Feed-Forward NN (FFNN) trained with the back-propagation algorithm with adaptive learning rate and momentum, and a Recurrent NN (RNN), trained with the Real Time Recurrent Learning (RTRL) algorithm. The results indicate that the best prediction performance can be achieved by the use of RNN.

A real time simulation model of glucose-insulin metabolism for type 1 diabetes patients

In this paper, a simulation model of glucose-insulin metabolism for Type 1 diabetes patients is presented. The proposed system is based on the combination of Compartmental Models (CMs) and artificial Neural Networks (NNs). This model aims at the development of an accurate system, in order to assist Type 1 diabetes patients to handle their blood glucose profile and recognize dangerous metabolic states. Data from a Type 1 diabetes patient, stored in a database, have been used as input to the hybrid system. The data contain information about measured blood glucose levels, insulin intake, and description of food intake, along with the corresponding time. The data are passed to three separate CMs, which produce estimations about (i) the effect of Short Acting (SA) insulin intake on blood insulin concentration, (ii) the effect of Intermediate Acting (IA) insulin intake on blood insulin concentration, and (iii) the effect of carbohydrate intake on blood glucose absorption from the gut. The outputs of the three CMs are passed to a Recurrent NN (RNN) in order to predict subsequent blood glucose levels. The RNN is trained with the Real Time Recurrent Learning (RTRL) algorithm. The resulted blood glucose predictions are promising for the use of the proposed model for blood glucose level estimation for Type 1 diabetes patients.

A neural network approach for insulin regime and dose adjustment in type 1 diabetes

A decision support system based on a neural network approach is proposed to advise on insulin regime and dose adjustment for type 1 diabetes patients.

Circadian blood pressure during the early course of type 1 diabetes. Analysis of 1,011 ambulatory blood pressure recordings in 354 adolescents and young adults

OBJECTIVE Little information is available on the early course of hypertension in type 1 diabetes. The aim of our study, therefore, was to document circadian blood pressure profiles in patients with a diabetes duration of up to 20 years and relate daytime and nighttime blood pressure to duration of diabetes, BMI, insulin therapy, and HbA1c. RESEARCH DESIGN AND METHODS Ambulatory profiles of 24-h blood pressure were recorded in 354 pediatric patients with type 1 diabetes (age 14.6 +/- 4.2 years, duration of diabetes 5.6 +/- 5.0 years, follow-up for up to 9 years). A total of 1,011 profiles were available for analysis from patients not receiving antihypertensive medication. RESULTS Although daytime mean systolic pressure was significantly elevated in diabetic subjects (+3.1 mmHg; P < 0.0001), daytime diastolic pressure was not different from from the height- and sex-adjusted normal range (+0.1 mmHg, NS). In contrast, both systolic and diastolic nighttime values were clearly elevated (+7.2 and +4.2 mmHg; P < 0.0001), and nocturnal dipping was reduced (P < 0.0001). Systolic blood pressure was related to overweight in all patients, while diastolic blood pressure was related to metabolic control in young adults. Blood pressure variability was significantly lower in girls compared with boys (P < 0.01). During follow-up, no increase of blood pressure was noted; however, diastolic nocturnal dipping decreased significantly (P < 0.03). Mean daytime blood pressure was significantly related to office blood pressure (r = +0.54 for systolic and r = +0.40 for diastolic pressure); however, hypertension was confirmed by ambulatory blood pressure measurement in only 32% of patients with elevated office blood pressure. CONCLUSIONS During the early course of type 1 diabetes, daytime blood pressure is higher compared with that of healthy control subjects. The elevation of nocturnal values is even more pronounced and nocturnal dipping is reduced. The frequency of white-coat hypertension is high among adolescents with diabetes, and ambulatory blood pressure monitoring avoids unnecessary antihypertensive treatment.

Identification of a SIRT1 mutation in a family with type 1 diabetes.

Type 1 diabetes is caused by autoimmune-mediated β cell destruction leading to insulin deficiency. The histone deacetylase SIRT1 plays an essential role in modulating several age-related diseases. Here we describe a family carrying a mutation in the SIRT1 gene, in which all five affected members developed an autoimmune disorder: four developed type 1 diabetes, and one developed ulcerative colitis. Initially, a 26-year-old man was diagnosed with the typical features of type 1 diabetes, including lean body mass, autoantibodies, T cell reactivity to β cell antigens, and a rapid dependence on insulin. Direct and exome sequencing identified the presence of a T-to-C exchange in exon 1 of SIRT1, corresponding to a leucine-to-proline mutation at residue 107. Expression of SIRT1-L107P in insulin-producing cells resulted in overproduction of nitric oxide, cytokines, and chemokines. These observations identify a role for SIRT1 in human autoimmunity and unveil a monogenic form of type 1 diabetes.

Hepatic and intramyocellular glycogen stores in adults with type 1 diabetes and healthy controls.

Glycogen levels in liver and skeletal muscle assessed non-invasively using magnetic resonance spectroscopy after a 48-h pre-study period including a standardized diet and withdrawal from exercise did not differ between individuals with well-controlled Type 1 DM and matched healthy controls.

Age Related Differences in Individual Quality of Life Domains in Youth with Type 1 Diabetes.

BACKGROUND: Investigating individual, as opposed to predetermined, quality of life domains may yield important information about quality of life. This study investigated the individual quality of life domains nominated by youth with type 1 diabetes. METHODS: Eighty young people attending a diabetes summer camp completed the Schedule for the Evaluation of Individual Quality of Life-Direct Weighting interview, which allows respondents to nominate and evaluate their own quality of life domains. RESULTS: The most frequently nominated life domains were 'family', 'friends', 'diabetes', 'school', and 'health' respectively; ranked in terms of importance, domains were 'religion', 'family', 'diabetes', 'health', and 'the golden rule'; ranked in order of satisfaction, domains were 'camp', 'religion', 'pets', and 'family' and 'a special person' were tied for fifth. Respondent age was significantly positively associated with the importance of 'friends', and a significantly negatively associated with the importance of 'family'. Nearly all respondents nominated a quality of life domain relating to physical status, however, the specific physical status domain and the rationale for its nomination varied. Some respondents nominated 'diabetes' as a domain and emphasized diabetes 'self-care behaviors' in order to avoid negative health consequences such as hospitalization. Other respondents nominated 'health' and focused more generally on 'living well with diabetes'. In an ANOVA with physical status domain as the independent variable and age as the dependent variable, participants who nominated 'diabetes' were younger (M = 12.9 years) than those who nominated 'health' (M = 15.9 years). In a second ANOVA, with rationale for nomination the physical status domain as the independent variable, and age as the dependent variable, those who emphasized 'self care behaviors' were younger (M = 11.8 years) than those who emphasized 'living well with diabetes' (M = 14.6 years). These differences are discussed in terms of cognitive development and in relation to the decline in self-care and glycemic control often observed during adolescence. CONCLUSIONS: Respondents nominated many non-diabetes life domains, underscoring that QOL is multidimensional. Subtle changes in conceptualization of diabetes and health with increasing age may reflect cognitive development or disease adjustment, and speak to the need for special attention to adolescents. Understanding individual quality of life domains can help clinicians motivate their young patients with diabetes for self-care. Future research should employ a larger, more diverse sample, and use longitudinal designs.

Diabetic nephropathy in African-Americans with type 1 diabetes: The new Jersey 725 study

Diabetic nephropathy is the most common cause of end-stage renal disease (ESRD) in the United States. African-Americans and patients with type 1 diabetes (T1D) are at increased risk. We studied the rate and factors that influenced progression of glomerular filtration rate (GFR) in 401 African-American T1D patients who were followed for 6 years through the observational cohort New Jersey 725 study. Patients with ESRD and/or GFR<20 ml/min were excluded. The mean (SD) baseline GFR was 106.8 (27.04) ml/min and it decreased by 13.8 (mean, SD 32.2) ml/min during the 6-year period (2.3 ml/min/year). In patients with baseline macroproteinuria, GFR decreased by 31.8 (39.0) ml/min (5.3 ml/min/year) compared to 8.2 (mean, SD 27.6) ml/min (1.3 ml/min/year) in patients without it (p<0.00001). Six-year GFR fell to <20 ml/min in 5.25% of all patients, but in 16.8% of macroproteinuric patients.^ A model including baseline GFR, proteinuria category and hypertension category, explained 35% of the 6-year GFR variability (p<0.0001). After adjustment for other variables in the model, 6-year GFR was 24.9 ml/min lower in macroproteinuric patients than in those without proteinuria (p=0.0001), and 12.6 ml/min lower in patients with treated but uncontrolled hypertension compared to normotensive patients (p=0.003). In this sample of patients, with an elevated mean glycosylated hemoglobin of 12.4%, glycemic control did not independently influence GFR deterioration, nor did BMI, cholesterol, gender, age at diabetes onset or socioeconomic level.^ Taken together, our findings suggest that proteinuria and hypertension are the most important factors associated with GFR deterioration in African-American T1D patients.^

A Mobile Feedback System for Integrated E-health Platforms to improve Self-Care and Compliance of Diabetes Mellitus Patients

Exploiting the full potential of telemedical systems means using platform based solutions: data are recovered from biomedical sensors, hospital information systems, care-givers, as well as patients themselves, and are processed and redistributed in an either centralized or, more probably, decentralized way. The integration of all these different devices, and interfaces, as well as the automated analysis and representation of all the pieces of information are current key challenges in telemedicine. Mobile phone technology has just begun to offer great opportunities of using this diverse information for guiding, warning, and educating patients, thus increasing their autonomy and adherence to their prescriptions. However, most of these existing mobile solutions are not based on platform systems and therefore represent limited, isolated applications. This article depicts how telemedical systems, based on integrated health data platforms, can maximize prescription adherence in chronic patients through mobile feedback. The application described here has been developed in an EU-funded R&D project called METABO, dedicated to patients with type 1 or type 2 Diabetes Mellitus

Automatic physical activity pattern recognition and meal intake modelling in type 1 diabetes for their inclusion in artificial pancreas systems

La diabetes comprende un conjunto de enfermedades metabólicas que se caracterizan por concentraciones de glucosa en sangre anormalmente altas. En el caso de la diabetes tipo 1 (T1D, por sus siglas en inglés), esta situación es debida a una ausencia total de secreción endógena de insulina, lo que impide a la mayoría de tejidos usar la glucosa. En tales circunstancias, se hace necesario el suministro exógeno de insulina para preservar la vida del paciente; no obstante, siempre con la precaución de evitar caídas agudas de la glucemia por debajo de los niveles recomendados de seguridad. Además de la administración de insulina, las ingestas y la actividad física son factores fundamentales que influyen en la homeostasis de la glucosa. En consecuencia, una gestión apropiada de la T1D debería incorporar estos dos fenómenos fisiológicos, en base a una identificación y un modelado apropiado de los mismos y de sus sorrespondientes efectos en el balance glucosa-insulina. En particular, los sistemas de páncreas artificial –ideados para llevar a cabo un control automático de los niveles de glucemia del paciente– podrían beneficiarse de la integración de esta clase de información. La primera parte de esta tesis doctoral cubre la caracterización del efecto agudo de la actividad física en los perfiles de glucosa. Con este objetivo se ha llevado a cabo una revisión sistemática de la literatura y meta-análisis que determinen las respuestas ante varias modalidades de ejercicio para pacientes con T1D, abordando esta caracterización mediante unas magnitudes que cuantifican las tasas de cambio en la glucemia a lo largo del tiempo. Por otro lado, una identificación fiable de los periodos con actividad física es un requisito imprescindible para poder proveer de esa información a los sistemas de páncreas artificial en condiciones libres y ambulatorias. Por esta razón, la segunda parte de esta tesis está enfocada a la propuesta y evaluación de un sistema automático diseñado para reconocer periodos de actividad física, clasificando su nivel de intensidad (ligera, moderada o vigorosa); así como, en el caso de periodos vigorosos, identificando también la modalidad de ejercicio (aeróbica, mixta o de fuerza). En este sentido, ambos aspectos tienen una influencia específica en el mecanismo metabólico que suministra la energía para llevar a cabo el ejercicio y, por tanto, en las respuestas glucémicas en T1D. En este trabajo se aplican varias combinaciones de técnicas de aprendizaje máquina y reconocimiento de patrones sobre la fusión multimodal de señales de acelerometría y ritmo cardíaco, las cuales describen tanto aspectos mecánicos del movimiento como la respuesta fisiológica del sistema cardiovascular ante el ejercicio. Después del reconocimiento de patrones se incorpora también un módulo de filtrado temporal para sacar partido a la considerable coherencia temporal presente en los datos, una redundancia que se origina en el hecho de que en la práctica, las tendencias en cuanto a actividad física suelen mantenerse estables a lo largo de cierto tiempo, sin fluctuaciones rápidas y repetitivas. El tercer bloque de esta tesis doctoral aborda el tema de las ingestas en el ámbito de la T1D. En concreto, se propone una serie de modelos compartimentales y se evalúan éstos en función de su capacidad para describir matemáticamente el efecto remoto de las concetraciones plasmáticas de insulina exógena sobre las tasas de eleiminación de la glucosa atribuible a la ingesta; un aspecto hasta ahora no incorporado en los principales modelos de paciente para T1D existentes en la literatura. Los datos aquí utilizados se obtuvieron gracias a un experimento realizado por el Institute of Metabolic Science (Universidad de Cambridge, Reino Unido) con 16 pacientes jóvenes. En el experimento, de tipo ‘clamp’ con objetivo variable, se replicaron los perfiles individuales de glucosa, según lo observado durante una visita preliminar tras la ingesta de una cena con o bien alta carga glucémica, o bien baja. Los seis modelos mecanísticos evaluados constaban de: a) submodelos de doble compartimento para las masas de trazadores de glucosa, b) un submodelo de único compartimento para reflejar el efecto remoto de la insulina, c) dos tipos de activación de este mismo efecto remoto (bien lineal, bien con un punto de corte), y d) diversas condiciones iniciales. ABSTRACT Diabetes encompasses a series of metabolic diseases characterized by abnormally high blood glucose concentrations. In the case of type 1 diabetes (T1D), this situation is caused by a total absence of endogenous insulin secretion, which impedes the use of glucose by most tissues. In these circumstances, exogenous insulin supplies are necessary to maintain patient’s life; although caution is always needed to avoid acute decays in glycaemia below safe levels. In addition to insulin administrations, meal intakes and physical activity are fundamental factors influencing glucose homoeostasis. Consequently, a successful management of T1D should incorporate these two physiological phenomena, based on an appropriate identification and modelling of these events and their corresponding effect on the glucose-insulin balance. In particular, artificial pancreas systems –designed to perform an automated control of patient’s glycaemia levels– may benefit from the integration of this type of information. The first part of this PhD thesis covers the characterization of the acute effect of physical activity on glucose profiles. With this aim, a systematic review of literature and metaanalyses are conduced to determine responses to various exercise modalities in patients with T1D, assessed via rates-of-change magnitudes to quantify temporal variations in glycaemia. On the other hand, a reliable identification of physical activity periods is an essential prerequisite to feed artificial pancreas systems with information concerning exercise in ambulatory, free-living conditions. For this reason, the second part of this thesis focuses on the proposal and evaluation of an automatic system devised to recognize physical activity, classifying its intensity level (light, moderate or vigorous) and for vigorous periods, identifying also its exercise modality (aerobic, mixed or resistance); since both aspects have a distinctive influence on the predominant metabolic pathway involved in fuelling exercise, and therefore, in the glycaemic responses in T1D. Various combinations of machine learning and pattern recognition techniques are applied on the fusion of multi-modal signal sources, namely: accelerometry and heart rate measurements, which describe both mechanical aspects of movement and the physiological response of the cardiovascular system to exercise. An additional temporal filtering module is incorporated after recognition in order to exploit the considerable temporal coherence (i.e. redundancy) present in data, which stems from the fact that in practice, physical activity trends are often maintained stable along time, instead of fluctuating rapid and repeatedly. The third block of this PhD thesis addresses meal intakes in the context of T1D. In particular, a number of compartmental models are proposed and compared in terms of their ability to describe mathematically the remote effect of exogenous plasma insulin concentrations on the disposal rates of meal-attributable glucose, an aspect which had not yet been incorporated to the prevailing T1D patient models in literature. Data were acquired in an experiment conduced at the Institute of Metabolic Science (University of Cambridge, UK) on 16 young patients. A variable-target glucose clamp replicated their individual glucose profiles, observed during a preliminary visit after ingesting either a high glycaemic-load or a low glycaemic-load evening meal. The six mechanistic models under evaluation here comprised: a) two-compartmental submodels for glucose tracer masses, b) a single-compartmental submodel for insulin’s remote effect, c) two types of activations for this remote effect (either linear or with a ‘cut-off’ point), and d) diverse forms of initial conditions.