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.

Nutritional impact on health and performance in intensively reared rabbits

The present work summarizes research related to the definition of nutrient recommendations for feeds used in the intensive production of rabbit's meat. Fibre is the main chemical constituent of rabbit diets that typically contain 320 to 360 and 50 to 90 g/kg of insoluble and soluble fibre, respectively. Instead, the dietary contents of cereal grains (∼120 to 160 g/kg), fat (15 to 25 g/kg) and protein concentrates (150 to 180 g/kg) are usually low with respect to other intensively reared monogastric animals. Cell wall constituents are not well digested in rabbits, but this effect is compensated by its stimulus of gut motility, which leads to an increasing rate of passage of digesta, and allows achieving an elevated dry matter intake. A high feed consumption and an adequate balance in essential nutrients are required to sustain the elevated needs of high-productive rabbits measured either as reproductive yield, milk production or growth rate in the fattening period. Around weaning, pathologies occur in a context of incomplete development of the digestive physiology of young rabbits. The supply of balanced diets has also been related to the prevention of disorders by means of three mechanisms: (i) promoting a lower retention time of the digesta in the digestive tract through feeding fibre sources with optimal chemical and physical characteristics, (ii) restricting feed intake after weaning or (iii) causing a lower flow of easily available substrates into the fermentative area by modifying feed composition (e.g. by lowering protein and starch contents, increasing its digestibility or partially substituting insoluble with soluble fibre), or by delaying age at weaning. The alteration in the gut microbiota composition has been postulated as the possible primary cause of these pathologies.

Effect of cage type on the behaviour pattern of rabbit does at different physiological stages

Interest in commercially farmed rabbit welfare has increased in recent years. As a result, new alternative housing systems have been developed, although they require evaluation in order to demonstrate their potential for improving welfare. The aim of this trial was to study the behavioural traits of rabbit does housed in 2 different types of cage (TC): conventional vs. alternative with an elevated platform, at different physiological stages (PS); lactation and gestation. Behavioural observations were carried out on 12 rabbit commercial does using continuous 24 h video recording. Independently of PS and TC, rabbit does spent most of their time on foot mats (on av. 57.7%). However, due to the use of platforms (on av. 23.0% of time), lactating does spent 36.6% less time on foot mats (P<0.001) and gestating does spent 27.0% less time on wire mesh (P<0.001) in alternative cages than in conventional cages. Alternative cages allowed for standing posture, but this behaviour was only observed in gestating does (on av. 4.6 times a day). Frequency of drinking was higher in conventional than in alternative cages (24.6 vs. 19.1 times a day; P<0.05). Gestating does housed in conventional cages reached the highest duration and frequency of interacting with neighbours (276 s/d and 4.6 times/d; P<0.05). The frequency of interacting with kits was lower in alternative than in conventional cages (2.4 vs. 8.6 times a day; P<0.01). Doe behaviour was influenced by the time of day, with less activity during the midday hours. During dark hours, rabbit does more frequently performed restless behaviour such as hyperactivity or nursing, matching the time at which rabbit does spent more time on the platform. The platform was frequently used by rabbit does, regardless of their physiological stage, and during late lactation phase, when mothers were not receptive to nursing, does housed in alternative cages used the platform as a mean to flee from kits trying to suckle. Use of the platform might lead to hygienic problems due to retained faeces on the platform and faeces and urine falling onto animals located in the lower part of the cage. The absence of stereotypies in rabbit does of this trial, suggested that animal welfare was not compromised by the type of housing (conventional or alternative cages).

Animal performance and meat characteristics in steers reared in intensive conditions fed with different vegetable oils

Enhancing the quality of beef meat is an important goal in terms of improving both the nutritional value for the consumer and the commercial value for producers. The aim of this work was to study the effects of different vegetable oil supplements on growth performance, carcass quality and meat quality in beef steers reared under intensive conditions. A total of 240 Blonde D? Aquitaine steers (average BW = 293.7 ± 38.88 kg) were grouped into 24 batches (10 steers/batch) and were randomly assigned to one of the three dietary treatments (eight batches per treatment), each supplemented with either 4% hydrogenated palm oil (PALM) or fatty acids (FAs) from olive oil (OLI) or soybean oil (SOY). No differences in growth performance or carcass quality were observed. For the meat quality analysis, a steer was randomly selected from each batch and the 6th rib on the left half of the carcass was dissected. PALM meat had the highest percentage of 16:0 ( P< 0.05) and the lowest n-6/n-3 polyunsaturated fatty acids (PUFA) ratio ( P< 0.05), OLI had the highest content of t 11-18:1 ( P< 0.01) and c 9,t 11-18:2 ( P< 0.05) and SOY showed the lowest value of monounsaturated fatty acids (MUFA) ( P< 0.001), the highest percentage of PUFA ( P< 0.01) and a lower index of atherogenicity ( P = 0.07) than PALM. No significant differences in the sensory characteristics of the meat were noted. However, the results of the principal component analysis of meat characteristics enabled meat from those steers that consumed fatty acids from olive oil to be differentiated from that of steers that consumed soybean oil.