Alterações da glicemia : uma análise de clusters

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceA diabetes tipo 2 é considerada a epidemia do século XXI. Os valores de corte de glicemia, utilizados no diagnóstico da diabetes tipo 2 e da pré-diabetes, são estabelecidos por convenção. No caso da pré-diabetes, não existe acordo entre as diferentes sociedades (Organização Mundial de Saúde e American Diabetes Association), no que respeita ao valor de corte da glicemia em jejum e da HbA1c. Também na classe da pré-diabetes, existem indivíduos que já apresentam complicações da diabetes, outros irão progredir, e ainda aqueles que nunca progredirão na doença. Assim, a classificação baseada apenas nos valores de glicemia parece ser insuficiente, não só para o diagnóstico, mas também para identificar, o risco de progressão de cada indivíduo. A diabetes tipo 2 tem etiologia multifatorial, e complexa. Com base nos presentes critérios, podemos estar a agrupar indivíduos com diferentes fenótipos sob o mesmo grupo de diagnóstico. A abordagem igual de fenótipos diferentes pode contribuir para a ineficácia da prevenção, do diagnóstico e da terapêutica, o que se traduz no fardo socioeconómico que a diabetes tipo2 representa. A análise de clusters tem como objetivo pôr em evidência grupos naturais numa determinada população. Permitindo a análise de dados complexos, revela padrões de características que definem grupos diferentes. Em particular, os Self-organizing Maps (SOM), são uma metodologia robusta de análise de clusters, que permitem a redução dos dados a uma grelha de 2 dimensões, conservando a topologia dos mesmos. Este trabalho teve como objetivo utilizar a análise de clusters, nomeadamente o SOM, para revelar grupos, que representem diferentes fenótipos da doença metabólica e que possam ser úteis na compreensão dos mecanismos fisiopatológicos, e na melhoria da prevenção, diagnóstico e tratamento destes doentes. Aplicámos, em primeiro lugar, um algoritmo de SOM a 1010 indivíduos da coorte Prevadiab2, reduzindo assim a dimensionalidade dos dados. Para este algoritmo utilizamos parâmetros (27) antropométricos e bioquímicos, reconhecidamente importantes na fisiopatologia da diabetes tipo 2. De seguida, com base num cluster hierárquico (método Ward), definimos os clusters finais. Identificámos 10 clusters, com diferentes perfis antropométricos e metabólicos. Todos os clusters apresentam indivíduos com normoglicemia e com hiperglicemia (pré-diabetes e/ou diabetes) em diferentes proporções. Nos 5 clusters que contêm pessoas com diabetes, encontramos também indivíduos com pré-diabetes, e surpreendentemente, encontramos ainda pessoas com normoglicémia, embora os últimos estejam presentes em menor proporção. A aplicação do SOM, a uma população, que inclui pessoas com normoglicemia e hiperglicemia, permitiu identificar grupos com diferentes fenótipos antropométricos e metabólicos. Mais relevante, os resultados obtidos levantam várias questões relevantes relativas aos mecanismos fisiopatológicos subjacentes aos diferentes fenótipos. A resposta a estas questões pode ter um impacto determinante na melhoria da prevenção, do diagnóstico e da eficácia terapêutica da diabetes tipo 2.Type 2 diabetes (T2D) is considered the 21st century epidemic. Glycemic cutoff values, for T2D and prediabetes diagnosis, are established by convention. In the case of prediabetes, there is no agreement between the different societies (World Health Organization and American Diabetes Association) regarding the cutoff value of fasting glycaemia and HbA1c. Also, in the prediabetes class, there are individuals who already show diabetes complications, others will progress, and those who will never progress in the disease. Thus, classification based solely on glycemic values seems to be insufficient, not only for diagnosis, but also to identify the risk of progression of each individual. T2D has a multifactorial and complex etiology. Based on actual criteria, we may be grouping individuals with different phenotypes under the same diagnostic group. Applying equal approach to the different phenotypes may contribute to the inefficacy of prevention, diagnosis and therapy, which translates into the socioeconomic burden that T2D represents. Cluster analysis aims to highlight natural groups in a given population. By allowing the analysis of complex data, it reveals patterns of characteristics that define different groups. In particular, Self-Organizing Maps (SOM) is a robust clustering methodology, that reduces the data into a 2-dimension topological greed. The aim of this study was to do a cluster analysis using a SOM, to reveal groups representing different metabolic disease phenotypes, which may be useful in understanding pathophysiological mechanisms and in improving the prevention, diagnosis and treatment of these subjects. First, we applied a SOM to 1010 individuals from the Prevadiab2 cohort, reducing the data dimensionality. For this algorithm we used anthropometric and biochemical parameters (27), which are recognized as important in T2D pathophysiology. Then, based on a hierarchical cluster (Ward method), we define the final clusters. We identified 10 clusters, with different anthropometric and metabolic profiles. All clusters have individuals with normoglycemia and hyperglycemia (prediabetes and/or diabetes) in different proportions. In the 5 clusters that contain people with diabetes, we also find individuals with prediabetes, and surprisingly, even normoglycemic subjects, although the latter are present in a smaller proportion. The application of SOM to a population, including normoglycemic and hyperglycemic people, allowed the identification of groups with different anthropometric and metabolic phenotypes. More relevant, the results obtained raise several important questions regarding the pathophysiological mechanisms underlying the different phenotypes. The answer to these questions can have a decisive impact in improving the prevention, diagnosis and therapeutic efficacy of T2D subjects

ADVANCED RISK MANAGEMENT OF AN ARCTIC MARINE SEISMIC SURVEY OPERATION

This research is motivated by the lack of a robust risk management framework addressing the high risks in Arctic Marine Seismic Survey Operations (AMSSO), and the lack of transparent decision-making in Arctic shipping risk management globally. The literature review carried out herein reveals that the AMSSO and Arctic navigation involve significant risks caused by human elements and the unique features of this region. These known risk factors combine to constitute a ship-ice collision risk. This last represents the goal of the research investigation. With the complexity of the AMSSO system, three technical chapters are proposed to analyse and reduce the risks in the AMSSO. The first technical chapter deals with local risk analysis of the system. Herein, a Fuzzy Rule-based methodology is developed employing the probability distribution assessment in the form of belief degrees with Bayesian Network (BN) and Failure Mode and Effect Analysis (FMEA) for estimating the risk parameters of each hazard event using a computer-aided analysis. A case study of the application of the proposed risk model – Fuzzy Rule-based Bayesian Network (FRBN) –, in the Greenland, Iceland and Norwegian Seas (GNIS) AMSSO is carried out to identify the most critical hazard event in the prospect oil field. The second technical chapter deals with the global safety performance of the Ship-Ice Collision model dovetailing the Evidential Reasoning (ER) technique and Analytic Hierarchy Process (AHP) with the FRBN. A trial application of the global safety performance of the Ship-Ice Collision case in a prospect oil field is carried out to determine the safety level of AMSSO, measured against a developed benchmark risk. The outcome of the investigation reveals the Risk Influence Factor (RIF) of each hazard event in AMSSO. Since the risk level is far above the tolerable region of the developed benchmark risk, several Risk Control Options (RCOs) are investigated in the last technical chapter to reduce and control the critical risks. This technical chapter finalises the risk management framework developed in this research. In a trial application of reducing a critical risk in AMSSO, AHP-TOPSIS is utilised to find a balance between cost and benefit in selecting the most appropriate RCO at the heart of several RCOs and their associated criteria. The novelty of this research lies in the fact that it tackles the major concerns in risk analysis (concerns such as dynamic event risk analysis, hazard data uncertainties, and hazard event dependencies) of a complex system. More also, it adopts a hybrid methodology that offers a non-monotonic utility output to select the most appropriate RCO amongst several RCOs and conflicting criteria, to reduce the critical risks in AMSSO, in an economically viable strategy