Evidence synthesis and decision modelling for Metabolic Syndrome

Metabolic Syndrome (MetS) may be defined as a clustering of risk factors for diabetes mellitus (T2DM) and cardiovascular disease (CVD) which puts individuals at increased risk of developing these conditions and consequently leads to a reduction in life expectancy and increased morbidity. Although there are a number of definitions of MetS, essentially having any three of the following five risk factors confers a diagnosis of MetS; (i) impaired fasting glucose levels, (ii) raised blood pressure, (iii) raised triglycerides, (iv) low levels of high-density lipoprotein cholesterol (HDLC), and (v) increased waist circumference. A comprehensive decision model has been developed to combine different levels of evidence in a Markov model. This model is based in the behavior of MetS and its possible progression to T2DM and CVD, in order to evaluate the potential impact of a MetS based intervention at population level. Evidence synthesis methods are going to be incorporated in the model to integrate different levels of information. Firstly, a Mixed Treatment Analysis (MTC) of Randomized Controlled Trials (RCTs), which have evaluated a number of lifestyle and pharmacological interventions in individuals with MetS was undertaken. This information also assessed the possibility of reversing a diagnosis of MetS. Secondly, a systematic review of published literature was conducted to assess the evidence related with the association between the MetS and development of T2DM and/or CVD. A Bayesian approach to the problem has also been advocated which enables flexibility to develop a Markov model of this complexity. WinBugs offers a comfortable solution for the evaluation of a Markov model, given its Gibbs sampler. Main findings of this thesis are related with large amount of uncertainty, presuming a difficulty to provide a clear decision related to the application of MetS for prevention of T2DM and CVD.