Epidemiological modelling of cardiovascular disease in those with metabolic syndrome

Metabolic syndrome, a clustering of cardiometabolic risk factors, significantly increases the risk of developing both cardiovascular disease (CVD) and T2DM. The exact pathogenesis remains unknown, however it is hypothesised that when metabolic susceptibility is present in individuals who are overweight or obese, the development of multiple cardiometabolic risk factors (metabolic syndrome) ensues. Metabolic syndrome is estimated to affect approximately 20-30% of the Western world, with rates expected to rise as a result of an ageing and increasingly obese population, in turn increasing CVD and diabetes. With the potential health and economic burden those with metabolic syndrome pose for society, considerable efforts are being focused on determining the most effective and cost-effective prevention strategies. While data from clinical trials provides the highest level of evidence regarding the efficacy of prevention strategies, their results alone are insufficient to critically inform clinical practice and health policy. Epidemiological modelling is a relatively new discipline that translates the results of clinical trials to estimate the potential benefits and costs. This thesis comprises a series of epidemiological models, developed to assess the effectiveness and cost-effectiveness of a variety of primary preventive strategies for CVD in those with metabolic syndrome. First-line therapy guidelines for the treatment of metabolic syndrome pertain to lifestyle changes; the uptake of physical activity to recommended guidelines (and reduction of total time spent in sedentary pursuits), as well as dietary changes. Analysis of the uptake of physical activity to recommended guidelines or the reduction of TV-viewing time in those with metabolic syndrome demonstrated that both behavioural changes were effective in the prevention of cardiovascular deaths. With regard to potential dietary changes, the consumption of dark chocolate in those classified as metabolic syndrome exhibited effective and potentially cost-effective results. Analysis of a therapeutic treatment option, a multi-component drug (polypill), demonstrated the polypill was an effective means of reducing CVD. Cost-effectiveness analyses, however, illustrated that the proposed cost of the polypill would not produce a cost-effective outcome. Other work contained in this thesis pertains to the development of epidemiological models. To determine the benefits and costs of a prevention strategy, models rely not only on efficacy data of the prevention strategy sourced from clinical trials, but also require the prediction of expected event rates. Risk prediction algorithms specific for use in a metabolic syndrome population are lacking, thus works as part of this thesis included validation of the most appropriate risk prediction tool. Analyses demonstrated the Framingham risk prediction algorithm published in 1991 was the most appropriate tool for risk prediction of cardiovascular events in those classified as metabolic syndrome. This was superior to other published Framingham risk prediction algorithms, as well as algorithms specific for those with diagnosed diabetes (United Kingdom Prospective Diabetes Study (UKPDS)). The findings of this doctorate project are based on extrapolation of the best-available current evidence. The results offer utility for health policy and practice. Considering the increasing burden of disease, and limited health care resources, epidemiological modelling will increasingly be required.