Background: Metabolic Syndrome (MetS) can lead to an increased risk of developing diabetes mellitus and cardiovascular disease. A consequence is a reduction in life expectancy and increased morbidity. Although MetS has different definitions, a diagnosis is generally confirmed if a person has three of the following five risk factors: (i) impaired fasting glucose levels, (ii) raised blood pressure, (iii) raised triglycerides, (iv) low levels of highdensity lipoprotein cholesterol (HDL-C), and (v) increased waist circumference. The following treatments were compared: diet, Dash diet (a specific type of diet), exercise, lifestyle interventions (diet + exercise), anti-diabetic, and a control group. The network of evidence for the various treatment comparisons (in terms of reversal of MetS) and the number of estimates available for each pairwise comparison are shown in Figure 1. Objectives: To estimate the clinical effectiveness of the treatments of the MetS. Methods: Both direct and indirect evidence available for all pairwise comparisons of the treatments is synthesized using mixed treatment comparisons methods (Caldwell et al., 2005). Classical methods are compared with the Bayesian approach. Results: Table 1 reports results with classical and Bayesian methods (log odds ratio scale and SD). The classical results presented are (a) the direct estimates, (b) synthesis of all indirect estimates, and (c) when both are available the combination of direct and indirect estimates. The Bayesian results are for (a) fixed effect model, and (b) a random-effects model. Conclusions: Bayesian methods make an effective use of all the evidence available and enable an estimate of all pairwise comparisons to be obtained, even when there is both no direct or indirect pairwise evidence available. In addition, the Bayesian fixed effect approach reduces the uncertainty (in comparison to the classical approach) even when there is direct evidence available as all observations contribute to the estimation of all comparisons. In the case of MetS, the Bayesian random-effects model gives estimates with considerable uncertainty due to the relatively small number of studies. The use of a Bayesian approach also enables the integration of this MTC analysis within a broader economic decision model framework.