Metabolic syndrome is taking epidemic proportions, especially in developed countries. Each risk factor component of the syndrome independently increases the risk of developing coronary artery disease. The risk factors are obesity, dyslipidemia, hypertension, diabetes type 2, insulin resistance, and microalbuminuria. Nuclear receptors is a family of receptors that has recently received a lot of attention due to their possible involvement in metabolic syndrome. Putting the receptors into context with their co-factors and ligands may reveal therapeutic targets not found by studying the receptors alone. Therefore, in this thesis, interactions between genes in nuclear receptor pathways were analysed with the goal of investigating if these interactions can supply leads to biomarkers for metabolic syndrome. Metabolic syndrome donor gene expression data from the BioExpressä, database was analysed with the APRIORI algorithm (Agrawal et al. 1993) for generating and mining association rules. No association rules were found to function as biomarkers for metabolic syndrome, but the resulting rules show that the data mining technique successfully found associations between genes in signaling pathways.