An exploratory enalysis of the relation between metabolic syndrome factors and microRNA data

Abstract

The metabolic syndrome (MetS) is associated with increased risk for cardiovascular disease. The detection and treatment of the underlying factors of the metabolic syndrome have a significant influence on the reduction of the cardiovascular disease. In this study, we analyze relations between MetS components and RNA molecules (microRNAs) regulating gene expression at the posttranscriptional level, in order to determine the predictive value of different microRNAs for subjects with metabolic syndrome. We apply correlation and linear regression to analyze the relationship between microRNAs and selected arterial markers. Logistic regression models were used to explore the statistical relationship between microRNAs and categorical variables. Results show that statistically significant linear relationship exists between arterial markers and several microRNAs, however, the observed relationship is very weak (<0.25). Since cardiovascular diseases are usually multifactorial diseases, caused by various mechanisms, it is more likely, that the combination of microRNAs will have stronger predictional or diagnostic power. Moreover, it is possible that more valuable results can be obtained by analyzing relations between microRNAs and binary variable determining the absence/existence of metabolic syndrome. Hence, we plan to use canonical correlation analysis to investigate linear combinations of microRNAs which have a maximum correlation with arterial markers.