Classification Method for Predicting the Development of Myocardial Infarction by Using the Interaction between Genetic and Environmental Factors

Abstract

Multifactorial diseases, such as lifestyle-related diseases, for example, cancer, diabetes mellitus, and myocardial infarction, are believed to be caused by the complex interactions between various environmental factors on a polygenic basis. In addition, it is believed that genetic risk factors for the same disease differ on an individual basis according to their susceptible environmental factors. In the present study, to predict the development of myocardial infarction (MI) and classify the subjects into personally optimum development patterns, we have extracted risk factor candidates (RFCs) that comprised a state that is a derivative form of polymorphisms and environmental factors using a statistical test. We then selected the risk factors using a criterion for detecting personal group (CDPG), which is defined in the present study. By using CDPG, we could predict the development of MI in blinded subjects with an accuracy greater than 75%. In addition, the risk percentage for MI was higher with an increase in the number of selected risk factors in the blinded data. Since sensitivity using the CDPG was high, it can be an effective and useful tool in preventive medicine and its use may provide a high quality of life and reduce medical costs.