Gene - nutrition interactions in the onset of obesity as Cardiovascular Disease risk factor based on a computational intelligence method

Identification of gene-gene and gene-environment interactions that contribute in the onset of a multi-factorial disease supports the prevention of diseases like the Cardiovascular Disease (CVD). Body Mass Index (BMI), a measure of human obesity, is an independent risk factor of CVD. Furthermore, it is known that a subjectpsilas BMI is affected both by his/her lifestyle, e.g. nutrition, and genetic profile. Aim of the paper is to predict a subjectpsilas onset of obesity using lifestyle and genetic information. The prediction is performed by a computational intelligence based system using a Parameter Decreasing Method (PDM) combined with an Artificial Neural Network (ANN). The system uses an initial set of 63 input variables corresponding to sex, average nutrition intake measurements, and genetic variations to identify the 32 most important ones that affect BMI. The selected variables are the ones to interact with each other towards the complex trait of BMI, which is used as a 2-class output variable (BMI les 25 vs. BMIges25) in the ANN. The system achieved a mean accuracy of the system evaluated by a 3-cross validation resampling technique equal to 77.89%.