Understanding coronary atherosclerosis in relation to obesity : is getting the distribution of body fatness using dual-energy X-ray absorptiometry worth the effort ? A novel perspective using Bayesian Networks

A relative excess of fat in the upper body region has been proven to be associated with increased coronary artery disease (CAD) risk. Dual-energy X-ray absorptiometry (DXA) is probably the most accurate and precise method available to study fat regional distribution and to directly measure total body fat and lean soft tissue mass. However, while several studies have investigated the abilities of obesity anthropometric measures in predicting CAD, only few studies have evaluated DXA as CAD predictor; particularly, a comparison between a model including information coming from anthropometric measurements and a model in which fat is precisely measured by DXA, is still lacking. In order to verify if CAD severity, as measured by Gensini score, is better predicted when a prognostic model includes DXA measurements rather than anthropometric measures, we compared performance obtained by two Bayesian Networks (BNs) including standard anthropometric measures and DXA, respectively.