Novel Body Shape Descriptors for Abdominal Adiposity Prediction Using Magnetic Resonance Images and Stereovision Body Images

Abstract

Objective: The purpose of this study was to design novel shape descriptors based on three-dimensional (3D) body images and to use these parameters to establish prediction models for abdominal adiposity. Methods: Sixty-six men and fifty-five women were recruited for abdominal magnetic resonance imaging (MRI) and 3D whole-body imaging. Volumes of abdominal visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were measured from MRI sequences by using a fully automated algorithm. The shape descriptors were measured on the 3D body images by using the software developed in this study. Multiple regression analysis was employed on the training data set (70% of the total participants) to develop predictive models for VAT and SAT, with potential predictors selected from age, BMI, and the body shape descriptors. The validation data set (30%) was used for the validation of the predictive models. Results: Thirteen body shape descriptors exhibited high correlations (P < 0.01) with abdominal adiposity. The optimal predictive equations for VAT and SAT were determined separately for men and women. Conclusions: Novel body shape descriptors defined on 3D body images can effectively predict abdominal adiposity quantified by MRI.