Sun Jingjing, Xu Bugao, Lee Jane, Freeland-Graves Jeanne H
Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas, USA.
Center for Computational Epidemiology and Response Analysis, University of North Texas, Denton, Texas, USA.
Obesity (Silver Spring). 2017 Oct;25(10):1795-1801. doi: 10.1002/oby.21957. Epub 2017 Aug 26.
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.
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.
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.
Novel body shape descriptors defined on 3D body images can effectively predict abdominal adiposity quantified by MRI.
本研究的目的是基于三维（3D）人体图像设计新型形状描述符，并使用这些参数建立腹部肥胖的预测模型。
招募了66名男性和55名女性进行腹部磁共振成像（MRI）和3D全身成像。通过使用全自动算法从MRI序列中测量腹部内脏脂肪组织（VAT）和皮下脂肪组织（SAT）的体积。使用本研究开发的软件在3D人体图像上测量形状描述符。对训练数据集（占总参与者的70%）进行多元回归分析，以建立VAT和SAT的预测模型，潜在预测因子选自年龄、BMI和身体形状描述符。验证数据集（30%）用于预测模型的验证。
13个身体形状描述符与腹部肥胖呈现高度相关性（P < 0.01）。分别为男性和女性确定了VAT和SAT的最佳预测方程。
在3D人体图像上定义的新型身体形状描述符可以有效预测通过MRI量化的腹部肥胖。
