Department of Nutritional Sciences, The University of Texas at Austin, Austin, Texas, USA.
Obesity (Silver Spring). 2014 Mar;22(3):852-62. doi: 10.1002/oby.20489. Epub 2013 Dec 2.
Abdominal visceral adiposity is related to risks for insulin resistance and metabolic perturbations. Magnetic resonance imaging (MRI) and computed tomography are advanced instruments that quantify abdominal adiposity; yet field use is constrained by their bulkiness and costliness. The purpose of this study is to develop prediction equations for total abdominal, subcutaneous, and visceral adiposity via anthropometrics, stereovision body imaging (SBI), and MRI.
Participants (67 men and 55 women) were measured for anthropometrics and abdominal adiposity volumes evaluated by MRI umbilicus scans. Body circumferences and central obesity were obtained via SBI. Prediction models were developed via multiple linear regression analysis, utilizing body measurements and demographics as independent predictors, and abdominal adiposity as a dependent variable. Cross-validation was performed by the data-splitting method.
The final total abdominal adiposity prediction equation was -470.28 + 7.10 waist circumference - 91.01 gender + 5.74 sagittal diameter (R2 = 89.9%), subcutaneous adiposity was -172.37 + 8.57 waist circumference - 62.65 gender - 450.16 stereovision waist-to-hip ratio (R2 =90.4%), and visceral adiposity was -96.76 + 11.48 central obesity depth - 5.09 central obesity width + 204.74 stereovision waist-to-hip ratio - 18.59 gender (R2 = 71.7%). R2 significantly improved for predicting visceral fat when SBI variables were included, but not for total abdominal or subcutaneous adiposity.
SBI is effective for predicting visceral adiposity and the prediction equations derived from SBI measurements can assess obesity.
腹部内脏脂肪与胰岛素抵抗和代谢紊乱的风险有关。磁共振成像（MRI）和计算机断层扫描（CT）是定量腹部脂肪的先进仪器；然而，由于其体积大和成本高，实际应用受到限制。本研究的目的是通过人体测量学、立体视觉体层成像（SBI）和 MRI 开发用于预测总腹部、皮下和内脏脂肪的预测方程。
对 67 名男性和 55 名女性参与者进行人体测量和 MRI 脐部扫描评估腹部脂肪量。通过 SBI 获得体围和中心肥胖。通过多元线性回归分析，利用身体测量和人口统计学数据作为独立预测因子，腹部脂肪作为因变量，建立预测模型。采用数据分割法进行交叉验证。
总腹部脂肪的最终预测方程为-470.28 + 7.10 腰围-91.01 性别+5.74 矢状径（R2 = 89.9%），皮下脂肪为-172.37 + 8.57 腰围-62.65 性别-450.16 SBI 腰围-臀围比（R2 = 90.4%），内脏脂肪为-96.76 + 11.48 中心肥胖深度-5.09 中心肥胖宽度+204.74 SBI 腰围-臀围比-18.59 性别（R2 = 71.7%）。当纳入 SBI 变量时，预测内脏脂肪的 R2 显著提高，但对总腹部或皮下脂肪则不然。
SBI 可有效预测内脏脂肪，且 SBI 测量值的预测方程可用于评估肥胖。
