Funding agencies: NIH 3 R21 DK081206-02S1; This research was supported by grant R21 DK081206 from the National Institutes of Health.
Predictive equations for central obesity via anthropometrics, stereovision imaging and MRI in adults
Version of Record online: 2 DEC 2013
Copyright © 2013 The Obesity Society
Volume 22, Issue 3, pages 852–862, March 2014
How to Cite
Lee, J. J., Freeland-Graves, J. H., Pepper, M. R., Yao, M. and Xu, B. (2014), Predictive equations for central obesity via anthropometrics, stereovision imaging and MRI in adults. Obesity, 22: 852–862. doi: 10.1002/oby.20489
Disclosure: The authors declared no conflict of interest.
Author contributions: Jane J. Lee implemented the study, conducted data analysis, and composed the manuscript. Jeanne H. Freeland-Graves served as the primary project supervisor and a corresponding author. M. Reese Pepper implemented the study and collected the research data. Ming Yao and Bugao Xu provided the 3-D stereovision imaging system and provided technical support for the 3-D measurements of central obesity. All of the authors participated in data analysis and preparation of the manuscript.
- Issue online: 5 MAR 2014
- Version of Record online: 2 DEC 2013
- Accepted manuscript online: 24 APR 2013 03:23AM EST
- Manuscript Accepted: 25 MAR 2013
- Manuscript Received: 10 DEC 2012
- NIH. Grant Number: 3 R21 DK081206-02S1
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.