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Prediction Models for Assessment of Abdominal Adiposity
Author(s) -
Freeland-Graves Jeanne H,
Lee Jane J,
Yao Ming,
Xu Bugao
Publication year - 2012
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.26.1_supplement.113.5
Subject(s) - umbilicus (mollusc) , medicine , anthropometry , subcutaneous fat , abdomen , circumference , demographics , obesity , magnetic resonance imaging , nuclear medicine , adipose tissue , radiology , surgery , mathematics , geometry , demography , sociology
Abdominal visceral adiposity increases risk for diabetic/cardiovascular diseases. This study developed prediction equations for visceral and subcutaneous adiposity via 3‐ dimensional stereovision body imaging (SBI). 121 adults were measured for anthropometrics via SBI and magnetic resonance imaging (MRI). Abdominal fat volumes were evaluated by MRI umbilicus scans. Adjusted measurements for stereovision (SV) umbilicus and hip circumferences, central obesity depth (COD), and frontal widest girth of abdominal area (WG) were measured via SBI. Prediction models were developed by stepwise multiple linear regression, with body measurements and demographics as independent predictors; visceral/subcutaneous adiposity was dependent. Strongest predictors were: subcutaneous fat (mm 2 ), − 439.35 + 7.45 SV umbilicus circumferences (cm) − 40.09 gender − 1.71 age (yr) (R 2 =0.88, p<0.001); visceral fat (mm 2 ), 90.93 + 11.90 COD (cm) – 4.30 WG (cm) +0.51 age (yr) −1.90 SV hip circumference (cm) + 1.30 SV umbilicus circumference (cm) (R 2 =0.72, p<0.001). Inclusion of SV significantly improved R 2 . In contrast, visceral fat with only anthropometrics and demographics reduced the R 2 to 0.57. Subcutaneous adiposity prediction did not improve with SBI. Thus, SBI is an effective technique to create visceral adiposity prediction equations that can be applied to assess obesity in clinical settings. NIH 3R21DK081206‐02S1.