Predictive Equations for Body Fat and Abdominal Fat With DXA and MRI as Reference in Asian Indians

Abstract

To develop accurate and reliable equations from simple anthropometric parameters that would predict percentage of total body fat (%BF), total abdominal fat (TAF), subcutaneous abdominal adipose tissue (SCAT), and intra-abdominal adipose tissue (IAAT) with a fair degree of accuracy. Anthropometry, %BF by dual-energy X-ray absorptiometry (DXA) in 171 healthy subjects (95 men and 76 women) and TAF, IAAT, and SCAT by single slice magnetic resonance imaging (MRI) at L3-4 intervertebral level in 100 healthy subjects were measured. Mean age and BMI were 32.2 years and 22.9 kg/m(2), respectively. Multiple regression analysis was used on the training data set (70%) to develop equations, by taking anthropometric and demographic variables as potential predictors. Predicted equations were applied on validation data set (30%). Multiple regression analysis revealed the best equation for predicting %BF to be: %BF = 42.42 + 0.003 x age (years) + 7.04 x gender (M = 1, F = 2) + 0.42 x triceps skinfold (mm) + 0.29 x waist circumference (cm) + 0.22 [corrected] x weight (kg) - 0.42 x height (cm) (R (2) = 86.4%). The most precise predictive equation for estimating IAAT was: IAAT (mm(2)) = -238.7 + 16.9 x age (years) + 934.18 x gender (M = 1, F = 2) + 578.09 x BMI (kg/m(2)) - 441.06 x hip circumference (cm) + 434.2 x waist circumference (cm) (R (2) = 52.1%). SCAT was best predicted by: SCAT (mm(2)) = -49,376.4 - 17.15 x age (years) + 1,016.5 x gender (M = 1, F = 2) +783.3 x BMI (kg/m(2)) + 466 x hip circumference (cm) (R (2) = 67.1). We present predictive equations to quantify body fat and abdominal adipose tissue sub-compartments in healthy Asian Indians. These equations could be used for clinical and research purposes.