Prediction of Abnormal Body Fat Percentage by Anthropometrics Parameters Using Receiver Operating Characteristic Curve

The World Health Organization has defined obesity "as the abnormal or excessive fat accumulation that represents a risk to health". Although obesity is characterized by an excessive amount of body fat, it is commonly measured using body mass index which is unable to differentiate between elevated body fat content and increased lean mass. The indicator that best predicts obesity is the one that quantify adipose tissue and, therefore, the estimation of body fat percentage (BFP). Skinfolds have been used to measure the BFP, based on the Siri and Brozec formula. There are no official cut-off points for BFP, as the associated data is relatively insufficient worldwide. Studies agreed that fewer than 25% in men and 30% in women are commonly used as normal BFP. The aim of this study is to evaluate the capability of the anthropometrics variables to discriminate subjects with abnormal BFP. A database of 1053 subjects with 28 anthropometrics measures was used. Area under the receiver operating characteristic curves (AUC_ROC), sensibility (SEN), specificity (SPE) and negative predictive value (NPV) was calculated to evaluate the predictive ability of anthropometric variables measured. Three circumferences (Arm, waist and hip) and four skinfolds (calf, suprailiac, abdominal and thigh) were the variables with the best abnormal BFP detection capability, with an AUC_ROC>0.800 (SEN>0.760 and SPE>0.673). Having a high probability of detecting subjects with normal BFP (NPV>0.970). Easier variables to acquire, such as waist, arm, and hip circumferences, could be used in low-income countries where it is not easy to have a plicometer.