Improved prediction of body fat by measuring skinfold thickness, circumferences, and bone breadths

Garcia, A.L. , Wagner, K., Hothorn, T., Koebnick, C., Zunft, H.J. and Trippo, U. (2005) Improved prediction of body fat by measuring skinfold thickness, circumferences, and bone breadths. Obesity Research, 13(3), pp. 626-634.

Abstract

OBJECTIVE: To develop improved predictive regression equations for body fat content derived from common anthropometric measurements. RESEARCH METHODS AND PROCEDURES: 117 healthy German subjects, 46 men and 71 women, 26 to 67 years of age, from two different studies were assigned to a validation and a cross-validation group. Common anthropometric measurements and body composition by DXA were obtained. Equations using anthropometric measurements predicting body fat mass (BFM) with DXA as a reference method were developed using regression models. RESULTS: The final best predictive sex-specific equations combining skinfold thicknesses (SF), circumferences, and bone breadth measurements were as follows: BFM(New) (kg) for men = -40.750 + {(0.397 x waist circumference) + [6.568 x (log triceps SF + log subscapular SF + log abdominal SF)]} and BFM(New) (kg) for women = -75.231 + {(0.512 x hip circumference) + [8.889 x (log chin SF + log triceps SF + log subscapular SF)] + (1.905 x knee breadth)}. The estimates of BFM from both validation and cross-validation had an excellent correlation, showed excellent correspondence to the DXA estimates, and showed a negligible tendency to underestimate percent body fat in subjects with higher BFM compared with equations using a two-compartment (Durnin and Womersley) or a four-compartment (Peterson) model as the reference method. DISCUSSION: Combining skinfold thicknesses with circumference and/or bone breadth measures provide a more precise prediction of percent body fat in comparison with established SF equations. Our equations are recommended for use in clinical or epidemiological settings in populations with similar ethnic background.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Garcia, Dr Ada
• Authors: Garcia, A.L., Wagner, K., Hothorn, T., Koebnick, C., Zunft, H.J., and Trippo, U.
• College/School: College of Medical Veterinary and Life Sciences > School of Medicine, Dentistry & Nursing
• Journal Name: Obesity Research
• ISSN: 1071-7323