Prediction of segmental lean mass using anthropometric variables in young adults

J Sports Sci. 2012;30(8):777-85. doi: 10.1080/02640414.2012.670716. Epub 2012 Mar 27.

Abstract

The aim of the present study was to develop and cross-validate anthropometrical prediction equations for segmental lean tissue mass (SLM). One hundred and seventeen young healthy Caucasians (67 men and 50 women; mean age: 31.9 ± 10.0 years; Body Mass Index: 24.3 ± 3.2 kg · m(-2)) were included. Body mass (BM), stretch stature (SS), 14 circumferences (CC), 13 skinfolds (SF) and 4 bone breadths (BB) were used as anthropometric measurements. Segmental lean mass of both arms, trunk and both legs were measured by dual energy X-ray absorptiometry as the criterion method. Three prediction equations for SLM were developed as follows: arms = 40.394(BM) + 169.836(CCarm-tensed) + 399.162(CCwrist) - 85.414(SFtriceps) - 39.790(SFbiceps) - 7289.190, where Adj.R (2) = 0.97, P < 0.001, and standard error of estimate (SEE) = 355 g;trunk = 181.530(BM) + 155.037(SS) + 534.818(CCneck) + 175.638(CCchest) - 88.359(SFchest) - 147.232(SFsupraspinale) - 46522.165, where Adj.R(2) = 0.97, P < 0.001, and SEE = 1077g; and legs = 55.838(BM) + 88.356(SS) + 235.579(CCmid-thigh) + 278.595(CCcalf) + 288.984(CCankle) - 84.954(SFfront-thigh) - 53.009(SFmedial calf) - 28522.241, where Adj.R (2) = 0.96, P < 0.001, and SEE = 724 g. Cross-validation statistics showed no significant differences (P < 0.05) between observed and predicted SLM. Root mean squared errors were smallest for arms (362 g), followed by legs (820 g) and trunk (1477 g). These new prediction equations allow an accurate estimation of segmental lean mass in groups of young adults, but estimation errors of 8 to 14% can occur in certain individuals.

Publication types

  • Validation Study

MeSH terms

  • Absorptiometry, Photon
  • Adult
  • Anthropometry / methods*
  • Body Composition
  • Body Mass Index
  • Female
  • Humans
  • Male
  • Skinfold Thickness
  • Thinness / diagnosis*
  • White People / statistics & numerical data
  • Young Adult