Body fat and skinfold thicknesses: A dimensional analytic approach

Use of allometry and skinfold thicknesses to estimate the fat mass of circumpubertal boys

Background: In the pubertal period, each part of the body grows at different rates. This makes the relationship between the parts nonlinear. Particularly, the fat mass (FM) of boys tends to decrease, further exacerbating the issue of linearity in growth.Aim: To propose predictive models of FM from skinfolds (SF) adjusted allometrically.Subjects and methods: 75 pubertal boys (14.4 ± 0.9 years) composed the sample by self-selected pubic hair. FM was determined by DXA (FM_DXA) (kg), stature (cm), body mass (kg) and nine SF (mm) by anthropometric measurements. The SF were individually adjusted allometrically. FM_DXA (Y) values and each anthropometric independent variable-IV (X) underwent logarithmic transformation. Linear regressions were used. The significance of the prediction of the FM_DXA of each model (ANOVA) and the concordance analysis (Bland-Altman) were performed.Results: Predictive allometric models of FM_DXA showed high determination coefficients (r²) and reduced standard errors of estimation (SEE). The adjusted model involved reduced agreement limits, demonstrating its accuracy.Conclusions: It was possible to propose models with allometric adjustments to estimate FM of pubertal boys. Allometry may aid in the development of predictive linear models of body composition, since it has an indicative capacity of the variables for estimation.

Estimating total body fat using a skinfold prediction equation in Brazilian children

BACKGROUND

The double burden of obesity and underweight is increasing in developing countries and simple methods for the assessment of fat mass in children are needed.

AIM

To develop and validate a new anthropometric predication equation for assessment of fat mass in children.

SUBJECTS AND METHODS

Body composition was assessed in 145 children aged 9.8 ± 1.3 (SD) years from São Paulo, Brazil using dual energy X-ray absorptiometry (DEXA) and skinfold measurements. The study sample was divided into development and validation sub-sets to develop a new prediction equation for FM (PE).

RESULTS

Using multiple linear regression analyses, the best equation for predicting FM (R(2) = 0.77) included body weight, triceps skinfold, height, gender and age as independent variables. When cross-validated, the new PE was valid in this sample (R(2) = 0.80), while previously published equations were not.

CONCLUSION

The PE was more valid for Brazilian children that existing equations, but further studies are needed to assess the validity of this PE in other populations.