Scaling of linear anthropometric dimensions in living humans

Objectives Some previous studies suggest that humans do not conform to geometric similarity (isometry) in anthropometric dimensions of the upper and lower limbs. Researchers often rely on a single statistical approach to the study of scaling patterns, and it is unclear whether these methods produce similar results and are equally robust. This study used one bivariate and one multivariate method to examine how linear anthropometric dimensions scale in a sample of adult humans. Materials and methods Motion capture marker data from 104 adults of varying height and mass were used to calculate anthropometric dimensions. We analyzed scaling patterns in pooled and separate sexes with two methods: (1) bivariate log-log regression and (2) multivariate principal component analysis (PCA). We calculated 95% highest density/confidence intervals for each method and defined positive/negative allometry as estimates lying outside those intervals. Results Results identified isometric scaling of the upper arm, thigh, and shoulder, positive allometry of the forearm and shank, and negative allometry of the pelvis in the pooled sample using both statistical methods. Patterns of allometry in the pooled sample were similar between methods but differed in magnitude. Sex-specific results differed in both pattern and magnitude between log-log regression and PCA. Only one measurement (shoulder width) departed from isometry in the sex-specific log-log regressions. Discussion Our findings suggest that especially in sex-specific analyses, the pattern and magnitude of allometry are sensitive to statistical methodology. When body mass was selected as the size variable, most human linear anthropometric dimensions in this sample scaled isometrically and were therefore geometrically similar within sexes.

Automated summary

The study found isometric scaling of the upper arm, thigh, and shoulder, positive allometry of the forearm and shank, and negative allometry of the pelvis in the pooled sample using both statistical methods. The scaling patterns were similar between methods but differed in magnitude, and sex-specific results differed in both pattern and magnitude between log-log regression and PCA.