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A multivariate analysis of fatness and relative fat patterning.

W H Mueller, R M Reid

    American Journal of Physical Anthropology
    |February 1, 1979
    PubMed
    Summary

    Principal components analysis revealed three key body fat components: overall fatness, trunk-extremity pattern, and upper-lower body pattern. These patterns showed genetic influence and correlated with socioeconomic status in rural Colombian adults and children.

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    Area of Science:

    • Anthropometry
    • Human Biology
    • Obesity Research

    Background:

    • Body composition analysis is crucial for understanding health and disease.
    • Previous studies have explored body fat distribution but lacked comprehensive multivariate analysis.
    • Understanding fat patterning and its determinants is essential for public health initiatives.

    Purpose of the Study:

    • To identify components of obesity and relative fat patterning using principal components analysis.
    • To investigate the influence of age, sex, and socioeconomic status on fatness and fat patterning.
    • To explore the familial resemblance of fatness and fat patterning.

    Main Methods:

    • Skinfold measurements were taken from four samples in rural Colombia (boys, girls, men, women).

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  • Principal components analysis was used to identify components of fatness and relative fat patterning.
  • Canonical correlation analysis examined relationships between socioeconomic status and body composition.

    • Main Results:

    • Three consistent components emerged across samples: fatness (70-80% variance), trunk-extremity pattern (10-15%), and upper-lower body pattern (10-15%).
    • Fatness and trunk-extremity patterns varied with age in children but not adults.
    • Higher fatness correlated with greater patterning, and familial resemblance was observed for both fatness and patterning, suggesting genetic influence.

    Conclusions:

    • Principal components analysis is a valuable tool for quantitative anthropometric studies.
    • Body fatness and patterning have distinct components with varying influences from age and genetics.
    • Socioeconomic status is more closely linked to overall fatness than to fat distribution patterns.