Body mass index as a phenotypic expression of adiposity: Quantitative contribution of muscularity in a population-based sample

Academic Article

Abstract

  • Objective: Although widely applied as a phenotypic expression of adiposity in population and gene-search studies, body mass index (BMI) is also acknowledged to reflect muscularity even though relevant studies directly measuring skeletal muscle (SM) mass are lacking. The current study aimed to fill this important gap by applying advanced imaging methods to test the hypothesis that, after controlling first for adiposity, SM mass is also a significant determinant of BMI in a population-based sample. Design: Whole-body magnetic resonance imaging scans were completed in Coronary Artery Risk Development in Young Adults study subjects aged 33-45 years. Physical activity (PA) levels, alcohol intake and adequacy of food intake were assessed by standardized questionnaires. Subjects: The study included 58 African-American (AA) and 78 Caucasian (C) men; and 63 AA and 64 C women. Measurements: Whole-body adipose tissue (AT) and SM volumes. Results: AT was significantly predicted by not only BMI, but also PA and alcohol intake with total model R2 's of 0.68 (P<0.0001) for men and 0.89 (P<0.0001) for women. Men had more SM than AT at all levels of BMI whereas SM predominated in women at lower BMIs (C<26 kg/m 2; AA< 28 kg/m2). In men, both AT and SM contributed a similar proportion of between-subject variation in BMI. In contrast, in women AT contributed 30% ∼more than SM to the variation in BMI. Developed allometric models indicated SM associations with AT, PA and race after adjusting for height. There was little association of age, lifestyle factors or race with BMI after controlling for both AT and SM. Conclusion: Variation in muscularity provides a mechanistic basis for the previously observed nonspecificity of BMI as a phenotypic expression of adiposity. These quantitative observations have important implications when choosing adiposity measures in population and gene-search studies. © 2009 Macmillan Publishers Limited All rights reserved.
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    Digital Object Identifier (doi)

    Author List

  • Heymsfield SB; Scherzer R; Pietrobelli A; Lewis CE; Grunfeld C
  • Start Page

  • 1363
  • End Page

  • 1373
  • Volume

  • 33
  • Issue

  • 12