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New insights into scaling of fat‐free mass to height across children and adults
Author(s) -
Wang Zimian,
Zhang Junyi,
Ying Zhiliang,
Heymsfield Steven B.
Publication year - 2012
Publication title -
american journal of human biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.559
H-Index - 81
eISSN - 1520-6300
pISSN - 1042-0533
DOI - 10.1002/ajhb.22286
Subject(s) - demography , medicine , body mass index , confidence interval , linear regression , population , fat free mass , fat mass , mathematics , statistics , sociology
Objective: Forbes expressed fat‐free mass (FFM, in kg) as the cube of height (H, in m): FFM = 10.3 × H 3 . Our objective is to examine the potential influence of gender and population ancestry on the association between FFM and height. Methods: This is a cross‐sectional analysis involving an existing dataset of 279 healthy subjects (155 males and 124 females) with age 5–59 years and body mass index (BMI) 14–28 kg/m 2 . FFM was measured by a four‐component model as the criterion. Results: Nonlinear regression models were fitted: FFM = 10.8 × H 2.95 for the males and FFM = 10.1 × H 2.90 for the females. The 95% confidence intervals for the exponential coefficients were (2.83, 3.07) for the males and (2.72, 3.08) for the females, both containing hypothesized value 3.0. Population ancestry adjustment was considered in the H‐FFM model. The coefficient of the H‐FFM model for male Asians is smaller than that for male Caucasians ( P = 0.006), while there is no statistically significant difference among African‐Americans, Caucasians and Hispanics: 10.6 for the males (10.1 for Asians, 10.8 for African‐Americans, 10.7 for Caucasians and 10.4 for Hispanics) and 9.6 for the females (9.3 for Asians, 9.8 for African‐Americans, 9.6 for Caucasians and 9.5 for Hispanics). Age adjustment was unnecessary for the coefficient of the H‐FFM model. Conclusion: Height is the most important factor contributing to the magnitude of FFM across most of the lifespan, though both gender and ancestry effects are significant in the H‐FFM model. The proposed H‐FFM model can be further used to develop a mechanistic model to explain why population ancestry, gender and age influence the associations between BMI and %Fat. Am. J. Hum. Biol., 2012. © 2012 Wiley Periodicals, Inc.