Inverted BMI rather than BMI is a better proxy for percentage of body fat
Alan Michael Nevill, Antonios Stavropoulos‐Kalinoglou, Giorgos S. Metsios, Yiannis Koutedakis, ROGER L. HOLDER, George D. Kitas, Mohammed A. Mohammed
Abstract
Alan Michael Nevill, Antonios Stavropoulos‐Kalinoglou, Giorgos S. Metsios, Yiannis Koutedakis, ROGER L. HOLDER, George D. Kitas, Mohammed A. Mohammed
Abstract
BACKGROUND: Percentage of body fat (BF%) is a known risk factor for a range of healthcare problems but is difficult to measure. An easy to measure proxy is the weight/height(2) ratio known as the Body Mass Index (BMI kg/m(2)). However, BMI does have some inherent weaknesses which are readily overcome by its inverse iBMI (1000/BMI, cm(2)/kg). METHODS: The association between BF% and both BMI and iBMI together with their distributional properties was explored using previously published data from healthy (n = 2993) and diseased populations (n = 298). RESULTS: BMI is skewed whereas iBMI is symmetrical and so is better approximated by the normal distribution. The relationship between BF% and BMI is curved, but that of iBMI and BF% is linear and thus iBMI explains more of the variation in BF% than BMI. For example a unit increase in BMI for a group of thin women represents an increase of 2.3% in BF, but for obese women this represents only a 0.3% increase in BF-a 7-fold difference. The curvature stems from body mass being the numerator in BMI but the denominator in BF% resulting in a form of hyperbolic curve which is not the case with iBMI. Furthermore, BMI and iBMI have different relationships (interaction) with BF% for men and women, but these differences are less marked with iBMI. CONCLUSIONS: Overall, these characteristics of iBMI favour its use over BMI, especially in statistical models.
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BACKGROUND: Percentage of body fat (BF%) is a known risk factor for a range of healthcare problems but is difficult to measure. An easy to measure proxy is the weight/height(2) ratio known as the Body Mass Index (BMI kg/m(2)). However, BMI does have some inherent weaknesses which are readily overcome by its inverse iBMI (1000/BMI, cm(2)/kg). METHODS: The association between BF% and both BMI and iBMI together with their distributional properties was explored using previously published data from healthy (n = 2993) and diseased populations (n = 298). RESULTS: BMI is skewed whereas iBMI is symmetrical and so is better approximated by the normal distribution. The relationship between BF% and BMI is curved, but that of iBMI and BF% is linear and thus iBMI explains more of the variation in BF% than BMI. For example a unit increase in BMI for a group of thin women represents an increase of 2.3% in BF, but for obese women this represents only a 0.3% increase in BF-a 7-fold difference. The curvature stems from body mass being the numerator in BMI but the denominator in BF% resulting in a form of hyperbolic curve which is not the case with iBMI. Furthermore, BMI and iBMI have different relationships (interaction) with BF% for men and women, but these differences are less marked with iBMI. CONCLUSIONS: Overall, these characteristics of iBMI favour its use over BMI, especially in statistical models.
Key concepts: Body mass index, Mathematics, Proxy (statistics), Demography, Obesity, Classification of obesity, Medicine, Body Shape Index