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.
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 obesewomen 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.
Authors: Simonete Silva; Alcibíades Bustamante; Alan Nevill; Peter T Katzmarzyk; Duarte Freitas; António Prista; José Maia Journal: PLoS One Date: 2016-03-03 Impact factor: 3.240