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dc.contributor.authorNevill, Alan M.
dc.contributor.authorStavropoulos-Kalinoglou, Antonios
dc.contributor.authorMetsios, Giorgos S.
dc.contributor.authorKoutedakis, Yiannis
dc.contributor.authorHolder, Roger L.
dc.contributor.authorKitas, George D.
dc.contributor.authorMohammed, Mohammed A.
dc.date.accessioned2011-08-22T10:07:28Z
dc.date.available2011-08-22T10:07:28Z
dc.date.issued2011
dc.identifier.citationAnnals of Human Biology
dc.identifier.issn0301-4460
dc.identifier.issn1464-5033
dc.identifier.doi10.3109/03014460.2011.606832
dc.identifier.urihttp://hdl.handle.net/2436/140269
dc.descriptionPublished on-line ahead of print
dc.description.abstractBackground: 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/height2 ratio known as the Body Mass Index (BMI kg/m2). However, BMI does have some inherent weaknesses which are readily overcome by its inverse iBMI (1000/BMI, cm2/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 ¼ 2993) and diseased populations (n ¼ 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
dc.language.isoen
dc.publisherInforma UK, Ltd.
dc.relation.urlhttp://informahealthcare.com/doi/abs/10.3109/03014460.2011.606832
dc.subjectBody mass index
dc.subjectInverted body mass index
dc.subjectBody fat
dc.subjectTransformation
dc.titleInverted BMI rather than BMI is a better proxy for percentage of body fat
dc.typeJournal article
dc.identifier.journalAnnals of Human Biology
html.description.abstractBackground: 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/height2 ratio known as the Body Mass Index (BMI kg/m2). However, BMI does have some inherent weaknesses which are readily overcome by its inverse iBMI (1000/BMI, cm2/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 ¼ 2993) and diseased populations (n ¼ 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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