BACKGROUND: Body Mass Index (BMI), which is defined as the ratio between weight (in kg) and height (in m2), is often used in clinical practice as well as in large scale epidemiological studies to classify subjects as underweight, normal weight, overweight or obese. Although BMI does not directly measure the percentage of Body Fat (BF%), it is widely applied because it is strongly related with BF%, it is easy to measure and it is an important predictor of mortality. Among children, age and sex-specific reference values of BMI, known as percentiles, are used. However, it is not clear how strong the relationship between BMI and BF% is among children and whether the association is linear. We performed a cross-sectional study aiming at evaluating the strength and shape of the relationship between BMI and BF% among school-aged children aged 6-12 years. FINDINGS: The study was conducted on a sample of 361 football-playing male children aged 6 to 12 years in Rome, Italy. Age, weight, height and skinfold thickness were collected. BF% was estimated using 4 skinfold equations whereas BMI was converted into BMI-for-age z-score. The relationship between these variables was examined using linear regression analyses. Mean BMI was 18.2 (± 2.8), whereas BF% was influenced by the skinfold equation used, with mean values ranging from 15.6% to 23.0%. A curvilinear relationship between BMI-for-age zscore and BF % was found, with the regression line being convex. The association between BMI-for-age zscore and BF% was stronger among overweight/obese children than among normal/underweight children. This curvilinear pattern was evident in all 4 skinfold equations used. CONCLUSIONS: The association between BMI-for-age zscore and BF% is not linear among male children aged 6-12 years and it is stronger among overweight and obese subjects than among normal and underweight subjects. In this age group, BMI is a valid index of adiposity only among overweight and obese subjects.
BACKGROUND: Body Mass Index (BMI), which is defined as the ratio between weight (in kg) and height (in m2), is often used in clinical practice as well as in large scale epidemiological studies to classify subjects as underweight, normal weight, overweight or obese. Although BMI does not directly measure the percentage of Body Fat (BF%), it is widely applied because it is strongly related with BF%, it is easy to measure and it is an important predictor of mortality. Among children, age and sex-specific reference values of BMI, known as percentiles, are used. However, it is not clear how strong the relationship between BMI and BF% is among children and whether the association is linear. We performed a cross-sectional study aiming at evaluating the strength and shape of the relationship between BMI and BF% among school-aged children aged 6-12 years. FINDINGS: The study was conducted on a sample of 361 football-playing male children aged 6 to 12 years in Rome, Italy. Age, weight, height and skinfold thickness were collected. BF% was estimated using 4 skinfold equations whereas BMI was converted into BMI-for-age z-score. The relationship between these variables was examined using linear regression analyses. Mean BMI was 18.2 (± 2.8), whereas BF% was influenced by the skinfold equation used, with mean values ranging from 15.6% to 23.0%. A curvilinear relationship between BMI-for-age zscore and BF % was found, with the regression line being convex. The association between BMI-for-age zscore and BF% was stronger among overweight/obesechildren than among normal/underweight children. This curvilinear pattern was evident in all 4 skinfold equations used. CONCLUSIONS: The association between BMI-for-age zscore and BF% is not linear among male children aged 6-12 years and it is stronger among overweight and obese subjects than among normal and underweight subjects. In this age group, BMI is a valid index of adiposity only among overweight and obese subjects.
Authors: Robert J Kuczmarski; Cynthia L Ogden; Shumei S Guo; Laurence M Grummer-Strawn; Katherine M Flegal; Zuguo Mei; Rong Wei; Lester R Curtin; Alex F Roche; Clifford L Johnson Journal: Vital Health Stat 11 Date: 2002-05
Authors: C A Aguirre; G D C Salazar; D V Lopez de Romaña; J A Kain; C L Corvalán; R E Uauy Journal: Eur J Clin Nutr Date: 2014-08-06 Impact factor: 4.016
Authors: Caroline E Boeke; Emily Oken; Ken P Kleinman; Sheryl L Rifas-Shiman; Elsie M Taveras; Matthew W Gillman Journal: BMC Pediatr Date: 2013-06-24 Impact factor: 2.125
Authors: Stephanie T Broyles; Amanda E Staiano; Kathryn T Drazba; Alok K Gupta; Melinda Sothern; Peter T Katzmarzyk Journal: PLoS One Date: 2012-09-25 Impact factor: 3.240