Relation of BMI to fat and fat-free mass among children and adolescents

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1 (2005) 29, 1 8 & 2005 Nature Publishing Group All rights reserved /05 $ PAPER Relation of BMI to fat and fat-free mass among children and adolescents DS Freedman 1 *, J Wang 2, LM Maynard 1, JC Thornton 2, Z Mei 1, RN Pierson Jr 2, WH Dietz 1 and M Horlick 2,3 1 Division of Nutrition and Physical Activity, Centers for Disease Control and Prevention K-26, Atlanta, GA, USA; 2 Body Composition Unit, Department of Medicine, Obesity Research Center, St Luke s-roosevelt Hospital, NY, USA; and 3 Children s Hospital of New York, Columbia University, NY, USA OBJECTIVE: Although the body mass index (BMI, kg/m 2 ) is widely used as a surrogate measure of adiposity, it is a measure of excess weight, rather than excess body fat, relative to height. We examined the relation of BMI to levels of fat mass and fat-free mass among healthy 5- to 18-y-olds. METHODS AND PROCEDURES: Dual-energy X-ray absorptiometry was used to measure fat and fat-free mass among 1196 subjects. These measures were standardized for height by calculating the fat mass index (FMI, fat mass/ht 2 ) and the fat-free mass index (FFMI, fat-free mass/ht 2 ). RESULTS: The variability in FFMI was about 50% of that in FMI, and the accuracy of BMI as a measure of adiposity varied greatly according to the degree of fatness. Among children with a BMI-for-age Z85th P, BMI levels were strongly associated with FMI (r ¼ across sex age categories). In contrast, among children with a BMI-for-age o50th P, levels of BMI were more strongly associated with FFMI (r ¼ ) than with FMI (r ¼ ). The relation of BMI to fat mass was markedly nonlinear, and substantial differences in fat mass were seen only at BMI levels Z85th P. DISCUSSION: BMI levels among children should be interpreted with caution. Although a high BMI-for-age is a good indicator of excess fat mass, BMI differences among thinner children can be largely due to fat-free mass. (2005) 29, 1 8. doi: /sj.ijo Published online 27 July 2004 Keywords: body mass index; X-ray densitometry Introduction The prevalence of overweight among children and adolescents in the US is about three-fold higher than in the 1960s, with 15% of schoolchildren now classified as overweight. 1 Although the body mass index (BMI, kg/m 2 ) is widely used as a surrogate measure of adiposity, it is a measure of excess weight relative to height, rather than excess body fat. The interpretation of BMI among children and adolescents is further complicated by the changes that occur in weight, height, and body composition during growth. 2,3 Several studies have examined the relation of BMI to body fatness as determined by dual-energy X-ray absorptiometry *Correspondence: Dr DS Freedman, CDC Mailstop K 26, 4770 Buford Hwy, Atlanta, GA , USA. DFreedman@CDC.gov Supported by NIH Grant DK For overnight deliveries: Rhodes Bldg, Rm 5161, 3005 Chamblee-Tucker Rd, Atlanta, GA , USA. Received 9 February 2004; revised 22 April 2004; accepted 10 May 2004; published online 27 July 2004 (DXA), densitometry, and other laboratory methods. BMI levels among adults are highly correlated with %body fat, and in combination with race, sex, and age, multiple R 2 - values of have been reported. 4 7 Associations among children and adolescents have been more variable, 8 21 and relatively weak correlations (ro0.6) have been reported in several subgroups. 8,10,11,13,15,21 These weaker associations among children and adolescents may be attributable to the asynchronous changes that occur in the levels of fat mass and fat-free mass during growth. The purpose of the current study is to describe the relation of BMI to fat mass and fat-free mass among healthy 5- to 18-yolds. Because of the limitations in expressing body composition data as kilograms (eg, kg of fat-free mass) and as percentages (eg, %body fat), it has been suggested that the fat mass index (FMI, kg of fat mass/ht 2 )andthefat-freemassindex(ffmi, kg of fat-free mass/ht 2 ) be used as indicators of nutritional status The current analyses use BMI, FMI, and FFMI, and because all are standardized for height 2, the contribution of both fat mass and fat-free mass to BMI can be easily assessed.

2 2 Methods Sample Between 1995 and 2000, 1196 healthy volunteers (ages, 5 18 y) were recruited in the New York City area for the Pediatric Rosetta Body Composition Project through local newspaper notices, announcements at schools and activity centers, and by word of mouth. The Institutional Review Board of St Luke s-roosevelt Hospital Center approved the study protocol, and consent was obtained from each volunteer s parent or guardian. When appropriate, assent was also obtained from each volunteer. A questionnaire was used to establish ethnicity; the criterion was consistent ethnic background (white, black, Hispanic, or Asian) for both parents and all grandparents. Asian volunteers were of Chinese or Korean background, and Hispanic subjects were mostly of Dominican or Puerto Rican origin. Anthropometry Body weight was measured to the nearest 0.1 kg (Weight Tronix) and height to the nearest 0.5 cm using a stadiometer (Holtain) with subjects wearing a hospital gown and foam slippers. National US data ( ) were used to calculate sex- and age-specific percentiles (P) and Z-scores for weight, height, and BMI. 25,26 These Z-scores and percentiles would therefore represent a child s BMI relative to the BMIs of American children between 1963 and As suggested, 27,28 childhood overweight is defined as a BMI Z95th P. Dual-energy X-ray absorptiometry Whole-body DXA scans were performed using Lunar models DPX, with pediatric software version 3.8G, and DPX L, with pediatric software 1.5G (GE Lunar Corporation, Madison, WI, USA). 29 The scan mode was chosen according to the weight guidelines provided by the manufacturer, and each scan provided an estimate of %body fat. Among adults who had repeated scans in our laboratory, the coefficient of variation (CV) for %body fat was 3.3% and the intraclass correlation coefficient was An anthropomorphic spine phantom made up of calcium hydroxyapatite embedded in a Lucite block ( cm) was scanned with both DXA instruments for quality control (1) each morning prior to subject evaluation, and (2) immediately before and after all DXA maintenance visits. The measured phantom bone mineral density was stable throughout the study period at g/cm 2. Bottles (8 l) of ethanol and water, simulating fat and fat free soft tissues, respectively, were scanned monthly as soft tissue quality control markers. Over the study period, measured R-values ranged from to (CV ¼ 0.127%) for ethanol, and from to (CV ¼ 0.103%) for water. Similar to the standardization of weight for height 2 in BMI, we standardized the two components of weight, fat mass and fat-free mass, for height 2. FMI was calculated as fat mass (kg)/m 2, and FFMI as fat-free mass (kg)/m 2. These indices have been proposed as indicators of nutritional status, and because FMI þ FFMI ¼ BMI, their use allows the contribution of fat mass and fat-free mass to BMI to be easily examined. In agreement with a previous report, 23 there were strong associations between %body fat and FMI (r ¼ 0.95), and between height and fat-free mass (r ¼ 0.81). Statistical analyses We focused on the relation of BMI-for-age to levels of FFMI and FMI. Descriptive analyses compared mean levels of these indices across race, sex, and age groups, and lowess (locally weighted scatterplot smoother), a robust smoothing technique, 31 was also used to examine these associations. We also compared mean levels of FMI and FFMI across BMI categories, and Pearson correlation coefficients were used to examine the relation of BMI to FMI and FFMI. Stratified analyses examined whether the relation of BMI to levels FMI and FFMI varied across categories of BMI. We also examined the relation of BMI to height-adjusted levels of fat mass (kg) and fat-free mass (kg) in regression analyses; in addition to BMI and height, these models also included race and various interactions (product terms) with height as predictors. (Analyses that used FMI and FFMI as dependent variables, without the inclusion of height as a covariate, yielded very similar results.) To assess possible nonlinearity, BMI-for-age and height were modeled using restricted cubic splines, 32 which are more flexible than polynomials because a change in the fit of one part of the curve does not influence the entire curve. Results Various characteristics of the children, by race, sex, and age, are shown in Table 1. Despite the similar BMI levels of boys and girls, levels of fat mass and FMI were higher among girls, while FFMI levels were higher among boys. There were also differences across race/ethnicity groups, particularly among girls, with black and Hispanic children having a higher BMIfor-age than Asian and white children. However, while the relatively high BMI of Hispanic children was primarily due to high FMI levels, the high BMI of black children was largely due to levels of FFMI. Blacks had the highest mean FFMI in each sex age group, and among 12- to 18-y-old boys, blacks also had the lowest mean FMI (3.9 kg/m 2 ) level. Sex and age differences in the three indices are also shown in Figure 1. Before age 12 y, BMI levels were very similar between boys and girls because the higher (B0.5 kg/m 2 ) FMI of girls (middle panel) was counterbalanced by the higher FFMI of boys (right panel). After age 12 y, the sex difference in FMI was greater than that for FFMI so that girls had slightly higher BMI levels than boys. FMI levels decreased after age 12 y among boys.

3 Table 1 Descriptive characteristics by sex, race/ethnicity, and age group 3 Boys Girls Age (y) Whites Blacks Hispanics Asians Whites Blacks Hispanics Asians 5 11 N Age (y) Weight-for-age Z Height-for-age Z BMI (kg/m 2 ) BMI-for-age Z BMI-for-age P Fat mass (kg) FMI (kg/m 2 ) Fat-free mass (kg) FFMI (kg/m 2 ) N Age (y) Weight for-age Z Height-for-age Z BMI (kg/m 2 ) BMI-for-age Z BMI-for-age P Fat mass (kg) FMI (kg/m 2 ) Fat-free mass (kg) FFMI (kg/m 2 ) Figure 1 Levels of BMI (left panel), FMI (middle panel), and FFMI (right panel) by sex and age. Data were smoothed using lowess within each sex; these smoothed values are somewhat comparable to median levels.

4 4 Figure 2 Levels of FMI (x-axis) vs FFMI (y-axis) among the 1196 children, by age. The solid symbols represent boys, and the triangles represent overweight children. The diagonal line represents a BMI of 20 kg/m 2, and emphasizes that various combinations of FFMI and FMI that can result in a similar BMI. Levels of FMI and FFMI are shown for each of the 1196 children in Figure 2, and the variability (standard deviation) in FFMI was about 40 55% of that for FMI. Among 5- to 8-yold boys, for example, standard deviations were 1.2 kg/m 2 (FFMI) and 2.5 kg/m 2 (FMI). Although overweight children (triangles) had substantially higher levels of FMI than other children, with their data being shifted to the right, they also had higher FFMI levels (data shifted upwards). Various combinations of FMI and FFMI can result in similar BMI levels, and points near the diagonal lines in Figure 2 are children who have a BMI of B20 kg/m 2. For example, among 9- to 11-y-old boys, there was a 10-y-old with an FMI of 1.6 kg/m 2 and an 11-y-old with an FMI of 6.9 kg/m 2. However, despite this more than four-fold difference in FMI, the two boys had very similar BMI levels (20.0 and 19.6 kg/m 2 ) because the 5.3 ( ) kg/m 2 difference in FMI was counterbalanced by a 5.6 kg/m 2 difference in FFMI. Levels of BMI, FMI, and FFMI were then compared across categories of BMI-for-age (Table 2). Mean levels are shown for children with a BMI o25th P, and other values represent the difference in mean levels between the lowest BMI category and each of the four higher BMI-for-age categories. Among 5- to 8-y-old boys, for example, the mean BMI among those with levels between the 25th and 49th P s was 15.2 kg/m 2,a level that was 1.2 kg/m 2 higher than the mean of 14.0 kg/m 2 among boys with a BMI o25th P. This BMI difference was attributable to a 0.2 kg/m 2 difference in mean FMI levels and a 1.0 kg/m 2 difference in mean FFMI levels. Among boys, the differences in FFMI between these two BMI categories (o25th P vs 25 49th P) were similar to or greater than those for FMI. Although differences between the two lowest BMI categories were less consistent among girls, in both sexes, much of the difference in BMI levels among relatively thin children was attributable to FFMI. Differences in FMI were not consistently greater than those for FFMI until the 75th P of BMI was reached, and among overweight (BMI Z95th P) participants, the difference in FMI was 2.5- to 3-fold greater than that for FFMI. Among 15- to 18-y-old girls, for example, those who were overweight had a 11.1 kg/m 2 higher mean FMI and a 4.5 kg/m 2 higher mean FFMI than those who had a BMI o25th P. As shown in Table 3, BMI levels were more strongly associated with FMI (r ¼ ) than with FFMI (r ¼ ). Stratified analyses, however, indicated that these associations varied markedly across the three categories of BMIfor-age. Among 5- to 8-y-old boys who had a BMI o50th P, for example, BMI levels showed little association with FMI

5 Table 2 Mean levels and differences in various characteristics according to BMI-for-age 5 BMI-for age P, Boys BMI-for-age P, Girls Age (y) Characteristic o Z95 o Z N BMI (kg/m 2 ) a FMI (kg/m 2 ) * * FFMI (kg/m 2 ) N BMI (kg/m 2 ) FMI (kg/m 2 ) * * FFMI (kg/m 2 ) * N BMI (kg/m 2 ) FMI (kg/m 2 ) * FFMI (kg/m 2 ) * N BMI (kg/m 2 ) FMI (kg/m 2 ) * * FFMI (kg/m 2 ) All comparisons for mean levels of FMI and FFMI with those in the BMI o25th P category were statistically significant at the 0.05 level except where noted: *PZ0.05. a Values represent differences in the mean level of characteristics between the specified BMI-for-age category and the BMI o25th P category. For example, the mean BMI of 5- to 8-y-old boys who had a BMI between the 25th and 49th P was 15.2 kg/m 2, a value that is 1.2 kg/m 2 higher than in the BMI o25th P category. Table 3 Relation of BMI levels to fat and fat-free mass by sex and age Boys Girls Age (y) Mean7s.d. Overall correlation BMI-for-age a o50th P 50 84th P Z85th P Mean7s.d. Overall correlation BMI-for-age o50th P 50 84th P Z85th P 5 8 BMI (kg/m 2 ) F b F FMI (kg/m 2 ) c FFMI (kg/m 2 ) BMI (kg/m 2 ) F F FMI (kg/m 2 ) FFMI (kg/m 2 ) BMI (kg/m 2 ) F F FMI (kg/m 2 ) FFMI (kg/m 2 ) BMI (kg/m 2 ) F F FMI (kg/m 2 ) FFMI (kg/m 2 ) a The BMI-for-age cutpoints roughly divided the participants into thirds. b Mean7s.d. level of BMI within each BMI-for-age category. c Pearson correlation coefficients, adjusted for race (four categories) and age, between BMI and the specified characteristic. Sample sizes within categories of sex, age, and BMI-for-age ranged from 38 to 66. (r ¼ 0.22), possibly due to the low variability (s.d.b1 kg/m 2 ) of BMI levels in this category. However, despite this low variability, BMI levels were strongly associated with FFMI (r ¼ 0.83) among these relatively thin boys. In contrast, among relatively heavy (BMI Z85th P) 5- to 8-y-old boys, BMI was more strongly associated with FMI (r ¼ 0.96) than with FFMI (r ¼ 0.57). Fairly similar differences were seen in other sex and age groups, but the differences in the relation of BMI to FMI across the BMI-for-age categories tended to be more pronounced among boys than among girls.

6 6 Figure 3 Predicted levels of fat mass (left panels) and fat-free mass (right panels), by sex, BMI Z-score, and height. The regression models included BMI Z-score, height, race, and interactions (product terms) between race and BMI Z-score, and between race and age. Restricted cubic splines were used to model BMI Z-score and age, and predicted levels are shown for black children at the mean heights of 8-, 12-, and 17-y-olds. A BMI-for-age Z-score of 1.0 is approximately equal to the 85th P, and a Z-score of is equal to the 95th P. Figure 3 shows the predicted relation of BMI-for-age to kg of fat mass (left panels) and kg of fat-free mass (right panels). These regression models, which in addition to BMI Z-score also included height, race, and various interactions as independent variables, yielded multiple R 2 -values of 0.90 (boys) and 0.92 (girls) for fat mass, and 0.94 (girls) and 0.96 (boys) for fat-free mass. Predicted values of fat mass and fatfree mass are shown for boys and girls, at three heights (the mean heights of 8-, 12-, and 17-y-olds). Among boys (top), levels of fat mass varied only slightly at BMI Z-scores below 1.0 (approximately equal to the 85th P), but increased substantially at higher BMI Z-scores (Po0.001 for nonlinearity). In contrast, the association between BMI and fat-free mass among boys was strongest at BMI Z-scores below 1.0 (right, upper panel). Although the relation of BMI-for-age to fat mass among girls (bottom, left panel) also varied by BMI Z-score, the nonlinearity was less pronounced than among boys. There was no evidence of nonlinearity (P ¼ 0.13) in the association between BMI and fat-free mass among girls. Discussion Our results show that the accuracy of BMI as a surrogate measure of adiposity among children varies according to the degree of fatness: BMI performs well among relatively heavy children (eg, BMI-for-age Z85th P) but not among thinner children. We found, for example, that differences in fat-free mass between children with a BMI below the 25th P and those with a BMI between the 25th and 49th P s were frequently larger than were differences in fat mass. Although BMI was strongly associated with FMI among relatively heavy children, among children with a BMI o50th P, the moderate associations between BMI and FFMI were as least as strong as were the associations between BMI and FMI. Other investigators have reported that the accuracy of BMI as an indicator of obesity among children increases with the level of BMI (relative to sex and age) or %body fat. 11,14,18 For example, a study of 2554 schoolchildren 11 reported a correlation of r ¼ 0.58 between BMI and (skinfold derived) %body fat among boys who were relatively fat (upper tertile), whereas there was no association (r ¼ 0.01) among leaner boys. Among boys in the current study, correlations between BMI and FMI ranged from r ¼ 0.85 to 0.96 (BMI Z85th P) vs correlations of r ¼ 0.22 to 0.45 (BMI o50th P). These differences across BMI categories agree with the nonlinearity that we observed between BMI-for-age and fat mass (Figure 3). The shape of these associations indicates that substantial differences in body fatness would be expected only at relatively high levels (eg, Z85th P) of BMI.

7 The nonlinear relation of BMI to body fatness may also account, in part, for the differing associations (R 2 ¼ ) that have been observed across studies For example, BMI was strongly associated with %body fat (r ¼ ) among Pima Indian children (whose mean BMI was B5 kg/ m 2 higher than participants in the current study), 16 whereas a correlation of r ¼ 0.46 was observed among adolescent girls with anorexia nervosa (mean BMI ¼ 13.5 kg/m 2 ). 21 In agreement with the sex and age differences in body fatness, it has also been reported that the associations with BMI are stronger among females than among males, 4,10,11 and among 7- to 10-y-old boys than among 14- to 16-y-old boys. 13 The variability that we observed in the levels of both fat and fat-free mass emphasizes that the same absolute BMI value can result from various combinations of FMI and FFMI. Variability in both FMI and FFMI has been described in a cross-sectional study of 8-y-olds, 33 and is apparent in the longitudinal changes that occur throughout childhood and adolescence. 34 We also found, as have others, 17,35,36 that the similar BMI levels of boys and girls obscures the sex differences in the levels of fat mass (higher among girls) and fat-free mass (higher among boys). Furthermore, whereas the age-related increases in BMI among adults are primarily due to increases in fat mass, 37 our data indicate that the age differences in BMI among boys after age 12 y are almost entirely due to differences in fat-free mass. Other data indicate that longitudinal increases in BMI among children and adolescents are largely attributable to fat-free mass, 17 and it has also been suggested 33 that the adiposity rebound is due to increases in fat-free, rather than fat, mass. Although fat-free mass makes up about 75% (girls) to 80% (boys) of the body weight of children and adolescents, relatively few studies have examined this component of body composition. In part, this is because fat-free mass has typically been expressed in absolute units (kg of fat mass) or as a percentage of body weight. 24 However, the strong association between fat free mass and height (r ¼ 0.73 in the current study) complicates the interpretation of fat free mass when expressed in kg. Furthermore, because %fat free mass is perfectly correlated with %body fat, it provides no additional information on body composition. It has therefore been suggested that kg of fat-free mass and fat mass be standardized for height, in much the same way that weight is standardized for height in BMI. Although the exponent for height in FMI should be greater than 2.0 to minimize the correlation between this index and height, 38 we obtained similar results in analyses that used FMI or height-adjusted (through linear regression) kg of fat mass. There is a strong (r ¼ 0.95 in the current study) association between FMI and %body fat, and we obtained similar results with both measures (data not shown). However, because BMI is the sum of FMI and FFMI, the use of these indices allows one to assess easily the contribution of fat mass and fat-free mass to a given BMI. For example, as compared with children who had a BMI o25th P, the difference in fat mass of overweight (BMI Z95th P) children was approximately three-fold greater than the difference in fat-free mass (Table 2). In contrast, the difference in fat-free mass among children with a BMI between the 25th and 49th P s (vs those with a BMI o25th P) was frequently greater than the fat mass difference. These findings emphasize that BMI comparisons among non-overweight children need to be interpreted carefully as they may be largely due to differences in fatfree mass. A possible limitation of the current study is that children were recruited between 1995 and 2000 through newspaper advertisements and through announcements at schools and after school activity centers. Although these participants may not be representative of the general population, we found that their mean levels of weight, height, and BMI differed only slightly from children and adolescents examined in the Bogalusa Heart Study ( ) and NHANES 4 ( ). Among white and black children, for example, mean BMI Z-scores were 0.52 (current study), 0.47 (Bogalusa Heart Study), and 0.39 (NHANES 4). In addition, although the strong association that we observed between levels of BMI and FMI among overweight children would likely also apply to obese adults, there are exceptions. 39 For example, the high BMI levels of many athletes are largely attributable to increased fat-free mass. Our results emphasize the limitations of BMI as a marker of adiposity among non-overweight children and adolescents. Because of the variability in the levels of both fat mass and fat-free mass, and the many combinations of FMI and FFMI that result in the same BMI, this weight height index needs to be carefully interpreted. Because high levels of BMI-for-age are associated with substantial increases in fat mass, this index is most useful as a measure of obesity. Among relatively thin children (BMI o50th P), BMI differences may be largely due to differences in fat-free mass. References 1 Ogden CL, Flegal KM, Carroll MD, Johnson CL. Prevalence and trends in overweight among US children and adolescents, JAMA 2002; 288: Franklin MF. Comparison of weight and height relations in boys from 4 countries. Am J Clin Nutr 1999; 70: 157S 162S. 3 Horlick M. Body mass index in childhoodfmeasuring a moving target. J Clin Endocrinol Metab 2001; 86: Gallagher D, Visser M, Sepulveda D, Pierson RN, Harris T, Heymsfield SB. How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? Am J Epidemiol 1996; 143: Deurenberg P, Yap M, van Staveren WA. Body mass index and percent body fat: a meta analysis among different ethnic groups. Int J Obes Relat Metab Disord 1998; 22: Gallagher D, Heymsfield SB, Heo M, Jebb SA, Murgatroyd PR, Sakamoto Y. Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin 2000; 72: Jackson AS, Stanforth PR, Gagnon J, Rankinen T, Leon AS, Rao DC, Skinner JS, Bouchard C, Wilmore JH. 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8 8 index: the Heritage Family Study. Int J Obes Relat Metab Disord 2002; 26: Hannan WJ, Wrate RM, Cowen SJ, Freeman CP. Body mass index as an estimate of body fat. Int J Eat Disord 1995; 18: Goulding A, Gold E, Cannan R, Taylor RW, Williams S, Lewis Barned NJ. DEXA supports the use of BMI as a measure of fatness in young girls. Int J Obes Relat Metab Disord 1996; 20: Daniels SR, Khoury PR, Morrison JA. The utility of body mass index as a measure of body fatness in children and adolescents: differences by race and gender. Pediatrics 1997; 99: Schaefer F, Georgi M, Wuhl E, Scharer K. Body mass index and percentage fat mass in healthy German schoolchildren and adolescents. Int J Obes Relat Metab Disord 1998; 22: Pietrobelli A, Faith MS, Allison DB, Gallagher D, Chiumello G, Heymsfield SB. Body mass index as a measure of adiposity among children and adolescents: a validation study. J Pediatr 1998; 132: Sarria A, Garcia Llop LA, Moreno LA, Fleta J, Morellon MP, Bueno M. Skinfold thickness measurements are better predictors of body fat percentage than body mass index in male Spanish children and adolescents. Eur J Clin Nutr 1998; 52: Bray GA, DeLany JP, Harsha DW, Volaufova J, Champagne CC. Evaluation of body fat in fatter and leaner 10 y old African American and white children: the Baton Rouge Children s Study. Am J Clin Nutr 2001; 73: Widhalm K, Schonegger K, Huemer C, Auterith A. Does the BMI reflect body fat in obese children and adolescents? A study using the TOBEC method. Int J Obes Relat Metab Disord 2001; 25: Lindsay RS, Hanson RL, Roumain J, Ravussin E, Knowler WC, Tataranni PA. Body mass index as a measure of adiposity in children and adolescents: relationship to adiposity by dual energy X ray absorptiometry and to cardiovascular risk factors. J Clin Endocrinol Metab 2001; 86: Maynard LM, Wisemandle W, Roche AF, Chumlea WC, Guo SS, Siervogel RM. Childhood body composition in relation to body mass index. Pediatrics 2001; 107: Mast M, Langnase K, Labitzke K, Bruse U, Preuss U, Muller MJ. Use of BMI as a measure of overweight and obesity in a field study on 5 7 year old children. Eur J Nutr 2002; 41: Taylor RW, Jones IE, Williams SM, Goulding A. Body fat percentages measured by dual energy X ray absorptiometry corresponding to recently recommended body mass index cutoffs for overweight and obesity in children and adolescents aged 3 18 y. Am J Clin Nutr 2002; 76: Mei Z, Grummer Strawn LM, Pietrobelli A, Goulding A, Goran MI, Dietz WH. Validity of body mass index compared with other body composition screening indexes for the assessment of body fatness in children and adolescents. Am J Clin Nutr 2002; 75: Kerruish KP, O Connor J, Humphries IR, Kohn MR, Clarke SD, Briody JN, Thomson EJ, Wright KA, Gaskin KJ, Baur LA. Body composition in adolescents with anorexia nervosa. Am J Clin Nutr 2002; 75: VanItallie TB, Yang MU, Heymsfield SB, Funk RC, Boileau RA. Height normalized indices of the body s fat free mass and fat mass: potentially useful indicators of nutritional status. Am J Clin Nutr 1990; 52: Hattori K, Tatsumi N, Tanaka S. Assessment of body composition by using a new chart method. Am J Hum Biol 1997; 9: Wells JC. A critique of the expression of paediatric body composition data. Arch Dis Child 2001; 85: Kuczmarski RJ, Ogden CL, Guo SS, Grummer Strawn LM, Flegal KM, Mei Z, Wei R, Curtin LR, Roche AF, Johnson CL. CDC growth charts: United States. Adv Data 2000; 314: 1 27; ( cdc.gov/nccdphp/dnpa/growthcharts/sas.htm). 26 Ogden CL, Kuczmarski RJ, Flegal KM, Mei Z, Guo S, Wei R, Grummer-Strawn LM, Curtin LR, Roche AF, Johnson CL. Centers for Disease Control and Prevention 2000 growth charts for the United States: improvements to the 1977 National Center for Health Statistics version. Pediatrics 2002; 109: Himes JH, Dietz WH. Guidelines for overweight in adolescent preventive services: recommendations from an expert committee. The Expert Committee on Clinical Guidelines for Overweight in Adolescent Preventive Services. Am J Clin Nutr 1994; 59: Kuczmarski RJ, Flegal KM. Criteria for definition of overweight in transition: background and recommendations for the United States. Am J Clin Nutr 2000; 72: Mazess RB, Barden HS, Bisek JP, Hanson J. Dual energy X ray absorptiometry for total body and regional bone mineral and soft tissue composition. Am J Clin Nutr 1990; 51: Russell Aulet M, Wang J, Thornton J, Pierson Jr RN. Comparison of dual photon absorptiometry systems for total body bone and soft tissue measurements: dual energy X rays versus gadolinium 153. J Bone Miner Res 1991; 6: Cleveland WS. The elements of graphing data. Wadsworth Advanced Books and Software: Monterey, CA; pp Harrell Jr FR. Regression modeling strategies with applications to linear models, logistic regression, and survival analysis. Springer- Verlag: New York; pp Wells JC. Body composition in childhood: effects of normal growth and disease. Proc Nutr Soc 2003; 62: Siervogel RM, Maynard LM, Wisemandle WA, Roche AF, Guo SS, Chumlea WC, Towne B. Annual changes in total body fat and fat free mass in children from 8 to 18 years in relation to changes in body mass index: the Fels Longitudinal Study. Ann NY Acad Sci 2000; 904: Ruxton CH, Reilly JJ, Kirk TR. Body composition of healthy 7 and 8 year old children and a comparison with the reference child. Int J Obes Relat Metab Disord 1999; 23: Mast M, Kortzinger I, Konig E, Muller MJ. Gender differences in fat mass of 5 7-year old children. Int J Obes Relat Metab Disord 1998; 22: Schutz Y, Kyle UU, Pichard C. Fat-free mass index and fat mass index percentiles in Caucasians aged y. Int J Obes Relat Metab Disord 2002; 26: Wells JC, Cole TJ, ALSPAC study team. Adjustment of fat free mass and fat mass for height in children aged 8 y. Int J Obes Relat Metab Disord 2002; 26: Prentice AM, Jebb SA. Beyond body mass index. Obes Rev 2001; 2:

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