Gender Differences in Total and Regional Body ...

Journal of Clinical Densitometry: Assessment of Skeletal Health, vol. 12, no. 2, 229e237, 2009 Ó Copyright 2009 by The International Society for Clinical Densitometry 1094-6950/09/12:229e237/$36.00 DOI: 10.1016/j.jocd.2008.12.008

Original Article

Gender Differences in Total and Regional Body Composition Changes as Measured by Dual-Energy X-Ray Absorptiometry in Korean Children and Adolescents Jung Sub Lim,*,1 Jin Soon Hwang,2 Gi Jeong Cheon,3 Jun Ah Lee,1 Dong Ho Kim,1 Kyung Duk Park,1 and Kyung Hee Yi4 1

Department of Pediatrics, Korea Cancer Center Hospital, Seoul, Republic of Korea; 2Department of Pediatrics, Ajou University School of Medicine, Suwon, Republic of Korea; 3Department of Nuclear Medicine, Korea Cancer Center Hospital, Seoul, Republic of Korea; and 4Department of Pediatrics, Wonkwang University Sanbon Medical center, Sanbon, Republic of Korea

Abstract The objective of this study was to gain normal reference values and to evaluate gender differences in total and regional body composition changes according to age and Tanner stage by dual-energy X-ray absorptiometry in Korean. Four hundred and forty-nine healthy subjects, aged 5e20 yr were enrolled. Until 9 yr of age, males had a higher lean tissue mass and a lower percent body fat (%BF) than the females (12.6% vs 10.0%, p ! 0.01). These differences were not evident from 10 to 12.9 yr because of early pubertal progression in girls. After 13 yr, a significant sex difference in the body compositions were observed again. In late teens, females have higher %BF than males (25.6% vs 12.3%, p ! 0.01). However, females have lower android-gynoid percent fat ratio than males at Tanner stages (TSs) 4 and 5 ( p ! 0.01). These differences are because of significant increase of gynoid lean tissue mass after 13 yr in males. The reference data would be useful for future research related to growth and obesity in Korean children and adolescents. Key Words: Body composition; body fat distribution; DXA; korean; puberty.

for the care of children with obesity. Although anthropometrical measures, such as the body mass index (BMI) and skin-fold thickness, are used clinically, they are not accurate measurements of the body composition (7). Other established reference methods, such as hydrodensitometry and isotope distribution evaluation, are not suitable for routine pediatric clinical practice (8). However, dual-energy X-ray absorptiometry (DXA) is easy to perform, safe, and clinically accessible (9,10). DXA has undergone validation studies with other reference methods and is known to be accurate and reproducible in pediatric populations (11e13). As a result, most of the body composition reference data have been measured by DXA in children (14e18). DXA also has the advantage of measuring the regional body composition including android and gynoid regions (19e21). It is well known that the body composition, especially fat distribution, is different depending on age, body weight,

Introduction During growth, the human body increases in size and demonstrates changes of body composition. Body composition reflects the nutritional state and normal growth. Hence, its evaluation is clinically important for the assessment of abnormal nutrition, growth failure, and the effects of chronic disease as well as the effect of treatment in pediatric patients. These days, pediatricians confront the current epidemic of pediatric obesity (1) and its immediate and long-term complications (2e6). Therefore, the evaluation of the body composition provides important information Received 07/18/08; Revised 12/18/08; Accepted 12/22/08. *Address correspondence to: Jung Sub Lim, MD, PhD, Department of Pediatrics, Korea Cancer Center Hospital, Gongneungdong 215, Nowon-gu, Seoul 139-706, Republic of Korea. E-mail: [email protected]

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230 gender, race, and ethnicity (19e21). Asian adults have more body fat (BF) compared with other ethnic groups (22,23), and this difference is present since childhood (24). Some have reported that the body composition differences, especially fat distribution, between racial groups, might explain the differences in the metabolic risks for metabolic diseases (25). However, information of body composition on Asian populations is scarce, especially in children and adolescents. Most of the earlier studies have been on Caucasians with comparisons excluding the analysis of diverse Asian ethnic differences. The aim of this study was to make reference data of body composition according to age and pubertal changes in Korean children and adolescents. This information might be useful in identifying children and adolescents who are at risk of complications from obesity or in identification of abnormal body composition secondary to chronic disease or medication. In addition, we evaluated the total and regional differences in body composition between males and females.

Lim et al. on Fig. 1 as previously described (21). For the android region, the lower boundary was the top of the pelvic line of demarcation. The upper boundary was placed above the pelvic line of demarcation at a position that was equivalent to 20% of the distance between the pelvis and the femoral neck. The gynoid region was defined as the upper boundary positioned below the pelvic cutoff line by 1.5 times the height of the android region. The lower boundary was defined as equal to 2 times the height of the android region. The percent BF (%BF) of the total body and regional ROI was calculated using the following equation: %BF 5 FM/(LTM þ FM þ BMC) 100%. The android to gynoid fat ratio (A/G fat) is the ratio of android %BF over gynoid %BF. All obtained data were analyzed by encore software 10x (GE Lunar Corporation). The coefficients of variation (CV) for BMC, LTM, FM, %BF, and A/G fat among 30 subjects with repeated measurements were 1.04, 0.64, 2.40, 2.44, and 5.84, respectively.

Statistical Analyses

Subjects and Methods Subjects

Anthropometric and body composition data are expressed as mean standard deviation (SD). The differences among age groups and TSs were tested with the one-way analysis

Four hundred and forty-nine children and adolescents were enrolled in this study between June 2007 and May 2008. All of the participants and their ancestors were of Korean ethnicity. There were 217 males and 232 females with an age range of 5e20 yr. The inclusion criteria were: a BMI between the 5th to 95th percentile of normal Korean children and the absence of chronic disease and medications that would affect body composition. Those with endocrine disorders affecting growth and development were also excluded. Weight was measured to the nearest 0.1 kg on an electronic scale (150A; Cas co., Ltd., Seoul, Korea) and height was measured to the nearest 0.1 cm using a stadiometer (DS-102; Dong Sahn Jenix Co., Ltd, Seoul, Korea). A pediatric endocrinologist assessed the TS of the subjects who agreed to the examination, in the presence of their parents. Bone age was assessed according to Greulich and Pyle by 1-yr intervals (26). The Institutional Review Board of the Korea Cancer Center Hospital approved this study. Informed consent was obtained from all parents and subjects. If participants were older than 17 yr, written informed consent was obtained from the participants only.

Body Composition Assessment Whole-body DXA scans were performed using the Lunar Prodigy advance system with pediatric software, version enCore 2005 9.15.010 (GE Lunar Corporation, General Electric, Madison, WI). Each scan provided the bone mineral content (BMC), lean tissue mass (LTM) and fat mass (FM) of the subjects. A trained technician performed the measures based on the manufacturer’s guidelines, including positioning. All measurements were analyzed automatically and reanalyzed by the same person after manual adjustments for each region of interest (ROI), especially for the android and gynoid regions. The ROI for the android and gynoid regions is shown Journal of Clinical Densitometry: Assessment of Skeletal Health

Fig. 1. The region of interest for bone (left), android (top, right) and gynoid (bottom, right) in dual-energy X-ray absorptiometry. Volume 12, 2009

Body Composition in Children

Journal of Clinical Densitometry: Assessment of Skeletal Health

Table 1 Anthropometric Characteristics for Females and Males Females Age group (yr) N 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Total

8 12 14 13 30 19 23 15 12 15 13 14 14 16 14 232

Males

Age (yr) Bone age (yr) Weight (kg) Height (cm) BMI (kg/m2) N 5.5 0.3 6.5 0.3 7.5 0.2 8.6 0.2 9.4 0.3 10.5 0.3 11.5 0.3 12.6 0.3 13.6 0.3 14.6 0.3 15.4 0.2 16.3 0.3 17.4 0.3 18.5 0.3 19.5 0.3 12.5 4.0

5.4 0.7 6.6 1.0 7.4 0.8 9.1 1.3 10.2 1.1 10.9 1.7 12.0 1.1 13.6 1.4 14.5 1.2 15.7 1.6 16.2 1.3 18.3 1.7 18.1 1.6 17.8 0.4

19.0 1.3 22.3 2.7 23.9 2.4 27.1 2.8 32.6 5.6 35.6 6.6 38.8 7.2 48.3 5.0 50.8 5.8 51.5 5.7 53.8 6.5 52.9 4.0 56.4 5.4 53.9 5.5 56.4 5.2 41.5 13.3

110.2 4.0 118.4 4.4 122.9 5.6 132.2 4.8 134.9 6.1 141.9 7.6 147.2 7.6 155.3 5.9 158.0 4.6 158.5 3.3 161.8 3.6 161.0 6.2 160.3 3.7 162.7 5.2 161.1 5.1 146.3 16.5

15.7 1.5 15.9 1.2 15.8 1.3 15.5 1.3 17.8 1.9 17.6 2.4 17.8 2.1 20.0 1.6 20.4 2.3 20.5 2.5 20.6 2.4 20.4 1.5 21.9 1.6 20.3 1.1 21.8 1.9 18.8 2.8

14 13 17 12 16 13 14 24 17 13 16 9 8 18 13 217

Age (yr) Bone age (yr) Weight (kg) Height (cm) BMI (kg/m2) 5.4 0.3 6.5 0.2 7.4 0.3 8.5 0.2 9.4 0.3 10.5 0.3 11.3 0.2 12.4 0.3 13.5 0.3 14.5 0.3 15.4 0.2 16.4 0.3 17.3 0.3 18.3 0.3 19.4 0.4 12.3 4.2

4.9 0.5 5.7 0.6 6.8 1.1 7.7 0.8 8.6 1.0 10.3 1.5 11.4 1.5 12.3 1.6 13.9 1.7 15.8 1.7 16.9 1.4 18.6 0.9 18.8 0.5 18.7 0.6

19.7 1.1 22.9 2.3 26.4 3.6 28.7 2.7 31.9 4.9 38.1 5.7 40.9 8.3 42.7 10.7 52.3 10.5 58.7 7.2 61.9 9.6 61.3 8.1 64.0 3.9 68.1 6.6 69.1 6.0 45.7 17.8

111.1 2.4 120.8 3.9 125.9 3.8 129.4 3.7 135.4 6.3 139.5 5.4 144.8 9.6 151.8 8.9 160.4 9.2 167.5 5.3 171.1 5.4 170.4 2.8 175.5 5.4 173.6 4.3 176.3 4.6 149.6 21.6

16.0 0.8 15.9 1.2 15.8 1.3 15.5 1.3 17.8 1.9 17.6 2.4 17.8 2.1 20.0 1.6 20.4 2.3 20.5 2.5 20.6 2.4 20.4 1.5 21.9 1.6 20.4 2.1 21.8 1.9 18.8 2.8

All values are mean standard deviation.

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Lim et al. Table 2 Body Composition Values for Females and Males Females

Age group (yr) 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Total

Males

BMC (g)

LTM (kg)

FM (kg)

%BF

BMC (g)

LTM (kg)

FM (kg)

%BF

621 70 758 85 832 113 1025 121 1142 155 1277 243 1453 290 1763 263 2023 232 2004 228** 2225 220** 2198 253* 2304 159** 2267 262** 2326 255** 1606 603*

15.3 0.6* 17.9 1.7* 19.0 1.6* 21.9 2.3 23.9 3.1 26.5 3.8 29.3 4.0 34.8 3.3 36.1 3.3* 36.2 2.2** 37.2 1.9** 36.6 2.9** 39.1 3.7** 37.8 3.7** 38.9 3.7** 30.0 8.1**

2.3 1.1 2.8 1.2* 3.6 1.2 3.3 1.3 7.1 2.7* 7.5 4.0* 7.5 4.2 10.8 3.3 12.0 3.8* 12.6 4.0 * 14.1 4.9** 13.5 2.8* 15.4 3.0** 13.2 3.6** 14.3 3.5** 9.4 5.3**

12.3 5.1 12.9 4.1 ** 15.1 4.3 12.3 4.2 21.4 5.6* 20.4 8.2 18.7 7.5 22.6 5.3 23.5 5.7** 24.3 5.7** 25.8 6.2** 25.7 4.1** 27.8 4.1** 24.6 5.1** 25.6 5.2** 21.1 7.2**

649 48 833 100 916 128 1004 110 1120 153 1256 164 1476 303 1629 310 1955 384 2331 339 2628 447 2475 324 2877 308 2769 249 2988 275 1752 813

16.2 0.7 19.4 1.4 21.5 1.9 23.1 1.3 25.6 3.0 28.1 3.2 30.1 5.4 35.3 7.0 41.0 7.6 46.5 4.6 51.0 5.3 50.1 4.7 55.5 3.3 55.0 3.9 57.0 4.4 36.4 14.3

1.9 0.7 1.9 0.9 3.4 1.9 4.0 1.9 4.9 3.2 8.3 3.5 8.5 4.7 9.8 5.8 8.4 4.6 9.5 3.5 8.3 5.3 9.4 5.4 5.3 3.0 9.4 4.9 8.4 4.4 6.9 4.8

10.0 3.3 8.3 3.2 12.5 5.6 13.8 5.7 14.7 7.9 21.3 7.1 20.2 9.2 19.9 9.2 15.7 7.5 16.0 4.8 12.6 6.5 14.3 6.7 8.3 4.3 13.6 6.1 12.0 5.5 14.6 7.5

All values are mean standard deviation. Abbr: BMC, bone mineral content; LTM, lean tissue mass; FM, fat mass; %BF, percent body fat. Significant difference between females and males in the same age group (independent t-test): *p ! 0.05; **p ! 0.01.

The mean values for age, bone age, height, weight, and BMI are described in Table 1. The anthropometric characteristics of the study population were not different from the Korean standards for the same age (27).

and had a slight increase thereafter. For males, the peak LTM was attained at 17 yr of age and was almost the same as the BMC (Fig. 2B). There was almost no overlap between the BMC and LTM values between males and females after 17 yr of age, although significant difference begins at the age of 14 yr. There was a strong relationship between the BMC and LTM in males and females. However, the males had stronger correlations than the females (males: r 5 0.983, p ! 0.001; females: r 5 0.962, p ! 0.001). The FM increased according to age until the age of 12 yr for both males and females. However, males showed no definite increase of FM thereafter, whereas the females showed a continuous increase in their FM until 17 yr of age (Fig. 2C). As a result, the %BF for males increased from 10.0% to 19.9% until the age of 12 yr, and then decreased to 12.0%, whereas the %BF for females increased from 12.3% to 27.8% until 17 yr of age and then decreased to 25.6% (Fig. 2D). Therefore, the mean %BF was higher for females than for males in all age groups except 10, 11, and 12 yr.

Total Body Composition According to the Age Group

Total Body Composition According to Tanner Stage

The DXA values for the total BMC, LTM, FM, and %BF according to age and gender are shown in Table 2 and Fig. 2. The total BMC increased steadily in females with a plateau at 15 yr of age. In boys, the BMC increased steadily with the plateau at 17 yr of age (Fig. 2A). The LTM changes were similar to the BMC changes, but occurred a little earlier. For females, the LTM increased steadily until the age of 13 yr,

The TS was significantly correlated with all the components of body composition in both males and females when age and weight were controlled ( p ! 0.01). The BMC and LTM increased according to TS in both males and females ( p ! 0.01). Males had a higher BMC and LTM at the same TS when compared with females. The females had continuously increasing FM, whereas the males had increases in

of variance (ANOVA). The age groups were defined as follows: for example, the age group 7 was defined as 7.00e7.99. The differences of the measured values between males and females with the same chronological age and TS were analyzed by the independent t-test. Stepwise multiple regression analyses were performed to develop an anthropometry-based equation for estimating body composition. Curve fit was carried out with triweight log estimation according to age. All statistical analyses and curve fit were performed using SPSS 15.0 (SPSS, Chicago, IL). For all analyses, p ! 0.05 was considered statistically significant.

Results Population Characteristics


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Fig. 2. Each body composition change is plotted by age in female and male. The bold line and squares represent male. The dotted line and triangles represent female. (A) Bone mineral content (BMC); (B) lean tissue mass (LTM); (C) fat mass (FM); (D) percent body fat (%BF). FM until TS 3, and then, it decreased significantly after TS 4. As a result, the total %BF for the males was significantly lower than for the females at TSs 4 and 5 (Table 3). As puberty starts in a female earlier than a male, we analyzed TS 1 subjects younger than 9 yr of age separately. In those younger than 9 yr, the males had higher LTM and a lower %BF than the females, whereas there were no differences in age, weight, height, and BMI (Table 4).

Regional Body Composition The FM of the android and gynoid regions increased in a similar fashion as the total FM until 12 yr of age and TS 3. After 13 yr, although the android LTM and FM showed little change, the gynoid LTM increased dramatically compared with the gynoid FM, especially in male (Fig. 3). As a result, the A/G fat for males was higher than that of females in TSs 4 and 5 (0.97 0.24 vs 0.84 0.16, p ! 0.001), although there Journal of Clinical Densitometry: Assessment of Skeletal Health

was a great deal of overlap by age (Fig. 4). In general, the A/G fat in males increased steadily according to age, whereas it remained stable in females after 10 yr of age.

Anthropometry-Based Equations for Body Composition The equations for the BMC, LTM, and FM for males and females were calculated with weight, height, and age as independent variables, and the results are presented in Table 5. Predictor variables were included in the regression equation only if the R2 value was improved by 3%. Although the TS was significantly correlated with all components of body composition, including TS did not increase R2.

Discussion The results of this study demonstrated the influence of age, TS, and gender on body composition in Korean children and Volume 12, 2009

4.5 3.8 8.8 5.0 10.3 4.8 8.5 4.3 8.4 4.8 22.8 4.5 32.1 5.8 39.5 5.5 47.3 5.4 53.6 5.0 1001 264 1524 339 1854 272 2305 417 2741 352

All values are mean standard deviation. Abbr: BMC, bone mineral content; LTM, lean tissue mass; FM, fat mass; %BF, percent body fat. Significant difference between girls and boys in the same age group (independent t-test): *p ! 0.05; **p ! 0.01.

7.6 2.1 11.8 1.7 13.4 0.8 15.7 1.6 17.8 1.7 8.3 2.0 12.5 1.0 12.7 1.0 14.3 1.2 17.3 1.8 93 23 15 24 62 14.0 4.9 21.0 7.1** 20.7 7.1 21.9 6.7 25.6 4.8** 3.4 1.6* 7.3 3.4** 8.2 3.5 10.5 4.7 13.8 3.4** 19.4 30** 24.8 3.4** 28.8 3.1** 34.1 4.1** 37.4 3.3** 7.6 1.7 10.6 1.1 11.7 0.6** 13.9 2.1** 16.9 1.7* 57 30 23 35 86 1 2 3 4 5

7.7 1.5** 10.0 1.1** 11.0 1.0** 12.9 1.6** 16.8 2.0

860 140 1219 321 1388 204** 1802 354** 2214 239**

FM (kg) LTM (kg) BMC (g) LTM (kg)

FM (kg)

%BF

N

Age

Bone age

Male Female

BMC (g) Bone age Age N Tanner stage

Table 3 Age, Bone Age and Body Composition According to Tanner Stage

14.1 7.8 19.4 8.1 19.5 8.0 14.1 5.6 12.6 6.2

Lim et al. %BF

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adolescents. To our knowledge, this is the first Asian study of body composition, in the age range from 5 to 20 yr measured by DXA, in a single ethnic group. The results from this study might be used as a reference standard for Korean children in the research of metabolic disease related to body composition. It could also be used for explaining the difference of metabolic syndrome pattern of Koreans compared with other ethnic groups. The LTM is achieved just after the period of maximum growth in height and weight in Koreans. The peak BMC was obtained 0 or 2 yr later than LTM, corresponding to the age of the final height achieved. This value was a little earlier than previously reported in Caucasians (14). Compared with young adult Korean data, our subjects over 17 yr of age had almost the same LTM and BMC values (28). However, it does not represent that Koreans reach peak LTM and BMC in the late second decade of life, as both LTM and BMC are closely related to height and weight. The average Korean children and adolescent height and weight have increased over the past 10 yr because of improved nutrition. The bone age of our children were advanced by 0.5e2.0 yr compared with the chronological age in late teens. This finding reflects that contemporary Korean children show a trend toward earlier maturation. The %BF of TS 5 was 25.6% in females and 12.6% in males, which is almost the same as that obtained in Korean young adults, 20e30 yr of age, by bioelectrical impedance analysis (28). In Dutch children, the %BF of TS 5 was 25.5% in females and 9.3% in males (15). In the study reported by Ellis et al, the %BF from 15 to 18 yr of age in Caucasians, African Americans, and Hispanics ranged from 20.7 to 26.4 in females and from 14.8 to 16.7 in males (16,17). By comparing the anthropometry-based equations for the FM, the FM of Korean children and adolescents was not higher than that of other ethnicities. A multiple ethnic group comparison study including Korean Americans as Asians also did not show any significant difference in the %BF between Asians and Caucasians (19,20). Therefore, the average %BF in Koreans does not appear to be higher than the average %BF for Caucasians. The results of this study showed that Korean males had a greater LTM and BMC, whereas Korean females had a greater %BF, except for the age group 10e12 yr. In children younger than 9 yr, females had a higher total and regional %BF. This was because of the approx 6.1% higher LTM in the males (18.99 vs 20.15 kg). In a study using computed tomography (CT) scanning of children 5e10 yr of age closely matched for age, weight, and height, there was an approx 10% greater muscle mass observed in Caucasian boys (29). In addition, the bone age of the female subjects was 1 yr more advanced when compared with the males (7.4 vs 6.3 yr, p ! 0.05), but the chronological age and anthropometric data showed no difference. Other reports have not shown these differences, most likely because their subjects were not confined to a single ethnicity and bone age was not consistently measured. Therefore, there are conflicting results as to whether sexual dimorphism is present in the Volume 12, 2009


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Table 4 Anthropometric and Body Composition in Subjects Younger Than 9 yr N Age (yr) Bone age (yr) Height (cm) Weight (kg) BMI (kg/m2) BMC (g) Female 49 7.3 1.2 Male 58 7.0 1.2

7.4 1.7* 6.3 1.3

122.3 8.8 23.7 3.7 122.3 8.0 24.6 4.2

FM (kg)

LTM (kg)

%BF

15.8 1.3 840 174 3.17 1.33* 18.99 2.78 13.5 4.4* 16.3 1.3 855 164 2.78 1.68 20.15 2.95 11.1 5.0

All values are mean standard deviation. Abbr: BMC, bone mineral content; LTM, lean tissue mass; FM, fat mass; %BF, percent body fat. Significant difference between girls and boys in the same age group (independent t-test): *p ! 0.05; **p ! 0.01.

body composition of prepubertal children; many of the differences are likely because of confounding factors in the analyses. From the ages of 10e12.9 yr, there were no sex differences in the total and regional body composition. This can be explained as follows. First, in this age group, most females enter puberty whereas males do not. The growth hormone

secretion rate increases during TS 2, with the highest rate achieved during TSs 3 and 4 in females; however, most males did not reach TS 4 when the growth hormone reached peaks as described previously (30). As growth hormone increases the LTM and BMC, it masks the sexual differences in the LTM and BMC in this age group. Second, males showed a ‘‘fat wave’’ at this age; this increase in the %BF during

Fig. 3. Regional body composition change of android and gynoid regions is plotted by age in female and male. The bold line and squares represents male. The dotted line and triangles represent female. (A) android lean tissue mass (LTM); (B) android fat mass (FM); (C) gynoid lean tissue mass (LTM); (D) gynoid fat mass (FM). Note significant increase of gynoid LTM. Journal of Clinical Densitometry: Assessment of Skeletal Health

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Fig. 4. Android to gynoid fat ratios (A/G fat) are plotted. The squares represent male and the triangles represent female. (A) A/G Fat according to Tanner stage (TS); (B) A/G fat according to age. Although there is a clear difference of A/G fat in TSs 4 and 5, many Korean females had high A/ G fat: hence most of the female values overlap with the male values by age. *p ! 0.01. peripubertal periods has also been reported in a previous study (14). In addition, Korean females did not demonstrate much of an increase in peripheral fat relative to central fat,

when compared with other ethnic groups. He et al reported that Asian females have less gynoid fat relative to android fat, when compared with other racial groups (20). For males, from 12 yr of age, there is a greater LTM acquisition rate and a stable FM, whereas females have a continuous increase in the FM and a stable LTM after 15 yr of age. As a result, differences in the FM and %BF between the genders were evident at the age of 14 yr. Sex hormone effects might play a role in these differences. Testosterone plays an important role in increasing LTM during puberty, whereas estrogen facilitates FM accumulation and fat distribution in the gluteal/femoral regions (31). The regional body composition differences are presented as the A/G fat. The A/G fat, a DXA-derived ratio, demonstrates the relative proportion of ‘‘android’’ to ‘‘gynoid’’ fat. Korean males showed a higher A/G fat than females during late puberty as previously noted in other races (21). The main reason for this difference was the greater increase in gynoid LTM and no increase in android fat at least in Korean males. This study had the following limitations. We did not include subjects with BMI over the 95th percentile. Therefore, we did not determine whether obese subjects would have a higher %BF or higher abdominal fat composition when compared with Caucasians. In a comparison study of obese Korean with obese Caucasian adult women, Koreans were found to have a greater %BF and waist-hip ratio (32). Japanese subjects had a greater %BF compared with Caucasian subjects when the BMI was over 25 (22). The other limitation is that, although we did not include who showed precocious puberty, the adolescent who showed abnormal growth pattern, such as earlier puberty or delayed puberty, might influence each body composition values as our data are based on age. Hence, we analyzed body composition according to TS also. In conclusion, for Korean children and adolescents, the total and regional body composition findings of this study are in general agreement with the overall pattern of pubertal change in other ethnic groups. However, we found some patterns unique to Korean subjects. These Korea-specific findings of body composition can be used as reference standards for research, especially obesity-related metabolic diseases in the future.

Table 5 Anthropometry-Based Equations for Estimating Average Body Composition in Females and Males Females Regression equation BMC 5 31.7 Wt þ 44.2 Age 265.7 LTM 5 0.51 Wt þ 0.29 Age þ 5.14 FM 5 0.096 Age þ 0.614 Wt 0.194 Ht þ 13.508

Males R2

SEE

Regression equation

R2

SEE

0.942 0.938 0.893

145 g 2.03 kg 1.73 kg

BMC 5 30.5 Wt þ 62.6 Age 402.7 LTM 5 0.53 Wt þ 1.15 Age 1.90 FM 5 0.645 Age þ 0.564 Wt 0.21 1 Ht þ 20.620

0.941 0.954 0.667

199 g 3.08 kg 2.78 kg

Stepwise multiple regression analysisdprediction variables were as follows: age (yr), weight (kg), and height (cm), BMC (g), LTM (kg), FM (kg). Abbr: BMC, bone mineral content; Wt, weight; Ht, height; LTM, lean tissue mass; FM, fat mass; SEE, standard error estimate. Journal of Clinical Densitometry: Assessment of Skeletal Health

Volume 12, 2009


Acknowledgments We express our gratitude to all of the volunteers who participated in this study. We also thank Jin Seong Jeong for measuring body composition by DXA, and Hyun Jae Lee and Qi Q Zhou for technical support in analyzing the DXA results. This work was supported partially by the korean society of pediatric Endocrinology Research Fund.

References 1. Ebbeling CB, Pawlak DB, Ludwig DS. 2002 Childhood obesity: public-health crisis, common sense cure. Lancet 360:473e482. 2. Freedman DS, Dietz WH, Srinivasan SR, Berenson GS. 1999 The relation of overweight to cardiovascular risk factors among children and adolescents: the Bogalusa Heart Study. Pediatrics 103:1175e1182. 3. Must A, Strauss RS. 1999 Risks and consequences of childhood and adolescent obesity. Int J Obes Relat Metab Disord 23(Suppl 2):S2e11. 4. Young-Hyman D, Schlundt DG, Herman L, et al. 2001 Evaluation of the insulin resistance syndrome in 5- to 10-year-old overweight/obese African-American children. Diabetes Care 24: 1359e1364. 5. Ludwig DS, Ebbeling CB. 2001 Type 2 diabetes mellitus in children: primary care and public health considerations. JAMA 26(286):1427e1430. 6. Srinivasan SR, Myers L, Berenson GS. 2002 Predictability of childhood adiposity and insulin for developing insulin resistance syndrome (syndrome X) in young adulthood: the Bogalusa Heart Study. Diabetes 51:204e209. 7. Sweeting HN. 2007 Measurement and definitions of obesity in childhood and adolescence: a field guide for the uninitiated. Nutr J 26(6):32. 8. Brodie D, Moscrip V, Hutcheon R. 1998 Body composition measurement: a review of hydrodensitometry, anthropometry, and impedance methods. Nutrition 14:296e310. 9. Ellis KJ, Shypailo RJ, Pratt JA, Pond WG. 1994 Accuracy of dual-energy x-ray absorptiometry for body-composition measurements in children. Am J Clin Nutr 60:660e665. 10. Njeh CF, Fuerst T, Hans D, et al. 1999 Radiation exposure in bone mineral density assessment. Appl Radiat Isot 50:215e236. 11. Gutin B, Litaker M, Islam S, et al. 1996 Body-composition measurement in 9-11-y-old children by dual-energy X-ray absorptiometry, skinfold-thickness measurements, and bioimpedance analysis. Am J Clin Nutr 63:287e292. 12. Fuller NJ, Wells JC, Elia M. 2001 Evaluation of a model for total body protein mass based on dual-energy X-ray absorptiometry: comparison with a reference four-component model. Br J Nutr 86:45e52. 13. Sopher AB, Thornton JC, Wang J, et al. 2004 Measurement of percentage of body fat in 411 children and adolescents: a comparison of dual-energy X-ray absorptiometry with a fourcompartment model. Pediatrics 113:1285e1290. 14. Ogle GD, Allen JR, Humphries IR, et al. 1995 Body-composition assessment by dual-energy x-ray absorptiometry in subjects aged 4e26 y. Am J Clin Nutr 61:746e753. 15. Boot AM, Bouquet J, de Ridder MA, et al. 1997 Determinants of body composition measured by dual-energy X-ray absorptiometry in Dutch children and adolescents. Am J Clin Nutr 66:232e238.


237 16. Ellis KJ. 1997 Body composition of a young, multiethnic, male population. Am J Clin Nutr 66:1323e1331. 17. Ellis KJ, Abrams SA, Wong WW. 1997 Body composition of a young, multiethnic female population. Am J Clin Nutr 65: 724e731. 18. Sala A, Webber CE, Morrison J, et al. 2007 Whole-body bone mineral content, lean body mass, and fat mass measured by dual-energy X-ray absorptiometry in a population of normal Canadian children and adolescents. Can Assoc Radiol J 58: 46e52. 19. He Q, Horlick M, Thornton J, et al. 2002 Sex and race differences in fat distribution among Asian, African-American, and Caucasian prepubertal children. J Clin Endocrinol Metab 87: 2164e2170. 20. He Q, Horlick M, Thornton J, et al. 2004 Sex-specific fat distribution is not linear across pubertal groups in a multiethnic study. Obes Res 12:725e733. 21. Novotny R, Going S, Teegarden D, et al. 2007 ACT Research Team. Hispanic and Asian pubertal girls have higher android/ gynoid fat ratio than whites. Obesity 15:1565e1570. 22. Gallagher D, Heymsfield SB, Heo M, et al. 2000 Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr 72: 694e701. 23. Kagawa M, Kerr D, Uchida H, Binns CW. 2006 Differences in the relationship between BMI and percentage body fat between Japanese and Australian-Caucasian young men. Br J Nutr 95: 1002e1007. 24. Shaw NJ, Crabtree NJ, Kibirige MS, Fordham JN. 2007 Ethnic and gender differences in body fat in British schoolchildren as measured by DXA. Arch Dis Child 92:872e875. 25. Ehtisham S, Crabtree N, Clark P, Shaw N, Barrett T. 2005 Ethnic differences in insulin resistance and body composition in United Kingdom adolescents. J Clin Endocrinol Metab 90: 3963e3969. 26. Greulich WW, Pyle SI. 1950 Radiographic atlas of skeletal development of the hand and wrist. Stanford University Press, Palo. Califronia. 27. Moon JS, Lee SY, Nam CM, et al. 2008 2007 Korean National Growth Charts: reveiw of developmental process and an outlook. Korean J Pediatr 51:1e25. 28. Cui LH, Shin MH, Kweon SS, et al. 2007 Relative contribution of body composition to bone mineral density at different sites in men and women of South Korea. J Bone Miner Metab 25: 165e171. 29. Arfai K, Pitukcheewanont PD, Goran MI, et al. 2002 Bone, muscle, and fat: sex-related differences in prepubertal children. Radiology 224:338e344. 30. Albertsson-Wikland K, Rosberg S, Karlberg J, Groth T. 1994 Analysis of 24-hour growth hormone profiles in healthy boys and girls of normal stature: relation to puberty. J Clin Endocrinol Metab 78:1195e1201. 31. Rosenbaum M, Leibel RL. 1999 Clinical review 107: role of gonadal steroids in the sexual dimorphisms in body composition and circulating concentrations of leptin. J Clin Endocrinol Metab 84:1784e1789. 32. Chung S, Song MY, Shin HD, et al. 2005 Korean and Caucasian overweight premenopausal women have different relationship of body mass index to percent body fat with age. J Appl Physiol 99: 103e107.

Volume 12, 2009