International Journal of Obesity (1997) 21, 1167±1175 ß 1997 Stockton Press All rights reserved 0307±0565/97 $12.00 Age- and maturity-related changes in body composition during adolescence into adulthood: The Fels Longitudinal Study

SS Guo, WC Chumlea, AF Roche and RM Siervogel

Division of Human Biology, Departments of Community Health and of Pediatrics, Wright State University School of Medicine, Yellow Springs, OH, USA

OBJECTIVES: To examine patterns of change in total body fat (TBF), percent body fat (%BF), and fat-free mass (FFM), from 8±20 y of age and the effect of rate of skeletal maturation. To determine the degree of tracking of body composition for individuals from childhood into adulthood. RESEARCH DESIGN: Annual serial data for TBF, %BF and FFM from underwater weighing using a multicomponent body composition model were collected from 130 Caucasian males and 114 Caucasian females between 1976 and 1996. Rate of maturation was de®ned as FELS skeletal age (SA) less chronological age (CA). Random effects models were used to evaluate general patterns of change and tracking of individual serial data over the 12 y age range. RESULTS: Changes in TBF followed a quadratic model for males and for females with declining rates of change. Changes for %BF followed a cubic model for males and females. General patterns of change for FFM followed a cubic model for males and a quadratic model for females. TBF for males and females increased with age, but the rates of change declined with age. %BF for females increased from age 8±20 y. For males, %BF increased with age, but the positive rates of change declined and became a negative when aged about 13 y and reached a minimum at about the age of 15 y. The rate of change for %BF increased thereafter. FFM for males and females increased with age, but the rates of change decreased with age. The extent of tracking is inversely related to the length of the time interval. At the same age, rapidly-maturing children have signi®cantly larger amounts of TBF, %BF and FFM than slow- maturing children. Tracking in body composition for individuals persisted from childhood to adulthood. CONCLUSIONS: (1) There are gender-associated differences in these patterns of change for %BF and FFM but not for TBF; (2) TBF, %BF and FFM increased with increased rates of maturation; (3) signi®cant tracking in body composition for individuals persists from childhood to adulthood.

Keywords: body composition; maturation; patterns of change

Introduction %BF and FFM for individuals from childhood into adulthood has not been reported. Understanding and quantifying the patterns of change in body composi- Changes in body composition occur in conjunction tion and the tracking of levels of body composition with growth in body size and shape during adoles- from childhood into adulthood, would allow the early cence.1 Growth produced changes in total body fat recognition of children and adolescents with aberrant (TBF), percent body fat (%BF) and fat-free mass changes and=or unusual levels of body composition. (FFM) during childhood affect adult body composi- This knowledge would facilitate their subsequent tion and fat distribution, all of which, in turn, affect management. risk factors for cardiovascular and related diseases.1±4 Our knowledge of the changes in body composi- However, there is a paucity of knowledge regarding tion, from childhood into adulthood, depends on the the long-term patterns of change in body composition availability of long-term serial studies of children, but from childhood into adulthood. The tracking of available serial data are scant. Most reports of changes indices of body composition such as the body mass in body composition during childhood and adoles- index (BMI) from childhood into adulthood has been cence have used cross-sectional data,1,8,9 or were reported.5,6 BMI values at an age are highly correlated based upon changes in estimates of body composition with concurrent measures of TBF and %BF7 and from indirect methods.10,11 Analyses of limited serial indicate potential risk for obesity later in adulthood.6 data, have been reported by Chumlea and collea- However, the tracking of measured values for TBF, gues.12,13 These investigators reported annual incre- ments in body composition variables for 69 children Correspondence: Shumei S Guo, PhD, Department of aged 10±18 y, using data from the Fels Longitudinal Community Health, Wright State University School of Medicine, Study. These short term serial data, permitted simple 1005 Xenia Avenue, Yellow Springs, OH 45387-1695, USA. descriptions of changes, based upon two or three E-mail: [email protected] Received 19 February 1997; revised 14 July 1997; accepted 6 consecutive annual examinations only. These data August 1997 were insuf®cient for modelling individual patterns of Age- and maturity-related changes in body composition SS Guo et al 1168 change over time or for relating changes to subsequent pant's birthdays from 1976 to 1988. Because of the adult outcomes. timing of scheduled visits and the ages of the partici- It is well recognized that there is a relationship pants at the beginning and the end of this period of data between the maturation of a child and his or her collection, some participants had incomplete data sets growth and development, especially during adoles- within the age range of the analysis. Some participants cence. Using data from the Fels Longitudinal Study, were older than 8 y when the measurements began in Reynolds14 demonstrated that early sexual maturity in 1976, and some were younger than 23 y when the most girls and boys, aged 6±16 y, based upon secondary recent data for the present analysis were recorded. The gender characteristics, was related to increased rates absence of these values did not alter the descriptions of of growth in radiographic measures of muscle and the changes that occurred in body composition. The subcutaneous adipose tissue. Similar ®ndings have inclusion of serial data up to the age of 23 y in the been reported by others.15,16 However, these earlier analysis improved the accurate description of the pat- studies were not able to incorporate the rate or level of terns of change near the age of 20 y. maturation, with concurrent changes in body compo- sition over long time periods, into multivariate serial analytical models. Body composition methods The present study examined the pattern of age- Measures of TBF, %BF and FFM were obtained from related changes in TBF, %BF and FFM during a 12 y a multicomponent body composition model. The age- age range, from childhood and adolescence to young and gender-speci®c values for the concentrations of adulthood. We addressed three questions. First, what the major constituents of FFM were used to calculate values for its density in different age groups for each were the age- and gender-speci®c patterns of change 18 18 in TBF, %BF and FFM from 8±20 y of age? The gender. We smoothed the Lohman values for the patterns of change in TBF, %BF and FFM were density of FFM by using a ®xed-knot cubic spline technique and calculated FFM from body density modelled, and the annual rates of change for body 19 composition were derived. Secondly, what was the using these smoothed values. Let d1 be the density effect of the rate of biological maturation on the and d2 be the density of fat (0.9 g=cc). The value for %BF is: patterns of change in body composition values?   Indices of rates of maturation were incorporated into 1 d d d %BF ˆ 1 2 2  100; the analysis to evaluate their effects on changes in BD d d d d body composition during adolescence. Thirdly, what 1 2 1 2 was the extent of the tracking of body composition and TBF and FFM can be obtained from %BF as: from childhood into young adulthood? The likelihood TBF ˆ W  %BF and FFM ˆ W 1 %BF†: of individuals remaining in the same percentile chan- nel in body composition over time was evaluated. The Body density was determined from underwater weigh- sensitivity and speci®city of childhood and adolescent ing and residual volume, which was measured on a body composition measures, as predictors of adult Gould 2100 computerized spirometer.19 In the multi- values, were computed. component model, the density of the fat-free compo- nent varied due to changes in its water and bone mineral content.18,19 Stature was measured to 0.1 cm on a Holtain stadiometer, (Seritex, Carlstadt, NJ, USA) and weight was measured to 0.1 kg on a beam Subjects and Methods balance scale.

The data for the present study were from 130 Cauca- Maturational data sian males and 114 Caucasian females, all of whom Skeletal ages were assessed using the FELS method20 are participants in the Fels Longitudinal Study in from left hand-wrist radiographs obtained on the same southwestern Ohio. The Fels Longitudinal Study 17 day as the body composition measures. Rate of started in 1929 and has been described in detail. maturation was de®ned as skeletal age (SA) of the Participants enter the Fels Longitudinal Study at or left hand-wrist less chronological age (CA), that is, before birth and are followed at regularly scheduled SA 7 CA. For each participant, the average of visits. Body composition measures start at the age of SA 7 CA over the period from 8 y to maturity was 8 y. For the present analysis, we included participants obtained. A participant with an average value > 1 who had at least 6 serial body composition measures indicated a tendency for rapid maturation; a partici- taken between the ages of 8 and 23 y. All procedures pant with an average value < 7 1 had a tendency for were approved by the Institutional Review Board of slow maturation and `intermediate' otherwise. Wright State University. To study changes in body composition from 8±20 y of age, individual, annual serial measures of TBF, Preliminary analysis %BF and FFM from 8±23 y of age were analyzed. Each individual's serial data were divided into seven, These measurements were taken near each partici- 2 y segments: 8±10, 10±12, 12±14, 14±16, 16±18, 18± Age- and maturity-related changes in body composition SS Guo et al 1169 20 and 21±23 y. If a participant had more than one participants who remained in the upper tertile group measurement within a two year age range, the average from one age to another. Speci®city (Sp) refers to the was computed and used in the analysis. Means and percentage of participants who remained in the com- s.d. were calculated for weight, stature, TBF, %BF bined middle and lower tertile group from one age to and FFM for males and females separately within another. Tracking of TBF, %BF and FFM was eval- each two year age segment. uated between 5 y age intervals, 8±13 and 13±18 y, and a 10 y age interval, 8±18 y. These selected age intervals approximately represent a prepubertal to General patterns of change pubertal period, a pubertal to postpubertal period This analysis focused on the overall behavior of body and a prepubertal to postpubertal period. composition values for individuals across the age range. If body composition measurements at different ages for individuals follow a multivariate normal distribution, then the mean and covariance structure of these data are suf®cient to describe the process of Results changes in body composition over time. To describe the patterns of change for TBF, %BF and FFM from childhood into adulthood, the corre- General patterns of change sponding age-speci®c values for each individual were Gender-speci®c means and standard deviations for plotted by age. Each individual's serial data, were TBF, %BF and FFM, are presented in Table 1 for ®tted by a family of low-degree polynomials (includ- each 2 y age segment. The means for TBF and %BF ing linear quadratic and cubic) with four possible were signi®cantly larger (P < 0.05) in females than in covariance models, to describe the variance-covar- males from 8±20 y of age and the gender difference in iance structure between pairs of serial data within these mean values increased with age. The means for each body composition variable. The likelihood ratio FFM, stature and weight were similar for males and tests and the Akaike Information Criterion21 were females from the age of 8±14 y, but after 14 y of age, used to select the appropriate model. The likelihood the means for FFM for males were signi®cantly larger ratio method, tests various nested models using than those for females. Similarly, the males were asymptotic chi-square statistics. The Akaike Informa- signi®cantly taller and heavier than females > 14 y. tion Criterion (AIC) was used as a overall measure of The selection of the appropriate model for each adequacy of ®t of the speci®c models. The computa- body composition variable, was based on the greatest tion was performed using `SAS PROC MIXED' AIC value and the likelihood ratio tests (Table 2). program.22 Two of the covariance models did not converge and were not considered further. A quadratic model with random intercepts and slopes was chosen for TBF for Tracking both males and females. The best ®t for %BF for both Tracking for body composition was determined from males and females was a cubic model with random the predicted values of the individual ®tted model as intercepts and slopes. For FFM in males, a cubic the extent to which individuals remained in the same model with random intercepts and slopes was percentile channel over time. The percentile channel selected, but in the females, a quadratic model with was de®ned as the upper tertile of the study sample for random intercepts and slopes was selected. The each age. Sensitivity (Se) refers to the percentage of regression parameter estimates with their standard

Table 1 Means Æ s.d. for body composition from underwater weighing and for anthropometry by age segments and gender*

8^10 y 10^12y 12^14 y 14^16y 16^18y 18^20 y

Males TBF (kg) 4.73 Æ 3.97* 6.35 Æ 5.06* 8.18 Æ 5.50* 9.08 Æ 7.23* 8.00 Æ 7.14* 9.72 Æ 7.27* BF(%) 14.89 Æ 10.01* 16.45 Æ 8.87* 17.62 Æ 7.93* 14.95 Æ 8.01* 11.40 Æ 6.98* 12.99 Æ 7.00* FFM (kg) 24.47 Æ 3.44 28.91 Æ 3.87 35.19 Æ 6.00 48.03 Æ 8.59* 57.43 Æ 8.06* 60.20 Æ 7.52* Stature (cm) 133.10 Æ 5.57 143.81 Æ 5.92 154.50 Æ 7.71 169.45 Æ 7.96* 176.72 Æ 7.04* 179.17 Æ 7.00* Weight (kg) 29.19 Æ 5.90 35.26 Æ 7.85 43.36 Æ 10.10* 57.10 Æ 12.78 65.42 Æ 12.2* 69.90 Æ 12.5* BMI (kg=m2) 16.35 Æ 2.26 16.92 Æ 2.83* 17.99 Æ 3.06* 19.76 Æ 3.63 20.91 Æ 3.53 21.74 Æ 3.49 Females TBF (kg) 6.39 Æ 3.80 8.87 Æ 4.36 11.21 Æ 4.88 13.56 Æ 6.10 15.16 Æ 6.46 16.25 Æ 6.41 BF(%) 20.12 Æ 9.42 22.54 Æ 7.53 23.15 Æ 6.99 23.65 Æ 6.83 25.10 Æ 6.75 26.33 Æ 7.01 FFM (kg) 24.06 Æ 3.97 29.90 Æ 5.11 35.96 Æ 6.24 42.20 Æ 5.90 43.67 Æ 5.39 43.96 Æ 5.51 Stature (cm) 133.75 Æ 6.10 144.53 Æ 7.61 156.17 Æ 7.73 163.63 Æ 7.07 164.91 Æ 7.01 165.42 Æ 6.82 Weight (kg) 30.45 Æ 5.82 37.86 Æ 8.09 47.16 Æ 9.28 55.75 Æ 9.71 58.82 Æ 9.66 60.19 Æ 9.71 BMI (kg=m2) 16.91 Æ 2.24 17.95 Æ 2.64 19.20 Æ 2.75 20.77 Æ 3.10 21.61 Æ 3.12 21.96 Æ 2.95

*Sign®cant sex-associated differences at P < 0.05. TBF ˆ total body fat; %BF ˆ percent body fat; FFM ˆ fat-free mass; BMI ˆ body mass index. Age- and maturity-related changes in body composition SS Guo et al 1170 Table 2 Goodness of ®t of models and estimated regression coef®cients for the selected models for total body fat (TBF), percent body fat (%BF) and fat-free mass (FFM)

Models AIC 72 log likelihoods Chi-square statistics P value

TBF (kg) for males TBF* ˆ 2.4 ‡ 0.65 age 70.003 age2 Random intercept 71494.72 2985.45 ± ± Random intercept, slope* 71429.88 2851.76 133.69 < 0.001 TBF (kg) for females TBF* ˆ 76.28 ‡ 1.84 age 70.03 age2 Random intercept 71281.03 2562.06 ± ± Random intercept, slope* 71228.15 2448.30 113.76 < 0.001 BF (%) for males BF* ˆ 766.75 ‡ 18.63 age 71.28 age2 ‡ 0.028 age3 Random intercept 71631.41 3258.82 ± ± Random intercept, slope* 71612.96 3217.92 40.90 < 0.001 BF (%) for females BF* ˆ 712.6 ‡ 7.01 age 70.44 age2 ‡ 0.01 age3 Random intercept 71419.54 2835.07 ± ± Random intercept, slope* 71401.17 2794.35 40.72 < 0.001 FFM (kg) for males FFM* ˆ 131.93 727.67 age ‡ 2.28 age2 70.05 age3 Random intercept 71584.84 3165.67 ± ± Random intercept, slope* 71540.34 3072.67 93.00 < 0.001 FFM (kg) for females FFM* ˆ 728.26 ‡ 7.54 age 70.19 age2 Random intercept 7120.95 2422.90 ± ± Random intercept, slope* 71193.03 2378.05 33.85 < 0.001

*Selected models based upon chi-square tests for P < 0.05. For all the selected models, while there are general patterns of change over time for each variable, there are also differences among individuals. These differences are re¯ected in the baselines (intercept) and the rates of change (slope).

errors for each selected model are summarized in 8 y to 16.3 kg at age 20 y; the corresponding increase Table 3. in males was from 4.7 kg at age 8 y to 9.7 kg at age The regression parameter estimates of these models 20 y. Similarly, the average %BF for the females were used to predict corresponding values for TBF, increased from 20±26% BF. In the males, the means %BF and FFM, for each individual. Gender-speci®c for %BF increased from age 8±14 y, then decreased plots of the means of these predicted values for TBF, from age 14±18 y, but increased again from age 18± %BF and FFM are shown in Figures 1±3, respectively. 20 y. The overall increase in FFM from age 8±14 y The predicted values were close to the corresponding was similar for both males and females, but after- observed values. This concordance indicated that the wards, the males demonstrated a much greater chosen models adequately described the general pat- increase in FFM than the females. Average amounts terns of change in the body composition of these of FFM increased about 10 kg from age 8±14 y in children into young adulthood. both males and females. After the age of 14 y, the From these tabular and graphic results, it appears increase in mean FFM was 9.0 kg for females, but that on average from childhood into adulthood, TBF for males, the average amount of FFM increased and %BF increased in both the females and males, but about 25.0 kg. These changes in FFM are re¯ected more in the females than the males. The average by corresponding changes in stature and weight increase in TBF for females was from 6.4 kg at age (Table 1).

Table 3 The regression parameter estimates and effects of maturity on total body fat (TBF), percent body fat (%BF) and fat-free mass (FFM) for males and females

Intercept Age Age2 Age3 Slow-maturing Intermediate

Males TBF(kg) 2.40 0.65 70.003 ± 75.24(1.75)* 72.76(1.36)* (2.72) (0.32) (0.01) ± ± ± BF(%) 766.74 18.63 71.28 0.027 76.71(2.60)* 73.16(1.98) (12.46) (2.51) (0.17) (0.004) ± ± FFM(kg) 131.92 727.67 70.05 ± 77.05(1.7)* 74.95(1.4) (10.89) (2.23) (0.003) ± ± ± Females TBF(kg) 76.28 1.84 70.03 ± 73.40(1.77) 72.73(1.01)* (2.82) (0.37) (0.01) ± ± ± BF(%) 712.60 7.01 70.44 0.01 72.98(2.55) 73.38(1.01)* (14.20) (2.95) (0.20) (0.004) ± ± FFM(kg) 728.26 7.54 70.19 ± 76.09(2.12)* 71.81(1.20) (2.55) (0.32) (0.01) ± ± ±

*The values are expressed relative to rapid-maturity ˆ zero, signi®cant at P < 0.05. For example, on the average, a rapid-maturer has larger TBF than the intermediate-maturer by 2.76 kg. age2 ˆ age6age. age3 ˆ age6age6age. Age- and maturity-related changes in body composition SS Guo et al 1171 General patterns of rates of change By taking the ®rst derivative of the selected models for TBF, %BF and FFM with respect to time, we generated gender-speci®c velocity curves for each body composition variable (Figures 4±6). Males and females had positive annual velocities for TBF, indi- cating that TBF increased continuously from the age of 8±20 y. The velocity in TBF for females was larger than that for males, particularly at young ages, and declined with age. The maximum velocity for TBF was 0.6 kg=y for males and 1.4 kg=y for females at about age 8 y for both. Percent body fat increased continuously from the age of 8±20 y for females, but the velocity for %BF decreased for much of this age range. The maximum velocity for girls was 1.9%=y at about the age of 8 y, and the minimum velocity was 0.6%=y at about the age of 15 y, then the velocity started to increase again. Figure 1 Average status values in total body fat (TBF) from age For males, %BF increased from age 8 y to about 12 y, 8±20 y for males and females. but %BF velocity during this age range decreased from 3.5%=y to zero. After the age of 12, %BF in males decreased because of the negative velocity in %BF which reached a minimum of 7 0.9%=y at about age 15 y. After the age of 15 y, the velocity in %BF for males remained negative but increased toward zero at age 18 y. By age 20 y, the velocity in %BF in males was 1%=y. The velocity for FFM, for males increased from the age of 8 y to reach a maximum velocity of 7.0 kg=y at about age 15 y. The velocity for FFM in males then decreased to 3.5 kg=y at age 20 y. The velocity for FFM in females decreased continuously over the same period. The maximum velocity for FFM for females was about 4.5 kg=y at around the age of 8 y. Females only had larger annual velocities in FFM than the males from the age of 8±11 y and the velocity for FFM for females decreased to zero by the age of 20 y.

Figure 2 Average status values in percent body fat (%BF) from age 8±20 y for males and females. Rates of Maturation The associations of patterns of change in TBF, %BF and FFM with rates of maturation are presented in Table 3. Independent of age, rapidly maturing males had larger values for TBF, %BF and FFM than slowly maturing males and females. At the same age, rapidly- maturing females had signi®cantly larger mean FFM values than slowly-maturing females. Rapidly-matur- ing males had signi®cantly larger mean TBF values than the intermediate maturing males. Rapidly-matur- ing females had signi®cantly larger mean TBF BF values than intermediate-maturing females.

Tracking The sensitivity for children to remain in the upper tertile from one age to another are presented in Table 4. The tracking analyses for TBF showed that, of the males whose TBF values were in upper tertile at age 8 y, about 74% remained in the upper tertile at age Figure 3 Average status values in fat-free mass (FFM) from age 13 y (sensitivity). Of the females with TBF values in 8±20 y for males and females. the upper tertile at age 8 y, 68% remained in the upper Age- and maturity-related changes in body composition SS Guo et al 1172 tertile group at 13 y. This proportion decreased as the length of the time interval increased. For example, only 60% of males with values in the upper tertile at age 8 y were also in the upper tertile at age 18 y, and the proportion remaining in the same percentile zone decreased to 46% for females. However, of the males in the upper tertile for TBF at age 13 y, 86% remained in the upper tertile at age 18 y as did 77% of the females. This pattern of a stronger degree of tracking from age 13±18 y than from 8±13 y or 8±18 y was fairly consistent for both males and females for %BF and FFM also. Tracking for 5 y intervals was slightly greater for those age 13 y than for those aged 8 y, indicating better tracking from the pubertal to the postpubertal period (13±18 y) than from the prepuber- tal to the pubertal period (8±13 y). These same pat- terns persisted for the individuals in the middle and Figure 4 Velocity in total body fat (TBF) from age 8±20 y for males and females. lower tertiles (speci®city) across the same age ranges for TBF, %BF and FFM. The 5 y tracking for %BF was 91% for males aged 8 y and 81% at age 13 y. Tracking in %BF in males decreased from the prepubertal to the pubertal period and from the pubertal to postpubertal period. The extent of tracking continued to decrease as the time- intervals increased. Tracking in %BF (both sensitivity and speci®city) was less marked in females than males for all age intervals. Furthermore, for females, 5 y tracking from the prepubertal to the pubertal period was similar to that from the pubertal to postpubertal period. Of the males whose FFM values were in upper tertile at age 8 y, about 79% remained in the upper tertile at age 13 y (sensitivity). Of the female partici- pants with FFM values in the upper tertile at age 8 y, 89% remained in the upper tertile group at age 13 y. For the 13±18 y intervals, sensitivity was greater for males and only slightly greater for females aged 8 y Figure 5 Velocity in percent body fat (%BF) from age 8±20 y for than for males of the same age, indicating better males and females. tracking from the pubertal to the postpubertal period than from the prepubertal to the pubertal period. The degree of tracking decreased slightly as the length of the time intervals increased, but more so in males than females. For example, of those males with FFM values in the upper tertile at age 8 y, only 70% remained in the upper tertile at age 18 y, while 83% of females remained in the upper tertile for FFM at age 18 y. This greater degree of tracking in FFM in the females is probably because most girls have com- pleted their growth in FFM during this period.

Discussion and Conclusions

The present study con®rms previous reports of changes in body composition during adolescence. In Figure 6 Velocity in fat-free mass (FFM) from age 8±20 y for this study, long-term serial data, over a 12-year males and females. period, were analyzed to examine age- and maturity- Age- and maturity-related changes in body composition SS Guo et al 1173 Table 4 Tracking in body composition from childhood to adulthood at 5 y and 10 y intervals

TBF %BF FFM

Age Intervals (y) Sensitivity Specificity Sensitivity Specificity Sensitivity Specificity

Males 8±13 0.74 0.87 0.91 0.79 0.79 0.89 13±18 0.86 0.93 0.81 0.90 0.91 0.95 8±18 0.60 0.80 0.72 0.86 0.70 0.84 Females 8±13 0.68 0.84 0.71 0.86 0.89 0.94 13±18 0.77 0.88 0.71 0.86 0.91 0.96 8±18 0.46 0.72 0.43 0.71 0.83 0.91

TBF ˆ total body fat: %BF ˆ percent body fat: FFM ˆ fat-free mass: related changes in measured body composition for but the velocity in TBF declined (Figure 3). The groups and for individuals from childhood into young females consistently had larger values for TBF than adulthood. The statures and weights of the present males, and the extent of these gender differences sample were within the corresponding 5th and 95th increased after 13 y. For females, %BF also increased percentiles for stature and weight, for persons at the from age 8±20 y (Figure 2), but the velocity in %BF same ages from the National Center for Health Sta- declined and reached a minimum at about age 15 y tistics.23 Body composition measures were obtained (Figure 5). The signi®cant increase in body fat in from underwater weighing using a multi-component females during pubescence and their greater degree of body composition model and the effects of rate of fatness, compared with males, is largely due to their maturation on long-term patterns of change in body greater production of estrogen.1 The %BF values for composition were examined. males increased from age 8±12 y, then decreased until Numerous studies have reported changes in body about age 18 y, but started to increase thereafter. composition during growth from the prepubertal, Haschke24 reported an increase in %BF from age 7± pubertal, to postpubertal periods,1,8,24 but these studies 12 y followed by a decrease in values from age 12± were based predominantly upon cross-sectional data. 17 y in males, whereas Chumlea and co-workers13 Only a few studies have used serial data to analyze reported an annual decrease of 1.15% in %BF from changes in body composition, but these previous serial age 10±18 y.13 studies had small sample sizes.1,13 Furthermore, pre- The results from the application of the random vious studies calculated changes in body composition effects model, indicated that the general patterns of as differences between two successive measurements change for FFM followed a cubic model in males and or applied a simple linear regression model. Differ- a quadratic model in females (Figure 3). FFM values ences between pairs of successive measurements pro- were similar between males and females before age vide inadequate descriptions of the long-term patterns 12±14 y, but males had substantially larger FFM of change and individual data can be excluded when values than females thereafter. Values for FFM there are missing values. A linear regression assumes increased with age in each gender, but the increase changes over time are constant, which may not tended to decline continuously for females as indi- accurately re¯ect the actual patterns of change. The cated by the decreasing velocity in FFM for females present study took advantage of the availability of (Figure 6). For males, the increase in FFM was just long-term serial data and improved statistical methods beginning to decline in the postpubertal period. The to investigate long-term patterns of change in body velocity in FFM which reached a peak of 7 kg=y was composition. still 4 kg=y at age 20 in males. The gender differences A random effects model was used to determine the in the present ®ndings were slightly smaller than those patterns of change over time in body composition and reported by Forbes1 who considered the differences the parameters were used to characterize individual to be largely due to the greater production of testos- differences. This model analyzed the complete set of terone by the males. The increase in FFM of 36 kg for serial data. The random effects model handled the males from age 10±20 y in the present study, was occurrence of missing values and included measure- larger than the increase of 33 kg reported by Forbes1. ments made at various time intervals. Missing values The increase in FFM for females from age 10±20 y were estimated, using maximum likelihood proce- was 19 kg which was also larger than that of 16 kg dures, with the assumption that the patterns of reported by Forbes.1 These study differences, in the change for individuals followed paths similar to amounts of change in FFM may be partly due to those of their peers. The random effects model also whether serial data for individuals or cross-sectional allowed for the inclusion of covariates such as gender data were analyzed or a multicomponent model was and rate of maturation. used. The general pattern of change in TBF values was The major changes in the components of FFM from one of increase throughout the study period (Figure 1) the prepubertal to young adulthood are due to altera- Age- and maturity-related changes in body composition SS Guo et al 1174 tions in the water and bone mineral content of the in muscle and fat than later maturing children at the FFM8,24 leading to increases in the density of FFM same chronological age. with growth and maturation.18 There are also gender, The participants in the present study were born race and maturational differences.25,26 It is necessary between 1958 and 1988. Potentially, there could be to use a multicomponent model to estimate body secular trends in the body composition measures, composition in children and adolescents, from under- affecting these results for the older children. To water weighing, because the multicomponent model examine this possibility, we separated the participants assumes that the total body consists of fat, water, bone into four groups by their birth years: 1953±1963, and protein, and that each of these components has a 1963±1973, 1973±1983 and 1983 ‡ . Values for ®xed density. Several studies have documented the TBF, %BF and FFM were compared among the four advantages of multi-compartment model over the groups at age 8, 13, and 18 y. There were consistent traditional 2-compartment model.18,27,28 increasing trends from older to younger groups by Within the study age period, the velocity curves for birth years, but these trends were not signi®cant. TBF and FFM have their maximum values at age 8 y However, these trends in increased fatness among for females (Figure 4 and Figure 6). The study period the younger birth groups corresponds with the began at age 8 y, so that there are no body composi- reported increased prevalence of obesity in US chil- tion measures for younger participants. If data for dren.29 younger participants were available, it is possible that We have demonstrated, for the ®rst time, that the the maximum velocity in TBF and FFM could have tracking of measured values of TBF, %BF and FFM been earlier. We are uncertain as to the age of persists over 5 and 10 y time periods from childhood maximum velocity for TBF and FFM because of the to adulthood. The occurrence of tracking at these age constraint of performing underwater weighing for levels has signi®cant implications for the clinical young children. With the availability of pediatric management, public health programs and epidemio- software for dual energy X-ray absorptiometry logical studies of US children. Clinical and public machines, future investigators should be able to health programs are aimed at altering the percentile remove this uncertainty for the age at maximum levels of children with high values for BMI that are velocity for TBF and FFM in females. associated with increased health risk. Values for BMI The selected random effects models ®t the indivi- are highly correlated with TBF and %BF.7 Clearly, dual serial data well. As expected, the variations when a high value is found for TBF or %BF (or a low between the predicted and the observed values in value for FFM) it should be regarded as a signi®cant the individual curves were greater than those in the guide to future body composition values and poten- mean curves. There were differences among indivi- tially indicative of increased health risk in adult- duals in the intercepts (baseline values at 8 y) and the hood,30 because childhood body composition values slopes (rates of change). The individual parameter are closely related to corresponding values in young estimates from the present results allow future studies adulthood. The present ®ndings indicate that high of covariate in¯uences on patterns of change in body values of TBF and %BF for individuals in childhood composition. These in¯uences include genetic factors, strongly persist into adulthood and this persistence is physical activity, diet, hormone and maturational even stronger for adolescence into adulthood. There- levels. fore, the presence of obesity in a child early in In the present study, relative skeletal age expressed adolescence, has a high probability of continuing, as relative to chronological age (CA), that is SA±CA, that child develops into an adult. Body composition was used as an index of maturity. This measure of should be measured or accurately estimated in clinical maturity de®ned whether an individual was rapidly- and epidemiological studies of children, even perhaps maturing, slowly-maturing or near-average. A mean at younger ages than those covered in the present relative skeletal age for each child was obtained over investigation. the study period and incorporated into the random effects model, to study the effects of maturity levels on body composition. At the same chronological age, rapidly-maturing children have signi®cantly larger Conclusions values for TBF, %BF and FFM than slowly-maturing children. At the same chronological age, rapidly- maturing males and females have signi®cantly larger From the present results, we are able to describe the values for TBF and %BF, than those maturing at near patterns of change in body composition from child- average rates and signi®cantly larger values for FFM hood into adulthood, the effect of the rate of matura- than those who are maturing slowly. These results tion on body composition and the degree of tracking using relative skeletal age, support the similar ®ndings in body composition for an individual. These ®ndings by other investigators1,14,16,31 who have used this and con®rm reports from earlier cross-sectional and short- other measures of maturity (age at menarche, peak term serial studies. For TBF, there is a continual height velocity and secondary gender characteristics); increase, but a declining rate of change. For %BF earlier maturing children have greater rates of growth the same pattern exists as for TBF for females, but for Age- and maturity-related changes in body composition SS Guo et al 1175 males the pattern of change in %BF re¯ects the anthropometry and bioelectrical impedance. In: Yasumura S, concurrent changes in FFM. For FFM, there is an Harrison JE, McNeill KG, Woodhead AD, Dilmanian FA (eds). In Vivo Body Composition Studies. Recent Advances. increase in girls that slows down in adolescence, but Basic Life Sciences, Volume 55. Plenum Press: New York, in males, there is a continual rapid increase into young 1990. pp 391±393. adulthood. For the same age, rapidly-maturing chil- 11 Slaughter MH, Lohman TG, Boileau RA, Horswill CA, Still- dren have signi®cantly larger TBF, %BF and FFM man RJ, Van Loan MD, Bemben DA. Skinfold equations for than the slowly-maturing children. For the same age, estimation of body fatness in children and youth. Hum Biol 1988; 60: 709±723. rapidly-maturing females have signi®cantly larger 12 Chumlea WC, Knittle JL, Roche AF, Siervogel RM, Webb P. TBF and %BF than the intermediate-maturing females Size and number of adipocytes and measures of body fat in and signi®cantly larger FFM than the slow-maturing boys and girls 10 to 18 years of age. Am J Clin Nutr 1981; 34: females. Signi®cant tracking in body composition 1791±1797. exists. The extent of tracking is inversely related to 13 Chumlea WC, Siervogel RM, Roche AF, Webb P, Rogers E. Increments across age in body composition for children 10 to the length of time intervals between two measure- 18 years of age. Hum Biol 1983; 55: 845±852. ments. 14 Reynolds EL. Sexual maturation and the growth of fat, Many aspects of these ®ndings have been reported muscle and bone in girls. Child Development 1946; 17: 121± previously but for shorter time periods and smaller 144. (and sometimes cross-sectional) samples.7,12±14,31 15 Roche AF, Wainer H, Thissen D. The RWT method for the prediction of adult stature. Pediatrics 1975; 56: 1026±1033. With the use of a large serial data set, we have 16 Tanner JM. Growth at Adolescence. Blackwell Scienti®c con®rmed the pattern of change in body composition Publications: Oxford, 1962, pp 121±130. among children and that these patterns are affected by 17 Roche AF. Growth, Maturation and Body Composition: The a child's level of maturity. More importantly, we have Fels Longitudinal Study 1929±1991. Cambridge University demonstrated that the pattern of these changes in body Press: Cambridge, UK, 1992. pp 282. 18 Lohman T. Applicability of body composition techniques and fatness persist into young adulthood, demonstrating constants for children and youths. Exerc Sports Sci Rev 1986; the signi®cant tracking of body composition and 14: 325±357. obesity. 19 Guo S, Roche AF, Houtkooper L. 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