1 Division of Adult and Community Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA.
2 Division of Adolescent and School Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA.
3 The University of TexasHouston Health Science Center, Houston, TX.
Received for publication July 23, 2001; accepted for publication May 28, 2002.
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ABSTRACT |
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adolescence; body mass index; child; growth; longitudinal studies; obesity
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INTRODUCTION |
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In epidemiologic studies, body fatness has been conceived in various ways using a variety of methods, including body composition, relative weight, and body measurements of skinfold thicknesses and circumferences for assessment of regional fat distribution (4). Body fatness may be represented by measures of fat mass in absolute terms or relative to total body mass, often expressed as "percent body fat." Use of sophisticated body composition techniques is impractical in most population-based epidemiologic research; therefore, estimation of fat mass and percentage of body fat by anthropometry, alone or with bioelectrical impedance, is useful (5).
In addition to fat mass and percent body fat, skinfold thickness measures are also available, and values at multiple body sites can be summed to produce a measure of total body fatness or to reflect regional fat distribution.
Relative weight is usually assessed as body weight in relation to height, an approach long taken by the life insurance industry (e.g., Metropolitan relative weight). An index of relative weight takes the general form weight/heightn. In epidemiologic studies, n has usually been set at 2the value corresponding to the Quetelet index or body mass index, calculated as weight (kg) divided by height squared (m2).
The anatomic distribution of body fat has also been studied in relation to risk of cardiovascular events (e.g., acute myocardial infarction) and risk factors (e.g., elevated blood lipid levels and high blood pressure) (6). Some laboratory techniques provide direct measures of tissue composition at sites such as the abdomen. Field methods are limited to anthropometry, with various derived indices based on anatomic diameters, circumferences, and skinfold thicknesses. Abdominal circumference, for example, is a useful measure of regional fat distribution (7).
Obesity indices derived from body composition and anthropometric methods have been used widely in population studies. However, to our knowledge, the comparative growth patterns of such indices relative to each other have not been examined in population-based longitudinal studies (812). Studying growth patterns of body fat measured by different indices is important in order to understand the development of obesity and the influence of age and sex on individual differences in body fatness. Comparing growth patterns for different indices may further facilitate interpretation of inconsistent study results involving different obesity measures. Using data from Project HeartBeat!, we undertook the current analysis to construct and describe the growth patterns, from age 8 years to age 18 years, of five obesity indices often used in epidemiologic research: body mass index, percent body fat, abdominal circumference, sum of two skinfolds, and sum of six skinfolds.
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MATERIALS AND METHODS |
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The design and methods of Project HeartBeat! have been described previously (1315). A unique feature of the design is overlap in ages at examination of successive cohort pairs among the three age cohorts. That is, members of cohort 1, aged 8 years at entry, were age 11 at their last scheduled examinations; members of cohort 2, aged 11 years at entry, were age 14 at their last scheduled examinations; and members of cohort 3 were aged 14 years at entry. This feature permitted evaluation of cohort effects at ages of overlap and strengthened the mixed longitudinal design. Statistical evaluation of the overlapping data revealed few differences attributable to cohort membership and supported our decision to combine data from the three cohorts to assess patterns of development over the entire age span of the study.
The study sample consisted of 678 participants (table 1); 49.1 percent were female, 74.6 percent were White, 20.1 percent were Black, and 5.3 percent were of other race/ethnicity. The mean number of examinations was 8.3 over 4 years of data collection. The distribution of the examinations was reported earlier (13). Withdrawals (153 participants, 22.6 percent) were mainly due to relocation (73/153, 48 percent), the remainder being due to loss of interest, competing activities, and the like. The withdrawals were similarly distributed by sex and ethnicity, and children who withdrew were slightly older at baseline than the rest of the study participants (a mean age of 11.6 years vs. 10.6 years). No other significant differences in baseline characteristics were found between withdrawals and nonwithdrawals after age effects were accounted for.
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Measurements
Using standard protocols, two trained and certified technicians worked together to obtain each anthropometric measurement (16). Participants were barefoot and wore surgical scrub suits over underwear while the measurements were taken. Weight was measured to the nearest 0.1 kg and height to the nearest 0.1 cm. Body mass index was calculated as weight (kg)/height (m)2. Abdominal circumference was measured to the nearest 0.1 cm at the level of the greatest frontal extension of the abdomen between the bottom of the rib cage and the top of the iliac crest. Skinfolds were measured in triplicate to the nearest 0.1 mm at six sites. The three measurements made at each site were averaged before the sum of skinfolds was calculated in two different ways: 1) the commonly used sum of the triceps and subscapular skinfolds and 2) the sum of six skinfolds (the triceps, subscapular, midaxillary, and abdominal skinfolds, the distal thigh, and the lateral calf).
Percent body fat is calculated by the sex-specific formulae of Guo et al. (17) on the basis of a combination of bioelectrical impedance and body measurements. These formulae were chosen because they have been cross-validated in several studies that included children and young adults and they had the smallest standard errors among the various formulae for bioimpedance (17, 18). The standard errors of these prediction equations appear to be typical of those of other body composition techniques, such as body density from underwater weighing (19). The use of prediction equations such as these has two advantages over simpler anthropometry: 1) they estimate a specific aspect of body composition (here, body fat) and 2) carefully cross-validated formulae provide more accurate estimates than single measurements and indices (5, 19).
Data analysis
Multilevel statistical analysis was used to estimate average age trajectories by sex and race/ethnicity for each of the five obesity indices (20). The multilevel analysis explicitly accounts for correlations due to repeated measurements made over time on individuals. This is accomplished by introducing into the regression model a complex variance term that estimates variance within and between individual subjects and also the appropriate covariance terms. The model parameters were estimated by the method of iterated generalized least squares, which under normality assumptions leads to maximum likelihood estimates. Tests of statistical hypotheses were carried out through the use of Wald tests (ratio of an estimated parameter to its standard error) or deviance tests (changes in 2 ln(likelihood)). A p value of 0.05 was used as the criterion for all statistical testing. No correction was made for repeated testing.
Population predicted values were calculated and plotted for each race-sex subgroup in five figures, one for each index. These predictions are subject to sampling variability that can be calculated as a function of age from the estimated variances and covariances for the model. To preserve the simplicity of the figures, we do not display these confidence bands graphically.
Although Blacks constituted 20.1 percent of the total study population, as planned in accordance with funding restrictions, the absolute numbers of observations for Blacks from ages 8 years to 8.5 years and 15 years to 18 years were relatively small (table 2). Accordingly, we decided that estimates of trajectories in these age intervals might not be stable and could be misleading. Hence, trajectories for Black participants are limited to ages 8.515 years.
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A backward-elimination procedure was used for selecting independent variables. Step 1 was to eliminate age3, sex x age3, and race x age3 together from the model if the deviance associated with these three terms was not statistically significant; step 2 was to eliminate age2, sex x age2, and race x age2 together if the deviance associated with these three terms was not significant. Age, sex x age, and race x age were checked at step 3. The backward-elimination procedure would be terminated and the model considered final if, at a certain step, the group of relevant variables being tested for removal was statistically significant.
This elimination procedure was adopted prior to modeling to allow the same flexibility of change, in each obesity index, with age in different sex and ethnic subgroups at the same exponent of age. Age was centered before modeling by subtracting the mean age of all participants at examination.
Descriptive statistical analyses were performed with the SPSS statistical package (21). The longitudinal modeling of trajectories of obesity indices was conducted using MLwiN, version 2.1 (22).
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RESULTS |
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Trajectories of the five obesity indices
Figures 15 show the trajectories of each of the five obesity indices by sex and race.
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Percent body fat
Trajectories for percent body fat were strikingly different from those for body mass index. Patterns of change in mean percent body fat with age also differed importantly by sex (figure 2). In non-Black males, the trajectory peaked at approximately 24.8 percent at age 9 years; it decreased sharply and continuously over subsequent ages, to reach approximately 15.5 percent at age 18. By contrast, among non-Black females, percent body fat remained nearly constant at about 25.526 percent, although some decrease occurred from age 8 to age 12 and a very slight increase occurred thereafter. Among Black males, percent body fat increased from about 19.4 percent at 8.5 years to a peak of approximately 20.8 percent at age 10.2 years, decreasing to approximately 15.8 percent at age 15 years. By contrast, mean percent body fat among Black girls increased continuously, though not markedly, from approximately 24.5 percent at age 8.5 years to approximately 27.4 percent at age 15.
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DISCUSSION |
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Comparisons of selected baseline anthropometric measures with those of combined samples from National Health and Nutrition Examination Surveys I (19711975) and II (19761980) indicated that Project HeartBeat! children were slightly larger and had somewhat thicker skinfolds than children in the national surveys, and variances of those measures in Project HeartBeat! were generally comparable to those in the national surveys (15). A separate comparison of the 5th, 50th, and 95th percentiles of weight, stature, and body mass index with recent growth chart percentiles for US children (Centers for Disease Control and Prevention) showed that non-Black children in Project HeartBeat! exhibited distributions of basic body measurements similar to those of children in recent US data; Black children in Project HeartBeat! deviated more from US Black children in body mass at the 95th percentile (data not shown). We conclude that generalizations from our findings are appropriate for the non-Black sample, while those for Black children should be considered only approximate.
It is clear that growth patterns among the non-Black children differed remarkably by index, especially as regards sex differences. All indices varied importantly with age, and, except for body mass index and abdominal circumference, expected sex differences emerged at older ages, with girls having more adiposity than boys.
Notable was the similarity in the growth patterns of abdominal circumference and body mass index; both increased in a monotonic fashion with age, with little or no sex difference emerging as children reached late adolescence. This contrasts with percent body fat, for which there was a strong divergence between males and females, with males, as expected, decreasing in body fat with age and females remaining nearly constant with age. This sex pattern in the growth of body fat is well known (23, 24). The lack of a sex difference in body mass index and its tendency to increase among boys in late adolescenceas opposed to body fat, which diminishes in boysis evident in other population samples (8).
The skinfold sums tended to reflect a growth pattern similar to that of percent body fat. Whether we considered the sum of six skinfolds or the sum of two skinfolds, the picture was much the same; addition of the four further measures beyond the triceps and subscapular skinfolds did not contribute noticeably to differentiation of groups by age or sex.
When trajectories for Black participants were examined, much the same relation among indices was apparent. Body mass index and abdominal circumference were similar in pattern. However, each of the remaining indices appeared to distinguish males and females more sharply among Blacks than was the case for non-Blacks. Only for percent body fat was there a suggestion of a possibly important difference from non-Blacks, in that for females percent body fat appeared to increase from age 8.5 years to 15 years, while for non-Black females it remained essentially unchanged over this age range. This resulted in Black girls attaining higher levels of body fat than non-Black girls by age 12 years. Similar trends have been documented in other recent longitudinal studies of adiposity in girls (9, 10).
The contrast between the age-related pattern of body mass index and abdominal circumference and that of percent body fat, sum of two skinfolds, and sum of six skinfolds indicates that different indices measure different aspects of adiposityfor example, "relative weight" for body mass index and abdominal circumference and "body fat" for the other indices studied here. Evidently, boys relative weight increases in late adolescence mainly because of the growth of lean mass and skeletal mass at the shoulder and girls mainly because of the growth in adiposity and skeletal mass at the hip. These aspects of body composition are not distinguished by relative weight or circumference-based measures. Hence, there is a lack of a clear sex difference in relative-weight indices of adiposity. Among Black children in Project HeartBeat!, a clear sex difference was seen in all of the indices, including body mass index and abdominal circumference. Nevertheless, the latter two trajectories did not have the shape of the body fat growth curve. The growth curve of body fat was quite distinct from that of relative weight. This distinctness of the growth trajectory of body fat is readily explained on the basis of these data. It results from the loss of body fat in absolute terms in males during the peak of adolescent somatic growth and the corresponding depression in the growth rate of body fat in females, although in females the average growth rate does not fall below zero (23). The different body measurement-based indices of adiposity represent different tissue content, as evidenced by the differences in growth pattern. This may partly explain some of the conflicting results from studies on relations between "obesity" and cardiovascular disease risk factors in children.
Growth of body fat has previously been studied through analysis of body composition (24, 25), radiography and skinfold thickness (26, 27), and body mass index (28). The least developed of these methods is assessment of body composition in terms of means, variances, and percentiles of fat mass, percent body fat, or fat-free mass in normal population samples. Malina and Bouchard (29) summarized the available data in terms of mean values by age and sex. In contrast to body composition, extensive normative data are available for the triceps and subscapular skinfolds and body mass index (3033).
Developmental changes, particularly with regard to sex differences in body fat and body fat distribution, have been studied (23, 34, 35). However, to our knowledge, no longitudinal study to date has considered a series of indices commonly used in epidemiologic research and made a comparative study of their growth patterns. The data from Project HeartBeat! are consistent with regard to the growth of percent body fat in other studies.
Little has been written previously about the lack of sex differences and differentiation in the body mass index, and very little is known about the growth of abdominal circumference. The lack of sex differences in average values in the latter two indices, as well as their monotonic patterns of age change in the two sexes, is at variance with what is widely known about the growth of body fat in humans. Body mass index and abdominal circumference are composite measures of the whole body, and it is known that each contains information from tissues other than adipose mass. Underlying bone, muscle, and organs will be reflected in body mass index and abdominal circumference, indicating that they are, at best, indirect indicators of body fat. This is reflected in their growth trajectories, which do not differentiate typical male/female patterns. Body mass index is almost as much related to fat-free mass as it is to body fat mass (15, 36), and abdominal circumference, however convenient it is as an index of abdominal fat, is evidently only marginally related to visceral fat (37). These shortcomings are evident in the different growth trajectories of "two" types of indirect indices of childhood adiposity. Study of the relation of these different types of adiposity indices to blood pressure and blood lipid levels will be required to better interpret the extent to which energy balance per se, vis à vis body fat, influences cardiovascular disease risk factors in children. Alternatively, sexual maturation, with its attendant accelerated changes in both lean and fat mass at adolescence, is what is responsible for the association of body measurements with lipid levels and blood pressure.
Conclusions
The five indices studied here, all accepted as measurements of adiposity, exhibit different growth patterns in childhood. Obesity indices with distinct growth patterns should not be expected to relate in the same way to changes in blood pressure or blood lipid concentrations. Consideration of choices among these indices for further studies will depend on subsequent analysis of their predictive relation to cardiovascular disease risk factors and other variables of interest and on factors of cost, staff time, and participant burden.
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ACKNOWLEDGMENTS |
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The authors appreciate the cooperation of the Conroe Independent School District and the generous support of The Woodlands Corporation.
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NOTES |
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REFERENCES |
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