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Comparison of methods to assess change in children’s body composition^1,2,3

¹ From the Unit on Growth and Obesity, Developmental Endocrinology Branch, National Institute of Child Health and Human Development (JE, JRM, MK, DR, and JAY), and the Nutrition Department (NGS and CS) and the Department of Nuclear Medicine (JCR), Warren Magnuson Clinical Center, National Institutes of Health, Department of Health and Human Services, Bethesda, MD.

² Supported by grant Z01-HD-00641 from the National Institutes of Health (JAY) and the National Center for Minority Health and Health Disparities. JAY and NGS are commissioned officers in the US Public Health Service, Department of Health and Human Services.

³ Address reprint requests to JA Yanovski, Unit on Growth and Obesity, DEB, NICHD, NIH, 10 Center Drive, Building 10, Room 10N262 MSC 1862, Bethesda, MD 20892-1862. E-mail: yanovskj{at}mail.nih.gov.

	ABSTRACT

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Background: Little is known about how simpler and more available methods to measure change in body fatness compare with criterion methods such as dual-energy X-ray absorptiometry (DXA) in children.

Objective: Our objective was to determine the ability of air-displacement plethysmography (ADP) and formulas based on triceps skinfold thickness (TSF) and bioelectrical impedance analysis (BIA) to estimate changes in body fat over time in children.

Design: Eighty-six nonoverweight and overweight boys (n = 34) and girls (n = 52) with an average age of 11.0 ± 2.4 y underwent ADP, TSF measurement, BIA, and DXA to estimate body fatness at baseline and 1 ± 0.3 y later. Recent equations were used to estimate percentage body fat by TSF measurement (Dezenberg equation) and by BIA (Suprasongsin and Lewy equations). Percentage body fat estimates by ADP, TSF measurement, and BIA were compared with those by DXA.

Results: All methods were highly correlated with DXA (P < 0.001). No mean bias for estimates of percentage body fat change was found for ADP (Siri equation) compared with DXA for all subjects examined together, and agreement between body fat estimation by ADP and DXA did not vary with race or sex. Magnitude bias was present for ADP relative to DXA (P < 0.01). Estimates of change in percentage body fat were systematically overestimated by BIA equations (1.37 ± 6.98%; P < 0.001). TSF accounted for only 13% of the variance in percentage body fat change.

Conclusion: Compared with DXA, there appears to be no noninvasive and simple method to measure changes in children’s percentage body fat accurately and precisely, but ADP performed better than did TSF or BIA. ADP could prove useful for measuring changes in adiposity in children.

Key Words: Air-displacement plethysmography • bioelectrical impedance • skinfold thickness • dual-energy X-ray absorptiometry • DXA • adiposity • body fat mass • change • growth • children

	INTRODUCTION

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Over the past 40 y, the prevalence of pediatric overweight, defined as a body mass index (BMI) 95th percentile for age, sex, and race, has risen dramatically, so that >15% of children aged 6–19 y are now considered overweight (1). The increase in prevalence of overweight is greatest in black and Hispanic children and adolescents (1, 2). Evaluation of efforts to reverse the increasing prevalence of overweight in childhood requires the ability to assess changes in body composition, especially body fatness, accurately.

The measurement of change in adiposity in children is challenging because of the effects of maturation and growth on lean muscle mass, fat mass, and hydration status (3). Currently, methods available to examine changes in children’s body composition include relatively simple field methods, such as bioelectrical impedance (BIA) and skinfold-thickness measurements, that were shown to be neither consistently precise nor accurate (4-6), and more cumbersome laboratory methods, such as hydrodensitometry, isotope dilution, and dual-energy X-ray absorptiometry (DXA), that were shown to be more accurate and precise but can be inconvenient and difficult to use in pediatric populations.

Air-displacement plethysmography (ADP) is a fairly new method of body composition assessment that was shown in cross-sectional studies to be reasonably precise, accurate, and easy to use in both adults and children (7-10). Limited data describe the ability of ADP to accurately assess changes in body composition (11), but to our knowledge no available data exist in pediatric cohorts.

We, therefore, examined estimates of body fatness at 2 different time points to assess estimates of change in body fatness in normal-weight and overweight African American and white children and adolescents. Percentage body fat by ADP, BIA, and skinfold thickness was compared with estimates of percentage body fat by DXA, the reference method.

	SUBJECTS AND METHODS

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Subjects
Eighty-six African American and white subjects (52 girls and 34 boys; Table 1) were recruited for nonintervention (natural history) studies of physiology and metabolism and for weight reduction trials through notices mailed to children in the school systems of Montgomery County, MD, Prince George’s County, MD, Fairfax County, VA, Arlington County, VA, and Washington, DC; by advertisements in local newspapers; and, in the case of overweight children, by referral from local physicians. The study was approved by the National Institutes of Health Intramural Clinical Research Review Board. Each child gave written assent, and a parent gave written consent, for protocol participation.

Subjects were studied 2 times, at baseline and again 0.98 ± 0.34 (mean ± SD) y later, to assess change in body fatness as measured by ADP, DXA, BIA, and anthropometry.

Air-displacement plethysmography
Subjects were studied in the morning, after an overnight fast, and were instructed to void before being measured. Body density was assessed with use of the BOD POD ADP body composition system (Life Measurement Incorporated, Concord, CA), according to the manufacturer’s directions and procedures previously described (10, 14). All ADP studies were performed by trained research assistants. Subjects were assessed in minimal clothing (either underwear or a tight-fitting bathing suit) and wearing a swim cap. Body mass was measured on a scale calibrated with a known weight before each subject’s measurement. Body volume was determined while subjects sat in the BOD POD chamber. To correct for lung air volume, the thoracic gas volume (V_TG) was measured during tidal breathing and during exhalation against a mechanical obstruction. Subjects were excluded from the study if measured V_TG could not be obtained. The skin surface area artifact was also calculated by the ADP software to account for the changes in air temperature close to the subject’s skin. Body density was then calculated as the ADP-measured body mass divided by (total body volume + 0.40 x V_TG – surface area artifact).

The Siri equation was used to determine body fatness from body density (15). ADP-Siri was previously shown to be a reasonably accurate method of body fat estimation (10). The estimation of body fatness from body density assumes a constant density of the fat-free mass (FFM). Because the water content of the FFM was previously reported to change with age, we also used the Lohman age-adjusted equations to determine body fatness from body density (3).

Dual-energy X-ray absorptiometry
Body composition was assessed by DXA with use of the Hologic QDR 2000 (Waltham, MA) pencil-beam densitometer (n = 38) and the Hologic 4500A fan-beam densitometer (n = 48). DXA estimates of body fat were used as the criterion method to which all other estimates of body fat were compared. Baseline and follow-up measurements for each subject were done with the same DXA machine. Analyses divided by densitometer type found no differences in the relation between change in percentage body fat and other demographic variables (t tests; data not shown).

Anthropometric measurements
All anthropometry was assessed with use of standardized technique (16) by 1 of 3 experienced dietitians. With the arm hanging loosely at the subject’s side, the triceps skinfold thickness (TSF) was "measured at the midline of the posterior aspect of the arm, over the triceps muscle, at a point midway between the lateral projection of the acromion process of the scapula and the inferior margin of the olecranon process of the ulna" (16). TSF measurements, taken to the nearest 0.5 mm, were taken in triplicate, using Lange calipers (Cambridge Science Industries, Cambridge, MA). The average of the measurements was used for the analysis. The calipers were checked for calibration before use with 5-mm and 15-mm calibration blocks that were provided by the manufacturer. Calipers with erroneous calibration were returned to the manufacturer for repair.

Percentage fat was calculated with use of the equation of Dezenberg et al (17) from TSF measurements [(0.332 x weight) + (0.230 x triceps) + (0.641 x sex) + (0.857 x ethnicity) – 8.004; sex is 1 for male and 2 for female and ethnicity is 1 for white and 2 for African American]. Height was measured 3 times with use of a stadiometer (Holtain Ltd, Crymych, United Kingdom) that was calibrated to the nearest 0.1 cm before each subject was measured. Weight was obtained with use of a platform digital scale (Scale-Tronix, Wheaton, IL) that was calibrated to the nearest 0.1 kg.

Bioelectrical impedance analysis
Resistance and reactance were measured with use of a Bioelectrical Body Composition Analyzer (models 101Q, 106, and Quantum II; RJL Systems, Detroit) as recommended by the manufacturer. These analyzers yield identical resistance and reactance readings (18). Subjects removed socks, shoes, and any metal jewelry before measurement. Source electrodes were placed on the posterior surface of the right hand at the distal end of the third metacarpal and on the anterior surface of the right foot at the distal end of the second metatarsal, and were at least 5 cm distal to the receiving electrodes, which were placed between the styloid processes of the radius and ulna and between the medial and lateral malleoli of the ankle. Subjects were measured while lying supine on a nonconductive surface. Bioelectric resistance was measured after introduction of a 50-kHz electrical signal with a maximum current of either 500 µA (model 101Q), 800 µA (model 106), or 425 µA (model Quantum II). FFM was calculated with use of the equation of Lewy et al (19) for African American healthy children (FFM = 0.84 x [height²/resistance (ht²/R)] + 1.10), whereas the equation of Suprasongsin et al (20) was used to calculate FFM of healthy white children [FFM = 0.524 x (ht²/R) + 0.415 x weight – 0.32]. Percentage body fat was determined by subtracting FFM from total body mass and dividing by body weight.

Statistical analysis
Data from 86 children were used in the analysis. Parametric data were analyzed on a Macintosh PowerPC with use of STATVIEW 5.0.1 software (Abacus Concepts Inc, Berkeley, CA). Methods used to assess agreement were Bland-Altman pairwise comparisons (21), simple regression, analysis of variance with use of race and sex as between-group factors (with no interaction terms), and Student t tests. All tests were 2-tailed. The 95% CIs for the Bland-Altman limits of agreement were also calculated.

Bland-Altman comparisons were considered to have magnitude bias if there was a significant correlation between the differences (changes in percentage body fat by DXA – changes in percentage body fat by the test method) and the mean percentage body fat of the criterion and test methods, implying that, as the best estimate of the change in percentage body fat departs from the actual mean change in percentage body fat, the error increases.

Three subjects did not complete anthropometric measurements, and 4 subjects did not undergo BIA measurements and were, therefore, excluded from the analyses of these measurements. Variables compared were percentage body fat obtained from DXA (software version 5.64 for QDR-2000, and software version 11.2 for QDR-4500A), application of the Siri equation (percentage fat = 495/density – 450) and the age-adjusted Lohman (3) equation to ADP body density measurements, the Dezenberg et al (17) race- and sex-specific equation derived from TSF measurements, the Lewy et al (19) BIA equation for African Americans, and the Suprasongsin et al (20) BIA equation for whites.

	RESULTS

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Cross-sectionally, percentage body fat measured by DXA was highly correlated with estimates of percentage body fat by ADP-Siri (P < 0.001; R² = 0.88, SEM = 0.10; R² = 0.84, SEM = 0.10 for baseline and follow-up measurements, respectively), ADP-Lohman (P < 0.001; R² = 0.85, SEM = 0.10; R² = 0.81, SEM = 0.098 for baseline and follow-up measurements, respectively), and TSF-Dezenberg (P < 0.001; R² = 0.51, SEM = 0.080; R² = 0.60, SEM = 0.086 for baseline and follow-up measurements, respectively). However, percentage body fat by DXA was not as highly correlated with estimates of percentage body fat by BIA-Lewy and Suprasongsin (P < 0.001; R² = 0.20, SEM = 0.050; R² = 0.19, SEM = 0.048 for baseline and follow-up measurements, respectively).

At each time point, ADP-Siri accurately estimated percentage body fat compared with DXA in all subjects (Figure 1 A and B), although significant magnitude bias was found at the baseline measurement (Figure 1A; P = 0.02). ADP-Lohman significantly underestimated estimates of percentage body fat compared with DXA by 4.00% ± 7.54% at baseline (Figure 1C; P < 0.001) and 3.19% ± 8.60% at follow-up (Figure 1D; P < 0.001) but did not have significant magnitude bias. TSF-Dezenberg significantly underestimated percentage body fat by DXA by 2.84% ± 13.0% at baseline (Figure 1E; P < 0.001) and by 2.98% ± 12.6% at follow-up (Figure 1F; P < 0.001). In addition, Bland-Altman plots revealed the presence of significant magnitude bias at both time points (P < 0.001). Similarly, estimates of percentage body fat by BIA underestimated measurements of percentage body fat by DXA by 6.46% ± 24.0% at baseline (Figure 1G; P < 0.001) and by 5.09% ± 23.3% at follow-up (Figure 1H; P < 0.001) and had significant magnitude bias (P < 0.01).

View larger version (56K): [in this window] [in a new window]   FIGURE 1.. Bland-Altman plots for magnitude bias in the estimation of percentage body fat (%BF) by dual-energy X-ray absorptiometry (DXA) at baseline by (A) air-displacement plethysmography (ADP) with use of the Siri equation (P = 0.02, R2 = 0.059, SEM = 0.027), (C) ADP with use of the Lohman adjustment of the Siri equation (NS, R2 = 0.001, SEM = 0.003), (E) skinfold-thickness measurements with use of the Dezenberg et al equation (P < 0.001, R2 = 0.38, SEM = 0.069), and (G) bioelectrical impedance analysis with use of the Lewy et al and Suprasongsin et al equations (P < 0.001, R2 = 0.14, SEM = 0.041), as well as at follow-up 1 y later by (B) ADP with use of the Siri equation (NS, R2 = 0.026, SEM = 0.018), (D) ADP with use of the Lohman adjustment of the Siri equation (NS, R2 = 1.3 x 10–5, SEM = 0.0004), (F) skinfold thicknesses with use of the Dezenberg et al equation (P < 0.001, R2 = 0.53, SEM = 0.081), and (H) bioelectrical impedance analysis with use of the Lewy et al and Suprasongsin et al equations (P < 0.01, R2 = 0.082, SEM = 0.032). The dashed lines indicate the mean differences, which was significantly different from zero for panels A, E, F, G, and H (described in text); the shaded area represents the limits of agreement ( difference ± 2 SD); , subjects scanned with use of the Hologic QDR2000; , subjects scanned with the Hologic QDR4500A.

Figure 2A

View larger version (42K): [in this window] [in a new window]   FIGURE 2.. Correlation plots of estimates of change in percentage body fat (%BF) by dual-energy X-ray absorptiometry (DXA) with estimates of change in %BF by (A) air-displacement plethysmography (ADP) with use of the Siri model (P < 0.001, R2 = 0.59, SEM = 0.084), (B) ADP with use of the Lohman age-specific model (P < 0.001, R2 = 0.57, SEM = 0.082), (C) bioelectrical impedance analysis (BIA) with use of the Lewy et al and Suprasongsin et al equations (P < 0.001, R2 = 0.44, SEM = 0.074), and (D) skinfold-thickness measurements with use of the Dezenberg et al equation (P < 0.001, R2 = 0.13, SEM = 0.040).

Table 2

Figure 3

View larger version (82K): [in this window] [in a new window]   FIGURE 3.. Bland-Altman plots for magnitude bias in the estimation of change in percentage body fat (%BF) by dual-energy X-ray absorptiometry (DXA) and (A) air-displacement plethysmography (ADP) with use of the Siri equation (P < 0.01, R2 = 0.12, SEM = 0.037), (B) ADP with use of the Lohman adjustment of the Siri equation (P < 0.01, R2 = 0.16, SEM = 0.043), (C) bioelectrical impedance with use of the Lewy et al and Suprasongsin et al equations (P < 0.001, R2 = 0.57, SEM = 0.084), and (D) skinfold-thickness measurements with use of the Dezenberg et al equation (P = NS, R2 = 0.002, SEM = 0.005). The dashed lines indicate the mean differences; the shaded area represents the limits of agreement ( difference ± 2 SD).

	DISCUSSION

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
We compared the ability of ADP, skinfold-thickness measurements, and BIA to accurately assess changes in body fatness of normal-weight and overweight African American and white boys and girls with DXA as the criterion method. We used both the Siri equation and the Lohman age-adjusted equation to estimate percentage body fat from ADP density measurements. Both ADP methods estimated the change in percentage body fat imperfectly, but acceptably, compared with DXA, with the Siri estimates of change in percentage body fat agreeing better with DXA than estimates made with the Lohman age-adjusted model. Estimates of percentage body fat change from BIA had both systematic and magnitude biases, whereas estimates based on skinfold thicknesses accounted for little of the variance in percentage body fat change. We found no significant differences from DXA in estimation of change in percentage body fat in African American subjects for both ADP methods; however, we only had 28 African American subjects in our sample compared with 58 white subjects. It is, therefore, possible that we found no significant differences because of insufficient power for this subgroup.

Our finding of good agreement between ADP change and DXA change estimates is supported by one prior study of 22 adults who underwent weight loss (11). To our knowledge, there are no prior pediatric studies that examine assessment of change in body composition by ADP.

The observation that change in percentage body fat is better estimated by ADP-Siri than by ADP-Lohman is consistent with our previous cross-sectional study (10) that examined the ability of ADP to accurately estimate percentage body fat, which found better agreement between DXA and estimates of percentage body fat made with the Siri equation than estimates made with the Lohman model. Taken together, we believe these data show that it is not necessary to apply the Lohman model’s corrections to the Siri equation to determine change in percentage body fat in growing children and adolescents. Despite ADP’s ability to predict mean change in percentage body fat accurately in the sample, both ADP-Siri and ADP-Lohman had significant magnitude bias. ADP, therefore, does not fully substitute for DXA as a measure of change in percentage body fat.

We also examined the accuracy of other field methods in the assessment of change in percentage body fat compared with DXA. The Lewy-Suprasongsin BIA equations significantly overestimated change in percentage body fat in our sample, and there was a positive relation between change in percentage body fat and measurement error. On cross-sectional analysis, these BIA models also tended to overestimate percentage body fat compared with DXA. This finding is consistent with some (4), but not all (5, 6), previous investigations that compared BIA estimates of body fatness with DXA. We found that the Dezenberg et al (17) equation of TSF did not account for >10% of the variance in DXA estimates of change in percentage body fat. When examined cross-sectionally, the TSF method both estimated percentage body fat inaccurately compared with DXA and had significant magnitude bias. This finding was consistent with prior studies suggesting that estimates of percentage body fat with skinfold-thickness measurements could be inaccurate (4, 5).

Strengths of this study include the use of DXA, a robust and well-accepted measure as the criterion method, the wide range of changes in percentage body fat of study subjects, and the use of Bland-Altman comparisons in the interpretation of results. Limitations include the relatively small sample size, and the use of 2 different densitometers for DXA measurement. Although the use of 2 DXA machines might have contributed to measurement error, baseline and follow-up measurements for each subject were performed with use of the same densitometer. Further, when these data were examined separately for each densitometer, no differences were found. Tylavsky et al (23) found that even though fan-beam technology did not estimate body composition in the same way as pencil-beam technology, there were no differences in the estimation of change in body composition between the densitometers in adults.

We conclude that, compared with DXA, there appears to be no noninvasive and simple method to measure changes in children’s percentage body fat accurately and precisely. Change in body fat appears to be acceptably estimated by ADP with use of the Siri equation. Even though significant magnitude estimation biases do exist for this technique, ADP-Siri appears to be a superior approach to skinfold-thickness measurement or BIA for determination of changes in body composition in growing children.

	ACKNOWLEDGMENTS

 
None of the authors had any financial or personal interest in any company or organization sponsoring the research. JAY, JRM, JCR, and JE were responsible for the study design; NGS, CS, MK, JE, and DR were responsible for the data collection; JAY, JRM, and JE were responsible for the data analysis; and JE, JRM, NGS, CS, MK, DR, JCR, and JAY wrote the manuscript.

	REFERENCES

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 

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