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Validity of predictive equations developed to estimate body fat from anthropometry and bioelectrical impedance analysis in 8–10 year-old children
Clinical Nutrition 31 (2012) 364e371
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Clinical Nutrition journal homepage: http://www.elsevier.com/locate/clnu
Original article
Validity of predictive equations developed to estimate body fat from anthropometry and bioelectrical impedance analysis in 8e10 year-old childrenq Lara Nasreddine a, Farah Naja a, Andrew P. Hills b, Sara Kassem Youssef a, Joelle Chahine a, Nahla Hwalla a, * a b
Faculty of Agricultural and Food Sciences, Department of Nutrition and Food Sciences, American University of Beirut, P.O. Box: 110236, Riad El-Solh, Beirut 1107 2020, Lebanon Mater Mother’s Hospital, Mater Medical Research Institute and Griffith Health Institute, Griffith University, Brisbane, Australia
a r t i c l e i n f o
s u m m a r y
Article history: Received 22 June 2011 Accepted 16 November 2011
Background & aim: To date, no studies have examined the validity of skinfold-based (SF) equations and Bioelectrical Impedance Analysis (BIA) in predicting body fat in children of Middle-Eastern origin. The objective of this study was to examine the predictive validity of previously published SF-based equations and BIA in estimating body fat in 8e10 year-old Lebanese children, and to develop new prediction equations for use in this population group. Methods: 158 subjects participated in the study. Percent body fat (% BF) estimates derived from SF-based equations and BIA were compared against the deuterium dilution technique (DDL). Multivariate linear regression analyses were conducted for the development of new prediction equations to estimate %BF using anthropometric variables. Results: Bland-Altman analysis showed that SF-based equations and BIA significantly underestimated %BF as compared to DDL. Mean differences in %BF ranged between 1.3 and 6.5% in boys and 4.5e9.5% in girls. New anthropometry-based equations were proposed for the prediction of %BF in Lebanese pre-pubertal children. Conclusion: Previously published prediction equations underestimated %BF in Lebanese pre-pubertal children. The validity of the new prediction equations developed in this study to estimate %BF in Lebanese children needs to be investigated in future studies. Ó 2011 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.
Keywords: Body fat Skinfold thickness Deuterium dilution technique Bioelectrical impedance Prediction equations Children
1. Introduction The prevalence of pediatric obesity is increasing rapidly worldwide and emerging as a major risk factor for several chronic diseases of public health significance.1 Body mass index (BMI), which is calculated by dividing weight (kg) by the square of height (m2), is widely used to define and assess overweight and obesity. Although BMI is simple to measure and is a valuable tool in monitoring trends of obesity, it also has numerous disadvantages.2 Essentially, it does not distinguish between increased mass in the form of fat, lean tissue or bone. This can hence lead to a significant
Abbreviations: %BF, Percent body fat; BIA, Bioelectrical impedance analysis; DDL, Deuterium dilution technique; DXA, Dual energy X-ray absorptiometry; FM, Fat mass; FFM, Fat free mass; HC, Hip circumference; TBW, Total body water; SF, skinfold; WC, waist circumference. q Conference presentation: Part of this work was presented in the XI International Congress on Obesity, 2010, Stockholm, Sweden. * Corresponding author. Tel.: þ961 1 343002; fax: þ961 1 744460. E-mail addresses: [email protected] (L. Nasreddine), [email protected] (F. Naja), [email protected], a.hills@griffith.edu.au (A.P. Hills), [email protected] (S.K. Youssef), [email protected] (J. Chahine), [email protected] (N. Hwalla).
level of misclassification in large-framed and/or muscular children who are rated as overweight or obese by BMI. Since the pathology and morbidity associated with obesity is driven by excess fat mass,3 the ideal monitoring tool should directly assess adiposity.2 Adiposity can be evaluated by several field and laboratorybased methods, which may vary in their sophistication, accuracy, feasibility and cost.4 Some of these methods, such as underwater weighing, are inappropriate for use in young children, whereas availability and cost may limit the use of others in large epidemiological studies [e.g. Dual-energy X-ray absorptiometry (DXA) and magnetic resonance imaging].4 Compared with other methods, skinfold thickness (SFs) assessment and bioelectrical impedance analysis (BIA) are practical, inexpensive, and relatively easy to use in large field studies and have been suggested as reliable for evaluating body fatness in children.5e7 For that purpose, several prediction equations based on SF measurements or BIA have been developed to estimate body fat in children.7e10 However, ethnic-related differences in the ability of these equations to accurately predict body fat have been reported.6,11,12 To date, no studies have examined the validity of published SF-based equations and BIA to predict body fat in children of Middle-Eastern
0261-5614/$ e see front matter Ó 2011 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved. doi:10.1016/j.clnu.2011.11.008
L. Nasreddine et al. / Clinical Nutrition 31 (2012) 364e371
origin. The present study aims at examining the applicability and validity of existing SF-based equations and BIA in estimating body fat among 8e10 years old Lebanese children and to develop new prediction equations for use in this age group. The deuterium dilution technique (DDL), a 2-compartment model for body composition assessment, was used as the reference method for assessing total body water (TBW), and for derivation of fat-free mass (FFM) and fat mass (FM). Even though inconsistencies pertaining to the validity of the DDL technique in estimating body fat have been reported in the literature,13e15 particularly with respect to the TBW/FFM ratio which can be influenced by growth, maturation, gender and biological factors, several studies have reported DDL as a valid technique for body composition evaluation in children.13,15 In a recent study on Mexican 6e14 years old children, Ramirez et al16 showed that the DDL technique is accurate, precise and free of bias, as compared to their four-compartment model, and concluded that DDL may be a reliable technique to assess body composition in this age group. 2. Materials and methods 2.1. Subjects A total of 158, 8-10 year-old children (77 boys and 81 girls) were recruited from local schools in Beirut, Lebanon. A non-random purposive sampling approach was used to enroll children across a wide BMI range for each year of age and sex. Additional inclusion criteria required that participants be healthy, at Tanner stage 1 of puberty, and free from any diagnosed medical condition or treatment that may interfere with body composition assessment. Pubertal status of each participant was assessed according to the criteria of Tanner by trained investigators. Underweight children (BMI <-2 SD) according to WHO growth standards,17 were excluded.Informed written consent from parents and assent from children were obtained. The research protocol was approved by the Institutional Review Board, American University of Beirut. 2.2. Anthropometric measurements Anthropometric and body composition assessments were completed in the school setting. Height and body weight were measured according to standard procedures, using a portable stadiometer (Holtain, Crymych, UK) and a SECAÔ calibrated electronic weighing scale (Hamburg, Germany), respectively.18,19 BMI was calculated as weight (kg) divided by height squared (m2). Definitions of overweight and obesity were based on sex and agespecific þ1 and þ2 BMI Z scores, respectively, according to WHO growth standards.17 Waist circumference (WC) was measured using a calibrated plastic measuring tape, at the umbilicus level, with subjects standing and following normal expiration.18 Hip circumference (HC) was measured at the level of the greater trochanter, which is the large eminent part of the femur. It was measured horizontally to the nearest 0.1 cm at the level of the greatest lateral extension of the hips, with subject standing erect and arms loosened to the sides.20 Using standard procedures,18,19 SF thicknesses were measured with a Holtain skinfold caliper to the nearest 0.1 mm at six anatomical sites on the right side of the body (biceps, triceps, subscapular, iliac crest , thigh and calf). Skinfold measurements were taken according to the International Society for the Advancement of Kinanthropometrythe (ISAK) protocol.18,19 The triceps skinfold site was located as the point on the posterior side of the arm, and the biceps skinfold site on the anterior side of the arm.
365
Both of these sites were taken at the level of the mid acromialeradiale landmark which is defined as the mid-point of the straight line joining the most lateral part of the acromion and the proximal and lateral border of the head of the radius. The subscapular skinfold site was located 2 cm along the line running laterally and obliquely from the subscapular landmark, which is the inferior angle of the scapula, at an angle of 45 . The triceps, biceps and subscapular skinfold sites were located with the subject in a relaxed standing position, the arms hanging by the sides. The iliac crest skinfold site was located at the center immediately above the point on the iliac crest where the line from the middle of the armpit meets the iliac crest, following the longitudinal axis of the body. The calf skinfold site was located at the middle of the calf at the level of the maximal circumference, with the knee bent at a 90 angle. The thigh skinfold site was located at the mid-point of the long axis between the inguinal point and the mid-point of the posterior superior border of the patella. The site was marked with the leg flexed at a 90 angle.18,19 All skinfold measurements were taken twice and the average of the two values adopted. Measurement error was minimized by having the same trained researcher perform all measurements within acceptable technical error of measurement limits for each procedure .18,21 2.3. Body composition assessment 2.3.1. TBW measurement The DDL technique was used to assess TBW. Before consuming an oral dose of the stable isotope, subjects were asked to provide a 5 mL urine sample after an overnight fast to determine the basal deuterium level in the body (pre-dose sample). A 10% deuterium oxide dose of 0.5 g/kg body weight was given orally, after which a light breakfast was offered to participants, but physical activity was not allowed during the study day. A second (post-dose) urine sample was collected 5 h later to allow for complete equilibration within body water compartments.22 The enrichment of the predose and post-dose urine samples, the dose given and the local tap water were measured by isotope ratio mass spectrometry (IRMS, 20:20 Hydra Model, PDZ Europa, Crewe, UK). Deuterium dilution space was determined using the following equation:
TBWðkgÞ ¼
TA ðEa EtÞ 1 a Es Ep 1:041
Where T is the amount of tap water in which the dose was diluted in grams, A is the amount of dose taken by the participant in grams, a is the amount of the dose in grams retained for mass spectrometer analysis, and Ea, Et, Ep and Es are the isotopic enrichment in delta units relative to standard mean ocean water of the dilute dose, the tap water used, the pre-dose urine sample and the post-dose urine sample. The constant (1.041) was used to adjust for the non-aqueous exchange of hydrogen atoms in the body. FFM was derived from TBW using a hydration coefficient, that is, the fraction of FFM comprised of water. Lohman’s age- and gender-specific constants for hydration of FFM for children were used to calculate FFM.23 The absolute FM was derived by subtracting FFM from body weight, based on the two-compartment body composition model and %BF was then calculated. 2.3.2. Skinfold-based prediction equations In this study, five child-specific skinfold-based equations were tested for the prediction of %BF and included those developed by Bray et al,8 Dezenberg et al,10 Goran et al,9 and Slaughter et al,7 (Appendix 1). Prediction equations were selected if they were based on anthropometric variables (weight, height, skinfold
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thicknesses) and on a 2-compartment model for body composition assessment while targeting pre-adolescent children.8,10 2.3.3. BIA A tetrapolar single frequency (200 mA at 50 kHz) electrical bioimpedance analyzer (Imp DF50, ImpediMed Limited, Brisbane, QLD, Australia) was used to measure TBW, extracellular body water (ECW), intracellular body water (ICW), FM and FFM. BIA measurement was taken on the right side of the body and standardized testing procedures followed.24 Body composition values were derived using the instrument’s software without modification. Factors known to influence hydration status and BIA measurement including room temperature, exercise, eating and drinking were all controlled. Before each testing session, the device was checked using the test cell provided by the manufacturer. 2.4. Statistical analysis Gender-specific descriptive statistics were conducted for anthropometric measurements, SFs and %BF estimates. Results are shown as means and standard deviations. Differences in %BF derived from DDL, BIA and SF were assessed using repeated measures ANOVA with Bonferroni corrections. Pearson correlation coefficients were determined to evaluate the correlation between % BF estimated by DDL and each of the methods under investigation. The Bland and Altman approach25 was used to compare the methods listed in Appendix 1 against the reference method of determining %BF. Based on this approach, the difference between 2 methods of measuring %BF can be evaluated by plotting this difference against the average value for the 2 methods. Accordingly, the difference in %BF obtained by DDL (i.e. the reference method) and by each of the methods under investigation was compared with the average value of %BF estimates provided by DDL and each of the methods under consideration.25 This difference was plotted against and analyzed with respect to the average body fat using simple linear regression. The hypothesis that the slope was equal to zero was tested in each case. This is equivalent to testing whether the 2 methods have the same error variance and allows to test for differential effects across the body fat range.26 Mean difference, 95% CI and limits of agreement (mean difference 2SD) between the various methods under investigation and DDL for estimating %BF were determined. The wider the limits of agreements relative to the mean value, the lower the level of agreement between the methods. A one-sample Student’s t test was performed to assess whether mean difference between DDL and each of the methods under investigation was significantly different from zero. The closest the mean difference to zero, the higher the agreement between the reference method and the specific method under investigation. Multivariate linear regression analyses using a forward stepwise model were conducted for development of %BF prediction equations from anthropometric variables. Independent variables used in regression analyses included age, weight, height, WC, HC, and each of the six skinfolds (biceps, triceps, subscapular, iliac crest, thigh and calf). %BF values obtained from DDL were used as the dependent variable in the predictive models. Several regression diagnostics were performed for each model. Linearity was tested by the lack of fit test which indicated a linear association between percent body fat and the dependant variables used in each model. Cases that violated the linearity assumption were excluded. Outliers, defined as cases with standardized residuals greater than 3.3, were excluded.27 Normality of residuals was assessed by WilkeseShapiro test and subjects whose residuals caused violations were excluded. CookseWeisberg test of heteroscedasticity did not show significance and homoscedasticity was confirmed. Cases
deleted due to violations of assumptions in any one model were excluded from all analyses to ascertain comparability of the models. Cases excluded were 7 subjects (4 boys and 3 girls). The best predictors of %BF on the basis of the highest R2 and the lowest Standard Error of the Estimate (SEE) for each combination of anthropometric measurements were identified. Statistical analyses were conducted using the Statistical Analysis Package for Social Sciences (SPSS, version 18), and the level of significance was set at P < 0.05. 3. Results Mean age ( SD) was of 9.29 years (0.77) for boys and 9.09 years (0.75) for girls. Sex-specific anthropometric characteristics of the study sample are shown in Table 1. Based on the new WHO growth standards,17 57.1% of boys and 65.4% of girls were normalweight, while 42.8% of boys and 34.5% of girls were overweight (BMI > þ1 SD). Correlations between %BF estimates derived from the applied SF-based equations were high, ranging between 0.82 and 0.98 in boys and between 0.83 and 0.97 in girls. Similar correlations were observed between BIA and SF-based equations (0.82e0.88 in boys and 0.83 to 0.85 in girls) (data not shown). Correlations between the methods under investigation and DDL were also high, ranging between 0.87 and 0.93 in boys and between 0.76 and 0.83 in girls (P < 0.05 for all) (Table 2). Despite these significant correlations, repeated measures ANOVA with Bonferroni corrections documented significant differences between %BF estimated by DDL and each of the other methods in both boys and girls (P < 0.05), with all methods underestimating %BF. Based on the Bland and Altman approach, mean differences in % BF between the various methods under investigation and DDL were significantly different from zero for all methods under investigation, indicating that SF-based equations and BIA are not interchangeable with DDL for %BF estimation, in both genders (Table 3, Figs. 1 and 2). In boys, mean differences in %BF ranged between 1.3% and 6.5%, with the lowest mean difference being observed for BIA and Bray’s equation. The limits of agreement relative to the mean difference were wide for all methods under investigation, indicating poor agreement between the methods. The slopes for Goran and Dezenberg equations were significantly different from zero indicating a bias in estimating %BF as average body fat increased. In girls, mean differences in %BF ranged between 4.5% and 9.5% with the lowest mean difference being reported for Dezenberg and Table 1 Anthropometric characteristics of the study subjects (n ¼ 158). Variable
Body weight, kg Height, cm BMI, kg/m2 WC, cm HC, cm Biceps, mm Triceps, mm Subscapular, mm Iliac crest, mm Calf, mm Thigh, mm BMI classificationa Normal weight BMI þ1SD Overweight BMI > þ1SD
Boys (n ¼ 77)
Girls (n ¼ 81)
Mean SD
Mean SD
34.67 9.73 134.64 7.9 18.86 3.82 64.56 9.62 73.85 9.77 9.196 4.86 12.00 5.81 10.79 7.34 9.56 6.93 15.25 8.22 21.87 10.43 n (%) 44 (57.1%)
30.71 132.87 17.19 59.94 70.28 7.35 10.44 8.61 8.28 13.17 19.35
53 (65.4%)
33 (42.8%)
28 (34.5%)
BMI, body mass index; WC, waist circumference; HC, hip circumference. a BMI classification based on the 2007 WHO growth standards.13
7.34 7.94 2.68 7.08 8.43 2.58 4.04 4.56 4.77 5.60 8.88
L. Nasreddine et al. / Clinical Nutrition 31 (2012) 364e371 Table 2 Comparison of %BF estimates derived by DDL, BIA and SF-based equations in 8e10 year-old Lebanese children. Method
DDL BIA Bray Slaughter 1 Slaughter 2 Goran Dezenberg
%BF estimate mean SDa
Correlation between %BF estimated by DDL and other methodsb
Boys (n ¼ 77)
Boys (n ¼ 77)
Girls (n ¼ 81)
e 0.930 0.912 0.888 0.895 0.877 0.879
e 0.790 0.828 0.829 0.785 0.760 0.822
26.29 25.08 25.10 21.24 20.54 19.79 21.86
9.60 10.12 8.73 9.83 9.62 5.56 6.52
Girls (n ¼ 81) 26.74 20.22 22.37 19.69 17.23 18.33 22.21
7.11 8.68 5.81 5.75 5.96 4.39 5.27
%BF, Percent body fat; DDL, Deuterium dilution method; BIA, bioelectrical impedance analysis. a Significant differences between %BF obtained using DDL and all methods under study in both boys and girls (p < 0.05). b Pearson Correlation Coefficients between %BF estimated by DDL and each of the other methods were significant for all the methods under study (P < 0.05) in both boys and girls.
Bray’s equations. The limits of agreement relative to the mean difference were wide for all methods under investigation, suggesting poor agreement between the methods. Except for Slaughter equation (2), the slopes for all other equations as well as for BIA were significantly different from zero, indicating bias in estimating %BF as average body fat increased. The prediction equations developed and their associated R2 and SEE values are shown in Table 4 for each combination of anthropometric measurements that yielded the best prediction of body fat on the basis of the highest R2 and lowest SEE values. 4. Discussion This is the first study to address the validity of previously published child-specific equations and BIA to assess %BF in children of Middle-Eastern origin. Our results showed that these methods significantly underestimated body fat as compared to DDL. The limits of agreement were considerably wide for all methods under investigation and bias in the estimation of %BF was noted as body fat increased. These findings suggest that the methods under consideration are not interchangeable with DDL for the assessment of %BF in pre-pubertal Lebanese children. This supports previous literature on ethnic disparities in the ability of published SF-based equations and BIA to accurately predict body fat.6,11,12 The same concern was raised by Treuth et al12 who showed that, in a multiethnic sample of pre-pubertal girls (n ¼ 101), BIA, anthropometry and isotope dilution were not directly interchangeable for %BF assessment and that estimates of body composition in this population group were highly method-dependent.
367
In this study, even though BIA had higher agreement with DDL in boys than in girls, it still significantly underestimated %BF in both genders, with the underestimation worsening as body fat increased, particularly among girls. A possible explanation for this observation lies in the nature of the BIA method, which is designed to measure body water by the resistance to an alternating current. It is suggested that as body fat increases, the resistance in the BIA measurement is systematically biased. This is possibly due to the increase in FM, which has a lower hydration than lean tissue and consequently alters the electrical conductivity of the alternating current.26 This is supported by data reported by Hewitt et al.28 who documented a significant negative relation between the difference in %BF calculated from density alone and that calculated from density, water, and bone and the water content of fat free mass.28 Consequently, as the percentage of water in the FFM increased, the underestimation of fat increased. This is consistent with our findings and with those reported by Bray et al.26 who evaluated 5 different BIA equations for the prediction of body fat in 12 year-old African-American and white children and showed that BIA provides a biased underestimate of body fat that worsened significantly as body fat increased. In the present study, equations developed by Slaughter7 were applied and found to significantly underestimate %BF in both genders. Despite their widespread use in pediatric research, Slaughter equations were reported to perform well in some settings, and not in others.12,29,30 It is important to mention that, in the present study, the sample included children who were strictly pre-pubertal, whereas Slaughter equations were based on a more liberal interpretation of pre-pubertal status.11 The mixed-pubertal nature of Slaughter’s sample may have included a large proportion of children in early pubertal developmental stages with higher body fat levels than strictly pre-pubertal children.11 This concern was also raised by Roemmich et al.31 who reported that Slaughter equations7 did not agree well with their four-compartment model for the assessment of body composition in children and recommended further refinement of these equations. The underestimation of %BF by Slaughter equations as observed in the present study is also in agreement with findings reported for 3-8 year-old white and Hispanic US children,4 5e15 year old Sri Lankan children32 and for a multi-ethnic sample of pre-pubertal girls.12 In the current study, Bray’s equation8 showed the highest agreement with DDL, however, it still underestimated %BF in the study sample. This was not the case with other studies where Bray’s equation was found to significantly overestimate %BF in prepubertal African-American, non-Black Hispanics, and nonHispanic Caucasian girls when compared to DXA.33 Applying Goran’s equation9 to our study population showed disagreement with %BF estimated by DDL (highest mean difference, significant bias), indicating that this equation is an inappropriate predictor of %BF in Lebanese pre-pubertal children. This was also
Table 3 Differences between each of the methods for estimating %BF and the criterion method (DDL) based on the Bland-Altman approach. Method
Boys (n ¼ 77)
Girls (n ¼ 81) a
Mean difference SD (95% CI) BIA Bray Slaughter 1 Slaughter 2 Goran Dezenberg
a
1.31 3.74 (2.17; 0.45) 1.72a 3.92 (2.64; 0.81) 5.58bc 4.59 (6.66; 4.52) 5.75b 4.40 (6.75; 4.75) 6.50b 5.43 (7.73; 5.27) 4.43c 4.97 (5.56; 3.30)
Slope
Limits of agreement
Mean differencea SD (95% CI)
Slope
Limits of agreement
0.294 0.493 0.125 0.010 1.040b 1.006b
8.79, 6.17 9.56, 6.12 14.76, 3.6 14.55, 3.05 17.36, 4.36 14.37, 5.51
6.76a 5.32 (7.96; 5.56) 4.79b 4.08 (5.76; 3.82) 7.47a 4.08 (8.44; 6.51) 9.51c 4.42 (10.49; 8.52) 8.41d 4.73 (9.46; 7.35) 4.53b 4.09 (5.44; 3.62)
0.469b 0.578b 0.597b 0.381 0.699b 0.681b
17.4, 3.88 12.95, 3.37 15.63, 0.69 18.35, 0.67 17.87, 1.05 12.71, 3.65
DDL, Deuterium dilution method; BIA, bioelectrical impedance analysis. a,b,c Mean difference values with the same superscript are not statistically significant from one another. a All values of the mean difference are significantly different from 0. b Slope value significantly different from 0.
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Fig. 1. Bland and Altman plots comparing, in boys, % BF as determined by (a) DDL and BIA; (b) DDL and Bray’s equation; (c) DDL and Slaughter 1 equation (triceps & calf); (d) DDL and Slaughter 2 equation (triceps and subscapular); (e) DDL and Goran’s equation; (f) DDL and Dezenberg’s equation. Mean difference in %BF estimates for each subject are plotted against the mean value of the two respective methods. Horizontal lines are drawn at the mean difference and at the limits of agreement defined as the mean difference 2SD of the difference. Differences in body fat estimates between DDL and each specific method are plotted against and analyzed with respect to the average body fat using simple linear regression (regression line shown in red). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
reported by Dezenberg et al.10 who found that Goran’s equation performed poorly in Caucasian and African-American children, a fact that led them to propose other equations for the assessment of %BF in 4e10 year-old children. It is important to note that applying Dezenberg’s equation to our study sample also resulted in a significant underestimation of %BF compared with DDL. Dezenberg et al.10 have based their prediction equations on a sample of
pre-pubertal and pubertal European and African-American children, two age groups who may be at different stages in FFM accretion, which may limit the applicability of the generated equations to pre-pubescent children and possibly to other ethnicities. This concern was also raised by Cameron et al11 who showed that the Dezenberg equation results in a 15% underestimation of % BF in African pre-pubertal children. L’Abbée et al have however
L. Nasreddine et al. / Clinical Nutrition 31 (2012) 364e371
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Fig. 2. Bland and Altman plots comparing, in girls, % BF determined by (a) DDL and BIA; (b) DDL and Bray’s equation; (c) DDL and Slaughter 1 equation (triceps & calf); (d) DDL and Slaughter 2 equation (triceps and subscapular); (e) DDL and Goran’s equation; (f) DDL and Dezenberg’s equation. Mean difference in %BF estimates for each subject are plotted against the mean value of the two respective methods. Horizontal lines are drawn at the mean difference and at the limits of agreement defined as the mean difference 2SD of the difference. Differences in body fat estimates between DDL and each specific method were plotted against and analyzed with respect to the average body fat using simple linear regression (regression line shown in red). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
reported an overestimation of %BF by the Dezenberg equation compared to the isotope dilution technique in a sample of 30 children (6e7 years old) from the Netherlands.30 Overall, the findings of this study show that all the applied SFbased equations were not interchangeable with DDL and underestimated %BF in 8e10 year-old Lebanese children. This suggests that
fat patterning in Lebanese children may differ from that of the groups on which the predictive equations were developed. It also implies that less fat may be situated subcutaneously in Lebanese children as compared to American/European children.29 This supports previous studies, where significant differences in fat patterning were reported among children from different ethnicities,34 a fact that has prompted
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Table 4 Regression equations developed for the estimation of %BF in Lebanese pre-pubertal children. Regression equation Boys %BF ¼ 3.946 þ 0.011(A) 0.205(Ht) þ 0.646(HC) þ 0.329(Tr) þ 0.257(Th) %BF ¼ 3.454 e 0.0004(A) 0.243(Ht) þ 0.763(HC) þ 0.432(Tr) þ 0.167(IC) Girls %BF ¼ 12.387 þ 0.104(A) 0.084(Ht) þ 0.726(Tr) þ 0.311(IC) þ 0.241(Th) %BF ¼ 5.418 þ 0.079(A) 0.063(Wt) þ 0.761(Tr) þ 0.310(IC) þ 0.245(Th)
Model R2
SEE
0.906
2.901
0.895
3.070
0.815
3.260
0.812
3.288
A: Age; Ht: Height; HC: Hip circumference; Tr: Triceps SF; Th: Thigh SF; IC: Iliac crest SF; Wt: Weight.
In conclusion, this paper responds to the increasing need for assessment of body composition in diverse ethnic groups, and provides evidence that internationally published predictive equations for the estimation of %BF do not apply to Lebanese and probably to other Middle-Eastern children. By proposing new anthropometry-based prediction equations fort the estimation of % BF in pre-pubertal children, this study should encourage researchers in the region to test the developed equations for validity and applicability and to develop new population-specific body fat prediction equations. Conflict of interest This study is free of any conflict of interest. Acknowledgments
several researchers to propose ethnic and population specific regression equations for the estimation of %BF from skinfold and anthropometric variables.6,11,12 In the present study, new equations for the estimation of %BF in Lebanese children have been developed, using DDL as the reference method. The inclusion of triceps, thigh and/or iliac crest SFs in these regression equations was found to improve the prediction of %BF in both boys and girls. This supports several previous studies where triceps and thigh SF measurements appeared as strong predictors of body fatness in children7,8,11,12,35 and where iliac crest SF was found to be significantly correlated with body fat, especially in girls who have not yet reached menarche.36 In addition to the inclusion of SF measurements in the regression equations, inclusion of additional anthropometric measurements such as weight and height was found to improve the prediction of %BF in the study sample. Although Wells et al.37found that variation in height did not result in significant bias in estimating body fat when comparing children at the age of 8 years; it was possible to use height as a variable when assessing body composition of children of varying heights. The inclusion of weight in one of the regression equations for girls is in agreement with findings reported by Treuth et al.12 who predicted body fat in prepubertal girls from weight, suprailiac, triceps, subscapular and thigh skinfolds with an R2 of 0.89. This explains the likelihood of the integration of these variables in one of our prediction equations. Moreover, the inclusion of circumference measurements such as HC in the equations developed for boys in this study was also found to improve the prediction of %BF in the study sample. In the literature, predictive equations including HC were reported for the estimation of %BF in adults38 but none has been reported for children. The strength of this study lies in the selection of a sample of children of a defined age group and in the use of DDL as the reference method for body fat assessment, a method that was shown to give valid measurements of body fatness in children.26 The study also included, by design, subjects with a wide BMI distribution, to increase the applicability of the developed prediction equations. In addition, in order to minimize measurement error in this study, the same trained researcher performed all measurements using standard procedures. The study also had some limitations. Children were given a light breakfast after collection of the baseline urine sample, as it was not possible to ask them to fast for 5 h, the time needed for isotope equilibration.22 Food intake causes an expansion in TBW due to metabolic water being produced by the metabolism of food-derived energy.6 Under the conditions of light physical activity during the study and assuming an average respiratory quotient of 0.85, the rate of expansion of the body water pool during the equilibration period would approximate 0.04% per hour.6 Such a small measurement error is not expected to introduce major biases in this study’s results.
L.N. contributed to study design, data collection and interpretation and drafted the manuscript. NH contributed to study design, data interpretation and critically revised the manuscript. F.N. contributed to data analysis, interpretation and revised the manuscript. A.H contributed to analysis of samples and revised the manuscript. S.K.Y and J.C contributed to data collection in partial fulfillment of their MSc Degree. All authors read and approved the final manuscript. Role of the funding source This work was supported by the International Atomic Energy Agency (RAS/6/050), Lebanese National Council for Scientific Research (CNRS) and University Research Board at the American University of Beirut. The study sponsors had no involvement in the study. Appendix 1. Predictive equations selected for validation against DDL for the assessment of %BF among 8e10 year-old Lebanese children.
Prediction equation Bray8 Dezenberga,10 Goran9 Slaughter (1)7 Slaughterb (2)7
%BF ¼ 7.26 þ (0.76 Bi) þ (0.36 Calf) þ (0.24 Thigh) FM(kg) ¼ (0.332 Weight) þ (0.263 Tri) þ (0.760 Gender) þ (0.704 Ethnicity) 8.004 FM (kg) ¼ (0.23 Sub) þ (0.18 Weight) þ (0.13 Tri) 3.0 %BF ¼ 0.735 (Tri þ Calf) þ 1.0 %BF ¼ 0.610 (Tri þ Calf) þ 5.1 %BF ¼ 1.21 (Tri þ Sub) 0.008 (Tri þ Sub)2 1.7 %BF ¼ 1.3 (Tri þ Sub)- 0.013 (Tri þ Sub)2 2.5
%BF, percent body fat; Bi, biceps; Tri, triceps; Sub, subscapular; FM, fat mass. a Dezenberg’s equation: ethnicity ¼ 1 (for Caucasian); gender is 1 for male and 2 for females. b If the sum of triceps & subscapular skinfold exceeded 35 mm, the following equations were used: Males: %BF ¼ 0.783*(triceps þ subscapular) þ 1.6; Females: % BF ¼ 0.546*(triceps þ subscapular) þ 9.7.
References 1. Lobstein T, Baur L, Uauy R, IASO International Obesity TaskForce. Obesity in children and young people: a crisis in public health. Obes Rev 2004;5(Suppl. 1):4e104. 2. McCarthy HD, Cole TJ, Fry T, Jebb SA, Prentice AM. Body fat reference curves for children. Int J Obes (Lond) 2006;30:598e602. 3. Fortuno A, Rodriguez A, Gomez-Ambrosi J, Fruhbeck G, Diez J. Adipose tissue as an endocrine organ: role of leptin and adiponectin in the pathogenesis of cardiovascular diseases. J Physiol Biochem 2003;59:51e60.
L. Nasreddine et al. / Clinical Nutrition 31 (2012) 364e371 4. Eisenmann JC, Heelan KA, Welk GJ. Assessing body composition among 3- to 8-year-old children: anthropometry, BIA, and DXA. Obes Res 2004;12:1633e40. 5. Lohman TG. Assessment of body composition in children. Pediatr Exerc Sci 1989;1:19e30. 6. Eckhardt CL, Adair LS, Caballero B, Avila J, Kon IY, Wang J, et al. Estimating body fat from anthropometry and isotopic dilution: a four-Country comparison. Obes Res 2003;11:1553e62. 7. Slaughter MH, Lohman TG, Boileau RA, Horswill CA, Stillman RJ, Van Loan MD, et al. Skinfold equations for estimation body fatness in children and youth. Hum Biol 1988;60:709e23. 8. Bray GA, DeLany JP, Harsha DW, Volaufova J, Champagne CC. Evaluation of body fat in fatter and leaner 10-y-old African American and white children: the Baton Rouge children’s study. Am J Clin Nutr 2001;73:687e702. 9. Goran MI, Driscoll P, Johnson R, Nagy TR, Hunter G. Cross-calibration of bodycomposition techniques against dual-energy X-ray absorptiometry in young children. Am J Clin Nutr 1996;63:299e305. 10. Dezenberg CV, Nagy TR, Gower BA, Johnson R, Goran MI. Predicting body composition from anthropometry in pre-adolescent children. Int J Obes 1999;23:253e9. 11. Cameron N, Griffiths PL, Wright MM, Blencowe C, Davis NC, Pettifor JM, et al. Regression equations to estimate percentage body fat in African prepubertal children aged 9 y. Am J Clin Nutr 2004;80:70e5. 12. Treuth MS, Butte NF, Wong WW, Ellis KJ. Body composition in prepubertal girls: comparison of six methods. Int J Obes 2001;25:1352e9. 13. Wells KCK, Fuller NJ, Dewit O, Fewtrell M, Elia M, Cole T. Four-component model of body composition in children: density and hydration of fat-free mass and comparison with simpler models. Am J Clin Nutr 1999;69:904e12. 14. Fields D, Goran M. Body composition techniques and the four-compartment model in children. J Appl Physiol 2000;89:613e20. 15. Gately PJ, Radley D, Cooke CB, Carroll S, Oldroyd B, Truscott JG, et al. Comparison of body composition methods in overweight and obese children. J Appl Physiol 2003;95:2039e46. 16. Ramirez E, Valencia ME, Moya-Camarena SY, Alemán-Mateo H, Méndez RO. Four-compartment model and validation of deuterium dilution technique to estimate fat-free mass in Mexican youth. Nutrition 2009;25:194e9. 17. De Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J. Development of a World Health Organization (WHO) growth reference for school-aged children and adolescents. Bull World Health Organ 2007;85:660e7. 18. Marfell-Jones M, Olds T, Stewart A, Carter L. International standards for anthropometric assessment. Potchefstroom, South Africa: International Society for the Advancement of Kinanthropometry; 2006. 19. International Society for the Advancement of Kinanthropometry. International standards for anthropometric assessment. Canberra: ISAK; 2001. 20. Lohman T, Roche A, Martorell R. Anthropometric standardization reference manual. Champaign: Human Kinetics Books; 1988.
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21. Kagawa M, Byrne NM, King NA, Pal S, Hills AP. Ethnic differences in body composition and anthropometric characteristics in Australian Caucasian and urban Indegenous children. Br J Nutr 2009;102:938e46. 22. Colley RC, Byrne NM, Hills AP. Implications of the variability in time to isotopic equilibrium in the deuterium dilution technique. Eur J Clin Nutr 2007;61:1250e5. 23. Lohman TG. Applicability of body composition techniques and constants for children and youths. Exerc Sport Sci Rev 1986;14:325e57. 24. Heyward VH. Practical body composition assessment for children, adults, and older adults. Int J Sports Nutr 1998;8:285e307. 25. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1:307e10. 26. Bray GA, DeLany JP, Volaufova J, Harsha DW, Champagne CC. Prediction of body fat in 12-y-old African American and white children: evaluation of methods. Am J Clin Nutr 2002;76:980e90. 27. Rousseeuw PJ, Leroy AM. Robust regression and outlier detection. Wiley Series in Probability and statistics. New Jersey: John Wiley and Sons, Inc.; 2003. 329p. 28. Hewitt MJ, Going SB, Williams DP, Lohman TG. Hydration of the fat- free body mass in children and adults: implication for body composition assessment. Am J Physiol 1993;265:E88e95. 29. Deurenberg P, Yap M, van Staveren WA. Body mass index and percent body fat: a meta analysis among different ethnic groups. Int J Obes 1998;22:1164e71. 30. L’Abbée C, Visser GH, Liem ET, Kok DEG, Sauer PJJ, Stolk RP. Comparison of methods to assess body fat in non-obese six to seven-year-old children. Clin Nutr 2010;29:317e22. 31. Roemmich JN, Clark PA, Weltman A, Rogol AD. Alterations in growth and body composition during puberty. I. Comparing multicompartment body composition models. J Appl Physiol 1997;83:927e35. 32. Wickramasinghe VP, Lamabadusuriay SP, Cleghorn GJ, Davies PS. Use of SkinFold thickness in Sri Lankan children: comparison of several prediction equations. Indian J Pediatr 2008;75:1237e42. 33. Loftin M, Nichols J, Going S, Sothern M, Schmitz KH, Ring K, et al. Comparison of the validity of anthropometric and bioelectric impedance equations to assess body composition in adolescent girls. Int J Body Compos Res 2007;5:1e8. 34. Sisson SB, Katzmarzyk PT, Srinivasan SR, Chen W, Freedman DS, Bouchard C, et al. Ethnic differences in subcutaneous adiposity and waist girth in children and adolescents. Obes (Silver Spring) 2009;17:2075e81. 35. Lohman TG, Caballero B, Himes JH, Davis CE, Stewart D, Houtkooper L, et al. Estimation of body fat from anthropometry and bioelectrical impedance in Native American children. Int J Obes 2000;24:982e8. 36. Sánchez-AndrésA. Fatness and fat patterning in relation to age changes and menarche. Int J Anthropol 1991;6:197e205. 37. Wells JCK, Cole TJ, ALSPAC study team. Adjustment of fat-free mass and fat mass for height in children aged 8 y. Int J Obes 2002;26:947e52. 38. Ramirez-Zea M, Torun B, Martorell R, Stein AD. Anthropometric predictors of body fat as measured by hydrostatic weighing in Guatemalan adults. Am J Clin Nutr 2006;83:795e802.
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Clinical Nutrition journal homepage: http://www.elsevier.com/locate/clnu
Original article
Validity of predictive equations developed to estimate body fat from anthropometry and bioelectrical impedance analysis in 8e10 year-old childrenq Lara Nasreddine a, Farah Naja a, Andrew P. Hills b, Sara Kassem Youssef a, Joelle Chahine a, Nahla Hwalla a, * a b
Faculty of Agricultural and Food Sciences, Department of Nutrition and Food Sciences, American University of Beirut, P.O. Box: 110236, Riad El-Solh, Beirut 1107 2020, Lebanon Mater Mother’s Hospital, Mater Medical Research Institute and Griffith Health Institute, Griffith University, Brisbane, Australia
a r t i c l e i n f o
s u m m a r y
Article history: Received 22 June 2011 Accepted 16 November 2011
Background & aim: To date, no studies have examined the validity of skinfold-based (SF) equations and Bioelectrical Impedance Analysis (BIA) in predicting body fat in children of Middle-Eastern origin. The objective of this study was to examine the predictive validity of previously published SF-based equations and BIA in estimating body fat in 8e10 year-old Lebanese children, and to develop new prediction equations for use in this population group. Methods: 158 subjects participated in the study. Percent body fat (% BF) estimates derived from SF-based equations and BIA were compared against the deuterium dilution technique (DDL). Multivariate linear regression analyses were conducted for the development of new prediction equations to estimate %BF using anthropometric variables. Results: Bland-Altman analysis showed that SF-based equations and BIA significantly underestimated %BF as compared to DDL. Mean differences in %BF ranged between 1.3 and 6.5% in boys and 4.5e9.5% in girls. New anthropometry-based equations were proposed for the prediction of %BF in Lebanese pre-pubertal children. Conclusion: Previously published prediction equations underestimated %BF in Lebanese pre-pubertal children. The validity of the new prediction equations developed in this study to estimate %BF in Lebanese children needs to be investigated in future studies. Ó 2011 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.
Keywords: Body fat Skinfold thickness Deuterium dilution technique Bioelectrical impedance Prediction equations Children
1. Introduction The prevalence of pediatric obesity is increasing rapidly worldwide and emerging as a major risk factor for several chronic diseases of public health significance.1 Body mass index (BMI), which is calculated by dividing weight (kg) by the square of height (m2), is widely used to define and assess overweight and obesity. Although BMI is simple to measure and is a valuable tool in monitoring trends of obesity, it also has numerous disadvantages.2 Essentially, it does not distinguish between increased mass in the form of fat, lean tissue or bone. This can hence lead to a significant
Abbreviations: %BF, Percent body fat; BIA, Bioelectrical impedance analysis; DDL, Deuterium dilution technique; DXA, Dual energy X-ray absorptiometry; FM, Fat mass; FFM, Fat free mass; HC, Hip circumference; TBW, Total body water; SF, skinfold; WC, waist circumference. q Conference presentation: Part of this work was presented in the XI International Congress on Obesity, 2010, Stockholm, Sweden. * Corresponding author. Tel.: þ961 1 343002; fax: þ961 1 744460. E-mail addresses: [email protected] (L. Nasreddine), [email protected] (F. Naja), [email protected], a.hills@griffith.edu.au (A.P. Hills), [email protected] (S.K. Youssef), [email protected] (J. Chahine), [email protected] (N. Hwalla).
level of misclassification in large-framed and/or muscular children who are rated as overweight or obese by BMI. Since the pathology and morbidity associated with obesity is driven by excess fat mass,3 the ideal monitoring tool should directly assess adiposity.2 Adiposity can be evaluated by several field and laboratorybased methods, which may vary in their sophistication, accuracy, feasibility and cost.4 Some of these methods, such as underwater weighing, are inappropriate for use in young children, whereas availability and cost may limit the use of others in large epidemiological studies [e.g. Dual-energy X-ray absorptiometry (DXA) and magnetic resonance imaging].4 Compared with other methods, skinfold thickness (SFs) assessment and bioelectrical impedance analysis (BIA) are practical, inexpensive, and relatively easy to use in large field studies and have been suggested as reliable for evaluating body fatness in children.5e7 For that purpose, several prediction equations based on SF measurements or BIA have been developed to estimate body fat in children.7e10 However, ethnic-related differences in the ability of these equations to accurately predict body fat have been reported.6,11,12 To date, no studies have examined the validity of published SF-based equations and BIA to predict body fat in children of Middle-Eastern
0261-5614/$ e see front matter Ó 2011 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved. doi:10.1016/j.clnu.2011.11.008
L. Nasreddine et al. / Clinical Nutrition 31 (2012) 364e371
origin. The present study aims at examining the applicability and validity of existing SF-based equations and BIA in estimating body fat among 8e10 years old Lebanese children and to develop new prediction equations for use in this age group. The deuterium dilution technique (DDL), a 2-compartment model for body composition assessment, was used as the reference method for assessing total body water (TBW), and for derivation of fat-free mass (FFM) and fat mass (FM). Even though inconsistencies pertaining to the validity of the DDL technique in estimating body fat have been reported in the literature,13e15 particularly with respect to the TBW/FFM ratio which can be influenced by growth, maturation, gender and biological factors, several studies have reported DDL as a valid technique for body composition evaluation in children.13,15 In a recent study on Mexican 6e14 years old children, Ramirez et al16 showed that the DDL technique is accurate, precise and free of bias, as compared to their four-compartment model, and concluded that DDL may be a reliable technique to assess body composition in this age group. 2. Materials and methods 2.1. Subjects A total of 158, 8-10 year-old children (77 boys and 81 girls) were recruited from local schools in Beirut, Lebanon. A non-random purposive sampling approach was used to enroll children across a wide BMI range for each year of age and sex. Additional inclusion criteria required that participants be healthy, at Tanner stage 1 of puberty, and free from any diagnosed medical condition or treatment that may interfere with body composition assessment. Pubertal status of each participant was assessed according to the criteria of Tanner by trained investigators. Underweight children (BMI <-2 SD) according to WHO growth standards,17 were excluded.Informed written consent from parents and assent from children were obtained. The research protocol was approved by the Institutional Review Board, American University of Beirut. 2.2. Anthropometric measurements Anthropometric and body composition assessments were completed in the school setting. Height and body weight were measured according to standard procedures, using a portable stadiometer (Holtain, Crymych, UK) and a SECAÔ calibrated electronic weighing scale (Hamburg, Germany), respectively.18,19 BMI was calculated as weight (kg) divided by height squared (m2). Definitions of overweight and obesity were based on sex and agespecific þ1 and þ2 BMI Z scores, respectively, according to WHO growth standards.17 Waist circumference (WC) was measured using a calibrated plastic measuring tape, at the umbilicus level, with subjects standing and following normal expiration.18 Hip circumference (HC) was measured at the level of the greater trochanter, which is the large eminent part of the femur. It was measured horizontally to the nearest 0.1 cm at the level of the greatest lateral extension of the hips, with subject standing erect and arms loosened to the sides.20 Using standard procedures,18,19 SF thicknesses were measured with a Holtain skinfold caliper to the nearest 0.1 mm at six anatomical sites on the right side of the body (biceps, triceps, subscapular, iliac crest , thigh and calf). Skinfold measurements were taken according to the International Society for the Advancement of Kinanthropometrythe (ISAK) protocol.18,19 The triceps skinfold site was located as the point on the posterior side of the arm, and the biceps skinfold site on the anterior side of the arm.
365
Both of these sites were taken at the level of the mid acromialeradiale landmark which is defined as the mid-point of the straight line joining the most lateral part of the acromion and the proximal and lateral border of the head of the radius. The subscapular skinfold site was located 2 cm along the line running laterally and obliquely from the subscapular landmark, which is the inferior angle of the scapula, at an angle of 45 . The triceps, biceps and subscapular skinfold sites were located with the subject in a relaxed standing position, the arms hanging by the sides. The iliac crest skinfold site was located at the center immediately above the point on the iliac crest where the line from the middle of the armpit meets the iliac crest, following the longitudinal axis of the body. The calf skinfold site was located at the middle of the calf at the level of the maximal circumference, with the knee bent at a 90 angle. The thigh skinfold site was located at the mid-point of the long axis between the inguinal point and the mid-point of the posterior superior border of the patella. The site was marked with the leg flexed at a 90 angle.18,19 All skinfold measurements were taken twice and the average of the two values adopted. Measurement error was minimized by having the same trained researcher perform all measurements within acceptable technical error of measurement limits for each procedure .18,21 2.3. Body composition assessment 2.3.1. TBW measurement The DDL technique was used to assess TBW. Before consuming an oral dose of the stable isotope, subjects were asked to provide a 5 mL urine sample after an overnight fast to determine the basal deuterium level in the body (pre-dose sample). A 10% deuterium oxide dose of 0.5 g/kg body weight was given orally, after which a light breakfast was offered to participants, but physical activity was not allowed during the study day. A second (post-dose) urine sample was collected 5 h later to allow for complete equilibration within body water compartments.22 The enrichment of the predose and post-dose urine samples, the dose given and the local tap water were measured by isotope ratio mass spectrometry (IRMS, 20:20 Hydra Model, PDZ Europa, Crewe, UK). Deuterium dilution space was determined using the following equation:
TBWðkgÞ ¼
TA ðEa EtÞ 1 a Es Ep 1:041
Where T is the amount of tap water in which the dose was diluted in grams, A is the amount of dose taken by the participant in grams, a is the amount of the dose in grams retained for mass spectrometer analysis, and Ea, Et, Ep and Es are the isotopic enrichment in delta units relative to standard mean ocean water of the dilute dose, the tap water used, the pre-dose urine sample and the post-dose urine sample. The constant (1.041) was used to adjust for the non-aqueous exchange of hydrogen atoms in the body. FFM was derived from TBW using a hydration coefficient, that is, the fraction of FFM comprised of water. Lohman’s age- and gender-specific constants for hydration of FFM for children were used to calculate FFM.23 The absolute FM was derived by subtracting FFM from body weight, based on the two-compartment body composition model and %BF was then calculated. 2.3.2. Skinfold-based prediction equations In this study, five child-specific skinfold-based equations were tested for the prediction of %BF and included those developed by Bray et al,8 Dezenberg et al,10 Goran et al,9 and Slaughter et al,7 (Appendix 1). Prediction equations were selected if they were based on anthropometric variables (weight, height, skinfold
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L. Nasreddine et al. / Clinical Nutrition 31 (2012) 364e371
thicknesses) and on a 2-compartment model for body composition assessment while targeting pre-adolescent children.8,10 2.3.3. BIA A tetrapolar single frequency (200 mA at 50 kHz) electrical bioimpedance analyzer (Imp DF50, ImpediMed Limited, Brisbane, QLD, Australia) was used to measure TBW, extracellular body water (ECW), intracellular body water (ICW), FM and FFM. BIA measurement was taken on the right side of the body and standardized testing procedures followed.24 Body composition values were derived using the instrument’s software without modification. Factors known to influence hydration status and BIA measurement including room temperature, exercise, eating and drinking were all controlled. Before each testing session, the device was checked using the test cell provided by the manufacturer. 2.4. Statistical analysis Gender-specific descriptive statistics were conducted for anthropometric measurements, SFs and %BF estimates. Results are shown as means and standard deviations. Differences in %BF derived from DDL, BIA and SF were assessed using repeated measures ANOVA with Bonferroni corrections. Pearson correlation coefficients were determined to evaluate the correlation between % BF estimated by DDL and each of the methods under investigation. The Bland and Altman approach25 was used to compare the methods listed in Appendix 1 against the reference method of determining %BF. Based on this approach, the difference between 2 methods of measuring %BF can be evaluated by plotting this difference against the average value for the 2 methods. Accordingly, the difference in %BF obtained by DDL (i.e. the reference method) and by each of the methods under investigation was compared with the average value of %BF estimates provided by DDL and each of the methods under consideration.25 This difference was plotted against and analyzed with respect to the average body fat using simple linear regression. The hypothesis that the slope was equal to zero was tested in each case. This is equivalent to testing whether the 2 methods have the same error variance and allows to test for differential effects across the body fat range.26 Mean difference, 95% CI and limits of agreement (mean difference 2SD) between the various methods under investigation and DDL for estimating %BF were determined. The wider the limits of agreements relative to the mean value, the lower the level of agreement between the methods. A one-sample Student’s t test was performed to assess whether mean difference between DDL and each of the methods under investigation was significantly different from zero. The closest the mean difference to zero, the higher the agreement between the reference method and the specific method under investigation. Multivariate linear regression analyses using a forward stepwise model were conducted for development of %BF prediction equations from anthropometric variables. Independent variables used in regression analyses included age, weight, height, WC, HC, and each of the six skinfolds (biceps, triceps, subscapular, iliac crest, thigh and calf). %BF values obtained from DDL were used as the dependent variable in the predictive models. Several regression diagnostics were performed for each model. Linearity was tested by the lack of fit test which indicated a linear association between percent body fat and the dependant variables used in each model. Cases that violated the linearity assumption were excluded. Outliers, defined as cases with standardized residuals greater than 3.3, were excluded.27 Normality of residuals was assessed by WilkeseShapiro test and subjects whose residuals caused violations were excluded. CookseWeisberg test of heteroscedasticity did not show significance and homoscedasticity was confirmed. Cases
deleted due to violations of assumptions in any one model were excluded from all analyses to ascertain comparability of the models. Cases excluded were 7 subjects (4 boys and 3 girls). The best predictors of %BF on the basis of the highest R2 and the lowest Standard Error of the Estimate (SEE) for each combination of anthropometric measurements were identified. Statistical analyses were conducted using the Statistical Analysis Package for Social Sciences (SPSS, version 18), and the level of significance was set at P < 0.05. 3. Results Mean age ( SD) was of 9.29 years (0.77) for boys and 9.09 years (0.75) for girls. Sex-specific anthropometric characteristics of the study sample are shown in Table 1. Based on the new WHO growth standards,17 57.1% of boys and 65.4% of girls were normalweight, while 42.8% of boys and 34.5% of girls were overweight (BMI > þ1 SD). Correlations between %BF estimates derived from the applied SF-based equations were high, ranging between 0.82 and 0.98 in boys and between 0.83 and 0.97 in girls. Similar correlations were observed between BIA and SF-based equations (0.82e0.88 in boys and 0.83 to 0.85 in girls) (data not shown). Correlations between the methods under investigation and DDL were also high, ranging between 0.87 and 0.93 in boys and between 0.76 and 0.83 in girls (P < 0.05 for all) (Table 2). Despite these significant correlations, repeated measures ANOVA with Bonferroni corrections documented significant differences between %BF estimated by DDL and each of the other methods in both boys and girls (P < 0.05), with all methods underestimating %BF. Based on the Bland and Altman approach, mean differences in % BF between the various methods under investigation and DDL were significantly different from zero for all methods under investigation, indicating that SF-based equations and BIA are not interchangeable with DDL for %BF estimation, in both genders (Table 3, Figs. 1 and 2). In boys, mean differences in %BF ranged between 1.3% and 6.5%, with the lowest mean difference being observed for BIA and Bray’s equation. The limits of agreement relative to the mean difference were wide for all methods under investigation, indicating poor agreement between the methods. The slopes for Goran and Dezenberg equations were significantly different from zero indicating a bias in estimating %BF as average body fat increased. In girls, mean differences in %BF ranged between 4.5% and 9.5% with the lowest mean difference being reported for Dezenberg and Table 1 Anthropometric characteristics of the study subjects (n ¼ 158). Variable
Body weight, kg Height, cm BMI, kg/m2 WC, cm HC, cm Biceps, mm Triceps, mm Subscapular, mm Iliac crest, mm Calf, mm Thigh, mm BMI classificationa Normal weight BMI þ1SD Overweight BMI > þ1SD
Boys (n ¼ 77)
Girls (n ¼ 81)
Mean SD
Mean SD
34.67 9.73 134.64 7.9 18.86 3.82 64.56 9.62 73.85 9.77 9.196 4.86 12.00 5.81 10.79 7.34 9.56 6.93 15.25 8.22 21.87 10.43 n (%) 44 (57.1%)
30.71 132.87 17.19 59.94 70.28 7.35 10.44 8.61 8.28 13.17 19.35
53 (65.4%)
33 (42.8%)
28 (34.5%)
BMI, body mass index; WC, waist circumference; HC, hip circumference. a BMI classification based on the 2007 WHO growth standards.13
7.34 7.94 2.68 7.08 8.43 2.58 4.04 4.56 4.77 5.60 8.88
L. Nasreddine et al. / Clinical Nutrition 31 (2012) 364e371 Table 2 Comparison of %BF estimates derived by DDL, BIA and SF-based equations in 8e10 year-old Lebanese children. Method
DDL BIA Bray Slaughter 1 Slaughter 2 Goran Dezenberg
%BF estimate mean SDa
Correlation between %BF estimated by DDL and other methodsb
Boys (n ¼ 77)
Boys (n ¼ 77)
Girls (n ¼ 81)
e 0.930 0.912 0.888 0.895 0.877 0.879
e 0.790 0.828 0.829 0.785 0.760 0.822
26.29 25.08 25.10 21.24 20.54 19.79 21.86
9.60 10.12 8.73 9.83 9.62 5.56 6.52
Girls (n ¼ 81) 26.74 20.22 22.37 19.69 17.23 18.33 22.21
7.11 8.68 5.81 5.75 5.96 4.39 5.27
%BF, Percent body fat; DDL, Deuterium dilution method; BIA, bioelectrical impedance analysis. a Significant differences between %BF obtained using DDL and all methods under study in both boys and girls (p < 0.05). b Pearson Correlation Coefficients between %BF estimated by DDL and each of the other methods were significant for all the methods under study (P < 0.05) in both boys and girls.
Bray’s equations. The limits of agreement relative to the mean difference were wide for all methods under investigation, suggesting poor agreement between the methods. Except for Slaughter equation (2), the slopes for all other equations as well as for BIA were significantly different from zero, indicating bias in estimating %BF as average body fat increased. The prediction equations developed and their associated R2 and SEE values are shown in Table 4 for each combination of anthropometric measurements that yielded the best prediction of body fat on the basis of the highest R2 and lowest SEE values. 4. Discussion This is the first study to address the validity of previously published child-specific equations and BIA to assess %BF in children of Middle-Eastern origin. Our results showed that these methods significantly underestimated body fat as compared to DDL. The limits of agreement were considerably wide for all methods under investigation and bias in the estimation of %BF was noted as body fat increased. These findings suggest that the methods under consideration are not interchangeable with DDL for the assessment of %BF in pre-pubertal Lebanese children. This supports previous literature on ethnic disparities in the ability of published SF-based equations and BIA to accurately predict body fat.6,11,12 The same concern was raised by Treuth et al12 who showed that, in a multiethnic sample of pre-pubertal girls (n ¼ 101), BIA, anthropometry and isotope dilution were not directly interchangeable for %BF assessment and that estimates of body composition in this population group were highly method-dependent.
367
In this study, even though BIA had higher agreement with DDL in boys than in girls, it still significantly underestimated %BF in both genders, with the underestimation worsening as body fat increased, particularly among girls. A possible explanation for this observation lies in the nature of the BIA method, which is designed to measure body water by the resistance to an alternating current. It is suggested that as body fat increases, the resistance in the BIA measurement is systematically biased. This is possibly due to the increase in FM, which has a lower hydration than lean tissue and consequently alters the electrical conductivity of the alternating current.26 This is supported by data reported by Hewitt et al.28 who documented a significant negative relation between the difference in %BF calculated from density alone and that calculated from density, water, and bone and the water content of fat free mass.28 Consequently, as the percentage of water in the FFM increased, the underestimation of fat increased. This is consistent with our findings and with those reported by Bray et al.26 who evaluated 5 different BIA equations for the prediction of body fat in 12 year-old African-American and white children and showed that BIA provides a biased underestimate of body fat that worsened significantly as body fat increased. In the present study, equations developed by Slaughter7 were applied and found to significantly underestimate %BF in both genders. Despite their widespread use in pediatric research, Slaughter equations were reported to perform well in some settings, and not in others.12,29,30 It is important to mention that, in the present study, the sample included children who were strictly pre-pubertal, whereas Slaughter equations were based on a more liberal interpretation of pre-pubertal status.11 The mixed-pubertal nature of Slaughter’s sample may have included a large proportion of children in early pubertal developmental stages with higher body fat levels than strictly pre-pubertal children.11 This concern was also raised by Roemmich et al.31 who reported that Slaughter equations7 did not agree well with their four-compartment model for the assessment of body composition in children and recommended further refinement of these equations. The underestimation of %BF by Slaughter equations as observed in the present study is also in agreement with findings reported for 3-8 year-old white and Hispanic US children,4 5e15 year old Sri Lankan children32 and for a multi-ethnic sample of pre-pubertal girls.12 In the current study, Bray’s equation8 showed the highest agreement with DDL, however, it still underestimated %BF in the study sample. This was not the case with other studies where Bray’s equation was found to significantly overestimate %BF in prepubertal African-American, non-Black Hispanics, and nonHispanic Caucasian girls when compared to DXA.33 Applying Goran’s equation9 to our study population showed disagreement with %BF estimated by DDL (highest mean difference, significant bias), indicating that this equation is an inappropriate predictor of %BF in Lebanese pre-pubertal children. This was also
Table 3 Differences between each of the methods for estimating %BF and the criterion method (DDL) based on the Bland-Altman approach. Method
Boys (n ¼ 77)
Girls (n ¼ 81) a
Mean difference SD (95% CI) BIA Bray Slaughter 1 Slaughter 2 Goran Dezenberg
a
1.31 3.74 (2.17; 0.45) 1.72a 3.92 (2.64; 0.81) 5.58bc 4.59 (6.66; 4.52) 5.75b 4.40 (6.75; 4.75) 6.50b 5.43 (7.73; 5.27) 4.43c 4.97 (5.56; 3.30)
Slope
Limits of agreement
Mean differencea SD (95% CI)
Slope
Limits of agreement
0.294 0.493 0.125 0.010 1.040b 1.006b
8.79, 6.17 9.56, 6.12 14.76, 3.6 14.55, 3.05 17.36, 4.36 14.37, 5.51
6.76a 5.32 (7.96; 5.56) 4.79b 4.08 (5.76; 3.82) 7.47a 4.08 (8.44; 6.51) 9.51c 4.42 (10.49; 8.52) 8.41d 4.73 (9.46; 7.35) 4.53b 4.09 (5.44; 3.62)
0.469b 0.578b 0.597b 0.381 0.699b 0.681b
17.4, 3.88 12.95, 3.37 15.63, 0.69 18.35, 0.67 17.87, 1.05 12.71, 3.65
DDL, Deuterium dilution method; BIA, bioelectrical impedance analysis. a,b,c Mean difference values with the same superscript are not statistically significant from one another. a All values of the mean difference are significantly different from 0. b Slope value significantly different from 0.
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Fig. 1. Bland and Altman plots comparing, in boys, % BF as determined by (a) DDL and BIA; (b) DDL and Bray’s equation; (c) DDL and Slaughter 1 equation (triceps & calf); (d) DDL and Slaughter 2 equation (triceps and subscapular); (e) DDL and Goran’s equation; (f) DDL and Dezenberg’s equation. Mean difference in %BF estimates for each subject are plotted against the mean value of the two respective methods. Horizontal lines are drawn at the mean difference and at the limits of agreement defined as the mean difference 2SD of the difference. Differences in body fat estimates between DDL and each specific method are plotted against and analyzed with respect to the average body fat using simple linear regression (regression line shown in red). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
reported by Dezenberg et al.10 who found that Goran’s equation performed poorly in Caucasian and African-American children, a fact that led them to propose other equations for the assessment of %BF in 4e10 year-old children. It is important to note that applying Dezenberg’s equation to our study sample also resulted in a significant underestimation of %BF compared with DDL. Dezenberg et al.10 have based their prediction equations on a sample of
pre-pubertal and pubertal European and African-American children, two age groups who may be at different stages in FFM accretion, which may limit the applicability of the generated equations to pre-pubescent children and possibly to other ethnicities. This concern was also raised by Cameron et al11 who showed that the Dezenberg equation results in a 15% underestimation of % BF in African pre-pubertal children. L’Abbée et al have however
L. Nasreddine et al. / Clinical Nutrition 31 (2012) 364e371
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Fig. 2. Bland and Altman plots comparing, in girls, % BF determined by (a) DDL and BIA; (b) DDL and Bray’s equation; (c) DDL and Slaughter 1 equation (triceps & calf); (d) DDL and Slaughter 2 equation (triceps and subscapular); (e) DDL and Goran’s equation; (f) DDL and Dezenberg’s equation. Mean difference in %BF estimates for each subject are plotted against the mean value of the two respective methods. Horizontal lines are drawn at the mean difference and at the limits of agreement defined as the mean difference 2SD of the difference. Differences in body fat estimates between DDL and each specific method were plotted against and analyzed with respect to the average body fat using simple linear regression (regression line shown in red). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
reported an overestimation of %BF by the Dezenberg equation compared to the isotope dilution technique in a sample of 30 children (6e7 years old) from the Netherlands.30 Overall, the findings of this study show that all the applied SFbased equations were not interchangeable with DDL and underestimated %BF in 8e10 year-old Lebanese children. This suggests that
fat patterning in Lebanese children may differ from that of the groups on which the predictive equations were developed. It also implies that less fat may be situated subcutaneously in Lebanese children as compared to American/European children.29 This supports previous studies, where significant differences in fat patterning were reported among children from different ethnicities,34 a fact that has prompted
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Table 4 Regression equations developed for the estimation of %BF in Lebanese pre-pubertal children. Regression equation Boys %BF ¼ 3.946 þ 0.011(A) 0.205(Ht) þ 0.646(HC) þ 0.329(Tr) þ 0.257(Th) %BF ¼ 3.454 e 0.0004(A) 0.243(Ht) þ 0.763(HC) þ 0.432(Tr) þ 0.167(IC) Girls %BF ¼ 12.387 þ 0.104(A) 0.084(Ht) þ 0.726(Tr) þ 0.311(IC) þ 0.241(Th) %BF ¼ 5.418 þ 0.079(A) 0.063(Wt) þ 0.761(Tr) þ 0.310(IC) þ 0.245(Th)
Model R2
SEE
0.906
2.901
0.895
3.070
0.815
3.260
0.812
3.288
A: Age; Ht: Height; HC: Hip circumference; Tr: Triceps SF; Th: Thigh SF; IC: Iliac crest SF; Wt: Weight.
In conclusion, this paper responds to the increasing need for assessment of body composition in diverse ethnic groups, and provides evidence that internationally published predictive equations for the estimation of %BF do not apply to Lebanese and probably to other Middle-Eastern children. By proposing new anthropometry-based prediction equations fort the estimation of % BF in pre-pubertal children, this study should encourage researchers in the region to test the developed equations for validity and applicability and to develop new population-specific body fat prediction equations. Conflict of interest This study is free of any conflict of interest. Acknowledgments
several researchers to propose ethnic and population specific regression equations for the estimation of %BF from skinfold and anthropometric variables.6,11,12 In the present study, new equations for the estimation of %BF in Lebanese children have been developed, using DDL as the reference method. The inclusion of triceps, thigh and/or iliac crest SFs in these regression equations was found to improve the prediction of %BF in both boys and girls. This supports several previous studies where triceps and thigh SF measurements appeared as strong predictors of body fatness in children7,8,11,12,35 and where iliac crest SF was found to be significantly correlated with body fat, especially in girls who have not yet reached menarche.36 In addition to the inclusion of SF measurements in the regression equations, inclusion of additional anthropometric measurements such as weight and height was found to improve the prediction of %BF in the study sample. Although Wells et al.37found that variation in height did not result in significant bias in estimating body fat when comparing children at the age of 8 years; it was possible to use height as a variable when assessing body composition of children of varying heights. The inclusion of weight in one of the regression equations for girls is in agreement with findings reported by Treuth et al.12 who predicted body fat in prepubertal girls from weight, suprailiac, triceps, subscapular and thigh skinfolds with an R2 of 0.89. This explains the likelihood of the integration of these variables in one of our prediction equations. Moreover, the inclusion of circumference measurements such as HC in the equations developed for boys in this study was also found to improve the prediction of %BF in the study sample. In the literature, predictive equations including HC were reported for the estimation of %BF in adults38 but none has been reported for children. The strength of this study lies in the selection of a sample of children of a defined age group and in the use of DDL as the reference method for body fat assessment, a method that was shown to give valid measurements of body fatness in children.26 The study also included, by design, subjects with a wide BMI distribution, to increase the applicability of the developed prediction equations. In addition, in order to minimize measurement error in this study, the same trained researcher performed all measurements using standard procedures. The study also had some limitations. Children were given a light breakfast after collection of the baseline urine sample, as it was not possible to ask them to fast for 5 h, the time needed for isotope equilibration.22 Food intake causes an expansion in TBW due to metabolic water being produced by the metabolism of food-derived energy.6 Under the conditions of light physical activity during the study and assuming an average respiratory quotient of 0.85, the rate of expansion of the body water pool during the equilibration period would approximate 0.04% per hour.6 Such a small measurement error is not expected to introduce major biases in this study’s results.
L.N. contributed to study design, data collection and interpretation and drafted the manuscript. NH contributed to study design, data interpretation and critically revised the manuscript. F.N. contributed to data analysis, interpretation and revised the manuscript. A.H contributed to analysis of samples and revised the manuscript. S.K.Y and J.C contributed to data collection in partial fulfillment of their MSc Degree. All authors read and approved the final manuscript. Role of the funding source This work was supported by the International Atomic Energy Agency (RAS/6/050), Lebanese National Council for Scientific Research (CNRS) and University Research Board at the American University of Beirut. The study sponsors had no involvement in the study. Appendix 1. Predictive equations selected for validation against DDL for the assessment of %BF among 8e10 year-old Lebanese children.
Prediction equation Bray8 Dezenberga,10 Goran9 Slaughter (1)7 Slaughterb (2)7
%BF ¼ 7.26 þ (0.76 Bi) þ (0.36 Calf) þ (0.24 Thigh) FM(kg) ¼ (0.332 Weight) þ (0.263 Tri) þ (0.760 Gender) þ (0.704 Ethnicity) 8.004 FM (kg) ¼ (0.23 Sub) þ (0.18 Weight) þ (0.13 Tri) 3.0 %BF ¼ 0.735 (Tri þ Calf) þ 1.0 %BF ¼ 0.610 (Tri þ Calf) þ 5.1 %BF ¼ 1.21 (Tri þ Sub) 0.008 (Tri þ Sub)2 1.7 %BF ¼ 1.3 (Tri þ Sub)- 0.013 (Tri þ Sub)2 2.5
%BF, percent body fat; Bi, biceps; Tri, triceps; Sub, subscapular; FM, fat mass. a Dezenberg’s equation: ethnicity ¼ 1 (for Caucasian); gender is 1 for male and 2 for females. b If the sum of triceps & subscapular skinfold exceeded 35 mm, the following equations were used: Males: %BF ¼ 0.783*(triceps þ subscapular) þ 1.6; Females: % BF ¼ 0.546*(triceps þ subscapular) þ 9.7.
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