THE DEVELOPMENT OF PREDICTION EQUATIONS FOR BMI AND FAT MASS FROM SIMPLE ANTHROPOMETRY IN 6-TO-8-YEAR-OLD CHILDREN

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1 Chiang Mai Med J 2007;46(1): Original article THE DEVELOPMENT OF PREDICTION EQUATIONS FOR BMI AND FAT MASS FROM SIMPLE ANTHROPOMETRY IN 6-TO-8-YEAR-OLD CHILDREN Sainatee Pratanaphon, M.Sc., Samatchai Chamnongkich, Ph.D., Korakot Hensangvilai, B.Sc. Department of Physiotherapy, Faculty of Associated Medical Sciences, Chiang Mai University Abstract Early identification of excessive weight is needed for preventing obesity. The aim of this study was to determine the interrelationships among simple anthropometry variables for measuring body fatness, and formulate the prediction equations for body mass index (BMI) and fat mass (FM) in children. The body composition of 664 children aged 6-8 years old was assessed by the following methods: BMI, waist circumference (WC), triceps skinfold thickness (TST) and conicity index (CI). Fat mass, fat free mass (FFM) and percentage of body fat (%BF) were derived from Dezenberg s equation. Pearson correlations showed that in boys, BMI had a high correlation with WC, TST, FM, FFM and %BF. Correlation between WC and CI, TST, FM, FFM and %BF were also high. In contrast, the correlation between CI and other anthropometric variables was low, except for WC. Similar outcomes were observed in girls. The three BMI prediction equations based body weight and/or TST and/or WC (R 2 = , CV 6-7%), and the FM prediction equations based on body weight and TST (R 2 = , CV 0.4-6%) were developed with high coefficient of determination. Thus, BMI, WC and TST were useful tools for evaluating childhood adiposity, whereas CI was the poorest index. Chiang Mai Med J 2007;46(1): Keywords: predicted equation, fat mass, body mass index, waist circumference, skinfold thickness The prevalence of obesity among children and adolescents is increasing worldwide. (1) It has caused an adverse effect on health, when tracking childhood to adult. The early age of adiposity rebound may predispose an individual to the development of overweight and obesity. (2) Therefore, early identification of body fatness should start during childhood. Many practical and reproducible measures evaluating childhood obesity have been used in the clinical setting and in epidemiological studies. A growing consensus is to use body mass Address requests for reprints: Sainatee Pratanaphon, M.Sc., Department of Physical Therapy, Faculty of Sciences, Chiang Mai University, Chiang Mai 50200, Thailand. asischnr@chiangmai.ac.th Received 19 October 2006, and in revised form 14 December 2006

2 32 Pratanaphon S, Chumnongkitch S, Hensangvilai K. index (BMI) cutoffs to classify childhood obesity. (3-4) Triceps skinfold thickness (TST) has also been used as an index of body fat mass in children. (5) Several studies have reported a high correlation between fatness and BMI, (3,6-7) skinfold thickness (5-6) and %BF derived from skinfolds. (8) Moreover, waist circumference (WC) was suggested as a tool for screening children with central obesity because of its sensitivity and specificity. (9-11) WC was also found to be a sensitive marker of cardiovascular risk in children. (12) The conicity index (CI), which evaluates waist circumference in relation to height and weight, appears to have a prognosis value similar to that of the waist hip ratio in adults. (13) However, its ability to assess fat adiposity has not been evaluated in children. It is known that body composition varies with age, gender, race, and maturation. (14) Therefore, the aims of this study were to evaluate the correlation among these anthropometric measures for measuring body fat mass in children aged 6-8 years old, and also develop the BMI and FM equations for this population. Methods Initially, 995 children aged 6-8 years old attending Chiang Mai kindergarten school, Amphur Muang, Chiang Mai were asked to participate in the study. Parents and children provided informed consent. Approval for this study was obtained from the Ethics Committee of the Faculty of Associated Medical Sciences, Chiang Mai University. Data collection was done during the period of July 2004-August Measurements of height, body mass, triceps skinfold and waist circumference were carried out. Stature was measured to the nearest 0.1 cm using a wall stadiometer, and body mass was measured to the nearest 0.5 kg using a mechanical beam medical scale (Health O Meter 402 KL) according to standard practice. (15) BMI was calculated as body weight divided by height squared (kg/ m 2 ). Triceps skinfold was measured to the nearest millimeter 3 times with a Lange skinfold caliper on the right side of the body. The triceps skinfold locus, which was halfway between the acromion and olecranon process on the back of the arm, was measured with the elbow stretch out. (15) The equation developed by Dezenberg et al. (16) was used to calculate fat mass (FM), and then percentage body fat (%BF), and fat free mass (FFM) was derived accordingly. Waist circumference was measured to the nearest centimeter with a flexible tape measure while the subjects were in a standing position at the end of gentle expiration. (9,17) The following areas were used: laterally midway between the lowest portion of the rib cage and iliac crest, and anteriorly midway between the xiphoid process of the sternum and umbilicus. Two measurements were taken. (17) If the two measurements were identical, then that value was used. If the two differed, then a third measurement was taken to determine which value was correct. The conicity index (CI) was defined by Valdez et al. (13) as: Statistical analysis Physical characteristics were described as mean and standard deviation. The independent t-test was used to compare mean between the anthropometric and physical characteristics of the subjects. Pearson correlation coefficients were used to determine the relationship between the anthropometric variables. All statistical analyses were carried out using SPSS

3 Prediction equations for BMI and FM in children 33 software version 10.0 for Windows (SPSS Inc., Chicago, IL). Variable selection and equation development An all-possible-subsets regression analysis was performed separately for BMI and FM, with the possible independent variables of age, body mass, height, CI, TST and WC included in each analysis. The equations were formulated from all the possible combinations of independent variables. They were selected by measures of goodness-of-fit statistics, including the R 2 values adjusted for the df, standard error of the estimate (SEE), and Cook s statistic. (18) Pure error was a performance measure of a prediction equation when applied to an independent sample. The pure error should be similar to the SEE value of the same equation from its validation. (19) Results Physical characteristics Overall, 664 from 995 children (66.7%) agreed to participate in the study. Table 1 shows the physical characteristics of the study population, measured separately for boys and girls. Boys and girls were similar in age, body mass and height, and TST. Boys had significantly higher BMI, WC and CI values compared to girls. Correlation among simple anthropometry Low correlation between age and the anthropometric measurements such as BMI, WC, CI and TST were observed in both sexes (r= for boys, r= for girls). Correlation between height and the anthropometric variables was slight to moderate (r= for boys, r= for girls). In contrast, body weight showed a strong correlation with BMI, WC and TST (r= for boys, r= for girls), except for CI (r=0.32 for boys, r=0.51 for girls). Thus, body mass was considered to correlate in part with the above anthropometry. As shown in Table 2 and 3, the CI revealed the poorest correlation with other anthropometric variables for boys (r= , p<0.01) and a moderate correlation for girls (r= , p<0.01), except for WC, which correlated moderately for both boys and girls (r=0.75 for boys, r=0.78 for girls). In contrast, BMI showed a strong correlation with WC, TST, and variables derived from Dezenberg s equation, including FM, FFM and %BF (r= for boys,

4 34 Pratanaphon S, Chumnongkitch S, Hensangvilai K. r= for girls). WC correlated highly with TST, FM, FFM, and %BF (r= for boys, r= for girls). It was noticeable in both gender that almost all of the variables derived from Dezenberg s equation had high correlations with BMI, WC and TST, except for CI. BMI and FM prediction equations The results of the BMI and FM equations are shown in Table 4. The BMI values that derived from body weight for the SEE were 1.2 kg/m 2 for both sexes. The average BMI for boys and girls was 16.6 and kg/m 2, respectively. Thus, the corresponding coefficients of variation (CV), calculated from SEE divided by the average BMI, were 7% for boys and 7% for girls. Similar results were also found in BMI equations derived from body weight and TST. The BMI values for the SEE were 1.1 kg/m 2 for boys and 1.0 kg/m 2 for girls. The average BMI for boys and girls were the same as mentioned above, which resulted in a CV of 7% for boys and 6% for girls. The SEE value for boys and girls was 1.2 and 1.1 kg/m 2, respectively. The corresponding CVs for the third BMI equation, which derived from body weight and WC, were 7% for both sexes. During the development of the FM equation, the independent variables such as BW, BMI, TST and WC showed a good R 2 and SEE (Table 5). However, only BW and TST were selected for use in the equation because less CV was associated with them. The FM values derived from BW and TST for the SEE were 0.02 kg for boys and 0.33 kg for girls. The average FM for boys was 4.78 kg, resulting in a CV of 0.4%. The mean FM for girls was 5.39 kg and the corresponding CV was 6%. Discussion In this study, girls showed less BMI, WC and CI than boys, whereas no difference in TST between sexes (Table 1). In contrast, Pecoraro s study (20) found no sex difference in 6-year-old Italian children tested for BMI and TST. It was reported that children s maturation status and growth pattern might result in large variations of body composition between-population, inter-and intra-individual. (4) In addition, differences in ethnic backgound, anthropometry and sample size were responsible for the gender difference. (9) Therefore, these factors may contribute to the different results between this study and Pecoraro. (20) From Pearson s correlation, the body weight (BW) had a high correlation with the anthropometric variables rather than age and stature. The subject s age in this study was in the same range, in which the adiposity rebound occured as previously reported. (21) Thus, at this age, body weight plays a crucial role on a childs body fatness. Although each measurement came from different methods and assumptions, BMI, WC, and TST demonstrated a strong correlation with each other in both sexes (r= for boys and r= for girls) (Tables 2 and 3). These results suggested that the BMI, WC and TST are useful tools for evaluating childhood adiposity. The results of this study support those from previous studies, which evaluated the effectiveness of anthropometric indices such as BMI and skinfold thickness for measuring obesity in children aged 8-12 years old. (7) The moderate- to-high correlations between WC and measuresments of truncal fatness in children 3-8 years of age were also found in Eisenmann s study. (8) Moreover, WC was recommended as a sensitive

5 Prediction equations for BMI and FM in children 35 marker of cardiovascular risk in children with a mean age of 7.7±2 years, (12) and it was used for identifying overweight and obese children at risk of developing metabolic complications. (11,22) Therefore, WC is not only a simple tool for defining childhood obesity, but is also suggested as a clinical measure along with the BMI. On the other hand, CI showed slight-tomoderate correlations to other anthropometric variables for both sexes, except WC. It had been reported that the childhood body composition between nations varies with gender-, ethnic background-, and maturation. (14,23) In addition, to prevent obesity in children at the age of adiposity rebound in Chiang Mai, the BMI and FM predicting equations were firstly developed for children aged 6-8 years old. The selection of variables used in the equation was based in part on their association with BMI and FM. As shown in Table 5, of all the independent variables, BW, TST and WC had the highest R 2 and the lowest SEE values with BMI, whereas, BW, BMI, TST and WC showed the highest R 2 and the lowest SEE values with FM. It was found that body weight was a major contributor in predicting BMI as well as FM. The positive regression coefficients of either TST or WC were partial correlations

6 36 Pratanaphon S, Chumnongkitch S, Hensangvilai K. after the consideration of body weight. The independent variables for the BMI equations included body weight, and either TST or WC for boys and girls (Table 4). By increasing the number of independent variables such as BW, and either TST or WC in BMI equations, R 2, SEE and the pure error were improved (Table 4). However, the corresponding CV of those three BMI equations was similar for each, 7%. Therefore, this may suggest that the precision of the three equations for predicting BMI value was the same. Nevertheless, to prevent possible errors from stature measurement and save time, the BMI equation based on BW is suggested. In contrast, although many independent variables such as BW, BMI, TST and WC showed high R 2 and low SEE values with FM (Table 5), only BW and TST gave less CV in fat mass prediction. Thus, the best FM prediction equation included BW and TST as the independent variables. It has been known that early identification of childhood obesity and prevention are important, especially at the age of obesity rebound, which predisposes development of excess weight. (2) According to a limitation the in number of available practitioners and tools used in the community, such as schools, these new BMI and FM equations have been proposed, based on simple anthropometry. However, one limitation of this study was that all subjects included were from the urban area of Chiang Mai province. Therefore, validation of these equations may be warranted before use in other areas of the country.

7 Prediction equations for BMI and FM in children 37 Conclusion The anthropometric measurements, including BMI, WC, and TST, were found to be useful methods for determining obesity in elementary school children, whereas CI was the poorest index of body fatness when compared with the others. Three BMI equations and the FM equation were developed for elementary schoolchildren in the urban area of Chiang Mai. In all equations, the body mass was the major contributing factor for determining BMI and FM. The additional variables using BMI equations was either TST or WC, whereas TST was used for formulating the FM equation. References 1. Kavey REW, Daniels SR, Lauer RM, Atkins DL, Hayman LL, Taubert K. American Heart Association Guidelines for primary prevention of atherosclerotic Cardiovascular Disease beginning in childhood. Circulation 2003;107: Rolland-Cachera MF, Deheeger M, Bellisle F, Sempé M, Guilloud-Bataille M, Patois E. Adiposity rebound in children: a simple indicator for predicting obesity. Am J Clin Nutr 1984;39: Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 2000;320: Wang Y. Epidemiology of childhood obesity-methodological aspects and guidelines: what is new? Intern J Obes 2004;28:S Sarri A, Moreno LA, Garcia-Llop LA, Fleta J, Morellon MP, Bueno M. Body mass index, triceps skinfold and waist circumference in screening for adiposity in male children and adolescents. Acta Paediatr 2001;4: Sarri A, Garcia-Llop LA. Skinfold thickness measurements are better predictors of body fat percentage than body mass index in male Spanish children and adolescents. Eur J Clin Nutr 1998;52: Chan YL, Leung SSF, Lam WWM, Peng XH, Metreweli C. Body fat estimation in children by magnetic resonance imaging, bioelectrical impedance, skinfold and body mass index: A pilot study. J Pediatr Child Health 1998;34: Eisenmann JC, Heelean KA, Welk GJ. Assessing body composition among 3-to 8-year-old children: anthropometry, BIA, and DXA. Obes Res 2004;12: Higgins PB, Gower BA, Hunter GR, Goran MI. Defining health-related obesity in prepubertal children. Obes Res 2001;9: Goran MI, Gower BA, Treuth M, Nagy TR. Prediction of intra-abdominal and subcutaneous abdominal adipose tissue in healthy pre-pubertal children. Int J Obes Relat Metab Disord 1998;22: Taylor RW, Jones IE, Williams SM, Goulding A. Evaluation of waist circumference, waist-to-hip ratio, and the conicity index as screening tools for high trunk fat mass, as measured by dual-energy X- ray absorptiometry, in children aged 3-19 y. Am J Clin Nutr 2000;72: Maffeis C, Pietrobelli A, Grezzani A, Provera S, Taté L. Waist circumference and cardiovascular risk factors in prepubertal children. Obesity Res 2001; 9: Valdez R, Seidell JC, Ahn YI, Weiss KM. A new index of abdominal adiposity as an indicator of risk for cardiovascular disease. A cross population study. Int J Obes Relat Metab Disord 1993;17: Florentino RF. The burden of obesity in Asia: Challenges in assessment, prevention and management. Asia Pacific J Clin Nutr 2002;11(suppl):S Harrison G, Buskirk E, Lindsay CJ, et al. Skinfold thickness and measurement technique. In: Lohman TG, Roche AF, Martorell R, editors. Anthropometric Standardization reference manual. Champaign, IL: Human Kinetics Publishers; p Dezenberg CV, Nagy TR, Gower BA, Johnson R, Goran MI. Predicting body composition from anthropometry in preadolescent children. Int J Obes 1999;23: Lohman TG. Applicapability of body composition techniques and constants for children and youth. Exerc Sport Sci Rev 1986;14: SPSS base 10.0 applications guide. SPSS Inc. Chicago, IL; 1999: Sun SS, Chumlea WC, Heymsfield SB, el al. Development of bioelectrical impedance analysis predic-

8 38 Pratanaphon S, Chumnongkitch S, Hensangvilai K. tion equations for body composition with the use of a multicomponent model for use in epidemiologic surveys. Am J Clin Nutr 2003;77: Pecoraro P, Guida B, Caroli M, et al. Body mass index and skinfold thickness versus bioimpedance analysis fat mass prediction in children. Acta Diabetal 2003;40: Dietz WH. Critical periods in childhood for the development of obesity. Am J Clin Nutr 1994;59: Daniels SR, Khourty PR, Morrison JA. Utility of different measures of body fat distribution in children and adolescents. Am J Epidemiol 2000;152: Goran MI. Measurement issues related to studies of childhood obesity: assessment of body composition, body fat distribution, physical activity, and food intake. Pediatrics 1998;101: การพ ฒนาสมการเพ อทำนายค าด ชน มวลกายและมวลไขม น โดยการว ดองค ประกอบของร างกายแบบง ายในเด กอาย 6-8 ป สายนท ปรารถนาผล, วท.ม., สมรรถช ย จำนงค ก จ, Ph.D., กรกฏ เห นแสงว ไล, วท.บ. ภาคว ชากายภาพบำบ ด คณะเทคน คการแพทย มหาว ทยาล ยเช ยงใหม บทค ดย อ การจำแนกภาวะน ำหน กต วเก นต งแต เร มแรกเป นส งสำค ญในการป องก นโรคอ วน การศ กษาน ม ว ตถ ประสงค เพ อศ กษาความส มพ นธ ระหว างว ธ การว ดองค ประกอบของร างกาย แบบง ายในการประเม นภาวะอ วนและสร างสมการเพ อทำนายค าด ชน มวลกายและมวลไขม นในเด ก ผ เข าร วมการศ กษาเป นเด กอาย 6-8 ป จำนวน 664 ราย ทำการหาค าด ชน มวลกาย (BMI) ว ดเส นรอบ วงของเอว (WC) ความหนาของไขม นใต ผ วหน งท ตำแหน งกล ามเน อไตรเซบส (TST) และหาค า conicity index (CI) คำนวณค ามวลไขม น (FM) มวลกายไร ไขม น (FFM) และเปอร เซ นต ไขม น (%BF) จากสมการของ Dezenberg เม อพ จารณาด วยความส มพ นธ เป ยร ส นพบว าในเด กชาย ค า BMI ม ความส มพ นธ ก นส งก บ WC, TST, FM, FFM และ %BF และเส นรอบวงของเอวก ม ความ ส มพ นธ ก นส งก บ CI, TST, FM, FFM และ %BF เช นก น ตรงก นข ามก บความส มพ นธ ระหว าง CI และองค ประกอบของร างกายอ นๆ ซ งม ความส มพ นธ ก นน อย ยกเว นเส นรอบวงของเอว ส งเกต พบผลคล ายก นน ในเด กหญ ง สามสมการในการทำนายค า BMI ซ งข นอย ก บน ำหน กต วและ/หร อ TST และ/หร อ WC (R 2 = , CV 6-7%) และสมการทำนายค า FM ซ งข นอย ก บน ำหน กต วและ TST (R 2 = , CV 0.4-6%) จ งถ กพ ฒนาข นโดยม ค าส มประส ทธ สหส มพ นธ ส ง สร ปได ว า BMI, WC และ TST เป นเคร องม อท เป นประโยชน ในการประเม นไขม นของเด ก ในขณะท CI เป นต ว ช ว ดไขม นในเด กท แย ท ส ด เช ยงใหม เวชสาร 2550;46(1): คำสำค ญ: สมการทำนายมวลไขม น ด ชน มวลกาย เส นรอบวงของเอว ความหนาของไขม นใต ผ วหน ง