Artigos

INTRODUCTION

Monitoring body mass and body fat mass (FM) throughout childhood and adolescence is crucial for identifying potential health and nutritional risks that may persist into adulthood 1–3. Body composition analysis plays a crucial role in detecting excessive body FM and mass, with Dual-energy X-ray absorptiometry (DXA) recognized as a reference technique for this purpose 4,5. However, despite its accuracy, DXA's widespread use in population studies is limited due to costliness and the need to carry out the tests in a specialized laboratory with trained staff 6,7.
In this way, the growth standards/references are the main tool for monitoring children and adolescents’ nutritional status at a population level 8,9. This non-invasive and easily applicable method eliminates the need for expensive equipment 10. Additionally, even though the BMI growth charts don't directly measure adiposity, they exhibit high sensitivity in detecting overweight and obesity, displaying a robust positive correlation with body fat 11,12. However, there are valid concerns about the development of these growth standards/references, particularly regarding their consideration of the complex interplay of environmental, genetic, and cultural factors influencing growth patterns between regions/countries 13,14,15. Studies pointed out that growth can be affected be several factors including environmental, genetic predispositions, and socio-economic-political-emotional (SEPE) conditions 13,14,15,16. Furthermore, cultural practices and dietary habits significantly shape growth trajectories, emphasizing the need for comprehensive and culturally sensitive approaches in developing growth standards/references 15.
Several international growth standard/references have been widely utilized over the years 8,9. A growth standard represents the growth of a ‘healthy’ population, serving as a role model. Conversely, a growth reference outlines the growth patterns observed within a specific sample of individuals, reflecting their current growth trends 17.
For instance, the 2006 World Health Organization (WHO) 18 growth charts qualify as growth standards as they were developed based on a sample of health children with optimal health and nutrition conditions, including exclusive breastfeeding for at least three to four months of age 8,18. In contrast, growth charts from the Centers for Disease Control and Prevention (CDC, 2000) 19, the WHO (2007) 20, and the International Task Obesity Force (IOTF, 2000/2012) 21,22 are considered growth references, as they capture growth patterns observed in various populations but may not necessarily represent an ideal standard 8,17,.
In addition, to these well-known international growth references, there is the MULT growth reference (2023) 23,24, developed using longitudinal data from multiethnic children and adolescents across 10 countries, which has recently been released. Alongside these international growth references, there is the Brazil-specific BMI growth reference developed by Conde & Monteiro in 2006 25.
Furthermore, while these growth references play a crucial role in assessing nutritional status at the population level, there remains a lack of consensus regarding which one is most appropriate for Brazilians, particularly for adolescents 8,9. Therefore, the aim of this study was to compare the nutritional status classified by the height and BMI references from the CDC (2000) 19, BRAZIL (2006) 25, WHO (2007) 20, IOTF (2012) 22, and MULT (2023) 23,24 and to evaluate their diagnostic accuracy in detecting obesity based on body adiposity in a sample of adolescents from northeastern Brazil.




METHODS

Data from adolescents were obtained from a previous study carried out by researchers at the Federal University of Rio Grande do Norte (UFRN), Brazil, between January 2018 and April 2019 6. That study aimed to develop and cross-validate fat-free mass (FFM) predictive equations using bioelectrical impedance analysis, with DXA as the reference method 6. Its convenience sample consisted of 257 adolescents (128 girls), aged 10 to 19 years, from the city of Natal, Northeast Brazil, who were recruited through publicity among participants in university extension projects of the Physical Education Department of UFRN and two social projects maintained by the federal government 6.
For this study, the total non-probabilistic sample of the previously carried out study was considered, therefore, we used the G*Power 3.1.9.7 package to calculate the power of the sample by the exact two-tailed test of comparison of proportions, adopting an effect size of 0.1, ? error of 0.05, and constant proportion of 0.5 6,26. Thus, the calculated power of the sample was 0.88. Demographic data such as sex (determined by the presence or absence of the Y chromosome at birth), age (in months), anthropometric measurements of weight and height, and body composition measurements based on DXA were selected 6. These anthropometric and body composition examinations were performed by highly trained health professionals who followed a standardized examination protocol to ensure data quality 4, 6, 27,28. Additionally, all subjects were measured without personal belongings that could interfere with the anthropometric and DXA assessments 6,27. All data collections were conducted in a single visit by each participant to the laboratory to perform, after an overnight fasting, sequentially, anthropometric measurements, and DXA assessments 6.
The whole-body composition analysis was conducted using the Lunar Prodigy equipment, NRL 41990 model (GE Lunar, Madison, WI, United States) 6. The equipment was calibrated weekly, using the Calibration Phantom, according to the manufacturer's instructions 6. Participants were scanned while lying supine along the longitudinal axis of the table 6. Their feet were placed together and secured at the level of the fingers to immobilize the legs, while their hands were positioned in a prone position within the scanning region 6,27. Participants were instructed to remain still during the scanning process, and the staff followed the procedures recommended by the manufacturer and Nanna et al., (2015) 4,6.
The participants' body composition was estimated by the iDXA, version 13.6 enCore™ 2011 software (GE Healthcare Lunar, Madison, WI, USA), as described in a study involving a sample of Brazilian Army cadets 6,29. Total body measurements were conducted to assess FM, bone mineral content (BMC), and lean soft tissue (LST) 6,29. The FFM was calculated by the sum of the BMC and LST (FFM = BMC + LST) values 6,29. The reproducibility of the variables estimated by DXA was determined using the coefficient of variation (CV%) and the technical error of measurement (TEM) 6,29.
In the selection process, the quality data analysis was performed and participants with implausible height-for-age and/or BMI-for-age values were excluded (n = 1) 30. Implausible values were those below -6 standard deviations (SD) or above +6 SD of the WHO (2007) reference height values or those below -5 SD or above +5 SD of the WHO (2007) reference BMI values 20,30.
Concerning the obesity classification, the FM estimated by DXA was used to calculate a Fat Mass Index (FMI), as proposed by Kelly et al., (2009) 31. The FMI is obtained by dividing the FM (kg) by the squared height (m) 31. The obesity classification according to the participant´s FMI was performed based on the cut-off’s values for FMI (percentile 95th) proposed by De Oliveira et al., (2023) 32, while for the BMI was performed according to the values proposed by the CDC (2000) 19, BRAZIL (2006) 25, WHO (2007) 20, IOTF (2012) 22, and MULT (2023) 24 BMI references.
The diagnostic accuracy, defined as the proportion of all tests that give a correct result, was performed for the CDC (2000) 19, BRAZIL (2006) 25, WHO (2007) 20, IOTF (2012) 22, and MULT (2023) 24 BMI growth references based on the obesity diagnostic classified by the FMI 31,32. Furthermore, sensitivity, specificity, and positive and negative likelihood ratios (+LR/-LR) were calculated in R using the epiR package, with a significance level of 5% and a 95% confidence interval 33,34.
In order to compare the growth references, the prevalence of normal height and stunting was estimated applying the height references from CDC (2000) 19, WHO (2007) 20, and MULT (2023) 23. Moreover, the prevalence of underweight, normal weight, overweight, and obesity was calculated according to the BMI reference values of the CDC (2000) 19, BRAZIL (2006) 25, WHO (2007) 20, IOTF (2012) 22, and MULT (2023) 24. For the MULT (2023) 24 BMI reference, the optimized cut-off points 35 were applied for both sexes: a) underweight for percentile < 3rd; b) overweight for percentile ? 85th; c) obesity for percentile ? 98th. To assess the agreement between the CDC (2000) 19, BRAZIL (2006) 25, WHO (2007) 20, IOTF (2012) 22, and MULT (2023) 23,24 growth references, the Bland-Altman 36 plots were performed based on the z-scores of BMI and height. All these statistical analyses were performed in R software version 4.2.3 for macOS 37.
Regarding ethical aspects, the Natal survey was conducted according to the ethical principles for research involving human subjects, laid down in the Declaration of Helsinki and the Brazilian resolution CNS 466/12 38,39. It was approved by the Research Ethics Committee of the University Hospital Onofre Lopes — HUOL/UFRN under number 34804414.7.0000.5292 6. The legal guardians of the adolescents were informed about the research and gave their consent for participation by signing the Informed Consent Form (ICF) 6. In addition to that, the participants themselves, if capable of understanding, also signed an assent form 6. The details of these protocols, as well as their approvals from the ethics committees, can be found in previous studies 6,27,40.


RESULTS

After excluding implausible values, 256 subjects (50.0% males) were included in the analysis. The demographic characteristics of the sample are shown in Table 1.
Regarding diagnostic accuracy, as shown in Table 2, the IOTF (2012) 22 obesity percentiles presented the lowest values for specificity (0.60; CI95%:0.15-0.95) and +LR (2.44; CI95%:0.83-7.14). Further, MULT (2023) 24 presented the highest values for sensitivity (0.98; CI95%: 0.95-0.99) and +LR (4.88; CI95%:0.85-28.17), and CDC (2000) 19, BRAZIL (2006) 25, and WHO (2007) 20, presented the same values for sensitivity (0.97; CI95%: 0.94-0.98) and +LR (4.86; CI95%:0.84-28.06).
The nutritional status of the participants is presented in Figures 1 and 2. The prevalence of stunting was higher applying the height references from CDC (2000) 19 (9.0%), and MULT (2023) 23 (8.6%) when compared to the WHO (2007) 20 (3.9%). On the other hand, the highest underweight prevalence (7.8%) was achieved by applying the CDC (2000) 19 BMI growth chart, and the highest obesity prevalence (4.3%) was by applying the WHO (2007) 20, CDC (2000) 19 and BRAZIL (2006) 25 BMI growth charts. The IOTF (2012) 22 reference presented the lowest prevalence of obesity (3.5%), and the MULT (2023) 24 BMI reference showed the lowest prevalence for underweight (2.3%).
In the agreement analysis (Figure 3), the Bland-Altman plots showed the lowest critical difference (CD) between the height references of WHO (2007) 20 and MULT (2023) 23 (CD = 0.30). For BMI, the lowest CD was between the MULT (2023) 24 and IOTF (2012) 22, and the highest was between WHO (2007) 20 and MULT (2023) 24 (CD = 0.61). Moreover, the difference between MULT (2023) 23 and WHO (2007) 20 height references presented a negative tendency, while the difference between MULT (2023) 24 and WHO (2007) 20 BMI references presented positive values in the right extremity.


DISCUSSION

This study presented valuable insights into the nutritional diagnosis based on body composition analysis in a sample of adolescents from northeastern Brazil. Notably, the MULT (2023) 24 BMI reference demonstrated its relevance, especially in establishing obesity cutoffs, presenting the best diagnostic accuracy among the BMI growth references for this sample.
When compared to WHO (2007) 20 growth reference, the MULT (2023) 23 presented a higher height median, leading to a correspondingly higher prevalence of stunting. These findings are similar to the studies conducted in European countries and Australia, which also have noticed this pattern, indicating that children and adolescents in these regions displayed taller stature than those referenced in the WHO (2007) 20 height reference 41–43.
Additionally, research conducted by the NCD Risk Factor Collaboration (NCD-RisC/2020) across 200 countries and territories reveals that emerging economies have experienced the most significant growth in height over the last three decades 44. This positive trend in height in developing nations is due to improvements in their healthcare systems and socioeconomic conditions 18,20,45. This explains why height references based on data from children born before the 1990’s, like those from CDC (2000) 19 and WHO (2007) 20, tend to show lower median heights in contrast to references derived from more recent decades. MULT (2023) 23 height reference offers an advantage over the WHO (2007) 20 reference because it was developed using height data from multiethnic children born more recently, potentially overcoming the secular trend in height.
Regarding obesity classification, even though there were no significant variations among the growth references in this study, the MULT (2023) 24 BMI reference showed the most optimal performance. This observation aligns with findings from a previous study we conducted, which assessed international growth references for diagnosing obesity in schoolchildren from Santos, Brazil 46. In that study, the MULT (2023) 24 BMI reference presented the best +LR and – LR combination based on body fat mass evaluated via skinfold measurements 46. Additionally, another study involving schoolchildren and adolescents from the United States highlighted that among international BMI growth references, the MULT (2023) 24 exhibited the highest diagnostic accuracy for identifying obesity based on fat mass index (FMI) calculated using DXA 32.
Moreover, while the CDC recommends its own growth reference for children and adolescents aged 2 to 19 years in the United States, there is evidence that the CDC (2000) 19 BMI growth reference overestimates the prevalence of obesity in certain populations, such as student-athletes aged 11 to 19 years 47. Similar prevalence overestimations have been noted in other countries, including South Africa and Saudi Arabia 48,49.
The BRAZIL (2006) 25 BMI reference presented similar performance to the WHO (2007) 20 reference, which is consistent with the findings of a study conducted with a national sample of adolescents aged from 11 to 17 years old from the five Brazilian regions 50. In that national study, the authors observed that the BRAZIL (2006) 25 and WHO (2007) 20 presented a close prevalence for overweight (BRAZIL = 20.6%; WHO = 20.1%) 50. Another study conducted with schoolchildren aged 7 to 10 years old from the city of Florianopolis, Brazil also found similarities on the performance of the BRAZIL (2006) 25 and WHO (2007) 20 in detecting excess body fat 51. It suggested that the classification accuracy could be improved by refining cutoffs, which is an advantage of the IOTF (2012) 22 and MULT (2023) 24 classification systems 51. These systems use the adult BMI cutoff value of 30 kg/m2 to establish the obesity percentile at the final age, aligning with a well-known adult risk factor for non-communicable diseases and making the transition from adolescence to adulthood smoother 22,24,52.
However, while the IOTF (2012) 22 BMI reference has been recommended for worldwide use and has shown good accuracy for the European population, it exhibited relatively lower +LR for our sample. This discrepancy could be attributed to the composition of IOTF (2000/2012) 21,22 population sample, as it lacks representation from African countries, which may impact its coefficient of variation. Additionally, the IOTF's age limit of 2 to 18 years may be a limitation, as there is evidence of growth beyond 18 years, particularly for boys 21,22,53.
The major strengths of this study include the use of anthropometric data collected by trained health professionals, aimed at reducing measurement errors and social desirability bias, as well as the use of DXA to assess body composition, considered a reference method for estimating fat mass 4. However, limitations include a small, non-representative sample, only from one capital in the northeast region of the country, in addition to the absence of a reference to determine obesity in children and adolescents based on body composition, underscoring the need for further validation of FMI use and appropriate cutoffs for obesity 32. Although this study has limitations regarding the sample, it is important to highlight that we conducted DXA examination, which provides great accuracy in diagnosing body fat 4. Due to its high cost, DXA is rarely used in large-scale population studies, underscoring the significance and quality of this study, even though its conclusions cannot be generalized regionally or nationally 4,54.
Furthermore, a systematic review underscored the ongoing increase in overweight and obesity prevalence among Brazilian adolescents, highlighting the crucial need for effective nutritional surveillance in the country 55. Our study contributes to this by evaluating the accuracy of existing growth charts in detecting obesity, based on DXA, which is a reference method. Thus, this emphasizes the relevance of the MULT (2023) 24 growth reference in diagnosing the nutritional status of adolescents based on body composition. However, it also suggests that further research is essential to validate its effectiveness on a broader scale for diagnosing obesity among children and adolescents nationally and internationally.
In summary, this study underscores the practical applications of MULT growth references (2023) 23,24 in shaping public health policies. Developed using data from multiethnic children born more recently (1990s-2000s), presenting growth charts from birth to 20 years old for height and BMI, and demonstrating remarkable accuracy for diagnosing obesity, it seems to reflect the contemporary growth trends 32,46. In this way, by incorporating the MULT (2023) 23,24 growth reference into nutritional surveillance programs, healthcare practitioners and policymakers can more effectively identify at-risk populations and tailor interventions to address the ongoing challenges of stunting, overweight and obesity.

ACKNOWLEDGMENTS

The authors are grateful to the staff and participants of the Natal survey, and to the Federal University of Rio Grande do Norte (UFRN) for conducting this study and making it available to researchers.

FUNDING

All phases of this study were supported in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) - Finance Code 001 (grant numbers 88887.368190/2019-00, 88887.356471/2019-00).

REFERENCES

1. Ripka WL, Esmanhoto E, Ulbricht L. Body fat percentage in adolescents from Curitiba-PR metropolitan region: reference data using LMS method. RBCDH 2018; 20(5):373–380.
2. Wi?ch P, Sa?aci?ska I, B?czek M, Bazali?ski D. The nutritional status of healthy children using bioelectrical impedance and anthropometric measurement J Pediatr (Rio J) 2022; 98(2):161–167.
3. Ward ZJ, Long MW, Resch SC, Giles CM, Cradock AL, Gortmaker SL. Simulation of Growth Trajectories of Childhood Obesity into Adulthood. N Engl J Med 2017; 377(22):2145–2153.
4. Nana A, Slater GJ, Stewart AD, Burke LM. Methodology Review: Using Dual-Energy X-Ray Absorptiometry (DXA) for the Assessment of Body Composition in Athletes and Active People. Int J Sport Nutr Exerc Metab 2015; 25(2):198–215.
5. Mason J, Morris C, Long DE, Sanden MN, Flack K. Comparison of Body Composition Estimates among Norland Elite®, Lunar iDXA®, and the BodPod® in Overweight to Obese Adults. Meas Phys Educ Exerc Sci 2020; 24(1):65–73.
6. Costa RF da, Silva AM, Masset KV da SB, Cesário T de M, Cabral BG de AT, Ferrari G, Dantas PMS. Development and Cross-Validation of a Predictive Equation for Fat-Free Mass in Brazilian Adolescents by Bioelectrical Impedance. Front Nutr 2022; 9:820736.
7. Sahoo K, Sahoo B, Choudhury A, Sofi N, Kumar R, Bhadoria A. Childhood obesity: causes and consequences. J Family Med Prim Care 2015; 4(2):187-192.
8. De Oliveira MH, Dos Santos Pereira D, Melo DS, Silva JC, Conde WL. Accuracy of international growth charts to assess nutritional status in children and adolescents: a systematic review. Rev Paul Pediatr 2022; 40:e2021016.
9. Ferreira AA. Evaluation of the growth of children: path of the growth charts. Demetra (Rio J.) 2012; 7(3):191–202.
10. Akindele MO, Phillips JS, Igumbor EU. The relationship between body fat percentage and body mass index in overweight and obese individuals in an urban African setting. J Public Health Afr 2016; 7(1):15–19.
11. Lazarus R, Baur L, Webb K, Blyth F. Body mass index in screening for adiposity in children and adolescents: systematic evaluation using receiver operating characteristic curves. Am J Clin Nutr 1996; 63:500-506.
12. Birch L, Perry R, Hunt LP, Matson R, Chong A, Beynon R, Shield JPH. What change in body mass index is associated with improvement in percentage body fat in childhood obesity? A meta-regression. BMJ Open 2019; 9(8):e028231.
13. Hruschka DJ, Hackman JV, Stulp G. Identifying the limits to socioeconomic influences on human growth. Econ Hum Biol 2019; 34:239–251.
14. Wells JC. Worldwide variability in growth and its association with health: Incorporating body composition, developmental plasticity, and intergenerational effects. Am J Hum Biol 2017; 29(2):e22954.
15. Urlacher SS, Blackwell AD, Liebert MA, Madimenos FC, Cepon?Robins TJ, Gildner TE, Snodgrass JJ, Sugiyama LS. Physical growth of the Shuar: Height, Weight, and BMI references for an indigenous Amazonian population. Am J Hum Biol 2016; 28(1):16–30.
16. Bogin B. Social-economic-political-emotional (SEPE) factors regulate human growth. Hum Biol Public Health 2021; 1:1–20.
17. Khadilkar V. The growing controversy about growth charts: WHO or regional? Int J Pediatr Endocrinol 2013; 2013(Suppl 1):O6.
18. WHO Multicentre Growth Reference Study Group. WHO Child Growth Standards: length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age - methods and development. Acta Paediatr 2006; 95(S450):76–85.
19. Kuczmarski RJ, Ogden CL, Guo SS. 2000 CDC Growth Charts for the United States: Methods and Development. Vital Heal Stat 2002; 266:1–190.
20. Onis M De, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ 2007; 85(9):660–667.
21. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 2000; 320(7244):1240–1243.
22. Cole TJ, Lobstein T. Extended international (IOTF) body mass index cut-offs for thinness, overweight and obesity. Pediatr Obes 2012; 7(4):284–294.
23. De Oliveira MH, Araújo J, Ramos E, Conde WL. MULT: New height references and their efficiency in multi-ethnic populations. Am J Hum Biol 2023; 35(5):e23859.
24. De Oliveira MH, Araújo J, Severo M, Rodrigues KAS, Conde WL. MULT: A new BMI reference to assess nutritional status of multi?ethnic children and adolescents. Am J Hum Biol 2023; 35(11):e23946.
25. Conde WL, Monteiro CA. Body mass index cutoff points for evaluation of nutritional status in Brazilian children and adolescents. J Pediatr (Rio J) 2006; 82(4):266–272.
26. GPower. GPower 3.1.9.7. The growing controversy about growth charts: WHO or regional? Int J Pediatr Endocrinol Universität Düsseldorf, Düsseldorf, Germany; 2020. Available at: http://www.gpower.hhu.de/. Acessed [Jan 2024].
27. Costa RF da, Masset KV da SB, Silva AM, Cabral BG de AT, Dantas PMS. Development and cross-validation of predictive equations for fat-free mass and lean soft tissue mass by bioelectrical impedance in Brazilian women. Eur J Clin Nutr 2022; 76(2):288–296.
28. Lohman TG, Roche AF, Martorell R. Anthropometric Standardization Reference Manual. Champaign, IL: Human Kinetics Books; 1988.
29. Langer RD, Borges JH, Pascoa MA, Cirolini VX, Guerra-Junior G, Goncalves EM. Validity of bioelectrical impedance analysis to estimate fat-free mass in army cadets. Nutrients 2016; 8(3):121.
30. WHO Expert Committee. Physical Status: The use and interpretation of anthropometry. WHO Library Cataloguing. Geneva: WHO; 1995.
31. Kelly TL, Wilson KE, Heymsfield SB. Dual Energy X-Ray Absorptiometry Body Composition Reference Values from NHANES. PLoS One 2009; 4(9):e7038.
32. De Oliveira MH, Araújo J, Severo M, Pereira DB dos S, Mazzeti CM da S, Conde WL. Accuracy of the international growth charts to diagnose obesity according to the body composition analysis in US children and adolescents [Unpublished manuscript]. University of São Paulo 2023; 1–25.
33. Van Stralen KJ, Stel VS, Reitsma JB, Dekker FW, Zoccali C, Jager KJ. Diagnostic methods I: Sensitivity, specificity, and other measures of accuracy. Kidney Int 2009; 75(12):1257–1263.
34. Stevenson, M. epiR: Tools for the Analysis of Epidemiological Data. R package version 2.0.74. CRAN; 2024. Available from: https://cran.r-project.org/web/packages/epiR/index.html
35. De Oliveira MH, Costa RF, Conde WL. A new tool to assess the nutritional status of children and adolescents based on the MULT growth reference [Unpublished manuscript]. University of São Paulo 2023; 1–20.
36. Bland MJ, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986; 327(8476):307–310.
37. R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2022.
38. Brazilian National Health Council. Resolution N° 466/12 on research involving human subjects. Diário Oficial da União 2012: 12 Dec.
39. World Medical Association. World Medical Association declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA 2013; 310(20):2191–2194.
40. Costa RF, Masset KB, Sousa EC, Cabral BT, Dantas PMS. Development and cross-validation of predictive equations of fat-free mass by bioelectrical impedance for Brazilian men aged 20 to 59 years old. Motricidade 2018; 14(SI):26–32.
41. Rosario AS, Schienkiewitz A, Neuhauser H. German height references for children aged 0 to under 18 years compared to WHO and CDC growth charts. Ann Hum Biol 2011; 38(2):121–130.
42. Hughes I, Harris M, Cotterill A, Garnett S, Bannink E, Pennell C, Sly P, Leong GM, Cowell C, Ambler G, Werther G, Cutfiled W, Choong CS. Comparison of Centers for Disease Control and Prevention and World Health Organization references/standards for height in contemporary Australian children: Analyses of the Raine Study and Australian National Children’s Nutrition and Physical Activity cohorts. J Paediatr Child Health 2014; 50(11):895–901.
43. Bonthuis M, van Stralen KJ, Verrina E, Edefonti A, Molchanova EA, Hokken-Koelega ACS, Schaefer F, Jager KJ. Use of national and international growth charts for studying height in European children: Development of up-to-date European height-for-age charts. PLoS One 2012; 7(8):1–11.
44. NCD Risk Factor Collaboration (NCD-RisC). Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: a pooled analysis of 2181 population-based studies with 65 million participants. Lancet 2020; 396(10261):1511–1524.
45. World Health Organization (WHO). Reducing stunting in children: equity considerations for achieving the Global Nutrition Targets 2025. Geneva: WHO; 2018.
46. De Oliveira MH, Costa RF da, Fisberg M, Kruel LFM, Conde WL. Comparison of international height and BMI-for-age growth references and their correlation with adiposity in Brazilian schoolchildren. Br J Nutr 2024; 1-10.
47. Etchison WC, Bloodgood EA, Minton CP, Thompson NJ, Collins MA, Hunter SC, Dai H. Body mass index and percentage of body fat as indicators for obesity in an adolescent athletic population. Sports Health 2011; 3(3):249–252.
48. Moselakgomo K V, Van Staden M. Diagnostic comparison of Centers for Disease Control and Prevention and International Obesity Task Force criteria for obesity classification in South African children. Afr J Prim Health Care Fam Med 2017; 9(1):e1-7.
49. El Mouzan MI, Al Herbish AS, Al Salloum AA, Foster PJ, Al Omar AA, Qurachi MM, Kecojevic T. Comparison of the 2005 growth charts for Saudi children and adolescents to the 2000 CDC growth charts. Ann Saudi Med 2008; 28(5):334–340.
50. Pelegrini A, Silva DAS, Gaya ACA, Petroski EL. Comparison of three criteria for overweight and obesity classification in Brazilian adolescents. Nutr J 2013; 12(5):1-8.
51. Leal DB, de Assis MAA, Conde WL, Bellisle F. Performance of references based on body mass index for detecting excess body fatness in schoolchildren aged 7 to10 years. Rev Bras Epidemiol 2014; 17(2):517–530.
52. World Health Organization (WHO). Obesity: preventing and managing the global epidemic: report of a WHO consultation. World Health Organ Tech Rep Ser 2000; 894:1-253.
53. Kato S, Ashizawa K, Satoh K. An examination of the definition “final height” for practical use. Ann Hum Biol 1998; 25(3):263–270.
54. Achamrah N, Colange G, Delay J, Rimbert A, Folope V, Petit A, Grigioni S, Déchelotte P, Coëffier M. Comparison of body composition assessment by DXA and BIA according to the body mass index: A retrospective study on 3655 measures. PLoS One 2018; 13(7):e0200465.
55. Sbaraini M, Cureau FV, Ritter JDA, Schuh DS, Madalosso MM, Zanin G, Goulart MR, Pellanda LC, Schann BD. Prevalence of overweight and obesity among Brazilian adolescents over time: a systematic review and meta-analysis. Public Health Nutr 2021; 24(18): 6415-6426.