Journal of Kerman University of Medical Sciences

Development and Validation of a Simple Equation to Fat Mass and Percentage of Body Fat in Children and Adolescents

Document Type : Original Article

Authors

Maryam Asadi 1
Ahmad Zare Javid 2
Parvaneh Kazemi 3
Morteza Sharifat 4
Mahsa Samadani 4
Hossein Bavi Behbahani 4

1 Department of Nutrition, Shoushtar Faculty of Medical Sciences, Shoushtar, Iran

2 Nutrition and Metabolic Diseases Research Center, Clinical Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.

3 Department of physiology, Tehran markaz Azad University, Tehran, Iran

4 Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

https://doi.org/10.34172/jkmu.2024.02
Abstract
 
 Background: This study aimed to develop and validate a simple equation to fat mass (FM) and percentage of body fat (PBF) in children and adolescents.
Methods: Participants consisted of 404 children and adolescents (176 males and 228 females, 5-18 years old) randomly divided into Derivation (n = 279) and Validation (n = 125) groups. FM and PBF were measured by a bioelectrical impedance analyzer. Based on demographic variables retrieved from the derivation group, 10 FM and 10 PBF predictive equations were developed using multiple regression. Finally, the most accurate model (using the coefficient of determination - R2) was chosen and validated on the validation group.
Results: The best FM and PBF equations, which were derived from demographic characteristics, were: FM (kg) = Weight (kg) x 0.15 + BMI x 1.53 + Sex x 3.40 – Age (years) x 0.37 – 26.20; where sex = 1 for male and 0 for female. R = 0.97, R2 = 0.94, standard error of the estimate (SEE) = 3.74 kg. PBF (kg) = 0.31 x Height (cm) - Weight (kg) x 0.59 + BMI x 2.98 + Sex x 6.17 – Age (years) x 0.76 – 52.84; where sex = 1 for male and 0 for female. R = 0.90, R2 = 0.82, SEE = 4.88 kg.
Conclusion: Our predictive equations accurately predicted FM and PBF using simple parameters (height, weight, BMI, sex, and age) in children and adolescents.

Highlights

Maryam Asadi(google scholar)(pubmed)

Ahmad Zare Javid(google scholar)(pubmed)

Parvaneh Kazemi(google scholar)(pubmed)

Morteza Sharifat(google scholar)(pubmed)

mahsa samadani(google scholar)(pubmed)

Hossein Bavi Behbahani(google scholar)(pubmed)

Keywords

Percentage of body fat
Fat mass
Predictive
Equation
Children
Adolescents
Subjects

  1.  

    1. Dias KA, Green DJ, Ingul CB, Pavey TG, Coombes JS. Exercise and vascular function in child obesity: a meta-analysis. Pediatrics. 2015;136(3):e648-59. doi: 10.1542/peds.2015- 0616.
    2. Lindsay AC, Sitthisongkram S, Greaney ML, Wallington SF, Ruengdej P. Non-responsive feeding practices, unhealthy eating behaviors, and risk of child overweight and obesity in Southeast Asia: a systematic review. Int J Environ Res Public Health. 2017;14(4):436. doi: 10.3390/ijerph14040436.
    3. Singh AS, Mulder C, Twisk JW, van Mechelen W, Chinapaw MJ. Tracking of childhood overweight into adulthood: a systematic review of the literature. Obes Rev. 2008;9(5):474- 88. doi: 10.1111/j.1467-789X.2008.00475.x.
    4. Kline AM. Pediatric obesity in acute and critical care. AACN Adv Crit Care. 2008;19(1):38-46. doi: 10.1097/01. AACN.0000310750.85663.ae.
    5. Wyatt SB, Winters KP, Dubbert PM. Overweight and obesity: prevalence, consequences, and causes of a growing public health problem. Am J Med Sci. 2006;331(4):166-74. doi: 10.1097/00000441-200604000-00002.
    6. Sybilski AJ, Raciborski F, Lipiec A, Tomaszewska A, Lusawa A, Furmańczyk K, et al. Obesity--a risk factor for asthma, but not for atopic dermatitis, allergic rhinitis and sensitization. Public Health Nutr. 2015;18(3):530-6. doi: 10.1017/ s1368980014000676.
    7. Bass R, Eneli I. Severe childhood obesity: an under-recognised and growing health problem. Postgrad Med J. 2015;91(1081):639- 45. doi: 10.1136/postgradmedj-2014-133033.
    8. World Health Organization (WHO). Obesity and Overweight. Available from: https://www.who.int/newsroom /fact-sheets/ detail/obesity-and-overweight (Accessed April 20, 2020.
    9. Ripka WL, Orsso CE, Haqq AM, Prado CM, Ulbricht L, Leite N. Validity and accuracy of body fat prediction equations using anthropometrics measurements in adolescents. Eat Weight Disord. 2021;26(3):879-86. doi: 10.1007/s40519- 020-00918-3.
    10. Silva AM, Fields DA, Sardinha LB. A PRISMA-driven systematic review of predictive equations for assessing fat and fat-free mass in healthy children and adolescents using multicomponent molecular models as the reference method. J Obes. 2013;2013:148696. doi: 10.1155/2013/148696.
    11. Karthik L, Kumar G, Keswani T, Bhattacharyya A, Chandar SS, Bhaskara Rao KV. Protease inhibitors from marine actinobacteria as a potential source for antimalarial compound. PLoS One. 2014;9(3):e90972. doi: 10.1371/ journal.pone.0090972.
    12. Butcher A, Kabiri LS, Brewer W, Ortiz A. Criterion validity and sensitivity to change of a pediatric bioelectrical impedance analysis scale in adolescents. Child Obes. 2019;15(2):142-8. doi: 10.1089/chi.2018.0183.
    13. Barreira TV, Staiano AE, Katzmarzyk PT. Validity assessment of a portable bioimpedance scale to estimate body fat percentage in white and African-American children and adolescents. Pediatr Obes. 2013;8(2):e29-32. doi: 10.1111/j.2047- 6310.2012.00122.x.
    14. Kriemler S, Puder J, Zahner L, Roth R, Braun-Fahrländer C, Bedogni G. Cross-validation of bioelectrical impedance analysis for the assessment of body composition in a representative sample of 6- to 13-year-old children. Eur J Clin Nutr. 2009;63(5):619-26. doi: 10.1038/ejcn.2008.19.
    15. Lim JS, Hwang JS, Lee JA, Kim DH, Park KD, Jeong JS, et al. Cross-calibration of multi-frequency bioelectrical impedance analysis with eight-point tactile electrodes and dual-energy X-ray absorptiometry for assessment of body composition in healthy children aged 6-18 years. Pediatr Int. 2009;51(2):263- 8. doi: 10.1111/j.1442-200X.2008.02698.x.
    16. Laurson KR, Eisenmann JC, Welk GJ. Body fat percentile curves for US children and adolescents. Am J Prev Med. 2011;41(4 Suppl 2):S87-92. doi: 10.1016/j.amepre.2011.06.044.
    17. Holmes CJ, Racette SB. The utility of body composition assessment in nutrition and clinical practice: an overview of current methodology. Nutrients. 2021;13(8):2493. doi: 10.3390/nu13082493.
    18. Frignani RR, Passos MA, de Moraes Ferrari GL, Niskier SR, Fisberg M, de Pádua Cintra I. Reference curves of the body fat index in adolescents and their association with anthropometric variables. J Pediatr (Rio J). 2015;91(3):248-55. doi: 10.1016/j. jped.2014.07.009.
    19. Cossio-Bolaños M, de Arruda M, Sulla Torres J, Urra Albornoz C, Gómez Campos R. Development of equations and proposed reference values to estimate body fat mass among Chilean children and adolescents. Arch Argent Pediatr. 2017;115(5):453-61. doi: 10.5546/aap.2017.eng.453.
    20. Cortés-Castell E, Juste M, Palazón-Bru A, Monge L, Sánchez- Ferrer F, Rizo-Baeza MM. A simple equation to estimate body fat percentage in children with overweightness or obesity: a retrospective study. PeerJ. 2017;5:e3238. doi: 10.7717/ peerj.3238.
    21. Lozano-Berges G, Gómez-Bruton A, Matute-Llorente Á, Julián- Almárcegui C, Gómez-Cabello A, González-Agüero A, et al. Assessing fat mass of adolescent swimmers using anthropometric equations: a DXA validation study. Res Q Exerc Sport. 2017;88(2):230-6. doi: 10.1080/02701367.2017.1284976.
    22. Zheng Y, Liang J, Zeng D, Tan W, Yang L, Lu S, et al. Association of body composition with pubertal timing in children and adolescents from Guangzhou, China. Front Public Health. 2022;10:943886. doi: 10.3389/fpubh.2022.943886.
    23. Heyward VH, Wagner DR. Applied Body Composition Assessment. Human Kinetics; 2004.
    24. Itani L, Tannir H, El Masri D, Kreidieh D, El Ghoch M. Development of an easy-to-use prediction equation for body fat percentage based on BMI in overweight and obese Lebanese adults. Diagnostics (Basel). 2020;10(9):728. doi: 10.3390/diagnostics10090728.
    25. Kanellakis S, Skoufas E, Karaglani E, Ziogos G, Koutroulaki A, Loukianou F, et al. Development and validation of a bioelectrical impedance prediction equation estimating fat free mass in Greek - Caucasian adult population. Clin Nutr ESPEN. 2020;36:166-70. doi: 10.1016/j.clnesp.2020.01.003.
    26. Lohman TG, Roche AF, Martorell R. Anthropometric Standardization Reference Manual. Champaign, IL: Human Kinetics Books; 1988.
    27. Androutsos O, Gerasimidis K, Karanikolou A, Reilly JJ, Edwards CA. Impact of eating and drinking on body composition measurements by bioelectrical impedance. J Hum Nutr Diet. 2015;28(2):165-71. doi: 10.1111/jhn.12259.
    28. Matthews EL, Hosick PA. Bioelectrical impedance analysis does not detect an increase in total body water following isotonic fluid consumption. Appl Physiol Nutr Metab. 2019;44(10):1116-20. doi: 10.1139/apnm-2019-0106.
    29. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1(8476):307-10.
    30. Cicek B, Ozturk A, Unalan D, Bayat M, Mazicioglu MM, Kurtoglu S. Four-site skinfolds and body fat percentage references in 6-to-17-year old Turkish children and adolescents. J Pak Med Assoc. 2014;64(10):1154-61.
    31. Hussain Z, Jafar T, uz Zaman M, Parveen R, Saeed F. Correlations of skin fold thickness and validation of prediction equations using DEXA as the gold standard for estimation of body fat composition in Pakistani children. BMJ Open. 2014;4(4):e004194. doi: 10.1136/bmjopen-2013-004194.
    32. Ranasinghe C, Gamage P, Katulanda P, Andraweera N, Thilakarathne S, Tharanga P. Relationship between body mass index (BMI) and body fat percentage, estimated by bioelectrical impedance, in a group of Sri Lankan adults: a cross sectional study. BMC Public Health. 2013;13:797. doi: 10.1186/1471-2458-13-797.
    33. Hastuti J, Kagawa M, Byrne NM, Hills AP. Anthropometry to assess body fat in Indonesian adults. Asia Pac J Clin Nutr. 2018;27(3):592-8. doi: 10.6133/apjcn.092017.02.
    34. Castro-Porras LV, Rojas-Russell ME, Villanueva-Sánchez J, López-Cervantes M. An anthropometry-based equation of fat mass percentage as a valid discriminator of obesity. Public Health Nutr. 2019;22(7):1250-8. doi: 10.1017/ s1368980018004044.
    35. Abizanda P, Romero L, Sánchez-Jurado PM, Martínez-Reig M, Alfonso-Silguero SA, Rodríguez-Mañas L. Age, frailty, disability, institutionalization, multimorbidity or comorbidity. Which are the main targets in older adults? J Nutr Health Aging. 2014;18(6):622-7. doi: 1007/s12603-014-0033-3.
    36. Silveira EA, Barbosa LS, Noll M, Pinheiro HA, de Oliveira C. Body fat percentage prediction in older adults: agreement between anthropometric equations and DXA. Clin Nutr. 2021;40(4):2091-9. doi: 10.1016/j.clnu.2020.09.032.
    37. Al Hammadi H, Reilly JJ. Classification accuracy of body mass index for excessive body fatness in Kuwaiti adolescent girls and young adult women. Diabetes Metab Syndr Obes. 2020;13:1043-9. doi: 10.2147/dmso.s232545.
    38. Williams J, Wake M, Campbell M. Comparing estimates of body fat in children using published bioelectrical impedance analysis equations. Int J Pediatr Obes. 2007;2(3):174-9. doi: 10.1080/17477160701408783.
    39. Orta Duarte M, Flores Ruelas Y, López-Alcaraz F, del Toro- Equihua M, Sánchez-Ramírez CA. Correlation between percentage of body fat measured by the Slaughter equation and bio impedance analysis technique in Mexican schoolchildren. Nutr Hosp. 2014;29(1):88-93. doi: 10.3305/ nh.2014.29.1.6992.
    40. Lyra CO, Lima SC, Lima KC, Arrais RF, Pedrosa LF. Prediction equations for fat and fat-free body mass in adolescents, based on body circumferences. Ann Hum Biol. 2012;39(4):275-80. doi: 10.3109/03014460.2012.685106.
    41. Kuczmarski RJ, Fanelli MT, Koch GG. Ultrasonic assessment of body composition in obese adults: overcoming the limitations of the skinfold caliper. Am J Clin Nutr. 1987;45(4):717-24. doi: 10.1093/ajcn/45.4.717.
    42. Damilakis J, Adams JE, Guglielmi G, Link TM. Radiation exposure in X-ray-based imaging techniques used in osteoporosis. Eur Radiol. 2010;20(11):2707-14. doi: 10.1007/ s00330-010-1845-0.
    43. Voogt MP, Opmeer BC, Kastelein AW, Jaspers MW, Peute LW. Obstacles to successful implementation of eHealth applications into clinical practice. In: Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth. IOS Press; 2018. p. 521-5. doi: 10.3233/978-1- 61499-852-5-521.
    44. Gade W, Schmit J, Collins M, Gade J. Beyond obesity: the diagnosis and pathophysiology of metabolic syndrome. Clin Lab Sci. 2010;23(1):51-61.
    45. Millstein RA, Carlson SA, Fulton JE, Galuska DA, Zhang J, Blanck HM, et al. Relationships between body size satisfaction and weight control practices among US adults. Medscape J Med. 2008;10(5):119.
Journal of Kerman University of Medical Sciences
Volume 31, Issue 1
March and April 2024
Pages 8-16

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