Korean J Fam Pract 2020; 10(1): 68-73  https://doi.org/10.21215/kjfp.2020.10.1.68
Application of Relative Fat Mass Equation in Korean Adults
Mi Ji Lee, Young Hye Kim, Jin Gu Kim, Seon Yeong Lee*, Kyunam Kim, Jongwoo Kim, Jeong Ki Paek
Department of Family Medicine, Sanggye Paik Hospital, Inje University College of Medicine, Seoul, Korea
Seon Yeong Lee
Tel: +82-2-950-1150, Fax: +82-2-950-4093
E-mail: sylee@paik.ac.kr
ORCID: http://orcid.org/0000-0002-8274-3654
Received: May 31, 2019; Revised: October 8, 2019; Accepted: January 15, 2020; Published online: February 20, 2020.
© The Korean Academy of Family Medicine. All rights reserved.

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Background: Body mass index (BMI) has limitations in determining body fat percentage and body fat distribution, and causes misclassification of body fat-defined obesity. As high body fat percentage is associated with mortality, an accurate assessment of body fat percentage is considered clinically important. Recently, Woolcott and Bergman reported a relative fat mass (RFM) equation which calculated the body fat percentage using the height and waist circumferences. However, as RFM has been studied only in European-, Mexican-, and African-Americans, an assessment in Asians was needed. Therefore, we aimed to evaluate the applicability of RFM in Korean adults.
Methods: This study included 7,733 adults who visited a Sanggye Paik Hospital Health Promotion Center from May 1, 2016 to November 12, 2018. BMI and RFM were calculated by measuring height, weight, and waist circumference. The total body fat (TBF) percentage was measured by bioelectrical impedance analysis. We compared the BMI, RFM, and TBF percentage to assess the applicability of RFM in Korean adults.
Results: RFM had a statistically significant correlation with TBF percentage in both male and female (male: β=0.808, R2=0.653, female: β=0.766, R2=0.587, P<0.001). In the Bland-Altman plot, RFM showed good agreement with the TBF percentage within the 95% confidence interval.
Conclusion: The RFM equation can be used to predict TBF percentage in Korean adults.
Keywords: Obesity; Body Mass Index; Adiposity
References
  1. WHO. Obesity and overweight [Internet]. Geneva: World Health Organization;c2017 [cited 2019 Jan 2].
  2. Guh DP, Zhang W, Bansback N, Amarsi Z, Birmingham CL, Anis AH. The incidence of co-morbidities related to obesity and overweight: a systematic review and meta-analysis. BMC Public Health 2009; 9: 88.
    Pubmed KoreaMed CrossRef
  3. Shapiro CL. Cancer survivorship. N Engl J Med 2018; 379: 2438-50.
    Pubmed CrossRef
  4. Nazare JA, Smith J, Borel AL, Aschner P, Barter P, Van Gaal L, et al.; INSPIRE ME IAA Investigators. Usefulness of measuring both body mass index and waist circumference for the estimation of visceral adiposity and related cardiometabolic risk profile (from the INSPIRE ME IAA study). Am J Cardiol 2015; 115: 307-15.
    Pubmed CrossRef
  5. Ashwell M, Gibson S. Waist-to-height ratio as an indicator of 'early health risk': simpler and more predictive than using a 'matrix' based on BMI and waist circumference. BMJ Open 2016; 6: e010159.
    Pubmed KoreaMed CrossRef
  6. Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. World Health Organ Tech Rep Ser 1995; 854:1-452.
    Pubmed
  7. Rothman KJ. BMI-related errors in the measurement of obesity. Int J Obes (Lond) 2008; 32 Suppl 3: S56-9.
    Pubmed CrossRef
  8. Hung SP, Chen CY, Guo FR, Chang CI, Jan CF. Combine body mass index and body fat percentage measures to improve the accuracy of obesity screening in young adults. Obes Res Clin Pract 2017; 11: 11-8.
    Pubmed CrossRef
  9. Romero-Corral A, Somers VK, Sierra-Johnson J, Thomas RJ, Collazo-Clavell ML, Korinek J, et al. Accuracy of body mass index in diagnosing obesity in the adult general population. Int J Obes (Lond) 2008; 32: 959-66.
    Pubmed KoreaMed CrossRef
  10. Heitmann BL, Erikson H, Ellsinger BM, Mikkelsen KL, Larsson B. Mortality associated with body fat, fat-free mass and body mass index among 60-yearold Swedish men-a 22-year follow-up. The study of men born in 1913. Int J Obes Relat Metab Disord 2000; 24: 33-7.
    Pubmed CrossRef
  11. Lahmann PH, Lissner L, Gullberg B, Berglund G. A prospective study of adiposity and all-cause mortality: the Malmö Diet and Cancer Study. Obes Res 2002; 10: 361-9.
    Pubmed CrossRef
  12. Slosman DO, Casez JP, Pichard C, Rochat T, Fery F, Rizzoli R, et al. Assessment of whole-body composition with dual-energy x-ray absorptiometry. Radiology 1992; 185: 593-8.
    Pubmed CrossRef
  13. Mazess R, Collick B, Trempe J, Barden H, Hanson J. Performance evaluation of a dual-energy x-ray bone densitometer. Calcif Tissue Int 1989; 44: 228-32.
    Pubmed CrossRef
  14. Habib SS. Body mass index and body fat percentage in assessment of obesity prevalence in Saudi adults. Biomed Environ Sci 2013; 26: 94-9.
    Pubmed CrossRef
  15. Lean ME, Han TS, Deurenberg P. Predicting body composition by densitometry from simple anthropometric measurements. Am J Clin Nutr 1996;63: 4-14.
    Pubmed CrossRef
  16. Stevens J, Ou FS, Cai J, Heymsfield SB, Truesdale KP. Prediction of percent body fat measurements in Americans 8 years and older. Int J Obes (Lond) 2016; 40: 587-94.
    Pubmed KoreaMed CrossRef
  17. Gómez-Ambrosi J, Silva C, Catalán V, Rodríguez A, Galofré JC, Escalada J, et al. Clinical usefulness of a new equation for estimating body fat. Diabetes Care 2012; 35: 383-8.
    Pubmed KoreaMed CrossRef
  18. Chambers AJ, Parise E, McCrory JL, Cham R. A comparison of prediction equations for the estimation of body fat percentage in non-obese and obese older Caucasian adults in the United States. J Nutr Health Aging 2014; 18:586-90.
    Pubmed KoreaMed CrossRef
  19. Woolcott OO, Bergman RN. Relative fat mass (RFM) as a new estimator of whole-body fat percentage - a cross-sectional study in American adult individuals. Sci Rep 2018; 8: 10980.
    Pubmed KoreaMed CrossRef
  20. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986; 1: 307-10.
    Pubmed CrossRef
  21. Lukaski HC, Siders WA. Validity and accuracy of regional bioelectrical impedance devices to determine whole-body fatness. Nutrition 2003; 19: 851-7.
    Pubmed CrossRef
  22. Bolanowski M, Nilsson BE. Assessment of human body composition using dual-energy x-ray absorptiometry and bioelectrical impedance analysis. Med Sci Monit 2001; 7: 1029-33.
    Pubmed
  23. Sun G, French CR, Martin GR, Younghusband B, Green RC, Xie YG, et al. Comparison of multifrequency bioelectrical impedance analysis with dualenergy X-ray absorptiometry for assessment of percentage body fat in a large, healthy population. Am J Clin Nutr 2005; 81: 74-8.
    Pubmed CrossRef
  24. Bioelectrical impedance analysis in body composition measurement: National Institutes of Health Technology Assessment Conference Statement. Am J Clin Nutr 1996; 64(3 Suppl): 524S-32S.
    Pubmed CrossRef
  25. Gallagher D, Visser M, Sepúlveda D, Pierson RN, Harris T, Heymsfield SB. How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? Am J Epidemiol 1996; 143: 228-39.
    Pubmed CrossRef
  26. ackson AS, Stanforth PR, Gagnon J, Rankinen T, Leon AS, Rao DC, et al. The effect of sex, age and race on estimating percentage body fat from body mass index: the Heritage Family Study. Int J Obes Relat Metab Disord 2002;26: 789-96.
    Pubmed CrossRef
  27. Ho-Pham LT, Campbell LV, Nguyen TV. More on body fat cutoff points. Mayo Clin Proc 2011; 86: 584; author reply 584-5.
    Pubmed KoreaMed CrossRef
  28. Oreopoulos A, Lavie CJ, Snitker S, Romero-Corral A. More on body fat cutoff points-reply-I. Mayo Clin Proc 2011; 86: 584-5.
    KoreaMed CrossRef


This Article

e-submission

Archives