Nutrition Research and Practice

The Korean Nutrition Society (한국영양학회)
권호 표지
DOI QR코드

DOI QR Code

Development and validation of prediction equations for the assessment of muscle or fat mass using anthropometric measurements, serum creatinine level, and lifestyle factors among Korean adults

  • Lee, Gyeongsil (Department of Family Medicine, Seoul National University Hospital) ;
  • Chang, Jooyoung (Department of Biomedical Sciences, Seoul National University Graduate School) ;
  • Hwang, Seung-sik (Department of Public Health Science, Graduate School of Public Health, Seoul National University) ;
  • Son, Joung Sik (Department of Family Medicine, Seoul National University Hospital) ;
  • Park, Sang Min (Department of Family Medicine, Seoul National University Hospital)
  • Received : 2020.01.16
  • Accepted : 2020.06.01
  • Published : 2021.02.01
Download PDF
⟨ Previous Next ⟩

Abstract

BACKGROUND/OBJECTIVES: The measurement of body composition, including muscle and fat mass, remains challenging in large epidemiological studies due to time constraint and cost when using accurate modalities. Therefore, this study aimed to develop and validate prediction equations according to sex to measure lean body mass (LBM), appendicular skeletal muscle mass (ASM), and body fat mass (BFM) using anthropometric measurement, serum creatinine level, and lifestyle factors as independent variables and dual-energy X-ray absorptiometry as the reference method. SUBJECTS/METHODS: A sample of the Korean general adult population (men: 7,599; women: 10,009) from the Korean National Health and Nutrition Examination Survey 2008-2011 was included in this study. The participants were divided into the derivation and validation groups via a random number generator (with a ratio of 70:30). The prediction equations were developed using a series of multivariable linear regressions and validated using the Bland-Altman plot and intraclass correlation coefficient (ICC). RESULTS: The initial and practical equations that included age, height, weight, and waist circumference had a different predictive ability for LBM (men: R2 = 0.85, standard error of estimate [SEE] = 2.7 kg; women: R2 = 0.78, SEE = 2.2 kg), ASM (men: R2 = 0.81, SEE = 1.6 kg; women: R2 = 0.71, SEE = 1.2 kg), and BFM (men: R2 = 0.74, SEE = 2.7 kg; women: R2 = 0.83, SEE = 2.2 kg) according to sex. Compared with the first prediction equation, the addition of other factors, including serum creatinine level, physical activity, smoking status, and alcohol use, resulted in an R2 that is higher by 0.01 and SEE that is lower by 0.1. CONCLUSIONS: All equations had low bias, moderate agreement based on the Bland-Altman plot, and high ICC, and this result showed that these equations can be further applied to other epidemiologic studies.

Keywords

References

  1. Gadde KM, Martin CK, Berthoud HR, Heymsfield SB. Obesity: pathophysiology and management. J Am Coll Cardiol 2018;71:69-84. https://doi.org/10.1016/j.jacc.2017.11.011
  2. Heymsfield SB, Wadden TA. Mechanisms, pathophysiology, and management of obesity. N Engl J Med 2017;376:254-66. https://doi.org/10.1056/NEJMra1514009
  3. Kang S, Kyung C, Park JS, Kim S, Lee SP, Kim MK, Kim HK, Kim KR, Jeon TJ, Ahn CW. Subclinical vascular inflammation in subjects with normal weight obesity and its association with body fat: an 18F-FDG-PET/CT study. Cardiovasc Diabetol 2014;13:70-70. https://doi.org/10.1186/1475-2840-13-70
  4. Marques-Vidal P, Pécoud A, Hayoz D, Paccaud F, Mooser V, Waeber G, Vollenweider P. Normal weight obesity: relationship with lipids, glycaemic status, liver enzymes and inflammation. Nutr Metab Cardiovasc Dis 2010;20:669-75. https://doi.org/10.1016/j.numecd.2009.06.001
  5. Martinez KE, Tucker LA, Bailey BW, LeCheminant JD. Expanded normal weight obesity and insulin resistance in US adults of the national health and nutrition examination survey. J Diabetes Res 2017;2017:9502643-43. https://doi.org/10.1155/2017/9502643
  6. Romero-Corral A, Somers VK, Sierra-Johnson J, Korenfeld Y, Boarin S, Korinek J, Jensen MD, Parati G, Lopez-Jimenez F. Normal weight obesity: a risk factor for cardiometabolic dysregulation and cardiovascular mortality. Eur Heart J 2010;31:737-46. https://doi.org/10.1093/eurheartj/ehp487
  7. Al-Gindan YY, Hankey C, Govan L, Gallagher D, Heymsfield SB, Lean ME. Derivation and validation of simple equations to predict total muscle mass from simple anthropometric and demographic data. Am J Clin Nutr 2014;100:1041-51. https://doi.org/10.3945/ajcn.113.070466
  8. Kulkarni B, Kuper H, Taylor A, Wells JC, Radhakrishna KV, Kinra S, Ben-Shlomo Y, Smith GD, Ebrahim S, Byrne NM, Hills AP. Development and validation of anthropometric prediction equations for estimation of lean body mass and appendicular lean soft tissue in Indian men and women. J Appl Physiol (1985) 2013;115:1156-62. https://doi.org/10.1152/japplphysiol.00777.2013
  9. Lee DH, Keum N, Hu FB, Orav EJ, Rimm EB, Sun Q, Willett WC, Giovannucci EL. Development and validation of anthropometric prediction equations for lean body mass, fat mass and percent fat in adults using the National Health and Nutrition Examination Survey (NHANES) 1999-2006. Br J Nutr 2017;118:858-66. https://doi.org/10.1017/S0007114517002665
  10. Lee RC, Wang Z, Heo M, Ross R, Janssen I, Heymsfield SB. Total-body skeletal muscle mass: development and cross-validation of anthropometric prediction models. Am J Clin Nutr 2000;72:796-803. https://doi.org/10.1093/ajcn/72.3.796
  11. Wen X, Wang M, Jiang CM, Zhang YM. Anthropometric equation for estimation of appendicular skeletal muscle mass in Chinese adults. Asia Pac J Clin Nutr 2011;20:551-6.
  12. Snow RJ, Murphy RM. Factors influencing creatine loading into human skeletal muscle. Exerc Sport Sci Rev 2003;31:154-8. https://doi.org/10.1097/00003677-200307000-00010
  13. Kweon S, Kim Y, Jang MJ, Kim Y, Kim K, Choi S, Chun C, Khang YH, Oh K. Data resource profile: the Korea national health and nutrition examination survey (KNHANES). Int J Epidemiol 2014;43:69-77. https://doi.org/10.1093/ije/dyt228
  14. Aparisi Gomez MP, Ponti F, Mercatelli D, Gasperini C, Napoli A, Battista G, Cariani S, Marchesini G, Bazzocchi A. Correlation between DXA and laboratory parameters in normal weight, overweight, and obese patients. Nutrition 2019;61:143-50. https://doi.org/10.1016/j.nut.2018.10.023
  15. Kim DH, Lee EJ, Lee JY, Lee DC. The association and the characteristics of the smoking status and differences in physical activity level in Korean adults: the Sixth Korea National Health and Nutrition Examination Survey (KNHANES VI-1), 2013. Korean J Fam Pract 2015;5 Suppl 2:510-6.
  16. Oh JY, Yang YJ, Kim BS, Kang JH. Validity and reliability of Korean Version of International Physical Activity Questionnaire (IPAQ) short form. J Korean Acad Fam Med 2007;28:532-41.
  17. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1:307-10. https://doi.org/10.1016/S0140-6736(86)90837-8
  18. Kong KA. Statistical methods: reliability assessment and method comparison. Ewha Med J 2017;40:9-16. https://doi.org/10.12771/emj.2017.40.1.9
  19. Bredella MA. Sex differences in body composition. Adv Exp Med Biol 2017;1043:9-27. https://doi.org/10.1007/978-3-319-70178-3_2
  20. Lemieux S, Prud'homme D, Bouchard C, Tremblay A, Després JP. Sex differences in the relation of visceral adipose tissue accumulation to total body fatness. Am J Clin Nutr 1993;58:463-7. https://doi.org/10.1093/ajcn/58.4.463