Nutr Res Pract.  2021 Feb;15(1):95-105. 10.4162/nrp.2021.15.1.95.

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 G
• Chang J
• Hwang Ss
• Son JS
• Park SM

• KMID: 2511676
• DOI: http://doi.org/10.4162/nrp.2021.15.1.95

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.

Keyword

Anthropometry; body composition; dual-energy X-ray absorptiometry; predictive value of tests; validation study

Figure

• 

  Fig. 1 Bland–Altman plot of the derivation group (left) and validation group (right) for LBM, ASM, and BFM estimates by dual-energy X-ray absorptiometry and prediction equations among men.LBM, lean body mass; pLBM, predicted lean body mass; ASM, appendicular skeletal muscle mass; pASM, predicted skeletal muscle mass; BFM, body fat mass; pBFM, predicted body fat mass.

• 

  Fig. 2 Bland–Altman plot of the derivation group (left) and validation group (right) for LBM, ASM, and BFM estimates using dual-energy X-ray absorptiometry and prediction equations among women.LBM, lean body mass; pLBM, predicted lean body mass; ASM, appendicular skeletal muscle mass; pASM, predicted skeletal muscle mass; BFM, body fat mass; pBFM, predicted body fat mass.

Reference

1. Gadde KM, Martin CK, Berthoud HR, Heymsfield SB. Obesity: pathophysiology and management. J Am Coll Cardiol. 2018; 71:69–84. PMID: 29301630.

2. Heymsfield SB, Wadden TA. Mechanisms, pathophysiology, and management of obesity. N Engl J Med. 2017; 376:254–266. PMID: 28099824.

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 18 F-FDG-PET/CT study. Cardiovasc Diabetol. 2014; 13:70–70. PMID: 24708764.

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; 20:669–675. PMID: 19748248.

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; 2017:9502643–9502643. PMID: 28812029.

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; 31:737–746. PMID: 19933515.

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; 100:1041–1051. PMID: 25240071.

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; 115:1156–1162. PMID: 23950165.

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; 118:858–866. PMID: 29110742.

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. PMID: 10966902.

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; 20:551–556. PMID: 22094840.

12. Snow RJ, Murphy RM. Factors influencing creatine loading into human skeletal muscle. Exerc Sport Sci Rev. 2003; 31:154–158. PMID: 12882483.

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. PMID: 24585853.

14. Aparisi Gómez 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; 61:143–150. PMID: 30711863.

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):510–516.

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; 28:532–541.

17. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986; 1:307–310. PMID: 2868172.

18. Kong KA. Statistical methods: reliability assessment and method comparison. Ewha Med J. 2017; 40:9–16.

19. Bredella MA. Sex differences in body composition. Adv Exp Med Biol. 2017; 1043:9–27. PMID: 29224088.

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; 58:463–467. PMID: 8379501.