Using Anthropometric Indices Predictive Equations for Estimating Whole-Body Fat Mass Instead of Whole Body Dual-Energy X-Ray Absorptiometry Scan

Document Type : Original Article (s)

Authors

1 Assistant Professor, Department of Medical Physics and Medical Engineering, School of Medicine AND Biosensor Research Center, Isfahan University of Medical Sciences AND Isfahan Osteoporosis Diagnosis and Body Composition Center, Isfahan, Iran

2 Assistant Professor, Department of Medical Physics and Medical Engineering, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

3 PhD Student, Department of Medical Physics and Medical Engineering, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

4 Isfahan Osteoporosis Diagnosis and Body Composition Center, Isfahan, Iran

5 PhD Student, Department of Biostatistics and Epidemiology, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran

Abstract

Background: This study was designed to compare the predicted whole-body fat mass via various anthropometric indices including waist circumference (WC), waist-to-height ratio (WHtR), hip circumference (HC), waist-to-hip ratio (WHR) and body mass index (BMI). Cost and radiation dose reduction are the advantages of this prediction compared to whole body dual-energy X-ray absorptiometry (DXA) scan.Methods: Whole-body composition was measured via dual-energy X-ray absorptiometry for 143 adult patients referred to Isfahan Osteoporosis Diagnosis Center, Isfahan, Iran. Values of weight, height, waist and hip circumferences were measured and body mass index, waist-hip ratio and waist-to-height ratio was calculated. Datasets were split randomly into two parts, the derivation set with 100 subjects and validation set with 43 subjects. Multiple regression analysis with back ward stepwise elimination procedure was used for derivation set and then, the estimates were compared with the actual measurements.Findings: Using multiple linear regression analyses, the best equation for predicting whole-body fat mass (R2 = 0.808) included body mass index and gender.Conclusion: The present study showed that body mass index is the best anthropometric predictor of whole-body fat mass (adjusted R2 = 0.680 and squared errors of prediction = 999.42).

Keywords

References

  1. Chen ZA, Roy K, Gotway Crawford CA. Obesity prevention: the impact of local health departments. Health Serv Res 2013; 48(2 Pt 1): 603-27.
  2. Hodge AM, Zimmet PZ. The epidemiology of obesity. Baillieres Clin Endocrinol Metab 1994; 8(3): 577-99.
  3. Pongchaiyakul C, Kosulwat V, Rojroongwasinkul N, Charoenkiatkul S, Thepsuthammarat K, Laopaiboon M, et al. Prediction of percentage body fat in rural thai population using simple anthropometric measurements. Obes Res 2005; 13(4): 729-38.
  4. Zimmet P, Caterson I. The Asia-pacific perspective: redefining obesity and its treatment. Sydney, Australia: Health Communications Australia Pty Ltd; 2000.
  5. Lee CM, Huxley RR, Wildman RP, Woodward M. Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: a meta-analysis. J Clin Epidemiol 2008; 61(7): 646-53.
  6. Taylor RW, Brooking L, Williams SM, Manning PJ, Sutherland WH, Coppell KJ, et al. Body mass index and waist circumference cutoffs to define obesity in indigenous New Zealanders. Am J Clin Nutr 2010; 92(2): 390-7.
  7. Roche AF, Heymsfield SB, Lohman TG. Human body composition. Champaign, IL: Human Kinetics Publishers; 1996.
  8. Goulding A, Taylor RW, Gold E, Lewis-Barned NJ. Regional body fat distribution in relation to pubertal stage: a dual-energy X-ray absorptiometry study of New Zealand girls and young women. Am J Clin Nutr 1996; 64(4): 546-51.
  9. Taylor RW, Jones IE, Williams SM, Goulding A. Evaluation of waist circumference, waist-to-hip ratio, and the conicity index as screening tools for high trunk fat mass, as measured by dual-energy X-ray absorptiometry, in children aged 3-19 y. Am J Clin Nutr 2000; 72(2): 490-5.
  10. Boudousq V, Kotzki PO, Dinten JM, Barrau C, Robert-Coutant C, Thomas E, et al. Total dose incurred by patients and staff from BMD measurement using a new 2D digital bone densitometer. Osteoporos Int 2003; 14(3): 263-9.
  11. Njeh CF, Fuerst T, Hans D, Blake GM, Genant HK. Radiation exposure in bone mineral density assessment. Appl Radiat Isot 1999; 50(1): 215-36.
  12. Leslie WD. Prediction of body composition from spine and hip bone densitometry. J Clin Densitom 2009; 12(4): 428-33.
  13. Albanese CV, Diessel E, Genant HK. Clinical applications of body composition measurements using DXA. J Clin Densitom 2003; 6(2): 75-85.
  14. Lei SF, Liu MY, Chen XD, Deng FY, Lv JH, Jian WX, et al. Relationship of total body fatness and five anthropometric indices in Chinese aged 20-40 years: different effects of age and gender. Eur J Clin Nutr 2006; 60(4): 511-8.
  15. Litwin SE. Which measures of obesity best predict cardiovascular risk? J Am Coll Cardiol 2008; 52(8): 616-9.
Journal of Isfahan Medical School
Volume 32, Issue 292 - Serial Number 292
May and June 2014
Pages 1046-1053

Files

History

  • Receive Date: 12 March 2014
  • Revise Date: 02 May 2024
  • Accept Date: 06 April 2022

Share

How to cite

Statistics