Anthropometric predictive equations for estimating body composition

Salamat, Mohammad Reza; Shanei, Ahmad; Salamat, Amir Hossein; Khoshhali, Mehri; Asgari, Mahdi

Anthropometric predictive equations for estimating body composition

Authors

¹ Department of Medical Physics and Medical Engineering, Medical School, Isfahan University of Medical Sciences, Isfahan, Iran, Iran

² Department of Research and Development, Isfahan Osteoporosis Diagnosis and Body Composition Center, Isfahan University of Medical Sciences, Isfahan, Iran

³ Department of Biostatistics and Epidemiology, Health School, Isfahan University of Medical Sciences, Isfahan, Iran

Abstract

Background: Precise and accurate measurements of body composition are useful in achieving a greater understanding of human energy metabolism in physiology and in different clinical conditions, such as, cardiovascular disease and overall mortality. Dual-energy x-ray absorptiometry (DXA) can be used to measure body composition, but the easiest method to assess body composition is the use of anthropometric indices.
This study has been designed to evaluate the accuracy and precision of body composition prediction equations by various anthropometric measures instead of a whole body DXA scan.
Materials and Methods: We identified 143 adult patients underwent DXA evaluation of the whole body. The anthropometric indices were also measured. Datasets were split randomly into two parts. Multiple regression analysis with a backward stepwise elimination procedure was used as the derivation set and then the estimates were compared with the actual measurements from the whole-body scans for a validation set.
The SPSS version 20 for Windows software was used in multiple regression and data analysis.
Results: Using multiple linear regression analyses, the best equation for predicting the whole-body fat mass (R ² = 0.808) included the body mass index (BMI) and gender; the best equation for predicting whole-body lean mass (R ² = 0.780) included BMI, WC, gender, and age; and the best equation for predicting trunk fat mass (R ² = 0.759) included BMI, WC, and gender.
Conclusions: Combinations of anthropometric measurements predict whole-body lean mass and trunk fat mass better than any of these single anthropometric indices. Therefore, the findings of the present study may be used to verify the results in patients with various diseases or diets.

Keywords

References

1.	Chen ZA, Roy K, Gotway Crawford CA. Obesity prevention: The impact of local health departments. Health Serv Res 2013;48:603-27.
2.	Hodge AM, Zimmet PZ. 5 The epidemiology of obesity. Baillieres Clin Endocrinol Metab 1994;8:577-99.
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;13:729-38.
4.	Leslie WD, Weiler HA, Nyomba BL. Ethnic differences in adiposity and body composition: The First Nations bone health study. Appl Physiol Nutr Metab 2007;32:1065-72.
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;61:646-53.
6.	Bajaj HS, Brennan DM, Hoogwerf BJ, Doshi KB, Kashyap SR. Clinical Utility of Waist Circumference in Predicting All-cause Mortality in a Preventive Cardiology Clinic Population: A PreCIS Database Study. Obesity 2009;17:1615-20.
7.	Camhi SM, Bray GA, Bouchard C, Greenway FL, Johnson WD, Newton RL, et al. The relationship of waist circumference and BMI to visceral, subcutaneous, and total body fat: Sex and race differences. Obesity (Silver Spring) 2010;19:402-8.
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;64:546-51.
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 yr. Am J Clin Nutr 2000;72:490-5.
10.	Kyle U, Genton L, Hans D, Pichard C. Validation of a bioelectrical impedance analysis equation to predict appendicular skeletal muscle mass (ASMM). Clin Nutr 2003;22:537-43.
11.	Kim J, Heshka S, Gallagher D, Kotler DP, Mayer L, Albu J, et al. Intermuscular adipose tissue-free skeletal muscle mass: Estimation by dual-energy X-ray absorptiometry in adults. J Appl Physiol 2004;97:655-60.
12.	Clasey JL, Bouchard C, Teates CD, Riblett JE, Thorner MO, Hartman ML, et al. The Use of Anthropometric and Dual-Energy X-ray Absorptiometry (DXA) Measures to Estimate Total Abdominal and Abdominal Visceral Fat in Men and Women. Obes Res 1999;7:256-64.
13.	Leslie WD. Prediction of body composition from spine and hip bone densitometry. J Clin Densitom 2009;12:428-33.
14.	Albanese CV, Diessel E, Genant HK. Clinical applications of body composition measurements using DXA. J Clin Densitom 2003;6:75-85.
15.	LaForgia J, Dollman J, Dale MJ, Withers RT, Hill AM. Validation of DXA body composition estimates in obese men and women. Obesity (Silver Spring) 2009;17:821-6.
16.	Lei S, Liu M, Chen X, Deng F, Lv J, Jian W, 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 2005;60:511-8.
17.	Litwin SE. Which measures of obesity best predict cardiovascular risk? J Am Coll Cardiol 2008;52:616-9.
18.	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;92:390-7.

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