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



Salamat MR1 ; Shanei A2, 3 ; Asgari M2, 3 ; Salamat A4 ; Khoshhali M5
Authors
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Authors Affiliations
  1. 1. 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. 2. Department of Medical Physics and Medical Engineering, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
  3. 3. Department of Medical Physics and Medical Engineering, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
  4. 4. Isfahan Osteoporosis Diagnosis and Body Composition Center, Isfahan, Iran
  5. 5. Department of Biostatistics and Epidemiology, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran

Source: Journal of Isfahan Medical School Published:2014

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). © 2014, Isfahan University of Medical Sciences(IUMS). All rights reserved.
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