Original Article

European Journal of Clinical Nutrition (2007) 61, 582–589. doi:10.1038/sj.ejcn.1602556; published online 29 November 2006

Influence of methods used in body composition analysis on the prediction of resting energy expenditure

Guarantor: MJ Müller.

Contributors: MJM contributed to study design, OK, ABW and PZ contributed to data collection, OK and ABW contributed to data analysis, OK, ABW, MJM, CCG and MH contributed to discussion of data, and OK, ABW and MJM writing of the paper. There are no conflicts of interest.

O Korth1, A Bosy-Westphal1, P Zschoche2, C C Glüer2, M Heller2 and M J Müller1

  1. 1Institut für Humanernährung und Lebensmittelkunde der Christian-Albrechts-Universität zu Kiel, Kiel, Germany
  2. 2Klinik für Diagnostische Radiologie, Universitätsklinikum Schleswig Holstein, Campus Kiel, Germany

Correspondence: Professor MJ Müller, Institut für Humanernährung und Lebensmittelkunde, Christian-Albrechts-Universität zu Kiel, Düsternbrooker Weg 17, D-24105 Kiel, Germany. E-mail: mmueller@nutrfoodsc.uni-kiel.de

Received 20 December 2005; Revised 18 August 2006; Accepted 12 September 2006; Published online 29 November 2006.

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Abstract

Objective:

 

There are considerable differences in published prediction algorithms for resting energy expenditure (REE) based on fat-free mass (FFM). The aim of the study was to investigate the influence of the methodology of body composition analysis on the prediction of REE from FFM.

Design:

 

In a cross-sectional design measurements of REE and body composition were performed.

Subjects:

 

The study population consisted of 50 men (age 37.1plusminus15.1 years, body mass index (BMI) 25.9plusminus4.1 kg/m2) and 54 women (age 35.3plusminus15.4 years, BMI 25.5plusminus4.4 kg/m2).

Interventions:

 

REE was measured by indirect calorimetry and predicted by either FFM or body weight. Measurement of FFM was performed by methods based on a 2-compartment (2C)-model: skinfold (SF)-measurement, bioelectrical impedance analysis (BIA), Dual X-ray absorptiometry (DXA), air displacement plethysmography (ADP) and deuterium oxide dilution (D2O). A 4-compartment (4C)-model was used as a reference.

Results:

 

When compared with the 4C-model, REE prediction from FFM obtained from the 2C methods were not significantly different. Intercepts of the regression equations of REE prediction by FFM differed from 1231 (FFMADP) to 1645 kJ/24 h (FFMSF) and the slopes ranged between 100.3 kJ (FFMSF) and 108.1 kJ/FFM (kg) (FFMADP). In a normal range of FFM, REE predicted from FFM by different methods showed only small differences. The variance in REE explained by FFM varied from 69% (FFMBIA) to 75% (FFMDXA) and was only 46% for body weight.

Conclusion:

 

Differences in slopes and intercepts of the regression lines between REE and FFM depended on the methods used for body composition analysis. However, the differences in prediction of REE are small and do not explain the large differences in the results obtained from published FFM-based REE prediction equations and therefore imply a population- and/or investigator specificity of algorithms for REE prediction.

Keywords:

resting energy expenditure, fat-free mass, body composition, 4-compartment model, REE prediction

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