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Influence of methods used in body composition analysis on the prediction of resting energy expenditure.
Eur J Clin Nutr 2007; 61(5):582-9EJ

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 37.1+/-15.1 years, body mass index (BMI) 25.9+/-4.1 kg/m2) and 54 women (age 35.3+/-15.4 years, BMI 25.5+/-4.4 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 (FFM(ADP)) to 1645 kJ/24 h (FFM(SF)) and the slopes ranged between 100.3 kJ (FFM(SF)) and 108.1 kJ/FFM (kg) (FFM(ADP)). 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% (FFM(BIA)) to 75% (FFM(DXA)) 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.

Authors+Show Affiliations

Institut für Humanernährung und Lebensmittelkunde der Christian-Albrechts-Universität zu Kiel, Kiel, Germany.No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Comparative Study
Journal Article

Language

eng

PubMed ID

17136038

Citation

Korth, O, et al. "Influence of Methods Used in Body Composition Analysis On the Prediction of Resting Energy Expenditure." European Journal of Clinical Nutrition, vol. 61, no. 5, 2007, pp. 582-9.
Korth O, Bosy-Westphal A, Zschoche P, et al. Influence of methods used in body composition analysis on the prediction of resting energy expenditure. Eur J Clin Nutr. 2007;61(5):582-9.
Korth, O., Bosy-Westphal, A., Zschoche, P., Glüer, C. C., Heller, M., & Müller, M. J. (2007). Influence of methods used in body composition analysis on the prediction of resting energy expenditure. European Journal of Clinical Nutrition, 61(5), pp. 582-9.
Korth O, et al. Influence of Methods Used in Body Composition Analysis On the Prediction of Resting Energy Expenditure. Eur J Clin Nutr. 2007;61(5):582-9. PubMed PMID: 17136038.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Influence of methods used in body composition analysis on the prediction of resting energy expenditure. AU - Korth,O, AU - Bosy-Westphal,A, AU - Zschoche,P, AU - Glüer,C C, AU - Heller,M, AU - Müller,M J, Y1 - 2006/11/29/ PY - 2006/12/1/pubmed PY - 2007/6/26/medline PY - 2006/12/1/entrez SP - 582 EP - 9 JF - European journal of clinical nutrition JO - Eur J Clin Nutr VL - 61 IS - 5 N2 - 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 37.1+/-15.1 years, body mass index (BMI) 25.9+/-4.1 kg/m2) and 54 women (age 35.3+/-15.4 years, BMI 25.5+/-4.4 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 (FFM(ADP)) to 1645 kJ/24 h (FFM(SF)) and the slopes ranged between 100.3 kJ (FFM(SF)) and 108.1 kJ/FFM (kg) (FFM(ADP)). 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% (FFM(BIA)) to 75% (FFM(DXA)) 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. SN - 0954-3007 UR - https://www.unboundmedicine.com/medline/citation/17136038/Influence_of_methods_used_in_body_composition_analysis_on_the_prediction_of_resting_energy_expenditure_ L2 - http://dx.doi.org/10.1038/sj.ejcn.1602556 DB - PRIME DP - Unbound Medicine ER -