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Research Article| Volume 59, ISSUE 8, P1181-1189, August 2010

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Poor prediction of resting energy expenditure in obese women by established equations

Published:January 04, 2010DOI:https://doi.org/10.1016/j.metabol.2009.11.011

      Abstract

      The objective of the study was to evaluate the accuracy of established prediction equations that calculate resting energy expenditure (REE) in obese women. This was a cross-sectional study. In 273 mildly to severely obese women (age, 41.7 ± 13.2 years; body mass index, 42.8 ± 7.0 kg/m2), REE was measured by indirect calorimetry (mREE), along with fat mass (FM) and fat-free mass (FFM) by bioelectrical impedance analysis. Eleven established equations were used to predict REE (pREE), with 9 equations basing on the anthropometric parameters body weight and height and 2 equations including body composition parameters (FM, FFM). All equations provided pREE values that significantly correlated with mREE (r > 0.66, P < .001), although 8 equations systematically underestimated mREE (P < .05). Of note, even the best equation was not able to accurately predict mREE with a deviation of less than ±10% in more than 70% of the tested women. Furthermore, equations using body composition data were not superior in predicting REE as compared with equations exclusively including anthropometric variables. Multiple linear regression analyses revealed 2 new equations—one including body weight and age and another including FM, FFM, and age—that explained 56.9% and 57.2%, respectively, of variance in mREE. However, when these 2 new equations were applied to an independent sample of 33 obese women, they also provided an accurate prediction (±10%) of mREE in only 56.7% and 60.6%, respectively, of the women. Data show that an accurate prediction of REE is not feasible using established equations in obese women. Equations that include body composition parameters as assessed by bioelectrical impedance analysis do not increase the accuracy of prediction. Based on our results, we conclude that calculating REE by standard prediction equations does not represent a reliable alternative to indirect calorimetry for the assessment of REE in obese women.
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      References

        • Ogden C.L.
        • Carroll M.D.
        • Curtin L.R.
        • et al.
        Prevalence of overweight and obesity in the United States, 1999-2004.
        JAMA. 2006; 295: 1549-1555
        • Johannsen D.L.
        • Welk G.J.
        • Sharp R.L.
        • et al.
        Differences in daily energy expenditure in lean and obese women: the role of posture allocation.
        Obesity. 2008; 16: 34-39
        • Levine J.A.
        • Lanningham-Foster L.M.
        • McCrady S.K.
        • et al.
        Interindividual variation in posture allocation: possible rote in human obesity.
        Science. 2005; 307: 584-586
        • Ravussin E.
        • Lillioja S.
        • Abbott W.
        • et al.
        Variability of 24 hour energy-expenditure, resting metabolic-rate, and sleeping metabolic-rate in man.
        Clin Res. 1986; 34: A73
        • Haugen H.A.
        • Chan L.N.
        • Li F.
        Indirect calorimetry: a practical guide for clinicians.
        Nutr Clin Pract. 2007; 22: 377-388
        • FAO/WHO/UNU
        Energy and protein requirements. Report of a joint FAO/WHO/UNU expert consultation.
        WHO, Geneva1985 (WHO technical report service no. 724)
        • Mifflin M.D.
        • Stjeor S.T.
        • Hill L.A.
        • et al.
        A new predictive equation for resting energy expenditure in healthy individuals.
        Am J Clin Nutr. 1990; 51: 241-247
        • Harris J.
        • Benedict F.
        A biometric study of basal metabolism in man.
        (WHO technical report service no. 724) Carnegie Institution, Washington, DC1919 (WHO technical report service no. 724)
        • de Luis D.A.
        • Aller R.
        • Izaola O.
        • et al.
        Prediction equation of resting energy expenditure in an adult Spanish population of obese adult population.
        Ann Nutr Metab. 2006; 50: 193-196
        • Bernstein R.S.
        • Thornton J.C.
        • Yang M.U.
        • et al.
        Prediction of the resting metabolic rate in obese patients.
        Am J Clin Nutr. 1983; 37: 595-602
        • Siervo M.
        • Boschi V.
        • Falconi C.
        Which REE prediction equation should we use in normal-weight, overweight and obese women?.
        Clin Nutr. 2003; 22: 193-204
        • Owen O.E.
        • Kavle E.
        • Owen R.S.
        • et al.
        A reappraisal of caloric requirements in healthy women.
        Am J Clin Nutr. 1986; 44: 1-19
        • Muller M.J.
        • Bosy-Westphal A.
        • Klaus S.
        • et al.
        World Health Organization equations have shortcomings for predicting resting energy expenditure in persons from a modern, affluent population: generation of a new reference standard from a retrospective analysis of a German database of resting energy expenditure.
        Am J Clin Nutr. 2004; 80: 1379-1390
        • Lazzer S.
        • Agosti F.
        • Silvestri P.
        • et al.
        Prediction of resting energy expenditure in severely obese Italian women.
        J Endocrinol Invest. 2007; 30: 20-27
        • Tappy L.
        Metabolic consequences of overfeeding in humans.
        Curr Opin Clin Metab. 2004; 7: 623-628
        • Horgan G.W.
        • Stubbs J.
        Predicting basal metabolic rate in the obese is difficult.
        Eur J Clin Nutr. 2003; 57: 335-340
        • Ravussin E.
        • Gautier J.F.
        Metabolic predictors of weight gain.
        Int J Obes. 1999; 23: 37-41
        • da Rocha E.E.M.
        • Alves V.G.F.
        • Silva M.H.N.
        • et al.
        Can measured resting energy expenditure be estimated by formulae in daily clinical nutrition practice?.
        Curr Opin Clin Metab Care. 2005; 8: 319-328
        • Gallagher D.
        • Albu J.
        • He Q.
        • et al.
        Small organs with a high metabolic rate explain lower resting energy expenditure in African American than in white adults.
        Am J Clin Nutr. 2006; 83: 1062-1067
        • Elia M.
        • Body-Composition Analysis
        An evaluation of 2 component models, multicomponent models and bedside techniques.
        Clin Nutr. 1992; 11: 114-127
        • Onur S.
        • Haas V.
        • Bosy-Westphal A.
        • et al.
        l-Tri-iodothyronine is a major determinant of resting energy expenditure in underweight patients with anorexia nervosa and during weight gain.
        Eur J Endocrinol. 2005; 152: 179-184
        • AlAdsani H.
        • Hoffer L.J.
        • Silva J.E.
        Resting energy expenditure is sensitive to small dose changes in patients on chronic thyroid hormone replacement.
        J Clin Endocrinol Metab. 1997; 82: 1118-1125
        • Dilba B.
        • Johannsen M.
        • Trabert J.
        • et al.
        Anteiliger Einfluss eines achtwöchigen Sport- und Diätprogramms auf Körpergewicht, Risikofaktoren und Fitness adipöser Patientinnen.
        Akt Ernaehr Med. 2006; 31: 328-333
        • Weir J.B.D.
        New methods for calculating metabolic rate with special reference to protein metabolism.
        J Physiol (Lond). 1949; 109: 1-9
        • Bland J.M.
        • Altman D.G.
        Statistical methods for assessing agreement between 2 methods of clinical measurement.
        Lancet. 1986; 1: 307-310
        • Das S.K.
        • Saltzman E.
        • McCrory M.A.
        • et al.
        Energy expenditure is very high in extremely obese women.
        J Nutr. 2004; 134: 1412-1416
        • Alvarez V.P.
        • Dixon J.B.
        • Strauss B.J.G.
        • et al.
        Single frequency bioelectrical impedance is a poor method for determining fat mass in moderately obese women.
        Obes Surg. 2007; 17: 211-221
        • Cox-Reijven P.L.
        • Soeters P.B.
        Validation of bio-impedance spectroscopy: effects of degree of obesity and ways of calculating volumes from measured resistance values.
        Int J Obes. 2000; 24: 271-280
        • Bosy-Westphal A.
        • Reinecke U.
        • Schlorke T.
        • et al.
        Effect of organ and tissue masses on resting energy expenditure in underweight, normal weight and obese adults.
        Int J Obes. 2004; 28: 72-79
        • Muller M.J.
        • Bosy-Westphal A.
        • Kutzner A.
        • et al.
        Metabolically active components of fat-free mass and resting energy expenditure in humans: recent lessons from imaging technologies.
        Obesity reviews. 2002; 3: 113-122
        • Bader N.
        • Bosy-Westphal A.
        • Dilba B.
        • et al.
        Intra- and interindividual variability of resting energy expenditure in healthy male subjects—biological and methodological variability of resting energy expenditure.
        Brit J Nutr. 2005; 94: 843-849
        • Wang Z.
        • Heshka S.
        • Gallagher D.
        • et al.
        Resting energy expenditure–fat free mass relationship: new insights provided by body composition modeling.
        Am J Physiol Endocrinol Metab. 2000; 279: E539-E545
        • Bosy-Westphal A.
        • Wolf A.
        • Buehrens F.
        • et al.
        Familial influences and obesity-associated metabolic risk factors contribute to the variation in resting energy expenditure: the Kiel Obesity Prevention Study.
        Am J Clin Nutr. 2008; 87: 1695-1701
        • Gougeon R.
        • Lamarche M.
        • Yale J.F.
        • et al.
        The prediction of resting energy expenditure in type 2 diabetes mellitus is improved by factoring for glycemia.
        Int J Obes. 2002; 26: 1547-1552