Calorimetry in obese women: comparison of two different operating indirect calorimeters together with the predictive equation of Harris and Benedict

  • T. Hagedorn
  • C. Savina
  • C. Coletti
  • M. Paolini
  • L. Scavone
  • B. Neri
  • Lorenzo M. Donini
  • C. Cannella
Original Article

Abstract

In patients with obesity, it is important to know the exact metabolic function in order to assess balanced nutritional support, reducing the risks of the initial situation. For an adequate detection of the resting metabolic rate (RMR), commonly indirect calorimeters are used. In the present study, two different indirect calorimeters [an expiratory collection open-circuit system (Fitmate) and a ventilated open-circuit system (Quark RMR)], were correlated and analysed for better adequacy. The predictive equation of Harris–Benedict (HBE) was confronted with the measured RMR of the better evaluated indirect calorimeter. 42 obese women (age 55 ± 12 years and BMI 42.9 ± 6.8 kg/m2) were included in the study after selecting patients according to the predefined inclusion and exclusion criteria. Measurement durations of each 15 min were performed after an overnight fast with both calorimeters. Received values of Fitmate and Quark were compared while the calorimeter with the more reliable values was compared with the HBE. Significant correlations (P < 0.001) between the devices were achieved, although a significant difference of 1,051 kJ/day (14.9%) between Fitmate (7,542 ± 1,230 kJ/day) and Quark RMR (6,491 ± 806 kJ/day) was detected. The mean calculated RMR of the HBE (7,181 ± 716 kJ/day), in comparison with Quark RMR was significantly different (P < 0.001). The correlation of the two indirect calorimeters, their different functioning, led to significant differences between devices and RMR measurements. The HBE was found to overestimate the measured RMR of Quark RMR. Though the HBE was developed on lean subject, it cannot be considered as a reliable equation for obese subjects.

Keywords

Calorimetry Calorimeter validation Obesity Harris–Benedict equation 

References

  1. 1.
    American Association for Respiratory Care (AARC) (2004) Metabolic measurement using indirect calorimetry during mechanical ventilation—2004 revision & update. Respir Care 49:1073–1079Google Scholar
  2. 2.
    Battezzatti A, Viganò R (2001) Indirect calorimetry and nutritional problems in clinical practice. Acta Diabetol 38:1–5CrossRefGoogle Scholar
  3. 3.
    Cadena M, Sacristan E, Infante O, Escalante B, Rodriguez F (2005) Steady state condition in the measurement of VO2 and VCO2 by indirect calorimetry. Engineering in Medicine and Biology 27th Annual Conference pp 7773–7776Google Scholar
  4. 4.
    Camerini G, Adami GF, Marinari GM, Campostano A, Ravera G, Scopinaro N (2001) Failure of preoperative resting energy expenditure in predicting weight loss after gastroplasty. Obes Res 9:589–591CrossRefGoogle Scholar
  5. 5.
    Close WH, Dauncey MJ, Ingram DL (1980) Heat loss from humans measured with a direct calorimeter and heat-flow meters. Br J Nutr 43:87–93CrossRefGoogle Scholar
  6. 6.
    Compher C, Frankenfield D, Keim N, Roth-Yousey L (2006) Best practice methods to apply to measurement of resting metabolic rate in adults: a systematic review. J Am Diet Assoc 106:881–903CrossRefGoogle Scholar
  7. 7.
    Daly JM, Heymsfield SB, Head CA, Harvey LP, Nixon DW, Katzeff H, Grossman GD (1985) Human energy requirements: overestimation by widely used prediction equation. Am J Clin Nutr 42:1170–1174Google Scholar
  8. 8.
    Das SK, Saltzman E, McCrory MA, Hsu LKG, Shikora SA, Dolnikowski G, Kehayias JJ, Roberts SB (2004) Energy expenditure is very high in extremely obese women. J Nutr 134:1412–1416Google Scholar
  9. 9.
    Deurenberg P, Yap M, van Staveren WA (1988) Body mass index and percent body fat: a metanalysis among different ethnic groups. Int J Obes 22:1164–1171CrossRefGoogle Scholar
  10. 10.
    Frankenfield DC, Roth-Yousey L, Compher C (2005) Comparison of predictive equations for resting metabolic rate in healthy non-obese and obese adults: A systematic review. J Am Diet Assoc 105:775–789CrossRefGoogle Scholar
  11. 11.
    Frankenfield DC, Rowe WA, Smith JS, Cooney RN (2003) Validation of several established equations for resting metabolic rate in obese and non-obese people. J Am Diet Assoc 103:1152–1159CrossRefGoogle Scholar
  12. 12.
    Gougeon R, Lamarche M, Yale J-F, Venuta T (2002) The prediction of resting energy expenditure in type 2 diabetes mellitus is improved by factoring for glycemia. Int J Obes 26:1547–1552CrossRefGoogle Scholar
  13. 13.
    Greenberg AS, Obin MS (2006) Obesity and the role of adipose tissue in inflammation and metabolism. Am J Clin Nutr 83:461S–465SGoogle Scholar
  14. 14.
    Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, Gordon DJ, Krauss RM, Savage PJ, Smith SC Jr, Spertus JA, Costa F (2005) American Heart Association; National Heart, Lung, and Blood Institute. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 112:2735–2752CrossRefGoogle Scholar
  15. 15.
    Haugen HA, Chan L-N, Li F (2007) Indirect calorimetry: a practical guide for clinicians. Nutr Clin Pract 22:377–388CrossRefGoogle Scholar
  16. 16.
    Haugen HA, Melanson EL, Tran ZV, Kearney JT, Hill JO (2003) Variability of measured resting metabolic rate. Am J Clin Nutr 78:1141–1144Google Scholar
  17. 17.
    Horner NK, Lampe JW, Patterson RE, Neuhouser ML, Beresford SA, Prentice RL (2001) Indirect calorimetry protocol development for measuring resting metabolic rate as a component of total energy expenditure in free-living postmenopausal women. J Nutr 131:2215–2218Google Scholar
  18. 18.
    Jackson DM, Pace L, Speakman JR (2007) The measurement of resting metabolic rate in preschool children. Obesity 15:1930–1932CrossRefGoogle Scholar
  19. 19.
    Kunz I, Schorr U, Klaus S, Sharma M (2000) Resting metabolic rate and substrate use in obesity hypertension. Hypertension 36:26–32Google Scholar
  20. 20.
    Leitzmann C, Müller C, Michel P, Brehme U, Hahn A, Laube H (2003) Ernährung in Prävention und Therapie. Hippokrates. 2. Auflage. StuttgartGoogle Scholar
  21. 21.
    Levine JA (2005) Measurement of energy expenditure. Publ Health Nutr 8:1123–1132Google Scholar
  22. 22.
    Livingston EH, Kohlstadt I (2005) Simplified resting metabolic rate-predicting formulas for normal-sized and obese individuals. Obes Res 13:1255–1262CrossRefGoogle Scholar
  23. 23.
    Maiolo C, Melchiorri G, Iacopino L, Masala S, De Lorenzo A (2003) Physical activity energy expenditure measured using a portable telemetric device in comparison with mass spectrometer. Br J Sports Med 37:445–447CrossRefGoogle Scholar
  24. 24.
    Martin K, Wallace P, Rust PF, Garvey WT (2004) Estimation of resting energy expenditure considering effects of race and diabetes status. Diabetes Care 27:1405–1411CrossRefGoogle Scholar
  25. 25.
    Matarese LE (1997) Indirect calorimetry: technical aspects. J Am Diet Assoc 97:S154–S160CrossRefGoogle Scholar
  26. 26.
    McClave SA, Lowen CC, Kleber MJ, McConnell JW, Jung LY, Goldsmith LJ (2003) Clinical use of the respiratory quotient obtained from indirect calorimetry. J Parent Enteral Nutr 1:21–26CrossRefGoogle Scholar
  27. 27.
    McClave SA, Spain DA, Skolnick JL, Lowen CC, Kieber MJ, Wickerham PS, Vogt JR, Looney SW (2003) Achievement of steady state optimizes results when performing indirect calorimetry. J Parent Enteral Nutr 27:16–20CrossRefGoogle Scholar
  28. 28.
    Mifflin MD, St Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO (1990) A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr 51:241–247Google Scholar
  29. 29.
    Miodownik S, Melendez J, Carlon VA, Burda B (1998) Quantitative methanol-burning lung model for validating gas-exchange measurements over wide range of FIO2. J Appl Physiol 84(6):2177–2182Google Scholar
  30. 30.
    Moreira da Rocha EE, Alves VGF, Da Fonseca RBV (2006) Indirect calorimetry: methodology, instruments and clinical application. Curr Opin Clin Nutr Metab Care 9:247–256CrossRefGoogle Scholar
  31. 31.
    National Heart Lung and Blood Institute (NHLBI) (2000) Obesity education initiative. The practical guide. Identification, evaluation, and treatment of overweight and obesity in adults: the evidence reportGoogle Scholar
  32. 32.
    Nieman DC, Austin MD, Benezra L, Pearce S, McInnis T, Gross SJ (2006) Validation of Cosmed’s FitMateTM in measuring oxygen consumption and estimating resting metabolic rate. Res Sports Med 14:89–96Google Scholar
  33. 33.
    Nieman DC, Trone GA, Austin MD (2003) A new handheld device for measuring resting metabolic rate and oxygen consumption. J Am Diet Assoc 103:588–593CrossRefGoogle Scholar
  34. 34.
    Owen OE, Kavle E, Owen RS, Polansky M, Caprio S, Mozzoli MA, Kendrick ZV, Bushman MC, Boden G (1986) A reappraisal of caloric requirements in healthy women. Am J Clin Nutr 44:1–19Google Scholar
  35. 35.
    Pennock B, Donahoe M (1993) Indirect calorimetry with a hood: flow requirements, accuracy, and minute ventilation measurement. J Appl Physiol 74:485–491Google Scholar
  36. 36.
    Pi-Sunyer FX (2002) The obesity epidemic: pathophysiology and consequences of obesity. Obes Res 10:97S–104SCrossRefGoogle Scholar
  37. 37.
    Reeves MM, Capra S (2003) Variation in the application of methods used for predicting energy requirements in acutely ill adult patients: a survey practice. Eur J Clin Nutr 57:1530–1535CrossRefGoogle Scholar
  38. 38.
    Reeves MM, Davies PSW, Bauer J, Battistutta D (2004) Reducing the time period of steady state does not affect the accuracy of energy expenditure measurements by indirect calorimetry. J Appl Physiol 97:130–134CrossRefGoogle Scholar
  39. 39.
    Roffey DM (2006) Day-to-day variance in measurement of resting metabolic rate using ventilated-hood and mouthpiece and nose-clip indirect calorimetry systems. JPEN 30:426–432Google Scholar
  40. 40.
    Schols AMWJ, Schoffelen PFM, Ceulemans H, Wouters EFM, Saris WHM (1992) Measurement of resting energy expenditure in patients with chronic obstructive pulmonary disease in a clinical setting. J Parent Enteral Nutr 16:364–368CrossRefGoogle Scholar
  41. 41.
    Scott C (1993) Resting metabolic rate variability as influenced by mouthpiece and nose clip practice procedures. J Burn Care Rehabil 14:573–577CrossRefGoogle Scholar
  42. 42.
    Segal KR (1987) Comparison of indirect calorimetric measurements of resting energy expenditure with ventilated hood, face mask, and mouthpiece. Am J Clin Nutr 45:1420–1423Google Scholar
  43. 43.
    Segal KR, Presta E, Gutin B (1984) Thermic effect of food during graded exercise in normal weight and obese men. Am J Clin Nutr 40:995–1000Google Scholar
  44. 44.
    Severinghaus JW (1989) Water vapour errors of calibration errors in some capnometers: Respiratory conventions misunderstood by manufacturers? Anaesthesiology 70:990–998CrossRefGoogle Scholar
  45. 45.
    Smith DA, Dollman J, Withers RT, Brinkman M, Keeves JP, Clark DG (1997) Relationship between maximum aerobic power and resting metabolic rate in young adult women. J Appl Physiol 82:156–163Google Scholar
  46. 46.
    Soares MJ, Piers LS, O’Dea K, Shetty PS (1998) No evidence for ethnic influence on basal metabolism: an examination of data from India and Australia. Br J Nutr 79:333–341CrossRefGoogle Scholar
  47. 47.
    St Jeor ST, Cutter GR, Perumean-Chaney SE, Hall SJ, Herzog H, Bovee V (2004) The practical use of charts to estimate resting energy expenditure in adults. Top Clin Nutr 19:51–56Google Scholar
  48. 48.
    Turley KR, McBride PJ, Wilmore JH (1993) Resting metabolic rate measured after subjects spent the night at home versus at a clinic. Am J Clin Nutr 58:141–144Google Scholar
  49. 49.
    Vogels N, Diepvens K, Westerterp-Plantenga MS (2005) Predictors of long-term weight maintenance. Obes Res 13:2162–2168CrossRefGoogle Scholar
  50. 50.
    Walsh TS (2003) Recent advances in gas exchange measurement in intensive care patients. Br J Anesthesiology 91:120–131CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • T. Hagedorn
    • 1
  • C. Savina
    • 1
  • C. Coletti
    • 1
  • M. Paolini
    • 1
  • L. Scavone
    • 1
  • B. Neri
    • 2
  • Lorenzo M. Donini
    • 2
  • C. Cannella
    • 2
  1. 1.Rehabilitation Clinical Institute “Villa delle Querce”RomeItaly
  2. 2.Department of Medical Physiopathology (Food Science Section)“Sapienza” University of RomeRomeItaly

Personalised recommendations