Endokrin Bozukluğu Olan Hastalarda Bazal Metabolik Hızın Belirlenmesinde İndirekt Kalorimetre ile Diğer Enerji Denklemlerinin Karşılaştırılması

Amaç: Bu çalışma, endokrin hastalıklara sahip ve ayaktan tıbbi tedavi alan hastaların bazal metabolizma hızı hesaplanmasında kullanılan enerji denklemleri ile indirekt kalorimetre sonuçlarını karşılaştırarak, bu hasta grubunun enerji gereksinmesinin belirlenmesinde en doğru sonucu veren denklemlerin belirlenmesi amacı ile yapılmıştır. Bireyler ve Yöntem: Çalışma, Aralık 2016-Şubat 2017 ayları arasında Başkent Üniversitesi Ankara Hastanesi Endokrinoloji Bölümü’ne başvuran, 18-86 yaş arası, indirekt kalorimetre (IC) (COSMED, Fitmate GS) ile bazal enerji harcamaları ölçülen ve çalışmaya katılma konusunda gönüllü olan 150 hasta (%74 kadın, %26 erkek) üzerinde yapılmıştır. Bireylerin kişisel özellikleri ve yaşam tarzları anket formu ile sorgulanmıştır. Antropometrik ölçümleri ve vücut bileşimi analizleri ölçülmüş ve kaydedilmiştir. Ayrıca bireylerin antropometrik ölçümleri ve vücut bileşimleri enerji denklemlerinde kullanılarak bireylerin bazal metabolik hızları (BMH) 42 ayrı enerji denklemi ile hesaplanmıştır. Bulgular: İndirekt kalorimetre kullanımının mümkün olmadığı durumlarda endokrin hastası bireylerin BMH’nin belirlenmesinde, tüm bireylerde Harris-Benedict (HB) 1984, erkek bireylerde Lazzer (BC), yetişkin bireylerde Nelson (BC), yaşlı bireylerde HB 1984, HB 1919 ve De Lorenzo, hafif kilolu bireylerde Henry, obez ve morbid obez bireylerde ise Huang ve Japanese (sadeleştirilmiş) denklemlerinin kullanımının en doğru sonuçları vereceği belirlenmiştir. Kadın bireyler ile zayıf ve normal bireylerin BMH’lerinin belirlenmesinde ise IC ile yeterli uyuma sahip hiçbir denklem belirlenememiştir. Sonuç: Endokrin hastalığa sahip bireylerde IC kullanımının mümkün olmadığı durumlarda BMH’nin belirlenmesinde HB 1984 denkleminin kullanımının en doğru sonuçları vereceği belirlenmiştir.

Comparison of Indirect Calorimetry and Predictive Equations for Determination of Basal Metabolic Rate of Patients with Endocrine Disorders

Aim: The purpose of this study was to specify the equations yielding the most accurate result for the determination of basal metabolic rate of outpatients with endocrine disorders by comparing the indirect calorimetry results with predictive equations. Subjects and Method: This study was conducted on 150 voluntary patients (female 74%, male 26%) aged between 18 to 86 years, who admitted to Başkent University Ankara Hospital Endocrinology Department between December 2016 and February 2017. The basal metabolic rate (BMR) was measured by indirect calorimetry (IC) (COSMED, Fitmate GS). Demographics and information related to individual lifestyles were obtained by a questionnaire. Anthropometric and body composition analysis were measured and recorded. Furthermore, BMR was calculated with 42 different predictive equations by using the anthropometric and body composition measurements. Results: Harris-Benedict (HB) 1984 equation was found to be the most accurate equation for determination of BMR in endocrine patients when the use of indirect calorimetry is not possible. In addition, (1) Lazzer (BC), (2) Nelson (BC), (3) HB 1984, HB 1919, De Lorenzo, (4) Henry, and (5) Huang-Japanese (simplified) gave the most accurate estimations for males, adults, elderly, overweight, and obese or morbid obese subjects, respectively. None of the BMR equations showed similar results with IC in females, and in underweight or normal weight subjects. Conclusion: In cases where indirect calorimetry is not available, the HB 1984 equation can be used to estimate basal metabolic rates of endocrine patients.

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  • 1. Pekcan G. Beslenme Durumunun Saptanması, Hacettepe Üniversitesi Sağlık Bilimleri Fakültesi Beslenme ve Diyetetik Bölümü, Sağlık Bakanlığı yayım no:726, Klasmat matbaacılık, Ankara, 2008.
  • 2. DeLegge MH, Guenter P. Energy. In: Gottschlich MM, editor. The A.S.P.E.N Nutrition Support Core Curriculum: A Case-Based Approach: The Adult Patient. USA, Silver Spring: 2007. p.19-29.
  • 3. Psota T, Chen KY. Measuring energy expenditure in clinical populations: rewards and challenges. Eur J Clin Nutr. 2013;67:436–42.
  • 4. Breen H, Ireton-Jones C. Predicting energy needs in obese patients. Nutr Clin Pract. 2004;19:284–9.
  • 5. Harris JA, Benedict FG. A biometric study of basal metabolism in man. Proc Natl Acad Sci USA. 1918;4(12): 370–3.
  • 6. Da Rocha EE, Alves VG, Silva MH, Chiesa CA, da Fonseca RB. Can measured resting energy expenditure be estimated by formula in daily clinical nutrition practice? Curr Opin Clin Nutr Metab Care. 2005;8:319–28.
  • 7. Wang Z, Heshka S, Zhang K, Boozer CN, Heymsfield SB. Resting energy expenditure: systematic organization and critique of prediction methods. Obes Reas. 2001;9:331–6.
  • 8. Anbar R, Beloosesky Y, Cohen J, Madar Z, Weiss A, Theilla M, et al. Tight calorie control in geriatric patients following hip fracture decreases complications: A randomized, controlled study. Clin Nutr. 2014;33:23–8.
  • 9. Von Pettenkofer M. Ueber einen neuen Respirations apparat. [On a new device for respiration analyses.] 1st ed. Munich, K Akademie in Commission, 1861.
  • 10. Atawer WO, Rosa EB. Description of neo respiration calorimeter and experiments on the conservation of energy in the human body. Vol:36, Washington, DC, Government Printing Office, 1899.
  • 11. Frankenfield DC. On heat, respiration, and calorimetry. Nutrition. 2010;26:339–50.
  • 12. Schadewaldt P, Nowotny B, Straßburger K, Kotzka J, Roden M. Indirect calorimetry in humans: a postcalorimetric evaluation procedure for correction of metabolic monitor variability. Am J Clin Nutr. 2013;97:763–73.
  • 13. Frankenfield D, Roth-Yousey L, Compher C. Comparison of predictive equations for resting metabolic rate in healthy nonobese and obese adults: a systematic review. J Am Diet Assoc. 2005;105:775–89.
  • 14. Henry CJ. Basal metabolic rate studies in humans: measurement and development of new equations. Public Health Nutr. 2005;8:1133–52.
  • 15. da Rocha EEM, Alves VGF, Fonsenca RBV. Indirect calorimetry: methodology, instruments and clinical application. Curr Opin Clin Nutr Metab Care. 2006;9:247– 56.
  • 16. Schoeller DA. Making indirect calorimetry a gold standard for predicting energy requirements for institutionalized patients. J Am Diet Assoc. 2007;107:390– 2.
  • 17. Melanson EL, Ingebrigtsen JP, Bergouignan A, Ohkawara K, Kohrt WM, Lighton JR. A new approach for flowthrough respirometry measurements in humans. Am J Physiol Regul Integr Comp Physiol. 2010;298:1571–9.
  • 18. Chung-Kang T, Hua-Shui H, Chih-Te H, Hui-Ying H, Chiu- Shong L, Cheng-Chieh L, et al. Predictive equation of resting energy expenditure in obese adult Taiwanese. ORCP. 2011;5:313-9.
  • 19. Jesus P, Achamrah N, Grigioni S, Charles J, Rimbert A, Folope V, et al. Validity of predictive equations for resting energy expenditure according to the body mass index in a population of 1726 patients followed in a Nutrition Unit. Clin Nutr. 2015;34:529-35.
  • 20. Alves VGF, da Rocha EEM, Gonzalez MC, da Fonseca RBV, do Nascimento Silva MH, Chiesa CA. Assessment of resting energy expenditure of obese patients: Comparison of indirect calorimetry with formulae. Clin Nutr. 2009;28:299–304.
  • 21. Boullata J, Williams J, Cottrell F, Hudson L, Compher C. Accurate determination of energy needs in hospitalized patients. J Am Diet Assoc. 2007;107:393-401.
  • 22. Frankenfield DC, Ashcraft CM, Galvan DA. Prediction of resting metabolic rate in critically ill patients at the extremes of body mass index. JPEN J Parenter Enteral Nutr. 2013;37:361-7.
  • 23. De Waele, Opsomer T, Honoré PM, Diltoer M, Mattens S, Huyghens L, et al. Measured versus calculated resting energy expenditure in critically ill adult patients. Do mathematics match the gold Standard. Minerva Anestesiol. 2015;81:272-82.
  • 24. Daly JM, Heymsfield SB, Head CA, Harvey LP, Nixon DW, Katzeff H, et al. Human energy requirements: Overestimation by widely used prediction equations. Am J Clin Nutr. 1985;42:1170-4.
  • 25. Compher C, Frankenfield D, Keim N, Roth-Yousey L. Evidence Analysis Working Group. Best practice methods to apply to measurement of resting metabolic rate in adults: A systematic review. J Am Diet Assoc. 2006;106:881-903.
  • 26. Alberda C, Snowden L, McCargar L, Gramlich L. Energy requirements in critically ill patients: how close are our estimates? Nutr Clin Pract. 2002;17:38-42.
  • 27. Malone AM. Methods of assessing energy expenditure in the intensive care unit. Nutr Clin Pract. 2002;17:21-8.
  • 28. Wouters-Adriaens MP, Westerterp KR. Low resting energy expenditure in Asians can be attributed to body composition. Obesity (Silver Spring). 2008;16:2212-6.
  • 29. Rodrigues JCD, Lamarca F, de Oliveira CL, Cuppari L, Lourenço LA, Avesania CM. Agreement between prediction equations and indirect calorimetry to estimate resting energy expenditure in elderly patients on hemodialysis. eSPEN Journal. 2014; 9(2):91-6.
  • 30. Nagano A, Yamada Y, Miyake H, Domen K, Koyama T.Comparisons of predictive equations for resting energy expenditure in patients with cerebral infarct during acute care. J Stroke Cerebrovasc Dis. 2015;24:1879-85.
  • 31. Ireton-Jones C, Jones JD. Improved equations for predicting energy expenditure in patients: The Ireton- Jones equations. Nutr Clin Pract. 2002;17(1):29-31.
  • 32. Nelson KM, Weinsier RL, Long CL, Schutz Y. Prediction of resting energy expenditure from fat-free mass and fat mass. Am J Clin Nutr. 1992;56(5):848-56.
  • 33. Schofield WN. Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr. 1985;39:5-41.
  • 34. Harris JA, Benedict FG. A biometric study of human basal metabolism. Proc Natl Acad Sci USA. 1918;4:370–3.
  • 35. Cunningham JJ. Body composition as a determinant of energy expenditure: a synthetic review and a proposed general prediction equation. Am J Clin Nutr. 1991;54:963-9.
  • 36. Siervo M, Bertoli S, Battezzati A, Wells JC, Lara J, Ferraris C, et al. Accuracy of predictive equations for the measurement of resting energy expenditure in older subjects. Clin Nutr. 2014;33(4):613-9.
  • 37. Anderegg BA, Worrall C, Barbour E, Simpson KN, DeLegge M. Comparison of resting energy expenditure prediction methods with measured resting energy expenditure in obese, hospitalized adults. JPEN, J Parenter Enteral Nutr. 2009;33(2):168-75.