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Centro de Investigaciones Regionales Dr. Hideyo Noguchi, Universidad Autónoma de Yucatán

2019, Número 3

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Rev Biomed 2019; 30 (3)


Comparación de métodos de estimación del gasto energético en reposo en adultos jóvenes de Yucatán, México

Espadas-Herrera JC, González-Ramírez L, Ávila-López JC, Janssen-Aguilar R, Molina-Seguí F, Huerta-Quintanilla R, Hernández-Hernández AM, Canto-Lugo E, Laviada-Molina HA

Idioma: Español
Referencias bibliográficas: 36
Paginas: 105-115
Archivo PDF: 362.33 Kb.


RESUMEN

Introducción. Extrapolar ecuaciones matemáticas para la predicción del gasto energético en reposo (GER) a poblaciones diferentes a la original donde fueron desarrolladas, puede comprometer la precisión del cálculo.
Objetivo. Comparar diversas ecuaciones predictivas del GER contra la calorimetría indirecta (CI) para la determinación de la mejor ecuación como alternativa en adultos jóvenes universitarios en Yucatán.
Material y Métodos. Estudio transversal con 34 mujeres y 30 hombres (20.25 ± 1.5 años) clasificados de acuerdo a su índice de masa corporal (IMC): subgrupos sin sobrepeso y con sobrepeso. La medición del GER fue a través de un calorímetro indirecto portátil. El análisis estadístico incluyó la evaluación del sesgo con intervalos de confianza del 95%, precisión, exactitud y correlación de Pearson (p=0.01).
Resultados. A pesar de la alta correlación entre las ecuaciones predictivas del GER y la CI, no fue posible identificar alguna con clara superioridad frente al resto. La ecuación de MifflinSt Jeor mostró algunas ventajas al evaluar al grupo total.
Conclusión. en la práctica clínica, las ecuaciones predictivas son un instrumento útil y económico que no debe ser descartado a pesar de sus limitaciones.


REFERENCIAS (EN ESTE ARTÍCULO)

  1. Psota T, Chen KY. Measuring energy expenditure in clinical populations: rewards and challenges. Eur J Clin Nutr. 2013 May; 67(5): p. 436–442. DOI: https://doi. org/10.1038/ejcn.2013.38

  2. Frankenfiel DC, Ashcraft C. Estimating Energy Needs in Nutrition Support Patients. JPEN J Parenter Enteral Nutr. 2011 Sep; 35(5): p. 563-570. DOI: https://doi. org/10.1177/0148607111415859

  3. Parra A, Cherem L, Galindo D, Díaz M, Pérez AB, Hernández C. Comparación del gasto energético en reposo determinado mediante calorimetría indirecta y estimado mediante fórmulas predictivas en mujeres con grados de obesidad I a III. Nutr Hosp. 2013 Abr; 28(2): p. 357-364. DOI: https://doi.org/10.3305/ nh.2013.28.2.6188

  4. Haugen HA, Chan LN, Li F. Indirect calorimetry: a practical guide for clinicians. Nutr Clin Pract. 2007 Ago; 22(4): p. 377-388. DOI: https://doi. org/10.1177/0115426507022004377

  5. Kennedy S, Ryan L, Fraser A, Clegg ME. Comparison of the GEM and the ECAL indirect calorimeters against the Deltatrac for measures of RMR and diet-induced thermogenesis. J Nutr Sci. 2014; 3: p. e52. DOI: https:// doi.org/10.1017/jns.2014.58

  6. Frankenfield DC, Ashcraft CM. Toward the Development of Predictive Equations for Resting Metabolic Rate in Acutely Ill Spontaneously Breathing Patients. JPEN J Parenter Enteral Nutr. 2016 Sep; 41(7): p. 1155-1161. DOI: https://doi.org/10.1177/0148607116657647

  7. Frankenfield DC, Coleman A. An evaluation of a handheld indirect calorimeter against a standard calorimeter in obese and nonobese adults. JPEN J Parenter Enteral Nutr. 2013 Sep; 37(5): p. 652-658. DOI: https://doi.org/10.1177/0148607112473340

  8. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenber P, Elia M, Gómez JM, et al. Bioelectrical impedance analysis-- part I: review of principles and methods. Clin Nutr. 2004 Oct; 23(5): p. 1226-1243. DOI: https://doi.org/10.1016/j. clnu.2004.06.004

  9. Zanella P, Ávila C, de Souza C. Estimating Resting Energy Expenditure by Different Methods as Compared With Indirect Calorimetry for Patients With Pulmonary Hypertension. Nutr Clin Pract. 2018 Abr; 33(2): p. 217- 223. DOI: https://doi.org/10.1177/0884533617727731

  10. Frankenfield DC, Rowe WA, Smith JS, Cooney RN. Validation of several established equations for resting metabolic rate in obese and nonobese people. J Am Diet Assoc. 2003 Sep; 103(9): p. 1152-1159. DOI: https://doi. org/10.1053/jada.2003.50575

  11. Weir JB. New methods for calculating metabolic rate with special reference to protein metabolism. J Physiol. 1949 Ago; 109: p. 1-9.

  12. Cooper JA, Watras AC, O’Brien MJ, Luke A, Dobratz JR, Earthman CP, et al. Assessing validity and reliability of resting metabolic rate in six gas analysis systems. J Am Diet Assoc. 2009 Ene; 109(1): p. 128-132. DOI: https://doi.org/10.1016/j.jada.2008.10.004

  13. Harris JA, Benedict FG. A Biometric Study of Human Basal Metabolism. Proc Natl Acad Sci USA. 1918 Dic; 4(12): p. 370-373

  14. Mifflin MD, St Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO. A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr. 1990 Feb; 51(2): p. 241-247. DOI: https://doi. org/10.1093/ajcn/51.2.241

  15. Energy and protein requirements. Report of a joint FAO/ WHO/UNU Expert Consultation. World Health Organ Tech Rep Ser. 1985 Oct; 724: p. 1-206.

  16. Valencia ME. Energía. In Bourges H, Casanueva E, Rosado J. Recomendaciones de Ingestión de Nutrimentos para la Población Mexicana: bases fisiológicas. México: Editorial Médica Panamericana; 2009.

  17. Sheiner L, Beal S. Some suggestions for measuring prediction performance. J Pharmacokinet Biopharm. 1981 Abr; 9: p. 503-512. DOI: https://doi.org/10.1007/ BF01062337

  18. Frankenfield D, Coleman A, Alam S, Cooney RN. Analysis of estimation methods for resting metabolic rate in critically ill adults. JPEN J Parenter Enteral Nutr. 2009 Ene-Feb; 33: p. 27-36. DOI: https://doi.org/10.1016/j. jada.2005.02.005

  19. Menditto A, Patriarca M, Magnusson B. Understanding the meaning of accuracy, trueness and precision. Accred Qual Assur. 2007 Oct; 12(1): p. 45–47. DOI: https://doi. org/10.1007/s00769-006-0191-z

  20. Walker RN, Heuberger R. Predictive equations for energy needs for the critically ill. Respir Care. 2009; 54: p. 509–52

  21. 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 May; 105(5): p. 775-789. DOI: https://doi.org/10.1177/0148607108322399

  22. Valencia ME, Moya SY, McNeill G, Haggarty P. Basal metabolic rate and body fatness of adult men in northern Mexico. Eur J Clin Nutr. 1994 Mar; 48(3): p. 205-211.

  23. Parra A, Pérez-Lizaur A. Comparación de la estimación del gasto energético basal por cuatro ecuaciones versus calorimetría indirecta en mujeres con peso normal, sobrepeso y obesidad. 2012 Apr-Jun; 20(2): p. 63-66.

  24. Korth O, Bosy-Westphal A, Zschoche P, Glüer CC, Heller M, Müller MJ. Influence of methods used in body composition analysis on the prediction of resting energy expenditure. Eur J Clin Nutr. 2007 May; 61(5): p. 582- 589. DOI: https://doi.org/10.1038/sj.ejcn.1602556

  25. Rao ZY, Wu XT, Liang BM, Wang MY, Hu W. Comparison of five equations for estimating resting energy expenditure in Chinese young, normal weight healthy adults. Eur J Med Res. 2012 Sep; 17: p. 26. DOI: https://doi.org/10.1186/2047-783X-17-26

  26. Weijs PJ. Validity of predictive equations for resting energy expenditure in US and Dutch overweight and obese class I and II adults aged 18-65 y. Am J Clin Nutr. 2008 Oct; 88(4): p. 959-970. DOI: https://doi. org/10.1093/ajcn/88.4.959

  27. Quiroz G, Serralde A, Saldaña M, Gulias A, Guevara M. Validating an energy expenditure prediction equation in overweight and obese Mexican patients. Nutr Hosp. 2014 Oct; 30(4): p. 749-755. DOI: https://doi.org/10.3305/ nh.2014.30.4.7639

  28. Melanson EL, Coelho LB, Tran ZV, Haugen HA, Kearney JT, Hill JO. Validation of the BodyGem handheld calorimeter. Int J Obesity. 2004 Oct; 12: p. 1479- 1484. DOI: https://doi.org/10.1038/sj.ijo.0802643

  29. Alam DS, Hulshof PJM, Roordink D, Meltzer M, Salam MM, van Raaij J. Validity and reproducibility of resting metabolic rate measurements in rural Bangladeshi women: comparison of measurements obtained by Med Gem and by Deltatrac device. Eur J Clin Nutr. 2005 May; 59: p. 651-657. DOI: https://doi.org/10.1038/ sj.ejcn.1602122

  30. Fields DAKJT, Copeland KC. MedGem hand-held indirect calorimeter is valid for resting energy expenditure measurement in health children. Obesity. 2006 Oct; 14: p. 1755-1761. DOI: https://doi.org/10.1038/oby.2006.202

  31. Compher C, Hise M, Sternber A, Kinosian BP. Comparision between MedGem and Deltatrac resting metabolic rate measurements. Eur J Clin Nutr. 2005 Oct; 59: p. 1136-1141. DOI: https://doi.org/10.1016/j. jada.2007.12.012

  32. Hlynsky J, Birmingham CL, Johnston M, Gritzner S. The agreement between the MedGem indirect calorimeter and a standard indirect calorimeter in anorexia nervosa. Eating Weight Disord. 2005 Dic; 10: p. e83-e87. DOI: https://doi.org/10.1038/sj.ejcn.1602223

  33. Nieman , DC , Trone GA, Austin MD. A new handheld device for measuring resting metabolic rate and oxygen consumption. J Am Diet Assoc. 2003 May; 103: p. 588- 593. DOI: https://doi.org/10.1053/jada.2003.50116

  34. St.-Onge MP, Rubiano F, Jones A, Heymsfield SB. A new hand-held indirect calorimeter to measure post-prandial energy expediture. Obesity Res. 2004 Abr; 12: p. 704- 709. DOI: https://doi.org/10.1038/oby.2004.82

  35. Stewart C, Goody CM, Branson R. Comparison of two systems of measuring energy expediture. JPEN Parent Enteral Nutr. 2005 May-Jun; 29: p. 212-217. DOI: https://doi.org/10.1177/0148607105029003212

  36. Froehle AW. Climate variables as predictors of basal metabolic rate: new equations. Am J Hum Biol. 2008 May; 20(5): p. 510-529. DOI: https://doi.org/10.1002/ ajhb.20769




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