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Obezitesi Olan Diyabetli Bireylerde Dinlenme Metabolik Hızının Belirlenmesinde Kullanılan Yöntemler

Year 2022, Volume: 7 Issue: 1, 151 - 155, 31.01.2022

Abstract

Tip 2 diabetes mellitus (DM), dünya nüfusunun önemli bir bölümünü etkileyen ve obezite ile ilişkilendirilen kronik bir hastalıktır. Tip 2 diyabetli obezitesi olan bireylerin tedavisinde birincil strateji, yaşam tarzı değişiklikleri yoluyla ağırlık kaybının sağlanmasıdır. Toplam enerji harcamasının önemli bir bileşeni olan dinlenme metabolik hızının (DMH) hesaplanmasında en güvenilir yöntem indirekt kalorimetredir. İndirekt kalorimetreye ulaşım mümkün olmadığında enerji denklemleri kullanılabilmektedir. Çeşitli araştırmalarla geliştirilen farklı enerji denklemleri bulunmaktadır. Gougeon, Huang, Ikeda ve Martin formülleri diyabetlilere yöneliktir. Yapılan çalışmalarda, FAO/WHO/UNU, Harris-Benedict, Huang denklemlerinin ve biyoelektrik impedans analizinin indirekt kalorimetreye kıyasla DMH’ı daha yüksek tahmin ettiği gösterilmiştir. Gougeon, obezitesi olan diyabetlilere yönelik geliştirmiş olduğu denkleme, değişken olarak plazma glikozunu eklemiş ve daha doğru DMH sonucu elde ettiklerini belirtmiştir. Ayrıca, Mifflin denkleminin obezitesi olan diyabetiklerde daha doğru sonuç verdiği gösterilmiştir. Sonuç olarak, enerji denklemleri, sağlıklı bireylerde doğru sonuçlar verebilmesine karşın daha yaşlı veya hasta bireylerde yeterince doğru sonuçlar vermemektedir. Bu nedenle, enerji denklemleri bireysel olarak hassasiyetle seçilmelidir. İndirekt kalorimetreye ulaşılamadığında, doğruluk oranı yüksek denklemlerin kullanımı enerji gereksinimini belirlemede kolaylık sağlayabilir.

References

  • International Diabetes Federation. IDF Diabetes Atlas, 9th edition. Brussels, Belgium: International Diabetes Federation, 2019.
  • American Diabetes Association (ADA). Standards of medical care in diabetes: lifestyle management. Diabetes Care. 2020;48(Suppl 1):38–65.
  • Pekcan G. Beslenme Durumunun Saptanması, Hacettepe Üniversitesi Sağlık Bilimleri Fakültesi Beslenme ve Diyetetik Bölümü, Sağlık Bakanlığı Yayım No:726, Ankara, Klasmat Matbaacılık, 2008.
  • Psota T, Chen KY. Measuring energy expenditure in clinical populations: Rewards and challenges. Eur J Clin Nutr. 2013;67(5):436–442.
  • 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(4):763– 773.
  • Jesus P, Achamrah N, Grigioni S, Charles J, Rimbert A, Folope V, Petit A 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. Clinical Nutrition. 2015; 34(3):529-535.
  • Frankenfield DC, Ashcraft CM, Galvan DA. Prediction of resting metabolic rate in critically ill patients at the extremes of body mass index. J Parenter Enteral Nutr. 2013; 37(3):361-367.
  • De Waele E, 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(3):272-82.
  • Gündoğdu T, Acar Tek N. Anoreksiya nervoza hastalarında enerji harcamasının belirlenmesinde kullanılan yöntemler. SDÜ Sağlık Bilimleri Dergisi. 2019;10(3):320-326.
  • Wouters-Adriaens MP, Westerterp KR. Low resting energy expenditure in Asians can be attributed to body composition. Obesity (Silver Spring). 2008; 16(10): 2212-2216.
  • Bernstein RS, Thornton JC, Yang MU, Wang J, Redmond AM, Pierson RN Jr, et al. Prediction of the resting metabolic rate in obese patients. Am J Clin Nutr. 1983;37(4):595–602.
  • 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(6):963–969.
  • Ganpule AA, Tanaka S, Ishikawa-Takata K, Tabata I. Interindividual variability in sleeping metabolic rate in Japanese subjects. Eur J Clin Nutr. 2007;61(11):1256–1261.
  • Gougeon R, Lamarche M, Yale JF, Venuta T. The prediction of resting energy expenditure in type 2 diabetes mellitus is improved by factoring for glycemia. Int J Obes Relat Metab Disord. 2002;26(12):1547-1552.
  • Harris JA, Benedict FG. A biometric study of basal metabolism in man; Carnegie Inst: Washington DC, USA, 1919. Volume:279.
  • Huang KC, Kormas N, Steinbeck K, Loughnan G, Caterson ID. Resting metabolic rate in severely obese diabetic and nondiabetic subjects. Obes Res. 2004;12(5):840-845.
  • Ikeda K, Fujimoto S, Goto M, Yamada C, Hamasaki A, Ida M et al. A new equation to estimate basal energy expenditure of patients with diabetes. Clin Nutr. 2013;32(5):777–82.
  • Lazzer S, Bedogni G, Lafortuna CL, Marazzi N, Busti C, Galli R, et al. Relationship between basal metabolic rate, gender, age, and body composition in 8,780 white obese subjects. Obesity (Silver Spring). 2010;18(1):71–78.
  • Lührmann PM, Herbert BM, Krems C, Neuhäuser-Berthold M. A new equation especially developed for predicting resting metabolic rate in the elderly for easy use in practice. Eur J Nutr. 2002:41(3):108–113.
  • Martin K, Wallace P, Rust PF, Garvey WT. Estimation of resting energy expenditure considering effects of race and diabetes status. Diabetes Care. 2004;27(6):1405–1411.
  • 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;51(2):241–247.
  • Muller MJ, Bosy-Westphal A, Kutzner D, Heller M. Metabolically active components of fat-free mass and resting energy expenditure in humans: Recent lessons from imaging technologies. Obes Rev. 2002;3(2):113–122.
  • Nachmani M, Lahav Y, Zeev A, Grosman-Rimon L, Eilat-Adar S. Weight change adjusted equations for assessing resting metabolic rate in overweight and obese adults. Obes Res Clin Prac. 2021. Doi: 10.1016/j.orcp.2021.03.001. Available from: https://pubmed.ncbi.nlm.nih.gov/33773945/
  • Owen OE. Resting metabolic requirements of men and women. Mayo Clin Proc. 1988:63(5):503–510.
  • Rodrigues AE, Mancini MC, Dalcanale L, de Melo ME, Cercato C, Halpern A. Padronizaçao do gasto metab´olico de repouso e proposta de nova equaçao para uma populaçao feminina brasileira. Arq Bras Endocrinol Metab. 2010:54(5):470-476.
  • Schofield WN. Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr. 1985;39(Suppl 1):5–41.
  • Food and Agriculture Organization (FAO). Energy and protein requirements: Report of a joint FAO/WHO/UNO expert consultation. Geneva: World Health Organization; 1985.
  • de Figueiredo Ferreira M, Detrano F, Coelho GM, Barros ME, Serrão Lanzillotti R, Firmino Nogueira Neto J, et al. Body composition and basal metabolic rate in women with type 2 diabetes mellitus. J Nutr Metab. 2014;2014:574057.
  • Luy SC, Dampil OA. Comparison of the Harris-Benedict equation, bioelectrical impedance analysis, and indirect calorimetry for measurement of basal metabolic rate among adult obese Filipino patients with prediabetes or type 2 diabetes mellitus. J ASEAN Fed Endocr Soc. 2018;33(2):152-159.
  • Cancello R, Soranna D, Brunani A, Scacchi M, Tagliaferri A, Mai S, et al. Analysis of predictive equations for estimating resting energy expenditure in a large cohort of morbidly obese patients. Front Endocrinol (Lausanne). 2018;9:367.
  • Merghani TH, Alawad A, Ballal MA. Measured versus predicted resting metabolic rate in obese diabetic and obese non-diabetic subjects. IOSR Journal of Dental and Medical Sciences. 2013;10(2): 63- 67.
  • Buch A, Diener J, Stern N, Rubin A, Kis O, Sofer Y, et al. Comparison of equations estimating resting metabolic rate in older adults with type 2 diabetes. J Clin Med. 2021;10:1644.
  • Ferrannini E. Sodium-glucose transporter-2 inhibition as an antidiabetic therapy. Nephrol Dial Transplant. 2010;25(7):2041-2043.
  • Fontvieille AM, Lillioja S, Ferraro RT, Schulz LO, Rising R, Ravussin E. Twenty-four-hour energy expenditure in Pima Indians with type 2 (noninsulin- dependent) diabetes mellitus. Diabetologia. 1992;35(8):753-759.
  • Consoli A, Nurjhan N, Capani F, Gerich J. Predominat role of gluconeogenesis in increased hepatic glucose prodution in NIDDM. Diabetes. 1989;38(5):550–557.
  • Bursztein S, Elwyn DH, Askanazi J, Kinney JM. Energy, metabolism, indirect calorimetry and nutrition. Baltimore: Williams&Wilkins, 1989. 266p.
  • Miyake R, Ohkawara K, Ishikawa-Takata K, Morita A, Watanabe S, Tanaka S. Obese Japanese adults with type 2 diabetes have higher basal metabolic rates than non-diabetic adults. J Nutr Sci Vitaminol (Tokyo). 2011;57(5):348–354.
  • Tabata S, Kinoshita N, Yamada S, Matsumoto H. Accuracy of basal metabolic rate estimated by predictive equations in Japanese with type 2 diabetes. Asia Pac J Clin Nutr. 2018;27(4):763–769.
  • Piaggi P, Thearle MS, Bogardus C, Krakoff J. Fasting hyperglycemia predicts lower rates of weight gain by increased energy expenditure and fat oxidation rate. J Clin Endocrinol Metab. 2015;100(3):1078-1087.
  • Ryan M, Salle A, Guilloteau G, Genaitay M, Livingstone MBE, Ritz P. Resting energy expenditure is not increased in mildly hyperglycemia obese diabetic patients. Br J Nutr. 2006;96(5):945-948.

Methods Used to Determine the Resting Metabolic Rate of Obese Diabetic Individuals

Year 2022, Volume: 7 Issue: 1, 151 - 155, 31.01.2022

Abstract

Diabetes mellitus (DM) is a chronic disease affecting a significant portion of the world’s population and is associated with obesity. The primary strategy in the treatment of obese individuals with type 2 DM is to achieve weight loss through lifestyle changes. Indirect calorimetry is the most reliable method for calculating resting metabolic rate (RMR), an important component of total energy expenditure. When indirect calorimetry is not available, energy equations can be used. There are various equations developed in several studies. Gougeon, Huang, Ikeda and Martin’s equations are aimed at diabetic patients. Studies have shown that FAO/WHO/UNU, Harris-Benedict, Huang equations, and bioelectric impedance analysis predict a higher RMR compared to indirect calorimetry. Gougeon added plasma glucose level as a variable to the equation developed for obese diabetics and stated that they obtained a more accurate RMR results. Additionally, the Mifflin equation has been shown to be more accurate in obese diabetics. In conclusion, although energy equations can give accurate results in healthy individuals, they don’t provide accurate results in older individuals or patients. Therefore, energy equations should be selected individually and sensitively. When indirect calorimetry is not available, the use of equations with a high accuracy rate may provide convenience in determining the energy requirement.

References

  • International Diabetes Federation. IDF Diabetes Atlas, 9th edition. Brussels, Belgium: International Diabetes Federation, 2019.
  • American Diabetes Association (ADA). Standards of medical care in diabetes: lifestyle management. Diabetes Care. 2020;48(Suppl 1):38–65.
  • Pekcan G. Beslenme Durumunun Saptanması, Hacettepe Üniversitesi Sağlık Bilimleri Fakültesi Beslenme ve Diyetetik Bölümü, Sağlık Bakanlığı Yayım No:726, Ankara, Klasmat Matbaacılık, 2008.
  • Psota T, Chen KY. Measuring energy expenditure in clinical populations: Rewards and challenges. Eur J Clin Nutr. 2013;67(5):436–442.
  • 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(4):763– 773.
  • Jesus P, Achamrah N, Grigioni S, Charles J, Rimbert A, Folope V, Petit A 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. Clinical Nutrition. 2015; 34(3):529-535.
  • Frankenfield DC, Ashcraft CM, Galvan DA. Prediction of resting metabolic rate in critically ill patients at the extremes of body mass index. J Parenter Enteral Nutr. 2013; 37(3):361-367.
  • De Waele E, 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(3):272-82.
  • Gündoğdu T, Acar Tek N. Anoreksiya nervoza hastalarında enerji harcamasının belirlenmesinde kullanılan yöntemler. SDÜ Sağlık Bilimleri Dergisi. 2019;10(3):320-326.
  • Wouters-Adriaens MP, Westerterp KR. Low resting energy expenditure in Asians can be attributed to body composition. Obesity (Silver Spring). 2008; 16(10): 2212-2216.
  • Bernstein RS, Thornton JC, Yang MU, Wang J, Redmond AM, Pierson RN Jr, et al. Prediction of the resting metabolic rate in obese patients. Am J Clin Nutr. 1983;37(4):595–602.
  • 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(6):963–969.
  • Ganpule AA, Tanaka S, Ishikawa-Takata K, Tabata I. Interindividual variability in sleeping metabolic rate in Japanese subjects. Eur J Clin Nutr. 2007;61(11):1256–1261.
  • Gougeon R, Lamarche M, Yale JF, Venuta T. The prediction of resting energy expenditure in type 2 diabetes mellitus is improved by factoring for glycemia. Int J Obes Relat Metab Disord. 2002;26(12):1547-1552.
  • Harris JA, Benedict FG. A biometric study of basal metabolism in man; Carnegie Inst: Washington DC, USA, 1919. Volume:279.
  • Huang KC, Kormas N, Steinbeck K, Loughnan G, Caterson ID. Resting metabolic rate in severely obese diabetic and nondiabetic subjects. Obes Res. 2004;12(5):840-845.
  • Ikeda K, Fujimoto S, Goto M, Yamada C, Hamasaki A, Ida M et al. A new equation to estimate basal energy expenditure of patients with diabetes. Clin Nutr. 2013;32(5):777–82.
  • Lazzer S, Bedogni G, Lafortuna CL, Marazzi N, Busti C, Galli R, et al. Relationship between basal metabolic rate, gender, age, and body composition in 8,780 white obese subjects. Obesity (Silver Spring). 2010;18(1):71–78.
  • Lührmann PM, Herbert BM, Krems C, Neuhäuser-Berthold M. A new equation especially developed for predicting resting metabolic rate in the elderly for easy use in practice. Eur J Nutr. 2002:41(3):108–113.
  • Martin K, Wallace P, Rust PF, Garvey WT. Estimation of resting energy expenditure considering effects of race and diabetes status. Diabetes Care. 2004;27(6):1405–1411.
  • 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;51(2):241–247.
  • Muller MJ, Bosy-Westphal A, Kutzner D, Heller M. Metabolically active components of fat-free mass and resting energy expenditure in humans: Recent lessons from imaging technologies. Obes Rev. 2002;3(2):113–122.
  • Nachmani M, Lahav Y, Zeev A, Grosman-Rimon L, Eilat-Adar S. Weight change adjusted equations for assessing resting metabolic rate in overweight and obese adults. Obes Res Clin Prac. 2021. Doi: 10.1016/j.orcp.2021.03.001. Available from: https://pubmed.ncbi.nlm.nih.gov/33773945/
  • Owen OE. Resting metabolic requirements of men and women. Mayo Clin Proc. 1988:63(5):503–510.
  • Rodrigues AE, Mancini MC, Dalcanale L, de Melo ME, Cercato C, Halpern A. Padronizaçao do gasto metab´olico de repouso e proposta de nova equaçao para uma populaçao feminina brasileira. Arq Bras Endocrinol Metab. 2010:54(5):470-476.
  • Schofield WN. Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr. 1985;39(Suppl 1):5–41.
  • Food and Agriculture Organization (FAO). Energy and protein requirements: Report of a joint FAO/WHO/UNO expert consultation. Geneva: World Health Organization; 1985.
  • de Figueiredo Ferreira M, Detrano F, Coelho GM, Barros ME, Serrão Lanzillotti R, Firmino Nogueira Neto J, et al. Body composition and basal metabolic rate in women with type 2 diabetes mellitus. J Nutr Metab. 2014;2014:574057.
  • Luy SC, Dampil OA. Comparison of the Harris-Benedict equation, bioelectrical impedance analysis, and indirect calorimetry for measurement of basal metabolic rate among adult obese Filipino patients with prediabetes or type 2 diabetes mellitus. J ASEAN Fed Endocr Soc. 2018;33(2):152-159.
  • Cancello R, Soranna D, Brunani A, Scacchi M, Tagliaferri A, Mai S, et al. Analysis of predictive equations for estimating resting energy expenditure in a large cohort of morbidly obese patients. Front Endocrinol (Lausanne). 2018;9:367.
  • Merghani TH, Alawad A, Ballal MA. Measured versus predicted resting metabolic rate in obese diabetic and obese non-diabetic subjects. IOSR Journal of Dental and Medical Sciences. 2013;10(2): 63- 67.
  • Buch A, Diener J, Stern N, Rubin A, Kis O, Sofer Y, et al. Comparison of equations estimating resting metabolic rate in older adults with type 2 diabetes. J Clin Med. 2021;10:1644.
  • Ferrannini E. Sodium-glucose transporter-2 inhibition as an antidiabetic therapy. Nephrol Dial Transplant. 2010;25(7):2041-2043.
  • Fontvieille AM, Lillioja S, Ferraro RT, Schulz LO, Rising R, Ravussin E. Twenty-four-hour energy expenditure in Pima Indians with type 2 (noninsulin- dependent) diabetes mellitus. Diabetologia. 1992;35(8):753-759.
  • Consoli A, Nurjhan N, Capani F, Gerich J. Predominat role of gluconeogenesis in increased hepatic glucose prodution in NIDDM. Diabetes. 1989;38(5):550–557.
  • Bursztein S, Elwyn DH, Askanazi J, Kinney JM. Energy, metabolism, indirect calorimetry and nutrition. Baltimore: Williams&Wilkins, 1989. 266p.
  • Miyake R, Ohkawara K, Ishikawa-Takata K, Morita A, Watanabe S, Tanaka S. Obese Japanese adults with type 2 diabetes have higher basal metabolic rates than non-diabetic adults. J Nutr Sci Vitaminol (Tokyo). 2011;57(5):348–354.
  • Tabata S, Kinoshita N, Yamada S, Matsumoto H. Accuracy of basal metabolic rate estimated by predictive equations in Japanese with type 2 diabetes. Asia Pac J Clin Nutr. 2018;27(4):763–769.
  • Piaggi P, Thearle MS, Bogardus C, Krakoff J. Fasting hyperglycemia predicts lower rates of weight gain by increased energy expenditure and fat oxidation rate. J Clin Endocrinol Metab. 2015;100(3):1078-1087.
  • Ryan M, Salle A, Guilloteau G, Genaitay M, Livingstone MBE, Ritz P. Resting energy expenditure is not increased in mildly hyperglycemia obese diabetic patients. Br J Nutr. 2006;96(5):945-948.
There are 40 citations in total.

Details

Primary Language Turkish
Subjects Health Care Administration
Journal Section Derlemeler
Authors

Gülşah Kaner 0000-0001-5882-6049

Buse Bakır 0000-0001-5884-5063

Early Pub Date January 30, 2022
Publication Date January 31, 2022
Submission Date July 6, 2021
Published in Issue Year 2022 Volume: 7 Issue: 1

Cite

APA Kaner, G., & Bakır, B. (2022). Obezitesi Olan Diyabetli Bireylerde Dinlenme Metabolik Hızının Belirlenmesinde Kullanılan Yöntemler. İzmir Katip Çelebi Üniversitesi Sağlık Bilimleri Fakültesi Dergisi, 7(1), 151-155.
AMA Kaner G, Bakır B. Obezitesi Olan Diyabetli Bireylerde Dinlenme Metabolik Hızının Belirlenmesinde Kullanılan Yöntemler. İKÇÜSBFD. January 2022;7(1):151-155.
Chicago Kaner, Gülşah, and Buse Bakır. “Obezitesi Olan Diyabetli Bireylerde Dinlenme Metabolik Hızının Belirlenmesinde Kullanılan Yöntemler”. İzmir Katip Çelebi Üniversitesi Sağlık Bilimleri Fakültesi Dergisi 7, no. 1 (January 2022): 151-55.
EndNote Kaner G, Bakır B (January 1, 2022) Obezitesi Olan Diyabetli Bireylerde Dinlenme Metabolik Hızının Belirlenmesinde Kullanılan Yöntemler. İzmir Katip Çelebi Üniversitesi Sağlık Bilimleri Fakültesi Dergisi 7 1 151–155.
IEEE G. Kaner and B. Bakır, “Obezitesi Olan Diyabetli Bireylerde Dinlenme Metabolik Hızının Belirlenmesinde Kullanılan Yöntemler”, İKÇÜSBFD, vol. 7, no. 1, pp. 151–155, 2022.
ISNAD Kaner, Gülşah - Bakır, Buse. “Obezitesi Olan Diyabetli Bireylerde Dinlenme Metabolik Hızının Belirlenmesinde Kullanılan Yöntemler”. İzmir Katip Çelebi Üniversitesi Sağlık Bilimleri Fakültesi Dergisi 7/1 (January 2022), 151-155.
JAMA Kaner G, Bakır B. Obezitesi Olan Diyabetli Bireylerde Dinlenme Metabolik Hızının Belirlenmesinde Kullanılan Yöntemler. İKÇÜSBFD. 2022;7:151–155.
MLA Kaner, Gülşah and Buse Bakır. “Obezitesi Olan Diyabetli Bireylerde Dinlenme Metabolik Hızının Belirlenmesinde Kullanılan Yöntemler”. İzmir Katip Çelebi Üniversitesi Sağlık Bilimleri Fakültesi Dergisi, vol. 7, no. 1, 2022, pp. 151-5.
Vancouver Kaner G, Bakır B. Obezitesi Olan Diyabetli Bireylerde Dinlenme Metabolik Hızının Belirlenmesinde Kullanılan Yöntemler. İKÇÜSBFD. 2022;7(1):151-5.



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