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Techniques and Methods

Adult energy requirements predicted from doubly labeled water

International Journal of Obesity volume 42, pages 1515–1523 (2018)Cite this article

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Abstract

Background

Estimating energy requirements forms an integral part of developing diet and activity interventions. Current estimates often rely on a product of physical activity level (PAL) and a resting metabolic rate (RMR) prediction. PAL estimates, however, typically depend on subjective self-reported activity or a clinician’s best guess. Energy-requirement models that do not depend on an input of PAL may provide an attractive alternative.

Methods

Total daily energy expenditure (TEE) measured by doubly labeled water (DLW) and a metabolic chamber from 119 subjects obtained from a database of pre-intervention measurements measured at Pennington Biomedical Research Center were used to develop a metabolic ward and free-living models that predict energy requirements. Graded models, including different combinations of input variables consisting of age, height, weight, waist circumference, body composition, and the resting metabolic rate were developed. The newly developed models were validated and compared to three independent databases.

Results

Sixty-four different linear and nonlinear regression models were developed. The adjusted R2 for models predicting free-living energy requirements ranged from 0.65 with covariates of age, height, and weight to 0.74 in models that included body composition and RMR. Independent validation R2 between actual and predicted TEE varied greatly across studies and between genders with higher coefficients of determination, lower bias, slopes closer to 1, and intercepts closer to zero, associated with inclusion of body composition and RMR covariates. The models were programmed into a user-friendly web-based app available at: http://www.pbrc.edu/research-and-faculty/calculators/energy-requirements/ (Video Demo for Reviewers at: https://www.youtube.com/watch?v=5UKjJeQdODQ)

Conclusions

Energy-requirement equations that do not require knowledge of activity levels and include all available input variables can provide more accurate baseline estimates. The models are clinically accessible through the web-based application.

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References

  1. Reeves MM, Capra S. Predicting energy requirements in the clinical setting: are current methods evidence based? Nutr Rev. 2003;61:143–51.

    Article  Google Scholar 

  2. Tharion WJ, Lieberman HR, Montain SJ, et al. Energy requirements of military personnel. Appetite. 2005;44:47–65.

    Article  Google Scholar 

  3. Schoeller DA, van Santen E. Measurement of energy expenditure in humans by doubly labeled water method. J Appl Physiol Respir Environ Exerc Physiol. 1982;53:955–9.

    CAS  Google Scholar 

  4. Green AJ, Smith P, Whelan K. Estimating resting energy expenditure in patients requiring nutritional support: a survey of dietetic practice. Eur J Clin Nutr. 2008;62:150–3.

    Article  CAS  Google Scholar 

  5. Judges D, Knight A, Graham E, Goff LM. Estimating energy requirements in hospitalised underweight and obese patients requiring nutritional support: a survey of dietetic practice in the United Kingdom. Eur J Clin Nutr. 2012;66:394–8.

    Article  CAS  Google Scholar 

  6. Harris JA, Benedict FG. A biometric study of human basal metabolism. Proc Natl Acad Sci USA. 1918;4:370–3.

    Article  CAS  Google Scholar 

  7. 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;51:241–7.

    Article  CAS  Google Scholar 

  8. Westerterp KR. Doubly labelled water assessment of energy expenditure: principle, practice, and promise. Eur J Appl Physiol. 2017;117:1277–85.

    Article  CAS  Google Scholar 

  9. FAO/ WHO/UNU Expert Consultation. Human energy requirements: report of a joint FAO/ WHO/UNU expert consultation. Food Nutr Bull. 2005;26:166

    Article  Google Scholar 

  10. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40:181–8.

    Article  Google Scholar 

  11. Livingston EH, Kohlstadt I. Simplified resting metabolic rate-predicting formulas for normal-sized and obese individuals. Obes Res. 2005;13:1255–62.

    Article  Google Scholar 

  12. Goran MI. Estimating energy requirements: regression based prediction equations or multiples of resting metabolic rate. Public Health Nutr. 2005;8:1184–6.

    Article  Google Scholar 

  13. Vinken AG, Bathalon GP, Sawaya AL, Dallal GE, Tucker KL, Roberts SB. Equations for predicting the energy requirements of healthy adults aged 18-81y. Am J Clin Nutr. 1999;69:920–6.

    Article  CAS  Google Scholar 

  14. Heo M, Faith MS, Pietrobelli A, Heymsfield SB. Percentage of body fat cutoffs by sex, age, and race-ethnicity in the US adult population from NHANES 1999-2004. Am J Clin Nutr. 2012;95:594–602.

    Article  CAS  Google Scholar 

  15. Stevens J, Ou FS, Cai J, Heymsfield SB, Truesdale KP. Prediction of percent body fat measurements in Americans 8 years and older. Int J Obes (Lond). 2016;40:587–94.

    Article  CAS  Google Scholar 

  16. 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.

    Article  CAS  Google Scholar 

  17. Institute of Medicine (U.S.). Panel on Macronutrients., Institute of Medicine (U.S.). Standing Committee on the Scientific Evaluation of Dietary Reference Intakes. Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty acids, Cholesterol, Protein, and Amino acids. Washington, DC: National Academies Press; 2005.

    Google Scholar 

  18. Levine JA, Eberhardt NL, Jensen MD. Role of nonexercise activity thermogenesis in resistance to fat gain in humans. Science. 1999;283:212–4.

    Article  CAS  Google Scholar 

  19. Hand GA, Shook RP, Paluch AE, et al. The Energy Balance Study: the design and baseline results for a longitudinal study of energy balance. Res Q Exerc Sport. 2013;84:1–12.

    Article  Google Scholar 

  20. Roberts SB, Dietz W, Sharp T, Dallal GE, Hill JO. Multiple laboratory comparison of the doubly labeled water technique. Obes Res. 1995;3(Suppl 1):3–13.

    Article  Google Scholar 

  21. Van Der Ploeg GE, Withers RT, Laforgia J. Percent body fat via DEXA: comparison with a four-compartment model. J Appl Physiol (1985). 2003;94:499–506.

    Article  Google Scholar 

  22. Trabulsi J, Troiano RP, Subar AF, et al. Precision of the doubly labeled water method in a large-scale application: evaluation of a streamlined-dosing protocol in the Observing Protein and Energy Nutrition (OPEN) study. Eur J Clin Nutr. 2003;57:1370–7.

    Article  CAS  Google Scholar 

  23. DeGregory KW, Kuiper P, DeSilvio T, et al. A review of machine learning in obesity. Obes Rev. 2018;19:668–85.

    Article  CAS  Google Scholar 

  24. Sabounchi NS, Rahmandad H, Ammerman A. Best-fitting prediction equations for basal metabolic rate: informing obesity interventions in diverse populations. Int J Obes (Lond). 2013;37:1364–70.

    Article  CAS  Google Scholar 

  25. Murakami H, Kawakami R, Nakae S, et al. Accuracy of wearable devices for estimating total energy expenditure: comparison with metabolic chamber and doubly labeled water method. JAMA Intern Med. 2016;176:702–3.

    Article  Google Scholar 

  26. Evenson KR, Goto MM, Furberg RD. Systematic review of the validity and reliability of consumer-wearable activity trackers. Int J Behav Nutr Phys Act. 2015;12:159.

    Article  Google Scholar 

  27. Plasqui G, Westerterp KR. Physical activity assessment with accelerometers: an evaluation against doubly labeled water. Obes (Silver Spring). 2007;15:2371–9.

    Article  Google Scholar 

  28. Plasqui G, Bonomi AG, Westerterp KR. Daily physical activity assessment with accelerometers: new insights and validation studies. Obes Rev. 2013;14:451–62.

    Article  CAS  Google Scholar 

  29. Westreich D, Lessler J, Funk MJ. Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. J Clin Epidemiol. 2010;63:826–33.

    Article  Google Scholar 

  30. Heymsfield SB, Pietrobelli A. Body size and human energy requirements: reduced mass-specific total energy expenditure in tall adults. Am J Hum Biol. 2010;22:301–9.

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Mathematical Sciences, United States Military Academy, West Point, NY, USA

    Andrew Plucker & Diana M. Thomas

  2. Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA, USA

    Nick Broskey, Corby K. Martin, Steven B. Heymsfield & Leanne A. Redman

  3. University of Wisconsin, Madison, WI, USA

    Dale Schoeller

  4. Department of Pediatrics, Children’s Mercy, Kansas City, MO, USA

    Robin Shook

  5. Mayo Clinic, Rochester, MN, USA

    James A. Levine

  6. Ipsen Foundation, Paris, France

    James A. Levine

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  1. Andrew Plucker
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Correspondence to Diana M. Thomas.

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Plucker, A., Thomas, D.M., Broskey, N. et al. Adult energy requirements predicted from doubly labeled water. Int J Obes 42, 1515–1523 (2018). https://doi.org/10.1038/s41366-018-0168-0

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