Relavent Research

Summary:

Measuring energy expenditure (EE) is an important part of clinical nutrition care, especially for patients with metabolic disorders, critical illness, or those not responding to nutrition therapy. EE can vary widely in conditions like obesity, chronic kidney disease, liver cirrhosis, HIV, cancer cachexia, and in ICU patients. Accurate assessment of resting or basal EE helps clinicians personalize nutrition support and avoid under- or overfeeding. This review discusses how EE is measured, current challenges, new technologies, and considerations when interpreting EE in obese, pediatric, and elderly patients—especially in relation to body composition changes.

Psota T, Chen KY. Measuring energy expenditure in clinical populations: rewards and challenges. Eur J Clin Nutr. 2013 May;67(5):436-42. doi: 10.1038/ejcn.2013.38. Epub 2013 Feb 27. PMID: 23443826; PMCID: PMC3928639.

Summary:

Most predictive equations used to estimate resting energy expenditure (REE) are inaccurate in people with very low or very high BMI. In this study of 1,726 patients, common formulas such as Harris–Benedict misestimated REE by more than 10% in over half of underweight and many obese individuals. Equations that included body composition (like the Huang equation) performed slightly better but still lacked precision. The authors conclude that indirect calorimetry remains the most accurate way to measure metabolism, particularly for those with extreme BMI values.

Jésus P, Achamrah N, Grigioni S, Charles J, Rimbert A, Folope V, Petit A, Déchelotte P, Coëffier M. 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 Jun;34(3):529-35. doi: 10.1016/j.clnu.2014.06.009. Epub 2014 Jun 28. PMID: 25016971.

Summary:

In overweight and obese Australian adults, standard equations used to predict resting metabolic rate (RMR) were accurate only about 40% of the time, regardless of gender or weight category. Even new population-specific formulas offered no improvement. The study concludes that direct measurement of RMR using indirect calorimetry is essential in clinical weight management, as predictive equations fail to reliably estimate true energy needs.

Thomas G. Wright, Brian Dawson, Geoffrey Jalleh, Kym J. Guelfi,

Accuracy of resting metabolic rate prediction in overweight and obese Australian adults,

Obesity Research & Clinical Practice,

Volume 10, Supplement 1, 2016, Pages S74-S83,ISSN 1871-403X,

Summary:

In this 2-year study of overweight women, resting metabolic rate (RMR) briefly dropped after weight loss (about –54 kcal/day) but returned to normal once weight stabilized. The adaptation was small, temporary, and unrelated to weight regain, regardless of race. The findings suggest that when measured accurately under stable conditions, metabolic adaptation plays little long-term role in regaining weight, underscoring the importance of standardized indirect calorimetry for assessing true metabolic changes.

Martins C, Gower BA, Hill JO, Hunter GR. Metabolic adaptation is not a major barrier to weight-loss maintenance. Am J Clin Nutr. 2020 Sep 1;112(3):558-565. doi: 10.1093/ajcn/nqaa086. PMID: 32386226; PMCID: PMC7458773.

Summary:

This review highlights that many patients—especially those with obesity, spinal cord injury, ALS, cerebral palsy, or advanced age—have lower energy needs than predictive equations suggest. Providing excess calories can worsen outcomes in critical and long-term care. Because energy requirements vary widely across these conditions, indirect calorimetry is recommended to accurately assess true caloric needs and prevent overfeeding when managing nutrition in vulnerable or immobilized patients.

Magnuson B, Peppard A, Auer Flomenhoft D. Hypocaloric considerations in patients with potentially hypometabolic disease States. Nutr Clin Pract. 2011 Jun;26(3):253-60. doi: 10.1177/0884533611405673. PMID: 21586410.

Summary:

This study found that resting metabolic rate (RMR) varies with more than just body size or composition. Among 324 healthy young adults, models that included factors like fat-free mass, fat mass, age, sex, and daily sun exposurepredicted RMR more accurately (up to 75% accuracy) than 11 existing equations. The findings suggest that lifestyle and environmental factors—such as sun exposure—can meaningfully influence metabolism, and that improved predictive models may help refine RMR estimation, especially when indirect calorimetry isn’t available.

Zhou W, Su H, Tong J, Du W, Wang B, Chen P, Wan H, Zhou M. Multiple factor assessment for determining resting metabolic rate in young adults. Sci Rep. 2024 May 23;14(1):11821. doi: 10.1038/s41598-024-62639-2. PMID: 38783110; PMCID: PMC11116489.

Summary:

This study found that standard RMR prediction formulas like Mifflin-St Jeor and Harris-Benedict significantly overestimate calorie needs in African American adults. Using indirect calorimetry, researchers showed that fat-free mass and race were major predictors of true RMR, with African Americans having about 144 kcal/day lower RMRon average. The difference was explained partly by variations in truncal fat-free mass, reflecting organ metabolism. The authors conclude that predictive formulas using only height, weight, age, and gender lack accuracy, reinforcing the value of direct metabolic testing for individualized nutrition planning.

Reneau J, Obi B, Moosreiner A, Kidambi S. Do we need race-specific resting metabolic rate prediction equations? Nutr Diabetes. 2019 Jul 29;9(1):21. doi: 10.1038/s41387-019-0087-8. PMID: 31358726; PMCID: PMC6662665.10.1002/ncp.10657. Epub 2021 Mar 26. PMID: 33769598.

Summary:

Although indirect calorimetry (IC) remains the gold standard for measuring energy expenditure, this review found that in most ICU patients, simple weight-based estimates (≈25 kcal/kg/day) are sufficient for early nutrition support. Studies show that precise IC measurements do not improve clinical outcomes compared with approximate methods, and partial feeding (50–80% of needs) is often equally effective. However, IC remains valuable for patients with atypical conditions—such as severe obesity, amputation, fluid overload, or prolonged inflammation (e.g., COVID-19)—where predictive formulas become unreliable.

McClave SA, Omer E. Point-Counterpoint: Indirect Calorimetry Is not Necessary for Optimal Nutrition Therapy in Critical Illness. Nutr Clin Pract. 2021 Apr;36(2):268-274. doi: 10.1002/ncp.10657. Epub 2021 Mar 26. PMID: 33769598.