ArticleThe American journal of clinical nutrition2025
New insights into total and resting energy expenditure using state-of-the-art methods in cancer survivors: a cross-sectional study.
Article in The American journal of clinical nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Risk of protein intake deficiency during treatment with GLP-1 and GIP/GLP-1 receptor agonists: considerations for secondary sarcopenia.Advances in therapy · 2026Review
- Muscle loss must be interpreted in the context of fluid imbalance: a dual-parameter model predicts functional outcomes after acute stroke.European geriatric medicine · 2026Article
- Dynamic machine learning model integrating resting energy expenditure for predicting postoperative complications after gastrectomy for gastric cancer.Journal of cancer research and clinical oncology · 2026Article
- Predictive equations commonly used in the clinic underestimate resting energy expenditure compared with whole-room indirect calorimetry in colorectal cancer survivors.The American journal of clinical nutrition · 2026Article
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Authors and funding
12 authors.
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Abstract
backgroundPrecise measurement of energy expenditure is essential for guiding nutritional care after cancer treatment. However, commonly used predictive equations may be inaccurate for individuals recovering from cancer. Leveraging state-of-the-art methods can offer valuable insights into the actual energy requirements of cancer survivors upon treatment completion.
objectivesThe aim of this study was to characterize total (TEE) and resting energy expenditure (REE) and to assess the accuracy of predictive equations against measured values in posttreatment colorectal cancer survivors (CRCSs).
methodsIn this cross-sectional study, 4 TEE and 22 REE equations were compared against doubly labeled water and whole-room indirect calorimeter, respectively. Accuracy was assessed via paired t test, Bland-Altman analysis, and the proportion of predictions within 10% of measured values. Pearson correlations investigated relationships between percentage bias and variables of interest [e.g., age, weight, BMI (in kg/m
resultsTwenty participants (equal sex distribution; mean ± SD age: 61.4 ± 14.1 y; BMI: 28.8 ± 6.4) were included. Most had a history of colon cancer (55%) and stage III disease (75%). Predictive equations (predicted TEE range: ∼2060-2500 kcal/d; predicted REE range: ∼1230-1730 kcal/d) commonly underestimated measured TEE (∼2460 ± 680 kcal/d) (n = 2, 50%) and REE (∼1700 ± 330 kcal/d) (n = 19, 86.4%). Dietary Reference Intake equations with estimated physical activity level had the highest individual-level accuracy for TEE prediction but still resulted in substantial intra-individual variability (∼≤1400 kcal error). BMI and body composition were positively related to percentage bias in TEE but not REE equations. For REE, the Johnstone and Harris-Benedict equations showed the best individual-level agreement but still exhibited high intraindividual variability, with errors up to ∼≤460 kcal and ∼≤530 kcal, respectively.
conclusionsThe majority of CRCSs exhibit higher energy expenditure than estimated by standard prediction equations, underscoring the need to validate these equations in populations with cancer to optimize accuracy. Improved methods for assessing energy expenditure are needed to guide long-term survivorship care.
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