Examining Resting and Total Energy Expenditure in Patients with Cancer
Bibliographic record
Abstract
Energy expenditure forms the basis of all dietary recommendations. In patients with cancer, resting energy expenditure (REE) can be impacted by tumor burden, high systemic inflammation, and/or altered body composition. Total energy expenditure (TEE) and physical activity levels (PAL) have been characterized primarily in patients with advanced disease or severe weight loss, which may not represent many individuals with cancer. The overall aims of this research were to investigate the determinants of REE and to characterize TEE in relation to body weight, body composition, PAL, and current energy recommendations in patients with colorectal cancer (CRC). Additionally, this research aimed to examine the validity of REE predictive equations and a portable indirect calorimeter in patients with solid tumors. REE was measured by indirect calorimetry and TEE was ascertained using doubly labeled water. Body composition was determined using dual X-ray absorptiometry or computerized tomography image analysis. This research showed that REE was not accurately estimated by predictive equations; even the most accurate equation (Mifflin St.-Jeor) still under-predicted REE by up to 32.4 % (-440 kcal/day) and over-predicted REE by up to 18.1% (261 kcal/day). Error was influenced by age and fat mass. A portable indirect calorimeter also did not accurately measure REE (average error ± two standard deviations: -467 to 363 kcal/day). In newly diagnosed patients with CRC, body weight, body composition, age, and sex predicted approximately 80% of REE variability. However, inflammation change and stage IV cancer predicted the REE change over time in patients with stage III or IV CRC. In individuals with mostly earlier stage CRC, TEE was 2473 ± 499 kcal/day (range: 1562 to 3622 kcal/day), or 29.7 ± 6.3 kcal/kg body weight (range: 20.4 to 48.5 kcal/kg body weight/day). Average PAL was 1.43 ± 0.27, which was higher than previously reported in cancer, despite a high prevalence of elevated REE. Energy estimation using 25 kcal/kg underestimated TEE (-12.6 ± 16.5%, p=0.002) and individual agreement with all other energy recommendations was poor. The major finding of this research was that REE and TEE (and consequently PAL) were highly variable in patients with cancer, which is not captured by current predictive equations, portable tools, or energy recommendations. Furthermore, body composition is a major determinant of REE at one timepoint and factors such as inflammation and cancer stage impact REE change across time. This research highlights the heterogeneity in energy metabolism in patients with cancer and will contribute to the formation of evidence-based dietary recommendations, considering disease stage, cancer type, body weight, body composition, and/or physical activity, with the ultimate goal of improving cancer care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".