The importance of protein sources to support muscle anabolism in cancer: An expert group opinion
Bibliographic record
Abstract
This opinion paper presents a short review of the potential impact of protein on muscle anabolism in cancer, which is associated with better patient outcomes. Protein source is a topic of interest for patients and clinicians, partly due to recent emphasis on the supposed non-beneficial effect of proteins; therefore, misconceptions involving animal-based (e.g., meat, fish, dairy) and plant-based (e.g., legumes) proteins in cancer are acknowledged and addressed. Although the optimal dietary amino acid composition to support muscle health in cancer is yet to be established, animal-based proteins have a composition that offers superior anabolic potential, compared to plant-derived proteins. Thus, animal-based foods should represent the majority (i.e., ≥65%) of protein intake during active cancer treatment. A diet rich in plant-derived proteins may support muscle anabolism in cancer, albeit requiring a larger quantity of protein to fulfill the optimal amino acid intake. We caution that translating dietary recommendations for cancer prevention to cancer treatment may be inadequate to support the pro-inflammatory and catabolic nature of the disease. We further caution against initiating an exclusively plant-based (i.e., vegan) diet upon a diagnosis of cancer, given the presence of elevated protein requirements and risk of inadequate protein intake to support muscle anabolism. Amino acid combination and the long-term sustainability of a dietary pattern void of animal-based foods requires careful and laborious management of protein intake for patients with cancer. Ultimately, a dietary amino acid composition that promotes muscle anabolism is optimally obtained through combination of animal- and plant-based protein sources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".