Barriers to cancer nutrition therapy: excess catabolism of muscle and adipose tissues induced by tumour products and chemotherapy
Bibliographic record
Abstract
Cancer-associated malnutrition is driven by reduced dietary intake and by underlying metabolic changes (such as inflammation, anabolic resistance, proteolysis, lipolysis and futile cycling) induced by the tumour and activated immune cells. Cytotoxic and targeted chemotherapies also elicit proteolysis and lipolysis at the tissue level. In this review, we summarise specific mediators and chemotherapy effects that provoke excess proteolysis in muscle and excess lipolysis in adipose tissue. A nutritionally relevant question is whether and to what degree these catabolic changes can be reversed by nutritional therapy. In skeletal muscle, tumour factors and chemotherapy drugs activate intracellular signals that result in the suppression of protein synthesis and activation of a transcriptional programme leading to autophagy and degradation of myofibrillar proteins. Cancer nutrition therapy is intended to ensure adequate provision of energy fuels and a complete repertoire of biosynthetic building blocks. There is some promising evidence that cancer- and chemotherapy-associated metabolic alterations may also be corrected by certain individual nutrients. The amino acids leucine and arginine provided in the diet at least partially reverse anabolic suppression in muscle, while n-3 PUFA inhibit the transcriptional activation of muscle catabolism. Optimal conditions for exploiting these anabolic and anti-catabolic effects are currently under study, with the overall aim of net improvements in muscle mass, functionality, performance status and treatment tolerance.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".