Role of n-3 fatty acids in muscle loss and myosteatosis
Bibliographic record
Abstract
Image-based methods such as computed tomography for assessing body composition enables quantification of muscle mass and muscle density and reveals that low muscle mass and myosteatosis (fat infiltration into muscle) are common in people with cancer. Myosteatosis and low muscle mass have emerged as independent risk factors for mortality in cancer; however, the characteristics and pathogenesis of these features have not been resolved. Muscle depletion is associated with low plasma eicosapentaenoic (20:5n-3) and docosahexaenoic (22:6n-3) in cancer and supplementation with n-3 fatty acids has been shown to ameliorate muscle loss and myosteatosis in clinical studies, suggesting a relationship between n-3 fatty acids and muscle health. Since the mechanisms by which n-3 fatty acids alter body composition in cancer remain unknown, related literature from other conditions associated with myosteatosis, such as insulin resistance and obesity is considered. In these noncancer conditions, it has been reported that n-3 fatty acids act by increasing insulin sensitivity, reducing inflammatory mediators, and altering adipokine profiles and transcription factors; therefore, the plausibility of these mechanisms of action in the neoplastic state are considered. The aim of this review is to summarize what is known about the effects of n-3 fatty acids with regards to muscle condition and to discuss potential mechanisms for effects of n-3 fatty acids on muscle health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".