Fish oil supplementation and maintaining muscle mass in chronic disease: state of the evidence
Bibliographic record
Abstract
PURPOSE OF REVIEW: Providing eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), in the form of fish oils, to benefit muscle is an emerging area of interest. The aim of this work was to evaluate the current literature that has assessed muscle mass as an outcome during a fish oil intervention in any chronic disease. RECENT FINDINGS: The vast majority of studies published in the last 3 years (12 of 15) have been conducted in the oncological setting, in patients undergoing treatment for cancers of the gastrointestinal tract, breast, head and neck, lung, cervix, and hematological cancers. Three studies were conducted in patients with chronic obstructive pulmonary disease (COPD). Fish oil was provided as part of nutrient mixtures in 12 studies and as capsules in three studies. SUMMARY: Overall, the evidence for an effect of fish oil supplementation on muscle mass in patients with cancer undergoing treatment and in COPD remains unequivocal and reveals limited new knowledge in the area of fish oil supplementation in the cancer setting. Recent literature continues to provide mixed evidence on the efficacy of fish oil on muscle mass and function. The present review highlights challenges in comparing and interpreting current studies aimed at testing fish oil supplementation for 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".