A Brief Narrative Review of the Underlying Mechanisms Whereby Omega-3 Fatty Acids May Influence Skeletal Muscle: From Cell Culture to Human Interventions
Bibliographic record
Abstract
Skeletal muscle is essential for human locomotion as well as maintaining metabolic homeostasis. Age-related reduction in skeletal muscle mass, strength, and function (i.e., sarcopenia) is a result of pathophysiological processes that include inflammation, alteration of molecular signaling for muscle protein synthesis and degradation, changes in insulin sensitivity, as well as altered skeletal muscle satellite cell activity. Finding strategies to mitigate skeletal muscle loss with age is deemed paramount as the percentage of the population continues to shift towards having more older adults with sarcopenia. Recent research indicates omega-3 fatty acid supplementation can influence anabolic or catabolic pathways in skeletal muscle. Our brief review will provide a synopsis of some underlying mechanisms that may be attributed to omega-3 fatty acid supplementation's effects on skeletal muscle. We will approach this review by focusing on cell culture, animal (pre-clinical models), and human studies evaluating omega-3 fatty acid supplementation, with suggestions for future research. In older adults, omega-3 fatty acids may possess some potential to modify pathophysiological pathways associated with sarcopenia; however, it is highly likely that omega-3 fatty acids need to be combined with other anabolic interventions to effectively ameliorate sarcopenia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".