The Influence of Omega-3 Fatty Acids on Skeletal Muscle Protein Turnover in Health, Disuse, and Disease
Bibliographic record
Abstract
Ingestion of omega-3 fatty acids is known to exert favourable health effects on a number of biological processes such as improved immune profile, enhanced cognition, and optimised neuromuscular function. Recently, data have also emerged demonstrating a positive influence of omega-3 fatty acid intake on skeletal muscle. For instance, there are reports of clinically-relevant gains in muscle size and strength in healthy older persons with omega-3 fatty acid intake as well as evidence that omega-3 fatty acid ingestion alleviates the loss of muscle mass and prevents decrements in mitochondrial content during periods of muscle-disuse. Cancer cachexia that is characterized by a rapid involuntary loss of lean mass, has also been shown to be attenuated by omega-3 fatty acid provision. The primary means by which omega-3 fatty acids alter positively impact skeletal muscle mass is via incorporation of eicosapentaenoic acid (EPA; 20:5n-3) and docosahexaenoic acid (DHA; 22:6n-3) into membrane phospholipids of the sarcolemma and intracellular organelles. Enrichment of EPA and DHA in these membrane phospholipids is linked to enhanced rates of muscle protein synthesis, decreased expression of factors that regulate muscle protein breakdown, and improved mitochondrial respiration kinetics. However, exactly how incorporation of EPA and DHA into phospholipid membranes alters these processes remains unknown. In this review, we discuss the interaction between omega-3 fatty acid ingestion and skeletal muscle protein turnover in response to nutrient provision in younger and older adults. Additionally, we examine the role of omega-3 fatty acid supplementation in protecting muscle loss during muscle-disuse and in cancer cachexia, and critically evaluate the molecular mechanisms that underpin the phenotypic changes observed in skeletal muscle with omega-3 fatty acid intake.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".