Erythrocyte Omega-3 Index as a Biomarker for Skeletal Muscle Omega-3 Composition: A 22-Week Study
Bibliographic record
Abstract
Omega-3 fatty acid supplementation has been shown to positively influence various biological processes including immune function, cognition and neuromuscular performance, with recent evidence suggesting beneficial effects in human skeletal muscle. Measurement of omega-3 fatty acids in skeletal muscle requires skeletal muscle biopsies that are invasive and require specialized training. In contrast, measurements of erythrocyte and plasma omega-3 saturation require minimally invasive venous blood draws. This novel analysis, conducted as part of a larger study (, investigated the correlation between the relative abundance of eicosapentaenoic acid (EPA; 20:5n-3) and docosahexaenoic acid (DHA; 22:6n-3) in human skeletal muscle, red blood cells and plasma. Using a repeated measures design, 15 females and 14 males supplemented 5 g/d of EPA+DHA (3.2 g EPA; 1.8 g DHA) for 8 weeks followed by 14 weeks of washout. Skeletal muscle biopsies and venous blood draws were obtained at weeks 0, 6, 8, 16, 20, 22. Analysis revealed a strong correlation between EPA in skeletal muscle and erythrocytes at week-0 (p < 0.001; r2 = 0.721) which weakened over the supplementation and washout periods. For DHA, there was a moderate correlation at week-0 (p < 0.001; r2 = 0.505) which remained consistent through the 22-week course. For EPA+DHA, there was a strong correlation between skeletal muscle and erythrocytes peaking at week-8 (p < 0.001; r2 = 0.813), before weakening slightly throughout the washout period. Finally, there was no significant association between skeletal muscle and plasma levels throughout the supplementation and washout periods. This analysis provides insights into the association and incorporation of the omega-3 fatty acids EPA and DHA on a tissue-dependent basis. Our results suggest that the Omega-3 Index (EPA+DHA) may be a useful, non-invasive bio-marker of skeletal muscle omega-3 composition during supplementation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".