Omega-3 fatty acids and changes in LBM: alone or in synergy for better muscle health?
Bibliographic record
Abstract
Myopenia or muscle wasting due to ageing, chronic disease, and various medical interventions has been associated with increased mortality, morbidity, and poorer physical function. Attempts through nutrient and exercise interventions have been made to prevent this deterioration. In addition, while a measure of lean body mass (LBM) is associated with health outcomes, LBM function may be a better prognostic tool. Long-chain omega-3 fatty acids (LCn-3s) are nutrients that may mitigate LBM losses in noncancer populations. The purpose of this review is to determine whether LCn-3s have a role in LBM sparing in noncancer populations, to establish a minimum dose and duration of LCn-3s that will result in LBM change, and to summarise the potential effects of LCn-3s on LBM function when combined with an anabolic stimulus. Overall, in noncancer populations, LCn-3s have limited utility in sparing LBM during energy balance, energy restriction, or in conjunction with aerobic exercise. Further investigations are required to determine the appropriate dose and duration of LCn-3s for optimal LBM function. Finally, compelling evidence exists for LCn-3s in conjunction with an anabolic stimulus to improve LBM function and quality. Functionality of LBM tissue is an important outcome for population health, and LCn-3s show some promise, albeit pending further study.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".