An Update on Protein, Leucine, Omega-3 Fatty Acids, and Vitamin D in the Prevention and Treatment of Sarcopenia and Functional Decline
Bibliographic record
Abstract
Aging is associated with sarcopenia and functional decline, leading to frailty and disability. As a modifiable risk factor, nutrition may represent a target for preventing or postponing the onset of these geriatric conditions. Among nutrients, high-quality protein, leucine, vitamin D, and omega-3 polyunsaturated fatty acids (n-3 PUFA) are of particular interest for their demonstrated effects on skeletal muscle health. This narrative review aims to examine the recent observational and interventional evidence on the associations and the role of these nutrients in the muscle mass, strength, mobility, and physical function of free-living older adults, who are either healthy or at risk of frailty. Recent evidence supports a higher protein intake recommendation of 1.0–1.2 g/kg/day in healthy older adults; an evenly distributed mealtime protein intake or minimal protein per meal may be beneficial. In addition, vitamin D supplementation of 800–1000 IU, particularly when vitamin D status is low, and doses of ~3 g/day of n-3 PUFA may be favorable for physical function, muscle mass, and strength. Reviewed studies are highly heterogenous, yet the quantity, quality, and timing of intakes should be considered when designing intervention studies. Combined protein, leucine, vitamin D, and n-3 PUFA supplements may convey added benefits and may represent an intervention strategy in the prevention of sarcopenia and functional decline.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".