Assessing the Effects of Dietary Protein Supplementation on Sarcopenia in Community-Dwelling Older Adults
Bibliographic record
Abstract
Sarcopenia, an age-associated skeletal muscle disorder characterized by muscle loss, is associated with disability in elderly populations. This literature review summarizes the impact of protein intake and supplementation on the indicators of severe sarcopenia-muscle mass, muscle strength, and physical function in community-dwelling older adults. We performed a literature search on PubMed, EMBASE, and MedLine, and included studies that evaluated the effects of protein intake with or without exercise intervention and on sarcopenia in community-dwelling older adults. Information regarding study participants, protein intervention, and sarcopenia-related outcomes were collected. Protein supplementation with or without exercise positively improves muscle mass, and aspects of muscle strength and physical function in sarcopenic and pre-frail older adults, while it elicited inconclusive effects in healthy populations. Greater dietary animal-based and soy-based protein diets can improve muscle mass in older adults. In conclusion, protein supplementation can improve muscle mass and reduce the risk of sarcopenia in sarcopenia and pre-frail older adults, while future studies should continue to investigate the effects of protein supplementation on indicators of sarcopenia in healthy older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.002 | 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".