The Effect of Vitamin D Supplementation to Parameter of Sarcopenia in Elderly People: a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background Vitamin D plays an essential role in promoting skeletal muscle metabolism. Several studies show that vitamin D may help the elderly prevent sarcopenia. Nevertheless, the outcome remains debatable. Our meta-analysis aimed to summarize the effect of vitamin D supplementation on sarcopenia-related parameters. Methods We searched PubMed, Cochrane, Springer, SAGE Journals, and Scopus abstracts on 10th December 2021 for relevant studies. We included articles that studied the effect of vitamin D on muscle mass, muscle strength, and physical performance. The aim was to measure the muscle mass, muscle strength, and physical performance both at baseline and at the end of the intervention. Results A total of 6,628 participants from 35 studies were included. Most of the studies used oral vitamin D, whereas only one study used intramuscular injection. The effect of vitamin D supplementation showed no effect on appendicular skeletal muscle mass (SMD = .05 [95% CI, .33 – .44], p = .79). Regarding muscle strength, vitamin D supplementation did not have a significant effect on muscle strength which is handgrip strength (p = .26). Respecting physical performance, vitamin D supplementation did not affect TUG (Timed Up and Go) (p = .45). Conclusions Vitamin D supplementation had minimal effect on sarcopenia-related parameters. Further research into understanding the role of Vitamin D in preventing the progressivity of sarcopenia still needs to be explored.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".