Combining Amino Acid and Vitamin D Supplementation with Exercise Training Increases Skeletal Muscle Mass and Prevent Bone Mineral Density Loss in Participants with Low Muscle Mass
Bibliographic record
Abstract
This study evaluated the impact of exercise training with amino acid and vitamin D supplementation on muscle and bone mass in participants with low muscle volume. Twenty-nine Japanese participants (56-84 years old) were enrolled and assigned into the supplement (n=15) and non-supplement (n=14) groups. All participants underwent a 6-month exercise program. Supplements and nutrition support were provided to the participants in the supplement group for 12 weeks. Body composition and whole bone mineral density (BMD) were measured using dual energy x-ray absorptiometry. The outcomes, including body composition, whole BMD, and skeletal muscle mass index (SMI), were evaluated twice: pre- and post-intervention. The SMI was 6.51(6.28; 7.14) and 5.58 (5.24; 6.05) (kg/m2) in men and women, respectively. The average SMI change was 0.13% (-0.05%; 0.31%) and 2.33% (-0.88%; 5.48%); [mean (lower; upper quartile)]. The average BMD loss in the non-supplement group was -2.78%, and the BMD increased in the supplement group by 4.34%; there was an absolute difference between the two groups (p<0.05). After the intervention, serum myostatin was changed (p=0.001, non-supplement>supplement), serum vitamin D was increased (p=0.03; supplement>non-supplement), and BMD was maintained (p=0.03, supplement>non-supplement). There was a significant difference in the serum myostatin level at baseline and at 6-month in the non-supplement group, with a mean difference of 483.78 ng/ml (p=0.01). There was no significant improvement in the total lean mass, and handgrip strength. Resistance exercise combined with an amino acid supplement affects muscle and bone mass in the short-term intervention.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".