The Effect of Multi-Ingredient Protein versus Collagen Supplementation on Satellite Cell Properties in Males and Females
Bibliographic record
Abstract
INTRODUCTION: Skeletal muscle satellite cells (SC) contribute to the adaptive process of resistance exercise training (RET) and may be influenced by nutritional supplementation. However, little research exists on the impact of multi-ingredient supplementation on the SC response to RET. PURPOSE: We tested the effect of a multi-ingredient supplement (MIS) including whey protein, creatine, leucine, calcium citrate, and vitamin D on SC content and activity as well as myonuclear accretion, SC and myonuclear domain compared with a collagen control (COL) throughout a 10-wk RET program. METHODS: Twenty-six participants underwent a 10-wk linear RET program while consuming either the MIS or COL supplement twice daily. Muscle biopsies were taken from the vastus lateralis at baseline and 48 h after a bout of damaging exercise, before and after RET. Muscle tissue was analyzed for SC and myonuclear content, domain, acute SC activation, and fiber cross-sectional area (fCSA). RESULTS: MIS resulted in a greater increase in type II fCSA following 10 wk of RET (effect size (ES) = 0.89) but not myonuclear accretion or SC content. Change in myonuclei per fiber was positively correlated with type I and II and total fiber hypertrophy in the COL group only, indicating a robust independent effect of MIS on fCSA. Myonuclear domain increased similarly in both groups, whereas SC domain remained unchanged following RET. SC activation was similar between groups for all fiber types in the untrained state but showed a trend toward greater increases with MIS after RET (ES = 0.70). CONCLUSIONS: SC responses to acute damaging exercise and long-term RET are predominantly similar in MIS and COL groups. However, MIS can induce greater increases in type II fCSA with RET and potentially SC activation following damage in the trained state.
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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) | 0.000 | 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) | 0.003 | 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".