Statins and Dietary and Serum Cholesterol Are Associated With Increased Lean Mass Following Resistance Training
Bibliographic record
Abstract
BACKGROUND: Age-related muscle loss (sarcopenia) is a prevalent condition associated with disability and mortality. Exercise and optimal nutrition are interventions to prevent and treat sarcopenia, yet little is known, outside of protein, of the effect of common nutrition recommendations and medication use on exercise-related muscle gain. METHODS: Forty-nine community-dwelling, 60- to 69-year-old men and women completed 2 weeks of nutrition education (American Dietetic Association recommendations) followed by 12 weeks of high intensity resistance exercise training (RET) with postexercise protein supplementation and 3x/wk dietary logs. RESULTS: We observed a dose-response relationship between dietary cholesterol (from food logs) and gains in lean mass that was not affected by variability in protein intake. Serum cholesterol and the serum cholesterol lowering agent statin were also independently associated with greater increases in lean mass. Dietary cholesterol was not associated with serum cholesterol or the significant reduction in blood pressure observed, but trends were observed for altered plasma C-reactive protein. CONCLUSION: These data suggest that dietary and serum cholesterol contribute to the skeletal muscles' response to RET in this generally healthy older population and that some statins may improve this response.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".