Superior Aerobic Capacity and Indices of Skeletal Muscle Morphology in Chronically Trained Master Endurance Athletes Compared With Untrained Older Adults
Bibliographic record
Abstract
The study aim was to comprehensively assess physiological function and muscle morphology in chronically trained older individuals against untrained young and older individuals. In a cross-sectional design, 15 young untrained controls (YC) (20 ± 2.7 years, 78.9 ± 13.3 kg), 12 untrained older controls (OC) (69.8 ± 4.1 years, 77.5 ± 14.2 kg), and 14 endurance-trained master athletes (MA) (67.1 ± 4.1 years, 68.7 ± 6.5 kg) underwent assessments of body composition, aerobic capacity, strength, muscle architecture, and fiber-type morphology. Skeletal muscle index was lower and body fat greater in OC versus MA. Estimated VO2max (mL·kg-1·minute-1) was similar between MA and YC, but lower in OC. Isometric leg strength normalized to fat-free mass was similar between groups, whereas normalized isometric arm strength was greater in YC than MA. Myosin heavy chain (MHC) I fiber area was greater in MA than OC, while MHC II fiber area was greater in YC than OC. MHC II fiber myonuclear domain size was greater in YC than OC and MA, whereas MA had greater MHC I and MHC II fiber capillarization than OC and YC. Satellite cell content was similar between groups. Chronic endurance training enhances indices of muscle morphology and improves body composition and aerobic capacity in older age, with potentially important implications for healthspan extension.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".