Endurance training fails to correct age‐related declines in cardiovagal control
Bibliographic record
Abstract
This study tested the hypothesis that long‐term endurance training (ET) preserves cardiovagal control in aging individuals. Baroreflex sensitivity (BRS) and the rapid heart rate response at exercise onset (ΔHR) reflect levels of cardiovagal control. BRS declines with age and cardiovascular disease but, reportedly, this effect can be reversed by ET. It remains unclear if ET also preserves ΔHR (reflecting removal of parasympathetic cardiac inhibition). Both BRS (sequence method, 5–10 min of baseline) and ΔHR to three, 30s contractions at 40% of their maximum voluntary contraction strength, were assessed in a group of young individuals (Y; age=26±4) and in groups of older individuals who were healthy (O; age=56±4), endurance trained (ET; age=55±4) or entering cardiac rehabilitation (CR; age=59±4) (n=15 for each group). Compared with Y (10±9 bpm), ΔHR was less in CR (3±2 bpm; P<0.05) but not the other groups (O: 7±5, ET: 5±3 bpm; NS). Similarly, compared with Y (30±15 ms/mmHg), BRS was less in CR (14±12 ms/mmHg) but not the other groups (O: 20±12, ET: 25±15 ms/mmHg). Contrary to previous results, ET did not preserve BRS at levels expressed in Y. However, age correlated strongly with BRS and ΔHR (r=−0.4, p=0.001; r=−0.3, p=0.013 respectively) across all groups. The results suggest that age exerts a dominant impact on cardiovagal control and that this effect remains difficult to restore with long‐term ET. Supported by CIHR.
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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 | 0.000 | 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".