Central and Peripheral Adaptations to Exercise in Cardiovascular Disease
Bibliographic record
Abstract
Heart failure (HF), directly affected by hypertension, is a leading cause of mortality in Canada, and manifests with myocardial dysfunction. Current drug therapies have merit, however their efficacy is limited as evident by the 50% five-year survival rate for HF. Therefore, non-pharmacological treatments are required to manage hypertension and prevent HF. This thesis is an investigation into the molecular effects of exercise training in models of HF. Endurance training (ET) has long been known to exert hypotensive effects and improve the quality of life of individuals afflicted with HF. Recently, high intensity interval training (HIIT) has been touted as a time efficient alternative in this patient population. However, clinical evidence on the safety and efficacy of HIIT in patients with compromised cardiac function remains scarce. This thesis demonstrated in hypertensive rats, ET increased mitochondrial content (~25%; P<0.05), shifted fibre type to a slow/oxidative phenotype (increased IIA, decreased IIX and IIB) and increased capillarization and endothelial nitric oxide synthase (eNOS) content. In contrast, HIIT did not uniformly increase mitochondrial content, was not as robust at inducing fast-to-slow fibre type transitions, and reduced capillarization and eNOS protein with increased hypoxia inducible factor 1 alpha (HIF1α). Within the heart, ET decreased fibrosis by ~40%, promoted a 20% increase in the left ventricular capillary/fibre ratio, an increase in eNOS protein, and a decrease in HIF1α. HIIT did not decrease fibrosis, increased left ventricular mass by 20%, and increased by brain natriuretic peptide 50% in the absence of concomitant angiogenesis. Altogether, the skeletal and cardiac muscle data indicated ET and HIIT had divergent effects in hypertensive rats. In addition to hypertension, congenital heart defects represent a cause of HF. Therefore, individuals with complex adult congenital heart disease embarked on 6 months of ET. All subjects increased their exercise capacity and tolerance supporting the use of ET in humans with complex cardiovascular disease. In summary, while the current thesis illustrates the beneficial effects of ET on skeletal and cardiac muscles, as well as in complex patients, there remains a critical need for additional research on the effects of HIIT in settings of compromised cardiovascular function.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".