Acute effects of ultramarathon running at altitude on the cardiovascular system: a scoping review
Bibliographic record
Abstract
Abstract Introduction The popularity of ultra-distance running, particularly at altitude, has increased significantly over the last 20 years. While the health benefits of moderate exercise are well-documented, the long-term cardiovascular effects of prolonged endurance exercise, especially at high altitudes, remain uncertain. Most existing research has focused on sea-level events, leaving the specific impacts of altitude on the cardiovascular system underexplored. This review aims to summarize current knowledge on the effects of ultra-distance running at altitude on the cardiovascular system and compare these adaptations with known changes at sea-level. Methods A systematic review was conducted, focusing on studies that examined the effects of ultra-distance running at altitudes above 1000 meters. A total of 24 cross-sectional studies with 1075 participants were included, assessing variables related to cardiac structure, function, and biomarkers pre- and post-race. The studies were appraised for risk of bias using the Newcastle-Ottawa Scale. Results The reviewed studies indicated significant cardiovascular changes post-race, including reductions in systolic and diastolic blood pressure, decreased parasympathetic activity and increased sympathetic modulation demonstrated by changes in heart rate variability (HRV) parameters and left ventricular systolic and diastolic function reductions on echocardiography. Notable increases in biomarkers such as cardiac troponins (cTnI and cTnT) and creatine kinase (CK) were also observed. Conclusion Ultra-distance running at altitude elicits significant acute cardiovascular responses, including reduced autonomic function, blood pressure, and cardiac performance, as well as elevated levels of cardiac biomarkers. Though high altitude is a unique environment, the changes seen among altitude runners are similar to those of ultra-distance running at sea-level suggesting that the exercise volume in this sport plays a more significant impact than the effect of altitude. These findings may prompt and guide future recommendations for managing cardiovascular risk in endurance athletes competing at altitude. While parameters reviewed here were transient, further research and analysis is needed to understand the long-term implications and pooled effects of this activity.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".