Blood cardiac biomarkers responses are associated with 24 h ultramarathon performance
Bibliographic record
Abstract
Purpose Clinical significance of cardiac biomarkers response in ultra-endurance runners are not completely elucidated because events vary in distance/duration and competitors modulate running intensity according to individual running capacity. The aim of this study was to examine the relationship between self-selected exercise intensity with cardiac biomarkers comparing experienced (EXP, N=11) and novice (NOV, N=14) runners able to finish a 24h ultramarathon (24UM) with significant differences in performance. Methods Cardiac biomarkers (i.e. CKMB/totalCK, cTnT and NT-proBNP), inflammatory markers (i.e. leukocytes and CRP) and cortisol were analyzed before and after a 24UM. Results EXP finished the race with significant ( p < 0.05) longer distance than NOV (158.8 ± 15.8 vs 116.8 ± 10.3 Km). Two-way mixed ANOVA showed significant time × performance level interaction with greater increase of cTnT ( F (1,23) = 6.18, p = 0.021), NT-proBNP ( F (1,23) = 9.27, p = 0.006) and cortisol ( F (1, 23) = 5.13, p = 0.03) in the EXP group. CKMB/totalCK ( F (1, 23) = 71.90, p < 0.0001) decreased while leukocytes ( F (1, 23) = 100.06, p < 0.0001) and CRP ( F (1, 23) = 93.37, p < 0.0001) increased in both groups (main effect of time). Correlations were found between 24UM distance and cortisol (r = 0.58; p = 0.002), CKMB (r = 0.47; p = 0.017), cTnT (r = 0.44; p = 0.027) or NT-proBNP (r = 0.56; p = 0.003). Cortisol and NT-proBNP were also significantly correlated (r = 0.51; p = 0.01). Conclusions Although there is no clear evidence of cardiac risk when comparing cardiac biomarkers levels with clinical cut-off values, cardiac biomarkers are associated with running performance and pituitary-adrenocortical system response. In EXP runners, higher levels of cardiac biomarkers and cortisol suggest a more hemodynamically challenged heart during prolonged endurance exercise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".