Hypertensive response to exercise: a marker of altered metabolism in endurance athletes?
Bibliographic record
Abstract
Background Inappropriate high blood pressure response to exercise in non athletes is known to be a predictor for future resting systemic hypertension. However, the relevance of exercise hypertension in endurance athletes is not known. Objective Assess the differences in heart rate variability (HRV), 24-h ambulatory blood pressure monitoring (ABPM) and lipid profile in athletes with an hypertensive response to exercise, and athletes with normal response to exercise. Design This study was a prospective cross-sectional study. 47 provincial and national athletes without previous diagnosis of systemic hypertension or other cardiovascular disease, training at least 10 h/week were consecutively recruited. 38 athletes completed the study. Intervention: Athletes underwent an ABPM, a 24-h HRV assessment (Holter), a maximal exercise test and blood samples. Hypertensive response to exercise was defined as systolic blood pressure (SBP) ≥220 mm Hg or diastolic blood pressure (DBP) > 100 mm Hg. Results Two athletes had systemic hypertension (SBP: 139 ± 3, DBP: 81 ± 8 mm Hg) on 24 h-ABPM and 14 athletes showed hypertensive response to exercise (SBP: 243 ± 20, DBP: 77 ± 13 mm Hg). Lower values of high density lipoprotein (HDL) (1.27 ± 0.19 vs 1.51 ± 0.23 g/L, p=0.04) and Apo-A1 (1.31 ± 0.14 vs 1.56 ± 0.15 g/L, p=0.003) were observed in athletes with hypertensive responses to exercise. The latter also had higher values of night time SBP on ABPM compared to athletes with a normal response to exercise (116 ± 6 vs 106 ± 8 mm Hg, p=0.02). No difference was found between both groups regarding HRV indices. Conclusion Higher values of night time SBP on ABPM and lower values of HDL and Apo-A1 were observed in athletes with hypertensive response to exercise. These observations may be the first sign of minor metabolic disturbance in endurance athletes, although parameters remain within de normal values.
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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".