186 THE ASSOCIATION BETWEEN HYPERTENSION, PHYSICAL ACTIVITY, ENDOTHELIAL FUNCTION, AND INFLAMMATION
Bibliographic record
Abstract
Objective: The purpose of the current study was to evaluate the associations between hypertension, physical activity (PA), endothelial function (EF), and inflammation. Design and Methods: Men and women (n = 326, mean age: 59.6 ± 9.5 years) were recruited. All participants underwent a Forearm Hyperaemic Reactivity test measuring brachial artery reactivity, a proxy of EF, and their relative uptake ratio (RUR) was established. Participants completed a self-report questionnaire on leisure time PA. Average metabolic equivalent hours/week was calculated. Hypertension was defined as self-report of physician diagnosis or currently taking antihypertensive medications, which were verified by chart review. Blood samples were taken and analysed for inflammatory markers; C-reactive protein (CRP) and sedimentation rate (SR). Results: We observed a significant main effect of hypertension on RUR (F = 5.64, p = .02), whereby hypertensive patients had a reduced RUR, indicating poorer EF. Additionally, the analysis showed a main effect of hypertension on CRP level (F = 11.14, p = .001) such that participants with hypertension had higher CRP levels. No effect of hypertension was observed for SR. There were no effects of PA on any of the outcomes, nor were any interaction effects (hypertension and PA) observed. Conclusions: The results suggest that EF and inflammation are associated with hypertension but unaffected by PA. It is possible that the effects of PA are less pronounced in an older population where EF is already compromised. Additionally, the sample population tended to have relatively low levels of PA which may contribute to the null finding. Additional interventional studies are needed to evaluate these relationships.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".