Flow-Mediated Dilation in Athletes
Bibliographic record
Abstract
PURPOSE: Controversy exists on whether endothelial function is enhanced in athletes. We sought to systematically review the literature and determine whether endothelial function, as assessed by flow-mediated dilation (FMD), is greater in athletes across all ages relative to that in their age-matched counterparts. METHODS: We conducted a systematic search on MEDLINE, Cochrane, Scopus, and Web of Science since their inceptions until July 2013 for articles evaluating FMD in athletes. A meta-analysis was performed to compare the standardized mean difference (SMD) in FMD of the brachial artery between athletes and age-matched control subjects. Subgroup analyses and meta-regression were used to identify sources of heterogeneity. RESULTS: Twenty-one articles were included in this analysis, comprising 530 athletes (452 endurance trained, 49 strength trained, and 29 endurance and strength trained) and 376 control subjects. After data pooling, FMD was higher in athletes than that in control groups (SMD, 0.48; P = 0.008). In subgroup analyses, young athletes (<40 yr) presented increased baseline brachial artery diameter (mean difference, 0.40 mm; P < 0.00001) and similar FMD (SMD, 0.27; P = 0.22) compared with those in controls. In contrast, master athletes (>;50 yr) showed similar baseline brachial artery diameter (mean difference, 0.04 mm; P = 0.69) and increased FMD (SMD, 0.99; P = 0.0005) compared with those in controls. CONCLUSIONS: The current meta-analysis provides evidence that master athletes but not young athletes exhibit greater FMD compared with that in age-matched healthy controls, thus suggesting that the association between high levels of exercise training and increased FMD is age dependent.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".