Relationship between brachial artery flow-mediated dilatation, hyperemic shear stress, and the metabolic syndrome
Bibliographic record
Abstract
Metabolic syndrome (MetSyn) may predispose to cardiovascular disease (CVD) by causing vascular dysfunction. This study aimed to determine the association of MetSyn with vascular function, as assessed by brachial artery flow-mediated dilatation (FMD) and hyperemic shear stress (HSS). A total of 1,417 male firefighters without established diabetes and CVD were classified for MetSyn, according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP) definition. MetSyn was present in 267 individuals (19%). Although FMD was lower in those with versus without MetSyn (8.1 +/- 4.1 vs 8.7 +/- 4.0%; p = 0.02), this was not significant after adjusting for baseline differences (age, smoking, and brachial artery diameter) (p = 0.2). However, HSS was significantly lower in those with versus without MetSyn (72.0 +/- 27.8 vs 80.9 +/- 24.8 dyne/cm(2); p < 0.001), and there was a significant inverse graded relationship with the number of NCEP criteria present (mean HSS for those with 0, 1, 2, 3, 4, and 5 criteria: 83.2 +/- 22.5, 82.2 +/- 24.7, 76.5 +/- 27.2, 74.3 +/- 27.4, 66.5 +/- 28.4, 67.1 +/- 27.6 dyne/cm(2); p < 0.001 for trend). The individual NCEP criteria of abdominal obesity, systolic hypertension, and impaired fasting glucose were independent predictors for HSS. In conclusion, MetSyn was not associated with impaired FMD. Alternatively, HSS, a measure of microvascular function, was significantly lower in those with MetSyn. Thus, MetSyn may contribute to CVD by causing microvascular dysfunction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".