Preserved Forearm Endothelial Responses with Acute Exposure to Progesterone: A Randomized Cross-Over Trial of 17-β Estradiol, Progesterone, and 17-β Estradiol with Progesterone in Healthy Menopausal Women1
Bibliographic record
Abstract
Regularly menstruating women are relatively protected from cardiovascular disease. Epidemiological and endothelial function studies attribute this protection to estradiol (E(2)), but both progesterone (P) and E(2) are normally present. A range of vascular effects of added progestins have been described, from neutral to detrimental, but the effects of P per se on endothelial function in humans have not been reported. We therefore investigated the acute effects of E(2), P, and E(2) combined with P, on endothelium-dependent and -independent forearm blood flow responses. Using venous occlusion plethysmography, forearm blood flow (FBF) was measured during acute brachial artery infusions, achieving physiologic levels of 17-beta-E(2), P, and 17-beta-E(2) with P in healthy menopausal women with no cardiovascular disease risk factors. Vehicle or hormones were infused, in random order, on 4 days, 1 week apart. Flow responses were measured during coinfusions of hormone with the endothelium-dependent vasodilator acetylcholine and the endothelium-independent vasodilator sodium nitroprusside. Twenty-seven healthy menopausal women were studied, and all had normal baseline endothelial responses. Small ( approximately 15%), statistically nonsignificant increases in endothelium-dependent flow responses were seen after all acute hormone treatments. No impairment in response was seen with P alone or in combination with 17-beta-E(2). In healthy menopausal women without cardiovascular disease risk factors and without baseline defects in endothelial function, acute exposure to physiologic levels of 17-beta-E(2), P, and 17-beta-E(2) with P produced equivalent endothelium-dependent responses. These data suggest that P does not have detrimental vascular effects in humans.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".