Impact of the menstrual cycle on peripheral vascular function in premenopausal women: systematic review and meta-analysis
Bibliographic record
Abstract
Fluctuations in endogenous hormones estrogen and progesterone during the menstrual cycle may offer vasoprotection for endothelial and smooth muscle (VSM) function. While numerous studies have been published, the results are conflicting, leaving our understanding of the impact of the menstrual cycle on vascular function unclear. The purpose of this systematic review and meta-analysis was to consolidate available research exploring the role of the menstrual cycle on peripheral vascular function. A systematic search of MEDLINE, Web of Science, and EMBASE was performed for articles evaluating peripheral endothelial and VSM function across the natural menstrual cycle: early follicular (EF) phase versus late follicular (LF), early luteal, mid luteal, or late luteal. A meta-analysis examined the effect of the menstrual cycle on the standardized mean difference (SMD) of the outcome measures. Analysis from 30 studies ( n = 1,363 women) observed a “very low” certainty of evidence that endothelial function increased in the LF phase (SMD: 0.45, P = 0.0001), with differences observed in the macrovasculature but not in the microvasculature (SMD: 0.57, P = 0.0003, I 2 = 84%; SMD: 0.21, P = 0.17, I 2 = 34%, respectively). However, these results are partially explained by differences in flow-mediated dilation [e.g., discrete (SMD: 0.86, P = 0.001) vs. continuous peak diameter assessment (SMD: 0.25, P = 0.30)] and/or menstrual cycle phase methodologies. There was a “very low” certainty that endothelial function was largely unchanged in the luteal phases, and VSM was unchanged across the cycle. The menstrual cycle appears to have a small effect on macrovascular endothelial function but not on microvascular or VSM function; however, these results can be partially attributed to methodological differences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".