Early Menarche as a Protective Factor Against Cardiovascular Events: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Women are often neglected in cardiovascular health prevention. Age at menarche (AAM) has been linked to cardiovascular (CVD) disease in women and is potentially identified as one of the significant CVD risk factor. However, there is still limited comprehensive evidence addressing this issue. This systematic review and meta-analysis aimed to investigate how early menarche affects the outcome of all-cause mortality, CVD mortality, total cardiovascular disease event, stroke (ischemic, hemorrhagic, and total stroke), and coronary heart disease (CHD). METHOD: The Cochrane Library, MEDLINE, Embase, ScienceDirect, and Google Scholar databases were searched from March 2013 to March 2023 for cohorts investigating the effect of early onset of menarche on CVD events with a minimum follow-up period of 5 years. Studies that observed specific population and/or included women with a history of CVD at baseline were excluded. The Newcastle-Ottawa scale was used for risk of bias assessment for each cohort included. The data were presented as dichotomous measure using risk ratios. I2 statistics were utilized to evaluate the heterogeneity of presented data. RESULTS: Thirteen cohorts included 18 626 799 female patients with ages ranging from 43 to 62.6 years. These reported 6 estimates each for CHD (5 483 298 patients) and all-cause mortality (1 595 878 patients), 5 estimates each for total stroke (2 941 321 patients) and CVD mortality (1 706 742 patients), 4 estimates each for total CVD events (3 988 311 patients) and ischemic stroke (2 434 580 patients), and 1 estimate for hemorrhagic stroke (66 104 patients). Our study found that events of CHD were significantly lower in early menarche (RR 0.57; 95% CI 0.41-0.78; P <.00001), as well as total stroke (RR 0.51; 95% CI 0.35-0.73; P =.0003), CVD mortality (RR 0.47; 95% CI 0.22-0.98; P =.04), total CVD events (RR 0.44; 95% CI 0.25-0.76; P =.003), ischemic stroke (RR 0.31; 95% CI 0.15-0.61; P <.0008), and hemorrhagic stroke (RR 0.12; 95% CI 0.07-0.20; P <.00001); and insignificantly higher in all-cause mortality (RR 0.90, 95% CI 0.76-1.06, P =.20). CONCLUSION: In our study, cardiovascular events are lower in women with early menarche; hence, the later age of menarche is a potential risk factor to be considered when assessing CVD risk in a patient. However, our sample characteristics were heterogenous, and we did not consider other female hormonal factors that might potentially contribute to the CVD outcomes observed; thus, further studies are needed to clarify.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.026 | 0.022 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".