Reassessing the association between age at menarche and cardiovascular disease: observational and Mendelian randomization analyses
Bibliographic record
Abstract
AIMS: Observational studies have shown a U-shaped association between age at menarche (AAM) and cardiovascular disease (CVD). We assessed non-linearity of the observational association and the potential causal relationship between AAM and CVD using data from the European Prospective Investigation into Cancer and Nutrition-Cardiovascular Disease (EPIC-CVD) study and the UK Biobank. METHODS AND RESULTS: We included women without pre-existing myocardial infarction (MI) or stroke at baseline. We estimated hazard ratios for incident MI, ischaemic and haemorrhagic stroke later in life using Cox regression in observational analyses and conducted non-linear Mendelian randomization (MR) based on fractional polynomials and linear MR based on inverse-variance weighted regression. We analysed 283 210 women with a median AAM of 13 (IQR 12-14) years in both EPIC-CVD and the UK Biobank, of which 8468 experienced a MI, 5501 an ischaemic and 1887 a haemorrhagic stroke. The association between AAM and MI and ischaemic stroke was U-shaped with higher risks in women aged ≤12 and ≥16 compared with those aged 13 years at menarche. Our MR analyses found no evidence for non-linearity between genetically proxied AAM and any CVD endpoint later in life, but each year higher genetically proxied AAM was related to a lower risk of MI (hazard ratio 0.92 [95% CI 0.86-0.99]), but not to ischaemic and haemorrhagic stroke. CONCLUSION: This study supported non-linear observational associations between AAM and MI and ischaemic stroke. MR analyses suggested a causal relationship between higher AAM and risk of MI without an indication for non-linearity. There was no support for a potential causal link with ischaemic and haemorrhagic stroke.
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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 | 0.138 | 0.310 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".