Assessing the causal and independent impact of parity-related reproductive factors on risk of breast cancer subtypes
Bibliographic record
Abstract
BACKGROUND: Observational evidence proposes a protective effect of having children and an early first pregnancy on breast cancer development; however, the causality of this association remains uncertain. Here, we assess whether parity-related reproductive factors impact breast cancer risk independently of each other and other causally related or genetically correlated factors: adiposity, age at menarche, and age at menopause. METHODS: We used genetic data from UK Biobank for reproductive factors and adiposity, and the Breast Cancer Association Consortium for risk of overall, estrogen receptor (ER) positive and negative breast cancer, and breast cancer subtypes. We applied univariable and multivariable Mendelian randomization (MR) to estimate genetically predicted direct effects of ever parous status, ages at first birth and last birth, and number of births on breast cancer risk. RESULTS: We found limited evidence for a genetically predicted protective effect of an earlier age at first birth on breast cancer risk. While the univariable analysis revealed later age at first birth decreased ER-negative breast cancer risk (odds ratio (OR): 0.76; 95% confidence interval: 0.61, 0.95 per standard deviation (SD) increase in age at first birth), this effect attenuated with separate adjustment for age at menarche (potential confounder) (OR: 0.83; 0.62, 1.06) and age at menopause (genetically correlated factor) (OR: 0.80; 0.66, 1.01). Furthermore, we found evidence that a later age at first birth decreased HER2-enriched breast cancer risk but only after adjusting for number of births (potential mediator) (OR: 0.28; 0.11, 0.57 per SD increase in age at first birth). In the multivariable analysis, we found little evidence for genetically predicted effects of ever-parous status, age at last birth, or number of births on breast cancer risk; however, analyses of ever-parous status and age at last birth were limited by weak instruments. CONCLUSIONS: This study found minimal evidence of a genetically predicted protective effect of earlier age at first birth on breast cancer risk, while identifying some evidence for a genetically predicted adverse effect on ER-negative breast cancer risk. However, weak instruments limited the multivariable analysis of ever parous status and age at last birth, which may be improved with larger sample sizes.
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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 | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".