Causal Effects of Breast Cancer Risk Factors across Hormone Receptor Breast Cancer Subtypes: A Two-Sample Mendelian Randomization Study
Bibliographic record
Abstract
BACKGROUND: It is unclear if established breast cancer risk factors exert similar causal effects across hormone receptor breast cancer subtypes. We estimated and compared causal estimates of height, body mass index (BMI), type 2 diabetes, age at menarche, age at menopause, breast density, alcohol consumption, regular smoking, and physical activity across these subtypes. METHODS: We used a two-sample Mendelian randomization approach and selected genetic instrumental variables from large-scale genome-wide association studies. Publicly available summary-level Breast Cancer Association Consortium data (n = 247,173; 133,384 cases, 113,789 controls) for the following subtypes were included: luminal A-like (45,253 cases), luminal B-/HER2-negative-like (6,350 cases), luminal B-like (6,427 cases), HER2-enriched (2,884 cases), and triple-negative (8,602 cases). We employed multiple Mendelian randomization methods to evaluate the strength of causal evidence for each risk factor-subtype association. RESULTS: Collectively, our analyses indicated that increased height and decreased BMI are probable causal risk factors for all five subtypes. For the other risk factors, the strength of evidence for causal effects differed across subtypes. Heterogeneity in the magnitude of causal effect estimates for age at menopause and breast density was explained by null findings for triple-negative tumors. Regular smoking was the sole risk factor for which there was no evidence of a causal effect on any subtype. CONCLUSIONS: This study suggests that established breast cancer risk factors differ across hormone receptor subtypes. IMPACT: Our results are valuable for the development of primary prevention strategies, improvement of breast cancer risk stratification in the general population, and identification of novel breast cancer risk factors.
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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 | 0.120 | 0.219 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".