Investigating the Effect of Estradiol Levels on the Risk of Breast, Endometrial, and Ovarian Cancer
Bibliographic record
Abstract
Abstract Background High levels of estrogen are associated with increased risk of breast and endometrial cancer and have been suggested to also play a role in the development of ovarian cancer. Cancerogenic effects of estradiol, the most prominent form of estrogen, have been highlighted as a side effect of estrogen-only menopausal hormone therapy. However, whether high levels of endogenous estrogens, produced within the body, promote cancer development, has not been fully established. Objective We aimed to examine causal effects of estradiol on breast, endometrial, and ovarian cancer. Methods Here we performed a two-sample Mendelian randomization (MR) to estimate the effect of endogenous estradiol on the risk of developing breast, endometrial, and ovarian cancer, using the UK Biobank as well as 3 independent cancer cohorts. Results Using 3 independent instrumental variables, we showed that higher estradiol levels significantly increase the risk for ovarian cancer (OR = 3.18 [95% CI, 1.47-6.87], P = 0.003). We also identified a nominally significant effect for ER-positive breast cancer (OR = 2.16 [95% CI, 1.09-4.26], P = 0.027). However, we could not establish a clear link to the risk of endometrial cancer (OR = 1.93 [95% CI, 0.77-4.80], P = 0.160). Conclusion Our results suggest that high estradiol levels promote the development of ovarian and ER-positive breast cancer.
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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 | 0.011 | 0.026 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".