Menopausal Hormone Therapy Formulation and Breast Cancer Risk
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether the increased risk of breast cancer is dependent on the formulation of menopausal hormone therapy (HT) used. METHODS: We performed a population-based case-control study of women aged 50 years or older using data from the U.K. Clinical Practice Research Datalink. Women with incident cases of breast cancer were age-matched (1:10) with a control group of women with comparable follow-up time with no history of breast cancer. Exposures were classified as ever or never for the following menopausal HT formulations: bioidentical estrogens, animal-derived estrogens, micronized progesterone, and synthetic progestin. Logistic regression analyses were performed to estimate the adjusted effect of menopausal HT formulation on breast cancer risk. RESULTS: Between 1995 and 2014, 43,183 cases of breast cancer were identified and matched to 431,830 women in a control group. In adjusted analyses, compared with women who never used menopausal HT, its use was associated with an increased risk of breast cancer (odds ratio [OR] 1.12, 95% CI 1.09-1.15). Compared with never users, estrogens were not associated with breast cancer (bioidentical estrogens: OR 1.04, 95% CI 1.00-1.09; animal-derived estrogens: OR 1.01, 95% CI 0.96-1.06; both: OR 0.96, 95% CI 0.89-1.03). Progestogens appeared to be differentially associated with breast cancer (micronized progesterone: OR 0.99, 95% CI 0.55-1.79; synthetic progestin: OR 1.28, 95% CI 1.22-1.35; both OR 1.31, 0.30-5.73). CONCLUSION: Although menopausal HT use appears to be associated with an overall increased risk of breast cancer, this risk appears predominantly mediated through formulations containing synthetic progestins. When prescribing menopausal HT, micronized progesterone may be the safer progestogen to be used.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".