Menopause Is a Determinant of Breast Aromatase Expression and Its Associations With BMI, Inflammation, and Systemic Markers
Bibliographic record
Abstract
Context: Most estrogen-dependent breast cancers occur after menopause, despite low levels of circulating estrogens. Breast expression of the estrogen-biosynthetic enzyme, aromatase, is proposed to drive breast cancer development after menopause. However, the effects of menopause on breast aromatase expression are unknown. Objective: To determine the effect of menopause on breast aromatase expression in relation to body mass index (BMI), white adipose tissue inflammation (WATi), and systemic markers of metabolic dysfunction. Design, Setting, and Participants: Cross-sectional study of 102 premenopausal (age 27 to 56) and 59 postmenopausal (age 45 to 74) women who underwent mastectomy for breast cancer treatment/prevention. Outcome: Breast tissue was assessed for the presence of crown-like structures and the expression and activity of aromatase. Systemic markers examined include interleukin (IL)-6, insulin, glucose, leptin, adiponectin, high-sensitivity C-reactive protein (hsCRP), cholesterol, and triglycerides. Multivariable analysis was performed for aromatase messenger RNA (mRNA) in relation to BMI, WATi, and blood markers. Results: Postmenopausal women had higher BMI and more breast WATi than premenopausal women. Fasting levels of IL-6, glucose, leptin, hsCRP, and homeostatic model assessment 2 insulin resistance score were higher in the postmenopausal group. BMI was positively correlated with aromatase mRNA in both pre- and postmenopausal women. Aromatase levels were higher in breast tissue of postmenopausal women, with levels being higher in inflamed vs noninflamed, independent of BMI. Adipocyte diameter and levels of leptin, hsCRP, adiponectin, and high-density lipoprotein cholesterol were more strongly correlated with aromatase in postmenopausal than premenopausal women. Conclusions: Elevated aromatase in the setting of adipose dysfunction provides a possible mechanism for the higher incidence of hormone-dependent breast cancer in obese women after menopause.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".