Estrogen Metabolism and Mammographic Density in Postmenopausal Women: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Prospective studies have consistently found that postmenopausal breast cancer risk increases with circulating estrogens; however, findings from studies of estrogens and mammographic density (MD), an intermediate marker of breast cancer risk, have been inconsistent. We investigated the cross-sectional associations of urinary estrogens, and their 2-, 4-, and 16-hydroxylated metabolites with MD. METHODS: Postmenopausal women without breast cancer (n = 194), ages 48 to 82 years, and reporting no current menopausal hormone therapy use were enrolled at a clinic in Western NY in 2005. Urinary estrogens and estrogen metabolites were measured using mass spectrometry. Percent MD and dense area (cm(2)) were measured using computer-assisted analyses of digitized films. Linear regression models were used to estimate associations of log-transformed estrogen measures with MD while adjusting for age, body mass index (BMI), parity, and past hormone therapy use. RESULTS: Urinary concentrations of most individual estrogens and metabolites were not associated with MD; however, across the interdecile range of the ratio of parent estrogens (estrone and estradiol) to their metabolites, MD increased by 6.8 percentage points (P = 0.02) and dense area increased by 10.3 cm(2) (P = 0.03). Across the interdecile ranges of the ratios of 2-, 4-, and 16-hydroxylation pathways to the parent estrogens, MD declined by 6.2 (P = 0.03), 6.4 (P = 0.04), and 5.7 (P = 0.05) percentage points, respectively. All associations remained apparent in models without adjustment for BMI. CONCLUSION: In this study of postmenopausal women, less extensive hydroxylation of parent estrogens was associated with higher MD. IMPACT: Hydroxylation of estrogens may modulate postmenopausal breast cancer risk through a pathway involving MD.
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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 | 0.003 | 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".