Mammographic density, plasma vitamin D levels and risk of breast cancer in postmenopausal women
Bibliographic record
Abstract
Mammographic density is a strong risk factor for breast cancer, but the underlying biology for this association is unknown. Studies suggest that vitamin D may reduce breast cancer risk and dietary vitamin D intake has been associated with reduced breast density. We conducted a case-control study nested within the Nurses' Health Study cohort consisting of 463 and 497 postmenopausal cases and controls, respectively. We examined the association between mammographic density and plasma levels of 25-hydroxyvitamin D [25(OH)D] and 1,25-dihydroxyvitamin D [1,25(OH)(2)D]. We assessed whether plasma vitamin D metabolites modify the association between breast density and breast cancer. Percent mammographic density was measured from digitized film mammograms. Generalized linear models were used to determine mean percent breast density per quartile of vitamin D metabolite. Logistic regression models were used to calculate relative risks and confidence intervals. All models were adjusted for matching variables and potential confounders. We found no cross-sectional association between circulating levels of 25(OH)D or 1,25(OH)(2)D with mammographic density. Women in the highest tertile of mammographic density and lowest tertile of plasma 25(OH)D had 4 times greater risk of breast cancer than women with the lowest mammographic density and highest plasma 25(OH)D levels (RR = 3.8; 95% CI: 2.0-7.3). The overall interaction between mammographic density and plasma 25(OH)D was nonsignificant (p-het = 0.20). These results indicate that the association between mammographic density and breast cancer is independent of plasma vitamin D metabolites in postmenopausal women. Further research examining vitamin D, mammographic density and breast cancer risk is warranted.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".