Vitamin D and Calcium Intakes from Food or Supplements and Mammographic Breast Density
Bibliographic record
Abstract
BACKGROUND: A better understanding of factors that affect breast density, one of the strongest breast cancer risk indicators, may provide important clues about breast cancer etiology and prevention. This study evaluates the association of vitamin D and calcium, from food and/or supplements, to breast density in premenopausal and postmenopausal women separately. METHODS: A total of 777 premenopausal and 783 post-menopausal women recruited at two radiology clinics in Quebec City, Canada, in 2001 to 2002, completed a food frequency questionnaire to assess vitamin D and calcium. Breast density from screening mammograms was assessed using a computer-assisted method. Associations between vitamin D or calcium and breast density were evaluated using linear regression models. Adjusted means in breast density were assessed according to the combined daily intakes of the two nutrients using generalized linear models. RESULTS: In premenopausal women, total intakes of vitamin D and calcium were inversely related to breast density (beta = -1.4; P = 0.004 for vitamin D; beta = -0.8; P = 0.0004 for calcium). In multivariate linear regression, simultaneous increments in daily total intakes of 400 IU vitamin D and 1,000 mg calcium were associated with an 8.5% (95% confidence interval, 1.8-15.1) lower mean breast density. The negative association between dietary vitamin D intake and breast density tended to be stronger at higher levels of calcium intake and vice versa. Among postmenopausal women, intakes of vitamin D and calcium were not associated with breast density. CONCLUSION: These findings show that higher intakes of vitamin D and calcium from food and supplements are related to lower levels of breast density among premenopausal women. They suggest that increasing intakes of vitamin D and calcium may represent a safe and inexpensive strategy for breast cancer prevention.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".