Influence of Insulin-like Growth Factors on the Strength of the Relation of Vitamin D and Calcium Intakes to Mammographic Breast Density
Bibliographic record
Abstract
Diets with higher vitamin D and calcium contents were found associated with lower mammographic breast density and breast cancer risk in premenopausal women. Because laboratory studies suggest that the actions of vitamin D, calcium, insulin-like growth factor (IGF)-I, and IGF-binding protein-3 (IGFBP-3) on human breast cancer cells are interrelated, we examined whether IGF-I and IGFBP-3 levels could affect the strength of the association of vitamin D and calcium intakes with breast density. Among 771 premenopausal women, breast density was measured by a computer-assisted method, vitamin D and calcium intakes by a food frequency questionnaire, and levels of plasma IGF-I and IGFBP-3 by ELISA methods. Multivariate linear regression models were used to examine the associations and the interactions. The negative associations of vitamin D or calcium intakes with breast density were stronger among women with IGF-I levels above the median (beta = -2.8, P = 0.002 and beta = -2.5, P = 0.002, respectively) compared with those with IGF-I levels below or equal to the median (beta = -0.8, P = 0.38 and beta = -1.1, P = 0.21; P(interaction) = 0.09 and 0.16, respectively). Similar results were observed within levels of IGFBP-3 (P(interaction) = 0.06 and 0.03, respectively). This is the first study to report that the negative relation of vitamin D and calcium intakes with breast density may be seen primarily among women with high IGF-I or high IGFBP-3 levels. Our findings suggest that the IGF axis should be taken into account when the effects of vitamin D and calcium on breast density (and perhaps breast cancer risk) are examined at least among premenopausal women.
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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 | 0.001 |
| 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) | 0.000 | 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".