Abstract P6-10-15: Association between vitamin D supplementation and mammographic density change over time in women at high risk for breast cancer
Bibliographic record
Abstract
Abstract Background: Vitamin D deficiency has been linked to breast cancer risk, but less is known about vitamin D and changes over time in mammographic density (MD), a strong predictor of breast cancer risk. Studies that have evaluated the association between MD and vitamin D have primarily been cross-sectional designs and focused on average-risk postmenopausal women. Methods: Using data from a prospective cohort study (1991-2013), we examined whether vitamin D supplementation was associated with MD at baseline and changes in MD over time. High-risk women had a first-degree family history of breast cancer, atypical hyperplasia, lobular or ductal carcinoma in situ. They completed baseline questionnaires with self-reported vitamin D supplement use (Y/N) and had serial mammograms with qualitative assessment of MD (BIRADS categories: 1=0-24%, 2=25-50%, 3=51-75%, 4=76-100%). GEE logistic regression and unordered polytomous regression models were used to assess the association between change in MD in the short-term (<3 years) and long-term (≥3 years) with vitamin D use (stayed dense: BIRADS 3/4 for both exams; stayed nondense: BIRADS 1/2; increased: BIRADS 1/2 to 3/4; decreased: BIRADS 3/4 to 1/2). Primary confounders were included in every model (age, race, body mass index [BMI], menopausal status) and other additional confounders were selected based on 10% change-of-coefficient rule. Results: Of 1171 women who had vitamin D supplement information and a baseline mammogram, 615 had two mammograms within 3 years from baseline and 461 had a long-term follow-up mammogram. Median age was 49 (range, 17-88), median BMI 23.6 kg/m2 (range, 14.9-53.4), and mean follow-up time 6 years (range, 9 months-18 years). Among women with a BMI<25, no vitamin D supplementation was associated with dense baseline MD (BIRADS 3/4) after adjusting for age, race, menopausal status, and annual household income (OR=1.61, 95% CI=1.12-2.33). Those who reported vitamin D use were about 50% less likely to demonstrate long-term increases in MD (see table below). Vitamin D and short-term and long-term mammographic density changes Stay denseIncreaseDecreaseStay nondense nn; OR (95% CI)n; OR (95% CI)n; OR (95% CI)Vitamin D*29136; 0.80 (0.38-1.66)60; 1.00 (0.53-1.89)209; 1.41 (0.91-2.17)Vitamin D**29136; 0.82 (0.37-1.80)60; 0.74 (0.37-1.47)209; 1.37 (0.87-2.16)Vitamin D***21132; 0.46 (0.20-1.04)69; 1.08 (0.58-1.99)134; 1.32 (0.78-2.23)Vitamin D****21132; 0.49 (0.21-1.14)69; 1.12 (0.60-2.11)134; 1.34 (0.78-2.30)*Short-term: adjusted for age, BMI, race and menopausal status; **Short-term: additional adjustment for highest education level, annual household income; ***Long-term: adjusted for age, BMI, race and menopausal status; ****Long-term: additional adjustment for highest education level, age of first childbirth, time intervals from the first to the last mammogram Discussion: Although vitamin D supplementation was not associated with short-term changes in MD, we did observe a trend toward an association with long-term change among high-risk women. If replicated in larger studies, our study gives added evidence that MD changes may need longer observation time. Citation Format: Katherine D Crew, Tong Xiao, Mary Beth Terry. Association between vitamin D supplementation and mammographic density change over time in women at high risk for breast cancer [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P6-10-15.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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) | 0.002 | 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 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".