Abstract P6-10-15: Association between vitamin D supplementation and mammographic density change over time in women at high risk for breast cancer
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».