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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 tête enseignante, 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 ».