Abstract A4: C-peptide, mammographic density, and risk of invasive breast cancer
Notice bibliographique
Résumé
Abstract Objectives: C-peptide, a stable marker of insulin secretion, has been associated with breast cancer incidence in some, but not all, earlier studies. Recent evidence links plasma C-peptide levels with dietary patterns, making C-peptide a potentially modifiable risk factor for breast cancer. Mammographic measures of breast density (i.e., dense area, non-dense area, and percent dense area) are associated with breast cancer risk independently of established risk factors, yet the relationship between C-peptide and these density parameters has not been thoroughly studied. We estimated the associations between plasma C-peptide levels, measures of mammographic density, and subsequent breast cancer risk among non-diabetic participants in the Nurses' Health Study (NHS) and Nurses' Health Study II (NHS2) cohorts. Methods: C-peptide concentrations were assayed by ELISA from prospectively collected plasma samples of non-diabetic breast cancer cases and controls nested in the NHS (762 cases, 1,146 controls) and the NHS2 (323 cases, 642 controls). C-peptide levels were defined as batch-specific quintiles, using cutpoints derived from controls. Associations between C-peptide and breast density measures were estimated among controls with available mammograms, and calculated as the difference in means between quintiles of C-peptide level using general linear models adjusted for age, menopausal status, age at menarche, alcohol consumption, BMI, and waist-to-hip circumference ratio. We used conditional logistic regression models to estimate odds ratios associating quintile of C-peptide concentration with breast cancer incidence while adjusting for established breast cancer risk factors, with and without further adjustment for breast density. Incidence associations were modeled separately in the two cohorts, evaluated for homogeneity, and pooled by inverse-variance weighting. Results: The association between C-peptide and mammographic breast density was measured in 1,272 breast cancer controls merged from both cohorts. Compared with subjects in the first quintile, subjects in the highest C-peptide quintile had a larger area of non-dense tissue (difference in adjusted means = 8.3 cm2, 95% CI: −4.8 cm2, 21 cm2), a smaller area of dense tissue (difference in adjusted means = −5.1 cm2, 95% CI: −13 cm2, 3.1 cm2), and a lower percent dense area (difference in adjusted means = −3.4%, 95% CI: −7.6%, 0.7%). C-peptide level was strongly associated with BMI (difference in mean BMI, highest vs. lowest quintile = 5.6, 95% CI: 4.8, 6.4). In the combined analysis, C-peptide was positively associated with breast cancer incidence in models adjusted for age, menopausal status, postmenopausal hormone use, BMI, age at first birth, age at menarche, alcohol consumption, and family history of breast cancer (pooled OR, highest vs. lowest quintile = 1.37, 95% CI: 1.02, 1.84). The association strengthened following simultaneous adjustment for dense and non-dense breast area (pooled OR, highest vs. lowest quintile = 1.57, 95% CI: 1.05, 2.38). Incidence associations were similar within strata of menopausal status. Conclusions: C-peptide may be modestly associated with larger non-dense area in the breast (and with lower percent dense area by extension). C-peptide was positively associated with breast cancer risk, with and without adjustment for breast density measures. Our results suggest that C-peptide is a biomarker of elevated breast cancer risk that does not act through an effect on breast density. Citation Information: Cancer Prev Res 2011;4(10 Suppl):A4.
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 | 0,001 | 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 ».