PURPOSE: We previously reported an inverse association between flavonoid intake and breast cancer incidence, which has been confirmed by others, but no studies have considered simultaneously potential interactions of flavonoids with multiple genetic polymorphisms involved in biologically relevant pathways (oxidative stress, carcinogen metabolism, DNA repair, and one-carbon metabolism). METHODS: To estimate interaction effects between flavonoids and 13 polymorphisms in these four pathways on breast cancer risk, we used population-based data (n = 875 cases and 903 controls) and several statistical approaches, including conventional logistic regression and semi-Bayesian hierarchical modeling (incorporating prior information on the possible biologic functions of genes), which also provides biologic pathway-specific effect estimates. RESULTS: Compared to the standard multivariate model, the results from the hierarchical model indicate that gene-by-flavonoid interaction estimates are attenuated, but more precise. In the hierarchical model, the average effect of the deleterious versus beneficial gene, controlling for average flavonoid intake in the DNA repair pathway, and adjusted for the three other biologically relevant pathways (oxidative stress, carcinogen metabolism, and one-carbon metabolism), resulted in a 27 % increase risk for breast cancer [odds ratio = 1.27; 95 % confidence interval (CI) = 0.70, 2.29]. However, the CI was wide. CONCLUSIONS: Based on results from the semi-Bayesian model, breast cancer risk may be influenced jointly by flavonoid intake and genes involved in DNA repair, but our findings require confirmation.
RCT Entities:
PURPOSE: We previously reported an inverse association between flavonoid intake and breast cancer incidence, which has been confirmed by others, but no studies have considered simultaneously potential interactions of flavonoids with multiple genetic polymorphisms involved in biologically relevant pathways (oxidative stress, carcinogen metabolism, DNA repair, and one-carbon metabolism). METHODS: To estimate interaction effects between flavonoids and 13 polymorphisms in these four pathways on breast cancer risk, we used population-based data (n = 875 cases and 903 controls) and several statistical approaches, including conventional logistic regression and semi-Bayesian hierarchical modeling (incorporating prior information on the possible biologic functions of genes), which also provides biologic pathway-specific effect estimates. RESULTS: Compared to the standard multivariate model, the results from the hierarchical model indicate that gene-by-flavonoid interaction estimates are attenuated, but more precise. In the hierarchical model, the average effect of the deleterious versus beneficial gene, controlling for average flavonoid intake in the DNA repair pathway, and adjusted for the three other biologically relevant pathways (oxidative stress, carcinogen metabolism, and one-carbon metabolism), resulted in a 27 % increase risk for breast cancer [odds ratio = 1.27; 95 % confidence interval (CI) = 0.70, 2.29]. However, the CI was wide. CONCLUSIONS: Based on results from the semi-Bayesian model, breast cancer risk may be influenced jointly by flavonoid intake and genes involved in DNA repair, but our findings require confirmation.
Authors: Tasha R Smith; Edward A Levine; Nancy D Perrier; Mark Steven Miller; Rita I Freimanis; Kurt Lohman; L Douglas Case; Jianfeng Xu; Harvey W Mohrenweiser; Jennifer J Hu Journal: Cancer Epidemiol Biomarkers Prev Date: 2003-11 Impact factor: 4.254
Authors: Hyo-Sung Jeon; Kyung Mee Kim; Sun Ha Park; Su Yeon Lee; Jin Eun Choi; Ga Young Lee; Sin Kam; Rang Woon Park; In-San Kim; Chang Ho Kim; Tae Hoon Jung; Jae Yong Park Journal: Carcinogenesis Date: 2003-07-17 Impact factor: 4.944
Authors: Henry J Lin; Ann Sofie Johansson; Gun Stenberg; Alicia M Materi; Jae Man Park; Aihua Dai; Haiyan Zhou; Jason S Y Gim; Irving H Kau; Steven I Hardy; Michael W Parker; Bengt Mannervik Journal: Biochim Biophys Acta Date: 2003-06-26
Authors: Benjamin A Rybicki; David V Conti; Andrea Moreira; Mine Cicek; Graham Casey; John S Witte Journal: Cancer Epidemiol Biomarkers Prev Date: 2004-01 Impact factor: 4.254
Authors: J Peterson; P Lagiou; E Samoli; A Lagiou; K Katsouyanni; C La Vecchia; J Dwyer; D Trichopoulos Journal: Br J Cancer Date: 2003-10-06 Impact factor: 7.640