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Predictors of Mammographic Density: Insights Gained from a Novel Regression Analysis of a Twin Study

¹ Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, School of Population Health, The University of Melbourne, and ² BreastScreen Victoria, St Vincent's Hospital, and The University of Melbourne, Melbourne, Australia; ³ Department of Preventive and Social Medicine, University of Otago, Dunedin, New Zealand; ⁴ Cancer Epidemiology Centre, The Cancer Council Victoria, Victoria, Australia; ⁵ Division of Epidemiology and Statistics, Ontario Cancer Institute, ⁶ Blackburn Cardiovascular Genetics Laboratory, Robarts Research Institute, ⁷ Division of Preventive Oncology, Cancer Care Ontario, and ⁸ Imaging Research, Sunnybrook and Women's College Hospital, Ontario, Canada

Requests for reprints: John Hopper, The University of Melbourne, Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, The University of Melbourne, 723 Swanston Street, Carlton, Victoria 3053, Australia. Phone: 61-3-8344-0697; Fax: 61-3-9349-5815. E-mail: j.hopper{at}unimelb.edu.au

Understanding which factors influence mammographically dense and nondense areas is important because percent mammographic density adjusted for age is a strong, continuously distributed risk factor for breast cancer, especially when adjusted for weight or body mass index. Using computer-assisted methods, we measured mammographically dense areas for 571 monozygotic and 380 dizygotic Australian and North American twin pairs ages 40 to 70 years. We used a novel regression modeling approach in which each twin's measure of dense and nondense area was regressed against one or both of the twin's and co-twin's covariates. The nature of changes to regression estimates with the inclusion of the twin and/or co-twin's covariates can be evaluated for consistency with causal and/or other models. By causal, we mean that if it were possible to vary a covariate experimentally then the expected value of the outcome measure would change. After adjusting for the individual's weight, the co-twin associations with weight were attenuated, consistent with a causal effect of weight on mammographic measures, which in absolute log cm²/kg was thrice stronger for nondense than dense area. After adjusting for weight, later age at menarche, and greater height were associated with greater dense and lesser nondense areas in a manner inconsistent with causality. The associations of dense and nondense areas with parity are consistent with a causal effect and/or within-person confounding. The associations between mammographic density measures and height are consistent with shared early life environmental factors that predispose to both height and percent mammographic density and possibly breast cancer risk. (Cancer Epidemiol Biomarkers Prev 2008;17(12):3474–81)