NDLI: Predictors of mammographic density: insights gained from a novel regression analysis of a twin study.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Dite, Gillian S. Gurrin, Lyle C. Byrnes, Graham B. Stone, Jennifer Gunasekara, Anoma McCredie, Margaret R. E. English, Dallas R. Giles, Graham G. Cawson, Jennifer Hegele, Robert A. Chiarelli, Anna M. Yaffe, Martin J. Boyd, Norman F. Hopper, John L.
Spatial Coverage	North America Australia
Description	Country affiliation: Australia Author Affiliation: Dite GS ( The University of Melbourne, Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, The University of Melbourne, 723 Swanston Street, Carlton, Victoria 3053, Australia.)
Abstract	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 cm(2)/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.
File Format	HTM / HTML
ISSN	10559965
e-ISSN	15387755
DOI	10.1158/1055-9965.EPI-07-2636
Journal	Cancer Epidemiology Biomarkers & Prevention
Issue Number	12
Volume Number	17
Language	English
Publisher	American Association for Cancer Research
Publisher Date	2008-12-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Discipline Cancer epidemiology Breast Anatomy & Histology Mammography Adipose Tissue Body Height Body Weight Breast Neoplasms North America Predictive Value Of Tests Regression Analysis Risk Factors Twins, Dizygotic Twins, Monozygotic Research Support, Non-u.s. Gov't Twin Study
Content Type	Text
Resource Type	Article
Subject	Epidemiology Oncology

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