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
The University of Melbourne, Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, The University of Melbourne, 723 Swanston Street, Carlton, Victoria 3053, Australia.
Cancer Epidemiol Biomarkers Prev. 2008 Dec;17(12):3474-81. doi: 10.1158/1055-9965.EPI-07-2636.
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.
了解哪些因素会影响乳房X线摄影中的致密区和非致密区很重要，因为根据年龄调整后的乳房X线摄影密度百分比是乳腺癌的一个强大的、呈连续分布的风险因素，尤其是在根据体重或体重指数进行调整时。我们使用计算机辅助方法，对571对澳大利亚和北美40至70岁的同卵双胞胎以及380对异卵双胞胎的乳房X线摄影致密区进行了测量。我们采用了一种新颖的回归建模方法，将每个双胞胎的致密区和非致密区测量值与该双胞胎和其孪生同胞的一个或两个协变量进行回归分析。通过纳入双胞胎和/或孪生同胞的协变量来评估回归估计值的变化性质，以判断其是否与因果模型和/或其他模型一致。所谓因果关系，是指如果能够通过实验改变一个协变量，那么结果测量的期望值将会改变。在对个体体重进行调整后，孪生同胞与体重的关联减弱，这与体重对乳房X线摄影测量值的因果效应一致，就绝对对数平方厘米/千克而言，体重对非致密区的影响比对致密区的影响强三倍。在调整体重后，初潮年龄较晚和身高较高与更大的致密区和更小的非致密区相关，这种方式与因果关系不一致。致密区和非致密区与产次的关联与因果效应和/或个体内部混杂因素一致。乳房X线摄影密度测量值与身高之间的关联与共同的早期生活环境因素一致，这些因素会导致身高和乳房X线摄影密度百分比升高，进而可能增加患乳腺癌的风险。
