Inference about causation from examination of familial confounding: Application to longitudinal twin data on mammographic density measures that predict breast cancer risk

Jennifer L Stone, Gillian S Dite, Graham G. Giles, Jennifer Cawson, Dallas R. English, John L. Hopper

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20 Citations (Scopus)

Abstract

Background: Mammographic density is a strong risk factor for breast cancer. It is unknown whether there are different causes of variation in mammographic density at different ages. Methods: Mammograms and questionnaires were obtained on average 8 years apart from 327 Australian female twin pairs (204 monozygous and 123 dizygous). Mammographic dense area and percentage dense area were measured using a computer-assisted method. The correlational structure of the longitudinal twin data was estimated under a multivariate normal model using FISHER. Inference about causation from examination of familial confounding was made by regressing each twin's recent mammographic density measure against one or both of her and her co-twin's past measures. Results: For square root dense area and percentage dense area (age- and body mass index-adjusted), the correlations over time within twins were 0.86 and 0.82, and the cross-twin correlations were 0.71 and 0.65 for monozygous pairs and 0.25 and 0.20 for dizygous pairs, respectively. As a predictor of a twin's recent dense area, the regression coefficient (SE) for the co-twin's past dense area reduced after adjusting for her own past measure from 0.84 (0.03) to 0.09 (0.03) for monozygous pairs and from 0.63 (0.04) to 0.04 (0.03) for dizygous pairs. Corresponding estimates for percentage dense area were 0.73 (0.04), 0.10 (0.03), 0.42 (0.05), and 0.03 (0.03). Conclusion: Mammographic density measures are highly correlated over time and the familial/genetic components of their variation are established before mid-life. Impact: Mammographic density of young women could provide a means for breast cancer control.

Original languageEnglish
Pages (from-to)1149-1155
Number of pages7
JournalCancer Epidemiology, Biomarkers & Prevention
Volume21
Issue number7
DOIs
Publication statusPublished - Jul 2012
Externally publishedYes

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