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P17 6 th <strong>International</strong> <strong>Workshop</strong> on Breast Densitometry and Breast Cancer Risk Assessment MULTIPLE METABOLIC RISK FACTORS AND MAMMOGRAPHIC BREAST DENSITY IN THE NEW YORK CITY MULTIETHNIC BREAST CANCER PROJECT Parisa Tehranifar, 1,2,3 Diane Reynolds, 4 Xiaozhou Fan, 1 Bernadette Boden-Albala, 5 Natalie Engmann, 1 Julie D. Flom, 1 Mary Beth Terry 1,2 1 Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY ; 2 Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY ; 3 The Center for the Study of Social Inequalities and Health, Columbia University Mailman School of Public Health, New York,, NY; 4 School of Nursing, Long Island University, Brooklyn Campus, Brooklyn, NY; 5 Division of Social Epidemiology, Department of Health Evidence and Policy, Department of Neurology, ICAHN School of Medicine at Mount Sinai, New York, NY Acknowledgement: We would like to thank the study participants for contributing data to this analysis and the following research staff for assisting with data collection and recruitment activities: Diane Levy, Wendy Lewis, Gladys Rivera, Zoe Quandt, Joy White, Jessica Cabildo and Renata Khanis. This research was funded by grants from the National Cancer Institute (grant numbers: U54 CA101598; K07 CA151777), Susan B. Komen Foundation Career Catalyst award (grant number KG110331) and the National Institute of Environmental Health Sciences Center Support (grant number ES009089). Background: Metabolic abnormalities have been individually associated with breast cancer risk. More recently, studies have also shown modest positive associations between the presence of multiple metabolic conditions, as frequently defined by the metabolic syndrome (MetS), and breast cancer risk in postmenopausal women. Few studies have examined whether the presence of multiple metabolic risk factors are similarly associated with breast density, an intermediate marker of breast cancer risk. We examined whether obesity, diabetes, hypertension, and elevated cholesterol, individually and in combination, are associated with breast density. Methods: We digitized film mammograms and measured percent density and dense area using a computer-assisted method in a sample of 191 women, recruited from a single radiology facility during their screening mammography appointment (age range=40-61 years; 42% African American, 22% African Caribbean, 22% White, 11% Hispanic, 3% other ethnicities). We used in-person computer assisted interviews to collect detailed epidemiologic data at the time of recruitment. We used linear regression models to examine the associations of each metabolic condition and the number of co-occurring metabolic conditions (0, 1, 2, and 3 or 4 conditions) with percent density and dense breast area. ABSTRACTS Results: In age adjusted models of individual metabolic conditions, hypertension and obese body mass index (≥30 kg/m 2 ) were inversely associated with percent density, and high blood cholesterol was inversely associated with dense area. The presence of multiple metabolic conditions was associated with lower percent density and dense area in multivariable models (2 conditions and 3 or 4 conditions vs. 0 conditions were associated with 9.4% (95% CI:-13.2, -5.5) and 10.7% (95% CI:-15.2, -6.2) reduction in percent density and with 5.5 cm 2 (95% CI: -10.8, -0.3) and 8.4 cm 2 (95% CI: -14.5, -2.4) smaller dense area). Conclusions: The presence of multiple metabolic conditions was associated with lower levels of relative and absolute measures of breast density in a predominantly racial minority sample of women. The reduction in breast density associated with multiple metabolic conditions was greater than the influence of any single condition on breast density. Our results suggest that the positive association between metabolic abnormalities and breast cancer may not involve pathways related to breast density, and may be stronger after accounting for the influence of metabolic conditions on breast density. 50
6 th <strong>International</strong> <strong>Workshop</strong> on Breast Densitometry and Breast Cancer Risk Assessment P18 GENETIC TESTING FOR BREAST CANCER RISK ESTIMATION – A COST - EFFECTIVENESS ANALYSIS Henri Folse 1 (corresponding and presenting author), Tuan Dinh 1 , Richard Allman 2 1 Archimedes, Inc., 2 Genetic Technologies, Ltd. Genetic testing based on seven single-nucleotide polymorphisms (7SNP) can improve individualized estimates of lifetime risk of breast cancer relative to the Gail risk test alone, for the purpose of recommending MRI screening for women at high risk. ABSTRACTS An individual-based, continuous-time simulation model of breast cancer and health care processes was used to simulate women in a virtual trial comparing the use of the 7SNP test to the Gail risk test alone to categorize patients as either low risk or high risk. Low risk patients received annual mammogram, while high risk patients received annual MRI. Cancer incidence was based on Surveillance, Epidemiology, and End Results (SEER) data and validated to the Cancer Prevention Study II (CPS-II) Nutrition Cohort data set. Risk factors are drawn from the National Health and Nutrition Examination Survey (NHANES-4) and Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) data sets. Mammogram characteristics were derived from the Breast Cancer Surveillance Consortium (BCSC) dataset. Other parameters were derived from published literature. The 7SNP test (vs Gail alone) saved 0.00734 quality-adjusted life-years (QALYs) per person at a cost of $1,971 per person ($268,386 per QALY). Limiting the 7SNP test to only those patients with a lifetime Gail risk of 16 – 28% resulted in a cost of $163,264 per QALY. These results were sensitive to the age at which the test is given, the discount rate, and the costs of the genetic test and MRI. The cost-effectiveness of using the 7SNP test for patients with intermediate Gail risk is similar to that of other recommended strategies, including annual MRI for patients with a lifetime risk greater than 20% or BRCA1/2 mutations. 51
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