Breast Cancer Risk From Modifiable and Nonmodifiable Risk Factors Among White Women in the United States

Paige Maas, Myrto Barrdahl, Amit D. Joshi, Paul L. Auer, Mia M. Gaudet, Roger L. Milne, Fredrick R. Schumacher, William F. Anderson, David Check, Subham Chattopadhyay, Laura Baglietto, Christine D. Berg, Stephen J. Chanock, David G. Cox, Jonine D. Figueroa, Mitchell H. Gail, Barry I. Graubard, Christopher A. Haiman, Susan E. Hankinson, Robert N. HooverClaudine Isaacs, Laurence N. Kolonel, Loic Le Marchand, I. Min Lee, Sara Lindström, Kim Overvad, Isabelle Romieu, Maria Jose Sanchez, Melissa C. Southey, Daniel O. Stram, Rosario Tumino, Tyler J. VanderWeele, Walter C. Willett, Shumin Zhang, Julie E. Buring, Federico Canzian, Susan M. Gapstur, Brian E. Henderson, David J. Hunter, Graham G. Giles, Ross L. Prentice, Regina G. Ziegler, Peter Kraft, Montse Garcia-Closas, Nilanjan Chatterjee

Research output: Contribution to journalArticlepeer-review

150 Scopus citations


Importance: An improved model for risk stratification can be useful for guiding public health strategies of breast cancer prevention.

Objective: To evaluate combined risk stratification utility of common low penetrant single nucleotide polymorphisms (SNPs) and epidemiologic risk factors.

Design, Setting, and Participants: Using a total of 17 171 cases and 19 862 controls sampled from the Breast and Prostate Cancer Cohort Consortium (BPC3) and 5879 women participating in the 2010 National Health Interview Survey, a model for predicting absolute risk of breast cancer was developed combining information on individual level data on epidemiologic risk factors and 24 genotyped SNPs from prospective cohort studies, published estimate of odds ratios for 68 additional SNPs, population incidence rate from the National Cancer Institute-Surveillance, Epidemiology, and End Results Program cancer registry and data on risk factor distribution from nationally representative health survey. The model is used to project the distribution of absolute risk for the population of white women in the United States after adjustment for competing cause of mortality.

Exposures: Single nucleotide polymorphisms, family history, anthropometric factors, menstrual and/or reproductive factors, and lifestyle factors.

Main Outcomes and Measures: Degree of stratification of absolute risk owing to nonmodifiable (SNPs, family history, height, and some components of menstrual and/or reproductive history) and modifiable factors (body mass index [BMI; calculated as weight in kilograms divided by height in meters squared], menopausal hormone therapy [MHT], alcohol, and smoking).

Results: The average absolute risk for a 30-year-old white woman in the United States developing invasive breast cancer by age 80 years is 11.3%. A model that includes all risk factors provided a range of average absolute risk from 4.4% to 23.5% for women in the bottom and top deciles of the risk distribution, respectively. For women who were at the lowest and highest deciles of nonmodifiable risks, the 5th and 95th percentile range of the risk distribution associated with 4 modifiable factors was 2.9% to 5.0% and 15.5% to 25.0%, respectively. For women in the highest decile of risk owing to nonmodifiable factors, those who had low BMI, did not drink or smoke, and did not use MHT had risks comparable to an average woman in the general population.

Conclusions and Relevance: This model for absolute risk of breast cancer including SNPs can provide stratification for the population of white women in the United States. The model can also identify subsets of the population at an elevated risk that would benefit most from risk-reduction strategies based on altering modifiable factors. The effectiveness of this model for individual risk communication needs further investigation.

Original languageEnglish (US)
Pages (from-to)1295-1302
Number of pages8
JournalJAMA Oncology
Issue number10
StatePublished - Oct 1 2016

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


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