RT Journal Article
T1 Breast Cancer Risk From Modifiable and Nonmodifiable Risk Factors Among White Women in the United States.
A1 Maas, Paige
A1 Barrdahl, Myrto
A1 Joshi, Amit D
A1 Auer, Paul L
A1 Gaudet, Mia M
A1 Milne, Roger L
A1 Schumacher, Fredrick R
A1 Anderson, William F
A1 Check, David
A1 Chattopadhyay, Subham
A1 Baglietto, Laura
A1 Berg, Christine D
A1 Chanock, Stephen J
A1 Cox, David G
A1 Figueroa, Jonine D
A1 Gail, Mitchell H
A1 Graubard, Barry I
A1 Haiman, Christopher A
A1 Hankinson, Susan E
A1 Hoover, Robert N
A1 Isaacs, Claudine
A1 Kolonel, Laurence N
A1 Le Marchand, Loic
A1 Lee, I-Min
A1 Lindström, Sara
A1 Overvad, Kim
A1 Romieu, Isabelle
A1 Sanchez-Perez, Maria-Jose
A1 Southey, Melissa C
A1 Stram, Daniel O
A1 Tumino, Rosario
A1 VanderWeele, Tyler J
A1 Willett, Walter C
A1 Zhang, Shumin
A1 Buring, Julie E
A1 Canzian, Federico
A1 Gapstur, Susan M
A1 Henderson, Brian E
A1 Hunter, David J
A1 Giles, Graham G
A1 Prentice, Ross L
A1 Ziegler, Regina G
A1 Kraft, Peter
A1 Garcia-Closas, Montse
A1 Chatterjee, Nilanjan
AB An improved model for risk stratification can be useful for guiding public health strategies of breast cancer prevention. To evaluate combined risk stratification utility of common low penetrant single nucleotide polymorphisms (SNPs) and epidemiologic risk factors. 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. Single nucleotide polymorphisms, family history, anthropometric factors, menstrual and/or reproductive factors, and lifestyle factors. 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). 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. 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.
YR 2016
FD 2016
LK http://hdl.handle.net/10668/10126
UL http://hdl.handle.net/10668/10126
LA en
DS RISalud
RD Apr 9, 2025