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. Hoover & 25 others Claudine 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 journalArticle

Abstract

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
Volume2
Issue number10
DOIs
StatePublished - Oct 1 2016

Fingerprint

Breast Neoplasms
Single Nucleotide Polymorphism
Epidemiologic Factors
Health Surveys
Population
Hormones
SEER Program
Reproductive History
National Cancer Institute (U.S.)
Risk Reduction Behavior
Smoke
Registries
Life Style
Prostatic Neoplasms
Body Mass Index
Cohort Studies
Public Health
Smoking
Odds Ratio
Communication

ASJC Scopus subject areas

  • Medicine(all)

Cite this

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

In: JAMA oncology, Vol. 2, No. 10, 01.10.2016, p. 1295-1302.

Research output: Contribution to journalArticle

Maas, P, Barrdahl, M, Joshi, AD, Auer, PL, Gaudet, MM, Milne, RL, Schumacher, FR, Anderson, WF, Check, D, Chattopadhyay, S, Baglietto, L, Berg, CD, Chanock, SJ, Cox, DG, Figueroa, JD, Gail, MH, Graubard, BI, Haiman, CA, Hankinson, SE, Hoover, RN, Isaacs, C, Kolonel, LN, Le Marchand, L, Lee, IM, Lindström, S, Overvad, K, Romieu, I, Sanchez, MJ, Southey, MC, Stram, DO, Tumino, R, VanderWeele, TJ, Willett, WC, Zhang, S, Buring, JE, Canzian, F, Gapstur, SM, Henderson, BE, Hunter, DJ, Giles, GG, Prentice, RL, Ziegler, RG, Kraft, P, Garcia-Closas, M & Chatterjee, N 2016, 'Breast Cancer Risk From Modifiable and Nonmodifiable Risk Factors Among White Women in the United States', JAMA oncology, vol. 2, no. 10, pp. 1295-1302. https://doi.org/10.1001/jamaoncol.2016.1025
Maas, Paige ; Barrdahl, Myrto ; Joshi, Amit D. ; Auer, Paul L. ; Gaudet, Mia M. ; Milne, Roger L. ; Schumacher, Fredrick R. ; Anderson, William F. ; Check, David ; Chattopadhyay, Subham ; Baglietto, Laura ; Berg, Christine D. ; Chanock, Stephen J. ; Cox, David G. ; Figueroa, Jonine D. ; Gail, Mitchell H. ; Graubard, Barry I. ; Haiman, Christopher A. ; Hankinson, Susan E. ; Hoover, Robert N. ; Isaacs, Claudine ; Kolonel, Laurence N. ; Le Marchand, Loic ; Lee, I. Min ; Lindström, Sara ; Overvad, Kim ; Romieu, Isabelle ; Sanchez, Maria Jose ; Southey, Melissa C. ; Stram, Daniel O. ; Tumino, Rosario ; VanderWeele, Tyler J. ; Willett, Walter C. ; Zhang, Shumin ; Buring, Julie E. ; Canzian, Federico ; Gapstur, Susan M. ; Henderson, Brian E. ; Hunter, David J. ; Giles, Graham G. ; Prentice, Ross L. ; Ziegler, Regina G. ; Kraft, Peter ; Garcia-Closas, Montse ; Chatterjee, Nilanjan. / Breast Cancer Risk From Modifiable and Nonmodifiable Risk Factors Among White Women in the United States. In: JAMA oncology. 2016 ; Vol. 2, No. 10. pp. 1295-1302.
@article{4de9aebf5cbf46e7a83ad059dc0f8282,
title = "Breast Cancer Risk From Modifiable and Nonmodifiable Risk Factors Among White Women in the United States",
abstract = "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.",
author = "Paige Maas and Myrto Barrdahl and Joshi, {Amit D.} and Auer, {Paul L.} and Gaudet, {Mia M.} and Milne, {Roger L.} and Schumacher, {Fredrick R.} and Anderson, {William F.} and David Check and Subham Chattopadhyay and Laura Baglietto and Berg, {Christine D.} and Chanock, {Stephen J.} and Cox, {David G.} and Figueroa, {Jonine D.} and Gail, {Mitchell H.} and Graubard, {Barry I.} and Haiman, {Christopher A.} and Hankinson, {Susan E.} and Hoover, {Robert N.} and Claudine Isaacs and Kolonel, {Laurence N.} and {Le Marchand}, Loic and Lee, {I. Min} and Sara Lindstr{\"o}m and Kim Overvad and Isabelle Romieu and Sanchez, {Maria Jose} and Southey, {Melissa C.} and Stram, {Daniel O.} and Rosario Tumino and VanderWeele, {Tyler J.} and Willett, {Walter C.} and Shumin Zhang and Buring, {Julie E.} and Federico Canzian and Gapstur, {Susan M.} and Henderson, {Brian E.} and Hunter, {David J.} and Giles, {Graham G.} and Prentice, {Ross L.} and Ziegler, {Regina G.} and Peter Kraft and Montse Garcia-Closas and Nilanjan Chatterjee",
year = "2016",
month = "10",
day = "1",
doi = "10.1001/jamaoncol.2016.1025",
language = "English (US)",
volume = "2",
pages = "1295--1302",
journal = "JAMA oncology",
issn = "2374-2437",
publisher = "American Medical Association",
number = "10",

}

TY - JOUR

T1 - Breast Cancer Risk From Modifiable and Nonmodifiable Risk Factors Among White Women in the United States

AU - Maas, Paige

AU - Barrdahl, Myrto

AU - Joshi, Amit D.

AU - Auer, Paul L.

AU - Gaudet, Mia M.

AU - Milne, Roger L.

AU - Schumacher, Fredrick R.

AU - Anderson, William F.

AU - Check, David

AU - Chattopadhyay, Subham

AU - Baglietto, Laura

AU - Berg, Christine D.

AU - Chanock, Stephen J.

AU - Cox, David G.

AU - Figueroa, Jonine D.

AU - Gail, Mitchell H.

AU - Graubard, Barry I.

AU - Haiman, Christopher A.

AU - Hankinson, Susan E.

AU - Hoover, Robert N.

AU - Isaacs, Claudine

AU - Kolonel, Laurence N.

AU - Le Marchand, Loic

AU - Lee, I. Min

AU - Lindström, Sara

AU - Overvad, Kim

AU - Romieu, Isabelle

AU - Sanchez, Maria Jose

AU - Southey, Melissa C.

AU - Stram, Daniel O.

AU - Tumino, Rosario

AU - VanderWeele, Tyler J.

AU - Willett, Walter C.

AU - Zhang, Shumin

AU - Buring, Julie E.

AU - Canzian, Federico

AU - Gapstur, Susan M.

AU - Henderson, Brian E.

AU - Hunter, David J.

AU - Giles, Graham G.

AU - Prentice, Ross L.

AU - Ziegler, Regina G.

AU - Kraft, Peter

AU - Garcia-Closas, Montse

AU - Chatterjee, Nilanjan

PY - 2016/10/1

Y1 - 2016/10/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85013768524&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85013768524&partnerID=8YFLogxK

U2 - 10.1001/jamaoncol.2016.1025

DO - 10.1001/jamaoncol.2016.1025

M3 - Article

VL - 2

SP - 1295

EP - 1302

JO - JAMA oncology

JF - JAMA oncology

SN - 2374-2437

IS - 10

ER -