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Risk Factors for Breast Cancer for Women Aged 40 to 49 Years: A Systematic Review and Meta-analysis FREE

Heidi D. Nelson, MD, MPH; Bernadette Zakher, MBBS; Amy Cantor, MD, MPH; Rongwei Fu, PhD; Jessica Griffin, MS; Ellen S. O'Meara, PhD; Diana S.M. Buist, PhD, MPH; Karla Kerlikowske, MD, MS; Nicolien T. van Ravesteyn, MSc; Amy Trentham-Dietz, PhD; Jeanne S. Mandelblatt, MD, MPH; and Diana L. Miglioretti, PhD
[+] Article and Author Information

From the Oregon Evidence-based Practice Center, Oregon Health & Science University, and Providence Cancer Center, Portland, Oregon; Group Health Research Institute, Seattle, Washington; San Francisco Veterans Affairs Medical Center and University of California, San Francisco, California; Erasmus Medical Center, Rotterdam, the Netherlands; Carbone Cancer Center, University of Wisconsin-Madison, Madison, Wisconsin; and Lombardi Comprehensive Cancer Center, Washington, DC.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.

Note: Each cancer registry and the Statistical Coordinating Center have received institutional review board approval for either active or passive consenting processes or a waiver of consent to enroll participants, link data, and perform analytic studies. All procedures are compliant with the Health Insurance Portability and Accountability Act, and all registries and the Statistical Coordinating Center have received a federal Certificate of Confidentiality and other protection for the identities of women, physicians, and facilities participating in this research. A list of the BCSC investigators and procedures for requesting BCSC data for research purposes is provided at http://breastscreening.cancer.gov/.

Acknowledgment: The authors thank the participating women, mammography facilities, and radiologists for the data they have provided for this study. Rose Relevo, MLIS, MS, and Robin Paynter, MA-LIS, conducted literature searches, and Katie Reitel, BA, provided assistance; all are affiliated with Oregon Health & Science University.

Grant Support: By a National Cancer Institute Activities to Promote Research Collaboration supplement (U01CA086076-10S1). Data collection was supported by the National Cancer Institute–funded BCSC cooperative agreements (U01CA63740, U01CA86076, U01CA86082, U01CA63736, U01CA70013, U01CA69976, U01CA63731, and U01CA70040). Providence Health & Services provided additional support for Dr. Nelson, and the Veterans Affairs Fellowship in Health Issues of Women Veterans provided support for Dr. Cantor. The collection of BCSC cancer data used in this study was supported in part by several state public health departments and cancer registries throughout the United States. For a full description of these sources, please see www.breastscreening.cancer.gov/work/acknowledgement.html.

Potential Conflicts of Interest: Dr. Nelson: Grant (money to institution): NCI; Support for travel to meetings for the study or other purposes (money to institution): NCI. Dr Fu: Grant (money to institution): AHRQ. Ms. Griffin: Grant (money to institution): NCI. Dr. O'Meara: Grant (money to institution): NCI; Employment: Group Health Research Institute. Dr. Buist: Grant (money to institution): NCI. Ms. van Ravesteyn: Grant (money to institution): NCI. Dr. Trentham-Dietz: Grant (money to institution): NCI; Support for travel to meetings for the study or other purposes: NCI. Dr. Mandelblatt: Grant (money to institution): NCI; Support for travel to meetings for the study or other purposes (money to institution): NCI. Dr. Miglioretti: Grant (money to institution): NIH. Disclosures can be also viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M11-2350.

Requests for Single Reprints: Heidi D. Nelson, MD, MPH, Oregon Evidence-based Practice Center, Oregon Health & Science University, Mail Code BICC, 3181 SW Sam Jackson Park Road, Portland, OR 97239-3098; e-mail, mailto:nelsonh@ohsu.edu.

Current Author Addresses: Drs. Nelson, Zakher, Cantor, and Fu and Ms. Griffin: 3181 SW Sam Jackson Park Road, Mail Code BICC, Portland, OR 97239.

Drs. O'Meara, Buist, and Miglioretti: 1730 Minor Avenue, Suite 1600, Seattle, WA 98101.

Dr. Kerlikowske: 4150 Clement Street, San Francisco, CA 94121.

Ms. van Ravesteyn: PO Box 2040, Rotterdam 3000, the Netherlands.

Dr. Trentham-Dietz: 307 WARF Building, 610 Walnut Street, Madison, WI 53726.

Dr. Mandelblatt: Suite 4100, 3300 Whitehaven Street NW, Washington, DC 20007.

Author Contributions: Conception and design: H.D. Nelson, D.S.M. Buist, K. Kerlikowske, N. van Ravesteyn, A. Trentham-Dietz, J. Mandelblatt, D. Miglioretti.

Analysis and interpretation of the data: H.D. Nelson, B. Zakher, A. Cantor, R. Fu, J. Griffin, E.S. O'Meara, D.S.M. Buist, K. Kerlikowske, A. Trentham-Dietz, J. Mandelblatt.

Drafting of the article: H.D. Nelson, B. Zakher, A. Cantor, R. Fu, J. Griffin, E.S. O'Meara, K. Kerlikowske, J. Mandelblatt.

Critical revision of the article for important intellectual content: H.D. Nelson, B. Zakher, A. Cantor, R. Fu, D.S.M. Buist, K. Kerlikowske, N. van Ravesteyn, A. Trentham-Dietz, J. Mandelblatt, D. Miglioretti.

Final approval of the article: H.D. Nelson, B. Zakher, R. Fu, J. Griffin, E.S. O'Meara, D.S.M. Buist, K. Kerlikowske, N. van Ravesteyn, J. Mandelblatt, D. Miglioretti.

Provision of study materials or patients: H.D. Nelson, D.S.M. Buist, K. Kerlikowske.

Statistical expertise: H.D. Nelson, R. Fu, K. Kerlikowske, D. Miglioretti.

Obtaining of funding: H.D. Nelson, D.S.M. Buist, K. Kerlikowske, J. Mandelblatt, D. Miglioretti.

Administrative, technical, or logistic support: H.D. Nelson, J. Griffin, D.S.M. Buist.

Collection and assembly of data: H.D. Nelson, B. Zakher, A. Cantor, R. Fu, J. Griffin, D.S.M. Buist, K. Kerlikowske, D. Miglioretti.


Ann Intern Med. 2012;156(9):635-648. doi:10.7326/0003-4819-156-9-201205010-00006
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Background: Identifying risk factors for breast cancer specific to women in their 40s could inform screening decisions.

Purpose: To determine what factors increase risk for breast cancer in women aged 40 to 49 years and the magnitude of risk for each factor.

Data Sources: MEDLINE (January 1996 to the second week of November 2011), Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews (fourth quarter of 2011), Scopus, reference lists of published studies, and the Breast Cancer Surveillance Consortium.

Study Selection: English-language studies and systematic reviews of risk factors for breast cancer in women aged 40 to 49 years. Additional inclusion criteria were applied for each risk factor.

Data Extraction: Data on participants, study design, analysis, follow-up, and outcomes were abstracted. Study quality was rated by using established criteria, and only studies rated as good or fair were included. Results were summarized by using meta-analysis when sufficient studies were available or from the best evidence based on study quality, size, and applicability when meta-analysis was not possible. Data from the Breast Cancer Surveillance Consortium were analyzed with proportional hazards models by using partly conditional Cox regression. Reference groups for comparisons were set at U.S. population means.

Data Synthesis: Sixty-six studies provided data for estimates. Extremely dense breasts on mammography or first-degree relatives with breast cancer were associated with at least a 2-fold increase in risk for breast cancer. Prior breast biopsy, second-degree relatives with breast cancer, or heterogeneously dense breasts were associated with a 1.5- to 2.0-fold increased risk; current use of oral contraceptives, nulliparity, and age 30 years or older at first birth were associated with a 1.0- to 1.5-fold increased risk.

Limitations: Studies varied by measures, reference groups, and adjustment for confounders, which could bias combined estimates. Effects of multiple risk factors were not considered.

Conclusion: Extremely dense breasts and first-degree relatives with breast cancer were each associated with at least a 2-fold increase in risk for breast cancer in women aged 40 to 49 years. Identification of these risk factors may be useful for personalized mammography screening.

Primary Funding Source: National Cancer Institute.

Editors' Notes
Context

  • Knowing which factors influence breast cancer risk for women younger than 50 years might help target screening efforts.

Contribution

  • This review found that the following factors increased risk for breast cancer in women aged 40 to 49 years: extremely dense breasts or first-degree relatives with breast cancer (≥2-fold increase); prior breast biopsy, second-degree relatives with cancer, or heterogeneously dense breasts (1.5- to 2.0-fold increase); current oral contraceptive use, nulliparity, and age 30 years or older at first birth (1.0- to 1.5-fold increase).

Caution

  • Confounding may have affected some risk estimates.

Implication

  • Risk factor information could help personalize decisions about breast cancer screening in women aged 40 to 49 years.

—The Editors


Current practice guidelines on mammography screening differ in their recommendations for women in their 40s (13). The U.S. Preventive Services Task Force recommends individualized, informed decision making about when to start mammography screening based on a woman's values about benefits and harms (4). Risk-based screening has been recommended for other health conditions in the United States and may provide a similar evidence-based approach for breast cancer. However, applying this approach to clinical practice has been problematic because it is unclear how women and clinicians can effectively consider individualized risk factor information in their discussions of benefits and harms.

Microsimulation models of mammography screening developed as part of the Cancer Intervention and Surveillance Modeling Network (CISNET) indicated that women with approximately 2-fold increased risk for breast cancer who started biennial screening at age 40 years had similar benefits (life-years gained) and harms (false-positive results) as average-risk women who started screening at age 50 years (5). The risk threshold was higher when the CISNET models considered reduction in breast cancer deaths as a benefit (risk ratio [RR], 3.3) or annual rather than biennial screening (RR, 4.3). These results suggest that identifying women with at least a 2-fold increased risk for breast cancer could be useful in determining whether to initiate mammography screening before age 50 years.

Much research has been published describing personal and clinical risk factors associated with breast cancer. However, studies generally included women of various ages, measured and reported risk factors in different ways, and provided wide ranges of risk estimates. Consequently, results of broad-based epidemiologic studies may not be clinically applicable to the screening decisions of individual women and in some cases may be misleading.

The purpose of this systematic review and meta-analysis is to determine what factors increase risk for invasive breast cancer, specifically for women aged 40 to 49 years, and to estimate the magnitude of risk for each factor compared with average-risk women. It focuses on women who are eligible for screening mammography under current practice guidelines in the United States and considers average-risk women to be those without the risk factor or who represent the mean or majority of women in the cohort, depending on the risk factor. This project was conducted in collaboration with development of the CISNET models of mammography screening based on increasing levels of risk and builds on previous work (67).

Data Sources and Searches

A standard protocol was developed and followed for this review. In conjunction with a research librarian, we used the National Library of Medicine's Medical Subject Headings keyword nomenclature to search MEDLINE (1996 to the second week of November 2011), the Cochrane Central Register of Controlled Trials (fourth quarter of 2011), and the Cochrane Database of Systematic Reviews (fourth quarter of 2011) for relevant English-language studies and systematic reviews. We also conducted secondary referencing by manually reviewing reference lists of papers and by using Scopus to search citations of key studies. Searches included studies published during the past 16 years to provide data that are relevant to current cohorts of women considering mammography screening and to correspond to the time frame of risk factor data collected by the Breast Cancer Surveillance Consortium (BCSC) that were also used in this study.

Study Selection

We developed inclusion and exclusion criteria for abstracts and articles based on the target population, risk factors, and outcome measures. We included randomized, controlled trials; observational studies; systematic reviews; and meta-analyses. After an initial review of abstracts, we retrieved full-text articles and conducted a second review by using additional inclusion criteria defined specifically for each risk factor, including eligibility of the data for statistical meta-analysis. When sufficient studies were not available for a meta-analysis, we used the best evidence as determined by consensus among the investigators on the basis of study quality, size, and applicability.

The target population consisted of women aged 40 to 49 years who were eligible for screening mammography. Studies were excluded if they enrolled women who were not candidates for routine screening because they had prior breast or ovarian cancer, ductal carcinoma in situ or other noninvasive breast cancer, current breast physical findings, presence of deleterious BRCA1/BRCA2 mutations in self or relatives, or prior chest radiation for such conditions as lymphoma. Included studies were conducted in countries with patient populations and health care services similar to those of the United States to ensure applicability. Studies that reported outcomes in age groups that differed from the 40- to 49-year age category were included if most participants were aged 40 to 49 years and all were younger than 55 years. When studies reported outcomes by menopausal status rather than age, we used results for premenopausal women as long as the group included a majority of women in their 40s.

The main outcome measure was incidence of invasive breast cancer at age 40 to 49 years or invasive and noninvasive breast cancer as a combined outcome when this was the only measure reported in a study.

Risk factors included race and ethnicity, body mass index (BMI), physical activity, alcohol use, smoking, family history of breast cancer, breast density, prior breast procedures, and reproductive factors (age at menarche; parity; age at birth of first child; breastfeeding; oral contraceptive use; menopausal age, status, and type; and menopausal hormone therapy). We included studies meeting the following criteria: studies of risk factors for recent or current status that reflected exposure within 1 year of breast cancer diagnosis; studies of physical activity that reported categories of exercise descriptively (inactive, some, or regular) or quantified by metabolic equivalents; studies of alcohol use and smoking that reported use status (current, former, or never), recency, and amounts of use (drinks per week or packs per day); and studies of oral contraceptive and menopausal hormone therapy use that investigated any formulation (combination, progestin, or estrogen only) and used various definitions of ever and never use. We excluded studies of nonoral forms of contraception and those evaluating formulations not applicable to the target population (8).

We included studies that defined parity as the number of full-term births, full-term pregnancies, live births, or pregnancies lasting 6 months or more regardless of outcome, consistent with standard medical definitions (9). We included studies of breastfeeding that used a nonbreastfeeding group of parous women as the reference category and determined breastfeeding activity (ever or never) and total duration.

We included studies that reported menopausal status and history of hysterectomy or oophorectomy if the event preceded the breast cancer diagnosis in women in their 40s. We reviewed studies of mammographic breast density that used several methods to categorize density, but we reported results only from studies that used the Breast Imaging Reporting and Data System (BI-RADS) classification (1 = almost entirely fat, 2 = scattered fibroglandular densities, 3 = heterogeneously dense, and 4 = extremely dense) because of its clinical relevance to practice in the United States (10).

Data Extraction and Quality Assessment

For the included studies, an investigator abstracted the following data: study design, setting, participant characteristics (including age, race and ethnicity, and diagnosis), enrollment criteria, exposures (dose and duration), procedures for data collection, number enrolled and number lost to follow-up, methods of exposure and outcome ascertainment, analytic methods (including adjustment for confounders), and results for each outcome. A second investigator confirmed the accuracy of key data.

We used predefined criteria developed by the U.S. Preventive Services Task Force to assess the quality of studies (11). Two investigators independently rated the quality of each eligible study (good, fair, or poor), and final ratings were determined by consensus among raters. We used only studies rated as good or fair to determine risk estimates.

We assessed applicability of studies by using the PICOTS (population, intervention, comparator, outcomes, timing of outcomes measurement, and setting) approach (12). In addition, applicability of case–control studies was based on the control group population. For all studies, applicability was high if participants were recruited predominantly from community populations rather than clinical populations. For each risk factor, we also determined the consistency of results (that is, the degree of similarity in the effect sizes of the different studies). Studies were considered consistent if outcomes were generally in the same direction of effect and ranges of effect sizes were narrow. Applicability and consistency were determined by consensus of the investigators who reviewed the studies and conducted the meta-analyses.

Data Synthesis and Analysis

For eligible studies, we combined data in several meta-analyses to obtain more precise estimates of the relationship between risk factors and breast cancer. All included studies had cohort or case–control designs, and only studies reporting estimates that adjusted for at least 1 potential confounder in their analysis were included in the meta-analysis. To determine the appropriateness of meta-analysis, we considered clinical and methodological diversity and assessed statistical heterogeneity.

We abstracted or calculated estimates of RRs (odds ratio, rate ratio, or hazard ratio) and their SEs from each study and used them as the effect measures. Because the incidence of breast cancer was low, we considered the estimates of odds ratios to be equivalent to estimates of relative risks (rate ratio or hazard ratio). This assumed that the underlying event rates in the case–control studies, for example, reflected the low incidence rate in the population.

For most risk factors, studies reported RRs based on similar cut points across included studies and we used estimates based on the reported cut points. For BMI, the cut points were too disparate to be combined as reported in the published studies. Therefore, we combined BMI categories to correspond to the World Health Organization definitions of underweight (<18.5 kg/m2), normal weight (18.5 to <25 kg/m2), overweight (25 to <30 kg/m2), and obese (≥30 kg/m2) (13). We combined the underweight and normal weight categories because too few women were included in the underweight group. When studies categorized BMI by using other cut points, we calculated RRs by assuming that BMI is log-normally distributed and that a linear association exists between breast cancer risk and BMI on the logit scale. We estimated distribution parameters of BMI from published information in each study.

We assessed the presence of statistical heterogeneity among the studies by using standard chi-square tests and the magnitude of heterogeneity by using the I2 statistic (14). We used a random-effects model to account for variation among studies. In general, when there is no variation among studies, the random-effects model yields the same results as a fixed-effects model without a study effect (15). To explore heterogeneity, we used meta-regression to assess the effect of the degree of adjustment for confounders in the original studies. This was quantified by the total number of adjusted variables and the number of adjusted risk factors considered in the review as well as other study-level variables, such as quality, study design, and breast cancer type. We also conducted sensitivity analyses to assess the robustness of results that considered variation from different definitions of risk factors and reference groups, inclusion of noninvasive breast cancer as an outcome, and outlying studies. The results of the sensitivity analyses indicated no major differences from the main analysis.

For specific risk factors (BMI, age at menarche, and age at birth of first child), we recalculated RRs from the meta-analysis by using reference groups that differed from the original studies to approximate average risk in the population. The new reference groups were chosen to align with the distribution or mean of risk factors in the target population to provide more clinically relevant risk estimates. Data describing distributions or means in the target population were obtained from various sources representing U.S. national samples (1622) (Appendix Table).

Table Jump PlaceholderAppendix Table.  

Population Distribution of Risk Factors for Women Aged 40 to 49 Years

Comparison With BCSC Data

We included data from the BCSC to supplement the systematic review because some risk factors were not reported in published studies. The BCSC is a national collaboration of 5 mammography registries and 2 affiliate sites in the United States that prospectively collects data on breast imaging, risk factors, and breast cancer outcomes (23). We analyzed BCSC data collected from 1994 to 2010 for women aged 40 to 49 years at the time of screening mammography. Risk factor data were obtained at the time of each screening mammography and reported in categories similar to those defined by the systematic review. Results for 380 585 women aged 40 to 49 years were provided in proportional hazards models adjusted for age, race, family history of breast cancer, and BMI and stratified by site. We used partly conditional Cox regression (24) to incorporate multiple observations per woman (allowing her to enter the analysis at each eligible screening mammography) and accounted for multiple observations per woman by using the robust sandwich estimator of the SE (25). Women were followed until they were diagnosed with invasive breast cancer; until they were censored at the first occurrence of ductal carcinoma in situ, death, age 50 years, or end of complete cancer follow-up or eligibility for her site; or for 5 years after the examination date. Mean length of follow-up was 3.3 years.

All analyses for the meta-analysis and BCSC data were performed by using Stata, version 11.0 (StataCorp, College Station, Texas), and were reported as RRs with 95% CIs.

Role of the Funding Source

The National Cancer Institute funded this work but had no additional role in the design, conduct, or reporting of the review and analysis.

A total of 9036 abstracts were identified by search criteria; of these, 884 full-text articles were reviewed and 95 met the inclusion criteria as well as the criteria for good or fair quality (Figure). Sixty-one studies of 8 risk factors (BMI, alcohol use, smoking, age at menarche, parity, age at first birth, breastfeeding, and oral contraceptive use) provided data for meta-analysis. Two published meta-analyses of family history of breast cancer reported results specifically for women in their 40s (2627). Single studies provided estimates for 3 risk factors because either they were the only studies that met the inclusion and quality criteria for the risk factor (prior breast procedure) or studies did not provide data that could be used in a meta-analysis (breast density and physical activity). Individual studies providing data for risk estimates are described in the Supplement . Data from the BCSC provided the only estimates for 3 risk factors that had no published studies that met the inclusion criteria (race and ethnicity, menopausal status and type, and menopausal hormone therapy). No data were available to evaluate age at menopause.

Grahic Jump Location
Figure.

Summary of evidence search and selection.

BMI = body mass index; OC = oral contraceptive.

* Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews.

† Reference lists, Scopus, and studies suggested by experts.

‡ Some articles are included for more than 1 risk factor.

§ Published meta-analyses.

∥ No articles met inclusion criteria for race and ethnicity, menopausal stage and type (surgical or nonsurgical), age at menopause, and menopausal hormone use.

¶ Although some studies met inclusion criteria for the systematic review, they did not provide data for the meta-analysis because they used dissimilar categories or different measures from the other included studies.

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Personal Risk Factors

Personal risk factors included race and ethnicity, BMI, physical activity, alcohol use, and smoking (Table 1). Data from the BCSC indicated no statistically significant increased risks for breast cancer by race and ethnicity when white race was used as the reference group.

Table Jump PlaceholderTable 1.  

Breast Cancer Risk Associated With Personal Factors for Women Aged 40 to 49 Years

For BMI, a meta-analysis of 18 studies (17, 2844) indicated reduced risks for women in the overweight (RR, 0.86 [95% CI, 0.82 to 0.90]) and obese (RR, 0.74 [CI, 0.68 to 0.81]) categories compared with women in the normal and underweight category. Results were similar for overweight and obese categories in the BCSC, but the CI included 1.0 for overweight. Data specifically for underweight women were not provided by the published studies, although the BCSC data indicated that risk for underweight women was not significantly different from that of normal weight women.

Ten studies of physical activity met inclusion criteria (4554) but could not be combined in the meta-analysis because they used different measures of activity and reported results in dissimilar categories. All studies reported no statistically significant differences in breast cancer risk based on physical activity. Results from a large, good-quality study designed to specifically assess the relationship between physical activity and premenopausal breast cancer provided the estimates in Table 1(46). Data on physical activity were not available from the BCSC.

Although 12 studies reporting various measures of alcohol use met inclusion criteria (31, 36, 5564), results from only 3 studies could be combined in the meta-analysis (55, 61, 64). Using no alcohol use as the reference group, results indicated higher risk estimates with increasing amounts of alcohol consumption; however, all CIs included 1.0. Smoking use (never vs. ever) and status (never vs. current or former) had no significant associations with breast cancer based on meta-analyses of 12 studies of never versus ever use (31, 62, 6574) and 7 studies of never versus current or former use (31, 62, 67, 7073). No BCSC data on alcohol use or smoking were available.

Family History, Breast Density, and Breast Procedures

In an analysis of data from 52 epidemiologic studies (26), breast cancer risk was significantly increased for women with first-degree relatives with breast cancer (RR for 1 relative, 2.14 [CI, 1.92 to 2.38]; 2 relatives, 3.84 [CI, 2.37 to 6.22]; and ≥3 relatives, 12.05 [CI, 1.70 to 85.16]) (Table 2). Data from the BCSC also showed higher risk for women with a first-degree relative with breast cancer (RR, 1.86 [CI, 1.69 to 2.06]). In both the meta-analysis and the BCSC results, risk was higher among women with first-degree relatives who were diagnosed at younger ages than those diagnosed at older ages. Risk ratios for women with relatives younger than 40 years compared with women with no first-degree relatives were 3.0 (CI, 1.8 to 4.9) in the meta-analysis (26) and 2.17 (CI, 1.86 to 2.53) for women with relatives younger than 50 years in the BCSC. Risk was lower for women with relatives diagnosed at age 60 years or older (RR, 1.7 [CI, 1.3 to 2.1]) (26). In a meta-analysis of 2 studies (27), risk was also significantly increased for women with 1 or more second-degree relatives compared with none (RR, 1.7 [CI, 1.4 to 2.0]).

Table Jump PlaceholderTable 2.  

Breast Cancer Risk Associated With Family History, Breast Density, and Breast Procedures for Women Aged 40 to 49 Years

A published study of BCSC data reported risk estimates for breast cancer by using BI-RADS breast density categories and defined BI-RADS category 2 (scattered fibroglandular densities) as the reference group (75). Results indicated increased risk for categories 3 (RR, 1.62 [CI, 1.51 to 1.75]) and 4 (RR, 2.04 [CI, 1.84 to 2.26]) and reduced risk for category 1 (RR, 0.46 [CI, 0.37 to 0.58]).

A published study of BCSC data reported increased breast cancer risk for women who had prior benign results on breast biopsy (RR, 1.87 [CI, 1.64 to 2.13]) (76). Additional BCSC data that included prior biopsies or fine-needle aspirations also indicated increased risk (RR, 1.51 [CI, 1.36 to 1.67]).

Reproductive Factors

Reproductive factors included age at menarche, parity, age at first birth, breastfeeding, oral contraceptive use, menopausal status, and menopausal hormone therapy (Table 3). In our meta-analysis of 13 studies (31, 34, 45, 7786), menarche at age 15 years or older was associated with reduced risk for breast cancer compared with the reference age of 13 years (RR, 0.87 [CI, 0.78 to 0.97]). Results from the BCSC were similar.

Table Jump PlaceholderTable 3.  

Breast Cancer Risk Associated With Reproductive Factors for Women Aged 40 to 49 Years

A meta-analysis of 17 studies of parity (31, 34, 45, 74, 7780, 8290) indicated that nulliparous women had a significantly higher risk for breast cancer than parous women (RR, 1.16 [CI, 1.04 to 1.26]), similar to BCSC estimates (Table 3). In a meta-analysis of 13 studies (31, 34, 74, 7780, 82, 8487, 90), risk was significantly reduced for women with 3 or more births compared with nulliparous women (RR, 0.73 [CI, 0.61 to 0.87]). In a meta-analysis of 5 studies of age at first birth (34, 45, 77, 83, 88), women having their first child at age 30 years or older had a higher risk for breast cancer than a reference group of women aged 25 to 29 years (RR, 1.20 [CI, 1.02 to 1.42]) but a slightly lower risk than nulliparous women (RR, 1.25 [CI, 1.08 to 1.46]). Results from the BCSC indicated no significantly increased risk for these groups but did indicate decreased risk for women aged 20 years or younger at the time of the first birth.

In a meta-analysis of 14 studies (34, 74, 78, 80, 8283, 86, 9096), breastfeeding was associated with reduced risk for breast cancer (RR, 0.87 [CI, 0.77 to 0.98]), particularly when it continued for 12 months or longer (RR, 0.85 [CI, 0.73 to 0.99]) (34, 78, 80, 8283, 9196). Breastfeeding data were not available from the BCSC.

Twelve studies of oral contraceptive use provided estimates for the meta-analysis of ever use compared with never use (31, 74, 7778, 97104), and 8 studies provided estimates for recency of use, with the most recent category defined as within 5 years (7778, 99104). None of these associations was statistically significant, although all point estimates were increased (Table 3). Data from the BCSC indicated significantly higher risk for breast cancer for current oral contraceptive use than for former or never use (RR, 1.30 [CI, 1.13 to 1.49]).

Data from the BCSC showed reduced breast cancer risk for perimenopausal and postmenopausal women (either surgical or nonsurgical menopause) compared with premenopausal women. The BCSC data also indicated that women with no uterus currently using menopausal hormone therapy had a reduced risk for breast cancer compared with nonusers (RR, 0.70 [CI, 0.52 to 0.94]), whereas those with a uterus had no significant association. Presumably, women without a uterus were using estrogen alone, whereas those with a uterus were using estrogen combined with progestin.

Sixty-six studies identified in the systematic review contributed data for breast cancer risk estimates for 13 unique risk factors, whereas data from the BCSC provided estimates for 11 risk factors, 3 of which were not included in published studies. Both sources provided estimates for some risk factors that were expressed in alternate ways, such as in dichotomous as well as ordinal categories. A summary of evidence for the systematic review describes the number and design of included studies; breast cancer outcomes; and ratings for quality, consistency, and applicability for each risk factor (Table 4). Overall, studies were consistent and applicability was high, largely because conditions of the study population were incorporated into inclusion criteria.

Table Jump PlaceholderTable 4.  

Summary of Evidence for Studies Providing Data for Risk Estimates

Results indicated that women in their 40s with extremely dense breasts on mammography or at least 1 first-degree relative with breast cancer had at least a 2-fold increased risk for breast cancer (Table 5). This level of risk corresponds to the risk threshold of the CISNET models, which demonstrated similar benefits and harms for increased-risk women starting biennial screening at age 40 years and average-risk women starting at age 50 years (5). Risk was even higher among women with 2 or more first-degree relatives with breast cancer or first-degree relatives diagnosed before age 40 years.

Table Jump PlaceholderTable 5.  

Factors Significantly Associated With Increased Breast Cancer Risk for Women Aged 40 to 49 Years

The following 3 factors were associated with a 1.5- to 2.0-fold increased risk: a prior benign breast biopsy result, a second-degree relative with breast cancer, and heterogeneously dense breast tissue. Current use of oral contraceptives, nulliparity, and first birth at age 30 years or older were associated with a 1.0- to 1.5-fold increased risk, although some results differed by data sources, which suggests inconsistency. Several factors were associated with lower-than-average risk, including BMI of 25 kg/m2 or higher; low breast density; age 15 years or older at menarche; birth of 3 or more children; breastfeeding; perimenopausal or postmenopausal status; and use of menopausal, estrogen-only hormone therapy.

Although the results of this review are consistent with previous research, our estimates of risk are unique and relevant to current clinical dilemmas about mammography screening for women in their 40s. Although most women who develop breast cancer have no known risk factors, information about risk may be particularly useful when making decisions about screening. Of note, several risk factors identified in single studies or in studies of women of various ages were not statistically significant in our analysis. These findings may be useful to women, clinicians, and health systems considering risk-based screening who find a long list of potential risk factors difficult to navigate. Focusing on high breast density and first-degree family history of breast cancer may be a more clinically feasible approach to personalized screening.

Family history is an important, well-established risk factor for breast cancer, and its role in breast cancer screening and prevention extends beyond mammography. Current recommendations from the U.S. Preventive Services Task Force for women with family histories of breast cancer include genetic counseling and mutation testing if appropriate (105) and consideration of medications to reduce risk for primary breast cancer (106).

Several studies have reported associations between mammographic breast density and breast cancer (107111), but only 1 met inclusion criteria for this review because it reported results with BI-RADS classifications that are used in U.S. clinical practice and provided risk estimates specific to the target population (75). The use of breast density in screening and prevention is currently unclear, although research suggests that it may be important for estimating risk (111114) and for determining the age at which screening should begin and appropriate screening intervals (115). However, clinical trials of these strategies have not been done and use of breast density in current practice poses such challenges as variability of reporting among radiologists (116).

Our risk estimates were derived from epidemiologic data, and their application in predicting individual risk has not been evaluated. They may be particularly useful for developing more complex risk prediction models. Although several such models exist (for example, the Gail model), they were not developed specifically for women in their 40s, were not based on recent research, and have low discriminatory accuracy in predicting individual risk (117). Improving risk models and demonstrating their effectiveness in clinical applications are necessary future steps in this work.

Although our risk estimates may be useful in informing clinical practice, effective methods to modify risk factors to reduce breast cancer incidence are largely untested. Our results indicated reduced breast cancer risk for women with BMIs in the overweight and obese categories. However, an inverse association has been found in postmenopausal women (118). Given the higher incidence of breast cancer in postmenopausal women, an appropriate clinical recommendation would be to modify weight gain after menopause rather than at a younger age. Some risk factors may reflect underlying biological effects that cannot be modified for disease prevention purposes, such as parity. In contrast, breast density was reduced in high-risk women receiving tamoxifen in clinical trials (119). How to apply this information to individual patients is currently unclear.

This evidence review and meta-analysis is limited in several ways. Studies reported different measures, dissimilar categories for exposures and outcomes, and reference groups that did not represent average risk in the target population. Studies also varied in the degree of adjustment for confounders in their risk estimates. All of these variations could lead to potential bias in the combined estimates of RRs. In addition, some women outside the targeted age group were included because studies provided data in categories that did not align with ours. Publication bias and selective reporting are also potential limitations but are difficult to assess.

To address these issues, we developed inclusion criteria that considered the quality and applicability of studies consistently across risk factors, included only fair- or good-quality studies, selected best-evidence estimates for risk factors that lacked estimates from a meta-analysis, and redefined reference groups to approximate the mean or distribution of the target population. In addition, we analyzed primary BCSC data to supplement or support the meta-analysis results. To address concerns of heterogeneity, we performed several sensitivity analyses, including an analysis based on the degree of adjustment for confounders, and we found no important differences in results.

Data in the meta-analysis and the BCSC were derived from observational studies that were subject to inherent potential biases, such as unmeasured and uncontrolled confounders. Our analysis was limited to the effects of individual risk factors, and we did not assess the risk associated with multiple concurrent factors. Our inclusion criteria led to the selection of studies enrolling a specifically defined population, and results may not be applicable to different populations. Also, we focused on factors that increase risk for breast cancer in collaboration with the development of the CISNET models. Clinical application of our estimates of factors associated with reduced risk is limited because the CISNET models were not designed for similar reduced-risk scenarios.

This comprehensive systematic review and meta-analysis of risk factors for breast cancer in women aged 40 to 49 years, as well as a primary analysis of the same risk factors using BCSC data, indicated that having either extremely dense breast tissue on mammography or first-degree relatives with breast cancer is associated with at least a 2-fold increased risk for breast cancer. Identification of these risk factors may be useful for personalized mammography screening.

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CrossRef
 
Marchbanks PA, McDonald JA, Wilson HG, Folger SG, Mandel MG, Daling JR, et al..  Oral contraceptives and the risk of breast cancer. N Engl J Med. 2002; 346:2025-32. PubMed
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Moorman PG, Millikan RC, Newman B.  Oral contraceptives and breast cancer among African-american women and white women. J Natl Med Assoc. 2001; 93:329-34. PubMed
 
Newcomb PA, Longnecker MP, Storer BE, Mittendorf R, Baron J, Clapp RW, et al..  Recent oral contraceptive use and risk of breast cancer (United States). Cancer Causes Control. 1996; 7:525-32. PubMed
CrossRef
 
Rosenberg L, Palmer JR, Rao RS, Zauber AG, Strom BL, Warshauer ME, et al..  Case-control study of oral contraceptive use and risk of breast cancer. Am J Epidemiol. 1996; 143:25-37. PubMed
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McCormack VA, dos Santos Silva I.  Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev. 2006; 15:1159-69. PubMed
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Thomas DB, Carter RA, Bush WH Jr, Ray RM, Stanford JL, Lehman CD, et al..  Risk of subsequent breast cancer in relation to characteristics of screening mammograms from women less than 50 years of age. Cancer Epidemiol Biomarkers Prev. 2002; 11:565-71. PubMed
 
Ursin G, Ma H, Wu AH, Bernstein L, Salane M, Parisky YR, et al..  Mammographic density and breast cancer in three ethnic groups. Cancer Epidemiol Biomarkers Prev. 2003; 12:332-8. PubMed
 
Vachon CM, van Gils CH, Sellers TA, Ghosh K, Pruthi S, Brandt KR, et al..  Mammographic density, breast cancer risk and risk prediction. Breast Cancer Res. 2007; 9:217. PubMed
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Barlow WE, White E, Ballard-Barbash R, Vacek PM, Titus-Ernstoff L, Carney PA, et al..  Prospective breast cancer risk prediction model for women undergoing screening mammography. J Natl Cancer Inst. 2006; 98:1204-14. PubMed
CrossRef
 
Chen J, Pee D, Ayyagari R, Graubard B, Schairer C, Byrne C, et al..  Projecting absolute invasive breast cancer risk in white women with a model that includes mammographic density. J Natl Cancer Inst. 2006; 98:1215-26. PubMed
 
Tice JA, Cummings SR, Ziv E, Kerlikowske K.  Mammographic breast density and the Gail model for breast cancer risk prediction in a screening population. Breast Cancer Res Treat. 2005; 94:115-22. PubMed
CrossRef
 
Schousboe JT, Kerlikowske K, Loh A, Cummings SR.  Personalizing mammography by breast density and other risk factors for breast cancer: analysis of health benefits and cost-effectiveness. Ann Intern Med. 2011; 155:10-20. PubMed
 
Nicholson BT, LoRusso AP, Smolkin M, Bovbjerg VE, Petroni GR, Harvey JA.  Accuracy of assigned BI-RADS breast density category definitions. Acad Radiol. 2006; 13:1143-9. PubMed
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Nelson HD, Fu R, Humphrey L, Smith ME, Griffin JC, Nygren P.  Comparative Effectiveness of Medications to Reduce Risk of Primary Breast Cancer in Women. Comparative Effectiveness Review (Prepared by Oregon Evidence-based Practice Center under contract 290-97-0018). Rockville, MD: Agency for Healthcare Research and Quality; September 2009.
 
van den Brandt PA, Spiegelman D, Yaun SS, Adami HO, Beeson L, Folsom AR, et al..  Pooled analysis of prospective cohort studies on height, weight, and breast cancer risk. Am J Epidemiol. 2000; 152:514-27. PubMed
CrossRef
 
Cuzick J, Warwick J, Pinney E, Duffy SW, Cawthorn S, Howell A, et al..  Tamoxifen-induced reduction in mammographic density and breast cancer risk reduction: a nested case-control study. J Natl Cancer Inst. 2011; 103:744-52. PubMed
CrossRef
 

Figures

Grahic Jump Location
Figure.

Summary of evidence search and selection.

BMI = body mass index; OC = oral contraceptive.

* Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews.

† Reference lists, Scopus, and studies suggested by experts.

‡ Some articles are included for more than 1 risk factor.

§ Published meta-analyses.

∥ No articles met inclusion criteria for race and ethnicity, menopausal stage and type (surgical or nonsurgical), age at menopause, and menopausal hormone use.

¶ Although some studies met inclusion criteria for the systematic review, they did not provide data for the meta-analysis because they used dissimilar categories or different measures from the other included studies.

Grahic Jump Location

Tables

Table Jump PlaceholderAppendix Table.  

Population Distribution of Risk Factors for Women Aged 40 to 49 Years

Table Jump PlaceholderTable 1.  

Breast Cancer Risk Associated With Personal Factors for Women Aged 40 to 49 Years

Table Jump PlaceholderTable 2.  

Breast Cancer Risk Associated With Family History, Breast Density, and Breast Procedures for Women Aged 40 to 49 Years

Table Jump PlaceholderTable 3.  

Breast Cancer Risk Associated With Reproductive Factors for Women Aged 40 to 49 Years

Table Jump PlaceholderTable 4.  

Summary of Evidence for Studies Providing Data for Risk Estimates

Table Jump PlaceholderTable 5.  

Factors Significantly Associated With Increased Breast Cancer Risk for Women Aged 40 to 49 Years

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Letters

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Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

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Personalized screening counseling of women 40-49 years of age should be based on risk factors for breast cancer death
Posted on May 28, 2012
PhilippeAutier, Research Director
International Prevention research Institute
Conflict of Interest: None Declared

Screening is more effective when the incidence of the target disease is high, that is, more deaths will be prevented while a lower proportion of positive screening tests will be false positives. The idea of optimizing screening effectiveness in women 40 to 49 years of age by targeting women with greater risk to develop a breast cancer is appealing. The review of breast risk factors among by Nelson and Colleagues (1) may provide guidance on how to provide personalized counseling to pre- menopausal women about the likely benefits and side-effects of mammography screening. The review found that breast radiological density, family history, reproductive history, use of oral contraceptives and prior benign breast biopsy were factors associated with higher breast cancer incidence in women aged 40-49 years. However, since the primary goal of breast screening being to decrease the risk to die from breast cancer, screening would be more effective for women at higher risk to die from this cancer, and not just a higher risk to be diagnosed with that cancer. The caveat that risk factors for breast cancer occurrence would have a similar influence on the risk of breast cancer death is not correct. Few studies have been done on risk factors associated with breast cancer death, but they show that reproductive factors have little influence on the risk to die from breast cancer (2). High breast density is associated with more aggressive cancer and this factor is thus relevant to both incidence and mortality (3). Adiposity is associated with reduced breast cancer risk in pre-menopausal women, and therefore, this factor was not selected by the review. However, the risk of breast cancer death in pre-menopausal women increases with adiposity (4) and obese women would probably benefit more from screening than lean women. Women giving birth in their forties become increasingly common, and breast cancers occurring in the two years following childbirth are known to be more lethal (5). So, these women should probably be offered screening shortly after discontinuation of breast feeding. The risk of breast cancer death associated with use of oral contraceptives and prior benign breast biopsy are not known. In conclusion, compared to risk factors for breast cancer occurrence, personalized screening counseling based on risk factors for breast cancer mortality may be more effective and identify different women likely to benefit from screening.

References

1. Nelson HD, Zakher B, Cantor A, Fu R, Griffin J, OMeara ES, et al. Risk factors for breast cancer for women aged 40 to 49 years. Ann Int Med. 2012; 156: 635-48.

2. Barnett GC, Shah M, Redman K, Easton DF, Ponder BAJ, Pharoah PDP Risk factors for the incidence of breast cancer: Do they affect survival from the disease? J Clin Oncol. 2008; 26:3310-6.

3. Chiu SY, Duffy S, Yen AF, Tab?r L, Smith RA, Chen H. Effect of baseline breast density on breast cancer incidence, stage, mortality, and screening parameters: 25-year follow-up of a swedish mammographic screening. Cancer Epidemiol Biomarkers Prev. 2010; 19; 1219-28.

4. Loi S, Milne RL, Friedlander ML, McCredie MRE, Giles GG, Hopper JL, Phillips K. Obesity and outcomes in premenopausal and postmenopausal breast cancer. Cancer Epidemiol Biomarkers Prev. 2005; 14:1686-91.

5. Daling JR, Malone KE, Doody DR, Anderson BO, Porter PL. The relation of reproductive factors to mortality from breast cancer. Cancer Epidemiol Biomarkers Prev. 2002;11: 235-41.

Conflict of Interest:

None declared

Authors’ Response to Letters
Posted on June 19, 2012
Heidi D. Nelson, MD, MPH, Bernadette Zakher, MBBS, Amy Cantor, MD, MPH
Oregon Health & Science University, Portland, OR
Conflict of Interest: None Declared

We agree with comments from Dr. Autier that identifying factors associated with increased risk of breast cancer mortality, not just breast cancer incidence, to guide breast cancer screening for women in their forties would be useful. However, it is also correct that few studies have reported these associations.

Our systematic review focused on the many published studies of risk factors for breast cancer incidence specifically for women in their forties who would be candidates for mammography screening under current U.S. guidelines (1). Our results indicated that extremely dense breasts on mammography and first-degree relatives with breast cancer were each associated with at least a 2-fold increase in risk; prior benign breast biopsy, second-degree relatives with breast cancer, and heterogeneously dense breasts with 1.5 to 2.0 fold increase; and current oral contraceptive use, nulliparity, and age at first birth 30 years and older with 1.0 to 1.5 increase. Several other risk factors were not statistically significantly associated with breast cancer incidence. Increased BMI was associated with reduced breast cancer risk, an inverse relationship that reverses for women above age 50. We did not emphasize risk factors related to reduced risk because this project was intended to identify risks above general population levels in collaboration with the development of population screening models that did not consider reduced-risk scenarios (2).

Although our risk estimates may help inform clinical decision making about screening, they were derived from epidemiologic data and their application in predicting risks for individual women has not been evaluated. Next steps in this work would be to improve risk models and demonstrate their effectiveness in clinical applications. Mortality estimates would also contribute to this effort.

Heidi D. Nelson, MD, MPH

Bernadette Zakher, MBBS

Amy Cantor, MD, MPH

Oregon Health & Science UniversityPortland, Oregon 97239

References

1. Nelson HD, Zakher B, Cantor A, Fu R, Griffin J, O'Meara ES, et al. Risk factors for breast cancer for women aged 40 to 49 years. Ann Intern Med. 2012; 156: 635-48.

2. van Ravesteyn NT, Miglioretti DL, Stout NK, Lee SJ, Schechter CB, Buist DSM, et al. What level of risk tips the balance of benefits and harms to favor screening mammography starting at age 40 years? Ann Intern Med. 2012;156:609-617.

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