Affiliations of authors: Department of Epidemiology and Biostatistics, University of California, San Francisco, CA (KK, SRC); General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, CA (KK); Department of Medicine, University of California, San Francisco, CA (KK, JC, JAT, EZ); Department of Radiology, University of California, San Francisco, CA (JS); San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco (SRC)
Correspondence and reprint requests to: Karla Kerlikowske, MD, San Francisco Veterans Affairs Medical Center, General Internal Medicine Section, 111A1, 4150 Clement Street, San Francisco, CA 94121 (e-mail: kerliko{at}itsa.ucsf.edu).
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ABSTRACT |
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INTRODUCTION |
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In addition to its effects on breast tissue, estrogen can also affect bone mineral density (BMD). Indeed, BMD may be a marker of lifetime exposure to estrogen. Consistent with a role as such a marker, BMD is a risk factor for breast cancer. The Study of Osteoporotic Fractures (13) demonstrated that women in the highest quartile of distal radius BMD or metacarpal cortical bone mass had a two- to threefold higher incidence of breast cancer than women in the lowest BMD quartile. Three subsequent studies have found weaker associations. The Fracture Intervention Trial (14) found that women in the highest quartile of hip BMD had a nonstatistically significant 1.5-fold higher incidence of breast cancer than women in the lowest quartile. The Rotterdam study (15) found that women in the highest tertile of spine BMD had a twofold higher incidence of breast cancer than women in the middle tertile but found no association with hip BMD and incidence of breast cancer. The Dubbo Osteoporosis Epidemiology Study (16) found a twofold higher incidence of breast cancer in women with increased spine BMD and a modest increase in women with increased hip BMD as compared with women with low BMD.
Although both BMD and mammographic density are risk factors for breast cancer, and both may be markers of estrogen exposure, the relationship between mammographic breast density and BMD has not been well studied. Here, we evaluated the relationship between breast density and hip and spine BMD and the relationship between breast density in combination with hip and spine BMD and breast cancer risk among women participating in the San Francisco Mammography Registry (SFMR).
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SUBJECTS AND METHODS |
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The SFMR (http://mammography.ucsf.edu/SFMR/) is a population-based mammography registry of women undergoing mammography in any of 16 radiology facilities in San Francisco and Marin Counties. The present study included women aged 28 years or older in the registry who underwent a bilateral mammography examination in San Francisco, indicated by the radiologist as being performed for screening, and a BMD measurement of the hip within 2 years of each other at the University of California, San Francisco (UCSF); California Pacific Medical Center (CPMC); or San Francisco Kaiser Permanente Medical Center (KPMC) between February 1, 1992, and December 31, 2002. We excluded all women who had a diagnosis of breast cancer before their first screening examination in the SFMR because treatments for breast cancer, including tamoxifen and radiation, may alter breast density on subsequent examinations (17) and because mammograms performed before the establishment of the SFMR are not easily accessible. We also excluded women who had had breast augmentation, reduction, or reconstruction; history of mastectomy; or bilateral breast cancer. We included women with invasive breast cancer or ductal carcinoma in situ (as case subjects) in the analysis if the screening mammogram and BMD measurement occurred before their breast cancer diagnosis. At least two women without breast cancer (control subjects) were selected from the same mammography and BMD facility as case subjects. Breast cancer case subjects were identified by linkage of the SFMR with the Northern California Surveillance, Epidemiology, and End Results (SEER1) program and the California Cancer Registry.
Annual approval was obtained from the UCSF Institutional Review Board to collect registry and BMD information that included a waiver of signed consent.
Measurements and Procedures
Screening mammography examinations were linked to clinical BMD databases at UCSF, CPMC, and KPMC to identify women who had had a total hip (N = 15 254) or total spine (lumbar vertebrae one to four; N = 14 475 of the 15 254) BMD measurement by dual X-ray absorptiometry within 2 years of a screening mammography examination. If a woman had more than one screening examination or more than one BMD measurement, we selected the mammography examination and BMD measurement that were performed closest in time to each other. BMD was measured in g/cm2 using a Hologic Delphi/A or W scanner at UCSF, a Hologic Delphi/W scanner at CPMC, and a Hologic QDR 1000 scanner at KPMC (Hologic, Inc., Waltham, MA). Peak bone mass and standard deviation (SD) were based on reference data supplied by the manufacturer. These devices were maintained using standard quality control procedures recommended by the manufacturer to assure that the BMD calibrations remained constant within plus or minus 1%. Although no attempt was made to cross-calibrate the devices, studies on Hologic dual X-ray absorptiometry devices in clinical practice have shown them to be within plus or minus 2% of each other (18).
We retrieved screening examinations to digitize and measure percentage mammographic breast density in 208 women with breast cancer (case subjects) and a random sample of 436 women without breast cancer (control subjects). For women with invasive breast cancer or ductal carcinoma in situ, we selected the craniocaudal screening examination of the breast that did not have breast cancer. The craniocaudal view was selected because it excludes the pectoralis muscle, which has been shown to create artifacts when measuring breast density (19). For the control subjects, we selected either the right or left craniocaudal view because breast density measures of the right and left breast are highly correlated (19).
Mammographic breast density was quantified using a validated computer-based threshold method as described previously (3). The participant's craniocaudal screening view was digitized on a Lumisys LumiScan 200 radiographic films digitizer (Kodak, Inc.) (100-mm pixel size, 12-bit dynamic range) and archived onto a CD jukebox. This semiautomated, computer-assisted method involves dividing the mammographic image into a distribution of gray values, with darker regions indicative of fat tissue and lighter regions representing dense tissue. The method is based on the interactive selection of two thresholds in the image of a digitized mammogram. One threshold separates the breast image from the background (breast area) and the other identifies the regions that represent radiographically dense tissue (mammographic density). The percentage of dense tissue in the breast was determined by dividing the number of pixels outlined in the dense regions by the total area of the breast as calculated with dedicated computer software (3). A single radiologist trained in assessing mammographic breast density with the UCSF Mammog-raphy Density Workstation read all study films from case and control subjects (N = 644) (3). A qualitative assessment of breast density was also assigned in clinical practice for women (N = 3105) undergoing screening mammography at UCSF, but not the other centers, using breast density categories established by the American College of Radiology and reported in the Breast Imaging Reporting and Data System (BI-RADS) (20). The four breast density categories were as follows: 1) almost entirely fat, 2) scattered fibroglandular densities, 3) heterogeneously dense, and 4) extremely dense.
At the time of each screening examination, women completed a survey that included demographic information and breast health history questions. The survey included the following race/ethnicity categories: 1) African American/Black, 2) Caucasian/White, 3) Hispanic/Latina, 4) American Indian, 5) Chinese, 6) Japanese, 7) Filipina, 8) Vietnamese, 9) other Asian, and 10) other non-Asian. In addition, the survey included questions about family history of breast cancer in a first-degree relative (mother, sister, or daughter), current postmenopausal hormone therapy use, menopausal status, age at first live birth, and weight and height. Women were considered to be current hormone therapy users if they reported using female hormones for menopause at the time of a screening examination. Women were considered to be postmenopausal if both ovaries had been removed, if they reported that their periods had stopped permanently, if they were using hormone therapy, or if they were aged 55 years or older. Age at first live birth was stratified into three categories: younger than 30 years, 30 years or older, and nulliparous. Height and weight were determined using self-reported information collected at the time of either screening mammography or BMD measurement.
Data and Statistical Analysis
Frequency distributions of demographic characteristics and clinical risk factors were computed for all women with a screening examination and BMD of the hip or spine within 2 years of each other. For all analyses, race/ethnicity was collapsed into three categories that include white, Asian or Pacific Islander, and all other races. For women without breast cancer, the association of breast density with clinical risk factors was computed by determining the proportion of women with a BI-RADS density of 3 or 4 and the average percentage breast density for those who had a quantitative assessment of breast density. The association of BMD with clinical risk factors was computed by determining mean hip and spine BMD for all women without breast cancer. Statistically significant differences for categorical variables were determined using a chi-square test and logistic regression when adjusting for age. Statistically significant differences for continuous variables were determined using standard t test and t test of adjusted means when adjusting for age.
Spearman rank partial correlation coefficients were used to assess the overall association between BMD measured as a continuous variable and BI-RADS breast density. Pearson's correlation coefficient was used to assess the overall association between BMD measured as a continuous variable and percentage breast density as a continuous variable. We also performed correlation analyses stratified by age and menopausal status and postmenopausal hormone therapy use adjusting for body mass index, race/ethnicity, and age at first live birth. We stratified women into two groups (younger than aged 65 years and aged 65 years or older) because the strongest association between BMD and breast cancer risk has been found among an elderly population (13), suggesting that if breast density and BMD are correlated, then the strongest correlation may also be observed in the elderly.
For case and control subjects, we calculated mean time between screening examinations and BMD measurements. In addition, we calculated the mean time between screening mammography and breast cancer diagnosis and between BMD measurement and cancer diagnosis. Chi-square tests were used to determine statistical significance when comparing clinical and demographic characteristics of case and control subjects.
Multivariable logistic regression was used to determine whether percentage mammographic breast density and BMD are independent risk factors for breast cancer after adjusting for age at diagnosis, race/ethnicity, family history of breast cancer, age at first live birth, and body mass index. BMD was divided into quartiles, and percentage mammographic density was divided into sextiles based on the distribution of women without breast cancer.
The attributable risks of breast cancer for varying percentages of mammographic breast density were calculated using the formula Attributable risk = (RR-1)Pc/RR, in which RR is the relative risk associated with a given percentage mammographic density and Pc is the prevalence of that category in the breast cancer case subjects. The odds ratio (see Table 6) was assumed to approximate the relative risk.
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RESULTS |
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To investigate the validity of BI-RADS density assessments, percentage mammographic breast density measurements, and BMD measurements, we examined the association between the reported demographic and clinical characteristics (5,13) and breast density or BMD. We found that the proportion of women without breast cancer with BI-RADS density assessment categories 3 or 4 and mean percentage mammographic density decreased with increasing age, menopause, and body mass index and increased with late age at first live birth and postmenopausal hormone use (Table 2). As expected, both hip and spine BMD decreased with increasing age and menopause and increased with increasing body mass index and postmenopausal hormone use (Table 3). Asian women had a lower age-adjusted hip BMD than white women (0.792 ± 0.002 versus 0.834 ± 0.001, respectively); the difference was attenuated but not eliminated after adjusting for body mass index and postmenopausal hormone use (data not shown). Thus, the BI-RADS density assessments, percentage mammographic breast density measurements, and BMD measurements appear to be valid measures.
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We determined the proportion of breast cancers attributable to breast density for women in each category of percentage mammographic density (Table 7). For women with a percentage mammographic density of greater than 42.6%, the attributable risk of breast cancer was 38%; for women with a percentage mammographic density of greater than 54%, the attributable risk of breast cancer was 29%; and for women with a percentage mammographic density between 23.0% and 42.7%, the attributable risk of breast cancer was only 4% (Table 7).
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DISCUSSION |
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Factors that increase the exposure of breast tissue to estrogens, such as older age at first birth, menopause, obesity, or postmenopausal hormone therapy, have been shown to be associated with an increased risk of breast cancer (2224). Consistent with the hypothesis that prolonged exposure to endogenous estrogens influences breast cancer risk is the observation that increased postmenopausal estradiol levels are associated with increased breast cancer risk (25). High estradiol levels among postmenopausal women are also positively associated with high BMD (26,27), and some studies have shown that postmenopausal women with the highest BMD are at increased risk of breast cancer risk (13,28). However, one study has shown that BMD may not be associated with breast cancer risk independent of its relationship with endogenous hormones and body mass index (29), suggesting that increasing BMD with increasing body mass index among postmenopausal women is largely the result of the associated increase in serum estrogens (30). By comparison, mammographic breast density does not appear to be strongly associated with serum estradiol levels among postmenopausal women (31). Moreover, in our study, the strength of the association of increased breast density and breast cancer risk is not affected by controlling for body mass index. Thus, mammographic density (1,2) and post-menopausal hormone levels (25) are both strongly associated with breast cancer risk and may act independently of each other, whereas BMD may not act independently of its relationship with endogenous estrogens.
What mechanisms other than cumulative estrogen exposure may account for the association of increased breast density and risk of breast cancer? First-degree relatives of women with increased mammographic density have an increased risk of developing breast cancer (32). Thus, genes that determine breast density may also affect breast cancer risk. Growth factors that affect the breast have also been shown to be associated with mammographic density (31,33). Studies have demonstrated that, in premenopausal women, mammographic density is associated with higher insulin-like growth factor 1 levels, which in turn are associated with an increased risk of breast cancer (31,33).
We did not find that BMD was associated with risk of breast cancer. Our study has twice as many women with breast cancer as other studies (13,14,16,28), so a lack of statistical power is unlikely to explain our findings. In the study with the strongest association between BMD and breast cancer risk (13), the study population had a mean age of 71 years and lower mean hip and spine BMDs than those observed in our study (hip = 0.75 g/cm2 versus 0.82 g/cm2, respectively; spine = 0.84 g/cm2 versus 0.92 g/cm2, respectively). In addition, the studies with the strongest association with BMD and breast cancer risk found the association with appendicular (arm) BMD (13,28,34). Studies that measured axial (hip and spine) BMD found weaker and more inconsistent associations with BMD (1416). Lastly, BMD is not merely a marker for estrogen exposure. Other growth factors that we did not measure may be involved in the association between BMD and breast cancer. It is also possible that different methods were used to assess spine and hip BMD in different facilities, even though these measurements were made on the same types of machine. Any variation in this measurement across clinical practices could limit our ability to find an association with BMD and breast cancer. Our inability to corroborate research studies that used measurements made in a research protocol suggests that the hip and spine BMD measured in clinical practice may not be a useful marker to predict breast cancer risk in clinical practice.
Our study included a large sample size with primarily postmenopausal women of diverse racial and ethnic groups, which increases the generalizability of the results. In addition, our study is the first, to our knowledge, to report the associations of breast density, BMD, and breast cancer and the largest study to date that has examined the BMDbreast cancer relationship. One limitation of the study is that BMD was measured in clinical practice for reasons not assessed by our study. If primarily healthy women elect to undergo BMD measures in clinical practice, then a health selection bias could be introduced, with lower-than-expected numbers of breast cancers among women with high BMD limiting our ability to find an association between BMD and breast cancer. A second potential limitation is the possibility of cancer detection bias. However, the cancer rates reported for the SFMR (35) are within the range of those reported in the literature, in which follow-up has been reported to be 99.6% (36). In addition, cancer reporting to the SFMR from the SEER program has been estimated to be more than 94.3% complete (37). Thus, cancer detection bias is unlikely to explain our results. Lastly, masking bias is a possibility. However, this bias is not likely to explain our results because, after we excluded cancers associated with a normal mammography result from the analyses, we observed the same association between breast density and breast cancer risk and between BMD and breast cancer risk as we did when such cancers were included.
Our findings suggest that breast density is strongly associated with increased risk of breast cancer and that BMD, although a marker of lifetime exposure to estrogen, is not. Given that breast density is independently associated with an increased risk of breast cancer after taking into account reproductive and hormonal risk factors, it is possible that a component of breast density that is not estrogen mediated may contribute to breast cancer risk. This theory is supported by the observation that high breast density is as strongly associated with estrogen receptorpositive breast cancer as with estrogen receptornegative breast cancer (38) and with breast cancer in premenopausal women as with breast cancer in postmenopausal women (1). Further investigation into factors that have been shown to affect breast density and breast cancer risk may reveal the biologic basis between breast density and breast cancer.
BMD is measured by dual energy absorptiometry, an accurate, precise, and automated technique. BMD has become a widely accepted clinical tool to assess fracture risk and to guide the use of therapies to prevent fractures. By analogy, breast density is a strong risk factor present in a substantial proportion of breast cancer cases and therefore may eventually be useful to assess breast cancer risk and guide the use of preventive therapies for breast cancer. An accurate, precise, and automated means to assess breast density in clinical practice is needed, however, before breast density can be used to identify women who may benefit from breast cancer prevention measures.
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NOTES |
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This work was supported by a National Cancer Institute-funded Breast Cancer Surveillance Consortium cooperative agreement (U01CA63740).
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Manuscript received August 26, 2004; revised December 16, 2004; accepted January 4, 2005.
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