Longitudinal Changes in Volumetric Breast Density in Healthy Women across the Menopausal Transition

Breast density is a measure of the stromal and epithelial tissue in the breast as seen on a mammogram and is a strong risk factor for breast cancer (1–3). Breast density is strongly influenced by age, and studies suggest that many women experience a natural decline in the amount of dense breast tissue with aging (4, 5). The most accelerated declines are often observed over the menopausal transition, corresponding with Pike hypothesis that the rate of breast tissue aging decreases over menopause, and that the magnitude of this decrease may be influenced by exposure to breast cancer risk factors (6).

Previous studies of longitudinal changes in breast density have primarily used area-based breast density assessment to estimate the decline in percent density (the proportion of the total breast area comprised of dense tissue), and found an average annual decline in percent density of 0.5%–2%, with the greatest reductions occurring over the menopausal transition (4, 5, 7–9). Fewer studies have examined changes in area-based absolute density (the amount of dense area), which is believed to be a more etiologically relevant phenotype of breast density for breast cancer risk than percent density, as it reflects the amount of tissue at risk of carcinogenesis (10). One study estimated that women undergoing menopause had a decline in dense area that was 3.39 cm² larger than age-matched women who remained premenopausal during the same time period (4). Cross-sectional studies comparing breast density in premenopausal and postmenopausal women support this finding (11), with a recent study including women from 22 countries estimating that postmenopausal women had a mean dense area that was 3.5 cm² lower than premenopausal women (12).

However, not all women experience a decline in breast density with menopause. Current research suggests that women with higher baseline breast density have accelerated declines (5, 7), and combination postmenopausal hormone users have attenuated declines or increases over time across the menopausal transition (5, 7, 13). However, findings on the effects of reproductive-related factors and obesity on change in density over time have been mixed across studies (4, 5, 7–9).

Longitudinal changes in breast density are associated with breast cancer risk, and women who experience the greatest declines over time have a reduced risk of breast cancer (14, 15). Therefore, identifying factors influencing change across the menopausal transition may improve targeted prevention efforts. Automated, volumetric breast density measures are increasingly used in clinical settings and can monitor changes in breast density over time (16); however, literature quantifying longitudinal change in volumetric breast density is sparse.

The objective of our study was to use volumetric breast density assessment to measure changes in breast density over the menopausal transition in healthy, cancer-free women, and identify risk factors that affect change during this time period.

Participants were sampled from the San Francisco Mammography Registry (SFMR), a population-based mammography registry collecting demographic, risk factor, and mammographic information on women undergoing mammography in the San Francisco Bay Area. We included four SFMR facilities that obtained raw digital images from Hologic-Selenia mammography machines since 2006. Passive permission to participate in research is obtained at each mammography visit.

Eligible women had at least two full field digital mammograms between 2007 and 2013, with one premenopausal mammogram, performed prior to self-reported menopause and at least one subsequent postmenopausal mammogram. At least 2 years were required between the premenopausal and postmenopausal mammogram. The “menopausal transition” is defined as the time between the premenopausal and postmenopausal mammogram for each woman. Women with a personal history of breast cancer, breast implants, or mastectomy and women without craniocaudal mammogram views were excluded. All mammograms between the premenopausal and postmenopausal mammogram (perimenopausal mammograms) were collected for this analysis, for a total of 2,586 women and 6,112 mammograms (mean, 2.4 per woman).

Demographics and risk factor data were self-reported at each mammography visit. Menopause status was determined by asking women if their menstrual periods had stopped and if they were using postmenopausal hormone therapy (HT). Women were classified as postmenopausal if they reported their periods had stopped, for any reason, or if they reported use of postmenopausal HT, regardless of their self-report of menstrual periods. All other women were considered premenopausal. Covariates collected at the time of the premenopausal mammogram included age, race/ethnicity, body mass index [BMI; continuous (kg/m²) and categories (<25 kg/m², 25–29 kg/m², and ≥30 kg/m²)], first-degree family history of breast cancer, parity, age at first birth (nulliparous, <30 years, or ≥30 years), and current alcohol use (none, ≤1 drink per day, or ≥2 drinks per day). Use of postmenopausal HT was collected at all mammograms subsequent to the premenopausal mammogram and was classified as unknown, no current use, and current use. Among current users, further classification by formulation (estrogen vs. estrogen and progesterone) was available. BMI was collected at each mammogram and change in BMI (kg/m²) was calculated between premenopausal and each subsequent mammogram.

Raw (“for processing”) mammogram image formats were collected and stored and Volpara automated breast density software was run on all mammograms.

Volpara (version 1.5.3, Matakina Technology) is a fully-automated software that measures volumetric breast density on full field digital mammography machines. The Volpara proprietary algorithm identifies an area of the breast that is entirely fatty tissue and uses this reference point to estimate the thickness of dense tissue at each pixel in the image, not including the skin. Further detail on the Volpara algorithm is published elsewhere (17). Estimates of dense breast volume (DV) are obtained by summing the estimated dense tissue across all pixels in the breast image, and volumetric percent density (VPD) is obtained by dividing the estimated DV from the total breast volume. Breast density was assessed on craniocaudal (CC) mammography views. For women with both CC views available on all mammograms (n = 2,551), we calculated breast density on the CC view of a randomly chosen side, using the same side for each subsequent mammogram. Among women who had usable CC views from only a single side, we used the available side (n = 35) for all images.

Characteristics of the study sample at the premenopausal mammogram are summarized by frequency and percentage or median and quartiles. Linear regression, adjusted for age, was used to estimate the effects of premenopausal risk factors on DV at the premenopausal mammogram. These analyses were performed on DV after log transformation and estimates and were then back-transformed to the original scale. We fit linear mixed effects models including all available mammograms across the menopausal transition to estimate the annualized change in DV from the premenopausal mammogram, accounting for correlations within women over time with woman-specific random effects. The associations between baseline and time-dependent (HT only) risk factors on annualized change in DV were assessed by fitting interactions between each risk factor and time (years) since premenopausal mammogram. Separate models were fit for each covariate interaction, and were adjusted for age, time (years), premenopausal BMI, change in BMI, and DV at the premenopausal mammogram. We found that premenopausal DV was strongly associated with annualized changes in DV and premenopausal risk factors (e.g., BMI) were strongly associated with premenopausal DV; therefore, all longitudinal mixed models were additionally adjusted for the interaction between premenopausal DV and time. Models were also fit using relative change in DV (as a percent of baseline). Supplementary analyses examined distribution of annualized changes in DV by premenopausal density and models assessing change in VPD (Supplementary Tables S1 and S2). All analyses were conducted in SAS 9.4.

Characteristics of the study sample at the premenopausal mammogram are reported in Table 1. Of the 2,586 women included, 1,802 (70%) had two mammograms (premenopausal and postmenopausal mammogram only), 628 (24%) had three (additionally one perimenopausal mammogram), and 156 (6%) had four (additionally two perimenopausal mammograms). The median age of women at the premenopausal mammogram was 51 [interquartile range (IQR), 49–52] years. The median BMI at the premenopausal mammogram was 23.3 (IQR: 21.2–26.5) kg/m² and the median change in BMI from premenopausal to postmenopausal mammogram was 0 (IQR: −0.5–0.9) kg/m².

Associations between demographics and risk factors with DV at the premenopausal mammogram are shown in Table 2. Older age, parity, and younger age at first birth were associated with lower DV at the premenopausal mammogram (all P < 0.01). Women with a first-degree family history of breast cancer had greater premenopausal DV compared with women without a family history (P < 0.001), and women who reported ≥2 drinks per day of alcohol consumption had greater premenopausal DV compared with women consuming <2 drinks per day or women reporting no alcohol use (P < 0.001). The greatest differences in premenopausal DV were seen comparing women with a BMI < 25 kg/m², who had a mean DV of 49.8 cm³, to women with BMI's of 25–29 kg/m² or >30 kg/m², who had a mean premenopausal DV of 60.4 and 64.6 cm³, respectively (P < 0.001). DV also varied by race/ethnicity, with Caucasian women having the highest mean premenopausal DV of 58.0 cm³, compared with Asian women, who had the lowest premenopausal DV of 45.6 cm³, and women of other racial/ethnic groups who had a mean of 58.1 cm³ (P < 0.001).

The distribution of changes in DV across the menopausal transition is displayed in Fig. 1. The estimated decline in DV per year was 2.22 [95% confidence interval (CI), −2.74 to −1.71] cm³ with a median time of 3.1 (IQR, 2.5–3.5) years from premenopausal to postmenopausal mammogram (Fig. 2). The median DV at the premenopausal mammogram was 54.0 (IQR, 37.7–77.8) cm³, and women above the median DV at the premenopausal mammogram had greater declines across the menopausal transition, with average annualized declines of 3.46 (95% CI, −4.40 to −2.73) cm³ compared with 0.97 (95% CI, −1.71 to −0.22) in women at and below the median premenopausal DV, respectively (P_interaction < 0.001). Among women with premenopausal DV above the median, 80% (1,380/1,732) experienced any decline and 32.9% of these women had a postmenopausal DV below the median. For women below the median DV, 58% (1,022/1,767) experienced any decline and 89.8% remained below the median DV at the postmenopausal mammogram (Supplementary Table S1).

The estimated decline in DV in overweight and obese women did not differ (−2.02; 95% CI, −2.65 to −1.40 cm³ vs. −2.72; 95% CI, −3.84 to −1.61 cm³) compared with normal weight women (−2.31; 95% CI, −3.84 to −0.77 cm³; P_interaction = 0.56). In addition, we found no differences in the rate of change in DV over time by race, parity, age at first birth, family history of breast cancer, or current alcohol use (Table 3).

Use of postmenopausal HT during the menopausal transition trended toward larger declines in DV per year compared with nonusers, with women on HT on average having declines of 3.58 cm³ compared with 2.14 cm³ in nonusers, although this difference was not significant (P = 0.18). Further breakdown of HT use by formulation showed no significant differences between nonusers (−2.13 cm³), estrogen-only users (−3.16 cm³), and users of estrogen and progestin combination therapy (−3.09 cm³; P_interaction = 0.64).

Results from models examining change in DV relative to baseline DV were broadly consistent with absolute change models, although the difference in change between baseline DV below compared with above the median was no longer statistically significant (P = 0.22; Table 4).

This longitudinal analysis of DV across the menopausal transition found a decline in DV of 2.2 cm³ per year across the menopausal transition. We found that premenopausal DV was a strong predictor of greater annualized declines in DV and that other risk factors, while strongly affecting premenopausal DV, had no significant effect on annualized changes in DV.

Our finding of a decline of 2.2 cm³ per year across the menopausal transition is broadly consistent with the annualized decline in dense breast area estimated by Boyd and colleagues of 6.8 cm² in healthy women across 5 years during the menopausal transition (∼1.36 cm² per year; ref. 4). No studies, to our knowledge, have serial measures of volumetric breast density across the menopause, preventing direct comparisons of our findings. However, our own recent study found annualized declines of 0.28 cm³ in premenopausal women and 0.82 cm³ in postmenopausal women, unselected for proximity to the menopausal transition (16) The mean decline in our study is significantly greater per year during the menopausal transition, which is consistent with longitudinal research using area-based measures that suggest the greatest annualized reductions occur during the perimenopausal years (7).

Consistent with previous research using area-based density assessment, we found that higher premenopausal DV is a predictor of greater annualized declines in DV (4, 5, 7, 8), and that race/ethnicity, family history of breast cancer, parity, age at first birth, and alcohol use at the premenopausal mammogram did not significantly modify longitudinal changes (5, 7, 8). In analyses modeling change in DV as a percent change relative to premenopausal DV, we found similar results, although differences by initial DV were not significant. In both absolute and relative models of change, we adjusted for baseline density, which is strongly affected by demographic characteristics and premenopausal risk factors; therefore, it is possible that these factors have no effect on change in breast density aside from their effect on the premenopausal density. Interestingly, we found that alcohol had a strong effect on premenopausal DV, showing an increasing DV with increasing levels of alcohol use, although previous literature using area-based density has been mixed (18–20).

We found no differences in reduction in DV over time by BMI. Some (5, 7), but not all (8, 9), longitudinal studies using area-based density assessment have found attenuated reductions over time in overweight and obese women, although all examined percent density, whereas we examine absolute DV. BMI is strongly inversely associated with area-based percent density; therefore it is possible that previous research found attenuated declines in overweight and obese women because the premenopausal breast density in these women was lower, thus allowing for relatively smaller changes over time (5). In our study, overweight and obese women had the highest premenopausal DV, therefore we would expect these women to experience greater reductions in DV over time. However, once adjusting for premenopausal DV, we found no additional effect of premenopausal BMI to slow declines in density. While the focus of our study was to identify premenopausal predictors of decline in DV, it is probable that changes in BMI over time are more relevant for influencing change in DV. One study using volumetric measurement found that reductions in BMI were associated with subsequent decreases in DV (21).

Postmenopausal HT use is associated with increased breast density in cross-sectional studies, and has been associated with attenuated declines or increases over time and across the menopausal transition (13, 22). Our findings that postmenopausal HT had no significant effect on changes in DV were unexpected, particularly the finding of no distinction between formulations of HT, which have shown important differences with respect to breast density in other studies (13, 23, 24). Maskarinec and colleagues (5) reported that combined HT users had attenuated declines in area-based percent density that were 3.3% less than declines in nonusers, although declines in users of estrogen-only HT were only 1.6% less than declines in nonusers, per decade of follow-up. On the basis of previous literature, we would expect that women on HT in perimenopause or menopause would increase, maintain, or at least experience attenuated reductions in breast density relative to nonusers. However, the effects on breast density are likely dependent on duration of use (13, 25), which was not available in our analysis. It is possible that newer formulations of HT at lower doses have smaller effects of breast density, or that changes are less apparent when using volumetric assessment. Future research is needed to further examine this finding.

The menopausal transition is typically characterized by decreases in dense tissue, but also weight gain, which can increase both nondense and total breast volume. For this reason, we focused our primary analysis on DV, consistent with the hypothesis that the absolute dense tissue is reflective of the number of cells at risk of carcinogenesis, thus potentially serving as a better indicator of breast cancer risk compared with percent measures which are confounded by body size (10). Furthermore, longitudinal assessment of changes in DV may be less influenced by mammography acquisition features, such as compressed breast thickness, which is known to be highly correlated with baseline factors such as BMI. Thus, this correlation may potentially bias estimates of factors that influence changes in these measures over time. We include volumetric percent density changes in a supplementary analysis, as it is frequently used in clinical and research settings, although a fuller assessment of how acquisition parameters affects changes in different phenotypes of volumetric breast density over time is warranted.

Longitudinal changes in qualitative and area-based breast density have consistently been associated with breast cancer risk, with the greatest changes in breast density corresponding to the largest differences in risk (8, 9, 14, 25, 26). Furthermore, Kerlikowske and colleagues demonstrated that the use of multiple longitudinal measures of breast density improved clinical risk stratification for breast cancer (27). This suggests that longitudinal trajectories of breast density may be a more relevant indicator of changes in breast cancer risk than measurement at a single timepoint. As women tend to experience accelerated changes in breast density over the menopause (4, 5, 7–9), these changes may be indicators of postmenopausal breast cancer risk, thus the ability to capture longitudinal trajectories across menopause may offer enhanced risk stratification in the clinical setting. However, to date, studies of change in breast density and changes in risk have used two-dimensional breast density. Given the potential for use in clinical decision making, future research is needed to identify what magnitude of change in volumetric breast density is meaningful to reduce breast cancer risk.

A major strength of our study is the prospective collection of risk factor data and multiple mammograms in healthy women across the menopausal transition, and the use of automated volumetric breast density measurement. Our study has several important limitations, including the use of self-reported menopause status, which is subject to measurement error. Errors in self-report are unlikely to be dependent on premenopausal risk factors; however, these nondifferential errors may have biased the effects of risk factors on changes in DV toward the null. Postmenopausal HT use and formulation were self-reported, and lack of duration information makes it difficult to determine whether the lack of effect of DV over time is real, or if the short average duration of use or newer lower dose formulations account for the lack of an effect of HT on changes over time.

In summary, we found that the mean change in DV over an average of 3 years across the menopausal transition was 2.2 cm³, and that women with higher premenopausal DV experienced the greatest declines in DV over this period. Future research is warranted to determine what magnitude of change and timing of these changes in volumetric breast density is relevant for breast cancer risk.

J. Shepherd reports receiving commercial research grants from Hologic, Inc. and General Electric and is a consultant/advisory board member for Naked Labs. C.M. Vachon reports receiving commercial research grant from and is a consultant/advisory board member for GRAIL. No potential conflicts of interest were disclosed by the other authors.

Conception and design: N.J. Engmann, D.H. Whaley, R.A. Hiatt, J. Heine, D.L. Miglioretti, K. Kerlikowske, C.M. Vachon

Development of methodology: N.J. Engmann, D.H. Whaley, J. Heine, K. Kerlikowske, C.M. Vachon

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): C. Scott, L. Ma, A. Mahmoudzadeh, D.H. Whaley, A.D. Norman, J. Shepherd, K. Kerlikowske

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): N.J. Engmann, C. Scott, M.R. Jensen, S.J. Winham, K.R. Brandt, A. Mahmoudzadeh, D.H. Whaley, C.B. Hruska, J. Heine, J. Shepherd, V.S. Pankratz, D.L. Miglioretti, K. Kerlikowske, C.M. Vachon

Writing, review, and/or revision of the manuscript: N.J. Engmann, C. Scott, M.R. Jensen, S.J. Winham, K.R. Brandt, D.H. Whaley, C.B. Hruska, A.D. Norman, R.A. Hiatt, J. Heine, J. Shepherd, V.S. Pankratz, D.L. Miglioretti, K. Kerlikowske, C.M. Vachon

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): C. Scott, L. Ma, A.D. Norman, R.A. Hiatt, J. Shepherd, K. Kerlikowske, C.M. Vachon

Study supervision: R.A. Hiatt, J. Shepherd, C.M. Vachon

Other (IT support): F.-F. Wu

Other (graduate mentor of first author): R.A. Hiatt

We acknowledge the women within the San Francisco Mammography Registry who enabled this research. This work was supported in part by the NIH, NCI (R01 CA177150, P01 CA154292, and R01 CA207084).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.