Abstract
Background
Understanding how changes in body mass index (BMI) relate to changes in mammographic density is necessary to evaluate adjustment for BMI gain/loss in studies of change in density and breast cancer risk. Increase in BMI has been associated with a decrease in percent density, but the effect on change in absolute dense area or volume is unclear.
Methods
We examined the association between change in BMI and change in volumetric breast density among 24,556 women in the San Francisco Mammography Registry from 2007-2013. Height and weight were self-reported at the time of mammography. Breast density was assessed using single x-ray absorptiometry measurements. Cross-sectional and longitudinal associations between BMI and dense volume (DV), non-dense volume (NDV) and percent dense volume (PDV) were assessed using multivariable linear regression models, adjusted for demographics, risk factors, and reproductive history.
Results
In cross-sectional analysis, BMI was positively associated with DV (β=2.95 cm3, 95% CI 2.69, 3.21) and inversely associated with PDV (β=-2.03%, 95% CI -2.09, -1.98). In contrast, increasing BMI was longitudinally associated with a decrease in both DV (β=-1.01 cm3, 95% CI -1.59, -0.42) and PDV (β=-1.17%, 95% CI -1.31, -1.04). These findings were consistent for both pre- and postmenopausal women.
Conclusion
Our findings support an inverse association between change in BMI and change in PDV. The association between increasing BMI and decreasing DV requires confirmation.
Impact
Longitudinal studies of PDV and breast cancer risk, or those using PDV as an indicator of breast cancer risk, should evaluate adjustment for change in BMI.
Introduction
High mammographic density is a risk factor for primary breast cancer (1-4). Mammographic density is typically assessed by comparing the proportion of dense and non-dense area measured on a two-dimensional mammographic image. Recently, novel methods that incorporate the thickness of the breast have been proposed as enhancements to area assessment (5-8). Single X-ray absorptiometry (SXA), which calculates breast volume using a calibrated phantom included in the mammographic image, has demonstrated equivalent breast cancer risk prediction to area methods (8). Some associations between breast cancer risk factors and mammographic density differ between area and volumetric methods. In particular, higher body mass index (BMI) has been associated with lower dense area (DA) (2, 9, 10) but higher dense volume (DV) (7, 11-13) in cross-sectional analyses.
Neither mammographic density nor BMI are static values. It is understood that percent dense area (PDA) decreases during aging, and that the rate of decline is greatest during the menopausal transition (14-16). It is unclear how simultaneous change in BMI influences the decline in density. A limited number of longitudinal studies have shown an inverse relationship between change in BMI and change in PDA (17, 18), and have reported either a null (17) or positive (18) association between change in BMI and change in absolute DA. To date, no studies have evaluated the longitudinal association of change in BMI on change in mammographic density using volumetric methods, and it is unclear if the difference between area and volumetric assessment seen in cross-sectional analyses will affect this longitudinal relationship. This association must be established to understand the impact of adjusting for changing BMI in longitudinal studies of volumetric density and breast cancer risk.
We examined the cross-sectional and longitudinal associations between BMI and mammographic density using the SXA volumetric density measurement method. Our analysis was conducted in the San Francisco Mammography Registry (SFMR), a large cohort of women undergoing breast imaging in the San Francisco Bay Area. Based on prior cross-sectional analyses using volumetric methods (7, 11-13), we hypothesized that an increase in BMI over the study period would be associated with an increase in both dense volume (DV) and non-dense volume (NDV), and a decrease in percent dense volume (PDV).
Materials and methods
Study population
The SFMR was established in 1994 and participates in the NCI-funded Breast Cancer Surveillance Consortium (BCSC). The SFMR has received Institutional Review Board approval for passive permission to enroll participants, link data, and perform analyses. All procedures were Health Insurance Portability and Accountability Act compliant and the SFMR received a Federal Certificate of Confidentiality and other protections for the identities of women, physicians, and facilities.
At the time of screening mammogram at an SFMR facility, women completed a self-administered, one-page questionnaire providing basic demographic, risk factor, and reproductive history information. Women were given the option to opt out of participating in research and the opt out rate for SFMR facilities averages 1.8% (range: 0.8-3.3%). Women were eligible for the current analysis if they were age 18 and older with two or more mammograms in 2007-2013 spaced at least nine months apart. Women with a history of breast cancer, mastectomy, breast implants, or breast surgery were excluded. Mammograms that were performed within 6 months of a breast cancer diagnosis were also excluded.
The eligible study sample consisted of 30,000 women who contributed 75,489 mammograms. A total of 1,149 mammograms were removed due to poor placement of the SXA phantom in the mammographic image resulting in volumetric density measurement error. As a result, 368 women had only one mammogram to contribute to the analysis and were further excluded. Women were excluded if their reported height between mammograms varied by more than 3 inches (470 women, 1,197 mammograms) and if data were missing on BMI or other covariates (4,606 women, 11,122 mammograms). These exclusions resulted in a final study population of 24,556 women who contributed 61,653 mammograms.
BMI assessment
Women self-reported their current height in inches and weight in pounds at the time of their screening mammogram, and these data were used to calculate BMI (weight in kg divided by the square of the height in meters). BMI was analyzed both in its original continuous form and categorized according to World Health Organization guidelines: <18.5 kg/m2 for underweight, 18.5-<25 kg/m2 for normal weight, 25-<30 kg/m2 for overweight, and >=30 kg/m2 for obese.
Volumetric breast density assessment
DV and NDV were measured using the SXA technique and software developed by UCSF investigators (version 7.1). Total breast volume was computed by adding DV and NDV, and PDV was calculated by dividing DV by total breast volume. A complete description of the specific SXA imaging methods, development, and calibration processes has been previously published (19, 20). Briefly, a specialized SXA phantom was inserted in the corner of the x-ray field during the mammography examination. The phantom was designed to conform to the same thickness of the breast and was composed of materials mimicking known fat/glandular content. The placement of the phantom was specifically designed to not interfere with standard screening procedures and to account for tilt of the compression surfaces during the examination (8, 20). Grayscale values for the pixels in the breast image were then compared to the grayscale values in the phantom, and unique volumes of adipose (non-dense) and fibroglandular (dense) breast tissue were determined from the two-dimensional mammographic image (20).
SXA defines the total lack of dense breast tissue (i.e. 0% density) as pure fat as opposed to adipose, which contains fat and water. Therefore, in the division of breast tissue into dense and non-dense content, the SXA method includes water from adipose tissue in the fibroglandular (dense) content. This is analogous to fat saturated MRIs that have been used to measure breast density in young women (21), but in contrast to other volumetric and area techniques. However, direct comparisons demonstrate that SXA measures are highly correlated with area density measures (Shepherd 2012) and with other volumetric density techniques (Wang 2013). The mean difference in PDV measurements between repeat readings using the SXA technique has been demonstrated to be less than 2.5% (8). Further, SXA has been monitored over time using a quality control phantom to ensure stability of measurements with no systematic changes observed (13, 22).
Covariate assessment
Data on demographics, reproductive history, family history, and other breast cancer risk factors were obtained via the one-page questionnaire administered at the time of the screening mammogram. The covariates selected as potential confounders or effect modifiers were based on known predictors of mammographic density and availability within the SFMR data. Covariates assessed at the time of the first screening mammogram and considered unchanging included age at first mammogram, race, ever given birth, age at first birth, education, first-degree family history of breast cancer, and prior history of breast biopsy. Time-varying covariates included age at current mammogram, menopausal status, postmenopausal hormone therapy (HT) use, and hormonal birth control use. Consistent with previous analyses using BCSC definitions, women were considered postmenopausal if they reported that menstrual periods had stopped for more than 12 months, if they reported a bilateral oophorectomy, or if they were 55 years of age or older. Women were otherwise considered premenopausal. Current use of postmenopausal HT and hormonal birth control were assessed at the time of the screening mammogram. The questionnaire did not include specific formulations of HT or history of prior use.
Statistical analysis
There were no statistically significant differences in distributions of density, BMI, or covariates between women who were excluded from the analysis and those who were retained (data not shown). We calculated descriptive statistics for demographic and reproductive characteristics. We assessed the cross-sectional association between BMI at first mammogram and volumetric density measures at first mammogram using a generalized linear regression model adjusted for all covariates. For the longitudinal analyses we estimated the annual change in BMI and volumetric density measures as (Δ value / Δ time, days) × 365.25 days/year to account for varying time lapse between mammograms. We categorized BMI change over the study period based on change from initial BMI (at first mammogram): ≥10% loss, 5-10% loss, stable within ±5%, 5-10% gain, and ≥10% gain. We used ANOVA to compare the percent gain/loss based on initial BMI, using the underweight or normal range (<25 kg/m2) as the reference. We summarized the annual change in each volumetric density measure over the study period and calculated adjusted means and confidence intervals using generalized linear regression.
We assessed the association between annual change in BMI and annual change in DV, NDV, and PDV using a random intercept mixed effects model. The mixed effects model is appropriate for data in which each subject may contribute a varying number of observations, and the random intercept allows for individual subject variation in baseline density measures (23). The model was adjusted for all covariates listed above and time-varying factors were updated at each successive mammogram. We stratified all analyses by menopausal status because the association between BMI and breast cancer risk has been shown to vary between pre- and postmenopausal women (24, 25). Because previous analyses have shown that declines in PDA over the menopausal transition may be modified by initial BMI and postmenopausal HT use (14, 15), we tested for effect modification by BMI at the first mammogram and by HT (never user, consistent user, initiated use during the study period, discontinued use during the study period). We further considered effect modification by race/ethnicity, since BMI and the distribution of breast density have been shown to differ by race/ethnicity (26).
All analyses were performed using SAS Version 9.2 (SAS Institute, Cary, North Carolina).
Results
A majority of the study population was Caucasian (66.3%) with 25.1% Asian or Pacific Islander (Table 1). The average age at the first mammogram was 56.4 years, and 64.1% of women were postmenopausal. Over half of the study population was classified as normal weight at the first mammogram (62.6%), while 24.2% were classified as overweight and 10.9% were classified as obese. About 12% of the study population was using postmenopausal HT at the time of the first mammogram. Women contributed an average of 2.54 mammograms, and the average time between first and last mammogram was 2.4 years (range 0.8-5.9 years).
Table 1. Selected patient characteristics measured at first mammogram (N=24,556); BCSC SFMR 2007-2013.
| Study population N (%) | |
|---|---|
| General characteristics | |
| Age at mammogram (years); Mean (SD) | 56.4 (10.9) |
| BMI (kg/m2); Mean (SD) | 24.5 (4.6) |
| Underweight: <18.5 | 583 (2.4) |
| Normal weight: 18.5 - <25 | 15,363 (62.6) |
| Overweight: 25 - <30 | 5,938 (24.2) |
| Obese: 30+ | 2,672 (10.9) |
| Race | |
| Caucasian | 16,268 (66.3) |
| African-American | 519 (2.1) |
| Asian / Pacific Islander | 6,184 (25.2) |
| Other | 1,585 (6.4) |
| Education level | |
| < High school | 747 (3.0) |
| High school diploma | 1,820 (7.4) |
| Some college | 4,970 (20.2) |
| College degree | 17,019 (69.3) |
| First degree family history of breast cancer | 4,787 (19.5) |
| Previous breast biopsy | 5,785 (23.6) |
| Reproductive history | |
| Menopausal status | |
| Premenopausal | 8,355 (34.0) |
| Postmenopausal | 15,734 (64.1) |
| Ever given birth | 16,315 (66.4) |
| Age at first birth | |
| Nulliparous | 8,183 (33.3) |
| < 20 years | 835 (3.4) |
| 20 - 29 years | 7,681 (31.3) |
| 30 - 39 years | 7,031 (28.6) |
| 40+ years | 826 (3.4) |
| Hormone therapy use (at first mammogram) | 3,001 (12.2) |
| Birth control hormone use (at first mammogram) | 1,493 (6.1) |
| Tamoxifen or raloxifene use (at first mammogram) | 204 (< 1.0) |
Percentages may not add to 100% due to unknown values (<10% for any characteristic)
In the fully-adjusted cross-sectional analysis, BMI at first mammogram was positively associated with both DV (β=2.95 cm3, 95% CI 2.69, 3.21) and NDV (β=51.03 cm3, 95% CI 49.93, 52.13) at first mammogram, and was inversely related to PDV at first mammogram (β=-2.03%, 95% CI -2.09, -1.98) (Table 2). The associations with NDV and with PDV were stronger among women who were premenopausal at first mammogram compared to those who were postmenopausal (p value for interaction by menopausal status: p=0.79 for DV, p<0.01 for NDV, p<0.01 for PDV). In cross-sectional analysis, no significant interaction by postmenopausal HT use (p value for interaction by HT use: p>0.15 for DV, NDV, and PDV) or by race (p value for interaction by race: p>0.28 for DV, NDV, and PDV) was observed
Table 2. Cross-sectional association between BMI and volumetric density measures at first mammogram; BCSC SFMR 2007-2013.
| N (women) | Mean (SD) at first mammogram | β (SE) a, b | 95% CI | P value | |
|---|---|---|---|---|---|
| All women | 24,556 | ||||
| Dense breast volume (cm3) | 142.3 (76.7) | 2.95 (0.13) | 2.69, 3.21 | < 0.001 | |
| Non-dense breast volume (cm3) | 416.8 (331.0) | 51.03 (0.56) | 49.93, 52.13 | < 0.001 | |
| Percent dense breast volume (%) | 32.5 (19.5) | -2.03 (0.03) | -2.09, -1.98 | < 0.001 | |
| Premenopausal at first mammogram | 8,355 | ||||
| Dense breast volume (cm3) | 166.7 (83.7) | 3.05 (0.27) | 2.51, 3.58 | < 0.001 | |
| Non-dense breast volume (cm3) | 327.1 (303.2) | 53.64 (0.98) | 51.72, 55.55 | < 0.001 | |
| Percent dense breast volume (%) | 42.3 (21.3) | -2.88 (0.06) | -3.00, -2.77 | < 0.001 | |
| Postmenopausal at first mammogram | 15,734 | ||||
| Dense breast volume (cm3) | 129.1 (69.1) | 2.97 (0.16) | 2.67, 3.28 | < 0.001 | |
| Non-dense breast volume (cm3) | 465.6 (336.2) | 49.96 (0.69) | 48.61, 51.31 | < 0.001 | |
| Percent dense breast volume (%) | 27.3 (16.2) | -1.67 (0.03) | -1.73, -1.61 | < 0.001 |
Adjusted for age at first mammogram, race, education, first degree family history of breast cancer, history of breast biopsy, ever given birth (yes/no), menopausal status at first mammogram (all women), and hormone use at first mammogram
Regression coefficient (β) represents the change in volumetric density measure associated with a 1 kg/m2 difference in BMI, holding the covariates above constant.
Stratified results exclude 467 women with unknown menopausal status at first mammogram
P value for interaction by menopausal status: p=0.79 for DV, p<0.01 for NDV, p<0.01 for PDV
A majority of women maintained stable weight within ± 5% of their initial BMI during the study period (73.6%) (Table 3). A greater proportion of women who were initally overweight or obese lost over 5% of their initial BMI compared to those with BMI < 25 kg/m2 (p<0.01). Among premenopausal women, a higher proportion of those who were initially overweight or obese gained over 5% of their initial BMI compared to those who initially had BMI <25 kg/m2 (p=0.01). No difference in weight gain by initial BMI was observed among postmenopausal women (p=0.86).
Table 3. Change in BMI from first mammogram to last mammogram, stratified by menopausal status and by BMI at first mammogram (kg/m2) (N=24,556); BCSC SFMR 2007-2013.
| N (women) | > 10% loss N (%) | 5-10% loss N (%) | Stable ± 5% N (%) | 5-10% gain N (%) | > 10% gain N (%) | |
|---|---|---|---|---|---|---|
| All women | 24,556 | 839 (3.4) | 2,177 (8.9) | 18,059 (73.6) | 2,584 (10.5) | 897 (3.6) |
| Premenopausal (all exams) | 7,266 | |||||
| Underweight or normal: <25 | 5,203 | 45 (0.9) | 271 (5.2) | 4,163 (80.0) | 558 (10.7) | 166 (3.2) |
| Overweight: 25 - <30 | 1,487 | 71 (4.8) | 172 (11.6) | 983 (66.0) | 185 (12.4) | 78 (5.2) |
| Obese: 30+ | 576 | 57 (9.9) | 65 (11.3) | 356 (61.8) | 78 (13.5) | 20 (3.5) |
| Postmenopausal (all exams) | 15,715 | |||||
| Underweight or normal: <25 | 9,682 | 168 (1.7) | 735 (7.6) | 7,489 (77.4) | 956 (9.9) | 331 (3.4) |
| Overweight: 25 - <30 | 4,093 | 239 (5.8) | 513 (12.5) | 2,787 (68.1) | 417 (10.2) | 137 (3.4) |
| Obese: 30+ | 1,940 | 191 (9.9) | 272 (14.0) | 1,224 (63.1) | 181 (9.3) | 72 (3.7) |
Stratified results exclude 486 women with unknown menopausal status at one or more mammogram(s) and 1,089 women who transitioned from pre- to postmenopausal during the study period.
The mean annual change in DV, NDV, and PDV over the study period was -0.56 cm3/year, 6.09 cm3/year, and -0.81 %/year, respectively. A 1 kg/m2 annual increase in BMI was associated with a statistically significant decrease in DV (β=-1.01 cm3/year, 95% CI -1.59, -0.42), increase in NDV (β=26.2 cm3/year, 95% CI 23.5, 28.9), and decrease in PDV (β=-1.17%/year, 95% CI -1.31, -1.04) (Table 4). These associations remained significant after adjustment for change in total breast volume (Supplementary Table S1). When stratified by menopausal status and initial BMI, the significant annual decrease in DV with increasing BMI was observed among premenopausal women who were intitally overweight or obese, but not among those who were initially underweight or normal BMI (p value for interaction by initial BMI: p<0.01). Among postmenopausal women, the annual decrease in DV with increasing BMI was not statistically significant within initial BMI strata and no interaction was observed (p value for interaction by initial BMI: p=0.67). In longitudinal analysis, we observed no significant overall interaction by postmenopausal HT use (p value for interaction by HT use: p>0.15 for DV, NDV, and PDV) or race (p value for interaction by race: p>0.19 for DV, NDV, and PDV).
Table 4. Association between annual increase in BMI and annual change in volumetric mammographic breast density, stratified by menopausal status and BMI at first mammogram (kg/m2): BCSC SFMR 2007-2013.
| Annual change in dense breast volume (cm3 per year) | Annual change in non-dense breast volume (cm3 per year) | Annual change in percent dense breast volume (% per year) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| N (women) | β (SE) a | 95% CI | p value | β (SE) a | 95% CI | p value | β (SE) a | 95% CI | p value | |
| All women | 24,556 | -1.01 (0.30) | -1.59, -0.42 | 0.001 | 26.2 (1.38) | 23.45, 28.87 | < 0.001 | -1.17 (0.07) | -1.31, -1.04 | < 0.001 |
| Premenopausal (all exams) | 7,266 | -1.73 (0.73) | -3.17, -0.30 | 0.02 | 32.64 (2.63) | 27.47, 37.80 | < 0.001 | -1.83 (0.14) | -2.10, -1.56 | < 0.001 |
| Underweight or normal: <25 | 5,203 | 0.92 (0.74) | -0.52, 2.37 | 0.21 | 31.17 (2.65) | 25.97, 36.37 | < 0.001 | -2.84 (0.23) | -3.30, -2.38 | < 0.001 |
| Overweight: 25 - <30 | 1,487 | -2.71 (0.98) | -4.63, -0.80 | 0.01 | 33.56 (4.50) | 24.74, 42.37 | < 0.001 | -1.47 (0.21) | -1.87, -1.07 | < 0.001 |
| Obese: 30+ | 576 | -3.29 (1.90) | -7.01, 0.44 | 0.08 | 32.63 (6.48) | 19.93, 45.32 | < 0.001 | -0.86 (0.17) | -1.18, -0.53 | < 0.001 |
| Postmenopausal (all exams) | 15,715 | -0.74 (0.32) | -1.36, -0.12 | 0.02 | 24.08 (1.63) | 20.89, 27.27 | < 0.001 | -0.95 (0.08) | -1.10, -0.80 | < 0.001 |
| Underweight or normal: <25 | 9,682 | -0.32 (0.32) | -0.95, 0.31 | 0.32 | 22.93 (2.95) | 17.15, 28.70 | < 0.001 | -1.51 (0.20) | -1.91, -1.11 | < 0.001 |
| Overweight: 25 - <30 | 4,093 | -0.94 (0.66) | -2.23, 0.36 | 0.16 | 31.09 (1.73) | 27.70, 34.47 | < 0.001 | -1.08 (0.11) | -1.29, -0.87 | < 0.001 |
| Obese: 30+ | 1,940 | -0.53 (0.61) | -1.73, 0.67 | 0.39 | 20.57 (2.56) | 15.55, 25.58 | < 0.001 | -0.35 (0.09) | -0.52, -0.17 | < 0.001 |
Adjusted for age at first mammogram, age at first birth, history of breast biopsy, education, ever given birth (yes/no), first degree family history of breast cancer, hormone use during study period, menopausal status (all women), and race
Stratified results exclude 486 women with unknown menopausal status at one or more mammogram(s) and 1,089 women who transitioned from pre- to postmenopausal during the study period.
P value for interaction by menopausal status: DV p=0.43, NDV p=0.01, PDV p<0.01
P value for interaction by BMI at first mammogram among:
premenopausal women: DV p<0.01, NDV p=0.87, PDV p<0.01
postmenopausal women: DV p=0.67, NDV p<0.01, PDV p<0.01
Discussion
Consistent with our expectations, we observed positive cross-sectional relationships between BMI and both DV and NDV, and an inverse cross-sectional relationship between BMI and PDV. We further observed that DV and PDV declined on average over the study period, and that a longitudinal increase in BMI was associated with an accelerated decrease in PDV. However, contrary to our expectations, we found that a longitudinal increase in BMI was also associated with an accelerated decrease in DV. This finding was consistent among pre- and postmenopausal women, and was strongest among premenopausal women who were intially overweight or obese.
Our findings of a positive cross-sectional relationship between BMI and DV are consistent with studies using the SXA method (13) and other volumetric techniques (11, 12, 27), but in contrast with studies using area assessment (10, 28). Differences in study populations may contribute to the difference in the association of BMI and DV between area and volumetric methods; however, two studies (7, 13) compared this association using area and volumetric density measured from the same mammographic images and confirmed this contrasting result. This indicates that measurement method may be largely responsible for the difference. Unlike area methods, which rely on a dichotomous separation of dense and non-dense area, the volumetric SXA method calculates a continuous value for DV based on the comparison of each pixel on the mammographic image to a known phantom (20). Continuous assessment of DV may provide a more accurate measurement of dense tissue than dichotomous area methods. Further, the SXA method includes water in adipose tissue in its calculation of DV (8). A recent study comparing area and SXA volume measurements reported that correlations between DA and DV were stronger among lean women than among obese women as a result of this inclusion (13). The contribution of water from adipose tissue may partially account for the positive cross-sectional association between BMI and DV.
Our observed 1.17% annual decline in PDV associated with a unit annual increase in BMI is similar in magnitude to the two previous studies of change in BMI and PDA over time, which reported annual declines of 0.36% (17) and 1.44% (18). However, our observation of a decrease in DV with increasing BMI does not support their findings of no association (17) or positive association (18) between change in BMI and DA. The association between change in BMI and change in DA observed by Reeves et al (17) was in the same direction as our finding, but their results among 833 women were not statistically significant. Although we observed a significant annual decrease in DV, it should be noted that the magnitude, approximately 1 cm3/year, is small compared to the initial average DV in our study population (142.3 cm3) and compared to the difference in DV that was associated with increased breast cancer risk in a case-control analysis of 864 women using SXA (8). Women in the highest quintiles of DV (192+ cm3) had significantly higher risk compared to those in the lowest quintile (<122 cm3).
We observed that the longitudinal association between BMI and DV was strongest among premenopausal women who were initially overweight or obese. These women were more likely to gain more than 5% of their initial weight during the study period than their lean counterparts. It is possible that the association between change in BMI and DV was easiest to observe among women who gained more weight, since a larger change in BMI may allow us to see the associated small change in DV. Furthermore, age-related changes in breast density typically occur during early perimenopause (14-16) and a decline in DV may be more pronounced and easily observable among these women. The inverse association between BMI and DV was seen among postmenopausal women, but did not vary by initial BMI; and likewise, no difference in weight gain by initial BMI was observed among postmenopausal women.
It is understood that the number and size of breast lobules decrease with increasing age, known as breast tissue involution (29, 30), which is consistent with a decrease in DV over time. Age remained a significant factor in our multivariable models for DV that included change in BMI as well as menopausal status and parity, both of which have been associated with the rate of breast involution (29). Thus, breast involution with age may partially explain our finding of a decrease in DV. Beyond the effect of age, the biological mechanism that may link an annual increase in BMI to an annual decrease in absolute DV is unclear. Like other volumetric methods, the SXA technique calculates breast volume using the two-dimensional mammographic image. Our finding could reflect differences in capturing dense breast tissue on a mammographic image for large- versus small-breasted women as opposed to a true reduction in DV with increasing BMI. However, additional adjustment for total breast volume in the model did not change the association between change in BMI and change in DV. It is unlikely that the inclusion of adipose water in the SXA assessment of DV explains our findings of a decrease in DV with increasing BMI because this would this inclusion would tend to increase DV with increasing BMI. Further, water has been estimated to account for only 8% (21) to 20% (31) of adipose content, and therefore the impact of this measurement method on our results is likely to be minor. However, future studies using SXA are necessary to confirm our findings.
Our study is strengthened by the large number of participants and the concurrent collection of height and weight data at the time of the screening mammogram. In addition, volumetric density was assessed using a validated method that has been found to similarly predict breast cancer risk compared to area assessment (8). Our results must be interpreted in context of the study limitations, however. First, height and weight were self-reported. However, validity of self-reported height and weight measures has been assessed within a subset of the BCSC cohort (13) and the Spearman correlation coefficient between BMI from self-reported and measured values was 0.949 (95% CI 0.938, 0.957). Second, our ability to adjust for confounding variables was limited by the information collected on the SFMR questionnaire, however we adjusted for the main factors known to impact breast density including age, BMI, menopausal status, family history of breast cancer, and parity. In particular, breast tissue involution with age may partially explain our longitudinal findings, despite adjustment to the extent possible for factors associated with involution. Finally, the SXA technique has been shown to be consistent over time (22); however, placement of the breast during the mammogram examination may introduce some variability in the measurement of breast density over the course of the study.
The roles of BMI and mammographic density in breast carcinogenesis are unclear and are complicated by the dynamic nature of body and breast composition over time. Prior analyses of BMI and PDA find that these are independent breast cancer risk factors in both pre- and postmenopausal women (10, 30, 32), and that studies of either factor should control for the other to avoid negative confounding (10, 32). We observed significant associations between change in BMI and change in volumetric density measures over an average of 2.4 years of follow-up. While short-term change in BMI may not be strongly associated with breast cancer risk, adult weight gain has been consistently associated with increased risk (33, 34). Thus, change in BMI may be a confounder in long-term longitudinal studies of volumetric density and breast cancer risk; and researchers should consider adjusting for change in BMI to fully understand the independent effect of change in volumetric density on breast cancer risk. Further, longitudinal studies using change in volumetric density as an indicator of changing breast cancer risk should carefully evaluate the potential for confouding by gain or loss in BMI and consider adjustment as necessary. The inverse association we observed between change in BMI and change in DV after adjustment for factors associated with breast involution is not easily explained, and confirmation is required to ensure that our results are not due to chance or an inherent manifestation of our measurement method.
Supplementary Material
Acknowledgments
Financial Support: This work was supported by the National Cancer Institute-funded Breast Cancer Surveillance Consortium (P01 CA154292 (K. Kerlikowske, J. Shepherd, J.A. Tice, B.L. Sprague) and HHSN261201100031C (K. Kerlikowske, B.L. Sprague)) and U54 CA163303 (B.L. Sprague, J. Shepherd). 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. We thank the participating women, mammography facilities, and radiologists for the data they have provided for this study. A list of the BCSC investigators and procedures for requesting BCSC data for research purposes are provided at: http://breastscreening.cancer.gov/.
The collection of cancer and vital status data used in this study was supported in part by state public health departments and cancer registries throughout the US. For a full description of these sources, please see: http://breastscreening.cancer.gov/work/acknowledgement.html.
Footnotes
Conflicts of interest: None
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