PURPOSE: We examined whether obesity and a history of diabetes, hypertension, and elevated cholesterol, individually and in combination, are associated with breast density, a strong risk factor for breast cancer. METHODS: We measured percent density and dense area using a computer-assisted method (n = 191; age range = 40-61 years). We used linear regression models to examine the associations of each metabolic condition and the number of metabolic conditions (zero, one, two, and three or four conditions) with breast density. RESULTS: Among individual metabolic conditions, only high blood cholesterol was inversely associated with percent density (β = -5.4, 95% confidence interval [CI]: -8.5, -2.2) and dense area (β = -6.7, 95% CI = -11.1, -2.4). Having multiple metabolic conditions was also associated with lower breast density, with two conditions and three or four conditions versus zero conditions associated with 6.4% (95% CI: -11.2, -1.6) and 7.4% (95% CI: -12.9, -1.9) reduction in percent density and with 6.5 cm(2) (95% CI: -13.1, -0.1) and 9.5 cm(2) (95% CI: -17.1, -1.9) decrease in dense area. CONCLUSIONS: A history of high blood cholesterol and multiple metabolic conditions were associated with lower relative and absolute measures of breast density. The positive association between metabolic abnormalities and breast cancer risk may be driven by pathways unrelated to mammographic breast density.

Importance

Many established breast cancer risk factors are used in clinical risk prediction models, although the proportion of breast cancers explained by these factors is unknown.

Objective

To determine the population-attributable risk proportion (PARP) for breast cancer associated with clinical breast cancer risk factors among premenopausal and postmenopausal women.

Design, setting, and participants

Case-control study with 1:10 matching on age, year of risk factor assessment, and Breast Cancer Surveillance Consortium (BCSC) registry. Risk factor data were collected prospectively from January 1, 1996, through October 31, 2012, from BCSC community-based breast imaging facilities. A total of 18 437 women with invasive breast cancer or ductal carcinoma in situ were enrolled as cases and matched to 184 309 women without breast cancer, with a total of 58 146 premenopausal and 144 600 postmenopausal women enrolled in the study.

Exposures

Breast Imaging Reporting and Data System (BI-RADS) breast density (heterogeneously or extremely dense vs scattered fibroglandular densities), first-degree family history of breast cancer, body mass index (>25 vs 18.5-25), history of benign breast biopsy, and nulliparity or age at first birth (≥30 years vs <30 years).

Main outcomes and measures

Population-attributable risk proportion of breast cancer.

Results

Of the 18 437 women with breast cancer, the mean (SD) age was 46.3 (3.7) years among premenopausal women and 61.7 (7.2) years among the postmenopausal women. Overall, 4747 (89.8%) premenopausal and 12 502 (95.1%) postmenopausal women with breast cancer had at least 1 breast cancer risk factor. The combined PARP of all risk factors was 52.7% (95% CI, 49.1%-56.3%) among premenopausal women and 54.7% (95% CI, 46.5%-54.7%) among postmenopausal women. Breast density was the most prevalent risk factor for both premenopausal and postmenopausal women and had the largest effect on the PARP; 39.3% (95% CI, 36.6%-42.0%) of premenopausal and 26.2% (95% CI, 24.4%-28.0%) of postmenopausal breast cancers could potentially be averted if all women with heterogeneously or extremely dense breasts shifted to scattered fibroglandular breast density. Among postmenopausal women, 22.8% (95% CI, 18.3%-27.3%) of breast cancers could potentially be averted if all overweight and obese women attained a body mass index of less than 25.

Conclusions and relevance

Most women with breast cancer have at least 1 breast cancer risk factor routinely documented at the time of mammography, and more than half of premenopausal and postmenopausal breast cancers are explained by these factors. These easily assessed risk factors should be incorporated into risk prediction models to stratify breast cancer risk and promote risk-based screening and targeted prevention efforts.

Sub-Saharan Africa suffers an inordinate burden of disease and does not have the numbers of suitably trained health care workers to address this challenge. New concepts in health sciences education are needed to offer alternatives to current training approaches.A perspective of integrated training in population health for undergraduate medical and nursing education is advanced, rather than continuing to take separate approaches for clinical and public health education. Population health science educates students in the social and environmental origins of disease, thus complementing disease-specific training and providing opportunities for learners to take the perspective of the community as a critical part of their education.Many of the recent initiatives in health science education in sub-Saharan Africa are reviewed, and two case studies of innovative change in undergraduate medical education are presented that begin to incorporate such population health thinking. The focus is on East Africa, one of the most rapidly growing economies in sub-Saharan Africa where opportunities for change in health science education are opening. The authors conclude that a focus on population health is a timely and effective way for enhancing training of health care professionals to reduce the burden of disease in sub-Saharan Africa.

Background: The U.S. Hispanic/Latino population is heterogeneous both socioculturally and by the proportion of European, Indigenous American, and African ancestry of the regions from which individuals originate. A previous study reported that genetic ancestry was associated with breast cancer survival among Latinas, independent of sociodemographic and tumor characteristics, suggesting that a genetic factor associated with ancestry may affect breast cancer survival.Methods: We evaluated the association of genetic ancestry with breast cancer outcomes among 506 Latina women with invasive breast cancer in the Pathways Study, a cohort study within Kaiser Permanente, an integrated health care delivery system. Proportional hazards models were used to assess the effect of ancestry on breast cancer recurrence (53 events), breast cancer-specific mortality (31 events) and all-cause mortality (54 events), with a mean follow-up time of 6 years.Results: Indigenous American ancestry was not associated with breast cancer recurrence [HR = 1.00 per 10% increase; 95% confidence interval (CI), 0.86-1.16], breast cancer mortality (HR = 0.95; 95% CI, 0.77-1.17), or all-cause mortality (HR = 0.93; 95% CI, 0.80-1.08). Adjustment for sociodemographic variables, tumor characteristics, and treatment did not alter the associations.Conclusions: Our results suggest that previously reported differences in breast cancer survival by genetic ancestry may be overcome by improving health care access and/or quality.Impact: Improving health care access and quality may reduce breast cancer disparities among U.S. Latinas. Cancer Epidemiol Biomarkers Prev; 26(9); 1466-9. ©2017 AACR.

Sub-Saharan Africa suffers an inordinate burden of disease and does not have the numbers of suitably trained health care workers to address this challenge. New concepts in health sciences education are needed to offer alternatives to current training approaches.A perspective of integrated training in population health for undergraduate medical and nursing education is advanced, rather than continuing to take separate approaches for clinical and public health education. Population health science educates students in the social and environmental origins of disease, thus complementing disease-specific training and providing opportunities for learners to take the perspective of the community as a critical part of their education.Many of the recent initiatives in health science education in sub-Saharan Africa are reviewed, and two case studies of innovative change in undergraduate medical education are presented that begin to incorporate such population health thinking. The focus is on East Africa, one of the most rapidly growing economies in sub-Saharan Africa where opportunities for change in health science education are opening. The authors conclude that a focus on population health is a timely and effective way for enhancing training of health care professionals to reduce the burden of disease in sub-Saharan Africa.

Background

Complex systems models of breast cancer have previously focused on prediction of prognosis and clinical events for individual women. There is a need for understanding breast cancer at the population level for public health decision-making, for identifying gaps in epidemiologic knowledge and for the education of the public as to the complexity of this most common of cancers.

Methods and findings

We developed an agent-based model of breast cancer for the women of the state of California using data from the U.S. Census, the California Health Interview Survey, the California Cancer Registry, the National Health and Nutrition Examination Survey and the literature. The model was implemented in the Julia programming language and R computing environment. The Paradigm II model development followed a transdisciplinary process with expertise from multiple relevant disciplinary experts from genetics to epidemiology and sociology with the goal of exploring both upstream determinants at the population level and pathophysiologic etiologic factors at the biologic level. The resulting model reproduces in a reasonable manner the overall age-specific incidence curve for the years 2008-2012 and incidence and relative risks due to specific risk factors such as BRCA1, polygenic risk, alcohol consumption, hormone therapy, breastfeeding, oral contraceptive use and scenarios for environmental toxin exposures.

Conclusions

The Paradigm II model illustrates the role of multiple etiologic factors in breast cancer from domains of biology, behavior and the environment. The value of the model is in providing a virtual laboratory to evaluate a wide range of potential interventions into the social, environmental and behavioral determinants of breast cancer at the population level.

Background: Reductions in breast density with tamoxifen and aromatase inhibitors may be an intermediate marker of treatment response. We compare changes in volumetric breast density among breast cancer cases using tamoxifen or aromatase inhibitors (AI) to untreated women without breast cancer.Methods: Breast cancer cases with a digital mammogram prior to diagnosis and after initiation of tamoxifen (n = 366) or AI (n = 403) and a sample of controls (n = 2170) were identified from the Mayo Clinic Mammography Practice and San Francisco Mammography Registry. Volumetric percent density (VPD) and dense breast volume (DV) were measured using Volpara (Matakina Technology) and Quantra (Hologic) software. Linear regression estimated the effect of treatment on annualized changes in density.Results: Premenopausal women using tamoxifen experienced annualized declines in VPD of 1.17% to 1.70% compared with 0.30% to 0.56% for controls and declines in DV of 7.43 to 15.13 cm³ compared with 0.28 to 0.63 cm³ in controls, for Volpara and Quantra, respectively. The greatest reductions were observed among women with ≥10% baseline density. Postmenopausal AI users had greater declines in VPD than controls (Volpara P = 0.02; Quantra P = 0.03), and reductions were greatest among women with ≥10% baseline density. Declines in VPD among postmenopausal women using tamoxifen were only statistically greater than controls when measured with Quantra.Conclusions: Automated software can detect volumetric breast density changes among women on tamoxifen and AI.Impact: If declines in volumetric density predict breast cancer outcomes, these measures may be used as interim prognostic indicators. Cancer Epidemiol Biomarkers Prev; 26(6); 930-7. ©2017 AACR.

Background

Given that breast cancer and normal dense fibroglandular tissue have similar radiographic attenuation, we examine whether automated volumetric density measures identify a differential change between breasts in women with cancer and compare to healthy controls.

Methods

Eligible cases (n = 1160) had unilateral invasive breast cancer and bilateral full-field digital mammograms (FFDMs) at two time points: within 2 months and 1-5 years before diagnosis. Controls (n = 2360) were matched to cases on age and date of FFDMs. Dense volume (DV) and volumetric percent density (VPD) for each breast were assessed using Volpara™. Differences in DV and VPD between mammograms (median 3 years apart) were calculated per breast separately for cases and controls and their difference evaluated by using the Wilcoxon signed-rank test. To simulate clinical practice where cancer laterality is unknown, we examined whether the absolute difference between breasts can discriminate cases from controls using area under the ROC curve (AUC) analysis, adjusting for age, BMI, and time.

Results

Among cases, the VPD and DV between mammograms of the cancerous breast decreased to a lesser degree (- 0.26% and - 2.10 cm³) than the normal breast (- 0.39% and - 2.74 cm³) for a difference of 0.13% (p value < 0.001) and 0.63 cm³ (p = 0.002), respectively. Among controls, the differences between breasts were nearly identical for VPD (- 0.02 [p = 0.92]) and DV (0.05 [p = 0.77]). The AUC for discriminating cases from controls using absolute difference between breasts was 0.54 (95% CI 0.52, 0.56) for VPD and 0.56 (95% CI, 0.54, 0.58) for DV.

Conclusion

There is a small relative increase in volumetric density measures over time in the breast with cancer which is not found in the normal breast. However, the magnitude of this difference is small, and this measure alone does not appear to be a good discriminator between women with and without breast cancer.

Background

Mammographic breast density declines during menopause. We assessed changes in volumetric breast density across the menopausal transition and factors that influence these changes.

Methods

Women without a history of breast cancer, who had full field digital mammograms during both pre- and postmenopausal periods, at least 2 years apart, were sampled from four facilities within the San Francisco Mammography Registry from 2007 to 2013. Dense breast volume (DV) was assessed using Volpara on mammograms across the time period. Annualized change in DV from pre- to postmenopause was estimated using linear mixed models adjusted for covariates and per-woman random effects. Multiplicative interactions were evaluated between premenopausal risk factors and time to determine whether these covariates modified the annualized changes.

Results

Among the 2,586 eligible women, 1,802 had one premenopausal and one postmenopausal mammogram, 628 had an additional perimenopausal mammogram, and 156 had two perimenopausal mammograms. Women experienced an annualized decrease in DV [-2.2 cm³ (95% confidence interval, -2.7 to -1.7)] over the menopausal transition. Declines were greater among women with a premenopausal DV above the median (54 cm³) versus below (DV, -3.5 cm³ vs. -1.0 cm³; P < 0.0001). Other breast cancer risk factors, including race, body mass index, family history, alcohol, and postmenopausal hormone therapy, had no effect on change in DV over the menopausal transition.

Conclusions

High premenopausal DV was a strong predictor of greater reductions in DV across the menopausal transition.

Impact

We found that few factors other than premenopausal density influence changes in DV across the menopausal transition, limiting targeted prevention efforts.