Search

Background: Elevated mammographic density (MD) for a woman's age and body mass index (BMI) is an established breast cancer risk factor. The relationship of parity, age at first birth, and breastfeeding with MD is less clear. We examined the associations of these factors with MD within the International Consortium of Mammographic Density (ICMD). Methods: ICMD is a consortium of 27 studies with pooled individual-level epidemiological and MD data from 11,755 women without breast cancer aged 35–85 years from 22 countries, capturing 40 country-& ethnicity-specific population groups. MD was measured using the area-based tool Cumulus. Meta-analyses across population groups and pooled analyses were used to examine linear regression associations of square-root (√) transformed MD measures (percent MD (PMD), dense area (DA), and non-dense area (NDA)) with parity, age at first birth, ever/never breastfed and lifetime breastfeeding duration. Models were adjusted for age at mammogram, age at menarche, BMI, menopausal status, use of hormone replacement therapy, calibration method, mammogram view and reader, and parity and age at first birth when not the association of interest. Results: Among 10,988 women included in these analyses, 90.1% (n = 9,895) were parous, of whom 13% (n = 1,286) had ≥ five births. The mean age at first birth was 24.3 years (Standard deviation = 5.1). Increasing parity (per birth) was inversely associated with √PMD (β: − 0.05, 95% confidence interval (CI): − 0.07, − 0.03) and √DA (β: − 0.08, 95% CI: − 0.12, − 0.05) with this trend evident until at least nine births. Women who were older at first birth (per five-year increase) had higher √PMD (β:0.06, 95% CI:0.03, 0.10) and √DA (β:0.06, 95% CI:0.02, 0.10), and lower √NDA (β: − 0.06, 95% CI: − 0.11, − 0.01). In stratified analyses, this association was only evident in women who were post-menopausal at MD assessment. Among parous women, no associations were found between ever/never breastfed or lifetime breastfeeding duration (per six-month increase) and √MD. Conclusions: Associations with higher parity and older age at first birth with √MD were consistent with the direction of their respective associations with breast cancer risk. Further research is needed to understand reproductive factor-related differences in the composition of breast tissue and their associations with breast cancer risk. [ABSTRACT FROM AUTHOR]

Simple Summary: Females prenatally exposed to diethylstilbestrol have an elevated risk of severe cervical dysplasia and some cancers, while testicular cancer is increased in males. We assessed these associations in the prenatally exposed female's offspring (third generation). Based on third-generation females' self-reports, diethylstilbestrol exposure was not associated with risks of overall cancer, breast cancer, or severe cervical dysplasia. Borderline ovarian cancer risk in exposed vs. unexposed was elevated but compatible with chance. Based on mothers' reports, diethylstilbestrol exposure did not increase the risk of overall or other cancers in third-generation females. Overall cancer risk in exposed males appeared elevated but compatible with chance. Testicular cancer risk was not elevated in exposed males, and there were no prostate cancers reported. These data do not provide evidence that diethylstilbestrol is associated with cancer risk in third-generation females or males. The third generation is relatively young and requires follow-up to assess risk as the cohort ages. Background: Females exposed prenatally to diethylstilbestrol (DES) have an elevated risk of cervical dysplasia, breast cancer, and clear cell adenocarcinoma (CCA) of the cervix/vagina. Testicular cancer risk is increased in prenatally exposed males. Epigenetic changes may mediate the transmission of DES effects to the next ("third") generation of offspring. Methods: Using data self-reported by third-generation females, we assessed DES in relation to the risk of cancer and benign breast and reproductive tract conditions. Using data from prenatally DES-exposed and unexposed mothers, we assessed DES in relation to cancer risk in their female and male offspring. Cancer risk was assessed by standardized incidence ratios (SIR) and 95% confidence intervals (CI); the risks of benign and malignant diagnoses were assessed by hazard ratios (HR) and 95% CI. Results: In self-reported data, DES exposure was not associated with an increased risk of overall cancer (HR 0.83; CI 0.36–1.90), breast cancer, or severe cervical dysplasia. No females reported CCA. The risk of borderline ovarian cancer appeared elevated, but the HR was imprecise (3.46; CI 0.37–32.42). Based on mothers' reports, DES exposure did not increase the risk of overall cancer (HR 0.80; CI 0.49–1.32) or of other cancers in third-generation females. Overall cancer risk in exposed males appeared elevated (HR 1.41; CI 0.70–2.86), but the CI was wide. The risk of testicular cancer was not elevated in exposed males; no cases of prostate cancer were reported. Conclusions: To date, there is little evidence that DES is associated with cancer risk in third-generation females or males, but these individuals are relatively young, and further follow-up is needed. [ABSTRACT FROM AUTHOR]

Background To support mammography screening decision making, we developed a competing-risk model to estimate 5-year breast cancer risk and 10-year nonbreast cancer death for women aged 55 years and older using Nurses' Health Study data and examined model performance in the Black Women's Health Study (BWHS). Here, we examine model performance in predicting 10-year outcomes in the BWHS, Women's Health Initiative-Extension Study (WHI-ES), and Multiethnic Cohort (MEC) and compare model performance to existing breast cancer prediction models. Methods We used competing-risk regression and Royston and Altman methods for validating survival models to calculate our model's calibration and discrimination (C index) in BWHS (n = 17 380), WHI-ES (n = 106 894), and MEC (n = 49 668). The Nurses' Health Study development cohort (n = 48 102) regression coefficients were applied to the validation cohorts. We compared our model's performance with breast cancer risk assessment tool (Gail) and International Breast Cancer Intervention Study (IBIS) models by computing breast cancer risk estimates and C statistics. Results When predicting 10-year breast cancer risk, our model's C index was 0.569 in BWHS, 0.572 in WHI-ES, and 0.576 in MEC. The Gail model's C statistic was 0.554 in BWHS, 0.564 in WHI-ES, and 0.551 in MEC; IBIS's C statistic was 0.547 in BWHS, 0.552 in WHI-ES, and 0.562 in MEC. The Gail model underpredicted breast cancer risk in WHI-ES; IBIS underpredicted breast cancer risk in WHI-ES and in MEC but overpredicted breast cancer risk in BWHS. Our model calibrated well. Our model's C index for predicting 10-year nonbreast cancer death was 0.760 in WHI-ES and 0.763 in MEC. Conclusions Our competing-risk model performs as well as existing breast cancer prediction models in diverse cohorts and predicts nonbreast cancer death. We are developing a website to disseminate our model. [ABSTRACT FROM AUTHOR]

High mammographic density is a well-established risk factor for breast cancer; however, data from Black women are limited. It is largely unknown how mammographic density is associated with breast cancer subtypes among Black women. We examined the association between percent mammographic density (PMD) and breast cancer risk among participants in the Black Women's Health Study. Digital screening mammograms were available for 363 cases and 5541 non-cases. Cumulus software was used to assess PMD. We used inverse probability of sampling weights and Cox proportional hazards models, adjusted for age and body mass index, to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) overall and by age at mammography and estrogen receptor (ER) status of the breast tumors. Multivariable models included additional breast cancer risk factors. Tests of statistical significance were 2-sided. In simple models, women in the highest quartile of PMD had 53% increased odds of breast cancer compared to those in the lowest quartile (HR 1.53; 95% CI: 1.11, 2.11). HRs were 1.37 (95% CI: 0.83, 2.24) among women <55 years of age and 1.68 (95% CI: 1.10, 2.56) among women aged ≥55 years. HRs were 1.49 (95% CI: 1.02, 2.16) for ER+ cancer and 1.45 (95% CI: 0.73, 2.87) for ER- cancer. Associations were largely unchanged in multivariable models. In this study of U.S. Black women, higher PMD was associated with ER+ and ER- breast cancer risk. Findings from this study reinforce the importance of breast density as a risk factor for breast cancer in Black women., (© 2024 UICC.)

Background: The American Cancer Society recommends physicians inform average risk women about endometrial cancer (EC) risk on reaching menopause, but new diagnoses are rising fastest in women <50 years. Educating these women about EC risks requires knowledge of risk factors. However, EC in young women is rare and challenging to study in single study populations., Methods: We included 13,846 incident EC patients (1,639 < 50 years) and 30,569 matched control individuals from the Epidemiology of Endometrial Cancer Consortium. We used generalized linear models to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for 6 risk factors and EC risk. We created a risk score to evaluate the combined associations and population attributable fractions of these factors., Results: In younger and older women, we observed positive associations with BMI and diabetes, and inverse associations with age at menarche, oral contraceptive use, and parity. Current smoking was associated with reduced risk only in women ≥50 years (PHet<0.01). BMI was the strongest risk factor [OR≥35 vs <25 kg/m2=5.57 (95% CI:4.33-7.16) for <50 years; OR≥35 vs <25 kg/m2=4.68 (95% CI : 4.30-5.09) for ≥50 years; PHet=0.14]. Possessing ≥4 risk factors was associated with ∼9-fold increased risk in women <50 years and ∼4-fold increased risk in women ≥50 years (PHet<0.01). Together, 59.1% of ECs in women <50 and 55.6% in women ≥50 were attributable to these factors., Conclusions: Our data confirm younger and older women share common EC risk factors. Early educational efforts centered on these factors may help mitigate the rising EC burden in young women., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: High mammographic density is one of the strongest breast cancer risk factors; however, determinants of high mammographic density are understudied in Black women. We assessed growth and development factors across the lifecourse in relation to mammographic density., Methods: Within the Black Women's Health Study (BWHS), we used Cumulus software to assess percent mammographic density from digital screening mammograms for 5,905 women ages 40-74. We fit linear regression models to quantify the association of lifecourse characteristics including birth weight, childhood somatotype, age at menarche, body mass index (BMI) at age 18, height, BMI at mammography, and adulthood waist-to-hip ratio with density overall and by age. We also performed a path analysis to assess the total and mediating effects of the growth and development factors on density., Results: BMI at age 18, height, BMI at mammography, and waist-to-hip ratio were significantly and inversely associated with density. On path analysis, total effects of childhood somatotype (standardized  = -0.05, p <0.001), BMI at age 18 (standardized  = -0.13, p <0.001), BMI at mammography (standardized  = -0.22, p <0.001), and waist-to-hip ratio (standardized  = -0.04, p <0.001) were associated with density., Conclusions: Several factors across the lifecourse - greater childhood somatotype, BMI at age 18, height, BMI at mammography, and waist-to-hip ratio - were associated with lower mammographic density in this cohort of Black women., Impact: Body size closer to the time of mammography may be more meaningful in determining mammographic density, though early life adiposity also influences mammographic density.

Background: To support mammography screening decision making, we developed a competing-risk model to estimate 5-year breast cancer risk and 10-year nonbreast cancer death for women aged 55 years and older using Nurses' Health Study data and examined model performance in the Black Women's Health Study (BWHS). Here, we examine model performance in predicting 10-year outcomes in the BWHS, Women's Health Initiative-Extension Study (WHI-ES), and Multiethnic Cohort (MEC) and compare model performance to existing breast cancer prediction models., Methods: We used competing-risk regression and Royston and Altman methods for validating survival models to calculate our model's calibration and discrimination (C index) in BWHS (n = 17 380), WHI-ES (n = 106 894), and MEC (n = 49 668). The Nurses' Health Study development cohort (n = 48 102) regression coefficients were applied to the validation cohorts. We compared our model's performance with breast cancer risk assessment tool (Gail) and International Breast Cancer Intervention Study (IBIS) models by computing breast cancer risk estimates and C statistics., Results: When predicting 10-year breast cancer risk, our model's C index was 0.569 in BWHS, 0.572 in WHI-ES, and 0.576 in MEC. The Gail model's C statistic was 0.554 in BWHS, 0.564 in WHI-ES, and 0.551 in MEC; IBIS's C statistic was 0.547 in BWHS, 0.552 in WHI-ES, and 0.562 in MEC. The Gail model underpredicted breast cancer risk in WHI-ES; IBIS underpredicted breast cancer risk in WHI-ES and in MEC but overpredicted breast cancer risk in BWHS. Our model calibrated well. Our model's C index for predicting 10-year nonbreast cancer death was 0.760 in WHI-ES and 0.763 in MEC., Conclusions: Our competing-risk model performs as well as existing breast cancer prediction models in diverse cohorts and predicts nonbreast cancer death. We are developing a website to disseminate our model., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)