Search

We aimed to examine the association between the use of metformin and other anti‐diabetic medications and breast cancer incidence within two large prospective cohort studies. We followed 185,181 women who participated in the Nurses' Health Study (NHS; 1994–2016) and the NHSII (1995–2017), with baseline corresponding to the date metformin was approved for type 2 diabetes (T2D) treatment in the US Information on T2D diagnosis, anti‐diabetes medications, and other covariates was self‐reported at baseline and repeatedly assessed by follow‐up questionnaires every 2 years. Breast cancer cases were self‐reported and confirmed by medical record review. Hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between medication use and breast cancer were estimated using Cox proportional hazards regression models, adjusting for breast cancer risk factors. During 3,324,881 person‐years of follow‐up, we ascertained 9,192 incident invasive breast cancer cases, of which 451 were among women with T2D. Compared with women without T2D (n = 169,263), neither metformin use (HR = 0.97; 95% CI = 0.81–1.15) nor other anti‐diabetic medications use (HR = 1.11; 95% CI = 0.90–1.36) associated with significantly lower breast cancer incidence. Among women with T2D (n = 15,918), compared with metformin never users, metformin ever use was not significantly inversely associated with breast cancer (HR = 0.92; 95% CI = 0.74–1.15). Although we observed that past use of metformin was inversely associated with breast cancer in the T2D population (HR = 0.67; 95% CI = 0.48–0.94), current use (HR = 1.01; 95% CI = 0.80–1.27) and longer duration of metformin use were not associated with breast cancer (each 2‐year interval: HR = 1.01; 95% CI = 0.95–1.07). Overall, metformin use was not associated with the risk of developing breast cancer among the overall cohort population or among women with T2D. [ABSTRACT FROM AUTHOR]

PurposeAccurate risk assessment is necessary for decision-making around breast cancer prevention. We aimed to develop a breast cancer prediction model for postmenopausal women that would take into account their individualized competing risk of non-breast cancer death.MethodsWe included 73,066 women who completed the 2004 Nurses' Health Study (NHS) questionnaire (all ≥57 years) and followed participants until May 2014. We considered 17 breast cancer risk factors (health behaviors, demographics, family history, reproductive factors) and 7 risk factors for non-breast cancer death (comorbidities, functional dependency) and mammography use. We used competing risk regression to identify factors independently associated with breast cancer. We validated the final model by examining calibration (expected-to-observed ratio of breast cancer incidence, E/O) and discrimination (c-statistic) using 74,887 subjects from the Women's Health Initiative Extension Study (WHI-ES; all were ≥55 years and followed for 5 years).ResultsWithin 5 years, 1.8 % of NHS participants were diagnosed with breast cancer (vs. 2.0 % in WHI-ES, p = 0.02), and 6.6 % experienced non-breast cancer death (vs. 5.2 % in WHI-ES, p

Background: We investigated the associations of reproductive factors known to influence breast cancer risk with the expression of breast stem cell markers CD44, CD24, and ALDH1A1 in benign breast biopsy samples. Methods: We included 439 cancer-free women with biopsy-confirmed benign breast disease within the Nurses' Health Study (NHS) and NHSII. The data on reproductive and other breast cancer risk factors were obtained from biennial questionnaires. Immunohistochemistry (IHC) was performed on tissue microarrays. For each core, the IHC expression was assessed using a semi-automated platform and expressed as % of cells that stained positive for a specific marker out of the total cell count. Generalized linear regression was used to examine the associations of reproductive factors with a log-transformed expression of each marker (in epithelium and stroma), adjusted for other breast cancer risk factors. Results: In multivariate analysis, the time between menarche and age at first birth was inversely associated with CD44 in epithelium (β per 5 years = -0.38, 95% CI -0.69; -0.06). Age at first birth and the time between menarche and age at first birth were inversely associated with ALDH1A1 (stroma: β per 5 years = -0.43, 95% CI -0.76; -0.10 and β = -0.47, 95% CI -0.79; -0.15, respectively; epithelium: β = -0.15, 95% CI -0.30; -0.01 and β = -0.17, 95% CI -0.30; -0.03, respectively). Time since last pregnancy was inversely associated with stromal ALDH1A1 (β per 5 years = -0.55, 95% CI -0.98; -0.11). No associations were found for CD24. The observed associations were similar in premenopausal women. In postmenopausal women, lifetime duration of breastfeeding was inversely associated with stromal ALDH1A1 expression (β for ≥24 vs. 0 to <1 months = -2.24, 95% CI 3.96; -0.51, p-trend = 0.01). Conclusion: Early-life reproductive factors may influence CD44 and ALDH1A1 expression in benign breast tissue. [ABSTRACT FROM AUTHOR]

BackgroundThe Breast Cancer Risk Assessment Tool (BCRAT, "Gail model") is commonly used for breast cancer prediction; however, it has not been validated for women age 75 years and older.MethodsWe used Nurses' Health Study (NHS) data beginning in 2004 and Women's Health Initiative (WHI) data beginning in 2005 to compare BCRAT's performance among women age 75 years and older with that in women age 55 to 74 years in predicting five-year breast cancer incidence. BCRAT risk factors include: age, race/ethnicity, age at menarche, age at first birth, family history, history of benign breast biopsy, and atypia. We examined BCRAT's calibration by age by comparing expected/observed (E/O) ratios of breast cancer incidence. We examined discrimination by computing c-statistics for the model by age. All statistical tests were two-sided.ResultsSeventy-three thousand seventy-two NHS and 97 081 WHI women participated. NHS participants were more likely to be non-Hispanic white (96.2% vs 84.7% in WHI, P < .001) and were less likely to develop breast cancer (1.8% vs 2.0%, P = .02). E/O ratios by age in NHS were 1.16 (95% confidence interval [CI] = 1.09 to 1.23, age 57-74 years) and 1.31 (95% CI = 1.18 to 1.45, age ≥ 75 years, P = .02), and in WHI 1.03 (95% CI = 0.97 to 1.09, age 55-74 years) and 1.10 (95% CI = 1.00 to 1.21, age ≥ 75 years, P = .21). E/O ratio 95% confidence intervals crossed one among women age 75 years and older when samples were limited to women who underwent mammography and were without significant illness. C-statistics ranged between 0.56 and 0.58 in both cohorts regardless of age.ConclusionsBCRAT accurately predicted breast cancer for women age 75 years and older who underwent mammography and were without significant illness but had modest discrimination. Models that consider individual competing risks of non-breast cancer death may improve breast cancer risk prediction for older women.

Obesity is well established as a cause of postmenopausal breast cancer incidence and mortality. In contrast, adiposity in early life reduces breast cancer incidence. However, whether short-term weight change influences breast cancer risk is not well known. We followed a cohort of 77,232 women from 1980 to 2006 (1,445,578 person-years), with routinely updated risk factor information, documenting 4196 incident cases of invasive breast cancer. ER and PR status were obtained from pathology reports and medical records yielding a total of 2033 ER+/PR+ tumors, 595 ER-/PR- tumors, 512 ER+/PR- tumors. The log incidence breast cancer model was used to assess the association of short-term weight gain (over past 4 years) while controlling for average BMI before and after menopause. Short-term weight change was significantly associated with breast cancer risk (RR 1.20; 95 % CI 1.09-1.33) for a 4-year weight gain of ≥15 lbs versus no change (≤5 lbs) (P_trend < 0.001). The association was stronger for premenopausal women (RR 1.38; 95 % CI 1.13-1.69) (P_trend = 0.004) than for postmenopausal women (RR 1.10; 95 % CI 0.97-1.25) (P_trend = 0.063). Short-term weight gain during premenopause had a stronger association for ER+/PR- (RR per 25 lb weight gain = 2.19; 95 % CI 1.33-3.61, P = 0.002) and ER-/PR- breast cancer (RR per 25 lb weight gain = 1.61; 95 % CI 1.09-2.38, P = 0.016) than for ER+/PR+ breast cancer (RR per 25 lb weight gain = 1.13; 95 % CI 0.89-1.43, P = 0.32). There are deleterious effects of short-term weight gain, particularly during pre-menopause, even after controlling for average BMI before and after menopause. The association was stronger for ER+/PR- and ER-/PR- than for ER+/PR+ breast cancer.

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]

Background: The objective of this study was to evaluate the role of low‐carbohydrate diets after breast cancer diagnosis in relation to breast cancer–specific and all‐cause mortality. Methods: For 9621 women with stage I–III breast cancer from two ongoing cohort studies, the Nurses' Health Study and Nurses' Health Study II, overall low‐carbohydrate, animal‐rich low‐carbohydrate, and plant‐rich low‐carbohydrate diet scores were calculated by using food frequency questionnaires collected after breast cancer diagnosis. Results: Participants were followed up for a median 12.4 years after breast cancer diagnosis. We documented 1269 deaths due to breast cancer and 3850 all‐cause deaths. With the use of Cox proportional hazards regression and after controlling for potential confounding variables, we observed a significantly lower risk of overall mortality among women with breast cancer who had greater adherence to overall low‐carbohydrate diets (hazard ratio for quintile 5 vs. quintile 1 [HRQ5vsQ1], 0.82; 95% CI, 0.74–0.91; ptrend =.0001) and plant‐rich low‐carbohydrate diets (HRQ5vsQ1, 0.73; 95% CI, 0.66–0.82; ptrend <.0001) after breast cancer diagnosis but not animal‐rich low‐carbohydrate diets (HRQ5vsQ1, 0.93; 95% CI, 0.84–1.04; ptrend =.23). However, greater adherence to overall, animal‐rich, or plant‐rich low‐carbohydrate diets was not significantly associated with a lower risk of breast cancer–specific mortality. Conclusions: This study showed that greater adherence to low‐carbohydrate diets, especially plant‐rich low‐carbohydrate diets, was associated with better overall survival but not breast cancer–specific survival among women with stage I–III breast cancer. Greater adherence to low‐carbohydrate diets, especially plant‐rich low‐carbohydrate diets, was associated with better overall survival but not breast cancer–specific survival among women with stage I–III breast cancer. [ABSTRACT FROM AUTHOR]

Screening mammography aims to identify breast cancer early and secondarily measures breast density to classify women at higher or lower than average risk for future breast cancer in the general population. Despite the strong association of individual mammography features to breast cancer risk, the statistical literature on mammogram imaging data is limited. While functional principal component analysis (FPCA) has been studied in the literature for extracting image‐based features, it is conducted independently of the time‐to‐event response variable. With the consideration of building a prognostic model for precision prevention, we present a set of flexible methods, supervised FPCA (sFPCA) and functional partial least squares (FPLS), to extract image‐based features associated with the failure time while accommodating the added complication from right censoring. Throughout the article, we hope to demonstrate that one method is favored over the other under different clinical setups. The proposed methods are applied to the motivating data set from the Joanne Knight Breast Health cohort at Siteman Cancer Center. Our approaches not only obtain the best prediction performance compared to the benchmark model, but also reveal different risk patterns within the mammograms. [ABSTRACT FROM AUTHOR]

With mammography being the primary breast cancer screening strategy, it is essential to make full use of the mammogram imaging data to better identify women who are at higher and lower than average risk. Our primary goal in this study is to extract mammogram-based features that augment the well-established breast cancer risk factors to improve prediction accuracy. In this article, we propose a supervised functional principal component analysis (sFPCA) over triangulations method for extracting features that are ordered by the magnitude of association with the failure time outcome. The proposed method accommodates the irregular boundary issue posed by the breast area within the mammogram imaging data with flexible bivariate splines over triangulations. We also provide an eigenvalue decomposition algorithm that is computationally efficient. Compared to the conventional unsupervised FPCA method, the proposed method results in a lower Brier Score and higher area under the ROC curve (AUC) in simulation studies. We apply our method to data from the Joanne Knight Breast Health Cohort at Siteman Cancer Center. Our approach not only obtains the best prediction performance comparing to unsupervised FPCA and benchmark models but also reveals important risk patterns within the mammogram images. This demonstrates the importance of utilizing additional supervised image-based features to clarify breast cancer risk. [ABSTRACT FROM AUTHOR]

Background Physical activity is generally associated with better outcomes following diagnosis; however, few studies have evaluated change in pre- to postdiagnosis activity and repeated measures of activity by intensity and type. Methods We evaluated physical activity and survival following a breast cancer diagnosis in the Nurses' Health Study and Nurses' Health Study II (n = 9308 women, n = 1973 deaths). Physical activity was evaluated as updated cumulative average of metabolic equivalent of task (MET)-h/wk (assigned per activity based on duration and intensity) and change in pre- to postdiagnosis activity. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results Higher postdiagnosis activity was inversely associated with breast cancer–specific mortality in categories from ≥9 MET-h/wk (vs <3 MET h/wk, HR≥9 to <18 = 0.74 [95% CI = 0.55 to 0.99]; HR≥27 = 0.69 [95% CI = 0.50 to 0.95]; P trend = .04) and all-cause mortality from ≥3 MET-h/wk (HR≥3 to <9 = 0.73 [95% CI = 0.61 to 0.88]; HR≥27 = 0.51 [95% CI = 0.41 to 0.63]; P trend < .001). Associations were predominantly observed for estrogen receptor–positive tumors and in postmenopausal women. Walking was associated with lower risk of all-cause mortality (≥9 vs <3 MET-h/wk, HR= 0.69 [95% CI = 0.57 to 0.84]) as was strength training. Relative to stable activity pre- to postdiagnosis (±3 MET-h/wk), increases from ≥3 to 9 MET-h/wk were associated with lower all-cause mortality risk (P trend < .001). Results were robust to adjustment for prediagnosis physical activity. Conclusions Physical activity was associated with lower risk of death following diagnosis. Increased pre- to postdiagnosis activity corresponding to at least 1-3 h/wk of walking was associated with lower risk of death. These results provide further impetus for women to increase their activity after a breast cancer diagnosis, though reverse causation cannot be fully excluded. [ABSTRACT FROM AUTHOR]

Background: Guidelines recommend shared decision making (SDM) for mammography screening for women ≥ 75 and not screening women with < 10-year life expectancy. High-quality SDM requires consideration of women's breast cancer (BC) risk, life expectancy, and values but is hard to implement because no models simultaneously estimate older women's individualized BC risk and life expectancy. Methods: Using competing risk regression and data from 83,330 women > 55 years who completed the 2004 Nurses' Health Study (NHS) questionnaire, we developed (in 2/3 of the cohort, n = 55,533) a model to predict 10-year non-breast cancer (BC) death. We considered 60 mortality risk factors and used best-subsets regression, the Akaike information criterion, and c-index, to identify the best-fitting model. We examined model performance in the remaining 1/3 of the NHS cohort (n = 27,777) and among 17,380 Black Women's Health Study (BWHS) participants, ≥ 55 years, who completed the 2009 questionnaire. We then included the identified mortality predictors in a previously developed competing risk BC prediction model and examined model performance for predicting BC risk. Results: Mean age of NHS development cohort participants was 70.1 years (± 7.0); over 10 years, 3.1% developed BC, 0.3% died of BC, and 20.1% died of other causes; NHS validation cohort participants were similar. BWHS participants were younger (mean age 63.7 years [± 6.7]); over 10-years 3.1% developed BC, 0.4% died of BC, and 11.1% died of other causes. The final non-BC death prediction model included 21 variables (age; body mass index [BMI]; physical function [3 measures]; comorbidities [12]; alcohol; smoking; age at menopause; and mammography use). The final BC prediction model included age, BMI, alcohol and hormone use, family history, age at menopause, age at first birth/parity, and breast biopsy history. When risk factor regression coefficients were applied in the validation cohorts, the c-index for predicting 10-year non-BC death was 0.790 (0.784–0.796) in NHS and 0.768 (0.757–0.780) in BWHS; for predicting 5-year BC risk, the c-index was 0.612 (0.538–0.641) in NHS and 0.573 (0.536–0.611) in BWHS. Conclusions: We developed and validated a novel competing-risk model that predicts 10-year non-BC death and 5-year BC risk. Model risk estimates may help inform SDM around mammography screening. [ABSTRACT FROM AUTHOR]

Background Insulin resistance and hyperinsulinemia play important roles in the progression of multiple chronic disease and conditions. Diet modulates insulin response; however, evidence is limited regarding whether diets with higher insulinemic potential increase the risk of invasive breast cancer. Objectives We aimed to prospectively evaluate the association between a food-based empirical dietary index for hyperinsulinemia (EDIH) and the incidence of invasive breast cancer. Methods We prospectively followed 76,686 women from the Nurses' Health Study (NHS; 1984–2016) and 93,287 women from the Nurses' Health Study II (NHSII; 1991–2017). Diet was assessed by food-frequency questionnaires every 4 y. The insulinemic potential of diet was evaluated using the previously established EDIH based on circulating C-peptide concentrations. Higher scores indicate higher insulinemic potential of the diet. Covariates included reproductive, hormonal, and anthropometric factors (height and BMI at age 18 y); race; socioeconomic status; total alcohol intake; total caloric intake; and physical activity. Results During 4,216,106 person-years of follow-up, we documented 10,602 breast cancer cases (6689 NHS, 3913 NHSII). In the pooled multivariable-adjusted analyses, women in the highest, compared with the lowest, EDIH quintile (Q) were at higher breast cancer risk (HRQ5 vs. Q1 = 1.15; 95% CI: 1.07, 1.24; P -trend < 0.01). Although heterogeneity by estrogen receptor (ER) status was nonsignificant, the strongest association between EDIH and breast cancer was observed for ER-negative tumors (HRQ5 vs. Q1 = 1.21; 95% CI: 1.00, 1.46; P -trend = 0.02). Among tumor molecular subtypes, the strongest associations were observed for human epidermal growth factor receptor 2 (HER2)–enriched tumors (HRQ5 vs. Q1 = 1.62; 95% CI: 1.01, 2.61; P -trend = 0.02). Conclusions A dietary pattern contributing to hyperinsulinemia and insulin resistance was associated with greater breast cancer risk, especially ER-negative and HER2-enriched tumors. Our findings suggest that dietary modifications to reduce insulinemic potential may reduce the risk of breast cancer. [ABSTRACT FROM AUTHOR]

Background: Some previous studies suggested that high supplemental vitamin C intake may be associated with an increased risk of breast cancer, although evidence is inconsistent.Objectives: Our objective was to study the association between vitamin C intake and breast cancer risks using regularly updated assessments of intake over a long follow-up.Methods: We prospectively followed 88,041 women aged 33 to 60 years from the Nurses' Health Study (1980-2014) and 93,372 women aged 26 to 45 years from the Nurses' Health Study II (1991-2013). A total of 11,258 incident invasive breast cancers among 181,413 women were diagnosed. Data on vitamin C intake were collected every 2-4 years via a validated FFQ and specific questions on dietary supplement use. Multivariate HRs and 95% CIs for incident invasive breast cancer were estimated with Cox models.Results: During follow-up, 82% of participants ever used supplements containing vitamin C, including multivitamins. Cumulative total vitamin C intake (HR for quintiles 5 compared with 1 = 0.97; 95% CI: 0.91-1.03; Ptrend = 0.81), dietary vitamin C intake (HR for quintiles 5 compared with 1 = 0.98; 95% CI: 0.92-1.04; Ptrend = 0.57), and supplemental vitamin C intake (HR for quintiles 5 compared with 1 in users = 1.02; 95% CI: 0.94-1.09; Ptrend = 0.77) were not associated with breast cancer risks. Results were unchanged when different exposure latencies were considered. The results did not differ by menopausal status, postmenopausal hormone therapy use, or BMI. No differences were observed by estrogen receptor status of the tumor.Conclusions: Our results do not support any important association between total, dietary, or supplemental vitamin C intake and breast cancer risks. [ABSTRACT FROM AUTHOR]