This cohort study used data from the Breast Cancer Surveillance Consortium to examine associations between incidence of invasive breast cancer and breast density, body mass index, and age among women aged 65 years or older. Is breast density associated with invasive breast cancer among women aged 65 years or older, and does body mass index (BMI) modify any associations between breast density and breast cancer risk?In this cohort study of 221 714 screening mammograms among 193 787 US women aged 65 years or older, heterogeneous or extreme breast density vs scattered fibroglandular density was associated with an increased risk of invasive breast cancer among women aged 65 to 74 years and among those aged 75 years or older with a BMI of 25 or higher. No interaction between BMI and breast density was found in either age group.The findings suggest that breast density is associated with an increased risk of invasive breast cancer among women aged 65 years or older. Breast density is associated with breast cancer risk in women aged 40 to 65 years, but there is limited evidence of its association with risk of breast cancer among women aged 65 years or older.To compare the association between breast density and risk of invasive breast cancer among women aged 65 to 74 years vs women aged 75 years or older and to evaluate whether the association is modified by body mass index (BMI).This prospective cohort study used data from the Breast Cancer Surveillance Consortium from January 1, 1996, to December 31, 2012, for US women aged 65 years or older who underwent screening mammography. Data were analyzed from January 1, 2018, to December 31, 2020.Breast Imaging Reporting and Data System breast density category, age, and BMI.The 5-year cumulative incidence of invasive breast cancer by level of breast density  and age (65-74 vs ≥75 years) was calculated using weighted means. Cox proportional hazards models were fit to estimate the association of breast density with invasive breast cancer risk. The likelihood ratio test was used to test the interaction between BMI and breast density.df, 2; P = .26; for age ≥75 years, 2.06; df, 2; P = .36).A total of 221 714 screening mammograms from 193 787 women were included in the study; a total of 38% of the study population was aged 75 years or older. Of the mammograms, most were from women aged 65 to 74 years (64.6%) and non-Hispanic White individuals (81.4%). The 5-year cumulative incidence of invasive breast cancer increased in association with increasing breast density among women aged 65 to 74 years  and among those aged 75 years or older . Extreme or heterogeneous breast density was associated with increased risk of breast cancer compared with scattered fibroglandular breast density in both age categories . Women with almost entirely fatty breasts had a decrease of approximately 30%  in the risk of invasive breast cancer compared with women with scattered fibroglandular breast density . Associations between breast density and breast cancer risk were not significantly modified by BMI (for age 65-74 years: likelihood ratio test, 2.67; The findings suggest that breast density is associated with increased risk of invasive breast cancer among women aged 65 years or older. Breast density and life expectancy should be considered together when discussing the potential benefits vs harms of continued screening mammography in this population. We also assessed whether associations were modified by BMI. The goal of this study was to identify population subgroups who may be at increased risk of breast cancer, especially because the US Preventive Service Task Force guidelines state that the current evidence is considered insufficient to recommend routine breast cancer screening for women aged 75 years or older.19 The BCSC registries collect information on demographics, risk factors, clinical history, pathologic features, and mammography indication and results.20 Breast cancer diagnoses and tumor characteristics are obtained by linking BCSC data to pathology services; regional Surveillance, Epidemiology, and End Results programs; and/or state tumor registries. Data are pooled at a central Statistical Coordinating Center (SCC). Each registry and the SCC received institutional review board approval for active or passive consenting processes or a waiver of consent to enroll participants, link data, and perform analytic studies. All procedures were compliant with the Health Insurance Portability and Accountability Act, and all registries and the SCC have received a Federal Certificate of Confidentiality and other protection for the identities of women, physicians, and facilities who are subjects of this research. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.This cohort study used data from the National Cancer Institute–funded BCSC, a community-based, geographically and racially/ethnically diverse cohort study that broadly represents the population of women who receive screening mammography in the US.21 For women with more than 1 mammogram during the study period, we selected the first mammogram performed within each of 2 age subdivisions: 65 to 74 years and 75 years or older. We excluded women with a breast cancer diagnosis before or within 3 months after their first eligible mammogram because we set out to examine incident breast cancer risk. Women were also excluded if they had breast implants or had undergone mastectomy or if their breast density or BMI was not available. Data were analyzed from January 1, 2018, to December 31, 2020.We included 7 BCSC registries located in New Hampshire, North Carolina, the San Francisco Bay Area, western Washington, New Mexico, Colorado, and Vermont. The sample included 221 714 mammograms from 193 787 women aged 65 years or older that were performed between January 1, 1996, and December 31, 2012, and included the BCSC’s comprehensive longitudinal collection of breast density as recorded by interpreting radiologists.22: a, almost entirely fat; b, scattered fibroglandular densities; c, heterogeneously dense; and d, extremely dense. We grouped the heterogeneously dense (27.8%) and extremely dense (3.7%) categories together  because extremely dense breasts are infrequent in older women.11 The main outcome variable was incident invasive breast cancer within 3 months to 10 years after the assessment of breast density.Breast density was the study’s primary exposure and was classified by interpreting radiologists as part of their clinical interpretation of mammograms using the 4 American College of Radiology BI-RADS breast density categories12 However, in subgroup analysis, we dichotomized BMI as less than 25  vs 25 or higher (overweight or obese) to avert small sample sizes. Race/ethnicity was grouped as non-Hispanic White, non-Hispanic Black, Asian or Pacific Islander, Hispanic, other (Native Hawaiian or Pacific Islander and American Indian or Alaskan Native), and mixed race/ethnicity. Age at the time of mammography was categorized based on US Preventive Service Task Force screening cutoffs17: women aged 65 to 74 years, for whom screening is recommended, and women aged 75 years or older, for whom there is no recommendation for or against screening. Use of hormone therapy at the time of mammography was classified dichotomously based on women’s self-report. Prior diagnoses of benign breast disease were collected by BCSC registries from pathology reports and radiology systems. We grouped all diagnoses from each patient’s pathology reports into 1 category based on the most invasive finding: nonproliferative, proliferative without atypia, proliferative with atypia, or lobular carcinoma in situ.24 We classified a biopsy as indicating an unknown diagnosis if a woman reported undergoing a prior biopsy but pathology results were not available.Covariate information was obtained from self-report at the time of mammography and included age, first-degree family history of breast cancer, race/ethnicity, weight, height, current postmenopausal hormone therapy use at the time of mammography, and history of breast biopsy. Self-reported height and weight were used to calculate BMI , which was categorized as less than 18.5, 18.5 to 24.9, 25.0 to 29.9, 30.0 to 34.9, or 35.0 or greater based on World Health Organization criteria.25 was used to estimate the 5-year cumulative incidence of invasive breast cancer by breast density, BMI, and age group after accounting for mortality from causes other than breast cancer.27 The model was adjusted for the patients’ BCSC registry and factors associated with breast cancer risk, including family history of breast cancer, race/ethnicity, hormone therapy use, and benign breast disease .By design, all women who had missing age and breast density data as well as those with missing values for specific covariates were excluded from all data analyses based on those variables. Using ascertained breast density as the unit of analysis, we evaluated the distribution of breast density by demographic and clinical factors that are associated with breast cancer risk in older women. We used separate models for women aged 75 years or older vs those aged 65 to 74 years. The Fine and Gray method disease . Results disease . The folA total of 221 714 screening mammograms from 193 787 women were included in the study; 38% of the study population was aged 75 years or older. During a mean follow-up of 6.3 years, 5069 invasive breast cancers were diagnosed. Of the included mammograms, most were from women aged 65 to 74 years (64.6%) and 35.4% were from women aged 75 years or older . Most maIn general, we observed higher rates of breast cancer associated with increasing BI-RADS density by BMI categories in both age groups . Among wIn the group of women aged 65 to 74 years with overweight or obesity, the 5-year cumulative incidence of invasive breast cancer was 12.3 per 1000 women  among those with fatty breasts and 23.9 per 1000 women  among those with heterogeneously or extremely dense breasts. In the group of women aged 65 to 74 years with a BMI less than 25, the cumulative incidence was 8.2 per 1000 women  among those with fatty breasts and 20.6 per 1000 women  among those with heterogeneously or extremely dense breasts.In the group of women aged 75 or older with overweight or obesity (BMI ≥25), the 5-year cumulative incidence of invasive breast cancer was 15.0 per 1000 women  among those with fatty breasts and 27.2 per 1000 women  among those with heterogeneously or extremely dense breasts. In the group of women aged 75 years or older with a BMI less than 25, the cumulative incidence was 13.5 per 1000 women  among those with fatty breasts and 18.3 per 1000 women  among those with heterogeneously or extremely dense breasts.P = .14 for interaction for age × density).Compared with scattered fibroglandular breast densities, heterogeneous or extreme breast density was associated with an increased risk of breast cancer among women aged 65 to 74 years regardless of BMI  . Similardf, 2; P = .26) and those 75 years or older .Among women aged 65 to 74 years, those with almost entirely fatty breasts had a decreased risk of invasive breast cancer compared with women with scattered fibroglandular breast densities regardless of BMI . Similar associations were observed in the group of women aged 75 years or older overall  and among those with a BMI of 25 or higher , but there was no association among those with a BMI less than 25 . There was no significant interaction between BMI and breast density among women aged 65 to 74 years .35 Although Lam et al37 found that the risk of breast cancer increased in association with increased breast density and vice versa after adjusting for a patient’s BMI, the study included a small group of women aged 75 years or older, and the estimates did not take into account factors associated with breast cancer. Similarly, in a study of 61 844 women, of whom 4137 (6.7%) were aged 75 years or older, Vacek et al36 observed an increased breast cancer risk in association with increasing breast density but did not stratify results based on age. We extended the results from these studies by quantifying the association between BI-RADS breast density categories and breast cancer risk in a population-based cohort of older women who underwent screening mammography in US community practice.Although this study showed a significant association between breast density and increased breast cancer risk among older women, the estimates in our study were lower than the estimates reported in a meta-analysis by McCormack and dos Santos Silva38 evaluation of the diffusion of such innovations among older women with an aim of developing individualized screening strategies will be important, particularly among women aged 75 years or older, for whom guidelines regarding screening mammography remain unclear. With the aging of the population in the US and worldwide, it is becoming increasingly important to implement life expectancy–based screening strategies39 that consider breast cancer risk so that older women who are likely to benefit can continue to undergo screening and risk of harms such as overdiagnosis and overtreatment can be reduced among those unlikely to benefit.As newer and more advanced breast density assessment techniques are developed,This study has strengths. We used, to our knowledge, the largest population-based sample of socioeconomically diverse older women undergoing screening mammography in US community practice. In addition, the study was conducted with prospective cohort design and included complete breast cancer follow-up. We were also able to account for the several confounding factors, including family history of breast cancer, race/ethnicity, postmenopausal hormone therapy use, BMI, and benign breast disease. Furthermore, we examined the association of breast density with breast cancer separately by age group, which included a large proportion of women aged 75 years or older.41 Of note, interobserver agreement on the BI-RADS categories is moderate.42 Although the lack of quantitative measures of breast density in this study may be considered a limitation, a strength of the BI-RADS measurement categories is their clinical relevance; the categorical assessment of BI-RADS density is the measure provided clinically on all mammograms performed in the US. The BI-RADS measurements have evolved over time, with the 2013 (5th) edition being the most recent. Although this study’s population included women from 1996 to 2012, we used the latest BI-RADS measurements to evaluate the association of breast density with breast cancer risk. As the methods for measuring breast density evolve, future studies should continue to evaluate associations between breast cancer risk and breast density, especially among older women. Another limitation of this study was the use of self-reported height and weight; a considerable amount of these data was missing, mostly because some BCSC facilities did not collect this information during some or all years.This study also has limitations. It relied on BI-RADS breast density measurements reported by interpretive radiologists as part of clinical practice at multiple radiology facilities.43In this cohort study, we observed an association between increased density as categorized by the BI-RADS and an increased risk of invasive breast cancer, but low breast density  was associated with a decreased risk of invasive breast cancer across age groups. The positive associations found in this study between breast density and breast cancer among women aged 75 years or older suggest that breast density and life expectancy should be considered together when discussing the potential benefits and harms of continued screening mammography in this population.


Background: Women bear a large share of disease burden caused by household air pollution due to their great involvement in domestic activities. Pollutant emissions are believed to vary by exposure patterns such as cooking and space heating. Little is known about the independent effect of solid cooking fuel combustion on breast cancer risk. We aimed to examine the association of indoor coal and wood combustion for cooking with breast cancer risk.Methods: During June 2004–July 2008, participants aged 30–79 from 10 diverse regions across China were enrolled in the China Kadoorie Biobank. Primary cooking fuel use information in up to three residences was self-reported at baseline. Multivariable logistic regression models yielded adjusted odds ratios (ORs) and 95% confidence intervals (CIs).Results: A total of 290,396 female participants aged 30–79 were included in the main analysis. Compared with long-term clean fuel users, the fully adjusted ORs were 2.07 (95%CI: 1.37–3.13) for long-term coal users, 1.12 (95% CI: 0.72–1.76) for long-term wood users, and 0.98 (95% CI: 0.55–1.74) for those who used mixed solid fuels to cook. Those who had switched from solid to clean fuels did not have an excess risk of breast cancer .Conclusion: Long-term solid fuel combustion for cooking may increase the risk of breast cancer. The strength of association is stronger among coal users than wood users. Targeted interventions are needed to accelerate the access to clean and affordable energy. Household air pollution (HAP) causes immense disease burden throughout the world. Around 3.8 million people died prematurely from illness attributed to HAP . GloballHousehold air pollution from solid cooking fuel  has been categorized as a Group 2A carcinogen . SpecialWe used the baseline data from China Kadoorie Biobank (CKB) . It was Registered participants went to the local assessment stations after signing the informed consent. Trained health staffs conducted a computer-assisted interview with participants to collect a set of information, including demographics, lifestyle behaviors, and medical history via a standard electronic questionnaire. All participants also underwent physical measurements and a 10 ml blood sample collection. Ethical approval of CKB was obtained from the Ethical Review Committee of the Chinese Center for Disease Control and Prevention and the Oxford Tropical Research Ethics Committee.Participants were asked to recall their cooking frequency, type of cooking fuels and ownership of ventilated stoves for up to three most recent residences (each lived at least 1 year), and duration (in years) in each residence. Participants were asked, “In your present & two previous houses, how often did you cook at home?” Participants chose from the options of daily, weekly, monthly, rarely/never, no cooking facility . For thoCovariates of potential interest comprised of demographic characteristics, lifestyle factors, household air pollution, reproductive history and family history, which were selected based on previous literature on this topic , 16, 26.n = 302,510) and excluded 2,238 participants who did not report cooking information at three residences or used other unspecified fuels, leaving 300,272 for baseline characteristics estimation. We further excluded participants who did not cook regularly at three residences  and those with fluctuating exposure condition  . Finally, a total of 290,396 participants were included in the main analyses.We restricted our analyses to females  of breast cancer. Model 1 was adjusted for age and study region . Model 2Several sensitivity analyses were further performed. First, we additionally adjusted for potential covariates, including physical activity, family history of cancer and consumption of preserved vegetables. Second, we excluded participants who smoked daily/on most days; those who were exposed to environmental tobacco smoke daily or almost every day; those who were nulliparous; those who had ever used oral contraceptive pills. Third, we selected the lag period of 5 years and 10 years, discounting the exposure during this period. Finally, we explored the association of HAP from solid cooking fuel use with breast cancer mortality, using time in study as the time scale. All analyses were performed using Stata software 15.1 .Of the 300,272 females [mean (SD) age 51.46 (10.48) years], 51.1 % always used solid cooking fuel and 18.0% always used clean fuel in all residences. Females who always used solid fuels tended to be older, more likely to live in rural region, less educated, more exposed to passive smoking, less likely to use oral contraceptive pills and had lower household income in comparison with clean fuel users .We documented 551 participants diagnosed with breast cancer. Compared with long-term clean cooking fuel use, long-term coal combustion was associated with a higher risk of breast cancer  . Fully aThere was no statistical effect measure modification by environmental tobacco smoking (ETS), cooking stove ventilation, menopausal status or contraceptive use . The strIn the sensitivity analyses, the association of solid fuel exposure and breast cancer risk was unaltered after adjusting for potential confounders and excluding regular smokers, nulliparous women and those who had ever used oral contraceptive drugs . When 5-In this study, we observed inconsistent associations of solid cooking fuel exposure with breast cancer risk. The adjusted ORs of breast cancer were not statistically significant among persistent solid fuel users in general . In line with our finding, a case-control study of women on Long Island demonstrated no increased risk of breast cancer incidence in females who frequently burned wood in their home . HoweverWe observed no elevated breast cancer risk among women who had ceased using solid fuels. The point estimate of risk was lower in those who had switched from solid to clean fuels than long-term solid fuel users [OR]. Those who had ceased using solid fuels may get less exposed to solid fuel burning than long-term solid fuel users [duration in years: median (IQR): 16 (9–25) vs. 30 (21–41)]. Previous CKB study has demonstrated that the excess risk of all-cause mortality decreased by more than 60% in 5 years after cessation of indoor solid fuel burning . The preThe association between household air pollution and breast cancer is biologically plausible. Incomplete combustion of solid fuels releases many pollutants to the indoor and outdoor air, such as carbon monoxide, particulate matter, carcinogenic polycyclic aromatic hydrocarbons (PAHs) –9. Of alPersistent coal users had a higher risk of breast cancer than persistent wood users. We cannot directly compare our estimate for coal exposure with prior studies. To our knowledge, this is the first study which reports the association of breast cancer with coal combustion for cooking. Our results should be interpreted with caution due to relatively small number of cases. Given the sample sizes in the subgroups, we have sufficient power for coal combustion analysis (approximately 100%) but not for wood combustion (<50%). Although the play of chance cannot be ruled out, our analysis may suggest that pollutants from coal combustion could have more hazardous effect on breast cancer development than those from wood combustion. Different fuel properties and environmental condition contributes to the different formation and changes of trace organics emitted from combustion which may have adverse effects on breast carcinogenesis . ResultsThe chief strengths of this study include the large number of cooking fuel users , geographical diversity and completeness of data collection. Moreover, to discount the exposure that is thought irrelevant to the outcome, we conducted sensitivity analyses and selected 5-year or 10-year lag period. Our study has several limitations as well. First, the cross-sectional design of this study precludes a causal inference between solid fuel exposure and risk of breast cancer. Further prospective studies are needed to confirm the causal relationship. Second, like other CKB studies, recall-bias is possible because of the self-reported nature of the baseline survey. Nevertheless, about 78% of the participants in the resurvey reported the same type of cooking fuel as in the baseline survey, and the kappa value for cooking information was acceptable (0.6) . The phyHousehold air pollution from solid cooking fuel combustion may elevate the risk of female breast cancer. The strength of the association is higher in long-term coal users than in long-term wood users. This study may have global implications as many countries are in the transition to clean energy. Efforts to disseminate clean and affordable alternatives (electricity and gas) are gaining momentum in LMICs . AdoptioThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethical Review Committee of the Chinese Center for Disease Control and Prevention and the Oxford Tropical Research Ethics Committee. The patients/participants provided their written informed consent to participate in this study.TL and SW designed the study. SW acquired the data. TL analyzed and drafted the manuscript. All authors contributed to the interpretation of data and revised the article. All authors read and approved the final article.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


KCNK2, C5orf56, SCAMP2, and SIN3A) and the other index SNPs are located close to GSTM4, AMPD2, CASTOR2, and RP11-168G16.2. Here we present risk loci with consistent direction of associations in African and European descendants. The study suggests that replication across multiple ancestry populations can help improve the understanding of breast cancer genetics and identify causal variants.Our study describes breast cancer risk loci using a cross-ancestry GWAS approach. We first identify variants that are associated with breast cancer at P < 0.05 from African ancestry GWAS meta-analysis (9241 cases and 10193 controls), then meta-analyze with European ancestry GWAS data (122977 cases and 105974 controls) from the Breast Cancer Association Consortium. The approach identifies four loci for overall breast cancer risk  and two loci for estrogen receptor-negative disease (1q41 and 7q11.23) at genome-wide significance. Four of the index single nucleotide polymorphisms (SNPs) lie within introns of genes ( GWAS have enhanced our understanding for the genetic basis of breast cancer, but the majority of them were performed for European ancestry populations. Here, the authors use a cross-ancestry approach and report seven new variants associated with breast cancer risk among women of African ancestry. Studies have shown a significant contribution of genetic factors to breast cancer risk3, yet the landscape of this contribution has not been fully elucidated. Mutations in high- and moderate-penetrance genes confer relatively high risks of breast cancer but are rare in the population and account for <5–10% of cases4. Genome-wide association studies (GWAS) have been successful in identifying common low-penetrance genetic variation and approximately 200 risk loci have now been identified7. The risk loci so far identified have provided clues to elucidating breast cancer tumorigenesis through previously unknown mechanisms. Additionally, when combined into risk scores, these polymorphisms can be used for breast cancer risk prediction8.Breast cancer is the most common cancer in women worldwide and accounted for 2.1 million new cases and 627,000 deaths in 201813, it is unclear whether the same genetic risk factors are also important in other populations, which may limit the applicability of the findings to other groups14. The earliest GWAS conducted in African ancestry populations identified genetic variants at 5p15.33 (TERT-CLPTM1L) associated with estrogen receptor (ER) negative breast cancer15. A larger analysis of African ancestry individuals which included several consortia identified a SNP at 3q26.21 also associated with ER-negative breast cancer16. Some common susceptibility loci are shared across populations, and the shared disease-associated variants are more likely to be causal14.Despite the usefulness of GWAS, the majority of the GWAS studies have been performed among European ancestry populationsHere we present, using a cross-ancestry GWAS approach in 248,000 women, genetic risk variants at 1p13.3, 5q31.1, 15q24, and 15q26.3 for overall breast cancer, and at 1q41 and 7q11.23 for ER-negative disease. The consistency of the directions of the risk for these loci in African and European samples increases the likelihood of their being causal variants.P < 5 × 10−8 on cross-ancestry meta-analysis, with odds ratios (OR) ranging from 0.95 to 1.05  and the others were in intergenic regions. The direction of the associations was consistent for the pooled African and European estimates. The estimates for overall and ER-negative breast cancer were generally consistent across the five contributing studies of African ancestry participants , two independent signals at 15q24 , and one each at 5q31.1 (5:132149322:G:GGCCGCCGCC) and 15q26.3 (rs117793215) for overall breast cancer risk. Another independent SNP at 1q41 that was associated with ER-negative breast cancer was rs5780828  between the protective allele of rs17024629  at 1q13.3 and increased expression of GSTM1, GSTM2, and GSTM4, which are located 19 kb, 31 kb, and 51 kb downstream of the SNP. At 5q31, the top SNP rs2522057, located 15 kb downstream of IRF1, was most significantly associated with the gene’s expression levels. At 15q24.1, rs1869959, located 35 kb upstream of MPI and 12 kb upstream of ULK3, was significantly associated with the expression of these two genes. The other top SNP at the 15q24 locus, rs60381548, located intron of SIN3A, 30 kb downstream of PTPN9, 162 kb downstream of SNUPN, and 212 kb upstream of SNX33 was correlated with all four genes. The 1q41 locus revealed a significant association between rs67931591 and PTPN14. The SNP at 7q11.23 was significantly correlated with STAG3L2, a pseudogene. Previous published report on normal breast tissues from the GTEx revealed associations between rs2522057 and the SLC22A5 gene, and between rs1869959 and the ULK3 gene , which encodes the protein potassium channel subfamily K member 2, a member of the two-pore-domain background potassium channel family. Potassium channels are known to play a role in cancer and studies using TCGA data have shown associations with DNA methylation in the KCNK genes and triple negative breast cancer. Additionally, overexpression of KCNK5, KCNK9, and KCNK12 and under-expression of KCNK6 and KCNK15 were associated with triple negative breast cancer17. Other studies investigated expression of KCNK2 gene as potential prognostic markers. For example, Innamaa et al.18 found increased KCNK2 expression in human ovaries and a role in cell proliferation and apoptosis for KCNK2 modulators in ovarian cancer cell lines. Li et al.19 found differential expression of KCNK2, KCNK15 and KCNK17 in liver cancer cells compared to healthy tissue. KCNK2 has also been reported in amplified regions in a genome-wide scan of chromosomal alterations in esophageal squamous cell carcinoma20.The SNPs identified in this study lie in regions that are close to genes that have been previously implicated in cancer. Interestingly we found three variants located within the introns of genes. One of the variants, rs67931591 was found in SCAMP2 intron and rs60381548 at 15q24.2 in the SIN3A gene). The SIN3A gene was associated with rs60381548 in the eQTL analysis of breast tumor in the present study. Switch-independent 3 family A (SIN3A) is a transcriptional regulator, that along with its paralog and corepressor play important roles in normal breast development, cancer and metastasis23. Furthermore, SIN3A mediates STAT3 transcriptional repressor activity24 and along with genes involved in histone modification such as HDAC and Lysine specific demethylase (LSD), inhibits several cancer genes including CASP7, TGFB2, CDKN1A, HIF1A, TERT and MDM225. Studies have shown key roles for SIN3A in breast cancer including sensitivity to chemotherapy25 and breast cancer progression27.We found two independent SNPs at the 15q24 locus at about 582 kb apart , and IRF1 that encodes interferon regulatory factor 1. SLC22A5 is an estrogen-dependent gene whose expression is associated with ER status in breast cancer cell lines and tissue specimens34. Significantly decreased levels of SLC22A5 have been reported in colorectal cancer tissues compared to normal tissues in eQTL studies35. Moreover, eQTL studies report associations between rs2522057 and gene expression in several tissues including breast mammary tissue, lymphocytes, esophageal mucosa, lung, skeletal muscle, skin, thyroid and whole blood30. We found an association between rs2522057 and IRF1 expression levels in the eQTL analysis of breast cancer in this study lending support to the likelihood of involvement of the IRF1 gene in the mechanism of the SNP on breast cancer carcinogenesis. Additionally, IRF1 has been shown to have tumor suppressor functions in breast cancer through its inhibition of NF-kB36 and CASP8 activation and induction of apoptosis37.Similarly, the AMPD2 (high adenosine monophosphate deaminase 2) and has recently been shown to predict worse outcomes in undifferentiated pleomorphic sarcoma38. Earlier studies39 found high expression levels of AMPD2 in hepatocellular carcinoma, though the levels did not differ substantially from those in the non-tumorous organ. It is noteworthy that our eQTL analysis did not find a significant association with AMPD2 expression. The carcinogen metabolism genes, GSTM1, GSTM2, and GSTM4 are also located in this region and our eQTL analysis of breast tumor revealed highly significant associations between rs17024629 and these genes. The GSTM1 null genotype has been associated with several cancers including cancers of the colorectum, oral cavity, lung, cervix, and stomach47. In eQTL studies, GSTM4 was significantly associated with gene expression in several tissues including the aorta, lungs, tibia nerve and whole blood30.The majority of GWAS-identified SNPs were located in non-coding regions of the genome, and three loci in the present study were found in intergenic regions. The closest gene to rs17024629 is CASTOR2 gene . The CASTOR proteins are arginine sensors that function as negative regulators of the TORC1 signaling pathway, an often dysregulated pathway in human cancer, through the GATOR complex, inhibiting mTORC149. The rs181337095 SNP is located 6 kb 5′ of RP11-168G16.2, an antisense DNA.The rs1637365 SNP at the 7q11.23 locus is near the p value cutoff of 10−4, and the chance that some of the identified SNPs could be spurious findings cannot be ruled out. Another noteworthy point is that identification of genetic variants in GWAS is just the first step of the discovery of true causal variants and genes associated with breast cancer. Further studies are needed, including in vitro and in vivo functional studies to elucidate the mechanisms by which identified putative causal variants are acting and identify the targeted genes, Finally, although the direction and strength of the associations were consistent between African and European populations, and mostly consistent with Latino populations, we could not find statistically significant replication of the identified variants, which are likely due to the modest sample sizes of the Latino study.A potential limitation of this study is the different genotyping platforms used by the different consortia. However, stringent QC measures pre- and post-imputation were carried out. Additionally, the meta-analysis did not reveal significant heterogeneity across studies. Secondly, the sample size for ER-negative breast cancer cases was relatively small, thus reducing the precision of the estimates and providing less power for detecting risk loci. The third limitation is related to the additional SNPs identified at the same loci with the index SNPs from the conditional regression analysis. The regression procedures were based on a liberal Our study found six loci that could provide further insights into pathways for breast cancer carcinogenesis. The genetic variants that shared across ancestry populations makes them possible causal variants. Functional studies on these loci are desirable to identify causal variants and elucidate the mechanisms of breast cancer carcinogenesis. In addition, future studies can evaluate these variants for breast cancer risk prediction, particularly in African ancestry populations.16 and the Ghana Breast Health Study (GBHS)51, with a combined sample size of 19434 participants including 9241 cases and 10193 controls 16 and the BCAC European ancestry data6. The AABC was genotyped using the Illumina Human 1M-Duo BeadChip. After QC, a total of 3007 cases  and 2720 controls remained in the analysis52. Genotyping in the ROOT consortium was done using Illumina HumanOmni 2.5-8v1 array and 1657 cases  and 2029 controls passed QC. In the BCAC-African ancestry consortium, genotyping was done using the Illumina OncoArray (260K GWAS backbone) and after removing overlapped samples between OncoArray with AABC, AMBER and ROOT and samples failed in QC, a total of 2271 cases  and 1406 controls remained for analysis. The Illumina MEGA array was used for genotyping in the AMBER consortium, and 1407 cases  and 2408 controls remained in analysis passed QC. In the GBHS, Illumina Global Screening Array was used for genotyping, and 899 cases  and 1630 controls were included in analysis. Imputation for all studies was done using the cosmopolitan reference panel in the 1000 Genomes Project (Phase 3 release).Genotyping and quality control (QC) procedures have been described in detail for the three consortia53.In addition, we examined the association between the identified SNPs of interest and breast cancer risk in a GWAS of Latinos (2385 cases and 6416 controls). Details of the genotyping, QC and data analysis have been publishedP values using unconditional logistic regression, adjusting for 10-year age group, sample type , study  versus others) and PCs that associated with breast cancer at P < 0.1. The GBHS estimated per-allele ORs and 95% CI for each SNP on allele counts (dosages) using unconditional logistic regression adjusting for the first ten principal components, self-reported ethnicity and age. In the Oncoarray African ancestry samples, a total of 27 million SNPs with MAF ≥ 0.1% and imputation quality score ≥0.3 were included in the analysis. PCs were estimated using EIGENSTRAT. ORs and P value of each SNP were estimated using unconditional logistic regression, adjusting for age, study (Women of African Ancestry Breast Cancer Study—WAABCS versus other) and the first ten PCs.In the ROOT and AABC GWAS studies, genotyped SNPs were analyzed and imputed with imputation score >0.3 and minor allele frequency >0.01 to account for uncertainty in imputation. Unconditional logistic regression was used to examine the association of each SNP and breast cancer risk adjusting for age, study site and eigenvectors from Principal Components Analysis (PCA). In the ROOT GWAS, the first four eigenvectors were used to control for population stratification as only the first 4 eigenvectors were associated with case status. The AABC GWAS adjusted for the first 10 eigenvectors from the PCA. OR and 95% confidence intervals (CI) were calculated from the multivariable logistic regressions. All tests of statistical significance were two sided. Using similar methods, separate analyses were conducted to compare ER-positive and ER-negative breast cancers with controls. The AMBER consortium estimated ORs and 6.The BCAC European study used a two-stage imputation approach, using SHAPEIT2 for phasing and IMPUTE version 2 for imputation. The first ten principal components and country were adjusted for in the logistic regression, and per-allele ORs and standard errors were computed54. Variants associated with breast cancer at P < 0.05 from the African ancestry meta-analysis were then combined in another fixed effects meta-analysis with the coefficients from the BCAC European ancestry data. Heterogeneity in both meta-analyses was assessed using the I2 statistic. SNPs that were significant genome-wide (P < 5 × 10−8) in the cross-ancestry meta-analysis, and >500 kb away from the 180 loci known to be associated with breast cancer risk were identified6. Conditional analysis below confirmed the identified loci. All analyses were done separately for ER-positive, ER-negative, and overall breast cancer risk.Regression coefficient estimates from the five contributing African ancestry studies were combined in a fixed effects meta-analysis using METAL55, that utilizes summary statistics and population-specific linkage disequilibrium (LD) from 1000 Genomes Project, for the computation of conditional beta coefficients. SNPs significant at p < 10−4 after adjusting for lead SNP were considered as independent signals. The p < 10−4 cutoff was derived by applying a factor of 3000  to the GWAS significance of 5 × 10−8. This procedure was repeated until no additional independent signals were significant. In addition to the conditional analysis involving the lead SNP and one other candidate SNP, we also determined joint ORs including all independent loci in the same model. Separate analyses were done for African and European ancestry data, and the estimates from the conditional analysis were combined in a meta-analysis.In order to identify independent SNPs in the identified loci, conditional analysis was done in each of the regions, including all variants in the flanking ±500 kb region of the lead SNP. The 15q24 region had two SNPs about 582 kb apart that were both genome-wide significant (see results for details). Hence, all variants in the region extending from 500 kb upstream of the proximal SNP and 500 kb downstream of the other SNP were included in the conditional analysis for this region. We used the GCTA software with the –COJO option56. Using data from ENCODE57 and the Roadmap Epigenomics Consortium58, we examined the chromatin states including core 15-state model and 25-state model using 12 imputed marks, H3K4me1 and H3K27ac (enhancers), and H3K4me3 and H3K9ac (promoters) for each identified SNP and other SNPs in strong LD with these lead SNPs (>0.8). We also assessed evolutionary conserved regions, DNase hypersensitivity sites, and variant effect on regulatory motifs, proteins bound and eQTL hits from previous studies.The functional annotations of the SNPs were determined using HaploReg v4.1n = 164 and European ancestry, n = 778) were used in the analysis. A linear regression model estimated additive effects for each SNP, adjusting for age, ancestry, copy number variation, batch effect, and molecular subtype. Separate analyses were done for African and European ancestry samples and the estimates were meta-analysed to obtain overall estimates. Bonferroni significance levels were applied to determine statistical significance. We also checked associations between the identified loci and gene expression in several tissues, including normal breast, that had been published from previous eQTL analyses on the Haploreg website.We carried out a cis-eQTL analysis to understand possible target genes in the six loci. All genes within ±1 MB around each index SNP were evaluated and gene expression in breast tumors from TCGA breast cancer patients  for previously reported associations for the identified lead SNPs and all other SNPs in LD with r2 > 0.4 and phenotypes.We assessed the GWAS catalog (Informed consent was obtained from all subjects included in the analysis. The relevant ethical review boards at all participating institutions approved study protocols.Supplemental information


Prior studies indicate that the age of onset of breast cancer is an important element in considering communication between patients and the health care team. Younger women aged 45 and under diagnosed with breast cancer are often at a higher risk of being more vulnerable to psychosocial issues compared to older women aged 46 years and above. Few studies have examined age differences in patient perceptions of treatment-related discussion and communication during transition with their health care team.The aims of this survey were (1) to better understand breast cancer survivors’ perspectives regarding communication with health care providers during treatment and during transition to posttreatment care; and (2) to determine the differences between younger women with breast cancer (≤45 years of age) and older women (≥46 years of age). It was hypothesized that (1) breast cancer survivors’ psychosocial and finance-related communications with health care providers may lack effectiveness; (2) younger women experience greater needs for patient-centered communication with physicians and health care providers, especially about psychosocial care and transition to posttreatment care; and (3) younger breast cancer patients (≤45 years of age) need more information on survivorship and follow-up care.U test was performed to assess differences in participants’ perceptions about communication with health care providers by age group: younger (≤45 years of age) and older (≥46 years of age) women.An internet-based survey was conducted with 143 women in Central Texas with 35% (n=50) aged 45 years or under and 65% (n=93) aged 46 years and above. The Mann-Whitney P=.02). For the question about the timing of follow-up appointments, the overall mean score was 64.29; the mean score of the younger group was 54.44, and that of the older group was 68.88 (P=.05). All the group scores related to psychosocial and financial support discussions with health care providers were low, with a rollup average of only 30.02 out of 100, suggesting that this is an important area for improving patient-centered communication.Statistically significant results pertained to rating health care team and patient discussions about transition from treatment to posttreatment using scores of 0 as “no discussion” and 100 as “in-depth discussion.” For the questions about management of posttreatment care, the overall mean score of the groups was 56.26 and that of the younger group was 43.96; the mean score of the older group was 61.96 (For all patients, transition from treatment to posttreatment requires a greater level of engagement and communication with the health care team. It appears that younger patients aged ≤45 years require more in-depth and personalized messaging to better understand their posttreatment care requirements. Although the median age at presentation is approximately 62 years in the United States , approxiCompared to older women, younger women generally have more aggressive cancers, lower survival rates, and are more likely to experience recurrence of cancer -10. CommIn this study, we used an internet-based survey tool to better understand the differences in the perceptions on health care provider team support between younger and older breast cancer survivors during treatment and the transition from treatment to posttreatment care. We hypothesized that (1) there may be breakdowns in communication during treatment and transitions in care between breast cancer and health care providers and (2) that younger women  have greater needs for patient-centered communication, especially that related to psychosocial care and the transition to posttreatment care. An additional hypothesis was that younger breast cancer patients (<45 years of age) need more information on survivorship and follow-up care during transition, as they are expected to live longer and may also have a higher rate of recurrence .The Breast Cancer Resource Center (BCRC) was the recipient of a 5-year cooperative agreement with the US Center for Disease Control (CDC) under a grant entitled “Multiple Approaches to Support Young Breast Cancer Survivors and Metastatic Breast Cancer Patients.”The grant is focused on improving services and access to resources for young breast cancer survivors diagnosed under the age of 45 years and for metastatic breast cancer patients. A multifaceted needs assessment, consisting of focus groups, key informant interviews, and an internet-based survey, was conducted to determine what unmet needs exist in Central Texas.Participants were able to access it via the internet from a link provided by BCRC to its constituents and collaborating organizations from August to November 2020. There were no incentives for participants who completed the survey. Results were screened for automated agents or bots and duplicate entries. BCRC sought institutional review board approval from the University of Texas at the Austin Office of Research Support and Compliance and was granted an exemption (reference: FWA # 00002030).The participants were recruited using convenience sampling via email from a number of Texas-based cancer and breast cancer advocacy groups. The Cancer Alliance of Texas was also involved in the recruitment process by distributing surveys among its participating organizations, agencies, institutions, and individual members. BCRC formed an advisory council, consisting of physicians, researchers, stakeholders, and survivors, to support the CDC grant activities, including assisting in dissemination of the survey. The survey was also advertised on the BCRC Facebook pages, and the link was emailed to anyone who had been a BCRC client since 2018. The web-based survey was a voluntary, open survey, and it was created and distributed by BCRC to breast cancer survivors in Texas who had a previous or current breast or metastatic breast cancer diagnosis.The survey consisted of 44 questions and was created using Qualtrics . Content from earlier focus groups and key informant interviews informed the questions included in the survey and the draft survey was tested with survivors and members of the project’s advisory group. Anonymous responses were captured directly in Qualtrics, and they were later downloaded directly from the software for analysis.Survey participants were informed of the average length of time the survey would take, the purpose of the survey, how the responses would be used, and who the investigator was. Adaptive questioning was used to reduce the number and complexity of the questions. Survey participants could go back and review their responses before submitting. The survey collected various demographic data including ethnicity, education level, and income and insurance status. Women were asked to rate the helpfulness of their health care team, as it related to aspects of treatment. Participants were queried about their concerns regarding treatment-related issues and the level of discussion with their health care providers about treatment-related, and psychosocial- and finance-related topics using a scale ranging from 0 for “not at all a concern/no discussion” to 100 for “extreme concern/in-depth discussion.” Cronbach α values for concerns about treatment-related issues, the level of discussion about treatment-related topics, and the level of discussion about psychosocial- and finance-related topics were. 81, .95, and .97, respectively.U test was performed to examine differences in participants’ perceptions about communication with health care providers by age group: younger (<45 years old) and older (≥46 years old) participants. When analyzing missing data, we confirmed that the variables of interest were missing completely at random ; thus, the results using the listwise deletion method were reported [Descriptive statistics and chi-square tests were conducted to examine associations of the sociodemographic characteristics, marital status, and the length of time since initial treatment with the age group. Because of the relatively small sample size, the Mann-Whitney reported . All staA total of 143 participants completed the survey and provided their year of birth. Among those participants, 140 identified themselves as women and 2 as other. The median age of the participants was 49.0 years with 35% (n=50) aged <45 years and 65% (n=93) aged >46 years. The median age of the younger breast cancer participants was 41.5 years, and that of the older breast cancer participants was 56.0 years. Among all the respondents, 83.6% (n=117) were White with 16.4% (n=23) reporting “other.” Latino or Hispanic ethnicity was claimed by 17.0% (n=24). The differences in education levels between the groups were significant and generally high with 18.9% (n=27) having a high school degree or some college education; 48.3% (n=69) held associate or bachelor’s degrees, and 32.9% (n=47) mentioned advanced master’s, professional, or doctoral degrees. Approximately 78.3% (n=112) of the participants were insured through their employer or had private insurance at the time of their diagnosis. The characteristics of all the participants are presented in P=.04). The question about concerns related to fertility preservation was significant, with means of 25.60 for all the participants, 45.70 for the younger participants, and 4.59 for older participants (P=.002).Among the set of questions about their concerns regarding treatment-related effects, only 2 responses showed statistically significant differences between the younger (<45 years of age) and the older (>46 years of age) participants, as observed in The next set of questions focused on transitions to posttreatment care. This was the area where we found the greatest number of significant differences between the younger (<45 years of age) and older participants (>46 years of age), and where the second hypothesis that younger breast cancer survivors experience less support from their health care teams appeared to be best demonstrated. The questions asking about transitions to posttreatment care were ranked based on the extent to which treatment-related topics were discussed by the health care team during the transition to posttreatment care on a scale from 0 indicating “no discussion” to 100 indicating “in-depth discussion.” The statistically significant questions where the younger participants responded with lower scores than the older breast cancer survivor participants regarding the extent to which their health care provider discussed transition topics included the following:P=.02).Which doctor (oncologist vs primary care) would manage posttreatment care? The younger participants’ mean score was 43.96 vs the older participants’ mean score of 61.96 (P=.03).When to contact your oncologist or primary care doctor or your OB-GYN (obstetrician-gynecologist)? The younger participants’ mean score was 32.77, and the older participants’ mean score was 51.96 (P=.02).What long-term effects to anticipate ? The younger participants’ mean score was 43.52, and that of the older participants was 62.38 (P=.05).How frequently you should have follow-up appointments? The younger participants’ mean score was 54.44, and that of the older participants was 68.88 , what long-term effects to anticipate, and how often they would need follow-up scans or tests. For younger and older participants, the mean scores for what would be considered critically important aspects of cancer survivorship fell below 60 points on the 100-point scale of 0 for “no discussion” and 100 for “in-depth discussion.”For the questions in the areas of communications with health care team members regarding psychosocial and finance-related topics, our results were comparable to the findings in a nationally representative sample in which limited proportions of cancer survivors reported high-quality discussions with providers after diagnosis, ranging from 29% (n=349) for emotional and social needs to 62% (n=745) for follow-up care recommendations, indicating that 76% experienced suboptimal communication with their cancer care providers .  These Regarding younger breast cancer survivors as compared to older survivors, patient-specific communication assumes additional importance, as shown by the findings of Champion et al . This woThis need for support is further supported by the study of Johnson et al addressing breast cancer in adolescents and young adults . In thisP>.05) and employed the listwise deletion method, a common method to generate unbiased and conservative estimates [Our study was cross-sectional, thus limiting the ability to draw causal inferences. We could not control for certain variables, such as the cancer site, stage or subtype, provider type, or specialty due to sample size limitations and lack of information. The mix of younger (≤45 years of age) and older (≥46 years of age) participants in our survey, with 35% being younger, is higher than the national ratio of 11% younger (≤45 years of age) patients . Howeverstimates .The survivors’ cancer history was self-reported. Our sample was predominantly composed of non-Hispanic Whites and communication differences may exist among patients from diverse racial, ethnic, and cultural backgrounds. There was also the possibility of recall bias, particularly for respondents further from treatment. Our study was conducted during the period of sequester in Central Texas due to the COVID-19 pandemic, which may account for a slightly lower response rate.Breast cancer survivors’ perceptions of conversations with health care professionals revealed missed opportunities for older and younger patients regarding understanding of concerns related to costs, the need for financial services, emotional/mental support counseling, and the need for providing patients’ spouses and children with posttreatment support. Participants in this survey emphasized additional support for spouses, children, and family members of those diagnosed with metastatic breast cancer. This research also revealed missed opportunities for enhancing patient-provider communication among younger breast cancer patients during treatment regarding genetic screening and fertility preservation services.Younger and older breast cancer survivors transitioning from treatment to posttreatment care would benefit from being offered access to psychosocial and financial counseling following breast cancer treatment. These gaps and barriers imply the need for oncology care teams to increase their focus on communications and clarity regarding transitions in care, follow-up care, late or long-term treatment effects, financial support, and psychosocial needs, with special focus on younger breast cancer patients.


The processed food dietary pattern was not significantly associated with breast cancer risk. The results of this study show that a traditional dietary pattern and a cereal-dairy breakfast dietary pattern may reduce the risk of developing breast cancer in this population.A total of 396 breast cancer cases and 396 population-based controls from the South African Breast Cancer study (SABC) matched on age and demographic settings was included. Validated questionnaires were used to collect dietary and epidemiological data. Dietary patterns were derived using principal component analysis with a covariance matrix from 33 food groups. Odds ratios and 95% confidence intervals were estimated using conditional logistic regression. A traditional, a cereal-dairy breakfast and a processed food dietary pattern were identified, which together explained 40.3% of the total variance in the diet. After adjusting for potential confounders, the traditional dietary pattern and cereal-dairy breakfast dietary pattern were inversely associated with breast cancer risk (highest tertile versus lowest tertile) (OR = 0.72, 95%CI: 0.57–0.89,  Diets in South Africa have shifted from nutritious traditional meals towards diets characterized by higher consumption of nutrient-poor, energy-dense foods ,2,3,4. DIt is already established that modifiable lifestyle factors such as diet, body weight and physical activity play a crucial role in cancer prevention . HoweverThere are various different methods to investigate the association between dietary intake and cancer risk, and dietary pattern analysis has emerged as a complementary method over investigating individual nutrients or foods , as it aStudies investigating a posteriori dietary patterns in association with noncommunicable diseases in the adult South African population are limited. One of these studies showed that dietary patterns comprising predominantly processed foods were positively associated with the risk of being overweight or obese . ObesityThe subjects included in this study were part of the South African Breast Cancer (SABC) study ,17,18. B2.Trained investigators and fieldworkers conducted face-to-face interviews at the time of recruitment using previously validated questionnaires ,20. InfoParticipants were asked about their habitual dietary intake over the past month, and dietary intake data were collected as soon as possible after breast cancer diagnoses (at recruitment) before any cancer treatment. A validated and culture-specific quantified food frequency questionnaire (QFFQ) was used, together with food models, food portion pictures and household utensils alongside the South African Food Composition Tables to determine habitual dietary intake ,22,23,24All individual foods and beverages contained in the QFFQ were categorized into 33 food groups (measured in grams/day) based on similarity of the nutrient content . Certain individual foods were classified as individual groups on their own since they were consumed often within the population .The International Agency for Research on Cancer and the University of the Witwatersrand Committee for Research on Human Subjects granted ethical approval for the South African Breast Cancer study (M140980). Permission to conduct research at Chris Hani Baragwanath academic hospital was obtained from the Gauteng Province Medical Advisory Committee. All subjects gave written informed consent prior to participation.2).Descriptive analyses were performed, and differences between cases and controls were assessed using paired sample t-test  and Wilcoxon Signed Rank test  for continuous variables and paired Chi-square test for categorical variables (presented as percentages). Specifications of the World Health Organization were used to calculate BMI, using measured height and weight (kg/mp < 0.001) and indicated homogeneity of variance of the different foods consumed.Principal component analysis with a covariance matrix was used to derive a number of independent linear combinations, based on a set of food groups, to retain habitual dietary patterns. This method reduces foods or food groups based on a linear combination of correlated foods or food groups into a smaller set of principle components (dietary patterns) . Althoug2 vs. BMI ≥ 30 kg/m2). For the latter two variables, unconditional logistic regression was used.Conditional logistic regression models were used to compute odds ratios, and associated 95% confidence intervals were used to determine the association between breast cancer risk and each dietary pattern. Each identified dietary pattern was divided into tertiles based on the 33rd and 66th percentiles of controls to compare the highest to the lowest tertiles to determine the association with breast cancer risk. One standard deviation increase in each dietary pattern (continuous variable) was also used to determine the association with breast cancer risk. Analysis was stratified by hormonal breast cancer receptor subtypes, menopausal status (pre vs. post) and obesity  ethnicity , individual income , level of education , smoking (smokers and non-smokers), height (continuous), waist circumference (continuous), habitual physical activity/d (active and less active), age at menarche (continuous), full-term pregnancy (yes/no), age at first pregnancy (<24 vs. >24 years of age), age at menopause (<48 vs. >48 years of age), time since menopause, parity (≤3 children vs. >3 children), ever breast-feeding (yes/no), duration of exclusive breast-feeding (months), use of exogenous hormones including hormonal birth control to avoid pregnancy  and hormone replacement therapy/combined hormone replacement therapy after menopause, family history of breast cancer (yes/no) alcohol consumption, HIV positivity (yes/no), miss-reporting of energy (under reporting vs. over reporting) and total energy intake in kJ (continuous). Only ethnicity, individual income per month, waist circumference, physical activity and menopausal status influenced the crude output by more than 10% and were therefore included in model 2.Model 3 included all adjustments made in model 2 and additional dietary factors  to evaluate the additional impact of dietary factors on the association with breast cancer risk in the respective food groups. Sensitivity analysis was conducted by excluding HIV positive breast cancer cases and controls but did not alter the results (results not shown).The traditional dietary pattern had the lowest total energy content , followed by the cereal-dairy breakfast pattern , and the processed food dietary pattern showed the highest total energy content . The processed food dietary pattern had the highest content of saturated fat  and added sugar  while showing the lowest content of dietary fibre (mean = 21.7 g ± 8.7 g). The protein-to-carbohydrate-to-fat ratio of each dietary pattern is as follows , divided by total energy intake from total protein, carbohydrate, and fat): traditional dietary pattern = 1:5.3:2.8, cereal-dairy breakfast pattern = 1:5.1:2.3 and processed food dietary pattern = 1:4.8:2.5. The processed food dietary pattern also showed the lowest micronutrient content compared with the traditional and cereal-dairy breakfast dietary patterns.p-trend < 0.001), for postmenopausal women , women with PR+ breast cancer , for women with a BMI <30 kg/m2  and for women with a BMI >30 kg/m2 . Interestingly, in the fully adjusted model, the inverse association observed in women with BMI > 30 kg/m2 lost statistical significance. The crude analysis, comparing the highest to the lowest tertiles of the cereal-dairy breakfast pattern showed inverse associations with breast cancer risk overall , for postmenopausal women  and for women with a BMI <30 kg/m2 . Similar inverse associations with breast cancer risk were observed in the fully adjusted model. No statistically significant associations with breast cancer risk were observed for the pattern on processed food. Similar results were observed when dietary patterns were assessed per one standard deviation increase in breast cancer risk  also showed inverse associations with breast cancer risk overall, in postmenopausal women and in women with a BMI < 30 kg/m2. No significant association was observed between the processed food dietary pattern  and breast cancer risk.In this black urban population of South African women, a traditional, a cereal-dairy breakfast and a processed food dietary pattern were identified, which together explained 40.3% of the total variance in the diet. After adjusting for potential confounders, the traditional dietary pattern  showed inverse associations with breast cancer risk overall, in postmenopausal women, in women with PR+ breast cancer and in women with a BMI < 30 kg/mNumerous case–control and cohort studies, mainly conducted in Caucasian women, have investigated the association between a posteriori defined dietary patterns and breast cancer risk. In general, these studies identified the prudent dietary pattern, characterized by foods such as vegetables, fruit, whole grains, fatty fish, poultry and low fat dairy products, as the dietary pattern is inversely associated with breast cancer risk ,37,38,392.The a posteriori approach in our study did not identify the same prudent dietary pattern that was observed in previous studies, which also used a posteriori approaches ,38. ThisWhile the amount of foods consumed differed, the traditional dietary pattern in our study contained similar food groups  to the prudent dietary patterns identified in other studies using a posteriori approaches. However, the traditional dietary pattern in our study did not contain any fruits while also containing food groups that are not usually included in a prudent dietary pattern, such as organ and offal meat, soup powders, and mono- and polyunsaturated fats such as margarine (excluding fatty fish).Organ and offal meat are more affordable meat options in South Africa and are often chosen over costlier lean meat cuts, especially red meat, in lower-income households . Organ mOf the three dietary patterns, the traditional dietary pattern had the lowest total energy, saturated fat and added sugar content while having the highest amounts for dietary fibre, vitamins and minerals. The traditional diets’ lower energy content indicates that organ/offal meats and margarine were consumed in smaller portion sizes and less frequently. This together with the higher amounts of fibre and micronutrients in the traditional dietary pattern may explain why the traditional dietary pattern was inversely associated with breast cancer risk in this study.In our study, a cereal-dairy breakfast dietary pattern was inversely associated with breast cancer risk. This may be related to the negative saturated fat loading in this dietary pattern together with the relatively high calcium content of the diet, being the highest of all three identified dietary patterns. Although limited evidence suggests a protective association between diets high in calcium and breast cancer risk, evidence is, however, inconclusive and warrants further investigation .p = 0.12) [p < 0.001) [Westernized or unhealthy dietary patterns are often characterized by consumption of fast and deep fried foods, processed meats, saturated fats, sugar sweetened beverages, alcoholic beverages and other highly processed foods. In general, findings from previous studies investigating the association between ‘Westernized’ or unhealthy dietary patterns and breast cancer risk have been inconclusive. For example, a systematic review and meta-analysis, conducted in 2010 and including 17 case–control and cohort studies, did not show any significant association between the highest versus lowest categories of Western/unhealthy dietary patterns  . However< 0.001) .In contrast, no significant association between the processed food dietary pattern and breast cancer risk was observed in our study. The results of this study are in line with a former study conducted in black women from Soweto, which investigated the association between ultra-processed food consumption (identified using the NOVA food classification system and breast cancer risk) ,42. In tIn addition, the total variance explained by other studies, investigating a posteriori dietary patterns in association with breast cancer risk, ranged from 10% to 75% ,43,44,45The strengths of this study include the fact that cases were recruited prior to any breast cancer treatment, that the questionnaires used to obtain data were proven to be validated, and that the data used in the analysis were standardized and administered by trained personnel. The limitations include the relatively limited sample size of this study; the nature of a case–control study design, which is prone to differential biases of cases; and the use of a QFFQ to collect dietary data, which relies on the memory of participants and is therefore more prone to recall bias. Dietary intake and physical activity were measured over the past month when habitual dietary intake/physical activity of case participants could have changed due to illness and may contribute to random misclassification and under estimation of dietary intake. In addition, although dietary intakes were captured throughout the year (in different participants) seasonal variability of foods (not adjusted for) may have influenced usual reporting of dietary intakes. Ideally large-scale longitudinal studies should confirm the results of this case–control studies that was conducted in the absence of any South African cohort study.The results of this study show that a traditional dietary pattern and a cereal-dairy breakfast dietary pattern, consisting of a lower total energy, saturated fat and added sugar and higher fibre, calcium and other key micro-nutrient contents, may reduce the risk of developing breast cancer in this population. Food groups associated with these dietary patterns may play key roles in breast cancer prevention interventions. Following a processed food dietary pattern was not associated with breast cancer risk in our study. However, the higher total energy, saturated fat and added sugar content and lower dietary fibre and key micronutrients content of this processed food dietary pattern may increase the risk of being overweight and obese and ultimately breast cancer risk.


AXIN2) gene and susceptibility to different cancer has attracted much interest. The present study aimed to evaluate the association between AXIN2 rs2240308 C>T, rs1133683 C>T, rs7224837 A>G polymorphisms with susceptibility to breast cancer. The association of genetic polymorphisms at Axis inhibition protein 2 (A polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay was designed to genotype the AXIN2 rs2240308 C>T, rs1133683 C>T, rs7224837 A>G polymorphisms among 150 breast cancer patients and 150 healthy subjects. The frequencies of these genetic variants were in agreement with Hardy-Weinberg equilibrium in healthy controls (p>0.05). The frequencies of AXIN2 rs2240308 C>T, rs1133683 C>T, rs7224837 A>G genotypes were similar in breast cancer patients and controls. There was no a significant association between the AXIN2 SNP and risk of breast cancer. The impact of AXIN2 polymorphisms in the breast cancer development remains unclear. Our results indicated that AXIN2 rs2240308, rs7224837 and rs1133683 polymorphisms did not contribute to increased risk of breast cancer. More studies with larger sample sizes and diverse ethnicities are warranted to verify our finding. BRCA1 and BRCA2 genes, which responsible for a lifetime risk between 50 and 85% and account for approximately 15% of familial breast cancer cases .Breast cancer is the most common type of epithelial cancer among women globally, with an average risk of 12% to develop in normal women life . Breast cancer has also the highest incidence rate and the second death rates among all types of cancer . Breast cancer is a multifactorial, polygenic and heterogeneous disease which aggressive subtypes are characterized by faster growth rates, increased capability to invade and metastasize, leading to poorer clinical outcomes . The incidence of breast cancer has been increasing in the world where it accounts for 6.9% of all cancers and 684,996 deaths in 2020 . However, worldwide differences in incidence and mortality of this disease are largely explained by age and country-level income . It is evident that the increasing trend in incidence is mainly attributed to reproductive and lifestyle patterns such as older age at first birth, decrease in childbearing and breastfeeding, lower physical activity, and obesity . Breast cancer is a complex disease involving hereditary and environmental risk factors. A genetic (inherited) component in breast cancer is well established in the etiology of breast cancer . Estimation of heritability based on twin studies found 25 to 31% to be explained by genetic factors . High-risk women are primarily identified on the basis of family history and mutation screening of the To date, a large effort to investigate the genetic component of breast cancer has been performed . The Wnt signaling pathway plays a crucial role during embryogenesis, while aberrations in this pathway are implicated in a variety of human cancers . Axis inhibition protein 2 (AXIN2) also known as axin-like protein (Axil), a key component of the Wnt signaling pathway, plays an important role in the regulation of cell proliferation, cytometaplasia, migration, apoptosis and other important cellular functions, and it has demonstrated a close relationship with the development of some cancers . AXIN2 is a scaffold protein which is required for the phosphorylation of β-catenin. Stabilization of AXIN2 protein in cancer cell lines inhibited Wnt signaling, migration, and under low serum conditions, reduced colony formation . In addition to the many AXIN2 mutations reported in various cancer types, a few reports have linked AXIN2 SNPs to the disease risk in breast cancer. AXIN2 mutations have also been associated with the development of oral clefts, such as cleft lip and cleft palate .AXIN2 gene is located on chromosome 17q23-q24, which belongs to a heterozygosity region that frequently loss in different cancers in human . It has been suggested that polymorphisms at AXIN2 gene are associated with the risk of developing of breast cancer. Moreover, it can be targeted by miR143HG/miR-1275 to regulate breast cancer progression by modulating the Wnt/β-catenin pathway . Although numerous genetic association studies on breast cancer have been published, there are few a few studies about association of AXIN2 gene with breast cancer risk . To date, several number polymorphisms have been identified at AXIN2 region, including rs2240308 (exon1), rs9915936 (exon5), rs1133683 (exon5), and rs4072245 (intron7), which some of them most frequently studied in relation to risk of cancer. Here, the authors aimed to evaluate the association between AXIN2 rs2240308 C>T, rs1133683 C>T, rs7224837 A>G polymorphisms with susceptibility to breast cancer.The Subjects and Basic CharacteristicsAll procedures in this study were carried out in accordance with the ethical standards of the institutional or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. The objective of the study was fully explained to all participants and a written informed consent was obtained from each participants. This study was approved by the Ethics Committee of the Azad University. This study included 150 diagnosed with breast cancer and 150 healthy subjects without history of cancer.DNA extractionGenomic DNA was extracted from 200 μL of peripheral blood of all subjects with EDTA as an anticoagulant. A Commercially available kit for DNA extraction was used for DNA extraction, according to the manufacturer’s instructions kit . AXIN2 rs2240308, rs7224837 and rs1133683 polymorphisms were genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) using previously reported primers . PCR wasStatistical analysisχ2) test. The associations of AXIN2 rs2240308, rs7224837 and rs1133683 polymorphisms with breast cancer risk was calculated by using the odds ratio (OR) and 95% confidence interval (CI) . Statistical analyses were carried out using SPSS version 19.0  for Windows.The chi-square test or Fisher’s exact test was performed to examine the differences between cases and controls in terms of mean age and gender. Hardy-Weinberg equilibrium (HWE) was used for the distributions of AXIN2 rs2240308, rs7224837 and rs1133683 polymorphisms in healthy subjects performed by the chi square  have performed a study to found association of 15 genetic variants located in 8 genes associated with Wnt signaling in Saudi Arabian women. Their results showed a significant association of genetic variants including AXIN2 rs3923087, β-catenin rs4135385, DKK3 rs6485350, SFRP3 rs7775 and TCF7L2 rs12255372 with breast cancer risk in the population. However, the study did not find a significant association of AXIN2 rs4791171, rs11079571 and rs3923086, β-catenin rs13072632, and SFRP3 rs288326 polymorphisms with breast cancer. Moreover, their results revealed that AXIN2 rs3923086 has a protective influence in breast cancer patients older than 43 years and AXIN2 rs3923087 heterozygous AG genotype was associated with reduced risk in both the age groups (≤43 or >43) as well as in the overall study. Wang et al., (2008) examined a number of SNPs in the AXIN2 gene including the four variants in this study and reported a significantly elevated risk with premenopausal breast cancers. In a Japanese population a SNP at codon 50 of the AXIN2 gene was shown to have a significantly reduced risk of developing lung cancer while association with head and neck and colorectal cancer was not observed .The association between AXIN2 and breast cancer has been demonstrated in limited populations and their results were somewhat inconclusive. To the best knowledge, this was the first study evaluated the association of AXIN2 polymorphisms with breast cancer in Iranian women. This study results revealed that AXIN2 rs2240308, rs7224837 and rs1133683 polymorphisms did not contribute to increased risk of breast cancer. Therefore, further studies in Iranian population should be conducted to explain the association between AXIN2 polymorphisms and breast cancer. Aristizabal-Pachon et al., for the first time, showed a significant association between AXIN2 rs151279728 and rs2240308 polymorphisms and breast cancer susceptibility in a Brazilian population, which indicating the role of Wnt/β-catenin pathway dysfunction in breast cancer tumorigenesis . In 2019, Dai et al., in a population of 415 n the Chinese Han breast cancer women and 528 healthy subjects evaluated the association of AXIN2 polymorphism with breast cancer. Their findings revealed that the rs11079571 and rs3923087 were associated with an increased risk of breast cancer, but not rs3923086. Moreover, they showed that the minor allele of rs3923087 polymorphism was associated with lymph node metastases and the ATA haplotype was correlated with an increased risk of breast cancer . Menezes et al., (2009) in a study assessed 75 families with nonsyndromic cleft lip with or without cleft palate (CL/P) and 93 control families without a history of cancer to find the role of Recently, Li et al., (2021) in a meta-analysis based on 72 studies with 22,087 cases and 18,846 controls evaluated the association of AXIN2 rs11079571, rs1133683 and rs35285779 polymorphisms with overall cancer risk. The meta-analysis results revealed that AXIN2 rs11079571, rs1133683 and rs35285779 polymorphisms were significantly associated with overall cancer risk. Gong et al., (2015) in a meta-analysis based on 8 studies with 1,559 cancer cases and 1503 controls evaluated the association of AXIN2 rs2240308 polymorphism with cancer risk. Their results revealed that this polymorphism was significantly associated with risk of cancer in overall. AXIN2 rs2240308 polymorphism was not associated with cancer risk in Turkish and Japanese populations. Their results showed that AXIN2 rs2240308 polymorphism was correlated with decreased risk of cancer by ethnicity. Yu et al., (2017) in a meta-analysis based on 14 studies with 2,215 cases and 2,481 controls found that AXIN2 rs2240308 polymorphism was not associated with cancer risk in overall. Similar with the previous meta-analysis, they showed that this genetic variant was associated with a decreased cancer risk in Asians.In conclusion, this case-control study indicated that AXIN2 rs2240308, rs7224837 and rs1133683 polymorphisms did not contribute to increased risk of breast cancer. Further multicenter, prospective studies with larger sample size would have improved the accuracy of the present findings.Conceptualization: Soheila Sayad, Jamal Jafari-Nedooshan, Mahdieh Abdi-Gamsae. Data curation: Seyed Alireza Dastgheib, Hossein Neamatzadeh. Formal analysis: Seyed Alireza Dastgheib, Meraj Farbod. Investigation: Mahdieh Abdi-Gamsae, Hossein Neamatzadeh. Methodology: Seyed Alireza Dastgheib, Hossein Neamatzadeh, Fatemeh Asadian. Sampling: Jamal Jafari-Nedooshan, Meraj Farbod, Fatemeh Asadian. Resources: Mojgan Karimi-Zarchi, Mahdieh Abdi-Gamsae, Jamal Jafari-Nedooshan. Software: Soheila Sayad, Seyed Alireza Dastgheib, Hossein Neamatzadeh. Validation: Mojgan Karimi-Zarchi, Jamal Jafari-Nedooshan. Writing-original draft: Soheila Sayad, Mahdieh Abdi-Gamsae, Hossein Neamatzadeh. Writing-review and editing: all authors.


We investigated the associations of reproductive factors with the percentage of epithelium, stroma, and fat tissue in benign breast biopsy samples.This study included 983 cancer-free women with biopsy-confirmed benign breast disease (BBD) within the Nurses’ Health Study and Nurses’ Health Study II cohorts. The percentage of each tissue type  was measured on whole-section images with a deep-learning technique. All tissue measures were log-transformed in all the analyses to improve normality. The data on reproductive variables and other breast cancer risk factors were obtained from biennial questionnaires. Generalized linear regression was used to examine the associations of reproductive factors with the percentage of tissue types, while adjusting for known breast cancer risk factors.As compared to parous women, nulliparous women had a smaller percentage of epithelium  and fat  and a greater percentage of stroma . Among parous women, the number of children was inversely associated with the percentage of stroma . The duration of breastfeeding of ≥ 24 months was associated with a reduced proportion of fat . In a separate analysis restricted to premenopausal women, older age at first birth was associated with a greater proportion of epithelium and a smaller proportion of stroma.Our findings suggest that being nulliparous as well as having a fewer number of children (both positively associated with breast cancer risk) is associated with a smaller proportion of epithelium and a greater proportion of stroma, potentially suggesting the importance of epithelial-stromal interactions. Future studies are warranted to confirm our findings and to elucidate the underlying biological mechanisms.The online version contains supplementary material available at 10.1186/s13058-021-01447-2. Breast cancer remains the most commonly diagnosed cancer in women in the USA and worldwide . The vasReproductive factors related to childbearing are also recognized as breast cancer risk factors. Parity, younger age at first birth, and breastfeeding are associated with reduced breast cancer risk –8. A lonSome previous studies of associations between reproductive factors and mammographic breast density, a well-established strong risk factor reflective of relative amounts of fibroglandular vs. fatty tissue content on the mammogram, found inverse associations of parity and positive associations of age at first birth and duration of breastfeeding with breast density –18. In oIn this study, we aimed to assess the associations of several reproductive variables  with the extent of epithelial, stromal, fibroglandular, and fat tissue in non-malignant breast tissue from benign breast biopsy samples using prospective data in cancer-free women from the Nurses’ Health Study (NHS) and Nurses’ Health Study II (NHSII) and a deep-learning computational pathology method for tissue composition assessment.Our analysis included cancer-free women (controls) from the nested case-control study of breast cancer conducted among the subcohort of women with biopsy-confirmed benign breast disease (BBD) in the NHS and NHSII cohorts , 23. TheEarly NHS questionnaires  asked whether the participant had ever been diagnosed with “fibrocystic disease” or “other BBD” and whether she had been hospitalized in relation to this diagnosis. Beginning in 1982, the NHS questionnaires specifically asked about a history of biopsy-confirmed BBD (fibrocystic disease or other BBD). The initial 1989 NHS II questionnaire and all subsequent biennial questionnaires also asked the participants to report any diagnosis of BBD and to indicate whether it was confirmed by biopsy or aspiration.Cases were women with biopsy-confirmed BBD who reported a diagnosis of breast cancer during 1976–1998 for the NHS and 1989–1999 for the NHSII following their BBD diagnosis. Using incidence density sampling, four women with biopsy-confirmed BBD who were free of breast cancer at the time of the matching case’s diagnosis (controls) were matched to the respective case on the year of birth and year of benign breast biopsy . We atteThe study protocol was approved by the institutional review boards of the Brigham and Women’s Hospital and Harvard T.H. Chan School of Public Health and those of participating registries as required. Consent was obtained or implied by the return of questionnaires.Hematoxylin and eosin (H&E) breast tissue slides were retrieved for biopsy-confirmed BBD patients who gave permission to review their biopsy records. The slides were independently reviewed by one of three pathologists in a blinded fashion, i.e., the evaluating pathologists were blinded to the type of BBD noted on the original diagnosis , 27. AnyH&E slides were digitized into whole-slide images at × 20 (n = 93) or × 40 (n = 890) using the Panoramic SCAN 150 . For women with good-quality slides, up to six slides from different tissue blocks were digitized. H&E slides that were not digitized were due to poor quality, slides too thick to fit into the scanner, and plastic mounting coverslips. Attempts to create new H&E slides were not always possible due to missing  blocks, old style blocks not created using tissue cassettes, or poor-quality blocks . Slides Whole-slide images were processed using a deep-learning computational pathology method to segment BBD tissues into epithelial, stroma, and fat regions. Tissue image analysis included normal terminal duct lobular units (TDLUs) and BBD lesions . Details of the image analysis method and its performance are described elsewhere . BrieflyFor each whole-slide image, our method computed total, epithelial, stromal, and adipose tissue areas in pixels. We next calculated the average percent of each tissue type out of the total area across all available slides for each woman , weighted by the total tissue area of the slides. We examined the associations of reproductive factors with the percentage of each of these individual tissue regions as well as combined epithelial and stromal tissue (fibroglandular area).The data on age at menarche, parity, age at first birth, and breastfeeding were available from baseline and biennial questionnaires, completed closest to the date of the biopsy. Among all eligible controls with tissue readings, the completeness of the data on parity was 98.3%. Among parous women with tissue readings, information on age at first birth and breastfeeding was available for 98.4% and 94.1% of the sample, respectively. Age at menarche was available for 99.6% of the sample. For 4 women with missing age at menarche, a median value in the study sample was imputed, as done in previous studies –33. TimeAge at first birth was categorized as < 25, 25–29, and ≥ 30 years. Parity was defined both as a binary variable  as well as categorical . Additionally, the number of children among parous women was modeled as a continuous variable. Age at first birth was modeled both as a categorical  and as a continuous variable. Information on breastfeeding was collected one time (on the 1986 questionnaire), and women were asked to report the total months of breastfeeding. Lifetime duration of breastfeeding  was classified as none to < 1, 1 to < 12, 12 to < 24, and ≥ 24 months. Age at menarche was modeled both as a categorical  and as a continuous variable. The time interval between menarche and first birth as well as the time since the last pregnancy was modeled as continuous variables.Information on breast cancer risk factors was obtained from the biennial questionnaires closest to the date of the biopsy. Women were considered to be postmenopausal if they reported (1) no menstrual periods within the 12 months before biopsy with natural menopause, (2) bilateral oophorectomy, or (3) hysterectomy with one or both ovaries retained and were 54 years or older for ever smokers or 56 years or older for never smokers , 35.We used multivariate linear regression to examine the associations of parity, age at first birth, breastfeeding, and the interval between menarche and first birth with the proportion of epithelial, stromal, fibroglandular, and fat tissues. Because tissue type measures were non-normally distributed, we used log-transformed values in all the regression analyses to improve normality. The risk estimates were adjusted for age (continuous), body mass index , a family history of breast cancer (yes vs. no), alcohol use , age at menarche , menopausal status/postmenopausal hormone use , and study cohort . Additionally, in the analysis of the association of breastfeeding, the estimates were adjusted for parity and age at first birth. In the analysis of the associations of parity and age at first birth, the risk estimates were mutually adjusted for these two variables. In the analysis for the interval between menarche and first birth, the estimates were adjusted for parity.The analyses of all reproductive variables except nulliparity and age at menarche were limited to parous women only. Parity, age at first birth, and age at menarche were modeled both as continuous and categorical, and breastfeeding was modeled as categorical. The lowest category for parity (1 child), age at first birth (< 25 years), and breastfeeding (0 to < 1 month) were used as the reference. To assess the overall trend for each of the categorical reproductive variables, we used respective medians within each category. The time since the last pregnancy as well as the duration of the interval between menarche and first birth were modeled as continuous variables. As in our prior study on reproductive factors and mammographic breast density, some of the associations were limited to either pre- or postmenopausal women; in the secondary analysis, we examined these associations separately in premenopausal women; the small sample of postmenopausal women (n = 290) in our study did not allow us to draw meaningful conclusions for this stratum. Finally, to account for the potential influence of BBD lesions on the study findings, we additionally adjusted all models for the type of the BBD.In addition to the main approach, we used the SAS Proc Glimmix procedure which accounts for non-normal data distributions to examine the associations with tissue types using their original continuous scale. The analyses were performed using the SAS software .In this study of 983 cancer-free women, 299 (30.4%) had non-proliferative disease, 559 (56.9%) had proliferative disease without atypia, and 125 (12.7%) had atypical hyperplasia, consistent with previously reported distributions of these BBD subtypes . The disIn our study sample, the average age at the biopsy was 42 years (range 19–58 years). A majority of the women were premenopausal at the biopsy (62.3%). The majority of women were parous , and the majority of parous women had at least two children  and breastfed for at least 1 month . The average age at first birth was 25 years (range 15–40 years) for premenopausal women and 25 years (range 16–37 years) for postmenopausal women. Age-adjusted characteristics of pre- and postmenopausal women in the study by nulliparous status are presented in Table In multivariate analysis Table , being nParity was associated with a reduced proportion of stroma , and having a first child at age 25–29 years was associated with a larger proportion of epithelial tissue , though there was no clear pattern for this association. The time since the last pregnancy as well as the duration of the interval between age at menarche and first birth was not associated with any of the tissue measures. None of the reproductive factors was associated with the proportion of fibroglandular tissue. The patterns of associations of the reproductive factors with tissue types were similar in the statistical analyses with the secondary modeling approach  . In contBreast tissue changes during pregnancy have been suggested as possible reasons for the long-term protective effect on breast cancer risk . The infIn our study, older age at first birth was associated with a greater proportion of epithelium and a smaller proportion of stroma, but these findings were limited to premenopausal women. We did not find any associations of the length of the time period between menarche and first birth with any of the tissue types. Gabrielson et al. found a marginally positive association of age at first birth with the proportion of epithelium, but the results were not significant in the small stratum (n = 55) of premenopausal women , and GerWe report inverse associations of breastfeeding duration with the proportion of fat tissue. Consistent with our findings, Gabrielson et al. found inverse associations, though only marginally significant associations of breastfeeding with the percentage of adipose tissue in the breast  . StudiesTo our knowledge, this is the largest study to date exploring the associations of several reproductive variables with the proportion of epithelium, stroma, fibroglandular, and fat tissues. The analysis used data from the Nurses’ Health Study and Nurses’ Health Study II, established cohorts with more than 30 years of follow-up, confirmed benign breast disease status and comprehensive information on breast cancer risk factors. Our study has a few limitations. Despite the prospective nature of the cohort, a measurement error for a few select reproductive variables especially in postmenopausal women is possible. For example, previous studies had conflicting findings on the accuracy of recall for age at menarche –54 whichWe investigated the associations of several reproductive variables related to childbearing with the extent of epithelial, stromal, fibroglandular, and fat tissue. Our findings suggest that nulliparous women are more likely to have a lower percentage of epithelium and fat and a greater percentage of stroma as compared to parous women, potentially suggesting the importance of epithelial-stromal interactions. Parous women with a greater number of children appeared to have a smaller proportion of stroma. In premenopausal women, younger age at first birth was associated with a larger proportion of epithelium and a smaller proportion of stroma. Future studies are warranted to confirm our findings and to elucidate the underlying biological mechanisms.Additional file 1: Table S1. Distribution of various tissue elements by the type of benign breast disease. Table S2. Associations of reproductive factors with tissue types in benign breast biopsy samples using proc glimmix procedure . Table S3. Associations of reproductive variables with tissue types in benign breast biopsy samples of premenopausal women using proc glimmix procedure . Table S4. Associations of reproductive variables with percentage of different tissue types (log-transformed) in benign breast biopsy samples , additionally adjusted for BBD subtype. Table S5. Associations of reproductive variables with percentage of different tissue types (log-transformed) in benign breast biopsy samples of premenopausal women , additionally adjusted for BBD subtype.