Breast disorders cause great anxiety for women especially when they occur in pregnancy because breast cancer is the most common cause of cancer related deaths in women. Majority of the disorders are Benign Breast Diseases (BBD) with various degrees of associated breast cancer risks. With increasing breast cancer awareness in Nigeria, we sought to determine the prevalence and characteristics of breast disorders among a cohort of pregnant women.® technique was performed at recruitment and follow up visits at third trimester, six weeks postpartum and six months postpartum. Women with breast disorders were referred for Breast Ultrasound Scan (BUS) and those with Breast Imaging Reporting and Data System (BIRADS) ≥4 had ultrasound guided biopsy. Statistical analysis was performed using Stata version 14.A longitudinal study of 1248 pregnant women recruited in their first trimester- till 26 weeks gestational age consecutively from selected antenatal clinics (ANCs), in Ibadan, Southwest Nigeria. A pretested interviewer- administered questionnaire was used to collect information at recruitment. Clinical Breast Examination (CBE) using MammaCareMean age of participants was 29.7 ± 5.2 years and mean gestational age at recruitment was 20.4 ± 4.4 weeks. Seventy-two participants (5.8%) had a past history of BBD and 345 (27.6%) were primigravidae. Overall, breast disorder was detected among 223 (17.9%) participants and 149 (11.9%) had it detected at baseline. Findings from the CBE showed that 208 (69.6%) of 299 breast disorders signs found were palpable lumps or thickenings in the breast, 28 (9.4%) were persistent pain, and 63 (21.1%) were abscesses, infection and mastitis. Twenty out of 127 (15.7%) participants who had BUS performed were classified as BIRADS ≥3. Lesions found by BUS were reactive lymph nodes (42.5%), prominent ducts (27.1%), fibroadenoma (9.6%), breast cysts (3.8%) and fibrocystic changes (2.5%). No malignant pathology was found on ultrasound guided biopsy.Breast lump is a major breast disorder among pregnant women attending antenatal clinics in Ibadan. Routine clinical breast examination and follow up of pregnant women found with breast disorders could facilitate early detection of pregnancy associated breast cancer in low resource settings. Breast disorders are very common among women –3 and soDuring pregnancy, many women suffer from different types of breast problems including fibroadenoma, mastitis, breast abscess, galactocoele and pregnancy associated breast cancer (PABC) which is very common because of the relative young age at breast cancer diagnosis in the population –13. PregBreast cancer screening is a viable option for early detection of breast cancer . In sub-Our study is the first to examine women for breast disorders at intervals in pregnancy and postpartum using CBE, BUS and ultrasound guided biopsy when necessary and it determined the prevalence and characteristics of breast disorders in pregnancy and 6 months postpartum A similar study was conducted previously in the eastern part of Nigeria and its objective was to compare the detection rate of CBE with BUS among pregnant women . Due to The study aimed to determine the prevalence and characteristic of breast disorders as detected by CBE and classified by ultrasonography and ultrasound guided biopsy in pregnancy and six months postpartum. The secondary aim of the study was to identify differences in the characteristics of women with breast disorders and women without breast disorders. The hypothesis was that breast disorders detection in pregnancy or during postpartum can aid early diagnosis of pregnancy-associated breast cancer.Study centers and settings include the antenatal and postnatal clinics of the University College Hospital (UCH), Adeoyo Maternity Hospital (AMH) and Primary Health Centre (PHC) Agbongbon in Ibadan representing tertiary, secondary and primary public health facilities based on three level of care in Ibadan, Southwestern Nigeria. Ibadan is the capital and a city in Oyo State, Nigeria. Oyo state has a population of 5,580,894 according to 2006 census and 1,633,333 are reproductive age women (15-49 years) . TherefoThis is a longitudinal study of pregnant women attending antenatal clinics (ANCs) in Ibadan, Nigeria. All pregnant women within first trimester till- 26 weeks gestational age were eligible. Pregnant women with previous breast cancer history were excluded.The study sample size of 970 was calculated using the sample size formula for estimating relative risk . SeveralAfter obtaining informed consent, participants were consecutively recruited during their ≤26 weeks gestational age. They were followed up in their third trimester, six weeks postpartum and six months postpartum considering variations in hormones and the difficulty in detecting lumps in pregnancy and during lactation.A pretested and face validated questionnaire was used to collect information on socio-demographic characteristics, medical history, lifestyle and reproductive variables. Test-retest method was used to ascertain the reliability of the questionnaire. The questionnaire was administered to ten pregnant women twice with two-week interval at a different health centre (PHC Bashorun) from the study centre. Variables from the questionnaires were computed. Total composite scores for pre-test and post-test were then compared. A correlation value of 0.9 was achieved.® method of Breast Self Examination (BSE) and CBE. MammaCare® method is validated and recommended for effective CBE and BSE [Research nurses and research assistants were trained prior to the commencement of the study. Research Nurses were introduced to the study and instructed on breast health and MammaCare and BSE . It invop < 0.001), which represents a moderate agreement among the Nurses.Trained Research nurses practiced on breast models and examined human volunteers attending ANC at the University College Hospital, Ibadan. During the training, the Clinical Breast Examination tool, which included a checklist, was developed and reviewed to ensure consistency and reduce selection bias while defining breast disorders. In addition, inter rater reliability analysis was performed by rating each nurse performance and agreement on detecting lumps on a breast model. The inter-rater reliability for the raters kappa coefficient was 0.55 . Ultrasound guided biopsy was performed for those with BIRADS 4 lesion by a Consultant Radiologist and biopsy read at the Pathology Department of UCH.p-value was < 0.05 from bivariate analysis were selected into the final GLMM.Descriptive statistics was used for calculating the percentages. Generalized Linear Marginal Model (GLMM) with Generalized Estimating Equation (GEE) was used to determine the trend of breast disorders in pregnancy and in lactation and it was also used to determine association between selected patients characteristics with breast disorder status. The GLMM assumed an exchangeable variance covariance structure. Variables whose All statistical analyses were done using Stata version 14 .Ethical approval was gotten from the University of Ibadan/University College Hospital (UI/UCH) Ethics Committee and the Oyo State Ministry of Health Ethics Committee, Ibadan, Nigeria and all participating women gave their written informed consent.A total of 1248 pregnant women within ages 16–46 were recruited. The mean ± SD age was 29.7 ± 5.2 years and mean ± SD gestational age at recruitment was 20.4 ± 4.4 weeks. Seven hundred and eight (56.6%) women had above secondary level education. All study participants were of African heritage with majority (92.5%) from Yoruba ethnic group, 72 (5.8%) had a history of breast disease, 345 (27.6%) were primigravidae and 58 (4.7%) had family history of cancer (Table One hundred and eighty three (14.7%) of study participants had ever done CBE prior to the study, but only 50 (27.3%) from these women had it done during ANC booking or registration. Other places mentioned were: whenever they noticed any breast change and they made breast complaint to health practitioners 55 (30.1%), during normal routine check up 20 (10.9%), and during breast health sensitization or seminar programmes in their community, place of worship or place of work 8 (4.4%).All the pregnant women had CBE done during their clinic visit at recruitment and 701 56.2%) had CBE done in their third trimester. At postpartum, 922 73.9%) were seen at their various postnatal clinics and those who could not make their clinic visit were called at 6 weeks postpartum also, 728 (58.3%) were seen or called at 6 months postpartum  women referred did BUS. Eighteen (14.2%) had BIRADS 3 and 98 (77.2%) had BIRADS 2  and prominent ducts (27.1%). Other findings were fibroadenoma (9.6%), fibrocystic changes (2.5%) and breast cysts (3.8%). Figure 2.5% and No malignant pathologies were found in the biopsy specimen of the three women referred for breast ultrasound guided biopsy. Diagnoses made were florid adenosis, lactational adenoma and fibroadenoma/pregnancy associated changes with infection.p < 0.001). Compared to the risk of detecting breast disorders by CBE at recruitment, the odds ratio for breast disorders was 0.80 (95% CI: 0.62–1.03) at third trimester, 0.54 (95% CI: 0.41–0.69) at 6 weeks postpartum and 0.35 (95% CI:0.25–0.49) at 6 months postpartum.The prevalence of breast disorders detected by CBE reduces with time on the generalized linear model with GEE (p = 0.05) from bivariate analysis and some important variables. Variables included in the final GLM model were time, age group, number of pregnancies, study centre, weight, education, occupation, ever had history of benign breast diseases, BMI (group) and relative history of any cancer type. Women who had B.Sc. equivalent level of education had lower odds of breast disorders compared to those with none or primary level of education , women with a history of benign breast diseases had higher odds of breast disorders compared to those without the history , and women with three or more pregnancies had higher odds of breast disorders compared to those with one. .Table Benign breast diseases are very common among young women in their reproductive ages, irrespective of whether pregnant, lactating or not , 2. The ® experts [At baseline, the prevalence of breast disorders in pregnancy detected by CBE was 11.9%. Throughout pregnancy and 6 months postpartum, the total prevalence of breast disorder was 17.9%. Findings from some community surveys in Africa, whereby breast screening was carried out among women in general reported lesser proportions. For example, a study conducted in Ghana found 4.8% of 1419 premenopausal women had clinically palpable breast lumps of which screening was conducted by mammocare experts . Abuidri® for CBE at the 3 study centers. Despite these possibilities, Mammacare® techniques used has been scientifically validated for CBE and it has been proven to detect lumps as small as 0.3 cm [A higher prevalence of breast disorders in pregnancy than non-pregnant state is not unusual because of the influence of female’s hormones and growth factors causing several physiological changes during pregnancy and lactation , 29, 30.s 0.3 cm .In Nigeria, majority of breast cancer cases are known to occur among women in their reproductive ages and the cancer types associated has been proven to be very aggressive , 2, 31. Although the prevalence of women with breast disorders was high, interestingly, the risk of having breast disorders detected by CBE in pregnancy for an individual reduces with time over the period of follow up . Some benign breast diseases may have been masked by the increase in breast density or they underwent secretory, cystic and necrotic changes or they simply regress spontaneously after childbirth. Examples of BBD known for these attributes are fibroadenoma and fibrocystic changes found in some non African studies , 33, 34.A palpable mass is the most common sign of breast disease detected by CBE as reported by studies conducted in South Africa and Rwanda, respectively , 36. ThiA slight left laterality of breast disorders was found in this study. Studies conducted in Ile-Ife, Ilesha, Port-Harcourt in Nigeria, reported a right sided preponderance of benign breast diseases. Their studies however did not indicate whether it was found in pregnancy or not but it was found among women in general , 27, 37.Findings from breast ultrasound scan showed majority of women had reactive axillary lymph nodes and prominent ducts. The prominent ducts are due to physiological changes in the breast during pregnancy and lactation. It appears as hypoechoic tubular structures on b-mode ultrasound scan; it represents ducts filled with colostrum and echogenic milk in late pregnancy and during lactation .Lumps in pregnancy might not necessary be different from non pregnant state . In thisEzeonu et al.  also revAccording to Bell et al. , fibroadEven though three participants were referred for biopsy, breast cancer diagnosis was not made in this study. Ezeonu et al.  attributAn association was found between number of pregnancies and breast disorders in this study. After adjusting for confounders, women with 3 or more pregnancies had lower odds for breast disorders compared to those who had one. This is interesting as increasing parity is an established factor for reduced breast cancer risk among women in general, this was also noted in a Nigerian study . FibroadOther patients’ characteristic associated with lower odds of breast disorders in this study is having a bachelor’s level of education compared to those with none or primary level of education. Variables associated with higher odds for breast disorders were a history of previous benign breast diseases and being a full time housewife or unemployed.Approximately 40% of women referred for ultrasound scan did not show up, in addition, there were delay from CBE to BUS uptakes. These may affect some of the findings in this study as lower proportion of masses were found in the ultrasound reports compared to CBE. Fear and activities of alternative or traditional medicine are reasons that could account for not appearing for BUS . MisclasThis study found a substantial number of pregnant women with breast lumps detected by CBE. We found fibroadenoma to be the commonest breast disease in pregnancy and at six weeks postpartum, while breast cyst and galactocoele were found at six weeks and six months postpartum. The incidence of breast disorders detected by CBE was 9.2 per 100 person years in pregnancy and lactation. The risk of breast disorders detected through CBE reduced with time in pregnancy and lactation.We recommend breast screening like CBE to be done twice during pregnancy, at ANC registration and once in third trimester. For lactating women, CBE should be done at 6 weeks postpartum. However, those found with breast disorders should be referred immediately for BUS and ultrasound guided biopsy.Antenatal and postnatal clinics should be considered a place for intensified screening and teaching of breast education. This is a feasible and very useful option for early breast cancer diagnosis. As close to 60% of women in their reproductive ages who attend ANC at least once in pregnancy according to National Demographic Health Survey 2013  would be


Breast cancer is an important cause of morbidity and mortality for women, yet a significant proportion of variation in individual risk is unexplained. It is reasonable to infer that unexplained breast cancer risks are caused by a myriad of exposures and their interactions with genetic factors. Most epidemiological studies investigating environmental contribution to breast cancer risk have focused on a limited set of exposures and outcomes based on a priori knowledge. We hypothesize that by measuring a rich set of molecular information with omics  and comparing these profiles using a case-control design we can pinpoint novel environmental risk factors. Specifically, exposome-wide association study approaches can be used to compare molecular profiles between controls and either breast cancer cases or participants with phenotypic measures associated with breast cancer . Current challenges in annotating compound peaks from biological samples can be addressed by creating libraries of environmental chemicals that are breast cancer relevant using publicly available high throughput exposure and toxicity data, and by mass spectra fragmentation. This line of discovery and innovation will extend understanding of how environmental exposures interact with genetics to affect health, and provide evidence to support new breast cancer prevention strategies. Breast cancer is the most common cancer in women worldwide, and is a major cause of death for women in mid-life . In factIncreasing evidence suggests that environmental factors, rather that inherited genetic factors, are the major causes of chronic diseases, including breast cancer. Studies of cohorts of 44,788 pairs of Western European monozygotic twins estimated that the genetic population attributable fraction for breast cancer was 31% (95% CI: 11–51%) . In otheWhile several environmental factors, including history of hormone replacement therapy, reproductive history, alcohol consumption, and physical activity are associated with breast cancer risk ,8, risk Animal studies and epidemiology suggest that environmental chemicals likely play diverse biological roles in the development of breast cancer, with some exposures important early in life and others important in mid- or late-life. For example, our recent review of biological evidence and epidemiologic studies found a higher risk of breast cancer for exposures to dioxins, air pollution, dichlorodiphenyltrichloroethane (DDT), and perflurooctanesulfonamide (PFOSA) during breast development, and for occupational exposures to solvents and other mammary carcinogens such as gasoline components . However, most epidemiological studies investigating environmental contribution to breast cancer risk have focused on a limited set of exposures and outcomes based on a priori knowledge and, as a result, are likely to miss important relationships. In addition, these studies have almost exclusively assessed the risks of exposure to single chemicals. In order to reduce the burden of breast cancer, there is an urgent need to find the undiscovered environmental risk factors. One promising approach is to conduct exposome-wide association studies (EWAS) that measure a rich set of molecular information from both exogenous and endogenous sources in biospecimens and identify associations with breast cancer ,12. SystThe objective of this research program is to measure a myriad of environmental exposures , evaluate their interactions with genetic factors, and assess their associations with health outcomes and/or biological pathways related to breast cancer  . We hypoN = 621 cases) and nineteen with estrogen receptor positive breast cancer (N = 418 cases), including saturated fatty acids (from fats/oils), vitamin E derivatives (from desserts or vitamin supplements) and androgens , with ORs ranging from 0.6 to 2.2.Metabolomics is recognized as a powerful platform for detecting small molecules in biospecimens ,20. ThesThe main challenge with the use of non-targeted metabolomics based on high-resolution mass spectrometry (HRMS) is the annotation  of small molecules. The first step for the annotation of metabolomics features relies on matching accurate mass of detected features to known small molecules present in public databases such as the Human Metabolome Database (HMDB)  or the MData resources that are now available from the US Environmental Protection Agency (EPA) and other sources provide valuable information to help build environmental chemical databases that are relevant to breast cancer. To start, very large exogenous chemical libraries are available such as the EPA’s Distributed Structure-Searchable Toxicity   or PubChAdditional opportunities to develop relevant chemical databases for annotating tentative chemical matches would use available data from EPA on expected population exposures and mechanisms of toxicity. For example, EPA has used high throughput modeling along with information on production volumes, chemical properties, and some chemical use information to predict population exposures for almost 500,000 chemicals. These data may be used to prioritize chemicals for a database. For example EPA’s modeling predicted 1880 chemicals would have median population exposures above 0.1 mg/kg-day . ChemicaAnother challenge with annotation of metabolomics features is that it is often not possible to identify compounds simply based on accurate mass since several molecules with different structures exhibit similar mass. Therefore, successful studies should utilize HRMS to produce MS/MS fragmentation spectra. The generated MS/MS spectra will provide structural information specific to each molecule to guide identification of compounds. It is also important to note that non-targeted metabolomics analysis using HRMS is less sensitive, compared to targeted HRMS analysis, and therefore exposures to very low levels of environmental factors may be missed. New tools and approaches to improve the sensitivity of non-targeted HRMS metabolomics analysis would be greatly encouraged. Adductomics is another top-down technique that measures modifications of blood proteins like hemoglobin (Hb) or human serum albumin (HSA) to characterize exposures to reactive electrophiles that are inherently toxic but cannot be measured directly in biospecimens . By charThe integration of data from multiple omics platforms would provide a more holistic understanding of gene-environment interactions in breast cancer pathogenesis compared to single omics approaches analyzed in isolation. However, multi-omics integration faces several challenges such as data harmonization, large number of false positive results, inadequate data visualization tools, and metabolic pathway databases. This research program would need to be carried out by a multidisciplinary team with expertise in omics measurements, bioinformatics, breast cancer biology, toxicology , and epidemiology. EPA data on short term toxicity testing and exposure modeling are publicly available and can be used to build libraries of breast cancer-relevant chemicals. The proposed research could leverage existing cohorts, which have archived biological specimens and have collected extensive health information, including known risk factors for breast cancer and breast cancer outcomes or phenotypes relevant to breast cancer. California is well positioned to carry out this research idea, with several state-of-the-art omics platforms, researchers with relevant expertise, and access to existing cohorts of adult women and young girls with archived biological specimens suitable for omics and information on breast cancer risk factors . Projects designed to meet these objectives will need budgets in the range of $1 M/year for 4–5 years.We anticipate that this research program will build and demonstrate a new approach combining omics and phenotypic measurements to investigate associations of an unprecedentedly wide spectrum of exposures  with phenotypic measures, which will provide unique opportunities to discover novel environmental risk factors for breast cancer and bring new insights into molecular mechanisms of these relationships. Two key innovative elements are application and integration of omics approaches to identify differences between participants with and without breast cancer or breast cancer risk factors, and the creation of a new breast cancer-related chemical library of pharmaceuticals, food and consumer product chemicals, and pollutants that have biological activities similar to known breast carcinogens. The use of MS/MS fragmentation profiles will provide critical structural information to get more confident identification of compounds. Use of newly available databases of short-term toxicity and exposure predictions for thousands of chemicals to identify breast cancer-related chemicals is also novel as an approach for building a metabolomics compound database. Findings from this program will provide a newly extended list of breast cancer-related environmental factors and strengthen scientific support for reducing exposure to such factors by providing novel data about human exposures from diet, lifestyle factors, and chemicals that are associated with health outcomes and/or pathways related to breast cancer. We also expect that this program will identify endogenous mediators of the relationships between exposures and breast cancer-related phenotypes, providing new insights about molecular mechanisms underlying exposure–outcome relationships. This will further our understanding of how environmental exposures interact with genetics and impact women’s health, and will ultimately help develop prevention strategies and lower the incidence of breast cancer. Projects that would stratify women based on menopausal status, race/ethnicity and socio-economic level would provide an opportunity to also evaluate disparities in estimated breast cancer risk from exposure to environmental factors and identify disproportionately affected populations.We hypothesize that by measuring a rich set of molecular information with omics  and comparing these profiles using a case-control design we can pinpoint novel environmental risk factors for breast cancer. Specifically, exposome-wide association study (EWAS) approaches can be used to compare molecular profiles between controls and either breast cancer cases or participants with phenotypic measures associated with breast cancer . Current challenges in annotating compound peaks from biological samples can be addressed by creating libraries of environmental chemicals that are breast cancer relevant using publicly available high throughput exposure and toxicity data, and by mass spectra fragmentation. This line of discovery and innovation will extend understanding of how environmental exposures interact with genetics to affect health, and provide evidence to support new breast cancer prevention strategies.


The tumor protein p73 (TP73) is a homolog of TP53 family. Ectopic p73 overexpression largely mimics p53 activities as a tumor suppressor and activates the transcription of p53-responsive genes and as a result induce apoptosis. This study aimed to investigate the association between p73 G4A polymorphism and the risk of breast cancer in a northeastern Iranian population.This case-control study was performed on 105 patients who admitted in educational hospitals of Mashhad University of Medical Sciences, Iran during 2013–2015, with breast cancer as case group and 120 healthy women as the control group. PCR-CTPP method was used to investigate the relationship between the p73 G4A polymorphism and the risk of breast cancer.There was no significant association between the AA genotype of the p73 G4A polymorphism and breast cancer in case and control groups. Although G allele frequency was higher in the case group, the abundance of this allele between case and control groups was not statistically meaningful and, as a result, not associated with the risk of breast cancer in this study group.There was no association between G4A p73 polymorphism and the risk of breast cancer in a northeastern Iranian population. Cancer is the second leading cause of death worldwide after cardiovascular disease . Breast Tumor protein 73 (TP73) is a homolog of p53, coded via a polymorphic gene in 1p36.3 region . p73 is Up to now, at least 19 polymorphisms have been recognized in p73. Ten polymorphisms are in exon region, eight in intron region and one is in the region of gene promoter. However, functionality of most of these polymorphisms has not been recognized yet . The 1p3The aim of this study was to investigate the relation between G4A polymorphism in p73 gene and the risk of breast cancer in northeast population of Iran.This case-control study was performed on 105 clinically and pathologically confirmed breast cancer patients who admitted in educational hospitals of Mashhad University of Medical Sciences, Iran during 2013–2015 as case group and 120 age-matched healthy women as control group were enrolled.All participants were asked to complete the consent form, approved by the Institutional Ethical Committee and Research Advisory Committee of Mashhad University of Medical Sciences, Mashhad, Iran.Demographic variables such as age, menopause status, family history of breast cancer, and marriage status were obtained from all participants.Five mililiter of venous blood was taken from each case study and put in test tubes containing EDTA. Genomic DNA of leukocytes was extracted using commercially available kit  according to the manufacturer's protocol. Genotyping of p73 G>A was determined by polymerase chain reaction with confronting two-pair primers (PCR-CTPP), modified from a technique . The regPCR amplifications for both of primer pairs were performed in a 20 μl reaction volume containing 2 μL buffer, 50 μM dNTPs, 2.5 μM MgCl2, 0.5 unit of Taq polymerase with 100ng of genomic DNA. The amplifications were done as follows: initial denaturation at 95°C for 5 min followed by 35 cycles of denaturation at 95°C for 1 min, annealing at 66 °C for 45 sec, DNA extension at 72°C for 2 min and final extension at 72°C for 5 min.All amplification cycles were performed in Biometra Thermal cycler . The PCR products were separated by electrophoresis on 2% agarose gels containing ethidium bromide and visualized by UV light. The product size of G and A alleles was shown in P-value less than 0.05 were considered statistically significant.Case and control groups were compared according to the difference in their genotype frequency via chi-square test using SPSS software version 17.0 software . The amount of genetic divergence within population was tested via Hardy-Weinberg. Moreover, Independent t-test, logistic regression test was used and P=0.07). The frequency of genotype between case and control groups was as shown in The present study was performed on 105 women with breast cancer with average age of 46.1±10.2 yr and 120 healthy control subjects as a control group with an average age of 43.8±11.8 yr old (P=0.17). Moreover, there was no association between the different models of G4A p73 polymorphism such as dominant, recessive, over dominant and codominant and increased risk of breast cancer .The genotype frequency of p73 gene G4A polymorphism does not show any significant association with the risk of breast cancer in either patient or control groups (t cancer . The genP=0.589). The highest and lowest average age of patients with cancer was observed in genotype GG and AA, respectively .Furthermore, pathological type of breast cancer stratified analysis in patients group were shown in P=0.31) among our patients' participants.As shown in In this study, there was no statistically significant association between G4A polymorphism in exon 2 of the p73 gene and the risk of breast cancer in our study population. Moreover, although the G mutant allele frequency was higher in breast cancer patients than in controls, this difference was not statistically meaningful.p73 is a p53 homolog that has a similar structure and function to it. p73 can activate the transcription of p53-responsive genes and plays an essential role in cell cycle events and DNA damage mechanisms. Although the role of p73 G4C14>A4T14 polymorphism and the risk of cancer has been investigated for different types of cancers in populations with various genetic and social backgrounds , to dateIn contradiction to some studies, our study showed that there was no meaningful association between p73 G4A polymorphism and the risk of breast cancer. In a study on Chinese population, there was a significant association between GC/GC genotype and the weak prognosis of breast cancer. Furthermore, cases with GC/GC genotype had a higher risk of advanced breast cancer compared to those with either GC/AT or AT/AT genotype . An epidThe impact of different genotypes of p73g4a polymorphism on tumor development can be explained by different functions of p73 protein in various tissues and tumors. Some SNP of p73 are identified but none of them can be substituted by amino acids in proteins , 23. G4AThere was no statistically significant association between G4A polymorphism in exon 2 of the p73 gene and the risk of breast cancer in a sample of northeast Iranian population.Ethical issues  have been completely observed by the authors.


Reproductive factors associated with breast cancer risk may also affect the prognosis. This study aimed to evaluate the associations of multiple reproductive factors with breast cancer prognosis and the modifying effects of menopausal status. We obtained data from 3805 breast cancer patients recruited between October 2008 and June 2016 in Guangzhou. The subjects were followed up until 30 June 2018. The hazard ratios (HRs) and 95% confidence intervals (95% CIs) were calculated using multivariate Cox models to estimate the associations. It was found that there were U‐shaped patterns for the associations of age at first birth and durations from first/last birth to diagnosis with breast cancer prognosis. The adverse effects of old age at first birth  and long intervals from first  or last  birth to diagnosis on progression‐free survival (PFS) were significantly more pronounced among premenopausal women. Additionally, long interval (>5 years) between first and second birth was associated with a better PFS [HR (95% CI): 0.64 (0.42‐0.97)]. These results suggested that age at first birth, durations from first/last birth to diagnosis, and intervals between first and second birth should be taken into account when following the patients and assessing the prognosis of breast cancer, particularly for premenopausal patients. These findings would also have implications for further insight into the mechanisms of breast cancer development. This study found U‐shaped patterns for the associations of age at first birth and durations from first/last birth to diagnosis with breast cancer prognosis, particularly for premenopausal women; a relatively long interval between first and second birth may benefit prognosis of breast cancer. The details were described elsewhere.2.2When first hospitalized for primary breast cancer, the subjects were interviewed in‐person by trained investigators using a structured questionnaire. We collected the following information: demographic characteristics, menstrual and reproductive history, life styles , and family history of breast cancer. Reproductive history included total number of pregnancies, the outcome of each pregnancy, and age at each birth.Clinicopathological characteristics and body mass index (BMI) were collected from medical records. The status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) was determined by pathologists using immunohistochemical tests. Detailed definitions of ER, PR, and HER2 status were previously described in detail.2.3All participants were followed up at least every 3 months during the first year, and every 6 months during the second and the third year; thereafter, patients were followed up once every year until death or 30 June 2018. The follow‐up data, including survival condition , updated contact information, postdiagnostic life style, and treatment information, were obtained from 3273 (86.1%) breast cancer patients by means of phone call and outpatient department visits. The endpoints of this study were overall survival (OS) and progression‐free survival (PFS), defined as the time from diagnosis until death and the time from diagnosis to the date of progression , respectively. Survival status was censored at the date of the latest interview or 30 June 2018.2.4Log‐rank test was used to estimate the associations of breast cancer prognosis with demographic and clinical characteristics at baseline to determine the potential confounders. To explore the nonlinear effect on breast cancer survival, the maternal age‐related factors  were modeled as continuous variables and fitted in a Cox proportional hazard model using restricted cubic splines with knots at the 5th, 35th, 65th, and 95th percentiles. The classifications of those variables were then defined based on the results of the restricted cubic spline for breast cancer PFS. Cox proportional hazard models were performed to investigate the associations between reproductive characteristics and breast cancer prognosis with hazard ratios (HRs) and 95% confidence intervals (95% CIs), adjusting for age at diagnosis, clinical stage, education, menopausal status, ER, and HER2 status.P < .05.To assess the joint effects of different reproductive factors on breast cancer prognosis, we cross‐classified the patients by different reproductive factors. Further stratification analyses were performed by menopausal status  and ER status (positive vs negative) to see whether the associations were modified by clinicopathological characteristics. Interactions between those factors were estimated by the product terms in the Cox regression models. All analyses were performed with R software version 3.4 with a two‐sided significance level of 2.5The data that support the findings of this study are available on request from the corresponding author.33.1As shown in Table 3.2P‐value was not statistically significant ; there was also a pronounced U‐shape association for the intervals between first/last birth and diagnosis . For OS, the patterns were similar to that of PFS .Restricted cubic spline analyses were used to estimate the nonlinear relationship of maternal age‐related factors with breast cancer prognosis Figures , 2. For Pinteraction = .048). Similarly, for the intervals between first/last birth and diagnosis, the longer intervals (≥20 years) were nonsignificantly associated with a poor prognosis (OS and PFS) as compared to the intervals of 10 to 19 years . There were no marked joint effects across other reproductive factors on the PFS either.We further examined the joint effects of multiple reproductive factors Table . Long in4In this study, we found that there were U‐shaped patterns for the effects of age at first birth and age intervals from first/last birth to diagnosis on the prognosis of breast cancer; particularly, the adverse effects of late age at first birth and long intervals from first/last birth to diagnosis were more pronounced among premenopausal women than postmenopausal women, and the interactions were significant. Additionally, long interval between first and second birth was related to a better prognosis of breast cancer.The results from studies on age at first birth as a prognostic factor have been debatable. Some have reported no association,The results from previous studies on breast cancer prognosis associated with the intervals between first/last birth and diagnosis were not consistent either. While several studies have reported that women who had given birth within 2‐5 years before diagnosis of breast cancer were in relation to an increased mortality,Interestingly, we further found that the above effects of late first birth and greater intervals from first/last birth to diagnosis were more evident among premenopausal patients. The underlying biologic mechanisms were not clear and possibly involved the different hormonal milieu in premenopausal patients who had high concentrations of circulating estrogens and progesterone.It is worth noting that a long interval between first and second birth was independently associated with a reduced risk of progression. Previous studies on breast cancer risk have shown that a short interval between first and second birth was associated with a significantly increased risk of breast cancer,The present study examined the separate and joint effects of multiple reproductive characteristics on breast cancer prognosis in China, which added valuable evidence to improve our understanding on the prognostic impact of reproductive factors on breast cancer. With further stratification analyses, we were able to detect the effect modification by ER and menopausal status. Nevertheless, only PFS was applied in the stratification analyses as the number of deaths was limited, though the progression was able to reflect the survival condition of patients. In addition, the numbers of subjects with short intervals from first/last birth to diagnosis were relatively small and the corresponding interactions were not able to be evaluated. Thus, the findings should be interpreted cautiously. Finally, there was a lack of information on prior use of hormonal therapy and socioeconomic status in our study, which may have confounded the results; however, the adjustment for education and clinicopathological characteristics partly compensated this defect.In conclusion, it was found that the effects of age at first birth and durations from first/last birth to diagnosis on breast cancer prognosis occurred in U‐shaped patterns instead of linear relationships as suggested in previous studies, which should be taken into account when following the patients and assessing the prognosis of breast cancer, particularly for premenopausal patients. These findings would also have implications for further insight into the mechanisms of breast cancer development.None declared. Click here for additional data file.


Per- and poly- fluoroalkyl substances (PFASs) are a large family of synthetic chemicals, some of which are mammary toxicants and endocrine disruptors. Their potential as breast carcinogens is unclear. Our objective was to evaluate the risk of breast cancer associated with serum PFAS concentrations in a nested case-control study within the California Teachers Study.Participants were 902 women with invasive breast cancer (cases) and 858 with no such diagnosis (controls) who provided 10 mL of blood and were interviewed during 2011–2015, an average of 35 months after case diagnosis. PFASs were measured using automated online SPE-HPLC-MS/MS methods. Statistical analyses were restricted to six PFASs with detection frequencies ≥ 95%: PFOA (Perfluorooctanoic acid), PFNA (Perfluorononanoic acid), PFUnDA (Perfluoroundecanoic acid), PFHxS (Perfluorohexane sulfonic acid), PFOS (Perfluorooctane sulfonic acid), and MeFOSAA (2-(N-Methyl-perfluorooctane sulfonamido) acetic acid. Unconditional logistic regression was used to calculate adjusted odds ratios (ORs), estimating the breast cancer risk associated with each PFAS.p ≤ 0.05) among the 107 cases with hormone-negative tumors but not the 743 with hormone-positive tumors.For all cases of invasive breast cancer, none of the adjusted ORs were statistically significant but marginally significant ORs < 1.0 were observed for PFUnDA and PFHxS (p-trend = 0.08). Adjusted ORs < 1.0 for PFUnDA and PFHxS were statistically significant  contains supplementary material, which is available to authorized users. Per- and poly- fluoroalkyl substances (PFASs) are a large family of synthetic chemicals that have been used in the U.S. since the 1950’s in a variety of industrial applications and consumer products, including non-stick cookware, food packaging, foam fire retardants, water and stain resistant clothing, carpeting and other textiles . CharactConcern for potential cancer risks stems from a large body of laboratory studies that indicate the potential for myriad toxic effects that could play a role in carcinogenesis, including tumor induction, developmental toxicity, hepatoxicity, immunotoxicity, and endocrine disruption , 25–32. Cancer risks in humans, however, remain unclear , 37. Of Most epidemiologic studies aimed at evaluating cancer risks associated with PFAS exposures have been conducted in occupational cohorts, predominantly comprised of men with insufficient numbers of women to meaningfully examine breast cancer risks –48. PopuTo date, only two sets of studies have been designed specifically to evaluate breast cancer risks associated with PFAS exposures. A case-control study of premenopausal women nested within the Danish National Birth Cohort, while reporting a weakly protective effect for perfluorohexane sulfonic acid (PFHxS) and a weakly elevated risk for perfluorooctane sulfonamide (PFOSA), overall found no consistent statistically significant risks for breast cancer , 52. AnaThe objective of the current study was to evaluate the risk of breast cancer associated with serum levels of several PFASs among participants of a large case-control study, nested within the California Teachers Study (CTS), a statewide prospective cohort established specifically to study breast cancer.The study population was comprised of 902 invasive breast cancer cases and 858 controls drawn from the California Teachers Study (CTS). The CTS, an on-going prospective cohort study initiated in 1995–1996 primarily to study breast cancer, is comprised of 133,479 female California public school professionals. Details of the creation and conduct of the CTS are described elsewhere . BrieflyCases and controls included in the present analysis were drawn from CTS members who had provided a blood sample and completed an interview-administered questionnaire as part of their participation in a separately-funded breast cancer case control study nested within the CTS cohort. Case selection criteria for the nested CTS case control study included: diagnosis with invasive breast cancer  between January 1, 2006 and August 1, 2014; age less than 80 years at diagnosis; no prior history of invasive or in situ breast cancer at cohort entry; and a continuous resident of California from cohort entry until time of diagnosis. Controls were drawn from a probability sample of at-risk CTS cohort members frequency matched to breast cancer cases by age at baseline (5-year age groups), race/ethnicity and CCR regional cancer registry of residence. Participation rates were approximately 55% and 65% for controls and cases, respectively. Compared to non-participants, participants were slightly older (89% versus 86% aged ≥ 40 years) and more likely to be white (91% versus 86%). Active refusals were the most common reason for non-participation, with 29% of controls and 21% of cases refusing, followed by inability to contact/lack of response (12% for controls and 9% for cases). Approximately 4% of controls and 5% of cases were excluded due to illness or death. Interview data and blood specimens were collected from 913 invasive breast cancer cases and 1270 controls during the course of the nested CTS study (May 2011 to October 2015).data not shown)). Furthermore, samples collected during the last 2 months of the CTS nested case control study were not included in the current analysis due to budgetary and timeline constraints of this separately-funded study. Ultimately the participants in the current analyses consisted of all 902 invasive breast cancer cases and 858 controls in the CTS nested case control study who provided a blood specimen and completed an interview-administered questionnaire at blood draw between October 2011 and August 2015. Blood specimens were collected an average of 35 months after case diagnosis .Prior analyses of serum PFAS levels among controls in the nested CTS study indicated that PFAS levels were highest during the early months of serum collection  – a timeBlood was collected, usually in participants’ homes, by licensed phlebotomists into a 10 mL BD® tube  with clot activator, double gel for transport, and silicone coated interior, using standard phlebotomy techniques. Prior to field processing, specimens were kept on cool packs for at least 30 minutes. Within hours of collection, phlebotomists spun down the clotted blood samples in the field using portable centrifuges to separate the serum portion. Processed samples were then frozen and stored at − 20 °C for 4–6 weeks until transported either via local courier (on cool-packs) or overnight (on dry-ice via FedEx) to the laboratory for chemical analysis. Samples remained frozen during this transportation process. Upon receipt at the laboratory, specimens were stored at − 20 °C until analysis.Measurement of PFASs in collected sera was conducted using the method as detailed previously . BrieflyInformation on potential covariates was derived from a series of surveys, including a self-administered baseline questionnaire completed in 1995–1996 at the initiation of the CTS, five mailed follow-up surveys, as well as a survey administered by a phlebotomist interviewer at the time of blood draw. Data from the interview administered at blood collection were used to characterize age at blood draw in years (40–49/50–59/60–69/70–79/≥80), menopausal status (Post/Pre and Peri/unknown), age (in years) at menopause , years since the onset of menopause  and hormone therapy (HT) use . Season of blood collection  was based on the date that the blood sample was collected, as recorded by the phlebotomist. Data from responses to the 1995–1996 baseline questionnaire were used to characterize: age at CTS enrollment in years (20–80); race/ethnicity (White/Black/Hispanic/Asian/PI/other); body mass index (BMI) (16.0–24.9/25.0–29.9/30.0–53.3/unknown); physical activity in hours/week (< 0.5/0.5–3.9/≥4.0/unknown); alcohol consumption in grams/day (none/< 20/≥20/unknown); pack years of smoking (never smoker/≤10/11–20/21–30/≥31/unknown); family history of breast cancer (in a first degree relative -- no/yes/unknown); age at menarche in years (≤11/12–13/≥14/never/unknown); age (in years) at first full-term pregnancy (no full term pregnancy/≤24/25–29/≥30/unknown); breast feeding history  (never pregnant/pregnant without live birth/0/< 6/6–11/≥12/unknown). Detailed information on HT use  was also gathered from the baseline questionnaire. A variable for weight change (loss of  >5 pounds/gain of  >5 pounds/no change of  >5 pounds/unknown) was calculated based on difference between self-reported weight on the fifth mailed questionnaire (administered in 2012–2013) and the 1995–1996 baseline questionnaire. Dietary consumption (grams/day) of fat (tertiles/unknown), pork (none/< median/ ≥ median/unknown), and total red meat (tertiles/unknown) were derived from information on dietary factors obtained from responses to a modified version of the Block questionnaire included on the 1995–1996 baseline questionnaire .In addition to these survey-based factors, neighborhood characteristics of socioeconomic status and urbanization were also considered as potential covariates as some of our prior work have suggested they may be related to serum PFAS levels . These wThis initial set of potential covariates was chosen to include established breast cancer risk factors and factors that our prior analyses had identified as correlates to serum PFAS concentrations in this study population.All analyses were conducted in SAS Version 9.4. The limit of detection (LOD) was defined as three-times the standard deviation of the blank. Because the LOD varied by batch, for the purpose of summarization, we calculated the average LOD across all batches, weighted by the number of samples in each batch. Samples with PFAS concentrations below the LOD were imputed as LOD/cument}2 , 65. To 10-transformed in all subsequent analyses to enable the application of parametric statistical approaches. Smoothing splines were considered in generalized additive models (using PROC GAM) and evaluated to assess potential non-linearities in the relationship between each PFAS and the log-odds of breast cancer but no evidence of non-linearity was observed.Prior to conducting the risk analyses, a number of exploratory and descriptive analyses were conducted. Frequency distributions of potential covariates were evaluated and chi-square statistics were calculated to assess the statistical significance of differences in distribution by case status. Spearman rank correlations between PFASs were calculated. As many of the PFAS were highly correlated we considered each PFAS separately in our analyses. Because the PFAS concentrations were highly-skewed, values were log10 ) and categorical variables . All regression models included terms for the matching design variables . Crude ORs were calculated from simple models including only these matching design variables. Adjusted ORs were generated from multivariate models, adjusting for potential confounding factors. Starting with the full set of potential covariates (as described in the previous section), covariates for inclusion in the final multivariable models were selected via a two-step process. First, a backwards elimination approach was used, starting with a model that forced inclusion of the PFAS variable, age, race, and region of residence, and retaining all covariates for which the p-value for the Wald chi-square was < 0.05. We then further evaluated potential confounders by adding each of the excluded variables back into the model one at a time and evaluated the effect of the change in the estimated odds ratios for the PFAS. Factors that changed the estimated odds ratio for the PFAS by 10% or more were retained in our final multivariable regression models. While we conducted this process separately for each PFAS, it resulted in the same set of covariates for all PFASs. The final set of covariates appear as footnotes to Table The risk of breast cancer associated with each PFAS was estimated by unconditional logistic regression, using PROC LOGISTIC to generate odds ratios (OR) and 95% confidence intervals (95% CI). PFAS concentrations were considered alternatively in separate sets of models as continuous variables  tumors versus non-hormonally responsive tumors that were estrogen receptor negative and progesterone receptor negative (ER-/PR-). Furthermore, because toxicological evidence suggests that the effects of exposure to xenobiotic endocrine disruptors such as PFASs may differ depending on the endogenous hormonal milieu in which they occur , 67, we Selected characteristics of the study population are detailed in Table p > 0.05) for all PFASs, with the exception of MeFOSAA for which the median was marginally higher in controls compared to cases (p = 0.04).Serum PFAS levels are summarized in Table data not shown).Statistically significant positive correlations were observed between all the PFASs with Spearman Rank Correlations ranging from 0.21 (for PFHxS and PFUnDA) to 0.63 (for PFOS and PFOA). Correlations between the PFASs were generally similar among cases and controls  but not when modeled as log-linear continuous variables .The risks associated with serum PFAS concentrations for all invasive breast cancers in our full study population are summarized in Table When stratified by menopausal status, the overall pattern of risk estimates were similar to that observed in the full study population Table . The sugp = 0.06). All other analyses conducted among the a priori selected subsets of interest yielded results similar to those observed in the full study population (data not shown).When the data were stratified by tumor hormone responsiveness, no statistically-significant risks were observed for the 743 cases with ER+ or PR+ tumors Table . HoweverOverall the findings from this study do not provide evidence that concentrations of PFASs measured in serum collected after diagnosis are related to breast cancer risk in this population of middle-aged and older California women. Our results are consistent with the null findings reported in a pair of C8 Science Panel Studies conducted among Ohio and West Virginia residents with known drinking water PFOA contamination , 50 and The significantly elevated breast cancer risks found among the Greenlandic Inuits –55 may bThe increased breast cancer risks observed among the Greenlandic Inuits may also reflect greater susceptibility to PFAS exposures. Analyses of genetic polymorphisms in genes that are involved in estrogen metabolism and estrogen biosynthesis  indicated polymorphic frequencies in this Inuit population that are distinctly different than those reported in Euro-Caucasian populations that have been the focus of the other breast cancer studies to date . While tAnother potential explanation for the disparate findings across the small body of studies conducted to date could be the variability in methodologic approaches, especially with regards to exposure assessment. Our approach for estimating PFAS exposure was more similar to that used in the Greenlandic Inuit study but our findings are more consistent with the null results reported from the Danish , 52 and An important distinction between our study and the Inuit study is that while both relied on blood collected post-diagnosis, in the Inuit study the blood was collected prior to treatment while in our study this was not the case. To our knowledge, the effects of breast cancer treatment, if any, on levels of serum PFAS have not been explored and remain unknown. If breast cancer treatment causes declines in PFAS levels, this would have limited our ability to detect an increase in breast cancer risk and could have resulted in spurious inverse associations. Unfortunately, complete information on treatment is not available for the CTS cohort.Exposure assessment in the C8 Science Panel Study, while retrospective, was not based on body burden measurements but rather was derived from complex modeling incorporating information from self-reported residential histories, historical data on PFOA drinking water contamination, occupational histories and a job exposure matrix to estimate cumulative PFOA exposures. While these estimates were validated with serum PFOA levels in a subset of participants, the possibility of exposure misclassification remains.A unique strength of the Danish study is its reliance on quantitatively-measured values of PFASs in blood samples collected 10 to 15 years prior to breast cancer diagnosis/enrollment. Notably, however, the blood samples, were collected during early pregnancy. This poses some complication in the interpretation of the study’s results. Pregnancy-related changes in blood volume and alterations in concentrations of serum albumin  to whichOverall we interpret the results from our study as null. Our analyses, however, indicated some suggestion of an inverse association between breast cancer risk and some PFASs, specifically PFUnDA and PFHxS, that appeared to be driven by risks among cases with non-hormonally responsive tumors. We find these results to be particularly perplexing. Given the body of literature on the potential estrogen disrupting properties of these compounds (with some studies suggesting anti-estrogenic properties), the most logical explanation for these findings would be that they are a reflection of anti-estrogenic activity. If this were the case, we would expect these effects to be more pronounced among cases with hormonally responsive tumors, as the etiology of these tumors is thought to be more strongly driven by hormonal factors than tumors that are not hormonally responsive , 75. We,To our knowledge, it is not known whether breast cancer treatment affects serum PFAS levels but it is conceivable that certain treatments, such as chemotherapy and hormonal therapy, could alter PFAS levels. One small study that compared serum levels of chlorinated hydrocarbons in breast cancer patients pre- and post-treatment reported significant differences in levels among patients who had received chemotherapy . It is ddata not shown). Moreover, age at menarche did not correlate with serum PFAS levels (data not shown). A significant but very weak inverse correlation (r < 0.10), however, was seen between age at menopause and serum level of PFHxS but not PFUnDA (data not shown). Because the serum PFAS levels in our study were measured after menarche and menopause , it is not possible to directly assess whether the suggestively protective effects we observed for some of the PFASs could be mediated by effects on the timing of menarcheal and menopausal onset.However, these suggestively inverse findings should not be entirely dismissed. A significant inverse association between breast cancer risk and PFHxS was also reported by the Danish study   and for When interpreting the results of our study it is important to consider potential mechanistic pathways. Three pathways have been proposed by which environmental chemicals such as the PFASs can impact breast cancer risk: 1) developmental toxicity mediated by endocrine disruption, leading to permanent morphologic changes in breast tissue and/or alterations in sex-steroid hormonal signaling pathways; 2) genotoxicity, either direct or indirect; and 3.) hormonal tumor promotion. Within each of these pathways, dose and timing of exposure are critical factors in determining effects .For the PFASs, evidence is most compelling from both laboratory and human studies for developmental toxicity associated with exposures encountered during fetal through pre/peri-pubertal development , 32, 67.Genotoxic effects are initiated through genetic damage which may lead to mutations that eventually progress to cancer. Mammary tissue appears to be most susceptible to the genotoxic effects of carcinogens during puberty and pregnancy when the mammary cells are not fully differentiated and are undergoing rapid proliferation . HoweverThe mechanistic pathway for which our study is perhaps best suited to provide insight is that of hormonally-mediated tumor promotion. Many of the primary risk factors for breast cancer are thought to be mediated by cumulative lifetime exposure to high levels of endogenous estrogens  and the The estrogen disrupting properties of PFASs remain unclear. A fairly large body of laboratory studies, and to a lesser extent human studies, suggest myriad and complex potential estrogen disrupting effects which are far from well-understood. In laboratory studies, PFASs have been shown to both increase and decrease estradiol levels , 66, 67,While we conducted a number of analyses among subsets of our study population designed to crudely capture groups of women who might have distinctly higher or lower levels of circulating endogenous estrogens , these analyses proved to be non-illustrative. Without the ability to control for endogenous estrogen levels, both our generally null findings, and suggestively protective findings for some PFASs, may be an artifact of an inability to account for endogenous estrogen levels which could impact the estrogenic or anti-estrogenic effects of PFAS, if they exist.The suggestion of a potentially protective effect associated with a few of the PFASs however warrants further attention, especially in light of toxicological evidence suggesting potential anti-estrogenic properties for some of these compounds under certain conditions. In particular, it appears that endogenous estrogen levels may be especially important in determining the mode of action of PFASs, including whether they exert estrogenic or anti-estrogenic properties. Future studies of this issue that can incorporate information about endogenous estrogen levels or genetic determinants of estrogen biosynthesis and metabolism would be particularly informative.In summary, it is important to consider our findings in the context of the strengths and limitations of our study. The age structure of our study population limited our ability to evaluate risks associated with in utero exposures, which may be the most etiologically relevant time period for breast cancer risk. Furthermore, the collection of blood samples post-diagnosis and post-treatment calls into question the possibility of reverse causality. As with any epidemiologic case control study, the potential for selection bias and uncontrolled confounding also must be considered. However, our reliance on data collected from a case control study nested within an on-going cohort study that was specifically designed to study breast cancer, somewhat minimizes these concerns. The eligibility criteria for our study population was well-specified, cases and controls were carefully matched, and we saw little evidence of survival bias. The wealth of data collected on known breast cancer risk factors allowed for the evaluation of a comprehensive set of potential confounders.The results from this study do not support an association between serum levels of PFASs and risk of breast cancer. Interpretation of these results, however, should not be overextended to conclude that PFASs are not of concern for breast cancer. In the context of the large body of laboratory data demonstrating endocrine disrupting properties of these PFASs at concentrations similar to those seen in some human populations, further study is warranted. It is especially important to elucidate mechanisms of action. Exposure to many of the PFASs examined in our study are declining due to regulatory and voluntary phase-outs enacted in response to heath concerns. These compounds however are being replaced with other PFASs that have not yet been studied , 23, 83.Additional file 1:Table S1. Comparison of serum PFAS levels (ng/mL) in this study with levels in other epidemiologic studies of breast cancer risk. (DOCX 25 kb)


Mammographic breast density, adjusted for age and body mass index, and a polygenic risk score (PRS), comprised of common genetic variation, are both strong risk factors for breast cancer and increase discrimination of risk models. Understanding their joint contribution will be important to more accurately predict risk.Using 3628 breast cancer cases and 5126 controls of European ancestry from eight case-control studies, we evaluated joint associations of a 77-single nucleotide polymorphism (SNP) PRS and quantitative mammographic density measures with breast cancer. Mammographic percent density and absolute dense area were evaluated using thresholding software and examined as residuals after adjusting for age, 1/BMI, and study. PRS and adjusted density phenotypes were modeled both continuously  and categorically. We fit logistic regression models and tested the null hypothesis of multiplicative joint associations for PRS and adjusted density measures using likelihood ratio and global and tail-based goodness of fit tests within the subset of six cohort or population-based studies.P > 0.21) or goodness of fit tests (P > 0.09), whether assessed continuously or categorically. The joint association (OR) was 2.60 in the highest categories of adjusted PD and PRS and 0.34 in the lowest categories, relative to women in the second density quartile and middle PRS quintile.Adjusted percent density (odds ratio (OR) = 1.45 per SD, 95% CI 1.38–1.52), adjusted absolute dense area , and the 77-SNP PRS  were associated with breast cancer risk. There was no evidence of interaction of the PRS with adjusted percent density or dense area on risk of breast cancer by either the likelihood ratio  contains supplementary material, which is available to authorized users. BRCA1 and BRCA2 mutation carriers   or tail-based (P > 0.23) goodness of fit tests  or categorical (quartiles PD/quintiles PRS) variables; this included evaluation by likelihood ratio tests [[OR SD or ca 0.91,1.0, χ2LRT =The multiplicative association can be seen in the similarities of the risk estimates from the joint association of PRS and density measures to the risk expected from the individual main effect models Tables ; Fig. 1.This is the first study to have examined the joint association of a 77-SNP PRS and continuous mammographic density measures on breast cancer risk. We found that the combined associations of the PRS and adjusted density measures on breast cancer are well described by a multiplicative model. These results imply that either adjusted percent density or absolute dense area measures can be incorporated as continuous measures into risk models with PRS in a straightforward manner, without interaction terms. Moreover, the two measures are close to uncorrelated; as a result, the risk discrimination afforded by using both measures is much greater than using either alone.Our findings are consistent with those from the few prior studies evaluating PRS and density measures. Two studies evaluated the contribution of the PRS to the Breast Cancer Surveillance Consortium or BCSC model, which includes the BI-RADS four category density measure , 19. BotOur findings are consistent with joint effects of PRS with other breast cancer risk factors. Two studies have examined the joint association of a PRS with reproductive variables , alcohol intake, postmenopausal hormone therapy, and BMI on breast cancer. The first involved a 77-SNP PRS examined in up to 28,241 cases and 30,445 controls in BCAC, finding that most associations were consistent with a multiplicative association . An earlOur study used quantitative density measures assessed from digitized film mammograms, primarily using the Cumulus software. Although this measure allowed us to evaluate a more precise density measure than the four BI-RADS categories evaluated previously, it is not used in clinical practice as it is not fully automated. Some centers now use commercially available automated density measures such as Volpara which provide a continuous volumetric density in addition to a BI-RADS-like categorical estimate. We and others have shown that these automated measures have a similar ability to predict risk as the BI-RADS clinical density measure and the area-based measures used in the current report , 44. It Since the 77-SNP PRS was established and validated, additional common genetic variants have been identified for breast cancer risk, and these will allow a more informative PRS to be developed –4, 35. TP < 10− 4 with ER-negative breast cancer [Some SNPs have been identified to be more strongly associated with ER-positive or ER-negative disease , 45, 46.t cancer , 6. Mammt cancer , 47–49. Important strengths of this analysis include the largest dataset to date to examine the combined associations of PRS and mammographic density on breast cancer risk, the use of quantitative density measures that were standardized across studies, and a common genotyping platform with standard quality control procedures for the majority of studies. We recognize, however, that our results are strictly generalizable to women of European ancestry only. We also note some overlap between our studies with those used to identify the associated SNPs and develop the PRS. However, while this could have led to some overfitting and hence overestimation of the risk gradient for the PRS, this would not have affected the primary conclusion that associations of the PRS and density are almost completely independent and not confounded. Also, two of the largest cohort studies in our sample  consisting of over 1300 cases were not included in the studies used for development of the 77-SNP PRS and estimation of the PRS risk gradients (1.7 and 1.4 per unit SD for PD) were similar to those estimated from the remaining studies. Finally, as noted above, additional work will be necessary to confirm our findings with an updated PRS and novel mammographic density measures , 51 as tIn summary, we confirm continuous mammographic density measures and PRS are two of the strongest risk factors for breast cancer and can be included in risk models without interaction terms. Absolute risk associated with higher density will be larger for women at high SNP-based inherited risk.Additional file 1:Table S1. Study design and characteristics for eight participating studies. Table S2. Characteristics of cases and non-cases by study (% of non-missing). Table S3. Associations  of adjusted percent density (PD), adjusted dense area (DA) and polygenic risk score (PRS) with breast cancer by study. Adjusted for age and BMI. Table S4. Goodness of fit test P values for joint effect of adjusted* density measures with 77-SNP polygenic risk score (PRS) on breast cancer, based on six population-based studies. PRS and density measures are continuous . (DOCX 34 kb)


Body size is an important modifiable risk factor for postmenopausal breast cancer. However, it remains unclear whether direct measures of fat mass are better indicators of risk than anthropometric measures, or whether central adiposity may contribute to risk beyond overall adiposity. We analyzed data from 162,691 postmenopausal women in UK Biobank followed from 2006 to 2014. Body size was measured by trained technicians. Multivariable‐adjusted Cox regression was used to estimate relative risks. Analyses were stratified by age at recruitment, region and socioeconomic status, and adjusted for family history of breast cancer, age at menarche, age at first birth, parity, age at menopause, previous hormone replacement therapy use, smoking, alcohol intake, height, physical activity and ethnicity. We observed 2,913 incident invasive breast cancers during a mean 5.7 years of follow‐up. There was a continuous increase in risk of postmenopausal breast cancer with increasing adiposity, across all measures. The point estimate, comparing women in the top (median 37.6 kg) to bottom (median 17.6 kg) quartile of body fat mass was 1.70 . The magnitudes of the associations between per SD increase in BMI and body fat mass with breast cancer risk were similar, suggesting impedance measures of fat were not substantially better indicators of risk than anthropometric measures. After adjusting for body fat mass, the associations between anthropometric measures of central adiposity and breast cancer risk were attenuated. The magnitude of risk, across all measures of adiposity, was greater in women who had been postmenopausal for 12 or more years. What's new?Obesity is associated with an increased risk of postmenopausal breast cancer. However, whether direct anaylses of fat mass using bioelectrical impedance measures are better indicators of breast cancer risk than anthropometric measures, such as body mass index (BMI) and waist circumference, remains unclear. In this study, based on data for postmenopausal women obtained from UK Biobank, the authors found that BMI, waist circumference, and direct bioimpedance measures of fat showed similarly strong associations with postmenopausal breast cancer risk. Central adiposity was no longer associated with breast cancer risk after adjusting for overall body fat mass. BMIbody mass indexCIconfidence intervalDXAdual‐energy X‐ray absorptiometryHRThormone replacement therapyRRrelative riskAlthough there are several important known risk factors for breast cancer, excess adiposity is one of the few modifiable ones. Previous studies have demonstrated an association between obesity and increased risk for postmenopausal breast cancer,Due to high cost and difficulty of implementation, most large prospective cohort studies have been unable to directly address this question, instead examining the association between obesity and breast cancer risk using only traditional anthropometric measures such as body mass index (BMI), waist circumference and waist‐to‐hip ratio, which do not directly distinguish between lean and fat mass.http://www.ukbiobank.ac.uk/). UK Biobank has approval from the North West Multi‐centre Research Ethics Committee, the Confidentiality Advisory Group in England and Wales and the Community Health Index Advisory Group in Scotland. All participants provided written informed consent.Data were obtained from UK Biobank . Details of the rationale, design and survey methods for UK Biobank have been described elsewhereThe complete UK Biobank dataset includes 502,620 UK adults  aged 40–70 at recruitment during 2006–2010. Participants completed a touchscreen questionnaire during the baseline assessment center visit that included questions on socio‐demographics, lifestyle, health and medical history and sex‐specific factors. A number of physical measurements, including body size and composition, were also assessed on the whole cohort during the baseline assessment center visit. A repeat assessment of all baseline measures was conducted in 20,345 participants at the UK Biobank Coordinating Centre between August 2012 and June 2013.n = 18,372), were premenopausal  or had unknown menopausal status after applying the above categorization criteria , were current users of hormone replacement therapy (HRT) , or had missing data on any measure of body size and composition . Current HRT users were excluded because HRT use is known to attenuate associations of adiposity with breast cancer risk in postmenopausal women.Women were eligible for these analyses if they were postmenopausal at recruitment. Women were defined as being postmenopausal at recruitment if they reported that their periods had stopped. Women with unknown self‐reported menopausal status were defined as postmenopausal if they were aged 53 or over at recruitment based on previously established criteriaHRT) n = ,680, or 2). Dual‐energy X‐ray absorptiometry (DXA) was used to measure fat mass/percentage on a subset of participants beginning in 2014 using the GE‐Lunar iDXA .At the UK Biobank baseline interview, trained staff measured standing height using the Seca 202 device . The Wessex non‐stretchable sprung tape measure  was used to measure waist circumference and hip circumference, from which we derived waist‐to‐hip‐ratio by dividing waist circumference by hip circumference. The Tanita BC‐418MA body composition analyzer  was used to measure body and trunk fat mass and percentage using bio‐impedance. BMI was calculated by dividing weight (kg) by the square of standing height , date of death, date of loss to follow‐up or end of follow‐up for cancer incidence . Women diagnosed with any cancer other than breast cancer (with the exception of non‐melanoma skin cancer) during follow‐up were censored at date of diagnosis.χ2 statistic. Tests for linear trend were performed with categories coded in an ordinal fashion using the repeat assessment median of each category to attain measures that are more representative of the true long‐term values.Multivariable‐adjusted Cox regression with attained age as the underlying time variable was used to estimate hazards ratios (referred to as relative risks [RRs]) and 95% confidence intervals (CIs) for the association between breast cancer risk and body size and composition measures: height, weight, BMI, body fat mass, body fat percentage, waist circumference, hip circumference, waist‐to‐hip ratio, trunk fat mass and trunk fat percentage. For the risk analyses, women were categorized into quartiles according to their baseline body size/composition. To facilitate comparison across measures of adiposity, we also estimated RRs per standard deviation (SD) increase and show the results from tests for linear trend corrected using the repeat assessment median within each category (grouped into quartiles) and the corresponding All analyses were stratified by 5‐year age at recruitment categories, region of recruitment and socioeconomic status (based on quintiles of Townsend deprivation index),χ2 statistic for trend across quartile medians was used to quantify the extent to which each particular index of adiposity was related to risk.r >0.88). Assessment of interaction terms between each exposure of interest and the underlying time variable did not suggest any significant deviation from proportional hazards.The RRs were also estimated for each body size/composition variable as a function of years since menopause  by using a time‐varying covariate. Comparisons were made between women who were <12 years and ≥12 years since menopause because this cut point divided the number of cases approximately equally. Likelihood ratio tests were used to assess whether time since menopause was an effect modifier in the associations between body size/composition variables and breast cancer risk. All analyses were conducted using STATA version 15.0 .Two thousand nine hundred and thirteen invasive breast cancer cases were diagnosed among 162,691 postmenopausal women at risk during a mean follow‐up of 5.7 (SD 1.1) years. Table Table 2). Women in the top quartile of body fat mass , had the greatest increase in breast cancer risk , compared to women in the lowest quartile . The magnitudes of associations with breast cancer risk per SD increases in BMI and body fat mass were similar with overlapping confidence intervals: RR 1.21; 95% CI 1.15–1.27 for BMI and RR 1.25; 95% CI 1.19–1.31 for body fat mass in multivariable‐adjusted models , and waist‐to‐hip ratio was least informative (χ2 = 202.0). After adjusting for body fat mass, associations of breast cancer risk with per SD increases in waist circumference, hip circumference and waist‐to‐hip ratio were attenuated to the null. However, the confidence intervals around the adjusted associations were moderately wide so small residual associations cannot be ruled out. Results using standard categories for BMI and waist circumferenceAll indicators of central obesity were significantly associated with postmenopausal breast cancer risk Fig. , Table 4p‐values for interaction were <0.02 for all adiposity variables except waist‐to‐hip ratio, BMI and body and trunk fat percentage  demonstrated a moderately greater magnitude of risk associated with postmenopausal breast cancer when comparing body fat to its surrogate measure, BMI.In these data, all measures of adiposity showed stronger associations with postmenopausal breast cancer risk among women who were ≥12 years since menopause compared to women with <12 years since menopause. These findings are consistent with those from two previous large prospective studies which found that the positive association between adiposity and postmenopausal breast cancer risk was more marked in women aged 65 or olderElevated estrogen levels may stimulate carcinogenesis through increased cell proliferation as well as through pro‐angiogenic and anti‐apoptotic effects.A 2003 review on central obesity found that waist circumference and waist‐to‐hip ratio were no longer associated with postmenopausal breast cancer risk after adjusting for BMI or weight,Recent Mendelian randomization studies report an inverse association between BMI predicted using single nucleotide polymorphisms identified by genome‐wide association studies and postmenopausal breast cancer risk.A notable strength of our study is the availability of objectively measured, rather than self‐reported, adult body size and composition measures. Self‐reported measures of adiposity such as weight tend to be underestimated, especially among overweight and obese women.Bioelectrical impedance measurements have been validated against hydrodensitometry, which is regarded as the gold standard for measuring body fat.In conclusion, in this large prospective study, we found strong positive associations between all body size/composition variables and postmenopausal breast cancer risk. Anthropometric indices, with the exception of waist‐to‐hip ratio, were as informative for breast cancer risk as impedance measures of fat mass. Our results also suggest no association between postmenopausal breast cancer risk and abdominal adiposity beyond its contribution to overall adiposity.Supporting Information Figure 1Click here for additional data file.Supporting Information Table 1Click here for additional data file.Supporting Information Table 2Click here for additional data file.


Several dietary factors have been reported to be associated with risk of breast cancer, but to date, unequivocal evidence only exists for alcohol consumption. We sought to systematically assess the association between intake of 92 foods and nutrients and breast cancer risk using a nutrient-wide association study.Using data from 272,098 women participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) study, we assessed dietary intake of 92 foods and nutrients estimated by dietary questionnaires. Cox regression was used to quantify the association between each food/nutrient and risk of breast cancer. A false discovery rate (FDR) of 0.05 was used to select the set of foods and nutrients to be replicated in the independent Netherlands Cohort Study (NLCS).Six foods and nutrients were identified as associated with risk of breast cancer in the EPIC study . Higher intake of alcohol overall was associated with a higher risk of breast cancer (hazard ratio (HR) for a 1 SD increment in intake = 1.05, 95% CI 1.03–1.07), as was beer/cider intake and wine intake , whereas higher intakes of fibre, apple/pear, and carbohydrates were associated with a lower risk of breast cancer . When evaluated in the NLCS (2368 cases), estimates for each of these foods and nutrients were similar in magnitude and direction, with the exception of beer/cider intake, which was not associated with risk in the NLCS.Our findings confirm a positive association of alcohol consumption and suggest an inverse association of dietary fibre and possibly fruit intake with breast cancer risk. Dietary factors have been extensively investigated as possible risk factors for breast cancer, but overall evidence for associations is inconsistent and inconclusive . Aside fFruits and vegetables are of particular interest due to their rich content of nutrients and phytochemicals, which are thought to have anticarcinogenic effects . HoweverWe systematically evaluated an extensive list of dietary factors in relation to breast cancer risk using a nutrient-wide association study (NWAS) approach. The NWAS takes an analogous strategy to that of genome-wide association studies (GWAS), separately estimating associations for each food and nutrient measured, and using multiple comparison adjustments to select promising associations for replication in an independent study . This meThis NWAS involved investigation of intakes of 92 foods and nutrients (for which data were available) in relation to breast cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC) study, and calculation of the associated False Discovery Rate (FDR) to select dietary factors to evaluate in the independent replication cohort, the Netherlands Cohort Study (NLCS).The EPIC study includes 521,330 men and women aged 25 to 70 years at recruitment, which occurred between 1992 and 2000 . ParticiWomen without a pre-baseline diagnosis of cancer were eligible for inclusion in these analyses; those who did not complete dietary or lifestyle questionnaires or with missing data on relevant confounders were excluded.Incident breast cancers were identified through population-based cancer registries or active follow-up, and mortality data were obtained from cancer or mortality registries . Breast The NLCS includes 120,852 participants, of whom 62,573 are women, aged 55 to 69 years when recruited in 1986 from the general population in 204 municipalities in the Netherlands with computerised population registries . At recrFor efficiency, a case-cohort approach was used for questionnaire processing and follow-up. Breast cancer cases were identified from the entire cohort, but accumulated person-years at risk in the entire cohort were estimated from a subcohort of 2589 women who were randomly sampled from the cohort immediately after recruitment. For cases and members of the subcohort, we excluded women with a prevalent cancer other than non-melanoma skin cancer at recruitment, as well as those with incomplete or inconsistent dietary data or missing confounder data.Incident breast cancer cases were identified by record linkage to the Netherlands Cancer Registry and the Dutch National Pathology Registry.In the EPIC study, the diet of participants was assessed at enrolment using validated country-specific or study centre-specific dietary questionnaires or food records , 16, 17.Information on dietary intake in the NLCS was collected at recruitment using a 150-item semi-quantitative food frequency questionnaire that estimated the average frequency and amounts of foods and beverages habitually consumed in the previous 12 months. The food frequency questionnaire has been validated and tested for reproducibility , 20. Nut2), physical activity  [q value < 0.05) within EPIC were carried forward for replication in the NLCS.Cox regression models with age as the time scale were fitted to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for risk of breast cancer as a function of each food or nutrient. In the EPIC study, age at recruitment was the entry time, and age at cancer diagnosis (except non-melanoma skin cancer), death, emigration, or last follow-up, whichever occurred first, was the exit time. In the NLCS, the total person-years at risk were estimated from the subcohort, and Prentice-weighted Cox proportional hazards models with robust standard error estimates were used to account for the case-cohort design . Intakes active) ; NLCS, n active) . The setWe performed the NWAS overall, as well as separately by premenopausal/postmenopausal status at baseline. Associations with breast cancer for the identified foods and nutrients in the EPIC study were also assessed by ER and PR expression in tumours for the 60% of EPIC cases and 46% of NLCS cases for whom receptor status data were available.All analyses were performed in R version 3.6.1.Of the 343,985 eligible women in the EPIC study without a pre-baseline diagnosis of cancer, we excluded 3343 participants who did not complete dietary or lifestyle questionnaires. A further 68,544 were excluded because they had missing values for relevant covariates, leaving 272,098 women available for analysis. In these women, 10,979 incident invasive breast cancers were identified during a median follow-up time of 15 years. After 20.3 years of follow-up, 3339 incident invasive breast cancer cases were identified in the NLCS. Women with incomplete or inconsistent dietary data  and those with missing data on confounders (451 cases and 326 subcohort members) were excluded, leaving 2368 invasive breast cancer cases (including 93 cases who were subcohort members) and 1608 non-case subcohort members in this analysis. Women in the NLCS subcohort were older than women in the EPIC study  (Tables The mean (SD) intakes of the 92 foods and nutrients that were evaluated in the EPIC study are presented in Additional file N = 5738 cases) but not among premenopausal women (N = 5241 cases)  for which higher consumption was associated with higher risk, and three dietary factors  for which higher intake was associated with lower risk of breast cancer (FDR < 0.05). The positive association of alcohol, and inverse associations of fibre, apple/pear, and carbohydrate intake with breast cancer risk were confirmed in the NLCS.In the EPIC study, associations of the identified foods and nutrients with breast cancer risk did not differ substantially by hormone receptor status, but intakes of apple/pear and carbohydrates appeared to have no association with risk of ER/PR-negative tumours. Our analyses stratified by menopausal status showed that results for postmenopausal women alone were very similar to the overall results. No foods or nutrients met the FDR threshold when analysis was restricted to premenopausal women. More research is needed to verify the lack of findings for premenopausal breast cancer and to evaluate underlying mechanisms.. Further, whilst the analogy to GWAS is somewhat appropriate, especially in terms of the approach to statistical analysis, it is far from perfect. The variants typed on a genome-wide array are typically not chosen because of any hypothesised association, but rather to provide adequate coverage of genetic variation in the whole genome. On the other hand, the 92 foods and nutrients in our study were assessed and derived and made available in the EPIC database because of prior plausibility of their association with disease outcomes. Further, the food and nutrient intakes are not independent. Thus, the NWAS approach is more closely aligned to a systematic analysis of candidate genes than it is to the hypothesis-agnostic approach of GWAS.Advantages of this study include its large size and long duration of follow-up, and the NWAS approach which involved a comprehensive assessment of foods and nutrients whilst accounting for multiplicity of tests, and replication of findings in an external cohort. Possible explanations for the lack of consistent associations between dietary factors and breast cancer in epidemiological studies include measurement error arising from the dietary assessment method, and inadequate dietary variation or narrow range of intakes in individual studies. Moreover, associations for dietary factors, if they do exist, may be of a small magnitude for which many studies have had inadequate statistical power to detect. A strength of the EPIC study is the variation in diet. The wide range of dietary intakes of foods and nutrients in this heterogeneous population, a key aspect of the study design , 25, andThis study reaffirms the well-established positive association between alcohol intake and breast cancer risk , 26–28 aIn this NWAS, inverse associations between dietary fibre and carbohydrate intake and breast cancer risk were identified and confirmed in the independent NLCS cohort. The 2017 WCRF/AICR Continuous Update Project report concluded that there is only limited evidence, for which no conclusions can be drawn, for associations of dietary fibre and carbohydrate intake with risk of breast cancer –33.The inverse association between total dietary fibre intake and breast cancer risk in the EPIC study has been reported previously ; howeverThe inverse association for apple/pear intake found in the current analysis could be reflecting fibre intake but might not be solely due to the fibre content of these fruits. Indeed, the association was slightly weaker but persisted after adjustment for fibre and carbohydrate intake. We speculate that it is possible that apple/pear intake is indicative of fruit intake in general since these are commonly consumed fruits in Western populations, and thus, their intake may be well captured in dietary questionnaires. In the EPIC study, apples and pears made the greatest contribution to total fruit intake . In a meThe inverse association of carbohydrate intake with breast cancer risk in this NWAS could be, at least in part, due to total carbohydrate intake capturing fibre and fruit consumption. Notably, the magnitude of the association for carbohydrates was identical to that for fibre and apple/pear intake. In addition, in the EPIC study, fruit was the second biggest food group source of carbohydrates (contributing 13%) . NeverthVegetables have garnered interest due to their rich phytochemical content and have been widely investigated for possible associations with breast cancer. In our study, no individual vegetables nor vegetable groups were associated with risk of breast cancer. Consistent with our results, a meta-analysis of 10 prospective studies , and a pPrevious analyses in the EPIC study have found a weak association between saturated fat intake and breast cancer risk , 42, wheThe fact that few foods and nutrients were found to be associated with breast cancer risk in this study, and other studies , 6, 29, The associations identified in this study are supported by biologically plausible mechanisms. In particular, it is thought that dietary fibre intake may exert a beneficial effect for prevention of breast cancer by decreasing circulating oestrogen levels via inhibition of intestinal reabsorption of oestrogens excreted in bile and concomitant increased faecal excretion of oestrogens –48. AlcoThis study confirms the well-established increased risk of breast cancer associated with alcohol consumption and suggests that higher intake of dietary fibre and possibly fruit might be associated with reduced breast cancer risk.Additional file 1: Means and standard deviations. Mean intake and standard deviation of the 92 foods and nutrients that were evaluated in the EPIC study.Additional file 2: Model estimates. Estimates from the models for the 92 dietary factors that were evaluated in the EPIC study.