The UK Women’s Cohort Study (UKWCS) was established to explore links between diet and chronic disease, in particular cancer. Previous cohort studies exploring diet and cancer have often produced results with small, not statistically significant effect sizes, due in part to the fact that diet is a complex exposure with measurement being subject to a varietyof errors and bias.The original cohort was planned to have two phases: first the baseline data collection (1995 to 1998) using a postal questionnaire including a detailed FFQ, developed from the FFQ used in the Oxford arm of the European Prospective Investigation into Cancer and Nutrition (EPIC) study.The aims of the UKWCS were to explore relationships between diet  and chronic disease .Women were selected from approximately 500 000 responders to a direct mail questionnaire sent by the World Cancer Research Fund to people living in England, Wales and Scotland, using direct mail lists targeted towards females . These m,A total of 174 local research ethics committees were contacted and permission to carry out the baseline UKWCS study was obtained.The whole cohort has been contacted for a second time, Phase 2. All baseline responders were mailed between 1999 and 2002. Non-responders were mailed a postcard reminder and then followed up with a telephone call; 14 172 (40%) completed a follow-up health and lifestyle questionnaire similar to that used at baseline, and 12 453 (35%) also completed a 4-day food diary. There were some small differences in baseline characteristics by response status at Phase 2. For example, responders were slightly more likely to: be educated to degree level; have a lower body mass index (BMI); be less likely to smoke; eat less meat and more fruit and vegetables; and take dietary supplements, compared with non-responders .Table 2Nine sub-studies have re-contacted samples of the cohort . Sub-stuThe first of these was a sub-group of 1918 women who completed a repeat FFQ, approximately 2 years after baseline. This allowed assessment of stability of dietary patterns, and an estimation of random measurement error in the FFQ.To investigate the effect of non-starch polysaccharide (nsp) on plasma nutrient concentrations, participants were restricted to those living within a 30-mile radius of Leeds, or a 1-h drive, to facilitate collection of blood samples. A total of 274 participants had blood samples analysed for a range of micronutrients. Higher levels of nsp were not associated with lower plasma concentrations of the micronutrients measured, even allowing for the higher nutrient levels generally found in high-fibre foods.n = 8409) and non-supplement users (n = 5413) were compared from the first half of the UKWCS baseline data. Supplement users had a healthier lifestyle profile and an adequate nutritional intake, suggesting that supplement users do not need to take supplements to meet a nutrient deficiency.Supplement (The Taste Genetics (TaGI) sub-study began in 2003. A sub-sample of 5 500 women who were ideal responders were mailed and asked to provide data regarding phenylthiocarbamide (PTC) taster status, food preference and food behaviour. PTC is a chemical that mimics the bitter taste sensation and is detectable in varying levels by different individuals. Respondents were categorized as ‘non-tasters’, ‘tasters’ or ‘supertasters’, based on their response to PTC-impregnated filter papers using a Labelled Magnitude Scale (LMS).,The Iron Status sub-study was nested within Phase 2 data collection to determine the relationships between food and nutrient intakes, HFE genotype and iron status. Foods and nutrients associated with iron stores, with adjustment for gene mutations associated with haemochromatosis, were explored. A subgroup of 15 000 participants provided cheek cell samples by mail to assess two mutations linked to iron metabolism. All women who were homozygous or heterozygous for the C282Y gene mutation, along with a random sample of 3000 women, provided a blood sample for measurementof iron storage markers and DNA. Cheek cell samples, blood samples or both, were genotyped for C282Y andH63D mutations from 6779 women; 2489 women also had their iron status assessed using serum ferritin and transferrin saturation.A total of 3596 Phase 2 respondents with eating frequency coded from the first food diary in waves 1–4 (mailed between 24 April 1999 and 4 February 2000) were mailed a questionnaire and further 4-day food diary in 2006, to explore snacking habits and body weight status; 2253 women responded.The aims of this sub-study were to determine the acceptability of web-based cognitive function testing in a large cohort of older women, and to establish cross-sectional associations between traits and components of successful ageing. In 2010/11, a random sample of 2000 surviving women, who were not cancer registered, were invited to complete a questionnaire which included a personality assessment and an invitation to complete a reaction time task online. There were 920 questionnaires returned, a response rate of 46%; 426 (64%) additionally provided data on reaction time using an online task.In 2009, the Iron and Blood Pressure sub-study was conducted; sampling was linked to the Iron Status sub-study. The aim was to use Mendelian randomization to examine the relationship between maternal iron status [using maternal serum ferritin as the environmental measure of exposure (modifiable risk factor)] and maternal C282Y genotype as the instrumental variable, with offspring blood pressure and adiposity measures (body mass index and waist circumference) in adulthood. The non-exposed were randomly sampled from women with a wild-type genotype and one or more children. In all, 1686 children from C282Y carrier mothers and 1690 from wild-type mothers were contacted; 517 (17%) consented to take part, and 348 offspring of 277 mothers completed the study. Of these, 170 (49%) were children of C282Y mutation genotype mothers, of whom 12 were children of 11 homozygous mothers (aa), and 158 were children of heterozygous mothers (ag).,In 2014–15, we are contacting a random sub-sample of participants who have completed Phase 2 data collection, and are still alive, and ask them to complete a new web-based 24-h dietary recall using the myfood24 tool.,The UKWCS has also been included in a number of consortia. The United Kingdom Dietary Cohort Consortium has included the UKWCS in pooled analyses of dietary data collected using food diaries in seven prospective studies. This work has focused on diet in relation to risk of colorectal and breast cancer.The baseline questionnaire included a range of socio-economic, lifestyle and health status questions including self-reported anthropometric measures and weight change over time. Detailed reproductive history; information on past health experience, sibling and parental health as well as physical activity assessment were obtained. A 217-item food frequency questionnaire was included, based on the questionnaire used in EPIC,Phase 2 questions asked in detail about 17 different types of dietary supplements. More detailed supplement use was recorded in the 4-day diaries. Over 12 000 participants completed food diaries for 4 days, with a separate page for each day to record individual supplements taken including: brand; name; amount taken; and dosage. This information was captured into a database of supplements taken by participants, and matched via a drop-down menu against supplements listed in a supplement ingredient database. This enabled the allocation of ingredient amounts to the supplements taken. In total there were 3996 different marketed supplement types listed in the database and each was given a separate supplement identification code. This contained brand name, supplement descriptions, ingredient composition and units, which were obtained from product labels provided by participants, suppliers’ websites or provided directly from manufacturers upon request.Although a relatively healthy cohort, around a quarter of the women were overweight and 10% obese. A third were not meeting the recommended intakes for fruit and vegetables and 80% had a low level of physical activity .Table 4Deaths and cancer registrations for the cohort are being recorded. Cause of death was coded in the International Classification of Diseases (ICD) 9 and now in 10. Prevalent cancers were identified from knowledge of pre-existing cancers from the NHSCR/HSCIC data linked to the baseline questionnaire date. Any cases occurring after the date of the questionnaire return are counted as incident. Cancer diagnoses are registered under ICD codes by local cancer registries and collated by the National Health Service Central Register, now the HSCIC. Cancer and death registrations for women in UKWCS are extracted quarterly by the NHSCR/HSCIC. This information is linked to UKWCS identification codes. Cardiovascular mortality records for participants have also been identified from the national registry data; 258 fatal cardiovascular disease (CVD) cases  were observed over an average follow-up period of 14.3 years.DNA is available from stored cheek cell samples on 5434 women and from blood samples for 2624 women. Blood samples are available from 2485 and plasma samples from 2576 women. Samples are from 2005 and stored at −20°C.Ptrend = 0.001), and for processed meat 1.59 . Larger effect sizes were found in postmenopausal women for all meat types compared with premenopausal women. An inverse association for risk of breast cancer with total fibre and cereal fibre intake has been demonstrated in premenopausal, but not postmenopausal women.trend = 0.05).Findings have used baseline FFQ data to provide evidence for the relationship between risk of breast cancer and dietary intakes. The study has demonstrated a positive association between meat intake and risk of breast cancer.Exploration of common dietary patterns found no important associations with risk of premenopausal breast cancer, although a fish-eating dietary pattern that excludes meat from the diet may confer some benefit with regard to risk of postmenopausal breast cancer.,An exploration of dietary acrylamide intake in relation to breast cancer risk in the cohort found no evidence of association. A weak association may exist with premenopausal breast cancer, but this requires further investigation.www.ukwcs.leeds.ac.uk.Other work is now being undertaken exploring diet in relation to colorectal and female reproductive hormone cancers. A full list of publications associated with the UKWCS can be found on the study website P < 0.01. Total fibre intake was associated with total  stroke events and the HR per 6 g/day total fibre intake was 0.89 (95% CI 0.81 to 0.99).Analysis of fatal CHD, stroke or CVD risk in the full sample found no association with total dietary fibre and fibre from different food sources.Other results have focused on particular nutrients from the diet or supplements linked to non-cancer outcomes. Haem, but not non-haem or total iron, is associated with serum ferritin concentrations.Women with personal or family histories of some cardiovascular or intestinal disorders were more likely to take supplements containing vitamin C, though not necessarily at high doses. High-dose vitamin C intake was linked to healthier behaviours and a history of breast cancer, total cancer and other illnesses within close family members.Diets of the women have been matched to food-cost databases; results show that a healthier diet, defined by a healthy diet indexOne of the main strengths of the study is the recruitment of participants into the cohort with a wide range of exposure to different dietary patterns; this decreases measurement error bias and increases power.www.ukwcs.leeds.ac.uk] or via email to [ukwcs@leeds.ac.uk] or [j.e.cade@leeds.ac.uk].All collected source data are maintained and stored by the Nutritional Epidemiology Group, University of Leeds. Specific proposals for collaboration are welcomed. Further information can be found on the UKWCS website  or via email to [ukwcs@leeds.ac.uk] or [j.e.cade@leeds.ac.uk].Specific proposals for collaboration are welcomed. Further information can be found on the UKWCS website [The UKWCS was originally funded by the World Cancer Research Fund. Other support has been received from the Food Standards Agency (grant NO5 023), the Medical Research Council (ref: G1100235/1), the Wellcome Trust (Fellowship WT87789), the Economic and Social Research Council, Kelloggs Sales and Marketing UK Ltd. The open access fee was funded by University of Leeds.


Early life social environment may influence breast cancer through shaping risk factors operating in early life, adolescence and adulthood, or may be associated with breast cancer risk independent of known risk factors. We investigated the associations between early life socioeconomic status (SES) and mammographic density, a strong risk factor for breast cancer, and the extent to which these associations were independent of risk factors across the lifecourse.n = 700 composed of 441 individuals and 127 sibling sets).We used data from an adult follow-up study of two U.S. birth cohorts of women (average age = 43 years) with prospectively collected data starting during the pregnancy of the mother and continuing through early childhood of the offspring. We collected data on factors in later life periods through computer-assisted interviews with the offspring as adults, and obtained routine clinical mammograms for measurement of percent density and dense and nondense breast areas using a computer assisted method. We used generalized estimating equation models for multivariable analysis to account for correlated data for sibling sets within the study sample . Highest vs. lowest parental educational attainment was associated with higher percent density in models that adjusted for age at mammogram and adult BMI , but the association was attenuated and no longer statistically significant after further adjustment for early life factors. There were no associations between early life SES indicators and non-dense area after adjustment for adult BMI. Neither adult education nor adult income was statistically significantly associated with any measure of mammographic density after adjusting for age and adult BMI.Highest vs. lowest family income level around the time of birth was associated with smaller dense breast area after adjustment for early life factors  and risk factors in later life periods, including adult body mass index (BMI) and adult SES (β = −8.2 cmWe did not observe consistent associations between different measures of early life SES and mammographic density in adulthood. Breast cancer risk is influenced by exposures that affect breast development and tissue structure and occur throughout the life-course, beginning with in utero and childhood periods . The majHigher adult SES, particularly greater educational attainment, has consistently been associated with increased breast cancer risk –13, but Using prospectively collected early life data, we examined the associations between early life SES  and adult mammographic breast density, a measure of the amount of dense (fibroglandular) breast tissue as visualized on a mammogram and a strong and independent risk factor for future breast cancer risk –23. To fWe used data from the Early Determinants of Mammographic Density (EDMD) Study, an adult follow-up study of participants from several U.S. birth cohorts, established to investigate early life exposures and breast cancer risk , 29. TheThe study was approved by the Institutional Review Boards at Columbia University Medical Center, Kaiser Permanente, Brigham and Women’s Hospital and Brown University. All participants provided informed consent prior to data collection.Early life SES indicators included maternal and paternal education and annual family income around the time of each participant’s birth. Highest maternal and paternal educational attainment were categorized into less than high school graduate, high school graduate, trade or technical training or some college including associate degree, and college or higher degrees. We categorized childhood annual family income collected in US dollars into 5, 4 and 3 levels. The analysis using these different categorization of family income yielded the same overall results and we restrict our presentation to the categories of < $5000, $5000-7999 and ≥ $8000. We also considered additional early life data that have been associated with mammographic density , 34. TheWe collected detailed reproductive history data from participants in adulthood and derived the following variables: age at menarche , age at first live birth (in years), parity (nulliparous and parous with number of live births), and menopausal status .2, calculated from self-reported height and weight).We used data on current smoking status , alcohol intake in last 12 months , moderate to vigorous physical activity in the last 3 months (categorized into no exercise and tertiles of exercise in number of minutes per week) and current body mass index  by dividing the dense area by total breast area and multiplying the result by 100, and non-dense area by subtracting dense area from total breast area. We read mammograms in batches of about 50 films, with each batch including films from both cohort sites. All films for a participant and films from sibling sets were read together in the same batch. We re-read 10% of the films from each batch to measure within-batch reproducibility of readings. We also repeated reading on additional 10% of films from every batch to measure batch-to-batch variability. The within-batch correlation coefficient was 0.96 for percent density and the intra-class coefficient for between-batch reliability was 0.95.We used cranio-caudal film mammograms taken closest to the date of interview, and used films from the right breast only if the left breast films were not available. To assess mammographic density, a trained investigator used a computer program (Cumulus) to outline the total breast area and dense tissue area. We converted the number of pixels within the outlined boundaries to cmWe tested whether birth cohort site modified the associations between the three early life SES indicators  and three measures of density  by including an interaction term between the cohort site and each early life SES indicator in the regression models of mammographic density. The interaction terms with early life SES and site were not statistically significant and the inclusion of the interaction term did not significantly improve the model fit. We therefore combined the data across the two cohort sites. As the results for paternal and maternal education at birth were similar in all analyses, we limit the presentation of our results to maternal education only.We examined the association of each early life SES indicator and each mammographic density measure separately, using generalized estimating equation models for multivariable analysis to account for possible correlated data for sibling sets within the study sample. We began by fitting a minimally adjusted model that included age at mammogram and adult BMI. We added to this model other early life factors, followed by the addition of adult SES indicators of income and education. Finally, we added two sets of breast cancer risk factors at later life periods: 1) reproductive factors: age at menarche, age at first birth, menopausal status, and 2) lifestyle factors: smoking status, current alcohol consumption and physical activity. We repeated our final analyses using a combined parental education variable from paternal and maternal education, and stratified our multivariable models separately by race/ethnicity and menopausal status; however, the results of these additional analyses were not different from the overall results, and are therefore not presented.2 for dense area (IQR: 20.5–47.7) and 102.1 cm2 for nondense area (IQR: 46.4–447.7). The distributions of paternal and maternal education were similar with over two-thirds of the participants having parents with high school or less education. Participants had relatively high educational attainment with over 80% obtaining some education beyond high school. Maternal smoking at the time of pregnancy with the study participants was relatively common (40%), while over half of the participants themselves never smoked regularly. Participants had low alcohol intake  and low physical activity levels .Table 2, 95% confidence interval [CI] = −13.3, −3.2, Panel 1, Model 3). Maternal education was not associated with dense area in any models. As compared with maternal education of less than a high school education, maternal educational attainment of college or higher degree was associated with higher percent density after adjustment for age at mammogram and adult BMI , but this association was reduced with adjustment for early life and adult factors in the fully adjusted model . There were no significant differences in percent density for other maternal education categories, or in dense area for other categories of childhood family income. Non-dense area was also not associated with either parental education and childhood family income in any of the models.Table Further adjustment for reproductive and lifestyle factors had minimal influences on the estimates of the associations for early life SES and either measure of density (data not shown).Table In examining early life SES in relation to mammographic density, we aimed to further clarify the role of early life factors in the development of breast cancer risk, and identify possible life-course pathways. Specifically, we examined whether parental education and family income at birth were associated with mammographic density, assessed from mammograms obtained in midlife, after accounting for: 1) birth and childhood characteristics relevant to breast cancer risk, and 2) established risk factors for breast cancer in later life periods. We observed significantly smaller dense area for the highest family income level at birth and higher percent density for the highest parental educational attainment, but only the association between family income and dense area remained after adjustment for factors from different periods of life.Percent density and dense area respectively capture the relative and absolute amount of fibroglandular tissues on mammograms. Both measures have similar positive associations with the risk of breast cancer and most risk factors for breast cancer , 27, 35.The patterns of parental education in percent density parallel well-documented positive associations between adult SES and breast cancer risk –13, whicThe main strengths of our study include the collection of prospective data on early life factors and detailed data on breast cancer risk factors in adulthood, which together with highly reliable measures of mammographic density offer a thorough evaluation of the influence of early life socioeconomic factors on breast cancer risk. We lacked data for exposures during puberty and adolescence, critical life periods for breast cancer risk development, that are characterized by rapid growth, breast development and hormone production and slow differentiation of breast tissue –43. CertIn conclusion, we did not find consistent associations between early life SES and mammographic density in adulthood. The observed associations differed by the type of SES indicator and measure of mammographic density, and were limited to comparisons between the highest and lowest categories of early life SES. Higher parental education predicted higher percent density whereas higher parental family income predicted lower dense breast area, with the direction of the latter association being inconsistent with the known link between higher adult SES and increased breast cancer risk.


Most studies on endocrine-disrupting chemicals and breast cancer have focused on single compounds and have produced inconclusive findings.We assessed the combined estrogenic effects of mixtures of xenoestrogens in serum and their relationship to breast cancer risk.A total of 186 incident pretreatment breast cancer cases and 196 frequency-matched controls were randomly sampled from a large population-based multicase–control study in Spain. The total effective xenoestrogen burden attributable to organohalogenated xenoestrogens (TEXB-α) and endogenous hormones and more polar xenoestrogens (TEXB-β) was determined in serum samples using high-performance liquid chromatography and E-Screen bioassay. Odds ratios for breast cancer comparing tertiles of serum TEXB-α and TEXB-β were estimated using logistic models, and smooth risk trends were obtained using spline models.Cases had higher geometric mean TEXB-α and TEXB-β levels  than controls . The fully adjusted odds ratios for breast cancer  comparing the second and third tertiles of TEXB-α with the first tertile were 1.77  and 3.45 , respectively, and those for TEXB-β were 2.35  and 4.01 , respectively. A steady increase in risk was evident across all detected TEXB-α levels and a sigmoidal trend was observed for TEXB-β. Individual xenoestrogens showed weak and opposing associations with breast cancer risk.This is the first study to show a strong positive association between serum total xenoestrogen burden and breast cancer risk, highlighting the importance of evaluating xenoestrogen mixtures, rather than single compounds, when studying hormone-related cancers.Pastor-Barriuso R, Fernández MF, Castaño-Vinyals G, Whelan D, Pérez-Gómez B, Llorca J, Villanueva CM, Guevara M, Molina-Molina JM, Artacho-Cordón F, Barriuso-Lapresa L, Tusquets I, Dierssen-Sotos T, Aragonés N, Olea N, Kogevinas M, Pollán M. 2016. Total effective xenoestrogen burden in serum samples and risk for breast cancer in a population-based multicase–control study in Spain. Environ Health Perspect 124:1575–1582; http://dx.doi.org/10.1289/EHP157 Malignant breast tumors are the leading cause of cancer in women worldwide in terms of incidence and mortality . DespiteLaboratory studies, specifically rodent models, support the implication of environmental pollutants in breast cancer development . EndocriIn the present study, we measured the combined estrogenic activity of mixtures of xenoestrogens in serum samples and evaluated its relationship to breast cancer risk in a subsample of cases and controls from a large population-based multicase–control study in Spain (MCC-Spain).http://www.mccspain.org) is a population-based multicase–control study conducted between 2008 and 2013 in 12 Spanish provinces to identify environmental, personal, and genetic factors related to five common cancers: breast, prostate, colorectal, stomach, and chronic lymphocytic leukemia. The study design has been previously reported  and n-hexane:methanol:2-isopropanol (40:45:15 v/v) (phase B)], with the most lipophilic compounds eluting in the shortest time. After HPLC fractionation, duplicated dry extracts of each fraction were combined, resuspended in experimental steroid-free medium , and tested for estrogenic activity with the E-Screen bioassay  in serum samples using a standardized bioassay for the combined estrogenic effect of mixtures of xenoestrogens , which hbioassay . The combioassay . Thus, TThe limit of detection for TEXB-α and TEXB-β was 0.1 Eeq pM/mL, which corresponded to the minimum concentration needed to produce a significantly different proliferative effect from that observed in steroid-free control cells. For 6.3% and 3.7% of participants with TEXB-α and TEXB-β determinations below the limit of detection, respectively, a level equal to the limit of detection divided by the square root of 2 was imputed. TEXB-α and TEXB-β levels could not be determined in 44.2% and 35.1% of serum samples, respectively, because MCF-7 cells treated with their extracts grew less than steroid-free control cells, which hampered reading the proliferative effect in the estradiol dose-response curve. For quality control, 10 serum samples were analyzed in triplicate through independent extraction, HPLC fractionation, and E-Screen bioassay. The interassay coefficients of variation for TEXB-α and TEXB-β were 18.5% and 11.1%, respectively.p,p´-dichlorodiphenyldichloroethylene , were quantified using high-resolution gas chromatography with microelectron capture detection and using p-chlorodibenzophenone as the internal standard. The limit of detection for all of these chemicals was set at 0.05 ng/mL, representing the smallest analyte amount that gave a signal-to-noise ratio > 3. For 5.2%, 1.8%, 3.4%, 8.4%, and 2.1% of participants with serum concentrations of PCB-138, PCB-153, PCB-180, HCB, and p,p´-DDE, respectively, below the limit of detection, a level equal to the limit of detection divided by the square root of 2 was imputed. Total cholesterol and triglycerides were enzymatically quantified in 10 μL of serum using a Cobas 400 analyzer (Roche), and total lipids were derived from the short formula based on these measured lipid species , and  species .p,p´-DDE) based on their control distributions.Participants were grouped into tertiles of serum TEXB-α and TEXB-β levels based on their distributions among the controls. Odds ratios and 95% confidence intervals (CIs) for breast cancer comparing the second and third tertiles with the first tertile of serum TEXB-α and TEXB-β were estimated using logistic regression models. We also estimated the odds ratio for women with undetermined estrogenicity in the bioassay compared with all other women with determined estrogenicity. Tests for linear risk trends across serum TEXB-α and TEXB-β tertiles were performed by including an ordinal variable with the median level of each tertile among controls in logistic regression models. To further explore the shape of the dose–response relationships of serum TEXB-α and TEXB-β levels with breast cancer risk, we used restricted quadratic splines for log-transformed TEXB-α and TEXB-β levels with knots at the 10th, 50th, and 95th percentiles of their control distributions (the first knot was set at the 10th percentile to exceed levels below the limit of detection) . We alsoLogistic regression models were fitted with increasing degrees of adjustment. The first model adjusted for province , age (continuous), body mass index (continuous), education level , and serum total lipid levels (continuous). The second model further adjusted for breast cancer risk factors, including smoking status , number of births , age at first birth (continuous), menopausal status , use of hormone replacement therapy (never or ever), previous breast biopsy (no or yes), and family history of breast cancer . Finally, the third model mutually adjusted serum TEXB-α and TEXB-β levels for each other. Effect modifications were contrasted by including interaction terms of serum TEXB-α and TEXB-β tertiles with each of the above covariates in logistic regression models. Analyses were performed using Stata v.13.1 (StataCorp) and R .in situ) and 196 population-based controls with available serum samples for estrogenicity analyses. The mean age and mean body mass index among cases and frequency-matched controls were 59.8 years and 26.3 kg/m2, respectively. Compared with controls, cases were more likely to be nulliparous, ever smokers, and ever users of hormone therapy, and to have lower education levels and higher prevalences of breast biopsies and affected first-degree relatives; however, only the difference in the prevalence of breast biopsies was statistically significant , we excluded 5 prevalent or recurrent cases of breast cancer at baseline interview, 5 cases who initiated chemotherapy or hormone therapy before blood extraction, 1 case who withdrew initial consent, and 15 additional women (7 cases and 8 controls) with insufficient serum samples. Thus, the final sample included 186 incident pretreatment cases of breast cancer  and were virtually uncorrelated with TEXB-β levels . Apart from differences by geographic region, no other significant trend in breast cancer risk factors or serum chemical concentrations was observed across tertiles of serum TEXB-α and TEXB-β levels among controls, in part because of the limited number of control women within each tertile . Serum concentrations of PCB-138, PCB-153, PCB-180, HCB, and  tertile . Comparep,p´-DDE .p for linear trend = 0.003 and 0.001, respectively). Compared with the first tertile, the odds ratios for the second and third tertiles of serum TEXB-α were 1.77  and 3.45 , and those for the second and third tertiles of serum TEXB-β were 2.35  and 4.01 . The increase in breast cancer risk was marked and sustained over all serum TEXB-α levels > 0.5 Eeq pM/mL  for HCB.Individual organohalogenated xenoestrogens contained in the α fraction showed weak and opposing associations with breast cancer risk . In modeIn subgroup analyses, the increased risk for breast cancer in the third versus the first tertile of serum TEXB-α tended to be higher in women with normal weight and those with a family history of breast cancer , although none of these effect modifications was statistically significant . The posThis is the first study to show a graded positive association between serum total xenoestrogen burden, as determined by the α fraction of the TEXB bioassay, and breast cancer risk. Women in the third tertile of serum TEXB-α had a 3.45-fold increase in breast cancer risk compared with those in the first tertile. The β fraction of the TEXB bioassay was also positively associated with the risk for breast cancer; this result was somewhat expected given that circulating endogenous estrogens were included in this fraction. Finally, none of the individual organohalogenated xenoestrogens analyzed in this study was significantly associated with breast cancer risk.Most studies in this field have focused on serum or adipose concentrations of a single chemical or a small number of chemicals, ignoring the cumulative effects of mixtures. Recognizing this limitation, the World Health Organization Report has concluded that it is critical to move beyond the analysis of one chemical at a time to explore the effects of EDC mixtures . The TEXp,p´-DDE concentrations between cases and controls, and none of these single compounds was positively correlated with serum TEXB-α levels among controls, reflecting their modest contribution to the total xenoestrogen burden. Similarly, a previous study reported no correlation between individual organohalogenated xenoestrogens and their combined estrogenic activity in adipose tissue (Most women in this study (83.5%) had detectable serum concentrations of all of the measured organochlorine chemicals, reflecting the ubiquity of their exposure in the general population. We found no differences in PCB, HCB, or e tissue .The extensive HPLC fractionation performed before the TEXB bioassay was designed to separate organohalogenated lipophilic xenoestrogens in the α fraction from endogenous hormones and from more polar xenoestrogens in the β fraction . In our Although the risk for breast cancer increased progressively across all detected TEXB-α levels > 0.5 Eeq pM/mL, the association for TEXB-β followed a sigmoidal trend, with a sharp increase in risk between 2 and 40 Eeq pM/mL and a downturn at higher levels. Nonmonotonic responses are remarkably common in studies of natural hormones and EDCs . The upwAlthough the present study had limited power to detect effect modifications, we observed somewhat greater effects of TEXB-α on breast cancer risk in women of normal weight and in those with a family history of breast cancer. A stronger effect in leaner women was also evident in a previous case–control study using the TEXB bioassay in adipose tissue ; in thatContrary to previous findings in adipose tissue extracts , the comThe strengths of this study include the population-based case–control design and the use of a reliable biomarker for the combined estrogenic effect of EDC mixtures. However, several limitations must be mentioned. First, the response rate among population controls was moderate, with higher participation rates among women with higher levels of education. To control for this potential selection bias, all analyses were adjusted for education level. Second, owing to the case–control design, serum samples were collected after diagnosis in breast cancer cases, which might have led to a reverse causation bias if serum concentrations of hormones or xenoestrogens had changed after disease onset. To minimize the potential for reverse causation, we restricted the analysis to patients with incident breast cancer who did not receive neoadjuvant chemotherapy or hormone therapy before blood extraction. However, because most growing breast tumors are estrogen-demanding, serum estrogen levels might have decreased after disease onset, leading to a potential dilution in the associations, particularly for TEXB-β because endogenous hormones have higher binding affinity to estrogen receptors and shorter biological half-lives than xenoestrogens. Third, adipose tissue extracts were not collected in our study, and TEXB in serum samples was taken as a surrogate of the overall estrogenic activity at the mammary gland. Although many EDCs are lipophilic and accumulate in breast fatty tissue, their concentrations in serum are relatively low and depend on serum lipid content. For this reason, all analyses relating serum TEXB levels to breast cancer risk were adjusted for serum total lipids. Fourth, estrogenicity could not be determined in over one third of the serum samples because breast cancer cells treated with their extracts grew less than did steroid-free control cells in the TEXB bioassay. Although there is no clear explanation for this lack of growth, samples with undetermined estrogenicity had significantly higher levels of all measured organohalogenated xenoestrogens; therefore, they might also have had elevated concentrations of other unmeasured common-source xenobiotics that prevented or hampered cellular growth. Nevertheless, because undetermined estrogenicity was unrelated to case–control status, our analyses based on determined samples will provide an unbiased estimate of the association between TEXB and breast cancer risk. Finally, the inherent time-consuming and serum-demanding characteristics of the TEXB bioassay, together with the substantial proportion of undetermined samples, greatly limited the effective sample size and power of the present study, which precluded more extensive analyses according to tumor subtypes.The combined estrogenic activity of mixtures of organohalogenated xenoestrogens in serum samples was positively associated with breast cancer risk, even though no single compound showed a significant effect when analyzed individually. The increase in risk was strong and progressive across all detected estrogenic levels. Our findings show the importance of evaluating mixtures of EDCs, rather than single chemicals, in epidemiological studies on hormone-related cancers. This study provides new evidence linking breast cancer to combined exposures to EDCs, something to be considered by policy agencies in charge of controlling their production and distribution.


To determine the frequency of breast cancer (BC) patients with hereditary risk features in a wide retrospective cohort of patients in Spain.Sporadic breast cancer group (R0); Individual risk group (IR); Familial risk group (FR); Individual and familial risk group (IFR) with both individual and familial risk criteria.a retrospective analysis was conducted from 10,638 BC patients diagnosed between 1998 and 2001 in the GEICAM registry “El Álamo III”, dividing them into four groups according to modified ESMO and SEOM hereditary cancer risk criteria: 7,641 patients were evaluable. Of them, 2,252 patients (29.5%) had at least one hereditary risk criteria, being subclassified in: FR 1.105 (14.5%), IR 970 (12.7%), IFR 177 (2.3%). There was a higher frequency of newly diagnosed metastatic patients in the IR group . In contrast, in RO were lower proportion of big tumors (> T2) , nodal involvement  and lower histological grades (20.9% G3 for the R0 vs 29.8%) when compared to patients with any risk criteria.Almost three out of ten BC patients have at least one hereditary risk cancer feature that would warrant further genetic counseling. Patients with hereditary cancer risk seems to be diagnosed with worse prognosis factors. Breast cancer is the most frequent malignancy in women . In SpaiIt is widely described in the literature that up to 25% of breast cancer patients have a familial/hereditary background, that can be explained through a genetic condition only in a small percentage . PopulatRecently, the Spanish Medical Oncology Society (SEOM) have suggested clinical criteria for genetic test selection of hereditary breast cancer patients through a clinical guideline . In the The implications of genetic testing are nowadays going beyond the prevention and early detection area, influencing also therapeutic decisions with the use of specific treatments, such as oral PARP inhibitors or platinum-based regimens. Currently, several clinical trials are ongoing for different clinical scenarios with these treatments, from the metastatic disease to the adjuvant setting, being in the spotlight of the oncology breast cancer community .In summary, a better understanding of the epidemiological landscape of breast cancer patients with hereditary risk features is of interest.El Álamo Project is a retrospective observational study that includes 26,658 breast cancer patients diagnosed between 1990 and 2001 across 43 Spanish Hospitals and distributed in three cohorts: El Alamo I with 4,532 patients diagnosed between 1990 and 1993, El Alamo II with 10,849 patients diagnosed between 1994 and 1997 and El Alamo III with 11,277 patients diagnosed between 1998 and 2001. El Alamo project has the aim to describe patterns of presentation, management and outcomes of breast cancer in Spain [El Álamo III, included for the first time the familial background of patients, in addition to clinical and personal features linked to hereditary risk . With more than eleven thousand invasive breast cancer patients diagnosed in 11 of the 17 Spanish regions [ regions , this isThe objectives of this study are to analyze the frequency and clinical/pathological characteristics of Spanish invasive breast cancer patients with hereditary risk features.All procedures performed in this study were in accordance with the ethical standards of the participant institutions and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. “Comité Etico de Investigación Clínica del Area 1” IRB reviewed and approved the Alamo project.El Álamo III project that included 11,277 breast cancer patients. El Alamo focused on female breast cancer and only some centers recruited a small number of male patients , so they were excluded from this analysis. Non invasive carcinoma breast cancer cases  were also excluded.This is a retrospective analysis from Questionnaires including data regarding individual tumor information and familial features were completed by the clinical investigators  histology and/or bilateral (synchronous or metachronous) breast cancer or breast cancer at any age together with ovarian cancer. The familial criteria were: the presence of first or second degree relatives with the following features: 3 relatives (including the patient) with breast and /or ovarian cancer or 2 relatives (including the patient), if the relative fulfill any of the individual criteria above mentioned, regardless degree; or 2 relatives (including the patient) if is first degree and diagnosed with breast and/or ovarian cancer.Sporadic breast cancer group (R0) (Control group) without individual or familial risk criteria; Individual risk group (IR) with no familial or not determined (ND) familial risk, but with individual risk criteria; Familial risk group (FR) with no individual or ND individual risk, but with familial risk criteria; Individual and familial risk group (IFR) with both individual and familial risk criteria. Global hereditary risk group (GHR) comprises the three last categories, namely IR or FR or IFR.According to the individual and familial criteria, patients were divided into 4 different subgroups: ® version 21 (IBM corporation).Chi-square and unpaired t student/Anova were used to compare categorical and continuous variables respectively. All statistical tests had a significance level of 0.05 unless stated otherwise. Data were analysed using SPSSFrom 1998 to 2001, 10,638 women with invasive breast cancer were included in the study. Patients who had enough information to be sub-classified as one of the four risk subgroups accounted for 7,641 (71.8%). The individual and familial risk criteria for hereditary breast cancer of the global sample are described in Of these evaluable patients, 2,252 patients (29.5%) had at least one hereditary risk criteria, constituting the global hereditary risk group (GHR). The 5,389 (70.5%) remaining patients, with no risk features, were considered the R0 group . Table 3In the univariate analysis we found that R0 group presented a lower proportion of big tumors (≥ T2) than the GHR group , a lower proportion of nodal involvement  and lower histological grades . Metastases at diagnosis were present in similar proportion in both groups . As expected, a higher proportion of TNBC was found in the GHR group, given that the TN phenotype is included in the criteria to define hereditary cancer . In ordeComparing each specific GHR subtype with sporadic cases, we observed that the differences seen before are only observed for the IR group. In contrast, similar clinic-pathological features were seen between R0 and IFR and FR groups respectively . MoreoveAccording to this large and representative sample of the Spanish breast cancer landscape, we can say that three out of ten patients have, at least, one hereditary breast cancer risk feature, and, in consequence, could be candidate for genetic testing and counselling. Overall, patients with hereditary cancer risk features have larger tumors and more frequently nodal involvement in comparison to patients without hereditary cancer risk features, while both subgroups have a similar rate of distant metastases at initial diagnosis. However, these differences probably are related to the greater aggressiveness observed in patients fulfilling the individual criteria. Interestingly, when patients with TNBC were excluded for this analysis, presence of nodal involvement and higher grades, although not tumor size, remained higher in patients with hereditary risk features.Strong points of this study are the large number of patients analyzed and the representativeness’ of Spanish population, since two thirds of the regions are represented. Few studies exist in Spain analyzing the frequency of different familial cancer from a population point of view, with the exception of melanoma , pancreaAnother weakness, in order to classify patients in a given risk group, is the fact that nearly 60% of patients had an unknown HER2 status, in consequence, a substantial number of patients, could not be evaluated regarding the TN phenotype, one of the major risk factors for hereditary breast cancer. Within the time-frame of data collection (patients diagnosed from 1998 to 2001), although the role of HER2 was well known as a prognostic factor, the determination of this biomarker was not widely used, given that appropriate targeted therapy was only available for metastatic patients.Patients with hereditary cancer risk features have worse pathological risk factors, according to T and N status, and to histological grade, all well-known bad prognosis factors. Data of prognosis from patients with known BRCA 1 and 2 mutations are conflicting in literature. A recent meta-analysis did not detect differences in breast cancer specific survival rate in BRCA2 mutation carriers when compared to sporadic ones . I contrOne could think that patients concerned with their familial background are more prone to intensive surveillance, both by themselves and by their health care givers, what should be translated into earlier diagnostic presentations, something that is not reflected by our data. This is true in other familial cancers, such as melanoma, where patients at familial risk in Spain present with better prognosis pathological factors . HoweverIt is important to analyze if our data are comparable to other countries. Our results are according to what is described in general literature . HoweverFinally, the practical consequences of our findings should be taken into consideration. In general, it is estimated that, according to different institutional series in Spain and western countries, BRCA 1 or 2 mutations are present from 7% to 20% of selected and unselected patients in western countries –27. With


Most mammography screening programs are not individualized. To efficiently screen for breast cancer, the individual risk of the disease should be determined. We describe a model that could be used at most mammography screening units without adding substantial cost.The study was based on the Karma cohort, which included 70,877 participants. Mammograms were collected up to 3 years following the baseline mammogram. A prediction protocol was developed using mammographic density, computer-aided detection of microcalcifications and masses, use of hormone replacement therapy (HRT), family history of breast cancer, menopausal status, age, and body mass index. Relative risks were calculated using conditional logistic regression. Absolute risks were calculated using the iCARE protocol.Comparing women at highest and lowest mammographic density yielded a fivefold higher risk of breast cancer for women at highest density. When adding microcalcifications and masses to the model, high-risk women had a nearly ninefold higher risk of breast cancer than those at lowest risk. In the full model, taking HRT use, family history of breast cancer, and menopausal status into consideration, the AUC reached 0.71.Measures of mammographic features and information on HRT use, family history of breast cancer, and menopausal status enabled early identification of women within the mammography screening program at such a high risk of breast cancer that additional examinations are warranted. In contrast, women at low risk could probably be screened less intensively.The online version of this article (doi:10.1186/s13058-017-0820-y) contains supplementary material, which is available to authorized users. Risk prediction models for breast cancer use lifestyle factors , family Computer-aided detection (CAD) is designed to support radiologists at mammographic screening units in diagnosing early breast cancer. These software can indicate suspicious microcalcifications and masses. We used fully automated CAD and breast density measurement systems and predicted the probability for a woman with a negative mammogram result to be diagnosed with breast cancer within 2 years. We wanted to create an easily implementable prediction tool for individualized breast cancer screening without adding substantial cost or effort to the health care system.We merged established risk factors, such as use of hormone replacement therapy (HRT), family history of breast cancer, menopausal status, body mass index (BMI), and mammographic density with microcalcifications and masses, using U.S. Food and Drug Administration-approved CAD software . We wereIn Sweden, women aged 40–74 years are invited every 18–24 months to the national screening program . Women wcBIRADS.Full-field digital mammograms from the mediolateral oblique (MLO) and craniocaudal (CC) views of the left and right breasts were used to measure mammographic density using the area-based STRATUS method  identifies suspicious microcalcifications and masses and presents the findings to the radiologist or as digital text information. Raw mammograms of the MLO and CC views of right and left breasts were used to identify microcalcifications and masses. On the basis of the distribution of microcalcifications among control subjects, the number of microcalcifications was categorized into five groups: 0, 1–10, 11–20, 21–40, and >40 microcalcifications. The number of masses was given as the true number. Level of density and number of microcalcifications and masses, as well as the differences in density and number of microcalcifications and masses between breasts, were used in the model.On the basis of self-reported information, dichotomous variables were created for current use of HRT, history of breast cancer in first-degree relatives, and menopausal status. Current use of HRT was defined as use within the last 12 months. BMI and age were assessed at the time of study entry, which was the time the baseline mammogram was taken. Screening-detected breast cancer was defined as breast cancer diagnosed within 3 months of a screen. An interval breast cancer was defined as a breast cancer diagnosed at least 3 months after a negative screen but before the date of the next scheduled screen .Descriptive statistics were presented for participant characteristics and to describe mammographic features in the tumor breast side  versus the nontumor breast side in the cases. Differences between the breasts were calculated without assuming knowledge of the tumor breast side. These absolute differences were calculated as the standard deviation (SD) of the two breasts for each mammographic feature and were used as continuous predictors in the final model.The continuous predictors in the conditional logistic regression model were tested for the best transformation using the Sauerbrei method  with fraAbsolute risks were calculated using the Individualized Coherent Absolute Risk Estimator (iCARE) package in R . The SweUsing the same data, a cross-validated AUC was calculated and compared with values generated by the established Tyrer-Cuzick and Gail risk models. The numbers of invasive and in situ cases that were diagnosed during follow-up were tabulated by quintile of the 2-year absolute risks predicted at baseline. The increase in number of diagnosed cases by quintile of baseline risk was calculated and tested for linear trend.All statistical tests were two-sided at a significance level of 0.05 and calculated using SAS version 9.4 software  for descriptive statistics and relative risks. Absolute risks were evaluated with R 3.3.0 software using the iCARE package 1.0.0.p = 0.05) and had a family history of breast cancer   in the breast corresponding to the tumor side in cases (Table p = 2.5 × 10−7). Comparing density pairwise between the tumor side and nontumor side in cases showed a mean difference of 1.1% (p = 3.4 × 10−3)  and 12.2% in control subjects (p = 4.0 × 10−20) and the contralateral side in cases and control subjects . The comparison between tumor and nontumor sides in cases showed a mean difference of 2.7 microcalcifications (p = 1.9 × 10−3)  but not on the contralateral side in cases and control subjects. The pairwise comparison between tumor and nontumor sides in cases showed a mean difference of 0.26 masses (p = 9.2 × 10−3) , microcalcifications (p = 4.0 × 10−16), and masses (p = 0.02).In the lower part of Table Relative risks of breast cancer within 3 years from a negative mammographic screening result at baseline were calculated using two models (Table n = 383) and in situ (n = 50) cancers . When stratifying on mode of detection , we observed that all mammographic features, including the absolute differences between the breasts, were more likely to identify interval cancers than screening-detected cancers  using mammographic density adjusted for BMI and age at mammography, 0.64 (95% CI 0.62–0.67) when adding family history of breast cancer and HRT use, and 0.71 (95% CI 0.69–0.73) after adding microcalcifications and masses (Table p = 0.04). The corresponding numbers for the Tyrer-Cuzick and Gail models were 35.3 cases (p = 0.01) and 15.1 cases (p = 0.14), respectively  of the total number of patients with breast cancer (n = 570) who developed breast cancer during the study follow-up were predicted at baseline in the highest quintile of the 2-year absolute risk score . In future studies, it will also be of importance to understand the relationship between the localization of the mammographic features and subsequent tumors and how different cut-off points for defining interval cancer will influence the risk estimates.The KARMA cohort is large, but the follow-up time is just some few years. The obvious weakness of our study is the low number of breast cancer cases. For women with missing data on a risk factor, imputation was performed according to the protocol established with each risk model. We calculated a cBIRADS score that mimics the established BI-RADS score to help clinical implementation, but we do not know how the true BI-RADS score would influence our model. As a unique strength, we built our model on one of the few existing population-based prospective screening cohorts with detailed information on factors that possibly influence the risk of breast cancer.Our model includes three mammographic features that could easily be derived from raw mammograms. By adding information on some few established risk factors, it has the potential to individualize screening and improve clinical care by identifying women in need of additional examination procedures. At the same time, there may be a substantial proportion of women who will have very little benefit from mammography screening, owing to their low risk of breast cancer.


There was no significant increase in serous tumours, a modest increase in mucinous tumours, but a substantial increase in endometrioid  and clear‐cell tumours . Among parous women, each additional birth was associated with an overall 6% reduction in ovarian cancer risk; this association also varied by histotype (p = 0.0006), with the largest reduction in risk for clear‐cell tumours  and weak, if any, effect for endometrioid, high‐grade serous, or mucinous tumours. We found little association with age at first or last birth. There was about a 10% risk reduction per 12‐months breastfeeding , with no significant heterogeneity by histotype, but statistical power was limited. In this large prospective study, ovarian cancer risk associated with parity varied substantially by tumour histotype. Nulliparity was associated with a substantially greater overall risk than expected from the effect of a single birth, especially for clear cell and endometrioid tumours, perhaps suggesting that infertility is associated with these histotypes.Ovarian cancer risk is known to be reduced amongst women who have had children, but reported associations with breastfeeding are varied. Few studies have had sufficient power to explore reliably these associations by tumour histotype. In a prospective study of 1.1 million UK women, 8719 developed ovarian cancer during follow‐up. Cox regression yielded adjusted relative risks (RRs) overall and by tumour histotype amongst women with different childbearing patterns. Nulliparous women had a 24% greater ovarian cancer risk than women with one child, with significant heterogeneity by histotype ( What's new?Evidence suggests that women who bear children are at decreased risk of ovarian cancer. Whether breastfeeding contributes to this reduction in risk remains unclear. In this prospective study, each birth in parous women reduced overall ovarian cancer risk by 6%, while nulliparity was associated with a 24% increase in risk compared to women with one child. Among parous women, risk reduction varied by tumour type, with the greatest reduction observed for clear cell tumours and almost no reduction detected for serous tumours. Ovarian cancer risk was additionally reduced by 10% for every 12 months of breastfeeding. Information on data access for the Million Women Study is available at www.millionwomenstudy.org/data_access/. Follow‐up was via record linkage to the National Health Service (NHS) Central Register : all participants were ‘flagged’, so that the study investigators were routinely notified of cancer registrations or deaths. Cancer diagnoses were coded by the NHS Central Register using the 10th revision of the WHO International Classification of Diseases (ICD‐10),The Million Women Study is a cohort of 1.3 million UK women, aged 56 (standard deviation (SD) 5) at the time of recruitment (1996–2001). Participants were invited to take part in the study via letters sent with invitations to attend mammographic screening for breast cancer. The women completed questionnaires at recruitment, including information on reproductive, sociodemographic, and health factors. The study methods have been described in detail elsewhere,All participants gave written consent to follow‐up at recruitment. The study received ethical approval from the Oxford and Anglia Multi‐Centre Research Ethics Committee (MREC 97/01).Information on reproductive factors was taken from the recruitment questionnaire. Parity was defined by the number of full‐term pregnancies, derived from the response to the question: ‘How many children have you had? ’ and the dates of birth for each child. The latter information, as recorded by the women, was also used for analyses of the woman's age at first or last birth.Total duration of breastfeeding was calculated as the sum of the duration of breastfeeding each child, as reported in response to the question: ‘for how many months did you breastfeed each child, if at all?’ Average duration of breastfeeding per child breastfed was calculated as the sum of duration of breastfeeding each child, divided by the number of children breastfed.The outcome of interest was ovarian cancer (ICD‐10 C56). For analyses of histotype, the outcome was split into the four most common specific histological groups: serous , mucinous (ICD‐O codes 8470–8490), endometrioid , and clear cell ; the remainder of cases were classified as other/unspecified.For some analyses, serous and mucinous tumours were further subdivided, as previously described.n = 44,775); if they reported a previous bilateral oophorectomy  ; or if they had missing information on parity . The remaining women  contributed person‐years from the date of recruitment into the study until the date of registration for ovarian cancer, the date of death, or last date of follow‐up (31 December 2014)—whichever was soonest. Women were censored at diagnosis of any cancer (other than non‐melanoma skin cancer). About 1% of participants were lost to follow‐up by the end of 2014 and such women are censored at the date when they were lost, contributing person‐years until then.Women were excluded if they had been diagnosed with any invasive cancer  prior to recruitment  of ovarian cancer according to parity, age at first or last birth, and breastfeeding. Attained age was the underlying time variable. There was no evidence of significant violation of the proportional hazards assumption, as assessed by graphical methods and tests based on Schoenfeld residuals.In analyses of parity, we first estimated the RR for parous versus nulliparous women. We then estimated the RR for nulliparous vs. para‐1 women and the RR per birth in parous women by simultaneously modelling parity as a binary variable indicating nulliparous women and as a linear trend in parous women . Heterogeneity of these two RRs was tested using a contrast test. In categorical analyses of parity and ovarian cancer risk by histological type among parous women, parity was categorised into 3 groups  due to the small number of cases of less common subtypes with high parity.Analyses of the timings of births were restricted to parous women with complete information on age at first and last births, and were stratified by parity . Both age at first birth, and age at last birth, were treated as ordered categorical variables . Tests for trend used the categorical variables coded with the median age in each age‐group . Separate models were run for age at first birth and age at last birth.Analyses of breastfeeding used the total duration of breastfeeding , or the average duration of breastfeeding per child breastfed, as ordered categorical variables . Tests for trend used the categorical variable coded by the median duration of breastfeeding in each group . For analyses of breastfeeding, women were excluded if they had missing information on the duration of breastfeeding . All breastfeeding analyses were restricted to parous women, and stratified by parity .2, 25–29 kg/m2, 30+ kg/m2), smoking history , and quintiles of socioeconomic status based on the Townsend deprivation index.All analyses were stratified by geographical region (10 regions corresponding to the areas covered by the cancer registries), and adjusted for use of the oral contraceptive pill , tubal ligation , family history of breast cancer , hysterectomy , use of menopausal hormones , body mass index (BMI)  were explored as potential confounders, but were not included in the final model as their inclusion made no appreciable difference to the main estimate of effect.Tests of heterogeneity in the relationship between ovarian cancer and parity or breastfeeding by histotype were performed using a competing risks approach.For analyses where comparisons were made between more than two exposure categories, relative risks are presented in figures with group‐specific confidence intervals (95% g‐s CI) for the log risk in each group .A total of 1,144,762 women were included in this analysis, with a mean age at recruitment of 56.1 years (SD 4.8). Overall, 89% of participants reported at least one full‐term pregnancy. Table n = 3916), 15% mucinous (n = 896), 11% endometrioid (n = 622) and 7% clear cell (n = 414). Of the remaining cases (n = 2871), the majority were recorded as unspecified carcinoma or adenocarcinoma.During 16.7 million person‐years of follow‐up (an average of 14.6 [SD 3.7] years per woman), 8719 incident ovarian cancers were reported, with a mean age at diagnosis of 65.6 years (SD 6.5). Of the 5848 epithelial cancers with the four most common tumour types, 67% were serous , after adjustment for age, region, socioeconomic status, tubal ligation, family history of breast cancer, hysterectomy, BMI, smoking, and use of contraceptive or menopausal hormones.The reduction in risk of ovarian cancer was greatest for the first birth, with almost a 20% reduction in risk compared to nulliparous women ; there were further reductions in risk with subsequent births, but of smaller magnitude (<10% per birth)  and almost a 70% higher risk of clear cell tumours , with a smaller increase in risk of mucinous tumours , and a small and non‐significant increase in risk of serous tumours, the most common histotype  . There was no significant heterogeneity in the association between low‐grade and high‐grade serous tumours (heterogeneity: p = 0.5), or between mucinous borderline tumours and mucinous carcinoma (heterogeneity: p = 0.3).Given the substantial difference in the proportional reduction in risk associated with the first compared to subsequent births, it was not valid to model the effects of parity simply as a relative risk per birth including nulliparous women. Instead, a model was used that allowed for distinct relative risks to be estimated for nulliparous women compared to para‐1 women, and for additional births amongst parous women. In this model, nulliparous women had a 24% higher risk of ovarian cancer than women with one child , and there was significant heterogeneity by histotype (heterogeneity: p = 0.01). Nulliparous women had about a 50% higher risk of endometrioid , with a 25% reduction in risk per birth of clear cell tumours, , a lesser reduction in the risk of endometrioid tumours , a relatively small reduction in the risk of serous tumours , and no significant reduction in risk of mucinous tumours  . When serous tumours were divided, there was a significant reduction in risk of serous borderline and low‐grade carcinoma , but not of high‐grade serous carcinoma  (heterogeneity: p = 0.01). There was no significant heterogeneity in the association between mucinous borderline tumours and mucinous carcinoma (heterogeneity: p = 0.4).Among parous women, each additional birth was associated with a 6% reduction in risk of ovarian cancer on average . This association was heterogeneous by histotype (heterogeneity: p = 0.0003). There was no significant trend seen with increasing age at first or last birth, either for ovarian cancer overall, or for the four main histotypes , 68%  reported ever breastfeeding, with a mean duration of 8.1 (SD 9.4) months overall, or an average of 3.8 (SD 3.5) months per child breastfed. Women who reported breastfeeding (of any duration) had a small but significant reduction in the risk of ovarian cancer overall, compared to women who never breastfed . This was most evident amongst women who reported a total duration of breastfeeding of ≥6 months  . A similar trend in reduced risk of ovarian cancer was seen with increasing duration of breastfeeding per child, although confidence intervals were wide in some of the longer duration groups .Amongst parous women with information on breastfeeding , 68% 55,695 repo), 68% 55,695 repop < 0.001), and a 14% reduction in the relative risk of ovarian cancer per 6‐month increase in duration of breastfeeding per child , with no significant heterogeneity by histotype , but statistical power was limited , and the virtually complete follow‐up and ascertainment of incident ovarian cancer, with only 1% of the cohort being lost to follow‐up over 14 years. A weakness is the relatively short durations of breastfeeding seen in our population: among those who reported breastfeeding, the median average duration was 3 months per child breastfed (mean: 3.8 months), and details of whether breastfeeding was exclusive to other forms of intake for the child were lacking. Another potential weakness was the lack of central histopathology review. However, as any potential misclassification of tumour histotypes would tend to blur differences, the observed variation in risk by ovarian cancer histotype is unlikely to be due to misclassification, and may even be under‐estimated here.The substantially greater risk of endometrioid and clear cell tumours seen in nulliparous women might be at least partly related to infertility. Certain conditions may both reduce a woman's fertility and increase her risk of specific histotypes of ovarian cancer. Endometriosis may be an example of one such condition, as it reduces fertility,We do not have information on which of our participants were nulliparous because of infertility/subfertility, as opposed to personal choice or social circumstances. However, several other studies have shown that infertility is significantly more common amongst nulliparous women than amongst parous women.There remains considerable uncertainty about the mechanism for the reduced risk of ovarian cancer seen with parity and breastfeeding, assuming it is causal—other than possible confounding by causes of infertility or subfertility. Popular hypotheses include the possibilities that pregnancy and lactation act by interrupting the pro‐inflammatory environment of incessant ovulation,In this largest prospective study of ovarian cancer to date, we found that the known reduced overall risk of ovarian cancer with increasing parity varies in magnitude between the first and subsequent births, and varies between different ovarian cancer histotypes. The findings are consistent with hypotheses that the different histotypes have different aetiologies, and that there are different effects of infertility and of births on ovarian cancer risk.


While a range of common genetic variants have been identified to be associated with risk of sporadic breast cancer in several Western studies, little is known about their role in South Asian populations. Our objective was to examine the association between common genetic variants in breast cancer related genes and risk of breast cancer in a cohort of Sri Lankan women.A case-control study of 350 postmenopausal women with breast cancer and 350 healthy postmenopausal women was conducted. Genotyping using the iPLEX GOLD assay was done for 56 haplotype-tagging single nucleotide polymorphisms (SNPs) in 36 breast cancer related genes. Testing for association was done using an additive genetic model. Odds ratios and 95% confidence intervals were calculated using adjusted logistic regression models.XRCC2), rs6917 (PHB), rs1801516 (ATM), and rs13689 (CDH1)] were significantly associated with risk of breast cancer. The rs3218550 T allele and rs6917 A allele increased breast cancer risk by 1.5-fold and 1.4-fold, respectively. The CTC haplotype defined by the SNPs rs3218552|rs3218550|rs3218536 on chromosome 7 (P = 0.0088) and the CA haplotype defined by the SNPs rs1049620|rs6917 on chromosome 17 (P = 0.0067) were significantly associated with increased risk of breast cancer. The rs1801516 A allele and the rs13689 C allele decreased breast cancer risk by 0.6-fold and 0.7-fold, respectively.Four SNPs [rs3218550  contains supplementary material, which is available to authorized users. Breast cancer is a multifactorial disease that results from the association between various genetic, environmental, hormonal and lifestyle factors . It is tFGFR2 gene at chromosome 10q; rs889312 near MAP3K1 at 5q; rs3803662 located between CASC16 and the LOC643714 gene at 16q; intronic variant rs3817198 located in LSP1 at 11p and rs13281615 located at 8q24 in a region lacking any annotated genes . They demonstrated the functional significance of this intronic variant using a combination of luciferase reporter gene assays and electrophoretic mobility shift assays. Similarly, Mazhar et al. reported that two intronic variants of FGFR2  and a non-coding transcript of CASC16 [rs3803662 (P = 0.012)] were related to sporadic breast cancer in Pakistani women ; rs6917,NC_000017.11:g.49404181G > A,NG_023046.1:g.15700C > T,NM_001281496.1:c.*811C > T [prohibitin-1 gene (PHB)/17q21.33]; rs1801516,NC_000011.9:g.108175462G > A,NG_009830.1:g.86904G > A,NM_000051.3:c. *5557G > A,NP_000042.3:p.Asp1853Asn [ataxia telangiectasia-mutated (ATM)/11q22.3]; and rs13689,NC_000016.9:g.68868522 T > C,NG_008021.1:g.102328 T > C,NM_001317184.1:c.*1120 T > C [E-cadherin gene (CDH1)/16q22.1].Four SNPs showed significant association with breast cancer:P = 0.009837, OR = 1.525, 95% CI = 1.107–2.101] and rs6917  were found to be significantly associated with breast cancer. Two other SNPs: rs1801516  and  also had significant association with breast cancer (Table XRCC2 gene on chromosome 7 (P = 0.008762) and the CA haplotype defined by the SNPs rs1049620|rs6917 on the PHB gene on chromosome 17 (P = 0.006718) were significantly associated with increased risk of breast cancer (Table The SNPs rs3218550   [P = 0.03]  and for the homozygous variant,   . Howevertibility . The pubnificant . Accordinificant . These fr of p53 . FunctioOur study had several strengths, including an adequate sample size from a homogenous ethnicity , thus minimizing any bias arising from population stratification. Study limitations include the fact that the analysis did not take into consideration probable differences in lifestyle factors and the selected SNPs may not give as comprehensive a view of genetic variation as sequencing does. It is possible that the other SNPs which showed a null association with breast cancer either do not modify the susceptibility to breast cancer in the Sri Lankan population or their effects are minimal and can be detected only with larger study samples. We plan to address these issues in subsequent studies. A detailed analysis of the phenotypic and clinical characteristics of this cohort in relation to the genotypic results is the subject of another study.XRCC2, PHB, CDH1 and ATM genes, respectively may influence susceptibility to breast cancer among Sri Lankan postmenopausal women. Considering the vital functional roles of XRCC2 and PHB genes in HRR and cell cycle regulation, the significant differences in genetic susceptibility to breast cancer in the Sri Lankan population observed with the SNPs rs3218550 and rs6917 and the haplotypes defined by them may indicate a true association. However, the exact biological mechanisms by which these polymorphisms regulate overall breast cancer risk needs further evaluation using functional studies. These findings have important implications as data from multiple breast cancer susceptibility alleles may be pooled together to detect women at varying levels of breast cancer risk. This sort of stratification could help guide preventive and screening strategies. Thus, these results may have potential implications in the early detection, prevention and treatment of sporadic breast cancer patients and deserve further investigation through functional assays.The findings of this study indicate that common genetic variations in the Additional file 1:Table S1. List of haplotype-tagging single nucleotide polymorphisms selected for genotyping. Table S1 shows the list of haplotype-tagging single nucleotide polymorphisms which were selected for genotyping including detailed information about the genes, chromosomes, locations and putative functional scores of the genetic variants. (DOCX 23 kb)Additional file 2:Table S2 Relationship between the genotyped single nucleotide polymorphisms and breast cancer risk. Table S2 shows the list of haplotype-tagging single nucleotide polymorphisms which were genotyped in the study cohort and their association with breast cancer risk. (DOCX 19 kb)


There are well-known associations of ionizing radiation with female breast cancer, and emerging evidence also for male breast cancer. In the United Kingdom, female breast cancer following occupational radiation exposure is among that set of cancers eligible for state compensation and consideration is currently being given to an extension to include male breast cancer.We compare radiation-associated excess relative and absolute risks of male and female breast cancers.Breast cancer incidence and mortality data in the Japanese atomic-bomb survivors were analyzed using relative and absolute risk models via Poisson regression.We observed significant (p ≤ 0.01) dose-related excess risk for male breast cancer incidence and mortality. For incidence and mortality data, there are elevations by factors of approximately 15 and 5, respectively, of relative risk for male compared with female breast cancer incidence, the former borderline significant (p = 0.050). In contrast, for incidence and mortality data, there are elevations by factors of approximately 20 and 10, respectively, of female absolute risk compared with male, both statistically significant (p < 0.001). There are no indications of differences between the sexes in age/time-since-exposure/age-at-exposure modifications to the relative or absolute excess risk. The probability of causation of male breast cancer following radiation exposure exceeds by at least a factor of 5 that of many other malignancies.There is evidence of much higher radiation-associated relative risk for male than for female breast cancer, although absolute excess risks for males are much less than for females. However, the small number of male cases and deaths suggests a degree of caution in interpretation of this finding.Little MP, McElvenny DM. 2017. Male breast cancer incidence and mortality risk in the Japanese atomic bomb survivors – differences in excess relative and absolute risk from female breast cancer. Environ Health Perspect 125:223–229; http://dx.doi.org/10.1289/EHP151 Female breast cancer is the most commonly occurring cancer among women in developed and developing regions of the world . Male brFemale breast cancer has been associated with exposure to moderate and high doses (> 100 mGy) of ionizing radiation in the Japanese atomic bomb survivors Life Span Study (LSS) cohort and in women who received radiotherapy . A pooleIn the United Kingdom, the Industrial Injuries Advisory Council  is curreIn this paper, we analyze male and female breast cancer incidence and mortality in the latest versions of the LSS incidence  and mortThe LSS breast cancer incidence data used is the publicly available version of the data set analyzed by i  and dose group d with mean breast dose idD (in Sv), sex s  and mean attained age ida, age at exposure ide and time since exposure idt  is given in Model 1:Poisson regression methods were used to investigate breast cancer risks. A linear relative risk model was fitted in which the expected number of deaths in stratum idPYλi(1 + αsDidexp[β1[ida – 50]/10 + β2[idt – 30]/10+β3[ide – 20]/10]) [1]idPY is the number of person years of follow-up. λi is the expected cancer rate in stratum i, and αs is the excess relative risk (ERR)/Sv, both estimated from the model fit, along with all other model parameters . The values of 50, 20, 30 years subtracted from the attained age, age at exposure, and time since exposure are the approximate mean values of these variables in the two data sets; we did this to stabilize parameter estimates. Similar models of breast cancer risk have been fitted previously to these and other breast cancer data sets  [2]The neutron component of breast cancer dose incorporates a weighting factor  of 10, to account for the known higher effectiveness of this type of radiation compared with that of high energy gamma rays .i with certain values of explanatory variables (idjZ)Nj=1  is given in Model 3:We also evaluated the excess absolute risk (EAR), modeling that required us to construct a parametric function of the baseline (zero dose) risks. We assumed that the expected number of cases or deaths in stratum idPY(f((idjZ)Nj= 1,(γj)Nj= 1) + αsDidexp[β1[ida – 50]/10 + β2[idt – 30]/10 + β3[ide – 20]/10]) [3]or analogous to Model 2, we also consider Model 4:idPY(f((idjZ)Nj= 1,(γj)Nj= 1) + αsDidexp[β1s[ida – 50]/10 + β2s[idt – 30]/10 + β3s[ide – 20]/10]) [4]Here the baseline cancer rate is given by Model 5:j)Nj=1, the latter determined by the model fit; the contrast with the semi-parametric rates, λi, in Models 1 and 2 should be noted. In order to adequately fit breast cancer incidence and mortality, taking account of all factors other than radiation in the two data sets, we considered models for f constructed from a candidate set of variables that included city, sex, all terms ln[age/50]k, ln[years since exposure/30]k, and [age at exposure – 20]k with integral k taking values between 1 and 6, and all second order interactions of these . In order to avoid over-parameterized models, the Akaike Information Criterion (AIC)  , 1981 waidPY(f((idjZ)Nj= 1,(γj)Nj= 1) (1 + αsDidexp[β1s[ida – 50]/10 + β2s[idt – 30]/10 + β3s[ide – 20]/10]) [6]However, these models are in some ways less flexible than the Models 1 and 2 with semi-parametrically modeled baseline rates, and in particular cannot be readily fitted to the male breast cancer data by itself, because there are too few cases. Results are generally similar to those using the semi-parametric relative risk models, so we shall not refer to them in this article.s, and in the temporally modifying parameters , β1, β2, β3. Notice that idt = ida – ide so we only fit submodels of Models 1 to 4 with at most two of the three parameters β1, β2, β3  allowed to be nonzero. All model parameters are estimated via Poisson maximum likelihood Nj= 1,(γj)Nj= 1) + g(D)h((jZ)Nj= 1,(γj)Nj= 1) for some functions f, g, h, then the probability of causation (PC) associated with radiation is given in Model 7:If cancer rate following assumed radiation dose g(D)h((jZ)Nj= 1,(γj)Nj= 1)/ {f((jZ)Nj= 1,(γj)Nj= 1) + g(D)h((jZ)Nj= 1,(γj)Nj= 1)} [7]Further details on the rationale are given elsewhere . In partsDidexp[β1[ida – 50]/10 + β2[idt – 30]/10 + β3[ide – 20]/10]/ {1 + αsDidexp[β1[ida – 50]/10 + β2[idt – 30]/10 + β3[ide – 20]/10]} [8]αWe estimated PC for male breast cancer using the model fitted here and compared it with PC estimated for various other sites, using relative risk models fitted by the We evaluated PC for attained age of 65 years, which is approximately the median age of occurrence for male breast cancer in the LSS, assuming exposure to 50 mSv at 35 and 55 years old. Although 50 mSv is about twice the mean lifetime cumulative dose, 24.9 mSv, in the United Kingdom’s National Registry for Radiation Workers, there are 20,373 workers (11.7% of the cohort) with cumulative doses above this level .p = 0.003)  higher by a factor of ~ 15 than the analogous trend risk of 1.86 Sv–1  for females (p = 0.050) ; whereas for females, the EAR is higher by a factor of ~ 20, 7.25/104 person-year Sv , a difference that is highly statistically significant (p < 0.001). The comparison of EARs between the sexes is much the same without adjustment for time since exposure  . The bre females . This di= 0.050) . These rexposure .p = 0.010) , higher by a factor of ~ 5 than the analogous trend risk of 1.86 Sv–1  for females (p > 0.2) ; whereas for females, the EAR is higher by a factor of ~ 10, 1.53/104 person-year Sv , a difference that is highly statistically significant (p < 0.001). The comparison of EARs between the sexes is much the same without adjustment for time since exposure  . The bre females . This dip > 0.2) . As for exposure .p = 0.094) difference between the time since exposure trends in the mortality relative risk data, with males exhibiting a strong decrease in risk over time compared with a modest increase over time for females [adjustment per decade of time since exposure of 0.11 and 1.26 respectively (data not shown)] ] .p = 0.05) for breast cancer incidence, where it is higher by a factor of ~ 15, but the elevation is also quite pronounced for breast cancer mortality, higher by a factor of ~ 5 . However, the male breast cancer absolute excess risks are lower by a factor of ~ 10 to ~ 20 than those for females , reflecting the much lower baseline cancer rates for males than for females.The analyses of this paper suggest that male breast cancer has an ERR that exceeds that for female breast cancer. This elevation of male relative risk compared to female is particularly strong , and low ratio of male:female absolute excess risks , is largely accounted for by the ratio of male:female baseline breast cancer rates . Comparison of Models 1 and 3 would lead one to expect that it is approximately the case that:Indeed, the findings of a high ratio of male:female relative excess risks of breast cancer / ≈  [9]The adequacy of this approximation may be judged by the fact that the left-hand side of Model 9 is 27.68/1.86 = 14.88 for incidence  and 9.48Because of the much lower EARs of male compared with female breast cancer, our findings imply minimal impact on assessments of individual or population breast cancer risk following all but therapeutic levels of radiation exposure, compared with those using models proposed by national  and intep = 0.09) between the sexes, with the male ERR concentrated in the earlier years of follow-up compared with the female. This does not contradict the pattern shown in Supplemental Material A Table A3, which shows that, as one would expect, all (radiogenic and other) male breast cancer cases are overwhelmingly concentrated in the later years of follow-up, a simple consequence of the ageing of the cohort.There are no strong indications of differences between the sexes in the temporal modifications . This is perhaps a function of lack of statistical power due to the very small numbers of cases and deaths in males. There are borderline significant indications that time since exposure modifications in relative risk may differ  registries, evaluated ionizing radiation as a risk factor, and observed a trend for an increasing risk of breast cancer with an increasing number of self-reported radiographic examinations, which was statistically significant for exams performed between 1933 and 1963, although not for any later period (1964–1987) . After rBRCA2 gene are a risk factor for both male and female breast cancer, but mutations in the BRCA1 gene appears to be a risk much more for female breast cancer than for male breast cancer  that time since exposure modifications in relative risk may differ between the sexes. However, the small number of cases and deaths in the data sets that we have analyzed argues for a degree of caution in interpretation of this finding.Women experience menarche and menopause, which are not experienced by men, and the timing of these events appear to influence both the baseline risk of breast cancer  and its t cancer . Mutatiot cancer , as addir  PDFClick here for additional data file.