Many of the established risk factors for breast cancer implicate circulating hormone levels in the aetiology of the disease. Increased levels of postmenopausal endogenous oestradiol (E2) have been found to increase the risk of breast cancer, but no such association has been confirmed in premenopausal women. We carried out a meta-analysis to summarise the available evidence in women before the menopause.We identified seven prospective studies of premenopausal endogenous E2 and breast cancer risk, including 693 breast cancer cases. From each study we extracted odds ratios of breast cancer between quantiles of endogenous E2, or for unit or s.d. increases in (log transformed) E2, or (where odds ratios were unavailable) summary statistics for the distributions of E2 in breast cancer cases and unaffected controls. Estimates for a doubling of endogenous E2 were obtained from these extracted estimates, and random-effect meta-analysis was used to obtain a pooled estimate across the studies.Overall, we found weak evidence of a positive association between circulating E2 levels and the risk of breast cancer, with a doubling of E2 associated with an odds ratio of 1.10 .Our findings are consistent with the hypothesis of a positive association between premenopausal endogenous E2 and breast cancer risk. However, provided assumptions concerning the distribution of oestrogen are satisfied, it is possible  to estimDealing with methodological differences in the designs of the individual studies is more problematic. There is substantial within-subject variation in oestrogen levels across the menstrual cycle, and it is possible that the relationship between oestrogen levels and breast cancer risk differs according to the phase of the cycle. Pooling results from studies conducted using oestrogen levels measured in different phases may obscure this, as may merely adjusting for phase (without taking account of a possible interaction) within the individual studies. Further, the methods used to adjust for phase in the menstrual cycle vary between studies, from simple methods, such as matching on day of cycle or stratifying on phase, to the more sophisticated approach of obtaining residuals from spline regressions of the oestrogen curve throughout the menstrual cycle. In addition, most studies adjust or match for several other breast cancer risk factors, the choice of which varies from study to study. These methodological differences will inevitably introduce a degree of heterogeneity into the results of the studies and, given the available information from each study, this heterogeneity cannot be completely removed through careful statistical analysis. However, despite these differences, we believe that as the studies are all investigating the same basic question, a formal meta-analysis is justified. Such a meta-analysis needs to acknowledge that the differences in study design mean that it is virtually certain that there will be heterogeneity in the magnitude of the observed associations and to quantify this. If the extent of the heterogeneity was large, then this would cast doubt on the validity of a pooled estimate.We therefore carried out a systematic review of the literature to identify all prospective studies of endogenous premenopausal oestrogens on breast cancer risk. To estimate the effect of a doubling of circulating premenopausal oestrogen levels on the risk of breast cancer, we combined the results from seven prospective studies, which include 693 breast cancer cases. We used a random-effect meta-analysis to take account of the anticipated heterogeneity, resulting from the differences in methodology between studies described above.Studies were identified by performing a literature search on Medline and Embase from 1950 to February 2009, of articles containing the words ‘breast’ in combination with any of the terms ‘cancer’, ‘carcinoma’, ‘neoplasia’ or ‘tumour’ and the terms ‘estrogen’, ‘estriol’, ‘estradiol’, ‘estrone’, ‘hormone’ or ‘steroid’ in the title. Articles were restricted to prospective studies of circulating premenopausal oestrogens on breast cancer risk. The search (carried out by KW) produced 7895 articles, of which 148 titles were identified as potentially eligible. Overall, 13 studies were included based on their abstracts, but 6 were excluded after reading the full-text articles, 2 because the subjects were all postmenopausal, 2 because the cases were all a subset of one of the studies included in the meta-analysis, and 2 studies measured hormones other than oestrogens. The remaining seven studies were all nested matched case–control studies  were manThe distribution of E2 is positively skewed and a number of the individual studies considered the effect of E2 on breast cancer risk after logarithmic transformation of E2. We took the same approach, and assumed that a log-normal distribution for E2 is reasonable, so as to yield estimates of the odds ratio associated with any multiplicative increase of circulating E2. We present the odds ratio for a doubling of circulating E2. Strictly, odds ratios that are adjusted for (and/or matched on) different sets of covariates are not comparable . HoweverThe diversity in the ways in which results were reported meant that a range of techniques were needed in order to estimate the log odds ratio for a doubling in E2 and its standard error in the various studies. Maximally adjusted, matched odds ratios were calculated or extracted from all studies, except for e(E2), 10(E2) and e(E2). These estimates (and corresponding standard errors) were used to directly estimate the log odds ratio for a doubling of E2 and its standard error in these studies.Three studies (e(E2) in controls and the overall s.d. of loge(E2) in order to estimate the mean of loge(E2) in each quantile, as described by e(E2) were estimated as the intercept and slope of normal quantile–quantile plots of the quantiles of loge(E2), respectively, separately for cases and controls. The pooled s.d. in cases and controls was used as the overall s.d., after testing for evidence of a difference in s.d. values in cases and controls using an F test. The odds ratios reported by In three further studies (e(E2) in cases and controls for each study are summarised in Table 2. We pooled the 10 estimates of s.d. of loge(E2) across cases and controls and across studies, first pooling the luteal and follicular estimates of s.d. from the study by e(E2) in cases and controls  to 14%  increase in the odds of developing breast cancer, depending on which estimate is included from the The combined estimated odds ratio for a doubling of free E2 (from the two studies that provided estimates) was 1.27  or 1.41  when including the luteal and follicular estimates from There was little evidence of between-study heterogeneity, with the proportion of variability between studies falling between 20% and 33%, depending on which estimate was included from the study by Our analysis provides some evidence of a positive association between circulating E2 levels and breast cancer risk in premenopausal women. We estimate that a doubling of circulating E2 increases a woman's risk of breast cancer by 10% .The breast cancer odds ratio for a doubling of postmenopausal E2 has been estimated to be 1.29 (95% CI: 1.15, 1.44; vs bottom quartiles of several androgens in the study by vs bottom quintiles of the same androgens, the estimates range from 1.7 to 2.2 (The endogenous androgens testosterone, androstenedione, DHEA and DHEAS, which do not have the cyclical variation of oestrogen, have been found to have similar effects on breast cancer risk in premenopausal and postmenopausal women. Odds ratios for breast cancer for premenopausal women in the top 7 to 2.2 . This suAll studies used E2 as the exposure, and in addition oestriol was measured in one study , and wasAll studies included in the meta-analysis matched cases and controls. All estimates in the primary meta-analysis are from fully matched/adjusted analyses, apart from those from the study by We assumed in our analysis that a log-normal distribution for E2 is reasonable. In three of the studies (It is possible that the effect of a doubling of E2 on breast cancer risk differs according to the phase of the menstrual cycle. This, coupled with the fact that different methods were used to match or adjust for time of blood sample collection in the menstrual cycle in the various studies, may limit the utility of a single pooled estimate. However, a number of factors suggest that our pooled estimate may well be generalisable to the different phases of the cycle. First, we did not find any statistically significant evidence of heterogeneity in the magnitude of associations across studies. Second, although one study . Third, In postmenopausal women, circulating levels of free E2 were found to have a larger effect on breast cancer risk than total E2 . We estiIt is worth considering whether a woman's circulating E2 level could be added to models for projecting the risk of breast cancer, such as the Gail model or the ‘Gail model 2’  demonstrThis meta-analysis has demonstrated weak evidence of a positive association between premenopausal endogenous E2 levels and breast cancer risk. More studies are needed to accurately quantify this association, in order to provide meaningful estimates and to allow a comparison with the association in postmenopausal women. Repeated measurements in each woman may be helpful to reduce measurement error in E2. Further estimates in the follicular and luteal phases of the cycle separately would aid the discussion of whether E2 in a particular phase in the cycle is important, or whether mean levels across the cycle are implicated.


Literary data suggest apparently ambiguous interaction between menopausal status and obesity-associated breast cancer risk based on the principle of the carcinogenic capacity of estrogen. Before menopause, breast cancer incidence is relatively low and adiposity is erroneously regarded as a protective factor against this tumor conferred by the obesity associated defective estrogen-synthesis. By contrast, in postmenopausal cases, obesity presents a strong risk factor for breast cancer being mistakenly attributed to the presumed excessive estrogen-production of their adipose-tissue mass. Obesity is associated with dysmetabolism and endangers the healthy equilibrium of sexual hormone-production and regular menstrual cycles in women, which are the prerequisites not only for reproductive capacity but also for somatic health. At the same time, literary data support that anovulatory infertility is a very strong risk for breast cancer in young women either with or without obesity. In the majority of premenopausal women, obesity associated insulin resistance is moderate and may be counteracted by their preserved circulatory estrogen level. Consequently, it is not obesity but rather the still sufficient estrogen-level, which may be protective against breast cancer in young adult females. In obese older women, never using hormone replacement therapy (HRT) the breast cancer risk is high, which is associated with their continuous estrogen loss and increasing insulin-resistance. By contrast, obese postmenopausal women using HRT, have a decreased risk for breast cancer as the protective effect of estrogen-substitution may counteract to their obesity associated systemic alterations. The revealed inverse correlation between circulatory estrogen-level and breast cancer risk in obese women should advance our understanding of breast cancer etiology and promotes primary prevention measures. New patents recommend various methods for the prevention and treatment of obesity-related systemic disorders and the associated breast cancer.  Many studies support an apparently Janus-faced ambiguous interaction between obesity and breast cancer risk depending on the menopausal status of patients -3. In yoNevertheless, several authors could find confusing and disturbing associations between obesity and breast cancer risk in postmenopausal women. Obese postmenopausal women, who had never used hormone replacement therapy (HRT) exhibited fairly high breast cancer risk . By contIn premenopausal cases, the results of clinical studies justify that obesity induces mild or moderate decrease in circulating estrogen levels reflected by their inclination to anovulatory infertility and long or irregular menstrual cycles . Based oEven larger studies, which equivocally strengthen the protective effect of obesity for premenopausal breast cancer risk, confess that the responsible mechanisms are completely obscure. Evidences, provided by clinical endocrinological studies regarding correlations between defective hormonal status of obese women and decreased breast cancer incidence, are inconsistent or fairly contradictory . ConsideObesity is a well-known cancer risk factor associated with different grades of insulin resistance and a disturbance of male to female circulating sexual steroid levels , 20. HypThe aim of the present study is to clarify the realistic correlations between breast cancer risk and obesity-associated hormonal alterations during the whole life of women. Moreover, this study tries to reveal the sources of the misleading clinical and epidemiologic results suggesting the breast cancer protective effect of obesity in the young. These associations are examined in an analytical review based on the results of prospective, case-control and meta-analytic studies.1). The main stream of obesity related alterations is a self-generating, progressive insulin resistance in thorough interplay with the dysmetabolism and inflammation of adipose tissue mass and with an imbalance of sexual steroid production in the endocrine organs.Clinical and experimental evidences prove that obesity, particularly visceral fatty tissue deposition leads to insulin resistance, associated with diverse immunologic, metabolic and hormonal alterations mediating breast cancer risk  is a defect of insulin-mediated cellular glucose uptake, which may elicit many disorders in the gene regulation of cellular metabolism, growth, differentiation and mitotic activity. The progression of this disorder predisposes patients to a variety of diseases, such as metabolic syndrome, type-2 diabetes, cardiovascular lesions and malignancies at different sites .in the first, compensated phase of insulin resistance maintains serum glucose level within the normal range by means of an increased secretory capacity of insular β-cells . Insulindevelops in the second, partially uncompensated phase of insulin resistance when the enhanced insulin synthesis of pancreatic islet cells is not enough to maintain euglycemia. This is a quartet of elevated fasting glucose, high serum triglyceride, low HDL cholesterol level and hypertension, being characteristic of viscerally obese patients . Each ofHyperglycemia is advantageous for the unrestrained DNA synthesis of tumor cells. It provokes deliberation of free radicals causing derangements in both DNA and enzymes having important role in repair mechanisms [chanisms , 33. Higchanisms . A prospchanisms .Dyslipidemia is a complex disturbance of the lipid metabolism associated with insulin resistance and hyperinsulinemia [ulinemia . Serum lulinemia , 38. Resulinemia .Hypertension usually shows close positive correlation with obesity, insulin resistance and hyperinsulinemia [ulinemia , 41. Eleulinemia , 42. Theulinemia . In postulinemia .Metabolic syndrome is associated with both increased breast cancer risk and high mortality rates in postmenopausal cases , 46. Timis the uncompensated phase of insulin resistance when the secretory capacity of insular β-cells becomes exhausted and the decreased serum insulin level results in hyperglycemia. The disrupted glucose homeostasis, the excessive formation of free radicals and the protein glycation depress the activities of the antioxidant scavengers and enzymes. These noxious processes cause serious damages in all biological structures even at a molecular level , 48. Thehyperandrogenism and anovulatory infertility through several pathways as insulin is a potent regulator of sexual steroid production in the endocrine organs [ovarian androgen production at the expense of reduced estrogen synthesis [Obesity related insulin resistance and excessive insulin synthesis in women may contribute to e organs . Hyperinynthesis . High inynthesis . Therapeynthesis , 59. A rynthesis .adrenal androgen synthesis as well by means of increased adrenal sensitivity to adrenocorticotropin. At the same time, high insulin level may favor the luteinizing hormone(LH) secretion of pituitary. Increased LH level also stimulates androgen biosynthesis by the activation of adrenal gland and ovarian theca cells [Hyperinsulinemia promotes ca cells , 62. Accca cells . In centca cells , 65.Insulin resistance, hyperinsulinemia and excessive IGF-I activity mediate defective estrogen synthesis by counteraction to aromatase enzyme gene expression at cellular level. The aromatase enzyme complex catalyzes the conversion of androgens to estrogens in a wide variety of tissues. In premenopausal women, the ovaries are the principle sources of estradiol; by contrast, in postmenopausal women when the ovaries cease to produce estrogen, it is synthesized in a number of extragonadal sites . These sIn premenopausal insulin resistant women with type-2 diabetes, the ovaries exhibit decreased capacity to convert androgen to estrogen, probably due to a reduction of ovarian aromatase activity . ConversEstrogens decrease low grade inflammatory reactions and may in parallel reduce the glucocorticoid responses. Low estrogen levels after menopause allow the predominance of glucocorticoids and the manifestation of the metabolic syndrome. These observations suggest that the disturbed equilibrium between sex hormones and glucocorticoids may be a critical element in the manifestation of metabolic syndrome-related pathologies .Visceral adipose tissue has important functions in energy homeostasis, metabolic equilibrium, immune responses and in the regulation of cell proliferation. Secretion of signaling molecules; adipokines regulates the cellular microenvironment both locally and systemically -73.The chronic low grade inflammation associated with obesity is an important player in tumor development and progression. Increased IGF-I level may mediate the inflammation of adipose tissue via its effects on immunologically active cells including macrophages and T cells . IGF-I madipocytokines; such as leptin, adiponectin resistin, ghrelin etc. [Adipokines include unique products of fatty tissue known as lin etc. . Leptin lin etc. . High lelin etc. . Converslin etc. . The ballin etc. .Proinflammatory cytokines  produced in excessive adipose tissue mass increase nitric oxide (NO) production, a substrate for reactive oxygen species (ROS). Accumulation of cytokines and ROS further contribute to insulin resistance resulting in elevated circulating glucose and free fatty acid levels. All signaling molecules of fatty tissue including cytokines, hormones and growth factors are involved in the regulation of the proliferation, invasion and metastatic spread of tumor cells . ProinflOut of several hormones affecting body mass and adipose tissue deposition, estrogens promote, maintain and control the ideal distribution of body fat . These sEstrogen regulates the metabolism, differentiation, growth and cell kinetic mechanisms of adipocytes. In healthy premenopausal women central adipocytes show higher insulin sensitivity and exhibit a higher turnover rate despite similar fat content as compared with male cases . ConversObesity and overweight are important concomitants of insulin resistance and they are strongly associated with disturbed equilibrium of male to female sexual hormone concentrations in women . HealthyHealthy premenopausal women are typically protected from cardiovascular diseases and hypertension as compared with men and this has been hypothesized to be because of the protective effects of estrogens. Conversely, obese, diabetic young women, and postmenopausal cases may lose this protected state as their bioavailable estradiol levels are strongly reduced .Estrogen may have crucial role in the maintenance of normal serum lipid levels. Postmenopausal women have higher total cholesterol, LDL cholesterol and triglyceride levels, whereas lower HDL cholesterol levels as compared with premenopausal cases. These changes may be regarded as a shift toward a more atherogenic lipid profile in estrogen deficient milieu . PostmenEstradiol has cardiovascular protective impact by its anti-hypertensive activity as well. Estrogens can downregulate components of the renin-angiotensin system (RAS) and reduce the expression and activity of angiotensin I-converting enzyme , 95. EstClinical and experimental studies support that estradiol has also antioxidant activities , 99 thatpancreatic islet cells [Estrogens have beneficial effects on the energy metabolism and glucose homeostasis by means of several pathways . Estrogeet cells . Estrogeet cells . EstrogeIn the liver, estrogen regulates insulin sensitivity by the balanced activation of glycogen synthase and glycolytic enzymes to maintain the equilibrium of glycogen synthesis and glycogenolysis. In ER-α knockout mice, hepatic insulin resistance was associated with decreased glucose uptake in skeletal muscles [ muscles .In the peripheral tissues, ERs advantageously modulate the insulin stimulated glucose uptake through regulation of the phosphorylation of insulin receptor protein. In hyperinsulinemia, high concentrations of estradiol can inhibit the excessive insulin signaling in adipocytes [ipocytes , which mERs have crucial roles in cellular glucose uptake by regulation of intracellular glucose transporters (GLUTs) and enhancing both GLUT4 expression and translocation . In humaEstrogens and ER signals have pivotal role in the regulation of growth hormone (GH) activity by means of modulation of its secretion and cellular GH receptor function. Estrogens play a major and positive role in the regulation of GH-IGF-I axis in both genders , which mPostmenopausal hormone replacement therapy (HRT) reduced abdominal obesity, insulin resistance, new-onset diabetes, hyperlipidemia, blood pressure, adhesion molecules and procoagulant factors in women without diabetes and reduced insulin resistance and fasting glucose in women with diabetes .Low grade systemic inflammation may be associated with estrogen deficient states, namely menopause and ovariectomy. At early stages of estrogen deficiency estrogen administration decreased the inflammation associated risk of developing cardiovascular disease . NeverthPhytoestrogens may advantageously influence the level of adipokines in insulin resistant women. In postmenopausal cases, diet, physical exercise and daily oral intake of soy isoflavones had a beneficial lowering effect on serum leptin, and TNF-α levels and showed a significant increase in mean serum levels of adiponectin . EpidemiEstrogen advantageously regulates serum levels of available growth factors. In clinical studies estradiol lowered, whereas testosteron increased total IGF-I level and estradiol specifically suppressed unbound, free IGF-I level . CrosstaThe presumed synergistic contribution of ERs and GFRs to cancer development and progression would be a permanent danger without contraregulatory impact. Inhibition of growth factor signaling in apparently ER-negative breast cancer cells successfully restored ER expression suggesting a dynamic, inverse relationship between the two receptor systems . Moreoveobese premenopausal women, peripheral, subcutaneous adiposity is typical and there is a lower incidence of obesity associated dysmetabolism. By contrast, in obesepostmenopausal cases, circulating levels of estrogen are dramatically decreased and adipose tissue distribution becomes more male-like. Predominance of visceral adiposity and the associated metabolic and hormonal disorders mean high risk for obesity related diseases, included breast cancer [Fat deposition is thoroughly affected by male to female sexual steroid level . Circulat cancer .The most important data characterizing the grade of obesity are body mass index (BMI) and weight in kilogram , 5, 118 Some authors reported on the value of WC, HC and WHC ratio in the prediction of premenopausal breast cancer occurrence , 122. CoMagnetic resonance imaging (MRI) was used to quantify separately the mass of visceral and subcutaneous abdominal fat depositions in obese adolescent girls . Mass ofIn conclusion, neither BMI nor circumference measurement may exactly separate the metabolically indifferent, subcutaneous fat and the dangerous visceral fat, which may partially explain the controversial correlations between obesity grade and breast cancer risk.Circulating female sexual steroid levels and fertility continuously decrease during the life of women. The ability to conceive is at its peak in young women under 30 years of age with a continuous decline from the fourth decade, which suggests decreasing fertility even in the premenopausal phase . Menopau1).In premenopausal women, the good equilibrium of sex steroid synthesis defines health and reproductive capacity, whereas good, symptom-free adaptation to the estrogen deficient environment is a prerequisite of postmenopausal health. Changes in the hormonal equilibrium during women’s lives strongly influence the obesity associated breast cancer risk  the coexistence of anovulatory infertility and insulin resistance represents common risk for the cancers of highly hormone dependent breast, endometrium and ovary , 148. MoHyperprolactinemia is also associated with cycle disorders, reproductive dysfunction and hyperandrogenism in women, however, it may be sharply discerned from PCOS and other disorders related to androgen excess. An increased overall cancer risk was found in hyperprolactinemia patients but further investigations are necessary to confirm these results . Data ofHow can we explain the increased prevalence of breast, endometrial and ovarian cancers in women with fertility disorders, even without obesity? In premenopausal cases, slight or moderate decrease in circulating female sexual steroid levels may be enough to block the delicate mechanism of ovulation. At the same time, a slightly estrogen deficient milieu confers preferential cancer risk for the female organ triad having high estrogen demand , 136.In women, multiparity and risk for malignancies at several sites exhibit an inverse relationship -157. HigNulliparity may be associated with ovulatory disorders in the majority of cases and there are studies, which equivocally exhibited increased prevalence of breast cancer in nulliparous women , 162. Fuin vitro fertilization (IVF) in anovulatory cases [Possible role of fertility medications in the increased risk of breast cancer has been hypothesized. However, large studies were not able to find any associated risk of breast cancer after ovulation provocation and ry cases . Moreovery cases . Recentlry cases . Howeverry cases . These rIn postmenopausal estrogen deficient, obese cases the regional distribution of fat deposition near uniformly affects the visceral region in close correlation with their high breast cancer risk .Obese postmenopausal women never using HRT are obviously insulin resistant. Moreover, ageing after menopause is a further factor increasing the cancer risk as it is associated with a continuous estrogen loss and parallel advancing insulin resistance [sistance . In old obese HRT user postmenopausal women, the incidence of breast cancer is equivocally reduced [In  reduced . The pro reduced . In HRT  reduced  yields f reduced  and by i reduced . All theA great challenge is to explain the beneficial anticancer impact of HRT in obese postmenopausal women based on the misbelief that they have excessive circulatory estrogen level. To solve this contradiction, some authors assumed that mediators other than estrogen, such as insulin and insulin-like growth factors might confer the obesity associated breast cancer risk after menopause . Oral contraceptive (OC) use replaces the natural menstrual cycle with relatively steady levels and fluctuations of artificial sex hormones.Recent epidemiological studies have confirmed that combined oral contraceptives provide substantial protection against endometrial and ovarian cancer in the endangered anovulatory women , 169. EfIn PCOS cases, treated with OCs, the volume of cystic ovaries is reduced, ovarian testosterone secretion is decreased and there are favorable effects on carbohydrate and lipid metabolism as well . A recenUse of HRT has highly controversial associations with breast cancer risk. Till now, the carcinogenic capacity of postmenopausal estrogen therapy was a prevailing concept [ concept . By cont concept , 172. ReIn 2011, the WHI Randomized Controlled Trial strengthened that the estrogen treatment in women with prior hysterectomy resulted in a significantly lower risk for breast cancer than in untreated controls . Breast Today, increasing numbers of evidence from the clinical trials suggest that unopposed estrogen as hormone treatment does not increase the risk of breast cancer, and may even reduce it. The earlier concepts regarding the breast cancer risk of hormone replacement therapy are completely changing -178.HRT decreases the accumulation of central fatty tissue in obese postmenopausal women and provides them long term metabolic and health benefits , 167. HR environmental estrogens (xenoestrogens) occurs through diet, household products and cosmetics, but concentrations of single compounds in breast tissue are generally lower than needed for assayable estrogenic responses [Exposure to esponses . ExposurEndocrine disruptors (EDs) are environmental chemicals that are able to bind hormonal receptors. These compounds may disrupt the endocrine regulation of reproduction and adaptive mechanisms and promote the development of endocrine disorders. Since several EDs have a structure similar to that of endogenous steroid hormones such as estrogens, they have an affinity for steroid hormone receptors and may alter the normal hormone-mediated reactions [eactions . EnvironPoor natural light exposure mediates deleterious metabolic and hormonal alterations by excessive melatonin secretion; such as insulin resistance, deficiencies of estrogen, thyroxin and vitamin D [itamin D . Long teBefore the introduction of antiestrogens, high dose estrogen was successfully administered as endocrine therapy for postmenopausal women with advanced breast cancer. Nowadays, the ambiguous, unreliable therapeutic effects and high toxicity of antiestrogens suggest the necessity of finding new strategies for breast cancer treatment . RecentlPhytoestrogen containing health supplement compositions are recommended by some patents for the prevention or treatment of postmenopausal breast cancer and for the alleviation of menopausal symptoms [symptoms . Soybeansymptoms . A recensymptoms .Obesity-associated hormonal disorders confer breast cancer risk during the whole life of women without any ambiguous interaction between obesity and menopausal status. Erroneous results regarding the breast cancer protective effect of obesity in young women derive mainly from the deceivingly lower tumor incidence among them. Further misleading factor may be that hormonal disorders related to anovulatory infertility are strong cancer risk factors for the breast both in obese and non-obese young women. Low incidence rate of breast cancer in young obese women may be a very important misleading finding. In the majority of premenopausal obese cases a predominantly subcutaneous adipose tissue deposition results in milder insulin resistance being counteracted by their preserved hormonal cycle. Consequently, in obese premenopausal women their circulatory estrogen level confers protective effect against breast cancer rather than obesity.Tumor cell kinetics may also provide an explanation for the low breast cancer incidence rate in obese premenopausal cases. A long term exposition to harmful environmental and systemic factors is necessary for cancer initiation; moreover the tumor growth from the initiation to clinically diagnosable size takes further several years. This double delay of clinical appearance of breast cancers may frequently result in a postmenopausal tumor, though it was initiated in the premenopausal period. Anovulatory infertility and the associated hormonal defects seem to be stronger breast cancer risk factors than adiposity alone and these alterations are not rare even among control cases with normal weight. Studies on obesity-related breast cancer risk disregarded the random occurrence of these hormonal alterations both among obese cases and lean controls. Moreover, oral contraceptive use is fairly widespread among premenopausal women, which may reverse the mild hormonal defects and attenuates their cancer risk either in obese or in lean cases. Strict selection of healthy, lean control women for obesity associated breast cancer studies and taking into account the OC use among obese cases and controls will justify the health advantage of normal body weight over obesity.Recognition of the inverse correlation between circulatory estrogen-level and breast cancer risk in obese women should advance our understanding of breast cancer etiology. Moreover, it would promote primary cancer prevention measures and the introduction of causal cancer therapy.2 and 3). Circulatory endogenous estrogen was blamed to provoke cancer initiation and promotion in the highly hormone responsive female organs, however, this misbelief yielded fairly controversial results in the clinical and experimental fields of medicine. Nowadays, overwhelming literary data suggest that estrogens have advantageous impact on dysmetabolism and hyperandrogenism, which are essential sources of tumor development.Changing concepts concerning breast cancer etiology provide completely new strategies against mammary tumors (Tables Recent patents disclose oral contraceptive and metformin derivative treatment possibilities for breast cancer prevention and therapy. Moreover, a new invention provides special estrogen derivatives being metabolically more advantageous for the treatment of PCOS than oral contraceptives. As anovulatory disorders in young women are strong risk factors for breast cancer, insulin resistance screening of asymptomatic occult cases in the population and their hormone treatment would be effective primary breast cancer prevention.In postmenopausal cases, obesity, metabolic syndrome, type-2 diabetes and hysterectomy are strong risk factors for mammary malignancies. Hormone replacement therapy in these endangered cases would be advantageous preventive measure.The present practices of breast cancer therapy have fairly controversial results. Worldwide administration of antiestrogen compounds for breast cancer treatment yielded thorough disappointment. Antiestrogens are cytostatic agents blocking the most important regulatory mechanisms associated with estrogen receptors. They do not have only toxic effects but also strong carcinogenic capacity. Recently, scientists in some centers confess, that one armed estrogen treatment is rather cancer protective than carcinogenic. Certain patents disclose the beneficial effects of phytoestrogens in breast cancer prevention and therapy. Moreover, publications on the high dose estrogen treatment of advanced breast cancer cases are regularly returning and the results are fairly encouraging. Today, we are on the route to regard estrogens as beneficial anti-cancer drugs rather than carcinogenic agents.


Studies have suggested that statins may inhibit tumor cell growth and possibly prevent carcinogenesis. The objective of this study was to investigate the association between persistent statin use and the risk of primary cancer in adults.This retrospective study was conducted by using the computerized data sets of a large health maintenance organization (HMO) in Israel. The study population was 202,648 enrollees aged 21 or older who purchased at least 1 pack of statin medication from 1998 to 2006. The follow-up period was from the date of first statin dispensation (index date) to the date of first cancer diagnosis, death, leaving the HMO, or September 1, 2007, whichever occurred first. Persistence was measured by calculating the mean proportion of follow-up days covered (PDC) with statins by dividing the quantity of statin dispensed by the total follow-up time.During the study period, 8,662 incident cancers were reported. In a multivariable model, the highest cancer risk was calculated among nonpersistent statin users. A strong negative association between persistence with statin therapy and cancer risk was calculated for hematopoietic malignancies, where patients covered with statins in 86% or more of the follow-up time had a 31%  lower risk than patients in the lowest persistence level (≤12%) .Our study demonstrated that persistent use of statins is associated with a lower overall cancer risk and particularly the risk of incident hematopoietic malignancies. In light of widespread statin consumption and increases in cancer incidence, the association between statins and cancer incidence may be relevant for cancer prevention. Medscape, LLC is pleased to provide online continuing medical education (CME) for this journal article, allowing clinicians the opportunity to earn CME credit.This activity has been planned and implemented in accordance with the Essential Areas and policies of the Accreditation Council for Continuing Medical Education through the joint sponsorship of Medscape, LLC and Preventing Chronic Disease. Medscape, LLC is accredited by the ACCME to provide continuing medical education for physicians. AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.Medscape, LLC designates this Journal-based CME activity for a maximum of 1 www.medscape.org/journal/pcd (4) view/print certificate.All other clinicians completing this activity will be issued a certificate of participation. To participate in this journal CME activity: (1) review the learning objectives and author disclosures; (2) study the education content; (3) take the post-test with a 70% minimum passing score and complete the evaluation at Release date: August 08, 2012; Expiration date: August 08, 2013Upon completion of this activity, participants will be able to:Assess the pleiotropic effects of statinsDistinguish the effect of statin therapy on the risk of incident cancer in the current studyIdentify the type of cancer most inhibited by statins in the current studyEvaluate variables which might affect the efficacy of statins in preventing cancerCME EDITORPreventing Chronic Disease. Disclosures: Teresa Ramsey and Camille Martin disclosed no relevant financial relationships.Teresa Ramsey, Editor; Camille Martin, Editor, CME AUTHORCharles P. Vega, MD, Health Sciences Clinical Professor; Residency Director, Department of Family Medicine, University of California, Irvine. Disclosure: Charles P. Vega, MD, has disclosed no relevant financial relationships.AUTHORS AND CREDENTIALSDisclosures: Miriam Lutski, MSc; Varda Shalev, MD; Avi Porath, MD; Gabriel Chodick, PhD have disclosed no relevant financial relationships.Affiliations: Miriam Lutski, MSc, Sackler Faculty of Medicine, Tel Aviv University, Israel; Varda Shalev, MD, Sackler Faculty of Medicine, Tel Aviv University, Israel, and Maccabi Healthcare Services, Tel Aviv, Israel; Avi Porath, MD, Gabriel Chodick, PhD, Maccabi Healthcare Services, Tel Aviv, IsraelCancer is the second most common cause of death in the United States, exceeded only by heart diseases . Annual 3-Hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase inhibitors (statins) effectively reduce cholesterol levels and decrease the incidence of cardiovascular and cerebrovascular events . Large rAfter statins were introduced into clinical practice, they were shown to have effects other than lipid lowering, referred to as pleiotropic effects . More thStatins have been associated with a significantly lower risk of breast , colorecOur previous study  demonstrWe conducted this study among members of Maccabi Healthcare Services (MHS), established in 1941. MHS has become Israel’s second-largest health maintenance organization, with a membership of 2 million countrywide. All data for this analysis were obtained from MHS automated databases.The cohort of statin users has been described previously ,15. BrieData on cancer occurrence during the study follow-up period were obtained from the Israel National Cancer Register (ICR). Established in 1960, the ICR collects information on diagnosed cancer cases from all medical institutions in the country with a completeness of above 93.5% for solid tumors and approximately 90% for nonsolid tumors  with statins by dividing the quantity of statin packs dispensed by the total follow-up days. PDC was categorized into quintiles .Demographic variables at index date included baseline values of age, sex, marital status, place of residency, years of stay in Israel (for new immigrants) and religiosity . These categories were determined on the basis of self-reported data obtained by MHS for marketing purposes. Socioeconomic level was categorized into quartiles and determined according to the poverty index of the member’s census enumeration area . The poverty index, ranging from 0 (lowest) to 20 (highest), is based on several parameters including household income, educational qualifications, crowding, material conditions, and car ownership . HistoryChi-square test for categorical variables and Kruskal-Wallis test for continuous variables were performed to determine significant differences in baseline characteristics among quintiles of PDC. To address the effect of statin type, we conducted sensitivity analyses of simvastatin users . Cox’s proportional hazards  model wiDuring the follow-up period , 9,256 patients (4.6%) died and 2,787 (1.4%) left MHS. The mean age of the total population was 57.3 years . In geneA total of 8,662 incident cancer cases were reported during the follow-up period . The incAfter adjusting for potential confounders and statin efficacy, an inverse association between persistence with statin therapy and cancer risk was observed for all-site and site-specific cancers . In a muP = .001 for linear trend with an HR of 0.80 (0.76–0.86) for 5th PDC quintile compared with nonpersistent statin users (data not shown). The fully adjusted HR for hematopoietic cancers was 0.69  for the highest PDC quintile; for lymphoma the HR was 0.69 , and for leukemia the HR was 0.58   .P = .02). In stratified analyses, substantially lower risk of cancer was calculated for patients aged 50 or older and for patients treated with high-efficacy statins . Early meta-analysis  of 14 stWhen our analyses were stratified by sex, the significant negative association between continuation with statins and lymphoma risk was limited to men only. The reduction of hematopoietic cancer risk by sex also has been reported in other studies of prescription medications ,25, but Our study had several methodologic strengths, including its historical prospective design, a large and unselected study population, systematic data collection, and a long follow-up period. The threat of methodologic biases was further reduced by an individual evaluation of statin persistence based on dispensing information, which is the most feasible method of estimating medication use in large populations . The useHowever, some potential limitations should be discussed. Statin users are frequently under continuous surveillance of various specialists, and more screening tests could have led to surveillance bias. However, such bias is usually associated with earlier cancer detection and higher observed cancer risk and thus cannot explain the negative association between PDC with statins and cancer risk observed in our cohort. A healthy user bias is another potential bias. Persistent users may be more likely to have more aspects of a healthy lifestyle such as diet, exercise, and avoidance of risky behaviors . To avoiData on some variables that can be associated with statins and cancer, such as physical activity, diet, and family history of cancer, were missing in our analysis. However, none of these is an established risk factor for hematologic neoplasms. Smoking, a well-established risk factor for several types of cancer, is an additional confounder for which data were incomplete. Statins are more likely to be prescribed for cigarette smokers because of their higher risk of atherosclerotic cardiovascular disease. However, results from subanalyses for patients with valid smoking status were similar to overall analysis. The results of sensitivity analyses by follow-up duration suggest that the threat of methodologic biases such as misclassification of exposure were unlikely.Our study showed that overall cancer risk decreased with increasing level of statin efficacy, but we found no significant differences between lipophilic and hydrophilic statins. Several studies ,30 have In light of widespread statin consumption and the indications for long-term or lifelong use, the association between statin use and lower cancer risk may contribute to improved public health. For example, the incidence of leukemia in Israel is 32 per 100,000 for Jewish men aged 60 to 69 years (


The Gail model (GM) is a risk-assessment model used in individual estimation of the absolute risk of invasive breast cancer, and has been applied to both clinical counselling and breast cancer prevention studies. Although the GM has been validated in several Western studies, its applicability outside North America and Europe remains uncertain. The Singapore Breast Cancer Screening Project (SBCSP) is a nation-wide prospective trial of screening mammography conducted between Oct 1994 and Feb 1997, and is the only such trial conducted outside North America and Europe to date. With the long-term outcomes from this study, we sought to evaluate the performance of GM in prediction of individual breast cancer risk in a Asian developed country.The study population consisted of 28,104 women aged 50 to 64 years who participated in the SBSCP and did not have breast cancer detected during screening. The national cancer registry was used to identify incident cases of breast cancer. To evaluate the performance of the GM, we compared the expected number of invasive breast cancer cases predicted by the model to the actual number of cases observed within 5-year and 10-year follow-up. Pearson's Chi-square test was used to test the goodness of fit between the expected and observed cases of invasive breast cancers.The ratio of expected to observed number of invasive breast cancer cases within 5 years from screening was 2.51 . The GM over-estimated breast cancer risk across all age groups, with the discrepancy being highest among older women aged 60 - 64 years . The model also over-estimated risk for the upper 80% of women with highest predicted risk. The overall E/O ratio for the 10-year predicted breast cancer risk was 1.85 (1.68-2.04).The GM over-predicts the risk of invasive breast cancer in the setting of a developed Asian country as demonstrated in a large prospective trial, with the largest difference seen in older women aged between 60 and 64 years old. The reason for the discrepancy is likely to be multifactorial, including a truly lower prevalence of breast cancer, as well as lower mammographic screening prevalence locally. The Gail model (GM) for evaluation of the absolute individual risk of invasive breast cancer in women has been used extensively in Western populations for individual counseling and cancer prevention trial design. Given that international breast cancer incidence varies widely and that the validation of GM has been conducted predominantly in Western populations, it is relevant to examine its performance in different settings. The age-standardized rate of breast cancer was 59.5 per 100,000 persons per year among Singaporean women in the period of 2004 to 2008 ; this fiin situ breast cancer. This model was subsequently modified by investigators from the Breast Cancer Prevention Trial [The original GM was derived from data of the Breast Cancer Detection Demonstration Project (BCDDP), a program consisting of five annual mammography screens across 29 centers in the US . By meanon Trial  by usingon Trial , has beeon Trial  used daton Trial , who fouon Trial .From 1968 to 2007, Singapore experienced an almost threefold increase in breast cancer incidence. The incidence rate of breast cancer from 2003 to 2007 was 2.9 times that in 1968 to 1972 and 5.65% higher than in 1998 to 2002 . Our stun = 166,600). A total of 69,473 (41.7%) of these women were randomly selected and invited for a single free mammography screening from 1 October 1994 to 28 February 1997. Prior to mammography, all attendees completed a questionnaire to determine eligibility, and only those who were eligible proceeded to further interview and mammography. Women who had cancers of the breast or other sites (except non-melanoma skin cancer) and had mammography or breast biopsy in the 12 months prior to screening or were pregnant were excluded from screening. In total, 28,235 women from 50 to 64 years of age participated in this initial mammography screening program with a single two-view mammogram examination. No subsequent free mammography screening was offered.The SBCSP was a population-based mammography screening project conducted between October 1994 and February 1997 among female Singaporeans. The design of the SBCSP has been described in detail previously . BrieflyAmong the 28,235 participants, 131 women were detected with breast cancer during initial screening and were excluded from the study population. Therefore, a total of 28,104 women provided the basis of this study. Electronic matching with the Singapore Cancer Registry and the National Death Register was used to identify any breast cancers occurring among these women from the date of the screen until December 2007. Permission was obtained from the Ministry of Health (Singapore) and the Ministry of Home Affairs (Singapore), respectively, for data access. One hundred forty-four and 409 invasive breast cancers were diagnosed within 5 years and 10 years from screening, respectively. Women not diagnosed with breast cancer during each period formed the corresponding control group for comparison with the diagnosed cases.Risk factor categories in the GM were formed by using information from the screening questionnaire: age at menarche , age at first live birth , previous breast biopsy, and number of first-degree relatives with breast cancer. Number of previous breast biopsies and presence of atypical hyperplasia in biopsy were not ascertained in the questionnaire, which asked only whether women had had any previous breast biopsy. The questionnaire asked each woman whether she had a mother, any sister(s), or any daughter(s) with breast cancer and not the actual number of affected first-degree relatives. Assuming that women who reported that they had only sister(s) or only daughter(s) with breast cancer were recorded as having one affected first-degree relative, we recategorized our information on family history to three categories of 0, 1, or 2 or more affected first-degree relatives.The 5-year and 10-year absolute risks of each individual woman were calculated from their age of screening until end of follow-up at 5 years and 10 years, respectively. The calculation was done by using publicly available SAS codes for GM prediction from the NCI's Breast Cancer Risk Assessment Tool website . The expP value of less than 0.05 was considered statistically significant. All analyses were performed by using SAS 9.1 .Model calibration was assessed by comparison of the expected (E) and observed (O) number of breast cancer cases by GM risk factor categories, 5-year age group, and quintiles of predicted risk. Quintiles were formed by ranking all women by their predicted risk from the GM in ascending order. Confidence intervals (CIs) were calculated for E/O ratio on the basis of exact theory, and O was assumed to follow a Poisson distribution. The Pearson chi-squared test was used to test the goodness of fit between E and O. Comparison of categorical variables between diagnosed cases and the control group was performed by using the chi-squared test or Fisher exact test as appropriate. A Matsuno and colleagues  recentlyin situ (DCIS) invasive breast cancer. The distribution of the study cohort by risk factors in the GM is shown in Table Up to December 2007, 575 of 28,104 individuals developed ductal carcinoma The various breast cancer risk factors had a similar influence on the incidence of both DCIS and invasive breast cancers as compared with the corresponding estimated impact on risks as calculated by the GM Table . HoweverA parallel comparison between the observed breast cancers developed in the 5 years after screening for categories defined by breast cancer risk factors and the calculated expected numbers of invasive breast cancers predicted by using GM's 5-year predicted risk for Caucasian women was performed Table . OverallFour hundred nine breast cancers developed in the 10 years after screening as compared with the 758 expected cancer cases predicted by using the GM Table .The GM is widely used in Western countries for predicting the absolute risk of invasive breast cancer. Several other prospective studies looking at the accuracy of the GM were previously performed but these studies were done predominantly in Western populations: New York , Canada The SBCSP was a national prospective study of 28,235 predominantly Asian women. This is the only prospective trial conducted outside North America and Europe. Our results demonstrate, for the first time, an association between breast cancer incidence and the GM risk factors in an Asian population. Given the mature 10-year follow-up data of this study, the number of expected invasive breast cancer cases which was based on the GM was 1.85 times higher than the actual number observed in our local population. We conclude that the use of the GM considerably overestimates population risk for breast cancer incidence in Singaporean women who are not in a structured program of regular mammography screening. Given that our study was conducted in an Asian population, this result would be relevant to our Asian population and have a relevant impact on local chemoprevention studies and screening policies.In our cohort, mammography was performed only once; thereafter, patients continued usual care. In Singapore, the incidence of breast cancer was 59.5 per 100,000 per year in the period from 2004 to 2008 [The use of screening would often result in a transient rise in numbers of pre-invasive and invasive lesions and a resultant fall in events in the years immediately following the period of screening because prevalent cases that were detected at initial screening were eliminated in subsequent years. This would result in fewer cases observed in the year immediately following the screening period and thus result in over-prediction. This was also observed in our cohort: the incidence of invasive breast cancer  fell from an initial 37.5 to a mere 9.2 in the second year and 12.8 in the third year following screening. In the unscreened population, the invasive breast cancer incidence per 10,000 women remained relatively similar throughout all follow-up years after screening. However, the original model reported by Gail and colleagues [Singapore is a small, dense, and highly electronically networked society. All Singaporeans have a unique identification number that enables accurate matching of electronic data with a central electronic cancer registry. Cancer notification is mandatory in Singapore and is managed through pathology departments. This enables near complete ascertainment of breast cancer incidence by linkage of SBCSP members with the cancer registry data.There is minimal impact made on the results of our study despite the minor differences in coding of covariates in the SBCSP data when compared with the original GM. While the original GM includes the number of previous breast biopsies  and presence of atypical hyperplasia as risk factors, our analysis used an indicator of whether a woman had a previous breast biopsy  in place of these factors for the SBCSP. These coding differences are unlikely to affect predictions meaningfully. In the NCI's SAS macro for estimating absolute breast cancer risk under GM, the relative risk of a women with one previous breast biopsy under GM (1.27 for those at least 50 years old) is very similar to that of a women with a previous breast biopsy (1.29 for those at least 50 years old). For unknown presence of atypical hyperplasia, the relative risk of all women with a previous breast biopsy done is multiplied by a factor of 1. In addition, the percentage of women with a previous breast biopsy in SBCSP is 5.2, which is lower than the 15% based on the BCDDP data used for the estimation of relative risk in the GM, further lowering the influence of this covariate on our study results.The models overestimate risk in most of the categories. The discrepancy may arise from additional risk factors varying across populations not taken into account by the GM. In our study, the GM overestimates risk for women in whom age of menarche was at least 12 years, women who were more than 50 years old and who had no biopsy, or women without a first-degree relative affected and age at first live birth of 20 to 29 years, and these particular groups would benefit less from the use of mammography. It is of interest that the relative risks associated with family history are larger in our study as compared with those found in other studies performed in Western populations. This is especially so for women with more than one affected first-degree relative with breast cancer, in whom the larger relative risks could be due to the small number of such women in our study. Matsuno and colleagues  also repThe good performance of the IT-GM and the IT1-GM using independent data of Florence-EPIC (Florence-European Prospective Investigation into Cancer and Nutrition) indicates that the data from the Italian Multicentre Case Control Study of Diet and Breast Cancer might be useful for revising the GM to include additional risk factors, particularly modifiable risk factors, such as dietary consumption patterns. In principle, the case control data used in this study, together with cancer registry data, can be used to construct such models of absolute risk, and the current findings encourage us to do so.The extent of over-prediction of number of invasive breast cancers by GM within 10 years from screening (E/O ratio = 1.85) was smaller than that for the 5-year period (E/O ratio = 2.51). There are a few possible reasons for this decrease in E/O ratio. One possible explanation is the increase in breast cancer incidence with age. This results in larger numbers of breast cancer diagnosed in an older population. A larger incidence of breast cancer would better approximate the risks of breast cancer incidence calculated by the GM. In addition, it is possible that, over time, an increased proportion of women adopted regular mammographic screening, thereby resulting in an increase of breast cancer detection and consequent narrowing of our E/O ratio as calculated by using the GM. There was a significant lack of fit between the observed and expected breast cancer numbers based on GM for the SBCSP study cohort. By age cohort, females who were 50 to 59 years old appeared to derive a better benefit from a one-time screening mammography in comparison with females who were 60 to 64 years old.The Matsuno models gave a breast cancer number that was closer to the observed count as compared with the Caucasian-American GM model, but overestimation remained at 5-year follow-up Tables , 5 and 6Decarli and colleagues  pointed Based on rapid fertility and lifestyle changes in the Singapore population (particularly in the 1970s), an age-cohort model is thought to provide possibly the best prediction for breast cancer trends seen . In our Over-diagnosis is used to describe a condition that is diagnosed but that otherwise would not cause symptoms or go on to cause death. This occurs when the cancer either does not progress or grows at such a slow rate that the individual succumbs to other comorbidities. Given the increasing prevalence of cancer screening together with the improvements in screening technologies and interventions, the issue of over-diagnosis is now more evident. The 15-year follow-up results of the Malmo mammographic screening trial suggest that the risk of over-diagnosis of a mammographically detected cancer is 24% . While eFor patients, early detection adds to the increased risks of procedures such as breast biopsies as well as the risks of unnecessary treatment and medical expenses. Over-diagnosis also adds to patient anxiety and has social implications. A thorough discussion between clinician and patient should be carried out to ensure that the patient understands the implications and limitations of screening prior to proceeding with it. Strategies to reduce the risk of over-diagnosis would include a review of the current risk factors in our local population and a refinement of our risk prediction models to allow us to better advise our patients with regard to the need for screening.Much of our data are based on information obtained from our national cancer and diseases registry, which has excellent coverage. However, it is true that certain individuals who had migrated from the country and changed citizenship would subsequently be lost to follow-up on such a national database. However, the numbers of migrants remain small and should not affect our results significantly. It should be noted that our study represents the only systematic one-time mammography screening study performed to date and differs from other studies that were conducted on the basis of annual/biennial mammograms. With increased ascertainment of incidental tumors, the incidence of breast cancer would be higher with annual screening as compared with a single screening mammogram.In summary, we report long-term results from a large national prospective breast cancer mammographic screening study from Singapore, the only one conducted outside North America and Europe. Our study validates the GM risk factors individually but demonstrates that the GM overestimates individual risk for breast cancer in the setting of a developed country in Asia. With industrialization and its accompanying lifestyle shifts, Asia has witnessed a dramatic rise of breast cancer incidence, representing a huge global burden. Consequently, it has become imperative to evaluate local breast cancer epidemiology in order to validate existing models of breast cancer risk. With our results, future work should focus on the development of appropriately calibrated models for better prediction of risk, which would benefit individual counseling and cancer prevention research.in situ; E: expected number of breast cancer cases; E/O: ratio of expected to observed number of breast cancer cases; EPIC: European Prospective Investigation into Cancer and Nutrition; GM: Gail model; NCI: National Cancer Institute; O: observed number of breast cancer cases; SBCSP: Singapore Breast Cancer Screening Project; SEER: Surveillance: Epidemiology: and End Results.AABCS: Asian American Breast Cancer Study; BCDDP: Breast Cancer Detection Demonstration Project; CI: confidence interval; DCIS: ductal carcinoma The authors declare that they have no competing interests.MHT participated in the design and coordination of the study and helped to perform the statistical analysis and write the manuscript. PA participated in the design and coordination of the study. WYC, WSO and NQJL helped to perform the statistical analysis and write the manuscript. GHH, PHT, CSW, KSC, and KYC reviewed the manuscript and provided advice. All authors read and approved the final manuscript.


We evaluated the performance of the Gail model for a Korean population and developed a Korean breast cancer risk assessment tool (KoBCRAT) based upon equations developed for the Gail model for predicting breast cancer risk.Using 3,789 sets of cases and controls, risk factors for breast cancer among Koreans were identified. Individual probabilities were projected using Gail's equations and Korean hazard data. We compared the 5-year and lifetime risk produced using the modified Gail model which applied Korean incidence and mortality data and the parameter estimators from the original Gail model with those produced using the KoBCRAT. We validated the KoBCRAT based on the expected/observed breast cancer incidence and area under the curve (AUC) using two Korean cohorts: the Korean Multicenter Cancer Cohort (KMCC) and National Cancer Center (NCC) cohort.p = 0.017), while the KoBCRAT produced higher 5-year and lifetime risk for the cases than for the controls . The observed incidence of breast cancer in the two cohorts was similar to the expected incidence from the KoBCRAT . The AUC using the KoBCRAT was 0.61 for the KMCC and 0.89 for the NCC cohort.The major risk factors under the age of 50 were family history, age at menarche, age at first full-term pregnancy, menopausal status, breastfeeding duration, oral contraceptive usage, and exercise, while those at and over the age of 50 were family history, age at menarche, age at menopause, pregnancy experience, body mass index, oral contraceptive usage, and exercise. The modified Gail model produced lower 5-year risk for the cases than for the controls (Our findings suggest that the KoBCRAT is a better tool for predicting the risk of breast cancer in Korean women, especially urban women. Breast cancer is the second most common malignancy among women in Korea. The incidence of breast cancer is increasing rapidly, at an average rate second only to that of thyroid cancer. In 2009, 13,399 new female cases of breast cancer developed and 1,878 women died of this disease Several breast cancer screening modalities are currently available, including clinical breast examination, mammography, breast ultrasonography, and breast magnetic resonance imaging. Biannual mammography is provided as an organized cancer screening program for women who are 40 years and older in Korea. Given the high incidence of breast cancer, its significance, and the various available screening tests, a model estimating an individual's risk of developing breast cancer that could be easily applied in the clinical setting may be useful for recommending supplementary screening tests and conducting chemoprevention or screening intervention trials Several risk factors for breast cancer have been identified The incidence of breast cancer and various lifestyle or reproductive risk factors differ markedly among ethnic groups The objectives of this study were to develop the Korean Breast Cancer Risk Assessment Tool (KoBCRAT) using major risk factors for breast cancer, incidence and mortality rate from Korean data, and Gail's equations for projecting individual breast cancer probability in a Korean population; to evaluate the performance of the Gail model and the KoBCRAT; and to validate the KoBCRAT.This study was based on the Seoul Breast Cancer Study (SeBCS) recruited from 1994 to 2007. The cases  consisted of women diagnosed with histologically confirmed breast cancer who admitted to three teaching hospitals located in Seoul, accounting for about 15–18% of total breast cancer cases in Korea. The controls  were composed of non-cancer patients visiting the same hospitals as the cases from 1994 to 1997, health examinees visited to same hospitals from 1998 to 2000, and healthy women without cancer who participated in the community health screening program provided by teaching hospitals located in urban area from 2001 to 2007. After getting written informed consent, information on demographic characteristics, reproductive factors, and lifestyle habits were collected by trained interviewers using a structured questionnaire. The cases and controls were frequency-matched using 5-year age groups  and the enrollment year . To select age-matched cases and controls, a random selection method was applied. As a result, controls <30 years old or ≥40 years old and cases in their thirties, when the number of participants was higher than in the other group, were randomly excluded. Ultimately, 3,789 sets of cases and controls were selected for analysis. The distribution of characteristics among the cases and controls were compared using chi-square tests. The study design and the present study were approved by the Seoul National University institutional review board (IRB number: C-0909-048-295).We assessed a variety of factors that have been consistently associated with breast cancer This study used the same statistical approach as in the Gail model Individual probabilities of developing breast cancer were projected by combining information on an individual's relative risk, baseline hazard rate, and competing risk. The breast cancer incidence rate was obtained from Korean National Cancer Registry data and the total mortality rate and breast cancer specific mortality rate were obtained from Statistics of deaths by cause from the Korea National Statistical Office (Appendix 1). We measured the discriminatory accuracy of the model using the area under the receiver operating curve (AUC). All statistical analyses were performed using SAS software  and Microsoft Excel 2007 .To compare the performance of the KoBCRAT with that of the Gail model, we calculated and compared the mean risk for 3,789 age-matched case-control pairs from the SeBCS using parameter estimators as in the original Gail model, as well as Korean incidence and mortality data. Also, we compared the mean risk for 3,789 age-matched case-control pairs using the KoBCRAT. We also validated the KoBCRAT in two large, independent, prospective Korean cohort studies with incident breast cancer cases: the Korean Multi-Center Cohort (KMCC) study, a community general population cohort recruited from four rural regions We calculated the means and standard deviations of 5-year and lifetime breast cancer risks up to the age of 90 years old in breast cancer case-control sets using the KoBCRAT, the Gail model, and the modified Gail model and compared the differences in mean values between cases and controls from each model by T- test.In the KMCC and the NCC cohort, we compared the expected and observed numbers of incident breast cancer cases overall. The expected number of breast cancer cases was calculated by adding the individual absolute risk for each person calculated by the KoBCRAT and compared using a chi-squared test. The ratio of expected to observed numbers of cases (E/O ratio) and its 95% CI  were calculated with the following equation The mean age of the 3,789 cases and 3,789 controls was 49.0±9.47 years. Details about the characteristics of cases and controls were presented and compared in Appendix 2. The same variables as risk factors of breast cancer were included in the model when each of forward selection, backward elimination, and stepwise regression was applied. The risk factors and their levels and corresponding ORs are given in p = 0.017). With the KoBCRAT, the 5-year and lifetime risk scores were significantly higher for the cases than for the controls, indicating good performance. .The performance of the Gail model compared with the KoBCRAT in our 3,789 cases and 3,789 controls is presented in p-values of expected and observed numbers of cases obtained by the chi-squared test were not significant, showing good model calibration . The discriminatory power measured by AUC was 0.61  in the KMCC female participants and 0.89  in the female participants of the NCC cohort.We validated the KoBCRAT model using the two Korean cohort studies such as KMCC and the NCC cohort . The oveWe established the KoBCRAT using the Gail's equation. The risk factors for breast cancer in the KoBCRAT were selected according to the age categories of <50 and ≥50 year. These risk factors were a family history of breast cancer in the first-degree relatives, age at menarche, menopausal status, age at first full-term pregnancy, duration of breast feeding, oral contraceptive usage, and exercise for women aged <50 years. Risk factors for women aged ≥50 years were a family history of breast cancer in the first-degree relatives, age at menarche, age at menopause, experience of pregnancy, BMI, oral contraceptive usage, and exercise. The Gail model over-predicted the risk of non-cases compared with breast cancer cases for Koreans, whereas the KoBCRAT showed good performance. Estimated numbers of incident breast cancer measured by the KoBCRAT agreed well with the observed numbers in two independent cohorts, showing good validity.Although the Gail model selected the same risk factors across all age groups, the risks were calculated separately for two age ranges: <50 years and ≥50 years Among the risk factors included in the KoBCRAT, family history of breast cancer, age at menarche, menopausal status and age at menopause, pregnancy, age at first full-term pregnancy, duration of breast feeding, BMI, and oral contraceptive usage were consistently significant risk factors in previous Korean studies The Gail model performs well in female populations of the United States. Based on the Gail 2 model, modification of the original Gail model Several breast cancer risk assessment tools have been proposed for Korea. A model with a cohort design and an 8-year follow-up The c-statistics in the KoBCRAT were 0.63 in women aged <50 years and 0.65 in those aged ≥50 years, showing modest discriminative power between breast cancer cases and controls. These values are not inferior to those in the Gail model for Western populations  The E/O ratios of the Gail model for source populations from the United States, United Kingdom, and Italy were 0.75–1.19 Attempts to increase the performance of the Gail model have involved the addition of other risk factors, such as genetic information and breast density; however, the c-statistics were only modestly increased by 0.025 and 0.047 This study has some limitations. The KoBCRAT had moderate discriminatory accuracy although the c-statistic values were similar to those of the Gail model, which might render it inadequate for cancer diagnosis or screening. Therefore, we should design a modified model including other risk factors, such as breast density, bone mineral density, and genetic and molecular biomarker information. Second, previous studies assessed model calibration using E/O ratios according to risk factor or age group categories Considering the rapid increase in breast cancer incidence in recent decades in Korea, the development of breast cancer models targeting the Korean female population is needed. We developed a breast cancer risk assessment tool and conducted validation with two cohorts. The KoBCRAT showed good calibration and modest discrimination, particularly for women living in urban areas or with estrogen receptor-positive tumors. Despite several limitations, the KoBCRAT is more applicable to Korean women than the Gail model based on Western populations. We expect that the KoBCRAT will contribute to future breast cancer clinical trials focused on primary prevention and early detection in Korean women. Further work is needed to increase the discriminatory power of the KoBCRAT for Korean females as a whole.File S1Supporting information. Table S1. Age-specific incidence for breast cancer, breast cancer–specific mortality rate, total mortality rate, and baseline risk of breast cancer in Korea. Table S2. Distribution of basic characteristics of cases and controls. Figure S1. Natural logarithms of the odds ratio for individual relative risk of breast cancer development(DOC)Click here for additional data file.


Breast density is both a modifiable risk factor for breast cancer and an indicator of the sensitivity of mammography. Reliable measurement in the screening population could enable personalised screening to maximise early detection of cancer. In the Predicting Risk of Cancer at Screening (PROCAS) study, we are investigating different approaches to measuring breast density, and we present comparative data from one subjective method and two automated volumetric methods.The screening mammograms of 4,109 women enrolled in PROCAS were visually assessed independently by two experienced film readers, who recorded their estimates of percentage density on visual analogue scales (VAS). The mammograms were also processed by Quantra™ (Hologic Inc.) and Volpara (Matakina Technology Ltd). They were ranked according to density by each method, and the top 10% and 1% compared.Of the 617 mammograms ranked in the most dense 10% by at least one method, only 127 were high density by all three methods. The overlap between the two volumetric methods was 214; between VAS and Quantra™ it was 195 and between VAS and Volpara it was 147. For the 48 mammograms in the top 1% by density, the overlaps were 13, 9 and 7, respectively.The lack of overlap between the methods was surprising and has serious implications for the implementation of personalised screening. The optimum measure of breast density in digital mammograms has yet to be identified; different methods may yield the strongest links with cancer risk and sensitivity of mammography, and further research is needed to elucidate these relationships.


Several common breast cancer genetic susceptibility variants have recently been identified. We aimed to determine how these variants combine with a subset of other known risk factors to influence breast cancer risk in white women of European ancestry using case-control studies participating in the Breast Cancer Association Consortium.FGFR2), 8q24-rs13281615, 11p15-rs3817198 (LSP1), 5q11-rs889312 (MAP3K1), 16q12-rs3803662 (TOX3), 2q35-rs13387042, 5p12-rs10941679 (MRPS30), 17q23-rs6504950 (COX11), 3p24-rs4973768 (SLC4A7), CASP8-rs17468277, TGFB1-rs1982073 and ESR1-rs3020314). Interactions were tested for by fitting logistic regression models including per-allele and linear trend main effects for SNPs and risk factors, respectively, and single-parameter interaction terms for linear departure from independent multiplicative effects.We evaluated two-way interactions between each of age at menarche, ever having had a live birth, number of live births, age at first birth and body mass index (BMI) and each of 12 single nucleotide polymorphisms (SNPs) . The assumption of multiplicative combined effects for these established genetic and other risk factors in risk prediction models appears justified. FGFR2, LSP1, MAP3K1, TOX3, MRPS30, COX 11, SLC4A7, and at chromosomes 8p24 and 2q35 [P < 10-7) coming from candidate gene approaches is CASP8 [TGFB1 [ESR1 [Breast cancer is known to have both a genetic and non-genetic etiology. Several common genetic susceptibility variants have recently been identified, predominantly by genome-wide association studies (GWAS). These include single nucleotide polymorphisms (SNPs) at loci containing the genes is CASP8 ; more eq8 [TGFB1  and ESR1It is important to determine how these common SNPs combine with other known risk factors such as age at menarche, parity, age at first birth and body mass index (BMI) ,9 to infFGFR2), 8q24-rs13281615, 11p15-rs3817198 (LSP1), 5q11-rs889312 (MAP3K1), 16q12-rs2803662 (TOX3), 2q35-rs13387042, 5p12-rs10941679, 17q23-rs6504950, 3p24-rs4973768 and CASP8-rs17468277) and two for which there is less clear evidence of a main effect (TGFB1-rs1982073 and ESR1-rs3020314). The potential effect modifiers considered were age at menarche, ever having had a live birth, number of live births, age at first birth and BMI. A secondary aim was to evaluate these interactions in susceptibility to breast cancer subtypes defined by ER and progesterone receptor (PR) status. Data for white women of European ancestry were combined from 21 case-control studies participating in the Breast Cancer Association Consortium (BCAC).We, therefore, aimed to assess effect modification for 12 SNPs, 10 of which have been clearly associated with breast cancer risk . SNP CASP8- rs17468277 is in complete linkage disequilibrium with CASP8-rs1045485, which has previously been reported to be associated with breast cancer [Genotyping methods have been previously described ,7,12,14.Overall genetic associations were evaluated for each of the 12 SNPs by estimating odds ratios (ORs) and their 95% confidence intervals (CI) via logistic regression, assuming multiplicative per-allele effects for the risk allele, as first reported in the literature  and a single interaction term for the product of the number of risk alleles and the value of the risk factor. This was tested statistically by a likelihood ratio test comparing this model to that without the interaction term. Effect modification by BMI was assessed separately for women <55 and ≥55 years of age.P-values adjusted for multiple testing [P-value for the comparison of this model to the main effects only model applied to the same data. The minimum P-value was recorded for each of 10,000 replicates and the adjusted P-values were estimated as the proportion of replication P-values less than the corresponding unadjusted P-value. Results rounded to two decimal places were identical to those obtained using a standard non-parametric permutation test [In addition, a parametric bootstrap test was used to estimate interaction  testing . For eacion test .All statistical analyses were carried out using Stata: Release 10   with theThe 21 participating studies contributed 26,349 cases and 32,208 controls of self-reported white European race/ethnicity, all with available data for at least one of the 12 SNPs considered and at least one of the other risk factors considered . Of these, 17,603 cases from 18 studies  were interviewed within two years after their breast cancer diagnosis and 29,187 controls came from the same 18 studies. Forty-six percent of cases and 38% of controls were under age 55 years at diagnosis and interview, respectively. ER and PR status was known for 19,561 and 16,962 cases, respectively. Details by study are provided in Table 2) was associated with a 20% (95% CI = 10 to 29%) lower risk of breast cancer for women under age 55 years. The one unexpected observation was that obesity was not associated with breast cancer risk in women aged 55 years and older .When analyses were restricted to population-based studies, the expected associations with breast cancer were observed for the risk factors, with one exception. After adjustment for age and study, each one-year increase in age at menarche was associated with a 4% (95% CI = 2 to 5%) decrease in breast cancer risk, and being parous was associated with a 16% (95% CI = 10 to 22%) decreased risk. For parous women, each additional live birth was associated with an 11% (95% CI = 8 to 13%) decrease in risk, while each five-year increment in age at first birth was associated with a 7% (95% CI = 4 to 10%) increase in risk. Obesity  was for the modification of the association with 11p15-rs3817198 (LSP1) by number of live births. Per-allele OR estimates increased from 1.04 (95% CI = 0.97 to 1.11) for women who had had just one live birth to 1.24 (95% CI = 1.11 to 1.38) for women with at least four live births, and an interaction OR of 1.05 per live birth and per allele was estimated. This trend was also observed when data from only population-based studies and from only studies with at least 1,000 cases and 1,000 controls were considered . Evidence for this interaction was observed when the analysis was restricted to ER-positive and PR-positive disease . However, considering that 72 tests for interaction were carried out, chance cannot be excluded as an explanation for these results. The multiple-test-adjusted P-value for the modification of the 11p15-rs3817198 association by number of live births was 0.12. The adjusted p-values for all other interactions tested were all ≥0.61.The strongest evidence of interaction , parity (per live birth) and age at first birth (per five-year age increase), our study had 90% power at a significance level of 0.0007  to detect interaction ORs of at least 1.06 for all loci tested except CASP8-rs17468277, for which the minimum was 1.08. For BMI (per five-unit increase) the minimum interaction OR detectable with 90% power in both age strata (<55 and ≥55) was 1.08 for the more common variants and 1.10 for CASP8-rs17468277. For parity, considered as never or ever having had a live birth, the study had similar power to detect interaction ORs of at least 1.20 for CASP8-rs17468277 and 1.16 for the remaining loci.FGFR2), 8q24-rs13281615, 11p15-rs3817198 (LSP1), 5q11-rs889312 , 2q35-rs13387042, 5p12-rs10941679, 17q23-rs6504950, 3p24-rs4973768 and CASP8-rs17468277) nor the putative associations with TGFB1-rs1982073 or ESR1-rs3020314. This was also true for disease subtypes defined by ER and PR status.This combined analysis of more than 25,000 cases and 30,000 controls found no conclusive evidence that age at menarche, parity, age at first birth or BMI modify the established associations of breast cancer risk with 10q26-rs298158 (LSP1). However, the observed trend of increasing relative risk with increasing parity was not statistically significant after correction for multiple testing. It should be noted that the interaction OR was 1.05 per allele and per live birth. This corresponds to an estimated per-allele OR increasing from 1.04 for women with one child to 1.24 for women with four or more children, for a SNP with an estimated average OR of 1.08 across all levels of parity. Such weak interactions would only result in very small differences in estimates of joint effects relative to those from models assuming multiplicative effects. This finding in this very large study highlights the difficulty of identifying modifying effects of this magnitude.The strongest evidence of effect modification was for number of live births and 11p15-rs3817198  and number of children (P = 0.9) [A recent study by Travis  [et al. . Of noteOne of the strengths of the BCAC is the large combined sample size achieved through international collaboration. This has proven to be very effective in confirming or ruling out association with breast cancer for common SNPs identified through GWAS and candidate gene studies ,14,20,21A potential limitation of our study derives from heterogeneity in data collection methods across studies. All studies except two (neither population-based) used structured questionnaires administered by a variety of means, including in-person interviews, phone-interviews and self-administration. Nevertheless, the measurement of age at menarche, ever having had a live birth, number of live births and age at first birth seem likely to be robust to these differences in data collection method. Our results for BMI may be more likely to be affected by heterogeneity in data collection methods, although standardized measurement within studies and adjustment for study as a covariate should limit this to a loss of power, rather than any systematic bias. We repeated our primary analyses excluding cases interviewed before, or more than two years after, their breast cancer diagnosis and results were not substantially different. This suggests that between-study differences in the reference time at which BMI was reported did not influence the inference from our study. A further limitation of our study was that we did not collate information on hormone therapy (HT) use from the majority of participating studies and so were unable evaluate interactions between SNPs and BMI by HT use in older women. This requires further investigation because HT has been observed to modify the effect of obesity on post-menopausal breast cancer risk . Since mIn summary, in the largest collaborative analyses of gene-environment interactions carried out to date, we have observed no conclusive evidence for modification of the per-allele relative risk associated with common breast cancer susceptibility variants by age at menarche, parity, age at first birth or BMI. This finding is consistent with those from a recently published smaller prospective study. These results imply that the combined effects of these common susceptibility alleles and other established risk factors can be assumed to multiplicative in risk predicted models for breast cancer.ABCFS: Australian Breast Cancer Family Study; AOCS: Australian Ovarian Cancer Study; BBCC: Bavarian Breast Cancer Cases and Controls; BBCS: British Breast Cancer Study; BCAC: Breast Cancer Association Consortium; BMI: body mass index; CEPH, Centre d'Etude Polymorphisme Humain; CGPS: Copenhagen General Population Study; CI: confidence interval; CNIO-BCS: Spanish National Cancer Centre Breast Cancer Study; ER: estrogen receptor; GENICA: Gene Environment Interaction & Breast Cancer in Germany; GESBC: Genetic Epidemiology Study of Breast Cancer by Age 50; GWAS: genome-wide association study; HT: hormone therapy; KBCP: Kuopio Breast Cancer Project; kConFab: Kathleen Cuningham Foundation Consortium for Research into Familial Breast Cancer; LMBC: Leuven Multidisciplinary Breast Centre; MARIE: Mammary Carcinoma Risk Factor Investigation; MCBCS: Mayo Clinic Breast Cancer Study; MCCS: Melbourne Collaborative Cohort Study; NC-BCFR: Northern California Breast Cancer Family Registry; OFBCR: Ontario Familial Breast Cancer Registry; OR: odds ratio; PBCS: NCI Polish Breast Cancer Study; PR: progesterone receptor; SASBAC: Singapore and Sweden Breast Cancer Study; SBCS: Sheffield Breast Cancer Study; SEARCH: Study of Epidemiology and Risk factors in Cancer Heredity; SNP: single nucleotide polymorphism; UCIBCS: UCI Breast Cancer Study; USA: United States of America; USRT: US Radiologic Technologists Study.The authors declare that they have no competing interests.RLM performed the statistical analyses and wrote the manuscript. MMG also carried out statistical analyses. RLM, MMG, ABS, PAF, FJC, PDP, MGC, ELG and JCC formed part of the writing group which was responsible for the interpretation of results and critical revision of the manuscript for important intellectual content. JB, NM, IdSS, OF, NJ, JF, JLH, MCS, AJS, MSL, SJS, DMF, VMK, PA, ILA, GG, JAK, HB, DL, PH, KC, JJL, GCT, LB, GS, SEB, EMJ, RH, MWB, LH and DFE also contributed to the interpretation of results and/or critical revision of the manuscript. RLM, PAF, FJC, JB, JIAP, MPZ, IdSS, LJG, HAC, AZ, LB, MES, JL, JLH, GSD, CA, MCS, AJS, MSL, DMF, VK, ILA, GG, JAK, NW, AC, MWRR, SSC, MS, AMD, HB, YDK, TB, GENICA, DL, AS, PH, JJL, GCT, kConFab, AOCS, GGG, LB, GS, SEB, BGN, HF, EMJ, DWW, ASW, CV, JEO, ZF, AV, DFJ, JH, KH, LH, MKH, JM, DFE, PDP, MGC and JCC participated in the original design, subject recruitment, acquisition of data and/or biospecimen collection for the studies that contributed data. RLM, MMG, OF, NJ, AZ, MCS, AJS, SJS, AM, ILA, AC, AMD, SA, HB, GENICA, DL, JR, SWG, AKI, HH, SEB, BGN, ZF, MK, ABE, MKH and JM carried out the genotyping and/or quality control of genotype and other data. All authors were involved in drafting the manuscript and read and approved the final version.Additional data tables S1 to S8. Additional Data Table 1: Design details for each study. Additional Data Table 2: Per-allele breast cancer odds ratios (OR) estimates and 95% confidence intervals  for SNPs, stratified by age at menarche, ever having had a live birth, number of live births, age at first birth and BMI; based on data from all studies. Additional Data Table 3: Per-allele breast cancer odds ratio (OR) estimates and 95% confidence intervals  for SNPs, stratified by age at menarche, ever having had a live birth, number of live births, age at first birth and BMI; based on data from population-based studies only. Additional Data Table 4: Per-allele breast cancer odds ratio (OR) estimates and 95% confidence intervals  for SNPs, stratified by age at menarche, ever having had a live birth, number of live births, age at first birth and BMI; based on data only from studies with at least 1,000 cases and 1,000 controls. Additional Data Table 5: Per-allele ER-positive breast cancer odds ratios (OR) and 95% confidence intervals  for SNPs, stratified by age at menarche, ever having had a live birth, number of live births, age at first birth and BMI; based on data from all studies. Additional Data Table 6: Per-allele ER-negative breast cancer odds ratios (OR) and 95% confidence intervals  for SNPs, stratified by age at menarche, ever having had a live birth, number of live births, age at first birth and BMI; based on data from all studies. Additional Data Table 7: Per-allele PR-positive breast cancer odds ratios (OR) and 95% confidence intervals  for SNPs, stratified by age at menarche, ever having had a live birth, number of live births, age at first birth and BMI; based on data from all studies. Additional Data Table 8: Per-allele PR-negative breast cancer odds ratios (OR) and 95% confidence intervals  for SNPs, stratified by age at menarche, ever having had a live birth, number of live births, age at first birth and BMI; based on data from all studies.Click here for file