BRCA1 or BRCA2 mutation, or other risk factors. An accurate assessment of breast cancer risk is required to balance the benefits and risks of surveillance, yet published studies have used narrow risk assessment schemata for enrollment. Breast density limits the sensitivity of film-screen mammography but is not thought to pose a limitation to MRI, however the utility of MRI surveillance has not been specifically examined before in women with dense breasts. Also, all MRI surveillance studies yet reported have used high strength magnets that may not be practical for dedicated imaging in many breast centers. Medium strength 0.5 Tesla MRI may provide an alternative economic option for surveillance.Evidence-based screening guidelines are needed for women under 40 with a family history of breast cancer, a BRCA1 or BRCA2 germline mutation.We conducted a prospective, nonrandomized pilot study of 30 women age 25–49 years with dense breasts evaluating the addition of 0.5 Tesla MRI to conventional screening. All participants had a high quantitative breast cancer risk, defined as ≥ 3.5% over the next 5 years per the Gail or BRCAPRO models, and/or a known BRCA1 mutation carrier.The average age at enrollment was 41.4 years and the average 5-year risk was 4.8%. Twenty-two subjects had BIRADS category 1 or 2 breast MRIs (negative or probably benign), whereas no category 4 or 5 MRIs  were observed. Eight subjects had BIRADS 3 results, identifying lesions that were "probably benign", yet prompting further evaluation. One of these subjects was diagnosed with a stage T1aN0M0 invasive ductal carcinoma, and later determined to be a Using medium-strength MRI we were able to detect 1 early breast tumor that was mammographically undetectable among 30 young high-risk women with dense breasts. These results support the concept that breast MRI can enhance surveillance for young high-risk women with dense breasts, and further suggest that a medium-strength instrument is sufficient for this application. For the first time, we demonstrate the use of quantitative breast cancer risk assessment via a combination of the Gail and BRCAPRO models for enrollment in a screening trial. The sensitivity of mammography has been observed to be lower in women <50 years of age 63–86%), compared with women ≥50 years 89–94%) -3. The U–86%, com% -3. TheAbout 25% of all women have dense breast tissue, and this physiology is more common in younger women . Indeed,BRCA1 or BRCA2 breast and ovarian cancer susceptibility genes . Additionally, the high cost of breast MRI is severely limiting. Not only is there a large cost associated with the purchase of a high field strength magnet, but also there are additional costs for housing and maintaining the unit. The use of medium field strength magnets has been criticized for their low signal-to-noise ratio (SNR) per sampling time. However, the use of a medium field strength magnet, such as a 0.5 Tesla, would come at significant cost savings of up to 1/3 that of a higher field strength MRI system. Our study provides preliminary evidence that a medium field strength breast MRI system can be effectively used for high-risk surveillance.et al. [et al. [There is evidence that the use of a medium field strength breast MRI system is comparable to a 1.5 T system. In a study published by Kuhl et al. , MRI was [et al.  imaged 4The appearance of T1-weighted gradient echo images generated from a contrast study depends on the SNR, the contrast-to-noise ratio (CNR) and the contrast agent used. The SNR is a function of the magnetic field strength, magnet shim, flip angle, voxel size, receiver gain, RF coil and image processing parameters. Magnetic field strength directly affects the SNR and the spin relaxation properties of the tissue. The SNR is linear with field strength--everything else being equal, 1.5 T magnets produce images with three times the SNR of images from 0.5 T magnets. In addition, the T1 of a given tissue/sample type is larger at higher field strengths. This implies that at a higher field strength, the spin relaxation of a given tissue/sample may be changed more that the same tissue/sample at a lower strength and thus may increase the difference observed in pre- and post-contrast images.The CNR is a measure of the average intensity of an object compared to the average intensity of the noise floor for a given object and pulse sequence. The CNR is a function of the relation of the pulse sequence timing parameters  to the spin relaxation properties  of the object. Because the contrast agent changes the spin relaxation properties, it changes the CNR of the image. Above a minimum SNR threshold, the ability to detect a lesion using MRI is a function of the change in CNR of pre- and post-contrast images and the voxel size acquired. If the configuration of a 1.5T MRI scanner and a 0.5T MRI scanner is such that both are above the minimum level of SNR, have comparable CNR changes after adding a contrast agent, and have identical voxel sizes and scan durations, the ability to detect lesions is similar.Our study demonstrates that medium field strength MRI can detect tumors that have been missed by conventional screening mammography. There are, however, certainly limitations to this initial study, including its small size; up to 3 months time differential between screening mammogram and MRI; and lack of longitudinal follow up. No conclusions can be drawn regarding the overall sensitivity or specificity of screening with the 0.5 T MRI.BRCA1 or BRCA2 mutation, or other risk factors, largely because of the lack of randomized, controlled trials to inform the development of such guidelines [There has been a dearth of evidence-based screening guidelines for women age < 40 with a family history of breast cancer, a idelines -50.As evidence accumulates regarding the efficacy of bilateral prophylactic mastectomy, data regarding the efficacy of screening is of paramount importance so that women can make informed choices. In a large screening study of 251 mutation carriers, a high rate of interval tumors found on breast self-examination led to the suggestion that more frequent  mammography should be considered, particularly in younger women , resultiThe variations in sensitivity and specificity for breast MRI exist for several reasons, including technical factors , interpret al. [et al. [BRCA1 or BRCA2 mutations found a specificity of 95.4% based on biopsy of BIRADS level 4 and 5 lesions, but did not take into account the effect of breast density, nor the non-biopsy interventions engendered by level 3 lesions [All high-risk MRI screening studies reported thus far have used high field-strength magnets. Stoutjesdijk et al.  found th [et al.  reported lesions . High-riThe largest breast MRI surveillance study reported to date, based on 1909 eligible women including 358 germ-line mutation carriers, found a specificity of 89.8% for workup of level 3, 4, and 5 lesions . HoweverBRCA1/2 mutation risk via BRCAPRO. Kriege et al. [BRCA1 and BRCA2 mutation carriers, since only 6.6% (19/288) of that high-risk population carried such mutations [We contend that the benefits of breast cancer surveillance cannot be satisfactorily balanced against the risks of screening without an accurate assessment of absolute breast cancer risk. We report for the first time the use of quantitative breast cancer risk assessment for use in enrollment in a breast cancer screening pilot study by combining both the established Gail model and a purely genetic model which estimates e et al.  used thee et al.  for riske et al. . The Gaiutations .BRCA1 or BRCA2 gene, based on our accumulated knowledge of hereditary breast-ovarian cancer syndrome. Since 23 of our 30 subjects were enrolled based on their BRCAPRO risk rather than their risk based on the Gail model, this group represents a rather unique "high-risk" population, with perhaps more in common with studies in BRCA1/2 carriers such as that of Warner et al. [BRCA1 or BRCA2 mutations may be reluctant to enroll in a trial restricted to mutation carriers, since their genetic status would be known by virtue of their participation. Enrollment according to quantitative risk can allow women who are not yet prepared for genetic testing to participate in a high-risk surveillance trial.Supplementation of the Gail model with the BRCAPRO model allows for a much more efficient ascertainment of women at high risk for breast cancer specifically due to the possibility that they carry a mutant We have advanced the rationale for quantitative risk assessment in chemoprevention trials, with the advantages of improved power using a smaller study population, shorter study duration and lower cost . The samThe concomitant use of the Gail and BRCAPRO-based models allows for the identification of a broad array of high-risk women . The repThe author(s) declare that they have no competing interests.WSR designed the study, recruited and consented the patients, obtained funding, and drafted the manuscript. JJL and SGG contributed to the study evaluation and drafted the manuscript. JHS was responsible for the performance and interpretation of the mammography and MRI. MH was the research coordinator and helped design the study. VGV participated in the study design and data interpretation. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Mammographic breast density is one of the strongest known risk factors for breast cancer. We present a novel technique for estimating breast density based on 3D T1-weighted Magnetic Resonance Imaging (MRI) and evaluate its performance, including for breast cancer risk prediction, relative to two standard mammographic density-estimation methods.The analyses were based on MRI (n = 655) and mammography (n = 607) images obtained in the course of the UK multicentre magnetic resonance imaging breast screening (MARIBS) study of asymptomatic women aged 31 to 49 years who were at high genetic risk of breast cancer. The MRI percent and absolute dense volumes were estimated using our novel algorithm (MRIBview) while mammographic percent and absolute dense area were estimated using the Cumulus thresholding algorithm and also using a 21-point Visual Assessment scale for one medio-lateral oblique image per woman. We assessed the relationships of the MRI and mammographic measures to one another, to standard anthropometric and hormonal factors, to BRCA1/2 genetic status, and to breast cancer risk (60 cases) using linear and Poisson regression.P < 0.0001). Both show strong associations with established anthropometric and hormonal factors. Mammographic percent dense area, and to a lesser extent MRI percent dense volume were lower in BRCA1 carriers  but there was no association with BRCA2 carrier status. The study was underpowered to detect expected associations between percent density and breast cancer, but women with absolute MRI dense volume in the upper half of the distribution had double the risk of those in the lower half (P = 0.009).MRI percent dense volume is well correlated with mammographic percent dense area (R = 0.76) but overall gives estimates 8.1 percentage points lower (The MRIBview estimates of volumetric breast density are highly correlated with mammographic dense area but are not equivalent measures; the MRI absolute dense volume shows potential as a predictor of breast cancer risk that merits further investigation. BRCA1 or BRCA2 genes .The details of the remaining women are given in table S1 in Additional data file  program . The senAt the time of recruitment 576 of the 749 women (76.9%) completed a questionnaire containing questions relating to lifestyle risk factors, reproductive and hormonal history and various standard anthropometric measures Table .The primary analyses were based on three measures of proportional density  and two measures of absolute density (MRI dense volume and Cumulus mammographic dense area), as described above. All analyses of mammographic density refer to the MLO projection unless otherwise specified.The relationships between the density measures for the left and right breasts were assessed using Pearson linear correlation coefficients and t-tests. The mean unsigned difference between breasts (i.e. not taking into account which of a woman's breasts is the denser) is also presented. The relationship between the MRI percent dense volume and Cumulus percent dense area was assessed using linear regression. Correlation coefficients and weighted kappa statistics  were used to describe inter-reader (MRI) and intra-reader ) consistencies.vs never smokers; women who had ever been pregnant vs never pregnant; women who had ever used hormone replacement therapy vs those who had not; women who had previously taken an oral contraceptive vs those who had not; women who had ever taken tamoxifen as part of a prevention trial vs those who had not. Women who reported that their menstrual periods had stopped due either to a natural menopause, a hysterectomy or an oophorectomy were considered as being postmenopausal.Linear regression was used to examine associations between the density measures and a range of standard variables, with the two MRI measures log-transformed to closer approximate a normal distribution. Continuous variables were divided into quartiles; age at study registration, weight, body mass index , hip, waist and chest circumferences and waist:hip ratio . Parity was defined as the number of full-term pregnancies; women with four or more were considered as a single group. The following binary variables were also considered: current/previous smokers BRCA1/2 genetic groups were adjusted for age at registration ≥ 45 years and associations between genetic status and density were tested using linear regression. The analyses were repeated additionally adjusting for BMI and parity.The mean values of each density measure for the BRCA1 mutation carriers, BRCA2 mutation carriers and uninformative/non-carriers. The power to detect a doubling of risk between the upper and lower halves of the density distribution was 72% for the MRI percent dense volume and 59% for the Cumulus percent dense area, rising to 92% and 82% for a 2.5-fold increase in risk.Finally, Poisson regression was used to test whether each of the density measures was associated with cancer risk in this population by quartile of density, and also grouping the two lower and two upper quartiles in order to improve the stability of the estimates. Each individual's at-risk period was defined as starting at their study registration and continuing until the earliest of breast cancer diagnosis, death, prophylactic bilateral mastectomy, other loss to the ONS flagging system or 31 July 2007. The analyses were performed adjusting only for age group, adjusting for genetic status and/or BMI and parity, and also separately for All statistical tests are two-sided and were conducted using Stata version 10.0 .P = 1.0 for MRI, P = 0.32 for Cumulus, P = 0.31 for VA) , compared with women with a Cumulus-density < 30% [slope = 0.43 ] (P = 0.0036 for the difference between the two slopes). The 30% cut-off was based on visual inspection of the scatter-plot. On average the MRI percent dense volume was 14.9 percentage points lower (95% CI 13.5 to 16.4) than the Cumulus percent dense area (P < 0.0001) for the women with mammographically denser breasts, but there was no significant difference for the less dense breasts (P = 0.20). Despite the strong correlation there were a few women for whom the MRI and Cumulus estimates were highly divergent. The correlation between the MRI absolute dense volume and the Cumulus absolute dense area was clearly present but was weaker than for the corresponding percent densities (R = 0.61) [Additional data file The percent dense volume measured by MRI was positively correlated with the percent dense area estimated using the Cumulus program , in order to establish whether the relationships seen in unselected populations are also true in this high-risk group of women, and to see whether breast density as measured by our novel MRI-density estimation technique is related to the same factors as mammographic breast density Tables  and 2.P < 0.001 in each case) and with WHR (P = 0.003), as expected. Although there was a significant negative trend in Cumulus percent density with increasing age, a significant decrease was seen only for women in the oldest age quartile . Post-menopausal women, women who had ever been pregnant and women who had ever used tamoxifen also had lower percent dense areas .The Cumulus percent dense area was negatively associated with BMI, weight, hip, waist and chest circumferences and with parity (trend P = 0.023).Lower MRI percent dense volume was significantly associated with all of the same variables, and additionally showed a possible negative association with previous oral contraceptive use (P = 0.021) but not with any of the other tested variables.The Cumulus measure of absolute dense area was significantly associated with the same variables as the Cumulus percent density, apart from age, tamoxifen use and ever-pregnancy, but the associations were in each case less significant than for percent dense area. The MRI measure of absolute dense volume showed a modestly significant association with chest circumference  and chest circumference (trend by quartiles). However, chest circumference was only available for 491 women and so, to avoid excessive reduction in sample sizes, subsequent analyses were conducted under two models: 1) adjusted for age ≥ 45 years only; 2) adjusted for age ≥ 45 years, BMI quartiles and parity.BRCA1/BRCA2 genotype. TP53 carriers and others with a Li-Fraumeni syndrome family history were not included in this part of the analysis because of their small numbers. For each of the percent and absolute density measures, women with an uninformative genetic test (the baseline group) tended to have denser breasts than either the BRCA1 or BRCA2 carriers or non-carriers. The comparison with non-carriers was only significant for the MRI percent density, and then only when BRCA1 and BRCA2 non-carriers were combined .Figures BRCA1 mutation carriers had significantly lower MRI percent dense volume (P = 0.010), Cumulus percent dense area (P = 0.001), VA percent dense area (P < 0.001) and Cumulus absolute dense volume (P = 0.002) than the uninformative group; these effects remained significant after adjustment for BMI and parity , and this result was non-significant after adjustment for BMI and parity (P = 0.31). P = 0.00 than the Figures , but theBRCA1 and BRCA2 carrier probabilities  using multivariate regression. BRCA1 carrier probability was negatively associated with Cumulus percent density  but not with MRI percent dense volume . BRCA2 carrier probability was not associated with MRI or Cumulus percent densities .The data were reanalysed in terms of the Boadicea-estimated Thirty-eight of the 749 women developed breast cancer during the course of the MARIBS study; a further 22 developed breast cancer between their last MARIBS screening round and the cut-off date of 31/07/2007. Unfortunately we were unable to obtain any mammogram for 12 of the women with a breast cancer during the study, so the comparison of the MRI and Cumulus/Visual density measures are not based on equal numbers.BRCA1 and BRCA2 genotype/carrier probability (Upper two quartiles vs lower two quartiles: IRR (incidence rate ratio) = 2.04 (1.16 to 3.59) P = 0.013 for MRI; IRR = 2.24 (1.18 to 4.26) P = 0.014 for VA). These incidence rate ratios ceased to be significant when further adjusted for parity and BMI, although this may be due to the smaller numbers of women for whom these data were available  P = 0.021; IRR = 1.35 (1.01 to 1.80) P = 0.042 for trend in quartiles) (Table BRCA1 and BRCA2 carriers (BRCA1 IRR = 3.62 (1.32 to 9.90) P = 0.012; BRCA2 IRR = 3.06 (1.14 to 8.19) P = 0.026) but was not apparent in the non-carriers/uninformative women (P = 0.66) . Specifically, among the set of 176 BRCA1 mutation carriers analysed by Mitchell et al, 36% developed breast cancer, compared with only 17% of our 94 BRCA1 carriers, hence it is possible that their group of carriers were biased towards women with denser breasts . This would have obscured any more general association between BRCA1 mutation status and low density. Conversely, the opposite may well be true, in that we specifically excluded women who had developed breast cancer prior to recruitment (mean age at entry = 40 years). According to the same logic, this could have biased our set of BRCA1 carriers towards those with a lower than average density. The same would not necessarily have been seen for BRCA2 carriers, in whom a greater proportion would be expected to be asymptomatic at the age of entry regardless of the presence or absence of potential modifiers such as density. The observation of lower density in BRCA1 mutation carriers would seem to be at odds with a report that only BRCA1/p53-deficient mice developed lateral branches and alveoli without pregnancy [BRCA1/2 carriers and non-carriers from a Chicago study [We found carrying a regnancy . To our go study .BRCA1/2 mutation carriers.The chief goal of breast density estimation is for the study and prediction of breast cancer risk. High breast density is well established as one of the strongest known risk factors for breast cancer, with clearer associations for quantitative measures of percent density (such as Cumulus) than for qualitative measures . HoweverBRCA1/2 mutation carriers, comparing women with percent density ≥ 50% with those with density < 50%, but found the comparison to be non-significant when densities between 25 to 50% were compared with < 25% [BRCA1 and BRCA2 carriers. Only 17% of the women in our study had a Cumulus percent density ≥ 50% , giving us a 30% power to detect a difference of the same magnitude as that reported by Mitchell et al. This difference in the distribution of densities between the studies may be due to differences in ascertainment criteria, as discussed above, or may simply reflect the use of different digitizers.Mitchell et al reported a breast cancer odds ratio of 2.29 (95% CI 1.23 to 4.26) for a combined group of Our results also appear at first glance to be different from those seen in the general population; a recent meta-analysis of 42 studies reported an almost five-fold increase in risk for the densest category (≥ 75%) versus the least dense (< 5%) . HoweverTP53 mutation carriers for whom x-ray mammography is not considered safe.The MARIBS study was originally designed to have suitable power to detect differences in sensitivity between MRI and XRM breast screening , rather We also examined the value of the craniocaudal view in our data set in the light of a recent report that the predictive value of visually-assessed mammographic percent density based on a mediolateral oblique view alone was substantially improved when combined with density estimates from the craniocaudal view . We founBRCA1 and BRCA2 carriers. It is possible that this is a false positive result. If it is true, it is curious that MRI dense volume showed almost no significant associations with any of the tested anthropometric or hormonal factors. However, given that breast cancer is believed to be initiated in the stromal or epithelial cells, it is plausible that the total quantity of target tissue is a more relevant predictor of breast cancer risk than the proportional density. In this context it is interesting that the absolute MRI breast volume was the only density measure not found to be lower in BRCA1 mutation carriers. While clearly related to percent dense volume, absolute dense volume is not capturing the same information; we found that 37% of women were in a different half of the percent MRI dense volume distribution than the absolute dense volume distribution, explaining why the results for the two measures were so different. No such breast cancer association was seen for the Cumulus absolute dense area, suggesting that the differences between mammographic and MRI density are more marked when not adjusted for total breast area/volume, and that the three-dimensional MRI dense volume provides a better estimate of the amount of tissue at risk of carcinogenesis. These findings are necessarily preliminary and will need to be confirmed in further studies.Interestingly, the one density outcome for which we did see a consistent association with breast cancer risk, even after adjusting for genetic status, BMI and parity, was the absolute MRI dense volume, a measure which has not been studied before. There appeared to be an approximate doubling of risk between the upper and lower halves of the distribution, rising to an over three-fold increase in The MRIBview algorithm for the MRI-based estimation of the volume of dense tissue in the breast provides a viable alternative to quantitative mammographic density estimation and may be of particular value in women at high genetic risk of breast cancer for whom MRI breast screening is already recommended. Although the nature of the available dataset limited our power to detect associations between density and breast cancer risk, MRI percent dense volume did not appear to perform markedly differently from Cumulus percent dense area. However, the association between absolute MRI dense volume and breast cancer risk is a novel, and potentially important finding that requires replication in a specifically designed case-control study.BMI: body mass index; CC: cranio-caudal; CI: confidence interval; IQR: inter quartile range; IRR: incidence rate ratio; MARIBS: magnetic resonance imaging breast screening; MLO: medial-lateral oblique; MLPA: multiple ligation dependant probe amplification; MRI: magnetic resonance imaging; ONS: office of national statistics; RR: relative risk; UCL: University College London; VA: visual assessment; WHR: waist-hip ratio; XRM: X-ray mammographyRE has received an educational grant from VISTA diagnostics. ML has received salary as a Director of a company  developing dedicated MRI scanners for breast cancer. The company also receives grant funding from the UK Technology Strategy Board. This company does not fund this research or manuscript in any way. ML is employed by the Institute of Cancer Research (University of London) which holds patents on density measurement using MRI and holds the contracts of staff contributing to this research. His department has research agreements with Philips, Siemens and General Electric, major imaging companies that might benefit from this work; these research agreements are not related to the research in this manuscript. ML has also performed paid consultancy for Roche for work unrelated to this manuscript. The other authors declare that they have no competing interests.DT performed all statistical analyses, prepared the figures and drafted the manuscript. ML, RE, DE and DGE conceived of the study and participated in its design and co-ordination. GK-L coordinated the data management. FL obtained, entered and managed the pedigree data. MK created the MRIBview program. SR and EB performed the MRI density estimates. IW traced and data-managed the mammograms. CB performed the mammographic visual density assessments. SG and SJR performed the BRCA1/2 genetic analyses. RW conceived of the study, participated in its design and coordination, performed the Cumulus density estimation and helped to draft the manuscript. All authors read and approved the final manuscript.table S1 (genetic status of participants and probabilities of carrying BRCA1 or BRCA2 mutations for untested women and women with uninformative BRCA1/BRCA2 screening tests as predicted by the BOADICEA program), Table S2  and Table S3 .Click here for fileFigure S1 .Click here for fileFigure S2 (a scatter plot showing the relationship between MRI absolute dense volume and Cumulus absolute dense area).Click here for fileThe full MARIBS authorship list.Click here for file


Various perinatal factors, including birth weight, birth order, maternal age, gestational age, twin status, and parental smoking, have been postulated to affect breast cancer risk in daughters by altering the hormonal environment of the developing fetal mammary glands. Despite ample biologic plausibility, epidemiologic studies to date have yielded conflicting results. We investigated the associations between perinatal factors and subsequent breast cancer risk through meta-analyses.We reviewed breast cancer studies published from January 1966 to February 2007 that included data on birth weight, birth order, maternal age, gestational age, twin status, and maternal or paternal smoking. Meta-analyses using random effect models were employed to summarize the results.We found that heavier birth weights were associated with increased breast cancer risk, with studies involving five categories of birth weight identifying odds ratios (ORs) of 1.24  for 4,000 g or more and 1.15 (95% CI 1.04 to 1.26) for 3,500 g to 3,999 g, relative to a birth weight of 2,500 to 2,599 g. These studies provided no support for a J-shaped relationship of birthweight to risk. Support for an association with birthweight was also derived from studies based on three birth weight categories  and two birth weight categories . Women born to older mothers and twins were also at some increased risk, but the results were heterogeneous across studies and publication years. Birth order, prematurity, and maternal smoking were unrelated to breast cancer risk.in utero exposures reflective of higher endogenous hormone levels could affect risk for development of breast cancer in adulthood.Our findings provide some support for the hypothesis that  Intrauterine environmental exposures to endogenous or exogenous hormones, notably estrogens, may influence the subsequent development of breast cancer in offspring . During Here we review the epidemiologic studies that have assessed the association between perinatal factors and breast cancer risk in daughters. A meta-analytical approach was applied in order to clarify further the possible role played by the intrauterine environment in the etiology of breast cancer.The data retrieved for the systematic review were based on searches of all published papers, letters, abstracts, and review articles on birth weight, birth order, maternal age, gestational age, twin status, and maternal or paternal smoking and breast cancer using the MEDLINE database from January 1966 through February 2007. We used keywords combining text words, with terms for six perinatal factors combined with terms for breast cancer ; three categories ; and two categories . Birth order was examined using two different categorical schemes: 1 (referent) versus ≥2; and 1 (referent), 2 to 4, and ≥5. Maternal age was classified into three categories: <25 years old (referent), 25 to 29 years old, and ≥30 years old. Gestational age was also analyzed in two ways: ≤36 weeks versus ≥37 weeks (referent); and ≤32 weeks versus ≥33 weeks (referent). To examine twin status, three classification schemes were employed: twin versus singleton (referent); monozygotic twin (or sister twins if zygosity was not reported) versus singleton (referent); and dizygotic twin (or sister-brother twins if zygosity was not reported) versus singleton (referent). Maternal or paternal smoking was considered as follows: no smoking during pregnancy (referent) versus smoking during pregnancy. If the criteria utilized in an article were slightly different from our criteria, then we included the data and described the difference in a footnote.ORs and 95% confidence intervals (CIs) were recalculated from published frequency tables of individual studies using the Mantel-Haenszel common OR estimate. However, the reported OR (95% CI) was used when the published studies did not provide further details as to the frequencies of the exposure variables. If the manuscript reported the results after performing a stratified analysis, then we re-calculated the crude OR by combining across strata. A random-effects model was used to obtain summary ORs and 95% CIs.Heterogeneity was assessed by heterogeneity test using Cochran Q statistics . PublicaWhen significant heterogeneity or publication bias was found, we performed subgroup analyses by study design (case-control study versus cohort study), and the source of information (data linkage versus self-report) to assess the impact on between-study variations (heterogeneity). Many of the large-scale studies, especially cohort studies, were published after 2000; thus, we classified publication years into before 2000 versus 2000 or later in order to assess publication biases associated with small study sizes. Few studies focused on either Asians or African-Americans, and so it was not possible to examine ethnicity effects. All statistical analyses were conducted using STATA .We identified 34 studies that assessed the association between birth weight and breast cancer risk Table : 19 caseP_Q test > 0.05 for all categories). In the five-category meta-analysis, ORs were 1.11 (95% CI 0.90 to 1.33) for birth weight <2,500 g, 1.11 (0.99 to 1.25) for 3,000 to 3,499 g, 1.15 (1.04 to 1.26) for 3,500 to 3,999 g, and 1.24 (1.04 to 1.48) for ≥4,000 g relative to the referent category of 2,500 to 2,999 g. In the three-category meta-analysis, ORs were 1.06 (95% CI 0.98 to 1.14) for 3,000 to 3,999 g and 1.15 (1.01 to 1.31) for ≥4,000 g relative to the referent category of <3,000 g. In the two-category meta-analysis, ORs were 1.09 (95% CI 1.02 to 1.18) for the category of >3,000 g  relative to the referent category of ≤3,000 g .Among 34 studies of birth weight and breast cancer, we selected studies that employed the same categories of birth weight. To evaluate whether a J-shaped relationship existed, we grouped birth weight into more than three categories. The findings of meta-analysis of eight studies that utilized five categories of birth weight  and 11 studies that used three categories  are shown in Figures P value by the mean difference of birth order ). There was no difference in risk for women of birth orders 2 to 4 (OR 0.97 [95% CI 0.91–1.03]).For the meta-analysis, we included 14 studies (13 case-control studies and one cohort) that used two birth order categories: 1 (referent) and ≥2. There was significant heterogeneity across all studies (We identified 28 studies (22 case-control and six cohort) that assessed the association between maternal age and breast cancer risk Table . Seven sP_Q test < 0.01 for 25 to 29 years and for ≥30 years). Heterogeneity was also present across case-control studies and studies published after 2000 (P_Q test < 0.01). The ORs (95% CI) were 1.18 (1.05 to 1.11) for 25 to 29 years and 1.23 (1.07 to 1.15) for ≥30 years across all studies.In our meta-analyses, we included the 18 studies that reported categorical data and examined three age categories  that assessed the association between prematurity and breast cancer risk (Table P-Q test = 0.55), whereas we found no association between prematurity (≤36 weeks) and risk , and the association was not significant (OR 1.20 [95% CI 0.74 to 1.95]).There was no significant heterogeneity across studies (turity ≤3 weeks anWe examined 13 studies (eight case-control and five cohort) that assessed the association between twin status and risk Table . Most stP-Q test = 0.13), and the meta-analysis of 13 studies examining twin status (without regard to zygosity) found an OR of 1.22 (95% CI 1.01 to 1.11). There was no evidence of any publication bias (P-Egger test or P-Begg test >0.1). There were little evidence of heterogeneity (P-Q test > 0.1 for monozygotic or dizygotic twins), and breast cancer risk was not significantly increased among either monozygotic (OR 0.95 [95% CI 0.85 to 1.07]) or dizygotic (OR 1.17 [95% CI 0.99 to 1.37]) twins, albeit based on limited statistical power. In subgroup analysis by study design, cohort studies identified significantly increased risk (OR 1.23 [95% CI 1.00 to 1.11]) for breast cancer in twins versus singletons, with no study heterogeneity (P-Q test = 0.07). Case-control studies showed no association with twin status (OR 1.39 [95% CI 0.91 to 1.12]). There was no evidence of any publication bias (P-Egger test or P-Begg test > 0.05) among the case-control or cohort studies. In subgroup analysis by study design and zygosity, there were no heterogeneity in studies (P-Q test > 0.1). In subgroup analysis by study year, significant heterogeneity by publication year was identified (P = 0.01), and the OR (95% CI) for studies published before 2000 was 1.06 (0.97 to 1.47), whereas the OR (95% CI) for studies published in 2000 or later was 1.27 (1.03 to 1.58).The Q test for heterogeneity was not significant . The meta-analysis for maternal smoking . Twin status was associated with 1.2-fold higher risk for breast cancer relative to a singleton birth. Although we found some evidence of increased risk associated with older maternal age , there were heterogeneous findings across study designs.Most studies identified an increased risk for breast cancer with heavier birth weight, with the association being particularly strong for premenopausal or early-onset breast cancers ,22,29,30Although some studies identified a J-shaped relationship between birth weight and breast cancer risk ,35,37,45in utero exposuresto factors such as insulin-like growth factor-I or estrogens [Although the mechanisms underlying the association between high birth weight and breast cancer risk remain unclear, it has been suggested that heavier birth weights may result from increased strogens -76. Thesstrogens ,77. Howestrogens ,79. One strogens . FurtherOur analysis found no association of breast cancer risk with birth orders between 2 and 4, but we did note a somewhat reduced risk associated with higher birth orders (at least 5), although the results were heterogeneous across studies. Biologically, pregnancy estrogen levels appear to be higher during first pregnancies and decline in successive pregnancies . FurtherIn our meta-analysis, we found some evidence that having been born to an older mother was associated with higher breast cancer risk, although the results were heterogeneous across studies. Our data failed to support the previous studies that suggested a J-shaped relationship between maternal age and breast cancer risk. It was previously suggested that older maternal age may have an adverse effect on the primordial mammary gland of their daughters because of altered hormonal profiles  or may lWe observed no association between prematurity and breast cancer risk. Biologically, women having abruptio placentae or an extremely premature birth (<32 week) have been shown to have elevated levels of human chorionic gonadotropin and α-fetoprotein, which could inhibit the differentiation of stem cells in human breast tissue cells . GestatiTwin pregnancies are associated with an approximate doubling of estrogen levels compared with singleton pregnancies ,87. DizyStudies of parental smoking, especially maternal smoking, and daughter's breast cancer risk have yielded inconsistent results. Biologically, maternal smoking, rather than paternal smoking, has a greater impact on the fetus. In the meta-analytic results, both factors failed to exhibit a significant association with risk. Some studies have reported that maternal smoking in pregnancy reduces serum estrogen levels ,92. A reThese meta-analyses are based on results from studies involving heterogeneous designs and methodology. We did note between-study heterogeneity for the associations of birth order, maternal age, and twin status. To resolve the heterogeneous findings, we considered the influence of study design and the date of study publication on the results by subgroup analyses. However, heterogeneity in studies could only be explained partially.Effects of maternal age, birth order, prematurity , and maternal smoking were found to be heterogeneous across study designs, but birth weight and twinning were comparable. Self-reported measures of perinatal factors may be vulnerable to misclassification biases, with differential or nondifferential effects ,95. BecaOur findings may be somewhat inflated because of our dependence on crude rather than adjusted ORs or RRs. A possible misclassification bias for zygosity might have resulted in studies that used sex as a proxy for zygosity . Becausein utero, thereby increasing the subsequent risk of breast cancer. Findings of an increase in breast cancer risk among daughters exposed to diethylstilbestrol in utero supports this hypothesis [in utero may be involved in the subsequent development of breast cancer, further biologic data are needed to elucidate the relationship fully.It has been hypothesized that certain perinatal factors, including birth weight and order, twin pregnancies, prematurity, maternal age, and smoking, may reflect higher estrogenic environments pothesis ,98. Althpothesis -74 throupothesis ,99,100. CI = confidence interval; OR = odds ratio; RR = relative risk.The authors declare that they have no competing interests.SKP collected and selected the all of breast cancer studies, analyzed the data in the study, drafted the manuscript, critically revised the manuscript for important intellectual content, and takes responsibility for the study concept and design, the integrity of the data, and the accuracy of the data analysis. DK participated in design of the study, drafting of the manuscript and interpretation of results, and critically revised the manuscript for important intellectual content. KAM was responsible for the study concept and design, interpreted the findings, and revised the manuscript for important intellectual content. MGC participated in the interpretation of the data and revision of the manuscript. YK was involved in data analysis and revision for important intellectual content. KYY contributed to interpreting the findings and critically revised the manuscript for important intellectual content. LAB led conception and design of the study, the analysis and interpretation of the findings, and revision to the manuscript, and obtained part funding for this research. All authors read and approved the final manuscript.


Breast cancer (BC) is a major public health problem, with rising incidence in many regions of the globe. Although mortality has recently dropped in developed countries, death rates are still increasing in some developing countries, as seen in Brazil. Among the reasons for this phenomenon are the lack of structured screening programs, a long waiting period between diagnosis and treatment, and lack of access to health services for a large proportion of the Brazilian population.Since 2004, an intervention study in a cohort of women in Southern Brazil, denominated Porto Alegre Breast Health Intervention Cohort, is being conducted in order to test the effectiveness and cost-effectiveness of a model for BC early detection and treatment. In this study, over 4,000 women from underserved communities aged 40 to 69 years are being screened annually with mammography and clinical breast examination performed by a multidisciplinary team, which also involves nutritional counseling and genetic cancer risk assessment. Risk factors for BC development are also being evaluated. Active search of participants by lay community health workers is one of the major features of our program. The accrual of new participants was concluded in 2006 and the study will last for 10 years. The main goal of the study is to demonstrate significant downstaging of BC in an underserved population through proper screening, attaining a higher rate of early-stage BC diagnoses than usually seen in women diagnosed in the Brazilian Public Health System. Preliminary results show a very high BC incidence in this population , despite a low prevalence of classical risk factors.This study will allow us to test a model of BC early diagnosis and treatment and evaluate its cost-effectiveness in a developing country where the mortality associated with this disease is very high. Also, it might contribute to the evaluation of risk factors in a population with a different ethnic background from that studied in developed countries. If our model is proven effective, it may be replicated in other parts of the globe where BC is also a major public health problem. Breast cancer (BC) incidence rates have increased significantly worldwide, and today is the most common non-skin cancer in women in almost all parts of the globe. The incidence is higher in the United States and Northern Europe, intermediate in Southern and Eastern Europe and South America, and lower in Asia .Although BC incidence rates are still increasing in developed countries, BC mortality rates have declined in recent years in these nations, what can be explained by greater breast cancer awareness, by the guarantee of health care access and by the adoption of public policies towards early tumor detection. In countries who have successfully implemented nationwide mammography screening programs, such as the United Kingdom , the NetIn contrast, in Brazil and other developing countries, both mortality and incidence rates have steadily increased in recent years. For example, the age-standardized mortality (ASM) related to BC in Brazil has grown from 10.74 per 100,000 women in 2000 to 12.32 in 2005 , which pThe State of Rio Grande do Sul  presents the second highest breast cancer incidence rate in the country, with an estimated rate of 85.50 new cases per 100,000 women in 2008 – a number comparable to the USA and North Europe. The State's capital, Porto Alegre, has an even higher BC incidence rate, with 119.72 new cases per 100,000 women projected for the current year [In Brazil, approximately 75% of the population has access to health care only through the Brazilian Public Health System (PHS) , which iIn Brazil, the current national recommendation for breast cancer screening calls for mammography at two-year intervals, targeted at women between the ages of 50 and 69 years . These rAlthough the government guideline containing these recommendations has been published in 2004, there is not actually a national screening program, since there is no provision of financing nor training of health care professionals capable to provide service to all eligible women between ages 50 and 69 years. In the areas of the city of Porto Alegre included in this project, there used to be no mammographic reference service. Women with breast symptoms used to be referred to the city central area, where they were offered the services of a breast specialist necessary for diagnostic procedures  and treatment, with very long waiting times for each step of the process. A national survey conducted in 2003 confirms the lack of a structured screening program in the country, showing that 49.7% of women above the age of 50 have never been submitted to a mammography, and, among those who had done the exam, 18% did it more than 2 years ago .Considering the above, the Associação Hospitalar Moinhos de Vento, in a partnership with the City Health Department of Porto Alegre and a non-profit organization (Breast Institute of Rio Grande do Sul), initiated a prospective breast cancer screening study, denominated Porto Alegre Breast Health Intervention Cohort (NMPOA – Núcleo Mama Porto Alegre). The two primary objectives of this project are:(1) To test the effectiveness of a centrally structured mammography screening and early treatment program, based on active women search, close contact with the BHU, promoting a multidisciplinary and humanized care for an underserved population from a developing country;(2) To verify the profile of breast cancer risk factors in the sample studied, evaluating the frequency and possible contribution of established risk factors and attempting to identify new ones.A secondary objective of the study is the estimation of the cost-effectiveness of such a program.The NMPOA project is a breast cancer screening and early treatment program, with evaluation of risk factors for breast cancer in an intervention cohort. From April 2004 through March 2006, 9,218 women aged 15 years or older visiting one of the 19 participating BHUs from Porto Alegre were evaluated in a cross-sectional study, which included: (1) an entry form questionnaire regarding risk factors for breast cancer – including family history (FH) of cancer – and presence of breast symptoms, and (2) breast examination by a trained professional . All patients with breast complaints or an abnormal physical exam were referred to NMPOA for further evaluation. Women between 40 and 69 years of age were invited to participate in the screening cohort. Women without symptoms and outside the targeted age group were advised to attend the BHU once a year for physical examination. Figure The accrual was done at the participating BHUs both actively  and passively (women that attended the BHU for any reason being invited to the project). Although women from all ages above 15 could be enrolled in the cross-sectional study, the main emphasis was the invitation of women between the ages of 40 and 69 years, since that would be the group which would enter the screening cohort. The total number of women between 40 and 69 years old in the region was approximately 16,000, according to the most recent national census (year 2000), and the accrued number was about 4,000 in this age range.The screening program is based on annual mammographies in women between the ages of 40 and 69 years, which are always preceded by physical examination performed by a breast surgeon or a trained nurse. One of the main features of the program is its close control of the frequency of visits, based on a computer system that generates monthly reports on the due dates of all participants. The close contact and continuous feedback to the BHUs professionals guarantees that this strategy is put into practice with maximum effort, in order to achieve optimal adherence to the screening program.The proposal of the NMPOA project includes the performance of all clinical, imaging and pathology exams, as well as clinical visits and surgical treatment, in the same center. The health care approach is multi-disciplinary, including nurses, breast surgeons and a nutritionist. All mammographies are performed on the same day of the clinical visit, and they remain stored at the center. This integration of screening, diagnosis and treatment in the same center is an effort to shorten the time of delivery of appropriate treatment in each newly diagnosed breast cancer case.All mammographic exams done at NMPOA are interpreted only by radiologists who are specialized in breast cancer and follow rigorous international standards of quality control. The radiologist classifies mammographies using density and risk criteria, purposed by the BI-RADS (Breast Imaging Report and Data System) classification . Women wThe imaging screening consists of mammograms in patients older than 40 and ultrasound starting at an age 10 years younger than the relative's age when diagnosed with BC, not starting before the age of 25 .Participants of the cross-sectional study older than 18 years answering positively to at least one of the FH questions described below were referred to genetic cancer risk assessment. If the risk assessment yielded moderate or high genetic risk (estimated lifetime breast cancer risks greater than 20% or a family history suggestive of a hereditary breast cancer syndrome), the participant was invited to attend clinical visits and imaging screening at the NMPOA center, being followed by breast physicians and geneticists every six months. The main goals of the genetic evaluations are to determine: (1) the prevalence of a first-degree FH of cancer and characterize the type of cancer, age at diagnosis and relationship of the cancer-affected relative to the proband; (2) the lifetime breast cancer risk estimates in women attending the cohort and reporting a family history of breast and other cancers; (3) the prevalence of hereditary breast cancer phenotypes in a population-based sample of women in Latin America; (4) the contribution of germline mutations in known breast cancer predisposition genes among women from the cohort reporting a FH of cancer and fulfilling criteria for the clinical diagnosis of a breast cancer predisposition syndrome. Preliminary reports on the results of the genetic evaluation have been published elsewhere .BRCA mutations, such as early-onset, bilateral or male breast cancer, multiple primary cancers of the breast and ovary, first-degree or multiple-case family history of breast or ovarian cancer. In addition, a question about FH of BC and/or colon cancer was included due to a previous suggestion of a higher than expect prevalence of such association in cancer genetic clinics of Porto Alegre.The questions regarding FH in the cross-sectional study focused on features that have been associated with an increased likelihood of clinically significant Additional risk factors evaluated in the cross-sectional study included smoking, obesity, alcohol consumption and history of breast diseases. Current smokers were defined as those who smoked at least one cigarette per day; never smokers those who have never tried a cigarette in their life; and former smoker were defined as those who had stopped smoking for at least one year. Educational level of the participants  was also inquired, and was measured by the years of schooling.For the approximately 4,000 already registered women in the annual mammographic screening, a complete clinical form was filled when they joined the cohort, containing gynecological-obstetric data (such as number of pregnancies and hormone use), family history, medical history and physical exam, which includes anthropometric measurements and breast examination. Every time the participant returns for clinical evaluation and annual mammography, the answers are updated, since many of these risk factors are dynamic. BI-RADS data from all mammographies, including density score, are also stored. If a tumor is diagnosed, all data concerning staging, pathology report, hormone receptors and both surgical and oncological treatment are registered.Participants who do not wish to continue in the screening program  are requested to fill a form with the motive of withdrawal. This is also being recorded, in order to understand reasons for non-compliance, a major problem in Brazil ,18-22.For the patients who need complimentary investigation , as well as in women who undergo surgical and oncological treatment for cancer, length between each step of care is been recorded. We intend to compare these data with other national registries in the PHS  in orderThe best parameter to show the effectiveness of our program would be breast cancer associated mortality. However, considering the very long breast cancer related death survival in asymptomatic women , and taking into account the well established relationship between tumor staging and survival , we chosIn search of an adequate comparison value, we considered that the staging of all breast cancers diagnosed during the study period in our city in a similar population  would be suitable. These data are available upon request in the City Health Department. The total number of women between 40 and 69 years today in Porto Alegre is 247,000 , of whicThe proportion of cases at or below stage I in the NMPOA cohort and the same proportion in the city will be compared by the Fisher's exact test. Continuous variables, like the time frames between the steps of care, will be compared with Student's t-test. We are also planning to perform a cost-effectiveness analysis of this intervention, in which a Markov model will be employed.This project was approved by the ethical review board (ERB) of the City Health Department and of Associação Hospitalar Moinhos de Vento. The genetics project was also approved by the ERB from Hospital de Clínicas de Porto Alegre, where all biological material are stored. All subjects were provided a copy of the written consent before study entry and were assured of their anonymity and confidentiality of data collected.The screening cohort has already enrolled 4,037 women. Considering the number of women registered in the cross-sectional study who are reaching the age of 40 and will enter the cohort until the year 2014, this number will probably reach 4,500 until the end of the project.Table There were 1286 women who responded positively to one of the high genetic risk questions in the cross-sectional study, 902 of whom were further referred to genetic cancer risk assessment sessions. Of these, 214 had a pedigree suggestive of a hereditary breast cancer predisposition syndrome. Preliminary results from the genetic evaluation have been published elsewhere .Incidence of cancer so far has been 117 cases per 100,000 person-years , a number very similar to the number of cases predicted in the city for the year 2008, 119 cases per 100,000 women. Compliance to the annual screening has been approximately 60% so far, and the rate of participants who had their last mammogram not more than 2 years ago is almost 80%  . Although we cannot be sure about the total number of women reached by the active and passive search, this number suggests a low adherence to preventive health care in this population. This is in accordance with national data, which showed that almost 50% of women above 50 years old have never done mammography, and almost 10% had done their last exam three or more years before .The incidence of breast cancer in this population is high, being comparable to developed countries and largely exceeding the Brazilian average incidence of 51 cases per 100,000. The cancer rate becomes quite intriguing when we confront it with the baseline data of our cohort: the majority of breast cancer risk factors in this population have a low prevalence , fostering the idea that unknown risk factors might be playing a major role in this region and reinforcing the importance of understanding the distribution of breast cancer risk factors in different populations. The incidence of cancer observed so far in the screening cohort suggests that this sample is representative of our city.Our longitudinal collection of risk factor data, especially the genetic risk factors, is very important for an accurate measurement, since this factor can change over time, as new information about cancer development in relatives of the patient becomes acknowledged. The design and sample size of our study will probably be sufficient to respond the primary question of our study, regarding the effectiveness of the model. However, we recognize that the correlation of risk factors and breast cancer will probably be underpowered. Nevertheless, we believe that our study can contribute with others in the field, and, in a pooled analysis through meta-analytic techniques, help us understand the high incidence of the disease in a large area in our country.We consider that the high rate of compliance so far is one the main strengths of your study. This is achieved by patient education, with information about the importance of the screening program, and intense contact with the BHUs, with provision of lists of patients who should come next month. We also supply transportation for women with very low financial resources, what helps to keep attendance. Every three or four months, we select a region with important problems of attendance and provide a special bus to take them to the PABHC.This project aims to establish a model of breast health attention directed to underserved women that rely only on health care provided by the public sector. The prompt access of women in our study to the reference service, as well as the clinical exam done by a specialized professional and high quality mammography are fundamental issues for early diagnosis of breast cancer. This is a major step to reduce breast cancer mortality worldwide.ASM: Age-standardized mortality; BI-RADS: Breast Imaging Report and Data System; BC: Breast cancer; BHU: Basic healthcare unit; ERB: Ethical review board; FH: Family history; HRT: Hormone replacement therapy; OC: Oral contraceptives; NMPOA: Núcleo Mama Porto Alegre ; PHS: Public Health System.The authors declare that they have no competing interests.MC was responsible for study design and manuscript review, and also participated in the manuscript drafting. RR was responsible for manuscript drafting and data analysis. BW, PAP, DD, PK, LF, JF, MG and PP participated in the study design. AB, PAP, JZ, JG and GS participated in data collection and were involved in patients' care. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


A case-control study of 689 breast cancer patients seen at Tata Memorial Hospital during the period 1980-84 was carried out. During the same period 711 females who attended the hospital without a history of benign breast lesions or gynaecological complaints were selected as controls. Patients were interviewed by trained investigators to collect data on reproductive factors, menstrual history, tobacco smoking and chewing habit, dietary practices (vegetarian and non-vegetarian diet) and alcohol consumption. Cases and controls were stratified into four age groups  and three places of residence . The adjusted relative risk (RR) for unmarried women compared with married women was 2.3. Nulliparous women had a 2.2-fold higher risk than parous women. Late age at marriage (30 years and above) and late age at first pregnancy (30 years and above) showed excess risks of 2.5 and 5.4 compared with women married at the age of 14 years and age at first pregnancy of < or = 14 years. Three or more pregnancies was associated with a 40-50% reduction in risk (P < 0.01). Non-vegetarian diet, literacy status and a history of stillbirth and abortion did not emerge as significant risk factors for breast cancer in our study. These findings, in a low-risk population, were consistent with those reported from high-risk populations.


Some 1.38 million offspring up to age 55 years with 50.6 million person-years were included. Poisson regression analysis included age at diagnosis, birth cohort, socio-economic status and region of residence as other explanatory variables. The only significant associations were an increasing risk for breast cancer by birth order and a decreasing risk for melanoma by birth order and, particularly, by family size. When details of the women's own reproductive history were included in analysis, birth orders 5–17 showed a relative risk of 1.41. The effects on breast cancer may be mediated through increasing birth weight by birth order. For melanoma, socio-economic factors may be involved, such as limited affordability of sun tourism in large families. Testis cancer showed no significant effect and prostate cancer was excluded from analysis because of the small number of cases. © 2001 Cancer Research Campaign


Breast-cancer incidence and mortality rates in different countries were found to be correlated with height, weight and age at menarche, all of which have been identified as risk factors in cohort or case-control studies of breast cancer. There were, however, correlations with total fat and animal protein consumption per capita even after controlling for the 3 anthropometric variables. This suggests that, while some of the effects of diet on breast-cancer rates may be mediated through effects on these known risk factors, there may be more direct effects as well.


Education inequalities in cancer incidence have long been noted. It is not clear, however, whether such inequalities persist in the United States, especially for less common malignancies and after adjustment for individual risk factors.Within the NIH–AARP Diet and Health Study, we examined the association between education and the risk of developing cancers in a prospective cohort of 498 455 participants who were 50–71 year old and without cancer at enrollment in 1995/96. During a maximum 8.2 years of follow–up we identified 40 443 cancers in men and 18 367 in women. In age-adjusted models, the least educated men (<high school), compared to those with the most education (post–graduate), had increased risks of developing cancers of the esophagus , head and neck , stomach , colon , rectum , liver , lung , pleura , bladder  and combined smoking–related cancers . In contrast, lower education level was associated with a decreased risk of melanoma of the skin  and local prostate cancers . Women with the least education had increased risks of colon , lung , kidney  and combined smoking–related cancers  but a lower risk of melanoma of the skin , endometrial  and invasive breast cancers . Adjustment for smoking and other risk factors did not eliminate these associations, except those for cancers of the head and neck, colon, and liver in men and kidney in women.We found a higher risk of malignant disease, particularly smoking– related cancers, among those in the lowest educational attainment category. Only some of the educational gradient is attributable to smoking. The persistence of substantial education inequalities in cancer incidence poses a challenge for etiologic research and public health policy. Low socioeconomic status (SES) has been associated with increased risks of morbidity and mortality in different age groups within a variety of countries.A number of demographic, behavioral and biologic factors, including smoking, energy balance, cancer screening, hormone use and age at first birth, likely lie on the causal pathway between education and cancer.Previous studies have investigated education in relation only to a single cancer or a few common malignancies. Only a few earlier studies in Europe have prospectively investigated multiple cancer sites, including relatively rare malignancies, with sufficient data to adjust for individual risk factors.The National Institutes of Health and AARP (formerly known as the American Association of Retired Persons) formed the NIH–AARP Diet and Health Study in 1995/96 when a 16–page paper questionnaire was mailed to 3.5 million AARP members aged 50–71 in 6 states  and 2 metropolitan areas . These states and metropolitan areas were selected because of the high quality of their cancer registries with a secondary goal of targeting areas with high minority populations. The cohort was designed to have a wide range of exposures in order to study the associations between health and lifestyle factors, especially diet. The study cohort and methods have been previously described in more detail.We obtained information on education, age, race, smoking, diet, alcohol consumption, weight, height, marital status, and personal and family history of cancer. Women answered an additional set of questions regarding their age at first birth, number of children, menopausal hormone use, and history of hysterectomy and oophorectomy. In addition, 334 643 participants reported their cancer screening behaviors on a second questionnaire mailed in 1996. A total of 566 402 participants provided sufficient information to be included in the cohort. Persons with prevalent cancers (n = 52 586), without information on education (n = 15 349), or who had moved or died before their questionnaire was received (n = 12) were excluded, leaving 498 455 participants  for analysis.2); <25, 25−<30, 30−<35, 35+ kg/m2), physical activity , marital status (yes/no), and family history of cancer (yes/no). For analyses of cancers of the breast, colon, ovary, and prostate, a variable for screening behavior during the three years prior to baseline  was included in the models. Information about menopausal hormone therapy (MHT) use  was included as a covariate in the analyses of cancers in women. For malignancies specific to women, we included a variable that combined a woman's age at first birth and number of children . Each value of a categorical variable, including one for missing information, was included in the model as a separate variable, with the reference level excluded from the model.We asked participants to report their highest grade or level of education completed in one of 7 categories: 8 yrs, 8–11 yrs, high school graduate, post high school training and technical college, some college, college graduate and post–graduate. Those who reported less than 8 years of education or 8–11 years of education were classified into a single category, less than high school. For each education category, we calculated age–adjusted incidence rates per 100,000 person-years by five year age intervals for individual cancer sites and all cancers in men and women separately. To examine whether smoking confounds the education-cancer associations, smoking status was included as a covariate in the age–adjusted models. Covariates entered into regression models included age (continuous), a 31-level smoking variable , race/ethnicity , energy intake , alcohol consumption , body mass index . Participants were matched on their first and last names, sex, address histories, date of birth and Social Security Number (available for 85% of the participants). Address histories were constructed by annual linkage of cohort members to the National Change of Address database maintained by the U.S. Postal Service. We have shown that this method ascertains approximately 90% of incident cancers.a priori ‘smoking–related’ cancer was defined to comprise a malignancy of the head and neck, esophagus, lung, bladder, or pancreas.Our end points were first primary incident cancers, defined according to the Surveillance Epidemiology and End Result (SEER) criteria, with minor modifications for malignancies of the head and neck, esophagus, pancreas, and prostate, as described previously.The NIH–AARP Diet and Health Study protocol was approved by the U.S. National Cancer Institute Special Studies Institutional Review Board.We used SAS software v 8.2  to calculate age–adjusted incidence rates. For cross tabulations and Cox proportional hazard regression models, we used Intercooled Stata 8.0 statistical software . A participant's exit date was the time of the first of four possible events: 1) diagnosis with cancer; 2) a move outside the 11 states; 3) death; or 4) end of the study on December 31, 2003. We calculated relative risks (RR), equivalent to hazard ratios, and 95% confidence intervals (CI) from age– and multivariable–adjusted proportional hazards analyses, with time on study defined as the difference between the date of questionnaire return and the participant's exit date. We calculated tests for trend by including in Cox models a variable constructed from estimates of the number of years of education for each category of educational attainment. Specifically, we estimated 8 years of school for those with less than a high school education, 12 years for those who graduate high school, 13 years for post-high school trained individuals, 14 years for those who reported some college, 16 years for college graduates and 18 for post-graduate degree holders. We evaluated effect modification by stratified analysis and statistically with the use of a cross-product term. We present data only when at least 10 cancer cases occurred within an education category. All analyses were sex–stratified, with post–graduate education serving as the reference group. All tests were two sided and a p–value of less than or equal to 0.05 was considered statistically significant.The relations between education and various risk factors are shown for men and women in The average follow–up time for the entire cohort was 6.86 years for a total contribution of 3 418 703 person years. In age–adjusted models, we found a significantly increased risk of any cancer for men with less than high school compared to men with post–graduate education  . The assAmong women, the age–adjusted risk of any cancer for participants with less than high school compared to those with postgraduate education was reduced  . For smoCompared to the age–adjusted results, site specific risk estimates from models that were further adjusted for smoking habits were somewhat attenuated, but remained statistically significant  and 3. FAmong rarer cancers, pleural tumors were strongly and inversely associated with education in men (multivariate model: 4.56. 2.13–9.75).The smoking-related cancer data suggested effect modification for smoking status itself: cross-product terms for education and smoking were statistically significant for both men (p<0.0001) and women (p = 0.0019). Stratified analyses showed that the inverse association between education and smoking-related cancers association was not present among never smokers but was restricted to current and former smokers. No effect modification was apparent for age, race, body mass index, physical activity, alcohol consumption, birth cohort (50–59 vs. 60+), self reported health  and preexisting disease (yes vs. no) (data not shown).”In this large prospective cohort of United States men and women aged 50 to 71, substantial inverse education gradients persist for incident cancer. In fully adjusted models, we found higher risks among the least, compared to the most, educated individuals, especially for combined smoking–related cancers . In addition, we found inverse education gradients for cancers of the stomach and rectum (men only) and colon (women only). Some direct associations with education and cancer risk also emerged, notably those for melanoma of the skin (both men and women), localized prostate cancer (men), and invasive breast and endometrial cancer (women).The NIH-AARP cohort is a large prospective cohort with detailed information on a variety of covariates which allowed us to control for multiple risk factors at the individual level in an analysis of first primary rare and common malignancies in both men and women. Other prospective studies conducted in Europe have reported similar results, although these studies did not control for risk factorsThe smoking–adjusted analyses are revealing in two ways. First, for some sites, particularly lung and smoking–related cancers combined, adjustment for smoking leads to substantial attenuation of the inverse education–cancer association in men and women. Given that smoking is clearly related to education  and smokAfter adjustment for age and smoking, the inclusion of other covariates in the regression models resulted in little additional attenuation of the education–cancer associations. Although residual confounding for such imperfectly measured variables as total energy intake, alcohol consumption, and physical activity cannot be ruled out, these additional factors explain relatively little of the education–cancer connection.We did not have information on H. pylori infection status to incorporate in the multivariate analyses of gastric cancer. However, when investigated by Nagel et al in a large nested case control study in Europe, the inverse association of gastric cancer remained, albeit non-significant, even after controlling for H. pyloriThe data reveal a strong inverse gradient for pleural cancer in men. This finding from a prospective cohort study, possible only because of the study's large size, appears unexplained by smoking and may reflect occupational or environmental exposure to asbestos.Education level was weakly but significantly positively associated with invasive breast cancers in women, which is consistent with findings from other studies.Studies of educational attainment and prostate cancer have yielded inconsistent results. In our cohort, the education–prostate cancer association was weakly positive, statistically significantly so only for localized disease. The point estimates were similar for localized and advanced prostate cancer, however; the power to detect the positive association with advanced disease was limited. The weak positive association for prostate cancer was largely unaffected by multivariate analysis, which is not surprising given the paucity of strong risk factors for this malignancy.The direct association of education level with melanoma of the skin in our cohort is in line with previous findings. It is important to note that the AARP membership tends to be more educated, on the average, than the U.S. population as a whole. Nevertheless, the cohort has a wide range of educational attainment, including over 30,000 people, or 6.6% of the study population, with less than a high school education. This wide range of educational attainment allows us to make informative comparisons of cancer incidence across education categories.Education captures many aspects of the constructs ‘social class’ and ‘socioeconomic status’ and is widely used as an indicator of social ‘difference’ in epidemiologic studies. A particular advantage of investigating education is avoiding reverse causation bias: incident cancer may lead to downward occupational mobility and reduced income but generally will not affect educational status achieved by early adulthood.In summary, the data from the NIH–AARP cohort show that substantial education gradients in incident cancer risk persist in the United States. A few malignancies are positively associated with educational attainment; these positive associations are primarily of etiologic interest, given that lowering educational attainment is hardly an appropriate strategy for preventing melanoma of the skin or cancers of the breast, prostate, and endometrium. The majority of the observed education associations, however, are inverse, and these are evident especially for smoking– related malignancies. Smoking likely accounts for some—although not all––of the increased cancer risk among lower educated men and women. To the extent that smoking is the mediating causal factor, reducing the differential in smoking rates is a reasonable strategy for addressing SES–cancer inequalities. To the extent that smoking does not account for the inverse associations, further research to identify the causal factors underlying the education–cancer gradients is clearly warranted.The persistent education-cancer differences in the United States (and many other countries) remain a cause for concern. They also, however, present an opportunity to understand more deeply the etiology of cancer and ultimately reduce its incidence.


Median age at first screening was 54.1 years (range: 40.0–71.5), median SMF volume 70.25 cm3 (interquartile range: 51.0–103.0) and mean SMF% 26.3%, s.d.=8.0% (range: 12.7–58.8%). Age-adjusted logistic regression models showed a positive relationship between age at last menstrual period and SMF%, odds ratio (OR) per year later: 1.05 . Number of pregnancies was inversely related to SMF volume, OR per extra pregnancy: 0.78 . There was a suggestion of a quadratic relationship between birthweight and SMF%, with lowest risks in women born under 2.5 and over 4 kg. Body mass index (BMI) at university (median age 19) and in 2001 (median age 62) were positively related to SMF volume, OR per extra kg m−2 1.21 (1.15–1.28) and 1.17 (1.09–1.26), respectively, and inversely related to SMF%, OR per extra kg m−2 0.83 (0.79–0.88) and 0.82 (0.76–0.88), respectively, P<0.001. Standard Mammogram Form% and absolute SMF volume are related to several, but not all, breast cancer risk factors. In particular, the positive relationship between BMI and SMF volume suggests that volume of dense breast tissue will be a useful marker in breast cancer studies.We conducted a cross-sectional study nested within a prospective cohort of breast cancer risk factors and two novel measures of breast density volume among 590 women who had attended Glasgow University (1948–1968), replied to a postal questionnaire (2001) and attended breast screening in Scotland (1989–2002). Volumetric breast density was estimated using a fully automated computer programme applied to digitised film-screen mammograms, from medio-lateral oblique mammograms at the first-screening visit. This measured the proportion of the breast volume composed of dense (non-fatty) tissue (Standard Mammogram Form (SMF)%) and the absolute volume of this tissue (SMF volume, cm The magnitude of the relationship between breast density and breast cancer has led to the use of breast density as a biomarker for breast cancer risk . InvestiLimitations of visual and area-based methods of assessing breast density, such as subjectivity, variations in density with breast compression and X-ray exposure and the time involved in visual assessment of mammograms have led to interest in automated, volumetric methods of breast density. The aim of this study was to explore the use of the Standard Mammogram Form (SMF™) tool (The women included in the study are members of the Glasgow Alumni Cohort (Those women living in Scotland were asked to give consent for access to screening mammograms taken under the Scottish Breast Screening Programme (1989–2002). Cranio-caudal and medio-lateral oblique (MLO) films were digitised on site as we have previously described  of the breast that is dense (SMF volume) and (ii) the percentage of the volume of the breast which is dense (SMF%).The end result of the SMF algorithm is two volumetric measures of breast density, (i) the absolute volume ) for skewed data and means (s.d.) for normally distributed data. Key exposure variables were cross-tabulated against quartiles of SMF volume, SMF% and SCC. Logistic regression models were used to estimate odds ratios (ORs) between exposure variables and breast density, using SMF volume and SMF% dichotomised at the median and SCC split at 50% or greater density compared to under 50% density. Logistic regression was chosen in favour of ordered logistic regression, since initial analyses showed that the common OR for any dichotomy of the outcome variables was not constant, that is, there was not a proportional relationship between the exposure variables and adjacent categories of SMF. All models were adjusted for age (linear variable) at the time of mammography. Confounding was investigated by comparing the magnitude of the estimates from age-adjusted and further adjusted models. Interaction models to test for the presence of a differing relationship between each of the risk factors and pre- compared to post-menopausal breast density were performed. For all models, the linear term for the risk factor was used, rather than the categorical variable, with the exception of birthweight, which appeared to show a quadratic relationship with SMF% and therefore it would have been inappropriate to test the linear association.n=1285). Of the respondents, 935 women (73%) were still living in Scotland. Two hundred and seventy-seven of these women (30%) had never had a screening mammogram, and two women refused access to their films.There were 3566 women in the original Glasgow Alumni Cohort, of whom 2169 (61%) were sent a postal questionnaire in 2001. These were the women who could be traced through the National Health Service Central Register and were still alive. The response rate was 59% (n=1199) of the remaining 590 women.The SMF algorithm was run on all 3968 mammograms belonging to 649 of the remaining 656 women (films of seven women were omitted inadvertently). The SMF programme failed on one image (<0.1%) and produced an invalid result for 29 (1.4%) further images. These invalid results can arise from a lack of data, for example, an inability for compute breast thickness if this was not recorded in the medical records, or can occur if the breast did not fit onto one film. Twenty-three (3.5%) women (122 images) were excluded as they reported having had breast cancer in the 2001 questionnaire. Thirty-one women (134 mammograms) had attended university after 1968 so were excluded, since the proportion of students attending the Student Health Service fell dramatically after this date . The median age at the time of responding to the questionnaire was 61.7 years (range: 51.0–77.8). The median age at first breast screening was 54.1 years (range: 40.0–71.5), including eight women who were over 65 years at the time of their first mammogram.3 (IQR: 51.0–103.0). The percentage of the volume of breast tissue that is dense (SMF%) approached a normal curve, with a mean of 26.3% and s.d.=8.0% (range: 12.7–58.8%). Fifty-one women (8.6%) fell into the lowest SCC category (0%) and 110 (18.6%) in the highest category (over 75%). Using the dichotomised SCC variable, 38.9% were classified as having dense breasts (over 50% dense). Relationships between the SMF measures and SCC have been described previously (The distribution of the volume of dense tissue (SMF) was positively skewed. The median volume of dense tissue was 70.25 cmReproductive and anthropometric characteristics of the included women are shown in −2) when at university, by the time the women were aged 51–78 years, this proportion approached 40%.Two-thirds of the women included had ever been pregnant, with the majority of these women having had two or three pregnancies. Most women had their first pregnancy between the age of 24 and 30 years, reflecting the delay in childbearing among university graduates. Forty-seven women reported their mother having had breast cancer; the 50 women who did not answer this question were assumed to have a negative family history of breast cancer in subsequent analyses. Over half of the women did not report their birthweight. Of those who did, the majority, were between 3.0 and 3.9 kg. Under 10% of the women were overweight =0.079. Similarly, ever having been pregnant and the number of pregnancies was inversely associated with SMF volume in post-menopausal but not pre-menopausal women. Comparing the magnitude of the ORs, there was a suggestion that a maternal history of breast cancer was associated with a higher risk of high SMF volume in pre- but not post-menopausal women, but this was based on only 14 pre- and 31 post-menopausal women with a maternal history of breast cancer. Similarly, the presence of an inverse relationship between birthweight and SMF% was only seen in post-menopausal women. Finally, the positive associations of BMI with SMF volume and the inverse associations with SMF% were seen in all women.In general, the patterns of association described above were only present for post-menopausal women. For example, post-menopausal women whose menarche had been early had a higher risk of high SMF%, whereas this was not seen for pre-menopausal women, The results presented in this paper highlight for the first time differences in relationships between breast cancer risk factors and several measures of breast density, both area-based and volumetric. The most consistent of these was SMF volume, which was positively associated with higher BMI, ever being pregnant, having had more children and maternal breast cancer. Higher SCC and SMF% were associated with later age at LMP and lower BMI, which themselves lower the risk.Two limitations of the study are important. First, the SMF method, using GenerateSMF version 2.2, groups all non-fatty tissue, including fibrous (both intra- and extra-lobular), glandular and vascular tissues together, which we refer to as ‘dense’, whereas ideally, the estimated dense volume should only be of glandular tissue. Thus, the computed volumes include both the epithelial tissue components considered relevant to risk, as well as non-epithelial dense components, , less clearly related to risk (A second limitation of the study is that, despite theoretical predictions, we do not know the predictive value of SMF. We have previously reported that SMF% correlates well with a frequently used visual assessment of density (The results which we found for SMF volume and SMF% are broadly consistent with those reported by others in relation to the percentage of the area of the breast which is dense. Previous studies have found positive relationships between age at menarche and breast density , most reWe found a relationship between SMF volume, but not SMF%, and a maternal history of breast cancer, although this did not quite reach conventional levels of statistical significance. This accords with a recent review, which noted that most relationships reported are weak, and that family history and mammographic density are independent risk factors . We had Our most interesting result concerns current BMI, a well-established post-menopausal risk factor . Using dDespite continuing reported associations between risk factors and breast density, such work needs to be refined. First, the relative amounts of dense and non-dense tissues should be considered as two separate outcomes, as suggested previously (Second, the mechanisms underlying associations between breast density and risk need investigation. Differences in an area-based measure of breast density and level of acculturation by Chinese women in the United States has been found (In summary, our findings suggest that the novel technique of estimating the volume of dense breast tissue, which involves computerised modelling of mammographic breast density using a fully automated system, may be useful in large epidemiological studies. Work is underway on whether SMF can predict breast cancer risk.


Sir,British Journal of Cancer (We thank the authors for their interest in our study and apologise to them for having omitted citing their study (It is not clear that an analysis in the United Kingdom based on country of birth is comparable to one, such as ours, based on self-reported ethnicity. Also, by limiting our study to one area of East London and one referral hospital we have been able to control for socioeconomic status and variations in treatment practise and, thus, the outcome.We would also like to defend our age-related observations, even with the broad age groupings available to us. It was clear that the disease indeed does occur at younger ages, accounting for the different age ranges of the population, in the black population in the area of our study. We consider, therefore, that our conclusion is not a mistake although we do accept that our findings need to be substantiated in a national study of breast cancer using self-reported ethnicity to characterise the patterns of breast cancer better in British black women.