Breast cancer is a heterogeneous disease, divided into subtypes based on the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Subtypes have different biology and prognosis, with accumulating evidence of different risk factors. The purpose of this study was to compare breast cancer risk factors across tumor subtypes in a large, diverse mammography population.Women aged 40–84 without a history of breast cancer with a screening mammogram at three United States health systems from 2006 to 2015 were included. Risk factor questionnaires were completed at mammogram visit, supplemented by electronic health records. Invasive tumor subtype was defined by immunohistochemistry as ER/PR+HER2−, ER/PR+HER2+, ER, and PR−HER2+, or triple‐negative breast cancer (TNBC). Cox proportional hazards models were run for each subtype. Associations of race, reproductive history, prior breast problems, family history, breast density, and body mass index (BMI) were assessed. The association of tumor subtypes with screen detection and interval cancer was assessed using logistic regression among invasive cases.The study population included 198,278 women with a median of 6.5 years of follow‐up (IQR 4.2–9.0 years). There were 4002 invasive cancers, including 3077 (77%) ER/PR+HER2−, 300 (8%) TNBC, 342 (9%) ER/PR+HER2+, and 126 (3%) ER/PR−HER2+ subtype. In multivariate models, Black women had 2.7 times higher risk of TNBC than white women . Breast density was associated with increased risk of all subtypes. BMI was more strongly associated with ER/PR+HER2− and HER2+ subtypes among postmenopausal women than premenopausal women. Breast density was more strongly associated with ER/PR+HER2− and TNBC among premenopausal than postmenopausal women. TNBC was more likely to be interval cancer than other subtypes.These results have implications for risk assessment and understanding of the etiology of breast cancer subtypes. More research is needed to determine what factors explain the higher risk of TNBC for Black women. Breast cancer is a heterogeneous disease, with subtypes that may have different etiologies. This study evaluated traditional breast cancer risk factors across tumor subtypes, finding distinct sets of risk factors for each subtype. We additionally discuss implications for screening and risk assessment. Tumors that are ER and/or PR positive and HER2 negative (ER/PR+HER2−) are the most common, accounting for approximately 73% of breast cancers.The existing literature suggests that breast cancer subtypes have unique etiologies.The purpose of the study was to compare breast cancer risk factors across breast cancer subtypes, with a specific focus on assessing associations of BMI and breast density and the interactions of these factors with menopause status.22.1BRCA1/2 mutation. We additionally excluded women with less than 6 months of follow‐up, including women diagnosed with breast cancer within 6 months of mammography to maintain temporality between risk factor ascertainment and cancer diagnosis. Finally, women who died but did not have a known date of death or date of the last contact were excluded from analyses. Details of the study populations and exclusions are provided in Figure The study population included women aged 40–84 who received a screening mammogram at Massachusetts General Hospital (MGH) from 2006 to 2015, Newton‐Wellesley Hospital (NWH) from 2006 to 2015, or the University of Pennsylvania Health System (Penn) from 2010 to 2015. Women were excluded from the analysis if they had prior breast cancer, breast implants, or a known 2.2N = 3160, 1.6%). Imputation was used to estimate height and then calculate BMI for an additional 18,163 patients (9.2%) that did not have self‐reported or EHR height data but did have weight data, using the median value for that site. Implausible BMI values—those <12 or >82—were considered missing.N = 136). Missing information on BI‐RADS (Breast Imaging Reporting and Data System) breast density was extracted from radiology reports using natural language processing as described previously (N = 8855).N = 92). Menopause status was defined based on age and self‐reported menstruation status; patients were automatically considered postmenopausal if they had stopped menstruating or were over 55 years of age, and premenopausal otherwise.We used a tiered strategy for the assessment of risk factors with the primary source being a risk factor questionnaire completed by the patient at the time of their mammogram. All risk factors were self‐reported, with the exception of age and breast density. Missing information was then supplemented by electronic health records (EHR) when appropriate. If self‐report BMI information was missing, EHR weight and height and/or BMI were used if the body measurement occurred within 1 year prior or within 6 months after screening mammogram .Breast cancer diagnoses through December 31, 2018 were identified from health system cancer registries as well as the Massachusetts, Pennsylvania, New Jersey, and Delaware state cancer registries. Invasive cancers were characterized based on the expression of ER, PR, and HER2 from immunohistochemistry as reported to the cancer registries. In addition, HER2 expression was manually abstracted from EHR for cases diagnosed prior to 2010. Tumor subtypes were defined as ER and/or PR+HER2−, ER and/or PR+HER2+, ER and PR−HER2+, or ER and PR and HER2− (TNBC). Additionally, we categorized whether invasive breast cancer cases were screen‐detected or not screen‐detected for cases diagnosed through 2016. We defined cases as screen‐detected if there was a positive mammogram  within 1 year prior to cancer diagnosis date. We further defined cases as interval cancers if there was a negative mammogram  within 1 year of cancer diagnosis, consistent with established definitions.2.4Cox proportional hazards modeling was used to estimate the hazard ratios (HRs) for breast cancer subtypes for each risk factor. We ran separate models with each tumor subtype as the outcome, using a time origin of 6 months post mammogram date, with censoring upon the diagnosis of DCIS , other tumor subtypes, death, date last contact for patients with the missing date of death or December 31, 2018 for patients not known to have died. We tested the interactions of BMI with menopause status, breast density with menopause status, and BMI with breast density for each tumor subtype, based on interactions reported in prior studies.3Table The associations of known breast cancer risk factors with breast cancer subtypes were assessed using multivariable models Table . For ER/p < 0.001). Overweight and obesity were more strongly associated with ER/PR+HER2− breast cancer among postmenopausal women than premenopausal women . Interactions were not statistically significant for the ER/PR+HER2+ or ER/PR−HER2+ breast cancer. Associations of BMI with TNBC were of similar magnitude as seen in other subtypes but were only statistically significant for postmenopausal overweight women . We also observed a significant interaction between menopause status and breast density for both ER/PR+HER2− (p < 0.001) and TNBC (p = 0.019), with a stronger association among premenopausal than postmenopausal women . There was no significant interaction between menopause status and dense breasts for combined HER2+ subtypes  and ER/PR+HER2+ , ER/PR−HER2+ (family history and density) and ER/PR+HER2− . Additionally, we found that TNBCs were less likely to be screen‐detected and more likely than other subtypes to be diagnosed as interval cancers. These results have implications both for risk assessment and understanding of the etiology of breast cancer subtypes.Our results are consistent with a recent large pooled analysis of six cohorts or case–control studies that found that breast density was associated with increased risk of all intrinsic molecular subtypes.While it is well known that Black women have higher risk of TNBC, it is striking that Black women had nearly threefold increased risk even with comprehensive adjustment for breast cancer risk factors in a screened population, a magnitude that has been observed in previous studies which adjusted for fewer risk factors.We observed that older age was associated with an increased risk of TNBC. This may seem to be inconsistent with the prior literature reporting younger age to be associated with increased risk of TNBC.We found that prior biopsy and atypical hyperplasia were strongly associated with ER/PR+cancers irrespective of HER2 status but were not associated with TNBC, recognizing that the HR for the association with for ER/PR−HER2+ was relatively large but not statistically significant. Prior biopsy and atypical hyperplasia likely reflect changes in the breast that suggest higher subsequent risk of hormone receptor‐positive and HER2 positive tumors, but these changes do not appear to correlate with TNBC. This finding further points to unique etiologic mechanisms for TNBC.As expected based on prior studies,Our finding that interval cancers are more likely to be triple negative is consistent with existing literature. A population‐based study in Ireland found that triple‐negative tumors were over three times more likely to be interval cancers than screen‐detected.The strengths of our study include the prospective design among a large population of women undergoing mammography at three large centers and included the assessment of established breast cancer risk factors along with BMI and breast density, allowing us to assess interactions among risk factors. Additionally, the study includes a significant number of Black women, who are at high risk of dying of cancer but have been underrepresented in research studies to date. The limitations of our study include missing data on some risk factors‐ an inherent problem in studies using data collected for clinical purposes. However, given the prospective design, we do not expect that missing data would be differential by breast cancer diagnosis. We lacked data on the use of hormone replacement therapy (HRT), which is strongly associated with both breast density and breast cancer risk.Our results add to the literature describing differences in risk factors across breast cancer subtypes. We found that breast density may be a particularly strong risk factor for TNBC among premenopausal women, and that the other risk factors evaluated in this study do not explain racial differences in TNBC between Black and white women. These results highlight the urgency of exploring novel risk factors, such as genetics, epigenetics, biomarkers, and environmental exposures to understand the risk for less common but aggressive triple‐negative and ER/PR−HER2+ breast cancer subtypes, as existing risk factors appear largely irrelevant to risk of these tumors.The authors have the following corporate relationships to disclose: Emily Conant: Dr. Conant has grants and is on the advisory board for iCAD, Inc. and for Hologic, Inc. Kevin Hughes: Dr. Hughes receives honoraria from Hologic  and Myriad Genetics, Dr. Hughes has financial interests in CRA Health (Formerly Hughes RiskApps) which recently was sold to Volpara. CRA Health develops risk assessment models/software with a particular focus on breast cancer and colorectal cancer. Dr. Hughes is a founder of the company. Dr. Hughes is the Co‐Creator of Ask2Me.Org which is freely available for clinical use and is licensed for commercial use by the Dana Farber Cancer Institute and the MGH. Dr. Hughes's interests in CRA Health and Ask2Me. Org were reviewed and are managed by Massachusetts General Hospital and Partners Health Care in accordance with their conflict of interest policies. Aditya Bardia: Dr. Bardia is a consultant or on the advisory board for Pfizer, Novartis, Genentech, Merck, Radius Health, Immunomedics, Taiho, Sanofi, Daiichi Pharma/Astra Zeneca, Puma, Biotheranostics Inc., Phillips, Eli Lilly, Foundation Medicine. Dr. Bardia is contracted to do research with or has grants (to institution) with Genentech, Novartis, Pfizer, Merck, Sanofi, Radius Health, Immunomedics, Daiichi Pharma/Astra Zeneca. The remaining authors have no conflicts to disclose.This study was deemed exempt from review by the University of Pennsylvania Institutional Review Board.Table S1‐S5Click here for additional data file.


Our results suggest that the relationship between BMI and breast cancer could be due to a relationship between weight and breast cancer, downgraded by inappropriately adjusting for height; potential importance of anthropometric measures other than total body fat; breast cancer risk associations with BMI and weight are across a continuum. 2 when predicting breast cancer risk for postmenopausal women, and if the weight association differs by underlying familial risk. We studied 6,761 women postmenopausal at baseline with a wide range of familial risk from 2,364 families in the Prospective Family Study Cohort. Participants were followed for on average 11.45 years and there were 416 incident breast cancers. We used Cox regression to estimate risk associations with log-transformed weight and BMI after adjusting for underlying familial risk. We compared model fits using the Akaike information criterion (AIC) and nested models using the likelihood ratio test. The AIC for the weight-only model was 6.22 units lower than for the BMI-only model, and the log risk gradient was 23% greater. Adding BMI or height to weight did not improve fit . Conversely, adding weight to BMI or height gave better fits . Adding height improved only the BMI model . There was no evidence that the BMI or weight associations differed by underlying familial risk (P > 0.2). Weight is more informative than BMI for predicting breast cancer risk, consistent with nonadipose as well as adipose tissue being etiologically relevant. The independent but multiplicative associations of weight and familial risk suggest that, in terms of absolute breast cancer risk, the association with weight is more important the greater a woman's underlying familial risk.We considered whether weight is more informative than body mass index (BMI) = weight/heightOur results suggest that the relationship between BMI and breast cancer could be due to a relationship between weight and breast cancer, downgraded by inappropriately adjusting for height; potential importance of anthropometric measures other than total body fat; breast cancer risk associations with BMI and weight are across a continuum. Considering BMI alone assumes that height, a risk factor on its own , which pooled data from the Breast Cancer Family Registry (BCFR) Cohort and the Kathleen Cuningham Foundation Consortium for research into Familial Breast Cancer (kConFab) Follow-up Study . The BCFBRCA1 and BRCA2 mutation status information, wherever available, using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) version 3 . The underlying familial risk was based on the predicted 1-year risk of breast cancer based on pedigree information and A total of 6,761 women from 2,364 families with complete self-reported data on height and weight were included. The mean age at baseline was 61.22 years (SD = 9.41) and the mean duration of follow-up was 11.45 years (SD = 5.39). During the follow-up, there were 416 incident breast cancers diagnosed . Details of participants are shown in P < 0.05 when they were fitted by themselves. We calculated robust 95% confidence intervals (CI) that accounted for clustering by family and corresponding SE on the log(HR) scale. We used the log(HR)/SE to compare the strengths of association with each anthropometric measure in terms of differentiating cases from controls. All statistical tests were two-sided and estimates with a P value < 0.05 were considered nominally significant. We used Stata version 16 for all analyses  for the associations of weight, height, and BMI with breast cancer risk, stratified by study site and birth cohort, with considering underlying familial risk. On the basis of their distributions being skewed, and on maximizing the log likelihoods of fitted models following the principles of Box and Cox transformations , we log-analyses .We fitted models for each of weight, BMI, and height alone, and then separate models for each pairwise combination of weight, height, and BMI. We also adjusted for and fitted interactions with 1-year BOADICEA risk, i.e., the interactions between 1-year BOADICEA risk and each of BMI, weight, and height, and the interactions between 1-year BOADICEA risk and baseline age. In addition, BMI, weight, and height, 1-year BOADICEA risk and baseline age were all fitted as restricted cubic spline terms in the aforementioned Cox regression models to allow for the possible nonlinear relationships between these anthropometric measures and breast cancer risk .i − AICmin, where AICi and AICmin are the AIC values for a fitted model and a better model, respectively, indicates the information loss when a fitted model is used rather than the better model. The strength of evidence against the better-fitted model is considered to be substantial if 0 ≤ ΔAIC ≤ 2, considerably less if 4 ≤ ΔAIC ≤ 7, essentially none if ΔAIC > 10 0.5, where ΔLL is the change in log likelihood from adding that parameter.The Akaike information criterion (AIC) was used to compare model fits ; lower AAIC > 10 . The likAIC > 10  was usedBRCA1 and BRCA2 mutation carriers. Third, we excluded ever/current users of hormone replacement therapy. Fourth, we included BMI, weight, and 1-year BOADICEA risk on a natural scale.The following sensitivity analyses were conducted. First, we included only breast cancers known to be invasive. Second, we excluded Northern California – Cancer Prevention Institute of California, Institutional Review Board (2001–033) and Stanford University (45842), which ensure that all studies are conducted in accordance with the ethical principles of the Belmont Report.New York – Columbia University Medical Center, Institutional Review Board (AAA7794), which ensures that all studies are conducted in accordance with the ethical principles of US Common Rule and Declaration of Helsinki.Philadelphia – Fox Chase Cancer Center, Institutional Review Board (95–009), which ensures that all studies are conducted in accordance with the ethical principles of the Belmont Report.Utah – Huntsman Cancer Institute, University of Utah, Institutional Review Board (00004965), which ensures that all studies are conducted in accordance with the ethical principles of the Belmont Report and US Common Rule.Ontario – Mount Sinai Hospital Research Ethics Board (#02–0076-U) and University Health Network Research Ethics Board (#96-U107-CE), which ensure that all studies are conducted in accordance with the ethical principles of Declaration of Helsinki.Australia – University of Melbourne, Human Ethics Sub-Committee (1441420.1), which ensures that all studies are conducted in accordance with the ethical principles of Declaration of Helsinki.kConFab – Peter MacCallum Cancer Centre, the Peter Mac Ethics Committee (97/27), which ensures that all studies are conducted in accordance with the ethical principles of Declaration of Helsinki.All participants in the BCFR and kConFab provided written informed consent before cohort enrollment. Human research ethics committees at the participating institutions granted ethics approval for the six sites of the BCFR and for kConFab:http://www.bcfamilyregistry.org/ and www.kconfab.org.For access to the data used in this this study, please see P for likelihood ratio test = 0.3; ΔAIC = 0.90 and 0.83, respectively). Conversely, adding weight to both the BMI-only and the height-only models improved fits . Adding BMI gave a better fit to the height-only model , and adding height also gave a better fit to the BMI-only model .In support of this, adding height or BMI to the weight-only model did not improve fit (both P > 0.2).The HR for familial risk was 1.93  when fitted in the base model. While the HRs varied from 1.94 to 1.96 when additionally fitted with one or more of weight, BMI, and height, virtually no changes in the effects of familial risk were found. As previously found for BMI , for botSimilar results were found when we excluded adjustment for familial risk (see Supplementary Table S1). The associations with weight, BMI, and height were also little changed after adjusting for familial risk, consistent with the lack of multiplicative interactions above.P for nonlinear >0.2) . There was also no evidence of nonlinearity with 1-year BOADICEA risk or baseline age .Using the restricted cubic spline regression gave similar results, i.e., weight alone gave the most parsimonious model fit; BMI and height added information to the height-only model and BMI-only model, respectively; no evidence of multiplicative interactions between 1-year BOADICEA risk and weight or height or BMI (Supplementary Table S2). There was no evidence for nonlinear breast cancer risk associations with log-transformed BMI or weight, or height on a natural scale  weight alone provided the most parsimonious model fit, and BMI and height added additional information to the models by themselves; (ii) there was no evidence for multiplicative interactions between 1-year BOADICEA risk and weight or BMI or height; (iii) there was evidence that log-transformed BMI or weight, or height on a natural scale, had linear associations with breast cancer risk . The aforementioned results still held when all the variables were analyzed on a natural scale, although the evidence was slightly weaker , except for the marginal evidence for the nonlinear association between weight and breast cancer risk after adjusting for familial risk (P for nonlinearity = 0.049).After adjusting for 1-year BOADICEA risk, in the models excluding We found evidence that weight alone gives more information on breast cancer risk prediction for postmenopausal women than does BMI alone. None of the pairwise combinations of BMI, weight, and height provided more information on breast cancer risk than the weight-only model. On the contrary, while height added information to the BMI-only model, this did not provide a better fitting model than weight alone. This suggests that the adjustment of weight for height inherent in BMI is not optimal for breast cancer risk prediction. Therefore, BMI alone does not appear to capture the relationship between body measures and breast cancer risk as well as weight alone. The evidence for this was consistent across all the sensitivity analyses. Fitting log(BMI) corresponds to a special case of a model of log(weight) and log(height) in which the log(height) coefficient is forced to be in the opposite direction of the log(weight) coefficient, and of twice the absolute magnitude . We founχ12 = 74), while for BMI it was 1.21 . Therefore ΔAIC = 32, providing substantial evidence that BMI alone gave a worse fit than weight alone. BMI alone also did not adequately describe the relationship of body composition and body size to systolic blood pressure for which weight was a better predictor (Similar observations have been found for breast cancer and other human traits. For example, a large-scale postmenopausal breast cancer study using the UK Biobank  found thredictor .These findings challenge current beliefs about the association between anthropometric measures and breast cancer risk. First, the World Cancer Research Fund International Continuous Update Project considered BMI and weight change, but not weight, as risk factors for breast cancer , and intSecond, our findings suggest the potential importance of anthropometric measures other than total body fat , rather 2are taken to be at baseline risk and therefore not regarded as being at increased breast cancer risk, even though each increase in weight, and therefore BMI, could be associated with an increase in postmenopausal breast cancer risk. That is, the risk associations with weight and BMI are increasing across their ranges, and not a step-function as is implicit by only considering categories. Thus, describing breast cancer risk in terms of weight as a continuum, rather than in terms of BMI categories, would more accurately inform, and perhaps better translate, public health and clinical decisions relevant to breast cancer, i.e., no “healthy” category of BMI should be defined—each increase matters for both BMI and weight, in terms of postmenopausal breast cancer risk.Third, our findings clarify that the breast cancer risk associations with BMI and weight, whether log-transformed or not, and height on its natural scale, are across a continuum and at least approximately linear, rather than a step function and restricted to the extreme and arbitrarily assigned categories labelled with negative connotations . SimilarBMI, weight, height and familial risk are all positively associated with postmenopausal breast cancer risk. The lack of evidence for multiplicative interactions between the familial risk and BMI or weight or height in this study implies that, on the absolute scale, the additive positive associations of BMI or weight or height and the underlying familial risk with breast cancer risk must increase with any of them . This maThere are two main limitations in this study. One is that we did not consider risk separately by breast cancer subtypes defined by hormone receptor status, because these data were not available for a large proportion of cases. Risk associations with different subtypes of breast cancer vary according to BMI categories, which could reflect age and different etiologies . The secIn conclusion, there was evidence that weight is more informative than BMI for predicting postmenopausal breast cancer risk, and breast cancer control based on body composition might need to be broader and more nuanced than just being focused on ‘adiposity’ and ‘obesity’. The contribution of weight to breast cancer risk would be more important the greater a woman's familial risk of breast cancer.Supplementary DataClick here for additional data file.


Australia has one of the world’s highest breast cancer incidences. For most women who are not classified as at high risk, eligibility and frequency of breast cancer screening in Australia is based solely on their age. Breast cancer risk models can help to optimise early detection and management of breast cancer. We evaluated six commonly used models over 15 years follow-up using an Australian community-based cohort of 7608 women aged 50–65 years. The BOADICEA and IBIS models best discriminated women who were at higher risk of developing breast cancer from those at lower risk, but no model apart from BOADICEA accurately predicted absolute risk across the risk spectrum. The BOADICEA model could be of clinical use for women of similar demography. p-difference ≤ 0.04). No model except BOADICEA calibrated well across the spectrum of 15-year risk . The performance of BOADICEA and IBIS was similar across age groups and for women with or without a family history. For middle-aged Australian women, BOADICEA and IBIS had the highest discriminatory accuracy of the six risk models, but apart from BOADICEA, no model was well-calibrated across the risk spectrum. Prospective validation of risk models is needed to assess their clinical utility, particularly over the longer term. We evaluated the performance of six commonly used breast cancer risk models . 15-year risk scores were estimated using lifestyle factors and family history measures from 7608 women in the Melbourne Collaborative Cohort Study who were aged 50–65 years and unaffected at commencement of follow-up two (conducted in 2003–2007), of whom 351 subsequently developed breast cancer. Risk discrimination was assessed using the C-statistic and calibration using the expected/observed number of incident cases across the spectrum of risk by age group  and family history of breast cancer. C-statistics were higher for BOADICEA  0.56–0.62) and IBIS  than the other models ( Breast cancer is the most common cancer and cause of cancer death for women worldwide, with approximately 2.3 million incident cases in 2020, and carries a substantial burden of disease ,2. AustrBreast cancer risk models are currently used in familial cancer clinics by genetic counsellors to stratify women and inform risk-tailored advice on the optimal age range, frequency, and modality of screening for those at high risk ,4. For mThe Melbourne Collaborative Cohort Study (MCCS) is a prospective cohort that includes 24,469 women from Melbourne, Australia, aged between 27 and 76 years (99% were 40–69 years) at recruitment . All parWe used the latest versions  of the risk models: BOADICEA version 5.0.0, using updated Australian incidence rates ; IBIS ve2). History of hyperplasia or benign breast disease were not available. As our aim was to compare models using solely family history and lifestyle risk factor information, results from germline genetic testing for pathogenic variants in BRCA1 and BRCA2 (or susceptibility genes) and mammographic density were not included in our analyses due to lack of availability of these data for most women. At follow-up 2, MCCS participants completed a questionnaire that asked about their demographic characteristics and lifestyle-related factors, including, for example, age, alcohol intake, age at menarche, parity, number of sisters, brothers, and children, age at first birth, menopausal status, and use of the oral contraceptive pill and menopausal hormone therapy. Summary family history data on affected relatives were obtained from questionnaires at follow-up 2 (first-degree relatives) and follow-up 1 (second-degree relatives). Data from the most recent questionnaires were used and supplemented with that from older questionnaires if unavailable. To reconstruct pedigrees, the following assumptions were made about the year of birth (YOB) of participants’ relatives: mothers and aunts (25 years before the participant’s YOB), grandmothers (50 years before the participant’s YOB), sisters (participant’s YOB), and daughters (25 years after the participant’s YOB). Missing ages for affected and unaffected mothers, aunts, and grandmothers were imputed to 70 years, whereas for sisters, they were imputed to the youngest of the participants’ age at follow-up 2 or aged 70 years . Weight at follow-up 2 was measured to the nearest 100 g using a digital electronic scale, while height was measured at baseline to the nearest 1 mm, using a stadiometer. Body mass index (BMI) was defined as weight (kg) divided by height squared  during follow-up to 1 March 2019. Follow-up began at follow-up 2 attendance and ended at: (i) diagnosis of invasive breast cancer, (ii) follow-up time reaching 15 years, (iii) age 80 years (maximum age for estimating risk in BOADICEA), or (iv) censor date of 1 March 2019, whichever came first. Expected cancer counts for the defined cohort were estimated for each model by summing the predicted risks over all eligible participants. Deaths from causes other than breast cancer were included as competing risks for all models except for two, BOADICEA and BRCAPRO-BCRAT, because they do not currently have an option to account for competing causes of death.We compared the performance of the models with up to 15-year risk in terms of discrimination and calibration. Calibration was assessed by comparing the number of expected cases (E) within the cohort with the number observed (O). Model discrimination was assessed using a concordance statistic (C-statistic)  and plotp-value < 0.05 indicates miscalibration [The assessment of model calibration at the individual level was graphically represented from the model’s goodness of fit using the calibration belt routine. This method uses likelihood-based tests on a data-driven forward selection of polynomial regression models to assess the goodness of fit of the 15-year risk estimates from the six models, where a ibration . Calibra2; 23% had a first- or second-degree family history of breast cancer . C-statistics did not vary significantly across different age subgroups, whereas C-statistics from all models (except for BCRAT) were slightly higher for women who had a family history of breast cancer than for those who did not have any affected relatives. The overall discrimination of 15-year breast cancer risk (measured by C-statistic) across the six models ranged between 0.51 and 0.59 . IBIS anp-value < 0.03), where they generally underpredicted risk at the low end of risk and overpredicted risk at high end of risk  , particu of risk . p-value < 0.02), whilst iCARE-lit had higher discrimination at 5-year and 10-year risk compared with 15-year risk  had the widest range of predicted risk distribution. The exceptions to this wider predicted risk distribution by pedigree-based models were the two BRCAPRO models, likely because they only allow for the effects of ase risk . InteresA previous study that analysed a combined Australian and North American cohort reported higher 10-year C-statistics for BCRAT (0.64), BRCAPRO (0.62), IBIS (0.66), and BOADICEA (0.65) . This maIBIS and BOADICEA consistently outperformed other risk models in discrimination, including in family registries that encompass participants from Australia, USA, and Canada . We showWe have previously demonstrated a doubling of discriminatory accuracy for IBIS and BOADICEA (C-statistic from 0.56–0.57 to 0.62) with the addition of a polygenic risk score to predictors examined  using a subsample of the MCCS . AdditioWe evaluated six breast cancer risk models within an Australian average-risk cohort of women aged 50 to 65 years and found that IBIS and BOADICEA had the highest discriminatory accuracy and that their discriminatory accuracy remained consistent over time. However, apart from BOADICEA, no model was well-calibrated across the risk spectrum. Breast cancer risk models can help strengthen preventive efforts such as screening programs for average-risk Australian women via tailored surveillance advice. Models with lifestyle-related factors and family history will further benefit from the inclusion of information on genetics and mammographic density.


Cox regression and logistic regression analyses evaluated breast cancer risk from selenium exposure, stratified for the SNPs and adjusted for risk factors. A total of 1946 women were diagnosed with breast cancer. Women with T/T alleles in rs1050450 had lower breast cancer risk compared with C/C, HR 0.81 (0.68–0.96). Interaction by rs1050450 limited a protective effect of higher selenium intake to T/T carriers, HR 0.68 (0.43–1.08) for intermediate intake and HR 0.63 (0.40–1.00) for high intake. No interactions or risk differences were seen for other SNPs or for serum selenium or genetically elevated selenium. The results indicate that genetic variation in rs1050450 might affect breast cancer risk and that selenium exposure could be a possible modifiable risk factor for breast cancer among women with that variation.Selenium has been suggested to be protective regarding breast cancer risk but no overall effect has been established. Genetics may modify the effect. This study compares the effect of selenium exposure on breast cancer risk between women with different alleles in single-nucleotide polymorphisms (SNPs). The Malmö Cancer and Diet Study, a cohort including 17,035 women and >25 years of follow-up on breast cancer diagnosis, was used. Five promising SNPs regarding interaction with selenium exposure were selected from the literature: rs1050450, rs4880, rs3877899, rs7579, and rs71304. Selenium exposure was assessed in three ways: genetically elevated ( Due to its significant role in antioxidation, the essential mineral selenium has been of interest to study in cancer development, including breast cancer . CurrentRecently, several large studies on breast cancer risk have been performed using different selenium exposure measurements. No association between genetically elevated levels of circulating selenium and breast cancer risk was found in a recent Mendelian randomization (MR) study . Null reThe aim of this study was to determine whether the effect of selenium exposure on breast cancer risk is modified by relevant SNPs, and whether any independent effect of these SNPs on breast cancer risk exists, in a low-selenium setting. Selenium exposure was assessed by applying a triangulation approach encompassing a genetic score for higher selenium levels, dietary selenium intake and serum selenium levels. Five SNPs  were evaluated regarding effect modification.This study was designed within the MDCS, a population cohort based in the Swedish city of Malmö. As previously described, citizens of Malmö aged 44 to 74 were invited to the cohort and participants were included from 1991 to 1996 . In totan = 16,429). Women with prevalent breast cancer at baseline (n = 538) were then excluded from the analyses based on dietary selenium intake . Only women with valid data regarding serum selenium were then included for analyses based on serum levels (n = 2037). Serum selenium values were available from a case–control study within the MDCS from 2017, including incident breast cancer cases to 31 December 2013 (n = 1186) and an equal number of controls [Inclusion and exclusion were different for each of the three exposure assessments and are visualized in 013 n = 16 and an 013 n = 16 and an controls .p < 1 × 10−15) were removed. Missing SNPs were imputed using a reference panel from the Haplotype Reference Consortium [The genotyping in MDCS has been described previously . Stored nsortium . GeneticFive SNPs with a reasonable mechanistic pathway and evidence of effect on selenoproteins and/or breast cancer development were identified in previous research  ,13,14,15p = 5 × 10−8 and excluding SNPs in linkage disequilibrium (LD r2 ≤ 0.1), as well as one SNP, rs6859667, with a minor allele frequency less than 5% [The allele score design was adopted from the MR study on predicted levels of circulating micronutrients and breast cancer risk by Papadimitriou et al. (2021), and each SNP was weighted for its effect on selenium levels  were not included in the serum selenium analyses in the present study. The serum selenium variable was split into tertiles by rank, and this was performed separately for each season as described above.Serum selenium was analyzed in a previous study, with methodology described in more detail . In shorThe Swedish personal number was used to cross-link MDCS to registry data up until 31 December 2019. Breast cancer diagnosis including the date of diagnosis was identified in the Swedish Cancer Registry, date of death was identified in the Swedish Cause of Death Registry, and emigration was identified in The Swedish Population Registry.Missing values were explored among the 15,891 women included in the dietary intake analyses and considered to be missing at random. A total of 96.0% had complete data with no missing values. The variable with the most missing values was ‘Age at first childbirth’ including 269 women (1.7%) with missing information. The missing values were imputed using chained equations. Predictive mean matching was chosen as the model type for scale variables. In total, 25 new datasets, each with 15,891 individuals, were created from 10 iterations each. The multiple imputation model included all factors in the dietary intake analyses—endpoint, time at risk (logarithmic), exposure , risk factors  and rs10Descriptive statistics for tertiles of allele score in relation to established risk factors for breast cancer were investigated in a cross table. Descriptive statistics for dietary selenium intake and serum selenium have been published previously for this cohort ,23.Kaplan–Meier curves were used to evaluate the assumption of proportional hazards for dietary intake and genetically elevated selenium. Cox regression analyses were used to evaluate the risk of breast cancer and render HRs and 95% confidence intervals. In analyses considering genetically elevated selenium, allele score tertiles were compared, using ‘low’ as the reference. The underlying time variable was age at censoring. The indicator of event was a first-time breast cancer diagnosis, including diagnoses both before and after baseline of MDCS. Other reasons for censoring were death, emigration or end of follow-up. The analyses were also stratified for the included SNPs ; and in a separate analysis, an interaction term of the allele score and the SNP variable was included along with the two variables separately. Separate Cox regression analyses were performed for rs1050450, rs4880, rs3877899, rs7579 and rs713041, comparing the number of effect alleles  and the risk of breast cancer.In analyses based on dietary selenium intake, only a first-time breast cancer diagnosis after baseline was used as the indicator of event and, consequently, the time at risk started at the cohort baseline and the time in cohort was used as the underlying time variable. Analyses compared tertiles of dietary selenium intake and were first adjusted for age at baseline, and then for additional established risk factors for breast cancer; age at menarche, age at menopause, BMI, education, socio-economic status, marital/cohabiting status, number of children, age at first childbirth, ever use of contraceptive pills, oophorectomy, alcohol consumption and use of hormone replacement therapy. Analyses were stratified for SNPs and interaction was tested as described above. Sensitivity analyses including only complete cases were also performed.The analyses based on serum selenium were treated as a case–control design. Hence, tertiles of serum selenium levels were compared with logistic regression, rendering odds ratios (ORs) regarding risk of incident breast cancer from baseline until end of follow-up. Analyses were adjusted for the same factors as the dietary intake analyses. Analyses were stratified and interaction was tested as described above. Sensitivity analyses were also performed, using the multiple imputation based on women with valid serum selenium as described above.All analyses were performed in IBM SPSS 27.0.1.0.Descriptive statistics for the included and excluded women are presented in Descriptive statistics for low, intermediate and high allele scores are presented in In When comparing tertiles of allele score, 16,429 women were at risk for breast cancer from birth to the end of follow-up, resulting in a mean follow-up time of 80.0 years, a total of 1,280,698 person years (py) and 1956 individuals with a breast cancer diagnosis. In When comparing tertiles of dietary intake, 15,891 women were at risk of breast cancer from inclusion in MDCS to the end of follow-up or censoring. With a mean follow-up time of 21.5 years, and a total of 342,129 py, 1418 women were diagnosed with breast cancer. In In total, 2037 women with data on serum selenium levels were followed for a mean time of 17.0 years from baseline. During follow-up, 1047 out of those women were diagnosed with breast cancer. No overall differences between tertiles or any interactions with individual SNPs were seen. In In the complete case analyses, results were similar to adjusted analyses (results not shown). In the sensitivity analyses for serum selenium, using the alternative multiple imputation model, the adjusted ORs were similar (results not shown).The two main findings in this study were that rs1050450 in GPX-1 is associated with decreased risk of breast cancer for T/T carriers, and modifies the effect of dietary selenium intake on breast cancer risk. A protective effect of intermediate or high selenium intake was seen among women carrying C/T or T/T alleles in a stepwise fashion, compared to women with the C/C allele. The overall effect of selenium intake and serum selenium has previously been reported for this cohort, and we found no further interactions with the other four SNPs investigated ,23. We fLoss of heterozygosity in the GPX-1 gene is a common event in breast cancer, as well as in other cancers, and suggests an important role for GPX-1 in cell homeostasis ,32. GPX-n = 190), odds ratio (OR): 1.88 (1.08–3.28), while the results were more in line with the present study for ductal breast cancer (n = 659) with an OR: 0.73 (0.47–1.14) and for overall breast cancer (n = 975) with an OR: 0.83 (0.54–1.28) [Our results are in line with findings from a study including 136 Polish women with breast cancer where women carrying T/T or C/T in rs1050450 had lower risk compared to C/C . In cont54–1.28) . Other s54–1.28) . A lower54–1.28) . However54–1.28) . In a me54–1.28) . Thus, oRegarding the interaction of rs1050450, one study found that women with T/T had the lowest levels of lipid peroxidation and that the GPX-1 activity was associated with lipid peroxidation, but that selenium levels did not affect the association . Those rChallenges of observational studies include validly measuring exposure and outcome. Outcome data were collected from the Swedish Cancer Registry with high validity and completeness . LimitatFor rare alleles in some SNPs, the number of events was low in the present study. That resulted in zero events in some categories when adjusting the Cox regression for all breast cancer risk factors, and those analyses were discarded. Cox regression analyses should be interpreted with caution if <10 events per category, although most analyses are stable for up to five events per category . Across The reason for including prevalent breast cancers as endpoint in the model evaluating genetically elevated selenium but not in the models evaluating dietary intake and serum levels was to avoid the risk of reverse causation, since there is a risk that a prevalent breast cancer disease could have affected the dietary intake and the serum selenium levels, as well as some of the risk factors evaluated at baseline. However, prevalent disease at baseline would not affect the studied genetics, and thus there is no risk of reverse causation.Our findings support a lower risk of breast cancer for women with the SNP rs1050450 T/T in GPX-1, and a protective effect of dietary intake of selenium among these women. However, due to few events and no evidence of a similar effect with regard to serum selenium or genetic score, the evidence is weak and our findings should be seen as indications and need to be replicated in other cohorts. However, our findings are in line with ideas presented in recent reviews, and further mechanistic and epidemiological studies are warranted since selenium exposure could be a modifiable risk factor for some women.


Mammographic density (MD) of the breast and body mass index (BMI) are inversely associated with each other, but have inconsistent associations with respect to the risk of breast cancer. Skeletal muscle mass index (SMI) has been considered to reflect a relatively accurate fat and muscle percentage in the body. So, we evaluated the relation between SMI and MD. A cross-sectional study was performed in 143,456 women who underwent comprehensive examinations from 2012 to 2016. BMI was adjusted to analyze whether SMI is an independent factor predicting dense breast. After adjustment for confounding factors including BMI, the odds ratios for MD for the dense breasts was between the highest and lowest quartiles of SMI at 2.65 for premenopausal women and at 2.39 for postmenopausal women. SMI was a significant predictor for MD, which could be due to the similar growth mechanism of the skeletal muscle and breast parenchymal tissue. Further studies are needed to understand the causal link between muscularity, MD and breast cancer risk. It is known that the risk of breast cancer varies four to six times, based on the MD1. The difference in MD may reflect the effects of hormones and growth factors and might be related to the causal pathway of breast cancer3. Additionally, because cells at risk of turning malignant are mainly located in the fibro glandular tissue, the total amount of this tissue can also be considered as a risk factor for breast cancer4. Therefore, MD is a significant predictor for the risk of breast cancer, and factors influencing breast density might also contribute to the risk of breast cancer. However, the biological basis for determining MD is still unclear3.Mammographic density (MD) reflects the relative amount of connective and epithelial tissue (dense area) and fat tissue (non-dense area) in the breast14. Increasing age and post-menopausal state with decreased estrogen levels is known to be a major determinant of MD15. Anthropometric factors, such as body mass index (BMI), waist–hip ratio, and waist circumference are also known to be related to MD9. In epidemiological studies, the most widely used index to evaluate body fat is BMI, which is calculated as the weight (kg) divided by height squared (m2). However, considering the inverse association between BMI and MD and the positive association between MD and breast cancer risk, the positive association between BMI and breast cancer risk remains unexplained in postmenopausal women17. The association between obesity and breast cancer risk in premenopausal women is not consistent across studies19. We supposed that the inconsistency could be because anthropometric factors used in previous studies are indicators of weight or mass including both muscle and fat, and these parameters do not reflect the respective amounts of each of muscle and adipose tissue.There have been many studies on the factors related to breast density; however, different results have been reported due to heterogeneity and the small number of subjects examined and different statistical methods used for the analysisWe obtained the skeletal muscle mass index (SMI) using an equipment that measures the ratio of adipose tissue to muscle and evaluated the association between SMI and MD. Moreover, SMI indicates not only the proportion of skeletal muscle in the body, but are also considered better markers for assessing adiposity and its metabolic consequences compared to BMI. This is because SMI reflects a relatively accurate fat percentage in the body. Therefore, we analyzed whether the SMI can be a better predictor for MD, compared to the other anthropometric parameters used in previous studies. In addition, by attempting to adjust BMI in the analysis of the relationship between SMI and MD, it was intended to identify the cause of the paradoxical relationship between BMI and MD.n = 145,898), the data at first year was used for analysis. We also excluded participants with missing anthropometric measures (n = 1387), and those with incomplete mammography (n = 895). Finally, a total of 143,456 women were included in the study. This study was approved by the Institutional Review Board of Kangbuk Samsung Hospital (KBSMC 2018-02-056), which waived the requirement for informed consent due to the use of deidentified data obtained as part of routine health screening exams, retrospectively. All methods were performed in accordance with relevant guidelines and regulations.This study was a retrospective analysis of the data of 291,636 patients who underwent comprehensive annual or biennial examinations from 2012 to 2016 at the Samsung Healthcare Center in Seoul and Suwon, South Korea. Mammography was included as a part of the basic examination for all women over 30 years of age. The participants were employees of various companies, and were required to undergo an annual or biennial mandatory screening, which is required by the Industrial Safety and Health Law in Korea. The spouses of some male employees were also included due to the company’s welfare policies, and some women were self-registered. Of the data of 291,636 patients, if any participant underwent multiple examinations through several years . MD was assessed from the craniocaudal and mediolateral oblique views of the each breast, using the breast imaging reporting and data system (BI-RADS) with 4 categories: almost entirely fat, scattered fibroglandular densities, heterogeneously dense, or extremely dense by several experienced radiologistsAnthropometric measurements were obtained by the trained staff at the time of mammography. Height was measured to the nearest 0.1 cm using a stadiometer while the participant stood with heels together, arms to the side, legs straight, shoulders relaxed, and the head positioned so the gaze was straight ahead. Body weight was measured to the nearest 0.1 kg using a digital scale, with women in light clothing without shoes. Waist circumference (cm) was measured using a soft tape midway between the lowest rib margin and the iliac crest in a standing position. BMI was calculated as weight in kilograms divided by height in meters square.21.The percentage of body skeletal muscle was estimated using a multifrequency bioelectrical impedance analyzer (BIA) with eight-point tactile electrodes . SMI was calculated as SMI (%) = skeletal muscle mass (kg)/body weight (kg) × 100, based on the methods used by Janssen et al.Data about the history and health-related behaviors, family history, menstrual and reproductive history were collected through a self-administered questionnaire. Blood samples were taken from the antecubital vein by trained staff after the participant had fasted for at least 8 h, to measure the biochemical parameters. Calcium level adjusted to albumin was calculated as serum calcium + 0.8 × .24. Chi-square test for categorical data and t test for continuous variables were used to compare the baseline characteristics between the groups. The associations of variables with breast density were evaluated using RW, which is a way quantify the relative importance of correlated variables in regression analysis and it could produce clear results even if the predictors have very high collinearity25. Pearson correlations and scatter plots were used for understanding the relationship between anthropometric factors, age and SMI24. Logistic regression analyses were conducted to investigate the impact on MD of anthropometric factors by estimating odds ratios (ORs) with 95% confidence internals (CIs). We adjusted for parameters like age, parity, age at menarche, history of estrogen replacement therapy, and calcium level adjusted to albumin. In addition, we adjusted for BMI to evaluate whether SMI was an independent factor for a dense breast. All reported p values are two tailed, and comparisons where p < 0.05 were considered statistically significant.Statistical analyses were performed using the STATA version 13.0  and R version 4.0.3 with packages for relative weights (RW) analysis and scatterAmong the 143,456 participants, there were 115,013 premenopausal women (80.2%) and 28,443 postmenopausal women (19.8%). Table Pearson correlations and scatter plots between anthropometric factors, age and SMI are presented in Fig. To examine the effect of each factor on MD, we performed a logistic regression analysis of the known risk factors for dense breasts from previous studies and RW analysis. Younger age, nulliparity and low calcium level after adjustment to albumin were significantly associated with dense breasts, regardless menopausal state. Age at menarche, history of estrogen replacement therapy had different effects on dense breasts according to menopausal state Table . RW analTable To investigate whether SMI was related to MD independent of BMI, we analyzed the association of MD with SMI after further adjustment for BMI. It was seen that SMI was still a statistically significant factor associated with MD. Premenopausal women in the highest quartile of SMI were 2.65 times more likely to have dense breast tissues than those in the lowest quartile (95% CI 2.52–2.79), while postmenopausal women in the highest quartile of SMI were 2.39 times more likely to have dense breast tissues than those in the lowest quartile (95% CI 2.02–2.82) Table .Table 4MTo our knowledge, this is the first study about the association between skeletal muscularity and MD, and we demonstrated that SMI was the most important predictor among the known predictors of MD by using RW analysis. SMI was also an independent predictor of dense breasts after adjustment for known factors associated with MD, including age and BMI. These results suggest that SMI is a measure to overcome the inconsistency in BMI observed in previous MD-related studies; this could be explained by the strong relationship between skeletal muscularity and breast parenchymal tissue.25. Additionally, the role of epidermal growth factor family of growth factors is known in the development of epithelial tissues, particularly the mammary gland and the gastrointestinal tract, and their role in skeletal tissue growth was also reported26. Thus, based on this growth mechanism, the relationship between SMI and breast parenchymal tissue is highly relevant, and might explain the relationship with MD.In infancy, childhood, puberty, and adulthood, the development of body is affected by a variety of hormones. Among them, gonadal sex steroids, growth hormone and insulin-like growth factor-1 are known to play a major role in development of mammary gland as well as skeletal muscle9. In our study, as in previous studies, factors representing adiposity, such as BMI, weight, and waist circumference were found to be significantly associated with MD in both pre and postmenopausal women. However, these measurements cannot distinguish between muscle and adipose tissue; hence, it may have a limitation for use as a marker to represent adipose tissue.There are many studies on the relationship between MD and adiposity. Easily measurable but potentially inaccurate estimates of adiposity such as weight, BMI, waist circumference, weight gain have proven to be relevant to MD11. These studies that suggest that body or abdominal fat assessed by DXA or abdominal CT might have an association with the percentage of dense area in breast, independent of BMI. The eight-polar BIA used in our study was reported to be superior to the DXA or body weight used in other studies, for the accuracy of fat mass and muscle mass measurements28. The SMI measured by BIA indicates the ratio of the skeletal muscle and adipose tissue in the body. The significant association between MD and SMI independent of BMI in our study might further strengthen the results of previous studies, and the SMI measured by BIA could be better indicator of predicting MD.Attempts have been made to measure adiposity or muscularity accurately using imaging methods, such as whole-body dual x-ray absorptiometry (DXA) or abdominal computed tomography (CT) scans29. This irregularity of the menstrual cycle induces a decrease in ovarian function, resulting in a decrease of estrogen synthesis. Low level of the sex hormone is associated with inhibition of the growth of the fibroglandular tissue of the breast, resulting in a low MD and a reduced risk of breast cance19. However, in late-adulthood, the increase in adiposity due to weight gain increases only the breast fat tissue (non-dense area) without an increase in the fibroglandular tissue (dense area), resulting in a low MD. Although results vary based on the hormones used or the molecular subtype, it is known that obesity in postmenopausal women increases the risk of breast cancer, which is in contrast to the results in premenopausal women31. It could be because an increase in fat mass caused a higher level of estrogen, induced by aromatization of androstenedione32.Based on previous studies, although adiposity seems to be highly related to MD and breast cancer, it is still difficult to understand the biological pathway and has different effect according to menopause state. In early-adulthood, estrogen synthesis in subcutaneous fat accounts for only about 5% of the total, but when the fat mass is excessive, negative feedback is received in the hypothalamic-pituitary-axis15. Reproductive hormones that decrease with aging affect MD, which is prominent in the premenopausal period33. In particular, the hormonal changes that occur during the menopausal transition are reflected in the drastic decline in MD34. In our study, age showed the highest RW value except for SMI, confirming the strong correlation between age and MD. Therefore, in order to more clearly identify the association between SMI and MD, we adjusted for age in analyses.Increasing age is strongly associated with declines in MD, which occurs in both pre- and post-menopausal women, regardless of race36. In our study, postmenopausal women with dense breasts consumed larger amount of alcohol than those with non-dense breasts, while premenopausal women did not. Vitamin D is known to inhibit estrogen synthesis and its receptor function, and inhibit breast epithelial and stromal proliferation, suggesting an inverse relationship with breast cancer and MD38. Based on this theory, several studies have examined the association between MD and vitamin D, but the meta-analysis did not yield significant results14. In our study, vitamin D levels were low in women with high MD regardless of the menopausal status. Considering that our study was based on the data from vitamin D measurements of 97,072 participants, which was much larger than the data in a previous meta-analysis (n = 5863), this result could be significant. For a clear conclusion, a further study with correction of the confounding factors might be needed.We also conducted a verification of factors that have been previously associated with MD. Alcohol consumption has been known to be associated with an increased breast cancer and MD in most studiesThe limitation of this study is that quantitative measurements were not used for the measurement of MD. Quantitative measurements are expressed as continuous variables, which might provide more accurate statistical results than the classification based on the BI-RADS method that we used. Second, all participants were Koreans, and they have more dense breasts than other races. Therefore, the results cannot be extrapolated to the general population.We found a positive correlation between the muscularity and breast density. SMI was independent and strong predictor of dense breast. Considering our results and growth mechanism, the proportion of muscularity in the body are likely to be correlated with the proportion of parenchyma in the breast. It offers the possibility to explain the reason for the paradoxical relationship between BMI, MD and breast cancer risk, and for disagreement between previous studies. Further studies are needed to understand the causal link between muscularity, MD and breast cancer risk.


Smoking was strongly associated with breast cancer in previous studies. Whether smoking promotes breast cancer through DNA methylation remains unknown.Two-sample Mendelian randomization (MR) analyses were conducted to assess the causal effect of smoking-related DNA methylation on breast cancer risk. We used 436 smoking-related CpG sites extracted from 846 middle-aged women in the ARIES project as exposure data. We collected summary data of breast cancer from one of the largest meta-analyses, including 69,501 cases for ER+ breast cancer and 21,468 cases for ER− breast cancer. A total of 485 single-nucleotide polymorphisms (SNPs) were selected as instrumental variables (IVs) for smoking-related DNA methylation. We further performed an MR Steiger test to estimate the likely direction of causal estimate between DNA methylation and breast cancer. We also conducted colocalization analysis to evaluate whether smoking-related CpG sites shared a common genetic causal SNP with breast cancer in a given region.p < 1.83 × 10−11) in the MR Steiger test. Further colocalization analysis showed that there was strong evidence (based on PPH4 > 0.8) supporting a common genetic causal SNP between the CpG site of cg2583948 [with IMP3 expression (PPH4 = 0.958)] and ER+ breast cancer. There were no causal associations between smoking-related DNA methylation and ER− breast cancer.We established four significant associations after multiple testing correction: the CpG sites of cg2583948 , cg0760265 , cg0420946 , and cg2037583  were associated with the risk of ER+ breast cancer. All the four smoking-related CpG sites had a larger variance than that in ER+ breast cancer (all These findings highlight potential targets for the prevention of ER+ breast cancer. Tissue-specific epigenetic data are required to confirm these results. The data indicated that one in every four cancer cases diagnosed is breast cancer, and one in every six cancer deaths is breast cancer in women in 2020. Breast cancer caused 685,000 deaths in 2020, and it became the fifth leading cause of cancer mortality worldwide. It has been confirmed that cigarette smoking, one of the most important environmental risk factors, represents a significant effect on breast cancer risk , breast cancer has been confirmed as the most commonly diagnosed cancer in women (cer risk –4.Cigarette smoking was reported to induce reactive oxygen species (ROS), oxidative stress, and DNA methylation, which play vital roles in carcinogenesis , 6. The MR is considered as “nature’s randomized control trial” , using gThe design of our study is shown in n = 846) and found 474 smoking-related CpG sites proxied by at least one mQTL . Of these, 406 CpGs (86%) were proxied by a single SNP  project, which used the Illumina Infinium HumanMethylation450 (HM450) BeadChip to generate epigenetic data on cord blood and peripheral blood samples from 1,018 mother–offspring pairs at five time points  . A recenngle SNP . On thisweak IVs .We collected summary data of breast cancer from the largest available GWAS summary statistics to date, a meta-analysis of 67 studies including 69,501 ER+ and 21,648 ER− cases, and 105,974 controls for breast cancer . Of thescausal effect = βZY/βZX . Generally, a valid instrument should satisfy three assumptions (p < 5 × 10−8); (2) not associated with confounders of DNA methylation or breast cancer; and (3) should only be associated to the breast cancer through the smoking-related DNA methylation.As shown in To evaluate the causal effects of smoking-related DNA methylation on the risk of breast cancer, we conducted a two-sample MR analysis  using twIt is possible that smoking-related DNA methylation may have a causal effect on breast cancer. Another possibility concerns reverse causation, whereby SNPs used as proxies for DNA methylation have their primary effect through breast cancer rather than through DNA methylation. For this, we performed the MR Steiger test  to estimIn addition, for smoking-related DNA methylation (CpG sites) where there was evidence supporting a causal relationship on breast cancer, we applied colocalization analysis to investigate whether the SNP responsible for influencing smoking-related DNA methylation at each CpG site was the same SNP influencing changes to breast cancer risk. As recommended by the developers of the colocalization, a posterior probability of hypothesis  of 80% or higher was considered evidence of colocalization.In our study, most CpG sites were proxied by a single SNP, so it was difficult to evaluate horizontal pleiotropy using conventional methods like evaluation of MR-Egger regression intercept. We conducted a phenome-wide association test  to assesp-value  < 0.05; (2) the MR Steiger test showed a direction of effect from smoking-related DNA methylation to breast cancer; and (3) colocalization analysis supported the idea that smoking-related DNA methylation and breast cancer shared a common genetic causal SNP in a given region.Based on the analyses as mentioned above, we could conclusively establish a robustly causal association when satisfying the following conditions: (1) the results of Wald ratio or IVW reached the multiple comparisons adjusted p-values were two-sided, and the statistical significance was set at the level of adjusted p-value < 0.05 after false discovery rate correction.MR analyses and the MR Steiger test were performed in R (version 4.0.3) with the R package “TwoSampleMR” , and colF statistics ranging from 19 to 952, reflecting a strong instrument strength for smoking-related DNA methylation. All the participants had an identical genetic background , as a consistent selection in exposure data, and to our knowledge, there was no sample overlap between the exposure and outcome GWASs.The characteristics of the participants from the ARIES project for smoking-related DNA methylation and meta-analysis for breast cancer are shown in q-value after false discovery rate < 0.05): using rs8035987 as an instrument, we found that the smoking-related DNA methylation level of cg2583948 was associated with the risk of ER+ breast cancer . Similarly, the CpG sites of cg0760265 , cg0420946 , and cg2037583  showed causal effects on ER+ breast cancer risk. For ER− breast cancer, we found no significant smoking-related CpG sites after false discovery rate correction. Moreover, in these four smoking-related CpG sites identified in ER+ breast cancer, only cg25839482 showed a nominally significant effect on ER− breast cancer risk , but failed to pass the multiple test correction   Figure 2p < 1.83× 10−11), suggesting a forward causal direction from smoking-related DNA methylation to breast cancer risk (p = 5.86E-07) , the effect of methylation on the risk of ER+ breast cancer achieved a nominal significance but did not pass the multiple testing threshold . We finally identified the DNA methylation at cg2583948, which locates at the promoter region of IMP3 and inhibits IMP3 expression  to proxy the CpG site of cg2607605 .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Breast cancer is the most common malignancy in women. Genetic risk factors associated with breast cancer incidence have been identified.  This study is aimed at determining the association of XRCC3 Thr241Met (rs861539), XRCC4 G(-1394) T (rs6869366) DNA repair and BAX G(-248) A (rs4645878), and BCL2 C(-938) A (rs2279115) apoptotic gene polymorphisms with breast cancer.  Genetic analysis was performed using peripheral blood samples. Gene polymorphisms were detected by using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) technique. 175 patients and 158 healthy controls were enrolled in the study. P = 0.002). The risk of metastasis was 11 times with this genotype. It was associated with 6 times more risk of having a tumor larger than 2 cm. The risk of breast cancer was 2.77 times more in individuals carrying the Met/Met genotype of XRCC3 Thr241Met (rs861539) (P = 0.009). The risk of having advanced clinical stage (stage III+IV) with the Met/Met genotype was 4 times more increased. No relationship with breast cancer was found with XRCC4 G(-1394) T (rs6869366) and BCL2 C(-938) A (rs2279115) gene polymorphisms.  Breast cancer risk was 5.43 times more in individuals with AA genotype of Bax G(-248) A (rs4645878) ( Multicenter trials using subjects with genetic variations are needed to establish the relationship between breast cancer and single gene polymorphism. Breast cancer is one of the most common malignancies seen in women and one of the leading reasons of cancer-related mortality in developed countries. The lack of clear knowledge about the molecular mechanisms responsible for the development of breast cancer empowers the need for detailed and all round studies on this subject. Although its etiology is not clearly known, several genetic risk factors related to the high incidence have been defined. Studies have suggested that DNA repair and apoptosis mechanisms could have a role in the development of breast cancer. It has been reported that DNA repair and apoptosis gene polymorphisms could affect breast cancer risk , 2.DNA repair mechanisms play major roles in the sustainability of genomic integrity. Various types of DNA damages have been repaired with various types of DNA repair mechanisms. DNA double-strand breaks could result from factors like free radicals of endogenous origin, exogenous chemicals, and ionizing radiation . Mammal Aiding in preserving the stability of the chromosome, X-ray repair cross-complementing group 3 (XRCC3) gene is enrolled the HR pathway . The proApoptosis is programmed cell death at physiologic and pathologic circumstances. The disruptions in the apoptotic pathways could lead to development of cancer by affecting cellular hemostasis . ApoptotIn our study, we investigated the relationship between breast cancer risk and genetic variations in DNA repair [XRCC3 Thr241Met and XRCC4 G(‐1394) T] and apoptosis [BAX G(-248) A and BCL-2 C(‐938) A] pathways. Our results could aid in linking the presence of gene polymorphism with clinical findings.The study population consisted of 175 female patients with breast cancer who admitted to the breast disease outpatient clinic of Istanbul Education and Research Hospital. The control group consisted of 158 women with the same demographic status as the disease group. The demographical information of the patients was obtained by one on one interviews. Histopathological diagnosis and data were obtained with the permission of the Pathology Department. The distributions of clinical characteristics of the patients are shown in The study was approved by the ethics committee of Istanbul Education and Research Hospital. The funding for genetic analysis was provided by the Education and Planning Committee of the same hospital. Genetic analysis was performed in Department of Medical Biology, Cerrahpasa School of Medicine, Istanbul University-Cerrahpasa.Blood samples were taken into vacuumed, sterile K3-EDTA tubes (2 ml), and stored at −20°C until analysis. At the day of analysis, total genomic DNAs were prepared using DNA isolation kit  according to the manufacturer's instructions.μl reaction mixture containing 100 ng DNA, 1 X PCR buffer (with KCI), 0.04 mM dNTPs, 0.04 U Taq DNA polymerase, 10 pmol forward and reverse primers, and variable amount of H2O. The PCR conditions were presented in Genotyping of XRCC3 Thr241Met , XRCC4 Gt-test was used in portraying the differences among two continuous variables. Chi square (χ2) or Fischer's exact test (two sided) were utilized in evaluation the genotypes and alleles, and test for deviation of genotype distribution from Hardy–Weinberg equilibrium (HWE). P values of <0.05 were considered statistically significant. The odds ratio (OR) and their 95% confidence intervals (CIs) were calculated to estimate the strength of the association. The data were analyzed using Statistical Package for the Social Sciences .Mean and standard deviations (SDs) were shown as continuous variables. Student's P = 0.44). Smoking rate was 35% in the patients and 37% in the controls. Smoking status was not significantly different between patients and controls (P = 0.81).Our study consisted of 175 patients and 158 healthy controls. Patients  and controls  were not different in terms of age  T, BAX G(-248) A, and BCL2 C(-938) A genotypes were in accordance with the HWE among the cases and controls.P = 0.002). BAX-248 AA genotype was seen in 3% of the control group and 14% of the patients. The frequency of the G allele was 69% in the patients and 74% in the controls. The frequency of the A allele was 31% in the patients and 36% in the controls. The difference was not statistically significant (P = 0.009). XRCC3 241 Met/Met genotype was seen in 9% of the controls and 19% of the patients. The frequency of the XRCC3 241Thr allele was 55% in the patients and 65% in the controls. The frequency of the XRCC3 241Met allele was 45% in the patients and 35% in the controls. These results were not statistically significant  A gene polymorphism, homozygote expression (AA genotype) of BAX-248A allele was associated with 5 times increased risk of breast cancer  and tumor size (P = 0.02). Patients with AA genotype had 11 times increased risk of having metastasis . The patients with the AA genotype had 6 times increased risk of having tumor sizes more than 2 cm . In addition, XRCC3 241 Met/Met genotype defined as the risk genotype was associated with clinical stage (P = 0.02). Patients with Met/Met genotype had 4 times increased risk of being clinical stage III + IV . On the other hand, XRCC4 G(-1394) T and BCL2 C(-938) gene polymorphism and all of the disease parameters did not have any statistically significant relationship. Also, we did not find any significant relationship between XRCC3 Thr241Met, XRCC4 G(-1394) T, BAX G(-248) A, and BCL2 C(-938) A polymorphisms and the ER/PR/HER2 and triple negative status of the patients .We investigated the association between the clinical characteristics of the patients and XRCC3 Thr241Met, XRCC4 G(-1394) T, BAX G(-248) A, and BCL2 C(-938) A genotypes. The BAX G(-248) AA genotype defined as the risk genotype was associated with metastatic status  T, BAX G(-248) A, and BCL2 C(-938) A gene polymorphisms. These genes code for the proteins enrolled in DNA injury repair and apoptosis which are important processes in carcinogenesis. Several studies addressed the polymorphisms on these genes. The results from the previous studies show variations. The genetic differences endemic in a geographical area could be one of the reasons. The frequency of genetic variants associated with a gene polymorphism in a particular population is an important determinant of the breast cancer risk. Thus, differences in the incidence of variant alleles associated with a polymorphism among societies may lead to different results. The genes we studied encode proteins registered in DNA damage repair and apoptosis, which are important processes in carcinogenesis and breast cancer. The polymorphisms we have studied regarding these genes are polymorphisms that have been found to be related to different parameters and cancer risk in various cancers but have not been studied in the Turkish population.In our study, women with homozygote BAX-248A allele (AA genotype) had 5 times more risk of developing breast cancer. In addition, status of metastasis and tumor size was associated with this genotype. These findings were similar with the study of Kholoussi et al. . They foThe relationship between BAX G(-248) A gene polymorphism and clinical parameters in different types of cancer has been studied previously with various different results. Wang et al. studied the effects of BAX G(-248) A gene polymorphism and survival in gastric cancer patients receiving postoperative chemotherapy. In their study, having at least one variant genotype in BAX G(-248) A was associated with increase in the recurrence risk and poorly affecting survival . Gu et aThe analysis we conducted on XRCC3 Thr241Met polymorphism portrayed that, homozygote expression of 241Met allele (241Met/Met) was associated with 3 times increased risk of developing breast cancer. This genotype was also associated with clinical stages of III + IV. Chai et al. performed a meta-analysis on XRCC3 Thr241Met gene polymorphism and breast cancer arriving at similar results as our study. In that study having 241 Met/Met genotype in XRCC3 Thr241Met gene polymorphism was reported as a risk factor for breast cancer especially in the Asian population , 21. QurIn our study, we did not find a relationship with BCL2 C(-938) A gene polymorphism and breast cancer like Searle et al. . NeitherIn our study, no statistically significant relationship was found between XRCC4 G(-1394) T gene polymorphism and breast cancer. In the study of Chiu et al., on the other hand, homozygote or heterozygote expression of –1394 T allele increased the risk of breast cancer. The difference between this study and our study could result from the difference in the incidence of the variant allele . While tApoptosis and DNA repair are important processes establishing the road leading to cancer development. The proteins utilized during these process play important roles. Gene polymorphisms have key roles in the activity of these proteins. The risks and roles of these polymorphisms and their relationship with clinical parameters have been shown in the literature. Our study has shown that these polymorphisms have significant roles in the development of breast cancer, studying four different gene polymorphisms in the Turkish population for the first time. While we found a relationship only between BAX G(-248) A and XRCC3 Thr241Met between the onset or risk of the disease and the polymorphisms we studied, we found a relationship only between the XRCC3 Thr241Met polymorphism in terms of the severity of the disease.


Mammography density of Asian women is known to be higher than Western women. After 2009, the Korean National Cancer Screening Program (NCSP) has started to notify mammography density (MD). To investigate the effect of MD notification, we integrated screening results with national health insurance claim data from 2009 to 2013. We performed a cross-sectional observational study which investigated the crude detection rate (CDR), positive predictive value (PPV), and incidence rate of Interval Cancer (IC). IC was defined as breast cancer, where the interval between the screening date and date of diagnosis was more than 12 months and less than 24 months among participants with previous normal results. CDR and PPV per 100,000 results increased from 510.9 to 756.2 and from 1842.5 to 3364.9, respectively. The incidence rate of IC per 100,000 negative results increased from 623.3 to 676.2. Women younger than 50 years had a high incidence of ICs. After notifying MD, the incidence rate of IC less increased comparing with CDR or PPV. Screening mammography could be more useful to Asian women when reporting MD. Mammographic screening is one of many screening tests available worldwide. It is used in the early detection of breast cancer, resulting in less intensive treatment and a better overall prognosis. Thus, the effectiveness of mammographic screening is based on its ability to detect of breast cancer earlier, when the lesion is small, thereby interrupting the natural history of the disease. In the past two decades, widespread mammographic screening and effective treatment modalities have led to a shift in the tumor stage at presentation, thereby reducing mortality . At least 9 separate randomized clinical trials, comprising more than 600,000 women, were conducted in Canada, the United States, the United Kingdom, Russia, and Sweden. All of them showed reduction in breast cancer-related mortality using mammographic screening .Despite the high sensitivity reported for population-based screening programs (ranging from 74.7–89.4%), a percentage of tumors remain undetected, manifesting clinically between a normal screening result and the following screening . These tumors, called interval cancers (IC), are a major limitation of screening programs. The detection rate of IC has been recognized as a valid indicator of both screening quality, and the sensitivity of the screening procedure . The number of published studies that report IC on a national level is scarce .Several previous studies have confirmed the importance of breast density, both in predicting overall breast cancer occurrence, and reducing mammography sensitivity leading to a higher proportion of ICs diagnosed . In Korea, the incidence rate of IC among participants with known mammographic density has been previously examined. However, a previous report used a screening interval of 12 months, while the real interval is 24 months. The aim of this study was to survey the effect of MD notification on the crude detection rate (CDR), positive predictive value (PPV), and incidence rate of IC, using the actual screening interval (24 months), in South Korea.Study populationThe National Cancer Screening Program (NCSP) recommends that all Korean women older than 40 years participate in biennial mammographic screening. The baseline cohort comprised 9,469,234 women aged 40 years and older who underwent screening via the NCSP between 2009 and 2013. Among them, 5,907 subjects with a previous diagnosis of breast cancer were excluded, as were 6,051,915 subjects with unknown mammographic breast density. Therefore, a total of 3,417,319 Korean women aged 40 years and older were enrolled in a final analysis. The year of examination was determined by the year of birth; women with an odd number birth year underwent biennial screening on odd number years. Ethical ApprovalsThe current study collected data from the NCSP database, which included information on the participant’s demographic characteristics and screening results. Written informed consent was received from participants for the collection of their screening results. Permission was granted by the Ministry of Health and Welfare, and the investigators used data maintained and de-identified by the National Health Insurance Sharing Service (NHIS). We collected data regularly from the NHIS and the need for informed consent for this specific study was waived because the NCSP database is quite large and unidentical personal dataset was released for public health or academic study from NHIS ( NHIS-2018-1-211). Additionally, the study was approved by the Institutional Review Board (IRB) of the Inje University Haeundae-paik hospital, Korea (IRB no. 2017-07-639-001). MeasurementSelection and processing of variablesParticipant identification (ID), age, screening date, and screening results were selected from the NCSP breast screening data, and disease codes were selected from the health insurance claim data. Participants were classified according to age at the time of screening as 40–49, 50–59, 60–69, and <70 years, and the screening results were reported as normal, benign disease, suspected breast cancer, or deferred. According to the guidelines on reporting results and recommendations of the Korean NCSP, deferred cases are those in which a judgment of normal, benign lesions, or suspected cancer cannot be made based only on the current examination, and additional examination, such as ultrasound or magnified views, compared with previous mammograms, or re-examination after some period is recommended for accurate diagnosis. Using the disease codes in the health insurance claim data, breast cancers were classified as either malignant neoplasm or carcinoma in situ. The date of breast cancer detection was defined as the first visit day  to a medical institution for chemotherapy or surgery. That is, participants were defined as having breast cancer if they were diagnosed with malignant neoplasm or carcinoma in situ, and received either medication or surgery related to breast cancer. Screening data and insurance claim data on breast cancer were merged according to the subjects’ ID.Key breast cancer screening performance indicators and their definitionsFrom the results of breast cancer screening, suspected and deferred breast cancer diagnoses were defined as positive screening outcomes, while results showing normal findings and benign lesions were defined as negative. Among the participants receiving breast cancer screening, breast cancer treatment occurring within 6 months of screening was defined as breast cancer detected by screening, while the absence of treatment for breast cancer within the period was defined as non-detection.Crude detection rate (CDR) for breast cancer screeningThe crude detection rate for breast cancer screening was defined as the number of participants with positive results in whom breast cancer was detected per 100,000 breast cancer screening participantsPositive predictive value (PPV) of breast cancer screeningThe PPV of screening for breast cancer was defined as the number of detected patients with breast cancer per 100,000 who received positive results on breast cancer screening.Interval cancer rate (ICR)IC was defined as a breast cancer diagnosis within the interval between screening dates of more than 12 months and less than 24 months in participants with previous negative screening results. ICR was defined as the number cases of breast cancer detected among 100,000 participants with negative breast cancer screening results.Statistical analysisData analyses were performed using SAS statistical software . The study subjects were selected using breast cancer screening, and health insurance claim data as described above. The variables were presented as counts and percentages, and the PPV, and incidence of IC were calculated. CDR of breast cancer screening after notification of mammography densityThe CDR of positive breast cancer screening increased from 510.9 per 100,000 in 2009, to 756.2 per 100,000 participants in 2013  . During PPV of screening for breast cancer after notification of mammography densityThe PPV of positive breast cancer screening rapidly increased from 1,842.5 per 100,000 in 2009, to 3,364.9 per 100,000 in 2014  . The proIncidence rate of IC in breast cancer after notification of mammography density The incidence rate of IC among negative participants increased from 623.3 per 100,000 in 2009, to 676.2 per 100,000 participants in 2011 and the incidence rate of both interval carcinoma in situ, and invasive cancer increased  but showWhen considered with MD, the incidence rate of IC was relatively less increasing comparing with CDR from 2009– 2013. Furthermore, this finding is consistent regardless of tumor type (invasive cancer and carcinoma in situ). The ICR among those younger than 50 years was the highest of all the groups.IC is an important indicator of the quality of a breast cancer screening program and a predictor for its success in reducing breast cancer mortality . According to risk of IC, several studies showed a few risk factors for IC, such as MD , age , screening method  , and screening interval , and the their confounding effect.Sankatsingb et al., (2018) found that the rate of IC at initial screening among women aged 49–51 years was significantly lower for digital mammography than for serial film mammography. This finding corresponds to the results of the DMIST trial, which showed a higher diagnostic accuracy for digital mammography than film mammography or in pre-, and peri-menopausal women with dense breasts, under the age of 50 . The difference in the prevalence of IC could be dependent on the year of screening. O’Brien, et al., (2015) showed that in the first year after screening, the interval cancer rate was 5.8 per 10,000 screens, which increased to 13.2 per 10,000 screens in the second year after screening. In previous Korean study using other NCSP data, the results of breast cancer screening was inconsistent dependent on either both screening interval or MD measurement. Lee et al., (2016) reported that the incidence rate of IC was 51.7–76.3 per 100,000 negative patients in which they used 12 months- interval. The CDR of invasive cancer in the present study was higher than that of National Cancer Report in the same period , because the latter study included all participants regardless of MD reporting.In contrast, there were no significant differences in the CDR of breast cancer screening between the month before, and the month after the implementation of breast density notification legislation, on breast density reporting by radiologists in United States . Asian women, including Korean women, characteristically have higher-density breasts than women from other ethnic groups . Consequently, high accuracy is difficult to achieve with mammography screening alone. In addition, the age-specific incidence of female breast cancer in Asia peaks at 40–49 years, whereas in Western countries, the peak is at 60–70 years . Asian countries must, therefore, take measures to address the accuracy of breast cancer screening in women aged 40–49 years . According to the Japan Strategic Anti-cancer Randomized Trial (J-START) to investigate the efficacy of adjunctive ultrasonography, it reported that interval cancers were detected in the ultrasonography plus mammography (0.05%) group, compared with the mammography-only group (0.10%). Additional ultrasonography could reduce interval cancer among women aged 40–49 years . Other investigators  had previously shown low interval cancer rates among women screened with adjunctive ultrasonography. Ongoing controversy over the optimal approach to breast cancer screening has led to discordant professional society recommendations, particularly for women aged 40–50 years. One potential solution, risk-based screening, has been on the rise, where decisions around the starting age, stopping age, frequency, and modality of screening are based on individual risk, so as to maximize the early detection of aggressive cancers, and minimize the harms of screening through optimal resource utilization . This study had a number of limitations. First, as health insurance claim data were used to detect breast cancer in the central cancer registry, there may have been some differences between the actual incidence of breast cancer in the Central Cancer Registry, and the cases detected in the present study. In fact, the data from the present study excluded patients with end stage breast cancer receiving only palliative treatment with no anticancer treatment or surgery. However, the number of detected breast cancer ranged from 97.3% to 101.1% of the values from the central cancer registry during 2009–2012. In addition, as most patients with cancer qualified for special case calculation, the difference between the number of the detected breast cancers in this study and the number in the Central Cancer Registry was unlikely to be large enough to significantly influence our results. Second, the detection of breast cancer may be influenced by symptoms, level of exposure to risk factors such as pregnancy, breast feeding, hormone treatment, and family history of breast cancer, socioeconomic, and educational level. These factors could influence either the breast density, or behaviors after notified a screening result. We did not include these personal factors.Participants were not well-informed on interval cancer, and the limitations of mammography, because the rate of IC was relatively low in the biennial screening interval. Being in a specific age- group, or having a strong family history, in addition to reported mammographic density could assist clinicians with choosing supplemental examination. The present study showed the specific group who needs attention by both clinicians, and policy-makers. In conclusion, both CDR, and incidence of IC have increased during the 5 year period (2009–2013). After reporting MD, the incidence rate of IC relatively less increased comparing with CDR or PPV. Screening mammography was probably considered to be useful in Asian women when reporting MD. Though women whose last screening results were normal or benign disease, participants should be announced to take care of their breast health considering other risk factors, including age, and MD. Conceptualization: J.Lee. Data curation: M.Oh. Formal analysis: M.Oh. Methodology: J.Lee. Software: M.Oh. Investigation: M.Oh, J.Lee. Writing- original draft: J.Lee. Writing-review & editing: J.Lee, M.Oh