SALL1 rs10521222 and HLA-DQA1 rs9271608) and lifestyles, including alcohol intake, lifetime cumulative exposure to estrogen, and overall and visceral obesity, are the most common and strongest predictive markers for breast cancer across the analyses. The risk profile that combined those variables presented their synergistic effect on the increased breast cancer risk in a gene–lifestyle dose-dependent manner. Our study may contribute to improved predictability for breast cancer and suggest potential interventions for the women with the risk genotypes and lifestyles to reduce their breast cancer risk.Molecular and genetic immune-related pathways connected to breast cancer and lifestyles in postmenopausal women are not fully characterized. In this study, we explored the role of pro-inflammatory cytokines such as C-reactive protein (CRP) and interleukin-6 (IL-6) in those pathways at the genome-wide level. With single-nucleotide polymorphisms (SNPs) in the biomarkers and lifestyles together, we further constructed risk profiles to improve predictability for breast cancer. Our earlier genome-wide association gene-environment interaction study used large cohort data from the Women’s Health Initiative Database for Genotypes and Phenotypes Study and identified 88 SNPs associated with CRP and IL-6. For this study, we added an additional 68 SNPs from previous GWA studies, and together with 48 selected lifestyles, evaluated for the association with breast cancer risk via a 2-stage multimodal random survival forest and generalized multifactor dimensionality reduction methods. Overall and in obesity strata , we identified the most predictive genetic and lifestyle variables. Two SNPs ( Activation of innate immunity creates a tissue microenvironment high in reactive oxygen and nitrogen species, leading to potential DNA damage and alterations in nearby cells5. The inflammatory response also elevates the circulating levels of cancer-promoting inflammatory cytokines such as C-reactive protein (CRP) and interleukin-6 (IL-6)2. These key pro-inflammatory biomarkers reflect different molecular pathways in the immune cascade in acute and chronic immune responses but may be interrelated in carcinogenesis, yielding a congruent association with breast cancer risk. For example, IL-6, upregulated by macrophages and adipose tissue, promotes breast tumor initiation and progression7. CRP, a major acute-phase reactant and a biomarker of chronic low-grade inflammation, partially induced by IL-6, has been associated with increased risk of breast cancer9. The carcinogenetic mechanisms of these markers are partially understood. IL-6 regulates aromatase activity responsible for estrogen production in adipose tissue, which is important in developing postmenopausal breast cancer11. CRP levels are attenuated by prolonged inhibition of cyclooxygenase-2 action (promoting estrogen formation in adipose tissue)12. Thus, IL-6 and CRP may be involved in inflammatory pathways connected to breast cancer tumorigenesis.Chronic inflammation may play an important role in the pathogenesis of non-inflammatory diseases, such as breast cancer, from tumor initiation through progression17, while only a few reported a marginal effect on breast cancer risk6. The gene–phenotype pathway may not be connected to CRP and IL-6 alone, but also modulated by lifestyle pathways linked to obesity 25, lipid metabolism26, high-fat diet, exercise, smoking, and alcohol34. Further, the inflammatory cytokines and the genetic markers have demonstrated different associations with breast cancer according to obesity35 and related lifestyle factors such as physical activity and dyslipidemia38. Thus, studying how those lifestyle factors modify and interact with gene and phenotype, leading to increased breast cancer susceptibility, may contribute to the understanding of the complex genotype–phenotype pathway and is important to develop a genetically targeted intervention tool for use in primary breast cancer prevention efforts.Given the relationships between those inflammatory markers and breast cancer risk, genetic variants involved in the biomarkers’ functional and structural regulation may have potential implication in the causal pathway, affecting the risk of breast cancer. Previous genomic epidemiology studies for the associations between CRP/IL-6-related genome-wide genetic variants and breast cancer risk are limited and mostly showed null results39, obesity, and breast cancer . Using a large-scale postmenopausal women cohort from the Women’s Health Initiative Database for Genotypes and Phenotypes (WHI dbGaP) Study, we previously performed a genome-wide association (GWA) gene–environment (G × E) interaction study for CRP and IL-6 by addressing the pleiotropic effect of those biomarkers on the gene–phenotype relationship; we identified 88 top GWA single-nucleotide polymorphisms (SNPs)42. We have now extended the scope of modeled genetic factors by including 68 additional SNPs in relation to CRP and IL-6 from previous GWA studies that focused on European ancestry with independent replications44. We examined the association of those top GWA-based SNPs with primary invasive breast cancer risk overall and in obesity-related strata in which the SNPs were associated with CRP and IL-6 at genome-wide significance in our earlier GWA study42. This approach may allow us to elucidate an empirical pathway through which a substantial proportion of the susceptibility of GWA SNPs in CRP and IL-6 influences breast cancer risk through interactions with specific lifestyles  analysis and ranked them according to their predictive value and accuracy for breast cancer. In addition, we applied a generalized multifactor dimensionality reduction (GMDR) model to characterize high-order gene–gene interactions and selected the best genetic prediction model50. Briefly, healthy women were enrolled in the WHI study between 1993 and 1998 at 40 clinical centers across the United States if they were 50–79 years old, postmenopausal, expected to stay near the clinical centers for at least 3 years after enrollment, and able to provide written informed consent. Participants were eligible for the WHI dbGaP study if they had met eligibility requirements for submission to dbGaP and provided DNA samples. The Harmonization and Imputation GWASs under the dbGaP study accession (phs000200.v12.p3) consist of 6 sub-studies  which was coordinated by dbGaP to contribute to a joint imputation and harmonization effort for GWASs within the 2 representative study arms, Clinical Trials and Observational Studies. The detailed study designs and rationale are described elsewherees Table . Of the es Table , in our 54 and (2) preliminary analyses including univariate and stepwise multiple regression analyses and a multicollinearity test. Those variables include demographic and socioeconomic factors ; family histories of breast and colorectal cancers and diabetes; medical histories ; lifestyles (cigarette smoking and exercise); dietary factors ; and reproductive histories  use, and use of exogenous estrogen [E] only and E plus progestin [E + P]). We also included anthropometric variables, including height, weight, and waist and hip circumferences, which had been measured by trained staff.The coordinating clinical centers conducted data quality assurance periodically and collected participant information through self-administered questionnaires. In this study, we initially selected 48 variables measured at screening for our analysis on the basis of (1) their association with inflammation and breast cancer through the literature review55.The breast cancer outcomes were determined via a centralized review of medical charts by a committee of physicians on the basis of pathology or cytology reports. The time from enrollment to breast cancer development, censoring, or study end point was calculated and represented in years. Cancer cases were coded using the National Cancer Institute’s Surveillance, Epidemiology, and End-Results guidelines56. Briefly, the genotypes were normalized to the reference panel GRCh37, and imputation was conducted via 1000 Genomes reference panels57. SNPs for harmonization were checked for pairwise concordance among all samples across the GWASs. The initial data QC included SNP filtering with a missing-call rate of < 2% and a Hardy–Weinberg equilibrium of p ≥ 1E–04. The second QC step included SNPs with 58 but excluded individuals with a KING kinship estimate > 0.08859.We extracted genotyped data from the WHI dbGaP Harmonized and Imputed GWASs. Details of the data-cleaning process have been previously discussedt tests (for continuous variables) and chi-squared tests . If continuous variables were skewed or had outliers, Wilcoxon’s rank-sum test was conducted. Our previous GWA analysis evaluated the gene–lifestyle interactions via stratifications defined by body mass index , waist circumstance , waist-to-hip ratio , metabolic equivalents , and % calories from SFA . The results  from the sub-GWASs were combined in a meta-analysis assuming a fixed-effect model. In this study, we performed an association study of the 88 SNPs identified in subgroups by obesity and obesity-related lifestyle variables with breast cancer risk in the identical subgroups. The additional 68 SNPs from other GWA studies were pulled together overall and in subgroups for the purpose of analysis.Differences in participants’ baseline characteristics and allele frequencies by breast cancer development were examined with unpaired 2-sample n = 5000 in this study), creating a forest of trees61. An ensemble cumulative hazard estimate was calculated from each tree and averaged over all trees for each individual and used to compute a predicted cumulative breast cancer incidence rate. Also, using this ensemble estimate and creating the out-of-bag (OOB) data , the OOB concordance index (c-index) was estimated, which is a measure of prediction performance conceptually similar to the area under the receiver operating characteristic (AUROC) curve62. The rank of each variable was determined on the basis of its predictability for breast cancer according to 2 predictive parameters: (1) minimal depth (MD), in which variables that have a small MD and split the tree close to the root are considered highly predictive and (2) variable importance (VIMP), computed as the difference between the OOB c-indexes from the original OOB data and from the permuted OOB data, in which variables that have greater VIMP values are the more predictive63. Because they use different prediction algorithms, we expect the variables’ ranking to differ to some degree. The RSF, a machine-learning and nonparametric tree-based ensemble method, accounts for nonlinearity and high-order interactions among variables, which may not be handled by a traditional regression method64. The RSF may thus provide a more accurate risk estimation.In the current study, we conducted the RSF analysis. The RSF initially generates bootstrap samples using approximately 63% of the original data and grows a tree from each sample via a splitting rule to maximize survival differences across daughter nodes. This tree-building process is repeated numerous times , cross-validation consistency (CVC), and sign p value. The model with the highest TBA, CVC 10/10, and p < 0.05 based on 1000-times permutation testing was considered the best model.Further, we applied a GMDR model that is described in detail elsewhere65. GMDR v.1.0. and R v.3.5.2.  were used.Multiple Cox proportional hazards regressions, with a test of proportional hazards via a Schoenfeld residual plot and ρ evaluation, were conducted to obtain hazard ratios (HRs) and 95% confidence intervals (CIs) for the single and combined effects of SNPs and lifestyle factors on breast cancer, with adjustment for covariates Table . A 2-taiThe allele frequencies of 156 GWA CRP/IL-6-related SNPs and baseline characteristics of participants are displayed in Tables With the 156 GWA SNPs and 48 lifestyle factors, we performed the two-stage RSF and GMDR Figure  to deterSALL1 rs10521222; HLA-DQA1 rs9271608; DUSP1 rs17658229; APOC1 rs4420638; and TRAIP rs2352975) and 3 lifestyles (duration of OC and E + P use and BMI) as the most influential variables for breast cancer. Second, we estimated the c-index  from the nested RSF model (Table TRAIP rs2352975 was the best predictive with the highest TBA of 0.5382 and CVC of 10/10 (p < 0.001).Next, with the 12 lifestyles and selected SNPs together, overall and in subgroups, we conducted the second multimodal RSF to construct risk profiles with the most predictive variables. Particularly, in the overall group, we first computed the 2 measures MD and VIMP  and 5 lifestyle factors .For each of the obesity strata , we continuously applied those multimodal Tables –9 and GMTRAIP rs2352975 CT + TT, DUSP1 rs17658229 CC, HLA-DQA1 rs9271608 GG, SALL1 rs10521222 TT, and APOC1 rs4420638 GG. Also, by using a cutoff value bisecting variables  group Table , the besWe further evaluated the combined effect of SNPs and lifestyle factors and their joint effect with E + P use on breast cancer risk Table  and deteSALL1 rs10521222 and HLA-DQA1 rs9271608) and, consistent with previous studies68, 5 lifestyle factors such as alcohol intake, lifetime cumulative exposure to estrogen (post OC and E + P use), and overall and visceral obesity. The risk profiles that combined those influential variables presented a synergistic effect on the increased risk for breast cancer in a gene–lifestyle dose-dependent manner.An increasing number of population-based cancer genomic studies have incorporated environmental factors in the molecular causal pathway. Comprehending how lifestyle factors interact with genes and phenotypes, influencing risk for breast cancer, is important for constructing improved risk profiles, leading to the development of a gene–lifestyle combination intervention for primary cancer prevention efforts. Our 2-stage multimodal RSF and GMDR analyses identified the strongest predictive genetic and lifestyle variables overall and in obesity strata. The genetic effects in this study were associated with the SNPs involved in inflammatory cytokine pathways. The most common markers for breast cancer risk across the strata are 2 SNPs related to CRP  in tumorigenesis has recently been investigated as a tumor suppressor for ovarian and Wilms’ tumors73, hepatoblastoma, and gastric carcinoma75. However, the function of SALL1 in cancer development has not been determined. Few recent studies of in vivo RNAi screen and in vivo/in vitro breast cancer cells have implicated SALL1 as a tumor suppressor in breast cancer by inhibiting cancer cell growth, proliferation, and cell-cycle arrest, through the Nucleosome Remodeling and Deacetylase network76 or by regulating CDH1, a contributor to epithelial-to-mesenchymal transition77. Our finding of the SALL1 SNP’s association with CRP at the GWA level and with breast cancer risk is supported by these previous biologic studies and further suggests the involvement of SALL1 in immune mechanisms of breast cancer tumorigenesis.One SNP near HLA-DQA1 belongs to the human leukocyte antigen (HLA) class II alpha chain paralogues, which increase immune system sensitivity by distinguishing its own proteins from foreign invaders79. HLA class II, the human version of the major histocompatibility complex (MHC) class II, regulates the antitumoral cellular immune response by presenting MHC antigen in tumor cells to the immune system, stimulating tumor infiltration of CD4 + T cells82. Several previous studies reported that the SNPs of HLA class II have implications in the carcinogenesis of specific cancers , but limited studies in association with breast cancer have been conducted and were restricted to subjects other than Caucasians; further, the results were inconsistent81 or null82. Our study is the first to report the association of the HLA-DQA1 SNP with breast cancer risk in non-Hispanic white women, suggesting that HLA class II plays a decisive role in the pathogenesis of breast cancer in this population by diminishing the efficacy of the antitumoral immune response. Also, this association would have been missed without the incorporation of obesity factors, which calls for further study of the biologic mechanism.90. Notably, in postmenopausal women, few studies have examined the combined and joint effect of alcohol intake with other lifestyles68 or relevant genetic variants92 on breast cancer risk; in particular, the gene–lifestyle study results did not support a significantly increased risk among women who carried specific risk genotypes and had higher alcohol intake92. Molecular biologic mechanisms of alcohol-associated tumorigenesis in breast cancer may involve complicated pathways: an elevated level of estrogen by testosterone conversion; an increased level of insulin-like growth factors from the liver due to alcohol consumption94; and disruption of folate metabolism95. Also, acetaldehyde, derived from the metabolism of ethanol, is a carcinogenic metabolite that causes formation of DNA adducts and inhibits DNA repair and methylation patterns96. Further, high and regular alcohol intake may lead to a dietary deficiency of essential nutrients, making individuals susceptible to tumorigenesis90. Corresponding to this alcohol-response tumorigenic environment, and supported by previous research66, our study showed that more than moderate alcohol intake, jointly with the risk SNPs, substantially elevated the risk of breast cancer synergistically; and this synergistic effect occurred more strongly in the non-obese subgroups.A number of epidemiologic studies have revealed that alcohol intake, even of a small amount , can increase breast cancer risk in both pre- and post-menopausal women99, with an affinity for androgen and mineralocorticoid receptors, leading to cell proliferation and anti-apoptosis100. Further, the joint effect of E + P use with the SNPs was profound in the non-obese subgroups, suggesting complementary pathways of sex hormones and obesity .Another influential lifestyle factor in our study is the opposed E + P use that contributes to the lifetime cumulative exposure to estrogen. Synthetic progestin is a well-established risk factor for breast cancerr = 0.9)101. In addition, we confined our study population to non-Hispanic white postmenopausal women, limiting the generalizability of our study findings to other populations. Due to insufficient statistical power, we were unable to investigate the molecular subtypes of breast cancer. Despite several benefits from the 2-stage RSF multimodal and GMDR approaches, it can overfit the model owing to complicated analysis tasks, particularly in relatively small subgroups, so our results need to be replicated in an independent study with a large sample size.The amounts of daily dietary alcohol intake were obtained from self-reported food frequency questionnaires and then validated to be highly correlated with 1 month of food-diary records (Overall, in this study, the SNPs in proinflammatory cytokines previously identified as genome-wide significant had a synergistic effect on breast cancer risk by combining with lifestyle factors, including alcohol intake, lifetime cumulative exposure to estrogen, and obesity. Our findings warrant molecular biologic studies such as gene signature and aberrant cell signaling in relation to breast cancer in postmenopausal women who have a history of alcohol intake and estrogen use by different levels of obesity and related lifestyles. Our study may contribute to improved prediction accuracy and the ability to assess breast cancer risk, and suggest potential interventions for women who carry the risk genotypes, such as partial or absolute abstinence from alcohol intake, shorter duration of hormone therapy, and better weight control, potentially leading to an improved impact on the epigenetic aberrations and thus reducing the risk of breast cancer.Supplementary Information.


Model performance was assessed by the area under the receiver operating curve (AUC). In all 1127 women, a multivariable model incorporating five family history components  and having a mammogram in the last 3 years, performed well (AUC = 0.86). For the 168 uncertain risk women, only paternal family history of BC remained significant (AUC = 0.71). Clinicians should pay particular attention to these five family history components when assessing FBC risk, especially prostate cancer which is not in the current national guidelines.Family history of breast and related cancers can indicate increased breast cancer (BC) risk. In national familial breast cancer (FBC) guidelines, the risk is stratified to guide referral decisions. We aimed to identify characteristics associated with the recommendation for referral in a large cohort of women undergoing FBC risk assessment in a recent primary care study. Demographic, family history, psychological and behavioural factors were collected with family history questionnaires, psychological questionnaires and manual data extraction from general practice electronic health records. Participants were women aged 30–60 with no previous history of breast or ovarian cancer. Data from 1127 women were analysed with stepwise logistic regression. Two multivariable logistic models were developed to predict recommendations for referral: using the entire cohort (The online version of this article (10.1007/s12687-020-00452-w) contains supplementary material, which is available to authorized users. BRCA1 and BRCA2 genes  or manage in primary care (no event). Box The dependent variable was the final recommended clinical pathway for the participants following the risk assessment: For continuous data, we conducted median imputation for missing data; for categorical data, missing data were recoded as a separate category of ‘not reported’.p < 0.05 was considered significant. Bonferroni correction was used for psychological measures with multiple items to account for correlation between measures.STATA version 13.1 was used for the statistical analysis. Demographics for non-participating women were obtained by electronic search from anonymised primary care health records. Age (median) and ethnicity (white and non-white) were compared between participating and non-participating women using two-sample Wilcoxon rank-sum test and Pearson Chi-square test respectively.Univariate analysis was performed using logistic regression for continuous and binary categorical variables. For non-binary categorical variables, likelihood ratio test was performed against a base model that included age and Index of Multiple Deprivation (IMD) as a priori demographic variables.Significant variables were then included in the multivariable analysis, with age and IMD quintiles considered a priori. Variables were then removed in stepwise backward elimination to give the final model predicting a recommendation of referral to secondary care. The final model was then applied to the subgroup of participants who were in the uncertain risk group. The area under the receiver operating curve (AUC) was generated to test model performance, a measure of discriminatory accuracy of the model to distinguish between outcome events.From the initial electronic records search, 10,080 women were potentially eligible based on the inclusion criteria. After further review of eligibility by general practitioners, 7012 women were invited to participate; 1127 (16%) consented and completed the familial breast cancer risk assessment in primary care. Reasons for exclusion by general practitioners were unknown due to data protection and privacy laws. Ethnicity and age data for the 8953 potentially eligible women who did not join the study were extracted from the primary care records. These non-participants included both those that were not invited to participate (3068) and those who declined invitation to participate (5885). It was not possible to distinguish from the anonymised non-participant data those who were not invited.The median time from participants returning the family history questionnaire (risk assessment) to participants receiving an outcome letter (refer or manage in primary care) was 27 days (interquartile range 15 to 43 days). The demographics for the 1127 participants are presented in Table p = 0.003), both groups had a median age of 46 years. For those with reported ethnicity, participation rate was 17% (1099/6608) for white participants and 11% (22/195) for non-white participants; this difference was of borderline significance (p = 0.047).For the 8953 non-participating women, the median age was 46 years (interquartile range 39–52); 37% (3271/8953) had no ethnicity recorded in their primary care records. Despite this being a known problem with routinely collected primary care data sources, primary care ethnicity data are generally comparable with the UK census , of which 64 (38%) were recommended referral  with family history of non-FDR breast cancer aged < 40 did not meet the NICE referral criteria; they were given the ‘Uncertain’ category and after discussion with the specialists, referral was not indicated for 10 participants.From the univariate analysis of all factors listed in Box In the multivariable analysis including all 1127 participants, the family history variable with the strongest association of recommendation for referral was breast cancer in younger women , followed by bilateral breast cancer, relevant cancers  and breast cancer in the paternal lineage . Of these 680 participants who did not have/report a mammogram in the last 3 years, only 9% were not compliant, the rest were not in the national breast cancer screening age group of 50–70.Combining these significant factors with age and IMD quintiles a priori produced a predictive model with high discriminatory accuracy  did not allow for a cluster effect analysis. Nevertheless, socioeconomic variation at participant level was measured using the IMD. The study population was predominantly white and above average on the socioeconomic gradient. The mammography uptake in this cohort suggests a responders bias, where women who are more concerned of their familial breast cancer risk are more likely to participate in the study. This, together with the lack of ethnic and socioeconomic variation, limits the generalisation of findings to other population groups.We were not able to account for socioeconomic variation at the general practice level as the small number of practices .


Moreover, we reported significant interaction effects of older age and period in recent years in addition to the interplay of older age and risk factor HBMI on MRs and DALYs. Increased age and obesity contribute to substantially raised breast cancer MRs and DALYs in China and around the globe. These discoveries shed light on protective health policies and provision of healthy lifestyle for improving the subsequent breast cancer morbidity and mortality for China, as well as other related Asian regions that are presently facing the same public health challenges. Limited studies quantified the age, period, and cohort effects attributable to different risk factors on mortality rates (MRs) and disability-adjusted life years (DALYs) due to breast cancer among Chinese women. We used data from the Global Burden of Disease Study (GBD) in 2017. Mixed-effect and hierarchical age–period–cohort (HAPC) models were used to assess explicit and implicit fluctuations in MRs and DALYs attributable to different breast cancer associated risk factors. As the only risk factor, high body mass index (HBMI) showed continuously increasing trends in MRs and DALYs across ages, periods, and cohorts. Age, recent periods (2010–2015), and risk factor HBMI showed significant positive effect on MRs and DALYs ( Breast cancer is the most commonly diagnosed cancer or disease among women all over the world and linked with considerable years of life lost, leading to increased cancer-related mortality and morbidity ,2,3. BreAge is one of the most imperative breast cancer incidence-related risk factors. The breast cancer incidence varies drastically with race and ethnicity, even among young women. White women are faced increasing breast cancer risk in age above 45 years in comparison with black women. Older breast cancer patients face poor survival rates than young ones, and studies demonstrated that older age was an independent prognostic factor for adverse disease outcomes ,7.2 [Obesity affects 13% of the population in the world or more than six hundred million adults around the globe, and it is defined as a body mass index (BMI) ≥30 kg/m2 . Obesity2 ,10,11. A2 ,12,13,142 . Obesity2 . The Con2 ,18.A meta-analysis demonstrated a strong association between physical inactivity and increased risk of breast cancer . HoweverNone of the studies reported the association of any single specific factor with breast cancer while evaluating the global burden of disease morbidity and mortality, and they concluded that multiple factors are responsible for the onset and development of breast cancer ,22. EpidAlthough breast cancer incidence is lower in China as compared to Western countries, China is currently facing increasing trends in its incidence and mortality . In viewThe Global Burden of Disease (GBD) study in 2017 included a yearly evaluation casing 195 countries and territories from 1990 to 2017. It covered 476 risk–outcome cases including 359 diseases and injuries, with 84 behavioral, environmental, occupational, and metabolic risks or groups of risks from 1990 to 2017 by age, sex, year, and location. It extracted relative risk and exposure estimates from 46,749 randomized controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. There are two primary sources of data for China: surveillance data from the China Disease Surveillance Points (DSP) system and vital registration (VR) data collected by the Chinese Center for Disease Control and Prevention (CDC). Gagkidu et al. explained the methodology in detail along with the approach of the GBD 2017 .2 for adults), alcohol use, low physical activity (Low PA), and smoking, were extracted for women aged 20–84 years old.We only extracted the data of the Chinese female population from this study. From 1990 to 2015, the data for association between breast cancer MRs and DALYs with various risk factors like age (20–80) and period (1990–2015), as well as classified risk factor variable with classes of high body mass index . Mortality data source of breast cancer from vital registration (VR), cancer registration systems, and verbal autopsy (VA) data, and mortality estimates were used as input data into the Cause of Death Ensemble Model (CODEm). The CODEm predicted MRs based on all available data, and then the COD correct algorithm was used for each single cause estimation.DALYs are a summary measure of population health widely used to quantify disease burden; they are composed of years lived with disability (YLDs) and years of life lost due to premature mortality (YLLs). YLLs represent the mortality component of DALYs, and YLLs due to breast cancer were calculated by using standard life expectancy and the number of deaths according to age. YLDs, the morbidity component of the DALYs, were calculated by multiplying the prevalence (number of cases) of each sequela by its disability weight. For a given risk–outcome pair, the attributable DALY estimate was obtained by multiplying the total DALYs by the population attributable fraction (PAF) for the risk–outcome pair for each age, sex, location, and year.As the aim of the study was quantifying the variations in breast cancer MRs and DALYs across age, periods, cohorts, and interactions with different risk factors, an individual model was constructed for both outcomes for the Chinese female population.http://ghdx.healthdata.org/gbd-results-tool.The GBD study was based on adjustable breast cancer risk factors, i.e., the justification of data quality and the eminence of the estimate models . MR and To measure the potential changes related to risk factors, we described the breast cancer outcomes (MRs and DALYs) stratified by different risks for age, period, and cohort. Furthermore, in order to assess the variation and median trend of mortality and DALYs irrespective of risk factors , we also reported the boxplot by age, period, and cohort for each outcome.A sequence of explicit assessments of variances in MRs and DALYs within individuals across different ages (age effect) and population-wide changes in MRs and DALYs over time (period effect) were measured by successions of longitudinal mixed-effect models (MEMs) with random and fixed effects of individual-level and random coefficients. Furthermore, implicit assessments of the random variances (effects) in outcomes connected to different risks across ages, periods, and cohorts were also performed. We selected the MEMs because of their ability to handle asymmetric data and non-discrete covariates. Moreover, the identifiability problem could be overcome by the hierarchical structure of the models with no assumption of linear and additive effects of age–period–cohort (APC) at the same level of analysis ,27,28.MEMs also permit individuals (observations) to have their coefficients on different levels, whereby some observations are above and some below the population mean (regression intercept); similarly, some individuals have a higher rate of change than the population mean (regression slope) and some have a lower rate of change than population mean. In this situation, the model accommodates more flexibility and can estimate the differences in outcome at different levels and over time in the presence of heterogeneity. In the present study, three models were employed. Among them, model 1 contained age, levels of period (year), and dummy-classified risk factor variables with four risks. The years and risk factors coefficients described the period and risks effect, respectively. Model 2 included the additional age interaction with year in model 1. Interaction terms allowed us to assess how effects of period may fluctuate with age (cohort effect). Model 3 involved the interaction of age and risk factors by extending model 2. We aimed to evaluate in what way these interactions described some of the period effects, plus how risk factor effects may vary through age. In order to provide information about how these variables were linked to MRs and DALYs, we compared these models with the traditional generalized linear model (GLM) by including the full set of variables. The goodness of fit was evaluated using the Akaike information criterion (AIC), Bayesian information criterion (BIC), and log-likelihood statistic for the estimated models.p < 0.05.A hierarchical age–period–cohort (HAPC) analysis of longitudinal panel data ,29,30, eLongitudinal analyses of mortality rates (MRs) and DALYs due to breast cancer across ages attributable to different risk factors like high body mass index (HBMI), alcohol use, low physical activity (Low PA), and smoking for different years among Chinese women are depicted in Rising trends of MRs and DALYs were noticed with increased age in all risk factors under consideration. In particular, higher MRs and DALYs were observed for the risk factor HBMI from 1990 to 2015; specifically, the age groups of 50 to 75 years for MRs and 50 to 60 years for DALYs were more stressed from 2000 onward. Mortality and DALYs attributable to HBMI peaked in 2015 as compared to previous years in the age groups of 70–75 years and 55–60 years, respectively. These rates were almost three times greater than the MRs of 2010 to 2015. On the other hand, at young ages, low breast cancer MRs and DALYs were observed with reference to HBMI . The medp < 0.01), as well as a significant interaction effect of age and HBMI with MRs (p < 0.01), when controlling for other factors  with DALYs  with DALYs as compared to the reference group , suggesting that the early cohorts were positively associated with MRs due to breast cancer see . MoreoveFurthermore, the random effects of individual age, period, and cohort in each risk were reported to assess the interaction of different levels of age, period, and cohort with various risks. Results indicated that women with older ages from 50 to 75 years had HBMI, which was found to be a significant risk factor of MRs. Similarly, extensive higher random effects of period and risks on MRs were identified in a period of 10 years from 2005 to 2015 among women who had HBMI. The interaction of cohorts and risks showed a declining trend of random effects on MRs from the overall cohort 1910–1965 among women with HBMI and rising trends in early cohorts, i.e., 1910–1935 . On the In associating the random effects among age, period, cohort, and interaction with risks, we again confirmed that all random effects were higher in women with HBMI, and older ages, recent years, and early cohorts contributed to increasing MRs and DALYs attributable to HBMI.The comparison of fixed and random effects across ages, periods, and cohorts is displayed in It is well documented that, over the past two decades, the prevalence of obesity tripled (11%–29%) among Chinese adults . In the In the current study, controlling for age, the period effect was found to be positively associated with MRs in 2015. Additionally, results of the model presented that HBMI was positively associated with MRs when adjusting for age and period, and a significant interaction effect of age and period was observed in 2010 and 2015 with MRs, along with a significant interaction effect of age and HBMI with MRs when controlling for other factors. One of the Indian epidemiological studies reported increased age-standardized breast cancer incidence rate (39%) over 26 years (1990–2016), and this increase was observed in every single state. HBMI, high plasma glucose levels, and passive smoking were considered to have roles in breast cancer DALYs . YouldenThe current study demonstrated that, in associating the random effects among age, period, cohort, and interaction with risks, it was established that all random effects were higher in women with HBMI, and that older ages, recent years, and early cohort contributed to increasing MRs and DALYs in relation to HBMI. Moreover, both random- and fixed-effect models confirmed similar results to strengthen the findings.Biological mechanisms underlying the relationship between breast cancer and obesity clarify the reasons that obesity contributes to DALYs and deaths. Excess circulating hormones are present, particularly estrogen, which causes the development of carcinogenesis and mutations by prompting free-radical production, revealing genotoxicity. Hyperinsulinemia is another factor causing breast cancer, which encourages insulin-like growth factor (IGF–1) production and activity . TherefoThe current study has certain limitations such as the unavailability of data for all the risk factors including alcohol intake, physical inactivity, use of oral contraceptives, and socioeconomic status. Moreover, if exact BMI and physical inactivity values (proper measures) for breast cancer cases were available, they would have led to better depiction and interpretation of results. In addition, obesity may lead to other diseases or an outcome of some health issues; thus, it is always challenging to explain direct mechanisms involved in its association.In conclusion, increased age and obesity contribute to substantial breast cancer MRs and DALYs in China and around the globe. These results support the promotion of exercise and proper diet in Chinese women in order to provide help in disease prevention and management.


Multiple common variants identified by genome-wide association studies have shown limited evidence of the risk of breast cancer in Chinese individuals. In this study, we aimed to uncover the relationship between estrogen levels and the genetic polymorphism of estrogen metabolism-related enzymes in breast cancer (BC) and establish a risk prediction model composed of estrogen-metabolizing enzyme genes and GWAS-identified breast cancer-related genes based on a polygenic risk score.Unrelated BC patients and healthy subjects were recruited for analysis of estrogen levels and single nucleotide polymorphisms (SNPs) in genes encoding estrogen metabolism-related enzymes. The polygenic risk score (PRS) was used to explore the combined effect of multiple genes, which was calculated using a Bayesian approach. An independent sample t-test was used to evaluate the differences between PRS scores of BC and healthy subjects. The discriminatory accuracy of the models was compared using the area under the receiver operating characteristic (ROC) curve.P = 0.17); however, there was a significant difference between BC and healthy subjects in M2 (P = 4.9*10− 5). The ROC curve results showed that the accuracy of M2 (AUC = 62.18%) in breast cancer risk identification was better than that of M1 (AUC = 54.56%).The estrogen homeostasis profile was disturbed in BC patients, with parent estrogens  and carcinogenic catechol estrogens  significantly accumulating in the serum of BC patients. We then established a PRS model to evaluate the role of SNPs in multiple genes. PRS model 1 (M1) was established from SNPs in 6 GWAS-identified high risk genes. On the basis of M1, we added SNPs from 7 estrogen metabolism enzyme genes to establish PRS model 2 (M2). The independent sample t-test results showed that there was no difference between BC and healthy subjects in M1 (Estrogen and related metabolic enzyme gene polymorphisms are closely related to BC. The model constructed by adding estrogen metabolic enzyme gene SNPs has a good predictive ability for breast cancer risk, and the accuracy is greatly improved compared with that of the PRS model that only includes GWAS-identified gene SNPs. Breast cancer is the most common malignant disease among women worldwide, accounting for 24% of new cancer cases and 15% of cancer deaths in 2018, and incident cases are expected to increase by more than 46% by 2040, according to the GLOBOCAN Cancer Tomorrow prediction tool, which will seriously endanger women’s lives and health . With coThe occurrence and development of breast cancer are closely related to genetic and environmental factors. In 1989, Gail proposed the breast cancer risk prediction model, which included factors such as age at evaluation, age at menarche, age at first live birth, race, number of breasts, and family history of breast cancer , 5. SomeGWASs also have their own limitations. First, a major limitation of genome-wide approaches is the need to adopt a high level of significance to account for multiple tests. Second, GWASs explain only a modest fraction of the missing heritability . EstrogeBased on the above analysis, our research aims to reveal the form of estrogen homeostasis disorders in breast cancer and explore the association between metabolic enzyme gene polymorphisms and breast cancer occurrence from the overall level of estrogen metabolism. Furthermore, we developed a risk score comprising GWAS-selected SNPs and estrogen metabolic enzyme gene SNPs to optimize the breast cancer risk prediction model.The standards and other chemical reagents were described in our previously published study .Serum samples were collected during the follicular and luteal phases of 64 premenopausal women (mean age: 45.5 ± 5.04 years) first diagnosed with BC and 49 matched healthy women (mean age: 43.7 ± 8.80 years) to detect the level of estrogens. Blood samples were also collected from 140 premenopausal women (mean age: 43.3 ± 6.24 years) first diagnosed with BC and 140 matched healthy women (mean age: 40.2 ± 3.52 years) to extract DNA and analyze SNP genotypes. All samples and related data were obtained from the Affiliated Hospital of Xuzhou Medical University, Xuzhou, China, from June 2017 to May 2019. Patients with BC were enrolled from the Department of Nail Surgery, whereas healthy subjects were enrolled from the physical examination center. Blood samples were collected before any therapy.The enrollment criteria were as follows: no history of smoking; BMI ranging from 19 to 26; and no history of chemotherapy, radiotherapy, or estrogen-related endocrine therapy during blood sample collection. The characteristics of the patients at baseline can be seen in Table The LC-MS/MS method was performed according to our previously published method .http://www.geneskybiotech.com). Detailed information about the basic SNP information can be found in Table DNA was extracted from peripheral whole blood with a Tiangen DNA extraction kit . The main metabolic enzymes CYP19A1, CYP1A1, CYP1B1, HSD17B1, COMT, UGTs, and SULTs are involved in the regulation of estrogen metabolism. In this study, according to a previous study and pharmacogenomic database, 1 gene locus that is more common or affects the function and activity of metabolic enzymes was screened from each metabolic enzyme. At the same time, we used GWAS-identified breast cancer-related SNPs according to a previous study . All selSPSS 22.0 software was used to perform statistical analysis. We used the mean ± SEM to express all estrogen data and Student’s t-test to test differences between the two groups. Multivariate analysis was performed using SIMCA 14.0 software.HWE was examined among controls using a goodness-of-fit chi-squared test. The odds ratio (OR) and 95% confidence interval (CI) were calculated using a logistic regression model to assess the association between the SNPs and the risk of breast cancer.P value in the per-allele model as a candidate. The basic formula is as follows:We established a PRS to estimate the multigene contribution of estrogen-metabolic enzyme gene loci for breast cancer susceptibility, which was created using marginally significant SNPs associated with breast cancer risk based on the per-allele models. For SNPs in strong linkage disequilibrium located on the same gene or chromosome, we chose the one variant with the lowest P < 0.01) and 2/4-OHE1 (P < 0.05). OPLS-DA was constructed as an unsupervised statistical method to identify potential estrogen homeostatic changes between the two groups. As shown in Fig. Using LC-MS/MS quantitative analysis, we measured the expression levels of 11 serum estrogens and metabolites in 64 patients with premenopausal BC (mean age: 45.5 ± 5.04 years) and 49 matched controls (mean age: 43.7 ± 8.80 years). We found that there was no significant difference in age between the BC group and NC group. As shown in Fig. P > 0.05 explained that the samples at enrollment were representative of the group. As seen in Table We enrolled 140 patients first diagnosed with breast cancer and 140 corresponding healthy women in this study. The mean age at diagnosis (for patients with cancer) was 43.3 ± 6.24 years, and the mean age of healthy women at enrollment was 40.2 ± 3.52 years. Blood samples were collected from these participants to extract DNA and analyze the SNP genotype. We found that there was no significant difference in age between the BC group and NC group. The chi-square test was used to test the HWE value, and P = 0.007), CYP1B1 rs1056827 (P = 0.004), CYP1B1 rs1056836 (P = 0.002) and SULT1A1 rs1042028 (P = 0.029) showed significant differences in distribution. Compared with the wild-type genotypes of CYP1A1 rs1048943 (TT) or SULT1A1 rs1042028 (CC), the heterozygous variant genotypes of CYP1A1 rs1048943 (TC) or SULT1A1 rs1042028 (CT) showed significantly higher risk in breast cancer, with ORs of 2.37  and 2.21 (95% CI = 1.20–4.05), respectively. Compared with the wild-type genotypes of CYP1B1 rs1056827 (CC), the homozygous variant genotypes (AA) showed a significantly higher risk in breast cancer, yielding an OR of 6.90 (95% CI = 1.50–31.76). Compared with the wild-type genotypes of CYP1B1 rs1056836 (GG), the heterozygous variant genotypes significantly reduced the risk of breast cancer, yielding an OR of 0.37 (95% CI = 0.21–0.67). In addition, no associations with breast cancer risk were observed for the estrogen metabolic enzyme gene SNPs CYP19A1 (rs700519), HSD17B1 (rs605059), COMT (rs4680), or UGT2B7 (rs7439366) or the GWAS-selected SNPs ZNF365 (rs10822013), FGFR2 (rs2981579), RAD51B (rs3784099), TOX3 (rs3803662), MAP3K1 (rs889312), or HCN1 (rs981782).Table P value in CYP1A1, and rs1056836 is a protective gene loci, we chose the risk variant rs10526827 in CYP1B1. The PRS scores are expressed as the means ± SEM to find the difference between the two groups. Under M1 and M2, the PRS data of the two groups obeyed a normal distribution; therefore, we used an independent sample t-test to evaluate the difference between the two groups of data. As shown in Table P = 4.9*10− 5); however, there was no significant difference between NC and BC in M1 (P = 0.17). Finally, the ROC curve was calculated to evaluate how the risk models discriminated between women with and without breast cancer  is an estrogen-dependent tumor, and the occurrence of BC is closely related to the imbalance of estrogen homeostasis. The accumulation of estrogen and its toxic metabolites in vivo is a significant risk factor for BC development. Different types of estrogens have different physiological and pathological activities and can play an important role in the process of cancer development through different mechanisms. Parent estrogens are postulated to promote tumorigenesis directly through the stimulation of the estrogen receptor (ER) . The endIn this study, the LC-MS/MS quantitative analysis method was used to determine the serum estrogens in the BC group and NC group. Comparing the levels of serum estrogens in the follicular phase and luteal phase of premenopausal breast cancer patients with healthy female volunteers, we found that the levels of parent and hydroxylated estrogen in the BC group were significantly higher than those in the NC group, which indicated that estrogen metabolism disorder is closely related to the occurrence and development of breast cancer. Using OPLS-DA, we also noticed that E1, E2, 4-OHE2, 2-OHE2, and 2/4-OHE1 are BC-related disease markers. This result was consistent with the epidemiologic characteristics of patients with BC .A large number of studies have confirmed that breast cancer exhibits heritability , 28. HowEstrogen is an important risk factor for breast cancer. However, no research has incorporated estrogens into the breast cancer risk prediction model. A possible major reason is that there is no clinically effective estrogen evaluation method because the steady state of estrogen is affected by various physiological and pathological factors, such as menstrual cycle fluctuations. However, estrogen homeostasis is regulated by various metabolic enzymes. Therefore, we believe that estrogen metabolic enzyme gene polymorphisms are closely related to estrogen homeostasis and the occurrence and development of breast cancer. In this study, univariate logistic regression analysis showed that CYP1A1, CYP1B1, and SULT1A1 gene polymorphisms are closely related to the occurrence of breast cancer. It is worth noting that these gene polymorphisms are also associated with other estrogen-dependent tumors such as endometrial cancer and ovarian cancer. Hiroshi Hirata et al. found that the SULT1A1 rs9282861 (rs1042028) was related to endometrial cancer . A meta-P = 0.001) may result in decreased function of the CYP1B1 enzyme, reducing the production of 4-hydroxy estrogen and even catechol estrogen-3,4-quinone  to form adducts with DNA. At the same time, this study also proved that the variant alleles of CYP1A1 rs1048943  and CYP1B1 rs1056827  are closely related to the risk of breast cancer, which is consistent with most research [CYP1A1 and CYP1B1 are the major phase I drug metabolism enzymes that catalyze the hydroxylation of estrogens. The increasing polarity of estrogens may be related to the risk of breast cancer . Our expresearch , 35. TheSULTs catalyze the sulfate conjugation of a broad range of substrates and play an important role in the metabolism of endogenous and exogenous compounds, including thyroid and steroid hormones, neurotransmitters, drugs and procarcinogens . SULTs cPrevious studies investigated associations between the PRS of multiple SNPs and breast cancer risk to study the cumulative effect of genes on the disease. Mavaddat et al. constructed a 77-SNP PRS for breast cancer and found a threefold increase in risk when comparing the polygenic scores of the highest 1% and the middle quintiles . Harlid P-value (4.9*10− 5) of the M2 PRS model was far less than that of M1 (0.17). Moreover, the ROC curve (62.18%) of the M2 model was better than that of the M1 model (54.56%). Therefore, the model constructed by adding estrogen metabolic enzyme gene SNPs had a good ability in breast cancer risk prediction, and the accuracy was greatly improved.In this study, we evaluated possible relationships between the increased breast cancer risk estrogen metabolic enzyme gene SNPs and GWAS-identified gene SNPs in an Asian population. Among them, the GWAS-identified SNPs were not associated with breast cancer risk in the per-allele model or dominant model in our study. This finding was inconsistent with a previous study . FurtherThere are several limitations of this study that should be noted. First, the sample size was relatively small. In this study, only 140 premenopausal women first diagnosed with BC and 140 matched healthy women were recruited based on our criteria; thus, we did not have enough statistical power to detect the effect of the genetic variants on some of the parameters. Second, because funding was limited, it did not include comprehensive metabolic enzymes and adequate breast cancer risk gene loci. Due to these reasons, the AUC was small and the model have not been tested. In the future, we will study additional estrogen-metabolizing enzyme genes and other breast cancer risk genes in our research. At the same time, we will also include recognized breast cancer risk factors such as age at evaluation, age at menarche, age at first live birth, race, number of breasts, and family history of breast cancer and construct a breast cancer risk prediction model composed of phenotype and genotype to obtain a more valuable ROC value. In addition, the sample size needs to be further expanded, and it is better to include more data information of different races.Estrogens and related metabolic enzyme gene polymorphisms are closely related to BC. The model constructed by adding estrogen metabolic enzyme gene SNPs has good predictive ability for breast cancer risk, and the accuracy is greatly improved compared with that of the PRS model that only includes GWAS-identified gene SNPs.


Breast diseases in women, whether benign or malignant, are very commonly encountered. The breast is the commonest site for female cancer in Egypt (38.8%). Breast cancer screening can reduce morbidity and mortality and improve the survival rate for this malignancy. Mammogram can be used as a screening technique beside its role as diagnostic, especially in women over 40 years of age.To assess knowledge, attitude and practice regarding breast cancer and awareness about mammogram as a screening method among a group of females attending Ain Shams University outpatient clinics.A cross-sectional study was performed from August to September 2017. A systematic random sample was taken from attending females (18–70 years) in Ain Shams University outpatient clinics . They were interviewed using a questionnaire inquiring about sociodemographic background, participants’ knowledge, attitude, and practice towards breast cancer and its screening.The mean age ± SD of attending females (18–70 years) was 37 ± 11 years. Most study participants had correct information about mammography. They showed poor knowledge level about risk factors. Mass media such as TV and internet were identified as the main source of information on breast cancer by 43% and 23.9%, respectively. In general, participants had positive attitude towards breast cancer screening by mammography. Around 90% agreed that mammogram was the best way to find a very small lump in the breast, and 91.4% agreed that women who have regular screening by mammogram have better disease outcome than those who do not screen. Regarding mammography practice rate, a small percent of participants (8.1%) was advised by their doctors to conduct a screening mammography. The level of knowledge was significantly and positively correlated with their attitude towards breast cancer screening.The poor knowledge and practices of women illustrate the need for health education program directed to Egyptian females to improve their knowledge about breast cancer—especially its risk factors—and its screening. Using TV and Internet as media for spreading information about this disease is crucial. Breast cancer is the most frequently diagnosed cancer among women which now represents one in four of all cancers in women. It is also the most common cause of death among women worldwide ..Recently, there is a shift toward Western lifestyles in developing countries due to rapid societal and economic changes. These styles consequently result in dietary, reproductive, and hormonal changes which are risk factors for the dramatic increase in cancer rates. Although, the incidence rates of breast cancer are higher in developed countries, mortality is much greater in developing countries, due to difficulty in early detection of the disease and lack of access to treatment as well ..Globally, breast cancer is the most diagnosed cancer and the leading cause of cancer death among females, representing 23% of the total cancer cases and 14% of the cancer deaths. Breast cancer is now also the leading cause of death among women from all cancers in developing countries  was done on migrants. A third question was also removed as it annoyed all pilot study participants. The question was “Does pain caused by tight bra lead to breast cancer?”An interview questionnaire derived from Amin  was usedThe questionnaire included (a) 14 background questions; age, marital status, educational level, occupation, residence, number of children, monthly expenditure, presence of family physician, and if the participant reached menopause or not. Participating women were asked to rate the most important channel—from their point of view—of health education to deliver information about breast cancer and its screening . (b) Participants’ knowledge about breast cancer epidemiology, and its screening was tested through 12 questions . An answer was scored “one” for correct answer and “zero” for wrong answer or “Don’t know.” Scores of the twelve questions were then summed and treated as a continuous variable. (c) Participants’ attitude was explored through 14 questions . A five-point Likert Scale was used to allow the individual to express their attitude towards a particular statement. Scores for the 14 questions were summed and treated as a continuous variable. Participants’ answers ranged from “Strongly agree to strongly disagree.” 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, while 5 = strongly agree. The range of the total score is 14–70 (14 questions × 1–5). (d) Practice; two questions were asked to participants (If her doctor ever asked her to do a mammogram and if the participant had a mammogram due to any reason).Data was coded, entered, and analyzed using SPSS  .P value of < 0.05 was considered statistically significant.Descriptive statistics was done using the number and percentage for categorical variables, mean ± SD, and range for quantitative variables. Bar chart was used to express the most important sources of information. Pearson’s correlation was used to find relation between women’s knowledge and their attitude towards mammogram as a screening technique. Linear regression was performed to find out factors which independently affect participants’ knowledge. Our sample consisted of 381 females, aged (18–71) years with mean ± SD (37 ± 11) years. The majority were housewives and residents of Cairo. The most frequent level of education was secondary education (33.1%) followed by “below secondary education” (25.5%). Most of them were married (84.3%) and (90%) reported having children; mean ± SD (3 ± 1) children. Female participants reported starting age of pregnancy at 12 years and kept getting pregnant until 48 years. The majority (81.9%) were in childbearing age (Table The most common sources used by study participants for getting information was TV (42.9%) followed by Internet and the health education received in some primary health care centers . The least used sources were radio and newspapers (3.9% each) Fig. .Fig. 1TMost of study participants had correct knowledge about mammography  . Regarding knowledge about risk factors, they showed poor knowledge level   . More than half (57.4%) disagreed that they do not have enough time to schedule for mammograms; nevertheless (51.4%) agreed that they have more important problems to take care of Table .Table 3r = 0.133, p = 0.01) (Table The knowledge score of study participants ranged between 0 and 11 points (out of 12) with mean ± SD (7 ± 2) while attitude score ranged between 35 and 65 points (out of 70) with mean ± SD 49.4 ± 5.7. Knowledge and attitude score were significantly and positively correlated  (Table Linear regression shows that age and level of women’s education independently affected their level of knowledge (y) Table .Table 6The current study showed that the majority of study participants had low level of knowledge regarding breast cancer risk factors which was disappointing given their exposure to health education sessions and also contact with health workers such as doctors and nurses. This finding agreed with Boulos and Ghali  study amp < 0.001) which disagrees with Dahiya et al. [p < 0.01). This controversy is probably due to that Indian females below 30 years received very good health education sessions in schools and universities as stated by participants. Another cross-sectional study done in Buraidah, Saudi Arabia, on teachers working in female schools [We found that knowledge about breast cancer risk factors improved significantly with age  in the current study did not know that old age at first child was another risk factor for breast cancer which agreed with Boulos and Ghali study  (57.1% oMost of participating women in this study (73.5%) were not aware that the probability of breast cancer increases with age but Boulos and Ghali  study shAs for warning signs of breast cancer, most of participating women in this study (87%) did not know that breast lumps or benign tumors increase the risk for breast cancer. Sixty-two percent were aware that most breast lumps are not cancer, compared to 16.2% of female students at Ain Shams University Hospitals [. Moreover, in our study, 93% were aware that undetectable breast masses by hand could be detected by radio diagnostic procedures as they are more sensitive. This result agreed with Newton and Grethlein [While 74.2% of female students in Boulos and Ghali’s  study idrethlein  who reve. This result disagrees with Ojewusi and Arulogun’s study [In this study, 86% of participants were aware that mammogram was recommended above 40 years and this result was found in a group of women with good knowledge about healthy lifestyle and healthy practices including spacing between births and family planning . This re’s study , women w’s study .Regarding the source of health information and knowledge, 43% of women in this study had their information from mass media (TV) and this agreed with Boulos and Ghali’s  study  rather than being embarrassing (63.2%) or painful (39.3%). This contradicts with the findings of Donnelly et al.  where thWomen in the current study showed favorable attitude towards the importance of mammogram in early detection of breast lumps as the majority (89.4%) agreed that having a mammogram will help them find breast lumps easily, 89.8% agreed that having a mammogram is the best way for them to find a small lump in the breast while 91.4% agreed that finding lumps through routine mammogram, may improve their treatment for breast cancer. Moreover, 53.8% agreed that having a mammogram will decrease their chances of dying from breast cancer. This result agrees with Donnelly et al. , where tWhen participants of our study were asked if they have more important problems to manage, 51.4% agreed to this statement. This may be explained by the fact that there is a big percentage of female Egyptians who lead the household (61%) according to a report by United Nations . This poThe current study results also showed that higher education improves participants’ knowledge which agreed with Gürdal et al.  in TurkeThis study was a hospital-based study and may not truly reflect the level of awareness among the general population. Our sample included only the outpatients from Ain Shams University clinics who were present at time of data collection, so it cannot be generalized to all women of different socio-economic classes.Exploring awareness of female attendants at Ain Shams University outpatient clinics revealed that those women had good level of knowledge regarding breast cancer epidemiology and its status in Egypt; however, their knowledge regarding the risk factors of this disease was poor and need to be improved so that they can identify high-risk groups. Generally, participants had positive attitude towards performing mammogram for screening of breast cancer; nevertheless, they were overwhelmed with other life problems keeping them away from performing screening. Age and educational level independently affected participants’ knowledge.This study clearly illustrates the need for health education programs directed to Egyptian females attending Ain Shams University outpatient clinics to improve their knowledge about breast cancer especially its risk factors and its screening by mammogram after 40 years.There is a crucial need to conduct health education sessions for females early in their childbearing period in schools and universities to raise their awareness about breast cancer and its risk factors. Training of health care professionals (HCPs) to educate women about this serious and potentially fatal type of cancer is a must as HCPs can be a trustable source of information.


Risk-based BC screening using multifactorial risk assessment appears to be acceptable to most women. This suggests that the implementation of this approach is likely to be well-supported by Canadian women.Risk-stratified screening for breast cancer (BC) is increasingly considered as a promising approach. However, its implementation is challenging and needs to be acceptable to women. We examined Canadian women’s attitudes towards, comfort level about, and willingness to take part in BC risk-stratified screening. We conducted an online survey in women aged 30 to 69 years in four Canadian provinces. In total, 4293 women completed the questionnaire (response rate of 63%). The majority of women (63.5% to 72.8%) expressed favorable attitudes towards BC risk-stratified screening. Most women reported that they would be comfortable providing personal and genetic information for BC risk assessment (61.5% to 67.4%) and showed a willingness to have their BC risk assessed if offered (74.8%). Most women (85.9%) would also accept an increase in screening frequency if they were at higher risk, but fewer (49.3%) would accept a reduction in screening frequency if they were at lower risk. There were few differences by province; however, outcomes varied by age, education level, marital status, income, perceived risk, history of BC, prior mammography, and history of genetic test for BC (all  The Canadian Task Force on Preventive Health Care recommends that women aged 50–74 be screened with mammography every 2 to 3 years . Still, Nonetheless, the implementation of this novel approach faces considerable organizational, social, ethical, and legal challenges. Obtaining real-world evidence and population’s engagement can help resolve these challenges ,10,11. TThe Canadian PERSPECTIVE I&I study  is one of the major ongoing initiatives examining the potential of risk-stratified BC screening ,19,20. Ahttps://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=1710000501), we estimated a required sample size of 4268 women and applied quotas based on age and province. The Research Ethics Committees of the CHU de Québec-Université Laval and the McGill University approved the study (registration number: F9–42434).A population-based, cross-sectional survey of women aged 30 to 69 years from the four largest provinces in Canada  was conducted from in February 2019. Based on the age distribution of women in the four provinces in 2018 (https://www.ipsos.com/en), a survey firm with a panel of individuals from the general population in Canada who have previously consented to be contacted for research. Potential participants were emailed a link to the questionnaire.Based on previous studies ,15,21,22Attitudes towards BC risk assessment and risk-based screening were measured using three questions: “What do you think of the idea of using information like age, cancers in your family, having children, lifestyle factors, breast density and weight to identify women who are at high, average or low risk of developing breast cancer?”; and “ What do you think of the idea of using results from genetic testing  to identify women who are at high, average or low risk of developing breast cancer?”; and “What do you think of the idea of changing how often women are invited for breast screening based on them being at high or low risk of developing breast cancer?” Participants were asked to use a 5-point Likert scale to report whether they viewed BC risk assessment and risk-stratified screening as a “very bad idea”, “bad idea”, “neither a good or a bad idea”, “good idea”, “very good idea”, “don’t know”, or “prefer not to answer”. As done previously ,15, respBeing comfortable in providing information for BC risk assessment was measured using three questions: “How comfortable would you feel providing personal information , so you can find out your breast cancer risk level?”, “How comfortable would you feel providing a small sample of blood or saliva for genetic testing , so you can find out your breast cancer risk level?”, and “How comfortable would you feel having a mammogram to assess your breast density (amount of dense tissue compared to the amount of fatty tissue in the breast) so you can find out your breast cancer risk level?” Again, participants were given a 5-point Likert scale with choices ranging from “very comfortable” to “very uncomfortable.” The responses options were dichotomized into either “very comfortable/comfortable” or “very uncomfortable/uncomfortable/neither comfortable nor uncomfortable/prefer not to answer”.Willingness to have BC risk assessed and tailored screening frequency was evaluated by the following four questions: “Would you be willing to have your breast cancer risk level assessed using information mentioned above? This would mean being told whether you are at average, lower than average, or higher than average risk of developing breast cancer”, “If your estimated level of breast cancer risk was higher than average, would you be willing to have your breast cancer screening more often than every 2 to 3 years?”, “If your estimated level of breast cancer risk was average or lower than average, would you be willing to have your breast cancer screening less often than every 2 to 3 years?”, and “If your estimated level of breast cancer risk was much lower than average, would you be willing not to be offered any breast screening?”. Responses were collected on a 5-point Likert scale with options dichotomized as “yes, definitely/yes, probably” vs. “no, definitely, not/no, probably, not/not sure/prefer not to answer”.Sociodemographic variables were assessed for participants’ age, ethnicity, marital status, educational level, employment status, annual family income, and province of residence. Other covariates considered include perceived health status, personal history of BC, perceived lifetime susceptibility to BC, personal experience of mammography and genetic testing for BC, and family experience of genetic testing for BC.Descriptive statistics were used to present the characteristics of the study sample and to report proportions of participants according to each dichotomized outcome variables. Mutually adjusted logistic regression models were used to explore the associations between participants’ characteristics and the three outcomes of interest. We tested and found no sign of multicollinearity. Given the exploratory nature of the study and the correlation between the outcomes variables, we did not adjust for multiple comparisons . Insteadn = 74), our analytical sample included 4219 participants.p-values ≤ 0.01). In contrast, women with higher annual family income, higher perceived lifetime risk of BC, who previously had a mammogram or had a family history of genetic testing for BC were more likely to be in favour of risk-stratified screening .As shown in Panel A of p values ≤ 0.01). In contrast, women with higher family income, good perceived health status, higher perceived lifetime risk of BC, previous experience of mammogram and those with a family history of genetic test for BC were more likely to report being “comfortable or very comfortable” in providing this information .As shown in Panel B of p-values ≤ 0.01). Older women were also more willing forego screening if their estimated BC risk was much lower than average . In contrast, women with higher family income, higher perceived lifetime risk of BC, prior mammogram, and family history of genetic test for BC were more willing to have their BC risk assessed and to be screened more frequently if their estimated risk was higher than average . Women with higher perceived lifetime risk of BC and prior mammogram were less willing to have less frequent or no screening if they were found to be at lower than average risk . It was the opposite for those with personal or familial history of genetic test for BC .As shown in Panel C of A large majority of Canadian women in the present study expressed favorable views of BC risk assessment and of a risk-stratified approach to BC screening. Most women reported being comfortable with providing personal and genetic information for BC risk assessment and showed a willingness to have their BC risk assessed if offered. Most women would then accept an increase in screening frequency if they were at higher than average risk, but fewer would accept a reduction if they were at lower risk. There were few differences by province of residence. It is worth mentioning that Canada has a universal healthcare system, but the healthcare organization is under provincial jurisdiction. Across Canada, BC screening programs are government-funded and are offered by different Provincial Health Services Authority agencies. Thus, BC screening programs might vary from one province to another. For instance, Ontario and British Columbia have a high-risk BC program while Alberta and Quebec do not have such a program. In high-risk programs, women have more frequent mammograms and use other diagnostic tests. However, attitudes were found to vary by age, education level, marital status, income, perceived risk of BC, personal history of BC, experience with mammography, and personal and family history of genetic test for BC .These findings are consistent with those from previous quantitative studies conducted in the UK ,14,15, SSeveral participants’ characteristics were significantly associated with their views on risk-stratified screening. Specifically, those with a lower educational level and total family income exhibited less favorable views. This is consistent with findings from the UK  and SwedThis study has several strengths. To our best knowledge, this is the largest study on this topic. This allowed us to explore factors associated with women’s views of risk-stratified screening, information needed to determine implementation strategies. Further, the use of a web-based survey provided a cost-effective approach to include younger women aged 30 to 49 who are not currently eligible for screening in provincial programs. Limitations include the potential for selection bias with respect to those women who chose to participate and complete the study questionnaire. In addition, we included women from only four provinces, which limits the generalisability of the findings across Canada, even though we found little differences by province. However, several characteristics of our sample, including marital status, education, income, ethnicity, and self-rated health, are fairly comparable to those of the participants of the Statistics Canada’s Canadian Community Health Surveys ,30.The present study provides evidence suggesting that the implementation of risk-stratified BC screening is likely to be well-supported by Canadian women. Further studies are needed to understand why some women are reluctant to provide their personal and genetic information for BC risk assessment. Future studies should also look on how the interactions between different factors shape women’s views on stratified BC screening. Finally, additional studies are also needed to generate evidence on the acceptability of this approach by healthcare professionals, on the ways to communicate BC risk, and to assess healthcare organisational readiness and costs.


Whether breast volume is a risk factor for breast cancer is controversial. This study aimed to evaluate whether a significant association between breast volume and risk of breast cancer, based on linear measurements, was present by applying propensity score matching (PSM).The study was designed as a hospital-based case-control study. Between March 2018 and May 2019, 208 cases and 340 controls were retrospectively reviewed. Information on menarche, smoking, feeding mode, oral contraceptives, reproductive history and family history was obtained through a structured questionnaire. Breast volume was calculated using a formula based on linear measurements of breast parameters. Cox regression and PSM were used to estimate odds ratios and 95% confidence intervals for breast cancer using risk factors adjusted for potential confounders.P = 0.014). Binary logistic regression showed that the risk of breast cancer was slightly higher in the case group with larger breast volumes than in the control group. However, there was no significant statistical difference between the two groups using an independent sample Mann-Whitney U test (P = 0.438) or conditional logistic regression (P = 0.446).There was a significant difference in breast volume between the two groups before propensity score matching (After PSM for potential confounding factors, there is no significant difference in breast volume estimated by BREAST-V formula between the case group and the control group. The risk of breast cancer may not be related to breast volume in Chinese women. In recent years, breast cancer has become the most common cancer among women. In America, the number of new cases of breast cancer in women from 2011 to 2015 was 126.0 per 100,000 women per year, there were expected to be 266,120 new cases of female breast cancer, with an estimated 40,920 deaths from breast cancer in 2018 , feedingP approximately 0.09), possibly because they were likely to have smaller breasts. This also suggested a relationship between breast volume and the risk of breast cancer. However, in a population-based nested case-control study, Thurfjell et al. [Hsieh et al.  found thl et al.  found thl et al.  describeTo better provide a theoretical basis for breast cancer prevention, we conducted the present study to determine the association between breast volume and the risk of breast cancer. Longo’s  formula This retrospective hospital-based case-control study was conducted to investigate the association between breast volume and the risk of breast cancer among women; it involved 208 cases and 340 controls from March 2018 to May 2019 from two hospitals in Guangdong Province, one of which was a tertiary hospital with nearly 3000 beds. Eligible participants were asked to complete a questionnaire under the supervision of trained interviewers; therefore, the loss of sample size due to lost interviews was not considered. The case group included women diagnosed with breast cancer through pathological examination, while the control group included women who underwent health screening examinations, including molybdenum target X-ray breast examinations, and women who had breast cancer within 1 month were excluded. The case group and control group were matched in a ratio of 1:1. To achieve a power of 85% and a two-tailed type I error rate of α = 0.05, each group required at least 181 patients. Considering the propensity score matching success rate, we collected more than this number of cases and more controls to obtain optimal match. The case group included adult women who had recently been diagnosed with breast cancer and were preparing for surgery or adjuvant chemotherapy, excluding those who were pregnant or breastfeeding or who had a history of breast cancer surgery, breast masses, breast augmentation, or communication difficulties. Healthy adult women who were examined at the hospital to ensure that they did not have breast cancer were enrolled in the control group, excluding those who were pregnant or breastfeeding or who had undergone breast augmentation, suspected breast cancer, or communication difficulties.Information on sociodemographic characteristics, menarche age, alcohol consumption, smoking, history of proliferative benign breast disease, feeding mode, oral contraceptives, reproductive history, and family history of breast cancer was obtained through a structured questionnaire. The breast parameters of the two groups were measured and breast volume was calculated using a formula based on linear measurements of breast parameters .The dependent variable for this study was cancer status , and the only exposure factor was breast volume, which was calculated from linear measurements using the BREAST-V formula . Breast All data in this study were collected during in-person interviews after consent was obtained from all study participants; bilateral breast data were collected. The measurer was single-blind to grouping. Anatomical distances included in the BREAST-V formula were the sternal notch-to-nipple distance, fold-to-nipple distance, and fold-to-fold projection distance when the measured person was in a standing position.M ± S, and the independent sample t test was used for comparisons between two groups; the data with a skewed distribution were described by M , and the Mann-Whitney U test was used for comparisons between two groups. The count data were described by a ratio or composition ratio, and the chi-square test was used for comparisons between two groups. A level of P < 0.05 was used to indicate significance; all statistical tests were two-tailed.This study was a case-control study. All data were entered by two people after verification and statistical processing was performed using SPSS 24.0. The normally distributed data were described as Propensity score matching was performed to control for potential confounders, and the match tolerance value was 0.005. The propensity scores were determined by using age, BMI, age at menarche, age at first pregnancy, number of pregnancies, feeding mode, history of proliferative benign breast disease, history of oral contraceptives, smoking, alcohol consumption, history of hyperthyroidism, and family history of breast cancer. The propensity value calculated according to the logistic regression was matched according to the 1:1 nearest neighbor matching method, and then the two matched groups were regarded as independent groups. The baseline data were statistically analyzed before and after matching. Binary logistic regression analysis was used before PSM, while conditional logistic regression analysis was performed with the help of a Cox regression model in SPSS 24.0 to evaluate the effect of breast size on the risk of breast cancer after matching. A virtual survival time was recorded for each row before and after matching. Survival time was regarded as a time variable, outcome was regarded as a status variable, and the remaining variables were regarded as covariates. The default “case group” had a short survival time, and the “control group” had a long survival time. The odds ratio (OR) for breast cancer was calculated in the highest vs lowest quartile of breast volume as the ratio between the observed prevalences, and it was expressed with a 95% confidence interval.A total of 208 women were included in the case group, and 340 women were included in the control group. PSM was performed with SPSS 24.0, and 185 women were successfully matched after balancing the confounding factors of the two groups of patients.The measured bilateral breast data were averaged, and the breast volume was calculated using the BREAST-V formula. Breast volume was regarded as a continuous variable and the only dependent variable was whether breast cancer was present.U test was used for comparisons between groups. The results showed that, except for age (P = 0.668) and BMI (P = 0.211), the differences in other indicators were statistically significant . The menarche age in the case group was earlier than that in the control group, the age at first pregnancy was later than that of the control group, and the number of pregnancies was less than that of the control group. There were no significant differences in the count data between the groups using the chi-square test  , chi-square test or the Fisher’s exact test between the two groups after PSM. The baseline data between the groups reached equilibrium , and the variance was not uniform (P < 0.001). The Mann-Whitney U test using two independent samples showed a significant difference (P = 0.014) . The odds ratio between the highest and the lowest groups based on quartile groups was 1.515; however, this result was not statistically significant (P = 0.089)  Table . Binary  Table . U test (P = 0.438) (Table P = 0.446)  Table  or condiA Chinese study showed that abortion does not increase the risk of breast cancer, and the latest meta-analysis did not find a relationship between abortion and breast cancer risk . A studyA pooled analysis of 6 prospective cohort studies  indicate2, those with a bra cup size of “D or larger” had a significantly higher incidence of breast cancer than women who reported “A or smaller” , although this association was limited to leaner women [Egan et al.  concludeer women . Some exer women . In addiFor the measurement of breast volume, the gold standard is the water displacement method . Apart fIn the present study, there was no significant difference in age between the case group and the control group before PSM. The reason may be that we excluded the lower age population in the process of collecting the case group data, and we found that the age of the physically examined population ranged from 40 to 70, which is similar to the high-risk age range for breast cancer onset. The reason for the lack of a significant difference in BMI between the two groups may be due to an insufficient sample size, in addition to not having ruled out the existence of Berkson bias. The prevalence of cigarette smoking among women in China is quite low compared to that of males, and our observations were characteristic of smoking in China. Therefore, the recruited participants seem to be a group of very healthy persons.The advantage of this study is that our breast volume data were obtained using a simple and convenient method based on linear measurements of the breast; on the other hand, PSM was used to balance potential confounding between the case group and control group to ensure the comparability of the two groups to a certain extent. Compared with overfitting in traditional multivariate regression, overfitting in this study was reduced to some degree since the propensity score was applied to all the selected patients and finally matched according to the tendency score. In terms of the sample size, given the problem of unsuccessful matching, the control group had more data collected than the case group, which guaranteed the matching success rate to some extent.The limitations of this study are as follows. First, there are inherent biases from this study. It is acknowledged that selection bias will have a strong effect on representativeness and prevalence estimates. To reduce this risk, only patients with incident breast cancer were chosen, while controls were recruited from among healthy persons instead of from among patients’ visitors to reduce confounding by family history. Second, recall bias is a well-known source of systematic error and may lead to spurious associations between exposures and the outcome of interest. In this study, recently diagnosed cases were included, and direct interviews of both the case and control groups were performed by the same trained interviewers to reduce recall bias and improve the accuracy of the information obtained. Third, young women who are breastfeeding have differences in breast volume from those who are not, and older women have reduced breast volume due to fat reduction; in addition, breast size data could not be obtained for patients who had undergone a double mastectomy. Therefore, although this study used PSM, imbalance cannot be completely eliminated, which is one of the shortcomings of this research. Finally, although the hospitals selected have a wide range of radiation, they are limited, and our findings cannot be generalized to the entire Chinese population because all participants were recruited from the same province area.Although breast density is one of the risk factors for breast cancer , 14, regIn this case-control study, we found a significant difference in breast volume between the two group. Regrettably, this difference disappeared after PSM. However, the effect of breast volume on the risk of breast cancer can not be ignored. It is suggested that subsequent research can be carried out with multiple centers and large sample sizes.Additional file 1. Supplementary file 1: Table S1. Questionnaire.