P = 0.005) and untreated (P = 0.01). EC cases with prior LS‐associated cancer more often reported family history of LS‐associated cancer (P = 0.04) and breast cancer (P = 0.05). EC patients with a germline pathogenic MMR gene variant were more likely to report a prior cancer than cases with a MMR proficient tumor (P = 0.0001), but more than half (54.5%) of MMR carriers reported no prior cancer. Women developing EC after tamoxifen treatment for breast cancer were significantly more likely to develop EC of malignant mixed mullerian tumor subtype , present with stage IV disease , and have poorer survival . While report of prior cancer is an indicator of MMR pathogenic variant status, molecular analysis of all ECs at diagnosis is warranted to detect all patients with LS. Results also indicate the importance of longer‐term monitoring of women treated with tamoxifen for symptoms of EC, and the need for studies assessing the biological mechanism underlying the poorer prognosis of this subset of EC patients.We hypothesized that endometrial carcinoma (EC) patients with a prior cancer diagnosis, after accounting for EC arising after tamoxifen‐treated prior breast carcinoma, are more likely to have an underlying genetic basis. We used information from a population‐based study to compare measured risk factors, tumor characteristics, survival, and known mismatch repair (MMR) pathogenic variant status for EC subgroups according to prior diagnosis of cancer ‐associated cancer). Family history of any cancer was increased for EC cases with prior breast cancer, both tamoxifen treated ( The highest incidence rates in 2012 were estimated to be 19.1 and 15.6 per 100 000 in North America and Western Europe, respectively,PTEN tumor suppressor gene causing Cowden Syndrome.Established nongenetic risk factors for EC include age and exposure to exogenous estrogens, or endogenous hyperestrogenic status associated with nulliparity, early age at menarche, late‐onset menopause and obesity.TP53 pathogenic variants) were shown to have a fivefold increased risk of developing a second primary cancer compared to the general population.BRCA1/2 pathogenic variant carriers have reported that women with a first diagnosis of breast carcinoma had a significantly higher risk of developing a pathologically similar contralateral breast carcinomaThere is evidence that individuals with moderate‐high‐risk pathogenic variants in cancer predisposition genes are more likely to develop multiple cancers in their lifetime. The cumulative risk of metachronous colorectal carcinoma was reported to be up to 69% among carriers of pathogenic germline MMR gene variants.We thus hypothesized that report of a prior cancer, after accounting for EC arising after tamoxifen‐treated prior breast carcinoma, might identify women with EC more likely to have an underlying genetic basis. We anticipated that EC patients with a prior cancer diagnosis might be more likely to report a family history of cancer than EC cases with no prior cancer diagnosis. In addition, we considered the possibility that classical epidemiological risk factors for EC (such as obesity) may be less important for EC patients with prior cancer report (and suspected genetic basis). To investigate these questions, we detailed prior cancer report and tamoxifen use for women with EC and controls participating in the population‐based Australian National Endometrial Cancer Study (ANECS). We compared epidemiological risk factors, reported family history, tumor characteristics, survival, and known MMR gene pathogenic variant status for EC subgroups according to cancer types diagnosed prior to recruitment, and for women without EC and no reported personal history of cancer.22.1All ANECS participants provided informed written consent, and approval was obtained from the QIMR Berghofer Medical Research Institute Human Research Ethics Committee, participating hospitals and cancer registries. Details of participant ascertainment, eligibility criteria, questionnaires, and data collection, including blood samples for genetic testing, and assessment of family history have been previously reported.The dataset used for this analysis was based on information for 1399 EC cases and 740 controls who were eligible to participate. Information about report of prior cancer was extracted from questionnaires (cases and controls), and supplemented by information from medical reports ascertained by clinical follow‐up studies, and from information about prior cancer noted in endometrial cancer pathology reports (cases only). Tamoxifen use for individuals with prior breast cancer was verified where possible from breast or EC pathology reports, and/or from clinical records.The detailed breakdown of all prior cancers in controls and EC cases is reported in Table We also analyzed a subset of EC cases with at least one diagnosis of a cancer type falling into the Lynch Syndrome (LS) spectrum (termed LS‐associated) prior to EC diagnosis. Cancers considered to be LS‐associated included cancer of the bile duct, bladder, brain, colon/rectum, duodenum, endometrium, gastrointestinal tract, ovary, pancreas, renal pelvis, and stomach. Of 15 women self‐reporting prior ovarian cancer, only two were determined to be true prior cancers, based on age at diagnosis and information from clinical follow‐up or pathology reports; for 11 women, their ovarian cancer diagnosis was actually concurrent with EC, and EC pathology reports for another two women indicated that their ovaries were normal and intact at hysterectomy for EC. For the purposes of this analysis, women who had ovarian cancer that was concurrent with EC were included among those with no prior cancer unless they had been diagnosed with another cancer prior to their EC diagnosis. A total of 27 EC cases had been diagnosed with at least one prior LS‐associated cancer.In addition to breast and LS‐associated cancers, there were 68 EC patients and 38 controls with reports of other cancer types, with some individuals reporting multiple prior cancer types Table . The rem2.22. Parity, defined as the number of pregnancies ≥6 months, was analyzed as 1 or ≥2 vs 0. Age at menarche was analyzed as 12‐13 or ≥14 vs ≤11. Risk factors for EC, for example, oral contraceptive use, smoking, and ≥3 months use of systemic menopausal hormone therapy  were entered into models as ever vs never use. We also evaluated all patient subgroups for EC risk associated with family cancer history comparing any vs no first‐ or second‐degree relative (FDR or SDR) reporting any invasive cancer, any LS cancer, and breast or ovarian cancer. For patient characteristics analyzed as ordinal variables, we assessed the trend across ordered groups using the nonparametric Cuzick's test for trend.The risk of EC associated with known epidemiologic risk factors was evaluated using age‐adjusted logistic regression models. Using controls reporting no prior cancer at interview (n = 653) as reference, risk estimates were evaluated for: (a) cases with no history of cancer prior to EC diagnosis (n = 1215); (b) cases diagnosed with breast cancer prior to EC who were treated with tamoxifen (n = 68); (c) cases diagnosed with breast cancer prior to EC with no known tamoxifen use (n = 33); and (d) cases who had been diagnosed with at least one LS‐associated cancer prior to EC (n = 27). Age  was entered into regression models as a continuous variable. Body mass index (BMI) was analyzed as a categorical variable comparing women with a BMI 25‐29, and ≥30 to those with BMI <25 kg/mWe used Cox regression models to evaluate overall and EC‐specific survival of cases with a prior breast cancer diagnosis and tamoxifen use, prior breast cancer and no known tamoxifen use, and cases diagnosed with at least one LS‐associated cancer prior to EC, compared to those with no history of cancer prior to EC diagnosis. Cox models were adjusted for the following: age at EC diagnosis as a continuous variable; tumor stage in three categories ; tumor histology in seven categories ; presence/absence of LVSI. Survival time was defined as the interval from date of first treatment for EC to date of death from any cause or due to EC, or censored at 31 December 2013.In addition, we evaluated the relationship between prior cancer and tumor/germline MMR status as previously determinedhttp://www.r-project.org/).All tests for association were two‐tailed, and performed using STATA SE v. 13 , and the R project for Statistical Computing version 3.2.2 (3P = 0.4), or considering individual cancer types. The proportion with reported tamoxifen‐treated breast cancer was nonsignificantly higher in cases compared to controls  increased the proportion of tamoxifen‐treated prior breast cancer among cases to 4.9%). There was no difference between cases and controls for report of prior LS‐associated cancers overall (1.9% vs 2.0%). There was some suggestion that cases were more likely to report two or more prior cancer types compared to controls (1.5% vs 0.7%), but this difference was not statistically significant (P = 0.1).As detailed in Table 3.1P ≤ 0.002). There was no evidence that endometrial cancer was diagnosed at an earlier age for patients with prior LS‐associated cancers .Results are detailed in Table P = 1.4 × 10−39). Oral contraceptive use, older age at menarche, parity, and use of systemic hormone therapy  were inversely associated with EC risk in women with no prior cancers (P ≤ 0.001), and there was a significant trend toward a lower risk of EC associated with older age at menarche (P = 0.0002) and higher numbers of full‐term births . The risk estimates for EC following a prior breast or LS‐associated cancer associated with the risk factors highlighted above were necessarily imprecise given the smaller sample sizes. Nevertheless, they were generally in the same direction as those reported for EC with no prior cancer, with elevated risk associated with increasing BMI, and decreased risk associated with OC use, increasing parity, later age at menarche, and postmenopausal HRT use. Possible exceptions were ever‐smoking, which did not appear to be inversely associated with EC among women with prior breast cancer and tamoxifen use, while age at menarche did not appear to be inversely associated with EC among women with prior breast cancer and no tamoxifen use.Endometrial carcinoma risk in women with no cancer prior to EC diagnosis was associated with known epidemiologic risk factors for this disease.3.2P = 2.5 × 10−4) or any LS cancer (P = 2.7 × 10−4), but no significant increased EC risk associated with having an FDR or SDR with breast cancer (P = 0.3) or ovarian cancer (P = 0.9). Despite small sample sizes, there was marginal evidence that women with prior breast cancer had increased risk of EC if they reported a family history of cancer in close relatives, be that family history of any cancer type , family history of breast cancer , or family history of LS‐associated cancers . All cases with prior LS‐associated cancer reported a family history of cancer, and for this subgroup, there was evidence for increased risk associated with reported family history of LS‐associated cancer  or family history of breast cancer .As shown in Table 3.3P = 1.3 × 10−6), and present with stage IV disease . In accordance with this observation, women with tamoxifen‐treated breast cancer had poorer overall survival  and EC‐specific survival  compared to women with no prior cancer. Women with prior breast cancer and no tamoxifen use or prior LS‐associated cancer exhibited similar survival to those with no prior cancer  or prior LS‐associated cancer were less likely to present with a grade 1 or 2 endometrioid adenocarcinoma than the reference group of EC cases with no prior cancer Table . In part3.4P = 0.0001). Prior cancers reported by pathogenic MMR gene variant carriers included breast cancer with tamoxifen use , prior LS‐associated cancer , and other prior cancer . Patients with MMR tumor deficiency but no MMR germline pathogenic variant identified were slightly more likely to report no prior cancer compared to the reference group of MMR proficient cases, but this difference was not statistically significant .The relationship between MMR status and prior cancer is summarized in Table 4We hypothesized that there may be a genetic contribution to EC risk in patients reporting prior cancer, after accounting for tamoxifen treatment for prior breast cancer. Our baseline comparison of reported prior cancer in controls compared to EC cases supported the known association between tamoxifen treatment for breast cancer and subsequent risk of EC. There was no evidence to suggest that any reported prior cancer, reported breast cancer without tamoxifen treatment, or LS‐associated cancers combined, was more common for cases compared to controls, confirming that reported prior cancer alone is not useful as a generic feature to identify women at increased risk of suspected familial EC.Recognizing that sample sizes for the subgroups of women with prior cancer were small, our results nonetheless suggest that epidemiological factors conferring increased risk of EC have similar directions of effect for EC cases with no prior cancer compared to those with prior breast cancer or LS‐associated cancer. We considered explanations for the possible exceptions to anticipated associations with epidemiological risk factors for women with prior cancer, noting that in all instances the relevant risk estimates included unity. Smoking is thought to reduce circulating endogenous estrogen and so result in a reduced risk of EC, as observed for EC after prior LS‐associated cancer or prior breast cancer in the absence of tamoxifen use. The observation that smoking was not associated with EC among women with prior breast cancer and tamoxifen use might be explained by the fact that exposure to tamoxifen and not endogenous estrogen is the major mechanism for risk in this subset of women. There is no obvious explanation for the observation that women with prior breast cancer and no tamoxifen use did not show an inverse association with increasing age at menarche, especially since this subgroup of women did show a statistically significant inverse EC risk associated with increasing parity. It thus seems reasonable to discount the observation as spurious, reflecting the small sample size for this group.All subgroups of individuals with prior cancer showed an increased proportion of reported family history of cancer (FDR and/or SDR) compared to EC cases with no prior cancer or controls with no prior cancer, even those with tamoxifen‐treated breast cancer Table . EC riskReport of prior LS‐associated cancer is considered an indicator of MMR pathogenic variant carrier status.P = 0.004 any cause, HR 1.91; P = 0.02 EC‐specific). In stark contrast, there was no evidence for a survival difference for women reporting prior breast cancer without tamoxifen treatment or prior LS‐associated cancers (P ≥ 0.3). Notably, although women with prior tamoxifen‐treated breast cancer had a higher prevalence of MMMT tumors compared to those with prior breast cancer and no tamoxifen use, this survival difference was not totally accounted for by poorer prognostic features of EC subtypes  would have minimally biased our overall results toward the null. Further, any bias in data quality should not have impacted case‐case analysis.This study has confirmed the relationship between EC risk and prior tamoxifen‐treated breast cancer, and also previous reports that tamoxifen‐treated breast cancers are more likely to lead to poorer prognosis EC. However, considering all possible sources of information regarding tamoxifen treatment, we also estimate that one‐third of all EC after breast cancer did not appear to be tamoxifen related. Epidemiological risk factors were largely similar across subgroups according to prior cancer report. Supporting our hypothesis that cancer‐causing genetic factors will be enriched in individuals with prior cancer, a family history of cancer was increased for all patient subgroups reporting prior cancer. Surprisingly, this included family history of LS‐associated cancers for EC cases with tamoxifen‐treated prior breast cancer, and family history of breast cancer for individuals with prior LS‐associated cancer. Further, while prior LS‐associated cancer is clearly an indicator of MMR pathogenic variant status, so is report of other prior cancer types. However, 55% of established carriers did not report prior cancer, supporting the increasingly accepted role of universal immunohistochemical or molecular analysis of all ECs at diagnosis to detect patients with LS.An important finding arising from this study is that women developing EC after tamoxifen treatment for breast cancer exhibited poorer survival—even after adjustment for known EC‐related prognostic features. We suggest that it is important to ensure longer‐term monitoring of women treated with tamoxifen for symptoms of EC. We also believe there is scope for studies assessing the biological mechanism underlying the poorer prognosis of this subset of EC patients.None declared. Click here for additional data file. Click here for additional data file.


The role of insulin resistance (IR) in developing postmenopausal breast cancer has not been thoroughly resolved and may be confounded by lifestyle factors such as obesity. We examined whether genetically determined IR is causally associated with breast cancer risk.We conducted Mendelian randomization (MR) analyses using individual-level data from our previous meta-analysis of a genome-wide association study (GWAS) . Four single-nucleotide polymorphisms were associated with fasting glucose (FG), 2 with fasting insulin (FI), and 6 with homeostatic model assessment–IR (HOMA-IR) but were not associated with obesity. We used this GWAS to employ hazard ratios (HRs) for breast cancer risk by adjusting for potential confounding factors.G6PC2 rs13431652; HR = 0.59, 95% CI: 0.35–0.99). Genetically driven FI and HOMA-IR were not significantly associated. Stratification analyses by body mass index, exercise, and dietary fat intake with combined phenotypes showed that genetically elevated IR was associated with greater breast cancer risk in overall obesity and inactive subgroups .No direct association was observed between comprising 12 IR genetic instruments and breast cancer risk . In phenotype-specific analysis, genetically elevated FG was associated with reduced risk for breast cancer (main contributor of this MR-effect estimate: We found complex evidence for causal association between IR and risk of breast cancer, which may support the potential value of intervention trials to lower IR and reduce breast cancer risk. Postmenopausal women have an increased risk of developing breast cancer. Eighty percent of new breast cancer cases occur in women aged 50 and older, and obesity is a well-established risk factor for postmenopausal breast cancer –3. The oThe results of previous epidemiologic studies for the association between IR and postmenopausal breast cancer are inconsistent: rate ratio of HOMA-IR and glucose = 1.50  in a nested case-control study , hazard A Mendelian randomization (MR) analysis may be a better method to address these challenges. It has been used to analyze genetic variants as an instrumental variable to evaluate the effect of an exposure  on an outcome  . This geIn this study, we conducted MR analysis by using our previous genome-wide association study (GWAS) data to test the hypothesis that genetically determined IR has a potential causal effect on postmenopausal breast cancer risk.We used data from our previous meta-analysis of a genome-wide gene-environment (G*E) interaction study , which i−8) as instrumental variables and pruned the list of such SNPs by linkage disequilibrium (LD) (r2 < 0.1). For each SNP, we employed results obtained from this GWAS in multiple Cox regression analysis for breast cancer risk by adjusting for covariates. The covariates were selected for their association with IR and breast cancer from stepwise regression analyses: age, education, family income, family history of breast cancer, depressive symptoms, smoking, exercise, alcohol intake, percentage of calories from saturated fatty acids, body mass index (BMI), waist-to-hip ratio, hysterectomy, ages at menarche and menopause, use of oral contraceptives, exogenous estrogen only, and estrogen plus progestin.Using the meta-analysis of G*E GWAS for IR and breast cancer risk, we identified IR-associated SNPs at genome-wide significance ; 2 independent SNPs associated with fasting insulin ; and 6 independent SNPs associated with HOMA-IR .Before conducting our MR analyses, we checked whether our data met basic assumptions required for valid inference. MR analysis typically assumes that genetic instruments are not weak . We estimated a sum of the T-squared statistics across phenotype-specific SNPs, assessing whether our SNP instruments were well powered for downstream MR analysis. With a threshold of 10, which is commonly used , we consMR studies may be confounded when modeled SNPs exhibit biological pleiotropy, or when SNPs independently affect breast cancer risk through intermediate traits other than IR. To determine the extent of pleiotropic signal in our study we conducted the following analyses. 1) Given obesity’s established role for breast cancer risk, we interrogated for the possible association of obesity  with thebreastcancer / βIR) [Having demonstrated that our genetic instruments are predictive for respective phenotypes and unlikely to be confounded by pleiotropic effects, we performed MR analysis using the inverse-variance weighted method . This qur / βIR) . The resThe heterogeneity of the MR estimate, which is additional evidence of pleiotropy, was evaluated by using Cochran’s Q test. A 2-tailed P value < 0.05 was considered statistically significant. A multiple-comparison adjustment was conducted by using the Benjamini-Hochberg method . R3.5.1 G6PC rs13431652 of the FG SNPs and PABPC1P2 rs77772624 and LINC00460 rs17254590 of the HOMA-IR SNPs were observed at genome-wide significance in the overall and the high-fat diet groups.The 12 IR SNPs identified for the different subgroups in our previous G*E GWAS are presented in G6PC rs13431652 ; and in the inactive subgroup, MTRR/LOC729506 rs13188458 . After a multiple-testing correction, MTRR/LOC729506 rs13188458 remained statistically significant . In the MR analysis of the combined effects of genetic instruments on breast cancer risk by phenotype  and breast cancer risk (adjusted by covariates) identified 2 SNPs whose genetically driven IR phenotype was associated with breast cancer outcome : in the henotype , the pooWe also performed subgroup analyses  stratifiWe performed an MR-Egger test to detect potential directional pleiotropy and found no significant evidence of apparent directional pleiotropy across the tested associations. We further conducted a sensitivity test for the association between genetic instrumental variables for IR and breast cancer risk by replacing current HRs for breast cancer with HRs that were adjusted for age and 10 genetic principal components only; similar results were observed and no evidence of apparent directional pleiotropy was observed.We conducted genetic analyses for IR phenotypes in relation to postmenopausal breast cancer risk in an MR framework, which could establish potential causality. If genetic instruments are not linked to the outcomes through any alternative pathway, the results of an MR study could resemble those of randomized clinical trials  and provG6PC2 rs13431652 was the main contributor of the MR effect estimate of FG on breast cancer. G6PC2 encodes the glucose-6-phosphatase catalytic 2 subunit. It regulates glycemia by opposing the action of glucokinase in pancreatic beta cells, thus modulating glycolytic flux and glucose-stimulated insulin secretion [Particularly, in the phenotype-specific analysis, genetically elevated FG was associated with a reduced risk for breast cancer. Previous prospective  and MR secretion . Individecretion .MTRR/LOC729506 was a strong contributor to the effect of genetically driven FI on increased risk of breast cancer; this association was observed only in the subgroup of inactive women. Mutations in MTRR can induce IR and T2DM in adipose tissue by provoking endoplasmic reticular stress, resulting in inhibited insulin signaling [In addition, our MR analysis of individual genetic instruments of FI indicated that rs13188458 in an intergenic region of ignaling . Previouignaling , but notOur study results should be interpreted with caution because of unmeasured confounding factors that could have introduced bias. MR analysis requires several assumptions, such as LD , weak genetic instruments, pleiotropy, and population structure  . In the MR analysis might also be subject to nonlinearity between exposure and outcome. The association between genetically driven IR and cancer risk may be influenced by the feedback mechanism , resulting in nonlinear processes, but such canalization tends to bias MR estimates toward the null, so it is unlikely to alter the statistical directions or significance . Our stuIn conclusion, we quantified the potential causal relationship between genetically elevated IR and risk of breast cancer and found complex evidence that lifetime exposure to IR is likely to influence the development of breast cancer in postmenopausal women. Further biologic research into this complicated association of IR with breast cancer by incorporating different behavior types may help clarify the mechanisms underlying the associations observed in our study. Nonetheless, our findings may provide additional evidence for conducting intervention trials to lower IR, thus reducing breast cancer risk.


Breast and cervical cancer cases are rising among service and industrial women workers in Vietnam. We conducted a cross-sectional study among 287 workers in three factories in Hanoi and Bac Ninh from July to September 2018 to describe the knowledge of these cancers among industrial workers in Northern Vietnam using a structured questionnaire. Factors associated with knowledge of breast and cervical cancer were identified using generalized linear models (GLM). In our study, approximately one-third of participants believed breast cancer was caused by the lack of breastfeeding, exposure to pollution, and chemicals. Less than 50% knew about sexually transmitted infections that can cause cervical cancer or were aware of a vaccine for cervical cancer. Having one sexual partner within the last year was positively associated with having a higher score of knowledge for both diseases. Receiving a medical checkup within the last 12 months and seeking health information via the internet were related to greater breast cancer knowledge. Targeted education campaigns are needed to ensure proper knowledge and improve awareness of breast cancer and cervical cancer among industrial workers. The World Health Organization estimated that breast cancer impacts 2.1 million women each year . We haveIn Vietnam, the rate of breast cancer has dramatically risen over the previous two decades. According to the result of Global Burden of Disease study, in 2000, the incidence rate of breast cancer was 8.57 per 100,000 women, up to 12.18 per 100,000 women in 2010, and 15.7 per 100,000 women in 2017 . The ageVietnam has had remarkable economic transformations in the past two decades . VietnamThis cross-sectional study was carried out in three factories in Hanoi and Bac Ninh from July to September 2018. Hanoi and Bac Ninh are the two technology development hotspots within the largest industrial zones in Northern Vietnam. We selected a total of 289 participants by convenience sampling according to the following eligibility criteria: (1) at least 18 years old; (2) have current labor contracts with the three aforementioned factories, and (3) have worked at those factories for at least six months. Participants had to have the capacity to communicate with the interviewers. We excluded participants who suffered from severe illness during the recruitment process.Briefly, participants who met the eligibility criteria were asked to be interviewed in the counseling room by a research member. Once participants agreed to participate in the study, they were fully informed about the purpose of the study, benefits, and drawbacks. Participants also signed written informed consents acknowledging their rights and protection of their confidentiality.A 20-minute face-to-face interview was conducted. The data collectors were program staff and researchers who underwent training. Factory staff were not involved in participant recruitment. Before the interview process, a pilot survey was carried out among 20 respondents. To examine the feasibility of recruitment, we selected workers of different ages and genders. Minor modifications in the wording of the standardized questionnaire  were incRespondents self-reported their gender, age, educational level, marital status, immigration status, monthly income, and the number of children. We also asked participants to self-report the number of years of experience in their respective fields and the number of working hours per day.In this study, a series of questions about knowledge about breast cancer and cervical cancer were asked regarding their causes, important signs and symptoms, and prevention methods. We developed a scoring system for the participants’ knowledge about these cancers. The scale Cronbach’s alpha value in both breast cancer knowledge and cervical cancer knowledge were 0.92. The total score of knowledge of breast cancer ranged from 0 to 18 points and knowledge of cervical cancer was from 0 to 19 points.Regarding drinking practices, we interviewed participants based on the Alcohol Use Disorders Identification Test—Consumption (AUDIT-C) . AUDIT-CParticipants’ smoking patterns were identified based on 3 major groups: current smokers, former smokers, and never smokers. Current smoker was defined as an adult who had smoked at least 100 cigarettes during his/her lifetime and had smoked within the previous 30 days. Former smoker was defined as an adult who had smoked 100 cigarettes in his/her lifetime but without smoking in the last 30 days. An adult was classified as never a smoker if he/she had never smoked around 100 cigarettes, or had never smoked, in their lifetime.We collected information on the frequency of receiving medical check-ups and using reproductive healthcare services within the last 12 months, which are common methods of accessing health-related information. Participants’ number of sexual partners in the last 12 months was also collected.p-value of less than 0.05 was considered statistically significant. For detecting multicollinearity, we used vif values. In our study, all vif values were less than 10, which indicated no multicollinearity between the variables.Researchers analyzed the data using STATA 12.0 . Socioeconomic characteristics, knowledge of breast cancer and cervical cancer, health risk behaviors, and health-seeking behaviors were presented. These variables were considered as potential covariates in the multivariate regression models. Factors associated with knowledge of breast cancer and cervical cancer were identified using generalized linear models (GLM). A This study protocol was approved by the Institutional Review Board of Hanoi Medical University (code: 01a-QD/VNCTN).Our study sheds light on the perceived knowledge of breast cancer and cervical cancer, along with their associated factors, among female factory workers in Northern Vietnam. Findings from this study highlighted the low to moderate level of adequate knowledge of breast cancer and cervical cancer among the workers, especially regarding causes and common signs/symptoms of the diseases. Importantly, only half of the respondents were aware of a vaccine that could help prevent cervical cancer. In this study, we observed that medical staff and the internet were the most common sources of health information for participants. We also identified multiple factors associated with having a higher score of knowledge for breast cancer and cervical cancer. Our results provide insights for physicians and policymakers to implement targeted interventions to improve awareness about breast cancer and cervical cancer, especially among factory workers.According to the theory of health behavior change, people will be more likely to engage in changing health behaviors by fostering their knowledge and beliefs to enhance their self-regulation skills and abilities . As knowThis study showed that the internet was considered as the most common source of health information  for breast cancer . HoweverIn this study, a high percentage (77.5%) of participants knew that genital infections were a major cause of cervical cancer. However, less than half of them mentioned certain sexually transmitted infections  and having multiple sexual partners as risk factors for cervical cancer. The limited cervical cancer knowledge may limit participants’ abilities to change lifestyle-related risk factors, as well as to seek prevention methods for cervical cancer . About hOnly half of the participants knew about the human papillomavirus (HPV) vaccine that can reduce the chances of developing cervical cancer. This percentage was lower than those from a study conducted among Vietnamese women, who believed that their knowledge about cervical cancer and HPV vaccines was sufficient to make a decision about vaccination . AnotherSome limitations of this study should be acknowledged. First, self-reporting data may lead to recall bias as well as social desirability bias. Second, cross-sectional study design may limit the ability to identify causal relationships between associated factors and the knowledge of participants. Furthermore, although we had conducted this study in three different factories, the generalization of our results cannot be established due to convenience sampling methods.Despite the limitations described, several implications can be drawn from the research. First, interventions for early detection and reduction of breast cancer and cervical cancer should be expanded to industrial workers, especially interventions that can increase knowledge about the causes and common signs or symptoms of these conditions among female workers. Such interventions may help prevent modifiable risk factors of breast and cervical cancers related to one’s lifestyle. Second, health communication campaigns should focus on people who have not had sexual intercourse or are not sexually active, since we found that these workers are at higher risk of having insufficient breast cancer and cervical cancer knowledge. Third, future education campaigns to ensure proper awareness about HPV vaccination and breast cancer prevention should be developed for factory workers. Finally, the internet should be explored as a medium to combat potentially inaccurate health information about breast and cervical cancer among workers.Our study suggested a low to moderate level of knowledge about breast cancer and cervical cancer, especially regarding the causes and common signs or symptoms of these diseases, among industrial workers. One important finding from this study is also limited awareness of the HPV vaccine to prevent cervical cancer. Developing targeted education campaigns is needed to further ensure a proper understanding of the cancers and their prevention strategies among this vulnerable population.


Sexually transmitted infections (STIs) are associated with pelvic inflammatory disease and tubal pathologies. Given the tubal origin of a proportion of ovarian cancers, STIs may be relevant in their aetiology.Chlamydia trachomatis, Mycoplasma genitalium, herpes simplex virus type 2, and against human papillomavirus oncogenes  were measured in prediagnosis plasma samples in a nested case–control study in the Nurses’ Health Studies (n = 337 cases 1:1 matched to controls). Logistic regression was used to estimate multivariable-adjusted relative risks (RRs) and 95% confidence intervals [CIs] comparing women seropositive vs. seronegative among all cases (invasive and borderline), invasive (n = 257), and invasive serous ovarian cancers; n = 170), and borderline ovarian tumours (n = 80).Antibodies indicating past infection with C. trachomatis seropositivity was associated with higher risk of ovarian cancer overall (RR = 2.07 [1.25–3.43]); results were similar for invasive, invasive serous, and borderline tumours. We observed no associations for the other STIs. Relative to women seronegative to all infections, strongest associations were observed for seropositivity to C. trachomatis plus another STI ; however, the RRs were not significantly different.C. trachomatis infection may increase ovarian cancer risk; additional studies are required. Chlamydia trachomatis and Mycoplasma genitalium and viral infections like human papillomavirus (HPV) and herpes simplex virus type 2 (HSV-2), can induce persistent changes in the female genital tract. C. trachomatis and M. genitalium are associated with pelvic inflammatory disease (PID), salpingitis, and tubal infertility.3 PID was associated with increased ovarian cancer risk in a recent meta-analysis,4 providing indirect evidence for a role for STIs in the aetiology of the disease. STI-induced tubal pathologies may be relevant given that a proportion of ovarian tumours likely originate in the fallopian tube,6 with tubal involvement or precursor serous tubal intraepithelial carcinomas (STICs) observed in up to 70% of high-grade serous ovarian cancers.7Sexually transmitted infections (STIs), including bacterial infections such as C. trachomatis and ovarian cancer risk are suggestive of an association12; data on other STIs are sparse.12 Infection with mucosal high-risk types of HPV  is recognised as a cause of cervical and other anogenital malignancies.13 Incidence of STIs is rising in many developed countries, with 1.6 million reported cases of C. trachomatis in the United States in 2016.14 There are relatively few modifiable risk factors for ovarian cancer; STI prevention would provide a target for primary prevention of this often-lethal disease. To investigate the association between STIs and ovarian cancer risk, we conducted a nested case–control study on seropositivity to C. trachomatis and M. genitalium, HSV-2, and multiple proteins of HPV16, HPV18, and HPV45, with risk of borderline ovarian tumours (BOT) and invasive epithelial ovarian cancer (iEOC) in the Nurses’ Health Study (NHS) and NHSII among 337 cases and 337 matched controls.Prior retrospective studies, and a single prospective study, on 15 The NHSII began in 1989 with 116,429 female registered nurses ages 25–42 years, using the same approach. Participants in both cohorts have been followed biennially to update information on lifestyle factors and ascertain disease diagnoses.The NHS was initiated in 1976 when 121,700 registered nurses, ages 30–55 years, completed and returned a mailed questionnaire.16 A second collection among 15,982 women (ages 44–68 years) was carried out between 2008 and 2011. Similar methods were used for all collections.19 Heparin plasma samples have been stored in liquid nitrogen freezers since collection. This study was approved by the Institutional Review Board of the Brigham and Women’s Hospital .In 1989–1990, 32,826 NHS participants provided a blood sample (ages 43–70 years); in 2000–2002, a subset of 18,743 of these participants (ages 53–80 years) provided a second sample. Between 1996 and 1999, 29,611 NHSII participants (ages 32–54 years) provided blood samples.n = 271; NHSII, n = 66) were diagnosed through June 1, 2016 (NHS) and June 1, 2015 (NHSII). Cases were matched to one control, who was alive and had intact ovaries at the time of case diagnosis (see Eligible cases were diagnosed with confirmed incident ovarian cancer (i.e. BOT or iEOC) after first blood collection. A total of 337 cases ) into subgroups with higher vs. lower antibody levels using the median in all C. trachomatis positive women (2668 MFI) as the cut point.In addition to evaluating the individual infections, we compared women seropositive for n = 53). We evaluated heterogeneity by rapidly fatal disease (died within 3 years of diagnosis) and less aggressive disease (survived at least 3 years following diagnosis) comparing models allowing vs. not allowing the association for the STI to vary by disease aggressiveness using a likelihood ratio test.24Conditional logistic regression was used to estimate relative risks (RRs) and 95% confidence intervals [CIs] for ovarian cancer overall. Unconditional logistic regression, adjusted for the matching factors, was used in analyses restricted to BOT, iEOC, and serous iEOC; there were too few cases of other histotypes  to assess separately , oral contraceptive (OC) use , tubal ligation , marital status , and weight change between ages 18 and blood collection ; these variables were selected a priori based on their association with ovarian cancer or STI prevalence. Additional adjustment for family history of breast and/or ovarian cancer, non-steroidal anti-inflammatory or aspirin use , or depressive symptoms  did not change effect estimates by ≥10%.n = 127) or who were nulliparous (n = 84) were excluded in sensitivity analyses. Statistical analyses were conducted in SAS 9.4 . All statistical tests were two sided, and p < 0.05 was considered statistically significant.Potential effect modification by menopausal status at blood collection and diagnosis , age at blood collection (<60 vs. 60+ years), and ever OC use was evaluated by including interaction terms and evaluating the Wald test. Women reporting tubal ligation (n = 257), and 66% of invasive cases were of serous histology (n = 170)  and predominantly postmenopausal (69%). Cases were diagnosed at median age of 68 years (range: 37–89); 76% were diagnosed with invasive disease  .Seropositivity to at least one STI was observed in 25% of the study population; seropositivity to s) Table . Eight pC. trachomatis infection was associated with a two-fold increased risk of ovarian cancer (RR: 2.07 [95% CI: 1.15–3.43]); results were similar for invasive (1.98 [1.21–3.23]) and invasive serous disease (2.31 [1.33–4.01]), and BOT (2.11 [1.04–4.28]) . No significant association for antibodies to other individual infections and ovarian cancer risk was observed (p ≥ 0.11) including in the HPV L1 analyses (data not shown). M. genitalium had a suggestive positive association despite the low seroprevalence (1.92 [0.78–4.72]).Seropositivity to C. trachomatis plus the other infections and ovarian cancer risk. Relative to women seronegative to all evaluated infections, seropositivity to C. trachomatis and any other infection was associated with a 2.74-fold higher risk of ovarian cancer (95% CI: 1.20–6.27), whereas C. trachomatis alone was associated with a 1.88-fold higher risk (95% CI: 1.03–3.42) .We next evaluated antibodies to C. trachomatis, RR: 1.94 [1.17–3.21]; Table M. genitalium and ovarian cancer risk (RR: 3.40 [1.25–9.27]). We observed no significant differences in the associations by menopausal status at blood collection or diagnosis, age at blood collection, or ever OC use. Further, the associations did not differ significantly for rapidly fatal and less aggressive disease (p ≥ 0.18).Associations excluding women with a tubal ligation were generally similar , though effect estimates were not significantly different. Associations were similar or slightly stronger when restricting to cases with invasive serous disease; this association is notable given that relatively few risk factors have been identified for this subtype. Our associations were robust across different subsets of women, including those with a prior tubal ligation and parous women.This study suggests that past/current infection with C. trachomatis may directly affect ovarian carcinogenesis as it induces DNA double-strand breaks, interferes with the DNA damage response,25 and inhibits apoptosis in the host cell.26 Importantly, C. trachomatis and M. genitalium in particular may indirectly influence ovarian cancer development through increasing inflammation in the genital tract. Specifically, these infections are an important cause of salpingitis and other tubal pathologies,27 including tubal damage-inducing PID.28 This may be particularly relevant for the serous histotype, as the fallopian tube has been identified as the origin of the majority of these tumours,6 with STICs as a suggested precursor lesion.29 Inflammation, and inflammation-related sequelae, are likely major mechanisms linking STIs and PID to higher ovarian cancer risk, and inflammation31 and its downstream impact on immune suppression may play a role in risk of ovarian cancer regardless of subtype. A recent meta-analysis of 13 studies reported a significant association between history of PID infection and ovarian cancer risk (RR: 1.25 [1.06–1.44]), though this association was limited to borderline disease , and no histology-specific estimates were provided.4 Price et al. recently estimated probabilities ranging from 12% to 16% of C. trachomatis infection leading to clinical PID.32 No data on PID or self-reported infection status were available in our study; thus we could not investigate this as a potential mediator.11 have assessed different IgG antibodies (e.g. heat shock protein 60—type 1 (cHSP60-1),8 elementary bodies of serovar D10). Results from these studies are mixed, in part, because the antibodies have higher cross-reactivity with other chlamydial and bacterial infections than the gold standard Pgp3-based antibody. Further, it is unclear whether having ovarian cancer or being under treatment may alter the expression of IgG antibodies. More recent data from Trabert et al. using the same laboratory methods as this study reported a positive association between antibodies to C. trachomatis infection and iEOC risk in a population-based case–control study in Poland (n = 244 cases) and in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial (n = 159 cases).12 In the latter study (as in our study), blood samples were collected prior to diagnosis of ovarian cancer, reducing impact of disease status  on immune functioning.Retrospective data on history of chlamydia infection and ovarian cancer riskC. trachomatis using the laboratory cut point). In the current study, we did not observe that, among seropositive women, higher titres of C. trachomatis antibody levels were more strongly associated with risk. While higher antibody levels may be associated with severity or number of past infections or with more severe tubal damage,33 antibody levels in response to an infection are a function of many other factors including age at infection and individual-level immune response. Further research is needed to better understand how antibody titres relate to disease severity and carcinogenic-related damage and inflammation to the fallopian tubes and ovaries.In the PLCO study, a significant association was only observed when a higher cut point was used to define Pgp3 positivity ; elevated cut point based on Poland study, RR = 2.25 [1.07–4.71]), whereas in the current study, a two-fold increase in risk was observed using the laboratory cut point (RR = 2.07 [1.25–3.43]). This may, in part, be due to the larger sample size in our study or underlying differences in the study populations  in the fallopian tube following infection.35M. genitalium seropositivity was associated with borderline, but not invasive, tumours in one small study,8 and the expression of viral microRNA, predominantly mapped to HSV-1 and HSV-2 genomes, was observed to be higher in serous ovarian carcinomas than in normal tissue.36 Trabert et al. observed a 47% higher risk of iEOC among women seropositive to M. genitalium in the Poland case–control study, but no association in PLCO; HSV-2 was also not related to risk in that study.12M. genitalium is thought to be a cause of PID and warrants further study in larger populations with a higher seroprevalence. HPV oncoproteins promote genomic instability and proliferation and inhibit apoptosis,37 and high-risk HPV types are causally linked to cervical cancer, other anogenital cancers, and cancers of the head and neck.13 These infections have been minimally explored in relation to ovarian cancer but, in general, have not been related to ovarian cancer development,38 suggesting that HPV does not infect ovarian or fallopian tube cells.We observed no significant association between antibodies to C. trachomatis and ovarian cancer risk appeared stronger when a woman was also seropositive to M. genitalium or HSV-2. This may reflect higher incidence of tubal pathologies following infection with multiple STIs or, alternatively, may be the sequelae of a constellation of other factors . A limitation of our study is the lack of data on covariates such as sexual history and history of PID and/or other pelvic diseases and their treatment, thus it is possible that our results are, in part, due to residual confounding by these factors. As no studies have examined the relationship of sexual history with ovarian cancer risk, it is unclear whether this may be an important confounder.The association between This study assessed antibodies indicating history of STI infection using a validated assay in blood samples collected median 7 years prior to diagnosis. Antibodies are relatively stable in blood, thus we expect misclassification of exposure to be minimal and non-differential. However, while we know that an infection was present at or at some time prior to blood collection, and thus diagnosis, we cannot assess when the infection initially occurred, or whether subsequent infections manifested in the interval between blood collection and diagnosis, or what proportion of women with negative serology had history of an infection but did not seroconvert. We expect these issues be similar among women who developed ovarian cancer after blood collection and women who remained cancer-free and thus would result in non-differential misclassification of the exposure.C. trachomatis infection, with strongest associations observed among women seropositive to C. trachomatis and another STI . Additional experimental studies to establish causality, epidemiologic studies including data on PID to investigate this potential mediator, and larger epidemiologic studies to evaluate disease subtype are needed to confirm these associations. If these results are confirmed, it would suggest that STI prevention and early detection efforts, including vaccine development, may represent an opportunity to reduce ovarian cancer risk.In summary, in a large, prospective epidemiologic study, we observed an increased risk of ovarian cancer overall and of the serous histotype among women seropositive for Supplemental Materials and MethodsSupplemental Tables S1 and S2


A posteriori DPs or M-HPs were derived with a Principal Component Analysis (PCA). Three DPs: ‘Non-Healthy’, ‘Prudent’, and ‘Margarine and Sweetened Dairy’ and two M-HPs: ‘Metabolic-Syndrome’ and ‘High-Hormone’ were identified. The ‘Polish-adapted Mediterranean Diet’ (‘Polish-aMED’) score was calculated. The risk of breast cancer risk was three-times higher (odds ratio (OR): 2.90; 95% confidence interval (95% Cl): 1.62–5.21; p < 0.001) in the upper tertile of the ‘Non-Healthy’ pattern (reference: bottom tertile) and five-times higher  in the upper tertile of the ‘High-Hormone’ profile (reference: bottom tertile). There was a positive association of ‘Metabolic-Syndrome’ profile and an inverse association of ‘Polish-aMED’ score with the risk of breast cancer, which disappeared after adjustment for confounders. No significant association between ‘Prudent’ or ‘Margarine and Sweetened Dairy’ DPs and cancer risk was revealed. Concluding, a pro-healthy diet is insufficient to reduce the risk of breast cancer in peri- and postmenopausal women. The findings highlight the harmful effect of the ‘High-Hormone’ profile and the ‘Non-Healthy’ dietary pattern on breast cancer risk. In breast cancer prevention, special attention should be paid to decreasing the adherence to the ‘Non-Healthy’ pattern by reducing the consumption of highly processed food and foods with a high content of sugar and animal fat. There is also a need to monitor the concentration of multiple sex hormones in the context of breast cancer risk.Breast cancer is the most diagnosed cancer in women worldwide. Studies regarding complex breast cancer aetiology are limited and the results are inconclusive. We investigated the associations between dietary patterns (DPs), metabolic-hormone profiles (M-HPs), and breast cancer risk. This case-control study involved 420 women aged 40–79 years from north-eastern Poland, including 190 newly-diagnosed breast cancer cases. The serum concentration of lipid components, glucose, and hormones  was marked in 129 post-menopausal women . The food frequency consumption was collected using a validated 62-item food frequency questionnaire.  Cancer is the second-leading cause of death worldwide . GloballBreast cancer aetiology is composed of many factors, including: age, genetic, reproductive, hormonal, and lifestyle factors, of which diet is of particular interest ,5. Therea posteriori analyses, mainly Principal Components Analysis (PCA) or Factor Analysis (FA), to identify dietary patterns [a priori approach, like the Mediterranean diet score [An approach that is focused on individual foods or nutrients to assess the association with cancer does not include the complex interactions between the various components of foods . Anotherpatterns ,11,12,13et score ,17,18,19et score ,17. Theset score ,21,22.In addition to the dietary factors, breast cancer is linked to metabolic and hormone-related factors, including endogenous sex hormone levels. The contribution of some hormones, especially oestrogens and androgens ,24,25,26Given that breast cancer is an etiologically complex disease, comprised of lifestyle, molecular, and metabolic factors, a transdisciplinary approach is the key to understanding the mechanisms linking diet, metabolic syndrome, and hormones with cancer. Previous studies have examined the association of dietary patterns ,20,21,22The aim of the study was to assess the associations of dietary patterns, including ‘Polish-adapted Mediterranean Diet’ (‘Polish-aMED’) score and metabolic-hormone profiles (M-HPs) with breast cancer risk in women from north-eastern Poland. Here, we further investigated these observations by also taking into consideration many potential confounders, including hormone receptor status.The study was approved by the Bioethics Committee of the Faculty of Medical Sciences, University of Warmia and Mazury in Olsztyn on 2 October 2013 (resolution no. 29/2013). All of the subjects gave their written informed consent to participate in the study, including to blood sample collection and to use clinical data for research.The present study was conducted in 2014–2017 among adult women from north-eastern Poland. The study protocol with a case-control design was developed. In total, the cancer-control sample involved 420 subjects, aged 40.0–79.9 (mean 59.9 SD 8.6) years.The cancer sample involved 190 women with a newly diagnosed (primary) and histologically confirmed breast cancer (invasive or in situ) identified by codes C50.0–50.9 of the International Classification of Diseases for Oncology . The timThe control sample involved 230 women who were excluded from breast cancer based on mammography (MM) and/or ultrasonography (USG) of the breasts. The time from cancer exclusion to subject recruitment in the study and data collected did not exceed six months b. ControBreast cancer diagnosis and pathologic characteristics of the tumours were confirmed through surgery results and oncology consultant reports. Information that was related to the hormone receptor status was obtained for tumours with available results of the immunohistochemical analyses of the breast cancer tissue in the medical record . These dDietary data were collected using a validated and interviewer-administered version of the 62-item Food Frequency Questionnaire (FFQ-6) . The wida priori: low (0–2 points), average (3–5 points), and high (6–8 points).The ‘Polish-adapted Mediterranean Diet’ (‘Polish-aMED’) score is a Polish version of the Mediterranean diet (MED) score described by Fung et al. . In deveWe limited the blood sample collection to the selected 129 postmenopausal women aged 45.0–79.9 (mean 62.1 SD 8.2) years, including 47 breast cancer cases and 82 controls. Cancer and control sub-samples were chosen through a convenient and non-random selection, without a significant difference in age or BMI. Blood samples from cases were collected after breast cancer diagnosis and prior to surgery or therapy initiation a. All wo®, Franklin Lakes, NJ, USA) between 8:00 a.m. and 10:00 a.m., all according to standardized procedures. Blood samples were allowed to stand for 30 min at 22 °C, and were then centrifuged at 3000 rpm for 10 min at 22 °C, and the separated serum was then obtained. For all analyses, laboratory staff were blind to the case-control status of samples. All methods were fully automated with automatic calibration and performed at the Laboratory of Biochemical Studies of Nutritional Status in the Department of Human Nutrition at the University of Warmia and Mazury in Olsztyn.At baseline, a 12 mL fasting blood sample was obtained from participants via venipuncture  and was collected in two 6 mL pre-labelled and red top tubes , according to the manufacturer’s instructions. Glucose was measured enzymatically with the hexokinase method. Enzymatic-colorimetric tests were used to determine TG, TC, and HDL-C. The low-density lipoprotein cholesterol (LDL-C) concentrations were calculated using the Friedwald’s formula. Serum endogenous hormones: oestradiol, progesterone, prolactin, and testosterone and cortisol and insulin concentrations were measured in serum samples with electrochemiluminescence immunoassays (ECLIA) using an automated immune-analyser Cobas e411 (Roche Diagnostics®). The minimum detectable concentrations (MDC) were as follows: glucose 4.32 mg/dL, TG 8.85 mg/dL, TC 3.87 mg/dL, HDL-C 3.0 mg/dL, oestradiol 5.0 pg/mL, progesterone 0.03 ng/mL, prolactin 0.047 ng/mL, testosterone 0.025 ng/mL, insulin 0.2 µU/mL, and cortisol 0.054 µg/dL. Women with hormone concentrations below the MDC  were assigned the value of the minimum level of detection.Glucose, triglycerides (TG), total-cholesterol (TC), and high-density lipoprotein cholesterol (HDL-C) concentrations were measured in serum samples using a Cobas Integra 400plus auto-analyser  with varimax rotation was applied to identify dietary patterns (DPs) and metabolic-hormone profiles (M-HPs) . Two sep2 test  or a Kruskal-Wallis test (continuous data). The association between the frequency consumption of 21 food groups and the ‘Polish-aMED’ score was evaluated using Pearson’s correlation coefficients. The percentage distribution of breast cancer cases was compared by tertiles or levels of DPs and M-HPs using the Pearson Chi2 test with Yates’ correction as necessary. All serum biomarker concentrations were log-transformed to normalize their variable distributions and Student’s t-test was then used to compare them between cases and controls. Logistic regression analysis was used to assess the associations of DPs or M-HPs with breast cancer risk. The odds ratio (OR) and 95% confidence interval (95% CI) were calculated. The references (OR = 1.00) were the control sample and the bottom tertile or lowest level of each DP or M-HP. Four models were created: unadjusted model, model 1—adjusted for the potential confounders mentioned above, model 2 and model 3—fully-adjusted models for the same confounders included in model 1 and for M-HPs Score or DPs Score, respectively (excluding the modelled variable from confounders set). The level of significance of the odds ratio was verified with the Wald’s test [p-value < 0.05 was considered to be statistically significant.Differences in baseline characteristics between cases and controls were verified with a Pearson Chid’s test . The staa priori ‘Polish-aMED’ score with the frequency consumption of: nuts and seeds, wholemeal cereals, fruit, legumes, vegetables, fish, and with the ratio of vegetable oils-to-animal fat, and a negative correlation with the frequency consumption of red and processed meats regarding components of the ‘Polish-aMED’ score  and higher in the upper tertiles of the ‘Non-Healthy’ DP (46.3% vs. 22.6%), ‘Metabolic-Syndrome’ (44.7% vs. 32.8%) and ‘High-Hormone’ Profiles , serum concentration of TG ≥ 150 mg/dL (20.0% vs. 7.4%), and HDL-C < 50 mg/dL (26.0% vs. 9.9%), as well as log of TG/HDL ratio ≥ 0.5 (18.0% vs. 6.2%) and LDL/HDL ratio ≥ 3.5  and four-times higher . A one-point increment in the ‘Non-Healthy’ DP score increased the risk of cancer by 32%  and 40% . The one-point increment in the ‘Polish-aMED’ score decreased the risk of cancer by 14% . This association disappeared after the adjustment  and six-times higher . A one-point increment in the ‘High-Hormone’ Profile score increased the risk of cancer by 6%  and 7% . A one-point increment in the ‘Metabolic-Syndrome’ Profile score increased the risk of cancer by 1%  [A unique strength of this study is the novel approach in investigating the breast cancer aetiology by taking into account both many hormones and metabolic components as overall metabolic-hormone profiles. This holistic approach obtained results regarding the combined effects of these biomarkers on breast cancer risk. Furthermore, we have included a number of potential confounders in the fully adjusted model, including socioeconomic, reproduction, lifestyle, clinical factors, and dietary pattern scores. Another strength of the study broadly assesses the respondents’ diets as a whole by identifying dietary patterns using two methods (teriori) ,46. To cteriori) . Lastly,teriori) . Thus, tThere are some study limitations, including its case-control design and sampling bias. A non-random sample selection may reduce the external validity of the study. The close matching of the controls with cases may cause an association existing in real life to be overestimated . HoweverThe study revealed that pro-healthy diet is insufficient to reduce the risk of breast cancer in peri- and postmenopausal women. These findings provide interesting insights into the strong harmful effect of the high adherence to the ‘High-Hormone’ profile and the ‘Non-Healthy’ dietary pattern on breast cancer risk, independently of many potential confounders, among women from north-eastern Poland.The results may improve the understanding of the complex aetiology of breast cancer, related to diet, hormone, and metabolic association and may prove useful in establishing primary preventive strategies. In the prevention of breast cancer, special attention should be paid to decreasing adherence to the ‘Non-Healthy’ pattern by reducing the consumption of highly processed food, foods with a high content of sugar and animal fat. There is also a need to monitor the concentration of multiple sex hormones in the context of breast cancer. Further, large prospective studies are needed to confirm the results on the role of diet, hormones, and metabolic syndrome in the aetiology of breast cancer, stratified by menopausal and hormone receptor status.


There is a growing interest in delivering more personalised, risk-based breast cancer screening protocols. This requires population-level validation of practical models that can stratify women into breast cancer risk groups. Few studies have evaluated the Gail model (NCI Breast Cancer Risk Assessment Tool) in a population screening setting; we validated this tool in a large, screened population.lifepool cohort) participating in Australia’s BreastScreen programme. We investigated the association between Gail scores and future invasive breast cancer, comparing observed and expected outcomes by Gail score ranked groups. We also used machine learning to rank Gail model input variables by importance and then assessed the incremental benefit in risk prediction obtained by adding variables in order of diminishing importance.We used data from 40,158 women aged 50–69 years  although there was some overestimation of cancer risk in the top decile of our study group . Women in the highest quintile (Q5) of Gail scores had a 2.28-fold increased risk of breast cancer  compared with the lowest quintile (Q1). Compared with the median quintile, women in Q5 had a 34% increased risk  and those in Q1 had a 41% reduced risk . Similar patterns were observed separately for women aged 50–59 and 60–69 years. The model’s overall discrimination was modest (area under the curve (AUC) 0.59, 95% CI 0.56–0.61). A reduced Gail model excluding information on ethnicity and hyperplasia was comparable to the full Gail model in terms of correctly stratifying women into risk groups.Over a median of 4.3 years, the Gail model predicted 612 invasive breast cancers compared with 564 observed cancers (expected/observed (E/O) = 1.09, 95% confidence interval (CI) 1.00–1.18). There was good agreement across decile groups of Gail scores (χThis study confirms that the Gail model (or a reduced model excluding information on hyperplasia and ethnicity) can effectively stratify a screened population aged 50–69 years according to the risk of future invasive breast cancer. This information has the potential to enable more personalised, risk-based screening strategies that aim to improve the balance of the benefits and harms of screening.The online version of this article (10.1186/s13058-018-1084-x) contains supplementary material, which is available to authorized users. National guidelines and programmes for universal age-based breast cancer screening were established in many countries following trials showing reduced breast cancer mortality –4. HowevRisk-stratified screening protocols require accurate estimates of risk using data that can be readily obtained by population-based programmes. The Gail model –16 is relifepool cohort comprises 53,800 women recruited since 2010 primarily from the Australian population-based mammography screening programme to facilitate research into breast cancer screening, epidemiology, and genetics. Using data from baseline questionnaires, we generated Gail risk estimates for active breast cancer screening participants in the historical target age range for screening (50–69 years) and compared predicted and observed risk of incident invasive breast cancer. In addition, we evaluated risk estimates from reduced Gail models, assessing the incremental benefit obtained by adding variables to the model in order of diminishing contribution to risk estimation.The Lifepool commenced recruitment in May 2010, restricted to women aged at least 40 years at enrolment. Up to January 2015, recruitment was primarily through an invitation included in appointment letters for women attending subsequent rounds of screening at the BreastScreen programme based in the Australian state of Victoria (BreastScreen Victoria). Other methods of recruitment were publicity at women’s health events, referrals by participants to friends and family, and inclusion as a research project on the national database Register4 [lifepool participants complete a detailed ‘baseline’ questionnaire capturing socio-demographic, lifestyle, and health-related information. Further details on the cohort including the questionnaire and other material can be found on-line (http://www.lifepool.org). The lifepool cohort is regularly linked to BreastScreen Victoria records and to the Victorian Cancer Registry to update information on the occurrence of any cancer diagnosed within the state of Victoria.egister4  in July Lifepool also provided linked data comprising: 1) BreastScreen Victoria screening episodes up to 27 June 2017 with information on screening dates and cancer diagnoses ; and 2) Victorian Cancer Registry breast cancer diagnoses  and, for women with any cancer registration, death records . Lifepool also provided participant withdrawals and ad hoc death notifications and cancer diagnosis outside Victoria. Data provision is described in Additional file Complete questionnaire data were provided for this study for all participants who completed baseline questionnaire data up to 11 September 2016. Gail risk scores were assigned using the source code available on the National Cancer Institute website , which gWe did not use the ‘family membership’ field in the Gail model source code designed for generating scores for groups of women (which would combine risk information from identified family members in the study group) as this information was unavailable in our data. Most race/ethnicity categories within the Gail model did not map to the ethnic profile of Australian women; as a best approximation, women who self-reported any Asian ethnicity were assigned to the Gail category ‘Asian-American’ (relabelled to ‘Asian’) and all other women to the category ‘White’ (labelled ‘Mixed’).We generated Gail 5-year probability of breast cancer (‘scores’) for each woman and compared incident invasive breast cancer outcomes by quantile groups of risk (partitioned by group-level quintiles and/or deciles), for three age ranges (50–69/50–59/60–69 years). Hazard functions were censored to diagnosis (invasive or in situ), death, or 31 December 2016 (whichever occurred first). Quantile groups (i.e. quintiles and deciles) were generated for each age range analysed to reflect how the Gail model would assign women to risk groups if used on specific age groups. Receiver operating curves (ROC) were generated for outcomes against continuous Gail scores for women with a minimum follow-up period of 3 years. To compare observed and estimated diagnoses, we generated the Gail predicted probability of breast cancer for each woman for her observation period by linear interpolation between annual-year Gail estimates. Of note, the order of Gail scores does not change with the specified duration of future risk so that women would be ranked the same if we described 1-year, 5-year or 10-year risk. However, the expected number of cancers in this study are dependent on the follow-up time for each woman, so that women with the same rank of baseline risk but different observation periods (e.g. 3 years versus 6 years) would have a different probability of a cancer being observed during the follow-up period. We then summed these observation period-based probabilities for each Gail 5-year risk quantile group to generate the expected number of cancers within that group, and compared this with the observed number of cancers using chi-squared tests and ratios of expected to observed cancers  calculated as for Constantino et al. ). StatisN = 6131) comprising a representative balance of cases (women who developed breast cancer) and controls (women who did not develop breast cancer) and a corresponding training set  weighted to have a ratio of 1:9 cases to controls. The model was trained on each training dataset and validated on the corresponding test dataset, generating 100 ranks of variable importance which were then combined in a single ranking of variables according to the number of times each variable appeared in that ranking. Gail scores were calculated for each model by step-wise addition of variables according to that ranking (Models 1–8), with these scores then categorised into quantile groups and then evaluated under a hazards framework as for the whole model.We evaluated Gail models using a reduced number of input variables, starting with the most important predictor of cancer risk in this cohort as identified using a machine learning approach. To maximise information to train and validate machine learning, we extended the dataset to all ages and women with invasive cancer diagnosed at the baseline mammogram Fig. . The eigA total of 40,158 women (75% of the cohort) were included in our analyses. Major exclusions were: 2806 women who resided outside the state of Victoria at the time of completing their questionnaire because their subsequent diagnoses were unlikely to appear on Victorian screening and cancer registry records; 988 women who were not linked to screening records; 3085 women who did not have a baseline screening mammogram within 60 days of completing their questionnaire; and 169 women with a personal history of breast cancer prior to their reference screen. We excluded a further 262 women who had had a breast cancer diagnosis (205 invasive and 57 DCIS) at their reference screen, and 5965 women outside the historical BreastScreen target age range of 50–69 years at their reference screen for logistic regression analyses . No women remaining in the sample had a LCIS diagnosis at or prior to their reference screen. Additional exclusions are presented in Fig. During a median follow-up of 4.3 years, 564 women 1.4%) were diagnosed with invasive breast cancer . E/O did not differ significantly for quantile groups Q1–Q4 and D9; however, the Gail model overestimated risk for women in decile group D10 , leading to a net overestimation in group Q5 . Similar patterns persisted within age groups 50–59 and 60–69 years .Observed and expected diagnoses are shown as rates according to decile groups of Gail model-predicted 5-year risk in Fig. p < 0.001; Q2 vs Q3: 0.63, 95% CI 0.47–0.84, p = 0.001) and those above the highest quintile had a 34% increased risk .Hazards ratios for invasive cancer incidence by Gail model 5-year risk quantile groups are shown in Table p < 0.0001). Hazard functions followed a significant trend across risk groups . The ROC area under the curve (AUC) using continuous Gail scores was 0.59 (95% CI 0.56–0.61) for women aged 50–69, 0.59 (95% CI 0.55–0.62) for women aged 50–59, and 0.57 (95% CI 0.54–0.60) for women aged 60–69 years.When compared with women with the lowest scores (Q1), the risk of invasive cancer increased by quintile group, being statistically significant for group Q3 and above. Group Q5 had a 2.28-fold increased risk compared to Q1 (hazard ratio (HR) 2.28, 95% CI 1.73–3.02, Machine learning models ranked the importance of Gail model variables as ordered in Table Comparing outcomes arising within a maximum of 6.5 years follow-up, we found that women aged 50–69 years within the highest quintile of Gail risk scores (Q5) had more than double the risk of invasive breast cancer compared with women in the lowest quintile (Q1). Compared with women in the median-risk group (Q3), Q1 had a 40% reduced risk and Q5 a 34% increased risk of incident invasive breast cancer. This suggests that the existing Gail model is suitable for assigning women into groups at significantly different risk of invasive breast cancer in the 5 years following a negative screen.We found good overall agreement between expected and observed cases of invasive breast cancer, confirming absolute risk estimates over an average of 4.3 years of follow-up except for women in the upper decile of Gail scores; while these women were appropriately classified as the highest-risk group, their absolute Gail risk scores overestimated the observed outcomes  per 100,000 women born in north-east Asia compared with 120 (107–133) per 100,000 women born in the USA or Canada) .Using machine learning, a reduced model resulted in hazard ratios comparable to the full Gail model, suggesting that a simplified model  could be equally effective in this population while saving significant effort and resources. Unsurprisingly, the stepwise addition of the variables ‘had biopsy’ made little difference since the number of biopsies was already included. The ethnicity variable would hold more value if the Gail model was modified to suit Australian ethnicity categories.The modest discriminatory accuracy of the Gail model (AUC = 0.59) is consistent with a recent meta-analysis of European validation studies (pooled AUC =0.58) , confirmThis study has various strengths. Analyses are based on data from a large prospective cohort of actively screened participants, with questionnaires completed during 2010–2014 and outcomes recorded up to end 2016, and therefore results are highly relevant to contemporary screening populations and programmes. Cancer outcomes were identified through direct linkage with cancer registrations, and screening histories by direct linkage with the screening programme. We accounted for censoring by using hazards models, and we report outcomes for groups based on quintile and decile values to demonstrate potential applications for this tool not only to identify women at very high risk of breast cancer but also to identify women at medium and reduced risk of breast cancer.lifepool cohort and these women may be more willing and/or able than other BreastScreen participants to provide the information required for the Gail model.Our study has several limitations. Firstly, we did not have records of cancers diagnosed outside the state of Victoria, although these are likely to be few. Secondly, we did not have complete death records. Based on Australian deaths data  . Since its inception, the Gail model has been modified to account for the variation in breast cancer risk observed in various populations [lifepool cohort data, optimally combining clinical, genetic, mammographic density, and behavioural risk factors. We will also report outcomes for younger and older women, by mode of detection , and incidence of DCIS as the lifepool cohort matures.As appropriate for validating a predictive tool, our analysis excluded from our study group women with a breast cancer diagnosis at or prior to their ‘baseline’ ulations –25. Riskulations , 34 and ulations , 38. FutThe findings from this study indicate that the Gail model, or a simplified version of this model, is an effective tool for stratifying active breast cancer screening participants aged 50–69 years to groups according to risk of invasive breast cancer diagnosed up to 5 years following risk assessment.Additional file 1:Table S1. Details of data provided by lifepool for this study. (DOC 33 kb)


Women who had more episodes of non-lactational mastitis had a higher risk of developing a breast cancer. Thus, the risk of breast cancer in women with non-lactational mastitis is significantly higher than those without non-lactational mastitis.Little is known regarding the association of non-lactational mastitis women with breast cancer risk. This population-based cohort study examined the breast cancer risk in women with non-lactational mastitis. We identified 3,091 women with non-lactational mastitis between 2000 and 2011 using the Taiwan National Health Insurance Research Database. We performed 1:4 propensity score matching by age, socioeconomic status and comorbidities and identified 12,364 women without non-lactational mastitis. The mean age of women with non-lactational mastitis was 37.9 years; these women had a higher breast cancer risk than the comparison group . The incidence rates of breast cancer in women with non-lactational mastitis and the comparison group were 14.79 and 7.57 per 10,000 person-years, respectively. Furthermore, non-lactational mastitis was a risk factor for breast cancer in women aged <50 years, women with lower socioeconomic status and women with hormonal medication ( Treatment for breast cancer has improved, such that early treatment of breast cancer is associated with a good survival rate3. It is important to identify women who have a higher risk of breast cancer to ensure that they can take necessary precautionary measures.Breast cancer is the most commonly diagnosed malignancy, accounting for 1 in 4 cancer cases among women worldwide4. Non-lactational mastitis is a breast inflammatory condition in non-breastfeeding women. The two major entities of non-lactational mastitis are periductal mastitis and idiopathic granulomatous mastitis and both of them primarily affect young women8.Mastitis refers to breast inflammation, comprising lactational and non-lactational mastitis. Lactational mastitis constitutes acute inflammation of the breast in connection with pregnancy or breastfeeding, and it occurs in 2–10% of breastfeeding women11. Consequently, various infectious and inflammatory diseases are strongly associated with a high risk of malignancy in the corresponding organs15. However, whether or not women with non-lactational mastitis, as opposed to those without non-lactational mastitis, have a higher risk of breast cancer remains unknown. Therefore, the aim of this population-based cohort study was to examine the breast cancer risk in women with non-lactational mastitis using a national database of Taiwan. We also evaluated the risk of breast cancer among women with different numbers of episodes of non-lactational mastitis.Little is known regarding the risk of breast cancer in women with non-lactational mastitis. Owing to many studies supporting strong associations between chronic inflammatory conditions and tumorigenesis, a link between inflammation and cancer has been recognizedhttp://nhird.nhri.org.tw/en/index.htm). All diagnoses in the database are coded in accordance with the International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM).Taiwan National Health Insurance (NHI) provides universal insurance coverage and is a single-payer system with the government as the sole insurer. It covers more than 99% of the Taiwanese population of 23.4 million people and has contracts with 97% of hospitals and clinics in Taiwan. Data regarding registration files and medical claims for all beneficiaries are linked through encrypted identification numbers in the National Health Insurance Research Database (NHIRD) . Because we used the encrypted NHIRD dataset, the IRB determined that informed consent was not needed. Our research was performed in accordance with relevant guidelines and regulations.Lactational mastitis in the current study was defined as mastitis that occurred in women within 1 year of giving birth to a baby. Non-lactational mastitis was defined as mastitis that occurred in women beyond 1 year after giving birth to a baby, or in women who had never given birth to a baby.Figure 16, hypertension17, chronic obstructive pulmonary disease18, thyroid disease20, diabetes21, hyperlipidaemia22 and obesity23.Comorbidities and hormonal medications were identified using ICD-9 codes from the NHIRD. Comorbidities that have been reported as potential risk factors for breast cancer include schizophreniaWe used income-related insurance payment amounts as a proxy measure of individual SES. The SES was classified into four groups: (1) lower than US$528/month ; (2) between US$528 and US$960/month ; (3) between US$960 and US$1526/month ; (4) greater than US$1526/month . We selected US$528/month  as the lowest income level for the SES cut-off point because this was the government-stipulated minimum wage for a full-time employee in Taiwan.If a woman had more than one occurrence of non-lactational mastitis and the treatment interval was longer than 3 months, the second occurrence was defined as new non-lactational mastitis. We measured the occurrence of non-lactational mastitis per year among women in non-lactational mastitis cohort. We divided this occurrence data into three tertiles and analysed the risk of breast cancer.We used the same procedure, excluding women with non-lactational mastitis, to generate the lactational mastitis cohort and analysed the breast cancer risk of women with lactational mastitis (Supplementary).24. Differences in each variable in the non-lactational mastitis and comparison cohorts were calculated by using the standardised mean difference (SMD)26; an SMD of <0.1 was considered to indicate a negligible difference25. We described the grouped age, comorbidities and medications in the two cohorts as numbers and percentages; we presented the mean ages as means and standard deviations.Four control women were matched to each woman with non-lactational mastitis who had a similar propensity score, based on nearest neighbour matching without replacement using calliper width within 0.1To estimate the risk of breast cancer in the non-lactational mastitis and comparison cohorts, hazard ratios (HRs), adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) were evaluated by using crude and adjusted Cox proportional hazard models, respectively. All statistical analyses were performed using SAS statistical software, version 9.4 . The figure for the cumulative incidence curve was plotted by R software. Significance was indicated by a two-sided p-value of <0.05.A total of 3,091 women with non-lactational mastitis were enrolled in this study   can cause the activation of inflammatory pathways33. Chronic inflammation is further compounded by potentially malignant transformation37. Repeated occurrence of non-lactational mastitis in women may indicate that they are in a status of long-term exposure to these PAMPs that damages their breast and drives their breast cancer risk higher. However, further studies are needed to reveal the association of non-lactational mastitis with breast cancer. Our findings may serve as an epidemiological support for population-based observations.Our results showed that the risk of breast cancer in women increased significantly as the occurrence of non-lactational mastitis increased. Non-lactational mastitis is known to be related to chronic inflammation secondary to infection-induced cytokines38; individual studies have been unable to reveal an association between non-lactational mastitis and breast cancer in women because they have been limited by either small sample size or cross-sectional design.The strength of this study is that it was a nationwide, population-based study. The NHI covers 99% of Taiwan’s population and has nearly complete medical records for the entire population. The results of this population-based cohort study are noteworthy, as non-lactational mastitis is not a very common disease, and the breast cancer incidence is approximately 188–194 per 100,000 women in Taiwan40. To reduce the misclassification of lactational mastitis as non-lactational mastitis, our cut-off for non-lactational mastitis was 1 year after giving birth to a baby. Third, residual and unmeasured confounding factors might exist; lifestyle-related variables (e.g. smoking and diet), family history and parity status are not recorded in the NHIRD. However, we were able to adjust for SES, comorbidities and hormonal medication, which are known risk factors for breast cancer. Finally, the follow-up period was short; however, the significance of non-lactational mastitis as a risk factor for breast cancer in the younger age group was dominant.There were some limitations in this study. First, ICD codes were used to determine the diagnosis of mastitis. However, there is not a specific code to identify the entity and aetiology of non-lactational mastitis in the ICD-9 coding system. Second, this study did not identify when women stopped breastfeeding after delivery of a baby. In the literature, the median duration of breastfeeding is reportedly 7–12 monthsIn summary, this study showed a significant risk of breast cancer in women with a history of non-lactational mastitis. Moreover, their risk of breast cancer was higher with increased frequency of non-lactational mastitis. The majority of the population in this study was in the younger age group (<40 years), and the follow-up period was short; therefore, further studies should be performed in a population with a wider age range and a longer follow-up period.Risk of breast cancer in women with non-lactational mastitis