Attributions of causality are common for many diseases, including breast cancer. The risk of developing breast cancer can be reduced by modifications to lifestyle and behaviours to minimise exposure to specific risk factors, such as obesity. However, these modifications will only occur if women believe that certain behaviours/lifestyle factors have an impact on the development of breast cancer.The Breast Cancer, Environment and Employment Study is a case-control study of breast cancer conducted in Western Australia between 2009 and 2011. As part of the study 1109 women with breast cancer and 1633 women without the disease completed a Risk Perception Questionnaire in which they were asked in an open-ended question for specific cause/s to the development of breast cancer in themselves or in others. The study identified specific causal beliefs, and assessed differences in the beliefs between women with and without breast cancer.The most common attributions in women without breast cancer were to familial or inherited factors (77.6%), followed by lifestyle factors, such as poor diet and smoking (47.1%), and environmental factors, such as food additives (45.4%). The most common attributions in women with breast cancer were to mental or emotional factors (46.3%), especially stress, followed by lifestyle factors (38.6%) and physiological factors (37.5%), particularly relating to hormonal history.While the majority of participants in this study provided one or more causal attributions for breast cancer, many of the reported risk factors do not correspond to those generally accepted by the scientific community. These misperceptions could be having a significant impact on the success of prevention and early detection programs that seek to minimise the pain and suffering caused by this disease. In particular, women who have no family history of the disease may not work to minimise their exposure to the modifiable risk factors. Breast cancer is one of the most commonly diagnosed invasive cancers and it accounts for a quarter of the female cancers diagnosed annually in Australia. In WestStudies worldwide have identified many factors to which women with breast cancer attribute their condition, including stress and other psychosocial factors; knocks, bruises or injury to the breast; religious causes; chemicals, food additives; proximity to electronic equipment or overhead power lines; viral or bacterial infection; and bad luck–9, althoThis study aimed to investigate perceptions about breast cancer causation in a study of women participating in a case-control study of breast cancer. Specifically, we aimed to investigate whether women attributed specific cause/s to the development of breast cancer in themselves or in others; what these causal beliefs might be; and to explore whether there may be differences in beliefs between women with breast cancer and women without the disease.Understanding how women attribute breast cancer causation is important information for health promotion, including breast awareness and promotion of screening programmes, clinical care and policy development. Causal attributions are known to affect the responses of women to health messages and their adoption of various behaviours aimed at risk reduction, including mammographic screening, 14. A pThis paper describes the beliefs regarding breast cancer causes held by women in Western Australia with and without the condition.Data for this study were collected as part of the Breast Cancer, Environment and Employment Study (BCEES), a case-control study of breast cancer conducted in Western Australia between 2009 and 2011–18. WithConsenting BCEES participants, 1205 with breast cancer (response fraction: 57.8%) and 1789 without breast cancer (response fraction: 41.1%), completed an initial questionnaire which comprised sections on demographics, reproductive history, family history of breast cancer, sleep habits, employment history, lifetime physical activity, lifestyle, and pesticide exposure. Information about breast cancer stage and treatment was not sought.Of the eligible women who did not participate, those who refused were older compared with those who did not respond. Case women who did not respond were more likely to live in very remote areas (4% compared with 2% who refused). There were no differences in socio-economic status between women who participated and those who did not.After completion and return of the initial questionnaire participants were mailed a Risk Perception Questionnaire (RPQ). The RPQ consisted of a series of possible exposures and the woman was asked if each exposure increased the risk of breast cancer, decreased it, had no effect or they didn’t know [paper submitted].The RPQ also included one of two possible open-ended questions: case women were asked "What do you believe caused your breast cancer?" while control women were asked "What do you believe causes breast cancer?". We allowed space for approximately 8-10 sentences. The RPQ was completed and returned by 1109 (92.0%) of case participants and by 1633 (91.3%) of control participants.Written responses from the RPQ were converted into digital text using Cardiff TeleForms® software and reviewed by project staff for accuracy and completeness.As there were 37 separate possible exposures listed in the main section of the RPQ, this paper describes only the responses to the single open-ended question. The quantitative data are the focus of a separate paper currently under review.The text data were analysed using NVivo 9 , a qualitative data management software package. The responses for each person were initially reviewed by a single investigator (AT), and words or phrases from the text were coded within a scheme based on concepts identified from a range of sources, 19–24, After the initial review, the BCEES study team reviewed the categories, some example responses and any uncategorised text in order to further define and refine the coding definitions. A total of 37 categories were identified (including a category for ‘don’t know’ responses), representing six major themes. Respondents who wrote that they didn’t know, but then listed a number of possible causes were coded for the specific factors rather than ‘Don’t know’.The coding definitions were then validated by two independent raters coding a random sample of responses. Discrepancies between raters were discussed with the BCEES study team and consensus was sought.Analysis was conducted on the six higher level categories representing major themes in the data. These are described below:Included breast density and size, reproductive and hormonal profile, injury, disease and infection, immune system imbalance, sleep disturbance, medications, age, gender, and diagnostic delay.Included environment, chemicals, food additives, radiation, domestic and personal care products, passive smoking, pesticides, and shiftwork.Included family history and inheritable genetic mutations, and a universal gene or cell that could turn malignant.Included stress, mental illness, and other psycho-social aspects.Included alcohol, poor diet, overweight/obese, hormone replacement therapy, physical inactivity, smoking, substance abuse, and unhealthy lifestyle.Included fate, religious causes and (bad) luckThe modifiable/lifestyle category comprised the factors which fall under personal control. All other higher-level categories were considered to contain non-modifiable factors.In the results below, quotations in the respondent’s exact words are used to illustrate causal attributions. The examples were chosen to demonstrate themes and are not necessarily representative of all respondents. Some editing has been necessary due to the length of some replies.Descriptive analyses were conducted using Stata 12 .Ethics approval for this project was received from the Human Research Ethics Committees of The University of Western Australia and the Department of Health Western Australia. All participants provided written informed consent.Case women were slightly younger, more likely to have a family history, and were more likely to have been born outside Australia and to have completed a university education than control women Table . More thIn the open ended questions Table , case wo"My inability to deal with a stressful situation over many years resulting in high anxiety and sleeplessness…compounded by a sense of powerlessness over my ability to change the circumstances causing the stress". "Excessive stress, anxiety and lack of sleep leading to neglect in diet and lack of exercise and consumption of alcohol – binge drinking". "Weakening of the immune system caused by stressful events…. making my body lose its natural balance + trigger the cancer". Almost half of the case women attributed their condition to some aspect of mental state, with a majority specifically citing stress. For many participants, specific stressful life events, including family-life or work-life, were conceptualised as ‘triggers’. Respondents often described a string of events or processes, sometimes extending over many years, that played a causal role in their diagnosis with breast cancer. Control women were less likely than case women to attribute the disorder to mental factors and stress. However, of those control women who did report stress as a casual factor, it was framed in similar terms to the case women."…I feel that everyone has a cell that contains cancer but some things trigger that cell to expand, maybe food, chemicals, pesticides or stress". "Imbalance in the body cells causes the cancer to become active; hereditary; certain type of genes carrying cancer cells". The attribution of breast cancer to familial/genetic factors was the most commonly identified cause among control women, a proportion considerably higher than that for case women Table . ControlA substantial number of participants supplied one or more modifiable or lifestyle factors as breast cancer causes Table . The mos"…HRT increases the risk, as does alcohol consumption, obesity, smoking, lack of regular exercise". "Lack of physical exercise resulting in weight gain in recent years. Stress; HRT" Women from both groups typically listed several different factors from this category:"… – alcoholic soft drinks – a diet high in sugar…" "…a lot of red meat, no veges and healthy foods…exercise…" Many women also referred to specific foods, both insufficient and in excess. These references were generally combined with a range of other factors:"…the modern environment with chemicals everywhere and in everything, including food, clothing, home, drink (water & manufactured)....polluted air…" "…the cumulative effect of pesticides sprayed on the fruit and vegetables and gardens (went into drinking water). Genetically modified food, growth hormones in animal products, stress". Slightly more control women reported one or more environmental factors as risks for the disease, compared with case women Table . Environ"…Also I think factors such as power lines & mobile phone towers play a big part for any type of cancer". "…a network computing cabling box sits in that room [work office]....the radiation given off from that electromagnetic field must be enormous". A number of participants also attributed breast cancer development to radiation and more often mentioned non-ionising radiation, such as low frequency magnetic fields, than ionising radiation, such as X-rays and gamma (γ)-radiation:"I accidently bumped my breast several times and I believe the result was a small lump that developed into cancer". Case women were more likely to report one or more physiological agents as a cause of breast cancer than were control women Table . The mosAlmost 80% of participants provided one or more causes of breast cancer in response to the open-ended question on the RPQ. This finding is consistent with other studies on breast cancer from Australia and Canada, 21, andper se. Case women will generally have had some discussion with their medical practitioners in an attempt to pinpoint the aetiology of the disease and may have tried to identify a specific cause for their own disease. Previous studies have shown that women with cancer of varying types tend to attribute their disease to fewer agents than they recognise as causal factors for cancer in general[A large proportion of respondents described more than one causal agent. However, case women gave fewer suggested causes for breast cancer and more often provided a ‘Don’t know’ response, compared with control women. This may reflect the fact that case women were asked what they thought caused their own disease rather than the cause of breast cancer  general.The range of factors cited by respondents was comparable to those reported in previous studies, both those that asked open-ended questions, 21 and In common with numerous other studies, 36, 37 The attribution of breast cancer development to inherited or genetic factors was the most commonly identified cause among control women, a finding previously reported by others, 24. ForBRCA gene mutations convey a much a higher risk to women who carry them, only a small number of women have the affected genotype, and known inheritable genetic mutations account for less than 5% of breast cancers with other familial risk bringing this to about 9% of breast cancer cases[While the er cases, 39. TheThe most frequent attribution within the modifiable/lifestyle category by case women was HRT (13.7%). HRT has been identified as a risk factor for breast cancer, and this discovery was the subject of widespread media coverage and policy debate, 41. GivWithin this category, control women were more likely to attribute breast cancer to lifestyle factors such as diet, alcohol, smoking, or lack of exercise. Alcohol consumption is a recognised risk factor for breast cancer, 42, andSome lifestyle factors were referred to, by both case and control women, as protective rather than causative: more vegetables and exercise, less fat, red meat and alcohol, and keeping a healthy weight. However, with the exception of low alcohol consumption, sufficient exercise and avoiding weight gain, there is little evidence that specific lifestyle factors protect against breast cancer development, 47.Slightly more control women reported one or more environmental factors as risks for breast cancer. However specific chemicals were rarely identified by participants, who generally referred to broad factors such as ‘pollutants’, ‘toxins’ or ‘additives’ in describing environmental risks.A number of chemicals found in the environment, such as benzene (found in vehicle exhausts), are known carcinogens although with no definite connection to breast cancer, 42. SomExposure to ionising radiation, such as x-rays, is a known risk factor for cancers of all types, but no This study used an undirected, open-ended question to elicit the widest possible range of views as to breast cancer causation, similarly to some studies, and in The relatively low response fraction for BCEES may have resulted in an unrepresentative sample. However, the similarity of the findings from this study with previous work across the globe encourages the belief that BCEES participants adequately represent the views of the wider population.The inclusion of possible risk factors, such as power lines, in the 37-item checklist section of the RPQ which preceded the open-ended question, may have stimulated reporting of these items, to the detriment of other risk factors, such as age and gender, that did not form part of the checklist.Overall, many of the risk factors cited by the participants do not correspond to those generally accepted by the scientific community. Some ofWhat is clear however, is that there is a discrepancy between attributed and scientifically-recognised risk factors and that this may have a significant impact on the success of prevention and early detection programs that seek to minimise the pain and suffering caused by this disease. Specifically, the inflated perception of genetic factors as a cause of breast cancer amongst women without breast cancer is a potential threat to the success of prevention and early detection programs. There is a need for research to identify what impact this poor understanding of the role of genetics may be having on mammographic screening, and a role for health promotion in raising awareness of the true role of genetics in breast cancer risk. In addition, the relatively poor recognition of the causative role of some modifiable lifestyle factors is a significant barrier to effective public health campaigns aimed at disease prevention.There is a need for health promotion campaigns to raise awareness and improve knowledge of the scientifically-recognised risk factors, in order to allow women to make informed choices about their health and healthcare options. There is a role for additional research to quantify the impact of these misperceptions on breast cancer risk and for health promotion to improve knowledge and awareness of risk factors with a strong evidence base.


Background. Although individual metabolic risk factors are reported to be associated with breast cancer risk, controversy surrounds risk of breast cancer from metabolic syndrome (MS). We report the first systematic review and meta-analysis of the association between MS and breast cancer risk in all adult females. Methods. Studies were retrieved by searching four electronic reference databases  and cross-referencing retrieved articles. Eligible for inclusion were longitudinal studies reporting associations between MS and breast cancer risk among females aged 18 years and older. Relative risks and 95% confidence intervals were calculated for each study and pooled using random-effects models. Publication bias was assessed quantitatively (Trim and Fill) and qualitatively (funnel plots). Heterogeneity was examined using Q and I2 statistics. Results. Representing nine independent cohorts and 97,277 adult females, eight studies met the inclusion criteria. A modest, positive association was observed between MS and breast cancer risk . No publication bias was observed. Conclusions. MS is associated with increased breast cancer risk in adult women. Breast cancer, the most common cancer in women worldwide, accounted for 1.7 million new cases in 2012, comprising a quarter of all new cancer cases .Metabolic syndrome (MS) is a cluster of pathophysiological disorders comprising central obesity, insulin resistance, high blood pressure, and dyslipidemia. Reaven's definition of MS in 1988  was follMS has been identified as a risk factor for several cancers, particularly breast, pancreatic, colorectal, and prostate cancers –15. IndiPrevious epidemiologic studies on MS and breast cancer risk show contrary results. For example, only four , 43, 51 A recent systematic review and meta-analysis of MS and postmenopausal breast cancer found that MS was moderately associated with the risk of postmenopausal breast cancer . However a priori inclusion criteria for this study were as follows: (1) observational studies using cohort (both prospective and retrospective), case-control, or nested case-control study designs; (2) studies examining the association between MS  and breast cancer incidence, as defined by the authors; (3) studies with adult females ≥ 18 years of age as participants; (4) English-language studies published as journal articles, doctoral dissertations, or masters' theses; (5) published and indexed studies up to June 30, 2012; and (6) studies reporting sufficient data  for calculating a common effect size. Neither lobular carcinoma in situ nor ductal carcinoma in situ breast cancer cases were excluded from the study.TheStudies not meeting all inclusion criteria were excluded from this review. Excluded studies were those that (1) were not published as full reports, such as conference abstracts and letters to the editors; (2) only examined individual components of MS; (3) measured the MS variables at time of cancer diagnosis; (4) used cancer mortality, rather than incidence, as the outcome; and (5) were published in a language other than English.A comprehensive and systematic search was conducted using four electronic databases: PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science, and ProQuest . Since the term MS dates back to the late 1950s, with variations in use as early as the 1920s, the start dates of each of the databases were used as the commencement date for study search: Web of Science (1900), CINAHL (1952), PubMed (1966), and ProQuest (1861). In addition, cross-referencing from retrieved studies was also performed. Major keywords used in the search for potentially eligible studies included “metabolic syndrome”  and “breast cancer” (“neoplasm and breast”). Using the most recent publication, trials published as duplicate reports  were only included once. All electronic searches were conducted using the graphical user interface for each database. The last search was conducted on June 30, 2012. An initial cut-off point for the inclusion of studies was not used given the difficulty in establishing such a point, as well as our concern about the potential loss of studies that met our eligibility criteria.At the first screening, one author (RB) screened all abstracts and selected articles for full-text examination. At the second level of the study selection process, two of the authors (RB and TH) examined the full-text articles and then selected the included studies following mutual discussion and consensus.k) statistic [Two of the authors (RB and TH) reviewed every study selected and independently extracted data from studies onto electronic coding forms. These forms could hold up to 52 items per study. Attempts were made to contact authors of three of the original studies for missing information , 62, 64,tatistic , the oveRisk of bias was assessed using a modified version of Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist . The iteRisk estimates were used to examine the association between MS and risk of breast cancer. These were derived from reported relative risks, odds ratios, hazard ratios, incident rate ratios, or standardized incidence ratios, together with corresponding 95% confidence intervals (CIs), from the original studies. Where necessary and possible, all metrics were converted to risk ratios (RRs). Adjusted risk estimates were pooled for analysis from multivariable models in the original studies. However, for two case-control studies that were included , 51, adjz-score two-tailed alpha value ≤ 0.05 was considered to be statistically significant. In addition, 95% CIs were calculated for each result from each study as well as for pooled estimates. Heterogeneity was calculated using the Q [I2 statistics [Q statistic was considered to be evidence of statistically significant heterogeneity. While somewhat arbitrary, I2 values of 25%, 50%, and 75% were considered to represent low, moderate, and high amounts of heterogeneity [N test was used to compute the number of missing null studies that would be needed to nullify the overall pooled RR as being statistically significant [p ≤ 0.05) were considered to be outliers.All RR results were pooled using a random-effects model, an approach that incorporates between-study heterogeneity into the model . A z-scong the Q  and I2 atistics . An alphogeneity . Publicaogeneity . In addinificant . Statist post hoc: (1) deletion of results from two case-control studies because odds ratios were used instead of RR [ post hoc data analysis was also conducted with studies that included participants with diabetes and/or taking medications for diabetes, deleted from the model [Influence analysis was conducted with each study result deleted from the model once, in order to examine the effects of each on the overall pooled results. Cumulative meta-analysis, ranked by year, was also conducted in order to examine the accumulation of results over time. A separate pooled analysis, limited to postmenopausal women, was conducted because studies show that MS in postmenopausal women increases the risk of breast cancer , 51, 62.ad of RR , 51, (2)ad of RR , 14, 51,ad of RR , 48, 51.he model , 64. Allhe model . in situ breast cancer cases but there were only seven such cases in that study [in situ and invasive) as well as invasive cancers separately, and results remained unchanged [A general description of the included studies is shown in at study . Anothernchanged .Risk of bias results are shown in z = 3.13; p < 0.002; Q = 26.28, p < 0.001; I2 = 69.55%) (p = 0.06–0.82).Overall, a statistically significant increase of 47% in the risk for incident breast cancer was observed for adult females with MS  . With thz = 1.98; p = 0.05; Q = 14.13, p = 0.01; I2 = 64.61%). When limited to studies with only prospective designs, the RR decreased by 30% but remained statistically significant with very low heterogeneity . When limited to postmenopausal women, breast cancer risk increased by 34% and was still statistically significant with high heterogeneity . When limiting the results to studies that controlled for four or more of the important confounders . Lastly, when data were analyzed after deleting from the model those studies that had participants with diabetes or taking medications for diabetes [z = 1.61; p = 0.11; Q = 17.4, p = 0.02; I2 = 76.96%).With each study deleted from the model once, results remained positive and statistically significant . The poodiabetes , 64, theThe purpose of this aggregate data meta-analysis was to examine the association between MS and the risk for breast cancer in adult females. Overall, the results suggest that there was a modest positive association between MS and risk of breast cancer. This finding is strengthened by the robustness of results from other analyses. These include (1) examination for publication bias, (2) influence analysis with each study being deleted from the model once, (3) deletion of the two case-control studies with odds ratios from the overall model, (4) limiting the analysis to prospective designs, (5) including only postmenopausal women in the analysis, and (6) limiting the results to studies that controlled for four or more of the important confounders. In addition, the results from cumulative meta-analysis, ranked by year, indicate an increasingly statistically significant association since 2011. In contrast, despite a slightly increased mean RR, overlapping CIs were observed when studies that included participants with diabetes or taking medications for diabetes were deleted from the model , 43, 64.Assessment for risk of bias indicated that a majority of studies were at low risk regarding study design, cancer assessment, and sample size. However, a majority were at high risk or unclear risk in terms of handling of missing data and nonparticipation of subjects at each stage of follow-up. It is suggested that future studies provide complete information on the handling of missing data and on the nonparticipation of subjects at each stage of follow-up.When limited to postmenopausal women, a stronger association between MS and breast cancer was observed. This association was stronger in case-control and retrospective cohort study designs compared to prospective cohort study designs. These findings concur with those from a recent meta-analysis on MS and breast cancer risk in postmenopausal women . Several in vitro through several mechanisms [There are several potential mechanisms linking MS with an increased risk of breast cancer. First, obese postmenopausal women produce higher levels of estrogens, which in turn increase the biologically available fraction of circulating estradiol by reducing plasma concentration of sex hormone binding globulin (SHBG) . Low plachanisms . It can chanisms . Insulinchanisms , therebychanisms .The increasing prevalence of MS and its association with breast cancer, among other comorbidities, point toward the critical need to develop public health strategies to manage MS. Given the increasingly large global burden of metabolic risk factors, even a small association with breast cancer can have a substantial public health impact. Risk assessment tools can be developed which incorporate MS as a risk factor for breast cancer. Healthcare providers will then be better equipped to identify high-risk women for primary and secondary prevention.This study has several strengths. First, to the best of our knowledge, this is the first systematic review and meta-analysis examining the association between MS and risk of breast cancer in all adult women. The analysis incorporates all women and a subanalysis of postmenopausal women. The overlapping meta-analysis on metabolic syndrome and breast cancer was confined to postmenopausal women only . Second,This study also has several potential limitations. These include (1) the different methods used to assess exposure, identify cancer, control for confounders, and define MS, (2) limiting studies to those published in English, which may have inflated the results , (3) the in situ and invasive cancers separately in relation to metabolic syndrome. Finally, a focus on obese women with respect to MS and breast cancer seems appropriate.In order to inform and undergird a biological rationale for the observed positive association between MS and breast cancer risk in adult females, future research should comprise analyses based on a standard definition of MS and employ objective and standard biomarkers for assessing each MS component. In addition, adjustments for all important potential confounders need to be made. It would be helpful if future studies examined the relationship between MS and breast cancer risk separately in perimenopausal and premenopausal women since breast cancer in women may be estrogen-independent. Along those lines, not all studies adjusted for hormone replacement therapy, a potential confounder. Future studies should report this information. Furthermore, they need to examineIn conclusion, the overall results of this meta-analysis suggest that there is a modest positive association between MS and risk of breast cancer in adult females.


Background. The incidence of premenopausal breast cancer is rising throughout South Asia. Our objective was to determine the role of risk factors associated with Westernization for premenopausal breast cancer in Bangladesh. Methods. We conducted a matched case-control study between January 1, 2007, and December 31, 2010, at four hospitals in Bangladesh. Cases were premenopausal women diagnosed with invasive breast cancer. Controls were premenopausal women with no personal history of breast cancer. Logistic regression was used to calculate the odds ratios (OR) for breast cancer. Results. We identified 129 age-matched pairs. The mean age of breast cancer diagnosis was 37.5 years. Each year decrease in the age of menarche significantly increased the risk of breast cancer . The risk was also increased with a current body mass index of ≥25 kg/m2 . Age at first childbirth, parity, and breastfeeding were not significantly associated with premenopausal breast cancer risk (P > 0.05). Conclusions. Age at menarche and adult weight gain were associated with premenopausal breast cancer risk. Other factors associated with Westernization may not be relevant to premenopausal breast cancer risk in Bangladesh. The World Health Organization (WHO) estimates that more than 60% of new cancer cases occur in low- and middle-income countries of Africa and Asia as well as Central and South America , 2. ThesThe adoption of western lifestyles and changes in diet has led to an increase in the number of overweight and obese women, as well as changing reproductive patterns, such as an earlier menarche, delayed childbearing, low parity, and decreased breastfeeding –7. TheseTo date, most epidemiological studies have evaluated risk factors for breast cancer in western populations . The epidemiology of breast cancer in most Asian populations is less well understood. Recent studies from several East Asian countries  have shown that women in these countries increasingly share risk factors for breast cancer with women from western countries –12. In cBangladesh is a low-income country situated in South Asia, bordered by Burma, India, and the Bay of Bengal . In receThis multicenter, hospital-based, case-control study was conducted between January 1, 2007, and December 31, 2010, at four hospitals in Bangladesh. Of the four study sites, three are located in urban Bangladesh and include the Dhaka Medical College Hospital, Bangabandhu Sheikh Mujib Medical University, and Anowara Medical Services. The fourth study site, the Amader Gram Breast Care Program, is located in city of Khulna and mostly caters to rural patients.in situ or invasive) were also not eligible for this study. Cases were recruited from the inpatient and outpatient departments of four study sites.We defined a case as a premenopausal woman aged 25 years or older who was diagnosed with invasive breast cancer in the 12 months prior to study enrollment. Women were considered to be premenopausal if they had reported a menstrual period within the 12 months prior to their breast cancer diagnosis. Women who had not experienced a menstrual period for 12 consecutive months or women who had a hysterectomy were considered postmenopausal and were excluded from the study. Women who had previous history of breast cancer . We defined four categories of BMI according to the World Health Organization guidelines [2, (2) normal range, 18.50 to 24.99 mg/m2, (3) overweight, ≥25 mg/m2, and (4) obese ≥30 mg/m2. For both cases and controls, we relied on self-reported values of weight in order to calculate the BMI at the age of 18 years. Interviews were conducted by a member of the Bangladesh-based research team.We developed a study questionnaire to record information on demographic, reproductive, and anthropometric factors. The questionnaire collected information on date of birth, income (Bangladeshi taka per month), education, age at menarche, age at first childbirth, total number of children, duration of breastfeeding (in months), current height (meters), weight at the age of 18 years (kg), and weight at the time of breast cancer diagnosis . For each study subject we calculated body mass index (BMI) by the weight in kilograms divided by the square of the height in meters . For all odds ratios we calculated 95% confidence intervals (CI). We performed bivariable (unadjusted) and multivariable (adjusted) analyses. A P value of 0.05 or less was considered statistically significant. All P values were two-tailed. The data was analyzed using Statistical Analysis Software (SAS) version 9.2.Cases and controls were matched 1 : 1 on the date of birth, within 12 months. We performed univariable analysis to compare the baseline characteristics of cases and their matched controls. The McNemar chi-squared test was used for categorical variables and Student's P = 0.0002) and age at first childbirth . There was no significant difference between cases and controls for mean parity  and duration of breastfeeding .We identified 129 matched pairs. P = 0.009) but a significantly higher weight at the time of breast cancer diagnosis . The average weight gain since the age of 18 years was significantly higher for cases, when compared with controls . Compared to controls, a significantly greater proportion of cases had a BMI in excess of ≥25 kg/m2 at the time of breast cancer diagnosis .Compared to controls, cases had a significantly lower mean weight at the age of 18 years . Age at first childbirth, parity, and breastfeeding were not associated with the risk of breast cancer.P = 0.18). Compared to women with a normal current BMI (18.5–24.99 kg/m2), the unadjusted OR of breast cancer for women with a BMI of ≥25 kg/m2 was 7.61 . The increased risk of breast cancer with a BMI of ≥25 kg/m2 remained significant when the model was adjusted for other factors .The BMI at the age of 18 years was not associated with breast cancer risk. However, there was a trend towards increasing risk of breast cancer with weight gain since the age of 18. Each kilogram of weight gain from the age of 18 years was associated with a 7% increase in breast cancer risk .The main objective of this study was to evaluate the role of reproductive and anthropometric factors with the risk of premenopausal breast cancer in Bangladesh. Of the reproductive factors, only an early age at menarche significantly increased the risk of premenopausal breast cancer. From the age of 15 years, each year decrease in age of menarche increased the risk of breast cancer approximately twofold. Other reproductive factors such as a delayed first childbirth, low parity, and breastfeeding were not related to premenopausal breast cancer risk.2 or above was associated with significantly higher risk of breast cancer.We did not observe an association between the BMI at the age of 18 years and premenopausal breast cancer risk. However, current BMI of 25 kg/mP < 0.0001) [The increased risk of premenopausal breast cancer with early menarche in our study is consistent with the literature from western populations , 25–27,  0.0001) . The ave 0.0001) , 14. An  0.0001) . However2 or higher was associated with 50% reduction in the risk of premenopausal breast cancer, compared to women who had a body mass index of 21 kg/m2 or less . We observed a strong association between current BMI and premenopausal breast cancer risk in this Bangladeshi population. In our study, a current BMI of 25 kg/m2 or higher was associated with fivefold increase of risk of breast cancer as compared to women with a BMI of 18.5–24.9 kg/m2 . Similar estimates were reported in the South Indian study [2  and BMI of ≥30 kg/m2 .Most studies to date suggest that being overweight and obesity are associated with a lower risk of premenopausal breast cancer but are associated with a greater risk of postmenopausal breast cancer –30. In aan study . In that2 at 18 years, the risk of premenopausal breast cancer was halved among women with a BMI of 27.5 kg/m2 . Other studies have found no association [P = 0.91).Regarding early adulthood, most studies show an inverse relationship between body size at the age of 18 and the risk of breast cancer, irrespective of menopausal status –37. In tociation , 39. We P = 0.18). It is possible that the effects of body mass index and weight change on premenopausal breast cancer risk differ according to ethnicity [Weight gain in adulthood appears to be associated with an increased risk of postmenopausal breast cancer , 40, 41 thnicity . Larger In the current study, delayed first childbirth and parity were not related to breast cancer risk in our study population in contrast to the existing epidemiologic evidence clearly demonstrating that childbearing is a protective factor , 49, andP < 0.0001) [The combined evidence from epidemiological studies suggests that breastfeeding is protective against premenopausal breast cancer. An analysis of 47 epidemiological studies estimated that each additional year of breastfeeding protects against breast cancer by a factor of 4.3%  . A previ 0.0001) . It may Our study has several important limitations. The majority of controls in our study were recruited from the two hospital outpatient departments (DMCH and BSMMU) in Dhaka. However, a small proportion in Dhaka was recruited from the community. We cannot differentiate the source of our controls, which might affect our estimates. The sample size and number of matched pairs in this study are relatively small that may limit the precision of the effect of covariates on our primary outcome (breast cancer). Most covariates in our study were modeled as continuous variables. The small number of incident cases of breast cancer precluded us from formulating subcategories of exposures. Also, we did not evaluate other measures of central obesity such as waist circumference or waist-to-hip ratio. We relied on the recall of study subjects in order to estimate weight and body mass index at 18 years. We were also unable to collect adequate data on hormone receptor status of the breast cancers. Stratifying cases by estrogen receptor status may unmask associations, which are relevant to reproductive risk factors, as well as body mass index. Lastly, very few women from either the cases or control groups were able to report a family history of cancer (data not shown). This may be for social and/or cultural reasons, which have been suggested as a cause of underreporting of cancer family histories in other Asian studies .Our observations suggest that overweight and obesity in adulthood and in particular weight gain during early adulthood may play an important role in the increasing risk of premenopausal breast cancer in Bangladeshi women. Further research is warranted to better understand the complex interactions between environmental, hormonal, and genetic factors in the etiology of breast cancer in Bangladesh and other South Asian populations. Meanwhile, it is important for public health and other relevant agencies in all countries to promote healthy lifestyles, including exercise and maintaining a healthy body weight in order to mitigate the rapid rise in some cancers, as well as cardiovascular disease and diabetes.


Breast cancer, the second leading type of cancer in Korean women, has shown increasing incidence over the past 10 years. However, the curves of incidence by age group cast doubt on the birth cohort effect hypothesis. To explain the curves, here I suggest two alternative hypotheses of breast density and viral infection based on pre-existing evidences. Evaluating these hypotheses would require important clues to find unknown risk factors of breast cancer and to plan more effective strategies for breast cancer control in Korean women. Breast cancer ranks first in incidence and mortality rates for women throughout the world . The numHowever, when incidences are examined by age group, an unusual phenomenon occurs since, as seen in Figure 2 of Bae , the peaAs such, it can be suspected that the characteristics of breast cancer incidence in Korean women could involve reasons other than a birth cohort effect from lifestyle westernization. In an effort to explain these reasons, an opportunity to identify the causal factors specific for Korean women is created and effective preventative measures can be established. Therefore, here I present two hypotheses based on literature reviews on breast cancer.The evidence for this hypothesis is described in detail in the forthcoming published report by Kim & Bae . SummaryThe identification of dense breasts on mammography increases the breast cancer occurrence rate by 4-6 times in Western Caucasian women; as such, it has become the strongest known risk factor to date -17. In pOn the other hand, according to a report that risk of dense breasts is maintained even after 10 years , examiniAs breast cancer in developing countries is becoming more common in relatively younger age groups, the possibility of infection is being actively suggested, leading to the emergence of the concept of breast cancer being an infectious disease ,27. WithMMTV was isolated from mice with breast cancer that were raised in the same cage, so it is natural to suspect that MMTV can also cause breast cancer in humans . The worEBV, widely known to cause Burkitt’s lymphoma , receiveHPV, known as a virulence factor for uterine cervical cancer , reporteThe mechanism by which HPV infection causes breast cancer is explained by HPV infiltration through the mammalian duct from the genital to oral route of the sex partner or propagation through the blood ,46. MoreIn this paper, two hypotheses that were proposed to explain the trends in increasing incidence of breast cancer in Korean women for the last 20 years were investigated. To obtain evidence that support the first hypothesis that dense breasts are related to breast cancer occurrence, the direction of future studies should be to: (1) verify whether the distribution of dense breasts by age correlates with the annual incidence of breast cancer; (2) use a case-control study method to identify the association between dense breasts and well-known breast cancer risk or protective factors; and (3) conduct a cohort study to verify the relative risk for the onset of breast cancer based on patterns of change in breast density in subsequent screening mammography. If these studies can prove the hypotheses, it would be necessary to establish a new screening strategy, in which patients with dense breasts are selected as a risk group; breast ultrasonography, in addition to mammography, is performed regularly; and the interval of follow-ups is better coordinated . To obta


ORs) and 95% confidence intervals (CIs) were calculated to estimate the association between life satisfaction and lifestyle, knowledge of breast cancer, physical examination, and detection rate of breast diseases. Overall life satisfaction was positively associated with women's healthy lifestyle. Compared with less satisfied women, satisfied women were less likely to be smokers , have more exercise , eat less fried , smoked , pickled , and grilled  foods. Satisfied women were more likely to have knowledge of breast cancer , and have regular physical examinations . Compared to less satisfied women, we found significantly lower detection rate of benign breast diseases , and lower but nonsignificant detection rate of breast cancer  in satisfied women. Women with a higher overall life satisfaction are more likely to have healthy lifestyle, knowledge of breast cancer, and regular physical examination, thus resulting in a lower detection rate of breast diseases in screening.To investigate the association between overall life satisfaction and healthy lifestyle, knowledge of breast cancer, physical examination, and detection rate of breast cancer and benign breast disease in Chinese women. In a multicentered breast disease screening program in China, we enrolled 33,057 women aged 45–65 years without prior diagnosis of breast cancer. After completing an epidemiological questionnaire, all participants were examined by clinical breast examination, breast ultrasound, and mammography independently. All breast cancer cases and a selected sample of benign breast diseases were confirmed pathologically. Univariate and multivariate odds ratios ( Breast cancer has become the most frequently diagnosed malignant tumor in women worldwide, and more than 1.6 million Chinese women would be diagnosed with breast cancer each year The association between psychological factors and risk of breast cancer has been established Our study was designed to evaluate the association between overall life satisfaction and lifestyle, knowledge of breast cancer as well as physical examination, and to estimate the relationship between overall life satisfaction and the detection rates of breast cancer and benign breast diseases among Chinese females based on the Multimodality Independent Screening Trial (MIST) in China. Overall life satisfaction in this study was regarded as a comprehensive concept used to evaluate subjective well‐being of life n = 181) were confirmed by pathological examination. A total of 33,057 women are included in the final analysis. This study was approved by the Tianjin Medical University Cancer Institute and Hospital (TMUCIH) Institutional Review Board. All the participants had written informed consent before breast disease screening.Our study is based on data from the MIST, a multicentered breast disease screening program performed by the Chinese Anti‐Cancer Association (CACA) from 2008 to 2010. The screening procedure has been presented in detail elsewhere Overall life satisfaction was collected from a question “How would you rate your satisfaction with overall life”. A single response was selected from the five categories: very satisfied, satisfied, just so‐so, less satisfied, and not satisfied. In the analysis, we combine the first two categories as “satisfied”, and the last three categories as “less satisfied”.Lifestyle factors of women in this study mainly include smoking , alcohol consumption , physical activity (times/week), and diet. Diet measures consist of diet patterns (proportion of vegetables and meat) and frequency of unhealthy foods  intake weekly. Information on diet and physical activity are only available in the 2009 and 2010 questionnaire.Knowledge of breast cancer was measured by the question: do you have knowledge of breast cancer, such as risk factors or prevention ? Sources of your breast cancer knowledge included newspaper and books, radio and television, health education, friends or relatives with breast cancer, or other ways. The number of sources is used for analysis. Physical examination was measured by the question “do you participate in regular physical examinations ”.2). Family history of cancer is defined as cancer occurring in first‐degree relatives, including grandparents, parents, sisters and brothers, and children.In this study, demographic factors include region, age, ethnicities, education level, occupation, height, body weight, household income, source of health insurance, marital status, age at marriage, miscarriages, number of children, family history of cancer, and self‐assessment of personality, health condition, interpersonal relationship, and major setbacks or unfortunate events. Height and weight are measured by investigators, and body mass index (BMI) is calculated using the following formula: BMI = (weight in kg)/, age , ethnicities , education level , occupation , household income , marital status , age at marriage , health insurance , BMI , characters , health condition , interpersonal relationship , major setbacks or unfortunate events , number of children , miscarriages , and family history of cancer .Secondly, we estimate the association between overall life satisfaction and lifestyle, the association between overall life satisfaction and knowledge of breast cancer, physical examination, and the association between overall life satisfaction and detection rates of breast cancer and benign breast diseases by using univariate‐ and multivariate‐adjusted models, including factors significantly associated with overall life satisfaction in prior analysis.P‐values are two sided.Data analysis is performed with SPSS 17.0 . Odds ratios (ORs) and 95% confidence intervals (CIs) are provided in the results and all Among the 33,057 women included in the analysis, 30,149 (91.2%) are reported satisfied with overall life, while 2908 (8.8%) are reported less satisfied. Table Overall life satisfaction is associated with healthy lifestyle. Compared to less satisfied women, satisfied women are less likely to be smokers , more likely to do exercise , and less likely to eat fried , smoked , pickled , and grilled  foods. Significant associations are not found between life satisfaction and alcohol consumption as well as diet pattern in multivariate analysis, though there are significant associations in the univariate analysis  Table . HoweverAmong the 33,057 participants, 102 were pathologically confirmed to have breast cancer, 90 cases in the satisfied group and 12 cases in the less satisfied group. Overall life satisfaction is significantly associated with lower detection rate of benign breast diseases , and a lower but nonsignificant association with the detection rate of breast cancer  Table .In this study, we found that women with higher overall life satisfaction were associated with significantly lower detection rate of benign breast diseases, and a lower, though nonsignificant detection rate of breast cancer. The inverse association may be explained, at least in part, by the positive association between overall life satisfaction and healthy lifestyle, knowledge of breast cancer, and regular physical examination.It is well established that lifestyle play an important role on breast cancer risk. A large number of researches have evaluated the relationship between unhealthy lifestyle, including smoking, alcohol drinking, and unhealthy diet, and the risk of breast cancer. Recently, European Prospective Investigation into Cancer and Nutrition (EPIC) cohort reported a lower risk of breast cancer in women with healthy lifestyle combining healthy diet, never smoking, no alcohol consumption, and high physical activities Previous studies were inconsistent on the association between life satisfaction and breast cancer risk. A null result was reported from the Finnish Twin Cohort comprising 12,032 women in Fenland, after 21 years of follow‐up In our study, 19.8% were reported to have no knowledge about breast cancer, and this proportion is comparable to the 19.4% reported in a cross‐sectional study conducted in Eastern China, which also identified association between overall life satisfaction and knowledge of breast cancer Generally, our study found a healthier lifestyle  among women satisfied with their overall lives. Previous studies also showed certain psychosocial factors might affect the behavior of smoking We acknowledge that this is a cross‐sectional study and there are intrinsic limitations in explanation for temporal relationships between a low overall life satisfaction and late age at marriage, interpersonal relationship, and most unhealthy lifestyle in Table Overall life satisfaction is a comprehensive index related to almost any aspect of life. In our study, overall life satisfaction is related to factors including region, age, age at marriage, occupation, household income, insurance, character, international relationship, health condition, and major unfortunate events. Therefore, an overall well‐being of women will be beneficial to the protection of breast diseases. Further prospective studies are warranted to investigate the association between overall life satisfaction and breast cancer.The authors declare that they have no competing interests.


The objective of this study is to compare different methods for measuring breast density, both visual assessments and automated volumetric density, in a breast cancer screening setting. These measures could potentially be implemented in future screening programmes, in the context of personalised screening or screening evaluation.th edition) and with two commercially available automated software programs (Quantra and Volpara volumetric density). BI-RADS density  was assessed by three radiologists. Quantra (v1.3) and Volpara (v1.5.0) provide continuous estimates. Different comparison methods were used, including Bland-Altman plots and correlation coefficients .Digital mammographic exams (N = 992) of women participating in the Dutch breast cancer screening programme (age 50–75y) in 2013 were included. Breast density was measured in three different ways: BI-RADS density  for Quantra, which was higher than the Volpara estimate . The mean difference between Quantra and Volpara was 5.19% (95% CI: 5.04–5.34) (ICC: 0.64). There was a clear increase in volumetric percent dense volume as BI-RADS density increased. The highest accuracy for predicting the presence of BI-RADS c+d (heterogeneously or extremely dense) was observed with a cut-off value of 8.0% for Volpara and 13.8% for Quantra.Although there was no perfect agreement, there appeared to be a strong association between all three measures. Both volumetric density measures seem to be usable in breast cancer screening programmes, provided that the required data flow can be realized. Fibroglandular breast tissue, which is referred to as dense tissue, is known to mask breast carcinomas on mammograms , 2. In aWolfe proposed a breast pattern scale in 1976  and the Breast density is not structurally assessed at screening examinations in the Netherlands. BI-RADS density is only recorded in the clinical setting. The Breast Density Inform Law in the USA  did leadIn the Netherlands, women ages 50–75 years are invited to participate in breast cancer screening every two years. We included 1000 mammographic examinations of participants who were screened at the Nijmegen screening unit in 2013. The dataset consists of multiple small sets of consecutive exams. The dates of retrieval were chosen at random, and we therefore believe that the dataset as a whole can be seen as a random sample of the Nijmegen screening population. Both a mediolateral oblique (MLO) and a craniocaudal (CC) view were obtained per breast. In five participants, only the left (N = 3) or the right (N = 2) views were available. Five examinations were excluded because the women had breast prostheses, which would distort the automated breast density measurements. In addition, three exams could not be read by the volumetric breast density software. This resulted in a dataset of 992 mammograms.According to the Dutch law, medical ethics approval is not needed for this type of study, with no extra burden for participants and anonymized data. Written informed consent was not required for this study because the data were obtained in the context of an agreement between the regional screening organisations and the Dutch Reference Centre for Screening. Women automatically consent to the use of their data for scientific purposes by participating in screening. The screening organisations are responsible for data delivery in accordance with privacy regulations, particularly regarding anonymizing data and potentially removing data of participants who objected to the exchange of personal data with specific organisations (opt-out procedure).The Dutch screening programme uses Full-Field Digital Mammography (FFDM). All exams in this study were performed on the same Hologic Selenia system . Breast density was measured in three different ways: BI-RADS density , Quantra volumetric density (automated software), and Volpara volumetric density (automated software). The R2 Quantra Volumetric Assessment software (version 1.3) was integrated in the Cenova DICOM server . Version 1.5.0 of the Volpara Algorithm  was used.BI-RADS breast density was assessed by three experienced screening radiologists. An initial pilot was performed where the radiologists scored the first 250 mammograms , which was concluded with a consensus meeting to ensure that the radiologists were applying the scale in a similar way. The ACR guidelines were discussed during the meeting, and discrepancies in the pilot scores were addressed. The consensus meeting had a favourable effect on the agreement between the radiologists. The scores before the consensus meeting were not included in our main analyses. Instead, the mammograms were scored again by the radiologists  several weeks after the consensus meeting.th edition, American College of Radiology) BI-RADS density classification ), and two-way mixed intraclass correlation coefficients (ICC) were calculated for comparison of the different volumetric density measures. The following formula was used to calculate the ICC :ICC=σp2An ICC of +1.0 indicates that the measures give perfectly matching scores, with ICC values >0.7 often being considered as ‘good’ , 27. HowFinally, Bland-Altman plots are presented as agreement measures. The Bland-Altman plot consists of differences between two measurements on the y-axis and the mean of the two methods on the x-axis. Limits of agreement can be calculated by multiplying the standard deviation (σ) of the differences with 1.96 (+/-1.96σ). This is based on the assumptions that: (a) the variation in differences is similar across the range of values for the mean, and (b) the differences follow a normal distribution. The original (untransformed) differences were used for the Bland-Altman analyses. The observed difference between Quantra and Volpara is expected to be in between the limits of agreement in 95% of (future) measurements. Bias is defined as the mean difference between the two methods. The standard error of the bias is calculated as:All statistical analyses were performed using SAS , apart from the ICC calculations that were performed with SPSS . Figures were made with GraphPad Prism . Two-sided p-values smaller than 0.05 were considered to be statistically significant.w ranged from 0.82 (95% CI: 0.79–0.86) to 0.87 (95% CI: 0.83–0.91). Based on the Landis and Koch guidelines (reworded by Altman), the intra-rater reliability could thus be seen as ‘very good’. The intra-rater agreement ranged from 62.8% (n = 157) to 84.8% (n = 212) (n = 212) , with a w of the inter-rater comparisons ranged from 0.80 to 0.84, which corresponds to ‘good’ or ‘very good’ reliability. In nine cases, the radiologists all scored differently. The number of discordant pairs with a difference of more than one category was limited .All three radiologists agreed in 570 out of 992 (57.5%) assessments. 3 (IQR: 49–101) with Quantra compared to 50 cm3 (IQR: 39–70) with Volpara. Total breast volume, on the other hand, was higher for Volpara: 774 cm3 (IQR: 509–1119) compared to 577 cm3 (IQR: 368–842) for Volpara and Quantra, respectively. Based on the VDG, 12.3% and 30.7% of the women had ‘extremely dense’ (VDG 4) and ‘heterogeneously dense’ breasts (VDG 3), respectively.The volumetric breast density measures are presented in 3 (95% CI: 22.0–26.3) for dense volume. Compared with the Volpara measurement, the Quantra estimate of percent dense volume is expected to range between +0.5% and +9.9% in 95% of the measurements (limits of agreement). The Pearson’s r and the ICC were 0.91 (95% CI: 0.90–0.92) and 0.64 (95% CI: -0.07–0.88), respectively. The limits of agreement of absolute dense volume were -43.6 cm3 and +91.9 cm3, with a Pearson’s r of 0.82 (95% CI: 0.80–0.84) and an ICC of 0.55 (95% CI: 0.24–0.72).w: 0.80, 95% CI: 0.77–0.82; proportion agreement: 65.4%) (: 65.4%) . VolparaThe ROC analyses on predicting the presence of BIRADS c+d (high density) with percent dense volume resulted in the following area under the curve (AUC) values: 0.948 (95% CI: 0.935–0.960) with Volpara and 0.948 (95% CI: 0.935–0.961) with Quantra . The higThe median age at examination was 59 years (IQR: 54–64). The median percent dense volume, Volpara and Quantra estimates, appeared to decrease with age . The assWe studied three different methods to assess breast density, namely the BI-RADS density scale and two software programs (Quantra and Volpara). Quantra gave higher estimates of percent dense volume and absolute dense volume than Volpara. There was a positive association between percent dense volume and the BI-RADS density scale for both programs. In addition, the VDG (Volpara measure) seemed to be a good approximation of BI-RADS density in our study. Absolute dense volume only appeared to be associated with BI-RADS density when using the Volpara estimates. These density measures may potentially be used in the evaluation of screening performance and to identify risk groups.w tends to suggest ‘good’ to ‘very good’ reliability based on the Landis and Koch guidelines, even though these categories may be somewhat arbitrary. The proportions of agreement improved after the consensus meeting (data not shown), but there are still relatively large discrepancies between the radiologists (up to 32.4% for observer 2 vs. 3). For this reason, density assessment by individual radiologists is not useful for selecting women for future alternative screening regimens in population-based organised breast cancer screening programmes or risk management. Furthermore, the intra- and inter-rater variability may differ between radiologists, for example based on experience level .–41.35–41 density . We obseOR 1.08) .et al. indicated that Volpara may slightly underestimate the true density (as measured with MRI) [et al. is, to our knowledge, the first study to include both Volpara and Quantra. They observed a strong correlation between the two measures, as well as a strong correlation of both with MRI [et al. did report on absolute dense volume in their comparison study of Quantra and Volpara [There was a relatively strong correlation in percent dense volume between the two automated volumetric methods . The correlation for absolute dense volume, on the other hand, appeared to be somewhat weaker, with lower correlation coefficients . The first results from validation studies, comparing volumetric density to MRI results, are now appearing in the literature. Gubern-Mérida ith MRI) . Wang etwith MRI . However Volpara . Althouget al. [et al. showed that the highest Volpara density quartile was associated with a 2.93 (percent dense volume) or 1.63 (absolute dense volume) higher risk than the lowest quartile [et al. studied several breast density measures: both Volpara (Q5 vs. Q1: OR 8.26) and Quantra (Q5 vs. Q1: OR 3.94) estimates of percent dense volume were associated with an increased breast cancer risk [The effect of breast density on breast cancer risk is relevant for personalised (primary and secondary) prevention, where it can potentially be used as a risk stratification factor. Little evidence has yet been published on the association between volumetric density and breast cancer risk to date, although previous studies have suggested that volumetric density may be more strongly associated with breast cancer risk due to its predicted biological association , 13. Accet al. . A studyquartile . Finallycer risk .The associations between volumetric density and other established breast cancer risk factors may provide some insight into the etiological role of volumetric density. We studied the association with age, where we observed a similar inverse association as has previously been determined using other density measures. Studies have shown that most risk factors have a similar association with Volpara volumetric breast density as they do with area-based measures –46. OnlyOne of the limitations of our study is that we did not have any information on breast cancer risk, which would ultimately be needed to validate both breast density measures and potentially implement them in a breast cancer screening setting if they are to be used for risk stratification. More research is needed as well on the association between volumetric density and sensitivity of digital mammography. This information is required to identify a clinically relevant breast density cut-off value above which additional screening  may be cost effective. Studies are also needed on the potential inclusion of volumetric density in risk models. Strengths of the current study include the use of both Volpara and Quantra, which we were able to study in relation to the newest BI-RADS density classification. In addition, we included both percent dense volume and absolute dense volume. Finally, our study sample was relatively large compared to previous studies .Before volumetric density measurements can be implemented in breast cancer screening, the infrastructure on storing unprocessed mammogram data has to be developed further. This would involve large amounts of data. However, the advantage of this data storage is that multiple automated tools can easily be compared over time. Furthermore, if at any time an algorithm would be introduced that performs considerably better, it could also be applied to historical data. This is especially important for monitoring density changes, for example between geographic areas and within women. Due to the lack of intra- and inter-observer variability, in contrast to the BI-RADS density classification, changes in density can be more readily detected if random measurement error is small.Volpara and Quantra clearly differed from each other. However, there appeared to be a strong association of these measures with each other and with the BI-RADS density scale. Further research on the differences between the measures is needed before they can be implemented in breast cancer screening programmes. This applies both to the logistics surrounding breast density measurements and the role of breast density in screening programmes. If studies indeed show that breast density is important for evaluating performance or could be useful for risk stratification, then both Quantra and Volpara may be considered.S1 Fig(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.


MF) exposure has been suggested as a risk factor for breast cancer in both men and women. Due to the rarity of this disease in men, most epidemiologic studies investigating this relationship have been limited by small sample sizes. Herein, associations of several measures of occupational MF exposure with breast cancer in men were investigated using data from the population‐based case–control component of the Canadian National Enhanced Cancer Surveillance System. Lifetime job histories were provided by 115 cases and 570 controls. Average MF exposure of individual jobs was classified into three categories  through expert blinded review of participant's lifetime occupational histories. The impact of highest average and cumulative MF exposure, as well as exposure duration and specific exposure‐time windows, on cancer risk was examined using logistic regression. The proportion of cases (25%) with a highest average exposure of ≥0.3 μT was higher than among controls (22%). We found an elevated risk of breast cancer in men who were exposed to ≥0.6 μT  when compared to those with exposures <0.3 μT. Those exposed to occupational MF fields for at least 30 years had a nearly threefold increase in risk of breast cancer  when compared to those with background levels of exposure. Findings for the other time‐related MF variables were inconsistent. Our analysis, in one of the largest case–control studies of breast cancer in men conducted to date, provides limited support for the hypothesis that exposure to MF increases the risk breast cancer in men.Occupational magnetic field ( While breast cancer is one of the most common cancers among women, it rarely occurs in men. In Canada, breast cancer accounted for less than 1% of newly diagnosed cancer cases in men in 2014 There are important advantages to studying the relationship between occupational and environmental exposures and breast cancer in men as they have far fewer reproductive risk factors than women. For example, the risk of breast cancer in women is influenced by age at menarche, age at menopause, and several other reproductive characteristics , and hormone replacement therapy A greater number of studies have examined associations of MF exposure with breast cancer in women. According to literature summaries found in the IARC Monograph This study population consisted of participants from the Canadian National Enhanced Cancer Surveillance System (NECSS). The NECSS was a population‐based case–control study conducted among men and women aged 20–74 from eight of 10 Canadian provinces . A detailed description of the design of the NECSS has been published previously A total of 115 incident participating cases of breast cancer in men were obtained from provincial cancer registries as described previously μT. As described previously μT cut‐point for MF exposure was based on the distribution of residential exposures from a previous Canadian study of residential MF exposures and childhood leukemia μT was estimated as the 82nd percentile for adult exposures in the same homes μT was chosen to be double the 0.3 μT, where highly exposed jobs included, among others, sheet metal workers, electricians, and electric utility workers , blinded to case–control status, as described previously rs Table . MF expot‐tests and chi‐square tests for continuous and categorical variables, respectively. For all analyses, Ps < 0.05 were considered statistically significant.Descriptive characteristics of cases and controls for known or suspected risk factors were expressed using means and standard deviations for continuous variables, and percentages for categorical variables. Differences between cases and controls were assessed using μT), 0.3 to <0.6, and ≥0.6 μT E), duration of employment (D), and whether employment was full or part time (F) for each job, calculated using the formula:Ei = 0 for jobs <0.3 μT, 1 for jobs 0.3 to <0.6 μT, 2 for jobs ≥0.6 μT; Di, duration of employment (years); Fi, 1 for full time, 0.5 for part time or seasonal employment; j, total number of jobs held.Several MF exposure metrics were constructed for the analyses. The highest average exposure to occupational MFs was classified as none  exposure, time since first exposure, and age at first exposure to MFs were also considered in separate models.To evaluate the impact of assigning weight to the intensity of MF exposure in a linear fashion in the MF index, a sensitivity analysis was also conducted assigning a weight of 4 to jobs in the ≥0.6 2), and leisure‐time physical activity. Leisure‐time physical activity levels were evaluated based on the self‐reported number of hours of strenuous activity per month and split into tertiles on the basis of the distribution among controls. BMI was characterized using a categorical variable  Associations between MF exposure and breast cancer were evaluated using unconditional logistic regression. To evaluate the potential for confounding of these relationships by other breast cancer risk factors, three models were created: an unadjusted model, a model adjusted for age only, and a multivariate model that included risk factors identified a priori. Variables included in the full multivariate model were age (continuous), body mass index  across all models (age‐adjusted and fully adjusted), all confidence intervals included unity, and no trend across increasing levels of MF intensity was evident.Relationships between occupational MF exposure metrics and breast cancer are presented in Table When time‐related exposure variables were considered, results were more mixed. No clear associations between cumulative MF exposure (measured by MF index) and breast cancer in men were observed, as all confidence intervals included 1.0 Table . In a paNone of the time since last exposure, time since first exposure, or age at first exposure to occupational MFs was associated with male breast cancer Table . As a seDuration of occupational exposure to MFs was positively associated with breast cancer in the ≥30 years exposure category in the unadjusted model, with an OR = 4.20 (95% CI = 1.52–11.56). The estimated ORs for this category remained elevated in the age‐adjusted  and multivariate  models.Finally, jobs whose MF exposure could not be classified were coded as unexposed in the main analysis to maximize the available sample size for breast cancer cases. To evaluate the impact of including these jobs, a sensitivity analysis excluding jobs where MF exposure could not be classified was performed Table , and resμT was elevated. Our study also examined the influence of occupational MF exposure using several exposure‐time windows, specifically time since most recent (last) exposure, time since first exposure, and age at first exposure to MFs. There were no clear associations with breast cancer for any of these measures. These findings are in contrast to findings from an earlier U.S. study where an elevated risk was seen among men exposed to occupational MFs ≥30 years prior to diagnosis, particularly among men who were under age 30 at the time of this exposure Overall, this study did not detect a clear association between occupational MF exposure and breast cancer in men based on highest average exposure, although the estimated OR for a highest average MF exposure of ≥0.6 μT average MF exposure threshold for ≥30 years. The estimated ORs for ≥30 years exposure duration in both the age and multivariate adjusted models  are similar to that seen in one previous study of male breast cancer, where an OR = 2.1 (95% CI = 0.7–6.2) was observed for 30 or more years of employment in jobs exposed to MFs Using a duration of exposure metric, an increased risk of breast cancer in men was observed for employment in jobs considered above the 0.3 While the underlying biological mechanisms linking MF exposure with breast cancer risk has not been established μT), providing a more nuanced exposure characterization than some previous studies that have only considered whether specific job types would or would not include exposure to MFs As one of the largest studies of occupational MF exposure and breast cancer in men conducted to date However, despite these strengths, this study does have some limitations. First, while the characterization of occupational MF exposure in this study was more detailed than in some previous work, no objective measures of MFs were included and characterization was based on the coding of job titles. Thus, there remains potential for exposure misclassification if individuals were employed in a job that was considered “exposed” but did not in fact have exposure to MFs over the course of their work (or vice versa). Furthermore, a much larger proportion of jobs in the male breast cancer group (18.2%) compared to the control group (0.5%) could not have their MF exposure classified. The main reason for this difference was that, due to the large number of potential controls in NECSS and the labor‐intensive nature of the occupational coding process used to characterize MF exposure, only controls with good‐quality occupational history data were included. Conversely, given that the number of male breast cancer cases was much smaller in comparison, all cases were included for job history coding, regardless of the quality of the data. This difference in quantity of missing data in the case and control groups further increases the risk of misclassification bias. However, while the main analysis included all jobs without classification as “unexposed” to maximize sample size for breast cancer cases, sensitivity analysis removing these jobs produced similar findings, suggesting that they were not driving any observed associations between MF exposure and male breast cancer. Finally, our analysis did not consider residential MF exposures, such that any individuals who had low occupational but higher residential MF exposures would be included in the unexposed group, which could have biased risk estimates toward the null. However, given that studies of breast cancer in women have not shown strong associations between residential MF exposure and breast cancer Additionally, while this study is one of the larger studies of breast cancer in men, there are still a relatively small number of individuals who were exposed to high levels of occupational MFs. As evidenced by the relatively wide confidence intervals associated with a number of the estimated ORs, it is possible that this study was still underpowered to detect real effects of occupational MF exposure on male breast cancer risk. Future work with larger number of cases, potentially through pooling of information across multiple male breast cancer datasets, is necessary to help clarify the relationships between occupational MF exposure and male breast cancer risk. Finally, while several established breast cancer risk factors for men were included in the multivariate model, information concerning family history of breast cancer was not available, thus the potential confounding influence of this risk factor could not be considered. However, we know of no reason to suspect that family history of breast cancer is related to occupational MF exposure, and hence it is unlikely to bias our associations.In conclusion, while there was some suggestion that highest average exposure to MFs increases the risk of breast cancer in men, the associations with time‐related variables were less consistent and only observed with a long duration of exposure. Future research where multiple breast cancer datasets of men are pooled could be helpful to increase study power and yield more insights.None declared.Table S1. Sensitivity analysis using alternate magnetic field index.Table S2. Sensitivity analysis of magnetic field exposure and male breast cancer in multivariate models including socioeconomic status variables.Click here for additional data file.


Background: DNA methyltransferase-3B (DNMT3B) is an important enzyme responsible for maintaining the DNA methylation pattern in eukaryotic cells. In this study we have investigated the correlation between the 46359C→T polymorphism in the DNMT3B gene and the risk of breast cancer incidence among sporadic breast cancer patients in Fars Province, Southern Iran.Methods: In this case-control study, 100 breast cancer patients and 138 healthy control subjects were genotyped for the DNMT3B gene by the polymerase chain reaction-restriction fragment length polymorphism method.Results: The genotype frequency in the case  group significantly (P=0.008) differed from the control  group. We observed a decreased association between the CT genotype and lymph node involvement in breast cancer patients. Our results have shown that in comparison to the homozygous CC genotype carriers the DNMT3B-CT genotype has a significantly lower risk for breast cancer .Conclusion: Our case-control study showed that the CT genotype was significantly associated with decreased breast cancer risk. Consistent with these results, a significant decrease of CT genotype among lymph node positive breast cancer patients was observed. However, a larger study population with more clinical data is needed to confirm these results. During the past decade it became evident that epigenetic alteration plays an important role in neoplastic transformation.3-5Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death among women.6-8 Aberrant promoter methylation of tumor suppressor genes is closely related with loss of their function.9,10 DNA methyltransferases, of which three active forms have been identified  catalyze DNA methylation. DNMT1 maintains the levels and patterns of methylated DNA during mitosis, whereas DNMT3A and DNMT3B are primarily responsible for de novo methylation.3,11,12 De novo hypermethylation of promoter CpG islands has been identified as a possible mechanism for tumor suppressor gene inactivation in human cancer cells.13,14 DNMT3B plays an important role in tumorigenesis, and overexpression of DNMT3B has been reported in tumors. However DNMT1 and DNMT3A have been found to be only modestly overexpressed at lower frequencies.15,16 Up regulation of DNMT3B is dramatically associated with a higher histopathological grade of breast tumors as well as proliferation of marker Ki67 and negative estrogen receptor-α expression – all indicative of possible DNMT3B involvement in breast tumor progression and metastasis.17DNA methylation is a major epigenetic mechanism that has an important role in chromosomal stability and gene expression in mammalian cells.18 Many reports have shown that the DNMT3b C/T polymorphism may change the enzyme methylating activity and thereby influence the incidence of cancer susceptibility.18-20 However, there is no consensus in the literature regarding an association between DNMT3B genotypes and the risk of different cancers.18-20 To the best of our knowledge, the association between DNMT3B polymorphism and breast cancer risk has been reported in a single British study.19 To date, this has not been explored in Iranian populations.The DNMT3B gene, located on chromosome 20q11.2, contains a C to T transition polymorphism  in the promoter region of the DNMT3B gene, -149 base pairs from the transcription start site.Therefore, we investigated the association between DNMT3B genotype and the risk of breast cancer incidence among sporadic breast cancer patients in Fars Province, Southern Iran.Study SubjectsA total of 100 sporadic breast tumor samples (95 fresh and 5 paraffin-embedded) were obtained from the Department of Pathology, Shiraz University of Medical Sciences, Shiraz, Iran from 2003 to 2006. Fresh samples were snap-frozen immediately after surgery and stored at -70°C. All samples were subjected to re-evaluation of the original histological diagnosis by an expert pathologist who also selected representative areas of the tissue sections for DNA extraction and further molecular analysis. Each patient’s clinicopathological information that included age, tumor size, type, grade and site, estrogen and progesterone receptor and lymph node involvement status was obtained from hospital records. The 138 healthy control females, matched for age with the case subjects, were selected from a pool of cancer-free subjects who volunteered to join the epidemiology survey during the same period. For the control group, normal genomic DNA was prepared from blood lymphocytes. This investigation was approved by the Ethics Committee of Shiraz University of Medical Sciences. DNA Extraction and DNMT3B Genotyping18 Briefly, we used 25 μl of PCR mixture that contained 100-300 ng of DNA template, 12.5 pmol of each primer , 0.1 mmol/L of each deoxynucleotide triphosphate, 1×PCR buffer , 2.0 mmol/L MgCl2, and 1.25 U Taq polymerase . The PCR amplification profile consisted of an initial denaturation step at 95°C for 5 min, 35 cycles of denaturation at 95°C for 30 s, annealing at 65°C for 30 s, and extension at 72°C for 30 s. This was followed by a further extension step at 72°C for 10 min. The 380 bp PCR products were digested overnight with 5 units of AvrII  at 37°C and separated on 2% agarose gels. The digested product was visualized by red gel staining under UV illumination. The variant T allele has a AvrII restriction site that results in two bands (208 bp and 172 bp) whereas the wild-type C allele lacks the AvrII restriction site, thus producing a single 380-bp band. Therefore the heterozygote alleles were expected to have three bands  and peripheral blood lymphocytes (control group) using a Cinnagen genomic DNA purification kit . The purity and concentration of DNA were assessed by spectrophotometric measurement of absorbance at 260 and 280 nm. DNMT3B C/T polymorphism was analyzed by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). The PCR sense (5´-TGCTGTGACAGGCAGAGCAG-3´) and antisense (5´-GGTAGCCGGGAACTCCACGG-3´) primers were used to amplify the target DNA as previously described.ion site .Statistical Analysis<0.05 was considered statistically significant. All data were analyzed using SPSS 12.0 software.The difference in frequency distributions of the DNMT3B genotypes and allelotypes between the patients and the control group were analysed using the chi-square test. The odds ratios (ORs) and 95% confidence intervals (CIs) for the DNMT3B genotype were calculated by logistic regression analysis, with adjustment for age. A P value The clinicopathological characteristics of the study subjects are shown in 15,21 In agreement with this hypothesis, Shen et al. have reported that carriers of T alleles, particularly heterozygous (CT), had a significant increase in lung cancer risk compared to the homozygous CC genotype.18 However, we found that the CT genotype was significantly associated with decreased risk (2 fold) of breast cancer . Since we were unable to adjust for environmental risk factors  we could not exclude the possibility that such confounding factors might have led to a type I error. Possibly both factors were involved, therefore this discrepancy could be due to different functions of DNMT3B in different cell types. It has been reported that several spliced forms of DNMT3B, with different enzyme activity are expressed in a tissue specific manner.15,22,23 We also observed a decreased association between the CT genotype and lymph node involvement in breast cancer patients, which suggested that genetic susceptibility might play an important role in metastatic properties of aggressive breast cancer tumors. The results of other investigations regarding the association between DNMT3B single nucleotide polymorphism (SNPs) and the risk of cancer were conflicting. Wu et al. demonstrated that the C/T polymorphism was not associated with up regulation of DNMT3B and increased risk of hepatocellular carcinoma.23 These researchers observed a similar pattern of DNMT3B genotype among hepatocellular carcinoma patients (n=100) and healthy subjects (n=140).23 An investigation in north China showed that the C/T polymorphism was not associated with susceptibility to gastric cardiac adenocarcinoma.24 Montgomery et al. genotyped 352 cases and 258 controls from a British population and found that carriers of C alleles showed significant increases in breast cancer risk.19 Their findings did not agree with the hypothesis in which the carrier of T alleles should have higher susceptibility to cancer. They suggested this inconsistency might be an artifact that resulted from a chance variation or it might point to differing influences of promoter methylation in this type of cancer. In contrast to the research of Montgomery et al., the results of two other studies showed that carriers of T alleles, particularly the TT genotype, notably increased the risks of squamous cell carcinoma of the head and neck (SCCHN) and prostate cancer.25,26 The findings of the current study were not consistent with the previous conflicting data. However, the current study’s limitation was the inability to adjust the study data for confounding factors. As the results of different studies have shown, there is no consensus regarding the association between DNMT3B genotypes and the risk of cancer. These inconsistent results may be due to factors such as small sample size, different ethnic groups, geographic areas and inadequate adjustment for confounding factors.The mechanism of the association between DNMT3B 149 C→T polymorphism and the risk of cancer is not clearly understood. According to the underlying hypothesis, the C→T transition may up regulate DNMT3B expression, resulting in increased susceptibility toward aberrant de novo methylation of CpG islands of the promoter in some tumor suppressor genes and thereby increase cancer risk.Our case-control study showed that the CT genotype was significantly associated with decreased risk of breast cancer in our studied groups. Consistent with these results, we observed a significant decrease in CT genotype among lymph node positive breast cancer patients. Further studies with larger samples size and more clinical data are required to confirm these results.