Typically, lifetime risk is calculated by the period method using current risks at different ages. Here, we estimate the probability of being diagnosed with cancer for individuals born in a given year, by estimating future risks as the cohort ages.We estimated the lifetime risk of cancer in Britain separately for men and women born in each year from 1930 to 1960. We projected rates of all cancers (excluding non-melanoma skin cancer) and of all cancer deaths forwards using a flexible age-period-cohort model and backwards using age-specific extrapolation. The sensitivity of the estimated lifetime risk to the method of projection was explored.The lifetime risk of cancer increased from 38.5% for men born in 1930 to 53.5% for men born in 1960. For women it increased from 36.7 to 47.5%. Results are robust to different models for projections of cancer rates.The lifetime risk of cancer for people born since 1960 is >50%. Over half of people who are currently adults under the age of 65 years will be diagnosed with cancer at some point in their lifetime. What is the probability of developing cancer for someone born in a given year? The lifetime risk of developing cancer is the probability that a person will be diagnosed with cancer over the course of his or her lifetime. The lifetime risk is widely used as a popular measure of how widespread cancer is in a particular population . It is cmi(y) (per 100 000 person years) (historic and projected from 2010 based) for the period ‘y' 1951–2012 and projected for 2013–2060 by age ‘i'  and sex were downloaded from the Office for National Statistics website , as well as numbers of all cancer excluding non-melanoma skin cancer (ICD-10 codes: C00-C97 excluding C44) diagnoses Ri(y)  and numbers of all cancer deaths Di(y) , by the 5-year age group and sex, were provided by Cancer Research UK. They in turn received the data from ONS and the relevant national cancer registries.National population estimates The lifetime risk of cancer is estimated from the rates of cancer (incidence) and all-cause mortality. The basic idea is that at each age there is a chance of being diagnosed with cancer and a chance of dying. When the cancer incidence rates include second primaries, it is necessary to consider the mortality from causes other than cancer  up to different ages. Informally, the cumulative risk to age 80 years can be thought of as the probability of being diagnosed with cancer before the age of 80 years, assuming that there are no competing causes of death. That is, the risk in someone who does not die of something else before the age of 80 years. Hence, the cumulative risk of cancer to age 65 years will be very similar to the lifetime risk curtailed at the age of 65 years, but the cumulative risk to age of 100 years will be substantially greater than the lifetime risk . The advantage of the cumulative risk of cancer up to age of 85 years (say) is that it does not depend on all-cause mortality rates. Thus, an increase in longevity will lead to an increase in the lifetime risk of cancer, but will have no effect on the cumulative risk.ri(y) and mortality di(y) rates were modelled using age-period-cohort (APC) models and extrapolated out to the year 2040 using the statistical method that was published by In order to estimate the rates for years for which there are no data , it is necessary to model the existing rates and extrapolate. Full details are provided in A factorial-design sensitivity analysis was performed to investigate the effect of various assumptions and model parameters on the estimated lifetime risk. In particular, we varied the link function in the generalised linear model, the extent to which the linear drift is attenuated over time, the number of knots in the splines used for the age, period and cohort effects in the model and the method used for estimating rates before 1975. Exponential and power-5 link functions were used. In addition to moderate attenuation (75% over 17 years), we considered no attenuation and rapid attenuation (75% over 6 years). As an alternative to using 1975 incidence rates for all earlier years, we used the model fits for 1960–1974 and similarly for 1960–1970 for cancer mortality rates. In this sensitivity analysis, back projected rates for 1960 were used for all previous years (1930–1960).The estimated lifetime risks of developing cancer for men and women born in different years are presented in The estimated lifetime risk of cancer for both men and women born in 1930 was 38.5% in men and 36.7% in women . Over 30By comparing x, for x between 0 and 120 years). The cumulative risk rises rapidly after the age of 65 years. Indeed, it can be seen that the cumulative risk up to age 70 years is less than half the lifetime risk. In other words, over half of the lifetime risk of cancer for the 1960 birth cohort comes from cancer diagnosed beyond the age of 70 years. The cumulative risk for men is 12.9, 29.6 and 49.8% for ages 0–64, 0–74 and 0–84 years, respectively. For women, the cumulative risk is 15.1, 26.4 and 39.9%, respectively.It is interesting to speculate whether if people lived long enough virtually everyone would get cancer. The cumulative risk to age 120 years for men is nearly 90% but it is just over 70% for women. Thus, it would seem that virtually all men would get cancer if they did not die of other causes first. But in women, whereas the majority of women would get cancer if they did not die of other causes first, a substantial minority would not.The lifetime risk of cancer for men born since 1950 is >50%. For women it is slightly less. The lifetime risk for men born in 1960 is much greater than for men born in 1930: it increased from 38.5 to 53.5% or by a factor of 1.39 and we would expect this increase to continue into the future. Much of this increased risk is due to increasing longevity: the cumulative risk until the age of 84 years was 46.6% for men born in 1930 and it has increased by a factor of just 1.07% to 49.8% for men born in 1960. Similar increases are seen in women, but the impact of increasing longevity is less dramatic—lifetime risk increases by a factor of 1.30, whereas the cumulative risk up to the age of 84 years increases by 1.11 (1960 birth cohort compared with that of 1930).Men born since 1950 have >1 in 2 chance of being diagnosed with cancer at some point in their lifetime. It is noticeable that the lifetime risk increased more rapidly for cohorts between 1930 and 1940 than between 1950 and 1960 . To the Beyond the effect of increasing longevity, increasing age-specific rates of cancer have also had an impact on the increasing lifetime risk of cancer. Whereas smoking-related cancers have become less common in men, other cancers have become more common.In women, breast and lung cancers have increased substantially since the mid 1970's. The increase in breast cancer is related to lifestyle changes, such as increasing obesity  is small and variations in cancer rates in children, teenagers and young adults will have little impact on the eventual lifetime risk. By contrast, someone born in 1960 is unlikely to have died and would, in 2012, be 51 years so most of their lifetime risk is extrapolated.The sensitivity analysis  shows thThe results of this analysis should enhance public health messages and improve resource planning for both commissioners and providers of healthcare in the UK. It may also assist clinicians and patients to weigh the lifetime risk of developing cancer versus other challenging health risks. Whereas the results of this analysis are specific to the British population, the methodology can be applied to other populations.


The Predicting Risk of Cancer at Screening study in Manchester, UK, is a prospective study of breast cancer risk estimation. It was designed to assess whether mammographic density may help in refinement of breast cancer risk estimation using either the Gail model (Breast Cancer Risk Assessment Tool) or the Tyrer-Cuzick model .Mammographic density was measured at entry as a percentage visual assessment, adjusted for age and body mass index. Tyrer-Cuzick and Gail 10-year risks were based on a questionnaire completed contemporaneously. Breast cancers were identified at the entry screen or shortly thereafter. The contribution of density to risk models was assessed using odds ratios (ORs) with profile likelihood confidence intervals (CIs) and area under the receiver operating characteristic curve (AUC). The calibration of predicted ORs was estimated as a percentage [(observed vs expected (O/E)] from logistic regression.The analysis included 50,628 women aged 47–73 years who were recruited between October 2009 and September 2013. Of these, 697 had breast cancer diagnosed after enrolment. Median follow-up was 3.2 years. Breast density  was a slightly stronger univariate risk factor than the Tyrer-Cuzick model  or the Gail model . It continued to add information after allowing for Tyrer-Cuzick  or Gail .Breast density may be usefully combined with the Tyrer-Cuzick model or the Gail model.The online version of this article (doi:10.1186/s13058-015-0653-5) contains supplementary material, which is available to authorized users. Breast cancer risk models estimate the chance that a woman will develop breast cancer in the future, and a more accurate assessment is needed to guide prevention and screening strategies . Risk isMammographic density appears as white (radiopaque) areas on a mammogram, and it is often measured visually as a percentage of the total breast area. Dense breasts have more fibroglandular tissue and less fat than non-dense breasts, and it is well established that women with these features are at a higher risk of breast cancer . DensityAnother visually assessed density measure is the percentage of the area of the breast containing fibroglandular tissue. Methods for this have been observed to produce a 4–6-fold risk difference for dense versus non-dense breasts , and theThe objective of this study was to assess whether visually assessed percentage density might improve the Tyrer-Cuzick and Gail risk models for risk assessment of women attending screening in the United Kingdom. We did so using a prospective screening cohort of women enrolled in the Predicting Risk of Breast Cancer at Screening (PROCAS) study from Manchester, UK .http://www.uhsm.nhs.uk/research/Documents/PROCAS%20Questionnaire.pdf). Each completed questionnaire was imported into a database and verified using a set of rules to check for inconsistencies. Screening mammograms were collected and stored. The earliest mammograms were film (20 %), but the majority used GE Senographe Essential full-field digital mammography .All women invited for routine mammographic screening between October 2009 and September 2013 across 15 screening areas in Greater Manchester, UK, were mailed a questionnaire, study information and a consent form. The two-page questionnaire was designed to collect family history as well as hormonal and lifestyle risk factors for breast cancer .The primary clinical endpoint was diagnosis of breast cancer  from entry screen onwards, as identified through the National Health Service Breast Screening Programme (NHSBSP) system and the Somerset and North West Cancer Intelligence services. In secondary analysis, we considered invasive breast cancer only. Prospective breast cancers occurred between October 2009 and September 2014, and the median follow-up was 3.2 years. Cancers detected less than 100 days after enrolment were defined as being detected at the entry screen.http://www.ems-trials.org/riskevaluator/) . Logistic regression models were fitted to assess the calibration of predicted logarithmic ORs, and observed risk was the predicted risk multiplied by an estimated calibration coefficient. Observed and expected ORs were plotted using a normal kernel smoother with bandwidth chosen by 10-fold cross validation  of cancers were diagnosed at entry screen, 28 (4 %) between 100 days and 2.5 years after enrolment and 110 (16 %) more than 2.5 years after enrolment. For three women, the timing was unknown at the time of analysis. Ethnic or other origin was recorded for 48,807 women (96 %), of whom 46,491 (92 %) were reported as white (453 Jewish); the remainder were Asian (n = 739), black (n = 556), mixed race of ethnicity (n = 262) and other (n = 759).The majority . The percentage variance in density explained by reader differences was estimated to be 11 %. Half of the absolute percentage differences between readers were less than or equal to 10.00 %; the IQR was 4.75–17.75 %. The intraclass correlation coefficient between left and right sides was 93 %.2). Twelve percent of women reported first-degree relatives with breast cancer, and 14 % disclosed a prior breast biopsy.Distributions of breast cancer risk factors in the cohort are shown in Table 2 . However, the Gail model performed better for cancers detected after entry screen  of expected for Tyrer-Cuzick and 46 % (95 % CI 26–65 %) of expected for the Gail model. Figure 2 9.2), similarly for Tyrer-Cuzick (LR χ2 48.8). There was little evidence of interaction by age group .Overall, the Gail model was not more informative than age . However, Table P < 0.001). It was higher in women with cancer  than in those without  (P < 0.001). After adjusting density for age, BMI and type of mammogram in the density residual, we observed that the LR χ2 was doubled from 27.4 to 61.4, and it was a stronger univariate risk factor than either model .Visually assessed breast density was inversely correlated with BMI (Spearman correlation coefficient −0.38) and age (Spearman correlation coefficient −0.19). Density was less for digital mammograms  than for film   of the cancers were diagnosed at entry, but the distribution of breast density and residual was similar in cancers diagnosed at screening versus later Fig. . Further2 statistics. Age at menopause is not in the Gail model, so this is part of the reason why overall the Tyrer-Cuzick model performed better. It was also interesting to note that a self-reported previous biopsy conferred more information than did presence of affected first-degree relatives. Mammographic density was a stronger risk factor than the risk factors used by the Tyrer-Cuzick or Gail models, after adjustment for age and BMI.The results of univariate analysis of breast cancer risk factors shown in Table Although the risk models were found to provide useful information for risk stratification without mammographic density, their relative risks were not well calibrated. A partial reason is that some risk factors were not as strong as expected, the most noticeable being family history .Our study differs from some other work in that we used a percentage visual assessment rather than the four-category BI-RADS scale. An advantage is that the visual percentage is not designed to assess masking issues, where dense tissue may make it more difficult to detect cancer in mammograms. This is relevant because in the most recent version (fifth edition) of the BI-RADS scale, specific reference to percentage density was removed  to focusEarlier studies have also shown that continuous measures of percentage density are useful in combination with classical risk factors. Chen et al.  used a cSome limitations of the study include the following. Firstly, the risk factors were self-reported via a questionnaire. Secondly, breast density was used to predict risk at the same screen, and the subgroup analysis by time of diagnosis since enrolment was limited. More follow-up is needed to help to address this issue. Thirdly, the visually assessed score required human judgement, which might make it unreliable for routine use in a screening program , althougTwo further criticisms might be made of the study. Firstly, it might be argued that the Gail model predicts the absolute risk of invasive cancer and does not include DCIS. However, the relative risk model was fitted to invasive cancer and DCIS , which i2 was doubled, and a larger proportion of women could be accurately classified to be at more than a moderately high risk when density was combined with either model. However, the AUC values remained modest, so there is still more to be done.We used a prospective cohort from the United Kingdom to test whether visually assessed mammographic density added to the Tyrer-Cuzick and Gail models for women who attend breast cancer screening. Discrimination measured by changes in LR χTo our knowledge, this study is the first in which the Tyrer-Cuzick risk model has been evaluated in a prospective screening setting. Although the analysis was limited by the cross-sectional nature and issues such as lead time, the Tyrer-Cuzick model was found to provide useful information for risk assessment. The Gail model has been well validated for use in North America, but in this UK setting the Gail model was outperformed by the Tyrer-Cuzick model.In conclusion, the data in this report are of relevance for designing improvements to the national screening program in the United Kingdom. By combining the Tyrer-Cuzick model with breast density, we identified 72 % of the population which had an average or below average risk of breast cancer, and they had proportionally fewer breast cancers. Such results might be used to inform modelling of the effect of different strategies for risk-adapted screening and prevention.


Background: Mammography as the most common secondary prevention method has known to be helpful in detecting breast cancer at the early stages. Low level of participation among women toward mammography uptake due to cultural beliefs is a great concern. This study aimed at exploring the perceptions of women about response costs of mammography adherence (MA) in Yazd, Iran.Methods:  A qualitative study using semi-structured interviews was performed. Fourteen women,one oncology nurse, and a breast cancer survivor were purposefully interviewed. Interviews were transcribed verbatim and analyzed by directed content analysis method based on protection motivation theory (PMT).Results: One main theme was emerged from the analysis namely called "response costs".Two main categories were also emerged from the data; (1) psychological barriers with six subcategories including "embarrassment," "worry about being diagnosed with cancer," "preoccupation with underlying disease," "misconception about mammography," "need for an accompanying person," and "internalizing the experiences of the others," and (2) maladaptive coping modes which encompassed three subcategories: "religious faith," "fatalism," and"avoidance and denial."Conclusion: Useful information was provided about the response costs of mammography utilization based on the perceptions of women. Cognitive barriers may be decreased by conducting modifications in women’s awareness and attitude toward MA as well as changing the national health system infrastructures. Incorporating religious and cultural belief systems into MA educational programs through motivational messages is recommended. Such a shift in the lifestyle, which is associated with reproductive, hormonal, and dietary risk factors, has led to an increase in the burden of breast cancer.3 Epidemiological studies based on the age-specific incidence in Yazd province showed breast cancer as the most prevalent cancer.4 Despite the annual increase in the incidence rate of the disease among Iranian women, there is a lack of recent evidence on its various aspects in Yazd, Iran.5It is noteworthy that the age of onset for breast cancer among Iranian women is reported to be up to ten years earlier than that in the developed countries.6 Although mammography screening programs are being implemented for all older than 40 women in several countries like the United Kingdom and the United States,7 there is no formal mammography screening program in Iran. Unfortunately, 70% of Iranian women with breast cancer are diagnosed in the advanced stages, when it is too late for successful treatment.9 It is estimated that patients with breast cancer living in the lower-income countries have lower 5-year survival rates in comparison with their counterparts in the higher-income countries at any stage of the diagnosis.10 Lack of mass media education and screening programs, poverty, poor access to health care facilities, cultural barriers, and poor breast awareness are associated with lower breast cancer survival in the developing countries.11Mammography screening has been shown to be associated with early detection and, therefore, mortality and morbidity reduction of breast cancer.12Based on those mentioned above and, also, the recommendation of the World Health Organization (WHO) on mammography as the only effective breast cancer screening method, it seems to be necessary to encourage Iranian women for mammography adherence (MA).13 Moreover, Lwin reported the lack of mammography awareness among Singaporean women in a way that they did not believe on the necessity of mammography behavior.14 According to the findings noted by Thomas et al, Iranian women experience several barriers in relation with breast cancer prevention and control such as the lack of knowledge, personal functioning, motivation, availability of information, health communication, and support.15 In the present qualitative study, the framework for interview questions and data analysis was based on the protection motivation theory (PMT). PMT was originally proposed by Rogers in 1975. It is encompasses of two independent appraisal pathways resulting from fear appeals; threat appraisal and coping appraisal.16 people might respond adaptively or maladaptively to the health information.17 In the threat appraisal pathway, an increase in severity, vulnerability, and fear may result in a decrease in the probability of occurrence of the maladaptive response. In the coping appraisal pathway, people evaluate the usefulness of the behaviors (response efficacy) and their ability to perform, successfully, preventive behaviors . Two important factors that might increase the likelihood of maladaptive response are intrinsic and extrinsic rewards , and response costs (barriers). The key point in this theory is adaptive behavioral change, which is reinforced through motivation.18 Protection motivation is the result of these two cognitive pathways .20 Because of the differences exist in cultural contexts, the perceptions of women in Yazd about the breast cancer preventive behaviors may be different from those of the other countries. Thus, the findings in the other cultures may not be applicable for Iranian women. This study was designed to explore the perceptions of women about response costs of MA and to provide more insight on this cultural sensitive topic.There are some perceived barriers influencing the mammography behavior; for instance, Donnelly et al in a study on the Qatari women showed that socioeconomic factors and awareness of recommended national guidelines of breast cancer screening may influence breast health seeking behaviors.In this qualitative study, directed content analysis was employed to analyze the data obtained by in-depth interview approach. The semi-structured interviews were conducted after designing a flexible framework for interview questions based on the PMT. The method section was provided based on the consolidated criteria for reporting qualitative research (COREQ) guideline. In the current study, the following key question was explored: What are the perceptions of women about the response costs of MA?a) having the age of 35 years or higher, (b) not suffering from mental disorders based on their records, and (c) the ability to communicate. The inclusion criteria for the nurse were as follow: (a) being with at least one year of nursing experience in an oncology unit, (b) having, at least, a bachelor degree in nursing, and (c) being female. Breast cancer survivor and oncology nurse were elected as critical cases in this study. The critical cases were considered to be interviewed hoping to address the relations that were being studied.21 Since oncology nurses have close interaction with breast cancer patients and their families, they were considered as key informants or experts in the study. Their perceptions and experiences were valuable to arrive a deep understanding of the effective factors on women’s breast health-seeking behaviors. Since the real experience of the participants is important in a qualitative study, a breast cancer survivor was chosen as a key informant, as well. The breast cancer survivors may have a good experience and deep understanding about the process of diagnosis and treatment of the disease. Moreover, as factors like nulliparity and the old age at the first live labor could enhance the chance of breast cancer occurrence, two old girls were interviewed as the persons at the higher risk for the disease. Thus, it was possible to get more insight about the subject. Maximum variation in sampling was considered with the participants’ age, marital status, education, occupation, and the breast disease history.The study participants were 12 women, one oncology nurse, and a breast cancer survivor, all from Yazd, Iran, who were invited to participate in the study through purposive sampling. The eligibility criteria for the women were considered to be as the following: . Meanwhile it was very flexible because of using semi-structured and in-depth individual interviews. So, when needed, some necessary changes were made in the interview questions by the interviewer. The first researcher was the main interviewer and data analyzer. A set of 14 audio-taped interviews was carried out from September to December 2014. The other researchers, who were faculty members affiliated with the Yazd University of Medical Sciences, Yazd-Iran and Tarbiat Modares University, Tehran-Iran, monitored and revised the data analysis process, step by step, to fulfill the systematic analysis. Fourteen participants aged 35 and older participated in the study. All were living in Yazd city, Iran. The interviews were performed with prior appointment with the participants and they lasted for 30-90 minutes in a quiet and calm room at a clinic near to the participant’s home. The interviews started with open and general questions; however, the questions became gradually more detailed as the interviews were being progressed. The interviewer tried to keep the participants in the main path of the study in order to achieve the research objectives. The main focus of the interview questions were “what is the meaning of cancer in your idea?,” “what factors may prevent you from doing mammography?,” and “what are the ideas of the persons close to you about MA?”The current study was an attempt to identify the response costs of MA among women in Yazd, as a part of a more comprehensive exploratory mixed method study for the first author’s doctoral dissertation. Directed qualitative content analysis based on the constructs of the PMT was used to data analysis, but, in this paper the results of the qualitative data analysis on the construct of response costs were, only, reported.22 Each interview was transcribed verbatim. The texts were imported into MAXQDA software version 10 for computer-assisted qualitative data analysis. The researcher read the texts several times to obtain a whole sense on the data. Unit of analysis and meaning units were considered. Open coding technique was adopted. The initial codes were organized into categories and subcategories. Different methods have been used in order to increase the trustworthiness of data analysis. For example, sufficient time was allocated for data collection and analysis. Constant comparative analysis was used throughout the process of analysis. The researcher bracketed her thoughts, preconceptions, and beliefs before and while performing the data analysis to ensure credibility. Some other strategies such as prolonged engagement with participants, faculty member’s revisions, and member checks were used to enhance the rigority of the data.In this study, qualitative directed content analysis was performed using Burnard’s 14-stage approach.embarrassment, worry about being diagnosed with cancer, preoccupation with underlying diseases, misconceptions about mammography, need for an accompanying person, and internalizing the experiences of the others. The subcategories underlying the category of maladaptive coping modes were religious faith, fatalism, and avoidance and denial. Brief overview of the thematic structure of the results could be found in Analysis of the understandings of the Iranian women about their perceived response costs of MA led to formation of one main theme, two categories, nine subcategories, and 143 initial codes. Response costs factor was considered as the main theme. Psychological barriers and maladaptive coping modes were determined as categories. The subcategories underlying the category of psychological barriers were EmbarrassmentSome participants stated that they were ashamed of mammography screening. In this regard, the old girls with no partner had comments different from the married women. They reported to experience a higher level of embarrassment than the married women because of their narrow territory to seek mammography, late referral to a gynecologist, being ashamed of disease follow-up, misunderstanding of the others about the reasons forgoing to a gynecologist, and, finally, not being comfortable to announce the family about the gynecologist visit. For instance, some participants reflected these concerns as follows:In Yazd, when a girl goes to a gynecologist clinic, the others have a reproachful glareat her and think on what has happened that she visits a gynecologist. The family also has the same opinion”, (P8). Another participant told: “It is mentally difficult for a girl to announce her family about visiting a gynecologist. One can easily speak about visiting a dentist, but the case for a gynecologist appointment is not so easy” (P7).“Worry about being diagnosed with cancer Worry about the mammography result is another barrier. In other words, worry about hearing bad news (being diagnosed with cancer) and have no preparedness to confront with breast cancer may reduce the women’s motivation for MA. For example, one of the participants said:If now I undergo mammography, the result may be OK. But I am afraid that if six months later I did the procedure, the result would be bad. Then, how should I deal with the disease. We always are waiting to hear good news. Nevertheless, I expect to be affected by breast cancer, as the disease has been occurred in my family” (P9).“Preoccupation with underlying diseasesWomen announced that suffering from another physical health problem may be a competing priority with breast cancer preventive behavior that may lead to a negative attitude toward mammography. For instance, one of the participants with diabetes said: “Since I have diabetes, I do not like to do anything with my physical health. I am mostly dealing with my blood sugar. Two years ago, I visited a doctor who prescribed me a medication that disturbed the level of my blood sugar. I am afraid that I visit a doctor for the breast issues, and then I had to take some medications which may increase the level of my blood sugar” (P2).Misconceptions about mammographySince I have many breast cysts and I have undergone a surgery once, I am worry about the stimulation and rupture of the cysts during mammography procedure. Won’t they [the cysts] be pressured a lot while mammography procedure?” (P6). Uncertainty about the harmfulness of the mammography rays was another concern mentioned by women: “I don’t know what kind of radiations mammography has? However, I have heard that it may be harmful for the body and should not be performed frequently” (P13).The most of the women participated in the study believed that mammography might be harmful for breast tissue. Some of them noted that breast cysts could be ruptured during mammography procedure. For example, one participant reflected her concern as it follows: “Need for an accompanying personI would like to be accompanied with a close person when going to mammography. When I find someone to be accompanied with, a long period of time has been passed” (P5).In the current study, the participants expressed that they need for a reliable and close person to accompany them while going to the mammography center. Because of this, they might lose the time or quite forget it. One of the participants said: “Internalizing the experiences of the othersAccording to the women’s statements, they could be highly influenced by each other. In other words, when women talk about mammography, they may describe their unpleasant experiences about it and thus decide not to uptake the mammography test:I am a hairdresser. Once, one of my customers came to me just after undergoing mammography. She was very pale and had nausea. It seems that she was compressed. For that reason, I never want to undergo mammography” (P6).“Everyone asks an intimate friend; one who understands her and is available. It is not easy for me to visit a doctor and consult her” (P8).Also in relation to breast cancer, the women may ask their questions from unaware and close persons rather than health care providers because of having easy access to them. One participant explained this matter as follows: “Religious faithParticipants’ religious beliefs have been found to be strong barriers for seeking mammography. the belief in the possibility of not being affected by breast cancer and considering it as a gift of God or God’s will were other issues discussed by the participants. This consideration has been well expressed by one of the participants:It is natural for people to believe in not being affected by breast cancer. Maybe God has created us in this way. If it was in another way, perhaps we were tortured. God may have put it inside us” (P10).“It is God’s will that people are being affected by breast cancer. God draws some of us for the disease. God is the one who draws” (P7).Another woman said: “Participants mentioned that the best way for dealing with cancer is turning to spirituality, praying, and delegating the problem to God. For instance, one of the participants commented as follows:If I were diagnosed with breast cancer, I would pray for God more and more as I believe that the doctors cannot do anything for me and only God can cure me” (P10).“Being affected by cancer is related to our deeds, and when someone has cancer, God examines her or him” (P3).Another woman stated that having breast cancer maybe a type of punishment from God because of mistakes people do in the past. She, also, noted that: “FatalismAll these things occur by chance. I think that chance plays a very important role in every event” (P3). Another participant expressed that the cause of death for each person is being determined, previously, and an individual is considered to have a limited control over his/her death: “Everybody must die in a way; one in a car accident, another experiences stroke, and some others may be diagnosed with breast cancer. There must always be a cause for death” (P4).Women participated in the current study mainly emphasized on the role of chance as an important factor which may limit the women’s power to prevent breast cancer. One of the participants quoted: “Avoidance and denialSome of the participants declared that they never think about breast cancer. Issues related to this subcategory were optimistic bias and an unreal assumption of being safe which may be considered as restrictive factors for mammography uptake. For instance, a participant said:You know, I think I will never be affected by breast cancer, as in a proverb it is said ‘human beings think that all other people will die except themselves’” (P1).“Another participant reflected the fact as follows:When I think about something, for example when I have flank pain and think about it, it seems that the pain aggravates. It is really in this way; thinking to pain worsens it. I try not to think about breast cancer and if it comes to my mind, I remove it” (P11).“The findings of the current study demonstrated a wide range of factors including psychological barriers and maladaptive coping modes that may affect the women’s adherence to mammography. Some of the aspects are new and there is no evidence about in the literature. These new findings are valuable as they show the effect of cultural context on the breast health-seeking behaviors of the Iranian women.23 The new and unique aspect of this study was the comments of the old girls about the shyness of referring to a gynecologist clinic or seeking help for breast cancer prevention. To the best of our knowledge, no report is available in the literature in this regard. The interviewer (the first author) is from Yazd city, Iran and notes that the old girls especially in the older ages are not willing to present any abnormal sign of breast or genital organs because of their fear about the opinions of the people around. In some cases, they might lose some suitable opportunities for marriage in the future. Therefore, in such a cultural context, it is considered inappropriate for an old girl to go to a gynecologist; in the other hand, the family may become remarkably worried about. Unfortunately, the old girls in Yazd are dealing with this problem and there is no evidence to compare the aforementioned results and, so, further investigation is needed.In the present study, according to the participant’s statements, embarrassment associated with mammography was considered as a vital response cost. This is consistent with those found by Fleming et al.24Moreover, worry about being diagnosed with cancer was mentioned as another psychological barrier by the participants, which is in line with the findings reported by Whitaker et al.25 another psychological barrier found in the present study was preoccupation with underlying diseases which was considered as a part of the competing concerns.Similar with those reported by Lamyian et al,26 Also, satisfaction with medical care was found to be important especially among asymptomatic women who decide to uptake mammography.27 Another factor was being worry about x-rays as a major barrier for mammography, which is consistent with the results of Sadikoglu et al.28There were, also, some misconceptions about the mammography procedure emerged during the qualitative analysis in the present study. For example, there was a belief that mammography might have severe complications for the breast tissue and, so, some participants especially those who had the history of breast cysts avoided from mammography uptake. The participants declared that they were less likely to undergo mammography because they follow what they hear from the others. Therefore, the impact of subjective norms is recalled as an important factor in the process of personal decision-making.29According to the participants’ comments, it was difficult for them to go to mammography clinics alone and they prefer to be accompanied by a close person like a sister or a close friend. It was concluded that embarrassment and fear of being diagnosed with cancer may affect the women’s self-esteem in MA. This issue could certainly waste the golden time for the early detection of breast cancer. Therefore, proper communication among the health care providers and women referring to the mammography centers may enhance the mutual trust and peace of mind. Accordingly, Meguerditchian et al showed that communication skills could be principal predictors of MA.30 Some of the participants in the present study believed that being affected by breast cancer might be a God’s gift. The others expressed that the disease could be a probe or punishment from God, as they may already have committed a sin or guilt in their lifetime. These findings are similar to the results of a qualitative study by Azaiza and Cohen.31 Moreover, Hatefnia et al showed that religious beliefs have significant association with mammography uptake among Iranian women.32 The study participants believed that medical treatment for breast cancer is useless and God could cure them, only. Mitchell et al declared that it is likely for delay to be happened in presenting a self-discovered breast lump among women with strong religious beliefs; especially belief in “religious intervention in place of treatment.” The researchers suggested the interaction between clinicians and clergies to correct wrong beliefs to enhance breast health-seeking behaviors.33As all the study participants in the present study were Muslim, religious belief was another issue that was emphasized by the participants. This concept was titled as maladaptive coping modes and grouped into three broad sub-categories. In a previous study, Zarghami stated that religion and spirituality were as significant barriers for Iranian women to seek medical screening and care in early stages of breast cancer; nevertheless, the role of such religious factors on cancer screening behaviors among women is not well understood.34 The participants in the present study claimed that human beings have no control on his/her death and cancer diagnosis will, certainly, result in death. Spurlock and Cullins pointed out that fatalism could negatively influence breast cancer screening behaviors.35 Also, the results of the study conducted by Donnelly et al showed that Arab women living in Qatar reported being diagnosed with cancer as a bad luck.36 It was concluded that women’s perceptions on cancer fatalism is a cultural issue and should be further investigated especially in countries such as Iran that holds culturally diverse population groups.Fatalism was conceptualized as “passively denying personal control” and encompasses the dimensions such as personal perceived lack of control over external events, luck, predetermination of a disease, and belief of inevitable death by a serious disease.37The most of the women in this study noted that they have never thought about the likelihood of being diagnosed with breast cancer and the role early detection in the disease survival. They always supposed to have immunity against the disease; thus, they feel no need to have a mammogram. This finding is consistent with those of reported by Hasson et al. who revealed that 16.5% of the participants reported no thinking about undergoing mammography.The current results gave us a more detailed picture on how Iranian women perceive response costs of breast health-seeking behaviors. Although, women are responsible for their own health within the context of breast cancer prevention, governmental policy makers are, also, responsible for provision of women’s health care facilities. In Iran, despite the high incidence of breast cancer, mammography screening is not an integrated routine service in the health care system. It was concluded that women in Yazd perceive some cultural-based barriers for MA. It is necessary to identify the priorities and the existing gaps in the country through accomplishing interdisciplinary scientific researches and specific interventions. Beside the development of women’s health policy in Iran, it is suggested that religious and cultural belief systems be incorporated into the educational programs applying persuasion- based health monitoring approach. Health educators may have good opportunities to design theory-based educational interventions within which the belief systems are being incorporated with the hope to initiate adaptive coping reactions.Research protocol was approved by the Research Council and Ethics Committee affiliated by Yazd Shahid Sadoughi University of Medical Sciences, Yazd, Iran. Voluntary participation, anonymity, confidentiality, and withdrawing from the study at any time without any penalty were some issues that were explained for the participants. All the interviews were recorded with a sound recorder after obtaining permission and signing an informed written consent form by the participants.The authors declare no conflict of interest.MK was the main investigator and contributed to development of research protocol, implementation of the research, and drafted the manuscript. SSMM supervised the study and scientific integrity of data collection and revision of the manuscript. ML was advisor to the qualitative study, participated in the design of the study and interpretation of data and revising the manuscript. MAM and Ht were advisor to the study, participated in the interpretation of data and revising the manuscript. All authors have read and approved the final manuscript.This study was financially supported by Shahid Sadoughi University of Medical Sciences, Yazd-Iran. The authors are grateful to all women who participated in the study.


Dioxins are environmental and persistent pollutants mostly emitted from combustion facilities . Known to be endocrine disrupting chemicals, dioxins are suspected to increase breast cancer (BC) risk. Although diet is considered the primary source of dioxin exposure, no previous study has been published on dietary dioxin exposure in relation to BC risk. We aimed to assess dietary dioxin exposure among women from the E3N cohort and estimate BC risk associated with this exposure.The study included 63,830 women from the E3N cohort who completed a diet history questionnaire (DHQ) in 1993 and were followed until 2008. Dietary dioxin exposure was estimated by combining consumption data from the E3N DHQ and food dioxin contamination data from a French national monitoring program. Hazard ratios (HR) and 95% confidence intervals (CI) were estimated by Cox models adjusted for BC risk factors.vs. Q1: 0.65; 95% CI: 0.45, 0.96; P for trend across quartiles = 0.0463).Mean dietary dioxin exposure was estimated at 1.3 ± 0.4 pg/kg body weight (BW)/day. A 0.4 pg/kg BW/d increase in dioxin intake was not associated with overall BC risk . A significant decrease in risk of estrogen receptor negative (ER-)/progesterone receptor negative (PR-) tumors was observed among post-menopausal women in the upper quartile of estimated dioxin intake  contains supplementary material, which is available to authorized users. Because they are highly lipophilic and persistent pollutants, dioxins can contaminate the food chain and accumulate in adipose tissues [Polychlorinated dibenzo tissues . Therefo tissues -3. Most -p-dioxin (TCDD) is the most toxic congener and has been classified as carcinogenic to humans by the International Agency for Research on Cancer (IARC) and the US Environmental Protection Agency (US EPA) [Dioxins are known to be endocrine disrupting chemicals  and susp(US EPA) ,6.Few epidemiological studies have investigated breast cancer risk associated with dioxin exposure, and there have been inconsistent results. Among the cohort of residents exposed to TCDD following the Seveso accident Italy, 1976), no increased breast cancer risk was observed among women living in the areas most exposed to TCDD 6, no inc. NeverthTo our knowledge no epidemiological study has explored the association between dioxin exposure through diet and breast cancer risk. Therefore, our study aimed to assess dietary dioxin exposure among women from the French E3N prospective cohort and estimate breast cancer risk associated with this exposure.Etude Epidémiologique auprès de femmes de la Mutuelle Générale de l'Education Nationale) study is an ongoing prospective cohort involving 98,995 French female volunteers, born between 1925 and 1950 and members of a national teachers’ health insurance plan [The E3N (nce plan . The E3Nin situ breast cancer (n = 483) and those for whom follow-up information was unavailable (n = 822). We further excluded 3,302 women with missing body weight (BW) in 1993 and 20 women who had never menstruated. Finally 63,830 women were included in our study population.Women included in our study population were participants of the E3N study, who had completed the E3N diet history questionnaire (DHQ) in 1993 and were followed until June 2008. Among the 74,522 women with dietary data, women having under- or over-reported their consumption, that is, those in the bottom and top 1% of the energy intake to basal metabolic rate ratio computed on the basis of age, height and weight, were excluded . In addition, we excluded women who reported cancer diagnosis (except non-melanoma skin cancer) before completing the E3N DHQ  along with those who developed From the third self-administered questionnaire to the ninth, data on health status and medical history of women, including cancer occurrence, were collected and updated. A total of 3,465 women reported primary invasive breast cancer between 1993 and 2008 and 92% of them were confirmed by pathology reports. Information on estrogen receptor (ER) and progesterone receptor (PR) were extracted from these reports, and invasive breast cancer cases were classified as ER+/PR+ , ER+/PR- (n = 561) or ER-/PR- (n = 414). Because of the small number of cases, the ER-/PR+ invasive breast cancers were not considered in the analyses (n = 107). Because the proportion of false-positive self-reported breast cancer in the E3N cohort was <5%, breast cancers not confirmed by pathology report (n = 285) were considered as cases. Deaths were identified through family contact or from insurance files, and causes of death were obtained from the French National Service on Causes of Death .Dietary data were collected from a validated self-administered DHQ, specifically developed for the assessment of the previous year's usual diet of the E3N population and sent in 1993 . The E3NConseil Supérieur d’Hygiène Publique de France (CSHPF)) [An assessment of the level of food contamination by dioxins (PCDDs and PCDFs) in France was published in 2000 by the French High Council for Public Health ((CSHPF)) . These cDietary dioxin exposure in our study was estimated for each woman, by combining consumption data from the E3N DHQ and lipid levels from the food composition table developed by the E3N team with food dioxins contamination data from the CSHPF report , Ci,k is the consumption of the food k by the woman i (in g/day), Fk is the proportion of lipids of the food k (in %), Lk is the average dioxin contamination of the food k  reported in the E3N questionnaire of 1993.where The intake of PCDDs and PCDFs was calculated in toxic equivalent (TEQ) using toxic equivalence factors (TEF) first developed by the North Atlantic Treaty Organization (NATO) in 1989 . In 2001Participants contributed person-years of follow up from the date they completed the E3N DHQ to the date of diagnosis of primary invasive breast cancer, date of death, date of return of the ninth questionnaire or June 2008, whichever occurred first. Estimated dietary dioxin exposure was categorized according to quartiles of its distribution in the study population. Baseline characteristics of the participants were described by quartiles of estimated dietary dioxin exposure using mean and standard deviation (SD) for quantitative variables and frequency and percentage for qualitative variables.2; considered as a time-dependent variable), total energy intake excluding alcohol , alcohol intake , education (undergraduate/postgraduate with a 1- to 4-year university degree/postgraduate with a 5+ year university degree), physical activity at baseline ), smoking status , age at menarche , previous use of oral contraceptives (never/ever), previous use of progestin before menopause (never/ever), menopausal status combined with use of menopausal hormone treatment (MHT) , age at menopause , age at first full-term pregnancy combined with number of live births (no child/≤2 children and age at first birth <30 years/≤2 children and age at first birth ≥30 years/≥3 children), breastfeeding (never/ever), previous family history of breast cancer (yes/no), previous history of personal benign breast disease , previous mammography . Further adjustments for food group consumption  were made. All adjustment variables had less than 5% missing data that were replaced by their modal or median value in the population. Information on estimated dietary dioxin exposure was available for all women of the study population.Hazard ratios (HR) and 95% confidence intervals (CI) for breast cancer were estimated for an increase of 0.43 pg TEQ/kg BW/day  and for quartiles of estimated dietary dioxin exposure with first quartile used as reference, using Cox proportional hazard models, stratified by 5-year-interval birth cohorts (from 1925 to 1950). Women’s age was used as the timescale. To evaluate potential dose-response associations, tests for linear trend across quartiles were derived from the Wald test of the models with the semi-continuous variable. All models were adjusted for known breast cancer risk factors and potential confounders: height (cm), body mass index (BMI) before and after menopause  for data analysis.We also estimated risk of invasive breast cancer according to hormone receptor status (ER/PR). We excluded cases for whom joint hormone-receptor status (n = 787) was missing from the corresponding analyses. Interactions between BMI and estimated dietary dioxin exposure were tested among premenopausal and postmenopausal women in relation to breast cancer risk because of opposite associations between BMI and risk of breast cancer before and after menopause  and the patterns ). Becauspatterns ,29 whilepatterns . All tesWe analyzed data from 63,830 women with a median follow up of 14.9 years, corresponding to 888,505 person-years and during which 3,465 incident invasive breast cancers occurred. The food groups most consumed were fruit and vegetables (mean ± SD: 724.8 ± 288.5 g/d), dairy products (341.0 ± 200.1 g/d) and cereal products . There was also no association between estimated dietary dioxin exposure and ER+/PR+ or ER+/PR- breast cancer risk. However a borderline statistically significant decrease in ER-/PR- breast cancer risk was observed across quartiles of estimated dietary dioxin exposure . When stratifying by menopausal status, we observed a statistically significant decrease in ER-/PR- breast cancer risk  . There was no significant interaction between BMI and estimated dietary dioxin exposure among pre- and postmenopausal women in relation to invasive breast cancer risk. There was no effect modification by smoking, alcohol consumption, dietary patterns, gain or loss of weight during follow up and breastfeeding, and there was no association between estimated dietary dioxin exposure and breast cancer risk in any above-defined subgroups  mostly reported a weak increase in the risk of breast cancer ,14 or thet al. found a lower risk associated with higher adipose tissue concentrations of dioxin-like organochlorines in postmenopausal women [et al. observed a lower risk of ER-/PR- breast cancer in women with serum levels of PCBs in the highest tertile compared to the lowest [et al. highlighted the fact that the effect of TCDD may vary according to the target organ as well as windows of exposure [Our results suggest that exposure to dioxins in the low range observed in our study may be associated with a decreased risk of hormone-independent (ER-/PR-) breast cancer, especially after menopause. Inverse associations between endocrine disrupting chemicals and ER- breast cancer risk have been found previously. Raaschou-Nielsen al women . Gammon e lowest . Some ste lowest . A smalle lowest . Howevere lowest ). Some se lowest ,40 or Ahe lowest . These oe lowest ,43. In aexposure . For exaStrengths of our study include the prospective design, the large size of the study population and number of breast cancer cases, a long follow-up that provides a large latency period for potential cancer occurrence, adjustment for most known breast cancer risk factors updated throughout follow-up, and validated dietary data. The participation rate which is still high 18 years after the start of the E3N study (≥75% at each follow-up questionnaire) and the low percentage of missing data attests of the quality of the information collected. Because our statistical models were adjusted for several individual breast cancer risk factors, it is unlikely that residual confounding changes our risk estimates. Moreover, we analyzed the potential confounding effect of flavonoid intake. Flavonoids are dietary phytochemicals that have been suggested to protect against dioxin toxicity  and to pNevertheless, our study has some limitations. First, to determine dietary dioxin exposure, we used consumption data obtained through a semi-quantitative diet history questionnaire. DHQ are often used for their applicability to a large number of participants. However, as DHQ are self-administered questionnaires on habitual food consumption over the past 12 months, we cannot exclude some inaccuracy of consumption data and a non-differential bias due to the desire of conformity to social norms. Nevertheless, the potential lack of precision has been minimized by the collection of food frequency for each food item and the estimation of portion sizes through a photo-booklet. Moreover, both the dietary questionnaire and photo-booklet have been validated ,19.Second, the food contamination data by dioxins came from the first French national monitoring program, which aimed to assess dioxin levels of foods sampled between 1996 and 1998 . BecauseAlso, we could not consider the source of the food consumed, that is, women living and consuming food produced near a dioxin emitting source may consume foods more contaminated than average. This could have led to an underestimation of the exposure for those women and to misclassification bias, although non differential so the risk would be potentially drawn toward 1. The estimated dietary dioxin exposure may not reflect the total exposure to dioxins of the E3N women. We cannot eliminate the contribution of exposure from environmental sources, even if its contribution remains probably minor compared to that of food. In contrast, because most women from the E3N cohort are teachers or from affiliated occupations, we can assume the occupational dioxin exposure to be negligible and homogeneous among study participants.Our estimation of the dietary dioxin exposure cannot be extrapolated to the French general population. Indeed, the intake of dioxins through the diet depends on the relative intake of foods of high or low levels of dioxin contamination and their quantity consumed . Moreover, we can expect a healthy participation effect in our cohort, as it is composed of volunteers with a high level of education and health consciousness. This could result in a possible underrepresentation of high fat (so high dioxin) consumptions; however this should not have biased our results.Among women from the E3N prospective cohort, estimated dietary dioxin exposure was not associated with breast cancer risk. Further studies should include both dietary and non-dietary  exposures as well as different windows of exposure  in order to further investigate the complex association between low-dose dioxin exposure and hormone receptor-defined breast cancer risk.


Ptrend = 0.02). Among parous women, an increasing number of full-term pregnancies (FTP) was inversely associated with risk . Ever breast-feeding was inversely associated with CBC risk only among women with ER-negative first tumors . Older age at first FTP was inversely associated with CBC risk among women with ER-negative first tumors , but suggestively positively associated with risk among women with ER-positive first tumors (Pheterogeneity = 0.03). Young age at menarche and low parity, both risk factors for first primary breast cancer, were also associated with overall CBC risk. Reductions in risk associated with breast-feeding were limited to women with ER-negative first tumors, who are at higher CBC risk than women with ER-positive primaries.Several reproductive factors are known to be associated with risk of breast cancer; however, relationships between these factors with risk of second primary asynchronous contralateral breast cancer (CBC) have not been widely studied. The Women’s Environmental, Cancer, and Radiation Epidemiology (WECARE) Study is a population-based case-control study of 1521 CBC cases and 2212 individually matched controls with unilateral breast cancer. Using multivariable conditional logistic regression models, we examined associations between reproductive factors and CBC risk, and whether associations differed by estrogen receptor (ER) status and menopausal status of the first breast cancer. Older age at menarche was inversely associated with CBC risk (≥14 vs. ≤11 years risk ratio (RR) = 0.82, 95 % confidence interval (CI) 0.65–1.03,  Reproductive factors, including menstrual and reproductive history, are well-established predictors of breast cancer risk. Young age at menarche and late age at menopause, representing an increased number of lifetime menstrual cycles, are associated with increased risk  is greater than the risk of developing a primary breast cancer in the general population  has been described in detail elsewhere  were diagnosed between 1985 and 2009 with a first invasive breast cancer that did not spread beyond regional lymph nodes at diagnosis and a second contralateral primary breast cancer at least 1 year after the first breast cancer diagnosis (reference date); (2) were younger than 55 years at first diagnosis; (3) had no previous or intervening cancer diagnosis except non-melanoma skin cancer or cervical carcinoma in situ; (4) were alive at the time of contact and able to provide informed consent to complete the interview and provide a biospecimen; and (5) resided in the same cancer registry reporting region for both diagnoses. Eligible controls with an intact contralateral breast were identified using the same eligibility criteria, and individually matched to cases  on the following criteria: year of birth (5-year strata), year of diagnosis (4-year strata), cancer registry region, and race/ethnicity. For each control, reference date was created by adding the at-risk period of her matched case to the date of her breast cancer diagnosis. WECARE I cases and controls were counter-matched on radiation exposure, such that two members of the case-control triad had received radiation therapy for their first breast cancer. In total, 2354 CBC cases and 3599 UBC controls met eligibility criteria and were approached for inclusion in the study. Of those eligible women, 1521 cases (64.6 %) and 2212 (61.5 %) controls completed the interview, provided a biospecimen , and provided written informed consent.Study participants were interviewed by telephone using a structured questionnaire aimed at evaluating known or suspected breast cancer risk factors, including personal demographics, medical history, menstrual and reproductive history, family history of cancer, use of hormones, smoking, and alcohol intake. Risk factor status was assessed during the period prior to first diagnosis, as well as between first diagnosis and reference date . Reproductive risk factors that were assessed included: age at menarche, number of pregnancies, duration and outcome of each pregnancy, date each pregnancy ended, duration of breast-feeding for each live birth, menopausal status, age at menopause and reason for menopause. A full-term pregnancy (FTP) was defined as a pregnancy that resulted in a stillbirth or at least one live birth. Breast-feeding duration was calculated by summing months of breast-feeding duration for each live birth. Detailed data on treatment and tumor characteristics, including ER and progesterone receptor (PR) status, were obtained directly from cancer registry records or by abstracting medical records, including pathology and surgical reports, radiation oncology clinic notes, and systemic adjuvant treatment data. Self-reported treatment data were used for participants with missing information in their medical records .Multivariable-adjusted risk ratios (RRs) and corresponding 95 % confidence intervals (CIs) were estimated by fitting conditional logistic regression models to the combined WECARE I and WECARE II data. We evaluated the following reproductive risk factors among all women: age at menarche , parity (parous vs. nulliparous), menopausal status (2 years prior to first breast cancer diagnosis) and age at menopause . In order to reduce the potential misclassification of menopausal status due to treatment-induced menstrual irregularities, we considered women premenopausal at first diagnosis if they reported menstruating or being pregnant in the 2 years before diagnosis. We examined number of FTP , age at first FTP , time between menarche and first FTP , time since last FTP  and duration of breast-feeding . We also evaluated whether time since last birth was associated with CBC risk, using the method outlined by Heuch et al.  to avoidTime-varying reproductive factors, including number and age at FTP and breast-feeding, were evaluated at the time of first diagnosis as well as at the reference date. Analyses produced similar results and, with the exception of menopausal status, estimates are shown for reproductive variables as of the reference date.In additional analyses, we examined whether associations between reproductive factors and CBC risk differed according to ER status of the first primary tumor, menopausal status at first diagnosis, and time since first diagnosis. Because PR status was unknown for approximately 25 % of first primary tumors, we did not examine whether associations differed by joint ER and PR status. However, among tumors with data on both markers, approximately 90 % of ER-positive first tumors were also PR-positive; similarly, about 90 % of ER-negative first tumors were also PR-negative. Likelihood ratio tests were used to assess heterogeneity by potential effect modifiers. Analyses were conducted using SAS v. 9.4 .Characteristics of the 1521 CBC cases and 2212 UBC controls included in our analysis are shown in Table Ptrend = 0.02) (Table Ptrend = 0.005) but there was no clear trend between increasing age at first FTP and CBC risk (Ptrend = 0.82). A long interval between menarche and first FTP was suggestively associated with increased CBC risk, though no statistically significant trend was observed . No clear trends between CBC risk and either breast-feeding duration or time since last birth were observed among parous women. Lastly, menopausal status at first diagnosis was not associated with CBC risk.Women who reported age at menarche of 14 years or older had an 18 % lower risk of CBC compared to those who had their first menses at age 11 years or younger . Although there was no statistically significant trend observed with increasing duration, women with ER-negative tumors who had breast-fed for 25 months or longer had the greatest reduction in CBC risk relative to those who had never breast-fed . High parity was associated with reduced CBC risk regardless of ER status of the first tumor (Pheterogeneity = 0.95). Older age at FTP was associated with a lower risk of CBC among women with ER-negative first tumors, while a statistically non-significant positive association was observed among those with ER-positive disease . No statistically significant heterogeneity by ER status was observed for the time between menarche and first FTP; however, results indicate modest heterogeneity for longer intervals between menarche and FTP with risk increasing among women with ER-positive first tumors and risk decreasing among those with an ER-negative first tumor (Pheterogeneity = 0.10).Table Ptrend < 0.001, Pheterogeneity = 0.0001), with similar RR estimates irrespective of ER status (Table Pheterogeneity = 0.07). Though tests of heterogeneity were not statistically significant, a long interval between menarche and first FTP was associated with an increased risk of CBC only among postmenopausal women who were diagnosed with an ER-positive first tumor (Ptrend = 0.009), whereas among premenopausal women diagnosed with an ER-negative first tumor, a long interval was associated with reduced risk (Ptrend = 0.05).Results of analyses jointly stratified analyses by ER status of first tumor and menopausal status 2 years prior to first diagnosis are shown in Table us Table . A histoResults from our large, population-based case-control study indicate that age at menarche and parity are related to CBC risk. Consistent with our findings from the first phase of the WECARE Study  may have also limited our ability to detect heterogeneity in some stratified analyses. In order to qualify for inclusion into our study, participants had to be alive at time of recruitment; we cannot rule out the possibility that women who died before this time had different distributions of reproductive factors than those who were alive. In order to limit the potential for resulting survival bias, we restricted eligibility to women whose cancer had not spread beyond regional lymph nodes, and matched cases and controls on both age and year categories of first diagnosis.Our results provide further evidence that some reproductive factors known to be associated with first primary breast cancers are additionally associated with CBC risk. Specifically, we confirmed our previous findings from the WECARE I study population, which indicated that older age at menarche and increasing parity appear protective for CBC. While not associated with overall CBC risk, increasing age at first FTP appeared inversely associated with risk among women with ER-negative first primaries. Additionally, while we found no association between breast-feeding and CBC risk our previous study, here, we report an inverse association that appears limited to women with ER-negative first tumors, who are at a higher risk of CBC, particularly ER-negative subtypes . These results add to others which suggest that breast-feeding may play a protective role in the etiology of ER-negative breast cancers, and provide the first evidence that this protective effect persists for second breast tumors. Further studies are needed to clarify these associations.


Observational epidemiological studies have shown that high body mass index (BMI) is associated with a reduced risk of breast cancer in premenopausal women but an increased risk in postmenopausal women. It is unclear whether this association is mediated through shared genetic or environmental factors.We applied Mendelian randomization to evaluate the association between BMI and risk of breast cancer occurrence using data from two large breast cancer consortia. We created a weighted BMI genetic score comprising 84 BMI-associated genetic variants to predicted BMI. We evaluated genetically predicted BMI in association with breast cancer risk using individual-level data from the Breast Cancer Association Consortium (BCAC) . We further evaluated the association between genetically predicted BMI and breast cancer risk using summary statistics from 16,003 cases and 41,335 controls from the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) Project. Because most studies measured BMI after cancer diagnosis, we could not conduct a parallel analysis to adequately evaluate the association of measured BMI with breast cancer risk prospectively.2 increase, 95% confidence interval [CI]: 0.56–0.75, p = 3.32 × 10−10). The associations were similar for both premenopausal  and postmenopausal breast cancer . This association was replicated in the data from the DRIVE consortium . Single marker analyses identified 17 of the 84 BMI-associated single nucleotide polymorphisms (SNPs) in association with breast cancer risk at p < 0.05; for 16 of them, the allele associated with elevated BMI was associated with reduced breast cancer risk.In the BCAC data, genetically predicted BMI was found to be inversely associated with breast cancer risk (odds ratio [OR]  =  0.65 per 5 kg/mBMI predicted by genome-wide association studies (GWAS)-identified variants is inversely associated with the risk of both pre- and postmenopausal breast cancer. The reduced risk of postmenopausal breast cancer associated with genetically predicted BMI observed in this study differs from the positive association reported from studies using measured adult BMI. Understanding the reasons for this discrepancy may reveal insights into the complex relationship of genetic determinants of body weight in the etiology of breast cancer. Using Mendelian randomization analysis, Wei Zheng and colleagues probe potentially causal associations between BMI and breast cancer risk in both pre- and postmenopausal women. Body mass index (BMI) has been linked to breast cancer risk in conventional population studies.In these studies, high BMI is associated with reduced risk of breast cancer in premenopausal women but with increased risk in postmenopausal women. These changed risks may be caused by BMI or caused by environmental factors that are associated with BMI.We sought to use a research tool from the genetics field to understand BMI’s causal role in breast cancer.We took advantage of previously identified genetic sequence variations that are associated with BMI in European populations and used these variants to predict BMI. These variants are set at birth and are not affected by environmental factors; thus, outcomes associated with high BMI as predicted by genetic variants are more likely to be caused by high BMI itself rather than by environmental factors that are associated with high BMI.Using databases containing individual genetic sequences and breast cancer diagnoses in a European population, we tested whether genetically predicted BMI was associated with diagnosis of breast cancer in either pre- or postmenopausal women.We found that genetically predicted high BMI was associated with decreased breast cancer risk, in both cancer databases. Unexpectedly, this was true for both pre- and postmenopausal women.Our results from postmenopausal women contradict prior findings from population studies, which used measured, rather than genetically predicted, BMI.BMI predicted using genetic variants identified to date may be more closely related to body weight in early life or midlife, which is negatively associated with risk of breast cancer. Measured high BMI later in life may be influenced by environmental factors that are associated with increased risk of breast cancer.More research is needed on the interrelationship of genetic factors, environment, and BMI in the risk of breast cancer. The association between body mass index (BMI) and breast cancer risk has been extensively investigated in observational epidemiologic studies. Most prospective cohort studies reported an inverse association between BMI and premenopausal breast cancer risk –7. A modSeveral explanations have been proposed for the opposite direction of the association between BMI and breast cancer risk by menopausal status. For example, it is postulated that overweight and obese women are more likely to experience anovulatory menstrual cycles, potentially leading to lower exposure to ovarian hormones and thus reducing the risk of breast cancer in premenopausal women ,12. AmonRecent genome-wide association studies (GWAS) have identified multiple loci associated with BMI. A genetic score, comprising BMI-associated single nucleotide polymorphisms (SNPs) capturing the portion of BMI determined by genetic factors, can be used in Mendelian randomization (MR) as the instrumental variable to evaluate the association between BMI and breast cancer risk by eliminating concerns of reverse causation and reducing the likelihood of selection bias and confounding in conventional epidemiologic studies. This is because the alleles associated with BMI should be randomly assigned to offspring from parents during gamete formation. In this study, data from two large consortia were used to conduct a MR analysis to assess the association between BMI and breast cancer risk.http://ccge.medschl.cam.ac.uk/research/consortia/icogs/). Genotype data were obtained either by direct genotyping using a custom Illumina iSelect genotyping array (iCOGS) that contains 211,155 SNPs [We obtained data from two large consortia, the Breast Cancer Association Consortium (BCAC) and the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) Project. All participating studies obtained written, informed consent from all subjects and received study protocol approval from their respective Institutional Review Boards. Our first analysis included 39 studies contributing participants of European ancestry to the BCAC Collaborative Oncological Gene Environment Study (COGS) project . This an155 SNPs  or by im155 SNPs . Populat155 SNPs .To further assess the association between genetically predicted BMI and breast cancer risk, we analyzed data from the DRIVE project, for which summary-level statistics from 16,003 breast cancer cases and 41,335 controls of European ancestry from 11 participating studies were available . DRIVE pp < 5 × 10−8) in populations of European ancestry were selected for this study. We selected independent SNPs, defined as r2 < 0.1 based on International HapMap Project phase 3 data. For any two SNPs with an r2 ≥ 0.1, the SNP with the lower p-value for association with BMI was selected. In total, 84 SNPs were selected for analysis. In BCAC data, 50 of the 84 SNPs were successfully genotyped, and the remaining 34 SNPs were imputed with high quality (imputation r2 > 0.8).SNPs associated with variation in BMI were identified from the NHGRI-EBI Catalog of Published Genome-Wide Association Studies in August 2015 . Furtherβi is the effect of the ith SNP for BMI reported in previous studies [SNPi is the dosage of the effect allele (range: 0 to 2) of the ith SNP. To scale the GS to the unit of BMI, we first performed a linear regression among controls, observed BMI ~ GS + error, where the expectation of error is zero. From this regression we obtained β0 (slope = 18.99) and β1 (effect = 0.451). Then, we used the values of β0 and β1 to compute BMI-GS using the formula, BMI-GS = β0+ β1* GS. The BMI-GS is a linear transformation of GS, and thus, these two variables were perfectly correlated (r = 1.0).Genetic scores for BMI (BMI-GS) used for MR were computed using previously described methods –22. The  studies  and SNPPooled analyses and meta-analysis were conducted to evaluate the association of BMI-GS with breast cancer risk. In pooled analysis, subjects from all BCAC studies were analyzed with adjustment for the BCAC study sites. In meta-analysis, we estimated the risk of breast cancer associated with BMI-GS in each of the BCAC studies, and then combined the results using a fixed effects model. Sensitivity analyses were performed using an unweighted BMI-GS to evaluate the robustness of the association . The per2), versus <25.5. Traditional World Health Organization BMI cutoffs were not used because of the narrow range of the BMI predicted by BMI-GSs (range: 24.14–28.53).Logistic regression was used to calculate adjusted odds ratios for the association between BMI-GS . The potential causal association between BMI (X) and breast cancer risk (Y) was modeled using BMI-associated SNPs as the instrumental variable [βYX) was calculated by using the Wald estimator: βYG is the natural log-scale odds ratio (OR) for breast cancer risk associated with the instrumental variable; βXG is the regression coefficient of the instrumental variable for BMI obtained from previous GWAS [SYG and SXG are the corresponding standard errors. We used an inverse-variance weighted method [We performed stratified analyses by factors that could potentially modify the association, including age, menopausal status, and postmenopausal HT. We assessed heterogeneity by hormone receptor status. Potential confounders included in logistic regression models were BCAC study site, age, and the eight principal components as described previously . In somevariable . Specifious GWAS . The staous GWAS : SEYX= (p < 0.001), postmenopausal HT use (p  = 0.004), smoking (p < 0.001), and weight (p  < 0.001). Results were unchanged after adjusting for observed BMI  . Using drved BMI .2 increase in BMI using meta-analyses was 0.65 , which was similar to that derived from the pooled analyses, OR = 0.68 . There was no apparent evidence for heterogeneity in the OR among BCAC studies (heterogeneity p = 0.06) . As a part of the sensitivity analysis, we also performed pooled analysis adjusting for breast cancer risk factors as covariables. As expected, adjustment of these variables slightly attenuated the association. However, the association remained highly statistically significant (p = 0.45), and in postmenopausal women, it was similar for women with and without use of HT . There was some evidence for a stronger association for ER-positive tumors than ER-negative tumors . Associations were similar in population-based studies  and non-population-based studies . Analyses using categorical variables of genetically predicted BMI showed inverse results similar to analyses treating predicted BMI as a continuous variable. We also stratified subjects by age  and found an inverse association between genetically predicted BMI and breast cancer risk for all age groups ≤ 65 y (p = 0.29). The BMI predicted using unweighted GS was also associated with reduced breast cancer risk (p = 1.64×10−7)  . MR-Egges for MR . In poolnificant . The OR s ≤ 65 y . No assocer risk . The eff64×10−7) . The strp < 0.05 .In pooled analysis of the BCAC data, 15 of the 84 SNPs analyzed in the study showed an inverse association with breast cancer risk, and one showed positive association with breast cancer risk at p < 0.05  Tables. p < 0.05  Tables. ociation . Five ofUsing data from BCAC, we conducted pooled analyses to evaluate the association of observed BMI with breast cancer risk by study design. Data from prospective cohort studies showed a positive association between observed BMI and breast cancer risk among postmenopausal women, while an inverse association was seen among premenopausal women . Data fr2 increase in BMI, is larger than that estimated in observational studies using measured BMI [Utilizing data from two large consortia, we found in this large MR study a consistent inverse association between BMI predicted by GWAS-identified genetic variants and premenopausal breast cancer risk in all subgroups examined, which is qualitatively consistent with the majority of published epidemiologic studies using measured BMI, although our predicted association, a 46% reduction in risk per 5 kg/mured BMI ,3,5,8,292 increase in BMI [Our MR analyses demonstrate an inverse association between genetically predicted BMI and postmenopausal breast cancer risk, with a predicted effect similar to that seen in premenopausal women. In contrast, previous large observational studies indicate a 5%–15% increased risk for postmenopausal breast cancer per 5 kg/me in BMI . In our The finding for an inverse association between BMI predicted using GWAS-identified SNPs and postmenopausal breast cancer risk differs from findings reported previously in studies using measured BMI, revealing a complex relationship of genetic determinants of BMI, weight gain, and breast cancer risk. A recent study found that a BMI-GS composed of 31 GWAS-identified SNPs (the majority of which are included in our study) was positively associated with annual weight gain between age 20 y and the time of the study baseline interview when participants were middle-aged . On the Several studies found that early-life BMI was inversely associated with breast cancer risk, and this inverse association is consistent in premenopausal ,32 and pAfter menopause, the primary source of estrogen is formed in adipose tissue, ,34 causiIn our study, we observed associations of high BMI-GS with early age at menarche, low prevalence of postmenopausal HT use, and high prevalence of cigarette smoking. It is known that high body weight is associated with an early age at menarche , and oveWe evaluated whether postmenopausal HT use may modify the association between BMI-GS and breast cancer risk or whether the association may vary by tumor hormone receptor status. Unlike some conventional observational studies on observed BMI-postmenopausal breast cancer association ,42, we dOur study has certain limitations. To date, GWAS-identified SNPs represent a small, but statistically significant, portion of the explained variance of observed BMI—approximately 2.7% ,44,45. NUsing data from approximately 146,000 women involved in two large consortia, we provide strong evidence of an inverse association between genetically predicted BMI and breast cancer risk for both premenopausal and postmenopausal women. The present study adds to the body of knowledge on the influence of body mass on breast cancer risk and points to further work required to elucidate the mechanisms responsible for the complex relationship between BMI and breast cancer risk. Our study, along with recent findings of an association of BMI-GS with weight gain in early adult life but weight loss in late adult life, suggests that weight gain later in adulthood may explain, at least partially, the positive association reported from previous studies between measured adult BMI and postmenopausal breast cancer risk, providing further support for lifestyle modification to reduce obesity as the primary prevention of breast cancer.S1 FigThe presence of funnel plot asymmetry indicates bias.(EPS)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 Table(DOCX)Click here for additional data file.S9 Table(DOCX)Click here for additional data file.S10 Table(DOCX)Click here for additional data file.S11 Table(DOCX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.