The purpose of this study was to explore women's views of the design of a large pragmatic cost-effectiveness randomised controlled trial of the policy of offering a health professional-delivered intervention to promote early presentation with breast symptoms in older women and thereby improve survival, with a view to informing protocol development. The trial will recruit over 100,000 healthy women aged 67+, and outcome data will be collected on those who develop breast cancer. The scale of the trial and the need for long-term follow-up presented a number of design challenges in relation to obtaining consent, ascertaining and contacting participants who developed breast cancer, and collecting outcome data.Qualitative study involving 69 women participating in 7 focus groups and 17 in-depth interviews. 15 women had a previous diagnosis of breast cancer and 54 did not.The women held strong views and had a good understanding of the rationale of the design of clinical trials. The women recognised that in a very large trial with long-term follow-up it was necessary to incorporate design features to make the trial feasible and efficient. Most strikingly, they supported the idea of opt-out consent and identifying women with breast cancer using routine datasets.This model of user involvement engaged women well with the design challenges of the trial and led to improvements to the protocol. The study strengthens the case for user involvement, in particular through focus groups and in-depth interviews, in the design of trials. It is widely accepted that involving service users in the design of research makes for more relevant research questions, higher levels of participation, better study design, and better interpretation of findings . The DepWe have designed a randomised controlled trial to evaluate the policy of offering a complex low-risk intervention to promote early symptomatic presentation of breast cancer in older women, and thereby improve survival. The intervention, known as the Promoting Early Presentation (PEP) Intervention, consists of a 10-minute interaction with a health professional supported by a booklet. It is designed to provide older women with information about the symptoms and risk of developing breast cancer, and the motivation, confidence and skills to present promptly on discovering a breast change ,5. The rWe are now planning a much more ambitious cluster randomised controlled trial which will examine whether the policy of offering the PEP Intervention following final routine mammogram on the National Health Service (NHS) Breast Screening Programme (at the age of 67-73) promotes prompt presentation and improves survival compared with not offering the PEP Intervention. The trial will need to recruit tens of thousands of women over the whole of England because only a small proportion of participants will develop breast cancer  over the follow-up period of five to eight years. We have chosen a cluster design in this pragmatic trial to avoid the disruption to services caused by individual randomisation in clinic, and because there will be lower risk of missing potential participants and of contamination. In addition, the PEP Intervention must be delivered by a specially trained radiographer; with individual randomisation, such a radiographer would have to be present at every screening clinic, rather than simply at those in clusters randomised to offer the PEP Intervention.We incorporated a number of features into the design of the trial to ensure that it would be feasible. These related to the consent model and the methods of ascertaining, contacting and interviewing participants with breast cancer.Individual trial participants can, potentially, give consent to randomisation, participation and follow-up. Consent from individuals to be randomised as a part of a cluster in a trial is rarely possible ,10. ConsIn our trial, because of the low risk associated with participation, we proposed that consent to participate and be followed up, in both control and intervention arms, should be obtained using "opt-out" consent, rather than the more traditional written opt-in consent. We planned to seek this consent at the time of postal invitation to participate sent with the invitation for a screening appointment, from women randomised both the control and intervention arms. All participating women, in both control and intervention arms would be provided with full written information about the trial, including the contact details of a researcher with whom to discuss participating if they wished, and would be able to opt out of participating or being followed up by post, telephone, online or in person when attending for breast screening. If they opted out, they would remain a member of relevant cluster (to be offered either the PEP Intervention or not), but no data would be collected about them and they would not be offered the PEP Intervention should they be randomised to a cluster in the PEP Intervention arm. We planned to obtain a further level of informed consent from women in intervention clusters to receive the PEP Intervention on arrival at the screening appointment: this would be opt-in verbal consent (providing the woman had not opted out earlier).We proposed to identify trial participants who developed breast cancer by flagging their NHS numbers on routine datasets, a method commonly used in trials to identify deaths among participants . This woWe proposed to conduct interviews (having obtained written opt-in consent), over the telephone rather than face-to-face, at about eight to ten weeks after diagnosis. These interviews would collect key outcome data: nature and duration of symptoms. Other key outcome data in the trial would be collected from cancer registry data, for example, date of diagnosis and stage.We wished to ensure that these proposed methods were not considered a threat to the dignity, safety, rights or well-being of the trial participants. We carried out a qualitative study using focus groups and in-depth interviews to examine women's perceptions of these elements of trial design.We carried out focus groups and in-depth interviews with women aged 60 to 75, some who had been previously diagnosed with breast cancer and others who had not had breast cancer. We recruited women with breast cancer with the help of two cancer charities: Macmillan, which advertised for volunteers on its website, and Breast Cancer Care, which sent out a flyer with its newsletter. We also directly contacted a Asian women's breast cancer support group. We recruited women who had not had a previous diagnosis of breast cancer using research recruitment agencies, who approached women in public areas and from existing databases of people willing to take part in research, to invite them to participate. We aimed to achieve a mix of women by age and income and living in rural and urban locations.During each focus group and interview, the facilitator explained the trial design issues and used a topic guide. In the focus groups, the facilitator supplemented this with a vignette describing one imaginary trial participant's experience of taking part in the trial ('Barbara's story'). All focus groups and interviews were audio-recorded. We paid all participants £20 plus travel expenses.Interviews and focus groups were transcribed verbatim and the data managed and analysed using the Framework method . This inThe National Centre for Social Research Ethics Committee gave ethics approval for this qualitative study. All participants provided verbal consent to take part.We carried out seven focus groups and seventeen in-depth interviews in total. Table We carried out two focus groups and nine in-depth interviews with women previously diagnosed with breast cancer . The focus groups were held with two established groups of women, one based in London and one in a market town in the Midlands: a support group, and a volunteer group providing support for women with breast cancer at a hospital. All these women were of white British ethnic origin.We carried out five focus groups  and eight in-depth interviews with women who had not had breast cancer . One of these focus groups was made up of Bengali-speaking women. Four of the in-depth interviews with women without breast cancer were with Black British women.The focus groups were held in private rooms in community centres and restaurants. The in-depth interviews took place at the participants' homes, except for two with women diagnosed with breast cancer, which were carried out over the telephone.The women  expressed support for the opt-out model for initial consent to participate and to be followed up using routine datasets. The women expressed understanding the need for trials to recruit a high proportion of potential participants, that recruitment should not be biased, and that trials had to be cost-effective; they felt that opt-out consent would help to achieve this. They also felt that opt-out consent would be much easier for potential participants, and was fair and inclusive. They thought that it might reduce bias in recruitment: because women might otherwise be inadvertently excluded simply because it was not convenient at the time or easy to complete an opt-in form, rather than because they really wished not to take part."I like the idea of opting out so then I'm included and I won't feel then left out or have to chase anybody up to be in it." The women felt that the only reason they might wish to opt out was because of the time commitment involved in taking part, as they felt taking part in the trial was not likely to be painful or intrusive.Participants thought that it was sufficient to inform women about the follow-up using routine datasets and that seeking formal consent for this aspect of participation was unnecessary.The idea of opt-out consent to being recontacted in the event of developing breast cancer generated a more mixed response. The women who had not had breast cancer felt unsure about how they might feel about this if they developed breast cancer. Some realised that even if women opted out of being recontacted at recruitment to the trial, they might later change their mind. The general view across the participants (regardless of characteristics) was that any kind of consent (opt-in or opt-out) for future contact at the time of recruitment may not be valid several years later under different circumstances."Well, to be honest, until you've had cancer, you don't know, you can't imagine how you feel. So you might quite happily tick a box and say, 'Yes if I get cancer I'll talk to you', but again that might be a different kettle of fish if you develop cancer. So I don't think you know yourself how you would cope and how you would feel." The women supported the idea of verbal consent to receive the intervention. While comfortable with providing written consent, they expressed the view that the main advantage of written over verbal consent was to protect the researchers or the NHS, not the participants. They said that written consent could be perceived as too formal, which might inhibit people from taking part."I think I'd be quite happy just to be of any use or any help, you know, in any way at all. And I wouldn't feel that I had to sign a piece of paper to say yes I will do this. That's almost being - I think signing things is almost a bit more beholden." They felt in that in a trial of this kind of intervention written consent was not necessary, because the potential for harm was low and that the participant would be capable of making an informed decision to receive the intervention or not.The women recognised that written consent would generate paperwork and this would incur costs and storage issues."[written consent is] not really necessary is it? I wouldn't have thought so. I think verbal is fine. I really do. I mean we've got enough paperwork anyway. I know it doesn't sound much but if you're doing thousands and thousands of women there's a cost to that you know, and I don't see the point of it. I really don't." The Bengali group noted that verbal consent might be more valid for people who did not have a good command of written English, including those with a low level of literacy in any language. In addition, they would be able to provide consent without a family member, or other, reading out written material to them, thus promoting their confidentiality.Several participants noted that consent to take part in research, however it was given or recorded, would only be valid if the participant was fully informed about the implications of taking part. Indeed ensuring that respondents felt informed was felt to be of greater significance than the mode of consent gained - verbal or written."It is important... just to keep people informed. In most cases... if you're not kept informed is when people get cross and angry about things, 'Well nobody has told me this'..." Participants recognised the need to identify women with breast cancer through routine datasets in the context of this trial and were generally comfortable with the idea. There was some expectation, especially among the women with breast cancer, that this was routinely done.Participants felt that women should be informed in clear language about the intention to follow them up through routine datasets and the reasons for doing so. Some expressed the view that it was important to outline specifically the plans for using the data."It has to be made clear in the beginning that if you take part in this, we will use your date of birth and your NHS number. We will then track you this year and this year." Participants had heard about losses of identifiable electronic data held by the government. Some were concerned that the routine datasets might not be stored securely during the research but felt less concerned knowing that the datasets would not contain names and addresses, only NHS numbers. They felt that the risks of retaining data were appropriately balanced against the importance of the research. They felt the need for assurances that the data would be used only for the purposes of the trial, that access would be restricted to appropriate research staff trained to handle and analyse the data and that the data would not be available to pharmaceutical or insurance companies.The participants acknowledged that taking part in the interview to collect data on the nature and duration of symptom had the potential to lead to some negative emotions. They generally approved of the idea that the breast care team  should coordinate making contact with a woman with breast cancer to ask them to participate in an interview to collect outcome data This was because the nurse would be known and trusted, and could advise the researchers about the appropriateness of contact at a particular time. It was considered important that the breast care team provided clear and concise information about the nature of the interview to allow women to make an informed decision about taking part.Participants felt that women would be comfortable declining to be contacted by the researchers and would not feel an inappropriate level of obligation or pressure to take part.Women with breast cancer identified some disadvantages of contact being coordinated by the breast care team. Some had not experienced a high level of continuity of care from their breast care teams, and thought that women with similar experiences might find this approach unacceptable.Among the women with experience of breast cancer, views about the proposed timing of recontact - eight to ten weeks after diagnosis - varied. Some felt that this timing was appropriate because women would probably have recovered from the initial distress surrounding diagnosis and that the interview might provide a welcome distraction during treatment. However, some felt that this was too early because women might still be having treatment or recovering. None felt that contact should be made earlier than eight weeks after diagnosis. Participants thought that the women should be able to choose the timing of the interview.Several participants thought that data on time from symptom discovery to presentation should be collected face-to-face rather than by a telephone interview. Some participants thought that telephone interviewing would be unpleasantly impersonal, but others suggested possible advantages of telephone interviewing: it might be less embarrassing, less threatening, less time-consuming, cheaper and more convenient if the woman was busy, ill or living in a rural area.This qualitative study including focus groups and in-depth interviews in women with and without personal experience of breast cancer found broad support for an unconventional consent model, a process of identifying women with breast cancer using routine datasets and a proposed method of recontact to take part in an interview. We found limited support for the idea of collecting data on nature and duration of breast cancer symptoms using a telephone rather than a face-to-face interview.The results provided many insights leading to improvements to the protocol and the information provided for participants. In particular, we gained support for our proposal to seek opt-out consent for randomisation or follow-up using routine data; women did not see this as a threat to their rights, safety or well-being. However, seeking opt-out consent to be recontacted on developing breast cancer was not considered sufficient; in fact, some expressed the view that consent of any kind at recruitment to be recontacted some time later under very different circumstances, in other words, in the event of the woman developing breast cancer, might not be valid, because she might feel very different about it at that time - more positively or negatively disposed towards it. We also gained support for the idea of verbal consent to receive the intervention, once opt-out consent to participate had been given.In relation to follow-up using routine data, the results suggested that not only did we need to develop robust systems for data governance but also to reassure participants about these systems. It also suggested that we should accommodate women's choices about when to be interviewed about nature and duration of symptoms in the event of developing breast cancer. The study suggested that face-to-face interviews with women to collect data on the nature and duration of symptoms might be seen to respect the well-being of participants more effectively than telephone interviews.offered the PEP Intervention (which they would still be at liberty to decline) and to be followed up using routine data. It is clear that consent to receive any intervention should be obtained actively, using opt-in methods  ..offered Opt-in consent may be seen as more defensible ethically and legally than opt-out consent as it relies on a participant actively confirming that they have consented  rather than solely omitting to withdraw consent. Opt-out consent may be seen as a threat to participant autonomy, a fundamental principle of medical ethics, because failure to withdraw consent does not confirm that the participant has actively consented. An opt-out consent model has, however, been used with NHS research ethics committee approval in trials of low risk interventions . Opt-outA striking finding was the idea that written opt-in consent was not always desirable and that verbal opt-in consent might be preferable. The participants thought that written opt-in consent was too formal, and that it might inhibit women from taking part or make them feel beholden to the researchers. They also felt that that it protected the researchers or the NHS, not research participants. The interpretation of written opt-in consent as a contract or a liability waiver has been noted before . Anotherinformed consent to take part in research, rather than focusing on whether it is written or verbal, or whether it is opt-in or opt-out http://www.nres.npsa.nhs.uk/applications/guidance/consent-guidance-and-forms/. Research ethics committee also refer to the Department of Health guidance on consent to receive physical interventions [The guidance provided to National Health Service Research Ethics Committees emphasises the importance of achieving ventions  .The most common forms of user involvement in randomised controlled trials is in the drafting of trial information leaflets and promoting recruitment, and users sometimes sit on steering committees . We haveThe focus group and in-depth interview model of user involvement requires trained qualitative researchers, a significant amount of work developing materials and are, therefore, expensive. However, for our trial, using this model generated surprising insights to strengthen the protocol, which is, arguably, less likely to occur with more common and conventional models of user involvement such as sitting on steering groups.Researchers are sometimes negative about user involvement in trials, suggesting that it takes too long, that expectations are too high, and that there is a conflict of interest between the "role of a patient advocate and reliable assessment of cost-effectiveness" . Using cA model of user involvement in research design, employing focus groups and in-depth interviews, engaged women well with the design challenges of the trial and led to improvements to the protocol. The study strengthens the case for user involvement of this kind in the design of trials.Opt-out consent to participate in trials is acceptable to potential research participants and should be considered for low risk interventions. Use of routine datasets is also acceptable, given appropriate governance arrangements, and should also be considered in trials where long term follow up for relatively rare events is needed.The authors declare that they have no competing interests.LF, LL and AJR designed the randomised controlled trial and had the idea for the study. LF commissioned the study, contributed to study design and wrote the paper. CMN, JG, and CT contributed to study design, and collected and analysed the data. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2288/10/110/prepub


High breast density, a strong predictor of breast cancer may be determined early in life. Childhood anthropometric factors have been related to breast cancer and breast density, but rarely simultaneously. We examined whether mammographic density (MD) mediates an association of birth weight, childhood body mass index (BMI), and height with the risk of breast cancer.13,572 women (50 to 69 years) in the Copenhagen mammography screening program (1991 through 2001) with childhood anthropometric measurements in the Copenhagen School Health Records Register were followed for breast cancer until 2010. With logistic and Cox regression models, we investigated associations among birth weight, height, and BMI at ages 7 to 13 years with MD (mixed/dense or fatty) and breast cancer, respectively.8,194 (60.4%) women had mixed/dense breasts, and 716 (5.3%) developed breast cancer. Childhood BMI was significantly inversely related to having mixed/dense breasts at all ages, with odds ratios  ranging from 0.69 (0.66 to 0.72) at age 7 to 0.56 (0.53 to 0.58) at age 13, per one-unit increase in z-score. No statistically significant associations were detected between birth weight and MD, height and MD, or birth weight and breast cancer risk. BMI was inversely associated with breast cancer, with hazard ratios of 0.91 (0.83 to 0.99) at age 7 and 0.92 (0.84 to 1.00) at age 13, whereas height was positively associated with breast cancer risk  and age 13, 1.08 (1.00 to 1.16)). After additional adjustment for MD, associations of BMI with breast cancer diminished  and age 13, 1.01 (0.93 to 1.11)), but remained with height  and age 13, 1.09 (1.01 to 1.17)).Among women 50 years and older, childhood body fatness was inversely associated with the breast cancer risk, possibly via a mechanism mediated by MD, at least partially. Childhood tallness was positively associated with breast cancer risk, seemingly via a pathway independent of MD. Birth weight was not associated with MD or breast cancer in this age group. A sub2),11,13-15Although few studies have linked early-life factors to mammographic density (MD)-25, theyTherefore, we linked measured data on body size at ages 7 to 13 years and records of birth weight, to records of MD and breast cancer after age 50 years, and explored whether MD mediates or modifies the association of birth weight, BMI, and height with breast cancer risk.The study cohort consists of 13,572 women older than 50 years who participated in the Copenhagen mammography screening program between 1991 and 2001 and had childhood anthropometric data in the Copenhagen School Health Records Register (CSHRR).z-scores were calculated based on children from 1955 through 1960, when the prevalence of overweight and obesity were low and stable, and were performed by using the LMS (lambda-mu-sigma) method[z-scores were calculated by using the LMS method and based on cohort-specific values, as increases in height occurred from the 1930 to 1983 birth years.The CSHRR is a database of health examinations records on 350,263 children born between 1930 and 1983 and who attended schools in Copenhagen municipality. The heaThe Copenhagen mammography screening program started in 1991 and targTwo views were taken on the first screen, a craniocaudal and an oblique. MD was dichotomized into fatty breast, equivalent to Breast Imaging Reporting and Data System  density code 1 and part of code 2, and mixed/dense breast, equivalent to part of BI-RADS code 2, 3, or 4. Women with a negative screening test and fatty breasts were scheduled to have only an oblique view at the next screen, whereas women with a negative screening test and mixed/dense breasts were scheduled for two views. The dichotomous outcome for MD was successfully used earlier, showing expected associations with breast cancer risk.Women were divided into four birth-year intervals  to account for birth-cohort effects. By using the CPR number, we linkin situ cancers (ICD-10 codes C50 and D05) between screening (1991 to 2001) and 31 December 2009, and to the Civil Registration System[We linked the records of 13,958 women by using the CPR number to the Danish Cancer Registry to extran System to extraWe used logistic regression to investigate association of MD with birth weight, height, and BMI in separate models, in two steps: crude model (model 1) and in a model adjusted for birth cohort and age at the time of screening (model 2). We used Cox proportional hazards regression with age as the underlying time, to investigate associations of birth weight, height, and BMI, separately, with the risk of breast cancer, in three steps: a crude model  (model 1), a model 1 additionally stratified by birth cohort (model 2), and a model 2 additionally adjusted for MD (model 3). The follow-up started on the date of screening (1991 through 2001) until a date of breast cancer diagnosis, death, emigration, or December 31, 2009, whichever came first.z-scores at ages 7 through 13 were modeled as continuous variables, and a separate model was fit at each age.Comparison of hazard ratios (HRs) estimating the effect of birth weight, BMI, and height on the risk of breast cancer from model 2 (without MD) and model 3 (with MD) was made for evaluation of a possible mediating role of MD. Birth weight, BMI, and height z-scores , and evaluated the shape of associations with MD/breast cancer, both visually and by log-likelihood tests against the corresponding linear model. The potential effect modification of an association between birth weight, BMI, and height and breast cancer by age and MD was evaluated by introducing interaction terms into the Cox model, and tested by the Wald test.Main analyses were performed in Stata 11.2. We additionally fit generalized additive models for binary/survival data with natural splines for birth weight, height, and BMI A sensitivity analysis was performed on an extended population, including breast cancer cases among 98 women who did not have data on MD. Additional sensitivity analyses were performed excluding women diagnosed with breast cancer within 1 and 2 years of their mammogram, to address the possibility of masking bias driving the association between MD and breast cancer risk. Finally, a sensitivity analyses was performed on the associations of BMI and height with MD and breast cancer, respectively, excluding women with missing data on birth weight.The study was entirely based on a data from Danish health registers and approved by the Danish Data Inspection Agency by Danish law serving as ethical approval of register-based research, which does not require an informed consent from study participants. Thus, no contact has been made with participating women, relatives, or their practicing doctors, and no consent was needed.The majority (60.4%) of women had mixed/dense breasts at a mean age of 54.6 years. Mean birth weight and BMI at all (7 through 13 years) ages was lower in women with mixed/dense breasts than in those with fatty breast, whereas no differences in height were found  comparing the highest to the lowest 25th percentile of BMI distribution at age 13.A significant and inverse association between age and MD diminished after adjusting for birth cohort Table . The youz-score unit, or 0.84 (0.67 to 1.05) when comparing the upper with the lower 25th percentile of BMI distribution. After adjustment for MD, the association at age 7 was attenuated to 0.97 (0.88 to 1.06) and 0.97 (0.77 to 1.21), respectively, and became positive for all other ages (Table Having mixed/dense breasts more than doubled the breast cancer risk (hazard ratio (HR); 95% CI, 2.34; 1.97 to 2.78) compared with women with fatty breasts  indicating that the positive association between height and breast cancer may be limited to women with mixed/dense breasts . Finally, in analyses limited to women with available data on birth weight, we found similar associations of BMI and height with MD and breast cancer as in the main analyses with the maximum available data for each anthropometric measure (not shown).High BMI in childhood was strongly inversely associated with developing mixed/dense breasts and marginally with breast cancer after age 50. MD may explain the inverse association between childhood BMI and breast cancer risk, at least in part. Tallness in childhood was not significantly associated with MD, but it was marginally positively associated with breast cancer risk. Birth weight was not significantly related to MD or breast cancer risk.2 in BMI at age 15[z-score at age 13 years. A study of 628 Scottish women detected an inverse association between having a high-risk mammogram (≥ 25% dense) and BMI at age 18[Inverse associations between BMI in childhood and MD in this cohort agree with existing evidence based primarily on Caucasian women in European and American populations,18,22-24t age 15; this ist age 18. FinallyOur findings disagree with two studies failing to detect association between MD and self-reported weight at age 10 in 201 US Chinese immigrants and selfet al.[et al.[, in a nested case–control study, retrospectively collected body fatness of women in Nurses’ Health Study at ages 5, 10, and 20 years, by using nine-level figure drawing. Conversely, although we adjusted for birth cohort and age in full model, lacking information on other breast cancer risk factors, Harris et al. matched cases and controls on age, menopausal status, postmenopausal hormone use, and race/ethnicity, and furthermore adjusted for age at menarche, parity/age at first birth, history of breast cancer, and alcohol use, but did not adjust for birth cohort.A weak protective effect of BMI at ages 7 through 13 on breast cancer risk in this cohort is confirmatory,11,13-15et al., where t.[et al., in a neet al. relied on self-reports confirmed by medical records in women who gave permission to obtain medical records and mammograms. Additionally, our study was conducted predominantly on postmenopausal women, whereas Harris et al. included a younger population of women at screening with a shorter follow-up (mean time between screening and breast cancer diagnoses of 4.7 years versus 8.6 years in our study), resulting in predominantly premenopausal breast cancer.Furthermore, breast cancer and MD definitions differ between the studies. Whereas we obtained mammograms and breast cancer information objectively via registries, without possibility for population selection by refusal to participate/release information, Harris et al. allowed these in analyses[Finally, whereas we excluded 98 breast cancer cases diagnosed at screening from main analyses, because of lack of MD, Harris analyses. In a seP value = 0.08), indicating that the positive association between height and breast cancer may be limited to women with mixed/dense breasts  and 1.09 (0.91 to 1.32), respectively, where we, for breast cancer diagnosed between 50 and 59 years, found OR of 1.01 (0.75 to 1.36).We found no association between birth weight and breast cancer, in contrast to the vast literature,6-10. Alt cancer. Howevert cancer includedn et al. found a et al.[et al.[et al.[Thus, these two sets of partially overlapping Danish data point at a lack of an association between birth weight and breast cancer from ages 50 to 59 years and a slight indication of a negative association at ages 60 and older. A careful look at the existing literature also supports this notion. Reviews by Ruder et al. suggest [et al. showed t[et al. reports [et al.. Similar[et al.-38 found[et al.,37 or neThe mechanisms behind our finding that MD may be a mediator explaining the inverse association between childhood body fatness and breast cancer risk are not well understood. A pathway suggesting direct influence of childhood body fatness on the development of mammary tissue during adolescence is likelet al.[et al.[The current study benefited from a large cohort of women with prospectively collected data on anthropometric childhood factors, MD, and breast cancer, with minimal possibility of recall, information, or selection bias. We detected a strong effect of birth cohort on MD, finding that younger cohorts of women (born in 1945 to 49) had significantly higher MD than women from the oldest cohorts (1930 to 1934), in agreement with Hellman et al.. Our stu[et al. about thet al.[et al., who therefore did not to adjust for it in their analyses[et al. showed that association of BMI at age 8 to 10 with two different measures of MD was robust to adjustment for adult BMI: effect estimates for percentage dense breast volume were attenuated by a half, but remained inverse and statistically significant, whereas estimates for absolute dense breast volume remained virtually unchanged[Limitations of this study include the lack of adjustment for other relevant breast cancer covariates at the time of screening, including menopausal status, age at menarche, age at first giving birth, parity, hormone replacement therapy (HRT) use, socioeconomic status, education, physical activity, alcohol use, and others, and possible bias in our estimates due to confounding. However, studies with covariate information available suggest that full adjustment for these enhanced crude associations between birth weight and MD, making them statistically significant,23, wheranalyses. Finallynchanged. Thus, enchanged.Another limitation is the possibility of BMI tracking, implying that the findings of inverse associations between childhood BMI and MD in adult life would be expected if BMI were tracked through life. As correlations between child and adult BMI strengthen with age, if the observed associations were due to BMI tracking, we would expect the associations of BMI and MD to be much stronger at 13 years of age versus 7 years of age; however, this is not the case here  and low (fatty breasts) MD, as no other measure of MD was available. In contrast, a wide variety of measures of MD were used in related studies, including the Boyd semiquantitative scale with six levels (A to F),23, Wolfet al.. SpecifiOur study indicates that birth weight is not related to MD or breast cancer risk after age 50 years. High BMI in girls is a strong determinant of a favorable MD, whereas height is not related to MD. Childhood body fatness is inversely associated with the breast cancer risk, possibly via a mechanism mediated by MD, at least in part, whereas childhood tallness is positively associated with the breast cancer risk, however, via a pathway that seems independent of MD.BMI: Body mass index; CI: confidence intervals; CPR: unique personal identification (number); CSHRR: Copenhagen School Health Records Register; HR: hazard ratio; HRT: hormone replacement therapy; MD: mammographic density; MPD: mammographic percentage density; OR: odds ratio.The authors declare that they have no competing interests.All authors made substantial contributions to conception and design, analysis, and interpretation of data, and critical review of the manuscript. ZJA carried out the design of the study, performed the statistical analyses, and wrote the manuscript. JLB secured funding, assisted in designing the study, interpretation of the results, and the revision of the manuscript. KB coordinated the data acquisition and assisted with the statistical analyses. IV made mammographic measurements, helped to draft the manuscript, and assisted in interpretation of data. TIAS participated in the acquisition of data, helped with the design of the study, interpretation of the results, and the revision of the manuscript. EL conceived of the study and participated in the designing of the study and the interpretation of the results. All authors participated in the drafting and revision of the manuscript. All authors read and approved the final manuscript.


Recent studies indicate that the selected micro-and macro-elements and vitamins may significantly influence the risk of cancer. Folic acid is a vitamin B which plays an important role in several processes in organism. Folates are important cofactors in the transfer and utilization of one-carbon-groups and play a key role in the remethylation of methionine thus providing essential methyl groups for numerous biological reactions. Furthermore, folates donate one-carbon units in the process of DNA-biosynthesis with implications for the regulation of gene expression, transcription, chromatine structure, genomic repair and genomic stability. Deficiency of folic acid has been reported to be associated with numerous disorders, including heart disease, stroke and cancers.The MTHFR gene produces a key enzyme in folate metabolism which catalyses the reactions essential for nucleotide biosynthesis and DNA methylation.The aim of study was to analyze an association of folic acid concentrations and genetic variants in the MTHFR gene with breast cancer risk in patients with BRCA1 mutation.Study group consisted of 155 breast cancer patients and 155 healthy women from the paired control group matched to cases by year of birth, cancer family history, adnexectomy, smoking. From all individuals blood sample was collected and from cancer cases was taken before treatment. Folic acid concentration was quantitatively measured in blood plasma by HPLC chromatography . Two functional SNPs in the MTHFR gene, 677 C>T (rs1801133) and 1298 A>C (rs1801131), both associated with reduced enzyme activity, have been tested by TaqMan on LightCycler 480.(Roche Diagnostic). Individuals were divided into four quartiles depending on folic acid concentration and number of cases and controls in each quartile was compared. Analysis was made depending on menopausal status defined as ≤ 50 and >50 years old.In a group of >50 years old, individuals classified in the first quartile  had a lower risk of breast cancer than patients with higher folate level. Whereas in a group of ≤50 years old, individuals classified in the 3 quartile  had a significantly lower risk of breast cancer than those with folate level between 32,02 – 54,42 μmol/l. Analysis of correlation between the level of folic acid and genetic variants 677 C/T and 1298 A/C in the MTHFR gene performed in 2 groups (>50 and ≤50 years) revealed:- for carriers of 1298 nCC and ≤50 years old significantly lower risk of breast cancer in individuals classified between 16,6-31,88 μmol/l in comparison to patients with lower and higher folate level.- for carriers of 677 CC and ≤50 years old lower risk of breast cancer in individuals classified between 19,86-32,02 μmol/l than patients with lower and higher folate level.- for carriers of 677nCC and ≤50 years old significantly lower risk of breast cancer in individuals classified between 5,94-14,8 μmol/l in comparison to patients with higher folate level.- for carriers of 1298 AA and >50 years old lower risk of breast cancer in individuals classified in the first quartile  in comparison to patients with higher folate level.- for carriers of 1298 nCC and >50 years old lower risk of breast cancer in individuals classified in the first quartile  in comparison to patients with higher folate level.- for carriers of 677 CC and >50 years old lower risk of breast cancer in individuals classified in the first quartile  in comparison to patients with higher folate level.- for carriers of 677 nCC and >50 years old lower risk of breast cancer in individuals classified in the first quartile  in comparison to patients with higher folate level.We can conclude that concentration of folic acid in the range of 18 - 32 μmol/l is associated with lower risk of breast cancer for women ≤50 years, with the possible exception for 677 nCC (CT+TT) where the lower risk of breast cancer is classified between 5,94-14,8 μmol/l.Concentration of folic acid lower than 17 μmol/l is associated with lower risk of breast cancer for women >50 years.


Both the percent of mammographic density and absolute dense (fibroglandular) area are strong breast cancer risk factors. The role of non-dense (fat) breast tissue is not often investigated, but we hypothesize that this also influences risk. In this study we investigated the independent effects of dense and fat tissue, as well as their combined effect on postmenopausal breast cancer risk.We performed a nested case-control study within the EPIC-NL cohort . We used multivariate logistic regression analyses to estimate breast cancer odds ratios adjusted for body mass index and other breast cancer risk factors.Large areas of dense (upper (Q5) vs lower quintile (Q1): OR 2.8 95% CI 1.7 to 4.8) and fat tissue  were independently associated with higher breast cancer risk. The combined measure showed that the highest risk was found in women with both a large (above median) area of dense and fat tissue.Fibroglandular and breast fat tissue have independent effects on breast cancer risk. The results indicate that the non-dense tissue, which represents the local breast fat, increases risk, even independent of body mass index (BMI). When studying dense breast tissue in relation to breast cancer risk, adjustment for non-dense tissue seems to change risk estimates to a larger extent than adjustment for BMI. This indicates that adjustment for non-dense tissue should be considered when studying associations between dense areas and breast cancer risk. In the last two decades, many researchers have observed a strongly elevated breast cancer risk in women with a high percent mammographic density ,2. PerceIncreasingly, the absolute area of dense tissue is reported in the literature along with the percent density measure. The reason for this is that the dense area is considered to represent the actual target tissue for tumor development ,4. As peet al. studied this indirectly, by assessing whether a large area of dense tissue is more harmful in small or large breasts, or in other words, in combination with a small or large area of fat tissue. In a combined population of African American and white women, Stuedal et al. observed that the association between percent and absolute mammographic density and breast cancer risk was weaker in women with larger breasts [Until now, there has been little attention given to the role of non-dense breast tissue, despite the fact that fat cells are known to be highly active endocrine cells that secrete numerous hormones which are thought to be important for the development of breast cancer ,13. The  breasts . One of  breasts . Althoug breasts , most ex breasts -23. It i breasts -27.et al. also studied which breast tissue characteristics best predict breast cancer risk: dense area, non-dense area, percent density or a combination thereof. They observed that the dense area was a better predictor than percent density. Further addition of non-dense area did not alter the fit of the model, suggesting no additional role for the fat tissue in influencing breast cancer. Unfortunately, no information on breast cancer risk factors, other than age, was available for inclusion in their models [Stone r models .To obtain more insight into the role of the fat breast tissue, we investigated the independent effects of the size of dense tissue and the size of non-dense tissue in the breast and their combined effects on breast cancer risk in a nested case control study of postmenopausal Caucasian women.The study population comprises participants of the Prospect-EPIC study , which iIncident breast cancer cases were identified by linking the cohort data with the regional cancer registry, IKMN , and the national cancer registry, LKR (Landelijke Kanker Registratie). Until 2007, 607 breast cancer cases had been identified among the 17,000 breast cancer screening participants. For each breast cancer case, the researchers used incidence density sampling to select three controls from the cohort . In totaAll participants gave their informed consent and the study was approved by The Institutional Review Board of the University Medical Centre Utrecht.μm and 12 bits per pixel.Mammographic density was assessed on the left mediolateral oblique mammograms (MLO) taken at the time of recruitment into the cohort, on average six years before diagnosis. The mammograms  were digitized with a Canon CFS300 scanner  with a pixel resolution of 50 2, was determined by multiplying the number of pixels in the respective areas by the size of one pixel. Percent density was calculated by dividing the dense area by the total breast area and multiplying by 100. All mammograms were read by one single reader (ML), blinded to participant characteristics, in batches of 53 mammograms each. Mammograms of cases and corresponding controls were in the same batch but randomly ordered. Each batch included two or three duplicate mammograms to allow estimation of the within-batch intraclass correlation coefficient. Also, a test batch was read before, after and three times in between the batches to determine the between-batch intraclass correlation coefficient. The intraclass correlation coefficients for the within-batch correlation as well as the between-batch correlation were 0.96, 0.96 and 1.00 for percent density, dense area and breast area, respectively.For assessing the mammographic density, Cumulus software  was used . With thPatient characteristics were described within quintiles of dense area and non-dense area among controls. For continuous variables we used means with standard deviations if normally distributed and medians with interquartile ranges of not normally distributed data (number of children). For categorical variables, we show proportions. To examine the presence of a linear trend in the distribution of the risk factors over the dense area and non-dense area quintiles, we used a test for linearity for normally distributed continuous data, a Jonckheere Terpstra test for skewed continuous data (number of children) and a chi-square linear trend test for proportions.Correlations between density measures with BMI and each other are calculated using the Pearson correlation coefficient. The non-linear density measures were transformed using natural logarithm in order to get normally distributed measures.We examined the relationship between breast measures and breast cancer risk by calculating odds ratios and their 95% confidence intervals (95% CI) for quintiles of percent density, dense area and non-dense area. Quintiles were based on the distributions of controls. Multivariate logistic regression was used to adjust for age at mammography, age at menarche, age at first delivery , number of children, age at menopause, hormone therapy (HT) use , birth control pill use , first degree relative with a history of breast cancer, that is, mother or sister (no/yes), height and BMI. To investigate the independent effects of the dense and the non-dense areas, the analyses of the dense area were additionally adjusted for the non-dense area. Despite the strong correlation between BMI and the non-dense area, we included the non-dense area in addition to BMI to examine whether the local breast fat has an effect on breast cancer risk independent of BMI. The analyses of the non-dense area were additionally adjusted for the dense area. The analyses are also additionally adjusted for BMI.In order to investigate the joint effect of dense and non-dense tissue, these variables were dichotomized by the median value of their distributions in the controls. Four groups were defined, that is, women with small areas of dense and non-dense tissue , women with a small area of dense but a large area of non-dense tissue (low/high), women with a large area of dense, but a small area of non-dense tissue (high/low) and those with a large area of both dense and non-dense tissue (high/high). Odds ratios were adjusted for the same confounders as described above. The relative excess risk (RERI) of women with above median areas of dense and non-dense tissue was calculated to assess the level of additive interaction.P-values are two-sided and if below 0.05 the results were considered statistically significant. Analyses were conducted using PASW Statistics 17.0 .All At recruitment, breast cancer cases were 60 years (SD 5.4) and controls 59 years (SD 5.6) on average. The breast cancer cases were older at the birth of their first child than the controls (26 (SD 4.2) vs 25 (SD 4.0) years) and also older when they became postmenopausal (48 (SD 5.6) vs 47 (SD 5.7) years). The mean age at diagnosis of the breast cancer cases was 66 years (SD 5.8) and the median time between the recruitment and the diagnosis was 6 years (interquartile range: 4 to 9 years).Distributions of breast cancer risk factors among quintiles of dense tissue and non-dense tissue in controls are presented in Table 2 (IQR 9.5 to 30.3) versus 14.9 cm2 (IQR 6.9 to 29.7)) and non-dense tissue (119.3 cm2 (IQR 87.5 to 163.5)) versus 116.4 cm2 (IQR 84.7 to 147.5)). The median percent density also was higher in breast cancer cases than in controls (13.3% (6.4 to 23.4) versus 11.3% (4.7 to 23.8)) (not in Table The measurements of the different breast tissues showed that the breast cancer cases compared to the controls (unadjusted) had a larger median area of dense tissue (18.5 cmP < 0.001) and strongly negatively correlated with a higher non-dense area . The dense area itself, however, is also negatively correlated with the non-dense area . The non-dense area is strongly positively correlated with BMI , and the dense area weakly negatively .In Table P for trend: 0.002). The risk estimates for dense area seem to be somewhat stronger than those for percent density although both risk estimates are within the confidence intervals of each other .As shown in Table Comparing the analysis without adjusting for non-dense area and with adjusting for non-dense area, it can be observed that adding the non-dense breast area somewhat increases risk estimates even after BMI has already been included in the model, although the confidence intervals partly overlap (fourth vs second 'OR' column of Table P for trend: < 0.001).After adjustment for dense area, a large non-dense area was also related with a higher breast cancer risk  multicollinearity may have affected these models. To circumvent this problem we repeated analyses, including BMI and the residuals of non-dense area regressed on BMI. This did, however, not lead to different results.2) area of dense tissue and a small  area of non-dense tissue showed a slightly higher breast cancer risk than with a small area of both tissue types . The highest risk was observed in women with large areas of both dense and non-dense tissue . This risk for women with high dense and non-dense area combined was slightly higher than the sum of the risk for women with large areas of either one of the high risk tissues, but not statistically significantly . Women with a larger breast size do not necessarily have a higher breast cancer risk, as can be observed from the median breast sizes for the risk categories in Table Women with a large (above median, that is, >14.9 cmIn this paper, we investigated the role of dense and fatty breast tissue in relation to breast cancer risk. We found that not only a large area of fibroglandular tissue is associated with a higher breast cancer risk, but also that a large area of fat breast tissue, even after taking BMI into account, has an independent effect on breast cancer risk. Women who have both a large area of fibroglandular tissue and a large area of fat tissue seem to have the highest breast cancer risk. It should be noted that the relationship with breast cancer is stronger for the dense than for the non-dense area. Therefore, the resulting risk depends on the composition of the breast and not simply on the size of the breast. When studying dense breast tissue in relation to breast cancer risk, adjustment for non-dense tissue seems to change risk estimates to a larger extent than adjustment for BMI. This indicates that adjustment for non-dense tissue should be considered when studying associations between dense area and breast cancer risk.et al. who showed that high circulating estrogen levels and high breast density increase breast cancer risk independently from one another [et al. [The harmful effect of a large area of dense tissue on breast cancer risk is established, and could be explained by the fact that proliferating cells are the actual target tissue for breast cancer development ,34. Also another . Evidenc [et al. , who sho [et al. -27.et al. [A large area of fatty breast tissue could also increase breast cancer risk through adipocytokines, such as leptin and adiponectin, which are secreted by the fat tissue. Leptin promotes breast cancer cell growth, whereas adiponectin reduces cell proliferation and enhances apoptosis. A higher body mass index is associated with increased secretion of leptin and decreased secretion of adiponectin . The balet al. .et al. investigated the role of the non-dense area in a group of women in the United Kingdom comparable to our study population. Although the highest quintile of non-dense area gave a somewhat higher odds ratio than the other quintiles, they did not find a significant association between non-dense area and breast cancer risk. Stone et al. concluded that the model with the dense area alone was the most parsimonious model [Currently, two studies have investigated the role of the breast fat tissue with different results. Stone us model . In thiset al. studied the role of the non-dense tissue indirectly by investigating the effect of the breast size on the relationship between mammographic density and breast cancer risk. They found that the association between breast density and breast cancer risk was weaker in women with larger breasts. This could indicate that the fat tissue is protective against breast cancer [et al.Stuedal t cancer . This dit cancer . This maet al., their findings might also be explained by a higher proportion of dense tissue in larger breasts having a more 'supportive' role than it does in smaller breasts and, therefore, it may be less correlated with the number of epithelial cells at risk and, hence, it is more weakly associated with breast cancer risk [According to Stuedal cer risk . If truecer risk . The groA strength of our case-control study is that it is nested in a large cohort study, in which mammograms and questionnaire information were collected long before breast cancer developed, reducing the chance of recall bias. The questionnaires contained extensive information about the potential breast cancer risk factors, allowing extensive confounder adjustment. Also, due to the long follow-up time, we were able to study breast density well before diagnosis, making it unlikely that density is influenced by the presence of a tumor, or that our findings are influenced by so-called masking bias . A weaknWe observed that besides the size of dense tissue, the size of non-dense tissue also plays a role in the development of breast cancer. When studying dense breast tissue in relation to breast cancer risk, adjustment for non-dense tissue may give more valid results than adjustment for BMI. Although not statistically significant, the results also give some indication that a large area of fibroglandular tissue could be related to higher breast cancer risk when surrounded by a large, compared to a small area of fat tissue. Further research is warranted to confirm this effect.BMI: Body Mass Index; EPIC: European Prospective Investigation into Cancer and Nutrition; HT: hormone therapy; IKMN: Regional Cancer Registry; IQR: interquartile range; LKR: National Cancer Registry; MLO: mediolateral oblique mammograms; OR: odds ratio; RERI: relative excess risk; SD: standard deviation.The authors declare that they have no competing interests.ML collected and assessed the mammograms, carried out the analysis and drafted the manuscript. PH is responsible for the initiation and follow-up of the cohort, for providing input on the interpretation of the results and helping to draft the manuscript. LP participated in discussions of the project and revised it critically for important intellectual content. GH participated in designing the study and helped edit the manuscript. WB provided input on the interpretation of the results from a radiological point of view and helped edit the manuscript. CG designed the study, provided input on the interpretation of the results and helped to draft the manuscript. All authors commented on the draft manuscript and approved the final manuscript.


Several studies over the last decade have suggested that the modern practice of keeping our bodies exposed to artificial light at night, or LAN, increases cancer risk, especially for cancers (such as breast and prostate cancers) that require hormones to grow. Women who work night shifts have shown higher rates of breast cancer,The study, conducted by University of Connecticut epidemiologist Richard Stevens and colleagues at the University of Haifa, showed that higher population-weighted country-level LAN levels were associated with higher incidence of breast cancer.“We took the top-level view and said, ‘If there really is causation going on, LAN levels worldwide should correlate well with breast cancer incidence,’” Stevens says. “This is a necessary but not sufficient condition for a potentially large effect. If we had seen no relationship between country LAN level and breast cancer risk, that would have been good evidence against a large effect of LAN on breast cancer risk.”Tulane University cancer biologist David Blask points out the implications go beyond shiftwork. “This study suggests that all of us who live in industrialized society have the potential to have our circadian system disrupted by too much light at night, and this risk is potentially not restricted to a smaller percentage of the population that is exposed because of their occupation,” Blask says.Harvard epidemiologist Eva Schernhammer agrees that the positive result from this study adds more evidence to the idea that LAN exposure contributes to breast cancer risk. But as an ecological study,The study authors point out that because of the ecological nature of the study, it did not control for behavior that would reduce individuals’ exposure to LAN, such as sleeping. If people are actually asleep, then little to no light would reach their retinas, Stevens says, adding, “Three of four good prospective studies have reported a lower risk of breast cancer in women who report a long sleep duration.”The new study highlights the need to understand the mechanisms behind the association between cancer and LAN, which aren’t clear, Stevens says. Previously, Blask and colleagues famously showed that a key factor in the connection is melatonin, a hormone produced in nighttime darkness that promotes sleep.CLOCK gene than women with breast cancer.CLOCK, was associated with lower risk of breast cancer.CLOCK promoter methylation.Other studies are implicating over- or underexpression of genes known to be involved in the body’s circadian clock. For instance, Stevens and colleagues at Yale including Yong Zhu found that healthy control women showed lower expression of the Another big question is how much of a contribution LAN makes to cancer risk. “Light at night is likely to be one of a number of factors that contributed to the increase in breast cancer over the last few decades,” says Les Reinlib, the program director who coordinates NIEHS grants related to health effects of LAN. “It seems to be significant, and if it is, then that’s something we can control.”Ways to reduce circadian disruption resulting from LAN exposure11⟫ Consider extending the dark period at night to 9 or 10 hours. Install room-darkening shades in bedrooms.⟫ Avoid even brief light exposures. Turn off the lights, television, and computer in the bedroom when you are sleeping. Avoid watching television or working on the computer right before you shut your eyes.⟫ If you get up in the night, forgo the usual bathroom lights for a dim red nightlight. Red light suppresses melatonin production less than other wavelengths.⟫ Do not take melatonin tablets unless directed by a physician. The spike in circulating melatonin may actually worsen, not alleviate, circadian disruption.


Telomeres at the ends of eukaryotic chromosomes play a critical role in maintaining the integrity and stability of the genome and participate in the initiation of DNA damage/repair responses.TERT-07, TERT-54 and POT1-03) in telomere maintenance genes previously found to be significantly associated with breast cancer risk. We used sister-sets obtained from the New York site of the Breast Cancer Family Registry (BCFR). Among the 313 sister-sets, there were 333 breast cancer cases and 409 unaffected sisters who were evaluated in the current study. We separately applied conditional logistic regression and generalized estimating equations (GEE) models to evaluate associations between the three SNPs and breast cancer risk within sister-sets. We examined the associations between genotype, covariates and telomere length among unaffected sisters using a GEE model.We performed a case-control study to evaluate the role of three SNPs  use. Increased duration of HRT use was significantly associated with reduced telomere length. The means of telomere length were 0.77 (SD = 0.35) for never HRT use, 0.67 (SD = 0.29) for HRT use <5yrs and 0.59 (SD = 0.24) for HRT use ≥5yrs after adjusting for age of blood donation and race and ethnicity.We found that exogenous hormonal exposure was inversely associated with telomere length. No significant associations between genetic variants and telomere length or breast cancer risk were observed. These findings provide initial evidence to understand hormonal exposure in the regulation of telomere length and breast cancer risk but need replication in prospective studies. TERT), a catalytic subunit of telomerase, have been found to increase, decrease or have no association with breast cancer risk TERT rs2736109, rs3816659 and POT1-03 rs33964002) in telomere maintenance genes associated with breast cancer risk Telomeres are nucleotide repeats (TTAGGG) with relevant proteins at the ends of eukaryotic chromosomes, which play a critical role in maintaining the integrity and stability of the genome In the current study, we evaluated the association between the three genetic variants and breast cancer risk among 313 sister-sets discordant for breast cancer from the New York site of the Breast Cancer Family Registry (BCFR). We also investigated the impact of these genetic variants as well as other epidemiologic factors on telomere length in unaffected sisters.The study population was selected from families participating in the New York site of the BCFR, six international collaborating sites. The description of the source of study participants and recruitment and data collection methods have been described in detail elsewhere Genomic DNA was extracted from white blood cells (WBC) by the salting out procedure. WBCs were lysed with SDS  in a nuclei lysis buffer and treated with RNaseA , RNaseT1  and Proteinase K . Proteins were co-precipitation with NaCl (330 µl of saturated NaCl/1 ml of solution) by centrifugation. Genomic DNA was recovered from the supernatant by precipitation with 100% ethanol, washed in 70% ethanol and dissolved in TE buffer. Telomere length quantification was performed with the quantitative PCR (Q-PCR) method described by Cawthon Hardy-Weinberg Equilibrium (HWE) was tested to compare the observed and expected genotype frequencies among affected and unaffected sisters, respectively TERT-07, TERT-54 and POT1-03 genotypes were compatible with those expected from the Hardy–Weinberg equilibrium. Overall, the three genetic variants in telomere maintenance genes had no significant impact on breast cancer risk after adjusting for age at blood donation . The TT genotyping of TERT-54 and AA genotyping of POT1-03 were inversely associated with breast cancer risk but the associations were not statistically significant (the ORs were 0.83 (95%CI: 0.44–1.57) and 0.48 (95%CI: 0.23–1.00), respectively). The results obtained from the GEE models were consistent in magnitude with those from the conditional logistic regression models supporting an overall lack of association between these genotypes and breast cancer even after considering that sisters were more likely to be concordant on genotype.The distributions of donation . The homp = 0.01)). The β value for HRT use ≥5 yrs was −0.16 (95%CI: −0.28–−0.03). Duration of OC use was also associated with significant shortening of telomere length . The β value for OC use <5 yrs was −0.10 (95%CI: −0.19–−0.01). Ever use of hormones (HRT or OC) was associated with shorter telomere length for both premenopausal and postmenopausal women (data not shown); but only postmenopausal women with HRT use ≥5yrs had statistically significantly shorter telomeres . Most of the observed significant associations were maintained after adjusting both for age, race and ethnicity, except for ever use of OC  compared with Caucasians (0.69). The β values of the GEE models were 0.13 (95%CI: 0.02–0.23) and 0.23 (95%CI: 0.13–0.34), respectively. No significant correlation was found between telomere length and other covariates within unaffected sisters  .POT1-03 and TERT-54 variants and telomere length. We observed a significant inverse association for TERT-07 GA genotype and telomere length  block at 12p13 and a region at 14q21 were also correlated with multiple cancer risks TERT-CLPTM1L locus might lead to gradual shortening of telomeres over time ATF7IP, a gene that enables expression of TERT and regulates the RNA component encoded by TERCTERT and POT1) that were significantly correlated with breast cancer risk in a large  case-control study, the Long Island Breast Cancer Study Project Recently, increasing evidence suggests that telomere maintenance genes, such as TERC was significantly associated with shorter telomere length, but this result could not be replicated in a family-based study possibly due to its minor effect, which raises another issue, statistical power Another recent study found that a SNP near p = 0.04) while longer duration of exogenous hormone use was linked to shorter telomere length in a subgroup of 18 women with surgical menopause  p<0.01) In addition, long-term environmental exposures and gene-environment interactions may also contribute to the observed inconsistencies. Several studies have shown that environmental risk factors like cigarette smoking One strength of the current study is the family-based design that efficiently reduces potential confounding related to population admixture In conclusion, we found that exogenous hormonal exposure was inversely correlated with telomere length, but genetic variants were not associated with telomere length or breast cancer risk. Further prospective investigations focused on various hormonal exposures and telomere alterations may help in our understanding the role of these factors in regulating breast cancer risk.


Serum lipids, diabetes, and obesity, individual components of metabolic syndrome, are associated with biliary tract cancer and stone risk, but the associations of metabolic syndrome or insulin resistance with biliary tract cancers and stones are not well studied.β-cell function were assessed, using homeostasis assessment models.In this population-based case-control study in Shanghai, China , metabolic syndrome was defined as the presence of any three of the five components, including high waist circumference, high triglycerides, low high-density lipoprotein cholesterol (HDL), high blood pressure, and diabetes. Insulin resistance and P trend <0.0001). The observed association persisted among subjects without a history of diabetes. The association between insulin resistance and gallbladder cancer was borderline (P trend=0.06). There was a significant inverse association between β-cell function and gallbladder cancer risk (P trend <0.001).Metabolic syndrome was significantly associated with gallbladder cancer (odds ratio (OR)=2.75, 95% confidence interval (95% CI)=1.82–4.15) and biliary stones , with a significant dose effect with increasing number of metabolic syndrome components (Our findings suggest that metabolic syndrome and insulin resistance have a role in the aetiology of biliary tract cancers and biliary stones, and if confirmed, they imply that lifestyle control of these factors may lower the risk of biliary stones and biliary tract cancer. Biliary tract cancers, including cancers of the gallbladder, extrahepatic bile duct, and ampulla of Vater, are rare but fatal malignancies ). While β-cell function (HOMA2-%β), and homeostasis insulin resistance (HOMA2-%R)) with biliary tract cancer and stone risk in the same population.To clarify further the role of metabolic syndrome in biliary tract cancer and biliary stones, we adapted a risk prediction approach to examine the associations of individual components and the collection of several components of metabolic syndrome with the risk of biliary tract cancers and biliary stones in a population-based case-control study conducted in Shanghai, China. Further, since insulin resistance is an early process of metabolic syndrome, we also examined markers of insulin resistance , an additional measurement was taken and the average of the closest two values was used. We also used self-reported height and weight to calculate usual adult body mass index  5 years before interview. Diabetes and hypertension were based on self-reported information.At interview, weight, height, and waist and hip circumference were measured. Physical measurements were taken twice. When the difference between the two measurements exceeded Overnight fasting blood samples were collected from over 80% of the participants who gave consent. Within 4 h of collection, study personnel transported the samples to the central processing laboratory at the Shanghai Cancer Institute for processing, and samples were later shipped to the NCI repository on dry ice via Federal Express. Serum lipids were measured at the Laboratory of Biochemistry, Institute of Cardiovascular Diseases, Zhongshan Hospital, Shanghai Medical University (Fudan University). Assay kits for total cholesterol (oxidation enzymatic assay)  were pre−1), low HDL (<1.04 mmol l−1 for both men and women), high blood pressure (self-reported), diabetes (self-reported), and high waist circumference (⩾90 cm in men and ⩾80 cm in women).Metabolic syndrome was defined based on the criteria established by the Chinese β-cell function (HOMA2-%β) and insulin resistance (HOMA2-%R)  for P trend for the number of components ranging from 0 to 5 factors.We examined each of the five components of metabolic syndrome individually, the total effect of metabolic syndrome (any three of the five components), and the degree of metabolic syndrome by calculating a T-tests and Kruskal–Wallis statistics  were used for the bivariate comparison of cases and controls.Measurements of the insulin sensitivity markers were categorised into tertiles based on the distribution in the non-diabetic control population. Correlations between log-transformed insulin sensitivity markers were calculated using Spearman correlation coefficients. −2 is considered normal, 23 to <25 kg m−2 overweight, and ⩾25 kg m−2 obese . A total of 368 gallbladder, 191 extrahepatic bile duct, and 68 ampulla of Vater cancer cases, as well as 1037 patients with biliary stones, and 959 population controls were included in the metabolic syndrome analysis. The analysis of insulin resistance was restricted to subjects who donated blood samples and did not have a history of diabetes (self-reported) since diabetes and anti-diabetic medications can influence insulin sensitivity indices; this resulted in 219 cases with gallbladder cancer, 116 with extrahepatic bile duct cancer, with 926 biliary stones, and 553 population controls. Cases were frequency-matched; therefore, we used unconditional multivariable logistic regression to calculate odds ratios (ORs) and 95% confidence intervals (95% CIs) adjusted for age, gender, and BMI. Body mass index was grouped according to the WHO classification for Asian populations where 18.5 to <23 kg m−2 obese . To redu−1) were associated with excess risks of gallbladder cancer , extrahepatic bile duct cancer , and biliary stones , while low serum HDL was associated with excess risks of gallbladder cancer , extrahepatic bile duct cancer , and biliary stones . Diabetes (self-reported) was also associated with excess risks of gallbladder cancer and biliary stones . Hypertension had a significant inverse association with all three biliary diseases, while the effect of waist circumference was null.Associations of individual metabolic syndrome components with biliary tract cancer and biliary stone risk are shown in P for trend <0.0001), with those having 4–5 components having risks as high as 4.5-fold when compared with those having 0–1 components (Metabolic syndrome (any three of the five components) was significantly associated with increased risks of all three cancers: gallbladder cancer , bile duct cancer , and biliary stones , after adjustment for age, gender, and BMI . Similarmponents . Similarβ (least insulin resistant) had a reduced risk of gallbladder cancer , bile duct cancer , and biliary stones  compared with individuals in the lowest tertile of HOMA2-%β (most insulin resistant). Fasting insulin and HOMA2-%R were not significantly associated with biliary tract cancers or stones.Associations for markers of insulin resistance among individuals without a history of diabetes are shown in β was positively correlated with BMI and stones. HOMA2-%R was positively correlated with BMI, waist circumference, and stones. Serum levels of insulin and glucose were positively correlated with BMI, waist circumference, and stones.Correlations among the metabolic and insulin sensitivity biomarkers were high among non-diabetic controls . HOMA2-%In this population-based study, metabolic syndrome and insulin resistance (among those without a history of diabetes) were associated with excess risks of biliary tract cancer and stones. There was a dose-response relationship between increasing number of metabolic syndrome components and risk of biliary tract cancer and stones. The observed association between metabolic syndrome and biliary tract cancer and stones was more pronounced in women than in men, and it persisted among non-obese (BMI<25) and non-diabetic individuals.Interestingly, the association between some components of metabolic syndrome, such as HDL, and cancer was stronger than that between metabolic syndrome (defined as having three or more of the five components) and cancer. This results because the definition of metabolic syndrome is stricter than the definition of the individual components, so that individuals have to have three or more components to be classified as having metabolic syndrome. Cancer patients who have only one or two of the components would not be classified as having metabolic syndrome. Therefore, the prevalence of metabolic syndrome is lower than that of each individual component in cancer patients, resulting in smaller ORs for metabolic syndrome than for the individual components.The observed association with metabolic syndrome does not appear to be explained entirely by a single component of the syndrome, since there was a significant dose-response relationship between the number of metabolic syndrome components and the risk of biliary tract cancer and stones. This dose-response relationship suggests that the presence of multiple components may have an additive effect on the risk of biliary tract cancers and stones. It is noteworthy that, although only three of the five individual components were positively associated with stone and cancer risks in the study, individuals having four or five components had the highest risk (4.5-fold). This observation suggests that the severity of metabolic syndrome exacerbates the development of stones and cancer.The association of metabolic syndrome with biliary tract cancer and stones is expected and biologically plausible, as several of the components have been individually linked to stone and cancer risk in the study. For example, earlier we reported that, independent of BMI, diabetes was associated with a 2-fold risk of biliary stone and cancer, with biliary stones and low HDL accounting for 60% and 17% of the diabetes effect on gallbladder cancer risk , and theThe lack of an association with waist circumference was somewhat surprising, but could be explained in part by the inherent limitation of the cross-sectional design of the study. Waist circumference in our study was measured at interview, which may have been affected by the presence of cancer (and weight loss), and may not represent usual adult waist size. Similarly, in our earlier analysis, BMI at interview was not associated with biliary tract cancer or stones, while usual adult BMI (5 years before interview) was associated with an excess risk , suggesting that stones may mediate part of the association between metabolic syndrome and cancer. It has been shown that inflammation has a key role in stone formation as well as in the pathogenesis of metabolic syndrome and insulin resistance . TherefoStrengths of our study include the relatively large size and minimal selection bias due to the population-based design and high response rate. Because subject selection and response are unrelated to the exposure of interest  selection bias in our study should be minimal. In addition, misclassification of outcome (cancer and stones) is also minimal due to the detailed evaluation of cancer status from clinical and pathology reports combined with near complete assessment of stone status through careful review of medical records and ultrasound examinations. We also minimised the impact of confounding by adjusting for known confounders, such as age, gender, and BMI, although residual confounding may still exist. An example of possible residual confounding is treatment for dyslipidaemia. However, if individuals were on treatment they would have had lower levels of triglyceride, which would bias the association towards the null. Therefore, our observed association is conservative and is an underestimate of the effect of dyslipidaemia and metabolic syndrome.Limitations of the study should be noted. Misclassification of biomarkers among cases, such as HDL, insulin, and glucose, is possible, since blood samples were taken at enrollment; therefore, the measurements in some cases may have been affected by weight loss among cases as a result of cancer. Although we are not able to remove the effect of reverse causation entirely, we attempted to minimise its impact by limiting the analysis to those with <10% weight loss in the previous 5 years. Of note, due to the study design, we were unable to address the relationship between duration of metabolic syndrome and cancer/stone risk.In summary, our study showed an increased risk of biliary tract cancer and biliary stones among individuals with metabolic syndrome and insulin resistance without a history of diabetes. These findings, if confirmed, suggest that lifestyle changes to control metabolic syndrome may help lower the risk of biliary stones and cancer.


Identify predictors of breast cancer mortality in women who exercised below , at (7.5 to 12.5 MET-hours/wk), or above (≥12.5 MET-hours/wk) recommended levels.Cox proportional hazard analyses of baseline pre-diagnosis MET-hours/wk vs. breast cancer mortality adjusted for follow-up age, race, baseline menopause, and estrogen and oral contraceptive use in 79,124 women  from the National Walkers' and Runners' Health Studies.−5). Breast cancer mortality was 4.0-fold greater  for C-cup, and 4.7-fold greater  for ≥D-cup vs. A-cup when adjusted for BMI and other covariates. Adjustment for cup size and BMI did not eliminate the association between breast cancer mortality and ≥7.5 MET-hour/wk walked or run .One-hundred eleven women  died from breast cancer during the 11-year follow-up. The decline in mortality in women who exercised ≥7.5 MET-hours/wk was not different for walking and running (P = 0.34), so running and walking energy expenditures were combined. The risk for breast cancer mortality was 41.5% lower for ≥7.5 vs. <7.5 MET-hours/wk , which persisted when adjusted for BMI . Other than age and menopause, baseline bra cup size was the strongest predictor of breast cancer mortality, i.e., 57.9% risk increase per cup size when adjusted for MET-hours/wk and the other covariates , and 70.4% greater when further adjusted for BMI (HR: 1.704, 95%CI: 1.344 to 2.156, P = 10Breast cancer mortality decreased in association with both meeting the exercise recommendations and smaller breast volume. In young, lean women, larger breast size has occasionally been associated with greater breast cancer risk This paper examines the dose-response relationships of breast cancer mortality to baseline pre-diagnosis exercise dose and bra cup size in a large cohort of women runners and walkers. There are several potential advantages to the focusing specifically on running and walking rather than all types of exercise. Running and walking occur in discrete bouts, and particularly for running, must be done regularity to be sustained. In addition, running and walking exercise energy expenditures can be calculated from weekly distance run or walked, which appears to be a better metric for studying dose-response than the traditional time-based calculations used by other studies The National Walkers' and Runners' Health Studies have been described previously Height and weight were determined by asking the participant, “What is your current height ?” and, “What is your current weight (pre-pregnancy weight if pregnant)?” Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. Self-reported waist, hip, and chest circumferences were elicited by the question, “Please provide, to the best of your ability, your body circumference in inches: waist___, hip___, and chest___,” without further instruction. Elsewhere, we have reported the strong correlations between self-reported and clinically measured heights (r = 0.96) and weights (r = 0.96) Intakes of meat, fish and fruit were based on the questions “During an average week, how many servings of beef, lamb, or pork do you eat”, and “…pieces of fruit do you eat”. Alcohol intake was estimated from the corresponding questions for 4-oz. (112 ml) glasses of wine, 12-oz. (336 ml) bottles of beer, and mixed drinks and liqueurs. Alcohol was computed as 10.8 g per 4-oz glass of wine, 13.2 g per 12 oz. bottle of beer and 15.1 g per mixed drink. Correlations between these responses and values obtained from 4-day diet records in 110 men were r = 0.46 and r = 0.38 for consumptions of meat and fruit, respectively. Family history of breast cancer was based on whether the respondent had a mother or sister who had breast cancer before age 55 (phase I), and from listing of cancers by site in all first-degree relatives (phase II). For consistency, the phase I definition of family history was used for all participants.2 at death or end of follow-up), race, menopause, and oral contraceptive and estrogen use. The covariates were selected for their significant relationship with breast cancer mortality. Results are presented as hazard ratios (HR) and their percent risk reduction {calculated as 100*(HR-1)} for categories of running energy expenditure relative to falling short (<7.5 MET-hours/week), achieving (7.5 to 12.5 MET-hours/week), or exceeding the minimum exercise energy expenditure recommended for health (>12.5 MET-hours/week) Mortality surveillance was completed through December 31, 2008 using the National Death Index One-hundred eleven  of the 79,124 runners and walkers died from breast cancer as the underlying cause of death during (mean±SD) 11.0±2.11 years of mortality surveillance. When adjusted for follow-up age, race, menopause, oral contraceptive, and estrogen and estrogen/progesterone medication use, the decline in breast cancer mortality in women who met or exceeded the current physical activity recommendations was not significantly different for walking and running (P = 0.34). In addition, relative to <7.5 MET-hours/wk at baseline, the reduction in risk was similar for 7.5 to 12.5 MET-hour/wk and >12.5 MET-hour/wk for running and walking combined . Thus, t−5) when also adjusted for BMI. Baseline breast volume, as indicated by bra cup size, was inversely related to MET-hour/wk run or walked . Other tPublic health guidelines recommend that adults “should do at least 150 minutes (2 hours and 30 minutes) a week of moderate-intensity, or 75 minutes (1 hour and 15 minutes) a week of vigorous-intensity aerobic physical activity, or an equivalent combination of moderate- and vigorous-intensity aerobic activity” The reduction in breast cancer mortality with running and walking we observed could be due to reduced risk for incident breast cancer, improved survival in women diagnosed with breast cancer, or both. Our data revealed no significant difference in the reduction in breast cancer mortality between running and walking. However, the power to detect small differences in risk was probably limited. Although the protective effects of physical activity on breast cancer risk are reasonably well-established 2, which is leaner than the general population. In the current study, the increased risk for breast cancer mortality with breast size remained significant when adjusted for baseline circumferences of the waist, hip and chest, was not necessarily exclusive to premenopausal or postmenopausal breast cancer, and became even more significant when adjusted for baseline BMI.Other than age and menopause status, baseline bra cup size was the most significant risk factor for fatal breast cancer in the women runners studied here. In young, lean women, larger breast size has been occasionally associated with breast cancer risk Breasts consist of radiographically dense (breast epithelium and stroma) and nondense tissue (adipose tissue) whose proportions differ across women. Those whose breast are ≥75% dense tissue have a four- to six-fold higher breast cancer risk than women with <25% dense tissue Hormones and genetics may also contribute to the greater risk from larger breasts. Higher circulating estrogen levels are associated with greater breast cancer risk Breast size may also affect survival. Women with breast volumes ≥850 ml have shorter disease-free and metastasis-free survival for ER-positive tumors There are important limitations to these analyses. The date and disease stage at diagnosis and the type of breast cancer treatment are not known because diagnosis would have occurred after the baseline survey. Physical activity, bra cup size, and other baseline variables were from self-report from the participants' baseline questionnaires. Bra cup size may not be a very precise measurement of breast volume. It has been suggested that the majority (70% to 100%) of women do not wear the correct size bra Exercise levels, bra size, and other subject characteristics could have changed prior to the onset of breast cancer. However, imprecision in breast volume and exercise energy expenditure based upon self-reported bra size and usual distance run are expected to attenuate their associations