Estimately, 70–80% of cancer cases are diagnosed in late stages in Kenya with breast cancer being a common cause of mortality among women where late diagnosis is the major ubiquitous concern. Numerous studies have focused on epidemiological and health policy dynamics essentially underestimating the determining factors that shape people’s choices and cues to health care service uptake. The study sought to evaluate the knowledge, attitude and health seeking behavior towards breast cancer and its screening in a quest to explain why women present for prognosis and treatment when symptomatic pointers are in advanced stages, impeding primary prevention strategies.Eight focus groups (6–10 members per group) and four key informant interviews were conducted among adult participants from rural and urban settings. Sessions were audio-recorded and transcribed. A thematic analysis of the data was based on the concepts of the health belief model. Data analysis was conducted using NVIVO10.Most women perceived breast cancer as a fatal disease and conveyed fear of having early screening. Rural women preferred self-prescribed medications and the use of alternative medicine for long periods before presenting for professional care on suspicion that the lump is cancerous. Accessibility to equipped health facilities, lack of information to establish effective follow-up treatment and low-income status were underscored as their major health seeking behavior barriers whereas, urban women identified marital status as their main barrier. Key informant interviews revealed that health communication programs emphasized more on communicable diseases. This could in part explain why there is a high rate of misconception and suspicion about breast cancer among rural and urban women in the study setting.Creating breast cancer awareness alongside clear guidelines on accessing screening and treatment infrastructure is critical. It was evident, a diagnosis of breast cancer or lump brings unexpected confrontation with mortality; fear, pain, cultural barriers, emotional and financial distress. Without clear referral channels to enable those with suspicious lumps or early stage disease to get prompt diagnosis and treatment, then well-meaning awareness will not necessarily contribute to reducing morbidity and mortality.The online version of this article (10.1186/s12905-018-0571-7) contains supplementary material, which is available to authorized users. Cancer is the third highest cause of mortality in Kenya after infectious and cardiovascular diseases. Leading cancers are breast and cervical for women , 2. SeveHealth care access is considered a multidimensional concept encompassing both financial and non-financial dimensions , 6. It hAs studies shed more understandings on the the risks and benefits of early breast cancer screening, indepth understanding of women perceived risk and barriers have become integral , 17, 18.The qualitative research was conducted between November 2013 and March 2014. A qualitative design using focus group discussion (FGD) and key informant interview (KII) methods was applied as an ideal approach to explore perceived motivators and barriers to healthy behaviours . The iteKakamega is a County in Western Kenya lying about 30 KM north of the Equator. The county has a population of about 1.7 million  with a population density of about 544 people per square kilometer and is Kenya’s second most populous county after Nairobi . AccordiThe researcher divided the 12 constituencies into two groups; urban and rural using Kakamega First County Development Plan, 2013. Map of the study areas, Kakamega couty constituencies and study locations . The concepts were pretested with small groups outside the target study area and revisions made where necessary. Information from KII (the first one to be conducted) helped inform some of the questions to ask in FGD. Thus, gaining further understanding on the validity of DPHN perspective in resonance to the population they attend to and considering they often provide inputs in appraising government health sector operational guidelines and policies which have a direct bearing on the populations they serve. Focus groups lasted between 43 to 65 min. Rural focus groups were conducted in Swahili while discussions with urban women took place in Swahili and English. The items in FGD interviews were initially formulated in English then translated into Swahili for the use in the different study locations. Data collected in Swahili versions were translated back to English to ensure consistitency with the data collected in English versions. The five-phase cycle of compiling, disassembling, re-assembling (and arraying), interpreting and concluding were used to process the data as earlier described .All the data from the KIIs and FGDs were then uploaded to the Computer Assisted Qualitative Data Analysis Software (CAQDAS) QSR Nvivo10 for windows for management and analysis. Coding was done as earlier described  with levFindings from the focus group discussions and key informant interviews are presented per the main categories from level 3 coding that emerged.chamas were common source of building financial capacity and borrowing among the women. The vast majority of women rated their health status as good.Sixty-eight women aged 18–60 years (age: 18–35: 36 and 36–60: 32) from the selected rural and urban constituencies of Kakamega county Kenya participated in eight focus groups (average 8.5 participants per group). Of these 63 (92.6%) were Christians and 5 (7.4%) were Muslims. Participants were predominantly married (72.1%). The educational level of the respondents varied considerably; 45.6% had primary education, 35.3% secondary, 11.8% middle level college training and 2.9% university degree while the rest had no formal schooling. There were 32 women from the rural setting and 36 urban. The average family size was 6 persons, with an average of 4 children per woman. All working women had government medical cover, the National Health Insurance Fund (NHIF) through their jobs, granting coverage for health service use at governmental facilities for themselves and their dependents. Despite having NHIF cover, the women expressed a generalized preference for private health care providers. They however explained that, their choice to use public rather than private services was often mediated by a series of social, economic, and geographical considerations. None of the women had private health insurance cover instead self-help savings and credit groups, commonly known as “Some men do not allow their women to go to the hospital. Most men do not even allow their women to go out of the homestead and ‘fetch water for domestic use’, how then will they even allow women to go for cancer screening at the hospital?”There were disparities between the urban and rural when it came to health seeking behavior barriers  identified long distance to health facility, lack of information on breast cancer and its screening and low-income status as major health seeking behavior barriers. An older woman from rural group 4 said:“The long distance to the cancer facility would cost a lot of transport fare that I cannot afford and this makes it difficult for me.”A younger woman from rural group 1, said:“The number of staff in the hospitals should be increased. Sometimes you will find there is only one nurse who has to do everything in a hospital. When a woman comes in for a BCS, the nurse is most likely to attend to the patients whose lives are at risk first. There should be devolution. The number of staff should be increased.”An interview with Key informant revealed that the health centres and hospitals do not have enough nurses to perform regular breast cancer screening. She narrated:“Some women go to churches that believe in prayers for healing, the churches do not allow their followers to go to the hospital. Such women do not go for breast cancer screening.”Through indepth discussions it was evident cultural religion orientation was a contributing barrier among rural women compared to their urban counterparts. A rural participant from rural group 3 said;“If a woman is known to be sleeping around with many men, and she discovers that she has breast cancer, she will prefer not to go and seek treatment because she is afraid that she might go to the hospital, get tested for HIV and told that she has AIDS.”The study further revealed that most women are skeptical of having early breast cancer screening as attributed to fear of getting a positive breast cancer diagnosis; stigmatization associated with it and breast cancer related cultural beliefs alongside misconceptions as well as the, what next? doubts. The action taken when a woman realized she had a breast lump was influenced by the community perception of the origin of the disease for the older urban participants, conventional practices and beliefs for most rural participants and level of knowledge by younger-rural women. Most urban women described the community perception and association of breast cancer to promiscuity, infidelity, and equating it to HIV/AIDS thus hindering women who suspected a lump in their breast was cancerous from seeking early treatment in hospitals. An older woman from the urban group 8 narrated:“Women believe it is a sore, so we take traditional herbs called “miyeka”.”Rural women on the other hand preferred using herbal remedies, self-medicating with painkillers or going to traditional medicine men for complementary and alternative remedy when they suspected they had a cancerous lump in their breast. A woman from rural group 3 said:We also established that younger-rural participants and older participants were not aware or clear of simple methods such as breast self-examination or where they could get early breast-screening services. Several older participants asked to be taught how to detect a lump in their breast while younger women explained that they were not confident on performing breast self-examination on themselves. A young participant explained in frustration how nurses instructed them to palpate their breast but she did not understand how palpation was done or what the nurses meant by palpate. When asked how to check for lumps in their breast, a participant from urban group 8 said:“We do not know. You should teach us on how one knows that they have breast cancer.” This is despite the fact that health centres in Kakamega County schedule a day in a month for breast health education. A key informant from a rural group explained that most facilities in the county conduct breast screening at facility level once a month. However, she also added that it was likely that women did not know that they could access such services for free due to lack of information.“I know they are cells that multiply in the breast leading to death.”Younger participants seemed to have a better comprehension of breast cancer, its early manifestations, early breast cancer screening and predispositions’ to the disease compared to older participants. A participant from rural group 1 defined breast cancer as:“I know that it is brought about by smoking and using a lot of fat in your food.”Specifically, young urban participants seemed more knowledgeable about lifestyle issues that predispose individuals to breast cancer. They mentioned smoking, too much sugar and salt in food, use of bleaching pills and self-medicated pills. A woman in urban group 7 said:Other misconceptions across the women included being born with “risky bugs” in their breasts, breast cancer being a consequence of having HIV and prostitution as well as surgery of the breast being associated with breast cancer status and death.“Breast cancer is a death sentence.”Most women were concerned of breast cancer and were overwhelmingly convinced that it is a serious terminal disease with no cure. Terms such as ‘deadly and death’ were commonly used in the description of breast cancer. A participant in rural group 2 said:“I understand that it has no cure. It is an incurable disease.”Another participant in rural group 1 said:“The women in the community believe that breast cancer is a disease that does not have a cure and so will not bother to go to the hospital.”Several participants also described the perceived seriousness of the disease as extremely dangerous and incurable. A young woman in urban group 5 stated:“I do not think going for breast cancer screening is a necessity. One woman that I know went for breast cancer screening and was told that she had it and that her breast had to be removed... When her breast was cut, the cancer spread throughout her body and following that, I and many of my friends who knew her can’t go to the hospital.”Participants were also afraid of CBE. A participant from urban group 8 narrated:“If I am found with cancer at an early stage, it means that I will not spend much on the treatment of the disease. It will be cheaper for me.”Some urban participants seemed well aware of the benefits of early breast cancer screening. They noted that knowing their breast cancer status early would result to the early treatment of the disease and this would increase the chances of survival. They also explained how early screening uptake reduces the high cost of treatment of the disease if it is detected early. A woman from urban group 6 said:Despite this knowledge among some of the participants, they were still sceptical of having early breast cancer screening as it would lead to psychological stress, depression and even early death unlike when they were not aware.“There is no much emphasis on breast cancer awareness campaigns.”Most of the participants had very limited knowledge of breast health awareness programs. Participants could only mention programs aimed at communicable diseases  awareness creation. Participants from rural group 2 said:“In this community, if a woman does not know the importance of early breast cancer screening, they will not go for screening. But after they have been taught on the importance, many of them flock to the screening rooms and get screened for cancer. An example is cervical cancer. Most women have had a pap smear done on them because there have been consistent seminars and campaigns on the importance of doing a pap.”The participants pointed out the reason why they do not have much information on breast cancer was because it was not being given as much prominence as other diseases like malaria per their views on assessment of public health information availed to them most of the times. An interview with a key informant from urban setting revealed that if participants were given information on breast cancer and its screening; there would be rapid uptake of early breast cancer screening among women. She said:“If they gave us information on breast cancer, it would motivate me to go for screening.”An urban participant in group 5 when asked what measures can be put in to motivate women to go for early screening said:“Through seminars- they should take CHW’s on seminars, and when they come they should educate the community on breast cancer by doing door to door education.”The participants also asked for free and regular breast cancer screening services to be made available not only in mother-child health clinics but also in the overall hospitals services. When the women were asked on the best channel of communication of information on breast cancer and its screening, urban participants suggested the use of media, and mother-child health clinics. Most young participants suggested use of community health workers (CHW), social media and text messages as the best avenues for the communication of information. Rural participants suggested the use of community health workers. A rural participant said:barazas) and village chief’s as the best channel for communication of information on breast cancer and its screening. Key informant 2 suggested:“Verbal health education- use of wall charts, the mass media, chief barazas and use community leaders .”Older women suggested use of door to door education, churches, village meetings  and lack of health professionals to perform needed screening tests as barriers to breast cancer screening. In the recent past, gainful strides have been achieved in availing a more inclusive health insurance cover, increased adaptation of treatment guidelines, expansion and upgrading of radiotherapy equipment across the country in addition to more research opportunities , 21, 37.Against the backdrop of improvement efforts, the present findings still mirror earlier observations of low uptake and with socio-economic factors of health having been implicated in influencing individuals and communities’ health seeking behaviors , 43. FurAmong the urban women, decision to visit a health facility on the discovery they had breast cancer was influenced by their knowledge and information about breast cancer. Women who are knowledgeable about breast cancer and its risk factors are known to be more likely to comply with such early detection behaviors than those who are not . Rural wCreating breast cancer awareness alongside clear guidelines on accessing screening and treatment infrastructure is critical. The messaging should aim at instilling hope and eradicating the myths and misconseptions harboured about the disease. It was evident, a diagnosis of breast cancer/lump with lack of clear course of expertise support, brings unpexpected confrontation with mortality; the fear, pain, cultural barriers, emotional and financial distress are very real. Without clear referral channels to enable those with suspicious lumps or early stage disease to get prompt diagnosis and treatment, then well-meaning awareness will not necessarrily contribute to reducing morbidity and mortality.Additional file 1:Interview guide for the focus group discussions and in-depth interviews (key informants) (DOCX 44 kb)Additional file 2:Map of the study areas. (DOCX 2073 kb)Additional file 3:Perceived barriers to early breast cancer screening uptake and treatment as mentioned by FGD participants. (DOCX 18 kb)


GPX1) is one of the antioxidant enzyme that remove the reactive oxygen species in a continuous process. Since the identification of a well‐characterized functional polymorphism named p.Pro198Leu (rs1050450 C>T) in GPX1 gene, abundant studies have evaluated the association between p.Pro198Leu polymorphism and tumor risk in diverse population. But, the available results related to breast cancer are conflicting and absent in Africa. The present case–control study was planned to assess the presence of GPX1 Pro198Leu polymorphism in Rwanda population to determine whether it is associated with the risk of developing breast cancer.Glutathione peroxidase 1 gene  was detected.No significant difference in the frequencies of Pro/Pro (49%) and Pro/Leu (51%) genotypes in cancer cases and in controls (50% each) were found. The allelic frequencies of Pro and Leu were 74% versus 26% and 75% versus 25% in breast cancer cases and controls respectively. No association was observed in allele frequencies of Pro and Leu, and familial history. Only an overall association of GPX1 Pro198Leu polymorphism could not be a risk factor for breast cancer in Rwanda. However, large‐scale studies on the effect of this polymorphism on the factors disturbing the redox homeostasis are needed for conclusive understanding.The result of this study suggested that  All breast cancer cases were recruited in 2016 at Rwanda Military Hospital and King Faisal Hospital, both located at Kigali, Rwanda. From each breast cancer case, clinical and pathological data  were collected. Table 2.3Approximately, 5 ml of the whole blood was taken by venepuncture into 5 ml vacutainers  containing EDTA. Genomic DNA was isolated from all peripheral blood samples by using a commercial kit  according to manufacturer's recommendations. DNA obtained was immediately used for PCR amplification or stored at −20°C until use.2.42, 200 μmol/L of each dNTP, 200 nmol/L of each primer, 0.25 U Platinum Taq DNA polymerase (Invitrogen), and 100 ng of genomic DNA in 1X PCR buffer. PCR reaction was performed as follows: After a first denaturation at 94°C for 7 min, follow 35 cycles of 94°C for 30 s, 60°C for 30 s, 72°C for 40 s. At the end of the last cycle, the mixtures were incubated at 72°C for 7 min. For every reaction, a negative control, in which DNA template was omitted from the amplification mixture, was included.p.Pro198Leu polymorphism screening was carried out by PCR amplification and DNA sequencing  and sequenced with forward strand on an ABI 3130XL DNA analyzer, using BigDye Terminator v3.1 Cycle Sequencing Kit .Sequencing reactions were performed in a final volume of 10 μl, containing 1 μl of 2.5X Big Dye ready reaction mix v.3.1, 10 pmol of forward primer and 100 ng of purified PCR product. The mixtures were incubated at 96°C for 1 min and 25 cycles were performed, including denaturation at 96°C for 10 s, primer annealing at 50°C for 5 s and extension at 60°C for 4 min. The reactions were set to 30 μl. To eliminate the excess of labeled ddNTPs, sequencing reaction products were purified using Sephadex G‐50 gel‐exclusion chromatography . The sequences were analysed using Sequence Scanner v2.0 software (Applied Biosystems).2.5GPX1 genotypes with the occurrence of bladder cancer and to examine the correlation between GPX1 genotypes and cancer stage or grade. The statistical relationship was considered as significant if the derived p‐value was ≤.05. The estimated genotypic and allelic frequencies were associated with 95% confidence intervals calculated using the modified Wald test (Agresti‐Coull).Statistical tests were performed using the OpenEpi software. Chi‐square test with Yates’ correction was used to evaluate the association of 3GPX1 exon 2 for all 41 cases of BC and 42 controls were successfully conducted and revealed rs1050450 C>T substitution in exon2 resulting in p.Pro198Leu in both cancer specimens and controls. An example of obtained electrophoregrams is illustrated in Figure Amplification and direct sequencing of In our study, only two genotypes were detected (Pro/Pro and Pro/Leu). However, homozygous Leu genotype was found neither in cancer cases nor in controls. The genotype and allele frequencies of GPX1 Pro198Leu polymorphism are summarized in Table p = .05).Overall, genotypic frequencies obtained in cancer cases were similar to that obtained in controls. Indeed, the frequencies of Pro/Pro and Pro/Leu genotypes were, respectively, 49% (20/41) and 51% (21/41) in cancer cases, and were 50% each in controls. Statistical analysis showed that no significant difference between cancer cases and healthy controls .The allelic frequencies of Pro and Leu were 74% versus 26% and 75% versus 25% in breast cancer cases and controls, respectively. No statistically significant difference allelic frequencies was observed of GPX1 Pro/Leu alleles between cancer and control groups (p > .05).GPX1 Pro198Leu polymorphism was also evaluated according to familial history. As indicated in Table p = .02).GPX1 Pro198Leu polymorphism was also evaluated according to tumor grade and clinical stage and the results are reported in Table p = .143). Regarding the distribution of cases according to tumor grade, statistical analysis could not be applied due to limited number of cases mainly in grade I.Allele frequency analysis showed that Pro allele prevails in both stages II and III, with 69.2% and 76.9%, respectively, and significant association was obtained  in GPX1 gene, a lot of studies have been conducted to evaluate the association between p.Pro198Leu polymorphism and risk of cancer development. Therefore, a great interest was given to the association between p.Pro198Leu polymorphism and breast cancer risk in various populations and a strong association was reported in Denmark, USA, UK and Poland . No significant difference was also found between Leu and Pro alleles between cases and controls (p = .5). Previous studies highlighted the presence of homozygous Leu/Leu genotypes and showed that Leu allele is strongly associated with breast cancer development suggesting that individuals carrying variant Leu allele (Pro/Leu and Leu/Leu) were associated with an increased cancer risk . Pro/Leu genotype was significantly higher in stage II as compared to stage III where the Pro/Pro genotype prevails. The presence of Leu allele seems to have a protective effect against breast cancer development and progression. This finding is supported by a recent study conducted in Poland showing that carrying Leu variant was associated with a significant 40% decrease in the breast cancer risk, suggesting a protective effect of GPX1 Leu variant (Jaworska et al., GPX1 Leu variant has been found also in the case of lung and laryngeal cancers (Jaworska et al., In the present study, a strong association was found between GPX1 Pro198Leu polymorphism and clinical stage (Overall, this study is very informative giving evidence of the absence of association between p.Pro198Leu polymorphism and breast cancer development in Rwanda and highlighting the rarity of homozygous Leu/Leu genotype in Rwandan people. However, the main limitation of this study is the small sample size making difficult to assess a clear association between such polymorphism and clinicopathological features.The results of this preliminary study showed that 198Leu genotype is rare in Rwanda and suggest that GPX1 Pro198Leu polymorphism is not a risk factor for breast cancer development. However large scale investigation in Rwandan and other African patients are needed to draw consistent information on the implication of this polymorphism in breast cancer in African patients.The authors declare no conflict of interest.


In the United States, breast cancer is the most common female cancer and the second leading cause of female cancer death . Breast In 2015, the ACS issued an update to its 2003 screening mammography guidelines . The 200The newly issued 2015 ACS screening mammography guidelines depart from the 2003 guidelines in several ways . First, So what prompted this substantial change in recommended screening guidelines? An editorial by Keating and Pace  identifiSecond, the panel used the Surveillance, Epidemiology, and End Results (SEER) data using 5-year age groups to estimate the benefits and harms of mammography screening compared with previous estimates using 10-year age groups. The panel found that the breast cancer deaths between women aged 45 to 49 years compared with those of women aged 50 to 54 years, rather than women aged 40 to 44 years. Thus, the panel recommended screening mammography to begin at age 50.Third, the panel commissioned an analysis of annual vs. biennial screening by the Breast Cancer Surveillance Consortium BCSC; . The BCSThe panel found that the evidence of CBE and mortality reduction with CBE to be of low quality and unconvincing. Thus, the panel recommended against CBE.BRCA; and (3) a personal history of radiation therapy to the chest wall received at a young age. The recommendations do not refer to women who are considered at higher risk.Overall, the changes in the ACS recommendations refer to women of average breast cancer risk. So, who are they? Average-risk women are those who have none of the following characteristics: (1) a personal history of breast cancer; (2) a suspected or confirmed genetic mutation such as BRCA; (3) personal history of radiation therapy to the chest wall received at a young age; (4) first-degree relatives diagnosed with breast cancer before the age of 45 or ovarian cancer at any age (particularly if more than one relative was diagnosed) or a male relative with breast cancer; (5) Ashkenazi Jewish heritage; and/or (6) other breast health problems or conditions, such as breast density, lobular carcinoma in situ, ductal carcinoma in situ, atypical ductal hyperplasia, or atypical lobular hyperplasia.Who is considered higher risk among women younger than 45 years? They are women with (1) a personal history of breast cancer; (2) a suspected or confirmed genetic mutation such as How do the Second, the USPSTF does not recommend screening mammography in women 75 and older. Third, the USPSTF found insufficient evidence to recommend CBE in women over 40. Fourth, the USPSTF recommends against teaching breast self-exam. And finally, current evidence regarding digital mammography or magnetic resonance imaging is insufficient to assess the additional benefits and harms over film mammography.So we return to the question, "When should I have a mammogram?" The answer can still be "that depends," because there is no specific right or wrong answer for women of average risk. Although some advanced practitioners may argue that the recommendations are confusing, it is hoped that others may view the new 2015 ACS recommendations in another light: one where the dialogue becomes patient-driven, patient-centered, and patient-valued.Advanced practitioners can begin the conversation with patient-driven comments: "Let’s talk about average risk. Are you at average risk? That depends on your current age as well as your family and personal histories. Maybe you are not at average risk. Thus, the guidelines do not pertain to you." It may be likely that advanced practitioners in oncology may see women who are not at average risk. They may be seeing survivors and family members of cancer survivors.Among our advanced practitioner colleagues in primary care or women’s health practices, they may be using the USPSTF guidelines for average-risk women. In this instance, the USPSTF guidelines may be more in line with the updated ACS guidelines, where average-risk women can have the opportunity to start biennial screening before the age of 50 years. The USPSTF guidelines are silent on the topic of annual screening before the age of 50, but the 2015 ACS guidelines for average-risk women begin at age 45. Thus, it is most likely that the topic of screening average-risk young women is covered in the ACS guidelines. So, both the new ACS and the USPSTF guidelines for young women cover the answer to "When should I have a mammogram?" Most importantly, maintaining a balanced discussion focused on risk and benefits as well as personal preferences and values can keep the dialogue moving.Given the weight of the new evidence about average risk, there is greater opportunity to apply evidence to practice to help women make informed decisions that are best for them. So, let the conversation begin!


Circulating concentrations of lipid biomarkers are associated with risk of cardiovascular diseases (CVD). The evidence for a relationship with cancer risk, however, is not entirely consistent. This study aims to assess the relationships of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), apolipoprotein (a) (apo(a)), apoB-100, and lipoprotein(a) (Lp(a)) with risk of common cancer forms and total cancer mortality in comparison to incidence and mortality of CVD.n = 2739), and cases of cancer (n = 1632), cancer mortality (n = 761), CVD (n = 1070), and CVD mortality (n = 381). Concentrations of lipid biomarkers were measured in pre-diagnostic blood samples. Hazard ratios (HR) and 95% confidence intervals (CI) were estimated using Prentice-weighted Cox regression models.We selected a case-cohort sample out of the prospective EPIC–Heidelberg study, including a random subcohort . Higher levels of Lp(a) were associated with an increase in prostate cancer risk  1.43, 1.02–2.03) and high levels of apo(a) were associated with a decrease in lung cancer risk  0.52, 0.30–0.91). High TC, HDL-C, apo(a), and Lp(a) levels were associated with a reduction in total cancer mortality  0.71, 0.54–0.93; and HRLp(a) 0.74, 0.57–0.98). All lipid biomarkers were associated with risk of myocardial infarction, whereby TC, apoB-100, TG, and Lp(a) were positively and HLD-C and apo(a) inversely associated with risk. Only high levels of TG were associated with an increased risk of stroke. None of the lipids were associated with risk of colorectal cancer and with risk of CVD mortality after multivariable adjustments.High levels of circulating apoB-100 and TG were inversely associated and high HDL-C levels were positively associated with breast cancer risk (highest vs. lowest quartile (Q4 vs. Q1), HRThis prospective study demonstrates inverse associations of lipid biomarkers with cancer incidence and mortality, with the exception of positive associations of HDL-C and Lp(a) with breast and prostate cancer risk, respectively. Thus, the observed cancer risk pattern clearly differs from the CVD risk pattern.The online version of this article (doi:10.1186/s12916-017-0976-4) contains supplementary material, which is available to authorized users. Circulating concentrations of lipid biomarkers are consistently associated with cardiovascular disease (CVD) events and thus are considered major indicators of metabolic health. In particular, high levels of low-density lipoprotein cholesterol (LDL-C) have been consistently associated with up to 1.7-fold increases in risk of CVD in observational studies .Lipids and lipoproteins in blood, including LDL-C and HDL-C, are responsible for cholesterol metabolism in humans. HDL-C is a small and dense lipoprotein, containing proteins and lipids, which removes fat molecules and cholesterol from cells and transports them back to the liver for excretion or re-utilization. Apo(a) is the major protein component of HDL-C in plasma and enables efflux of fat molecules by accepting fats from within cells. In contrast, apoB-100 is the primary apolipoprotein of chylomicrons, very low-density lipoprotein, intermediate-density lipoprotein, and LDL particles, and as such is responsible for transport of fat molecules to all peripheral cells. Another major component of chylomicrons and very low-density lipoprotein are TGs, which play an important role as transporters of dietary fat and as energy sources. In contrast to these lipid biomarkers, the metabolism of Lp(a) is largely unknown. Lp(a) is commonly described as an LDL-like lipoprotein particle consisting of one apo(a) molecule covalently bound to apoB-100 . While hWith regard to cancer incidence, the associations of lipid biomarkers, and particularly of apolipoproteins (apo) and Lp(a), have been investigated much less thoroughly than for CVD, and results from different prospective studies performed thus far are not entirely consistent. Elevated blood concentrations of TC and TG and lower concentrations of HDL-C and apo(a) were associated with an increased risk of overall cancer in the most recent meta-analysis based on 28 epidemiologic studies . HoweverEpidemiologic studies on cancer mortality are less numerous than those on CVD mortality, but low TC levels have been consistently associated with an up to 2-fold increase in overall cancer mortality in these studies –25. ThesSuch inconsistent results and limited investigations for some of the lipids and cancer entities make it difficult to define a common metabolic lipid profile characteristic for cancer risk similar to the one for CVD. It is also unclear whether these lipids are causally related to cancer risk or whether they are merely markers of general lifestyle determinants. In light of these uncertainties, we conducted a case-cohort study within the European Prospective Investigation into Cancer and Nutrition (EPIC)–Heidelberg cohort on the associations of lipid biomarkers with risk of common cancer forms and total cancer mortality, adjusted for multiple potential lifestyle factors. For comparison purposes, we report the associations of lipid parameters with risk of stroke, myocardial infarction (MI), and CVD death.We conducted a case-cohort study embedded within the EPIC–Heidelberg cohort. EPIC–Heidelberg is part of the larger European EPIC project and comprises 25,546 men and women from the local general population of Heidelberg and surroundings , 30. Parn = 2739). An exclusion criterion for any subject was non-availability of a blood sample. For the current study, 627 incident cases of breast (ICD-10: C50), 554 cases of prostate (ICD-10: C61), 195 cases of lung (ICD-10: C34), and 256 cases of colorectal cancer (ICD-10: C18-20) were included. In addition, 515 incident cases of stroke  and 555 cases of MI (ICD-10: I21) were included. Within the full EPIC–Heidelberg cohort, 1516 participants deceased until end of follow-up (Dec 2012), of which 761 (50%) died of cancer (any type), 381 (25%) of cardiovascular events, and the remaining 374 (25%) of miscellaneous conditions. All causes of death were also coded according to the ICD-10. We chose a case-cohort design in order to economize on samples and costs and since several failure time outcomes can be analyzed with the same comparison group, as the random sample subcohort is selected independently of the outcome [In EPIC–Heidelberg, chronic disease occurrences were prospectively ascertained through active follow-up through study subjects and their next-of-kin, combined with linkages with hospitalization records, and cancer and pathology registries. Data on vital status and causes of death were collected from municipal and regional mortality registries. For the present case-cohort study, verified incident cases of breast, colorectal, prostate, and lung cancer diagnosed up to the end of 2012, verified incident cases of MI and stroke, and verified cases of death up to the end of 2009 were included. A randomly selected subcohort of approximately 10% of the entire EPIC–Heidelberg cohort participants served as the reference pool  for basic clinical chemistry measurements, including serum concentrations of TC, HDL-C, TG, apo(a), apoB-100, and Lp(a). All measurements were made using the Roche Cobas 6000 analytical system for clinical chemistry, according to the manufacturer’s protocols. A small proportion (< 10%) of the analytes were not successfully measured and therefore the number of subjects may vary depending on the applied statistical model. The Friedewald formula (LDL = TC – HDL – TG/5) was applied to calculate LDL-C values .Spearman partial correlation coefficients (r) adjusted for sex and age at recruitment were calculated in the subcohort for selected baseline variables and lipid biomarkers. For analyses on cancer risk and mortality, we applied Prentice-weighted Cox proportional hazard regression models with age as the underlying time-scale to calculate hazard ratios (HR) and 95% confidence intervals (CI). All observations in the subcohort were left-truncated at age at recruitment and right-censored at end of follow-up, death, or loss of follow-up, whichever came first. Cases were included at the time of their event, following the Prentice weighting scheme . An exten = 915, 34% of the female study population), those with lipid-lowering medication use , those with prevalent liver insufficiency (n = 79), and those with prevalent kidney insufficiency (n = 66) in sensitivity analyses. The last two conditions were self-reported in a questionnaire . Risk associations for calculated LDL-C levels were comparable to those obtained by apoB-100 and can be found in Additional file r = 0.93) in our data.Prentice-weighted Cox regression models were used to estimate both crude and multivariable adjusted HR and 95% CIs for the associations between lipid biomarkers, cancer risk, risk of CVD, and mortality. The crude model includes sex and age as co-variables. Confounding factors were included in the multivariable adjusted models if they changed the HR by more than 10%, were significantly associated with either the exposure or the outcome, or were considered as important risk factors based on biological plausibility. Variables retained in adjusted models were body height, waist circumference, BMI, lifetime alcohol consumption, intake of red meat and fiber, smoking habits , socioeconomic status (school level), physical activity, diabetes, hypertension, and use of lipid-lowering drugs. Cox models with breast cancer as an outcome were further adjusted for oral contraceptive use, hormone replacement therapy, menopausal status, and number of full-term pregnancies. Heterogeneity by sex , current alcohol consumption at recruitment (nondrinkers vs. drinkers), menopausal status at baseline, age 55 at diagnosis and hormone replacement therapy (HRT) use at baseline (for breast cancer only), smoking status , and obesity was based on Q statistics, adjusted for age and, if applicable, for sex. To assess possible reverse causation bias, sensitivity analyses were conducted excluding cases diagnosed within the first 2 or 5 years of follow-up. We further excluded subjects with HRT use at baseline .All statistical tests were two-sided and Baseline characteristics of the EPIC–Heidelberg subcohort participants are displayed in Table r = 0.85), and apoB-100 levels correlated strongly and positively with TC (r = 0.85) and less strongly but positively with TG . BMI and body weight correlated negatively with HDL-C  and positively with TG  in the subcohort. TC, TG, and apoB-100 levels were highest, and HDL and apo(a) levels lowest, among current smokers compared to never smokers (P for difference by smoking status < 0.01). Among postmenopausal women in the subcohort, current HRT users had lower Lp(a) levels compared to non-users . Lp(a) did not correlate with any of the lipid biomarkers (r < 0.2) or further lifestyle factors (P > 0.05).In the subcohort, HDL-C levels correlated strongly and positively with apo(a) (Incident breast cancer risk was 1.45-fold higher among participants within the highest quartile of circulating apo(a) levels at baseline compared to those within the lowest quartile, whereas risks were 30% lower with high TG and high apoB-100 levels  levels were significantly associated with a 1.5 higher risk of incident prostate cancer , while medium Lp(a) levels (Q2 and Q2) or other lipid markers were not associated with risk Fig. . MultivaLung cancer risk was significantly lower with higher levels of apo(a)  and higher levels of HDL-C , but significantly increased with high apoB-100 levels , whereas the other lipid biomarkers were not significantly associated with risk of lung cancer  0.64, Q4 cut-offs 6.60, 1.70, and 1.90, respectively; Table Lp(a) 0.74), but did not materially alter the other results. The four most common causes of cancer death in our study were cancer of the lung , colorectum , breast , and pancreas . High HDL-C levels were inversely associated with lung , high apoB-100 levels inversely associated with breast , and high Lp(a) levels inversely with pancreas cancer mortality , whereas colorectal cancer mortality showed no association with any of the lipids.High TC, HDL-C, and apo(a) levels were all associated with a reduction in total cancer mortality  levels were inversely associated with CVD mortality whereas apoB-100 and TG were positively associated with CVD mortality Fig. . Howeverpre 1.64, 95% CI 1.17–2.30 and HRpost 0.83, 0.51–1.35; P = 0.024; and apo(a): HRpre 2.13, 1.33–3.40) and HRpost 0.83, 0.46–1.51; P = 0.008). Within postmenopausal women at baseline, significant heterogeneity by HRT use was observed for the association of Lp(a) levels with breast cancer risk (P = 0.002), with a significant Lp(a)-associated increase in breast cancer risk among HRT users compared to a significant inverse association among non-users . Regarding lung cancer, the inverse associations of high HDL-C and apo(a) levels with risk of lung cancer were confined to current smokers due to significant heterogeneity by smoking status : P < 0.001, HRcurrent 0.20, 0.09–0.48). The associations of TC and HDL-C levels with cancer mortality were also modified by smoking status, such that high TC and HDL-C levels were associated with a significantly reduced cancer mortality among never and former smokers, but not among current smokers after adjustments for sex, age, and BMI . We did not observe further heterogeneity by HRT use or smoking status for any of the other diseases or mortality endpoints, and we also observed no heterogeneity by sex, current alcohol consumption at baseline (≤ 6 vs. > 6 g/day) or obesity  for the associations of lipid biomarkers with risk of site-specific cancer or cancer mortality.We observed significant heterogeneity by menopausal status at baseline, but not by age 55 at diagnosis, for the associations of HDL-C and apo(a) with breast cancer risk, with a significant increase in risk among premenopausal but not postmenopausal women  1.19, 0.83–1.71; HRTG 0.73, 0.50–1.07; HRTC 0.65, 0.43–0.99). Excluding participants using lipid-lowering drugs at baseline resulted in significant risk estimates for apoB-100 with stroke  and slightly stronger estimates for TC and apoB-100 with MI , whereas other results were not different to the full study population.Excluding participants with less than 2 years of follow-up between recruitment and cancer diagnosis did not materially alter any of the results, whereas the extension to 5 years slightly attenuated the associations of TG with breast cancer, Lp(a) with prostate cancer, and apo(a) with total cancer mortality. Exclusion of participants with prevalent liver or kidney insufficiency also had negligible effects on risk estimates (data not shown). Excluding women using HRT at baseline resulted in a reduction of breast cancer cases of up to 65% and diminished the associations of HDL-C, apo(a), and TG to non-significance but resulted in significant associations of TC with risk of breast cancer in similar strength to the associations within the full study population  ≤ 1.05, apoB ≥ 1.56, TG ≥ 2.26, and Lp(a) ≥ 120, high TG and Lp(a) levels were associated with risk of stroke and high TC, TG, apoB, and Lp(a) levels with risk of MI after multivariable adjustment in comparable strength to quartile analyses mirroring these cut-points  were positively and HLD-C and apo(a) inversely associated with risk, as expected. Further, high levels of TG were associated with an increased risk of stroke. For cancer, by contrast, we observed that high levels of circulating apoB-100 and TG were inversely and high HDL-C levels were positively associated with breast cancer risk. Further, higher levels of Lp(a) were associated with an increase in prostate cancer risk and high levels of apo(a) were associated with a decrease in lung cancer risk, whereas none of the lipids were associated with risk of colorectal cancer. With regard to mortality, high TC, HDL-C, apo(a), and Lp(a) levels were associated with a reduction in total cancer mortality, whereas none of the lipids were associated with risk of CVD mortality. Thus, the observed cancer risk pattern clearly differs from the CVD risk pattern.A blood lipid profile characterized by high TC, LDL-C, TG, apoB-100, and Lp(a) and by low HDL-C and apo(a) is a well-established risk profile for CVD events, in particular for MI and other coronary heart diseases, and our study fully confirms these associations. A direct translation of this CVD risk pattern to a similar cancer risk pattern appears to be difficult, however, as results from prospective studies are not fully consistent and precise biological mechanisms linking lipid biomarkers to cancer development or cancer death are largely unknown. Several biological mechanisms have been proposed to account for an association between altered blood cholesterol levels and cancer incidence or mortality, including immune system disturbances, altered protein function, low circulating levels of fat-soluble anti-oxidants, increased tumor angiogenesis, reduced tumor apoptosis and increased tumor cell proliferation , 22. Expr = –0.35) and the inverse association of excess body weight with premenopausal breast cancer risk, as observed in EPIC but also other prospective studies [Previous prospective investigations showed inverse or null associations for most blood lipids with risks of breast, prostate, colorectal, or lung cancer. For breast cancer, we observed a positive association with HDL-C levels above 1.90, which contradicts with the latest meta-analysis in 2015 and prospective cohort studies that have been conducted thereafter , 12, 16. studies , 46. Opp studies . HRT use studies , 48. AmoEvidence for the associations of lipid biomarkers with risk of lung cancer is limited to two prospective studies, as far as we know , 11. SimA limited number of epidemiological studies have investigated the associations of total cholesterol with overall cancer mortality and, as in our study, they all showed that low total cholesterol levels are associated with greater cancer mortality , 24, 27.Despite the abovementioned possible biological mechanisms, a common metabolic profile characteristic for cancer similar to the one for CVD does not clearly emerge from our and previous prospective investigations. The opposite directions of the associations between HDL-C, apoB-100, and TG with breast cancer and the inverse associations between most of the lipid parameters and cancer death point towards distinct differences by endpoint. Indeed, cell line and animal studies have shown that intracellular cholesterol homeostasis varies among different cancer types, and therefore cholesterol could play differing roles depending on cancer type .Mendelian randomization studies, where genetic variants as proxies for altered levels of an exposure are used to assess the causal relationship with the outcome, might shed light on the complex and still unknown associations of lipids with risk of cancer and cancer mortality. However, these Mendelian randomization studies have scarcely been conducted so far and are limited to overall cancer (two studies) or prostate cancer (one study) and did not show any causal relationships of circulating lipids, instrumented by genetic risk scores, with risk –52.A potential limitation of our study could be reverse causation bias. However, exclusion of cases with less than 2 years of follow-up did not alter any of the observed associations. Further, measuring lipid biomarkers at one point in time might not reflect fluctuations of levels over time. Lp(a) concentrations are relatively refractory to changes in lifestyle or drug use  and, thuIn conclusion, in this large prospective case-cohort study we found that high apo(a) levels were associated, as expected from the literature, with a decreased lung cancer risk. In contrast to previous investigations, high HDL-C levels were associated with an increased breast cancer risk and high TG and apoB-100 levels were inversely associated with breast cancer risk. Finally, TC, HDL-C, apo(a), and Lp(a) levels were associated with a reduction in cancer mortality. Thus, the observed cancer risk pattern clearly differs from the CVD risk pattern. More prospective studies with a detailed assessment of lifestyle and medical variables at baseline are needed to further evaluate (1) the specific role of HDL-C and Lp(a) in breast cancer development and their interaction with exogenous sex hormones, (2) the role of lipid biomarkers in lung cancer development, (3) the role of Lp(a) in any cancer development, (4) the effect of all lipid biomarkers on cancer mortality, and (5) the possible effect modification by HRT use and smoking status. Complementary, Mendelian randomization studies might potentially add valuable insights of any causal relationship.Additional file 1:Hazard ratios and 95% confidence intervals for associations of low-density lipoprotein cholesterol with incident cancers, cancer mortality, incident cardiovascular disease (CVD), and CVD mortality. (DOC 68 kb)Additional file 2:Hazard ratios and 95% confidence intervals for associations of lipid biomarkers with incident breast cancer. (DOC 64 kb)Additional file 3:Hazard ratios and 95% confidence intervals for associations of lipid biomarkers with incident prostate cancer. (DOC 63 kb)Additional file 4:Hazard ratios and 95% confidence intervals for associations of lipid biomarkers with incident lung cancer. (DOC 64 kb)Additional file 5:Hazard ratios and 95% confidence intervals for associations of lipid biomarkers with incident colorectal cancer. (DOC 64 kb)Additional file 6:Hazard ratios and 95% confidence intervals for associations of lipid biomarkers with incident stroke. (DOC 64 kb)Additional file 7:Hazard ratios and 95% confidence intervals for associations of lipid biomarkers with incident myocardial infarction. (DOC 64 kb)Additional file 8:Hazard ratios and 95% confidence intervals for associations of lipid biomarkers with cardiovascular disease mortality. (DOC 64 kb)Additional file 9:Hazard ratios and 95% confidence intervals for associations of lipid biomarkers with incident cancers, cancer mortality, incident cardiovascular disease (CVD) and CVD mortality, using clinical cut-offs. (DOC 66 kb)


Height  and BMI at age 18  were positively associated with PR expression. Body size at age 5–10 years was inversely associated with Ki67 . Premenopausal BMI (≥25 vs.<20 kg/m2) was positively associated with cytoplasmic IGF-1R . Our data suggest that anthropometrics, breastfeeding, and alcohol intake may influence the molecular characteristics of normal breast tissue, elucidating the mechanisms by which these risk factors operate. However, larger studies are required to confirm these results.Studies have suggested that hormone receptor and Ki67 expression in normal breast tissue are associated with subsequent breast cancer risk. We examined the associations of breast cancer risk factors with estrogen receptor (ER), progesterone receptor (PR), insulin-like growth factor-1 receptor (IGF-1R), and Ki67 expression in normal breast tissue. This analysis included 388 women with benign breast disease (ages 17–67 years) in the Nurses’ Health Studies. Immunohistochemical staining was performed on tissue microarrays constructed from benign biopsies containing normal breast epithelium and scored as the percentage of epithelial cells that were positively stained. Ordinal logistic regression (outcomes in tertiles), adjusting for age and potential confounders, was performed to estimate odds ratios (OR) and 95% confidence intervals (CI) for the associations with risk factors. Alcohol consumption was positively associated  that also promote cancer formation. By examining healthy breast tissue biopsied from 388 women diagnosed with benign breast diseases, the researchers showed that alcohol consumption, height and body mass index were all positively associated with the expression of various breast tissue markers, while breastfeeding and early-life body size were inversely associated. The findings point to a connection between lifestyle and breast tissue-specific molecular characteristics that underpin cancer risk. The majority of breast cancer cases occur among the women of age 50 years or older.3 Besides age, reproductive factors ,5 exogenous hormone use,7 height,8 and family history of breast cancer9 also increase the risk. Further, lifestyle factors such as alcohol consumption,10 lack of physical activity,11 and postmenopausal obesity12 are among the established risk factors for breast cancer. Although the exact mechanisms underlying these associations are not yet known, they are likely to involve hormone-dependent pathways as many of these factors are hormonally related.Breast cancer is the most common cancer in women worldwide15 As proliferative effects of these hormones are largely mediated through their respective receptors,17 expression of these receptors in breast tissue may indicate tissue-specific responsiveness to these hormones. Ki67 is a cell proliferation marker; it is present only during active phases of the cell cycle.18 Highly proliferating cells, as reflected by high levels of Ki67, are prone to mutation initiation and expansion. Prior studies22 including ours24 have suggested that expression levels of estrogen receptor (ER), progesterone receptor (PR), IGF-1 receptor (IGF-1R), and Ki67 in cancer-free breast tissue may be associated with subsequent breast cancer risk.Estrogen, progesterone, and insulin-like growth factor-1 (IGF-1), with their proliferative effects, are thought to be key hormones in breast cancer etiology.24 Here, using the same biomarker data that were generated in the previous studies, we examined the associations of breast cancer risk factors with ER, PR, IGF-1R, and Ki67 expression in histopathologically normal breast epithelium among women with a diagnosis of benign breast disease (BBD) to help elucidate underlying mechanisms for these risk factors.Little is known about whether breast cancer risk factors influence molecular characteristics of normal breast before cancer develops. Previously, we have immunohistochemically (IHC) stained for ER, PR, IGF-1R, and Ki67 in benign breast biopsies within Nurses’ Health Study (NHS) and NHSII and examined the associations with subsequent breast cancer risk.The mean age at benign breast biopsy was 45.1 years. The majority of women were premenopausal (70%) and parous (92%). Parous women, on average, had 3.1 pregnancies. Seventeen percent of women had proliferative lesions with atypical hyperplasia. Other demographic characteristics are shown in Table r = 0.34), and PR and membranous IGF-1R (r = 0.24) expression among both premenopausal and postmenopausal women (Table r = 0.22), PR (Spearman r = 0.23), and membranous IGF-1R (Spearman r = 0.29). In postmenopausal women, Ki67 was positively correlated with cytoplasmic IGF-1R (Spearman r = 0.36) but inversely correlated with membranous IGF-1R (Spearman r = −0.43) and PR (Spearman r = −0.34).Spearman correlations were modest between PR and ER (en Table . In premp-trend = 0.001) and lower median levels of Ki67 expression  (Table Older women (≥50 vs.<40 years) had slightly higher median levels of ER expression  and Body mass index (BMI) at age 18  were positively associated with PR expression in normal breast tissue (Table p-trend = 0.03) was inversely associated with Ki67 expression. Similarly, BMI at age 18 (≥22 vs.<20 kg/m2) was inversely associated with Ki67 expression . Current BMI was positively associated with cytoplasmic IGF-1R among premenopausal women   Table  but was p-trend = 0.0003)  Table .Table 3Ap-trend = 0.008). Alcohol intake was also positively associated with PR expression in the age-adjusted models  but the associations were attenuated after multivariable adjustment   was associated with higher levels of ER expression  Table .Table 4ASimilar patterns of association were observed in sensitivity analyses after restricting the analyses to premenopausal women Table  and afteIn this analysis of normal breast tissue, we found evidence of associations between breast cancer risk factors and expression of tissue markers. Specifically, older women had higher ER and lower Ki67 expression in normal breast tissue. Anthropometric measures including height, body size at ages 5–10 years, and BMI at age 18 were associated with higher levels of PR and lower levels of Ki67 expression. Among premenopausal women, current BMI was associated with higher levels of cytoplasmic IGF-1R expression. Among parous women, breastfeeding was inversely associated with ER expression. Higher alcohol consumption was associated with higher levels of ER and PR expression. Our data suggest that these breast cancer risk factors may influence the molecular characteristics of normal breast tissue even before cancer develops.30 In study populations where the majority or all women were premenopausal, investigators have observed 3–7% ER-positive cells30, 12–29% PR-positive cells,27 and 3% Ki67-positive cells,30 in normal breast epithelium of cancer-free women. The estimates varied slightly across studies possibly due to differences in population characteristics . In studies where menstrual phase information was available, ER expression was higher among women in follicular vs. luteal phase.33 Compared to premenopausal women, postmenopausal women had higher levels of ER (26–42%)30 and lower levels of Ki67 expression (0.34%).30 However, it is unknown whether expression levels of these markers in normal breast tissue from women with BBD are higher compared to those from women without BBD, as there is little data from women without BBD.Although previous studies were smaller in size, the distributions of marker expression we observed in the current study are comparable to those from previous studies of normal breast tissue.30 we observed age was positively associated with ER but inversely associated with Ki67 expression in normal breast tissue. The inverse association between age and Ki67 expression is also consistent with physiologic changes women experience as they age. As women reach menopause, the ovaries stop producing estrogens and progesterone and their breasts undergo significant involution. In another study, some normal epithelium of involuting breasts was composed almost entirely of ER-positive cells,30 suggesting that ER-positive cells may be more slowly involuted than ER-negative cells. Although Ki67 was inversely associated with age, some studies suggested that the proportion of cells co-expressing ER and Ki67 may increase with age.30 In normal breast, ER-positive cells are unlikely to co-express Ki6734 and are thought to stimulate cell proliferation primarily in surrounding cells via paracrine activities;34 thus, high levels of ER and Ki67 co-expression may indicate the loss of control in cell division of ER-positive cells possibly due to high cumulative exposure to carcinogens.Consistent with findings from previous studies,38 and its intermediate markers 40 among women who were overweight and obese during early life. Studies have shown that women who were lean during their childhood have denser breasts39 and higher levels of circulating IGF-141 in adulthood compared to those who were overweight or obese. These findings suggest that childhood body leanness may have long-term effects on breast tissue through these mechanisms that may be involved in cellular proliferation. Similarly, we also observed height, another anthropometric measure that correlates with early-life nutritional status and timing of puberty , was inversely associated with Ki67 expression. Furthermore, we did not observe an association between current BMI and Ki67 expression, which further supports the hypothesis that the early-life period, especially from menarche to the first childbirth, represents a window of susceptibility in which breast tissues are particularly susceptible to carcinogens.42Our findings of inverse associations between childhood body size and Ki67 expression are in line with the data from previous studies that have shown reduced risk of breast cancer27 other studies have reported positive associations of current BMI with ER43 and PR expression.32 The difference in study findings may be due to the failure to take menstrual phase into account. Consistent with findings from a previous study,27 we also found positive associations of alcohol consumption with ER and PR expression, further supporting that alcohol may increase risk of breast cancer through hormonally-related mechanisms. Although previous studies have not found evidence of association with breastfeeding,32 we found strong inverse associations of breastfeeding with ER expression among parous women. However, little is known about the exact underlying mechanisms for these associations.In this study, current BMI was not associated with ER and PR expression. While one study reported null findings,31 while PR levels do not.31 Lastly, our analyses were conducted among women with a previous diagnosis of BBD. Although the exact relationship is not clear, hormone receptor and Ki67 expression levels in normal terminal duct lobular units (TDLUs) among women who had benign breast biopsies may be different from those among healthy women who have never had a breast biopsy . Thus, our results may not be generalizable to women without BBD.We acknowledge several limitations of this study. Given the small sample size, we had limited power to detect modest associations; however, to the best of our knowledge, the current study is the largest study of this topic. We performed multiple testing; thus, some of the positive findings could be due to chance. With the exploratory nature of this study, we did not take account of multiple testing in the analyses but have interpreted our results with caution. This hypothesis-generating study provides a basis for future study. Sampling variability in tissue microarray (TMA) coring may have contributed to measurement error in outcome. Furthermore, we do not have information on the phase of the menstrual cycle during which the benign biopsies were obtained. Studies have shown that levels of ER and Ki67 expression in normal breast tissue vary across cycleDespite these limitations, this study is the largest study to date to investigate the determinants of ER, PR, IGF-1R, and Ki67 expression levels in normal TDLUs. This study also included early-life factors such as BMI at age 18 and body size at ages 5–10 years. Furthermore, by using an automated image analysis system, we reduced observer error and measurement error in outcome.In summary, breast cancer risk factors including age, alcohol consumption, breastfeeding, height, and early-life body size were associated with ER, PR, and Ki67 expression in normal breast tissue. These findings contribute to our understanding of breast cancer biology and suggest that these risk factors may influence the molecular characteristics of normal breast even before cancer develops. However, given our small sample size and lack of information on menstrual phase, further studies are required to confirm these results.44 The NHS is an ongoing cohort study that began in 1976, including 121,700 female registered nurses aged 30–55 years. The NHSII is an ongoing cohort study that began in 1989, including 116,429 female registered nurses aged 25–42 years. In both cohorts, participants completed initial mailed, self-administered questionnaires that collected information on participants’ health behaviors, lifestyle factors, reproductive factors, and medical histories. Subsequent biennial follow-up questionnaires were used to assess updated information on a variety of known and suspected risk factors for chronic diseases, as well as newly diagnosed diseases including BBD confirmed by biopsy, and breast cancer. Cumulative response rates for both cohorts are > 90% and are similar among women with and without BBD diagnosis.This analysis includes participants in the nested case-control study of breast cancer conducted among the subcohort of women who reported a diagnosis of biopsy-confirmed BBD in the NHS and NHSII cohorts. Details of this nested case-control study and the BBD assessment have been previously described.n = 24). To assess the role of further reverse causation, we performed sensitivity analysis after excluding 53 women who developed breast cancer within 10 years since their benign biopsies.Cases were women with biopsy-confirmed BBD who reported a diagnosis of breast cancer during 1976–1998 for the NHS and 1989–1999 for the NHSII following their BBD diagnosis. When possible, four controls were selected for each case, matched on year of birth and year of benign breast biopsy, among women with biopsy-confirmed BBD who remained free of breast cancer at the time the matching case was diagnosed. We attempted to obtain BBD pathology records and archived biopsy specimens for all cases and controls from their hospital pathology departments; our ability to obtain biopsy blocks did not significantly differ by case and control status. Both cases and controls were included in the analysis because all tissue samples were collected prior to cancer diagnosis. To reduce potential reverse causation due to subclinical tissue change, women were excluded if they had evidence of in situ or invasive carcinoma at biopsy or reported a diagnosis of breast cancer within 6 months of their biopsy  and 10 years (r = 0.65).46 Reported body sizes at ages 5 and 10 were averaged to represent childhood body size. Cumulative average of alcohol consumption was estimated by taking the average of alcohol consumption from age 18 to the years prior to benign biopsy. Cumulative average of physical activity was calculated by taking the average of physical activity  conducted during adulthood from enrollment in the cohort to the questionnaire cycle prior to benign biopsy. We also evaluated the associations with current levels of alcohol consumption and physical activity and found similar results. To examine the timing and spacing of births in relation to marker expression, we derived a birth index term as described previously.47 A higher birth index represents a higher number of births occurring at earlier ages. Height, BMI at age 18, birth index, alcohol consumption, and physical activity were categorized into tertiles.Participants reported their risk factor information via questionnaires that were assessed prior to or around the time of biopsy. Body sizes at ages 5 and 10 years were recalled in 1988 (NHS) and 1989 (NHSII) using Stunkard’s nine-level pictogram 24 Briefly, six TMA blocks were constructed in the Dana Farber Harvard Cancer Center Tissue Microarray Core Facility  by obtaining up to three 0.6-mm cores of histopathologically normal TDLUs from each formalin-fixed paraffin-embedded breast biopsy blocks. The “normal” TDLUs selected were regions of histopathologically normal tissue that were adjacent to benign lesions. Women were excluded if there was no breast tissue, or not enough tissue remaining on the block for coring.Details of the TMA construction have been previously described.For each IHC stain, a 5-μm paraffin section was cut from each TMA block and immunostained with its antibody  after deparaffinization and rehydration. After blocking endogenous peroxidase activity, heat-induced epitope retrieval was performed on sections using the retrieval buffer  and then the antibodies were applied to the sections at room temperature. Appropriate positive and negative controls were included in all staining runs.23 For each stain, we used the Tissue Studio software to define an intensity and size threshold for nucleus identification and to define an intensity threshold for nuclear stain positivity. The automated analysis software was trained for scoring only the appropriate epithelial regions of the tissue. For each woman, we estimated the mean percentage of cells that were stain-positive across the cores, by weighting each core by its total cell count. The automated scoring data were moderately correlated with those manually scored by an expert pathologist (L.C.C.) on the subset of TMAs (73–327 cores) (Spearman r = 0.40–0.48).23 Because the data were skewed, we categorized into tertiles .For ER, PR, and Ki67, immunostaining results of each core were interpreted using an automated computational image analysis system  as previously described.48 for cytoplasmic and membranous staining separately by an expert pathologist (L.C.C.). Because our previous study24 suggested that the associations between IGF-1R expression and breast cancer risk may vary by cytoplasmic vs. membranous staining, we examined cytoplasmic and membranous staining separately. Further, cytoplasmic IGF-1R is thought to indicate internalized IGF-1R levels after IGF-1 has bound to it for IGF signaling.50 For each woman, we estimated the mean score across the cores and collapsed into three categories .For IGF-1R, immunostaining results of each core were manually scored on a 0–5 scale according to the proportion of stain-positive cells 24 with varying grant aims and funding support, different numbers of TMAs were stained for each marker  resulting in varying numbers of women with evaluable stainig. Among these 388 women, 158 women for ER, 215 women for PR, 245 women for IGF-1R, and 262 women for Ki67 were included in the analyses.A total of 388 women  with at least one core of histopathologically normal TDLUs with evaluable staining were included in this study. Because this study was a secondary data analysis of markers that were initially stained for previous studiesn = 102 ER, 138 PR, 165 IGF-1R, 191 Ki67) in sensitivity analyses. All statistical tests were two-sided with 5% type I error. Analyses were conducted with SAS version 9 (SAS Institute).In each risk factor analysis, we excluded particiants if they were missing age (an important confounder), exposure (risk factors), or outcome (tissue markers) data. Ordinal logistic regression was performed on ordinal categories of tissue marker expression  to estimate ORs and 95% CIs for the associations between the markers and the risk factors, adjusting for age. The ORs from the ordinal logistic regression indicates the odds of increasing one ordinal marker expression category  associated with risk factors . We tested for model assumptions  using chi-square score test and did not find evidence for violation. In multivariable models, we also adjusted for potential confounders . Additional adjustment for menopausal status, current BMI (or change in BMI since age 18), age at menarche, and age at first birth did not change the results thus were not included in the final models. We performed a test for trend using category-speciic median values. Because expression levels of tissue markers may vary by menopausal status, we restricted our analyses to 272 women who were premenopausal at BBD biopsy (http://www.nurseshealthstudy.org/researchers.The data that support the findings of this study are available from the Nurses’ Health Studies but restrictions apply to the availability of these data, and so they are not publicly available. However, data are available from the authors upon reasonable request and with permission of Nurses’ Health Studies External Advisory Board. Additional data sharing information and policy details can be accessed at Supplementary Table 1-4


No significant risk associations of overall survival were found for post-diagnostic intake of vegetables and fruits. Line dose-response analyses indicated the likely results for both pre- and post- diagnostic dietary intake. No significant association was found between intake of vegetables and fruits and breast cancer-specific mortality. In addition, intake of cruciferous vegetables was not associated with death from breast cancer. Our findings indicated a borderline inverse association between pre-diagnostic intake of fruit and overall survival of breast cancer, whereas intake of vegetables was not associated with survival.We aimed to conduct a meta-analysis investigating the association between consumption of vegetables and fruits and breast cancer survival. A comprehensive search of the PubMed and EMBASE was performed from the inception to September 30, 2016. The summary hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using a random effects model. Ten studies, with a total of 31,210 breast cancer cases, were included in the meta-analysis. The summary HRs (95% CIs) of overall survival (highest vs. lowest) were 1.08 (0.88–1.33; I For a long time, researchers have focused on the beneficial effects of consumption of vegetables and fruits (VF) on the prevention of cancers, including breast cancer3. According to the 2007 WCRF/AICR Second Expert Report followed by the Updated Breast Cancer 2010 Report, no conclusions can be reached for the evidence of fruits and vegetables consumption and breast cancer incidence4.Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of cancer-related death in women worldwide. During the past 25 years, breast cancer death rates have been increasing in economically developing countries14. The majority of these studies12 have reported a non-significant inverse association between VF intake and overall (OS) or breast cancer-specific survival (BCSS). However, two studies10 reported inverse associations of consumption of vegetables and fruits with all-cause mortality, and a trend for this relationship was observed in another study5. One study10 also found the inverse association with breast cancer-specific mortality. The 2014 report of the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) systematic analysis on surviving breast cancer15 showed that no significant associations were found between intake of VFs and breast cancer prognosis. The 2014 report presented results of studies published until 30th June, 2012. The purpose of this study was to summarize results on the association between consumption of VFs and breast cancer survival. In this report, we updated the literatures search up to September 30, 2016 and performed quality assessment in detail. We conducted several subgroup analyses according to menopausal state, intake dose and adjustments; and specifically, we conducted analysis for intake of cruciferous vegetables (CVs).With a growing number of breast cancer survivors, some studies have investigated the association between consumption of VF and breast cancer survival). The most common reasons for exclusion were a lack of data on intake of VF or breast cancer prognosis (n = 23 studies). One study16 was excluded because its data duplicated in other articles. Another study17 modeled VF intake as a continuous variable. Three studies were excluded because no hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) were reported or no information was used to calculate these estimates. Three studies20 were excluded because they reported dietary patterns or score.The flow chart summarizing the process of study selection is shown in Fig. 114 and 1 randomized control trial [RCT]9). Overall, 10 articles reported on OS14 and 5 on BCSS14. During a median follow-up duration of 6.6 years, there were a total of 5,329 total deaths  and 1,293 (ranged from 125 to 398) breast cancer specific mortality cases among 31,210 women with breast cancer. Articles were published between 1999 and 2016. Studies were conducted in the following geographic regions: 7 from the United States, 3 from Europe . Five studies12 collected information on dietary intake before diagnosis. In one study7, diet was assessed on average 3 months after diagnosis when 2/3 of the patients had not started chemotherapy treatment. One study6 assessed the average frequency of intake over the previous year, and two studies10 assessed dietary intake within 1 year of diagnosis. Four studies13 collected information on dietary intake after diagnosis, and one study14 before and after diagnosis. The quality scores of each study were summarized in Supplementary Table Table 10 reported an association between pre-diagnostic intake of vegetables and fruits combined and OS among breast cancer patients, and a non-significant association was found . Four studies12 reported associations between pre-diagnostic intake of vegetables and fruits and OS, respectively, with a combined HR of 0.96  for vegetables alone, and 0.83  for fruits alone.As shown in Fig. 10 were part of this dose-response analysis for the association between pre-diagnostic intake of vegetables and fruits and OS. The summary HR per 1 serving/day increment was 0.98 (95% CI: 0.95–1.01), with evidence of moderate heterogeneity . Three studies12 were included in the dose–response analysis for pre-diagnostic intake of vegetables and fruits and OS, respectively, with a summary HR of 0.93  intake of vegetables only and 0.95  intake of fruits only.As shown in Fig. 14 reported an association between post-diagnostic intake of vegetables and fruits combined and OS among breast cancer patients, and a non-significant association was found . Three studies11 reported associations between post-diagnostic intake of vegetables and fruits and OS, respectively, with a combined HR of 1.08  for vegetables alone, and 1.04  for fruits alone.As shown in Fig. 9 was part of this dose-response analysis for the association between intake of vegetables and fruits and OS, with a HR per 1 serving/day increment of 1.00 (95% CI: 0.97–1.03). Three studies11 were included in the dose–response analysis for intake of vegetables only, with the summary HR of 1.00 . Two studies11 were included in the dose–response analysis for intake of fruits only, with the summary HR of 0.99  and high dose . Combining studies with adjustments for tumor stages, treatment, BMI, smoking led to non-significant associations .Meta-regression analysis showed that no variables were significant factors for the association between pre- and post-diagnostic intake of vegetable and fruits and OS in women with breast cancer. In the leave-one-out sensitivity analysis, the pooled HR and 95% CI were not significantly altered  of 1.03 . Another study7 showed a null association between pre-diagnosis intake of CVs and OS for both pre- and post-menopausal women with breast cancer.Two studiesWe have evaluated the association between intake of vegetables and fruits and breast cancer outcomes using a meta-analysis of previously published studies. Overall, results of this meta-analysis suggest that intake of vegetables and fruit is not significantly associated with total and specific death from breast cancer. In subgroup meta-analysis, a borderline reduced risk of total death was observed for pre-diagnosis intake of fruits alone among women with breast cancer. In addition, intake of cruciferous vegetables was not associated with death from breast cancer.21. Lifestyle factors become increasingly important, which may help to lower the risk of dying from the disease. However, researches exploring the science behind surviving breast cancer are relatively scarce. The evidence from well-conducted RCTs may be stronger than that from cohort studies. The Women’s Healthy Eating and Living (WHEL) RCT9 observed that women in the intervention arm who consumed 5 vegetable servings, 16 oz vegetable juice, 3 fruit servings, 15–20% of energy, and 30 g fiber per day did not lower risk of recurrence and death from breast cancer. A few cohort studies14 have examined the effects of dietary factors on mortality outcomes in breast cancer survivors with inconsistent results. The 2014 breast cancer survivors report from WCRF/AICR15 suggested that a higher consumption of VF may not be related to surviving breast cancer.As early diagnosis and treatments for breast cancer improve, breast cancer prognosis becomes good in most of the developed countries, with a five-year survival rate of more than 85% in women with localized and regional diseaseΙ error.In the current study, we found pre-diagnosis intake of fruits alone was associated with borderline decrease in risk of total death among women with breast cancer, which also were confirmed according to dose-response analysis. However, only four studies were included for pre-diagnosis intake of fruits alone among women with breast cancer, we should be caution that this relationship was only due to chance, because we could not exclude a type 23. In cell culture and animal studies, carotenoids have retinoid-like effects on cellular differentiation and apoptosis and also exhibit inhibitory effects on mammary cell growth25. Vitamin C exhibits antioxidant actions including the neutralisation of free radicals, which may impact cancer progression27. Furthermore, dietary intake of folate was associated with a reduced death by inducing incorporation of uracil into DNA and inducing altered gene expression of critical tumor suppressor and proto-oncogenes via its role in DNA methylation30. Epidemiological studies31 and clinical trial32 have suggested that a high fiber intake was related to reductions in blood levels of estrogen. Recent study has indicated that higher preoperative serum estrogen level had a negative prognostic effect in postmenopausal women with breast cancer, especially in the ER-negative subgroup33.Several potential mechanisms might explain a protective effect of vegetables and fruits on breast cancer prognosis, which may be through high intakes of fiber, micronutrients, including vitamins C, E, folate and carotenoidsStrengths of this analysis include independent search and data abstraction by two researchers experienced in conducting systematic reviews; performing quality assessment in detail; exploring the aforementioned associations according to pre- and post-diagnosis, respectively; examining both high vs. low and dose–response relationships; conducting several subgroup and meta-regression analyses.9 included in our meta-analysis used 24 h-diet recalls, which are often considered more robust dietary assessment methods. Previous studies35 have suggested the relatively low correlation in report their intake of vegetables or fruit (Spearman’s correlation coefficients of 0.6 for fruit consumption and 0.4 for vegetable consumption). However, it was assessed before the outcome of interest  and thus is most likely non-differential. In addition, analyses of the highest vs. the lowest intake are limited because they do not account for true differences among studies. For example, the definition of the lowest exposure ranged from 08 to <2.559 servings/d of vegetables intake, and the highest intake levels of vegetables ranged from 183 g/d (about 1.8 servings/d)12 to >4.89 servings/d. Although statistically not significant, subgroup analyses demonstrated that OS was improved when the meta-analysis was restricted to high dose intakes of vegetables/fruits subgroup. The non-significant results might be due to low statistical power since only two studies were included in the high dose categories. More studies with larger sample size and long duration of follow-up are warranted.This meta-analysis has limitations. First, some misclassification of vegetables and fruits intake is likely. To reduce the errors in assessment of dietary intake, we included studies evaluating all fruits or vegetables. However, populations from different regions, ethnicities and time periods may have differences in their classifications and types consumed. Dietary intake was assessed with a food frequency questionnaire (FFQ) in all but one study which could have introduced measurement errors due to poor recall. One study5, the authors found there was significant association with intakes of vegetables and reduced mortality among women without metastatic lymph nodes , but no significant association among women with metastatic lymph nodes . Therefore, further investigations on vegetables and fruits intake and breast cancer prognosis by clinical tumor characteristics are needed. On the other hand, disease detection  is another potential confounder because screen-detected cases show lower stage and longer survival than cancer found incidentally37. However, data on mode of detection were not available in the study.Second, since not all potential confounders were adjusted for in the individual studies, there remains the possibility of residual or unmeasured confounding. For example, not all studies were adjusted for clinical characteristics and cancer treatments, both of which are the most important prognostic factors. Our meta-analysis found no associations between intake of vegetable and fruit and total mortality among breast cancer across studies whether or not with adjustments for clinical characteristics. In one of the included studies39. A healthy lifestyle may lower the occurrence of breast cancer. Although the evidence available is limited42, cigarette smoking, overweight/obesity and being physical inactivity (before and after a diagnosis) are suggested to be linked to increased total and specific cause of mortality in women with breast cancer. Stratified and meta-regression analyses indicated that risk of death for consumption of fruits and vegetables were not significantly changed by adjustment for BMI, smoking, and physical activity. However, only a few studies employed adjustments for these factors. Hence, we cannot fully exclude the possibility of residual confounding.Higher consumption of fruit and vegetables may be a marker for generally “healthier” dietary and lifestyle patterns, such as drinking less alcohol, a lower prevalence of overweight/obesity and cigarette smoking, and being physically active10 reported that high vegetables and fruits intake significantly improved disease prognosis among ER/PR-negative breast cancer patients. Therefore, further investigations on vegetables and fruits intake and breast cancer prognosis by ER/PR status are needed.Third, studies included in this meta-analysis were major conducted in Western countries  and in middle-aged and older person; so, the results should be extrapolated to other populations with caution. In addition, the number of studies included in the meta-analysis was small, reducing the power of the meta-analysis and thereby reduced the generalizability of our findings. The clinical behavior of ER-positive and ER-negative breast cancers differ significantly. We did not carry out stratified analysis by ER/progesterone receptor (PR) status because few studies provided the necessary data. One studyFinally, publication bias should always be considered in a meta-analysis of published data, because the results of large cohort studies (even null results) are more likely to be published. However, we could not perform such an analysis due to the small studies included.In conclusion, intake of vegetables alone was not significantly associated with overall survival of breast cancer, however, intake of fruit alone might has, if any, a small effect on the overall survival among women with breast cancer. Considering the limited number of included studies, further studies with larger sample size, well-controlled confounding factors, long enough follow-up time, and more accurate assessment of dietary intake are warranted.http://www.ncbi.nlm.nih.gov/pubmed/, from January 1, 1966) and EMBASE  databases. The following search terms were used: 1) breast cancer OR breast neoplasm OR breast carcinoma; 2) mortality OR survival OR prognosis OR disease progression OR outcome OR death; and 3) nutrition OR diet OR lifestyle OR fruit OR vegetable. All results were updated on September 30, 2016. In addition, we carried out manual searches of the reference lists from retrieved articles to identify additional studies. Only full-length original articles published in English were considered, and no attempt was made to include abstracts or unpublished data. This meta-analysis was planned, conducted, and reported according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement28.We (HJJ and GYT) conducted a literature search using the MEDLINE  the exposure of interest was consumption of vegetable and/or fruits assessed before or after diagnosis (≥12 months); (2) the outcome of interest was BCSS or OS among women with breast cancer; and (3) reported HRs (95% CIs) or information to calculate these estimates . Two authors (HJJ and GYT) independently assessed titles and abstracts of the references identified by the search strategy according to the selection criteria. Any differences in opinion with regard to eligibility were resolved by a third investigator (ZSJ). When unclear from the title or abstract whether the paper fulfilled the criteria, or when there is disparity among authors, a full text copy were obtained. We did not assess breast cancer recurrence as endpoints of the meta-analysis, given that the definition used for recurrence varied across studies. We excluded studies assessing the association for nutrients provided by vegetables and fruit, such as fiber, carotenoids and vitamin C. Case reports, reviews, animal studies, and The following data were extracted using a standardized data collection form: author’s name, publication year, country or region, name of study, number of participants, age, number of breast cancer cases and number of deaths (OS and/or BCSS), timing of data collection (before or after diagnosis) and type of diet assessment, range of exposure and adjustments. We used the maximally adjusted results when several risk estimates with various adjustments were reported. Data were extracted independently by two investigators (HJJ and GYT). Discrepancies were resolved by discussion and repeated examination of the studies to reach a consensus.44, which assesses the quality of case control and cohort studies against three parameter: selection , comparability , and exposure/outcome . A score of ≥7 stars is indicative of a high quality study. For the assessment of the study quality of one RCT9, we used the five-point Jadad scale of randomisation (0 or 1), double-blinding , treatment of withdrawals and dropouts (0 or 1), and allocation concealment (0 or 1)46. Studies were categorized as low quality (score ≤ 2) or high quality (score at least 3)47.For the assessment of the study quality of cohort studies, we used the Newcastle-Ottawa Scale (NOS) checklist48. The study from Dal Maso et al.10 did not use the lowest category as a reference, we therefore recalculated the RR and its 95% CI. We also reported results for intake of CVs. When estimates were available specifically for both pre- and post-menopausal women with breast cancer7, they were combined according to a fixed effects model to obtain an overall estimate. The summary measures were presented as forest plots where the size of data markers (squares) corresponds to the inverse of the variance of the natural logarithm of RR from each study, and the diamond indicates pooled RR.All statistical analyses were performed using STATA, version 11.0  statistical software. A two-tailed P value of <0.05 represents significance. The primary outcome analyzed was OS, and another point of interest was BCSS. Adjusted HR estimates were pooled for the highest vs. lowest level and linear dose-responses using random effects meta-analysis, which considers both within- and between-study variation and provides more conservative estimates than a fixed-effects modelI2-statistics and p values associated with Q statistics. P value of <0.10 represents statistically significant heterogeneity. I2 values explain the amount of total variation among studies that is due to heterogeneity. I2 value of >50% signify severe heterogeneity while a value of <25% represents no significant heterogeneity49. We explored heterogeneity between studies using three strategies. First, stratified analyses were carried out to estimate the effects of the following variables on outcomes: intake dose (the highest level of vegetable intake: ≥3.1 servings/d vs. >1.8 servings/d), menopausal status, confounding including clinical tumor stage, receiving treatment, BMI, physical activity and cigarette smoking. Because of the small number of studies reporting on BCSS, the stratified analyses were carried out only for OS. Second, we carried out stepwise meta-regression analyses. Third, we re-ran the meta-analysis removing studies once at a time to determine whether a particular study accounted for the heterogeneity.Heterogeneity among articles was estimated using the 51. Dose–response analysis is performed on the basis of the data for categories of intake levels on median dose, number of cases and participants and adjusted logarithm of the HR with its SE. Lack of this information led us to estimate the dose-response slopes using variance-weighted least squares (vwls) regression analysis51. We then combined the GLST-estimated study-specific slopes with studies that reported slope estimates to derive an overall average slope. This analysis was restricted to the studies reporting three or more exposure levels. In the absence of such median intake value, we used the midpoint of each category. For an open-ended upper category of intake, we assumed the size of the interval to be the same as the previous category. If the lowest category was open-ended, the lower boundary was assumed to be zero. When studies used different measurement units (e.g. grams per day), we standardized fruits and vegetables intake into servings per day using a standard portion size of 106 g52. Publication bias was not evaluated due to the small number of studies included.The dose–response results in the forest plots are presented in one serving/day increment, which was according to the method of generalized least-squares trend estimation (GLST) developed by Greenland and Orsinisupple.