Breast cancer risk stratification categorizes a woman’s potential risk of developing the disease as near-population, intermediate, or high. In accordance, screening and follow up for breast cancer can readily be tailored following risk assessment. Recent efforts have focussed on developing more accessible means to convey this information to women. This study sought to document the relevance of an informational e-platform developed for these purposes.To begin to assess a newly developed breast cancer risk stratification and decision support e-platform called PERSPECTIVE  among women who do not know their personal breast cancer risk (Phase 1). Changes (pre- and post- e-platform exposure) in knowledge of breast cancer risk and interest in undergoing genetic testing were assessed in addition to perceptions of platform usability and acceptability.N = 156) of differing literacy and education levels, aged 30 to 60, with no previous breast cancer diagnosis were recruited from the general population and completed self-report e-questionnaires.Using a pre-post design, women (p < .001). Increases in knowledge relating to obesity, alcohol, breast density, menstruation, and the risk estimation process remained even when sociodemographic variables age and education were controlled. There were no significant changes in genetic testing interest post-exposure. Mean ratings for e-platform acceptability and usability were high: 26.19 out of 30 (SD 0.157) and 42.85 out of 50 (SD 0.267), respectively.Mean e-platform viewing time was 18.67 min (SD 0.65) with the most frequently visited pages being breast cancer-related risk factors and risk assessment. Post-exposure, participants reported  significantly higher breast cancer-related knowledge (An informative breast cancer risk stratification e-platform targeting healthy women in the general population can significantly increase knowledge as well as support decisions around breast cancer risk and assessment. Currently underway, Phase 2, called PERSPECTIVE, is seeking further content integration and broader implementation . All women are at risk of developing breast cancer . In bothBreast cancer risk stratification follows a disease screening and prevention approach whereby women from a targeted population are classified into subgroups based on their risk of developing the illness . Using tTo complement this process, breast cancer information, tailored education, and support are essential in raising awareness and following up on recommendations . Of noteIn a randomized controlled trial published in The Lancet, Hersch et al.  reportedIn addition, the literature supports that decision-making, such as the choice to undergo genetic testing for breast cancer, can be influenced by prior knowledge and emotions . EmotionPERSPECTIVE  is a Quebec-based project that aims to promote early detection of breast cancer in a cost-effective manner through risk stratification and communication e-tools. It broadens the reach of mammography screening programs to target women, especially those under 50, who may be at higher risk of developing breast cancer but are overlooked due to higher age-based screening guidelines.In Phase 1, a web-based e-platform was developed. This initiative sought to provide comprehensive information on breast cancer risk in an optimal manner to women of different literacy and education levels  through in-depth semi-structured interviews. These women reported that the e-platform was relevant, easy to navigate, and that they learned new information on their relative breast cancer risk. Presented herein, Phase 1 utilized PERSPECTIVE as a pre-assessment tool to be used by women with no knowledge of their personal breast cancer risk. Thus, family history was not collected and personal risk assessment was not calculated.The password protected e-platform, available in English and French, contains 28 pages of content and images on breast cancer risk stratification, risk factors, prevention, personal risk assessment, risk classification levels, and recommended screening measures. All content was developed in accordance with the evention , and revThe aim of the current study was to begin to assess the PERSPECTIVE e-platform among women in the general population of various literacy and education levels, who were not aware of their personal breast cancer risk.Objective 1: To assess participants changes (pre- and post- e-platform exposure) in knowledge of breast cancer risk, while controlling for sociodemographic variables of age and education.Objective 2: To assess participants changes (pre- and post- e-platform exposure) in understanding and interest in subsequent genetic testing.Objective 3: To determine the usability and acceptability of the e-platform. The former refers to the quality of a person’s experience interacting with content, including ease of learning, efficiency of use, memorability, error frequency and severity, and subjective satisfaction [sfaction . Acceptasfaction .A pre-post design was used whereby measures were collected before and after participants interacted with the e-platform.Individuals were eligible to participate if they were female, aged between 30 and 60, with no previous breast cancer diagnosis, and had unrestricted access to the internet. Eligibility regarding age was determined in accordance with screening recommendations using a consensus-based method for the risk-classification approach of PERSPECTIVE, whereby women at high risk should begin screening at age 30 . All parIndividuals interested in learning more about the study contacted the study team through email or telephone. A team member answered questions and screened for eligibility. At the time of eligibility, participants were made aware that the e-platform was available to be viewed in English and/or French, the choice of language being theirs. If eligible, informed consent was obtained over the phone, then electronically. Following consent, a username and password for the PERSPECTIVE e-platform were provided. Participants completed the pre-exposure online questionnaire on LimeSurvey before viewing the e-platform for up to 30 min. The post-exposure questionnaire was completed immediately after e-platform viewing. Figure A 10-item questionnaire on breast cancer knowledge developed by Loiselle and Garland for the purpose of this study was completed pre- and post- e-platform exposure. Items are based on breast cancer risk information available in the literature and PERSPECTIVE.would you have genetic testing?”A 6-item questionnaire, developed by Loiselle and Garland for the purpose of this study, pertains to motivation and importance of knowing one’s breast cancer risk and interest in subsequent genetic testing by asking “(very difficult to understand) to 5 (very easy) (α = 0.76) [The Acceptability scale developed by Tariman et al.  (targeti = 0.76) . A score = 0.76) .(strongly disagree) to 5 (strongly agree). The SUS contains 2 factors: usable (α = 0.91) and learnable (α = 0.70) [The System Usability Scale (SUS) developed by Brooke  measures = 0.70) . Convert = 0.70) . to 5 (very much). Separate scores are calculated for positive and negative affect and each ranges from 10 to 50. A higher total score on each scale reveals either higher positive or negative mood. Internal consistency coefficients range between 0.86–0.90 for positive and 0.84–0.87 for negative affect. Test-retest reliability (1 week) was reported to be 0.79 for positive and 0.81 for negative affect [The Positive and Negative Affect Schedule (PANAS), a self-report mood questionnaire with two 10-item scales  was admie affect .Paired sample t-tests were performed to compare participant changes pre- and post- e-platform exposure for breast cancer risk knowledge, mood, and interest in subsequent genetic testing. Repeated measures ANCOVA was performed to control for potential pre-post differences in knowledge according to age and education.n = 85) identified French as their primary language, 34% (n = 53) identified English, and 11.5% (n = 18) other. 64.1% (n = 100) reported being Caucasian and 62.8% (n = 98) born in Canada. 50.7% (n = 79) had a bachelor’s degree or higher (Table The mean age of participants was 42.6 (SD 9.5). 54.5% (The average PERSPECTIVE viewing time was 18.67 min (SD 0.65). For both the French and English versions, the most frequently visited pages were risk factors and risk assessment content.Statistically significant pre-post changes in percentages of correct responses for breast cancer risk knowledge were obtained for obesity, genetic mutations, alcohol, risk factor stratification, breast density, and menstruation Table . When agStatistically significant changes were found in feeling interested, distressed, scared, enthusiastic (neg), proud (neg), nervous, determined (neg), and afraid after viewing the e-platform Table .Table 3BRCA1 and BRCA2, breast density, and lifestyle choices affecting breast cancer risk . ‘How helpful was this e-platform in providing information on breast cancer risk" and overall satisfaction were rated highest, 4.60 and 4.43 respectively, on 5.0 . ‘Did not have to learn new technical skills’, and ‘Information was easy to understand’ were the SUS items with the highest scores, 4.62 and 4.46 respectively on 5.0 . Whether changes in knowledge were sustained and interest in genetic testing evolved overtime cannot be ascertained. With any convenience sampling, women who chose to take part in this study were presumably interested in the topic and this may have biased findings. In addition, whereas the e-platform was designed for various literacy and educational levels, participants’ overall educational levels were higher than the general public .An informative e-platform directed specifically at women in the general population can increase awareness, knowledge and support informed decision making for women in the breast cancer risk stratification process. The study provides insight into important features of e-platforms that provide health and illness-related information, as well as relationships between affect and  breast screening information. In accordance with the shift towards personalization of health care according to patients’ needs and preferences, tailored recommendations based on personal breast cancer risk present an important contribution to the field. The e-platform developed and evaluated herein represents a first phase. Phase 2, which involves the integration and implementation of PERSPECTIVE is currently underway. A version of the e-platform will be assessed among  women undergoing genetic testing to determine their polygenic risk score from low penetrance common genetic variants. The e-platform will incorporate screening and follow-up recommendations into its features – being both practical and convenient.


Also, the nights worked throughout life  were significant in the analysis. Nurses who had or ever had breast cancer valued their self-perceived health more negatively (p < 0.001) and referred a lower quality of sleep (p < 0.001) than the non-cases nurses. The occupational factors derived from night work could have several impacts on nurses’ health and their family-work balance. Promoting healthy lifestyles, informing about shift work risks, and adjusting shift work schedules are critical methods to decrease the possible effects of circadian disruption in nurses.Shift work that involves circadian disruption has been highlighted as a likely carcinogenic factor for breast cancer in humans. Also, unhealthy lifestyle habits observed in night work nurses could be causally related to an increase in the incidence of estrogen-positive breast tumours in this population. Assessing baseline risk of breast cancer in nurses is essential. The objective of this study was to analyze the risk of breast cancer that nurses had in relation to their lifestyle and labour factors related to shift work. A cross-sectional descriptive study through a questionnaire about sociodemographic variables, self-perception of health, and working life was designed. The sample consisted of 966 nurses. The relationship between variables was tested. A binary logistic regression and a classification and regression tree were performed. The most significant labour variables in relation to the risk of breast cancer were the number of years worked (more than 16 years;  Shift work, including night shift work, has been associated with circadian disruption in several epidemiological studies conducted on nurses ,2,3,4,5 In fact, long-term rotating shifts  and night work have been linked to the presence of tumours with oestrogen-positive (ER+) and progesterone (PR+) receptors ,14,15, aOne of the most noted causes has been related to the loss of synchronisation between the circadian rhythm and the sleeping patterns of nurses during continuous and long-duration rotating shift work ,24,25,26Circadian and sleep disturbances .Cross-sectional, questionnaire-based descriptive study on the population of Registered Nurses in Spain, both men and women  . The samThe inclusion criteria were (a) Being a nurse and being registered in the General Council of Nurses of Spain; (b) Working in private and/or public centres; (c) Wish to participate, having read and signed the informed consent. On the other hand, the exclusion criteria were (a) Not being Registered Nurses; (b) Being under 18; (c) Working outside Spain.Cambridge Business English Dictionary defines shift work as: “a system in which different groups of workers work somewhere at different times of the day and night”. Besides, the IARC defined night work as: “one that requires at least three hours of work between midnight and 5 a.m.” ,21,43. TTo gather the appropriate information, an online questionnaire was used. The main risk variables related to breast cancer and night work which were used in this investigation were extracted and adapted ad hoc based on other questionnaires found in the scientific evidence ,14,45,46Sociodemographic data .Self-perception of health. An ad hoc evaluation tool consisting of 5 direct questions was designed to assess nurses’ perception of their own health. The valuation scale was a Likert type with ten response possibilities ranging from 1 “very low” to 10 “very high”. The questions used were: “How do you value your overall health?”, “How do you value the quality of your rest?”, “How do you value the effect shift work has on your health?”, “How do you value your level of work stress?” and “How do you value your satisfaction with your current job?”.General data on disease and cancer .Lifestyle habits .Family burdens .Sleeping aids .Exposure to tobacco (consumption habits (did you ever smoked?), exposure to tobacco in the workplace and at home).Labour information , number of years working regularly 3 nights per month or more, number of worked nights accumulated throughout life, and sick leaves throughout life and in the last year).The variables considered in this study were:The study development took place from December 2019 to November 2020. Google Forms©  was used to create the online questionnaire. Participants could not access the questionnaire until they had previously done the following: (a) Having read and understood an introductory letter to the study and its objectives; (b) Having confirmed voluntary and anonymous participation in the study; (c) Declaring working as a nurse in Spain and being currently registered. The data obtained from the questionnaires were entered in Excel , R Commander 4.0.0 , and SPSThe online questionnaire link was provided via email to the nurses who were registered in the General Nursing Council of Spain and previously had accepted to receive emails, news and information bulletins from this institution. The General Council of Official Nursing Colleges of Spain is the only regulatory body and competent authority of the nursing profession in Spain, therefore the registration in this government-authorized licensing body is obligatory to obtain a nursing license and be legally authorized to work, thus being considered a Registered Nurse (RN). Given this fact, a high number of nurses were consulted via email thanks to this collaboration, although we were not able to control this intervention due to the Data Protection policy of this institution. The social networks of official entities and professional groups of renowned prestige in the area of nursing in Spain  also collaborated with the dissemination of the questionnaire.Absolute frequencies, percentages, and measures of position and dispersion, depending on the type of variable, described the variables of interest. Student’s T-test for independent samples and the Chi-squared test were used to contrast differences and relationship (OR) between the variables and breast cancer risk. The Mann–Whitney U nonparametric test for independent samples was used to analyse heterogeneity in the self-perception of health category.Binary logistic regression allowed building a model to study the presence of breast cancer and identify those risk variables that played a relevant role. The Hosmer-Lemeshow test was used, and Odds Ratios (OR) were estimated with their confidence intervals.Root Node: represents a single input variable and a split point on that variable. It does not have any parent node and gives two children nodes based on the question.Internal Node: it will have a parent node and gives two children nodes.Terminal or Leaf Node: contains an output variable which is used to make a prediction. This node will have a parent node but will not have any children nodes.Finally, the CART (Classification and Regression Trees) data mining method ,49 was uFor this study, the Declaration of Helsinki  was taken into consideration and explicit written permission was obtained from participants through their informed consent. Data obtained in this study were to be duly guarded by the research team, ensuring the confidential use and processing in accordance with the Organic Law on Protection of Personal Data and the Guarantee of Digital Rights. Approval was obtained from the Research Ethics Committee of the province of Huelva, belonging to the Regional Government of Andalusia, with code TD-CMTE-2020. Likewise, approval was obtained from the Research Ethics Committee of the General Council of Official Nursing Colleges of Spain with code PI 2109/02/CE.The questionnaire was answered by a total of 966 nursing professionals, of whom 502 (51.97%) were healthy, 99 (10.25%) had or ever had some form of cancer, and 365 (37.78%) had another type of disease. Of those who had or ever had cancer, for 56.57%, it was breast cancer (56 subjects). Common diseases found in both men and women were hypertension, diabetes, obesity, asthma, or seasonal allergy. In women, isolated cases of thyroid cancer, cervical cancer, melanoma, leukaemia, and lymphoma were also found. p = 0.367) and 89.6% of the sample were women (p = 0.705). Subsequent bivariate analysis categorized by sex revealed only age  as significant in relation to breast cancer  and breast cancer patients (56) were compared with the variables of interest. The median age of the sample was 41 years (ncer see . Accordincer see .p = 0.684) and sex-categorized analysis (p = 0.648). On the other hand, significant associations with breast cancer were found in participants with a partner (p = 0.041) and those who cared for dependents at home outside the working hours (p < 0.001). Indeed, being single  and without family caring responsibilities  were related with breast cancer risk reduction . Similarly, the presence of familial cancer was significant (p = 0.005), resulting in risk reduction when there was no family cancer history   .p = 0.045). The higher number of breast cancer cases had normal BMI. Similarly, statistically significant differences (p < 0.001) were detected depending on the intensity of physical activity at work. Most breast cancer cases classified their exertion at work as moderate. However, no significant differences were found for the physical activity during free time (p = 0.631). Statistically significant differences were found regarding the frequency of exposure to tobacco smoke at home (p = 0.001) and the compliance with the smoking ban in the workplace (p = 0.010), but not with having ever smoked (p = 0.953). In terms of lifestyle habits, BMI showed significant differences (p < 0.001). In fact, not taking sleep medications resulted in risk reduction  (p = 0.441).Lastly, the 79.2% of the nurses claimed not to take any sleep medication, although this variable was relevant in breast cancer cases  . The mosp < 0.001; OR = 2.708, 95% CI = 1.548, 4.735) and being not currently shift working  showed a statistical association with the risk of breast cancer. Considering the working history of the participating subjects, having exceeded 16 years of work was presented as one of the most significant variables in this study . Nurses who had or ever had breast cancer had worked a mean of 26.1 years (SD = 8.1), while healthy nurses had worked a mean of 15.0 years (SD = 9.2). On the other hand, the percentage of cases with breast cancer was also higher in professionals with 500 or more nights worked  and when more than 3 nights per month had been worked for more than 10 years , finding a statistically significant difference in both situations (p < 0.001). The mean number of nights worked was 627.9 (SD = 639.4) in the case of healthy subjects and 1017.4 nights in those who had or ever had breast cancer (SD = 837.9). 2.3% of respondents never worked night shifts. Finally, the cumulative number of sick leaves, both in the last year  and throughout the professional life , have shown a statistically significant association with breast cancer   .p < 0.001), sleep and rest quality (p < 0.001), and stress at work (p = 0.002)  among the sample nurses, being lower in breast cancer cases (6.45) and higher in the healthy cases (8.11). The lowest value of this category was found in the quality of sleep and rest, with a mean of 6.28 (SD = 1.96) and decreasing to 5.29 in breast cancer cases. The highest value was identified when considering whether shifts affect the health of people, with a mean of 9.08 (SD = 1.37). In relation to stress at work, the mean value was 7.57 (SD = 1.86). The stress perception was higher among breast cancer cases (8.23) as compared to healthy cases (7.49). Finally, the satisfaction with the working conditions was valued with a mean of 7.28 (SD = 1.87). Among all the health self-perception variables, statistical differences were found in terms of overall health (= 0.002) .The following two regression methods were performed in this study to identify those variables that play a relevant role in breast cancer risk.p = 0.811), correctly classifying 91.3% of cases. In addition, all variables included in this model had significant values lower than 0.05 and OR values greater than the unit  have breast cancer. A second node differed according to the valuation of the overall health , resulting in a breast cancer percentage of 68% (0.68) in the 25 subjects  who valued their overall health below 5.5, and 7.3% of breast cancer cases in the subsequent 533 subjects (96% of the sample) whose scores were equal to or higher than 5.5. For the 4% of cases with worse health perception , an internal node differed according to the satisfaction with the current working conditions. The percentage of breast cancer cases reached 82.4% (0.82) when the level of job satisfaction was higher or equal than 5.5, and otherwise the percentage of cases was 38% (0.38). Returning to those cases with overall health higher or equal to 5.5 , the following internal node differed again according to the valuation of the overall health . 502 nurses (90%) whose self-perception of health was ≥7 showed a 5.8% (0.06) of breast cancer cases. Otherwise, the 6% (33) of nurses whose self-perception of health was between 5.5 and 7 points comprised 29.4% (0.29) of cancer cases. Finally, an internal node for sleep quality  is shown for the cases with overall health between 5.5 and 7. 62% (0.62) of breast cancer cases occurred in 1% (6) of nurses who perceived their sleep quality under 6. On the other hand, 19% of breast cancer cases occurred in the 5% (28) of nurses who perceived their sleep quality over 6 .The main objective of this study was to analyse the relationship between shift work, especially night shift work, and the risk of developing breast cancer in nursing professionals in Spain, in order to obtain a descriptive image of the labour and lifestyle factors that can influence the risk of breast cancer in this group.p < 0.001; OR = 4.190; 95% CI = 2.118, 8.287). In accordance with other studies [The logistic regression model was successfully validated and showed significant values. Among the measurements of night work that appear to be associated with the risk of breast cancer, having worked a mean of three night shifts per month for 10 years or more  has been highlighted, although the number of total years worked was also significant , taking as a reference the performance of professional activity for more than 16 years and that 95% of the sample reported having worked in rotating shifts at some point of his or her career, although at the time of the survey it was 80.8%. Also noteworthy in this study is the increased risk of breast cancer when 500 nights or more have been worked throughout life , having a partner  and the risk of breast cancer found in this study. It is therefore worth noting that the work-family balance can affect the role of nurses [On the other hand, the analysis of nurses’ self-perception of health variables and the CART classification and regression tree have made it possible to highlight the importance of attending to the assessment of general health, sleep quality, stress level, and level of job satisfaction referred to by nurses themselves. The present study has confirmed that nurses who had or ever had breast cancer exhibited a higher level of work-related stress and worse self-perceived health than healthy nurses. Equally, it is important to consider the impact of stress at home, as it can lead to work-family related conflicts and health problems ,34,53,54f nurses  and the f nurses , and thif nurses , tirednef nurses , sleep pf nurses  and manyp < 0.001), although it is true that the quality of rest had a surprisingly low mean value between the two groups (6.28). Despite that, only 20% of respondents resorted to sleep medication, even though this variable showed a statistical significance for breast cancer in the model . Hypnotics intake has been associated with cancer due to the alterations on sleep patterns, such as insomnia, that occur during the disease at any stage and that persist in survivors [The risk profile analysis of nursing professionals in this study identified that professionals who had breast cancer valued worse the quality of their rest (5.29) than healthy professionals 6.39), with significant differences being detected between both groups . With reference to BMI, the 14.3% of the sample who had breast cancer were obese and the 30.4% had overweight. Obesity has been associated with consecutive night shifts (more than 8 shifts per month) and cumulative years of night work (more than 20 years) [p = 0.010) and at home (p = 0.001). Finally, according to data on lifestyle habits, physical activity in the working context was statistically significant in relation to breast cancer, reporting the majority of cases when it was classified as “moderate”. This may be contrary to other results that report a beneficial association of physically active jobs to reduce breast cancer risk . Meanwhi0 years) ,77,78, a0 years) . In this0 years) ,79,80,81It is worth noting that the responses to the questionnaire on current work have identified that not undertaking shifts nor night work were risk factors for breast cancer, given that most of breast cancer cases did not work on shifts by the time of the survey. These results were in line with a literature review on returning to work following a breast cancer process in Spain , in whicWith a view to future research, investigating nurses’ breast cancer risk factors will still play an important role due to its importance in screening and prevention. Detailed information about nurses’ working hours and night work-related circadian disruption may also be relevant to increase visibility regarding this occupational health issue. In this sense, it would be interesting to measure total nocturnal output of melatonin in shift workers to make a correlation with total and nocturnal melatonin levels. Cortisol levels also require attention due to its balance with melatonin levels and regulation during day light. It must be noted that future research should also study specific parameters of circadian disruption, such as the expression of peripheral clock genes and clock-controlled genes. Periodic assessment of parameters such as waist circumference, waist-to-hip ratio, BMI, glycemia, glycosylated haemoglobin, triglycerides, and total HDL and LDL cholesterol would be useful to evaluate the risk of night shift-related diseases, such as obesity, type 2 diabetes, dyslipidaemia and metabolic syndrome . It is appropriate to adopt prevention and action guidelines that highlight associations between diet, weight, and physical activity to prevent the development of breast cancer and possible metabolic syndrome on shift workers, taking into account cases in premenopausal and postmenopausal women whenever possible ,85,86,87The results obtained in this study may have applicability in different spheres. In the university education system, it would be relevant to talk about the risk of shift-based breast cancer and cardiovascular risk in order to raise awareness among students of the repercussions of intensive night work during the first years of the career and the risks associated with circadian disruption in order to prevent them.For the management and human resources systems of healthcare companies, this study would allow managers and supervisors to consider an equitable distribution of shifts and breaks, as well as a limitation of weekly night shifts performed by nurses and overtime (extra-duties).Similarly, for night workers in general, and for nurses in particular, this study should encourage interventions to promote a healthy and balanced life in order to counteract the negative effects of work on rotating and night shifts. In this way, workers should be informed of the potential risks of performing nights intensively or for several years, and it would be appropriate to provide balanced diets, to install suitable spaces and lighting for work  and restAs for nurses who have or have had breast cancer, this study makes it possible to wonder whether shift-related breast cancer could be considered an occupational disease. In such case, it would be desirable to demonstrate individual risk by counting nights and analysing biomarkers. If possible, people who have had breast cancer should be encouraged to return to work through a process of time adaptation and activities.With regard to the limitations of the study, it should be kept in mind that it was a cross-sectional study and these results, although in line with previous evidence, must be considered with prudence to avoid interpretation bias. In addition, breast cancer diagnosis was not clinically confirmed because data was collected using self-reported information. It is also important to consider the recall bias as another study limitation related to the retrospective study design. In this sense, some variables such as weight, height or total number of worked nights could have been influenced by recall bias. On the other hand, our analysis could be influenced by a small sample size, as it covered only 56 cases of breast cancer, which could potentially represent participation bias although the sample and number of responses has been estimated as sufficient to overcome low representativeness. Many variables have not been considered in this study, such as menopausal status, age at menarche, age at first full-term parity, breastfeeding history, sleeping patterns or comorbidities. In this way, the authors encourage its detailed study in future investigations. Another limitation that has been perceived refers to precision of the intensity grades of the physical activity at work and self-perception of health. In this study, the exertion scores have been stated as light, moderate, hard or very hard, similarly to those scores found in the Borg Rating of Perceived Exertion Scale . HoweverThis study has highlighted a statistically significant increase in the risk of breast cancer in nurses in association with total years of work experience (the risk increases by over 16 years worked) and the total number of years of more than 3 nights per month (the risk increases by over 10 years worked). In addition, other factors such as the total number of nights worked (the risk increases by more than 500 nights), taking medication to sleep, and having had sick leaves have been associated. Similarly, certain variables related to health self-assessment, such as poor overall health quality, stress levels, or low sleep quality, have been significant in pointing out an increased risk of breast cancer.Given the incidence of breast cancer in nurses and the frequency of night shift working further research is needed to clinically measure the possible effects of shift work on breast cancer risk, circadian misalignments and other metabolic diseases that could affect nursing professionals. However, many interventions could be developed from the actual moment in order to prevent and inform about this carcinogenic factor.


Although blood DNA methylation (DNAm) profiles are reported to be associated with breast cancer incidence, they have not been widely used in breast cancer risk assessment. Among a breast cancer case–cohort of 2774 women (1551 cases) in the Sister Study, we used candidate CpGs and DNAm estimators of physiologic characteristics to derive a methylation‐based breast cancer risk score, mBCRS. Overall, 19 CpGs and five DNAm estimators were selected using elastic net regularization to comprise mBCRS. In a test set, higher mBCRS was positively associated with breast cancer incidence, showing similar strength to the polygenic risk score (PRS) based on 313 single nucleotide polymorphisms (313 SNPs). Area under the curve for breast cancer prediction was 0.60 for self‐reported risk factors (RFs), 0.63 for PRS, and 0.63 for mBCRS. Adding mBCRS to PRS and RFs improved breast cancer prediction from 0.66 to 0.71. mBCRS findings were replicated in a nested case–control study within the EPIC‐Italy cohort. These results suggest that mBCRS, a risk score derived using blood DNAm, can be used to enhance breast cancer prediction. Blood samples were collected between 2003 and 2009 from a prospective cohort of 50 844 cancer‐free women. Among a case–cohort subsample  selected in 2014, DNA methylation was profiled using HumanMethylation450 BeadChips. Elastic net regularization derived a methylation‐based breast cancer risk score (mBCRS). In a validation sample, mBCRS values were higher among cases and improved breast cancer prediction. AUCarea under the curveCIconfidence intervalCpGcytosine–phosphate–guanineDCISin situductal carcinoma DNAmDNA methylationEPICEuropean Prospective Investigation into Cancer and NutritionHRhazard ratiomBCRSmethylation‐based breast cancer risk scoreOPERAodds ratio per adjusted standard deviationORodds ratioPRSpolygenic risk scoreSDstandard deviationSNPsingle nucleotide polymorphism1Breast cancer risk is assessed using established risk factors to estimate a woman’s probability of developing the disease , 2, 3. MGenome‐wide DNA methylation (DNAm) profiles are epigenomic indicators of transcriptional activity, cell function, and identity . DNAm atUsing blood samples collected from cancer‐free women, we previously showed that blood DNAm, at individual CpGs and CpG set‐based DNAm estimators, is associated with incidence of breast cancer , 32, 33.22.1https://sisterstudy.niehs.nih.gov/English/coll‐data.htm.The Sister Study is a nationwide, ongoing, prospective cohort of 50 884 women residing in the United States and Puerto Rico who were enrolled between 2003 and 2009 . To be ein situ (DCIS) or invasive breast cancer during the time between enrollment and the end of February 2014. Approximately 3% (n = 1336) of the eligible women from the larger cohort who were cancer‐free at enrollment were randomly selected (the ‘random subcohort’). Of the women selected into the random subcohort, 72 developed incident breast cancer by the end of the study follow‐up period .Blood samples were collected at enrollment (2003–2009) when none of the women had been diagnosed with breast cancer . A case–2.2P > 0.000001, < 3 beads, or values outside three times the interquartile range), four were outliers for their methylation beta value distributions, one had missing phenotype data, and six were from women whose date of diagnosis preceded blood collection  tha tha18] t2.5L1 (lasso regression) and L2 (ridge regression) regularization; the lambda penalization parameter was identified using a pathwise coordinate descent algorithm (using the ‘cv.glmnet’ R package) [Among women in the Sister Study case‐cohort sample, we randomly selected 70% to comprise a training set; the remaining 30% were used as the testing set for internal validation. Because age is a risk factor for breast cancer, cases were systematically older than noncases at the time of their blood draw. We corrected for this by calculating inverse probability of selection weights. Using the weighted training set, elastic net Cox regression with 10‐fold cross‐validation was applied (using the ‘glmnet’ R package) to identify a subset of DNAm estimators and individual CpGs that predict breast cancer incidence (DCIS and invasive combined). The elastic net alpha parameter was set to 0.5 to balance package) . To geneP‐values from Cox regression models for case‐cohort designs with Barlow weights, robust standard errors, and age as the timescale [n = 12) or PRS (n = 27). Although questionnaire‐based and genetic risk information was not available for the EPIC‐Italy nested case–control study, we calculated standardized residuals for mBCRS adjusted only for age and examined breast cancer associations using odds ratios (ORs) estimated by unconditional logistic regression models.mBCRS and PRS associations with breast cancer incidence were examined using covariate‐adjusted standardized residuals in the testing set by estimating hazard ratios (HRs) and 95% confidence intervals (CI) and calculating 2‐sided imescale , 42. BecAfter accounting for the previously mentioned questionnaire‐based risk factors, we examined risk gradients for mBCRS, PRS, and age, alone and in combination. A risk gradient is defined as the ability to differentiate cases from controls on a population basis and is estimated as odds ratio per adjusted standard deviation (OPERA) using a logistic regression model . In both3BRCA1 and BRCA2 and among these women, the self‐reported mutation prevalence was 5% and 6%, respectively. The training set had more invasive cancers (80%) and fewer DCIS (20%) than the testing set . After age‐based inverse probability of selection weighting of the training set, there was no difference between cases and noncases by age at blood draw; however, cases had higher weighted mean PRS values, lower levels of physical activity, higher alcohol consumption, older ages at first birth, more affected family members, and a greater proportion with a history of breast biopsy  or age at blood draw  ; the strongest positive correlation was observed for the CD8+CD28‐CD45RA‐ cell type , and the strongest negative correlation was observed for cg02456218  . In the random subcohort members of the Sister Study testing set, the PRS and age were not correlated , but was positively correlated with body mass index  Fig. . In the 3.2P = 2.1 × 10−11), showing similar strength to the 313 SNP PRS . In tertile‐based analyses, Sister Study participants with mBCRS scores in the highest third had over a twofold increase in breast cancer risk ; in the EPIC‐Italy sample, participants with mBCRS scores in the highest third had an over fourfold increase in risk  ; again, associations were similar in a multivariable model  ; however, no differences were observed when comparing mBCRS and occurrence of estrogen receptor positive and negative invasive breast cancers  Table . In the 6) Table . To exam05) Fig. . mBCRS a05) Fig. . In a ca3.3P‐diff = 1.7 × 10−4) , 1.58 , and 1.35 , respectively Table . In the −4) Fig. .4We used epigenome‐wide DNAm array data from the Sister Study, a large prospective cohort of women, to construct a novel DNAm‐based risk score for breast cancer. Using published methods, we calculated a set of 36 DNAm estimators of biological age and physiologic characteristics and also included a candidate set of 100 individual CpGs previously reported to be associated with breast cancer . ElasticLike the individual CpGs, most of the DNAm estimators selected for mBCRS have been reported as markers of breast cancer risk. PhenoAgeAccel was associated with breast cancer incidence in both the Sister Study and EPIC cohorts , 33, andEstablished breast cancer risk factors include the PRS, which is based on 313 SNPs associated with the disease , 48, 49.Our study is not without limitations. All women enrolled in the Sister Study cohort had a biological sister previously diagnosed with breast cancer, and they are therefore at higher risk of breast cancer than the general population . Althoug5mBCRS, a novel risk score derived using blood DNAm array data, predicts breast cancer incidence. mBCRS captures risk that is distinct from both genetic and questionnaire‐based information and is similar in magnitude to that captured by the 313 SNP PRS. The addition of a methylation‐based risk score for breast cancer, mBCRS, to existing genetic and questionnaire‐based information resulted in markedly improved breast cancer prediction.The authors declare no conflict of interest.JKK and JAT conceived the study idea, drafted, and edited the manuscript. JKK performed the data analysis. ZX and MS helped with data management. KMO, CRW, and DPS provided constructive edits to the manuscript. All authors approved of the final version of the manuscript.https://publons.com/publon/10.1002/1878‐0261.13087.The peer review history for this article is available at Fig. S1. Correlation matrix for individual mBCRS components.Fig. S2. Histogram depicting the distribution of mBCRS values in the training set by case status.Fig. S3. Histogram depicting the distribution of the standardized residuals for mBCRS in the training set by case status.Fig. S4. mBCRS component correlations with age.Fig. S5. Correlation between age and PRS values.Fig. S6. mBCRS correlations with selected reproductive factors.Fig. S7. mBCRS correlations with selected breast cancer risk factors.Fig. S8. LOWESS plot of mBCRS by case status.Fig. S9. Correlation between mBCRS and time to diagnosis among cases.Table S1. List of 36 DNAm estimators of biological age and physiologic characteristics included as input to derive the methylation‐based breast cancer risk score.Table S2. List of 100 CpGs associated with breast cancer risk identified by Xu et al. (2019) and included as input to derive the methylation‐based breast cancer risk score.Table S3. Characteristics of the training set, weighted to account for differences in age at blood draw.Table S4. DNAm‐based components and coefficients selected to comprise mBCRS.Table S5. mBCRS tertile associations with breast cancer incidence in the Sister Study internal validation set and the EPIC‐Italy external validation set.Table S6. mBCRS associations (per covariate‐adjusted SD) with breast cancer incidence in the Sister Study testing set, stratified by age at blood draw, body mass index, menopause status and proband age at diagnosis.Table S7. mBCRS associations (per covariate‐adjusted SD) with breast cancer incidence by invasiveness and estrogen receptor status, and tests for etiologic heterogeneity in the Sister Study testing set.Table S8. Multivariate OPERA (95% CI) estimates of odds ratios per adjusted standard deviation from separate and combined analysis of mBCRS component classes , adjusted for breast cancer risk factors and standardized.Click here for additional data file.


Background: This report provided the effect of 15 preventable factors on the risk of breast cancer incidence.Study design: A systematic review and meta-analysis.Methods: A detailed research was conducted on PubMed, Web of Science, and Scopus databases in January 2020. Reference lists were also screened. Prospective cohort studies addressing the associations between breast cancer and 15 factors were analyzed. Between-study heterogeneity was investigated using the χ2, τ2, and I2 statistics. The probability of publication bias was explored using the Begg and Egger tests and trim-and-fill analysis. Effect sizes were expressed as risk ratios (RRs) with 95% confidence intervals (CIs) using a random-effects model.Results: Based on the results, out of 147,083 identified studies, 197 were eligible, including 19,413,702 participants. The RRs (95% CI) of factors associated with breast cancer were as follows: cigarette smoking 1.07 ; alcohol drinking 1.10 ; sufficient physical activity 0.90 ; overweight/obesity in premenopausal 0.92  and postmenopausal 1.18 ; nulliparity 1.16 ; late pregnancy 1.37 ; breastfeeding 0.87 ; ever using oral contraceptive 1.00 ; ever using estrogen 1.13 ; ever using progesterone 1.02 ; ever using estrogen/progesterone 1.60 ; ever taking hormone replacement therapy 1.26 ; red meat consumption 1.05 ; fruit/vegetable consumption 0.87 ; and history of radiation therapy, based on single study 1.31 .Conclusions: This meta-analysis provided a clear picture of several factors associated with the development of breast cancer. Moreover, the useful information in this study may be utilized for ranking and prioritizing preventable risk factors to implement effective prevention programs. According to the biennial report of the American Cancer Society, the breast cancer incidence rate had increased slightly by 0.3% per year . Several factors, such as genetic characteristics, lifestyle factors, and medical conditions, may play a role in the development of breast cancer. The risk factors for breast cancer can be divided into two categories, namely (a) fixed risk factors that may contribute to the development of breast cancer, however, cannot be changed, such as age, female gender, genetic characteristics , immunologic biomarkers , racial or ethnic characteristics , family history , and late menopause ; and (b) modifiable risk factors that play a role in the development of breast cancer and can be changed, such as alcoholic beverage consumption, cigarette smoking, physical inactivity, high body mass index (BMI) , high dietary fat, and dietary fiber intake . These factors are largely modifiable and preventable, and therefore, can be considered when designing effective prevention programs.Breast cancer is the most commonly occurring cancer in women, regardless of race or ethnicity, impacting over 2 million women worldwide annually, responsible for over 600,000 deaths in 2018  Efforts to improve screening programs and the early detection and treatment of breast cancer are important; nevertheless, it is of priority to take action to address preventable factors that play an important role in the development of breast cancer. Some measures, such as ranking and prioritizing the risk factors that contribute to the development of breast cancer and implementing prevention programs, can reduce the incidence of breast cancer and prevent thousands of new cases each year. Effective intervention strategies and prevention programs need a comprehensive understanding and a clear picture of the contributing factors that promote breast cancer. To the best of our knowledge, no comprehensive systematic review has yet been conducted to address all the potential preventable factors playing a pivotal role in the development of breast cancer. This meta-analysis was performed to address the associations between breast cancer and 15 factors that might be potentially modifiable and preventable, and consequently, might provide an opportunity to be addressed in prevention programs aimed to reduce the incidence of breast cancer. The eligibility criteria in this study were based on population, intervention, comparison, outcomes, and study design (PICOS). Accordingly, the women having any of the 15 preventable factors mentioned below were included in the exposure group and those without any of the 15 preventable factors mentioned below in the unexposed group. The outcome was considered breast cancer and the prospective cohort studies were reviewed. The outcome of interest was having pathologically confirmed breast cancer, of any type , among the general population, regardless of age, gender, race, ethnicity, and geographical region. The exposures of interest are listed below:Cigarette smoking (current/former smokers versus nonsmokers) Drinking alcohol (current/former drinkers versus non-drinkers) Physical activity (sufficient versus insufficient) Body mass index  Parity (nulliparous versus primiparous/multiparous) Late pregnancy (≥30 years versus <30 years) Breastfeeding  Ever using oral contraceptive (OCP) (yes versus no) Ever using estrogen (yes versus no) Ever using progesterone (yes versus no) Ever using estrogen/progesterone (yes versus no) Ever taking hormone replacement therapy (HRT) (yes versus no) Intake of red meat (highest intake versus lowest intake) Intake of fruit/vegetable (highest intake versus lowest intake) History of radiation therapy (yes versus no) 2 was classified as normal weight, 25.0-29.9 kg/m2 as overweight, and ≥ 30.0 kg/m2 as obese. At least 30 minutes of moderate- to vigorous-intensity physical activity per day (or 150 minutes per week) was considered sufficient for adults . Pregnancy over the age of 30 is considered high-risk . Accordingly, reproductive ages of > 30 were considered late pregnancy in this study. The duration of breastfeeding is recommended for at least 6 months continued up to 2 years of age or longer . Accordingly, various periods of breastfeeding, including ≥ 6, ≥ 12, and ≥ 24 months were considered in this research. The consumption of at least five total servings (400 grams) of fruit and vegetables per day is recommended . However, the majority of the included studies did not report fruit and vegetable consumption according to the recommendations of the World Health Organization. Therefore, the highest intake versus the lowest intake of fruit and vegetables were compared in the present study. There is no universal recommendation for red meat consumption. In this respect, the highest intake versus the lowest intake of red meat was also compared in this research.A BMI of 18.5-24.9 kg/m Prospective cohort studies addressing the association between breast cancer and any of the above factors were included in the meta-analysis, irrespective of their language and publication date and the participants' nationality, race, gender, and age. Wherever reported, full adjusted forms of risk ratio (RR) controlled was used for at least one or more potential confounding factors. A detailed search was conducted on PubMed, Web of Science, and Scopus databases in January 2020. The reference lists of the included studies were also explored. The search process was performed based on the following keywords:  and  The search results of all databases were combined using EndNote, and duplicates were deleted. Afterward, six authors  formed three two-person groups. Each group screened the titles and abstracts of one-third of the search results separately and independently and excluded ineligible studies. The full texts of potentially relevant studies were retrieved for further evaluation. The data from the relevant studies were extracted by 6 authors  using an electronic data collection form prepared in Stata . was used to assess the methodological quality of the included studies. Based on this scale, a maximum of 9 stars were assigned to each study. Studies that received 7 or more stars were labeled high-quality; otherwise, studies were classified as low-quality.The Newcastle-Ottawa Scale (NOS) 2) test  and tau-square (τ2) test and quantified by the I2 statistic . According to the I2 value, heterogeneity was classified as low (<50%), moderate (50-74%), or high (≥75%). The possibility of publication bias was explored by the Egger  and Begg  tests and the trim-and-fill method .The heterogeneity across studies was examined using the Chi-square  and Review Manager software (version 5.3).The effect measure of choice was the RR with 95% confidence intervals. The results were reported based on a random-effects model2≥50%), the source of heterogeneity was investigated using the MetaPlot Stata command based on the sequential algorithm.If the between-study heterogeneity was moderate to high , the overall RR for smokers versus nonsmokers was 1.07 . The overall effect measure showed that smoking significantly increased the risk of breast cancer by 7% (P <0.001). Between-study heterogeneity was moderate (I2=54%). The overall effect became a bit stronger  after performing a sensitivity analysis . Between-study heterogeneity was moderate (I2=65%). The overall effect became a bit stronger  after performing a sensitivity analysis.Based on 48 studies, the overall RR for current smokers versus never smokers was 1.06 . The overall effect measure showed that current smoking significantly increased the risk of breast cancer by 6% (P<0.001). Between-study heterogeneity was low (I2=29%). There was no evidence of publication bias Based on 42 studies, the overall RR for former smokers versus never smokers was 1.07 . The overall effect measure showed that former smoking significantly increased the risk of breast cancer by 7% , the overall RR for drinkers versus nondrinkers was 1.10 . The overall effect measure showed that drinking significantly increased the risk of breast cancer by 10% (P<0.001). Between-study heterogeneity was moderate (I2=63%). The overall effect became slightly weaker  after performing a sensitivity analysis . Between-study heterogeneity was moderate (I2=66%). The overall effect became slightly weaker  after performing a sensitivity analysis.Based on 46 studies, the overall RR for current drinkers versus never drinkers was 1.09 . The overall effect measure showed that current drinking significantly increased the risk of breast cancer by 9% (P<0.001). Between-study heterogeneity was low (I2=43%). There was no evidence of publication bias .Based on 10 studies the overall RR for former drinkers versus never drinkers was 1.22 . The overall effect measure showed that former drinking significantly increased the risk of breast cancer by 22% , the overall RR for sufficient versus insufficient physical activity was 0.90 . The overall effect measure showed that physical activity reduced significantly the risk of breast cancer by 9% (P<0.001). Between-study heterogeneity was moderate (I2=63%). The overall effect became slightly stronger  after performing a sensitivity analysis. There was no evidence of publication bias .Body mass index — Based on 52 studies (Supplementary File 4), the overall RR for overweight/obesity versus normal weight was 1.10 . The overall effect measure showed that overweight/obesity significantly increased the risk of breast cancer by 10% (P<0.001). Between-study heterogeneity was high (I2=76%). The overall effect became slightly stronger  after performing a sensitivity analysis .analysis . There wSupplementary File 5), the overall RR for overweight/obesity versus normal weight in the premenopausal period was 0.92 . The overall effect measure showed that overweight/obesity had no significant effect on the risk of breast cancer (P=0.140). Between-study heterogeneity was low (I2=50%). On the other hand, based on 24 studies (Supplementary File 6), the overall RR for overweight/obesity versus normal weight during the postmenopausal period was 1.18 . The overall effect measure showed that overweight/obesity significantly increased the risk of breast cancer by 18% (P<0.001).The effect of body mass index on the incidence risk of breast cancer was evaluated in pre- and post-menopausal periods separately. Based on 15 studies (Parity — Based on 67 studies (Supplementary File 7), the overall RR for nulliparous versus primiparous/multiparous was 1.16 . The overall effect measure showed that nulliparity significantly increased the risk of breast cancer by 16% (P<0.001). Between-study heterogeneity was high (I2=97%). The overall effect became slightly stronger  after performing a sensitivity analysis ; however, the Begg test did indicate evidence of publication bias (P=0.001). Trim-and-fill analysis estimated 19 missing studies , the overall RR for late pregnancy of ≥30 years versus normal pregnancy of <30 years was 1.37 . The overall effect measure showed that late pregnancy significantly increased the risk of breast cancer by 37% (P<0.001). Between-study heterogeneity was high (I2=90%). The overall effect became slightly weaker  after performing a sensitivity analysis ; nevertheless, the Begg test did indicate evidence of publication bias (P=0.001); however, the trim-and-fill analysis estimated no missing studies.The Egger test revealed no evidence of publication bias . The overall effect measure showed that breastfeeding reduced significantly the risk of breast cancer by 13% (P<0.001). Between-study heterogeneity was high (I2=82%). The overall effect became slightly weaker  after performing a sensitivity analysis .analysis . There wEver using OCP — Based on 45 studies (Supplementary File 10), the overall RR for using OCP versus not using OCP was 1.00 . Using OCP did not affect breast cancer (P=0.870). Between-study heterogeneity was moderate (I2=64%). The overall effect became slightly stronger and significant  after performing a sensitivity analysis .analysis . There wEver using estrogen — Based on 23 studies (Supplementary File 11), the overall RR for using estrogen versus not using estrogen was 1.13 . The overall effect measure showed that using estrogen significantly increased the risk of breast cancer by 13% (P<0.001). Between-study heterogeneity was high (I2=88%). The overall effect became slightly weaker  after performing a sensitivity analysis .analysis . There wEver using progesterone — Based on 5 studies (Supplementary File 12), the overall RR for using progesterone versus not using progesterone was 1.02 . The overall effect measure showed that using progesterone had no significant effect on breast cancer (P=0.820). Between-study heterogeneity was moderate (I2=67%). The overall effect became slightly weaker  after performing a sensitivity analysis .analysis . There wEver using estrogen/progesterone — Based on 17 studies (Supplementary File 13), the overall RR for using estrogen/progesterone versus not using estrogen/progesterone was 1.60 . The overall effect measure showed that using estrogen/progesterone significantly increased the risk of breast cancer by 60% (P<0.001). Between-study heterogeneity was high (I2=95%). The overall effect became slightly weaker  after performing a sensitivity analysis .analysis . There wEver taking hormone replacement therapy — Based on 62 studies (Supplementary File 14), the overall RR for taking HRT versus not taking HRT was 1.26 . The overall effect measure showed that taking HRT significantly increased the risk of breast cancer by 26% (P<0.001). Between-study heterogeneity was high (I2=88%). The overall effect became slightly stronger  after performing a sensitivity analysis .analysis . There wRed meat consumption — Based on 22 studies (Supplementary File 15), the overall RR for the highest intake versus the lowest intake of red meat was 1.05 . The overall effect measure showed that the consumption of red meat had no significant effect on breast cancer (P=0.030). Between-study heterogeneity was moderate (I2=52%). The overall effect became slightly stronger  after performing a sensitivity analysis (P=0.108), while the Egger test did indicate evidence of publication bias (P=0.022). However, the trim-and-fill analysis estimated no missing studies.analysis . The BegFruit/vegetable consumption — Based on 14 studies (Supplementary File 16), the RR for the highest intake versus the lowest intake of fruit/vegetables infrequently was 0.87 . The overall effect measure showed that fruit/vegetable consumption significantly reduced the risk of breast cancer by 23% (P=0.001). Between-study heterogeneity was low (I2=0%). There was no evidence of publication bias .History of radiation therapy — Only one prospective cohort study was found that investigated the effect of previous radiation therapy on the incidence of breast cancer. Based on the results of this study, the RR for ever-exposing to radiation therapy versus never-exposing to radiation therapy was 1.31 . The effect measure showed that exposure to radiation therapy had no significant effect on breast cancer.. In other words, the mere fact that the RRs of some risk factors for breast cancer are higher than the RRs of other risk factors is not a sufficient basis for ranking and prioritizing risk factors. Instead, the prevalence of risk factors in the community is an essential criterion that needs to be taken into account when ranking and prioritizing risk factors. When the effect of a particular risk factor on the outcome of interest is strong (a high RR), however, the prevalence of that risk factor is low in the community, the overall impact of the risk factor on the disease burden in the community is low. In contrast, when a particular risk factor is common in the community, the overall impact of the factor on the outcome of interest may be tremendous even if the association between the risk factor and the outcome is not very strong (a low RR). Therefore, ranking and prioritizing the behavioral and nutritional factors affecting the risk of breast cancer depend on both the strength of the associations (the magnitude of RRs) and the prevalence of the factors in the community. Furthermore, it is impossible to consider risk or protective factors as separate elements, rather, they should be considered a collection. Risk factors facilitate the occurrence of diseases, while protective factors inhibit their occurrence. When a balance exists between risk and protective factors or when protective factors overcome risk factors, the disease will not occur. In contrast, the disease will occur when risk factors are stronger than protective factors .According to our findings, estrogen/progesterone uptake and late pregnancy were the first and second most powerful risk factors for breast cancer, respectively, whereas, sufficient fruit/vegetable consumption and sufficient physical activity were the first and second most powerful protective factors against breast cancer, respectively. The magnitudes of the effect measures reported in this systematic review may be used for ranking and prioritizing the relative importance of risk and protective factors. However, it should be kept in mind that these factors vary in terms of their physiological modus operandi and their units of exposure; therefore, direct comparisons are often unwarranted . Cigarette smoke contains over 7,000 toxic chemical compounds, including human carcinogens . These toxins and carcinogens can result in direct DNA damage. Since DNA controls the normal growth and function of cells, damage to DNA can alter the growth patterns of cells. These abnormal gastric epithelial cells with DNA damage can turn into cancer.Our results revealed a positive association between cigarette smoking and the development of breast cancer. Ambrosone et al. conducted a meta-analysis of observational studies in 2008 and reported the effect of cigarette packs/years on breast cancer risk. They reported a dose-dependent fashion RR=1.44  . DNA lesions can cause cell mutations, which can convert a normal cell into cancer . Moreover, alcohol can act as an irritant and lead to mucosal damage. The damaged cells may try to repair themselves, which may cause DNA changes that can be a step toward cancer .This meta-analysis indicated that drinking alcohol increased the risk of developing breast cancer. Acetaldehyde, the first and most toxic metabolite of ethanol, is a human carcinogen and can induce DNA lesions by inhibiting DNA methylation and by interacting with retinoid metabolism . Consistent with our findings, Key et al. performed a meta-analysis of studies addressing the association between alcohol and breast cancer in 2006. They concluded that excess risk associated with alcohol drinking was 22% ; each additional 10 g ethanol/day was associated with a risk increase by 10%  . In addition, Bagnardi et al.  conducted a dose-response meta-analysis to address the effect of alcohol consumption and site-specific cancer risk. Based on the results of the mentioned meta-analysis, the relative risk of female breast cancer was reported to be 1.04 , 1.23 , and 1.61  for light, moderate, and heavy drinking, respectively. Although the approach of this meta-analysis to address the effect of drinking alcohol on breast cancer risk was different from ours, its results were consistent with ours confirming that drinking alcohol can increase the risk of breast cancer.According to our results, the risk of breast cancer of former drinkers was higher than that of current drinkers. One possible explanation for this finding is that former drinkers might be heavy drinkers who had drunk alcohol for many years; however, they were forced to quit drinking alcohol because of severe hepatobiliary and gastrointestinal complications P<0.170); nonetheless, it had a significant impact on the postmenopausal period (P<0.001). These findings were consistent with the results of a previous meta-analysis conducted by Cheraghi et al.  in 2012. They reported that overweight and obesity had no significant effect on the incidence of breast cancer during the premenopausal period, whereas it might increase the postmenopausal risk of breast cancer. Evidence, based on the meta-analyses of observational studies, indicated that excess BMI not only increased the postmenopausal risk of breast cancer but also heightened the risk of gynecologic cancer in women, such as endometrial cancer , cervical cancer , and ovarian cancer .Our results showed a positive and significant causal relationship between breast cancer and overweight and obesity as a whole. However, the effect of BMI on the incidence risk of breast cancer was different in pre- and post-menopausal periods separately. It was revealed that overweight/obesity had no significant effect on the risk of breast cancer in the premenopausal period  was associated with an increased risk of breast cancer RR=2.2  after 15 years . Several mechanisms have been suggested to explain the association by which HRT increases the risk of breast cancer. The results of experimental studies showed that rigorous cell proliferation occurs upon hormonal exposure in patients with hormone receptor-positive breast cancer. Zghair et al. indicated that breast cancer type 1 susceptibility protein (BRCA1) was the predominant marker gene responsible for estrogen regulation. They reported that exposure to high levels of estrogen, as well as exposure to high levels of iron during the postmenstrual period, exerted synergistic effects on cellular proliferation in BRCA1-linked hormone-responsive breast cancer . Additionally, both in vivo and in vitro investigations have been demonstrated that combination therapy with estradiol and estrogen/norethisterone increases the overexpression of proliferation of progesterone receptor membrane component 1 in breast cancer cells . Furthermore, Wiebe et al. reported that progesterone metabolite 5α-pregnane stimulated breast cell proliferation and detachment, and therefore, played an important role in the development of breast cancer .Based on our findings, using OCP, estrogen, progesterone, a combination of estrogen/progesterone, and HRT significantly increased the risk of breast cancer. The results of several previously conducted meta-analyses approved our findings. Steinberg et al. conducted a meta-analysis of case-control studies using community controls that analyzed the effect of conjugated equine estrogens on breast cancer. They reported that the risk of breast cancer after 10 years of estrogen use increased by at least 15% and up to 29% . On the other hand, Namiranian et al. showed in a meta-analysis that the age of first pregnancy after 30 years was associated with an increased risk of breast cancer odds ratio=1.52  . Pregnancy is associated with extensive changes to the breasts, making breast cells less likely to multiply and develop tumors. This issue explains the protective effect of pregnancy on younger women. However, after the age of 35 years, breast tissue is more likely to have accumulated cells carrying cancer-causing mutations, or clusters of abnormal cells with the potential to become cancerous. However, the important question is why the first pregnancy after age 35 increases the risk of breast cancer. The answer to this question lies in a signaling pathway called the JAK-STAT5 pathway. During pregnancy, pre-existing precancerous cells activate the PRLR-Jak2-STAT5 signaling pathway, accelerating their progression to fully cancerous cells. Blocking Jak2-STAT5 activity can reduce breast cancer risk associated with late-age pregnancy. This pathway can be blocked by various molecules, including Ruxolitinib, AG490, and C188-9 .The results of the present study indicated that breastfeeding decreased the risk of breast cancer by 13%, while late pregnancy significantly increased the risk of breast cancer by 40%. Consistent with our findings, Unar-Munguía recently conducted a meta-analysis in 2017 and showed that the relative risk for breast cancer in women who had breastfed exclusively was 0.72 , compared to women who had never breastfed . Another meta-analysis conducted by Wu et al. reported a dose-response inversed relationship between physical activity and breast cancer risk. According to the results of this meta-analysis, the risk of breast cancer decreased by 2% for every 25 metabolic equivalents (MET)-h/week increment in non-occupational physical activity, 3% for every 10 MET-h/week increments in a recreational activity, and 5% for every 2 h/week increments in moderate plus vigorous recreational activity . The mechanism by which physical activity reduces the risk of breast cancer is controversial. The results of empirical studies proposed that exercise-induced transient systemic acidosis will alter the in situ tumor microenvironment and delay tumor adaptation to regional hypoxia and acidosis in the later stages of carcinogenesis. Smallbone et al. demonstrated that repeated episodes of transient systemic acidosis would interrupt critical evolutionary steps in the later stages of carcinogenesis resulting in a substantial delay in the evolution of the invasive phenotype. They suggested that transient systemic acidosis might mediate the observed reduction in cancer risk associated with increased physical activity .Our results indicated that sufficient physical activity significantly reduced the risk of breast cancer. Based on the findings of a meta-analysis conducted by Chen et al., there was an inverse association between physical activity and risk of breast cancer OR=0.87  . Another meta-analysis conducted by Gandini et al. reported similar results. Based on the results of the mentioned meta-analysis, the relative risk of breast cancer for those who consumed vegetables was 0.75 , and for those who consumed fruit was 0.94  . It has been postulated that the anti-carcinogenic effects of fruits and vegetables may be attributed to the antioxidant effect of their vitamin content, especially vitamin C and beta-carotene. Antioxidants neutralize reactive oxygen free radicals, which cause DNA damage , which in turn may result in genetic modifications and carcinogenesis ,.Based on our findings, the intake of fruit and vegetable had a significant protective effect against breast cancer. The results of a meta-analysis recently conducted by Zhang et al. showed that the intake of vegetable-fruit-soybean dietary patterns could reduce the risk of breast cancer RR=0.87   conducted a meta-analysis to address the effect of red and processed meat consumption on breast cancer incidence. They concluded that red meat consumption was associated with a 6% higher breast cancer risk . The findings of another meta-analysis conducted by Guo et al. showed similar results. They reported that the relative risk of breast cancer for the highest versus the lowest consumption of red meat was 1.10  . Current evidence recommends consuming no more than moderate amounts of red meat, such as beef, pork, and lamb, and eat little, if any, processed meat. The recommendation is to limit consumption to no more than about three portions per week, which are equivalent to about 350-500 grams (about 12-18 ounces) cooked weight of red meat . There is strong evidence that the intake of either red or processed meat is the cause of colorectal, stomach, and breast cancers .Based on our findings, red meat consumption had a weak, yet, significant positive association with breast cancer. Farvid et al.  This review had a few limitations and potential biases. There were some studies, mostly old, that seemed potentially eligible to be included in this meta-analysis; nevertheless, neither their full texts nor their corresponding authors were accessible. This issue might have introduced a selection bias in our results. Furthermore, some epidemiological studies that addressed the associations between breast cancer and some risk factors were excluded from the meta-analysis since they were not consistent with the inclusion criteria defined for this review. This issue might also have raised the possibility of selection bias. Despite its limitations, this meta-analysis had three priorities over the previously conducted ones. First, many of the previous meta-analyses were carried out several years ago and needed to be updated based on current evidence. Second, in this study, 15 modifiable risk factors were examined, for some of which, no meta-analysis has been conducted before. Third, only the results of prospective cohort studies were employed that were the gold standard for observational studies with higher credibility. This meta-analysis provided a clear picture of several factors playing pivotal roles in the development of breast cancer. These results are helpful and may be utilized for ranking and prioritizing preventable risk factors to implement effective interventions and community-based prevention programs. It is reemphasized that both the strength of associations and the prevalence of factors in the community should be taken into account when ranking and prioritizing breast cancer-associated factors. These results were obtained as a part of an MSc thesis in Epidemiology. The authors would like to appreciate the Vice-Chancellor for Research and Technology of the Hamadan University of Medical Sciences, Hamadan, Iran, for approval and financial support of this study. The authors have no conflict of interest to declare. The Vice-Chancellor of Research and Technology, Hamadan University of Medical Sciences, funded this study (No. 9611247562). The funder had a role in the study design, data collection, analysis, publication decision, or manuscript preparation.Using estrogen/progesterone and late pregnancy are the first and second most powerful risk factors for breast cancer, respectively.Sufficient fruit/vegetable consumption and sufficient physical activity were the first and second most powerful protective factors against breast cancer, respectively.Ranking and prioritizing risk factors are essential for prevention programs.Both the strength of association and the prevalence of risk factors are important for ranking.


Primary prevention approaches are urgently needed to curb the growing global breast cancer burden. Mammographic density, also known as breast density, is one of the most significant and common independent risk factors for breast cancer. As such, it is an exciting new potential target to reduce the incidence of breast cancer; we could in the future identify women with high mammographic density at an increased risk of breast cancer and take steps to reduce that risk. However, more research is needed to understand the biological mechanisms that underlie high mammographic density and how these might be used to develop new prevention strategies. Here, we review the current knowledge around the biology of mammographic density with a particular focus on immune system factors that could provide new therapeutic targets.Mammographic density is an important risk factor for breast cancer; women with extremely dense breasts have a four to six fold increased risk of breast cancer compared to women with mostly fatty breasts, when matched with age and body mass index. High mammographic density is characterised by high proportions of stroma, containing fibroblasts, collagen and immune cells that suggest a pro-tumour inflammatory microenvironment. However, the biological mechanisms that drive increased mammographic density and the associated increased risk of breast cancer are not yet understood. Inflammatory factors such as monocyte chemotactic protein 1, peroxidase enzymes, transforming growth factor beta, and tumour necrosis factor alpha have been implicated in breast development as well as breast cancer risk, and also influence functions of stromal fibroblasts. Here, the current knowledge and understanding of the underlying biological mechanisms that lead to high mammographic density and the associated increased risk of breast cancer are reviewed, with particular consideration to potential immune factors that may contribute to this process. The breast is indisputably one of the most susceptible tissues in a woman’s body to cancer. A quarter of all cancers in women originate in the breast and the global burden of this disease is increasing . PrimaryThere are many non-hereditary risk factors for breast cancer including age, obesity, menstrual and reproductive history, and mammographic density. Mammographic density is one the strongest independent risk factors for breast cancer, accounting for 30% of breast cancer occurrences ,6. WomenThe term ‘mammographic density’ refers to the radiographic appearance of the breast on a mammogram ,12 and iThe most common method for classifying mammographic density in the clinic is the breast imaging reporting and data system (BIRADS) developed by the American College of Radiologists. This method is based on a qualitative assessment by the radiologist and classifies breasts on a scale of a to d; a is ‘mostly fatty’ where the mammogram appears mostly dark and transparent; b is ‘scattered density’; c is ‘heterogeneously dense’; and d is ‘extremely dense’ where the mammogram appears mostly white and opaque . ApproxiIn 1976, Wolfe proposed that mammographic density is positively associated with breast cancer risk ,21,22. ABreast cancer is not a single disease. There are a number of different breast cancer subtypes that each have a different disease etiology and can have a different prognosis. If a risk factor elevates risk of a specific breast cancer subtype, this can provide some clues as to the underlying mechanisms that drive increased risk. Several studies have attempted to determine if there is an association between mammographic density and increased risk of a specific breast cancer subtype, however, the results of these studies have not yielded consistent results. While some studies reported that mammographic density was not specifically associated with increased risk of estrogen receptor (ER) positive breast cancer, others reported an inverse relationship with density and ER positivity and a positive relationship with progesterone receptor positive breast cancers ,24,25,26Although it appears that there is no correlation between mammographic density and a specific subtype of breast cancer, there is some indication that mammographic density may be associated with tumour characteristics that generally indicate a poor prognosis. In a study by Yaghjyan et al., over 1000 case control pairs were examined, and reported that mammographic density measured by the Cumulus method was associated with more invasive, larger and higher-grade tumours . This waIn summary, high mammographic density is associated with increased risk of all breast cancer subtypes. Although delayed detection of breast cancer due to the masking effect of mammographic density contributes to a poorer prognosis, high mammographic density has clearly been demonstrated to be an important independent risk factor for developing breast cancer. However, it is important to consider that the epidemiological studies that demonstrate the association between high mammographic density and breast cancer risk do not demonstrate a causal relationship. Whilst it is possible that high mammographic density causes increased risk of breast cancer, it is also possible that elevated risk of breast cancer causes high mammographic density . Furthermore, the association may be due to a common cause, whereby an as yet unidentified factor independently increases both mammographic density and breast cancer risk .Many factors contribute to a woman’s mammographic density, and there are likely to be complex interactions at the biological level that ultimately determine how dense the tissue is. The relative abundance of epithelium, stroma and adipose tissue that develops in the breast during puberty largely determines the mammographic density of the adult tissue . StudiesBRCA1 or BRCA2 are not associated with increased mammographic density [Mammographic density is partially inheritable. Asian and African American women are more likely to have extremely dense breasts compared to non-Hispanic white women ,38. Alth density  and mamm density . However density . Further density . The indAge and BMI also affect mammographic density. BMI is strongly positively associated with breast area and non-dense area  and therFactors associated with a woman’s reproductive history and exposure to ovarian hormones may affect mammographic density. An inverse correlation between parity and mammographic density exists whereby there is a 4% decrease in breast cancer risk for every live birth in premenopausal women ,51. HoweThere is evidence that mammographic density is altered by exposure to drugs that affect ovarian hormone signaling. In postmenopausal women, it is well-established that hormone replacement therapy (HRT) increases mammographic density, and also is associated with an increase in breast cancer risk ,55,56,57It is not surprising that drugs that affect ovarian hormone signaling have a profound effect on mammographic density. Ovarian hormones provide the stimulus for breast development and as such the composition and function of the breast is fundamentally dependent on them. However, Huo et al. demonstrated that there is no difference in estrogen or progesterone receptor staining in paired tissue samples from high and low mammographic density areas within the breast . FurtherThe human breast, or mammary glands, are a unique bilateral tissue that undergoes major development during puberty, pregnancy, and during lactation . MammaryTo understand how the mammary gland epithelium might contribute to mammographic density, a number of studies have compared histology of breast biopsies or breast tissue collected from autopsies of known mammographic density determined by the mammogram. These studies demonstrate that there is a greater abundance of epithelial cells, and epithelial cell hyperplasia in radiographically dense breast tissue ,76,77,78The mammary gland stromal compartment is comprised of fibroblasts, immune cells, and ECM proteins such as collagen fibres . The rolSome of the studies that investigated the abundance of mammary epithelium in mammographic density also analysed the proportions of stroma. This was analysed in multiple ways; some studies determined the proportion of stroma in breast tissues by measuring the area of non-epithelial cells and collagen . More reFibroblasts are the most prominent cell type within the mammary stroma. They are non-inflammatory, non-vascular, non-epithelial cells. The primary role for mammary fibroblasts is production and regulation of turnover of collagen and other components of the extracellular matrix . FibroblCAFs can modulate the immune microenvironment by recruiting immune cells such as M2 macrophages and T cells to dampen anti-tumour immunity by expression of chemotactic cytokines . They alThe extracellular matrix and collagen are key components of the mammary stroma and are produced primarily by mammary fibroblasts . ECM proHigh abundance of collagen has been implicated in breast cancer risk. Previous studies used a transgenic mouse with increased stromal collagen in the mammary glands. These mice exhibited three times the amount of tumour formation compared to control mice, and the tumours were more invasive and had greater metastasis . This stIn the mammary gland, collagen is regulated by other proteins in the stroma produced by surrounding fibroblasts and immune cells . Matrix Immune cells are a key component of the mammary gland and are vital in facilitating mammary gland development and in immune surveillance to protect against potentially tumorigenic cells . HoweverInfiltration of immune cells can influence the growth and progression of breast cancer, and can be an indicator of prognosis. Infiltration of CD8+ cytotoxic T cells and CD56+ NK cells has been observed to be favourable the patient outcomes with breast cancer, as both these cells play a role in anti-tumour immunity ,109. DenMacrophages are highly plastic cells and key immune cells in the mammary gland. They have multiple functions in regulating mammary gland development during the ovarian cycle, pregnancy, lactation, and remodelling the mammary gland back to its pre-pregnant state . They alThe function of macrophages in the mammary gland depends on their phenotype. Macrophages differentiate into different phenotypes when exposed to different cytokine signals in the microenvironment. Polarisation to classically activated macrophages, or M1 macrophages, occurs in response to infection or injury, and can be induced by exposure to microbial by-products such as lipopolysaccharide (LPS) or by cytokines interferon gamma (IFNG) and TNFA ,116. TheAlternatively activated, or M2 macrophages, are predominantly polarised by signals from cytokines IL-4 and IL-13 and can be identified by surface marker expression of CD163 or CD206 ,123. M2 Due to their involvement with tumour progression, a subset of M2 macrophages have been identified in the tumour microenvironment as tumour associated macrophages (TAMs) ,128,129.Monocyte chemotactic protein 1, or CCL2, is a pro-inflammatory cytokine. It is produced by many cell types including epithelial cells, tumor cells, fibroblasts, and adipocytes, as well as immune cells such as monocytes and macrophages ,131. TheMmtv-Ccl2 transgenic mouse model, constitutive expression of CCL2 in the mammary glands reduced tumour latency and tumor free survival when challenged with the chemical carcinogen DMBA [Many studies have investigated the role of CCL2 in breast cancer and suggest that CCL2 increases breast cancer risk and tumor progression. In an gen DMBA . Intravegen DMBA . This hagen DMBA . In humagen DMBA ,138,139.gen DMBA ,141. Howgen DMBA ,143.Mmtv-Ccl2 mice, constitutive expression of CCL2 in the mammary glands results in greater stromal thickness and collagen deposition surrounding the epithelium [CCL2 has been implicated in many fibrotic diseases. CCL2 is highly expressed in the lungs of patients with lung fibrosis . Animal ithelium .The roles of CCL2 in breast cancer risk and fibrosis suggest that CCL2 could promote increased mammographic density. In women, paired high and low mammographic density tissue samples revealed that CCL2 expression is elevated in the epithelium of high mammographic density breast tissue . This stTransforming growth factor beta 1 (TGFB1) is a highly pleiotropic cytokine with a diverse number of cellular functions. It is involved in proliferation, differentiation, apoptosis, extracellular deposition, as well as modulating inflammation and immune responses ,150. TGFTGFB1 plays many roles in breast cancer, however, these functions can be both tumor suppressive and tumor promoting. The tumor suppressive functions are shown in murine studies of early tumorigenesis when constitutive expression of TGFB1 in the mammary gland results in decreased mammary tumor susceptibility when challenged with the chemical carcinogen 7,12-Dimethylbenz(a)anthracene (DMBA) . When TGTGFB1 is a key regulator of fibroblast activity and regulation of ECM deposition and turnover, and is therefore, implicated in many fibrotic diseases. Animal models have demonstrated the link between TGFB1 and fibrosis, for example overexpression of TGFB1 in rat lungs induces pulmonary fibrosis . In a moThere have been few studies that have investigated the role of TGFB in mammographic density. ChipSeq analysis revealed several genes involved in TGFB signaling exhibited decreased expression in breast tissue of women with high mammographic density compared to those with low mammographic density . FurtherMyeloperoxidase (MPO), produced by monocytes, and eosinophil peroxidase (EPO), produced by eosinophils, are peroxidase enzymes released by cells during inflammation and primarily known for their role in oxidative defense against invading bacterial pathogens . HoweverTumour necrosis factor alpha (TNFA) is a pleiotropic cytokine involved in inflammation . It is pThere have been few studies investigating the role of TNFA in mammographic density. One study measured the relationship between circulating inflammatory markers in relation to mammographic density obtained from screening mammograms, this study found no association between serum TNFA concentration and mammographic density . HoweverChronic inflammation is one of the hallmarks of cancer, and can promote mammary tumour initiation, growth, invasion, and metastasis. Elevated markers of inflammation such as C-reactive protein (CRP), IL-6 and TNFA are associated with an increased risk of breast cancer, increased risk of recurrence, and reduced tumour free survival . The useStudies have implicated inflammation as a driver of mammographic density. In a study that compared mammograms to healthy breast tissue taken from breast surgeries, it was reported that COX2 expression was greater in the stroma of high mammographic density breasts compared to low mammographic density breasts . This wan = 143; 6 months). Large cohort studies have found promising associations between NSAID use and lower mammographic density, although not all studies have found an association [As pharmaceutical inhibitors of COX2, NSAID use has the potential to decrease breast cancer risk in women with high mammographic density. It is currently unclear whether NSAID use reduces mammographic density; a randomized controlled trial suggested aspirin (325 mg/day) was not effective in lowering mammographic density , howeverociation ,206,207.ociation , and curMammographic density is strong independent risk factor for breast cancer. Women with high mammographic density have a four to six fold increased risk of breast cancer compared to women with low mammographic density when matched for age and BMI. Mammographic density is influenced by age, hereditary factors, BMI, parity, menopausal status, and hormonal therapies. Histological studies have revealed that high mammographic density is characterised by high proportions of stroma containing fibroblasts and an immune cell profile indicative of an inflammatory, tumour promoting environment. However, the underlying biological mechanisms that mediate mammographic density are yet to be elucidated.Chronic inflammation has been established to contribute to breast cancer risk and development. There are number of immune and inflammatory factors that contribute to breast cancer risk, as well influence the stromal environment. Peroxidase enzymes, TGFB, TNFA and CCL2 have been demonstrated to influence the stromal environment through effects on fibroblast activity and immune cells, as well as contributing to breast cancer risk and development. Of particular therapeutic interest, anti-inflammatory drugs hold promise in reducing mammographic density-associated breast cancer risk and further large-scale studies could provide new options for primary prevention. However, further research on the immune signaling factors active in breast tissue are needed to establish causal biological pathways. Understanding the drivers of mammographic density and the associated increased breast cancer risk may provide targets for preventative therapies in the future for women with high mammographic density.