This study aims to evaluate the impact of the decision aid on women’s knowledge, risk perception, acceptance of risk assessment and change of screening frequency, and decision-making. Here we report the results of a pre and post-survey in which women who are clients of BreastScreen Victoria were invited to complete an online questionnaire before and after viewing the decision aid. 3200 potential participants were invited, 242 responded with 127 participants completing both surveys. After reviewing the decision aid there was a significant change in knowledge, acceptance of risk-stratified breast cancer screening and of decreased frequency screening for lower risk. High levels of acceptance of risk stratification, genetic testing and broad support for tailored screening persisted pre and post review. The DEFINE decision aid has a positive impact on acceptance of lower frequency screening, a major barrier to the success of a risk-stratified program and may contribute to facilitating change to the population breast screening program in Australia.Internationally, population breast cancer screening is moving towards a risk-stratified approach and requires engagement and acceptance from current and future screening clients. A decision aid ( Potential harms of population screening such as overdiagnosis and false positives7 have come to light as well as significant advancements in screening technology. Despite these changes only small shifts such as the 2014 inclusion of the family history policy8 and breast density notification in some Australian states9, have altered to reflect this. Thirty years on, age and gender are still the predominant factors used to assess screening eligibility and breast cancer risk in the general population.Since the introduction of population-based breast cancer screening significant inroads have been made in the understanding of breast cancer risk11, has the potential to further reduce breast cancer mortality by lowering rates of interval cancers in higher risk individuals13. Planning for the change of program structure is underway for breast cancer screening both internationally and in Australia15. Several international projects are underway in the UK17, Canada18, Europe19, Italy20 and USA22 to further understand the possible advantages of this approach including the role genomics may play in risk assessment.Risk-based screening, whereby recommendations are dependent on breast cancer risk category, the calculation of which would include mammographic density24. However, there was less acceptance of decreased screening frequency if women are found to be lower than average risk26. Successful implementation of a risk-stratified approach to population breast cancer screening will depend on engagement and understanding from consumers, which will require comprehensive, and culturally sensitive communication made available in plain language.Women who currently participate in routine population breast cancer screening are interested and supportive of a risk-stratified approach, including the use of genomics to assess riskhttps://www.defineau.org) was developed incorporating current breast screening participants’ values, educational and decisional needs to support the process of choosing between current or risk-stratified screening27. An iterative process was undertaken with input from experts in a variety of fields as well as consumers who directed the content and improved readability.A purpose built, online Decision aid (DA) called DEFINE  women completed the pre-DA review survey. 127 (53%) women completed both the pre and post-surveys. Demographic data is summarised in Table The participants were well-experienced BreastScreen attendees with 197 of the 241 (82%) participants who responded to the question (one missing response) having had more than three mammograms. From the initial cohort of 242 respondents, 29% had a prior breast biopsy and 22% reported a family history of breast cancer. Table M) = 41.0 (Standard deviation (SD) 22.1), post-review M = 44.1 (SD 22.6). The increase of scores indicates women perceived their breast cancer risk as slightly higher after reviewing the DA although this was not statistically significant  −0.9 to 5.3, P value = 0.161).There was a normal distribution of responses on how likely a participant felt she was to develop breast cancer in her lifetime and no significant change between responses for pre and post DA review  choosing the middle response of rarely worrying about breast cancer, 24% (n = 30) often or sometimes worrying, and 27% (n = 34) never worrying.Breast cancer worry was normally distributed with 50% of all respondents . There was a weakly positive association between a participant’s level of worry about developing breast cancer and their perceived risk of developing breast cancer .Overall anxiety, measured by OASIS, was low  compared to post  reviewing the DA  and similarly for the risk factor knowledge test before (M = 4.6 SD = 2.0) and after  . There was a weakly positive correlation between the post-test breast cancer risk factor knowledge test scores and self-reported knowledge on risk factors  suggesting participants had high levels of insight into their understanding of breast cancer risk factors.There was a significant increase in self-rated breast cancer risk factor knowledge pre (P < 0.001). Figure Similarly, there was a statistically significant increase in how much women felt they knew about individualised breast screening with an increase mean score from 49.0 (SD = 29) to 74.4  post reviewing the DA .Initial levels of interest in risk-stratified screening were very high. Of the 241 respondents who answered the question to the survey pre-DA review, 226 participants 93.7%) were definitely or probably interested in a risk-stratified approach to breast cancer screening. Analysis of the 127 participants who completed both pre and post-surveys saw a shift from 122 to 126 women into either the “Yes probably” or “Yes definitely” group, illustrated in Fig. .7% were P = 0.779).There was a slightly higher rate of interest in the women who completed both pre and post DA review questionnaires compared to the 115 participants who did not complete the survey for after DA review, however this was not statistically significant (P = 0.074) and similarly, there was no association between women’s levels on anxiety as measure on the OASIS scale and their interest in risk-stratified breast screening .There was no association between levels of breast cancer worry and interest in risk-stratified breast screening .In the pre-DA survey, there were very high levels of acceptance for having an annual mammogram if high risk  compared to the acceptance of having a 3-year screening interval if deemed lower than average risk . Figure P = 0.564).Levels of acceptance for annual mammograms if higher than average risk was exceptionally high with only one of the 125 respondents who answered the question (0.8%) not interested prior to reviewing the DA and two of the 127 respondents (1.6%) in the post review survey. On binary analysis there was no significant change between the two responses (P = 0.004).In terms of a 5-year screening interval of much lower than average risk, this was acceptable to only 43% of women (54 of 127 respondents) pre-DA review, however this increased 54% (68 of 127 respondents) after reviewing the website. Grouping the outcomes for acceptance of a 5-year interval into binary categories there was a statistically significant shift towards acceptance (P = 0.343).There was no relationship between breast cancer worry and accepting a five year interval for screening if lower risk  interested in the pre-DA survey and 117 of the 123 participants who answered the question (95.1%) interested in the post-DA survey. Similarly, in regard to accepting a risk assessment, this was very high in the pre-DA survey with 125 participants (98.4%) accepting this before and 124 (100%—3 missing responses) in the post-DA survey. This compares to 95% (109 of 115) of respondents who did not complete the post-DA survey who were accepting of a risk assessment. Figure .2% inter95 women (75% of respondents) gave adequate responses to the free text response describing risk-stratified screening after reviewing the DA indicating adequate knowledge. 64 of those women had positive values and preferred individualised screening and three participants had negative values and preferred the current program. 67 participants or 53% of the cohort have been classified as making an informed decision. Seven participants were unsure which option they prefer and 20 did not respond to the question of preference.Satisfaction with decision was overall very high with 113 of the 125 respondents who answered the question (90%) rating a 0–6 high score. The remaining 12 respondents (10%) were in the intermediate 7–12 range and no participants had low levels of decision satisfaction.Responses to specific questions around utility and usefulness of the website are summarised in Table www.defineau.org) improves knowledge and in principle acceptance of a risk-stratified breast screening approach as well as allowing for informed decision-making. These results are in keeping with assessments of other DAs which consistently report significant improvements in knowledge47 and were confirmed in a Cochrane review of 105 studies reporting on DAs which have a significant impact on knowledge48.Our findings confirm that the DEFINE online DA  had 1–3 prior mammograms. Given the disparate group sizes between less and more prior screening rounds, meaningful differences could not be ascertained in terms of acceptability of differing screening frequencies, or risk stratification more broadly. It is plausible a group of women with less experience in the current system would have different attitudes towards a lower frequency of screening and this would be an interesting future study.24; however, our current results quantify those concerns to be small.The implications of our work for risk-stratified breast screening into an Australian context is reassuring in that we demonstrate women currently participating would not be dissuaded by a change in the program. We show women participating in our current program are interested in and motivated by this approach. There is broad acceptance of having risk assessed within our existing system as well as in the inclusion of genetic testing to this risk calculation. We have previously reported a proportion of current BreastScreen clients may have concerns about data security and privacy issues associated with genetic testingThe first limitation of this study is the skewed sample of women which were recruited. By recruiting women already actively involved in population breast screening it is difficult to generalise these findings to the broader population including those women who have little or no experience with our current breast screening program. Our cohort is not a representative sample of the broader BreastScreen community given they were highly experienced with over 80% having had at least 3 prior mammograms and with an average age older than the average BreastScreen Victorian client. The impact of this experience and older age is challenging to quantify but is foreseeable as a confounding factor in risk perception and acceptability.38 and an higher age than the average screening population, we may not have a representative sample of the overall screening population in Victoria. The other indicator we may have sampled a very well-informed group was that 73% of the cohort reported only ‘some’ of the website information was new to them. The use of email for recruitment will also have sub-select participants with higher technological literacy which potentially biased our cohort. This method of recruitment was selected as it most closely aligned with our tool being web based; however, a paper or in person-based recruitment strategy may have uncovered different results.Secondly, with only an 8% uptake rate from the initial email invitation and an overrepresentation of higher socioeconomic postcodesThirdly, we note just over half the participants completed both the pre and post-questionnaires. The group that completed only the pre-DA questionnaire was slightly younger with a marginally lower interest in risk-stratified screening and this further may limit the generalisability of our findings.Another limitation is the hypothetical nature of the responses. There may be disparity between intention and behaviour and women in this cohort were not committing to an actual change or acceptance, rather indicating potential intentions to an approach not yet available to them.We have custom built an effective decision aid which can support women’s decision making around a risk-stratified model and have demonstrated that even brief intervention significantly improved understanding and acceptability of a risk-stratified approach to population breast screening in Australia.51 but we have demonstrated this hesitancy can be lessened with brief educational and values assessment intervention; however, future work is needed assess whether this translates into real-world behaviour change with higher levels of uptake as well as the impact on a screening naïve population.International work from Canada, UK and Europe has consistently shown women are hesitant about reducing their screening interval14. We recognise the importance of patient involvement, education, and communication in a new population health intervention. Future directions for this DA are planned addressing key stake holders perspectives on the implementation of DAs into a risk-stratified breast screening approach in Australia.The transition from our current one size fits all population breast screening model to a tailored or personalised approach is well underwaySingle arm mixed methods survey conducted pre and post-review of an online DA, DEFINE.24. Stepwise, iterative development of the online DA was guided by recommendations from the International Patient Decision Aid Standards (IPDAS) Collaboration54 and has been described previously27. DEFINE can be viewed at https://www.defineau.org and fulfills 26 of the 41 criteria on the IDPASi v3 criteria53.The DA content was directed by results from focus groups conducted with participants who all attend BreastScreen VictoriaWomen currently participating in the BreastScreen Victoria (BSV) program who had a negative screen in the prior 6 months were invited via email to participate in the study. Through electoral roll data , BSV actively recruits women aged 50–74 years of age with an invitation letter at the age of 50. Women are eligible to participate from the age of 40 but are not actively invited by letter. Inclusion criteria for BSV is having identified as female on the electoral roll and being over the age of 50; however, there are clients within BSV outside of the target age range of 50–74. Women with breast cancer come out of the BSV program at diagnosis but are able to reenter at 5 years post-diagnosis. For this reason, women who had a prior breast cancer diagnosis were not excluded in this study in an attempt to represent the greater BSV population accurately.Participants were randomly selected from the BSV database for a range of postcodes and ages. The invitation email included a link to the online pre-DA review survey. This initial survey ended with the link to DEFINE. After submitting the pre-review survey, an automated email was sent to the participant containing the post-DA review survey, allowing individual responses to be paired. At the start of the post-review website survey was a compulsory question asking “Have you reviewed the DEFINE website?”. Participants could not start the post-DA review questionnaire without ticking ‘yes’ to this question.Ethics approval was obtained through Peter MacCallum Cancer Centre Ethics Committee (EC00235) Project number 17/194L. All procedures performed were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.All participants, including consumers, signed written informed consent forms approved by the ethics committeeThe study team constructed the mixed methods questionnaire as an iterative process. Where possible validated tools were used or existing tools modified for purpose. Table Demographic characteristics: Measured were: age, postcode (surrogate for socio-economic status), level of education, language spoken at home, experience with mammograms, prior breast biopsy, personal history of breast cancer and family history of breast cancer. Genetic testing was inquired about without specifying which genes had been tested.1. Incorrect distractors (such as stress or the use of antiperspirant deodorants) were excluded to reduce the chance of confirmation bias reinforcing myths about breast cancer risk factors. Self-reflection of knowledge was assessed by a single question where participants rated their knowledge of breast cancer risk factors on a scale of 1–100 where 1 is poor and 100 is excellent.Breast cancer risk factors knowledge: A knowledge measure of breast cancer risk factors was purpose-built using eight known breast cancer risk factors. Participants were required to “tick” as many of the factors they knew, and direct pre-post comparison was performed. The factors listed were – Mammographic density, family history, alcohol intake, body mass index, age, age of first pregnancy, age at menopause and being nulliparousRisk-stratified Breast screening knowledge: The only compulsory answer within the survey was a free text response to the question “What do you know about “individualised” or “personalised” breast screening?”. The free text responses were interrogated using a structured qualitative content analysis approach. Discrete codes, ‘accurate description’, ‘partially accurate’ or ‘value/belief about the program’, were defined and responses were compared pre and post for a change in understanding.Self-reported knowledge of risk-stratified breast screening was quantified and compared with a question asking participates to self-rate their knowledge of risk-stratified screening where 1 is poor and 100 is excellent.55.Breast cancer worry and general anxiety: A single question was posed to assess overall worry about developing breast cancer “How much do you worry about getting breast cancer?” with the potential responses being ‘never, rarely, sometimes, often, or most of the time’ which was adapted from the UK Health Information National Trends Survey56. This five-item tool measures each response using a 5-point Likert scale with questions being equally weighted to give a total score of 0–25. A score of 0–5 indicates mild or no anxiety, 6–10 indicates moderate, 11–15 severe and a score of 16–25 indicates extreme anxiety. We were interested in examining the role anxiety plays in decision-making57, and although anxiety is only one of many factors which may impair decision making we chose to focus on this as it has been documented to have a particularly pertinent role in breast cancer screening decisions58.General anxiety was measured using the Overall Anxiety Severity and Impairment Scale (OASIS)59. Risk perception can be a strong motivator for uptake of health behaviours such as cancer screening60.Breast cancer risk perception: Risk perception was assessed using three validated questions using numeric, verbal comparative scales adapted from Gurmankin Levy et al.If BreastScreen offered you the chance to take part in individualised breast screening in place of the current BreastScreen program, would you be:” of which the response options were: definitely interested, potentially interested, unlikely to be interested, or definitely not interested. The second question “Would you be interested in breast screening if each woman was managed differently depending on her personal breast cancer risk?” had potential responses of: yes definitely, yes probably, probably not, definitely not.Intention to participate: Two questions assessed intention to participate in a risk-stratified breast screening program. The first being: “If you were offered an assessment of your personal breast cancer risk by BreastScreen at your next mammogram, would you be” and “If calculating your personal breast cancer risk meant having a blood test to look at your genes, would you be” with available responses on a four-point Likert scale: definitely interested, potentially interested, unlikely to be interested and definitely not interested.Acceptability of risk assessment and genetic testing: Two questions examining acceptability of risk assessment as well as acceptance of genetic testing were asked: “Hypothetically, if your personal breast cancer risk was lower than the average woman you could be advised to have a mammogram every 3 years. Would you be happy to have screening every 3 years if you were found to be low risk?”. Available responses included yes definitely, yes probably, probably not, or definitely not.Acceptability of change of screening: Three questions addressed the issue of acceptance of differing screening frequencies . Prior to these questions, there was a descriptor explaining the current Australian recommendations for biennial mammography. These purpose built questions were aimed at assessing the acceptability of individualised screening and the impact of the DA on level of acceptability. The first question was “The other two scenarios were gauging acceptance of a mammogram every 5 years if their risk were much lower than average and to have an annual mammogram if they were found to be higher risk.62. In order to measure informed decision making we assessed three separate outcomes and combined three outputs of the post-website review survey: knowledge of risk-stratified screening from the free text response, attitudes of risk-stratified screening modified from the validated 6 questions developed by Dormandy63, and preference for current or risk-stratified screening. Participants were considered to have made an informed decision when they had adequate knowledge, positive values, and preferred risk-stratified screening or if they had adequate knowledge, negative values and preferred the current screening model. Adequate knowledge was defined as a response in the post-website review questionnaire to the open-ended question to “What do you know about “individualised” or “personalised” breast screening?” with ‘accurate description’ and ‘partly accurate description’ responses defined as adequate knowledge.Informed decision making: The multidimensional measure of informed choice (MMIC) acknowledges informed decision-making requires adequate knowledge, and alignment with values/attitudes and intention. This has been validated in the context of antenatal screening for Down’s syndrome64 evaluating satisfaction of a decision made. This measure was validated with a population of women deciding whether to take hormone replacement therapy65.Satisfaction with decision-making: Satisfaction with decision scale is a validated six item scale modified from O’Connor’s decisional conflict scaleEach question is answered on a five-point Likert scale of strongly agree, agree, neither agree or disagree, disagree, or strongly disagree. Scores were totalled and categorised into high, moderate, or low satisfaction.Website feedback and satisfaction: Nine questions assessing length of time spent reading website, clarity of information, ease of navigation and use of extra links embedded into text on the website which provided additional references.Seven questions addressing responses to the website including emotional response and helpfulness of the website. The final question to the survey was open text “Do you have any suggestions as to how the website could be improved?”.67.Study data were collected between February and July 2020 and managed using REDCap electronic data capture tools hosted at The University of Melbournet tests were two-tailed. Correlation between anxiety, risk perception and acceptability was performed using Spearman’s rank-order correlation. Statistical analysis was performed using STATAIC16.Categorical data were analysed with descriptive statistics. Pre and post-questionnaire data were analysed using a paired t test for continuous variables and Fisher’s exact test for categorical variables. Binary comparison assessing a pre-post review change in interest in risk-stratified screening and acceptance of differing screening frequencies was performed using a McNemar’s test. Comparisons were only performed using matched pre-post data and Content analysis for the free text response question regarding knowledge of risk-stratified screening was conducted initially by JL using an inductive approach coding into categories. These were co-coded by LF and where discrepancies arose, these were resolved by discussion between study investigators.Further information on research design is available in the Supplementary table 1Reporting summary


Breast cancer has become the most common malignancy among women, posing a severe health risk to women worldwide and creating a heavy social burden. Based on current observational studies, the dietary factor may have a causal relationship with breast cancer. Therefore, exploring how dietary composition affects breast cancer incidence will provide nutrition strategies for clinicians and women. We performed a two-sample Mendelian randomization (MR) analysis to find the causal effect of four kinds of relative macronutrient intake  on the risk of breast cancer and its subtypes . The Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) test, MR-Egger intercept test, Cochran’s Q statistic, funnel plot, and leave-one-out (Loo) analysis were all used in a sensitivity analysis to test the robustness of MR. Genetically, a higher relative protein intake was found as a protective factor for Luminal A and overall breast cancer, which was inconsistent with recent findings. A higher relative sugar intake could genetically promote the risk of Luminal B and HER2-positive breast cancer. Conclusions: A higher protein proportion in diet genetically reduces the risk of breast cancer, while higher relative sugar intake does the opposite. Breast cancer in women has overtaken lung cancer as the most commonly diagnosed cancer worldwide. Approximately 2.3 million new cases of female breast cancer were diagnosed, which represents almost 12% of all cancer cases globally . As for ER status, we used the summary data obtained from the GWAS conducted by Michailidou et al., including 21,468 ER-negative cases, 69,501 ER-positive cases, and 105,974 controls [https://bcac.ccge.medschl.cam.ac.uk/bcacdata/oncoarray/oncoarray-and-combined-summary-result/gwas-summary-results-breast-cancer-risk-2017/ (accessed on 13 February 2023)].We obtained the breast cancer risk genome-wide association studies (GWASs) summary data from the Breast Cancer Association Consortium (BCAC), which recruited over 100 groups with data on more than 200,000 individuals. Summary data on breast cancer risk came from these GWASs, including 133,384 breast cancer cases and 113,789 controls of European ancestry. In one GWAS, Zhang et al. used a novel two-stage polytomous regression method to characterize tumor heterogeneity by ER, progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status and tumor grade . Moreovecontrols . In addihttps://biobank.ctsu.ox.ac.uk/crystal/refer.cgi?id=118240, accessed on 19 February 2023). In contrast, FFQ was used by all replication cohorts. The “macronutrient densities” are acquired by dividing the macronutrient intake by total energy intake. However, suppose the relative intake of macronutrients does not increase linearly with the total energy intake. In that case, the simple construction of macronutrient proportions may not be the optimal correction for total energy intake. As a result, the macronutrient intakes may need to be properly corrected for total energy intake, leading to residual correlations between macronutrient and total energy intake, which may vary by macronutrient. Meddens et al.  included in the macronutrient composition and breast cancer risk GWAS. We considered the smallest sample size as an overlap in the same study. The EPIC and WHI sample overlap was 3.03%  and 3.47%  in the GWAS conducted by Zhang et al., respectively. The EPIC and WHI sample overlap was 3.08%  and 3.74%  in the GWAS conducted by Michailidou et al., respectively. Moreover, the proportion of sample overlap for the breast cancer risk GWAS conducted by Zhang et al. and Michailidou et al. was 6.50%  and 6.82% , respectively. The cohort information of the three studies and the overlapped samples are all shown in p = 8.46 × 10−3, p = 2.21 × 10−3, p = 7.91 × 10−4, p = 1.4 × 10−3, p = 9.2 × 10−3, p = 1.79 × 10−2). However, the causal relationship cannot be testified via MR analysis because the P value failed to pass the Bonferroni correction (p = 1.25 × 10−2). Further sensitivity analyses have found no pleiotropy and heterogeneity for the above estimates. In the subgroup MR analysis of the relative intake of protein and HER2-enriched breast cancer, the IVW showed a protective trend in breast cancer incidence , while MR-Egger manifested an inconsistent nonsignificant estimate. Therefore, we tightened the p value of IVs to 5 × 10−9, and SNP rs445551 was removed. Further analyses showed inconsistent but nonsignificant estimates using three methods  = 0.64; 95% confidence interval (CI) = 0.45–0.89;  methods . Finallyp < 1.00 × 10−4). When outliers  has been testified to enhance the efficacy of standard cancer therapy. FMD, a dietary structure low in calories, sugars, and protein but relatively high in fat content, could strengthen endocrine therapeutics tamoxifen and fulvestrant by lowering circulating IGF1, insulin, and leptin by upregulating EGR1 and PTEN to inhibit the AKT–mTOR signal pathway . ClaudioWe have failed to find the causal relationship between the risk of breast cancer and the relative intake of fat. At the same time, a large-scale, case–control study has revealed that replacing fat intake with carbohydrates of equal calories could lower the risk of breast cancer. Moreover, the same association was also shown for fat and protein . Sabina The strength of our study is that we, for the first time, investigated with the MR analysis the causal relationship between the relative intake of four macronutrients and the risk of breast cancer. In addition, the proportion of sample overlap in our study was small, guaranteeing the independence of exposure and outcome and making the estimates of MR more robust. Moreover, we used different methods to perform sensitivity analysis to detect outliers and correct potential pleiotropy and heterogeneity. On the other hand, most current diet-related research has concentrated on specific foods or ingredients, which may not offer comprehensive insights into the optimal diet for overall health. Instead, dietary patterns, which encompass a range of foods, nutrients, and beverages, can be valuable tools for assessing the overall impact of diet on health outcomes. The exposure information in our study is derived from large cohorts via dietary questionnaires, a process that reflects, to the greatest extent, the dietary structure of the participants in the cohorts. In addition, it indirectly describes the proportion of the four macronutrients in the dietary pattern using energy density.Our study also had some limitations. Firstly, the original dietary information obtained from UK Biobank was collected via a 24HDR questionnaire. Moreover, participants were asked to recall as accurately as possible how many portions of each food item they had consumed the previous day. Compared with the FFQ used by other cohorts, feedback from the 24HDR may have more random variation since the FFQ can obtain dietary intakes for a standard day in the previous week or month. Secondly, the IV numbers for relative fat, protein, and sugar intake were too small, potentially weakening the strength of the MR analysis due to the convergence bias. Thirdly, only European ancestry was tested in our study, so it is unreasonable to interpret our findings in other populations. Finally, the findings in our study are based on the current background of GWASs, which have identified the causal relationship between macronutrient intake and the risk of breast cancer in genetics. This can provide some reference for the formulation of clinical nutrition strategies but still requires large-scale clinical trials for validation.This study utilized the genetic method to investigate the causal relationship between the relative intake of macronutrients and the risk of breast cancer. We found that a higher relative intake of protein was inversely associated with the risk of Luminal A and overall breast cancer. On the contrary, a higher relative intake of sugar would promote incidences of Lumina B and HER2-positive breast cancer. However, what we have found was partially inconsistent with the current finding, so further validation needs to be performed via clinical trials.


Processing speed was significantly slower in the cancer groups than in the healthy group (both p < 0.001), but not between the cancer groups. Age, living status and education were significantly associated with the FACT-Cog . The correlations between the FACT-Cog score and BSI score were strong (r = 0.60 p < 0.01), and between the HADS anxiety and depression scales were strong  but correlations were weaker between performance based cognitive tests and measures of psychological functioning. Breast cancer groups indicated more cognitive impairment and reduced psychological functioning compared to the healthy group. However, there was no differences between the breast cancer groups. Chinese breast cancer survivors experienced excess cognitive impairment not associated with usual ageing. Assessment and intervention to address cognitive impairment should be made available to breast cancer survivors.To identify cognitive function in Chinese breast cancer survivors. Research questions were: is cognitive function was associated with breast cancer and/or chemotherapy treatment and/or psychological functioning:? and did women with breast cancer experience more cognitive and psychological issues than age-matched women without cancer? Breast cancer survivors with chemotherapy , breast cancer survivors without chemotherapy  and matched healthy controls  completed a battery of cognitive and psychosocial functioning. Demographic characteristics were also collected. The Perceived Cognitive Impairment score for cancer groups was significantly higher than for the healthy group ( Breast cancer survival is relatively high although treatments such as surgery, chemotherapy (CT), radiation therapy, endocrine therapy, and specific biological therapies can have side effects that impact on quality of life for breast cancer survivors3. One of the side effects of breast cancer treatment is cognitive changes4. This was first reported towards the end of the 1990s5, where cancer patients complained of impaired cognitive function after treatment. Cogntive functions affected include impaired memory, attention, speed of processing, and word finding, prompting researchers to assess cognitive function particularly in breast cancer survivors receiving chemotherapy. Chemotherapy was believed to be the main cause of cognitive impairment in cancer patients. A prospective, longitudinal, national multi-center study conducted by Janelsins et al.6 completed FACT-COG evaluations of breast cancer survivors and age-matched non-cancer controls before and after chemotherapy, and at 6-month follow-up, and found that patients with breast cancer who were treated in the community reported more cognitive difficulties up to 6 months after treatment with chemotherapy compared to age-matched noncancer controls. Cognitive difficulties can persist and in a study by Koppelmans et al.7, the cognitive performance of breast cancer survivors who had received chemotherapy more than 20 years earlier was still significantly worse than that of healthy controls.Breast cancer has been reported as the most common global cancer accounting for 11.7% of new cases8.Imaging studies have identified structural brain differences in breast cancer survivors who have had chemotherapy and those who have not, and between breast cancer survivors and healthy controls. Cerebral white matter seemed to be particularly vulnerable to the effects of chemotherapy. For example, abnormal microstructural changes in white-matter tracts concerned with cognition were discovered 10 years after completion of chemotherapy in breast cancer survivors compared to those who did not have chemotherapy9 showed that 22% and Wefel et al.10 found 21% of breast cancer patients had cognitive impairment before chemotherapy, suggesting that the cancer itself, surgery or menopause may be implicated. In another prospective study11 the correlation between chemotherapy and cognitive impairment was less clear with different subgroups emerging over the follow-up period of chemotherapy treatment being completed—some with a decline in cognitive function that was persistent or improved, and some with no effects. Authors also found that cognitive deficits were unrelated to anxiety or depression, and that chemotherapy cognitive outcomes were not related to menopausal status. Similarly, McDonald et al.12 found that cancer groups had decreased frontal hyperactivation compared with controls at 1 month after completion of chemotherapy. Ahles et al.13 analyzed the results of multiple longitudinal studies and proposed two hypotheses. The first is that the biology of cancer may contribute to lower cognitive performance; and the second that common risk factors for breast cancer and cognitive changes may exist13. In subsequent studies, cognitive manifestations are often referred to as cancer-related cognitive impairment (CRCI)14.Cognitive impairment presents as what is often referred to as "chemo brain". However, some prospective studies have found that cognitive impairment may predate chemotherapy. For example, Ahles et al.15. Urban areas of China recorded the highest incidence and mortality rates for breast cancer, and the most common type of breast cancer diagnosis was invasive ductal carcinoma. The incidence of breast cancer is expected to continue to rise in China15. Only two studies were located that have investigated cognition in Chinese populations of women with breast cancer: Zheng et al.16 conducted a prospective study of cognitive functioning in newly diagnosed primary breast cancer survivors taking measurements at 18 months and at 3 years post diagnosis, using a battery of assessments including the Logical Memory Subtest from the Chinese Version of the Wechsler Memory Scale , the Chinese Version of the Category Fluency Test (language/executive function), and the Chinese Version of the Stroop Test (attention/executive function). Results indicated a significant improvement in attention and executive function, immediate memory and delayed memory significantly from 18 to 36 months after breast cancer diagnosis. This study did not measure cognitive functioning prior to 18 months post breast cancer diagnosis, did not include a control group, did not compare participants who received chemotherapy and those who did not, and only included survivors in one geographical area of China. Li17 conducted a cross sectional study with breast cancer survivors from one medical centre immediately post surgery using self-reported measurs of cognition and psychological status, and also did not include a control group or comparison group of participants with and without chemotherapy. Neither study used the tools recommended by the International Cognition and Cancer Task Force18 which were the Hopkins Verbal Learning Test-Revised (HVLT-R), the Trail Making Test (TMT), and the Controlled Oral Word Association (COWA) test.However, most of this evidence has come from developed countries. Breast cancer was the most common cancer and the fifth contributor to cancer mortality among women in China in 2015Therefore, this study addressed some of the limitations of existing studies by collecting data across China, using a control group, comparing chemotherapy and non-chemotherapy groups and using both self-reported and performance based measures. The aims of this study were (1) to explore the cognitive functioning of Chinese breast cancer survivors within 6 months of diagnosis, (2) to compare cognitive functioning of breast cancer survivors who had chemotherapy, with those who did not have chemotherapy and with aged matched healthy Chinese women, (3) to explore the relationship between cognitive functioning and psychological functioning, and (4) to compare cognitive performance scores from tools using self-report, and tools using performance of cognitive tasks.A multicentre, cross-sectional study was conducted that explored the associations between breast cancer treatment with chemotherapy or breast cancer treatment without chemotherapy and CRCI in female breast cancer survivors who had treatment at one of five centres in China. These results were compared with age matched health controls at each centre. The five Chinese centres were, Jing 'an central hospital in Shanghai, Cancer hospital in Yunnan, Central hospital in Zhengzhou, Xiangya hospital in Hunan and Zhuangzu autonomous region people's hospital in Guangxi Fig. .Figure 1The participants were recruited from the centres, into three groups. Group A was defined as the breast cancer with chemotherapy group, Group B was defined as the breast cancer with no chemotherapy group, and Group C was defined as the age-matched healthy group. Controls were obtained from the same source population in each centre, and they could be family members or friends of the participants, as well as unrelated participants such as nursing assistants and healthy women recruited from the community. All participants completed the same assessments that included demographic information, cognitive function, and psychological states.This study was a co-operative study between Huashan hospital affiliated to Fudan university and the University of Sydney. Institutional review boards at the Jing 'an District Central Hospital of Huashan hospital affiliated to Fudan university and the University of Sydney (ID: 2019/722) approved the study before participants enrolled. All research was performed in accordance with relevant guidelinesand regulations, written informed consent was obtained from all participants. Chinese researchers from five centres were trained by two researchers from the University of Sydney (LM and SZH) about how to complete the outcome measures. The training was focused on the assessment of cognitive and psychological functioning.Women who were due for breast surgery at their respective hospitals were recruited by researchers. To ensure privacy, interested participants were presented with the entire study protocol and the informed consent procedure in a separate meeting room. Recruited participants were asked to sign the informed consent which was reviewed by the investigator. Participants were free to withdraw from the study at any time as highlighted in the informed consent information. Participants were assessed by the same researcher in a separate room throughout the process.The inclusion criteria for Group A were: (1) Female breast cancer survivors undergoing their first treatment with systemic chemotherapy or had completed it. (2) Between 25 and 75 years of age at time of diagnosis; (3) Fluent in Chinese and able to read and write; (4) Able to understand the content of the study and agrees to participate in this study. The exclusion criteria for Group A were: (1) Diagnosis of a neurodegenerative disease; (2) Use of psychotropic medication; (3) Previous history of cancer; (4) Neurobehavioral risk factors including history of neurological disorder ; (5) Moderate to severe head trauma.The inclusion criteria for Group B were: (1) female breast cancer survivors with a first diagnosis before chemotherapy or had not received it; (2) between 25 and 75 years of age at time of diagnosis; (3) fluent in Chinese and able to read and write; (4) Able to understand the content of the study and agrees to participate in the study. These patients were screened with the same exclusion criteria as Group A.p =  < 0.001, the sample size should be 75 for each group.Healthy controls were recruited age matched to the ages of the breast cancer participants. The exclusion criteria for the Group A were adapted to them. All study participants provided written informed consent to participate. The sample size for each group was calculated using the ABS sample calculator. Using the estimation of the total population of breast cancer, an alpha of 0.05% and a confidence level of 95% and An investigator-developed questionnaire was used to collect demographic information including age, Body Mass Index (BMI), living status, education, job, and medical information including type of cancer, stage of cancer, radiation therapy status, hormone therapy status, targeted therapy status and any other complications. Medical information was validated by a medical record review by researchers.19, The Hopkins Verbal Learning Test (HVLT)20, The Trail Making Test (TMT)21, the Hospital Anxiety and Depression Scale (HADS)22 and the Brief Symptom Inventory23. Two of the assessments were in-person neuropsychological tests, The Hopkins Verbal Learning Test (HVLT) and the Trail Making Test (TMT), which were administered by trained researchers. The assessments took approximately 40 min to complete.Eligible participants consented and completed a survey/questionnaire that assessed demographic and medical information, cognitive functioning and psychological status  and negatively expressed sentences . The FACT-Cog produces a total score and four subscale scores including:(1) Perceived Cognitive Impairments (CogPCI), (2) Impact of Perceived Cognitive Impairments on Quality of Life (CogQOL), (3) Comments from Others (CogOth), and (4) Perceived Cognitive Abilities (CogPCA). Participants rate items on the CogPCI and CogOth scales on a five-point Likert scale  to show the frequency of each event had occurred in the past 7 days. The CogQoL and CogPCA scales are rated on five-point scales  to show the extent to which each event had occurred in the past 7 days. Positively expressed sentences items (for PCA) are reversed to be calculated, so that lower scores reflect fewer cognitive impairments and better QoL.The Functional Assessment of Cancer Therapy-Cognitive Function, FACT-Cog (Version 3) was used to measure self-reported cognitive functioning and consists of 37 items designed to assess cognitive complaints. The application of the Chinese version of FACT-Cog has been tested with good reliability and validityImmediate and delayed verbal memory, speed of processing, and executive function were measured using the Hopkins Verbal Learning Test (HVLT) and the Trail Making Test (TMT).25. Having six versions also allows the HVLT to be used with participants at frequent intervals26.The Hopkins Verbal Learning Test (HVLT) has six different forms that each consist of a 12-item word list, composed of four words from each of three semantic categories . The participant is asked to listen to the word list read by the examiner and then tries to memorize the words. The word list is read to the participant at the rate of one word every 2 s. Words recalled by the participant are recorded. The same procedure occurs for two more trials. After the third learning trial, the participant is read 24 words. consisting of 12 distractors  and 12 items that were on the list, and is asked to say “yes” or “no” to identify each word on the original list. The Discrimination Index represents a measure of delayed memory and is calculated by the true positive minus the false positive. The HVLT has many advantages such as the time is takes to administer (10 min), it can be used with moderately and severely cognitively impaired participants and does not appear to have a ceiling effect in normal subjects21. The TMT has two parts (A and B), both of which are timed. The TMTA asks participants to connect the 25 numbers on the paper in order (processing speed). TMTB asks participant to connect the 25 numbers in order with shapes on the page (boxes and circles) to measure executive ability by connecting the circles quickly without lifting the pen from the paper.The Trail Making Test (TMT) can identify mild cognitive impairment. The TMT is often used in English-speaking countries because of the use of the English alphabet on the TMT-B. The revised Chinese version was used for measurement in this study22. The HADS consists of 14 self reported items scored from 0 to 3. The anxiety and depression scales are scored on scales of 0–21. Higher total scores indicate higher levels of anxiety and depression. The Chinese version of the HADS has acceptable internal consistency and validity and was evaluated as a reliable tool for the assessment of cancer survivors22.The Chinese Hospital Anxiety and Depression Scale (HADS) was used to measure depression and anxiety23. The Brief Symptom Inventory (BSI) consists of items covering nine symptom areas: somatization, obsession-compulsion, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation and psychoticism; and produces three indicators of distress: global severity index (GSI), Positive symptom distress index (PSDI), and positive symptom total (PST). These indicators measure current or past levels of symptomatology, intensity of symptoms, and the number of reported symptoms. Respondents rank each feeling item  on a 5-point scale ranging from 0  to 4 (extremely) in terms of any distress over the past 7 days. The GSI is considered the most sensitive indicator of the participant distress. The PST adds the items with non-zero responses and calculates the number of symptoms experienced. The PSDI is the sum of the values of the items receiving non-zero responses divided by the PST. This index provides information about the average level of distress experienced.. Psychological assessment was conducted using and Chinese Brief Symptom Inventory (BSI), identifing self-reported psychological symptomsData related to self-reported cognitive and psychological measures were analysed using SPSS for Windows (version 25). Characteristics of the sample were assessed with descriptive statistics including mean (M) and SD or frequency distributions. In comparing subject characteristics, cognitive and psychological measures among three groups used analysis of variance (ANOVA) for continuous variables, and chi-square or Fischer exact tests were used for testing differences in categorical measures. Pearson’s correlation coefficient was used to examine associations between the total score and four subscale scores of the FACT-Cog, with cognitive performance tests and psychological measure scores. To further explore associations between the FACT-Cog subscale scores of PCI and PCA and other measures of cognitive functioning, we obtained partial correlations, controlling for the covariates of age, depressive symptoms.Informed consent was obtained from all individual participants included in the study. No individual data was presented so consent to publish individual data was not required.A total of 302 women were recruited for the study. There were 106 in Group A (breast cancer survivors with chemotherapy), 100 in Group B (breast cancer survivors with no chemotherapy) and 96 in Group C (non-cancer controls), and these were evenly recruited across the five recruitment centres across China. Table p < 001). There were more participants with cancer that were not working compared to the healthy controls (p < 001). Among the cancer participants, 38.8% of the chemotherapy group (A) and 40.7% of the non-chemotherapy group (B) were not working, compared to 19.6% of the healthy controls. Most breast cancer survivors had invasive ductal carcinoma (IDC), accounting for 83% of participants. Early breast cancer (0–II stage) was most common, accounting for 82.9% of participants. There were significant differences in BMI, living arrangement, education, disease type, symptom stage, other symptoms, radiotherapy, endocrine therapy and radiotherapy among the three groups of participants.Of the 302 participants included for analysis, the 106 women in Group A were aged 36–75 years , the 100 women in Group B were aged 29–71 years  and the 96 women in Group C were aged 31–73 years . Participants were fairly balanced on age and living arrangements , except there were significantly more high school–educated participants in the healthy group compared with the breast cancer groups (p = 0.014), living arrangements (p = 0.049) and education (p = 0.003) (see Table p < 0.01). There was also a difference between participants with moderate and low levels of education and those with high levels of education (p < 0.01). Participants living alone had higher cognitive impairment.The FACT-Cog total scores across groups were compared and results showed that there were statistically significant differences in the FACT-Cog total scores of participants of different ages (p = 0.04), and the PCI score of Group B was also significantly worse than the healthy group (p = 0.032).The FACT-Cog results indicated that the total scores and sub-scores of the two cancer groups (chemotherapy and no chemotherapy) were similar and worse than those in the healthy group (see Table p < 0.01). However, there was no significant difference in scores between the two cancer groups (p = 0.311) For the delayed memory test component, the accuracy of the two groups of breast cancer patients was lower than that of the healthy group, and the error rate was higher, but the difference was not significant (p = 0.311)  compared to the healthy group (9.6%). Executive ability (4.8%) in the chemotherapy group and 4.0% in the non-chemotherapy group was worse than that in the healthy group (1.1%) . PCA also had a low correlation with processing speed and executive ability —see Table The total FACT-COG score, PCI and Oth subscales had a low correlation with performance-based cognition (HVLT and TMT) (r = 0.16–0.20p < 0.01), and between the HADS anxiety and depression scales were strong  indicating that symptom report and anxiety and depression were higher amongst breast cancer survivors reporting cognitive impairment  scores of the chemotherapy breast cancer group and the breast cancer group without chemotherapy were higher than those of the healthy group indicating that cognitive function was impacted by breast cancer. However, there was no significant difference in PCI scores between the chemotherapy group and the non-chemotherapy breast cancer group suggesting that chemotherapy may not be a significant factor. The HVLT fast memory and delayed memory scores of participants in both the chemotherapy and non-chemotherapy groups were also significantly worse than that of healthy women which is consistent with previous findings where even long term, cognitive performance can remain significantly worse for breast cancer survivors30.Cognitive changes associated with cancer and cancer treatment are complex. They are also an important issue for cancer survivors31. This review concluded that it is still uncertain if cognitive impairments are a result of treatment for breast cancer, the cancer itself, or psychological factors31.It is important to acknowledge that results indicate that chemotherapy alone may not be a significant factor in the cognitive dysfunction of breast cancer survivors. These results provide some direction for further exploration of the mechanisms of CRCI such as exploring other aspects of breast cancer presentation that could contribute to CRCI. This is consistent with reviews on the role of chemotherapy and cognitive impairment, especially with breast cancer survivors who are also commonly exposed to surgery, aneasthesia, radiotherapy and hormone treatments which can also have cognitive repercussions32.Breast cancer survivors with lower cognitive performance scores did not necessarily self-report more cognitive problems. This suggests that they were not aware of any changes in cognitive abilities since the diagnosis of cancer, or that they have compensated for their deficits. A lack of a relationship between self-report of cognitive functioning and performance on neuropsychological testing is commonly reported. It may be that breast cancer survivors with CRCI can still perform well on formal performance measures, but it may take a lot more cerebral effort to do so, which they are aware of and report on self-report measures. Breast cancer survivors commonly self-report higher levels of cognitive problems compared to healthy controls that may not be identified on formal testing33 investigated the relationship between self-reported cognition and performance-based cognitive assessment. In 29 cases of breast and colorectal cancer, they found no association between them. This study may have been limited by the small sample size. In a recent review article, Hutchison et al.34 reviewed 24 studies and found that only eight studies noted a correlation between self-reported cognitive function and performance-based cognitive function, and the results of these studies were inconsistent. The authors speculate that these inconsistencies may be due to the different self-report and performance-based assessment methods used in the studies, and the lack of assessments that use functional activities that are ecologically valid . Some studies have confirmed the relationship between self-reported cognitive functioning and performance on neuropsychological testing, but few studies in China have jointly assessed both and analysed the correlation between them34.Findings indicated that the FACT-Cog scale and subscale scores including PCI, OTH, PCA, and QOL were associated with fast memory, delayed memory, and executive function. Vardy et al.35. Neuropsychological tests usually assess performance at a single time point, whereas self-report measures assess performance over more extended periods , and in different settings. Additionally, neuropsychological assessments usually have to be administered in quiet and structured conditions, which is an artificial setting compared to a real-life environment32. Therefore, it is recommended that more comprehensive tests be used to detect subtle cognitive impairments in breast cancer survivors.It could be that self-report and performance-based tests of cognitive function assess performance at different periods of time, or even assess different aspects of cognition which may explain inconsistencies36. There are two possible reasons. One is that the negative emotions caused by breast cancer affect the overall function of the patient, including cognitive function. The other is that the decline of cognitive function is accompanied by the patient's emotional loss. The relationship between the two is unclear. In addition, we used a broader range of emotional problems to analyse the accompanying emotional problems not only anxiety and depression. Obsession-compulsion and depression were highly correlated with self-reported cognitive function.We conducted a correlation analysis of cognitive function and psychological functioning (as measured by the HADS and BSI) and found that the correlation between self-reported cognitive complaints and psychological functioning for breast cancer survivors was higher than that between performance-based cognitive performance and psychological functioning. This finding is consistent with previous researchFinally, we recommend using a multi-factorial model of cognitive decline to ascertain any potential relationships between CRCI and measures of cognitive functioning. We explored the effects of demographics, such as age, and cancer treatment on cognitive function. The study found that age, type of living situation, and level of education were associated with cognitive decline, while emotions as measured by the HADS and BSI also interfered with cognitive functioning.Despite being a multi-centre study the participants were mainly from urban areas, so results cannot be generalized to the cognitive function and psychological status of women with different socio-economic backgrounds and those who live in rural areas. As this was a cross-sectional study it was not possible to determine if cognitive impairments detected were persistent or if they improved over time. A future longitudinal study would be able to investigate this further. Breast cancer survivors may be exposed to several drugs with side effects during the 6 months after diagnosis and it is not clear which specific components of chemotherapy amy be related to cognitive changes. Finally, the control group had a higher level of education compared to the treatment groups. This may or may not have had an impact on the cognitive functioning results in this study.This study found that cognitive impairments (whether measured by self-report or performance) were associated with Chinese breast cancer survivors compared to healthy controls, however, these cognitive impairments were not always significantly different between breast cancer survivors who had received chemotherapy and those who had not. Cognitive impairments (both self-reported and performance-based) were associated with psychological concerns such as anxiety and depression. It appears that all breast cancer survivors need to be screened for cognitive changes and offered interventions to assist them with everyday functioning. It is unclear if Chinese breast cancer survivors have different experiences of cognitive impairment compared to studies of other populations. Qualitative studies using in-depth interviews would be helpful to understand the experience of participants with a high degree of cognitive impairment and to determine the nature of any psychosocial distress associated with cognitive functioning in this group of breast cancer survivors.Supplementary Information.


Numerous reviews of the epidemiology and risk factors for breast cancer have been published previously which heighted different directions of breast cancer.The present review examined the likelihood that incidence, prevalence, and particular risk factors might vary by geographic region and possibly by food and cultural practices as well.A systematic review (2017-2022) was conducted following Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, reporting on epidemiological and risk factor reports from different world regions. Medical Subject Heading (MeSH) terms: “Breast neoplasm” “AND” country terms such as “Pakistan/epidemiology”, “India/epidemiology”, “North America/epidemiology”, “South Africa/epidemiology” were used to retrieve 2068 articles from PubMed. After applying inclusion and exclusion terms, 49 papers were selected for systematic review.Results of selected articles were summarized based on risk factors, world regions and study type. Risk factors were classified into five categories: demographic, genetic and lifestyle risk factors varied among countries. This review article covers a variety of topics, including regions, main findings, and associated risk factors such as genetic factors, and lifestyle. Several studies revealed that lifestyle choices including diet and exercise could affect a person’s chance of developing breast cancer. Breast cancer risk has also been linked to genetic variables, including DNA repair gene polymorphisms and mutations in the breast cancer gene (BRCA). It has been found that most of the genetic variability links to the population of Asia while the cause of breast cancer due to lifestyle modifications has been found in American and British people, indicating that demographic, genetic, and, lifestyle risk factors varied among countries.There are many risk factors for breast cancer, which vary in their importance depending on the world region. However, further investigation is required to better comprehend the particular causes of breast cancer in these areas as well as to create efficient prevention and treatment plans that cater to the local population. Breast cancer (BC) is a major public health issue that affects women all over the world. It is the most often diagnosed cancer and the second biggest cause of cancer-related deaths among women globally . Breast Understanding and treating carcinoma of the breast on a global basis depends heavily on epidemiology. Breast cancer is the most prevalent kind of cancer in women globally, and its effects on people’s health as well as the general population cannot be overstated . We can Globally women have been affected by several types of breast cancer, which are differentiated based on hormone levels, aetiology, clinical screening and availability of various treatment options. Commonly, invasive breast cancer types are classified into estrogen receptors (ER), progesterone receptors (PR) and human epidermal growth factor 2 (HER2). In Asia, the incidence of hormone-positive BC is relatively high as compared to other regions .Determination of risk factors involved in the progression of breast cancer is especially important. Genetic factors such as gene mutations and family history are major threats to the development of cancer in first-degree relatives. Numerous biological processes, including histone modifications, polycomb/trithorax protein complexes, short non-coding or antisense RNAs, and DNA methylation, mediate epigenetic events. These various adjustments are intricately linked. The ability of genes to be expressed throughout typical stages of development is closely conditioned by epigenetic control . HistoneChemicals known as endocrine disruptors prevent the endocrine system, which is in charge of producing and controlling hormones in the body, from operating normally. These substances have the potential to imitate or obstruct natural hormones, resulting in hormonal imbalances and possibly harmful consequences on health . IncreasSystematic review of the literature utilizing PubMed was performed according to PRISMA 2020 guidelines  Figure, 19. PubAll publications were retrieved from PubMed in September 2022, with Medical Subject Heading (Mesh) Terms; a new and thoroughly revised version of lists of subject headings compiled by the National Library of Medicine (NLM) for its bibliographies and cataloging. The Mesh term “Breast neoplasm” was used with the Boolean operator “AND” and other related Mesh Terms related to regions/country names and “Epidemiology” to search all the records available from 2017 to 2022.A detailed list of retrieved articles related to BC epidemiology based on risk factors was collected for quantitative analysis. The initial screening was based on the title and abstract, while the final inclusion was based on full texts where available. EndNote software was used to combine and sort out duplicated articles based on the inclusion and exclusion criteria. All authors reviewed the retrieved articles and included only those articles, which were fulfilling the following conditions.Full-text articles published in PubMed Indexed journals, indexed with Mesh Terms as stated above.Abstracts, short commentaries, and studies focusing on treatment, and/or in languages other than English were excluded. Systematic reviews and letters to the editors were not included in this review. Qualitative studies regarding treatment therapies, survival rates, and diagnostic irregularities were excluded because of their inappropriate focus on the aim of our review.The first authors of this manuscript independently performed data extraction. All disagreements were discussed and resolved by all other authors in this study. The following data taken from each article was entered into a spreadsheet: Study reference, year published, study design, study region and risk factors.Three investigators independently rate the quality of included study as good, fair or poor. Final ratings were determined by consensus among all reviewers, only those studies rated as good or fair were included.An extensive search was conducted in PubMed using advanced search strategies to identify articles related to breast neoplasms in different regions. The search terms utilized were “breast neoplasm” and “Pakistan/epidemiology”, which resulted in 33 articles being extracted. After a rigorous process of inclusion and exclusion criteria, 11 articles on prevalence studies were selected for further analysis.Similarly, the search terms “breast neoplasms” and “India/epidemiology” were used, resulting in 63 articles being extracted. Out of these, 14 articles were deemed suitable for epidemiological studies after applying the selection criteria. The search terms “breast neoplasms” and “North America/epidemiology” produced a total of 883 articles, and 11 of these were selected for the study. The search terms “breast neoplasms” and “South Africa/epidemiology” produced 181 articles, with 6 being selected. Finally, the search terms “breast neoplasms” and “Israel/Turkey/Central Asia/Bangladesh/UAE/Saudi Arabia/Europe/Epidemiology” produced 908 articles, and 21 were selected for the study.The above results demonstrate the comprehensive nature of the literature search and thorough application of the inclusion and exclusion criteria.An initial search in PubMed utilizing MeSH terms (described above) resulted in the extraction of 2068 articles. Duplicate articles (n=193) were removed, leaving 1875 articles for further review. The remaining articles were evaluated by examining their titles and abstracts, and after applying the selection criteria, 49 studies were included in the present review, as shown in the PRISMA flow chart Figure 1In the Asian region, studies have found that breast cancer incidence rates are higher in Asian Indian and Pakistani Americans than in non-Hispanic white Americans . Risk faIn Africa, inherited mutations in the BRCA1/2 genes have been found to be a significant issue among Nigerian women . Low vitIn the USA, studies also identified risk factors such as use of hair dye and chemical straightener , unhealtCertain occupations and industries, such as healthcare and the service sector, are also associated with increased risk . Weight In Europe, studies have found that joint tobacco smoking and alcohol intake , occupatIn Israel, studies have found that breast cancer incidence is increasing among younger women . GeneticThe risk factors for breast cancer are subdivided into demographic, genetic, hormonal, lifestyle, and other categories as shown in With a frequency of 40, hormonal variables are the most common risk factor for breast cancer in Africa. With a prevalence of 20 each, genetic, demographic, and other  factors are also significant. In America, lifestyle factors are the most significant risk factor for breast cancer, with a prevalence of 80. Genetic, demographic, and hormonal factors also contribute, with a prevalence ranging from 20 to 40.In Europe, other factors such as oxidative stress, infections, diseases, and occupations have the highest prevalence, with a prevalence of 80. Hormonal, genetic, demographic, and lifestyle factors also play a role, with a prevalence ranging from 20 to 60.In Israel, genetic and demographic factors have an equal prevalence of 40, followed by hormonal and lifestyle factors with a prevalence of 20 each. Other factors have a prevalence of 20.Patients with breast cancer have multiple risk factors associated with their disease . DependiA person’s demographic group or a particular subset of the population can have an impact on the occurrence, distribution, assessment, and management of breast cancer through certain characteristics. Demographic considerations can shed light on the patterns and trends in the incidence of breast cancer in various communities . Age, weBreast cancer risk is heavily influenced by hereditary variables, and several genetic variants are known to dramatically enhance the risk of developing the illness. BRCA1 and BRCA2 gene mutations are the most well-known genetic changes linked to breast cancer. These genes are crucial for preserving the stability of the genetic material in the cell since they are involved in mending damaged DNA. The chance of developing breast and ovarian cancers is considerably increased by inheriting a deleterious mutation in either the BRCA1 or BRCA2 gene . An assoA complex interaction of factors, including genetics, way of life, socioeconomic circumstances, healthcare infrastructure, and cultural beliefs, characterizes the epidemiology of breast cancer in Africa. The female hormones progesterone and estrogen can affect the development of breast tissue and cells, and both their levels and activities are linked to an increased risk of breast cancer. A hormone called estrogen promotes the growth and upkeep of female reproductive tissues. High amounts of estrogen or continuous exposure to estrogen can raise the likelihood of breast cancer because it can encourage cell development in the breast. Imbalance of hormonal profile in the female population is the major risk factor for developing BC . ProlifeSusceptibility of inherited mutations in America and Africa is modest however; nearly one-third of the female population of Europe and Israel is under threat of BC progression due to genetic mutations Figure 2Lifestyle modifications impart beneficial effects on women’s health. Women who are exposed to smoking, containing toxic aromatic compounds and consuming alcohol, are more prone to developing breast cancers , 63. PhyBased on race and ethnic origin, the incidence rate is higher in black women as compared to white women . A recenObesity is linked with majority of chronic diseases including breast carcinoma. A higher risk is observed in menopausal women who are obese and have a sedentary lifestyle as compared to the females having normal BMI . UnhealtAir pollution, drug abuse and infections have a deleterious influence on the European population that affected 80% of the population Figure 2In addition to physical workouts, a healthy diet and consumption of essential vitamins reduce the risk of BC. Several studies have shown that intake of vitamin D with treatment has positive outcomes in cancer patients thus slowing the progression of the disease whereas its deficiency can increase the BC risk . MoreoveQuercetin (QCT), a flavonoid derived from many fruits and vegetables, is endowed with manifold biological properties, such as the ability to elicit a strong inhibitory effect on the growth of several tumor cell lines . QuercetObesity and breast cancer have a complicated and varied association. Insulin resistance and persistent low-grade inflammation are both linked to obesity. These elements can foster a body environment that is conducive to the growth of breast cancer . Due to In Israel, breast cancer is by far the most prevalent type of cancer among women. Because of variables like longer life expectancies, altered reproductive habits, and lifestyle choices, prevalence rates have been continuously increasing. The mortality rate has been declining, nevertheless, in part because of breakthroughs in therapy, early detection, and screening techniques. Women between the ages of 50 and 74 can receive mammograms through Israel’s national breast cancer screening program. The goal of this initiative is to identify breast cancer early, when it can be treated more successfully. The decreasing mortality rates have been attributed to routine screening and early diagnosis . ParticuThis paper has reviewed studies of incidence, prevalence, and risk factors for breast cancer in India, Pakistan, Kazakhstan, Turkey, USA, Europe and the United Arab Emirates. The evidence shows that diet, obesity, and genetic factors, such as BRCA mutations and DNA repair gene polymorphisms vary from region to region. These findings emphasize the need to be aware of the particularities of each region of the world with respect to breast cancer risk. This will facilitate early detection and improve prognosis. Our study provides valuable insights into the epidemiology, risk factors, and outcomes of breast cancer in various populations and highlights the need for further research and intervention efforts to reduce the burden of breast cancer in these regions.Not all world regions were included and study methodologies varied. In addition, the traditions, customs, and genetic backgrounds of the residents of different geographic regions is only superficially known. Thus, more specific research is needed that specifically target distinct populations or examine particular risk factors in order to enhance the comprehensiveness and accuracy of findings.The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding author/s.AR played a major role in the literature search, collecting and screening of abstracts, full-text articles, data extraction and quality assessment of the included studies and prepare the first draft of the manuscript, AK provide the Idea and design for systematic review, supervised and guided AR in data collection and reporting and review and revised the manuscript. FA and ZA: screening of abstracts, full-text articles, data extraction and quality assessment of the included studies and revision of the manuscript. MA, MK, MS, RA: critical revision of manuscript and resolution of discrepancies. All authors made significant intellectual contributions and approved the final version of the manuscript.


Leisure sedentary behavior (LSB) is associated with the risk of cancer, but the causal relationship between them has not been clarified. The aim of this study was to assess the potential causal association between LSB and risk of 15 site‐specific cancers.The causal association between LSB and cancer were assessed with univariate Mendelian randomization (UVMR) and multivariate Mendelian randomization (MVMR). 194 SNPs associated with LSB  were adopted as the instrument variables. Sensitivity analyses were performed to ensure the robustness of the results.p = 0.04) ，breast cancer  . Although causal association was not found between television watching and ovarian cancer, it was seen in low grade and low malignant potential serous ovarian cancer . However, significant results were not obtained in the UVMR analysis between driving, computer use and the 15 types of cancer. Further MVMR analysis indicated that the above results are independent from most metabolic factors and dietary habits, but mediated by educational attainment.UVMR analysis revealed that television watching significantly increased the risk of endometrial cancer (OR = 1.29, 95% CI = 1.02–1.64, LSB in form of television watching has independent causal association with the risk of endometrial cancer, breast cancer, and ovarian cancer. The detailed information of these studies is shown in Table 2.3F‐statistic, equal to (n–2)R2/(1–R2), can be used to evaluate the effect of weak instrumental variables.R2 was the degree of sedentary behaviors explained by the SNP, and was calculated using the formula R2 = 2 × (1 – EAF) × EAF×β2/(se2 × n) . Weak instrument bias is considered when F > 10. In addition, variation between individual genetic variant (I2GX) was calculated to assess potential weak instrument bias in the MR‐Egger regression analysis. Low risk of measurement bias was considered at I2GX > 95%.Weak instrument bias refers to bias produced by genetic variants with low power to explain exposure owing to insufficient sample size. According to the literature, the 2.4β‐value   was also calculated, and heterogeneity was considered as significant if I2 > 25%.p‐value for outcomes should be excluded, or random‐effects models and weight‐median are more appropriate for estimating MR effect , weighted‐median, MR‐Egger, MR‐PRESSO, and weighted‐mode, were performed to account for presence of horizontal pleiotropy, heterogeneity, potential violations of the MR assumptions, and invalid instrument‐exposure associations Figure .30, 3Horizontal pleiotropy, outliers, and heterogeneity were visually analyzed by using scatterplots, leave‐one‐out plots, and funnel plots.p‐value threshold, we added genetic variants for sedentary behavior with higher p‐value  and repeated the MR analysis.To determine if the results were robust to the 2.5GWAS studies of LSB have revealed some degree of genetic association between sedentary behaviors and other traits including triglycerides, total cholesterol, HDL, LDL, BMI, T2D, and educational attainment.To investigate whether the effect of LSB on cancers may be mitigated by its effect on other traits and prevent potential MR assumption violations, MVMR was used to estimate the direct effect of multiple exposures on different outcomes Figure .40 The2.6p < 0.05. Correction for p‐value was made using the Bonferroni method in the secondary analysis of LSB on metabolic factors (p < 0.05/9 = 0.006). Associations were considered suggestive at 0.006 < p < 0.05. Causal associations were estimated using the odds ratio (OR). All analyses were finished using the R package TwoSampleMR (version 0.5.6). Data analyses were conducted between January 2021 and June 2022.Primary univariate and multivariate analyses of the relationship between LSB and cancers were conducted with a two‐sided significance threshold of 33.1F‐value of IVs related to television watching, computer use, and driving ranged from 23.94 to 144.19, 24.07 to 79.56, and 24.00 to 45.01, respectively. None of them were considered to have weak instrument bias (F > 10)  Table . As calc3.2Complete results of UVMR of the association between 3 sedentary behaviors and 15 site‐specific cancers are summarized in Figure p = 0.010). It occurred in endometrioid subtype  rather than non‐endometrioid subtype  (Table p‐intercept >0.05). However, there was significant heterogeneity except for non‐endometrioid subtype  increase in watching television increased endometrial cancer risk by nearly 30%  Table . There wpe Table . After cp = 0.007) in the IVW fixed‐effects analyses. The association appeared to be stronger for the estrogen receptor negative (ER–) subtype  than for estrogen receptor positive (ER+) subtype   Table . In the 5) Table , S6. Giv5) Table . In addip = 0.018) without significant pleiotropy and heterogeneity   Tables . This asAside from the above results, television watching made no significant difference to the other 11 cancers. There was no evidence of horizontal pleiotropy or heterogeneity except for prostate cancer. But even when we used control analyses to correct the heterogeneity, no significant results were obtained Tables .3.3p = 0.019) and cervical cancer . Both of the latter findings, however, were supported by only one MR method, whereas all other methods consistently showed null results for computer use and endometrial and cervical cancer , TC , LDL , and T2D , while negatively associated with HDL  and years of schooling . For dietary habits, TV viewing was a risk factor of most unhealthy dietary habits , but was negatively associated with other dietary habits . Computer use was showed to be positively associated with years of schooling  and intake of cereal . However, there were no significant associations between computer use and all metabolic factors as well as all unhealthy dietary habits. As for driving, it was only showed to be associated with year of schooling   Tables . InteresFor endometrial cancer, adjustment of BMI, T2D, and educational attainment made the previously causal association non‐significant, indicating that BMI, T2D, and educational attainment were mediators in this association. Adjustment of BMI also indicated it as the mediators in the association between television watching and low‐grade serous ovarian cancer. Besides, only educational attainment changed the association between television watching and breast cancer, indicating it as a mediator Figure .4Our study explored the causal relationship between LSBs and 15 site‐specific cancers, using MR analyses. After complete sensitivity analyses, the associations were converged on three common female cancers – endometrial, breast, and ovarian cancer. Furthermore, the associations varied among subtypes of the three female cancers. However, computer use and driving were not seen as risk factors for the selected cancers. Further MVMR analyses indicated the direct effect of television watching on the three female cancers. Given the high stability of the positive results, we considered television watching as a risk factor for endometrial cancer, breast cancer, and ovarian cancer. Whether computer use and driving were risk factors was not conclusive.We found that television watching increased the risk of endometrial cancer, especially in endometroid subtype. This is in accordance with the previous large meta‐analyses that found a consistent association between television watching and incidence of endometrial cancer.We indicated television watching as a risk factor for breast cancer. Contrary to our findings, no significant causal association was shown in two other meta‐analyses.We also demonstrated causal associations between LSB and ovarian cancer in line with a large meta‐analysis.Another key point to note is that causal effects of driving and computer use on the selected cancers were either non‐significant or unestablished in several sensitivity analyses. For driving trait, inadequate SNPs and low statistical power might be the reasons. For computer use, the follow‐up pre‐UVMR and MVMR analysis gave the possible explanation. Pre‐UVMR analysis revealed high genetic correlations between sedentary behaviors and educational attainment, which were negative for TV viewing and driving, and positive for computer use. In addition, causal associations were found between TV viewing and cardio‐metabolic factors as well as unhealthy dietary habits, which were insignificant for computer use. It seemed that people spending more time in using computer had higher educational attainment and healthier dietary habits. And education had been presumed to causally influence health because it generally confers greater access to salubrious resources such as economic security, healthy lifestyles, social ties, fulfilling jobs, a sense of personal control, and learned effectiveness.The multivariable MR analyses showed an effect of television watching on female cancer independent of most cardio‐metabolic factors and dietary habits. It also indicated vertical pleiotropy of BMI, T2D, intake of fizzy drink and biscuits, as the direct effects of TV viewing on cancers were attenuated compared with the total effects. This provides genetic insights in how sedentary behaviors are associated with cancers. LSB could cause increased snack intake and decreased energy expenditure, accompanied by weight gain and obesity, which could increase risk of cancer. Obesity facilitates carcinogenesis through a number of pathways, including insulin resistance, perturbations in the insulin‐like growth factor axis, and low‐grade systemic inflammation.The multivariable MR in which we corrected for education indicated pleiotropy due to education. Pre‐UVMR analysis in our study revealed correlations between sedentary behaviors and educational attainment in line with previous study. Besides, we have also verified the inverse association between educational attainment and sedentary behaviors in line with a previous study. This study also found that higher educational attainment levels were positively with vigorous physical activity levels and alcohol consumption.The strengths of our study are, initially, that it is the first to explore and find a causal association between sedentary behavior and three common female cancers using MR and that different associations were found within different subtypes with reliable evidence. The results suggest a potential role of LSB in cancer prevention. We may need to promote lifestyle changes that reduce sedentary time for the general public. Second, the instrumental variables used in this study were derived from a large GWAS with significant statistical power. In addition, this study carried out sensitivity analyses using several different methods to test the heterogeneity and pleiotropy of IVs and correct the directionality of causal associations. The test results confirmed one another, making the results more reliable. Finally, MVMR was performed on the positive results obtained from UVMR, which revealed the direct effect of LSB on the corresponding cancers.The first limitation of this study is that the population included in this study was European, and it is not clear whether the conclusions of the study can be extended to other ethnic groups. Second, SNPs obtained by statistical methods rather than biological methods inevitably have pleiotropy, for which we used multiple sensitivity analyses to minimize the impact of pleiotropy. We also performed MVMR to correct the effects of confounders. However, this does not rule out the effects of other unknown, potentially confounding variables. In addition, the acquisition of leisure sedentary time was based on subjective measurements rather than with measurement tools; although this avoided measurement bias, it was pointed out that subjective and objective measurement standards are non‐uniform.Jinwei Chen: Conceptualization (lead); data curation (lead); formal analysis (lead); visualization (lead); writing – original draft (lead). Kai‐Bin Yang: Conceptualization (lead); formal analysis (lead); visualization (lead); writing – original draft (lead). Youyu Qiu: Conceptualization (supporting); formal analysis (lead); visualization ; writing – original draft (lead); writing – review and editing . Weijie Lai: Data curation (lead); visualization . Sifan Qi: Data curation (lead); visualization (supporting). Gaoyuan Wang: Data curation ; visualization (supporting). Lin Chen: Data curation ; writing – original draft . Kunpeng Li: Data curation (supporting); writing – original draft . Dan Zhou: Data curation (supporting); writing – original draft (supporting). Qing Liu: Formal analysis ; visualization (supporting); writing – original draft (lead). Ling‐Long Tang: Formal analysis (supporting); visualization (supporting); writing – original draft . Xu Liu: Formal analysis (supporting); visualization (supporting); writing – review and editing . Xiao‐Jing Du: Conceptualization (lead); project administration (lead); writing – review and editing . Rui Guo: Conceptualization (supporting); supervision (lead); writing – review and editing . Jun Ma: Conceptualization (lead); funding acquisition (lead); project administration (lead); supervision (lead); writing – review and editing (lead).This study was supported by grants from the National Natural Science Foundation of China , the Key‐Area Research and Development Program of Guangdong Province (2019B020230002), and the Overseas Expertise Introduction Project for Discipline Innovation .No potential conflict of interest was reported by the authors.Not applicable.The study strictly followed the Declaration of Helsinki. The data were anonymized and publicly available, thus institutional ethics committee approval and consent from the participants were not required.Figures S1–S99.Click here for additional data file.Tables S1–S9.Click here for additional data file.