The aims of this study were as follows: to estimate the mortality and years of life lost, assessed by disability-adjusted life years (DALYs), due to breast cancer attributable to physical inactivity in Brazilian women; to compare the estimates attributable to physical inactivity and to other modifiable risk factors; and to analyse the temporal evolution of these estimates within Brazilian states over 25 years (1990–2015), compared with global estimates. Databases from the Global Burden of Disease Study for Brazil, Brazilian states, and other parts of the world were used. Physical inactivity has contributed to a substantial number of deaths  and DALYs  due to breast cancer in Brazil. Physical inactivity was responsible for more deaths and DALYs (~12.0%) due to breast cancer than other modifiable risk factors (~5.0%). The Brazilian states with better socioeconomic indicators had higher age-standardized rates of mortality and morbidity due to breast cancer attributable to physical inactivity. From 1990 to 2015, mortality due to breast cancer attributable to physical inactivity increased in Brazil  and decreased  around the world. These findings support the promotion of physical activity in the Brazilian female population to prevent and manage breast cancer. Breast cancer was also responsible for substantial years of life lost around the world, becoming a type of cancer that, in addition to high mortality, is also responsible for early morbidity3. This neoplasm has multifactorial aetiology that includes both genetic and modifiable lifestyle factors2. Among the lifestyle factors, physical inactivity, obesity, inadequate diet and excessive alcohol use stand out as modifiable risk factors, which, if avoided, could help in the prevention and management of breast cancer2. It is not known whether studies have investigated the burden of breast cancer mortality due to all of these modifiable risk factors in the same population.Breast cancer is a disease more common in the female population, and in 2017, it was the leading cause of death among females around the world4. Likewise, leptin released from adipose tissue, which has been associated with post-menopausal breast cancer, significantly decreases following aerobic activity4. Therefore, physical activity may have positive effects on reducing the incidence of breast cancer by modifying endocrine function and improving the immune system5. A systematic review and meta-analysis on the association between physical inactivity and chronic disease outcomes found a strong correlation between physical inactivity and the risk of five chronic diseases, including breast cancer6. In addition, an aggregation of 35 prospective cohort studies reported a 14% reduction in the risk of breast cancer for physically active individuals6.Physical inactivity appears to reduce the risk of breast cancer through a number of mechanisms, including body fat reduction, which in turn reduces oestrogen and insulin concentrations. Indeed, these hormones have mitogenic effects on mammary cells8. Breast cancer patients, who exercised while undergoing cancer treatments, were not only able to tolerate the exercise prescriptions but also presented a more favourable trend towards the alleviation of decrements in functional capacity, fatigue levels, and depression when compared to patients who did not exercise. Additionally, and very importantly, no adverse events were reported, and patients seemed to have no problems engaging in regular physical activity8. In this sense, physical activity is also important in the tertiary prevention of breast cancer.There are short and persistent adverse, even toxic, effects of breast cancer treatment that might be prevented, attenuated, treated, or rehabilitated through regular physical activity11. The information on the burden of mortality or morbidity of a disease due to several causes is important because it provides essential data for the epidemiological monitoring of the disease. However, it does not allow for the identification and quantification of causal chain risk factors that impact the burden of disease12. The study of physical inactivity as a specific risk factor compared with other modifiable risk factors in the global burden of mortality and morbidity of breast cancer may be useful for public policies to intervene not only related to the disease but also related to associated risk factors, which may result, in the medium and long term, in lower incidence of the disease and better quality of life of the affected population12.Studies analysing data on the global burden of breast cancer mortality and morbidity have found no association with a single specific cause but with several causal factors of the disease14. The results of these national health databases showed that there is an inequality in the burden of diseases and risk factors among the states of Brazil14. This inequality can be reflected by socioeconomic differences and the level of development of each of the Brazilian regions. In this sense, the investigation of the evolution of breast cancer mortality and morbidity due to physical inactivity in the Brazilian states over 25 years may be useful for health policies.Brazil is a middle-income country that has recently improved its national health databases and implemented a series of surveys to better assess noncommunicable diseases, risk factors, prevalence and disease burdenThe aim of this study was to estimate the mortality and years of life lost, assessed by disability-adjusted life years (DALYs), due to breast cancer attributable to physical inactivity in the female population from Brazil and Brazilian states. We also aimed to compare the estimates of mortality and DALYs due to breast cancer attributable to physical inactivity with estimates attributable to other modifiable risk factors. Finally, we aimed to analyse the temporal trends of the estimates of mortality and DALYs due to breast cancer attributable to physical inactivity over 25 years (1990–2015) in comparison with global estimates and according to the socioeconomic status of Brazilian states.In 1990 and in 2015, there were 983,231  and 2,377,627  incident breast cancer cases worldwide, respectively. In Brazil, 26,558  incident breast cancer cases were estimated in 1990 and 79,015  in 2015. These values represented, in 1990, an age-standardized rate of incident breast cancer cases  of 48.88 (95%U.I.: 46.10–52.72) around the world and of 54.73 (95%U.I.: 45.52–60.30) in Brazil. In 2015, the age-standardized rate of incident breast cancer cases was 65.49 (95%U.I.: 61.67–68.79) around the world and 74.02 (95%U.I.: 61.90–85.86) in Brazil. From 1990 to 2015, there was a large increase in the age-standardized rate of incident breast cancer cases around the world  and in Brazil . Table Around the world, 324,867  deaths due to breast cancer due to all causes were estimated in 1990 and 523,487  in 2015. In Brazil, 7,264  deaths from breast cancer were estimated in 1990 and 16,964  in 2015. Supplementary Table Around the world, 9,578,973  DALYs due to breast cancer attributable to all causes were estimated in 1990 and 15,137,828  in 2015. In Brazil, 236,482  DALYs were estimated in 1990 and 503,463  in 2015. Supplementary Table Around the world, 63.265 (95%U.I.: 52.177–73.225) deaths due to breast cancer attributable to all risk factors  were estimated in 1990 and 103,829  in 2015. In Brazil, 1,212  deaths from breast cancer were estimated in 1990 and 3,166  in 2015. Supplementary Table Around the world, 1,709,585  DALYs due to breast cancer attributable to all risk factors  were estimated in 1990 and 2,743,041  in 2015. In Brazil, 35,113  DALYs were estimated in 1990 and 82,164  in 2015. Supplementary Table Regarding all-cause mortality and DALYs due to physical inactivity, Supplementary Table In relation to breast cancer mortality due to physical inactivity, 29,605  deaths were estimated in 1990 and 46,720  in 2015 around the world. These values represented an age-standardized rate of deaths per 100,000 inhabitants of 1.54 (95%U.I.: 1.11–1.97) in 1990 and 1.31 (95%U.I.: 0.95–1.66) in 2015. In Brazil, 875  deaths were estimated in 1990 and 2,075  in 2015, which represented an age-standardized mortality rate of 1.99 (95%U.I.: 1.46–2.52) in 1990 and 2.00 (95%U.I.: 1.47–2.54) in 2015. From 1990 to 2015, there was a decline in the age-standardized rate of deaths due to breast cancer attributable to physical inactivity  around the world, while Brazil had an increase . Table In relation to breast cancer DALYs due to physical inactivity, 812,664  DALYs were estimated in 1990 and 1,252,121  in 2015 around the world. These values represented an age-standardized mortality rate of 40.32 (95%U.I.: 29.10–51.99) in 1990 and 34.48 (95%U.I.: 25.04–44.41) in 2015. In Brazil, 28,089  DALYs were estimated in 1990 and 60,585  in 2015, which represented an age-standardized mortality rate of 55.80 (95%U.I.: 41.48–70.51) in 1990 and 55.26 (95%U.I.: 40.39–70.58) in 2015. From 1990 to 2015, DALYs due to breast cancer attributable to physical inactivity remained stable around the world , while Brazil had an increase . Table Physical inactivity was responsible for 12.0% and 12.2% of all deaths due to breast cancer in Brazil in 1990 and 2015, respectively. The other risk factors  were responsible for 4.7% and 6.5% of all deaths due to breast cancer in Brazil in 1990 and 2015, respectively. Regarding DALYs due to breast cancer, physical inactivity was responsible for 11.9% and 12.0% of all DALYs in 1990 and 2015, respectively. The other risk factors  were responsible for 2.9% and 4.3% of all DALYs due to breast cancer in Brazil in 1990 and 2015, respectively  and DALYs  due to breast cancer attributable to physical inactivity. The Brazilian states that showed higher age-standardized rates of deaths  due to breast cancer attributable to physical inactivity were Rio de Janeiro, southeastern Brazil ; Rio Grande do Sul, southern Brazil ; and São Paulo, southeastern Brazil . The Brazilian states that showed higher age-standardized rates of DALYs  due to breast cancer attributable to physical inactivity were Rio de Janeiro, southeastern Brazil ; Rio Grande do Sul, southern Brazil ; and São Paulo, southeastern Brazil   has been reported15, and in the U.S., there was a decline in breast cancer mortality rates from 1990 to 2009 in all ethnic groups16. From 1990 to 2015, public health policies were created in Brazil, which resulted in improvements in cancer screening and treatment services, such as the National Oncology Policy17. The improvement in health services may reflect in the larger diagnosis of cancer cases, which justifies the increased incidence in 2015. Additionally, this improvement in health services is perhaps reflected in the stability of the age-standardized rate of deaths and DALYs; however, the continued progress of these services is important for a significant decline19. According to the Brazilian Society of Radiotherapy, in 2013, 335 radiotherapy devices were needed to treat patients, but only 230 were available. As a result, approximately 90,000 patients stopped receiving radiotherapy in that year19. Thus, more effective actions for the prevention, screening and treatment of breast cancer are urgent in Brazil.Among the main findings of this study, it was found that over 25 years, estimates of deaths and DALYs due to breast cancer attributable to all causes were stable in Brazil and decreased around the world. A survey conducted in the Eastern Mediterranean Region analysed the trends of deaths and DALYs from 2005 to 2015 by different cancers and found that mortality and DALY rates due to breast cancer stabilized over a 10-year period20 analysed 22 prospective cohort studies that totalled 123,574 participants followed for a period ranging from 4.3 to 12.7 years and found that 5,462 subjects had breast cancer outcomes . Compared to those who reported low/no lifetime recreational pre-diagnosis physical activity, participants who reported high lifetime recreational pre-diagnosis physical activity levels had a significantly lower risk of breast cancer-related death. Significant risk reductions for breast cancer-related death was also demonstrated for more recent pre-diagnosis recreational physical activity, post-diagnosis physical activity, and meeting recommended post-diagnosis physical activity guidelines 20. Another systematic review with meta-analysis21 reported results similar and added the information that the association between pre- or post-diagnosis physical activity and total mortality among breast cancer survivors did not differ according to BMI, menopausal status, or tumour oestrogen receptor status. These reviews concluded that regular physical activity is a protective factor for pre- and post-diagnosis breast cancer mortality and support the results of this study.Among the risk factors for breast cancer, this study found that 12% of deaths and DALYs in Brazil were due to physical inactivity, and 4% to 6% were due to other risk factors . This percentage represented more than 3,000 deaths and more than 89,000 DALYs in Brazil due to breast cancer attributable to physical inactivity in the years analysed (1990 and 2015). A systematic review with meta-analysis22. Physical activity decreases oestradiol and increases sex hormone-binding globulin, which is a steroid-binding plasma glycoprotein whose main function is to reduce the amount of oestradiol23. Another factor that can lead to breast cancer is hyperinsulinaemia, which promotes the synthesis and activity of insulin-like growth factor (IGF-1)24. Physical activity can reduce insulin levels and insulin resistance, thereby decreasing fasting glucose and total IGF-125. Chronic inflammation is another factor that contributes to breast cancer development and progression through processes such as the polarization of immunosuppressive tumour-associated macrophages via cytokines and the subsequent production of tumour growth factors26. Physical exercise causes a reduction in the circulating amount of proinflammatory biomarkers and increases the circulating amount of anti-inflammatory substances27.The biological mechanisms that explain the relationship between breast cancer and physical inactivity clarify why physical inactivity has contributed to deaths and DALYs. One of the factors that cause breast cancer is the excess of circulating sex hormones, especially oestrogen, which can lead to the formation of mutations and carcinogenesis by stimulating the production of free radicals exhibiting genotoxicity9, as this observation was also noted in the present study. Thus, this study adds to the growing body of evidence that healthy years of life are lost due to breast cancer, not only from all causes but specifically due to physical inactivity. Brazil had a stabilization of age-standardized rates of deaths and DALYs due to breast cancer attributable to physical inactivity over 25 years, and on the other hand, a decrease was observed around the world. The expansion and improvement of services for the screening and treatment of breast cancer and more effective actions to stimulate physical activity in the population may result in a decline in these rates in Brazil19. In the last 25 years, Brazil has implemented important public policies to encourage physical activity, such as the National Health Promotion Policy28, the creation of community physical activity programmes and the inclusion of physical activity in health monitoring and surveillance systems29. Additional actions are required, such as improving the built environment of the cities to enable physical activity30. Brazilian studies have shown that air pollution, few green areas and poor quality of urban infrastructure are still challenges32 that affect the engagement of the population in physical activities30. Another necessary action is to direct actions to encourage the practice of physical activity among children33. Data from the Active Healthy Kids Global Alliance have demonstrated that only half of children in Brazil meet physical activity recommendations and that there are few government incentives to promote physical activity in this age group33.No studies that estimated the number of DALYs due to breast cancer attributable to physical inactivity were found. The studies found estimated the number of DALYs due to breast cancer attributable to all causes34, and this finding may justify our results. This study found that age-standardized rates of deaths and DALYs due to breast cancer attributable to physical inactivity were higher in Brazilian states with better socioeconomic conditions. According to the World Health Organization35, the increase in income and improvements in living standards in developing countries (which are observed more frequently in states with better socioeconomic conditions) have been accompanied by an increase in the incidence of breast cancer in women. This finding may be due to factors such as longer life, greater exposure to risk factors, higher fat intake, lower levels of physical activity, obesity and lower pregnancy rates than those of women who live in cities with worse economic conditions35. Another factor that may explain the high mortality and DALYs due to breast cancer attributable to physical inactivity in Brazilian states with better socioeconomic conditions is the fact that in these states, health services are better structured and serve more people than in states with worse socioeconomic conditions18. Serving more people may result in more diagnoses of breast cancer according to Brazilian Oncology Observatory18.We found an increase in mortality and DALYs due to breast cancer attributable to physical inactivity with advancing age. The literature has shown that physical inactivity increases with age19. For this particular population, regular physical activity has physical and psychological benefits21. In addition, it can prevent recurrence of the disease21. Thus, physical activity should be prioritized at any stage of breast cancer.The number of breast cancer survivors in Brazil was not investigated in the present study; however, this number may also have increased during the 25 years, since there have been improvements in health services2. Thus, the present study reinforces the need for public policies focused on primary health care5. On the other hand, it can be inferred from this study that modifiable risk factors can help in tertiary prevention8, since different research has shown that adopting a healthy lifestyle should be a therapeutic adjunct for breast cancer patients21. This study did not stratify breast cancer cases according to the staging of the disease, but the numbers of deaths and DALYs attributable to physical inactivity and other risk factors provide benchmark for public health in Brazil in its prioritization of healthy lifestyles for women who both have and have not received a diagnosis of breast cancer.The findings from this study can be useful for improving public health in Brazil because we found a large number of deaths and DALYs due to breast cancer attributable to modifiable risk factors. This information reinforces the need to act in the prevention of breast cancer not only through mammography screening but also through a healthy lifestyle. Adopting a healthy lifestyle has a lower cost to public health than the future treatment of the disease36. Second, this study did not exclude genetic cases of breast cancer mutations and did not stratify breast cancer according to the different types of treatment  to identify which has the greatest relation with physical inactivity37. Third, our study does not include all risk factors for breast cancer, such as the dosage of hormone replacement therapy and other pharmaceutical factors considered to be risks for this disease. Fourth, the present study only includes physical activity measured by questionnaires that are considered subjective measures of physical activity and are associated with measurement bias38. Fifth, the non-stratification of physical activity by domains is another limitation. Sixth, the use of a simulation model may be considered another limitation, although this model has evidence of validity39.This study has important limitations. First, this study did not stratify by the type of breast cancer to identify which type has the greatest relation with physical inactivityThe strength of this study was the estimation of mortality and DALYs by physical inactivity throughout Brazil. From this information, it was possible to estimate mortality rates and DALYs due to breast cancer attributable to physical inactivity. This is the first study that used data from all states to make these estimates, which increases the accuracy and resolution of the information. Thus far, it has been known that physical inactivity was a risk factor for breast cancer, but it had not been estimated the degree to which it was related to morbidity (DALYs) and mortality, especially across two time periods (1990 and 2015).It could be concluded that physical inactivity has contributed to a substantial number of deaths and DALYs due to breast cancer in Brazil and around the world. Physical inactivity was responsible for more deaths and DALYs due to breast cancer than other modifiable risk factors. Estimates of mortality and morbidity from breast cancer as a result of physical inactivity in Brazil increased between 1990 and 2015 compared to global estimates. Brazilian states with better socioeconomic indicators had higher mortality and morbidity rates from breast cancer due to physical inactivity. These findings support the promotion of physical activity in the Brazilian female population to help prevent and manage breast cancer.40.The Global Burden of Disease Study (GBD) 2015 includes an annual assessment covering 195 countries and territories from 1990 to 2015. It covers 310 diseases and injuries, 2,619 sequelae and 79 risk factors by age and sex. Detailed descriptions of the methodology and approach of the GBD 2015 have been published elsewhereData from this study included only information on the female population. For data on the association between breast cancer and physical activity and between breast cancer and other risk factors, the information corresponds to women aged ≥25 years.th revision of the International Statistical Classification of Diseases (ICD-10) to one of the 29 GBD cancer groups, were mapped. ICD-10 codes for the incidence, morbidity and mortality due to breast cancer were C50-C50.929, D05-D05.92, D24-D24.9, D48.6-D48.62, D49.3, and N60-N60.999.All neoplasms, as defined by the 109. Crude data were processed to make them comparable and to account for “garbage codes”, which are codes assigned to causes that are not usable from the perspective of public health40. These causes were redistributed to the most likely underlying cause of death based on the regression model1. Through the use of a Cause of Death Ensemble modelling (CODEm) approach with cause-specific covariates, mortality estimates for each individual cause were computed41. These estimates were scaled to fit into an independently modelled all-cause mortality estimate using the CoDCorrect algorithm39.Input data for cancer mortality estimates came from the vital statistics mortality data and the cancer registry incidence data. The latter were transformed into mortality estimates using separately modelled mortality-to-incidence ratios (MIR)39. For each sequela, years lost due to disability (YLDs) were calculated by multiplying the prevalence of each sequela by its disability weight. Finally, DALYs were calculated by summing premature death (YLLs) and YLDs. More information about these estimates can be found elsewhere39.Cancer survival was calculated using a MIR-based scaling factor. A 10-year prevalence of each cancer and each incidence cohort was calculated using these cancer survival estimates. The total prevalence was divided into four sequelae with variable disability weights: (1) diagnosis and treatment, (2) remission, (3) metastasis, and (4) terminal phase. A constant duration for sequelae (1), (3), and (4) was assumed. Sequela duration (2) was the remaining prevalence after subtracting the fixed sequelae duration. Years of life lost (YLLs) were computed by multiplying deaths by the normative standard life expectancy at each age of death12.Surveys of the general adult population performed using random sampling procedures that assessed self-reported physical activity in all life domains  were included. Due to the absence of a consistent relationship at the individual level between the amount of physical activity performed in each domain and total activity, it was not possible to use studies that included only recreational/leisure activities12.For the global estimates, data were primarily derived from two standardized questionnaires, the Global Physical Activity Questionnaire (GPAQ) and the International Physical Activity Questionnaire (IPAQ), although any other survey instrument that asked about intensity, frequency and duration of physical activities performed across all activity domains was included14. More details can be found at http://ghdx.healthdata.org/gbd-2015/data-input-sources and in a previous publication12.In the case of Brazil, surveys such as the telephone-based Surveillance of Risk and Protective Factors for Chronic Diseases, the Brazil World Health Survey, and the International Prevalence Study on Physical Activity were also consulted12. Estimates were made for subjects classified as physically inactive (<600 METS-min/week)12.To standardize all estimates of physical inactivity in Brazil and around the world, data from the population aged 25 years or more were considered. Reported physical activity was accumulated for durations of at least ten consecutive minutes across all life domains. Physical activity frequency, duration and intensity were used to calculate the total metabolic equivalent (MET) minutes per week12.In addition to physical inactivity, the GBD study defined other modifiable risk factors for breast cancer. This definition took into account the quality of the information, the evidence, and the quality of the prediction models12. Thus, it was considered a risk if there were some alcohol consumption/day. To generate estimates of alcohol consumption in grams per day, data from population surveys were used in combination with estimates of per capita consumption from the Food and Agriculture Organization42 and the Global Information System on Alcohol and Health database43. Per capita consumption is an aggregate measure of recorded, unrecorded, and tourist per capita consumption of alcohol (UNWTO database45) derived from sales, production, and other economic statistics. While population-based surveys provide accurate estimates of the prevalence of lifetime abstainers, former drinkers and current drinkers, they typically underestimate real alcohol consumption levels12. As a result, the per capita consumption figures across all ages and both sexes from the FAO and GISAH are considered to be a better estimate of overall volume of consumption12. Per capita consumption, however, does not provide the age- and sex-specific consumption estimates needed to compute alcohol-attributable burden of disease. Therefore, we use the age-sex pattern of consumption among drinkers modelled from the population survey data and the overall volume of consumption from the FAO and GISAH to determine the total amount of alcohol consumed by the country and states.For alcohol use, the theoretical minimum-risk exposure level (TMREL) was assumed to be no alcohol use 43. In this study, the TMREL of BMI was 20–25 kg/m². Thus, a BMI > 25 kg/m² was considered a risk. We used data from multi-country survey programmes, national surveys, and longitudinal studies that were available in the Global Health Data Exchange (GHDx) and provided either self-report or measured data on height and weight. A complete description of the data-seeking and update process for the GHDx is provided elsewhere12. In addition, a systematic review of national and subnational studies was conducted for studies published until 31 December 201512. We adjusted self-reported data for overweight prevalence, obesity prevalence, and mean BMI using hierarchical mixed-effects regression models, fit using maximum likelihood separately by sex.The TMREL of BMI was determined based on the BMI level that was associated with the lowest risk of all-cause mortality in prospective cohort studies12. We used dietary data from multiple sources, including nationally and sub-nationally representative nutrition surveys, household budget surveys, and the United Nations FAO food balance sheets and supply utilization accounts. All dietary data were standardized to 2000 kcal/day. For each dietary factor, we estimated the global age pattern of consumption based on nutrition surveys  and applied that age pattern to the FAO data.Exposure to a diet high in sugar-sweetened beverages was defined as an average daily consumption of greater than 2.5 grams per day of beverages with ≥50 kcal per 226.8-gram serving, including carbonated beverages, sodas, energy drinks, and fruit drinks, but excluding 100% fruit and vegetable juices. In this study, the TMREL of diets high in sugar-sweetened beverages was 0–5 grams/day. Thus, consumption greater than 5 grams/day was considered a risk12. Brazil’s surveys for all these risk factors are available at http://ghdx.healthdata.org/gbd-2015/data-input-sources.Further details about the modelling used for the risk factors were previously published12. For this approach, we used the simulation model by the CODEm to estimate indicators by age, sex, country, state, year, and cause, that is, an analytical tool that tests several possible statistical models of causes of death and creates a combined set of models that offers the best predictive performance. The DisMod-MR 2.1 software , a meta-regression tool, was used for the derivation of simultaneous estimates of incidence, prevalence, remission, disability, and mortality attributable to risk factors, such as physical inactivity39. In this study, the results of these models were used to proportionally distribute deaths from breast cancer from all causes, all risk factors, and physical inactivity39. Modelling details can be found in the literature12.The contribution of all causes, all risk factors and physical inactivity to mortality and DALYs due to breast cancer was estimated using a comparative risk assessment approach in which observed health outcomes are compared to those that would have been observed with a counterfactual set of exposures where no one was exposed46.Incident breast cancer cases, absolute number of deaths, mortality rates and DALYs  were used as metrics. The sum of DALYs across the population, or the burden of the disease, can be considered a measurement of the gap between current health status and the ideal health situation39.In the GBD study, 95% uncertainty intervals (95%U.I.) were calculated to provide information on the variability of estimates resulting from errors due to the sampling process and non-sample errors due to adjustments of data sources and modelling39 for all evaluated locations by calculating per capita income, formal education at 15 years of age and fertility rate. This index was used to compare the metrics used among Brazilian states. For this analysis, the Spearman correlation coefficient was applied.The GBD 2015 created the Socioeconomic Development Index (SDI)The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request. All data generated or analysed during this study are included in this published article (and its supplementary information files).Supplementary Information


To improve breast cancer risk stratification to enable more targeted early detection/prevention strategies that will better balance risks and benefits of population screening programmes.9362 of 57,902 women in the Predicting-Risk-Of-Cancer-At-Screening (PROCAS) study who were unaffected by breast cancer at study entry and provided DNA for a polygenic risk score (PRS). The PRS was analysed alongside mammographic density  and standard risk factors (Tyrer-Cuzick-model) to assess future risk of breast cancer based on tumour stage receptor expression and pathology.195 prospective incident breast cancers had a prediction based on TC/DR/PRS which was informative for subsequent breast cancer overall [IQ-OR 2.25 (95% CI 1.89–2.68)] with excellent calibration-(0.99). The model performed particularly well in predicting higher stage stage 2+ IQ-OR 2.69 (95% CI 2.02–3.60) and ER + BCs (IQ-OR 2.36 (95% CI 1.93–2.89)). DR was most predictive for HER2+ and stage 2+ cancers but did not discriminate as well between poor and extremely good prognosis BC as either Tyrer-Cuzick or PRS. In contrast, PRS gave the highest OR for incident stage 2+ cancers, [IQR-OR 1.79 (95% CI 1.30–2.46)].A combined approach using Tyrer-Cuzick/DR/PRS provides accurate risk stratification, particularly for poor prognosis cancers. This provides support for reducing the screening interval in high-risk women and increasing the screening interval in low-risk women defined by this model.The online version of this article (10.1007/s10549-019-05210-2) contains supplementary material, which is available to authorized users. BRCA1 or BRCA2 and single nucleotide polymorphisms (SNPs). SNPs account for more of the familial risk than all pathogenic variants in high or moderate-risk breast cancer genes  thus the ratio to the overall mean population breast cancer incidence using TC/DR/SNP18 was improved to the lower figure of 0.40 (95% CI 0.26–0.59) from 0.54 (95% CI 0.35–0.82) using TC/DR. Similarly, the addition of SNP18 to TC/DR increased the proportion of women in the moderate/high-risk group (≥ 5% 10-year risk) from 16.4% (1487/9062) to 18.3% (1656/9062) and the proportion of total breast cancers in these groups also increased from 65/195 (33.3%) using TC/DR to 78/195 (40%) using TC/DR/SNP18 and proportionately more of these women developed BC 4.37% (65/1487) versus 4.71% (78/1656) This results in comparable ratios of population to breast cancers of 2.03 (95% CI 1.52–2.69) and 2.19 (95% CI 1.67–2.85) respectively. Therefore, adding SNP18 to TC/DR led to greater separation of the population into high and low-risk groups, and potentially for stage 2+. Only 8/2944 (0.27%) women in the low-risk group developed an incident stage 2+ BC on combined TC/DR/SNP18 analysis compared to 33/1656 (2.11%) in the mod/high-risk group, a 7.8 fold difference. Using just TC/DR there were 6/2426 (0.25%) in low risk compared to 26/1487 (1.75%) in the moderate/high-risk group a sevenfold difference. Overall, in the incident population after saliva collection of 9062 women just over half (4600/9062-50.7%) could be divided into either low or moderate/high risk using TC/DR/SNP18 compared to 3913 (43.1%) using TC/DR alone. This resulted in better prediction ratios overall, with an OR from low to moderate/high of 5.44 (95% CI 3.52–8.61) with TC/DR/SNP18 compared to 4.36 (95% CI 2.77–7.02) using TC/DR alone.The predictive value for ER-negative breast cancer with TC/DR/SNP18 was weaker although numbers were small. Overall, only 35/405 (8.6%) invasive breast cancers were ER−, with 32 (7.9%) being triple negative.P < 0.0001. The proportion of stage 2+ cancers in the low risk group by TC/DR/SNP18 was 16/85 (18.8%) compared to 48/155 (30.9%) in the moderate/high-risk group (P = 0.05). The rate of stage 2+ cancer post-prevalent was 7.41 times higher (95% CI 3.57 to 17.28) in the moderate/high-risk group  compared to the low-risk group at 0.44 (95% CI 0.22–0.87); P < 0.0001. Of the 167 women who have died in the whole DNA/PROCAS cohort, thirteen died as a result of a breast cancer diagnosis since entering PROCAS. Nine of the thirteen deaths (69%) were in those with a 10-year risk > 3.5% and nine of the thirteen were stage 2+ at diagnosis.Of all cancers diagnosed in the TC/DR/SNP18 group, 136/495 (27.5%) were stage 2 or above. 48 (35%) of these cases were in the high/moderate-risk group (33/68–48.5% of incident BC) compared with only 16 (11.7%) in the low-risk group (8–11.7% of incident BC); The TC/DR/SNP18 score may be useful to define a group of women attending for their first population screening mammogram (aged 46–52 years in the UK) who do not require screening for a 10-year period. For example, the group aged 46–52 years at < 2% 10-year risk in PROCAS had a mean 10-year incidence of only 1.4% (5 of 547 developed breast cancer after 3383.27 years of follow up) and for the 262 women at < 1.5% 10-year risk there was only 1 breast cancer in 1648.43 years giving a rate of 0.6 per 1000 women in 10 years.Using the full PROCAS dataset of 57,902 women unaffected at entry, the DR was not predictive of ER− cancers. The median DR was 0.99 (IQR 0.84–1.19) for ER− tumours and 1.04 (0.87–1.26) for ER + cancers; the IQR OR compared to no cancer was 1.58 (95% CI 1.46–1.70) for ER+ and 1.31 (95% CI 1.06–1.31) for ER–. Similarly, TC 10-year risk median IQR was also less predictive of ER− at 2.84 (2.18–3.59) for ER− versus 2.91 (2.27–4.07) for ER+. This gave an IQR OR of 1.19 (95% CI 0.99–1.43) for ER− and 1.39 (95% CI 1.30–1.48) for ER+. In contrast using the full PROCAS dataset, DR was highly predictive of HER2+ breast cancer with an IQR OR of 1.72 (95% CI 1.39–2.15) compared to 1.54 (95% CI 1.44–1.66) for all breast cancer.We have shown that combining TC, DR and SNP18 improves the accuracy of breast cancer risk stratification over each factor independently and helps predict which women are more likely to develop better and worse prognosis cancers. Previously, we demonstrated that there was a higher proportion of cancers that are interval and stage 2+ in women at high/moderate-risk than for those at low risk . The preThe high proportion of stage 2+ and interval cancers in women with high mammographic density is likely to be due in part to ‘masking’ of smaller cancers in areas of dense fibroglandular tissue on mammography. This does not, however, explain the overall better prediction of post-prevalent stage 2+ cancers using SNP18.BRCA1 pathogenic variants, where the cancers are predominantly ER− [Before any such change to screening intervals is introduced the effectiveness of this change could be enhanced by better identification of the worse prognosis ER− breast cancers. Although TC risk does to some extent predict these cancers they are less effective than for ER+ cancers. SNP18 has little predictive value as would be expected, as the majority of individual SNPs are associated with ER+ tumours , with onntly ER− . The recWe have identified a strong link between mammographic density and HER2+ breast cancer and this has also been highlighted recently .There are limitations of the present study. We have used a definition for extremely good prognosis breast cancer that is likely to contain the great majority of potential over-diagnosis. However, grade 1 stage 1 breast cancers are still capable of causing breast cancer deaths and if women are removed from screening they are likely to become higher stage before becoming symptomatic. Equally, intermediate grade DCIS could become at least a grade 2 invasive breast cancer if untreated, with the possibility of a later breast cancer-related death. There was some missing data from the present study and two women were lost to follow-up and 18/407 (4.4%) invasive breast cancers had missing grade and ER status. Strengths include the large number of fully genotyped women in the prospective analysis with mammographic density information.In conclusion, in this study, we report that the risk groups not only define incidence rates but also the pathology and prognosis of the breast cancers that develop in them, with important implications for screening and preventive strategies. The current study confirms the added accuracy of risk prediction using a combined TC/DR/SNP18 approach and that it can define a sizeable group (32.5%) of women who have a low (< 2%) 10-year risk. Furthermore, cancers identified in this group are significantly more likely to have an extremely good prognosis . This woBelow is the link to the electronic supplementary material.Supplementary material 1 (DOC 49 KB)


Metabolomics is a promising molecular tool to identify novel etiologic pathways leading to cancer. Using a targeted approach, we prospectively investigated the associations between metabolite concentrations in plasma and breast cancer risk.n = 127, acylcarnitines, amino acids, biogenic amines, glycerophospholipids, hexose, sphingolipids) were measured by mass spectrometry in pre-diagnostic plasma samples and tested for associations with breast cancer incidence using multivariable conditional logistic regression.A nested case-control study was established within the European Prospective Investigation into Cancer cohort, which included 1624 first primary incident invasive breast cancer cases (with known estrogen and progesterone receptor and HER2 status) and 1624 matched controls. Metabolites (n = 2248), and after control for multiple tests, concentrations of arginine , asparagine (OR = 0.83 (0.74–0.92)), and phosphatidylcholines (PCs) ae C36:3 (OR = 0.83 (0.76–0.90)), aa C36:3 (OR = 0.84 (0.77–0.93)), ae C34:2 (OR = 0.85 (0.78–0.94)), ae C36:2 (OR = 0.85 (0.78–0.88)), and ae C38:2 (OR = 0.84 (0.76–0.93)) were inversely associated with breast cancer risk, while the acylcarnitine C2 (OR = 1.23 (1.11–1.35)) was positively associated with disease risk. In the overall population, C2 (OR = 1.15 (1.06–1.24)) and PC ae C36:3 (OR = 0.88 (0.82–0.95)) were associated with risk of breast cancer, and these relationships did not differ by breast cancer subtype, age at diagnosis, fasting status, menopausal status, or adiposity.Among women not using hormones at baseline  contains supplementary material, which is available to authorized users. Breast cancer is the most common cancer among women worldwide . Known mThe metabolome reflects endogenous processes and environmental and lifestyle factors –13. MetaIn the current study, we employed a targeted metabolomics approach to prospectively investigate the associations between 127 metabolites measured by mass spectrometry in pre-diagnostic plasma samples and risk of breast cancer, overall, and by breast cancer subtype, accounting for established breast cancer risk factors.n = 367,903) were aged 35–75 years old at inclusion. At recruitment, detailed information was collected on dietary, lifestyle, reproductive, medical, and anthropometric data [EPIC is an ongoing multi-center cohort study including approximately 520,000 participants recruited between 1992 and 2000 from ten European countries [Incident cancer cases were identified through record linkage with cancer registries in most countries and through health insurance records, cancer and pathology registries, and active follow-up of study subjects in France, Germany, and Greece. For each EPIC center, closure dates of the study period were defined as the latest dates of complete follow-up for both cancer incidence and vital status .All participants provided written informed consent to participate in the EPIC study. This study was approved by the ethics committee of the International Agency for Research on Cancer (IARC) and all centers.Subjects were selected among participants who were cancer-free (other than non-melanoma skin cancer) and had donated blood at recruitment into the cohort. Cancers were coded according to the Third Edition of the International Classification of Diseases for Oncology (code C50). Women diagnosed with first primary invasive breast cancer at least 2 years after blood collection and before December 2012, for whom estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) statuses of the tumors were available, were selected as cases for the current study.For each breast cancer case, one control was chosen at random among appropriate risk sets comprising all female cohort members who were alive and without cancer diagnosis (except non-melanoma skin cancer) at the time of diagnosis of the index case. Using incidence density sampling, controls were matched to cases on center of recruitment, age (± 6 months), menopausal status , phase Initially, 1626 cases and 1626 controls were eligible for the study, but after the exclusion of pregnant women at blood collection, a final population of 1624 cases and 1624 controls were included in the analysis.IDQ p180 platform  and following the procedure recommended by the vendor. A QTRAP5500 mass spectrometer  was used to measure 147 metabolites . Samples from matched case-control sets were assayed in the same analytical batch. Laboratory personnel were blinded to case-control status of the samples.All plasma samples were assayed in the Biomarkers laboratory at IARC, using the Absolute. Values lower than the lower limit of quantification (LLOQ), or higher than the upper limit of quantification (ULOQ), as well as lower than batch-specific limit of detection (LOD) , were considered out of the measurable range. Metabolites were excluded from the statistical analyses if more than 20% of observations were outside the measurable range (n = 20). A total of 127 metabolites  were finally retained for statistical analyses. Of these 127 metabolites, 113 had all values included in the measurable range. For the remaining 14 metabolites, values outside the quantifiable range  were imputed with half the LLOQ or half the batch-specific LOD, respectively.Metabolites were analyzed in samples from 3247 distinct subjects (one subject included in 2 pairs). Completeness of measures and coefficients of variation  are shown in Additional file Characteristics of cases and controls were described using mean and standard deviation (SD) or frequency. Geometric means were used to describe non log-transformed metabolite concentrations among cases and controls. Log-transformed metabolite concentrations were used in all other analyses. Partial Pearson’s correlations between metabolites, adjusted for age at blood collection, were estimated among controls.α = 0.05 by permutation-based stepdown minP adjustment of P values, as this method better accounts for the dependence of the tests [P values using Bonferroni correction (P < 0.05/127) and controlling for the false discovery rate (FDR) at α = 0.05 [We used conditional logistic regression to estimate the risk of breast cancer per standard deviation (SD) increase in metabolite concentration. The analysis was conditioned on the matching variables. Likelihood ratio tests were performed to compare linear models with cubic polynomial models in order to assess departure from linearity. Multiple testing was addressed by controlling for family-wise error rate at he tests , 22. Forα = 0.05 . All staMetabolites showing a statistically significant association with risk of breast cancer after correcting for multiple testing were categorized into quintiles based on the distribution of the concentrations among controls, and odds ratios (OR) for risk of breast cancer were estimated in each category. For tests of linear trend, participants were assigned the median value in each quintile and we modeled the corresponding variable as a continuous term. To identify potential confounders, models of the metabolites of interest (continuous and quintiles) were adjusted separately for each potential confounder and estimates obtained were compared with estimates from models with matching variables only. Only variables that changed parameter estimates by more than 10% were retained in the multivariable model. Variables tested were as follows: age at first menstrual period (continuous), number of full-term pregnancies (0/1/2/≥ 3), age at first full-term pregnancy (never pregnant/quartiles), breastfeeding , ever use of oral contraceptive (yes/no), ever use of MHT (yes/no/missing), smoking status (never/former/current), level of physical activity (Cambridge index : inactiv2/≥25 kg/m2), and country, by introducing interaction terms in the models. Subgroup analyses were conducted on the raw models. For WC, unconditional logistic regression adjusted for each matching factor was used. P values were not corrected for multiple tests since heterogeneity was investigated only for metabolites showing statistically significant associations with risk overall, after correction for multiple testing.For those metabolites showing a significant association with breast cancer risk after controlling for multiple testing, heterogeneity was investigated by menopausal status at blood collection, use of exogenous hormones at blood collection, fasting status at blood collection, age at diagnosis (age 50 or older/younger than age 50), breast cancer subtype , time between blood collection diagnosis (2–8.6 years/more than 8.6 years), and at recruitment waist circumference (WC) (< 80 cm/≥80 cm), BMI  and by cancer subtype.Analyses were conducted using SAS software for Windows  and R software (packages Epi and NPC) , 26.Cases were diagnosed on average 8.3 years after blood collection, at a mean age of 60.8 years. The majority of tumors were ER-positive (80.7%), PR-positive (68.2%), and HER2-negative (78.2%) Table . Mean coOverall, positive, moderate correlations were observed among some of the amino acids, phosphatidylcholines (PCs), lysoPCs, and sphingomyelins  and phosphatidylcholine PC ae C36:3  remained significantly associated with risk of breast cancer   and of PC ae C36:3   and PC ae C36:3 (P homogeneity = 0.017) with breast cancer, with statistically significant associations restricted to hormone non-users  and no associations observed in users  , asparagine , PC aa C36:3 , PC ae C34:2 , PC ae C36:2 , and PC ae C38:2 .In an analysis of the 127 metabolites restricted to hormone non-users (48) Fig. , we idenP homogeneity all > 0.12, Fig. P homogeneity of 0.50 for C2 and 0.12 for PC ae C36:3) or by time between blood collection and diagnosis (2–8.6/≥8.6 years (median); P homogeneity of 0.17 for C2 and 0.98 for PC ae C36:3) (data not shown).No significant heterogeneity was observed for the association of C2 and PC ae C36:3 with breast cancer by menopausal status, fasting status at blood collection, breast cancer subtype, age at diagnosis, WC (n = 1084 cases), PC ae C36:3 and PC aa C36:3 had adjusted P values close to statistical significance .Stratification by breast cancer subtypes for all metabolites  and the amino acids arginine and asparagine were inversely associated with disease risk. In the overall population (hormone users and non-users), only C2 and PC ae C36:3 were associated with risk of breast cancer independently from breast cancer subtype, age at diagnosis, fasting and menopausal status at collection, or adiposity.Acylcarnitine C2 plays a key role in the transport of fatty acids into the mitochondria for β-oxidation , 28. In Phospholipids are a major component of cell membranes and play a major role in cell signaling and cell cycle regulation. Previous studies of phospholipids showed that PC ae C36:3 concentrations were decreased in type 2 diabetes  and thatIn line with the inverse association observed between arginine and risk of breast cancer in hormone non-users, decreased plasma concentrations of arginine has been observed in breast cancer patients  comparedP values, and that an inverse association close to statistical significance was observed for PC ae C38:1. However, none of the metabolites identified in the present work were associated with risk of breast cancer in this previous study, which did not investigate heterogeneity by use of hormones.Prospective data on metabolomics and risk of breast cancer are limited , 16–18, In a previous study applying NMR-based metabolomics analyses in the SU.VI.MAX cohort , severalHeterogeneity by subtype was investigated only in the PLCO study, showing that some metabolites  and risk of breast cancer, and an inverse association between PC ae C36:3 and risk of breast cancer. These associations were limited to women not using hormones, as were inverse associations with arginine, asparagine, PCs aa C36:3, ae C34:2, ae C36:2, and ae C38:2. These metabolites might be biomarkers of future breast cancer development. These results need to be replicated in other epidemiological studies, and more research is needed to identify determinants of these metabolites.Additional file 1:Table S1.) and metabolites concentrations by case-control status (Table S2..); Supplementary figures showing age-adjusted correlations between metabolites in control participants  and adjusted P values for associations between metabolites and different breast cancer subtypes . Abbreviations: BMI: Body Mass Index; EPIC: European Prospective Investigation into Cancer and nutrition; ER: estrogen receptor; FDR: False Discovery Rate; HER2: Human epidermal growth factor receptor 2; IARC: International Agency for Research on Cancer; MHT: menopause hormone therapy; MS: Mass spectrometry; NMR: nuclear magnetic resonance; OR: odds ratio; PC: phosphatidylcholine; PR: progesterone receptor; SD: standard deviation; WC: waist circumference. (PDF 1041 kb)Supplementary tables describing the completeness of the metabolites measures (


Accurate breast cancer risk assessment for women attending routine screening is needed to guide screening and preventive interventions. We evaluated the accuracy of risk predictions from both visual and volumetric mammographic density combined with the Tyrer-Cuzick breast cancer risk model.th to 75th percentile of the adjusted percent density distribution in control participants (IQ-OR).A case-control study  of women aged 40–79 years was performed using self-reported classical risk factors. Breast density was measured by using automated volumetric software and Breast Imaging and Reporting Data System (BI-RADS) density categories. Odds ratios (95% CI) were estimated by using logistic regression, adjusted for age, demographic factors, and 10-year risk from the Tyrer-Cuzick model, for a change from the 25diff = 0.014) or volumetric percent density (Pdiff = 0.065). In this setting, 4.8% of women were at high risk (8% + 10-year risk), using the Tyrer-Cuzick model without density, and 7.1% (BI-RADS) compared with 6.8% (volumetric) when combined with density.After adjustment for classical risk factors in the Tyrer-Cuzick model, age, and body mass index (BMI), BI-RADS density had an IQ-OR of 1.55 (95% CI = 1.33 to 1.80) compared with 1.40 (95% CI = 1.21 to 1.60) for volumetric percent density. Fibroglandular volume  was a weaker predictor than was BI-RADS density (PThe addition of volumetric and visual mammographic density measures to classical risk factors improves risk stratification. A combined risk could be used to guide precision medicine, through risk-adapted screening and prevention strategies. Clinical BI-RADS breast density and volumetric density may be used in combination with classical questionnaire risk factors to assess risk.Combining breast density with other risk factors increases the number of women accurately identified at high and lower risk of breast cancer.The Tyrer-Cuzick risk assessment tool has been updated to support using BI-RADS or volumetric breast density.Breast cancer mortality rates in the USA have fallen substantially since 1990 because of early detection of the disease and better treatment . HoweverThere is increasing interest in tailoring breast cancer screening and prevention strategies based on individual risk estimation . A varieA comprehensive breast cancer risk model is desirable. A large body of evidence supports the role of breast density as a risk factor , but relThe case-control study discussed in this article was designed specifically to improve risk assessment for breast cancer. The analysis reported here aimed to extend the Tyrer-Cuzick model by determining the risk associated with 2 measures of mammographic density after adjustment for the other factors in the model. Both of the mammographic density methods considered are feasible for clinical practice (BI-RADS and fully automated volumetric density). The prespecified hypothesis was that both measures of breast density would add information for breast cancer risk assessment to classical risk factors.This case-control study was approved by the institutional review boards at the University of Virginia and Sunnybrook Research Institute. The study was compliant with the Health Insurance Portability and Accountability Act. Patients participating on site gave written consent. Patients participating remotely through electronic media were granted waiver of consent.All women 18–89 years of age diagnosed with breast cancer at a single institution between 2003 and 2013 who had a digital contralateral mammogram at the time of diagnosis were eligible as patient participants. Participant status (invasive breast cancer) was confirmed through chart review. All women without a breast cancer diagnosis but identified as having a digital mammogram between 2003 and 2008  were eligible as control participants. To ensure a similar age distribution, control participants were selected based on frequency matching of current age. Risk factor information was collected for patient and control participants between May 2012 and December 2013, using a self-reported electronic questionnaire that was administered in the breast imaging clinic, breast surgery clinic, or medical oncology clinic. Women who were eligible as patient participants but not seen in more than 2 years from initiation of patient recruitment were sent a letter for either survey completion by mail or online through an electronic token. Women were excluded if they had breast augmentation, prior contralateral mastectomy, or bilateral breast cancer at the time of initial diagnosis because these may affect breast density measurement. The study had sufficient power to detect breast density as a risk factor because of the larger sample size compared with those of several earlier studies on this issue , 15.The public institution provides reduced fee health care based on need, such that women with greater burden of disease and low resources are frequently referred for care. Thus, we expected some differences between patient participants and control participants because control participants would mostly include women attending regular screening provided by a health plan, but patient participants might not. As a result, we considered several demographic factors for inclusion as adjustments in the analysis. These were the concentric geographical area surrounding the institution, health insurance, and whether the woman had been assessed for financial assistance, ethnicity, education, and body mass index (BMI). Age in the 5-year groups was adjusted following the study design.Classic hormonal and reproductive risk factors included on the questionnaire were combined for adjustment using 10-year risk from the Tyrer-Cuzick (version 7.02) . The queth-edition lexicon because of the time frame of the study population.Full-field digital mammograms (“for processing”) DICOM files from Senographe 2000D, Senographe DS, and Senographe Essential  and Lorad Selenia and Selenia Dimensions  machines were retrieved. Breast density was measured using a fully automated volumetric software program . The primary breast density value was the estimated percentage of the breast by volume occupied by fibroglandular tissue. Absolute fibroglandular and fat volume  were secondary predictors. For patient participants, mammograms from the contralateral breast that were taken before and closest to the time of diagnosis of breast cancer were used. For control participants, the images closest to the questionnaire were used. The mean density from all 4 mammographic views  was used for control participants, but only measurements from the contralateral side were used for patient participants. BI-RADS density category was obtained from clinical records, which were based on both breasts and the 4Weighted kappa coefficients assessed the association between volumetric and clinical BI-RADS categories. Mammographic density was incorporated into the Tyrer-Cuzick risk model by developing a measure of breast density independent from age at mammogram and BMI at the time the questionnaire was filled, and it was defined as the difference between observed and expected density. Expected density was modeled in control participants by fitting a generalized additive model  of naturLogistic regression was used to estimate odds ratios after adjustment for age at mammogram , region , and demographic factors . Heterogeneity was tested using an interaction test. Tests for differences in predictive ability between 2 density measures were based on a nonparametric bootstrap test. An adjusted concordance index (mC) was used to assess discrimination further, formed by fitting a linear regression of the predictor against adjustment factors and taking the residual , 23.Sensitivity analysis assessed the assumption of a linear relationship between risk and the density residual, using a generalized additive model. Interactions between density, age, and BMI were also assessed assuming a linear effect of the density residual in a logistic regression model. The proportions of participants at increased risk (greater than 8% 10-year risk) and those at decreased risk (less than 2% 10-year risk) as previously used were estimated using density combined with the Tyrer-Cuzick model.P < 0.05 was called significant. For analysis, we used the statistical software R version 3.4.1 and with the mgcv package , clinical detection of a lump (n = 96), reporting other breast symptoms including pain and nipple discharge (n = 14), imaging other than mammography (n = 21), and unknown ( n = 3).Patient participants were more likely than control participants were to live farther away from the institution, use Medicaid, have no health insurance, and be assessed for financial assistance, whereas, proportionally, more control participants had a higher level of education and were white . CompareP < 0.001, There was significant heterogeneity in risk associated with classical factors between the primary service area, which is most representative of women attending routine screening, and outlying areas, where symptomatic women who were less likely to have private medical insurance formed a greater proportion of patient participants (P < 0.0001). BMI was significantly negatively correlated with percent density  or BI-RADS density (P = 1.0).P = 0.11).Women in the highest BI-RADS density category (9% of control participants) were 3 times  or volumetric percentage density . This was partly because there was some evidence that adjusted fat volume was negatively associated with breast cancer risk, with a slightly stronger effect for premenopausal women  compared with postmenopausal women .Absolute fibroglandular volume . There was some evidence of a larger risk difference between fatty and scattered BI-RADS density than between heterogeneous and scattered BI-RADS density , which wP = 0.15; volumetric density P = 0.7), nor of an interaction with Tyrer-Cuzick risk . There was some evidence of attenuation in risk from density with age , but we did not model this further because we were concerned about overfitting.Further sensitivity analysis found little evidence of interaction between density and BMI after adjustment for demographic factors and Tyrer-Cuzick 10-year risk ; the work of A.B. and J.C. was funded by Cancer Research UK (Grant number C569/A16891). The funding bodies had no role in the study design, analysis, interpretation of the data, or writing the manuscript.wbz006_Supplement_Figure_1Click here for additional data file.wbz006_Supplement_Table_1-2Click here for additional data file.


Persistent organic pollutants (POPs) bioaccumulate in the food chain and have been detected in human blood and adipose tissue. Experimental studies demonstrated that POPs can cause and promote growth of breast cancer. However, inconsistent results from epidemiological studies do not support a causal relationship between POPs and breast cancer in women. To identify individual POPs that are repeatedly found to be associated with both breast cancer incidence and progression, and to demystify the observed inconsistencies between epidemiological studies, we conducted a systematic review of 95 studies retrieved from three main electronic databases. While no clear pattern of associations between blood POPs and breast cancer incidence could be drawn, POPs measured in breast adipose tissue were more clearly associated with higher breast cancer incidence. POPs were more consistently associated with worse breast cancer prognosis whether measured in blood or breast adipose tissue. In contrast, POPs measured in adipose tissue other than breast were inversely associated with both breast cancer incidence and prognosis. Differences in biological tissues used for POPs measurement and methodological biases explain the discrepancies between studies results. Some individual compounds associated with both breast cancer incidence and progression, deserve further investigation. Persistent organic pollutants (POPs) are a group of chemical substances of synthetic origin used for industrial, agricultural or domestic purposes, that persist in the environment and bioaccumulate in the food chain due to their lipophilic properties ,2. POPs Given the abundance of adipose tissue in the human breast, mammary epithelial cells exposure to POPs sequestered in breast adipose tissue may promote carcinogenesis and progression of mammary cancers . In factAlthough experimental studies demonstrate that POPs can cause and promote growth of breast cancer, several observational studies conducted in humans yielded inconsistent results regarding the implication of POPs in women breast cancers ,24,25,26A systematic review was conducted following a pre-established protocol and according to the general methodology of Cochrane reviews . ConsideAn electronic search of the following databases was performed, from inception to December 2018: MEDLINE (via PubMed), EMBASE and CENTRAL . Search strategies were developed for each of these databases with text words and index terms referring to POPs, breast cancer risk and breast cancer prognosis, and excluding animal studies . No langAny observational or intervention study that evaluated the association between POPs and breast cancer risk, survival or a meaningful breast cancer prognostic factor, whatever the design was eligible for inclusion. No restrictions were applied regarding language or type  of publication. Women included in the studies before or after breast cancer diagnosis, regardless of age, menopausal status, breast cancer type, disease stage and treatment regimen, were eligible. No participants were excluded based on ethnicity.Studies that measured exposure to any lipophilic POP, in a lipid rich biological human sample , whatever the method of measurement, were eligible.Breast cancer risk, measured by breast cancer incidence, prevalence or breast mammographic density (a recognized breast cancer risk factor) and breast cancer survival, including overall survival , breast cancer-specific survival , and breast cancer-free survival (breast cancer recurrence), were the primary outcomes. Studies that assessed the association of POPs with meaningful breast cancer prognostic factors  were also eligible.The references identified by the search strategy were reviewed by one author (K.E-I.) in a two-step process. First, the title and abstract of each study were screened to exclude obviously non-eligible studies and second, the full text of retained articles was examined and subjected to evaluation using the predefined eligibility criteria. Whenever required, a second review author (C.D.) was consulted. When required, further information was sought from the authors by email.Data extraction was performed using an exhaustive standardized form designed for this review. Information about study design , participants and tumors characteristics at diagnosis , exposure assessment , measured outcomes and reported results  were collected. For observational studies, special attention was paid to distinguishing between adjusted and unadjusted results, and to the variable selection method used in multivariate analyses. Studies definition of each characteristic or variable retained was recorded. In the case of multiple publications related to the same study, the publication reporting the outcomes of interest to the present review or the one with the longest follow-up of these outcomes was considered as the reference, and information was supplemented by secondary publications as required. Abstracts with insufficient information and data to permit inclusion were excluded from the qualitative synthesis . Data weBased on the “STrengthening the Reporting of OBservational studies in Epidemiology.” (STROBE) statements , and theAssessment of the risk of bias was performed twice by a review author (K.E-I.), both for the risk of bias in each study and for the overall risk of bias across studies. When required, a second reviewer (C.D.) was consulted.Differences between studies, including study design, participant characteristics , tumor characteristics , exposure measurement  and different levels of risk of bias were considered for exploring possible sources of heterogeneity. Given that high heterogeneity between studies was expected, quantitative synthesis of data was not considered appropriate. A systematic qualitative synthesis of study characteristics and results was performed for risk, mortality, and prognostic factors associations with POPs exposure, and separately for each type of tissue sample. The results were considered adjusted only when all important confounders were considered into the models. For breast cancer risk, authors should have considered at minimum age, body mass index or any other estimation of body fat, and breastfeeding or parity as potential confounders. For breast cancer mortality, authors should have adjusted at minimum for age. In addition, studies of breast adipose POPs should have considered breastfeeding or parity as potential confounders. A positive association was defined as an observed higher risk or mortality with higher POPs exposure whereas a negative association was defined as an observed inverse association.n = 81) whereas studies of breast cancer prognosis included five cohort studies for mortality and nine cross-sectional studies for breast cancer prognostic factors. Overall, POPs were measured in peripheral blood in 63 studies, in breast adipose tissue in 32 studies, in adipose tissue other than breast in five studies and in breast tumors in four studies .Characteristics of the 61 studies that examined associations between peripheral blood POPs and breast cancer risk are summarized in Characteristics of the 26 studies that examined associations between breast adipose tissue POPs and breast cancer risk are summarized in Four studies examined associations between POPs in breast tumors and breast cancer risk, of which three included breast tissue surrounding malignant tumors for cases. Two studies compared malignant tumors of cases to benign tumors of controls whereas the two other studies compared malignant tumors of cases to normal tissue of controls. All these four studies were hospital-bases case-control studies with hospital controls, in which cases were on average 50 to 60 years old in the two studies that reported age at diagnosis. One study included exclusively invasive cancers, whereas the three other studies did not report proportion of invasive cancers. No study reported proportion of menopausal patients or the proportion of ER-positive breast cancers .Two studies examined associations between POPs in buttock adipose tissue and breast cancer risk. The recent one was a cohort-nested case-control study with incidence density (risk-set) sampling and included 409 postmenopausal breast cancer patients aged 58 years old in average, of which 78% had ER-positive breast cancers. The proportion of invasive tumors was not reported. The other study was a hospital-based case-control study with community controls including 265 breast cancer patients aged 62 years old in average. The proportions of postmenopausal patients, invasive tumors and ER-positive tumors were not reported .Characteristics of the 14 studies that examined the association between POPs and breast cancer prognosis are summarized in Six studies examined mortality outcomes, with three measuring POPs in peripheral blood, one measuring POPs in breast adipose tissue and two measuring POPs in adipose tissue other than breast. Patients were aged between 58 and 66 years old in average with only two studies reporting the proportion of postmenopausal women (66% and 100% respectively), and two studies reporting proportion of invasive cancers (71% and 86% respectively). The proportion of ER-positive breast cancers varied between 72% and 78% in the three studies that have reported patients ER status .Ten studies examined breast cancer prognostic factors, with three measuring POPs in peripheral blood, six measuring POPs in breast adipose tissue and one measuring POPs in adipose tissue other than breast. Patients were aged between 52 and 65 years old in average with varying proportions of premenopausal and postmenopausal women and none with at least 80% of postmenopausal patients. The proportion of invasive cancers varied between 85% and 100% and the proportion of ER-positive breast cancers varied between 50% and 86% with only two studies including at least 80% of ER-positive breast cancers .Overall, studies reporting associations between peripheral blood POPs and breast cancer risk ranged from moderate to critical risk of bias, whereas studies reporting associations between adipose tissue POPs and breast cancer risk were more likely to be at serious or critical risk of bias.Overall, studies reporting associations between POPs, measured in peripheral blood or in adipose tissue, and mortality outcomes were at serious risk of bias, whereas studies reporting associations with prognostic factors ranged from serious to critical risk of bias.Among the 61 studies that examined associations between peripheral blood POPs and breast cancer risk, 30 reported a positive association with at least one POP, six reported a negative association with at least one POP, and 20 reported no association. Cohort-nested case-control studies with cumulative density (exclusive) sampling, population-based case-control studies not nested in a defined cohort and hospital-based case-control studies with both community- and hospital-controls were more likely to observe an association. Studies that included at least 80% of postmenopausal patients were more likely to observe an association whereas studies that included less than 80% of postmenopausal patients were more likely to observe no association. Studies that included at least 80% of invasive cancers and those with non-reported proportions of invasive breast cancers were more likely to observe an association. Studies with non-reported proportions of ER-positive breast cancers were more likely to observe an association whereas studies including less than 80% ER-positive breast cancers were slightly more likely to observe no association .Among the 26 studies that examined associations between breast adipose tissue POPs and breast cancer risk, 10 reported a positive association with at least one POP, two reported a negative association with at least one POP, and seven reported no association. Studies reporting the proportion of invasive breast cancers were slightly more likely to observe an association. Studies with non-reported proportions of ER-positive breast cancers were more likely to observe a positive association. The four studies of POPs in breast tumors did not report minimally adjusted estimates of risk .The two studies that examined associations between POPs in buttock adipose tissue and breast cancer risk reported negative associations .All three studies that examined peripheral blood POPs reported positive associations with both breast cancer all-cause and specific mortality, of which one study also reported a negative association with all-cause mortality . The onlAmong the three studies that examined peripheral blood POPs and breast cancer prognostic factors, one study reported a positive association with tumor size and lymph-node involvement. Among the six studies of breast adipose tissue POPs, three studies examined associations with meaningful breast cancer prognostic factors but none of them reported minimally adjusted estimates. The only study of buttock adipose tissue POPs and breast cancer prognostic factors did not report minimally adjusted estimates .One to 71 individual POPs were measured in studies of breast cancer risk (median = 9). Organochlorines were measured in 69 studies, of which 43 in blood, 21 in breast adipose tissue, two in adipose tissue other than breast and three in breast tumor. Polychlorinated biphenyls (PCBs) were measured in 57 studies, of which 38 were in blood, 16 in breast adipose tissue, one in adipose tissue other than breast and two in breast tumors. Dioxins were measured in four studies, of which two in blood and two in breast adipose tissue. Perfluoroalkyl substances (PFAS) were measured in three studies in blood. Bisphenol A (BPA) was measured in two studies in blood, polybrominated flame retardants (PBBs and PBDEs) in one study in blood and one study in breast adipose tissue whereas mono-ethyl phthalate (MEP) and parabens were measured in blood in one study respectively . One to 35 individual POPs were measured in studies of breast cancer prognosis (median = 25). Organochlorines were measured in five studies of breast cancer mortality, of which three were in blood, one in breast adipose tissue and two in adipose tissue other than breast, whereas six studies measured organochlorines in relation to prognostic factors, of which three were in blood, four in breast adipose tissue and one in adipose tissue other than breast. PCBs were measured in four studies of breast cancer mortality, of which three were in blood, one in breast adipose tissue and one in adipose tissue other than breast, whereas four studies measured PCBs in relation to prognostic factors, of which two in blood, four in breast adipose tissue and one in adipose tissue other than breast . ParabenThe magnitude of the reported associations between POPs and breast cancer risk and mortality are summarized in p,p’-Dichlorodiphenyldichloroethylene , total or not specified DDE, β- Hexachlorocyclohexane (β-HCH), Dieldrin, PCB 118, PCB 138, PCB 170, PCB 180. Three individual POPs were consistently positively associated with breast cancer risk in breast adipose tissue: p,p’-DDE, total or not specified DDE, PCB 105. When considering POPs positively associated with breast cancer risk in at least one study and no reported negative associations, six individual POPs were positively associated with breast cancer risk in both blood and breast adipose tissue: p,p’-DDE, total or not specified DDE, Hexachlorobenzene (HCB), β-HCH, PCB 118 and PCB 180 , Dieldrin, PCB 174, PCB 177. Total PCBs and three individual POPs were positively associated with breast cancer mortality in breast adipose tissue: PCB 118, PCB 153, PCB 167  and when non-collapsible effect measures such as odds ratios are used . The biaThe strengths of the present systematic review include the use of the Cochrane Reviews rigorous methodology, the extensive and highly sensitive search strategy to retrieve as many relevant studies as possible, the use of a pre-established protocol, the assessment of the risk of bias, and the systematic analysis of results, in addition to considering sources of heterogeneity between studies results. Limitations include the lack of high-quality evidence inherent in observational study designs and the overall critical risk of bias in included studies. Finally, although the present systematic review has identified some individual POPs associated with both breast cancer risk and prognosis that deserve further investigation, it should be emphasized that different POPs have different metabolic profiles and can have synergistic or antagonistic effects, and that proportions of different POPs may vary from one person to another. Thus, approaches considering the simultaneous exposure to different POPs may be more relevant than the isolated analysis of individual POPs. Over the past three decades, numerous epidemiological studies have attempted to assess the association between exposure to POPs and breast cancer. Despite the apparent inconsistencies between studies, which were mainly due to methodological biases and to differences in the biological sample used for exposure measurement, when considering all studies  and all outcomes together (risk and prognosis), there was a trend toward a positive association between exposure to POPs and breast cancer that deserves further investigation. Future studies need to use rigorous methodology by including the relevant study population, using an appropriate biological sample for POPs measurement, controlling properly for confounding and assessing combined effects of POPs.


Breast cancer risk factors have been examined extensively in Western setting and more developed Asian cities/countries. However, there are limited data on developing Asian countries. The purpose of this study was to examine breast cancer risk factors and the change of selected risk factors across birth cohorts in Malaysian women.An unmatched hospital based case-control study was conducted from October 2002 to December 2016 in Selangor, Malaysia. A total of 3,683 cases and 3,980 controls were included in this study. Unconditional logistic regressions, adjusted for potential confounding factors, were conducted. The breast cancer risk factors were compared across four birth cohorts by ethnicity.Ever breastfed, longer breastfeeding duration, a higher soymilk and soy product intake, and a higher level of physical activity were associated with lower risk of breast cancer. Chinese had the lowest breastfeeding rate, shortest breastfeeding duration, lowest parity and highest age of first full term pregnancy.Our study shows that breastfeeding, soy intake and physical activity are modifiable risk factors for breast cancer. With the increasing incidence of breast cancer there is an urgent need to educate the women about lifestyle intervention they can take to reduce their breast cancer risk. Breast cancer risk factors have been examined extensively and the common ones include early age of menarche, late age of menopause, short breastfeeding duration, late age of first full term pregnancy, nulliparity and low parity –5. HowevMalaysia offers a unique opportunity to examine breast cancer risk factors in Asian populations because of its multi-cultural and multi-religious setting, both of which might influence lifestyle and reproductive characteristics, and hence, breast cancer risk. Notably, the three main ethnicities in Malaysia, namely, Malay, Chinese and Indian, represent the three largest ethnic groups in Asia. Breast cancer is the most common cancer among Malaysian women and accounted for 31% of total female cancers . The ageIn this paper, we report the association between clinical, exogenous hormonal, menstrual, reproductive, anthropometric and lifestyle factors with breast cancer from a hospital-based case-control study of 7,663 women in Malaysia. We also present the change of selected breast cancer-related factors across birth cohorts and their implication for breast cancer in Malaysia and potentially other developing Southeast Asian countries.The study was approved by the Independent Ethics Committee, Ramsay Sime Darby Health Care (reference nos: 201109.4 and 201208.1), and the Medical Ethics Committee, University Malaya Medical Centre (reference no: 842.9). All participants provided written informed consent. The study was performed in accordance with the Declaration of Helsinki.The Malaysian Breast Cancer Genetic Study (MyBrCa), initiated in 2002, is a hospital-based case-control study of breast cancer risk factors. The study participants are recruited from two participating hospitals in Selangor, Malaysia: University Malaya Medical Centre (UMMC), a public hospital, and Subang Jaya Medical Centre (SJMC), a private hospital. All patients diagnosed clinically with breast carcinoma are eligible for inclusion as cases. Cases from UMMC were recruited since October 2002, and from SJMC, since September 2012. Controls are healthy women between ages 40 and 74 with no personal history of breast cancer and recruited in the Malaysian Mammography Study (MyMammo) at UMMC and SJMC. At SJMC, MyMammo is a subsidized opportunistic mammogram screening programme that was initiated in 2011; while at UMMC, MyMammo started recruitment in 2014 from patients attending routine opportunistic screening in UMMC.All participants were interviewed by trained interviewers at the hospitals. The participants completed questionnaire that included items related to demographics, personal and family history of cancers, history of breast surgery, menstrual and reproductive history, use of oral contraceptive and hormone replacement therapy (HRT), breast cancer diagnosis (cases only) and history of and motivation of attending mammography screening (controls) only. The participants provided a blood sample that was processed and stored.To date, a total of 4,056 cases and 4,145 controls were recruited and interviewed. Only participants recruited before 1 January 2017 were included in this study. After removing duplicates, males and non-breast cancer cases, the remaining cohort consists of 3,683 cases and 3,980 controls.Ever had breast surgery was defined as whether the participant had surgery for a benign lump or cyst in the breast. Women who had sisters/mothers/daughters with breast cancer were categorized as having a first-degree family history of breast cancer. Ever used oral contraceptives and HRT was defined as at least one month of usage. Post-menopausal status was defined as no menses for the past one year. The participants were categorized as parous if they had at least one full term pregnancy (live or still birth). BMI was calculated as dividing weight (kg) by the square of height (m). Soy products intake included the consumption of tofu, fermented soybeans, tofu pudding, and soy products other than soymilk. The participants reported their average duration of strenuous, moderate and gentle physical activity of three periods: childhood (before 18 years old), young adulthood (18–30 years), and the recent years. Weekly metabolic equivalent (MET)-hours were obtained by multiplying the corresponding MET value of each intensity of physical activity  with the average time spent on physical activity .Cases and controls were compared using chi-square tests for categorical variables and t-tests for continuous variables. Unconditional logistic regressions were conducted to assess the association between risk factors and breast cancer, adjusting for potential confounders and other risk factors. The first models were adjusted for age, ethnicity, and hospital; for history of breast surgery, and anthropometric and lifestyle variables, the models were adjusted for age, ethnicity and education, and only participants from private hospital were included. In the second models, other breast cancer risk factors such as age of menarche, age of menopause, ever had full term pregnancy, first degree family history of breast cancer, and age of first full term pregnancy were added when appropriate. Conditional logistic regression using hospital-, ethnicity- and age- (±5 years) matched samples and unconditional logistic regressions stratified by pre- and post-menopausal status were also conducted. However, the results were similar to the unconditional and unstratified analysis thus they are not reported here.The participants were categorized based on their year of birth into four birth cohorts: those born before 1949, between 1950–59, between 1960–69, and after 1969, and their breast cancer risk factors were compared across the birth cohorts. To compare across ethnicity, analysis of variances (ANOVAs) were conducted for continuous variables while chi-square tests were conducted for categorical variables. To determine whether there was a change of trend in the selected variables across birth cohorts, trend analyses were conducted by entering the birth cohort variable as continuous parameter in the regression models.All analyses were conducted using R .We conducted unconditional logistic regression to examine the association of clinical, exogenous hormonal, menstrual and reproductive factors with breast cancer . CompareOf the menstrual and reproductive factors examined, breastfeeding had the strongest protective effect against breast cancer . Among p2) had 33% reduced risk and those who are obese (BMI ≥ 27.5kg/m2) had 53% reduced risk, after controlling for other risk factors . The increase was more noticeable among Chinese; there was an increase from 50% among the oldest cohort to 79% among the youngest cohort. Only Chinese had a significant increase of breastfeeding duration across birth cohorts (p<0.001); however, breastfeeding duration among Chinese remained low compared with Malays and Indians.There was a significant decrease of total soy intake among Chinese (p<0.001) and Malay (p<0.05) across birth cohorts. Compared with Malays and Indians, Chinese consumed significantly less soy products (p<0.001). However, the number of Malays and Indians who reported their intake of soy products were small compared with that of Chinese.In this hospital-based case-control study of 7,663 Malaysian women, we showed that a higher breastfeeding rate and duration, soy intake and level of physical activity were associated with a reduced risk of breast cancer among Southeast Asian women. Although Southeast Asian countries are experiencing a substantial increase in the burden of breast cancer, there have been limited studies in risk factors for breast cancer in these populations. Before the Malaysian Breast Cancer Genetic study, the previous largest study on breast cancer risk factors in Southeast Asia was from Indonesia and included 526 cases and 1,052 controls , 18. A lThe strongest predictor of breast cancer in our study was breastfeeding, and the inverse association between breastfeeding and breast cancer risk is well documented , 20–22. Our study also found that higher intake of soymilk and soy products is associated with lower risk of breast cancer. Soy is a major food in many parts of Asia and retrospective cross-sectional cohort studies in China and Japan show that increased soy protein intake is associated with reduced breast cancer risk in pre- and post-menopausal women , 24. A sAnother lifestyle factor that is shown to be associated with decreased breast cancer risk in our study is physical activity. This is consistent with the latest World Cancer Research Fund report which showed strong probable evidence that regular physical activity of various intensity decreases the risk of breast cancer among post-menopausal women while among pre-menopausal women, regular vigorous physical activity is associated with reduced risk . A recenIn our study, two risk factors were associated with breast cancer risk in the contradictory direction. First, the consumption of alcohol was associated with a decreased risk of breast cancer in our study. The association of alcohol consumption with increased breast cancer risk has long been established . HoweverIn addition, our study did not find a significant association between parity, age of first full term pregnancy, age of menarche and menopause and breast cancer, which is inconsistent with other studies , 37–41. Since this is a hospital-based case-control study rather than population-based, it might be subject to selection bias. The two hospitals where our participants were recruited were located in urban areas and rural Malaysian women were not included. However, it is noteworthy that these hospitals treat more than 10% of the breast cancer cases in Malaysia. The controls of our study were enriched for women who had a family history of breast cancer because they were participants of an opportunistic mammography screening programme.In conclusion, our study shows that breastfeeding, soy intake and physical activity are modifiable risk factors for breast cancer; and with the increasing incidence of breast cancer there is an urgent need to educate the women about lifestyle intervention they can take to reduce their risk of breast cancer.S1 FileThis file contains the questionnaire items used in this study.(DOCX)Click here for additional data file.