When stratified by histologic subtype, UV-B flux was positively associated with risk of serous tumors in NHS (Ptrend < 0.01), but inversely associated in NHSII (Ptrend = 0.01). Adjusted for confounders, ovarian cancer risk was not associated with vitamin D intake from food or supplements or with predicted 25(OH)D levels. Our study does not strongly support a protective role for vitamin D in ovarian cancer risk.Experimental evidence and ecologic studies suggest a protective role of vitamin D in ovarian carcinogenesis. However, epidemiologic studies using individual level data have been inconsistent. We evaluated ultraviolet (UV)-B radiation, vitamin D intake, and predicted plasma 25-hydroxyvitamin D [25(OH)D] levels as long-term surrogates of vitamin D exposure within the Nurses’ Health Study (NHS) and NHSII. We estimated incidence rate ratios (RRs) and 95% confidence intervals (CIs) for risk of overall ovarian cancer and by histologic subtype using Cox proportional hazards models. Between 1976 and 2010 in NHS and 1989 and 2011 in NHSII, we identified a total of 1,225 incident epithelial ovarian cancer cases  over 4,628,648 person-years of follow-up. Cumulative average UV-B exposure was not associated with ovarian cancer risk in NHS (P Ovarian cancer, a relatively rare but lethal disease, is the fifth leading cause of cancer death among U.S. women . SeveralVitamin D, best known for its role in normal bone development ,5, has nAlthough several ecological studies suggest that women who live in more northern latitudes have higher rates of ovarian cancer incidence and/or mortality ,16,17,18Given the need for additional rigorous epidemiologic studies, we assessed the risk of overall ovarian cancer and ovarian cancer histologic subtypes using multiple surrogates of long-term vitamin D exposure in the prospective Nurses’ Health Study (NHS) cohort, in which most women who developed ovarian cancer over follow-up were postmenopausal at diagnosis, and the prospective NHSII cohort, where the majority of ovarian cancer cases were premenopausal at diagnosis.Characteristics of women according to category of UV-B flux at the midpoint of follow-up (1998/1999) are shown in trend ≥ 0.08; trend < 0.01) for UV-B flux at age 30 years. In a sensitivity analysis, results were similar when 1986 was used as the baseline year instead of 1976 (data not shown). In contrast, a general inverse pattern of association was observed between UV-B flux exposure at different times of life and ovarian cancer risk among NHSII participants. NHSII women in the highest category of cumulative average UV-B flux from 1989–2011 had a RR = 0.67  of developing ovarian cancer compared to women with the lowest estimated UV-B flux exposure. Heterogeneity between cohorts was apparent at virtually every time point (Pheterogeneity ≤ 0.06). Over the course of 2,646,148 person-years of follow-up, 970 NHS women developed incident ovarian cancer. In NHSII, 225 women were diagnosed over 1,982,500 person-years of follow-up. With the exception of average annual UV-B flux exposure at age 30, UV-B flux exposure was not significantly associated with ovarian cancer risk among NHS women (Pheterogeneity ≥ 0.13). Meta-analysis revealed 20% to 40% decreased risk of ovarian cancer among premenopausal women living in areas of high UV-B flux (>113 R-B units) compared to those residing in areas of low UV-B flux (<113 R-B units) at various times of life (interaction ≤ 0.06). Heterogeneity by menopausal status was not observed in either cohort individually (Pinteraction ≥ 0.11), although power was limited. Additionally, we did not observe heterogeneity in the association between cumulative average UV-B flux and ovarian cancer risk by age, BMI, or OC use within NHS (Pinteraction ≥ 0.12) or NHSII (Pinteraction ≥ 0.14).An important difference between the cohorts is the higher proportion of postmenopausal women in NHS compared to NHSII . As a re of life . In contvs. borderline) or aggressiveness . NHS women residing in areas of high versus low UV-B flux (>113 versus < 113 R-B units) were at increased risk of serous ovarian cancer . In contrast, within NHSII, the comparable RR for serous ovarian tumors was 0.59 . Residing in areas of higher UV-B flux was not associated with mucinous or endometrioid/clear cell tumors in either cohort (Ptrend ≥ 0.14), although case numbers were low. Whereas UV-B flux was not associated with borderline tumors, a similar heterogeneous pattern between cohorts was observed for invasive disease (Pheterogeneity < 0.01), which may be driven by the large proportion of serous tumors within this group . UV-B flux was not associated with risk of developing an aggressive invasive tumor, but was associated with less aggressive invasive tumors, again with opposing directions of association between the cohorts (Pheterogeneity < 0.01). Considering the lack of a biological rationale for etiologic differences in serous tumor development or tumor aggressiveness by menopausal status, we explored additional known dissimilarities between the cohorts  levels were marginally positively associated with ovarian cancer risk in the NHS. Participants in the highest predicted tertile of predicted 25(OH)D had a RR = 1.27 D score as long-term surrogates of vitamin D exposure in two prospective cohorts. We did not observe consistent evidence linking the vitamin D pathway with reduced ovarian cancer risk. Among NHS women, these exposures were not associated with a lower risk of ovarian cancer, and in some instances were associated with an increased risk, particularly of serous and less aggressive invasive tumors. However, among NHSII women, living in areas of high UV-B flux generally was associated with a lower risk of ovarian cancer, including serous and less aggressive invasive tumors. The predicted 25(OH)D score also was associated with a lower risk in NHSII, although this association was attenuated when adjusted for BMI. Interestingly, BMI did not confound the association with UV-B flux. interaction ≥ 0.11) was not observed in either cohort individually, although sample sizes within strata were small. Stratifying by subtypes of ovarian cancer revealed that discrepant associations between cohorts were particularly evident for risk of serous and less aggressive invasive tumors. While menopausal status may account for the discrepant results between cohorts, we cannot exclude the possibility of a cohort effect. By design, the oldest woman in NHSII is still younger than the youngest woman in NHS for any given year . Known differences between the cohorts with respect to duration of estrogen-only postmenopausal hormone use, duration of breastfeeding, total physical activity, or smoking status [Divergent UV-B flux associations were observed for ovarian cancer between the two cohorts. We hypothesized that the observed associations with overall ovarian cancer differed because younger women tend to develop tumors with better prognosis than older women. Similar opposing relationships were observed when stratified by menopausal status, in which UV-B flux appeared associated with reduced premenopausal ovarian cancer risk, but a possibly increased postmenopausal ovarian cancer risk depending on timing of exposure. Interaction by menopausal status (Pg status  did not interaction ≥ 0.12), evidence of a positive association between UV-B flux and overall ovarian cancer risk was observed among NHS women who had ever used oral contraceptives (Ptrend = 0.05), but not among never users (Ptrend = 0.93).A shift in reproductive behaviors is apparent among participants of the two cohorts, reflecting general population trends in the U.S. Oral contraceptives first became commercially widely available in 1960 and were quickly adopted by married women to regulate childbearing . Most woIn general, the epidemiologic literature does not support a strong association between vitamin D exposure and ovarian cancer risk . The hypA population-based case-control study in Australia observed reduced risk of ovarian cancer among women in the highest tertile of lifetime ambient UV radiation . The association appeared stronger for women diagnosed with borderline tumors  and displayed similar ~25% to 30% decreased risks for invasive serous, mucinous, and endometrioid tumors . While wtrend = 0.04) [Vitamin D intake from food or supplements was not associated with overall ovarian cancer risk in either NHS or NHSII. Our results are consistent with the majority of studies assessing dietary intake of vitamin D from food and/or supplements as a risk factor for ovarian cancer. Two population-based case-control ,39 and t = 0.04) . The lac = 0.04) . Thus, vSimilar inconsistencies in ovarian cancer risk have been observed in studies that measured circulating levels of 25(OH)D, which accounts for multiple determinants of overall vitamin D status  ,47,48. IThe use of two large prospective cohorts with detailed information on ovarian cancer risk factors collected and updated over 34 years of follow-up are major strengths of our study. The wealth of data available allowed us to examine ovarian cancer risk associated with estimates of ambient UV exposure at various times of life as well as other surrogates for vitamin D exposure. We derived estimates of UV-B exposure based on the Robertson-Berger meter network, which is calibrated to provide measurements of solar UV-B irradiation that correspond with the human erythemal (sunburn) response  as an obOur study has several limitations that must be acknowledged. Participants within our cohorts are female registered nurses recruited from a small proportion of U.S. states. The target population was chosen to maximize internal validity through enhanced reporting accuracy of exposures and health outcomes and a high rate of follow-up, with the trade-off that the women would not be representative of the general U.S. population. Even so, as underlying biology is likely very similar across ethnic groups and social class, observations made in our cohorts may apply more broadly. Further, the states from which participants were recruited represent a large proportion (100 to 180 R-B units) of the average annual UV-B irradiation in the U.S. As participants moved over the course of follow-up, the distribution of average annual UV-B flux estimates expanded to cover the entire range of average annual UV-B flux (93 to 196 R-B units). The use of residence-based UV-B flux is limited by the fact that individual level exposure to solar irradiation may vary depending on actual time spent outdoors and sun-protective behaviors of participants, resulting in exposure misclassification. However, given the prospective study design and the 2-year time lag between exposure assessment and ovarian cancer diagnosis we incorporated into our analysis , any misclassification would have been non-differential with respect to disease, attenuating effect estimates. Although a single assessment may not fully reflect lifetime sun protective behaviors, our results were similar after we adjusted analyses for time spent outdoors and sunscreen use, which were asked on the 1980 questionnaire in NHS and on the 1993 questionnaire in NHSII. Another limitation to consider is that while we adjusted for established ovarian cancer risk factors, confounding by potential unmeasured risk factors such as timing, formulation and/or potency of oral contraceptives, or unidentified environmental exposures cannot be ruled out. The positive association between UV-B flux and overall ovarian cancer risk observed among NHS women appeared limited to women who ever used oral contraceptives, indicating that unmeasured confounding may have influenced our results. The NHS is an ongoing prospective cohort following 121,700 female registered nurses who were 30–55 years of age and from 11 U.S. states  in 1976. NHSII, a similar cohort begun in 1989, enrolled 116,430 female registered nurses aged 25–42 years from 14 US states . At baseline and biennially thereafter, participants from each cohort completed self-administered questionnaires that were used to gather and update detailed information on anthropometric and lifestyle variables, menstrual and reproductive factors, and medical history, as well as identify newly diagnosed cancers and other diseases. Blood samples were collected during 1989 and 1990 from a subset of 32,826 NHS participants and from 1996 to 1999 from 29,611 NHSII participants. Follow-up was high, obtaining >90% of total possible person-years within each cohort. Vital status was ascertained through next-of-kin, the U.S. Postal Service, and the National Death Index; both methods have identified an estimated 98% of deaths in the cohort .Completion of the self-administered questionnaire was considered to imply informed consent. The NHS and NHSII protocols were approved by the Institutional Review Board at the Brigham and Women’s Hospital, Boston, MA, USA.We identified women reporting a new ovarian cancer diagnosis on each questionnaire. For all reported cases and deaths due to ovarian cancer, we either asked permission from the participant or next of kin to obtain medical records pertaining to the ovarian cancer diagnosis or retrieved information about the case from the appropriate state or federal cancer registry. A gynecologic pathologist blinded to exposure status reviewed the medical records or registry data to confirm the diagnosis, as well as abstract stage, histology, and invasiveness. In a subset of 215 ovarian cancer cases, concordance between the medical records and the pathologist’s review was 98% for invasiveness and 83% for histologic type . Our anaAverage annual UV-B flux is a composite measure of biologically effective UV radiation at the earth’s surface based on latitude, altitude, and cloud cover ,54. Weigp < 0.01) [In 1980, a 61-item semi-quantitative food frequency questionnaire (FFQ) was sent to NHS participants to obtain dietary information. Expanded FFQs were subsequently sent approximately every 4 years. Dietary information was first collected from NHSII women in 1991 and every 4 years thereafter using a similar FFQ that asked about annual average intake of over 130 individual foods and 22 beverages. All FFQs inquired about how often, on average, participants consumed each food using standard portion sizes. Nine responses were possible, ranging from never or less than once per month to 6 or more times per day. Intake of dietary vitamin D and other nutrients was calculated by multiplying the portion size of a single serving of each food by its reported frequency of intake, then multiplying the total amount consumed by the nutrient content of the food, and summing the nutrient contributions of all food items using U.S. Department of Agriculture food composition data , while a < 0.01) .Multivariable linear regression models to predict 25(OH)D levels within each cohort previously were developed and validated . Based o2), total leisure-time physical activity , energy-adjusted [Predictors of plasma 25(OH)D levels were categorized as follows: race/ethnicity , BMI D in NHS/NHSII), or unknown date of birth were excluded. Person-time of follow-up was calculated for each eligible participant from the return date of the baseline questionnaire until the date of ovarian cancer diagnosis, diagnosis of another cancer (except non-melanoma skin cancer), bilateral oophorectomy, pelvic irradiation, death, or end of follow-up , which ever occurred first. Women with a cancer diagnosis (except non-melanoma skin cancer), bilateral oophorectomy, or menopause due to pelvic irradiation before the baseline year (vs. less aggressive invasive disease). To estimate long-term exposure to vitamin D, we calculated cumulative updated averages for UV-B flux, vitamin D intake, and predicted 25(OH)D to dampen variation due to measurement error . Further2). We tested the proportional hazards assumption by comparing the log likelihood of models with and without interaction terms between time and the main exposures. p-values were based on two-sided tests and considered statistically significant at p < 0.05. All analyses were conducted using SAS version 9.3 . All multivariable models were adjusted for established ovarian cancer risk factors: number of pregnancies (continuous), duration of oral contraceptive use , tubal ligation , menopausal status , ever use of postmenopausal hormones , and first-degree family history of ovarian cancer . Models assessing ovarian cancer risk associated with vitamin D intake from food were additionally adjusted for total caloric intake (continuous). We evaluated duration of estrogen-only postmenopausal hormone use , duration of breastfeeding (continuous), smoking status , BMI (continuous), and total physical activity  as potential confounders. With the exception of BMI, addition of these covariates did not substantially change RR estimates and, therefore, were not included in the final models. Tests for linear trend were conducted by assigning the median exposure value to each category and treating the variable as continuous to calculate Wald’s statistic. Heterogeneity in exposure associations between cohorts was calculated using the Q statistic and DerSimonian and Laird random effects meta-analysis  was usedIn summary, our results do not support a role for vitamin D in ovarian carcinogenesis. We observed some evidence for reduced risk of ovarian cancer associated with UV-B flux exposure among premenopausal, but not postmenopausal women. A biological rationale for a heterogenous effect of vitamin D by menopausal status is unclear, but appears consistent with observations in biomarker studies ,49,50. O


There is no universal consensus on the relationship between body mass index (BMI) and breast cancer. This meta-analysis was conducted to estimate the overall effect of overweight and obesity on breast cancer risk during pre- and post-menopausal period.All major electronic databases were searched until April 2012 including Web of Knowledge, Medline, Scopus, and ScienceDirect. Furthermore, the reference lists and related scientific conference databases were searched.i) and rate ratio (RRa) for cohort studies and odds ratio (OR) in case-control studies.All prospective cohort and case-control studies investigating the association between BMI and breast cancer were retrieved irrespective of publication date and language. Women were assessed irrespective of age, race and marital status. The exposure of interest was BMI. The primary outcome of interest was all kinds of breast cancers confirmed pathologically. Study quality was assessed using the checklist of STROBE. Study selection and data extraction were performed by two authors separately. The effect measure of choice was risk ratio ; and RRa = 0.99 , but a direct and significant correlation during postmenopausal period: OR = 1.15 ; RRi = 1.16 ; and RRa = 0.98 .Of 9163 retrieved studies, 50 studies were included in meta-analysis including 15 cohort studies involving 2,104,203 subjects and 3,414,806 person-years and 35 case-control studies involving 71,216 subjects. There was an inverse but non-significant correlation between BMI and breast cancer risk during premenopausal period: OR = 0.93 ; RRThe results of this meta-analysis showed that body mass index has no significant effect on the incidence of breast cancer during premenopausal period. On the other hand, overweight and obesity may have a minimal effect on breast cancer, although significant, but really small and not clinically so important. Breast cancer is the most common cancer in women both in the developed and the developing countries, comprising 16% of all female cancers. It is estimated that breast cancer led to 519,000 death in women in 2004 The etiology of breast cancer is not well known. However, several risk factors have been suggested to have an influence on the development of this malignant tumor including genetic, hormonal, environmental, sociobiological and physiological factors There is no universal consensus on the relationship between BMI and breast cancer. To date, a few meta-analyses have been conducted to estimate a summary measure of the effect size of overweight and obesity on breast cancer. However, these studies were limited to the English language studies cited by Medline We planned to include cohort and case-control studies addressing the association between body mass index and breast cancer. We developed a search strategy using and combing a set of keywords including breast cancer, body mass index, waist hip ratio, obesity, overweight, body size, cohort studies, case-control studies, and observational studies. We search all major electronic databases including Web of Knowledge (January 1945 to April 2012); Medline (January 1950 to April 2012); Scopus (January 1973 to April 2012); ScienceDirect (January 1823 to April 2012).In order to find additional references, we scanned the reference lists of all retrieved studies. In addition, we contacted authors of retrieved studies for additional unpublished studies. Furthermore, the following conference databases were searched for unpublished data until April 2012:www.asco.orgAmerican Society of Clinical Oncology; available from: www.cancer.orgAmerican Cancer Society; available from: www.iarc.frInternational Agensy for Research on Cancer; available from: 2). Based on the World Health Organization classification We included prospective cohort studies and case-control studies investigating the association between BMI and breast cancer irrespective of publication date and language. The retrospective cohort and matched case-control studies were excluded. We included those apparently healthy women irrespective of age, race and marital status. The exposure of interest was obesity and overweight using BMI. The term ‘BMI’ is a commonly used index to classify overweight and obesity in adults and is defined as the weight in kilograms divided by the square of the height in meters (kg/mTwo authors (ZC and ADI) read the retrieved publications separately in order to identify the studies that would meet the inclusion criteria of this review. The authors were not blinded to the authors' names of the publications, journals, and results. Any disagreements were resolved by adjudication with a third author (JP). The inter-authors reliability based on kappa statistics was 85%.Two authors (ZC and ADI) extracted the data from the included studies. The variables which were extracted for data analysis included study design, year and location of study conduction, sample size, number of outcomes, mean age, gender, and body mass index. The extracted data were entered in the electronic data sheet. In cases of missing data or need for clarification, study authors were contacted.We intended to assess the risk of bias of the included studies using the recommended checklist of STROBE i) and rate ratio (RRa) and that of case-control studies was odds ratio (OR). RRi was defined as the probability of a disease in exposed people to the probability of the disease in unexposed people in a cohort study. RRa was defined as the proportion of a disease in exposed people to a specified person-year  in a cohort study. OR was the proportion of the exposed population in whom disease has developed over the proportion of the unexposed population in whom disease has developed in a case-control study The effect measure of choice for cohort studies was risk ratio . Both Review Manager 5 2 or Chi2) at the 5% significance level (P<0.05). We quantified inconsistency across studies results using I2 statistic 2 or Tau2) statistic We explored statistical heterogeneity using the chi-squared  (a) (not shown) in cohort studies.The effect of BMI on breast cancer risk during pre- and postmenopausal period was assessed using odds ratio (OR)  and 2 ini)  and 4 ani = 0.97 ; and RRa = 0.99 . That means women who were overweight or obese during premenopausal ages were at lower risk of breast cancer compared to women with normal weight although the observed inverse correlation was not statistically significant.The results of both case-control and cohort studies showed that increase in BMI during premenopausal period reduced the risk of breast cancer: OR = 0.93 ; RRi = 1.16 ; and RRa = 0.98 . That means the women who were overweight or obese during postmenopausal period were significantly at higher risk of breast cancer.The results of both case-control studies and cohort studies showed that overweight and obesity in postmenopausal period increased slightly the risk of breast cancer: OR = 1.15 ; RRi and OR values, obese women had lower risk of breast cancer compared to overweight women during premenopausal period. However, the correlation was reversed during postmenopausal period so that obese women were at higher risk of breast cancer compare to overweight women although the difference was not statistically significant.The effect of overweight and obesity on the breast cancer risk was evaluated separately. According to the RRWe classified the cohort studies based on the selected items of the recommended checklist of STROBE into high-quality (33%) 2 test and the I2 statistics. The results of Chi2 test indicated that case-control studies were significantly heterogeneous (P<0.001). The I2 statistics for premenopausal period was 72% and for that of postmenopausal period was 80% (P = 0.220). The I2 statistics for premenopausal period was 32.8% and for postmenopausal period was 34.5%  among the results of the included studies. However, care must be taken in the interpretation of the statistical tests for heterogeneity. The Chi2 test has low power when the sample size is small. On the other hand, the test has high power in detecting a small amount of heterogeneity that may be clinically unimportant when there are many studies in a meta-analysis There was evidence of heterogeneity .In addition, our work brought some new information about the relationship between BMI and breast cancer, including (a) consolidation of the data to obtain summary measure of odds ratio, risk ratio, and rate ratio estimates regarding the effect of BMI on breast cancer; (b) non-significant inverse correlation between overweight and obesity and the incidence of breast cancer during premenopausal period; (c); significant direct correlation between overweight and obesity and the incidence of breast cancer during postmenopausal period; (d) the impact of various variables on the correlation between BMI and breast cancer such as studies designs, period of menopause, various types of BMI, and quality of the studies.The results of this meta-analysis showed that body mass index has no significant effect on the incidence of breast cancer during premenopausal period. On the other hand, overweight and obesity may have a minimal effect on breast cancer, although significant, but really small and not clinically so important.


Breast cancer is one of the leading causes of cancer deaths amongst Ugandan women. Most women live through challenging and emotional experiences having been diagnosed with breast cancer. The purpose of this study was to explore the lived experiences of women diagnosed and living with breast cancer.This was an exploratory qualitative study using a convenience sample (n = 12) of women confirmed with breast cancer and reporting to the Radiology department for imaging. In-depth individual interviews were conducted and findings were summarized into themes, representative of the participants’ lived experiences.All women in this study initially went through emotional trauma of living with breast cancer. However, with time, they seemed to accept and cope with their situation. Four major themes summarizing their experiences included: Thought of Death, Strength to live, Loss of female identity and sexuality and Coping mechanisms.This study provided a unique insight of the experiences of women living with breast cancer. By listening to their narratives, one could see emotional pain, anger, anxiety, strength to live and many more. Although women with breast cancer get clinical management, there is need to design holistic palliative healthcare services including counseling to assist then cope with life. Breast cancer is one of the common cancers amongst Ugandan women in addition to Kaposi's sarcoma and cervical cancer , 2. The The experience of living with breast cancer is unique to every woman , 10. SucIt has also been reported that women diagnosed with breast cancer can lose self-esteem and struggle to acquire emotional coping mechanisms especially after disfigurement following mastectomy , 19. ForBreast cancer diagnosis confronts women with the fact that life may never be the same again. Landmark and Wahl  reportedThe study was conducted within the Radiology department of Mulago National referral and teaching hospital in Uganda. It was an exploratory qualitative study using in-depth individual interviews. Questions asked were open ended and explored women's experiences of living with breast cancer. The questions were first piloted before carrying out the interviews. Convenience purposive sampling was used to select the women who participated in the interviews. The women were selected from among those diagnosed and confirmed with breast cancer and reporting to the Radiology department for imaging.thwoman since no new themes were emerging. A quiet place was chosen for the interviews. The responses were tape-recorded and later transcribed by the researcher, each interview transcription taking place immediately after carrying out the interview.There were twelve women included in the study that had been diagnosed and confirmed with breast cancer and had reported to the Radiology department for imaging. All women confirmed with breast cancer were eligible to participate in the study regardless of age, occupation, background or any other clinical characteristics. The final number of women to be included in the study was not predetermined, but was arrived at by the principle of data saturation. Data collection was terminated at the 12Thematic analysis was used for data analysis. This involved proof-reading raw data and coding it into categories of similar meaning. The categories were related to each other leading to the emergence of themes that were used to report findings. Data analysis commenced immediately after the first interview using an iterative process.The women provided verbal consent after explaining to them the purpose of this study. No participant was identified by name or opinion and all individual responses were kept confidential by the researcher. Data was securely kept in electronic format and secured by a password. Every participant was told that they were free to refuse participating at any time, even free to terminate the interview without any prejudices against them. They were told that this study would not affect their treatment and care in any way. A nursing assistant helped out in accessing potential participants as a third party. At the end of each interview, a nursing assistant once again counseled each lady about breast cancer as a way of calming down any tensions and memories the interview could have invoked.Lastly, the study received ethical approval from School of Medicine review board of Makerere University.Of the twelve women in this study, 2 were married, 3 were divorced and 7 were not in any relationship. Four of the women lived in town while the remaining eight came from rural communities. All the women had received at least primary education. 4 women were between 30 and 40 years, 6 were between 40 and 50 years and the remaining 2 were between 50 and 60 years of age. The results indicated that living with breast cancer provided different experiences related to physical, psychological, social and existential issues. These experiences were summarized into various themes below:“It was such a shock that I saw myself in the grave”. “When I was told that I have breast cancer, I knew all was over for me. I immediately started thinking of how am going to leave my children in this world all alone ”. “The news shocked me because I knew my days were numbered. I immediately thought about updating my will because death was just around the corner for me”. “When the doctor told me that I have cancer, I regarded myself as a moving dead body. The whole world seemed meaningless to me". However, the most shocking experience was given by a lady who said that: "I was shattered?. I have always been close to God, but this was unbelievable to be happening to me? I called people at home to start preparing my grave".This theme seemed to have dominated in the early stages of being diagnosed with breast cancer as women expressed their concerns about the eminent death. There were emotional reactions evident that seemed to be traumatic to the women as they contemplated dying. All women experienced shock after telling them they had breast cancer and all of them thought about death at that point in time as evidenced in the quotes below;"I have realized that I need to use my remaining time to be good to my family and accomplish what I needed to achieve in life”. “Although I know my life is limited, I will seek strength from the Lord such that I work for my children till I cannot work anymore”. “At first, I cried every day. But after sometime, I realized that it is not the end of the world and I still had something good to contribute not only to my family, but also to my country. Now am at peace”. “After some time of reflection, I realized that I was not the first to get breast cancer and I will not be the last. This gave me strength to continue doing my work and am now pushing on with it".This theme was especially active after overcoming the fear of death due to breast cancer. Most of the energy from women seemed to be channeled towards fighting for life and living on. This power to live was related to aspirations for the future, fear for relationships and fear for losing families. A common thread in this theme was the strength for the women to push on even after being diagnosed with breast cancer as evidenced in the following quotes:"I know my breast was going to be cut off. I was not crying much for the loss of the breast, but for the loss of my husband because I knew he could not be with me anymore”. “It is the breasts that make up a woman and if I lose them, am becoming a man”. “I do not think I will ever give birth again because if my breasts are cut off due to cancer, how will I breast feed my babies”. “After re-collecting myself, I told all my close friends and relatives to keep quiet about my situation since my wedding was in three months time. The man would have abandoned me and the wedding if he was told that I had breast cancer".This also dominated the responses as all the women felt that being diagnosed with breast cancer was equivalent to losing their female sexuality. This appeared to be associated with repercussions that were likely to develop in their relationships with partners and in their identity as women as evidenced in the following quotes:"Ever since I was told that I have breast cancer, am now very close to God and have established a personal relationship with Him. He is now my comfort”. “Members of my church have helped a lot in living with the situation. I now have no more fears”. “I thank my husband, children, relatives and friends who have given me encouragement and support. I now feel healthy and carrying on with my activities”. “I take my medicine as given to me by the doctor. I think this has kept me well and alive. I still have hope that with this medicine and prayers to my God, I will get cured of this cancer".This also emerged as an important theme from the women. All the women in this study demonstrated a strong will to live on with breast cancer and employed diverse methods of coping with the situation. Common among these were; psycho-social support from families and friends, getting closer to God (religious support), engaging in productive activities and remaining healthy with the cancer treatment. Some of these are evidenced in the quotes below:Results from this study indicate that living with breast cancer comes with a lot of psycho-social challenges to women in support of what Land mark and Wahl reported . The iniHowever, after some time, the women appeared to have accepted and adapted to the situation to live with breast cancer. It is most likely that after reflection about their situation, the women realized that they still had a life to live if they positively accept their situation. Therefore, fighting to survive seemed to give the women a new meaning to life, an observation reported in other studies –24. AddiIn this study, it was apparent that women went through a lot of emotions when they were told that they had breast cancer, however as time passed on, these reactions seemed to regress and women gained more confidence. This is probably because of the time lag that allowed women to mediate and reflect upon their situation. This self-reflection could have allowed them to gain more control of their lives and the spirit to continue living. Keller  reportedHowever, this time of reflection for women is likely to be more fruitful if they receive emotional and social support from family, relatives, friends and even health professionals. The importance of psycho-social support emerged as a strong motivator for the women to cope. Conversely, if a woman with breast cancer is abandoned by her people, it may trigger a sense of worthlessness, and eventually accelerate her death. This emotional support is particularly needed from spouses, friends and relatives to give the women strength. Unfortunately, like some women expressed, some of these anchors of support disappear on learning that the woman has breast cancer. This therefore stresses the importance of continued support for women living with breast cancer.The diagnosis of breast cancer in most cases awakens fear, anxiety, worthlessness, anger, doubt and self-pity. However, like this study has shown, it appears like all these are transitory and women eventually develop coping mechanisms to live on . In thisAdditionally, women diagnosed and living with breast cancer are still normal and active people and should be supported to engage in economically and socially productive activities. This also applies to positions of employment and relationship situations. This study has demonstrated that women living with breast cancer wish to regain their former patterns of living and should not be isolated by those who are essentially supposed to assist them achieve this goal. Landmark et al  advise tIt should be observed that none of the women mentioned receiving psycho-social support from health care providers. Although women diagnosed with breast cancer receive cancer treatment from health workers, they still need professional support from these health workers like counseling and continued holistic palliative care. There is need for policy makers in ministries of health to design nation- wide health care support interventions for women living with breast cancer as seen in cases of HIV/AIDS.Although this study has documented some of the lived experiences of women living with breast cancer, there are limitations worth noting. Firstly, the small number of participants in this study may not necessarily portray the experiences of all women living with breast cancer in Uganda. Many more exploratory studies of this nature are needed to supplement this. Secondly, this study did not discriminate between women who had been newly diagnosed with breast cancer and those who had lived with it for some time and probably receiving treatment. These two categories of women probably have differing experiences and more studies are needed to compare experiences of various strata of women diagnosed and living with breast cancer. However, the study provides a basis for many more studies on this subject.This study has demonstrated that women go through challenging experiences after being diagnosed with breast cancer starting including even thoughts of death and anxiety. Although they develop coping mechanisms, these women need psycho-social support from friends, family, spouses and relatives to cope. Health workers are particularly encouraged to go beyond cancer treatment, but offer holistic approaches including counseling to such women. The ministries of health are particularly called upon to design palliative care interventions for women living with breast cancer as has been done with HIV/AIDS.


There is plausible biological evidence as well as epidemiologic evidence to suggest coffee consumption may lower endometrial cancer risk. We evaluated the associations between self-reported total coffee, caffeinated coffee and decaffeinated coffee, and endometrial cancer risk using the Women’s Health Initiative Observational Study Research Materials obtained from the National Heart, Lung, and Blood Institute Biological Specimen and Data Repository Coordinating Center. Our primary analyses included 45,696 women and 427 incident endometrial cancer cases, diagnosed over a total of 342,927 person-years of follow-up. We used Cox-proportional hazard models to evaluate coffee consumption and endometrial cancer risk. Overall, we did not find an association between coffee consumption and endometrial cancer risk. Compared to non-daily drinkers (none or <1 cup/day), the multivariable adjusted hazard ratios for women who drank ≥4 cups/day were 0.86  0.63, 1.18) for total coffee, 0.89  for caffeinated coffee, and 0.51  for decaf coffee. In subgroup analyses by body mass index (BMI) there were no associations among normal-weight and overweight women for total coffee and caffeinated coffee. However among obese women, compared to the referent group (none or <1 cup/day), the hazard ratios for women who drank ≥2 cups/day were: 0.72  for total coffee and 0.66  for caffeinated coffee. Hazard ratios for women who drank ≥2 cups/day for decaffeinated coffee drinkers were 0.67 (0.43-1.06), 0.93 (0.55-1.58) and 0.80 (0.49-1.30) for normal, overweight and obese women, respectively. Our study suggests that caffeinated coffee consumption may be associated with lower endometrial cancer risk among obese postmenopausal women, but the association with decaffeinated coffee remains unclear. Known risk factors for endometrial cancer, such as use of unopposed estrogen, obesity, early menarche, and late age at menopause, support a hormonal etiology ,3,4,5,6.To date three prospective cohort studies have evaluated the association between coffee consumption and endometrial cancer risk ,18,19. TWe utilized the Women’s Health Initiative (WHI) Observational Study (OS) Research Materials obtained from the National Heart, Lung, and Blood Institute (NHLBI) Biological Specimen and Data Repository Coordinating Center to prospectively examine the association between coffee and endometrial cancer risk. With a large study population and access to detailed information on coffee consumption, these data provided a unique opportunity for evaluating the independent associations of caffeinated coffee and decaffeinated coffee overall and by BMI status.The WHI OS is a multi-center, ethnically diverse prospective cohort study that enrolled 93,676 eligible women aged 50-79 years. Details regarding study design, participant enrollment and eligibility have been previously described . Brieflyn = 5355), hysterectomy  or both-previous cancer other than non-melanoma skin cancer and hysterectomy (n = 6720) at baseline. We additionally excluded participants for whom there was no information available on follow-up (n = 260) or coffee consumption (n = 1935). Our primary analyses included 45,696 participants who contributed a total of 342,927 total person-years of follow-up. The average follow-up among the 45,696 participants was 7.5 years. From the 93,676 women at baseline, we excluded participants from analysis who reported having a previous cancer other than non-melanoma skin cancer , cups of caffeinated coffee each day and cups of decaffeinated coffee each day. Participants who did not drink coffee on a daily basis served as the referent category for all analyses. Women also reported coffee consumption on a similarly worded questionnaire administered at year 3 of follow-up.Adjudication of endometrial cancer cases in the WHI has been described previously . Briefly2; continuous), age at menarche , age at menopause , parity , tea consumption , diabetes status , physical activity ), history of oral contraceptive use , family history of endometrial cancer  and total daily energy intake . We created a parsimonious model using stepwise selection and likelihood ratio tests, and adjusted for age, ethnicity, unopposed estrogen use, combined estrogen-progestin use, smoking, and BMI. Given its strong association with coffee consumption, we separately adjusted for cigarette smoking by duration of regular smoking , and pack-years of smoking , and similar results were obtained using each approach. In models assessing caffeinated coffee and decaffeinated coffee, types of coffee were mutually adjusted for each other. Simultaneously adjusting for all other considered variables did not change our findings. Follow-up time for each participant was accrued from enrollment to date of diagnosis of endometrial cancer, date of hysterectomy, death, or date of last follow-up contact, whichever came first. Women who had a diagnosis of any other cancer during follow-up were not censored and were considered to be at risk of endometrial cancer. The assumption of proportional hazards in the Cox PH models was tested by including an interaction term with time, for all beverage consumption variables individually. There was no significant evidence of violation of the PH assumption in all reported models.Baseline characteristics of participants were compared by cups of total coffee, caffeinated coffee and decaffeinated coffee consumption categories using chi-square tests or ANOVA as applicable. All coffee variables were categorized as: none or <1 cup/day (referent category), 1 cup/day, 2-3 cups/day and ≥4 cups/day for the overall analyses, and none or <1 cup/day, 1 cup/day and ≥2 cups/day for subgroup analyses, due to small numbers. Cox proportional hazard (PH) regression models were used to estimate hazard ratios (HR) and 95% confidence intervals (95% CI) for total coffee, caffeinated coffee, and decaffeinated coffee consumption. We considered as potential confounders factors associated with coffee consumption as well as known or suspected risk factors for endometrial cancer. Variables initially considered were: age , ethnicity , unopposed estrogen use , combined estrogen-progestin use , smoking , BMI  adjusted for age only and for variables reported in the parsimonious multivariable adjusted models, for all coffee variables. For the sub-group analyses we only report results from the parsimonious models, as age adjusted hazard ratios were not different. We additionally performed analyses restricted to participants that reported drinking exclusively caffeinated coffee or drinking decaffeinated coffee at baseline. In the overall analyses, we formally tested for interaction between BMI and coffee consumption by adding an interaction term to our multivariable regression models.We conducted further sub-group analyses by BMI as reported at baseline  prior to the year 3 time point were not included.p values <0.05 were considered statistically significant, with no adjustment for multiple comparisons. All analyses were performed using SAS version 9.2 .Two-sided The majority of women (71.1%) reported drinking coffee (caffeinated or decaffeinated) on a daily basis at baseline. Of these daily coffee consumers, the proportion of women that drank caffeinated coffee only, decaffeinated coffee only, and both was 53.4%, 19.3%, and 27.3%, respectively. Compared to non-daily drinkers, total coffee consumers were more likely to be white and current smokers, and less likely to be obese and non-smokers . SimilarWe observed no overall inverse associations for total coffee, and caffeinated coffee and a marginally statistically significant inverse association between decaffeinated coffee and endometrial cancer risk. Compared to non-drinkers, the multivariable adjusted hazard ratios (HR) and 95% confidence intervals (95% CI) for women who drank ≥4 cups were: 0.86 (95% CI: 0.63-1.18) for total coffee, 0.89 (0.63-1.27) for caffeinated coffee only, and 0.51 (0.25-1.03) for decaffeinated coffee only .n = 8834); HR: 0.88 (95% CI: 0.57-1.57). We did not find any association among overweight women who drank ≥2 cups of decaffeinated coffee each day (HR: 0.93 (0.55-1.58)).In sub-group analyses by BMI category, there were no statistically significant associations among normal weight and overweight women for total coffee, or caffeinated coffee consumption with endometrial cancer risk . For decn = 7511) or decaffeinated drinkers only (n = 4649), strengthened the effect estimates for both caffeinated coffee (HR: 0.60 (95% CI: 0.39-0.93)) and decaffeinated coffee (HR: 0.70 (0.37-1.33)). The interactions between the three coffee variables and BMI were not statistically significant; with p values of 0.38, 0.30 and 0.51 for total coffee, caffeinated coffee and decaffeinated coffee, respectively.In obese women, there was a borderline-significant lower risk (HR 0.72 (95% CI 0.50-1.04)) among women who drank ≥2 cups of total coffee/day, compared to obese women who were non-daily drinkers . This inAdjustment for cigarette smoking by duration of smoking or pack-years of smoking separately while adjusting for variables included in the main models did not change our results. After adjusting for duration of smoking, the multivariable adjusted HRs and 95% CI among obese women who drank ≥2 cups of caffeinated coffee and decaffeinated coffee were 0.65 (0.44-0.95) and 0.79 (0.49-1.29) respectively, compared to non-daily drinkers. Similarly, after adjusting for pack-years of smoking associations were 0.66 (0.45-0.97) and 0.78 (0.47-1.27) for caffeinated coffee and decaffeinated coffee, respectively.n = 3898). However, the inverse association between ≥2 cups caffeinated coffee consumption and endometrial cancer risk among obese women was strengthened (HR: 0.40 (95% CI: 0.18-0.91)).We observed similar results in analyses further restricted to women who reported drinking the same number of coffee cups/day category at both baseline and year 3 (vs. non-daily tea consumption) or within BMI categories (data not shown).We also examined the association between tea consumption and endometrial cancer risk, while adjusting for variables in the parsimonious models and coffee consumption. No statistically significant associations were observed overall , we observed a lower risk of endometrial cancer among obese women who drank ≥2 cups/day of coffee. Though these observations were based on a small number of cases, the results suggest that coffee consumption may lower endometrial cancer risk among obese women, who also have the highest absolute risk in comparison to thin women.In this large prospective cohort study of post-menopausal women, we found some evidence of an overall inverse association between decaffeinated coffee and endometrial cancer risk, and no such overall associations for total coffee, or caffeinated coffee. However, within the subgroup of obese women (baseline BMI ≥ 30 kg/mThree other studies ,18,19 haThe unopposed estrogen hypothesis provides a compelling explanation for the etiology of most endometrial cancer . It posiWe observed an overall association with decaffeinated coffee, although the pattern was not consistently observed across all body mass index categories. Decaffeinated coffee intake has been linked to decreased C-peptide levels in postmenopausal women . ContrarCYP1A2 gene are associated with differential metabolism of caffeine [Major strengths of our study include its prospective design, detailed information on coffee consumption and potential covariates, and adjudication of endometrial cancer cases. Due to the prospective nature of the study and limited loss to follow up, the likelihood of recall bias and selection bias are minimal. Additionally, the large study population allowed us to examine the independent effects of caffeinated and decaffeinated coffee consumption. Our study is limited by its reliance on self-reported values for coffee consumption without specification of serving size per cup, which may have attenuated our results to towards the null. We additionally did not have information on other sources of caffeine, such as soft drinks and energy drinks, nor were data available on use of additives such as sugar, honey, and cream. Furthermore, we did not have access to DNA samples and were not able to evaluate potential gene-diet interactions. For example, polymorphisms of caffeine  and estrcaffeine . ExaminaIn conclusion, we extend evidence from previous publications in suggesting a lower risk of endometrial cancer among obese women for caffeinated coffee. Our results for decaffeinated coffee consumption and endometrial cancer risk were slightly suggestive of an inverse association; however we observed an inconsistent pattern in subgroups of women defined by body mass index. Furthermore, our study adds to an increasing body of evidence that suggests an inverse association between coffee consumption and several types of cancer including, bladder, breast, endometrial, hepatocellular, pancreatic and prostate cancer .


Breast cancer risk reduction has the potential to decrease the incidence of the disease, yet remains underused. We report on the development a web-based tool that provides automated risk assessment and personalized decision support designed for collaborative use between patients and clinicians.Under Institutional Review Board approval, we evaluated the decision tool through a patient focus group, usability testing, and provider interviews . This included demonstrations and data collection at two scientific conferences .Overall, the evaluations were favorable. The patient focus group evaluations and usability testing (N = 34) provided qualitative feedback about format and design; 88% of these participants found the tool useful and 94% found it easy to use. 91% of the providers (N = 23) indicated that they would use the tool in their clinical setting.BreastHealthDecisions.org represents a new approach to breast cancer prevention care and a framework for high quality preventive healthcare. The ability to integrate risk assessment and decision support in real time will allow for informed, value-driven, and patient-centered breast cancer prevention decisions. The tool is being further evaluated in the clinical setting. Involving women in decisions related to their own health is integral to the adoption of preventative health measures. This is particularly true in efforts intended to reduce breast cancer incidence. For example, although more than 2.4 million women in the United States are projected to benefit from breast cancer risk-reducing medications such as tamoxifen or raloxifene ,2, fewerMaking well-informed decisions about breast cancer risk reduction requires women to weigh the benefits and risks of the options and determine how they value the possible outcomes of each. In addition, women’s risk of breast cancer and preferences for interventions around breast cancer screening and risk reduction vary widely and must be included in their decision making. In this setting, there is a need for: 1) personalized risk assessment, 2) unbiased, comprehensive education about the options for risk reduction, and 3) an opportunity for meaningful consideration of the risks and benefits involved on the part of the patient.Decision aids are tools designed to support informed medical decision making and can facilitate efficient communication between patients and clinicians about these risks and benefits. These tools offer enhanced knowledge about interventions, allowing women the opportunity to weigh the risks and benefits of their choices. They have been shown to help patients increase their knowledge of their options, to reduce their decisional conflict, and to make decisions that align with their goals and values ,8. CliniWhile these tools have been shown to be both feasible and beneficial, they have a number of limitations. Most are not tailored to patients’ individual breast cancer risk or health characteristics. Also, they are not designed for use within the primary care setting, where risk reduction efforts are likely to have the strongest impact . FurtherTo address these decision-making challenges to breast cancer risk reduction, we developed a comprehensive framework to guide and inform consultations . Based oThe design of the decision aid prototype and assessment of its clinical feasibility and effectiveness has been explained in depth previously . It was To extend the generalizability and broaden the applicability of the decision aid, we transitioned the prototype into a web-based tool called BreastHealthDecisions.org designed for multiple clinical settings, including primary care clinics. In this paper, we describe the process used to develop BreastHealthDecisions.org, the results of the usability testing, and how stakeholder input was incorporated into the generalizable web-based tool.BreastHealthDecisions.org was developed through an iterative research process that allowed for continuous improvement of the decision support tool. Research involved assembling a team of expert advisors, developing the decision aid prototype, and evaluative work with key stakeholders.Our earlier framework and decision aid were guided by the decision analytic framework developed by Howard and colleagues ,23. The The content, format, and style of the decision aid were developed using current literature on risk communication -28 as weThe web tool, developed by Drs. Ozanne and Esserman and colleagues at UCSF, generates tailored patient-specific risk assessments using established risk models  based on patient data -32. Conshttp://www.BreastHealthDecisions.org to support open access for the decision aid. The web-based version is designed for use by both patients and their providers, specifically breast specialists, gynecologists, and primary care physicians. The tool uses patent risk factors including family history of cancer and patient biomarkers–such as atypia, breast density, and genetic mutations–to generate estimates of risk and intervention benefits.We have obtained the domain name The web-based tool was designed with the following core characteristics:individual tailoring to patients’ breast cancer risk and overall health using validated risk assessment models,• primary care setting, by providing content relevant to the general population of women,• applicability to the risk of side effects from risk reduction measures, one of the main barriers to use,• addresses the at the time of the consultation, facilitating communication between patients and clinicians, and• designed for use web-based platform, providing an easily accessible platform for both patients and clinicians that can be updated rapidly and disseminated broadly.• To evaluate the content and usability of BreastHealthDecisions.org, qualitative data was collected through usability testing with patients and health care providers. Discussions with the primary users of the decision aid and relevant experts were conducted to provide us with information about how to optimize the decision aid for future use in clinical settings. We aimed to elicit feedback on the content, structure, ease of use, and design of the decision aid.We targeted three groups of participants: patients, providers, and decision scientists and experts in shared decision-making. The participants provided verbal consent for the focus group and semi-structured interviews, according to the research protocol that was reviewed and approved by the institutional review boards at the University of California, San Francisco and Massachusetts General Hospital.The patient focus group consisted of breast cancer survivors who were recruited from a list of volunteers at a large cancer center. We chose breast cancer survivors to avoid raising patient anxiety in women who had not previously been diagnosed with breast cancer. We also chose them because they are knowledgeable about the issues surrounding breast cancer and could provide their opinion about the decision aid, but were not currently facing the specific decisions described in the decision aid. The health care provider participants were recruited at the 2009 San Antonio Breast Cancer Symposium (SABCS), held in San Antonio, TX. These participants, targeted as potential users of the decision aid, were asked to complete pilot testing at SABCS. Targeted evaluators included genetic counselors, primary care physicians, surgeons, oncologists, and nurse practitioners.The final group of participants consisted of experts in the field of decision sciences and shared decision-making. These participants were recruited from the 2009 International Shared Decision Making Conference (ISDM), held in Boston, MA.A case study for a high-risk woman was presented to the patient focus group participants and the group walked through the decision aid as it would be used in a consultation. After the presentation, a group discussion was initiated by an experienced facilitator using a semi-structured guide designed by the research team. The discussion lasted for approximately two hours and was transcribed. The participants commented on the usefulness of each section of the decision aid. Participants also gave advice about the relevance of the content, the ease of use of the design, and general comments about the structure of the tool.The health care provider participants viewed the web-based decision aid and completed one-on-one interviews with the principal investigator of the study. The interviews used a semi-structured interview guide that asked the participants about the appropriateness and usability of the decision aid in different clinical settings. The decision scientist experts went through a similar process, and were asked questions relating to the design and structure of the decision aid with respect to patient decision-making. All participants in the interview groups were also asked about the scientific relevance and validity of the content used in the decision aid and the usability of the decision aid. These participants were also invited to submit general comments about the structure and design of the tool.Each participant across the three groups was asked to indicate: 1) if they found the decision aid useful and 2) if they found the decision aid easy to use. Clinicians were asked additionally: 3) if they would use the tool in their clinics.Information gathered from the focus group evaluations and usability testing provided qualitative feedback about format and design of the decision aid. Six breast cancer survivors participated in the patient focus group recruited from a patient research registry. Twenty-three participants completed pilot testing of the decision aid at SABCS, and five participants completed usability testing of the decision aid at ISDM. Table Overall, the participants found the usability of the decision aid to be favorable. As shown in Table Based on the feedback of the pilot testing (previously reported)  and the The summary results indicate that the decision aid was designed in a way that was easy to follow and easy to use. The predominant opinion of the participants was that it was important for the tool to remain online to allow patients to refer to it after the consultation. It was also important to the participants for the decision aid to be tailored to each patient’s individual risk of developing breast cancer.Based on feedback from the advisory board and scientific advisors, the content was divided into five cohesive sections presented below. The design of the tool will allow users to skip modules that are less relevant and spend more time on modules of greater interest. Figure 1) Risk Assessment: Presents a personalized assessment of a woman’s risk of developing breast cancer. Five-year and lifetime risk scores are calculated using four validated models  -32 to pr2) Perspective & Goals: Compares an individual woman’s risk of breast cancer to other women and to risk of other causes of death. Individual breast cancer risks are compared to an average, age-matched woman to offer better risk discrimination and to improve risk communication. Individual breast cancer risks are also displayed in the context of data on leading causes of death, such as heart disease and lung cancer, to help prioritize breast cancer risk.3) Learning More About Your Risk: Presents information about tests that can help a woman learn more about her risk of developing breast cancer. Biomarkers  stratify patients by risk and identify the likely benefit from an intervention.4) Risk Reduction: Provides a comparison of options for reducing a woman’s risk of developing breast cancer. Presents information about lifestyle changes, chemoprevention, and surgical options while reflecting quality of life trade-offs.5) Summary & Next Steps: Captures the synopses and decisions related to risk-assessment and risk-reduction options for patients and can be incorporated into medical records.In this paper we have described how BreastHealthDecisions.org has been developed and evaluated as a personalized decision aid that can be used in a primary care setting to facilitate breast cancer risk reduction discussions. In developing the tool, we have used an iterative research process that incorporated feedback from a team of expert advisors, focus group data and usability interviews with key stakeholders and potential users of the decision aid. This process is aligned closely with that developed by Elwyn and colleagues that has been published since the collection of these data . The resThe efficacy of strategies to reduce the risk of developing breast cancer has been proven, but these interventions are not being implemented. In contrast, although the value of screening mammography is intensely debated, participation rates for screening are between 72 and 80%. Incorporating risk stratification and risk-reduction into routine clinical consultations would provide an opportunity to engage women in the larger context of their overall breast health, and could identify highest risk women who would derive the greatest benefit from these interventions.Studies of women’s decisions about breast cancer prevention and screening conclude that women have a tendency to overestimate both their risk of breast cancer and the risks of side effects associated with risk-reducing interventions, leading to suboptimal decision making. The majority of a cohort of 75 women who had undergone prophylactic bilateral mastectomy had a significantly exaggerated perception of their risk at the time they made their mastectomy decisions . These wAdditionally, women often focus on breast cancer risk, even though they may be at greater risk for other diseases such as heart disease or lung cancer, risks that can be reduced substantially through lifestyle changes. BreastHealthDecisions.org was designed to put an individual’s risk of breast cancer in context with the other health threats she may face in an effort to combat this bias and help women make more informed decisions about their health.Although we aimed to design our tool with the greatest possible generalizability, the web-based nature of BreastHealthDecisions.org necessitates the use of the internet and therefore will not be accessible to women or clinical settings without readily accessible computers. Despite this, it has been designed to be applicable to all women, regardless of their risk level, which will broaden its generalizability. The research team is currently working on corresponding paper-based tools for environments where use of the internet is limited. Additionally, the decision aid would be most effective if it were able to develop a personalized risk of breast cancer in addition to comparing individualized competing health risks. However, there are currently no risk assessment models that perform this type of comprehensive risk prediction.Variability among providers in communication skills and knowledge about breast cancer risk reduction may impact the effectiveness of the tool. For example, a provider may find it more important to spend time discussing the risk of heart disease with a patient, even if the patient herself is more concerned about breast cancer risk. Alternatively, the decision tool could prompt the provider to focus on breast cancer risk reduction even though the patient is first inclined to discuss other health issues. The tool is designed to account for this discrepancy in concern by presenting breast cancer risk information in the context of other risks, such as heart disease or diabetes.Evaluating BreastHealthDecisions.org through a focus group enabled us to collect a range of opinions about the usability and appropriateness of the tool. Using breast cancer survivors for our patient focus group meant that they were able to reflect on their personal experiences and concerns to provide feedback about the content and delivery of the tool, while their status as survivors made them less likely to react anxiously to the information. However, it is possible that survivors may have had a different set of issues than women at risk of developing breast cancer. Recruiting at scientific conferences resulted in a diverse set of clinicians and allowed for multiple testing environments.In both the pilot study and the focus group, we were unable to enroll participants from diverse socioeconomic backgrounds. The large majority of participants were affluent Caucasian women with post-secondary education. It is a goal for future evaluation to analyze the feasibility and effectiveness of using BreastHealthDecisions.org with a more diverse range of women.We are currently implementing the decision aid within the Athena Breast Health Network, a University of California-wide consortium. The decision aid will be used with women identified to be at elevated risk. In this implementation, we will measure indicators of informed decision-making and resource utilization.BreastHealthDecisions.org represents a new approach to integrate breast cancer risk assessment and reduction into routine care and provides a framework for high quality preventive healthcare. The ability to integrate risk assessment and decision support in real time allows for informed, value-driven, and patient-centered breast cancer risk reduction decisions. The expected outcome of our decision aid is improved patient decision-making regarding breast health: women will be more informed about their personal risks and the benefits that interventions hold for them, and will make decisions aligned with their preferences. BreastHealthDecisions.org is designed to encourage appropriate use of risk-reducing interventions by identifying women at highest risk, informing patients and supporting their preferences. Ultimately, improved decisions and appropriate use of risk-reducing interventions are expected to decrease the incidence and burden of breast cancer.The authors declare that they have no competing interestsEO conceived of the study, participated in its design, data collection, and drafted the manuscript. RH participated in the data analysis and helped to draft the manuscript. ZO participated in the data collection, and helped to draft the manuscript. LE participated in the design of the study. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6947/14/4/prepub


Associations of hormone-receptor positive breast cancer with excess adiposity are reasonably well characterized; however, uncertainty remains regarding the association of body mass index (BMI) with hormone-receptor negative malignancies, and possible interactions by hormone replacement therapy (HRT) use.n = 1,021) and ER+PR+  breast tumors within five-year age bands. Among postmenopausal women, the joint effects of BMI and HRT use were analyzed.Within the European EPIC cohort, Cox proportional hazards models were used to describe the relationship of BMI, waist and hip circumferences with risk of estrogen-receptor (ER) negative and progesterone-receptor (PR) negative ), and positively associated with risk among women ≥65 years (HR = 1.25 (1.16 to 1.34)). Adjusting for BMI, waist and hip circumferences showed no further associations with risks of breast cancer subtypes. Current use of HRT was significantly associated with an increased risk of receptor-negative (HRT current use compared to HRT never use HR: 1.30 (1.05 to 1.62)) and positive tumors (HR: 1.74 (1.56 to 1.95)), although this risk increase was weaker for ER-PR- disease (Phet = 0.035). The association of HRT was significantly stronger in the leaner women (BMI ≤22.5 kg/m2) than for more overweight women (BMI ≥25.9 kg/m2) for, both, ER-PR- (HR: 1.74 (1.15 to 2.63)) and ER+PR+ (HR: 2.33 (1.84 to 2.92)) breast cancer and was not restricted to any particular HRT regime.For risk of ER-PR- tumors, there was no association of BMI across the age bands. However, when analyses were restricted to postmenopausal HRT never users, a positive risk association with BMI (third versus first tertile HR = 1.47 (1.01 to 2.15)) was observed. BMI was inversely associated with ER+PR+ tumors among women aged ≤49 years (per 5 kg/mAn elevated BMI may be positively associated with risk of ER-PR- tumors among postmenopausal women who never used HRT. Furthermore, postmenopausal HRT users were at an increased risk of ER-PR- as well as ER+PR+ tumors, especially among leaner women. For hormone-receptor positive tumors, but not for hormone-receptor negative tumors, our study confirms an inverse association of risk with BMI among young women of premenopausal age. Our data provide evidence for a possible role of sex hormones in the etiology of hormone-receptor negative tumors. Breast cancer is a complex and heterogeneous disease with a variety of histopathological and molecular subforms with diverse clinical outcomes and relationships with established risk factors ,2. One iEpidemiological data indicate that associations between excess body mass index (BMI) and the risk of breast cancer may differ by the ER and PR status in breast tumors , and thaAnother important factor to be accounted for when assessing the relationships of excess adiposity with postmenopausal breast cancer risk is use of hormone replacement therapy (HRT). Various studies have shown interactions of BMI with postmenopausal HRT use as risk factors for breast cancer ,10-14. HThe present analysis extends on a previous report (2004) from the European Prospective Investigation into Cancer and Nutrition (EPIC) in which the relationships of anthropometry with risk of total breast cancer were investigated . Using mEPIC is a multi-center prospective cohort study designed to investigate the relationships between diet, nutrition and metabolic factors and cancer, consisting of about 370,000 women and 150,000 men aged mostly between 25 and 70 years . All parExtensive details about the standardized procedures for recruitment, measuring baseline anthropometry , questionnaires on current habitual diet, reproductive and menstrual history, exogenous hormone use  and HRT use), medical history, lifetime smoking and alcohol consumption history, occupation, level of education and physical activity and biological sample collection at study centers are given elsewhere ,16. All a priori excluded if they had a history of cancer prior to recruitment  or were missing a diagnosis or censoring date , thus leaving 345,158 participants. Three EPIC study centers did not provide any information on breast tumor receptor status and, therefore, were also systematically excluded from this analysis . Women were further excluded if they were missing questionnaire data (n = 526) or data on height and weight . This left a total cohort of 314,676 women from 10 countries for our present analysis.Of the 367,903 female participants in EPIC, women were 2). In Norway, Umea and a large proportion of the EPIC cohort in France (69%) subjects' height and weight were measured and self-reported by the cohort participants themselves, following detailed instructions [The details of standardized procedures for measuring anthropometry and baseline variables at study centers have previously been reported . Height,ructions ,17. Waisructions .Women were considered postmenopausal at recruitment if they had had no menstrual cycles in the last 12 months, were older than 55 years , or had a bilateral oophorectomy. Women who reported having had a hysterectomy but still had either one or both ovaries intact had their menopausal status classified using their reported menstrual history, age and exogenous hormone use. Women were classified with a peri/-or of unknown menopausal status if they had experienced one to nine menstrual cycles in the last 12 months, were aged 46 to 55 years if menstrual cycle history was missing, or were taking HRT at the time of recruitment. Women were deemed premenopausal if they had had ≥10 menstrual cycles in the last 12 months or were younger than 46 years if the menstrual cycle history was missing. Women who were < 46 years of age, still had one or both ovaries and were still menstruating were considered as premenopausal if they indicated exogenous hormone use.Information on postmenopausal hormone use was derived from country-specific baseline questionnaires covering ever and current use of HRT, brand name used at recruitment and age at start and duration of use . Of the th Revision (ICD-10). Information on tumor receptor status, as well as the available laboratory methods and quantification descriptions used to determine receptor status was collected by 20 centers. To standardize the quantification of receptor status received from the EPIC centers, the following criteria for a positive receptor status were used; ≥10% cells stained, any 'plus-system' description, ≥20 fmol/mg, an Allred score of ≥3, an IRS ≥2, or an H-score ≥10 [In all countries  incident breast cancer cases were identified using record linkage with cancer and pathology registries. In France, Germany, and Greece, cancer occurrence was prospectively ascertained through linkage with health insurance records and regular direct contacts with participants and their next of kin, and all reported breast cancer cases were then systematically verified against clinical and pathological records. Cancer incidence data were classified according to the International Classification of Diseases, 10core ≥10 .Vital status was collected from regional or national mortality registries. The last updates of endpoint data for cancer incidence and vital status were between 2005 and 2010, depending on the center. Women were considered at risk from the time of recruitment until breast cancer diagnosis or censoring . Three EPIC subjects had the same date of recruitment and date of exit and were excluded because they did not contribute to the underlying time at risk variable. An additional three subjects with a breast cancer diagnosis were excluded because it was unclear whether their diagnosis was a primary incidence case, thus a total of 9,530 breast cancer cases were included for this analysis. A total of 7,174 breast cancer cases had information on ER status ; of which, 5,906 had further information on PR status .Correlations between indices of adiposity were assessed using Pearson's partial correlation adjusted for study center and age at recruitment. Associations between BMI, waist and hip circumferences and the risk of breast cancer subtype were evaluated using Cox proportional hazards models to estimate hazard ratios (HR) and 95% confidence intervals (CI), with total breast cancer cases and breast cancer subtype outcomes defined as either jointly classified ER+PR+, ER+PR-, ER-PR+, ER-PR- or missing receptor status, ER-positive, ER-negative or missing receptor status, or, PR-positive, PR-negative or missing receptor status. All multivariable analyses were stratified by age in one-year categories and by study center, to prevent violations of the proportional-hazards assumption.To analyze associations of BMI, waist and hip circumferences with risks of breast cancer subtypes in all women, age bands were created by counting person years within each age limit, with left and/or right side censoring age limits defined as 'less than or equal to 49 years', 'between 50 and 54 years', 'between 55 and 59 years', 'between 60 and 64 years' and '65 years and older'. Incidence rates of breast cancer subtypes were calculated from person years and observed numbers of incident cases within age bands. Anthropometric measures were examined across the age bands on a continuous scale.BMI has been observed to interact with postmenopausal HRT use ,14,25, a2; BMI tertile 2: 22.6 to 25.8 kg/m2; BMI tertile 3: ≥25.9 kg/m2). Combined risk categories of BMI (tertiles) and HRT use were constructed using never users with a BMI in the lowest tertile as the reference category. Statistical interaction between categories of HRT use (never versus current use) and the integer score of BMI tertiles  entered as an ordered, quantitative variable was assessed by including an interaction term and the log likelihood ratio test.In postmenopausal women, BMI tertiles were created using cut-points based on the overall cohort distribution of body measurements , age at first child birth , parity , history of breastfeeding , use of oral contraceptives (OC) , smoking status , alcohol consumption (nonconsumers (< 1.5 g/day), 1.5 g to 10 g/day (as the reference category), 10 to 20 g/day, 20 to 30 g/day and greater than 30 g/day, missing), physical activity , educatDifferences between BMI, waist and hip circumferences and risk of breast cancer subtypes were analyzed using the data augmentation method as described by Lunn and McNeil, using a log likelihood ratio test to compare the model with and without interaction terms between the anthropometric variable and breast cancer subtype . Women wA sensitivity analysis that excluded all uncalibrated self-reported anthropometric measurements to assess the effect of including self-reported indices of BMI, waist and hip circumferences into the models found no difference between how measurements of anthropometry were obtained (data not shown) so, therefore, all self-reported anthropometric measurements were retained in the models. All tests of significance were two-sided and all analyses were completed using SAS version 9.2; SAS Institute, Cary, NC.A total cohort of 314,760 women was followed for a sum of 3,399,178 person years. Of these, 144,223 (45.8%) were postmenopausal at the time of recruitment, of which 93.6% had a natural menopause. Baseline characteristics of all women and postmenopausal women within this cohort are presented in Table n = 35,856 (76.0%)), combinations of estrogen plus progestin were used, while almost one out of four HRT users (24.0%) used formulae based on estrogens alone. Almost half of all breast cancer cases (46.2%) were diagnosed at the age of 60 years or older. The median time until diagnosis was 6.1 years for all incident cases, 6.3 years for ER-PR- tumors, 6.8 years for ER+PR+ tumors, and 5.0 years for tumors with unknown ER or PR receptor status. Incident cases with ER-PR- breast tumors were rather evenly distributed across the age bands, whereas the majority of cases with ER+PR+ breast tumors were diagnosed after the age of 55 . For ER+PR+ tumors, the negative risk association for BMI among women aged less than 49 years (per five-unit increase in BMI HR = 0.79 (95%CI: 0.68 to 0.91); P = 0.002) progressively turned into a significant positive risk association among women aged 65 years and older.Across the age bands, Cox regression models showed no distinct association for BMI with risk of ER-PR- breast cancer Figure . It was Phet = 0.05). Similar patterns of association were observed for BMI with risk of ER-negative versus ER-positive breast cancer subtypes, ignoring PR status ) and ER+PR+ tumors (per five-unit increase in BMI HR = 1.34 (1.22 to 1.47)) among the HRT never users older than 65 years  and ER+PR+ breast cancer (HR = 1.74 (95%CI: 1.56 to 1.95); Pvalue < 0.001), although the relative risk was significantly weaker for ER-PR- than for ER+PR+ tumors (Phet = 0.035).With regard to postmenopausal HRT use, Cox regression models showed that in comparison to HRT never users, postmenopausal women who were current HRT users at baseline had significantly increased risks of both ER-PR- breast cancer (HR = 1.30 (95%CI: 1.05 to 1.62); Ptrend = 0.06) (Table Pinteraction = 0.037)  and BMI tertiles, for ER-PR- breast cancer, using never users of HRT in the lowest BMI tertile as a reference category, the highest relative risk for current HRT use was seen for women in the first BMI tertile, while relative risks were somewhat weaker for women in the middle and upper tertiles of BMI Figure . Within 6) Table  contrast7) Table  and was Pinteraction < 0.001) no relationships with either receptor-negative or -positive breast cancer were observed after adjustment for BMI.Cancer registry data show that overall breast cancer incidence increases with age. However, after the age of 50, rates of hormone receptor-negative disease no longer increase with advancing age, whereas the incidence rates of ER-positive tumors continue, albeit at a reduced pace compared to earlier ages -33. Thiset al. (2009) [n = 131) for women in higher categories of BMI, waist and hip circumferences [For ER-PR- tumors, our findings across the age-bands are consistent with those from the meta-analysis by Suzuki . (2009)  of nine ferences . In addiferences  showed aet al. showed a 33% increase in risk of ER+PR+ tumors per five-unit increase in BMI among postmenopausal women  = 1.33 (95%CI = 1.20 to 1.48)), and a 10% reduction in risk of ER+PR+ disease among premenopausal women ) [For receptor-positive tumors, our findings on the relationships of risk with BMI are well in line with those from previous epidemiologic studies. The meta-analysis by Suzuki o 0.99)) , and anoo 0.99)) .The direct risk association of general obesity with both ER-PR- and ER+PR+ breast cancer in postmenopausal women not using HRT, may be explained by adiposity-related increases in circulating estrogen levels, due to conversion of androgens into estrogens within adipose tissue . The resThe mechanisms that underlie the inverse association of BMI with risk of ER+PR+ tumors among women of reproductive age are still largely unclear . AlthougAn important observation in our study is that current users of HRT, compared to women who did not use HRT, had higher relative risks of both receptor-negative and -positive, but with a stronger increase in risk for the ER+PR+ subtype. Some previous prospective studies have documented similar increases in risks of both ER-negative and -positive breast tumors ,47, espeAnother significant observation in our study was that for both breast cancer subtypes the increased risk association in current HRT users depended upon a women's BMI. For receptor-negative and -positive breast cancers, HRT-related increases in risk were stronger among the leaner women and was not restricted to a particular HRT type. Switching perspectives, this statistical interaction also manifested itself in a lack of BMI-related breast cancer risks for both receptor-negative and -positive tumors among current HRT users. A previous analysis of the EPIC data  and seveThe stronger association of HRT use in lean women in comparison to more overweight women could be potentially related to the different patterns of HRT use. As expected, we observed, a higher proportion of current use in lean women. Within past users of HRT, women of a higher BMI tended to have a shorter cumulative duration of HRT use. Although data was restricted to information collected at baseline, risk estimates in past and current users were not affected by adjustment for duration of use (data not shown).It is important to acknowledge the limitations of our study. The determination of ER and PR status in breast tumors is a standard part of breast cancer diagnosis used to predict endocrine therapy response . NeverthMajor strengths of the present study are its prospective design and large number of incident cases with receptor information. This large number of cases allowed in-depth analyses of BMI-related relative risk patterns across age bands, describing risk associations and their progressive changes from premenopausal to late postmenopausal age. Furthermore, the large case numbers allowed the examination of interaction effects between excess body weight and use of postmenopausal HRT and by HRT type.In summary, we found for receptor-negative tumors, increased BMI among postmenopausal women was associated with risk of receptor-negative tumors, however, only in never users of HRT. In previous studies, excess body weight was associated with an inverse risk only of receptor-positive breast cancer in women aged less than 49, which progressively turned into an increased risk of receptor-positive disease as women aged. Furthermore, women who reported current HRT use at baseline were at an increased risk of both receptor-negative and -positive breast cancer, although this increase was stronger for receptor-positive tumors. In both receptor-negative and -positive breast cancers our data show a stronger HRT-related increase in risk among leaner than among more obese women. This latter interaction also meant that BMI-related increases in risks of receptor-negative and -positive breast cancers were observed among nonusers of HRT only, whereas a possible BMI-related relative decrease (receptor-negative disease) or no increase in risk (receptor-positive disease) may be seen among current users of HRT.BMI: body mass index; CI: confidence interval; EPIC: European Prospective Investigation into Cancer and Nutrition; ER: estrogen receptor; HR: hazard ratio; HRT: hormone replacement therapy; OC: oral contraceptives; PR: progesterone receptor; RE: risk estimate.The authors declare that they have no competing interests.RR, AL, LD and RK contributed to the conception of the current analysis and all authors were involved in the design and acquisition of data from the EPIC cohort. RR, AL, LD and RK contributed to the analysis and all authors contributed to the interpretation of the data. RR, AL and RK drafted the manuscript and all authors revised the final draft critically for important critical content. All authors have given final approval of the version to be published.1Proportions and patterns of postmenopausal HRT use across BMI tertiles. 1BMI tertile 1: < 22.5 kg/m2; BMI tertile 2: 22.6 to 25.8 kg/m2; BMI tertile 3: > 25.9 kg/m2. Duration and time since HRT use are for past users of HRT, as reported at baseline recruitment.Click here for fileHazard ratios of ER-positive and ER-negative tumors for increased BMI across five-year age bands. All models are for a 5 kg/m2 increase in BMI and were stratified by age at recruitment and study center. Hazard ratio estimates are shown for all women and HRT never users.Click here for fileHazard ratios of PR-positive and PR-negative tumors for increased BMI across five-year age bands. All models are for a 5 kg/m2 increase in BMI and were stratified by age at recruitment and study center. Hazard ratio estimates are shown for all women and HRT never users.Click here for fileHazard ratios of joint ER+PR+ and ER-PR- tumors for increased waist circumference across five-year age bands. All models are for a 5 kg/m2 increase in BMI and were stratified by age at recruitment and study center. Hazard ratio estimates are shown for all women and HRT never users.Click here for fileHazard ratios of joint ER+PR+ and ER-PR- tumors for increased hip circumference across each age band. All models are for a 5 kg/m2 increase in BMI and were stratified by age at recruitment and study center. Hazard ratio estimates are shown for all women and HRT never users.Click here for file2Hazard ratios of ER+PR+ and ER-PR- tumors for postmenopausal estrogen plus progestin HRT user categories within tertiles of BMI. Hazard ratios of ER+PR+ and ER-PR- tumors for estrogen plus progestin HRT user categories within tertiles of BMI. All models are stratified by age at recruitment and EPIC center. BMI tertile 1: < 22.5 kg/m2; BMI tertile 2: 22.6 to 25.8 kg/m2; BMI tertile 3: > 25.9 kg/m2. Estrogen plus progestin HRT use at baseline recruitment. Assessment for interaction between BMI and HRT user categories was calculated using the log likelihood ratio test for models with and without the interaction term for HRT never and current use by BMI tertiles.Click here for fileHazard ratios of ER+PR+ and ER-PR- tumors across levels of BMI within categories of postmenopausal estrogen plus progestin HRT users. Hazard ratios of ER+PR+ and ER-PR- tumors across BMI tertiles and per 5 kg/m2 in estrogen plus progestin HRT user categories. All models are stratified by age at recruitment and EPIC center. BMI tertile 1: < 22.5 kg/m2; BMI tertile 2: 22.6 to 25.8 kg/m2; BMI tertile 3: > 25.9 kg/m2.Click here for fileHazard ratios of ER+PR+ and ER-PR- tumors for postmenopausal estrogen only HRT user categories within tertiles of BMI. Hazard ratios of ER+PR+ and ER-PR- tumors for estrogen-only HRT user categories within tertiles of BMI. All models are stratified by age at recruitment and EPIC center. BMI tertile 1: < 22.5 kg/m2; BMI tertile 2: 22.6 to 25.8 kg/m2; BMI tertile 3: > 25.9 kg/m2. Estrogen-only HRT use at baseline recruitment. Assessment for interaction between BMI and HRT user categories was calculated using the log likelihood ratio test for models with and without the interaction term for HRT never and current use by BMI tertiles.Click here for fileHazard ratios of ER+PR+ and ER-PR- tumors across levels of BMI within categories of postmenopausal estrogen only HRT users. Hazard ratios of ER+PR+ and ER-PR- tumors across levels of BMI tertiles and per 5 kg/m2 in estrogen-only HRT user categories. All models are stratified by age at recruitment and EPIC center BMI tertile 1: < 22.5 kg/m2; BMI tertile 2: 22.6 to 25.8 kg/m2; BMI tertile 3: > 25.9 kg/m2.Click here for file1 HRT user categoriesHazard ratios of ER+PR+ and ER-PR- tumors across BMI tertiles within E+P. 1Combined estrogen and progesterone HRT. All models were restricted to postmenopausal women with information on baseline HRT use and stratified by age at recruitment and study center. HRT never users within BMI tertile1 were used as the reference category. BMI tertile 1: ≤22.5 kg/m2; BMI tertile 2: 22.6 to 25.8 kg/m2; BMI tertile 3: ≥25.9 kg/m2.Click here for file1 HRT user categoriesHazard ratios of ER+PR+ and ER-PR- tumors across BMI tertiles within E-only. 1Estrogen-only HRT. All models were restricted to postmenopausal women with information on baseline HRT use and stratified by age at recruitment and study center. HRT never users within BMI tertile1 were used as the reference category. BMI tertile 1: ≤22.5 kg/m2; BMI tertile 2: 22.6 to 25.8 kg/m2; BMI tertile 3: ≥25.9 kg/m2.Click here for file