There is no reliable screening tool to identify people with high risk of developing pancreatic cancer even though pancreatic cancer represents the fifth-leading cause of cancer-related death in Korea. The goal of this study was to develop an individualized risk prediction model that can be used to screen for asymptomatic pancreatic cancer in Korean men and women.2 statistic.Gender-specific risk prediction models for pancreatic cancer were developed using the Cox proportional hazards model based on an 8-year follow-up of a cohort study of 1,289,933 men and 557,701 women in Korea who had biennial examinations in 1996–1997. The performance of the models was evaluated with respect to their discrimination and calibration ability based on the C-statistic and Hosmer-Lemeshow type χA total of 1,634 (0.13%) men and 561 (0.10%) women were newly diagnosed with pancreatic cancer. Age, height, BMI, fasting glucose, urine glucose, smoking, and age at smoking initiation were included in the risk prediction model for men. Height, BMI, fasting glucose, urine glucose, smoking, and drinking habit were included in the risk prediction model for women. Smoking was the most significant risk factor for developing pancreatic cancer in both men and women. The risk prediction model exhibited good discrimination and calibration ability, and in external validation it had excellent prediction ability.Gender-specific risk prediction models for pancreatic cancer were developed and validated for the first time. The prediction models will be a useful tool for detecting high-risk individuals who may benefit from increased surveillance for pancreatic cancer. Pancreatic cancer represents the fifth-leading cause of cancer-related death in Korea and the seventh worldwide. It has a dismal 5 year survival rate of 7.6% in Korea , mainly A screening strategy has not been established for sporadic pancreatic cancer. Because pancreatic cancer-specific symptoms occur late in the course of the disease, early detection will require screening asymptomatic individuals. Invasive pancreatic cancer develops from precancerous non-invasive precursor lesions called pancreatic intraepithelial neoplasia (PanIN), which progresses from PanIN1 to PanIN3 (carcinoma in situ) . Two majThe present study is the largest population-based cohort study of pancreatic cancer to date in Korea and provides a unique opportunity to develop a model for predicting the individual risk of developing pancreatic cancer.The study population consisted of men and women who participated in a biennial health examination conducted by Korean Health Insurance Corporation, including government employees, schoolteachers, company employees, and their dependents, between 1996 and 1997. A total of 1,289,933 men and 557,701 women aged 30 to 80 years who had no history of any cancer at baseline and during the first two years of follow-up, without any missing values for the primary risk factors , fasting glucose, urine glucose, cholesterol, smoking, age at smoking initiation, meal preference, frequency of meat consumption, eating habits), were included in the model development.To assess the performance of the models, we used an independent population medically evaluated by the National Health Insurance Corporation between 1998 and 1999 that was free of any cancer at baseline as a validation cohort. A total of 500,046 men and 627,629 women were included in the validation data set.The participants were followed from the date of the health examination until December 31, 2007, and the event was defined as the first diagnosis of pancreatic cancer . Individuals who did not develop cancer until the end of the follow-up were censored.This study was approved by the Institutional Review Board of the National Cancer Center, Korea (IRB no. NCCNCS09-305). The need for participant consent was waived by the ethics committee because this study involved routinely collected medical data that were anonymously managed at all stages, including data cleaning and statistical analysis.The incidence of pancreatic cancer among participants up to December 31, 2007, was identified through the Korean Central Cancer Registry (KCCR) database. The incidence of pancreatic cancer was classified according to ICD-10 codes (C25) . Deaths,The Cox proportional hazard model was used to develop gender-specific risk prediction models. Significant risk factors for pancreatic cancer were identified by crude and age-adjusted Cox regression. The time to event was defined as the time between the date of the health examination and the date of the first diagnosis of pancreatic cancer. Potential risk factors considered in the analyses included previous disease history , eating habits , meal preference (meat vs. vegetables), frequency of meat intake , drinking habit (≤2–3 times/month or ≥1–2 times/week), amount of alcohol consumed at a time, duration of smoking, amount of smoking per day , year of smoking cessation, physical activity , height (grouped by quartiles), BMI , systolic and diastolic blood pressure, total cholesterol, and fasting blood and urine glucose levels. More in-depth descriptions of the rationale of the categorization of these variables were published previously , 11. Thrf(x) = β1(x1−M1)+β2(x2−M2)+…+βk(xK−MK), β1,…,βK are Cox proportional hazard model coefficients, x1,…,xK are the values of K risk factors at baseline, and M1,…,MK are the average values of corresponding risk factors. Baseline survival probability S0(t) indicates the survival probability at time t for an individual at time t = 8 whose covariate values are equal to the mean value of each risk factor. The detailed procedures can be found in the The probability of developing pancreatic cancer within 8 years (t = 8) for an individual with K risk factors is estimated as follows:An independent population was used for external validation of the developed models, which allowed us to evaluate the performance of the models with respect to discrimination and calibration.Discrimination is a model’s ability to distinguish between non-events and events. This can be quantified by calculating the C-statistic for the survival model developed by Nam . The C-s2 statistic developed by Nam [2 statistic was calculated by dividing the data into 10 groups (deciles) based on the predicted probabilities produced by the model in ascending order. Then, for each decile, the average predicted probabilities were compared to the actual risk probabilities estimated by the Kaplan-Meier approach [Calibration measures how closely the predicted probabilities agree numerically with the actual outcomes. We used a Hosmer-Lemeshow (H-L) type χd by Nam . This χ2approach .All statistical analyses were performed using SAS, version 9.1  and Stata version 10 .Among 1,289,933 men and 557,701 women, 1,634 (0.13%) men and 561 (0.10%) women were newly diagnosed with pancreatic cancer during 8 years of follow-up, and the incidence rates in men and women were 11.56 and 9.39 cases per 100,000 person-years, respectively . Across Tables 2) and combined smoking status and the average value for the amount smoked per day into one variable termed “smoking” that comprised five categories: never, past, current <0.5 pack/day, current 0.5–0.99 pack/day, and current ≥1 pack/day. The pancreatic cancer risk increased as height and BMI increased. Higher blood glucose (≥140 mg/dL) was associated with an almost 30% increased risk compared to lower blood glucose (<140 mg/dL). Current smokers consuming more than 1 pack of cigarettes per day had double the risk compared to never smokers , and eating habits  are significant risk factors for pancreatic cancer. In the risk prediction model for men, age, height, BMI, blood glucose, urine glucose, smoking, and age at smoking initiation were finally included based on stepwise selection. To improve the model’s fit, we included a quadratic term of age (age smokers .2), height, BMI, blood glucose, urine glucose, and drinking habit were finally selected as factors. BMI greater than 25.0 was associated with a 1.4-fold increased risk compared to the normal range (18.5–22.9). Current smokers also had a 1.8-fold higher risk than never smokers  and 10.297 (p = 0.327) for men and women, respectively. The validation results are provided in Figs The C-statistics (95% CI) were 0.813 (0.800 to 0.826) for men and 0.804 (0.788 to 0.820) for women, respectively. The H-L type χThe absolute risk estimates for pancreatic cancer within 8 years are illustrated in For women, 8-year risk estimates are described in We divided derivation sets of men and women into five risk groups based on quintiles of the estimated probability of developing pancreatic cancer. The cumulative incidence probability and hazard ratio for each risk group is provided in Despite improvements in the clinical management of pancreatic cancer, limited advances have been made in the early detection of this highly lethal malignancy . Within However, very few studies in the literature have involved a prediction model for pancreatic cancer. One previous study proposed a method that combines PubMed knowledge and electronic health records to develop a weighted Bayesian Network Inference model for pancreatic cancer prediction . Though Alcohol consumption is a well-known risk factor for type II diabetes mellitus and chronic pancreatitis, both of which are associated with an increased risk of pancreatic cancer. However, the relationship between alcohol intake and pancreatic cancer risk has been too inconsistent to reach a conclusion on the association between alcohol intake and the risk of pancreatic cancer. A pooled analysis of the primary data from 14 prospective cohort studies  revealedThus far, no such early detection method has had sufficient sensitivity and specificity to serve as a tool for pancreatic cancer screening. In addition, the feasibility of pancreatic cancer screening among the general population is questionable owing to the overall low prevalence. Thus, existing research of screening has been restricted to high-risk individuals, such as those with Peutz-Jeghers syndrome, familial breast-ovarian cancer patients, and relatives of patients with familial pancreatic cancer with at least one affected first-degree relative . The larDespite the large sample size, a central limitation of this cohort study is its retrospective nature, as it utilized existing subject data that are usually documented for other reasons and can address longer follow-up times, but usually at the expense of poorer, less systematically obtained data . We usedAnother limitation in this study is death due to other reasons than pancreatic cancer was considered as ‘censored’ rather than a competing risk of pancreatic cancer occurrence. However, we could find some publications for development of the risk prediction model for pancreatic cancer using similar censoring scheme for death –29 and tSince this model is based on Korean population, for Asian population, further studies will be needed for other races.S1 Appendix(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


The relationship between non-steroidal anti-inflammatory drug (NSAID) consumption and breast cancer has been repeatedly studied, although the results remain controversial. Most case-control studies reported that NSAID consumption protected against breast cancer, while most cohort studies did not find this effect. Most studies have dealt with NSAIDs as a whole group or with specific drugs, such aspirin, ibuprofen, or others, but not with NSAID subgroups according to the Anatomical Therapeutic Chemical Classification System; moreover, scarce attention has been paid to their effect on different tumor categories .In this case-control study, we report the NSAID – breast cancer relationship in 1736 breast cancer cases and 1895 healthy controls; results are reported stratifying by the women’s characteristics  and by tumor characteristics.In our study, NSAID use was associated with a 24 % reduction in breast cancer risk , and similar results were found for acetic acid derivatives, propionic acid derivatives and COXIBs, but not for aspirin. Similar results were found in postmenopausal and premenopausal women. NSAID consumption also protected against hormone + or HER2+ cancers, but not against triple negative breast cancers. The COX-2 selectivity showed an inverse association with breast cancer (i.e. OR < 1), except in advanced clinical stage and triple negative cancers.Most NSAIDs, but not aspirin, showed an inverse association against breast cancer; this effect seems to be restricted to hormone + or HER2+ cancers.The online version of this article (doi:10.1186/s12885-016-2692-4) contains supplementary material, which is available to authorized users. The cyclooxygenase-prostaglandin inflammation pathway has been shown to play a relevant role in carcinogenesis, mainly via inhibition of the cyclooxigenase-2 (COX-2) isoform . ExperimRegarding breast cancer, results from epidemiological studies are inconsistent: cohort studies have reported very modest protective effects or no effect at all –5, whileThe number of epidemiological studies reporting results about the COX-2 inhibitors’ effect on breast cancer or about NSAIDs’ effect on different types of breast cancer  is still small and further studies are needed in order to clarify the specific effect of NSAID groups on different types of breast cancer , 10–12. The Multi Case-control (MCC-Spain) study is a population-based case-control study of common tumors in Spain and has been described elsewhere . It has Response rates were 71 % for breast cancer and 72 % for controls, with no differences in the main socio-demographic variables among those who participated and those who refused to participate. The Ethics Committees of participating hospitals approved the study protocols, and participants provided written informed consent at the time of enrollment.Participants were interviewed face-to-face by trained interviewers with a comprehensive epidemiological questionnaire that assessed socio-demographic information, personal and family history of cancer, anthropometric data, smoking habits, occupation, physical activity, water consumption, reproductive and medical history and medication/drugs use, family history, sun exposure, sleep habits, use of hygiene products and cosmetics, signs and symptoms. Diet was assessed with the use of a validated semi-quantitative Spanish Food Frequency Questionnaire (FFQ), which was modified to include regional products. The FFQ included 140 food items, and assessed usual dietary intake during the previous year.Participant’s weight was recorded by self-report, as estimated one year before diagnosis for cases and for controls. Body mass index (BMI) was estimated from self-reported weight and height 1 year before the diagnosis for cases and 1 year prior to the interview for controls. Similar estimates provided total energy consumption. Physical activity was recorded for all jobs and also recreational physical exercise.Detailed information was obtained on past medical conditions and the corresponding medications used. Participants were asked about past medical history of diabetes mellitus, high blood pressure, high levels of cholesterol and triglycerides, heart attack, embolism, other cardiovascular diseases, degenerative osteoarthritis, arthritis, migraine or cephalalgia, gout, ulcerative colitis, Crohn’s disease, renal calculus (nephrolithiasis or cystolithiasis), chronic obstructive pulmonary disease, asthma, bronchitis, irritable bowel syndrome, anemia, diverticulitis, celiac disease and cancer. The age at onset, the dates of diagnosis or occurrence and the type of treatment received for each condition was also registered.Drug use was recorded by indication. For each drug, the brand name, dose and duration of exposure were recorded to identify patients with regular drug consumption  and the duration of consumption. The drugs were coded following the Anatomical Therapeutic Chemical Classification System (ATC codes) to define groups with similar mechanisms of action . To be sAll drugs indicated for the treatment of inflammatory diseases were considered. The main ATC code included in the present analysis are codes B01AC06 and N02BA01 (Aspirin) and code M01 (Antiinflammatory and antirheumatic drugs). Data were also analyzed for subgroups with codes M01AA (Butilpirazolidins), M01AB , M01AC (Oxicams), M01AE , M01AH , M01AX (other NSAIDs) and their combinations. Finally, as cox2 inhibition has been suggested as the putative mechanisms for NSAID protective effect on breast cancer, we performed a subgroup analysis according to level of COX-selectivity. In this way, NSAIDs were grouped in cox1-selective/cox2-selective according to their log [IC80 ratio (WHMA COX-2/COX-1)] . NSAIDs 2]. The association between tumor characteristics  and NSAID consumption was studied using multinomial logistic regression. Results are reported as odds ratios (OR) with 95 % confidence intervals (CI). All reported p-values are two-tailed. Statistical analysis was carried out using the package Stata 12/SE .Unconditional logistic regression was used to assess the association between treatment of NSAID use and breast cancer, adjusting for age, recruitment area, education level, tobacco smoking history, BMI, family history of breast cancer, number of deliveries, age at first delivery, menarche age, and menopausal status. Stratified models were developed according to menopausal status and BMI [<25/≥25 kg/mA description of the 1736 cases and 1909 controls included in this study is provided in Table p values not shown). The protective effect of any NSAID was independent of BMI; however, the effect varied in subgroups: acetic acid derivatives were protective in women with BMI < 25 kg/m2  but not in overweight or obese women, while propionic acid derivatives  protected only in the latter group; p values for BMI – NSAID interaction were non-significant. Table 2.Results on the relationship between NSAID consumption and breast cancer overall and by menopausal status and BMI, according to women’s characteristics are reported in Tables Results for subgroups of breast cancer are reported in Tables When studying the effect of length of consumption Table , most asFinally, regarding the COX-selectivity of the NSAID  . Scarce The public health implications of the reduction in breast cancer risk when taking acetic acid and propionic acid derivatives should be highlighted as these groups account for about 80 % of NSAID consumption in the Spanish population. A note of caution should be remarked on aspirin results; the percentage of people declaring aspirin consumption seems low, which could be due to a reporting bias. Study participants were asked to report the diseases they were suffering from and the drugs they had been taking for treating them and we have also asked whether they were taking any other NSAID not reported before. It is possible that some people might not consider aspirin to be a drug, so failing to declare its usage.2 seem to be associated with the initiation and promotion of breast carcinogenesis [Most studies did not analyze NSAID effect on several types of breast cancer. According to our results, the inverse association of NSAID with breast cancer is more pronounced in postmenopausal cancers, ductal cancer, and hormone receptor or HER2 receptor positive tumors. This effect increases in women treated with COX-2 inhibitors, especially in early clinical stage, postmenopausal cancers and receptor positive tumors. The putative pathway for the NSAID protective effect is via COX inhibition. High levels of prostaglandins, derived from the activation of the COX/prostaglandin pathway, contribute to carcinogenesis in various ways  –40. Consogenesis . In a progenesis ; COX-2 oogenesis . UnfortuOn the other hand, COX inhibition would reduce aromatase activity . PeripheThis study has several limitations. First, NSAID consumption was self-reported, which could introduce a recall bias. For a recall bias to be responsible for the protective effects reported here, the bias would have to be differential in cases and controls, with controls remembering their previous NSAID consumption better; this seems counterintuitive as one would expect cases to be more motivated for remembering their previous exposures. In addition, if cases are less prone to report their NSAID usage, the same bias would be expected in all NSAID groups and in each stratum analyzed; however, our results were different according to the type of NSAID, which seems to contradict such a bias. Moreover, in order to minimize a differential recall bias, interviewers were blinded to the case-control status of the participants. Second, although our intention was to record data on aspirin dosage, most patients did not provide sufficiently detailed data on dosages of aspirin or other NSAID use. This fact prevents us from analyzing the dose-effect relationship. Third, we have adjusted for the usual confounders but residual confounding cannot be ruled out. Finally, any case-control study could be affected by a selection bias. Our study is population based, as controls have been selected from the same residence area as cases; the small differences in case and control educational levels suggest that the selection has been adequately carried out. Moreover, the high response rates obtained in this study  minimize the possibility of occurrence of such bias.Summarizing, although there is increasing evidence for a protective effect of NSAID against breast cancer risk, our results indicate that this effect is more pronounced in postmenopausal women and in estrogen/progesterone + receptor or HER2+ cancers. As this effect seems to be moderate, concerns remain about whether NSAID may play a role in chemoprevention or just indicate a pathway for identifying further more specific drugs that could be used for breast cancer chemoprevention in high risk women.


Public perception and anxiety of familial cancer have increased demands for clinical counseling, which may be well equipped for gene testing but less prepared for counseling of the large domain of familial cancer with unknown genetic background. The aim of the present study was to highlight the full scope of familial cancer and the variable levels of risk that need to be considered. Data on the 25 most common cancers were obtained from the Swedish Family Cancer Database and a Poisson regression model was applied to estimate relative risks (RR) distinguishing between family histories of single or multiple affected first-degree relatives and their diagnostic ages. For all cancers, individual risks were significantly increased if a parent or a sibling had a concordant cancer. While the RRs were around 2.00 for most cancers, risks were up to 10-fold increased for some cancers. Familial risks were even higher when multiple relatives were affected. Although familial risks were highest at ages below 60 years, most familial cases were diagnosed at older ages. The results emphasized the value of a detailed family history as a readily available tool for individualized counseling and its preventive potential for a large domain of non-syndromatic familial cancers. Hereditary cancer has become an important issue in oncology clinics because of the success in implementing genetic testing and/or screening methods for cancer syndromes. Most importantly, this information has been useful in preventing new tumors and cancer deaths18In the present article we aimed to describe a nationwide landscape of familial cancer by defining the risk by the number and diagnostic age of affected family members, each factors that need to be considered in clinical genetic risk assessment. The analyses are based on the latest version of Swedish Family-Cancer Database (FCD 2010) which is a unique source for family studies comprising almost 15 million people with clinical cancer data and other detailed personal information from 1958 through 2010.The FCD was used to estimate familial cancer risks for the 25 most common cancer sites. The database comprises information from the Multigeneration Register, censuses and death notifications provided by Statistics Sweden, and information from the Swedish Cancer Registry. In its latest update from 2010 the database included 14.7 million individuals, where all people born in Sweden from 1932 onwards (the offspring generation) were registered with linkage to their biological parents .th revision of the International Classification of Diseases (ICD-7) was used to identify the cancer type. Death information is assumed to be complete from 1961 onwards. In order to investigate cancer risks for offspring of affected parents as well as risk shared by siblings, all individuals with identified parents and non-missing information on birthday and sex were selected for the analysis, totaling in 8,148,737 index individuals. Among them 343,494 were diagnosed with one of the cancer sites under study.More than 1.7 million medically verified cancer cases were recorded in the Cancer Registry from 1958 to 2010. The 7Familial risk was assessed for the offspring generation by estimating relative risks (RRs) in terms of incidence rate ratios using a Poisson regression modelIn particular, independent groups for certain familial relationships were considered in different settings in order to examine the differences in the familial risk shared by parents and siblings, the dependence on diagnostic age, and the risk shared by multiple affected relatives. Risks for parental probands were calculated twice: First, with no limit on the parental diagnosis age and second, regarding only parental cancers if they were diagnosed until the age of 78 years. This cut-off was used for comparison reasons since it equaled with the maximum age for siblings in the FCD.The dependence of familial risk on diagnostic age was investigated considering FDRs in general. We distinguished between cancer diagnosis before and after the 60th birthday for both index persons and their relatives. The corresponding diagnostic age specific RRs were tested for equality by a likelihood ratio test. Familial risks were also analyzed to check for differences when both parents, one parent and one sibling, or no parent but multiple siblings were affected. This analysis was limited to a subgroup of most common cancer sites due to sample size constraints.The follow-up period started from the beginning of 1961, the birth year, or the immigration year, whichever came latest. The follow-up was terminated when a person was diagnosed with cancer, emigrated or died, or at the end of 2010, whichever came first. The register-based definition of period at risk was used for person-year calculations whereby a person was considered to be at familial risk irrespective of when family members were diagnosed with cancerFamilial risks were analyzed for 25 different cancer sites for which at least 10 affected parent-offspring and sibling pairs were detected. All cancer types under study showed significantly increased risks at 5% confidence level for offspring when their parents or siblings were affected, as shown in Analyzing the data for familial risk for cancers diagnosed at early and late ages , the resIt is noteworthy that among 23 cancer types with cases in the highest age category 19 showed a significant increase. Among the four remaining cancer types the sample size for cervical and thyroid cancer was small and the RR for kidney cancer was almost significant (RR = 1.29 with corresponding 95% CI 0.98–1.71). In agreement with prostate cancer, the other most common cancers, including colorectal, lung, bladder, and skin cancers showed the largest number of affected individuals in the highest age group and each with a significant familial risk. The exception for case numbers was breast cancer but the familial risk remained significant in the highest age group.Familial cancer risks when two or more FDRs were affected  with a concordant cancer and corresponding test statistics for significant differences among RRs were presented in The highest proportions of familial patients diagnosed at the age of 60 years or older were found for cancers of the prostate (21.9%), breast (14.4%), colorectum (13.8%), and lung (10.3%) as presented in Our study showed that the existence of affected family members is an important risk factor for all cancer types. For common cancers familial risk even increased with the number of affected family members. Until now, over 100 cancer predisposing variants have been identified and in addition over 300 low-risk loci have been mappedFamilial risk was assessed based on the FCD which is the world’s largest database for familial studies allowing for unbiased risk estimates in terms of selection and recall bias as the whole Swedish population was covered with registered data on family relationships and medically verified data was availableWe observed that familial RRs depend on age for most but not all cancers. This effect was already detected for some common cancers in a study based on a previous version of the FCDA high familial risk at an early age, shown above for most cancers, is in accord with the current understanding of cancer syndromes which disproportionately afflict young individualsBRCA1 gene, which were estimated to account for 60% to 76% of male breast cancers in high-risk families, are most likely to be causativeFamilial relative risks were further increased if multiple FDRs were affected. For melanoma, risk shared by parents was almost identical to sibling risks, but it was more than doubled if both parents or a parent and a sibling were affected. The aggregation of affected family members in these few families may be due to genetic predisposition through the high-risk susceptibility gene CDKN2 or, to lesser extent, by shared modifiable risk factors such as UV radiation, as melanoma is known to be a heterogeneous disease with differing etiologies321Most familial cancers were diagnosed at the age of 60 years or older. Among them, the number of families where 3 or more family members were diagnosed with a concordant cancer was negligible, with the exception of the most common cancers of the prostate and breast. The low proportion of families with 3 or more affect individuals is likely to define the genetic architecture of familial cancer; high-penetrance predisposition is rare compared to low-penetrance risk disposition signaled by two-case families.In conclusion, our results show that familial risk is a shared feature of all cancers and for many cancers multiple affected family members signal a high or very high risk that would necessarily require medical action. Some of such families are likely carriers of known high-risk cancer predisposition genes. However, the major proportion of familial cluster is probably caused by genes that remain to be discovered. Nevertheless, medical or behavioral intervention may be indicated, including screening recommendations or avoidance of carcinogenic exposures. The readily available information of family history deserves more attention in the first oncology contacts and established referral mechanisms for clinical counseling to evaluate screening and prevention strategies individually tailored to patients and their family members.How to cite this article: Frank, C. et al. Population Landscape of Familial Cancer. Sci. Rep.5, 12891; doi: 10.1038/srep12891 (2015).


Several studies suggest that overall and central-obesity are associated with increased breast cancer (BC) risk in postmenopausal-women. However, there are no studies investigating changes of central obesity and BC. We report on the association of BC risk with self-reported skirt size  changes between 20s and postmenopausal-age.Prospective cohort-study.UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) involving the nine trial centres in England.Postmenopausal-women aged >50 with no known history of BC prior to or on the day of completion of the study-entry questionnaire.At recruitment and at study entry, women were asked to complete a questionnaire. Women were followed-up via ‘flagging’ at the NHS Information Centre in England and the Hospital Episode Statistics.Time to initial BC diagnosis.Between 2 January 2005 and 1 July 2010, 92 834 UKCTOCS participants (median age 64.0) completed the study-entry questionnaire. During median follow-up of 3.19 years (25th–75th centile: 2.46–3.78), 1090 women developed BC. Model adjusted analysis for potential confounders showed body mass index (BMI) at recruitment to UKCTOCS , current SS at study entry  and change in SS per 10 years (CSS)  were associated with increased BC risk but not SS at 25 . CSS was the most predictive singe adiposity measure and further analysis including both CSS and BMI in the model revealed CSS remained significant  but not BMI .CSS is associated with BC risk independent of BMI. A unit increase in UK SS  every 10-years between 25 and postmenopausal-age is associated with postmenopausal BC risk by 33%. Validation of these results could provide women with a simple and easy to understand message.ISRCTN22488978. To the best of our knowledge, this is the first study investigating the association between central obesity using skirt size (SS) as a proxy and breast cancer risk. Between 25 and postmenopausal age, an increase in SS by one unit every decade increased the risk of postmenopausal breast cancer by 33% while decrease in SS was associated with lowering of risk.Our prospective cohort-study includes 94 000 women with comprehensive follow-up through data linkage to multiple national databases.There is a possibility of underestimation of self-reported SS. However, if current SS at study entry is uniformly underestimated then there is merely rescaling of CSS so that the strength of the association is unaffected. Furthermore, recall bias of the SS at 25 maybe a limitation but unless this inability in reporting is systematically related to future breast cancer, measurement error can only result in underestimating the strength of the true association between CSS and breast cancer risk.Given that obesity is now emerging as a global epidemic, from a public health prospective these findings are significant as they provide women with a simple and easy to understand message.Breast cancer is a major global health concern with 1 384 000 women being diagnosed every year.2–4A number of adiposity measures exist. The oldest and most common measure is body mass index (BMI) which is an empirical proxy for human body fat based on an individual’s weight and height. Central fat is another measure of obesity. It is usually estimated by waist circumference, waist–hip ratio, waist–height ratio or the conicity index which evaluates waist circumference in relation to height and weight. An adequate and easy to use proxy measure for waist circumference is clothing (trouser/skirt) size, which has been shown to provide a reliable and feasible estimate of waist circumference at the population level.6–16–24–There is a large body of evidence that high BMI is associated with increased breast cancer risk in postmenopausal women and reduced risk in premenopausal women.11The UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS),UKCTOCS is a multicentre randomised controlled trial of ovarian cancer screening in the general population. The trial invited 1.2 million women of whom 202 638 were recruited between 17 April 2001 and 27 September 2005 through 13 trial centres in England, Wales and Northern Ireland. The detailed trial design and eligibility criteria have been described elsewhere.Women provided written consent for use of their data in secondary studies. Ethical approval was obtained from the Joint University College London (UCL)/UCL Hospital Committees on the Ethics of Human Research .Participants recruited from England who returned the study-entry questionnaire prior to 2 July 2010 (censorship date—see ‘breast cancer notification’). Women recruited from Northern Ireland and Wales were excluded as HES was only available for women residing in England.Recruitment questionnaire: This was completed at recruitment into UKCTOCS and included information on the variables listed in Study-entry questionnaire: This was completed at follow-up (3–4 years after recruitment) and provided information on education, skirt size, continuing use of HRT, smoking, alcohol use, health status and cancer diagnosis postrecruitment . In the study-entry questionnaire two specific questions related to skirt size (SS) were asked: (1) ‘What was your SS when you were in your twenties?’ (2) ‘What is your SS now?’ Women could choose from 13 SS categories ranging from 6 to 30. The two SS questions were used to create a variable reflecting change in SS over time. The change in SS variable (CSS) was calculated as an increase in SS per 10 years. Note that a ‘one unit’ increase in SS means an increase by two nominal values, for example from size 10 to size 12 as odd sizes do not exist in the UK.Self-reported height and weight were taken from the recruitment questionnaire and used to calculate BMI  in that they were only able to participate in the current study because they ‘survived’  the period from their mid-20s (the first skirt size question) to study-entry questionnaire completion date (the second current skirt size question). To account for a woman's contribution to the analysis being conditional on having ‘survived’ during the unobserved period, the length of which depends on the age at study entry, the timescale used was age rather than date. Therefore, the time origin was fixed at age=25 for all participants and study entry was age at completion of study-entry questionnaire. Women were censored either at age at first breast cancer, death from any cause or 1 July 2010 whichever occurred first.Summaries of all identified potential confounding risk factors were performed. These variables were also included individually in a Cox regression model to obtain univariable estimates of their HR relating to breast cancer risk. In addition kernel density plots of all four adiposity measures were created and Pearson correlation coefficients were calculated for all adiposity measure-pairings. To obtain an appropriate confounder-adjusted estimate of the relative hazard of breast cancer of increasing SS, a Cox regression model including CSS in continuous form with all potential risk factors, regardless of statistical significance, was fitted (the ‘full model’). The functional form for CSS was determined using fractional polynomials, by considering powers from the standard predefined set {−2, −1, −0.5, 0, 0.5, 1, 2, 3} and modelling the most appropriate relationship between CSS and the (log) relative hazard. The full model was refitted with each of the adiposity measures forming the exposure variable, in turn. In addition, a variant formulation of the full model included both CSS and BMI and both SS at current study entry and BMI.Cox-Snell residuals were used to test the model fit and the proportional hazards assumption used in Cox regression was tested using the scaled Schoenfeld residuals for each variable in the final model. The (undue) influence of any outliers was assessed by calculating the change in the parameter estimate when an observation is deleted. To visualise the ‘dose-response’ relationship model, predicted relative hazards with 95% confidence bands for a range of CSS values were plotted. To obtain estimates of absolute risk for certain scenarios , the event density function was assumed to be exponentially distributed and the full model with CSS was fitted again using exponential regression. Survival curves with the same covariate pattern following both the Cox and exponential model fitting were plotted to ensure that the constant hazard was a reasonable assumption when averaged over the timeframe. An estimate of absolute risk was calculated by using the covariate-specific hazard from the exponential model for 1 and 5 years.To guard against the possibility of reverse causality whereby preclinical breast cancer may result in weight gain, the full model with CSS was refitted but with a time-lag of 1 year so that the start of the study period occurs 1 year after completion of the study-entry questionnaire.In the primary analysis, missing data in CSS or any other confounder was handled by case-wise deletion. To assess the robustness of estimation with such an approach to potential biases, multiple imputation (MI) was used to calculate the corresponding model estimates with missing data imputed appropriately. Specifically, a multivariate normal distribution formed by all potential predictors and the outcome variables (event and time) was used for the imputed random draws, and 20 fully imputed data sets were created with which to obtain valid estimates and SEs.A total of 157 997 women residing in England were recruited to the trial between 17 April 2001 and 29 September 2005. Of 11 659  women were not sent the study-entry questionnaire. The remaining 146 338 women were sent the study-entry questionnaire between 28 June 2005 and 17 March 2009 and 112 945 (77.2%) responded by 31 December 2010. A total of 10 689 of these women completed the current study-entry questionnaire after the censorship date of 1 July 2010 and were therefore excluded.Eight women did not give consent for flagging. The remaining 102 249 were successfully flagged with the NHS Information Centre. Of these, 62 were lost to follow-up by the NHS Information Centre prior to questionnaire completion, 9342 were diagnosed with breast cancer before or on the day of completion of the study-entry questionnaire and in 10, study-entry questionnaire completion date could not be calculated. The remaining 92 834 women were included in the study and were followed-up for a median of 3.19 years (25th–75th centile: 2.46–3.78 years). During the study period, 1090 women developed breast cancer, resulting in an absolute risk of breast cancer of 1.2% (1090/92 834) in this cohort of postmenopausal women, 75% of whom were aged over 60 years with no prior history of breast cancer. More information regarding the age distribution of the cohort is provided in online supplementary figure S1.The reproductive and lifestyle characteristics of the 92 834 women included in the analysis are summarised in The Pearson's correlation coefficients for the different adiposity measures are presented in A total of 5500 women were not included in the model due to missing values for either CSS or a confounder. The remaining 87 334 women, 1013 of whom developed breast cancer during follow-up were included in the full Cox model with CSS. Use of fractional polynomials of the CSS variable showed the most appropriate functional form was linear, with tests of better fit for other powers of degrees 1 (p=0.719) and 2 (p=0.981) showing no improvement. Confounder-adjustment revealed a stronger positive association between CSS and breast cancer risk  than without adjustment . For eacTests and plots based on the Schoenfeld residuals showed no evidence of any violation of the proportional hazards assumption. Online supplementary figure S2 plots these residuals for CSS with a superimposed smoother and shows that the effect of CSS on breast cancer risk is essentially stable over the entire age range considered in this study. The Cox-Snell residuals plotted against their cumulative hazard showed a virtual line of unity indicating good model fit (figure not shown). Similar diagnostic results were found using the other adiposity measures.A sensitivity analysis added a time-lag of 1 year to the primary analysis that used the full model with CSS. For this regression model only 86 284 women were included as 1 121 women were left-censored before 1 year, including 71 who had missing covariate data. The HR of 1.422  is slightly larger compared to the primary analysis but not substantively different .A multiple imputation analysis was carried out to investigate the effect of missing data on the model estimation. Data were missing for one or more variables in 5.9% and for CSS in 1.2% of the sample. Twenty completed data sets were created using multiple imputation and the subsequent HR=1.300   and SE eThis is to date the only study that we are aware of investigating the association of change in SS and breast cancer risk. Our data suggests that a unit increase in UK SS  every 10 years between the 20s and postmenopausal age increases the risk of postmenopausal breast cancer by 33%. We estimated an increase in 5-year absolute risk of postmenopausal breast cancer from 1 in 61 to 1 in 51 with each unit increase in SS per 10 years. Reductions in SS decreased the risk of breast cancer  though i5The major strengths of this study are its prospective cohort design, sample size, comprehensive follow-up through national registries and administrative databases and self-reporting resulting in high-quality data. Evaluation of numerous possible confounders including life style choices and socioeconomic status minimised bias. Owing to missing data in the covariates, case-wise deletion meant 5508 women were dropped from the Cox regression model. However, a sensitivity analysis using 20 completed sets of multiply imputed data showed that the missing data had little effect on the overall outcome of the study. Furthermore, the study conclusions were maintained after introducing a time-lag of 1 year to the study entry to rule out reverse causality. The absolute risk of breast cancer in this cohort over a median follow-up of 3.2 years was 1.2% as only postmenopausal women with no prior history of breast cancer, 75% of whom were aged over 60 years, were included in this analysis. In keeping with other studies, family history of breast cancer, infertility, age at last period, HRT use were associated with increased breast cancer risk while pregnancies lasting over 6 months were protective.–11The findings that CSS is associated with breast cancer risk  and that addition of BMI to the model does not significantly improve risk prediction, are consistent with previous studies addressing the relationship between body weight and breast cancer risk showing that adult weight gain is a better predictor of breast cancer risk than absolute weight or BMI.8Current SS , but not SS in the 20 s, was associated with a statistically significant increase in breast cancer risk and appears to be a better predictor compared to BMI. Our findings come in agreement with other studies39In our study changes in SS was a better predictor of breast cancer risk compared to absolute SS in terms of strength of association. Although the exact mechanism of these relationships need to be better understood, there is a suggestion that body fat around the waist is more metabolically active than adipose tissue elsewhere.41Convincing evidence is available that high-body weight, BMI and adult weight gain, which typically reflect an increase in body fat, are associated with increased breast cancer risk. To the best of our knowledge, this is the first study investigating the association of waist changes using SS as a proxy and breast cancer risk. Between 20s and postmenopausal age, an increase in skirt size by one unit every decade increases the risk of postmenopausal breast cancer by 33%. Validation of these results could provide women with a simple and easy to understand message.


In a linked Perspective, Barbara Fuhrman and Victor Cardenas discuss the potential biological mechanisms that may underlie the association between nevi and risk for breast cancer.Please see later in the article for the Editors' Summary PLOS Medicine:This Perspective discusses the following new studies published in doi:10.1371/journal.pmed.1001659Zhang M, Zhang X, Qureshi AA, Eliassen AH, Hankinson SE, et al. (2014) Association between Cutaneous Nevi and Breast Cancer in the Nurses' Health Study: A Prospective Cohort Study. PLoS Med 11(6): e1001659. Using data from the Nurses' Health Study, Jiali Han and colleagues examine the association between number of cutaneous nevi and the risk for breast cancer.doi:10.1371/journal.pmed.1001660Kvaskoff M, Bijon A, Mesrine S, Vilier A, Baglietto L, et al. (2014) Association between Melanocytic Nevi and Risk of Breast Diseases: The French E3N Prospective Cohort. PLoS Med 11(6): e1001660. Using data from the French E3N prospective cohort, Marina Kvaskoff and colleagues examine the association between number of cutaneous nevi and the risk for breast cancer.PLOS Medicine, Jiali Han and colleagues In this week's issue of p for trend = 0.003).The analysis conducted by Han and colleagues p for trend = 0.04). This association was attenuated and the trend no longer significant when investigators adjusted for benign breast disease or family history of breast cancer. A non-statistically significant increase in breast cancer risk of 8% was observed after adjusting for established breast cancer risk factors and measures of ultraviolet radiation exposure.The analysis conducted by Kvaskoff and colleagues p for trend = 0.03, p for heterogeneity = 0.04). In contrast, Han and colleagues found no significant modification of the risk association by menopausal status.While their findings are broadly consistent, subgroup findings in the two studies were different. Kvaskoff and colleagues found no significant association in postmenopausal women, but observed that, among premenopausal women, those who had reported “very many” nevi were 34% more likely to be diagnosed with breast cancer compared to counterparts who reported “none” even after adjustment for all potential confounders  are approximately 7 times higher than the risk of malignant melanoma Many unresolved questions remain. Will observed associations prove robust across studies and populations? Will the marker prove less informative in non-white populations or in those with less intense or more episodic sun exposure? Some previous studies suggest that research participants tend to undercount nevi and that reliability of nevi counts is limited


The role of passive smoking on breast cancer risk was unclear. This study aimed to evaluate the association between passive smoking and breast cancer risk among Chinese women.Ptrend=0.003, 0.006 and 0.009, respectively). An increased total smoker-years of any passive exposure significantly elevated the risk of breast cancer (Ptrend<0.001). Positive associations and dose-response relationships were found among postmenopausal women and all subtypes of estrogen receptor (ER) and progesterone receptor (PR) status of breast cancer.A hospital-based case-control study, including 877 breast cancer cases and 890 controls, frequency-matched by age and residence, was conducted. A structured questionnaire was used to collect information on passive smoking history through face-to-face interview by trained interviewers. Unconditional logistic regression models were used to estimate the association between passive smoking and breast cancer risk. A positive association between any passive smoking exposure and breast cancer risk was observed. Compared with women who were never exposed to passive smoking, women who were ever exposed had a higher breast cancer risk, with the adjusted odds ratio (OR) and 95% confidence interval (CI) of 1.35 (1.11-1.65). Similar result was found on home passive smoking exposure and breast cancer risk, but not on workplace passive smoking exposure. Women who were ever exposed to tobacco smoke at home had a higher risk of breast cancer compared with never exposed women, with the adjusted OR (95% CI) of 1.30 (1.05-1.61). Home passive smoking exposure showed significant dose-response relationships with breast cancer risk in smoker-years, cigarettes/day and total pack-years (Passive smoking was associated with an increased risk of breast cancer among non-smoking Chinese women. A stronger positive association with breast cancer risk was seen mainly among postmenopausal women. Many studies have examined the association between tobacco smoking and breast cancer risk –9. HowevIn China, traditionally, few women are smokers, but the rate of passive smoking has known to be high. A survey conducted between 2005 and 2007 showed a high rate of 43.6% . Even hiThe procedures and protocols of the study were approved by The Ethical Committee of School of Public Health, Sun Yat-sen University. All participants signed informed consent forms before the interview.Breast cancer cases and controls were recruited through two stages. The first stage was conducted from June 2007 to August 2008 and the second stage was from September 2011 to September 2013. Potential case patients were recruited from inpatients admitted to the surgical units of two affiliated hospitals of Sun Yat-sen University. Eligible cases were female subjects with histologically confirmed breast cancer diagnosed no more than 3 months before the interview, aged 25–70 years and natives of Guangdong province or having lived in Guangdong for at least 5 years. Women were excluded if they had a history of breast cancer or other cancers. Totally, 925 eligible cases were identified and 896 were interviewed, with a response rate of 96.9%.Control subjects were patients admitted to the same hospitals during the same time period as the case subjects. Eligibility criteria for controls were the same as described for the cases except that they had no history of any cancers. They were frequency matched with cases by age  and residence . These patients presented with a wide spectrum of non-neoplastic conditions including eye disorders , ear-nose-throat diseases , trifacial neuralgia, varicose veins, osteoarthritis, degenerate joint disease, orthopedics diseases, facial paralysis and acute appendicitis. In total, 939 controls were identified and 912 were interviewed, with 2.9% patients refused to participate.A structured questionnaire was used to collect information through face-to-face interview by trained interviewers. The collected information included socio-demographic and anthropometric parameters, dietary habits, menstrual and reproductive factors, use of hormone and contraceptive drugs, family history of cancer, alcohol drinking, active smoking, passive smoking history, disease history, and physical activity. The interview time was limited to exposures that occurred before diagnosis date for cases and the interview date for controls. Relevant medical information, medical diagnosis, and histological findings were abstracted from the hospital medical records.Women were classified as non-smokers if they reported never smoking or smoking less than 100 cigarettes over their lifetime . Passive2). Menopausal status was defined as at least 12 months since the last menstrual cycle. Women were considered to be premenopausal if they were currently menstruating, or if they were not menstruating because of a hysterectomy and younger than 50 years old. Women were defined as postmenopausal if they had either undergone a natural menopause, or surgery to remove both ovaries, or if their ovarian function was unknown but they were older than 50 years [Body mass index (BMI) was calculated by dividing weight (kg) by height  who reported past or current history of personal tobacco smoking. Analyses were based on the remaining 1767 non-smokers (877 cases and 890 controls).χ2 tests for categorical variables or t tests for continuous variables. Unconditional logistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (CI) for the association between passive smoking and breast cancer risk. Based on the comparison of baseline characteristics between cases and controls, the following variables, BMI, physical activity, age at menarche, age at first live birth, age at menopause, history of benign breast disease, and mother/sister/ daughter with breast cancer, were selected to be adjusted for as potential confounding factors. Age, residence and study stage were also controlled for in all logistic models. Tests for trend were performed by entering categorical variables as continuous parameters in the models. Stratified analyses by menopausal status were conducted. As breast cancer is a heterogeneous disease with estrogen receptor (ER) and progesterone receptor (PR) subtypes, stratified analyses by ER/PR status were also conducted. All analyses were performed using SPSS 13.0 . All tests were two-sided, with P values < 0.05 indicating statistical significance.Differences in characteristics were assessed by using either Compared to controls, cases were more likely to have higher BMI, an earlier age at menarche, a later age at first live birth, a later age at menopause, a history of benign breast disease and a family history of breast cancer, and were less likely to be physically active . All of As shown in Ptrend = 0.003, 0.006 and 0.009, respectively). Compared with women with no passive smoking exposure, the adjusted ORs (95% CIs) of more than 26 smoker-years, more than 16 cigarettes per day and more than 16 pack-years of exposure at home were 1.66 (1.21–2.26), 1.56 (1.17–2.09) and 1.61 (1.17–2.19), respectively (Passive smoking exposure at home was examined in detail. Women who were ever exposed to passive smoking at home (53.1% cases and 45.8% controls) had a higher risk of breast cancer compared with women who were never exposed to passive smoking, with the adjusted OR (95% CI) of 1.30 (1.05–1.61). Dose-response relationships between breast cancer risk and smoker-years, cigarettes per day and pack-years of exposure at home were observed (ectively .Ptrend = 0.313).Analysis on the association between passive smoking exposure at workplace and breast cancer risk (418 cases and 465 controls)  showed nPtrend < 0.001).A strong dose-response relationship and a positive association were observed between total smoker-years of passive smoking exposure at home and in the workplace and breast cancer risk. Compared with women who were never exposed to passive smoking, the adjusted ORs (95% CIs) was 0.95 (0.70–1.29) for 1–16 smoker-years, 1.34 (1.04–1.74) for 17–30 smoker-years, and 1.95 (1.43–2.66) for more than 31 smoker-years, respectively . But the significant dose-response relationship between total smoker-years of any exposure and breast cancer risk was found in both pre/post-menopausal women. No significant association was found between passive smoking in the workplace and breast cancer risk in both the pre/post-menopausal women  was 1.83 (1.29–2.60) for women with any passive smoking exposure and 1.80 (1.24–2.61) for women ever exposure to passive smoking at home. The significant dose-response relationships between breast cancer risk and smoker-years, cigarettes per day and total pack-years of exposure at home were also found only among postmenopausal women  among women who worked during the 5 years after excluding the influence of home exposure [We observed no significant association between workplace exposure to passive smoking and risk of breast cancer. Our results are in accordance with most other previous results , 15, 27.exposure .A cohort study conducted among California teachers observed an increased risk in the most highly exposed subgroup of postmenopausal women exposed in adulthood age ≥20 years)  . Another years .The data were collected using face-to-face interviews by trained interviewers and the response rates of cases and controls were relatively high. Furthermore, some major potential confounding factors were adjusted in all logistic regression models.This study had some limitations. First, selection bias was inevitable in hospital-based case-control studies. To minimize this bias, great attempt was made to recruit controls from patients with a wide spectrum of non-neoplastic conditions. Moreover, the high participation rate  and high comparability in socio-demographic factors between the two groups indicated that selection bias should not be a serious problem. Second, recall bias was also of concern in case-control studies. To reduce recall bias, we tried to interview patients as soon as diagnosis was made and take great effort to interview cases before their surgery. Third, limited sample size in some subgroups might lead to limited power to detect the associations. Fourth, there were some missing data on the duration or intensity of exposure, which may lead to an under estimation of the association. But the percentage of missing data was less than 5%. Fifth, this study had no information on genetic polymorphisms, which had been reported to modify the association of passive smoking with breast cancer risk .In summary, this study suggested that passive smoking was associated with an increased risk of breast cancer among non-smoking Chinese women. A stronger positive association with breast cancer was seen among postmenopausal women and all subtypes of ER/PR status of breast cancer. Future studies are needed to confirm these results.S1 Table(SAV)Click here for additional data file.


Women diagnosed with breast cancer frequently attribute their cancer to psychological stress, but scientific evidence is inconclusive. We investigated whether experienced frequency of stress and adverse life events affect subsequent breast cancer risk.Breast cancer incidence was analysed with respect to stress variables collected at enrolment in a prospective cohort study of 106,000 women in the United Kingdom, with 1783 incident breast cancer cases. Relative risks (RR) were obtained as hazard ratios using Cox proportional hazards models.There was no association of breast cancer risk overall with experienced frequency of stress. Risk was reduced for death of a close relative during the 5 years preceding study entry : 0.78–0.97), but not for death of a spouse/partner or close friend, personal illness/injury, or divorce/separation. There was a positive association of divorce with oestrogen-receptor-negative , but not with oestrogen-receptor-positive breast cancer. Risk was raised in women who were under age 20 at the death of their mother , but not of their father, and the effect was attenuated after excluding mothers with breast or ovarian cancer .This large prospective study did not show consistent evidence for an association of breast cancer risk with perceived stress levels or adverse life events in the preceding 5 years, or loss of parents during childhood and adolescence.The online version of this article (doi:10.1186/s13058-016-0733-1) contains supplementary material, which is available to authorized users. Breast cancer is the most commonly diagnosed cancer among females in the Western world, and its aetiology entails a multitude of genetic, reproductive, hormonal, and exogenous factors. Women with breast cancer frequently attribute the origin of their breast cancer, however, to psychological factors such as stress  althoughStress has been defined theoretically as the response of the body to the presence of external demands or, more subjectively, as the response to the individual’s appraisal of demands depending on their coping abilities , 3. PropPast epidemiological studies of exposure to psychological stress and breast cancer risk have predominantly reported breast cancer risk after adverse life events such as bereavement or divorce, as these are relatively easily ascertained, objective measures of major external stressors . Only a To address this issue, large prospective cohort studies are needed because such studies have the substantial advantage over those of retrospective design in that recall bias is avoided. We therefore investigated perceived frequency of stress, experience of adverse life events and bereavement at young ages in relation to breast cancer risk in a large prospective cohort study in the United Kingdom.The Breakthrough Generations Study is a cohort study of over 113,000 women in the UK, aged 16 or older, focused on breast cancer aetiology. The main recruitment phase was during 2003–2010, with baseline recruitment involving a postal questionnaire about established and candidate breast cancer risk factors, and donation of a blood sample. Participants are followed up approximately every 2.5–3 years by postal questionnaires, to obtain updated risk factor information as well as details of breast cancer diagnoses. Full details of the cohort methods have been published previously .Notification of incident cancers is obtained from follow-up questionnaires and ‘flagging’ at the National Health Service Central Registers (NHSCR), virtually complete registers of the population of the country, which notify cancer registrations in subjects ‘flagged’ for the study to authorised medical researchers as well as information on emigrations and deaths. Confirmation of self-reported diagnoses is obtained from the cancer registrations, participants’ general practitioners, pathology reports and other hospital records.The current analysis is based on all women who joined the Breakthrough Generations Study before 1 July 2012 without previous breast cancer, the recruitment cut-off date reflecting the date by which the first follow-up for the questionnaire was practically complete. Follow-up for breast cancer started at the date of receipt of the recruitment questionnaire and ended on the date of mailing for follow-up questionnaires, or if the follow-up questionnaire was not returned and the woman was covered by ‘flagging’ the earliest of the date the individual’s ‘flagging’, coverage ended , or the date after which ‘flagging’ notification is not yet complete (taken to be 1 March 2014). If the follow-up questionnaire was not returned and the woman was not covered by ‘flagging’, the follow-up was truncated at the date of her previous questionnaire.Data on stress variables were obtained from the baseline questionnaire, which asked whether subjects felt they had been experiencing stress over the last 5 years, to which they were invited to respond ‘never’, ‘occasionally’, ‘frequently’ or ‘continuously’. We combined the ‘never’ and ‘occasionally’ categories in the analyses due to the low number of subjects who reported ‘never’. The questionnaire also asked whether in the last 5 years they had experienced: death of a husband or long-term partner; death of a child, parent or other close relative; death of a close friend; divorce or separation; serious personal illness or injury; loss of a job; or another life event that they found very stressful. The pilot version of the baseline questionnaire did not include these questions and therefore the pilot subjects (n = 5387) were excluded from analyses of adverse life events. For analyses of age at death of parents, dates of death of parents were obtained from information in the questionnaire about first-degree relatives and were used to compute the subject’s age at bereavement.P values are two-sided.Relative risks (RR) with 95 % confidence intervals (CI) for breast cancer were obtained as hazard ratios using Cox proportional hazards models fitted in Stata 14.1  with attWe repeated the analyses for premenopausal and postmenopausal women separately, and by oestrogen-receptor status and extent (invasive/in situ) of breast cancer.During 2003–2012 a recruitment questionnaire was completed by 106,612 women without a history of breast cancer : 0.78–0.97)  in women who were aged less than 20 years at the time of loss of their mother, but not their father  and premenopausal and postmenopausal breast cancer separately  were similar, except for frequent stress, for which a reduced breast cancer risk was observed among postmenopausal  but not premenopausal  women. No association was observed, however, with continuous stress . With respect to divorce, relative risks were greater for premenopausal  than for postmenopausal  women, but neither were statistically significant.Risk in women who were aged 20 and under at loss of their mother was greater among premenopausal  than postmenopausal  women. Relative risks for all mothers were greater for in situ  than invasive  breast cancer. The association for in situ breast cancer was not attenuated after excluding mothers with breast or ovarian cancer , based on ten cases.Our study has the strengths that it is of prospective design, has a large number of cases with highly complete cohort follow-up, includes a wide range of stress variables and has information enabling analyses by oestrogen-receptor status. In addition, we were able to investigate the effect of adjustment for breast cancer risk factors on relative risk estimates, which is advantageous because the stress variables were associated with behavioural factors such as exercise, smoking, alcohol consumption and obesity.Women who reported frequent or continuous stress had a comparable risk of breast cancer to those who never or occasionally experienced stress. Our study was fourfold larger than previous studies on this topic, with the exception of the Nurses’ Health Study of care-giving stress, which had a similar number of cases and reported a protective effect for some but not all measures of stress . A lack Breast cancer risk in our study was not associated with having had any adverse life event during the 5 years preceding entry to the study, or with the total number of such events, a finding supported by previous investigations by the Women’s Health Initiative  and the We did not observe an association with divorce, except for a positive association for oestrogen-receptor-negative breast cancer only. No previous studies reported on associations by receptor status, and given that we are the first to report this association, it should be investigated by other studies. Our results hinted that the association might be restricted to premenopausal women only, which would be consistent as premenopausal women are more likely than postmenopausal women to have oestrogen-receptor-negative breast cancer . A twofoBereavement during childhood and adolescence has a well-established long-term impact on both mental and physical health , 40–42 aLimitations of our study are the lack of information on the intensity of stress, on stress in the workplace, and on the extent of social support or stress adaptive capacity, although the latter measure was not associated with breast cancer risk in a previous study . FurtherIn this large prospective study there was no consistent evidence that self-reported frequency of stress and experience of adverse life events affected subsequent breast cancer risk. Raised risks in women who were bereaved of their mother during childhood or adolescence were at least in part due to familial susceptibility.BMI, body mass index; CI, confidence interval; MET, metabolic equivalent; NHSCR, National Health Service Central Registers; RR, relative risk; UK, United Kingdom


This retrospective observational study evaluated the distribution of dense breasts by age group among healthy Korean women.Participants were women aged 30 years and older who voluntarily underwent screening mammography between January 2007 and December 2011. Women who received the Breast Imaging Reporting and Data System for mammographic density of 3 or 4 were defined as having dense breasts. The proportion of women with dense breasts  was calculated by dividing the number of participants with dense breasts by the total number of participants.Among the 231,058 women who participated, 78.15% were classified as having dense breasts. PDB was highest in the youngest age group (PDB=94.87%) and lowest in the oldest age group. The greatest difference in PDB between adjacent age groups was observed in the group aged 60-64 years.The results show that the proportion of dense breasts by age group increased in all age groups, except in those aged 35-39 years. These findings suggest an association between the age distribution of dense breasts and trends in breast cancer incidence. Further studies are needed to estimate the change in breast cancer incidence rate by age and the accumulation of fatty breast tissue in Korean women. Breast cancer is the second most common type of cancer among Korean women  and the Breast density has been shown to be associated with reproductive and hormonal factors , and amoMammographic density and the age-specific incidence of breast cancer differ markedly between women in Asian and Western countries -13. TherEvaluating the distribution of breast density by age group is necessary to determine whether there is an effect of breast density on the risk of breast cancer . Two stuStudy participants were recruited through cancer screening programs conducted by the Korea Medical Institute (KMI), as described in Bae et al. . The KMIStudy participants were Korean women aged 30 years and older who voluntarily underwent screening mammography between January 2007 and December 2011. Women who reported a prior history of breast cancer on a structured questionnaire were excluded because the mammography was a follow-up, rather than a screening test. Participant age was defined as the age at which the screening mammogram was received.For each participant, breast density was assigned one of four Breast Imaging Reporting and Data System (BI-RADS) categories. Dense breasts were defined as category 3 (heterogeneously dense [51-75% glandular]) or category 4 (extremely dense [>75% glandular]) .All participants consented to screening mammography and the use of personal data for research. This study protocol was approved by the institutional review board of Jeju National University Hospital (No. 2013-09-005).p-value of less than 0.05 was considered statistically significant. All statistical tests were performed using STATA version 12 .Participants were categorized by five-year age intervals . The proportion of participants with dense breasts  was calculated by dividing the number of women with dense breasts by the total number of participants. The dropping PDB (%) was determined by the difference in PDB between adjacent age groups. We evaluated the difference in the proportion of dense breasts by comparing each age group with the equivalent age group of the data published by Kim et al. . These dThe distribution of PDB by age group is shown in Our results show that the distribution of dense breasts by age group has changed over a ten-year period, with an increase in PDB observed for all age groups, except those aged 35-39 years. These results suggest that the different patterns in age at diagnosis of breast cancer between Asian and Western women may be related to the difference in PDB. It is also possible that changes in the PDB could be associated with the rising incidence of breast cancer.Our study had three main limitations. The first issue is the possibility of self-selection bias, a type of selection bias. Because the study participants voluntarily participated in the screenings, they are not representative of all Korean women. However, the aim of this study was not to evaluate any benefit from the screening intervention, but rather to describe the distribution of dense breasts. Since the participants did not know whether they had dense breasts as the time of the screening, they are likely to be representative of women who would volunteer for a screening mammography study. The second issue is the reliability of mammographic density determination. PDB was determined using the BI-RADS four-category scoring system for mammographic density , not theThe phenomenon of rapidly increasing incidence rates of breast cancer among Asian women could be explained by the combination of a diet rich in saturated fat and a sedentary lifestyle, as well as early menarche, decreased parity, and delayed childbearing, commonly referred to as the Westernization of lifestyle . Shin etBecause PDB is higher among women from Asian countries than women from Western countries , the accIn conclusion, this study suggests that over a ten-year period, PDB changes with age in Korean women. This suggests an association between the age distribution of women with dense breasts and trends in the incidence rate of breast cancer. Additional studies are needed to estimate the change in incidence rate by age group and the accumulation of breast fatty tissue in Korean women.


Risk of screen-detected breast cancer mostly reflects inherent risk, while risk of interval cancer reflects inherent risk and risk of masking (risk of the tumor not being detected due to increased dense tissue). Therefore the predictors of whether a breast cancer is interval or screen-detected include those that predict masking. Our aim was to investigate the associations between mammographic measures and (1) inherent risk, and (2) masking.We conducted a case-control study nested within the Melbourne collaborative cohort study of 244 screen-detected cases ) matched to 700 controls and 148 interval cases  matched to 446 controls. Dense area (DA), percent dense area (PDA), and non-dense area (NDA) were measured using the Cumulus software. Conditional and unconditional logistic regression were applied as appropriate to estimate the odds per adjusted standard deviation (OPERA) adjusted for age and body mass index (BMI), allowing for the association with BMI to be a function of age at diagnosis. Tests of fit were performed using the Bayesian information criterion (BIC) and the area under the receiver operating characteristic curve.For screen-detected cancer, the association with BMI had a marginally significant dependence on age at diagnosis, and after adjustment both DA and PDA were associated with risk (OPERA approximately 1.2) and gave a similar fit. NDA was not associated with risk. For interval cancer, the BMI risk association was not dependent on age at diagnosis and the best fitting model was PDA alone . Prediction of interval versus screen-detected cancer was best achieved by PDA alone  with no association with BMI. When the analysis was restricted to small tumors to reduce the influence of tumor growth, we obtained similar results.Inherent breast cancer risk is predicted by BMI and DA or PDA, but not NDA. Masking is predicted by PDA, and not by BMI. Understanding risk and masking could help tailor mammographic screening.The online version of this article (doi:10.1186/s13058-016-0722-4) contains supplementary material, which is available to authorized users. The regions of the breast that appear white or bright on a mammogram are referred to as being mammographically dense, and are usually measured in terms of their absolute area on the mammogram , or in terms of the percentage of the total area on the mammogram covered by dense area . These mammographic density (MD) measures, after adjustment for age and body mass index (BMI) due to negative confounding, are positively associated with risk of developing breast cancer –5. ThereThe mammographic regions that appear non-white are referred to as non-dense area (NDA), and their negative association with overall risk of breast cancer - without differentiation between risk of developing breast cancer, masking and growth rate - is controversial , 7. NDA Therefore, the concurrent associations between developing and masking of a breast tumor and the different mammography measures, DA, PDA and NDA, is unclear. The role of different rates of tumor growth also poses additional challenges.The risk of a woman being diagnosed with breast cancer, given age, BMI and MD measures, is the combination of: (1) her inherent risk of developing breast cancer; (2) her risk of having any existing tumor masked; and (3) the growth rate of her tumor should she develop one. We assumed the risk of screen-detected breast cancer is mostly influenced by inherent risk, while risk of interval breast cancer is due to a combination of inherent risk and risk of masking. Therefore, given a woman is diagnosed with breast cancer, the factors that differentiate her having a screen-detected versus an interval cancer will mostly be those that influence risk of masking. Restricting the analysis to small tumors should lessen the influence on the latter due to the growth rate of tumors.We have previously reported on the associations between DA, PDA and NDA, and breast cancer risk, while allowing for the associations with BMI to vary with age at diagnosis, using a case-control study nested within the Melbourne collaborative cohort study (MCCS) . Here weThe MCCS is a prospective cohort study of 41,514 people  aged between 27 and 76 years at study entry (99.3 % of whom were aged 40–69 years). Participants were recruited between 1990 and 1994 from the Melbourne metropolitan area. In 2009, through a record linkage between the MCCS and BreastScreen Victoria, a population-based screening program, we identified 20,444 (84 %) women in the MCCS who had attended BreastScreen Victoria at least once and were eligible for this study.We then designed a nested case-control study using incidence density sampling. Cases were women with a first diagnosis of invasive adenocarcinoma of the breast . Each case was matched randomly to four controls by year of birth, year of entry into the MCCS and country of origin . We selected the mammogram closest to baseline and of the contralateral breast with respect to the laterality of the tumor in the matching case. Only craniocaudal-projection images were used in this study. Further details about the nested case-control study based on the MCCS have been published elsewhere , 9.Screen-detected cases were identified at BreastScreen Victoria and interval cases were defined as those diagnosed within 2 years of a negative screening at BreastScreen Victoria . The cases were further categorized by tumor size as small tumors (<2 cm) and large tumors (≥2 cm), given that breast cancer stage is based on cutoffs of 2 cm or 5 cm. For this study, we excluded 61 screen-detected cases detected at their first screening and 52 cases diagnosed more than 2 years after a negative screen.There were 244 screen-detected cases (192 (79 %) with small tumors; 49 (20 %) with large tumors) matched to 700 controls and 148 interval cases (76 (51 %) with small tumors; 65 (44 %) with large tumors) matched to 446 controls.We estimated associations between the mammographic measures and risk according to the following different models: (1) BMI only; (2) BMI and a function of DA and NDA, with either as a linear combination or PDA; (3) BMI and only DA; and (4) all of the above models with mammographic measures without including BMI. The association between BMI and risk was fitted as a function of age at diagnosis of the case as a reference age, see below. BMI was measured at baseline attendance.X  on the scale of 1 (standard deviation) SD of X after adjusting the mean of X for all the other variables taken into account either by design or adjustment. This allows statistically independent comparisons of the disease-discrimination power of each of the different risk factors, as recently demonstrated 0.5, which is the standard deviation of DA’ after adjusting for NDA’. Similarly, we obtained the OPERA for NDA’.To estimate the OPERA associated with each mammographic measure, conditional logistic regression was fitted adjusting for age at mammogram with the standardized residuals corresponding to each mammographic measure, separately, for screen-detected and interval cancers. Letting BMI measured at cohort entry was standardized according to the mean and SD of the controls. To allow the association between BMI and risk to be dependent on age, an interaction term between the standardized BMI and reference age (age at diagnosis for the case and for its matched controls) was fitted in the models. The likelihood ratio test was applied to test the significance of the interaction between BMI and reference age.To estimate the OPERA for having interval versus screen-detected breast cancer we fitted unconditional logistic regression to data from cases only. BMI and all three mammographic measures were included in the same format as mentioned above and the models were adjusted for age at mammogram. For these analyses we presented only the estimates when fitting BMI as a constant because we found no evidence that the association between BMI and mode of detection depended on age at diagnosis.t test, assuming independence of normally distributed log(OPERA) estimates with a standard deviation consistent with the width of the confidence interval (CI). There is a slight overlap in the datasets used to estimate risk of small and large tumors due to the design properties of the nested case-control study and therefore, there is a possibility of overestimation of a significant difference.Relative goodness of fit was assessed by the Bayesian information criterion (BIC), and by the area under the receiver operating characteristic curve (AUC). We also tested for differences between AUCs using De Long’s test . To compWe conducted sensitivity analyses using unconditional logistic regression in which we made further adjustments for the following potential confounders that were assessed at cohort entry: BMI at age 18–21 years; age at menarche; parity and lactation; menopausal status; use of hormone replacement therapy (HRT); use of oral contraceptives (OC); alcohol consumption and energy intake; and the matching variables . These analyses were also repeated using only those women who had undergone mammography within 5 years of cohort entry.P value <0.05 was considered to be nominally statistically significant.We also conducted the following sensitivity analyses: (1) excluding cases diagnosed between 1 and 2 years after negative screening, and their matched controls; (2) excluding ever-users of HRT; and (3) excluding cases diagnosed within 2 years of the mammogram, and their matching controls. Statistical analyses were performed using Stata 12.1 . A two-sided P < 0.001), older at baseline when covariates were measured , and older when the mammogram closest to study entry was performed .Table P = 0.08 and 0.18, respectively). Interval cases had on average greater DA and PDA and lesser NDA compared to controls (P < 0.001). Compared to screen-detected cases, interval cases had on average greater DA and PDA and lesser total breast area and NDA (P < 0.01). Among screen-detected cases, those with small tumors had on average lesser DA (P = 0.01) but not lesser PDA (P = 0.11) than those with large tumors. Similarly, among interval cases, those with small tumors had on average lesser DA (P < 0.01) but not lesser PDA (P = 0.26) than those with large tumors. Screen-detected cases had a greater BMI than the controls (P = 0.04), whereas there was no significant difference in BMI between interval cases and controls (P = 0.68). Of the women who answered the question about history of family breast cancer, the proportion who reported any family history of breast cancer was higher among those with interval and screen-detected cancers  than it was among their respective controls  , although the difference between screen-detected cases and controls was marginally significant. Among screen-detected cases there was a greater percentage of women who had no children compared to controls .Screen-detected cases had on average similar DA and PDA compared to controls (P < 0.01), progesterone receptor (PR)-negative (PR-) , poorly differentiated tumors , positive nodal status , and larger tumor size, ≥2 cm .In terms of tumor characteristics, interval cases were diagnosed with more tumors with poorer prognosis than screen-detected cases; estrogen receptor (ER)-negative (ER-) . Both DA and PDA were positively associated with the risk of screen-detected breast cancer with a similar increase in risk of about 20 % per adjusted SD in all models. Models including either DA or PDA gave the best fit . NDA was not associated with risk of screen-detected cancer in any model.Table P ≥ 0.29). The best fitting model under the BIC involved PDA alone, with an increase in risk of about 124 %  per adjusted SD.Table P ≤ 0.12). The positive association with DA and PDA remained but the risk estimates were about 10 % per adjusted SD and marginally significant. In contrast, risk of large screen-detected cancers was not associated with BMI, whether fitted as dependent or independent of age at diagnosis (results not shown). The association between risk and DA or PDA was 59 % and 66 % per adjusted SD, respectively. The differences in the association between risk and DA or PDA according to size of tumor were nominally significant. There was no association between NDA and risk of small or large screen-detected breast cancer.Table Table Table The findings were similar when we adjusted for all the confounders and further restricted the analysis to mammograms performed within 5 years of cohort entry. No substantial differences in estimates were observed from the sensitivity analyses . Therefore, it is not surprising that they were associated with similar risk gradients once risk was expressed on the age-adjusted and BMI-adjusted scale using OPERA . The AUCOur finding that NDA was not associated with screen-detected disease is important given the controversy about the potential for NDA to be implicated in breast cancer risk . When anBoth DA and PDA gave a similar fit when analyzing risk of screen-detected cancer and when further restricted to small tumors. Recent findings from studies of single nucleotide polymorphisms (SNPs) associated with breast cancer risk found DA to be a better fit . PDA is When restricted to screen-detected cases with small tumors, it is more likely that the tumors were not present on previous mammography, in which case the risk estimates would better reflect those for inherent risk of the disease. This might explain the similarity in the risk model with the overall cases. Screen-detected large tumors, however, are plausibly more likely to have been present on previous mammography and therefore the risk of these tumors might be influenced by risk of (past) masking in addition to inherent risk and increased tumor growth rate. This is perhaps reflected by the fact that the association with BMI was not age-dependent, as we found from analyses of interval cancers, and of screen-detected versus interval cancers, which we contend are more about risk of masking.Risk of interval cancer, on the other hand, represents a combination of risk of developing the tumor and risk of masking. This is because, based on the European guideline for quality reassurance of screening programs , intervaWhen comparing interval with screen-detected breast cancers, if we assume that the majority of the screen-detected tumors were present only on the mammographic examination at which the tumor was identified and not on prior mammography, then the predictors are in effect referring to risk of masking. This is supported by our finding that the association between risk and BMI did not depend on age at diagnosis. Our results suggest that the percentage, rather than the absolute amount, of “whiteness” on a mammogram is a stronger risk factor for tumor masking. Other studies that have investigated PDA ) found similar results –27. ResuRisk of large tumors is hard to interpret due to the possible influence of tumor growth. MD risk gradients were significantly greater for large tumors compared with small tumors, for both detection modes. This observation could be due to the greater influence of increased tumor growth rate on large tumors. Two other Australian studies , 29 withOne strength of our study is that, as BMI is known to have differential associations with breast cancer risk , we realP = 0.32). If the tumors occurred at the same time, or if we were able to test this for true interval cases, this might mean that the larger tumors were on average growing at a faster rate. Misclassification of the detection mode of cases might also have occurred if screen-detected cases were wrongly classified as false-negative interval cases while true interval cases or occult tumors were wrongly classified as screen-detected cases. Other strengths and limitations of the study were discussed in our previous report [A limitation of our study is the sample size, especially for categories defined by detection and tumor size. We were not able to retrospectively review mammograms and identify the proportion of true interval, false-negative, and occult tumors . If thers report .In conclusion, we have gained greater insight into the roles of MD in breast cancer diagnosis by analyzing cases by their detection mode and tumor size. After properly taking into account the role of BMI as a risk factor for disease, we found that both DA or PDA were predictors of inherent risk and NDA played no role. For masking, PDA alone was the best predictor, and BMI was not a risk factor for this outcome. Consequently, screening strategies could be tailored; e.g., women with greater age-adjusted and BMI-adjusted DA, who are at higher inherent risk of the disease, could be recommended prevention strategies, early screening and/or more frequent screening, taking into account other measured risk factors such as family history. Women with greater PDA, irrespective of their BMI, who are at higher risk of masking, could be recommended for additional screening by ultrasound. Therefore, from the point of view of using MD measurements to improve screening, masking and inherent risk need to be thought of as separate, though interacting, issues.AMDRF, Australian Mammographic Density Research Facility; AUC, area under the receiver operating characteristic curve; BIC, Bayesian information criterion; BMI, body mass index; BSV, BreastScreen Victoria; CI, confidence interval; DA, dense area; DCIS, ductal carcinoma in situ; ER, estrogen receptor; HER2, human epidermal growth factor receptor-2; HRT, hormone replacement therapy; MCCS, Melbourne collaborative cohort study; MD, mammographic density; NDA, non-dense area; OPERA, odds per adjusted standard deviation; PDA, percent dense area; PR, progesterone receptor; SD, standard deviation