Toll-like receptors (TLRs) and the transcription factor nuclear factor-κB (NFκB) are important in inflammation and cancer.We examined the association between breast cancer risk and 233 tagging single nucleotide polymorphisms within 31 candidate genes involved in TLR or NFκB pathways. This population-based study in the Seattle area included 845 invasive breast cancer cases, diagnosed between 1997 and 1999, and 807 controls aged 65–79.MAP3K1, MMP9, TANK, and TLR9. These results were similar when the risk of breast cancer was examined within ductal and luminal subtypes. Subsequent exploratory pathway analyses using the GRASS algorithm found no associations for genes in TLR or NFκB pathways. Using publicly available CGEMS GWAS data to validate significant findings , rs889312 near MAP3K1 was confirmed to be associated with breast cancer risk . Further, two SNPs in TANK that were significant in our data, rs17705608 (P = 0.05) and rs7309 (P = 0.04), had similar risk estimates in the CGEMS data .Variant alleles in four genes were associated with breast cancer risk based on gene-level tests: Our findings suggest plausible associations between breast cancer risk and genes in TLR or NFκB pathways. Given the few suggestive associations in our data and the compelling biologic rationale for an association between genetic variation in these pathways and breast cancer risk, further studies are warranted that examine these effects. Tumor-promoting inflammation has been linked to cancer development in prior research -5, and hTNF, IL6, and MMP9[As modulators between inflammation and cancer, NFκB pathway genes play a central role in innate immunity and acute inflammatory response ,9. In no and MMP9-13. In t and MMP9,14,15. D and MMP9-18.While NFκB-related pathway genes are critical in innate and adaptive immune responses, genes in toll-like receptor (TLR) signaling pathways are also important as they activate NFκB in addition to other signaling pathways . In normNFKB1 has been shown to affect transcription [TNF have been shown to affect production of the cytokine TNF [TNF was associated with elevated TNF expression in vitro [TLR4, rs4986790 (D299G) and rs4986791 (T399I), have been shown to affect the extracellular domain of the TLR4 receptor [Further, there is evidence of variants in TLR or NFκB-related pathways affecting gene function. For example, an insertion/deletion (94ins/delATTG) in the promoter of cription . In micekine TNF . Likewisin vitro ,26. Addireceptor . Prior sThis study examined the association between tagging single nucleotide polymorphisms (tagSNPs) within candidate genes in either TLR or NFκB signaling pathways and breast cancer risk in post-menopausal women. We also conducted an exploratory analysis of multiple genes in TLR or NFκB pathways. We focused this study on older women as circulating levels of pro-inflammatory factors increase with age and breast cancer incidence is highest in this age group.Participant recruitment has been described previously . BrieflyInformation that detailed histology, estrogen receptor (ER) status, and progesterone receptor (PR) status was obtained from the Cancer Surveillance System. Tumors were categorized as luminal (ER or PR positive) or non-luminal (ER and PR negative) subtype. Histology was categorized by ICDO codes as ductal (8500), lobular (8520), ductal/lobular (8522), or other .AZI2, IFIH1, IKBKE, IRAK4, IRF3, MAP3K1, MAP3K7, MMP9, NFKB1, NFKB2, RELA, RELB, TANK, TBK1, TICAM1, TICAM2, TIRAP, TLR3, TLR4, TLR7, TLR9, TNF, TNFRSF1A, TNFRSF1B, TOLLIP, TRAF3, TRAF6, UBE2C, UBE3A, VISA, and ZBP1. Using the software SNAGGER [2 value of at least 0.80 and a minor allele frequency (MAF) of 0.05. The tagSNPs were chosen from regions representing the candidate genes plus 4,000 base pairs both 3′ and 5′ of the gene. SNP selection was prioritized based on functional importance, giving SNPs in coding regions priority over those in other regions. To ensure that at least one SNP from each bin would be successfully genotyped, more than one tagSNP was chosen where a bin included more than 10 SNPs. Additionally, coding SNPs within candidate genes with a MAF of at least 0.02 and also SNPs found to be associated with cancer risk in previous studies were included in the panel. For example, rs889312 in the region surrounding MAP3K1 was selected for analysis based on its significance in prior genome-wide association studies (GWAS) [As part of a study of breast cancer and inflammation, we examined 1,536 SNPs in pro- or anti-inflammatory genes. For this study, we selected a total of 233 SNPs from 31 genes in TLR or NFκB signaling pathways. The following genes were included:  SNAGGER  on publis (GWAS) .Genotyping was performed on 887 cases and 872 controls using the Illumina GoldenGate multiplex platform . Additional assays were run on the KASPAR platform at KBioscience for SNPs not covered on the Illumina platform or that appeared to be failing on Illumina after an interim review (N SNPs = 102). For the current analysis, all 233 SNPs were genotyped on Illumina and four were additionally typed on KASPAR. Of these four SNPs, three failed on Illumina and passed on KASPAR  and one was successfully typed on both platforms (rs5746026) that had a cross-platform concordance of 99.7%. We used results from Illumina to analyze rs5746026 as the call rate was 100%. Replicate aliquots were included for 143 (8%) of the 1,759 participants. Of these replicate-pairs, nine had discordant genotypes of at least 1% among passing SNPs. Monomorphic SNPs or those with call rates less than 90% were excluded from analysis. All SNPs included in this study had Hardy-Weinberg Equilibrium (HWE) p-values greater than 0.001 among Caucasian controls.To account for potential confounding due to population stratification, we used principal components analysis to restrict our sample to 1,652 white women . BrieflyP ≤ 0.05) based on the minP permutation test, we used logistic regression to examine the association between SNPs and the risk of ductal histology (N = 565) and luminal breast cancer (N = 744) subtype compared to all controls. These models were adjusted for continuous linear age at reference and were log-additive.Using these 845 cases and 807 controls, the relative risk of breast cancer associated with each SNP was approximated using logistic regression to compute odds ratios (OR) and 95% confidence intervals (CI). All models were adjusted for continuous linear age at reference and were log-additive. However, dominant models were fit when genotype cell counts were less than 5 for either cases or controls. We adjusted for multiple comparisons within a gene by using a minP permutation test with 10,000 replications to assess the significance of each gene . For genhttp://www.genome.jp/kegg/pathway/hsa/hsa04620.html). The first pathway included TLR3, TLR4, TLR7, TLR9, TIRAP, TICAM1, TICAM2, TOLLIP, IRAK4, TRAF3, TRAF6, MAP3K7, IRF3, and IKBKE. The second pathway included these genes in addition to NFKB1, NFKB2, RELA, and RELB. Prior to running any models with GRASS, we imputed any missing SNP values. All imputation was performed using BEAGLE 3.3 with a reference panel of phased genotype data from 283 European individuals sequenced by the 1000 Genomes Project [The gene set ridge regression in association studies (GRASS) algorithm was used to conduct exploratory pathway analyses for genes in TLR or NFκB pathways . We exam Project . PathwayP ≤ 0.05), the risk of breast cancer associated with each SNP was computed using logistic regression in the CGEMS data, after adjusting for age in 5-year groups. BEAGLE was used to impute seven SNPs that were not already present within the CGEMS data using phased genotype data from the 1000 Genomes Project as a reference panel. Six SNPs with successful imputation (r2 > 0.90) were used for analysis.Finally, we used publicly available data from the Cancer Genetics Markers of Susceptibility (CGEMS) Breast Cancer Genome-Wide Association Scan to validate our significant findings . A Holm All analyses were performed using Stata 11 or R version 2.10.1.Cases and controls did not vary substantially in demographic characteristics Table , but theMAP3K1, MMP9, TANK, and TLR9 was found to be significant at the gene level . In MMP9 we examined two coding SNPs and one intronic SNP. There was evidence that one of the coding SNPs, rs17576 (Q279R), was associated with an increased risk of breast cancer . Among controls, this SNP was not found to be in high LD with the other two SNPs we examined in MMP9 . Of the six SNPs we examined in TANK, two were significantly associated with a 20% decreased risk of breast cancer: rs17705608 located in the flanking 5′ UTR and rs7309 located in the 3′ UTR. These SNPs were in moderate LD among controls (r2 = 0.67) and had identical relative risk estimates . The four other intronic SNPs did not show evidence of affecting breast cancer risk and were not in LD with any other SNPs in this region . Of the two SNPs we examined in TLR9, only the synonymous coding SNP rs352140 (P545P) was associated with breast cancer risk .We examined variation in the risk of breast cancer associated with 233 SNPs representing 31 genes in TLR or NFκB pathways. After correcting for multiple comparisons using the minP permutation test, variation in TLR9 was not significant at the gene level for either ductal or luminal subtypes .The results for these four genes were almost identical when analyses were confined to cases with ductal and luminal subtypes respectively Table . For mosTLR3, TLR4, TLR7, TLR9, TIRAP, TICAM1, TICAM2, TOLLIP, IRAK4, TRAF3, TRAF6, MAP3K7, IRF3, and IKBKE, was not significant after performing a permutation test (P = 0.24). Likewise, after permutation testing the second pathway we examined, which included these same genes in addition to NFKB1, NFKB2, RELA, and RELB, was not significant (P = 0.28).As an exploratory pathway analysis, we used the GRASS algorithm to examine genes in the KEGG “Toll-like receptor signaling pathway” Figure . The firMAP3K1 was replicated, and without correction for multiple comparisons (P = 0.04 in CGEMS), with the suggestion of a slight increased risk of breast cancer . Although the associations with breast cancer were of similar magnitude and direction for most SNPs when comparing the two datasets, the risk of breast cancer associated with rs352140 in TLR9 was in the opposite direction  from that found in our data .We attempted to validate significant findings by assessing the risk of breast cancer associated with SNPs from our Seattle study using data from the CGEMS GWAS repository. Most SNPs found to be significant in our data were not found to be significant in the CGEMS data Table . Only rsMAP3K1, MMP9, TANK, and TLR9. Results were unchanged within cases with ductal or luminal subtypes. However, after replicating our results using the CGEMS GWAS data, only rs889312 from the region near MAP3K1 was associated with breast cancer risk.We found that the risk of breast cancer was associated with genetic variation in four genes in either TLR or NFκB pathways: MAP3K1 is a key player in TLR signaling pathways and produces downstream signaling for the NFκB pathway as well as the ERK and JNK kinase pathways [MAP3K1 have been found to be significant in three prior GWAS studies [2 = 0.68). A candidate gene study, which used 1,267 Dutch breast cancer cases and 20,973 controls from the BCAC GWAS, did not find rs889312 to significantly affect breast cancer risk , though they did find that this SNP was associated with lymph-node status (P = 0.04) [pathways ,40. Our  studies ,32,41. E studies . They co = 0.04) . HoweverMMP9, as MMPs influence cancer progression and contribute to tumor angiogenesis, growth, and metastasis by degrading the extracellular matrix and activating growth factors [MMP9 expression is regulated by NFκB [MMP9 to affect breast cancer risk, many prior studies have published results that support an association between MMP9 and breast cancer risk. Two previous analyses of expression found MMP9 plasma concentrations were greater in breast cancer cases compared to controls [MMP9 in tumor samples compared to normal breast tissue and an increased risk of breast cancer associated with the T allele for rs3918242 in MMP9  [MMP9 and breast cancer risk.We also investigated variation in  factors . MMP9 ex by NFκB , and in  by NFκB . Althougcontrols ,47. In a1.3–4.9) . In a ca1.3–4.9) . However1.3–4.9) ,51. Our TANK , which is a critical upstream component in the NFκB activation pathway and therefore could be a factor that relates to inflammation as well as cancer development and progression [TANK (rs17705608 and rs7309) were significantly associated with breast cancer risk in our study sample, interestingly no prior GWAS or candidate gene studies have reported on genetic variants in TANK affecting the risk of breast cancer. In the CGEMS GWAS data, neither of these SNPs was strongly associated with breast cancer risk .Another NFκB gene we investigated was gression ,13,52,53TLR9. No GWAS studies to date have found that breast cancer risk is influenced by variants in TLR9. We found that rs352140 in TLR9 was associated with breast cancer risk . Although this SNP is synonymous and does not alter the protein sequence, it could affect the protein via perturbations in mRNA splicing and stability, altered structure of mRNA, and (though less well-established) effects on protein folding [TLR9[TLR9 compared to controls, and that TLR9 mRNA expression was correlated with NFκB activity in breast cancer patients [TLR9 and breast cancer risk.As TLR pathways are central in tissue repair and regeneration ,54,55, w folding . Our res folding . Howevering [TLR9,59. Bergpatients . TherefoMyD88, TLR1, and TLR2). Given the biologic plausibility that genes within this pathway could affect cancer development and progression, it would be of interest for further studies to include pathway analyses, particularly those that have larger sample sizes, improved coverage of SNP variation, and other sources of variation such as epigenetic influences.In exploratory pathway analyses we did not observe an association between TLR-NFκB related genes and breast cancer risk. Although the results from these exploratory pathway analyses do not suggest that breast cancer risk is affected by combined variation in the genes that we examined from the KEGG “Toll-like receptor signaling pathway”, this study may have been limited to detect such an association given our sample size and the absence of some key genes within this pathway  [TNF (rs361525 and rs1800629) [LTA)  as well as a nonsynonomous coding SNP (rs767455) in the TNF receptor TNFRSF1A  [Although this study suggested variation in four genes4986790)  and TNF 1800629)  that aff1.1–1.4) .There were some limitations to this study that should be considered in the interpretation of our results. Our sample size may not have been sufficient to capture the true level of association between genetic variants with low frequency and breast cancer risk. Also, the assays we used may have misclassified or failed to detect variation in the genes we analyzed. However, misclassification is not likely a problem as the repeat samples were highly concordant. There could also be missed variation due to incomplete coverage of genes or due to our limited number of SNPs. It is also possible that we did not characterize important variation in these genes, since particular variants, such as deletions, variants in repeat regions, and copy number variants, were not detectable on the platforms we used for genotyping. Another limitation is that we did not genotype variants for every gene in TLR or NFκB related pathways. Therefore, potentially important associations between key genes in these pathways may have been missed. In addition, although we attempted to control for potential population stratification by restricting our sample to white women using principal components analysis, it is possible our analyses were subject to uncontrolled confounding from admixture.There were a number of strengths to this study. For one, our well-characterized study population is representative of post-menopausal women at risk of breast cancer in the Seattle metropolitan area. Also the population-based controls are representative of those at risk of disease. Further, our study sample is consistent with other populations that have been used to analyze breast cancer risk, raising the likelihood that associations from this study are generalizable to similar populations. Another strength of this study was our use of a tagSNP approach that maximized genetic coverage. Finally, by using data from the CGEMS GWAS to validate our findings we were able to draw stronger conclusions regarding the association between genetic variants in TLR or NFκB pathways and breast cancer risk.Overall, the results of this study do not suggest a strong association between breast cancer risk and the SNPs in the candidate genes we analyzed in TLR or NFκB pathways. Despite our findings, there is a compelling biologic rationale for an association between genetic variation in these pathways and breast cancer risk. Given the few suggestive associations in our data and results from prior studies that implicate plausible associations between breast cancer risk and genes in TLR or NFκB pathways, further studies are warranted that examine these effects.The authors declare that they have no competing interests.MMM and KEM provided the concept of the study as well as funding. MM and EWP provided laboratory methodology and expertise in evaluating the assay results. The analysis plan was developed by KEM, LGJ, AJR, and MMM under the direction of BM. Data analysis was performed by AJR and LGJ. AJR conducted the literature review and prepared the manuscript, including the Background, Materials and Methods, Results, Discussion, and Conclusions sections. All authors contributed substantially to revisions toward the final manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/13/219/prepubRisk of Breast Cancer Associated with SNPs in Non-significant TLR or NFκB Pathway Genes.Click here for file


Breast cancer is the second most common cancer among women in the Kilimanjaro Region of Tanzania. It was tested within a case–control study in this region whether a specific dietary pattern impacts on the breast cancer risk.A validated semi-quantitative Food Frequency Questionnaire was used to assess the dietary intake of 115 female breast cancer patients and 230 healthy age-matched women living in the same districts. A logistic regression was performed to estimate breast cancer risk. Dietary patterns were obtained using principal component analysis with Varimax rotation.P = 0.01), and for a “Fruity Diet”, characterized by a higher consumption of fish, mango, papaya, avocado and watery fruits . Both diets showed an inverse association with the ratio between polyunsaturated and saturated fatty acids (P/S ratio).The adjusted logistic regression estimated an increased risk for a “Fatty Diet”, characterized by a higher consumption of milk, vegetable oils and fats, butter, lard and red meat (OR = 1.42, 95 % CI 1.08–1.87; A diet characterized by a low P/S ratio seems to be more important for the development of breast cancer than total fat intake. Established factors for breast cancer development are age at menarche, age at menopause, age at first full-term pregnancy, breastfeeding and alcohol consumption at all ages –7. A higIn Tanzania, a low-income country where breast cancer is currently the second most common cancer in women, the lifestyle characterized by long-standing lactation or late age at menarche has been associated with a lower breast cancer risk . HoweverThe study was carried out in collaboration between the Kilimanjaro Christian Medical Centre (KCMC) in Moshi, Tanzania, and the Institute of Nutritional Sciences of the University of Giessen, Germany.U test [Breast cancer patients and controls were recruited in the Kilimanjaro Region between 2004 and 2007. The detailed study methodology has been described previously . In summU test .The present analyses focus on the dietary patterns of both cases and controls using the data of a semi-quantitative food frequency questionnaire (FFQ). The FFQ food list was prepared based on market surveys at different seasons and completed after a pre-test. The relative validity of the FFQ was assessed in 2005 and 2006 based on two non-consecutive 24-h recalls of 50 randomly selected women with a mean age of 40 years (23–70 years), who did not participate in the case–control study but lived in the same study region. The validation study covered two seasons with different food availability: dry and rainy season. Data collection was done by four trained enumerators. The training included estimation of quantities using common household measurements, for example, cups, spoons, customary packing size, and solid foods in pieces or slices. Foods were prepared according to local standard recipes and weighed using household kitchen scales by the research staff. Countable foods such as onions, eggs or bananas were classified according to their size into small, medium and large. Samples of food pieces were obtained from the local market, and mean weights were taken of each size. The matter of size was intensively discussed in the interviewer trainings to assure a common comprehension. A raw/cooked coefficient was applied when large deviations between cooked and raw foods were expected after preparation, for example, for dried cereals  and dried legumes. The coefficients were calculated by cooking experiments done by the nutritionist but without calculating any loss of vitamins and minerals. Seasonal food availability on individual level was assessed within the interview, especially for fruits, and a seasonal factor was applied accordingly.®, a nutrition software package, which generated tables of the individual food and nutrient intake per day, latter based on food composition tables from Tanzania, Kenya, Senegal, Mali and Germany [The FFQ data from both, the validation and the case–control study, were entered into NutriSurvey Germany , 24. Allt test. In addition, the Wilcoxon signed rank test was used to test for differences in the results of the 24-h recall and FFQ. There was no evidence for a seasonal effect in the food groups if the FFQ is used, except for non-alcoholic beverages. Differences in the intake of oils and fats assessed by the validated FFQ and its reference, the 24-h recall, could only be shown by one interviewer. This might be due to low quantification capacities of the studied population especially in this respective food group and especially during the 24-h recall. Furthermore, Spearman correlation was calculated with all interviewers grouped together for comparison with other studies that did not report whether they checked for interviewer bias. The correlation coefficient (rs) was highest in the food group “fruits”  followed by “cereals” , “beverages”  and the food groups “animal products” and “vegetables” . A negative non-significant correlation coefficient (rs) was found in the food group “oils and fats” . There had been no consistent statistical differences between FFQs and the 24-h recalls, and the correlations were low to modest but comparable to other studies except for oil and fats [For the validation study, the data sets were merged into six food groups to describe individual food intake: (1) cereals: bread, rolls, cereal products, grains, egg-free pasta; (2) vegetables: vegetables, pulses, potatoes, mushrooms; (3) animal products: eggs, dairy and cheese, meat, fish, poultry, sausages and other meat products; (4) beverages: non-alcoholic beverages, coffee, tea, water, alcoholic beverages; (5) fruits; (6) fats: oil, fats, butter. Since the values of most variables were not normally distributed, non-parametric tests were carried out in the subsequent analysis. The studied population had a low educational level, and considering the relative high number of interviewers in relation to the study population, the validation data were tested for interviewer effects before any statistical analysis was performed. The Kruskal–Wallis test chosen to test for homogeneity between the interviewers showed interviewer effects in 100 % of the food groups confirmed by the median one-way test at a level of 83 %. Therefore, further analysis was carried out stratified by interviewer. The Wilcoxon signed rank test was used to test the 24-h recall and the FFQ for seasonal variability. It is a non-parametric test equivalent to the paired and fats –27. The and fats  pointed However, it was recommended paying special attention to the training of interviewers and especially to the assessment of the oil and fat intake. In addition, a calculation for seasonal variability in fruit and vegetable intake was recommended to be used where applicable. The FFQ finally contained in total 65 food items.From data on individual food intake of the case–control study population, dietary patterns were created using PCA. Although there is a certain disagreement among statistical theorists about it –31, PCA If possible, the variables were entered as continuous variables. The variable “age at first full-term pregnancy” was categorized into three groups, “first full-term pregnancy ‘≤20 years’, ‘>20 years’, and ‘no pregnancy’”.Descriptive statistics, principal component analysis and logistic regression were performed using the statistical package of SPSS version 18 (SPSS Inc.).Ethical clearance was obtained from the Research and Ethics Committee of the KCMC, Moshi, Tanzania, and the Ethics Committee of the Faculty of Medicine of the University of Giessen, Germany.BMI 20 years = 1.31; 95 % CI, 1.11–1.55; ORhigh property = 0.22; 95 % CI, 0.09–0.55; and ORlactation = 0.99; 95 %CI, 0.98–1.00; all Ps < 0.01).Selected socioeconomic and reproductive characteristics of the study participants are presented in Table Mbege , bottled beer and wine .Median energy consumption in all women was 1,714 kcal per day , median protein intake was 47 g/day , median fat intake was 72 g/day  and median carbohydrate intake was 188 g/day (min 85 g/day–max 537 g/day). Median percentage of food energy from protein was 12 %, from fat 39 % and from carbohydrates 46 %. Median alcohol intake from alcoholic drinks was 8.2 g/day . Main alcoholic drinks were chapati (unleavened East African flat wheat bread), leguminous vegetables, bread, soda and red meat. Since most of these food items are usually purchased, we called it the “Diet of the Rich”. Pattern two is characterized by Mchicha, cucumber, okra, onions, carrots, tomatoes, maize, fish and avocado. Mchicha is the Swahili name for amaranth leaf, a traditional food in Tanzania often synonymously used for a dish consisting of amaranth leaves and, for example, onions, tomatoes and/or carrots in various amounts. The pattern was therefore named “Mchicha Diet”. The third pattern is characterized by ripe and green banana, sugar, different fruits, tubers, pulses and Mbege. The mountainous area of the Kilimanjaro Region is known for its various banana plants. Therefore, pattern three was called “Banana Diet”. Pattern four is characterized by a high consumption of milk, butter, lard, vegetable oils and fats, and a low consumption of sunflower oil and tea. All of the positively loading food items relate to fat, thus we called this pattern “Fatty Diet”. With increased affiliation to this Fatty Diet, bread consumption decreased  and red meat consumption increased .A PCA was conducted primarily on 36 food groups with Varimax rotation. The Kaiser–Meyer–Olkin measure verified the sampling adequacy for the PCA, KMO = 0.621, which is considered as mediocre , 41. FolMchicha, Banana and Fatty Diets  to a non-significant OR of 1.28 . The Banana and the Fatty Diets were still associated with an increased breast cancer risk on a significant level. The OR for the Fatty Diet increased to 3.04  among women with the highest consumption (4th quartile). With increased affiliation to the Fatty Diet, total fat intake increased significantly (P = 0.04), whereas percentage of energy from fat did not change (P = 0.83) and whereas the ratio of polyunsaturated fatty acids to saturated fatty acids (P/S ratio) was inversely associated with breast cancer risk .The non-conditional multivariate and logistic regression examining the associations between dietary behaviour and breast cancer showed an increased risk association with three out of the four dietary patterns: the ts Table . After iMbege—a local, often home-made opaque beer from bananas and millet. Acknowledging that alcohol is an accepted risk factor for breast cancer, the factor analysis was repeated excluding the alcoholic beverages from the food group list. In order to get comparable results to the first PCA, we generated six dietary patterns that described 40.3 % of the dietary variance, and four of them were comparable to the Diet of the Rich, Mchicha, Banana and Fatty Diets of the first PCA. Table Mchicha Diet and the Banana Diet were no longer associated with breast cancer risk, but the new Fruity Diet and again a Fatty Diet very similar to the first Fatty Diet were associated with increased risk . After energy adjustment, the OR for the Fatty Diet declined to 1.43 , whereas the OR for the Fruity Diet remained at the same level .The Banana Diet includes Several dietary patterns from two principal component analyses with Varimax rotation based on a FFQ were associated with increased breast cancer risk. Two patterns, both called Fatty Diet, are basically characterized by a higher consumption of milk, mixed vegetable oils and fats, butter and lard, but a low consumption of sunflower oil. Both Fatty Diets were associated with an increased risk in different logistical models. A diet rich in fat similar to our Fatty Diets was discussed by Schulz et al.  using reInvestigators from the Black Women’s Health Study, a prospective cohort study, identified a dietary pattern similar to our Fatty Diet called “Western Diet” also based on a PCA with Varimax rotation and factor loadings for dairy products and meat at similar level . HoweverMbege, increased significantly with increased affiliation to the Fatty Diet (P for trend < 0.001). Our data show a higher breast cancer risk for women mainly following the Banana Diet, which was also associated with a high consumption of Mbege, even though there is no risk association between alcohol intake and breast cancer risk in this study. According to the WRCF panel, there is ample and generally consistent evidence from case–control and cohort studies that alcoholic drinks are a cause of pre- and post-menopausal breast cancer [Mchicha Diet and the Banana Diet were no longer associated with breast cancer risk, although the latter is characterized by rapidly absorbable carbohydrates. Such carbohydrates have been associated with increased breast cancer risk [Michicha and Banana Diets, the analysis keeping alcoholic beverages as separate food groups should visualize an increased risk association. However, the odds ratio of Mbege as well as bottled beer and wine was estimated to be 1.00  indicating no risk association. In our study, the alcohol consumption was 8.2 g/day, which is well below the recommended maximum intake of one drink per day in the European code against cancer [The reported consumption of the local banana beer, t cancer . In ordecer risk , 54. Onet cancer . Thus, tPtrend < 0.001), which is caused by a reduced intake of polyunsaturated fatty acids mainly from sunflower oil (Ptrend < 0.001), but a stable saturated fatty acid intake (Ptrend = 0.19). Thus, we concluded that it is not the fish and fruit intake but the accompanying dietary fat consumption that is associated with breast cancer  is an internationally acknowledged indicator in epidemiological, economical and sociological studies. However, there is no international consensus on assessing SES, income and household expenditures being the most commonly used measures of SES . In low-We did not find a correlation between lactation, parity and property level. In the context of this study, “low property level” means people can call a bicycle or a radio their own. If they own both, they already belong to the group at “medium property level”. Thus, any extra income is used first to improve basic living conditions like nutrition, sanitation and health before it is used for education. Alderman  points oOur data show the impact of reproductive and lifestyle factors on breast cancer aetiology of women in the Kilimanjaro Region. With regard to eating habits and dietary patterns, the diversity of the Kilimanjaro diet is low, and it was less likely to miss important foods on the FFQ food list reducing the estimation bias for dietary behaviour. Due to low education levels and the poor infrastructure, we do not expect socially desirable answers, and participants are less likely to be informed about possible dietary impacts on health outcomes. The semi-quantitative FFQ allowed us to identify non-consumers and frequent consumers on the basis of eating habits, nutrient and energy intake in the case and control groups. Also, ready-to-use meals and eating out are uncommon in the Kilimanjaro region, which facilitated the identification of food groups based on single food items and less on complex meals.The sample is relatively small compared to studies in Westernized countries. The sample size required to achieve a high level of power in a logistic regression depends on the number of predictors and the size of the expected effect. Peduzzi et al.  showed tP < 0.001 indicating that correlations between items were sufficiently large for a PCA.Before running a PCA, the sampling adequacy was controlled using the KMO measure. The KMO measure, which was 0.62, is considered as mediocre in our case , 69. A lA major limitation is the PCA method. There have been discussions that the PCA method is less suitable for risk estimations of dietary patterns, because of difficulties to find plausible linkages between dietary patterns and the observed disease . TherefoThe knowledge about breast cancer and breast self-examination was very poor in our study population, and facilities for cancer diagnosis and treatment are still rare in countries like Tanzania . In ordeIn the absence of a general health insurance, patients have had to pay for getting access to the health facilities. With the aim to minimize confounding errors due to different livelihood systems between cases and controls, we decided to select the controls also from within the hospital setting. But thereby, other selection biases cannot be excluded.In conclusion, a dietary pattern rich in fat and characterized by a low P/S ratio may be associated with a higher risk of breast cancer. The fatty acid composition is probably more important than total fat intake for the breast cancer risk.


We developed a semi-automated tool to assess mammographic density (MD), a phenotype risk marker for breast cancer (BC), in full-field digital images and evaluated its performance testing its reproducibility, comparing our MD estimates with those obtained by visual inspection and using Cumulus, verifying their association with factors that influence MD, and studying the association between MD measures and subsequent BC risk.Three radiologists assessed MD using DM-Scan, the new tool, on 655 processed images  obtained in two screening centers. Reproducibility was explored computing pair-wise concordance correlation coefficients (CCC). The agreement between DM-Scan estimates and visual assessment  was quantified computing weighted kappa statistics (quadratic weights). DM-Scan and Cumulus readings were compared using CCC. Variation of DM-Scan measures by age, body mass index (BMI) and other MD modifiers was tested in regression mixed models with mammographic device as a random-effect term.The association between DM-Scan measures and subsequent BC was estimated in a case–control study. All BC cases in screening attendants (2007–2010) at a center with full-field digital mammography were matched by age and screening year with healthy controls (127 pairs). DM-Scan was used to blindly assess MD in available mammograms (112 cases/119 controls). Unconditional logistic models were fitted, including age, menopausal status and BMI as confounders.7%-17%=1.32 (95% CI=0.59-2.99), OR17%-28%=2.28 (95% CI=1.03-5.04) and OR>=29%=3.10 (95% CI=1.35-7.14). Our results confirm that DM-Scan is a reliable tool to assess MD in full-field digital mammograms.DM-Scan estimates were very reliable . They showed a reasonable agreement with visual MD assessment (weighted kappa ranging 0.79-0.81). DM-Scan and Cumulus measures were highly concordant (CCC ranging 0.80-0.84), but ours tended to be higher (4%-5% on average). As expected, DM-Scan estimates varied with age, BMI, parity and family history of BC. Finally, DM-Scan measures were significantly associated with BC (p-trend=0.005). Taking MD<7% as reference, OR per categories of MD were: ORThe online version of this article (doi:10.1186/2193-1801-2-242) contains supplementary material, which is available to authorized users. Mammographic density (MD), a strong risk factor for breast cancer, is increasingly used as a phenotype risk marker in clinical, genetic and epidemiological studies   contrast and brightness normalization, b) brightness correction according to breast thickness and c) segmentation of the breast and removal of regions of no-interest.To ensure that brightness values depend as much as possible on tissue density and not on other factors related with the acquisition process, a contrast and brightness normalization is desirable. Assuming that minimum and maximum tissue densities are always present on a mammography (subcutaneous fat and connective tissue respectively), minimum and maximum gray-level values should also appear in the histogram. Based on this idea, a histogram stretching operation can be set to normalize brightness and contrast. Options to manually modify brightness and contrast are also available.i,j has been applied to each pixel pi,j according to a user-defined parameter α Є [0:1] as specified below:X-ray attenuation depends not only on the density of the irradiated tissue, but also on its thickness. The thicker the tissue irradiated, the greater the attenuation and, consequently, the brighter the image. When the mammogram is taken, the breast is compressed between two parallel flat plates, which causes the breast to spread out and have a similar thickness along the plates. However, towards the edge of the breast, the thickness gradually decreases. This is a drawback when the goal is segmenting dense tissue, since thicker regions may look as dense tissue and vice versa. In order to avoid this problem, after estimating breast thickness  or the pectoral muscle if present  divided by the total distance between this border and the breast edge at row i, i.e., di,j = 0 when pi,j coincides to the border of the image, and di,j = 1 when pi,j coincides with the edge breast. Values of α = 1 leaves the image unchanged, while values of α < 1 attenuates the brightness as we approach to the internal part of the mammogram. A lower α corresponds to a greater attenuation.where dUsually, mammograms contain other objects besides the breast, such as labels and/or the pectoral muscle. Breast segmentation is semi-automatically performed by finding a threshold value T1 that discriminates between background and object pixels. The biggest object found is considered to be the breast, while the remainders are considered regions of non-interest and, therefore, removed. Nevertheless, this process cannot discard objects connected to the breast. To fix this problem, the user can modify the proposed T1 threshold and also manually invalidate other regions/objects not detected in the previous process.Once the image has been preprocessed and the breast has been segmented by means of T1, a second threshold T2 must be manually set to separate dense and fat tissue, which allows to measure the dense tissue (DT) and the non-dense or fat tissue (FT). Finally, PD is computed as DT / (DT + FT) 100. Figure Digital mammograms used in this project were collected as part of DDM-Spain (Determinants of Density in Mammograms in Spain), a cross-sectional study to investigate the main determinants of high breast density in Spanish women. More than 3500 women aged 45–68 years were recruited at 7 screening centres in Spain. Information regarding lifestyle factors was obtained by trained interviewers at the screening centre. Participants were also weighted and measured using a standardized protocol . Digital mammograms had already been processed and stored in DICOM format. None of these screening centers stored unprocessed (raw) images.In the present study, a set of 655 digital mammograms from women attending the screening centres located in Palma de Mallorca  and Barcelona  were selected. Both centres have full-field digital mammography devices . In addition, we quantified the agreement between the single visual quantitative scale, Boyd scale, and the two computer-assisted methods, DM-Scan and Cumulus, using weighted kappa statistics (quadratic weights). For this purpose, DM-Scan and Cumulus readings were categorized considering the 5 cut-offs proposed by the Boyd scale. Finally, the agreement between DM-Scan and Cumulus was evaluated computing concordance correlation coefficients and drawing the corresponding Bland-Altman graphics.The association between several determinants of MD density, such as age, menopausal status, BMI, family history of breast cancer, parity and use of hormonal replacement treatment, with the PD estimators obtained by each radiologist with DM-Scan was tested in a regression mixed model, with PD as the dependent variable and the screening center as a random effect term. The same procedure was used to estimate the association of these variables with PD measures obtained using Cumulus.In order to test the performance of DM-Scan to detect differences in PD in mammograms from healthy women and those who subsequently develop breast cancer, we set-up a case–control study including all breast cancer cases diagnosed in women attending the Burjasot screening center in Valencia, where full-field digital images had been used for more than 4 years . All breast cancer cases diagnosed in women attending screening there between the years 2007 and 2010 were included in this study. For each case, a matched control was randomly chosen among women who were screened the same year and had a similar age (+/− 2 years).For MD assessment, the left craniocaudal mammogram was selected, when the two views were available, otherwise the mediolateral oblique view of the left breast was used. When the time elapsed between the date of screening and the date of diagnosis was lower than 3 years, the craniocaudal or mediolateral oblique view of the contralateral breast was used. After excluding two cases with breast implants in the contralateral breast, a total of 127 cases and controls were identified. The mammogram was not available for 12 cases and 8 controls, rendering a final sample of 115 cases and 119 controls.For cases and controls, information on age, menopausal-status and self-reported BMI was extracted from the questionnaires administered by the screening program at the corresponding round.Unconditional logistic models were used to assess the association between DM-Scan estimates of density and BC risk, allowing for age, menopausal status and BMI as possible confounders. PD was categorized using as cut-offs the quartiles observed in the control group. The possible linear trend was assessed considering PD as a continuous variable. Finally, the discriminative power of DM-Scan readings was computed estimating the area under the receiver operating characteristic curve (AUC), adjusting by age and BMI as covariates. A nonparametric approach was used and 95% confidence intervals were obtained via bootstrap re-sampling (5000 samples).All statistical analyses were performed using the STATA version 12.0 software program .CEIC Dirección General de Salud Pública y Centro Superior de Investigación en Salud Pública). The breast cancer screening program in Valencia gathers information from all screening participants, all the required data were provided, in an anonymous way, by the screening personnel. Both studies were conducted in compliance with the Helsinki Declaration.DDM-Spain study protocol was formally approved by the Institutional Review Board at the Carlos III Institute of Health. Participants signed an informed consent. The case–control study was approved by the CSISP & Valencia General Directorate of Public Health Ethics Committee , and were highly concordant with those obtained using Cumulus, a similar tool developed to measure PD in digitalized film images. The reliability of PD estimations using DM-Scan is supported by the excellent agreement between the three readers in this study. Cumulus and DM-Scan estimates were equally associated with classical MD determinants, such as age, BMI, parity and family history of breast cancer. Finally, in the small case–control study designed to test the association between DM-Scan measures and breast cancer risk, ORs per category of PD showed a positive trend and, in spite of the reduced sample size, DM-Scan adjusted estimates proved to have a moderate but statistically significant discriminative power.Our results confirm that computer-assisted tools tend to provide lower PD compared with visual assessment. This is particularly true for mammograms classified in the highest categories of density by visual inspection, where the difference between visual and computed assessment is greater than 20%. When comparing the visual evaluation of PD with the results obtained with DM-Scan and/or Cumulus, two factors may explain the wide range of variability. Firstly, a visual reading may overestimate the PD when density is higher, given that the eye evaluates the image as a whole and tends to disregard pixels or tiny regions that do not correspond to the general density pattern of the area where they are located. The second factor, previously mentioned, is the inclusion of more subcutaneous fat tissue with semi-automatic tools. This is the price we have to pay in order to obtain more reproducible results . However, we failed to find an association between PD and menopausal status with any of these tools, probably due to the small number of premenopausal women. Regarding the use of hormonal replacement therapy, the number of current users was insufficient to analyze them as a single category, but MD was not higher in this group of women in the DDM-Spain study, and most of these women were under estrogen-only therapy  which allows for a better recognition of dense tissue. A full version of DM-Scan is freely available under request.We would like to highlight several strengths of this study: DDM-Spain is a population-based study, with information regarding breast cancer risk factors collected in a homogeneous way by trained interviewers who also measured weight and height under the same protocol using the same tools  images because Spanish screening centers discard them due to storage constraints. However, recent studies have confirmed that density measures in raw and processed images are strongly correlated, have equal reliability and are similarly associated with breast cancer . (PPT 255 KB)Authors’ original file for figure 1Authors’ original file for figure 2Authors’ original file for figure 3Below are the links to the authors’ original submitted files for images.


Limiting oxidative stress to the ovarian epithelium has been proposed as a first-line defense against ovarian cancer. Although evidence for an association between individual dietary antioxidant intake and ovarian cancer risk is conflicting, the combined evidence suggests a modest inverse association. Our study aimed to evaluate the association between total antioxidant capacity (TAC) and individual antioxidant intakes  and ovarian cancer risk.We conducted a population-based case–control study in New Jersey. Cases were women ages 21 years and older with newly diagnosed epithelial ovarian cancer who resided in six counties of New Jersey. Controls were women in the same age range who resided in the same geographic area. A total of 205 ovarian cancer cases and 390 controls were included. Dietary intake was ascertained using the Block food frequency questionnaire (FFQ), and TAC indices were constructed by linking FFQ-derived estimates to two standardized antioxidant capacity databases, the USDA Oxygen Radical Absorbance Capacity (ORAC) Database, and the University of Olso’s Antioxidant Food Database. Multivariate logistic regression models were used to calculate odds ratios and 95 % confidence intervals while controlling for major ovarian cancer risk factors.We found a strong inverse association with selenium from food sources . However, there was little evidence of an association with dietary TAC or the others individual antioxidants. In contrast, compared to non-users, supplement users had significant increased risk for all micronutrients, but no statistically significant increased risk was observed for combined intake from foods and supplements of any of these antioxidants.This study found an inverse association between selenium consumption from food sources and ovarian cancer risk, while there was little evidence of an association with TAC or any of the other individual antioxidants. Additional research is needed to confirm these findings. Ovarian cancer is associated with the highest mortality among all gynecologic cancers . OvarianIn ovarian cancer, more than 90 % of cancers originate from the surface epithelial cells . The etiAlthough evidence for an association between individual dietary antioxidant intake and ovarian cancer risk is conflicting , the comWe therefore sought to investigate the association between total antioxidant capacity (TAC) intake and ovarian cancer risk using data from standardized antioxidant databases in a population-based case–control study conducted in New Jersey ,10. As fNJ Ovarian Cancer Study has been described in detail elsewhere [The lsewhere ,10. It wlsewhere ,12, and In brief, cases were women with newly diagnosed histologically confirmed invasive epithelial ovarian cancer identified between January 2004 and May 2008. Ovarian cancer cases aged 21 years or older who spoke English or Spanish and resided in six counties of New Jersey  were eligible to participate. Case ascertainment was conducted by the NJSCR using rapid case ascertainment, supplemented with review of NJSCR data to identify cases diagnosed out of the area. During the study period, 493 women were identified. Of those, 61 passed away before they could be interviewed, for 9 cases their physicians advised that we should not contact them, and an additional 119 cases were not able to participate for various reasons . Of the remaining women, 233 (47 %) ovarian cancer cases completed the interview.Controls were women 21 years and older who resided in the same six counties as the cases  and spoke English or Spanish. Women who had a hysterectomy and/or a bilateral oophorectomy were not eligible to participate. To identify controls aged < 65 years, random digit dialing was conducted. Of the 355 eligible women identified, 175 (49 %) completed the interview. To identify women > 65 years of age, random selection was conducted from lists purchased from the Centers for Medicare and Medicaid. Of the 316 women who were contacted, 68 (22 %) completed the interview. However, the eligibility of 40 % was unknown. We conducted area sampling to identify additional controls older than 65 years, starting in 2003. Thirty consecutive households in randomly chosen neighborhoods were contacted by mail and by home visits. To better match the age distribution of the cases women aged 55 years and older were later included. Overall, 467 (40 %) controls completed the interview.Informed consent was provided by all women participating in the studies, and IRB approval was obtained from the CINJ, MSKCC, and the NJDHSS.A questionnaire was administered to participants to collect data on potential and established ovarian cancer risk factors. Information on demographic characteristics, residential history, pregnancy history, occupation, oral contraceptive use and other birth control methods, menstrual history and menopausal status, personal and family history of cancer and other illnesses, height and weight, physical activity, and exposure to other potential risk factors was included.To assess dietary intake, participants were asked to complete the Block Food Frequency Questionnaire (FFQ), version 98.2 . Patients reported their usual dietary intake in the six months prior to their date of entry into the study for controls and their diagnosis date for cases. A total of 205 ovarian cancer cases and 398 controls completed the FFQ. Eight controls were further excluded as they had both ovaries removed and were therefore ineligible, leaving 390 controls available for analysis.http://www.ars.usda.gov/sp2userfiles/place/12354500/data/orac/orac07.pdf), and the University of Olso’s Antioxidant Food Database [NutritionQuest provided nutrient calculations for individual antioxidants using the USDA Nutrient Database for Standard Reference. We derived TAC indices by linking FFQ-derived food consumption to two standardized antioxidant capacity databases, the USDA Oxygen Radical Absorbance Capacity (ORAC) Database .Total daily intake of food items known to be high in antioxidant content from the FFQ, including over 50 food items, such as fruits, vegetables, juices, grains, teas, chocolate and wine was computed first. Daily consumption was derived based on frequency and portion size, and food values were converted from their unit listed in the FFQ to grams per day, using values from the USDA Nutrient Database for Standard References , hydrophilic antioxidant capacity (H-ORAC), lipophilic antioxidant capacity (L-ORAC), and total phenolics (TP). Additionally, antioxidant values were derived using another database, the Antioxidant Food (FRAP) database, which is based on the electron transfer FRAP assay to extract antioxidant values. Both databases were used to assess the comparability of results using the two assays.umol of Trolox Equivalents per 100 grams per day for each food item and total mg gallic acid equivalents per 100 grams per day for each food item were calculated, and the total of all items were summed to develop each of the indices. Separate antioxidant indices were developed for total H-ORAC, L-ORAC, T-ORAC, and TP, from the ORAC database, and a TAC index was developed from the Antioxidant Food (FRAP) Database. As the ORAC database does not include values for antioxidant supplements, to compare the results of the TAC index from the ORAC database to that of the FRAP database, a TAC index without supplements based on the FRAP index was also developed.Antioxidant values were attributed to each food item based on the data in the antioxidant databases. The total >3), oral contraceptive use , hormone replacement therapy (HRT) use , tubal ligation, total energy intake (continuous), smoking status , alcohol consumption (continuous), and physical activity . Tests for trend were derived by assigning a median value to each tertile of the antioxidant variables. All analyses were conducted using SAS software version 9.2.The relationship between ovarian cancer and the TAC indices and individual antioxidants  was assessed by evaluating the indices as categorical variables. Tertiles of each TAC index were created based on the distribution of each index among controls. Age-adjusted ANCOVA and cross-tabulations were conducted to assess any association between the TAC indices and case–control status. Multiple unconditional logistic regression models were developed to estimate odds ratios and 95 % confidence intervals while controlling for potential confounders. Potential covariates included age, body mass index (BMI), education , race, age at menarche (continuous), menopausal status, parity  . Das andNumerous studies have indicated that selenium may have anticancer properties. Human studies have shown a decrease in the incidence of prostate, lung, and colorectal cancers ,38. AddiOur conflicting findings of a protective effect associated with dietary selenium intake versus an increased risk associated with supplement intake are unclear. Of note, the increased risk observed with selenium supplements only reached statistical significance after further adjustment for physical activity and smoking status. It is possible that related unmeasured confounders may be influencing the association. Studies of supplement intake and relation with cancer risk have been conflicting. A recent systematic review concludes that evidence does not support selenium supplementation in the prevention of cancer . Other rOnly one other study has developed a total antioxidant score in ovarian cancer patients, using data from the Teacher’s Health Study in California . HoweverThis study may be subject to the limitations of case–control studies, such as recall bias and selection bias. Particularly with our finding of increased risk with supplements, it is possible that cases may have reported use differently than controls. We also had a low participation rate. Potential non-participation bias was assessed by comparing characteristics of women who participated in this study to cases who did not in the New Jersey State Cancer Registry during the study time period. Cases that consented tended to be younger; however the racial/ethnic distribution and distributions by histology, stage and grade were similar. We did not have information on women who did not end up participating as controls. However, the distribution of risk factors in this study is similar to that reported in other studies, which gives us reassurance in the validity of our data.In conclusion, this study found a strong inverse association of selenium from food sources and ovarian cancer risk, while selenium supplement intake was associated with increased risk. However, we did not find any significant association between TAC intake and ovarian cancer risk. Supplement use in this study was found to be associated with increased ovarian cancer risk for all supplements studied. However, combined amounts of antioxidants from diet and supplement sources were not associated with increased risks. As several reports have recently raised concerns about the safety of vitamin supplements in recent years, including the 2007 WCRF/AICR Report which warned cancer patients and survivors against taking certain supplements , these fThe authors declare that they have no competing interests.DG wrote the first draft of the manuscript. EVB conceptualized the study and provided overall supervision for conducting the study. DG, EVB, MK conducted data analyses. LEP implemented case ascertainment. Additional expertise was provided by KD, SHO (cancer epidemiology), LRR (gynecologic oncology), S-EL (biostatistics), A-NTK . All authors revised the article critically for important intellectual content and approved the final version of the manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/12/211/prepub


Patterns of physical activity, body composition, and breastfeeding are closely related to health and are influenced by environmental, economic, and social factors. With the increase of sedentary lifestyle and overweight, many chronic diseases have also increased, including cancer. Breast cancer is the most common cancer in women worldwide, and the knowledge of its risk and protective factors is important to the adoption of primary prevention strategies. We aimed to investigate some risk and protective factors for breast cancer among women from Midwest Brazil. It is a case-control study of outpatient basis, carried out with 93 breast cancer cases and 186 controls. Socioeconomic, gynecological, anthropometric, and lifestyle variables were collected, and odds ratios (ORs) values were estimated , 95%). Per capita income equal to or lower than 1/2 Brazilian minimum wage , residence in rural area , and presence of family history of breast cancer  are risk factors for breast cancer. In turn, physical activity (past 6 months)  and leisure physical activity at 20 years old  are protective factors for the disease in women who live in Midwest of Brazil. Around the world, populations have changed rural areas for cities, becoming more sedentary and consuming great amounts of industrialized foods and beverages, which have contributed for the increase of obesity and chronic diseases, such as cancer. These changes began in Europe, North America, and other countries that became industrialized in XIX century. In recent decades, there was acceleration in urbanization process around the world, especially in low-middle-income countries, like Brazil. Patterns of physical activity, body composition, and breastfeeding are determined by environmental , economic , and social (education level) factors, influencing, thus, the occurrence of several types of cancer , 2.Breast cancer is the most common cancer in women worldwide and has been associated with higher socioeconomic status and urbanized populations , 3. FamiMost studies on the determinant factors for breast cancer, especially related to physical activity, have been conducted in high-income countries –8. Thus,Furthermore, in Brazil and other Latin American countries, there are few recent analytical studies on the risk and protective factors for breast cancer. According to a 2009 report by Brazilian National Cancer Institute , in GoiâThis outpatient-based case-control study was conducted in 2 reference public hospitals of Goiânia, capital of Goiás state, situated in Midwest of Brazil. Cases were interviewed at the Conjunctive Tissue Sector and Gynecology and Breast Clinic of Araújo Jorge Hospital (HAJ), a public institution that specializes in the prevention, diagnosis, and treatment of cancer, that provides health service to people from throughout Midwest of Brazil. Cases were also interviewed at the Breast Service of the General Hospital of the Federal University of Goiás . Controls were selected among users of the Gynecology Clinic at HC/UFG. Both hospitals (HAJ and HC/UFG) attend to Unique Health System (SUS) users, which have a similar socioeconomic profile.β) of 80%, 2 controls for each case, an odds ratio (OR) of 2.5 [To calculate the sample size, the following criteria were considered: significance level of 5%, study power (1-) of 2.5 , and a p) of 2.5 . AccordiThis study was approved by the ethics committees of UFG (protocol no. 026/08) and Araújo Jorge Hospital (protocol no. 019/08). Written informed consent was obtained from all participants.Data were collected between May 2008 and June 2010, through a questionnaire that had been previously tested in a pilot study. This questionnaire included socioeconomic variables , family history of cancer and breast cancer, gynecological variables , breastfeeding information, anthropometric measures, alcoholic drink consumption, physical activity, and smoking habit. Questions about alcoholic drink consumption, physical activity, and smoking habit were asked regarding the 6 months prior to the interview date. Data on body weight and physical activity were also investigated considering the young age (20 years old) .Interviewees provided data on occurrence and duration of breastfeeding (in months) for each of their children, which were added up to provide lifetime duration of breastfeeding. Anthropometric variables included height, current and previous (at 20 years-old) body weight, waist circumference, current and previous body mass index (BMI), and adult weight gain. The first 3 variables were obtained according to the protocol reported by Lohman et al. . Before The daily intake of ethanol was calculated as follows: the amount of each alcoholic beverage consumed per week was multiplied by its alcohol content and divided by 7 . We consThe subjects were questioned about physical activity (past 6 months) and were classified into 4 categories: active ; irregularly active A ; irregularly active B ; sedentary . This classification was defined according to the eighth version of the International Physical Activity Questionnaire (IPAQ), validated to Brazilian adult women . We alsoWe considered smokers as those who smoked regularly for at least 1 year  and ex-sData were analyzed using Statistical Software for Professional 8.0 . The normality test for quantitative variables was performed using normal curves in the Stata software. Variables were analyzed in the whole sample, without considering the menopausal status, like found in some studies –23, and t-test (parametric distribution) and the Mann Whitney test (nonparametric distribution). Regarding the qualitative variables, we used the chi-square test or Fisher's exact test to compare case and control groups, according to frequency distribution. For all variables related to outcome, we calculated the Wald statistic and the odds ratio (OR), with a significance level of 5% and a confidence interval (CI) of 95%. All variables with a P value lower than 0.20 for bivariate analyses were included in multivariate analyses. Logistic regression was performed using a hierarchical model structured in 2 levels. Socioeconomic variables, such as education level, per capita income, and area of residence were included in the first level . Height, physical activity, family history of cancer and of breast cancer, bearing children, and number of children were grouped in the second level . Bearing children and number of children, although considered as demographic variables, were included in the second level because they are influenced by socioeconomic status. Variables whose P value was lower than 0.05 remained in the logistic regression model [In bivariate analyses, to compare the 2 groups (case and control) in relation to quantitative variables, we applied Student's on model . We invited 98 cases and 186 controls to participate in research, but 5 cases (5.1%) refused to be interviewed, adding up 279 women (93 cases and 186 controls). The average age among cases was 51.93 years (range 30–83 years) and that among controls was 51.73 years (range 28–81 years). There were no significant differences between the case and control groups in relation to socioeconomic, gynecological, and anthropometric variables. The median family income per capita was lower than the Brazilian minimum wage , being US$ 147 for cases  and US$ 200 for controls  .Education level, per capita income, area of residence, family history of any kind of cancer, and family history of breast cancer in the first degree were significantly associated with breast cancer in the bivariate analysis .Anthropometric and lifestyle variables, such as alcohol consumption, smoking habit, and physical activity, are shown in We considered the following variables in the multivariate analysis: education level, per capita income, area of residence, bearing children, number of children, family history of cancer (any type), history of breast cancer, height, physical activity (past 6 months), and previous occupational and leisure physical activities. After adjusting the OR values, per capita income, area of residence, family history of breast cancer, physical activity (past 6 months), and previous leisure physical activity remained associated with breast cancer .Per capita income equal to or lower than 1/2 Brazilian minimum wage , residence in rural area , and presence of family history of breast cancer  were considered risk factors for breast cancer. Active women were at lower risk of developing breast cancer, compared to sedentary women , and that ones who had practiced leisure physical activity at young age (20 years-old) were less likely to develop the disease, compared to who had ever practiced  .The sample was very homogeneous concerning socioeconomic, anthropometric, and gynecological variables . This ca2) was approximately 28% among cases and 24% among controls. These values are higher than those found in Brazil, since the proportion of obesity in adult Brazilian women, between 2008 and 2009, was 16.9% [In as 16.9% . ObesityIn the present study, we found that having a low per capita income (under 1/2 Brazilian minimum wage) and living in a rural area were risk factors for breast cancer . One posIt should be added that approximately 2/3 of Brazilian population is exposed to pesticides, and the most vulnerable group to this exposure is rural workers . AccordiBRCA1 and BRCA2 genes, known tumor suppressors. A woman who carries a mutation in BRCA-1 has a risk of 60–80% for developing breast cancer [We also found that family history of breast cancer in first degree is a risk factor for breast cancer , and thit cancer , 31.Regarding physical activity, we found that women who practiced at least 150 minutes/week and/or ≥5 days/week of physical activity were at lower risk of developing breast cancer, compared to sedentary women. Moreover, that ones who practiced leisure physical activity at young age (20 years old) were also at lower risk, compared to that women who did not practice leisure activity. These results show that physical activity, in general, may protect against breast cancer. This protective effect is supported by WCRF and AICR's report and by other case-control studies . This reRegular physical activity probably has a protective effect on breast cancer by delaying menarche, promoting irregular and anovulatory cycles, reducing serum estrogen, increasing globulins that bind to sex hormones, reducing inflammation, improving immune function, and helping to control weight and to improve sensitivity to insulin , 6. Cons The lack of an association of some variables with breast cancer can be explained partially by the recall bias, which is very common in case-control studies. Another difficulty of case-control studies is the time required to consider exposure factors. Some studies , 33 consWe recommend that other breast cancer case-control studies are conducted on populations of higher socioeconomic status from the same region of Brazil. The risk and protective factors published by WCRF and AICR  may applThis investigation is the first case-control study on the risk and protective factors for breast cancer in a population from Midwest of Brazil. Findings from our research reinforce the associations between physical activity and family history of breast cancer, in first degree, and breast cancer. Moreover, our results reveal new data that can contribute to elucidate determinant factors for breast cancer, and to prevent this disease in epidemiological transition countries, such as Brazil .Low per capita income, living in a rural area, and family history of breast cancer are risk factors for breast cancer, whereas practicing physical activity, including at 20 years old, is a protective factor for the disease in women from Midwest of Brazil.


BRCA1 was mapped and cloned. In the past few years, several new loci associated with the various degree of breast cancer risk have been identified using “Candidate Gene Association Study (CGAS) and Genome-Wide Association Study (GWAS)” approaches. This editorial is meant to quickly glance over recent findings of these population-based association studies.I would like to welcome breast cancer research community to the first editorial of our newest journal “Breast Cancer: Basic and Clinical Research”. In pursuit of breast cancer culprits, we have come a long way since the early 90’s when the first breast cancer susceptibility gene  Breast cancer is one of the most terrifying diseases that human civilization has ever known. Legend has it that powerful queen Atossa of the ancient Babylon had breast cancer, and the specific mention of breast cancer can be found in “Hippocratic Corpus” written by Hippocrates and his peers dating back to fourth and fifth centuries B.C. . ThroughBRCA1, BRCA2, TP53, and PTEN . This increased risk is independent of lifestyle and environmental factors, and thought to be due to genetic susceptibility of individuals to develop breast cancer . The earand PTEN .BRCA1, BRCA2, TP53, PTEN, CHEK2, ATM, NBS1, RAD50, BRIP1, and PALB2 for inherited breast cancer  present in the human genome. The earlier studies focused on candidate gene approach and looked for SNPs in limited number of genes and their possible association with breast cancer. More recent studies known as Genome-Wide Association Studies (GWASs) are taking advantage of unbiased scan of the whole genome for SNPs associated with breast cancer risk.Several breast cancer research groups have studied the association of breast cancer risk with common variants (SNPs) of candidate genes. More comprehensive CGASs have been carried out by SEARCH breast cancer study group and the “Breast Cancer Association Consortium” (BCAC). Results of CGASs have been mixed and very confusing. Some of the association studies may not be directly comparable because they used different population groups, while in other studies, the sample size may not be sufficiently large enough.HER-2, in some studies a common variant HER-2 V655I was reported to be associated with breast cancer  was associated with reduced breast cancer risk at younger age, but increased risk at older age (UGT1A7) was associated with increased breast cancer risk at younger ages but decreased risk at older ages , Baynes et al. reported that in contrast to rare variants, the common variants in the cer risk . A studycer risk . A very cer risk .CASP8 D302H in the gene encoding Caspase 8, and TGFB1 L10P in the gene encoding transforming growth factor-β (TGFβ), in one allele (heterozygote) were associated with significant risk to invasive breast cancer  were associated with breast cancer, but the statistical significance of the association was only borderline  and epigenetic genes (for example DNMT3b-c31721t) with breast cancer risk has been reported but these observations need to be confirmed in larger epidemiological studies  breast cancer compared to noncarriers, while homozygous allele T of rs3803662 on chromosome 16q12 was associated with 1.64 fold risk of ER-positive breast cancer . Among ont study .FGFR2, which encode fibroblast growth factor receptor 2, with breast cancer risk was also reported in two independent GWASs (FGFR2), and rs12443621, rs8051542 and rs3803662 (TNRC9), rs889312 (MAP3K1), rs13281615 (8q) and rs3817198 (LSP1)  . Althougis point .FGFR2, which showed significant association with breast cancer  in ERBB4 with breast cancer  working group recommendations on replicating GWAS results .The next legitimate question is- what is the overall risk of a particular disease associated with these so called common variants? Although statistically significant, the effect of individual SNP is generally very small in terms of increasing breast cancer risk. Such risk is usually close to 1.2 to 1.5. However, the overall effect of combinations of SNPs on breast cancer risk may be substantial. This overall effect, which is also termed as polygenic effect (of SNPs) may be additive or synergistic and may account for most of the genetic risk associated with developing breast cancer. Although, only a handful of these common variants have been identified so far, the presumption is that there are many more of these, and they may genetically interact.For now, the breast cancer community will wait for the identification of all of the common variants in the genome, which would be associated with breast cancer, and then all of us- clinical and basic scientists, and other stake holders will debate what is in the best interest of naïve general public. Should we prepare for the genetic counseling of would be breast cancer patients even though the overall risk factor may still be below 1.5 to 2.0? In summary, each GWAS starts with an assumption that the evildoers are in the genome and that they most probably conspire together to increase the risk of developing a particular disease such as breast cancer. On an optimistic note, the gene hunters or SNP hunters to be more accurate, are busy hunting these evildoers, wherever they may be- in the introns, exons or the regulatory regions in the genome.


Many studies have reported an increased risk of developing a second primary malignancy (SPM) of the breast in women treated for thyroid cancer. In this study, we investigated several potential risk factors for this association. The aim of this retrospective cohort study was to identify a subgroup of women surgically treated for papillary thyroid cancer that may benefit from more careful breast cancer screening.A total of 101 women surgically treated for papillary thyroid cancer from 1996 to 2009 with subsequent follow-up were interviewed by phone regarding personal risk factors and lifestyle habits. Only 75 questionnaires could be evaluated due to a 25.7% rate of patients not retrieved or refusing the interview. Data analysis was performed using a multivariate logistic model.The standardised incidence ratio (SIR) for breast cancer was 3.58 (95% IC 1.14 - 8.37). Our data suggest a protective effect of multiparity on the development of a SPM of the breast . Significant associations were not found with other known risk factors including Body Mass Index (BMI), age at first tumour, concurrent metabolic diseases, smoking, physical activity and familiarity.This study confirms that a higher incidence of SPM of the breast is observed in women treated for papillary thyroid cancer. Additionally, this risk is increased by nulliparity, thus a strict breast screening program for nulliparous women treated for thyroid cancer may be advisable. Its overall age standardised rate in Italy is 9.1 cases/100.000 persons/year, even if its incidence is higher in women 14.3). The mortality rate is much lower, accounting 0.4 death/100.000 [arcinoma . Several. The morarcinoma , but theWomen with a histological diagnosis of papillary thyroid cancer and available follow-up data were selected from a database of patients surgically treated from 1996 to 2009 for thyroid disease at one of the Departments of Surgery of Policlinico Umberto I in Rome. A total of 101 women with papillary thyroid cancer were present in the database and were all included in this retrospective cohort study. A questionnaire was designed to guide a phone interview to investigate patients' personal risk factors and lifestyle habits . Confidence intervals were calculated from a poissonian distribution for standardised rates, according to .Of the 75 patients who answered the questionnaire, the mean age at the diagnosis of papillary cancer was 57.35 ± 12.75 years, with a mean follow-up period at the moment of the interview of 74.52 ± 12.75 months (6.21 ± 1.06 years). In the follow up period after the diagnosis of papillary thyroid cancer, 7 SPMs of the breast were observed. All these 7 patients had been already operated at the moment of the interview and their diagnosis of breast cancer had been histologically confirmed. The mean time interval between the diagnosis of thyroid cancer and the subsequent diagnosis of breast cancer was 5.80 ± 3.24 years. Two cases of detected breast cancer occurred within 2 years from the papillary carcinoma diagnosis and they were not considered in the SIR calculation. According to many other similar studies[et al. [O.R. 0.15; 95% IC 0.25 - 0.86) . This incidence is based on Piscitelli The characteristics of the study population are provided in Table Twenty-four patients (32%) did not follow a regular breast screening program. Because we only conducted a phone interview and not a clinical exam of the women, the actual number of SPMs of the breast could have been underestimated.Additionally, 72 of the 75 patients underwent radioactive iodine therapy.The incidence of thyroid cancer has almost doubled in Italy between the periods 1991-1995 and 2001-2005. This trend is almost exclusively due to an increase in papillary thyroid cancer (PTC) . The moret al. [et al. [In a meta-analysis conducted by Subramanian et al. , a poole [et al.  observed [et al. .et al. [in situ SPM breast cancer, we observed an increased risk of invasive breast cancer. Chen et al. (2001) [et al. (2008) [et al. and Brown et al., reflecting the general trend for the mean age at tumour onset in Italian populations [Although Canchola et al.  detected. (2001)  and Brow. (2008)  observedulations .et al. and Sawka et al. [According to Rubino a et al. ,14, thyrPreviously, the majority of studies on the incidence of SPM following thyroid cancer were based on cancer registries. Consequently, they could not take detailed personal data into account, and important information such as life style and personal risk factors of patients were not analysed. Using phone interviews, we investigated several potential risk factors involved in the association between thyroid and subsequent breast cancers. The aim of this retrospective cohort study was to identify a subgroup of women surgically treated for papillary thyroid cancer that may necessitate a more careful mammary screening.2. We were unable to detect a significant association between weight and risk using both univariate and multivariate analyses. Despite the fact that women with a first-degree relative suffering from breast cancer have a higher risk of developing primary breast cancer [Based on our analysis, we observed a significant protective effect of multiparity for SPM of the breast. Obesity has been associated with increased incidence of thyroid cancer . In our t cancer , a signiThis retrospective cohort study only included 101 patients, thus a larger patient population is needed to confirm our findings. Furthermore, we were unable to detect a significant association between young age and SPM, but the mean age for our sample population was higher than those reported in other studies ,4,10.Age of the first live birth as well as the number of children could be relevant information. Nevertheless, in this study we tried to keep the interview as short and simple as possible and to consider only dichotomous variables for calculation. Starting now from the consideration that childbearing is a risk factor, more addressed and large studies are needed to define in details extent and meaning of this finding. An additional limitation to the study was the loss of 25.7% of the initial 101 patients due to interview unavailability. It is possible that our results may have been different if the other 22 interviews had been collected, even if there is not a reason to presume a specific direction for this potential bias. In fact, personal characteristics of this sub-group, as recorded in the database, were similar to the rest of the sample.The pathophysiologic relationship between thyroid and breast cancer is not clearly understood. Potential hypotheses for the link between these tissues include a genetic predisposition, dysregulation of the immune system or a hormonal cause. Additionally, the observed increased incidence of breast SPM could be artificial because women previously treated for cancer may be more cautious with their health.et al. [in vitro that thyroid hormones (T3) may function to mimic or enhance the effect of oestrogens on breast cancer cell proliferation. Based on this information, it is clear that the effect of high l-thyroxine doses on breast tissue during suppressive hormonal therapy following thyroidectomy in thyroid cancer patients should to be investigated. According to the experimental hypothesis that high l-thyroxine doses could enhance the risk of breast cancer, hormone replacement therapy for postmenopausal women should also be investigated as a possible risk factor. This information was too detailed to be collected by a phone interview in this study, because of the large number of commercially available products.Hall et al.  demonstrThis study confirms that there is an increased incidence of breast SPM in women treated for papillary thyroid cancer. With the exception of nulliparity, we were unable to detect significant associations between the incidence of SPM and several known risk factors. From a methodological perspective, the questionnaire design was appropriate. It allowed for a detailed phone interview that only required 10 minutes of time from each patient. Based on our findings, a strict breast screening program for nulliparous women treated for thyroid cancer may be advisable. Although we were able to detect a significant association, larger studies are necessary to confirm our findings and investigate other potential risk factors .The authors declare that they have no competing interests.FC and AA operated the patients and did the follow up, FM designed the questionnaire and did the interviews, GDT was responsible for the epidemiological design and statistics. FC and FM wrote the paper, AA and GDT reviewed the text. All authors read and approved the final manuscript.the questionnaire for the interview. a 25 items questionnaire to collect information about risk factors related to personal habits and life-style. Tested for a phone interview of about 10 minutes.Click here for file


Glucocorticoids are widely prescribed drugs. In the human body, glucocorticoid is the main stress hormone and controls a variety of physiological and cellular processes, including metabolism and immune response. It belongs to the same steroid superfamily as estrogens, which are known to play a role in breast cancer. However, the effect of glucocorticoid use on the risk of breast cancer is not clear.We conducted a case-control study using population-based medical databases from Northern Denmark (1.8 million inhabitants) to investigate the association between glucocorticoid prescriptions and breast cancer risk. The study included 9,488 incident breast cancer cases diagnosed between 1994 and 2008 and 94,876 population controls. We estimated the odds ratios (ORs) and 95% confidence intervals (CIs) associating glucocorticoid use with breast cancer occurrence, controlling for prescriptions of postmenopausal hormone replacement therapy, anti-diabetics, immunosuppressive drugs, and hospital diagnosis of obesity, diabetes, chronic pulmonary diseases and autoimmune diseases.We found no effect on breast cancer risk in ever users (> 2 prescriptions) of any glucocorticoids (adjusted odds ratio (aOR) = 1.0; 95% CI: 0.96, 1.1), systemic glucocorticoids , or inhaled glucocorticoids , each compared to never users of any glucocorticoids. Associations for recent use (preceding two years) and former use (more than two years earlier) were near null in all dose categories . Intensity of systemic glucocorticoid use , regardless of duration , was also not associated with breast cancer risk.Overall, our study provides no evidence that glucocorticoid use affects the risk of breast cancer. Glucocorticoid is an adrenocortical hormone, belonging to the same steroid superfamily as estrogen, which is known to play a role in breast cancer risk . GlucocoSynthetic glucocorticoids affect immune function and are commonly used as anti-inflammatory and immunosuppressive therapy in diseases such as asthma, chronic obstructive pulmonary disease (COPD), inflammatory bowel disease (IBD), inflammatory arthritis, and other connective tissue disorders . The immTo our knowledge, only one study has previously been published on the relation between glucocorticoid use and breast cancer risk . Our preA recent review called for further research investigating the potential association between glucocorticoid use and breast cancer risk, criticizing our previous study for lack of clinical details and the possibility of confounding . We, theWe conducted this population-based case-control study among female residents of the Central and North Denmark Regions between 1 February 1994 and 31 December 2008. Together, these regions represent about one-third of the Danish population (approximately 1.8 million inhabitants). The Danish National Health Service provides tax-supported health-care to all residents of the country and refunds part of the patients' expenditures on most physician-prescribed drugs, including glucocorticoids .All health-related services are registered to individual patients by use of their civil personal registration (CPR) number, assigned to all Danish citizens since 1968 by the Danish Civil Registration System (CRS). This number encodes gender and date of birth  and faciThe Danish regions are served by pharmacies equipped with computerized accounting systems through which data are sent to a regional prescription database hosted by Aarhus University , with keThe sample size was determined by using the Danish Cancer Registry (DCR) to identify all women with an incident diagnosis of breast cancer in the region during the study period  contains information about all non-psychiatric hospital admissions since 1977, and since 1995 also outpatient visits . From thTo obtain information about parity and age at first birth we used the CRS . In the We categorized glucocorticoid use as never versus ever use. Ever users were further subdivided into two dose categories: one or two prescriptions or more than two prescriptions. We examined the association of overall glucocorticoid use with breast cancer and then examined the association of systemic and inhaled glucocorticoid use separately. To examine the temporality of glucocorticoid use and the risk of breast cancer, we divided ever use into recent use (only prescriptions within two years of the index date) and former use (prescriptions earlier than two years of the index date). In the group of any glucocorticoid users we further divided the number of prescriptions into low (one or two prescriptions), medium (three to nine) and high (more than nine). We based these cut-offs on the distribution of prescription counts among the controls with more than two prescriptions.Textbook of Rheumatology [To examine whether breast cancer risk was associated with the intensity and duration of glucocorticoid use, we restricted the analyses to systemic glucocorticoid users and calculated prednisolone equivalent cumulative doses based on the equivalency table in Kelly's matology  and grouThe covariates ever use of immunosuppressive drugs, ever use of postmenopausal HRT, COPD, asthma, IBD, obesity, and RA were coded individually as dichotomous variables. Other autoimmune diseases  to evaluate potential confounding and effect measure modification. We used conditional logistic regression analysis to compute crude and adjusted ORs (aORs) and their associated confidence intervals (95% CI), with simultaneous adjustment for the use of postmenopausal HRT, immunosuppressive drugs, hospital diagnosed obesity, diabetes (plus/minus a history of metformin use), COPD, asthma, IBD, RA and 'other autoimmune disease.' Since we used risk set sampling of controls, the ORs are unbiased estimates of the corresponding incidence rate ratio in the underlying population . We repoWe used Stata 11.0  and SAS 9.2  for the data analyses. This study was approved by the Danish Data Protection Agency (reference #2004-41-4693). According to Danish law, registry-based studies such as this one do not require further approval by an ethical review board or informed consent by the members of the study population.n = 31,130), a lower proportion of cases than controls had a first birth before age 25 (45% versus 49%) and had three or more children (25% versus 27%). In all other characteristics, there were no important differences between cases and controls.We identified 9,488 breast cancer cases and 94,876 population controls. Median age at the index date was 62.1 years. Characteristics of cases and controls are shown in Table n = 31,130), additional adjustment for parity and age at first birth did not change our overall estimates of null association. In the latter group, the aOR for > 2 glucocorticoid prescriptions ever, compared to never, was 1.0 .We found no effect on breast cancer risk in ever users (> 2 prescriptions) of any glucocorticoids compared to never users . The association among former users with a high number of prescriptions (> 9) was null . In addition, among recent users who only had prescriptions within the two years before their index date, we found null associations. Restricting the analysis to systemic or inhaled glucocorticoid use also showed no associations. Ever use (> 2 prescriptions) of systemic  or inhaled glucocorticoids , each compared to never use of any glucocorticoids, were both null Table  and all Overall, this population-based case-control study of more than 100,000 women provided no evidence of an association between glucocorticoid use and breast cancer risk. The null association was consistent when dividing the use of glucocorticoids into recent and former use, intensity and duration of use, and also consistent with our previous study .Our study has several strengths. The uniformly organized Danish healthcare system with complete hospital history and access to appropriate population controls allows a population-based case-control design. The use of population-based prescription registries, with a completeness approaching 100% ,14 ensurOur study also had limitations. We had no data regarding adherence with prescriptions, potentially leading to non-differential misclassification of some non-users as users. However, our drug exposure assessment was based on redeemed prescriptions, and because patients had to pay a proportion of the drug cost, our estimates are likely to reflect actual use, especially in women with more than two prescriptions. In support of this expectation, a validation study on postmenopausal HRT by Danish women showed good agreement between self-reported use and prescription data in the registry . We alsoin vivo studies have suggested that glucocorticoids may stimulate the expression of the sulfotransferase SULT1E1, which plays a role in deactivating estrogen [A more important limitation might be our inability to examine the impact of glucocorticoid use on breast cancer risk by specific breast cancer characteristics such as hormone receptor status. Studies have addressed the possibility of cross-talk between estrogen receptors and glucocorticoid receptors in mammary epithelial cells  and receestrogen . Thus, eestrogen  but we westrogen  and the estrogen . IGF-1 restrogen  and a meestrogen . Our stuThere are several mechanisms by which glucocorticoid prescriptions might affect breast cancer risk in subpopulations defined by molecular subcharacteristics. Evaluations of these associations would require a study with detailed biological data. Overall, however, our results provide no evidence of an increased risk of breast cancer in glucocorticoid users compared with never users.aOR: adjusted odds ratio; ATC: Anatomical Therapeutic Chemical; CI: confidence interval; COPD: chronic obstructive pulmonary disease; CPR number: civil personal registration number; CRS: Civil Registration System; DCR: Danish Cancer Registry; DNPR: Danish National Patient Registry; GR: glucocorticoid receptor; IBD: inflammatory bowel disease; ICD: International Classification of Diseases; IGF-1: insulin-like growth factor-1; HRT: hormone replacement therapy; OR: odds ratio; RA: rheumatoid arthritis.None of the authors received any fees, honoraria, grants or consultancies that would constitute a conflict of interest with the current study. The Department of Clinical Epidemiology, Aarhus University Hospital, receives funding for other studies from pharmaceutical companies in the form of research grants to (and administered by) Aarhus University. None of these studies have any relation to the present study.GVS designed the study, performed statistical analysis, interpreted the data, and drafted the manuscript. TLL conceived of the study and its design, provided epidemiological expertise, contributed to the interpretation of the data and critically revised the manuscript and contributed to major revisions. HTS and DCF conceived the study, and participated in its design, interpretation of data, and critically revised the manuscript. LP and SPU provided statistical expertise, analyzed the data, contributed to the interpretation of the data and critically reviewed the manuscript. All authors have read and approved the manuscript for publication.List of primary exposure drugs, cancer diagnosis, potential confounder drugs and diseases, and associated ICD and ATC codes. This file contains ATC codes for the primary exposure drugs and potential confounder drugs, ICD codes for the cancer diagnosis used, and ICD hospital diagnosis codes for the potentially confounder diseases.Click here for fileCumulative prednisolone equivalent dose calculation and list of systemic glucocorticoids with associated prednisolone conversion factors. This file contains a list of the prescribed systemic glucocorticoids during the study period, their associated prednisolone conversion factor used for the calculation, and presents how we calculated cumulative prednisolone equivalent doses.Click here for file