Few studies have supported an association between breast cancer and DDT, usually assessed with biomarkers that cannot discern timing of exposure, or differentiate between the accumulation of chronic low-dose versus acute high-dose exposures in the past. Previous studies suggest that an association may be evident only among women exposed to DDT during biologically susceptible windows, or among those diagnosed with estrogen receptor/progesterone receptor-positive (ER+PR+) breast cancer subtypes. Self-reported acute exposure to a fogger truck, which sprayed DDT prior to 1972, was hypothesized to increase the risk of breast cancer, particularly among women exposed at a young age or diagnosed with ER+PR+ breast cancer.We examined these possibilities in the Long Island Breast Cancer Study Project (LIBCSP) , which included exposure assessment by structured questionnaire and serum samples collected between 1996–1998, using adjusted logistic and polytomous regression to estimate ORs and 95% CIs.Women with ER+PR+ breast cancer had a 44% increased odds of ever seeing a pre-1972 fogger truck compared to other subtypes . However, there was little variation in the observed increase in breast cancer risk when considering all women who reported seeing a pre-1972 fogger truck at their residence , or during hypothesized susceptible windows. Self-reported acute exposure was not correlated with serum concentrations, a biomarker of long-term exposure.These findings support the hypothesis that seeing a fogger truck, a proxy measure for acute DDT exposure, may be associated with ER+PR+ tumors, the most commonly diagnosed breast cancer subtype among American women. Breast cancer is the leading cancer diagnosis among women in the United States (U.S.) . There ip,p’-dichlorodiphenyltrichloroethane  and breast cancer, although results have been inconsistent [p,p’-DDT and its most prominent metabolite, p,p’-dichlorodiphenyldichloroethylene , were widespread in the U.S. [p,p’-DDT exposure has been inconsistently linked to breast cancer despite having both estrogenic and carcinogenic properties [o’p-DDT and p’p-DDT) has been shown to increase mammary cell proliferation in animal models [p,p’-DDE, which has little to no estrogenic effect [There has long been suspicion of a relationship between the persistent organochlorine pesticide nsistent . By 1945the U.S. . Fogger the U.S. . p,p’-DDoperties ,12. Acutl models ,14 wherec effect .p,p’-DDE concentrations were associated with an increased risk of breast cancer [p,p’-DDT metabolites. Positive correlations are observed with age and are likely due to the relatively long half-lives . Whethep,p’-DDE  measured in life , and the in life .et al., 2007 [The aim of this study was to investigate the relationship between self-reported exposure to a fogger truck at a residence prior to the ban of DDT, as a proxy measure of an acute high dose exposure, and breast cancer incidence, particularly among women diagnosed with ER+PR+ tumors. In addition, the association between adolescent exposure to fogger trucks during two demonstrated developmentally sensitive time periods, defined as ≤20 years  and lesl., 2007 ), and bret al.[Data for this case–control investigation were drawn from the Long Island Breast Cancer Study Project (LIBCSP), which is described in detail in Gammon et al. Briefly,in situ or invasive breast cancer between August 1st, 1996 and July 31st, 1997, confirmed by a physician or the medical record. Cases were ascertained by contacting pathology departments of all 28 hospitals on Long Island and three tertiary care hospitals in New York City.All participants were residents of Nassau or Suffolk counties at the time of diagnosis and spoke English. Cases were women newly diagnosed with a first primary Controls were randomly selected women who had no history of breast cancer and were frequency matched to cases based on the expected age distribution of case women by 5-year age groups. Potential controls were identified by random digit dialing for those less than 65 years of age and by Health Care Finance Administration rosters for those 65 years and older.Written signed informed consent was obtained from all participants prior to interview. A total of 1,508 cases and 1,556 controls  completed the interview process. Participants were women aged from 20 to 98 years, and most likely to be postmenopausal (67%); in addition, the overwhelming majority of women self-identified as white (93%), rather than as black (5%), which is consistent with the racial distribution of these two counties at the time of data collection .Cases (97.7%) signed a medical record release form for the abstraction of clinical characteristics of breast cancer diagnosis and final study eligibility determination. For 95.2% of cases, medical records were successfully located and abstracted.Exposure to fogger trucks was assessed by questionnaire. The LIBCSP case–control interview occurred in the respondents’ homes and, for cases, within several months of the diagnosis of the first primary breast cancer. All participants were administered the main case–control questionnaire by uniformly trained interviewers. The main questionnaire included a comprehensive assessment of known and suspected risk factors for breast cancer, including a section on environmental exposures. This section included a question asking whether a fogger truck was seen at a participant’s residence and if yes the ages or the dates at which they began and ceased living at the residence. Study participants were able to list 2 residences where they witnessed a fogger truck. Any other exposures to fogger trucks at non-residences would not be included in this question. Participants were also asked whether they chased a fogger truck during childhood or adolescence; however, due to low levels of a positive response that question was not included in this analysis.To maximize information regarding windows of exposure, we utilized a birth cohort approach  ), and inPolytomous logistic regression  was usedEffect measure modification on a multiplicative scale was assessed by comparing the multivariable models with and without cross-product terms of the exposure and weight change . To deteThe analysis was further stratified by birth cohort, based on important years in the history of DDT use . The assp,p’-DDT and p,p’-DDE concentrations were dichotomized at the median. Linear regression was also employed to test whether self-report of a fogger truck predicted log-transformed p,p’-DDT or p,p’-DDE concentrations or the p,p’-DDT/ p,p’-DDE ratio in the serum with adjustment for lipid concentrations [To assess correlation with serum concentrations and self-report of a fogger truck at a residence, a kappa statistic  was calctrations , 5-year trations . An inteAll analyses were completed using SAS 9.2 .The majority of the LIBCSP study population was born in the two birth cohorts that were exposed to DDT at young ages, 1925–1945 and 1946–1972  and 558 (35.9%) cases and controls, respectively, reported ever seeing a fogger truck at a personal residence on Long Island. When restricted to those who lived at their residences prior to the DDT ban in 1972, 446 (32.7%) and 408 (29.0%) cases and controls, respectively, reported seeing a fogger truck. As shown in Table In subgroup analyses, 11.6% of cases and 11.8% of controls were found to be at or under the age of 20 when living at the residence where they saw the fogger truck. Slightly fewer, 9.9% of cases and 10.9% of controls were living at the residence ≤14 years of age. The associations between fogger truck and breast cancer were not stronger when limiting the exposed to participants who reported seeing a fogger truck at a residence that they lived ≤14 years of age  and ≤20 years of age   Table . When usAs shown in Table The covariate distributions for women with known ER/PR status were very similar to women without known ER/PR status (data not shown). However, the effect estimate for the association for ever seeing a fogger truck and breast cancer was slightly higher among case women with known ER/PR status  than among all women regardless of ER/PR status , although with overlapping confidence intervals.x2 = 1.86, df = 1, p = 0.2), there was a slightly elevated association between women who gained weight and reported ever seeing a fogger truck at a residence compared to women who had gained weight but did not report seeing a fogger truck . Breastfeeding history and menopausal status were not found to be significant modifiers of the relationship between fogger trucks and breast cancer. However, a positive association for reporting a fogger truck and breast cancer risk was observed for postmenopausal women .The OR for the association between exposure to a fogger truck and breast cancer was explored for effect measure modification by adult weight change, breastfeeding history and menopausal status at diagnosis as shown in Table Study participants were categorized by their birth cohorts, defined based on important periods of DDT use Table . An incrp-p’-DDT (k = −0.062) and p,p’-DDE (k = −0.060) did not indicate any agreement. Additionally, an adjusted linear regression model to predict log transformed p-p’-DDT and p-p’-DDE concentrations from self-report of a fogger truck at a residence did not identify an association .The kappa statistic between self-report of ever seeing a fogger truck and lipid-adjusted blood serum concentrations of on Table   ; suggestWe did not observe significant heterogeneity when investigating breast cancer risk by birth cohort (data not shown) and therefore it is unlikely that truck sightings are proxies for age at diagnosis. From the lack of an increase in risk observed for seeing a truck among the 1946–1972 birth cohort, we can also conclude that seeing a fogger truck was not a proxy for being at a young and potentially susceptible age during DDT use.Few studies have examined the association between DDT exposure during younger ages and subsequent breast cancer risk, and none have considered acute DDT exposures. Therefore, the study results reported here provide a novel perspective to the ongoing discussion about the relationship between DDT and breast cancer. The stronger association observed with a biologically susceptible subgroup, namely women with ER+PR+ breast cancer, provide support for an association between DDT and breast cancer risk. With the prevalence of DDT increasing with indoor residual spraying programs for malaria vector control outside the U.S. , the impIn this first report that considers acute exposure to DDT, a positive association was observed for spraying fogger trucks and ER+PR+ breast cancer. Given the high incidence of breast cancer, and in particular ER+PR+ tumors , this fip,p’-dichlorodiphenyltrichloroethane; p,p’-DDE: p,p’-dichlorodiphenyldichloroethylene; EPA: U.S. Environmental Protection Agency; LIBCSP: Long Island Breast Cancer Study ProjectER+PR+: Estrogen receptor/ progesterone receptor-positive; p,p’-DDT: The authors declared that they have no competing interests.AW, ST and MG conceived and designed the analysis. AW and MG drafted the manuscript. AW carried out the statistical analysis. MW completed the organochlorine assays. MG was Principal Investigator, AN was co-Principal Investigator, and ST, MW and SS were collaborators on the LIBCSP, and thus were responsible for all data collection . All authors contributed to the revisions of the manuscript and read and approved the final manuscript.Breast cancer subtype, stratified by birth cohorts, LIBCSP, 1996-1997.Click here for file


Although many low-penetrant genetic risk factors for breast cancer have been discovered, knowledge about the effect of multiple risk alleles is limited, especially in women <50 years. We therefore investigated the association between multiple risk alleles and breast cancer risk as well as individual effects according to age-approximated pre- and post-menopausal status.Ten previously described breast cancer-associated single-nucleotide polymorphisms (SNPs) were analysed in a joint European biobank-based study comprising 3584 breast cancer cases and 5063 cancer-free controls. Genotyping was performed using MALDI-TOF mass spectrometry, and odds ratios were estimated using logistic regression.P-trend 5.6 × 10−20 and 1.5 × 10−25, respectively). Odds ratio for breast cancer of 1.84 : 1.59–2.14; 10 SNPs) and 2.12  was seen for the maximum vs the minimum number of risk alleles. Additionally, one of the examined SNPs (rs981782 in HCN1) had a protective effect that was significantly stronger in premenopausal women .Significant associations with breast cancer were confirmed for 7 of the 10 SNPs. Analysis of the joint effect of the original 10 as well as the statistically significant 7 SNPs  found a highly significant trend for increasing breast cancer risk with increasing number of risk alleles (The strongly increasing risk seen when combining many low-penetrant risk alleles supports the polygenic inheritance model of breast cancer. In addition to the highly penetrant  and moderately penetrant  genetic variants conferring increased risk for breast cancer, low-penetrant risk has been linked with common genetic variants  by genome-wide association studies (GWASs) .Early GWASs on breast cancer reported findings of several new breast cancer susceptibility loci .P<0.05 in phase 3 of The six common susceptibility loci reported in 2007 by The study was performed within the European network of excellence Cancer Control using Population-based Registries and Biobanks (CCPRB). A total of 9395 samples (3882 cases and 5513 controls) were selected for genotyping . The stuThe Malmö Diet and Cancer Study (MDCS) is a prospective cohort study initiated in 1991. It comprises a total of 17 035 female residents of Malmö Sweden recruited between 1991 and 1996  is a preventive case-finding programme started in 1974 ; when donating blood, participants also signed a general consent form allowing research on their samples.The North Sweden Health and Disease Study (NSHDS) include the Västerbotten Intervention Programme (VIP) and the Mammography Screening Programme (MSP), initiated in 1985 and 1995, respectively. Participants in the VIP are screened at 40, 50 and 60 years of age and mammography screening and blood sampling is performed among women between 50 and 69 years of age . Unmatched controls were collected between 2000 and 2004, either from women who participated in the population-based cervical or breast cancer screening programme and found free of breast cancer or from older women in retirement homes who had not been diagnosed with breast cancer, to generally reflect the ages of the cases. By linkage to the Icelandic cancer registry in 2008, we identified cases diagnosed before 31 December 2007. A total of 866 cases  and 948 controls  had DNA available and were eligible to us.The use of these samples was approved by the data protection (200605037) and Science Ethics Committee in Reykjavik (VSNb2006050001/03-16 and VSNb2005070008/03-16).Cases with early onset or familial breast cancer, free from BRCA1/2 mutations, were recruited at the genetic counselling clinic in Silesia between 1997 and 2006. This collection included 391 cases  that were used in the present study. Samples from 306 unmatched controls  were collected between 2003 and 2009 from healthy women attending the same clinic, but who had no family history of breast cancer.The use of the Polish samples was approved by the Bioethical Commission at the Centre of Oncology in Gliwice (20 November 2001). All subjects signed an informed consent form before donating their samples.All GWAS-identified loci associated with breast cancer and published before 31 June 2007 were initially included in the study .The SEQUENOM MassARRAY Designer software  included eight of the above SNPs in a single multiplex assay. The SNP analyses were performed on a MALDI-TOF mass spectrometer (SEQUENOM MassArray) using standard iPLEX reagents and protocol (SEQUENOM) and 10 ng DNA as PCR template. Primer sets were from Metabion .μl) containing 2 ng of DNA template and primers   were subjected to 2 min at 50 °C and 10 min at 95 °C, followed by 50 PCR cycles of 95 °C for 15 s and 60 °C for 1 min.The SNPs rs2981582 and rs1045485 were analysed by a separate TaqMan ‘assay by design’ genotyping assay on a 7900HT instrument, using Master Mix No UNG from Applied Biosystems  according to the manufacturer's instructions. Reaction mixtures (6 N=270) were included as blinded duplicates to assess the quality of the genotyping assay.Approximately 3% of samples from the NSHDS, 5% of the samples from Iceland and 8% of the Polish cases  and P-trend was calculated using 0, 1 or 2 copies of the minor allele as a continuous variable. The OR of <1.0 indicates that the major allele is the risk allele. To examine heterogeneity between the age groups, adjusted case–case models using unconditional logistic regression analysis were used and P-values of <0.05 were considered statistically significant. The P-value for heterogeneity (Phet) of OR between cohorts was calculated using the Breslow–Day test.Individual samples producing results in <80% of the assays were excluded before statistical analyses in order to eliminate samples with poor-quality DNA and in concordance with P-trend for each numerical group of risk alleles. The same calculation was also performed using only the seven SNPs exhibiting significance. The maximum number of risk alleles was 20 and 14, respectively, that is, 2 for each SNP. Breast cancer risk for individuals with up to ⩾11/8 risk alleles was compared with the group with ⩽6/3 risk alleles. The median number of risk alleles among both cases and control population was 8  and 5 (model including 7 SNPs), and in order to estimate the risk increase/decrease in individuals with the highest and lowest numbers of risk alleles, 8/5 risk alleles was also set as a reference. The women were also stratified according to age (⩽50 vs >50 years) to assess potential differences in penetrance between age groups with increasing numbers of risk alleles.For each participant the total number of risk alleles was calculated, and logistic regression was used to estimate OR and P-values for trends reported in original reports are presented together with the results of the present analyses.To compare estimated risks in the present study with previous reports, OR and P<0.001).Of the initial 9395 samples selected for the project, 8647 (92.0%) were successfully retrieved and genotyped for ⩾80% of the SNPs. All SNPs had genotyping success rates of >90%, with an average of 97.8%. Results of all 3240 analyses performed on the 270 duplicate samples were in 100% concordance. All SNPs but one (rs4666451) passed the HWE cutoff  was <0.001 and for the remaining three SNPs  was <0.01  exhibited an age-adjusted OR near to what was originally reported .P<0.001) and had an OR that deviated from that reported (MAF was 0.35).Associations of the two remaining SNPs with breast cancer were not replicated. The SNP rs1045485 (CASP8) did not reach significance, although the point estimate of the per-allele OR among women >50 years  approaches that initially described by vs >50 years to approximate menopausal discrimination revealed different association in young vs older women for one of the SNPs (rs981782), whose protective effect was more pronounced in younger  than in older women . The original data set was also adjusted for study population but no difference in results was seen compared with the age-adjusted or unadjusted analysis (results not shown).Stratification of results according to study population  revealedP for trend: 5.6 × 10−20 and 1.5 × 1025, respectively; vs >50 years; results not shown).Finally, both cases and controls were classified according to the individual burden of risk alleles including both all 10 original SNPs and the SNPs statistically significantly associated with risk within this study . A successive increase in point estimate from an OR of 1 for the group with the minimum number of risk alleles  to an OR of 1.84  and 2.12  for the group carrying the maximum number of risk alleles  was detected  for heterogeneity between age groups was highly significant. Previous reports did not find this difference, which could be because of differences in age stratification and/or inclusion , whose carriers of the rs13387042 A allele demonstrate an increased risk. The 2q35 locus has also been verified in other non-Icelandic populations  association, might be attributable to insufficient statistical power.Methodological strengths include the exclusion of samples with <80% successful genotypes and by 100% concordant genotypes in 270 duplicate samples. Although the use of The FGFR2 and TOX3 SNPs have consistently been verified in published reports (In the present study we used a simple addition of the number of risk alleles, but still obtained almost exactly the same result as the study of Our findings, including total risk score, are well in line with previous studies. A novel finding for this study was the fact that the protective effect of the HCN1 SNP rs981782 was significantly stronger in women ⩽50 years of age. Odds ratios presented both here and in the other two studies consistently show that total risk scores based on low-penetrant SNPs adds only very modest improvement to risk prediction models based on medical data, and are therefore not likely to have an immediate clinical use. However, we can show that simple calculation of the number of risk alleles gives highly reproducible risk scores between studies and could be useful in further studies of the genetic predisposition to breast cancer.


This updated meta-analysis was conducted to assess the association between coffee consumption and breast cancer risk.We conducted a systematic search updated July 2012 to identify observational studies providing quantitative estimates for breast cancer risk in relation to coffee consumption. Pooled relative risks (RRs) with 95% confidence intervals (CIs) were calculated using a random-effects model, and generalized least square trend estimation was used to assess dose–response relationships.A total of 26 studies (16 cohort and 10 case–control studies) on coffee intake with 49497 breast cancer cases were included in the meta-analysis. The pooled RR showed a borderline significant influence of highest coffee consumption , low-to moderate coffee consumption , or an increment of 2 cups/day of coffee consumption  on the risk of breast cancer. In stratified analysis, a significant inverse association was observed in ER-negative subgroup. However, no significant association was noted in the others.Our findings suggest that increased coffee intake is not associated with a significantly reduced risk of breast cancer, but we observe an inverse association in ER-negative subgroup analysis. More large studies are needed to determine subgroups to obtain more valuable data on coffee drinking and breast cancer risk. Breast cancer is the most common type of female malignancy all over the world. Coffee, one of most known risk factors, may be crucial in the etiology of breast cancer We searched the databases MEDLINE and EMBASE to identify relevant studies published in English through July 2012. The following keywords were used in searching: “caffeine”, “coffee”, or “dietary factors”, combined with “breast cancer”, “breast carcinoma,” or “breast neoplasm”. We also reviewed references cited in the selected articles. The eligible studies had to meet the following criteria: (i) They had a case–control or cohort study design; (ii) The outcome of interest was primary breast cancer; (iii) The exposure of interest was coffee consumption. (iv) Relative risk (RR) and their 95% confidence intervals (CIs) could be extracted or calculated from relevant articles.The following information was extracted from each included study: first author's last name, study design, country of origin, study period, number of cases and subjects, adjustment for potential confounders, the exposure to coffee consumption, RR and corresponding 95% CIs for every category of coffee intake. For each study, low coffee consumption was defined as the reference category, high coffee consumption as the greatest degree of control, and moderate coffee consumption fell in between. All the data were extracted independently by two authors (Li XJ and Ren ZJ), and the disagreement was solved by discussion.2 statistics (I2>50% is considered significant). We calculated summary estimates of the RR using random-effects models, which consider both within- and between-study variation. Sensitivity analyses were also conducted, in which one study at a time was removed to analyze the influence of a single study on the pooled RR.Study-specific RRs/odds ratios (ORs) and 95% CIs for low to moderate consumption and high consumption level were extracted from each study, and then we pooled the overall RR/OR using the inverse of the corresponding variances as weights. Because breast cancer is rare, ORs in case–control studies yield similar estimates of RR To obtain the information on a dose–response relationship, we considered the increment of 2 cup per day Studies were not eligible if the required data were not reported or could not be estimated. If coffee consumption was indicated by milliliter, we defined 125 ml of coffee equal to 1 cup.The subgroup analyses according to geographic region, ER status, and menopausal status were performed to assess the potential effect of these variables on outcomes. The funnel plots with Begg's rank correlation and Egger regression tests were performed to detect publication bias We initially identified 186 potentially eligible studies. Most were excluded because the exposure or endpoint was not relevant to our analysis. After assessing the full-text of the 36 potentially relevant articles, we identified 26 eligible studies We identified 26 studies including 49497 incident cases of breast cancer and 863067 participants that were eligible for meta-analysis The multivariable-adjusted RRs in each study and the pooled RR of breast cancer for the highest versus lowest categories of coffee intake are presented in 23 studies were included for the dose–response analysis of coffee intake and risk of breast cancer There was no indication of publication bias from either visualization of the funnel plot, Begg's test (p = 0.84), or Egger's test (p = 0.54). The sensitivity analysis confirmed the stability of our results.This meta-analysis showed a borderline significant association between coffee consumption and decreased risk of breast cancer. The results of our meta-analysis were consistent with those in the earlier meta-analysis which contained 25250 cases The mechanisms by which coffee may affect breast carcinogenesis are complex and remain unclear. Coffee can both stimulate and suppress the development of mammary tumors in vitro CYP1A2*1F A/A genotype is associated with a greater proportion of ER− tumors among patients with breast cancer in a population-based study . Although the results appear inconsistent, it could be because of the different coffee-related variables measured. For instance, caffeine is only one out of the many different compounds contained in coffee, and thus caffeine intake is perhaps not a valid substitute for measuring the total effects of coffee consumption. In addition, the discrepancy might be attributed to other factors related to coffee drinking, such as brewing method, bean type, and caffeine content. Concerning relative small sample size, we cannot exclude the possibility that our findings in some subgroups may be a result of chance. More studies are needed to refute or confirm the associations that we observed in some subgroups.There were 5 studies The current meta-analysis had some advantages. First, the number of total cases included in the meta-analysis was substantial (n = 49497 cases). The pooled RRs of breast cancer for coffee intake were consistent with those in the previous meta-analysis which contained 25250 cases Despite these advantages, some limitations of our meta-analysis should be acknowledged. First, misclassification of coffee intake and thus misclassified coffee components are inevitable due to self-reported intake. Second, a meta-analysis is unable to solve problems with confounding factors that could be inherent in the included studies. Third, although many of studies had adjusted for important risk factors for breast cancer, unmeasured variables may also have influenced results of individual studies. Fourth, not all studies were included for the dose–response analysis due to lack of data. Fifth, studies included in this meta-analysis were mostly conducted in Europe, the United States, and Asia. Therefore, additional research in other populations is warranted to generalize the findings. Sixth, potential publication bias might influence the findings, yet little evidence of publication bias was observed. Finally, the results of meta-analysis in some subgroups (e.g. estrogen receptor status and Menopause status) need further to be investigated, because of limited data.In conclusion, our meta-analysis suggests that coffee consumption is not related to overall risk of breast cancer. However, our data suggest that high coffee consumption may decrease risk of ER–negative breast cancer, but these findings may be due to chance and warrant further study.Checklist S1A 27-item manuscript checklist.(DOC)Click here for additional data file.Table S1Summary characteristics of studies included in the meta-analysis.(DOC)Click here for additional data file.


Cytokines released from adipose tissues induce chronic low-grade inflammation, which may enhance cancer development. We investigated whether indices of obesity and circulating adipokine levels could predict incident cancer risk.This longitudinal community-based study included subjects from the Hong Kong Cardiovascular Risk Factors Prevalence Study (CRISPS) study commenced in 1995-1996 (CRISP-1) with baseline assessments including indices of obesity. Subjects were reassessed in 2000-2004 (CRISPS-2) with measurement of serum levels of adipokines including interleukin-6 (IL-6), soluble tumor necrosis factor receptor 2 , leptin, lipocalin 2, adiponectin and adipocyte-fatty acid binding protein (A-FABP). Incident cancer cases were identified up to 31 December 2011.2; p=0.063), was a significant independent predictor for incident cancers after adjustment for age, sex and smoking status. 99 of 1899 subjects reassessed at CRISPS-2 had developed cancers. Subjects who developed cancers had significantly higher level of hsCRP, IL-6, sTNFR2 and lipocalin 2. After adjustment for conventional risk factors, only IL-6  and sTNFR2  predicted cancer development.205 of 2893 subjects recruited at CRISPS-1 had developed incident cancers. More of the subjects who developed cancers were obese (22.1 vs 16.1%) or had central obesity (36.6 vs 24.5%) according to Asian cut-offs. Waist circumference , but not body mass index (adjusted HR 1.04 [1.00-1.08] per kg/mOur data supported the increased risk of malignancy by chronic low grade inflammation related to central obesity. The prevalence of obesity has increased dramatically in the last three decades globally. In a WoThe Hong Kong Cardiovascular Risk Factor Prevalence Study Cohort (CRISP) is a population-based prospective study on Chinese, mostly originated from Southern China. The study commenced as a cross-sectional survey in 1995-1996 (CRISP1)  with subBlood glucose, insulin and lipid levels were measured as described ,18. T2DMAll statistical analyses were performed using Statistical Package for the Social Sciences . Baseline variables were compared using chi-square, partial correlation or analysis of variance as appropriate. Variables were tested for normality using Kolmogorox-Smirnov test, and skewed variables were natural-logarithmically transformed before analysis. Biologically relevant variables were entered into Cox proportional hazard regression analyses by forced entry to identify the independent predictors for incident cancers. 2 for obesity and WC≥90cm in male or 80cm in female for central obesity [2) was used, the prevalence of obesity was 8.3% and 6.1% (p=0.197) for cancer and non-cancer group respectively. After adjustment for potential confounding factors, including age, sex, smoking status, WC , but not BMI , was found to be a significant independent predictor for incident cancers (2895 subjects (1412 men and 1483 women) were recruited at CRISPS-1. Two patients with known history of malignancy were excluded. After a median follow-up of 16.0 years (interquartile range 15.6-16.5 years), 205 patients had developed cancers (Table S1 in File S1) with an incidence rate of 4.62 per 1000 person-years. The commonest type of cancer was carcinoma of the lung (44 subjects), followed by colorectal cancer (36), breast cancer (20), prostate cancer (18) and cancers of the female reproductive tract (14). The median time for cancer development from baseline was 8.7 years (interquartile range 4.9 to 13.2 years).  obesity ). If the1944 subjects returned for CRISPS2 (2000-4) assessment. After excluding 45 subjects with known cancers or incomplete data at CRISP-2, a total of 1899  subjects were included in the analysis. 99 had developed cancers after a median follow up interval of 9.5 years (interquartile range 8.8-10.3). Baseline biomarkers in 2000-4  had significantly positive correlation with indices of obesity WC and BMI while adiponectin had significant negative correlation . Table 5After adjustment for age, sex, and smoking status (Model 1 of 2 increase in body mass index (BMI) above normal was found to increase the risk of developing many types of cancers by 24 to 59% in both sexes [2 raised all cancer-mortality by 1.09 times [2 and BMI≥30 kg/m2 respectively), we demonstrated that waist circumference, an indicator of central adiposity, but not BMI which represents general obesity, was a significant independent predictor of incident cancer development, even after adjusting for the effect of age, the strongest risk factor in this cohort. This differential effect of central and general adiposity on cancer risk has also been demonstrated in other studies in the Asian-Pacific region. In a case-control study of relatively lean Chinese population with mean BMI 21.9 kg/m2, an increase in waist-hip ratio increased the risk of prostate cancer by 3 folds while the risk incurred by BMI was not significant[There is now increasing evidence that obesity contributes to the development of many cancers, including cancers of colon, breast, liver, gall bladder, endometrium, kidney, prostate and pancreas ,25. A 5 th sexes . Study i09 times . Althoug09 times , a risin09 times . Moreove09 times . Since t09 times , visceragnificant. Again, Pro-inflammatory adipokines have been shown in vitro to play significant pathophysiological roles in different stages of tumorigenesis including cancer cell proliferation, angiogenesis and metastasis ,31 . ApaIL-6 and TNF-α are key cytokines involved in inflammation and immunity ,34. BothSmoking, a well known carcinogenic risk factor, has been associated with level of both IL6 and TNF-α,38. In tThe negative findings on adiponectin in this study was unexpected as this anti-inflammatory adipokine inhibited the growth of several breast cancer cell lines in vitro . In epidThis study has several limitations. Firstly, in view of the relatively small numbers of incident cancer cases, the findings of this study should be validated in studies involving larger sample size. Secondly, since baseline assessments were performed more than a decade ago and the list of adipokines is growing, not all adipokines were measured in this study. Thirdly, estradiol and its metabolites might be other important confounding factors which were not measured. Numerous studies have demonstrated the effects of estrogens on the development and progression of different cancers and estrIn conclusion, we have demonstrated in a population-based study that high waist circumference, and raised serum levels of the pro-inflammatory adipokines, IL-6 and TNFR2, can predict the development of cancers in a Chinese cohort with relatively low obesity prevalence. These associations are supportive of the role of chronic low grade inflammation, related to central obesity, in the development of cancers even though a causal relationship cannot be concluded. When more epidemiological data become available and with a better understanding of the roles of these adipokines in tumourigenesis, risk models can be constructed to identify high risk patients for the development of malignancy so that appropriate measures may be employed to reduce their risks. On the other hand, in patients with established malignancy, the inflammatory cytokines level may act as novel prognostic factors. Furthermore, the inhibition of the inflammatory cytokines provides potential new strategies for the treatment of cancers or for sensitizing tumors to chemotherapeutic agents. While IL-6 and TNF-alpha receptor antagonists are now used clinically for the treatment of a wide range of rheumatological or hematological disorders, it will be of interest to follow-up on patients treated with such agents for any potential beneficial effects on cancer development. File S1Supporting tables.(DOCX)Click here for additional data file.


P = 0.337) and a 1 g/d increment in mushroom intake conferred an RR of 0.97 (95% CI: 0.96–0.98) for breast cancer risk, with moderate heterogeneity . Besides, available menopause data extracted from included studies were used to evaluate the influence of menopausal statues. The summary RRs of mushroom consumption on breast cancer were 0.96 (95% CI: 0.91–1.00) for premenopausal women and 0.94 (95% CI: 0.91–0.97) for postmenopausal women, respectively. Our findings demonstrated that mushroom intake may be inversely associated with risk of breast cancer, which need to be confirmed with large-scale prospective studies further.Epidemiological studies have investigated the potential anticancer effects of mushroom intake. This review aims to clarify the evidence on the association of dietary mushroom intake with breast cancer risk and to quantify its dose-response relationship. Relevant studies were identified by a search of PubMed, Web of Science and Google Scholar up to December 31, 2013. Observational studies with relative risks (RRs) or hazard ratios (HRs) or odd ratios (ORs) and 95% confidence intervals (CIs) of breast cancer for three or more categories of mushroom intake were eligible. The quality of included studies was assessed by using Newcastle-Ottawa Scale. A dose-response meta-analysis was performed by utilizing generalized least squares trend estimation. Eight case-control studies and two cohort studies with a total of 6890 cases were ultimately included. For dose-response analysis, there was no evidence of non-linear association between mushroom consumption and breast cancer risk ( Breast cancer is the most common cancer and the leading cause of cancer death for female in both developed and developing countries, accounting for 23% of the total new cancer cases and 14% of the total cancer deaths in 2008 Lifestyle factors are considered to play an important role in the prevention of breast cancer since they could be modified in vivo and in vitroAs essential components of lifestyle, diet-related factors are thought to account for about 30% of cancers in developed countries Several studies reported an adverse association of edible mushroom intake with the risk of breast cancer We performed a systematic literature search on PubMed, Web of Science, and Google Scholar up to December 31, 2013 using the following key words: “mushroom” or “fungi” and “breast cancer”, “breast carcinoma”, “breast tumor” or “breast tumour”. The reference lists of selected articles were also scrutinized to obtain additional pertinent publications. Only articles written in English were included.Studies were eligible if they met the following criteria: (1) the study had a case-control or cohort design; (2) the exposure of interest was dietary intake of edible mushroom; (3) the outcome was the occurrence of breast cancer; (4) the study provided relative risks (RRs), hazard ratios (HRs) or odds ratios (ORs) with 95% CIs for ≧ 3 categories of exposure; (5) the number of cases and the total subjects or follow-up person-years for each category of mushroom intake were reported or derivable by published data. If an article reported results for premenopausal and postmenopausal women respectively, we separated this article into two independent studies by menopausal status.The following information were extracted from each included study: the first author's name, year of publication, study population, study design, age of participants, number of cases, menopausal status, daily mushroom consumption, OR, RR or HR with corresponding 95% CI for each category of mushroom consumption and adjusted potential confounders. The effect size that reflected the greatest degree of adjustment for potential confounders was included.The Newcastle-Ottawa Scale (NOS) The RR and HR are assumed to approximate the OR because of the low incidence of breast cancer P value for non-linearity was calculated by testing the null hypothesis that the coefficient of the second spline was equal to zero.For each study, the median or midpoint of upper and lower boundaries was assigned as the average intake of mushroom in each category. If the upper boundary of the highest category was not provided, we assumed that the upper boundary had the same amplitude as the closest category. We performed a dose-response model by using general least-squares trend estimation as described by Greenland and Longnecker Q test and 2I statistic and it was considered significant if P<0.10 for Q statistic or 2I>50%. When there was significant heterogeneity detected, data from included studies were combined by random-effects model; otherwise, the fixed-effects model was utilized. Meta-regression was initially conducted to find the source of heterogeneity, and then subgroup analysis was carried out if feasible. Sensitivity analyses were executed by deleting each study in turn to estimate the influence of individual studies on the pooled estimate. Besides, we evaluated publication bias by Begg's and Egger's regression tests.The between-study heterogeneity was assessed by the Cochran P<0.05 was considered statistically significant unless noted otherwise.All statistical analyses were conducted with Stata 10.0 and a The flow chart of literature search was shown in P = 0.337). A 1 g/d increment in mushroom consumption conferred an RR of 0.97 . The study-specific RRs per 1 g/d increase in mushroom consumption were presented in There was no evidence of significant departure from linearity among data from the 10 studies  among studies for premenopausal women, while no evidence of heterogeneity  was detected among studies for postmenopausal women. Significant associations were observed in both groups , study design (case-control or cohort), number of adjusted confounders (≧12 or <12), number of cases (≧400 or <400), whether adjusted for body mass index (BMI), whether adjusted for cigarette smoking, whether adjusted for alcohol drinking and whether adjusted for physical activity, respectively. As a result, none of these variables showed statistically significant associations in univeriate meta-regression models (P>0.05), suggesting that factors which mentioned above could not explain the heterogeneity among studies. Considering the negative result of meta-regression, subgroup analysis was not conducted further.Meta-regression was performed to explore potential sources of between-study heterogeneity. Firstly, an empty regression was run to estimate the baseline value for P<0.10) except the study conducted in premenopausal women by Zhang et al. was excluded . But after removed this study, there was still an adverse association of mushroom consumption and breast cancer risk . The results were not materially altered when other studies was deleted in turn, with RRs ranging from 0.96 (95% CI: 0.94–0.98) to 0.97 (95% CI: 0.95–0.99).Sensitivity analysis was carried out by omitted one study at a time and calculated the combined RR for remaining studies . The relP = 0.06 for Egger's regression test and 0.107 for Begg's test).For publication bias, funnel plot showed no obvious asymmetry . BesidesThe meta-analysis including 10 eligible studies indicated a linear dose-response association between mushroom intake and risk of breast cancer, with the summary RR being 0.97 (95% CI: 0.96–0.98) for per 1 g/d increment in mushroom consumption. Besides, the protective effects of mushroom intake on risk of breast cancer were consistently exhibited in premenopausal women and in postmenopausal women.in vitro and in vivo experiments Agaricus bisporus white button mushroom, a common edible mushroom consumed in most countries, had the ability to inhibit the growth of human breast cancer MCF-7 cells in part through activation of nuclear factor-κB with the production of p50/105 heterodimers. Additionally, in in vivo experiments, a reduction in tumor growth was observed when murine sarcoma 180 cells exposed to polysaccharides were implanted subcutaneously into mice Amauroderma rude, a well-known medicinal mushroom, has been reported could inhibit cancer cell survival and induce apoptosis, and suppression of c-myc expression appeared to be associated with these effects Potential benefits to breast cancer patients taking high dosage of the extract of specific medicinal mushrooms over long term have been well-studied with positive results Interestingly, the health benefits of mushroom intake for breast cancer presented no difference between premenopausal and postmenopausal women in our meta-analysis. Considering only three eligible studies included in the meta-analysis, it was not very appropriate to make a conclusion about whether the linear trend between mushroom intake and risk of breast cancer influenced by the menopausal status. By now, a few epidemiological studies have investigated this association by menopausal status and almost no experimental study has been conducted for the biological function of mushroom or mushroom extracts intake on the risk of breast cancer in terms of the hormone circumstance. It was worth to note that the association between mushroom intake and risk of breast cancer stratified by menopausal status need to be further explored in large perspective studies, and the underlying mechanisms should be uncovered by functional studies.The development of breast cancer is a multifactorial process, thus the association between mushroom and breast cancer may be confounded by many other factors The strengths for the current meta-analysis had been summarized. To the best of our knowledge, the meta-analysis firstly systematically explored the pooled effect for edible mushroom consumption on the risk of breast cancer. Besides, our analysis precisely estimated the pooled relative risks with an application of dose-response approaches. Moreover, we have convinced that the results of our systematic meta-analysis, in essence, were stable and reliable after performing sensitivity analyses and testing the publication bias. Despite the clear strengths, some limitations should be acknowledged. First, the number of eligible studies included in the meta-analysis was relatively insufficient. Besides, despite of 70% high-quality studies, only two of the ten included studies were prospectively designed, thus additional large-scale and well-designed studies are warranted. Second, we have no opportunity to identify the source of heterogeneity because of the limited information extracted from the original studies. Oral administration of mushrooms in suppressing breast cancer involves many confounders and thus benefits of mushroom consumption are less convincing, as opposed to extracts. Although most original studies had adjusted many potential confounding factors, other heterogeneous natures of studies, such as demographic, reproductive factors and other lifestyle characteristics, possibly made effect on the current results. However, we could not perform further analysis owing to lack of detailed data. Further, we couldn't ignore the heterogeneous effects of different mushroom types since there were too many different mushroom species in different countries. It's a big challenge for us to restrict edible mushroom species. Thus our result indicated the combined effects of many edible mushrooms.In conclusion, the results from this meta-analysis suggested that greater edible mushroom consumption may be associated with a lower risk of breast cancer. Our research provided a perspective that oral administration of mushrooms perhaps contribute to breast cancer primary prevention. Whereas available data are still sparse, the findings need to be updated and confirmed with well-designed prospective studies in future.Checklist S1PRISMA checklist. Preferred Reporting Items for Systematic Reviews and Meta-Analyses.(DOC)Click here for additional data file.


In contrast, adult weight gain displayed a positive association with MD, which was similar in both groups . Women who had gained more than 24 kg displayed higher MD . MD was also evaluated using Wolfe’s and Tabár’s classifications, with similar results being obtained. Once BMI, fat distribution and other confounders were considered, our results showed a clear dose–response gradient between the number of kg gained during adulthood and the proportion of dense tissue in the breast.High mammographic density (MD) is a phenotype risk marker for breast cancer. Body mass index (BMI) is inversely associated with MD, with the breast being a fat storage site. We investigated the influence of abdominal fat distribution and adult weight gain on MD, taking age, BMI and other confounders into account. Because visceral adiposity and BMI are associated with breast cancer only after menopause, differences in pre- and post-menopausal women were also explored. We recruited 3,584 women aged 45–68 years within the Spanish breast cancer screening network. Demographic, reproductive, family and personal history data were collected by purpose-trained staff, who measured current weight, height, waist and hip circumferences under the same protocol and with the same tools. MD was assessed in the left craniocaudal view using Boyd’s Semiquantitative Scale. Association between waist-to-hip ratio, adult weight gain (difference between current weight and self-reported weight at 18 years) and MD was quantified by ordinal logistic regression, with random center-specific intercepts. Models were adjusted for age, BMI, breast size, time since menopause, parity, family history of breast cancer and hormonal replacement therapy use. Natural splines were used to describe the shape of the relationship between these two variables and MD. Waist-to-hip ratio was inversely associated with MD, and the effect was more pronounced in pre-menopausal (OR = 0.53 per 0.1 units; 95 % CI = 0.42–0.66 Breast cancer screening programs using mammography are well extended in most developed countries, though there are variations in periodicity and age groups targeted. Mammograms reveal the characteristics of breast composition, because stroma and epithelium attenuate X-rays more than does fat and so appear light, whereas fat appears dark .The term mammographic density refers to the proportion of radiologically dense breast tissue, composed of stroma and epithelium, and is a marker of susceptibility to breast cancer , 3. EvenBMI is a well-established risk factor for post-menopausal breast cancer, even though it is inversely correlated with incidence of breast cancer among pre-menopausal women –12. FurtIn this study, we analyze the influence of adult weight gain and fat distribution on mammographic density in Spanish pre- and post-menopausal women attending breast cancer screening.Determinantes de la Densidad Mamográfica en España—Determinants of Mammographic Density in Spain) is a cross-sectional multicenter study based on 3,584 women, aged 45–68 years, recruited from seven specific screening centers within the Spanish Breast Cancer Screening Program network in the following Spanish Autonomous Regions: Aragon; Balearic Isles; Castile-Leon; Catalonia; Galicia; Navarre; and Valencia. Women were recruited from October 7, 2007 through July 14, 2008. All women aged 50–69 years, regardless of nationality or legal status, are screened under these government-sponsored programs every 2 years. In some regions, women aged between 45 and 49 years are also included. Women were contacted by telephone and invited to participate in the study. Those who agreed to be recruited were given an appointment with the interviewer at the screening center on the same day as that scheduled for their mammogram. Participants signed an informed consent. More details regarding the design of the study are provided elsewhere [The DDM-Spain study  was estimated as weight in kg divided by the square of height in meters.Menopausal status was self-reported and based on four specific questions: (1) What is your situation as regard menstruation? ; (2) How many periods have you had in the last 12 months? (3) Age at menopause ; and (4) cause of menopause. Post-menopausal status was then defined as absence of menstruation in the last 12 months. Women were re-interviewed by telephone and inconsistencies between questions were resolved. The few participants who had their uterus, but not their ovaries, surgically removed were classified as post-menopausal, if they were aged older than 49 years, because this is the average age of menopause in Spain.Four of the screening centers used analog mammography devices, while the other three used full-field digital machines. Mammographic density was assessed from the craniocaudal mammogram of the left breast using a visual semiquantitative score with six categories proposed by Boyd , namely,The association between MD, adult weight gain and fat distribution was evaluated by using ordinal logistic models with random center-specific intercepts . OrdinalThe correlation between adult weight gain, BMI and the remaining variables related with fat distribution was computed using Pearson’s correlation coefficients. Waist-to-hip ratio was selected as the overall measurement of fat distribution, given that it was less closely correlated with BMI than were the rest. The final model included adult weight gain, waist-to-hip ratio together with the rest of the abovementioned variables, except for use of hormonal replacement therapy because this exposure was not associated with MD in our study. For the purpose of constructing the final model, to be able to compute adult weight gain for women who failed to remember their weight at age 18 years (15 %), this variable was imputed as the median weight at age 18 reported by women drawn from the same screening center, height quintile and age group. Similarly, for the few women who did not know their bra size (0.7 %), this was imputed by taking the median size reported by women drawn from the same screening center and BMI quintile. A sensitivity analysis was performed but imputed information was excluded, to verify that such imputation did not alter the estimated effect of adult weight gain and waist-to-hip ratio on mammographic density. To test the consistency of the effect of these two anthropometric variables across categories of BMI, the final model was separately fitted for women stratified by observed BMI quintile.The final model was also separately fitted for pre- and post-menopausal women. Heterogeneity of effects between pre- and post-menopausal women was tested, by using the log-likelihood ratio test to compare the final model with a model that also included an interaction term between menopausal status and the corresponding explanatory variable. Furthermore, natural splines were used to explore the shape of the dose–response curve for the two variables of interest, waist-to-hip ratio and adult weight gain, without assuming a linear dose–response relationship. Splines were constructed using 4 knots, located in Harrell’s recommended percentiles, namely, 5, 35, 65 and 95 % . These sTo explore the consistency of the results yielded by using different density scales, the same model, including adult weight gain, waist-to-hip ratio, age, BMI, parity, family history of breast cancer and time since menopause, was fitted using Wolfe’s and Tabár’s classifications of mammographic density. Separate analyses were also performed on the pre- and post-menopausal groups, and heterogeneity of effects between these two groups was likewise checked.glamm function to fit random-intercept ordinal logistic models [All analyses were performed in Stata , using the c models .A total of 3,584 women were recruited and interviewed, with an average participation rate of 74.5 % (range 64.7–84.0 %). Ten women developed breast cancer within 6 months of mammography and were excluded from the analysis. Table P value = 0.033), but no differences in MD were observed between women who reported current or past use of hormonal menopausal therapy and non-users.Table All anthropometric variables were strongly and statistically significantly correlated with BMI, with Pearson’s correlation coefficients equal to or >0.80 for all of these, except bra size (0.63) and waist-to-hip ratio (0.43). To avoid a multi-colinearity problem, we decided to use waist-to-hip ratio as an indicator of fat distribution in subsequent analyses, because it was the measure showing the least correlation with BMI. Figure P value = 0.010). Indeed, the downward trend seemed to be attenuated among post-menopausal women with a higher waist-to-hip ratio , with the effect of this variable being more pronounced in pre-menopausal  than in post-menopausal women .The joint analysis of adult weight gain, fat distribution and the remaining explanatory variables is shown in Table io Table ; Fig. 2.P values for waist-to-hip ratio and adult weight gain, on fitting the final models shown in Table P value = 0.031). The association between these variables and density classified according to Tabár’s scale was less pronounced, however, and the dose–response trend for weight gain among pre-menopausal women was no longer in evidence.Table Our results show that, after adjusting for BMI, bra size and other possible confounders, adult weight gain was positively correlated with mammographic density, in both pre- and post-menopausal women. A strong inverse association was also observed between abdominal fat distribution, as measured by waist-to-hip ratio, and mammographic density.Previous studies have already reported an inverse association between abdominal fat distribution and mammographic density –21. A reAn interesting result of our study is the different effect of visceral adiposity on mammographic density in pre- versus post-menopausal women, with a steeper inverse correlation observed in the former group. The higher percentage of mammographic density observed in our study among women with a lower waist-to-hip ratio, for any BMI quintile, and the steeper effect in pre-menopausal women may be a marker of higher accumulated exposure to estrogens. Sex steroid hormones play important roles in the accumulation, metabolism and distribution of adipose tissue, creating a sexual dimorphic pattern –33. FemaOn the other hand, the strong inverse correlation between mammographic density and visceral adiposity partially explains the lack of connection between breast cancer incidence and BMI or fat distribution in pre-menopausal women. Adjustment for mammographic density has, in fact, been shown to reverse the direction of this association , 40. AmoWith regard to adult weight gain, our results indicated a positive association between this variable and mammographic density, once age, BMI and the remaining confounders had been taken into account. Previous studies have reported an inverse correlation between weight gain and mammographic density . A recenAdult weight gain is thought to be a better measure than BMI when it comes to assessing adiposity and its metabolic consequences, because weight gain largely reflects an increase in body fat independent of BMI . A recenAge, parity and time elapsed since menopause significantly decreased mammographic density. Family history of breast cancer was positively associated with mammographic density: It was considered a potential confounder, owing to the reported association between anthropometric variables, particularly higher waist circumference, and family history . FinallyThe DDM-Spain is the largest epidemiologic study analyzing mammographic density and breast cancer risk factors in Spanish women. Women were recruited from population-based Spanish breast cancer screening centers, and participation rates were high. According to the data supplied by the Spanish National Health Survey , our womOur study has also a series of limitations. First, one of our variables, adult weight gain, critically depended on subjects’ ability to remember their weight at age 18 years, something that 15 % of our women were unable to recall. Computing adult weight gain in such women using imputed weights at age 18 years implies a certain degree of non-differential misclassification. Indeed, as expected, the exclusion of imputed values resulted in steeper trends. Second, anthropometric measures were obtained by the pertinent interviewer following the standard protocol. The use of different interviewers at the recruiting screening centers introduces a certain amount of heterogeneity. The random effects term sought to take this unmeasured variability into account. Third, the number of pre-menopausal women might have been insufficient for the purpose of detecting significant differences in some associations, such as the dose–response curve for adult weight gain. This constraint was imposed by the type of women screened in Spain. It should be borne in mind that European breast-screening guidelines recommend mammographic examination in the age range from 50 to 69 years, and only three of our screening centers included women in their 40s. Finally, measurement of density was performed visually by a single radiologist using categorical scales. The use of quantitative methods has been recommended . Such meOur results confirm an inverse association between fat distribution and mammographic density, which was more pronounced in pre-menopausal women. Once BMI and fat distribution were taken into account, however, adult weight gain was positively associated with mammographic density in our subjects. This positive association appeared using both quantitative and qualitative density methods, and reflects a state of higher breast cancer susceptibility.


Endogenous estrogens can be bio-activated to endogenous carcinogens via formation of estrogen quinones. Estrogen-3,4-quinones react with DNA to form mutagenic depurinating estrogen-DNA adducts. The carcinogenicity of endogenous estrogens is related to unbalanced estrogen metabolism leading to excess estrogen quinones and formation of depurinating DNA adducts. The present studies were initiated to confirm that relatively high levels of depurinating estrogen-DNA adducts are present in women at high risk for breast cancer or diagnosed with the disease. These adducts may be biomarkers for early detection of breast cancer risk. The estrogen metabolites, conjugates and depurinating DNA adducts were identified and quantified by using ultraperformance liquid chromatography/tandem mass spectrometry to analyze urine samples from 40 healthy control women, 40 high-risk women and 40 women with newly diagnosed breast cancer. Estrogen metabolism was shifted from protective methoxylation and conjugation pathways in healthy control women towards activating pathways leading to formation of depurinating DNA adducts in women at high risk or with breast cancer. These results support the hypothesis that breast cancer is initiated by mutations derived from depurination of estrogen-DNA adducts. Therefore, relative levels of depurinating estrogen-DNA adducts could become biomarkers for early detection of breast cancer risk and aid in determining preventive strategies. In fact, these two compounds react with DNA to form analogous depurinating DNA adducts.ortho-quinones of benzene and estradiol (E2) induce similar hyperproliferation of human peripheral blood mononuclear cells.6Estrogens, which contain a benzene ring, can become endogenous carcinogens. Three lines of evidence support this concept. Exposure to estrogens is a risk factor for increased incidence of breast cancer.In an earlier study, we have shown that in healthy control women (n = 46) whose estrogen metabolism is considered balanced, the level of estrogen-DNA adducts in urine is low and/or the levels of estrogen catechol metabolites, and thiol and methyl conjugates are high. In contrast, in high-risk women (n = 12) and those with breast cancer n = 17), the unbalanced estrogen metabolism is reflected in higher levels of estrogen-DNA adducts in urine and/or lower levels of estrogen metabolites and conjugates., the unb1(E2)-1-N3Ade] and 4-hydroxyestrone(estradiol)-1-N7Guanine [4-OHE1(E2)-1-N7Gua]2 and 4-OHE2 has been observed in human breast epithelial (MCF-10 F) cells, which do not contain estrogen receptor-α, and are not affected by the presence of an anti-estrogen.2-3,4-quinone (Q).1(E2) are carcinogenic in the Syrian golden hamster and CD-1 mouse.Once released from DNA, the predominant depurinating estrogen-DNA adducts, 4-hydroxyestrone (estradiol)-1-N3Adenine [4-OHEin-situ and invasive carcinoma), all recruited at the Mayo Clinic. The results presented here provide further support that the ratios of depurinating DNA adducts to their respective estrogen metabolites and conjugates were significantly associated with risk status.In light of our earlier findings,1-sulfate 3, E2 4, E1 5, 2-OHE2 6, 2-OHE1 7, 16α-OHE2 10, 16α-OHE1 11, 2-OCH3E2 12, 2-OCH3E1 13, 4-OCH3E2 14, 4-OCH3E1 15, 2-OH-3-OCH3E2 16 and 2-OH-3-OCH3E1 17 were purchased from Steraloids, Inc. . 4-OHE2 8 and 4-OHE1 9 were synthesized as previously described.2-1-glutathione (SG) 18, 2-OHE2-4-SG 19, 2-OHE1-1-SG 20, 2-OHE1-4-SG 21, 2-OHE2-(1 + 4)-cysteine (Cys) 22, 2-OHE1-1-Cys 23, 2-OHE1-4-Cys 24, 2-OHE2-1-N-acetylcysteine (NAcCys) 25, 2-OHE2-4-NAcCys 26, 2-OHE1-1-NAcCys 27, 2-OHE1-4-NAcCys 28, 4-OHE2-2-SG 29, 4-OHE1-2-SG 30, 4-OHE2-2-Cys 31, 4-OHE1-2-Cys 32, 4-OHE2-2-NAcCys 33 and 4-OHE1-2-NAcCys 34 were synthesized by using the procedure of Cao et al.2-1-N7Gua 35, 4-OHE1-1-N7Gua 36, 4-OHE2-1-N3Ade 37, 4-OHE1-1-N3Ade 38, 2-OHE2-6-N3Ade 39 and 2-OHE1-6-N3Ade 40 were synthesized by following reported methods.Phenyl solid phase extraction (SPE) cartridges were purchased from Varian . Androstenedione 1, testosterone 2, ESpot urine samples were collected from 120 women at the Breast Diagnostic Clinic and Oncology Breast Clinic of the Mayo Clinic, Rochester, Minnesota. Women were recruited between October 2006 and December 2007 and their ages ranged between 23 and 84 years. Healthy control women: range, 23–63; median, 44.5 years; high-risk women: range, 35–76; median, 57.3 years; women with breast cancer: range, 35–84; median, 57.8 years.The 40 healthy control women had not received a diagnosis of breast cancer at the time of their urine collection and had a calculated Gail Model score of <1.66%. Among the 40 high-risk women, their Gail Model scores were 1.67%–11.7%. The Gail Model is a validated tool developed by the National Cancer Institute The protocol was approved by the Mayo Clinic and UNMC Institutional Review Boards. Signed consents included authorization to collect and bank urine samples and collect demographic and clinical information.A spot urine sample of about 50 ml was collected from each participant and 1 mg/ml ascorbic acid was added to prevent oxidation of the catechol moieties in the various estrogen compounds. The urine samples were aliquoted, frozen, and four 10-ml aliquots were transferred to the Eppley Institute, UNMC, on dry ice and stored at −80 °C until analysis. Thus, each analytical sample was thawed only once prior to analysis.The SPE method development and validation were described earlier.1 and E2, however, the levels of estrogen metabolites, conjugates and adducts changed marginally and in many cases decreased because of the incubation for 8 h at 37 °C. Hence, to avoid artifacts and errors that are introduced by maintaining the urine samples at 37 °C for 8 h, we carried out all the analyses without treating the samples with glucuronidase/sulfatase. For this reason, the reported levels of E1 and E2 are 10–15 times less than the total values.7We previously found that the treatment of urine with glucuronidase/sulfatase led to significant increases (10–20 fold) in the levels of EAll experiments were performed on a Waters  Quattro Micro triple quadrupole mass spectrometer by using electrospray ionization (ESI) in positive ion (PI) and negative ion (NI) mode, with an ESI-MS capillary voltage of 3.0 kV, an extractor cone voltage of 2 V, and a detector voltage of 650 V. Desolvation and cone gas flow were maintained at 400 and 60 l/h respectively. Desolvation temperature and source temperature were set to 200 and 100 °C, respectively. For all the studies, a methanol:water (1:1) mixture with 0.1% formic acid was used as the carrier solution. The parent and daughter ion data obtained for all standard compounds were used to generate the multiple reaction monitoring (MRM) method for UPLC/MS-MS operation.72O) and 20% B (0.1% formic acid in acetonitrile), changed to 79% A over 4 min, followed by a 6-min linear gradient to 45% A, resulting in a total separation time of 10 min. The levels of compounds were normalized to the concentration of creatinine (Cr). The elutions from the UPLC column were introduced to the Quattro Micro mass spectrometer. Resulting data were processed by using QuanLynx software (Waters) to quantify the estrogen metabolites, conjugates and DNA adducts.7UPLC/MS analyses of estrogen-related compounds  in urinep-values were calculated using GraphPad Prism software V 4.03 .Median values were calculated for all estrogen compounds obtained from healthy control, high risk and breast cancer groups. Ratios were compared for healthy control vs. high risk and for healthy control vs. breast cancer using one-way ANOVA. Additional post hoc analysis was done for multiple comparisons using Dunn’s method. All the statistics and Using the SPE/UPLC/MS-MS methodology recently developed by our laboratory,1(E2)-6-N3Ade adducts to the total ratio was found to be insignificant, whereas the predominant contribution was from the 4-OHE1(E2)-1-N3Ade and 4-OHE1(E2)-1-N7Gua adducts.29The ratios in healthy control women are generally low . In contAs seen in our earlier study, analysis of the ratio using one-way ANOVA reveals a significant difference between the high-risk and breast cancer groups compared to the healthy control group (p < 0.001) . Additio1(E2), 4-OCH3E1(E2) and 4-OHE1(E2)-thiol conjugate  values were higher for healthy controls compared with high-risk subjects and breast cancer cases (1(E2)-1-N3Ade and 4-OHE1(E2)-1-N7Gua values were lower for healthy controls compared with high-risk subjects and breast cancer cases (1(E2), 2-OCH3E1(E2), and 2-OHE1(E2)-thiol conjugate values were higher for healthy controls (1(E2)-6-N3Ade values did not show any trend. The rest of the metabolites (Each sample was analyzed in triplicate and the mean values were calculated for all 40 compounds . Finallyer cases . In conter cases . Comparecontrols , while tabolites  are not In conclusion, these results confirm our previous finding of high ratios of depurinating estrogen-DNA adducts to their corresponding metabolites and conjugates in urine samples from both high-risk women and women with breast cancer, compared to lower ratios in healthy control women.The ratio of depurinating estrogen-DNA adducts to their metabolites and conjugates may be a urine biomarker to quantify an individual’s risk and determine appropriate risk reducing strategies. Another use of the urine assay could be to assess the ability of natural antioxidants to balance estrogen activation and deactivation, thereby reducing the risk of developing breast cancer.


Lactobacillus casei Shirota (BLS) and soy isoflavone consumption since adolescence affected the incidence of breast cancer. In a population-based case-control study, three hundred and six cases with breast cancer and 662 controls aged 40 to 55 were matched for age and residential area and included in the analyses. Diet, lifestyle and other breast cancer risk factors were investigated using the self-administered questionnaire and interview. Odds ratios (ORs) of BLS and soy isoflavone consumption for breast cancer incidence were independently and jointly estimated using a conditional logistic regression. The ORs of BLS consumption (≥ four times a week against < four times a week) was 0.65 and statistically significant (p = 0.048). The analysis of association between soy consumption and breast cancer incidence showed the more the isoflavone consumption is, the lower the odds of breast cancer becomes. Adjusted ORs for breast cancer in the second, the third and the fourth quartiles of soy consumption against the first quartile were 0.76, 0.53 and 0.48, respectively . The BLS-isoflavone interaction was not statistically significant; however, a biological interaction was suggested. Regular consumption of BLS and isoflavones since adolescence was inversely associated with the incidence of breast cancer in Japanese women.The purpose of this study is to evaluate how beverages containing  One of Lactobacillus casei Shirota  have been sold in Japan since 1935. According to the manufacturer’s data, 84% of BLS were sold through a unique personal home and office delivery system in 1985, and the product took up an estimated 50% or more of the Japanese fermented dairy product market in 1970s and 1980s.Beverages containing Lactobacillus casei Shirota was shown to prevent recurrence of colon polyp in a randomized trial [Lactobacillus casei Shirota and NK cell-mediated antitumor activity [A Japanese case-control study showed regular consumption of BLS was inversely associated with occurrence of bladder cancer . Lactobaed trial  and to ped trial , 8. The activity .The association between soy consumption and occurrence of breast cancer has been evaluated in a number of epidemiological studies. Studies in Japan showed that soy consumption was inversely associated with occurrence of breast cancer in a cohort study by Yamamoto and colleagues , and HirThe mechanism of soy to prevent breast cancer may be ascribed to estrogenic and anti-estrogenic actions of isoflavones such as genistein and daidzein contained in soy. In a nested case-control study conducted along with the Japanese cohort study , an inveOn the other hand, consumption of BLS was shown to affect the intestinal flora in a randomized double-blind study , suggestBased on these findings, we evaluated the role of probiotic beverages in breast cancer prevention in Japanese women with implication of soy isoflavone consumption in a population-based case-control study. The study is to explore the cause of the recent rapid increase of breast cancer in Japan and the possibility of breast cancer prevention by lifestyle modification.22.1This population-based case-control study took place in 14 areas in Japan. The catchment areas were selected so that both rural and urban areas were covered. Cases were defined as Japanese women aged 40 to 55 at the time of consent, who had undergone operation for the International Union against Cancer Tumor Node Metastasis (UICC-TNM) Stage 0 or I unilateral or bilateral primary breast cancer at one of 14 study centers within one year prior to participation. Women who had breast cancer of UICC-TNM Stage II or higher and those with other types of cancer were excluded.Controls were Japanese women living in the catchment areas aged between 40 and 55 without past or present breast cancer. Women aged 40 to 55 were first picked out from the catchment areas based on the Basic Resident Register, and potential controls were randomly selected. Once a case was included in the study, controls matched to the case for age (the range of target age was divided into two-year brackets) and residential area were randomly selected from the pool of candidate controls and invited to participate. Invitation letters were sent until two controls were selected for a case.The study was approved by the institutional review board of all study centers. Cases and controls were recruited between October 9, 2007 and March 31, 2009. Informed consent was obtained from all cases and controls enrolled in the study.2.2A self-administered questionnaire survey and interviews were conducted. To avoid recall biases, participants were asked about their past BLS and soy consumption in interviews. Frequency and duration of BLS consumption and frequency and amount of soy (six items including miso-soup and tofu) consumption during three predetermined time periods; around the age 10 to 12, around the age 20, and 10 to 15 years prior to the study; were asked in the questionnaire. We conducted a self-administered questionnaire survey to ask about their diet , exercise, medical history, and family history during the year before their breast cancer diagnosis to cases and during the year before the survey to controls. We used a self-administered food frequency questionnaire created based on the food frequency questionnaire developed by the Japan National Cancer Center (JNCC) , of whicCases and matched controls were interviewed by the same trained interviewer who was blinded to the case/control status. A set of self-administered questionnaire forms was handed to each participant at the time of consent. Completed questionnaire forms were kept by participants until the interview and checked by interviewers for omission.2.3BLS and isoflavone consumption in the three predetermined time periods was averaged for the purpose of analysis. BLS consumption was classified into regular consumption (≥ 4 times a week) and no consumption (< 4 times a week). Isoflavone consumption was calculated based on the frequency and the amount of the six food items and classified into quartiles according to the daily consumption in the control group: > 43.75 mg/day (the fourth quartile), 28.81 to 43.75 mg/day (the third quartile), 18.76 to 28.81 mg/day (the second quartile), and < 18.76 mg/day (the first quartile).2.4Based on a hypothesized OR of 0.55 and a 15% prevalence of exposure to BLS in the control group, the target sample size was calculated to be 355 for cases and 710 for controls to achieve a 80% power with a 5% two-sided type I error. We calculated ORs and 95% confidence intervals (CIs) using a conditional logistic regression model. P-values for potential dose-response of soy isoflavone consumption on breast cancer were calculated based on the same regression model using linear scores. We also performed a subgroup analysis of association between BLS consumption and breast cancer occurrence according to menopausal status.Cases and controls were matched for age and area of residence for one analysis and for area of residence alone for a separate analysis. Robustness of the results was also examined in a sensitivity analysis using an unconditional logistic regression model, which adjusted the matching variables as covariates. All p-values were two-sided.Education, physical activity level, history of benign mammary tumor, family history of breast cancer, past/current use of female sex hormones before menopause, age at menarche, number of childbirth, breastfeeding experience, birth weight, BMI at the age 20, smoking status, and current energy intake were taken into account as potential confounders.We used the SAS LOGISTIC procedure  for all analyses. 2.5The sponsor organized the study group under one of the projects of Comprehensive Support Project for Oncology Research (CSPOR) , 18. The31) presents a flow chart of the case and control recruitment process. One control was excluded because we later found out her mother had filled out the questionnaire forms and had been interviewed. Baseline characteristics of 968 participants, including two cases and 32 controls not matched for age due to some adjustment in the sampling procedure, are shown in (Table 1). Statuses of estrogen receptor, progesterone receptor and human epidermal growth factor receptor in cases are shown in (Table 2). Crude ORs for isoflavone consumption in the second, the third and the fourth quartiles against the first quartile were 0.74 , 0.58 (0.38–0.88) and 0.52 (0.34–0.79), respectively.  Adjusted ORs were 0.76 (0.52–1.13), 0.53 (0.35–0.81) and 0.48 (0.31–0.73), respectively .Crude ORs (matched for age and area of residence) and adjusted ORs (matched for area of residence and adjusted for the age and other confounding factors) for breast cancer in women who had consumed BLS ≥ four times per week against those who had consumed < four times per week were 0.66  and 0.65 , respectively. , benign tumor , and family history of breast cancer .2).According to an analysis using a multiplicative factor, the interaction between BLS and isoflavones was not statistically significant (p = 0.87) but there was a trend of weaker dose-response relationship between isoflavone consumption and breast cancer as observed from the flat curve in women who consumed more BLS .A subgroup analysis according to menopausal status (matched for area of residence and adjusted for age and other confounding factors) showed an adjusted OR in premenopausal women was 0.78  and that in postmenopausal women was 0.43   showed adjusted ORs of BLS consumption ≥ four times per week to BLS consumption < four times per week were 0.86 , 0.58  and 0.84 , respectively.4Strengths of this study include (1) robustness of data, which were mostly comparable across the sensitivity analyses with different adjustments for confounders, (2) identification of known risk factors such as family history of breast cancer and history of benign tumor, (3) being a population-based study enrolling participants from multiple areas in Japan, (4) smaller biases due to an interview survey compared with a self-administered questionnaire survey, and 5) successful interviewer blinding. The interviewers were asked whether they had found out the case/control identity of the interviewees during interviews. They answered they had been able to identify cases and controls in 29% of the interview sessions, and they were incorrect about the case/control identification in 24% of the time. Therefore, the blinding was considered successful. As the limitations of this study recognized the following: While validated questionnaire forms  were use successfL. casei Shirota inhibited carcinogenesis by enhancing the NK cell activity [Daily consumption of BLS since adolescence had a significant inverse association with early breast cancer occurrence. A significant inverse association was also seen between consumption of soy isoflavones and breast cancer occurrence. The results are consistent with those from a case-control study conducted by Hirose and colleagues  in whichactivity . Increasactivity  are bothactivity , 22. Recactivity , 24. TheSo far no prospective study in human has evaluated how BLS consumption changes the intestinal flora and equol production. Now intestinal flora can be identified using an inexpensive new technology that produces results quickly, which is based on the reverse transcription-quantitative polymerase chain reaction analysis of microbacterial rRNA in human feces . This teThe stronger inverse association shown in our main analysis is consistent with the results of epidemiological studies showing an association between soy consumption in adolescence and decrease in breast cancer risk , 27 as w5This population-based case-control study in Japanese women showed an inverse association between BLS consumption since adolescence and breast cancer occurrence. Soy isoflavone consumption was also inversely associated with breast cancer occurrence as shown in the previous studies. Despite the study design that did not allow to indicate the recommended amount of probiotic beverages and soy isoflavone for the prevention of breast cancer, our study results suggested the benefit of consuming these products. Further studies may be able to recommend the lifestyle modification with diet including consumption of BLS and soy isoflavone.


Objective. The objectives of the study were to detect high-risk areas and to examine how racial and ethnic status affect the geographic distribution of female breast cancer mortality in Texas. Analyses were based on county-level data for the years from 2000 to 2008. Materials and Methods. Breast cancer mortality data were obtained from the Texas Cancer Registry, and the Spatial Scan Statistics method was used to run Purely Spatial Analyses using the Discrete Poisson, Bernoulli, and Multinomial models. Results and Conclusions. Highest rates of female breast cancer mortality in Texas have shifted over time from southeastern areas towards northern and eastern areas, and breast cancer mortality at the county level is distributed heterogeneously based on racial/ethnic status. Non-Hispanic blacks were at highest risk in the northeastern region and lowest risk in the southern region, while Hispanics were at highest risk in the southern region along the border with Mexico and lowest risk in the northeastern region. Breast cancer is the most common cancer and second leading cause of cancer mortality in the United States and Texas . It is eRelatively few studies have been conducted to examine how racial and ethnic status affects the geographic distribution of breast cancer mortality, and no study has been conducted to investigate this at the county level in the state of Texas. Identification of geographic patterns of breast cancer mortality based on racial and ethnic status could provide impetus to conduct further investigations and target health resources for prevention and treatment in specific geographic areas. The Spatial Scan Statistic method developed by Kulldorff , KulldorP value 0.0004) was located in the southeastern portion of Texas, including these 38 counties—Aransas, Atascosa, Austin, Bastrop, Bee, Bexar, Brazoria, Brooks, Caldwell, Calhoun, Colorado, De Witt, Duval, Fayette, Fort Bend, Galveston, Goliad, Gonzales, Guadalupe, Harris, Jackson, Jim Hogg, Jim Wells, Karnes, Kenedy, Kleberg, La Salle, Lavaca, Live Oak, Matagorda, McMullen, Nueces, Refugio, San Patricio, Victoria, Waller, Wharton, Willacy, and Wilson. A secondary cluster  included only one county: El Paso. In this paper, we present results of spatial analyses using the Spatial Scan Statistic method on female breast cancer in the state of Texas using county-level female breast cancer mortality data for the years 2000 to 2008. The objectives of the study were to detect high-risk areas of breast cancer mortality through the Discrete Poisson model, investigate whether there are high-risk areas where the distribution of breast cancer mortality differs based on the racial/ethnic status of the population using the Multinomial model, and examine the spatial distribution of breast cancer mortality cases for non-Hispanic blacks (NHBs), Hispanics and other races compared to non-Hispanic whites (NHWs) using the Bernoulli model.A previous study published by Zhan and Lin in 2003  detectedhttp://seer.cancer.gov/codrecode/1969+_d09172004/index.html), and the SEER program has defined major site groups based on the ICD-10. The Texas Cancer Registry is a statewide, population-based registry that collects high-quality, population-based data reported from various sources, including hospitals, cancer treatment centers, ambulatory surgery centers, pathology laboratories, and physician's offices through active and passive surveillance. It currently meets standards set by the National Program of Central Cancer Registries, Centers for Disease Control and Prevention for high-quality data, and is Gold-Certified by the North American Association of Central Cancer Registries.The case definition for this study was a death due to malignant neoplasm of breast cancer (C50) as listed by the International Classification of Diseases, 10th Revision (ICD-10) in the female population of Texas for the years 2000 to 2008. The breast cancer mortality data at the county level for the years 2000 to 2008 were obtained from the Texas Cancer Registry, Cancer Epidemiology and Surveillance Branch, Texas Department of State Health Services. The Texas Cancer Registry classifies cancer mortality data according to the Surveillance, Epidemiology, and End Results (SEER) “Cause of Death Recode”, as given by the SEER Cause of Death Recode 1969+ (9/17/2004)  were attributed to NHW, 3,503 (15.4%) to NHB, and 3,770 (16.5%) to Hispanics. At-risk population data at the county level for Texas covering the same time period were obtained from the US Census Bureau. Geographic coordinate data  that represent the locations of the 254 Texas county polygons were specified by the 2000 US Bureau of the Census.We used Spatial Scan Statistics to examine the presence of breast cancer clusters. We ran Purely Spatial Analyses using 4 different models . For the Discrete Poisson model, we assumed that the number of deaths in each county was Poisson distributed and ran the model with and without adjustment of racial/ethnic status of the population. For the Bernoulli model, we classified breast cancer deaths among NHW as controls and breast cancer deaths among other racial/ethnic groups as cases, thereby creating 3 comparisons . For the Multinomial model, we classified breast cancer deaths into 4 racial/ethnic categories . The level of statistical significance used for this study was 0.05. We used the SaTScan v9.1.1 released on March 9, 2011 to run the Spatial Scan Statistics. The Spatial Scan Statistics detect high-risk areas of cases by gradually scanning a window across space and noting the number of observed and expected observations inside the window. The window sizes are varied continuously up to a prespecified maximum size. The most likely cluster (the cluster least likely to be due to chance) is assigned to a window with the maximum likelihood. We used the scanning window in the shape of a circle and specified the maximum window size as one that included 50% of the at-risk population throughout our analyses. We also report secondary clusters that cause a rejection of the null hypothesis  and do not overlap the most likely cluster. MapInfo Professional version 8.0 was used to create the thematic map based on detected counties by Spatial Scan Statistics.This study did not need to be approved by the Committee for the Protection of Human Subjects because it used aggregated data on county-level breast cancer mortality in Texas.Results of spatial analysis using the Discrete Poisson model without any covariate adjustment suggests that there were five statistically significant clusters of counties with a high rate of female breast cancer mortality in Texas for the years 2000 to 2008 . The mosResults of spatial analysis using the Multinomial model suggest that there were three statistically significant clusters of counties where the distribution of risk for female breast cancer mortality based on race/ethnicity is statistically significantly different from the remaining regions of Texas . The mosWe found several statistically significant clusters for female breast cancer mortality at the county level in Texas through Purely Spatial Analyses. Five significant clusters were found through the Discrete Poisson model without any covariate adjustment, while the same model after adjusting for racial/ethnic status detected two significant clusters with different geographic distributions. The Multinomial model detected three significant clusters with different distributions of risk based on racial/ethnic status. The Bernoulli model found one significant cluster for NHB versus NHW, while two significant clusters were detected for Hispanics versus NHW and another two for “other races” versus NHW.Zhan and Lin (2003) conducted spatial cluster analysis using the Discrete Poisson model without any covariate adjustment on female breast cancer mortality data for the years 1990 to 1997 and found two significant clusters . The mosWe found three significant clusters using Multinomial analysis where risk for breast cancer mortality differed based on racial/ethnic status. The cluster in the northeast area of Texas had significantly highest risk for NHB and lowest risk of Hispanics, while the cluster in the southern area of Texas had significantly highest risk for Hispanics and lowest risk for NHB. It is important to note that risk is not substantially different among racial/ethnic groups in the northern Texas cluster. Findings from the Bernoulli model analysis support the results of the Multinomial analysis, indicating that breast cancer mortality is heterogeneously distributed based on racial/ethnic status. NHB and Hispanics are more likely to be diagnosed with later stages of breast cancer –13 and hWe identified one cluster in northeast Texas that had higher risk of breast cancer mortality for NHB compared to NHW though the Bernoulli model analysis. The location of this cluster was the same as that found by the Multinomial analysis; however, the risk was slightly lower. This could be due to fact that we included other races in the Multinomial model analysis and breast cancer incidence and mortality have been observed to be lower among this group compared to NHW, NHB, and Hispanics. For example, cancer incidence and mortality per 100,000 were 51.7 and 8.6, respectively, among Asian/Pacific Islanders for the years 2001 to 2005, while these same rates were 125.4 and 24.3 among NHW, 116.2 and 35.6 among NHB, and 84.6 and 17.2 among Hispanics .Using Bernouli model analysis, we identified two clusters, one in the western part of the state and another along the border with Mexico, where Hispanics had significantly higher risk of breast cancer mortality compared to NHW. The most likely cluster was the same as that found by the Multinomial analysis; however, the risk was slightly lower. For “other races,” two significant clusters were detected, with each covering four counties around the Houston and Dallas metropolitan areas, respectively. The cluster around Houston had higher risk compared to the Dallas area. This might be related to the fact that the proportion of total population for other races in Houston was higher compared to Dallas.This type of cluster analysis at the county level can provide useful information to policy makers for the following reasons.The Department of State Health Services divides all Texas counties into Health Service Regions (HSRs), identified numerically from 1 to 11, to provide comprehensive public health services to the citizens of Texas through 8 regional public health offices  22]. In. In22]. Approximately half of Texas counties did not have accredited permanent mammography facilities in 2008 as reported in the Texas Cancer Facts & Figures, 2008 . MoreoveResults of our spatial analyses at the county level provide useful information to guide future spatial analyses at finer scales in Texas. Moreover, they are useful jumping-off points to conduct subcluster analyses at the county level or finer scales for a particular population group. For example, the census block or tract level analysis of female breast cancer mortality among NHB could be conducted in the HSRs 4, 5, 6, and 7 covering secondary cluster 2 identified through the Multinomial model  to identThere are several limitations to the present study. We used breast cancer mortality data aggregated at the county level, which could affect the sensitivity of cluster detection. The geographic distribution of total population and number of breast cancer mortality cases at the county level in Texas is heterogeneous, with some counties having much lower mortality than other counties. This could affect study power so that some potential clusters might be missed. Lack of significance for some secondary clusters might be due to this reason or it might be due to the fact that the test is conservative, that is, we compared secondary clusters with the most likely cluster from the simulated datasets. Results may not be comparative to other studies using data aggregated at different geographic scales. Also, we included only race/ethnicity as a confounder in our analyses, yet there are many other known or hypothesized risk factors for breast cancer that we did not analyze, such as age at diagnosis , family Results of our analyses indicate that breast cancer mortality at the county level in Texas is distributed heterogeneously based on racial/ethnic status. The evidence suggests that highest rates of female breast cancer mortality have shifted over time from southeastern areas towards northern and eastern areas of the state. In Texas, NHB had highest risk for breast cancer mortality in the northeastern region and lowest risk in the southern region, while Hispanics had highest risk in the southern region along the border with Mexico and lowest risk in the northeastern region. These findings, along with continuing trends toward urbanization, growing numbers of Hispanic residents, and increasing levels of poverty for many minorities, provide challenges and opportunities for Texas policy makers and health advocates. More research is needed to make informed decisions about effective and efficient distribution of health care resources to reduce breast cancer disparities for Texas residents.