P450 1B1 (CYP1B1) is active in the metabolism of estrogens to reactive catechols and of different procarcinogens. Several studies have investigated the relationship between genetic polymorphisms of CYP1B1 and breast cancer risk with inconsistent results. A G → C transversion polymorphism in the heme-binding region in codon 432 of the gene results in amino acid change ; the Leu allele display increased catalytic efficiency for 4-hydroxylation of estradiol in some experimental systems.Cytochrome In this study, we utilized a polymerase chain reaction (PCR)-based restriction fragment length polymorphism (RFLP) assay to assess the relationship between this polymorphism and breast cancer risk in a case control study including 250 women with breast cancer and 250 controls from four University Teaching Hospitals in Southern Nigeria.CYP1B1 M1 genotype  was associated with a significant 59% increased risk of breast cancer  while homozygosity for the genotype (CYP1B1 M1 [Leu/Leu]) conferred a non-significant 51% increased risk of breast cancer. These risk profiles were modified in subgroup analysis. In premenopausal women, harboring at least one CYP1B1 (Leu) allele conferred a significant two-fold increased risk of breast cancer . No significant association was observed in postmenopausal women .Heterozygosity for the CYP1B1 gene is associated with increased risk of breast cancer and is particularly involved in breast cancer risk in premenopausal women of African descent.Our results suggest that the codon 432 polymorphism of the  CYP1B1 gene is located on chromosome 2p21-p22 and contains three exons [CYP1B1 might be involved in hormonal carcinogenesis through its ability to catalyze the metabolism of estradiol to 2-hydroxyestradiol and 4-hydroxyestradiol [Breast cancer has unique racial/ethnic variations in incidence and burden. While the incidence is rising globally, the increase is occurring faster in population groups that hitherto enjoyed low incidence. Reasons for the racial/ethnic variation in breast cancer incidence are unclear; however, differences in the distribution of polymorphisms in key candidate genes involved in estrogen/xenobiotic metabolism might contribute to variation in breast cancer susceptibility in different populations. The ee exons . The entee exons ; exon 3 ee exons . CYP1B1 stradiol . The forstradiol  and humastradiol ,8. In-sistradiol  which reCYP1B1 gene have been described in the Caucasian populations, of which four results in amino acid substitutions [CYP1B1 gene, we have chosen in this exploratory study to evaluate the role of the CYP1B1 Val432Leu polymorphism in breast cancer susceptibility in Nigerian women since there is experimental evidence suggesting that the variant CYP1B1 432Leu allele exceeded the wild-type CYP1B1 432Val allele with respect to estrogen hydroxylation activities. Given the carcinogenic and estrogenic potential of 4-OH-E2, it is plausible to speculate that inheritance of the CYP1B1 432Leu allele may contribute to increased breast cancer risk associated with estrogen-mediated carcinogenesis.Six polymorphisms of the itutions ,4. Two oitutions . The coditutions -14 and fitutions -18. TherStudy participants were recruited between September 2002 and April 2004 in four University Teaching Hospitals in Southern Nigeria including University of Benin Teaching Hospital, Benin City; Nnamdi Azikiwe University Teaching Hospital, Nnewi; University of Nigeria Teaching Hospital, Enugu; and University of Port Harcourt Teaching Hospital, Port Harcourt. The Institutional Review Board of University of Pittsburgh and the Ethics and Research Committees of the Nigerian institutions approved the study prior to commencement. A total of 500 study participants comprising 250 women with incident and prevalent breast cancer and 250 age- and institution-matched controls were recruited for the study. Women with confirmed breast cancer were recruited during surgical out-patient clinic visits or in-patient admissions while control subjects with non-malignant surgical diseases such as road traffic accident and other injuries (n = 168), intestinal obstructions (n = 49), appendicitis/pelvic inflammatory disease (26), urolithiasis and urinary tract infections (n = 5) and cholelithiasis (n = 2) were recruited from the same hospitals. Exclusion criteria include non-confirmation of diagnosis of breast cancer, a diagnosis of other malignant diseases, and refusal of donation of blood samples.Prior to recruitment, participants signed informed consent after detailed explanation of key points of the study including study objectives, risks and benefits, confidentiality and the rights of participants. Interviewer-administered questionnaires were used for data collection; questions were designed to gather demographic history including age, sex, religion, occupation, exposure to chemical fertilizers and pesticides, and rearing of domestic animals. In addition, obstetric and gynecological history including age at menarche, age at first full-term pregnancy, parity, breastfeeding, age at menopause , history of use of hormonal contraceptives and hormone replacement therapy and surgical oophorectomy was obtained. Information about lifestyle habits such as cigarette smoking and alcohol consumption was also obtained from study participants. Anthropometric measurements including height, weight, waist and hip circumferences were taken at the end of the interview.3-EDTA vacutainer tube from each of the study participants and stored in ice packs until it was centrifuged within 10 h of collection and buffy coats collected and stored in 3 ml tubes. All the samples were stored at -20°C in the various study sites in Nigeria and later transferred to the Nigerian coordinating center at the University of Benin Teaching Hospital in Polar Pack -20 C ice packs for frozen shipments and later shipped to University of Pittsburgh in dry ice using express services. Samples were stored at -80°C at the University of Pittsburgh until DNA extraction.Blood samples were collected at the end of the interview; details are reported elsewhere ,20; 10 mDNA extraction was carried out using QIAamp DNA Mini Kits (for buffy coats) and QIAamp DNA Midi Kits (for blood clots) protocols . The extracted DNA was stored at 4°C until used for PCR and RFLP analysis.CYP1B1 gene, including part of exon 3 that contains the polymorphism was carried out using forward primer: TCACTTGCTTTTCTCTCTCC and reverse primer: AATTTCAGCTTGCCTCCTG. A 50 μl PCR reaction mixture containing 2 μl of genomic DNA, 5 μl of deoxynucleotide triphosphates, 5 μl each of forward and reverse primers, 5 μl of 10× buffer, 1.5 μl of MgCl2 and 0.5 μl of Taq polymerase was placed in a thermalcycler. After denaturing for 10 min at 95°C, the DNA was amplified for 35 cycles at 95°C for 60 s, 58°C for 60 s, and 72°C for 60 s, followed by a 7 min extension at 72°C. A positive control containing genomic DNA and a negative control containing everything except DNA were included in the PCR experiment. Five μl of each PCR product, including the controls, were verified on a 2% agarose gel to ensure that the expected 650 bp product was generated.Genomic DNA from the cases and control subjects were analyzed for the presence of the G to C transversion mutation at codon 432 of the CYP1B1 gene by a PCR-based Restriction Fragment Length Polymorphism (RFLP) assay. PCR amplification of a 650 bp fragment of the CYP1B1-codon 432) generated a unique 650 bp fragment, while the 650 bp fragment was divided into unique 340 bp and 310 bp fragments when position 1294 contains a C. The gels were visualized by UV light and the RFLP gel electrophoresis products were read by two independent persons who were unaware of the identities of samples as either cases or controls.Restriction digest for the DNA fragment was carried out using Eco57I restriction enzyme. Fifteen μl of the PCR product was digested for 16 h overnight at 37°C with 1 unit of Eco57I (New England Biolabs). The product of the restriction digest was mixed with 10 μl of loading dye and verified on a 3% agarose gel (with Ethidium bromide) electrophoresis in a 1× Tris-Borate-EDTA buffer at 200 V for 60 min. The presence of a G at position 1294  software (Version 8.0). Conditional logistic regression was used to assess the association between the CYP1B1 gene were successful. Mean age of cases and control subjects were similar (46.3 ± 11.72 years and 47.3 ± 12.14 years respectively). Using univariate logistic regression models, the following variables including family history of breast cancer, education, ever married, age at fullterm pregnancy (FFTP), parity, duration of breastfeeding, abortion, use of hormone contraceptives, waist/hip ratio, and body mass index (BMI) were found to be significant predictors of breast cancer risk. However, only three variables including Family history of breast cancer , age at first fullterm pregnancy greater than 20 years  and waist/hip ratio  were significant predictors of breast cancer risk in the final multiple logistic regression model as shown in Table Five hundred participants comprising 250 women with breast cancer and 250 age- and institution-matched controls were recruited from four University Teaching Hospitals in Midwestern and Southeastern Nigeria. However, this report is based on data on 228 cases and 226 control subjects in whom RFLP polymorphism assays on CYP1B1  allele was less frequent in cases (0.86) compared to control subjects (0.89). The distribution of the CYP1B1 genotype is shown in Table CYP1B1 , CYP1B1  and CYP1B1 (Leu/Leu) in the cases were 0.73, 0.25, and 0.02, respectively while the corresponding frequencies in the control subjects were 0.81, 0.17, and 0.02, respectively. The distribution of CYP1B1  alleles in the control subjects was in Hardy-Weinberg equilibrium, overall and in both premenopausal and postmenopausal women.The CYP1B1  allele was less frequent among premenopausal cases (0.85) compared to the control subjects (0.92). As shown in Table CYP1B1  and CYP1B1 (Leu/Leu) genotypes were more common in cases compared with the control subjects.Among 142 premenopausal breast cancer cases and 142 premenopausal control women, the RFLP polymorphism assays were successful in 125 cases and 129 controls. The CYP1B1  and CYP1B1 (Leu) alleles in postmenopausal breast cancer cases and controls were similar. There were slight differences in the frequency of the CYP1B1 , CYP1B1  and CYP1B1 (Leu/Leu) genotypes among cases and control subjects, as shown in Table Of the 108 postmenopausal breast cancer cases and 108 postmenopausal control subjects, the PCR-based RFLP assays were successful in 103 cases and 97 control subjects. The distribution of the CYP1B1  genotype was associated with a significant increased risk of breast cancer , compared with the homozygous wild type CYP1B1  genotype. There were very few study subjects with the homozygous variant CYP1B1 (Leu/Leu), four among the cases and five among the control subjects; this genotype was not associated with breast cancer .As shown in Table CYP1B1 1B1  genotype in all women . The risk associated with the CYP1B1 (Leu/Leu)  and the combined CYP1B1  and CYP1B1 (Leu/Leu) genotypes  remained essentially unchanged.Adjustment for waist/hip ratio (WHR) slightly attenuated the risk associated with the CYP1B1  genotype was associated with a significantly increased risk of premenopausal breast cancer  did not reach significance . The risk of premenopausal breast cancer with the heterozygous CYP1B1  and homozygous CYP1B1 (Leu/Leu) genotypes combined was 2.04 (95% CI 1.10–3.78)  allele was not associated with risk of breast cancer in postmenopausal women . Controlling for WHR did not significantly alter the risk profiles  to 4-hydroxy catechols might contribute to breast cancer risk in Nigerian women.Breast cancer is a disease with unique phenotypic manifestations in different racial/population groups. It has been hypothesized that these population-specific characteristics of breast cancer may be partly due to differences in genetic susceptibility to the disease arising from variation in the frequency of different polymorphic alleles of key candidate genes involved in estrogen and xenobiotic metabolism in different populations. Our study was designed to evaluate the hypothesis that the CYP1B1 Val432Leu polymorphisms in the CYP1B1 codon 432 Val → Leu polymorphism across different populations groups. The frequency of the CYP1B1  allele among control subjects in our study (0.89) is closer to the frequency in African-Americans (0.70) [Comparison of our data with reports from other populations indicates wide variation in the distribution of the study 0.8 is closes (0.70) , Japan (s (0.70) , and Kors (0.70) .CYP1B1 (Leu) allele was significantly associated with breast cancer . Subgroup analysis based on menopausal status showed that the risk conferred by this polymorphism was essentially restricted to premenopausal women in whom the combination of CYP1B1  and CYP1B1 (Leu/Leu) genotypes was associated with over 2-fold increased risk of breast cancer . This association was not confirmed in postmenopausal women. The associations were not significantly modified by the adjustment of the data for waist/hip ratio  in either premenopausal or postmenopausal women, despite the finding of preferential 4-hydroxylation of estrogens in obese women on high fat diet [Our results suggest that harboring one fat diet .CYP1B1 polymorphisms and breast cancer in different populations. There appear to be no significant overall association between the CYP1B1 Val/Leu variant and breast cancer risk in Caucasian populations in a recent meta-analysis [CYP1B1 Val432Leu polymorphism and breast cancer risk [CYP1B1 Val/Val polymorphism was reached in two studies [2 . Although Bailey et al. [CYP1B1 Val432Leu polymorphism and breast cancer risk, they noted that Caucasian patients with the Val/Val genotype had a significantly higher percentage of breast cancer that were positive for estrogen receptors (ERs) or progesterone receptors (PRs), suggesting that this polymorphism may be functionally important for the expression of these steroid receptors.Several other investigators have evaluated the relationship between analysis . Howeveranalysis . Among tcer risk ,14, whilcer risk ,11,13; ocer risk . StatistCYP1B1 Val432Leu polymorphism and breast cancer risk [CYP1B1 Val432Leu polymorphism in breast cancer susceptibility in these populations [A small hospital-based case-control study involving 59 African-American women found no statistically significant association between the cer risk -27, one cer risk  reportedcer risk ,27 reporulations .CYP1B1 Val432Leu polymorphism and breast cancer risk failed to demonstrate an overall significant association in Asian women [Meta-analysis of studies on the association between an women . Of the an women ,16,26 foan women  reportedCYP1B1 Val432Leu polymorphism in different populations may be due to several reasons including differences in frequency of Val and Leu alleles in different populations, the relatively small sample size of some studies [The differences in breast cancer risk associated with the  studies ,13,25,26CYP1B1 might influence breast cancer risk are not completely known. CYP1B1 is expressed constitutively in extrahepatic tissues including lung and mammary tissue [CYP1A2 and CYP3A4, are involved in hepatic and extrahepatic estrogen hydroxylation, CYP1A1 and CYP1B1 display the highest levels of expression in breast tissue [CYP1B1 exceeds CYP1A1 in its catalytic efficiency as an estradiol (E2) hydroxylase, and differs from CYP1A1 in its main site of catalysis [CYP1B1 has its primary activity at the C-4 position of estradiol (E2), whereas CYP1A1 has its primary activity at the C-2 position. These two metabolites differ greatly in their carcinogenicity. Treatment with 4-OH-E2, but not 2-OH-E2, induced renal cancer in Syrian hamster [2 significantly increased 8-hydroxydeoxyguanosine levels, whereas 2-OH-E2 did not cause oxidative DNA damage [2 induced DNA single-strand breaks whereas 2-OH-E2 had a negligible effect [2-3,4-quinone produced two to three orders of magnitude higher levels of depurinating adducts than E2-2,3-quinone. Significantly higher 4-OH-E2/2-OH-E2 ratios were observed in breast tumor tissue than in adjacent normal breast tissue [CYP1B1 as a key player in the process.The mechanisms through which polymorphisms in y tissue . Althougt tissue ; CYP1B1 atalysis ,29,30. C hamster ,31. AnalA damage . Similare effect . Comparit tissue . These fCYP1B1 Val432Leu polymorphism and breast cancer risk conducted in African populations.To the best of our knowledge, ours is the largest case-control study on CYP1B1 Val → Leu polymorphism and hormone receptor status noted in Caucasian women [CYP1B1 (Leu) allele. The non-availability of facilities for hormone receptor assays during the recruitment of study participants in the Nigerian study sites limited our ability to actually evaluate the presence of such interaction if any.The epidemiologic literature on determinants of breast cancer in sub-Saharan African populations is scanty despite the evidence that breast cancer is already a public health problem in these developing countries ,36 and tan women . The obsCYP1B1 Val → Leu polymorphism in premenopausal breast cancer risk in Nigerian women. This polymorphism might be mediating its effect through increased metabolic conversion of estradiol, the main estrogen in premenopausal women, to 4-hydroxy estradiol and estrogen quinone and semiquinone intermediates. The results we report in this study need to be considered in conjunction with clinical and epidemiological information. In fact, breast cancer is predominantly a premenopausal disease in Nigerian women [CYP1B1  allele among postmenopausal women with hormone receptor positive breast cancer [CYP1B1 (Leu) allele suggest that breast cancer in sub-Saharan African populations may have a different etiopathogenesis than in Caucasian or Asian populations. The understanding of differences in estrogen and xenobiotic metabolism resulting from polymorphic variants in key candidate genes, and their interaction with environmental exposure has the potential to considerably improve our ability to characterize individual risk of breast cancer and enhance our ability to design individual and population-specific control and preventive measures.This study has demonstrated a role for the an women , with a an women . These dCYP1B1: Cytochrome P4501B1 gene; DNA: Deoxyribonucleic acid; PCR: Polymerase chain reaction; SNP: Single Nucleotide Polymorphism; CYP1A2: Cytochrome P4501A2 gene; CYP3A4: Cytochrome P4503A4 gene; E2: 17β-estradiol; 2-OH-E2: 2-Hydroxy-Estradiol; 2-OH-E2: 4-Hydroxy-Estradiol; ER: Estrogen receptor; PR: Progesterone receptor; SAS: Statistical Analysis Systems.The authors declare that they have no competing interests.MNO, CHB, ET, SJG, REF, LHK, participated in conceptualization, design of the study and preparation of manuscript; MNO, ERE, SNCA, JO and EEOU recruited study participants from Nigeria and organized the transfer of biological samples to the University of Pittsburgh; MNO, CHB, ET, SJG and JMZ carried out the genetic analysis and manuscript preparation.


The potential association between hypo- and hyperthyroid disorders and breast cancer has been investigated in a large number of studies during the last decades without conclusive results. This prospective cohort study investigated prediagnostic levels of thyrotropin (TSH) and triiodothyronine (T3) in relation to breast cancer incidence in pre- and postmenopausal women.P-value < 0.05 as statistically significant. All analyses were repeated for pre- and peri/postmenopausal women separately.In the Malmö Preventive Project, 2,696 women had T3 and/or TSH levels measured at baseline. During a mean follow-up of 19.3 years, 173 incident breast cancer cases were retrieved using record linkage with The Swedish Cancer Registry. Quartile cut-points for T3 and TSH were based on the distribution among all women in the study cohort. A Cox's proportional hazards analysis was used to estimate relative risks (RR), with a confidence interval (CI) of 95%. Trends over quartiles of T3 and TSH were calculated considering a P-trend: < 0.001). There were no such associations in pre-menopausal women, and no statistically significant interaction between T3 and menopausal status. Also, no statistically significant association was seen between serum TSH and breast cancer.Overall there was a statistically significant association between T3 and breast cancer risk, the adjusted RR in the fourth quartile, as compared to the first, was 1.87 (1.12 to 3.14). In postmenopausal women the RRs for the second, third and fourth quartiles, as compared to the first, were 3.26 (0.96 to 11.1), 5.53 (1.65 to 18.6) and 6.87 (2.09 to 22.6), (This is the first prospective study on T3 levels in relation to breast cancer risk. T3 levels in postmenopausal women were positively associated with the risk of breast cancer in a dose-response manner. Thyroid disorders and breast cancer both have a postmenopausal peak incidence, and a potential association between hypo- and hyperthyroid disorders and breast cancer has been investigated in a large number of studies during the last decades -19. HoweExperimental studies have shown that thyroid hormones can have estrogen-like effects in breast cancer, and that thyroid hormone receptors influence both normal breast cell differentiation and breast cancer cell proliferation ,3. SeverIt is, however, difficult to conclude from cross-sectional studies whether differences in thyroid hormone levels are associated with different risks of breast cancer, or if breast cancer itself alters thyroid hormone levels. To date, there is only one prospective cohort study on this issue, involving 61 breast cancer cases, where pre-diagnostic levels of TSH and T4 were related to subsequent risk of breast cancer . The preThe aim of the present study was to investigate prediagnostic serum levels of TSH and T3 in relation to breast cancer incidence in pre- and peri/postmenopausal women, respectively.Originally, 10,902 women participated in the Malmö Preventive Project. The project was established in 1974 when residents in Malmö, a city in southern Sweden, were invited to participate in a health survey. Entire birth cohorts, men and women, were examined until 1992 when the department closed. Approximately 70% of invited subjects participated.All participants answered a questionnaire concerning socio-demographic information, lifestyle habits, and medical history. Questions on reproductive factors, use of oral contraceptives (OC), and hormonal replacement therapy (HRT), were only included in women screened from April 1983 and onwards . There was no information on type of HRT. Body mass index (BMI) (kg/m2) was assessed by a trained nurse on baseline examination. A subject was considered to have a previous history of goiter if the question 'have you been treated for goiter' was answered with 'yes'. There was no available information on type of treatment.The participants were initially part of a preventive health care project. All former participants were informed about the present study by newspaper as required by the local ethical committee. All participants were offered to be excluded from the present study.Blood samples were taken after an overnight fast with the patient in the supine position. The serum samples were analyzed for T3 and TSH in women born in 1928 and 1941 and examined in 1983 and 1984. In women born in 1935 (examined from 1990 to 1992), TSH levels were measured in all, but T3 was only measured in a sub-set of all women. In women born in 1935, T3 was analyzed in those with pathological TSH values, a history of thyroid disease, or those with an enlarged thyroid gland at examination. In addition to this, the attending physician could also decide to analyze T3. The basis for the decision to analyze T3 in an individual subject was not recorded systematically.T3 was measured by a double anti-body radioimmunoassay  . Only siAmong 10,902 women, reproductive data including menopausal status had been assessed in 8,051 subjets. Serum TSH had been measured in 2,944 of these . Those with prevalent breast cancer (n = 46), goiter (n = 196) or both of these conditions (n = 6) were identified and excluded from the study.Finally, 2,696 women constituted our study population. Information on T3 was not available for all women born in 1935, and T3 had been assessed in 2,185 out of all 2,696 women. The present study was approved by the ethical committee at Lund University, Sweden: Dnr 652/2005 and Dnr 501/2006.in situ, were retrieved up until 31 December 2006 by record linkage with The Swedish Cancer Registry (until 31 December 2005) and, due to a one-year delay in registration, also from its regional branch, The Southern Swedish Regional Cancer Registry (cases diagnosed in 2006). Among 2,696 women, there were 173 incident breast cancer cases. Information on vital status was retrieved from the Swedish Cause-of-Death Registry.Breast cancer cases, invasive and Mean follow-up was 19.3 (5.08) years and total follow-up was 51,989 person-years.Quartile cut-points for T3 and TSH were based on the distribution among all women in the study cohort. Due to the change of method of analysis of TSH, separate cut-points were used for samples analyzed before and after this change.P-value < 0.05 was considered as statistically significant.Each woman was followed until the end of follow-up, 31 December 2006, or until she got breast cancer or died. The incidence of breast cancer per 100,000 person-years was calculated in different quartiles of T3 and TSH. A Cox's proportional hazards analysis was used to estimate corresponding relative risks (RR) with a confidence interval (CI) of 95%. Possible confounding factors, that is, established and potential risk factors for breast cancer, were introduced as covariates in a subsequent analysis. Age was entered as a continuous variable and all other factors were entered as categorical variables classified as in Tables P-value <0.05 was considered indicative of a statistically significant interaction.Breast cancer may be associated with different risk factors in peri/postmenopausal versus premenopausal women. Furthermore, both thyroid conditions and breast cancer have been associated with BMI ,22. FollIt has been reported that the relation between TSH and T3/T4 may be disturbed in breast cancer patients, hence the presence of undiagnosed breast cancer may affect thyroid hormone levels ,23,24. CSince T3 was not measured in the whole group born in 1935, the analysis of T3 was repeated including only women born in 1928 and 1941.Due to the potential effect of estrogens on thyroxin binding globulin (TBG) concentration, and hence the levels of T3, an additional analysis was executed excluding women treated with HRT or OC.Advanced age, peri/postmenopausal status, use of OC, HRT and high BMI, were more common in high T3 quartiles Table . With reP-value for trend < 0.001). No such associations were seen in premenopausal women. There were no statistically significant interactions between menopausal status and T3 (P: 0.48) or TSH (P: 0.83).Overall, the risk of breast cancer was statistically significantly higher in the fourth T3 quartile as compared to the first quartile , as compared to the corresponding RR in women with BMI below 25, 1.31 (0.72 to 2.41) (other data not shown).Adjusted RRs were very similar to crude RRs with regard to both T3 and TSH. When women who developed breast cancer within three years after screening were excluded from the analyses, all results were similar for T3, but for TSH in premenopausal women, the risk in the second quartile was even stronger Tables .The analyses of combinations of low and high T3/TSH levels showed a statistically significant association for crude RR in the highT3/highTSH groups, as compared to the low/low category (1.62: 1.01 to 2.60). The association was, however, not statistically significant in the adjusted analysis (1.63: 0.99 to 2.69). The data were also stratified for TSH, below and above median with regard to T3 quartiles. There was a statistically significant risk for breast cancer in the highest T3 quartile (2.69: 1.19 to 6.08) in women with a TSH above the median. The corresponding RR in women with low TSH levels was 1.62 (0.68 to 3.86). There was no statistically significant interaction between T3 and TSH in the whole cohort, in premenopausal or peri/postmenopausal women.When the analyses were repeated, excluding the women born in 1935, all results were similar, crude RR = 5.56 (1.66 to 18.57) for postmenopausal women in the highest T3 quartile (other data not shown).Also, the analyses were repeated excluding women treated with HRT and OC, and the results were similar, crude RR = 6.50 (1.99 to 21.2) for postmenopausal women in the highest T3 quartile (other data not shown).This is the first prospective study on T3 levels in relation to breast cancer risk. It shows that T3 levels in postmenopausal women are positively associated with the risk of breast cancer in a dose-response manner.It may be asked whether breast cancer cases in this cohort are representative of the whole breast cancer population. This cohort mainly comprised middle-aged women and 70% of the women invited to the health examination attended. As we have no information about exposure to the studied risk factors in women outside this cohort, observed incidence rates may not be applicable to all age groups or to the general population. However, as there was a wide distribution of T3 and TSH levels, it was possible to make internal comparisons between subjects with low and high values, respectively. We consider that our estimations of relative risks were not considerably affected by selection bias.Incomplete follow-up may affect the results. However, the Swedish Cancer Registry and the Swedish Cause-of-Death Registry have been validated and found to have a completeness of about 99% .Subjects with elevated T3 levels and a possible diagnosis of hyperthyroidism may already be within the health care system and due to this the diagnosis of breast cancer might be established earlier than for euthyroid subjects. However, general mammography screening was introduced in Sweden already in 1986 and stands for the majority of detection of breast cancer cases . It is uThe method of analysis for T3 remained the same throughout the study. Not all women in the study population had information on T3, but our analyses showed no difference in risk of breast cancer in relation to T3 when the group screened in 1990 was excluded. Hence, there was probably no major selection bias due to the inclusion of only some subjects with information on T3 in the later period.The method of analyses for TSH changed between 1983 and 1990 as did the reference values. This was taken into account in our statistical analysis.There is a well known circadian and seasonal variation concerning T3 and TSH levels . TrackinThe use of total T3 as a marker of T3 status involves an important methodological aspect. Most T3 in the circulation is bound to three transport proteins, thyroxin binding globulin (TBG), transthyretin and albumin. An increased TBG concentration leads to higher levels of total T3, and this is important to consider in studies of breast cancer risk, as exogenous estrogens, that are risk factors for breast cancer, that is, HRT and OC, led to increased TBG binding capacity . In ordegoiter, which probably limited the interference with the results in the present study. However, the current material did not allow any assessment of the risk of breast cancer following thyroxin therapy.In the questionnaire used to collect data from the participants, there was no information on co-morbidities or medication which may affect the thyroid function, for example, propanolol, salicylates and phenytoin. This is a limitation in the study, but as there is no known association between such factors and breast cancer risk, the effect from uncontrolled confounding was probably small. There was no information on thyroxin use specifically in the questionnaire, which is another limitation. This was handled by excluding women who affirmed any treatment for The current study is the largest prospective study to date on T3/TSH levels and breast cancer risk, but the number of cases was limited. Confidence intervals were wide and the statistical power relatively low. This led to an imprecision in the estimates related to T3, and there is a risk that the lack of association between TSH and breast cancer was due to a type II error.et al., including 1,136 breast cancer cases, reported that women with previous hypothyroid conditions had an increased risk of breast cancer [et al. used the SEER registry and found an increased risk of breast cancer in women with a previous diagnosis of thyroid cancer [A large number of epidemiological studies have been performed during the last 50 years on benign thyroid conditions in relation to breast cancer -14,30-32t cancer . An indid cancer . These tet al. examined 150 cases and 100 controls, and the results indicated that high T4 levels were associated with breast cancer [Some previous cross-sectional studies have found that high T3 levels are positively associated with breast cancer ,5,7, amot cancer ,24,34 ant cancer  and breat cancer . Finallyt cancer ,35,36, tt cancer , but thet cancer . Yet, mot cancer -40. One t cancer ,40. It it cancer . Indeed,t cancer . In our The presence of thyroid hormone receptors in human breast and breast cancer cell lines has been established -45, and It has been shown that T3 binds and stimulates the estrogen receptor, acting in synergy with estrogen on breast cancer cell lines, potentiating the estrogenic effect and enhancing cell proliferation . The rolObesity is associated with increased breast cancer risk in postmenopausal women . EstrogeThe significant positive association between T3 and breast cancer is specifically strong in postmenopausal women. Therefore, an imbalance between estrogen and T3 with an increased T3/E2 ratio may be more important than a pure synergistic effect between these two hormones for the risk of developing breast cancer. It has been suggested that this imbalance might enhance breast cancer development . This thIn conclusion, the present prospective cohort study, the first of its kind on prospectively measured T3 levels and breast cancer risk, indicates that high T3 levels in postmenopausal women are positively associated with the risk of breast cancer in a dose-response manner.BMI: body mass index; CI: confidence interval; E2: estrogen; HRT: hormone replacement therapy; IRMA: imunnoradiometric analysis; OC: oral contraceptives; RR: relative risk; T3: triiodohyronine; T4: thyroxin; TBG: thyroxin binding globulin; TPOab: thyroid peroxidase antibodies; TSH: thyrotropin.The authors declare that they have no competing interests.AT and JM made substantial contributions to the conception and design of the study. AT, JM and UBE contributed to the analysis of and interpretation of data. UBE and JM contributed to the acquisition of data. All authors were involved in drafting the manuscript and revising it critically for important intellectual content. All authors read and approved the final manuscript.


We studied 200 cases of cancer of the oral cavity and pharynx, of whom 57 women (median age = 64) and 200 hospital controls, frequency matched with cases by age and sex, in relation to smoking and drinking history, intake of 25 foods or food groups, indicators of oral hygiene and sexual activity, and history of sexually transmitted diseases. Odds ratios (OR) and 95% confidence intervals (CI) were obtained from unconditional multiple logistic regressions and adjusted for age, sex, area of residence, education, and smoking and drinking habits. In the multivariate model, high educational level and white-collar occupation, but not white race, were associated with halving of oral cancer risk. Smoking ≥30 cigarettes per day showed an OR of 20.8 (95% CI: 8.9–48.3), similar to smoking ≥4 cigars daily (OR = 20.5). Drinking ≥ 70 alcoholic drinks per week showed an OR of 5.7 (95% CI: 1.8–18.5). Hard liquors were by far the largest source of alcohol. Increased risk was associated with the highest tertile of intake for maize (OR = 1.9), meat (OR = 2.2) and ham and salami (OR = 2.0), whereas high fruit intake was associated with significantly decreased risk (OR = 0.4). Among indicators of dental care, number of missing teeth and poor general oral condition at oral inspection showed ORs of 2.7 and 2.6, respectively. Number of sexual partners, marriages or contacts with prostitutes, practice of oral sex and history of various sexually transmitted diseases, including genital warts, were not associated with oral cancer risk. 82% of oral cancer cases in Cuba were attributable to tobacco smoking, 19% to smoking cigars or pipe only. The fractions attributable to alcohol drinking (7%) and low fruit intake (11%) were more modest. Thus, decreases in cigarette and cigar smoking are at present the key to oral cancer prevention in Cuba. © 2001 Cancer Research Campaign


Up to 40% of referrals from primary care to ‘breast cancer family clinics’ prove to be of women whose assessed risk falls below the guidelines' threshold for management in secondary or tertiary care, despite recommendations that they should be screened out at primary care level. A randomised trial, involving 87 such women referred to the Tayside Familial Breast Cancer Service compared two ways of communicating risk information, letter or personal interview. Both were found to be acceptable to referred women and to their family doctors, although the former expressed a slight preference for interview. Only four women returned to their family doctors with continuing concerns about breast cancer. Nevertheless, understanding of information provided by either route was unsatisfactory, with apparent confusion about both absolute and relative risks of breast cancer. Substantial minorities appear to believe that they are at no increased risk at all, or even below the population level of risk, while others remain convinced that their personal risk has been underestimated. Family history record forms, completed by the referred women, preferably with the assistance of relatives, are crucial to full assessment of familial risk but one quarter of women referred to the Tayside Familial Breast Cancer Service currently do not complete and return these forms ahead of their clinic appointment. Further collaboration between primary care and the Breast Cancer Family Service is required to improve provision for concerned women whose risks fall below the threshold for special surveillance and to maximise effective use of the family history record form. Guidelines published in the UK recommend that women concerned about a family history of breast cancer should be assessed first in a primary care setting and only those whose risk exceeds a specified threshold should be referred to specialist services for counselling, screening and possible intervention . In real‘convey to individuals, especially those at low risk, accurate information in a sensitive and supportive manner’ must still be met  has been a multidisciplinary service run and staffed jointly by the Departments of Genetics, Breast Surgery and Radiology. Before this study began, all women referred to the TBFCC were offered an appointment, even if the family history appeared to place them at ‘low’ risk. When that assessment was confirmed, no further follow-up would be arranged, although clinical examination (sometimes supplemented by mammography), was offered before discharge. Inappropriate inclusion of these ‘lower risk’ women in surveillance programmes probably does not represent cost-effective use of limited resources . All referred women also received a standardised Family History Record form with a request to complete it as far as possible, preferably in consultation with relatives, and to return it as the first stage in their risk assessment. That form, together with the GP referral letter, augmented as appropriate in each case (and with relevant informed consent) by checking hospital records, Cancer Registry entries and Registrar General's Records of Births, Marriages and Deaths, provided the basis for a consensus decision, taken by the specialist genetics staff, whether to offer an appointment to the multi-disciplinary counselling/surveillance clinic. Enrolment in the trial thus required written informed consent, a completed Family History Record form and a clear decision that familial risk was below threshold level.Women who met these criteria were randomised by a genetics associate (using computer-generated random numbers) to receive the information in a personalised letter or to attend the genetics department for an interview  which gave an opportunity for questions to be asked and answered but did not include clinical breast examination or mammography. This was followed-up by a personal letter summarising the discussion. All letters included the information that, despite being below ‘threshold’ level, risk of breast cancer was still real. Women should therefore remain ‘breast aware’, report any breast symptoms promptly to their GP, notify TFBCC of any change in their family history of breast/ovarian cancer and participate in the National Breast Screening Programme from age 50. Letters were copied to the referring GP. The two subgroups were well matched for age and social class, the latter being assessed by postcode.Three months after the letter or interview, participants in the trial were asked to complete and return a ‘Satisfaction Questionnaire’, based on the instrument used in the Wales ‘TRACE’ study  software was used.During the study period, 380 women were referred to the TBFCC. Three quarters of these referrals came directly from Primary Care, the remainder being referred from the symptomatic breast clinic. Two hundred and eighty-one (74%) returned their ‘Family History Record’ form but 99 (26%) failed to do so, even after a personal reminder letter. Around half of these brought the form with them when they attended the clinic. Of those who did return the form, 64 (23%) did not give written consent to enter the trial. Only 18 of the 64 actively declined. The remainder simply did not return the consent document or returned it unsigned. Again, many brought the signed form to the multidisciplinary clinic but, even if ‘low’ risk status was confirmed, they could not then be randomised. There were therefore 217 women eligible for the study and, after full assessment as described above, 90 of these (41.5%) were judged to be below the guideline threshold level of genetic risk. They were therefore randomised to ‘letter’ (43) or ‘interview’ (47). Three were subsequently withdrawn; one, assigned to the ‘letter’ group, was found to have a cancer on initial examination at the symptomatic breast clinic . The other two provided, at interview, new information shifting them to the ‘moderate’ risk category. Among the other 45 ‘low risk’ women interviewed, five gave new information requiring additional checks on family history and three mentioned breast symptoms that led to investigation by a breast surgeon but all remained in the ‘low’ risk category and no significant breast pathology was found. These data are summarised in Seventy-one of the 87 randomised study patients (81.6%) completed and returned the 3 month ‘satisfaction’ questionnaire. The 87 patients had been referred by 82 GPs, of whom 64 (78%) responded to the follow-up questionnaire (with replies relating to 69 patients – 79%). Analyses of the responses are presented below.t-tests were applied to all comparisons.Independent samples t(69)=−0.636, P=0.527.‘Concerns about breast cancer’ (6 items). Interitem correlations were good so the six were averaged to generate an index of breast cancer concerns. No difference was found between ‘letter’ and ‘face-to-face’ groups, ‘Actions since referral’ (10 items). Correlations among items varied but all were significant. No differences between ‘letter’ and ‘face-to-face’ groups were significant.vs ‘face-to-face’ groups did not reach significance, t(69)=−1.676, P=0.098.‘Experiences since referral’ (12 items). Correlations between items were all significant so scores were averaged. The difference between averaged scores for ‘letter’ t(69)=−2.246, P=0.028.‘Personal breast cancer risk estimate’. There was a significant difference between ‘letter’ group (mean=2.0) and ‘face-to-face’ group (mean=2.38), meaning that those receiving their information at interview perceived their risk to be slightly higher than those informed by letter, t(69)=−0.705, P=0.483.‘Concern about personal breast cancer risk’. No significant difference was found between ‘letter’ and ‘face-to-face’ groups, t(64)=.424, P=0.673 and t(69)=0.194, P=0.846.‘Population lifetime risk of breast cancer’. Respondents were invited to estimate population risk in two formats see . For onet(64)=1.036, P=0.304 and t(69)=−0.249, P=0.804. Both population and personal risk estimates were, however, often wildly inaccurate and there were poor correlations between estimates expressed in the two different formats by the same respondent.‘Your own lifetime risk of breast cancer’. Again, this question was posed in two formats. Five responses were missing for one of these. No significant differences between the trial groups were found for either format: P range 0.020–0.001) although the mean scores for the ‘letter’ group were in the ‘quite satisfied’ to ‘very satisfied’ range.‘Satisfaction with the process’. For five of the 12 items in this set of questions, the ‘face-to-face’ group expressed significantly higher levels of satisfaction than the ‘letter’ group  A further seven  took the opposite view and clearly did not accept the judgement that they were at the lower end of the genetic risk spectrum In addition to the above quantifiable responses, women were invited to provide free text answers to open ended questions about their reactions to the information given. The majority either left these text boxes blank or indicated that they were content with the process. However, 7 (4 from the ‘letter’ and three from the ‘interview’ group) made statements indicating that they now believed they were at very low risk of breast cancer – possibly less than that of the general population.  between his referral and communication of the low assessed risk. Another was dissatisfied because he had no record of the outcome of his referral .When asked how they felt about a policy of evaluating risk before offering any clinic appointment and of declining appointments by explanatory letter for those judged to be below ‘threshold’ risk level, 46 of 64 respondents (72%) had no reservations. Seventeen had some reservations and one had serious reservations; where specific reasons were given, these related to the anticipated difficulty for some patients in completing a standard Family History Record form.No patient had complained to the GP about the way in which their risk status had been assessed or communicated and only four had returned to the GP with concerns about breast cancer in the 18–48 months since receiving their clinic report. Two had fresh complaints of breast discomfort, which were investigated in the regional breast unit and two had simply wished to discuss the information from the genetics clinic. No breast cancers were recorded at that point but one other patient has subsequently developed invasive breast cancer at age 62 years.Our findings show that in deriving the best possible estimate of future cancer risk, face-to-face interview adds little to a detailed family history form  verified and extended by access to hospital, Cancer Registry and Registrar General's records. Communication of risk information and its implications, however, still presents difficulties.Patients, whether informed by letter or by interview, seemed to be very uncertain of their actual risk level some 3 months later, at least when invited to give it a numerical value. The discrepancies between two alternative ways of presenting that information may suggest a lack of clarity in the questions or difficulties with numerical notation. Communication of risk in the setting of a breast cancer family clinic is well recognised as a problem area, with no method of communication proven to achieve accurate understanding (The GP questionnaires revealed no preference for either method of delivering the risk evaluation and, in general, a process whereby referred patients were assessed without necessarily being seen in person at a genetics clinic was considered acceptable.Given that there must be a threshold level of risk below which clinical and mammographic screening cannot be offered, some disappointment, and hence dissatisfaction with the service is inevitable. Studies, including one from Scotland , have shNo procedure, short of providing universal access to regular mammography, is likely to satisfy all women and any method of risk assessment will prove flawed in individual cases, as we have found. Nevertheless, our findings suggest that for most women at the lower end of the familial risk spectrum, communication of this information does not require a personal interview. A letter can be an adequate substitute. There is still scope for improvement without incurring unjustifiable costs. For example, the letter might include an invitation to contact the Genetics Centre to discuss continuing concerns. There may be a place for group sessions with specialists such as dieticians, counsellors and breast care nurses, where information on risk reducing ‘lifestyle’ modification may be offered and questions can be answered. Particular attention must also be given to methods of explaining risk, perhaps making use of high quality, specifically designed leaflets. Generic literature available from patient support groups, cancer charities and other clinics may also be useful but it will be important to harmonise the information they contain (The concern raised by several GPs about women who find it difficult to complete a Family History Record needs to be addressed. The fact that noncompletion of this form previously guaranteed access to the multidisciplinary clinic was perhaps a disincentive to its proper use and insistence on return of the form as a precondition for access to the cancer family service will almost certainly improve compliance. Rather than simply rejecting referrals in the absence of a completed form, however, it seems preferable to enlist the support of the primary care team in establishing why it has not been returned and in assisting those with genuine difficulties to collect and collate whatever family information may be available.


We explored the association of mammographic density, a breast cancer risk factor, with hormonal and proliferation markers in benign tissue from tumor blocks of pre-and postmenopausal breast cancer cases.Breast cancer cases were recruited from a case-control study on breast density. Mammographic density was assessed on digitized prediagnostic mammograms using a computer-assisted method. For 279 participants of the original study, we obtained tumor blocks and prepared tissue microarrays (TMA), but benign tissue cores were only available for 159 women. The TMAs were immunostained for estrogen receptor alpha (ERα) and beta (ERβ), progesterone receptor (PR), HER2/neu, Ki-67, and Proliferating Cell Nuclear Antigen (PCNA). We applied general linear models to compute breast density according to marker expression.A substantial proportion of the samples were in the low or no staining categories. None of the results was statistically significant, but women with PR and ERβ staining had 3.4% and 2.4% higher percent density. The respective values for Caucasians were 5.7% and 11.6% but less in Japanese women (3.5% and -1.1%). Percent density was 3.4% higher in women with any Ki-67 staining and 2.2% in those with positive PCNA staining.This study detected little evidence for an association between mammographic density and expression of steroid receptors and proliferation markers in breast tissue, but it illustrated the problems of locating tumor blocks and benign breast tissue samples for epidemiologic research. Given the suggestive findings, future studies examining estrogen effects in tissue, cell proliferation, and density in the breast may be informative. Although a vast body of literature describes a positive association between mammographic density and breast cancer risk with an estimated relative risk of 4 or higher for women in the highest as compared to the lowest density category , not mucAs risk factors that induce cell proliferation ,16, endoTo understand how hormone receptors and cell proliferation are related to breast density, this study examined the expression of ERα, ERβ, and PR as well as HER2, Ki-67, and Proliferating Cell Nuclear Antigen (PCNA) , in relaThe study was approved by the Institutional Review Boards of the University of Hawaii and Wake Forest University; all subjects provided informed consent in writing. We recruited subjects for the TMA study from 607 breast cancer cases who had participated in the Multiethnic Cohort (MEC)  and a neTMAs were prepared according to standard procedures ,35. In bThe TMAs were stained for the following markers: ERα, ERβ, PR, and PCNA , Ki-67 , and HER2/neu 1 . The basic staining procedure used an avidin-biotin-alkaline phosphatase method, modified for antigen retrieval from paraffin-embedded tissue using the procedure of Shi et al . FollowiWhen a trained pathologist (JMC) evaluated all stained specimens to confirm their malignancy status and the presence of epithelial tissue, 118 breast tissue samples were re-categorized as benign or malignant, i.e., the core had been taken from a malignant part of the block although the intent had been to get a benign sample or vice versa. Another 409–433 (depending on the marker) core sections with equivocal features, e.g., connective tissue or fat tissue only or bad dye quality, were excluded. As a result, at least one benign tissue sample was available for 159 women (mean = 1.7 specimens per woman). Quantification of staining was done on individual TMA core sections at a magnification of 20×, using a Nikon Labophot 2 microscope, a 3 megapixel digital camera , and color imaging software . The area of all nuclei in the core section was measured by a color selection corresponding to hematoxylin (A). The area of positively immunostained tissue was measured by a color selection corresponding to the Vector Red chromogen (B). For the nuclear stains used in this study, the percentage of staining was expressed as B/A × 100; results were averaged for subjects with several cores.2, a factor of 0.000676 was used. The intraclass correlation coefficients (ICC) to assess reliability were 0.96 : 0.95, 0.97) and 0.996  for the size of the dense and the total breast area, respectively. This resulted in an ICC of 0.974 for percent density . For the present study, the cranial caudal view closest to, but before, breast cancer diagnosis was selected; the mean time between the two dates was 10.0 ± 14.8 months.Breast density readings used for the present study had all been obtained previously as part of the NCC study . All mamvs. any epithelial staining. Linear regression models were used to analyze the association between histological markers as categorical variables and mammographic density (absolute density and percent density) as continuous variables. Associations were adjusted for variables previously shown to be associated with mammographic density including age at mammogram, body mass index (BMI), ethnicity, age at menarche, parity, age at first live birth, HT use at mammogram, menopausal status, and family history of breast cancer. Separate analyses stratified by ethnicity (Caucasian and Japanese) and by total size of the breast with the median as cutpoint were also performed.SAS statistical software package version 9.1 was used for all analyses . The dense breast area was square root transformed to normalize the distribution. For ease of interpretation, back transformed mean values are presented. For all histology markers, the mean percentage of stained cells of all available cores per sample was calculated. To assess marker agreement by subject, ICCs were computed. For all six markers, the distributions of samples were skewed with strong left tails. The interquartile ranges were 0.0–8.1%, 0.0–13.8%, 0.0–1.5%, 0.0–14.5%, 0.0–0.6%, 0.8–16.2% for ERα, ERβ, PR, HER2, Ki-67, and PCNA, respectively. Therefore, samples were divided into two categories; negative staining (<10% of cells stained) and positive staining (≥ 10% of cells). Because the number of women with positive staining for PR and Ki-67 was very low (4 and 13 respectively), we dichotomized the results into no 2, respectively). BMI and total breast area were strongly correlated 0.52 (p < 0.0001). Absolute mammographic density was highest in Caucasian women and in the subgroup of women with other ethnicities (43 cm2) and lower in Hawaiian (36 cm2) and Japanese women (34 cm2). Percent mammographic density was highest among the subgroup of women with other ethnicities (50%) and lowest in Hawaiian women (30%), while it was intermediate in Japanese (40%) and Caucasians (38%).Women in the TMA study were slightly younger, were more likely premenopausal, and had a lower BMI than in the original NCC study, but were otherwise similar Table . The stuA substantial proportion of the samples were in the low or no staining categories Table . The perp = 0.08) . Women with large breasts and positive staining for all markers, except Ki-67, had higher percent densities, especially for PR (6.2%), ERβ (6.4%), and PCNA (4%). Although not statistically significant, women with small breasts who stained for hormonal markers showed slightly lower percent density except for PR with 4.3% higher density in category 2. Those with positive Ki-67 staining had 4.4% higher density, whereas positive staining for PCNA made no difference among women with small breasts. With few exceptions, the associations with absolute area were similar to the findings with percent density. Restricting the analyses to postmenopausal women did not change the results; no significant associations were observed (data not shown).As an exploratory analysis, we stratified by total breast area to capture possible effects due to high adiposity. More women with large breasts had PR expression than women with small breasts  Table . The oppThis investigation of breast density and immunohistochemical marker expression in TMAs observed no significant associations in the entire study population, but it appeared that mammographic density was slightly higher for women with PR expression as compared to those with no PR expression. This observation was consistent across the two major ethnic groups and women with different breast sizes. The difference between low and high categories was 3–4% in density which, if a true finding, may translate into a 6–8% higher breast cancer risk . Only inSix previous reports examined the underlying histological markers for breast density; one study used forensic breast samples , one exaThe observation that in women with small breasts, percent density was slightly lower for those with higher ER expression, whereas it was higher in women with large breasts and positive ER expression suggests that a possible effect of ER expression on breast density, if it exists, may depend on the amount of local estrogens. Apart from ovarian production, estrogens are metabolized from androgens in adipose tissue . Thus, iThis epidemiologic investigation took advantage of the TMA approach that allows assessment of marker expression in a large number of pathologic samples under similar staining conditions ,42. AnotThis investigation illustrates the problems of obtaining benign breast tissue samples for a representative sample of participants in epidemiologic studies. To benefit from the advantages of the TMA technique in future research, it is recommended to select the areas for taking the tissue cores carefully and to minimize the loss of specimens during immunostaining. Based on our few suggestive findings, future mammographic density investigations may pursue estrogenic effects as assessed by PR and cell proliferation using Ki67 and other such markers to examine the question of local estrogen activity in relation to breast density. The slightly higher breast density with PR expression suggests a stronger estrogenic response in mammographically dense breasts possibly leading to stronger cell proliferation, an idea supported by associations of breast density with hyperplasia and other benign breast pathology ,10,45,46BMI: Body mass index; CI: Confidence interval; ER: Estrogen receptor; HT: Hormone therapy; HTR: Hawaii tumor registry; ICC: Intraclass correlation coefficient; MEC: Multiethnic cohort; NAF: Nipple aspirate fluid; NCC: Nested case control study; PCNA: Proliferating Cell Nuclear Antigen; PR: Progesterone receptor; TMA: Tissue microarray.The authors declare that they have no competing interests.MV carried out the statistical analysis and drafted the manuscript. GM conceived of the study, obtained funding, and directed the statistical analysis. EE participated in the design and the data collection and contributed to the statistical analysis. JSS contributed to the data collection and the statistical analysis. JK evaluated and selected the pathologic specimens and provided input on pathologic issues. BH provided access to the pathologic specimens and contributed to the study design. JMC performed the pathologic evaluation of the TMAs and contributed to the study design. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/9/182/prepub


The association between socioeconomic status and colon cancer was investigated in a prospective cohort study that started in 1986 in The Netherlands among 120,852 men and women aged 55-69 years. At baseline, data on socioeconomic status, alcohol consumption and other dietary and non-dietary covariates were collected by means of a self-administered questionnaire. For data analysis a case-cohort approach was used, in which the person-years at risk were estimated using a randomly selected subcohort (1688 men and 1812 women). After 3.3 years of follow-up, 312 incident colon cancer cases were detected: 157 men and 155 women. After adjustment for age, we found a positive association between colon cancer risk and highest level of education (trend P = 0.13) and social standing (trend P = 0.008) for men. Also, male, upper white-collar workers had a higher colon cancer risk than blue-collar workers . Only the significant association between social standing and colon cancer risk persisted after additional adjustment for other risk factors for colon cancer (trend P = 0.005), but the higher risk was only found in the highest social standing category . In women, there were no clear associations between the socioeconomic status indicators and colon cancer.


Background: Lifetime risk assessment tools are relatively limited in identifying breast cancer risk in younger women. The predictive value of mathematical models to estimate risk varies according to age, menopausal status, race/ethnicity, and family history. Current risk prediction models estimate population, not individual, levels of breast cancer risk; hence, individualized risk prediction models are needed to identify younger at-risk women who could benefit from timely risk reduction interventions. Clinical data collected as part of breast cancer screening studies may be modeled using Bayesian classification. Purpose: To train a proof-of-concept Bayesian classifier for breast cancer risk stratification. Patients and Methods: We trained a Bayesian belief network (BBN) model on cohort data , breast imaging, and biopsy data) from a prospective pilot screening trial in younger women (N = 591). Receiver operating characteristic curve analysis and cross-validation of the model were used to derive preliminary guidance on the robustness of this approach and to gain insights into what a cross-validation exercise could provide in terms of risk stratification in a larger population. Results: Independent predictors of biopsy outcome in the BBN model included personal breast disease history, breast size, EIS (low vs high risk) and imaging results, and Gail cutoff (5-year risk: <1.66% vs ≥1.66%). Area under the receiver operating characteristic curve and positive predictive value for benign and malignant biopsy outcomes were 0.88 and 97% and 0.97 and 42%, respectively. Patient-specific probability of biopsy outcome given positive EIS result and Gail model 5-year risk ≥1.66% indicated that the combined effect of these predictors on likelihood that a biopsy would prove malignant exceeded the sum of the individual effects; breast cancer likelihood is as follows: 3% (EIS negative and Gail model 5-year risk <1.66%) versus 9% (EIS positive and Gail model 5-year risk <1.66%) versus 27% (EIS negative and Gail model 5-year risk ≥1.66%) versus 45% (EIS positive and Gail model 5-year risk ≥1.66%). Conclusion: Clinical data collected as part of breast cancer screening studies can be modeled using Bayesian classification. The BBN model may be predictive and may provide clinically useful incremental risk information for individualized breast cancer risk assessment in younger women. Breast carcinoma is the most commonly diagnosed cancer and the second leading cause of cancer-related mortality among women in the United States.2-As breast cancer in younger age < 40 years) women is infrequently diagnosed in the early stages by utilizing current screening guidelines, improved cancer screening and detection methods are important in current research, particularly in younger, at-risk women.0 years wnot identified as being high risk.11While increased surveillance for at-risk women may be beneficial, the value of this approach is restricted by the rarity of breast cancer due to known genetic risk factors.BRCA1 or BRCA2 gene mutations, resulting in lifetime risk of cancer exceeding 20%.Magnetic resonance imaging (MRI) is being used increasingly as a screening modality in high-risk women with a significant family history of breast cancer or those with Tamoxifen may be considered in both premenopausal and postmenopausal women, and raloxifene may be considered in postmenopausal women, with lobular carcinoma in situ or with a 5-year breast cancer risk estimate of 1.66% or higher , in order to reduce the risk of estrogen receptor-positive breast cancer.not individual, levels of breast cancer risk. Currently, the only criterion generally used to identify high-risk young women who could benefit from chemoprevention is family/genetic history. The value of this risk estimation paradigm is limited by the rarity of breast cancer due to known gene mutations. Better individualized risk prediction models are needed to identify younger at-risk women who could benefit from risk reduction interventions and earlier chemoprevention.Lifetime relative risk assessment tools  are available to identify women older than 35 years who are at risk for breast cancer. However, the predictive value of mathematical models to estimate breast cancer risk varies according to age, menopausal status, race/ethnicity, and family history of breast cancer. Instruments such as the Gail model are imperfect for identifying increased cancer risk in younger women.Bayesian belief network (BBN) models have been used in research to better understand research data and biologic systems such as functional genomics. In recent years, these applications have been applied to better understand clinical problems, such as models developed to estimate breast cancer risk in mammographic microcalcifications and predict false-positive mammograms.Between August 2002 and March 2005, a total of 591 female military healthcare beneficiaries were enrolled into this institution review board—approved, single-arm, prospective pilot screening trial. The clinical protocol was reviewed and approved by institutional review boards of the Walter Reed Army Medical Center, Washington, DC, and the Keller Army Hospital, West Point, NY. Subjects who met the eligibility criteria were offered participation in this study. Subjects were recruited from the gynecology clinic or the Comprehensive Breast Center at Walter Reed Army Medical Center or the gynecology or family practice clinic at Keller Army Hospital. Study inclusion criteria consisted of younger women aged 18 to 49 years who provided informed consent prior to study enrollment and who were willing to be followed at the participating institution. Age was stratified for analysis as follows: younger than 30, 30 to 34, 35 to 39, and 40 to 49. Potential study subjects were excluded if they had breast surgery (including core biopsy) or were lactating within the preceding 3 months, had breast fine needle aspiration within the preceding 1 month, were pregnant, had electrically powered implanted devices , or were undergoing chemotherapy or radiation treatment. Data collected for each study subject included age, race/ethnicity, clinical history , hormonal information , breast density and size (bra cup size), Gail model risk estimate, results of clinical breast examination (CBE), screening breast electrical impedance scanning (EIS), conventional imaging, and biopsy results. All study participants underwent EIS of the breast by using the T-ScanTM 2000ED  as previously described.23The BBN model was trained by using a priori variables to estimate the likely diagnostic outcome of breast biopsy. The BBN model was developed by using commercially available machine-learning algorithms , which automatically learn network structures and joint probabilities from the prior probabilities in the data. BBN models are a type of directed acyclic graph, which means that they represent information in a hierarchical format. BBN models allow us to identify those variables that contain the most information and are thus most useful for estimating outcomes. The associations represented by BBN models are associations of conditional dependence, allowing us to estimate the posterior likelihood of a given outcome given prior observations.In order to refine the model, a stepwise training process was used. Quantitative and qualitative assessments were used to optimize variable preparation and selection in order to produce the most robust and useful model. The objective was to produce the optimum biopsy outcome estimate through iterative quality assurance and reduction of confounding information. This process used to develop the model is summarized as follows: (1) preliminary modeling to identify appropriate machine-learning parameters, data quality issues, and confounding features and feature analogues that reduce model accuracy, (2) global modeling to set appropriate machine-learning parameters, remove identified analogs and confounders, and perform full “queue learning” to observe global data structure, (3) naive modeling of the outcome of interest to identify the relative contribution of covariates, and (4) focused modeling by using “queue learning” on subsets of variables identified in the prior steps to derive a more focused BBN model than that obtained in global modeling. By excluding marginal or noncontributory variables, the remaining ones are explored more exhaustively.Cross-validation was performed on the final focused Bayesian classifier by using a train-and-test cross-validation methodology to produce classification accuracy estimates. Fivefold cross-validation was performed by randomizing the data set into 5 separate and unique train-and-test sets. Each set consists of a training set composed of 90% of patient records and a test set consisting of the remaining 10% of records. Once the model was constructed with a training set, the matching test set was entered into the model, generating a case-specific prediction for each record for independent variables of interest. A receiver operating characteristic curve was plotted for each test to calculate classification accuracy. The receiver operating characteristic curve was used to calculate area under the curve, a metric of overall model quality, and to calculate corresponding predictive values for biopsy outcome.P = .95). Fifty-five percent of participants had no family history of breast cancer, and family history was only marginally associated with biopsy outcome (P =.10). The findings of CBE were statistically associated with both age (P =.01) and disease (P ≤ .001); 31% of subjects had findings that were deemed not suspicious, whereas 4% of subjects had suspicious CBE findings. Five percent of study subjects had an estimated 5-year risk of breast cancer ≥1.66% according to the Gail model, and these findings were statistically associated with both disease and age of subject (P ≤ .001). Mammography was performed in 281 women and was found to be Breast Imaging Reporting and Data System (BIRADS) III or higher in 75 cases (27%); mammography was found to be statistically associated with both disease and age of subject (P ≤ .001). Breast ultrasound examination was performed in 258 women and was found to be BIRADS III or higher in 66 cases (26%); ultrasound was found to be statistically associated with disease (P ≤ .001) but not with age (P = .18).The study population comprised an ethnically diverse group of younger women (41% non-Caucasian), healthcare beneficiaries in a free access system of military medical care. Of the 591 study participants, 67% were younger than 40 years (mean age: 35 ± 6.9 years) and 90% were premenopausal (Table P = .12), mean age at first pregnancy (P = .39), and nulliparity (P = .93). Finally, there was no statistically significant difference between the mean age of our population (35 years) and the mean age at time of cancer diagnosis  or diagnosis of premalignant histopathology . We tabulated data by age group and biopsy outcome as shown in Tables We also studied other well-known risk factors in our population and identified 3 risk factors that were not statistically associated with biopsy outcome: mean age at menarche P = .2, mean an = 4) or malignant (n = 4). In the EIS-positive group, 10 underwent biopsy; 5 were benign, whereas 5 were either premalignant (n = 3) or malignant (n = 2). Of 13 premalignant or malignant lesions, EIS identified 5 (38.5%). The negative predictive value (NPV) of the EIS-negative group was 92%, whereas the positive predictive value (PPV) of the EIS-positive group was 50%.Of the 591 women enrolled in the study, 568 were found to be EIS negative (low risk) and 23 were found to be EIS positive (high risk). In the EIS-negative group, 95 underwent biopsy and 87 were benign on final histopathology. The remaining 8 were either premalignant  for differentiating malignancy and premalignant disease from benign findings Table . Cross-vTo demonstrate the use of this type of model, we walk through an example case of how we can use available information to estimate clinically relevant outcomes using the network. Knowledge of breast size (bra cup) B  can be selected and applied to the model to estimate biopsy outcome. Subsets of features can also be used to generate an inference table Table  that canFor the majority of younger women, namely those considered to be average risk for developing breast cancer under the current risk assessment and screening model, the only generally available risk assessment modality is CBE, which is imperfect as a screening tool as it has an unacceptably low sensitivity and high false-positive rate compared with mammography.26-33The early detection of breast cancer in younger women is very important, particularly because it demonstrates aggressive tumor biology with rapid tumor growth, demonstrates a relatively short preclinical disease phase, and has worse cancer-specific survival than in older women.28N = 591) and produced receiver operating characteristic curves, when cross-validated, with areas under the curve of 0.88, 0.97 and 0.75 for benign, malignant, and premalignant findings, respectively. This proof-of-principle study shows that clinical data collected as part of routine, current breast cancer screening studies can be developed into enhanced screening tools with improved sensitivity and specificity by using machine-learned BBN models. These networks can use readily available information to estimate clinically relevant outcomes, providing clinically useful incremental risk information for individualized breast cancer risk assessment in younger women.Recognizing the need for individualized breast cancer risk assessment tools for younger women, we conducted a feasibility study to determine whether a machine-learned BBN model could be developed to support individualized breast cancer risk stratification. The model trained and cross-validated in this study was based on data from a prospective pilot screening trial in younger women  model whenever possible; thus, these additional factors were likely surpassed by more specific imaging and personal history risk factors.In our BBN model, the features showing direct conditional dependence with biopsy outcome include (1) personal history of breast disease, (2) breast size (bra cup). (3) EIS (low vs high risk), (4) breast imaging results, and (5) Gail model risk cutoff (5-year risk <1.66 vs ≥1.66). Each of these variables is also significantly related to biopsy category when examined using the calculated χInterestingly, a number of attributes were found to have statistically significant association with patient age, including CBE findings, mammography BIRADS category, nulliparity, and Gail model 5-year risk score. Conversely, certain factors found to be significantly associated with biopsy outcome, using both bivariate statistical tests and the machine-learning process, were not associated with patient age: breast (bra cup) size, EIS screening examination, or MRI. There was no statistically significant difference in mean age at diagnosis of premalignant or malignant disease compared with the mean age of our study population, and when biopsy results were examined by age category, they still did not demonstrate any statistically significant associations. Finally, features considered as well-established breast cancer risk factors in the general population were not found to have statistically significant associations with biopsy outcome in our younger population, including family history of breast cancer, age at menarche, nulliparity, and age at first pregnancy. While these are considered as common risk factors for developing breast cancer, data suggest that these variables are not effective to determine individual risk of cancerous or premalignant lesions in our study population. Although we must be cautious not to overinterpret these findings, they do raise important questions about the appropriate risk measures in a younger population of ethnically diverse women when well-established risk factors have statistically significant association with subjects' age while our disease outcome appears to be age independent.Having trained, encoded, and validated the machine-learned BBN model, we can estimate the likely biopsy outcome given readily available clinical and imaging data. However, the BBN model not only allows the posterior estimation of the likely biopsy outcome but also identifies a hierarchy of conditional dependence that allows us to identify which pieces of information are most useful in calculating our estimate. This hierarchy also defines how independent variables influencing biopsy outcome also influence one another, providing a better understanding of how the estimate is derived and providing an opportunity to estimate missing parameters by using those currently available for any given patient. It is notable that the combined effect of these independent predictors on the likelihood of disease is greater than the sum of the individual effects. By way of example, mammography finding of BIRAD IV increases the likelihood of a malignant biopsy result in our study population by 5%, whereas a Gail model 5-year risk score of greater than 1.66% increases the likelihood of malignancy by 26%, yet together these findings increase the likelihood of disease by 42%—greater than the sum of their individual effects.Importantly, the current proof-of-principle model estimates probability of having a breast abnormality biopsied and having it show the underlying malignancy. The ultimate clinical utility of such a model with appropriate sample size, population disease incidence, and follow-up will be based on robust predictive value of the model for developing breast cancer. The most clinically relevant model will be based on easily obtainable nonimaging parameters to identify at-risk women, who could benefit from breast imaging—based screening and risk reduction interventions.A need exists for a breast cancer risk estimation paradigm that can be used along with relevant demographic, clinical, and other readily obtainable patient-specific data in younger women in order to provide an individualized cancer risk assessment, direct screening efforts that can lead to prophylaxis, and detect breast cancer at an early stage. The computational complexity of designing such risk stratification algorithms for the average-risk woman necessitates a large, multidimensional cohort and requires a selection and encoding methodology that is both robust and transparent so as to sustain clinical scrutiny and improve clinical practice. For our study, we integrated multidimensional clinical, imaging, and pathological data from a prospective cohort to test the feasibility of model development. Using this cohort, we trained a BBN model by using machine-learning algorithms and developed a risk classification model for the average-risk younger woman, with promising cross-validation results. Our proof-of-concept study shows that this type of model could be used to perform individualized screening on a regular basis, using available clinical data at low cost, and that it warrants further assessment and independent testing.The authors thank Tiffany Felix for her invaluable assistance supported in part by the Henry M. Jackson Foundation for the Advancement of Military Medicine and the members and staff of the United States Military Cancer Institute and the Clinical Breast Care Project for their consistent support of this collaborative research effort.


A number of recent studies have shown an association between breast-cancer risk and height, weight and dietary habits, especially fat consumption. In the present study, height and weight were determined for 179 consecutive, unselected, breast-cancer patients and age-matched controls selected from a computerized population register. Height and weight for these two groups were compared, including two different indices for overweight (Quetelet's index and Broca's index). Comparisons were repeated after subdivision into pre- and postmenopausal women. In all calculations, the mean values of patients and controls were very similar and without significant difference. It therefore seems improbable that increased height and weight or obesity constitute risk factors for breast cancer. Earlier studies may have shown differences as the result of selection mechanisms not present in this study.


We performed a survival analysis to assess the effect of meat consumption and meat type on the risk of breast cancer in the UK Women's Cohort Study. Between 1995 and 1998 a cohort of 35 372 women was recruited, aged between 35 and 69 years with a wide range of dietary intakes, assessed by a 217-item food frequency questionnaire. Hazard ratios (HRs) were estimated using Cox regression adjusted for known confounders. High consumption of total meat compared with none was associated with premenopausal breast cancer, HR=1.20 (95% CI: 0.86–1.68), and high non-processed meat intake compared with none, HR=1.20 (95% CI: 0.86–1.68). Larger effect sizes were found in postmenopausal women for all meat types, with significant associations with total, processed and red meat consumption. Processed meat showed the strongest HR=1.64 (95% CI: 1.14–2.37) for high consumption compared with none. Women, both pre- and postmenopausal, who consumed the most meat had the highest risk of breast cancer. Although evidence that links meat consumption with cancers of the stomach, colorectum and pancreas is increasing , developed from that of the European Prospective Investigation into Cancer and Nutrition (EPIC) study (P<0.05), or more than 90% power for a relative risk of 1.5. In terms of postmenopausal breast cancer, 395 cases would give approximately 90% power to detect a relative risk of 1.4 (P<0.05). Analysing the exposure as a continuous variable would provide even more power.Details of women fulfilling the eligibility criteria were submitted to the UK Office of National Statistics and subsequently flagged on the NHS central register. Incident cancers and cause of death were coded according to the International Classification of Diseases 9 and 10. The investigation censor date was 31st October 2004, with median follow-up of 8 years when a total of 1750 incident malignant cancer cases had been recorded, including 283 premenopausal and 395 postmenopausal breast cancers. Menopausal status was based on answers to the baseline questionnaire regarding menstrual and obstetric history and the age at baseline. Power calculations suggested 283 premenopausal breast cancer cases would give approximately 80% power to detect a relative risk of 1.4 comparing two levels of a binary exposure with equal numbers in each group  existed as a single item on the FFQ.Daily intakes of each of the four main meat types  were calculated by summing the daily intakes of the individual food items within each meat type as described above. Intakes of each item were determined by using the frequency categories to estimate the number of daily portions. These were then converted into weights by referring to standard portion sizes for each food item . Intakesvs processed meat consumption and consumption of different meat types. Processed meat formed a separate category to be compared against non-processed meat. Survival analyses were conducted in Stata version 9 using Cox regression weighted by the inverse of the probability of being sampled to take into account the large proportion of vegetarians in the cohort. The time variable used in the survival analysis was time in the study (person years), calculated as the time from the date the questionnaire was filled in until either a report of incident breast cancer, death or the censor date of the analysis, whichever came first. Women with extremely high or low total energy intake  were excluded, as were women with prevalent breast cancer.Exposures of interest were total meat consumption, non-processed Two models were developed. Model 1 adjusted only for age (continuous) and energy intake by the residuals method (split into quartiles) . Model 2As breast cancer may represent different diseases in the two menopause status groups, an initial analysis combined both groups, incorporating menopausal status as a confounder in the model. As a test for interaction between meat consumption and menopausal status confirmed a potential modifying effect of menopausal status, we have treated pre- and postmenopausal women independently. The proportional hazards assumption was checked using graphical methods of log–log curves and Schoenfeld goodness of fit tests , which c−2. Cohort participants were relatively health conscious, with low rates of smoking (11%) and alcohol consumption more than once per week (52%). Most did not use full-fat milk , and a large proportion reported taking dietary supplements . Meat eaters account for a higher percentage of present HRT users than vegetarians, although it must be taken into consideration that vegetarians tend to be younger and therefore less likely to be using HRT. In general, the cohort is well educated and middle class where 8784 (27%) had been educated to degree level and 20 879 (63%) worked in professional or managerial positions. More detail regarding the cohort women has been provided previously  was 1.12 . Non-processed meat consumption was positively associated with risk, HR=1.20 (95% CI: 0.86–1.68) for high consumers vs non-consumers with a relative risk per 50 g day−1 of 1.13 . The association with processed meat was not statistically significant although the risk in high consumers was similar to that observed in non-processed meat. The borderline non-significant association with red meat consumption tended to show the largest effect sizes of all meat types, HR=1.32 (95% CI: 0.93–1.88) for high consumption vs the reference category with relative risk per 50 g day−1 of 1.13 .The associations between meat consumption and premenopausal breast cancer are presented in vs the reference category, and when treated as a continuous variable, resulted in a significant linear trend and relative risk per 50 g day−1 of 1.10 . Relationships between both processed meat and red meat and postmenopausal breast cancer were also significant. Risks for the three meat types were similar when considering HRs of the categorical analysis, however, fitting meat in the model as a continuous predictor resulted in a much stronger relationship with processed meat, giving a relative risk per 50 g day−1 of 1.64 .In postmenopausal women, slight positive trends were observed across the low, medium and high meat categories with a more marked difference between those not consuming meat and those that do. However, splitting the meat categories into more groups by dividing the low consumers into low and very low consumers strengthened the dose response relationship with meat consumption. There was a tendency for the point estimates to be somewhat larger in postmenopausal than in premenopausal women (using Model 2), as shown in P-values become less significant with greater adjustments. Hazard ratios in the highest meat consumption category for Model 1 in premenopausal women were slightly lower than for Model 2 for all meat types with the exception of offal . Tests for trend were more significant in Model 2. The opposite is true for postmenopausal risk where HRs are lowered in the refined model and In the sensitivity results excluding vegetarians, estimates were broadly similar and conclusions unchanged, emphasising a dose response across the consumption categories of meat in both pre- and postmenopausal women. Sensitivity analyses for ambiguous menopausal status and women with cancer within 1 year of entry did not substantially alter HRs or overall trends. The links between meat consumption, cooking methods  and risk were investigated by considering interactions within Model 2; there was no evidence of changes in risk. Excluding HRT users from the analysis of postmenopausal women appeared to strengthen the relationship with breast cancer.The UKWCS is one of the largest cohorts investigating diet and cancer in women in the UK. It was designed to include participants with a wide range of dietary exposures to optimise comparisons between different levels of meat intake, as proposed previously  . A compaResults of a large case–control study (10 149 cases and 7990 controls) in northern Italy between 1983 and 1996 also found significant positive associations of breast cancer  with red meat consumption . The UKWCS are expected to have a higher proportion than this as family history of breast cancer may have encouraged them to become WCRF supporters. In addition, some may have taken up a vegetarian diet in the belief that it is protective against breast cancer. However, if these women are also genetically predisposed to breast cancer, then the chances of developing breast cancer are increased. This is more likely among the premenopausal women because genetic causes tend to lead to early onset of breast cancer. This could explain why, in the premenopausal women, vegetarians have a higher risk than others.Risks for pre- and postmenopausal women were examined separately, based on variability in some risk factors and because breast cancer may represent different diseases in these groups . The assN-acetyltransferase 2 . Risks were increased by almost 50% for even low consumers of processed meat. A case–control study in a subcohort of the Nurses' Health Study (466 cases) supports this, breast cancer  being 40% more likely in women consuming more than 0.07 portions of bacon daily in comparison with non-consumers  for high The strength of this study was the wide range of meat intake within the cohort which reduces measurement error (In conclusion, women generally consuming most total meat, red and processed meat were at the highest increased risk compared with non-meat consumers, though red and processed meat were only statistically significant postmenopausally. Effect sizes were smaller in non-processed meat and only statistically significant in premenopausal women. There were no statistically significant linear associations with consumption of poultry or offal in either pre- or postmenopausal women. This study indicates relationships with certain meats and breast cancer in both pre- and postmenopausal women and merits further investigation in a larger study.