Breast Cancer Research and Treatment

, Volume 117, Issue 1, pp 1–8 | Cite as

Is red meat intake a risk factor for breast cancer among premenopausal women?

  • Valerie H. Taylor
  • Monali Misra
  • Som D. Mukherjee


Breast cancer is the second leading cause of cancer deaths in women today and is the most common cancer among women. Although a number of risk factors such as genetics, family history, parity, age at first birth, and age at menarche and menopause have been established, most are difficult to modify. Diet, however, is a potentially modifiable approach for prevention and a variety of dietary patterns have been examined with respect to their role in breast cancer. One such dietary factor is red meat consumption. Red meat intake has been hypothesized to increase breast cancer risk but while both case–control and ecologic studies have supported a positive association, prospective cohort studies have been inconsistent. One explanation for this inconsistency may be related to menopausal status. We performed a meta-analysis on the association between breast cancer risk and red meat consumption in premenopausal women. A total of ten studies were identified. The summary relative risk was 1.24 (95% CI 1.08–1.42). Case–control studies (N = 7) had a risk of 1.57 (95% CI 1.23–1.99), while cohort studies (N = 3) had a summary relative risk of 1.11 (95% CI 0.94–1.31).


Breast cancer Diet 


Breast cancer is the second leading cause of cancer deaths in women today and is the most common cancer among women, excluding nonmelanoma skin cancers [1, 2]. The annual incidence rate of breast cancer (number of new breast cancers per 100,000 women) increased by ~4% during the 1980s but leveled off to 100.6 cases per 100,000 women in the 1990s. The death rates from breast cancer also declined significantly between 1992 and 1996, with the largest decreases among younger women. Medical experts attribute the decline in breast cancer deaths to earlier detection and more effective treatments [3]. While breast cancer is less common in women less than 30 years of age, they tend to have more aggressive breast cancers, which may explain why survival rates are lower among younger women [4]. Although a number of risk factors such as genetics, family history, parity, age at first birth, and age at menarche and menopause have been established, most are difficult to modify [1, 5]. Diet, however, is a potentially modifiable approach for prevention and a variety of dietary patterns have been examined with respect to their role in breast cancer. One such dietary factor is red meat consumption.

The link between red meat and breast cancer

A number of biological mechanisms have been putatively linked to the association between red meat and breast cancer. Red meat, depending upon processing methods, may be a source of heterocyclic amines (HCA), N-nitroso compounds, and polyaromatic hydrocarbons, all of which have been shown to be mammary carcinogens in rodents and in human breast cell cultures [6, 7, 8, 9, 10]. Exogenous hormone treatment of beef cattle has also been hypothesized as a causative mechanism. There has also been increasing concern regarding the treatment of cattle with naturally occurring or synthetic sex hormones as a means of enhancing muscle growth. It has been suggested that a population exposed to chronic low levels of estrogen may manifest an increase in estrogen-related illnesses such as breast cancer [11]. Red meat is also a source of heme-iron, a highly bioavailable form of iron, which has been shown to enhance estrogen-induced tumor formation [12, 13].

This biological plausibility has not consistently translated to a clear putative model. While there is good evidence from cell-culture and animal studies supporting the role of dietary components in mammary cell tumorigenesis [14, 15, 16] results from studies in women have been variable. Red meat intake has been hypothesized to increase breast cancer risk, but while case–control and ecologic studies have supported a positive association, prospective cohort studies have been inconsistent. Two meta-analyses on the topic resulted in different conclusions. In a meta-analysis by Boyd et al. [17], meat intake was considered to be a modest risk factor for breast cancer, with a summary relative risk of 1.54 (CI 1.31–1.82), but this conclusion was reached despite the fact the results were significant in only 1 of 5 cohort studies and 1 of 12 case–control studies reviewed. A second meta-analysis, involving a pooled analysis of 9 cohort studies, did not find any association [18].

One possible reason for this equivocal finding may be related to menopausal status. The majority of reported studies evaluated postmenopausal breast cancer risk [19, 20, 21, 22], but some investigations suggest a high risk of developing breast cancer in premenopausal women [23, 24]. The aim of this review is to examine the influence of red meat on breast cancer risk among premenopausal women.


A MEDLINE search, citing articles from 1966 onward, supplemented by a review of bibliographies was conducted to identify relevant studies. Breast cancer, diet, and red meat were used as keywords. Criteria used to select studies included: (1) English language, (2) published studies with original data in peer-reviewed journals, and (3) studies that confirmed red meat intake and menopausal status, providing data on premenopausal risk.


A total of ten studies were identified and reviewed with respect to an association between breast cancer, red meat and menopausal status. Of these, six were case–control studies, one was a nested case–control and three were cohort studies. Risk of breast cancer was reviewed for the highest level of consumption provided. Table 1 summarizes selected characteristics of the published studies that examined the role of red meat consumption in relation to breast cancer risk in premenopausal women with breast cancer. For the cohort studies, the summary risk was 1.11 and for the case–control studies it was 1.57. The total risk looking at data from all studies analyzed was 1.24. Levene test of heterogeneity I [2] was .005.
Table 1

Description of studies examining breast cancer risk associated with red meat intake in premenopausal women


Study design

No. of cases/controls


Type of controls

Dietary assessments

Case description


Lee et al. [25]






Age range: 24–57 (cases), 24–58 controls. Hospital admissions for breast cancer between 1986 and 1988

OR: 2.57 (1.36–4.87)10,11 (p = 0.003)

De Stefani et al. [26]






Hospital admissions in Uruguay, 1994–1997

OR: 3.01(0.77–11.7)11,16,3,6,8,12,13,14,15, p (trend) = 0.09

Ambrosone et al. [30]



United States



Aged 40–85. Diagnosed with breast cancer

OR: 1.2 (0.8–1.9)11,17,6,10,4,3,14,18, p (trend) = 0.3

Hermann et al. [32]






<51 years of age hospitalized between 1992 and 1995. Mean age 42.6 years (SD = 5.48 for cases, 5.77 for controls)

OR: 1.99 (1.25–3.18)11,9,3,8,4,13,2,6,10

Witte et al. [27]



United states, Canada



Women <50 cases identified between 1957 and 1989. Mean age at diagnosis was 41 90% Caucasian

OR: 0.6 (0.3–1.3)11,6,8,18,2,4,13, p (trend) = 0.13

Dai et al. [33]






Chinese women in Shanghai, age range: 25–64. Diagnosed between 1996 and 1998

OR: 1.8911,6,8,18,2,4,13 (p < 0.01)

Toniolo et al. [38]

Nested case–control New York University women’s Health study (1985–1991)

Cohort: 14,291, Premenopausal cases: 79

United States

Women in New York attending a breast screening clinic


85% of cases and controls were Caucasians. In the remaining 15%, African Americans, Latinas, and Asians were equally represented

OR: 1.89 (0.79–4.53)6,10,3,8,12,13

Holmes et al. [39]

Cohort Nurses Health study 1980–1994

Cohort: 53,952, cases: 854

United States

Nurses in the US


Started in 1976. Nurses aged 30–55

RR: 0.94 (0.72–1.22)11,1,8,10,4,6,3,12,19,2,13, p = 0.9

Cho et al. [40]

Cohort nurses health study II (1991–2003)

Cohort: 90,659, cases: 1021

United States

Premenopausal nurses in the US


Age range at baseline 36 ± 4.6, age at time of diagnosis 43.0 ± 4.5

RR: 1.27 (0.961.67)11,1,8,10,4,6,3,12,19,2,13,p for trend 0.28

Taylor et al. [41]

UK Women’s cohort

Cohort: 33,725, cases: 70/3334


Women aged 35–69


Women aged 35–69

RR: 1.20 (0.68–1.68)1,4,5,8,11,13,1417,19,20

FFQ food frequency questionnaire

Variables adjusted for 1 smoking, 2alcohol, 3 family history of breast cancer, 4 weight, 5 socioeconomic status, 6 age at menarche, 7 menopause, 8 parity, 9 breast feeding, 10 age of first live birth, 11 age, 12 history of benign breast disease, 13 energy intake, 14 vegetable intake, 15 total fat intake, 16 residence, 17 education, 18 fruit intake, 19 OCP use, 20 activity level

aFFQ: self-administered

bFFQ: interview

cFFQ: validated


All case–control studies that met inclusion criteria for this review were similar in that they studied a population of premenopausal women with breast cancer who were identified via hospital records and contacted within weeks of diagnosis. All participants were required to provide information via dietary interview and/or food frequency questionnaire (FFQ) about their dietary habits prior to the breast cancer diagnosis. These responses were compared to those of an identified control population.

The first case–control study examining breast cancer risk associated with red meat consumption in premenopausal women was carried out by Lee et al. in Singapore in 1991 [25]. This study examined a cohort of 125 premenopausal women who were identified and interviewed within 3 weeks of their breast cancer diagnosis. They were interviewed with regard to their food intake 1 year prior to diagnosis. This study found a statistically significant increased risk of breast cancer for premenopausal women (RR 2.57, 95% CI 1.36–4.87, p < 0.003); however, there were no significant changes seen in the postmenopausal group. Controls were selected from patients admitted to surgical wards over the same time period. A similar study was completed in Uruguay in 1997 [26]. Unlike the Lee study, the FFQ used in this study did not specify portion sizes; these were determined later by a nutritionist according to local practices. Again, an association between red meat and breast cancer was observed; the RR for premenopausal women was 3.01 (95% CI 0.77–11.7) and for postmenopausal women was 2.79 (95% CI 1.35–5.75). This was surprising given the homogeneity of the diet in Uruguay [26], as the finding of an effect of individual dietary factors on cancer risk is more likely in populations with a high variation in intake and less likely when variance is low. This study appears to support an association between breast cancer and red meat, but broad confidence intervals and the small sample size of only 32 cases and 22 controls weaken this conclusion. A limitation of both studies is that controls were chosen from hospitalized patients, as the effect of disease in hospitalized patients could modify dietary recall.

A study by Witte et al. [27] also examined the risk of breast cancer in premenopausal women and was one of the two case–control studies conducted in North America. This study examined women with bilateral breast cancer, who are known to have an increased risk of carrying a genetic mutation than women with unilateral breast cancer [28]. This study was unique in that, for each case, an unaffected sister served as the matched control. It was felt that the sibling controls had a similar motivation to cases in answering questions, reducing recall bias. Using siblings of patients as controls decreases potential bias related to odds ratio estimates, assuming that the exposure-specific risk of breast cancer is relatively constant over time [29]. All case–control studies examined in this review have inherent in them a recall bias, as respondents were asked to recollect dietary patterns in one or 2 years leading up to the diagnosis. This potential bias was amplified in this study as some subjects were asked about exposure information in the remote past, which for some respondents required recollection of dietary intake patterns more than 15 years previous. To investigate this potential bias, the authors stratified their results by year of diagnosis, a technique that did not change their ORs. Given the rarity of bilateral breast cancer both incident and prevalent cases were included in analysis and this could lead to biased results if some dietary risk factors affected survival among prevalent cases. The authors of this study concluded that there was no association between red meat intake and breast cancer but given the rare illness being investigated it is difficult to generalize this conclusion.

A case–control study by Ambrosone et al. [30] also examined premenopausal breast cancer patients in the United States. This study identified cases from two different hospitals in New York and took controls from the general population. Patients were contacted within 2 months postdiagnosis and asked about diet 2 years prior to diagnosis. This study had the lowest participation rate, with only 66% of eligible controls (n = 301) and 62% of eligible cases (n = 316) electing to take part. This leads to a risk of selection bias, as most case nonparticipation was due to physicians’ refusals to allow contact with patients (72%). Thus, the most ill patients may have been excluded, limiting generalizability. One interesting difference in this study was the fact that they included fruit and vegetable consumption in the list of confounding variables adjusted for. The rational for doing this was that this dietary factor may be associated with reduced risk and could therefore skew the results of the study [31]. A slightly increased risk was found for premenopausal women (RR 1.2 CI 0.8–1.9) as opposed to postmenopausal women (RR 1.0 CI 0.7–1.4).

A study by Hermannn et al. [32] looked at breast cancer risk in a population of 278 premenopausal German women. Unlike the other case–control studies, in this study, patients were contacted twice, once to obtain demographic and risk factor information and then later to do a FFQ. FFQs have been validated to assess consumption patterns over extended periods but as the time frame for recall lengthens, so does the risk of error caused by attrition and selection bias. Recall bias may have been a problem as the time span between questioned food intake and FFQ completion was up to 2 years. In some cases, the diagnosis of breast cancer may have caused changes in dietary habits, which would affect the precision and validity of the recalled dietary habits. This study found that red meat consumption was associated with a significantly increased risk of breast cancer (RR 1.99 CI 1.23–3.18). A study by Dai et al. [33] also looked at red meat intake in a population of Chinese women in Shanghai, an area traditionally known to have a low risk of breast cancer. For premenopausal women, the OR was 1.89 while for postmenopausal women the OR was 2.04. This was the only study to stratify by activity level, attempting to control for this possible confounder in the end results. We are not provided with any description of how activity was accessed, however, so the merit of this is unknown.

Use of food frequency questionnaires

One bias inherent in all the studies described earlier involves the use of a FFQ. Regarding measurement error, clearly the use of a FFQ or any self report measure to assess consumption can lead to misclassification of intake. For many cancers, illness may have caused changes in dietary habits, possibly influencing memory of past eating habits. Thus, recall bias may affect observed associations between dietary intake and cancer risk. The problems with the use of FFQs have been investigated by Giovannucci et al. [34]. He conducted a study in which dietary questionnaires obtained before and after breast cancer diagnosis were completed for cancer cases and controls. They concluded that selection and recall bias, i.e., the tendency of breast cancer cases to report past food consumption differently than controls, would by itself explain the results of case–control studies even in the absence of true association [34]. This conclusion has been challenged by investigators who did not find evidence of recall bias in a study of similar design [35].

One strategy to minimize error in dietary assessments is to obtain data on both food frequency and estimate of portion size, as this has been demonstrated to enhance the fidelity of diet estimates [36]. To adjust for subjects’ tendency to consistently overreport or underreport, it is also important to adjust for total energy intake [37] and to reduce potential error due to multiple comparisons, analyses should be conducted on a priori study hypothesis. It is also important to remember that in general, FFQs only provide information on the immediate past and are not able to estimate intake patterns during periods of exposure dating back several years. It may be that more remote events are actually periods of exposure that are critical. A selection bias is also built in to all the case–control studies reviewed in this paper. Among controls, if women who chose to participate are healthier, the results may be biased away from the null. Among patients, death or disease severity affected participation. If a dietary factor is related to this, then reports of associations may be somewhat distorted. Despite inherent problems, it is likely that FFQs do enable investigators to rank order subjects and identify relationships.

Cohort studies

One means of reducing bias conferred by the effect of disease status on recall is to obtain dietary information prior to diagnosis. Interviewing patients prior to diagnosis eliminates both the psychological effects of diagnosis and treatment, as well as the influence of health-related information on the perception of lifestyle behaviors. A nested case–control within the cohort of the New York University’s Women’s Health Study attempted to do this [38]. This group showed an increased risk of breast cancer (1.87 CI 1.09–3.21 p for trend .01) in all women studied (n = 180), and this risk did not change appreciably when premenopausal women were considered separately (1.89, 95% CI 0.79–4.53). This study appears to support an association between breast cancer and red meat, but again, broad confidence intervals weaken this conclusion. This study was also designed and analyzed as a nested case–control rather than a full cohort and this approach may have caused some further loss in statistical efficiency, although this loss should have been small (<20%) since there was an average of 4.6 controls per case, with true relative risks probably in the range between 1 and 2.

A cohort design with repeated measures would address weaknesses related to recall bias and this is an obvious strength of the three cohort studies looking at premenopausal breast cancer and red meat intake identified by this review. The first of these studies, the Nurses Health Study (NHS) I, looked at breast cancer risk in a cohort of 53,952 nurses in the United States from 1980 to 1994 [39]. A FFQ was administered on five separate associations between 1980 and 1995 with respondents followed until 1998. This study found no association between breast cancer risk and red meat for either premenopausal (RR = 0.94, 95% CI 0.72–1.22) or postmenopausal (RR 0.99, 95% CI 0.86–1.13) women. The results of this study were not supported by the most recent study to look at the association between premenopausal breast cancer and red meat [40]. This study used the NHS II cohort of 116,671 nurses, administered FFQs at two time points and followed participants from 1991 to 1999. The investigators here documented an association between meat consumption and breast cancer with a RR of 1.27(0.72–1.22) [40]. A recent study examining the link between red meat and breast cancer involved the UK Women’s Cohort, a cohort of over 35,000 women [41]. This study found that a high consumption of red meat was associated with premenopausal breast cancer. (RR 1.20, 95% CI 0.86–1.68) It is worth noting that the cohort in the NHS I was older than that followed in NHS II (30–55 vs. 25–43) and therefore closer to menopause. This may explain the difference in the impact of red meat in the two populations.

All these studies have a number of strengths. They were prospective, included a large number of cases, had little loss to follow up, and were not prone to the biases of case–control studies. They also assessed dietary intake at multiple points over an extended follow-up period.

The body of literature examining the relationship between premenopausal breast cancer and red meat is not substantive, and results are hampered by measurement problems and small sample sizes. Despite these problems, this quantitative summary of the published literature in this area indicates that red meat may contribute to breast cancer risk in the premenopausal population (Fig. 1). Different associations by menopausal status for some breast cancer risk factors, such as adiposity, support the hypothesis that a risk factor such as red meat could affect premenopausal women differently than postmenopausal women and contribute to illness risk in this population [42]. One mechanistic explanation for the possible association between red meat and breast cancer was examined in the study by Cho et al. [40]. This study stratified premenopausal breast cancer cases according to hormone receptor status and reported a statistically significant difference for estrogen and progesterone positive (ER+/PR+) vs. estrogen and progesterone negative (ER/PR) breast cancers (RR 1.97 vs. 0.89). Further analysis of the NHS II cohort that examined red meat consumption in adolescence also found an association between receptor positive tumors and red meat consumption [43].
Fig. 1

Forest plot of breast cancer risk associated with red meat intake in premenopausal women

Estrogen receptors are nuclear receptors that bind estrogen, resulting in DNA and protein synthesis, cell division, and breast cancer proliferation [44, 45, 46]. Progesterone receptors bind progesterone in a similar manner [47]. Breast tumors that express ERs and PRs behave differently, both clinically and biologically, than tumors that do not express to hormonal receptors and have better overall outcomes. It has been hypothesized that risk factors most closely associated with ER+/PR+ breast tumors may involve mechanisms related to estrogen and progesterone exposure, whereas the etiology of ER/PR breast cancer may be independent of hormonal exposure. Epidemiologic studies [48, 49] have found that several hormone-related lifestyle such as nulliparity, earlier age at menarche, higher body mass index, and use of oral contraceptive pills or hormone therapy are more strongly related to elevated risk of hormone receptor positive breast cancers but not hormone receptor negative cancers.

The incidence rates for hormone receptor–negative tumors have remained relatively constant in the United States while the incidence of hormone receptor positive tumors has been increasing, with an increase from 65.2 (per 100,000 person-years) in 1992 to 75.1 (per 100,000 person-years) in 1998 among women in the 40–49 year age group [50, 51]. This increasing trend of hormone receptor-positive breast cancers suggests a possible role of environmental or lifestyle factors in the development of this type of cancer. Given that a number of putative pathways put forward linking red meat consumption and breast cancer involve hormonal mechanisms, the effect of red meat on the development of hormone receptor–positive cancer may explain the possible association between this dietary factor and premenopausal breast cancer.


A mandate of the Canadian Cancer Statistics 2007 document is for research to identify modifiable risk factors for breast cancer. The American cancer society publishes nutrition guidelines to advise health care professionals and the general public about dietary practices that reduce cancer risk and these guidelines are based on existing scientific evidence. According to these guidelines, the evidence linking red meat consumption to breast cancer is rated at level B: no clear harm or benefit. As is shown by this review, emerging evidence indicates that this dietary variable may carry a different risk profile in premenopausal woman as opposed to their postmenopausal counterparts and this risk needs to be reflected in cancer guidelines. It is also important that further research looking at red meat and other dietary variables can be carried out in different populations, to identify groups that are at increased risk. Beginning to address this, it would be important to utilize a quantitative measurement of HCAs or estrogen levels, as these have been cited as possible causative mechanisms linking red meat to breast cancer. Most studies examining HCA exposure have used recent data on HCA concentrations in various meats prepared in the United States in the 1990s [52]. No biomarker has currently been established to serve as an independent measure of HCA intake, but recent studies have indicated that the concentration of a fried food mutagen 2-amino-1-methyl-6-phenylimidazo [4,5-b] pyridine (PhIP) levels in human hair can be used as a biological indicator of dietary HCAs [53, 54]. The use of radioimmunoassay to measure reproductive sex steroid hormones as a marker of endogenous estrogen levels would also provide information. These measurements used concurrently with FFQs would help quantify exposure and aid in establishing clearer biological causality.


  1. 1.
    Martin AM, Weber BL (2000) Genetic and hormonal risk factors in breast cancer. J Natl Cancer Inst 92(14):1126–1135PubMedCrossRefGoogle Scholar
  2. 2.
    International Agency for Research on Cancer (2002) Handbook of cancer prevention: breast cancer screening. IARC, FranceGoogle Scholar
  3. 3.
    Canadian Cancer Society/National Cancer Institute of Canada (2007) Canadian cancer statistics 2007. Canadian Cancer Society/National Cancer Institute of Canada, CanadaGoogle Scholar
  4. 4.
    American Cancer Society (2005) Breast cancer facts and Figs 2005–2006. American Cancer Society, GeorgiaGoogle Scholar
  5. 5.
    Kelsey JL, Bernstein L (1996) Epidemiology and prevention of breast cancer. Annu Rev Public Health 17:47–67PubMedCrossRefGoogle Scholar
  6. 6.
    Snyderwine EG (1994) Some perspectives on the nutritional aspects of breast cancer research. Food-derived heterocyclic amines as etiologic agents in human mammary cancer. Cancer 74(Suppl 3):1070–1077PubMedCrossRefGoogle Scholar
  7. 7.
    Rivera ES, Andrade N, Martin G et al (1994) Induction of mammary tumors in rat by intraperitoneal injection of NMU: histopathology and estral cycle influence. Cancer Lett 86(2):223–228PubMedCrossRefGoogle Scholar
  8. 8.
    Zarbl H, Sukumar S, Arthur AV, Martin-Zanca D, Barbacid M (1985) Direct mutagenesis of Ha-ras-1 oncogenes by N-nitroso-N-methylurea during initiation of mammary carcinogenesis in rats. Nature 315(6018):382–385PubMedCrossRefGoogle Scholar
  9. 9.
    Yuspa SH, Poirier MC (1988) Chemical carcinogenesis: from animal models to molecular models in one decade. Adv Cancer Res 50:25–70PubMedCrossRefGoogle Scholar
  10. 10.
    Gould MN, Grau DR, Seidman LA, Moore CJ (1986) Interspecies comparison of human and rat mammary epithelial cell-mediated mutagenesis by polycyclic aromatic hydrocarbons. Cancer Res 46(10):4942–4945PubMedGoogle Scholar
  11. 11.
    Andersson AM, Skakkebaek NE (1999) Exposure to exogenous estrogens in food: possible impact on human development and health. Eur J Endocrinol 140(6):477–485PubMedCrossRefGoogle Scholar
  12. 12.
    Wyllie S, Liehr JG (1998) Enhancement of estrogen-induced renal tumorigenesis in hamsters by dietary iron. Carcinogenesis 19(7):1285–1290PubMedCrossRefGoogle Scholar
  13. 13.
    Liehr JG, Jones JS (2001) Role of iron in estrogen-induced cancer. Curr Med Chem 8(7):839–849PubMedGoogle Scholar
  14. 14.
    Rose DP (1997) Dietary fat, fatty acids and breast cancer. Breast Cancer 4(1):7–16PubMedCrossRefGoogle Scholar
  15. 15.
    Rock CL, Kusluski RA, Galvez MM, Ethier SP (1995) Carotenoids induce morphological changes in human mammary epithelial cell cultures. Nutr Cancer 23(3):319–333PubMedCrossRefGoogle Scholar
  16. 16.
    Snyderwine EG (1999) Mammary gland carcinogenesis by 2-amino-1-methyl-6-phenylimidazo [4, 5-b] pyridine in rats: possible mechanisms. Cancer Lett 143(2):211–215PubMedCrossRefGoogle Scholar
  17. 17.
    Boyd NF, Stone J, Vogt KN, Connelly BS, Martin LJ, Minkin S (2003) Dietary fat and breast cancer risk revisited: a meta-analysis of the published literature. Br J Cancer 89(9):1672–1685PubMedCrossRefGoogle Scholar
  18. 18.
    Missmer SA, Smith-Warner SA, Spiegelman D et al (2002) Meat and dairy food consumption and breast cancer: a pooled analysis of cohort studies. Int J Epidemiol 31(1):78–85PubMedCrossRefGoogle Scholar
  19. 19.
    Thorand B, Kohlmeier L, Simonsen N, Croghan C, Thamm M (1998) Intake of fruits, vegetables, folic acid and related nutrients and risk of breast cancer in postmenopausal women. Public Health Nutr 1(3):147–156PubMedCrossRefGoogle Scholar
  20. 20.
    Graham S, Hellmann R, Marshall J et al (1991) Nutritional epidemiology of postmenopausal breast cancer in western New York. Am J Epidemiol 134(6):552–566PubMedGoogle Scholar
  21. 21.
    Kushi LH, Fee RM, Sellers TA, Zheng W, Folsom AR (1996) Intake of vitamins A, C, and E and postmenopausal breast cancer. The Iowa women’s health study. Am J Epidemiol 144(2):165–174PubMedGoogle Scholar
  22. 22.
    Deitz AC, Zheng W, Leff MA et al (2000) N-Acetyltransferase-2 genetic polymorphism, well-done meat intake, and breast cancer risk among postmenopausal women. Cancer Epidemiol Biomarkers Prev 9(9):905–910PubMedGoogle Scholar
  23. 23.
    Howe GR, Hirohata T, Hislop TG et al (1990) Dietary factors and risk of breast cancer: combined analysis of 12 case-control studies. J Natl Cancer Inst 82(7):561–569PubMedCrossRefGoogle Scholar
  24. 24.
    Ambrosone CB, Marshall JR, Vena JE et al (1995) Interaction of family history of breast cancer and dietary antioxidants with breast cancer risk (New York, United States). Cancer Causes Control 6(5):407–415PubMedCrossRefGoogle Scholar
  25. 25.
    Lee HP, Gourley L, Duffy SW, Esteve J, Lee J, Day NE (1991) Dietary effects on breast-cancer risk in Singapore. Lancet 337(8751):1197–1200PubMedCrossRefGoogle Scholar
  26. 26.
    De Stefani E, Ronco A, Mendilaharsu M, Guidobono M, Deneo-Pellegrini H (1997) Meat intake, heterocyclic amines, and risk of breast cancer: a case–control study in Uruguay. Cancer Epidemiol Biomarkers Prev 6(8):573–581PubMedGoogle Scholar
  27. 27.
    Witte JS, Ursin G, Siemiatycki J, Thompson WD, Paganini-Hill A, Haile RW (1997) Diet and premenopausal bilateral breast cancer: a case–control study. Breast Cancer Res Treat 42(3):243–251PubMedCrossRefGoogle Scholar
  28. 28.
    Ottman R, Pike MC, King MC, Casagrande JT, Henderson BE (1986) Familial breast cancer in a population-based series. Am J Epidemiol 123(1):15–21PubMedGoogle Scholar
  29. 29.
    Goldstein AM, Hodge SE, Haile RW (1989) Selection bias in case-control studies using relatives as the controls. Int J Epidemiol 18(4):985–989PubMedCrossRefGoogle Scholar
  30. 30.
    Ambrosone CB, Freudenheim JL, Sinha R et al (1998) Breast cancer risk, meat consumption and N-acetyltransferase (NAT2) genetic polymorphisms. Int J Cancer 75(6):825–830PubMedCrossRefGoogle Scholar
  31. 31.
    Freudenheim JL, Marshall JR, Vena JE et al (1996) Premenopausal breast cancer risk and intake of vegetables, fruits, and related nutrients. J Natl Cancer Inst 88(6):340–348PubMedCrossRefGoogle Scholar
  32. 32.
    Hermann RC, Yang D, Ettner SL, Marcus SC, Yoon C, Abraham M (2002) Prescription of antipsychotic drugs by office-based physicians in the United States, 1989–1997. Psychiatr Serv 53(4):425–430PubMedCrossRefGoogle Scholar
  33. 33.
    Dai Q, Shu XO, Jin F, Gao YT, Ruan ZX, Zheng W (2002) Consumption of animal foods, cooking methods, and risk of breast cancer. Cancer Epidemiol Biomarkers Prev 11(9):801–808PubMedGoogle Scholar
  34. 34.
    Giovannucci E, Stampfer MJ, Colditz GA et al (1993) A comparison of prospective and retrospective assessments of diet in the study of breast cancer. Am J Epidemiol 137(5):502–511PubMedGoogle Scholar
  35. 35.
    Friedenreich CM, Howe GR, Miller AB (1991) An investigation of recall bias in the reporting of past food intake among breast cancer cases and controls. Ann Epidemiol 1(5):439–453PubMedGoogle Scholar
  36. 36.
    Block G, Hartman AM, Dresser CM, Carroll MD, Gannon J, Gardner L (1986) A data-based approach to diet questionnaire design and testing. Am J Epidemiol 124(3):453–469PubMedGoogle Scholar
  37. 37.
    Willet WC (1998) Nutritional epidemiology. Oxford Press, New YorkGoogle Scholar
  38. 38.
    Toniolo P, Riboli E, Shore RE, Pasternack BS (1994) Consumption of meat, animal products, protein, and fat and risk of breast cancer: a prospective cohort study in New York. Epidemiology 5(4):391–397PubMedCrossRefGoogle Scholar
  39. 39.
    Holmes MD, Colditz GA, Hunter DJ et al (2003) Meat, fish and egg intake and risk of breast cancer. Int J Cancer 104(2):221–227PubMedCrossRefGoogle Scholar
  40. 40.
    Cho E, Chen WY, Hunter DJ et al (2006) Red meat intake and risk of breast cancer among premenopausal women. Arch Intern Med 166(20):2253–2259PubMedCrossRefGoogle Scholar
  41. 41.
    Taylor EF, Burley VJ, Greenwood DC, Cade JE (2007) Meat consumption and risk of breast cancer in the UK Women’s Cohort Study. Br J Cancer 96(7):1139–1146PubMedCrossRefGoogle Scholar
  42. 42.
    Huang WY, Newman B, Millikan RC, Schell MJ, Hulka BS, Moorman PG (2000) Hormone-related factors and risk of breast cancer in relation to estrogen receptor and progesterone receptor status. Am J Epidemiol 151(7):703–714PubMedGoogle Scholar
  43. 43.
    Linos E, Willet WC, Cho E, Colditz G, Frazier LA (2008) Red meat consumption during adolescence among premenopausal women and risk of breast cancer. Cancer Epidemiol Biomarkers Prev 17(8):2146–2151PubMedCrossRefGoogle Scholar
  44. 44.
    Wittliff JL (1984) Steroid-hormone receptors in breast cancer. Cancer 53(3 Suppl):630–643PubMedCrossRefGoogle Scholar
  45. 45.
    King WJ, Greene GL (1984) Monoclonal antibodies localize oestrogen receptor in the nuclei of target cells. Nature 307(5953):745–747PubMedCrossRefGoogle Scholar
  46. 46.
    Rayter Z (1991) Steroid receptors in breast cancer. Br J Surg 78(5):528–535PubMedCrossRefGoogle Scholar
  47. 47.
    Perrot-Applanat M, Cohen-Solal K, Milgrom E, Finet M (1995) Progesterone receptor expression in human saphenous veins. Circulation 92(10):2975–2983PubMedGoogle Scholar
  48. 48.
    Olsen A, Tjonneland A, Thomsen BL et al (2003) Fruits and vegetables intake differentially affects estrogen receptor negative and positive breast cancer incidence rates. J Nutr 133(7):2342–2347PubMedGoogle Scholar
  49. 49.
    Cotterchio M, Kreiger N, Theis B, Sloan M, Bahl S (2003) Hormonal factors and the risk of breast cancer according to estrogen- and progesterone-receptor subgroup. Cancer Epidemiol Biomarkers Prev 12(10):1053–1060PubMedGoogle Scholar
  50. 50.
    Li CI, Daling JR, Malone KE (2003) Incidence of invasive breast cancer by hormone receptor status from 1992 to 1998. J Clin Oncol 21(1):28–34PubMedCrossRefGoogle Scholar
  51. 51.
    Yasui Y, Potter JD (1999) The shape of age-incidence curves of female breast cancer by hormone-receptor status. Cancer Causes Control 10(5):431–437PubMedCrossRefGoogle Scholar
  52. 52.
    Byrne C, Sinha R, Platz EA et al (1998) Predictors of dietary heterocyclic amine intake in three prospective cohorts. Cancer Epidemiol Biomarkers Prev 7(6):523–529PubMedGoogle Scholar
  53. 53.
    Kobayashi M, Hanaoka T, Tsugane S (2007) Validity of a self-administered food frequency questionnaire in the assessment of heterocyclic amine intake using 2-amino-1-methyl-6-phenylimidazo [4, 5-b] pyridine (PhIP) levels in hair. Mutat Res 630(1–2):14–19PubMedGoogle Scholar
  54. 54.
    Reistad R, Nyholm S, Becher G, Alexander J (1999) 2-Amino-1-methyl-6-phenylimidazo [4,5-b] pyridine (ph1P) in human hair as biomarker for dietary exposure. Biomarkers 4:263–271CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2009

Authors and Affiliations

  • Valerie H. Taylor
    • 1
  • Monali Misra
    • 2
  • Som D. Mukherjee
    • 3
  1. 1.Department of Psychiatry and Behavioural NeuroscienceMcMaster UniversityHamiltonUSA
  2. 2.Department of Surgery, St. Josephs HealthcareMcMaster UniversityHamiltonUSA
  3. 3.Department of Oncology, Juravanski Cancer CenterMcMaster UniversityHamiltonUSA

Personalised recommendations