Abstract
Mediterranean populations’ lower breast cancer incidence has been attributed to a traditional Mediterranean diet, but few studies have quantified Mediterranean dietary pattern intake in relation to breast cancer. We examined the association of a Mediterranean diet scale (MDS) with mammographic breast density as a surrogate marker for breast cancer risk. Participants completed a dietary questionnaire and provided screening mammograms for breast density assessment using a computer-assisted method. Among 1,286 women, MDS was not clearly associated with percent density in multivariate linear regression analyses. Because of previous work suggesting dietary effects limited to smokers, we conducted stratified analyses and found MDS and percent density to be significantly, inversely associated among current smokers (β=−1.68, p=0.002), but not among non-smokers (β=−0.08, p=0.72) (p for interaction = 0.008). Our results confirm a previous suggestion that selected dietary patterns may be protective primarily in the presence of pro-carcinogenic compounds such as those found in tobacco smoke.
Keywords: breast neoplasms, Mediterranean diet, mammographic density
INTRODUCTION
Breast cancer is less frequent in Mediterranean populations than in northern Europeans (1). The lower incidence of breast cancer in Mediterranean populations has been attributed to a traditional Mediterranean diet, commonly characterized by high consumption of foods of plant origin, relatively low consumption of red meat, and high consumption of olive oil (1). Indeed, Trichopoulou et al. (1) have estimated that approximately 15% of the incidence of breast cancer could be prevented if the populations of highly developed western countries could shift to a traditional Mediterranean diet. Few studies, however, have quantified intake of a Mediterranean dietary pattern in relation to either breast cancer risk or surrogate markers of risk.
Breast density, the percentage of total breast area with a mammographically dense appearance, is a useful surrogate marker for breast cancer risk in epidemiologic studies (2). It is strongly associated with breast cancer risk (3, 4) is modifiable (5–7), and changes in density have recently been associated with changes in risk (8). Understanding whether MDS and breast density are associated could have implications for breast cancer prevention. The objective of this analysis, therefore, was to examine the association of a Mediterranean diet with breast density.
MATERIALS AND METHODS
Study sample
The study sample included participants in the Minnesota Breast Cancer Family Study (9). The Minnesota Breast Cancer Family Study was initiated in 1990 as a follow-up to a 1944 family study that included 544 breast cancer probands ascertained at the Tumor Clinic of the University of Minnesota Hospital. Eligible participants for the follow-up study included sisters, daughters, nieces, and granddaughters of the original probands, and spouses of male first- and second-degree relatives. Upon enrollment, women completed telephone interviews and dietary questionnaires. Women at least 40 years of age were also asked to provide a recent mammogram.
Of 9,084 women in the original cohort, we excluded those who were interviewed through a surrogate (N=2,903), who did not return a food frequency questionnaire (FFQ) (N=2,685), who reported an infeasible energy intake (<600 kcal/day or >5000 kcal/day) (N=224), or who left at least 30 missing responses on the FFQ (N=125). We additionally excluded 1,710 women without mammographic images assessed for breast density and 53 women with a breast cancer diagnosis at enrollment into the follow-up study, leaving 1,384 women available for these analyses.
The project was conducted in accordance with the ethical standards of the Mayo Clinic and the Fox Chase Cancer Center and was approved by the Institutional Review Boards at both institutions.
Data collection
Data collection methods for the study have been described previously (9, 10). Briefly, telephone interviews were completed for all available female relatives aged 18 years and older. The collected data included history of cancer, marital status, education, menstrual and pregnancy history, oral contraceptive use, physical activity, and history of smoking and alcohol intake. Menopausal status was assessed by the response to a question of whether the participant had a menstrual period within the last year, excluding periods brought on by hormones. After the telephone interview, each subject additionally received in the mail a body measurement questionnaire designed to elicit measures of height, weight, and circumferences of the waist (2 inches above the umbilicus) and hip (maximal protrusion) (11). To assess usual food and beverage intake over the past year, participants were asked to complete a 153-item semi-quantitative food frequency questionnaire adapted from Willett et al. (12), with frequency response options for each food item ranging from “never or less than once per month” to “six or more times per day.”
Breast density assessment
Women aged 40 years or older were asked to provide a recent mammogram to verify their breast cancer status and to allow estimation of breast density. If no mammogram had been taken in the previous year (2 years if <50 years of age at time of interview), they were instructed to obtain a new one through their personal physician. Percent breast density was estimated using the semi-automated breast density method developed by Dr. Martin Yaffe and colleagues at the University of Toronto (13). The method involves dividing the mammographic image into a distribution of gray values, then setting two thresholds: one that differentiates the edge of the breast from the rest of the mammogram, and the other that identifies the border of the region(s) in the pixel distribution representing the radiographically dense tissue in the image. Higher gray value pixels are thought to be a result of fibroglandular tissue, and lower gray values a result of fat tissue. Dividing the pixels related to fibroglandular tissue by the total number of pixels making up the entire breast allowed for an estimate of percent breast density. This measure has consistently been associated with breast cancer (14, 15), and has high intra-observer correlation (>0.95 for our reader on over 700 mediolateral images).
Statistical analyses
We quantified intake of a Mediterranean diet using a nine-item Mediterranean diet scale (16, 17). For each of the six items considered beneficial (vegetables, legumes, fruits and nuts, cereals, fish, and monounsaturated:saturated (M:S) fat ratio), women with intake above the median were assigned a value of 1, while those with intake below the median were assigned a value of 0. For two items considered detrimental (meat, dairy), women with intake above the median received a score of 0, while those with intake below the median were assigned a value of 1. For alcohol, women with intake between 5 and 25 g per day received a value of 1, and all others received a value of 0. The resulting item-specific values were then summed to create an overall diet score ranging from zero to nine.
We compared distributions of sociodemographic, lifestyle, reproductive, and dietary factors across MDS categories using previously defined cutpoints of 0–3, 4–5, and 6–9 (16). Categorical variables were compared using the Cochran-Mantel-Haenszel test statistic. Continuous variables were compared using analysis-of-variance. We used linear regression models, adjusting for age as a covariate, to examine associations of these same factors with percent breast density.
We assessed the association of MDS with percent density, after adjustment for covariates, using multivariate linear regression analysis. We used generalized estimating equations to account for autocorrelation resulting from including women from the same family (18, 19). MDS was modeled both as a continuous variable and as a categorical variable with the 0–3 category as the referent group. Variables were included as potential confounders in final models if they were significantly associated with either MDS or percent breast density. Final multivariate models included 1,286 women with complete covariate data and adjusted for age, total energy intake, menopausal status, education (< high school, high school graduate, some college, college graduate+), years of hormone replacement use (0, 1–5, 6+), BMI, WHR, age at menarche, a variable combining parity and age at first live birth (nulliparous, 1–2 children with age at first live birth >20 y, 1–2 children with age at first live birth ≤20 y, 3+ children with age at first live birth >20 y, 3+ children with age at first live birth ≤20 y), alcohol intake (g/day), and relation to proband (first-degree relative, second-degree relative, married-in). Categorical covariates were coded using dummy variables to allow for non-linear associations across categories. Other variables evaluated as confounders but not included in final models were smoking status, age at menopause, years of use of oral contraceptives, history of hysterectomy, and history of oophorectomy.
We examined the possibility of effect modification by menopausal status by examining p-values for interaction, estimated from a model including a variable x menopausal status interaction term. We used the same strategy to assess possible effect modification by relation to proband (first-degree relative, second-degree relative, married-in) and smoking status (current vs. non-smoker).
RESULTS
Among 1,286 women with complete covariate data, mean (SD) age was 57 (12) years, 72% were postmenopausal, mean (SD) BMI was 27.0 (5.7) kg/m2, and mean (SD) percent breast density was 22.6 (15.9). Women with higher MDS tended to be older and better educated, had lower WHR, and were less likely to be current smokers (table 1). MDS was also associated with postmenopausal status, use of hormone replacement, and lower breast density, probably because of its association with age. Not surprisingly, higher MDS was associated with higher intake of vegetables, legumes, fruits and nuts, cereals, fish, and with higher M:S fat ratio, but with lower intake of meats and dairy.
TABLE 1.
Mediterranean Diet Score1 | Percent Breast Density
|
|||||
---|---|---|---|---|---|---|
Variable | 0–3 (N=457) | 4–5 (N=520) | 6–9 (N=309) | Beta2 | SE | p-value |
Mean (±SD) age (y) | 54.5 (12.2) | 57.8 (11.6) | 59.4 (10.9)3 | −0.5 | 0.04 | <0.0001 |
Level of education (%) | ||||||
< high school | 13 | 10 | 10 | Referent | -- | -- |
high school graduate | 43 | 41 | 28 | 1.7 | 1.2 | 0.2 |
some college | 28 | 33 | 38 | 1.8 | 1.2 | 0.2 |
college graduate+ | 16 | 16 | 243 | 4.6 | 1.3 | 0.003 |
Mean (±SD) BMI (kg/m2) | 27.3 (6.4) | 27.0 (5.1) | 26.6 (5.5) | −1.2 | 0.08 | <0.0001 |
Mean (±SD) WHR | 0.84 (0.07) | 0.83 (0.08) | 0.82 (0.08) | −5.7 | 0.6 | <0.0001 |
Mean (±SD) age at menarche (y) | 13.0 (1.6) | 12.9 (1.4) | 12.9 (1.5) | 1.1 | 0.3 | <0.0001 |
Parity and age at first live birth (%) | ||||||
Nulliparous | 10 | 12 | 10 | Referent | -- | -- |
1–2, >20 years | 25 | 25 | 27 | −3.3 | 1.6 | 0.03 |
1–2, ≤20 years | 8 | 7 | 6 | −7.7 | 1.9 | <0.0001 |
3+, >20 years | 34 | 38 | 38 | −6.3 | 1.6 | <0.0001 |
3+, ≤20 years | 23 | 19 | 19 | −8.9 | 1.7 | <0.0001 |
Postmenopausal (%) | 63 | 76 | 793 | −6.4 | 1.4 | <0.0001 |
Hormone replacement use (%) | ||||||
0 years | 58 | 52 | 45 | referent | -- | -- |
1–5 years | 23 | 24 | 26 | −0.1 | 1.0 | 0.96 |
≥6 years | 20 | 24 | 294 | 3.4 | 1.1 | 0.002 |
Smoking status (%) | ||||||
Never | 49 | 58 | 59 | referent | -- | -- |
Former | 30 | 30 | 34 | 0.7 | 0.9 | 0.4 |
Current | 20 | 12 | 73 | −0.05 | 1.4 | 0.97 |
Relation to proband | ||||||
Married in | 34 | 38 | 38 | referent | -- | -- |
Second-degree relative | 46 | 46 | 45 | 1.6 | 0.9 | 0.07 |
First-degree relative | 19 | 17 | 17 | 2.6 | 1.3 | 0.05 |
Mean (±SD) energy intake (kcal) | 1803 (618) | 2010 (655) | 2096 (629)3 | −0.002 | 0.001 | <0.0001 |
Mean (±SD) servings/week | ||||||
Vegetables | 13.8 (9.1) | 22.2 (12.6) | 31.9 (15.1)3 | 0.0 | 0.04 | 0.99 |
Legumes | 2.1 (1.7) | 3.4 (3.2) | 4.9 (3.3)3 | 0.08 | 0.18 | 0.65 |
Fruits and nuts | 13.2 (9.4) | 21.2 (11.5) | 28.9 (14.2)3 | 0.04 | 0.04 | 0.39 |
Cereals | 5.7 (6.6) | 9.9 (8.7) | 12.4 (8.0)3 | −0.11 | 0.12 | 0.37 |
Fish | 0.8 (0.8) | 1.4 (1.7) | 2.3 (1.8)3 | 0.27 | 0.26 | 0.31 |
Dairy | 25.3 (13.7) | 24.3 (13.4) | 19.8 (11.8)3 | 0.03 | 0.08 | 0.70 |
Meats | 9.7 (6.1) | 9.2 (5.4) | 8.6 (5.2)5 | −0.27 | 0.09 | 0.002 |
Mean (±SD) alcohol intake (g) | 4.9 (11.5) | 4.2 (9.0) | 4.2 (6.4) | 0.11 | 0.05 | 0.02 |
Mean (±SD) ratio of monounsaturated:saturated fat | 1.1 (0.2) | 1.2 (0.2) | 1.2 (0.2)3 | −1.5 | 2.7 | 0.57 |
Mean (±SD) percent density | 24.1 (16.6) | 21.5 (15.9) | 22.2 (14.5)5 | -- | -- | -- |
Number of MDS categories (out of 9) for which the subject was assigned a positive value.
Betas represent absolute estimated mean change in percent breast density per unit increment in continuous variables (age, BMI, age at menarche, dietary intake) but per 0.1 increment in WHR. For nominally coded predictor variables, betas represent absolute mean change in percent breast density relative to referent category. Associations with age are unadjusted, whereas all other associations are adjusted for age.
p<0.0001, assessing the association between Mediterranean diet score and covariates; p-values were determined by Cochran-Mantel-Haenszel test statistic for categorical variables or by analysis-of-variance of continuous variables.
p<0.01
p<0.05
In age-adjusted analyses, breast density was associated with higher education, age at menarche, age at first live birth, alcohol intake, and being a first-degree relative to the breast cancer proband. It was inversely associated with BMI, WHR, parity, postmenopausal status, hormone replacement use, and intake of energy and meats (table 1).
In fully adjusted models, MDS was not associated with percent density in analyses including all women (table 2). The association varied by smoking status, however. While MDS was not associated with percent density among non-smokers, it was significantly, inversely associated with percent density among current smokers (β=−1.68, p=0.002; p for interaction = 0.008). To further explore this finding, we examined associations of percent density with the individual components of MDS, within current smokers. Vegetables, legumes, and cereals were the individual components of the MDS that were most strongly inversely related to percent density within this subgroup (Table 3).
Table 2.
N | Mediterranean Diet Score | Revised Mediterranean Diet Score2 | |||||
---|---|---|---|---|---|---|---|
Beta | SE | p-value | Beta | SE | p-value | ||
All women | 1,286 | ||||||
MDS (continuous) | −0.27 | 0.20 | 0.17 | −0.33 | 0.20 | 0.09 | |
MDS category | |||||||
0–3 | 457 | referent | -- | -- | referent | -- | -- |
4–5 | 520 | −1.32 | 0.87 | 0.13 | −1.22 | 0.92 | 0.18 |
6–9 | 309 | −0.54 | 0.92 | 0.56 | −1.40 | 0.98 | 0.15 |
| |||||||
Non-smokers | 1,110 | ||||||
MDS (continuous) | −0.08 | 0.22 | 0.72 | −0.12 | 0.21 | 0.58 | |
MDS category | |||||||
0–3 | 365 | Referent | -- | -- | referent | -- | -- |
4–5 | 457 | −1.15 | 0.92 | 0.21 | −0.61 | 1.00 | 0.54 |
6–9 | 288 | 0.13 | 1.02 | 0.90 | −0.58 | 1.08 | 0.59 |
Current smokers | 176 | ||||||
MDS (continuous) | −1.68 | 0.55 | 0.002 | −1.90 | 0.55 | 0.0005 | |
MDS category | |||||||
0–3 | 4 | referent | -- | -- | referent | -- | -- |
4–5 | 63 | −1.88 | 2.09 | 0.37 | −4.26 | 2.31 | 0.07 |
6–9 | 21 | −7.17 | 2.77 | 0.01 | −8.07 | 2.82 | 0.004 |
Adjusted for age, total energy intake, menopausal status, education, years of use of hormone replacement, BMI, WHR, age at menarche, parity and age at first live birth (combined variable), alcohol intake, and relation to proband. Betas represent absolute estimated mean change in percent breast density per unit increment in continuous Mediterranean Diet Score (MDS), or absolute estimated mean change in percent density relative to 0–3 category for categorized MDS.
Revised so that for alcohol component of MDS, individual receives a score of 1 for 0 g/day of alcohol, rather than for 5–25 g/day as in original scale, and a score of 0 otherwise.
Table 3.
Beta2 | SE | p-value | |
---|---|---|---|
Vegetables | −4.92 | 1.98 | 0.01 |
Legumes | −4.49 | 2.22 | 0.04 |
Fruits | −2.38 | 1.97 | 0.23 |
Cereals | −6.60 | 2.02 | 0.001 |
Fish | −0.96 | 2.18 | 0.66 |
Monounsaturated:saturated fat ratio | −3.29 | 1.79 | 0.07 |
Dairy | 1.42 | 2.19 | 0.52 |
Meat | 0.29 | 2.24 | 0.90 |
Alcohol | 1.93 | 1.76 | 0.27 |
Adjusted for age, total energy intake, menopausal status, education, years of use of hormone replacement, BMI, WHR, age at menarche, parity and age at first live birth (combined variable), and relation to proband. All models except for alcohol additionally adjusted for alcohol intake.
Betas represent absolute estimated mean change in percent breast density for above vs. below median for all items except alcohol. Beta for alcohol represents absolute estimated mean change in percent density for intake of 5–25 g/day vs. all others.
Previous investigations have hypothesized an anticancer effect for resveratrol, found in red wine and selected other foods. When we revised the alcohol component of the MDS to consider only g/day of alcohol from red wine, however, results were not appreciably different (not shown). In addition, because alcohol is known to increase breast cancer risk, we also revised the MDS such that women received a score of 1 if they consumed zero g/day of alcohol, instead of 5–25 g/day, and a score of 0 otherwise. Revising the alcohol component of the MDS in this way strengthened the inverse association between the MDS and percent density among all women, as well as among current smokers, although the association among all women remained non-significant (table 2). We saw no effect modification by menopausal status or family history of breast cancer.
DISCUSSION
In this first study to examine a Mediterranean diet in relation to breast density, we found evidence for an inverse association that appeared to be limited to current smokers. The association was strengthened slightly when the alcohol component of the MDS, which favors moderate consumption over no or excessive consumption, was revised to favor no consumption over any consumption.
Specific factors in the Mediterranean diet that may be relevant in protecting against breast cancer include its high content of selenium, glutathione, fiber, polyphenols, and vitamins E and C, and its favorable n-6/n-3 fatty acid ratio (20). Olive oil or oleic acid has also been of particular interest for its potential role in protecting against peroxidation and inducing transcriptional repression of Her-2/neu (21, 22). Previous breast density studies, however, have not offered convincing evidence for associations with individual components of a Mediterranean diet. A previous analysis of food and nutrient intake and breast density in the same sample of participants from the Minnesota Breast Cancer Family Cohort (23) showed associations of percent breast density with alcohol and vitamins C and E and inverse associations for saturated fat and dairy intake among pre-menopausal women – with the exception of alcohol, all contrary to expectation. Among post-menopausal women, while percent density was associated with white wine intake, it was inversely associated with intake of red wine, known to be a good source of polyphenols such as resveratrol. This previous analysis, however, used a subjective estimate of percent density determined by an experienced radiologist. Other breast density studies have reported no associations for fruits (24), nuts and seeds (25), or cereals (24, 25), and mixed findings regarding intake of vegetables (24, 26), fish (24, 26), dairy (24, 27), and monounsaturated fatty acids or olive oil (27–30)
A Mediterranean diet effect may be more pronounced when quantified as an overall dietary pattern than when examined in terms of its specific components. Among current smokers in our sample, only three of the nine MDS components were statistically significantly associated with breast density, while six of the nine components were associated in the expected direction. However, studies that have examined overall (rather than components of) Mediterranean diet are, in fact, also suggestive of an inverse association with breast cancer risk. In one six-month intervention study, women who were randomized to receive instruction in preparing a traditional Mediterranean diet, designed to increase their intake of whole grains, legumes, vegetables, fish, and olive oil, exhibited a significant, >40% decrease in endogenous estrogen levels relative to the control group (31). Other studies have examined a Mediterranean diet pattern in relation to breast cancer. In the Nurses’ Health Study, women in the highest quintile for the MDS had a risk for estrogen receptor negative breast cancer of 0.8 (trend p=0.03) relative to women in the lowest quintile (32). MDS was also inversely, albeit not significantly, associated (OR~0.6) with breast cancer risk in a small case-control study including primarily BRCA gene mutation carriers (33). In another study that used factor analysis to identify dietary patterns in women in northern Italy, breast cancer risk was inversely related to intake of a “salad vegetables” pattern, characterized by intake of raw vegetables and olive oil (34).
Others have remarked on the paradoxical role of the alcohol component of the Mediterranean diet, noting its cardiovascular benefits but, simultaneously, its risk with respect to cancer (35). Because alcohol is a known risk factor for breast cancer, we calculated a revised MDS in which women received a point for no alcohol rather than for moderate alcohol consumption. Although the association remained non-significant, revising the score resulted in a slight strengthening of the inverse association between the MDS and breast density. Our results suggest that the breast health benefits of a Mediterranean diet may be enhanced if the diet is modified to minimize alcohol intake. Revising the MDS to consider alcohol only from red wine rather than from all sources did not change results.
We observed an inverse association of the MDS only among current smokers. While our observation is based on a relatively small number of smokers, it is consistent with previous findings in this population for fruit-vegetable-cereal and salad-sauce-pasta/grain dietary patterns identified from principal components analysis (Tseng M, Vierkant RA, Kushi LH, Sellers TA, Vachon, CM. Dietary patterns and breast density in the Minnesota Breast Cancer Family Study. Accepted for publication, Cancer Causes Control). It is also consistent with the finding of inverse associations of “prudent” and “southern” dietary patterns with breast cancer only among smokers in other studies (36, 37). Ahn et al. (38) recently reported an increase in breast cancer risk with the lower activity glutathione S-transferase A1 (GSTA1) *B/*B genotype, but only among women with lower cruciferous vegetable consumption and among current smokers. Although we did not assess GSTA1 genotype for this analysis, our observation might reflect a protective effect of the Mediterranean diet that is masked if individuals are not distinguished by genotype but more visible in the presence of carcinogenic compounds found in tobacco smoke. Together, these findings suggest that genetic analyses may clarify mechanisms underlying interactions between dietary intake and smoking, but also that diet modification merits further investigation as a preventive measure among smokers.
Non-participation in the mammography phase of the study may have biased estimates of the association between dietary intake and breast density. A previous analysis (23) indicated that women at higher risk for dense breasts and women with a more health-conscious lifestyle were more likely to participate in this component of the study. Overrepresentation of such women in our sample likely biased our estimates towards the null. Strengths of the study include its relatively large sample size and its use of quantitative, highly reliable estimates of breast density.
Overall our results indicate an inverse association of a Mediterranean diet with breast density, although any protective effect may be limited to smokers. Our findings suggest that a Mediterranean diet is protective primarily in the presence of pro-carcinogenic compounds such as those found in tobacco smoke. Our findings also raise the question of whether other subsets of the population can be identified who might benefit more from diet modification as a means of reducing breast cancer risk.
Acknowledgments
The authors thank Ms. Fang-Fang Wu for her work in reading mammograms and estimation of mammographic percent density. This work was supported by grant 5 R03 CA097779-02 from the National Institutes of Health.
References
- 1.Trichopoulou A, Lagiou P, Kuper H, Trichopoulos D. Cancer and Mediterranean dietary traditions. Cancer Epidemiol Biomarkers Prev. 2000;9:869–73. [PubMed] [Google Scholar]
- 2.Byrne C. Studying mammographic density: implications for understanding breast cancer. J Natl Cancer Inst. 1997;89:531–533. doi: 10.1093/jnci/89.8.531. [DOI] [PubMed] [Google Scholar]
- 3.Maskarinec G, Meng L. A case-control study of mammographic densities in Hawaii. Breast Cancer Res Treat. 2000;63:153–161. doi: 10.1023/a:1006486319848. [DOI] [PubMed] [Google Scholar]
- 4.Ursin G, Ma H, Wu AH, et al. Mammographic density and breast cancer in three ethnic groups. Cancer Epidemiol Biomarkers Prev. 2003;12:332–338. [PubMed] [Google Scholar]
- 5.Brisson J, Brisson B, Cote G, Maunsell E, Berube S, Robert J. Tamoxifen and mammographic breast densities. Cancer Epidemiol Biomarkers Prev. 2000;9:911–915. [PubMed] [Google Scholar]
- 6.Greendale GA, Reboussin BA, Slone S, Wasilauskas C, Pike MC, Ursin G. Postmenopausal hormone therapy and change in mammographic density. J Natl Cancer Inst. 2003;95:30–37. doi: 10.1093/jnci/95.1.30. [DOI] [PubMed] [Google Scholar]
- 7.Freedman M, Martin San J, O’Gorman J, et al. Digitized mammography: A clinical trial of postmenopausal women randomly assigned to receive raloxifene, estrogen, or placebo. J Natl Cancer Inst. 2001;93:51–56. doi: 10.1093/jnci/93.1.51. [DOI] [PubMed] [Google Scholar]
- 8.Kerlikowske K, Ichikawa L, Miglioretti DL, et al. Longitudinal measurement of clinical mammographic breast density to improve estimation of breast cancer risk. J Natl Cancer Inst. 2007;99:386–95. doi: 10.1093/jnci/djk066. [DOI] [PubMed] [Google Scholar]
- 9.Sellers TA, Anderson VE, Potter JD, et al. Epidemiologic and genetic follow-up study of 544 Minnesota breast cancer families: design and methods. Genet Epidemiol. 1995;12:417–429. doi: 10.1002/gepi.1370120409. [DOI] [PubMed] [Google Scholar]
- 10.Sellers TA, King RA, Cerhan JR, et al. Fifty-year follow-up of cancer incidence in a historical cohort of Minnesota breast cancer families. Cancer Epidemiol Biomarkers Prev. 1999;8:1051–7. [PubMed] [Google Scholar]
- 11.Weaver TW, Kushi LH, McGovern PG, et al. Validation study of self-reported measures of fat distribution. Int J Obes Relat Metab Disord. 1996;20:644–50. [PubMed] [Google Scholar]
- 12.Willett WC, Sampson L, Browne ML, et al. The use of a self-administered questionnaire to assess diet four years in the past. Am J Epidemiol. 1988;127:188–199. doi: 10.1093/oxfordjournals.aje.a114780. [DOI] [PubMed] [Google Scholar]
- 13.Byng JW, Boyd NF, Fishell E. The quantitative analysis of mammographic densities. Phys Med Biol. 1994;39:1629–1638. doi: 10.1088/0031-9155/39/10/008. [DOI] [PubMed] [Google Scholar]
- 14.McCormack VA, dos Santos Silva I. Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev. 2006;15:1159–69. doi: 10.1158/1055-9965.EPI-06-0034. [DOI] [PubMed] [Google Scholar]
- 15.Vachon CM, Brandt KR, Ghosh K, et al. Mammographic breast density as a general marker of breast cancer risk. Cancer Epidemiol Biomarkers Prev. 2007;16:43–9. doi: 10.1158/1055-9965.EPI-06-0738. [DOI] [PubMed] [Google Scholar]
- 16.Trichopoulou A, Costacou T, Bamia C, Trichopoulos D. Adherence to a Mediterranean diet and survival in a Greek population. N Engl J Med. 2003;348:2599–608. doi: 10.1056/NEJMoa025039. [DOI] [PubMed] [Google Scholar]
- 17.Trichopoulou A, Kouris-Blazos A, Wahlqvist ML, et al. Diet and overall survival in elderly people. BMJ. 1995;311:1457–60. doi: 10.1136/bmj.311.7018.1457. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Williamson JM, Lipsitz SR, Kim KM. GEECAT and GEEGOR: computer programs for the analysis of correlated categorical response data. Comput Methods Programs Biomed. 1999;58:25–34. doi: 10.1016/s0169-2607(98)00063-7. [DOI] [PubMed] [Google Scholar]
- 19.Zeger SL, Liang KY. Longitudinal data analysis for discrete and continuous outcomes. Biometrics. 1986;42:121–130. [PubMed] [Google Scholar]
- 20.Simopoulos AP. The traditional diet of Greece and cancer. Eur J Cancer Prev. 2004;13:219–30. doi: 10.1097/01.cej.0000130011.99148.07. [DOI] [PubMed] [Google Scholar]
- 21.Alarcon de la Lastra C, Barranco MD, Motilva V, Herrerias JM. Mediterranean diet and health: biological importance of olive oil. Curr Pharm Des. 2001;7:933–50. doi: 10.2174/1381612013397654. [DOI] [PubMed] [Google Scholar]
- 22.Menendez JA, Vazquez-Martin A, Ropero S, Colomer R, Lupu R. HER2 (erbB-2)-targeted effects of the omega-3 polyunsaturated fatty acid, alpha-linolenic acid (ALA; 18:3n-3), in breast cancer cells: the “fat features” of the “Mediterranean diet” as an “anti-HER2 cocktail. Clin Transl Oncol. 2006;8:812–20. doi: 10.1007/s12094-006-0137-2. [DOI] [PubMed] [Google Scholar]
- 23.Vachon CM, Kushi LH, Cerhan JR, Kuni CC, Sellers TA. Association of diet and mammographic breast density in the Minnesota Breast Cancer Family Cohort. Cancer Epidemiol Biomarkers Prev. 2000;9:151–160. [PubMed] [Google Scholar]
- 24.Sala E, Warren R, Duffy S, Welch A, Luben R, Day N. High risk mammographic parenchymal patterns and diet: a case-control study. Br J Cancer. 2000;83:121–126. doi: 10.1054/bjoc.2000.1151. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Nagel G, Mack U, von Fournier D, Linseisen J. Dietary phytoestrogen intake and mammographic density -- results of a pilot study. Eur J Med Res. 2005;10:389–94. [PubMed] [Google Scholar]
- 26.Jakes RW, Duffy SW, Ng FC, et al. Mammographic parenchymal patterns and self-reported soy intake in Singapore Chinese women. Cancer Epidemiol Biomarkers Prev. 2002;11:608–613. [PubMed] [Google Scholar]
- 27.Masala G, Ambrogetti D, Assedi M, Giorgi D, Del Turco MR, Palli D. Dietary and lifestyle determinants of mammographic breast density. A longitudinal study in a Mediterranean population. Int J Cancer. 2006;118:1782–9. doi: 10.1002/ijc.21558. [DOI] [PubMed] [Google Scholar]
- 28.Boyd NF, Greenberg C, Lockwood G, et al. Effects at two years of a low-fat, high-carbohydrate diet on radiologic features of the breast: results from a randomized trial. J Natl Cancer Inst. 1997;89:488–496. doi: 10.1093/jnci/89.7.488. [DOI] [PubMed] [Google Scholar]
- 29.Nagata C, Matsubara T, Fujita H, et al. Associations of mammographic density with dietary factors in Japanese women. Cancer Epidemiol Biomarkers Prev. 2005;14:2877–2880. doi: 10.1158/1055-9965.EPI-05-0160. [DOI] [PubMed] [Google Scholar]
- 30.Nordevang E, Azavedo E, Svane G, Nilsson B, Holm LE. Dietary habits and mammographic patterns in patients with breast cancer. Breast Cancer Res Treat. 1993;26:207–215. doi: 10.1007/BF00665798. [DOI] [PubMed] [Google Scholar]
- 31.Carruba G, Granata OM, Pala V, et al. A traditional Mediterranean diet decreases endogenous estrogens in healthy postmenopausal women. Nutr Cancer. 2006;56:253–9. doi: 10.1207/s15327914nc5602_18. [DOI] [PubMed] [Google Scholar]
- 32.Fung TT, Hu FB, McCullough ML, Newby PK, Willett WC, Holmes MD. Diet quality is associated with the risk of estrogen receptor-negative breast cancer in postmenopausal women. J Nutr. 2006;136:466–72. doi: 10.1093/jn/136.2.466. [DOI] [PubMed] [Google Scholar]
- 33.Nkondjock A, Ghadirian P. Diet quality and BRCA-associated breast cancer risk. Breast Cancer Res Treat. 2006 doi: 10.1007/s10549-006-9371-0. [DOI] [PubMed] [Google Scholar]
- 34.Sieri S, Krogh V, Pala V, et al. Dietary patterns and risk of breast cancer in the ORDET cohort. Cancer Epidemiol Biomarkers Prev. 2004;13:567–572. [PubMed] [Google Scholar]
- 35.Rimm EB, Ellison RC. Alcohol in the Mediterranean diet. Am J Clin Nutr. 1995;61:1378S–1382S. doi: 10.1093/ajcn/61.6.1378S. [DOI] [PubMed] [Google Scholar]
- 36.Adebamowo CA, Hu FB, Cho E, Spiegelman D, Holmes MD, Willett WC. Dietary patterns and the risk of breast cancer. Ann Epidemiol. 2005;15:789–95. doi: 10.1016/j.annepidem.2005.01.008. [DOI] [PubMed] [Google Scholar]
- 37.Velie EM, Schairer C, Flood A, He JP, Khattree R, Schatzkin A. Empirically derived dietary patterns and risk of postmenopausal breast cancer in a large prospective cohort study. Am J Clin Nutr. 2005;82:1308–19. doi: 10.1093/ajcn/82.6.1308. [DOI] [PubMed] [Google Scholar]
- 38.Ahn J, Gammon MD, Santella RM, et al. Effects of glutathione S-transferase A1 (GSTA1) genotype and potential modifiers on breast cancer risk. Carcinogenesis. 2006;27:1876–82. doi: 10.1093/carcin/bgl038. [DOI] [PubMed] [Google Scholar]