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Nutritional Epidemiology

Diet Quality Is Associated with the Risk of Estrogen Receptor–Negative Breast Cancer in Postmenopausal Women¹

Teresa T. Fung^*,,2, Frank B. Hu^,,, Marjorie L. McCullough^**, P. K. Newby, Walter C. Willett^,, and Michelle D. Holmes

^* Department of Nutrition, Simmons College, Boston, MA; Department of Nutrition, Harvard School of Public Health, Boston, MA; ^** Epidemiology and Surveillance Research, American Cancer Society, Atlanta, GA; Jean Mayer U.S. Department of Agriculture Human Nutrition Research Center on Aging at Tufts University, Boston, MA; Department of Epidemiology, Harvard School of Public Health, Boston, MA; and Channing Laboratory, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA

² To whom correspondence should be addressed. E-mail: fung{at}simmons.edu.

	ABSTRACT

TOP ABSTRACT SUBJECTS AND METHODS RESULTS DISCUSSION APPENDIX LITERATURE CITED

 
Emerging evidence suggests that diet quality indices may serve as prognostic indicators of disease. However, the ability of these indices to predict breast cancer risk has not been evaluated previously. We assessed the association between several diet quality scores and the risk of breast cancer in postmenopausal women. The indices we used were the Healthy Eating Index (HEI), Alternate Healthy Eating Index (AHEI), Diet Quality Index-Revised (DQI-R), Recommended Food Score (RFS), and the alternate Mediterranean Diet Score (aMed). We calculated diet quality indices from dietary information collected in FFQ administered 5 times between 1984 and 1998 among women in the Nurses' Health Study cohort. Relative risks (RR) were computed using Cox proportional hazards models and adjusted for known risk factors for breast cancer. Separate analyses were conducted for estrogen receptor positive (ER+) and negative (ER–) tumors. Between 1984 and 2002, we documented 3580 cases of breast cancer, of which 2367 were ER+, and 575 were ER–. We did not observe any association between the diet quality indices and total or ER+ breast cancer risk. However, for ER– breast cancer, after adjusting for potential confounders, the RR comparing highest to lowest quintiles were 0.78 (95% CI = 0.59–1.04, P for trend = 0.01) for the AHEI, 0.69 (95% CI = 0.51–0.94, P for trend = 0.003) for the RFS, and 0.79 (95% CI = 0.60–1.03, P for trend = 0.03) for the aMed. These observations appeared to be the result of an inverse association (P for trend = 0.01) with the vegetable component of the scores. We conclude that women who scored high in AHEI, RFS, and aMed had a lower risk of ER– breast cancer. The HEI and DQI-R appeared to be of limited value in predicting breast cancer risk.

KEY WORDS: • breast cancer • diet • nutrition • estrogen receptor

Several diet quality indices have been developed to evaluate the healthfulness of individual diets. These indices are usually based on established nutrient requirements and well-publicized dietary guidelines. However, diet indices have not been tested extensively in their ability to predict the risk of chronic diseases. Previous studies focused on diet quality in relation to total mortality or incidence of broad categories of diseases (1–3). A strategy that combines all cancers together may not provide full insight because both dietary risk factors and the strength of association often differ by cancer site. For breast cancer, there are few established dietary risk factors other than alcohol (4), indicating that individual dietary factors in adult life have weak if any effect on breast cancer risk. It is therefore worthwhile to examine whether overall diet patterns affect risk, perhaps through additive or interactive effects of dietary behaviors not captured in studies of single nutrients.

We examined prospectively the association between several diet quality indices and the risk of breast cancer in postmenopausal women. The scores used in this study were Healthy Eating Index (HEI),³ Alternate Healthy Eating Index (AHEI), Diet Quality Index-Revised (DQIR), Recommended Food Score (RFS), and the alternate Mediterranean Diet Score (aMed). We also considered breast cancer tumors according to estrogen receptor (ER) status because evidence suggests that risk may vary by ER status (5).

	SUBJECTS AND METHODS

TOP ABSTRACT SUBJECTS AND METHODS RESULTS DISCUSSION APPENDIX LITERATURE CITED

 
    Study population. The Nurses' Health Study (NHS) began in 1976 when 121,700 female nurses aged 30–55 y living in 11 U.S. states responded to a questionnaire regarding medical, lifestyle, and other health-related information (6). Since 1976, questionnaires have been sent biennially to update exposures and disease outcome status. Follow-up was complete for >95% of the potential person time up to 2002. In 1980, the participants completed a 61-item FFQ. In 1984, the FFQ was expanded to 116 items. Similar FFQ were sent to the women in 1986, 1990, 1994, and 1998. We considered 1984 as baseline for this analysis because the expanded number of food items on the longer questionnaire was critical in calculating diet quality scores. The NHS is approved by the Institutional Review Board of the Brigham and Women's Hospital, Boston, MA.

For the present analysis, women were included if they completed the 1984 FFQ with <70 missing items and had a total energy intake range (as calculated from the FFQ) between 2060 and 14420 kJ/d (500 and 3500 kcal/d). Women with a history of cancer, except for nonmelanoma skin cancers, were excluded. Thus, we included 71,058 women in this analysis with follow-up for up to 18 y, from 1984 to 2002.

    Assessment of dietary intake. Dietary intake information was collected by FFQ designed to assess average food intake over the previous year. A standard portion size was given for each food item. Cohort members were asked to choose from 9 possible frequency of consumption responses, ranging from "never" to "more than 6 times a day" for each food. Total energy intake was calculated by summing the energy intake from all foods. For this analysis, we used information from the FFQ administered in 1984, 1986, 1990, 1994, and 1998. Previous validation studies among members of the NHS cohort revealed good correlations between nutrients assessed by the FFQ and multiple weeks of food records completed over the previous year (7). For example, correlation coefficients between the 1986 FFQ and diet records obtained in 1986 were 0.68 for saturated fat, 0.76 for vitamin C, and 0.73 for dietary cholesterol. The mean correlation coefficient between frequencies of intake of 55 foods from 2 FFQ 12 mo apart was 0.57 (8).

Scoring criteria for each diet quality index were described in detail elsewhere. A brief description is presented in the Appendix. For each index, a higher score represents a more healthful diet. Calculation of the HEI was based on criteria set in The Healthy Eating Index Final Report and adapted to this cohort by McCullough et al. (2,9). Briefly, the HEI contains 10 components consisting of grains, vegetables, fruit, milk, meat, total fat, saturated fat, cholesterol, sodium, and diet variety. These criteria reflect recommendations based on the USDA Food Guide Pyramid (10) and the 1995 Dietary Guidelines for Americans (11). Possible scores from each component ranged from 0 to 10, depending on level of intake, with a possible total score of 100 for the HEI. The AHEI scoring criteria (12) differed from those of the original HEI; it addressed quality within food groups by removing potatoes from vegetables, and including fruit, nuts and soy, white/red meat ratio, trans fat and the polyunsaturated:saturated fat ratio, cereal fiber, and adding long-term multivitamin use, and alcohol intake. The possible score for the multivitamin component was either 2.5 or 7.5 to avoid overweighting. The AHEI was based on 9 items, with a maximum possible score of 87.5.

The RFS was developed by Kant et al. (1) and adapted by McCullough et al. (12) for our FFQ. The RFS focused on fruits, vegetables, whole grains, lean meats or meat alternates, and low-fat dairy products. Participants received 1 point for each recommended food consumed at least weekly. Based on the length of our FFQ, the maximum possible scores were 49–56, depending on the version of the FFQ.

The DQIR score was based on methods developed by Haines et al. (13). and adapted for our FFQ by Newby et al. (14). Briefly, the DQIR consists of 10 components that measure intake of several food groups and nutrients as well as diet diversity and moderation. These components included grains, vegetables, fruit, total fat, saturated fat, cholesterol, iron, calcium, diet diversity, and moderation in added fat and sugar. The range of possible scores for each component was 0–10 points, depending on the level of intake; the maximum possible DQIR score was 100 points.

The aMed score was based on a Mediterranean diet scale by Trichopoulou et al. (15,16). The original score was based on the intake of 9 items: vegetables, legumes, fruits and nuts, dairy, cereals, meat and meat products, fish, alcohol, and the monounsaturated:saturated fat ratio. Participants with intake above the median intake received 1 point; otherwise they received 0 points. Meat and dairy product consumption below the median received 1 point. We modified the original scale by excluding potato products from the vegetable group, separating fruits and nuts into 2 groups, eliminating the dairy group, including whole-grain products only, including only red and processed meats for the meat group, and assigning 1 point for alcohol intake between 5 and 15 g/d. These modifications were based on dietary patterns and eating behaviors that have been consistently associated with lower risks of chronic disease in clinical and epidemiological studies. The score range for the aMed was 0–9.

    Case ascertainment. For this analysis, we used incident breast cancers obtained by self-report in the biennial questionnaire post-1984 to 2002. Permission was then obtained to review medical records for confirmation for all self-reported cases; 99% of self-reported cases were confirmed by medical records. We also included 1% of cases confirmed by the participants. Estrogen and progesterone receptor status was obtained from pathology reports and each receptor was classified as positive, negative, or uncertain. Deaths were reported by the postal service, family members, or by searching the National Death Index. In this study, we included only postmenopausal breast cancer cases to reduce potential etiologic heterogeneity.

    Statistical analysis. To reduce random within-person variation and best represent long-term dietary intake, we calculated cumulative averages of diet quality scores from each of the FFQ. For example, scores in 1984 were used to predict breast cancer occurrence from 1984 to 1986, and the average of 1984 and 1986 intake was used to model cancer risk in 1986–1988, and so on (17). We also used baseline (1984) diet as a predictor of breast cancer risk to assess the influence of long-term diet.

We used the Cox proportional hazards model to assess associations between dietary quality scores and the risk of breast cancer between 1984 and 2002. The regression analyses were adjusted for age, smoking status (never, past, current smoker up to 14 cigarettes/d, 15–24 cigarettes/d, 25+ cigarettes/d), BMI (5 categories), multivitamin (yes/no), energy intake (quintiles), physical activity in metabolic equivalent (MET) h/wk (quintiles), family history of breast cancer (yes/no), personal history of benign breast disease (yes/no), age at menopause, and use of postmenopausal hormone therapy (13 categories), BMI at age 18 (4 categories), and weight change since age 18 y (7 categories). Alcohol intake (4 categories) was adjusted in the analysis of HEI, DQIR, and RFS, but not in the AHEI or aMed scores because alcohol is one of the components of these indices. We also classified breast cancer by the ER status of the tumors. In stratified analysis, we censored women when they were diagnosed with breast cancer with ER status that was not the outcome of the particular analysis. For example, in analysis of ER+ tumors, we censored women when they were diagnosed with ER– tumors. We did not present data on progesterone receptor status because it did not affect the diet-breast cancer relation.

	RESULTS

TOP ABSTRACT SUBJECTS AND METHODS RESULTS DISCUSSION APPENDIX LITERATURE CITED

 
During 18 y of follow-up, we ascertained 3580 cases of breast cancer in postmenopausal women. Of these, 2367 were ER+ and 575 were ER–; the remainder (638 cases) could not be clearly classified. In this cohort, women who scored high on these indices were less likely to be smokers, had a higher level of physical activity, and were more likely to use postmenopausal hormones (Table 1).

	DISCUSSION

TOP ABSTRACT SUBJECTS AND METHODS RESULTS DISCUSSION APPENDIX LITERATURE CITED

Although numerous studies have examined the association between foods and nutrients and breast cancer risk, very few have focused on the entire diet, and none to our knowledge have used established diet quality indices as predictors of risk. In addition, subtypes of breast cancer have generally not been analyzed separately. Risk factors for breast cancer may differ according to the ER status of the tumor (5,18). Estrogen exposure is one of the strongest risk factors for breast cancer, but it may have less influence on ER– tumors than ER+ tumors. This is supported by results of chemoprevention trials on ER modulators in which the reduction of incidence was observed for ER+ tumors only (19,20). Therefore, in ER+ tumors, any potential influence of dietary factors may be difficult to detect given the strong influence of hormonal factors. Conversely, in ER– tumors, other risk factors, including diet, may exert a relatively larger influence and be more easily detectable.

The HEI, AHEI, and RFS did not predict combined cancer risk from all sites in our cohort (2). However, ER– breast cancer was only a small portion of all cancers diagnosed in our cohort; therefore, the lack of association with overall cancer risk is not inconsistent with our findings. In a cohort of Swedish women, those whose RFS score was in the highest quintile had a 24% lower risk (P for trend = 0.005) of overall cancer mortality than those with scores in the bottom quintile (3). On the other hand, the Mediterranean Diet Score (on which ours was based), was inversely associated with overall cancer mortality (15). Empirically derived dietary patterns using factor analysis identified a "prudent" pattern that has characteristics similar to the AHEI and aMed (21,22). This pattern was not associated with overall breast cancer risk in one study (22); however, when ER types were analyzed separately, an inverse association was observed with ER– cancer (23).

The ability of diet quality scores to predict breast cancer risk depends on how well these scores measure dietary risk factors for breast cancer. The most consistent dietary risk factor for breast cancer is alcohol (4), and folate is a possible modifier in this relation (24). Fruits and vegetables, the major sources of folate were inversely associated with overall breast cancer in a case-control study (25), but this association was mainly null in cohort studies (26,27). However, such studies generally did not distinguish between ER subtypes. When tumors of different ER status were analyzed separately, an inverse association was observed with ER– tumors in one cohort (28). In a recent study from our group, each serving of vegetables intake was associated with a 6% reduction in ER– breast cancer and each serving of fruit was associated with a 12% reduction, whereas no association was observed for ER+ tumors (23). In addition, a dietary pattern consisting of fruits, vegetables, whole grains, fish, and poultry was also associated with a lower risk of ER– tumors compared with diets low in those foods. Fat intake after menopause has little association with breast cancer risk (29), but premenopausal fat intake from red meats and dairy products may be associated with breast cancer in premenopausal women (30). All diet quality scores include fruits and vegetables, but they contribute only 10–20% of the total score except for the RFS to which they contribute 80% of the total score. In addition, the AHEI also awards points for multivitamin intake, which usually contains a substantial amount of folic acid.

The RFS, which had the strongest inverse relation with ER– tumors, does not include alcohol. The AHEI and aMed consider moderate alcohol intake to be a beneficial component of the diet given its association with lower cardiovascular disease risk (31); thus, it may not be optimal to use it as a recommendation for breast cancer prevention. Because both the AHEI and aMed indices are inversely associated with ER– breast cancer, moderate alcohol intake may not overwhelm the beneficial influence of other dietary components.

Because cancer development is usually slow, the long follow-up period in this study and the availability of dietary information over multiple years increase the possibility that we can capture important periods during which diet may exert an influence on breast cancer risk in adults. Our analysis was adjusted extensively for potential confounders. Although we did not observe an association with ER+ tumors, this does not exclude the possibility that diet earlier in life may have an influence on the development of ER+ tumors.

As illustrated in Table 2, the distribution of cases in each the quintile of the various scores varied; this is expected given the different criteria for what constitutes a healthy diet in these scores. Thus, we are comparing different constructs, and populations will score higher or lower on various indices, depending on the components of the score and the underlying consumption of those dietary components in a population. We thought it was most objective to compare associations based on distribution within each score (e.g., quintiles).

In conclusion, our findings suggest that ER– breast cancer tumors may be more strongly associated with diet than ER+ tumors. We found that high scores for the AHEI, RFS, and aMed were associated with a lower risk of ER– breast cancer, whereas the HEI and DQI-R were of limited value in predicting breast cancer risk. The dietary patterns reflected by these scores may serve as possible guidelines for cancer prevention, especially for ER– breast cancer in postmenopausal women.

	APPENDIX

TOP ABSTRACT SUBJECTS AND METHODS RESULTS DISCUSSION APPENDIX LITERATURE CITED

 

Components of diet quality scores

Score Components Healthy Eating Index Grains, vegetables, fruits, milk, meat, total fat (% energy), saturated fat (% energy), cholesterol, sodium, diet variety. Alternate Healthy Eating Index Vegetables, fruits, nuts and soy, cereal fiber, ratio of white to red meat, trans fat (% energy), polyunsaturated:saturated fat ratio, alcohol, duration of vitamin use. Diet Quality Index-Revised Grains, vegetables, fruits, total fat (% energy), saturated fat (% energy), cholesterol, iron, calcium, diet diversity, diet moderation Recommended Food Score Specific items of fruits, vegetables, whole grains, low saturated fat proteins, low-fat dairy products Alternate Mediterranean Diet Score Vegetables, legumes, fruits, nuts, whole grains, low intake of red and processed meats, alcohol, fish, monounsaturated:saturated fat ratio

	FOOTNOTES

 
¹ Funded by National Institutes of Health grants CA87969 and CA095589.

³ Abbreviations used: aMed, Alternate Mediterranean Diet Index; AHEI, Alternate Healthy Eating Index; DQI-R, Diet Quality Index- Revised; ER, estrogen receptor; HEI, Healthy Eating Index; MET, metabolic equivalent; NHS, Nurses' Health Study; RFS, Recommended Food Score; RR, relative risk.

Manuscript received 24 August 2005. Initial review completed 27 September 2005. Revision accepted 22 November 2005.

	LITERATURE CITED

TOP ABSTRACT SUBJECTS AND METHODS RESULTS DISCUSSION APPENDIX LITERATURE CITED

 

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This Article Abstract Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Fung, T. T. Articles by Holmes, M. D. Search for Related Content PubMed PubMed Citation Articles by Fung, T. T. Articles by Holmes, M. D.