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This Article Abstract Full Text (PDF) All Versions of this Article: 89/1/297    most recent ajcn.2008.26575v1 Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Citation Map 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 Google Scholar Google Scholar Articles by Kolahdooz, F. Articles by Webb, P. M Search for Related Content PubMed PubMed Citation Articles by Kolahdooz, F. Articles by Webb, P. M Agricola Articles by Kolahdooz, F. Articles by Webb, P. M

Dietary patterns and ovarian cancer risk^1,2,3

¹ From the Cancer and Population Studies Group, Queensland Institute of Medical Research, Brisbane, Australia (FK, TII, JCvdP, and PMW), and the University of Queensland, School of Population Health, Brisbane, Australia (FK).

² Recruitment for this study was funded by the National Health & Medical Research Council of Australia and the Cancer Council Queensland. FK is funded by a scholarship from the World Bank, and PW is funded by a National Health & Medical Research Council Senior Research Fellowship.

³ Reprints not available. Address correspondence to PM Webb, Cancer and Population Studies Group, Queensland Institute of Medical Research, Post Office Royal Brisbane Hospital, Queensland 4029, Brisbane, Australia. E-mail: penny.webb{at}qimr.edu.au.

	ABSTRACT

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Background: Evidence for a role of individual foods and nutrients in the causation of ovarian cancer is inconclusive. To date, few studies have considered dietary patterns in relation to ovarian cancer risk.

Objective: We conducted a population-based case-control study in Australia to identify and analyze dietary patterns in relation to ovarian cancer risk.

Design: Principal components analysis of 40 food groups was performed to identify eating patterns in 683 women with epithelial ovarian cancer and in 777 control women aged 18–79 y. Detailed information on risk factors was obtained through face-to-face interviews, whereas dietary information was obtained by administering a semiquantitative food-frequency questionnaire for subjects to complete themselves. Multivariable-adjusted odds ratios (ORs) for ovarian cancer risk were estimated with logistic regression modeling.

Results: Three major eating patterns were identified: "snacks and alcohol," "fruit and vegetable," and "meat and fat." A significant inverse association between the snacks and alcohol pattern and ovarian cancer risk (highest compared with lowest group, multivariable-adjusted OR: 0.59; 95% CI: 0.43, 0.82; P for trend: 0.001) was attenuated after further adjustment for white or red wine intake. The fruit and vegetable pattern was not associated with risk. The meat and fat pattern was associated with an increased risk of ovarian cancer (highest compared with lowest group, multivariable-adjusted OR: 2.49; 95% CI: 1.75, 3.55; P for trend < 0.0001). Further adjustment for body mass index strengthened this association.

Conclusions: A diet characterized by high meat and fat intake may increase the risk of epithelial ovarian cancer. A diet high in fruit and vegetables was not associated with reduced risk.

	INTRODUCTION

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Ovarian cancer is the eighth most common cancer and the fifth most common cause of cancer death in women in North America (1). It is usually diagnosed at an advanced stage and, therefore, despite improvements in treatment, the survival rate remains low at 45% after 5 y (1). Although the association between family history, parity, oral contraceptive use, and ovarian cancer risk is well defined, the role of other factors such as diet remains controversial. Studies have variously reported associations between intakes of dairy products (2–5), saturated fat (6–9), and fruit and vegetables (10, 11) and ovarian cancer risk, but these have not been confirmed in other studies. Overall, there is limited evidence for or against a contribution of diet in the causation of ovarian cancer (12).

Dietary intake is a complex exposure. The assessment of dietary patterns represents an alternative to the more usual approach, which focuses on single foods and nutrients, and allows evaluation of the effects of combinations of many foods simultaneously. This approach reduces the likelihood that associations observed with single foods and nutrients are confounded by other foods or nutrients that are commonly consumed with the food of interest and allows for the potentially complex interactions between different nutrients. Furthermore, the effect of single nutrients or food items might be too small to detect, whereas the effect of a dietary pattern, as a cumulative indicator of several nutrients or food items, could be large enough to detect at the population level (13).

Two previous studies assessed dietary patterns in relation to ovarian cancer risk and arrived at different conclusions. One hospital-based case-control study evaluated the association of ovarian cancer risk with nutrient-based dietary patterns and concluded that a vitamins and fiber pattern was inversely associated and a starch-rich pattern was positively associated with risk (14). A second prospective study with 311 cases found a significantly increased risk of ovarian cancer among women who consumed a plant-based diet but no association with high-protein or high-fat, high-carbohydrate, ethnic, or salad and wine diets; the authors concluded that their data did not show any convincing association between dietary patterns and ovarian cancer risk (15).

To further investigate this issue, we used the information reported on a validated food-frequency questionnaire (FFQ) from a population-based case-control study conducted in Australia to identify common patterns of food consumption and to relate these patterns to ovarian cancer risk.

	SUBJECTS AND METHODS

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Study participants and case ascertainment
Details of this study were described previously (16). Briefly, histologically confirmed incident cases with primary epithelial ovarian cancer that were diagnosed in Queensland between late 1990 and 1993 and in New South Wales and Victoria during 1991 and 1992 were ascertained. With their treating doctors’ consent, 1,014 eligible women aged between 18 and 79 y were invited to take part in the study. Women were excluded if they were too sick (19 women), could not complete the questionnaire because of language difficulty or psychiatric problems (68 women), or could not be contacted (12 women). Of the remaining 915 women, 50 died before the interview and 41 patients or their doctors refused to participate, which left 824 patients (90%) who were interviewed for the study. An additional 28 women were excluded from analysis because they were not on the electoral roll, and 3 were excluded because they did not have histologically confirmed epithelial ovarian cancer. The final case group consisted of 793 women.

Control subjects were selected at random from the electoral roll (enrollment to vote in Australia is compulsory), such that their age and regional distribution matched those expected for the cases. Of 1173 eligible control subjects, 855 (73%) were interviewed. Women provided detailed risk factor information in a standardized face-to-face interview and provided dietary information by filling out a semiquantitative FFQ. The FFQ was based on an instrument originally developed and validated by Willett et al (17) but was adapted for the Australian setting; a similar instrument was validated by other investigators in the Australian population (18, 19).

In total, 717 cases and 806 controls returned an FFQ. We excluded women if 10% of items on the FFQ were missing (26 cases and 21 controls) or if their estimated calorie intake was extreme (<2100 or >14,700 kJ; 8 cases and 8 controls) on the basis of recommended criteria (20). A final group of 683 cases and 777 controls were eligible for analysis.

Dietary assessment
The FFQ asked respondents to recall how often, on average, they had consumed a standard serving size of 119 separate food items in the previous 12 mo; frequency responses ranged from "never" to "4 or more times per day." In addition, we asked about the quantity of sugar habitually added to food or beverages, and the discretionary use of fat was assessed by the frequency with which visible fat on meat, foods fried at home, and fried takeout foods were consumed. For seasonal fruit and vegetables, participants indicated how often these foods were eaten when in season. To calculate food intake (in g), the reported frequency of intake for each food item was converted to a frequency per day, and that value was multiplied by the standard serving size of each food as specified in the FFQ. Seasonal foods were weighted according to the proportion of the year the food was available. Intakes of the 119 food items and 4 additional fat and sugar items were reclassified into 40 predefined food groups (Appendix A). Food items with similar nutrient contents were combined (eg, tuna and sardines were grouped as oily fish), whereas foods with unique nutrients (eg, coffee, tea, pizza, and vegemite) were retained as individual items. For each participant, the average daily intake of each food group was calculated by summing the intake of the individual foods in that food group.

	RESULTS

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The factor-loading matrix for the 3 identified major dietary patterns is shown in Table 1. The dietary patterns were labeled on the basis of food groups with high factor loadings. The snacks and alcohol pattern was characterized by high consumption of alcoholic beverages, pizza, nuts, and other snacks. The fruit and vegetable pattern featured high consumption of all types of fruit, vegetables, and whole-grain foods. The meat and fat pattern was characterized by high consumption of red and processed meat, eggs, fat spreads, and sweetened foods. These 3 eating patterns explained 22% of the total variance in dietary intake (10.7% for the first pattern, 6.2% for the second pattern, and 5.6% for the third pattern). Increasing the number of patterns did not materially increase the total proportion of variance in dietary intake explained by the model.

The characteristics of control women in the 3 dietary pattern groups are shown in Table 2. In comparison with controls in the lowest group of the snacks and alcohol pattern, those in the highest group were younger, were less likely to be postmenopausal, more often had a post–high school qualification, were less likely to have had a hysterectomy, used oral contraceptives more often and longer, and had a higher intake of alcohol and total energy (all P < 0.05). In contrast, compared with controls in the lowest group of the fruit and vegetable dietary pattern, those in the highest group were older and more likely to be postmenopausal, to have had a hysterectomy, and to have used hormone replacement therapy. The same group was also less likely to have used oral contraceptives, to have smoked, or to drink alcohol, but they had a higher total energy intake than did those in the lowest group of this pattern (all P < 0.05). Compared with control subjects in the lowest group of the meat and fat pattern, those in the highest group were younger, less likely to be postmenopausal, and had a higher total energy intake (all P < 0.05).

	DISCUSSION

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
We identified 3 major dietary patterns in this population of Australian women that together explained 22% of the variance in dietary intake as measured by the FFQ. Two of these patterns were similar to the fruit and vegetable and meat and fat patterns found in a previous prospective study of skin cancer in an Australian population in which a similar FFQ was used (22).

Our results showed that women with a high score for the snacks and alcohol pattern, which is characterized by high intakes of alcohol and snacks such as potato chips and pizza, had a 40% lower risk of ovarian cancer than did those who had a low score for this dietary pattern. In contrast, women with a high score on the meat and fat pattern, which is characterized by high intakes of red meat, fat spreads, and discretionary fat and low intakes of vegetables, had a 2.5-fold increased risk of ovarian cancer. The fruit and vegetable pattern, which is characterized by high intakes of vegetables, fruit, and legumes, was not associated with ovarian cancer risk.

The strong inverse association between the snacks and alcohol pattern and risk of ovarian cancer was largely explained by wine consumption, because, when we further adjusted the model for wine intake, the association was attenuated. This is consistent with our previous observation that increasing wine consumption is associated with a significantly reduced risk of ovarian cancer in this study population (adjusted OR for 1 glass wine/d compared with no alcohol intake: 0.56; 95% CI: 0.33, 0.93) (23). Although alcohol intake increases a woman's risk of breast cancer (24), pooled analyses of 10 case-control studies (25) and 10 cohort studies (26) suggest that total alcohol consumption is not associated with ovarian cancer risk. The data for wine intake, however, are less consistent; one study confirmed our observation of an inverse association (27), whereas others observed no association or an increased risk (28, 29). Resveratrol, a phytoalexin found in grape skin and red wine, has been reported to have antioxidant properties and thus may act as a chemopreventive agent (30, 31). Fruit and vegetables contain many potential cancer-preventive agents, including carotenoids, antioxidant vitamins, folate, and flavonoids, and the 1997 report from the World Cancer Research Fund (WCRF) suggested that increased intakes of fruit and vegetables might be associated with lower ovarian cancer risk (24). However, the latest WCRF report found no evidence of an association between fruit and vegetable intake and ovarian cancer risk, although limited evidence of an inverse association with nonstarchy vegetable intake and ovarian cancer risk was found (24). Similarly, a recent pooled analysis of 12 cohort studies found no significant association between total fruit and vegetable intake and ovarian cancer and concluded that fruit and vegetable consumption in adulthood has no important effect on ovarian cancer risk (11). These findings are consistent with the lack of association between the fruit and vegetable dietary pattern and ovarian cancer risk in our study.

A diet high in meat or fat may increase the risk of some types of cancer (24); however, previous studies that evaluated the role of dietary fat in ovarian cancer risk reported mixed results. A meta-analysis of 1 cohort and 7 case-control studies found that a high total fat intake was associated with a 24% increased risk of ovarian cancer, with a 20% increased risk of ovarian cancer associated with high saturated fat intake and a 70% increased risk among those with the highest intake of animal fat (7). However, a second pooled analysis of 12 cohort studies found no association between total, monounsaturated, polyunsaturated, trans-unsaturated, animal, or vegetable fat intake and ovarian cancer risk, although a weakly positive association was noted for saturated fat intake (9). The endogenous formation of nitroso compounds is one suggested mechanism whereby high meat intake could increase cancer risk. Intake of red and processed meats, but not of white meat or fish, was associated with an increased risk of colorectal cancer in a number of studies (32, 33); the 2007 WCRF report concluded that there was probable evidence of such an association and evidence of an association between intake of red and processed meats and cancers of the esophagus and lung and of an association between intake of red meat and cancers of the pancreas and endometrium (24). The data for ovarian cancer are currently more limited; however, many studies have reported elevated risks associated with higher meat or red meat consumption (34).

Two other groups evaluated dietary patterns in relation to ovarian cancer risk. A recent prospective study conducted in the California Teachers Study cohort identified 5 separate dietary patterns (15). Their plant-based dietary pattern was similar to our fruit and vegetable pattern, and they also observed no reduction in risk of ovarian cancer for women in the highest intake groups; instead, they saw a trend toward increased risk. However, in contrast with our study, they observed no increased risk for women with the highest scores for the high-protein and high-fat diet, which is similar to our meat and fat diet, and no reduction in risk of ovarian cancer for the salad and wine pattern. These differences may have been due in part to the fact that their analyses included only 311 cases or to the prospective nature of the study, which measured dietary patterns up to 10 y before diagnosis. The only other study to evaluate the association between dietary patterns and ovarian cancer risk used patterns defined by nutrient intake instead of foods (14). These authors’ results indicated an inverse association for a vitamins and fiber pattern and no association for the animal products and unsaturated fat patterns. These findings are also somewhat at odds with our data, but the differences may have been due to the different approaches used to define dietary patterns. We preferred to use the food-based approach because people eat foods, not individual nutrients, and we believe that a food-based approach is more easily interpreted.

Our study had a number of strengths and limitations. The major strengths of our study include its relatively large sample size, population-based design, and relatively high response rate in both cases and control subjects. In addition, the FFQ we used has been shown to be a valid measure of diet when compared with weighed food records (19, 35, 36). The strong influence of current diet on recall of past diet does, however, raise concerns regarding the possibility of bias among cases if their diet changed as a result of their diagnosis or because they experienced symptoms as a result of the presence of subclinical disease before diagnosis. Cases were interviewed at a median of 1.7 mo after diagnosis, and 88% of cases were interviewed within 6 mo of diagnosis. To minimize the possibility of recall bias, participants were asked about food consumption 1 y before the study (or, for cases, before their diagnosis). If cases had changed their diet as a result of their disease, one might expect this effect to be greatest among those with more advanced cancer; however, our results did not vary appreciably between women with early-stage disease and those with advanced disease, which suggests that our results were not simply due to biased recall. Residual confounding is also a possibility, although adjustment for known ovarian cancer risk factors had little effect on the effect estimates for the highest compared with lowest intake groups: snacks and alcohol pattern (crude OR: 0.56; 95% CI: 0.41, 0.76; adjusted OR: 0.59; 95% CI: 0.43, 0.82) and meat and fat pattern (crude OR: 2.21; 95% CI: 1.64, 2.98; adjusted OR: 2.49; 95% CI: 1.75, 3.55).

It is also possible that the women included in these analyses differed in some way from those who were excluded because they did not have complete dietary data. The only significant differences were that cases without dietary data were less likely to have had post-high school education than were those with data (P = 0.009), whereas controls without dietary data were more likely to be nulliparous (P = 0.049) and less likely to have had a hysterectomy (P = 0.01). Adjustment for these variables in the multivariable logistic regression models did not materially change the results obtained. Finally, the 3 major dietary patterns derived from our data explained only 22% of total variance, which suggested the existence of other eating patterns. The proportion of variability explained in our study, however, is similar to that reported by other authors (22, 37), and when we repeated the analysis using 4 instead of 3 dietary patterns, the proportion of variability explained did not improve.

In conclusion, our data are consistent with those of others in suggesting that a diet high in fruit and vegetables does not reduce a woman's risk of ovarian cancer. Our results also suggest that a diet high in red and processed meat and fat may increase risk. Although little evidence of a role of dietary fat in the causation of ovarian cancer exists, further investigation of the association with red and processed meat intake is warranted. The strong inverse association we observed with the snacks and alcohol dietary pattern was largely due to the strong inverse association between wine intake and ovarian cancer risk in this study population. Additional data are required to clarify whether components other than the alcohol in wine have a beneficial effect or whether this association is a consequence of uncontrolled confounding by other nondietary lifestyle factors that are associated with wine drinking.

	ACKNOWLEDGMENTS

 
We thank Maria Celia Hughes of the Queensland Institute of Medical Research for her assistance with batch processing of the FFQ and acknowledge Nirmala Pandeya for her statistical advice.

The authors’ responsibilities were as follows—FK: performed the statistical analysis and wrote the first draft of the manuscript; TII and JCvdP: provided technical assistance and helped write the manuscript; and PMW: conceived the study, oversaw the project, and helped write the manuscript. None of the authors had a personal or financial conflict of interest associated with this work.

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TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 

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This Article Abstract Full Text (PDF) All Versions of this Article: 89/1/297    most recent ajcn.2008.26575v1 Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Citation Map 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 Google Scholar Google Scholar Articles by Kolahdooz, F. Articles by Webb, P. M Search for Related Content PubMed PubMed Citation Articles by Kolahdooz, F. Articles by Webb, P. M Agricola Articles by Kolahdooz, F. Articles by Webb, P. M