1 Channing Laboratory, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA.
2 Department of Epidemiology, Harvard School of Public Health, Boston, MA.
3 Department of Nutrition, Harvard School of Public Health, Boston, MA.
Received for publication August 23, 2001; accepted for publication February 24, 2002.
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ABSTRACT |
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cohort studies; fats; ovarian neoplasms; prospective studies; questionnaires
Abbreviations: Abbreviations: CI, confidence interval; FFQ, food frequency questionnaire; NHS, Nurses Health Study; RR, relative risk.
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INTRODUCTION |
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MATERIALS AND METHODS |
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Assessment of food and nutrient intake
Dietary intake of fats and fat-rich foods was assessed in 1980, 1984, 1986, and 1990 by using a standard semiquantitative food frequency questionnaire (FFQ). A 61-item questionnaire was used for our baseline assessment of diet in 1980, whereas the FFQ used in 1984, 1986, and 1990 was expanded to include 131 foods. Women were asked to record how often they had consumed specified portion sizes of each food in the previous year (e.g., <1/month, 13/month, 1/week, 24/week, 56/week, 1/day, 23/day, 45/day, and 6/day).
In 1980, participants were asked about their intake of many foods high in fat or cholesterol. These included red meat as a main dish, red meat in a sandwich or mixed dish, processed meats, hamburgers, hot dogs, bacon, chicken with and without skin, fish, eggs, butter, margarine, whole milk, hard cheese, ice cream, french fries, potato chips, a variety of baked goods, peanut butter, and nuts. Additional questions inquired about the types of fats used for frying and baking and the type of margarine used (stick vs. tub). Participants were also asked about whether their intake of each food has significantly increased or decreased in the previous 10 years. The FFQs administered in 1984, 1986, and 1990 included additional questions on usual intake of canned tuna, dark-meat fish, other fish, shrimp and lobster, oil-and-vinegar salad dressing, cream, sour cream, and mayonnaise.
We used the food intake information reported on the FFQ to calculate each participants average total fat intake in the previous year as well as her intake of specific types of fat. These included animal, vegetable, dairy, saturated, monounsaturated, polyunsaturated, and trans fats and cholesterol. In addition, we measured consumption of specific fatty acids, including stearic, oleic, linoleic, -linolenic, arachidonic, docosahexaenoic, eicosapentaenoic, and conjugated linoleic acids.
To calculate a persons total fat intake, we multiplied the portion size of a single serving of each food item by its reported frequency of intake. We then multiplied the total amount of each food consumed by the fat content of the food and added together the fat contributions from all foods (1820). We repeated this procedure to calculate each womans usual intake of the specific types of fat under study.
Both the 1980 and the 1986 FFQs have been validated previously for use in this population (2124). A subset of the NHS cohort, consisting of 191 women living in the Boston, Massachusetts, area, completed two 136-item FFQs (used in 1986) 1 year apart. In the intervening year, they completed two 1-week diet records on which they recorded all foods consumed each day. Correlations between each FFQ and mean values from the two diet records were high for total, saturated, and polyunsaturated fats and cholesterol (range of coefficients, 0.480.73) (23). A similar validation study of the 61-item FFQ used in 1980, comparing food and nutrient intake as measured by two questionnaires, produced similar correlations (21), ranging from 0.35 for chicken with skin to 0.74 for butter (22). In addition, in a study with 185 participants, percentage of calories from fat as measured by the 1984 FFQ predicted serum triglyceride levels (24); the geometric mean triglyceride levels in women with less than 20, 20.125, 25.130, 30.135, 35.140, and more than 40 percent calories from fat were 156, 139, 129, 103, 85, and 70 mg/dl, respectively.
Information on ovarian cancer risk factors was collected by questionnaire throughout the follow-up period. Every 2 years, we updated data on most factors, including menopausal status, postmenopausal hormone use, smoking status, hysterectomy, and body mass index. Information on several factors was collected during part of the follow-up period; we updated information on these factors until the questions were no longer asked, at which point the last response was carried forward until the end of follow-up. For example, questions on oral contraceptive use were included on questionnaires until 1982, at which point fewer than 1 percent of women were still using oral contraceptives. We considered women who reported past use of oral contraceptives in 1982 to be past users for the remainder of follow-up and those who were current users in 1982 to be past users from 1984 onward. We updated parity (measured until 1984) and tubal ligation (measured until 1982) in a similar fashion. Information on age, age at menarche, age at first birth, talc use, and menstrual irregularity was collected once and then carried forward throughout the follow-up period.
Assessment of ovarian cancer
On each follow-up questionnaire, participants were asked whether they had received a physicians diagnosis of ovarian cancer during the previous 2 years. Women who reported this diagnosis were asked for permission to review their medical records. Records were reviewed by physicians blinded to the participants exposure status to confirm the diagnosis and to identify histologic type and subtype and invasiveness. Only confirmed cases of epithelial ovarian cancer were included in the analysis. In addition, the National Death Index was searched systematically to identify women who may have died prior to reporting a diagnosis by questionnaire, and we then contacted family members to obtain medical records. A validation study of the National Death Index as a means of case ascertainment indicated that approximately 98 percent of deaths are successfully identified (25).
Statistical analysis
We excluded cohort members from the analysis at baseline if they reported a diagnosis of cancer, a bilateral oophorectomy, or pelvic irradiation before the start of follow-up in 1980. We also excluded participants who did not complete the 1980 FFQ used for baseline measurement, those who left 10 or more food items blank, and those who reported an implausibly high or low total calorie intake (<500 or >3,500 kcal/day) on the 1980 FFQ. Person-years of follow-up were accrued from the date of return of the 1980 questionnaire until a diagnosis of ovarian or other cancer, the report of bilateral oophorectomy or pelvic irradiation, death, or the end of the follow-up period on June 1, 1996, whichever came first.
To limit misclassification caused by differences in body size and physical activity level, we adjusted fat intake for total energy intake by using the residual method (23); residuals were calculated based on the expected nutrient intake of a woman consuming 1,600 kcal per day, the average intake at baseline. In addition, we calculated the percentage of total energy consumed as fat by multiplying each womans total fat intake (in grams) by 9 kcal/g of fat and dividing the product by total calories consumed.
To represent overall intake during the follow-up period, we used the cumulative average of intake reported on all previous FFQs as the best estimate of exposure intensity and used this value to predict risk of disease in the subsequent interval (26). For example, for each woman, we averaged saturated fat intake reported in 1980 and 1984 and then used this average to predict ovarian cancer risk from 1984 to 1986; similarly, we used the average of saturated fat intake in 1980, 1984, and 1986 to predict risk from 1986 to 1988. To create a cumulative average of the intake of fat-rich foods, we first assigned to each of the intake categories on the FFQ a value corresponding to the average number of servings per day (e.g., 24 servings per week = 0.43 servings per day). Then, as described above, we used the average of all previous food intake measures to predict subsequent risk of disease.
Participants were divided into quintiles or deciles based on their intake of each type of fat and into categories based on their frequency of food intake. Person-time was allocated to each category of fat and food intake in 2-year increments, allowing each participant to change exposure status every 2 years. For years in which dietary questions were not included on the NHS questionnaire (1982, 1988, and 1992), each participants response from the previous questionnaire was carried forward. If a participant did not complete the dietary section of the questionnaire in any given year, she was assigned a missing value for all foods included on the FFQ that year. When a participant did not provide information on a specific food item, she was assigned a missing value for that food, and that food did not contribute to the calculation of fat intake.
Incidence rates for each category of fat and food intake were calculated by dividing the number of incident cases by the follow-up time in each category. Relative risks were estimated with rate ratios comparing the incidence of ovarian cancer in each category with that of the lowest category (referent) by using pooled logistic regression (27), and 95 percent confidence intervals were calculated. We used the Mantel extension test for trend with two-sided p values to evaluate the presence of a linear trend in the relative risk across categories. For food variables, the median values for each category were included in the regression model as a continuous variable. For nutrient variables, we included quintile number as a continuous variable in the model because quintile cutpoints differed between questionnaire years.
In addition to considering the effects of each type of fat individually, we evaluated the possibility of confounding by intake of other fat subtypes. To accomplish this, we included variables for intake of saturated, monounsaturated, polyunsaturated, and trans fats and cholesterol in a single regression model (23). We similarly evaluated the risk associated with intake of dairy fat, nondairy animal fat, and vegetable fat.
We addressed the possible confounding effect of a wide range of other ovarian cancer risk factors, including age, body mass index, parity, age at menarche, tubal ligation, hysterectomy, oral contraceptive use, physical activity, height, smoking status, age at first birth, menstrual cycle regularity, age at menopause, postmenopausal hormone use, and talc use. We also evaluated the presence of confounding by other nutrients, including lactose, protein, carbohydrate, alcohol, caffeine, and total calories. Variables were included in multivariate regression models if their addition to the model changed the relative risks for the fat intake by 10 percent or more compared with the crude relative risk or if it was determined that they were significant predictors of ovarian cancer independent of fat intake in our population.
We performed several subanalyses to determine whether associations between diet and ovarian cancer were limited to particular subgroups of our population. First, we evaluated the effect of fat intake on specific tumor subtypes (serous, mucinous, and endometrioid). To evaluate whether remote, long-term intake of specific foods affected cancer risk, we limited the analysis to those participants who indicated on their 1980 FFQ that intake of a particular food had not substantially changed in the previous 10 years. We then used the food intake frequency reported on their 1980 FFQ to predict ovarian cancer between 1980 and 1996.
To determine whether the fat intake-ovarian cancer relation varied by levels of other risk factors, we stratified our data by age (<50 vs. 50 years), menopausal status, and postmenopausal hormone use (premenopausal vs. postmenopausal and never use of hormones vs. postmenopausal and ever use of hormones), body mass index (<25 vs.
25 kg/m2), oral contraceptive use (ever use vs. never use), smoking status (never vs. current vs. past), and physical activity level (<4 vs.
4 hours/week). We then compared the association of fat intake and cancer risk across strata. Finally, we performed lagged subanalyses excluding cases diagnosed in both the first 2 and 4 years of follow-up to examine whether changes in diet preceding a diagnosis of ovarian cancer biased results.
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RESULTS |
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Characteristics of the cohort by quintile of total fat intake in 1980 are presented in table 1. Median total fat intakes for the five quintiles were 48.5, 62.7, 70.9, 77.6, and 83.5 g, respectively. By 1990, the range in total fat intake was smaller, with medians of 49.9 and 70.0 g in the first and fifth quintiles (results not shown). Women with higher total fat intakes were slightly more likely to be current smokers and less likely to participate in vigorous physical activity than were those with low intake. In addition, total fat intake was inversely associated with alcohol and lactose consumption. Other characteristics did not vary across quintiles.
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To determine whether the observed fat-ovarian cancer relation was influenced by participants altering their diet preceding a diagnosis of cancer, we excluded from our analyses cases diagnosed during both the first 2 and 4 years of follow-up. Results did not differ substantially from those presented (results not shown).
Finally, we did not find that the relation between fat intake and ovarian cancer risk differed substantially by age, oral contraceptive use, menopausal status/postmenopausal hormone use, body mass index, smoking status, or physical activity level (results not shown).
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DISCUSSION |
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Several mechanisms exist to explain how the frequent consumption of fats and animal products may be associated with ovarian cancer. The repeated rupture of the follicle associated with ovulation is believed to expose the ovarian epithelium to hormones in the surrounding fluid; high estrogen concentrations may increase the likelihood of tumor development (28). High consumption of fats may increase circulating estrogen levels, thus increasing the possibility of cell damage and proliferation (29). This theory is supported by studies indicating that vegetarian women with low-fat diets have lower urinary levels of total estrogens and estriol, higher fecal estrogen excretion, and higher levels of sex hormone-binding globulin than do nonvegetarian women with diets higher in fat (30, 31). However, differences in the diets of these groups are probably not limited to fat intake alone; it is unclear what other aspects of diet, such as fiber intake, may account for differences in hormone profiles. Other data do not support a positive association between fat intake and estrogen (23, 24). In a recent cross-sectional study of fat intake and plasma steroid hormone levels in 381 postmenopausal NHS participants, estradiol was inversely related to intake of total, vegetable, and marine omega3 fats (24).
Several epidemiologic studies have suggested that consumption of various types of fats is positively associated with ovarian cancer. In a large, population-based case-control study, significant increases in ovarian cancer risk were noted for high intake of saturated fat and total cholesterol (4). Consumption of animal fat, in particular, was significantly associated with cancer risk in two other case-control studies (5, 6), which reported a 7080 percent increase in risk with high intake. However, fat intake was not associated with ovarian cancer in other case-control studies (79). In the prospective Iowa Womens Health Study, Kushi et al. (10) found no evidence of a positive relation between intake of total, animal, vegetable, saturated, monounsaturated, or polyunsaturated fat and ovarian cancer, although nonsignificantly lower risks were observed for high intake of monounsaturated, polyunsaturated, and vegetable fats.
The absence of an association between fat intake and ovarian cancer in our data may be related to the fact that few members of our cohort reported a very low intake of dietary fat. The median values of total fat intake at baseline for our first and fifth quintiles were 48.5 and 83.5 g/day, equivalent to approximately 30 and 50 percent of calories from fat, respectively (table 1). A positive relation between fat and ovarian cancer risk may be discernible only over a greater range of intake. However, when we compared risk over deciles of total fat intake or when we evaluated fat intake as a percentage of total calories, we also did not find evidence of an association. Furthermore, the range of intake of our study does not differ substantially from those of other US studies reporting a positive association (4, 5). While it is also possible that the misclassification of fat intake as measured by the FFQ may have attenuated results to some degree, this is not a probable explanation of the absence of an association over extreme levels of intake, since it is very unlikely that participants were misclassified from one extreme category to the other (e.g., from 25 to
50 percent of calories from fat) (23).
The results of several studies are consistent with our findings concerning eggs (4, 10, 13) and cheese (5). However, given our null findings for saturated fat, animal fat, and cholesterol, which are substantial components of these foods, these results may also be due to chance. We did not find evidence of a positive association between intake of red meat and ovarian cancer. Increases in ovarian cancer risk of 60 and 170 percent with frequent intake of red meat were noted in case-control studies in Italy (11) and Japan (15), respectively. Intake of fried meats, in particular, was associated with a nonsignificant increased risk for the disease in a Finnish cohort (14). In two additional prospective studies, Seventh-day Adventist women who were lacto-ovo vegetarians had a lower risk of ovarian cancer than did nonvegetarian Adventist members (13) and non-Adventist members (12). However, frequent meat intake was not associated with ovarian cancer in several other analyses (10, 16).
To our knowledge, ours is the largest prospective study of diet and ovarian cancer to date. It is also one of few studies of diet and ovarian cancer to use a complete FFQ to assess nutrient intake, allowing us to adjust for the effects of total energy intake. While little evidence suggests that energy intake is related to ovarian cancer (6, 9, 10), control for calorie intake can limit misclassification in nutrient intake caused by differences in body size and physical activity level (23). In addition, repeated dietary assessment over the follow-up period will minimize random within-person variation in measurement of food and nutrient intake (23). Specific types of fat intake, as measured by this FFQ, have previously been associated with coronary heart disease (32) and non-Hodgkins lymphoma (33) in our cohort, suggesting that the observed lack of association with ovarian cancer is not likely to be the result of exposure misclassification.
In conclusion, we did not find intake of fats and high-fat foods to be associated with ovarian cancer risk. Further evaluation of this relation in large population studies may clarify the relation between intake of eggs and other fat-rich foods and ovarian cancer.
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ACKNOWLEDGMENTS |
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NOTES |
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REFERENCES |
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