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Keywords:

  • dietary pattern;
  • pancreatic cancer;
  • case-control study;
  • prevention

Abstract

  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. APPENDIX I

To investigate associations between broad dietary patterns and pancreatic cancer risk, we conducted a case-control study of 585 histologically confirmed pancreatic cancer cases and 4,779 population-based controls in 8 Canadian provinces between 1994 and 1997. Dietary intake was assessed using a FFQ. Major dietary patterns were identified by factor analysis. Unconditional logistic regression was used to describe associations between dietary pattern scores and risk of pancreatic cancer. Three dietary patterns were identified: Western, characterized by high intake of processed meats, sweets and desserts, refined grains and potatoes; fruits and vegetables, characterized by high intake of fresh fruits and cruciferous vegetables; drinker, characterized by high consumption of liquor, wine and beer. After adjustment for age, BMI, smoking, physical activity, province, educational attainment and total energy intake, the fruits and vegetables pattern was associated with a 49% reduction in pancreatic risk among men (OR = 0.51, 95% CI 0.29–0.90, p = 0.004) when comparing the highest and lowest quartiles of dietary pattern scores. No significant relationship was observed with the Western and drinker patterns. Although the response rate for eligible, recruited subjects was relatively low, our results suggest that the fruits and vegetables dietary pattern reduces pancreatic cancer risk among men. © 2004 Wiley-Liss, Inc.

Because of its incidence and poor prognosis, pancreatic cancer is one of the most important human malignancies. Approximately 216,300 new cases occur worldwide each year.1 In Canada in 2004, it is the fourth leading cause of cancer-related death in both men and women.2 Survival is extremely low, with a 5-year rate of <5%3 and a case-fatality ratio of 0.99.2

Few risk factors for pancreatic cancer have been consistently identified. About 30% of cases are attributable to smoking.1, 4 An increased frequency of pancreatic cancer has been suggested among individuals with a long-standing history of diabetes,5, 6 chronic pancreatitis,7, 8 pernicious anemia9 and inheritable syndromes such as familial adenomatous polyposis.10, 11 An elevated risk of pancreatic cancer has also been seen in families with a history of breast cancer and BRCA2 mutations.12, 13 It has been estimated that 30–50% of pancreatic cancers may be due to dietary factors,14 though for most dietary components the overall evidence is equivocal. A number of investigators have recorded a heightened risk in relation to excessive intake of fat,15 grilled and salted/smoked red meats,16, 17 dairy products18 and cholesterol,19, 20 though some of these associations were not confirmed in a large prospective cohort of women.21 In contrast, pancreatic cancer risk has been found to decrease with increased consumption of fresh fruits and vegetables20, 22 as well as foods containing no preservatives or additives19, 20 and with increased carbohydrate and energy intake.23 However, no studies to date have examined broader food consumption, which provides a more comprehensive representation of dietary intake involving a large number of dietary components working together. The present investigation was undertaken to explore the possible role of major dietary patterns in the etiology of pancreatic cancer within the Canadian NECSS.

Material and methods

  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. APPENDIX I

Study population

The NECSS is a multisite, population-based, case-control study involving 21,022 participants with one of 19 types of cancer identified through cancer registries in 8 of the 10 Canadian provinces (Alberta, British Columbia, Manitoba, Newfoundland, Nova Scotia, Ontario, Prince Edward Island and Saskatchewan). The present investigation is based on the pancreatic cancer portion, and the study population has been described elsewhere.24 Briefly, between April 1, 1994, and December 31, 1997, the participating provincial registries identified pancreatic cancer cases as early as possible in the registration process, to minimize loss of subjects because pancreatic cancer is a rapidly fatal disease. All cases included in the NECSS were confirmed histologically and defined according to the WHO's International Classification of Diseases, rubric 157.25 Data were intentionally collected by proxy only in Ontario, where 132 of 271 returned questionnaires (49%) were completed by proxy respondents. Proxy data were obtained for only 8 cases in the other provinces. However, like most other studies of pancreatic cancer that collected information directly from case subjects, the overall proportion of eligible cases that responded was low. Among men diagnosed with pancreatic cancer, 30% had died before interview and consent was not granted by physicians for an additional 15%. Among women, 28% had died before they could be contacted and the attending physician refused consent to approach patients for an additional 16%. Among those sent questionnaires, response rates were 55% for men and 56% for women. The vast majority of cases were ascertained within 1– 3 months of diagnosis; physician consent to send questionnaires to patients was obtained within 1 month, and approximately 70% of questionnaires were returned within 2 months of mailing.

The NECSS used frequency matching in selecting the control population to achieve age and gender distributions similar to those of all cancer cases combined. Based on the projected number of incident cancer cases by province, questionnaires were mailed to 8,117 subjects throughout the 1996 calendar year, using the same protocol as for cases. Questionnaires were returned for 573 controls (7.4%) because of a wrong or old address, with no updated address found. Strategies for control selection varied by province, depending on data accessibility. In Prince Edward Island, Nova Scotia, Manitoba, Saskatchewan and British Columbia, provincial health insurance plans were tapped to obtain a random sample of the population stratified by age and gender. In each of these provinces, >95% of residents are covered by public health-care plans. Active military personnel and their families as well as indigenous peoples were excluded because they were covered by other plans. In Ontario, Ministry of Finance data were used to derive a stratified random sample, while Newfoundland and Alberta adopted a random digit-dialing method to enroll a population sample. A total of 5,039 controls were selected to serve as a common control group for all types of cancer. Response rates of 65% and 71% were obtained from the respective male and female control populations.

Questionnaires, with telephone follow-up for clarification when necessary, were mailed to subjects to obtain information on residential and occupational history as well as other risk factors for cancer. Included were questions on smoking history, height, weight, physical activity, ethnicity and education.

Dietary assessment

Food consumption data were obtained via a semiquantitative FFQ developed after 2 instruments that have been widely validated: the short Block questionnaire26 and the Willett questionnaire.27 Subtle changes were made to the questionnaire items to take into account variations between American and Canadian dietary practices. The FFQ includes questions on 69 different food and beverage items as well as the frequency of consumption and the amounts consumed. Participants were asked how often they had consumed these foods per week in the time period 2 years prior to interview. Daily energy intake was determined by summing the caloric content of each food and beverage item, based on reference values given in the 1997 CNF. Food items were then classified into 38 groups (Appendix Table I). The food grouping design was based on the CNF, similarity of nutrient profiles or processing and culinary usage. Some individual foods were retained either because they did not fit within a broader group (e.g., nuts, eggs and tea) or because they represented distinct nutritional structures (e.g., wine, beer and supplements).

Statistical analysis

In total, data were collected on 630 cases (355 men, 275 women) and 5,039 controls (2,547 men, 2,492 women). We excluded controls with missing age or who were under 30 years of age (n = 226) because pancreatic cancer cases are usually 30 years or older. Furthermore, subjects with daily intake <800 kcal (28 cases, 44 controls) or >4,500 kcal (17 cases, 20 controls) were excluded because such intakes are unrealistic and, hence, of questionable validity. A total of 585 cases (335 men, 250 women) and 4,779 controls (2,422 men, 2,357 women) were eligible for analysis.

Based on the food and beverage items retrieved from the FFQ, major dietary patterns were derived by factor analysis, using an orthogonal rotation (VARIMAX) procedure. This procedure minimizes the correlation between factors and results in a simpler data structure with greater interpretability.28 The correlation matrix, rather than the covariance matrix, was employed since the variables were not all of the same type and magnitude.29 In determining the number of factors to retain, we considered eigenvalues (>1.5), the Scree plot and factor interpretability. This analysis suggested 3 broad dietary patterns in the data. A dietary pattern score was computed for each study subject by summing intakes of each food group weighted by its factor loading,30 which represents the relative contribution of that food group. Positive loading indicates that the dietary variable is positively associated with the factor, whereas negative loading reflects an inverse association with the factor. Labeling of dietary patterns was descriptive, based on our interpretation of the pattern structures.

To assess the relationships of the dietary patterns with selected lifestyle variables, we calculated the χ2 for trend and p value with the median value for each variable according to categories of dietary patterns. To determine the associations between dietary pattern scores and pancreatic cancer risk, subjects were divided into 4 categories according to quartiles of dietary pattern scores among the overall study population. ORs and 95% CIs were calculated using unconditional logistic regression. Risk estimates were initially adjusted only for age and total energy intake. Multivariate models were also used to adjust more fully for matching variables (age group and province), lifetime cigarette consumption (0, >0–15 and >15 pack-years), BMI (<23, 23–24.9, 25–26.9, 27–29.9 and ≥30 kg/m2), physical activity (total number of hours/month of moderate and strenuous activity with a weighting factor of 9/5 for strenuous activities),31 educational attainment (years) and total energy intake (as a continuous variable). Furthermore, analysis was run excluding all proxy interviews, and models were fitted for men and women separately. Tests for linear trend in logistic regression analysis were performed with scores derived from the median values of categorized variables and entered into the model as successive integers. All tests of statistical significance were 2-sided. Analyses were conducted with SPSS for Windows (release 10.02, 1999; SPSS, Chicago, IL).

Results

  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. APPENDIX I

Demographic and other selected characteristics of the study population are presented in Table I. Regardless of gender, the age distributions of cases and controls were fairly similar, and 98% of the study population was Caucasian. Cases were more likely to consume lower amounts of fruit and vegetables, with higher amounts of tobacco and greater total energy intake than controls. Both male and female cases were more likely to be interviewed without the use of a proxy respondent, while there were no appreciable differences between cases and controls with respect to BMI 2 years prior to the diagnosis of cancer.

Table I. Selected Characteristics of the Study Population (N = 5,146), NECSS, Canada, 1994–1997
CharacteristicsMale (n = 2,600)Female (n = 2,546)
CasesControlsCasesControls
n%n%n%n%
  • 1

    Subjects with unrealistically low (<800 kcal) or high (>4,500 kcal) intakes were excluded.

Age (years)        
 30–3431122642583
 35–39721356421225
 40–447210851252179
 45–49216111513532014
 50–543210146723926011
 55–5946142119291225411
 60–64601833615502033615
 65–69812452022602436616
 70–74782357625552236316
Ethnicity        
 Caucasian331992,25797242992,23797
 Other418381593
Cigarette consumption (pack-years)        
 0752255324107431,14850
 >0–15752271532592368030
 >151855699744843446820
Use of proxy        
 Yes872631542221
 No248742,26299196782,29499
BMI 2 years prior to diagnosis(kg/m2)        
 <23621941318893681336
 23–24.9601844520441748621
 25–27.9772357425351433915
 28–29.9712150022321335215
 ≥30651933315502030613
    CasesControlsCasesControls
Mean fruit/vegetable consumption (servings/day)4.34.64.74.8
Interquartile range (servings/day)2.62.42.62.7
Mean daily energy intake1 (kcal)2,0621,9151,8021,752
859877654756

Three major dietary patterns were identified (Table II). The first was labeled “Western” since, as in previous studies,32, 33 it reflected intake of foods related to a Western diet, such as processed meats, sweets and desserts, refined grains and potatoes. The second was labeled “fruits and vegetables” since it was heavily weighted by fresh fruits and cruciferous vegetables, with a modest involvement of green and dark yellow vegetables and less refined grains. The third was labeled “drinker”, reflecting elevated liquor, wine and beer consumption. These 3 patterns explained 16.7% (8.2%, 4.4% and 4.1%, respectively) of the total variation in food intake among individuals.

Table II. Factor-Loading Matrix for the 3 Dietary Patterns in the Study Population, NECSS, Canada, 1994–1997
Food groupMean intake (g/day)Dietary pattern
WesternFruits and vegetablesDrinker
  • 1

    For simplicity and ease of interpretation, factor loadings <0.15 in absolute value are indicated by a dash.

Processed meats24.300.521
Sweets and desserts39.830.49
Refined grains20.030.48−0.33
Potatoes134.140.43
Processed fish11.260.41
Organ meats4.880.39
Soft drinks263.700.36
Legumes and legume products28.620.360.28
Snacks7.130.34
Margarine5.510.33
Nuts6.130.32
Fried potatoes18.680.28
Mayonnaise6.230.26
Eggs16.380.24
Oils0.520.24
High-fat dairy products331.180.23
Red meats85.840.23
Fruits278.950.260.45
Cruciferous vegetables21.76 0.43
Green and other vegetables32.75 0.38
Dark yellow vegetables37.200.260.37
Low-fat dairy products444.970.180.33
Rice, pasta92.440.32
Whole grains34.710.32
Soups87.920.31
Poultry products16.540.31
Cold breakfast cereals86.840.29
Tomatoes62.230.190.28
Supplements0.03−0.240.27
Water681.820.24
Fruit juices244.610.24
Fish26.790.24
Liquor13.070.74
Wine29.060.73
Beer146.620.61
Tea331.180.21−0.24
Spices and herbs0.54−0.170.20
Coffee536.58−0.180.20

The relationships between selected lifestyle variables and the 3 dietary patterns are summarized in Table III. Age was positively associated with the Western pattern (p = 0.0002) but not with the fruits and vegetables or drinker pattern. Total energy intake was positively linked with the Western pattern (p = 0.002) but not with the fruits and vegetables or drinker pattern. No relationship between BMI and any of the 3 dietary patterns was apparent. Smoking was positively associated with both the Western (p = 0.0003) and the drinker (p = 0.011) patterns but not with the fruits and vegetables pattern. Physical activity was inversely related to the Western pattern (p = 0.001) but not to the fruits and vegetables or drinker pattern. Educational attainment was inversely associated with the Western pattern only (p = 0.0003).

Table III. Associations Between the 3 Dietary Patterns with Selected Lifestyle Variables, NECSS, Canada, 1994–1997
Quartile1Number of cases/controlsAge2 (years)Energy intake2 (kcal/day)BMI2 (kg/m2)Smoking2 (pack-years)Physical activity2 (hr/month)Educational attainment2 (years)
  • 1

    Subjects were divided into 4 categories according to quartiles of the lifestyle variable in the study population.

  • 2

    Median value.

Western pattern       
 1 (low)1121/1,163591,48424.6215.213.0
 2137/1,148601,68025.0412.212.0
 3147/1,142611,84525.5711.012.0
 4 (high)180/1,108632,09925.7910.611.0
 p <0.01<0.010.29<0.01<0.01<0.01
Fruits and vegetables pattern       
 1 (low)143/1,133581,66625.6147.811.5
 2160/1,117611,63825.2511.312.0
 3148/1,139611,80425.0213.912.0
 4 (high)134/1,172631,90024.8115.612.0
 p 0.490.220.550.900.600.74
Drinker pattern       
 1 (low)127/1,173641,81725.1111.2511
 2140/1,118601,70725.2111.6212
 3132/1,148581,66525.0613.2812
 4 (high)176/1,122601,80225.112.912.1212
 p 0.860.090.190.010.750.25

Table IV presents the ORs for pancreatic cancer according to the 3 dietary patterns. After adjustment for age and total energy intake, pancreatic cancer risk for the fruits and vegetables pattern was reduced among men (OR = 0.74, 95% CI 0.52–1.05, p = 0.036) when comparing the highest to the lowest quartile of dietary pattern scores. This reduction in risk was more pronounced in the fully adjusted model (OR = 0.55, 95% CI 0.32–0.93, p = 0.008). Moreover, exclusion of proxy data did not materially affect the results (OR = 0.51, 95% CI 0.29–0.89, p = 0.004). In contrast, pancreatic cancer risk was significantly increased with the drinker pattern (OR = 1.60, 95% CI 1.14–2.34, p = 0.004) after adjusting for age and total energy intake. However, the strength of this association was weakened when additional covariates were included in the model, and the trend was no longer significant (OR = 1.35, 95% CI 0.81–2.25, p = 0.18). No significant relationship was observed among females.

Table IV. OR for Pancreatic Cancer According to Quartile of Dietary Pattern Score, NECSS, Canada, 1994–1997
AdjustmentQuartilesp1 for trend
1 (low)234 (high)
  • 1

    Two-sided Wald tests.

  • 2

    Adjusted for age (in 5-year groups), smoking (0, >0–15 and >15 pack-years), BMI (5 categories), physical activity (total number of hr/month of moderate and strenuous activities), province (8 groups), educational attainment (years) and total energy intake (as a continuous variable).

  • 3

    Same as above but excluding subjects with proxy interview.

Western pattern     
 Males     
  Age- and energy-adjusted OR (95% CI)1.001.10 (0.76–1.58)0.95 (0.66–1.37)1.10 (0.76–1.57)0.72
  Fully adjusted model 12: OR (95% CI) (cases/controls) 1.57 (0.94–2.64)1.14 (0.66–1.96)1.30 (0.75–2.25)0.56
58/46179/53680/609118/659 
  Fully adjusted model 23: OR (95% CI) (cases/controls) 1.51 (0.88–2.59)1.23 (0.70–2.14)1.25 (0.71–2.23)0.49
42/49661/57561/64884/700 
 Females     
  Age- and energy-adjusted OR (95% CI)1.001.03 (0.70–1.49)1.34 (0.92–1.94)1.26 (0.85–1.88)0.12
  Fully adjusted model 1: OR (95% CI) (cases/controls) 1.18 (0.67–2.01)1.42 (0.82–2.47)1.40 (0.76–2.59)0.10
63/70258/61267/53362/449 
  Fully adjusted model 2: OR (95% CI) (cases/controls) 1.18 (0.69–2.02)1.35 (0.78–2.37)1.36 (0.73–2.54)0.14
52/72446/62753/54545/459 
Fruits and vegetables dietary pattern     
 Males     
  Age- and energy-adjusted OR (95% CI)1.001.21 (0.89–1.64)0.96 (0.69–1.34)0.74 (0.52–1.05)0.04
  Fully adjusted model 1: OR (95% CI) (cases/controls) 0.93 (0.61–1.43)0.78 (0.49–1.24)0.55 (0.32–0.93)<0.01
99/69597/55975/50464/507 
  Fully adjusted model 2: OR (95% CI) (cases/controls) 0.87 (0.55–1.35)0.80 (0.50–1.29)0.51 (0.29–0.90)<0.01
81/74571/60552/53844/531 
 Females     
  Age- and energy-adjusted OR (95% CI)1.000.99 (0.70–1.40)1.05 (0.73–1.51)1.06 (0.72–1.58)0.59
  Fully adjusted model 1: OR (95% CI) (cases/controls) 1.30 (0.73–2.31)1.30 (0.73–2.30)1.06 (0.58–1.96)0.98
79/68168/64159/57244/402 
  Fully adjusted model 2: OR (95% CI) (cases/controls) 1.35 (0.75–2.42)1.30 (0.73–2.34)1.06 (0.57–1.96)0.92
32/45251/57662/65251/675 
Drinker dietary pattern     
 Males     
  Age- and energy-adjusted OR (95% CI)1.001.42 (0.98–2.06)1.22 (0.84–1.76)1.60 (1.14–2.34)<0.01
  Fully adjusted model 1: OR (95% CI) (cases/controls) 1.38 (0.82–2.31)0.88 (0.52–1.50)1.51 (0.92–2.47)0.09
58/49272/47773/576132/720 
  Fully adjusted model 2: OR (95% CI) (cases/controls) 1.28 (0.74–2.19)0.81 (0.47–1.42)1.35 (0.81–2.25)0.18
40/51153/52955/621100/748 
 Females     
  Age- and energy-adjusted OR (95% CI)1.000.99 (0.70–1.40)1.05 (0.73–1.51)1.06 (0.72–1.58)0.59
  Fully adjusted model 1: OR (95% CI) (cases/controls) 0.86 (0.51–1.43)0.78 (0.45–1.36)0.95 (0.53–1.71)0.80
79/68168/64159/57244/402 
  Fully adjusted model 2: OR (95% CI) (cases/controls) 0.81 (0.48–1.36)0.75 (0.43–1.31)0.83 (0.46–1.52)0.53
63/69252/67148/58633/406 

Subgroup analyses of pancreatic cancer risk by age, BMI, educational attainment and smoking status revealed no effect modification (data not shown).

Discussion

  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. APPENDIX I

The present population-based, case-control study found a significant inverse association between the fruits and vegetables dietary pattern and pancreatic cancer risk in males. Our results are consistent with previous investigations, particularly those that examined associations between consumption of fruits and vegetables and pancreatic cancer risk.

A multicenter, population-based, case-control study in the United States34 discerned a statistically significant inverse trend in pancreatic cancer risk with increasing consumption of cruciferous vegetables in men (OR = 0.5, p = 0.04) and women (OR = 0.4, p = 0.002). In that study, frequent consumption of raw fruits was associated with a nonsignificant 20% decreased risk among men but with little or no risk reduction among women. In a population-based, case-control investigation in Japan,22 raw vegetable consumption was associated with decreased pancreatic cancer risk (OR = 0.71, 95% CI 0.51–0.99). However, when subgroup analysis was performed by gender, the inverse association was no longer significant in both males and females. In a population-based, case-control study in Shanghai,35 a significant inverse association between risk of pancreatic cancer and consumption of both fruits and vegetables was detected only among men. In addition, 3 other investigations,18, 19, 36 which did not take into account effect modification by gender, likewise report that fruits and vegetables may be preventive against pancreatic cancer.

The fruits and vegetables dietary pattern was loaded heavily on fresh fruits (e.g., apples, oranges, bananas and cantaloupes) and cruciferous vegetables (e.g., broccoli, cabbage, cauliflower and Brussels sprouts). These foods are important sources of nutrients, with vitamin C and carotenoids being the main contributors. They also contain high concentrations of unique phytochemicals with potential cancer-preventing properties. Several biologic mechanisms that might explain the protective influence of fruits and vegetables on carcinogenesis have been proposed. Vitamin C has been hypothesized to protect against cancer at many sites by preventing oxidative damage to polyunsaturated fatty acids and DNA through antioxidant and free radical–scavenging actions.37 Vitamin C has also been shown to inhibit N-nitrosation, a process by which carcinogenic substances, such as N-nitroso compounds, are formed in food and in the gastrointestinal tract that may be involved in the etiology of pancreatic cancer.38 Carotenoids are involved in putative cancer-preventing activity via their antioxidant properties, stimulation of gap junction intracellular communication, induction of detoxifying enzymes, inhibition of cellular proliferation39 and enhancement of immune function.40 I3C, a phytochemical abundant in cruciferous vegetables, increases the ratio of 2/16α-(OH)estrone, a metabolite ratio able to prevent or halt carcinogenesis.41 I3C has also been reported to operate through other pathways, including induction of apoptosis,42 inhibition of microtubule assembly43 and decreased effect of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone, a known tobacco carcinogen.44 These different mechanisms, which may operate simultaneously, provide support for the hypothesis that high fruits and vegetables dietary pattern scores may be related to reduced pancreatic cancer risk.

It has been argued that consumption of foods commonly considered to be healthy, such as fruits and vegetables, may be part of an overall prudent lifestyle. In this investigation, age, total energy intake, BMI, smoking status, physical activity and educational attainment were not significantly related to the fruits and vegetables dietary pattern. Therefore, it is unlikely that residual confounding from these variables may explain the observed inverse association between this dietary pattern and pancreatic cancer risk.

As expected, the observed relationships between lifestyle and both the Western and drinker dietary patterns were in the expected direction. Higher Western pattern scores were associated with older age, elevated energy intake, heavy cigarette smoking, limited physical activity and lower educational attainment. Higher drinker pattern scores were related to heavy cigarette smoking.

The major strengths of our study include the large number of pancreatic cancer cases, histologic diagnosis, dietary pattern analysis and general population sampling. The large sample size permitted subgroup analyses by gender, age group and smoking status with reasonable statistical power. Histologic confirmation of diagnosis reduced the possibility of disease misclassification. Our dietary pattern analysis extended beyond nutrients and foods and was designed to account for complex interactions among dietary components and their cumulative effects. A population-based approach facilitates extrapolation of results to the general population.

Nevertheless, some limitations may be kept in mind when commenting on our results. Since the assessment of dietary exposure was retrospective, recall bias cannot be completely excluded. A prospective cohort approach has several advantages when studying associations between nutritional factors and cancer risk. However, because of the relatively low incidence of pancreatic cancer, the largest cohort to date examining diet and pancreatic cancer risk confirmed only 178 pancreatic cancer cases in 18 years of follow-up.21 We could not adjust OR estimates for the potentially confounding effect of diabetes mellitus and family history of pancreatic cancer since this information was not collected at baseline. Nevertheless, we expected that diabetes did not confound the association between dietary patterns and pancreatic cancer risk because of the likelihood that diet represents the initial risk factor for both chronic diseases, rather than lying on the causal pathway. In addition, it has been suggested that the significance of diabetes is much weaker if cases of recent onset are excluded.1 As well, it is unlikely that the confounding effect of family aggregation of pancreatic cancer may explain the significantly reduced risk we found because genetic/familial predisposition is relatively rare.6, 45 Although the FFQ was modeled after 2 questionnaires that have been tested in the past on several populations for validity and reproducibility, the NECSS has not validated this instrument with respect to dietary patterns in the Canadian population. As a consequence, it may induce a degree of measurement error and obscure some relationships.

Extensive use was made of proxy respondents in Ontario, with information from 48% of eligible cases collected through proxy interviews. In contrast, only 0.2% of controls were interviewed by proxy. Bias may have been introduced in the present investigation if rapidly fatal cases had a different etiology from cases with longer survival or if the dietary patterns under study influenced survival. However, our findings did not change materially after exclusion of proxy data, indicating that the relative risk associated with dietary patterns was similar for proxy and nonproxy respondents. As in other epidemiologic studies of pancreatic cancer, another limitation is the consequence of early and high case fatality associated with the disease. Cases who died before the questionnaire study were not included. However, since there was no discrimination based on their demographic characteristics and lifestyle factors, such as age, smoking or educational attainment, the survivors were still representative of the study population and bias of this kind, if any, would not be substantial.

Although we were able to generate 3 distinct dietary patterns that are easy to interpret in the study population, the percentage of variance in food consumption among individuals that is explained by these patterns was relatively low. However, this overall variance was somewhat similar to that observed in other studies31, 46 that used 3 dietary patterns to predict cancer and cardiovascular disease risk. In addition, factor analysis requires some arbitrary but important decisions, including the number of factors to retain, their labels and the method of rotation. We conducted sensitivity analysis by performing subgroup analyses by age group (<61 years, ≥61 years) and smoking status (ever/never smokers) and by including subjects with extreme intakes (<800, >4,500 kcal), and we tested oblique instead of orthogonal rotation. These analyses did not significantly alter our findings, indicating that the identified patterns were robust. Labeling of the 3 patterns was somewhat subjective but similar to that documented in other populations.32, 33, 46

In conclusion, our current data suggest that the fruits and vegetables dietary pattern reduces pancreatic cancer risk among male Canadians. A dose–response relationship was apparent. Further epidemiologic investigations assessing the overall role of diet in pancreatic cancer are warranted since the only way to cope with the rapid and inexorable lethality of this cancer is prevention.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. APPENDIX I

The present study was supported by the National Cancer Institute of Canada, through a fellowship awarded to A.N., and by the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Engineering Research Council of Canada and the McLaughlin Foundation, through a research chair awarded to D.K. Health Canada supported the NECSS, in collaboration with the provincial cancer registries, through its program Health and the Environment. The Canadian Cancer Registries Epidemiology Research Group comprises a principal investigator from each of the provincial cancer registries: B. Paulse, Newfoundland Cancer Foundation; R. Dewar, Nova Scotia Cancer Registry; D. Dryer, Prince Edward Island Cancer Registry; N. Kreiger, Cancer Care Ontario; E. Kliewer, Cancer Care Manitoba; D. Robson, Saskatchewan Cancer Foundation; S. Fincham, Division of Epidemiology, Prevention and Screening, Alberta Cancer Board; and N. Le, British Columbia Cancer Agency.

References

  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. APPENDIX I
  • 1
    Steward BW, Kleihues P, eds. World cancer report. Lyon: IARC Press, 2003. 24852.
  • 2
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APPENDIX I

  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. APPENDIX I
Table I. Food Groupings Used for Analysis of Dietary Patterns, NECSS, Canada, 1994–1997
Food group or foodFood items included
Low-fat dairy productsSkim milk, 1% milk
High-fat dairy productsWhole milk, 2% milk, cheese, butter, ice cream
EggsEggs
Spices and herbsPepper, salt, garlic and onions added to food
MargarineMargarine on bread or vegetables
MayonnaiseMayonnaise, salad dressing
OilsOils used in cooking
Poultry productsChicken, turkey
SoupsSoups with vegetables
Processed meatsLuncheon meats, sausage, bacon, hot dogs, smoked meat
Cold breakfast cerealsBran or granola cereals, other cold cereals
FruitsApple, orange, banana, cantaloupe and other fruits
Fruit juicesOrange, citrus, grape, apple, other juice drinks or frozen juice
Cruciferous vegetablesBroccoli, cabbage, cauliflower, Brussels sprouts
Dark-yellow vegetablesCarrots, yellow (winter) squash
TomatoesTomatoes, tomato juice, tomato sauce
PotatoesPotatoes, sweet potatoes
Green and other vegetablesSpinach, other vegetables
NutsNuts
Red meatsBeef, pork or lamb as main dish; hamburger; mixed beef dishes
Organ meatsBeef, pork, turkey and chicken liver
FishFresh, frozen or canned fish
Processed fishSmoked, salted or dried fish
Legumes and legume productsTofu, soybeans, beans, lentils, peanut butter
Whole grainsDark or whole-grain bread, cooked cereals
Refined grainsWhite bread or rolls
Rice, pastaRice, macaroni, spaghetti or noodles
Sweets and dessertsChocolate, cookies, cake, doughnuts, pastry, pies
Fried potatoesFrench fries, fried potatoes
SnacksPotato chips
CoffeeCoffee
TeaTea
Soft drinksSoda, soft and powdered drinks
WaterTap water, bottled water
BeerBeer
WineWine
LiquorLiquor
SupplementsMultiple vitamin or mineral supplements