Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 7-Day Trial for You or Your Team.

Learn More →

Prospective Study of Major Dietary Patterns and Colorectal Cancer Risk in Women

Prospective Study of Major Dietary Patterns and Colorectal Cancer Risk in Women Abstract A number of prospective cohort studies have examined the relations of individual dietary variables to risk of colorectal cancer. Few studies have addressed the broader eating patterns that reflect many dietary exposures working together. Using data from a prospective study of 61,463 women, with an average follow-up period of 9.6 years (between 1987 and 1998) and 460 incident cases of colorectal cancer, the authors conducted a factor analysis to identify and examine major dietary patterns in relation to colorectal cancer risk. Using proportional hazards regression to estimate relative risks, the authors found no clear association between a “Western,” “healthy,” or “drinker” dietary pattern and colorectal cancer risk. However, the data suggested that consuming low amounts of foods that constitute a “healthy” dietary pattern may be associated with increased risks of colon and rectal cancers. An inverse association with the “healthy” dietary pattern was found among women under age 50 years, although the number of cancers in this age group was limited and interpretation of this finding should be cautious. In this age group, relative risks for women in increasing quintiles of the “healthy” dietary pattern, compared with the lowest quintile, were 0.74 (95% confidence interval (CI): 0.41, 1.31), 0.69 (95% CI: 0.39, 1.24), 0.59 (95% CI: 0.32, 1.07), and 0.45 (95% CI: 0.23, 0.88) (p for trend = 0.03). The role of overall eating patterns in predicting colorectal cancer risk requires further investigation. cohort studies, colorectal neoplasms, diet, factor analysis, statistical CI, confidence interval Colorectal cancer is the second-leading cause of cancer death in the United States and other developed countries (1), yet the etiology of this disease remains largely unknown. Given the role of the colorectal tract as a conduit for ingested food and the many potentially carcinogenic and anticarcinogenic substances contained in food (2), dietary exposures have been widely studied in relation to colorectal cancer. Foods and food groups that have shown some (albeit inconsistent) associations with colorectal cancer risk in various studies include fruits and vegetables, meat, dietary fiber, total fat, eggs, dairy products, coffee, tea, and alcohol (2). However, few studies have addressed the broader eating patterns that reflect many dietary exposures working together. One method of identifying and examining larger dietary patterns that may be associated with disease is factor analysis (3), which aggregates interrelated variables into composite “factors.” These factors represent dietary patterns in the study population and help distinguish individuals according to the combination of foods they choose to eat. These dietary patterns might better explain disease occurrence than individual dietary exposures. Therefore, using factor analysis, we identified and examined major dietary patterns and their relation to colorectal cancer risk in a large prospective cohort study of Swedish women. MATERIALS AND METHODS The Swedish mammography screening cohort From 1987 to 1990, a population-based mammography screening program was introduced in two counties in central Sweden. In Västmanland County, all women born between 1917 and 1948 received a mailed invitation to be screened via mammography between March 1987 and March 1989 (n = 41,786), together with a 6-page questionnaire; 31,735 women (76 percent) returned completed questionnaires. In Uppsala County, all women born between 1914 and 1948 were invited to the screening, and they received the questionnaire between January 1988 and December 1990 (n = 48,517); 34,916 of these women (72 percent) returned completed questionnaires. Hence, questionnaires completed prior to mammography were obtained from 66,651 women (73.8 percent) in the source population. The questionnaire included items about age, weight (kg), height (cm), education, and diet. For the present analyses, we excluded women who were outside of the age range 40–74 years (n = 165); those with missing (n = 707) or incorrect (n = 415) identification numbers; and those for whom we lacked information on the date of the questionnaire (n = 608), the date of moving out of the study area (n = 79), or the date of death (n = 16). After further exclusion of 793 women with extreme energy intake estimates that probably reflected careless completion of the dietary questionnaire (below or above the mean ±3 standard deviations for loge-transformed calories; cutpoints: 417 kcal and 3,729 kcal), the cohort was restricted to 63,868 women. Through linkage to the Swedish Cancer Registry, we identified and excluded all women with a previous diagnosis of cancer other than nonmelanoma skin cancer (n = 2,405). Thus, the study cohort comprised 61,463 women at the start of follow-up. Dietary assessment The self-administered food frequency questionnaire included questions on 67 commonly eaten foods. Participants were asked how often, on average, they had consumed these foods over the past 6 months. Eight predefined frequency categories ranging from “never/seldom” to “four or more times per day” were used. For each food item, we converted these frequencies to frequency per week. For nutrient calculations, we used age-specific portion sizes (ages 40–52, 53–65, and 66–74 years) based on mean values from 5,922 days of weighed food records kept by 213 women randomly selected from the study population. Food groupings The food grouping scheme was based on similarity of nutrient profiles or culinary usage among the foods and was somewhat similar to that used in previous studies (4, 5). Some individual food items were kept, either because it was inappropriate to incorporate them into a certain food group (e.g., eggs, margarine, tea, and pea soup) or because they were assumed to represent distinct dietary patterns (e.g., wine, liquor, beer, and soda). After the foods had been grouped, 24 variables were retained for the factor analysis (appendix table 1). APPENDIX TABLE 1. Food groupings used for analyses of dietary patterns in a population of Swedish women, 1987–1990 Food or food group  Food items included  Vegetables  Beets, carrots, cabbage, lettuce, spinach, tomatoes, cucumbers  Fruits  Apples, pears, oranges, grapefruit, bananas  Whole grains  Whole-grain soft bread, crisp bread, oatmeal and other whole-grain hot cereals  Refined grains  White bread, rice, spaghetti, waffles, pancakes  Cereal  Assorted breakfast cereals, muesli  Low-fat dairy products  Low-fat milk, reduced-fat (medium) milk, low-fat yogurt  High-fat dairy products  Butter, cheese, whole milk, whole yogurt, ice cream  Fish  Salmon, mackerel, sardines, tuna, herring, other fish, lobster, shrimp, crab, mussels  Poultry  Chicken  Meat  Beef, chopped meat, minced meat, liver, liver pâté  Processed meat  Bacon, sausage, blood pudding  Eggs  Eggs  Margarine  Margarine  Pea soup  Pea soup, bean soup  Potatoes  Boiled potatoes, fried potatoes, French fries  Snacks  Potato chips, other snack chips, popcorn, fried and salted nuts  Sweets  Assorted candy, caramels, chocolate, cookies, sugar (e.g., sugar cubes), sweet soups, marmalade, jams  Juice  Juice  Soda  Carbonated and uncarbonated sweetened drinks  Tea  Tea  Coffee  Coffee  Beer  Beer (various proofs)  Wine  Wine  Liquor  Liquor (various proofs)  Food or food group  Food items included  Vegetables  Beets, carrots, cabbage, lettuce, spinach, tomatoes, cucumbers  Fruits  Apples, pears, oranges, grapefruit, bananas  Whole grains  Whole-grain soft bread, crisp bread, oatmeal and other whole-grain hot cereals  Refined grains  White bread, rice, spaghetti, waffles, pancakes  Cereal  Assorted breakfast cereals, muesli  Low-fat dairy products  Low-fat milk, reduced-fat (medium) milk, low-fat yogurt  High-fat dairy products  Butter, cheese, whole milk, whole yogurt, ice cream  Fish  Salmon, mackerel, sardines, tuna, herring, other fish, lobster, shrimp, crab, mussels  Poultry  Chicken  Meat  Beef, chopped meat, minced meat, liver, liver pâté  Processed meat  Bacon, sausage, blood pudding  Eggs  Eggs  Margarine  Margarine  Pea soup  Pea soup, bean soup  Potatoes  Boiled potatoes, fried potatoes, French fries  Snacks  Potato chips, other snack chips, popcorn, fried and salted nuts  Sweets  Assorted candy, caramels, chocolate, cookies, sugar (e.g., sugar cubes), sweet soups, marmalade, jams  Juice  Juice  Soda  Carbonated and uncarbonated sweetened drinks  Tea  Tea  Coffee  Coffee  Beer  Beer (various proofs)  Wine  Wine  Liquor  Liquor (various proofs)  View Large Identification of colon and rectal cancer cases and follow-up of the cohort Cases were identified through matching of the cohort with computerized regional cancer registers for colon and rectal cancer diagnoses made in the two study counties through December 31, 1998. Quality control of the regional cancer registers was performed through comparison with medical journals for the follow-up period of 1987–1994. Completeness of follow-up for colorectal cancers was documented to be 98 percent, as was the completeness of the Swedish Cancer Registry as a whole (6). We identified 460 colorectal cancers in total. Colon cancers were defined as those occurring above the peritoneal delineation of the abdominal cavity (n = 291), and rectal cancers were those occurring below this delineation (n = 159). Ten case women were diagnosed with both colon and rectal cancer. For the purposes of subanalysis by colon cancer site, proximal colon cancers were defined as those arising between the cecum and the splenic flexure (n = 118), and distal colon cancers were defined as those arising between the descending colon and the sigmoid colon (n = 101). Seventy-two colon cancers were of unspecified location. Dates of death in the cohort were ascertained through the Swedish Death Register. For women who moved out of the study area, the date of moving was obtained by matching of the cohort with the Swedish Population Register, which is computerized and continuously updated. Statistical analysis Factor analysis (principal-components) was used to derive food patterns based on the 24 food variables in our data. We conducted the analysis using the FACTOR procedure in SAS (7). We rotated the factors by means of an orthogonal transformation (the varimax rotation function in SAS) to obtain a simpler structure with greater interpretability. In determining the number of factors to retain, we considered eigenvalues (>1) and the Scree test (3). An overall dietary pattern score was created for each individual by weighting her intake of each food contributing to that pattern by the relative contribution those foods made (factor loadings) (3). A positive loading indicates that the dietary variable is positively associated with the factor, and a negative loading indicates an inverse association with the factor. All data presented in this paper are from the varimax rotation. Labeling of dietary patterns was based on our interpretation of the data and was arbitrary, in that other labels might have been equally suited to the data. Cox proportional hazards models were used to estimate hazard rate ratios and 95 percent confidence intervals relating the factors to the occurrence of invasive colorectal cancer. Follow-up was censored at the date of death, the date of migration out of the study area, or the end of the follow-up period (December 31, 1998). As a basis for the trend tests, scores were constructed from the median values of categorized variables and were placed into the model as successive integers. RESULTS Three major dietary patterns were generated. Pattern 1 was labeled “healthy,” since it reflected the correlated intakes of foods commonly thought to be healthy, such as fruits and vegetables, fish and poultry, cereal and whole-grain breads, fruit juice, and low-fat dairy products (table 1). Pattern 2 was labeled “Western,” since it reflected the correlated intakes of foods associated with a Western diet: processed and red meats, soda and sweets, refined breads and potatoes, and high-fat dairy products. Pattern 3 was labeled “drinker,” since it primarily reflected the correlated intakes of wine, beer, and spirits. These dietary patterns were distinct in that most food items were important to only one major pattern (table 1). TABLE 1. Factor-loading matrix for the three major dietary patterns found in a population of Swedish women, 1987–1990   Pattern 1 (“healthy”)  Pattern 2 (“Western”)  Pattern 3 (“drinker”)  Vegetables  0.66  —*  —  Fruits  0.55  —  —  Fish  0.54  —  0.24  Whole grains  0.43  0.20  0.36  Low-fat dairy foods  0.40  —  −0.22  Poultry  0.36  —  0.30  Cereal  0.34  —  —  Eggs  0.32  0.21  0.19  Juice  0.27  —  —  Margarine  0.26  —  −0.22  Tea  0.19  —  0.17  Processed meats  —  0.58  —  Sweets  −0.17  0.54  —  Refined grains  —  0.54  —  High-fat dairy foods  —  0.46  —  Meats  0.33  0.46  0.20  Soda  —  0.45  —  Potatoes  —  0.43  −0.20  Pea soup  —  0.30  —  Coffee  —  0.18  —  Wine  —  —  0.67  Liquor  —  —  0.58  Beer  —  —  0.48  Snacks  —  0.16  0.37  % of variability explained  9.4  8.2  7.0    Pattern 1 (“healthy”)  Pattern 2 (“Western”)  Pattern 3 (“drinker”)  Vegetables  0.66  —*  —  Fruits  0.55  —  —  Fish  0.54  —  0.24  Whole grains  0.43  0.20  0.36  Low-fat dairy foods  0.40  —  −0.22  Poultry  0.36  —  0.30  Cereal  0.34  —  —  Eggs  0.32  0.21  0.19  Juice  0.27  —  —  Margarine  0.26  —  −0.22  Tea  0.19  —  0.17  Processed meats  —  0.58  —  Sweets  −0.17  0.54  —  Refined grains  —  0.54  —  High-fat dairy foods  —  0.46  —  Meats  0.33  0.46  0.20  Soda  —  0.45  —  Potatoes  —  0.43  −0.20  Pea soup  —  0.30  —  Coffee  —  0.18  —  Wine  —  —  0.67  Liquor  —  —  0.58  Beer  —  —  0.48  Snacks  —  0.16  0.37  % of variability explained  9.4  8.2  7.0  * For ease of interpretation, factor loadings below 0.15 are indicated by a dash. View Large Age and body weight (body mass index (weight (kg)/height (m)2)) were inversely associated with the “drinker” dietary pattern (table 2). These two variables were not clearly associated with either a “healthy” dietary pattern or a “Western” dietary pattern. Energy intake was positively related to both the “healthy” and “Western” dietary patterns but not to the “drinker” pattern. The percentage of women who had attended a university was positively associated with both the “healthy” and “drinker” dietary patterns, but it was not clearly associated with the “Western” pattern. TABLE 2. Relation of three dietary patterns to other lifestyle variables in a population of Swedish women, 1987–1990 Dietary pattern and quintile  Median age (years)  Median energy intake (kcal/day)  Median body mass index*  % with a university education  Entire cohort  53  1,302  24.2  4.6  “Healthy” dietary pattern           1 (low)  54  1,070  24.3  2.2   3  52  1,279  24.1  5.1   5 (high)  52  1,545  24.2  6.5  “Western” dietary pattern           1 (low)  54  990  24.5  4.7   3  52  1,296  24.2  5.0   5 (high)  52  1,660  24.0  3.9  “Drinker” dietary pattern           1 (low)  59  1,389  24.8  2.7   3  52  1,254  24.3  4.0   5 (high)  47  1,318  23.3  7.3  Dietary pattern and quintile  Median age (years)  Median energy intake (kcal/day)  Median body mass index*  % with a university education  Entire cohort  53  1,302  24.2  4.6  “Healthy” dietary pattern           1 (low)  54  1,070  24.3  2.2   3  52  1,279  24.1  5.1   5 (high)  52  1,545  24.2  6.5  “Western” dietary pattern           1 (low)  54  990  24.5  4.7   3  52  1,296  24.2  5.0   5 (high)  52  1,660  24.0  3.9  “Drinker” dietary pattern           1 (low)  59  1,389  24.8  2.7   3  52  1,254  24.3  4.0   5 (high)  47  1,318  23.3  7.3  * Weight (kg)/height (m)2. View Large There were no significant associations between the three major dietary patterns and colorectal cancer risk (table 3). There was a suggestion of an inverse association between the “healthy” dietary pattern and colorectal cancer; the highest risk was seen in the bottom quintile, and little change in risk was observed above this delineation. No linear trends or significant alterations of risk were observed across quintiles of the “Western” dietary pattern or the “drinker” dietary pattern. Multivariate-adjusted relative risk estimates were similar to estimates adjusted only for age and energy intake. Examination of cancer subsites separately showed results generally similar to those obtained for total colorectal cancer, although there was no suggestion of any inverse association between the “healthy” dietary pattern and distal colon cancers. Subgroup analyses of colorectal cancer risk by categories of age, body mass index, and family history showed no effect modification, except for an inverse association among women below age 50 years (the mean age of menopause in Sweden) with a “healthy” dietary pattern. In this age group, relative risks for increasing quintiles of the “healthy” dietary pattern, compared with the lowest quintile, were 0.74 (95 percent confidence interval (CI): 0.41, 1.31), 0.69 (95 percent CI: 0.39, 1.24), 0.59 (95 percent CI: 0.32, 1.07), and 0.45 (95 percent CI: 0.23, 0.88) (p for trend = 0.03). There were 116 colorectal cancer cases in this subgroup of younger women. There was no association for any dietary pattern among women aged 50 years or over. The range in median values of factor scores between the lowest and highest quintiles of each dietary pattern was −1.16 to +1.24 for the “healthy” pattern, −1.13 to +1.28 for the “Western” pattern, and −1.02 to +1.18 for the “drinker” pattern. TABLE 3. Relative risks of colorectal cancer according to the three major dietary patterns observed in a population of Swedish women, 1987–1998 Cancer site, dietary pattern, and adjustment factors*  Quintile   p for trend  1 (low load)  2   3   4   5 (high load)   RR†  95% CI†  RR   95% CI  RR   95% CI  RR  95% CI  Total colorectal cancer                       “Healthy” dietary pattern                        Age- and energy-adjusted  1.00‡  0.77  0.58, 1.01  0.69  0.52, 0.93  0.74  0.55, 0.99  0.78  0.57, 1.05  0.09    Multivariate-adjusted  1.00  0.77  0.58, 1.02  0.70  0.52, 0.95  0.75  0.55, 1.02  0.79  0.56, 1.10  0.18   “Western” dietary pattern                        Age- and energy-adjusted  1.00  0.87  0.64, 1.18  1.08  0.80, 1.46  1.14  0.83, 1.56  1.09  0.76, 1.57  0.27    Multivariate-adjusted  1.00  0.85  0.63, 1.16  1.04  0.76, 1.41  1.07  0.77, 1.49  0.97  0.66, 1.44  0.68   “Drinker” dietary pattern                        Age- and energy-adjusted  1.00  0.91  0.70, 1.19  1.14  0.88, 1.49  0.79  0.58, 1.09  1.14  0.84, 1.53  0.72    Multivariate-adjusted  1.00  0.88  0.67, 1.15  1.10  0.84, 1.44  0.78  0.57, 1.08  1.13  0.84, 1.53  0.67  Total colon cancer                       “Healthy” dietary pattern  1.00  0.75  0.53, 1.08  0.69  0.47, 1.01  0.83  0.57, 1.21  0.83  0.54, 1.26  0.58   “Western” dietary pattern  1.00  0.86  0.59, 1.25  0.97  0.66, 1.43  1.08  0.72, 1.62  0.93  0.57, 1.53  0.82   “Drinker” dietary pattern  1.00  0.72  0.50, 1.02  1.18  0.85, 1.64  0.78  0.52, 1.16  1.14  0.78, 1.66  0.44  Proximal colon cancer                       “Healthy” dietary pattern  1.00  0.55  0.32, 0.95  0.52  0.29, 0.94  0.59  0.32, 1.09  0.62  0.31, 1.20  0.17   “Western” dietary pattern  1.00  0.58  0.32, 1.07  0.69  0.37, 1.27  1.11  0.61, 2.03  0.70  0.32, 1.55  0.97   “Drinker” dietary pattern  1.00  0.66  0.38, 1.15  1.26  0.77, 2.07  0.54  0.27, 1.09  1.03  0.56, 1.90  0.98  Distal colon cancer                       “Healthy” dietary pattern  1.00  0.87  0.46, 1.64  0.91  0.47, 1.74  1.05  0.54, 2.03  1.24  0.61, 2.52  0.47   “Western” dietary pattern  1.00  1.08  0.57, 2.03  1.21  0.63, 2.32  0.91  0.43, 1.93  1.34  0.59, 3.05  0.73   “Drinker” dietary pattern  1.00  0.73  0.41, 1.32  0.97  0.55, 1.71  0.73  0.38, 1.41  0.99  0.53, 1.85  0.94  Rectal cancer                       “Healthy” dietary pattern  1.00  0.75  0.47, 1.20  0.73  0.44, 1.19  0.64  0.38, 1.09  0.77  0.44, 1.35  0.26   “Western” dietary pattern  1.00  0.91  0.53, 1.58  1.32  0.78, 2.25  1.23  0.69, 2.18  1.20  0.61, 2.35  0.38   “Drinker” dietary pattern  1.00  1.17  0.76, 1.81  0.96  0.59, 1.55  0.78  0.45, 1.35  1.03  0.60, 1.75  0.57  Cancer site, dietary pattern, and adjustment factors*  Quintile   p for trend  1 (low load)  2   3   4   5 (high load)   RR†  95% CI†  RR   95% CI  RR   95% CI  RR  95% CI  Total colorectal cancer                       “Healthy” dietary pattern                        Age- and energy-adjusted  1.00‡  0.77  0.58, 1.01  0.69  0.52, 0.93  0.74  0.55, 0.99  0.78  0.57, 1.05  0.09    Multivariate-adjusted  1.00  0.77  0.58, 1.02  0.70  0.52, 0.95  0.75  0.55, 1.02  0.79  0.56, 1.10  0.18   “Western” dietary pattern                        Age- and energy-adjusted  1.00  0.87  0.64, 1.18  1.08  0.80, 1.46  1.14  0.83, 1.56  1.09  0.76, 1.57  0.27    Multivariate-adjusted  1.00  0.85  0.63, 1.16  1.04  0.76, 1.41  1.07  0.77, 1.49  0.97  0.66, 1.44  0.68   “Drinker” dietary pattern                        Age- and energy-adjusted  1.00  0.91  0.70, 1.19  1.14  0.88, 1.49  0.79  0.58, 1.09  1.14  0.84, 1.53  0.72    Multivariate-adjusted  1.00  0.88  0.67, 1.15  1.10  0.84, 1.44  0.78  0.57, 1.08  1.13  0.84, 1.53  0.67  Total colon cancer                       “Healthy” dietary pattern  1.00  0.75  0.53, 1.08  0.69  0.47, 1.01  0.83  0.57, 1.21  0.83  0.54, 1.26  0.58   “Western” dietary pattern  1.00  0.86  0.59, 1.25  0.97  0.66, 1.43  1.08  0.72, 1.62  0.93  0.57, 1.53  0.82   “Drinker” dietary pattern  1.00  0.72  0.50, 1.02  1.18  0.85, 1.64  0.78  0.52, 1.16  1.14  0.78, 1.66  0.44  Proximal colon cancer                       “Healthy” dietary pattern  1.00  0.55  0.32, 0.95  0.52  0.29, 0.94  0.59  0.32, 1.09  0.62  0.31, 1.20  0.17   “Western” dietary pattern  1.00  0.58  0.32, 1.07  0.69  0.37, 1.27  1.11  0.61, 2.03  0.70  0.32, 1.55  0.97   “Drinker” dietary pattern  1.00  0.66  0.38, 1.15  1.26  0.77, 2.07  0.54  0.27, 1.09  1.03  0.56, 1.90  0.98  Distal colon cancer                       “Healthy” dietary pattern  1.00  0.87  0.46, 1.64  0.91  0.47, 1.74  1.05  0.54, 2.03  1.24  0.61, 2.52  0.47   “Western” dietary pattern  1.00  1.08  0.57, 2.03  1.21  0.63, 2.32  0.91  0.43, 1.93  1.34  0.59, 3.05  0.73   “Drinker” dietary pattern  1.00  0.73  0.41, 1.32  0.97  0.55, 1.71  0.73  0.38, 1.41  0.99  0.53, 1.85  0.94  Rectal cancer                       “Healthy” dietary pattern  1.00  0.75  0.47, 1.20  0.73  0.44, 1.19  0.64  0.38, 1.09  0.77  0.44, 1.35  0.26   “Western” dietary pattern  1.00  0.91  0.53, 1.58  1.32  0.78, 2.25  1.23  0.69, 2.18  1.20  0.61, 2.35  0.38   “Drinker” dietary pattern  1.00  1.17  0.76, 1.81  0.96  0.59, 1.55  0.78  0.45, 1.35  1.03  0.60, 1.75  0.57  * Age-adjusted models included age (continuous variable) and energy intake (continuous variable). Multivariate adjustment included age (continuous variable), energy intake (continuous variable), body mass index (in quartiles), and education (less than high school, high school, and university). † RR, relative risk; CI, confidence interval. ‡ Referent. View Large DISCUSSION In this population of Swedish women, we found no clear association between three distinct dietary patterns and colo-rectal cancer risk. Although dietary patterns could be discerned in our population, these patterns explained little of the variability in colorectal cancer incidence. Nevertheless, our data suggested that scoring very low with regard to the “healthy” dietary pattern (that is, consumption of fruits and vegetables, cereals and whole grains, fish, poultry, and low-fat dairy products) may be associated with increased risks of colon and rectal cancers. This association was clearer for women under age 50, although the number of cancers in this age group was low and interpretations should be made cautiously. Overall, our results are in agreement with those of a growing number of prospective cohort studies that have found no associations with dietary factors previously reported to be associated with colorectal cancer risk. For example, recent negative associations in large prospective cohort studies and clinical trials have called into question putative associations with intake of dietary fiber (8, 9) and consumption of fruits and vegetables (10, 11). Thus, variations in colorectal cancer incidence over time and geographic region that strongly suggest an etiologic role for environmental exposures (12–17) have yet to be adequately explained by changes in or differences in diet or other environmental factors. One previous study by Slattery et al. (4, 18) examined dietary patterns derived from principle-components factor analysis of colon cancer risk. That study, a case-control study, found a positive association with a “Western” dietary pattern and an inverse association with a “calcium and low-fat dairy products” pattern. None of the other seven dietary patterns extracted by Slattery et al., including a “moderation” dietary pattern that was similar to our “healthy” pattern, were significantly related to risk. However, direct comparisons are difficult, since the results of cohort studies have differed markedly from those of case-control studies with regard to such factors as intake of dietary fiber (8, 9, 19), fruits and vegetables (10, 11, 20), total energy (21, 22), and even coffee (23). Factor analysis involves decisions that can be called subjective or arbitrary—decisions that can have some impact on both the results and their interpretation (24). For example, the selection and grouping of foods for analysis from the larger pool of available food items can be guided by existing knowledge about how individual foods may be related to broader dietary patterns, but investigators may still differ in their decision-making criteria. There are also a number of different criteria for limiting the number of factors to be extracted from the data (24). Various approaches have been considered useful, such as extracting factors with eignenvalues greater than 1 (3) or graphing the eigenvalues and extracting factors that visibly explain an important degree of variation beyond what is explained by other factors (3). The methods by which the selected factors are then rotated and the manner in which the factors are ultimately labeled are also based on subjective criteria and are liable to different interpretations (24). In this context, it is interesting to note that our “Western,” “healthy,” and “drinker” dietary patterns are similar to those labeled “Western,” “moderation,” and “alcohol” in the case-control study by Slattery et al. (4). These factors are also similar to factors labeled “Western” and “prudent” in a subgroup of the Health Professionals Follow-up Study cohort (5), which suggests that these factors are more universal and may represent dietary patterns common to several populations. The strengths of our study include the relatively large sample size of our cohort, the study's population-based character, the diagnosis of colorectal cancer at specific subsites, the completeness of follow-up in the Swedish cancer register system, and the large number of cases. The prospective assessment of exposure in our study eliminated recall bias, which is a potential threat to the validity of case-control studies. However, we could not adjust our relative risk estimates for the potentially confounding effect of exercise, since this information was not collected at baseline. Energy intake, an approximate indicator of physical activity (25), was not associated with colorectal cancer in our data, and our results were not altered by adjustment for the effects of energy intake or body mass index. Since exercise has been shown to be associated with a “healthy” dietary pattern (4), bias due to confounding from physical activity may have contributed to the inverse association we observed among younger women. Moreover, although we constructed our 67-item food frequency questionnaire with the intention of including the most important foods (and sources of dietary variation) consumed by the Swedish population (26), it still may not have allowed estimation of energy intake with great precision, and thus adjustment for total caloric intake may not have fully accounted for between-person variation due to energy intake or physical activity. However, the associations with physical activity observed in previous studies were not limited to younger women (27), and therefore we would not expect confounding by physical activity, if it exists in our data, to be limited to this age group. Physical activity was not associated previously with either the “Western” dietary pattern or the “alcohol” dietary pattern (4). In addition, there may be other unidentified factors that might have accounted for our null findings to some degree. For example, if use of nonsteroidal antiinflammatory drugs such as aspirin, which has been associated with decreased risk of colorectal cancer (28), was more prevalent among persons with a “Western” dietary pattern in our data, this may have masked a true positive association. We know of no data that suggest a greater use of aspirin or other factors associated with decreased risk, such as colorectal cancer screening, among persons who consume foods associated with a “Western” diet, but we cannot rule out this possibility. Our data were further limited by the likelihood of some degree of measurement error in the individual dietary exposures. Nondifferential misclassification would tend to attenuate relative risk estimates (29), but such misclassification does not easily explain the discrepancy between the negative findings of most cohort studies and the inverse associations found in studies of retrospective design. The level of nonresponse in this study may also have led to a loss of precision due to reductions in sample size and outcome events, and to a reduced range of exposure to the foods and food groups that constitute the dietary patterns. Therefore, we cannot rule out the possibility of weak associations with dietary patterns that remained undetected in our data. In conclusion, our methods of extracting dietary patterns yielded patterns similar to some of those found in a previous case-control study of colorectal cancer (4). Unlike that study, however, we did not find any strong, significant association between dietary patterns and colorectal cancer risk. This discrepancy is consistent with the emerging schism between the results of cohort studies and the results of case-control studies with regard to several dietary risk factors for colorectal cancer—including previous findings of no association with dietary fiber in our own data and the finding that fruit and vegetable consumption was inversely associated with risk only among persons presumed to be deficient in antioxidant intake (30). On the other hand, our current data suggest the possibility that a “healthy” dietary pattern is protective among younger women. The role of overall eating patterns in the prediction of colorectal cancer risk should be investigated further. Correspondence to Dr. Paul Terry, Department of Epidemiology and Social Medicine, Albert Einstein College of Medicine, 1300 Morris Park Avenue, 13th Floor, Bronx, NY 10461 (e-mail: [email protected]). This study was supported by grants from the Swedish Council for Planning and Coordination of Research and the Swedish Cancer Society. REFERENCES 1. Parkin D. Estimates of the worldwide incidence of 25 major cancers in 1990. Int J Cancer  1999; 80: 827–41. Google Scholar 2. Potter J. Nutrition and colorectal cancer. Cancer Causes Control  1996; 7: 127–46. Google Scholar 3. Kim J-O, Mueller CW. Factor analysis: statistical methods and practical issues. Thousand Oaks, CA: Sage Publications, Inc, 1978. Google Scholar 4. Slattery ML, Boucher KM, Caan BJ, et al. Eating patterns and risk of colon cancer. Am J Epidemiol  1998; 148: 4–16. Google Scholar 5. Hu FB, Rimm E, Smith-Warner SA, et al. Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire. Am J Clin Nutr  1999; 69: 243–9. Google Scholar 6. Mattsson B, Wallgren A. Completeness of the Swedish Cancer Registry: non-notified cancer cases recorded on death certificates in 1978. Acta Radiol  1984; 23: 305–13. Google Scholar 7. SAS Institute, Inc. SAS/STAT user's guide, version 6. 4th ed. Vol 2. Cary, NC: SAS Institute, Inc, 1989. Google Scholar 8. Fuchs CS, Giovannucci E, Colditz GA, et al. Dietary fiber and the risk of colorectal cancer and adenoma in women. N Engl J Med  1999; 340: 169–76. Google Scholar 9. Alberts D, Martinez M, Roe D, et al. Lack of effect of high-fiber cereal supplement on the recurrence of colorectal adenomas. N Engl J Med  2000; 342: 1156–62. Google Scholar 10. Michels KB, Giovannucci E, Joshipura KJ, et al. Prospective study of fruit and vegetable consumption and incidence of colon and rectal cancers. J Natl Cancer Inst  2000; 92: 1740–52. Google Scholar 11. Schatzkin A, Lanza E, Corle D, et al. Lack of effect of a low-fat, high-fiber diet on the recurrence of colorectal adenomas. Polyp Prevention Trial Study Group. N Engl J Med  2000; 342: 1149–55. Google Scholar 12. Willett W. The search for the causes of breast and colon cancer. Nature  1989; 338: 389–94. Google Scholar 13. Tomatis L, ed. Cancer: causes, occurrence and control. (IARC Scientific Publication no. 100). Lyon, France: International Agency for Research on Cancer, 1990. Google Scholar 14. Haenszel W, Kurihara M. Studies of Japanese migrants. I. Mortality from cancer and other diseases among Japanese in the United States. J Natl Cancer Inst  1968; 40: 43–68. Google Scholar 15. Whitmore A, Zheng S, Wu A, et al. Colorectal cancer in Chinese and Chinese-Americans. Natl Cancer Inst Monogr  1985; 69: 43–6. Google Scholar 16. Doll R, Peto R. The causes of cancer: quantitative estimates of avoidable risks of cancer in the United States today. J Natl Cancer Inst  1981; 66: 1191–308. Google Scholar 17. Nelson N. Is chemoprevention overrated or under-funded? J Natl Cancer Inst  1996; 88: 947–9. Google Scholar 18. Slattery ML, Edwards SL, Boucher KM, et al. Lifestyle and colon cancer: an assessment of factors associated with risk. Am J Epidemiol  1999; 150: 869–77. Google Scholar 19. Howe GR, Benito E, Castelleto R, et al. Dietary intake of fiber and decreased risk of cancers of the colon and rectum: evidence from the combined analysis of 13 case-control studies. J Natl Cancer Inst  1992; 84: 1887–96. Google Scholar 20. Steinmetz K, Potter J. Vegetables, fruit, and cancer. II. Mech-anisms. Cancer Causes Control  1991; 2: 427–42. Google Scholar 21. Howe GR, Aronson KJ, Benito E, et al. The relationship between dietary fat intake and risk of colorectal cancer: evidence from the combined analysis of 13 case-control studies. Cancer Causes Control  1997; 8: 215–28. Google Scholar 22. Willett WC. Dietary fat intake and cancer risk: a controversial and instructive story. Semin Cancer Biol  1998; 8: 245–53. Google Scholar 23. Giovannucci E. Meta-analysis of coffee consumption and risk of colorectal cancer. Am J Epidemiol  1998; 147: 1043–52. Google Scholar 24. Martinez ME, Marshall JR, Sechrest L. Invited commentary: factor analysis and the search for objectivity. Am J Epidemiol  1998; 148: 17–21. Google Scholar 25. Willett W, Howe G, Kushi L. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr  1997; 65(suppl): 1220S–8S. Google Scholar 26. Statens Jordbruksnämd. Food consumption, 1966–1988. Jönköping, Sweden: Statens Jordbruksnämd, 1989. Google Scholar 27. Colditz GA, Cannuscio CC, Frazier AL. Physical activity and reduced risk of colon cancer: implications for prevention. Cancer Causes Control  1997; 8: 649–67. Google Scholar 28. Sjodahl R. Nonsteroidal anti-inflammatory drugs and the gastrointestinal tract: extent, mode, and dose dependence of anticancer effects. Am J Med  2001; 110(1A): 66S–9S. Google Scholar 29. Rothman KJ, Greenland S. Modern epidemiology. 2nd ed. Philadelphia, PA: Lippincott Williams & Wilkins Publishers, 1998. Google Scholar 30. Terry P, Giovannucci E, Michels KB, et al. Fruit, vegetables, dietary fiber, and risk of colorectal cancer. J Natl Cancer Inst  2001; 93: 525–33. Google Scholar http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png American Journal of Epidemiology Oxford University Press

Prospective Study of Major Dietary Patterns and Colorectal Cancer Risk in Women

Download PDF
2 pages

Loading next page...
 
/lp/oxford-university-press/prospective-study-of-major-dietary-patterns-and-colorectal-cancer-risk-eUVCrQRl0v

References (23)

Publisher
Oxford University Press
ISSN
0002-9262
eISSN
1476-6256
DOI
10.1093/aje/154.12.1143
Publisher site
See Article on Publisher Site

Abstract

Abstract A number of prospective cohort studies have examined the relations of individual dietary variables to risk of colorectal cancer. Few studies have addressed the broader eating patterns that reflect many dietary exposures working together. Using data from a prospective study of 61,463 women, with an average follow-up period of 9.6 years (between 1987 and 1998) and 460 incident cases of colorectal cancer, the authors conducted a factor analysis to identify and examine major dietary patterns in relation to colorectal cancer risk. Using proportional hazards regression to estimate relative risks, the authors found no clear association between a “Western,” “healthy,” or “drinker” dietary pattern and colorectal cancer risk. However, the data suggested that consuming low amounts of foods that constitute a “healthy” dietary pattern may be associated with increased risks of colon and rectal cancers. An inverse association with the “healthy” dietary pattern was found among women under age 50 years, although the number of cancers in this age group was limited and interpretation of this finding should be cautious. In this age group, relative risks for women in increasing quintiles of the “healthy” dietary pattern, compared with the lowest quintile, were 0.74 (95% confidence interval (CI): 0.41, 1.31), 0.69 (95% CI: 0.39, 1.24), 0.59 (95% CI: 0.32, 1.07), and 0.45 (95% CI: 0.23, 0.88) (p for trend = 0.03). The role of overall eating patterns in predicting colorectal cancer risk requires further investigation. cohort studies, colorectal neoplasms, diet, factor analysis, statistical CI, confidence interval Colorectal cancer is the second-leading cause of cancer death in the United States and other developed countries (1), yet the etiology of this disease remains largely unknown. Given the role of the colorectal tract as a conduit for ingested food and the many potentially carcinogenic and anticarcinogenic substances contained in food (2), dietary exposures have been widely studied in relation to colorectal cancer. Foods and food groups that have shown some (albeit inconsistent) associations with colorectal cancer risk in various studies include fruits and vegetables, meat, dietary fiber, total fat, eggs, dairy products, coffee, tea, and alcohol (2). However, few studies have addressed the broader eating patterns that reflect many dietary exposures working together. One method of identifying and examining larger dietary patterns that may be associated with disease is factor analysis (3), which aggregates interrelated variables into composite “factors.” These factors represent dietary patterns in the study population and help distinguish individuals according to the combination of foods they choose to eat. These dietary patterns might better explain disease occurrence than individual dietary exposures. Therefore, using factor analysis, we identified and examined major dietary patterns and their relation to colorectal cancer risk in a large prospective cohort study of Swedish women. MATERIALS AND METHODS The Swedish mammography screening cohort From 1987 to 1990, a population-based mammography screening program was introduced in two counties in central Sweden. In Västmanland County, all women born between 1917 and 1948 received a mailed invitation to be screened via mammography between March 1987 and March 1989 (n = 41,786), together with a 6-page questionnaire; 31,735 women (76 percent) returned completed questionnaires. In Uppsala County, all women born between 1914 and 1948 were invited to the screening, and they received the questionnaire between January 1988 and December 1990 (n = 48,517); 34,916 of these women (72 percent) returned completed questionnaires. Hence, questionnaires completed prior to mammography were obtained from 66,651 women (73.8 percent) in the source population. The questionnaire included items about age, weight (kg), height (cm), education, and diet. For the present analyses, we excluded women who were outside of the age range 40–74 years (n = 165); those with missing (n = 707) or incorrect (n = 415) identification numbers; and those for whom we lacked information on the date of the questionnaire (n = 608), the date of moving out of the study area (n = 79), or the date of death (n = 16). After further exclusion of 793 women with extreme energy intake estimates that probably reflected careless completion of the dietary questionnaire (below or above the mean ±3 standard deviations for loge-transformed calories; cutpoints: 417 kcal and 3,729 kcal), the cohort was restricted to 63,868 women. Through linkage to the Swedish Cancer Registry, we identified and excluded all women with a previous diagnosis of cancer other than nonmelanoma skin cancer (n = 2,405). Thus, the study cohort comprised 61,463 women at the start of follow-up. Dietary assessment The self-administered food frequency questionnaire included questions on 67 commonly eaten foods. Participants were asked how often, on average, they had consumed these foods over the past 6 months. Eight predefined frequency categories ranging from “never/seldom” to “four or more times per day” were used. For each food item, we converted these frequencies to frequency per week. For nutrient calculations, we used age-specific portion sizes (ages 40–52, 53–65, and 66–74 years) based on mean values from 5,922 days of weighed food records kept by 213 women randomly selected from the study population. Food groupings The food grouping scheme was based on similarity of nutrient profiles or culinary usage among the foods and was somewhat similar to that used in previous studies (4, 5). Some individual food items were kept, either because it was inappropriate to incorporate them into a certain food group (e.g., eggs, margarine, tea, and pea soup) or because they were assumed to represent distinct dietary patterns (e.g., wine, liquor, beer, and soda). After the foods had been grouped, 24 variables were retained for the factor analysis (appendix table 1). APPENDIX TABLE 1. Food groupings used for analyses of dietary patterns in a population of Swedish women, 1987–1990 Food or food group  Food items included  Vegetables  Beets, carrots, cabbage, lettuce, spinach, tomatoes, cucumbers  Fruits  Apples, pears, oranges, grapefruit, bananas  Whole grains  Whole-grain soft bread, crisp bread, oatmeal and other whole-grain hot cereals  Refined grains  White bread, rice, spaghetti, waffles, pancakes  Cereal  Assorted breakfast cereals, muesli  Low-fat dairy products  Low-fat milk, reduced-fat (medium) milk, low-fat yogurt  High-fat dairy products  Butter, cheese, whole milk, whole yogurt, ice cream  Fish  Salmon, mackerel, sardines, tuna, herring, other fish, lobster, shrimp, crab, mussels  Poultry  Chicken  Meat  Beef, chopped meat, minced meat, liver, liver pâté  Processed meat  Bacon, sausage, blood pudding  Eggs  Eggs  Margarine  Margarine  Pea soup  Pea soup, bean soup  Potatoes  Boiled potatoes, fried potatoes, French fries  Snacks  Potato chips, other snack chips, popcorn, fried and salted nuts  Sweets  Assorted candy, caramels, chocolate, cookies, sugar (e.g., sugar cubes), sweet soups, marmalade, jams  Juice  Juice  Soda  Carbonated and uncarbonated sweetened drinks  Tea  Tea  Coffee  Coffee  Beer  Beer (various proofs)  Wine  Wine  Liquor  Liquor (various proofs)  Food or food group  Food items included  Vegetables  Beets, carrots, cabbage, lettuce, spinach, tomatoes, cucumbers  Fruits  Apples, pears, oranges, grapefruit, bananas  Whole grains  Whole-grain soft bread, crisp bread, oatmeal and other whole-grain hot cereals  Refined grains  White bread, rice, spaghetti, waffles, pancakes  Cereal  Assorted breakfast cereals, muesli  Low-fat dairy products  Low-fat milk, reduced-fat (medium) milk, low-fat yogurt  High-fat dairy products  Butter, cheese, whole milk, whole yogurt, ice cream  Fish  Salmon, mackerel, sardines, tuna, herring, other fish, lobster, shrimp, crab, mussels  Poultry  Chicken  Meat  Beef, chopped meat, minced meat, liver, liver pâté  Processed meat  Bacon, sausage, blood pudding  Eggs  Eggs  Margarine  Margarine  Pea soup  Pea soup, bean soup  Potatoes  Boiled potatoes, fried potatoes, French fries  Snacks  Potato chips, other snack chips, popcorn, fried and salted nuts  Sweets  Assorted candy, caramels, chocolate, cookies, sugar (e.g., sugar cubes), sweet soups, marmalade, jams  Juice  Juice  Soda  Carbonated and uncarbonated sweetened drinks  Tea  Tea  Coffee  Coffee  Beer  Beer (various proofs)  Wine  Wine  Liquor  Liquor (various proofs)  View Large Identification of colon and rectal cancer cases and follow-up of the cohort Cases were identified through matching of the cohort with computerized regional cancer registers for colon and rectal cancer diagnoses made in the two study counties through December 31, 1998. Quality control of the regional cancer registers was performed through comparison with medical journals for the follow-up period of 1987–1994. Completeness of follow-up for colorectal cancers was documented to be 98 percent, as was the completeness of the Swedish Cancer Registry as a whole (6). We identified 460 colorectal cancers in total. Colon cancers were defined as those occurring above the peritoneal delineation of the abdominal cavity (n = 291), and rectal cancers were those occurring below this delineation (n = 159). Ten case women were diagnosed with both colon and rectal cancer. For the purposes of subanalysis by colon cancer site, proximal colon cancers were defined as those arising between the cecum and the splenic flexure (n = 118), and distal colon cancers were defined as those arising between the descending colon and the sigmoid colon (n = 101). Seventy-two colon cancers were of unspecified location. Dates of death in the cohort were ascertained through the Swedish Death Register. For women who moved out of the study area, the date of moving was obtained by matching of the cohort with the Swedish Population Register, which is computerized and continuously updated. Statistical analysis Factor analysis (principal-components) was used to derive food patterns based on the 24 food variables in our data. We conducted the analysis using the FACTOR procedure in SAS (7). We rotated the factors by means of an orthogonal transformation (the varimax rotation function in SAS) to obtain a simpler structure with greater interpretability. In determining the number of factors to retain, we considered eigenvalues (>1) and the Scree test (3). An overall dietary pattern score was created for each individual by weighting her intake of each food contributing to that pattern by the relative contribution those foods made (factor loadings) (3). A positive loading indicates that the dietary variable is positively associated with the factor, and a negative loading indicates an inverse association with the factor. All data presented in this paper are from the varimax rotation. Labeling of dietary patterns was based on our interpretation of the data and was arbitrary, in that other labels might have been equally suited to the data. Cox proportional hazards models were used to estimate hazard rate ratios and 95 percent confidence intervals relating the factors to the occurrence of invasive colorectal cancer. Follow-up was censored at the date of death, the date of migration out of the study area, or the end of the follow-up period (December 31, 1998). As a basis for the trend tests, scores were constructed from the median values of categorized variables and were placed into the model as successive integers. RESULTS Three major dietary patterns were generated. Pattern 1 was labeled “healthy,” since it reflected the correlated intakes of foods commonly thought to be healthy, such as fruits and vegetables, fish and poultry, cereal and whole-grain breads, fruit juice, and low-fat dairy products (table 1). Pattern 2 was labeled “Western,” since it reflected the correlated intakes of foods associated with a Western diet: processed and red meats, soda and sweets, refined breads and potatoes, and high-fat dairy products. Pattern 3 was labeled “drinker,” since it primarily reflected the correlated intakes of wine, beer, and spirits. These dietary patterns were distinct in that most food items were important to only one major pattern (table 1). TABLE 1. Factor-loading matrix for the three major dietary patterns found in a population of Swedish women, 1987–1990   Pattern 1 (“healthy”)  Pattern 2 (“Western”)  Pattern 3 (“drinker”)  Vegetables  0.66  —*  —  Fruits  0.55  —  —  Fish  0.54  —  0.24  Whole grains  0.43  0.20  0.36  Low-fat dairy foods  0.40  —  −0.22  Poultry  0.36  —  0.30  Cereal  0.34  —  —  Eggs  0.32  0.21  0.19  Juice  0.27  —  —  Margarine  0.26  —  −0.22  Tea  0.19  —  0.17  Processed meats  —  0.58  —  Sweets  −0.17  0.54  —  Refined grains  —  0.54  —  High-fat dairy foods  —  0.46  —  Meats  0.33  0.46  0.20  Soda  —  0.45  —  Potatoes  —  0.43  −0.20  Pea soup  —  0.30  —  Coffee  —  0.18  —  Wine  —  —  0.67  Liquor  —  —  0.58  Beer  —  —  0.48  Snacks  —  0.16  0.37  % of variability explained  9.4  8.2  7.0    Pattern 1 (“healthy”)  Pattern 2 (“Western”)  Pattern 3 (“drinker”)  Vegetables  0.66  —*  —  Fruits  0.55  —  —  Fish  0.54  —  0.24  Whole grains  0.43  0.20  0.36  Low-fat dairy foods  0.40  —  −0.22  Poultry  0.36  —  0.30  Cereal  0.34  —  —  Eggs  0.32  0.21  0.19  Juice  0.27  —  —  Margarine  0.26  —  −0.22  Tea  0.19  —  0.17  Processed meats  —  0.58  —  Sweets  −0.17  0.54  —  Refined grains  —  0.54  —  High-fat dairy foods  —  0.46  —  Meats  0.33  0.46  0.20  Soda  —  0.45  —  Potatoes  —  0.43  −0.20  Pea soup  —  0.30  —  Coffee  —  0.18  —  Wine  —  —  0.67  Liquor  —  —  0.58  Beer  —  —  0.48  Snacks  —  0.16  0.37  % of variability explained  9.4  8.2  7.0  * For ease of interpretation, factor loadings below 0.15 are indicated by a dash. View Large Age and body weight (body mass index (weight (kg)/height (m)2)) were inversely associated with the “drinker” dietary pattern (table 2). These two variables were not clearly associated with either a “healthy” dietary pattern or a “Western” dietary pattern. Energy intake was positively related to both the “healthy” and “Western” dietary patterns but not to the “drinker” pattern. The percentage of women who had attended a university was positively associated with both the “healthy” and “drinker” dietary patterns, but it was not clearly associated with the “Western” pattern. TABLE 2. Relation of three dietary patterns to other lifestyle variables in a population of Swedish women, 1987–1990 Dietary pattern and quintile  Median age (years)  Median energy intake (kcal/day)  Median body mass index*  % with a university education  Entire cohort  53  1,302  24.2  4.6  “Healthy” dietary pattern           1 (low)  54  1,070  24.3  2.2   3  52  1,279  24.1  5.1   5 (high)  52  1,545  24.2  6.5  “Western” dietary pattern           1 (low)  54  990  24.5  4.7   3  52  1,296  24.2  5.0   5 (high)  52  1,660  24.0  3.9  “Drinker” dietary pattern           1 (low)  59  1,389  24.8  2.7   3  52  1,254  24.3  4.0   5 (high)  47  1,318  23.3  7.3  Dietary pattern and quintile  Median age (years)  Median energy intake (kcal/day)  Median body mass index*  % with a university education  Entire cohort  53  1,302  24.2  4.6  “Healthy” dietary pattern           1 (low)  54  1,070  24.3  2.2   3  52  1,279  24.1  5.1   5 (high)  52  1,545  24.2  6.5  “Western” dietary pattern           1 (low)  54  990  24.5  4.7   3  52  1,296  24.2  5.0   5 (high)  52  1,660  24.0  3.9  “Drinker” dietary pattern           1 (low)  59  1,389  24.8  2.7   3  52  1,254  24.3  4.0   5 (high)  47  1,318  23.3  7.3  * Weight (kg)/height (m)2. View Large There were no significant associations between the three major dietary patterns and colorectal cancer risk (table 3). There was a suggestion of an inverse association between the “healthy” dietary pattern and colorectal cancer; the highest risk was seen in the bottom quintile, and little change in risk was observed above this delineation. No linear trends or significant alterations of risk were observed across quintiles of the “Western” dietary pattern or the “drinker” dietary pattern. Multivariate-adjusted relative risk estimates were similar to estimates adjusted only for age and energy intake. Examination of cancer subsites separately showed results generally similar to those obtained for total colorectal cancer, although there was no suggestion of any inverse association between the “healthy” dietary pattern and distal colon cancers. Subgroup analyses of colorectal cancer risk by categories of age, body mass index, and family history showed no effect modification, except for an inverse association among women below age 50 years (the mean age of menopause in Sweden) with a “healthy” dietary pattern. In this age group, relative risks for increasing quintiles of the “healthy” dietary pattern, compared with the lowest quintile, were 0.74 (95 percent confidence interval (CI): 0.41, 1.31), 0.69 (95 percent CI: 0.39, 1.24), 0.59 (95 percent CI: 0.32, 1.07), and 0.45 (95 percent CI: 0.23, 0.88) (p for trend = 0.03). There were 116 colorectal cancer cases in this subgroup of younger women. There was no association for any dietary pattern among women aged 50 years or over. The range in median values of factor scores between the lowest and highest quintiles of each dietary pattern was −1.16 to +1.24 for the “healthy” pattern, −1.13 to +1.28 for the “Western” pattern, and −1.02 to +1.18 for the “drinker” pattern. TABLE 3. Relative risks of colorectal cancer according to the three major dietary patterns observed in a population of Swedish women, 1987–1998 Cancer site, dietary pattern, and adjustment factors*  Quintile   p for trend  1 (low load)  2   3   4   5 (high load)   RR†  95% CI†  RR   95% CI  RR   95% CI  RR  95% CI  Total colorectal cancer                       “Healthy” dietary pattern                        Age- and energy-adjusted  1.00‡  0.77  0.58, 1.01  0.69  0.52, 0.93  0.74  0.55, 0.99  0.78  0.57, 1.05  0.09    Multivariate-adjusted  1.00  0.77  0.58, 1.02  0.70  0.52, 0.95  0.75  0.55, 1.02  0.79  0.56, 1.10  0.18   “Western” dietary pattern                        Age- and energy-adjusted  1.00  0.87  0.64, 1.18  1.08  0.80, 1.46  1.14  0.83, 1.56  1.09  0.76, 1.57  0.27    Multivariate-adjusted  1.00  0.85  0.63, 1.16  1.04  0.76, 1.41  1.07  0.77, 1.49  0.97  0.66, 1.44  0.68   “Drinker” dietary pattern                        Age- and energy-adjusted  1.00  0.91  0.70, 1.19  1.14  0.88, 1.49  0.79  0.58, 1.09  1.14  0.84, 1.53  0.72    Multivariate-adjusted  1.00  0.88  0.67, 1.15  1.10  0.84, 1.44  0.78  0.57, 1.08  1.13  0.84, 1.53  0.67  Total colon cancer                       “Healthy” dietary pattern  1.00  0.75  0.53, 1.08  0.69  0.47, 1.01  0.83  0.57, 1.21  0.83  0.54, 1.26  0.58   “Western” dietary pattern  1.00  0.86  0.59, 1.25  0.97  0.66, 1.43  1.08  0.72, 1.62  0.93  0.57, 1.53  0.82   “Drinker” dietary pattern  1.00  0.72  0.50, 1.02  1.18  0.85, 1.64  0.78  0.52, 1.16  1.14  0.78, 1.66  0.44  Proximal colon cancer                       “Healthy” dietary pattern  1.00  0.55  0.32, 0.95  0.52  0.29, 0.94  0.59  0.32, 1.09  0.62  0.31, 1.20  0.17   “Western” dietary pattern  1.00  0.58  0.32, 1.07  0.69  0.37, 1.27  1.11  0.61, 2.03  0.70  0.32, 1.55  0.97   “Drinker” dietary pattern  1.00  0.66  0.38, 1.15  1.26  0.77, 2.07  0.54  0.27, 1.09  1.03  0.56, 1.90  0.98  Distal colon cancer                       “Healthy” dietary pattern  1.00  0.87  0.46, 1.64  0.91  0.47, 1.74  1.05  0.54, 2.03  1.24  0.61, 2.52  0.47   “Western” dietary pattern  1.00  1.08  0.57, 2.03  1.21  0.63, 2.32  0.91  0.43, 1.93  1.34  0.59, 3.05  0.73   “Drinker” dietary pattern  1.00  0.73  0.41, 1.32  0.97  0.55, 1.71  0.73  0.38, 1.41  0.99  0.53, 1.85  0.94  Rectal cancer                       “Healthy” dietary pattern  1.00  0.75  0.47, 1.20  0.73  0.44, 1.19  0.64  0.38, 1.09  0.77  0.44, 1.35  0.26   “Western” dietary pattern  1.00  0.91  0.53, 1.58  1.32  0.78, 2.25  1.23  0.69, 2.18  1.20  0.61, 2.35  0.38   “Drinker” dietary pattern  1.00  1.17  0.76, 1.81  0.96  0.59, 1.55  0.78  0.45, 1.35  1.03  0.60, 1.75  0.57  Cancer site, dietary pattern, and adjustment factors*  Quintile   p for trend  1 (low load)  2   3   4   5 (high load)   RR†  95% CI†  RR   95% CI  RR   95% CI  RR  95% CI  Total colorectal cancer                       “Healthy” dietary pattern                        Age- and energy-adjusted  1.00‡  0.77  0.58, 1.01  0.69  0.52, 0.93  0.74  0.55, 0.99  0.78  0.57, 1.05  0.09    Multivariate-adjusted  1.00  0.77  0.58, 1.02  0.70  0.52, 0.95  0.75  0.55, 1.02  0.79  0.56, 1.10  0.18   “Western” dietary pattern                        Age- and energy-adjusted  1.00  0.87  0.64, 1.18  1.08  0.80, 1.46  1.14  0.83, 1.56  1.09  0.76, 1.57  0.27    Multivariate-adjusted  1.00  0.85  0.63, 1.16  1.04  0.76, 1.41  1.07  0.77, 1.49  0.97  0.66, 1.44  0.68   “Drinker” dietary pattern                        Age- and energy-adjusted  1.00  0.91  0.70, 1.19  1.14  0.88, 1.49  0.79  0.58, 1.09  1.14  0.84, 1.53  0.72    Multivariate-adjusted  1.00  0.88  0.67, 1.15  1.10  0.84, 1.44  0.78  0.57, 1.08  1.13  0.84, 1.53  0.67  Total colon cancer                       “Healthy” dietary pattern  1.00  0.75  0.53, 1.08  0.69  0.47, 1.01  0.83  0.57, 1.21  0.83  0.54, 1.26  0.58   “Western” dietary pattern  1.00  0.86  0.59, 1.25  0.97  0.66, 1.43  1.08  0.72, 1.62  0.93  0.57, 1.53  0.82   “Drinker” dietary pattern  1.00  0.72  0.50, 1.02  1.18  0.85, 1.64  0.78  0.52, 1.16  1.14  0.78, 1.66  0.44  Proximal colon cancer                       “Healthy” dietary pattern  1.00  0.55  0.32, 0.95  0.52  0.29, 0.94  0.59  0.32, 1.09  0.62  0.31, 1.20  0.17   “Western” dietary pattern  1.00  0.58  0.32, 1.07  0.69  0.37, 1.27  1.11  0.61, 2.03  0.70  0.32, 1.55  0.97   “Drinker” dietary pattern  1.00  0.66  0.38, 1.15  1.26  0.77, 2.07  0.54  0.27, 1.09  1.03  0.56, 1.90  0.98  Distal colon cancer                       “Healthy” dietary pattern  1.00  0.87  0.46, 1.64  0.91  0.47, 1.74  1.05  0.54, 2.03  1.24  0.61, 2.52  0.47   “Western” dietary pattern  1.00  1.08  0.57, 2.03  1.21  0.63, 2.32  0.91  0.43, 1.93  1.34  0.59, 3.05  0.73   “Drinker” dietary pattern  1.00  0.73  0.41, 1.32  0.97  0.55, 1.71  0.73  0.38, 1.41  0.99  0.53, 1.85  0.94  Rectal cancer                       “Healthy” dietary pattern  1.00  0.75  0.47, 1.20  0.73  0.44, 1.19  0.64  0.38, 1.09  0.77  0.44, 1.35  0.26   “Western” dietary pattern  1.00  0.91  0.53, 1.58  1.32  0.78, 2.25  1.23  0.69, 2.18  1.20  0.61, 2.35  0.38   “Drinker” dietary pattern  1.00  1.17  0.76, 1.81  0.96  0.59, 1.55  0.78  0.45, 1.35  1.03  0.60, 1.75  0.57  * Age-adjusted models included age (continuous variable) and energy intake (continuous variable). Multivariate adjustment included age (continuous variable), energy intake (continuous variable), body mass index (in quartiles), and education (less than high school, high school, and university). † RR, relative risk; CI, confidence interval. ‡ Referent. View Large DISCUSSION In this population of Swedish women, we found no clear association between three distinct dietary patterns and colo-rectal cancer risk. Although dietary patterns could be discerned in our population, these patterns explained little of the variability in colorectal cancer incidence. Nevertheless, our data suggested that scoring very low with regard to the “healthy” dietary pattern (that is, consumption of fruits and vegetables, cereals and whole grains, fish, poultry, and low-fat dairy products) may be associated with increased risks of colon and rectal cancers. This association was clearer for women under age 50, although the number of cancers in this age group was low and interpretations should be made cautiously. Overall, our results are in agreement with those of a growing number of prospective cohort studies that have found no associations with dietary factors previously reported to be associated with colorectal cancer risk. For example, recent negative associations in large prospective cohort studies and clinical trials have called into question putative associations with intake of dietary fiber (8, 9) and consumption of fruits and vegetables (10, 11). Thus, variations in colorectal cancer incidence over time and geographic region that strongly suggest an etiologic role for environmental exposures (12–17) have yet to be adequately explained by changes in or differences in diet or other environmental factors. One previous study by Slattery et al. (4, 18) examined dietary patterns derived from principle-components factor analysis of colon cancer risk. That study, a case-control study, found a positive association with a “Western” dietary pattern and an inverse association with a “calcium and low-fat dairy products” pattern. None of the other seven dietary patterns extracted by Slattery et al., including a “moderation” dietary pattern that was similar to our “healthy” pattern, were significantly related to risk. However, direct comparisons are difficult, since the results of cohort studies have differed markedly from those of case-control studies with regard to such factors as intake of dietary fiber (8, 9, 19), fruits and vegetables (10, 11, 20), total energy (21, 22), and even coffee (23). Factor analysis involves decisions that can be called subjective or arbitrary—decisions that can have some impact on both the results and their interpretation (24). For example, the selection and grouping of foods for analysis from the larger pool of available food items can be guided by existing knowledge about how individual foods may be related to broader dietary patterns, but investigators may still differ in their decision-making criteria. There are also a number of different criteria for limiting the number of factors to be extracted from the data (24). Various approaches have been considered useful, such as extracting factors with eignenvalues greater than 1 (3) or graphing the eigenvalues and extracting factors that visibly explain an important degree of variation beyond what is explained by other factors (3). The methods by which the selected factors are then rotated and the manner in which the factors are ultimately labeled are also based on subjective criteria and are liable to different interpretations (24). In this context, it is interesting to note that our “Western,” “healthy,” and “drinker” dietary patterns are similar to those labeled “Western,” “moderation,” and “alcohol” in the case-control study by Slattery et al. (4). These factors are also similar to factors labeled “Western” and “prudent” in a subgroup of the Health Professionals Follow-up Study cohort (5), which suggests that these factors are more universal and may represent dietary patterns common to several populations. The strengths of our study include the relatively large sample size of our cohort, the study's population-based character, the diagnosis of colorectal cancer at specific subsites, the completeness of follow-up in the Swedish cancer register system, and the large number of cases. The prospective assessment of exposure in our study eliminated recall bias, which is a potential threat to the validity of case-control studies. However, we could not adjust our relative risk estimates for the potentially confounding effect of exercise, since this information was not collected at baseline. Energy intake, an approximate indicator of physical activity (25), was not associated with colorectal cancer in our data, and our results were not altered by adjustment for the effects of energy intake or body mass index. Since exercise has been shown to be associated with a “healthy” dietary pattern (4), bias due to confounding from physical activity may have contributed to the inverse association we observed among younger women. Moreover, although we constructed our 67-item food frequency questionnaire with the intention of including the most important foods (and sources of dietary variation) consumed by the Swedish population (26), it still may not have allowed estimation of energy intake with great precision, and thus adjustment for total caloric intake may not have fully accounted for between-person variation due to energy intake or physical activity. However, the associations with physical activity observed in previous studies were not limited to younger women (27), and therefore we would not expect confounding by physical activity, if it exists in our data, to be limited to this age group. Physical activity was not associated previously with either the “Western” dietary pattern or the “alcohol” dietary pattern (4). In addition, there may be other unidentified factors that might have accounted for our null findings to some degree. For example, if use of nonsteroidal antiinflammatory drugs such as aspirin, which has been associated with decreased risk of colorectal cancer (28), was more prevalent among persons with a “Western” dietary pattern in our data, this may have masked a true positive association. We know of no data that suggest a greater use of aspirin or other factors associated with decreased risk, such as colorectal cancer screening, among persons who consume foods associated with a “Western” diet, but we cannot rule out this possibility. Our data were further limited by the likelihood of some degree of measurement error in the individual dietary exposures. Nondifferential misclassification would tend to attenuate relative risk estimates (29), but such misclassification does not easily explain the discrepancy between the negative findings of most cohort studies and the inverse associations found in studies of retrospective design. The level of nonresponse in this study may also have led to a loss of precision due to reductions in sample size and outcome events, and to a reduced range of exposure to the foods and food groups that constitute the dietary patterns. Therefore, we cannot rule out the possibility of weak associations with dietary patterns that remained undetected in our data. In conclusion, our methods of extracting dietary patterns yielded patterns similar to some of those found in a previous case-control study of colorectal cancer (4). Unlike that study, however, we did not find any strong, significant association between dietary patterns and colorectal cancer risk. This discrepancy is consistent with the emerging schism between the results of cohort studies and the results of case-control studies with regard to several dietary risk factors for colorectal cancer—including previous findings of no association with dietary fiber in our own data and the finding that fruit and vegetable consumption was inversely associated with risk only among persons presumed to be deficient in antioxidant intake (30). On the other hand, our current data suggest the possibility that a “healthy” dietary pattern is protective among younger women. The role of overall eating patterns in the prediction of colorectal cancer risk should be investigated further. Correspondence to Dr. Paul Terry, Department of Epidemiology and Social Medicine, Albert Einstein College of Medicine, 1300 Morris Park Avenue, 13th Floor, Bronx, NY 10461 (e-mail: [email protected]). This study was supported by grants from the Swedish Council for Planning and Coordination of Research and the Swedish Cancer Society. REFERENCES 1. Parkin D. Estimates of the worldwide incidence of 25 major cancers in 1990. Int J Cancer  1999; 80: 827–41. Google Scholar 2. Potter J. Nutrition and colorectal cancer. Cancer Causes Control  1996; 7: 127–46. Google Scholar 3. Kim J-O, Mueller CW. Factor analysis: statistical methods and practical issues. Thousand Oaks, CA: Sage Publications, Inc, 1978. Google Scholar 4. Slattery ML, Boucher KM, Caan BJ, et al. Eating patterns and risk of colon cancer. Am J Epidemiol  1998; 148: 4–16. Google Scholar 5. Hu FB, Rimm E, Smith-Warner SA, et al. Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire. Am J Clin Nutr  1999; 69: 243–9. Google Scholar 6. Mattsson B, Wallgren A. Completeness of the Swedish Cancer Registry: non-notified cancer cases recorded on death certificates in 1978. Acta Radiol  1984; 23: 305–13. Google Scholar 7. SAS Institute, Inc. SAS/STAT user's guide, version 6. 4th ed. Vol 2. Cary, NC: SAS Institute, Inc, 1989. Google Scholar 8. Fuchs CS, Giovannucci E, Colditz GA, et al. Dietary fiber and the risk of colorectal cancer and adenoma in women. N Engl J Med  1999; 340: 169–76. Google Scholar 9. Alberts D, Martinez M, Roe D, et al. Lack of effect of high-fiber cereal supplement on the recurrence of colorectal adenomas. N Engl J Med  2000; 342: 1156–62. Google Scholar 10. Michels KB, Giovannucci E, Joshipura KJ, et al. Prospective study of fruit and vegetable consumption and incidence of colon and rectal cancers. J Natl Cancer Inst  2000; 92: 1740–52. Google Scholar 11. Schatzkin A, Lanza E, Corle D, et al. Lack of effect of a low-fat, high-fiber diet on the recurrence of colorectal adenomas. Polyp Prevention Trial Study Group. N Engl J Med  2000; 342: 1149–55. Google Scholar 12. Willett W. The search for the causes of breast and colon cancer. Nature  1989; 338: 389–94. Google Scholar 13. Tomatis L, ed. Cancer: causes, occurrence and control. (IARC Scientific Publication no. 100). Lyon, France: International Agency for Research on Cancer, 1990. Google Scholar 14. Haenszel W, Kurihara M. Studies of Japanese migrants. I. Mortality from cancer and other diseases among Japanese in the United States. J Natl Cancer Inst  1968; 40: 43–68. Google Scholar 15. Whitmore A, Zheng S, Wu A, et al. Colorectal cancer in Chinese and Chinese-Americans. Natl Cancer Inst Monogr  1985; 69: 43–6. Google Scholar 16. Doll R, Peto R. The causes of cancer: quantitative estimates of avoidable risks of cancer in the United States today. J Natl Cancer Inst  1981; 66: 1191–308. Google Scholar 17. Nelson N. Is chemoprevention overrated or under-funded? J Natl Cancer Inst  1996; 88: 947–9. Google Scholar 18. Slattery ML, Edwards SL, Boucher KM, et al. Lifestyle and colon cancer: an assessment of factors associated with risk. Am J Epidemiol  1999; 150: 869–77. Google Scholar 19. Howe GR, Benito E, Castelleto R, et al. Dietary intake of fiber and decreased risk of cancers of the colon and rectum: evidence from the combined analysis of 13 case-control studies. J Natl Cancer Inst  1992; 84: 1887–96. Google Scholar 20. Steinmetz K, Potter J. Vegetables, fruit, and cancer. II. Mech-anisms. Cancer Causes Control  1991; 2: 427–42. Google Scholar 21. Howe GR, Aronson KJ, Benito E, et al. The relationship between dietary fat intake and risk of colorectal cancer: evidence from the combined analysis of 13 case-control studies. Cancer Causes Control  1997; 8: 215–28. Google Scholar 22. Willett WC. Dietary fat intake and cancer risk: a controversial and instructive story. Semin Cancer Biol  1998; 8: 245–53. Google Scholar 23. Giovannucci E. Meta-analysis of coffee consumption and risk of colorectal cancer. Am J Epidemiol  1998; 147: 1043–52. Google Scholar 24. Martinez ME, Marshall JR, Sechrest L. Invited commentary: factor analysis and the search for objectivity. Am J Epidemiol  1998; 148: 17–21. Google Scholar 25. Willett W, Howe G, Kushi L. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr  1997; 65(suppl): 1220S–8S. Google Scholar 26. Statens Jordbruksnämd. Food consumption, 1966–1988. Jönköping, Sweden: Statens Jordbruksnämd, 1989. Google Scholar 27. Colditz GA, Cannuscio CC, Frazier AL. Physical activity and reduced risk of colon cancer: implications for prevention. Cancer Causes Control  1997; 8: 649–67. Google Scholar 28. Sjodahl R. Nonsteroidal anti-inflammatory drugs and the gastrointestinal tract: extent, mode, and dose dependence of anticancer effects. Am J Med  2001; 110(1A): 66S–9S. Google Scholar 29. Rothman KJ, Greenland S. Modern epidemiology. 2nd ed. Philadelphia, PA: Lippincott Williams & Wilkins Publishers, 1998. Google Scholar 30. Terry P, Giovannucci E, Michels KB, et al. Fruit, vegetables, dietary fiber, and risk of colorectal cancer. J Natl Cancer Inst  2001; 93: 525–33. Google Scholar

Journal

American Journal of Epidemiology – Oxford University Press

Published: Dec 15, 2001

There are no references for this article.