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Patterns in food use and compliance with NCI dietary guidelines a
b
b
D. Elizabeth Randall , James R. Marshall , John Brasure & Saxon Graham
b
a
Nutrition Program , State University of New York , 301 Parker Hall, Buffalo, NY, 14214 b
Department of Social and Preventive Medicine , State University of New York , Buffalo, NY, 14214 Published online: 04 Aug 2009.
To cite this article: D. Elizabeth Randall , James R. Marshall , John Brasure & Saxon Graham (1991) Patterns in food use and compliance with NCI dietary guidelines, Nutrition and Cancer, 15:2, 141-158, DOI: 10.1080/01635589109514121 To link to this article: http://dx.doi.org/10.1080/01635589109514121
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Patterns in Food Use and Compliance With NCI Dietary Guidelines
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D. Elizabeth Randall, James R. Marshall, John Brasure, and Saxon Graham
Abstract We developed multidimensional measures of diet by identifying seven gender-specific patterns among 1,475 male and 780 female subjects selected as neighborhood controls in a case-control study of cancer at various sites. The purpose of this study was to examine these patterns for compliance with the National Cancer Institute (NCI) Dietary Guidelines. Diets were scored and each subject assigned fertile ranks for each pattern. Mean values for indicators of compliance were compared across tertiles for each pattern. No one pattern signified compliance with all guidelines. High-fat patterns in both genders and the low-cost pattern in women were the best indicators of poor compliance with NCI guidelines. (Nutr Cancer IS, 141-158, 1991)
Introduction
Diet is complex; it involves the repeated use of foods that, in combination, incorporate numerous factors, both nutrient and nonnutrient, that are implicated in cancer etiology. In an effort to lower the occurrence of cancer in the United States, the National Cancer Institute (NCI) has suggested dietary guidelines that may reduce cancer risk (1). These guidelines can be summarized as follows. • • • • • •
Reduce total fat intake to 30% or less of calories. Increase dietary fiber intake to 20-30 g daily, not to exceed 35 g. Include a variety of fruits and vegetables daily. Avoid obesity. Consume alcoholic beverages in moderation, if at all. Minimize consumption of salt-cured, salt-pickled, and smoked foods.
The nutritional impact of adopting these guidelines is fairly clear: The guidelines would involve a decreased fat intake, an increased fiber intake, and an increased use of a range of D.E. Randall is affiliated with the Nutrition Program and J.R. Marshall, J. Brasure, and S. Graham with the Department of Social and Preventive Medicine, State University of New York at Buffalo, Buffalo, NY 14214.
Copyright © 1991, Lawrence Erlbaum Associates, Inc.
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nutrients found predominantly in fruits and vegetables. The guidelines promote decreased exposure to the harmful effects of ethanol as well as to the other putative harmful factors contained in smoked and salted foods. Patterson and Block (2) attempted to investigate the extent to which food choices in the 24-hour dietary recall data from the NHANES II were consistent with recommendations to lower cancer risk. They were constrained, however, by the inability of intakes over a single day to represent usual diets. Past studies of diet and cancer commonly have addressed contributions of single nutrients to cancer risk, based on intakes estimated using a food frequency technique. The Western New York Diet Study (WNYDS), a case-control study of diet and cancer of the lung, esophagus, larynx, mouth, stomach, bladder, colon, and rectum, represents one such study (3-11). Because results of these studies and comparable epidemiological investigations of diet and cancer have produced estimated levels of risk lower than those expected on the basis of ecological investigations (12,13), we have speculated that the single-nutrient approach underestimates the importance of diet with respect to cancer risk. Findings from our earlier work indicate that arraying individuals by quartile of intake of a single nutrient simultaneously arrays them on other dietary traits as well (14). These traits include both protective and risk-increasing factors. We have pursued the identification of dietary patterns among the control subjects of the WNYDS (15) to develop dietary measures reflective of combinations of individual foods as consumed by a free-living population. Results indicate that several distinctive dietary patterns are associated with such nutrient risk factors as high-fat or low-fiber intakes. The purpose of this study is to examine these dietary patterns further, regarding their compliance with the NCI Dietary Guidelines, and thereby to identify specific features of the patterns that may contribute to risk. Our ultimate goal, which is beyond the scope of this paper, is to submit the patterns to case-control comparisons of risk for cancer at each of the sites included in the study. Methods Sample Characteristics Subjects were recruited as neighborhood control subjects for persons in Erie, Niagara, and Monroe counties diagnosed in 1975-1986 with cancer at various sites. The control subjects were further matched to the cases on age, gender, and race. The total control sample consists of 1,475 males and 780 females with mean ages of 62.9 and 64.5 years, respectively. More than 80% of the males were married; among females, 60% were married and 30% were widowed. Both white- and blue-collar occupations were well represented in the subjects' employment histories. The protocol of this study was approved by the committee for the protection of human subjects of the School of Medicine and Biological Sciences at the State University of New York at Buffalo. Identification of Dietary Factors Data were collected in a 2.5-hour interview that included a 128-item semiquantitative food frequency instrument, as well as additional items regarding commonly used food, preparation and storage methods, and discretionary use of condiments. Subjects were questioned concerning their food use during the previous 12-month period. The number of foods included in the final factor analysis was reduced to 95 by a) excluding items consumed with a mean frequency of less than 0.5 times monthly (smoked, dried, pickled, and canned meats and meat spreads; smoked and pickled fish; lamb; veal; snow peas; pumpkin; kale; eggplant; and greens), b) combining similar forms of infrequently used foods that, together, were consumed more frequently than 0.5 times monthly (dried fruit and ham), and c) eliminating items generally used only as condiments (spreads, pickles, and olives).
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Principal components analysis with orthogonal rotation of the factors was used to extract dietary patterns; seven patterns were identified for each gender. These factor analytic procedures are reported more fully elsewhere (14). The patterns, which appear in Tables 1 and 2, include only those foods most strongly associated with each factor, at a level of -0.2 > r > 0.3. Factors were named in an effort to reflect factually the foods contained within them. The high-fat patterns, for example, contain many foods dense in fat; foods in the traditional pattern are consistent with eating meat, potato, and common vegetables; and the healthful patterns include foods consistent with current trends to leaner foods. Each subject was assigned a score for each pattern by multiplying the factor weight of each food included in the pattern by the standardized value of the reported frequency of use and summing over all foods. Subjects were then given a tertile rank for each pattern based on the locations of their scores within the distributions of dietary pattern scores. The seven patterns extracted account for 20.3% and 21.7% of the variance in food use in males and females, respectively. Because additional patterns explain decreasing amounts, many patterns would be needed to account for even 50% of the variance; these patterns would be difficult to interpret (15,16). Associations between dietary patterns and cancer in a population will likely be found among those patterns contributing most to the variance in dietary intake. Indices of Compliance With Dietary Guidelines Nutrient values were assigned to all the 128 foods included in the original food frequency instrument and used to compute nutrient intake for each subject. Reported frequency of use of foods was converted to a common monthly unit, with a month consisting of four weeks or 28 days. The food frequency measure is known to overestimate nutrient intake, but it is successful in categorizing subjects by level of nutrient intake. We chose, therefore, to control for quantitative differences in intake and examine fat and fiber intakes expressed as density-based measures, namely percent of calories derived from fat and grams of dietary fiber per 1,000 kcal consumed. To assess variety in fruit and vegetable use, we calculated vegetable and fruit diversity separately by summing the number of vegetables and fruits, respectively, on the list of foods consumed at least monthly. We used self-reported height and weight data to calculate body mass index (BMI) using the equation BMI = wt(kg)/ht2(m) to assess obesity. Weekly servings of wine, beer, and distilled alcohol, separately, as well as total alcohol consumption were used to assess moderation in alcohol use. The weekly number of servings of all meats and fish on the original 128-item food list that were salt cured, salt pickled, or smoked {n = 15) was used to assess compliance with the recommendation to minimize consumption of these foods. Also for this recommendation, we calculated the number of times weekly that subjects consumed meats and poultry that were charbroiled. Results Fat Table 3 presents differences in fat consumption, expressed as mean percent of calories derived from fat, by tertiles of dietary pattern scores. At all levels of each pattern, subjects failed to achieve the goal of obtaining no more than 30% of calories from fat, although some patterns are more closely associated with attaining this goal than are others. The data suggest that few subjects complied with the recommendation in the NCI guidelines to consume no more than 30% of calories as fat. For example, closest compliance among men was found for subjects in the lowest tertile of the high-fat pattern. Based on the distribution of values, approximately 90% of subjects with diets of lowest fat composition had intakes between
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o
I
Table 1. Dietary Factors" for Malesb Factor 1: Salad Food Lettuce Celery Green pepper Cucumber Tomatoes Radishes Onions Carrots
Factor 2: Fruit
Weight
Food
0.63 0.62 0.61 0.57 0.54 0.53 0.52 0.44
Peaches Plums Pears Apples Oranges, tangerines Cherries Grapes Melons Bananas Berries Beer
% Variance
6.0
Weight 0.57 0.54 0.54 0.52
Factor 3: Healthful Food
Weight
0.56 0.48 0.45 Rice 0.41 Poultry 0.36 0.47 Mushrooms 0.34 0.39 Green beans 0.33 0.37 Summer squash 0.33 0.37 Asparagus 0.33 0.34 White bread -0.20 0.32 Cookies -0.22 -0.25 -0.23 Potatoes 3.7
Broccoli Spinach Cauliflower
2.7
a: Identified in the Western New York Diet Study from 1975 to 1986. b: For males, n = 1,'175.
Factor 4: Traditional
Factor 5: Snacks
Food
Weight
Food
Wax beans Green beans Potatoes Peas Beets Cabbage Roast beef Cakes
0.52 0.49 0.45 0.45 0.39 0.36 0.36 0.35 0.34 0.31
Cookies Candy Crackers Doughnuts, pastries Hamburger Ice cream Baked beans
Pies
Corn
2.2
Weight
Factor 6: High Fat Food
0.46 Eggs 0.42 Bacon 0.39 Sausage Steak 0.39 Beer 0.38 Salami, 0.36 pepperoni 0.33 Distilled alcohol Cookies Summer squash Winter squash Cereal, unsweetened 2.1
Weight
Factor 7: Whole Grain Food
Weight
0.44 0.43 0.41 0.34 0.33
Whole wheat bread 0.36 0.34 Cereal, cooked 0.32 Oat products -0.20 Cola Noodles -0.22 0.31 Hamburger -0.25 -0.26 French fries 0.30 Coffee -0.29 -0.20 Macaroni -0.35 -0.44 -0.22 Spaghetti -0.23 White bread -0.47 -0.40 1.8
1.8
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to
Table 2. Dietary Factors" for Females* Factor 1: Salad Food
Factor 2: Healthful
Weigh t
Food
Weight
Lettuce Onions Cucumber Tomatoes Celery Green pepper Radishes Carrots Apples Broccoli
0.62 0.62 0.57 0.56 0.56 0.54 0.52 0.47 0.32 0.30
Green beans Cauliflower Spinach Beets Summer squash Pineapple Liver Broccoli Wax beans Winter squash Cottage cheese Peas Mushrooms Cabbage
0.55 0.49 0.47 0.43 0.43 0.41 0.37 0.36 0.36 0.35 0.34 0.33 0.33 0.30
0.05
5.2
0.0056
67.9
0.0000
49.6
0.0000
Females (n = 780) Salad Healthful Low cost Fruit High fat Light Whole grain
6.3
7.6
9.5
(3.2)
(3.1)
(3.0)
6.5
7.7
9.2
(3.2)
(2.9)
(3.4)
7.5
8.0
8.0
(3.4)
(3.5)
(3.2) 10.4 (2.7)
5.2
7.8
(2.6)
(2.5)
7.4
7.6
8.4
(3.3)
(3.3)
(3.4)
6.5
7.8
9.0
(3.0)
(3.2)
(3.4)
6.8
7.9
8.6
(3.2)
(3.2)
(3.5)
1.8
>0.05
261.5
0.0000
6.1
0.0025
41.9
0.0000
19.0
0.0000
a: Scores are from Western New York Diet Study conducted from 1975 to 1986. b: Values in parentheses are standard deviations.
high-fat (F = 6.3, p < 0.002) patterns and negatively related to the traditional pattern (F = 5.0, p< 0.007). Among women, body mass was related to only the fruit pattern (F = 3.4, p < 0.04), with smaller body size associated with lower scores on this dietary pattern. Alcohol Mean weekly consumptions of wine, beer, and distilled alcohol, as well as total alcohol intake, are presented by level of dietary pattern scores in Tables 7 and 8. For males, highest wine intakes were found among subjects with highest scores for the salad and healthful patterns. Use of beer was found to be significantly greater for lower scores on the fruit and healthful patterns and higher scores for the high-fat pattern. Greater distilled alcohol intake was also associated positively with the high-fat dietary pattern. Among women, wine consumption was negatively associated with the fruit pattern and positively associated with the light and healthful patterns. Beer consumption was lower among women with high scores for the fruit and whole-grain patterns. Distilled alcohol use was negatively associated with fruit pattern scores and positively associated with scores on the light pattern.
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Table 7. Mean Weekly Number of Servings of Alcoholic Beverages by Tertile of Dietary Pattern Scores" for Males6 Pattern Score0 Dietary Pattern Salad Wine Beer Distilled alcohol Total alcohol
Low
Medium
High
F Statistic
0.9
1.2
(2.7)
1.9 (4.2)
12.3
(2.5)
0.8
>0.05
4.5
5.4
4.7
(10.2)
(13.0)
(10.1)
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Beer Distilled alcohol Total alcohol Healthful Wine Beer Distilled alcohol Total alcohol Traditional Wine Beer Distilled alcohol Total alcohol Snacks Wine Beer Distilled alcohol Total alcohol
0.0000
3.1
4.0
3.9
1.7
>0.05
(9.4)
(8.7) 10.6 (17.2)
(7.7) 10.5 (14.0)
2.9
>0.05
2.5
>0.05
8.5
(14.2) Fruit Wine
p Value
1.1
1.3
1.5
(3.3)
(3.1)
(3.5)
8.3
3.0
3.3
(16.1)
(6.1)
(7.6)
4.3
3.7
3.0
(9.2) 13.7 (19.7)
(9.3)
(7.2)
8.0
7.8
(12.2)
(11.6)
0.6
1.3
2.0
(2.1)
(2.9)
(4.3)
5.8
4.9
3.9
(11.8)
(11.5)
(10.0)
37.7 2.7
0.0000 >0.05
24.6
0.0000
20.4
0.0000
3.9
0.0211
3.3
3.6
4.2
1.2
>0.05
(9.2)
(7.6)
(8.9) 10.0 (15.4)
0.0
>0.05
2.7
>0.05
0.6
>0.05
9.8
9.8
(15.7)
(14.6)
1.6
1.3
1.1
(3.9)
(3.0)
(2.9)
4.7
4.6
5.3
(11.9)
(9.3)
(12.0)
3.6
3.9
3.6
0.1
>0.05
(9.7)
(7.3)
(8.6) 10.0 (16.6)
0.1
>0.05
4.3
9.8
9.7
(16.2)
(12.5)
1.6
1.4
1.0
(4.1)
(3.1)
(2.3)
5.1
5.1
4.3
(11.6)
(12.1)
(9.6)
3.8
4.1
3.2
(10.4) 10.5 (17.1)
(8.7) 10.6 (15.2)
(6.2) 8.5
0.0134
0.8
>0.05
1.3
>0.05
2.9
>0.05
(13.0) (continued)
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Table 7. (Continued) Pattern Scorec Dietary Pattern
Low
High fat Wine Beer Distilled alcohol Total alcohol
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Whole grain Wine Beer
High
1.1
1.3
1.5
(2.7)
(3.0)
(4.0)
1.6
3.2
9.7
(3.8)
(6.3)
(16.7)
F Statistic 1.7
p Value >0.05
81.7
0.0000
1.4
2.9
6.8
55.7
0.0000
(2.9)
(5.2)
(13.1) 18.0 (21.8)
133.3
0.0000
4.0
7.5
(6.0)
(8.6)
1.2 (3.6)
1.2
1.5
(2.9)
(3.4)
5.8
4.6
4.2
(12.1)
(10.0)
(11.2)
3.1
4.0
4.0
(7.3) 10.0 (14.6)
(8.5)
(9.9)
Distilled alcohol Total alcohol
Medium
9.8
9.8
(14.1)
(16.7)
1.7
>0.05
2.6
>0.05
2.1
>0.05
0.0
>0.05
a: Scores are from Western New York Diet Study conducted from 1975 to 1986. b: For males, n = 1,475. c: Values in parentheses are standard deviations.
Salted and Smoked Foods Highest mean frequency of use of these meats and fish was found among those men with highest scores on the traditional, snacks, and high-fat patterns and with lowest scores on the whole-grain patterns (Tables 9 and 10). Frequency of consumption of charbroiled meats and poultry was most strongly associated with the upper tertile of the high-fat pattern and lowest tertile of the whole-grain pattern. Among women, the use of salted meats and fish was most strongly associated with the highest scores on the low-cost and high-fat patterns. Charbroiled meats were more frequently consumed by women with highest scores on the salad and low-cost patterns. Discussion Data in this study identify areas for cancer risk reduction in each of the dietary patterns, even those patterns that appear to promote well-being. To assist in interpreting the nutritional consequences of our indices of compliance, we present results of correlational analyses of pattern scores and nutrient intakes in Table 11. Total Fat Data from this study indicate that several of the patterns are associated with lower fat intake. Few subjects in even these apparently prudent eating styles demonstrate compliance with the guideline to obtain fewer than 30% of calories from fat. Scores for the fruit and whole-grain patterns were indicative of lower fat composition for both genders. The high-fat pattern extracted for males is strongly indicative of noncompliance. A similarly labeled
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Table 8. Mean Weekly Number of Servings of Alcoholic Beverages by Tertile of Dietary Pattern Scores" for Females6 Pattern Scorec Dietary Pattern Salad Wine Beer Distilled alcohol Total alcohol
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Healthful Wine Beer Distilled alcohol Total alcohol Low cost Wine Beer Distilled alcohol Total alcohol Fruit Wine Beer Distilled alcohol Total alcohol High fat Wine Beer Distilled alcohol Total alcohol
Low
Medium
High
F Statistic 2.0
>0.05
0.1
>0.05
0.2
>0.05
0.9
>0.05
1.0
1.0
1.5
(2.9)
(2.6)
(4.7)
0.8
0.7
0.7
(3.4)
(2.8)
(2.1)
1.3
1.4
1.5
(3.2)
(3.2)
(3.5)
3.1
3.0
3.7
(5.8)
(5.5)
(6.7)
0.8
1.1
1.6
(2.4)
(2.8)
(4.8)
0.6
0.6
0.8
(3.1)
(2.6)
(2.7)
1.6
1.0
1.5
(4.0)
(2.7)
(3.1)
3.1
2.8
3.9
(5.9)
(5.2)
(6.8)
1.1
1.5
0.6
(3.3)
(4.5)
(2.5)
0.6
0.5
1.0
(2.9)
(1.9)
(3.3)
1.4
1.4
1.4
(3.4)
(3.2)
(3.3)
3.1
3.3
3.4
(5.9)
(6.3)
(6.0)
1.9
1.1
0.6
(5.0)
(2.8)
(1.6)
1.5
0.3
0.2
(4.5)
(1.3)
(0.8)
2.6
1.1
0.5
(2.3)
(1.1)
6.0
2.5
1.3
(8.6)
(4.1)
(2.4)
1.3
1.1
1.2
(4.0)
(3.4)
(3.1)
0.8
0.4
0.8
(3.5)
(1.5)
(3.0)
1.4
1.1
1.6
(3.3)
(2.7) 2.6 (5.2)
(3.8)
3.5
(6.6)
3.7
3.3
p Value
0.0373
0.4
>0.05
2.1
>0.05
2.3
>0.05
1.3
>0.05
2.4
>0.05
0.0
>0.05
0.2
>0.05
9.6
0.0001
18.3
0.0000
31.6
0.0000
49.5
0.0000
0.2
>0.05
1.8
>0.05
1.7
>0.05
2.3
>0.05
(6.1) (continued)
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Table 8. (Continued) Pattern Scorec Dietary Pattern Light Wine Beer Distilled alcohol Total alcohol
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Whole grain Wine Beer Distilled alcohol Total alcohol
Low
Medium
High
F Statistic 9.2
0.6
1.0
1.9
(2.4)
(2.5)
(4.9)
0.8
0.7
0.6
(3.2)
(2.6)
(2.5)
0.9
1.9
1.3
(2.8)
(3.8)
(3.1)
2.3
3.5
3.9
(5.2)
(5.9)
(6.8)
1.4
1.4
0.8
(4.1)
(4.0)
(1.9)
1.1
0.6
0.4
(3.7)
(2.6)
(1.5)
1.4
1.3
1.4
(3.3)
(3.4)
(3.2)
3.9
3.3
2.6
(6.9)
(6.4)
(4.5)
0.1
p Value 0.0001 >0.05
5.2
0.0055
4.7
0.0092
2.0 5.6 0.1 3.5
>0.05 0.0039 >0.05 0.0317
a: Scores are from Western New York Diet Study conducted from 1975 to 1986. b: For females, n = 780. c: Values in parentheses are standard deviations.
pattern among women has less predictive value, although the association of that pattern with noncompliance is statistically significant. The low-cost pattern identified among women also indicated noncompliance. Not only do persons following diets consistent with the high-fat pattern or the low-cost pattern among women consume a higher percent of calories from fat, but also they have factor scores associated with higher energy intake, implying high total fat intakes. Although the two patterns in women are not as strongly associated with percent fat composition, they do correlate strongly with total fat and energy intakes; women in the uppermost tertile of these patterns may not be as distinguishable by the composition of the foods they eat as they are by the total amounts they consume.
Dietary Fiber The salad and fruit patterns for both genders, and the healthful and whole-grain patterns for males, are strongly, positively associated with high-fiber density. Because of limitations of the food frequency measure, we hesitate to interpret these findings in terms of compliance with the quantitative recommendation to consume 20-30 g of fiber daily. The salad and fruit patterns are positively associated with energy intakes and even more strongly with total fiber intake, leading us to conclude that persons in the highest tertiles of these patterns achieve greater dietary fiber intake by consuming foods that are more fiber dense. For the healthful pattern, both dietary fiber density and total fiber intakes are higher among persons with highest scores. It would appear that the lower energy intakes of males with high scores for the whole-grain pattern result in a lack of correspondence between pattern scores and total fiber intake. These findings point to fruit and vegetable use, rather than whole-grain use, as being the most contributory to compliance with the recommendation to increase dietary fiber intake.
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Table 9. Mean Weekly Frequency of Consumption of Cured, Pickled, or Smoked Meat and Fish by Tertile of Dietary PatterrI Scores" Pattern Score* Dietary Pattern
Low
Medium
High
Salad
4.3
4.7
5.1
(3.6)"
(4.1)
(4.1)
F Statistic
p Value
Males (n == 1,475) Fruit Healthful Traditional
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Snacks High fat Whole grain
4.7
4.3
5.2
(3.8)
(3.7)
(4.2)
4.9
4.5
4.8
(3.9)
(3.4)
(4-4)
3.7
4.8
5.7
(3.5)
(3.5)
(4.5)
3.9
4.8
5.6
(3.6)
(3.9)
(4.1)
3.0
4.1
7.0
(2.7)
(3.1)
(4.5)
5.8
4.6
3.8
(4.1)
(3.9)
(3.5)
4.6
0.0105
6.4
0.0016
1.5
>0.05
30.8
0.0000
24.6
0.0000
162.4
0.0000
33.5
0.0000
10.4
0.0000
5.1
0.0066
119.8
0.0000
Females (n = 780) Salad Healthful Low cost Fruit High fat Light Whole grain
2.4 (2.4) 2.7 (2.6) 1.5 (1.8) 2.9 (2.6) 2.1 (2.5) 3.1 (2.6) 3.1 (2.8)
3.3
3.5
(2.9)
(3.1)
3.1
3.4
(2.9)
(3-D
2.9
4.8
(2.4)
(3.2)
3.0
3.3
(2.7)
(3.2)
3.0
4.1
(2.7)
(3.1)
3.0
3.2
(2.7)
(3.2)
3.0
3.1
(2.8)
(3.0)
1.1
31.9
>0.05 0.0000
0.5
>0.05
0.1
>0.05
a: Scores are frorr» Western New York Diet Study conducted from 1975 to 1986. b: Values in parentheses are standarc 1 deviations.
The high-fat patterns are associated with low-fiber density for males as well as for females. Because these patterns correlate positively with energy intake, total fiber intake may be similar across tertiles of pattern scores. For males, we observe no association between pattern scores and total fiber intake. Among women, high-fat as well as low-cost pattern scores relate positively to total fiber intake. Variety in Vegetables and Fruits Past research has indicated a positive association between diversity measures and quantitative measures of dietary intake (17). In this study, high scores for most patterns are consistent with the recommendation to increase variety in use of fruits and vegetables. Among men, however, the high-fat pattern is unrelated to vegetable diversity, and the whole-grain pattern was indicative of noncompliance with variety in vegetable use. Similar trends are observed for fruit diversity. Scores for the whole-grain pattern were associated with fruit diversity for women but show very little difference across tertiles of the whole-grain pattern among males.
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Table 10. Mean Weekly Frequency of Consumption o\ ' Charbroiled Meat and Poultry by Tertile of Dietary Pattern Scores" Pattern!Score" Dietary Pattern
Low
Medium
High
F Statistic
p Value
Males (n = 1,475) Salad Fruit Healthful Traditional
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Snacks High fat Whole grain
1.4
1.7
1.9
(2.2)
(2.3)
(2.4)
1.7
1.4
1.9
(2.3)
(2.1)
(2.4)
1.6
1.7
1.8
(2.2)
(2.3)
(2.4)
1.4
1.6
2.0
(2.0)
(2.2)
(2.6)
1.4
1.7
2.0
(2.0)
(2.1)
(2.7)
1.1
1.8
2.2
(1.8)
(2.2)
(2.7)
2.3
1.5
1.2
(2.8)
(2.0)
(1.8)
7.0
0.0009
6.4
0.0017
0.8
>0.05
7.2
0.0008
8.7
0.0002
32.3
0.0000
34.6
0.0000
15.1
0.0000
6.7
0.0014
16.9
0.0000
Females (n = 780) Salad Healthful Low cost Fruit High fat Light Whole grain
0.9
1.3
1.9
(1.6)
(2.0)
(2.8)
1.1
1.3
1.7
(2.3)
(1.9)
(2.4)
0.8
1.3
1.9
(1.6) 1.3 (1.8)
(2.5)
(2.3)
1.2
1.6
(1.9)
(2.8)
1.2
1.3
1.6
(2.5)
(1.9)
(2.2)
1.1
1.4
1.5
(1.8)
(2.6)
(2-2)
1.6
1.3
1.2
(2.7)
(2-0)
(1.9)
2.5
>0.05
2.2
>0.05
2.8
>0.05
2.3
>0.05
a: Scores are from Western New York Diet Study conducted from 1975 to 1986. b: Values in parentheses are standard deviations.
Obesity
Only small differences were observed for mean BMI by score for any of the dietary patterns. It is doubtful that the magnitude of the observed differences in body size by tertile of pattern scores would have any practical significance. The validity of the results is constrained by the fact that heights and weights were self-reported. Bias in reporting, especially with those persons at the highest weight levels underreporting their true weight, could be partially responsible for the weakness of the observed associations. It is also possible that there is little association between total food intake and obesity in this population.
Alcohol Among males, the high-fat pattern was most strongly related to noncompliance with the guideline to moderate alcohol use. Men in the uppermost tertile of this pattern consumed an average of 2.5 drinks daily. Although high scores for the salad and healthful patterns were
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Table 11. Correlations Between Dietary Factor Scores and Nutrient Intake"
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Nutrient Intake*" Dietary Factor
Energy
Fat
Dietary Fiber
Sodium
Salad Fruit Healthful Traditional Snacks High fat Whole grain
0.19 0.32 0.066 0.45 0.51 0.27 -0.32
Males (n = 1,475) 0.16 0.18 0.04* 0.40 0.41 0.41 -0.31
0.38 0.57 0.27 0.49 0.36 0.03* -0.06*
0.18 0.22 0.05* 0.41 0.41 0.34 -0.26
Salad Healthful Low cost Fruit High fat Light Whole grain
0.22 0.29 0.43 0.29 0.51 0.06* 0.26
Females (n = 780) 0.17 0.25 0.43 0.16 0.49 0.07* 0.16
0.48 0.45 0.20 0.53 0.25 0.20 0.37
0.19 0.25 0.44 0.16 0.43 0.05" 0.17
a: Values are from Western New York Diet Study conducted from 1975 to 1986. b: Values are Pearson product-moment correlation coefficients. c: Values are not statistically significant at p < 0.001 (two-tailed test).
associated with more frequent consumption of wine, consumption of total alcohol appears to comply with the guidelines. The fruit pattern predicts greater compliance with this recommendation for alcohol in all its forms. Ingestion of alcoholic beverages is lower overall among the women. As with men, the fruit pattern relates negatively to consumption of alcohol. However, among women, the high-fat pattern is unrelated to alcohol use. On the other hand, the light pattern in women was positively associated with both wine and distilled alcohol use. The association of the pattern with distilled alcohol is not monotonic, medium status on the pattern being associated with the highest level of distilled alcohol consumption. Salted and Smoked Foods The frequency of consuming salted meats and fish was most strongly associated in males with the high-fat pattern. The traditional and snacks patterns also had strong, positive associations. Scores for the whole-grain pattern, on the other hand, were negatively related. Among women, the low-cost pattern had the strongest association with use of salted meats and fish, although the high-fat pattern was also strongly positively related. These associations with patterns and salted foods is reflected in the strength of relationships with sodium intake (Table 9). With the exception of the healthful pattern, all dietary patterns were associated with frequency of consuming charbroiled foods among males. Scores on the high-fat pattern exhibited the strongest positive relationships, whereas scores for the whole-grain pattern had the strongest negative ones. Among females, only three of the dietary patterns were significantly associated with use of charbroiled foods, with the salad and low-cost patterns having the strongest positive relationships. We have attempted to summarize the findings in Table 10 by indicating, for each recommendation, whether the specific dietary patterns appear to be associated with compliance, noncompliance, or if the associations remain unclear. Our decisions regarding
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Z p
Table 12. Summary of Compliance of Dietary Patterns With NCI Guidelines to Lower Cancer Risk" Dietary Pattern Females (n = 780)
Males (n = 1,475) Guideline
Salad Fruit Healthful Traditional Snacks High fat Whole grain Salad Healthful Low cost Fruit High fat Light Whole grain C
Fat
Fiber Variety Vegetables Fruits Obesity Alcohol Salted and smoked food Salted Charbroiled
C
c
c
C
c c
c c
c
NC NC
C
C
c
c
C
c
c
c
NC
c c
c c
C
NC
c NC
NC
NC NC
C C
c c
C NC C
c
NC
a: Abbreviations are as follows: C, compliance; NC, noncompliance; no ibbreviation, inconclusive evidence.
NC NC
c
NC NC
c
cC
c
NC
c
compliance are somewhat arbitrary and are based on both statistically and practically significant differences in indicators of compliance across the tertiles of dietary pattern scores. The high-fat pattern among males fails to comply with any of the NCI dietary guidelines. For all other patterns, in both genders, each pattern complies with at least one of the recommendations. For both males and females, the fruit patterns demonstrate greatest compliance. In terms of total risk, however, persons following diets consistent with the high-fat patterns and females following the low-cost or light patterns may incorporate most risk factors. Persons identified as being in these categories may well represent target groups for nutrition interventions to lower cancer risks. It is our intent to submit the dietary patterns to case-control comparisons of cancer risk at several sites. Data presented in this paper will be supportive in interpreting the results of such analyses.
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Acknowledgments and Notes This research was funded by National Institutes of Health (Bethesda, MD) Grant No RO3 CA-51720-01. Address reprint requests to Dr. D. E. Randall, Nutrition Program, 301 Parker Hall, State University of New York at Buffalo, Buffalo, NY 14214. Submitted 20 June 1990; accepted in final form 31 October 1990.
References 1. Butrura, RR, Clifford, CK, and Lanza, E: "NCI Dietary Guidelines: Rationale." Am J Clin Nutr 48, 888-895, 1988. 2. Patterson, BH, and Block, G: "Food Choices and Cancer Guidelines." Am J Public Health 78, 282-286, 1988. 3. Graham, S: "Fats, Calories, and Calorie Expenditure in the Epidemiology of Cancer." Am J Clin Nutr 45, 342-346, 1987. 4. Graham, S: "Epidemiology of Retinoids and Cancer." JNCI 73, 1423-1428, 1984. 5. Byers, T, Vena, JE, Mettlin, C, Swanson, M, and Graham, S: "Dietary Vitamin A and Lung Cancer Risk: An Analysis by Histiologic Subtypes." Am J Epidemiol 120, 769-776, 1984. 6. Graham, S, Marshall, J, Haughey, B, Brasure, J, Freudenheim, J, et al.: "Nutritional Epidemiology of Cancer of the Esophagus." Am J Epidemiol 131, 454-467, 1990. 7. Graham, S, Haughey, B, Marshall, J, Brasure, J, Zielezny, M, et al.: "Diet in the Epidemiology of Gastric Cancer." Nutr Cancer 13, 19-34, 1990. 8. Graham, S, Marshall, J, Haughey, B, Mittelman, A, Swanson, M, et al.: "Dietary Epidemiology of Cancer of the Colon in Western New York." Am J Epidemiol 128, 490-503, 1988. 9. Freudenheim, J, Graham, S, Horvath, PJ, Marshall, JR, Haughey, BP, et al.: "Risks Associated With Source of Fiber and Fiber Components in Cancer of the Colon and Rectum." Cancer Res 50, 3295-3300, 1990. 10. Freudenheim, JL, Graham, S, Marshall, JR, Haughey, BP, and Wilkinson, G: "A Case-Control Study of Diet and Rectal Cancer in Western New York." Am J Epidemiol 131, 612-624, 1990. 11. Freudenheim, JL, Graham, S, Marshall, JR, Haughey, BP, and Wilkinson, G: "Lifetime Alcohol Intake and Risk of Rectal Cancer in Western New York." Nutr Cancer 13, 101-109, 1990. 12. Wynder, EL, and Gori, GB: "Contribution of the Environment to Cancer Incidence: An Epidemiologic Exercise." JNCI 58, 825-832, 1977. 13. Doll, R, and Peto, R: "The Causes of Cancer: Quantitative Estimates of Avoidable Risks of Cancer in the United States Today." JNCI 66, 1191-1308, 1981. 14. Randall, E, Marshall, J, Graham, S, and Brasure, J: "Food Frequency and the Multidimensionality of Diet." J Am Diet Assoc 89, 1070-1075, 1989. 15. Randall, E, Marshall, J, Graham, S, and Brasure, J: "Patterns in Food Use and Their Associations With Nutrient Intakes." Am J Clin Nutr 52, 739-745, 1990. 16. Nicklas, TA, Webber, LS, Thompson, B, and Berenson, GS: "A Multivariate Model for Assessing Eating Patterns and Their Relationship to Cardiovascular Risk Factors: The Bogalusa Heart Study." Am J Clin Nutr 49, 1320-1327, 1989. 17. Randall, E, Nichaman, MZ, and Contant, CF, Jr: "Diet Diversity and Nutrient Intake." J Am Diet Assoc 85, 830-836, 1985.
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Nutrition and Cancer Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/hnuc20
Patterns in food use and compliance with NCI dietary guidelines a
b
b
D. Elizabeth Randall , James R. Marshall , John Brasure & Saxon Graham
b
a
Nutrition Program , State University of New York , 301 Parker Hall, Buffalo, NY, 14214 b
Department of Social and Preventive Medicine , State University of New York , Buffalo, NY, 14214 Published online: 04 Aug 2009.
To cite this article: D. Elizabeth Randall , James R. Marshall , John Brasure & Saxon Graham (1991) Patterns in food use and compliance with NCI dietary guidelines, Nutrition and Cancer, 15:2, 141-158, DOI: 10.1080/01635589109514121 To link to this article: http://dx.doi.org/10.1080/01635589109514121
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Patterns in Food Use and Compliance With NCI Dietary Guidelines
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D. Elizabeth Randall, James R. Marshall, John Brasure, and Saxon Graham
Abstract We developed multidimensional measures of diet by identifying seven gender-specific patterns among 1,475 male and 780 female subjects selected as neighborhood controls in a case-control study of cancer at various sites. The purpose of this study was to examine these patterns for compliance with the National Cancer Institute (NCI) Dietary Guidelines. Diets were scored and each subject assigned fertile ranks for each pattern. Mean values for indicators of compliance were compared across tertiles for each pattern. No one pattern signified compliance with all guidelines. High-fat patterns in both genders and the low-cost pattern in women were the best indicators of poor compliance with NCI guidelines. (Nutr Cancer IS, 141-158, 1991)
Introduction
Diet is complex; it involves the repeated use of foods that, in combination, incorporate numerous factors, both nutrient and nonnutrient, that are implicated in cancer etiology. In an effort to lower the occurrence of cancer in the United States, the National Cancer Institute (NCI) has suggested dietary guidelines that may reduce cancer risk (1). These guidelines can be summarized as follows. • • • • • •
Reduce total fat intake to 30% or less of calories. Increase dietary fiber intake to 20-30 g daily, not to exceed 35 g. Include a variety of fruits and vegetables daily. Avoid obesity. Consume alcoholic beverages in moderation, if at all. Minimize consumption of salt-cured, salt-pickled, and smoked foods.
The nutritional impact of adopting these guidelines is fairly clear: The guidelines would involve a decreased fat intake, an increased fiber intake, and an increased use of a range of D.E. Randall is affiliated with the Nutrition Program and J.R. Marshall, J. Brasure, and S. Graham with the Department of Social and Preventive Medicine, State University of New York at Buffalo, Buffalo, NY 14214.
Copyright © 1991, Lawrence Erlbaum Associates, Inc.
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nutrients found predominantly in fruits and vegetables. The guidelines promote decreased exposure to the harmful effects of ethanol as well as to the other putative harmful factors contained in smoked and salted foods. Patterson and Block (2) attempted to investigate the extent to which food choices in the 24-hour dietary recall data from the NHANES II were consistent with recommendations to lower cancer risk. They were constrained, however, by the inability of intakes over a single day to represent usual diets. Past studies of diet and cancer commonly have addressed contributions of single nutrients to cancer risk, based on intakes estimated using a food frequency technique. The Western New York Diet Study (WNYDS), a case-control study of diet and cancer of the lung, esophagus, larynx, mouth, stomach, bladder, colon, and rectum, represents one such study (3-11). Because results of these studies and comparable epidemiological investigations of diet and cancer have produced estimated levels of risk lower than those expected on the basis of ecological investigations (12,13), we have speculated that the single-nutrient approach underestimates the importance of diet with respect to cancer risk. Findings from our earlier work indicate that arraying individuals by quartile of intake of a single nutrient simultaneously arrays them on other dietary traits as well (14). These traits include both protective and risk-increasing factors. We have pursued the identification of dietary patterns among the control subjects of the WNYDS (15) to develop dietary measures reflective of combinations of individual foods as consumed by a free-living population. Results indicate that several distinctive dietary patterns are associated with such nutrient risk factors as high-fat or low-fiber intakes. The purpose of this study is to examine these dietary patterns further, regarding their compliance with the NCI Dietary Guidelines, and thereby to identify specific features of the patterns that may contribute to risk. Our ultimate goal, which is beyond the scope of this paper, is to submit the patterns to case-control comparisons of risk for cancer at each of the sites included in the study. Methods Sample Characteristics Subjects were recruited as neighborhood control subjects for persons in Erie, Niagara, and Monroe counties diagnosed in 1975-1986 with cancer at various sites. The control subjects were further matched to the cases on age, gender, and race. The total control sample consists of 1,475 males and 780 females with mean ages of 62.9 and 64.5 years, respectively. More than 80% of the males were married; among females, 60% were married and 30% were widowed. Both white- and blue-collar occupations were well represented in the subjects' employment histories. The protocol of this study was approved by the committee for the protection of human subjects of the School of Medicine and Biological Sciences at the State University of New York at Buffalo. Identification of Dietary Factors Data were collected in a 2.5-hour interview that included a 128-item semiquantitative food frequency instrument, as well as additional items regarding commonly used food, preparation and storage methods, and discretionary use of condiments. Subjects were questioned concerning their food use during the previous 12-month period. The number of foods included in the final factor analysis was reduced to 95 by a) excluding items consumed with a mean frequency of less than 0.5 times monthly (smoked, dried, pickled, and canned meats and meat spreads; smoked and pickled fish; lamb; veal; snow peas; pumpkin; kale; eggplant; and greens), b) combining similar forms of infrequently used foods that, together, were consumed more frequently than 0.5 times monthly (dried fruit and ham), and c) eliminating items generally used only as condiments (spreads, pickles, and olives).
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Principal components analysis with orthogonal rotation of the factors was used to extract dietary patterns; seven patterns were identified for each gender. These factor analytic procedures are reported more fully elsewhere (14). The patterns, which appear in Tables 1 and 2, include only those foods most strongly associated with each factor, at a level of -0.2 > r > 0.3. Factors were named in an effort to reflect factually the foods contained within them. The high-fat patterns, for example, contain many foods dense in fat; foods in the traditional pattern are consistent with eating meat, potato, and common vegetables; and the healthful patterns include foods consistent with current trends to leaner foods. Each subject was assigned a score for each pattern by multiplying the factor weight of each food included in the pattern by the standardized value of the reported frequency of use and summing over all foods. Subjects were then given a tertile rank for each pattern based on the locations of their scores within the distributions of dietary pattern scores. The seven patterns extracted account for 20.3% and 21.7% of the variance in food use in males and females, respectively. Because additional patterns explain decreasing amounts, many patterns would be needed to account for even 50% of the variance; these patterns would be difficult to interpret (15,16). Associations between dietary patterns and cancer in a population will likely be found among those patterns contributing most to the variance in dietary intake. Indices of Compliance With Dietary Guidelines Nutrient values were assigned to all the 128 foods included in the original food frequency instrument and used to compute nutrient intake for each subject. Reported frequency of use of foods was converted to a common monthly unit, with a month consisting of four weeks or 28 days. The food frequency measure is known to overestimate nutrient intake, but it is successful in categorizing subjects by level of nutrient intake. We chose, therefore, to control for quantitative differences in intake and examine fat and fiber intakes expressed as density-based measures, namely percent of calories derived from fat and grams of dietary fiber per 1,000 kcal consumed. To assess variety in fruit and vegetable use, we calculated vegetable and fruit diversity separately by summing the number of vegetables and fruits, respectively, on the list of foods consumed at least monthly. We used self-reported height and weight data to calculate body mass index (BMI) using the equation BMI = wt(kg)/ht2(m) to assess obesity. Weekly servings of wine, beer, and distilled alcohol, separately, as well as total alcohol consumption were used to assess moderation in alcohol use. The weekly number of servings of all meats and fish on the original 128-item food list that were salt cured, salt pickled, or smoked {n = 15) was used to assess compliance with the recommendation to minimize consumption of these foods. Also for this recommendation, we calculated the number of times weekly that subjects consumed meats and poultry that were charbroiled. Results Fat Table 3 presents differences in fat consumption, expressed as mean percent of calories derived from fat, by tertiles of dietary pattern scores. At all levels of each pattern, subjects failed to achieve the goal of obtaining no more than 30% of calories from fat, although some patterns are more closely associated with attaining this goal than are others. The data suggest that few subjects complied with the recommendation in the NCI guidelines to consume no more than 30% of calories as fat. For example, closest compliance among men was found for subjects in the lowest tertile of the high-fat pattern. Based on the distribution of values, approximately 90% of subjects with diets of lowest fat composition had intakes between
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o
I
Table 1. Dietary Factors" for Malesb Factor 1: Salad Food Lettuce Celery Green pepper Cucumber Tomatoes Radishes Onions Carrots
Factor 2: Fruit
Weight
Food
0.63 0.62 0.61 0.57 0.54 0.53 0.52 0.44
Peaches Plums Pears Apples Oranges, tangerines Cherries Grapes Melons Bananas Berries Beer
% Variance
6.0
Weight 0.57 0.54 0.54 0.52
Factor 3: Healthful Food
Weight
0.56 0.48 0.45 Rice 0.41 Poultry 0.36 0.47 Mushrooms 0.34 0.39 Green beans 0.33 0.37 Summer squash 0.33 0.37 Asparagus 0.33 0.34 White bread -0.20 0.32 Cookies -0.22 -0.25 -0.23 Potatoes 3.7
Broccoli Spinach Cauliflower
2.7
a: Identified in the Western New York Diet Study from 1975 to 1986. b: For males, n = 1,'175.
Factor 4: Traditional
Factor 5: Snacks
Food
Weight
Food
Wax beans Green beans Potatoes Peas Beets Cabbage Roast beef Cakes
0.52 0.49 0.45 0.45 0.39 0.36 0.36 0.35 0.34 0.31
Cookies Candy Crackers Doughnuts, pastries Hamburger Ice cream Baked beans
Pies
Corn
2.2
Weight
Factor 6: High Fat Food
0.46 Eggs 0.42 Bacon 0.39 Sausage Steak 0.39 Beer 0.38 Salami, 0.36 pepperoni 0.33 Distilled alcohol Cookies Summer squash Winter squash Cereal, unsweetened 2.1
Weight
Factor 7: Whole Grain Food
Weight
0.44 0.43 0.41 0.34 0.33
Whole wheat bread 0.36 0.34 Cereal, cooked 0.32 Oat products -0.20 Cola Noodles -0.22 0.31 Hamburger -0.25 -0.26 French fries 0.30 Coffee -0.29 -0.20 Macaroni -0.35 -0.44 -0.22 Spaghetti -0.23 White bread -0.47 -0.40 1.8
1.8
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to
Table 2. Dietary Factors" for Females* Factor 1: Salad Food
Factor 2: Healthful
Weigh t
Food
Weight
Lettuce Onions Cucumber Tomatoes Celery Green pepper Radishes Carrots Apples Broccoli
0.62 0.62 0.57 0.56 0.56 0.54 0.52 0.47 0.32 0.30
Green beans Cauliflower Spinach Beets Summer squash Pineapple Liver Broccoli Wax beans Winter squash Cottage cheese Peas Mushrooms Cabbage
0.55 0.49 0.47 0.43 0.43 0.41 0.37 0.36 0.36 0.35 0.34 0.33 0.33 0.30
0.05
5.2
0.0056
67.9
0.0000
49.6
0.0000
Females (n = 780) Salad Healthful Low cost Fruit High fat Light Whole grain
6.3
7.6
9.5
(3.2)
(3.1)
(3.0)
6.5
7.7
9.2
(3.2)
(2.9)
(3.4)
7.5
8.0
8.0
(3.4)
(3.5)
(3.2) 10.4 (2.7)
5.2
7.8
(2.6)
(2.5)
7.4
7.6
8.4
(3.3)
(3.3)
(3.4)
6.5
7.8
9.0
(3.0)
(3.2)
(3.4)
6.8
7.9
8.6
(3.2)
(3.2)
(3.5)
1.8
>0.05
261.5
0.0000
6.1
0.0025
41.9
0.0000
19.0
0.0000
a: Scores are from Western New York Diet Study conducted from 1975 to 1986. b: Values in parentheses are standard deviations.
high-fat (F = 6.3, p < 0.002) patterns and negatively related to the traditional pattern (F = 5.0, p< 0.007). Among women, body mass was related to only the fruit pattern (F = 3.4, p < 0.04), with smaller body size associated with lower scores on this dietary pattern. Alcohol Mean weekly consumptions of wine, beer, and distilled alcohol, as well as total alcohol intake, are presented by level of dietary pattern scores in Tables 7 and 8. For males, highest wine intakes were found among subjects with highest scores for the salad and healthful patterns. Use of beer was found to be significantly greater for lower scores on the fruit and healthful patterns and higher scores for the high-fat pattern. Greater distilled alcohol intake was also associated positively with the high-fat dietary pattern. Among women, wine consumption was negatively associated with the fruit pattern and positively associated with the light and healthful patterns. Beer consumption was lower among women with high scores for the fruit and whole-grain patterns. Distilled alcohol use was negatively associated with fruit pattern scores and positively associated with scores on the light pattern.
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Table 7. Mean Weekly Number of Servings of Alcoholic Beverages by Tertile of Dietary Pattern Scores" for Males6 Pattern Score0 Dietary Pattern Salad Wine Beer Distilled alcohol Total alcohol
Low
Medium
High
F Statistic
0.9
1.2
(2.7)
1.9 (4.2)
12.3
(2.5)
0.8
>0.05
4.5
5.4
4.7
(10.2)
(13.0)
(10.1)
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Beer Distilled alcohol Total alcohol Healthful Wine Beer Distilled alcohol Total alcohol Traditional Wine Beer Distilled alcohol Total alcohol Snacks Wine Beer Distilled alcohol Total alcohol
0.0000
3.1
4.0
3.9
1.7
>0.05
(9.4)
(8.7) 10.6 (17.2)
(7.7) 10.5 (14.0)
2.9
>0.05
2.5
>0.05
8.5
(14.2) Fruit Wine
p Value
1.1
1.3
1.5
(3.3)
(3.1)
(3.5)
8.3
3.0
3.3
(16.1)
(6.1)
(7.6)
4.3
3.7
3.0
(9.2) 13.7 (19.7)
(9.3)
(7.2)
8.0
7.8
(12.2)
(11.6)
0.6
1.3
2.0
(2.1)
(2.9)
(4.3)
5.8
4.9
3.9
(11.8)
(11.5)
(10.0)
37.7 2.7
0.0000 >0.05
24.6
0.0000
20.4
0.0000
3.9
0.0211
3.3
3.6
4.2
1.2
>0.05
(9.2)
(7.6)
(8.9) 10.0 (15.4)
0.0
>0.05
2.7
>0.05
0.6
>0.05
9.8
9.8
(15.7)
(14.6)
1.6
1.3
1.1
(3.9)
(3.0)
(2.9)
4.7
4.6
5.3
(11.9)
(9.3)
(12.0)
3.6
3.9
3.6
0.1
>0.05
(9.7)
(7.3)
(8.6) 10.0 (16.6)
0.1
>0.05
4.3
9.8
9.7
(16.2)
(12.5)
1.6
1.4
1.0
(4.1)
(3.1)
(2.3)
5.1
5.1
4.3
(11.6)
(12.1)
(9.6)
3.8
4.1
3.2
(10.4) 10.5 (17.1)
(8.7) 10.6 (15.2)
(6.2) 8.5
0.0134
0.8
>0.05
1.3
>0.05
2.9
>0.05
(13.0) (continued)
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Nutrition and Cancer 1991
Table 7. (Continued) Pattern Scorec Dietary Pattern
Low
High fat Wine Beer Distilled alcohol Total alcohol
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Whole grain Wine Beer
High
1.1
1.3
1.5
(2.7)
(3.0)
(4.0)
1.6
3.2
9.7
(3.8)
(6.3)
(16.7)
F Statistic 1.7
p Value >0.05
81.7
0.0000
1.4
2.9
6.8
55.7
0.0000
(2.9)
(5.2)
(13.1) 18.0 (21.8)
133.3
0.0000
4.0
7.5
(6.0)
(8.6)
1.2 (3.6)
1.2
1.5
(2.9)
(3.4)
5.8
4.6
4.2
(12.1)
(10.0)
(11.2)
3.1
4.0
4.0
(7.3) 10.0 (14.6)
(8.5)
(9.9)
Distilled alcohol Total alcohol
Medium
9.8
9.8
(14.1)
(16.7)
1.7
>0.05
2.6
>0.05
2.1
>0.05
0.0
>0.05
a: Scores are from Western New York Diet Study conducted from 1975 to 1986. b: For males, n = 1,475. c: Values in parentheses are standard deviations.
Salted and Smoked Foods Highest mean frequency of use of these meats and fish was found among those men with highest scores on the traditional, snacks, and high-fat patterns and with lowest scores on the whole-grain patterns (Tables 9 and 10). Frequency of consumption of charbroiled meats and poultry was most strongly associated with the upper tertile of the high-fat pattern and lowest tertile of the whole-grain pattern. Among women, the use of salted meats and fish was most strongly associated with the highest scores on the low-cost and high-fat patterns. Charbroiled meats were more frequently consumed by women with highest scores on the salad and low-cost patterns. Discussion Data in this study identify areas for cancer risk reduction in each of the dietary patterns, even those patterns that appear to promote well-being. To assist in interpreting the nutritional consequences of our indices of compliance, we present results of correlational analyses of pattern scores and nutrient intakes in Table 11. Total Fat Data from this study indicate that several of the patterns are associated with lower fat intake. Few subjects in even these apparently prudent eating styles demonstrate compliance with the guideline to obtain fewer than 30% of calories from fat. Scores for the fruit and whole-grain patterns were indicative of lower fat composition for both genders. The high-fat pattern extracted for males is strongly indicative of noncompliance. A similarly labeled
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Table 8. Mean Weekly Number of Servings of Alcoholic Beverages by Tertile of Dietary Pattern Scores" for Females6 Pattern Scorec Dietary Pattern Salad Wine Beer Distilled alcohol Total alcohol
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Healthful Wine Beer Distilled alcohol Total alcohol Low cost Wine Beer Distilled alcohol Total alcohol Fruit Wine Beer Distilled alcohol Total alcohol High fat Wine Beer Distilled alcohol Total alcohol
Low
Medium
High
F Statistic 2.0
>0.05
0.1
>0.05
0.2
>0.05
0.9
>0.05
1.0
1.0
1.5
(2.9)
(2.6)
(4.7)
0.8
0.7
0.7
(3.4)
(2.8)
(2.1)
1.3
1.4
1.5
(3.2)
(3.2)
(3.5)
3.1
3.0
3.7
(5.8)
(5.5)
(6.7)
0.8
1.1
1.6
(2.4)
(2.8)
(4.8)
0.6
0.6
0.8
(3.1)
(2.6)
(2.7)
1.6
1.0
1.5
(4.0)
(2.7)
(3.1)
3.1
2.8
3.9
(5.9)
(5.2)
(6.8)
1.1
1.5
0.6
(3.3)
(4.5)
(2.5)
0.6
0.5
1.0
(2.9)
(1.9)
(3.3)
1.4
1.4
1.4
(3.4)
(3.2)
(3.3)
3.1
3.3
3.4
(5.9)
(6.3)
(6.0)
1.9
1.1
0.6
(5.0)
(2.8)
(1.6)
1.5
0.3
0.2
(4.5)
(1.3)
(0.8)
2.6
1.1
0.5
(2.3)
(1.1)
6.0
2.5
1.3
(8.6)
(4.1)
(2.4)
1.3
1.1
1.2
(4.0)
(3.4)
(3.1)
0.8
0.4
0.8
(3.5)
(1.5)
(3.0)
1.4
1.1
1.6
(3.3)
(2.7) 2.6 (5.2)
(3.8)
3.5
(6.6)
3.7
3.3
p Value
0.0373
0.4
>0.05
2.1
>0.05
2.3
>0.05
1.3
>0.05
2.4
>0.05
0.0
>0.05
0.2
>0.05
9.6
0.0001
18.3
0.0000
31.6
0.0000
49.5
0.0000
0.2
>0.05
1.8
>0.05
1.7
>0.05
2.3
>0.05
(6.1) (continued)
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Nutrition and Cancer 1991
Table 8. (Continued) Pattern Scorec Dietary Pattern Light Wine Beer Distilled alcohol Total alcohol
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Whole grain Wine Beer Distilled alcohol Total alcohol
Low
Medium
High
F Statistic 9.2
0.6
1.0
1.9
(2.4)
(2.5)
(4.9)
0.8
0.7
0.6
(3.2)
(2.6)
(2.5)
0.9
1.9
1.3
(2.8)
(3.8)
(3.1)
2.3
3.5
3.9
(5.2)
(5.9)
(6.8)
1.4
1.4
0.8
(4.1)
(4.0)
(1.9)
1.1
0.6
0.4
(3.7)
(2.6)
(1.5)
1.4
1.3
1.4
(3.3)
(3.4)
(3.2)
3.9
3.3
2.6
(6.9)
(6.4)
(4.5)
0.1
p Value 0.0001 >0.05
5.2
0.0055
4.7
0.0092
2.0 5.6 0.1 3.5
>0.05 0.0039 >0.05 0.0317
a: Scores are from Western New York Diet Study conducted from 1975 to 1986. b: For females, n = 780. c: Values in parentheses are standard deviations.
pattern among women has less predictive value, although the association of that pattern with noncompliance is statistically significant. The low-cost pattern identified among women also indicated noncompliance. Not only do persons following diets consistent with the high-fat pattern or the low-cost pattern among women consume a higher percent of calories from fat, but also they have factor scores associated with higher energy intake, implying high total fat intakes. Although the two patterns in women are not as strongly associated with percent fat composition, they do correlate strongly with total fat and energy intakes; women in the uppermost tertile of these patterns may not be as distinguishable by the composition of the foods they eat as they are by the total amounts they consume.
Dietary Fiber The salad and fruit patterns for both genders, and the healthful and whole-grain patterns for males, are strongly, positively associated with high-fiber density. Because of limitations of the food frequency measure, we hesitate to interpret these findings in terms of compliance with the quantitative recommendation to consume 20-30 g of fiber daily. The salad and fruit patterns are positively associated with energy intakes and even more strongly with total fiber intake, leading us to conclude that persons in the highest tertiles of these patterns achieve greater dietary fiber intake by consuming foods that are more fiber dense. For the healthful pattern, both dietary fiber density and total fiber intakes are higher among persons with highest scores. It would appear that the lower energy intakes of males with high scores for the whole-grain pattern result in a lack of correspondence between pattern scores and total fiber intake. These findings point to fruit and vegetable use, rather than whole-grain use, as being the most contributory to compliance with the recommendation to increase dietary fiber intake.
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Table 9. Mean Weekly Frequency of Consumption of Cured, Pickled, or Smoked Meat and Fish by Tertile of Dietary PatterrI Scores" Pattern Score* Dietary Pattern
Low
Medium
High
Salad
4.3
4.7
5.1
(3.6)"
(4.1)
(4.1)
F Statistic
p Value
Males (n == 1,475) Fruit Healthful Traditional
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Snacks High fat Whole grain
4.7
4.3
5.2
(3.8)
(3.7)
(4.2)
4.9
4.5
4.8
(3.9)
(3.4)
(4-4)
3.7
4.8
5.7
(3.5)
(3.5)
(4.5)
3.9
4.8
5.6
(3.6)
(3.9)
(4.1)
3.0
4.1
7.0
(2.7)
(3.1)
(4.5)
5.8
4.6
3.8
(4.1)
(3.9)
(3.5)
4.6
0.0105
6.4
0.0016
1.5
>0.05
30.8
0.0000
24.6
0.0000
162.4
0.0000
33.5
0.0000
10.4
0.0000
5.1
0.0066
119.8
0.0000
Females (n = 780) Salad Healthful Low cost Fruit High fat Light Whole grain
2.4 (2.4) 2.7 (2.6) 1.5 (1.8) 2.9 (2.6) 2.1 (2.5) 3.1 (2.6) 3.1 (2.8)
3.3
3.5
(2.9)
(3.1)
3.1
3.4
(2.9)
(3-D
2.9
4.8
(2.4)
(3.2)
3.0
3.3
(2.7)
(3.2)
3.0
4.1
(2.7)
(3.1)
3.0
3.2
(2.7)
(3.2)
3.0
3.1
(2.8)
(3.0)
1.1
31.9
>0.05 0.0000
0.5
>0.05
0.1
>0.05
a: Scores are frorr» Western New York Diet Study conducted from 1975 to 1986. b: Values in parentheses are standarc 1 deviations.
The high-fat patterns are associated with low-fiber density for males as well as for females. Because these patterns correlate positively with energy intake, total fiber intake may be similar across tertiles of pattern scores. For males, we observe no association between pattern scores and total fiber intake. Among women, high-fat as well as low-cost pattern scores relate positively to total fiber intake. Variety in Vegetables and Fruits Past research has indicated a positive association between diversity measures and quantitative measures of dietary intake (17). In this study, high scores for most patterns are consistent with the recommendation to increase variety in use of fruits and vegetables. Among men, however, the high-fat pattern is unrelated to vegetable diversity, and the whole-grain pattern was indicative of noncompliance with variety in vegetable use. Similar trends are observed for fruit diversity. Scores for the whole-grain pattern were associated with fruit diversity for women but show very little difference across tertiles of the whole-grain pattern among males.
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Nutrition and Cancer 1991
Table 10. Mean Weekly Frequency of Consumption o\ ' Charbroiled Meat and Poultry by Tertile of Dietary Pattern Scores" Pattern!Score" Dietary Pattern
Low
Medium
High
F Statistic
p Value
Males (n = 1,475) Salad Fruit Healthful Traditional
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Snacks High fat Whole grain
1.4
1.7
1.9
(2.2)
(2.3)
(2.4)
1.7
1.4
1.9
(2.3)
(2.1)
(2.4)
1.6
1.7
1.8
(2.2)
(2.3)
(2.4)
1.4
1.6
2.0
(2.0)
(2.2)
(2.6)
1.4
1.7
2.0
(2.0)
(2.1)
(2.7)
1.1
1.8
2.2
(1.8)
(2.2)
(2.7)
2.3
1.5
1.2
(2.8)
(2.0)
(1.8)
7.0
0.0009
6.4
0.0017
0.8
>0.05
7.2
0.0008
8.7
0.0002
32.3
0.0000
34.6
0.0000
15.1
0.0000
6.7
0.0014
16.9
0.0000
Females (n = 780) Salad Healthful Low cost Fruit High fat Light Whole grain
0.9
1.3
1.9
(1.6)
(2.0)
(2.8)
1.1
1.3
1.7
(2.3)
(1.9)
(2.4)
0.8
1.3
1.9
(1.6) 1.3 (1.8)
(2.5)
(2.3)
1.2
1.6
(1.9)
(2.8)
1.2
1.3
1.6
(2.5)
(1.9)
(2.2)
1.1
1.4
1.5
(1.8)
(2.6)
(2-2)
1.6
1.3
1.2
(2.7)
(2-0)
(1.9)
2.5
>0.05
2.2
>0.05
2.8
>0.05
2.3
>0.05
a: Scores are from Western New York Diet Study conducted from 1975 to 1986. b: Values in parentheses are standard deviations.
Obesity
Only small differences were observed for mean BMI by score for any of the dietary patterns. It is doubtful that the magnitude of the observed differences in body size by tertile of pattern scores would have any practical significance. The validity of the results is constrained by the fact that heights and weights were self-reported. Bias in reporting, especially with those persons at the highest weight levels underreporting their true weight, could be partially responsible for the weakness of the observed associations. It is also possible that there is little association between total food intake and obesity in this population.
Alcohol Among males, the high-fat pattern was most strongly related to noncompliance with the guideline to moderate alcohol use. Men in the uppermost tertile of this pattern consumed an average of 2.5 drinks daily. Although high scores for the salad and healthful patterns were
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Table 11. Correlations Between Dietary Factor Scores and Nutrient Intake"
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Nutrient Intake*" Dietary Factor
Energy
Fat
Dietary Fiber
Sodium
Salad Fruit Healthful Traditional Snacks High fat Whole grain
0.19 0.32 0.066 0.45 0.51 0.27 -0.32
Males (n = 1,475) 0.16 0.18 0.04* 0.40 0.41 0.41 -0.31
0.38 0.57 0.27 0.49 0.36 0.03* -0.06*
0.18 0.22 0.05* 0.41 0.41 0.34 -0.26
Salad Healthful Low cost Fruit High fat Light Whole grain
0.22 0.29 0.43 0.29 0.51 0.06* 0.26
Females (n = 780) 0.17 0.25 0.43 0.16 0.49 0.07* 0.16
0.48 0.45 0.20 0.53 0.25 0.20 0.37
0.19 0.25 0.44 0.16 0.43 0.05" 0.17
a: Values are from Western New York Diet Study conducted from 1975 to 1986. b: Values are Pearson product-moment correlation coefficients. c: Values are not statistically significant at p < 0.001 (two-tailed test).
associated with more frequent consumption of wine, consumption of total alcohol appears to comply with the guidelines. The fruit pattern predicts greater compliance with this recommendation for alcohol in all its forms. Ingestion of alcoholic beverages is lower overall among the women. As with men, the fruit pattern relates negatively to consumption of alcohol. However, among women, the high-fat pattern is unrelated to alcohol use. On the other hand, the light pattern in women was positively associated with both wine and distilled alcohol use. The association of the pattern with distilled alcohol is not monotonic, medium status on the pattern being associated with the highest level of distilled alcohol consumption. Salted and Smoked Foods The frequency of consuming salted meats and fish was most strongly associated in males with the high-fat pattern. The traditional and snacks patterns also had strong, positive associations. Scores for the whole-grain pattern, on the other hand, were negatively related. Among women, the low-cost pattern had the strongest association with use of salted meats and fish, although the high-fat pattern was also strongly positively related. These associations with patterns and salted foods is reflected in the strength of relationships with sodium intake (Table 9). With the exception of the healthful pattern, all dietary patterns were associated with frequency of consuming charbroiled foods among males. Scores on the high-fat pattern exhibited the strongest positive relationships, whereas scores for the whole-grain pattern had the strongest negative ones. Among females, only three of the dietary patterns were significantly associated with use of charbroiled foods, with the salad and low-cost patterns having the strongest positive relationships. We have attempted to summarize the findings in Table 10 by indicating, for each recommendation, whether the specific dietary patterns appear to be associated with compliance, noncompliance, or if the associations remain unclear. Our decisions regarding
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o
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Z p
Table 12. Summary of Compliance of Dietary Patterns With NCI Guidelines to Lower Cancer Risk" Dietary Pattern Females (n = 780)
Males (n = 1,475) Guideline
Salad Fruit Healthful Traditional Snacks High fat Whole grain Salad Healthful Low cost Fruit High fat Light Whole grain C
Fat
Fiber Variety Vegetables Fruits Obesity Alcohol Salted and smoked food Salted Charbroiled
C
c
c
C
c c
c c
c
NC NC
C
C
c
c
C
c
c
c
NC
c c
c c
C
NC
c NC
NC
NC NC
C C
c c
C NC C
c
NC
a: Abbreviations are as follows: C, compliance; NC, noncompliance; no ibbreviation, inconclusive evidence.
NC NC
c
NC NC
c
cC
c
NC
c
compliance are somewhat arbitrary and are based on both statistically and practically significant differences in indicators of compliance across the tertiles of dietary pattern scores. The high-fat pattern among males fails to comply with any of the NCI dietary guidelines. For all other patterns, in both genders, each pattern complies with at least one of the recommendations. For both males and females, the fruit patterns demonstrate greatest compliance. In terms of total risk, however, persons following diets consistent with the high-fat patterns and females following the low-cost or light patterns may incorporate most risk factors. Persons identified as being in these categories may well represent target groups for nutrition interventions to lower cancer risks. It is our intent to submit the dietary patterns to case-control comparisons of cancer risk at several sites. Data presented in this paper will be supportive in interpreting the results of such analyses.
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Acknowledgments and Notes This research was funded by National Institutes of Health (Bethesda, MD) Grant No RO3 CA-51720-01. Address reprint requests to Dr. D. E. Randall, Nutrition Program, 301 Parker Hall, State University of New York at Buffalo, Buffalo, NY 14214. Submitted 20 June 1990; accepted in final form 31 October 1990.
References 1. Butrura, RR, Clifford, CK, and Lanza, E: "NCI Dietary Guidelines: Rationale." Am J Clin Nutr 48, 888-895, 1988. 2. Patterson, BH, and Block, G: "Food Choices and Cancer Guidelines." Am J Public Health 78, 282-286, 1988. 3. Graham, S: "Fats, Calories, and Calorie Expenditure in the Epidemiology of Cancer." Am J Clin Nutr 45, 342-346, 1987. 4. Graham, S: "Epidemiology of Retinoids and Cancer." JNCI 73, 1423-1428, 1984. 5. Byers, T, Vena, JE, Mettlin, C, Swanson, M, and Graham, S: "Dietary Vitamin A and Lung Cancer Risk: An Analysis by Histiologic Subtypes." Am J Epidemiol 120, 769-776, 1984. 6. Graham, S, Marshall, J, Haughey, B, Brasure, J, Freudenheim, J, et al.: "Nutritional Epidemiology of Cancer of the Esophagus." Am J Epidemiol 131, 454-467, 1990. 7. Graham, S, Haughey, B, Marshall, J, Brasure, J, Zielezny, M, et al.: "Diet in the Epidemiology of Gastric Cancer." Nutr Cancer 13, 19-34, 1990. 8. Graham, S, Marshall, J, Haughey, B, Mittelman, A, Swanson, M, et al.: "Dietary Epidemiology of Cancer of the Colon in Western New York." Am J Epidemiol 128, 490-503, 1988. 9. Freudenheim, J, Graham, S, Horvath, PJ, Marshall, JR, Haughey, BP, et al.: "Risks Associated With Source of Fiber and Fiber Components in Cancer of the Colon and Rectum." Cancer Res 50, 3295-3300, 1990. 10. Freudenheim, JL, Graham, S, Marshall, JR, Haughey, BP, and Wilkinson, G: "A Case-Control Study of Diet and Rectal Cancer in Western New York." Am J Epidemiol 131, 612-624, 1990. 11. Freudenheim, JL, Graham, S, Marshall, JR, Haughey, BP, and Wilkinson, G: "Lifetime Alcohol Intake and Risk of Rectal Cancer in Western New York." Nutr Cancer 13, 101-109, 1990. 12. Wynder, EL, and Gori, GB: "Contribution of the Environment to Cancer Incidence: An Epidemiologic Exercise." JNCI 58, 825-832, 1977. 13. Doll, R, and Peto, R: "The Causes of Cancer: Quantitative Estimates of Avoidable Risks of Cancer in the United States Today." JNCI 66, 1191-1308, 1981. 14. Randall, E, Marshall, J, Graham, S, and Brasure, J: "Food Frequency and the Multidimensionality of Diet." J Am Diet Assoc 89, 1070-1075, 1989. 15. Randall, E, Marshall, J, Graham, S, and Brasure, J: "Patterns in Food Use and Their Associations With Nutrient Intakes." Am J Clin Nutr 52, 739-745, 1990. 16. Nicklas, TA, Webber, LS, Thompson, B, and Berenson, GS: "A Multivariate Model for Assessing Eating Patterns and Their Relationship to Cardiovascular Risk Factors: The Bogalusa Heart Study." Am J Clin Nutr 49, 1320-1327, 1989. 17. Randall, E, Nichaman, MZ, and Contant, CF, Jr: "Diet Diversity and Nutrient Intake." J Am Diet Assoc 85, 830-836, 1985.
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