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Original Research Communications |
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Objective: We examined the reproducibility and validity of dietary patterns defined by factor analysis using dietary data collected with a food-frequency questionnaire (FFQ).
Design: We enrolled a subsample of men (n = 127) from the Health Professionals Follow-up Study in a diet-validation study in 1986. A 131-item FFQ was administered twice, 1 y apart, and two 1-wk diet records and blood samples were collected during this 1-y interval.
Results: Using factor analysis, we identified 2 major eating patterns, which were qualitatively similar across the 2 FFQs and the diet records. The first factor, the prudent dietary pattern, was characterized by a high intake of vegetables, fruit, legumes, whole grains, and fish and other seafood, whereas the second factor, the Western pattern, was characterized by a high intake of processed meat, red meat, butter, high-fat dairy products, eggs, and refined grains. The reliability correlations for the factor scores between the 2 FFQs were 0.70 for the prudent pattern and 0.67 for the Western pattern. The correlations (corrected for week-to-week variation in diet records) between the 2 FFQs and diet records ranged from 0.45 to 0.74 for the 2 patterns. In addition, the correlations between the factor scores and nutrient intakes and plasma concentrations of biomarkers were in the expected direction.
Conclusions: These data indicate reasonable reproducibility and validity of the major dietary patterns defined by factor analysis with data from an FFQ.
Key Words: Diet • dietary pattern • factor analysis • biomarker • reproducibility • validity • men • Health Professionals Follow-up Study • food-frequency questionnaire
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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To overcome these limitations, several authors recently proposed to study overall dietary patterns by considering how foods and nutrients are consumed in combination (4, 6–13). In a dietary pattern analysis, the collinearity of nutrients and foods can be used to advantage because patterns are characterized on the basis of habitual food consumption. Examination of dietary patterns would more closely parallel real-world conditions, under which dietary intakes consist of nutrients that occur together in common foods (14).
Although the concept of studying dietary patterns has elicited considerable interest, no study has been conducted to examine the reproducibility and validity of these methods. We studied the reproducibility and validity of dietary patterns defined by factor analysis using dietary data collected with a food-frequency questionnaire (FFQ) and diet records among participants in the Health Professionals Follow-up Study, who were enrolled in a nutrient validation study in 1986.
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SUBJECTS AND METHODS |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Dietary assessment
The participants completed the same FFQ twice, 1 y apart (FFQ1 and FFQ2). The reproducibility and validity of nutrient and food intake measurements from the FFQ used in this study were described in detail elsewhere (16, 17). The FFQ includes 131 food items with specified serving sizes described by using natural portions (eg, 1 banana, 2 slices of pizza) or standard weight and volume measures of the servings commonly consumed in this study population. For each food item, participants indicated their average frequency of consumption over the past year in terms of the specified serving size by checking 1 of 9 frequency categories ranging from "almost never" to "
6 times/d." The selected frequency category for each food item was converted to a daily intake. For example, a response of "2–4/wk" was converted to 0.43 servings/d (3 servings/wk).
Participants also completed two 1-wk diet records 
6–7 mo apart. The first week's record began 3 mo after administration of FFQ1 and the second week's record began 2–3 mo before administration of FFQ2. Each subject was given a dietetic scale and was trained by the study dietitian to weigh and record all food consumed. The study dietitian reviewed the returned diet records and resolved questions or discrepancies with the participant. Foods reported in the diet records were coded by using the CBORD DIET ANALYZER SYSTEM (version 3.0.3, 1998; The CBORD Group, Ithaca, NY). Mixed dishes reported on the records that were not included in the CBORD system were coded by their component ingredients as described by the participant.
To obtain daily food intake measurements from the diet records that were comparable with those from the FFQs, an attempt was made to match each of the 1565 unique diet-record food codes to one or more items on the questionnaire. A total of 963 diet-record food codes were matched to a single food item on the questionnaire; multiple codes were frequently matched to the same questionnaire food item. For example, the diet-record codes for "avocado, whole" and "avocado, mashed" were the only 2 codes that were matched to the avocado food item on the questionnaire, whereas 56 diet-record codes for various brands of breakfast cereals were all matched to the same questionnaire item for cold breakfast cereal. For 254 diet-record foods that could not be matched to a single food item on the questionnaire, recipes were created and ingredients were assigned to separate food items. The remaining 348 diet-record foods (eg, asparagus, gravy, and clams) that did not match any of the questionnaire items were eliminated, usually because they were not consumed frequently in this population.
Food groupings
Because of the small number of subjects (n = 127) relative to the number of food items, we collapsed the individual food items into 40 predefined food groups (Table 1
). The grouping scheme was based on the similarity of nutrient profiles or culinary usage among the foods and was somewhat similar to that used in other studies (13). Note that other criteria could have been used to define the number and types of food groups to be included in the analyses. Some individual food items were preserved either because it was inappropriate to incorporate them into a certain food group (eg, eggs, margarine, pizza, soup, coffee, and tea) or they were suspected to represent distinct dietary patterns (eg, garlic, liquor, wine, beer, and French fries).
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Statistical analysis
Factor analysis (principal component) was used to derive food patterns based on the 40 food groups for each of the FFQs and the diet records. We conducted the analyses using the FACTOR PROCEDURE in SAS (21). The factors were rotated by an orthogonal transformation (Varimax rotation function in SAS) to achieve simpler structure with greater interpretability. In determining the number of factors to retain, we considered eigenvalues (>1), the Scree test (22), and the interpretability of the factors. We did not use the percentage of variance explained by each factor because this criterion depends largely on the total number of variables included in the analyses. The substantive meanings of the rotated factors were considered in conjunction with the above empirical criteria and the derived factors were labeled on the basis of our interpretation of the data as well as on prior literature.
The factor score for each pattern was computed by combining the observed variables with weights that were proportional to their component (factor) loadings (22).
Component or factor score for pattern
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| (1) |
where bij is the loading for the jth food item or group on the ith pattern, 
i is the associated eigenvalue, and Xj is the standardized value of jth food item or group.
Pearson correlation coefficients were used to evaluate the consistency of dietary patterns derived from dietary data collected with the 2 FFQs and diet records. To reduce the within-person variation in food intake derived from the diet records, we conducted factor analysis using the average consumption for each food group across two 1-wk diet records. We also calculated deattenuated correlation coefficients for the dietary patterns between the 2 FFQs and the diet records, corrected for week-to-week variation in diet records by using the following formula (23):
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| (2) |
where rt is the corrected correlation between the dietary pattern scores derived from the FFQ and diet records, ro is the observed correlation, 
is the ratio of estimated within-person and between-person variation in dietary pattern scores derived from the two 1-wk diet records, and k is the number of repeated observations of diet records (in this study k = 2).
Pearson correlation coefficients were also used to assess the relation between dietary patterns and computed nutrients from diet records and plasma biochemical measurements. The nutrients for the diet records were energy adjusted by using the regression method (24). We adjusted plasma concentrations of carotenoids, tocopherols, and retinol for age, plasma cholesterol, plasma triacylglycerol, and body mass index. All biochemical measurements were loge transformed to achieve normality.
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RESULTS |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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. Foods underestimated by the FFQs compared with the diet records (ie, the gold standard) included processed meats, eggs, butter, high-fat dairy products, mayonnaise and creamy salad dressings, refined grains, and sweets and desserts, whereas most of the vegetable and fruit groups, nuts, high-energy and low-energy drinks, and condiments were overestimated by the FFQs. Pearson correlations comparing daily intakes of the food groups derived from the 2 FFQs and the diet records are also listed in Table 2. Reproducibility correlations for the comparison of the 2 FFQs ranged from 0.36 for legumes to 0.92 for coffee (
: 0.70). Pearson correlation coefficients ranged from 0.09 for other vegetables to 0.83 for coffee (: 0.38) for the comparison between FFQ1 and the diet records and from 0.07 for other vegetables to 0.90 for coffee (: 0.42) for the comparison between FFQ2 and the diet records.
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. A positive loading indicates a positive association with the factor, whereas a negative loading indicates an inverse association with the factor. The larger the loading of a given food item or group to the factor, the greater the contribution of that food item or group to a specific factor. The 2 major patterns identified from the 3 sources of dietary data were similar. The first factor was loaded heavily with the following foods or food groups: vegetable groups, legumes, whole grains, fruit, oil and vinegar salad dressings, and fish and other seafood, and the second factor was loaded heavily with processed meat, red meat, butter, high-fat dairy products, refined grains, eggs, and French fries. Following the method of Slattery et al (13), we labeled the first factor as the prudent pattern and the second factor as the Western pattern. The factor analyses also identified other minor patterns. However, because they were inconsistent across the 3 sources of data and explained a small amount of variance, we did not include them in the subsequent correlation analyses.
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), indicating good reproducibility. The 2 factors identified from the FFQs were reasonably correlated with those from the diet records: correlation coefficients corrected for week-to-week variation in diet records ranged from 0.45 to 0.74. Correlations between FFQ2 and the diet records were higher than those between FFQ1 and the diet records. In addition, those patterns were reasonably correlated with nutrient intakes calculated from diet records (Table 5). In particular, the prudent pattern was positively correlated with intakes of fiber, magnesium, potassium, folate, vitamin B-6, and carotenes, and negatively correlated with intakes of total and saturated fat. In contrast, the Western pattern was positively correlated with intakes of total and saturated fat and negatively correlated with intakes of fiber, magnesium, potassium, folate, vitamin B-6, and carotenes. The correlations between the factor scores and plasma concentrations of biomarkers were in the expected direction. The analyses eliminating current smokers (n = 11) yielded nearly identical results (data not shown).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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For both patterns, there were some differences in the factor loadings for the food items between the FFQs and diet records, probably because of methodologic differences between the dietary assessment methods (24) and random statistical variations. However, the major patterns generated from the FFQ and diet records were similar, and the correlations of the dietary patterns between each FFQ and the diet records ranged from 0.45 to 0.74, suggesting the usefulness of an FFQ in assessing dietary patterns relative to diet records. Correlations between dietary patterns derived from the diet records and blood measurements were generally higher than those between dietary patterns derived from the FFQ and the blood measurements. This may be in part because dietary intakes assessed by diet records are more accurate. The temporal relation may also have played a part because the FFQ asked about diet over the past year, whereas the biochemical measures reflect relatively shorter-term intakes (24).
The dietary patterns derived from our data were qualitatively similar to those from previous studies using the factor analytic approach. Using dietary data collected by a diet-history questionnaire, Slattery et al (13) grouped >800 food items from the diet-history questionnaire into 35 separate food groups. Two major eating patterns were identified: the Western pattern and the prudent pattern. They found that the prudent pattern was associated with a lower risk of colon cancer, whereas the Western pattern was associated with a higher risk of colon cancer. They also identified several minor patterns (high-fat, high-sugar, high-dairy products; drinkers; and substitutors), which were not significantly associated with the risk of colon cancer. In a study of 939 Swiss adults, Gex-Fabry et al (26) found 2 similar major patterns: one was associated with a high intake of pork meat and sausages, pasta, and potatoes (satiating-capacity pattern), whereas the other was associated with a high intake of fresh fruit and vegetables, fish and other seafood, and poultry (healthfulness pattern).
One limitation of our study was that it included men only. Eating patterns may differ for women, even though a prior study suggested that major eating patterns apply to both men and women (13). Also, eating patterns are likely to vary with different socioeconomic statuses, ethnic groups, and cultures. Thus, it is necessary to replicate our study in other populations. In addition, because of changes in food preferences and food availability, the meaning of a dietary pattern could change over time. Finally, the 2 major patterns derived from our data explained only 20% of total variance, suggesting the existence of other eating patterns. However, in our study, dietary patterns other than the Western and prudent patterns were highly variable across the different dietary assessment methods, and may not be reproducible across studies. In a previous study (13), the Western and prudent dietary patterns explained 19% of the variance in men and 15% of the variance in women. However, these values should be interpreted with caution because they depend heavily on the total number of variables used in the factor analysis.
Because there are many potential differences in nutrient contents between dietary patterns, dietary pattern analysis cannot be specific about the particular nutrients responsible for the observed differences in disease risk, and thus may not be useful for assessing the biological relations between dietary components and disease risk. In particular, this approach would not be optimal if the effect was due to a specific nutrient (eg, neural tube defects resulting from a folic acid deficiency) because the effect of the nutrient would be diluted. Therefore, the dietary pattern approach may be more useful when traditional nutrient analyses have identified few dietary associations for the disease (eg, breast cancer). On the other hand, when many dietary associations have been shown for the disease (eg, coronary artery disease), dietary pattern analysis may also be useful because it examines not only nutrients and foods but the effects of overall diet as well. In addition, a dietary pattern can be used as a covariate when examining a specific nutrient to know whether the effect of the nutrient is independent of the overall dietary pattern.
In conclusion, our data indicate reasonable reproducibility and validity of the major dietary patterns defined by factor analysis using data from the FFQs. These findings suggest the potential use of the dietary pattern approach for studying diet-disease relations.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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2 Supported by research grants CA55075, HL60712, and Nutrition Training Grant T32DK07703 from the National Institutes of Health. FBH was the recipient of the Charles A King Trust Research Fellowship from The Medical Foundation, Boston.
3 Address reprint requests to FB Hu, Department of Nutrition, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA 02115. E-mail: frank.hu{at}channing.harvard.edu.
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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