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To assess the effect of different methods of classifying food use on principal components analysis (PCA)-derived dietary patterns, and the subsequent impact on estimation of cancer risk associated with the different patterns.
Dietary data were obtained from 232 endometrial cancer cases and 639 controls (Western New York Diet Study) using a 190-item semi-quantitative food-frequency questionnaire. Dietary patterns were generated using PCA and three methods of classifying food use: 168 single foods and beverages; 56 detailed food groups, foods and beverages; and 36 less-detailed groups and single food items.
Classification method affected neither the number nor character of the patterns identified. However, total variance explained in food use increased as the detail included in the PCA decreased (~8%, 168 items to ~17%, 36 items). Conversely, reduced detail in PCA tended to attenuate the odds ratio (OR) associated with the healthy patterns (OR 0.55, 95% confidence interval (CI) 0.35–0.84 and OR 0.77, 95% CI 0.49–1.20, 168 and 36 items, respectively) but not the high-fat patterns (OR 0.95, 95% CI 0.57–1.58 and OR 0.85, 0.51–1.40, 168 and 36 items, respectively).
Greater detail in food-use information may be desirable in determination of dietary patterns for more precise estimates of disease risk.
To assess the relative ability of principal components analysis (PCA)-derived dietary patterns to correctly identify cases and controls compared with other methods of characterising food intake.
Participants in this study were 232 endometrial cancer cases and 639 controls from the Western New York Diet Study, 1986–1991, frequency-matched to cases on age and county of residence.
Usual intake in the year preceding interview of 190 foods and beverages was collected during a personal interview using a detailed food-frequency questionnaire. Principal components analysis identified two major dietary patterns which we labelled ‘healthy’ and ‘high fat’. Classification on disease status was assessed with separate discriminant analyses (DAs) for four different characterisation schemes: stepwise DA of 168 food items to identify the subset of foods that best discriminated between cases and controls; foods associated with each PCA-derived dietary pattern; fruits and vegetables (47 items); and stepwise DA of USDA-defined food groups (fresh fruit, canned/frozen fruit, raw vegetables, cooked vegetables, red meat, poultry, fish and seafood, processed meats, snacks and sweets, grain products, dairy, and fats).
In general, classification of disease status was somewhat better among cases (54.7% to 67.7%) than controls (54.0% to 63.1%). Correct classification was highest for fruits and vegetables (67.7% and 62.9%, respectively) but comparable to that of the other schemes (49.5% to 66.8%).
Our results suggest that the use of principal components analysis to characterise dietary behaviour may not provide substantial advantages over more commonly used, less sophisticated methods of characterising diet.
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