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Is principal components analysis necessary to characterise dietary behaviour in studies of diet and disease?

Published online by Cambridge University Press:  02 January 2007

Susan E McCann ,
John Weiner ,
Saxon Graham  and
Jo L Freudenheim
Susan E McCann*
Affiliation:
Department of Social and Preventive Medicine, 270 Farber Hall, University at Buffalo, Buffalo, NY 14214, USA
John Weiner
Affiliation:
Department of Social and Preventive Medicine, 270 Farber Hall, University at Buffalo, Buffalo, NY 14214, USA
Saxon Graham
Affiliation:
Department of Social and Preventive Medicine, 270 Farber Hall, University at Buffalo, Buffalo, NY 14214, USA
Jo L Freudenheim
Affiliation:
Department of Social and Preventive Medicine, 270 Farber Hall, University at Buffalo, Buffalo, NY 14214, USA
*
*Corresponding author: Email [email protected]
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Abstract

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Objective:

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.

Subjects:

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.

Design:

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).

Results:

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%).

Conclusions:

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.

Keywords

Dietary patternsFood patternsStatistical methodsMisclassification
Type
Research Article
Information
Public Health Nutrition , Volume 4 , Issue 4 , August 2001 , pp. 903 - 908
Copyright
Copyright © CABI Publishing 2001

References

1Randall, E, Marshall, JR, Graham, S, Brasure, J. Frequency of food use data and the multidimensionality of diet. J. Am. Diet Assoc. 1989; 89: 1070–5.CrossRefGoogle ScholarPubMed
2Randall, E, Marshall, JR, Graham, S, Brasure, J. High risk health behaviors associated with various dietary patterns. Nutr. Cancer 1991; 16: 135–51.CrossRefGoogle ScholarPubMed
3Ursin, G, Ziegler, RG, Subar, AF, Graubard, BI, Haile, RW, Hoover, R. Dietary patterns associated with a low-fat diet in the national health examination follow-up study: identification of potential confounders for epidemiologic analyses. Am. J. Epidemiol. 1993; 137(8): 916–27.CrossRefGoogle ScholarPubMed
4Randall, E, Marshall, JR, Graham, S, Brasure, J. Patterns in food use and their associations with nutrient intakes. Am. J. Clin. Nutr. 1990; 52: 739–45.CrossRefGoogle ScholarPubMed
5Randall, E, Marshall, JR, Brasure, J, Graham, S. Patterns in food use and compliance with NCI dietary guidelines. Nutr. Cancer 1991; 15: 141–56.CrossRefGoogle ScholarPubMed
6Randall, E, Marshall, JR, Brasure, J, Graham, S. Dietary patterns and colon cancer in western New York. Nutr. Cancer 1992; 18: 265–76.CrossRefGoogle ScholarPubMed
7Slattery, ML, Boucher, KM, Caan, BJ, Potter, JD, Ma, KN. Eating patterns and risk of colon cancer. Am. J. Epidemiol. 1998; 148(1): 4–16.CrossRefGoogle ScholarPubMed
8Hu, FB, Rimm, EB, Stampfer, MJ, Ascherio, A, Spiegelman, D, Willett, WC. A prospective study of major dietary patterns and risk of coronary heart disease in men. Am. J. Clin. Nutr. 2001; 72(4): 912–21.CrossRefGoogle Scholar
9Hu, FB, Smith-Warner, SA, Feskanich, D, Stampfer, MJ, Ascherio, A, Sampson, L, Willett, W. Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire. Am. J. Clin. Nutr. 1999; 69: 243–9.CrossRefGoogle ScholarPubMed
10Willett, WC. Nutritional Epidemiology. 2nd edn. New York: Oxford University Press, 1998.CrossRefGoogle Scholar
11Johnson, DE. Applied Multivariate Methods for Data Analysts. Pacific Grove, CA: Duxbury Press, 1998.Google Scholar
12McCann, SE, Freudenheim, JL, Marshall, JR, Vena, JE, Laughlin, R, Brasure, JR, Swanson, MK, Graham, S. Diet in the epidemiology of endometrial cancer in Western New York. Cancer Causes Control 2000; 11(10): 965–74.CrossRefGoogle ScholarPubMed
13Logue, AW, Smith, ME. Predictors of food preferences in adult humans. Appetite 1986; 7: 109–25.CrossRefGoogle ScholarPubMed
14Portney, LG, Watkins, MP. Foundations of Clinical Research: Applications to Practice. Norwalk, CT: Appleton & Lange, 1993.Google Scholar
15American Institute for Cancer Research. Endometrium. In: Food, Nutrition and the Prevention of Cancer: A Global Perspective. Part II: Cancers, Nutrition and Food, Chapter 4.13. Washington, DC: American Institute for Cancer Research, 1997.Google Scholar