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Dietary patterns and their association with food and nutrient intake in the European Prospective Investigation into Cancer and Nutrition (EPIC)–Potsdam study

Published online by Cambridge University Press:  09 March 2007

Matthias B. Schulze*
Affiliation:
Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbruecke, Arthur-Scheunert-Allee114-116, 14558 Bergholz-Rehbruecke, Germany
Kurt Hoffmann
Affiliation:
Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbruecke, Arthur-Scheunert-Allee114-116, 14558 Bergholz-Rehbruecke, Germany
Anja Kroke
Affiliation:
Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbruecke, Arthur-Scheunert-Allee114-116, 14558 Bergholz-Rehbruecke, Germany
Heiner Boeing
Affiliation:
Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbruecke, Arthur-Scheunert-Allee114-116, 14558 Bergholz-Rehbruecke, Germany
*
*Corresponding author: Dr Matthias B. Schulze, fax +49 33200 88444, email mschulze@www.dife.de
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Abstract

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Dietary pattern analysis has recently received growing attention, as it might be more appropriate in studies of diet–disease associations than the single food or nutrient approach that has dominated past epidemiological research. Factor analysis is a technique which is commonly used to identify dietary patterns within study populations. However, the ability of factor solutions to account for variance of food and nutrient intake has so far remained unclear. The present study therefore explored the statistical properties of dietary patterns with regard to the explained variance. Food intake of 8975 men and 13 379 women, assessed by a food-frequency questionnaire, was aggregated into forty-seven separate food groups. Dietary patterns were identified by principal component analysis and subsequent varimax rotation. Seven factors were retained for both men and women, which accounted for about 31 % of the total variance. The explained variance was relatively high (>40 %) for cooked vegetables, sauce, meat, dessert, cake, bread other than wholemeal, raw vegetables, processed meat, high-fat cheese, butter and margarine. Factor scores were used to investigate associations between the factors and nutrient intake. The patterns accounted for relatively large proportions of variance of energy and macronutrient intake, but for less variance of alcohol and micronutrient intake, especially of retinol, β-carotene, vitamin E, Ca and ascorbic acid. In addition, factors were related to age, BMI, physical activity, education, smoking and vitamin and mineral supplement use.

Type
Research Article
Copyright
Copyright © The Nutrition Society 2001

References

Ainsworth, BE, Haskell, WL, Leon, AS, Jacobs, DR, Montoye, HJ, Sallis, JF & Paffenbarger, RS (1993) Compendium of physical activities: classification of energy costs of human physical activities. Medicine and Science in Sports and Exercise 25, 7180.CrossRefGoogle ScholarPubMed
Appel, LJ, Moore, TJ, Obarzanek, E, Vollmer, WM, Svetkey, LP, Sacks, FM, Bray, GA, Vogt, TM, Cutler, JA, Windhauser, MM, Lin, PH & Karanja, N (1997) A clinical trial of the effects of dietary patterns on blood pressure. New England Journal of Medicine 336, 11171124.CrossRefGoogle ScholarPubMed
Barker, ME, McClean, SI, Thompson, KA & Reid, NG (1990) Dietary behaviours and sociocultural demographics in Northern Ireland. British Journal of Nutrition 64, 319329.CrossRefGoogle ScholarPubMed
Boeing, H, Bohlscheid-Thomas, S, Voss, S, Schneeweiß, S & Wahrendorf, J (1997) The relative validity of vitamin intakes derived from a food frequency questionnaire compared to 24-hour recalls and biological measurements: results from the EPIC pilot study in Germany. International Journal of Epidemiology 26(Suppl. 1), S82-S90.CrossRefGoogle ScholarPubMed
Boeing, H, Wahrendorf, J & Becker, N (1999) EPIC–Germany – a source for studies into diet and risk of chronic diseases. Annals of Nutrition and Metabolism 43, 195204.CrossRefGoogle ScholarPubMed
Bohlscheid-Thomas, S, Hoting, I, Boeing, H & Wahrendorf, J (1997) Reproducibility and relative validity of group intake in a food frequency questionnaire developed for the German part of the EPIC project. International Journal of Epidemiology 26(Suppl. 1), S59-S70.CrossRefGoogle Scholar
Bohlscheid-Thomas, S, Hoting, I, Boeing, H & Wahrendorf, J (1997) Reproducibility and relative validity of food energy and macronutrient intake of a food frequency questionnaire developed for the German part of the EPIC project. International Journal of Epidemiology 26(Suppl. 1), S71-S81.CrossRefGoogle ScholarPubMed
Gex-Fabry, M, Raymond, L & Jeanneret, O (1988) Multivariate analysis of dietary patterns in 939 Swiss adults: sociodemographic parameters and alcohol consumption profiles. International Journal of Epidemiology 17, 548555.CrossRefGoogle ScholarPubMed
Gittelsohn, J, Wolever, TM, Harris, SB, Harris-Giraldo, R, Hanley, AJ & Zinman, B (1998) Specific patterns of food consumption and preparation are associated with diabetes and obesity in a native Canadian community. Journal of Nutrition 128, 541547.CrossRefGoogle Scholar
Hatcher, L (1994) An Easy Guide to Use the SAS System for Factor Analysis and Structural Equation Modeling.Google Scholar
Hu, FB, Rimm, E, Smith-Warner, SA, Feskanich, D, Stampfer, MJ, Ascherio, A, Sampson, L & Willett, WC (1999) Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire. American Journal of Clinical Nutrition 69, 243249.CrossRefGoogle ScholarPubMed
Klipstein-Grobusch, K, Georg, T & Boeing, H (1997) Interviewer variability in anthropometric measurements and estimates of body composition. International Journal of Epidemiology 26(Suppl. 1), S174-S180.CrossRefGoogle ScholarPubMed
Kroke, A, Klipstein-Grobusch, K, Voss, S, Moseneder, J, Thielecke, F, Noack, R & Boeing, H (1999) Validation of a self-administered food-frequency questionnaire administered in the European Prospective Investigation into Cancer and Nutrition (EPIC) Study: comparison of energy, protein, and macronutrient intakes estimated with the doubly labeled water, urinary nitrogen, and repeated 24-h dietary recall methods. American Journal of Clinical Nutrition 70, 439447.CrossRefGoogle Scholar
Mark, SD, Thomas, DG & Decarli, A (1996) Measurement of exposure to nutrients: an approach to the selection of informative foods. American Journal of Epidemiology 143, 514521.CrossRefGoogle Scholar
Martinez, ME, Marshall, JR & Sechrest, L (1998) Invited commentary: factor analysis and the search for objectivity. American Journal of Epidemiology 148, 1719.CrossRefGoogle ScholarPubMed
Randall, E, Marshall, JR, Graham, S & Brasure, J (1990) Patterns in food use and their associations with nutrient intakes. Americal Journal of Clinical Nutrition 52, 739745.CrossRefGoogle ScholarPubMed
Rappoport, L, Peters, GR, Downey, R, McCann, T & Huff-Corzine, L (1993) Gender and age differences in food cognition. Appetite 20, 3352.CrossRefGoogle ScholarPubMed
Riboli, E & Kaaks, R (1997) The EPIC Project: rationale and study design. International Journal of Epidemiology 26(Suppl. 1), S6-S14.CrossRefGoogle ScholarPubMed
Sacks, FM, Obarzanek, E & Windhauser, NM (1995) Rationale and design of the Dietary Approaches to Stop Hypertension Trial (DASH). A multicenter controlled-feeding study of dietary patterns to lower blood pressure. Annals of Epidemiology 5, 108118.CrossRefGoogle Scholar
Schofield, WN, Schofield, C & James, WPT (1985) Basal metabolic rate – review and prediction. Human Nutrition Clinical Nutrition 39(Suppl. 1), 196.Google Scholar
Schwerin, HS, Stanton, JL, Riley, AM, Schaefer, AE, Leveille, GA, Elliott, JG, Warwick, KM & Brett, BE (1981) Food eating patterns and health: a reexamination of the Ten-State and HANES I surveys. American Journal of Clinical Nutrition 34, 568580.CrossRefGoogle Scholar
Slattery, ML, Boucher, KM, Caan, BJ, Potter, JD & Ma, KN (1998) Eating patterns and risk of colon cancer. American Journal of Epidemiology 148, 416.CrossRefGoogle ScholarPubMed
Thomas, DG & Mark, SD (1997) Max_r: an optimal method for the selection of subsets of foods for the measurement of specific nutrient exposures Computer Methods and Programs in Biomedicine 54, 151156.CrossRefGoogle ScholarPubMed
Whichelow, MJ & Prevost, AT (1996) Dietary patterns and their association with demographic, lifestyle and health variables in a random sample of British adults. British Journal of Nutrition 76, 1730.CrossRefGoogle Scholar
Willett, WC (1998) Nutritional Epidemiology.2nd ed. New York: Oxford University Press.CrossRefGoogle Scholar
Williams, DEM, Prevost, TP, Whichelow, MJ, Cox, BD, Day, NE & Wareham, NJ (2000) A cross-sectional study of dietary patterns with glucose intolerance and other features of the metabolic syndrome. British Journal of Nutrition 83, 257266.CrossRefGoogle ScholarPubMed
Wolff, CB & Wolff, HK (1995) Maternal eating patterns and birth weight of Mexican American infants. Nutrition and Health 10, 121134.CrossRefGoogle ScholarPubMed