gms | German Medical Science

50. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds)
12. Jahrestagung der Deutschen Arbeitsgemeinschaft für Epidemiologie (dae)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie
Deutsche Arbeitsgemeinschaft für Epidemiologie

12. bis 15.09.2005, Freiburg im Breisgau

Identification of a high-fiber and low-fat food pattern associated with low prospective weight change

Meeting Abstract

  • Mandy Schulz - Deutsches Institut für Ernährungsforschung Potsdam-Rehbrücke, Nuthetal
  • Ute Nöthlings - Cancer Research Center of Hawaii, Honolulu
  • Kurt Hoffmann - Deutsches Institut für Ernährungsforschung Potsdam-Rehbrücke, Nuthetal
  • Manuela Bergmann - Deutsches Institut für Ernährungsforschung Potsdam-Rehbrücke, Nuthetal
  • Heiner Boeing - Deutsches Institut für Ernährungsforschung Potsdam-Rehbrücke, Nuthetal

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. Deutsche Arbeitsgemeinschaft für Epidemiologie. 50. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds), 12. Jahrestagung der Deutschen Arbeitsgemeinschaft für Epidemiologie. Freiburg im Breisgau, 12.-15.09.2005. Düsseldorf, Köln: German Medical Science; 2005. Doc05gmds274

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Veröffentlicht: 8. September 2005

© 2005 Schulz et al.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.de). Er darf vervielf&aauml;ltigt, verbreitet und &oauml;ffentlich zug&aauml;nglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.


Gliederung

Text

Introduction and Aims

In recent years, the integration and compression of full dietary information using dietary pattern approaches to assess the association between diet and body weight development has become of increasing interest among nutritional epidemiologists [1]. The aim of the study was to identify a dietary pattern predictive of subsequent annual weight change by using dietary composition information.

Material and Methods

Study subjects were 24,958 middle-aged men and women of the EPIC (European Prospective Investigation into Cancer and Nutrition)-Potsdam cohort [2]. To derive dietary patterns we used the reduced rank regression (RRR) method [3] with three response variables presumed to affect weight change: fat density, carbohydrate density, and fiber density. Annual weight change was computed by fitting a linear regression line to each persons’ body weight data (baseline, 2- and 4-year follow-up) and determining the slope. In linear regression models, the pattern score was related to annual weight change.

Results

We identified a food pattern of high consumption of whole grain bread, fruits, fruit juices, grain flakes/muesli, and raw vegetables and low consumption of processed meat, butter, high-fat cheese, margarine and meat to be predictive of subsequent weight change. Mean annual weight gain was gradually decreasing with increasing score of the pattern (p for trend: <0.0001), i.e. subjects scoring high at the pattern maintained their weight or had significantly lower weight gains over time compared with subjects with an opposite pattern. The significance of the food pattern in predicting annual weight change was confined to non-obese subjects however.

Conclusion

In this study population we identified a food pattern characterized by high-fiber and low-fat food choices that can help to maintain body weight or at least prevent excess body weight gain.


References

1.
Newby PK, Tucker KL. Empirically derived eating patterns using factor or cluster analysis: a review. Nutr Rev 2004; 62: 177-203.
2.
Boeing H, Wahrendorf J, Becker N. EPIC-Germany--A source for studies into diet and risk of chronic diseases. European Investigation into Cancer and Nutrition. Ann Nutr Metab 1999; 43: 195-204.
3.
Hoffmann K, Schulze MB, Schienkiewitz A, Nöthlings U, Boeing H. Application of a new statistical method to derive dietary patterns in nutritional epidemiology. Am J Epidemiol 2004; 159: 935-44.