Dietary patterns and risk of advanced prostate cancer: a principal component analysis in Uruguay
- 250 Downloads
In order to explore the role of broader eating patterns in the etiology of prostate cancer, we conducted a principal components analysis among Uruguayan men.
The study included 345 newly diagnosed cases of advanced prostate cancer and 690 hospitalized controls. The factor analysis was performed using the control population.
Factor analysis allowed the extraction of five patterns, labeled as prudent, traditional, substituter, drinker, and Western. Whereas the traditional and Western patterns were directly associated with risk of prostate cancer (OR for high quartile versus the low quartile of the Western diet was 2.35, 95% CI 1.44–3.85, p-value for trend < 0.0001), the prudent, drinker, and substituter patterns were not associated with risk of the disease. After adjustment of each pattern for the foods with high loadings, these three patterns did not modify substantially their original ORs.
The Western and traditional patterns could partially explain the high incidence of advanced prostate cancer in Uruguay, a main producer of beef in the World.
KeywordsProstate cancer Exploratory factor analysis Milk Eggs Mate Cholesterol Calcium
Supported by a grant from International Agency for Research on Cancer, Lyon, France.
- 1.Parkin DM, Whelan SL, Ferlay J, Teppo L, Thomas DB et al (eds) (2002) Cancer incidence in five continents. Volume VIII. IARC scientific publications Nº 155. Lyon, IARC, FranceGoogle Scholar
- 5.World Cancer Research Fund/American Institute for Cancer Research (2007) Food, nutrition, physical activity, and the prevention of cancer: a global perspective. AIRC, WashingtonGoogle Scholar
- 20.Harman HH (1976) Modern factor analysis, 3rd edn. Revised. The University of Chicago Press Ltd., LondonGoogle Scholar
- 21.Kim J-O, Mueller CW (1978) Factor analysis. Statistical methods and practical issues. Age University Paper No 14, CaliforniaGoogle Scholar
- 22.Kline P (2002) An easy guide to factor analysis. Routledge, New YorkGoogle Scholar
- 23.Horst P (1965) Factor analysis of data matrices. Holt, Rinehart, and Winston, New YorkGoogle Scholar
- 24.Thomson GH (1951) The factorial analysis of human ability. University of London Press, LondonGoogle Scholar
- 26.Breslow NE, Day NE (1980) Statistical methods in cancer research. Volume 1-The analysis of case-control analysis. IARC scientific publication No 32. IARC, LyonGoogle Scholar
- 27.TA STA (2005) Stata reference guide. Release 9. Stata Press, College StationGoogle Scholar
- 32.Platz EA, Till C, Goodman PJ, Parnes HL, Figg WD, Albanes D, Neuhouser ML, Klein EA, Thompson IA Jr, Kristal AR (2009) Men with low serum cholesterol have a lower risk of high-grade prostate cancer in the placebo arm of the Prostate Cancer Prevention Trial. Cancer Epidemiol Biomarkers Prev 18:2807–2813CrossRefPubMedGoogle Scholar
- 33.Ha B (2010) Prostate cancer linked to cholesterol levels. The Johns Hospital Science & TechGoogle Scholar
- 44.Jacobs DR Jr, Steffen LM (2003) Nutrient, foods, and dietary patterns as exposures in research: a framework for food synergy. Am J Clin Nutr 8 suppl:508S–513SGoogle Scholar