Dietary patterns and risk of bladder cancer: A factor analysis in Uruguay

Eduardo De Stefani, Paolo Boffetta, Alvaro L. Ronco, Hugo Deneo-Pellegrini, Giselle Acosta, María Mendilaharsu

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

Objective: To determine the major dietary patterns associated with bladder cancer risk, we conducted a principal components analysis (PCA) in a case-control study from Uruguay. Methods: A total of 255 newly diagnosed and microscopically confirmed cases of transitional cell carcinoma of the bladder and 501 hospitalized controls were included in the study. Both series were drawn from the four major public hospitals in Montevideo, Uruguay. Cases and controls were frequency matched on age and sex. Controls were submitted to factor (principal components) analysis. Results: We retained three factors that explained 25.1% of the total variance (including error variance). The first factor was labeled as the sweet beverages pattern. This factor was characterized by high loadings of coffee, tea, and added sugar and was strongly associated with risk of bladder cancer (OR 3.27, 95% CI 1.96-5.45). The second factor was labeled as the Western pattern and displayed high loadings of red meat, fried eggs, potatoes, and red wine. This pattern was directly associated with risk of bladder cancer (OR 2.35, 95% CI 1.42-3.89). Finally, the third factor was labeled as the prudent pattern and showed high loadings of fresh vegetables, cooked vegetables, and fruits. This pattern was not associated with risk of bladder cancer. Conclusions: According to our study, non-alcoholic beverages were the strongest risk factor for bladder cancer, whereas the Western pattern was also associated with a significant increase in risk of bladder cancer.

Original languageEnglish
Pages (from-to)1243-1249
Number of pages7
JournalCancer Causes and Control
Volume19
Issue number10
DOIs
StatePublished - Dec 2008
Externally publishedYes

Keywords

  • Bladder cancer
  • Dietary patterns
  • Factor analysis
  • Principal components

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