Dietary patterns derived from principal component analysis (PCA) and risk of colorectal cancer: a systematic review and meta-analysis

Eur J Clin Nutr. 2019 Mar;73(3):366-386. doi: 10.1038/s41430-018-0234-7. Epub 2018 Jul 26.

Abstract

Background and aim: Colorectal cancer (CRC) is highly prevalent worldwide, with dietary habits being a major risk factor. We systematically reviewed and meta-analysed the observational evidence on the association between CRC and dietary patterns (DP) derived from principal component analysis.

Design: PRISMA guidelines were followed. Web of Science, Medline/PubMed, EMBASE, and The Cochrane Library were searched to identify all eligible papers published up to the 31st July 2017. Any pre-defined cancer of the colon was included, namely colon-rectal cancer (CRC), colon cancer (CC), rectal cancer (RC), or proximal and distal CC, if available. Western (WDP) and prudent (PDP) dietary patterns were compared as a proxy to estimate "unhealthy" (Rich in meat and processed foods) and "healthy" diets (containing fruits or vegetables), respectively. Meta-analyses were carried out using random effects model to calculate overall risk estimates. Relative risks (RR) and 95% confidence intervals were estimated comparing the highest versus the lowest categories of dietary patterns for any of the forms of colon cancer studied.

Results: 28 studies were meta-analysed. A WDP was associated with increased risk of CRC (RR 1.25; 95% CI 1.11, 1.40), and of CC (RR 1.30; 95% CI 1.11, 1.52). A PDP was negatively associated with CRC (RR 0.81; 95% CI 0.73, 0.91). Sensitivity analyses showed that individuals from North-and South-American countries had a significantly higher risk of CRC than those from other continents.

Conclusion: A PDP might reduce the risk of CRC. Conversely, a WDP is associated with a higher risk of disease.

Publication types

  • Meta-Analysis
  • Research Support, Non-U.S. Gov't
  • Systematic Review

MeSH terms

  • Colorectal Neoplasms / epidemiology*
  • Diet / methods*
  • Diet / statistics & numerical data
  • Humans
  • Principal Component Analysis
  • Risk Assessment