Sex-specific associations of empirically derived dietary patterns with colorectal cancer risk in a Korean population: a case‒control study

Scientific Reports(2024)

Cited 0|Views12
No score
Abstract
Dietary patterns may be a crucial modifiable factor in colorectal cancer (CRC) risk. This study aimed to examine the associations of dietary patterns derived from two methods with CRC risk in Korea. In a study of 1420 CRC patients and 2840 control participants, we obtained dietary patterns by principal component analysis (PCA) and reduced rank regression (RRR) using 33 predefined food groups. The associations between dietary patterns and CRC risk were assessed using unconditional logistic regression models to calculate odds ratios (ORs) and 95% confidence intervals (CIs). We identified two similar dietary patterns, derived from PCA 1 (prudent) and RRR (healthy), characterized by higher consumption of green/yellow vegetables, light-colored vegetables, fruits, eggs, and milk in both men and women. In women, higher prudent and healthy pattern scores were significantly associated with a lower risk of CRC (prudent, OR Q4 vs. Q1 = 0.59, 95% CI 0.40–0.86, P for trend = 0.005; healthy, OR Q4 vs. Q1 = 0.62, 95% CI 0.43–0.89, P for trend = 0.007). In men, a significant inverse association between dietary pattern and risk of rectal cancer was found only for the healthy dietary pattern (OR Q4 vs. Q1 = 0.66, 95% CI 0.45–0.97, P for trend = 0.036). Compared with the dietary pattern derived by PCA, the RRR dietary pattern had a slightly stronger association with a lower risk of distal colon cancer (OR Q4 vs. Q1 = 0.58, 95% CI 0.35–0.97, P for trend = 0.025) and rectal cancer (OR Q4 vs. Q1 = 0.29, 95% CI 0.15–0.57, P for trend < 0.001) in women. Our findings suggest cancer prevention strategies focusing on a diet rich in vegetables, fruits, eggs, and milk. Moreover, the use of both PCA and RRR methods may be advantageous to explore the associations between dietary patterns and risk of CRC.
More
Translated text
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined