Cluster analysis of dietary patterns associated with colorectal cancer derived from a Moroccan case-control study.

ABSTRACT

INTRODUCTION: Colorectal cancer (CRC) is a global public health problem. There is strong indication that nutrition could be an important component of primary prevention. Dietary patterns are a powerful technique for understanding the relationship between diet and cancer varying across populations. OBJECTIVE: We used an unsupervised machine learning approach to cluster Moroccan dietary patterns associated with CRC. METHODS: The study was conducted based on the reported nutrition of CRC matched cases and controls including 1483 pairs. Baseline dietary intake was measured using a validated food-frequency questionnaire adapted to the Moroccan context. Food items were consolidated into 30 food groups reduced on 6 dimensions by principal component analysis (PCA). RESULTS: K-means method, applied in the PCA-subspace, identified two patterns 'prudent pattern' (moderate consumption of almost all foods with a slight increase in fruits and vegetables) and a 'dangerous pattern' (vegetable oil, cake, chocolate, cheese, red meat, sugar and butter) with small variation between components and clusters. The student test showed a significant relationship between clusters and all food consumption except poultry. The simple logistic regression test showed that people who belong to the 'dangerous pattern' have a higher risk to develop CRC with an OR 1.59, 95% CI (1.37 to 1.38). CONCLUSION: The proposed algorithm applied to the CCR Nutrition database identified two dietary profiles associated with CRC the 'dangerous pattern' and the 'prudent pattern'. The results of this study could contribute to recommendations for CRC preventive diet in the Moroccan population.