ArticleBMJ health & care informatics2023
Cluster analysis of dietary patterns associated with colorectal cancer derived from a Moroccan case-control study.
Article in BMJ health & care informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Poor Diet and Oral Cavity Cancer Risk in Smoking and Non-Smoking Men and Women: An Analysis of Three US Cohorts.Head & neck · 2026Article
- Artificial Intelligence in Food-Nutrition-Health Research: From Multimodal Data Integration to Precision Intervention.Journal of food science · 2026Review
- Real-world data on oncology in Morocco: a narrative review of four decades of national evidence.Journal of cancer research and clinical oncology · 2026Review
- Machine Learning Unveils Dietary Antioxidants as Influential Factors for Diabetes-Cancer Comorbidity: Insights From National Health and Nutrition Examination Survey.Food science & nutrition · 2026Article
- Machine Learning-Driven Precision Nutrition: A Paradigm Evolution in Dietary Assessment and Intervention.Nutrients · 2025Review
- Dietary patterns and colorectal cancer: a multicenter case-control study in an Iranian population.Scientific reports · 2025Article
- A generative deep neural network for pan-digestive tract cancer survival analysis.BioData mining · 2025Article
- The Association between Diet-Exercise Patterns and Cirrhosis: A Cross-Sectional Study from NHANES 2017-March 2020.Nutrients · 2024Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
introductionColorectal 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.
objectiveWe used an unsupervised machine learning approach to cluster Moroccan dietary patterns associated with CRC.
methodsThe 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).
resultsK-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).
conclusionThe 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.
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