Autoencoder-derived dietary patterns linked a processed meat pattern to higher colorectal cancer risk in men (HR 1.98) and a bread pattern to lower gastric cancer risk in women (HR 0.53).
Do specific Autoencoder-derived dietary patterns affect the risk of total and site-specific cancers in a large prospective cohort?
Autoencoders provide a robust dimensionality reduction method for identifying dietary patterns that are significantly associated with site-specific cancer risks.
Tasa de eventos absoluta: 0% vs 0%
Abstract Background PCA rarely exceeded 0.2, UMAP reached ∼0.4, and AE achieved 0.35, providing competitive cluster quality with the most balanced variable contributions. Ten dietary patterns were identified: Balanced, Selective, Rice, Bread, Vegetables, Dairy, Meat, Processed meat, Noodles, and Salty. External validation using KNHANES produced similar silhouette values (∼0.36) and preserved centroid positions, confirming transferability. Over a median follow-up of 9.4 years, 7,390 cancer cases occurred. No significant associations were observed for total cancer; however, site-specific analyses revealed that the Processed meat pattern in men was associated with higher colorectal cancer risk (HR = 1.98, 95% CI: 1.12-3.49), and the Selective pattern with higher gastric cancer risk (HR = 1.32, 95% CI: 1.03-1.70) compared to the Balanced pattern. In women, the Bread pattern was associated with lower gastric cancer risk (HR = 0.53, 95% CI: 0.32-0.89). Conclusion Among the dimensionality reduction techniques, AE achieved the most favorable balance of cluster quality and variable contribution balance, supporting its utility for developing dietary patterns. These findings demonstrate that machine learning-based dimensionality reduction methods, particularly AEs, can strengthen dietary pattern development and capture meaningful associations with cancer risk. Citation Format: Hyobin Lee, Dongseok Heo, Sukhong Min, Sinyoung Cho, So-Yoon Lee, Ji-Yeob Choi, Bongwon Suh, Daehee Kang. Comparison of machine learning-based dimensionality reduction methods for dietary patterns and their predictability of cancer risk in a large cohort study abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5053.
Lee et al. (Fri,) reported a other. Autoencoder-derived dietary patterns linked a processed meat pattern to higher colorectal cancer risk in men (HR 1.98) and a bread pattern to lower gastric cancer risk in women (HR 0.53).