Dietary patterns associated with the incidence of hypertension among adult Japanese males: application of machine learning to a cohort study

The previous studies that examined the effectiveness of unsupervised machine learning methods versus traditional methods in assessing dietary patterns and their association with incident hypertension showed contradictory results. Consequently, our aim is to explore the correlation between the incidence of hypertension and overall dietary patterns that were extracted using unsupervised machine learning techniques. Data were obtained from Japanese male participants enrolled in a prospective cohort study between August 2008 and August 2010. A final dataset of 447 male participants was used for analysis. Dimension reduction using uniform manifold approximation and projection (UMAP) and subsequent K-means clustering was used to derive dietary patterns. In addition, multivariable logistic regression was used to evaluate the association between dietary patterns and the incidence of hypertension. We identified four dietary patterns: ‘Low-protein/fiber High-sugar,’ ‘Dairy/vegetable-based,’ ‘Meat-based,’ and ‘Seafood and Alcohol.’ Compared with ‘Seafood and Alcohol’ as a reference, the protective dietary patterns for hypertension were ‘Dairy/vegetable-based’ (OR 0.39, 95