Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Jun;63(4):1293-1314.
doi: 10.1007/s00394-024-03342-w. Epub 2024 Feb 25.

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

Longfei Li  1   2 , Haruki Momma  2 , Haili Chen  2 , Saida Salima Nawrin  3 , Yidan Xu  2 , Hitoshi Inada  4   5 , Ryoichi Nagatomi  6   7
Affiliations

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

Longfei Li et al. Eur J Nutr. 2024 Jun.

Abstract

Purpose: 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.

Methods: 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.

Results: 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% CI 0.19-0.80, P = 0.013) and the 'Meat-based' (OR 0.37, 95% CI 0.16-0.86, P = 0.022) after adjusting for potential confounding factors, including age, body mass index, smoking, education, physical activity, dyslipidemia, and diabetes. An age-matched sensitivity analysis confirmed this finding.

Conclusion: This study finds that relative to the 'Seafood and Alcohol' pattern, the 'Dairy/vegetable-based' and 'Meat-based' dietary patterns are associated with a lower risk of hypertension among men.

Keywords: Dietary patterns; Hypertension; K-means; UMAP; Unsupervised machine learning.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no conflict of interests.

Figures

Fig. 1
Fig. 1
Flowchart of participants included in the analysis
Fig. 2
Fig. 2
Scatter plot and contour map. The graph illustrates the relative distances between participants in the Oroshisho cohort study using unsupervised machine learning and dimension reduction techniques based on BDHQ data. The data has been reduced to two dimensions (Dimension 1 (D1) and Dimension 2 (D2)) using UMAP. Each point on the graph represents an individual participant in the study. The contour lines are curves that show areas of constant Gaussian values in the 2D space, connecting points of similar 2D values. Number clusters (n = 4) were chosen optimally by contour map analysis. Exemplary 2D visualization of the relative distances between all participants in the Oroshisho cohort study using UMAP. Colors indicate cluster assignment using K-means clustering (K = 4). An interpretable name for the dietary pattern is then defined for each cluster based on the trend value (see ‘Methods’ for details)
Fig. 3
Fig. 3
Analysis of age distribution of clusters. Dot plot of the age distribution for each cluster. The color numbering indicates a representation of each cluster
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Zhao D, Qi Y, Zheng Z, Wang Y, Zhang XY, Li HJ, Liu HH, Zhang XT, Du J, Liu J. Dietary factors associated with hypertension. Nat Rev Cardiol. 2011;8(8):456–465. doi: 10.1038/nrcardio.2011.75. - DOI - PubMed
    1. NCD Risk Factor Collaboration Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19·1 million participants. Lancet. 2017;389(10064):37–55. doi: 10.1016/s0140-6736(16)31919-5. - DOI - PMC - PubMed
    1. Cardiovascular diseases (CVDs). https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases...). Accessed 21 June 2021.
    1. Ogihara T, Rakugi H. Hypertension in the elderly: a Japanese perspective. Drugs Aging. 2005;22(4):297–314. doi: 10.2165/00002512-200522040-00003. - DOI - PubMed
    1. Umemura S, Arima H, Arima S, Asayama K, Dohi Y, Hirooka Y, Horio T, Hoshide S, Ikeda S, Ishimitsu T, Ito M, Ito S, Iwashima Y, Kai H, Kamide K, Kanno Y, Kashihara N, Kawano Y, Kikuchi T, Kitamura K, Kitazono T, Kohara K, Kudo M, Kumagai H, Matsumura K, Matsuura H, Miura K, Mukoyama M, Nakamura S, Ohkubo T, Ohya Y, Okura T, Rakugi H, Saitoh S, Shibata H, Shimosawa T, Suzuki H, Takahashi S, Tamura K, Tomiyama H, Tsuchihashi T, Ueda S, Uehara Y, Urata H, Hirawa N. The Japanese Society of Hypertension Guidelines for the Management of Hypertension (JSH 2019) Hypertens Res. 2019;42(9):1235–1481. doi: 10.1038/s41440-019-0284-9. - DOI - PubMed