Dietary patterns associated with the incidence of hypertension among adult Japanese males: application of machine learning to a cohort study.
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 t...
| Publicado en: | European Journal of Nutrition Vol. 63; no. 4; pp. 1293 - 1315 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177559860&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177559860 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14366207 CR0 jtl: European Journal of Nutrition issn: 14366207 maglogo: N pubinfo: dt: Jun2024 vid: 63 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 177559860 175626580 177559860 177559860 10.1007/s00394-024-03342-w 177559860 ppf: 1293 ppct: 22 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Dietary patterns associated with the incidence of hypertension among adult Japanese males: application of machine learning to a cohort study. aug: au: Li, Longfei Momma, Haruki Chen, Haili Nawrin, Saida Salima Xu, Yidan Inada, Hitoshi Nagatomi, Ryoichi affil: https://ror.org/041zje040 School of Physical Education and Health, Heze University, 2269 University Road, Mudan District, 274-015, Heze, Shandong, China sug: subj: Hypertension Epidemiology Men Psychosocial Factors Dietary Patterns Adverse Effects Food Habits Hypertension Risk Factors Machine Learning Methods Risk Assessment Human Male Japan Prospective Studies Multiple Logistic Regression Odds Ratio Confidence Intervals Descriptive Statistics Age Factors Body Mass Index Smoking Educational Status Physical Activity Hyperlipidemia Diabetes Mellitus Seafood Dairy Products Plant-Based Diet Funding Source Adult Meat Adult: 19-44 years Male ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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