ArticleFood science and biotechnology2026
Bi-dimensional health space mapping: machine learning analysis of population health dynamics in Korean and Dutch cohorts.
Article in Food science and biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Health spans a broad spectrum, encompassing various biological and lifestyle factors. The complexity of biological systems necessitates for integrating diverse factors into a unified biomarker. We constructed a health space model that highlights metabolism and oxidative stress as key indicators for tracking healthy aging and mapping health trajectories. To ensure cross-ethnic relevance, we used data from the Dutch Nutrition Questionnaires plus and Korean National Health and Nutrition Examination Survey (KNHANES) cohorts. Our approach combines machine learning with logistic regression, applying a least absolute shrinkage and selection operator penalty to propensity score-matched datasets. External validation using an independent KNHANES cohort showed strong performance (AUC = 0.959 for metabolic stress; 0.973 for oxidative stress), confirming model reliability. These findings support the health space model as a holistic tool for monitoring physiological stress. Our research advances personalized health monitoring and offers a foundation for precision nutrition strategies aimed at reducing chronic disease risk.
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