ArticleYonsei medical journal2026
The Relationship between Biological Aging and Cognitive Function: A Machine Learning Model.
Article in Yonsei medical journal, 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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5 authors.
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Abstract
purposeThe relationship between cognitive impairment and biological age (BA) is unclear. This study aimed to investigate the association between BA and cognitive impairment. MATERIALS AND
methodsA total of 2202 participants from the 2011-2012 and 2013-2014 National Health and Nutrition Examination Survey were included. Klemera-Doubal method age (KDM-Age), phenotypic age (PhenoAge), and their acceleration values were calculated based on laboratory parameters. Six machine learning models were constructed to compare performance metrics such as area under the receiver operating characteristic curve (AUC-ROC) and accuracy, and Shapley additive explanations (SHAP) was used to interpret feature contributions.
resultsAfter fully adjusting for confounders, each 1-year increase in PhenoAge was associated with a 6%, 8%, and 5% higher risk of cognitive impairment on the Consortium to Establish a Registry for Alzheimer's Disease (CERAD), Digit Symbol Substitution Test (DSST), and Animal Fluency Test (AFT) tests, respectively (all
conclusionPhenoAge is a biological aging indicator with suggestive value for identifying the risk of cognitive impairment in older adults. Combining the RF model with key sociodemographic information may help facilitate early risk screening.
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