ORIGINAL RESEARCH article
Front. Genet.
Sec. Computational Genomics
Volume 16 - 2025 | doi: 10.3389/fgene.2025.1631228
This article is part of the Research TopicRefining Precision Medicine through AI and Multi-omics IntegrationView all articles
A Machine Learning Framework for Predicting Cognitive Impairment in Aging Populations Using Urinary Metal and Demographic Data
Provisionally accepted
- 1Chengdu University of Traditional Chinese Medicine, Chengdu, China
- 2Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
- 3Chongqing Hospital of Jiangsu Province Hospital, Chongqing, China
- 4Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan Province, China
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Cognitive impairment in older adults poses a significant global public health concern, with environmental metal exposure emerging as a major risk factor. However, the combined effects of multiple metals and the modulatory roles of demographic variables remain insufficiently explored. This study analyzed data from four NHANES cycles (1999–2000, 2001–2002, 2011–2012, 2013–2014), comprising 1,230 participants aged ≥ 60 years. Urinary concentrations of nine metals and creatinine were quantified in conjunction with demographic variables. Cognitive status was classified using data‑driven quartile thresholds on the Digit Symbol Substitution Test, CERAD Word‑Learning Test, and Animal Fluency tests. Six machine learning algorithms were trained and evaluated using sensitivity (SN), specificity (SP), accuracy (ACC), Matthews correlation coefficient (MCC) and AUC. The eXtreme gradient boosting (XGBoost) model demonstrated superior performance across all metrics (SN = 0.78, SP = 0.84, ACC =0.81, MCC =0.62, AUC = 0.90), and was selected for subsequent interpretation. SHAP analysis identified educational level, age, race/ethnicity, and creatinine as primary predictors. with elevated thallium and molybdenum levels and reduced barium levels also contributing to cognitive risk. Ultimately, a user-friendly webserver was deployed for the predictive model and is freely accessed at http://bio-medical.online/admxp/. The associated webserver enables accessible risk screening and underpins precision prevention strategies in aging populations.
Keywords: machine learning, cognitive impairment, metal, demographic, Shap
Received: 19 May 2025; Accepted: 16 Jun 2025.
Copyright: © 2025 Ren, Zhao, Yang, Liao, Zhang and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xuemei Liu, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
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