A machine learning framework for predicting cognitive impairment in aging populations using urinary metal and demographic data
IntroductionCognitive 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.Meth...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2025.1631228/full |
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| author | Fengchun Ren Xiao Zhao Qin Yang Huaqiang Liao Yudong Zhang Xuemei Liu |
| author_facet | Fengchun Ren Xiao Zhao Qin Yang Huaqiang Liao Yudong Zhang Xuemei Liu |
| author_sort | Fengchun Ren |
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| description | IntroductionCognitive 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.MethodsThis 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.ResultsThe 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. Elevated thallium and molybdenum levels and reduced barium levels also contributed to cognitive risk. Ultimately, a user-friendly webserver was deployed for the predictive model and is freely accessed at http://bio-medical.online/admxp/.DiscussionThe associated webserver enables accessible risk screening and underpins precision prevention strategies in aging populations. |
| format | Article |
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| institution | Kabale University |
| issn | 1664-8021 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Genetics |
| spelling | doaj-art-294a749cf5ea49439d32840c1ea933c92025-08-20T03:26:26ZengFrontiers Media S.A.Frontiers in Genetics1664-80212025-06-011610.3389/fgene.2025.16312281631228A machine learning framework for predicting cognitive impairment in aging populations using urinary metal and demographic dataFengchun Ren0Xiao Zhao1Qin Yang2Huaqiang Liao3Yudong Zhang4Xuemei Liu5Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, ChinaDepartment of Radiology, Chongqing Hospital of Jiangsu Province Hospital, The People’s Hospital of Qijiang District, Chongqing, ChinaDepartment of Infectious Diseases, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, ChinaDepartment of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, ChinaDepartment of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, ChinaDepartment of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, ChinaIntroductionCognitive 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.MethodsThis 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.ResultsThe 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. Elevated thallium and molybdenum levels and reduced barium levels also contributed to cognitive risk. Ultimately, a user-friendly webserver was deployed for the predictive model and is freely accessed at http://bio-medical.online/admxp/.DiscussionThe associated webserver enables accessible risk screening and underpins precision prevention strategies in aging populations.https://www.frontiersin.org/articles/10.3389/fgene.2025.1631228/fullmachine learningcognitive impairmentmetaldemographicSHAP |
| spellingShingle | Fengchun Ren Xiao Zhao Qin Yang Huaqiang Liao Yudong Zhang Xuemei Liu A machine learning framework for predicting cognitive impairment in aging populations using urinary metal and demographic data Frontiers in Genetics machine learning cognitive impairment metal demographic SHAP |
| title | A machine learning framework for predicting cognitive impairment in aging populations using urinary metal and demographic data |
| title_full | A machine learning framework for predicting cognitive impairment in aging populations using urinary metal and demographic data |
| title_fullStr | A machine learning framework for predicting cognitive impairment in aging populations using urinary metal and demographic data |
| title_full_unstemmed | A machine learning framework for predicting cognitive impairment in aging populations using urinary metal and demographic data |
| title_short | A machine learning framework for predicting cognitive impairment in aging populations using urinary metal and demographic data |
| title_sort | machine learning framework for predicting cognitive impairment in aging populations using urinary metal and demographic data |
| topic | machine learning cognitive impairment metal demographic SHAP |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2025.1631228/full |
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