# A machine learning framework for predicting cognitive impairment in aging populations using urinary metal and demographic data

## TL;DR

This study uses machine learning to predict cognitive impairment in older adults based on urinary metal levels and demographic data, offering a web tool for risk screening.

## Contribution

A novel machine learning framework combining urinary metal data and demographics to predict cognitive impairment in aging populations.

## Key findings

- XGBoost outperformed other models with high accuracy and AUC in predicting cognitive impairment.
- Educational level, age, race/ethnicity, and creatinine were key predictors of cognitive risk.
- Elevated thallium and molybdenum and reduced barium levels were linked to increased cognitive risk.

## Abstract

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. 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/.

The associated webserver enables accessible risk screening and underpins precision prevention strategies in aging populations.