Impact of heavy metals on allostatic load in adults: a NHANES study

Yiyin  Rong; Shenglin  Luo; Cong  Wu; Haozhe  Huang; Ming  Chen; Xingyu   Liu; Shaobo Wu

doi:10.71321/3b9xjm29

Authors

Yiyin Rong Xi’an Jiaotong University
Shenglin Luo Xi’an Jiaotong University
Cong Wu Xi’an Jiaotong University
Haozhe Huang Xi’an Jiaotong University
Ming Chen Xi’an Jiaotong University
Xingyu Liu Xi’an Jiaotong University
Shaobo Wu Xi'an Jiaotong university

DOI:

https://doi.org/10.71321/3b9xjm29

Keywords:

Urinary metals, Allostatic load score, National health and nutrition examination survey, Machine Learning

Abstract

Background: Allostatic load, defined as the cumulative strain resulting from the chronic stress response, is associated with adverse health outcomes. Heavy metals, prevalent in various environmental pollutants, exert cumulative effects on the human body through exposure via water or food sources. However, the relationship between heavy metals and allostatic load remains poorly understood. The aim of this study is to examine the association between urinary metal concentrations and allostatic load.

Methods: This study analyzed data from 4,231 adult participants in the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2010. We employed linear regression analysis, Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS), and quantile g-computation (qgcomp) to investigate the associations between twelve urinary metals and allostatic load. Additionally, we developed K-nearest neighbors (KNN), random forest (RF), and XGBoost models to predict allostatic load scores (ALS).

Results: Linear regression analysis indicated that the combined effects of the twelve urinary metals were negatively correlated with allostatic load. WQS, qgcomp, and BKMR analyses identified cesium (Cs), molybdenum (Mo), lead (Pb), platinum (Pt), and cobalt (Co) as the primary influencing factors (all p-values < 0.05). Furthermore, when predicting ALS based on heavy metal exposure, the random forest model outperformed the other machine learning algorithms, with a root mean square error (RMSE) of 2.377428, compared to 2.501523 for KNN and 2.377733 for XGBoost.

Conclusion: Our findings indicate that urinary metal concentrations are negatively associated with allostatic load, with Cs, Mo, Pt, Pb, and Co showing the most significant negative correlations. Further research is necessary to explore the causal relationships and underlying mechanisms. Additionally, our analysis demonstrated that the random forest model was the most effective for ALS prediction.

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