Qiannan Wei

Anhui University of Science and Technology, China

Title: Association Between Multiple Metal Exposures and Hyperglycemia Among Coal Miners

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Biography

Qiannan Wei, PhD, received her doctoral degree in Occupational and Environmental Health from Sun Yat-sen University in 2025. She is currently affiliated with the School of Public Health, Anhui University of Science and Technology. Dr. Wei has led or participated in three national and provincial/ministerial-level research projects. She has published multiple scientific papers, including six SCI-indexed articles as first author or co-first author. Her main research interests include environmental epidemiology, environment, and health.

Abstract

Objective: To investigate the association between urinary multi-metal co-exposure and hyperglycemia among coal miners, and to identify key contributing metals for occupational metabolic-risk prevention. Methods: A cross-sectional study was conducted among 1,418 coal miners who underwent routine occupational health examinations in a coal mining enterprise in Northern Shaanxi, China, in 2023. Demographic characteristics, lifestyle factors, occupational information, physical measurements, and biochemical indicators were collected through questionnaires, physical examinations, and laboratory tests. Ten urinary metals, including Pb, V, Fe, Zn, Cu, As, Cr, Cd, Mn, and Ni, were measured by inductively coupled plasma mass spectrometry and corrected for urinary creatinine. Hyperglycemia was defined as fasting plasma glucose ?6.1 mmol/L or physician-diagnosed diabetes with glucose-lowering medication. Multivariable unconditional logistic regression assessed single-metal associations, while weighted quantile sum regression, quantile g-computation, and Bayesian kernel machine regression evaluated mixture effects and major contributors. Results: Among all participants, 144 cases of hyperglycemia were identified, corresponding to a prevalence of 10.2%. Compared with the first quartile, the fourth quartiles of urinary Zn, Cu, and Fe were associated with higher odds of hyperglycemia, with odds ratios of 3.61 (95% CI: 2.14-6.32), 2.65 (95% CI: 1.61-4.47), and 1.98 (95% CI: 1.20-3.31), respectively. The weighted quantile sum model indicated a positive mixture effect (OR=2.27, 95% CI: 1.63-3.16), mainly driven by Zn, Cu, Ni, and Fe. Quantile g-computation also showed an increased risk (OR=1.55, 95% CI: 1.08-2.21), with Zn, Fe, and Cu contributing most to positive weights and V contributing most to negative weights. Bayesian kernel machine regression confirmed that higher overall metal mixture exposure was associated with increased hyperglycemia risk, with Zn, Cu, and Fe showing positive effects and V showing an inverse association. Conclusion: Urinary multi-metal mixture exposure was associated with elevated hyperglycemia risk among coal miners. Zn, Cu, and Fe were the main risk-related metals, whereas V may represent a potential protective signal.

Keywords

coal mining; urinary metals; mixture exposure; hyperglycemia; weighted quantile sum regression; quantile g-computation; Bayesian kernel machine regression; occupational health