The Combined Effects of Urine Zinc, Cadmium, Mercury, Lead, and Copper on Endometrial Cancer Staging

Issah Haruna, Russell R. Broaddus, Andrew B. Gladden, Kiran Subedi and Emmanuel Obeng-Gyasi ()
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Issah Haruna: Department of Built Environment, North Carolina A&T State University, Greensboro, NC 27411, USA
Russell R. Broaddus: Department of Pathology and Laboratory Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
Andrew B. Gladden: Department of Pathology and Laboratory Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
Kiran Subedi: Analytical Services Laboratory, College of Agriculture and Environmental Sciences, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA
Emmanuel Obeng-Gyasi: Department of Built Environment, North Carolina A&T State University, Greensboro, NC 27411, USA

IJERPH, 2025, vol. 22, issue 2, 1-16

Abstract: Endometrial cancer (EC) is a growing public health concern. This secondary data study of a case series leveraged existing samples and data to explore the potential link between exposure to heavy metals/essential elements and stage of EC. We analyzed urine samples from women with EC, measuring levels of toxic metals (cadmium, mercury, and lead) and essential elements (zinc and copper). Our findings revealed that higher levels of mercury, cadmium, and lead are associated with more advanced EC stages. Conversely, zinc showed a protective effect, potentially mitigating EC stage progression. Copper levels did not show a clear association with EC stage. These results highlight the potential impact of environmental exposures on EC stage and the crucial need for advanced statistical methods to understand the combined effects of these pollutants on health and the need for public health interventions. Further research is needed to explore the mechanisms by which these metals influence EC stage and long-term outcomes.

Keywords: environmental pollutants; EC; essential element; heavy metals; Bayesian kernel machine regression; linear regression univariate and bivariate exposure-response (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2025
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