Use of Generalized Weighted Quantile Sum Regressions of Tumor Necrosis Factor Alpha and Kidney Function to Explore Joint Effects of Multiple Metals in Blood

Kuei-Hau Luo, Hung-Pin Tu, Cheng-Hong Yang, Chen-Cheng Yang, Tzu-Hua Chen and Hung-Yi Chuang
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Kuei-Hau Luo: Graduate Institute of Medicine, College of Medicine, Kaohsiung Medicine University, Kaohsiung City 807, Taiwan
Hung-Pin Tu: Department of Public Health and Environmental Medicine, College of Medicine, Kaohsiung Medicine University, Kaohsiung City 807, Taiwan
Cheng-Hong Yang: Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 807, Taiwan
Chen-Cheng Yang: Graduate Institute of Medicine, College of Medicine, Kaohsiung Medicine University, Kaohsiung City 807, Taiwan
Tzu-Hua Chen: Department of Family Medicine, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung 80145, Taiwan
Hung-Yi Chuang: Graduate Institute of Medicine, College of Medicine, Kaohsiung Medicine University, Kaohsiung City 807, Taiwan

IJERPH, 2022, vol. 19, issue 12, 1-15

Abstract: Exposure to heavy metals could lead to adverse health effects by oxidative reactions or inflammation. Some essential elements are known as reactors of anti-inflammatory enzymes or coenzymes. The relationship between tumor necrosis factor alpha (TNF-α) and heavy metal exposures was reported. However, the interaction between toxic metals and essential elements in the inflammatory response remains unclear. This study aimed to explore the association between arsenic (As), cadmium (Cd), lead (Pb), cobalt (Co), copper (Cu), selenium (Se), and zinc (Zn) in blood and TNF-α as well as kidney function. We enrolled 421 workers and measured the levels of these seven metals/metalloids and TNF-α in blood; kidney function was calculated by CKD-EPI equation. We applied weighted quantile sum (WQS) regression and group WQS regression to assess the effects of metal/metalloid mixtures to TNF-α and kidney function. We also approached the relationship between metals/metalloids and TNF-α by generalized additive models (GAM). The relationship of the exposure–response curve between Pb level and TNF-α in serum was found significantly non-linear after adjusting covariates ( p < 0.001). Within the multiple-metal model, Pb, As, and Zn were associated with increased TNF-α levels with effects dedicated to the mixture of 50%, 31%, and 15%, respectively. Grouped WQS revealed that the essential metal group showed a significantly negative association with TNF-α and kidney function. The toxic metal group found significantly positive associations with TNF-α, serum creatinine, and WBC but not for eGFR. These results suggested Pb, As, Zn, Se, and mixtures may act on TNF-α even through interactive mechanisms. Our findings offer insights into what primary components of metal mixtures affect inflammation and kidney function during co-exposure to metals; however, the mechanisms still need further research.

Keywords: TNF-?; arsenic (As); cadmium (Cd); lead (Pb); cobalt (Co); copper (Cu); selenium (Se); zinc (Zn); weighted quantile sum (WQS) regression; generalized additive model (GAM) (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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