Abstract
In the real world, humans are exposed to multiple metal(loid)s (designated hereafter metals) that contain essential metals as well as toxic metals. Exposure to the metal mixture was assumed to be associated with renal function impairment; however, there is no consensus on available studies. Therefore, we here explored the association between multiple metals exposure and indicators of renal function in the general population from southeastern China. A total of 11 metals with 6 human essential metals and 5 toxic metals were determined in the selected 720 subjects. In addition, serum uric acid (SUA), serum creatinine (SCR), and the estimated glomerular filtration rate (eGFR) were measured or calculated as indicators of renal function. Using multiple flexible statistical models of generalized linear model, elastic net regression, and Bayesian kernel machine regression, the joint as well as the individual effect of metals within the mixture, and the interactions between metals were explored. When exposed to the metal mixture, the statistically non-significantly increased SUA, the significantly increased SCR, and the significantly declined eGFR were observed. In addition, the declined renal function may be primarily attributed to lead (Pb), arsenic (As), and nickel (Ni) exposure. Finally, interactions, such as the synergistic effect between Pb and Mo on SUA, whereas the antagonistic effect between Ni and Cd on SCR and eGFR were identified. Our finding suggests that combined exposure to multiple metals would impair renal function. Therefore, reducing exposure to toxic heavy metals of Pb, As, and Cd and limiting exposure to the human essential metal of Ni would protect renal function.
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The datasets generated and/or analyzed during the current study were not publicly available but are available from the corresponding author upon reasonable request.
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Acknowledgements
All participants included in our study and all members of our study team are greatly acknowledged.
Funding
This research was supported by grants from the National Natural Science Foundation of China (Nos. 21966022, 81903360), the Key Program of Natural Science Foundation of Jiangxi Province (No. 20192ACB20025), the Natural Science Foundation of Jiangxi Province (No. 20192BAB215048), and the Science and Technology Program of Jiangxi Province Health Commission (No. 202311106).
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Conceptualization: Guangqin Fan, Guihua Du, and Fankun Zhou; methodology: Guangqin Fan, Guihua Du, Xiaoguang Song, Fankun Zhou, Jie Xie, and Feng Chang; investigation: Xiaoguang Song, Lu Ouyang; Qi Li, Shiying Ruan, Shaoqi Rao, Shuo Yang, and Xin Wan; formal analysis: Guangqin Fan and Guihua Du; data visualization: Guihua Du; resources: Guangqin Fan and Xiaoguang Song; writing—original draft preparation: Guihua Du; writing—review and editing: Guangqin Fan; supervision: Guangqin Fan; funding acquisition: Guangqin Fan, Fankun Zhou, and Xiaoguang Song.
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Supplementary Information
Fig. S1
Grouped scatter plot of the Q(height) based eGFR (Q(height)-eGFR) and eGFR for the full age spectrum (FAS-eGFR) (PNG 131 kb)
Fig. S2
Estimated coefficients and relative 95%CI between single metal exposure and level of SUA (a), SCR (b), or eGFR (c) via generalized linear regression (GLM). In this model, the control covariates were age, gender, BMI, education level, household income, marriage status, smoking or passive smoking, and drinking. *(**) represents P<0.05(0.01) (PNG 120 kb)
Fig. S3
Bi-variate exposure-response functions of the metal mixture and level of SUA (a), SCR (b), or eGFR (c). This figure shows the association of each pair of the 11 metals with levels of SUA, SCR, or eGFR when all other heavy metals are fixed at their 50th percentile (PNG 1063 kb)
Fig. S4
Estimated coefficients and relative 95%CI between metal exposure and level of SUA (a), SCR (b), or eGFR (c) via GLM. In this model, the 10 metals, diabetes, hypertension, as well as the 8 factors that were mentioned in the covariates of method section were included as covariates when assessing the effect on the level of SUA, SCR, or eGFR. *(**) represents P<0.05(0.01) (PNG 98 kb)
Fig. S5
The univariate relationship between each metal and SUA(a-b), SCR(c-d), or eGFR (e-f) level by using BKMR for sensitivity analysis. The univariate relationship between each metal exposure and the SUA (a), SCR (c), or eGFR (e), where levels of all other metals are fixed to the 50th quantile. The shaded part was 95% CI for the curve. The estimated effect of single metal and relative 95% CI for the level of SUA (b), SCR (d), or eGFR(f) when levels of other metals were fixed at various quantiles. In the model of BKMR for sensitivity analysis, diabetes, and hypertension were included as covariates (PNG 565 kb)
Fig. S6
Joint effect and 95%CI of the 11 metals on level of SUA (a), SCR (b), or eGFR (c) when all the metals at particular percentiles were compared to their 50th percentile in the sensitivity analysis (PNG 127 kb)
Fig. S7
Bi-variate exposure-response functions of the metal mixture and level of SUA (a), SCR (b), or eGFR (c) in the sensitivity analysis. This figure shows the association of each pair of the 11 metals with levels of SUA, SCR, or eGFR when all other heavy metals are fixed at their 50th percentile (PNG 1165 kb)
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Du, G., Song, X., Zhou, F. et al. Association between multiple metal(loid)s exposure and renal function: a cross-sectional study from southeastern China. Environ Sci Pollut Res 30, 94552–94564 (2023). https://doi.org/10.1007/s11356-023-29001-x
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DOI: https://doi.org/10.1007/s11356-023-29001-x