J Korean Med Sci.  2024 Mar;39(8):e77. 10.3346/jkms.2024.39.e77.

Association Between Multiple Heavy Metal Exposures and Cholesterol Levels in Residents Living Near a Smelter Plant in Korea

• Moon SI
• Yim DH
• Choi K
• Eom SY
• Choi BS
• Park JD
• Kim H
• Kim YD

• KMID: 2553328
• DOI: http://doi.org/10.3346/jkms.2024.39.e77

Abstract

Background
Considering the interactions between heavy metals, a comprehensive evaluation of the effects of exposure to various types of co-interacting heavy metals on health is required. This study assessed the association between dyslipidemia markers and blood mercury, lead, cadmium, iron, zinc, and nickel levels in residents of an abandoned refinery plant.
Methods
A total of 972 individuals (exposed group: 567, control group: 405) living near the Janghang refinery plant in the Republic of Korea were included. Blood mercury, lead, cadmium, iron, zinc, nickel, cholesterol, and triglyceride levels were measured. The combined effect of the six heavy metals on dyslipidemia markers was evaluated using a Bayesian kernel machine regression (BKMR) model and compared with the results of a linear regression analysis. The BKMR model results were compared using a stratified analysis of the exposed and control groups.
Results
In the BKMR model, the combined effect of the six heavy metals was significantly associated with total cholesterol (TC) levels both below the 45th percentile and above the 55th percentile in the total population. The combined effect range between the 25th and 75th percentiles of the six metals on TC levels was larger in the exposed group than that in the total population. In the control group, the combined effects of the changes in concentration of the six heavy metals on the TC concentration were not statistically significant.
Conclusion
These results suggest that the cholesterol levels of residents around the Janghang refinery plant may be elevated owing to exposure to multiple heavy metals.

Keyword

Refinery Plant; Heavy Metals; Dyslipidemia; BKMR Analysis

Figure

• 

  Fig. 1 Pearson’s correlation matrices for the log-transformed levels of blood heavy metals.Hg = mercury, Pb = lead, Cd = cadmium, Fe = iron, Zn = zinc, Ni = nickel.

• 

  Fig. 2 Combined effect of the heavy metals on TC level on the total population. The Bayesian kernel machine regression model was used to assess the effect on the total population. The model was adjusted for age, sex, drinking, smoking, economic status, and body mass index. (A) The overall effect of the exposure to the combined metals: 95% CI of the estimate of the TC value at each quantile compared with that when all heavy metals are set at the median concentration (B) Single pollutant association: 95% CI of TC estimates for the interquartile range change of each heavy metal concentration when the concentrations of the remaining heavy metals are fixed at the 25th, 50th, and 75th percentiles (C) Univariate exposure-response functions and 95% confidence bands for each heavy metal, with the other pollutants fixed at the 50th percentile. (D) Bivariate exposure-response functions, that is, when the concentrations of other heavy metals are the medians and the concentrations of expos2 metal (row) are at the 25th, 50th, and 75th percentile, respectively, it shows the change in TC concentration according to the change in expos1 (column) level.Zn = zinc, Fe = iron, Ni = nickel, Cd = cadmium, Pb = lead, Hg = mercury, TC = total cholesterol, CI = confidence interval.

• 

  Fig. 3 Combined effect of heavy metals on TC level on the exposed group. The Bayesian Kernel Machine Regression model was used to assess the effect on the exposed group. The model was adjusted for age, sex, drinking, smoking, economic status, and body mass index. (A) The overall effect of the exposure to the combined metals: 95% CI of the estimate of the TC value at each quantile compared with that when all heavy metals are set at the median concentration (B) Single pollutant association: 95% CI of TC estimates for the interquartile range change of each heavy metal concentration when the concentrations of the remaining heavy metals are fixed at the 25th, 50th, and 75th percentiles (C) Univariate exposure-response functions and 95% confidence bands for each metal, with the other pollutants fixed at the 50th percentile. (D) Bivariate exposure-response functions, that is, when the concentrations of other heavy metals are the medians and the concentrations of expos2 metal (row) are at the 25th, 50th, and 75th percentile, respectively, it shows the change in TC concentration according to the change in expos1 (column) level.Zn = zinc, Fe = iron, Ni = nickel, Cd = cadmium, Pb = lead, Hg = mercury, TC = total cholesterol, CI = confidence interval.

• 

  Fig. 4 Combined effect of heavy metals on total cholesterol (TC) level on the control group. The Bayesian Kernel Machine Regression model was used to assess the effect on the control group. The model was adjusted for age, sex, drinking, smoking, economic status, and body mass index. (A) The overall effect of the exposure to the combined metals: 95% CI of the estimate of the TC value at each quantile compared with that when all heavy metals are at the median concentration (B) Single pollutant association: 95% CI of TC estimates for the interquartile range change of each heavy metal concentration when the concentrations of the remaining heavy metals are fixed at the 25th, 50th, and 75th percentiles (C) Univariate exposure-response functions and 95% confidence bands for each metal, with the other pollutants fixed at the 50th percentile. (D) Bivariate exposure-response functions, that is, when the concentrations of other heavy metals are the medians and the concentrations of expos2 metal (row) are at the 25th, 50th, and 75th percentile, respectively, it shows the change in TC concentration according to the change in expos1 (column) level.Zn = zinc, Fe = iron, Ni = nickel, Cd = cadmium, Pb = lead, Hg = mercury, TC = total cholesterol, CI = confidence interval.

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