Multiple metal exposure and renal tubular damage of residents in a metal-polluted region in Korea

Dahee Han; Hyuna Jang; Kyung-Hwa Choi; Jang-Hee Lee; Sang-Yong Eom; Young-Seoub Hong; Woo Jin Kim; Eunil Lee; Yong Min Cho

doi:10.5620/eaht.2025014

Environ Anal Health Toxicol > Volume 40:2025 > Article

Han, Jang, Choi, Lee, Eom, Hong, Kim, Lee, and Cho: Multiple metal exposure and renal tubular damage of residents in a metal-polluted region in Korea

Original Article

Environ Anal Health Toxicol 2025; 40: e2025014.

Published online: June 20, 2025

DOI: https://doi.org/10.5620/eaht.2025014

Multiple metal exposure and renal tubular damage of residents in a metal-polluted region in Korea

Dahee Han¹

, Hyuna Jang²

, Kyung-Hwa Choi³

, Jang-Hee Lee⁴

, Sang-Yong Eom⁵

, Young-Seoub Hong⁶

, Woo Jin Kim⁷

, Eunil Lee⁸

, Yong Min Cho^9,^*

¹Department of Environmental Chemical Engineering, Seokyeong University, Seoul, Republic of Korea

²Department of Environmental Science, Baylor University, Waco, Texas, USA

³Department of Preventive Medicine, Dankook University College of Medicine, Cheonan, Republic of Korea

⁴Institute of Environmental Health, SD Medical Research Institution, Yong-in, Republic of Korea

⁵Department of Preventive Medicine, Chungbuk National University College of Medicine, Cheongju, Republic of Korea

⁶Department of Preventive Medicine, Dong-A University College of Medicine, Busan, Republic of Korea

⁷Department of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, Republic of Korea

⁸Center of Occupational & Environmental Medicine, Korea University Ansan Hospital, Ansan, Republic of Korea

⁹Department of Nano Chemical and Biological Engineering, Seokyeong University, Seoul, Republic of Korea

^*Correspondence: neworder@skuniv.ac.kr

Received July 4, 2024 Accepted May 7, 2025

This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This study investigates the correlations among urinary metals, the effects of co-exposure to multiple metals, and the relative importance of each metal in renal tubular damage (RTD) among residents of a metal-contaminated area. Urine sampling and health surveys were conducted for 120 participants living near a smelter for the Forensic Research via Omics Markers (FROM) study. Nine urinary metals (V, Cr, Mn, Ni, Mo, Cd, Sb, Pb, and Hg) and RTD markers such as beta-2-microglobulin (β₂-MG) and N-acetyl-β-D-glucosaminidase (NAG) were analyzed. The effects of multiple metals on RTD and the relative importance of each metal were investigated using Bayesian kernel machine regression (BKMR). The nine metals were highly correlated with each other, suggesting co-exposure to multiple metals. In the results of BKMR, co-exposure to multiple metals significantly affected NAG levels across the entire urinary metal concentration range. Although β₂-MG levels increased with rising urinary metal concentrations, the increase was not statistically significant. V and Cd were the highest contributors to β₂-MG (posterior inclusion probability, PIP=0.853) and NAG (PIP=0.983), respectively. This study demonstrates co-exposure to metals among residents living in the metal-contaminated area and that co-exposure to multiple metals significantly increased NAG levels. Additionally, to the best of our knowledge, this is the first study to show that V is the highest contributor to the increase inβ₂-MG. This study extends previous research by evaluating co-exposure to a more comprehensive array of metals, there by offering a broader perspective on the potential health impacts of RTD among residents in metal-contaminated areas.

Keywords: Metals co-exposure, Renal tubular damage, Bayesian kernel machine regression, Beta-2-microglobulin, N-acetyl-β-D-glucosaminidase

Introduction

Metals in the environment, mainly originating from industrial activities such as mining and smelting, can pollute soil, water, and crops in the vicinity and increase the risk of adverse health effects in residents [1]. These populations are often simultaneously co-exposed to multiple substances, leading to correlations among the substances [2].

Associations between environmental exposure to individual metals and kidney injury have been reported in previous studies [3, 4]. Metals such as Pb [5-9], Cd [5, 10, 11], and Ni [12, 13] are significantly associated with decreases in the estimated glomerular filtration rate (eGFR). In addition, Cd [14-16] and Hg [17] are known to significantly increase beta-2-microglobulin (β₂-MG) and N-acetyl-β-D-glucosaminidase (NAG), which are indicators of renal tubular damage (RTD).

RTD generally progresses before eGFR decreases, with low-molecular-weight protein levels increasing during the early stages of kidney injury [18]. β₂-MG is not excreted under normal conditions; however, it cannot be reabsorbed and is excreted in the urine when the renal tubules are damaged. NAG, an enzyme in renal tubular epithelial cells, is excreted in the urine when these cells are damaged [19]. These indicators of RTD provide important information, even when serum creatinine levels do not significantly change and eGFR is altered [20]. In a previous study [15], β₂-MG and NAG levels significantly increased in a general population exposed to environmental Cd, although there was no significant decrease in eGFR.

With increasing interest in co-exposure to pollutants and their health effects, associations between exposure to multiple metals and kidney damage have been reported in several studies. Exposure to Pb and Cd decreases renal function in populations highly exposed to both metals [5, 14, 21]. Additionally, two studies suggested associations between exposure to multiple metals and a decline in kidney function using linear regression analyses [22, 23].

Recently, Bayesian kernel machine regression (BKMR) has been used to evaluate the non-linear dose-response and joint effects of the non-additive interactions among multiple pollutants while considering multicollinearity [24-27]. BKMR has also been applied to evaluate the chronic effects of metals on the kidneys, demonstrating that co-exposure to metals decreases eGFR and increases the risk of chronic kidney disease (CKD) [28-33]. Urinary metal concentrations are considered indicators of environmental chronic exposures, whereas blood metal concentrations are believed to reflect both recent and accumulated exposure[34, 35].Nevertheless, only a few studies have examined the association between co-exposure to metals and RTD in urine. An earlier study documented the combined effect of urinary Cd, As, and Hg on NAG [36], and another study indicated that serum Cd, As, Se, and Fe significantly increased β₂-MG [37].

Therefore, we aimed to investigate the effects of co-exposure to multiple metals and the relative importance of each metal in RTD in residents near a smelter, known to be chronically exposed to multiple metals. Before this, we evaluated correlations among urinary metals to determine whether multicollinearity exists.

Materials and Methods

Study population

The study area islocated near the Janghang Copper Refinery in Chungcheongnam-do, Republic of Korea. The Janghang Copper Refinery smelted Cu, Pb, and Sn from 1936 to1989. During its operation, pollutants emitted from the smelter contaminated the surrounding environment. The Korean Ministry of Environment confirmed that pollutants from the smelter contaminated the nearby soil and groundwater [38]. Additionally, residents near the smelter have been exposed to heavy metals [39]. This area is one of the representative brown fields in the Republic of Korea. Participants for the Forensic Research via Omics Markers (FROM) study were recruited from those who lived in the area for more than 5 years. The FROM study aims to develop biomarkers that can be used as indicators of environmental diseases and reflect the characteristics of polluted areas in the Republic of Korea [40]. Written informed consent was obtained from the 120 participants included in this study. Urine sample collection and survey were conducted in August 2021. Age, sex, cigarette smoking, alcohol consumption, monthly income, occupational history at the smelter, and history of hypertension, diabetes mellitus, and kidney diseases were assessed using a questionnaire. Body mass index (BMI) was calculated by measuring the height and weight of the participants. This study was approved by the Institutional Review Board of Dong-A University (2-1040709-AB-N-01-202105-BR-002-08).

Urinary metals

The following nine metals were analyzed in the urine samples: Hg, V, Cr, Mn, Ni, Mo, Cd, Sb, and Pb. Urine samples were stored at −80 ℃ and analyzed in a certified laboratory (SD Medical Research Institute, Yong-in, Gyeonggi, Republic of Korea), where analysis is conducted for urinary metals as part of the Korean national biomonitoring program (Korean National Environmental Health Survey). To analyze the concentrations of the eight metals, excluding Hg, urine samples were diluted with 2% nitric acid and analyzed using an inductively coupled plasma-mass spectrometer (7800 ICP-MS; Agilent, Santa Clara, CA, USA) [41]. The standard reagent concentration for these eight metals was 1,000 mg/L (Spex CertiPrep, Metuchen, NJ, USA). Urinary Hg was analyzed using a mercury analyzer (MA-3000; NIC, Osaka, Japan). Urine samples (0.1 mL) were directly injected into the analyzer (Ministry of the Environment, 2012). The standard reagent concentration for Hg was 10 mg/L (Agilent Technologies). Internal quality assurance and quality control protocols were applied using analytical procedures: as internal standards, rhodium and iridium were injected into all urine samples [41]. ClinChek urine material 8849 levels 1 and 2 (RECIPE Chemicals, Munich, Germany) were analyzed twice in each batch. Additionally, external quality control was performed by participating in the 68th German External Quality Assessment Scheme (G-EQUAS, Erlangen-Nuremberg, Germany).In the linearity test, R2in all the calibration curves was>0.999. The limits of detection (LOD) were 0.096, 0.041, 0.058, 0.080, 0.286, 0.036, 0.028, 0.067, and 0.026 μg/L for V, Cr, Mn, Ni, Mo, Cd, Sb, Pb, and Hg, respectively. Concentrations below the LOD were replaced as LOD/√2 [42]. All urinary metal concentrations were adjusted with creatinine levels.

Markers of renal tubular damage

β₂-MG and NAG were analyzed using a Chemistry Autoanalyzer (Cobas 702; Roche Diagnostics System, Basel, Switzerland) at a wavelength of 700 nm. Urine samples (0.5 mL) were injected directly into the analyzer. The reagents were purchased from Roche Diagnostics System and Nittobo Medical Co. (Tokyo, Japan).

Statistical analysis

All biomarkers were transformed using a natural logarithm as they showed a right-skewed distribution. Spearman’s correlation test was conducted to investigate the correlations among urinary metals. As the nine urinary metals were highly correlated, BKMR was conducted to visualize the dose-response curves of each metal and analyze the joint effects of multiple metals on RTD. In this analysis, the joint effects presented in the dose-response functions illustrate the relative changes in the outcome variables compared to when the exposure of interest is at its median level, while all other exposures are held constant at their respective medians. The BKMR method addresses multicollinearity and non-additive interactions among independent variables [24]. In addition, the posterior inclusion probabilities (PIPs) for each metal were calculated, to indicate their relative importance. A high PIP indicates high importance. The BKMR modelran for 5,000 iterations using the Markov Chain Monte Carlo sampling. Sex, age, BMI, monthly income, cigarette smoking, alcohol consumption, occupational history in a smelter, hypertension, and diabetes mellitus were adjusted. Statistical analyses were performed using R version 4.1.2 (R Foundation for Statistical Computing, Vienna, Austria), and BKMR was conducted using the‘bkmr’ package.

Results

General characteristics and biomarkers of the subjects

Table 1 presents the general characteristics, distribution of urinary metals, and RTD in 120 participants. The average age of the participants was 71.5 ± 7.4 years, with more women than men among the participants. The average BMI was 25.2 kg/m2.Most participants never smoked cigarettes or consumed alcohol (73.3% and 54.2%, respectively). The most common income was less than 1,500,000 Korean won per month (66.7%), and 14.2% had an occupational history in the smelter. Among the participants, 66 (55.0%) had a history of hypertension and 31 (25.8%) had a history of diabetes mellitus. Seven (5.8%) participants had a history of kidney disease.

All urinary metals were significantly positively correlated with at least three other metals (Fig. 1). In particular, V and Ni showed significant positive correlations with seven metals, except Hg. The correlation coefficient between V and Ni was the highest (Spearman’s rho=0.66, p<0.001), followed by Ni and Sb (Spearman’s rho=0.45, p<0.001), and Cr and Mn (Spearman’s rho=0.43, p<0.001). Hg exhibited negative correlations with five metals, although these correlations were not significant. Additionally, Hg was positively correlated with Cd (Spearman’s rho=0.23, p=0.011) and Sb (Spearman’s rho=0.19, p=0.041).

Effects of co-exposure to multiple metals on renal tubular damage

Fig. 2 and Table 2 show the dose-response relationship between each metal and RTD when other metals were fixed at the median. β₂-MG was significantly associated with V(posterior mean estimates = -0.58 [95% CI: -1.07, -0.08] at the 25th percentile; posterior mean estimates = 0.55 [95% CI: 0.16, 0.95] at the 75th percentile).In contrast, Ni showed a negative association with β₂-MG, which was not statistically significant. NAG levels were linearly associated with Hg, Cr, and Cd; however, these associations were not statistically significant.

β₂-MG levels increased with the increasing concentrations of multiple urinary metals; however, these changes were not statistically significant (Fig. 3). Meanwhile, a significant effect of multiple metals on NAG was demonstrated. NAG changed significantly across the entire concentration range compared to when all metals were fixed at the median.

The relative importance of each metal for β₂-MG and NAG is shown in Table3. V and Cd were the highest contributors to β₂-MG (PIP=0.853) and NAG (PIP=0.983), respectively.

Discussion

This study demonstrated co-exposure to metals among residents living in a metal-contaminated area by investigating significant correlations among urinary metals. Moreover, co-exposure to multiple metals significantly increased NAG levels. V and Cd contributed most to β₂-MG and NAG, respectively.

We found that the nine metals were highly correlated with each other, consistent with the results of previous studies. Huang et al. [43] reported significant correlations among several metals, including V, Cr, Ni, Mo, Cd, Pb, and Mn, in urine. Park et al. [44] noted correlations between Pb in blood and Sb, Cd, Pb, and Mo in urine. In the present study, the correlation coefficient between urinary V and Ni was the highest among all metals. They are present at high concentrations in heavy oil; as V in heavy oil increases, Ni also increases [45]. Additionally, the most important hydrodesulfurization catalysts are mainly composed of molybdenum oxide and oxides of vanadium, cobalt, and nickel [46]. In this study, Mo showed a significant correlation with Ni and V, which may have been caused by the use of fuel or catalysts containing these metals during the smelting process.

With single metal exposure, β₂-MG was significantly associated with V when other metals were fixed at the median. Although β₂-MG levels were significantly lower at 25thpercentile of V exposure compared to the median, this finding does not necessarily indicate a protective effect of lower V exposure. Rather, it reflects the estimated relative changes in β₂-MG based on the modeled dose-response function. Additionally, urinary V has a significant impact on β₂-MG among multiple metals in the BKMR model. An in vitro study showed that renal tubular epithelial cells degenerated in avian broilers that ingested high concentrations of V (30 and 45 mg/kg) [47]. However, studies examining the association between V and RTD in humans are scarce. While a few reports indicate that V is significantly associated with CKD in humans, the results are inconsistent [23, 29, 31].In contrast, Ni showed a negative, but not statistically significant association with β₂-MG.Although renal toxicity due to Ni has been studied in occupationally exposed populations, the results are inconsistent[48, 49]. In an in vitro study, Ni exposure (100 mg/L of Ni in drinking water) did not significantly alter β₂-MG and NAG levels in rats after 6 months of exposure [50].The authors suggested that the Ni dose was likely in sufficient to cause significant tubular damage. Considering the documented nephrotoxicity of Ni, the negative association between β₂-MG and Ni might imply reverse causality, where decreased kidney function leads to reduced excretion of metals in urine, rather than indicating a protective effect of Ni [51].

NAG levels had a positive linear association with Hg, Cr, and Cd, even though those associations were not statistically significant. In particular, Cd contributed most significantly to NAG in the BKMR model. Previous studies have identified the dose-response relationship between levels of urinary Cd and NAG [52, 53]. Eom et al. [54] reported that urinary Cd significantly increases NAG in groups with high Cd exposure, where urinary Cd exceeded 2 μg/g creatinine. However, these priority contributions in this study do not indicate a direct association between the metals and RTD. These results should be interpreted cautiously because urinary V and Ni are highly correlated with other metals.

Exposure to multiple metals significantly affected NAG levels across the entire concentration range. While β₂-MG increased with rising urinary metal concentrations, the change was not statistically significant. BKMR analysis was conducted to evaluate the non-linearity of dose response and interaction among highly correlated metals, as previously described [24-27]. Therefore, a negative estimate at a lower percentile of an individual metal does not necessarily imply a negative slope across the entire exposure range in the joint effect plot. The joint effect summarizes the collective nonlinear and interactive influences of multiple metals, which may not be directly inferred from the patterns of individual metals. Few studies have focused on the effects of exposure to multiple metals on RTD. Nordberg et al. [16] reported that β₂-MG and NAG levels increase more when individuals are simultaneously exposed to high concentrations of Cd and As than when exposed to a single metal. Nonetheless, the study could not examine a continuous dose-response curve due to categorizing urinary metal concentrations. Quan et al. [37] reported that β₂-MG and retinol-binding protein (RBP) are significantly increased by a metal mixture (As, Cd, Se, and Fe), especially high levels of Se, which contradicts our findings. In the study, LASSO penalized regression was used before BKMR analysis to select the metals with the greatest impact, and these selected metals were included in the BKMR model.

The present study has several limitations. First, since it was a cross-sectional study, it could not determine the causal relationship between exposure to metals and RTD. Second, applying the results to the general population is difficult due to the small number of participants, of whom 65.0% were women, and only 6.7% were smokers, as the volunteers were recruited from a specific area. Studies in broader areas and with different age groups are needed to determine causal relationships. Finally, although age was adjusted for during statistical analysis to minimize the effect of aging, renal damage due to aging could not be completely excluded, given that participants were ≥ 50 years old.

Nevertheless, this study shows that residents in the metal-contaminated area are co-exposed to multiple metals and suggests the effect of co-exposure to metals on RTD, rather than on chronic renal failure. While multiple metals are known to cause CKD, the association between exposure to multiple metals and RTD remains unclear. In addition, our study indicates that multiple metals contribute to RTD and that the most significant contributors differ between RTD markers. To the best of our knowledge, this is the first study to show that V is the highest contributor to an increase in β₂-MG. Furthermore, this study extends previous research by evaluating co-exposure to a more comprehensive array of metals, thereby offering a broader perspective on the potential health impacts. Co-exposure to metals needs more attention to prevent early kidney damage before the development of chronic disease in metal-polluted areas.

Conclusions

This study demonstrates that co-exposure to multiple metals significantly increases NAG levels in residents living near a smelter. Urinary V and Cd levels were the highest contributors to β₂-MG and NAG increases, respectively. Co-exposure to metals must be considered to prevent RTD before it progresses to the chronic phase. Finally, this study found that V significantly contributes to increasing the levels of β₂-MG.

Notes

Acknowledgement

This work was supported by the Korea Environment Industry & Technology Institute (KEITI) through the Core Technology Development Project for Environmental Diseases Prevention and Management, funded by the Korea Ministry of Environment (MOE) [2480000101/RS-2021-KE001378].

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships.

CRediT author statement

DH: Conceptualization, Writing – Original draft preparation, Methodology, Software, Visualization. HJ: Software. KHC: Writing – Review & Editing. JL: Resources, Validation. SYE, YSH, and WJK: Investigation, EL: Conceptualization, YMC: Supervision, Project administration, Funding acquisition. All authors have read and approved the final manuscript.

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Figure 1.

Correlations among urinary metals are represented by Spearman’s rho, with significance levels indicated as follows: *p<0.05, **p<0.01, ***p<0.001.

Figure 2.

Posterior mean estimates and 95% confidence intervals of each urinary metalwere assessed for renal tubular damage when other metals were fixed at the median: (a) β₂-MG; (b) NAG. All urinary concentrations of metals were naturally log-transformed.

Figure 3.

Effect of multiple metals on renal tubular damage: (a) β₂-MG; (b) NAG. Estimated changes (β and 95% confidence interval) in renal tubular damage when comparing specific percentiles of urinary metal concentrations (ranging from the 25th to the 75th percentiles) to the median adjusted for age, sex, BMI, cigarette smoking, alcohol consumption, occupational history in smelters, monthly income, hypertension, and diabetes mellitus.

Table 1.

General characteristics and biomarkers of the participants (N=120).

Variables	Categories	N (%)
Total participants		120 (100.0)
Age (in years); mean (SD)		71.5 (7.4)
	50–59	9 (7.5)
	60–69	31 (25.8)
	70–79	69 (57.5)
	80–89	11 (9.2)
Sex	Male	42 (35.0)
	Female	78 (65.0)
BMI (in kg/m²); mean (SD)		25.2 (4.0)
Cigarette smoking	Never	88 (73.3)
	Former	24 (20.0)
	Present	8 (6.7)
Alcohol consumption	Never	65 (54.2)
	Former	33 (27.5)
	Present	22 (18.3)
Monthly income (1,000 Korean won)	<1,500	80 (66.7)
	≥1,500,<3,000	16 (13.3)
	≥3,000	12 (10.0)
	Unknown	12 (10.0)
Occupational history in smelter	Yes	17 (14.2)
	No	103 (85.8)
Hypertension	Yes	66 (55.0)
	No	54 (45.0)
Diabetes mellitus	Yes	31 (25.8)
	No	89 (74.2)
Kidney disease	Yes	7 (5.8)
	No	113 (94.2)
Urinary metals, µg/g creatinine, GM (95% CI)	Hg	0.41 (0.37, 0.47)
	V	0.42 (0.37, 0.47)
	Cr	0.31 (0.25, 0.39)
	Mn	0.17 (0.14, 0.21)
	Ni	6.11 (5.53, 6.75)
	Mo	120.61 (108.04, 134.64)
	Cd	2.17 (1.92, 2.46)
	Sb	0.12 (0.10, 0.14)
	Pb	1.20 (1.03, 1.40)
β2-MG, µg/g creatinine, GM (95% CI)		178.85 (149.34, 214.20)
NAG, IU/g creatinine, GM (95% CI)		5.14 (4.48, 5.90)

β₂-MG, beta-2-microglobulin; NAG, N-acetyl-β-D-glucosaminidase; GM, geometric mean; CI, confidence interval; SD, standard deviation.

Table 2.

Posterior mean estimates and 95% confidence intervals for each urinary metal in relation to renal tubular damage, with other metals held constant at their median levels.

	Quantile	Posterior mean estimate (95% CI)
	Quantile	β₂-MG	NAG
Hg	25^th	-0.06 (-0.39, 0.28)	-0.24 (-0.51, 0.03)
	50^th	0.01 (-0.23, 0.26)	0.07 (-0.12, 0.25)
	75^th	0.07 (-0.27, 0.40)	0.25 (0.00, 0.51)
V	25^th	-0.58 (-1.07, -0.08)	-0.03 (-0.31, 0.26)
	50^th	-0.09 (-0.35, 0.16)	0.00 (-0.19, 0.19)
	75^th	0.55 (0.16, 0.95)	0.03 (-0.21, 0.27)
Cr	25^th	0.14 (-0.19, 0.46)	-0.09 (-0.33, 0.15)
	50^th	0.02 (-0.23, 0.26)	0.02 (-0.17, 0.20)
	75^th	-0.13 (-0.49, 0.24)	0.10 (-0.17, 0.36)
Mn	25^th	-0.03 (-0.28, 0.22)	-0.04 (-0.23, 0.15)
	50^th	0.01 (-0.28, 0.30)	0.00 (-0.21, 0.21)
	75^th	0.03 (-0.42, 0.49)	0.04 (-0.27, 0.35)
Ni	25^th	0.39 (-0.10, 0.89)	0.02 (-0.27, 0.31)
	50^th	0.03 (-0.21, 0.28)	0.00 (-0.18, 0.19)
	75^th	-0.38 (-0.83, 0.06)	-0.02 (-0.31, 0.27)
Mo	25^th	-0.09 (-0.40, 0.22)	0.04 (-0.18, 0.26)
	50^th	0.01 (-0.24, 0.25)	0.00 (-0.18, 0.19)
	75^th	0.10 (-0.25, 0.45)	-0.04 (-0.28, 0.21)
Cd	25^th	-0.07 (-0.50, 0.36)	-0.18 (-0.50, 0.15)
	50^th	0.01 (-0.25, 0.27)	-0.02 (-0.22, 0.17)
	75^th	0.08 (-0.25, 0.40)	0.17 (-0.07, 0.41)
Sb	25^th	-0.21 (-0.48, 0.05)	0.09 (-0.11, 0.29)
	50^th	-0.02 (-0.39, 0.36)	0.02 (-0.27, 0.30)
	75^th	0.21 (-0.52, 0.94)	-0.09 (-0.62, 0.45)
Pb	25^th	0.10 (-0.36, 0.55)	-0.01 (-0.35, 0.33)
	50^th	0.01 (-0.26, 0.28)	0.01 (-0.20, 0.22)
	75^th	-0.09 (-0.40, 0.21)	0.02 (-0.20, 0.24)

β₂-MG, beta-2-microglobulin; NAG, N-acetyl-β-D-glucosaminidase; CI, confidence interval; SD, standard deviation.

Table 3.

Posterior inclusion probabilities indicate the relative importance of each metal for renal tubular damage.

	β₂-MG	NAG
Hg	0.595	0.435
V	0.853	0.267
Cr	0.612	0.224
Mn	0.515	0.350
Ni	0.629	0.231
Mo	0.554	0.183
Cd	0.569	0.983
Sb	0.564	0.192
Pb	0.505	0.263