We performed a cross-sectional analysis of first evaluation data from 798 current and former lead workers enrolled in a longitudinal study of the adverse health effects of inorganic lead exposure. All participants provided written, informed consent. The study protocol was approved by institutional review boards at the SoonChunHyang University and the Johns Hopkins University Bloomberg School of Public Health. Participation in the study was voluntary, and workers were paid approximately $30 for their time and effort. As previously described (Schwartz et al. 2001; Weaver et al. 2003a), workers were recruited from 26 different facilities including lead battery, lead oxide, lead crystal, and radiator manufactures and secondary lead smelting. No medical exclusionary criteria (e.g., blood pressure, renal disease) were applied. Study participants were not currently occupationally exposed to other known renal toxicants.

Data collection was completed either at the Institute of Industrial Medicine of the SoonChunHyang University in Chonan or at the study plants, using previously reported methods (Schwartz et al. 2001; Weaver et al. 2003a). Data and biologic specimens collected included a standardized questionnaire on demographics and medical and occupational history; blood pressure; height and weight measurement; a blood specimen [for blood lead, blood urea nitrogen (BUN), serum creatinine, uric acid, and genotyping]; a spot urine sample [for N-acetyl-β-d-glucosaminidase (NAG), retinol-binding protein (RBP), and creatinine]; and tibia lead concentration. A 4-hr urine collection after oral administration of 10 mg/kg dimercaptosuccinic acid (DMSA) was also obtained to measure DMSA-chelatable lead and creatinine clearance (787 participants completed this collection).

We measured lead biomarkers and renal outcomes using previously reported methods (Schwartz et al. 2001; Weaver et al. 2003a). In brief, blood lead was measured (Fernandez 1975) with a Zeeman background-corrected atomic absorption spectrophotometer (model 8100; Hitachi Ltd. Instruments, Tokyo, Japan) at the Institute of Industrial Medicine, a certified reference laboratory for lead in Korea. Tibia lead was assessed via a 30-min measurement of the left midtibia diaphysis using ¹⁰⁹Cd to fluoresce the K-shell X-rays of lead. The lead X-rays were recorded with a radiation detector and then quantified and compared with calibration data to estimate the concentration of lead in bone (Todd and Chettle 1994; Todd and McNeill 1993). All point estimates, including negative values, were retained in the statistical analyses, to minimize bias and to avoid censoring of data (Kim et al. 1995). Urine lead levels in the 4-hr collection were measured at the Wadsworth Center of the New York State Department of Health (Albany, NY, USA) by electrothermal atomic absorption spectrometry with Zeeman background correction (model 4100ZL; Perkin-Elmer, Norwalk, CT, USA) (Parsons and Slavin 1999).

We measured BUN, serum creatinine, and uric acid using an automatic chemical analyzer (model TBA 40FR biochemical analyzer; Toshiba, Tokyo, Japan). Urine creatinine was measured with the Sigma kit (Sigma Chemical Company, St. Louis, MO, USA). Measured creatinine clearance was defined as [(urinary creatinine in milligrams per deciliter × urine volume in milliliters) ÷ serum creatinine in milligrams per deciliter] ÷ collection time in minutes. Calculated creatinine clearance was obtained from the Cockcroft-Gault equation (Cockcroft and Gault 1976). We measured NAG using the PPR NAG test kit (PPR Diagnostics, Ltd., London, UK), and RBP was measured using a modification of the method of Topping et al. (1986).

We isolated DNA for genotyping from whole blood samples using the QIAamp blood kit (QIAGEN, Hilden, Germany). The ALAD polymorphism assayed (δ-aminolevulinic acid dehydratase NCBI accession no. AY319481) results in two alleles: ALAD1 and the variant, ALAD2, which has a G-to-C transversion at codon 177. The protocol for ALAD genotyping involved an initial amplification that generated a 916 bp fragment (Schwartz et al. 1995; Wetmur et al. 1991; Ziemsen et al. 1986). A second amplification, using a pair of nested primers (kindly provided by J. Wetmur), generated an 887 bp fragment. The amplified fragment was cleaved at the diagnostic Msp1 site on the ALAD2 allele. The Glu298Asp polymorphism of the eNOS gene (endothelial nitric oxidase synthase; NCBI accession no. X76307) involves a G-to-T transversion at nucleotide position 894 within exon 7, which results in substitution of aspartic acid for glutamic acid at codon 298; the variant allele is referred to as the Asp or T allele. Genotype was determined by a modification of the assay of Hibi et al. (1998) as previously described (Weaver et al. 2003b). The VDR BsmI polymorphic site in intron 8 [vitamin D (1,25-dihydroxyvitamin D₃) receptor; NCBI accession no. AY342401] consists of the common allele, denoted b, and a variant, denoted B, in which the BsmI restriction enzyme site is absent. Amplification used primers originating in exon 7 and intron 8 as previously published (Schwartz et al. 2000b).

The primary goals of our analysis were to examine associations of ALAD, VDR, and eNOS genotype with uric acid in lead workers while controlling for other covariates and to evaluate whether ALAD, VDR, and eNOS genotypes modified associations between the lead dose biomarkers (tibia lead, blood lead, DMSA-chelatable lead) and uric acid while controlling for other covariates. Statistical analysis was completed using SAS software, version 8.2 (SAS Institute, Cary, NC, USA).

Initially, we examined variable distributions. The distributions of NAG and RBP showed departures from normality and were thus ln-transformed; the adequacy of this transformation was subsequently confirmed by examination of residuals from the final regression models. Linear regression modeling with a dichotomous genotype variable was used to compare uric acid by genotype, while controlling for the same covariates used in the interaction models. For ALAD, participants with the ALAD1-2 genotype were compared with the reference group of participants with ALAD1-1 genotype. Because of small numbers, all analyses combined homozygous and heterozygous variant genotype carriers for VDR (BB and Bb) and eNOS (Glu/Asp and Asp/Asp). Linear regression modeling with genotype and lead variable cross-product terms was used to evaluate effect modification by genotype on associations between lead biomarkers and uric acid. Models with tibia lead were repeated after removal of data from participants with the common allele whose tibia lead levels were higher than the range in those with the variant allele. Covariate selection for final regression models used a priori variables [age, sex, body mass index (BMI; defined as weight in kilograms divided by the square of height in meters)] in modeling that included other biologically relevant variables in separate models, as previously described (Weaver et al. 2005). Covariates retained included age, sex, BMI, categorical alcohol consumption (current, former, never), and, when noted in specific models, systolic blood pressure and serum creatinine. Models are presented with and without these last two variables because their interrelatedness with lead dose and uric acid suggests that neither modeling approach is entirely adequate (Weaver et al. 2005). Continuous independent variables were centered at the mean.

Because effect modification by age on associations between lead biomarkers and uric acid was observed in this population (Weaver et al. 2005), we dichotomized the population by median age and repeated both types of models described above (main effect and interaction) to evaluate associations of genotype in models of uric acid and effect modification by genotype on associations between lead biomarkers and uric acid in different age groups. Last, a model that included cross-products of tibia lead and combined ALAD/VDR genotype subsets was evaluated in participants in the older half of the population to determine if the genotype association previously reported in this population (lead workers with the ALAD1-1 genotype were statistically less likely to have the VDR bb genotype; Schwartz et al. 2000a) was a factor in the effect modification observed in this study. Only two participants in the older age group had both variant alleles (ALAD1-2/VDR Bb or BB), which precluded meaningful analysis of this group; this subset was removed from the model. The final model assessed differences in the slopes of the relations between tibia lead and uric acid in the group of participants with the ALAD1-2/VDR bb genotypes and the ALAD1-1/VDR Bb or BB genotypes, in separate cross-product terms, compared with the reference group of participants with the ALAD1-1 and VDR bb genotypes.

As in previous analyses (Weaver et al. 2003a), we evaluated models for linear regression assumptions and the presence of outlying points using added variable plots (Weisberg 1985), which are graphical summaries of the relation between y and a particular x adjusted for the other covariates. For each plot, two lines were overlaid: the regression line, and a line determined by a scatter plot smoothing method (lowess) that calculates a locally weighted least squares estimate for each point in the scatter plot (Cleveland 1979). This allows an examination of the data for outliers that are overly influential, as evidenced by inconsistency between the lowess and regression lines. When warranted, models were repeated without outliers. Models were also assessed for collinearity by examining variance inflation factors and conditional indices.

A total of 79 (9.9%) participants were heterozygous for the ALAD2 allele, and none was homozygous (Table 1). For VDR, 85 (10.7%) were genotype Bb, and 4 (0.5%) were BB. For eNOS, 114 (14.4%) participants were genotype Glu/Asp, and 6 (0.7%) were Asp/Asp. Mean (± SD) and frequencies for selected demographic, exposure, and outcome variables are presented by genotype in Table 1. Mean uric acid was normal in all genotype subsets. Medians for selected measures are presented by genotype in groups dichotomized by median age (Table 2).