75 were not first-degree relatives, and they were selected for the analysis of genetic effect modi- fication on the association between cadmium exposure and epigenetic markers. DirectHyb HumanHT-12 (version 3.0; Illumina, San Diego, CA, USA) was used for gene expres- sion analysis according to the manufacturer’s instructions at the SCIBLU (Swegene Centre for Integrative Biology at Lund University) facility at Lund University. Background sig- nals were filtered from the gene expression data by BioArray Software Environment (BASE) (Vallon-Christersson et al. 2009). The DirectHyb HumanHT-12 (HT-12) array (Illumina) included one DNMT1 transcript and five DNMT3B transcripts [the gene has alternatively spliced transcripts (National Library of Medicine 2012b)]. No LINE-1 tran- scripts were included on the array. Statistical analyses. We used the averages of the relative amounts of methylated cytosine residues in the four, seven, and four CpG sites measured in LINE-1, p16, and MLH1, respectively to determine the overall methyla- tion level for each genetic element analyzed in a given sample. We used Spearman’s rank correlation analysis to estimate univariate cor- relations between cadmium in blood or urine and methylation and gene expression, as well as correlations with potential covariates such as age, body weight, body mass index (BMI), use of coca leaves, and zinc and selenium con- centrations in whole blood. Table 1. Descriptive information of the women and biomarkers analyzed.a 5th–95th Variable Median percentile Age (years) 34 18–64 Body weight (kg) 57.0 44.0–79.9 Height (cm) 152 144–161 BMI (kg/m2) 24.7 18.8–35.0 Blood hemoglobin (g/L) 155 130–180 Urinary creatinine (g/L)b 0.90 0.52–1.4 Coca chewing (%) 46.5 Methylation (%) LINE1c 86.2 83.5–89.3 p16d 3.4 1.7–5.8 MLH1e 4.2 1.2–7.1 Blood measures Cadmium (μg/L) 0.36 0.20–0.76 Selenium (μg/L) 176 150–221 Zinc (mg/L) 6.8 5.5–8.2 Urinary measures Cadmium (μg/L)b 0.23 0.091–0.70 Arsenic (μg/L)b 230 21–545 Boron (mg/L)b 14.6 2.6–27.9 Cesium (μg/L)b 470 26.1–880 Lithium (mg/L)b 3.9 0.27–10.4 an = 202 subjects, except for creatinine (n = 194) and blood selenium (n = 201) analyses. bAdjusted for spe- cific gravity of urine (1.020 g/mL). cAverage of relative amounts of methylated cytosines in four CpGs, positions 305–331, GenBank accession no. X58075. dAverage of relative amounts of methylated cytosines in seven CpGs, positions 148–174, accession no. L27211. eAverage of relative amounts of methylated cytosines in four CpGs, positions –209 to –188, accession no. U07418. We first evaluated the gene methyla- tion by quintiles of cadmium exposure [see Supplemental Material, Figures 1–3 (http:// dx.doi.org/10.1289/ehp.1104600)] and by scatter plots. Because visual inspection of the plots suggested linear relationships, we used linear regression models to estimate associa- tions (β-coefficients) of cadmium in blood and urine with the epigenetic markers. We per- formed a multivariable-adjusted analysis con- trolling first for age and coca chewing (yes/no) because age and coca chewing were correlated with at least one of the epigenetic markers (p-values < 0.2) and because cadmium expo- sure–outcome coefficients changed by > 5% when they were included in models. To assess the robustness of associations, we addition- ally controlled for urinary arsenic, boron, lith- ium, and cesium (modeled as untransformed continuous variables). In the linear regression analyses, urine and blood cadmium concen- trations were natural log (ln) transformed to obtain normal distributions of the residuals and a better linear fit with the outcomes. The influence of DNMT1 genotypes (effect modification) on the association between uri- nary cadmium concentration and LINE-1 methylation was analyzed in individuals that were not first-degree relatives (n = 172). This analysis was performed by modeling a cross- product term between genotype (for DNMT1 as an ordinal variable with three categories, and for DNMT3B dichotomized because few individuals were variant homozygotes) and lower-order terms for genotype and ln- urinary cadmium concentrations. Analyses were then stratified by genotype to estimate associations between urinary cadmium and LINE-1 methylation according to each geno- type. In addition, we used stratified models to estimate associations between urinary cad- mium and DNMT1 expression according to genotype in the 75 participants that were not first-degree relatives. Associations between genotypes and the expression of DNMTs were estimated using analysis of variance with and without adjustment for age and coca chew- ing. We did not adjust p-values for multiple Cadmium influences DNA methylation comparisons. A p-value < 0.05 was considered statistically significant. All statistical analyses were performed using SPSS (version 18; SPSS, Chicago, IL, USA). Results Descriptive data for the study participants and element concentrations in blood and urine are presented in Table 1. In general, the women were relatively short, and, using the conven- tional cut-offs for overweight (BMI 25–29.9) and obesity (BMI > 30), about half of them were overweight and 18% obese. Blood con- centrations of hemoglobin, selenium, and zinc indicated adequate nutritional status. Cadmium concentrations were 0.013–1.5 µg/L in urine (median, 0.23 µg/L) and 0.17–1.1 µg/L in blood (median, 0.36 µg/L). Four women who reported smoking had cadmium con- centrations of 0.045–0.24 µg/L in urine and 0.18–0.52 µg/L in blood. Median LINE-1, p16, and MLH1 methylation levels were 86.2%, 3.4%, and 4.2%, respectively (Table 1). The epigenetic markers were positively correlated with each other: the correlation between p16 and MLH1 methylation being the strongest (rS = 0.78, p < 0.01), followed by LINE-1 and MLH1 (rS = 0.51, p < 0.01), and LINE-1 and p16 (rS = 0.42, p < 0.01). Urinary cadmium concentrations were negatively cor- related with LINE-1 methylation (rS = –0.19, p < 0.01). Blood cadmium concentrations were not correlated with LINE-1 (rS = 0.046) but were positively correlated with urinary cadmium concentration (rS = 0.46, p < 0.01). Neither urinary cadmium nor blood cadmium concentrations were correlated with p16 or MLH1 methylation (rS < 0.1). None of the nutritional markers (BMI, hemoglobin, sele- nium, and zinc) were significantly correlated with the epigenetic markers (rS < 0.11). In the linear regression analysis, the ln-transformed urinary cadmium concentra- tions of all women (n = 202) were inversely associated with LINE-1 methylation [for unadjusted model: β = –0.50; 95% confi- dence interval (CI): –0.86, –0.14; p = 0.0070; Table 2]. Estimates did not change much Table 2. Associations between ln-transformed blood and urinary cadmium concentration (μg/L) and percent methylation of LINE1, p16, and MLH1. Predictor Blood cadmium Unadjusteda Adjusted 1b Adjusted 2c Urinary cadmium Unadjusteda Adjusted 1b Adjusted 2c LINE1 β (95% CI) 0.16 (–0.46, 0.78) 0.40 (–0.27, 1.07) 0.45 (–0.23, 1.12) –0.50 (–0.86, –0.14) –0.44 (–0.83, –0.053) –0.42 (–0.82, –0.025) p-Value 0.61 0.24 0.19 0.0070 0.026 0.038 p16 MLH1 β (95% CI) p-Value β (95% CI) p-Value 0.11 (–0.38, 0.60) 0.30 (–0.23, 0.83) 0.24 (–0.29, 0.77) –0.13 (–0.42, 0.15) –0.048 (–0.36, 0.26) –0.11 (–0.42, 0.21) 0.65 0.26 0.37 0.37 0.76 0.51 0.14 (–0.52, 0.80) 0.26 (–0.46, 0.98) 0.19 (–0.53, 0.91) –0.062 (–0.45, 0.33) –0.0054 (–0.43, 0.42) –0.073 (–0.50, 0.36) 0.69 0.47 0.61 0.75 0.98 0.74 β is the unstandardized regression coefficient. aUnivariate analysis (n = 202): percent methylation = α1 + β1 cadmium in blood/urine. bAdjusted analysis (n = 202): percent methylation = α1 + β1 cadmium in blood/urine + β2 × age + β3 × coca chewing. cAdjusted analysis (n = 201): percent methylation = α1 + β1 cadmium in blood/urine + β2 × age + β3 × coca chewing + β4 arsenic in urine. Environmental Health Perspectives • volume 120 | number 6 | June 2012 881