Wright et al. where Yij is the methylation levels (Alu or LINE1­) in subject i at time j, b0 is the overall intercept, ui is the separate random intercept for subject i, b1 is the slope representing the overall effect of time, and b2 is the slope rep­ resenting the overall effect of the bone lead marker. All mixed­effects models were con ­ ducted using the PROC MIXED procedure Table 1. Summary statistics [mean ± SD or n (%)] at baseline for participants with and without bone lead measurements in the NAS. With bone lead measure (n = 517) Without bone lead measure (n = 162) ap-Values for differences between subjects with and without bone lead measurements from Wilcoxon rank sum or Fisher’s exact test. bn = 508 for patella and 517 for tibia. cn = 463. dn = 127. Table 2. Distribution of DNA methylation at baseline by categories of covariates. Characteristic Tibia lead (g/g) Patella lead (g/g) Blood lead (µg/dL) Alu (%) LINE-1 (%) Age (years) BMI (kg/m2) Lymphocytes (%) Pack-years of cigarettes Education Less than high school diploma High school diploma/some college Four years of college Missing Variable Tibia lead (g/g)a 13.0 > 13.0 to 22 > 22 p for trend Patella lead (g/g)a 17.0 > 29 > 17.0 to 29 p for trend Blood lead (g/dL)a 2.0 > 2.0 to 4.0 > 4.0 p for trend Lymphocytes (%)a > 21.0 to 28 p for trend Age (years) 21.0 > 28 < 70 70–79 > 80 < 25 25–30 > 30 p for trend BMI (kg/m2) p for trend Education 20.5 ± 14.8 27.4 ± 19.7b 4.1 ± 2.4c 26.3 ± 1.0 76.8 ± 1.9 72.4 ± 6.5 28.0 ± 3.8 25.5 ± 8.6 20.1 ± 25.3 46 (8.9) 314 (60.7) 143 (27.7) 14 (2.7) n (%) 166 (32.1) 176 (34.0) 175 (33.9) 167 (32.9) 160 (31.5) 181 (35.6) 114 (24.6) 205 (44.3) 144 (31.1) 165 (31.9) 186 (36.0) 166 (32.1) 186 (36.0) 265 (51.3) 66 (12.8) 97 (18.8) 290 (56.1) 130 (25.1) 46 (9.2) 314 (62.4) 143 (28.4) 174 (33.7) 206 (39.8) 137 (26.5) — — 3.9 ± 2.1d 26.2 ± 1.2 77.1 ± 2.1 72.5 ± 7.6 28.5 ± 4.5 25.7 ± 7.2 25.6 ± 32.7 10 (6.2) 88 (54.3) 39 (24.1) 25 (15.4) Methylation (mean ± SD) Alu (%) 26.40 ± 0.95 26.18 ± 1.02 26.32 ±1.04 0.50 26.36 ± 1.03 26.30 ± 0.91 26.22 ± 1.07 0.19 26.35 ± 0.85 26.18 ± 0.95 26.41 ± 1.16 0.52 26.26 ± 0.95 26.39 ± 1.08 26.23 ± 0.98 0.79 26.41 ± 0.97 26.28 ± 1.03 26.07 ± 1.01 0.02 26.22 ± 0.98 26.28 ± 0.94 26.39 ± 1.16 0.21 26.28 ± 1.05 26.25 ± 0.97 26.40 ± 1.09 0.25 26.22 ± 0.99 26.40 ± 1.09 26.25 ± 0.89 0.68 p-Valuea 0.59 0.27 0.16 0.95 0.45 0.56 0.02 0.85 LINE-1 (%) 77.01 ± 1.79 76.64 ± 2.02 76.73 ± 1.74 0.18 77.17 ± 1.79 76.61 ± 1.94 76.57 ± 1.80 0.003 76.97 ± 1.78 76.68 ± 2.06 76.78 ± 1.61 0.44 77.03 ± 1.88 76.69 ± 1.64 76.66 ± 2.05 0.07 77.05 ± 2.01 76.64 ± 1.80 76.65 ± 1.54 0.04 76.74 ± 1.89 76.72 ± 1.85 76.97 ± 1.85 0.31 76.07 ± 1.79 76.85 ± 1.84 76.88 ± 1.88 0.06 76.67 ± 1.58 76.71 ± 1.99 77.05 ± 1.96 0.09 in SAS, version 9.1 (SAS Institute Inc., Cary, NC, USA). To determine whether the associations between DNA methylation levels and the different lead biomarker were nonlinear, we also modeled the lead biomarker as a penal­ ized spline in a generalized additive models using R software (R Foundation for Statistical Computing; http://www.rp­roject.org). Results Between 1 March 1999 and 27 June 2007, 1,047 blood samples from 704 subjects were successfully analyzed for DNA methylation in both Alu and LINE1­. Of these 704 subjects, 168 did not have bone lead measurements, leaving 536 subjects available for analysis. Fourteen subjects with uncertainty > 10 g/g lead in bone for tibia or 15 g/g for patella were excluded, reducing this number to 522 subjects available for the analysis. Finally, 5 subjects with missing values in WBC dif­ ferentials were also excluded. Therefore, in the end, 517 subjects with a total of 787 observa­ tions were available for analysis. Table 1 lists the demographic characteris­ tics of the population at baseline (i.e., at the first measurement of DNA methylation) and a comparison with those subjects who had bone lead data but did not supply DNA for methylation analysis. In general, subjects with and without methylation data were very simi­ lar with respect to the covariates. Table 2 pres­ ents the unadjusted relationships between lead biomarkers and global DNA methylation, as well as the relationship among the covari­ ates and biomarkers of global DNA methyla­ tion at baseline. Consistent with our previous report (Bollati et al. 2009), DNA methylation values for both Alu and LINE­1 decreased with increasing age (Table 3). Trends are sim­ ilar for increasing levels of patella lead, but no relationship is evident for either tibia or blood lead. A trend toward increased methylation at LINE1­ CpG islands was seen for both pack­ years of cigarette smoking and increasing years of education, but not for Alu (Table 2). In our mixed­effects models adjusting for smoking, age, BMI, education, and the percentage of lymphocytes in the WBC dif­ ferential, we found that, again, patella lead levels were associated with decreasing levels of global DNA methylation in LINE1­ retro­ transposons but not with methylation at Alu elements (Table 3). We found no signifi­ cant association between either global DNA methylation marker and blood or tibia lead levels. At baseline, LINE­1 methylation was inversely associated with age (Table 2). Finally, we generated a smoothed plot of the relation­ ship between patella lead levels and LINE­1 element methylation (Figure 1). This plot sug­ gests that the relationship may be nonlinear, with the primary decrease occurring between Less than high school High school/some college Four years of college p for trend Cigarette smoking (pack-years) 0 30 > 30 p for trend 792 aCategories are based on the tertile of the variable. ovlume 118 | number 6 | June 2010 • Environmental Health Perspectives