Lead biomarkers and DNA methylation within retrotransposons in global DNA methylation and that this association would be more evident than an association with a biomarker of concurrent/ shortt­erm exposure such as blood lead. Methods This study was approved by the institutional review boards of Harvard School of Public Health, Brigham and Women’s Hospital, and the Boston Veteran’s Administration Hospital, and all participants gave written informed con­ sent. This study was conducted on a subsam­ ple of the Normative Aging Study (NAS), a multidisciplinary longitudinal study of aging established by the Veterans Administration in 1963 (Bell et al. 1966). Briefly, 2,280 men were enrolled in the NAS. Participants received their first medical examination between 1963 and 1969. Subsequently, subjects have reported for medical examinations and standard blood and urine tests every 3–5 years. During those visits, NAS participants filled out questionnaires on smoking history, education level, food intake, and other risk factors that may influence health. Beginning in 1991, those who gave their informed consent presented to the Ambulatory Clinical Research Center of Brigham and Women’s Hospital for a KX­r­ay fluorescence (KXRF) measurement of lead content in the tibia and patella. Study subjects were thus meas­ ured for bone lead between 1991 and 1999. Bone lead levels measured by KXRF. Bone lead measurements were taken at two sites, the mid­tibial shaft and the patella, with an ABIOMED KXRF instrument (ABIOMED, Inc., Danvers, MA, USA, and the Harvard Metals Epidemiology Research Group) (Hu et al. 1990). The tibia and patella have been targeted for bone lead research because these two bones consist mainly of pure cortical and trabecular bone, respectively. A 30m­in meas ­ urement was taken at the midshaft of the left tibia and at the left patella after each region had been washed with a 50% solution of iso­ propyl alcohol. The tibial midshaft was taken as the point equidistant between the tibial pla­ teau and the medial malleolus. The KXRF beam collimator was sited perpendicular to the flat bony surface of the tibia and at the patella. As a qualityc­ontrol measure, once a week a 15p­pm phantom was positioned and meas ­ ured 20 consecutive times overnight as a first­ order calibration check. Analysis of means and SDs was performed to disclose any significant shift in accuracy or precision. Once a month, the entire set of calibration phantoms (0, 5, 10, 15, 20, 30, 40, 50, and 100 ppm; true values checked by inductively coupled plasma­ mass spectrometry) was measured, and a cali­ bration curve was calculated as a final check on calibration. Blood lead levels measured by graph- ite atomic absorption spectrometry. Fresh blood for lead measurement was taken in a special lead­free tube containing EDTA and sent to ESA Laboratories, Inc. (Chelmsford, MA, USA). Blood samples were analyzed by a Zeeman background­corrected flameless atomic absorption spectrophotometer (graph­ ite furnace). The instrument was calibrated before use with National Institute of Standard and Technology materials. Ten percent of the samples were run in duplicate. In tests on ref­ erence samples from the Centers for Disease Control and Prevention, precision (% relative SD) ranged from 8% for concentrations < 30 µg/dL to 1% for higher concentrations. Blood lead levels are measured at each triennial NAS study visit. For purposes of this study, we used the blood lead measurement most proximal in time to the bone lead measurement. DNA methylation. Bisulfite treatment. DNA was extracted from stored frozen buffy coat of 7 mL whole blood, using the QiAmp DNA blood kits (QIAGEN, Valencia, CA, USA); 500 ng DNA (50 ng/µL) was treated using the EZ DNA Methylation­Gold Kit (Zymo Research, Orange, CA, USA) according to the manufacturer’s protocol. Final elution was performed with 30 µL ME­lution buffer. Polymerase chain reaction and pyro- sequencing. DNA methylation was quanti­ tated using bisulfitep­olymerase chain reaction (PCR) and pyrosequencing, using primers and conditions described previously (Baccarelli et al. 2009; Colella et al. 2003; Tarantini et al. 2009; Yang et al. 2004). We used LINE­1 and Alu element PCR for pyrosequencing­ based methylation analysis following previously published methods (Bollati et al. 2007; Tost et al. 2003), with the following modifications: A 50µ­L PCR was carried out in 25 µL GoTaq Green Master mix (Promega, Madison, WI, USA), 1 pmol biotinylated forward primer, 1 pmol reverse primer, 50 ng bisulfitet­reated genomic DNA, and water. A biotin­labeled primer was used to purify the final PCR prod­ uct using Sepharose beads. The PCR prod­ uct was bound to Streptavidin Sepharose HP (Amersham­Biosciences, Uppsala, Sweden) , and the Sepharose beads containing the immo­ bilized PCR product were purified, washed, denatured using a 0.2­ M NaOH solution, and washed again using the Pyrosequencing Vacuum Prep Tool (Pyrosequencing Inc., Westborough, MA), according to the manu­ facturer’s recommendations. Pyrosequencing primer (0.3 µM) was then annealed to the puri­ fied singles­tranded PCR product , and pyrose­ quencing was performed using the Pyromark MD System (Pyrosequencing Inc.). The degree of methylation was expressed as the percentage of 5m­ethylated cytosines (%5mC) divided by the sum of methylated and unmethylated cyto­ sines. The assays, which allow for the ampli­ fication of a representative pool of repetitive elements, quantitatively assess the proportion of methylated sites in Alu and LINE1­ repetitive Environmental Health Perspectives • volume 118 | number 6 | June 2010 elements dispersed throughout the genome. Measures of Alu and LINE­1 methylation are highly correlated with 5­methyl cytosine content measured through high­performance liquid chromatography and have been widely used as a surrogate of global methylation (Weisenberger et al. 2005; Yang et al. 2004). We used nonC­pG cytosine residues as builti­n controls to verify bisulfite conversion. Each sample was tested in two replicates, and their average was used in the statistical analysis. Statistical analysis. We excluded tibia and patella bone lead measurements with estimated uncertainties > 10 µg/g bone and > 15 µg/g bone, respectively, because these measurements usually reflect excessive patient movement during the measurement (Hu et al. 1998). Such procedures are standard in analysis of bone lead data (Aro et al. 1994). We used the generalized extreme studentized deviation manyo­utlier method (Rosner 1983) to remove extreme outliers among the con­ tinuous independent variables with a normal distribution, as performed in previous analyses (Carey et al. 1997; Hu et al. 1996; Rexrode et al. 1998; Whitsel et al. 2001). This proce­ dure removed 10 subjects with extreme tibia lead levels and 8 subjects with extreme patella lead levels. We also report results of analyses in which those subjects with extreme bone lead measurements were included. Statistical analysis of predictors of DNA methylation levels. We fit a mixed­effects model to account for repeatedly measured methylation levels. Analyses were performed to evaluate the association between lead biomark­ ers and DNA methylation levels. The potential confounding factors included were age, body mass index (BMI), percentage of lymphocytes among white blood cells (WBCs), education, and packy­ears of cigarettes. Blood lead levels were used as the index of lead exposure in a separate model. Adjustment for confounders was conducted in stages to identify which con­ founders had a strong influence on results. A random slope for the time elapsed from the first visit for DNA methylation was initially considered to account for subject–specific vari­ ability of methylation over time, but a random­ intercept­ only model was preferred based on the likelihood ratio test comparing the two models. We also examined whether the asso­ ciation between bone lead and global DNA methylation levels changed over time. We included an interaction term between the time elapsed from the first visit and each bone lead marker, but we did not see any statistically sig­ nificant interaction. Therefore, we eliminated this interaction term from the final model. The following equation describes the structure of our fitted models: Yij = (b0 + ui) + covariates + b1Timeij + b2BonePbij + eij [1], 791