patella lead levels between 0 and approximately 40 µg/g bone and a leveling out of the effect at levels higher than this. Discussion Cumulative exposure to lead and DNA methylation. In the present study we found an association between patella lead levels (a biomarker of cumulative lead exposure) with LINE1­ repetitive element methylation. Blood lead levels (a biomarker of recent exposure) were not associated with DNA methyla­ tion. Our group (Pilsner et al. 2009) recently reported an association between global DNA methylation in umbilical cord WBC DNA and maternal patella lead levels. Here we expand that work by studying an aging popu­ lation, as opposed to newborns and women of childbearing age. Our results suggest that cumulative past exposure to lead among elderly individuals is associated with decreased LINE1­ DNA methylation, even after adjust ­ ing for age. This association is therefore likely due to the effects of distant past exposure, rather than more contemporaneous lead exposure. Our study population is composed of elderly men, the majority of whom were retired at the time of the study, limiting any impact of recent or subacute occupational lead exposure not captured by blood or bone lead. Bone lead is a unique biomarker in that past exposures can be accurately reconstructed and their impact on health directly measured. Because of bone lead’s half life of ≥ 10 years, ultimately > 90% of body lead is found in bone (Leggett 1993). Few chemicals have such a well­ established cumulative exposure biomarker. This may mean that bone lead is uniquely suited as a paradigm biomarker in which to test whether DNA methylation pat­ terns in WBCs correlate with lifetime envi­ ronmental exposures. However, because bone is not a uniform tissue, we measured lead in two types of bone: trabecular and cortical. Tibia represents cortical bone, and its matrix turnover is slower than trabecular bone, which is represented by patella (Hu et al. 1998). Lead biomarkers and DNA methylation within retrotransposons Previous research shows that tibia lead is bio­ logically more inert than patella lead but is a better dosimeter of cumulative exposure (Hu et al. 2007; Kosnett et al. 2007; Todd and Chettle 1994). This is reflected in the longer half­life of cortical lead levels (approximately 12–15 years) relative to trabecular lead lev­ els (approximately 8–10 years). In general, the higher turnover rate of patella bone lead into blood means that lead within trabecular bone is more biologically active (Hu et al. 2007). This may explain why this particular biomarker was statistically associated with reduced methylation of LINE­1. Under the assumption that DNA­methylation changes in lymphocytes are most influenced by direct chronic exposure to circulating lead in blood, patella lead would be more likely to be associ­ ated with DNA methylation changes than tibia lead. Considering the differences in the kinetics of tibia and patella lead, the DNA methylation findings seen in this study sug­ gest that redistribution of lead from bone over time may be part of the mechanism by which patella lead is associated with reduced DNA methylation in LINE1­. If the associa ­ tion were purely due to cumulative lead expo­ sure, one would expect tibia lead to be more strongly associated with DNA methylation than patella lead. Global methylation markers. Changes in methylation patterns among markers of global methylation, such as LINE1­ and Alu, had been thought to represent a more passive response to environmental factors. Changes in methylation in these sequences could reflect the general impact of environmental fac­ tors on DNA methylation, as they were not believed to be functional and methylation would likely be less regulated compared with regions containing genes (Axume et al. 2007; Weisenberger et al. 2005). Such transposable elements comprise > 50% of DNA sequence and were initially believed to be nonfunc­ tional “junk” DNA. More recent work has demonstrated that they play a critical role in human development, tissue differentiation, and gene expression, as these elements can be transcribed and translated into functional proteins (Belancio et al. 2009; Han and Boeke 2005). LINE1­ elements comprise about 20% of the human genome, are approximately 6,000 base pairs long, and consist of two open reading frames, one of which codes for an enzyme with both endonuclease and reverse transcriptase properties (Garcia­Perez et al. 2007; Wallace et al. 2008a, 2008b). Using this enzyme, LINE­ 1 elements can replicate and insert themselves into different genomic regions. Decreased methylation of LINE­ 1 elements would be expected to lead to the transcriptional activation of those repetitive sequences that contain complete promoters (Wilson et al. 2007). The majority of LINE1­ elements cannot be active because they have been either truncated or mutated over our evolutionary history. However, the human genome averages 500,000 LINE­ 1 copies, of which appproximately 3,000 LINE­1 sequences are complete (Lander et al. 2001) and 200 are potentially transcribed (Brouha et al. 2003). This is still a large number of biologically active retrotransposons. If dem­ ethylation of these LINE1­ elements activates their transcription and they are inserted near or within genes, they can alter gene expression and ultimately cell function. Furthermore, LINE­ 1 elements regulate replication and insertion of short interspersed nuclear ele­ ments such as Alu. Alu insertion into genes can also regulate gene expression and/or func­ tion (Wallace et al. 2008a, 2008b). Such findings suggest that these retrotransposons may represent yet another level of epigenetic regulation of gene expression. Indeed, a grow­ ing body of evidence suggests that LINE­1 transposition plays a critical role both in cell differentiation and in regulating the mam­ malian transcriptome (Coufal et al. 2009; Zemojtel et al. 2007). Changes in methyla­ tion of LINE­1 secondary to environment exposures may have significant impacts on health (Wallace et al. 2008a, 2008b; Wilson et al. 2007). Within this context, our finding Table 3. Cross-sectional lead biomarker association with DNA methylation estimated from mixed-effects models with increasing adjustment for confounders. Tibia lead (IQR = 15 g/g) Model 1 (n = 787) Model 2 (n = 762) Model 3 (n = 694) Patella lead (IQR = 19 g/g) Model 1 (n = 772) Model 2 (n = 747) Model 3 (n = 679) Blood lead (IQR = 2 g/dL) Model 1 (n = 716) Model 2 (n = 694) Alu β (95% CI) 0.01 (–0.08 to 0.10) 0.01 (–0.09 to 0.11) 0.02 (–0.10 to 0.13) –0.01 (–0.10 to 0.07) –0.01 (–0.10 to 0.08) –0.03 (–0.14 to 0.08) 0.03 (–0.04 to 0.11) 0.03 (–0.05 to 0.10) LINE-1 β (95% CI) –0.11 (–0.28 to 0.05) –0.06 (–0.23 to 0.12) –0.07 (–0.29 to 0.14) –0.20 (–0.36 to –0.05)* –0.17 (–0.33 to 0.00)* –0.25 (–0.44 to –0.05)* –0.01 (–0.15 to 0.13) 0.04 (–0.10 to 0.19) n o i t a l y h t e m0.0 1 - E N I L t n e c –re1.0 P Abbreviations: CI, confidence interval; IQR, interquartile range. Model 1 was adjusted for age, BMI, and percent lympho- cytes; model 2 was adjusted for age, BMI, percent lymphocytes, education, and pack-years of cigarettes; and model 3 was adjusted for for age, BMI, percent lymphocytes, education, pack-years of cigarettes, and blood lead levels. *p ≤ 0.05; n for each model represents total observations rather than individuals. Environmental Health Perspectives • volume 118 | number 6 | June 2010 0 20 40 60 80 100 Patella lead level (µg/g bone) 120 Figure 1. Penalized spline showing nonlinear relationship between patella lead level and LINE-1 DNA methylation. The plot is adjusted for age, BMI, education, WBC differential, and pack-years of smoking. 793