Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Blood DNA methylation biomarkers of cumulative lead exposure in adults

Journal of Exposure Science & Environmental Epidemiology volume 31, pages 108–116 (2021)Cite this article

Abstract

Background

Lead is a ubiquitous toxicant following three compartment kinetics with the longest half-life found in bones. Patella and tibia lead levels—validated measures of cumulative exposure—require specialized X-ray-fluorescence-spectroscopy available only in a few centers worldwide. We developed minimally invasive biomarkers reflecting individual cumulative lead exposure using blood DNA methylation profiles—obtainable via Illumina 450K or IlluminaEPIC bead-chip assays.

Methods

We developed and tested two methylation-based biomarkers from 348 Normative Aging Study (NAS) elderly men. We selected methylation sites with strong associations with bone lead levels via robust regressions analysis and constructed the biomarkers using elastic nets. Results were validated in a NAS subset, reporting specificity, and sensitivity.

Findings

Participants were 73 years old on average (standard deviation, SD = 6), with moderate lead levels of (mean ± SD patella: 27 ± 18 µg/g; tibia:21 ± 13 µg/g). Methylation-based biomarkers for lead in patella and tibia included 59 and 138 DNA methylation sites, respectively. Estimated lead levels were significantly correlated with actual measured values, (r = 0.62 patella, r = 0.59 tibia) and had low mean square error (MSE) (MSE = 0.68 patella, MSE = 0.53 tibia). Means and distributions of the estimated and actual lead levels were not significantly different across patella and tibia bones (p > 0.05). Methylation-based biomarkers discriminated participants highly exposed (>median) to lead with a specificity of 74 and 73% for patella and tibia lead levels, respectively, with 70% sensitivity.

Interpretation

DNA methylation-based lead biomarkers are novel tools that can be used to reconstruct decades’ worth of individual cumulative lead exposure using only blood DNA methylation profiles and may help identify the consequences of cumulative exposure.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2

Similar content being viewed by others

Code and data availability

Statistical code and de-identified data collected for this study are available from the corresponding author on reasonable request.

References

  1. Bakulski KM, Rozek LS, Dolinoy DC, Paulson HL, Hu H. Alzheimer’s disease and environmental exposure to lead: the epidemiologic evidence and potential role of epigenetics. Curr Alzheimer Res. 2012;9:563–73.

    Article  CAS  Google Scholar 

  2. Campbell JR, Rosier RN, Novotny L, Puzas JE. The association between environmental lead exposure and bone density in children. Environ Health Perspect. 2004;112:1200–3.

    Article  CAS  Google Scholar 

  3. Campbell JR, Auinger P. The association between blood lead levels and osteoporosis among adults-results from the third national health and nutrition examination survey (NHANES III). Environ Health Perspect. 2007;115:1018–22.

    Article  CAS  Google Scholar 

  4. Holland MG, Cawthon D. Levels ATFoBL workplace lead exposure. J Occup Environ Med. 2016;58:e371–e374.

    Article  CAS  Google Scholar 

  5. Agency for toxic substances and disease registry. Case studies in environmental medicine (Lead Toxicity). 2017. https://www.atsdr.cdc.gov/csem/lead/docs/csem-lead_toxicity_508.pdf.

  6. Alarcon WA. Elevated blood lead levels among employed adults—United States, 1994–2013. MMWR Morb Mortal Wkly Rep. 2016;63:59–65.

    Article  Google Scholar 

  7. Weisskopf MG, Proctor SP, Wright RO, Schwartz J, Spiro A 3rd, Sparrow D, et al. Cumulative lead exposure and cognitive performance among elderly men. Epidemiology. 2007;18:59–66.

    Article  Google Scholar 

  8. Navas-Acien A, Schwartz BS, Rothenberg SJ, Hu H, Silbergeld EK, Guallar E. Bone lead levels and blood pressure endpoints: a meta-analysis. Epidemiology. 2008;19:496–504.

    Article  Google Scholar 

  9. Hu H, Shih R, Rothenberg S, Schwartz BS. The epidemiology of lead toxicity in adults: measuring dose and consideration of other methodologic issues. Environ Health Perspect. 2007;115:455–62.

    Article  CAS  Google Scholar 

  10. Hu H, Rabinowitz M, Smith D. Bone lead as a biological marker in epidemiologic studies of chronic toxicity: conceptual paradigms. Environ Health Perspect. 1998;106:1–8.

    Article  CAS  Google Scholar 

  11. Wilker E, Korrick S, Nie LH, Sparrow D, Vokonas P, Coull B, et al. Longitudinal changes in bone lead levels: the VA Normative Aging Study. J Occup Environ Med. 2011;53:850–5.

    Article  CAS  Google Scholar 

  12. Wright RO, Schwartz J, Wright RJ, Bollati V, Tarantini L, Park SK, et al. Biomarkers of lead exposure and DNA methylation within retrotransposons. Environ Health Perspect. 2010;118:790–5.

    Article  CAS  Google Scholar 

  13. Zhang G, Pradhan S. Mammalian epigenetic mechanisms. IUBMB Life. 2014;66:240–56.

    Article  CAS  Google Scholar 

  14. Bird A. DNA methylation patterns and epigenetic memory. Genes Dev. 2002;16:6–21.

    Article  CAS  Google Scholar 

  15. Zhong J, Agha G, Baccarelli AA. The role of DNA methylation in cardiovascular risk and disease: methodological aspects, study design, and data analysis for epidemiological studies. Circulation Res. 2016;118:119–31.

    Article  CAS  Google Scholar 

  16. Reese SE, Zhao S, Wu MC, Joubert BR, Parr CL, Haberg SE, et al. DNA methylation score as a biomarker in newborns for sustained maternal smoking during pregnancy. Environ Health Perspect. 2017;125:760–6.

    Article  CAS  Google Scholar 

  17. Kovatsi L, Georgiou E, Ioannou A, Haitoglou C, Tzimagiorgis G, Tsoukali H, et al. p16 promoter methylation in Pb2+-exposed individuals. Clin Toxicol. 2010;48:124–8.

    Article  Google Scholar 

  18. Rakyan VK, Down TA, Balding DJ, Beck S. Epigenome-wide association studies for common human diseases. Nat Rev Genet. 2011;12:529–41.

    Article  CAS  Google Scholar 

  19. Portela A, Esteller M. Epigenetic modifications and human disease. Nat Biotechnol. 2010;28:1057–68.

    Article  CAS  Google Scholar 

  20. Feinberg AP. Epigenomics reveals a functional genome anatomy and a new approach to common disease. Nat Biotechnol. 2010;28:1049–52.

    Article  CAS  Google Scholar 

  21. Bell B, Rose CL, Damon A. The Veterans Administration longitudinal study of healthy aging. Gerontologist. 1966;6:179–84.

    Article  CAS  Google Scholar 

  22. Aro AC, Todd AC, Amarasiriwardena C, Hu H. Improvements in the calibration of 109Cd K x-ray fluorescence systems for measuring bone lead in vivo. Phys Med Biol. 1994;39:2263–71.

    Article  CAS  Google Scholar 

  23. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30:1363–9.

    Article  CAS  Google Scholar 

  24. Chen Y-a, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013;8:203–9.

    Article  CAS  Google Scholar 

  25. Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D, et al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics. 2013;29:189–96.

    Article  CAS  Google Scholar 

  26. Logue MW, Smith AK, Wolf EJ, Maniates H, Stone A, Schichman SA, et al. The correlation of methylation levels measured using Illumina 450K and EPIC BeadChips in blood samples. Epigenomics. 2017;9:1363–71.

    Article  CAS  Google Scholar 

  27. Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinform. 2012;13:86.

    Article  Google Scholar 

  28. Fox J, Weisberg S. An R companion to applied regression. USA: SAGE Publications; 2011.

  29. Du P, Zhang X, Huang C-C, Jafari N, Kibbe WA, Hou L, et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinform. 2010;11:587.

    Article  CAS  Google Scholar 

  30. Peters TJ, Buckley MJ, Statham AL, Pidsley R, Samaras K, V, Lord R, et al. De novo identification of differentially methylated regions in the human genome. Epigenetics Chromatin. 2015;8:6.

    Article  Google Scholar 

  31. Fan J, Lv J. Sure independence screening for ultrahigh dimensional feature space. J R Stat Soc. 2008;70:849–911.

    Article  Google Scholar 

  32. Phipson B, Maksimovic J. Oshlack A missMethyl: an R package for analyzing data from Illumina’s HumanMethylation450 platform. Bioinformatics. 2016;32:286–8.

    Article  CAS  Google Scholar 

  33. Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010;11:R14.

    Article  Google Scholar 

  34. Barltrop D, Khoo HE. The influence of nutritional factors on lead absorption. Postgrad Med J. 1975;51:795–800.

    Article  CAS  Google Scholar 

  35. Smith PJ, Blumenthal JA. Dietary factors and cognitive decline. J Prev Alzheimers Dis. 2016;3:53–64.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. John LuZQ. The elements of statistical learning: data mining, inference, and prediction. J R Stat Soc. 2010;173:693–4.

    Article  Google Scholar 

  37. Bender R, Lange S. Multiple test procedures other than Bonferroni’s deserve wider use. BMJ. 1999;318:600–600.

    Article  CAS  Google Scholar 

  38. Park SK, Mukherjee B, Xia X, Sparrow D, Weisskopf MG, Nie H, et al. Bone lead level prediction models and their application to examine the relationship of lead exposure and hypertension in the Third National Health and Nutrition Examination Survey. J Occup Environ Med. 2009;51:1422–36.

    Article  CAS  Google Scholar 

  39. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14:R115.

    Article  Google Scholar 

  40. Ji JS, Schwartz J, Sparrow D, Hu H, Weisskopf MG. Occupational determinants of cumulative lead exposure: analysis of bone lead among men in the VA normative aging study. J Occup Environ Med. 2014;56:435–40.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

EC and ROW were supported by the National Institute of Environmental Health Sciences (NIEHS) (grant: P30ES023515); ACJ was supported by NIEHS (grant: R00ES023450); MAK was supported by NIEHS (grants: R01ES028805 and P30ES009089); MW was supported by NIEHS (grant: P30ES000002); JS was supported by NIEHS (grants: R01ES015172, P30ES000002, and R01ES027747); HH was supported by NIH (grants: R01ES021446, and R01ES005257); AAB was supported by NIEHS (grants: P30ES009089, R01ES021733, R01ES025225, and R01ES027747). The VA Normative Aging Study is supported by the Cooperative Studies Program/Epidemiology Research and Information Center of the U.S. Department of Veterans Affairs and is a component of the Massachusetts Veterans Epidemiology Research and Information Center, Boston, MA.

Author information

Authors and Affiliations

  1. Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Elena Colicino, Allan Just & Robert O. Wright

  2. Mailman School of Public Health, Columbia University, New York, NY, USA

    Marianthi-Anna Kioumourtzoglou & Andrea A. Baccarelli

  3. Veterans Affairs Boston Healthcare System, Boston, MA, USA

    Pantel Vokonas

  4. School of Public Health, University of California, Berkeley, Berkeley, CA, USA

    Andres Cardenas

  5. VA Boston Healthcare System and Boston University School of Medicine, Boston, MA, USA

    David Sparrow

  6. Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Marc Weisskopf & Joel D. Schwartz

  7. School of Health Sciences, Purdue University, West Lafayette, IN, USA

    Linda H. Nie

  8. School of Public Health, University of Washington, Seattle, WA, F-226B, USA

    Howard Hu

Authors
  1. Elena Colicino
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Allan Just
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marianthi-Anna Kioumourtzoglou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pantel Vokonas
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andres Cardenas
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. David Sparrow
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Marc Weisskopf
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Linda H. Nie
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Howard Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Joel D. Schwartz
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Robert O. Wright
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Andrea A. Baccarelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

EC analyzed the data, interpreted the results and wrote the manuscript. AJ, MAK, and AC provided a portion of the statistical codes and interpretation of the results. PV, DS, MW, LHN HH JS ROW AAB designed the population study, provided epigenetic and lead information of each participant and contributed to writing the manuscript. All authors approved the final version of the document for submission. EC and AAB had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Corresponding author

Correspondence to Elena Colicino.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Colicino, E., Just, A., Kioumourtzoglou, MA. et al. Blood DNA methylation biomarkers of cumulative lead exposure in adults. J Expo Sci Environ Epidemiol 31, 108–116 (2021). https://doi.org/10.1038/s41370-019-0183-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41370-019-0183-9

Keywords

This article is cited by

Search

Advanced search

Quick links