DNA Methylation-Based Biomarkers of Environmental Exposures for Human Population Studies

Curr Environ Health Rep. 2020 Jun;7(2):121-128. doi: 10.1007/s40572-020-00269-2.

Abstract

Purpose of review: This manuscript orients the reader to the underlying motivations of environmental biomarker development for human population studies and provides the foundation for applying these novel biomarkers in future research. In this review, we focus our attention on the DNA methylation-based biomarkers of (i) smoking, among adults and pregnant women, (ii) lifetime cannabis use, (iii) alcohol consumption, and (iv) cumulative exposure to lead.

Recent findings: Prior environmental exposures and lifestyle modulate DNA methylation levels. Exposure-related DNA methylation changes can either be persistent or reversible once the exposure is no longer present, and this combination of both persistent and reversible changes has essential value for biomarker development. Here, we present available biomarkers representing past and cumulative exposures using individual DNA methylation profiles. In the present work, we describe how the field of environmental epigenetics can leverage machine learning algorithms to develop exposure biomarkers and reduce problems of misreporting exposures or limited access technology. We emphasize the crucial role of the individual DNA methylation profiles in those predictions, providing a summary of each biomarker, and highlighting their advantages, and limitations. Future research can cautiously leverage these DNA methylation-based biomarkers to understand the onset and progression of diseases.

Keywords: Alcohol; Biomarkers; Cannabis use; DNA methylation; Environmental exposures; Lead exposure; Smoking.

Publication types

  • Research Support, N.I.H., Extramural
  • Review

MeSH terms

  • Adult
  • Alcohol Drinking / genetics
  • DNA Methylation*
  • Environmental Biomarkers / genetics*
  • Environmental Exposure / analysis*
  • Epigenesis, Genetic
  • Epigenomics / methods*
  • Female
  • Humans
  • Lead / analysis
  • Machine Learning
  • Male
  • Marijuana Use / genetics
  • Pregnancy
  • Smoking / genetics

Substances

  • Environmental Biomarkers
  • Lead