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Epigenetics and Inflammatory Markers: A Systematic Review of the Current Evidence.

Valentina Gonzalez-JaramilloEliana Portilla-FernándezMarija GlisicTrudy VoortmanMohsen GhanbariWichor BramerRajiv ChowdhuryTamar NijstenAbbas DehghanOscar H FrancoJana Nano
Published in: International journal of inflammation (2019)
Epigenetic mechanisms have been suggested to play a role in the genetic regulation of pathways related to inflammation. Therefore, we aimed to systematically review studies investigating the association between DNA methylation and histone modifications with circulatory inflammation markers in blood. Five bibliographic databases were screened until 21 November of 2017. We included studies conducted on humans that examined the association between epigenetic marks (DNA methylation and/or histone modifications) and a comprehensive list of inflammatory markers. Of the 3,759 identified references, 24 articles were included, involving, 17,399 individuals. There was suggestive evidence for global hypomethylation but better-quality studies in the future have to confirm this. Epigenome-wide association studies (EWAS) (n=7) reported most of the identified differentially methylated genes to be hypomethylated in inflammatory processes. Candidate genes studies reported 18 differentially methylated genes related to several circulatory inflammation markers. There was no overlap in the methylated sites investigated in candidate gene studies and EWAS, except for TMEM49, which was found to be hypomethylated with higher inflammatory markers in both types of studies. The relation between histone modifications and inflammatory markers was assessed by one study only. This review supports an association between epigenetic marks and inflammation, suggesting hypomethylation of the genome. Important gaps in the quality of studies were reported such as inadequate sample size, lack of adjustment for relevant confounders, and failure to replicate the findings. While most of the studies have been focused on C-reactive protein, further efforts should investigate other inflammatory markers.
Keyphrases
  • dna methylation
  • genome wide
  • case control
  • oxidative stress
  • gene expression
  • quality improvement
  • machine learning
  • artificial intelligence
  • transcription factor
  • current status
  • deep learning
  • genome wide identification