Epigenetic biomarkers of smoking, inflammation, and social differences

This thesis aims to investigate the interplay between smoking, DNA methylation, inflammation, and socioeconomic position. First, 16 different methylation-based biomarkers of smoking are compared in their explanation of smoking status, pack years, and cessation. The predictor with the best class separation and that explained the most variation in self-reported smoking was proposed by McCartney et al (2018), however using methylation measured at a single locus in the AHRR gene worked almost as well. Secondly, factors including sex, age, cell type composition, education and socioeconomic classification were investigated to see if these influenced the agreement between self-reported and methylation-based smoking. This showed that more misclassifications occurred in self-reported ex-smokers compared to other smoking groups, and more affluent people compared to individuals not achieving any qualifications or working more routine occupations. Self-reported and DNAme-predicted smoking were also compared in terms of inflammation, and this suggested that DNAme-predicted smoking measures may more closely relate to inflammation than self-reports. Lastly, epigenetic signatures of inflammation were investigated. This showed that many factors influence DNA methylation changes that occur with inflammation, including educational attainment and suggests that the social environment could play a role in epigenetic signatures of inflammation. In EWAS models where self-reported smoking was used, the addition of educational attainment had more of an impact on findings compared to methylation-based measures of smoking. An overarching aim of this thesis is to communicate the importance of interdisciplinary approaches to health research to fully consider how the health of an occurs as part of a greater whole.