Abstract Environmental exposures increase risk for multiple diseases, including dementia. The modern expansion of industrialization and the combustion of fossil fuels, coupled with extensive application of chemical fertilizers in farming, have led to an increase in the levels of toxic elements in the air, water and food. A key part of this work is to identify biological signatures associated with toxic element exposure, such as arsenic, and its effects on chronic disease conditions. This presentation will showcase a worked example illustrating how arsenic exposure will be assessed in urine samples. iAs, MMA, DMA and AsB will be analysed in urine by ion chromatography interfaced with inductively coupled plasma-mass spectrometry detection (ICP-MS) in Prof. Andy Meharg's lab, Queens's University, Belfast. DNAm data will be processed through Rnbeads 2.0. A principal-component analysis (PCA) was then used to identify technical variability (batch effect) in DNAm data. After removing batch effect, Clean DNAm data will be used in linear regression models to determine CpG loci associated with arsenic exposure.