The proteome is the collection of proteins that are present within a specific cell, tissue, or system within the body. Our circulating proteome refers to the proteins circulating in our bloodstream and is made up of proteins that are either produced in the circulatory system, or proteins that enter the bloodstream from other organs and tissues in the body. Why do we care about this? As we know, proteins are extremely important! They are influencer molecules that maintain our health, and they’re also often the mediators of disease. Furthermore, protein biomarkers have been identified across many age-related morbidities. As proteins are the primary effectors of disease, connecting the epigenome, proteome, and time to disease onset may help to create new, predictive biological signatures. DNA methylation (DNAm) has been linked to the levels of proteins in our blood and the risk people have of developing chronic diseases. DNAm reflects the body's exposure to chronic stress and inflammation and while this process is dynamic, DNAm may be more stable than protein measures, which can be variable across multiple time points. DNAm scores for proteins could therefore be used to identify individuals with high-risk biological signatures, many years prior to disease diagnosis. In this week’s Everything Epigenetics podcast, Danni and I chat about the circulating proteome, how machine learning can be used to create epigenetic scores, and how information from the blood can be used to stratify risk of disease. We focus on the results from a study Danni published last year that integrated epigenetic and protein measures from the blood to develop new biomarkers for disease prediction. Danni’s work integrates these blood-based markers with the medical records of thousands of individuals to model disease onset. Danni is in the final year of her PhD, on the Wellcome Trust Translational Neuroscience programme at the University of Edinburgh. In this episode of Everything Epigenetics, you’ll learn about: - Danni’s neuroscience background and what got her interested in the field - Why the Wellcome Trust Translational Neuroscience programme at the University of Edinburgh was a perfect fit for her - A walkthrough of the central dogma - A review of DNA methylation - What the circulating proteome is and why it’s important - The importance of proteins as biomarkers - The definition and importance of an EpiScore, a term that Danni coined - The strongest methylation signature we’ve seen to-date - Why using DNA methylation to predict protein levels may be useful - Considerations on using blood when investigating these markers - The definition and importance of protein quantitative trait loci - The cohorts Danni investigated in her paper “Epigenetic scores for the circulating proteome as tools for disease prediction” - How Danni applied machine learning methods in the context of cohort studies - How Danni created EpiScores for protein levels  - The value of using protein EpiScores for disease prediction and risk stratification - Inflammation as an important indicator of health - How EpiScores compare with polygenic risk scores - The importance of these risk scores in the context of age-related chronic disease - The challenges and future directions in Danni’s work - How people can be involved in large-scale cohort studies - What’s next for Danni Where to find Danni: Twitter: https://twitter.com/dannigadd Danni’s profile at the University of Edinburgh: https://www.ed.ac.uk/profile/danni-gadd Thank you for joining us at the Everything Epigenetics Podcast and remember you have control over your Epigenetics, so tune in next time to learn more about how.