No abstract

Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNAm signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent sample, (Generation Scotland; n=9,537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 137 EpiScore – disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification.

Chronic morbidities place longstanding burdens on our health as we age. Although protein biomarkers are critical for the early detection of such diseases, current studies are limited by low sample sizes, variability in proteomics methods and fluctuations in inflammatory protein expression. Here, we present a novel framework for protein-by-proxy analysis of incident disease. We show that DNA methylation proxies for nine inflammatory and seven neurology plasma proteins (generated in up to 875 individuals in the Lothian Birth Cohort 1936) predict the incidence of seven leading causes of morbidity in the Generation Scotland cohort (n=9,537), ascertained via electronic health data linkage over a follow-up period of up to 14 years. After correction for multiple testing and adjustment for common disease risk factors, these included proxy associations between CCL11 and depression (Hazard Ratio: HR = 1.45, P = 1.8 x 10-4), VEGFA and ischaemic heart disease (HR = 1.16, P = 0.02) and associations between incident diabetes and FGF-21 (HR = 1.39, P = 9.7 x 10-7), NEP (HR = 1.32, P = 2.8 x 10-6) and N-CDase (HR = 1.16, P = 0.02). Several of the protein-proxy associations with disease pinpoint proteins that are already therapeutic targets for the diseases in question. These results provide new opportunities to identify circulating biomarkers for disease detection and candidate pathways for drug targeting.

Background Blood-based markers of cognitive functioning might provide an accessible way to track neurodegeneration years prior to clinical manifestation of cognitive impairment and dementia. Results Using blood-based epigenome-wide analyses of general cognitive function, we show that individual differences in DNA methylation (DNAm) explain 35.0% of the variance in general cognitive function (g). A DNAm predictor explains ~4% of the variance, independently of a polygenic score, in two external cohorts. It also associates with circulating levels of neurology- and inflammation-related proteins, global brain imaging metrics, and regional cortical volumes. Conclusions As sample sizes increase, the ability to assess cognitive function from DNAm data may be informative in settings where cognitive testing is unreliable or unavailable.