Epigenetic Predictors of Health and Lifestyle
Centre for Genomic and Experimental Medicine scientists develop DNA-based predictors of a range of health and lifestyle factors: October 2018
The potential benefits of a DNA-based predictor of health and lifestyle extends to both the clinical and non-clinical context. A biological predictor of alcohol consumption or smoking could be more accurate than relying on self-reported measurements, which in turn could allow for improved disease risk prediction.
A recent publication in the journal Genome Biology from Dr Riccardo Marioni’s group in the Centre for Genomic and Experimental Medicine, describes the development of a number of different epigenetic predictors of a range of lifestyle factors associated with health and mortality.
Epigenetic modifications are changes to the genome that do not involve changes to the DNA nucleotide sequence and have an effect on gene regulation. DNA methylation is one such modification and is both cell specific and dynamic, meaning it can change over time. It can be influenced by genes and the external environment.
Using data derived whole blood samples in large cohort datasets (Generation Scotland and the Lothian Birth Cohort 1936) the authors have developed DNA methylation-based predictors of ten health and lifestyle factors including alcohol consumption, smoking status, body mass index (BMI), waist-to-hip ratio, body fat percentage, four different measures of cholesterol and educational attainment. These predictors are shown to explain some of the phenotypic variance seen in these conditions, helping to characterize individual differences, and demonstrate a clinical relevance as some also show an association with prediction of mortality. The power of these methylation-based predictors is well demonstrated in the case of smoking, with a clear distinction seen at the epigenetic level between current and never smokers, with a significant association in survival analysis.
Most of the health and lifestyle factors identified are also risk factors for health conditions such as cancer and cardiovascular disease. The ability of DNA methylation-based predictors to inform on these modifiable characteristics could lead to greater understanding of disease variance, prediction and stratification of patients, and can be used in addition to the prediction of mortality from phenotypic data.
It is notable that the methylation-based predictors associate with both lifestyle and surivival. We are now collaborating with other groups to see if they are also predictive of disease outcomes. Furthermore, we are working with methods experts to improve the accuracy of our predictors.