Improving the prediction of cardiovascular risk with machine-learning and DNA methylation data

Classically, the cardiovascular risk of individual is evaluated using phenomenological variables (PV)such as blood pressure, body mass, smoker status, gender, age etc. Here we show that, on prospective study (after 10-15 years)these PV display a poor agreement with case-control samples. We were able to obtain more accurate predictions using both DNA methylation data and PV as input features of a Random Forest model, achieving a ROC-AUC of 0.74. Furthermore, the Random Forest output correlates with the reliability of the predictions producing a ROC-AUC of 0.90 when only the most reliable predictions are taken into consideration.