Doctors use information about a person’s age, cholesterol, and blood pressure to predict who might develop cardiovascular disease (CVD) and prescribe medication, for example, statins to try to prevent CVD in people predicted to be at high risk. Technological advancements now make it possible to cheaply measure hundreds of molecular risk factors in addition to cholesterol along with polygenic risk scores (PRSs) which measure complex inherited genetic risk.

Researchers at the University of Cambridge, supported by NIHR Cambridge BRC,  studied whether these additional biomarkers could help better predict who will develop CVD. They analysed health information from nearly 300,000 participants in the UK Biobank who had no previous CVD and were not already prescribed statins. They compared standard risk prediction models used by doctors to those that also included over 100 metabolomic biomarkers, 11 routine clinical biomarkers, and PRSs. The findings, published in the European Heart Journal on Monday,  confirmed that this new model could nearly double the number of future CVD events doctors would be able to correctly predict and prevent by prescribing statins.

The study suggests that integrating genetic and metabolic blood tests with existing clinical data could significantly enhance how doctors identify people at high risk of heart disease, leading to earlier and more effective prevention strategies.