In a large retrospective analysis using measurements of thousands of plasma proteins in primary and secondary event populations, scientists from deCODE genetics and collaborators from U.S., Denmark and Iceland, reported today in JAMA how they employed AI to develop a protein score to predict major atherosclerotic cardiovascular disease events (ASCVD).
. A large part of the risk captured by the proteins is also captured by established risk factors, however, the protein score captures additional risk.
What is more, the protein risk score is a dynamic measure and as such has the potential of being modified upon treatment unlike some of the classic risk factors that are immutable, such as family history and prior ASCVD events. This dynamic feature of protein risk scores, that the levels of proteins rise and fall as a function of time to and from events, makes it well-suited to predict the timing of events.
"We believe that in the proteomic risk score, we may have a biomarker that will allow the world to conduct shorter clinical trials with fewer participants. This is going to make the development of new medicines less expensive and make them available sooner for those who need them. Furthermore, in it may allow for more effective prevention of ASCVD," said Kari Stefansson, CEO of deCODE genetics and one of the senior investigators of the study.Hannes Helgason et al, Evaluation of Large-Scale Proteomics for Prediction of Cardiovascular Events,
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