Artificial Intelligence Helps Predict Atherosclerotic Cardiovascular Disease
deCODE genetics, a subsidiary of Amgen, in collaboration with partners from the USA, Denmark, and Iceland, harnessed artificial intelligence to create a protein-based score aimed at forecasting significant atherosclerotic cardiovascular disease events (ASCVD) ().
The study is based on a large data set consisting of over 13,500 Icelanders who had not experienced major ASCVD before plasma sampling and over 6,000 participants in the FOURIER trial who had suffered ASCVD before plasma sampling. All of these samples had measurements of the levels of around 5,000 plasma proteins measured with the SomaScan platform.
‘Using artificial intelligence, deCODE genetics and its collaborative partners have formulated a protein score designed to anticipate significant atherosclerotic cardiovascular disease occurrences. #HeartDisease #ArtificialIntelligence #Atherosclerosis’
A large part of the risk captured by the proteins is also captured by established risk factors, however, the protein score captures additional risk.
Dynamic Potential of Protein Risk Scores
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. As a result, protein risk scores could become an important tool in clinical trials to get an early assessment of the efficacy of therapeutic intervention or for monitoring risk."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 clinical practice 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.
Reference:
- Evaluation of Large-Scale Proteomics for Prediction of Cardiovascular Events - (https:jamanetwork.com/journals/jama/article-abstract/2808522)