Scientists from deCODE genetics, in collaboration with researchers from the United States, Denmark, and Iceland, have developed a protein score capable of predicting major atherosclerotic cardiovascular disease events (ASCVD).
Published in the Journal of the American Medical Association (JAMA), the large-scale study leverages artificial intelligence and proteomics data, offering a promising tool for both clinical trials and preventative healthcare.
Unique Data Set
The study analyzed plasma samples from over 13,500 Icelanders who had not previously experienced a major ASCVD event, as well as over 6,000 participants from the FOURIER trial, who had a history of ASCVD.
About 5,000 plasma proteins were measured using the SomaScan platform.
Protein Risk Score
The protein risk score, generated based on proteomics data from a single plasma sample, accurately predicts the risk of ASCVD events.
While it captures much of the same risk information as established factors like medical history, it also adds incremental value by capturing additional risks.
Dynamic Measure
What sets this protein risk score apart is its dynamic nature. Unlike static risk factors such as family history or previous ASCVD events, the protein levels in this score fluctuate over time.
This makes the protein score especially valuable for predicting the timing of ASCVD events and evaluating the efficacy of treatments.
Implications for the Medical Field
According to Kari Stefansson, CEO of deCODE genetics and a senior investigator of the study, this new biomarker could enable shorter clinical trials with fewer participants.
This would make the development of new drugs more cost-effective and speed up their availability for patients in need.
Beyond its application in clinical trials, the protein risk score could become an important tool in clinical practice for more effective prevention of ASCVD events.
The dynamic nature of the protein risk score allows for real-time risk assessment, offering the potential for targeted treatment and more proactive patient care.
Conclusion
The development of an AI-based protein risk score offers a promising avenue for improving the prevention and treatment of cardiovascular diseases.
By providing a dynamic and cost-effective tool for assessing risk, this study has significant implications for the future of cardiovascular healthcare.
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