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Predicting Heart Attacks

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  • It was only in the nineteen seventies that a doctor discovered heart attacks were caused by blood clots. We've come a long way, but we're still searching for the holy grail of heart attack prevention: a way to predict if and when a person will suffer a heart attack. We're getting close.

    These new approaches use multiple new methods. They pair algorithms with artificial intelligence to predict heart attacks based on genetics, heart scans, and injectable molecular probes. The plan is to use these algorithms along with a Genomic Risk Score to create a screening test that can accurately determine heart attack risk.

    The scoring system was recently developed and uses genetic data from half a million people. The more a person has gene variants that are linked to a heart attack, the greater their risk.

    For people who already have atherosclerotic plaques in their arteries, the question is which of those plaques are likely to break off, clog an artery, and cause a heart attack? Inflammation is a great indicator because it causes changes to fat in the arteries. A ten year study shows measuring those fat changes was good at identifying who would die from a heart attack.

    Finally, probes are being used to search for certain molecules in arteries, such as LDL, the bad cholesterol. Scientists have made an artificial antibody with a fluorescent tag that binds to LDL to see if it can warn us of a heart attack.

    What's exciting is the large number of tests under development. We're optimistic that one day heart attack prevention will just be a simple doctor's visit.

More Information

The discoveries bringing an end to heart attacks
Can cutting-edge new treatments and therapies put an end to cardiovascular and circulatory disease for good?..

High Cholesterol Facts
NFind up-to-date facts about high cholesterol in the United States...

Genomic Risk Prediction of Coronary Artery Disease in 480,000 Adults
The genomic score developed and evaluated here substantially advances the concept of using genomic information to stratify individuals with different trajectories of CAD risk and highlights the potential for genomic screening in early life to complement conventional risk prediction...

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