Coronary heart disease (CHD) is the world's leading cause of death. Up to 90% of all CHD deaths are preventable, but effective prevention of this mortality requires more scalable, precise methods for assessing CHD status and monitoring treatment response. Unfortunately, current diagnostic methods have barriers to implementation, particularly in rural areas and lower-income countries. This gap may be bridged by highly scalable advances in DNA methylation testing methods and artificial intelligence. Herein, we review prior studies of CHD related to methylation alone and in combination with other biovariables. We compare these new methods with established methods for diagnosing CHD. Finally, we outline pathways through which methylation-based testing methods may allow the democratization of improved methods for assessing CHD globally.
Keywords: DNA methylation; artificial intelligence; coronary heart disease; epigenetics; machine learning.
Diagnosing coronary heart disease is both costly and difficult at the present time. As a result, many patients in both mature and developing economies die prematurely. New developments in artificial intelligence, epigenetics and laboratory tools may lead to better methods for diagnosing and monitoring heart disease. In this article, we review how advancements in these three areas converge to create methods that are more sensitive for detecting heart disease. They are also more affordable. As a result, it is likely that the new computer-guided laboratory tools will become more common in clinical settings throughout the world.