Etiologic Puzzle of Coronary Artery Disease: How Important Is Genetic Component?
Lăcrămioara Ionela ButnariuLaura FloreaMinerva Codruţa BădescuElena TarcaIrina-Iuliana CostacheEusebiu Vlad GorduzaPublished in: Life (Basel, Switzerland) (2022)
In the modern era, coronary artery disease (CAD) has become the most common form of heart disease and, due to the severity of its clinical manifestations and its acute complications, is a major cause of morbidity and mortality worldwide. The phenotypic variability of CAD is correlated with the complex etiology, multifactorial (caused by the interaction of genetic and environmental factors) but also monogenic. The purpose of this review is to present the genetic factors involved in the etiology of CAD and their relationship to the pathogenic mechanisms of the disease. Method: we analyzed data from the literature, starting with candidate gene-based association studies, then continuing with extensive association studies such as Genome-Wide Association Studies (GWAS) and Whole Exome Sequencing (WES). The results of these studies revealed that the number of genetic factors involved in CAD etiology is impressive. The identification of new genetic factors through GWASs offers new perspectives on understanding the complex pathophysiological mechanisms that determine CAD. In conclusion, deciphering the genetic architecture of CAD by extended genomic analysis (GWAS/WES) will establish new therapeutic targets and lead to the development of new treatments. The identification of individuals at high risk for CAD using polygenic risk scores (PRS) will allow early prophylactic measures and personalized therapy to improve their prognosis.
Keyphrases
- coronary artery disease
- genome wide
- cardiovascular events
- percutaneous coronary intervention
- copy number
- coronary artery bypass grafting
- systematic review
- dna methylation
- case control
- heart failure
- aortic stenosis
- risk factors
- transcription factor
- atrial fibrillation
- electronic health record
- type diabetes
- replacement therapy
- drug induced
- artificial intelligence
- respiratory failure
- cardiovascular disease
- data analysis
- medical education