Investigation on Cardiovascular Risk Prediction Using Genetic Information

• Published in: IEEE transactions on information technology in biomedicine Vol. 16; no. 5; pp. 795 - 808
• Main Authors: Pu, Li-Na, Zhao, Ze, Zhang, Yuan-Ting
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2012
• Subjects: Adult; Aged; Bioinformatics; Biological cells; Body Mass Index; Cardiovascular disease (CVD); Cardiovascular Diseases - genetics; Cardiovascular Diseases - physiopathology; Diseases; Female; Genetic Predisposition to Disease; genetic variants; Genome-Wide Association Study; Genomics; Heart; Humans; Male; Middle Aged; Models, Statistical; Predictive models; risk prediction
• ISSN: 1089-7771; 1558-0032
• DOI: 10.1109/TITB.2012.2205009

Cardiovascular disease (CVD) has become the primary killer worldwide and is expected to cause more deaths in the future. Prediction and prevention of CVD have therefore become important social problems. Many groups have developed prediction models for asymptomatic CVD by classifying its risk based on established risk factors (e.g., age, sex, etc.). More recently, studies have uncovered that many genetic variants are associated with CVD outcomes/traits. If treated as single or multiple risk factors, the genetic information could improve the performance of prediction models as well as promote the development of individually tailored risk models. In this paper, eligible genome-wide association studies for CVD outcomes/traits will be overviewed. Clinical trials on CVD prediction using genetic information will be summarized from overall aspects. As yet, most of the single or multiple genetic markers, which have been evaluated in the follow-up clinical studies, did not significantly improve discrimination of CVD. However, the potential clinical utility of genetic information has been uncovered initially and is expected for further development.