Investigation on cardiovascular risk prediction using genetic information

Area of Science:

• Genetics
• Cardiology
• Public Health

Background:

• Cardiovascular disease (CVD) is the leading global cause of death, necessitating improved prediction and prevention strategies.
• Current CVD risk prediction models rely on traditional risk factors, but incorporating genetic information offers potential for enhanced accuracy.
• Genetic variants are increasingly recognized for their association with CVD outcomes and traits.

Purpose of the Study:

• To review genome-wide association studies (GWAS) related to CVD.
• To summarize clinical trials investigating the use of genetic information for CVD prediction.
• To assess the current impact and future potential of genetic data in CVD risk assessment.

Main Methods:

• Overview of eligible genome-wide association studies (GWAS) for CVD outcomes and traits.
• Summary of clinical trials evaluating genetic markers in CVD prediction.
• Analysis of the performance of genetic information in improving CVD risk discrimination.

Main Results:

• Most single or multiple genetic markers evaluated in clinical studies have not significantly improved CVD discrimination.
• Initial studies suggest a potential clinical utility for genetic information in CVD risk prediction.
• Genetic data's role in enhancing prediction models and enabling tailored risk assessments is under investigation.

Conclusions:

• Genetic information holds promise for advancing cardiovascular disease prediction and personalized risk models.
• Further research and development are needed to fully realize the clinical utility of genetic markers in CVD.
• While current genetic markers show limited impact, the field is evolving towards more precise CVD risk stratification.