Mendelian randomization studies in coronary artery disease

Area of Science:

• Cardiovascular Epidemiology
• Genetic Epidemiology
• Biostatistics

Background:

• Observational studies identify numerous biomarkers associated with coronary artery disease (CAD) risk.
• Establishing causality from observational associations is challenging due to confounding and reverse causation.
• Mendelian randomization (MR) offers a robust approach to infer causality in biomarker-disease relationships.

Purpose of the Study:

• To review the opportunities and challenges of applying MR studies to identify causal risk factors for CAD.
• To highlight the utility of MR in distinguishing true causal relationships from mere associations.
• To discuss the potential of MR for identifying novel therapeutic targets for CAD.

Main Methods:

• MR studies utilize genetic variants as instrumental variables for biomarkers.
• Key assumptions include the genetic variant's association with the biomarker and no confounding effects on disease.
• The method leverages random distribution of confounders across genotype groups.

Main Results:

• Successful MR analyses, exemplified by LDL cholesterol, demonstrate that genetic variants influencing biomarkers also influence CAD risk.
• The approach validates biomarkers like LDL cholesterol as causal risk factors for CAD.
• MR studies involving lipid traits, inflammation, hypertension, diabetes, and obesity are discussed.

Conclusions:

• MR is a powerful tool for establishing causality between biomarkers and CAD.
• It overcomes limitations of observational studies by minimizing confounding and reverse causation.
• MR analysis holds significant promise for discovering effective therapeutic targets for CAD.