Circulating causal protein networks linked to future risk of myocardial infarction

Area of Science:

• Genomics and Proteomics
• Cardiovascular Disease Research
• Systems Biology

Background:

• Blood protein variations are associated with complex diseases like cardiovascular conditions.
• Understanding the interplay of local and systemic factors is crucial for cardiovascular disease etiology.
• A systems-level approach is needed for comprehensive understanding.

Purpose of the Study:

• To develop a causal network inference framework for serum proteomics data.
• To identify causal serum protein subnetworks and their targets.
• To investigate associations with cardiometabolic traits and cardiovascular disease risk.

Main Methods:

• Utilized data from the Age, Gene/Environment Susceptibility-Reykjavik Study (AGES) cohort (n=5376).
• Measured 7523 serum proteins.
• Employed a causal inference framework with cis-acting protein quantitative trait loci (pQTLs) as instrumental variables to mitigate confounding.

Main Results:

• Identified 185 high-confidence causal serum protein subnetworks interacting with 5611 targets.
• Discovered several subnetworks with hierarchical, directional relationships.
• Found significant associations between subnetworks, cardiometabolic traits, and future risk of myocardial infarction and heart failure.

Conclusions:

• The causal network inference framework provides a systems-level understanding of cardiovascular disease.
• Identified protein subnetworks represent key players in cardiometabolic health and disease.
• Findings highlight potential targets for understanding and managing cardiovascular disease risk.