Circulating causal protein networks linked to future risk of myocardial infarction

Area of Science:

• Proteomics
• Systems Biology
• Cardiovascular Disease Research

Background:

• Blood protein variations are linked to complex diseases like atherosclerotic cardiovascular disease (ACVD).
• Understanding the interplay of local and systemic factors is crucial for ACVD etiology.
• A comprehensive, systems-level approach is needed to understand ACVD development.

Purpose of the Study:

• To develop a causal network inference framework for serum proteins.
• To analyze one of the largest serum proteomics datasets (AGES study).
• To identify causal relationships between proteins and their role in ACVD.

Main Methods:

• Utilized the Age, Gene/Environment Susceptibility-Reykjavik Study (AGES) dataset (5,376 older adults).
• Analyzed 7,523 serum proteins using a causal network inference framework.
• Employed cis-acting protein quantitative trait loci (pQTLs) as instrumental variables to infer causal protein-protein interactions.

Main Results:

• Identified 185 significant causal protein subnetworks (FDR = 1%, n ≥ 10 members).
• These subnetworks interact with 5,611 target proteins, providing insights into systemic homeostasis.
• Several subnetworks showed significant associations with future myocardial infarction, heart failure, and cardiometabolic traits.

Conclusions:

• Causal protein networks play a significant role in the etiology of atherosclerotic cardiovascular disease.
• The identified subnetworks offer biological insights into systemic homeostasis and disease pathways.
• This framework advances our understanding of ACVD pathogenesis and potential therapeutic targets.