Integrative proteomic analyses across common cardiac diseases yield new mechanistic insights and enhanced prediction

Area of Science:

• Cardiovascular Science
• Proteomics
• Genetics

Background:

• Cardiac diseases are a major cause of death, with poorly understood molecular underpinnings.
• Identifying novel molecular mechanisms is crucial for developing effective treatments.

Approach:

• Leveraged proteomic data from 1,459 circulating proteins in 44,313 UK Biobank participants.
• Utilized multivariable-adjusted Cox regression and cis-Mendelian randomization to identify protein-disease associations and causal links.
• Performed interaction analyses to explore sex-specific differences in protein-disease relationships.

Key Points:

• Identified 820 significant protein-disease associations (441 proteins) for coronary artery disease, heart failure, atrial fibrillation, and aortic stenosis.
• Cis-Mendelian randomization suggested causal roles for some proteins, highlighting potential therapeutic targets like IL-4 receptor for heart failure.
• Proteomic data enhanced prediction models for major cardiac conditions compared to clinical risk factors alone.

Conclusions:

• This large-scale proteomic analysis provides a foundation for understanding cardiac disease mechanisms.
• Identified potential novel therapeutic targets for various cardiovascular conditions.
• Suggests the clinical utility of protein-based biomarkers for cardiac disease prevention and risk stratification.