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Optimized Protocol for the Extraction of Proteins from the Human Mitral Valve
Published on: June 14, 2017
Integrative proteomic analyses across common cardiac diseases yield mechanistic insights and enhanced prediction
Art Schuermans1,2,3, Ashley B Pournamdari1,4, Jiwoo Lee1,2
1Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
Insights
This study analyzed 1,459 proteins in over 44,000 UK Biobank participants, identifying numerous protein-disease links for major cardiac conditions. Proteomic data improved disease prediction, offering new therapeutic targets and prevention strategies.
Area of Science:
- Cardiovascular Medicine
- Proteomics
- Genetics
Background:
- Cardiac diseases are a leading cause of morbidity with incompletely understood molecular underpinnings.
- Understanding the circulating proteome's role in cardiac disease is crucial for developing effective treatments.
Purpose of the Study:
- To characterize the circulating proteome associated with incident coronary artery disease, heart failure, atrial fibrillation, and aortic stenosis.
- To identify potential causal links and therapeutic targets for cardiac diseases using proteomic data.
Main Methods:
- Analysis of 1,459 protein measurements in 44,313 UK Biobank participants.
- Multivariable-adjusted Cox regression and cis-Mendelian randomization were employed.
- Sex-stratified interaction analyses were conducted to explore differential associations.
Main Results:
- Identified 820 significant protein-disease associations (441 unique proteins) at a Bonferroni-adjusted P < 8.6 × 10⁻⁶.
- Cis-Mendelian randomization suggested causal roles for a subset of identified proteins.
- Proteomic data improved prediction models for coronary artery disease, heart failure, and atrial fibrillation compared to clinical factors alone.
Conclusions:
- The study provides a comprehensive proteomic map associated with major cardiac diseases.
- Identified proteins, such as spondin-1 and Kunitz-type protease inhibitor 1, represent potential therapeutic targets.
- These findings support the development of protein-based prevention and treatment strategies for cardiovascular conditions.
Abstract:
Cardiac diseases represent common highly morbid conditions for which molecular mechanisms remain incompletely understood. Here we report the analysis of 1,459 protein measurements in 44,313 UK Biobank participants to characterize the circulating proteome associated with incident coronary artery disease, heart failure, atrial fibrillation and aortic stenosis. Multivariable-adjusted Cox regression identified 820 protein-disease associations-including 441 proteins-at Bonferroni-adjusted P < 8.6 × 10-6. Cis-Mendelian randomization suggested causal roles aligning with epidemiological findings for 4% of proteins identified in primary analyses, prioritizing therapeutic targets across cardiac diseases (for example, spondin-1 for atrial fibrillation and the Kunitz-type protease inhibitor 1 for coronary artery disease). Interaction analyses identified seven protein-disease associations that differed Bonferroni-significantly by sex. Models incorporating proteomic data (versus clinical risk factors alone) improved prediction for coronary artery disease, heart failure and atrial fibrillation. These results lay a foundation for future investigations to uncover disease mechanisms and assess the utility of protein-based prevention strategies for cardiac diseases.
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