| Title: | Systematic analyses uncover plasma proteins linked to incident cardiovascular diseases |
| Journal: | Protein & Cell |
| Published: | 6 Aug 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40927895/ |
| DOI: | https://doi.org/10.1093/procel/pwaf072 |
| URL: | https://academic.oup.com/proteincell/advance-article-pdf/doi/10.1093/procel/pwaf072/64234994/pwaf072.pdf |
Publication 17767
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Abstract
Cardiovascular disease (CVD) research is hindered by limited comprehensive analyses of plasma proteome across disease subtypes. Here, we systematically investigated the associations between plasma proteins and cardiovascular outcomes in 53,026 UK Biobank participants over a 14-year follow-up. Association analyses identified 3,089 significant associations involving 892 unique protein analytes across 13 CVD outcomes. The most notable associations included NT-proBNP for atrial fibrillation (P = 6.31 × 10-313), followed by NPPB (P = 1.03 × 10-164) and GDF15 for heart failure (P = 1.21 × 10-166). Among 445 unique proteins significantly linked to 18 cardiovascular metrics, LEP (RVEDV: β = -9.03, P = 2.76 × 10-51) and FABP4 (RVEDV: β = -10.18, P = 2.42 × 10-32) emerged as the strongest correlates of cardiac structure and function. Our integrated prediction model performed excellently across the majority of CVD outcomes, achieving an AUC of 0.86 for abdominal aneurysm. Two-sample Mendelian randomization analysis revealed 225 proteins causally linked to CVDs, with LPA showing the strongest coronary artery disease association (OR = 1.13 [1.10-1.17], P = 2.38 × 10-15), many of which are targets of existing drugs, suggesting repurposing opportunities. Mediation analysis revealed broad-spectrum mediators (e.g., IGFBP4 and GDF15, each influencing 9 cardiovascular outcomes) and outcome-specific protein mediators, with modifiable risk factors such as smoking and BMI predominantly mediating protein-CVD associations.This comprehensive longitudinal study provides unprecedented insights into plasma proteome influences on cardiovascular health interactions, offering novel perspectives for CVD diagnosis, prediction, and prevention.</p>
10 Keywords
- Aged
- Biomarkers
- Blood Proteins
- Cardiovascular Diseases
- Female
- Growth Differentiation Factor 15
- Humans
- Male
- Mendelian Randomization Analysis
- Middle Aged
21 Authors
- Yi-Lin Chen
- Ji-Jing Wang
- Jia You
- Ji-Yun Cheng
- Ze-Yu Li
- Yi-Jun Ge
- Bing-Ran Yao
- Xiao-Yu He
- Yu Guo
- Yi Zhang
- Shi-Dong Chen
- Liu Yang
- Xin-Rui Wu
- Bang-Sheng Wu
- Ya-Ru Zhang
- Mei Cui
- Qiang Dong
- Jian-Feng Feng
- Mei Tian
- Wei Cheng
- Jin-Tai Yu
1 Application
| Application ID | Title |
|---|---|
| 19542 | Identifying multi-level biomarkers and disease mechanisms for major mental disorders |
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