| Title: | Enhanced prediction of atrial fibrillation risk using proteomic markers: a comparative analysis with clinical and polygenic risk scores. |
| Journal: | Heart |
| Published: | 4 Sep 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39237126/ |
| DOI: | https://doi.org/10.1136/heartjnl-2024-324274 |
Publication 13023
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Abstract
BACKGROUND: Proteomic biomarkers have shown promise in predicting various cardiovascular conditions, but their utility in assessing the risk of atrial fibrillation (AF) remains unclear. This study aimed to develop and validate a protein-based risk score for predicting incident AF and to compare its predictive performance with traditional clinical risk factors and polygenic risk scores in a large cohort from the UK Biobank.</p>
METHODS: We analysed data from 36 129 white British individuals without prior AF, assessing 2923 plasma proteins using the Olink Explore 3072 assay. The cohort was divided into a training set (70%) and a test set (30%) to develop and validate a protein risk score for AF. We compared the predictive performance of this score with the HARMS2-AF risk model and a polygenic risk score.</p>
RESULTS: Over an average follow-up of 11.8 years, 2450 incident AF cases were identified. A 47-protein risk score was developed, with N-terminal prohormone of brain natriuretic peptide (NT-proBNP) being the most significant predictor. In the test set, the protein risk score (per SD increment, HR 1.94; 95% CI 1.83 to 2.05) and NT-proBNP alone (HR 1.80; 95% CI 1.70 to 1.91) demonstrated superior predictive performance (C-statistic: 0.802 and 0.785, respectively) compared with HARMS2-AF and polygenic risk scores (C-statistic: 0.751 and 0.748, respectively).</p>
CONCLUSIONS: A protein-based risk score, particularly incorporating NT-proBNP, offers superior predictive value for AF risk over traditional clinical and polygenic risk scores, highlighting the potential for proteomic data in AF risk stratification.</p>
9 Authors
- Mengyi Liu
- Yuanyuan Zhang
- Ziliang Ye
- Panpan He
- Chun Zhou
- Sisi Yang
- Yanjun Zhang
- Xiaoqin Gan
- Xianhui Qin
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