Schuermans Art, Pournamdari Ashley B, Lee Jiwoo, Bhukar Rohan, Ganesh Shriienidhie, Darosa Nicholas, Small Aeron M, Yu Zhi, Hornsby Whitney, Koyama Satoshi, Januzzi James L, Honigberg Michael C, Natarajan Pradeep
medRxiv. 2023 Dec 19:2023.12.19.23300218. doi: 10.1101/2023.12.19.23300218.
Cardiac diseases represent common highly morbid conditions for which underlying molecular mechanisms remain incompletely understood. Here, we leveraged 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 <8.6×10 . -Mendelian randomization suggested causal roles that aligned with epidemiological findings for 6% of proteins identified in primary analyses, prioritizing novel therapeutic targets for different cardiac diseases (e.g., interleukin-4 receptor for heart failure and spondin-1 for atrial fibrillation). Interaction analyses identified seven protein-disease associations that differed Bonferroni-significantly by sex. Models incorporating proteomic data (vs. 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 novel disease mechanisms and assess the clinical utility of protein-based prevention strategies for cardiac diseases.
心脏疾病是常见的高发病症，但其潜在分子机制仍未完全明晰。在此，我们利用英国生物银行44313名参与者的1459项蛋白质测量数据，来描述与冠心病、心力衰竭、心房颤动和主动脉瓣狭窄相关的循环蛋白质组。多变量调整的Cox回归在Bonferroni校正后确定了820种蛋白质与疾病的关联，其中包括441种蛋白质，校正后的P值<8.6×10⁻⁶。孟德尔随机化分析表明，在初步分析中确定的6%的蛋白质具有与流行病学研究结果一致的因果作用，为不同心脏疾病确定了新的治疗靶点（例如，心力衰竭的白细胞介素-4受体和心房颤动的腱蛋白-1）。交互分析确定了7种蛋白质与疾病的关联在性别上存在Bonferroni显著性差异。纳入蛋白质组数据的模型（相对于仅使用临床风险因素）改善了对冠心病、心力衰竭和心房颤动的预测。这些结果为未来揭示新的疾病机制以及评估基于蛋白质的心脏疾病预防策略的临床效用奠定了基础。
