Shang Luxiang, Wang Weilin, Liu Yiying, Tang Baopeng, Hou Yinglong
Department of Cardiology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, China.
Medical Science and Technology Innovation Center, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
J Cell Mol Med. 2025 Aug;29(15):e70760. doi: 10.1111/jcmm.70760.
Atrial fibrillation (AF) is a common arrhythmia associated with significant morbidity and adverse outcomes. High-throughput proteomics offers a promising approach for identifying circulating biomarkers to improve AF risk stratification. This systematic review evaluated cohort studies published from 2010 to 2024 that employed proteomic approaches to investigate associations between circulating proteins and AF incidence. Studies were screened using predefined criteria, and proteins significantly associated with AF in fully adjusted models were extracted. Bioinformatics analyses, including pathway enrichment and protein-protein interaction (PPI) network mapping, were conducted to characterise the implicated biological processes. Proteins reproducibly associated with AF across studies were further evaluated through drug target annotation and assessed for potential causal relationships using Mendelian randomisation (MR). In total, 111 proteins were identified across nine cohorts, with significant enrichment in pathways related to extracellular matrix organisation and cell adhesion. PPI analysis identified interleukin-6, matrix metalloproteinase-2 and C-reactive protein as central network hubs. Thirteen proteins were reproducible across at least two studies, with NT-proBNP consistently reported in eight cohorts, supporting its role as a robust and reliable biomarker of AF risk. Drug target analysis showed that four proteins-ANGPT-2, adrenomedullin, GDF15 and PLAUR-are currently under clinical investigation. MR analysis did not confirm causal associations between these proteins and AF, suggesting they may reflect disease-related processes rather than directly drive AF onset. This review highlights reproducible proteomic biomarkers and their functional networks in AF, providing a foundation for future studies focused on biomarker-guided prevention and therapeutic development. Trial Registration: PROSPERO number: CRD420251063038.
心房颤动(AF)是一种常见的心律失常,与显著的发病率和不良后果相关。高通量蛋白质组学为识别循环生物标志物以改善房颤风险分层提供了一种有前景的方法。本系统综述评估了2010年至2024年发表的队列研究,这些研究采用蛋白质组学方法调查循环蛋白与房颤发病率之间的关联。使用预定义标准筛选研究,并提取在完全调整模型中与房颤显著相关的蛋白质。进行了生物信息学分析,包括通路富集和蛋白质-蛋白质相互作用(PPI)网络映射,以表征所涉及的生物学过程。通过药物靶点注释进一步评估了跨研究与房颤可重复相关的蛋白质,并使用孟德尔随机化(MR)评估了潜在的因果关系。总共在九个队列中鉴定出111种蛋白质,在与细胞外基质组织和细胞粘附相关的通路中显著富集。PPI分析确定白细胞介素-6、基质金属蛋白酶-2和C反应蛋白为中心网络枢纽。13种蛋白质在至少两项研究中具有可重复性,八项队列研究一致报告了N末端脑钠肽前体(NT-proBNP),支持其作为房颤风险的强大且可靠生物标志物的作用。药物靶点分析表明,四种蛋白质——血管生成素-2(ANGPT-2)、肾上腺髓质素、生长分化因子15(GDF15)和尿激酶型纤溶酶原激活物受体(PLAUR)——目前正在进行临床研究。MR分析未证实这些蛋白质与房颤之间的因果关联,表明它们可能反映了疾病相关过程,而非直接驱动房颤发作。本综述强调了房颤中可重复的蛋白质组学生物标志物及其功能网络,为未来专注于生物标志物指导的预防和治疗开发的研究奠定了基础。试验注册:PROSPERO编号:CRD420251063038