Kraaijenhof Jordan M, Nurmohamed Nick S, Bom Michiel J, Gaillard E L, Ibrahim Shirin, Beverloo Cheyenne Y Y, Planken R Nils, Hovingh G Kees, Danad Ibrahim, Stroes Erik S G, Knaapen Paul
Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands.
Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Eur Heart J Cardiovasc Imaging. 2025 Mar 3;26(3):489-499. doi: 10.1093/ehjci/jeae313.
Coronary computed tomography angiography (CCTA) offers detailed imaging of plaque burden and composition, with plaque progression being a key determinant of future cardiovascular events. As repeated CCTA scans are burdensome and costly, there is a need for non-invasive identification of plaque progression. This study evaluated whether combining proteomics with traditional risk factors can detect patients at risk for accelerated plaque progression.
This long-term follow-up study included 97 participants who underwent two CCTA scans and plasma proteomics analysis using the Olink platform. Accelerated plaque progression was defined as rates above the median for percent atheroma volume (PAV), percent non-calcified plaque volume (NCPV), and percent calcified plaque volume (CPV). High-risk plaque (HRP) was identified by positive remodelling or low-density plaque at baseline and/or follow-up. Significant proteins associated with PAV, NCPV, CPV, and HRP development were incorporated into predictive models. The mean baseline age was 58.0 ± 7.4 years, with 63 (65%) male, and a median follow-up of 8.5 ± 0.6 years. The area under the curve (AUC) for accelerated PAV progression increased from 0.830 with traditional risk factors and baseline plaque volume to 0.909 with the protein panel (P = 0.023). For NCPV progression, AUC improved from 0.685 to 0.825 (P = 0.008), while no improvement was observed for CPV progression. For HRP development, AUC increased from 0.791 to 0.860 with the protein panel (P = 0.036).
Integrating proteomics with traditional risk factors enhances the prediction of accelerated plaque progression and high-risk plaque development, potentially improving risk stratification and treatment decisions without the need for repeated CCTAs.
冠状动脉计算机断层扫描血管造影（CCTA）可提供斑块负荷和成分的详细成像，斑块进展是未来心血管事件的关键决定因素。由于重复进行CCTA扫描既繁琐又昂贵，因此需要对斑块进展进行非侵入性识别。本研究评估了将蛋白质组学与传统危险因素相结合是否能够检测出有斑块加速进展风险的患者。
这项长期随访研究纳入了97名参与者，他们接受了两次CCTA扫描，并使用Olink平台进行了血浆蛋白质组学分析。斑块加速进展被定义为动脉粥样硬化体积百分比（PAV）、非钙化斑块体积百分比（NCPV）和钙化斑块体积百分比（CPV）高于中位数的速率。通过基线和/或随访时的阳性重塑或低密度斑块来识别高危斑块（HRP）。将与PAV、NCPV、CPV和HRP发展相关的重要蛋白质纳入预测模型。平均基线年龄为58.0±7.4岁，其中63名（65%）为男性，中位随访时间为8.5±0.6年。加速PAV进展的曲线下面积（AUC）从仅使用传统危险因素和基线斑块体积时的0.830增加到加入蛋白质组时的0.909（P=0.023）。对于NCPV进展，AUC从0.685提高到0.825（P=0.008），而CPV进展未观察到改善。对于HRP发展，加入蛋白质组时AUC从0.791增加到0.860（P=0.036）。
将蛋白质组学与传统危险因素相结合可增强对斑块加速进展和高危斑块发展的预测，有可能改善风险分层和治疗决策，而无需重复进行CCTA检查。
