Misra Shubham, Singh Praveen, Sengupta Shantanu, Kushwaha Manoj, Rahman Zuhaibur, Bhalla Divya, Talwar Pumanshi, Nath Manabesh, Chakraborty Rahul, Kumar Pradeep, Kumar Amit, Aggarwal Praveen, Srivastava Achal K, Pandit Awadh K, Mohania Dheeraj, Prasad Kameshwar, Mishra Nishant K, Vibha Deepti
Department of Neurology, All India Institute of Medical Sciences, New Delhi, India.
Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, USA.
Eur J Clin Invest. 2025 Apr;55(4):e14372. doi: 10.1111/eci.14372. Epub 2024 Dec 10.
Rapid diagnosis of stroke and its subtypes is critical in early stages. We aimed to discover and validate blood-based protein biomarkers to differentiate ischemic stroke (IS) from intracerebral haemorrhage (ICH) using high-throughput proteomics.
We collected serum samples within 24 h from acute stroke (IS & ICH) and mimics patients. In the discovery phase, SWATH-MS proteomics identified differentially expressed proteins, which were validated using targeted proteomics in the validation phase. We conducted interaction network and pathway analyses using Cytoscape 3.10.0. We determined cut-off points using the Youden Index. We developed three prediction models using multivariable logistic regression analyses. We assessed the model performance using statistical tests.
We included 20 IS and 20 ICH in the discovery phase and 150 IS, 150 ICH, and six stroke mimics in the validation phase. We quantified 375 proteins using SWATH-MS. Between IS and ICH, we discovered 20 differentially expressed proteins. In the validation phase, the combined prediction model including three biomarkers: GFAP (aOR 0.04; 95%CI .02-.11), MMP-9 (aOR .09; .03-.28), APO-C1 (aOR 5.76; 2.66-12.47) and clinical variables independently differentiated IS from ICH (accuracy: 92%, negative predictive value: 94%). Adding biomarkers to clinical variables improved discrimination by 26% (p < .001). Additionally, nine biomarkers differentiated IS from ICH within 6 h, while three biomarkers differentiated IS from mimics.
Our study demonstrated that GFAP, MMP-9 and APO-C1 biomarkers independently differentiated IS from ICH within 24 h and significantly improved the discrimination ability of prediction models. Temporal profiling of these biomarkers in the acute phase of stroke is warranted.
卒中及其亚型的快速诊断在早期阶段至关重要。我们旨在利用高通量蛋白质组学发现并验证基于血液的蛋白质生物标志物，以区分缺血性卒中（IS）和脑出血（ICH）。
我们在急性卒中（IS和ICH）及模拟患者发病24小时内收集血清样本。在发现阶段，SWATH-MS蛋白质组学鉴定出差异表达的蛋白质，在验证阶段使用靶向蛋白质组学进行验证。我们使用Cytoscape 3.10.0进行相互作用网络和通路分析。我们使用约登指数确定截断点。我们使用多变量逻辑回归分析开发了三个预测模型。我们使用统计检验评估模型性能。
我们在发现阶段纳入了20例IS和20例ICH，在验证阶段纳入了150例IS、150例ICH和6例卒中模拟患者。我们使用SWATH-MS对375种蛋白质进行了定量。在IS和ICH之间，我们发现了20种差异表达的蛋白质。在验证阶段，包含三种生物标志物：胶质纤维酸性蛋白（GFAP）（调整后比值比0.04；95%置信区间0.02 - 0.11）、基质金属蛋白酶-9（MMP-9）（调整后比值比0.09；0.03 - 0.28）、载脂蛋白C1（APO-C1）（调整后比值比5.76；2.66 - 12.47）以及临床变量的联合预测模型能够独立区分IS和ICH（准确率：92%，阴性预测值：94%）。在临床变量中加入生物标志物可使辨别能力提高26%（p < 0.001）。此外，9种生物标志物可在6小时内区分IS和ICH，而3种生物标志物可区分IS和模拟患者。
我们的研究表明，GFAP、MMP-9和APO-C1生物标志物可在24小时内独立区分IS和ICH，并显著提高预测模型的辨别能力。有必要对这些生物标志物在卒中急性期进行时间进程分析。
