Subtyping strokes using blood-based protein biomarkers: A high-throughput proteomics and machine learning approach

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.