Subtyping strokes using blood-based biomarkers: A proteomics approach

Background and Objectives: 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 hemorrhage (ICH) within 24 hours using high-throughput proteomics. Methods: We collected serum samples within 24 hours from acute stroke (IS & ICH) and mimics patients. In the discovery phase, SWATH-MS proteomics identified differentially expressed proteins (fold change: 1.5, p<0.05, and confirmed/tentative selection using Boruta random forest) between IS and ICH which were validated using Multiple Reaction Monitoring (MRM) proteomics in the validation phase. Protein-protein interactions and pathway analysis were conducted using STRING version 11 and Cytoscape 3.9.0. Cut-off points were determined using Youden Index. Prediction models were developed using backward stepwise multivariable logistic regression analysis. Hanley-McNeil test, Integrated discrimination improvement index, and likelihood ratio test determined the improved discrimination ability of biomarkers added to clinical models. Results: Discovery phase included 20 IS and 20 ICH while validation phase included 150 IS, 150 ICH, and 6 stroke mimics. We quantified 365 proteins in the discovery phase. Between IS and ICH, we identified 20 differentially expressed proteins. In the validation phase, combined prediction model including three biomarkers: GFAP (OR 0.04; 95%CI 0.02-0.11), MMP9 (OR 0.09; 95%CI 0.03-0.28), APO-C1 (OR 5.76; 95%CI 2.66-12.47) and clinical variables independently differentiated IS from ICH (accuracy: 92%, sensitivity: 96%, specificity: 69%). Addition of biomarkers to clinical variables improved the discrimination capacity by 26% (p<0.001). Subgroup analysis within 6 hours identified that GFAP and MMP9 differentiated IS from ICH with a sensitivity> 90%. Conclusions: Our study identified that GFAP, MMP, and APO-C1 biomarkers independently differentiated IS from ICH within 24 hours and significantly improved the discrimination ability to predict IS. Temporal profiling of these biomarkers in the acute phase of stroke is urgently warranted. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was supported in part by the AIIMS Intramural Research Grant (F. No. 8-762/A-762/2019/RS). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was approved by the institutional ethics committee of All India Institute of Medical Sciences, New Delhi, India (Ref. No. IECPG-395/28.09.2017). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All proteomics data produced are available online at ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD032917. * IS : Ischemic Stroke ICH : Intracerebral hemorrhage SDE : Significantly Differentially Expressed CT : Computed Tomography MRI : Magnetic Resonance Imaging SWATH-MS : sequential windowed acquisition of all theoretical fragment ion mass spectra MRM : Multiple Reaction Monitoring FDR : False Discovery Rate PCA : Principal Component Analysis OR : Odds Ratio CI : Confidence Interval ROC : Receiver Operating Characteristic AUC : Area Under the Curve PPV : Positive Predictive Value NPV : Negative Predictive Value VIF : Variance Inflation Factor IDI : Integrated Discrimination Improvement GO : Gene Ontology KEGG : Kyoto Encyclopedia of Genes and Genomes.