Abstract

Background Neuroinflammatory responses reflecting disease progression are believed to be closely associated with the severity of prognosis in post-stroke.

Purpose This study developed a combined predicted model of inflammation-derived biomarkers and clinical-based indicators using machine learning algorithms for differentiation of the functional outcome in patients with subacute ischemic stroke.

Methods Clinical blood samples and patient data from individuals with subacute ischemic stroke were collected at admission. Based on activities of daily living assessments followed by a 3-month recovery, patients were categorized into two groups: those with little effective recovery (LE) and those with obvious effective recovery (OE). Serum samples underwent proteomic testing for initial candidates. Subsequently, multidimensional validation of candidates in models of ischemia-reperfusion at protein and mRNA levels was performed. T-test, Receiver Operating Characteristic (ROC), and LASSO analysis in an additional cohort were performed to confirm the clinical variables and candidate biomarkers in the discriminatory sensitivity and specificity between the LE and OE groups. Finally, models were developed based on candidates in the training dataset and predicted stroke recovery outcomes in another new dataset using ten standard two-categorical variable algorithms in machine learning.

Results We identified higher tissue inhibitor metalloproteinase-1 (TIMP1) and LGALS3 levels were positively correlated with the severity of prognosis after ischemic stroke rehabilitation. TIMP1 (AUC=0.904, 0.873) and LGALS3 (AUC=0.995, 0.794) were confirmed to address superior sensitivity and specificity in distinguishing ischemic stroke from healthy control and LE group from OE group. The TIMP1 and Lgals3 expression exhibited an evident increase in microglia following ischemia-reperfusion. In addition, inflammation-derived biomarkers (TIMP1, LGALS3) coupled with clinical-based indicators (HGB, LDL-c, UA) were built in a combined model with random forest to differentiate OE from LE in 3-month follow-up with high accuracy (AUC = 0.8).

Conclusion Our findings provided evidence supporting the critical prognostic potential and risk prediction of inflammation-derived biomarkers after ischemic stroke rehabilitation in complementary to current clinical-based parameters.

Competing Interest Statement

The authors have declared no competing interest.

Clinical Trial

The study was approved by the local ethical committee of The First Affiliated Hospital of Shenzhen University (No.20211025002-FS01) and carried out by the Declaration of Helsinki. Medical history, physical examination, and biochemical data were obtained at the enrollment.

Funding Statement

This study was supported by funds from the Natural Science Funding of China (No.82272598 to Jiao Luo, No.81901470 to Jiao Luo), the Science, Technology and Innovation Commission of Shenzhen (JCYJ20210324135804012 to Jiao Luo?JCYJ20220530150407015 to Yulong Wang), Natural Science Foundation of Guangdong Province, China (No. 2020A1515011203), the Postdoctoral Science Foundation of China (No. 2019M663100).

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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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 local ethical committee of The First Affiliated Hospital of Shenzhen University (No.20211025002-FS01) and carried out by the Declaration of Helsinki. Medical history, physical examination, and biochemical data were obtained at the enrollment.

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Yes

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Data Availability

There is no conflict of interest for all authors who share data and materials in this article.

AbbreviationsISischemic strokeLElittle effective recoveryOEobvious effective recoveryHChealthy controlADLactivities of daily livingTIMP1Tissue inhibitor of metalloproteinase 1LGALS3Galectin-3MMPsMatrix metalloproteinasesHGBhemoglobinLDL-clow-density lipoprotein cholesterolUAuric acidROCreceiver operating characteristicAUCarea under curveNVUneurovascular unitROSreactive oxygen speciesNOnitric oxideTNF-αtumor necrosis factor αMCAOmiddle cerebral artery occlusionLC-MS/MSliquid chromatography-tandem mass spectrometry analysisUHPLCultra high-performance liquid chromatographyPASEFparallel accumulation serial fragmentationFPRfalse-positive rateDEPsdifferentially expressed proteinsPCAprincipal component analysisDEGsdifferentially expressed genesGOgene ontologyTGFB1Transforming Growth Factor beta 1PTPRCProtein Tyrosine Phosphatase Receptor Type CVIMVimentinMYH9Myosin Heavy Chain 9CSF1RColony stimulating factor 1 receptorGSNGelsolinTTCtriphenyl tetrazolium chlorideOGD/Roxygen-glucose deprivation/ reoxygenationPVNparaventricular nucleus of hypothalamusECMextracellular matrixBBBblood-brain barrierPASEFparallel cumulative serial fragmentationDEPsdifferentially expressed proteinsDEGsdifferentially expressed genesGOGene OntologyALBAlbminsvmSupport Vector MachinerfRandom ForestnnetArtificial Neural and Networknbnaive bayesian classifierkknnK-Nearest NeighborglmnetLogistic RegressionC5.0SEMstandard error of the mean.