Predicting reperfusion injury and functional status after stroke using blood biomarkers: the STROKELABED study
Abstract Background Ischemic stroke is a leading cause of disability and mortality, particularly among the elderly. Recanalization therapies, including thrombolysis and thrombectomy, are essential for restoring blood flow and saving ischemic tissue. However, these interventions may trigger reperfusi...
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-04-01
|
| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12967-025-06498-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850284713546088448 |
|---|---|
| author | Alessia Vignoli Elena Sticchi Benedetta Piccardi Vanessa Palumbo Cristina Sarti Alessandro Sodero Francesco Arba Enrico Fainardi Anna Maria Gori Betti Giusti Ada Kura Leonardo Tenori Marzia Baldereschi |
| author_facet | Alessia Vignoli Elena Sticchi Benedetta Piccardi Vanessa Palumbo Cristina Sarti Alessandro Sodero Francesco Arba Enrico Fainardi Anna Maria Gori Betti Giusti Ada Kura Leonardo Tenori Marzia Baldereschi |
| author_sort | Alessia Vignoli |
| collection | DOAJ |
| description | Abstract Background Ischemic stroke is a leading cause of disability and mortality, particularly among the elderly. Recanalization therapies, including thrombolysis and thrombectomy, are essential for restoring blood flow and saving ischemic tissue. However, these interventions may trigger reperfusion injury, worsening inflammation and tissue damage, leading to blood-brain-barrier (BBB) disruption, cerebral edema (CE) and adverse functional outcomes. Here we propose a model integrating circulating inflammatory biomarkers with metabolomic and lipoproteomic data able to help clinicians in predicting BBB disruption, CE at 24 h post stroke onset and poor post-stroke functional outcome (Modified Rankin Scale (mRS > 2). Methods Peripheral blood from 87 patients was collected at admission and 24 h after stroke onset. The logistic LASSO regression algorithm was employed to identify the optimal combination of metabolites, lipoprotein-related parameters and circulating biomarkers to discriminate the groups of interest at the two time-points. Results Multivariable logistic regression models included as covariates: age, sex, onset-to-treatment time, treatment with lipid-lowering medications before stroke, history of heart failure, history of atrial fibrillation and history of diabetes. The regression models showed that methionine, acetate, GlyA and MMP-2 were significant predictors of BBB disruption, methionine, acetate, TIMP-1 and CXCL-10 predicted 24-hours CE, whereas a poor functional outcome at three months was predicted by CXCL-10, IL-12 and LDL-5. Conclusions As stroke has a heterogeneous pathophysiology, a personalized approach based on biomarkers, as presented in this study, shown to be effective in tackling patient individual risk and could help in developing novel diagnostic, prognostic, and therapeutic neuroprotective strategies for the management of stroke patients. |
| format | Article |
| id | doaj-art-aa92d3eccccd4ac48aa04fedc5fc4c84 |
| institution | OA Journals |
| issn | 1479-5876 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj-art-aa92d3eccccd4ac48aa04fedc5fc4c842025-08-20T01:47:29ZengBMCJournal of Translational Medicine1479-58762025-04-0123111010.1186/s12967-025-06498-zPredicting reperfusion injury and functional status after stroke using blood biomarkers: the STROKELABED studyAlessia Vignoli0Elena Sticchi1Benedetta Piccardi2Vanessa Palumbo3Cristina Sarti4Alessandro Sodero5Francesco Arba6Enrico Fainardi7Anna Maria Gori8Betti Giusti9Ada Kura10Leonardo Tenori11Marzia Baldereschi12Department of Chemistry “Ugo Schiff”, University of FlorenceDepartment of Experimental and Clinical Medicine, University of FlorenceStroke Unit, Careggi University HospitalStroke Unit, Careggi University HospitalStroke Unit, Careggi University HospitalNeurofarba Department, University of FlorenceStroke Unit, Careggi University HospitalNeuroradiology Unit, Department of Experimental and Clinical Biomedical Sciences, Serio” University of FlorenceDepartment of Experimental and Clinical Medicine, University of FlorenceDepartment of Experimental and Clinical Medicine, University of FlorenceDepartment of Experimental and Clinical Medicine, University of FlorenceDepartment of Chemistry “Ugo Schiff”, University of FlorenceNeuroscience Institute, National Research CouncilAbstract Background Ischemic stroke is a leading cause of disability and mortality, particularly among the elderly. Recanalization therapies, including thrombolysis and thrombectomy, are essential for restoring blood flow and saving ischemic tissue. However, these interventions may trigger reperfusion injury, worsening inflammation and tissue damage, leading to blood-brain-barrier (BBB) disruption, cerebral edema (CE) and adverse functional outcomes. Here we propose a model integrating circulating inflammatory biomarkers with metabolomic and lipoproteomic data able to help clinicians in predicting BBB disruption, CE at 24 h post stroke onset and poor post-stroke functional outcome (Modified Rankin Scale (mRS > 2). Methods Peripheral blood from 87 patients was collected at admission and 24 h after stroke onset. The logistic LASSO regression algorithm was employed to identify the optimal combination of metabolites, lipoprotein-related parameters and circulating biomarkers to discriminate the groups of interest at the two time-points. Results Multivariable logistic regression models included as covariates: age, sex, onset-to-treatment time, treatment with lipid-lowering medications before stroke, history of heart failure, history of atrial fibrillation and history of diabetes. The regression models showed that methionine, acetate, GlyA and MMP-2 were significant predictors of BBB disruption, methionine, acetate, TIMP-1 and CXCL-10 predicted 24-hours CE, whereas a poor functional outcome at three months was predicted by CXCL-10, IL-12 and LDL-5. Conclusions As stroke has a heterogeneous pathophysiology, a personalized approach based on biomarkers, as presented in this study, shown to be effective in tackling patient individual risk and could help in developing novel diagnostic, prognostic, and therapeutic neuroprotective strategies for the management of stroke patients.https://doi.org/10.1186/s12967-025-06498-zStrokeBiomarkersInflammationBlood brain barrierCerebral edemaFunctional outcome |
| spellingShingle | Alessia Vignoli Elena Sticchi Benedetta Piccardi Vanessa Palumbo Cristina Sarti Alessandro Sodero Francesco Arba Enrico Fainardi Anna Maria Gori Betti Giusti Ada Kura Leonardo Tenori Marzia Baldereschi Predicting reperfusion injury and functional status after stroke using blood biomarkers: the STROKELABED study Journal of Translational Medicine Stroke Biomarkers Inflammation Blood brain barrier Cerebral edema Functional outcome |
| title | Predicting reperfusion injury and functional status after stroke using blood biomarkers: the STROKELABED study |
| title_full | Predicting reperfusion injury and functional status after stroke using blood biomarkers: the STROKELABED study |
| title_fullStr | Predicting reperfusion injury and functional status after stroke using blood biomarkers: the STROKELABED study |
| title_full_unstemmed | Predicting reperfusion injury and functional status after stroke using blood biomarkers: the STROKELABED study |
| title_short | Predicting reperfusion injury and functional status after stroke using blood biomarkers: the STROKELABED study |
| title_sort | predicting reperfusion injury and functional status after stroke using blood biomarkers the strokelabed study |
| topic | Stroke Biomarkers Inflammation Blood brain barrier Cerebral edema Functional outcome |
| url | https://doi.org/10.1186/s12967-025-06498-z |
| work_keys_str_mv | AT alessiavignoli predictingreperfusioninjuryandfunctionalstatusafterstrokeusingbloodbiomarkersthestrokelabedstudy AT elenasticchi predictingreperfusioninjuryandfunctionalstatusafterstrokeusingbloodbiomarkersthestrokelabedstudy AT benedettapiccardi predictingreperfusioninjuryandfunctionalstatusafterstrokeusingbloodbiomarkersthestrokelabedstudy AT vanessapalumbo predictingreperfusioninjuryandfunctionalstatusafterstrokeusingbloodbiomarkersthestrokelabedstudy AT cristinasarti predictingreperfusioninjuryandfunctionalstatusafterstrokeusingbloodbiomarkersthestrokelabedstudy AT alessandrosodero predictingreperfusioninjuryandfunctionalstatusafterstrokeusingbloodbiomarkersthestrokelabedstudy AT francescoarba predictingreperfusioninjuryandfunctionalstatusafterstrokeusingbloodbiomarkersthestrokelabedstudy AT enricofainardi predictingreperfusioninjuryandfunctionalstatusafterstrokeusingbloodbiomarkersthestrokelabedstudy AT annamariagori predictingreperfusioninjuryandfunctionalstatusafterstrokeusingbloodbiomarkersthestrokelabedstudy AT bettigiusti predictingreperfusioninjuryandfunctionalstatusafterstrokeusingbloodbiomarkersthestrokelabedstudy AT adakura predictingreperfusioninjuryandfunctionalstatusafterstrokeusingbloodbiomarkersthestrokelabedstudy AT leonardotenori predictingreperfusioninjuryandfunctionalstatusafterstrokeusingbloodbiomarkersthestrokelabedstudy AT marziabaldereschi predictingreperfusioninjuryandfunctionalstatusafterstrokeusingbloodbiomarkersthestrokelabedstudy |