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 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.

Keywords: Biomarkers; Blood brain barrier; Cerebral edema; Functional outcome; Inflammation; Stroke.

Fig. 1

Heatmap of metabolite concentrations in the groups determined by the three outcomes of interest (altered BBB permeability, cerebral edema, mRS > 2). Metabolite levels were measured at t0 and t1. The Cliff’s Delta effect size is represented with red (blue) for higher (lower) levels in patients with worse prognosis as illustrated in the color key. White dots encode for metabolites with p-values FDR adjusted < 0.1

Fig. 2

Heatmap of concentrations of the lipoprotein-related main fractions in the groups determined by the three outcomes of interest (altered BBB permeability, cerebral edema, mRS > 2). Lipoprotein levels were measured at t0 and t1. The Cliff’s Delta effect size is represented with red (blue) for higher (lower) levels in patients with worse prognosis as illustrated in the color key. White dots encode for metabolites with p-values FDR adjusted < 0.1.

Fig. 3

Heatmap of concentrations of circulating biomarkers in the groups determined by the three outcomes of interest (altered BBB permeability, cerebral edema, mRS > 2). Circulating biomarker levels were measured at t0 and t1. The Cliff’s Delta effect size is represented with red (blue) for higher (lower) levels in patients with worse prognosis as illustrated in the color key. White dots encode for metabolites with p-values FDR adjusted < 0.1