Vignoli Alessia, Sticchi Elena, Piccardi Benedetta, Palumbo Vanessa, Sarti Cristina, Sodero Alessandro, Arba Francesco, Fainardi Enrico, Gori Anna Maria, Giusti Betti, Kura Ada, Tenori Leonardo, Baldereschi Marzia
Department of Chemistry "Ugo Schiff", University of Florence, Via della Lastruccia 3-13, Sesto Fiorentino, 50019, Italy.
Magnetic Resonance Center (CERM), University of Florence, Via Luigi Sacconi 6, Sesto Fiorentino, 50019, Italy.
J Transl Med. 2025 Apr 30;23(1):491. doi: 10.1186/s12967-025-06498-z.
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).
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.
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.
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.
缺血性中风是导致残疾和死亡的主要原因，在老年人中尤为如此。再灌注治疗，包括溶栓和取栓，对于恢复血流和挽救缺血组织至关重要。然而，这些干预措施可能会引发再灌注损伤，加重炎症和组织损伤，导致血脑屏障（BBB）破坏、脑水肿（CE）和不良功能结局。在此，我们提出一种将循环炎症生物标志物与代谢组学和脂蛋白组学数据相结合的模型，该模型能够帮助临床医生预测中风发作后24小时的BBB破坏、CE以及中风后功能预后不良（改良Rankin量表（mRS＞2））。
收集87例患者入院时及中风发作后24小时的外周血。采用逻辑LASSO回归算法确定代谢物、脂蛋白相关参数和循环生物标志物的最佳组合，以区分两个时间点的目标组。
多变量逻辑回归模型纳入的协变量包括：年龄、性别、发病至治疗时间、中风前使用降脂药物治疗、心力衰竭病史、心房颤动病史和糖尿病病史。回归模型显示，蛋氨酸、乙酸盐、甘氨酸A和基质金属蛋白酶-2是BBB破坏的重要预测因子，蛋氨酸、乙酸盐、金属蛋白酶组织抑制因子-1和CXC趋化因子配体10预测24小时CE，而CXC趋化因子配体10、白细胞介素-12和低密度脂蛋白-5预测三个月时功能预后不良。
由于中风具有异质性病理生理学，本研究中提出的基于生物标志物的个性化方法被证明在应对患者个体风险方面有效，并且有助于开发用于中风患者管理的新型诊断、预后和治疗性神经保护策略。
