血清代谢组分析揭示了预测血管内卒中治疗后恶性脑水肿的生物标志物。

IF 4.3 2区 医学 Q1 CLINICAL NEUROLOGY
Jiaqi Luo, Xiaolin Zhao, Mengxuan Xiao, Junlin Deng, Shuhua Xie, Huanhuan Fan, Wenting Lu, Yuqing Su, Tong Wu, Huanrong Ma, Xianghong Liu, Suyue Pan, Kaibin Huang
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引用次数: 0

摘要

代谢组学反映了人体的代谢状态,在识别相关生物标志物和探索病理过程方面具有重大的前景。本研究利用血清代谢组学预测急性缺血性卒中(AIS)患者血管内治疗(EVT)后的恶性脑水肿(MBE),并探讨其潜在机制。在这项前瞻性观察性研究中,我们招募了前循环大血管闭塞的患者,这些患者在AIS evt后成功进行了再通,包括伴有或不伴有出血转化的患者。符合条件的患者根据随后出现的MBE分为两组。根据倾向评分匹配来调整潜在的混杂因素,对血清样本进行了广泛针对性的代谢组学分析。然后,基于已确定的差异代谢物建立了一个预测模型,并在另一个独立队列中进行了验证。通过对46例匹配患者的血清样本进行广泛针对性的代谢组学分析,我们确定了30种代谢物在MBE患者和非MBE患者之间发生显著改变,包括酰基肉碱、溶血磷脂酰胆碱、核黄素和酪氨酸的显著差异。通过10倍帧移交叉验证,构建了包含9种差异代谢物的预测模型,在训练集、测试集和外部验证中均具有较好的预测效果,曲线下面积(AUC)分别为0.842、0.815和0.734。基于多个时间点的动态变化分析可能提示氧化能量供应减少和持续应激在MBE中的潜在作用。所鉴定的酰基肉碱和溶血磷脂酰胆碱可能在MBE的发病机制中起重要作用。基于这些差异代谢物的预测模型有望成为MBE早期检测的无创分析方法,并需要进一步的改进和验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Serum Metabolome Profiling Reveals Biomarkers to Predict Malignant Brain Edema Following Endovascular Stroke Therapy.

Metabolomics reflects the body's metabolic state and holds substantial promise for identifying associated biomarkers and exploring pathological processes. This study used serum metabolomics to predict malignant brain edema (MBE) following endovascular therapy (EVT) for acute ischemic stroke (AIS) and investigate the underlying mechanisms. In this prospective observational study, we enrolled patients with anterior circulation large vessel occlusion who underwent successful recanalization post-EVT for AIS, including those with or without concomitant hemorrhagic transformation. Eligible patients were stratified into two groups according to the subsequent presence of MBE. Following propensity score matching to adjust for potential confounders, widely targeted metabolomic analysis was conducted on the serum samples. A prediction model, based on the identified differential metabolites, was then developed and validated in another independent cohort. Through widely targeted metabolomic analysis of serum samples from matched 46 patients, we identified 30 metabolites that were significantly altered between patients with and without MBE, including notable differences in acylcarnitine, lysophosphatidylcholine, riboflavin, and tyrosine. A prediction model incorporating 9 differential metabolites was constructed using 10-fold frame shift cross-validation, demonstrating prediction performance in the training set, test set, and external validation, with areas under the curve (AUC) of 0.842, 0.815, and 0.734, respectively. Dynamic change analysis based on multiple time points may suggest a potential role of diminished oxidative energy supply and sustained stress in MBE. The identified acylcarnitine and lysophosphatidylcholine might play influential roles in the pathogenesis of MBE. The prediction model derived from these differential metabolites holds promise as a noninvasive assay for the early detection of MBE and warrants additional refinement and validation.

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来源期刊
Translational Stroke Research
Translational Stroke Research CLINICAL NEUROLOGY-NEUROSCIENCES
CiteScore
13.80
自引率
4.30%
发文量
130
审稿时长
6-12 weeks
期刊介绍: Translational Stroke Research covers basic, translational, and clinical studies. The Journal emphasizes novel approaches to help both to understand clinical phenomenon through basic science tools, and to translate basic science discoveries into the development of new strategies for the prevention, assessment, treatment, and enhancement of central nervous system repair after stroke and other forms of neurotrauma. Translational Stroke Research focuses on translational research and is relevant to both basic scientists and physicians, including but not restricted to neuroscientists, vascular biologists, neurologists, neuroimagers, and neurosurgeons.
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