Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
Department of Neurosurgery, Liaocheng People's Hospital, Liaocheng City, Shandong Province, China.
J Neurointerv Surg. 2023 Jul;15(7):701-707. doi: 10.1136/neurintsurg-2022-019047. Epub 2022 Jun 2.
The diagnosis of cerebral thrombosis origin is challenging and remains unclear. This study aims to identify thrombosis due to cardioembolism (CE) and large artery atherosclerosis (LAA) from a new perspective of distinct metabolites.
Distinct metabolites between 26 CE and 22 LAA origin thrombi, which were extracted after successful mechanical thrombectomy in patients with acute ischemic stroke in the anterior circulation, were analyzed with a ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) system. Enriched metabolic pathways related to the metabolites were identified. Least absolute shrinkage selection operator regression analyses and a filtering method were used to select potential predictors. Furthermore, four machine learning classifiers, including decision tree, logistic regression, random forest (RF), and k means unsupervised classification model, were used to evaluate the predictive ability of the selected metabolites.
UPLC-QTOF-MS analysis revealed that levels of 88 and 55 metabolites were elevated in LAA and CE thrombi, respectively. Kyoto Encyclopedia of Genes and Genomes analysis revealed a significant difference between the pathways enriched in the two types of thrombi. Six metabolites (diglyceride (DG, 18:3/24:0), DG (22:0/24:0), phytosphingosine, galabiosylceramide (18:1/24:1), triglyceride (15:0/16:1/o-18:0), and glucosylceramide (18:1/24:0)) were finally selected to build a predictive model. The predictive RF model was confirmed to be the best, with a satisfactory stability and prediction capacity (area under the curve=0.889).
Six metabolites as potential predictors for distinguishing between cerebral thrombi of CE and LAA origin were identified. The results are useful for understanding the pathogenesis and for secondary stroke prevention.
脑血栓形成的诊断具有挑战性，目前仍不清楚。本研究旨在从独特代谢物的新视角识别心源性栓塞（CE）和大动脉粥样硬化（LAA）引起的血栓。
在前循环急性缺血性脑卒中患者成功机械取栓后，从 26 例 CE 起源和 22 例 LAA 起源血栓中提取出独特代谢物，并用超高效液相色谱-四极杆飞行时间质谱联用（UPLC-QTOF-MS）系统进行分析。鉴定与代谢物相关的富集代谢途径。使用最小绝对收缩选择算子回归分析和过滤方法选择潜在预测因子。此外，使用四种机器学习分类器，包括决策树、逻辑回归、随机森林（RF）和 k 均值无监督分类模型，评估所选代谢物的预测能力。
UPLC-QTOF-MS 分析显示，LAA 和 CE 血栓中分别有 88 种和 55 种代谢物水平升高。京都基因与基因组百科全书分析显示，两种血栓中富集的途径存在显著差异。最终选择了 6 种代谢物（二甘酯（DG，18:3/24:0）、DG（22:0/24:0）、植物鞘氨醇、半乳糖酰神经酰胺（18:1/24:1）、甘油三酯（15:0/16:1/o-18:0）和神经酰胺葡萄糖（18:1/24:0））来构建预测模型。预测 RF 模型被证实是最佳模型，具有良好的稳定性和预测能力（曲线下面积=0.889）。
确定了 6 种代谢物作为区分 CE 和 LAA 来源脑血栓的潜在预测因子。这些结果有助于了解发病机制，并有助于二级预防中风。
