Thrombosis origin identification of cardioembolism and large artery atherosclerosis by distinct metabolites.

Background: 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.

Methods: 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.

Results: 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).

Conclusions: 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.