Plasma metabolic signatures for intracranial aneurysm and its rupture identified by pseudotargeted metabolomics

Abstract Background Whole-body metabolism is emerging as a critical player in the pathophysiology of intracranial aneurysms (IAs). However, the vital metabolic signatures for IA risk stratification and its potential biological underpinnings remain elusive. Therefore, our study aimed to develop an early diagnosis model and rupture classification model by analyzing plasma metabolic profiles of IA patients. Methods Plasma samples from a cohort of 105 participants, including 75 patients with IAs in ruptured and unruptured statuses and 30 control participants, were collected for analysis. A pseudotargeted metabolomics strategy based on LC-MS/MS was applied for integration analyses of plasma samples, and significant differential metabolites were identified through univariate and multivariate statistical analysis. Furthermore, an integrated machine learning strategy based on LASSO, random forest and logistic regression were used for feature selection and model construction. Results The composition of metabolites changed significantly with the severity of IA. Among them, glutathione metabolism and bile acid metabolism may play a crucial role. Two sets of biomarker panels (M6 & M5) were defined to discriminate IA and its rupture with the area under receiver operating characteristic curve (AUC) of 0.843 and 0.929 on the validation sets, respectively. Conclusion The present study comprehensively characterizes plasma metabolites in different statuses of IA and demonstrates the potential of metabolic markers as non-invasive risk diagnostic tools for IA.