Untargeted metabolomics to detect and identify plasma metabolic signatures associated with intracranial aneurysm and its rupture.

Abstract

The biological basis for metabolic differences between unruptured and ruptured intracranial aneurysm (UIA and RIA) populations and their potential role in triggering IA rupture remain unclear. The aim of this study was to analyze the plasma metabolic profiles of patients with UIA and RIA using an untargeted metabolomic approach and to develop a model for early rupture classification. Plasma samples were analyzed using an ultra-high-performance liquid chromatography high-resolution tandem mass spectrometry-based platform. Least absolute shrinkage and selection operator regression and random forest machine learning methods were employed for metabolite feature selection and predictive model construction. Among 49 differential plasma metabolites identified, 31 were increased and 18 were decreased in the plasma of RIA patients. Five key metabolites-canrenone, piperine, 1-methyladenosine, betaine, and trigonelline-were identified as having strong potential to discriminate between UIA and RIA patients. This combination of metabolites demonstrated high diagnostic accuracy, with an area under the curve exceeding 0.95 in both the training and validation datasets. Our finding highlights the significance of plasma metabolites as potential biomarkers for early detection of IA rupture risk, offering new insights for clinical practice and future research on IA management.