Metabolic signatures and diagnostic models of ischemic stroke and its hypertensive subtype: a non-targeted metabolomics and machine learning

Abstract

Ischemic stroke (IS) is a leading cause of death and disability worldwide, yet reliable early diagnostic biomarkers remain lacking. This study employed non-targeted metabolomics and machine learning to characterize metabolic profiles and identify diagnostic biomarkers for IS. Non-targeted metabolomics profiling was performed using LC-MS/MS on plasma samples from 100 IS patients, including 50 cases of hypertensive ischemic stroke (HIS) and 50 cases of non-hypertensive ischemic stroke (NHIS), together with metabolomics data of 30 healthy controls (HC) from a previously published studies. Three machine learning algorithms, LASSO, glmBoost, and plsRglm, were used to screen candidate metabolite markers and construct a diagnostic model. Furthermore, targeted metabolomics was performed for quantitative validation of core differential metabolites in an independent clinical cohort. A total of 59 significantly differential expressed metabolites (DEMs) between IS and HC were identified, mainly enriched in Ferroptosis, Glutathione metabolism, and Sphingolipid signaling pathways. L-Cystine, LPC 18:1, and LPC 14:0 were selected as candidate markers. The diagnostic model constructed based on these three metabolites achieved area under the curve (AUC) values of 0.901 (95% CI: 0.852–0.950) and 0.860 (95% CI: 0.798–0.879) in the training and validation sets, respectively. Targeted metabolomics validated the expression trends of these three metabolites, consistent with the untargeted findings. 50 DEMs were identified between HIS and NHIS, mainly involving amino acid biosynthesis, ether lipid metabolism, and glycerophospholipid metabolism. Indole metabolites were synergistically upregulated, and serum TNF-α levels were significantly increased in the HIS ( P < 0.05). A total of 58 DEMs were identified between HIS and HC, mainly enriched in sphingolipid signaling pathway, ferroptosis, glutathione metabolism, and nucleotide metabolism. The diagnostic model constructed based on three metabolites screened by machine learning achieved AUCs of 0.883 (95% CI: 0.833–0.983) and 0.790 (95% CI: 0.712–0.816) in the training and validation set, respectively. This study systematically delineated the metabolic reprogramming characteristics of IS, constructed diagnostic models for IS and HIS stroke with good diagnostic performance, and preliminarily validated the reliability of L-Cystine, LPC 18:1, and LPC 14:0 as candidate diagnostic markers, providing a scientific basis for the early diagnosis and precise intervention of IS.