Data Sheet 1_Metabolic signatures and diagnostic models of ischemic stroke and its hypertensive subtype: a non-targeted metabolomics and machine learning.pdf

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.</p>Conclusion<p>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.</p>