Background: Ischemic stroke is a diversified disease with various mechanisms. However, the current subtyping process is complicated and time-consuming. Metabolite-based biomarkers have promising potential to improve classification and profound understanding of the disease. We aimed to identify novel targeted metabolomics-based biomarkers that are clinically relevant to the diagnosis of large-artery atherosclerosis (LAA) stroke. Methods and Results: We acquired serum samples and clinical data from a hospital-based acute stroke registry. Patients experiencing ischemic stroke within a week of symptoms onset were recruited. Finally, 238 acute ischemic stroke participants (97 LAA patients and 141 non-LAA patients) were included. A targeted quantitative and quality-controlled liquid chromatography with tandem mass spectrometry metabolomics analysis was performed, several metabolomic signatures which showed statistically significant differences between LAA and non-LAA groups were selected. A multivariate regression model was created to independently predict the LAA stroke. We obtained 55 serum metabolomics signatures, and of these, 2 biogenic amines (putrescine and kynurenine) were selected as independent discriminants for LAA (odds ratio [OR], 0.0004; 95% confidence interval [CI], 4.819x10 -07 -0.3601 for putrescine; OR, 0.4325; 95% CI, 0.2232-0.8381 for kynurenine) after adjustment for patients’ sex, age, body mass index, stroke severity, and comorbidities. Conclusions: Targeted metabolomics approach provided an enhanced understanding of LAA stroke pathogenesis. We have identified putrescine and kynurenine as novel biomarkers for LAA stroke.
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