Abstract:

Objective To analyze plasma metabolic profiles of different ischemic stroke subtypes and construct machine learning (ML)-based models to identify these subtypes for accurate diagnosis of cryptogenic stroke. Methods A total of 277 patients with acute ischemic stroke (AIS) from 3 hospitals were prospectively enrolled and stratified according to the TOAST classification criteria. Venous blood samples were collected for ¹H-NMR metabolomics detection, resulting in the identification of 46 metabolites. Metabolomics and Mendelian randomization analyses were employed to identify the potential biomarkers, and PyCaret was used to develop and optimize the ML models. A Plasma Metabolite Machine Learning (PMML) model was developed to differentiate the ischemic stroke subtypes, and the efficacy of the model was validated using a cryptogenic stroke (SUE) cohort. Results Significantly different metabolite profiles were observed among patients with large artery atherosclerosis (LAA), small vessel occlusion (SVO), and cardioembolism (CE). Mendelian randomization analysis indicated that glucose and proline may serve as potential biomarkers for SVO and LAA. The PMML model developed based on the metabolic profiles of LAA, SVO, and CE demonstrated a high predictive accuracy for distinguishing these ischemic stroke subtypes with an accuracy of 0.8630 and an area under the ROC curve of 0.9586. In the SUE patients (the validation cohort), 23.9% of the patients predicted to have LAA exhibited vulnerable plaques, as compared with a rate of 10.9% in those predicted to have SVO or CE. Five patients predicted to have CE were diagnosed with paroxysmal atrial fibrillation via long-term electrocardiographic monitoring. Conclusion Different ischemic stroke subtypes have distinct plasma metabolic profiles. The PMML model developed based on plasma metabolic features of the 3 well-diagnosed stroke subtypes demonstrates a high accuracy in identifying these subtypes with a great potential for predicting the etiology of cryptogenic stroke.

Key words: acute ischemic stroke, cryptogenic stroke, plasma metabolites, machine learning, Mendelian randomization