The time window from
stroke onset is critical for the treatment decision. However, in unknown onset
stroke, it is often difficult to determine the exact onset time because of the lack of assessment methods, which can result in controversial and random treatment decisions. Previous studies have shown that serum
biomarkers, in addition to imaging assessment, are useful for determining the
stroke onset time. However, as yet there are no specific
biomarkers or corresponding methodologies that are accurate and effective for determining the onset time of unknown onset
stroke. Herein, we describe our novel advanced metabolites-based machine learning method (termed extreme gradient boost [XGBoost]) combined with recursive feature elimination, which accurately screened five metabolites from 1124 metabolites detected in serum. These metabolites were capable of both detecting
acute ischemic stroke and backtracking the
acute ischemic stroke onset time. To further investigate the pathological mechanisms of
acute ischemic stroke, we also examined characteristic metabolites in different brain regions, and found two metabolites that could distinguish the core
infarct area from the ischemic penumbra. Although this study is based on animal experiments, our machine learning framework and selected metabolites may provide a basis for clinical
stroke evaluation and treatment.