Hyperglycemia is a feature of worse
brain injury after
acute ischemic stroke, but the underlying metabolic changes and the link to cytotoxic
brain injury are not fully understood. In this observational study, we applied regression and machine learning classification analyses to identify metabolites associated with
hyperglycemia and a neuroimaging proxy for cytotoxic
brain injury. Metabolomics and lipidomics were carried out using liquid chromatography-tandem mass spectrometry in admission plasma samples from 381 patients presenting with an
acute stroke.
Glucose was measured by a central clinical laboratory, and a subgroup of patients (n = 201) had apparent diffusion coefficient (ADC) imaging quantified on magnetic resonance imaging (MRI) to estimate cytotoxic injury.
Uric acid was the leading metabolite in univariate analysis of both
hyperglycemia (OR 19.6, 95% CI 8.6-44.7, P = 1.44 × 10-12) and ADC (OR 5.3, 95% CI 2.2-13.0, P = 2.42 × 10-4). To further prioritize model features and account for non-linear correlation structure, a random forest machine learning algorithm was applied to separately model
hyperglycemia and ADC. The statistical techniques used have identified
uric acid and gluconic
acids as leading candidate markers common to all models (R2 = 68%, P = 2.2 × 10-10 for
uric acid; R2 = 15%, P = 8.09 × 10-10 for
gluconic acid). Both
uric acid and
gluconic acid were associated with
hyperglycemia and cytotoxic
brain injury. Both metabolites are linked to oxidative stress, which highlights two candidate targets for limiting
brain injury after
stroke.