TY - JOUR
T1 - Uric acid and gluconic acid as predictors of hyperglycemia and cytotoxic injury after stroke
AU - Ament, Zsuzsanna
AU - Bevers, Matthew B.
AU - Wolcott, Zoe
AU - Kimberly, W. Taylor
AU - Acharjee, Animesh
PY - 2021/4
Y1 - 2021/4
N2 - 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.
AB - 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.
KW - Hyperglycemia
KW - Machine learning
KW - Metabolomics
KW - Neuroimaging
KW - Stroke
UR - http://www.scopus.com/inward/record.url?scp=85092635597&partnerID=8YFLogxK
U2 - 10.1007/s12975-020-00862-5
DO - 10.1007/s12975-020-00862-5
M3 - Article
VL - 12
SP - 293
EP - 302
JO - Translational Stroke Research
JF - Translational Stroke Research
IS - 2
ER -