Uric Acid and Gluconic Acid as Predictors of Hyperglycemia and Cytotoxic Injury after Stroke
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ORIGINAL ARTICLE
Uric Acid and Gluconic Acid as Predictors of Hyperglycemia and Cytotoxic Injury after Stroke Zsuzsanna Ament 1,2
&
Matthew B. Bevers 3 & Zoe Wolcott 1,2 & W. Taylor Kimberly 1,2
&
Animesh Acharjee 4,5,6
Received: 13 May 2020 / Revised: 31 August 2020 / Accepted: 4 October 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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. Keywords Machine learning . Metabolomics . Stroke . Hyperglycemia . Neuroimaging
Introduction Hyperglycemia in stroke patients is an important marker of mortality and outcome after stroke in both diabetic and
nondiabetic populations [1, 2]. Even moderately elevated glucose levels are associated with both a higher risk of short-term mortality and an increased risk of poor functional recovery compared with lower glucose levels [3]. Hyperglycemia is
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s12975-020-00862-5) contains supplementary material, which is available to authorized users. * W. Taylor Kimberly [email protected]
3
Division of Stroke, Cerebrovascular and Crital Care Neurology, Brigham and Women’s Hospital, 75 Francis Street, Boston, MA 02115, USA
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College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, UK
5
Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
6
NIHR S
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