Background Hyperglycaemia is frequent in acute ischemic stroke and denotes a bad prognosis, even in the absence of pre-existing diabetes. However, in clinical trials treatment of elevated glucose levels with insulin did not improve stroke outcome, suggesting that collateral effects rather than hyperglycaemia itself aggravate ischemic brain damage. As reactive glucose metabolites, glyoxal and methylglyoxal are candidates for mediating the deleterious effects of hyperglycaemia in acute stroke. Methods In 135 patients with acute stroke, we used liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) to measure glyoxal, methylglyoxal and several of their glycated amino acid derivatives in serum. Results were verified in a second cohort of 61 stroke patients. The association of serum concentrations with standard stroke outcome scales (NIHSS, mRS) was tested. Results Glucose, glyoxal, methylglyoxal, and the glyoxal-derived glycated amino acid Nδ-(5-hydro-4-imidazolon-2-yl)ornithine (G-H1) were positively correlated with a bad stroke outcome at 3 months as measured by mRS90, at least in one of the two cohorts. However, the glycated amino acids Nε-carboxyethyllysine (CEL) and in one cohort pyrraline showed an inverse correlation with stroke outcome probably reflecting lower food intake in severe stroke. Patients with a poor outcome had higher serum concentrations of glyoxal and methylglyoxal. Conclusions The glucose-derived α-dicarbonyl glyoxal and glycated amino acids arising from a reaction with glyoxal are associated with a poor outcome in ischemic stroke. Thus, lowering α-dicarbonyls or counteracting their action could be a therapeutic strategy for hyperglycaemic stroke.
The increasing prevalence of depression requires more effective therapy and the understanding of antidepressants’ mode of action. We carried out untargeted metabolomics of the prefrontal cortex of rats exposed to chronic social isolation (CSIS), a rat model of depression, and/or fluoxetine treatment using liquid chromatography–high resolution mass spectrometry. The behavioral phenotype was assessed by the forced swim test. To analyze the metabolomics data, we employed univariate and multivariate analysis and biomarker capacity assessment using the receiver operating characteristic (ROC) curve. We also identified the most predictive biomarkers using a support vector machine with linear kernel (SVM-LK). Upregulated myo-inositol following CSIS may represent a potential marker of depressive phenotype. Effective fluoxetine treatment reversed depressive-like behavior and increased sedoheptulose 7-phosphate, hypotaurine, and acetyl-L-carnitine contents, which were identified as marker candidates for fluoxetine efficacy. ROC analysis revealed 4 significant marker candidates for CSIS group discrimination, and 10 for fluoxetine efficacy. SVM-LK with accuracies of 61.50% or 93.30% identified a panel of 7 or 25 predictive metabolites for depressive-like behavior or fluoxetine effectiveness, respectively. Overall, metabolic fingerprints combined with the ROC curve and SVM-LK may represent a new approach to identifying marker candidates or predictive metabolites for ongoing disease or disease risk and treatment outcome.
The increasing prevalence of depression worldwide requires more effectiveness in therapy approaches and a molecular understanding of antidepressants mode of action. We carried out untargeted metabolomics of the prefrontal cortex of rats exposed to chronic social isolation (CSIS), a rat model of depression, and/or fluoxetine treatment using liquid chromatography-high resolution mass spectrometry. The behavioral phenotype was assessed by the forced swim test. To analyze metabolomics data, univariate and multivariate analysis and biomarker capacity assessment using the classical receiver operating characteristic (ROC) curve were used. Support vector machine-linear kernel (SVM-LK), as a machine-learning algorithm was performed for binary classification. Upregulated myo-inositol following CSIS may represent a potential marker of depressive phenotype. Effective fluoxetine treatment reversed depressive-like behavior and increased sedoheptulose 7-phosphate, hypotaurine, and acetyl-L-carnitine contents, which were identified as potential markers. We identified 4 or 10 marker candidates with ROC curve greater than 0.9 for CSIS or fluoxetine effectiveness designation. SVM-LK has given accuracy of 61.50%, or 93.30%, and 7 or 25 predictive metabolites for CSIS vs. control and fluoxetine-treated CSIS vs. CSIS classification. Overall, metabolic fingerprints combined with ROC curve and SVM-LK may represent a new approach to identifying potential markers or predicting metabolites for group designation or classification.
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