L-arginine pathway metabolites can discriminate paroxysmal from permanent atrial fibrillation in acute ischemic stroke

An Integrated Metabolomic Screening Platform Discovers the Potential Biomarkers of Ischemic Stroke and Reveals the Protective Effect and Mechanism of Folic Acid

Folic acid has a protective effect against ischemic stroke. However, the protective pharmacological mechanism remains unclear. The aim of this study is to explore the protective effect of folic acid on ischemic stroke animals by an integrated metabolomic biomarker screening platform. Based on ultra-performance liquid chromatography-tandem mass spectrometry (UPLC/MS) coupled with multivariate data analysis, the changes in metabolites and pathways were characterized. We found that the metabolic alteration involved a total of 37 metabolites, of which 26 biomarkers such as γ-aminobutyric acid, lysine, glutamate, ribose, and valine can be regulated by folic acid via metabolic pathways of amino acid metabolism, carbohydrate metabolism, fatty acid metabolism, citrate cycle, and pyruvate metabolism, which may be the potential therapeutic targets of folic acid against ischemic stroke. Folic acid as an emerging potential natural anti-fibrosis agent has significant activity in protecting against middle cerebral artery occlusion-induced rat ischemic stroke model by delaying pathological development, reversing the metabolic biomarkers, and mainly regulating the perturbation in amino acid metabolism, carbohydrate metabolism, fatty acid metabolism, citrate cycle, and pyruvate metabolism. It also showed that the integrated metabolic biomarker screening platform could provide a better understanding of the therapeutic effect and mechanism of drugs.

The Impact of Diabetes Mellitus and Admission Hyperglycemia on Clinical Outcomes after Recanalization Therapies for Acute Ischemic Stroke: STAY ALIVE National Prospective Registry

It was previously reported that diabetes mellitus (DM) and admission hyperglycemia (aHG) were associated with poor clinical outcomes in patients with acute ischemic stroke (AIS) who were treated with intravenous thrombolysis (IVT) or mechanical thrombectomy (MT). Our study aimed to assess the prognostic effect of DM and aHG (≥7.8 mmol/L) on clinical outcomes in patients treated with recanalization therapies (IVT and MT). Our multicentric study was based on data from the prospective STAY ALIVE stroke registry between November 2017 and January 2020. We compared the demographic data, clinical parameters and time metrics between recanalized DM and non-DM groups, and we analyzed the impact of DM and aHG on 90-day functional outcome, 90-day mortality, symptomatic intracranial hemorrhage (sICH), and successful recanalization. Statistical analyses were also performed in two subgroups: (1) patients treated with IVT alone and (2) patients treated with MT. Altogether, we included 695 patients from the three participating stroke centers in Hungary. Regarding the overall population, patients with diabetes were older (72 vs. 67 years, p < 0.001) and comorbidities were more frequent. There were significant differences in the 90-day good functional outcome (48.9% vs. 66.7%, p < 0.001), 90-day mortality (21.9% vs. 11.6%, p < 0.001) and the rate of symptomatic intracranial hemorrhaging (sICH) (7.8% vs. 2.2%, p < 0.001) between the groups. Diabetes and aHG were independently associated with a poor clinical outcome (OR 2.02, 95% CI 1.31−3.11, p = 0.001; OR 2.09, 95% CI 1.39−3.14, p < 0.001) and mortality at 3 months (OR 2.45, 95% CI 1.35−4.47, p = 0.003; OR 2.42, 95% CI 1.37−4.28, p = 0.002) and sICH (OR 4.32, 95% CI 1.54−12.09, p = 0.005; OR 4.61, 95% CI 1.58−13.39, p = 0.005) in the overall population. However, the presence of DM and aHG was not correlated with successful reperfusion (OR 0.39, 95% CI 0.09−1.67, p = 0.205; OR 0.42, 95% CI 0.09−1.97, p = 0.274) after MT. Our study revealed that diabetes and hyperglycemia on admission were correlated with poor clinical outcomes at 3 months in patients with acute stroke regardless of the recanalization method. In addition, the variables were also associated with sICH after recanalization therapies. However, successful recanalization was not associated with DM and aHG in patients who underwent MT.

L-arginine, asymmetric and symmetric dimethylarginine for early outcome prediction in unselected cardiac arrest victims: a prospective cohort study

Early prediction of the mortality, neurological outcome is clinically essential after successful cardiopulmonary resuscitation. To find a prognostic marker among unselected cardiac arrest survivors, we aimed to evaluate the alterations of the L-arginine pathway molecules in the early post-resuscitation care. We prospectively enrolled adult patients after successfully resuscitated in- or out-of-hospital cardiac arrest. Blood samples were drawn within 6, 24, and 72 post-cardiac arrest hours to measure asymmetric and symmetric dimethylarginine (ADMA and SDMA) and L-arginine plasma concentrations. We recorded Sequential Organ Failure Assessment, Simplified Acute Physiology Score, and Cerebral Performance Category scores. Endpoints were 72 h, intensive care unit, and 30-day mortality. Among 54 enrolled patients [median age: 67 (61-78) years, 48% male], the initial ADMA levels were significantly elevated in those who died within 72 h [0.88 (0.64-0.97) µmol/L vs. 0.55 (0.45-0.69) µmol/L, p = 0.001]. Based on receiver operator characteristic analysis (AUC = 0.723; p = 0.005) of initial ADMA for poor neurological outcome, the best cutoff was determined as > 0.65 µmol/L (sensitivity = 66.7%; specificity = 81.5%), while for 72 h mortality (AUC = 0.789; p = 0.001) as > 0.81 µmol/L (sensitivity = 71.0%; specificity = 87.5%). Based on multivariate analysis, initial ADMA (OR = 1.8 per 0.1 µmol/L increment; p = 0.002) was an independent predictor for 72 h mortality. Increased initial ADMA predicts 72 h mortality and poor neurological outcome among unselected cardiac arrest victims.

Optimization of Large Vessel Occlusion Detection in Acute Ischemic Stroke Using Machine Learning Methods

The early detection of large-vessel occlusion (LVO) strokes is increasingly important as these patients are potential candidates for endovascular therapy, the availability of which is limited. Prehospital LVO detection scales mainly contain symptom variables only; however, recent studies revealed that other types of variables could be useful as well. Our aim was to comprehensively assess the predictive ability of several clinical variables for LVO prediction and to develop an optimal combination of them using machine learning tools. We have retrospectively analysed data from a prospectively collected multi-centre stroke registry. Data on 41 variables were collected and divided into four groups (baseline vital parameters/demographic data, medical history, laboratory values, and symptoms). Following the univariate analysis, the LASSO method was used for feature selection to select an optimal combination of variables, and various machine learning methods (random forest (RF), logistic regression (LR), elastic net method (ENM), and simple neural network (SNN)) were applied to optimize the performance of the model. A total of 526 patients were included. Several neurological symptoms were more common and more severe in the group of LVO patients. Atrial fibrillation (AF) was more common, and serum white blood cell (WBC) counts were higher in the LVO group, while systolic blood pressure (SBP) was lower among LVO patients. Using the LASSO method, nine variables were selected for modelling (six symptom variables, AF, chronic heart failure, and WBC count). When applying machine learning methods and 10-fold cross validation using the selected variables, all models proved to have an AUC between 0.736 (RF) and 0.775 (LR), similar to the performance of National Institutes of Health Stroke Scale (AUC: 0.790). Our study highlights that, although certain neurological symptoms have the best ability to predict an LVO, other variables (such as AF and CHF in medical history and white blood cell counts) should also be included in multivariate models to optimize their efficiency.

Q. M. Muskens A, Boersma E, de Groot NMS, Brundel BJJM. Evaluating Serum Heat Shock Protein Levels as Novel Biomarkers for Atrial Fibrillation

Background: Staging of atrial fibrillation (AF) is essential to understanding disease progression and the accompanied increase in therapy failure. Blood-based heat shock protein (HSP) levels may enable staging of AF and the identification of patients with higher risk for AF recurrence after treatment. Objective: This study evaluates the relationship between serum HSP levels, presence of AF, AF stage and AF recurrence following electrocardioversion (ECV) or pulmonary vein isolation (PVI). Methods: To determine HSP27, HSP70, cardiovascular (cv)HSP and HSP60 levels, serum samples were collected from control patients without AF and patients with paroxysmal atrial fibrillation (PAF), persistent (PeAF) and longstanding persistent (LSPeAF) AF, presenting for ECV or PVI, prior to intervention and at 3-, 6- and 12-months post-PVI. Results: The study population (n = 297) consisted of 98 control and 199 AF patients admitted for ECV (n = 98) or PVI (n = 101). HSP27, HSP70, cvHSP and HSP60 serum levels did not differ between patients without or with PAF, PeAF or LSPeAF. Additionally, baseline HSP levels did not correlate with AF recurrence after ECV or PVI. However, in AF patients with AF recurrence, HSP27 levels were significantly elevated post-PVI relative to baseline, compared to patients without recurrence. Conclusions: No association was observed between baseline HSP levels and the presence of AF, AF stage or AF recurrence. However, HSP27 levels were increased in serum samples of patients with AF recurrence within one year after PVI, suggesting that HSP27 levels may predict recurrence of AF after ablative therapy.

Arginine Derivatives in Cerebrovascular Diseases: Mechanisms and Clinical Implications

The amino acid L-arginine serves as substrate for the nitric oxide synthase which is crucial in vascular function and disease. Derivatives of arginine, such as asymmetric (ADMA) and symmetric dimethylarginine (SDMA), are regarded as markers of endothelial dysfunction and have been implicated in vascular disorders. While there is a variety of studies consolidating ADMA as biomarker of cerebrovascular risk, morbidity and mortality, SDMA is currently emerging as an interesting metabolite with distinct characteristics in ischemic stroke. In contrast to dimethylarginines, homoarginine is inversely associated with adverse events and mortality in cerebrovascular diseases and might constitute a modifiable protective risk factor. This review aims to provide an overview of the current evidence for the pathophysiological role of arginine derivatives in cerebrovascular ischemic diseases. We discuss the complex mechanisms of arginine metabolism in health and disease and its potential clinical implications in diverse aspects of ischemic stroke.