Abstract TMP100: Biology of Stroke: Role of ELL2, GLIPR1, MAPKAPK3 Genes in Identifying Atrial Fibrillation Cause of Stroke

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Glen Jickling ◽  
Frank Sharp ◽  
Edward Jauch ◽  
Keyword(s):  
Atrial Fibrillation ◽  
Training Data ◽  
Blood Collection ◽  
Large Artery ◽  
Hospital Data ◽  
Stroke Patients ◽  
Stroke Etiology ◽  
Signature Genes ◽  
Diagnostic Signature ◽  
Artery Disease

Background: An accurate test to identify atrial fibrillation in ischemic stroke populations would be of significant clinical utility. Using the Biomarkers of Acute Stroke Etiology (BASE) trial (NCT02014896) dataset, our goal was to utilize a database of genes appearing in literature determine if gene expression accurately differentiate patients with atrial fibrillation from those with large artery stroke. Methods: BASE enrolled suspected stroke patients presenting to 20 hospitals within 24 hrs of symptom onset. Final gold standard diagnosis and stroke etiology were determined by an adjudication committee using all hospital data but blinded to RNA test results. Whole blood, obtained in PAXgene tubes, was frozen at -20C within 72 hrs and analyzed at a core lab (Ischemia Care, LLC, Dayton, OH) using Affymetrix HTA micro arrays. Genes were filtered to those appearing in stroke literature resulting in 543 potential signature genes. A two-way random forest classifier was built through cross validation of the training data resulting in a 3 gene diagnostic signature with robust performance conserved across literature consisting of ELL2, GLIPR1, MAPKAPK3 genes. Results: Overall, 99 patients were enrolled with NIHSS>5, 68 (69%) with atrial fibrillation cause of stroke and 31 (31%) with large artery stroke; (48%) were male, and median (IQR) age was 74.4 (66.1,81.7). Median (IQR) time from symptoms to blood collection was 420 (322, 472) minutes. Coexistent pathology at presentation included high blood pressure 84 (85%), hyperlipidemia 45 (45%), diabetes 31 (31%), and coronary artery disease 38 (38%). Three genes were able to differentiate atrial fibrillation from large vessel stroke; C-statistic 0.86 (0.52-1.0, 95% CI), sensitivity 0.93 (0.56-1.0, 95% CI) and specificity of 0.58 (0.35-0.81, 95% CI ). Conclusion: RNA expression of ELL2, GLIPR1, MAPKAPK3 genes differentiates atrial fibrillation stroke patients from those with large artery stroke, and may have therapeutic and outcome implications.

Abstract WMP61: RNA Expression Signature to Diagnosis Stroke Etiology by Atrial Fibrillation versus Large Artery Atherosclerosis Cause: A BASE Clinical Trial Analysis

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Glen Jickling ◽  
Frank Sharp ◽  
Edward Jauch ◽  
Keyword(s):  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Symptom Onset ◽  
Training Data ◽  
Rna Expression ◽  
Large Artery ◽  
Sample Collection ◽  
Hospital Data ◽  
Stroke Patients ◽  
Stroke Etiology

Background: Identifying atrial fibrillation in embolic stroke of ischemic stroke populations would be of significant clinical utility. Using the Biomarkers of Acute Stroke Etiology (BASE) trial (NCT02014896) dataset, our goal was to determine if blood gene expression signatures accurately differentiated patients with atrial fibrillation from large artery stroke patients. Methods: The BASE trial enrolled suspected stroke patients presenting to 20 hospitals within 24 hrs of symptom onset. Final gold standard diagnosis and stroke etiology were determined by an adjudication committee using all hospital data but blinded to RNA test results. Whole blood, obtained in PAXgene tubes, was frozen at -20C within 72 hrs and analyzed at a core lab (Ischemia Care, LLC, Dayton, OH) using Affymetrix HTA micro arrays. Approximately 38,000 genes on the HTA microarray were filtered to eliminate genes with low expression or high CV (> 10%) when run on replicate samples leaving 9,513 potential signature genes. A two-way random forest classifier was built through cross validation of the training data resulting in a 23 gene diagnostic signature. Results: There were 58 patients enrolled between 18 and 24 hours of symptom onset, with NIHSS>5, 27 (47%) with atrial fibrillation cause of stroke and 31 (53%) with large artery stroke; 64% were male, and median (IQR) age was 69.7 (62.8, 81.0). Median (IQR) time from symptoms to sample collection was 1323.5 (1208.8, 1381.3) minutes. Coexistent pathology at presentation was high blood pressure 49 (84%), hyperlipidemia 28 (48%), diabetes 9 (16%), and coronary artery disease 15 (26%). The panel was able to distinguish atrial fibrillation from large vessel stroke with a C-statistic 0.92 (0.55-1.0, 95% CI), sensitivity 0.90 (0.51-1.0, 95% CI) and specificity of 0.85. Conclusion: RNA expression differentiates strokes due to atrial fibrillation from large artery stroke and may have therapeutic and outcome implications in ischemic stroke populations.

Abstract 50: RNA Expression for Diagnosis of Stroke Etiology Differentiating Large Artery and Cardioembolic Stroke: Analytical Validation of Testing From the BASE Clinical Trial

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Edward C Jauch ◽  
Keyword(s):  
Acute Stroke ◽  
Symptom Onset ◽  
Training Data ◽  
Cardioembolic Stroke ◽  
Blood Collection ◽  
Rna Expression ◽  
Large Artery ◽  
Hospital Data ◽  
Stroke Patients ◽  
Stroke Etiology

Background: An accurate test to differentiate large artery stroke (LAS) patients from those with cardioembolic stroke (CES) would be of significant clinical utility. Using the Biomarkers of Acute Stroke Etiology (BASE) trial (NCT02014896) dataset, our purpose was to utilize blood gene expression signatures for accurately differentiating LAS from CES acute stroke etiologies. Methods: The BASE trial enrolled suspected stroke patients presenting to 20 hospitals within 24 hrs of symptom onset. Final gold standard diagnosis and stroke etiology were determined by an adjudication committee using all hospital data but blinded to RNA test results. Whole blood, obtained in PAX tubes, was frozen at -20C within 72 hrs and analyzed at a core lab (Ischemia Care, Dayton, OH) using Affymetrix HTA microarrays. Genes on the HTA microarray were filtered to eliminate genes with low expression or high CV (> 10%) when run on replicate samples leaving 9,513 potential signature genes. A two-way random forest classifier was built through cross validation of the training data resulting in a 45 gene diagnostic signature. Results: This is a planned interim cohort study of the 1700 patients enrolled in the BASE trial that does not include lacunar strokes, TIA, or stroke mimics. Overall, 222 patients were enrolled with NIHSS>5, 70 (32%) with LAS and 152 (68%) with CES; 59% were male, and median (IQR) age was 70.7 yrs (62.0, 80.2). Median (IQR) time from symptom onset to blood collection was 1200 (448, 1568) minutes. Coexistent pathology at presentation included atrial fibrillation 90 (48%), hypertension 153 (82%), hyperlipidemia 87 (47%), diabetes 60 (32%), and coronary artery disease 70 (37%). Patients were randomly divided into training (148), early symptom onset (<18hrs) validation (39) and a late symptom onset (>18 hrs) validation (35). The diagnostic gene signature results in the early validation cohort distinguished LAS from CES; C-statistic 0.78 (0.50-1.0, 95% CI), sensitivity 0.90 (0.55-1.0, 95% CI) and specificity of 0.70 (0.43-1.0, 95% CI). Conclusion: Early RNA expression differentiates large artery stroke patients from those with cardioembolic stroke, and may have therapeutic and secondary prevention implications.

Ischaemic stroke: common causes

2017 ◽  
Author(s):  
Hugh Markus ◽  
Anthony Pereira ◽  
Geoffrey Cloud
Keyword(s):  
Atrial Fibrillation ◽  
Heart Disease ◽  
Ischaemic Stroke ◽  
Small Vessel Disease ◽  
Small Vessel ◽  
Large Artery ◽  
Lacunar Stroke ◽  
Vessel Disease ◽  
Common Causes ◽  
Artery Disease

This chapter on common causes of ischaemic stroke reviews the major pathologies underlying ischaemic stroke, namely large-artery disease, cardioembolism, and small-vessel disease. Large-vessel extra- and intracranial atherosclerotic cerebrovascular disease is covered. Cardioembolic aetiologies of stroke including atrial fibrillation and valvular heart disease are discussed. Small-vessel disease causing lacunar stroke and possible heterogonous pathologies underlying this subtype are covered. Dolichoectasia of arteries as a potential cause of stroke and the newer concept of embolic stroke of undetermined source are also discussed.

Benign Oligemia in Subacute Stage Is Associated with Borderzone Infarction in Stroke Patients Caused by Intracranial Large Artery Disease

2016 ◽  
Vol 77 (1-2) ◽  
pp. 80-86
Author(s):  
Jingjing Li ◽  
Xiang-Ya Chen ◽  
Yannie Soo ◽  
Thomas W. Leung ◽  
Jinsheng Zeng ◽  
...  
Keyword(s):  
Large Artery ◽  
Stroke Patients ◽  
Subacute Stage ◽  
Artery Disease

scholarly journals Coronary Artery Disease in Patients Presenting With Acute Ischemic Stroke or Transient Ischemic Attack and Elevated Troponin Levels

2022 ◽  
Vol 12 ◽  
Author(s):  
Mathieu Kruska ◽  
Anna Kolb ◽  
Christian Fastner ◽  
Iris Mildenberger ◽  
Svetlana Hetjens ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Ischemic Stroke ◽  
Cardiovascular Risk ◽  
Coronary Artery ◽  
Transient Ischemic Attack ◽  
Large Artery ◽  
Elevated Troponin ◽  
Stroke Patients ◽  
Artery Disease ◽  
Ischemic Attack

Background: There is little information concerning the invasive coronary angiography (ICA) findings of patients with acute ischemic stroke (AIS) or transient ischemic attack (TIA) with elevated troponin levels and suspected myocardial infarction (MI). This study analyzed patient characteristics associated with ICA outcomes.Methods: A total of 8,322 patients with AIS or TIA, treated between March 2010 and May 2020, were retrospectively screened for elevated serum troponin I at hospital admission. Patients in whom ICA was performed, due to suspected type 1 MI based on symptoms, echocardiography, and ECG, were categorized according to ICA results (non-obstructive coronary artery disease (CAD): ≥1 stenosis ≥50% but no stenosis ≥80%; obstructive CAD: any stenosis ≥80% or hemodynamically relevant stenosis assessed by FFR/iwFR).Results: Elevated troponin levels were detected in 2,205 (22.5%) patients, of whom 123 (5.6%) underwent ICA (mean age 71 ± 12 years; 67% male). CAD was present in 98 (80%) patients, of whom 51 (41%) were diagnosed with obstructive CAD. Thus, ICA findings of obstructive CAD accounted for 2.3% of patients with troponin elevation and 0.6% of all stroke patients. The clinical hallmarks of myocardial ischemia, including angina pectoris (31 vs. 15%, p &lt; 0.05) and regional wall motion abnormalities (49 vs. 32%, p = 0.07), and increased cardiovascular risk indicated obstructive CAD. While there was no association between lesion site or stroke severity and ICA findings, causal large-artery atherosclerosis was significantly more common in patients with obstructive coronary disease (p &lt; 0.05).Conclusion: The rate of obstructive CAD in patients with stroke or TIA and elevated troponin levels with suspected concomitant type I MI is low. The cumulation of several cardiovascular risk factors and clinical signs of MI were predictive. AIS patients with large-artery atherosclerosis and elevated troponin may represent an especially vulnerable subgroup of stroke patients with risk for obstructive CAD.

Arterial stiffness is associated with intracranial large artery disease among ethnic Chinese and South Asian ischemic stroke patients

2009 ◽  
Vol 27 (7) ◽  
pp. 1453-1458 ◽  
Author(s):  
Deidre A De Silva ◽  
Fung-Peng Woon ◽  
Hui-Yee Gan ◽  
Christopher P Chen ◽  
Hui-Meng Chang ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Arterial Stiffness ◽  
South Asian ◽  
Large Artery ◽  
Stroke Patients ◽  
Ethnic Chinese ◽  
Artery Disease

scholarly journals A Study of Clinical Profile of Large Artery Disease in Ischemic Stroke Patients

2020 ◽  
Vol 12 (1) ◽  
pp. 27-32
Author(s):  
Vinothkanna Selvaraj ◽  
◽  
Balaji Tamilselvan ◽  
Vasudevan Devanathan ◽  
Gowrishankar A ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Clinical Profile ◽  
Large Artery ◽  
Stroke Patients ◽  
Artery Disease

scholarly journals The impact of growth differentiation factor 15 on the risk of cardiovascular diseases: two-sample Mendelian randomization study

2020 ◽  
Author(s):  
Zhuo Wang ◽  
Fangkun Yang ◽  
Menghuai Ma ◽  
Qinyi Bao ◽  
Jinlian Shen ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Heart Failure ◽  
Atrial Fibrillation ◽  
Coronary Artery Disease ◽  
Mendelian Randomization ◽  
Large Artery ◽  
Nonischemic Cardiomyopathy ◽  
Growth Differentiation Factor 15 ◽  
Increased Risk ◽  
Artery Disease

Abstract Background: Growth differentiation factor 15 (GDF-15), a stress responsive cytokine, belongs to transforming growth factor β (TGF-β) cytokine superfamily. Some evidence support that it’s involved in inflammation, coagulation, oxidative stress, endothelial dysfunction, and hemostasis. However, it’s still controversial whether GDF-15 directly contributes to the morbidity and mortality of patients suffered with cardiovascular disease (CVD). Besides prospective cohort study and randomized controlled trial, Mendelian randomization (MR) is a genetic epidemiological method that exploits genetic variants as unbiased proxies for modifiable to determine the causal relationships between exposures and health outcomes. Herein, we introduced a two-sample MR approach to evaluate the causal relationships of circulating GDF-15 levels with major CVDs incidence.Methods: Genetic instruments and summary statistics for two-sample MR analysis were obtained from 5 independent large genome-wide association studies (GWAS) to investigate the causal correlation between circulating GDF-15 levels and 9 CVDs, respectively. Conventional inverse variance weighted (IVW) method was adopted to evaluate the causality of GDF-15 with different outcomes; weighted median and MR egger were used for sensitivity analyses.Results: Among 9 SNPs identified from 5 GWASs in 2.6 million individuals, 5 SNPs (rs1227731, rs3195944, rs17725099, rs888663, rs749451) coming from chromosome 19 and containing the PGPEP1 and GDF-15 genes were employed. Based on the instruments, circulating GDF-15 levels significantly linked to the increased risk of cardioembolic stroke, atrial fibrillation, coronary artery disease and myocardial infarction. However, no significant causal association was observed for circulating GDF-15 levels with the incidence of any ischemic stroke, large-artery atherosclerotic stroke, small vessel stroke, heart failure and nonischemic cardiomyopathy.Conclusions: The MR study provides with genetic evidence for the causal relationship of circulating GDF-15 levels with the increased risk of cardioembolic stroke, atrial fibrillation, coronary artery disease and myocardial infarction, but not any ischemic stroke, large-artery atherosclerotic stroke, small vessel stroke, heart failure and nonischemic cardiomyopathy. It indicates that GDF-15 might be a promising biomarker or potential therapeutic target for some CVDs.

scholarly journals Atherosclerotic Burden and Vascular Risk in Stroke Patients With Atrial Fibrillation

Stroke ◽  
2021 ◽  
Author(s):  
Jong-Ho Park ◽  
Jong-Won Chung ◽  
Oh Young Bang ◽  
Gyeong-Moon Kim ◽  
Kang-Ho Choi ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Cardiovascular Events ◽  
Hazard Ratio ◽  
Adjusted Hazard Ratio ◽  
Major Adverse Cardiovascular Events ◽  
Atherosclerotic Vascular Disease ◽  
Stroke Patients ◽  
Vascular Events ◽  
Atherosclerotic Burden ◽  
Artery Disease

Background and Purpose: Data on the effect on vascular outcomes of concomitant atherosclerotic vascular disease (ASVD) with atrial fibrillation (AF) after stroke are limited. This study evaluated the effect of ASVD with AF versus AF only on the risk of vascular events. Methods: We retrospectively analyzed a prospectively registered multicenter database involving 3213 stroke patients with AF. ASVD included extracranial atherosclerosis measured in the proximal portion of the internal carotid artery, intracranial atherosclerosis (all ≥50% stenosis), coronary artery disease, and peripheral artery disease and was categorized into 4 strata depending on the number of ASVDs (0, 1, 2, and 3–4). The independent associations of ASVD with major adverse cardiovascular events, stroke, and all-cause death were assessed. Results: A total of 2670 patients were included (mean age, 73.5±9.8 years; median CHA 2 DS 2 -VASc score, 5; interquartile range, 4−6). During the follow-up (mean, 1.7 years), a total of 672 (25.2%) major adverse cardiovascular events, 170 (6.4%) stroke events, and 501 (18.8%) all-cause deaths were noted. The adjusted hazard ratio for major adverse cardiovascular events versus no ASVD was 1.25 (95% CI, 1.00–1.56) for ASVD 1, 1.34 (95% CI, 1.02–1.76) for ASVD 2, and 1.93 (95% CI, 1.24–2.99) for ASVD 3–4. The adjusted hazard ratio for all-cause death versus no ASVD was 1.32 (1.01–1.74), 1.47 (1.06–2.03), and 2.39 (1.47–3.89), respectively. Among ASVD components, the presence of symptomatic or asymptomatic extracranial atherosclerosis was a more potent predictor of major adverse cardiovascular events (1.27 [1.05–1.54]) and all-cause death (1.45 [1.17–1.81]). Conclusions: ASVD burden with AF can be a cumulative marker of a high risk for untoward vascular outcomes. Among ASVD components, extracranial atherosclerosis seems to have a predominant effect.

Abstract WP255: Can Normal Electrocardiogram Rule Out Newly Diagnosed Atrial Fibrillation in Ischemic Stroke Patients

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Joshua Santucci ◽  
Takashi Shimoyama ◽  
Ken Uchino
Keyword(s):  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Cryptogenic Stroke ◽  
Cardiac Monitoring ◽  
Large Artery ◽  
Newly Diagnosed ◽  
Stroke Patients ◽  
Stroke Subtype ◽  
History Of

Introduction: Electrocardiogram (ECG) findings of premature atrial contraction and prolonged PR interval are associated with risk of onset atrial fibrillation (AF) in cryptogenic stroke. We sought to see if normal ECG and AF incidence is incompletely understood. Methods: From a prospective single-hospital stroke registry from 2018, we identified ischemic stroke patients who had ECG done on admission for review. We excluded patients with AF on admission ECG, history of AF, and implanted device with cardiac monitoring capability. Normal ECG was interpreted based on the standardized reporting guidelines for ECG studies evaluating risk stratification of emergency department patients. Stroke subtype was diagnosed according to the TOAST classification: large artery atherosclerosis (LAA), small vessel occlusion (SVO), cardioembolism, others/undetermined and embolic stoke of undetermined source (ESUS) criteria. We compared the incidence of newly diagnosed AF during hospitalization and from outpatient cardiac event monitoring between normal and abnormal ECG. Results: Of the 558 consecutive acute ischemic stroke patients, we excluded 135 with AF on admission ECG or history of AF and 9 with implanted devices. Of the remaining 414 patients that were included in the study, ESUS (31.2%) was the most frequent stroke subtype, followed by LAA (30.0%), SVO (14.0%), others/undetermined (15.7%), and cardioembolism (9.2%). Normal ECG was observed in 125 patients (30.2%). Cardioembolic subtype was less frequent in the normal versus abnormal ECG group (1.6% vs. 12.5%, p<0.001). New AF was detected in 17/414 patients (4.1%) during hospitalization. Of these 17 patients, none had normal ECG (0/125) and all had abnormal ECG (17/289, 5.9%) (p=0.002). After discharge, of 111 patients undergoing 4-week outpatient cardiac monitoring, new AF was detected in 16 (14.4%). Of these 16 patients, only 1 had a normal ECG (1/35, 2.9%) while 15 had abnormal ECG (15/76, 19.7%) (p=0.02). Conclusions: Normal ECG at admission for acute ischemic stroke is associated with low likelihood of detection of new atrial fibrillation in either the inpatient or outpatient setting.

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