Ischemic Stroke Subtypes Research Articles

Risk Factors and Etiological Classification of Ischemic Stroke Subtypes in Southwest Iran

Precise categorization of the causes of ischemic stroke (IS) is crucial for optimizing stroke treatment and assessing the prognosis of patients. This cross-sectional study aimed to determine the risk factors and various subcategories of IS in Iranian patients. The study included all patients with IS referred to Golestan Hospital (Ahvaz, Iran) for one year. Their demographics and clinical data were collected. The etiology of IS was classified based on the TOAST (Trial of ORG 10172 in Acute Stroke Treatment) criteria. A total of 1100 patients with IS were evaluated, 658 were male and 442 were female. They had an average age of 66 years (ranging between 20 and 99 years). The majority of them were in the 61-80 age group. The prevalence of risk factors for IS included hypertension (HTN) (71.4%), diabetes (50.4%), smoking (42.4%), history of previous stroke (28%), dyslipidemia (15.4%), and cardiovascular disease (22.5%). Three months after admission, the mortality rate was 10.7% and the majority of patients exhibited a lower level of disability based on the modified Rankin Score (mRS) compared to the time of admission. The frequency of all risk factors, except for HTN, differed significantly between genders (P<0.05). Furthermore, the prevalence of risk factors varied significantly among different stroke etiologic subgroups (P<0.05). The most common etiological factors identified by TOAST classification were associated with large artery atherosclerosis (LAA) and small artery occlusion (SAO). Significant variations were observed in the prevalence of different etiologic subtypes of stroke among genders and across different age groups.

A prospective study of age-related differences in modifiable risk factors, stroke subtype, and functional outcome in young and middle-aged patients with first-ever ischemic stroke

The objective of our study was to compare the age-related differences in the prevalence of modifiable risk factors (RF), stroke subtype and functional outcome at hospital discharge in young and middle-aged patients with first-ever ischemic stroke (IS). The study included 120 patients with acute first-ever IS, aged 18–59 years, admitted to the Neurology Clinic of Dr. Georgi Stranski University Hospital, Pleven. Of these, 41 (34.2%) were in the young-age group and 79 (65.8%) were middle-aged. The middle-aged patients had higher rates of arterial hypertension (81.0%), diabetes mellitus (45.6%), dyslipidemia (83.5%), low levels of HDL cholesterol (68.4%), high levels of LDL cholesterol (72.2%) and arterial fibrillation (7.6%). The most common subtype of IS in middle-aged patients was large artery atherosclerosis (40.5%), small vessel occlusion (29.1%) and cardioembolism (6.3%). In comparison, we found a higher incidence of IS in the young patients with other determined (34.1%) and undetermined aetiology (34.1%). Our data on functional outcomes at hospital discharge confirm age-related differences in the prevalence of modifiable RFs and stroke subtype in young and middle-aged patients with first-ever IS. These findings highlight the need to develop specific therapeutic approaches for early identification and effective control of health- and lifestyle-related RFs to reduce the incidence of the most common subtypes of IS.

Left atrial myocardial strain, assessed by the method of speckle-tracking echocardiography, as a marker of fibrosis in patients with ischemic stroke

Introduction. Cardioembolic stroke (CE) is one of the main etiopathogenetic subtypes of ischemic stroke and accounts for approximately 17% of all patients with ischemic stroke. Currently, there is a search for widely accessible radiological diagnostic methods that allow for the accurate diagnosis of left atrial (LA) dysfunction. Among these methods is transthoracic strain echocardiography (EchoCG), which provides additional information about the degree of deformation and change in shape of the LA during the cardiac cycle. To date, the association between strain EchoCG parameters and the severity of fibrotic changes in the LA among patients with CE remains unstudied.Objective. To evaluate the relationship between myocardial deformation parameters of the left atrium and the degree of fibrosis in patients with CE, as well as the impact of LA myocardial deformation changes on the prognosis of patients with CE.Materials and Methods. A prospective cross-sectional study included 132 patients who underwent evaluation and treatment in the Department of Emergency Neurology at the N.V. Sklifosovsky Research Institute for Emergency Medicine from 2020 to 2022. Group 1 consisted of 12 patients who died during hospitalization, while Group 2 comprised 120 patients who were discharged from the department after receiving treatment.Results. Deceased patients, compared to survivors, were characterized by the presence of LA myocardial remodeling. Statistically significant differences in LA myocardial deformation parameters were found between the study groups. Correlation analysis revealed statistically significant associations between LA myocardial deformation parameters and the severity of fibrotic changes. In the multifactorial logistic regression model, significant effects on the prognosis of patients were observed for LA strain peak systolic circumferential strain rate (LA-Scs) and LA strain peak systolic longitudinal strain rate (LA-Sls), with each 1% increase in the parameter associated with a 1.128-fold and 1.075-fold increase in the odds of a lethal outcome, respectively (OR: 1.128, 95% CI: 1.021–1.25, p = 0.017, and OR: 1.075, 95% CI: 1.009–1.15, p = 0.025).Conclusions. The conducted study established a strong correlation between LA myocardial deformation and LA fibrosis and the degree of fibrosis in the LA. LA myocardial deformation parameters can be used in risk stratification for the development of a lethal outcome in patients with CE.

FCER1G as a novel immune-associated blood biomarker in cardiogenic stroke

BackgroundCardioembolic stroke (CE) exhibits the highest recurrence rate and mortality rate among all subtypes of cerebral ischemic stroke (CIS), yet its pathogenesis remains uncertain. The immune system plays a pivotal role in the progression of CE. Growing evidence indicates that several immune-associated blood biomarkers may inform the causes of stroke. The study aimed to identify new immune-associated blood biomarkers in patients with CE and create an online predictive tool in distinguishing CE from noncardioembolic stroke (non-CE) in CIS. MethodsGene expression profiles that were publicly available were obtained from the Gene Expression Omnibus (GEO). The identification of differentially expressed genes (DEGs) was conducted using the Limma package. The hub module and hub genes were identified through the application of weighted gene coexpression network analysis (WGCNA). In order to identify potential diagnostic biomarkers for CE, both the random forest (RF) model and least absolute shrinkage and selection operator (LASSO) regression analysis were employed. Concurrently, the CIBERSORT algorithm was employed to evaluate the infiltration of immune cells in CE samples and examine the correlation between the biomarkers and the infiltrating immune cells. The diagnostic gene expression in blood samples was confirmed using qRT-PCR in a self-constructed dataset. Univariate and multiple logistic regression analyses were used to identify the risk factors for CE. Subsequently, the mathematical model of the nomogram was employed via Java's "Spring Boot" framework to develop the corresponding online tool, which was then deployed on a cloud server utilizing "nginx". ResultsEleven differentially expressed genes (DEGs) that were upregulated and seven DEGs that were downregulated were identified. Through bioinformatics analysis and clinical sample verification, it was discovered that Fc Fragment of IgE Receptor Ig (FCER1G) could serve as a novel potential blood biomarker for CE. FCER1G, along with other risk factors associated with CE, were utilized to develop a nomogram. The training and validation sets, which consisted of 65 CIS patients, yielded areas under the curve (AUCs) of 0.9722 and 0.9689, respectively. These results indicate a high level of precision in risk delineation by the nomogram. Furthermore, the associated online predictive platform has the potential to serve as a more efficacious and appropriate predictive instrument (https://www.origingenetic.com/CardiogenicStroke-FCER1G) for distinguishing between CE and non-CE. ConclusionBlood biomarker FCER1G has the potential to identify patients who are at a higher risk of cardioembolism and direct the search for occult AF.The utilization of this online tool is anticipated to yield significant implications in terms of distinguishing between CE and non-CE, as well as enhancing the optimization of treatment decision support.

Differential distribution of plasma apoA-I and apoB levels and clinical significance of apoB/apoA-I ratio in ischemic stroke subtypes.

Ischemic stroke (IS) is classified into clinical subtypes and likely influenced by various lipid components. Nevertheless, the roles of apolipoprotein A-I (apoA-I), apolipoprotein B (apoB), and apoB/apoA-I ratio in different IS subtypes remain underexplored. This study aimed to investigate the differential distribution of plasma apoA-I and apoB levels among IS subtypes and to evaluate the predictive value of the apoB/apoA-I ratio in assessing IS subtypes and disease severity. In this study, 406 IS patients were categorized into three IS-subtypes based on clinical manifestations and imaging assessment, including intracranial atherosclerosis-related IS patients (ICAS, n = 193), extracranial atherosclerosis-related IS patients (ECAS, n = 111), and small artery occlusion-related IS patients (SAO, n = 102). Plasma apoA-I and apoB levels were measured upon hospital admission. Random forest (RF) models were performed to assess predictive values of these apolipoproteins apoB, apoA-I and their ratio in assessing IS subtype stratification and disease severity. Serum apoA-I levels were significantly lower in ICAS compared to ECAS and SAO patients (p < 0.0001), while apoB levels were higher in ICAS patients (p < 0.0001). The apoB/apoA-I ratio was significantly higher in ICAS compared to ECAS and SAO patients (p < 0.0001). Correlation analyses found a significant correlation between the apoB/apoA-I ratio and conventional lipid components. Additionally, RF models and plots of variable importance and distribution of minimal depth revealed that the apoB/apoA-I ratio played the most influential predictor in predicting IS subtypes and stenosis severity. Our study shows the differential distribution of apoA-I and apoB IS subtypes and reveals the significance of the apoB/apoA-I ratio in assessing IS subtypes and arterial stenosis severity. Further studies are warranted to validate these findings and enhance their clinical applicability.

Machine Learning-Based Etiologic Subtyping of Ischemic Stroke Using Circulating Exosomal microRNAs.

Ischemic stroke is a major cause of mortality worldwide. Proper etiological subtyping of ischemic stroke is crucial for tailoring treatment strategies. This study explored the utility of circulating microRNAs encapsulated in extracellular vesicles (EV-miRNAs) to distinguish the following ischemic stroke subtypes: large artery atherosclerosis (LAA), cardioembolic stroke (CES), and small artery occlusion (SAO). Using next-generation sequencing (NGS) and machine-learning techniques, we identified differentially expressed miRNAs (DEMs) associated with each subtype. Through patient selection and diagnostic evaluation, a cohort of 70 patients with acute ischemic stroke was classified: 24 in the LAA group, 24 in the SAO group, and 22 in the CES group. Our findings revealed distinct EV-miRNA profiles among the groups, suggesting their potential as diagnostic markers. Machine-learning models, particularly logistic regression models, exhibited a high diagnostic accuracy of 92% for subtype discrimination. The collective influence of multiple miRNAs was more crucial than that of individual miRNAs. Additionally, bioinformatics analyses have elucidated the functional implications of DEMs in stroke pathophysiology, offering insights into the underlying mechanisms. Despite limitations like sample size constraints and retrospective design, our study underscores the promise of EV-miRNAs coupled with machine learning for ischemic stroke subtype classification. Further investigations are warranted to validate the clinical utility of the identified EV-miRNA biomarkers in stroke patients.

Long-term outcomes among ischemic stroke TOAST subtypes: A 12-year Cohort study in China

BackgroundDisparities in short-term ischemic stroke (IS) prognosis among Trial of Org 10172 in Acute Stroke Treatment (TOAST) subtypes were observed. Notably, little is known about the long-term prognosis of different subtypes in China. We aim to investigate the long-term outcome in IS patients and try to explore the potential interactive effects between IS subtypes and antithrombotic therapy. MethodsThis is a prospective cohort of stroke survivors. Patients diagnosed with first-ever IS at the Department of Neurology, West China Hospital, Sichuan University from January 2010 to December 2019 were recruited. They were followed until September 2022 to assess recurrence, mortality, and functional recovery. The multivariate Fine-Gray model assessed stroke recurrence, while Cox regression estimated hazard ratios. Modified Rankin Scale scores(mRS) were analyzed using the generalized linear mixed effects model. ResultsAt baseline, 589 of 950 participants (62.00 %) were male. The longest follow-up was 150 months, the shortest was 1.5 months, and the median follow-up was 81.0 months. Cardio-embolism (CE) bore the highest mortality risk compared to large artery atherosclerosis (LAA) (HR=4.43,95 %CI 1.61-12.23). Among survivors on anticoagulant therapy, CE exhibited a reduced risk of mortality (HR = 0.18, 95 % CI 0.04-0.80). In function recovery, small artery occlusion (SAO) demonstrated more favorable prognostic outcomes (β=-2.08, P<0.01, OR=0.13,95 %CI 0.03-0.47). Among survivors taking antiplatelet drugs, SAO demonstrated a slower pace of functional recovery compared to LAA (β=1.39, P=0.05, OR=3.99,95 %CI 1.01-15.74). ConclusionsLong-term outcomes post-first IS vary among TOAST subtypes. Anticoagulant therapy offers long-term benefits among patients of the CE. However, prolonged administration of antiplatelet drugs among SAO patients may be limited in improving function recovery. Physicians should carefully consider treatment options for different IS subtypes to optimize patient outcomes and stroke care effectiveness.

Rare causes of cryptogenic stroke

Stroke is one of the leading causes of mortality and disability in the world, which is why it is considered a key medical and social problem of modern medicine. According to international multicenter studies, ischemic stroke is the most frequent type of this condition. According to research, the pathogenetic subtype of ischemic stroke cannot be identified in a third of cases. Based on the TOAST classification, cryptogenic stroke requires exclusion of criteria for other pathogenetic subtypes. However, in clinical practice, the reasons for making this diagnosis are as follows: incomplete examination of the patient, negative test results, and identification of two or more causes. The high incidence and risk of recurrent ischemic events determines the need for a thorough diagnostic search in order to exclude rare causes and conduct reasonable secondary prophylaxis.

Glucose Fluctuations in Acute Ischemic Stroke.

The Oxfordshire Community Stroke Project denotes four subtypes of ischemic stroke (total and partial anterior infarct, posterior, and lacunar). Hyperglycemia has been associated with a larger infarct size and poor prognosis. The purpose of the study was to investigate the correlation of glucose fluctuations with the Oxford sub-categories and patient outcomes using a blinded continuous glucose monitoring system. This is a non-interventional prospective observational study. Stroke patients with symptoms onset in the last 24h, participated in the study.A glucose sensor was placed for 72 hours. Disability was assessed using the modified Rankin Scale. Stroke subtypes were compared withtotal mean glucose and time in rangeusing ANOVA analysis. Multiple ordinal logistic regression was employed to analyze outcomes and survival. The sample consisted of 105 diabetic and non-diabetic patients. The overall mean glucose was 127.06 mg/dL and the time in range (70-140 mg/dL) was 70.98%. There was no significant difference between the stroke sub-categories and the total mean glucose. For every one-point increase in the time in range, we expect a 1.5%reduction in the odds of having a worse outcome. Patients with total anterior infarct are 2.31 times more likely to have a worse outcome than lacunar patients. The utilization of the Oxford classification may not be necessary for managing acute ischemic stroke glucose levels. Achieving glucose regulation and an increase in time in rangecan be attained through meticulous control, potentially extending life expectancy. Continuous glucose monitors may aid in achieving this objective.

Effectiveness of statin treatment for recurrent stroke according to stroke subtypes

Understanding the effectiveness of statin treatment is essential for developing tailored stroke prevention strategies. We aimed to evaluate the efficacy of statin treatment in preventing recurrent stroke among patients with various ischemic stroke subtypes. Using data from the Clinical Research Collaboration for Stroke-Korea-National Institute for Health (CRCS-K-NIH) registry, we included patients with acute ischemic stroke admitted between January 2011 and July 2020. To evaluate the differential effects of statin treatment based on the ischemic stroke subtype, we analyzed patients with large artery atherosclerosis (LAA), cardio-embolism (CE), and small vessel occlusion (SVO). The primary outcomes were recurrent ischemic stroke and recurrent stroke events. The hazard ratio for outcomes between statin users and nonusers was compared using a Cox proportional hazards model adjusted for covariates. A total of 46,630 patients who met the inclusion criteria were analyzed. Statins were prescribed to 92%, 93%, and 78% of patients with LAA, SVO, and CE subtypes, respectively. The hazards of recurrent ischemic stroke and recurrent stroke in statin users were reduced to 0.79 (95% confidence interval [CI], 0.63-0.99) and 0.77 (95% CI, 0.62-0.95) in the LAA subtype and 0.63 (95% CI, 0.52-0.76) and 0.63 (95% CI, 0.53-0.75) in CE subtype compared to nonusers. However, the hazards of these outcomes did not significantly decrease in the SVO subtype. The effectiveness of statin treatment in reducing the risk of recurrent stroke in patients with LAA and CE subtypes has been suggested. Nonetheless, no significant effect was observed in the SVO subtype, suggesting a differential effect of statins on different stroke subtypes.

Deep Learning-Based Automatic Classification of Ischemic Stroke Subtype Using Diffusion-Weighted Images.

Accurate classification of ischemic stroke subtype is important for effective secondary prevention of stroke. We used diffusion-weighted image (DWI) and atrial fibrillation (AF) data to train a deep learning algorithm to classify stroke subtype. Model development was done in 2,988 patients with ischemic stroke from three centers by using U-net for infarct segmentation and EfficientNetV2 for subtype classification. Experienced neurologists (n=5) determined subtypes for external test datasets, while establishing a consensus for clinical trial datasets. Automatically segmented infarcts were fed into the model (DWI-only algorithm). Subsequently, another model was trained, with AF included as a categorical variable (DWI+AF algorithm). These models were tested: (1) internally against the opinion of the labeling experts, (2) against fresh external DWI data, and (3) against clinical trial dataset. In the training-and-validation datasets, the mean (±standard deviation) age was 68.0±12.5 (61.1% male). In internal testing, compared with the experts, the DWI-only and the DWI+AF algorithms respectively achieved moderate (65.3%) and near-strong (79.1%) agreement. In external testing, both algorithms again showed good agreements (59.3%-60.7% and 73.7%-74.0%, respectively). In the clinical trial dataset, compared with the expert consensus, percentage agreements and Cohen's kappa were respectively 58.1% and 0.34 for the DWI-only vs. 72.9% and 0.57 for the DWI+AF algorithms. The corresponding values between experts were comparable (76.0% and 0.61) to the DWI+AF algorithm. Our model trained on a large dataset of DWI (both with or without AF information) was able to classify ischemic stroke subtypes comparable to a consensus of stroke experts.

Role of Von Willebrand factor level as a biomarker in acute ischemic stroke

BackgroundVon Willebrand factor (VWF) is a large, multimeric glycoprotein that plays a role in thrombus formation; it is also an important mediator of inflammation. Our study aims to determine the association of VWF plasma level and acute ischemic stroke and determine plasma level of VWF in different subtypes of acute ischemic stroke. This case–control study was conducted on 90 subjects: 30 acute ischemic atherosclerotic stroke patients, 30 acute cardioembolic stroke patients and 30 healthy age and sex-matched control subjects. Stroke patients were recruited within the first week of stroke onset with an age range from 18 to 75 years. All subjects underwent complete neurological examination, duplex ultrasonography (U/S), CT brain, routine laboratory work-up and serum level of VWF.ResultsVWF serum levels were significantly elevated in patients of acute ischemic stroke, compared to control subjects. Higher plasma levels of VWF were observed in patients with acute ischemic atherosclerotic stroke.ConclusionSerum level of VWF can be used as a marker for acute ischemic stroke, especially the atherosclerotic subtype.

Lipoprotein (a) concentration as a risk factor for ischaemic stroke and its subtypes.

To investigate the relationship between serum lipoprotein (a) [Lp(a)] concentration and the risk of ischaemic stroke (IS) and its subtypes. Lp(a) plays a role in atherogenic, pro-thrombotic, and antifibrinolytic processes. Elevated plasma Lp(a) is a strong independent risk factor for the development and progression of atherosclerotic disease. The association between lipoproteins and IS is more complex than that reported for cardiovascular diseases, with inconsistent and contradictory results from epidemiological studies. 231 patients with acute IS (defined as cases) and 163 age- and sex-matched control subjects were included in this prospective case-control study. Demographic and clinical variables (i.e. age, sex, smoking, presence of chronic diseases and concomitant medication) and laboratory data (i.e. concentrations of total cholesterol, high-density lipoprotein-cholesterol, low-density lipoprotein-cholesterol, triglycerides, Lp(a), apolipoprotein A1, apolipoprotein B) were recorded. The mean age and the percentage of men did not significantly differ between groups. Compared to controls, there was a significantly higher percentage of cases reported with concomitant diseases: diabetes mellitus, myocardial infarction, ischaemic heart disease, peripheral arterial disease, and atrial fibrillation. The study showed a significantly higher serum Lp(a) concentration in cases than in control subjects (81.81 nmol/L [c.32.7 mg/dL] vs. 59.75 nmol/L [c.23.9 mg/dL]; p = 0.036) and found an association between Lp(a) levels stratified by quartiles and the risk for ischaemic stroke (Q1 [Lp(a) < 13 nmol/L] vs. Q4 [Lp(a) > 117 nmol/L]: OR 2.23; 95% CI 1.23-4.03; p = 0.008). A subgroup analysis based on the TOAST classification of IS also showed a significant association between Lp(a) value of more than 75 nmol/L (30 mg/dL) and the risk of large-artery atherosclerosis stroke compared to the controls (OR 2.4; 95% CI 1.39-3.93; p = 0.001), as well as a statistically non-significant association with other subtypes of IS. The influence of Lp(a) remained significant even after adjusting for established risk factors for IS (OR 1.99; 95% CI 1.05-3.76; p = 0.04; respectively for the large-artery atherosclerotic subtype: OR 2.54; 95% CI 1.39-4.67; p = 0.003). We found that Lp(a) is an independent risk factor for ischaemic stroke, and for the large-artery atherosclerotic subtype of ischaemic stroke.

Ischemic stroke subtypes: risk factors, treatments, and 1-month prognosis – The Lille, France Stroke Registry

ObjectivesFirst, this registry-based study aimed to comprehensively analyze patients' medical histories and treatments based on ischemic strokes’ etiology. We focused on the management of atrial fibrillation among patients diagnosed with cardioembolic stroke. Then, our objective was to identify prognostic factors associated with 28-day mortality. Materials and methodsAll ischemic strokes occurring in adults between 2014 and 2021 in Lille, France, were categorized using the TOAST classification. Comparative analyses of patients' medical characteristics were conducted across subtypes. Survival rates within 28 days post-stroke were assessed, and factors influencing mortality were identified using a multivariate Cox model. Results1912 ischemic strokes were recorded, due to cardioembolism (36%), large-artery atherosclerosis (9%), small-artery occlusion (9%), other determined causes (6%), or undetermined causes (39%). The median NIHSS score after cardioembolic stroke (6, IQR: 3-13) was twice that after small-artery occlusion (3, IQR: 2-5). Among patients with cardioembolic stroke, 26% were diagnosed post-admission with atrial fibrillation. For the 42% diagnosed pre-admission, only 54% had prior prescriptions for oral anticoagulants. Reperfusion therapies were administered in 21% of cases, with significant variations across subtypes. Mortality rates were higher after cardioembolic strokes (17%) than after small-artery occlusions (3%). Prognostic factors included etiology, high NIHSS score, atrial fibrillation, and previous heparin prescription. ConclusionsWhile atrial fibrillation was underdiagnosed and undertreated, patients with cardioembolic stroke exhibited high severity and elevated mortality rates. Etiology emerged as an independent predictor of early mortality, regardless of NIHSS score upon admission. These findings underscore the importance of targeted prevention to improve patient outcomes after ischemic stroke.

Risk and Population Attributable Fraction of Stroke Subtypes in Japan.

Associations of major risk factors for stroke with total and each type of stroke, as well as subtypes of ischemic stroke, and their population attributable fractions had not been examined comprehensively. Participants of the Japan Public Health Center-based prospective (JPHC) Study Cohort II without histories of cardiovascular disease and cancer (n = 14,797) were followed from 1993 through 2012. Associations of current smoking, hypertension, diabetes, overweight (body mass index ≥25 kg/m2), non-high-density lipoprotein cholesterol (non-HDLC) categories, low HDLC (<40 mg/dL), urine protein, and history of arrhythmia were examined in a mutually-adjusted Cox regression model that included age and sex. Population attributable fractions (PAFs) were estimated using the hazard ratios and the prevalence of risk factors among cases. Subjects with hypertension were 1.63 to 1.84 times more likely to develop any type of stroke. Diabetes, low HDLC, current smoking, overweight, urine protein, and arrhythmia were associated with risk of overall and ischemic stroke. Hypertension and urine protein were associated with risk of intracerebral hemorrhage, while current smoking, hypertension, and low non-HDLC were associated with subarachnoid hemorrhage. Hypertension alone accounted for more than a quarter of stroke incidence, followed by current smoking and diabetes. High non-HDLC, current smoking, low HDLC, and overweight contributed mostly to large-artery occlusive stroke. Arrhythmia explained 13.2% of embolic stroke. Combined PAFs of all the modifiable risk factors for total, ischemic, and large-artery occlusive strokes were 36.7%, 44.5%, and 61.5%, respectively. Although there are differences according to subtypes, hypertension could be regarded as the most crucial target for preventing strokes in Japan.

Associations Between Stroke Type, Ischemic Stroke Subtypes, and Post-Stroke Cognitive Trajectories.

It is unclear how post-stroke cognitive trajectories differ by stroke type and ischemic stroke subtype. We studied associations between stroke types (ischemic, hemorrhagic), ischemic stroke subtypes (cardioembolic, large artery atherosclerotic, lacunar/small vessel, cryptogenic/other determined etiology), and post-stroke cognitive decline. This pooled cohort analysis from four US cohort studies (1971-2019) identified 1,143 dementia-free individuals with acute stroke during follow-up: 1,061 (92.8%) ischemic, 82 (7.2%) hemorrhagic, 49.9% female, 30.8% Black. Median age at stroke was 74.1 (IQR, 68.6, 79.3) years. Outcomes were change in global cognition (primary) and changes in executive function and memory (secondary). Outcomes were standardized as T-scores (mean [SD], 50 [10]); a 1-point difference represents a 0.1-SD difference in cognition. Median follow-up for the primary outcome was 6.0 (IQR, 3.2, 9.2) years. Linear mixed-effects models estimated changes in cognition after stroke. On average, the initial post-stroke global cognition score was 50.78 points (95% CI, 49.52, 52.03) in ischemic stroke survivors and did not differ in hemorrhagic stroke survivors (difference, -0.17 points [95% CI, -1.64, 1.30]; P=0.82) after adjusting for demographics and pre-stroke cognition. On average, ischemic stroke survivors showed declines in global cognition, executive function, and memory. Post-stroke declines in global cognition, executive function, and memory did not differ between hemorrhagic and ischemic stroke survivors. 955 ischemic strokes had subtypes: 200 (20.9%) cardioembolic, 77 (8.1%) large artery atherosclerotic, 207 (21.7%) lacunar/small vessel, 471 (49.3%) cryptogenic/other determined etiology. On average, small vessel stroke survivors showed declines in global cognition and memory, but not executive function. Initial post-stroke cognitive scores and cognitive declines did not differ between small vessel survivors and survivors of other ischemic stroke subtypes. Post-stroke vascular risk factor levels did not attenuate associations. Stroke survivors had cognitive decline in multiple domains. Declines did not differ by stroke type or ischemic stroke subtype.

Bayesian colocalization of GWAS and eQTL signals reveals cell type-specific genes and regulatory variants for susceptibility to subtypes of ischemic stroke

A colocalization analysis of genome-wide association study (GWAS) signals and expression quantitative trait loci (eQTL) was conducted to pinpoint target genes and their regulatory nucleotide variants for subtypes of ischemic stroke. We utilized GWAS data from prominent meta-analysis consortia (MEGASTROKE and GIGASTROKE) and single-cell eQTL data in brain and blood tissues to enhance accuracy and minimize noise inherent in bulk RNA-seq. Employing Bayesian colocalization methods, we identified ten shared loci between GWAS and eQTL signals, targeting five eGenes. Specifically, RAPH1 and ICA1L were discovered for small vessel stroke (SVS), whereas SCYL3, CAV1, and CAV2 were for cardioembolic stroke (CS). However, no findings have been made for large artery stroke. The exploration and subsequent functional analysis of causal variants within the colocalized regions revealed their regulatory roles, particularly as enhancer variants (e.g., rs144505847 and rs72932755 targeting ICA1L; rs629234 targeting SCYL3; rs3807989 targeting CAV1 and CAV2). Notably, our study unveiled that all eQTL for CS were identified in oligodendrocytes, while those for SVS were across excitatory neurons, astrocytes, and oligodendrocyte precursor cells. This underscores the heterogeneous tissue-specific genetic factors by subtypes of ischemic stroke. The study emphasizes the need for intensive research efforts to discover causative genes and variants, unravelling the cell type-specific genetic architecture of ischemic stroke subtypes. This knowledge is crucial for advancing our understanding of the underlying pathophysiology and paving the way for precision neurology applications.

Laboratory indicators of hemostasis, lipid metabolism and endothelial dysfunction in men aged 18–50 years with different subtypes of ischemic stroke

Objective: to evaluate laboratory parameters of hemostasis, lipid metabolism and endothelial dysfunction and their relationship in men aged 18–50 years with atherothrombotic (ATS), lacunar (LS) and cardioembolic (CES) stroke. Material and methods. The study included 89 men with ATS (n=36), LS (n=34) and CES (n=19). Neuroimaging, ultrasound and laboratory blood serum analyses were performed in all patients. Results. The mean age of the patients was 42.6±5.3 years. The main risk factors for ATS, LS and CES included: arterial hypertension (75; 97.8 and 73.7% of cases, respectively), dyslipidemia (60; 41.3 and 42.1%), tobacco smoking (71.7; 67.4 and 52.6%), regular alcohol consumption (35; 19.6 and 36.8%), obesity (23.3; 8.7 and 15.8 %), diabetes mellitus (8.3; 6.5 and 10.5 %). Lower tissue plasminogen activator levels were found in patients with CES (2.66±1.77 ng/ml) compared to patients with LS (3.38±3.0 ng/ml) and ATS (3.48±2.45 ng/ml). Plasminogen activator inhibitor-1 levels were significantly increased in all stroke subtypes. The mean level of soluble thrombomodulin was highest in patients with LS (100.86±58.22 pg/ml) compared to patients with ATS (96.37±85.71 pg/ml) and CES (75.28±39.36 pg/ml). The level of asymmetric dimethylarginine was higher in patients with ATS (1.46±0.42 μmol/l) and in patients with LS (0.79±0.37 μmol/l), and in patients with CES (0.4±0.13 μmol/l) it was within the reference values. Conclusion. We noted differences in laboratory parameters of the hemostasis, lipid metabolism and endothelial dysfunction in men aged 18–50 years with different stroke subtypes (ATS, LS and CES), as well as clinical and laboratory correlations.

Demography and Etiology of Ischemic Stroke in 18–45 Years

Introduction: The objective of our study was to examine the clinical characteristics and underlying risk factors of acute ischemic stroke in patients aged 18–45. Materials and Methods: We prospectively studied etiology of patients aged 18–45 with acute ischemic stroke presenting to our hospital from September to December of the following year. We aimed for 80% statistical power at a 5% significance level with a precision of 0.15, which led us to a calculated sample size of 39. Results: Fifty patients were observed. The main risk factors identified were alcohol use (36%), smoking (30%), and hypertension (22%). Prothrombotic states were found to be a significant risk factor in cases where the stroke’s origin was undetermined. The most common stroke subtype was large-artery atherosclerosis, which was followed by strokes with other underlying causes. There was a statistically significant association between hypertension and ischemic stroke subtypes ( P = 0.01). Hypertension was associated with small-vessel disease (92.3%) and strokes with undetermined causes (56.5%), with P = 0.023 and P &lt;0.001, respectively. Conclusions: Hypertension was associated with multiple stroke subtypes in young individuals. Therefore, it is essential to strictly control hypertension through antihypertensive therapy and lifestyle changes. As alcohol use and smoking are becoming more prevalent among young individuals, early implementation of awareness programs targeting primary stroke prevention is highly desirable.

Machine learning identifies novel coagulation genes as diagnostic and immunological biomarkers in ischemic stroke.

Coagulation system is currently known associated with the development of ischemic stroke (IS). Thus, the current study is designed to identify diagnostic value of coagulation genes (CGs) in IS and to explore their role in the immune microenvironment of IS. Aberrant expressed CGs in IS were input into unsupervised consensus clustering to classify IS subtypes. Meanwhile, key CGs involved in IS were further selected by weighted gene co-expression network analysis (WGCNA) and machine learning methods, including random forest (RF), support vector machine (SVM), generalized linear model (GLM) and extreme-gradient boosting (XGB). The diagnostic performance of key CGs were evaluated by receiver operating characteristic (ROC) curves. At last, quantitative PCR (qPCR) was performed to validate the expressions of key CGs in IS. IS patients were classified into two subtypes with different immune microenvironments by aberrant expressed CGs. Further WGCNA, machine learning methods and ROC curves identified ACTN1, F5, TLN1, JMJD1C and WAS as potential diagnostic biomarkers of IS. In addition, their expressions were significantly correlated with macrophages, neutrophils and/or T cells. GSEA also revealed that those biomarkers may regulate IS via immune and inflammation. Moreover, qPCR verified the expressions of ACTN1, F5 and JMJD1C in IS. The current study identified ACTN1, F5 and JMJD1C as novel coagulation-related biomarkers associated with IS immune microenvironment, which enriches our knowledge of coagulation-mediated pathogenesis of IS and sheds light on next-step in vivo and in vitro experiments to elucidate the relevant molecular mechanisms.