Background

Currently, intensive lipid lowering is recommended in patients with atherosclerotic ischemic stroke or transient ischemic attack. However, the role of statin in cardioembolic stroke is unclear. We investigated the association of statin with pretreatment collateral status in cardioembolic stroke.

Methods

A collaborative study from two stroke centers in distinct geographic regions included consecutive patients with acute middle cerebral artery (MCA) infarction due to atrial fibrillation (AF) who underwent cerebral angiography. The relationship between pretreatment collateral grade and the use/dose of statin at stroke onset was assessed. The angiographic collateral grade was evaluated according to the ASITN/SIR Collateral Flow Grading System.

Results

Ninety-eight patients (76 statin-naïve, 22 statin users) were included. Compared with statin-naïve patients, statin users were older and more frequently had hypertension, hyperlipidemia and coronary heart disease. Excellent collaterals (grade 3-4) were more frequently observed in statin users (11 patients, 50%) than in statin-naïve patients (21 patients, 27.6%; p = 0.049). The use of atorvastatin 10 mg equivalent or higher doses of statin was associated with excellent collaterals (p for trend = 0.025). In multiple regression analysis, prestroke statin use was independently associated with excellent collaterals (odds ratio, 7.841; 95% confidence interval, CI, 1.96-31.363; p = 0.004).

Conclusions

Premorbid use of statin in AF patients is associated with excellent collateral flow. Although most statin trials excluded patients with cardioembolic stroke, our data suggests the possibility that statin may be beneficial in AF-related stroke

Background Although antiplatelet agents are frequently prescribed in moyamoya disease in routine clinical practice, there are no large-scale epidemiologic trials or randomized trial evidence to support their use in patients with moyamoya disease. Methods and Results Using the Korean National Health Insurance Service database, patients diagnosed with moyamoya disease between 2002 and 2016 were followed up for up to 14 years to assess, using time-dependent Cox regression in all patients and in a propensity score-matched cohort, the association of antiplatelet therapy and individual antiplatelet agents with survival. Among 25 978 patients with newly diagnosed moyamoya disease, mean age was 37.6±19.9 years, 61.6% were women, and total follow-up was 163 347 person-years. Among 9154 patients who were prescribed antiplatelet agents at least once during the follow-up period, the proportion prescribed cilostazol gradually increased from 5.5% in 2002 to 56.0% in 2016. Any antiplatelet use was associated with reduced risk of death (hazard ratio, 0.77; 95% CI, 0.70-0.84) in a multivariate model. Among individual antiplatelet agents, cilostazol was associated with greater reduction in mortality than the 5 other antiplatelet regimens. Subgroup analysis, according to the age group and history of ischemic stroke, and sensitivity analysis, using propensity score-matched analysis, revealed consistent results. Conclusions Antiplatelet therapy is associated with substantial improvement in survival in patients with moyamoya disease, and cilostazol is associated with greater survival benefit compared with other antiplatelet regimens. These results provisionally support the use of antiplatelet therapy in patients with moyamoya disease and the conduct of confirmatory randomized controlled trials.

Identifying the cerebral arterial branches is essential for undertaking a computational approach to cerebrovascular imaging. However, the complexity and inter-individual differences involved in this process have not been thoroughly studied. We used machine learning to examine the anatomical profile of the cerebral arterial tree. The method is less sensitive to inter-subject and cohort-wise anatomical variations and exhibits robust performance with an unprecedented in-depth vessel range. We applied machine learning algorithms to disease-free healthy control subjects (n = 42), patients with stroke with intracranial atherosclerosis (ICAS) (n = 46), and patients with stroke mixed with the existing controls (n = 69). We trained and tested 70% and 30% of each study cohort, respectively, incorporating spatial coordinates and geometric vessel feature vectors. Cerebral arterial images were analyzed based on the segmentation-stacking method using magnetic resonance angiography. We precisely classified the cerebral arteries across the exhaustive scope of vessel components using advanced geometric characterization, redefinition of vessel unit conception, and post-processing algorithms. We verified that the neural network ensemble, with multiple joint models as the combined predictor, classified all vessel component types independent of inter-subject variations in cerebral arterial anatomy. The validity of the categorization performance of the model was tested, considering the control, ICAS, and control-blended stroke cohorts, using the area under the receiver operating characteristic (ROC) curve and precision-recall curve. The classification accuracy rarely fell outside each images 90-99% scope, independent of cohort-dependent cerebrovascular structural variations. The classification ensemble was calibrated with high overall area rates under the ROC curve of 0.99-1.00 [0.97-1.00] in the test set across various study cohorts. Identifying an all-inclusive range of vessel components across controls, ICAS, and stroke patients, the accuracy rates of the prediction were: internal carotid arteries, 91-100%; middle cerebral arteries, 82-98%; anterior cerebral arteries, 88-100%; posterior cerebral arteries, 87-100%; and collections of superior, anterior inferior, and posterior inferior cerebellar arteries, 90-99% in the chunk-level classification. Using a voting algorithm on the queued classified vessel factors and anatomically post-processing the automatically classified results intensified quantitative prediction performance. We employed stochastic clustering and deep neural network ensembles. Ma-chine intelligence-assisted prediction of vessel structure allowed us to personalize quantitative predictions of various types of cerebral arterial structures, contributing to precise and efficient decisions regarding the cerebrovascular disease.

During the first hours after stroke onset, neurological deficits can be highly unstable: some patients rapidly improve, while others deteriorate. This early neurological instability has a major impact on long-term outcome. Here, we aimed to determine the genetic architecture of early neurological instability measured by the difference between the National Institutes of Health Stroke Scale (NIHSS) within 6 h of stroke onset and NIHSS at 24 h. A total of 5876 individuals from seven countries (Spain, Finland, Poland, USA, Costa Rica, Mexico and Korea) were studied using a multi-ancestry meta-analyses. We found that 8.7% of NIHSS at 24 h of variance was explained by common genetic variations, and also that early neurological instability has a different genetic architecture from that of stroke risk. Eight loci (1p21.1, 1q42.2, 2p25.1, 2q31.2, 2q33.3, 5q33.2, 7p21.2 and 13q31.1) were genome-wide significant and explained 1.8% of the variability suggesting that additional variants influence early change in neurological deficits. We used functional genomics and bioinformatic annotation to identify the genes driving the association from each locus. Expression quantitative trait loci mapping and summary data-based Mendelian randomization indicate that ADAM23 (log Bayes factor = 5.41) was driving the association for 2q33.3. Gene-based analyses suggested that GRIA1 (log Bayes factor = 5.19), which is predominantly expressed in the brain, is the gene driving the association for the 5q33.2 locus. These analyses also nominated GNPAT (log Bayes factor = 7.64) ABCB5 (log Bayes factor = 5.97) for the 1p21.1 and 7p21.1 loci. Human brain single-nuclei RNA-sequencing indicates that the gene expression of ADAM23 and GRIA1 is enriched in neurons. ADAM23, a presynaptic protein and GRIA1, a protein subunit of the AMPA receptor, are part of a synaptic protein complex that modulates neuronal excitability. These data provide the first genetic evidence in humans that excitotoxicity may contribute to early neurological instability after acute ischaemic stroke.

Objective

To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts.

Methods

We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI.

Results

The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10^-8; and LINC00539/ZDHHC20, p = 5.82 × 10^-9. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p _[BI] = 9.38 × 10^-25; p _[SSBI] = 5.23 × 10^-14 for hypertension), smoking (p _[BI] = 4.4 × 10^-10; p _[SSBI] = 1.2 × 10^-4), diabetes (p _[BI] = 1.7 × 10^-8; p _[SSBI] = 2.8 × 10^-3), previous cardiovascular disease (p _[BI] = 1.0 × 10^-18; p _[SSBI] = 2.3 × 10^-7), stroke (p _[BI] = 3.9 × 10^-69; p _[SSBI] = 3.2 × 10^-24), and MRI-defined white matter hyperintensity burden (p _[BI] = 1.43 × 10^-157; p _[SSBI] = 3.16 × 10^-106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy.

Conclusion

In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI.