Clinical and Genetic Atrial Fibrillation Risk and Discrimination of Cardioembolic From Noncardioembolic Stroke.

BACKGROUND: Stroke is a leading cause of death and disability worldwide. Atrial fibrillation (AF) is a common cause of stroke but may not be detectable at the time of stroke. We hypothesized that an AF polygenic risk score (PRS) can discriminate between cardioembolic stroke and noncardioembolic strokes. METHODS: We evaluated AF and stroke risk in 26 145 individuals of European descent from the Stroke Genetics Network case-control study. AF genetic risk was estimated using 3 recently developed PRS methods (LDpred-funct-inf, sBayesR, and PRS-CS) and 2 previously validated PRSs. We performed logistic regression of each AF PRS on AF status and separately cardioembolic stroke, adjusting for clinical risk score (CRS), imputation group, and principal components. We calculated model discrimination of AF and cardioembolic stroke using the concordance statistic (c-statistic) and compared c-statistics using 2000-iteration bootstrapping. We also assessed reclassification of cardioembolic stroke with the addition of PRS to either CRS or a modified CHA2DS2-VASc score alone. RESULTS: Each AF PRS was significantly associated with AF and with cardioembolic stroke after adjustment for CRS. Addition of each AF PRS significantly improved discrimination as compared with CRS alone (P<0.01). When combined with the CRS, both PRS-CS and LDpred scores discriminated both AF and cardioembolic stroke (c-statistic 0.84 for AF; 0.74 for cardioembolic stroke) better than 3 other PRS scores (P<0.01). Using PRS-CS PRS and CRS in combination resulted in more appropriate reclassification of stroke events as compared with CRS alone (event reclassification [net reclassification indices]+=14% [95% CI, 10%-18%]; nonevent reclassification [net reclassification indices]-=17% [95% CI, 15%-0.19%]) or the modified CHA2DS2-VASc score (net reclassification indices+=11% [95% CI, 7%-15%]; net reclassification indices-=14% [95% CI, 12%-16%]) alone. CONCLUSIONS: Addition of polygenic risk of AF to clinical risk factors modestly improves the discrimination of cardioembolic from noncardioembolic strokes, as well as reclassification of stroke subtype. Polygenic risk of AF may be a useful biomarker for identifying strokes caused by AF.

C1QC, VSIG4, and CFD as Potential Peripheral Blood Biomarkers in Atrial Fibrillation-Related Cardioembolic Stroke.

Atrial fibrillation (AF) is a major risk factor for ischemic stroke. We aimed to identify novel potential biomarkers with diagnostic value in patients with atrial fibrillation-related cardioembolic stroke (AF-CE).Publicly available gene expression profiles related to AF, cardioembolic stroke (CE), and large artery atherosclerosis (LAA) were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified and then functionally annotated. The support vector machine recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) regression analysis were conducted to identify potential diagnostic AF-CE biomarkers. Furthermore, the results were validated by using external data sets, and discriminability was measured by the area under the ROC curve (AUC). In order to verify the predictive results, the blood samples of 13 healthy controls, 20 patients with CE, and 20 patients with LAA stroke were acquired for RT-qPCR, and the correlation between biomarkers and clinical features was further explored. Lastly, a nomogram and the companion website were developed to predict the CE-risk rate. Three feature genes (C1QC, VSIG4, and CFD) were selected and validated in the training and the external datasets. The qRT-PCR evaluation showed that the levels of blood biomarkers (C1QC, VSIG4, and CFD) in patients with AF-CE can be used to differentiate patients with AF-CE from normal controls (P < 0.05) and can effectively discriminate AF-CE from LAA stroke (P < 0.05). Immune cell infiltration analysis revealed that three feature genes were correlated with immune system such as neutrophils. Clinical impact curve, calibration curves, ROC, and DCAs of the nomogram indicate that the nomogram had good performance. Our findings showed that C1QC, VSIG4, and CFD can potentially serve as diagnostic blood biomarkers of AF-CE; novel nomogram and the companion website can help clinicians to identify high-risk individuals, thus helping to guide treatment decisions for stroke patients.

Identification of Hub Genes Associated with Immune Infiltration in Cardioembolic Stroke by Whole Blood Transcriptome Analysis.

Cardioembolic stroke (CS) is the most common type of ischemic stroke in the clinic, leading to high morbidity and mortality worldwide. Although many studies have been conducted, the molecular mechanism underlying CS has not been fully grasped. This study was aimed at exploring the molecular mechanism of CS using comprehensive bioinformatics analysis and providing new insights into the pathophysiology of CS. We downloaded the public datasets GSE58294 and GSE16561. Differentially expressed genes (DEGs) were screened via the limma package using R software. CIBERSORT was used to estimate the proportions of 22 immune cells based on the gene expression profiling of CS patients. Using weighted gene correlation network analysis (WGCNA) to cluster the genes into different modules and detect relationships between modules and immune cell types, hub genes were identified based on the intersection of the protein-protein interaction (PPI) network analysis and WGCNA, and their clinical significance was then verified using another independent dataset GSE16561. Totally, 319 genes were identified as DEGs and 5413 genes were clustered into nine modules using WGCNA. The blue module, with the highest correlation coefficient, was identified as the key module associated with stroke, neutrophils, and B cells naïve. Based on the PPI analysis and WGCNA, five genes (MCEMP1, CLEC4D, GPR97, TSPAN14, and FPR2) were identified as hub genes. Correlation analysis indicated that hub genes had general association with infiltration-related immune cells. ROC analysis also showed they had potential clinical significance. The results were verified using another dataset, which were consistent with our analysis. Five crucial genes determined using integrative bioinformatics analysis might play significant roles in the pathophysiological mechanism in CS and be potential targets for pharmaceutic therapies.

A Mendelian randomization analysis of the relationship between cardioembolic risk factors and ischemic stroke.

Observational studies have shown that several risk factors are associated with cardioembolic stroke. However, whether such associations reflect causality remains unknown. We aimed to determine whether established and provisional cardioembolic risk factors are causally associated with cardioembolic stroke. Genetic instruments for atrial fibrillation (AF), myocardial infarction (MI), electrocardiogram (ECG) indices and N-terminal pro-brain natriuretic peptide (NT-pro BNP) were obtained from large genetic consortiums. Summarized data of ischemic stroke and its subtypes were extracted from the MEGASTROKE consortium. Causal estimates were calculated by applying inverse-variance weighted analysis, weighted median analysis, simple median analysis and Mendelian randomization (MR)-Egger regression. Genetically predicted AF was significantly associated with higher odds of ischemic stroke (odds ratio (OR): 1.20, 95% confidence intervals (CI): 1.16-1.24, P = 6.53 × 10-30) and cardioembolic stroke (OR: 1.95, 95% CI: 1.85-2.06, P = 8.81 × 10-125). Suggestive associations were found between genetically determined resting heart rate and higher odds of ischemic stroke (OR: 1.01, 95% CI: 1.00-1.02, P = 0.005), large-artery atherosclerotic stroke (OR: 1.02, 95% CI: 1.00-1.04, P = 0.026) and cardioembolic stroke (OR: 1.02, 95% CI: 1.00-1.04, P = 0.028). There was no causal association of P-wave terminal force in the precordial lead V1 (PTFVI), P-wave duration (PWD), NT-pro BNP or PR interval with ischemic stroke or any subtype.

Association of ACE2 variant rs4646188 with the risks of atrial fibrillation and cardioembolic stroke in Uygur patients with type 2 diabetes.

BACKGROUND: Atrial fibrillation (AF) is the most common cardiac arrhythmia. Type 2 diabetes (T2D) is an independent risk factor for AF. The cardioembolic stroke (CS) risk is increased when both conditions coexist. Whether angiotensin-converting enzyme 2 (ACE2) genetic variants predict increased risks AF and CS in Uygur patients with T2D remain elusive. METHODS: A total of 547 Uygur subjects (272 controls and 275 T2D patients) were recruited to the study from south Xinjiang. Eight ACE2 variants were identified by MassARRAY system. RESULTS: ACE2 rs2074192 (CC, adjusted RR = 2.55, 95% CI 1.35-4.80, P = 0.004), rs4240157 (CC + CT, adjusted RR = 2.26, 95% CI 1.27-4.04, P = 0.006) and rs4646188 (TT, adjusted RR = 2.37, 95% CI 1.16-4.86, P = 0.018) were associated with higher AF risk. ACE2 rs4240157 (CC + CT, adjusted RR = 2.68, 95% CI 1.36-5.27, P = 0.004) and rs4646188 (TT, adjusted RR = 2.56, 95% CI 1.06-6.20, P = 0.037) were further associated with higher CS risk. The 3 ACE2 variants were related to larger left atrial end-systolic diameter (LAD) (all P < 0.05), but not all of the 3 ACE2 variants were related to increased levels of serum sodium (rs4240157 and rs4646188, all P < 0.05), HsCRP (rs4240157 and rs4646188, all P < 0.05) as well as decreased serum potassium levels (rs2074192 and rs4646188, all P < 0.05). The 3 ACE2 variants exhibited heterogeneity on circulating RAAS activation. In particular, ACE2 rs4646188 was associated with higher levels of ACE (P = 0.017 and 0.037), Ang I (P = 0.002 and 0.001), Ang II (both P < 0.001) and ALD (P = 0.005 and 0.011). CONCLUSION: These results indicated ACE2 rs4646188 was associated with increased risk of AF and CS among diabetic patients in Uygurs, which could be a promising genetic predisposition marker for early and personalized prevention strategies for the aforementioned clinical pathologies.

Sleep Duration and Stroke: Prospective Cohort Study and Mendelian Randomization Analysis.

BACKGROUND AND PURPOSE: Studies of sleep duration in relation to specific types of stroke are scarce. Moreover, the results are inconclusive and causality remains unclear. Our objective was to investigate whether sleep duration is associated with risk of stroke and its types using observational and Mendelian randomization designs. METHODS: The prospective study included 79 881 women and men (45-79 years of age) who were followed up for incident stroke or death over a mean follow-up of 14.6 years (1 164 646 person-years) through linkage to Swedish Registers. For the Mendelian randomization study, single-nucleotide polymorphisms associated with sleep duration were identified from a genome-wide association study. Summarized data for genetic associations with stroke were obtained from publicly available data of the MEGASTROKE and the International Stroke Genetics Consortia. RESULTS: Compared with normal sleep duration, long sleep (≥9 hours per day) was associated with increased risk of total and ischemic stroke (hazard ratios [95% CI], 1.12 [1.03-1.22] and 1.14 [1.03-1.24], respectively), whereas short sleep (<7 h/d) was linked to higher risk of intracerebral hemorrhage (hazard ratio [95% CI], 1.21 [1.03-1.41]). The 2-sample Mendelian randomization analysis supported no causal association of short or long sleep duration with ischemic stroke as a whole. CONCLUSIONS: In a prospective study, long sleep duration was associated with increased risk of total and ischemic stroke, whereas short sleep was linked to increased risk of intracerebral hemorrhage. However, the Mendelian randomization analysis did not show a significant detrimental effect of short or long sleep duration on the risk of total stroke or stroke types.

Integrative Analysis of MAPK14 as a Potential Biomarker for Cardioembolic Stroke.

The aim of this study was to obtain the candidate genes and biomarkers that are significantly related to cardioembolic stroke (CS) by applying bioinformatics analysis. In accordance with the results of the weighted gene coexpression network analysis (WGCNA) in the GSE58294 dataset, 11 CS-related coexpression network modules were identified in this study. Correlation analysis showed that the black and pink modules are significantly associated with CS. A total of 18 core genes in the black module and one core gene in the pink module were determined. We then identified differentially expressed genes (DEGs) of CS at 3 h, 5 h, and 24 h postonset. After performing intersection, it was found that 311 genes were coexpressed at these three time points. These genes were majorly enriched in positive regulation of transferase activity and regulation of peptidase activity. The abovementioned coexpressed DEGs were subjected to protein-protein interaction analysis and subnetwork module analysis. Subsequently, we used cytoHubba to obtain 11 key genes from DEGs. The intersection of the core genes screened from WGCNA and the key genes selected from DEGs yielded the MAPK14 gene. The expression level of MAPK14 on the receiver operating characteristic (ROC) curves of CS at 3 h, 5 h, and 24 h showed that the area under the ROC curve (AUC) was 0.923, 0.934, and 0.941, respectively. In a nutshell, MAPK14 screened out by using WGCNA showed differential expression in CS. We conclude that MAPK14 can be used as a potential biological marker of CS and exhibits potential to predict the physiopathological condition of CS patients.

Cardioembolic Ischemic Stroke Gene Expression Fingerprint in Blood: a Systematic Review and Verification Analysis.

An accurate etiological classification is key to optimize secondary prevention after ischemic stroke, but the cause remains undetermined in one third of patients. Several studies pointed out the usefulness of circulating gene expression markers to discriminate cardioembolic (CE) strokes, mainly due to atrial fibrillation (AF), while only exploring them in small cohorts. A systematic review of studies analyzing high-throughput gene expression in blood samples to discriminate CE strokes was performed. Significantly dysregulated genes were considered as candidates, and a selection of them was validated by RT-qPCR in 100 patients with defined CE or atherothrombotic (LAA) stroke etiology. Longitudinal performance was evaluated in 12 patients at three time points. Their usefulness as biomarkers for AF was tested in 120 cryptogenic strokes and 100 individuals at high-risk for stroke. Three published studies plus three unpublished datasets were considered for candidate selection. Sixty-seven genes were found dysregulated in CE strokes. CREM, PELI1, and ZAK were verified to be up-regulated in CE vs LAA (p = 0.010, p = 0.003, p < 0.001, respectively), without changes in their expression within the first 24 h after stroke onset. The combined up-regulation of these three biomarkers increased the probability of suffering from CE stroke by 23-fold. In cryptogenic strokes with subsequent AF detection, PELI1 and CREM showed overexpression (p = 0.017, p = 0.059, respectively), whereas in high-risk asymptomatic populations, all three genes showed potential to detect AF (p = 0.007, p = 0.007, p = 0.015). The proved discriminatory capacity of these gene expression markers to detect cardioembolism even in cryptogenic strokes and asymptomatic high-risk populations might bring up their use as biomarkers.