Atrial fibrillation genetic risk differentiates cardioembolic stroke from other stroke subtypes

Association and causal impact of TERT genetic variants on peripheral blood leukocyte telomere length and cerebral small vessel disease risk in a Chinese Han population: a mendelian randomization analysis

BACKGROUND

Previous observational studies have highlighted potential relationships between the telomerase reverse transcriptase (TERT) gene, short leukocyte telomere length (LTL), and cerebrovascular disease. However, it remains to be established as to whether TERT gene variants are associated with an elevated risk of cerebral small vessel disease (CSVD), and whether there is a causal relationship between LTL and CSVD.

METHODS

Five TERT single nucleotide polymorphisms (SNPs) were analyzed in 307 CSVD patients and 320 healthy controls in whom LTL values were quantified. Allele models and four genetic models were used to explore the relationship between these SNP genotypes and CSVD risk. A Mendelian randomization analysis of CSVD risk was then performed using LTL-related SNPs and the polygenic risk score (PRS) constructed from these SNPs as genetic instrumental variables to predict the causal relationship between LTL and CSVD risk.

RESULTS

Model association analyses identified two SNPs that were significantly associated with CSVD risk. LTL was significantly correlated with age (P < 0.001), and the MR analysis revealed an association between short LTL and an elevated risk of CSVD. PRS-based genetic prediction of short LTLs was also significantly related to an elevated CSVD risk.

CONCLUSION

Multiple genetic models and MR results indicate that TERT gene SNPs may be related to an elevated risk of CSVD, and that shorter LTL may be causally linked to such CSVD risk.

Genetic Determinants of Vascular Dementia

Vascular dementia (VaD) is a prevalent form of cognitive impairment with underlying vascular etiology. In this review, we examine recent genetic advancements in our understanding of VaD, encompassing a range of methodologies including genome-wide association studies, polygenic risk scores, heritability estimates, and family studies for monogenic disorders revealing the complex and heterogeneous nature of the disease. We report well known genetic associations and highlight potential pathways and mechanisms implicated in VaD and its pathological risk factors, including stroke, cerebral small vessel disease, and cerebral amyloid angiopathy. Moreover, we discuss important modifiable risk factors such as hypertension, diabetes, and dyslipidemia, emphasizing the importance of a multifactorial approach in prevention, treatment, and understanding the genetic basis of VaD. Last, we outline several areas of scientific advancements to improve clinical care, highlighting that large-scale collaborative efforts, together with an integromics approach can enhance the robustness of genetic discoveries. Indeed, understanding the genetics of VaD and its pathophysiological risk factors hold the potential to redefine VaD on the basis of molecular mechanisms and to generate novel diagnostic, prognostic, and therapeutic tools.

Genetics and Pharmacogenetics of Atrial Fibrillation: A Mechanistic Perspective

The heritability of atrial fibrillation (AF) is well established. Over the last decade genetic architecture of AF has been unraveled by genome-wide association studies and family-based studies. However, the translation of these genetic discoveries has lagged owing to an incomplete understanding of the pathogenic mechanisms underlying the genetic variants, challenges in classifying variants of uncertain significance (VUS), and limitations of existing disease models. We review the mechanistic insight provided by basic science studies regarding AF mechanisms, recent developments in high-throughput classification of VUS, and advances in bioengineered cardiac models for developing personalized therapy for AF.

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.

Clinical Genetic Testing for Atrial Fibrillation: Are We There Yet?

Important progress has been made toward unravelling the complex genetics underlying atrial fibrillation (AF). Initial studies were aimed to identify monogenic causes; however, it has become increasingly clear that the most common predisposing genetic substrate for AF is polygenic. Despite intensive investigations, there is robust evidence for rare variants for only a limited number of genes and cases. Although the current yield for genetic testing in early onset AF might be modest, there is an increasing appreciation that genetic culprits for potentially life-threatening ventricular cardiomyopathies and channelopathies might initially present with AF. The potential clinical significance of this recognition is highlighted by evidence that suggests that identification of a pathogenic or likely pathogenic rare variant in a patient with early onset AF is associated with an increased risk of death. These findings suggest that it might be warranted to screen patients with early onset AF for these potentially more sinister cardiac conditions. Beyond facilitating the early identification of genetic culprits associated with potentially malignant phenotypes, insight into underlying AF genetic substrates might improve the selection of patients for existing therapies and guide the development of novel ones. Herein, we review the evidence that links genetic factors to AF, then discuss an approach to using genetic testing for early onset AF patients in the present context, and finally consider the potential value of genetic testing in the foreseeable future. Although further work might be necessary before recommending uniform integration of genetic testing in cases of early onset AF, ongoing research increasingly highlights its potential contributions to clinical care.

Lymphocyte Count Derived Polygenic Score and Interindividual Variability in CD4 T-cell Recovery in Response to Antiretroviral Therapy

Access to safe and effective antiretroviral therapy (ART) is a cornerstone in the global response to the HIV pandemic. Among people living with HIV, there is considerable interindividual variability in absolute CD4 T-cell recovery following initiation of virally suppressive ART. The contribution of host genetics to this variability is not well understood. We explored the contribution of a polygenic score which was derived from large, publicly available summary statistics for absolute lymphocyte count from individuals in the general population (PGSlymph) due to a lack of publicly available summary statistics for CD4 T-cell count. We explored associations with baseline CD4 T-cell count prior to ART initiation (n=4959) and change from baseline to week 48 on ART (n=3274) among treatment-naïve participants in prospective, randomized ART studies of the AIDS Clinical Trials Group. We separately examined an African-ancestry-derived and a European-ancestry-derived PGSlymph, and evaluated their performance across all participants, and also in the African and European ancestral groups separately. Multivariate models that included PGSlymph, baseline plasma HIV-1 RNA, age, sex, and 15 principal components (PCs) of genetic similarity explained ∼26-27% of variability in baseline CD4 T-cell count, but PGSlymph accounted for <1% of this variability. Models that also included baseline CD4 T-cell count explained ∼7-9% of variability in CD4 T-cell count increase on ART, but PGSlymph accounted for <1% of this variability. In univariate analyses, PGSlymph was not significantly associated with baseline or change in CD4 T-cell count. Among individuals of African ancestry, the African PGSlymph term in the multivariate model was significantly associated with change in CD4 T-cell count while not significant in the univariate model. When applied to lymphocyte count in a general medical biobank population (Penn Medicine BioBank), PGSlymph explained ∼6-10% of variability in multivariate models (including age, sex, and PCs) but only ∼1% in univariate models. In summary, a lymphocyte count PGS derived from the general population was not consistently associated with CD4 T-cell recovery on ART. Nonetheless, adjusting for clinical covariates is quite important when estimating such polygenic effects.

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.

Leveraging Multi-Ancestry Polygenic Risk Scores for Body Mass Index to Predict Antiretroviral Therapy-Induced Weight Gain

Widespread availability of antiretroviral therapies (ART) for HIV-1 have generated considerable interest in understanding the pharmacogenomics of ART. In some individuals, ART has been associated with excessive weight gain, which disproportionately affects women of African ancestry. The underlying biology of ART-associated weight gain is poorly understood, but some genetic markers which modify weight gain risk have been suggested, with more genetic factors likely remaining undiscovered. To overcome limitations in available sample sizes for genome-wide association studies (GWAS) in people with HIV, we explored whether a multi-ancestry polygenic risk score (PRS) derived from large, publicly available non-HIV GWAS for body mass index (BMI) can achieve high cross-ancestry performance for predicting baseline BMI in diverse, prospective ART clinical trials datasets, and whether that PRSBMI is also associated with change in BMI over 48 weeks on ART. We show that PRSBMI explained ∼5-7% of variability in baseline (pre-ART) BMI, with high performance in both European and African genetic ancestry groups, but that PRSBMI was not associated with change in BMI on ART. This study argues against a shared genetic predisposition for baseline (pre-ART) BMI and ART-associated weight gain.

Dissecting Polygenic Etiology of Ischemic Stroke in the Era of Precision Medicine

Ischemic stroke (IS), the leading cause of death and disability worldwide, is caused by many modifiable and non-modifiable risk factors. This complex disease is also known for its multiple etiologies with moderate heritability. Polygenic risk scores (PRSs), which have been used to establish a common genetic basis for IS, may contribute to IS risk stratification for disease/outcome prediction and personalized management. Statistical modeling and machine learning algorithms have contributed significantly to this field. For instance, multiple algorithms have been successfully applied to PRS construction and integration of genetic and non-genetic features for outcome prediction to aid in risk stratification for personalized management and prevention measures. PRS derived from variants with effect size estimated based on the summary statistics of a specific subtype shows a stronger association with the matched subtype. The disruption of the extracellular matrix and amyloidosis account for the pathogenesis of cerebral small vessel disease (CSVD). Pathway-specific PRS analyses confirm known and identify novel etiologies related to IS. Some of these specific PRSs (e.g., derived from endothelial cell apoptosis pathway) individually contribute to post-IS mortality and, together with clinical risk factors, better predict post-IS mortality. In this review, we summarize the genetic basis of IS, emphasizing the application of methodologies and algorithms used to construct PRSs and integrate genetics into risk models.

A Polygenic Risk Score Based on a Cardioembolic Stroke Multitrait Analysis Improves a Clinical Prediction Model for This Stroke Subtype

Background

Occult atrial fibrillation (AF) is one of the major causes of embolic stroke of undetermined source (ESUS). Knowing the underlying etiology of an ESUS will reduce stroke recurrence and/or unnecessary use of anticoagulants. Understanding cardioembolic strokes (CES), whose main cause is AF, will provide tools to select patients who would benefit from anticoagulants among those with ESUS or AF. We aimed to discover novel loci associated with CES and create a polygenetic risk score (PRS) for a more efficient CES risk stratification.

Methods

Multitrait analysis of GWAS (MTAG) was performed with MEGASTROKE-CES cohort (n = 362,661) and AF cohort (n = 1,030,836). We considered significant variants and replicated those variants with MTAG p-value < 5 × 10⁻⁸ influencing both traits (GWAS-pairwise) with a p-value < 0.05 in the original GWAS and in an independent cohort (n = 9,105). The PRS was created with PRSice-2 and evaluated in the independent cohort.

Results

We found and replicated eleven loci associated with CES. Eight were novel loci. Seven of them had been previously associated with AF, namely, CAV1, ESR2, GORAB, IGF1R, NEURL1, WIPF1, and ZEB2. KIAA1755 locus had never been associated with CES/AF, leading its index variant to a missense change (R1045W). The PRS generated has been significantly associated with CES improving discrimination and patient reclassification of a model with age, sex, and hypertension.

Conclusion

The loci found significantly associated with CES in the MTAG, together with the creation of a PRS that improves the predictive clinical models of CES, might help guide future clinical trials of anticoagulant therapy in patients with ESUS or AF.

PolarMorphism enables discovery of shared genetic variants across multiple traits from GWAS summary statistics

MOTIVATION

Pleiotropic SNPs are associated with multiple traits. Such SNPs can help pinpoint biological processes with an effect on multiple traits or point to a shared etiology between traits. We present PolarMorphism, a new method for the identification of pleiotropic SNPs from genome-wide association studies (GWAS) summary statistics. PolarMorphism can be readily applied to more than two traits or whole trait domains. PolarMorphism makes use of the fact that trait-specific SNP effect sizes can be seen as Cartesian coordinates and can thus be converted to polar coordinates r (distance from the origin) and theta (angle with the Cartesian x-axis, in the case of two traits). r describes the overall effect of a SNP, while theta describes the extent to which a SNP is shared. r and theta are used to determine the significance of SNP sharedness, resulting in a P-value per SNP that can be used for further analysis.

RESULTS

We apply PolarMorphism to a large collection of publicly available GWAS summary statistics enabling the construction of a pleiotropy network that shows the extent to which traits share SNPs. We show how PolarMorphism can be used to gain insight into relationships between traits and trait domains and contrast it with genetic correlation. Furthermore, pathway analysis of the newly discovered pleiotropic SNPs demonstrates that analysis of more than two traits simultaneously yields more biologically relevant results than the combined results of pairwise analysis of the same traits. Finally, we show that PolarMorphism is more efficient and more powerful than previously published methods.

AVAILABILITY AND IMPLEMENTATION

code: https://github.com/UMCUGenetics/PolarMorphism, results: 10.5281/zenodo.5844193.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Decoding the PITX2-controlled genetic network in atrial fibrillation

Atrial fibrillation (AF), the most common sustained cardiac arrhythmia and a major risk factor for stroke, often arises through ectopic electrical impulses derived from the pulmonary veins (PVs). Sequence variants in enhancers controlling expression of the transcription factor PITX2, which is expressed in the cardiomyocytes (CMs) of the PV and left atrium (LA), have been implicated in AF predisposition. Single nuclei multiomic profiling of RNA and analysis of chromatin accessibility combined with spectral clustering uncovered distinct PV- and LA-enriched CM cell states. Pitx2-mutant PV and LA CMs exhibited gene expression changes consistent with cardiac dysfunction through cell type-distinct, PITX2-directed, cis-regulatory grammars controlling target gene expression. The perturbed network targets in each CM were enriched in distinct human AF predisposition genes, suggesting combinatorial risk for AF genesis. Our data further reveal that PV and LA Pitx2-mutant CMs signal to endothelial and endocardial cells through BMP10 signaling with pathogenic potential. This work provides a multiomic framework for interrogating the basis of AF predisposition in the PVs of humans.

Genetic Architecture of Stroke of Undetermined Source: Overlap with Known Stroke Etiologies and Associations with Modifiable Risk Factors

OBJECTIVE

Ischemic stroke etiology remains undetermined in 30% of cases. We explored the genetic architecture of stroke classified as undetermined to test if mechanisms and risk factors underlying large-artery atherosclerotic (LAAS), cardioembolic (CES), and small-vessel stroke (SVS) contribute to its pathogenesis.

METHODS

We analyzed genome-wide data from 16,851 ischemic stroke cases and 32,473 controls. Using polygenic risk scores for LAAS, CES, and SVS, we assessed the genetic overlap with stroke of undetermined source and used pairwise genomewide association study (GWAS-PW) to search for shared loci. We then applied Mendelian randomization (MR) to identify potentially causal risk factors of stroke of undetermined source.

RESULTS

Genetic risk for LAS, CES, and SVS was associated with stroke of undetermined source pointing to overlap in their genetic architecture. Pairwise analyses revealed 19 shared loci with LAAS, 2 with CES, and 5 with SVS that have been implicated in atherosclerosis-related phenotypes. Genetic liability to both carotid atherosclerosis and atrial fibrillation was associated with stroke of undetermined source, but the association with atrial fibrillation was attenuated after excluding cases with incomplete diagnostic workup. MR analyses showed effects of genetically determinants of blood pressure, diabetes, waist-to-hip ratio, inflammatory pathways (IL-6 signaling, MCP-1/CCL2 levels), and factor XI levels on stroke of undetermined source.

INTERPRETATION

Stroke of undetermined source shares genetic and vascular risk factors with other stroke subtypes, especially LAAS, thus highlighting the diagnostic limitations of current subtyping approaches. The potentially causal associations with carotid atherosclerosis and atherosclerotic risk factors might have implications for prevention strategies targeting these mechanisms. ANN NEUROL 2022;91:640-651.

Genetically Based Atrial Fibrillation: Current Considerations for Diagnosis and Management

Atrial fibrillation (AF) is the most common atrial arrhythmia and is subcategorized into numerous clinical phenotypes. Given its heterogeneity, investigations into the genetic mechanisms underlying AF have been pursued in recent decades, with predominant analyses focusing on early onset or lone AF. Linkage analyses, genome wide association studies (GWAS), and single gene analyses have led to the identification of rare and common genetic variants associated with AF risk. Significant overlap with genetic variants implicated in dilated cardiomyopathy syndromes, including truncating variants of the sarcomere protein titin, have been identified through these analyses, in addition to other genes associated with cardiac structure and function. Despite this, widespread utilization of genetic testing in AF remains hindered by the unclear impact of genetic risk identification on clinical outcomes and the high prevalence of variants of unknown significance (VUS). However, genetic testing is a reasonable option for patients with early onset AF and in those with significant family history of arrhythmia. While many knowledge gaps remain, emerging data support genotyping to inform selection of AF therapeutics. In this review we highlight the current understanding of the complex genetic basis of AF and explore the overlap of AF with inherited cardiomyopathy syndromes. We propose a set of criteria for clinical genetic testing in AF patients and outline future steps for the integration of genetics into AF care. This article is protected by copyright. All rights reserved.

Chromatin architecture around stroke haplotypes provides evidence that genetic risk is conferred through vascular cells

Introduction: Genome-wide association studies (GWAS) have identified numerous stroke-associated SNPs. To understand how SNPs affect gene expression related to increased stroke risk, we studied epigenetic landscapes surrounding 26 common, validated stroke-associated loci. Methods: We mapped the SNPs to linkage disequilibrium (LD) blocks and examined H3K27ac, H3K4me1, H3K9ac, and H3K4me3 histone marks and transcription-factor binding-sites in pathologically relevant cell types (hematopoietic and vascular cells). Hi-C data were used to identify topologically associated domains (TADs) encompassing the LD blocks and overlapping genes. Results: Fibroblasts, smooth muscle, and endothelial cells showed significant enrichment for enhancer-associated marks within stroke-associated LD blocks. Genes within encompassing TADs reflected vessel homeostasis, cellular turnover, and enzymatic activity. Conclusions: Stroke-associated genetic variants confer risk predominantly through vascular cells rather than hematopoietic cell types.

Precision medicine in secondary prevention of ischemic stroke: how may blood-based biomarkers help in clinical routine? An expert opinion

PURPOSE OF REVIEW

One in eight patients unfortunately suffers a new stroke within 5 years of their first stroke, even today. Research in precision medicine could lead to a more individualized treatment allocation, possibly achieving lower recurrence rates of ischemic stroke. In this narrative review, we aim to discuss potential clinical implementation of several promising candidate blood biomarkers.

RECENT FINDINGS

We discuss specifically some promising blood-based biomarkers, which may improve the identification of underlying causes as well as risk stratification of patients according to their specific cerebrovascular risk factor pattern.

SUMMARY

Multimodal profiling of ischemic stroke patients by means of blood biomarkers, in addition to established clinical and neuroradiological data, may allow in the future a refinement of decision algorithms for treatment allocation in secondary ischemic stroke prevention.

A Higher Polygenic Risk Score Is Associated with a Higher Recurrence Rate of Atrial Fibrillation in Direct Current Cardioversion-Treated Patients

Background and Objectives: Recurrence of atrial fibrillation (AF) within six months after sinus rhythm restoration with direct current cardioversion (DCC) is a significant treatment challenge. Currently, the factors influencing outcome are mostly unknown. Studies have found a link between genetics and the risk of AF and efficacy of rhythm control. The aim of this study was to examine the association between eight single-nucleotide variants (SNVs) and the risk of AF development and recurrence after DCC. Materials and Methods: Regarding the occurrence of AF, 259 AF cases and 108 controls were studied. Genotypes for the eight SNVs located in the genes CAV1, MYH7, SOX5, KCNN3, ZFHX3, KCNJ5 and PITX2 were determined using high-resolution melting analysis and confirmed with Sanger sequencing. Six months after DCC, a telephone interview was conducted to determine whether AF had recurred. A polygenic risk score (PRS) was calculated as the unweighted sum of risk alleles. Multivariate regression analyses were performed to assess SNV and PRS association with AF occurrence and recurrence after DCC. Results: The risk allele of rs2200733 (PITX2) was significantly associated with the development of AF (p = 0.012, OR = 2.31, 95% CI = 1.206–4.423). AF recurred in 60% of patients and the allele generally associated with a decreased risk of AF of rs11047543 (SOX5) was associated with a greater risk of AF recurrence (p = 0.014, OR = 0.223, 95% CI = 0.067–0.738). A PRS of greater than 7 was significantly associated (p = 0.008) with a higher likelihood of developing AF after DCC (OR = 4.174, 95% CI = 1.454–11.980). Conclusions: A higher PRS is associated with increased odds of AF recurrence after treatment with DCC. PITX2 (rs2200733) is significantly associated with an increased risk of AF. The protective allele of rs11047543 (SOX5) is associated with a greater risk of AF recurrence. Further studies are needed to predict the success of rhythm control and guide patient selection towards the most efficacious treatment.

Detection of Subclinical Paroxysmal Atrial Fibrillation and Its Correlation with Candidate Genes in Patients with Cryptogenic Ischemic Stroke and TIA

Introduction: Cardioembolic etiology is assumed to be the most frequent cause of cryptogenic strokes. The detection of subclinical paroxysmal atrial fibrillation (AF) is important in the correct choice of preventive treatment. The aim of this prospective study was to detect the incidence of AF in patients with a cryptogenic stroke or transient ischemic attack (TIA) and to evaluate the association between the presence of AF and selected single-nucleotide polymorphisms (SNP).

Methods: Patients with a cryptogenic stroke/ TIA (n=100) and a control group (n=15) of volunteers without significant cardiovascular disease were included in the study during the period of 2014 to 2019. To detect AF they underwent 12 months of ECG monitoring using an implanted loop recorder (ILR). Genotyping for SNPs rs10033464, rs2200733, rs225132, and rs2106261 was performed by a high resolution melting analysis.

Results: We found AF to be present in 24 (24%) patients with a cryptogenic stroke/TIA, versus no subjects in the control group. The SNPs rs2106261, rs2200733, rs225132, and rs10033464 were not found to be associated with AF in our study (p=0.240; 1.000; 0.887; 0.589). However, a weak trend for a higher frequency of rs2106261 risk allele A homozygotes was observed in the patients with AF compared to the patients without AF (0.416 vs. 0.263, p=0.073). Homozygotes for allele A of rs2106261 were also present in a significantly higher frequency in AF patients compared to the controls (0.416 vs. 0.133, p = 0.012).

Conclusion: In our study paroxysmal AF was a probable etiological factor in 24% of patients with cryptogenic ischemic stroke / TIA during the 12 months of monitoring. The homozygous allele A of rs2106261 was identified to be the possible genetic risk factor of AF, but this should be verified in larger cohorts.

The study has been registered at www.clinicaltrials.gov, identifier NCT02216370.

A Review of Biomarkers for Ischemic Stroke Evaluation in Patients With Non-valvular Atrial Fibrillation

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide and results in a significantly increased ischemic stroke (IS) risk. IS risk stratification tools are widely being applied to guide anticoagulation treatment decisions and duration in patients with non-valvular AF (NVAF). The CHA₂DS₂-VASc score is largely validated and currently recommended by renowned guidelines. However, this score is heavily dependent on age, sex, and comorbidities, and exhibits only moderate predictive power. Finding effective and validated clinical biomarkers to assist in personalized IS risk evaluation has become one of the promising directions in the prevention and treatment of NVAF. A number of studies in recent years have explored differentially expressed biomarkers in NVAF patients with and without IS, and the potential role of various biomarkers for prediction or early diagnosis of IS in patients with NVAF. In this review, we describe the clinical application and utility of AF characteristics, cardiac imaging and electrocardiogram markers, arterial stiffness and atherosclerosis-related markers, circulating biomarkers, and novel genetic markers in IS diagnosis and management of patients with NVAF. We conclude that at present, there is no consensus understanding of a desirable biomarker for IS risk stratification in NVAF, and enrolling these biomarkers into extant models also remains challenging. Further prospective cohorts and trials are needed to integrate various clinical risk factors and biomarkers to optimize IS prediction in patients with NVAF. However, we believe that the growing insight into molecular mechanisms and in-depth understanding of existing and emerging biomarkers may further improve the IS risk identification and guide anticoagulation therapy in patients with NVAF.

Genetics of atrial fibrillation-practical applications for clinical management: if not now, when and how?

The prevalence and economic burden of atrial fibrillation (AF) are predicted to more than double over the next few decades. In addition to anticoagulation and treatment of concomitant cardiovascular conditions, early and standardized rhythm control therapy reduces cardiovascular outcomes as compared with a rate control approach, favouring the restoration, and maintenance of sinus rhythm safely. Current therapies for rhythm control of AF include antiarrhythmic drugs (AADs) and catheter ablation (CA). However, response in an individual patient is highly variable with some remaining free of AF for long periods on antiarrhythmic therapy, while others require repeat AF ablation within weeks. The limited success of rhythm control therapy for AF is in part related to incomplete understanding of the pathophysiological mechanisms and our inability to predict responses in individual patients. Thus, a major knowledge gap is predicting which patients with AF are likely to respond to rhythm control approach. Over the last decade, tremendous progress has been made in defining the genetic architecture of AF with the identification of rare mutations in cardiac ion channels, signalling molecules, and myocardial structural proteins associated with familial (early-onset) AF. Conversely, genome-wide association studies have identified common variants at over 100 genetic loci and the development of polygenic risk scores has identified high-risk individuals. Although retrospective studies suggest that response to AADs and CA is modulated in part by common genetic variation, the development of a comprehensive clinical and genetic risk score may enable the translation of genetic data to the bedside care of AF patients. Given the economic impact of the AF epidemic, even small changes in therapeutic efficacy may lead to substantial improvements for patients and health care systems.

Polygenic Risk Scores Augment Stroke Subtyping

Objective

To determine whether the polygenic risk score (PRS) derived from MEGASTROKE is associated with ischemic stroke (IS) and its subtypes in an independent tertiary health care system and to identify the PRS derived from gene sets of known biological pathways associated with IS.

Methods

Controls (n = 19,806/7,484, age ≥69/79 years) and cases (n = 1,184/951 for discovery/replication) of acute IS with European ancestry and clinical risk factors were identified by leveraging the Geisinger Electronic Health Record and chart review confirmation. All Geisinger MyCode patients with age ≥69/79 years and without any stroke-related diagnostic codes were included as low risk control. Genetic heritability and genetic correlation between Geisinger and MEGASTROKE (EUR) were calculated using the summary statistics of the genome-wide association study by linkage disequilibrium score regression. All PRS for any stroke (AS), any ischemic stroke (AIS), large artery stroke (LAS), cardioembolic stroke (CES), and small vessel stroke (SVS) were constructed by PRSice-2.

Results

A moderate heritability (10%–20%) for Geisinger sample as well as the genetic correlation between MEGASTROKE and the Geisinger cohort was identified. Variation of all 5 PRS significantly explained some of the phenotypic variations of Geisinger IS, and the R² increased by raising the cutoff for the age of controls. PRSLAS, PRSCES, and PRSSVS derived from low-frequency common variants provided the best fit for modeling (R² = 0.015 for PRSLAS). Gene sets analyses highlighted the association of PRS with Gene Ontology terms (vascular endothelial growth factor, amyloid precursor protein, and atherosclerosis). The PRSLAS, PRSCES, and PRSSVS explained the most variance of the corresponding subtypes of Geisinger IS suggesting shared etiologies and corroborated Geisinger TOAST subtyping.

Conclusions

We provide the first evidence that PRSs derived from MEGASTROKE have value in identifying shared etiologies and determining stroke subtypes.

Promising Biomarker Candidates for Cardioembolic Stroke Etiology. A Brief Narrative Review and Current Opinion

Determining the cause of stroke is considered one of the main objectives in evaluating a stroke patient in clinical practice. However, ischemic stroke is a heterogeneous disorder and numerous underlying disorders are implicated in its pathogenesis. Although progress has been made in identifying individual stroke etiology, in many cases underlying mechanisms still remain elusive. Since secondary prevention strategies are tailored toward individual stroke mechanisms, patients whose stroke etiology is unknown may not receive optimal preventive treatment. Cardioembolic stroke is commonly defined as cerebral vessel occlusion by distant embolization arising from thrombus formation in the heart. It accounts for the main proportion of ischemic strokes, and its share to stroke etiology is likely to rise even further in future decades. However, it can be challenging to distinguish cardioembolism from other possible etiologies. As personalized medicine advances, stroke researchers' focus is increasingly drawn to etiology-associated biomarkers. They can provide deeper insight regarding specific stroke mechanisms and can help to unravel previously undetected pathologies. Furthermore, etiology-associated biomarkers could play an important role in guiding future stroke prevention strategies. To achieve this, broad validation of promising candidate biomarkers as well as their implementation in well-designed randomized clinical trials is necessary. This review focuses on the most-promising candidates for diagnosis of cardioembolic stroke. It discusses existing evidence for possible clinical applications of these biomarkers, addresses current challenges, and outlines future perspectives.

Derivation and validation of genome-wide polygenic score for urinary tract stone diagnosis

Urinary tract stones have high heritability indicating a strong genetic component. However, genome-wide association studies (GWAS) have uncovered only a few genome wide significant single nucleotide polymorphisms (SNPs). Polygenic risk scores (PRS) sum cumulative effect of many SNPs and shed light on underlying genetic architecture. Using GWAS summary statistics from 361,141 participants in the United Kingdom Biobank, we generated a PRS and determined association with stone diagnosis in 28,877 participants in the Mount Sinai BioMe Biobank. In BioMe (1,071 cases and 27,806 controls), for every standard deviation increase, we observed a significant increment in adjusted odds ratio of a factor of 1.2 (95% confidence interval 1.13-1.26). In comparison, a risk score comprised of GWAS significant SNPs was not significantly associated with diagnosis. After stratifying individuals into low and high-risk categories on clinical risk factors, there was a significant increment in adjusted odds ratio of 1.3 (1.12-1.6) in the low- and 1.2 (1.1-1.2) in the high-risk group for every standard deviation increment in PRS. In a 14,348-participant validation cohort (Penn Medicine Biobank), every standard deviation increment was associated with a significant adjusted odds ratio of 1.1 (1.03 - 1.2). Thus, a genome-wide PRS is associated with urinary tract stones overall and in the absence of known clinical risk factors and illustrates their complex polygenic architecture.

Alternate approach to stroke phenotyping identifies a genetic risk locus for small vessel stroke

Ischemic stroke (IS), caused by obstruction of cerebral blood flow, is one of the leading causes of death. While neurologists agree on delineation of IS into three subtypes (cardioembolic stroke (CES), large artery stroke (LAS), and small vessel stroke (SVS)), several subtyping systems exist. The most commonly used systems are TOAST (Trial of Org 10172 in Acute Stroke Treatment) and CCS (Causative Classification System for Stroke), but agreement is only moderate. We have compared two approaches to combining the existing subtyping systems for a phenotype suited for a genome-wide association study (GWAS). We used the NINDS Stroke Genetics Network dataset (SiGN, 11,477 cases with CCS and TOAST subtypes and 28,026 controls). We defined two new phenotypes: the intersect, for which an individual must be assigned the same subtype by CCS and TOAST; and the union, for which an individual must be assigned a subtype by either CCS or TOAST. The union yields the largest sample size while the intersect yields a phenotype with less potential misclassification. We performed GWAS for all subtypes, using the original subtyping systems, the intersect, and the union as phenotypes. In each subtype, heritability was higher for the intersect compared with the other phenotypes. We observed stronger effects at known IS variants with the intersect compared with the other phenotypes. With the intersect, we identify rs10029218:G>A as an associated variant with SVS. We conclude that this approach increases the likelihood to detect genetic associations in ischemic stroke.

How Will Genetics Inform the Clinical Care of Atrial Fibrillation?

Susceptibility to atrial fibrillation (AF) is determined by well-recognized risk factors such as diabetes mellitus or hypertension, emerging risk factors such as sleep apnea or inflammation, and increasingly well-defined genetic variants. As discussed in detail in a companion article in this series, studies in families and in large populations have identified multiple genetic loci, specific genes, and specific variants increasing susceptibility to AF. Since it is becoming increasingly inexpensive to obtain genotype data and indeed whole genome sequence data, the question then becomes to define whether using emerging new genetics knowledge can improve care for patients both before and after development of AF. Examples of improvements in care could include identifying patients at increased risk for AF (and thus deploying increased surveillance or even low-risk preventive therapies should these be available), identifying patient subsets in whom specific therapies are likely to be effective or ineffective or in whom the driving biology could motivate the development of new mechanism-based therapies or identifying an underlying susceptibility to comorbid cardiovascular disease. While current guidelines for the care of patients with AF do not recommend routine genetic testing, this rapidly increasing knowledge base suggests that testing may now or soon have a place in the management of select patients. The opportunity is to generate, validate, and deploy clinical predictors (including family history) of AF risk, to assess the utility of incorporating genomic variants into those predictors, and to identify and validate interventions such as wearable or implantable device-based monitoring ultimately to intervene in patients with AF before they present with catastrophic complications like heart failure or stroke.

Genetically Determined Birthweight Associates With Atrial Fibrillation: A Mendelian Randomization Study

BACKGROUND

Atrial fibrillation is a common cardiovascular disorder, characterized by irregular electrical activity in the upper chambers of the heart. Both chronic cardiometabolic risk factors and genetics have been shown to contribute to the development of atrial fibrillation. Birthweight has also been associated with risk of atrial fibrillation.

METHODS

In the current study, we utilized a genetic approach to study the effect of birthweight on atrial fibrillation. We used 2-sample Mendelian randomization to consider the impact of birthweight on incident atrial fibrillation using summary data from the Early Growth Genetics Consortium GWAS of birthweight and a large biobank-based GWAS of atrial fibrillation.

RESULTS

Using the framework of 2-sample Mendelian randomization, we found that a 1-SD genetic elevation of birthweight was associated with increased risk of atrial fibrillation (odds ratio, 1.27 [95% CI, 1.14-1.41]; P=1×10^-5) with sensitivity analyses demonstrating robustness of this result.

CONCLUSIONS

Our findings clarify the directionality of the relationship between birthweight and atrial fibrillation, supporting the growing body of evidence that intrauterine growth has a lifelong impact on cardiovascular health.

Current Synthesis and Systematic Review of Main Effects of Calf Blood Deproteinized Medicine (Actovegin®) in Ischemic Stroke

Background: Stroke is one of the largest problems and clinical-social challenges within neurology and, in general, pathology. Here, we briefly reviewed the main pathophysiological mechanisms of ischemic stroke, which represent targets for medical interventions, including for a calf blood deproteinized hemodialysate/ultrafiltrate. Methods: We conducted a systematic review of current related literature concerning the effects of Actovegin^®, of mainly the pleiotropic type, applied to the injury pathways of ischemic stroke. Results: The bibliographic resources regarding the use of Actovegin^® in ischemic stroke are scarce. The main Actovegin^® actions refer to the ischemic stroke lesion items’ ensemble, targeting tissue oxidation, energy metabolism, and glucose availability through their augmentation, combating ischemic processes and oxidative stress, and decreasing inflammation (including with modulatory connotations, by the nuclear factor-κB pathway) and apoptosis-like processes, counteracting them by mitigating the caspase-3 activation induced by amyloid β-peptides. Conclusion: Since no available therapeutic agents are capable of curing the central nervous system’s lesions, any contribution, such as that of Actovegin^® (with consideration of a positive balance between benefits and risks), is worthy of further study and periodic reappraisal, including investigation into further connected aspects.

Multilevel omics for the discovery of biomarkers and therapeutic targets for stroke

Despite many years of research, no biomarkers for stroke are available to use in clinical practice. Progress in high-throughput technologies has provided new opportunities to understand the pathophysiology of this complex disease, and these studies have generated large amounts of data and information at different molecular levels. The integration of these multi-omics data means that thousands of proteins (proteomics), genes (genomics), RNAs (transcriptomics) and metabolites (metabolomics) can be studied simultaneously, revealing interaction networks between the molecular levels. Integrated analysis of multi-omics data will provide useful insight into stroke pathogenesis, identification of therapeutic targets and biomarker discovery. In this Review, we detail current knowledge on the pathology of stroke and the current status of biomarker research in stroke. We summarize how proteomics, metabolomics, transcriptomics and genomics are all contributing to the identification of new candidate biomarkers that could be developed and used in clinical stroke management.

Atrial Fibrillation Risk and Discrimination of Cardioembolic From Noncardioembolic Stroke

Background and Purpose- Classification of stroke as cardioembolic in etiology can be challenging, particularly since the predominant cause, atrial fibrillation (AF), may not be present at the time of stroke. Efficient tools that discriminate cardioembolic from noncardioembolic strokes may improve care as anticoagulation is frequently indicated after cardioembolism. We sought to assess and quantify the discriminative power of AF risk as a classifier for cardioembolism in a real-world population of patients with acute ischemic stroke. Methods- We performed a cross-sectional analysis of a multi-institutional sample of patients with acute ischemic stroke. We systematically adjudicated stroke subtype and examined associations between AF risk using CHA₂DS₂-VASc, Cohorts for Heart and Aging Research in Genomic Epidemiology-AF score, and the recently developed Electronic Health Record-Based AF score, and cardioembolic stroke using logistic regression. We compared the ability of AF risk to discriminate cardioembolism by calculating C statistics and sensitivity/specificity cutoffs for cardioembolic stroke. Results- Of 1431 individuals with ischemic stroke (age, 65±15; 40% women), 323 (22.6%) had cardioembolism. AF risk was significantly associated with cardioembolism (CHA₂DS₂-VASc: odds ratio [OR] per SD, 1.69 [95% CI, 1.49-1.93]; Cohorts for Heart and Aging Research in Genomic Epidemiology-AF score: OR, 2.22 [95% CI, 1.90-2.60]; electronic Health Record-Based AF: OR, 2.55 [95% CI, 2.16-3.04]). Discrimination was greater for Cohorts for Heart and Aging Research in Genomic Epidemiology-AF score (C index, 0.695 [95% CI, 0.663-0.726]) and Electronic Health Record-Based AF score (0.713 [95% CI, 0.681-0.744]) versus CHA₂DS₂-VASc (C index, 0.651 [95% CI, 0.619-0.683]). Examination of AF scores across a range of thresholds indicated that AF risk may facilitate identification of individuals at low likelihood of cardioembolism (eg, negative likelihood ratios for Electronic Health Record-Based AF score ranged 0.31-0.10 at sensitivity thresholds 0.90-0.99). Conclusions- AF risk scores associate with cardioembolic stroke and exhibit moderate discrimination. Utilization of AF risk scores at the time of stroke may be most useful for identifying individuals at low probability of cardioembolism. Future analyses are warranted to assess whether stroke subtype classification can be enhanced to improve outcomes in undifferentiated stroke.

Genetic Variation and Response to Neurocritical Illness: a Powerful Approach to Identify Novel Pathophysiological Mechanisms and Therapeutic Targets

Disease-specific therapeutic options for critically ill neurological patients are limited. The identification of new preventive, therapeutic, and rehabilitation strategies is of the utmost importance in the field of neurocritical care research. Population genetics offers powerful tools to identify and prioritize biological pathways to be targeted by novel interventions. New treatments with supportive genetic evidence have twice the chances of obtaining final FDA approval compared to those without this support. Large collaborations, public access to data, reproducible science, and innovative analytical methods have exponentially increased the pace of discoveries related to neurocritical care genetics.

Translational Genomics in Neurocritical Care: a Review

Translational genomics represents a broad field of study that combines genome and transcriptome-wide studies in humans and model systems to refine our understanding of human biology and ultimately identify new ways to treat and prevent disease. The approaches to translational genomics can be broadly grouped into two methodologies, forward and reverse genomic translation. Traditional (forward) genomic translation begins with model systems and aims at using unbiased genetic associations in these models to derive insight into biological mechanisms that may also be relevant in human disease. Reverse genomic translation begins with observations made through human genomic studies and refines these observations through follow-up studies using model systems. The ultimate goal of these approaches is to clarify intervenable processes as targets for therapeutic development. In this review, we describe some of the approaches being taken to apply translational genomics to the study of diseases commonly encountered in the neurocritical care setting, including hemorrhagic and ischemic stroke, traumatic brain injury, subarachnoid hemorrhage, and status epilepticus, utilizing both forward and reverse genomic translational techniques. Further, we highlight approaches in the field that could be applied in neurocritical care to improve our ability to identify new treatment modalities as well as to provide important information to patients about risk and prognosis.

Genomic risk score offers predictive performance comparable to clinical risk factors for ischaemic stroke

Recent genome-wide association studies in stroke have enabled the generation of genomic risk scores (GRS) but their predictive power has been modest compared to established stroke risk factors. Here, using a meta-scoring approach, we develop a metaGRS for ischaemic stroke (IS) and analyse this score in the UK Biobank (n = 395,393; 3075 IS events by age 75). The metaGRS hazard ratio for IS (1.26, 95% CI 1.22-1.31 per metaGRS standard deviation) doubles that of a previous GRS, identifying a subset of individuals at monogenic levels of risk: the top 0.25% of metaGRS have three-fold risk of IS. The metaGRS is similarly or more predictive compared to several risk factors, such as family history, blood pressure, body mass index, and smoking. We estimate the reductions needed in modifiable risk factors for individuals with different levels of genomic risk and suggest that, for individuals with high metaGRS, achieving risk factor levels recommended by current guidelines may be insufficient to mitigate risk.