Mendelian Randomization Studies in Stroke: Exploration of Risk Factors and Drug Targets With Human Genetic Data

Stroke ◽  
2021 ◽  
Author(s):  
Marios K. Georgakis ◽  
Dipender Gill
Keyword(s):  
Risk Factors ◽  
Genetic Variants ◽  
Drug Targets ◽  
Mendelian Randomization ◽  
Genetic Data ◽  
Lipid Lowering ◽  
Coagulation Cascade ◽  
Random Allocation ◽  
Large Artery ◽  
Stroke Subtypes

Elucidating the causes of stroke is key to developing effective preventive strategies. The Mendelian randomization approach leverages genetic variants related to an exposure of interest to investigate the effects of varying that exposure on disease risk. The random allocation of genetic variants at conception reduces confounding from environmental factors and thus strengthens causal inference, analogous to treatment allocation in a randomized controlled trial. With the recent explosion in the availability of human genetic data, Mendelian randomization has proven a valuable tool for studying risk factors for stroke. In this review, we provide an overview of recent developments in the application of Mendelian randomization to unravel the pathophysiology of stroke subtypes and identify therapeutic targets for clinical translation. The approach has offered novel insight into the differential effects of risk factors and antihypertensive, lipid-lowering, and anticoagulant drug classes on risk of stroke subtypes. Analyses have further facilitated the prioritization of novel drug targets, such as for inflammatory pathways underlying large artery atherosclerotic stroke and for the coagulation cascade that contributes to cardioembolic stroke. With continued methodological advances coupled with the rapidly increasing availability of genetic data related to a broad range of stroke phenotypes, the potential for Mendelian randomization in this context is expanding exponentially.

scholarly journals Cardiac Risk Factors for Stroke: A Comprehensive Mendelian Randomization Study

Stroke ◽  
2021 ◽  
Author(s):  
Simon Frerich ◽  
Rainer Malik ◽  
Marios K. Georgakis ◽  
Moritz F. Sinner ◽  
Steven J. Kittner ◽  
...  
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Genetic Predisposition ◽  
Mendelian Randomization ◽  
Genetic Data ◽  
Cardiac Risk ◽  
Large Artery ◽  
Artery Disease

Background and Purpose: Observational studies suggest an association of stroke with cardiac traits beyond atrial fibrillation, the leading source of cardioembolism. However, controversy remains regarding a causal role of these traits in stroke pathogenesis. Here, we leveraged genetic data to systematically assess associations between cardiac traits and stroke risk using a Mendelian Randomization framework. Methods: We studied 66 cardiac traits including cardiovascular diseases, magnetic resonance imaging–derived cardiac imaging, echocardiographic imaging, and electrocardiographic measures, as well as blood biomarkers in a 2-sample Mendelian Randomization approach. Genetic predisposition to each trait was explored for associations with risk of stroke and stroke subtypes in data from the MEGASTROKE consortium (40 585 cases/406 111 controls). Using multivariable Mendelian Randomization, we adjusted for potential pleiotropic or mediating effects relating to atrial fibrillation, coronary artery disease, and systolic blood pressure. Results: As expected, we observed strong independent associations between genetic predisposition to atrial fibrillation and cardioembolic stroke and between genetic predisposition to coronary artery disease as a proxy for atherosclerosis and large-artery stroke. Our data-driven analyses further indicated associations of genetic predisposition to both heart failure and lower resting heart rate with stroke. However, these associations were explained by atrial fibrillation, coronary artery disease, and systolic blood pressure in multivariable analyses. Genetically predicted P-wave terminal force in V1, an electrocardiographic marker for atrial cardiopathy, was inversely associated with large-artery stroke. Conclusions: Available genetic data do not support substantial effects of cardiac traits on the risk of stroke beyond known clinical risk factors. Our findings highlight the need to carefully control for confounding and other potential biases in studies examining candidate cardiac risk factors for stroke.

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

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Danyang Tian ◽  
Linjing Zhang ◽  
Zhenhuang Zhuang ◽  
Tao Huang ◽  
Dongsheng Fan
Keyword(s):  
Risk Factors ◽  
Ischemic Stroke ◽  
Mendelian Randomization ◽  
P Wave ◽  
Cardioembolic Stroke ◽  
Large Artery ◽  
Weighted Analysis ◽  
Inverse Variance ◽  
Weighted Median ◽  
Genetically Determined

AbstractObservational 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.

Patient Demographics, Characteristics, and Intrahospital Mortality of Different Ischemic Stroke Subtypes in a Tertiary Hospital during Five-Year Period

2021 ◽  
Vol 6 (1) ◽  
pp. 6
Author(s):  
Sintija Strautmane ◽  
Kristaps Jurjāns ◽  
Estere Zeltiņa ◽  
Evija Miglāne ◽  
Andrejs Millers
Keyword(s):  
Risk Factors ◽  
Ischemic Stroke ◽  
Reperfusion Therapy ◽  
Cardioembolic Stroke ◽  
Study Group ◽  
Large Artery ◽  
Patient Demographics ◽  
Stroke Subtypes ◽  
Female Predominance ◽  
Study Population

Background and Objectives. Ischemic stroke (IS) is one of the leading causes of disability, morbidity, and mortality worldwide. The goal of the study was to evaluate patient demographics, characteristics, and intrahospital mortality among different ischemic stroke subtypes. Materials and Methods. A retrospective observational non-randomized study was conducted, including only ischemic stroke patients, admitted to Pauls Stradins Clinical university hospital, Riga, Latvia, from January of 2016 until December 2020. Ischemic stroke subtypes were determined according to Trial of Org 10172 in Acute Stroke Treatment (TOAST) criteria as a stroke due to (1) large-artery atherosclerosis (atherothrombotic stroke (AS)), (2) cardioembolism (cardioembolic stroke (CS)), (3) small-vessel occlusion (lacunar stroke (LS)), (4) stroke of other determined etiology (other specified stroke (OSS)), and (5) stroke of undetermined etiology (undetermined stroke (US)). The data between different stroke subtypes were compared. Results. There was a slight female predominance among our study population, as 2673 (56.2%) patients were females. In our study group, the most common IS subtypes were cardioembolic stroke (CS), 2252 (47.4%), and atherothrombotic stroke (AS), 1304 (27.4%). CS patients were significantly more severely disabled on admission, 1828 (81.4%), and on discharge, 378 (16.8%), p < 0.05. Moreover, patients with CS demonstrated the highest rate of comorbidities and risk factors. This was also statistically significant, p < 0.05. Differences between the total patient count with no atrial fibrillation (AF), paroxysmal AF, permanent AF, and different IS subtypes among our study population demonstrated not only statistical significance but also a strong association, Cramer’s V = 0.53. The majority of patients in our study group were treated conservatively, 3389 (71.3%). Reperfusion therapy was significantly more often performed among CS patients, 770 (34.2%), p < 0.05. The overall intrahospital mortality among our study population was 570 (12.0%), with the highest intrahospital mortality rate noted among CS patients, 378 (66.3%), p < 0.05. No statistically significant difference was observed between acute myocardial infarction and adiposity, p > 0.05. Conclusions. In our study, CS and AS were the most common IS subtypes. CS patients were significantly older with slight female predominance. CS patients demonstrated the greatest disability, risk factors, comorbidities, reperfusion therapy, and intrahospital mortality.

Abstract T P153: Women and Men with Ischemic Stroke in SiGN Have Different Subtype Distributions and Risk Factors

Stroke ◽  
2014 ◽  
Vol 45 (suppl_1) ◽  
Author(s):  
Kathryn M Rexrode ◽  
Braxton D Mitchell ◽  
Kathleen A Ryan ◽  
Steven J Kittner ◽  
Hakan Ay ◽  
...  
Keyword(s):  
Risk Factors ◽  
Ischemic Stroke ◽  
Stroke Risk ◽  
Cardioembolic Stroke ◽  
Relative Distribution ◽  
Large Artery ◽  
Stroke Risk Factors ◽  
Stroke Subtypes ◽  
Small Artery Occlusion ◽  
Ischemic Stroke Risk

Introduction: The relative distribution of stroke risk factors, as well as ischemic stroke subtypes, in women compared with men is not well described. Hypothesis: We hypothesized that the distribution of ischemic stroke risk factors and subtypes would differ by sex, with a later onset in women and greater proportion of comorbidities. Methods: The NINDS Stroke Genetics Network (SiGN) consortium was established to evaluate genetic risk factors for ischemic stroke. A total of 23 separate studies performed Causative Classification of Stroke (CCS) typing using standardized criteria on ischemic stroke cases and contributed data on risk factors. We compared the distribution of ischemic stroke risk factors and CCS phenotypes between men and women with ischemic stroke. Results: Of the 16,228 ischemic strokes in SiGN, 8005 (49.3%) occurred in women. Median age at stroke was older in female than male stroke cases (73 vs. 66 years) (p=<0.0001). Among stroke cases, women were more likely than men cases to have hypertension or atrial fibrillation and less likely to have diabetes or coronary artery disease, or to smoke (p <0.003 for all). The distribution of stroke subtypes also differed by sex, with women less likely than men to have large artery infarction and small artery occlusion, and more likely to have cardioembolic stroke and undetermined stroke due to incomplete work-up (p values all <0.0001; see Table). Results were similar when the distribution of stroke subtypes was examined for those <70 years and ≥70 years, except for cardioembolic stroke remaining more common only among women ≥70. Conclusions: In this large group of carefully phenotyped ischemic strokes, the distribution of ischemic stroke subtypes and risk factor profiles differ significantly by sex. Evaluation of the causes of these differences may highlight areas for improved prevention and risk reduction in both genders.

scholarly journals Using the MR-Base platform to investigate risk factors and drug targets for thousands of phenotypes

2019 ◽  
Vol 4 ◽  
pp. 113 ◽  
Author(s):  
Venexia M Walker ◽  
Neil M Davies ◽  
Gibran Hemani ◽  
Jie Zheng ◽  
Philip C Haycock ◽  
...  
Keyword(s):  
Risk Factors ◽  
Web Application ◽  
Drug Targets ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
R Package ◽  
Sensitivity Analyses ◽  
Link Type ◽  
Study Results

Mendelian randomization (MR) estimates the causal effect of exposures on outcomes by exploiting genetic variation to address confounding and reverse causation. This method has a broad range of applications, including investigating risk factors and appraising potential targets for intervention. MR-Base has become established as a freely accessible, online platform, which combines a database of complete genome-wide association study results with an interface for performing Mendelian randomization and sensitivity analyses. This allows the user to explore millions of potentially causal associations. MR-Base is available as a web application or as an R package. The technical aspects of the tool have previously been documented in the literature. The present article is complementary to this as it focuses on the applied aspects. Specifically, we describe how MR-Base can be used in several ways, including to perform novel causal analyses, replicate results and enable transparency, amongst others. We also present three use cases, which demonstrate important applications of Mendelian randomization and highlight the benefits of using MR-Base for these types of analyses.

Abstract 3884: Stroke Subtypes in Hispanic Patients in the NINDS Stroke Genetics Network (SiGN): A Comparison of Two Classification Systems

Stroke ◽  
2012 ◽  
Vol 43 (suppl_1) ◽  
Author(s):  
Michael Katsnelson ◽  
Tatjana Rundek ◽  
Ralph Sacco ◽  
Hannah Gardener ◽  
Shaneela Malik ◽  
...  
Keyword(s):  
Risk Factors ◽  
Ischemic Stroke ◽  
Classification Systems ◽  
Blood Collection ◽  
Large Artery ◽  
Stroke Subtype ◽  
Reliable Determination ◽  
Stroke Treatment ◽  
Stroke Subtypes ◽  
Small Artery Occlusion

Objectives: Identification of gene variants of stroke subtypes is important for the development of tailored ischemic stroke therapies among various ethnic groups. Valid and reliable determination of ischemic stroke subtype is essential for achieving this goal and to standardize a classification scheme across multi-center studies and different populations. Causative Classification System for Ischemic Stroke (CCS) is a novel computerized subclassification tool developed to improve reliability and accuracy of classifying stroke types. The CCS algorithm relies on both phenotypic and causative stroke variables. A Hispanic subset of the SiGN, an important and distinct target population with greater risk of certain stroke subtypes, was evaluated with Trial of Org 10172 in Acute Stroke Treatment (TOAST) and CCS and the agreement between the two classification systems was analyzed. Methods: Over 6000 subjects at 15 sites across US and Europe were enrolled, with TOAST and CCS locally adjudicated. Blood collection and central data quality control (10% central readjudication) were performed on all participants. A subset of Hispanics was analyzed for the purpose of this study and the agreement between the TOAST and CCS were assessed by kappa statistic. Findings: Hispanics (n=595, 10.9%) compared to non-Hispanics (n=5457) were more likely to be younger (63.7 vs. 64.0), male (55% vs. 46%) and have fewer of the traditional stroke risk factors HTN (54% vs. 64%), Afib (11% vs. 14%), DM(23% vs. 25%), CAD(16% vs. 20%) and smoking(19% vs. 22%). While the TOAST showed no differences between stroke subtypes for Hispanic vs. non-Hispanics, in CCS, Hispanics were classified with more of large vessel (22% vs. 20%), cardioembolic (37% vs. 30%) and small vessel strokes (13% vs. 9%) and fewer with undetermined etiology (28% vs. 40%) as compared to non-Hispanics. TOAST and CCS offered moderate correlation across all stroke types in Hispanics: kappa of 0.66 for large artery atherosclerosis, 0.58 for cardioembolic, and 0.58 for small artery occlusion. Conclusion: CCS offers a more sensitive and accurate system for subphenotyping of strokes in Hispanics who tended to have relatively fewer risk factors and unclassified strokes. Further studies correlating the two classification systems and their relation to genotyping data are warranted.

scholarly journals Evaluating Lipid‐Lowering Drug Targets for Parkinson's Disease Prevention with Mendelian Randomization

2020 ◽  
Vol 88 (5) ◽  
pp. 1043-1047
Author(s):  
Dylan M. Williams ◽  
Sara Bandres‐Ciga ◽  
Karl Heilbron ◽  
David Hinds ◽  
Alastair J. Noyce ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Prevention ◽  
Drug Targets ◽  
Mendelian Randomization ◽  
Lipid Lowering ◽  
Lipid Lowering Drug ◽  
Lowering Drug

scholarly journals Factorial Mendelian randomization: using genetic variants to assess interactions

2019 ◽  
Vol 49 (4) ◽  
pp. 1147-1158 ◽  
Author(s):  
Jessica M B Rees ◽  
Christopher N Foley ◽  
Stephen Burgess
Keyword(s):  
Risk Factors ◽  
Instrumental Variables ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Real Data ◽  
Uk Biobank ◽  
Pharmacological Interventions ◽  
Natural Break ◽  
Using Data ◽  
Randomization Analysis

Abstract Background Factorial Mendelian randomization is the use of genetic variants to answer questions about interactions. Although the approach has been used in applied investigations, little methodological advice is available on how to design or perform a factorial Mendelian randomization analysis. Previous analyses have employed a 2 × 2 approach, using dichotomized genetic scores to divide the population into four subgroups as in a factorial randomized trial. Methods We describe two distinct contexts for factorial Mendelian randomization: investigating interactions between risk factors, and investigating interactions between pharmacological interventions on risk factors. We propose two-stage least squares methods using all available genetic variants and their interactions as instrumental variables, and using continuous genetic scores as instrumental variables rather than dichotomized scores. We illustrate our methods using data from UK Biobank to investigate the interaction between body mass index and alcohol consumption on systolic blood pressure. Results Simulated and real data show that efficiency is maximized using the full set of interactions between genetic variants as instruments. In the applied example, between 4- and 10-fold improvement in efficiency is demonstrated over the 2 × 2 approach. Analyses using continuous genetic scores are more efficient than those using dichotomized scores. Efficiency is improved by finding genetic variants that divide the population at a natural break in the distribution of the risk factor, or else divide the population into more equal-sized groups. Conclusions Previous factorial Mendelian randomization analyses may have been underpowered. Efficiency can be improved by using all genetic variants and their interactions as instrumental variables, rather than the 2 × 2 approach.

scholarly journals MR-LDP: a two-sample Mendelian randomization for GWAS summary statistics accounting for linkage disequilibrium and horizontal pleiotropy

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Qing Cheng ◽  
Yi Yang ◽  
Xingjie Shi ◽  
Kar-Fu Yeung ◽  
Can Yang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Linkage Disequilibrium ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Alternative Methods ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Causal Relationships ◽  
Disease Outcomes

Abstract The proliferation of genome-wide association studies (GWAS) has prompted the use of two-sample Mendelian randomization (MR) with genetic variants as instrumental variables (IVs) for drawing reliable causal relationships between health risk factors and disease outcomes. However, the unique features of GWAS demand that MR methods account for both linkage disequilibrium (LD) and ubiquitously existing horizontal pleiotropy among complex traits, which is the phenomenon wherein a variant affects the outcome through mechanisms other than exclusively through the exposure. Therefore, statistical methods that fail to consider LD and horizontal pleiotropy can lead to biased estimates and false-positive causal relationships. To overcome these limitations, we proposed a probabilistic model for MR analysis in identifying the causal effects between risk factors and disease outcomes using GWAS summary statistics in the presence of LD and to properly account for horizontal pleiotropy among genetic variants (MR-LDP) and develop a computationally efficient algorithm to make the causal inference. We then conducted comprehensive simulation studies to demonstrate the advantages of MR-LDP over the existing methods. Moreover, we used two real exposure–outcome pairs to validate the results from MR-LDP compared with alternative methods, showing that our method is more efficient in using all-instrumental variants in LD. By further applying MR-LDP to lipid traits and body mass index (BMI) as risk factors for complex diseases, we identified multiple pairs of significant causal relationships, including a protective effect of high-density lipoprotein cholesterol on peripheral vascular disease and a positive causal effect of BMI on hemorrhoids.

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