Mendelian Randomization Studies in Stroke: Exploration of Risk Factors and Drug Targets With Human Genetic Data
This article has been corrected.
VIEW CORRECTIONAbstract
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.
Stroke is one of the leading causes of death and disability worldwide.1,2 Given the difficulties associated with stroke treatment and rehabilitation, primary and secondary prevention is crucial.3 Stroke is a highly heterogeneous disease with multiple underlying causes. Developing and optimizing preventive strategies, therefore, requires a clear understanding of the underlying pathophysiology, including insight into causal risk factors for different etiological stroke subtypes. Yet, randomized controlled trials so far have often considered stroke as a single end point, thus not offering insights into causal effects of modifiable risk factors on particular etiological subtypes. While some observational studies have explored such associations with stroke subtypes,4 their results may not reflect causal effects, due to the possibility of confounding and reverse causation.5
Mendelian randomization (MR) has been applied with the aim of leveraging observational data to provide insight into causal disease mechanisms.6,7 It is an epidemiological approach that makes use of genetic variants associated with a risk factor of interest and explores their associations with disease outcomes.8,9 It is because genetic variants are randomly assigned at conception that MR analyses can offer insight into causal relationships, to thus inform on risk factors and promising drug targets for prioritization in clinical trials.8,9 Over recent years, the widespread availability of genetic data and the explosion of genome-wide association studies (GWAS) has led to an exponential increase in the number of published MR analyses in the fields of cardiovascular medicine and neurology.10,11 Among them, multiple recent studies have explored stroke, primarily focusing on ischemic stroke and its subtypes. In this review, we provide an overview of these studies, discuss the implications of MR in stroke research, and reflect on potential future applications in the field.
Basic Principles of MR
MR uses genetic variants associated with a risk factor (genetic instruments) to investigate causal effects of the risk factor with an outcome of interest (Figure 1).8,9 In its simplest form, MR explores the associations between single-nucleotide polymorphism genetic variants that are associated with levels of an exposure (such as a risk factor), to explore the effects of modifying that risk factor on an outcome (such as a disease state). For example, a genetic polymorphism at the rs7529229 single-nucleotide polymorphism, which lies within the gene encoding the IL6 (interleukin 6) receptor (IL6R), has been found to strongly predict the circulating levels of CRP (C-reactive protein), a molecule downstream to the IL6 signaling cascade.12 Furthermore, rs7529229 shows consistent associations with other upstream and downstream markers of the IL6 signaling pathway, thus suggesting that it is a genetic predictor for the level of IL6 signaling activity. These heritable differences in IL6 signaling are associated with changes in lifetime risk of coronary artery disease, in that genetic downregulation of the IL6 signaling cascade relates to lower rates of coronary artery disease.12 These results thus provide genetic evidence to support that higher IL6R signaling is causally associated with increased coronary artery disease risk.12
The evolution of genetic epidemiology and the emergence of large-scale GWASs has enabled the discovery of genetic variants associated with various risk factors of interest (Figure 2). Thus, modern MR studies may incorporate hundreds or even thousands of genetic variants to explore causal associations of exposure traits with outcomes of interest. To exploit this increase in the numbers of genetic variants modeled as instruments, novel statistical methods have been developed.13 These can provide additional statistical power, and also allow the underlying MR assumptions to be relaxed. The main MR assumptions refer to the validity of genetic variants to be used as instrumental variables.9 Specifically, the variants should (1) strongly predict the risk factor of interest, (2) only associate with the outcome through their relation to the risk factor, and (3) not relate to confounders of the exposure-outcome association. Genetic variants with pleiotropic effects including associations with potential confounders in the exposure-outcome association under study may not represent valid genetic instruments.9 Pleiotropy refers to the phenomenon where a gene or a genetic variant can influence >1 phenotypic traits and may represent a source of bias in MR analyses.14
Dissecting the Causes of Stroke and Stroke Subtypes With MR
In previous years, MR has increasingly been applied in the field of stroke, especially after the publication of the MEGASTROKE GWAS meta-analysis in 2018.15 By studying 67 000 cases of stroke and 454 000 controls, MEGASTROKE not only informed on the genetic determinants of stroke, but also provided a unique data resource for MR studies.15 Critically, the MEGASTROKE study publicly shared its summary genetic association data on publication, allowing all interested researchers access to work on relevant projects. The larger sample size offered by MEGASTROKE further allowed for an increase in power of MR analyses and enabled more precise exploration of TOAST (Trial of ORG 10172 in Acute Stroke Treatment)-defined etiological ischemic stroke subtypes as outcome of interest.17 Similarly, data from a GWAS meta-analysis of 1545 cases and 1481 controls are available for intracerebral hemorrhage, also offering subclassification into deep and lobar locations thus enabling investigation into the field of hemorrhagic stroke as well.18 As a result, a large body of published MR studies have focused on exploring associations of known vascular risk factors with different stroke subtypes. Such investigations have provided important insights into the causes of stroke. For the purposes of the current review, we searched the MEDLINE database for MR studies exploring risk factors for stroke or its subtypes up to November 1, 2020, using a combination of the search terms “mendelian,” “randomization” or “randomisation,” and “stroke” or “cerebrovascular disease.”
Blood Pressure
For example, while blood pressure is widely accepted as a major risk factor for stroke, MR studies provide further insight to suggest that higher genetically predicted systolic and diastolic blood pressure are differentially associated with the different stroke subtypes.19 Specifically, genetic predisposition to higher blood pressure was more strongly associated with large artery and small vessel stroke, as compared with cardioembolic stroke. Furthermore, genetically predicted blood pressure was associated with deep intracerebral hemorrhage and not hemorrhages in lobar locations, thus providing additional evidence for a less prominent role of hypertension in lobar intracerebral hemorrhage. Unlike deep intracerebral hemorrhage, lobar hemorrhages are more commonly caused by cerebral amyloid angiopathy and the absence of an association with blood pressure is consistent with results from observational studies.20,21 MR has further allowed a deeper pathophysiologic exploration into the associations of blood pressure traits with stroke subtypes. Using genetic variants associated with blood pressure traits at different ages, we recently explored associations of genetically predicted mean arterial and pulse pressure at different age categories with risk of different stroke subtypes.22 As expected, genetically predicted mean arterial pressure was consistently associated with both ischemic stroke and intracerebral hemorrhage across the life course. Interestingly, genetically predicted pulse pressure at later life also emerged as a risk factor for ischemic stroke, even after adjusting for genetically predicted mean arterial pressure.22 These results suggest that the increasing blood pressure pulsatility with age, possibly as a result of increasing stiffening of the arterial tree, is an independent risk factor for ischemic stroke.
Circulating Lipids and Lipoproteins
MR analyses of blood lipids have also offered novel insights. Despite the fact that statins are on the frontline of secondary preventive strategies for ischemic stroke,23 data from MR studies suggest that genetically predicted LDL (low-density lipoprotein)-cholesterol levels are associated only with a higher risk of large artery stroke, but not cardioembolic or small vessel stroke.24–27 Unlike atherosclerotic phenotypes, such as coronary artery disease and large artery stroke,26,28,29 we recently found that genetically predicted HDL (high-density lipoprotein)-cholesterol levels associate with a lower risk of small vessel disease phenotypes, such as small vessel stroke and white matter hyperintensities.30 Conversely, multiple analyses from different sources of data suggest that higher genetically predicted LDL-cholesterol and lower genetically predicted HDL-cholesterol levels are associated with a lower risk of intracerebral hemorrhage,25,31 a finding consistent with evidence from post hoc analyses of clinical trials.32,33 These differences between stroke subtypes highlight the importance of collecting data specific to different etiological stroke subtypes. Analyses by stroke subtype will be essential to investigating the efficacy of preventive strategies across stroke subtypes in future clinical trials.
MR studies also played a key role in the elucidating of the role of lipoproteins in vascular disease and stroke. A series of MR analyses provided evidence to support that it is actually the amount of atherogenic lipoprotein particles that is the causal determinant of atherosclerosis, rather than the concentration of LDL-cholesterol or triglycerides per se. Specifically, the genetically predicted concentration of apolipoprotein B that is directly proportional to the number of circulating atherogenic particles in the blood is prioritized in MR analyses for coronary artery disease, above genetically predicted LDL-cholesterol or triglyceride concentrations.34–38 Recently, similar results were also obtained for the effects of apolipoprotein B, LDL-cholesterol, and triglycerides on risk of large artery stroke.24 Furthermore, the levels of Lp(a) (lipoprotein(a)), which are genetically determined up to 60%,39 have been shown in observational and genetic analyses to contribute to the risk of coronary artery disease on top of LDL-cholesterol levels and traditional vascular risk factors.39–41 The same principles seem to also apply for ischemic stroke42 and particularly large artery stroke.43
Metabolic and Behavioral Traits
Recent studies have provided insights into the role of metabolic traits, as well as exposure to smoking and alcohol on risk of stroke and its subtypes. As opposed to the majority of observational studies in the field, MR analyses did not support associations between genetically predicted body mass index and risk of stroke subtypes.44–46 On the contrary, genetically predicted waist-to-hip ratio, a marker of abdominal obesity, was associated with a higher risk of large artery and small vessel stroke, as well as a higher risk of intracerebral hemorrhage.44 Interestingly, these were exactly the phenotypes for which there was also an increase in risk with genetic liability to type 2 diabetes, hyperglycemia, and insulin resistance.45,47 Furthermore, genetic predisposition to pancreatic β-cell dysfunction was associated with a higher risk of small vessel stroke, intracerebral hemorrhage, and brain atrophy.47
Regarding alcohol consumption, an elegant study in the China Kadoorie Biobank using 2 genetic variants related to alcohol metabolism found that genetically predicted alcohol intake showed a log-linear association with the risk of stroke.48 This was in contrast to findings from conventional observational analyses performed in the same cohort, as well as previous observational studies,48 which reported a U-shaped association between alcohol intake and stroke risk. Recent MR analyses further support that genetic predisposition to smoking initiation, as well as smoking intensity, is associated with higher risk of large artery and small vessel stroke, but not cardioembolic stroke or intracerebral hemorrhage.49
The examples of alcohol consumption and smoking behavior also demonstrate how MR can be equally applied to social traits. While there is typically less genetic contribution to social behaviors, genetic variants related to complex traits such as educational attainment and intelligence have been identified,50 and further applied in MR analyses.51,52 These efforts have offered insight into the mechanisms by which education may influence cardiovascular disease risk, with MR mediation analyses suggesting that approximately half of the favorable effect of education on cardiovascular disease risk may be related to lower rates of obesity, elevated blood pressure, and smoking.51 MR analyses disentangling the respective contributions of education and intelligence to cardiovascular disease risk support that it is education rather than intelligence per se that is driving the observed association.52
Other Exposures in Relation to Stroke Risk
Beyond conventional vascular risk factors, MR studies have also explored other exposures in relation to stroke risk. For example, there was an inverse association of genetically predicted estimated glomerular filtration rate with risk of large artery stroke,53 an inverse association between genetically predicted magnesium levels and cardioembolic stroke,54 as well as positive associations of genetically predicted homocysteine levels55,56 and genetic liability to depression with risk of small vessel stroke.57 A careful review of published studies also allows for the generation of different conclusions across articles studying the same research question, even when using the same data sources. For example, while Cai et al58 reported a significant association between genetic predisposition to short sleep duration and insomnia with risk of large artery stroke, no significant association was found in 2 more recent studies.59,60 While the discrepance in the results can be explained by specific differences in the applied methodology, such inconsistencies highlight the importance of carefully designing and cautiously interpreting the results from MR analyses. A nonexhaustive list of risk factors that have been explored in MR studies,19,22,24–27,30,42–49,51–88 and a summary of their results is provided in Figure 3.
Traits Phenotypically Related to Different Stroke Subtypes
As well as directly investigating causal associations of modifiable exposures with risk of stroke subtypes, available genetic data have further allowed exploration of traits phenotypically related to different stroke subtypes, such as carotid atherosclerosis, atrial fibrillation, venous thromboembolism, and neuroimaging markers of cerebral small vessel disease. For example, considering systemic iron status as a modifiable risk factor, MR analyses have identified protective effects of its higher genetically proxied levels on risk of coronary artery disease,89 but detrimental effects on risk of ischemic stroke and specifically of the cardioembolic subtype.90 This discrepancy was further explored in consequent MR analyses investigating the association of genetically predicted systemic iron status with risk of carotid plaque and venous thromboembolism, respectively.91 In this work, higher genetically predicted iron status was found to be associated with lower risk of carotid plaque but increased risk of venous thromboembolism, offering further insight into the findings for the analysis of coronary artery disease and cardioembolic stroke, respectively. By considering secondary traits related to stroke subtypes, it was possible to gather additional evidence supporting an effect of small, lifelong increases in systemic iron status on reducing risk of atherosclerotic outcomes but increasing risk of thromboembolic ones.91 Of relevance, more recent MR analyses have supported that small genetically predicted increases in systemic iron status have the overall effect of reducing life expectancy.92
Use of MR to Explore Drug Targets for Stroke
An interesting application of MR is for studying drug effects. Genetic variants within or in the vicinity of a gene encoding a drug target protein (cis-variants) can be used as instruments to study the effects of perturbing that drug target.93,94 For example, while genetic variants throughout the genome associated with LDL-cholesterol could be used to explore causal associations of circulating LDL-cholesterol with cardiovascular disease risk, LDL-cholesterol-associated genetic variants within the locus of a gene encoding an LDL-lowering target (eg, HMGCR for the statin target) can inform us on the effects of LDL-cholesterol lowering through this target specifically, on risk of cardiovascular disease.95 In other words, instead of studying the effects of lowering circulating LDL-cholesterol levels on cardiovascular disease in general, we can study the effects on risk of cardiovascular disease risk of specifically lowering LDL-cholesterol levels through HMGCR inhibition (Figure 4). This concept can be used to study drug targets that are in various stages of the development pipeline, including for exploration of efficacy, identification of adverse effects and investigation of repurposing potential.
Following this principle, MR analyses have investigated associations between perturbation of existing drug targets and risk of stroke and stroke subtypes. Using a GWAS meta-analysis of 750 000 individuals, we recently identified genetic variants that were significantly associated with systolic blood pressure within the loci of the genes encoding the targets of angiotensin-converting enzyme inhibitors, β-blockers, and calcium channel blockers.96 Using these variants as proxies for these antihypertensive drug classes, we showed associations with the risk of coronary artery disease and stroke, which were comparable to those derived from clinical trials testing these drugs against placebo.96,97 In analyses for stroke, an interesting finding was that the genetic proxies for calcium channel blockade showed a stronger inverse associations with risk or ischemic stroke than the proxies for β-blockade.19 This finding is consistent with results from large-scale meta-analyses of clinical trials, and supports that the effects of calcium channel blockers on risk of stroke are stronger than those for β blockers. A possible explanation for this is the opposing effect of the 2 drug classes on blood pressure variability.98,99 When exploring stroke subtypes, this effect was particularly strong for small vessel stroke, as well as for the related imaging phenotype of white matter hyperintensities, thus suggesting that the effect of blood pressure variability could be most relevant for small vessel disease.19
Similar analyses have also been performed for lipid-lowering medications. Specifically, using lipid-lowering genetic variants in the genes encoding known drug targets, researchers have explored the effects of proxies for statins (HMGCR gene),26,30 PCSK9 (proprotein convertase subtilisin/kexin type 9) inhibitors (PCSK9 gene),100 and CETP (cholesteryl ester transfer protein) inhibitors (CETP gene)30,61 on risk of ischemic and hemorrhagic stroke. While lipid-lowering variants mimicking statin use have been associated with a lower risk of ischemic stroke, this association was statistically significant only for large artery stroke.26 On the contrary, such variants have also been associated with a higher risk of intracerebral hemorrhage.30 Furthermore, genetic proxies for PCSK9 inhibitors, although showing a strong protective effect on risk of coronary artery disease, their effects on risk of ischemic stroke is less pronounced, possibly related to the heterogeneous nature of ischemic stroke, with only a minority of all strokes being attributed to atherosclerosis.100 Recently, we also showed that genetic proxies for CETP inhibitors are associated with a lower risk of small vessel stroke and white matter hyperintensities, but a higher risk of intracerebral hemorrhage.30,61 Future studies are expected to shed more light on these associations and also explore associations of newer lipid-lowering drug targets with the risk of stroke.
MR has also been leveraged to study anticoagulant and antithrombotic targets. In this way, further insight was obtained into the role of circulating platelet levels in different forms of cardiovascular disease.72,101 Genetic variants that proxy the effect of modifying circulating coagulation factor levels have also been identified and applied in MR analyses investigating their association with risk of different cardiovascular disease outcomes.102–104 For example, genetic variants at the F10 gene related to circulating factor Xa levels have been used to study the effect of anticoagulant drugs targeting this coagulation factor (eg, apixaban, edoxaban, and rivaroxaban), offering insight into their repurposing potential.102 Similarly, genetic variants at the F11 gene that relate to circulating factor XI levels have offered insight into the efficacy of anticoagulant drugs that lower circulating factor XI levels.103 Indeed, factor XI inhibition is emerging as a novel pharmacological strategy for reducing risk of pathological thrombosis.105
MR as a Method to Identify Novel Risk Factors and Drug Targets
MR analyses can be applied to discover novel risk factors and drug targets. For example, new drug targets may be identified either through agnostic approaches facilitated by the availability of large-scale data sets on multiple phenotypes, or by targeted hypothesis-driven explorations. In the recent years, major progress has been made towards the identification of drug targets for inflammation. In an approach leveraging genetic data for multiple circulating cytokines and growth factors, we recently showed that among the 23 studied molecules, genetically predicted levels of MCP (monocyte-chemoattractant protein)-1 showed the strongest associations with risk of ischemic stroke, and particularly large artery and cardioembolic stroke.69 Genetically predicted MCP-1 levels further showed significant associations with coronary artery disease and myocardial infarction.69 From this starting point, we later moved back towards conventional epidemiological approaches to explore using real-life data whether measured circulating MCP-1 levels at midlife associate with long-term risk of incident stroke. Indeed, in a meta-analysis of 6 studies involving 17 000 individuals, circulating MCP-1 levels were associated with a higher risk of incident ischemic stroke over a follow-up up to 22 years, independently of conventional vascular risk factors.62 In a complementary analysis of the same cohorts, circulating MCP-1 levels were also associated with a higher risk of coronary artery disease and vascular death.106 These results triangulate previous evidence from experimental studies supporting a key role of MCP-1 in the pathogenesis of atherosclerosis. In particular, knocking out MCP-1 or CCR2 (C-C chemokine receptor type 2, the main receptor for MCP-1) in models of atherosclerosis leads to attenuation of the plaque burden, whereas pharmacological inhibition of the either MCP-1 or CCR2 seems to decrease plaque size and reverse plaque progression.107–112 Further supporting these findings, in a study of human subjects undergoing carotid endarterectomy due to symptomatic or asymptomatic carotid stenosis, we found significant associations of plaque MCP-1 levels with histopathologic, molecular, and clinical markers of plaque instability, thus further supporting a role of MCP-1 in plaque rupture.113 This triangulation of evidence from different methods can be a powerful strategy for prioritizing drug targets to be taken forward to clinical trials (Figure 5).
We also recently identified genetic proxies for IL6R blockade and tested their associations with risk of stroke. Using genetic variants at the gene encoding IL6R that are associated with circulating C-reactive protein levels, a downstream molecule in the IL6 cascade, and validating them on the basis of their association with other upstream and downstream molecules on the IL6 signaling pathway (circulating IL6, soluble IL6-receptor, and fibrinogen), we showed that genetically proxied inhibition of the pathway is not only associated with a lower risk of coronary artery disease,114 but also with a lower risk of ischemic stroke.68 Of relevance, this association was specific for large artery and small vessel stroke. An additional advantage of such investigations is the opportunity to explore the effects of these variants on the risk of multiple other phenotypes in an approach termed phenome-wide association study. Using data from the UK Biobank, we found that genetically proxied IL6R inhibition was not only associated with a lower risk of multiple atherosclerotic phenotypes, but also with a lower risk of type 2 diabetes, as well as higher HDL levels, thus suggesting an overall favorable cardiometabolic profile.115 On the contrary, we found significant associations of genetically proxied IL6R inhibition with higher risk of skin and urinary tract infections, as well as atopic dermatitis,115 all expected adverse effects of inhibiting IL6R signaling.
Hypothesis-free, agnostic approaches can represent a powerful tool for identifying causal mediators of stroke for perusal as therapeutic targets. In such a study, Chong et al116 explored the effects of genetically predicted circulating levels of 653 proteins on risk of ischemic stroke and its subtypes to find evidence for significant effects of 7 circulating proteins on risk of ischemic stroke. Specifically, CD40, apolipoprotein A, ABO, and MMP12 (matrix metallopeptidase 12) came up as significant hits for large artery stroke, whereas ABO, TNFSF12 (tumor necrosis factor superfamily member 12), SCARA5 (scavenger receptor class A member 5), and factor XI emerged as significant causal proteins for cardioembolic stroke.116 As discussed above, there are similar genetic and randomized controlled trial data already supporting factor XI as a pharmacological target for thrombosis.103,105
New Perspectives
Continued methodological developments and the exponentially increasing availability of genetic data are encouraging for the future of MR in stroke. Mediation analyses in MR, for example, allow not only the exploration of novel causal risk factors for stroke, but also enable dissection of the mechanistic pathway.117 Such MR mediation analyses were recently applied to generate evidence that approximately one-tenth of the effect of waist-to-hip ratio on risk of ischemic stroke was mediated by systolic blood pressure.44 The range of MR statistical sensitivity analyses and pleiotropy robust methods also continues to grow.13 Furthermore, nonlinear techniques are now available to investigate the shape of the relationship between a risk factor and a disease outcome using MR118 Of particular relevance to exploring drug targets, the combination of MR approaches with colocalization methods allows for complementary evidence of a shared genetic cause underlying the association between a risk factor and a disease outcome at a particular genetic locus.119 Such strategies can use genetic association data relating to gene expression, circulating protein levels and disease outcomes to strengthen the evidence implicating a particular target in disease pathophysiology. Information on variant function is increasingly available and online platforms such as the Cerebrovascular Disease Knowledge Portal120 (https://cd.hugeamp.org/) that provide such information can offer useful insights when interpreting results from MR analyses.
Beyond ischemic stroke and intracerebral hemorrhage, genetic data are now constantly being made available for other vascular phenotypes. Recently, a large-scale GWAS meta-analysis was published on the risk of intracranial aneurysms and subarachnoid hemorrhages, identifying 17 risk loci.121 MR analyses using these data suggest significant associations of genetically predicted blood pressure and smoking with higher risk of intracranial aneurysm.121 Efforts have also been made to determine the genetic predictors of recovery after stroke,122 with MR analyses finding evidence to suggest that genetic predisposition to depression may be associated with worse functional outcomes after ischemic stroke.123 Larger genetic studies focusing on functional outcome after stroke could enable additional exploration of predictors and new therapeutic targets for stroke recovery, which could improve patient care and inform on mechanisms of neuroprotection in general. Similarly, larger GWASs for intracerebral hemorrhage, but also for ischemic stroke subtypes, would enable more powerful, and thus more informative, MR analyses. Finally, sex-stratified GWASs for stroke and its subtypes could allow MR analyses to explore sex-specific risk factors. Insights from such analyses would be important given the well-known sex differences in stroke epidemiology.
Limitations
Like any research method, MR is based on specific assumptions. A number of methodological topics still remain open for strong debate between experts in MR. Such issues include but are not limited to appropriate methods for instrument selection,124,125 primary analytical approaches that are more robust against pleiotropy,13 population stratification, and differences in population characteristics between the exposure and the outcome samples,126 the impact of selecting instruments from a GWAS conditioning on additional variables beyond age and sex,127 the role of collider and incident-event bias especially when analyzing outcome data,128,129 the problems associated with the use of binary exposures,130 and comparability of the effects of lifelong genetically predicted exposures with those derived from short-term intervention in clinical trials.131 All of these technical issues can strongly influence the conclusions of MR analyses and thus highlight the importance of cautious interpretation of findings.132 While advancements in statistical methods and the availability of large-scale data sets have made MR analyses relatively easy to perform, critical thought is needed when designing the analysis, as several assumptions need to be thoroughly examined.
Conclusions
Over the last decade, MR has significantly advanced our understanding of the pathophysiological mechanisms underlying distinct stroke subtypes, while concurrently identifying novel pharmacological targets. Continued methodological developments and increasing availability of genetic data are expected to further increase opportunities for MR in the field of stroke. To this end, great strides are being made towards making use of human genetic data for developing effective treatments for patients.
References
1.
GBD 2015 DALYs and HALE Collaborators. Global, regional, and national disability-adjusted life-years (dalys) for 315 diseases and injuries and healthy life expectancy (hale), 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388:1603–1658. doi: 10.1016/S0140-6736(16)31460-X
2.
GBD 2015 Mortality and Causes of Death Collaborators. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388:1459–1544. doi: 10.1016/S0140-6736(16)31012-1
3.
Feigin VL, Norrving B, George MG, Foltz JL, Roth GA, Mensah GA. Prevention of stroke: a strategic global imperative. Nat Rev Neurol. 2016;12:501–512. doi: 10.1038/nrneurol.2016.107
4.
Jackson C, Sudlow C. Are lacunar strokes really different? A systematic review of differences in risk factor profiles between lacunar and nonlacunar infarcts. Stroke. 2005;36:891–901. doi: 10.1161/01.STR.0000157949.34986.30
5.
Wang MT, Bolland MJ, Grey A. Reporting of limitations of observational research. JAMA Intern Med. 2015;175:1571–1572. doi: 10.1001/jamainternmed.2015.2147
6.
Smith GD, Ebrahim S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32:1–22. doi: 10.1093/ije/dyg070
7.
Burgess S, Foley CN, Zuber V. Inferring causal relationships between risk factors and outcomes from genome-wide association study data. Annu Rev Genomics Hum Genet. 2018;19:303–327. doi: 10.1146/annurev-genom-083117-021731
8.
Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, Hartwig FP, Holmes MV, Minelli C, Relton CL, et al. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res. 2019;4:186. doi: 10.12688/wellcomeopenres.15555.2
9.
Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601. doi: 10.1136/bmj.k601
10.
Bandres-Ciga S, Noyce AJ, Traynor BJ. Mendelian randomization-a journey from obscurity to center stage with a few potholes along the way. JAMA Neurol. 2020;77:7–8. doi: 10.1001/jamaneurol.2019.3419
11.
Holmes MV, Ala-Korpela M, Smith GD. Mendelian randomization in cardiometabolic disease: challenges in evaluating causality. Nat Rev Cardiol. 2017;14:577–590. doi: 10.1038/nrcardio.2017.78
12.
Swerdlow DI, Holmes MV, Kuchenbaecker KB, Engmann JE, Shah T, Sofat R, Guo Y, Chung C, Peasey A, Pfister R, et al; Interleukin-6 Receptor Mendelian Randomisation Analysis (IL6R MR) Consortium. The interleukin-6 receptor as a target for prevention of coronary heart disease: a Mendelian randomisation analysis. Lancet. 2012;379:1214–1224. doi: 10.1016/S0140-6736(12)60110-X
13.
Slob EAW, Burgess S. A comparison of robust Mendelian randomization methods using summary data. Genet Epidemiol. 2020;44:313–329. doi: 10.1002/gepi.22295
14.
Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23(R1):R89–R98. doi: 10.1093/hmg/ddu328
15.
Malik R, Chauhan G, Traylor M, Sargurupremraj M, Okada Y, Mishra A, Rutten-Jacobs L, Giese AK, van der Laan SW, Gretarsdottir S, et al; AFGen Consortium; Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium; International Genomics of Blood Pressure (iGEN-BP) Consortium; INVENT Consortium; STARNET; BioBank Japan Cooperative Hospital Group; COMPASS Consortium; EPIC-CVD Consortium; EPIC-InterAct Consortium; International Stroke Genetics Consortium (ISGC); METASTROKE Consortium; Neurology Working Group of the CHARGE Consortium; NINDS Stroke Genetics Network (SiGN); UK Young Lacunar DNA Study; MEGASTROKE Consortium. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat Genet. 2018;50:524–537. doi: 10.1038/s41588-018-0058-3
16.
Traylor M, Farrall M, Holliday EG, Sudlow C, Hopewell JC, Cheng YC, Fornage M, Ikram MA, Malik R, Bevan S, et al; Australian Stroke Genetics Collaborative, Wellcome Trust Case Control Consortium 2 (WTCCC2); International Stroke Genetics Consortium. Genetic risk factors for ischaemic stroke and its subtypes (the METASTROKE collaboration): a meta-analysis of genome-wide association studies. Lancet Neurol. 2012;11:951–962. doi: 10.1016/S1474-4422(12)70234-X
17.
Adams HP, Bendixen BH, Kappelle LJ, Biller J, Love BB, Gordon DL, Marsh EE Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke. 1993;24:35–41. doi: 10.1161/01.str.24.1.35
18.
Woo D, Falcone GJ, Devan WJ, Brown WM, Biffi A, Howard TD, Anderson CD, Brouwers HB, Valant V, Battey TW, et al; International Stroke Genetics Consortium. Meta-analysis of genome-wide association studies identifies 1q22 as a susceptibility locus for intracerebral hemorrhage. Am J Hum Genet. 2014;94:511–521. doi: 10.1016/j.ajhg.2014.02.012
19.
Georgakis MK, Gill D, Webb AJS, Evangelou E, Elliott P, Sudlow CLM, Dehghan A, Malik R, Tzoulaki I, Dichgans M. Genetically determined blood pressure, antihypertensive drug classes, and risk of stroke subtypes. Neurology. 2020;95:e353–e361. doi: 10.1212/WNL.0000000000009814
20.
Jackson CA, Sudlow CL. Is hypertension a more frequent risk factor for deep than for lobar supratentorial intracerebral haemorrhage? J Neurol Neurosurg Psychiatry. 2006;77:1244–1252. doi: 10.1136/jnnp.2006.089292
21.
Martini SR, Flaherty ML, Brown WM, Haverbusch M, Comeau ME, Sauerbeck LR, Kissela BM, Deka R, Kleindorfer DO, Moomaw CJ, et al. Risk factors for intracerebral hemorrhage differ according to hemorrhage location. Neurology. 2012;79:2275–2282. doi: 10.1212/WNL.0b013e318276896f
22.
Georgakis MK, Gill D, Malik R, Protogerou AD, Webb AJS, Dichgans M. Genetically predicted blood pressure across the lifespan: differential effects of mean and pulse pressure on stroke risk. Hypertension. 2020;76:953–961. doi: 10.1161/HYPERTENSIONAHA.120.15136
23.
Kernan WN, Ovbiagele B, Black HR, Bravata DM, Chimowitz MI, Ezekowitz MD, Fang MC, Fisher M, Furie KL, Heck DV, et al; American Heart Association Stroke Council, Council on Cardiovascular and Stroke Nursing, Council on Clinical Cardiology, and Council on Peripheral Vascular Disease. Guidelines for the prevention of stroke in patients with stroke and transient ischemic attack: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2014;45:2160–2236. doi: 10.1161/STR.0000000000000024
24.
Yuan S, Tang B, Zheng J, Larsson SC. Circulating lipoprotein lipids, apolipoproteins and ischemic stroke. Ann Neurol. 2020;88:1229–1236. doi: 10.1002/ana.25916
25.
Falcone GJ, Kirsch E, Acosta JN, Noche RB, Leasure A, Marini S, Chung J, Selim M, Meschia JF, Brown DL, et al; International Stroke Genetics Consortium. Genetically elevated LDL associates with lower risk of intracerebral hemorrhage. Ann Neurol. 2020;88:56–66. doi: 10.1002/ana.25740
26.
Hindy G, Engström G, Larsson SC, Traylor M, Markus HS, Melander O, Orho-Melander M; Stroke Genetics Network (SiGN). Role of blood lipids in the development of ischemic stroke and its subtypes: a Mendelian randomization study. Stroke. 2018;49:820–827. doi: 10.1161/STROKEAHA.117.019653
27.
Valdes-Marquez E, Parish S, Clarke R, Stari T, Worrall BB, Hopewell JC; METASTROKE Consortium of the ISGC. Relative effects of LDL-C on ischemic stroke and coronary disease: a Mendelian randomization study. Neurology. 2019;92:e1176–e1187. doi: 10.1212/WNL.0000000000007091
28.
Voight BF, Peloso GM, Orho-Melander M, Frikke-Schmidt R, Barbalic M, Jensen MK, Hindy G, Hólm H, Ding EL, Johnson T, et al. Plasma HDL cholesterol and risk of myocardial infarction: a Mendelian randomisation study. Lancet. 2012;380:572–580. doi: 10.1016/S0140-6736(12)60312-2
29.
Ference BA. Causal effect of lipids and lipoproteins on atherosclerosis: lessons from genomic studies. Cardiol Clin. 2018;36:203–211. doi: 10.1016/j.ccl.2017.12.001
30.
Georgakis MK, Malik R, Anderson CD, Parhofer KG, Hopewell JC, Dichgans M. Genetic determinants of blood lipids and cerebral small vessel disease: role of high-density lipoprotein cholesterol. Brain. 2020;143:597–610. doi: 10.1093/brain/awz413
31.
Sun L, Clarke R, Bennett D, Guo Y, Walters RG, Hill M, Parish S, Millwood IY, Bian Z, Chen Y, et al; China Kadoorie Biobank Collaborative Group; International Steering Committee; International Co-ordinating Centre, Oxford; National Co-ordinating Centre, Beijing; Regional Co-ordinating Centres. Causal associations of blood lipids with risk of ischemic stroke and intracerebral hemorrhage in Chinese adults. Nat Med. 2019;25:569–574. doi: 10.1038/s41591-019-0366-x
32.
Goldstein LB, Amarenco P, Szarek M, Callahan A, Hennerici M, Sillesen H, Zivin JA, Welch KM; SPARCL Investigators. Hemorrhagic stroke in the stroke prevention by aggressive reduction in cholesterol levels study. Neurology. 2008;70(24 pt 2):2364–2370. doi: 10.1212/01.wnl.0000296277.63350.77
33.
Amarenco P, Bogousslavsky J, Callahan A, Goldstein LB, Hennerici M, Rudolph AE, Sillesen H, Simunovic L, Szarek M, Welch KM, et al; Stroke Prevention by Aggressive Reduction in Cholesterol Levels (SPARCL) Investigators. High-dose atorvastatin after stroke or transient ischemic attack. N Engl J Med. 2006;355:549–559. doi: 10.1056/NEJMoa061894
34.
Richardson TG, Sanderson E, Palmer TM, Ala-Korpela M, Ference BA, Davey Smith G, Holmes MV. Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: a multivariable Mendelian randomisation analysis. PLoS Med. 2020;17:e1003062. doi: 10.1371/journal.pmed.1003062
35.
White J, Swerdlow DI, Preiss D, Fairhurst-Hunter Z, Keating BJ, Asselbergs FW, Sattar N, Humphries SE, Hingorani AD, Holmes MV. Association of lipid fractions with risks for coronary artery disease and diabetes. JAMA Cardiol. 2016;1:692–699. doi: 10.1001/jamacardio.2016.1884
36.
Ference BA, Kastelein JJP, Ginsberg HN, Chapman MJ, Nicholls SJ, Ray KK, Packard CJ, Laufs U, Brook RD, Oliver-Williams C, et al. Association of genetic variants related to CETP inhibitors and statins with lipoprotein levels and cardiovascular risk. JAMA. 2017;318:947–956. doi: 10.1001/jama.2017.11467
37.
Ference BA, Kastelein JJP, Ray KK, Ginsberg HN, Chapman MJ, Packard CJ, Laufs U, Oliver-Williams C, Wood AM, Butterworth AS, et al. Association of Triglyceride-Lowering LPL Variants and LDL-C-Lowering LDLR Variants With risk of coronary heart disease. JAMA. 2019;321:364–373. doi: 10.1001/jama.2018.20045
38.
Zuber V, Gill D, Ala-Korpela M, Langenberg C, Butterworth A, Bottolo L, Burgess S. High-throughput multivariable Mendelian randomization analysis prioritizes apolipoprotein b as key lipid risk factor for coronary artery disease [published online November 1, 2020]. Int J Epidemiol. 2021;3:893–901. doi: 10.1093/ije/dyaa216
39.
Burgess S, Ference BA, Staley JR, Freitag DF, Mason AM, Nielsen SF, Willeit P, Young R, Surendran P, Karthikeyan S, et al; European Prospective Investigation Into Cancer and Nutrition–Cardiovascular Disease (EPIC-CVD) Consortium. Association of LPA variants with risk of coronary disease and the implications for Lipoprotein(a)-lowering therapies: a Mendelian randomization analysis. JAMA Cardiol. 2018;3:619–627. doi: 10.1001/jamacardio.2018.1470
40.
Clarke R, Peden JF, Hopewell JC, Kyriakou T, Goel A, Heath SC, Parish S, Barlera S, Franzosi MG, Rust S, et al; PROCARDIS Consortium. Genetic variants associated with Lp(a) lipoprotein level and coronary disease. N Engl J Med. 2009;361:2518–2528. doi: 10.1056/NEJMoa0902604
41.
Erqou S, Kaptoge S, Perry PL, Di Angelantonio E, Thompson A, White IR, Marcovina SM, Collins R, Thompson SG, Danesh J; Emerging Risk Factors Collaboration. Lipoprotein(a) concentration and the risk of coronary heart disease, stroke, and nonvascular mortality. JAMA. 2009;302:412–423. doi: 10.1001/jama.2009.1063
42.
Larsson SC, Gill D, Mason AM, Jiang T, Bäck M, Butterworth AS, Burgess S. Lipoprotein(a) in alzheimer, atherosclerotic, cerebrovascular, thrombotic, and valvular disease: Mendelian randomization investigation. Circulation. 2020;141:1826–1828. doi: 10.1161/CIRCULATIONAHA.120.045826
43.
Pan Y, Li H, Wang Y, Meng X, Wang Y. Causal effect of Lp(a) [Lipoprotein(a)] level on ischemic stroke and alzheimer disease: a Mendelian randomization study. Stroke. 2019;50:3532–3539. doi: 10.1161/STROKEAHA.119.026872
44.
Marini S, Merino J, Montgomery BE, Malik R, Sudlow CL, Dichgans M, Florez JC, Rosand J, Gill D, Anderson CD; International Stroke Genetics Consortium. Mendelian randomization study of obesity and cerebrovascular disease. Ann Neurol. 2020;87:516–524. doi: 10.1002/ana.25686
45.
Larsson SC, Scott RA, Traylor M, Langenberg CC, Hindy G, Melander O, Orho-Melander M, Seshadri S, Wareham NJ, Markus HS; METASTROKE Collaboration and NINDS Stroke Genetics Network (SiGN). Type 2 diabetes, glucose, insulin, BMI, and ischemic stroke subtypes: Mendelian randomization study. Neurology. 2017;89:454–460. doi: 10.1212/WNL.0000000000004173
46.
Larsson SC, Bäck M, Rees JMB, Mason AM, Burgess S. Body mass index and body composition in relation to 14 cardiovascular conditions in UK Biobank: a Mendelian randomization study. Eur Heart J. 2020;41:221–226. doi: 10.1093/eurheartj/ehz388
47.
Georgakis MK, Harshfield EL, Malik R, Franceschini N, Langenberg C, Wareham NJ, Markus HS, Dichgans M. Diabetes mellitus, glycemic traits, and cerebrovascular disease: a Mendelian randomization study. medRxiv. Preprint posted online December 30, 2019. doi: 10.1101/2019.12.27.19015834
48.
Millwood IY, Walters RG, Mei XW, Guo Y, Yang L, Bian Z, Bennett DA, Chen Y, Dong C, Hu R, et al. Conventional and genetic evidence on alcohol and vascular disease aetiology: a prospective study of 500 000 men and women in china. Lancet. 2019;393:1831–1842. doi: 10.1016/S0140-6736(18)31772-0
49.
Larsson SC, Burgess S, Michaëlsson K. Smoking and stroke: a Mendelian randomization study. Ann Neurol. 2019;86:468–471. doi: 10.1002/ana.25534
50.
Lee JJ, Wedow R, Okbay A, Kong E, Maghzian O, Zacher M, Nguyen-Viet TA, Bowers P, Sidorenko J, Karlsson Linnér R, et al; 23andMe Research Team; COGENT (Cognitive Genomics Consortium); Social Science Genetic Association Consortium. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat Genet. 2018;50:1112–1121. doi: 10.1038/s41588-018-0147-3
51.
Carter AR, Gill D, Davies NM, Taylor AE, Tillmann T, Vaucher J, Wootton RE, Munafò MR, Hemani G, Malik R, et al. Understanding the consequences of education inequality on cardiovascular disease: Mendelian randomisation study. BMJ. 2019;365:l1855. doi: 10.1136/bmj.l1855
52.
Gill D, Efstathiadou A, Cawood K, Tzoulaki I, Dehghan A. Education protects against coronary heart disease and stroke independently of cognitive function: evidence from Mendelian randomization. Int J Epidemiol. 2019;48:1468–1477. doi: 10.1093/ije/dyz200
53.
Marini S, Georgakis MK, Chung J, Henry JQA, Dichgans M, Rosand J, Malik R, Anderson CD. Genetic overlap and causal inferences between kidney function and cerebrovascular disease. Neurology. 2020;94:e2581–e2591. doi: 10.1212/WNL.0000000000009642
54.
Larsson SC, Traylor M, Burgess S, Boncoraglio GB, Jern C, Michaëlsson K, Markus HS; MEGASTROKE project of the International Stroke Genetics Consortium. Serum magnesium and calcium levels in relation to ischemic stroke: Mendelian randomization study. Neurology. 2019;92:e944–e950. doi: 10.1212/WNL.0000000000007001
55.
Larsson SC, Traylor M, Markus HS. Homocysteine and small vessel stroke: a Mendelian randomization analysis. Ann Neurol. 2019;85:495–501. doi: 10.1002/ana.25440
56.
Casas JP, Bautista LE, Smeeth L, Sharma P, Hingorani AD. Homocysteine and stroke: evidence on a causal link from Mendelian randomisation. Lancet. 2005;365:224–232. doi: 10.1016/S0140-6736(05)17742-3
57.
Cai H, Cai B, Zhang H, Sun W, Wang Y, Zhou S, Ye Z, Zhang Z, Liang J. Major depression and small vessel stroke: a Mendelian randomization analysis. J Neurol. 2019;266:2859–2866. doi: 10.1007/s00415-019-09511-w
58.
Cai H, Liang J, Liu Z, Fang L, Zheng J, Xu J, Chen L, Sun W, Zhang H. Causal effects of sleep traits on ischemic stroke and its subtypes: a Mendelian randomization study. Nat Sci Sleep. 2020;12:783–790. doi: 10.2147/NSS.S265946
59.
Lu H, Wu PF, Li RZ, Zhang W, Huang GX. Sleep duration and stroke: a Mendelian randomization study. Front Neurol. 2020;11:976. doi: 10.3389/fneur.2020.00976
60.
Titova OE, Michaëlsson K, Larsson SC. Sleep duration and stroke: prospective cohort study and Mendelian randomization analysis. Stroke. 2020;51:3279–3285. doi: 10.1161/STROKEAHA.120.029902
61.
Anderson CD, Falcone GJ, Phuah CL, Radmanesh F, Brouwers HB, Battey TW, Biffi A, Peloso GM, Liu DJ, Ayres AM, et al; Global Lipids Genetics Consortium and International Stroke Genetics Consortium. Genetic variants in CETP increase risk of intracerebral hemorrhage. Ann Neurol. 2016;80:730–740. doi: 10.1002/ana.24780
62.
Georgakis MK, Malik R, Bjorkbacka H, Pana TA, Demissie S, Ayers C, Elhadad MA, Fornage M, Beiser AS, Benjamin EJ, et al. Circulating monocyte chemoattractant protein-1 and risk of stroke: meta-analysis of population-based studies involving 17 180 individuals. Circ Res. 2019;125:773–782. doi: 10.1161/CIRCRESAHA.119.315380
63.
Wang T, Tang Z, Yu X, Gao Y, Guan F, Li C, Huang S, Zheng J, Zeng P. Birth weight and stroke in adult life: genetic correlation and causal inference with genome-wide association data sets. Front Neurosci. 2020;14:479. doi: 10.3389/fnins.2020.00479
64.
Nüesch E, Dale C, Palmer TM, White J, Keating BJ, van Iperen EP, Goel A, Padmanabhan S, Asselbergs FW, Verschuren WM, et al; EPIC-Netherland Investigators; UCLEB Investigators; IN Day. Adult height, coronary heart disease and stroke: a multi-locus Mendelian randomization meta-analysis. Int J Epidemiol. 2016;45:1927–1937. doi: 10.1093/ije/dyv074
65.
Larsson SC, Burgess S, Mason AM, Michaëlsson K. Alcohol consumption and cardiovascular disease: a Mendelian randomization study. Circ Genom Precis Med. 2020;13:e002814. doi: 10.1161/CIRCGEN.119.002814
66.
Zhuang Z, Gao M, Yang R, Li N, Liu Z, Cao W, Huang T. Association of physical activity, sedentary behaviours and sleep duration with cardiovascular diseases and lipid profiles: a Mendelian randomization analysis. Lipids Health Dis. 2020;19:86. doi: 10.1186/s12944-020-01257-z
67.
Rosa M, Chignon A, Li Z, Boulanger MC, Arsenault BJ, Bossé Y, Thériault S, Mathieu P. A Mendelian randomization study of IL6 signaling in cardiovascular diseases, immune-related disorders and longevity. NPJ Genom Med. 2019;4:23. doi: 10.1038/s41525-019-0097-4
68.
Georgakis MK, Malik R, Gill D, Franceschini N, Sudlow CLM, Dichgans M; INVENT Consortium, CHARGE Inflammation Working Group. Interleukin-6 signaling effects on ischemic stroke and other cardiovascular outcomes: a Mendelian randomization study. Circ Genom Precis Med. 2020;13:e002872. doi: 10.1161/CIRCGEN.119.002872
69.
Georgakis MK, Gill D, Rannikmäe K, Traylor M, Anderson CD, Lee JM, Kamatani Y, Hopewell JC, Worrall BB, Bernhagen J, et al. Genetically determined levels of circulating cytokines and risk of stroke. Circulation. 2019;139:256–268. doi: 10.1161/CIRCULATIONAHA.118.035905
70.
Zhang X, Wang A, Zhang J, Singh M, Liu D, Zuo Y, Wu L, Song M, Wang W, Feigin V, et al. Association of plasma C-reactive protein with ischaemic stroke: a Mendelian randomization study. Eur J Neurol. 2020;27:565–571. doi: 10.1111/ene.14113
71.
Prins BP, Abbasi A, Wong A, Vaez A, Nolte I, Franceschini N, Stuart PE, Guterriez Achury J, Mistry V, Bradfield JP, et al; PAGE Consortium; International Stroke Genetics Consortium; Systemic Sclerosis consortium; Treat OA consortium; DIAGRAM Consortium; CARDIoGRAMplusC4D Consortium; ALS consortium; International Parkinson’s Disease Genomics Consortium; Autism Spectrum Disorder Working Group of the Psychiatric Genomics Consortium; CKDGen consortium; GERAD1 Consortium; International Consortium for Blood Pressure; Schizophrenia Working Group of the Psychiatric Genomics Consortium; Inflammation Working Group of the CHARGE Consortium. Investigating the causal relationship of C-reactive protein with 32 complex somatic and psychiatric outcomes: a large-scale cross-consortium Mendelian randomization study. PLoS Med. 2016;13:e1001976. doi: 10.1371/journal.pmed.1001976
72.
Harshfield EL, Sims MC, Traylor M, Ouwehand WH, Markus HS. The role of haematological traits in risk of ischaemic stroke and its subtypes. Brain. 2020;143:210–221. doi: 10.1093/brain/awz362
73.
Maners J, Gill D, Pankratz N, Laffan MA, Wolberg AS, de Maat MPM, Ligthart S, Tang W, Ward-Caviness CK, Fornage M, et al. A Mendelian randomization of γ′ and total fibrinogen levels in relation to venous thromboembolism and ischemic stroke. Blood. 2020; 136:3062–3069. doi: 10.1182/blood.2019004781
74.
Gill D, Monori G, Georgakis MK, Tzoulaki I, Laffan M. Genetically determined platelet count and risk of cardiovascular disease. Arterioscler Thromb Vasc Biol. 2018;38:2862–2869. doi: 10.1161/ATVBAHA.118.311804
75.
Malik R, Rannikmäe K, Traylor M, Georgakis MK, Sargurupremraj M, Markus HS, Hopewell JC, Debette S, Sudlow CLM, Dichgans M; MEGASTROKE consortium and the International Stroke Genetics Consortium. Genome-wide meta-analysis identifies 3 novel loci associated with stroke. Ann Neurol. 2018;84:934–939. doi: 10.1002/ana.25369
76.
Cao W, Zheng D, Zhang J, Wang A, Liu D, Zhang J, Singh M, Maranga IE, Cao M, Wu L, et al. Association between telomere length in peripheral blood leukocytes and risk of ischemic stroke in a Han Chinese population: a linear and non-linear Mendelian randomization analysis. J Transl Med. 2020;18:385. doi: 10.1186/s12967-020-02551-1
77.
Cao W, Li X, Zhang X, Zhang J, Sun Q, Xu X, Sun M, Tian Q, Li Q, Wang H, et al. No causal effect of telomere length on ischemic stroke and its subtypes: a Mendelian randomization study. Cells. 2019;8:159. doi: 10.3390/cells8020159
78.
Li J, Guasch-Ferré M, Chung W, Ruiz-Canela M, Toledo E, Corella D, Bhupathiraju SN, Tobias DK, Tabung FK, Hu J, et al. The Mediterranean diet, plasma metabolome, and cardiovascular disease risk. Eur Heart J. 2020;41:2645–2656. doi: 10.1093/eurheartj/ehaa209
79.
Larsson SC, Drca N, Mason AM, Burgess S. Resting heart rate and cardiovascular disease. Circ Genom Precis Med. 2019;12:e002459. doi: 10.1161/CIRCGEN.119.002459
80.
Leong A, Rehman W, Dastani Z, Greenwood C, Timpson N, Langsetmo L, Berger C, Fu L, Wong BY, Malik S, et al; METASTROKE. The causal effect of vitamin D binding protein (DBP) levels on calcemic and cardiometabolic diseases: a Mendelian randomization study. PLoS Med. 2014;11:e1001751. doi: 10.1371/journal.pmed.1001751
81.
Larsson SC, Traylor M, Mishra A, Howson JMM, Michaëlsson K, Markus HS; MEGASTROKE Project of the International Stroke Genetics Consortium. Serum 25-Hydroxyvitamin D Concentrations and Ischemic Stroke and Its Subtypes. Stroke. 2018;49:2508–2511. doi: 10.1161/STROKEAHA.118.022242
82.
Bell S, Gibson JT, Harshfield EL, Markus HS. Is periodontitis a risk factor for ischaemic stroke, coronary artery disease and subclinical atherosclerosis? A Mendelian randomization study. Atherosclerosis. 2020;313:111–117. doi: 10.1016/j.atherosclerosis.2020.09.029
83.
Marouli E, Kus A, Del Greco M F, Chaker L, Peeters R, Teumer A, Deloukas P, Medici M. Thyroid function affects the risk of stroke via atrial fibrillation: a Mendelian randomization study. J Clin Endocrinol Metab. 2020;105:dgaa239. doi: 10.1210/clinem/dgaa239
84.
Larsson SC, Allara E, Mason AM, Michaëlsson K, Burgess S. Thyroid function and dysfunction in relation to 16 cardiovascular diseases. Circ Genom Precis Med. 2019;12:e002468. doi: 10.1161/CIRCGEN.118.002468
85.
Nordestgaard AT, Nordestgaard BG. Coffee intake, cardiovascular disease and all-cause mortality: observational and Mendelian randomization analyses in 95 000-223 000 individuals. Int J Epidemiol. 2016;45:1938–1952. doi: 10.1093/ije/dyw325
86.
Efstathiadou A, Gill D, McGrane F, Quinn T, Dawson J. Genetically determined uric acid and the risk of cardiovascular and neurovascular diseases: a Mendelian randomization study of outcomes investigated in randomized trials. J Am Heart Assoc. 2019;8:e012738. doi: 10.1161/JAHA.119.012738
87.
Keenan T, Zhao W, Rasheed A, Ho WK, Malik R, Felix JF, Young R, Shah N, Samuel M, Sheikh N, et al. Causal assessment of serum urate levels in cardiometabolic diseases through a Mendelian Randomization Study. J Am Coll Cardiol. 2016;67:407–416. doi: 10.1016/j.jacc.2015.10.086
88.
Au Yeung SL, Schooling CM. Impact of urinary sodium on cardiovascular disease and risk factors: a 2 sample Mendelian randomization study. Clin Nutr. 2021;40:1990–1996. doi: 10.1016/j.clnu.2020.09.018
89.
Gill D, Del Greco M F, Walker AP, Srai SKS, Laffan MA, Minelli C. The effect of iron status on risk of coronary artery disease: a Mendelian randomization study-brief report. Arterioscler Thromb Vasc Biol. 2017;37:1788–1792. doi: 10.1161/ATVBAHA.117.309757
90.
Gill D, Monori G, Tzoulaki I, Dehghan A. Iron status and risk of stroke. Stroke. 2018;49:2815–2821. doi: 10.1161/STROKEAHA.118.022701
91.
Gill D, Brewer CF, Monori G, Trégouët DA, Franceschini N, Giambartolomei C, Tzoulaki I, Dehghan A; INVENT Consortium. Effects of genetically determined iron status on risk of venous thromboembolism and carotid atherosclerotic disease: a Mendelian randomization study. J Am Heart Assoc. 2019;8:e012994. doi: 10.1161/JAHA.119.012994
92.
Daghlas I, Gill D. Genetically predicted iron status and life expectancy. Clin Nutr. 2021;40:2456–2459. doi: 10.1016/j.clnu.2020.06.025
93.
Walker VM, Davey Smith G, Davies NM, Martin RM. Mendelian randomization: a novel approach for the prediction of adverse drug events and drug repurposing opportunities. Int J Epidemiol. 2017;46:2078–2089. doi: 10.1093/ije/dyx207
94.
Schmidt AF, Finan C, Gordillo-Marañón M, Asselbergs FW, Freitag DF, Patel RS, Tyl B, Chopade S, Faraway R, Zwierzyna M, et al. Genetic drug target validation using Mendelian randomisation. Nat Commun. 2020;11:3255. doi: 10.1038/s41467-020-16969-0
95.
Ference BA, Majeed F, Penumetcha R, Flack JM, Brook RD. Effect of naturally random allocation to lower low-density lipoprotein cholesterol on the risk of coronary heart disease mediated by polymorphisms in npc1l1, hmgcr, or both: a 2 x 2 factorial Mendelian randomization study. J Am Coll Cardiol. 2015;65:1552–1561. doi: 10.1016/j.jacc.2015.02.020
96.
Gill D, Georgakis MK, Koskeridis F, Jiang L, Feng Q, Wei WQ, Theodoratou E, Elliott P, Denny JC, Malik R, et al. Use of genetic variants related to antihypertensive drugs to inform on efficacy and side effects. Circulation. 2019;140:270–279. doi: 10.1161/CIRCULATIONAHA.118.038814
97.
Gill D, Walker VM, Martin RM, Davies NM, Tzoulaki I. Comparison with randomized controlled trials as a strategy for evaluating instruments in Mendelian randomization. Int J Epidemiol. 2019;49:1404–1406. doi: 10.1093/ije/dyz236
98.
Webb AJ, Fischer U, Mehta Z, Rothwell PM. Effects of antihypertensive-drug class on interindividual variation in blood pressure and risk of stroke: a systematic review and meta-analysis. Lancet. 2010;375:906–915. doi: 10.1016/S0140-6736(10)60235-8
99.
Rothwell PM, Howard SC, Dolan E, O’Brien E, Dobson JE, Dahlöf B, Poulter NR, Sever PS; ASCOT-BPLA and MRC Trial Investigators. Effects of beta blockers and calcium-channel blockers on within-individual variability in blood pressure and risk of stroke. Lancet Neurol. 2010;9:469–480. doi: 10.1016/S1474-4422(10)70066-1
100.
Hopewell JC, Malik R, Valdés-Márquez E, Worrall BB, Collins R; METASTROKE Collaboration of the ISGC. Differential effects of PCSK9 variants on risk of coronary disease and ischaemic stroke. Eur Heart J. 2018;39:354–359. doi: 10.1093/eurheartj/ehx373
101.
Gill D, Monori G, Georgakis MK, Tzoulaki I, Laffan M. Genetically determined platelet count and risk of cardiovascular disease. Arterioscler Thromb Vasc Biol. 2018;38:2862–2869. doi: 10.1161/ATVBAHA.118.311804
102.
Gill D, Burgess S. Use of a genetic variant related to circulating FXa (Activated Factor X) levels to proxy the effect of FXa inhibition on cardiovascular outcomes. Circ Genom Precis Med. 2020;13:551–553. doi: 10.1161/CIRCGEN.120.003061
103.
Gill D, Georgakis MK, Laffan M, Sabater-Lleal M, Malik R, Tzoulaki I, Veltkamp R, Dehghan A. Genetically determined FXI (Factor XI) levels and risk of stroke. Stroke. 2018;49:2761–2763. doi: 10.1161/STROKEAHA.118.022792
104.
de Vries PS, Sabater-Lleal M, Huffman JE, Marten J, Song C, Pankratz N, Bartz TM, de Haan HG, Delgado GE, Eicher JD, et al; INVENT Consortium; MEGASTROKE Consortium of the International Stroke Genetics Consortium. A genome-wide association study identifies new loci for factor VII and implicates factor VII in ischemic stroke etiology. Blood. 2019;133:967–977. doi: 10.1182/blood-2018-05-849240
105.
Büller HR, Bethune C, Bhanot S, Gailani D, Monia BP, Raskob GE, Segers A, Verhamme P, Weitz JI; FXI-ASO TKA Investigators. Factor XI antisense oligonucleotide for prevention of venous thrombosis. N Engl J Med. 2015;372:232–240. doi: 10.1056/NEJMoa1405760
106.
Georgakis MK, de Lemos JA, Ayers C, Wang B, Bjorkbacka H, Pana TA, Thorand B, Sun C, Fani L, Malik R, et al. Association of circulating monocyte chemoattractant protein-1 levels with cardiovascular mortality: a meta-analysis of population-based studies. JAMA Cardiol. 2021;6:587–592. doi: 10.1001/jamacardio.2020.5392
107.
Liehn EA, Piccinini AM, Koenen RR, Soehnlein O, Adage T, Fatu R, Curaj A, Popescu A, Zernecke A, Kungl AJ, et al. A new monocyte chemotactic protein-1/chemokine CC motif ligand-2 competitor limiting neointima formation and myocardial ischemia/reperfusion injury in mice. J Am Coll Cardiol. 2010;56:1847–1857. doi: 10.1016/j.jacc.2010.04.066
108.
Bot I, Ortiz Zacarías NV, de Witte WE, de Vries H, van Santbrink PJ, van der Velden D, Kröner MJ, van der Berg DJ, Stamos D, de Lange EC, et al. A novel CCR2 antagonist inhibits atherogenesis in apoE deficient mice by achieving high receptor occupancy. Sci Rep. 2017;7:52. doi: 10.1038/s41598-017-00104-z
109.
Gu L, Okada Y, Clinton SK, Gerard C, Sukhova GK, Libby P, Rollins BJ. Absence of monocyte chemoattractant protein-1 reduces atherosclerosis in low density lipoprotein receptor-deficient mice. Mol Cell. 1998;2:275–281. doi: 10.1016/s1097-2765(00)80139-2
110.
Boring L, Gosling J, Cleary M, Charo IF. Decreased lesion formation in CCR2-/- mice reveals a role for chemokines in the initiation of atherosclerosis. Nature. 1998;394:894–897. doi: 10.1038/29788
111.
Combadière C, Potteaux S, Rodero M, Simon T, Pezard A, Esposito B, Merval R, Proudfoot A, Tedgui A, Mallat Z. Combined inhibition of CCL2, CX3CR1, and CCR5 abrogates Ly6C(hi) and Ly6C(lo) monocytosis and almost abolishes atherosclerosis in hypercholesterolemic mice. Circulation. 2008;117:1649–1657. doi: 10.1161/CIRCULATIONAHA.107.745091
112.
Winter C, Silvestre-Roig C, Ortega-Gomez A, Lemnitzer P, Poelman H, Schumski A, Winter J, Drechsler M, de Jong R, Immler R, et al. Chrono-pharmacological targeting of the CCL2-CCR2 axis ameliorates atherosclerosis. Cell Metab. 2018;28:175–182.e5. doi: 10.1016/j.cmet.2018.05.002
113.
Georgakis MK, van der Laan SW, Asare Y, Mekke JM, Haitjema S, Schoneveld AH, de Kleijn DPV, de Borst GJ, Pasterkamp G, Dichgans M. Monocyte-chemoattractant protein-1 levels in human atherosclerosis associate with plaque vulnerability. medRxiv. Preprint posted online September 05, 2020. doi: 10.1101/2020.09.04.20187955
114.
Swerdlow DI, Holmes MV, Kuchenbaecker KB, Engmann JEL, Shah T, Sofat R, Guo YR, Chung C, Peasey A, Ster RP, et al. The interleukin-6 receptor as a target for prevention of coronary heart disease: a Mendelian randomisation analysis. Lancet. 2012;379:1214–1224. doi: 10.1016/S0140-6736(12)60110-X
115.
Georgakis MK, Malik R, Li X, Gill D, Levin MG, Vy HMT, Judy R, Ritchie M, Verma SS, Nadkarni GN, et al. Genetically downregulated interleukin-6 signaling is associated with a favorable cardiometabolic profile: a phenome-wide association study. medRxiv. Preprint posted online November 03, 2020. doi: 10.1101/2020.10.28.20220822
116.
Chong M, Sjaarda J, Pigeyre M, Mohammadi-Shemirani P, Lali R, Shoamanesh A, Gerstein HC, Paré G. Novel drug targets for ischemic stroke identified through Mendelian randomization analysis of the blood proteome. Circulation. 2019;140:819–830. doi: 10.1161/CIRCULATIONAHA.119.040180
117.
Burgess S, Daniel RM, Butterworth AS, Thompson SG; EPIC-InterAct Consortium. Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways. Int J Epidemiol. 2015;44:484–495. doi: 10.1093/ije/dyu176
118.
Sun YQ, Burgess S, Staley JR, Wood AM, Bell S, Kaptoge SK, Guo Q, Bolton TR, Mason AM, Butterworth AS, et al. Body mass index and all cause mortality in HUNT and UK Biobank studies: linear and non-linear Mendelian randomisation analyses. BMJ. 2019;364:l1042. doi: 10.1136/bmj.l1042
119.
Zheng J, Haberland V, Baird D, Walker V, Haycock PC, Hurle MR, Gutteridge A, Erola P, Liu Y, Luo S, et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nat Genet. 2020;52:1122–1131. doi: 10.1038/s41588-020-0682-6
120.
Crawford KM, Gallego-Fabrega C, Kourkoulis C, Miyares L, Marini S, Flannick J, Burtt NP, von Grotthuss M, Alexander B, Costanzo MC, et al; International Stroke Genetics Consortium. Cerebrovascular disease knowledge portal: an open-access data resource to accelerate genomic discoveries in stroke. Stroke. 2018;49:470–475. doi: 10.1161/STROKEAHA.117.018922
121.
Bakker MK, van der Spek RAA, van Rheenen W, Morel S, Bourcier R, Hostettler IC, Alg VS, van Eijk KR, Koido M, Akiyama M, et al; HUNT All-In Stroke; China Kadoorie Biobank Collaborative Group; BioBank Japan Project Consortium; ICAN Study Group; CADISP Group; Genetics and Observational Subarachnoid Haemorrhage (GOSH) Study investigators; International Stroke Genetics Consortium (ISGC). Genome-wide association study of intracranial aneurysms identifies 17 risk loci and genetic overlap with clinical risk factors. Nat Genet. 2020;52:1303–1313. doi: 10.1038/s41588-020-00725-7
122.
Söderholm M, Pedersen A, Lorentzen E, Stanne TM, Bevan S, Olsson M, Cole JW, Fernandez-Cadenas I, Hankey GJ, Jimenez-Conde J, et al; International Stroke Genetics Consortium, the NINDS-SiGN Consortium, and the Genetics of Ischaemic Stroke Functional Outcome (GISCOME) Network. Genome-wide association meta-analysis of functional outcome after ischemic stroke. Neurology. 2019;92:e1271–e1283. doi: 10.1212/WNL.0000000000007138
123.
Gill D, James NE, Monori G, Lorentzen E, Fernandez-Cadenas I, Lemmens R, Thijs V, Rost NS, Scott R, Hankey GJ, et al; International Stroke Genetics Consortium and the GISCOME Network. Genetically determined risk of depression and functional outcome after ischemic stroke. Stroke. 2019;50:2219–2222. doi: 10.1161/STROKEAHA.119.026089
124.
Swerdlow DI, Kuchenbaecker KB, Shah S, Sofat R, Holmes MV, White J, Mindell JS, Kivimaki M, Brunner EJ, Whittaker JC, et al. Selecting instruments for Mendelian randomization in the wake of genome-wide association studies. Int J Epidemiol. 2016;45:1600–1616. doi: 10.1093/ije/dyw088
125.
Georgakis MK. Genetic instrument selection for Mendelian randomization explorations of drug target effects. Int J Epidemiol. 2020;49:1412–1413. doi: 10.1093/ije/dyaa060
126.
Brumpton B, Sanderson E, Heilbron K, Hartwig FP, Harrison S, Vie GÅ, Cho Y, Howe LD, Hughes A, Boomsma DI, et al; Within-family Consortium; 23andMe Research Team. Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses. Nat Commun. 2020;11:3519. doi: 10.1038/s41467-020-17117-4
127.
Holmes MV, Davey Smith G. Problems in interpreting and using GWAS of conditional phenotypes illustrated by ‘alcohol GWAS’. Mol Psychiatry. 2019;24:167–168. doi: 10.1038/s41380-018-0037-1
128.
Yaghootkar H, Bancks MP, Jones SE, McDaid A, Beaumont R, Donnelly L, Wood AR, Campbell A, Tyrrell J, Hocking LJ, et al. Quantifying the extent to which index event biases influence large genetic association studies. Hum Mol Genet. 2017;26:1018–1030. doi: 10.1093/hmg/ddw433
129.
Gkatzionis A, Burgess S. Contextualizing selection bias in Mendelian randomization: how bad is it likely to be? Int J Epidemiol. 2019;48:691–701. doi: 10.1093/ije/dyy202
130.
Burgess S, Labrecque JA. Mendelian randomization with a binary exposure variable: interpretation and presentation of causal estimates. Eur J Epidemiol. 2018;33:947–952. doi: 10.1007/s10654-018-0424-6
131.
Ference BA, Holmes MV, Smith GD. Using Mendelian randomization to improve the design of randomized trials. Cold Spring Harb Perspect Med. 2021;11:a040980. doi: 10.1101/cshperspect.a040980
132.
Burgess S, O’Donnell CJ, Gill D. Expressing results from a Mendelian randomization analysis: separating results from inferences. JAMA Cardiol. 2021;6:7–8. doi: 10.1001/jamacardio.2020.4317
Information & Authors
Information
Published In
Copyright
© 2021 American Heart Association, Inc.
Versions
You are viewing the most recent version of this article.
History
Published online: 17 August 2021
Published in print: September 2021
Keywords
Subjects
Authors
Disclosures
Disclosures Dr Gill is employed part-time by Novo Nordisk. The other author reports no conflicts.
Sources of Funding
Dr Georgakis is supported by the German Research Foundation (DFG, Walter-Benjamin Programme, GE 3461/1-1) and has received funding from the Onassis Foundation, the German Academic Exchange Service, and the Vascular Dementia Research Foundation. Dr Gill is supported by the British Heart Foundation Centre of Research Excellence (RE/18/4/34215) at Imperial College London.
Metrics & Citations
Metrics
Citations
Download Citations
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Select your manager software from the list below and click Download.
- From Gene Discovery to Stroke Risk: C5orf24's Pivotal Role Uncovered, Molecular Neurobiology, (2025).https://doi.org/10.1007/s12035-025-04802-y
- Mendelian randomization of plasma proteomics identifies novel ALS-associated proteins and their GO enrichment and KEGG pathway analyses, BMC Neurology, 25, 1, (2025).https://doi.org/10.1186/s12883-025-04091-x
- Genetic variation in targets of antihyperglycemic drugs and inflammatory bowel disease’ risk: A mendelian randomization study, Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 19, 3, (103204), (2025).https://doi.org/10.1016/j.dsx.2025.103204
- Impact of Conventional Stroke Risk Factors on Early- and Late-Onset Ischemic Stroke: A Mendelian Randomization Study, Stroke, 56, 3, (640-648), (2025)./doi/10.1161/STROKEAHA.124.048015
- Causal Association Between Multidimensional Plasma Lipid Composition and Pediatric Asthma: A Mendelian Randomization Study, Pediatric Pulmonology, 60, 2, (2025).https://doi.org/10.1002/ppul.27508
- Genetic insights into the immunological basis of male infertility: a translational perspective, F&S Science, (2025).https://doi.org/10.1016/j.xfss.2025.02.002
- Mendelian randomization assessing causal relationship between fibrinogen levels and ischemic stroke, Journal of Stroke and Cerebrovascular Diseases, 34, 2, (108199), (2025).https://doi.org/10.1016/j.jstrokecerebrovasdis.2024.108199
- Exploring the causal relationship between inflammatory cytokines, metabolites, and Behcet's syndrome: Mendelian randomization, Cytokine, 186, (156849), (2025).https://doi.org/10.1016/j.cyto.2024.156849
- Causal relationship between dietary habits and Alzheimer's disease risk: a univariable and multivariable Mendelian randomization study, Food & Function, 16, 4, (1486-1494), (2025).https://doi.org/10.1039/D4FO03636K
- Traumatic Brain Injury Increasing Risk of Meningioma? From the Genetic Evidence, World Neurosurgery, 193, (747-753), (2025).https://doi.org/10.1016/j.wneu.2024.10.003
- See more
Loading...
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Personal login Institutional LoginPurchase Options
Purchase this article to access the full text.
eLetters(0)
eLetters should relate to an article recently published in the journal and are not a forum for providing unpublished data. Comments are reviewed for appropriate use of tone and language. Comments are not peer-reviewed. Acceptable comments are posted to the journal website only. Comments are not published in an issue and are not indexed in PubMed. Comments should be no longer than 500 words and will only be posted online. References are limited to 10. Authors of the article cited in the comment will be invited to reply, as appropriate.
Comments and feedback on AHA/ASA Scientific Statements and Guidelines should be directed to the AHA/ASA Manuscript Oversight Committee via its Correspondence page.