[Frontiers In Bioscience, Landmark, 23, 1338-1359, March 1, 2018]
Towards precision medicine in ischemic stroke and transient ischemic attack
1Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China, 2Department of Neurology and Institute of Neurology, First Affiliated Hospital, Fujian Medical University, Fuzhou, China, 3China National Clinical Research Center for Neurological Diseases, Beijing, China, 4Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China, 5Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China, 6Trinity College of Arts and Sciences, Duke University, Durham, NC, USA
TABLE OF CONTENTS
Although there is no consensus on the exact definition of precision medicine, it is generally agreed upon that the term entails diagnosis and therapy tailored to the individual patient. Precision medicine has seen major advances in the past two decades, many of which are relevant to ischemic stroke or transient ischemic attack (TIA). Advances include substantial improvements in high-throughput technologies, collaborations between the fields of biology and medicine, increasingly advanced biomedical informatics, the development of multimodal brain imaging techniques, as well as the widespread usage of electronic medical records and big data. Precision medicine in ischemic stroke or TIA is still in its infancy, but there have already been changes in clinical care from a one-size-fits-all model to a more precise, individualized approach. However, further studies are urgently needed to bridge the gaps between clinical studies and precision clinical practice. We discuss here the advances and challenges for precision medicine in ischemic stroke or TIA at its current stage, focusing on genetic predispositions, pharmacogenetics, omics, brain imaging and big data.
The concept of precision medicine (PM), or the use of prevention and treatment strategies that take individual variability into account, can be traced back to more than a century ago. The ABO blood group system, identified by Karl Landsteiner in 1900, is the foundation of modern transfusion therapy. In 2011, the report “Toward Precision Medicine” was approved by the Governing Board of the National Research Council and emphasized the urgency of building a knowledge network for biomedical research to modernize disease taxonomy. The report addressed the feasibility, need, scope, impact, and consequences of creating a “new taxonomy of human diseases based on molecular biology” (1). With the innovation of human genome sequencing technology and powerful methods to analyze biomedical informatics and big data, precision medicine can and should be incorporated into patient care. (2)
In 2015, the President of the United States announced a research initiative that aims to accelerate progress toward a new era of precision medicine (www.whitehouse.gov/precisionmedicine) (2). In 2016, China launched a national key research and development program for precision medical research. These initiatives represent unprecedented opportunities for experts in ischemic stroke to develop more detailed patient profiles, obtain more in-depth understanding of responses to treatment, and ultimately identify an optimal treatment for a given patient (3). Although the PM for ischemic stroke or TIA is still in its infancy, clinical care has already begun to shift from a one-size-fits-all model toward a more precise, individualized approach. In this review, we summarize the current progress of precision medicine for ischemic stroke or TIA, focusing on disease-causing genes, genetic predispositions, pharmacogenetics, omics, brain imaging and big data.
3. MONOGENIC STROKE DISORDERS
Several mendelian disorders can cause stroke, defined as monogenic stroke (4-6). Testing of the disease-causing genes is the gold standard for the diagnosis of monogenic stroke (Table 1). With the improvements in next-generation sequencing technologies, the potential causative genes are directly distinguished (7). The primary and secondary preventions of sporadic stroke might not be suitable for all monogenic stroke. For example, an enzyme replacement therapy (ERT) with recombinantα-galactosidase A (α-gal) has shown benefits for patients with Fabry’ disease early in their disease. Drugs affecting mitochondrial respiratory chain could not be used for patients with MELAS (mitochondrial myopathy, encephalopathy, lactic acidosis, and stroke). Detail discussion of these monogenic disorders is beyond the scope of this review. Although the monogenic stroke is relatively rare, our understanding of monogenic stroke would help us to further reveal of the etiology and pathophysiology of sporadic stroke.
4. GENETICS OF SPORADIC ISCHEMIC STROKE
Most strokes are sporadic strokes, which are complex diseases. In the past, the candidate gene association study approach was the most common method used for analysis. Using the the genome-wide linkage analysis, the genes encoding phosphodiesterase 4D (PDE4D) and 5-lipoxygenase activating protein (FLAP) conferred risk of ischemic stroke in an Icelandic population (DeCODE) (8, 9). However, some studies failed to replicate these findings and others reported conflicting conclusions (10-12).
The genome-wide association studies (GWAS) approach has revolutionized the field of complex genetics and is now having a major impact on the understanding of stroke genetics, confirming the heritability of ischemic strokes (4, 13). Studies have shown that heritability was 37.9% for all patients and varied markedly among different stroke subtypes, with 40.3% for large-vessel, 32.6% for cardioembolic and 16.1% for small-vessel stroke (14). The GWASs of ischemic stroke are listed in Table-2, though not all of them were further validated in the following replicated studies and large-scale meta-analysis (15-18). The METASTROKE collaboration was established by the ISGC (International Stroke Genetics Consortium) to combine the GWAS datasets for ischemic stroke, including 15 cohorts for the discovery stage and 18 cohorts for the replication stage (19). This meta -analysis verified previous associations for cardioembolic stroke near PITX2 (pituitary homeobox 2) and ZFHX3 (zinc finger homeobox 3 gene), as well as for large-vessel stroke at a 9p21 locus (Cyclin-Dependent Kinase Inhibitor, CDKN2A/CDKN2B) and HDAC9 (Histone deacetylase 9) (19). More and more studies have shown that beyond these specific genetic variations, the susceptible SNPs among European, African, and Asian populations were identical (20-22). The genetic variants confirmed in the METASTROKE collaboration were nominally associated (p<0.0.5). with ischemic stroke in the COMPASS (Consortium of Minority Population Genome-Wide Association Studies of Stroke) trial in African Americans (20).
The NINDS-SiGN (National Institute of Neurological Disorders Stroke Genetics Network) is an international consortium that has taken a systematic approach to phenotyping and has produced the largest GWAS in ischemic stroke to date. The results of the GWAS on NINDS-SiGN have confirmed four loci robustly associated with ischemic stroke, including PITX2 and ZFHX3 for cardioembolic stroke and HDAC9 for large-vessel stroke (22). The EuroCLOT study further confirmed the subtype-specific associations between genetic variants and ischemic stroke. The rs505922 of the ABO (ABO histo-blood group) gene has been associated with large-vessel and cardioembolic stroke, but not with small-vessel stroke (23). Ischemic stroke (IS) and coronary artery disease (CAD) share several risk factors, especially for large-vessel stroke. A large-scale meta-analysis was conducted to evaluate the extent of shared genetic determination between the two diseases, including GWAS data from METASTROKE, Coronary Artery Disease Genome wide Replication and Meta-analysis (CARDIoGRAM), and Coronary Artery Disease (C4D) Genetics consortia (24). In this joint meta-analysis, 17 loci passed genome-wide significance for large-vessel stroke or CAD (24). These included variants at the 9p21 locus (25, 26), ABO and HDAC9 (27, 28). Several novel loci, which were associated with ischemic stroke or large-artery stroke, have been identified recently, including MMP12(29), PTCSC3 (30), and ALDH2 (31). All of these studies shed light on the different pathogenic pathways underpinning stroke subtypes. It is necessary to further evaluate the functions and mechanisms of these susceptible genetic variants (32).
The prediction of genetic predispositions allowed for the identification of individuals at elevated risk of ischemic stroke. The CHARGE risk score project attempted to improve the prediction of future strokes based on associated SNPs and risk factors, but there was only limited improvement when compared to the classic Framingham stroke risk score (33). The polygenic risk score (polyGRS) is based on the idea that a few strong indicators, as well as several weaker indicators, can be jointly informative to determine IS risk. It was recently investigated and shown to be superior to weighted multi-locus genetic risk scores as an IS prediction model (34).
To date, GWASs have yielded relatively few loci associated with ischemic stroke, in spite of the large sample sizes. In addition to SNPs, CNVs should also be accounted for when determining susceptibility to stroke, but there is limited data. Furthermore, most studies focus on common variants, but the rare genetic variants might also contribute to the risk of developing stroke and influence its prognosis. Whole-genome sequence (WGS) and exome-based analysis have already been used in studies of genetic risk factors in ischemic stroke. Participants in the Genetics of Early Onset Stroke (GEOS) have been genotyped by the exome array, which revealed four highly associated variants for all strokes, and several others that were specific for stroke subtypes (35).
There are two major challenges for GWAS in terms of identifying genomic underpinnings of ischemic stroke, which are similar to the process for other complex diseases. One challenge is to identify causal genes within a GWAS-implicated locus, and the other is to identify causal variants for polygenic traits (36). Network analysis of GWAS data is a novel approach that uses information in protein–protein and protein–DNA interaction networks to address these two challenges (36). A Weighted Gene Co-expression Network Analysis (WGCNA) of carotid atherosclerotic plaques has been explored to identify biologically tractable candidates for stroke and stroke subtypes (35).
5. PHARMACOGENETICS AND PRECISION MEDICINE IN ISCHEMIC STROKE
Pharmacogenetics deals with the influence of genetic variants on drug response, including the efficacy and toxicity of drugs (37). It seeks to develop the optimal drug therapy with maximum efficacy and minimal adverse effects, with respect to patient genotypes. The Pharmacogenomics Knowledgebase (PharmGKB, http://www.pharmgkb.org), is a publicly available online knowledgebase responsible for the aggregation, curation, integration and dissemination of related knowledge. It was founded in 2000 and jointly funded by the National Institutes of Health (NIH) and the National Institute of General Medical Sciences (NIGMS), and is a partner of the NIH Pharmacogenomics Research Network (PGRN). The CPIC (Clinical Pharmacogenetics Implementation Consortium) and the DPWG (Dutch Pharmacogenetics Working Group) have updated freely-available, peer-reviewed drug-dosing guidelines for clinicians with access to pre-emptive genetic testing results. These guidelines are available in the PharmGKB.
The biggest challenge was the application of pharmacogenetic markers to clinical decisions related to drug prescription. There are more than 200 Food and Drug Administration (FDA) drug labels referring to pharmacogenetic biomarkers of drug safety or efficacy. However, only a very small proportion of these drug labels mandate clinicians to test for pharmacogenetic markers. Here, we focus on the pharmacogenetics of antiplatelet and anticoagulation agents, for which we have strong evidence for stroke preventions (Table 3).
5.1. Antiplatelet agents
Platelets contribute to ischemic strokes by playing a key role in the development of atherosclerotic lesions at sites of endothelial activation (38). Antiplatelet agents are commonly used to prevent cerebrovascular events, but the response to antiplatelet therapy is highly variable (39, 40). Up to 20 % of patients treated with antiplatelets continue to experience new thrombotic events, and 8.2.% of patients treated with dual antiplatelet therapy of clopidogrel with aspirin in the CHANCE (Clopidogrel in High-Risk Patients with Acute Nondisabling Cerebrovascular Events) trial experienced a new stroke within 90 days (41).
Aspirin irreversibly inhibits the cyclo-oxygenase (COX)-1 enzyme by acetylating the serine residue at position 529 (37). The relationship between the response to aspirin with genetic variants of COX-1 and several receptors on platelet surfaces was evaluated based on candidate gene association studies (42). Among healthy volunteers and patients with coronary artery disease, participants who were heterozygous for the -842A>G/50C >T haplotype showed significantly greater inhibition of platelet aggregation by aspirin when compared with common allele homozygotes (43, 44). However, these COX-1 polymorphisms were not associated with clinical outcomes (45, 46). The platelet glycoprotein (GP) IIb/IIIa receptors are responsible for binding with fibrinogen and cross-linking of platelets, and for the von Willebrand factor (vWF) (47, 48). In a meta-analysis, which included 11 related studies before Dec. 2007, the PlA1/PlA2 polymorphism of platelet glycoprotein IIIa (GP IIIa) gene was associated with aspirin resistance in healthy subjects, but not in patients with cardiovascular disease (42, 47, 49). No relation was found between aspirin resistance and genetic variants in the 807 C>T of GPIa, the 1622A> G of P2Y1, or the H1/H2 of P2Y12 (42).
In 2013, a GWAS explored the common related genetic variations for response to aspirin (50). The study populations included 565 Amish PAPI (Pharmacogenomics of Anti-Platelet Intervention) study subjects, 227 nonemergent PCI patients from the Sinai Hospital of Baltimore Study and 1,000 patients from the INVEST-GENES (International VErapamil SR/trandolapril Study GENEtic Substudy). This GWAS showed that the rs12041331 in the platelet endothelial aggregation receptor-1 (PEAR1) gene was strongly associated with both platelet response and cardiovascular events in patients treated with aspirin alone, as well as in those treated in combination with clopidogrel (50). The rs12041331 reproducibly influenced platelet aggregation in aspirin-treated Danish patients with coronary artery disease (51). PEAR1 is a platelet trans-membrane protein that becomes activated upon platelet contact. The precise biological mechanism of activation remains unclear.
Both the GWAS and the whole-exome sequencing study reported that several common variants of supervillin (SVIL). were associated with inhibition of platelet aggregation as detected by both the PFA-100 and optical aggregometry (52). In spite of the different SNPs in these two studies, these data suggest that genetic variants in SVIL expression contribute to variations in human platelet reactivity and indicate that SVIL plays a role in arterial thrombosis (52).
Clopidogrel is widely used for secondary prevention of ischemic stroke and TIA. Recently, the combination of clopidogrel with aspirin has been recommended for initiation within 24 hours of a minor ischemic stroke or TIA (39, 40). Clopidogrel inhibits adenosine diphosphate (ADP) and stimulates platelet aggregation by binding irreversibly to a specific platelet receptor of ADP, P2Y12 (53). Clopidogrel requires transformation into an active metabolite by cytochrome P450 (CYP) isoenzymes for its anti-platelet effect. CYP2C19 has a crucial role in the metabolism of clopidogrel (54).
The *2 (681G>A, dbSNP rs4244285), *3 (636G>A, dbSNP rs4986893) and *17 (−806C>T, dbSNP rs12248560) polymorphisms were the most common variants in the CYP2C19 gene (55, 56). A number of studies have suggested that the carriage of CYP2C19 loss-of-function alleles (*2 and *3) were associated with increased cardiovascular events in patients treated with clopidogrel, particularly after coronary stenting (57-62). The gain-of-function variant is associated with a lower risk of cardiovascular events and with a higher risk of bleeding (63-65). However, substantial heterogeneity was observed among the studies. The carriers of CYP2C19 loss-of-function alleles did not increase the risk for cardiovascular events, with a sample size of ≥500 patients (63). Although there was an association between the CYP2C19 genotype and clopidogrel responsiveness, there was no significant association between genetic variants and cardiovascular events in the original randomized controlled trial (RCT), which included the ACTIVE-A, CURE, CHARISMA and CLARITY-TIMI 28 trials (66, 67).
The CHANCE trial was a randomized, double-blind, multicenter, placebo-controlled clinical trial, which clarified that for minor ischemic stroke or TIA, greater protection against recurrent stroke could be realized through means other than treatment with aspirin alone. The pre-specified pharmacogenetics sub-study of the CHANCE trial had shown an association between CYP2C19 genetic variants and clinical efficacy of clopidogrel (41, 68). Clopidogrel combined with aspirin reduced the risk of stroke only in patients without any CYP2C19 loss-of-function allele (HR, 0.51; 95% CI, 0.35-0.75), when compared to treatment with aspirin alone. In noncarriers, the absolute risk reduction of stroke recurrence was 5.7% between patients on dual- versus mono-antiplatelet therapies. However, carriers did not benefit from clopidogrel in addition to aspirin (HR, 0.93; 95% CI, 0.69 to 1.26). The proportion of carriers was 58.5% among Chinese patients, which is much higher than the proportion among Europeans (68). The SPS3 (Secondary Prevention of Small Subcortical Strokes) study was an international multicenter randomized trial evaluating antiplatelet and antihypertensive approaches to prevent stroke recurrence. The genetic substudy of SPS3 enrolled white, black and hispanic patients. Among white patients, there was a higher probability of recurrent stroke in carriers with at least one CYP2C19 loss-of-function allele when compared to noncarriers (OR 5.19, 95%CI, 1.08-24.90). However, the association was not significant in the overall cohort (69). A recent meta-analysis of reports published through June 2016 was undertaken, which included 15 studies on patients with ischemic stroke or TIA on clopidogrel. This analysis further confirmed that carriers of loss-of-function alleles have a greater risk of stroke and composite vascular events when compared with non-carriers (RR, 1.92; 95% CI,1.57-2.35) (70-76). Patients carrying two loss-of-function alleles have a higher risk of recurrent stroke than those carrying one loss-of-function allele (70). Non-genetic factors might affect the association between CYP2C19 and the variability of responses to clopidogrel. A post-hoc analysis of the CHANCE trial has found that the interactions between the carriage of CYP2C19 loss-of-function allele and clinical efficacy of clopidogrel were attenuated by poor glycemic control, defined as the glycemic albumin level >15.5% (77).
The influence of genetic polymorphisms other than CYP2C19 on clopidogrel efficacy for acute ischemic stroke or TIA was also evaluated in this meta-analysis (70). In a case-control study that enrolled a total of 268 stroke patients undergoing extracranial or intracranial stenting on clopidogrel with aspirin, the SNPs of P2Y12 (rs2046934), PON1 (rs662). and COX-1 (rs1330344) were associated with recurrent clinical events (78). However, no association was found between the risk of vascular event recurrence and the allelic gene variants modulating clopidogrel absorption (ABCB1), metabolic activation (CES1, CYP3A5, CYP2B6, CYP2C8, CYP2C9 and CYP3A4), or biologic activity (P2Y1 and ITGB3), especially in patients with ischemic stroke or TIA (70-73, 75, 78-80).
Ticagrelor is an oral, reversible, direct-acting inhibitor of the adenosine diphosphate receptor P2Y12. It is primarily metabolized by the CYP3A4 enzyme, and has faster onset and produces more pronounced platelet inhibition than clopidogrel (81). The ONSET/OFFSET and RESPOND genotype studies have suggested a superior pharmacodynamic effect of ticagrelor when compared to clopidogrel, irrespective of CYP2C19 genotype. There was no effect of CYP2C19 loss-of-function allele on the antiplatelet effect of ticagrelor, when assessed by ADP-induced platelet aggregation, VerifyNow P2Y12 assay and vasodilator-stimulated phosphoprotein (VASP) -phosphorylation assay (82). A GWAS based on the PLATO (Platelet Inhibition and Patient Outcomes). trial has found modest effects of three genetic loci (SLCO1B1, UGT2B7, and CYP3A4). on ticagrelor plasma levels, which could not be translated into any detectable effects on clinical efficacy or safety (83). Another genetic substudy of the PLATO trial has shown that ticagrelor was more effective for acute coronary syndromes than clopidogrel, irrespective of CYP2C19 and ABCB1 polymorphisms (84). To date, there is no pharmacogenetic data for stroke patients treated with ticagrelor.
5.2. Anticoagulation agents
Warfarin has been widely used as an oral anticoagulant for secondary prevention of cardioembolic stroke. It is a mixture of the enantiomers of S-warfarin and R-warfarin. The S-warfarin is 3-5 times more potent than R-warfarin, and 85% of the S-warfarin are metabolized by cytochrome P450 2C9 (85). The CYP2C9* 2 (430C> T) and CYP2C9 * 3 (1075A> C) variants are two common variants of CYP2C9 that have observably reduced enzyme activity when compared to the wild genotype (86, 87). Patients with CYP2C9 *2 or *3 required a lower dose of warfarin and had a higher risk of bleeding. Vitamin K epoxide reductase complex subunit 1(VKORC1). is the main rate-limiting step in the biosynthesis of vitamin K-dependent proteins, and is the target for warfarin-based anti-coagulants. It has been documented that the SNP in VKORC1 gene (−1639G>A) was associated with a necessary decrease in warfarin dosage. The FDA has approved pharmacogenetic testing of CYP2C9 and VKORC1 for warfarin, and has provided genotype-specific ranges of doses. However, the combination of CYP2C9 and VKORC1 polymorphisms accounted only for about 30 to 40% of the total variation in the warfarin dose (88). The final dose of warfarin might be influenced by other clinical and demographic factors such as age, body weight, concurrent disease, as well as concomitant interactions with medication and food. The pharmacogenetic-based therapy of warfarin is still being debated. In the COAG (Clarification of Optimal Anticoagulation Through Genetics) trial, which was designed to test the effect of genotype-tailored dosing on anticoagulation control, genotyping-guided dosing was not shown to be superior to conventional warfarin dosing (89). In contrast, in the EU-PACT (EUropean Pharmacogenetics of AntiCoagulant Therapy-Warfarin) trial, the genotype-guided dosing was shown to improve anticoagulation control (90). There are some genetic clues for the response to this new oral anticoagulant. A GWAS analysis, which was performed on patients in the RELY (Randomized Evaluation of Long-term Anticoagulation Therapy) trial, identified that carrying the CES1 rs2244613 was associated with lower exposure to the active metabolite of dabigatran (91).
6. PROTEOMIC AND PRECISION MEDICINE IN ISCHEMIC STROKE
In a symposium titled “2D Electrophoresis: from protein maps to genomes” held in Siena, Italy in 1994, Marc Wilkins coined the term “proteome”, and the term subsequently appeared in print in 1995 (6). The term originated from the combination of “protein” and “genome”, and refers to the entire complement of proteins expressed by genomes, cells, tissues and organisms. In recent years, theory and approaches to proteomics have been applied to explore the risk factors of stroke and to understand response variability to drugs. A proteomic study was conducted on stable coronary ischemic Spanish patients taking aspirin (100mg/day). In has been suggested that the level of expression of proteins associated with mechanisms such as energetic metabolism, cytoskeleton, oxidative stress and cell survival is associated with variations in patient responses to aspirin (92, 93). However, to date, the data for stroke patients is limited in this area. The MITICO study was designed to assess the prognostic value of markers for inflammation as they might relate to the risk of recurrence of vascular disease (94). The proteomic substudy of MITICO indicated that increased desmoplakin I levels during the first 1-3 months after symptom onset could be a biomarker for statin responsiveness against a new vascular event (95). Platelet basic protein (PBP). has been identified by mass spectrometry as a candidate serum biomarker for TIA in a small-sample proteomics study (96). This proteomic approach might be a promising method for finding novel biomarkers to more precisely predict, diagnose and treat ischemic stroke. The CHANCE trial has identified that the glycated albumin (GA) could be a potential biomarker to predict the effects of dual- and mono-antiplatelet therapy in patients with minor stroke or TIA (97). It has been suggested that glycomics might be proposed as a new approach for precise antiplatelet therapy.
7. OTHER OMICS AND PRECISION MEDICINE IN ISCHEMIC STROKE
Reports of the “omics” on oncology and cardiovascular disease have spurred an interest in other types of studies that may promote further understanding of ischemic stroke. In one study, a total of 194 RNA samples from 76 acute ischemic stroke patients were analyzed on whole-genome microarrays. Two gene profiles were identified in this study, one comprised of 40 genes, and the other of 37 genes. With sensitivity and specificity of > 90%, these two studies were capable of differentiating cardioembolic stroke from large vessel stroke and differentiating cardioembolic stroke due to atrial fibrillation from non-atrial fibrillation causes, respectively (98). Another 41-gene profile has been able to discriminate lacunar from non-lacunar stroke with high sensitivity and specificity (99). The combination of the above three gene expression profiles in conjunction with a measure of infarct location can predict a probable cause for cryptogenic strokes. Among a total of 131 patients, cryptogenic strokes were predicted to be 58% cardioembolic, 18% arterial, 12% lacunar and 12% of unclear etiology (100). A case-control study employing methylation microarray analysis has identified obesity-induced, stroke-dependent changes in the KCNQ1 methylation pattern (101, 102). In a prospective nested case-control study that included 42 ischemic stroke patients, over 480 000 DNA methylation sites were analyzed across the genome. Lower cg03548645 DNA methylation levels of TRAF3 (tumor necrosis factor receptor-as ociated factor 3) were correlated with increased platelet aggregation on clopidogrel treatment. It suggested, for the first time, that epigenetics may significantly contribute to the response variability of clopidogrel and to the recurrence of ischemic events in patients with ischemic stroke (102). Metabolomic analysis was performed by liquid chromatography coupled to mass spectrometry in plasma samples from a total of 131 TIA patients. This study has identified that lysophosphatidylcholine (LysoPC(16:0), LysoPC(20:4) and LysoPC(22:6) is significantly associated with stroke recurrence and that assessment of lysophosphatidylcholine levels could improve the predictive power of conventional predictors (103).
8. BRAIN IMAGING AND PRECISION MEDICINE IN STROKE
Imaging should be regarded as the core of precision stroke research (104). Unlike tumor biopsies, lesion tissue samples cannot be obtained routinely in stroke research or in clinical practice. Brain imaging can detect lesions in stroke patients in-vivo and provide information fundamental to precise clinical determination of treatment and can also be an effective predictor of outcomes. In clinical practice, imaging plays a vital role in stroke assessment, diagnosis, etiology, management, treatment, and prevention (105, 106). In the hyper-acute phase of ischemic stroke, noncontrast CT scans are pivotal to precise selection of patients eligible for implementation of intravenous tissue-type plasminogen activator, as CT scans can rule out hemorrhages and more precisely identify the area of cerebral infarction (106). The combination of multimodal imaging techniques, such as diffusion-weighted imaging, noninvasive angiography, perfusion imaging with CT and MRI can provide clinicians with a wider eligibility window for administration of IV tPA in order to treat patients beyond the accepted therapeutic window of 4.5. hours (107). When endovascular therapy is considered, noninvasive intracranial vascular imaging is strongly recommended during the initial imaging evaluation of the acute stroke (105). The collateral status in proximal middle cerebral artery occlusion may have a radical impact on the response to endovascular therapy for acute ischemic stroke (104). Brain imaging provides extensive information on baseline pathophysiology, necessary when selecting eligible patients for novel treatments as well as for prediction of patient outcomes.
In stroke clinical trial design, brain imaging biomarkers are powerful criteria for patient selection. The results of imaging can be used to precisely target the key population for a novel treatment, reducing the sample size and saving costs. Trial imaging selection criteria may also be successful in reducing the heterogeneity in stroke mechanisms (104). Recently, endovascular therapy trials used various imaging paradigms to achieve positive findings with only a fraction of the expected sample sizes (108, 109).
Despite the emphasis on its importance, several national precision initiatives are not collecting imaging data. Although stroke is included in the “Million Hearts” Initiative for precision medicine, brain imaging data cannot be collected. Liebeskind et al. strongly recommended that brain imaging become an integral part of stroke precision medicine, which should be rooted in data closely relevant to clinical practice (104, 110). Thus, there now exists a substantial opportunity to establish an individualized approach to stroke management, but it must include leveraging big data found within imaging studies.
9. BIG DATA AND PRECISION MEDICINE IN STROKE
The implementation of precision medicine produces massive data, such as genomics, proteomics, metablolomics, and diverse cellular assays, which together comprise the most important components of big data (1). Access to large amounts of clinical health information provides a potentially powerful complement to genome-based precision medicine (111). Big data have emerged at the forefront of biomedical research and is refining precision medicine. The American Heart Association has recommended the formation of a science advisory committee to evaluate the move towards merging of electronic health record data, genomics and cardiovascular research (112).
Phenotype-based precision medicine is used to determine individually targeted interventions from large data sets such as electronic hospital records (EHRs) and administrative claims. Combined data from multiple sources can reduce biases that are intrinsic in homogeneous source data. Standardizing data elements is also essential to controlling these biases. The National Institute of Neurological Disorders and Stroke launched the stroke-specific Common Data Elements (CDEs) project, which proposes standardization of data elements in clinical and population translational research in stroke. CDEs are able to decrease the study time necessary to process data, expedite data sharing, and promote well-informed clinical practice guidelines, creating a robust foundation for using big data from multiple sources (113). The number of patients with consistent data could create an immense network for examining subgroups that would not be possible in individual trials.
EHRs have played a vital role in medical practice, especially in developed countries (114). EHRs include massive medical data such as laboratory data, images, vital signs, and other clinical information. These elements compose a major part of the data for phenotypic precision medicine. With the application of genetic and genomic data in clinical practice, EHRs also allow this information to be recorded with efficiency equivalent to that of clinical laboratory data. EHR-coupled biobanks, which contain biological specimens linked to data in the EHR, are experiencing increasingly widespread usage. In comparison to conventional cohort studies or clinical trials, the link between EHRs and genomic data generates large data sets more rapidly and inexpensively (115). The National Human Genome Research Institute launched the Electronic Medical Records and Genomics (eMERGE) Network, including 10 EHR-based DNA repositories and >350 000 subjects (116). The eMERGE initiative has had a tremendous impact on the domains of genomics and informatics. The EMR is regarded as a powerful and cost-effective tool for precision medicine. Based on its success, eMERGE, through the EMRs, is poised to lead the way in implementing genomic medicine in clinical care (116). These findings from precision medicine and “real world” data of EMRs will be translated quickly into clinical practice, and thus lead to improvements in health care through safer and more effective prescription guidelines, augmentation of primary and secondary prevention strategies, and enhanced understanding of disease biology.
In addition, EHRs provide a large number of patients with longitudinal data that may improve the ability to separate true-positive from false-positive associations. Stroke outcomes can be ascertained by linking multiple sources of coded data in the UK Biobank through stroke-specific codes (117). Claim data can provide data about rare diseases that are difficult to obtain from a single site. For example, the incidence, mortality, and risk factors for pregnancy-related stroke in the United States from 2000-2001 were estimated through the Nationwide Inpatient Sample (118). In addition, the J-ASPECT study demonstrated the impact of CSC capacity on in-hospital mortality in stroke (119). EHR-derived data have helped identify single-nucleotide polymorphisms in 9p21 that are noted to be associated with cardiovascular disease (120). Via advanced machine-learning techniques, a stroke prediction model was established from aggregated health records in patients with atrial fibrillation.
10. SUMMARY AND PERSPECTIVE
Ischemic stroke is a complex disease with heterogeneity in etiology, disease pathophysiology, clinical presentation, and response to treatment. A one-size-fits-all approach would not be suitable for all patients. Precision medicine provides a novel strategy for specialists to establish a treatment plan for one patient that is distinct from that of another, with the goal of improving population health and clinical management of IS. The entire implementation of precision medicine in ischemic stroke or TIA should be a three-stage progress (Figure 1). The first stage is the “discovery” stage. In this stage, the associations between potential novel omics, imaging, and phenotypic biomarkers with ischemic stroke or TIA should be documented in case-control or in cohort studies. The “replication” stage is the second stage. The biomarkers identified in the first stage should be replicated in a biomarker-tailored randomized controlled trial. Lastly, the “translation” stage serves to translate these practices to clinical practice by using point-of-care testing approaches or mobile medical devices. To date, most of studies for precision medicine in ischemic stroke or TIA were in the first stage.
Precision medicine would distinguish patients with ischemic stroke primarily based on genetics and imaging information, while considering other phenotypic and psychosocial characteristics. Studies of other “omics” have explored several potential novel biomarkers, which might provide critical mechanistic insights into key biologic processes of ischemic stroke or TIA. These biomarkers might be put forward to a new clinical subtype as substitute or modification for TOAST/OCSP classification in ischemic stroke. The system integration of the omics’ data, phenotypes, exposome and microbiome would give us comprehensive insight into the development of ischemic stroke or TIA and the response variability to drugs, to which further study and analysis is needed in the future.
Yi Lin, Zixiao Li contributed equally to this work. The authors have no disclosure. This study was supported by grants from the Ministry of Science and Technology of the People’s Republic of China (2006BA101A11, 2008ZX09312-008, 2011BAI08B01, 2011BAI08B02, 2012ZX09303, 2013BAI09B14, 2013BAI09B03, 2015BAI12B02, and 2015BAI12B04), grants from Beijing Municipal Science and Technology Commission (D15110700200000, D151100002015001, D151100002015002, Z161100000516223 and Z141107002514125), a grant from National Natural Science Foundation of China (81322019), a grant from China Postdoctoral Science Foundation Grant (2014T70093), a grant from Fujian Provincial Natural Science Foundation (2015J01394), and a grant from Beijing Biobank of Cerebral Vascular Disease (D131100005313003).
1. U. S. N. R. C. C. o. A. F. f. D. a. N. T. o. Disease.: Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. Washington (DC): National Academies Press (US); (2011)
2. F. S. Collins and H. Varmus: A new initiative on precision medicine. N Engl J Med, 372(9), 793-5 (2015)
3. E. M. Antman and J. Loscalzo: Precision medicine in cardiology. Nat Rev Cardiol, 13(10), 591-602 (2016)
4. G. J. Falcone, R. Malik, M. Dichgans and J. Rosand: Current concepts and clinical applications of stroke genetics. Lancet Neurol, 13(4), 405-18 (2014)
5. A. Lindgren: Stroke genetics: a review and update. J Stroke, 16(3), 114-23 (2014)
6. P. Sharma, S. Yadav and J. F. Meschia: Genetics of ischaemic stroke. J Neurol Neurosurg Psychiatry, 84(12), 1302-8 (2013)
7. B. Rabbani, N. Mahdieh, K. Hosomichi, H. Nakaoka and I. Inoue: Next-generation sequencing: impact of exome sequencing in characterizing Mendelian disorders. J Hum Genet, 57(10), 621-32 (2012)
8. S. Gretarsdottir, G. Thorleifsson, S. T. Reynisdottir, A. Manolescu, S. Jonsdottir, T. Jonsdottir, T. Gudmundsdottir, S. M. Bjarnadottir, O. B. Einarsson, H. M. Gudjonsdottir, M. Hawkins, G. Gudmundsson, H. Gudmundsdottir, H. Andrason, A. S. Gudmundsdottir, M. Sigurdardottir, T. T. Chou, J. Nahmias, S. Goss, S. Sveinbjornsdottir, E. M. Valdimarsson, F. Jakobsson, U. Agnarsson, V. Gudnason, G. Thorgeirsson, J. Fingerle, M. Gurney, D. Gudbjartsson, M. L. Frigge, A. Kong, K. Stefansson and J. R. Gulcher: The gene encoding phosphodiesterase 4D confers risk of ischemic stroke. Nat Genet, 35(2), 131-8 (2003)
9. A. Helgadottir, A. Manolescu, G. Thorleifsson, S. Gretarsdottir, H. Jonsdottir, U. Thorsteinsdottir, N. J. Samani, G. Gudmundsson, S. F. Grant, G. Thorgeirsson, S. Sveinbjornsdottir, E. M. Valdimarsson, S. E. Matthiasson, H. Johannsson, O. Gudmundsdottir, M. E. Gurney, J. Sainz, M. Thorhallsdottir, M. Andresdottir, M. L. Frigge, E. J. Topol, A. Kong, V. Gudnason, H. Hakonarson, J. R. Gulcher and K. Stefansson: The gene encoding 5-lipoxygenase activating protein confers risk of myocardial infarction and stroke. Nat Genet, 36(3), 233-9 (2004)
11. S. Bevan, M. Dichgans, A. Gschwendtner, G. Kuhlenbaumer, E. B. Ringelstein and H. S. Markus: Variation in the PDE4D gene and ischemic stroke risk: a systematic review and meta-analysis on 5200 cases and 6600 controls. Stroke, 39(7), 1966-71 (2008)
12. T. Matsushita, M. Kubo, K. Yonemoto, T. Ninomiya, K. Ashikawa, B. Liang, J. Hata, Y. Doi, T. Kitazono, S. Ibayashi, M. Iida, Y. Kiyohara and Y. Nakamura: Lack of association between variations of PDE4D and ischemic stroke in the Japanese population. Stroke, 40(4), 1245-51 (2009)
14. S. Bevan, M. Traylor, P. Adib-Samii, R. Malik, N. L. Paul, C. Jackson, M. Farrall, P. M. Rothwell, C. Sudlow, M. Dichgans and H. S. Markus: Genetic heritability of ischemic stroke and the contribution of previously reported candidate gene and genomewide associations. Stroke, 43(12), 3161-7 (2012)
15. E. G. Holliday, J. M. Maguire, T. J. Evans, S. A. Koblar, J. Jannes, J. W. Sturm, G. J. Hankey, R. Baker, J. Golledge, M. W. Parsons, R. Malik, M. McEvoy, E. Biros, M. D. Lewis, L. F. Lincz, R. Peel, C. Oldmeadow, W. Smith, P. Moscato, S. Barlera, S. Bevan, J. C. Bis, E. Boerwinkle, G. B. Boncoraglio, T. G. Brott, R. D. Brown, Jr., Y. C. Cheng, J. W. Cole, I. Cotlarciuc, W. J. Devan, M. Fornage, K. L. Furie, S. Gretarsdottir, A. Gschwendtner, M. A. Ikram, W. T. Longstreth, Jr., J. F. Meschia, B. D. Mitchell, T. H. Mosley, M. A. Nalls, E. A. Parati, B. M. Psaty, P. Sharma, K. Stefansson, G. Thorleifsson, U. Thorsteinsdottir, M. Traylor, B. F. Verhaaren, K. L. Wiggins, B. B. Worrall, C. Sudlow, P. M. Rothwell, M. Farrall, M. Dichgans, J. Rosand, H. S. Markus, R. J. Scott, C. Levi, J. Attia, C. Australian Stroke Genetics, C. International Stroke Genetics and C. Wellcome Trust Case Control: Common variants at 6p21.1. are associated with large artery atherosclerotic stroke. Nat Genet, 44(10), 1147-51 (2012)
16. J. Hata, K. Matsuda, T. Ninomiya, K. Yonemoto, T. Matsushita, Y. Ohnishi, S. Saito, T. Kitazono, S. Ibayashi, M. Iida, Y. Kiyohara, Y. Nakamura and M. Kubo: Functional SNP in an Sp1-binding site of AGTRL1 gene is associated with susceptibility to brain infarction. Hum Mol Genet, 16(6), 630-9 (2007)
17. M. A. Ikram, S. Seshadri, J. C. Bis, M. Fornage, A. L. DeStefano, Y. S. Aulchenko, S. Debette, T. Lumley, A. R. Folsom, E. G. van den Herik, M. J. Bos, A. Beiser, M. Cushman, L. J. Launer, E. Shahar, M. Struchalin, Y. Du, N. L. Glazer, W. D. Rosamond, F. Rivadeneira, M. Kelly-Hayes, O. L. Lopez, J. Coresh, A. Hofman, C. DeCarli, S. R. Heckbert, P. J. Koudstaal, Q. Yang, N. L. Smith, C. S. Kase, K. Rice, T. Haritunians, G. Roks, P. L. de Kort, K. D. Taylor, L. M. de Lau, B. A. Oostra, A. G. Uitterlinden, J. I. Rotter, E. Boerwinkle, B. M. Psaty, T. H. Mosley, C. M. van Duijn, M. M. Breteler, W. T. Longstreth, Jr. and P. A. Wolf: Genomewide association studies of stroke. N Engl J Med, 360(17), 1718-28 (2009)
18. M. Kubo, J. Hata, T. Ninomiya, K. Matsuda, K. Yonemoto, T. Nakano, T. Matsushita, K. Yamazaki, Y. Ohnishi, S. Saito, T. Kitazono, S. Ibayashi, K. Sueishi, M. Iida, Y. Nakamura and Y. Kiyohara: A nonsynonymous SNP in PRKCH (protein kinase C eta) increases the risk of cerebral infarction. Nat Genet, 39(2), 212-7 (2007)
19. M. Traylor, M. Farrall, E. G. Holliday, C. Sudlow, J. C. Hopewell, Y. C. Cheng, M. Fornage, M. A. Ikram, R. Malik, S. Bevan, U. Thorsteinsdottir, M. A. Nalls, W. Longstreth, K. L. Wiggins, S. Yadav, E. A. Parati, A. L. Destefano, B. B. Worrall, S. J. Kittner, M. S. Khan, A. P. Reiner, A. Helgadottir, S. Achterberg, I. Fernandez-Cadenas, S. Abboud, R. Schmidt, M. Walters, W. M. Chen, E. B. Ringelstein, M. O’Donnell, W. K. Ho, J. Pera, R. Lemmens, B. Norrving, P. Higgins, M. Benn, M. Sale, G. Kuhlenbaumer, A. S. Doney, A. M. Vicente, H. Delavaran, A. Algra, G. Davies, S. A. Oliveira, C. N. Palmer, I. Deary, H. Schmidt, M. Pandolfo, J. Montaner, C. Carty, P. I. de Bakker, K. Kostulas, J. M. Ferro, N. R. van Zuydam, E. Valdimarsson, B. G. Nordestgaard, A. Lindgren, V. Thijs, A. Slowik, D. Saleheen, G. Pare, K. Berger, G. Thorleifsson, W. T. C. C. C. Australian Stroke Genetics Collaborative, A. Hofman, T. H. Mosley, B. D. Mitchell, K. Furie, R. Clarke, C. Levi, S. Seshadri, A. Gschwendtner, G. B. Boncoraglio, P. Sharma, J. C. Bis, S. Gretarsdottir, B. M. Psaty, P. M. Rothwell, J. Rosand, J. F. Meschia, K. Stefansson, M. Dichgans, H. S. Markus and C. International Stroke Genetics: Genetic risk factors for ischaemic stroke and its subtypes (the METASTROKE collaboration): a meta-analysis of genome-wide association studies. Lancet Neurol, 11(11), 951-62 (2012)
20. C. L. Carty, K. L. Keene, Y. C. Cheng, J. F. Meschia, W. M. Chen, M. Nalls, J. C. Bis, S. J. Kittner, S. S. Rich, S. Tajuddin, A. B. Zonderman, M. K. Evans, C. D. Langefeld, R. Gottesman, T. H. Mosley, E. Shahar, D. Woo, K. Yaffe, Y. Liu, M. M. Sale, M. Dichgans, R. Malik, W. T. Longstreth, Jr., B. D. Mitchell, B. M. Psaty, C. Kooperberg, A. Reiner, B. B. Worrall and M. Fornage: Meta-Analysis of Genome-Wide Association Studies Identifies Genetic Risk Factors for Stroke in African Americans. Stroke (2015)
21. X. Zhou, T. Guan, S. Li, Z. Jiao, X. Lu, X. Huang, Y. Ji and Q. Ji: The association between HDAC9 gene polymorphisms and stroke risk in the Chinese population: A meta-analysis. Sci Rep, 7, 41538 (2017)
22. N. S. G. Network and C. International Stroke Genetics: Loci associated with ischaemic stroke and its subtypes (SiGN): a genome-wide association study. Lancet Neurol, 15(2), 174-184 (2016)
23. F. M. Williams, A. M. Carter, P. G. Hysi, G. Surdulescu, D. Hodgkiss, N. Soranzo, M. Traylor, S. Bevan, M. Dichgans, P. M. Rothwell, C. Sudlow, M. Farrall, K. Silander, M. Kaunisto, P. Wagner, O. Saarela, K. Kuulasmaa, J. Virtamo, V. Salomaa, P. Amouyel, D. Arveiler, J. Ferrieres, P. G. Wiklund, M. A. Ikram, A. Hofman, G. B. Boncoraglio, E. A. Parati, A. Helgadottir, S. Gretarsdottir, U. Thorsteinsdottir, G. Thorleifsson, K. Stefansson, S. Seshadri, A. DeStefano, A. Gschwendtner, B. Psaty, W. Longstreth, B. D. Mitchell, Y. C. Cheng, R. Clarke, M. Ferrario, J. C. Bis, C. Levi, J. Attia, E. G. Holliday, R. J. Scott, M. Fornage, P. Sharma, K. L. Furie, J. Rosand, M. Nalls, J. Meschia, T. H. Mosely, A. Evans, A. Palotie, H. S. Markus, P. J. Grant, T. D. Spector, C. I. Euro, C. Wellcome Trust Case Control, G. A. Monica Risk, Monograph, MetaStroke and C. International Stroke Genetics: Ischemic stroke is associated with the ABO locus: the EuroCLOT study. Ann Neurol, 73(1), 16-31 (2013)
24. M. Dichgans, R. Malik, I. R. Konig, J. Rosand, R. Clarke, S. Gretarsdottir, G. Thorleifsson, B. D. Mitchell, T. L. Assimes, C. Levi, C. J. O’Donnell, M. Fornage, U. Thorsteinsdottir, B. M. Psaty, C. Hengstenberg, S. Seshadri, J. Erdmann, J. C. Bis, A. Peters, G. B. Boncoraglio, W. Marz, J. F. Meschia, S. Kathiresan, M. A. Ikram, R. McPherson, K. Stefansson, C. Sudlow, M. P. Reilly, J. R. Thompson, P. Sharma, J. C. Hopewell, J. C. Chambers, H. Watkins, P. M. Rothwell, R. Roberts, H. S. Markus, N. J. Samani, M. Farrall, H. Schunkert, A. Gschwendtner, S. Bevan, Y. C. Chen, A. L. DeStefano, E. A. Parati, T. Quertermous, A. Ziegler, E. Boerwinkle, H. Holm, M. Fischer, T. Kessler, C. Willenborg, R. Laaksonen, B. F. Voight, A. F. Stewart, D. J. Rader, A. S. Hall, J. S. Kooner, M. Consortium, C. A. consortium, C. D. consortium and C. International Stroke Genetics: Shared genetic susceptibility to ischemic stroke and coronary artery disease: a genome-wide analysis of common variants. Stroke, 45(1), 24-36 (2014)
25. A. Gschwendtner, S. Bevan, J. W. Cole, A. Plourde, M. Matarin, H. Ross-Adams, T. Meitinger, E. Wichmann, B. D. Mitchell, K. Furie, A. Slowik, S. S. Rich, P. D. Syme, M. J. MacLeod, J. F. Meschia, J. Rosand, S. J. Kittner, H. S. Markus, B. Muller-Myhsok, M. Dichgans and C. International Stroke Genetics: Sequence variants on chromosome 9p21.3. confer risk for atherosclerotic stroke. Ann Neurol, 65(5), 531-9 (2009)
26. M. Matarin, W. M. Brown, A. Singleton, J. A. Hardy, J. F. Meschia and I. investigators: Whole genome analyses suggest ischemic stroke and heart disease share an association with polymorphisms on chromosome 9p21. Stroke, 39(5), 1586-9 (2008)
27. C. International Stroke Genetics, C. Wellcome Trust Case Control, C. Bellenguez, S. Bevan, A. Gschwendtner, C. C. Spencer, A. I. Burgess, M. Pirinen, C. A. Jackson, M. Traylor, A. Strange, Z. Su, G. Band, P. D. Syme, R. Malik, J. Pera, B. Norrving, R. Lemmens, C. Freeman, R. Schanz, T. James, D. Poole, L. Murphy, H. Segal, L. Cortellini, Y. C. Cheng, D. Woo, M. A. Nalls, B. Muller-Myhsok, C. Meisinger, U. Seedorf, H. Ross-Adams, S. Boonen, D. Wloch-Kopec, V. Valant, J. Slark, K. Furie, H. Delavaran, C. Langford, P. Deloukas, S. Edkins, S. Hunt, E. Gray, S. Dronov, L. Peltonen, S. Gretarsdottir, G. Thorleifsson, U. Thorsteinsdottir, K. Stefansson, G. B. Boncoraglio, E. A. Parati, J. Attia, E. Holliday, C. Levi, M. G. Franzosi, A. Goel, A. Helgadottir, J. M. Blackwell, E. Bramon, M. A. Brown, J. P. Casas, A. Corvin, A. Duncanson, J. Jankowski, C. G. Mathew, C. N. Palmer, R. Plomin, A. Rautanen, S. J. Sawcer, R. C. Trembath, A. C. Viswanathan, N. W. Wood, B. B. Worrall, S. J. Kittner, B. D. Mitchell, B. Kissela, J. F. Meschia, V. Thijs, A. Lindgren, M. J. Macleod, A. Slowik, M. Walters, J. Rosand, P. Sharma, M. Farrall, C. L. Sudlow, P. M. Rothwell, M. Dichgans, P. Donnelly and H. S. Markus: Genome-wide association study identifies a variant in HDAC9 associated with large vessel ischemic stroke. Nat Genet, 44(3), 328-33 (2012)
28. Y. Han, W. Sun, L. Wang, S. Tao, L. Tian, Y. Hao, W. Zhang, S. Wu, S. Li, H. Lv, S. L. Zheng, J. Sun and J. Xu: HDAC9 gene is associated with stroke risk in a Chinese population. Exp Biol Med (Maywood), 238(7), 842-7 (2013)
29. M. Traylor, K. M. Makela, L. L. Kilarski, E. G. Holliday, W. J. Devan, M. A. Nalls, K. L. Wiggins, W. Zhao, Y. C. Cheng, S. Achterberg, R. Malik, C. Sudlow, S. Bevan, E. Raitoharju, I. S. G. C. W. T. C. C. Metastroke, N. Oksala, V. Thijs, R. Lemmens, A. Lindgren, A. Slowik, J. M. Maguire, M. Walters, A. Algra, P. Sharma, J. R. Attia, G. B. Boncoraglio, P. M. Rothwell, P. I. de Bakker, J. C. Bis, D. Saleheen, S. J. Kittner, B. D. Mitchell, J. Rosand, J. F. Meschia, C. Levi, M. Dichgans, T. Lehtimaki, C. M. Lewis and H. S. Markus: A novel MMP12 locus is associated with large artery atherosclerotic stroke using a genome-wide age-at-onset informed approach. PLoS Genet, 10(7), e1004469 (2014)
30. T. H. Lee, T. M. Ko, C. H. Chen, M. T. Lee, Y. J. Chang, C. H. Chang, K. L. Huang, T. Y. Chang, J. D. Lee, K. C. Chang, J. T. Yang, M. S. Wen, C. Y. Wang, Y. T. Chen, C. S. Hsieh, S. Y. Chou, Y. M. Liu, H. W. Chen, H. T. Liao, C. W. Wang, S. P. Chen, L. S. Lu, Y. T. Chen and J. Y. Wu: Identification of PTCSC3 as a Novel Locus for Large-Vessel Ischemic Stroke: A Genome-Wide Association Study. J Am Heart Assoc, 5(3) (2016)
31. L. L. Kilarski, S. Achterberg, W. J. Devan, M. Traylor, R. Malik, A. Lindgren, G. Pare, P. Sharma, A. Slowik, V. Thijs, M. Walters, B. B. Worrall, M. M. Sale, A. Algra, L. J. Kappelle, C. Wijmenga, B. Norrving, J. K. Sandling, L. Ronnblom, A. Goris, A. Franke, C. Sudlow, P. M. Rothwell, C. Levi, E. G. Holliday, M. Fornage, B. Psaty, S. Gretarsdottir, U. Thorsteinsdottir, S. Seshadri, B. D. Mitchell, S. Kittner, R. Clarke, J. C. Hopewell, J. C. Bis, G. B. Boncoraglio, J. Meschia, M. A. Ikram, B. M. Hansen, J. Montaner, G. Thorleifsson, K. Stefanson, J. Rosand, P. I. de Bakker, M. Farrall, M. Dichgans, H. S. Markus, S. Bevan, W. T. C. C. C. A. S. G. C. t. M. C. Garnet Collaborative Research Group and C. the International Stroke Genetics: Meta-analysis in more than 17,900 cases of ischemic stroke reveals a novel association at 12q24.1.2. Neurology, 83(8), 678-85 (2014)
32. H. S. Markus and S. Bevan: Mechanisms and treatment of ischaemic stroke--insights from genetic associations. Nat Rev Neurol, 10(12), 723-30 (2014)
33. C. A. Ibrahim-Verbaas, M. Fornage, J. C. Bis, S. H. Choi, B. M. Psaty, J. B. Meigs, M. Rao, M. Nalls, J. D. Fontes, C. J. O’Donnell, S. Kathiresan, G. B. Ehret, C. S. Fox, R. Malik, M. Dichgans, H. Schmidt, J. Lahti, S. R. Heckbert, T. Lumley, K. Rice, J. I. Rotter, K. D. Taylor, A. R. Folsom, E. Boerwinkle, W. D. Rosamond, E. Shahar, R. F. Gottesman, P. J. Koudstaal, N. Amin, R. G. Wieberdink, A. Dehghan, A. Hofman, A. G. Uitterlinden, A. L. Destefano, S. Debette, L. Xue, A. Beiser, P. A. Wolf, C. Decarli, M. A. Ikram, S. Seshadri, T. H. Mosley, Jr., W. T. Longstreth, Jr., C. M. van Duijn and L. J. Launer: Predicting stroke through genetic risk functions: the CHARGE Risk Score Project. Stroke, 45(2), 403-12 (2014)
34. S. Debette, Y. Saba, D. Vojinovic, X. Jian, H. Adams, G. Chauhan, M. Sargurupremraj, S. Kaffashian, J. Ding, J. C. Bis, P. Nyquist, K. Mather, C. Van Duijn, L. J. Launer, M. A. Ikram, H. Schmidt, W. T. Longstreth, M. Fornage, S. Seshadri and C. w. g. neuro: 19th Workshop of the International Stroke Genetics Consortium, April 28-29, 2016, Boston, Massachusetts, USA: 2016.0.01 MRI-defined cerebrovascular genomics-The CHARGE consortium. Neurol Genet, 3(1 Suppl 1), S2-S11 (2017)
35. D. Woo, S. Debette and C. Anderson: 20th Workshop of the International Stroke Genetics Consortium, November 3-4, 2016, Milan, Italy: 2016.0.36 ISGC research priorities. Neurol Genet, 3(1 Suppl 1), S12-S18 (2017)
36. M. D. Leiserson, J. V. Eldridge, S. Ramachandran and B. J. Raphael: Network analysis of GWAS data. Curr Opin Genet Dev, 23(6), 602-10 (2013)
37. A. M. Billeci, G. Agnelli and V. Caso: Stroke pharmacogenomics. Expert Opin Pharmacother, 10(18), 2947-57 (2009)
38. G. Davi and C. Patrono: Platelet activation and atherothrombosis. N Engl J Med, 357(24), 2482-94 (2007)
39. W. N. Kernan, B. Ovbiagele, H. R. Black, D. M. Bravata, M. I. Chimowitz, M. D. Ezekowitz, M. C. Fang, M. Fisher, K. L. Furie, D. V. Heck, S. C. Johnston, S. E. Kasner, S. J. Kittner, P. H. Mitchell, M. W. Rich, D. Richardson, L. H. Schwamm, J. A. Wilson, C. o. C. on behalf of the American Heart Association Stroke Council, C. o. C. C. Stroke Nursing and D. Council on Peripheral Vascular: 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, 45(7), 2160-236 (2014)
40. Y. Wang, S. Zhang, L. Zhang, Q. Dong, L. Cui, C. Pu, W. Wang, X. Liu and A. Xu: Chinese guidelines for the secondary prevention of ischemic stroke and transient ischemic attack 2014. Chin J Neurol., 48(4), 258-273 (2014)
41. D. Tang, C. Yang, J. Zheng, G. Canton, R. G. Bach, T. S. Hatsukami, L. Wang, D. Yang, K. L. Billiar and C. Yuan: Image-based modeling and precision medicine: patient-specific carotid and coronary plaque assessment and predictions. IEEE Trans Biomed Eng, 60(3), 643-51 (2013)
42. T. Goodman, A. Ferro and P. Sharma: Pharmacogenetics of aspirin resistance: a comprehensive systematic review. Br J Clin Pharmacol, 66(2), 222-32 (2008)
43. M. K. Halushka, L. P. Walker and P. V. Halushka: Genetic variation in cyclooxygenase 1: effects on response to aspirin. Clin Pharmacol Ther, 73(1), 122-30 (2003)
44. A. O. Maree, R. J. Curtin, A. Chubb, C. Dolan, D. Cox, J. O’Brien, P. Crean, D. C. Shields and D. J. Fitzgerald: Cyclooxygenase-1 haplotype modulates platelet response to aspirin. J Thromb Haemost, 3(10), 2340-5 (2005)
45. L. Cao, Z. Zhang, W. Sun, W. Bai, W. Sun, Y. Zhang, X. Wang, B. Cai, X. Xie, Z. Duan, Q. Cai, D. Liu, Y. Xiong, M. Ma, X. Liu and G. Xu: Impacts of COX-1 gene polymorphisms on vascular outcomes in patients with ischemic stroke and treated with aspirin. Gene, 546(2), 172-6 (2014)
46. N. Clappers, M. G. van Oijen, S. Sundaresan, M. A. Brouwer, R. H. Te Morsche, W. Keuper, W. H. Peters, J. P. Drenth and F. W. Verheugt: The C50T polymorphism of the cyclooxygenase-1 gene and the risk of thrombotic events during low-dose therapy with acetyl salicylic acid. Thromb Haemost, 100(1), 70-5 (2008)
47. V. L. Yip and M. Pirmohamed: Expanding role of pharmacogenomics in the management of cardiovascular disorders. Am J Cardiovasc Drugs, 13(3), 151-62 (2013)
48. C. Le Hello, R. Morello, A. Lequerrec, C. Duarte, J. Riddell and M. Hamon: Effect of PlA1/A2 glycoprotein IIIa gene polymorphism on the long-term outcome after successful coronary stenting. Thromb J, 5, 19 (2007)
49. J. J. Verschuren, S. Trompet, J. A. Wessels, H. J. Guchelaar, M. P. de Maat, M. L. Simoons and J. W. Jukema: A systematic review on pharmacogenetics in cardiovascular disease: is it ready for clinical application? Eur Heart J, 33(2), 165-75 (2012)
50. J. P. Lewis, K. Ryan, J. R. O’Connell, R. B. Horenstein, C. M. Damcott, Q. Gibson, T. I. Pollin, B. D. Mitchell, A. L. Beitelshees, R. Pakzy, K. Tanner, A. Parsa, U. S. Tantry, K. P. Bliden, W. S. Post, N. Faraday, W. Herzog, Y. Gong, C. J. Pepine, J. A. Johnson, P. A. Gurbel and A. R. Shuldiner: Genetic variation in PEAR1 is associated with platelet aggregation and cardiovascular outcomes. Circ Cardiovasc Genet, 6(2), 184-92 (2013)
51. M. Wurtz, P. H. Nissen, E. L. Grove, S. D. Kristensen and A. M. Hvas: Genetic Determinants of On-Aspirin Platelet Reactivity: Focus on the Influence of PEAR1. PLoS One, 9(10), e111816 (2014)
52. L. C. Edelstein, E. J. Luna, I. B. Gibson, M. Bray, Y. Jin, A. Kondkar, S. Nagalla, N. Hadjout-Rabi, T. C. Smith, D. Covarrubias, S. N. Jones, F. Ahmad, M. Stolla, X. Kong, Z. Fang, W. Bergmeier, C. Shaw, S. M. Leal and P. F. Bray: Human genome-wide association and mouse knockout approaches identify platelet supervillin as an inhibitor of thrombus formation under shear stress. Circulation, 125(22), 2762-71 (2012)
53. L. Wang, H. L. McLeod and R. M. Weinshilboum: Genomics and drug response. N Engl J Med, 364(12), 1144-53 (2011)
54. D. Trenk, S. D. Kristensen, W. Hochholzer and F. J. Neumann: High on-treatment platelet reactivity and P2Y12 antagonists in clinical trials. Thromb Haemost, 109(5), 834-45 (2013)
55. S. A. Scott, K. Sangkuhl, C. M. Stein, J. S. Hulot, J. L. Mega, D. M. Roden, T. E. Klein, M. S. Sabatine, J. A. Johnson, A. R. Shuldiner and C. Clinical Pharmacogenetics Implementation: Clinical Pharmacogenetics Implementation Consortium guidelines for CYP2C19 genotype and clopidogrel therapy: 2013 update. Clin Pharmacol Ther, 94(3), 317-23 (2013)
56. K. W. Weitzel, A. R. Elsey, T. Y. Langaee, B. Burkley, D. R. Nessl, A. O. Obeng, B. J. Staley, H. J. Dong, R. W. Allan, J. F. Liu, R. M. Cooper-Dehoff, R. D. Anderson, M. Conlon, M. J. Clare-Salzler, D. R. Nelson and J. A. Johnson: Clinical pharmacogenetics implementation: approaches, successes, and challenges. Am J Med Genet C Semin Med Genet, 166C(1), 56-67 (2014)
57. J. L. Mega, S. L. Close, S. D. Wiviott, L. Shen, R. D. Hockett, J. T. Brandt, J. R. Walker, E. M. Antman, W. Macias, E. Braunwald and M. S. Sabatine: Cytochrome p-450 polymorphisms and response to clopidogrel. N Engl J Med, 360(4), 354-62 (2009)
58. T. Simon, C. Verstuyft, M. Mary-Krause, L. Quteineh, E. Drouet, N. Meneveau, P. G. Steg, J. Ferrieres, N. Danchin, L. Becquemont, S. T. E. French Registry of Acute and S. T. E. M. I. I. Non: Genetic determinants of response to clopidogrel and cardiovascular events. N Engl J Med, 360(4), 363-75 (2009)
59. J. S. Hulot, J. P. Collet, J. Silvain, A. Pena, A. Bellemain-Appaix, O. Barthelemy, G. Cayla, F. Beygui and G. Montalescot: Cardiovascular risk in clopidogrel-treated patients according to cytochrome P450 2C19*2 loss-of-function allele or proton pump inhibitor coadministration: a systematic meta-analysis. J Am Coll Cardiol, 56(2), 134-43 (2010)
60. J. L. Mega, T. Simon, J. P. Collet, J. L. Anderson, E. M. Antman, K. Bliden, C. P. Cannon, N. Danchin, B. Giusti, P. Gurbel, B. D. Horne, J. S. Hulot, A. Kastrati, G. Montalescot, F. J. Neumann, L. Shen, D. Sibbing, P. G. Steg, D. Trenk, S. D. Wiviott and M. S. Sabatine: Reduced-function CYP2C19 genotype and risk of adverse clinical outcomes among patients treated with clopidogrel predominantly for PCI: a meta-analysis. JAMA, 304(16), 1821-30 (2010)
61. J. S. Jang, K. I. Cho, H. Y. Jin, J. S. Seo, T. H. Yang, D. K. Kim, D. S. Kim, S. H. Seol, D. I. Kim, B. H. Kim, Y. H. Park, H. G. Je, Y. H. Jeong and S. W. Lee: Meta-analysis of cytochrome P450 2C19 polymorphism and risk of adverse clinical outcomes among coronary artery disease patients of different ethnic groups treated with clopidogrel. Am J Cardiol, 110(4), 502-8 (2012)
62. A. R. Shuldiner, J. R. O’Connell, K. P. Bliden, A. Gandhi, K. Ryan, R. B. Horenstein, C. M. Damcott, R. Pakyz, U. S. Tantry, Q. Gibson, T. I. Pollin, W. Post, A. Parsa, B. D. Mitchell, N. Faraday, W. Herzog and P. A. Gurbel: Association of cytochrome P450 2C19 genotype with the antiplatelet effect and clinical efficacy of clopidogrel therapy. JAMA, 302(8), 849-57 (2009)
63. M. Zabalza, I. Subirana, J. Sala, C. Lluis-Ganella, G. Lucas, M. Tomas, R. Masia, J. Marrugat, R. Brugada and R. Elosua: Meta-analyses of the association between cytochrome CYP2C19 loss- and gain-of-function polymorphisms and cardiovascular outcomes in patients with coronary artery disease treated with clopidogrel. Heart, 98(2), 100-8 (2012)
64. C. Frere, T. Cuisset, B. Gaborit, M. C. Alessi and J. S. Hulot: The CYP2C19*17 allele is associated with better platelet response to clopidogrel in patients admitted for non-ST acute coronary syndrome. J Thromb Haemost, 7(8), 1409-11 (2009)
65. K. A. Tiroch, D. Sibbing, W. Koch, T. Roosen-Runge, J. Mehilli, A. Schomig and A. Kastrati: Protective effect of the CYP2C19 *17 polymorphism with increased activation of clopidogrel on cardiovascular events. Am Heart J, 160(3), 506-12 (2010)
66. G. Pare, S. R. Mehta, S. Yusuf, S. S. Anand, S. J. Connolly, J. Hirsh, K. Simonsen, D. L. Bhatt, K. A. Fox and J. W. Eikelboom: Effects of CYP2C19 genotype on outcomes of clopidogrel treatment. N Engl J Med, 363(18), 1704-14 (2010)
67. D. L. Bhatt, G. Pare, J. W. Eikelboom, K. L. Simonsen, E. S. Emison, K. A. Fox, P. G. Steg, G. Montalescot, N. Bhakta, W. Hacke, M. D. Flather, K. H. Mak, P. Cacoub, M. A. Creager, P. B. Berger, S. R. Steinhubl, G. Murugesan, S. R. Mehta, K. Kottke-Marchant, A. M. Lincoff, E. J. Topol and C. Investigators: The relationship between CYP2C19 polymorphisms and ischaemic and bleeding outcomes in stable outpatients: the CHARISMA genetics study. Eur Heart J, 33(17), 2143-50 (2012)
68. Y. Wang, X. Zhao, J. Lin, H. Li, S. C. Johnston, Y. Lin, Y. Pan, L. Liu, D. Wang, C. Wang, X. Meng, J. Xu, Y. Wang and C. investigators: Association Between CYP2C19 Loss-of-Function Allele Status and Efficacy of Clopidogrel for Risk Reduction Among Patients With Minor Stroke or Transient Ischemic Attack. JAMA, 316(1), 70-8 (2016)
69. C. W. McDonough, L. A. McClure, B. D. Mitchell, Y. Gong, R. B. Horenstein, J. P. Lewis, T. S. Field, R. L. Talbert, O. R. Benavente, J. A. Johnson and A. R. Shuldiner: CYP2C19 metabolizer status and clopidogrel efficacy in the Secondary Prevention of Small Subcortical Strokes (SPS3) study. J Am Heart Assoc, 4(6), e001652 (2015)
70. L. N. Qiu, Y. Sun, L. Wang, R. F. Han, X. S. Xia, J. Liu and X. Li: Influence of CYP2C19 polymorphisms on platelet reactivity and clinical outcomes in ischemic stroke patients treated with clopidogrel. Eur J Pharmacol, 747, 29-35 (2015)
71. B. L. Hoh, Y. Gong, C. W. McDonough, M. F. Waters, A. J. Royster, T. O. Sheehan, B. Burkley, T. Y. Langaee, J. Mocco, S. L. Zuckerman, N. Mummareddy, M. L. Stephens, 2nd, C. Ingram, C. M. Shaffer, J. C. Denny, M. H. Brilliant, T. E. Kitchner, J. G. Linneman, D. M. Roden and J. A. Johnson: CYP2C19 and CES1 polymorphisms and efficacy of clopidogrel and aspirin dual antiplatelet therapy in patients with symptomatic intracranial atherosclerotic disease. J Neurosurg, 1-6 (2015)
72. D. M. Jia, Z. B. Chen, M. J. Zhang, W. J. Yang, J. L. Jin, Y. Q. Xia, C. L. Zhang, Y. Shao, C. Chen and Y. Xu: CYP2C19 Polymorphisms and Antiplatelet Effects of Clopidogrel in Acute Ischemic Stroke in China. Stroke, 44(6), 1717-9 (2013)
73. Y. J. Lin, J. W. Li, M. J. Zhang, L. Qian, W. J. Yang, C. L. Zhang, Y. Shao, Y. Zhang, Y. J. Huang and Y. Xu: The association between CYP2C19 genotype and of in-stent restenosis among patients with vertebral artery stent treatment. CNS Neurosci Ther, 20(2), 125-30 (2014)
74. I. Spokoyny, N. Barazangi, V. Jaramillo, J. Rose, C. Chen, C. Wong and D. Tong: Reduced Clopidogrel Metabolism in a Multiethnic Population: Prevalence and Rates of Recurrent Cerebrovascular Events. J Stroke Cerebrovasc Dis (2013)
75. S. E. Kimmel, B. French, S. E. Kasner, J. A. Johnson, J. L. Anderson, B. F. Gage, Y. D. Rosenberg, C. S. Eby, R. A. Madigan, R. B. McBane, S. Z. Abdel-Rahman, S. M. Stevens, S. Yale, E. R. Mohler, 3rd, M. C. Fang, V. Shah, R. B. Horenstein, N. A. Limdi, J. A. Muldowney, 3rd, J. Gujral, P. Delafontaine, R. J. Desnick, T. L. Ortel, H. H. Billett, R. C. Pendleton, N. L. Geller, J. L. Halperin, S. Z. Goldhaber, M. D. Caldwell, R. M. Califf, J. H. Ellenberg and C. Investigators: A pharmacogenetic versus a clinical algorithm for warfarin dosing. N Engl J Med, 369(24), 2283-93 (2013)
76. W. Sun, Y. Li, J. Li, Z. Zhang, W. Zhu, W. Liu, Q. Cai, X. Wang, L. Cao, W. Bai, X. Fan, M. Ma, R. Guo, X. Liu and G. Xu: Variant recurrent risk among stroke patients with different CYP2C19 phenotypes and treated with clopidogrel. Platelets, 26(6), 558-62 (2015)
77. T. Hachiya, Y. Kamatani, A. Takahashi, J. Hata, R. Furukawa, Y. Shiwa, T. Yamaji, M. Hara, K. Tanno, H. Ohmomo, K. Ono, N. Takashima, K. Matsuda, K. Wakai, N. Sawada, M. Iwasaki, K. Yamagishi, T. Ago, T. Ninomiya, A. Fukushima, A. Hozawa, N. Minegishi, M. Satoh, R. Endo, M. Sasaki, K. Sakata, S. Kobayashi, K. Ogasawara, M. Nakamura, J. Hitomi, Y. Kita, K. Tanaka, H. Iso, T. Kitazono, M. Kubo, H. Tanaka, S. Tsugane, Y. Kiyohara, M. Yamamoto, K. Sobue and A. Shimizu: Genetic Predisposition to Ischemic Stroke: A Polygenic Risk Score. Stroke, 48(2), 253-258 (2017)
78. X. Q. Li, N. Ma, X. G. Li, B. Wang, S. S. Sun, F. Gao, D. P. Mo, L. G. Song, X. Sun, L. Liu, X. Q. Zhao, Y. L. Wang, Y. J. Wang, Z. G. Zhao and Z. R. Miao: Association of PON1, P2Y12 and COX1 with Recurrent Ischemic Events in Patients with Extracranial or Intracranial Stenting. PLoS One, 11(2), e0148891 (2016)
79. X. Yi, J. Lin, Y. Wang, Q. Zhou, C. Wang, W. Cheng and L. Chi: Association of Cytochrome P450 Genetic Variants with Clopidogrel Resistance and Outcomes in Acute Ischemic Stroke. J Atheroscler Thromb (2016)
80. Y. Han, H. H. Lv, X. Liu, Q. Dong, X. L. Yang, S. X. Li, S. Wu, J. M. Jiang, Z. Luo, D. S. Zhu, Y. Zhang, Y. Zheng, Y. T. Guan and J. F. Xu: Influence of Genetic Polymorphisms on Clopidogrel Response and Clinical Outcomes in Patients with Acute Ischemic Stroke CYP2C19 Genotype on Clopidogrel Response. CNS Neurosci Ther, 21(9), 692-7 (2015)
81. L. Wallentin, R. C. Becker, A. Budaj, C. P. Cannon, H. Emanuelsson, C. Held, J. Horrow, S. Husted, S. James, H. Katus, K. W. Mahaffey, B. M. Scirica, A. Skene, P. G. Steg, R. F. Storey, R. A. Harrington, P. Investigators, A. Freij and M. Thorsen: Ticagrelor versus clopidogrel in patients with acute coronary syndromes. N Engl J Med, 361(11), 1045-57 (2009)
82. U. S. Tantry, K. P. Bliden, C. Wei, R. F. Storey, M. Armstrong, K. Butler and P. A. Gurbel: First analysis of the relation between CYP2C19 genotype and pharmacodynamics in patients treated with ticagrelor versus clopidogrel: the ONSET/OFFSET and RESPOND genotype studies. Circ Cardiovasc Genet, 3(6), 556-66 (2010)
83. C. Varenhorst, N. Eriksson, A. Johansson, B. J. Barratt, E. Hagstrom, A. Akerblom, A. C. Syvanen, R. C. Becker, S. K. James, H. A. Katus, S. Husted, P. G. Steg, A. Siegbahn, D. Voora, R. Teng, R. F. Storey, L. Wallentin and P. Investigators: Effect of genetic variations on ticagrelor plasma levels and clinical outcomes. Eur Heart J, 36(29), 1901-12 (2015)
84. L. Wallentin, S. James, R. F. Storey, M. Armstrong, B. J. Barratt, J. Horrow, S. Husted, H. Katus, P. G. Steg, S. H. Shah, R. C. Becker and P. investigators: Effect of CYP2C19 and ABCB1 single nucleotide polymorphisms on outcomes of treatment with ticagrelor versus clopidogrel for acute coronary syndromes: a genetic substudy of the PLATO trial. Lancet, 376(9749), 1320-8 (2010)
85. A. Munshi and V. Sharma: Genetic signatures in the treatment of stroke. Curr Pharm Des, 21(3), 343-54 (2015)
86. G. M. Cooper, J. A. Johnson, T. Y. Langaee, H. Feng, I. B. Stanaway, U. I. Schwarz, M. D. Ritchie, C. M. Stein, D. M. Roden, J. D. Smith, D. L. Veenstra, A. E. Rettie and M. J. Rieder: A genome-wide scan for common genetic variants with a large influence on warfarin maintenance dose. Blood, 112(4), 1022-7 (2008)
87. F. Takeuchi, R. McGinnis, S. Bourgeois, C. Barnes, N. Eriksson, N. Soranzo, P. Whittaker, V. Ranganath, V. Kumanduri, W. McLaren, L. Holm, J. Lindh, A. Rane, M. Wadelius and P. Deloukas: A genome-wide association study confirms VKORC1, CYP2C9, and CYP4F2 as principal genetic determinants of warfarin dose. PLoS Genet, 5(3), e1000433 (2009)
89. M. Pirmohamed, G. Burnside, N. Eriksson, A. L. Jorgensen, C. H. Toh, T. Nicholson, P. Kesteven, C. Christersson, B. Wahlstrom, C. Stafberg, J. E. Zhang, J. B. Leathart, H. Kohnke, A. H. Maitland-van der Zee, P. R. Williamson, A. K. Daly, P. Avery, F. Kamali, M. Wadelius and E.-P. Group: A randomized trial of genotype-guided dosing of warfarin. N Engl J Med, 369(24), 2294-303 (2013)
90. A. H. Maitland-van der Zee, A. K. Daly, F. Kamali, V. G. Manolopoulous, T. I. Verhoef, M. Wadelius, A. de Boer, M. Pirmohamed and E.-P. S. Group: Patients benefit from genetics-guided coumarin anticoagulant therapy. Clin Pharmacol Ther, 96(1), 15-7 (2014)
91. S. T. Ma, G. L. Dai, X. L. Bi, M. R. Gong, B. T. Sun, W. Z. Ju and H. S. Tan: (Computational Pharmacological Study on Clopidogrel Metabolism Enzymes Influenced by Fufang Danshen Dripping Pill). Zhong Yao Cai, 38(5), 1009-12 (2015)
92. A. J. Lopez-Farre, P. J. Mateos-Caceres, D. Sacristan, L. Azcona, E. Bernardo, T. P. de Prada, S. Alonso-Orgaz, M. Fernandez-Arquero, A. Fernandez-Ortiz and C. Macaya: Relationship between vitamin D binding protein and aspirin resistance in coronary ischemic patients: a proteomic study. J Proteome Res, 6(7), 2481-7 (2007)
93. P. J. Mateos-Caceres, C. Macaya, L. Azcona, J. Modrego, E. Mahillo, E. Bernardo, A. Fernandez-Ortiz and A. J. Lopez-Farre: Different expression of proteins in platelets from aspirin-resistant and aspirin-sensitive patients. Thromb Haemost, 103(1), 160-70 (2010)
94. J. Castillo, J. Alvarez-Sabin, E. Martinez-Vila, J. Montaner, T. Sobrino, J. Vivancos and M. S. Investigators: Inflammation markers and prediction of post-stroke vascular disease recurrence: the MITICO study. J Neurol, 256(2), 217-24 (2009)
95. A. J. Lopez-Farre, J. J. Zamorano-Leon, A. Segura, P. J. Mateos-Caceres, J. Modrego, P. Rodriguez-Sierra, L. Calatrava, J. Tamargo and C. Macaya: Plasma desmoplakin I biomarker of vascular recurrence after ischemic stroke. J Neurochem, 121(2), 314-25 (2012)
96. P. M. George, M. Mlynash, C. M. Adams, C. J. Kuo, G. W. Albers and J. M. Olivot: Novel TIA biomarkers identified by mass spectrometry-based proteomics. Int J Stroke, 10(8), 1204-11 (2015)
97. A. Lindgren: ERRATUM: Corrected : Stroke Genetics: A Review and Update. J Stroke, 17(1), 91 (2015)
98. G. C. Jickling, H. Xu, B. Stamova, B. P. Ander, X. Zhan, Y. Tian, D. Liu, R. J. Turner, M. Mesias, P. Verro, J. Khoury, E. C. Jauch, A. Pancioli, J. P. Broderick and F. R. Sharp: Signatures of cardioembolic and large-vessel ischemic stroke. Ann Neurol, 68(5), 681-92 (2010)
99. G. C. Jickling, B. Stamova, B. P. Ander, X. Zhan, Y. Tian, D. Liu, H. Xu, S. C. Johnston, P. Verro and F. R. Sharp: Profiles of lacunar and nonlacunar stroke. Ann Neurol, 70(3), 477-85 (2011)
100. G. C. Jickling, B. Stamova, B. P. Ander, X. Zhan, D. Liu, S. M. Sison, P. Verro and F. R. Sharp: Prediction of cardioembolic, arterial, and lacunar causes of cryptogenic stroke by gene expression and infarct location. Stroke, 43(8), 2036-41 (2012)
101. A. M. Gomez-Uriz, F. I. Milagro, M. L. Mansego, P. Cordero, I. Abete, A. De Arce, E. Goyenechea, V. Blazquez, M. Martinez-Zabaleta, J. A. Martinez, A. Lopez De Munain and J. Campion: Obesity and ischemic stroke modulate thec levels of KCNQ1 in white blood cells. Hum Mol Genet, 24(5), 1432-40 (2015)
102. C. Gallego-Fabrega, C. Carrera, J. L. Reny, P. Fontana, A. Slowik, J. Pera, A. Pezzini, G. Serrano-Heras, T. Segura, J. Marti-Fabregas, E. Muino, N. Cullell, J. Montaner, J. Krupinski and I. Fernandez-Cadenas: TRAF3 Epigenetic Regulation Is Associated With Vascular Recurrence in Patients With Ischemic Stroke. Stroke (2016)
103. M. Jove, G. Mauri-Capdevila, I. Suarez, S. Cambray, J. Sanahuja, A. Quilez, J. Farre, I. Benabdelhak, R. Pamplona, M. Portero-Otin and F. Purroy: Metabolomics predicts stroke recurrence after transient ischemic attack. Neurology, 84(1), 36-45 (2015)
104. D. S. Liebeskind, K. Malhotra and J. D. Hinman: Imaging as the Nidus of Precision Cerebrovascular Health: A Million Brains Initiative. JAMA Neurol, 74(3), 257-258 (2017)
105. W. J. Powers, C. P. Derdeyn, J. Biller, C. S. Coffey, B. L. Hoh, E. C. Jauch, K. C. Johnston, S. C. Johnston, A. A. Khalessi, C. S. Kidwell, J. F. Meschia, B. Ovbiagele, D. R. Yavagal and C. American Heart Association Stroke: 2015 American Heart Association/American Stroke Association Focused Update of the 2013 Guidelines for the Early Management of Patients With Acute Ischemic Stroke Regarding Endovascular Treatment: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke, 46(10), 3020-35 (2015)
106. E. C. Jauch, J. L. Saver, H. P. Adams, Jr., A. Bruno, J. J. Connors, B. M. Demaerschalk, P. Khatri, P. W. McMullan, Jr., A. I. Qureshi, K. Rosenfield, P. A. Scott, D. R. Summers, D. Z. Wang, M. Wintermark, H. Yonas, C. American Heart Association Stroke, N. Council on Cardiovascular, D. Council on Peripheral Vascular and C. Council on Clinical: Guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke, 44(3), 870-947 (2013)
107. R. E. Latchaw, M. J. Alberts, M. H. Lev, J. J. Connors, R. E. Harbaugh, R. T. Higashida, R. Hobson, C. S. Kidwell, W. J. Koroshetz, V. Mathews, P. Villablanca, S. Warach, B. Walters, R. American Heart Association Council on Cardiovascular, S. C. Intervention and D. the Interdisciplinary Council on Peripheral Vascular: Recommendations for imaging of acute ischemic stroke: a scientific statement from the American Heart Association. Stroke, 40(11), 3646-78 (2009)
108. O. A. Berkhemer, P. S. Fransen, D. Beumer, L. A. van den Berg, H. F. Lingsma, A. J. Yoo, W. J. Schonewille, J. A. Vos, P. J. Nederkoorn, M. J. Wermer, M. A. van Walderveen, J. Staals, J. Hofmeijer, J. A. van Oostayen, G. J. Lycklama a Nijeholt, J. Boiten, P. A. Brouwer, B. J. Emmer, S. F. de Bruijn, L. C. van Dijk, L. J. Kappelle, R. H. Lo, E. J. van Dijk, J. de Vries, P. L. de Kort, W. J. van Rooij, J. S. van den Berg, B. A. van Hasselt, L. A. Aerden, R. J. Dallinga, M. C. Visser, J. C. Bot, P. C. Vroomen, O. Eshghi, T. H. Schreuder, R. J. Heijboer, K. Keizer, A. V. Tielbeek, H. M. den Hertog, D. G. Gerrits, R. M. van den Berg-Vos, G. B. Karas, E. W. Steyerberg, H. Z. Flach, H. A. Marquering, M. E. Sprengers, S. F. Jenniskens, L. F. Beenen, R. van den Berg, P. J. Koudstaal, W. H. van Zwam, Y. B. Roos, A. van der Lugt, R. J. van Oostenbrugge, C. B. Majoie, D. W. Dippel and M. C. Investigators: A randomized trial of intraarterial treatment for acute ischemic stroke. N Engl J Med, 372(1), 11-20 (2015)
109. M. Goyal, A. M. Demchuk, B. K. Menon, M. Eesa, J. L. Rempel, J. Thornton, D. Roy, T. G. Jovin, R. A. Willinsky, B. L. Sapkota, D. Dowlatshahi, D. F. Frei, N. R. Kamal, W. J. Montanera, A. Y. Poppe, K. J. Ryckborst, F. L. Silver, A. Shuaib, D. Tampieri, D. Williams, O. Y. Bang, B. W. Baxter, P. A. Burns, H. Choe, J. H. Heo, C. A. Holmstedt, B. Jankowitz, M. Kelly, G. Linares, J. L. Mandzia, J. Shankar, S. I. Sohn, R. H. Swartz, P. A. Barber, S. B. Coutts, E. E. Smith, W. F. Morrish, A. Weill, S. Subramaniam, A. P. Mitha, J. H. Wong, M. W. Lowerison, T. T. Sajobi, M. D. Hill and E. T. Investigators: Randomized assessment of rapid endovascular treatment of ischemic stroke. N Engl J Med, 372(11), 1019-30 (2015)
111. S. K. Rostanski and R. S. Marshall: Precision Medicine for Ischemic Stroke. JAMA Neurol, 73(7), 773-4 (2016)
112. J. L. Hall, J. J. Ryan, B. E. Bray, C. Brown, D. Lanfear, L. K. Newby, M. V. Relling, N. J. Risch, D. M. Roden, S. Y. Shaw, J. E. Tcheng, J. Tenenbaum, T. N. Wang, W. S. Weintraub, P. American Heart Association, E. Public, G. Publications Committee of the Council on Functional, B. Translational, C. Council on Clinical, E. Council on, Prevention, C. Council on Quality of, R. Outcomes and C. Stroke: Merging Electronic Health Record Data and Genomics for Cardiovascular Research: A Science Advisory From the American Heart Association. Circ Cardiovasc Genet, 9(2), 193-202 (2016)
113. J. L. Saver, S. Warach, S. Janis, J. Odenkirchen, K. Becker, O. Benavente, J. Broderick, A. W. Dromerick, P. Duncan, M. S. Elkind, K. Johnston, C. S. Kidwell, J. F. Meschia, L. Schwamm, D. National Institute of Neurological and G. Stroke Stroke Common Data Element Working: Standardizing the structure of stroke clinical and epidemiologic research data: the National Institute of Neurological Disorders and Stroke (NINDS) Stroke Common Data Element (CDE) project. Stroke, 43(4), 967-73 (2012)
114. D. Blumenthal: Launching HITECH. N Engl J Med, 362(5), 382-5 (2010)
115. E. Bowton, J. R. Field, S. Wang, J. S. Schildcrout, S. L. Van Driest, J. T. Delaney, J. Cowan, P. Weeke, J. D. Mosley, Q. S. Wells, J. H. Karnes, C. Shaffer, J. F. Peterson, J. C. Denny, D. M. Roden and J. M. Pulley: Biobanks and electronic medical records: enabling cost-effective research. Sci Transl Med, 6(234), 234cm3 (2014)
116. O. Gottesman, H. Kuivaniemi, G. Tromp, W. A. Faucett, R. Li, T. A. Manolio, S. C. Sanderson, J. Kannry, R. Zinberg, M. A. Basford, M. Brilliant, D. J. Carey, R. L. Chisholm, C. G. Chute, J. J. Connolly, D. Crosslin, J. C. Denny, C. J. Gallego, J. L. Haines, H. Hakonarson, J. Harley, G. P. Jarvik, I. Kohane, I. J. Kullo, E. B. Larson, C. McCarty, M. D. Ritchie, D. M. Roden, M. E. Smith, E. P. Bottinger, M. S. Williams and M. N. e: The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future. Genet Med, 15(10), 761-71 (2013)
117. R. Woodfield, I. Grant, U. K. B. S. O. Group, U. K. B. Follow-Up, G. Outcomes Working and C. L. Sudlow: Accuracy of Electronic Health Record Data for Identifying Stroke Cases in Large-Scale Epidemiological Studies: A Systematic Review from the UK Biobank Stroke Outcomes Group. PLoS One, 10(10), e0140533 (2015)
118. A. H. James, C. D. Bushnell, M. G. Jamison and E. R. Myers: Incidence and risk factors for stroke in pregnancy and the puerperium. Obstet Gynecol, 106(3), 509-16 (2005)
119. K. Iihara, K. Nishimura, A. Kada, J. Nakagawara, K. Ogasawara, J. Ono, Y. Shiokawa, T. Aruga, S. Miyachi, I. Nagata, K. Toyoda, S. Matsuda, Y. Miyamoto, A. Suzuki, K. B. Ishikawa, H. Kataoka, F. Nakamura and S. Kamitani: Effects of comprehensive stroke care capabilities on in-hospital mortality of patients with ischemic and hemorrhagic stroke: J-ASPECT study. PLoS One, 9(5), e96819 (2014)
120. G. C. Wood, C. D. Still, X. Chu, M. Susek, R. Erdman, C. Hartman, S. Yeager, M. A. Blosky, W. Krum, D. J. Carey, K. A. Skelding, P. Benotti, W. F. Stewart and G. S. Gerhard: Association of chromosome 9p21 SNPs with cardiovascular phenotypes in morbid obesity using electronic health record data. Genomic Med, 2(1-2), 33-43 (2008)
Key Words: Ischemic Stroke, Transient Ischemic Stroke, Precision Medicine, Review
Send correspondence to: Yongjun Wang, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No. 6 Tiantanxili, Dongcheng District, Beijing, China, 100050, Tel: 0086-01067098350, Fax: 0086-01067013383, E-mail: firstname.lastname@example.org