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. Author manuscript; available in PMC: 2022 Feb 2.
Published in final edited form as: Circulation. 2020 Nov 13;143(5):470–478. doi: 10.1161/CIRCULATIONAHA.120.051927

Clinical Application of a Novel Genetic Risk Score for Ischemic Stroke in Patients with Cardiometabolic Disease

Nicholas A Marston 1,2,*, Parth N Patel 3,*, Frederick K Kamanu 1,2, Francesco Nordio 1,2, Giorgio M Melloni 1,2, Carolina Roselli 5, Yared Gurmu 6, Lu-Chen Weng 5, Marc P Bonaca 7, Robert P Giugliano 1,2, Benjamin M Scirica 1,2, Michelle L O’Donoghue 1,2, Christopher P Cannon 2, Christopher D Anderson 8, Deepak L Bhatt 2, Philippe Gabriel Steg 9, Marc Cohen 10, Robert F Storey 11, Peter Sever 12, Anthony C Keech 13, Itamar Raz 14, Ofri Mosenzon 14, Elliott M Antman 1,2, Eugene Braunwald 1,2, Patrick T Ellinor 5, Steven A Lubitz 5,†, Marc S Sabatine 1,2,†, Christian T Ruff 1,2,†
PMCID: PMC7856243  NIHMSID: NIHMS1656470  PMID: 33185476

Abstract

Background:

Genome-wide association studies have identified single nucleotide polymorphisms (SNPs) that are associated with an increased risk of stroke. We sought to determine whether a genetic risk score (GRS) could identify subjects at higher risk for ischemic stroke after accounting for traditional clinical risk factors in five trials across the spectrum of cardiometabolic disease.

Methods:

Subjects who had consented for genetic testing and who were of European ancestry from the ENGAGE AF-TIMI 48, SOLID-TIMI 52, SAVOR-TIMI 53, PEGASUS-TIMI 54, and FOURIER trials were included in this analysis. A set of 32 SNPs associated with ischemic stroke was used to calculate a GRS in each patient and identify tertiles of genetic risk. A Cox model was used to calculate hazard ratios for ischemic stroke across genetic risk groups, adjusted for clinical risk factors.

Results:

In 51,288 subjects across the five trials, a total of 960 subjects had an ischemic stroke over a median follow-up period of 2.5 years. After adjusting for clinical risk factors, increasing genetic risk was strongly and independently associated with increased risk for ischemic stroke (p-trend=0.009). When compared to individuals in the lowest third of genetic risk, individuals in the middle and top tertiles of genetic risk had adjusted hazard ratios of 1.15 (95% CI 0.98-1.36) and 1.24 (95% CI 1.05-1.45) for ischemic stroke, respectively. Stratification into subgroups revealed the performance of the GRS appeared stronger in the primary prevention cohort with an adjusted HR for the top versus lowest tertile of 1.27 (95% CI 1.04-1.53), compared with an adjusted HR of 1.06 (95% CI 0.81-1.41) in subjects with prior stroke. In an exploratory analysis of patients with atrial fibrillation and CHA2DS2-VASc of 2, high genetic risk conferred a 4-fold higher risk of stroke and an absolute risk equivalent to those with CHA2DS2-VASc of 3.

Conclusions:

Across a broad spectrum of subjects with cardiometabolic disease, a 32-SNP GRS was a strong, independent predictor of ischemic stroke. In patients with atrial fibrillation but lower CHA2DS2-VASc scores, the GRS identified patients with risk comparable to those with higher CHA2DS2-VASc scores.

Keywords: genetics, genomics, stroke, ischemic stroke, atrial fibrillation

Ischemic stroke, a sudden neurologic deficit caused by an interruption of cerebral blood flow, remains a leading cause of morbidity and mortality worldwide.1 Though several traditional risk factors such as hypertension, diabetes mellitus, and heart failure exhibit strong associations with ischemic stroke, a substantial proportion of ischemic stroke risk remains unexplained.2–4 Multiple lines of evidence suggest that heritable factors may contribute to the development of ischemic stroke,5, 6 with some reports estimating that 30-40% of variability in ischemic stroke risk can be explained by genetic variation.7 Over the last decade, advances in molecular genetics have better defined the genetic architecture underlying risk for ischemic stroke, bringing further attention to this underappreciated component of stroke susceptibility.8

Genetic risk scores represent a method of summating an individual’s genetic propensity for a given phenotype, and have garnered interest for their potential to improve risk prediction in many common diseases.9–11 Despite promise in other conditions, early attempts at using a genetic risk score (GRS) for ischemic stroke showed limited predictive ability,12–14 possibly due to the fewer number of stroke susceptibility loci identified and the biologic heterogeneity of ischemic stroke. In the wake of the MEGASTROKE meta-analysis of genome-wide association studies,15 multiple groups have developed genetic risk scores with increased predictive power.16, 17 Whether a GRS can predict ischemic stroke risk across a diverse group of patients with cardiovascular disease, after fully adjusting for clinical risk factors including atrial fibrillation, is still not known.

We sought to determine whether a GRS could identify subjects at higher risk for ischemic stroke after accounting for traditional clinical risk factors in five trials across the spectrum of cardiometabolic disease. We quantified the level of risk conferred by each genetic risk tertile, compared the magnitude of risk provided by high genetic risk to that provided by well-established clinical risk factors, and investigated GRS performance across key subgroups. Lastly, we explored whether genetic risk classification could refine stroke risk prediction in patients with atrial fibrillation, with specific attention to those with lower CHA2DS2-VASc scores in whom high genetic risk might inform the decision about initiating anticoagulation.

Methods:

Study Design and Population

We performed a genetic cohort analysis pooling individual patient-level data from five cardiovascular clinical trials: ENGAGE AF-TIMI 48,18 SOLID-TIMI 52,19 SAVOR-TIMI 53,20 PEGASUS-TIMI 54,21 and FOURIER.22 The study population represents a broad spectrum of cardiovascular disease including established atherosclerosis, prior myocardial infarction, diabetes, and atrial fibrillation. Brief descriptions of each trial are listed in the Supplemental Appendix. Patients who consented for genetic analysis, passed quality control, and were of European ancestry were included. Baseline characteristics for each trial are listed in Supplemental Table I.

Study protocols were approved by the institutional review board or ethics committee at each participating site. All subjects provided written informed consent. Due to the sensitive nature of the data collected for this study, requests to access the dataset from qualified researchers trained in human subject confidentiality protocols may be sent to the TIMI Study Group.

Genotyping and Imputation

Methods for genotyping and imputation have previously been published.11 Genotyping was performed using the Illumina Multi-Ethnic Genotyping Array (ENGAGE AF-TIMI 48, SAVOR-TIMI 53, and PEGASUS-TIMI 54), Affymetrix Biobank Array (SOLID-TIMI 52), and Infinium Global Array (FOURIER). Pre-imputation quality control was performed using PLINK v2.0,23 followed by imputation using the Michigan Imputation server24 and TOPMed Freeze5 reference panel.25 Post-imputation quality control was performed, followed by identification of patients with European ancestry using the 1000 Genomes phase 3 v5 reference panel26 and ADMIXTURE tool.27

Genetic Risk Score

The GRS for ischemic stroke was based on all 32 genome-wide associated single nucleotide polymorphisms (SNPs) from MEGASTROKE,15 listed in Supplemental Table II. In cases where the risk variant was not available, proxy SNPs were used to complete the set of 32 SNPs.28, 29 Using PLINK v2.0, the GRS was calculated for each patient by multiplying the imputed allelic dosage with the variant-specific weight (beta-coefficient for the association between the SNP and ischemic stroke) based on MEGASTROKE,15 and then summed across all variants. Patients were divided into low, intermediate, and high genetic risk for ischemic stroke based on tertiles.

Clinical Endpoint and Follow Up

The endpoint of interest was ischemic stroke. In each trial, ischemic stroke was formally adjudicated by an independent clinical endpoint committee blinded to treatment assignment. The median follow-up across trials ranged from 2.2 to 2.9 years.

Statistical Analysis

Individual patient-level data were pooled from the five clinical trials. Analyses were performed in the overall genetic cohort, primary versus secondary prevention cohorts, clinical subgroups of interest, the ENGAGE AF-TIMI 48 trial (all with atrial fibrillation), and across subgroups of CHA2DS2-VASc ranges in those with and without atrial fibrillation.30 Time-to-event data were used to create a Kaplan-Meier curve. A Cox proportional hazards model was used to calculate hazard ratios (HR) for ischemic stroke across genetic risk categories. Shoenfeld Residuals were used to test proportional hazards assumptions which were met. Analyses were adjusted for age, sex, genetic ancestry (by principal components 1-5), and clinical comorbidities including hypertension, hyperlipidemia, diabetes mellitus, smoking, vascular disease, congestive heart failure, and atrial fibrillation. A trend test was used to assess the difference in ischemic stroke across genetic risk categories. Using the components of the Revised Framingham Stroke Risk Score (R-FSRS)31 as well as geographic region (given the global nature of these trials) for the base model in the overall cohort, the Harrell’s C-index was used to determine whether the addition of genetics (genetic risk score and genetic ancestry) improved discrimination between patients who experienced stroke and those who did not. The C-index was also assessed in patients with a CHA2DS2-VASc of 2, along with the ability of the GRS to reclassify risk using the net re-classification improvement (NRI) at event rate.32 The 95% confidence interval for NRI was calculated by a resampling method. All p-values were two-sided and assessed at a threshold of 0.05.

Results

Study Cohort

Across the five trials studied, 51,288 subjects were eligible to be included in this analysis. The average age of the study population was 65.9 years, and 28.3% were female. Most subjects (81.7%) had coronary or peripheral artery disease. Many had traditional clinical risk factors for stroke, including hypertension (82.3%), hyperlipidemia (60.2%), and diabetes (41.9%). A smaller percentage of the cohort had prior transient ischemic attack or stroke (14.0%), were current smokers (18.2%), had atrial fibrillation (28.4%), or had a history of congestive heart failure (29.1%). A total of 960 subjects had an ischemic stroke over a median follow up of 2.5 years.

Identification of Genetic Risk Groups

Baseline characteristics by genetic risk tertile are shown in Table 1. Subjects in the highest tertile of genetic risk were more likely to be female, have had a prior stroke, and have comorbidities associated with stroke including hypertension, atrial fibrillation, and congestive heart failure. The relative contribution of each trial to each tertile of genetic risk is listed in Supplemental Table I.

Table 1:

Baseline characteristics by genetic risk tertiles.

Low Genetic Risk Intermediate Genetic Risk High Genetic Risk P-Value
Participants 17096 17096 17096
Demographics
 Age, years 66.1 (9.2) 65.9 (9.3) 65.6 (9.2) <0.001
  Age 65-74 years 6521 (38) 6281 (37) 6272 (37) 0.007
  Age ≥ 75 years 3306 (19) 3306 (19) 3116 (18) 0.01
 Female Sex 4808 (28) 4722 (28) 4989 (29) 0.005
Medical History
 Hypertension 13749 (80) 14110 (83) 14350 (84) <0.001
 Hyperlipidemia 10379 (61) 10293 (60) 10181 (60) 0.09
 Diabetes 7367 (43) 6989 (41) 7118 (42) <0.001
 Smoking 3062 (18) 3118 (18) 3169 (19) 0.33
 Atrial Fibrillation 4300 (25) 4721 (28) 5536 (32) <0.001
 Vascular Disease 14107 (83) 14056 (82) 13716 (80) <0.001
 CHF 4494 (26) 4867 (29) 5552 (33) <0.001
 Stroke/TIA 2106 (12) 2453 (14) 2634 (15) <0.001
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Values indicate n (%) or average (standard deviation). P-values represent Chi-Squared test for categorical variables and one-way ANOVA for continuous variables. CHF=congestive heart failure, TIA=transient ischemic attack.

Genetic Risk Predicts Ischemic Stroke

The Kaplan-Meier event rates for ischemic stroke at 3 years in the low, intermediate, and high genetic risk groups were 1.95% (n=272), 2.24% (n=322), and 2.56% (n=366), respectively (Figure 1). After adjusting for clinical risk factors, increasing genetic risk remained strongly associated with increased risk for ischemic stroke (p-trend=0.009). Compared with patients in the lowest genetic risk category, those at intermediate genetic risk had an adjusted HR of 1.15 (95% CI 0.98-1.36) for ischemic stroke and those in the top tertile of genetic risk had an adjusted HR of 1.24 (95% CI 1.05-1.45). The magnitude of risk conferred by high genetic risk was comparable to the individual risk provided from smoking, diabetes, or hypertension (Supplemental Figure I). Addition of the GRS to a Cox model of clinical variables from the Revised Framingham Stroke Risk Score (R-FSRS) plus geographic region did not significantly increase the C-index (0.64 (0.62-0.66) vs. 0.65 (0.63-0.66)).

Figure 1: Kaplan-Meier event rates for ischemic stroke by tertile of genetic risk at 3 years.

Figure 1:

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Among subgroups, the performance of the GRS was stronger in the 44,095 subjects without prior stroke (adjusted HR of top tertile vs lowest tertile 1.27, 95% CI 1.04-1.53, Figure 2), with no clear predictive utility in the 7,193 subjects with prior stroke (adjusted HR 1.06, 95% CI 0.81-1.41). In addition, risk prediction with the GRS was better in subjects without diabetes (Pinteraction=0.002) or congestive heart failure (Pinteraction=0.04) compared to subjects with those conditions (Supplemental Figure II). The GRS demonstrated similar predictive power across subgroups of sex and age, as well as in those with and without vascular disease and atrial fibrillation.

Figure 2: Hazard ratios for top tertile of genetic risk score in patients with and without prior stroke.

Figure 2:

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Hazard ratios are adjusted for age, sex, hypertension, hyperlipidemia, smoking, diabetes mellitus, atrial fibrillation, coronary artery disease, and congestive heart failure. In all groups, 95% confidence intervals are shown. GRS=genetic risk score, HR=hazard ratio.

Exploratory Analysis in ENGAGE AF-TIMI 48

A total of 11,187 patients from the overall cohort were enrolled in ENGAGE AF-TIMI 48, a trial of patients with atrial fibrillation and CHADS2 score of 2 or higher who were treated with anticoagulation. Of these, 395 (3.5%) had an ischemic stroke over a median follow up of 2.8 years. Even after adjusting for components of CHA2DS2-VASc, patients with high genetic risk were at 29% greater risk of ischemic stroke (adjusted HR 1.29, 95% CI 1.01-1.64, p=0.045) compared with those with low genetic risk (Supplemental Figure III). The magnitude of risk was beyond that of multiple components of the CHA2DS2-VASc score such as vascular disease, congestive heart failure, diabetes, and hypertension.

Among this atrial fibrillation cohort, the predictive ability of the GRS was significantly stronger in patients with lower CHA2DS2-VASc scores (p-trend=0.04), including a 4-fold increased risk in patients with a CHA2DS2-VASc of 2 (HR 3.97 (95% CI 1.04-15.2), Figure 3). More specifically, high genetic risk identified one-third of patients with CHA2DS2-VASc of 2 who had risk levels equivalent to CHA2DS2-VASc of 3. When the GRS was applied to patients with a CHA2DS2-VASc of 2, the C-index went from 0.68 (0.58-0.77) to 0.84 (0.77-0.91), and the NRI was 0.32 (0.04-0.69), with 33% of those without stroke correctly reclassified to a lower risk group (Supplemental Table III). A sensitivity analysis in patients without atrial fibrillation from the other four trials demonstrated a similar trend of greater prognostic value from the GRS in subjects with fewer risk factors (p-trend=0.02, Supplemental Figure IV).

Figure 3: Absolute risk of ischemic stroke in anticoagulated patients with atrial fibrillation stratified by CHA2DS2-VASc score and genetic risk in ENGAGE AF-TIMI 48.

Figure 3:

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Median follow up of 2.8 years.

When comparing the degree of risk stratification provided by CHA2DS2-VASc in patients with differing levels of genetic risk, there was a steeper gradient present in patients with lower genetic risk, ranging from a 0.6% rate of stroke in patients with CHA2DS2-VASc score of 2 to 5.5% in patients with CHA2DS2-VASc score >5 (p-trend<0.001). In patients with high genetic risk, a more modest increase in absolute stroke risk was observed, ranging from 2.8% in patients with CHA2DS2-VASc score of 2 to 5.1% in those with scores >5 (p-trend=0.01, Supplemental Figure V).

Discussion

With 51,288 patients and 960 incident ischemic strokes, this study represents one of the largest prospective analyses of stroke genetics to date. Such data provide a unique opportunity to study the clinical value of an ischemic stroke GRS at scale and across a diverse patient population. In this study, we demonstrate that a 32-SNP GRS predicts ischemic stroke in patients with a wide range of cardiometabolic diseases, appears to have greater utility in those without prior stroke, and can potentially refine stroke risk in patients with atrial fibrillation and lower CHA2DS2-VASc scores, suggesting a potential role for genetic risk scores in therapeutic decision-making.

In patients who are older and have cardiovascular risk factors, it is not clear whether a genetic predisposition still plays a role in ischemic stroke. However, we found that genetic risk is a significant and independent predictor of ischemic stroke risk, even in patients with cardiometabolic disease and a median age of 66 years. More precisely, those in the top third of genetic risk carried a 24% greater risk of ischemic stroke than those in the lowest third of genetic risk. To put this degree of risk into perspective, high genetic risk was similar to the risk provided by several well-established clinical risk factors such as smoking, diabetes, and hypertension in this population. The performance of the GRS was even more robust when applied to patients without diabetes or heart failure, and more broadly, demonstrated stronger risk prediction in patients with a lower burden of clinical risk factors.

Beyond the independent and additive value of genetics in determining ischemic stroke risk, a second question of interest is whether genetics can also identify increased risk for recurrent ischemic stroke. While only 14% of the overall cohort had prior stroke, this subgroup of subjects accounted for 31% of the ischemic strokes during the follow up period. We found that the 32-SNP GRS was unable to predict recurrent stroke in this secondary prevention population. Conversely, the predictive value of genetic risk was far stronger in patients without prior stroke. These findings imply that the genetic risk score used in this study may be most useful for stratifying risk for a first-ever stroke. Determining whether primary and secondary strokes have varying genetic drivers will require additional investigation.

While identifying increased stroke risk from a GRS may be valuable, the low absolute event rates in the general cardiometabolic population limit the ability of the GRS to change clinical practice. However, application of the GRS in patients with atrial fibrillation could be considered to help determine who should receive anticoagulation. We tested whether genetics could enhance stroke risk stratification in an exploratory analysis of the ENGAGE AF-TIMI 48 trial and found that GRS performance was greatest when applied to patients with lower CHA2DS2-VASc scores. Specifically, when CHA2DS2-VASc was 2, there was a 4-fold increased risk of stroke in patients with high genetic risk. This translated to an absolute stroke risk equivalent to patients with a CHA2DS2-VASc of 3, identifying a group of patients whose stroke risk is underappreciated.

Clinically, patients with a CHA2DS2-VASc of 1 often present the greatest challenge regarding anticoagulation management and the addition of genetic risk could provide greater clarity in such situations. While the ENGAGE AF-TIMI 48 trial had very few patients with low CHA2DS2-VASc scores, and all were on anticoagulation, our findings suggest that high genetic risk confers a multiple-fold higher stroke risk that could help guide management. Further studies are needed to validate these findings and ultimately address whether such patients with low CHA2DS2-VASc scores but high genetic risk would benefit from anticoagulation therapy.

Limitations

Our study has several limitations. First, we specifically studied subjects enrolled in five clinical trials across the spectrum of cardiometabolic disease and therefore our findings may not be fully applicable to a standard at-risk population. Additionally, our analysis was limited to subjects who were of European ancestry and had consented to genetic testing. Further data will be required before extrapolating genetic risk prediction to more diverse populations. With regard to our investigation in ENGAGE AF-TIMI 48, the proportion of patients in the trial with lower CHA2DS2-VASc scores was limited. Therefore, the confidence intervals for the magnitude of increased risk in those with a high GRS were wide, and thus our results in this population should be viewed as exploratory. Future research efforts should focus on genotyping other large cohorts of subjects with atrial fibrillation in order to validate these findings in this subgroup of interest. Finally, our study does not explore the biologic heterogeneity of stroke and the relative contributions of large artery atherosclerotic stroke, cardioembolic stroke, and small-vessel stroke to the ischemic stroke phenotype. Though it is likely that each of these stroke subtypes contributed to the overall rates of ischemic stroke found in our study, attempts at genetic risk prediction across subtypes would require deeper stroke phenotyping and would likely be underpowered due to the very small number of genetic loci that are known to be associated with each stroke subtype. As such, in this study, we elected to apply a GRS comprised of the 32 SNPs that had achieved genome-wide significance for stroke or any stroke subtype to predict all ischemic stroke. Many of these SNPs closely associate with blood pressure, hyperlipidemia, coronary artery disease, atrial fibrillation, and venous thromboembolism, suggesting that the GRS used in this study incorporates these various biologic mechanisms. We anticipate that further advances in stroke genetics will reveal additional loci that can refine efforts towards genetic risk prediction of stroke subtypes in the future.

Conclusion

Across five large clinical trials of subjects with cardiometabolic disease, a 32-SNP GRS was a strong, independent predictor of ischemic stroke. The predictive value of the GRS appeared strongest in subjects without prior stroke and in subjects with fewer clinical risk factors. Moreover, in patients with atrial fibrillation but lower CHA2DS2-VASc scores, the GRS identified patients with risk comparable to those with higher CHA2DS2-VASc scores.

Supplementary Material

Supplemental Publication Material

Clinical Perspective.

What is new?

  • This is the first study to show that a genetic risk score remains an independent predictor of ischemic stroke in subjects who are older and have established cardiometabolic disease.

  • The genetic risk score demonstrated stronger risk prediction in subjects without prior stroke, and in those with a lower burden of clinical risk factors.

What are the clinical implications?

  • Genetics may help refine stroke prediction in patients at apparent low clinical risk.

  • Further studies should focus on whether patients with atrial fibrillation and CHA2DS2-VASc scores of 0-1 but high genetic risk would benefit from anticoagulation therapy.

ACKNOWLEDGMENTS

NAM and PNP contributed to study design, literature search, statistical analysis, data interpretation, figures, and drafting of the manuscript. FKK, FN, GMM, CR, YG, and LW contributed to data preparation, study design, and statistical analysis. MPB, RPG, BMS, MLO, CC, CDA, DLB, PGS, MC, RFS, PS, ACK, IR, OM, EMA, and EB contributed to data interpretation and critical review of the manuscript. PTE, SAL, MSS and CTR contributed to study design, statistical analysis, data interpretation, figures, and critical review of the manuscript. MSS and CTR are the guarantors of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

SOURCES OF FUNDING

The trials were funded by Amgen, AstraZeneca, Daiichi Sankyo, and GlaxoSmithKline.

DISCLOSURES

NAM reports grant support from the National Institutes of Health and involvement in clinical trials with Amgen, Pfizer, Novartis, and AstraZeneca without personal fees, payments, or increase in salary. PNP reports no disclosures. FKK reports no disclosures. FN is now employed by Takeda Pharmaceuticals. GMM reports no disclosures. CR is supported by a grant from Bayer AG to the Broad Institute focused on the development of therapeutics for cardiovascular disease. YG is now employed at the FDA. LW reports support from the American Heart Association (18SFRN34110082). MPB discloses grant support from Amgen, AstraZeneca, Bayer, Sanofi and consulting fees from Amgen, AstraZeneca, Bayer, Sanofi. RPG reports grants from Amgen and Daiichi Sankyo, during the conduct of the study; personal fees from Akcea, grants and personal fees from Amarin, personal fees from American College of Cardiology, grants and personal fees from Amgen, personal fees personal fees from Bristol Myers Squibb, personal fees from CVS Caremark, grants and personal fees from Daiichi Sankyo, personal fees from GlaxoSmithKline, personal fees from Janssen, personal fees from Lexicon, grants and personal fees from Merck, personal fees from Pfizer, personal fees from Servier, outside the submitted work; and Institutional research grant to the TIMI Study Group at Brigham and Women’s Hospital for research he is not directly involved in from Abbott, Amgen, Aralez, AstraZeneca, Bayer HealthCare Pharmaceuticals, Inc., BRAHMS, Daiichi Sankyo, Eisai, GlaxoSmithKline, Intarcia, Janssen, MedImmune, Merck, Novartis, Pfizer, Poxel, Quark Pharmaceuticals, Roche, Takeda, The Medicines Company, Zora Biosciences. BMS reports research grants from AstraZeneca, Eisai, Novartis, and Merck and consulting fees from AstraZeneca, Biogen Idec, Boehringer Ingelheim, Covance, Dr Reddy’s Laboratories, Eisai, Elsevier Practice Update Cardiology, GlaxoSmithKline, Lexicon, Merck, Novo Nordisk, Sanofi, and St Jude’s Medical; and has equity in Health [at] Scale. MLO reports institutional research grants from Amgen, Janssen, The Medicines Company, Eisai, GlaxoSmithKline, and Astra Zeneca. CPC reports research grants from: Amgen, Boehringer-Ingelheim (BI), Bristol-Myers Squibb (BMS), Daiichi Sankyo, Janssen, Merck, Novo Nordisk, and Pfizer. Consulting fees from Aegerion, Alnylam, Amarin, Amgen, Applied Therapeutics, Ascendia, BI, BMS, Corvidia, Eli Lilly, HLS Therapeutics, Innovent, Janssen, Kowa, Merck, Pfizer, Rhoshan, Sanofi. CDA reports grants from the National Institutes of Health (R01NS103924, R01NS069763), the American Heart Association (18SFRN34250007), and Massachusetts General Hospital, sponsored research support from Bayer AG, and consulting fees for ApoPharma, Inc. DLB discloses the following relationships - Advisory Board: Cardax, CellProthera, Cereno Scientific, Elsevier Practice Update Cardiology, Level Ex, Medscape Cardiology, MyoKardia, PhaseBio, PLx Pharma, Regado Biosciences; Board of Directors: Boston VA Research Institute, Society of Cardiovascular Patient Care, TobeSoft; Chair: American Heart Association Quality Oversight Committee; Data Monitoring Committees: Baim Institute for Clinical Research (formerly Harvard Clinical Research Institute, for the PORTICO trial, funded by St. Jude Medical, now Abbott), Cleveland Clinic (including for the ExCEED trial, funded by Edwards), Contego Medical (Chair, PERFORMANCE 2), Duke Clinical Research Institute, Mayo Clinic, Mount Sinai School of Medicine (for the ENVISAGE trial, funded by Daiichi Sankyo), Population Health Research Institute; Honoraria: American College of Cardiology (Senior Associate Editor, Clinical Trials and News, ACC.org; Vice-Chair, ACC Accreditation Committee), Baim Institute for Clinical Research (formerly Harvard Clinical Research Institute; RE-DUAL PCI clinical trial steering committee funded by Boehringer Ingelheim; AEGIS-II executive committee funded by CSL Behring), Belvoir Publications (Editor in Chief, Harvard Heart Letter), Canadian Medical and Surgical Knowledge Translation Research Group (clinical trial steering committees), Duke Clinical Research Institute (clinical trial steering committees, including for the PRONOUNCE trial, funded by Ferring Pharmaceuticals), HMP Global (Editor in Chief, Journal of Invasive Cardiology), Journal of the American College of Cardiology (Guest Editor; Associate Editor), K2P (Co-Chair, interdisciplinary curriculum), Level Ex, Medtelligence/ReachMD (CME steering committees), MJH Life Sciences, Population Health Research Institute (for the COMPASS operations committee, publications committee, steering committee, and USA national co-leader, funded by Bayer), Slack Publications (Chief Medical Editor, Cardiology Today’s Intervention), Society of Cardiovascular Patient Care (Secretary/Treasurer), WebMD (CME steering committees); Other: Clinical Cardiology (Deputy Editor), NCDR-ACTION Registry Steering Committee (Chair), VA CART Research and Publications Committee (Chair); Research Funding: Abbott, Afimmune, Amarin, Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol-Myers Squibb, Cardax, Chiesi, CSL Behring, Eisai, Ethicon, Ferring Pharmaceuticals, Forest Laboratories, Fractyl, Idorsia, Ironwood, Ischemix, Lexicon, Lilly, Medtronic, MyoKardia, Pfizer, PhaseBio, PLx Pharma, Regeneron, Roche, Sanofi Aventis, Synaptic, The Medicines Company; Royalties: Elsevier (Editor, Cardiovascular Intervention: A Companion to Braunwald’s Heart Disease); Site Co-Investigator: Biotronik, Boston Scientific, CSI, St. Jude Medical (now Abbott), Svelte; Trustee: American College of Cardiology; Unfunded Research: FlowCo, Merck, Novo Nordisk, Takeda. PGS reports receives research grants from Amarin, Bayer, Sanofi, and Servier. Speaking or consulting fees from Amarin, Amgen, AstraZeneca, Bayer/Janssen, Boehringer-Ingelheim, Bristol-Myers-Squibb, Idorsia, Novartis, Novo-Nordisk, Pfizer, Regeneron, Sanofi, and Servier. MC discloses honoraria for Speakers Bureau and advisory Boards (moderate) from AstraZeneca. RFS reports research grants, consultancy fees and honoraria from AstraZeneca; consulting fees and honoraria from Bayer and Bristol Myers Squibb/Pfizer; research grants and consultancy fees from Cytosorbents, GlyCardial Diagnostics and Thromboserin; consultancy fees from Amgen, Haemonetics, Hengrui, Idorsia, PhaseBio, Portola and Sanofi Aventis; honoraria from Intas Pharmaceuticals and Medscape. PS reports research grants and honoraria for speakers bureau- Amgen and Pfizer. ACK reports grants and personal fees from Abbott, personal fees from Amgen, personal fees from AstraZeneca, grants and personal fees from Mylan, personal fees from Pfizer, grants from Sanofi, grants from Novartis, personal fees from Bayer, outside the submitted work. IR received personal fees from AstraZeneca, Bristol Myers Squibb, Boehringer Ingelheim, Con-center BioPharma and Silkim, Eli Lilly, Merck Sharp & Dohme, Novo Nordisk, Orgenesis, Pfizer, Sanofi, SmartZyme Innovation, Panaxia, FutuRx, Insuline Medical, Medial EarlySign, CameraEyes, Exscopia, Dermal Biomics, Johnson & Johnson, Novartis, Teva, GlucoMe, and DarioHealth. OM reports serving on Advisory Boards for Novo Nordisk, Eli Lilly, Sanofi, Merck Sharp & Dohme, Boehringer Ingelheim, Novartis, AstraZeneca, BOL Pharma; Research grant support through Hadassah Hebrew University Hospital: Novo Nordisk, AstraZeneca and Bristol-Myers Squibb; Speaker’s Bureau: AstraZeneca and Bristol-Myers Squibb, Novo Nordisk, Eli Lilly, Sanofi, Novartis, Merck Sharp & Dohme, Boehringer Ingelheim. EMA: Dr. Antman reports receiving grant support through his institution from Daiichi Sankyo. EB reports research grants through the Brigham and Women’s Hospital from Astra Zeneca, Daiichi Sankyo, Merck, and Novartis. Consultancies with Amgen, Cardurion, MyoKardia, NovoNordisk, Boehringer-Ingelheim/Lilly, IMMEDIATE, and Verve. Uncompensated consultancies and lectures with The Medicines Company. PTE reports grants and personal fees from Bayer AG, personal fees from Novartis, personal fees from Quest Diagnostics, outside the submitted work. SAL is supported by NIH grant 1R01HL139731 and American Heart Association 18SFRN34250007. Dr. Lubitz receives sponsored research support from Bristol Myers Squibb / Pfizer, Bayer AG, Boehringer Ingelheim, Fitbit, and IBM, and has consulted for Bristol Myers Squibb / Pfizer and Bayer AG. MSS reports research grant support through Brigham and Women’s Hospital from Amgen; AstraZeneca; Bayer; Daiichi-Sankyo; Eisai; GlaxoSmithKline; Intarcia; Janssen Research and Development; Medicines Company; MedImmune; Merck; Novartis; Pfizer; Poxel; Quark Pharmaceuticals; Takeda (All >$10,000 per year); Consulting for Amgen; Anthos Therapeutics; AstraZeneca; Bristol-Myers Squibb; CVS Caremark; DalCor; Dyrnamix; Esperion; IFM Therapeutics; Intarcia; Ionis; Janssen Research and Development; Medicines Company; MedImmune; Merck; Novartis (all ≤$10,000 per year except Amgen, Esperion & Ionis); Dr. Sabatine is a member of the TIMI Study Group, which has also received institutional research grant support through Brigham and Women’s Hospital from: Abbott, Aralez, Roche, and Zora Biosciences. CTR reports grants from Boehringer Ingelheim, grants from Daiichi Sankyo, grants from MedImmune, grants from National Institute of Health, personal fees from Bayer, personal fees from Bristol Myers Squibb, personal fees from Boehringer Ingelheim, personal fees from Daiichi Sankyo, personal fees from Janssen, personal fees from MedImmune, personal fees from Pfizer, personal fees from Portola, personal fees from Anthos, outside the submitted work; Dr. Ruff is a member of the TIMI Study Group, which has received institutional research grant support through Brigham and Women’s Hospital from: Abbott, Amgen, Aralez, AstraZeneca, Bayer HealthCare Pharmaceuticals, Inc., BRAHMS, Daiichi-Sankyo, Eisai, GlaxoSmithKline, Intarcia, Janssen, MedImmune, Merck, Novartis, Pfizer, Poxel, Quark Pharmaceuticals, Roche, Takeda, The Medicines Company, Zora Biosciences.

Non-Standard Abbreviations

GRS

Genetic Risk Score

SNPs

Single Nucleotide Polymorphisms

R-FSRS

Revised Framingham Stroke Risk Score

NRI

Net Re-classification Improvement

HR

Hazard Ratio

CI

Confidence Interval

Footnotes

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