Subsequent event risk in individuals with established coronary heart disease : design and rationale of the GENIUS-CHD Consortium

Circ Genom Precis Med. 2019;12:e002470. DOI: 10.1161/CIRCGEN.119.002470 April 2019 145 BACKGROUND: The Genetics of Subsequent Coronary Heart Disease (GENIUS-CHD) consortium was established to facilitate discovery and validation of genetic variants and biomarkers for risk of subsequent CHD events, in individuals with established CHD. METHODS: The consortium currently includes 57 studies from 18 countries, recruiting 185 614 participants with either acute coronary syndrome, stable CHD, or a mixture of both at baseline. All studies collected biological samples and followed-up study participants prospectively for subsequent events. RESULTS: Enrollment into the individual studies took place between 1985 to present day with a duration of follow-up ranging from 9 months to 15 years. Within each study, participants with CHD are predominantly of self-reported European descent (38%–100%), mostly male (44%–91%) with mean ages at recruitment ranging from 40 to 75 years. Initial feasibility analyses, using a federated analysis approach, yielded expected associations between age (hazard ratio, 1.15; 95% CI, 1.14–1.16) per 5-year increase, male sex (hazard ratio, 1.17; 95% CI, 1.13–1.21) and smoking (hazard ratio, 1.43; 95% CI, 1.35–1.51) with risk of subsequent CHD death or myocardial infarction and differing associations with other individual and composite cardiovascular endpoints. CONCLUSIONS: GENIUS-CHD is a global collaboration seeking to elucidate genetic and nongenetic determinants of subsequent event risk in individuals with established CHD, to improve residual risk prediction and identify novel drug targets for secondary prevention. Initial analyses demonstrate the feasibility and reliability of a federated analysis approach. The consortium now plans to initiate and test novel hypotheses as well as supporting replication and validation analyses for other investigators. ORIGINAL ARTICLE Subsequent Event Risk in Individuals With Established Coronary Heart Disease Design and Rationale of the GENIUS-CHD Consortium © 2019 The Authors. Circulation: Genomic and Precision Medicine is published on behalf of the American Heart Association, Inc., by Wolters Kluwer Health, Inc. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution, and reproduction in any medium, provided that the original work is properly cited. Riyaz S. Patel, MD* Vinicius Tragante, PhD* Amand F. Schmidt, PhD* et al *Drs Patel, Tragante, and Schmidt are joint first authors. †Drs Samani, Hingorani, and Asselbergs are joint senior authors. The full author list is available on page 157. Key Words: coronary artery disease ◼ genetics ◼ myocardial infarction ◼ prognosis ◼ secondary prevention Circulation: Genomic and Precision Medicine https://www.ahajournals.org/journal/ circgen March152019 Downloaded from http://ahajournals.org by on June 6, 2019 Patel et al; Design and Rationale of GENIUS-CHD Circ Genom Precis Med. 2019;12:e002470. DOI: 10.1161/CIRCGEN.119.002470 April 2019 146 Major public health initiatives and policy changes, along with advances in drug and interventional therapies have significantly reduced cardiovascular morbidity and mortality in most high-income countries.1–3 However, the improved survival rates following an initial presentation with coronary heart disease (CHD) has, paradoxically, led to a growing number of patients living with established CHD (eg, 16M in the United States and 3M in the United Kingdom)4,5 who remain at substantially high risk of subsequent cardiovascular events. These include myocardial infarction (MI), repeated revascularizations but also heart failure, stroke, and sudden death.4 Despite a large body of knowledge on the pathophysiology of first CHD events in general populations,6,7 little is known about factors that influence disease progression or subsequent events in patients with established CHD, beyond those consequent to the acute index event in the short-term (such as biomarkers of myocardial dysfunction or necrosis, left ventricular function, or arrhythmia).8 As a result, although guidelines and treatment thresholds have progressively evolved over the past 2 decades, the targeted risk factors per se have remained largely unaltered.9 Novel therapies beyond lipid lowering, antiplatelet agents, and drugs recommended for high blood pressure and heart failure have been slow to emerge. Importantly, multiple novel and existing agents (eg, darapladib, varespladib, and folic acid) have failed in very late stage clinical development despite promising observational data.10–13 In contrast, some traditional risk factors, such as obesity, which show robust associations with initial CHD onset,14 continue to show inverse or null associations with subsequent events once CHD has developed.15 Ultimately, the high (residual) risk in individuals with existing CHD despite optimal contemporary therapy emphasizes the need for studying risk of subsequent events and their related causal pathways. For example, in the intervention arm of the IMPROVE-IT study (Vytorin Efficacy International Trial), despite simvastatin and ezetimibe treatment following an acute coronary syndrome, at 7 years, almost a third of participants experienced the primary end point (a composite of cardiovascular death, major coronary event, coronary revascularization, or nonfatal stroke).16 Similarly, in the FOURIER trial (Further Cardiovascular Outcomes Research with PCSK9 [proprotein convertase subtilisinkexin type 9] Inhibition in Subjects with Elevated Risk), almost 10% of patients with established but stable CVD, experienced an event at 2.2 years despite highintensity statin and PCSK9 inhibition, with achieved median LDL-C (low-density lipoprotein cholesterol) levels of 30 mg/dL.17 These data point to the existence of risk factors beyond traditional ones such as LDL-C, and the need to elucidate their related causal pathways.18 By studying those with established CHD at high risk of subsequent events, we plan to gain novel insights into other drivers of atherosclerosis or features that identify patients who may benefit most from novel therapies.9 Genetic and biomarker studies in these individuals may help identify novel molecular pathways and future drug targets with the goal of advancing precision medicine. In the absence of a single-large resource to study the determinants of coronary heart disease prognosis, we have established the Genetics of Subsequent CHD (GENIUS-CHD) consortium.19 Assembling studies from across the globe that have recruited patients with different types of CHD at baseline, have acquired prospective follow-up, and have stored biological specimens, or genetic data, the consortium aims to: (1) investigate genetic and nongenetic determinants of risk for subsequent CHD, systematically and at scale and (2) facilitate access to data and expertise, as a platform to foster collaboration among investigators working in the field. Here, we describe the design of the consortium, including details of participating studies, available data, and samples, as well as the governance procedures and the consortium’s approach to data sharing and collaboration to further advance the stated scientific aims. In addition, we present some early findings from an investigation of the association of patient characteristics and certain routinely recorded measures on the risk of subsequent events among patients with different types of CHD at baseline. METHODS In accordance with Transparency and Openness Promotion Guidelines, the data, analytic methods, and study materials will be made available to other researchers for purposes of reproducing the results or replicating the procedures. Participating studies received local institutional review board approval and included patients who had provided informed consent at the time of enrollment. The central analysis sites also received waivers from their local institutional review board for collating and analyzing summary-level data from these individual studies. Full details on the eligibility criteria, definitions of terminology, management of the consortium, and planned projects are provided in Materials in the Data Supplement. RESULTS The design and structure of the GENIUS-CHD consortium are presented in Figure 1. Studies meeting the main eligibility criteria were identified and invited to participate (Methods in the Data Supplement). In brief, studies are eligible to join the GENIUS-CHD consortium if they meet 3 inclusion criteria: (1) included individuals with established CHD (defined as the presence of or confirmed history of acute coronary syndrome at baseline, or of coronary artery disease as evidenced by any revascularization procedure (percutaneous coronary Downloaded from http://ahajournals.org by on June 6, 2019 Patel et al; Design and Rationale of GENIUS-CHD Circ Genom Precis Med. 2019;12:e002470. DOI: 10.1161/CIRCGEN.119.002470 April 2019 147 intervention or bypass surgery) or demonstrable plaque in any epicardial vessel on direct coronary imaging); (2) acquired prospective follow-up of participants with ascertainment of one or more subsequent cardiovascular disease events as well as all-cause mortality; and (3) had stored blood samples, which are viable and suitable for DNA and biomarker analysis or previously collected such data before sample depletion. At the time of writing, 57 studies from 18 countries are participating in the consortium and are listed in Table1. Please refer to www.genius-chd.org for an updated list. Brief narrative descriptions of each study are provided in Methods in the Data Supplement. The majority of studies are either investigator-led clinical cohorts (n=42), but clinical trials (n=10) and nested case-cohort (inception-study design) studies (n=5) are also included. Of the total, 23 studies have included participants at the time of an acute coronary syndrome, while the remainder recruited those with stable CHD or a mixture of the 2 (eg, from cardiac catheterization labs). Collectively, 185 614 participants have been enrolled with CHD at baseline (including 812 803 person-years of follow-up); of which 170 343 are of selfreported European descent. Recruitment times varied between studies, ranging from the earliest recruitment in 1985 to studies that remain actively recruiting to the present day. All studies enrolled patients >18 years of age, although one study exclusively recruited only those with premature CHD (MI <45 years), while another recruited only older subjects (>70 years). The overall mean age within each study reflects this heterogeneity, ranging from 40 to 75 years of age, and proportion of male sex ranging from 44% to 91% (Table1). Available Data Core Phenotypes All studies collected data on age, sex, and ethnicity. Risk factor data are available for diabetes mellitus, obesity, and smoking status in almost all participating studies (96%), while data on concentrations of routine blood lipids (total cholesterol, LDL-C, HDL-C [high-density lipoprotein cholesterol], and triglycerides; 84%), and blood pressure values at enrollment (82%) were collected by the majority of studies. Data on statin use at baseline are available in 90% of all participating studies (Table2). Figure 1. Overview of the Genetics of Subsequent Coronary Heart Disease (GENIUS-CHD) consortium, illustrating inclusion criteria and governance structure. Following project approval by the steering committee, analyses scripts are prepared and distributed to all members, with sharing of summary-level outputs before meta-analysis at the coordinating centers. Further details can be found at www.genius-chd.org. QC indicates quality control. Downloaded from http://ahajournals.org by on June 6, 2019 Patel et al; Design and Rationale of GENIUS-CHD Circ Genom Precis Med. 2019;12:e002470. DOI: 10.1161/CIRCGEN.119.002470 April 2019 148 Table 1. Overview of Each Study Participating in the GENIUS-CHD Consortium Alias Cohort Name Country Study Design Recruitment Period CHD Type Total Recruited With CHD European Ancestry (%) Europeans Recruited With CHD Mean Follow-Up Time (SD) Age (SD) Male (%) PubMED ID 4C Clinical Cohorts in Coronary disease Collaboration United Kingdom Clinical Cohort 2009–2014 CAD 3345 54.8 1832 2.56 (0.95) 61.8 (12.14) 61.5 NA AGNES Arrhythmia Genetics in The Netherlands The Netherlands Clinical Cohort 2001–2005 ACS 1459 100.0 1459 6.73 (4.75) 57.8 (10.73) 79.2 20622880 ANGES Angiography and Genes Study Finland Clinical Cohort 2002–2005 Mixed 588 100.0 588 8.20 (4.47) 64.1 (9.59) 65.5 21640993 ATVB Italian Atherosclerosis, Thrombosis and Vascular Biology Group Italy Clinical Cohort 1997–2006 ACS 1741 100.0 1741 10.47 (4.45) 40.0 (4.40) 90.8 21757122 CABGenomics CABG Genomics United States Clinical Cohort 2001–2014 Mixed 2694 85.5 2303 6.9 (3.5) 64.4 (10.38) 79 25649697 CARDIOLINES Cardiolines The Netherlands Clinical Cohort 2011 Mixed 1269 75.0 1692 1.3 (0.5) 63.5 (11.6) 72.8 NA CDCS Coronary Disease Cohort Study New Zealand Clinical Cohort 2002–2009 ACS 2139 91.4 1956 5.21 (2.15) 67.4 (12.01) 71.3 20400779 COGEN The Copenhagen Cardiovascular Genetic study Denmark Clinical Cohort 2011–2017 Mixed 3709 95.0 3904 5.5 (1.01) 70.1 (17.4) 67.5 In press COROGENE Corogene Study Finland Clinical Cohort 2006–2008 ACS 1489 100.0 1489 7.7 (0.5) 64.7 (11.88) 70.9 21642350 CTMM Circulating Cells The Netherlands Clinical Cohort 2009–2011 Mixed 713 96.5 688 0.97 (0.37) 62.6 (10.08) 69 23975238 CURE Cure-Genetics Study Canada RCT 1998–2000 ACS 12 434 82.1 10 203 0.78 (0.28) 65.4 (11.19) 61.4 11102254 EGCUT Estonian Biobank Estonia Population 2002–2011 CAD 2783 100.0 2783 6.65 (2.93) 66.6 (10.99) 51.5 24518929 EMORY Emory Cardiovascular Biobank United States Clinical Cohort 2004 Mixed 5873 72.0 4229 4.49 (3.15) 65.4 (11.74) 68.7 20729229 ERICO Estratégia de Registro de Insuficiência Coronariana Brazil Clinical Cohort 2009–2014 ACS 738 61.0 450 2.85 (1.48) 63.8 (13.35) 56 23644870 FASTMI2005 The French Registry of Acute ST-elevation MI France Clinical Cohort 2005 ACS 3669 100.0 3669 1.72 (0.63) 67.3 (13.94) 68.5 17893635 FINCAVAS Finnish Cardiovascular Study Finland Clinical Cohort 2001–2008 Mixed 1671 100.0 1671 8.57 (3.99) 60.9 (11.04) 69.4 16515696 FRISCII FRISCII Study Sweden RCT 1996–1998 ACS 3147 99.3 3125 7.46 (2.09) 66.3 (9.82) 69.5 10475181 GENDEMIP Genetic Determination of Myocardial Infarction in Prague Czech Republic Clinical Cohort 2006–2009 ACS 1302 100.0 1302 1.13 (0.78) 56.5 (8.66) 74.4 23249639 GENEBANK Cleveland Clinic Genebank Study United States Clinical Cohort 2001–2007 Mixed 2345 100.0 2345 3.00 (0.00) 61.5 (11.06) 74.3 21475195 GENESIS-PRAXY Gender and Sex Determinants of Cardiovascular Disease: From Bench to Beyond-Premature Acute Coronary Syndrome Canada Clinical Cohort 2009–2013 ACS 784 99.4 779 1.00 (0.00) 48.3 (5.62) 69.1 22607849 GENOCOR Genetic Mapping for Assessment of Cardiovascular Risk Italy Clinical Cohort 2007–2010 Mixed 497 100.0 497 5.68 (1.20) 65.2 (8.47) 86.7 22717531 GEVAMI The Genetic Causes to Ventricular Arrhythmia in Patients During First ST-Elevation Myocardial Infraction Denmark Clinical Cohort 2011 ACS 1033 100.0 1033 3.93 (1.40) 59.5 (10.37) 79.3 25559012 GoDARTS incident Genetics of Diabetes Audit and Research in Tayside Scotland (I) Scotland Population 2004–2012 CAD 1261 99.8 1258 3.47 (2.95) 71.3 (10.91) 61.1 29025058 GoDARTS prevalent Genetics of Diabetes Audit and Research in Tayside Scotland (P) Scotland Population 2004–2012 CAD 2514 99.7 2507 6.48 (3.06) 69.1 (9.41) 65.9 29025058 (Continued ) Downloaded from http://ahajournals.org by on June 6, 2019 Patel et al; Design and Rationale of GENIUS-CHD Circ Genom Precis Med. 2019;12:e002470. DOI: 10.1161/CIRCGEN.119.002470 April 2019 149 GRACE_B Global Registry of Acute Coronary Events - Belgium Belgium Clinical Cohort 1999–2010 ACS 734 100.0 734 4.25 (1.80) 65.9 (11.91) 75.8 20231156 GRACE_UK Global Registry of Acute Coronary Events - UK United Kingdom Clinical Cohort 2001–2010 ACS 1443 100.0 1443 9.54 (2.68) 64.3 (12.21) 69.6 20231156 IDEAL Incremental Decrease in End Points Through Aggressive lipid Lowering (IDEAL) Canada RCT 1999–2005 ACS 8888 99.3 8823 4.63 (0.82) 61.8 (9.47) 80.8 16287954 INTERMOUNTAIN Intermountain Heart Collaborative Study United States Clinical Cohort 1993–2009 Mixed 7556 89.5 6763 8.56 (5.39) 61.2 (11.06) 66.7 20691829 INVEST International Verapamil SR Trandolopril Study Genetic Substudy INVEST-GENES United States/ International RCT 1997–2003 CAD 5979 38.0 2270 2.83 (0.82) 66.1 (9.70) 44 21372283, 17700361 JUMC Krakow-GENIUS-CHD Poland Clinical Cohort 2010–2014 Mixed 747 100.0 747 0.84 (0.34) 68.3 (10.26) 71.6 28444280, 27481134 KAROLA Karola Study Germany Clinical Cohort 1999–2000 Mixed 1206 100.0 1206 11.62 (3.01) 58.7 (8.15) 84.2 24829374 LIFE-Heart Leipzig (LIFE) Heart Study Germany Clinical Cohort 2006–2014 Mixed 5564 100.0 5564 1.62 (2.03) 63.9 (11.09) 77.2 22216169 LURIC The Ludwigshafen Risk and Cardiovascular Health Study Germany Clinical Cohort 1997–2000 Mixed 2320 100.00 2320 8.58 (3.18) 63.8 (9.92) 76.6 11258203 MDCS Malmo Diet and Cancer Study Sweden Population 1991–1996 CAD 4,546 100.00 4546 8.3 (8.0) 58.0 (7.6) 60.2 19936945 NE_POLAND North East Poland Myocardial Infarction Study Poland Clinical Cohort 2001–2005 ACS 646 100.0 646 7.20 (2.75) 62.3 (11.84) 75.4 26086777 NEAPOLIS Neapolis Campania Italia Italy Clinical Cohort 2008–2012 Mixed 1394 100.0 1394 1.07 (0.54) 67.6 (10.50) 74.5 24262617 OHGS Ottawa Heart Genomics Study Canada Clinical Cohort 2010–2013 Mixed 546 100.0 546 1.77 (0.27) 65.6 (11.11) 73.8 NA PERGENE Perindopril Genetic Association Study (EUROPA) The Netherlands RCT 1997–2000 CAD 8746 99.0 8656 4.20 (0.62) 59.9 (9.27) 85.6 19082699 PLATO The Study of Platelet Inhibition and Patient Outcomes International RCT 2006–2008 ACS 18 624 98.3 18 315 0.86 (0.24) 62.6 (10.96) 69.5 19332184 PMI Post Myocardial Infarction Study New Zealand Clinical Cohort 1994–2001 ACS 1057 91.1 963 8.56 (3.58) 62.8 (10.56) 78 12771003 POPular The Popular study The Netherlands Clinical Cohort 2005–2007 Mixed 1024 98.2 1006 1.00 (0) 63.8 (10.39) 74.6 20179285 POPular Genetics The Popular GENETICS Study The Netherlands and Belgium RCT 2011–2017 ACS 2481 94.3 2287 1.00 (0) NA 74.9 24952855 PROSPER Prospective Study of Pravastatin in the Elderly at Risk The Netherlands RCT 1997–1999 CAD 893 100.0 893 3.15 (0.71) 75.4 (3.38) 70.3 10569329 RISCA Recurrance and Inflammation in the Acute Coronary Syndromes Study Canada Clinical Cohort 2001–2002 ACS 1054 100.0 1054 1.22 (0.18) 61.8 (11.45) 75.9 18549920 SHEEP Stockholm Heart Epidemiology Program (SHEEP) Sweden Clinical Cohort 1992–1995 ACS 1150 100.0 1150 14.87 (5.91) 59.3 (7.21) 70.7 17667644 SMART Second Manifestations of Arterial Disease The Netherlands Clinical Cohort 1999–2010 Mixed 3057 98.2 3001 6.77 (3.86) 60.5 (9.31) 81.7 10468526 STABILITY Stabilization of Atherosclerotic Plaque by Initiation of Darapladib Therapy trial International RCT 2008–2010 CAD 10 786 86.1 9287 3.60 (0.57) 64.7 (9.10) 82 24678955 THI Texgen United States Clinical Cohort 2001–2008 ACS 3875 73.1 2834 5.50 (3.42) 63.6 (10.61) 74.9 21414601 Table 1. Continued Alias Cohort Name Country Study Design Recruitment Period CHD Type Total Recruited With CHD European Ancestry (%) Europeans Recruited With CHD Mean Follow-Up Time (SD) Age (SD) Male (%) PubMED ID (Continued ) Downloaded from http://ahajournals.org by on June 6, 2019 Patel et al; Design and Rationale of GENIUS-CHD Circ Genom Precis Med. 2019;12:e002470. DOI: 10.1161/CIRCGEN.119.002470 April 2019 150 Additional Phenotypes A list of selected additional phenotypes available by study is presented in Table I in the Data Supplement. Of note, 79% have available data on plasma CRP (C-reactive protein), while coronary disease burden information, from invasive angiography is available in 52% of studies. Finally, over a third of studies have also collected data on physical activity (38%) and socioeconomic status (37%). Samples Stored samples are available in most studies for future assay testing and stored frozen. The majority have stored plasma (75%), while others also have serum, blood EDTA, RNA, and urine (Table II in the Data Supplement). DNA and Genotyping More than two-thirds of the studies have DNA still available, either preextracted or as whole blood collected in EDTA and stored for future genotyping. All studies within the consortium have performed genotyping in some capacity, with genome-wide data available in a subset of studies (Table III in the Data Supplement). Subsequent Events and Follow-Up The most commonly collected end point was all-cause death, collected by all but 2 studies. CHD death during follow-up was collected in 70% of studies, while incident MI was reported by 82% of studies. Studies ascertained end points through different means, including telephone contact, in-person patient interviews, clinical chart reviews, and linkage to national mortality registers and hospital records (Table IV in the Data Supplement). Power Calculations Empirical power was estimated based on a conservative sample size of 150 000 subjects with an event rate of 10% (across the entire follow-up period with a mean of about 5 years); Figure2. Given that the GENIUS-CHD consortium is designed to answer multiple questions, power was estimated for a range of genetic single nucleotide polymorphisms (SNPs) and nongenetic (biomarkers and clinical risk factors) effects. Minor allele frequencies of 0.01, 0.05, 0.10, and 0.25 were examined, representing rare to common SNPs. For each minor allele frequency, power was calculated for a range of plausible SNP effects on biomarkers (mean difference [μ] 0.01, 0.03, and 0.05) and clinical end points (odds ratios of 1.02, 1.05, and 1.10). For the association of SNPs with biomarkers, power was 80% (α=0.05) or more unless the SNP was rare (minor allele frequency of 0.01) or the effect size was small (eg, 0.01 per allele). For the association of SNPs with clinical end points, power was close to 80% when the effect size was large (odds ratio ≥1.10) or the minor allele frequency was ≥0.10. TNT Treating to New Targets Canada RCT 1998–1999 CAD 10 000 94.1 9409 4.36 (1.47) 61.1 (8.82) 81.6 15755765 TRIUMPH Translational Research Investigating Underlying Disparities in Acute Myocardial Infarction Patient’s Health Status United States Clinical Cohort 2005–2008 ACS 2062 100.0 2062 0.97 (0.15) 59.8 (12.10) 72.2 21772003 UCORBIO Utrecht Coronary Biobank The Netherlands Clinical Cohort 2011–2014 Mixed 1493 72.4 1081 1.6 (0.9) 65.4 (10.27) 75.6 NA UCP Utrecht Cardiovascular Pharacogenetics Study The Netherlands Clinical Cohort 1985–2010 Mixed 1508 100.0 1508 8.00 (4.16) 64.1 (9.97) 75.4 25652526 UKB UK Biobank United Kingdom Population 2006–2010 CAD 12 045 94.2 11 342 6.39 (1.72) 69.9 (6.07) 80.6 1001779 VHS Verona Heart Study Italy Clinical Cohort 1996CAD 939 100.0 939 5.62 (2.97) 61.3 (9.74) 81 10984565 VIVIT Vorarlberg Institute for Vascular Investigation and Treatment Study Austria Clinical Cohort 1999–2008 CAD 1447 99.8 1444 7.43 (2.91) 64.5 (10.45) 72 24265174 WARSAW ACS Warsaw ACS Genetic Registry Poland Clinical Cohort 2008–2011 ACS 681 100.0 681 2.97 (1.16) 63.5 (11.84) 74.2 NA WTCC WTCCC CAD Study United Kingdom Clinical Cohort 1998–2003 Mixed 1926 100.0 1926 10.05 (2.81) 60.0 (8.13) 79.3 16380912, 17634449 Alias denotes the abbreviated name of study used in figures and analyses. PubMed IDs are provided for individual study descriptions; mean (SD) with proportions (%) are provided unless otherwise stated. ACS indicates acute coronary syndrome; CAD, coronary artery disease; GENIUS-CHD, Genetics of Subsequent Coronary Heart Disease; and RCT, randomized controlled trial. Table 1. Continued Alias Cohort Name Country Study Design Recruitment Period CHD Type Total Recruited With CHD European Ancestry (%) Europeans Recruited With CHD Mean Follow-Up Time (SD) Age (SD) Male (%) PubMED ID Downloaded from http://ahajournals.org by on June 6, 2019 Patel et al; Design and Rationale of GENIUS-CHD Circ Genom Precis Med. 2019;12:e002470. DOI: 10.1161/CIRCGEN.119.002470 April 2019 151 Table 2. Participant Characteristics of Each Study Contributing to GENIUS-CHD Alias BMI, kg/m2 (SD) Systolic BP (SD) Diastolic BP (SD) Diabetes mellitus (%) Current Smoking (%) Total cholesterol (SD), mmol/L LDL-C (SD), mmol/L HDL-C (SD), mmol/L Creatinine (SD) Statin use (%) Prior Revascularization (%) Prior MI (%) 4C 30.2 (5.7) 133.8 (23) 77.9 (12.2) 21.8 19.1 4.64 (1.10) NA 1.309 (0.42) 98.7 (81) 24.7 20.6 14.1 AGNES 26.6 (3.9) NA NA 7.9 61.0 5.26 (1.04) 3.25 (1.01) 1.198 (0.45) NA 10.0 0.0 0.0 ANGES 28.1 (4.4) NA NA 30.8 14.7 4.71 (0.84) 2.68 (0.77) 1.166 (0.33) 83.0 (37) 69.4 42.4 24.7 ATVB 26.8 (4.0) 132.4 (21) 83.5 (13.5) 8.2 79.5 5.83 (1.39) NA 1.080 (0.33) NA 55.4 NA NA CABGenomics 29.8 (5.6) NA NA 9.0 10.3 4.32 (0.94) 2.13 (0.85) 1.085 (0.35) NA 74.1 NA 37.0 CARDIOLINES 26.9 (3.8) 134.4 (23) 84.34 (14.6) NA 0.6 5.43 (1.1) 3.84 (1.0) 1.16 (0.3) 73.09 (15) NA NA NA CDCS 27.3 (4.7) 129.1 (22) 74.6 (11.7) 15.2 5.8 5.01 (1.09) 2.95 (1.03) 1.175 (0.34) 100.8 (41) 46.0 26.5 30.4 COGEN NA NA NA 16.7 26.2 NA NA NA NA NA NA NA COROGENE 27.6 (4.8) NA NA 18.2 34.4 4.58 (0.99) 2.43 (0.88) 1.250 (0.37) 84.0 (46) 5.2 NA NA CTMM 27.6 (4.4) 135.5 (19) 77.4 (11.2) 21.0 20.9 4.54 (1.06) 2.59 (0.98) 1.135 (0.32) 86.2 (40) NA NA 30.3 CURE 27.7 (4.5) 135.1 (22) 77.1 (13.6) 20.9 23.0 NA NA NA 93.1 (35) NA 14.8 31.7 EGCUT 29.0 (5.2) 135.7 (18) 80.4 (10.6) 18.9 19.8 5.70 (1.17) 3.84 (1.08) 1.340 (0.35) NA 27.7 15.4 35.3 EMORY 29.8 (6.7) 137.0 (22) 75.0 (15.0) 34.2 7.8 4.49 (1.04) 2.42 (0.93) 1.090 (0.34) 100.2 (56) 74.2 59.6 26.8 ERICO 27.0 (5.1) 134.8 (32) 99.4 (38.0) 39.4 31.2 NA NA NA NA 23.8 11.7 26.2 FASTMI2005 27.2 (4.8) 139.9 (28) 80.0 (17.0) 35.9 29.1 5.03 (1.22) 3.03 (1.07) 1.239 (0.43) 103.4 (62) 74.1 NA 18.2 FINCAVAS 27.8 (4.3) 140.2 (22) 82.2 (10.6) 18.4 24.3 4.70 (0.90) 2.62 (0.80) 1.300 (0.39) 90.8 (70) 57.3 32.6 39.0 FRISCII 26.8 (3.9) 143.4 (23) 82.0 (10.6) 12.8 27.0 5.81 (1.12) 3.72 (0.99) 1.151 (0.36) 90.6 (19) 12.3 12.1 27.2 GENDEMIP 28.6 (4.7) 137.1 (21) 84.0 (10.8) 19.0 61.0 5.42 (1.16) 3.58 (1.09) 1.183 (0.33) NA 16.7 30.2 40.8 GENEBANK 29.4 (5.4) 132.7 (21) 75.0 (12.0) 11.8 16.8 4.38 (0.93) 2.51 (0.82) 0.903 (0.26) NA 71.8 65.3 56.1 GENESIS-PRAXY 29.5 (6.5) 139.5 (27) 86.2 (17.2) 13.9 44.2 4.87 (1.19) 2.89 (1.13) 0.966 (0.30) 75.9 (20) 92.9 11.4 11.5 GENOCOR NA 129.5 (20) 75.4 (11.1) 13.3 64.4 4.82 (0.92) 3.10 (0.83) 1.082 (0.28) 94.8 (27) 72.1 13.7 63.2 GEVAMI 27.2 (4.3) 124.8 (18) 73.2 (11.1) 8.9 52.4 NA NA NA NA 13.4 0.0 0.0 GoDARTSincident 29.8 (5.6) 126.7 (17) NA 70.9 NA 4.57 (1.02) 2.43 (0.91) 1.277 (0.41) 107.0 (65) 49.6 0.2 1.2 GoDARTSprevalent 30.2 (5.4) 136.0 (20) NA 75.8 14.5 4.37 (0.84) 2.04 (0.74) 1.320 (0.38) 101.0 (34) 66.3 30.2 46.8 GRACE_B 27.0 (4.3) 138.3 (25) 78.7 (14.6) 81.1 49.3 5.19 (1.20) 3.06 (1.09) 1.343 (0.98) 102.6 (63) 79.4 NA 80.5 GRACE_UK 27.9 (5.0) 137.9 (27) 76.4 (16.5) 13.9 69.2 5.20 (1.27) 3.07 (1.14) 1.204 (0.49) 101.5 (38) 14.5 20.2 30.0 IDEAL 27.3 (3.8) 136.9 (20) 80.4 (10.2) 11.9 20.7 5.09 (1.00) 3.14 (0.90) 1.192 (0.31) 100.6 (17) 75.5 40.9 100.0 INTERMOUNTAIN 29.5 (6.1) 141.8 (24) 81.1 (13.3) 20.3 10.2 4.91 (1.12) 2.76 (0.94) 1.048 (0.35) 99.6 (67) 38.7 NA 6.6 INVEST 29.4 (5.6) 148.4 (18) 82.4 (10.5) 24.3 13.3 NA NA NA NA 52.7 48.1 23.3 JUMC 26.3 (4.5) 148.2 (25) 80.3 (12.4) 36.1 27.5 4.97 (1.08) 3.11 (1.14) 1.232 (0.37) 91.3 (42) 87.5 49.8 39.9 KAROLA 26.9 (3.3) 120.0 (16) 73.1 (9.1) 18.6 31.8 4.44 (0.84) 2.61 (0.76) 1.030 (0.28) 82.7 (28) 77.0 42.8 22.4 (Continued ) Downloaded from http://ahajournals.org by on June 6, 2019 Patel et al; Design and Rationale of GENIUS-CHD Circ Genom Precis Med. 2019;12:e002470. DOI: 10.1161/CIRCGEN.119.002470 April 2019 152 LIFE-Heart 28.9 (4.7) 139.0 (22) 80.0 (12.9) 33.9 27.8 5.16 (1.19) 3.12 (1.05) 1.227 (0.35) 88.8 (34) 45.8 NA 13.3 LURIC 27.5 (4.0) 142.2 (24) 81.0 (11.5) 44.1 24.6 4.94 (0.99) 2.98 (0.89) 0.965 (0.26) 88.7 (38) 58.9 48.3 57.8 MDCS 25.8 (4.0) 141.1 (20) 85.6 (10.0) 4.4 26.6 6.17 (1.1) 4.16 (1.0) 1.38 (0.4) 84.76 (16) 0.03 0.00 0.00 NE_POLAND 24.8 (3.8) 138.7 (27) 88.1 (15.6) 22.3 48.5 5.12 (1.04) 3.31 (0.97) 1.126 (0.34) 92.0 (36) 81.2 1.7 11.2 NEAPOLIS 28.0 (4.2) 129.4 (14) 75.7 (7.7) 42.7 26.9 4.49 (1.03) 2.45 (0.99) 1.233 (0.66) 101.0 (68) 82.6 41.9 40.9 OHGS 28.5 (4.9) 132.2 (19) 72.1 (11.3) 5.5 19.3 5.57 (1.05) 3.46 (0.88) 1.222 (0.34) 89.1 (21) 91.6 27.8 23.3 PERGENE 27.5 (3.5) 136.9 (15) 81.8 (8.1) 12.7 14.7 5.41 (1.04) NA NA 86.5 (26) 55.3 54.6 65.4 PLATO 28.2 (4.5) 135.6 (22) 79.5 (12.9) 22.8 35.2 5.40 (1.23) 3.27 (1.11) 1.279 (0.35) 85.6 (26) 79.7 15.1 20.6 PMI 26.5 (3.8) 116.5 (16) 66.5 (9.6) 12.5 28.0 5.97 (1.19) 3.98 (1.07) NA 88.0 (28) 44.6 NA 18.4 POPular 27.2 (4.1) 144.9 (22) 81.4 (12.1) 19.0 27.6 4.56 (0.94) 2.73 (1.15) 1.260 (0.32) 92.7 (27) 80.7 32.9 43.6 POPular Genetics NA NA NA NA NA NA NA NA NA NA NA NA PROSPER 26.6 (3.9) 150.0 (22) 81.1 (11.4) 10.4 17.3 5.55 (0.84) 3.74 (0.74) 1.174 (0.31) 109.2 (23) 0.0 26.5 86.9 RISCA 27.2 (4.4) NA NA 19.8 30.4 NA NA NA 100.6 (29) 46.6 28.3 27.8 SHEEP 26.8 (4.0) 131.8 (21) 79.6 (10.3) 18.2 50.1 6.20 (1.16) 4.22 (1.01) 1.082 (0.31) NA 0.0 0.0 0.0 SMART 27.4 (3.7) 137.0 (19) 80.1 (10.8) 17.1 24.2 4.66 (0.95) 2.64 (0.88) 1.231 (0.72) 92.3 (23) 77.5 100.0 44.5 STABILITY 29.9 (5.0) 131.7 (16) 79.1 (10.0) 38.4 21.4 NA 2.25 (0.85) 1.216 (0.32) NA 97.3 74.6 58.6 THI 29.6 (5.6) NA NA 30.4 21.1 NA NA NA NA 57.2 21.7 16.7 TNT 28.5 (4.5) 130.7 (17) 77.9 (9.4) 14.2 13.3 4.53 (0.61) 2.52 (0.45) 1.223 (0.28) 104.5 (17) 70.1 NA 58.2 TRIUMPH 29.6 (6.0) 117.7 (18) 68.1 (10.9) 29.1 37.4 NA 2.70 (1.02) 1.037 (0.33) 113.7 (81) 89.0 27.2 18.5 UCORBIO 27.2 (4.3) NA NA 21.4 23.1 4.80 (1.18) 2.64 (1.05) 1.205 (0.33) 92.0 (45) 63.9 NA 29.0 UCP NA 153.4 (25) 87.1 (13.3) NA NA 5.66 (1.10) 3.36 (1.01) 1.244 (0.33) 94.7 (25) 27.0 NA NA UKB 29.4 (4.9) 139.1 (20) 78.7 (10.9) 22.2 75.9 NA NA NA NA 82.9 59.6 36.7 VHS 26.8 (3.6) NA NA 18.4 69.1 5.51 (1.13) 3.69 (1.00) 1.175 (0.30) 96.7 (32) 46.4 17.6 59.4 VIVIT 27.4 (4.1) 137.4 (19) 80.6 (10.9) 31.0 19.4 5.36 (1.15) 3.33 (1.02) 1.348 (0.40) 89.9 (41) 49.9 20.6 30.4 WARSAW ACS 28.1 (4.7) 128.0 (23) 76.2 (13.2) 21.8 42.4 4.98 (1.06) 2.99 (1.02) 1.105 (0.33) 93.5 (44) NA NA 18.9 WTCC 27.6 (4.2) 143.6 (22) 84.3 (12.3) 11.7 12.8 5.31 (0.98) 3.12 (0.90) 1.198 (0.38) NA 71.6 67.2 72.0 Data were collected through a federated analysis. Alias denotes the abbreviated name of study used in figures and analyses. Mean (SD) and proportions (%) are provided unless otherwise stated. BMI indicates body mass index; BP, blood pressure; GENIUS-CHD, Genetics of Subsequent Coronary Heart Disease; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; MI, myocardial infarction; and NA, not applicable. Table 2. Continued Alias BMI, kg/m2 (SD) Systolic BP (SD) Diastolic BP (SD) Diabetes mellitus (%) Current Smoking (%) Total cholesterol (SD), mmol/L LDL-C (SD), mmol/L HDL-C (SD), mmol/L Creatinine (SD) Statin use (%) Prior Revascularization (%) Prior MI (%) Downloaded from http://ahajournals.org by on June 6, 2019 Patel et al; Design and Rationale of GENIUS-CHD Circ Genom Precis Med. 2019;12:e002470. DOI: 10.1161/CIRCGEN.119.002470 April 2019 153 Power of observational (ie, nongenetic) analysis was >99% for both continuous and binary exposures unless the odds ratio was close to 1. In addition to continuous and binary outcome data, GENIUS-CHD also collects time-to-event data. Given the similarity (in most empirical settings) between odds ratio and hazard ratios,20 similar power is to be expected for time-to-event analysis. Initial Analysis To examine the feasibility of the federated analysis approach, we sought to collect data on participant characteristics, cardiovascular and mortality outcomes and association analyses with common clinical exposures. A standardized dataset was developed, with a federated analysis conducted using standardized statistical scripts. The summary-level outputs generated were then shared with the coordinating centers for aggregating and metaanalysis (Methods in the Data Supplement). Participant Characteristics Detailed characteristics of participants by study are presented in Table2. Prevalence of risk factors varied by study, with diabetes mellitus ranging from 4% to 76%; smoking from 8% to 79%. Mean total cholesterol by study ranged from 166.3 to 239.8 mg/dL, mean body mass index ranged from 24.8 to 30.2 kg/m2 and mean systolic blood pressure from 117 to 153 mm Hg. The proportion of participants with prior revascularization or MI was high in most studies reflecting the inclusion criteria for the consortium (Table2). Review of returned outputs from the federated analysis revealed good quality data with estimates falling within expected ranges for age, sex, and other variables, such as body mass index (Figure I in the Data Supplement). Figure 2. Figure illustrating empirical power for detecting different effect sizes for biomarker variance and clinical events for both α 0.05 and 0.0001, by varying minor allele frequencies, for a conservative total number of 150 000 with an event rate of 10%. MAF indicates minor allele frequency; and OR, odds ratio. Downloaded from http://ahajournals.org by on June 6, 2019 Patel et al; Design and Rationale of GENIUS-CHD Circ Genom Precis Med. 2019;12:e002470. DOI: 10.1161/CIRCGEN.119.002470 April 2019 160 Behringer; Dr Held declares institutional research grant, advisory board member and speaker’s bureau from AstraZeneca; institutional research grants from Bristol-Myers Squibb Merck & Co, GlaxoSmithKline, Roche Diagnostics. Advisory board for Bayer and Boehringer Ingelheim; Dr Lindholm has received institutional research grants from AstraZeneca, and GlaxoSmithKline; Speaker fees from AstraZeneca, Speaker fees from AstraZeneca; Dr Siegbahn has received institutional research grants from AstraZeneca, Boehringer Ingelheim, BristolMyers Squibb/Pfizer, Roche Diagnostics, GlaxoSmithKline; Dr ten Berg reports receiving fees for board membership from AstraZeneca, consulting fees from AstraZeneca, Eli Lilly, and Merck, and lecture fees from Daiichi Sankyo and Eli Lilly, AstraZeneca, Sanofi and Accumetrics; Dr Wallentin reports institutional research grants, consultancy fees, lecture fees, and travel support from BristolMyers Squibb/Pfizer, AstraZeneca, GlaxoSmithKline, Boehringer Ingelheim; institutional research grants from Merck & Co, Roche Diagnostics; consultancy fees from Abbott; and holds a patent EP2047275B1 licensed to Roche Diagnostics, and a patent US8951742B2 licensed to Roche Diagnostics; Dr Asselbergs has received research funding from Regeneron, Pfizer, Sanofi. REFERENCES 1. Ford ES, et al. Proportion of the decline in cardiovascular mortality disease due to prevention versus treatment: public health versus clinical care. Annu Rev Public Health. 2011;32:5–22. doi: 10.1146/ annurev-publhealth-031210-101211 2. Sidney S, et al. Recent trends in cardiovascular mortality in the united states and public health goals. JAMA Cardiol. 2016;1:594–599. doi: 10.1001/jamacardio.2016.1326 3. Ford ES, et al. Explaining the decrease in U.S. deaths from coronary disease, 1980-2000. N Engl J Med. 2007;356:2388–2398. doi: 10.1056/NEJMsa053935 4. Benjamin EJ, et al.; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart Disease and Stroke Statistics-2017 Update: a report from the American Heart Association. Circulation. 2017;135:e146–e603. doi: 10.1161/CIR.0000000000000485 5. Scarborough PBP, et al. Coronary Heart Disease Statistics 2010 Edition. London: British Heart Foundation; 2010. 6. Yusuf S, et al.; Interheart Study Investigators. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet. 2004;364:937–952. doi: 10.1016/S0140-6736(04)17018-9 7. Deloukas P, et al. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet. 2013;45:25–33. 8. Fox KA, et al. Prediction of risk of death and myocardial infarction in the six months after presentation with acute coronary syndrome: prospective multinational observational study (GRACE). BMJ. 2006;333:1091. doi: 10.1136/bmj.38985.646481.55 9. Schiele F, et al. Coronary artery disease: risk stratification and patient selection for more aggressive secondary prevention. Eur J Prev Cardiol. 2017;24(3_suppl):88–100. doi: 10.1177/2047487317706586 10. Schwartz GG, et al.; dal-OUTCOMES Investigators. Effects of dalcetrapib in patients with a recent acute coronary syndrome. N Engl J Med. 2012;367:2089–2099. doi: 10.1056/NEJMoa1206797 11. White HD, et al. Darapladib for preventing ischemic events in stable coronary heart disease. N Engl J Med. 2014;370:1702–1711. 12. Boden WE, et al. Niacin in patients with low HDL cholesterol levels receiving intensive statin therapy. N Engl J Med. 2011;365:2255–2267. 13. Armitage JM, et al. Effects of homocysteine-lowering with folic acid plus vitamin B12 vs placebo on mortality and major morbidity in myocardial infarction survivors: a randomized trial. JAMA. 2010;303: 2486–2494. 14. Global BMIMC, et al. Body-mass index and all-cause mortality: individualparticipant-data meta-analysis of 239 prospective studies in four continents. Lancet. 2016;388:776–786. 15. Wang ZJ, et al. Association of body mass index with mortality and cardiovascular events for patients with coronary artery disease: a systematic review and meta-analysis. Heart. 2015;101:1631–1638. doi: 10.1136/heartjnl-2014-307119 16. Cannon CP, et al.; Improve-IT Investigators. Ezetimibe added to statin therapy after acute coronary syndromes. N Engl J Med. 2015;372:2387– 2397. doi: 10.1056/NEJMoa1410489 17. Sabatine MS, et al.; Fourier Steering Committee and Investigators. Evolocumab and clinical outcomes in patients with cardiovascular disease. N Engl J Med. 2017;376:1713–1722. doi: 10.1056/NEJMoa1615664 18. Ridker PM, et al.; Cantos Trial Group. Antiinflammatory therapy with canakinumab for atherosclerotic disease. N Engl J Med. 2017;377:1119– 1131. doi: 10.1056/NEJMoa1707914 19. Patel RS, et al. The Genius-CHD consortium. Eur Heart J. 2015;36:2674–2676. 20. Rothman KJ, Greenland S. Modern epidemiology. 2nd ed. Philadelphia: Lippincott-Raven, 1998. 21. Lee JC, et al.; UK IBD Genetics Consortium. Genome-wide association study identifies distinct genetic contributions to prognosis and susceptibility in Crohn’s disease. Nat Genet. 2017;49:262–268. doi: 10.1038/ng.3755 22. Ohman EM, et al.; Reach Registry Investigators. The Reduction of Atherothrombosis for Continued Health (REACH) registry: an international, prospective, observational investigation in subjects at risk for atherothrombotic events-study design. Am Heart J. 2006;151:786.e1–786.e10. doi: 10.1016/j.ahj.2005.11.004 23. Psaty BM, et al. The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium as a model of collaborative science. Epidemiology. 2013;24:346–348. doi: 10.1097/EDE.0b013e31828b2cbb 24. Collins R, et al. What makes UK Biobank special? Lancet. 2012;379:1173– 1174. doi: 10.1016/S0140-6736(12)60404-8 25. Chen Z, et al.; China Kadoorie Biobank (CKB) collaborative group. China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long-term follow-up. Int J Epidemiol. 2011;40:1652–1666. doi: 10.1093/ije/dyr120 26. Reilly MP, et al.; Myocardial Infarction Genetics Consortium; Wellcome Trust Case Control Consortium. Identification of ADAMTS7 as a novel locus for coronary atherosclerosis and association of ABO with myocardial infarction in the presence of coronary atherosclerosis: two genome-wide association studies. Lancet. 2011;377:383–392. doi: 10.1016/S0140-6736(10)61996-4 27. Dahabreh IJ, et al. Index event bias as an explanation for the paradoxes of recurrence risk research. JAMA. 2011;305:822–823. doi: 10.1001/jama.2011.163 28. Nicholls SJ, et al. Effect of evolocumab on progression of coronary disease in statin-treated patients: the glagov randomized clinical trial. JAMA. 2016;316:2373–2384. doi: 10.1001/jama.2016.16951 29. Hu YJ, et al. Impact of selection bias on estimation of subsequent event risk. Circ Cardiovasc Genet. 2017;10:001616 Downloaded from http://ahajournals.org by on June 6, 2019