Effect of PCSK9 Inhibition With Alirocumab in Patients With Probable Familial Hypercholesterolemia or Type III Hyperlipoproteinemia: Results From the ODYSSEY OUTCOMES Trial.
Aug 22, 2025DOI: 10.1161/jaha.124.041190Clinical Trial, Phase IIIAHA
Written by: Gregory P GebaKusha A MohammadiAmy DamaskCharles PauldingLuca A LottaGeorge HindyRobert PordyGaren ManvelianMichael D ShapiroVera A BittnerDeepak L BhattMichael SzarekGregory G SchwartzPh Gabriel StegSergio Fazio
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MS
Dr. Michael Shapiro
Preventive Cardiology · Wake Forest School of Medicine
DB
Dr. Deepak Bhatt
Interventional Cardiology · Mount Sinai
Methods
Qualified researchers may request access to study documents (including the clinical study report, study protocol with any amendments, blank case report form, and statistical analysis plan) that support the methods and findings reported in this article. Individual anonymized participant data will be considered for sharing (1) once the product and indication has been approved by major health authorities (eg, Food and Drug Administration, European Medicines Agency, Pharmaceutical and Medical Devices Agency) or development of the product has been discontinued globally for all indications on or after April 2020 and there are no plans for future development, (2) if there is legal authority to share the data, and (3) if there is not a reasonable likelihood of participant reidentification. Submit requests to https://vivli.org/.The primary efficacy and safety results for the ODYSSEY OUTCOMES trial have been published.4 In brief, the trial consisted of patients aged ≥40 years who had been hospitalized with ACS 1 to 12 months before randomization and had LDL‐C levels ≥70 mg/dL (1.81 mmol/L), non–high‐density lipoprotein cholesterol (HDL‐C) levels ≥100 mg/dL (2.59 mmol/L), or apolipoprotein B (Apo B) levels ≥80 mg/dL after at least 2 weeks of stable LLTs. Patients were randomly assigned (1:1) to blinded treatment with subcutaneous alirocumab 75 mg or matching placebo every 2 weeks. The dose of alirocumab was adjusted under blinded conditions to target an LDL‐C level of 25–50 mg/dL (0.6–1.3 mmol/L).To identify patients with HeFH in the ODYSSEY OUTCOMES trial, the DLCN criteria were used.5 As most patients were already on statins, the untreated LDL‐C concentration was calculated using the adjustment factor provided in the supporting documentation for the DLCN tool.5, 22 Untreated LDL‐C was calculated using LLT‐specific adjustment factors with a formula that was previously validated.8, 23 Back‐calculated, untreated LDL‐C and the presence of early‐onset coronary artery disease (age <55 years in men and <60 years in women) were the 2 criteria used to assess the DLCN score for each participant. We defined an individual as having HeFH if the DLCN score categorized them as having at least probable HeFH (DLCN score ≥6).This study was approved by the institutional review board/independent ethics committee, and was conducted in accordance with the principles of the Declaration of Helsinki, the Council for International Organizations of Medical Sciences International Ethical Guidelines, and the International Conference on Harmonization Good Clinical Practice guidelines. All patients provided written informed consent before participation.
A pharmacogenetic analysis was conducted in a subset of the patients from the ODYSSEY OUTCOMES study. A total of 11 880 patients provided consent to participate in this post hoc analysis and had a DNA sample that passed quality‐control measures. Patient samples were genotyped with the Illumina Global Screening Array, version 1.0 (Illumina, Inc., San Diego, CA) and underwent whole exome sequencing on the NovaSeq 6000 platform (Illumina, Inc.) with the xGen version 3.0 capture kit (IDT, Coralville, IA). Population structure was assessed using principal component analysis with plink version 1.9 (Cog.genomics.org).24, 25 Eigenvalues (axes of variation) from the top 10 principal components from this analysis were used as covariates in any subsequent genetic analyses. Genotype imputation was conducted with Minimac426, 27 with the TOPMed imputation panel.28
Source data for the polygenic risk score (PRS) was a meta‐analysis of genome‐wide association studies of lipid levels, comprising 188 577 individuals.29 The effect size estimates from this analysis were trained in an independent population (UK Biobank, 439 997 individuals with LDL‐C measures) to identify the optimal algorithm (maximum r 2 between the LDL‐C PRS and measured LDL‐C levels). The LDPred algorithm30 with a ρ value of 0.01 was selected. The weights derived from the LDPred algorithm, comprising up to 2 019 264 variants, were used to calculate the LDL‐C PRS in the ODYSSEY OUTCOMES population.
Genetic variants from the whole exome sequencing platform were evaluated to identify variants linked with HeFH.31 Identified variants met 1 of the following criteria: (1) ClinVar32 (July 2023 download) variant with an associated disease/condition of hypercholesterolemia and a review status of ≥1 star (variants with a 1‐star rating due to conflicting reports of pathogenicity were removed if ≥50% of the submissions did not report the variant as pathogenic or likely pathogenic); (2) predicted loss of function in LDLR (ie, a nonsense or splicing variant in LDLR); and (3) a copy number variant (ie, deletion) in LDLR. All variants had a minor allele frequency of <0.01. Predicted loss‐of‐function variants are expected to alter protein function (eg, frameshift, splice acceptor, splice donor, start lost, stop gained).
Patients with T3HLP were identified through a screening tool metric proposed by Murase et al.33 Using the baseline measurements of non–HDL‐C and Apo B levels, T3HLP was defined as a non–HDL‐C/Apo B ratio of ≥2.6. The initial validation was done using the large cohort of the UK Biobank (n=487 553), which has both phenotypic and genetic data (APOE genotypes). The UK Biobank is a population‐based prospective study that recruited individuals aged 40 to 69 years between 2006 and 2010.34 The generation of the UK Biobank genotyping array and quality control was previously described.35 The TOPMed reference panel was used for genome‐wide imputation.26, 28
Baseline characteristics for patients with HeFH and T3HLP were assessed. Changes in LDL‐C, HDL‐C, triglycerides, total cholesterol, and Apo B after treatment with alirocumab were evaluated from baseline to month 48. Changes in lipoprotein (a) after treatment with alirocumab were evaluated from baseline to month 12. MACEs (defined as the composite of death from coronary heart disease, nonfatal myocardial infarction, fatal or nonfatal ischemic stroke, and unstable angina requiring hospitalization) after treatment with alirocumab were assessed in patients with HeFH and T3HLP.
Continuous variables were summarized using mean±SD or median (interquartile range). For categorical or ordinal variables, frequencies and percentages were provided. Baseline characteristics were summarized by HeFH and T3HLP status. Additionally, changes in LDL‐C, HDL‐C, triglycerides, total cholesterol, Apo B, and lipoprotein (a) were summarized using descriptive statistics. Efficacy data were summarized by treatment arms for MACE outcomes in subgroups of HeFH and T3HLP. The proportion difference and 95% CIs were reported for descriptive purposes to make within‐group comparisons in patients with and without HeFH. Treatment HRs and 95% CIs were estimated using a Cox proportional hazard model, stratified by geographic region. A P value for interaction was calculated to assess the heterogeneity of treatment differences in subgroups by HeFH status. The proportional hazard assumptions were checked via the Schoenfeld test, and the influential observations were examined through the “difference in the estimates of β” values. The cumulative incidence of MACEs over time was estimated using Kaplan–Meier methods by treatment group. Due to the low sample size of patients with T3HLP, we provided only the incidence for descriptive comparisons by treatment arm and T3HLP status. In the genetic analyses, a linear regression model adjusting for age, sex, and 10 principal components were used to evaluate the relationship between the LDL‐C PRS and LLT‐adjusted (using the DLCN documentation noted above) baseline LDL‐C measures. All analyses were done in SAS 9.4 (SAS Institute, Cary, NC), plink 1.9/2.0, and R 4.1.2 (R Foundation for Statistical Computing, Vienna, Austria).
Results
Table 1 describes the demographics and baseline characteristics of all patients in the ODYSSEY OUTCOMES trial. Overall, the mean±SD age of all patients was 58.6±9.3 years, and the majority were men (75%) and White individuals (79%). The mean age of patients with HeFH was significantly lower than that of patients without HeFH (53.3±8.2 and 58.9±9.3 years, respectively; P<0.0001). Of the 18 924 patients in the ODYSSEY OUTCOMES study, 4.5% had probable HeFH and 1.3% had definite HeFH at baseline (Table 1). Within the pharmacogenomics subgroup, 63% of the original trial cohort (11 880/18924) had genetic data available for analysis; of these, 4.4% had probable HeFH and 1.5% had definite HeFH at baseline (Figure S1).
Demographics and Baseline Characteristics by FH Status
"Other" includes: Hawaiian/Pacific Islander, Alaska Native/American Indian, mixed race, unknown, prefer not to answer.
Genetic studies in the pharmacogenomics subgroup of patients revealed overall low genotype–phenotype concordance, as previously reported.36, 37, 38 A total of 295 patients were identified as having a hypercholesterolemia‐associated variant, and 45.8% of these patients met the criteria for probable (26.8%) or definite (19.0%) HeFH by clinical assessment alone (ie, LLT‐adjusted baseline LDL‐C or age at qualifying ACS event). The majority (n=281) of the identified genetic variants was annotated in ClinVar, including 46 of the 51 predicted loss‐of‐function (pLoF) variants in LDLR. Five patients had an LDLR pLoF not previously submitted to ClinVar (all 5 pLoFs were frameshift variants meeting FH variant curation expert panel consensus guidelines, ie, ClinGen of “very strong pathogenic” criteria),31 and LDLR copy number deletions were identified in 10 patients, summing to a total of 296 variants among the 295 carriers. Patients with loss‐of‐function variants (ie, pLoFs and copy number deletions) tended to have higher baseline LDL‐C levels (Table S1). Overall, variants in LDLR comprised the majority of FH‐associated variants; however, 25% of individuals with genetic FH carried variants in APOB. Among patients allocated to the alirocumab treatment arm, individuals with FH‐associated pLoF mutations (eg, nonsense, frameshift, splicing variants, or copy number deletions) showed a mean percentage decrease from baseline to month 4 in LDL‐C of 58.9%, comparable with the decreases in carriers of FH‐associated missense mutations (54.4%) and noncarriers (59.4%).
There was an enrichment in genetic HeFH carriers among patients with clinically defined probable (n=528) and definite (n=173) HeFH; the percentage of patients with genetic HeFH increased to 15.0% (n=79/528) and 32.4% (n=56/173), respectively. We applied a PRS for LDL‐C to explore genotype–phenotype concordance. Individuals in the top 10% of LDL‐C PRS were ≈28% more likely to meet the clinical criteria for probable or definite HeFH (7.6% of individuals in the top 10% by PRS qualified as having probable HeFH versus 5.9% in the overall pharmacogenomics cohort). Conversely, carriers of genetic HeFH were 7.8 times more likely to meet clinical HeFH criteria (probable/definite HeFH was observed in 45.8% of genetic HeFH carriers versus 5.9% overall). While individuals with higher PRS scores have greater risk of recurrent cardiovascular events, the risk is of far lesser magnitude in comparison with carriers of FH variants.
Patients with HeFH treated with alirocumab experienced marked reductions in LDL‐C, non–HDL‐C, and Apo B levels from baseline to month 48 (Figure 1). Although substantial reductions in lipid levels were observed for patients with both HeFH and non‐FH, lipid levels for patients with HeFH remained consistently higher for the study duration (Figure 1). A reduction in lipoprotein (a) levels also was observed for patients with both HeFH and non‐FH treated with alirocumab (Figure S2).
Apo B indicates apolipoprotein B; HDL‐C, high‐density lipoprotein cholesterol; HeFH, heterozygous familial hypercholesterolemia; and LDL‐C, low‐density lipoprotein cholesterolApo B indicates apolipoprotein B; HDL‐C, high‐density lipoprotein cholesterol; HeFH, heterozygous familial hypercholesterolemia; and LDL‐C, low‐density lipoprotein cholesterol
During a median 2.8 years of follow‐up, the incidence of MACEs was higher in placebo‐treated patients with HeFH than in those without HeFH. The MACE relative risk reduction with alirocumab was consistent among those with (HR, 0.85 [95% CI, 0.61–1.19]) and without HeFH (HR, 0.85 [95% CI, 0.77–0.93]; P for interaction=0.8328; Table 2; model assumptions were verified, see Figure S3). Of note, the absolute risk reduction was numerically larger in patients with HeFH compared with those without. The cumulative incidence of MACEs in patients with HeFH is shown in Figure S4. The incidence of other key secondary end points according to treatment group and HeFH status is summarized in Table S2 and indicates that the relative benefit of alirocumab did not depend on HeFH status.
Summary of MACEs in Subgroups of Patients With and Without HeFH
The P values for interaction given are the P values for the interaction term in the Cox proportional hazard model. The proportional hazard model assumptions were shown to hold, and no influential observations were identified for any of the analyses (Figure S3).
HeFH indicates heterozygous familial hypercholesterolemia; HR, hazard ratio; MACE, major adverse cardiovascular event; and RD, risk difference.
MACE is defined as composite of death from coronary heart disease, nonfatal myocardial infarction, fatal or nonfatal ischemic stroke, or unstable angina requiring hospitalization.
A similar proportion of placebo‐ and alirocumab‐treated patients with HeFH reported serious treatment‐emergent adverse events (26.4% and 24.1%, respectively), and the incidence of adverse events among patients with HeFH was similar to that of the overall ODYSSEY OUTCOMES cohort.
Patients with T3HLP were identified using a previously published but not yet validated T3HLP biomarker (ratio of non–HDL‐C/Apo B >2.6). Applying the T3HLP biomarker to the UK Biobank database validated its specificity (99%) and sensitivity (89%) to diagnose T3HLP among carriers of the diagnostic APOE2/2 genotype. A summary of lipid parameters according to APOE haplotype from the UK Biobank database is provided in Table 3.
Summary of Lipid Parameters by APOE Haplotype From the UK Biobank Database
HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; TC, total cholesterol, and VLDL‐C, very‐low‐density lipoprotein cholesterol.
When the validated biomarker was applied to the ODYSSEY OUTCOMES cohort, only 30 patients qualified for the diagnosis of T3HLP, of whom 22 had been assigned to receive alirocumab. Baseline characteristics according to T3HLP status are summarized in Table 4.
Demographics and Baseline Characteristics by T3HLP Status
HDL‐C indicates high‐density lipoprotein cholesterol; IQR, interquartile range; LDL‐C, low‐density lipoprotein cholesterol;; and T3HLP, type III hyperlipoproteinemia.
In the pharmacogenomics subgroup, 13 patients had the clinical T3HLP phenotype identified with the biomarker, and 7 of these (53.8%) patients had the APOE2/2 genotype (Table 5). Conversely, 23.3% (n=7) of all patients identified with the APOE2/2 genotype (n=30) presented with the T3HLP biomarker, whereas only 0.05% (n=6) of APOE2/2 noncarriers (n=11 850) were positive for the T3HLP biomarker (Table 5; formal statistical analyses not possible due to low numbers). None of the patients with T3HLP without the APOE2/2 genotype were carriers of genetic variants causing FH (LDLR, PCSK9, APOB) or associated with dyslipidemia (LCAT, CETP, ANGPTL3, LPL, APOC2, APOC3, APOA5, NPC1L1, or LRP1).
APOE Haplotypes by T3HLP Criteria Among Patients With T3HLP
T3HLP indicates type III hyperlipoproteinemia.
Treatment with alirocumab markedly lowered mean LDL‐C, non–HDL‐C, and Apo B levels from baseline to month 48 in patients with T3HLP. The lipid‐lowering effect was similar to that observed in those without T3HLP (Figure 2). Lipoprotein (a) levels for patients with and without T3HLP over time are shown in Figure S5.
Apo B indicates apolipoprotein B; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; and T3HLP, type III hyperlipoproteinemia. Apo B indicates apolipoprotein B; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; and T3HLP, type III hyperlipoproteinemia.
At baseline, the LDL‐C PRS predicted LLT‐adjusted mean LDL‐C levels (Figure S6). Figure S6A displays a genome‐wide LDL‐C PRS including candidate genes (LDLR, PCSK9, LPA, APOB, APOE, and HMGCR). Mean LDL‐C in the top decile was 195.6 mg/dL versus 169.9 mg/dL in the bottom decile (β per SD 7.1; P=2.3×10−40). The PRS gradient was slightly weakened after removing the candidate gene regions, with mean LDL‐C levels in the top and bottom deciles of 193.2 mg/dL and 172.0 mg/dL (Figure S6B) and with a β per SD of 5.9 (P=5.7×10−28).
Alirocumab treatment lowered mean LDL‐C levels irrespective of PRS status; the lipid‐lowering effect was similar across the top, middle, and bottom deciles (Figure S7).
The incidence of MACEs was numerically lower in patients with T3HLP treated with alirocumab versus those treated with placebo (9% versus 25%; Table 6). The incidence of other key secondary end points according to treatment group and T3HLP status is summarized in Table S3.
Summary of MACEs in Subgroups of Patients With and Without T3HLP
MACE indicates major adverse cardiovascular event; and T3HLP, type III hyperlipoproteinemia.
MACE is defined as composite of death from coronary heart disease, nonfatal myocardial infarction, fatal or nonfatal ischemic stroke, or unstable angina requiring hospitalization.
The incidence of treatment‐emergent adverse events among those with T3HLP was higher with placebo (75.0%) than with alirocumab (50.0%). Serious treatment‐emergent adverse events were experienced by 1 placebo‐treated patient (12.5%) and 3 alirocumab‐treated patients (13.6%), and the incidence of adverse events was not different from that in the overall ODYSSEY OUTCOMES cohort.
Discussion
We investigated the ODYSSEY OUTCOMES database to determine the lipid and clinical responses to alirocumab treatment in 2 groups of patients likely to carry genetic dyslipidemias (HeFH or T3HLP) and increased risk of cardiovascular disease (CVD) events due to lifelong exposure to dyslipidemia. The importance of lifetime exposure to high levels of LDL‐C is corroborated by our results, which show similar LDL‐C levels but higher CVD rates among patients with HeFH treated with alirocumab compared with patients without HeFH receiving placebo. Using clinical biomarkers, we determined that ≈6% of the total trial population could be classified as having probable HeFH. This is consistent with the entry criteria for this trial (elevated LDL‐C on maximally tolerated oral therapy and history of coronary artery disease). In contrast, only 30 patients with T3HLP were identified. This is also consistent with expectations because this condition is rare, recessive, and with low penetrance. Treatment with alirocumab decreased LDL‐C, non–HDL‐C, and Apo B levels in patients with HeFH or T3HLP to a similar extent compared with the overall study population. This is the first time that effective lipid lowering in patients with T3HLP has been shown with alirocumab or any PCSK9 inhibitor. In addition, patients with HeFH showed similar treatment HR for MACEs compared with the overall study population, although the absolute reduction in risk of MACE was numerically greater in the former group.
Our results provide additional support for the concept that individuals with ACS and hyperlipidemia may have unrecognized genetic dyslipidemias, conferring a higher risk of CVD events, and that these individuals are responsive to effective LLTs. The younger age of individuals with versus without HeFH in this study, together with the increased rate of MACEs in those with HeFH, highlights the need for early diagnosis of this condition. Our data show an overall modest concordance between genetic and clinical diagnosis of HeFH, with a number needed to test to find a genetically confirmed patient with HeFH of ≈40. These results suggest that genetic testing in patients with post‐ACS with elevated LDL‐C may be cost effective. Furthermore, given the relatively low cost of genetic testing in the United States, the benefit of cascade screening in HeFH is potentially very large. Patients with HeFH have been extensively investigated and shown to respond to PCSK9 inhibitor monoclonal antibodies,2, 39 but the clinical outcome studies have been designed without the intent to enrich for, or even clearly identify, individuals with HeFH. A similar effect on LDL‐C and CVD rates for patients with or without FH was also suggested in an analysis of the SPIRE (Studies of PCSK9 Inhibition and the Reduction of Vascular Events) program of clinical trials of bococizumab, a PCSK9 inhibitor monoclonal antibody whose development was halted.40 However, real‐world evidence suggests that these medications provide clinical benefits to patients with HeFH, and the indications for use already mention this condition.41, 42
The pharmacogenomics analysis highlights a previously well‐documented genotype–phenotype discordance.43 When evaluating variants in LDLR and other genes associated with HeFH, 2.5% of patients in the overall pharmacogenomics subgroup were carriers. Although rare, genetically confirmed HeFH status is a much stronger predictor of elevated LDL‐C and earlier age of CVD onset than LDL‐C PRS. Nevertheless, within the ODYSSEY OUTCOMES cohort, additional genetic drivers beyond the 4 known genes affecting LDLR function44 played a role in the development of the clinical HeFH phenotype.
T3HLP has been known for many decades to be caused by variants in APOE, most commonly homozygosity for the common APOE2 variant and less commonly from rare heterozygous variants in APOE. Our results confirmed the very low frequency of the T3HLP lipid abnormality, with only 30 individuals identified using the validated biomarker. Seventeen of these 30 individuals were also in the genetic subgroup, with the APOE2/2 genotype strongly represented among carriers (23% APOE2/2 carriers versus only 0.05% APOE2/2 noncarriers were T3HLP positive), supporting the notion that T3HLP was the correct diagnosis for most of these patients. Genetic testing is seldom used to diagnose T3HLP, due to insurance coverage issues and the APOE–Alzheimer connection that makes physicians wary of the consequences of knowing the APOE genotype.
This study includes limitations inherent to post hoc analyses. Other limitations include back‐calculated LDL‐C levels to derive HeFH diagnosis and a smaller pharmacogenomics subgroup, making it difficult to draw inferences about possible CVD risk reduction in patients with T3HLP. In addition, the phenotypic definition of T3HLP did not account for statin use. Moreover, the DLCN scoring process did not include family history data, as these did not include information on age at the time of event. Additionally, at entry into the trial, case report forms did not collect information regarding a prior clinical or genetic diagnosis of HeFH. Therefore, we do not know the proportion of patients with genetically proven or clinically presumed HeFH. The lack of interaction with/inability to inform participants and their relatives during this post hoc analysis was a missed opportunity and a lesson for future trials. Finally, it was assumed the T3HLP biomarker ratio is constant when untreated patients are taking high‐intensity statins.
In conclusion, our study shows that patients with HeFH, identified using established criteria, appear to benefit from PCSK9 inhibition similarly to patients with common hypercholesterolemia in terms of lipid responses and clinical outcomes. In addition, patients with T3HLP, identified using a biomarker ratio validated using the UK Biobank database, also benefit from PCSK9 inhibition similarly to patients with common dyslipidemia in terms of lipid responses.
Sources of Funding
This analysis was funded by Regeneron Pharmaceuticals, Inc. The sponsor was involved in the study design and collection, analysis, and interpretation of data, as well as data checking of information provided in the article.
Disclosures
G.P.G., K.A.M., A.D., C.P., L.A.L., G.H., R.P., G.M., and S.M. are all employees of and shareholders in Regeneron Pharmaceuticals, Inc. M.D.S. reports participation grant support from DCRI, Esperion, Ionis, Merck, NHLBI, New Amsterdam, Novartis, and Amgen, all paid direct to his institution; scientific advisory boards with Amgen, Agepha, Novartis, and Ionis; and consultancy for Regeneron Pharmaceuticals, Inc., Ionis, Novartis, Aidoc, Shanghai Pharma Biotherapeutics, and Novo Nordisk. V.A.B. reports grant support from Sanofi, AstraZeneca, DalCor, Esperion, Bayer, Novartis, and Amgen, all paid directly to her institution; and personal fees from Sanofi, Pfizer, and Verve Therapeutics. D.L.B. reports the following relationships: Advisory Board: Angiowave, Bayer, Boehringer Ingelheim, CellProthera, Cereno Scientific, Elsevier Practice Update Cardiology, High Enroll, Janssen, Level Ex, McKinsey, Medscape Cardiology, Merck, MyoKardia, NirvaMed, Novo Nordisk, PhaseBio, PLx Pharma, Stasys; Board of Directors: American Heart Association New York City, Angiowave (stock options), Bristol Myers Squibb (stock), DRS.LINQ (stock options), High Enroll (stock); Consultant: Broadview Ventures, GlaxoSmithKline, Hims, SFJ, Youngene; Data Monitoring Committees: Acesion Pharma, Assistance Publique‐Hôpitaux de Paris, Baim Institute for Clinical Research (formerly Harvard Clinical Research Institute, for the PORTICO [Portopulmonary Hypertension Treatment wIth Macitentan ‐ a Randomized Clinical Trial], funded by St. Jude Medical, now Abbott), Boston Scientific (Chair, PEITHO [Pulmonary Embolism Thrombolysis Study] trial), Cleveland Clinic, Contego Medical (Chair, PERFORMANCE 2 [Protection Against Emboli During Carotid Artery Stenting Using the Neuroguard IEP System]), Duke Clinical Research Institute, Mayo Clinic, Mount Sinai School of Medicine (for the ENVISAGE‐TAVI AF [Edoxaban Compared to Standard Care After Heart Valve Replacement Using a Catheter in Patients With Atrial Fibrillation] trial, funded by Daiichi Sankyo; for the ABILITY‐DM [Randomized Comparison of Abluminus DES+ Sirolimus‐Eluting Stents Versus Everolimus‐Eluting Stents in Coronary Artery Disease Patients With Diabetes Mellitus Global] trial, funded by Concept Medical; for Alleviant ALLAY‐HF, funded by Alleviant Medical), Novartis, Population Health Research Institute; Rutgers University (for the National Institutes of Health–funded MINT [Myocardial Ischemia and Transfusion] trial); Honoraria: American College of Cardiology (Senior Associate Editor, Clinical Trials and News, ACC.org; Chair, ACC Accreditation Oversight Committee), Arnold and Porter law firm (work related to Sanofi/Bristol‐Myers Squibb clopidogrel litigation), Baim Institute for Clinical Research (formerly Harvard Clinical Research Institute; RE‐DUAL PCI [Evaluation of Dual Therapy With Dabigatran vs. Triple Therapy With Warfarin in Patients With AF That Undergo a PCI With Stenting] clinical trial steering committee funded by Boehringer Ingelheim; AEGIS‐II [Study to Investigate CSL112 in Subjects With Acute Coronary Syndrome] 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), CSL Behring (American Heart Association lecture), Cowen and Company, Duke Clinical Research Institute (clinical trial steering committees, including for the PRONOUNCE [A Trial Comparing Cardiovascular Safety of Degarelix Versus Leuprolide in Patients With Advanced Prostate Cancer and Cardiovascular Disease] 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, Oakstone CME (Course Director, Comprehensive Review of Interventional Cardiology), Piper Sandler, Population Health Research Institute (for the COMPASS operations committee, publications committee, steering committee, and US national co‐leader, funded by Bayer), WebMD (continuing medical education steering committees), Wiley (steering committee); Other: Clinical Cardiology (Deputy Editor); Patent: sotagliflozin (named on a patent for sotagliflozin assigned to Brigham and Women's Hospital who assigned to Lexicon; neither he nor Brigham and Women's Hospital receive any income from this patent); Research Funding: Abbott, Acesion Pharma, Afimmune, Aker Biomarine, Alnylam, Amarin, Amgen, AstraZeneca, Bayer, Beren, Boehringer Ingelheim, Boston Scientific, Bristol‐Myers Squibb, Cardax, CellProthera, Cereno Scientific, Chiesi, CinCor, Cleerly, CSL Behring, Eisai, Ethicon, Faraday Pharmaceuticals, Ferring Pharmaceuticals, Forest Laboratories, Fractyl, Garmin, HLS Therapeutics, Idorsia, Ironwood, Ischemix, Janssen, Javelin, Lexicon, Lilly, Medtronic, Merck, Moderna, MyoKardia, NirvaMed, Novartis, Novo Nordisk, Otsuka, Owkin, Pfizer, PhaseBio, PLx Pharma, Recardio, Regeneron Pharmaceuticals, Inc., Reid Hoffman Foundation, Roche, Sanofi, Stasys, Synaptic, The Medicines Company, Youngene, 89Bio; Royalties: Elsevier (Editor, Braunwald's Heart Disease); Site Co‐Investigator: Abbott, Biotronik, Boston Scientific, CSI, Endotronix, St. Jude Medical (now Abbott), Philips, SpectraWAVE, Svelte, Vascular Solutions; Trustee: American College of Cardiology; and unfunded research: FlowCo. M.S. reports grants from Sanofi, Regeneron Pharmaceuticals, Inc., Lexicon, Resverlogix, Baxter, and Janssen; and personal fees from CiVi, Esperion, Silence, and NewAmsterdam. G.G.S. reports research support to the University of Colorado from AstraZeneca, Sanofi, Silence Therapeutics, and The Medicines Company; and is coinventor of pending US patent 62/806313 (“Methods for Reducing Cardiovascular Risk”) assigned in full to the University of Colorado. P.G.S. reports grants and nonfinancial support (co‐chair of the ODYSSEY OUTCOMES [Evaluation of Cardiovascular Outcomes After an Acute Coronary Syndrome During Treatment With Alirocumab] trial; as such, he received no personal fees, but his institution has received funding for the time he has devoted to trial coordination, and he has received support for travel related to trial meetings) from Sanofi; research grants and personal fees from Bayer (Steering Committee MARINER, grant for epidemiological study), Merck (speaker fees, grant for epidemiological studies), Sanofi (Co‐Chair of the ODYSSEY OUTCOMES trial; Co‐Chair of the SCORED [Effect of Sotagliflozin on Cardiovascular and Renal Events in Participants With Type 2 Diabetes and Moderate Renal Impairment Who Are at Cardiovascular Risk] trial; consulting, speaking), Servier (Chair of the CLARIFY registry; grant for epidemiological research), and Amarin (executive steering committee for the REDUCE‐IT [A Study of AMR101 to Evaluate Its Ability to Reduce Cardiovascular Events in High‐Risk Patients With Hypertriglyceridemia and on Statin] trial [Disease Reduction of Cardiovascular Events With Icosapent Ethyl–Intervention Trial]; consulting); personal fees from Amgen, Bristol‐Myers Squibb, Boehringer Ingelheim, Pfizer, Idorsia, Myokardia, Novo Nordisk, Novartis, Regeneron Pharmaceuticals, Inc., and AstraZeneca; and having a European application number/patent number issued on October 26, 2016 (No. 15712241.7) for a method for reducing cardiovascular risk, all royalties assigned to Sanofi.
Supporting information
Tables S1–S3
Figures S1–S7
MS
Dr. Michael Shapiro
Cardiology · Preventive CardiologyWake Forest University Health Sciencesmichaels
