Risk of Incident Diabetes Related to Lipoprotein(a), LDL Cholesterol, and Their Changes With Alirocumab: Post Hoc Analyses of the ODYSSEY OUTCOMES Randomized Trial Free

Lipoprotein(a) is a risk factor for atherosclerotic cardiovascular disease and its complications. An unexplained finding in several cohorts is the inverse association of lipoprotein(a) concentration with prevalent and incident (new-onset) type 2 diabetes (NOD) (1–7). Some studies have associated lower lipoprotein(a) concentration with higher insulin concentrations and greater insulin resistance (IR) (2,8–10) while others have not (11,12), with caveats of small sample size or numbers of NOD events.

Loss-of-function variants in the PCSK9 and HMGCR genes that encode proprotein convertase subtilisin/kexin type 9 (PCSK9) and 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) are additively associated with lower levels of LDL cholesterol (LDL-C) and greater lifetime risk of NOD (13). Statin drugs inhibit HMGCR, lower LDL-C, and appear to increase the risk of NOD (14). Thus, previous data predicted that pharmacological inhibitors PCSK9 that lower lipoprotein(a) modestly and LDL-C substantially might increase risk of NOD, when added to statins. However, that prediction was not corroborated in large, placebo-controlled trials evaluating alirocumab or evolocumab (monoclonal antibodies against PCSK9) (15,16) or inclisiran (a small interfering RNA targeting PCSK9 mRNA) (17). In the ODYSSEY OUTCOMES trial, which compared alirocumab with placebo in patients with recent acute coronary syndrome, there was no overall effect of assigned treatment on NOD (15). However, lipoprotein(a) levels were inversely associated with prevalent diabetes at baseline and with NOD during follow-up in the placebo group (18). Together, these findings raise questions about the interplay of lipoprotein(a), LDL-C, and IR on the risk of NOD, including whether lipoprotein(a) is related to measures of IR, and whether the risk of NOD is related to baseline levels of lipoprotein(a), LDL-C, insulin, or IR or changes in their levels under treatment with alirocumab.

We addressed these questions in ODYSSEY OUTCOMES participants without diabetes at baseline to determine whether baseline lipoprotein(a), LDL-C, insulin, or the homeostasis model assessment (HOMA)-IR independently predicted NOD, and whether reductions in lipoprotein(a) and LDL-C with alirocumab were associated with changes in insulin or risk of NOD.

The ODYSSEY OUTCOMES trial (NCT01663402) (19) included 18,924 patients with recent acute coronary syndrome and elevated atherogenic lipoproteins despite high-intensity or maximum-tolerated statin treatment. Patients were enrolled at 1,315 sites in 57 countries and instructed to attend study visits in the fasting state. The primary outcome of death from coronary heart disease, nonfatal myocardial infarction, fatal or nonfatal ischemic stroke, or hospitalization for unstable angina pectoris was reduced with alirocumab compared with placebo. The trial was approved by the institutional review board of each site, and all patients provided informed consent.

The current post hoc analyses related lipoprotein(a), LDL-C, insulin, and IR to NOD in trial participants without diabetes at baseline who received at least one dose of study treatment and participated in a biomarker substudy. Diabetes at baseline was defined by a history of diabetes, baseline hemoglobin A1C ≥ 6.5%, or baseline fasting glucose ≥126 mg/dL (7 mmol/L). NOD was a prespecified secondary safety outcome including events from the first injection of study medication to 70 days after the last injection of study medication and defined by a diabetes-related adverse event, new use of antihyperglycemic medication, two consecutive measurements of fasting glucose ≥7 mmol/L, or two measurements of hemoglobin A1C ≥6.5% after randomization. Potential cases of NOD were reviewed and adjudicated by a blinded panel of experts as previously described (15).

Fasting glucose and LDL-C were measured at Covance Laboratories (Los Angeles, CA) (19). Serum samples for lipoprotein(a) and fasting insulin were received on dry ice and stored at −80°C until measurements were conducted at Leiden University Medical Center, the Netherlands. Insulin was measured with the Roche Elecys electrochemiluminescence assay. Lipoprotein(a) was measured with the Roche Tina-Quant Gen2 immunoturbidimetric assay (Roche Diagnostics, Penzberg, Germany) (20). HOMA-IR, a practical clinical index of IR (21), was calculated as fasting glucose (mmol/L) × fasting insulin (µU/mL)/22.5.

Spearman correlations were determined between baseline lipoprotein(a), LDL-C, insulin, glucose, and HOMA-IR. For modeling purposes, baseline lipoprotein(a), insulin, glucose, and HOMA-IR were log₂-transformed. Logistic regression models adjusted for assigned study treatment were applied to determine relationships between baseline biomarker levels and NOD.

The estimated risk of NOD from 4 months through 4 years after randomization was determined as a function of percent change of lipoprotein(a), insulin, or LDL-C from baseline to month 4 of assigned treatment. Glucose was not measured at month 4; therefore, HOMA-IR could not be calculated. For each predictor variable, risk of NOD was estimated by natural cubic splines from logistic regression models, adjusted for baseline concentrations of all three variables and percent change in the other two. Splines with knots at the 25th, 50th, and 75th percentiles were plotted from 5th to 95th percentiles. To account for interpatient differences in duration at risk for NOD, the logarithm of follow-up time was included as an offset. From the splines, the estimated risk of NOD for 25% and 50% decreases in lipoprotein(a) or LDL-C (compared with no change) was determined within the alirocumab group overall and for subgroups dichotomized at baseline lipoprotein(a) 125 nmol/L (a proposed threshold for elevated cardiovascular risk [3]). Finally, a sensitivity analysis was performed excluding patients with baseline insulin levels ≥95th percentile in case those measurements were not performed under fasting conditions.

A two-tailed P value <0.05 was considered statistically significant. Analyses were performed in SAS version 9.4 (SAS Institute).

The analysis cohort comprised 8,107 patients with baseline biomarker measurements (alirocumab, n = 4,066; placebo, n = 4,041), of whom 7,699 also had month 4 measurements (alirocumab, 3,877; placebo, 3,822). Baseline characteristics (Table 1) were generally balanced between treatment groups. Most patients were male and White. Statins were used in 96.9% (high-intensity statins in 89.4%). A majority had fasting glucose and hemoglobin A1C levels indicating prediabetes. Figure 1 shows histograms of baseline distributions of lipoprotein(a), LDL-C, insulin, glucose, and HOMA-IR. Distributions of lipoprotein(a), insulin, and HOMA-IR were skewed. Median baseline lipoprotein(a), LDL-C, insulin, glucose, and HOMA-IR were 46.8 (quartlle 1 [Q1], quartile 3 [Q3]: 13.5, 161.8) nmol/L, 87 (74, 105) mg/dL, 11.8 (7.8, 18.2) µU/mL, 5.4 (5.0, 5.8) mmol/L, and 2.8 (1.8, 4.51), respectively.

Spearman correlations between baseline levels of these biomarkers are shown in Supplementary Table 1. As expected, fasting glucose was correlated with fasting insulin (r = 0.33) and HOMA-IR (r = 0.47), and fasting insulin and HOMA-IR were closely correlated (r = 0.98). Baseline lipoprotein(a) had weak, negative correlations with fasting glucose (r = –0.04, P = 0.0002), insulin (r = –0.06, P < 0.0001), and HOMA-IR (r = –0.06, P < 0.0001), and a positive correlation with LDL-C (r = 0.13, P < 0.0001), noting that LDL-C was uncorrected for cholesterol content of lipoprotein(a).

During median follow-up 2.4 (Q1, Q3: 2.1, 3.3) years, premature discontinuation of study medication occurred in 611 patients (15.0%) in the alirocumab group and 663 patients (16.4%) in the placebo group. An additional 293 patients (7.3%) in the alirocumab group had protocol-specified blinded substitution with placebo for alirocumab following two consecutive LDL-C measurements < 15 mg/dL on alirocumab (19). Study medication adherence between baseline and month 4 was similar in both treatment groups (22).

There were 782 cases of NOD (alirocumab group, 383; placebo group, 399; odds ratio [OR] 0.998, 95% CI 0.860, 1.158; P = 0.98). Assigned treatment did not influence the risk of NOD among patients with baseline LDL-C at or below the median level of 87 mg/dL (data not shown). Baseline lipoprotein(a) was a modest inverse predictor and baseline insulin was a strong direct predictor of NOD, whereas LDL-C had no association with NOD (Supplementary Table 2). For a halving of baseline lipoprotein(a), the OR for NOD was 1.050 (95% CI 1.014, 1.088; P = 0.006). For a doubling of baseline insulin, the HR for NOD was 1.579 (95% CI 1.487, 1.677; P < 0.0001). There was no interaction between baseline lipoprotein(a) and insulin on NOD (P_interaction = 0.26). Findings were similar in a model substituting HOMA-IR for insulin. A 50 mg/dL lower baseline LDL-C had no association with the risk of NOD.

In the alirocumab group, median reduction from baseline to month 4 in lipoprotein(a) was 12.0 (Q1, Q3: 33.2, 2.2) nmol/L (corresponding to −26.5% [−5.4%, −47.7%; P < 0.001]) (Table 1 and Fig. 2). Corresponding reduction for LDL-C was 55.6 (Q1, Q3: 71.0, 40.2) mg/dL (corresponding to −65.5% [−75.8%, −50.9%]). There was no significant change in insulin from baseline to month 4 with alirocumab (median 4.6%, Q1, Q3: –24.0%, 43.0%). There was no correlation between percentage change in lipoprotein(a) or LDL-C and percentage change in insulin (both r = 0.010, P = 0.54). In the placebo group, there were no significant changes from baseline to month 4 in lipoprotein(a), LDL-C, or insulin (Table 1 and Fig. 2).

In the alirocumab group, 369 patients experienced NOD after month 4. Splines of the risk of NOD after month 4 as a function of the percent change of lipoprotein(a), LDL-C, and insulin at month 4 in the alirocumab group are presented in Fig. 3. Lipoprotein(a) and LDL-C splines had opposite and significant relationships with NOD (spline effects P = 0.038 and P = 0.036, respectively). Estimated OR for NOD after month 4 associated with 25% and 50% reductions in lipoprotein(a) with alirocumab at month 4 were 1.12 (95% CI 1.01, 1.23) and 1.24 (95% CI 1.02, 1.52), respectively. Conversely, estimated OR for NOD associated with 25% and 50% reductions in LDL-C with alirocumab were 0.88 (95% CI 0.80, 0.97) and 0.77 (95% CI 0.64, 0.94). Although alirocumab had no overall effect on insulin levels at month 4, patients with 25% and 50% increases in insulin from baseline to month 4 had estimated ORs for NOD after month 4 of 1.006 (95% CI 1.001, 1.012) and 1.013 (95% CI 1.003, 1.023), respectively.

In the placebo group, corresponding splines for 375 NOD events (Supplementary Fig. 1) showed no significant relationship of changes in lipoprotein(a) or LDL-C with NOD. As expected, change in insulin from baseline to month 4 was directly related to subsequent risk of NOD in both treatment groups (spline effect P < 0.0001).

Supplementary Fig. 2 shows absolute risk of NOD after month 4 in the alirocumab group according to baseline lipoprotein(a) dichotomized at 125 nmol/L and in both baseline categories for lipoprotein(a) reductions of 0%, 25%, or 50% at month 4. Overall, risk of NOD was greater in patients with lower baseline lipoprotein(a). However, in both baseline lipoprotein(a) categories, greater percentage reduction in lipoprotein(a) was associated with greater risk of NOD. Absolute lipoprotein(a) reduction was larger in patients with higher baseline lipoprotein(a), but the incremental risk of NOD with 25% or 50% lipoprotein(a) reductions was similar in both baseline categories.

A sensitivity analysis excluding patients with baseline insulin at or above the 95th percentile (41.2 µU/mL) showed no meaningful differences from the primary analysis (Supplementary Table 3 and Fig. 3).

Observations indicating an inverse relationship between lipoprotein(a) levels and prevalent (1,7,18) or incident type 2 diabetes (1,2,5,9) have gained attention and potential clinical relevance as new targeted therapeutic drugs are developed that substantially lower lipoprotein(a) concentration (23). The present analyses of ODYSSEY OUTCOMES investigated relationships of NOD with levels of lipoprotein(a), LDL-C, and insulin or HOMA-IR at baseline and on treatment with alirocumab or placebo, with three key findings.

First, baseline lipoprotein(a) had a modest but significant inverse correlation with baseline insulin and the IR index, HOMA-IR. The inverse correlation of lipoprotein(a) and HOMA-IR (r = –0.06) was similar to that previously reported by Kaya et al. (r = –0.08) (2). The modest correlations indicate that an individual’s lipoprotein(a) level is a minor determinant of the degree of IR. However, on a population level, the inverse correlations imply that cohorts with lower versus higher lipoprotein(a) levels have higher versus lower levels of insulin and HOMA-IR. Baseline LDL-C also had weak inverse correlation with levels of insulin and HOMA-IR, with recognition that cholesterol in lipoprotein(a) is measured as part of LDL-C, and no correction for this was attempted.

Second, baseline lipoprotein(a) (inversely) and either insulin or HOMA-IR (directly) were independent predictors of NOD, while baseline LDL-C was not. Over a median 2.4-years’ follow-up, comparing hypothetical patients with a twofold difference in baseline lipoprotein(a), the patient with lower lipoprotein(a) had an estimated OR of 1.050 (95% CI 1.014, 1.088) for NOD. As a corollary, if two hypothetical patients had lipoprotein(a) concentrations of 10 and 160 nmol/L (i.e., a 16-fold difference), the former patient’s risk of NOD would be predicted to be 1.216 (95% CI 1.057, 1.401) times the latter. Not surprisingly, baseline IR remained a much stronger predictor of NOD than lipoprotein(a): a doubling of baseline HOMA-IR with other characteristics similar was associated with an estimated OR of 1.81 for NOD.

Third, underlying overall neutral effects of alirocumab on insulin and NOD were directionally opposite relationships of lipoprotein(a) and LDL-C changes to risk of NOD. Percent reduction in lipoprotein(a) with alirocumab from baseline to month 4 was independently associated with increased risk of NOD, while percent reduction in LDL-C with alirocumab was independently associated with decreased risk of NOD.

Genetically lower LDL-C levels due to loss-of-function variants in PCSK9, HMGCR, or NPC1L1 are associated with greater risk of NOD (13,24); conversely, elevated LDL-C in familial hypercholesterolemia is associated with a lower risk of NOD (25). LDL-C lowering with high-intensity statin therapy has been associated with increased risk of NOD (14). However, both ODYSSEY OUTCOMES and the Further Cardiovascular Outcomes Research with PCSK9 Inhibition in Subjects with Elevated Risk (FOURIER) trial demonstrated a null effect of PCSK9 monoclonal antibodies (added to statins) on NOD despite substantial further LDL-C reduction (15,16). The current data indicate that a greater degree of LDL-C lowering by alirocumab corresponds to a lower risk of NOD. Reasons for the apparent discordance of the current findings with predictions from PCSK9 genetics and clinical trial data with statins are uncertain. Possible explanations include the fact that cohorts in genetic studies are generally initially healthy and insulin-sensitive, while many patients with acute coronary syndrome have a heightened inflammatory state and most are insulin-resistant. Inflammation may promote NOD (26). Mitigation of proinflammatory effects of PCSK9 by anti-PCSK9 agents (27–29) might delay or prevent NOD and play a role in the current findings. In addition, PCSK9 monoclonal antibodies have a greater effect to lower free PCSK9 and LDL-C levels than most genetic instrumental variables, and the relationship of LDL-C with NOD might differ at the very low LDL-C levels achieved on combined statin/PCSK9 inhibitor treatment.

In contrast, the current findings in a large, high-risk cohort that levels of lipoprotein(a) inversely associate with measures of IR and risk of NOD corroborate several previous observations in modest-sized cohorts. Three previous studies in patients without diabetes showed an inverse association of lipoprotein(a) with insulin and glucose levels or with HOMA-IR (9,10,30), and one indicated a synergistic effect of low lipoprotein(a) and IR on risk of NOD (9). The cellular mechanisms linking lipoprotein(a) concentration, IR, and risk for NOD remain elusive. Specifically, it is unknown whether low lipoprotein(a) promotes development or progression of IR leading to NOD, or, conversely, whether elevated insulin or IR suppresses lipoprotein(a) levels. The current data cannot resolve this question because, at month 4, the reduction of lipoprotein(a) by alirocumab was not yet accompanied by a change in insulin, and 97.1% of NOD occurred after month 4 (median 39 months), when no insulin or HOMA-IR measurements were available.

Some studies suggest that a larger number of lipoprotein(a) Kringle IV type 2 repeats, in turn associated with a lower lipoprotein(a) concentration, drive an inverse association of lipoprotein(a) concentration with NOD (31,32). If so, it has been argued (32) that pharmacological reduction of lipoprotein(a) should not influence NOD, because it does not change an individual’s lipoprotein(a) isoforms. The current data indicating that lipoprotein(a) lowering by alirocumab is associated with NOD argue against the isoform-dependent hypothesis.

Some previous studies have found no relationship of lipoprotein(a) levels or genetic traits affecting lipoprotein(a) levels with prevalent type 2 diabetes or its genetic traits (12,33). The absence of association in these studies may reflect their cross-sectional rather than longitudinal design. In previous longitudinal studies, an inverse relationship of lipoprotein(a) with NOD was most pronounced in the lowest quantile of lipoprotein(a) (1,4–7). An analogous finding in the current analysis (Supplementary Fig. 2) is that the absolute incremental risk of NOD with a 25% or 50% reduction of lipoprotein(a) was similar in patients with lower or higher baseline lipoprotein(a), despite smaller absolute reductions from baseline in the former category. Thus, low achieved lipoprotein(a) level, rather than large reduction in lipoprotein(a) level, may be the more important determinant of increased NOD risk with lipoprotein(a)-lowering treatment.

Drugs other than alirocumab lower lipoprotein(a), but, to date, those effects have not been evaluated in relation to NOD. In the FOURIER trial (34), the PCSK9 inhibitor evolocumab reduced lipoprotein(a) to an extent similar to that of alirocumab in ODYSSEY OUTCOMES, but a relationship between lipoprotein(a) reduction and NOD was not explored. Niacin reduces lipoprotein(a) to an extent similar to that of PCSK9 inhibitors, impairs glucose tolerance, and increases NOD (35); however, relationships of lipoprotein(a) lowering by niacin to changes in IR or NOD have not been investigated. Cholesteryl ester transfer protein inhibitors raise high-density lipoprotein cholesterol concentration, which is inversely associated with risk of NOD, generally reduce lipoprotein(a) and LDL-C concentrations, and reduce risk of NOD (36). Again, relationships among these effects have not been evaluated. Thus, the current data are the first to indicate that pharmacological reduction of lipoprotein(a) is independently associated with risk of NOD, after accounting for insulin and LDL-C concentrations.

Strengths of the current analysis include a large sample size and number of NOD events. Among the limitations, post hoc analyses are exploratory. The duration of follow-up was relatively brief. We cannot exclude the possibility that relationships between change in lipoprotein(a) or LDL-C on alirocumab and subsequent risk of NOD might become more or less pronounced over the longer term. It is also uncertain whether the present findings apply to PCSK9 inhibitors other than alirocumab. There were too few Black and Asian participants to draw inferences about the consistency of findings across racial groups. Potential isoform-dependent effects of lipoprotein(a)-lowering were not determined. PCSK9 inhibitors produce modest reductions in lipoprotein(a) and large reductions in LDL-C; although we analyzed the independent effects of these changes, it is uncertain whether the findings will apply to drugs in development that produce large reductions in lipoprotein(a) with modest reductions in LDL-C, or to longer-term treatment with PCSK9 inhibitors. We did not attempt to correct LDL-C for the cholesterol content in lipoprotein(a); doing so might have affected the independent associations of lipoprotein changes with risk of NOD. Data on insulin were available only at baseline and month 4. Therefore, it was not possible to determine whether insulin levels or IR changed after month 4, when most NOD occurred. However, the neutral overall effect of alirocumab on NOD makes this possibility less likely. HOMA-IR is not the gold standard to assess IR, but alternatives such as glucose tolerance testing or euglycemic hyperinsulinemic clamp are unfeasible in a large multicenter trial. Some cases of NOD may reflect small absolute increases in glucose or hemoglobin A1C from baseline levels slightly below criteria for diabetes, with uncertain clinical significance.

In summary, in the ODYSSEY OUTCOMES trial of statin-treated patients with recent acute coronary syndrome, baseline lipoprotein(a) concentration was inversely related to risk of NOD, independent of concurrent levels of insulin or HOMA-IR and LDL-C. Furthermore, underlying an overall neutral effect of alirocumab on NOD, alirocumab-mediated lipoprotein(a) reduction was independently associated with increased risk of NOD, while alirocumab-mediated LDL-C reduction was independently associated with decreased risk of NOD. Data to date have not raised concern that treatment with PCSK9 inhibitors including alirocumab, evolocumab, or inclisiran affects the risk of NOD (15–17). However, drugs are in development that specifically and substantially lower lipoprotein(a) concentration much more than PCSK9 inhibitors (23), but with smaller effects on LDL-C. Trials evaluating these drugs will determine whether larger and longer-duration lipoprotein(a) reductions in the absence of large LDL-C reductions affect the risk of NOD. In addition, mechanistic studies are needed to determine whether and how alterations of lipoprotein(a) concentration affect insulin action.

Acknowledgments. The authors thank the patients, study coordinators, and investigators who participated in the ODYSSEY OUTCOMES trial. Sophie Rushton-Smith, PhD (MedLink Healthcare Communications, London, U.K.), provided limited editorial assistance (manuscript formatting for style and submission) funded by Sanofi.

Funding. The ODYSSEY OUTCOMES trial was funded by Sanofi and Regeneron Pharmaceuticals. The biomarker analysis was funded by Sanofi and Roche Diagnostics.

Duality of Interest. G.G.S. reports research grants from the U.S. Department of Veterans Affairs Cooperative Studies Program, research support to the University of Colorado from AstraZeneca, Sanofi, and Silence Therapeutics, and support from the University of Oxford for travel to trial meetings. M.Sz. reports serving as a consultant or receiving research support, or both, from CiVi Biopharma, Resverlogix, Lexicon Pharmaceuticals, Baxter, Esperion, Amarin, NewAmsterdam Pharma, Sanofi, and Regeneron Pharmaceuticals, Inc. J.W.J. reports his department has received research grants from and/or he was a speaker (with or without lecture fees) for Continuing Medical Education accredited meetings sponsored/supported by Abbott, Amarin, Amgen, Athera Healthcare, BIOTRONIK, Boston Scientific, DalCor Pharmaceuticals, Daiichi Sankyo, Edwards Lifesciences, GE HealthCare, Johnson and Johnson, Lilly, Medtronic, Merck & Co., Novartis, Novo Nordisk, Pfizer, Roche, Sanofi, Shockwave Medical, the Netherlands Heart Foundation, CardioVascular Research the Netherlands, the Netherlands Heart Institute, and the European Community Framework KP7 Programme. C.M.C. reports research collaboration with Roche Diagnostics. V.A.B. reports grant support from Sanofi, Regeneron Pharmaceuticals, Amgen, AstraZeneca, DalCor Pharmaceuticals, Esperion, and Novartis; consulting fees from Pfizer; honoraria from Medscape; and fees for data safety monitoring boards for the National Institutes of Health and for Verve Therapeutics. M.Sc. is an employee of Roche Diagnostics International Ltd. and may hold shares in the company. D.L.B. discloses the following relationships: Advisory Board for 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, and Stasys; Board of Directors for American Heart Association New York City, ANGIOWave (stock options), Bristol-Myers Squibb (stock), DRSLINQ (stock options), and High Enroll (stock); consultant for Broadview Ventures, GlaxoSmithKline, Hims, SFJ Pharmaceuticals, and Youngene Therapeutics; data monitoring committees for Acesion Pharma, Assistance Publique-Hôpitaux de Paris, Baim Institute for Clinical Research (formerly Harvard Clinical Research Institute, for the PORTICO trial, funded by St. Jude Medical, now Abbott), Boston Scientific (Chair, PEITHO trial), Cleveland Clinic, Contego Medical (Chair, PERFORMANCE 2), Duke Clinical Research Institute, Mayo Clinic, Mount Sinai School of Medicine (for the ENVISAGE trial, funded by Daiichi Sankyo; for the ABILITY-DM trial, funded by Concept Medical; for ALLAY-HF, funded by Alleviant Medical), Novartis, Population Health Research Institute, and Rutgers University (for the NIH-funded MINT Trial); and received honoraria from 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 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), CSL Behring (AHA lecture), Cowen and Company, 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, 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 USA national co-leader, funded by Bayer), WebMD (CME steering committees), Wiley (steering committee); a relationship with Clinical Cardiology (Deputy Editor); a patent for Sotagliflozin (named on a patent for sotagliflozin assigned to Brigham and Women's Hospital who assigned to Lexicon Pharmaceuticals; neither I nor Brigham and Women's Hospital receive any income from this patent); research funding from Abbott, Acesion Pharma, Afimmune, Aker BioMarine, Alnylam Pharmaceuticals, Amarin, Amgen, AstraZeneca, Bayer, Beren Therapeutics, Boehringer Ingelheim, Boston Scientific, Bristol-Myers Squibb, Cardax, CellProthera, Cereno Scientific, Chiesi, CinCor Pharma, Cleerly, CSL Behring, Eisai, Ethicon, Faraday Pharmaceuticals, Ferring Pharmaceuticals, Forest Laboratories, Fractyl Health, Garmin, HLS Therapeutics, Idorsia, Ironwood Pharmaceuticals, Ischemix, Janssen, Javelin Biotech, Lexicon Pharmaceuticals, Lilly, Medtronic, Merck, Moderna, MyoKardia, NirvaMed, Novartis, Novo Nordisk, Otsuka, Owkin, Pfizer, PhaseBio Pharmaceuticals, PLx Pharma, Recardio, Regeneron Pharmaceuticals, Reid Hoffman Foundation, Roche, Sanofi, Stasys Medical, Synaptic Medical, The Medicines Company, Youngene, and 89Bio; royalties from Elsevier (Editor, Braunwald’s Heart Disease); Site Co-Investigator for Abbott, Biotronik, Boston Scientific, CSI, Endotronix, St. Jude Medical (now Abbott), Philips, SpectraWAVE, Svelte, and Vascular Solutions; Trustee for American College of Cardiology; and unfunded research for FlowCo. S.F. is an employee of Regeneron Pharmaceuticals and may hold shares in the company. G.G. is an employee of Sanofi and may hold shares in the company. S.G.G. reports research grant support (e.g., steering committee or data and safety monitoring committee) or speaker/consulting honoraria (e.g., advisory boards) from Amgen, Anthos Therapeutics, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol-Myers Squibb, CSL Behring, Daiichi Sankyo/American Regent, Eli Lilly, Esperion Therapeutics, Ferring Pharmaceuticals, HLS Therapeutics, JAMP Pharma, Merck, Novartis, Novo Nordisk, PendoPharm/Pharmascience, Pfizer, Regeneron Pharmaceuticals, Sanofi, Servier, and Valeo Pharma; and salary support/honoraria from the Heart and Stroke Foundation of Ontario/University of Toronto (Polo) chair, Canadian Heart Research Centre and MD Primer, Canadian VIGOUR Centre, Cleveland Clinic Coordinating Centre for Clinical Research, Duke Clinical Research Institute, New York University Clinical Coordinating Centre, PERFUSE Research Institute, and the TIMI Study Group (Brigham Health). R.A.H. reports research grants from the Patient-Centered Outcomes Research Institute, National Institutes of Health, CSL, and Janssen; consulting for Atropos Health, Bitterroot Bio, Bridge Bio, Bristol-Myers Squibb, ForeSight Medical, and Element Science; and serving on the boards of directors for the American Heart Association (unpaid) and Cytokinetics. H.D.W. reports grant support paid to the institution for the ODYSSEY OUTCOMES trial from Sanofi and Regeneron Pharmaceuticals, for the STRENGTH Trial from Omthera Pharmaceuticals, for the HEART-FID Study from American Regent, for the Dal GenE study from DalCor Pharma UK Inc, for the AEGIS II Study from CSL Behring, for the Clear Outcomes Study from Esperion Therapeutics, for the SOLIST-WHF and SCORED studies from Sanofi Aventis Australia Pty Ltd, for the Librexia AF and ACS studies from Janssen, and for ISCHEMIA and the MINT studies from the National Institutes of Health. He also received personal fees as a steering committee member for DalCor Pharma UK, CSL Behring, Sanofi Australia Pty Ltd, Janssen, and Esperion Therapeutics. He was on advisory boards for CSL Behring and Genentech. P.G.S. reports grants, personal fees, and nonfinancial support from Sanofi; grants and personal fees from Amarin, Servier, and Bayer; and personal fees from Amgen, AstraZeneca, BMS Medical, Boehringer Ingelheim, Idorsia, Pfizer, and Novartis. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. G.G.S., M.Sz., and P.G.S. conceived and designed the study. G.G.S. and P.G.S. obtained funding and supervised the work. G.G.S., M.Sz., J.W.J., C.M.C., E.R., V.A.B., M.Sc., D.L.B., S.F., G.G., S.G.G., R.A.H., H.D.W., and P.G.S. acquired, analyzed, and/or interpreted the data. G.G.S. drafted the manuscript. M.Sz. did the statistical analysis. All authors critically revised the manuscript for important intellectual content. P.G.S., G.G.S., and M.Sz. developed the trial protocol and statistical analysis plan in conjunction with the other members of the executive steering committee, which includes representatives of the funders. All authors had full access to all the data in the study and final responsibility for the decision to submit for publication. G.G.S., M.Sz., and P.G.S. have accessed and verified the data. G.G.S. and P.G.S. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. These data were presented, in part, at the American College of Cardiology 2024, Atlanta, GA, 7 April 2024 (https://doi.org/10.1016/S0735-1097(24)03954-8).

Handling Editors. The journal editors responsible for overseeing the review of the manuscript were Steven E. Kahn and Jennifer B. Green.