1 List of Abbreviations

CDM Common data model
DPP4 Dipeptidyl peptidase-4
GLP1 Glucagon-like peptide-1
IRB Institutional review board
LEGEND Large-scale Evidence Generation and Evaluation across a Network of Databases
MACE Major adverse cardiovascular event
MDRR Minimum detectable risk ratio
OHDSI Observational Health Data Science and Informatics
OMOP Observational Medical Outcomes Partnership
PS Propensity score
RCT Randomized controlled trial
SGLT2 Sodium-glucose co-transporter-2
T2DM Type 2 diabetes mellitus

2 Responsible Parties

2.1 Investigators

Investigator Institution/Affiliation
George Hripcsak Department of Biomedical Informatics, Columbia University, New York, NY, USA
Rohan Khera Department of Internal Medicine, Yale University, New Haven, CT, USA
Harlan M. Krumholz Department of Internal Medicine, Yale University, New Haven, CT, USA
Yuan Lu Department of Internal Medicine, Yale University, New Haven, CT, USA
Patrick B. Ryan Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA
Martijn J. Schuemie Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA
Marc A. Suchard * Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA
* Principal Investigator

2.2 Disclosures

This study is undertaken within Observational Health Data Sciences and Informatics (OHDSI), an open collaboration. RK is a founder of Evidence2Health, and receives grant funding from the US National Institutes of Health. MJS and PBR are employees of Janssen Research and Development and shareholders in John & Johnson. GH receives grant funding from the US National Institutes of Health and the US Food & Drug Administration and contracts from Janssen Research and Development. HMK receives grants from the US Food & Drug Administration, Medtronics and Janssen Research and Development, is co-founder of HugoHealth and chairs the Cardiac Scientific Advisory Board for UnitedHealth. MAS receives grant funding from the US National Institutes of Health, the US Department of Veterans Affairs and the US Food & Drug Administration and contracts from Janssen Research and Development and IQVIA.

3 Abstract

Background and Significance: Type 2 diabetes mellitus (T2DM) is a major cause of morbidity and mortality globally and is associated with an elevated risk of cardiovascular events. Therapeutic options for T2DM have expanded over the last decade with the emergence of sodium-glucose co-transporter-2 (SGLT2) inhibitors and glucagon-like peptide-1 (GLP1) receptor agonists, which reduced the risk of major cardiovascular events in randomized controlled trials (RCTs). Cardiovascular evidence for older second-line agents, such as sulfonylureas, and direct head-to-head comparisons, including with dipeptidyl peptidase 4 (DPP4) inhibitors, are lacking, leaving a critical gap in our understanding of the relative effects of T2DM agents on cardiovascular risk and on patient-centered safety outcomes.

Study Aims: To determine real-world comparative effectiveness and safety of traditionally second-line T2DM agents using health information encompassing millions of patients with T2DM, with a focus on individuals at moderate cardiovascular risk and other key subgroups.

Study Description: We will conduct three large-scale, systematic, observational studies to make pairwise comparisons of all SGLT2 inhibitor, GLP1 receptor agonist, DPP4 inhibitor and sulfonylurea agents at the drug-, class- and population subgroup-level within our proposed Large-Scale Evidence Generations Across a Network of Databases for T2DM (LEGEND-T2DM) initiative. LEGEND-T2DM will leverage the Observational Health Data Science and Informatics (OHDSI) community that provides access to a standing global network of administrative claims and electronic health record (EHR) data sources. The 13 data sources already committed to LEGEND-T2DM cover \(>\) 190 million patients in the US and about 50 million internationally, and include two academic medical centers, IBM MarketScan and Optum databases, and the US Department of Veterans Affairs. LEGEND-T2DM will study:

  • Population: Adult, T2DM patients who newly initiate a traditionally second-line T2DM agent, including individuals with and without established cardiovascular disease

Preliminary work in our data sources reveals > 1 million such new-users of SGLT2 inhibitors, GLP1 receptor agonists, DPP4 inhibitors or sulfonylureas with no prior, observed use of other second-line agents to best emulate the idealized RCT one would aim to run if it were practical, to compare:

  • Comparators:
    • SGLT2 inhibitors: canagliflozin, dapagliflozin, empagliflozin, ertugliflozin
    • GLP1 receptor agonists: albiglutide, dulaglutide, exenatide, liraglutide, lixisenatide, semaglutide
    • DPP4 inhibitors: alogliptin, linagliptin, saxagliptin, sitagliptin, vildagliptin
    • Sulfonylureas: chlorpropamide, glimepiride, glipizide, gliquidone, glyburide, tolazamide, tolbutamide

LEGEND-T2DM will execute all pairwise class-vs-class and drug-vs-drug comparisons in each data source that meet a minimum patient count of 1,000 per arm and extensive study diagnostics that assess reliability and generalizability through cohort balance and equipoise to examine the relative risk of cardiovascular and safety outcomes:

  • Outcomes:
    • Primary: 3- and 4-point major adverse cardiovascular events
    • Secondary effectiveness: Acute myocardial infarction, acute renal failure, glycemic control, hospitalization for heart failure, measured renal dysfunction, stroke, sudden cardiac death
    • Secondary safety: Abnormal weight gain or loss, acute pancreatitis, all-cause mortality, bladder cancer, bone fracture, breast cancer, diabetic ketoacidosis, diarrhea, genitourinary infection, hyperkalemia, hypoglycemia, hypoglycemia, hypotension, joint pain, lower extremity amputation, nausea, peripheral edema, photosensitivity, renal cancer, thyroid tumor, venous thromboembolism, vomiting

For each data source and comparison, LEGEND-T2DM will employ a state-of-the-art design:

  • Design: Observational: active-comparator, new-user cohort study

and committed LEGEND-T2DM data sources provide:

  • Timeframe: Up to 6-year (SGLT2 inhibitors) to 20-year (sulfonylureas) follow-up for all outcomes

Our systematic framework will address residual confounding, publication bias and \(p\)-hacking using data-driven, large-scale propensity adjustment for measured confounding, a large set of negative control outcome experiments to address unmeasured and systematic bias, prespecification and full disclosure of hypotheses tested and their results. These approaches capitalize on mature OHDSI open source resources and a large body of clinical and quantitative research that the LEGEND-T2DM investigators originated and continue to drive. Finally, LEGEND-T2DM is dedicated to open science and transparency and will publicly share all our analytic code from reproducible cohort definitions through turn-key software, enabling other research groups to leverage our methods, data, and results in order to verify and extend our findings.

4 Amendments and Updates

Number Date Section of study protocol Amendment or update Reason
1 7-Oct-2021 Milestones Update Add EU PAS #43551 registration date

5 Milestones

Milestone Planned / actual date
EU PAS registration 01-Oct-2021 / 07-Oct-2021
Start of analysis 01-Nov-2021
End of analysis
Results presentation

6 Rationale and Background

The landscape of therapeutic options for type 2 diabetes mellitus (T2DM) has been dramatically transformed over the last decade [1]. The emergence of drugs targeting the sodium-glucose co-transporter-2 (SGLT2) and the glucagon-like peptide-1 (GLP1) receptor has expanded the role of T2DM agents from lowering blood glucose to directly reducing cardiovascular risk [2]. A series of large randomized clinical trials designed to evaluate the cardiovascular safety of SGLT2 inhibitors and GLP1 receptor agonists found that use of many of these agents led to a reduction in major adverse cardiovascular events, including myocardial infarction, hospitalization for heart failure, and cardiovascular mortality [36]. However, other T2DM drugs widely used before the introduction of these novel agents, such as sulfonylureas, did not undergo similarly comprehensive trials to evaluate their cardiovascular efficacy or safety. Moreover, direct comparisons of newer agents with dipeptidyl peptidase-4 (DPP4) inhibitors, with neutral effects on major cardiovascular outcomes [710], have not been conducted. Nevertheless, DPP4 inhibitors and sulfonylureas continue to be used in clinical practice and are recommended as second-line T2DM agents in national clinical practice guidelines.

Several challenges remain in formulating T2DM treatment recommendations based on existing evidence [11]. First, trials of novel agents did not pursue head-to-head comparisons to older agents and were instead designed as additive treatments on the background of commonly used T2DM agents. Therefore, the relative cardiovascular efficacy and safety of novel compared with older agents is not known, and indirect estimates have relied on summary-level data restricted to common comparators [1214] and are less reliable [15,16]. Second, trials of novel agents have tested individual drugs against placebo, but have not directly compared SGLT2 inhibitors with GLP1 receptor agonists in reducing adverse cardiovascular event risk. Moreover, there is no evidence to guide the use of individual drugs within each class and across different drug classes, particularly among patients at lower cardiovascular risk than recruited in clinical trials. Third, randomized trials focused on cardiovascular efficacy and safety, but were not powered to adequately assess the safety of these agents across a spectrum of non-cardiovascular outcomes. Finally, restricted enrollment across regions, and subgroups of age, sex, and race further limits the efficacy and safety assessment that may guide individual patients’ treatment.

Evidence gaps from these trials also pose a challenge in designing treatment algorithms, which rely on comparative effectiveness and safety of drugs. Perhaps, as a result, there is large variation in clinical practice guidelines and in clinical practice with regard to these medications, with many patients initiated on the newer therapies and many others treated with older regimens [1721]. Among the second-line options, there is much variation with respect to the order of drugs used. This lack of consensus about the best approach provides an opportunity for systematic, large-scale observational studies.

7 Study Objectives

To inform critical decisions facing patients with diabetes, their caregivers, clinicians, policymakers and healthcare system leaders, we have launched the Large-Scale Evidence Generation and Evaluation across a Network of Databases for Diabetes (LEGEND-T2DM) initiative to execute a series of comprehensive observational studies to compare cardiovascular outcome rates and safety of second-line T2DM glucose-lowering agents. Specifically, these studies aim

  1. To determine, through systematic evaluation, the comparative effectiveness of traditionally second-line T2DM agents, SGLT2 inhibitors and GLP1 receptor agonists, with each other and with DPP4 inhibitors and sulfonylureas, for cardiovascular outcomes.
  2. To determine, through systematic evaluation, the comparative safety of traditionally second-line T2DM agents among patients with T2DM.
  3. To assess heterogeneity in effectiveness and safety of traditionally second-line T2DM agents among key patient subgroups: Using stratified patient cohorts, we will quantify differential effectiveness and safety across subgroups of patients based on age, sex, race, renal impairment, and baseline cardiovascular risk.

8 Research Methods

LEGEND-T2DM will execute three systematic, large-scale observational studies of second-line T2DM agents to estimate the relative risks of cardiovascular effectiveness and safety outcomes.

  1. The Class-vs-Class Study will provide all pairwise comparisons between the four major T2DM agent classes to evaluate their comparative effects on cardiovascular risk (Objective 1) and patient-centered safety outcomes (Objective 2);
  2. The Drug-vs-Drug Study will furnish head-to-head pairwise comparisons between individual agents within and across classes (both Objectives 1 and 2); and
  3. The Heterogeneity Study will refine these comparisons for T2DM patients for important subgroups (Objective 3). In contrast to a single comparison approach, LEGEND-T2DM will provide a comprehensive view of the findings and their consistency across populations, drugs, and outcomes. We will model each study on our successful collaborative research evaluating the comparative effectiveness of antihypertensives recently published in The Lancet [22].

Table 8.1 list the four major T2DM agent classes and the individual agents licensed in the U.S. within each class. We will examine all \({4 \choose 2} = 6\) class-wise comparisons and all \({5 + 6 + 4 + 7 \choose 2} = 231\) ingredient-wise comparisons.

For each comparison, we are interested in the relative risk of each of the cardiovascular and safety outcomes described in Section 8.5.

Table 8.1: T2DM drug classes and individual agents within each class
DPP4 inhibitors GLP1 receptor antagonists SGLT2 inhibitors Sulfonylureas
alogliptin albiglutide canagliflozin chlorpropamide
linagliptin dulaglutide dapagliflozin glimepiride
saxagliptin exenatide empagliflozin glipizide
sitagliptin liraglutide ertugliflozin gliquidone
vildagliptin lixisenatide glyburide
semaglutide tolazamide
tolbutamide

8.1 Study Design

For each study, we will employ an active comparator, new-user cohort design [2325]. New-user cohort design is advocated as the primary design to be considered for comparative effectiveness and drug safety [2628]. By identifying patients who start a new treatment course and using therapy initiation as the start of follow-up, the new-user design models an randomized controlled trial (RCT) where treatment commences at the index study visit. Exploiting such an index date allows a clear separation of baseline patient characteristics that occur prior to index date and are usable as covariates in the analysis without concern of inadvertently introducing mediator variables that arise between exposure and outcome [29]. Excluding prevalent users as those without a sufficient washout period prior to first exposure occurrence further reduces bias due to balancing mediators on the causal pathway, time-varying hazards, and depletion of susceptibles [28,30]. Our systematic framework across studies further will address residual confounding, publication bias, and p-hacking using data-driven, large-scale propensity adjustment for measured confounding [31], a large set of negative control outcome experiments to address unmeasured and systematic bias [3234], and full disclosure of hypotheses tested [35]. Figure 8.1 illustrates our design for all studies that the following sections describe in more detail.