Comparative Cardiovascular Effectiveness of GLP-1 RAs vs. Insulin in Early-Onset Type 2 Diabetes

NCT ID: NCT06913881

Last Updated: 2025-04-06

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 92100 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2010-01-01
• Study Completion Date: 2025-01-31

Brief Summary

The incidence of early-onset type 2 diabetes (T2D) is increasing globally. People with early-onset T2D have faster beta-cell deterioration and more aggressive diabetes progression than their late-onset counterparts. However, there is no specific treatment guideline tailored to the early-onset population.

Glucagon-like peptide-1 receptor agonists (GLP-1 RA) belong to a new class of glucose-lowering drugs that have been shown to promote weight loss and reduce incidence of cardiovascular diseases (CVD) in individuals with (mostly late-onset) T2D. Given the much higher prevalence of obesity, a key risk factor for CVD, in early-onset T2D, the cardiovascular benefits of the GLP-1 RA may be more pronounced in early-onset than in late-onset T2D. In addition, insulin use is highly prevalent in people with early-onset T2D. However, no randomized controlled trial (RCT) has evaluated the comparative effectiveness of GLP-1 RA and insulin on CVD incidence and CVD risk factors in this population.

Our objective is to investigate the optimal treatment strategies in people with early-onset T2D. Specifically, we aim to (1) compare CVD risk in GLP-1 RA and insulin users and (2) examine changes in key CVD risk factors, such as HbA1c, BMI, and lipid profiles, before and after initiation of GLP-1 RA, SGLT2 inhibitors, and insulin.

Based on real-world data from Sweden we will emulate a target trial to minimize selection bias and confounding.

This study will generate robust evidence to guide clinicians in optimizing treatment for early-onset T2D. Given the rising burden of this condition and the lack of specific treatment recommendations, our findings have the potential to improve long-term cardiovascular outcomes in this high-risk population.

Detailed Description

Yuxia Wei Introduction The incidence of T2D in adolescence and young adults is increasing alarmingly worldwide[1]. Early-onset (age at diagnosis <40) T2D is more aggressive and has faster beta-cell deterioration than late-onset T2D[2]. However, they are treated in the same way with late-onset T2D and there is no specific guideline tailored to early-onset T2D. There is therefore an urgent need to investigate the optimal treatment strategy in this age group. Previous treatment studies on early-onset T2D mostly focus on T2D diagnosed in children and adolescence (<18 years)[3] while studies in young adults are scarce[4,5].

Glucagon-like peptide-1 receptor agonists (GLP-1 RA), a new class of glucose-lowering drugs that have substantial effect on weight reduction, have been shown to reduce incidence of cardiovascular disease (CVD) in people with (mostly late-onset) T2D in both randomized clinical trials and observational studies[6-9]. Since people with early-onset T2D have a much higher prevalence of obesity[2], we hypothesize that they may benefit more from GLP-1 RA than their late-onset counterparts.

The aim of this study is to evaluate the cardiovascular benefits of GLP-1 RA added to metformin in young adults with T2D (diagnosed at age 18-39 years). Since insulin use is prevalent in people with early-onset T2D, we will compare GLP-1 RA users to insulin users. For comparison reason, we will also assess CVD incidence in GLP-1 RA users versus insulin users in late-onset (age ≥40 years) T2D.

Methods Study population We will apply an emulated target trial framework (Table 2 shows the specification and emulation of the target trial) based on Swedish nationwide registers to emulate a randomized clinical trial of the comparative effectiveness of GLP-1 RA and insulin on CVD incidence. Potential participants are people diagnosed with T2D at age ≥18 years in 1997-2020 (n=617,727), with at least one record in the National Diabetes Register (NDR). The date of diabetes diagnosis is defined as the first record of diabetes in NDR or the National Patient Register (NPR), or the first record of glucose-lowering drug prescription in the National Prescribed Drug Register (NPDR), or the middle of the self-reported year at diagnosis recorded in NDR, whichever comes first.

We will include people diagnosed with T2D for more than 6 months and with metformin use within 6 months before the initiation of the treatment of interest (GLP-1 RA or insulin). Individuals will be excluded if they meet one of the following criteria any time (unless stated otherwise) before or at treatment initiation: (1) other types of diabetes diagnosis recorded in NDR or NPR; (2) diagnosis of acute pancreatitis one month prior to treatment initiation; (3) diagnosis of other pancreatic diseases (chronic pancreatitis, cyst of pancreatis, etc; ICD-10 code K86) recorded in NPR; (4) use of GLP-1 RA or insulin within 1 year prior to treatment initiation, (5) advanced kidney disease (eGFR<30 mL/min/1.73 m2, dialysis or kidney transplant [procedure codes: KAS[10]]); (6) end-stage liver disease (K70.4, K71.1, K72, Z94.4, procedure codes JJC); (7) cancer of the digestive system (ICD codes: C15-C26) or endocrine glands such as thyroid (ICD codes: C73-C75); (8) inflammatory bowel diseases (ICD-10 K50-K52) (Table 2).

Treatment Information on prescribed drug use will be retrieved from the NPDR, which was established in June of 2005. We will create two arms of participants based on treatment information in 2010-2020. The exposure arm are new users of GLP-1 RA (with at least one dispensation of GLP-1 RA, without restriction on dose) while the comparison arm are insulin users (with at least one dispensation of insulin, without restriction on dose). The ATC codes for different glucose-lowering drugs are listed in Table 1. The date of dispensation is regarded as the time of treatment initiation. Further additional treatment or alternative treatment after the treatment initiation is allowed as needed.

Outcomes The primary outcome is major adverse cardiovascular events (MACE), defined as a composite outcome of cardiovascular death (ICD-10 codes: I00-I99) recorded in the Causes-of-Death Register, and nonfatal myocardial infarction (ICD-10: I21, I22 in NPR), nonfatal stroke (I60-61, I63-64), and hospitalized heart failure (I50) recorded in NPR. The secondary outcome is an expended composite CVD outcome that included cardiovascular death (I00-I99), any ischemic heart disease (I20-I25), stroke (I60-61, I63-64), hospitalized heart failure (I50), coronary revascularization (procedure codes in National Patient Register: FNA, FNB, FNC, FND, FNE, FNG[6]), and peripheral arterial diseases (I70, I73.9). For CVD-related ICD codes recorded in NPR, we will use primary and up to 6 secondary diagnoses from inpatient and outpatient data to define the first CVD event. However, among those with CVD-related ICD codes recorded before treatment initiation, if the same ICD code is recorded again in NPR after treatment initiation, the person will be identified as having a new CVD event during follow-up only if the ICD code is recorded as the primary diagnosis in the inpatient data.

We will also analyze death from any cause as a secondary outcome. Covariates BMI and HbA1c are the main factors affecting the choice of different treatments. We will retrieve BMI and HbA1c data up to 5 years prior to treatment initiation from NDR. Information on country of birth, educational level and sex will be obtained from the Total Population Register. Data on comorbidities and hospitalizations will be obtained from NPR while treatment data will be obtained from NPDR.

Statistical analysis The main analysis will be an intention-to-treat analysis, and the follow-up duration will be calculated from the time of treatment initiation until the occurrence of outcome, death, emigration, or December 31, 2021 (June 8, 2022, for all-cause mortality).

We will use logistic regression models to estimate the cumulative incidence of outcomes at given time points during follow-up (such as 1 or 2 years) for each treatment group. The risk difference between different treatment groups and risk ratios will then be calculated based on cumulative incidence.

We will balance the baseline characteristics between different treatment groups using the inverse probability weighting method. The baseline characteristics include country of birth, highest attained level of education, year at diabetes diagnosis, age at treatment initiation, sex, (1997-2000, 20001-2005, 2006-2010, 2010-2020), diabetes duration at treatment initiation, HbA1c (continuous) and BMI (continuous) within 5 years before treatment initiation (the record closest to the time of treatment initiation will be used if multiple records exist for the individual), the time gap between HbA1c (or BMI) measurement and treatment initiation, the interaction term between HbA1c (or BMI) and the time gap, and comorbidities, hospitalizations and drug use as listed in Table 3. Specifically, for glucose-lowering drug use, we consider the following factors as covariates: SGLT2 inhibitor use, DPP-4 inhibitor use, and use of other glucose-lowering drugs within 6 months prior to treatment initiation. People without HbA1c (or BMI) data within 5 years before treatment initiation will be assigned the population median values with an additional binary variable to indicate if the values are imputed or not.

Other analyses We will use change in body weight before and after treatment initiation as a positive comparison outcome, among people with records of body weight both within 6 (or 3?) months prior to and after treatment initiation.

As a sensitivity analysis, we will also use a per-protocol analysis, in which participants will be additionally censored at the time of treatment discontinuation (if they did not receive a refill 90 days after the dispensation. Participants are considered to have discontinued the drug if they did not receive a refill 90 days after the dispensation of the assigned drug.

References

1. Xie J, Wang M, Long Z, et al. Global burden of type 2 diabetes in adolescents and young adults, 1990-2019: systematic analysis of the Global Burden of Disease Study 2019. BMJ 2022; 379: e072385.

2. Magliano DJ, Sacre JW, Harding JL, Gregg EW, Zimmet PZ, Shaw JE. Young-onset type 2 diabetes mellitus - implications for morbidity and mortality. Nat Rev Endocrinol 2020; 16(6): 321-31.

3. Misra S, Ke C, Srinivasan S, et al. Current insights and emerging trends in early-onset type 2 diabetes. Lancet Diabetes Endocrinol 2023; 11(10): 768-82.

4. Chan JCN, Paldánius PM, Mathieu C, Stumvoll M, Matthews DR, Del Prato S. Early combination therapy delayed treatment escalation in newly diagnosed young-onset type 2 diabetes: A subanalysis of the VERIFY study. Diabetes Obes Metab 2021; 23(1): 245-51.

5. Zeitler P, Galindo RJ, Davies MJ, et al. Early-Onset Type 2 Diabetes and Tirzepatide Treatment: A Post Hoc Analysis From the SURPASS Clinical Trial Program. Diabetes Care 2024; 47(6): 1056-64.

6. Svanström H, Ueda P, Melbye M, et al. Use of liraglutide and risk of major cardiovascular events: a register-based cohort study in Denmark and Sweden. Lancet Diabetes Endocrinol 2019; 7(2): 106-14.

7. Hernandez AF, Green JB, Janmohamed S, et al. Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial. Lancet 2018; 392(10157): 1519-29.

8. Marso SP, Daniels GH, Brown-Frandsen K, et al. Liraglutide and Cardiovascular Outcomes in Type 2 Diabetes. N Engl J Med 2016; 375(4): 311-22.

9. Marso SP, Bain SC, Consoli A, et al. Semaglutide and Cardiovascular Outcomes in Patients with Type 2 Diabetes. N Engl J Med 2016; 375(19): 1834-44.

10. Xie Y, Bowe B, Xian H, Loux T, McGill JB, Al-Aly Z. Comparative effectiveness of SGLT2 inhibitors, GLP-1 receptor agonists, DPP-4 inhibitors, and sulfonylureas on risk of major adverse cardiovascular events: emulation of a randomised target trial using electronic health records. Lancet Diabetes Endocrinol 2023; 11(9): 644-56.

Conditions

Type 2 Diabetes Mellitus (T2DM)

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: PROSPECTIVE

Study Groups

Early onset T2D with GLP1 RA

People with early-onset T2D and metformin who initiate GLP1-RA treatment

GLP-1

Intervention Type: DRUG

We will compare people who have picked up GLP1 to those who have picked up insulin at any Swedish pharmacy

Early onset T2D with insulin

People with early-onset T2D and metformin who initiate insulin treatment

No interventions assigned to this group

late onset T2D with GLP1 RA treatment

People with early-onset T2D and metformin who initiate GLP1-RA treatment

GLP-1

Intervention Type: DRUG

We will compare people who have picked up GLP1 to those who have picked up insulin at any Swedish pharmacy

Late onset T2D with insulin

People with late-onset T2D and metformin who initiate insulin

No interventions assigned to this group

Interventions

GLP-1

We will compare people who have picked up GLP1 to those who have picked up insulin at any Swedish pharmacy

Intervention Type: DRUG

Eligibility Criteria

Inclusion Criteria

• We will include people diagnosed with T2D for more than 6 months and with metformin use within 6 months before the initiation of the treatment of interest (GLP-1 RA or insulin).

Exclusion Criteria

• Individuals will be excluded if they meet one of the following criteria any time (unless stated otherwise) before or at treatment initiation: (1) other types of diabetes diagnosis recorded in NDR or NPR; (2) diagnosis of acute pancreatitis one month prior to treatment initiation; (3) diagnosis of other pancreatic diseases (chronic pancreatitis, cyst of pancreatis, etc; ICD-10 code K86) recorded in NPR; (4) use of GLP-1 RA or insulin within 1 year prior to treatment initiation, (5) advanced kidney disease (eGFR<30 mL/min/1.73 m2, dialysis or kidney transplant [procedure codes: KAS[10]]); (6) end-stage liver disease (K70.4, K71.1, K72, Z94.4, procedure codes JJC); (7) cancer of the digestive system (ICD codes: C15-C26) or endocrine glands such as thyroid (ICD codes: C73-C75); (8) inflammatory bowel diseases (ICD-10 K50-K52)

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Karolinska Institutet

OTHER

Sponsor Role: lead

Responsible Party

Sofia Carlsson

senior lecturer

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Karolinska Institutet, Institute of Environmental Medicine

Stockholm, , Sweden

Countries

Sweden

Other Identifiers

Karolinska Institutet (KI)

Identifier Type: -

Identifier Source: org_study_id