Efficacy of Glucagon-like Peptide-1 Receptor Agonists According to Type 2 Diabetes Subtypes

NCT ID: NCT06120556

Last Updated: 2024-01-30

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 130 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-06-10
• Study Completion Date: 2023-10-31

Brief Summary

The goal of this observational retrospective study is to understand whether glucagon-like peptide-1 receptor agonists (GLP-1RA), which are a group of antidiabetes drugs, may act differently in different subtypes of patients with type 2 diabetes.

The main questions it aims to answer are:

• people with type 2 diabetes belonging to specific subtypes respond better (or worse) to GLP-1RA?
• the beneficial effect of GLP-1RA may last longer in people with type 2 diabetes belonging to specific subtypes?
• what are the clinical characteristics that better explain the efficacy and durability of GLP-1 receptor agonists in type 2 diabetes management?

Clinical data from records of patients attending the diabetes outpatient clinic of our facility will be retrieved to compare the outcomes of GLP-1 receptor agonists in patients belonging to four subtypes of type 2 diabetes.

Detailed Description

Patients with type 2 diabetes are all characterized by hyperglycemia, however their probability to develop micro- and micro-vascular complications. A classification of adult-onset diabetes in 5 subtypes was recently proposed: severe autoimmune diabetes (SAID - including type 1 diabetes and latent autoimmune diabetes in adults LADA), severe insulin resistant diabetes (SIRD), severe insulin deficient diabetes (SIDD), mild age related diabetes (MARD), mild obesity-related diabetes (MOD). This classification has been validated in a multiple populations of patients with recent onset diabetes (within 5 years).

However, this classification requires the measurement of c-peptide/insulinemia or anti- glutamic acid decarboxylase (GAD) antibodies, limiting its applicability in everyday clinical practice. An alternative algorithm requiring easily available clinical characteristics, such as BMI, height, waist circumference, HbA1c, fasting blood glucose, lipid profile, age and age at diagnosis was recently introduced and validated.

In this retrospective observational study, the calculated sample size was of 128 patients, in 4 groups, with alpha 0.05, 1-beta 0.80, effect size 0.3.

The following data will be retrieved for eligible patients: age, sex, diabetes duration, age at diagnosis, antidiabetes therapy, body weight, height, waist circumference, fasting blood glucose, HbA1c, total and HDL and LDL cholesterol, triglycerides, creatinine, microalbuminuria. The algorithm available online (https://uiem.shinyapps.io/diabetes_clusters_app/), will be used to assign enrolled patients to the 4 subtypes of type 2 diabetes (SIDD, SIRD, MARD, MOD).

If available, information regarding micro- and macro-vascular complications of diabetes will be retrieved.

All data will be collected at baseline visit and every follow-up visit (the first follow-up visit should 6-12 months following prescription of a GLP-1 receptor agonist).

Conditions

Diabetes Mellitus, Type 2

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

Severe Insulin Resistant Diabetes (SIRD)

Patients with SIRD are characterized by high BMI and high insulin resistance and low HbA1c. These patients likely develop diabetic kidney disease.

Defined according to the simplified algorithm proposed by Bello-Chavolla et al. requiring the following data: age at diabetes diagnosis, age, BMI, height, waist circumference, HbA1c, fasting blood glucose, fasting triglycerides, HDL cholesterol

GLP-1 receptor agonist

Intervention Type: DRUG

Patients initiating a GLP-1 receptor agonist (i.e. liraglutide, dulaglutide, semaglutide) will be included in the study.

Mild Age-Related Diabetes (MARD)

Patients with MARD are characterized by late onset diabetes without extreme features.

Defined according to the simplified algorithm proposed by Bello-Chavolla et al. requiring the following data: age at diabetes diagnosis, age, BMI, height, waist circumference, HbA1c, fasting blood glucose, fasting triglycerides, HDL cholesterol

GLP-1 receptor agonist

Intervention Type: DRUG

Patients initiating a GLP-1 receptor agonist (i.e. liraglutide, dulaglutide, semaglutide) will be included in the study.

Mild Obesity-related Diabetes (MOD)

Patients with MOD are characterized by high BMI without insulin resistance. Defined according to the simplified algorithm proposed by Bello-Chavolla et al. requiring the following data: age at diabetes diagnosis, age, BMI, height, waist circumference, HbA1c, fasting blood glucose, fasting triglycerides, HDL cholesterol

GLP-1 receptor agonist

Intervention Type: DRUG

Patients initiating a GLP-1 receptor agonist (i.e. liraglutide, dulaglutide, semaglutide) will be included in the study.

Severe Insulin Deficient Diabetes (SIDD)

Patients with SIDD are characterized by high HbA1c and rapid progression to insulin therapy. These patients likely develop retinopathy, even in the first years after diagnosis.

Defined according to the simplified algorithm proposed by Bello-Chavolla et al. requiring the following data: age at diabetes diagnosis, age, BMI, height, waist circumference, HbA1c, fasting blood glucose, fasting triglycerides, HDL cholesterol

GLP-1 receptor agonist

Intervention Type: DRUG

Patients initiating a GLP-1 receptor agonist (i.e. liraglutide, dulaglutide, semaglutide) will be included in the study.

Interventions

GLP-1 receptor agonist

Patients initiating a GLP-1 receptor agonist (i.e. liraglutide, dulaglutide, semaglutide) will be included in the study.

Intervention Type: DRUG

Eligibility Criteria

Inclusion Criteria

• Italian patients with type 2 diabetes
• Onset of diabetes at ≥ 50 years
• Diagnosis of type 2 diabetes ≤ 5 years from enrollment
• BMI ≥ 25 kg/m2
• Patients receiving a GLP-1RA prescription for the first time with at least one follow-up visit at 6-12 months from first prescription

Exclusion Criteria

• Autoimmune diabetes, monogenic diabetes, secondary diabetes
• History of diabetic ketoacidosis

• Minimum Eligible Age: 50 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Azienda Ospedaliero-Universitaria Consorziale Policlinico di Bari

OTHER

Sponsor Role: lead

Responsible Party

Francesco Giorgino

Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Francesco Giorgino, PhD

Role: PRINCIPAL_INVESTIGATOR

University of Bari Aldo Moro

Locations

Azienda Ospedaliero-Universitaria Policlinico Bari

Bari, , Italy

Countries

Italy

References

Ahlqvist E, Storm P, Karajamaki A, Martinell M, Dorkhan M, Carlsson A, Vikman P, Prasad RB, Aly DM, Almgren P, Wessman Y, Shaat N, Spegel P, Mulder H, Lindholm E, Melander O, Hansson O, Malmqvist U, Lernmark A, Lahti K, Forsen T, Tuomi T, Rosengren AH, Groop L. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 2018 May;6(5):361-369. doi: 10.1016/S2213-8587(18)30051-2. Epub 2018 Mar 5.

Reference Type: BACKGROUND

PMID: 29503172 (View on PubMed)

Bello-Chavolla OY, Bahena-Lopez JP, Vargas-Vazquez A, Antonio-Villa NE, Marquez-Salinas A, Fermin-Martinez CA, Rojas R, Mehta R, Cruz-Bautista I, Hernandez-Jimenez S, Garcia-Ulloa AC, Almeda-Valdes P, Aguilar-Salinas CA; Metabolic Syndrome Study Group; Group of Study CAIPaDi. Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach. BMJ Open Diabetes Res Care. 2020 Jul;8(1):e001550. doi: 10.1136/bmjdrc-2020-001550.

Reference Type: BACKGROUND

PMID: 32699108 (View on PubMed)

Dennis JM, Shields BM, Henley WE, Jones AG, Hattersley AT. Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data. Lancet Diabetes Endocrinol. 2019 Jun;7(6):442-451. doi: 10.1016/S2213-8587(19)30087-7. Epub 2019 Apr 29.

Reference Type: BACKGROUND

PMID: 31047901 (View on PubMed)

Other Identifiers

AOUConsorziale

Identifier Type: -

Identifier Source: org_study_id