Clinical trial NCT06120556: Efficacy of Glucagon-like Peptide-1 Receptor Agonists According to Type 2 Diabetes Subtypes
Official title: Efficacy of Glucagon-like Peptide-1 Receptor Agonists According to Type 2 Diabetes Subtypes: an Italian Monocentric Retrospective Study
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Diabetes Mellitus, Type 2
Keywords: type 2 diabetes
GLP-1 receptor agonists
subtypes
phenotypes
Lead sponsor: Azienda Ospedaliero-Universitaria Consorziale Policlinico di Bari
Sponsor class: OTHER
First posted: 2023-11-07
Enrollment: 130
Sex: ALL
Minimum age (years): 50
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: All patients who attended the Day Service for diabetes of the Endocrinology Unit of the University Hospital A.O.U. Policlinico di Bari, Puglia, Italy from January 1st 2012 to October 1st 2023 will be consecutively evaluated for inclusion.

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).

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

Primary outcomes:
measure: Difference in HbA1c change from baseline (%) among SIDD, SIRD, MARD, MOD subtypes
timeFrame: Difference in HbA1c change from baseline will be evaluated at first follow-up visit (occurring 6-12 months from GLP-1RA initiation)
Secondary outcomes:
measure: Difference in time to failure among SIDD, SIRD, MARD, MOD subtypes
timeFrame: Difference in time to failure will be assessed up to the last available visit (up to 36 months)
measure: Difference in fasting blood glucose change from baseline (mg/dl) among SIDD, SIRD, MARD, MOD subtypes
timeFrame: Difference in fasting blood glucose change from baseline (mg/dl) will be evaluated at first follow-up visit (occurring 6-12 months from GLP-1RA initiation)
measure: Difference in body weight change from baseline (kg) among SIDD, SIRD, MARD, MOD subtypes
timeFrame: Difference in body weight change from baseline (kg) will be evaluated at first follow-up visit (occurring 6-12 months from GLP-1RA initiation)
measure: Difference in percentage of patients reaching HbA1c below 7% among SIDD, SIRD, MARD, MOD subtypes
timeFrame: Difference in percentage of patients reaching HbA1c below 7% will be evaluated at first follow-up visit (occurring 6-12 months from GLP-1RA initiation)
Other outcomes:
Locations:
city: Bari
country: Italy
facility: Azienda Ospedaliero-Universitaria Policlinico Bari
Study officials:
affiliation: University of Bari Aldo Moro
name: Francesco Giorgino, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
citation: 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.