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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:
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.
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).
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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 |
| |
| 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 |
| |
| 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 |
| |
| 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 |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| GLP-1 receptor agonist | Drug | Patients initiating a GLP-1 receptor agonist (i.e. liraglutide, dulaglutide, semaglutide) will be included in the study. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Difference in HbA1c change from baseline (%) among SIDD, SIRD, MARD, MOD subtypes | Difference in HbA1c change from baseline will be evaluated at first follow-up visit (occurring 6-12 months from GLP-1RA initiation) |
| Measure | Description | Time Frame |
|---|---|---|
| Difference in time to failure among SIDD, SIRD, MARD, MOD subtypes | In patients reaching HbA1c <7% at first follow-up visit, the difference in time to failure (defined as HbA1c equal or above 7%) | Difference in time to failure will be assessed up to the last available visit (up to 36 months) |
| Difference in fasting blood glucose change from baseline (mg/dl) among SIDD, SIRD, MARD, MOD subtypes |
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Inclusion Criteria:
Exclusion Criteria:
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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.
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| Name | Affiliation | Role |
|---|---|---|
| Francesco Giorgino, PhD | University of Bari Aldo Moro | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Azienda Ospedaliero-Universitaria Policlinico Bari | Bari | 70124 | Italy |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 29503172 | Background | 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. | |
| 32699108 |
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Individual participant data that underlie the results reported in this article, after de-identification (text, tables, figures, and appendices).
Beginning 9 months and ending 36 months following article publication.
Researchers who provide a methodologically sound proposal.
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| ID | Term |
|---|---|
| D003924 | Diabetes Mellitus, Type 2 |
| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
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|
mg/dl |
| 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) |
| Difference in body weight change from baseline (kg) among SIDD, SIRD, MARD, MOD subtypes | kg | Difference in body weight change from baseline (kg) will be evaluated at first follow-up visit (occurring 6-12 months from GLP-1RA initiation) |
| Difference in percentage of patients reaching HbA1c below 7% among SIDD, SIRD, MARD, MOD subtypes | 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) |
| Background |
| 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. |
| 31047901 | Background | 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. |
| D004700 | Endocrine System Diseases |