- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT05119179
Pharmacogenetics of the Response to GLP-1 in Mexican-Americans With Prediabetes
June 8, 2023 updated by: Absalon D Gutierrez, The University of Texas Health Science Center, Houston
This project uses both transcriptomic- and genomic-level data to identify mechanisms of individual responses to glucagon-like peptide-1 (GLP-1) in Mexican-Americans with prediabetes.
The GLP-1 hormone is essential for glucose reduction, weight loss, cardiovascular risk reduction, and renal protection.
Newly discovered mechanisms will illuminate causal links between disease genotype and phenotype, which may ultimately guide personalized therapeutic approaches for type 2 diabetes, prediabetes, obesity, cardiovascular disease, renal disease, and other related diseases.
Study Overview
Detailed Description
This clinical trial will uncover new mechanisms of inter-individual responses to endogenous and exogenous glucagon-like peptide-1 (GLP-1) in Hispanics/Latinos (H/Ls) with prediabetes.
The results move the management of prediabetes, type 2 diabetes mellitus (T2DM), and relevant metabolic diseases to a more individualized approach in an understudied and at-risk population.
Few options exist for prediabetes treatment, and the current pharmaceutical management of T2DM does not predict drug treatment failures, nor differences in individual treatment responses and adverse effects.
A precise, genetics-based approach will provide superior therapeutic management for patients.
GLP-1-based therapies reduce blood glucose, promote weight loss, decrease cardiovascular events, and improve renal function.
Prior genetic studies, most done in Caucasians, identified associations between genetic variants and decreased GLP-1-induced insulin secretion, in an effort to guide individualized treatment.
However, these associations do not provide a clear mechanistic relationship between genotype and phenotype.
Transcriptomic analyses will uncover many of these mechanisms.
Here, we propose to 1) test the association of single nucleotide polymorphisms (SNPs) that regulate expression (eQTLs) of 11 candidate genes in a range of relevant metabolic tissues with differential GLP-1 response, 2) perform RNA sequencing before and after treatment to identify eQTLs in blood that predict response to GLP-1 therapy and develop risk-based prediction models in H/Ls, and 3) determine the effects of genetic regulation of candidate genes and newly discovered eQTLs phenome-wide in a large existing biobank, BioVU.
For aims 1 and 2, responses will be measured in 300 study subjects with prediabetes recruited from an established Mexican-American cohort via the oral minimal model method, before and after GLP-1 therapy, quantifying GLP-1 hormone efficacy and GLP-1-induced pancreatic beta cell insulin release and peripheral insulin sensitivity.
Procedures include serial measurements of plasma glucose, insulin, C-peptide, and GLP-1, and peripheral blood collection for RNA sequencing.
Our central hypotheses are: (1) metabolic tissue-based eQTLs of GLP-1-associated genes will be associated with physiological response to endogenous and exogenous GLP-1,(2) identification of eQTLs associated with GLP-1 treatment-induced changes in whole blood will identify new gene targets, and (3) this data will lead to the creation of eQTL-based prediction models for related diseases.
The study is innovative because it uses a novel combination of eQTL analysis and oral minimal model to assess GLP-1 response, examines a population highly underrepresented in pharmacogenomic studies, and utilizes novel statistical methods and applications to study gene expression.
The significance of this newly acquired mechanistic information will ultimately guide precision therapeutic regimens for diabetes prevention and treatment, weight loss, cardiovascular risk reduction, and related metabolic complications in an understudied population.
Study Type
Interventional
Enrollment (Estimated)
300
Phase
- Phase 4
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Contact
- Name: Norma Perez-Olazaran
- Phone Number: (956) 755-0695
- Email: Norma.P.PerezOlazaran@uth.tmc.edu
Study Contact Backup
- Name: Rocio Uribe
- Phone Number: (956) 882-5165
- Email: Rocio.D.Uribe@uth.tmc.edu
Study Locations
-
-
Texas
-
Brownsville, Texas, United States, 78520
- Recruiting
- UTHealth Clinical Research Unit (CRU) at UT Brownsville
-
Contact:
- Rocio Uribe
- Phone Number: (956) 882-5165
- Email: Rocio.D.Uribe@uth.tmc.edu
-
Contact:
- Norma Perez-Olazaran
- Phone Number: 956-755-0695
- Email: Norma.P.PerezOlazaran@uth.tmc.edu
-
-
Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
18 years and older (Adult, Older Adult)
Accepts Healthy Volunteers
No
Description
Inclusion criteria:
- Men and women, ages 18 years and older
- Diagnosis of Prediabetes - defined as either impaired fasting glucose (fasting glucose of 100-125 mg/dL), impaired glucose tolerance (2-hour postprandial blood glucose of 140-199 mg/dL after 75-gram oral glucose challenge), and/or a hemoglobin A1C ranging from 5.7% to 6.4%
- High risk for progression to diabetes: defined as having at least one of the two following additional factors: Obesity (BMI ≥ 30 kg/m2) and/or metabolically unhealthy status. "Metabolically unhealthy status" is defined as at least two of the following: elevated blood pressure (SBP ≥ 130 mmHg and/or DBP ≥ 85 mmHg), elevated triglycerides ≥ 150 mg/dL, low HDL cholesterol (males < 40 mg/dL; females < 50 mg/dL), and elevated fasting glucose ≥ 100 mg/dL (Wu S et al., 2017).
- Women of childbearing age must agree to use an acceptable method of pregnancy prevention (barrier methods, abstinence, hormonal contraception, intrauterine contraception, or surgical sterilization) for the duration of the study.
- Patients must have the following laboratory values: Hematocrit ≥ 34 vol%, estimated glomerular filtration rate ≥ 60 mL/min per 1.73 m2, AST (SGOT) < 2.5 times ULN, ALT (SGPT) < 2.5 times ULN, alkaline phosphatase < 2.5 times ULN
Exclusion Criteria:
- History of Type 1 or Type 2 diabetes mellitus
- Pregnant or breastfeeding women
- Medications: metformin, DPP-4 inhibitors, GLP-1 receptor agonists, SGLT-2 inhibitors, thiazolidinediones, insulin, sulfonylureas, meglitinides, alpha-glucosidase inhibitors, and/or corticosteroids over the last 3 months.
- Active malignancy
- History of clinically significant cardiac, hepatic, pancreatic or renal disease.
- History of any serious hypersensitivity reaction to the study medication (or any other incretin mimetic)
- Prisoners or subjects who are involuntarily incarcerated
- Prior history of pancreatitis, medullary thyroid cancer, or multiple endocrine neoplasia type 2 (MEN 2)
- Family history of medullary thyroid cancer (a rare form of thyroid cancer) or MEN2. However, as many individuals may not be aware of the specific type of thyroid cancer, will also exclude any family history of thyroid cancer or MEN2.
- Hospitalization for COVID-19 in last 3 months
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
- Primary Purpose: Treatment
- Allocation: N/A
- Interventional Model: Single Group Assignment
- Masking: None (Open Label)
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
---|---|
Experimental: Semaglutide
Semaglutide 0.25 mg subcutaneously weekly for 4 weeks, followed by semaglutide 0.5 mg subcutaneously weekly for 8 weeks.
|
Glucagon-like Peptide 1 Receptor Agonist
Other Names:
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Mean change in beta cell responsivity
Time Frame: 12 weeks
|
A rate which measures the ability of beta cells to secrete insulin
|
12 weeks
|
Insulin Sensitivity
Time Frame: 12 weeks
|
Measurement of the efficacy of insulin action at peripheral tissues
|
12 weeks
|
Disposition Index
Time Frame: 12 weeks
|
Product of beta cell responsivity and insulin sensitivity (see above)
|
12 weeks
|
GLP-1-Induced Potentiation
Time Frame: 12 weeks
|
Measurement of GLP-1 (glucagon-like peptide 1) hormonal efficacy in relationship to postprandial insulin secretion
|
12 weeks
|
Mean change in GLP-1 Area Under the Curve (AUC)
Time Frame: 12 weeks
|
Comparison of GLP-1 AUC measurements before and after drug intervention
|
12 weeks
|
Gene expression changes for minor variants of eQTLs for TCF7L2
Time Frame: 12 weeks
|
eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
|
12 weeks
|
Gene expression changes for minor variants of eQTLs for KCNQ1
Time Frame: 12 weeks
|
eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
|
12 weeks
|
Gene expression changes for minor variants of eQTLs for WFS1
Time Frame: 12 weeks
|
eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
|
12 weeks
|
Gene expression changes for minor variants of eQTLs for THADA
Time Frame: 12 weeks
|
eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
|
12 weeks
|
Gene expression changes for minor variants of eQTLs for CNR1
Time Frame: 12 weeks
|
eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
|
12 weeks
|
Gene expression changes for minor variants of eQTLs for CTRB1
Time Frame: 12 weeks
|
eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
|
12 weeks
|
Gene expression changes for minor variants of eQTLs for CTRB2
Time Frame: 12 weeks
|
eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
|
12 weeks
|
Gene expression changes for minor variants of eQTLs for GLP1R
Time Frame: 12 weeks
|
eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
|
12 weeks
|
Gene expression changes for minor variants of eQTLs for CHST3
Time Frame: 12 weeks
|
eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
|
12 weeks
|
Gene expression changes for minor variants of eQTLs for MTNR1B
Time Frame: 12 weeks
|
eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
|
12 weeks
|
Gene expression changes for minor variants of eQTLs for SORCS1
Time Frame: 12 weeks
|
eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
|
12 weeks
|
Previously unidentified cis-eQTLs associated with change in gene expression due to GLP-1 challenge
Time Frame: 12 weeks
|
Study has statistical power to detect previously unidentified eQTLs
|
12 weeks
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Mean change in glucose Area Under the Curve (AUC)
Time Frame: 12 weeks
|
Comparison of glucose AUC measurements before and after drug intervention
|
12 weeks
|
Mean change in C-peptide Area Under the Curve (AUC)
Time Frame: 12 weeks
|
Comparison of C-peptide AUC measurements before and after drug intervention
|
12 weeks
|
Change in hemoglobin A1C
Time Frame: 12 weeks
|
Change in hemoglobin A1C (measured once on each study day) before and after intervention
|
12 weeks
|
Mean change in insulin Area Under the Curve (AUC)
Time Frame: 12 weeks
|
Comparison of insulin AUC measurements before and after drug intervention
|
12 weeks
|
Creation of eQTL-based disease prediction models
Time Frame: 5 years
|
Create and apply eQTL-based prediction models to investigate the clinical consequences of variable GLP-1- induced gene expression changes (identified as above) in large electronic health records (EHRs), and use these models to predict disease risk phenome-wide.
|
5 years
|
Polygenic prediction model for GLP-1 therapy-associated outcomes
Time Frame: 5 years
|
Creation of Polygenic prediction model using above data
|
5 years
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Collaborators
Investigators
- Principal Investigator: Absalon D Gutierrez, MD, The University of Texas Health Science Center, Houston
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Actual)
November 22, 2021
Primary Completion (Estimated)
November 1, 2025
Study Completion (Estimated)
July 1, 2026
Study Registration Dates
First Submitted
August 5, 2021
First Submitted That Met QC Criteria
November 1, 2021
First Posted (Actual)
November 15, 2021
Study Record Updates
Last Update Posted (Actual)
June 12, 2023
Last Update Submitted That Met QC Criteria
June 8, 2023
Last Verified
June 1, 2023
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- HSC-MS-21-0297
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
NO
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Yes
Studies a U.S. FDA-regulated device product
No
product manufactured in and exported from the U.S.
No
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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