Pharmacogenetics of the Response to GLP-1 in Mexican-Americans With Prediabetes

PreDiabetes Clinical Trial

Official title:

Pharmacogenetics of the Response to GLP-1 in Mexican-Americans With Prediabetes

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05119179
• Other study ID #: HSC-MS-21-0297
• Status: Recruiting
• Phase: Phase 4
• Start date: November 22, 2021
• Est. completion date: July 2026

Study information

• Verified date: June 2023
• Source: The University of Texas Health Science Center, Houston
• Contact: Norma Perez-Olazaran
• Phone: (956) 755-0695
• Email: Norma.P.PerezOlazaran[@]uth.tmc.edu
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 300
• Est. completion date: July 2026
• Est. primary completion date: November 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion criteria:

1. Men and women, ages 18 years and older

2. 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%

3. 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).

4. 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.

5. 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:

1. History of Type 1 or Type 2 diabetes mellitus

2. Pregnant or breastfeeding women

3. 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.

4. Active malignancy

5. History of clinically significant cardiac, hepatic, pancreatic or renal disease.

6. History of any serious hypersensitivity reaction to the study medication (or any other incretin mimetic)

7. Prisoners or subjects who are involuntarily incarcerated

8. Prior history of pancreatitis, medullary thyroid cancer, or multiple endocrine neoplasia type 2 (MEN 2)

9. 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.

10. Hospitalization for COVID-19 in last 3 months

Related Conditions & MeSH terms

• Glucose Intolerance
• PreDiabetes
• Prediabetic State

Intervention

Drug:
• Semaglutide
Glucagon-like Peptide 1 Receptor Agonist

Locations

Country	Name	City	State
United States	UTHealth Clinical Research Unit (CRU) at UT Brownsville	Brownsville	Texas

Sponsors (2)

Lead Sponsor	Collaborator
The University of Texas Health Science Center, Houston	Vanderbilt University Medical Center

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Mean change in beta cell responsivity	A rate which measures the ability of beta cells to secrete insulin	12 weeks
Primary	Insulin Sensitivity	Measurement of the efficacy of insulin action at peripheral tissues	12 weeks
Primary	Disposition Index	Product of beta cell responsivity and insulin sensitivity (see above)	12 weeks
Primary	GLP-1-Induced Potentiation	Measurement of GLP-1 (glucagon-like peptide 1) hormonal efficacy in relationship to postprandial insulin secretion	12 weeks
Primary	Mean change in GLP-1 Area Under the Curve (AUC)	Comparison of GLP-1 AUC measurements before and after drug intervention	12 weeks
Primary	Gene expression changes for minor variants of eQTLs for TCF7L2	eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.	12 weeks
Primary	Gene expression changes for minor variants of eQTLs for KCNQ1	eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.	12 weeks
Primary	Gene expression changes for minor variants of eQTLs for WFS1	eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.	12 weeks
Primary	Gene expression changes for minor variants of eQTLs for THADA	eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.	12 weeks
Primary	Gene expression changes for minor variants of eQTLs for CNR1	eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.	12 weeks
Primary	Gene expression changes for minor variants of eQTLs for CTRB1	eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.	12 weeks
Primary	Gene expression changes for minor variants of eQTLs for CTRB2	eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.	12 weeks
Primary	Gene expression changes for minor variants of eQTLs for GLP1R	eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.	12 weeks
Primary	Gene expression changes for minor variants of eQTLs for CHST3	eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.	12 weeks
Primary	Gene expression changes for minor variants of eQTLs for MTNR1B	eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.	12 weeks
Primary	Gene expression changes for minor variants of eQTLs for SORCS1	eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.	12 weeks
Primary	Previously unidentified cis-eQTLs associated with change in gene expression due to GLP-1 challenge	Study has statistical power to detect previously unidentified eQTLs	12 weeks
Secondary	Mean change in glucose Area Under the Curve (AUC)	Comparison of glucose AUC measurements before and after drug intervention	12 weeks
Secondary	Mean change in C-peptide Area Under the Curve (AUC)	Comparison of C-peptide AUC measurements before and after drug intervention	12 weeks
Secondary	Change in hemoglobin A1C	Change in hemoglobin A1C (measured once on each study day) before and after intervention	12 weeks
Secondary	Mean change in insulin Area Under the Curve (AUC)	Comparison of insulin AUC measurements before and after drug intervention	12 weeks
Secondary	Creation of eQTL-based disease prediction models	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
Secondary	Polygenic prediction model for GLP-1 therapy-associated outcomes	Creation of Polygenic prediction model using above data	5 years