Pharmacogenetics of the Response to GLP-1 in Mexican-Americans With Prediabetes
- Registration Number
- NCT05119179
- Brief 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.
- 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.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 300
Not provided
- 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 & Design
- Study Type
- INTERVENTIONAL
- Study Design
- SINGLE_GROUP
- Arm && Interventions
Group Intervention Description Semaglutide Semaglutide Semaglutide 0.25 mg subcutaneously weekly for 4 weeks, followed by semaglutide 0.5 mg subcutaneously weekly for 8 weeks.
- Primary Outcome Measures
Name Time Method Mean change in beta cell responsivity 12 weeks A rate which measures the ability of beta cells to secrete insulin
Disposition Index 12 weeks Product of beta cell responsivity and insulin sensitivity (see above)
Gene expression changes for minor variants of eQTLs for WFS1 12 weeks eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
Mean change in GLP-1 Area Under the Curve (AUC) 12 weeks Comparison of GLP-1 AUC measurements before and after drug intervention
Gene expression changes for minor variants of eQTLs for TCF7L2 12 weeks eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
Gene expression changes for minor variants of eQTLs for CHST3 12 weeks eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
GLP-1-Induced Potentiation 12 weeks Measurement of GLP-1 (glucagon-like peptide 1) hormonal efficacy in relationship to postprandial insulin secretion
Gene expression changes for minor variants of eQTLs for KCNQ1 12 weeks eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
Gene expression changes for minor variants of eQTLs for SORCS1 12 weeks eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
Insulin Sensitivity 12 weeks Measurement of the efficacy of insulin action at peripheral tissues
Gene expression changes for minor variants of eQTLs for THADA 12 weeks eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
Gene expression changes for minor variants of eQTLs for CNR1 12 weeks eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
Gene expression changes for minor variants of eQTLs for CTRB1 12 weeks eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
Gene expression changes for minor variants of eQTLs for GLP1R 12 weeks eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
Previously unidentified cis-eQTLs associated with change in gene expression due to GLP-1 challenge 12 weeks Study has statistical power to detect previously unidentified eQTLs
Gene expression changes for minor variants of eQTLs for CTRB2 12 weeks eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
Gene expression changes for minor variants of eQTLs for MTNR1B 12 weeks eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
- Secondary Outcome Measures
Name Time Method Polygenic prediction model for GLP-1 therapy-associated outcomes 5 years Creation of Polygenic prediction model using above data
Mean change in glucose Area Under the Curve (AUC) 12 weeks Comparison of glucose AUC measurements before and after drug intervention
Mean change in C-peptide Area Under the Curve (AUC) 12 weeks Comparison of C-peptide AUC measurements before and after drug intervention
Creation of eQTL-based disease prediction models 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.
Change in hemoglobin A1C 12 weeks Change in hemoglobin A1C (measured once on each study day) before and after intervention
Mean change in insulin Area Under the Curve (AUC) 12 weeks Comparison of insulin AUC measurements before and after drug intervention
Trial Locations
- Locations (1)
UTHealth Clinical Research Unit (CRU) at UT Brownsville
🇺🇸Brownsville, Texas, United States