Pharmacogenetics of the Response to GLP-1 in Mexican-Americans With Prediabetes
Purpose
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.
Condition
- PreDiabetes
Eligibility
- Eligible Ages
- Over 18 Years
- Eligible Genders
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- 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
- 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
Study Design
- Phase
- Phase 4
- Study Type
- Interventional
- Allocation
- N/A
- Intervention Model
- Single Group Assignment
- Intervention Model Description
- Single center, before and after clinical trial
- Primary Purpose
- Treatment
- Masking
- None (Open Label)
Arm Groups
| Arm | Description | Assigned Intervention |
|---|---|---|
|
Experimental Semaglutide |
Semaglutide 0.25 mg subcutaneously weekly for 4 weeks, followed by semaglutide 0.5 mg subcutaneously weekly for 8 weeks. |
|
Recruiting Locations
Brownsville, Texas 78520
More Details
- Status
- Recruiting
- Sponsor
- The University of Texas Health Science Center, Houston
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.