UTHealth Clinical Research Unit (CRU) at UT Brownsville
Brownsville, Texas, 78520, United States
Location status: Recruiting
Location contact
Norma Perez-Olazaran
CONTACT
Rocio Uribe
CONTACT
NCT Number: NCT05119179
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.
Interested in participating?
Request Info18 year and older
All sexes
Interventional
Phase 4
Brownsville, Texas, 78520, United States
Location status: Recruiting
Norma Perez-Olazaran
CONTACT
Rocio Uribe
CONTACT
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.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Glucagon-like Peptide 1 Receptor Agonist
Other names: Ozempic
Time frame: 12 weeks
A rate which measures the ability of beta cells to secrete insulin
Time frame: 12 weeks
Measurement of the efficacy of insulin action at peripheral tissues
Time frame: 12 weeks
Product of beta cell responsivity and insulin sensitivity (see above)
Time frame: 12 weeks
Measurement of GLP-1 (glucagon-like peptide 1) hormonal efficacy in relationship to postprandial insulin secretion
Time frame: 12 weeks
Comparison of GLP-1 AUC measurements before and after drug intervention
Time frame: 12 weeks
eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
Time frame: 12 weeks
eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
Time frame: 12 weeks
eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
Time frame: 12 weeks
eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
Time frame: 12 weeks
eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
Time frame: 12 weeks
eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
Time frame: 12 weeks
eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
Time frame: 12 weeks
eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
Time frame: 12 weeks
eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
Time frame: 12 weeks
eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
Time frame: 12 weeks
eQTLs (expresion quantitative trait loci) are genes which affect the mRNA expression of another target gene.
Time frame: 12 weeks
Study has statistical power to detect previously unidentified eQTLs
Time frame: 12 weeks
Comparison of glucose AUC measurements before and after drug intervention
Time frame: 12 weeks
Comparison of C-peptide AUC measurements before and after drug intervention
Time frame: 12 weeks
Change in hemoglobin A1C (measured once on each study day) before and after intervention
Time frame: 12 weeks
Comparison of insulin AUC measurements before and after drug intervention
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.
Time frame: 5 years
Creation of Polygenic prediction model using above data
Contact information is provided by the study sponsor or research team.
Norma Perez-Olazaran
CONTACT
Rocio Uribe
CONTACT
The University of Texas Health Science Center, Houston
Other
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View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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