PreDiabetes
Conditions
Keywords
prediabetes, Mexican-American, GLP-1, pharmacogenetics, expression quantitative trait loci
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.
Interventions
DRUGSemaglutide
Glucagon-like Peptide 1 Receptor Agonist
Sponsors
The University of Texas Health Science Center, Houston
Vanderbilt University Medical Center
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
TREATMENT
Masking
NONE
Intervention model description
Single center, before and after clinical trial
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Mean change in beta cell responsivity | 12 weeks | A rate which measures the ability of beta cells to secrete insulin |
| Insulin Sensitivity | 12 weeks | Measurement of the efficacy of insulin action at peripheral tissues |
| Disposition Index | 12 weeks | Product of beta cell responsivity and insulin sensitivity (see above) |
| GLP-1-Induced Potentiation | 12 weeks | Measurement of GLP-1 (glucagon-like peptide 1) hormonal efficacy in relationship to postprandial insulin secretion |
| 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 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 WFS1 | 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 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 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 GLP1R | 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. |
| 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. |
| 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. |
| 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 |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| 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 |
| 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 |
| 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. |
| Polygenic prediction model for GLP-1 therapy-associated outcomes | 5 years | Creation of Polygenic prediction model using above data |
Countries
United States
Contacts
CONTACTNorma Perez-Olazaran
CONTACTRocio Uribe
PRINCIPAL_INVESTIGATORAbsalon D Gutierrez, MD
The University of Texas Health Science Center, Houston
Outcome results
None listed