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. Author manuscript; available in PMC: 2025 Oct 28.
Published in final edited form as: J Endocrinol. 2025 Oct 23;267(1):e250174. doi: 10.1530/JOE-25-0174

Effects of a GLP-1 Receptor Polymorphism on Responses to Liraglutide

Mona Mashayekhi 1, Bilgunay Ilkin Safa 1, Hui Nian 2, Jessica K Devin 3, Jorge L Gamboa 4, Chang Yu 5, Rui Chen 6,7, Joshua A Beckman 8, John R Koethe 9,10, Heidi J Silver 10,11, Kevin Niswender 1, James M Luther 12, Nancy J Brown 13
PMCID: PMC12556785  NIHMSID: NIHMS2115585  PMID: 41042549

Abstract

The rs6923761 (Gly168Ser) missense variant in the glucagon-like peptide-1 receptor (GLP-1R) has been associated with favorable anthropometric and metabolic parameters in individuals with obesity but decreased responsiveness to incretin-based therapies. Here we performed a pre-specified analysis of a randomized controlled trial in individuals with obesity and pre-diabetes comparing treatment with the GLP-1R agonist liraglutide, the dipeptidyl peptidase 4 inhibitor sitagliptin or hypocaloric diet, and evaluated the effects of the rs6923761 variant on outcomes. We found significantly greater weight loss to liraglutide with each copy of the variant allele present, indicating a gene dose effect. In addition, individuals with the variant allele exhibited a significant reduction in the pro-thrombotic and pro-inflammatory factor plasminogen activator inhibitor-1 after liraglutide treatment. There was no effect of genotype on fasting glucose after liraglutide treatment, yet individuals with the variant allele exhibited decreased responsiveness to liraglutide and hypocaloric diet in measurements of fasting insulin, C-peptide, glucagon, as well as in HOMA-IR. In conclusion, we found that the GLP-1R rs6923761 variant exerts a dual impact on liraglutide efficacy -- enhancing weight loss while diminishing metabolic benefits. The observed associations could be consistent with constitutive activation of the GLP-1R in the presence of this variant with reduced activation/signaling in response to pharmacologic agents, a pattern that has been observed with the rs10305492 variant in animal models. Future studies are needed to investigate the molecular mechanisms of associations with the rs6923761 variant.

Keywords: GLP-1 receptor, rs6923761, liraglutide, weight loss, PAI-1

Introduction

The glucagon-like peptide-1 receptor (GLP-1R) contains several single nucleotide polymorphisms that have been studied in relation to receptor expression and function, as well as clinical effects (BouSaba et al. 2023; Melchiorsen et al. 2023). Several have been associated with cardiometabolic outcomes (Wessel et al. 2015; Scott et al. 2016; Song et al. 2024). In this report, we focus on the rs6923761 variant which results in a glycine to serine missense substitution at position 168 (Gly168Ser) and has a minor allele frequency of ~30% (ALFA, dbSNP database, Build 156). This variant has been extensively studied both in molecular and clinical studies and has a relatively high minor allele frequency in our target population.

Molecular studies of GLP-1R variants must be interpreted with caution due to the lack of primary cell culture systems with native expression of the GLP-1R, necessitating the use of in vitro transfection studies. In some studies, the rs6923761 variant results in lower receptor surface expression compared to wild type and lower intracellular calcium mobilization in response to agonists (Koole et al. 2011), while other studies found no effect on signaling (Fortin et al. 2010; Melchiorsen et al. 2023).

Observational studies of the effects of the rs6923761 variant on baseline cardiometabolic measures have also yielded mixed results. Several studies have found that individuals with the variant tend to have lower weight, BMI, triglycerides, waist circumference, waist-to-hip ratio, fat mass, and HOMA-IR, suggesting a beneficial metabolic phenotype (de Luis et al. 2014a, 2015a, 2018; Harrall et al. 2024). In addition, a GWAS meta-analysis of glucose measurements under non-standardized conditions found that this variant is modestly but significantly associated with lower random glucose levels (Lagou et al. 2023). By contrast, another study found that individuals homozygous for this variant had excess body mass and higher glucose (Michałowska et al. 2022). Further, this variant was not associated with changes in gastric emptying rate (Yau et al. 2018; Anderson et al. 2020).

Several studies have evaluated the relationship between the rs6923761 variant and responses to weight loss or glucose lowering treatments, again with mixed results (BouSaba et al. 2023). Individuals with the rs6923761 variant have equivalent weight loss to various hypocaloric diets (de Luis et al. 2013, 2014b, 2015b), yet have reduced weight loss after bariatric surgery (de Luis et al. 2014c). In a study of individuals with diabetes, those with this variant exhibit greater weight reduction in response to liraglutide (de Luis et al. 2015c). Further, this variant was associated with greater delay in gastric emptying in response to GLP-1R agonist treatment (Chedid et al. 2018), which may be partially responsible for improved weight or metabolic parameters. Yet smaller studies in individuals with PCOS (Jensterle et al. 2015) or obesity (Chedid et al. 2018; Maselli et al. 2022) did not find a significant effect of the variant on weight loss in response to liraglutide or exenatide.

With regards to effects of the rs6923761 variant on metabolic outcomes, a GWAS using observational data in individuals with type 2 diabetes found that for each copy of the variant allele, there was a smaller reduction in hemoglobin A1c in response to a GLP-1R agonist (Dawed et al. 2023). Additionally, the presence of this variant decreased beta-cell responsiveness to infused GLP-1 during a hyperglycemic clamp (Sathananthan et al. 2010). We and others have reported that individuals with this variant have reduced glucose lowering to dipeptidyl peptidase 4 inhibitors that block degradation of endogenous GLP-1 (Javorský et al. 2016; Űrgeová et al. 2020; Mashayekhi et al. 2021). By contrast, there was no association of this variant with reduction of hemoglobin A1c in a study examining the response to liraglutide in individuals with diabetes (Eghbali et al. 2024).

Overall, there is conflict in the literature regarding the weight and metabolic effects of the rs6923761 variant. Some studies have found that this variant allele is associated with lower weight and metabolic measures at baseline but decreased responsiveness to GLP-1R therapies. Yet other studies have not confirmed these associations, and there is a need to fill this knowledge gap with a prospective, controlled study. To evaluate the contribution of the rs6923761 GLP-1R variant on weight and metabolic measures, we performed a pre-specified analysis within a randomized controlled trial (RCT) comparing treatment with liraglutide (a GLP-1R agonist), sitagliptin (a dipeptidyl peptidase 4 inhibitor that blocks degradation of endogenous GLP-1) or hypocaloric diet in individuals with obesity and pre-diabetes.

Materials and Methods

Protocol

Details of the protocol, as well as the primary and secondary outcomes, have been published (Mashayekhi et al. 2023, 2024; Silver et al. 2023). Briefly, individuals aged 18 to 65 with obesity (BMI ≥ 30 kg/m2) and pre-diabetes as defined by the American Diabetes Association criteria were enrolled (NCT03101930). The study was approved by the Vanderbilt Institutional Review Board, registered at clinicaltrials.gov NCT03101930, and conducted according to the Declaration of Helsinki. All participants provided written informed consent after full explanation of the purpose and nature of all procedures used. Participants underwent a baseline study day for anthropometric measurements and sample collection and were then randomized in a 2:1:1 ratio to treatment with liraglutide 1.8mg/day (Novo Nordisk), sitagliptin 100mg/day (Merck and Co, Inc), or hypocaloric diet. Randomization was stratified by race. Treatment with liraglutide or sitagliptin was double blind and placebo controlled. Treatment with hypocaloric diet was unblinded. Participants underwent additional study days at two weeks to measure short-term effects of treatment, and at 14 weeks to measure chronic effects of treatment.

Laboratory Analyses

All samples were stored in aliquots at −80°C until the time of assay unless otherwise noted.

Metabolite Measurements

A YSI glucose analyzer (YSI Life Sciences, Yellow Springs, OH) was used to measure plasma glucose immediately after collection. Plasma insulin samples were collected in tubes containing aprotinin, and insulin was measured by radioimmunoassay (EMD Millipore, Billerica, MA). The assay cross-reacts with 38% intact proinsulin, but not with C-peptide (≤ 0.01%).

Samples for GLP-1, C-peptide, GIP and glucagon were collected in tubes containing aprotinin plus DPP-4 inhibitor. Active or intact GLP-1 (GLP-1 (7-36) and GLP-1 (7-37)), C-peptide, active GIP, and glucagon were measured via multiplex immunoassay (MSD: Meso Scale Discovery, Gaithersburg, MD). The cross-reactivity of the GLP-1 assay for liraglutide is 0.03%. The cross-reactivity of glucagon for glicentin is 30%.

Inflammatory Markers

Blood for PAI-1 was collected in 0.105 M acidified sodium citrate and samples were analyzed using commercially available two-site ELISA using chromogenic substrates (TriniLIZE, Trinity Biotech, Berkeley Heights, NJ and American Diagnostica Inc, Stamford, CT).

Plasma samples for MCP-1, IFNg, IL-6, IL-8, IL-10 and TNFa were collected in potassium EDTA, spun immediately at 3,000 RPM for 10 minutes, and aliquoted for storage at −80°C until measured by immunoassay (Meso Scale Diagnostics, Rockville, MD).

DNA Analysis

Participants were given the option of providing blood for DNA analysis and gave separate consent for genetic research. DNA was processed, stored, and genotyped at the Vanderbilt Technologies for Advanced Genomics (VANTAGE) core.

Genotyping for the rs6923761 variant was accomplished by one of two methods. Initially, genotyping was performed using an off-the-shelf TaqMan® assay C_25615272_20, on a QuantStudio™ 12K Flex Real-Time PCR System (ThermoFisher Scientific, Massachusetts, USA), according to the manufacturer’s instructions. Samples were genotyped in several batches, using known family trios for Mendelian inheritance controls across plates and within plates to check for batch effects. There were duplicates between plates and within plates, and all replicates were concordant.

Subsequently, this technology was sunset, and additional samples were genotyped using a Twist Biosciences kit (P/N: 104207) following a modified, miniaturized version of the manufacturers protocol. Using a custom probe set, genotyping was performed by 150bp paired-end sequencing on the NovaSeq 6000 platform targeting 1 million reads per sample. Raw sequencing data (FASTQ files) obtained from the NovaSeq 6000 was subjected to quality control analysis, including read quality assessment. Real Time Analysis Software (RTA) and NovaSeq Control Software (NCS) (1.8.0; Illumina) were used for base calling. MultiQC (v1.7; Illumina) was used for data quality assessments. The capture efficiency, alignment, insert size and other sample performance metrics were evaluated using the Dragen Enrichment v4.2.1 pipeline using a custom BED file. We validated a total of 54 results from the initial TaqMan assay using the new Twist technology, confirming accuracy of both methods.

Calculated Measures

Homeostatic Model Assessment of Insulin Resistance (HOMA-IR)

HOMA-IR was calculated from the fasting measurements using the following formula: (insulin (μU/mL)*glucose(mg/dL))/405.(Matthews et al. 1985)

Matsuda Index of Insulin Sensitivity

The Matsuda index was calculated using the formula previously published: [10,000/(fasting blood glucose (FBG)*fasting blood insulin (FBI)*mean glucose during mixed meal test*mean insulin during mixed meal test)1/2] (Matsuda & DeFronzo 1999).

Disposition Index

The oral disposition index (DI), also previously termed the insulin-secretion-sensitivity-index-2 (ISSI-2) was calculated as the product of the AUCI/G-R*Matsuda index during the mixed meal (Santos et al. 2016).

Insulinogenic Index

The insulinogenic index was calculated from the mixed meal test using the following formula: (change in C-peptide time 0-30 minutes)/(change in glucose time 0-30 minutes) (Singh & Saxena 2010).

Results

The original RCT reported data from 88 individuals (Mashayekhi et al. 2023); five participants did not consent to providing a DNA sample, and their data are excluded from the present report. Data from the remaining 83 individuals who completed the study and consented to genetic research are presented (Table 1; liraglutide N=42, sitagliptin N=21, hypocaloric diet N=20; G is the wildtype allele, A is the variant allele). We evaluated all outcomes using both a recessive (GG vs GA vs AA) and co-dominant (GG vs GA/AA) model.

Table 1:

Baseline characteristics by genotype

Measures/Characteristics GG GA AA GA/AA Combined P-values
N=38 N=36 N=9 N=45 N=83 GG vs GA vs AA GG vs GA/AA
Age 52.8 ± 7.9 47.6 ± 12.0 50.8 ± 11.2 48.2 ± 11.8 50.3 ± 10.8 0.27 0.14
Sex 0.06 0.22
 Male 39% (15) 19% (7) 56% (5) 27% (12) 33% (27)
 Female 61% (23) 81% (29) 44% (4) 73% (33) 67% (56)
Race 0.09 0.01 *
 Asian 5% (2) 0% (0) 0% (0) 0% (0) 2% (2)
 Black or African American 21% (8) 3% (1) 0% (0) 2% (1) 11% (9)
 White 71% (27) 94% (34) 100% (9) 96% (43) 84% (70)
 More than one race 3% (1) 3% (1) 0% (0) 2% (1) 2% (2)
Ethnicity 0.19 0.06
 Hispanic or Latino 11% (4) 0% (0) 0% (0) 0% (0) 5% (4)
 NOT Hispanic or Latino 89% (34) 97% (35) 100% (9) 98% (44) 94% (78)
 Unknown / Not Reported 0% (0) 3% (1) 0% (0) 2% (1) 1% (1)
Waist, cm 115.8 ± 12.2 115.9 ± 12.6 127.1 ± 12.1 118.2 ± 13.2 116.9 ± 12.8 0.06 0.62
Hips, cm 123.3 ± 11.4 127.1 ± 12.7 132.5 ± 11.6 128.3 ± 12.5 126.1 ± 11.9 0.11 0.08
Weight, kg 108.8 ± 19.9 109.0 ± 21.3 122.0 ± 19.8 111.6 ± 21.4 110.1 ± 21.1 0.19 0.57
BMI, kg/m 2 38.8 ± 5.7 38.6 ± 5.8 42.7 ± 6.6 39.4 ± 6.1 39.0 ± 6.0 0.17 0.63
HbA1c, % 5.82 ± 0.32 5.62 ± 0.26 5.71 ± 0.43 5.64 ± 0.30 5.73 ± 0.31 0.03 * 0.02 *
PAI-1, U/mL 18.6 ± 8.7 21.1 ± 8.1 21.1 ± 8.2 21.1 ± 8.0 19.4 ± 8.7 0.31 0.12
HOMA-IR 6.25 ± 5.33 5.64 ± 3.94 5.54 ± 2.69 5.62 ± 3.69 5.71 ± 4.44 0.91 0.81
Fasting blood glucose, mg/dL 96.1 ± 10.5 97.3 ± 9.0 92.7 ± 9.3 96.3 ± 9.1 95.7 ± 9.9 0.55 0.80
Fasting insulin, μU/m 25.81 ± 19.97 23.28 ± 16.10 24.52 ± 12.71 23.53 ± 15.35 23.86 ± 17.36 0.81 0.74
Fasting GLP-1, pg/mL 1.32 ± 1.42 0.86 ± 0.94 0.48 ± 0.48 0.79 ± 0.88 1.05 ± 1.19 0.13 0.08
Fasting C-peptide, pg/mL 2795 ± 1255 3140 ± 1259 2695 ± 660 3053 ± 1173 2868 ± 1215 0.32 0.17
Fasting glucagon, pg/mL 18.99 ± 10.42 16.29 ± 8.42 21.98 ± 14.97 17.40 ± 10.06 18.20 ± 10.03 0.42 0.23
Fasting GIP, pg/mL 23.36 ± 30.07 14.50 ± 11.49 14.29 ± 7.83 14.46 ± 10.79 18.23 ± 21.54 0.45 0.23
Matsuda index 4.78 ± 10.93 3.35 ± 2.21 2.71 ± 1.06 3.22 ± 2.04 4.14 ± 7.42 0.90 0.79
Disposition index 16.54 ± 54.50 7.09 ± 41.93 16.13 ± 14.18 8.94 ± 37.96 11.87 ± 44.91 0.34 0.53
Insulinogenic index 167.92 ± 367.99 582.18 ± 1561.94 277.90 ± 178.09 518.12 ± 1390.68 334.59 ± 1021.35 0.02 * 0.004 *
Treatment arm assignment (RCT randomization ratio 2:1:1 Liraglutide:Sitagliptin:Diet) 0.72 0.36
 Liraglutide 16 21 5 26 42
 Sitagliptin 11 8 2 10 21
 Diet 11 7 2 9 20
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All measures shown as mean ± SD. The GA/AA column combines individuals with the GA or AA genotypes (co-dominant model). The combined column shows data for the entire cohort (all genotypes). GLP-1 indicates glucagon-like peptide-1; GIP, gastric inhibitory polypeptide; HOMA-IR, Homeostatic Model Assessment for Insulin Resistance; BMI, Body Mass Index; PAI-1, Plasminogen activator inhibitor-1. Asterisk (*) indicates P<0.05 using Kruskal-Wallis or Wilcoxon rank-sum test for continuous variables and Pearson test for categorical variables.

Baseline Characteristics

The minor allele frequency in our sample was 32.5%. The variant allele was infrequent in Blacks, with one out of nine Black individuals having the A allele (11%, as compared to 61% in Whites); for all comparisons we included a sensitivity analysis in Whites only. Notably, race was equally distributed between treatment arms as previously reported (Mashayekhi et al. 2023).

Hemoglobin A1c was lower in individuals with the variant allele. The insulinogenic index, a surrogate of beta cell function and measure of efficient insulin response to glucose intake (Singh & Saxena 2010), was higher in individuals with the variant allele. This suggests improved glucose homeostasis at baseline in individuals with the variant allele. Other baseline characteristics were similar among genotypes. Within the liraglutide-treated group only, baseline plasminogen activator inhibitor-1 (PAI-1) was higher in individuals with the variant (Table 2). While we report all results within sitagliptin- and hypocaloric diet-treated groups (Table S1), we draw fewer conclusions from these analyses given that the sample size was smaller in these arms, limiting our power.

Table 2:

Baseline characteristics by genotype within liraglutide-treated group

Measures/Characteristics GG GA AA GA/AA Combined P-values
GG vs GA vs AA GG vs GA/AA
Liraglutide Treatment
Arm		 N=16 N=21 N=5 N=26 N=42
Age 52.44 ± 7.33 47.33 ± 11.55 49.40 ± 11.87 47.73 ± 11.40 49.52 ± 10.21 0.6 0.3
Sex 0.88 0.97
 Male 31% (5) 29% (6) 40% (2) 31% (8) 31% (13)
 Female 69% (11) 71% (15) 60% (3) 69% (18) 69% (29)
Race 0.16 0.05 *
 Asian 12% (2) 0% (0) 0% (0) 0% (0) 5% (2)
 Black or African American 19% (3) 5% (1) 0% (0) 4% (1) 10% (4)
 White 69% (11) 95% (20) 100% (5) 96% (25) 86% (36)
 More Than One Race 0% (0) 0% (0) 0% (0) 0% (0) 0% (0)
Ethnicity 0.62 0.33
 Hispanic or Latino 6% (1) 0% (0) 0% (0) 0% (0) 2% (1)
 NOT Hispanic / Latino 94% (15) 95% (20) 100% (5) 96% (25) 94% (40)
 Unknown / Not Reported 0% (0) 5% (1) 0% (0) 4% (1) 2% (1)
Waist, cm 114.4 ± 12.0 116.3 ± 12.1 120.7 ± 7.9 117.2 ± 11.4 116.1 ± 11.5 0.56 0.59
Hips, cm 123.4 ± 11.9 126.7 ± 13.5 132.5 ± 14.9 127.8 ± 13.5 126.1 ± 12.9 0.45 0.34
Weight, kg 108.2 ± 22.0 110.5 ± 22.1 113.1 ± 10.0 111.0 ± 20.2 109.9 ± 20.7 0.81 0.73
BMI, kg/m2 39.1 ± 6.3 38.7 ± 5.4 41.4 ± 8.1 39.2 ± 6.0 39.1 ± 6.0 0.73 0.90
HbA1c, % 5.79 ± 0.22 5.63 ± 0.28 5.56 ± 0.48 5.62 ± 0.31 5.68 ± 0.29 0.30 0.12
PAI-1, U/mL 17.84 ± 10.78 23.00 ± 7.16 20.68 ± 8.83 22.55 ± 7.37 20.83 ± 8.94 0.09 0.03 *
Fasting glucose, mg/dL 94.27 ± 8.20 97.58 ± 8.06 93.60 ± 10.09 96.81 ± 8.42 95.88 ± 8.33 0.77 0.50
Fasting insulin, μU/m 24.08 ± 18.70 25.05 ± 17.04 16.13 ± 4.89 23.33 ± 15.78 23.62 ± 16.73 0.67 0.76
Fasting GLP-1, pg/mL 1.42 ± 1.80 0.79 ± 0.98 0.21 ± 0.17 0.69 ± 0.92 0.96 ± 1.35 0.08 0.13
Fasting C-peptide, pg/mL 2550.6 ± 1241.5 3460.9 ± 1407.0 2392.8 ± 596.9 3238.0 ± 1344.0 2996.7 ± 1333.9 0.03 * 0.04 *
Fasting glucagon, pg/mL 16.30 ± 10.53 17.87 ± 9.42 11.84 ± 1.36 16.62 ± 8.72 16.51 ± 9.25 0.17 0.62
Fasting GIP, pg/mL 23.30 ± 23.80 15.08 ± 13.42 14.60 ± 9.06 15.0 ± 12.46 17.91 ± 17.44 0.70 0.42
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All measures shown as mean ± SD or percent (N). The GA/AA column combines individuals with the GA or AA genotypes (co-dominant model). The combined column shows data for the entire cohort (all genotypes). GLP-1 indicates glucagon-like peptide-1; GIP, gastric inhibitory polypeptide; HOMA-IR, Homeostatic Model Assessment for Insulin Resistance; BMI, Body Mass Index; PAI-1, Plasminogen activator inhibitor-1. Asterisk (*) indicates P<0.05 using Kruskal-Wallis or Wilcoxon rank-sum test for continuous variables and Pearson test for categorical variables.

Effects of Treatment on Weight and Metabolic Measures

As reported previously (Mashayekhi et al. 2023), both liraglutide and hypocaloric diet caused significant weight loss, while sitagliptin did not (Figure 1A). Within the liraglutide arm, weight loss at 14 weeks was greater for each variant A allele present, consistent with an allele dose effect. Weight loss to hypocaloric diet was similar regardless of genotype. There was no significant effect of genotype on the change in fasting glucose after liraglutide treatment, while fasting glucose was decreased by hypocaloric diet only in individuals with the wildtype GG genotype. Notably, individuals with the wildtype GG genotype had a greater reduction in fasting insulin, HOMA-IR, and glucagon in response to liraglutide and hypocaloric diet, and a trend toward lower fasting GIP (Table 3). Genotype had no effect on fasting GLP-1 in the sitagliptin and diet arms and could not be evaluated in the liraglutide arm due to known cross-reactivity of the assay with the drug. Differences between the primary analysis and the whites only sensitivity analysis are highlighted in Table 3.

Figure 1. Effects of treatment on weight and PAI-1.

Figure 1.

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Plots show mean ± SEM for (A) weight and (B) PAI-1 in GG (black circles), GA (blue triangles) and AA (open blue squares) individuals on the top, or GG (black circles) and GA/AA (orange squares) individuals on the bottom. Panels indicate treatment arm. Time after initiation of treatment is shown on the x-axis. Asterisk (*) indicates P<0.05 for estimates of change from baseline.

Table 3:

Effects of treatment by genotype

Measures Liraglutide Sitagliptin Diet
GG
N=16 		GA
N=21 		AA
N=5 		GA/AA
N=26 		GG
N=11 		GA
N=8 		AA
N=2 		GA/AA
N=10 		GG
N=11 		GA
N=7 		AA
N=2 		GA/AA
N=9
Weight, kg
 Baseline 108.16 ± 21.98 110.47 ± 22.14 113.14 ± 9.96 110.98 ± 20.23 104.08 ± 15.54 113.84 ± 20.31 154.10 ± 0.99 121.89 ± 24.68 114.43 ± 20.85 99.07 ± 19.42 112.25 ± 9.26 102.00 ± 18.10
 2-Weeks 107.78 ± 21.77 110.13 ± 22.73 111.62 ± 8.99 110.42 ± 20.65 103.85 ± 16.42 112.96 ± 20.79 154.40 ± 2.76 121.25 ± 25.34 112.59 ± 21.19 * 98.68 ± 19.21 109.65 ± 8.27 101.12 ± 17.57
 14-Weeks 106.11 ± 22.71 * 107.58 ± 23.82 * 108.34 ± 10.41 * 107.73 ± 21.71 * 103.90 ± 15.83 112.53 ± 20.72 153.80 ± 1.91 120.78 ± 25.24 111.98 ± 21.42 * 102.22 ± 16.61 * 105.83 ± 11.00 * 103.25 ± 14.40 *
Fasting glucose, mg/dL
 Baseline 94.27 ± 8.20 97.58 ± 8.06 93.60 ± 10.09 96.81 ± 8.42 100.09 ± 10.44 99.38 ± 6.21 84.00 ± 5.66 96.30 ± 8.69 94.64 ± 12.93 93.86 ± 13.79 99.00 ± 4.24 95.00 ± 12.25
 2-Weeks 84.00 ± 9.52 * 84.95 ± 6.03 * 84.60 ± 11.37 * 84.88 ± 7.10 * 97.30 ± 8.92 91.12 ± 6.38 * 93.00 ± 1.41 91.50 ± 5.70 * 90.27 ± 12.16 * 94.43 ± 11.24 98.00 ± 2.83 95.22 ± 9.91
 14-Weeks 83.93 ± 5.82 * 85.53 ± 7.35 * 88.80 ± 11.30 86.16 ± 8.08 * 99.00 ± 4.07 94.88 ± 6.31 90.00 ± 4.24 93.90 ± 6.10 90.63 ± 11.31 * † 92.00 ± 9.51 96.00 ± 4.24 93.14 ± 8.19
Fasting insulin, μU/m
 Baseline 24.08 ± 18.70 25.05 ± 17.04 16.13 ± 4.89 23.33 ± 15.78 21.73 ± 11.73 21.77 ± 16.67 43.16 ± 7.89 26.05 ± 17.45 32.42 ± 27.24 19.67 ± 13.80 26.86 ± 8.77 21.27 ± 12.75
 2-Weeks 16.95 ± 11.39 * † 21.45 ± 13.48 15.23 ± 7.54 20.21 ± 12.64 27.51 ± 13.32 23.81 ± 15.23 69.16 ± 73.24 * 33.89 ± 35.27 22.89 ± 21.31 * 14.56 ± 9.91 24.05 ± 11.43 16.67 ± 10.37
 14-Weeks 19.83 ± 11.47 22.89 ± 17.31 12.64 ± 6.59 20.92 ± 16.23 24.04 ± 15.50 21.66 ± 15.29 54.55 ± 37.17 28.24 ± 22.97 20.90 ± 17.75 * 18.24 ± 9.24 21.31 ± 9.56 19.12 ± 8.63
Fasting C-peptide, pg/mL
 Baseline 2550.58 ± 1241.47 3460.91 ± 1406.99 2392.83 ± 596.91 3238.39 ± 1344.45 2738.86 ± 928.70 2784.94 ± 951.01 3289.01 ± 597.80 2896.95 ± 878.86 3139.58 ± 1559.11 2625.07 ± 911.85 3018.82 2674.29 ± 855.61
 2-Weeks 2416.31 ± 1090.12 3242.98 ± 1061.68 2461.85 ± 957.48 3073.17 ± 1070.61 3075.23 ± 1191.82 2761.09 ± 886.64 4543.27 ± 3255.16 * 3157.13 ± 1591.13 2559.75 ± 1428.13 * 2296.17 ± 857.49 3576.10 2456.16 ± 913.79
 14-Weeks 2475.71 ± 1278.13 3254.27 ± 1715.30 2408.33 ± 1269.89 3085.08 ± 1648.43 2954.40 ± 968.17 2519.34 ± 1087.83 3720.36 ± 1020.24 2786.23 ± 1139.35 2412.39 ± 1287.52 * 2673.25 ± 492.39 3329.33 2782.59 ± 515.46
Fasting glucagon, pg/mL
 Baseline 16.30 ± 10.53 17.87 ± 9.42 11.84 ± 1.36 16.61 ± 8.72 16.12 ± 8.39 13.48 ± 5.01 35.06 ± 10.13 18.27 ± 11.06 25.03 ± 10.34 14.82 ± 8.19 46.54 18.78 ± 13.54
 2-Weeks 10.98 ± 6.84 * † 14.10 ± 5.21 13.78 ± 4.19 14.03 ± 4.92 16.35 ± 4.36 11.59 ± 6.20 30.17 ± 18.06 15.72 ± 11.69 16.79 ± 8.64 * 12.02 ± 3.34 45.29 16.17 ± 12.16
 14-Weeks 14.91 ± 11.93 15.54 ± 8.00 8.06 ± 3.68 14.05 ± 7.89 * 13.66 ± 4.91 12.47 ± 4.02 36.49 ± 8.85 18.48 ± 12.10 20.29 ± 12.66 * 13.17 ± 8.73 48.14 18.99 ± 16.28
Fasting GIP, pg/mL
 Baseline 23.30 ± 23.80 15.08 ± 13.42 14.60 ± 9.06 14.98 ± 12.46 18.03 ± 14.10 10.97 ± 4.90 16.55 ± 7.22 12.21 ± 5.53 28.75 ± 46.18 16.46 ± 10.94 8.17 15.42 ± 10.54
 2-Weeks 21.76 ± 15.44 23.67 ± 19.00 13.77 ± 10.36 21.52 ± 17.77 38.28 ± 17.55 * 22.61 ± 13.02 157.78 ± 209.84 * 52.65 ± 95.83 * 12.46 ± 8.97 * 10.48 ± 5.64 11.19 10.57 ± 5.23
 14-Weeks 13.17 ± 7.81 19.86 ± 21.62 12.00 ± 8.87 18.29 ± 19.83 53.63 ± 49.97 * 21.10 ± 5.45 34.20 ± 26.24 24.01 ± 11.90 13.46 ± 9.13 16.55 ± 6.29 8.61 15.23 ± 6.49
Fasting GLP-1, pg/mL
 Baseline 1.42 ± 1.80 0.79 ± 0.98 0.21 ± 0.17 0.68 ± 0.92 1.49 ± 1.27 0.92 ± 1.00 0.68 ± 0.00 0.87 ± 0.89 0.96 ± 0.90 1.07 ± 0.82 0.82 ± 0.96 1.00 ± 0.78
 2-Weeks NA NA NA NA 6.59 ± 5.12 * 4.60 ± 2.09 13.08 ± 14.28 * 6.30 ± 6.23 * 0.61 ± 0.59 0.60 ± 0.33 0.46 ± 0.44 0.56 ± 0.33
 14-Weeks NA NA NA NA 5.64 ± 3.76 * 4.84 ± 2.50 3.87 ± 2.48 4.64 ± 2.39 * † 0.64 ± 0.75 0.81 ± 0.65 0.65 ± 0.17 0.77 ± 0.54
HOMA-IR
 Baseline 5.48 ± 4.34 6.06 ± 4.13 3.77 ± 1.40 5.62 ± 3.84 5.35 ± 2.98 5.23 ± 3.75 9.01 ± 2.24 5.99 ± 3.75 8.18 ± 7.81 4.84 ± 3.95 6.52 ± 1.86 5.21 ± 3.57
 2-Weeks 3.67 ± 2.87 * † 4.49 ± 2.74 3.24 ± 1.78 4.24 ± 2.60 6.70 ± 3.59 5.28 ± 3.09 16.01 ± 17.06 * 7.67 ± 8.12 5.53 ± 5.99 * 3.52 ± 2.77 5.78 ± 2.60 4.03 ± 2.75
 14-Weeks 4.19 ± 2.64 * † 4.91 ± 4.00 2.86 ± 1.83 4.52 ± 3.74 5.91 ± 3.94 4.99 ± 3.29 11.93 ± 7.69 6.37 ± 4.85 5.01 ± 4.81 * 4.25 ± 2.37 5.00 ± 2.04 4.47 ± 2.14
PAI-1, U/mL
 Baseline 17.84 ± 10.78 23.00 ± 7.15 20.68 ± 8.83 22.55 ± 7.37 18.07 ± 7.20 19.66 ± 8.95 22.98 ± 14.65 20.32 ± 9.39 20.37 ± 6.75 16.93 ± 8.94 20.42 ± 2.82 17.70 ± 7.96
 2-Weeks 15.50 ± 7.63 18.55 ± 4.72 * 17.06 ± 9.17 18.27 ± 5.63 * 15.02 ± 6.68 19.34 ± 6.93 16.93 ± 4.64 18.86 ± 6.39 21.56 ± 9.33 18.55 ± 6.25 18.16 ± 0.69 18.46 ± 5.42
 14-Weeks 17.59 ± 7.90 17.07 ± 6.09 * 15.79 ± 2.57(‡) 16.83 ± 5.56 * 20.36 ± 7.87 17.12 ± 5.67 25.79 ± 7.44 18.85 ± 6.67 14.84 ± 4.95 * 16.88 ± 6.06 19.30 ± 3.71 17.57 ± 5.31
Matsuda Index
 Baseline 3.57±3.21 3.00±1.73 3.35±0.78 3.07±1.58 2.81±1.35 3.26±2.50 1.60±0.73 2.93±2.33 8.39±19.83 4.48±3.06 2.22±0.98 3.98±2.85
 2-Weeks 5.86±6.60 * 4.24±2.69 4.33±1.73 4.26±2.49 2.82±1.84 3.25±1.96 1.98±1.89 2.97±1.91 3.51±1.89 * 4.39±2.56 2.60±1.27 4.00±2.39
 14-Weeks 4.28±2.48 3.90±2.30 4.73±1.90 4.06±2.22 3.00±1.72 3.96±2.91 1.64±1.03 3.50±2.77 4.72±3.20 * 4.11±2.82 2.91±1.34 3.77±2.43
Disposition Index
 Baseline 1.18±39.27 2.76±36.66 22.63±8.66 6.73±33.80 37.52±85.33 −6.98±27.41 18.82±8.07 −1.82±26.65 15.11±17.14 35.54±59.65 −2.83±17.29 27.01±54.70
 2-Weeks −3.53±68.55(‡) 6.57±38.35 13.23±23.76 7.90±35.58 9.46±22.91 * † 15.51±41.31(‡) 28.78±34.89 18.83±37.82(‡) 18.89±50.69 31.71±22.58 14.22±4.18 27.82±21.08
 14-Weeks 14.30±30.00(‡) 9.61±14.44 17.03±17.59 11.03±15.01 45.10±86.85 13.46±21.46(‡) 10.80±8.15 12.93±19.16 5.11±31.30 27.96±13.64 34.04±36.17(‡) 29.70±18.73(‡)
Insulinogenic Index
 Baseline 183.03±511.53 799.14±2061.84 280.95±220.45 681.37±1815.94 155.97±276.30 292.82±152.16 241.83±147.44 280.08±142.12 160.94±252.38 303.29±357.99 334.76 307.23±331.62
 2-Weeks 202.45±245.70 156.52±197.76 * 100.40±50.07 143.77±175.65 * 376.90±388.83 242.89±278.71 198.48±18.36 233.02±242.25 480.47±562.54 * 232.60±96.28 266.66 237.47±88.83
 14-Weeks 122.91±115.50 213.99±198.85 * 132.90±75.20 199.89±184.69 * 79.93±407.17 246.17±237.28 387.93±199.27 277.67±226.05 421.33±799.25 * 221.90±119.26 520.25 271.62±161.91
Open in a new tab

All measures shown as mean ± SD or percent (N). The GA/AA column combines individuals with the GA or AA genotypes (co-dominant model). GLP-1 indicates glucagon-like peptide-1; GIP, gastric inhibitory polypeptide; HOMA-IR, Homeostatic Model Assessment for Insulin Resistance; BMI, Body Mass Index; PAI-1, Plasminogen activator inhibitor-1; NA, not analyzed; ND, not done. Asterisk in bold (*) indicates P<0.05 for estimates of change from baseline using regression model. Dagger (†) indicates significance only in all participants and not in white only sensitivity analysis. Double dagger (‡) indicates P<0.05 only in whites and not in all participants. ßGLP-1 levels after treatment with liraglutide were not reported due to cross-reactivity of the assay with drug.

Effects of Treatment on Inflammatory Measures

PAI-1, the principal inhibitor of endogenous fibrinolysis and a marker of inflammation, was significantly reduced by liraglutide, but only in individuals with the variant allele (Figure 1B). Of note, this occurred as early as two weeks, prior to any significant weight loss, suggesting the effect of liraglutide on PAI-1 is independent of changes in weight. In contrast, hypocaloric diet only reduced PAI-1 in individuals with the wildtype genotype. PAI-1 is increased in obesity and correlates with changes in weight (Folsom et al. 1993; Orenes-Piñero et al. 2015). In our study, change in weight accounted for approximately 4% of the reduction in PAI-1 after treatment, and adjusting for weight as a confounder did not affect the PAI-1 results. There was no effect of genotype on circulating chemokine and cytokines measured (MCP-1, IFNg, IL-6, IL-8, IL-10, TNFa) (data not shown).

Discussion

To understand the effect of the rs6923761 GLP-1R variant on responses to incretin-based therapies, we compared changes in weight and circulating metabolic and inflammatory measures after treatment with liraglutide, sitagliptin, or hypocaloric diet. We found that the presence of the rs6923761 variant was associated with lower baseline hemoglobin A1c, higher baseline insulinogenic index suggesting better beta cell function, and a greater reduction in weight and PAI-1 after liraglutide. Importantly, the effect on weight was allele dose-dependent, with greater weight loss for each copy of the variant A allele present. It is notable that PAI-1 was significantly reduced after liraglutide treatment at 2 weeks, prior to significant weight loss, suggesting that the effect is independent of changes in weight. The significant reduction of PAI-1 after liraglutide in individuals with the variant allele may predict cardiovascular outcomes given the role of PAI-1 in cardiovascular disease (Thögersen et al. 1998). While fasting glucose levels were similar across genotypes in the liraglutide arm, there was a notable decreased responsiveness of other metabolic measures to liraglutide and hypocaloric diet in individuals with the variant allele.

The molecular basis of the divergent effects of the rs6923761 variant on weight and metabolic measures are unknown and require further investigation. Prior in vitro studies rely on artificial transfection models where expression or downstream signaling may not reflect true physiology. Based on our findings and prior reports in the literature, we hypothesize that the variant may exert distinct molecular effects that depend on both the receptor’s location (central nervous system versus peripheral/pancreatic GLP-1R signaling) and the type of agonism (endogenous/physiologic versus pharmacologic). At baseline, individuals with the variant allele have lower weight in population studies (de Luis et al. 2015a) and lower hemoglobin A1c as seen in this study, suggesting a beneficial effect of the variant in response to endogenous GLP-1. After pharmacologic treatment with a GLP-1R agonist, individuals with the variant allele have greater weight loss suggesting enhanced signaling in the central nervous system, and greater reduction of PAI-1, but reduced effects on metabolic hormones, suggesting diminished signaling in the periphery/pancreas.

Notably, GLP-1R agonism has been shown to have tissue-specific effects with bias for certain signaling pathways in different tissues. For instance, anorectic effects of GLP-1R agonism in the central nervous system require PKA and downstream mTORC1 signaling (Le et al. 2023), whereas the insulin secretion effects of GLP-1R agonism in pancreatic beta cells require EPAC2 while also engaging PKA signaling (Holz 2004). Further, in a mouse model expressing the rarer rs10305492 (Ala316Thr) variant, there was improved fasting metabolic parameters but blunted responses to GLP-1R agonist therapy in vivo (El Eid et al. 2024). These findings were linked to constitutive activation of the GLP-1R at baseline but dampened responses to pharmacologic agonists. It is possible that a similar effect is present with the rs6923761 variant, which will need to be investigated in the future.

Additional contributing mechanisms may include greater delay in gastric emptying in individuals with the variant, as seen in a prior study (Chedid et al. 2018), contributing to greater weight loss after liraglutide. Further, pancreatic beta cells may have decreased responsiveness to GLP-1 therapy, as seen previously in a hyperglycemic clamp study (Sathananthan et al. 2010). Alternatively, it’s possible that individuals with the variant allele had reduced metabolic responses to treatment because they had better metabolic parameters at baseline (lower HbA1c and higher insulinogenic index).

The glucagon-like peptide-1 receptor (GLP-1R) has many other known variants, with some associated with worsening cardiometabolic parameters such as diabetic kidney disease (rs3765467 Song et al., 2024) and gestational diabetes (rs6458093 Luo et al., 2022), and some associated with improvements in cardiometabolic parameters such as lower fasting blood glucose, type 2 diabetes risk, and heart disease (rs10305492 Scott et al., 2016; Wessel et al., 2015). In addition, a few variants are associated with differences in response to pharmacologic treatment with DPP4 inhibitors (rs3765467 Han et al., 2016) or GLP-1R agonists (rs10305492 El Eid et al., 2024), yet the findings have been inconsistent in human studies overall (BouSaba et al. 2023). In a large study of over 8,000 individuals, 36 variants in the GLP-1R were identified of which 10 had reduced signaling in transfected cells (Melchiorsen et al. 2023). These 10 variants with reduced signaling were not associated with type 2 diabetes, although had a small association with increased fasting glucose and HbA1c.

Strengths of this study include the prospective randomized design, evaluation of effects at two timepoints after treatment, and comparison of pharmacologic GLP-1R activation with liraglutide versus endogenous GLP-1R agonism with sitagliptin. One limitation of the study is that we had few individuals with the AA variant in the sitagliptin- and hypocaloric diet-treated arms and thus draw few conclusions from those analyses. In addition, the GLP-1 assay used cross-reacts with liraglutide, so we could not evaluate GLP-1 in the liraglutide-treated individuals. Further, PAI-1 was higher at baseline in GA/AA individuals treated with liraglutide. Although all statistical analyses control for the baseline level, we cannot exclude a regression to the mean in this analysis. Finally, the study timeline of 14 weeks limits discovery of effects requiring longer duration of treatment.

A deeper understanding of the contribution of genetic variants to cardiometabolic outcomes and responses to pharmacotherapy is important as we aim to improve our ability to find the right drug for the right patient. Our findings may contribute to an understanding of the variability in responsiveness to GLP-1R agonists both for weight loss and metabolic outcomes. Individuals with the rs6923761 variant would likely benefit most from GLP-1R therapy to lose weight but not benefit as much to lower glucose.

In conclusion, the GLP-1R rs6923761 variant is associated with lower baseline hemoglobin A1c and greater reduction in weight and PAI-1 in response to liraglutide, but not sitagliptin or hypocaloric diet. Future studies into the functional effects of this variant may shed light on the mechanisms of these findings.

Statistical Analysis

Pre-specified analyses reported here include anthropometric, metabolic, and circulating measures by genotype. Descriptive statistics of patient baseline characteristics are presented as mean ± SD for continuous variables and frequencies and proportions for categorical variables. Between-group comparisons in Tables 1 and 2 were performed using Kruskal–Wallis for 3-way comparisons (GG vs GA vs AA), Wilcoxon rank sum for 2-way comparisons (GG vs GA/AA), or Pearson’s chi-squared test for categorical variables.

To evaluate the genotype effects on these endpoints, separate multivariable generalized least squares linear regression models were fitted. Genotype, treatment (liraglutide, sitagliptin or diet), time (2- or 14-week), baseline measurement as well as interaction between genotype, treatment and time were included as independent variables. A compound symmetry structure for within-subject correlation was used. Inferences on the contrasts of interest were conducted using Wald test. Estimates of change in outcome from baseline to 2- and 14-weeks were calculated based on the multivariable regression model.

Prior population studies of the rs6923761 variant largely analyzed outcomes using a co-dominant model (wildtype GG versus GA/AA combined). It is unknown whether this variant truly behaves in a co-dominant fashion with regards to signaling/function. We evaluated all outcomes using both a recessive and co-dominant model. Genotype was included in the model in three different ways: GG vs. GA/AA, GG vs. GA vs. AA or the number of A alleles.

As sex was distributed similarly among treatment arms and among genotype groups (Table 2) in liraglutide-treated individuals in this randomized trial, we did not adjust for sex in the statistical analysis of the data. Given the known confounding effect of race in some of our metabolic measures and the uneven distribution of race in the cohort, we performed a sensitivity analysis in whites only.

We also evaluated the extent to which weight explains the effect of liraglutide on PAI-1 using a traditional approach of mediation analysis with generalized least squares linear models. The proportion of mediation effect was estimated as indirect effect divided by total effect, and 95% confidence interval was constructed using bootstrap approach.

All the analyses were performed using the statistical software R 4.1.0

Supplementary Material

01
02

Figure S1. Effects of treatment on metabolic measures. Plots show mean ± SEM for (A) fasting glucose; (B) fasting insulin; (C) fasting C-peptide; (D) fasting glucagon; (E) fasting GIP; (F) fasting GLP-1; (G) HOMA-IR in GG (black circles) and GA/AA (orange squares) individuals. Fasting GLP-1 in liraglutide treatment group not analyzed given cross-reactivity of the assay with liraglutide. Panels indicate treatment arm. Time after initiation of treatment is shown on the x-axis. Asterisk (*) indicates P<0.05 for estimates of change from baseline.

Funding and Acknowledgements

Research reported in this publication was supported by the American Heart Association 17SFRN33520017 (M.M., H.N., D.M., C.Y., H.S., J.M.L, N.J.B), National Center for Advancing Translational Sciences 5UL1TR002243 and KL2TR002245 (M.M.), National Institute of Diabetes and Digestive and Kidney Diseases T32DK007061 (M.M.), National Heart, Lung and Blood Institute 1K23HL159351 (M.M.). This work utilized the core(s) of the Vanderbilt Diabetes Research and Training Center funded by grant DK020593 from the National Institute of Diabetes and Digestive and Kidney Disease. Novo Nordisk provided liraglutide and matching placebo.

The Vanderbilt University Medical Center VANTAGE Core provided technical assistance for sample preparation and sequencing, and VANTAGE is supported in part by Clinical and Translational Science Award Grant 5UL1 RR024975-03, Vanderbilt Ingram Cancer Center Grant P30 CA68485, Vanderbilt Vision Center Grant P30 EY08126, and National Institutes of Health/National Center for Research Resources Grant G20 RR030956.

The authors acknowledge contributions from study nurse Patricia Wright; study dietician Dianna Olson; research assistants Sara E. Howard, Bradley Perkins, Eric C. Olson; lab manager Anthony Dematteo.

Footnotes

Declaration of Interest

None unless noted below:

J.M.L: Dr. Luther has served on the advisory board for Mineralys and serves as a consultant for Novo Nordisk.

N.J.B.: Dr. Brown serves on the scientific advisory board for and holds options in Alnylam Pharmaceuticals. She serves as a consultant for eBioStar Tech and Antlia Bioscience. Dr. Brown owns equity in Abbvie and J and J Pharmaceuticals.

J.A.B.: Dr. Beckman serves as a consultant for Medtronic, Merck, Mingsight, JanOne, Novartis, Regeneron, and Tourmaline.

No other potential conflicts of interest relevant to this article were reported.

References

  1. Anderson B, Carlson P, Laurenti M, Vella A, Camilleri M, Desai A, Feuerhak K & Bharucha AE 2020. Association between allelic variants in the glucagon-like peptide 1 and cholecystokinin receptor genes with gastric emptying and glucose tolerance. Neurogastroenterology and Motility 32 e13724. (doi: 10.1111/nmo.13724) [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. BouSaba J, Vosoughi K, Dilmaghani S, Prokop LJ & Camilleri M 2023. Pharmacogenetic interactions of medications administered for weight loss in adults: a systematic review and meta-analysis. Pharmacogenomics 24 283–295. (doi: 10.2217/pgs-2022-0192) [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Chedid V, Vijayvargiya P, Carlson P, Van Malderen K, Acosta A, Zinsmeister A & Camilleri M 2018. Allelic variant in the glucagon-like peptide 1 receptor gene associated with greater effect of liraglutide and exenatide on gastric emptying: A pilot pharmacogenetics study. Neurogastroenterology and Motility 30 e13313. (doi: 10.1111/nmo.13313) [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Dawed AY, Mari A, Brown A, McDonald TJ, Li L, Wang S, Hong M-G, Sharma S, Robertson NR, Mahajan A et al. 2023. Pharmacogenomics of GLP-1 receptor agonists: a genome-wide analysis of observational data and large randomised controlled trials. The Lancet. Diabetes & Endocrinology 11 33–41. (doi: 10.1016/S2213-8587(22)00340-0) [DOI] [PubMed] [Google Scholar]
  5. Eghbali M, Alaei-Shahmiri F, Hashemi-Madani N, Emami Z, Mostafavi L, Malek M & Khamseh ME 2024. Glucagon-Like Peptide 1 (GLP-1) Receptor Variants and Glycemic Response to Liraglutide: A Pharmacogenetics Study in Iranian People with Type 2 Diabetes Mellitus. Advances in Therapy 41 826–836. (doi: 10.1007/s12325-023-02761-1) [DOI] [PubMed] [Google Scholar]
  6. El Eid L, Deane-Alder K, Rujan R-M, Mariam Z, Oqua AI, Manchanda Y, Belousoff MJ, Bernardino de la Serna, Sloop KW, Rutter GA et al. 2024. In vivo functional profiling and structural characterisation of the human Glp1r A316T variant. BioRxiv : The Preprint Server for Biology. (doi: 10.1101/2024.10.19.619191) [DOI] [Google Scholar]
  7. Folsom AR, Qamhieh HT, Wing RR, Jeffery RW, Stinson VL, Kuller LH & Wu KK 1993. Impact of weight loss on plasminogen activator inhibitor (PAI-1), factor VII, and other hemostatic factors in moderately overweight adults. Arteriosclerosis and Thrombosis: A Journal of Vascular Biology 13 162–169. (doi: 10.1161/01.atv.13.2.162) [DOI] [PubMed] [Google Scholar]
  8. Fortin J-P, Schroeder JC, Zhu Y, Beinborn M & Kopin AS 2010. Pharmacological characterization of human incretin receptor missense variants. The Journal of Pharmacology and Experimental Therapeutics 332 274–280. (doi: 10.1124/jpet.109.160531) [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Han E, Park HS, Kwon O, Choe EY, Wang HJ, Lee Y-H, Lee S-H, Kim CH, Kim L-K, Kwak SH et al. 2016. A genetic variant in GLP1R is associated with response to DPP-4 inhibitors in patients with type 2 diabetes. Medicine 95 e5155. (doi: 10.1097/MD.0000000000005155) [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Harrall KK, Glueck DH, Lange LA, Litkowski EM, Vanderlinden LA, Konigsberg IR, Cree MG, Perng W & Dabelea D 2024. GLP-1R gene polymorphisms and metabolic traits during childhood and adolescence: The EPOCH study. The Journal of Clinical Endocrinology and Metabolism. (doi: 10.1210/clinem/dgae880) [DOI] [PubMed] [Google Scholar]
  11. Holz GG 2004. Epac: A new cAMP-binding protein in support of glucagon-like peptide-1 receptor-mediated signal transduction in the pancreatic beta-cell. Diabetes 53 5–13. (doi: 10.2337/diabetes.53.1.5) [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Javorský M, Gotthardová I, Klimčáková L, Kvapil M, Židzik J, Schroner Z, Doubravová P, Gala I, Dravecká I & Tkáč I 2016. A missense variant in GLP1R gene is associated with the glycaemic response to treatment with gliptins. Diabetes, Obesity & Metabolism 18 941–944. (doi: 10.1111/dom.12682) [DOI] [PubMed] [Google Scholar]
  13. Jensterle M, Pirš B, Goričar K, Dolžan V & Janež A 2015. Genetic variability in GLP-1 receptor is associated with inter-individual differences in weight lowering potential of liraglutide in obese women with PCOS: a pilot study. European Journal of Clinical Pharmacology 71 817–824. (doi: 10.1007/s00228-015-1868-1) [DOI] [PubMed] [Google Scholar]
  14. Koole C, Wootten D, Simms J, Valant C, Miller LJ, Christopoulos A & Sexton PM 2011. Polymorphism and ligand dependent changes in human glucagon-like peptide-1 receptor (GLP-1R) function: allosteric rescue of loss of function mutation. Molecular Pharmacology 80 486–497. (doi: 10.1124/mol.111.072884) [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Lagou V, Jiang L, Ulrich A, Zudina L, González KSG, Balkhiyarova Z, Faggian A, Maina JG, Chen S, Todorov PV et al. 2023. GWAS of random glucose in 476,326 individuals provide insights into diabetes pathophysiology, complications and treatment stratification. Nature Genetics 55 1448–1461. (doi: 10.1038/s41588-023-01462-3) [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Le TD V, Liu D, Besing G-LK, Raghavan R, Ellis BJ, Ceddia RP, Collins S & Ayala JE 2023. Glucagon-like peptide-1 receptor activation stimulates PKA-mediated phosphorylation of Raptor and this contributes to the weight loss effect of liraglutide. ELife 12. (doi: 10.7554/eLife.80944) [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. de Luis DA, Aller R, Izaola O, de la Fuente B, Primo D, Conde R & Sagrado MG 2013. Evaluation of weight loss and adipocytokine levels after two hypocaloric diets with different macronutrient distribution in obese subjects with the rs6923761 gene variant of glucagon-like peptide 1 receptor. Annals of Nutrition & Metabolism 63 277–282. (doi: 10.1159/000356710) [DOI] [PubMed] [Google Scholar]
  18. de Luis DA, Bachiller R, Izaola O, de la Fuente B & Aller R 2014a. Relation of the rs6923761 gene variant in glucagon-like peptide 1 receptor to metabolic syndrome in obese subjects. Annals of Nutrition & Metabolism 65 253–258. (doi: 10.1159/000365295) [DOI] [PubMed] [Google Scholar]
  19. de Luis DA, Aller R, Izaola O, Lopez JJ, Gomez E, Torres B & Soto GD 2014b. Effect of rs6923761 gene variant of glucagon-like peptide 1 receptor on metabolic response and weight loss after a 3-month intervention with a hypocaloric diet. Journal of Endocrinological Investigation 37 935–939. (doi: 10.1007/s40618-014-0117-2) [DOI] [PubMed] [Google Scholar]
  20. de Luis DA, Pacheco D, Aller R & Izaola O 2014c. Role of the rs6923761 gene variant in glucagon-like peptide 1 receptor gene on cardiovascular risk factors and weight loss after biliopancreatic diversion surgery. Annals of Nutrition & Metabolism 65 259–263. (doi: 10.1159/000365975) [DOI] [PubMed] [Google Scholar]
  21. de Luis DA, Aller R, de la Fuente B, Primo D, Conde R, Izaola O & Sagrado MG 2015a. Relation of the rs6923761 gene variant in glucagon-like peptide 1 receptor with weight, cardiovascular risk factor, and serum adipokine levels in obese female subjects. Journal of Clinical Laboratory Analysis 29 100–105. (doi: 10.1002/jcla.21735) [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. de Luis DA, Aller R, Izaola O & Romero E 2015b. Effects of a high-protein/low-carbohydrate versus a standard hypocaloric diet on adipocytokine levels and cardiovascular risk factors during 9 months, role of rs6923761 gene variant of glucagon-like peptide 1 receptor. Journal of Endocrinological Investigation 38 1183–1189. (doi: 10.1007/s40618-015-0304-9) [DOI] [PubMed] [Google Scholar]
  23. de Luis DA, Diaz Soto G, Izaola O & Romero E 2015c. Evaluation of weight loss and metabolic changes in diabetic patients treated with liraglutide, effect of RS 6923761 gene variant of glucagon-like peptide 1 receptor. Journal of Diabetes and Its Complications 29 595–598. (doi: 10.1016/j.jdiacomp.2015.02.010) [DOI] [PubMed] [Google Scholar]
  24. de Luis DA, Ballesteros M, Lopez Guzman A, Ruiz E, Muñoz C, Penacho MA, Iglesias P, Maldonado A, Puigdevall V & Delgado M 2018. rs6923761 gene variant in glucagon-like peptide 1 receptor: Allelic frequencies and influence on cardiovascular risk factors in a multicenter study of Castilla-Leon. Clinical Nutrition (Edinburgh, Scotland) 37 2144–2148. (doi: 10.1016/j.clnu.2017.10.013) [DOI] [PubMed] [Google Scholar]
  25. Luo P, Fan Y, Xiong Y, Feng H, Yang Z, Zhang C & Mei B 2022. Genetic variants of the GLP-1R gene affect the susceptibility and glucose metabolism of gestational diabetes mellitus: a two-center nested case–control study. Diabetology & Metabolic Syndrome 14 190. (doi: 10.1186/s13098-022-00963-1) [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Maselli D, Atieh J, Clark MM, Eckert D, Taylor A, Carlson P, Burton DD, Busciglio I, Harmsen WS, Vella A et al. 2022. Effects of liraglutide on gastrointestinal functions and weight in obesity: A randomized clinical and pharmacogenomic trial. Obesity (Silver Spring, Md.) 30 1608–1620. (doi: 10.1002/oby.23481) [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Mashayekhi M, Wilson JR, Jafarian-Kerman S, Nian H, Yu C, Shuey MM, Luther JM & Brown NJ 2021. Association of a glucagon-like peptide-1 receptor gene variant with glucose response to a mixed meal. Diabetes, Obesity & Metabolism 23 281–286. (doi: 10.1111/dom.14216) [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Mashayekhi M, Beckman JA, Nian H, Garner EM, Mayfield D, Devin JK, Koethe JR, Brown JD, Cahill KN, Yu C et al. 2023. Comparative effects of weight loss and incretin-based therapies on vascular endothelial function, fibrinolysis and inflammation in individuals with obesity and prediabetes: A randomized controlled trial. Diabetes, Obesity & Metabolism 25 570–580. (doi: 10.1111/dom.14903) [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Mashayekhi M, Nian H, Mayfield D, Devin JK, Gamboa JL, Yu C, Silver HJ, Niswender K, Luther JM & Brown NJ 2024. Weight Loss-Independent Effect of Liraglutide on Insulin Sensitivity in Individuals With Obesity and Prediabetes. Diabetes 73 38–50. (doi: 10.2337/db23-0356) [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Matsuda M & DeFronzo RA 1999. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care 22 1462–1470. (doi: 10.2337/diacare.22.9.1462) [DOI] [PubMed] [Google Scholar]
  31. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF & Turner RC 1985. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28 412–419. (doi: 10.1007/BF00280883) [DOI] [PubMed] [Google Scholar]
  32. Melchiorsen JU, Sørensen KV, Bork-Jensen J, Kizilkaya HS, Gasbjerg LS, Hauser AS, Rungby J, Sørensen HT, Vaag A, Nielsen JS et al. 2023. Rare Heterozygous Loss-of-Function Variants in the Human GLP-1 Receptor Are Not Associated With Cardiometabolic Phenotypes. The Journal of Clinical Endocrinology and Metabolism 108 2821–2833. (doi: 10.1210/clinem/dgad290) [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Michałowska J, Miller-Kasprzak E, Seraszek-Jaros A, Mostowska A & Bogdański P 2022. Association of GLP1R variants rs2268641 and rs6923761 with obesity and other metabolic parameters in a Polish cohort. Frontiers in Endocrinology 13 1000185. (doi: 10.3389/fendo.2022.1000185) [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Orenes-Piñero E, Pineda J, Roldán V, Hernández-Romero D, Marco P, Tello-Montoliu A, Sogorb F, Valdés M, Lip GYH & Marín F 2015. Effects of Body Mass Index on the Lipid Profile and Biomarkers of Inflammation and a Fibrinolytic and Prothrombotic State. Journal of Atherosclerosis and Thrombosis 22 610–617. (doi: 10.5551/jat.26161) [DOI] [PubMed] [Google Scholar]
  35. Santos JL, Yévenes I, Cataldo LR, Morales M, Galgani J, Arancibia C, Vega J, Olmos P, Flores M, Valderas JP et al. 2016. Development and assessment of the disposition index based on the oral glucose tolerance test in subjects with different glycaemic status. Journal of Physiology and Biochemistry 72 121–131. (doi: 10.1007/s13105-015-0458-0) [DOI] [PubMed] [Google Scholar]
  36. Sathananthan A, Man CD, Micheletto F, Zinsmeister AR, Camilleri M, Giesler PD, Laugen JM, Toffolo G, Rizza RA, Cobelli C et al. 2010. Common genetic variation in GLP1R and insulin secretion in response to exogenous GLP-1 in nondiabetic subjects: a pilot study. Diabetes Care 33 2074–2076. (doi: 10.2337/dc10-0200) [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Scott RA, Freitag DF, Li L, Chu AY, Surendran P, Young R, Grarup N, Stancáková A, Chen Y, Varga TV et al. 2016. A genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease. Science Translational Medicine 8 341ra76. (doi: 10.1126/scitranslmed.aad3744) [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Silver HJ, Olson D, Mayfield D, Wright P, Nian H, Mashayekhi M, Koethe JR, Niswender KD, Luther JM & Brown NJ 2023. Effect of the glucagon-like peptide-1 receptor agonist liraglutide, compared to caloric restriction, on appetite, dietary intake, body fat distribution and cardiometabolic biomarkers: A randomized trial in adults with obesity and prediabetes. Diabetes, Obesity & Metabolism 25 2340–2350. (doi: 10.1111/dom.15113) [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Singh B & Saxena A 2010. Surrogate markers of insulin resistance: A review. World Journal of Diabetes 1 36–47. (doi: 10.4239/wjd.v1.i2.36) [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Song J, Zhuang Y, Pan X, Chen Y & Xie F 2024. Variants in PPARD-GLP1R are related to diabetic kidney disease in Chinese Han patients with type 2 diabetes mellitus. Heliyon 10 e35289. (doi: 10.1016/j.heliyon.2024.e35289) [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Thögersen AM, Jansson JH, Boman K, Nilsson TK, Weinehall L, Huhtasaari F & Hallmans G 1998. High plasminogen activator inhibitor and tissue plasminogen activator levels in plasma precede a first acute myocardial infarction in both men and women: evidence for the fibrinolytic system as an independent primary risk factor. Circulation 98 2241–2247. (doi: 10.1161/01.cir.98.21.2241) [DOI] [PubMed] [Google Scholar]
  42. Űrgeová A, Javorský M, Klimčáková L, Židzik J, Šalagovič J, Hubáček JA, Doubravová P, Gotthardová I, Kvapil M, Pelikánová T et al. 2020. Genetic variants associated with glycemic response to treatment with dipeptidylpeptidase 4 inhibitors. Pharmacogenomics 21 317–323. (doi: 10.2217/pgs-2019-0147) [DOI] [PubMed] [Google Scholar]
  43. Wessel J, Chu AY, Willems SM, Wang S, Yaghootkar H, Brody JA, Dauriz M, Hivert M-F, Raghavan S, Lipovich L et al. 2015. Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility. Nature Communications 6 5897. (doi: 10.1038/ncomms6897) [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Yau AMW, McLaughlin J, Maughan RJ, Gilmore W, Ashworth JJ & Evans GH 2018. A Pilot Study Investigating the Influence of Glucagon-Like Peptide-1 Receptor Single Nucleotide Polymorphisms on Gastric Emptying Rate in Caucasian Men. Frontiers in Physiology 9 1331. (doi: 10.3389/fphys.2018.01331) [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

01
NIHMS2115585-supplement-01.docx (25.6KB, docx)
02

Figure S1. Effects of treatment on metabolic measures. Plots show mean ± SEM for (A) fasting glucose; (B) fasting insulin; (C) fasting C-peptide; (D) fasting glucagon; (E) fasting GIP; (F) fasting GLP-1; (G) HOMA-IR in GG (black circles) and GA/AA (orange squares) individuals. Fasting GLP-1 in liraglutide treatment group not analyzed given cross-reactivity of the assay with liraglutide. Panels indicate treatment arm. Time after initiation of treatment is shown on the x-axis. Asterisk (*) indicates P<0.05 for estimates of change from baseline.

NIHMS2115585-supplement-02.pdf (2.2MB, pdf)

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