Abstract
This cross-sectional study examines the US prescribing trends of glucagon-like peptide 1 (GLP-1) receptor agonists among individuals with type 2 diabetes and/or obesity.
Introduction
Research has highlighted the benefits of glucagon-like peptide 1 receptor agonists (GLP-1RAs) in managing blood glucose and reducing weight in individuals with type 2 diabetes (T2D) and/or obesity.1 However, current trends on how these drugs are prescribed across indications and subpopulations remain unknown. This study examined prescribing trends of GLP-1RAs among individuals with T2D and/or obesity.
Methods
This cross-sectional study used an integrated electronic health record (Epic Cosmos; Epic Systems Corp) covering more than 300 million US residents.2 The Emory University Institutional Review Board exempted this study from review and informed consent as this was a secondary analysis of deidentified data. The study followed the STROBE reporting guideline.
We examined trends in GLP-1RA prescriptions from January 1, 2010, to January 1, 2025, among subpopulations with T2D only, obesity only, and T2D and obesity. Prevalent T2D was identified using DiCAYA algorithms.3 Obesity was determined by diagnosis code or body mass index (BMI) (weight in kilograms divided by height in meters squared) of 30 or higher. We included a subgroup with T2D and BMI of 27 or higher. The outcome was the proportion of individuals prescribed a GLP-1RA (Figure 1) each year.
Figure 1. Annual Glucagon-Like Peptide 1 Receptor Agonist (GLP-1RA) or Dual Glucose-Dependent Insulinotropic Polypeptide and GLP-1RA Prescription Rates Among Different Subpopulations, 2010 to 2024.
Note that dulaglutide showed a comparable rate to semaglutide, possibly reflecting longer market availability or formulary preference. P for trend values are reported only for groups showing the most notable increases. BMI indicates body mass index (calculated as weight in kilograms divided by height in meters squared); SC, subcutaneous; T2D, type 2 diabetes.
We calculated the Gini index to measure differences in prescription rates in 2024 by sex, age, self-reported race and ethnicity, social vulnerability index, rural-urban commuting area, and insurance type. Trend analysis was conducted by fitting linear regressions (eMethods in Supplement 1). Statistical analyses were performed using R, version 4.2.3 (R Foundation). A 2-sided P < .05 was considered significant.
Results
We identified 17.9 million individuals with T2D only (mean [SD] age, 68.6 [13.9] years; 46.6% female and 53.4% male), 59.8 million with obesity only (mean [SD] age, 48.7 [18.9] years; 60.4% female and 39.6% male), and 12.9 million with T2D and obesity (mean [SD] age, 62.9 [13.9] years; 53.0% female and 47.0% male). From 2010 to 2024, prescription rates increased substantially, with the most growth in the T2D and obesity group (2.4% to 34.3%), followed by the T2D and BMI of 27 or higher (2.1% to 31.5%), T2D-only (0.6% to 13.7%), and obesity-only (0.04% to 7.1%) groups (Figure 1). In the obesity-only group, semaglutide prescriptions increased from 0.4% in 2022 to 2.7% in 2024, followed by tirzepatide from 0.06% in 2023 to 1.9% in 2024. In the T2D-only group, semaglutide prescriptions grew from 0.2% in 2019 to 5.3% in 2024, and tirzepatide from 0.2% in 2022 to 2.5% in 2024. A similar trend was observed in the T2D and obesity group, with semaglutide prescriptions (subcutaneous, for T2D) increasing from 0.6% in 2019 to 14.6% in 2024 and tirzepatide (for T2D) from 1.3% in 2022 to 8.1% in 2024.
In 2024, insurance-based differences in prescriptions were observed (Gini indices: obesity only, 0.48; T2D only, 0.32; T2D and obesity, 0.29) (Figure 2). Sex-based differences were most notable in the obesity-only group (Gini index, 0.32). Age-based differences were present across groups (Gini index: obesity only, 0.44; T2D only, 0.30; T2D and obesity, 0.15).
Figure 2. Differences in Glucagon-Like Peptide 1 Receptor Agonist (GLP-1RA) and Dual Glucose-Dependent Insulinotropic Polypeptide (GIP) and GLP-1RA Prescriptions Among Subpopulations in 2024.
Differences were measured using the Gini index. The social vulnerability index (SVI) is a tool developed by the Centers for Disease Control and Prevention to help public health officials identify communities that are more vulnerable to external stresses on human health. A high SVI score indicates greater vulnerability. Race and ethnicity were self-reported. T2D indicates type 2 diabetes.
aAmerican Indian or Alaska Native and Native Hawaiian or Pacific Islander.
bOther includes multiracial or multiethnic or those with missing information.
cOther health insurance includes unspecified or unknown, Medicare Advantage, Medicare Replacement, Tricare, Worker’s Compensation, Veteran’s Affairs, and third-party liability.
Discussion
This cross-sectional study found an increase in GLP-1RA prescriptions, with notable differences across subpopulations by insurance type, sex, and age. Tirzepatide and semaglutide grew the fastest, possibly due to their superior glycemic, weight loss, and guideline-emphasized cardiorenal benefits.4 Although GLP-1RAs are generally covered for T2D, coverage for obesity is limited (eg, Medicare excludes antiobesity drugs). Off-label semaglutide (for T2D) use in the obesity-only group underscores access barriers.5 Higher prescription rates among privately insured individuals indicate that socioeconomic factors influence access.6 Despite evidence about weight management, prescriptions remain low among people with obesity only. Study limitations included that medications were recorded based on prescriptions, not dispensations; variation in health system data contribution duration to Cosmos may have biased the trend analysis; and missing BMI may have led to underidentification of obesity, potentially limiting generalizability. This study highlights increasing GLP-1RA prescriptions and sociodemographic disparities, underscoring the need for policies to improve access, particularly among people with obesity only.
eMethods.
eReferences.
Data Sharing Statement
References
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Associated Data
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Supplementary Materials
eMethods.
eReferences.
Data Sharing Statement
