Trends in Glucagon-Like Peptide 1 Receptor Agonists Utilization for Weight Management in Children

Abstract

Background:

The increasing prevalence of pediatric obesity has led to a need for effective treatment strategies beyond lifestyle interventions. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have emerged as pharmacologic options for adolescents with obesity, with liraglutide and semaglutide receiving U.S. Food and Drug Administration (FDA) approval for this indication. However, real-world utilization patterns, effectiveness, and safety in pediatric populations remain understudied outside of clinical trials, which are essential for the development of clinical guidelines, health care policy, and future research.

Methods:

We used de-identified data from Epic Cosmos, encompassing 289 million patient records, to identify adolescents aged 12–18 years who initiated semaglutide or liraglutide for weight management between July 2020 and July 2023, excluding those with type 2 diabetes. High-quality cohorts included patients with ≥5 body mass index (BMI) measurements before and after medication initiation (n = 970), with a subset receiving medication ≥2 years (n = 198). Changes in BMI before and after treatment were assessed using Wilcoxon signed-rank tests, and demographic associations were analyzed using chi-squared tests.

Results:

Of 7,496 adolescents initiating medication, 28.14% of the high-quality cohort (n = 970) achieved significant weight loss. Semaglutide showed higher efficacy than liraglutide (33.96% vs. 23.02%) with greater median BMI reduction (−12.12% vs. −10.04%). In the ≥2-year cohort (n = 198), 27.78% maintained significant weight loss. Medication-specific complications differed notably: semaglutide users experienced more nausea (77.24% vs. 45.32%) with earlier onset (1.19 vs. 2.19 months) and unique instances of pancreatitis (3.97%), while liraglutide users had higher gastroparesis rates (7.16% vs. 2.00%).

Conclusions:

This study highlights increasing GLP-1RA utilization in pediatric obesity management, with semaglutide demonstrating greater adoption and effectiveness. However, disparities in access remain a concern. Further research is needed to identify predictors of treatment response and explore the impact of concurrent behavioral interventions on weight outcomes.

Introduction

The rising prevalence of childhood obesity represents a significant public health challenge in the United States, with approximately 1 in 5 youth affected.¹ Childhood obesity is defined as a body mass index (BMI) at or above the 95th percentile for age and sex, while severe obesity is classified as a BMI at or above 120% of the 95th percentile.² The prevalence of obesity increases with age, with rates among adolescents (12–19 years) rising from 16.0% in 1999 to 20.9% in 2018.³ Given the well-documented associations between pediatric obesity and long-term adverse health outcomes, including type 2 diabetes, cardiovascular disease, metabolic dysfunction-associated steatotic liver disease, and reduced quality of life,⁴ effective treatment strategies are critically needed. In addition, childhood obesity is linked to mental health conditions such as depression, anxiety, and disordered eating behaviors, further emphasizing the need for early and effective interventions.⁵

Historically, treatment options for pediatric weight management have been primarily limited to lifestyle modifications, including dietary changes and increased physical activity. While these interventions remain the foundation of obesity treatment, long-term success is often modest, particularly in cases of severe obesity.⁶ The limited efficacy of lifestyle interventions alone underscores the need for adjunctive therapies to support weight management in youth with obesity. From the 2023 Clinical Practice Guideline for the Evaluation and Treatment of Children and Adolescents with Obesity, there is a recommendation to discuss adjunctive treatment options with those that are eligible.⁴ This article will focus on pharmacologic treatment for weight management, specifically glucagon-like peptide-1 receptor agonist (GLP-1RA). Bariatric surgery is also an option for adjunctive treatment in the management of severe obesity but is outside the scope of this article.

Prior to the introduction of GLP-1RAs, pharmacologic options for pediatric weight management were extremely limited. Medications such as orlistat, phentermine, and topiramate were either not FDA-approved for pediatric obesity or had modest efficacy with significant side effects, limiting their use.⁷ The approval of GLP-1RAs for adolescent weight management represents a major advancement in obesity treatment, offering a new therapeutic option for youth struggling with excess weight.⁸

GLP-1RAs, originally developed for type 2 diabetes, have demonstrated significant efficacy in weight reduction through mechanisms that include appetite regulation, delayed gastric emptying, and central effects on satiety.⁹ In December 2020, liraglutide (Saxenda®) became the first GLP-1RA approved by the U.S. Food and Drug Administration (FDA) for individuals aged 12 years and older, followed by the approval of semaglutide (Wegovy®) in December 2022.¹⁰ Clinical trials have shown that these medications can lead to substantial weight loss in adolescents, with some studies reporting an average weight reduction of 10% or more.¹¹ Despite their promise, questions remain regarding their long-term safety, tolerability, and real-world effectiveness outside of controlled clinical trial settings.

While GLP-1RAs have expanded treatment options for pediatric obesity, several barriers may limit their use. Access to these medications is often constrained by insurance coverage restrictions, with many insurers requiring documentation of failed lifestyle interventions before approving prescriptions.¹² In addition, the high cost of GLP-1RAs poses a significant challenge for many families, potentially contributing to disparities in access.¹² Common side effects, including nausea, vomiting, and gastrointestinal distress, may also impact adherence and tolerability, particularly in adolescent populations.

Given the recent approval of GLP-1RAs for pediatric weight management, there is a critical need to understand real-world prescribing patterns and utilization trends compared with the existing data derived from clinical trial settings. This retrospective, multi-center study aims to characterize the temporal trends in GLP-1RAs use for weight management in the pediatric population. We believe such real-world data can provide insights into prescribing trends, adherence rates, and potential disparities in access, which in turn are essential for informing clinical guidelines, health care policy, and future research on obesity treatment in youth.¹³

We hypothesize that GLP-1RA utilization has increased since their approval, with higher prescription rates in states with a greater prevalence of pediatric obesity. By examining prescribing patterns and regional variations, this study seeks to provide critical insights into the adoption of GLP-1RAs in pediatric obesity care and identify potential barriers to their widespread implementation.

Methods

Data source

Data used in this study came from Epic Cosmos (Epic Systems Corporation, Verona, WI), a dataset created in collaboration with a community of Epic health systems. As of December 2024, Cosmos represented more than 289 million patient records from over 1,626 hospitals and 37,700 clinics across all 50 states, D.C., Lebanon, and Saudi Arabia. Encounters in Cosmos include inpatient, outpatient (including telephone and telemedicine visits), and emergency department visits documented in Epic. Given the de-identified nature of the data, this study qualified as nonhuman subjects research and did not require IRB oversight. Data extraction utilized the Cosmos Data Science platform given the granularity of the data points required.

To protect patient privacy, Epic requires published categories based on Cosmos data to have more than 10 patients. For small values where actual numbers could be calculated from other categories, we either combined categories until the group had more than 10 patients or did not report for that category, per policy.

Study population

Inclusion criteria

This study included patients aged 12–18 years at the start of medication (Semaglutide or Liraglutide) with 1 year of data available before and after medication initiation. The study timeframe spanned from July 1, 2020, to July 31, 2023, with data collection extending from July 1, 2019, to July 31, 2024. Patients who initiated medication at age 18 were included in the study, even if data extended up to 2 years beyond this age. This reflects real-world clinical practice, where pediatric providers often continue care during the transition to adult services. The physiology of a postpubertal adolescent is not expected to differ significantly from that of a 20-year-old.

Exclusion criteria

Patients with a diagnosis of type 2 diabetes mellitus (ICD-10 codes E11.*) were excluded from the initial cohort. Furthermore, patients with monogenic (Proprotein convertase subtilisin/kexin type 1 (PCSK1) deficiency, Proopiomelanocortin (POMC) deficiency, Leptin receptor (LEPR) deficiency), syndromic (Prader Willi, Bardet Biedl, Alstrom), and hypothalamic obesity; those that had no BMI data; those with BMI >100 (documentation errors); and those with insufficient data (less than 5 data points before and after starting treatment) were excluded from the study. See Figure 1 for patient flow diagram.

FIG. 1.

Patient flow diagram illustrating inclusion and exclusion of subjects.

Data collection

The dataset included outpatient prescription orders of semaglutide and liraglutide containing patient information, date of birth, medication details (name, strength, mode of administration, dosage, frequency), prescribing department specialty, start date, end date, and age at medication initiation. We also collected BMI data comprising patient identifiers, measurement dates, BMI values, department specialties, and encounter types. Additional information included encounter details (dates, department specialties, encounter types, and various status indicators), demographics (birthdate, ethnicity, race information, RUCA (Rural-Urban Commuting Area) code, socioeconomic indicators, household composition, housing type, minority status, and sex), social determinants of health (SDoH) information, complication data (presence of complications by diagnosis, with start/end dates and primary status), and obesity diagnosis information (presence of diagnosis, with start/end dates and primary status).

Cohort definition

From the extracted medication orders, patients were grouped by their minimum age at medication initiation. Patients who first started medication between ages 12–18 during the study timeframe were identified. If no medication end date was recorded, the study end date was used as the end date. We defined two specific cohorts: Cohort 1 (“High Quality Data”) included patients with at least 5 BMI measurements before starting medication and at least 5 measurements after starting and remaining on medication (n = 970); Cohort 2 comprised patients from Cohort 1 who had been on medication for at least 2 years (n = 198).

Data processing

BMI measurements were processed by limiting premedication values to within 1 year before starting medication. We removed measurements with BMI >100, although no patients were excluded by this criterion. Measurements where a patient was not on medication after the minimum start date were also removed. For each patient, BMI measurements were split into before and after medication initiation. We determined the minimum length (smallest number of total before vs. after measurements) and selected chronologically ordered values from the end of both before and after periods using this minimum length.

Statistical analysis

A one-sided Wilcoxon signed-rank test was performed comparing BMI values before and after medication initiation (hypothesis: after < before; alternative: after ≥ before). For each patient, we recorded the number of measurements, before median value, after median value, percent difference, test statistic, and P value. Patient demographics were described for all high-quality patients (n = 970), and those who lost significant weight were compared with those who did not using chi-squared tests. Separate chi-squared tests were performed for patients on liraglutide and semaglutide. Only categories containing more than 10 patients were included per Cosmos’ policy regarding patient privacy. We also described the total monthly number of new outpatient prescription orders for both medications and the geographic distribution of all patients who received a prescription (n = 7496), provided the total patients for that state exceeded 10, per Epic policy. SDoH data were too incomplete to report on. Wilcoxon tests were performed using the scipy.stats package.¹⁴ Chi-squared analysis was performed using JASP v19.3.0.¹⁵

Results

Cohort characteristics

From the initial 40,631 unique patients identified, 22,950 were aged 12–18 years, and 7496 started a GLP-1RA (either semaglutide or liraglutide) during the study timeframe. Of these, 6724 patients had at least one BMI measurement while on medication, and 970 patients met criteria for the high-quality dataset (Cohort 1). See Table 1.

Table 1.

Demographic Characteristics of All Patients and Those with Significant Weight Loss

	All patients	Significant weight loss	No significant weight loss	P value
n	970	273	697
Race				0.401
Black or African American	217 (22.4%)	54 (19.8%)	163 (23.4%)
Other Race	61 (6.3%)	20 (7.3%)	41 (5.9%)
White	631 (65.1%)	180 (65.9%)	451 (64.7%)
Ethnicity				0.74
^*Unspecified	65 (6.7%)	16 (5.9%)	49 (7.0%)
Hispanic or Latino	203 (20.9%)	60 (22.0%)	143 (20.5%)
Not Hispanic or Latino	702 (72.4%)	197 (72.2%)	505 (72.5%)
Multiracial				0.575
No	789 (81.3%)	219 (80.2%)	570 (81.8%)
Yes	181 (18.7%)	54 (19.8%)	127 (18.2%)
RUCA				0.798
Rural	144 (14.8%)	39 (14.3%)	105 (15.1%)
Urban	825 (85.1%)	232 (85.0%)	593 (85.1%)
Sex				0.691
Female	624 (64.3%)	178 (65.2%)	446 (64.0%)
Male	344 (35.5%)	94 (34.4%)	250 (35.9%)
Age at medication start				0.256
^**12–13	126 (12.6%)	28 (10.0%)	98 (13.6%)
14	119 (11.9%)	29 (10.4%)	90 (12.5%)
15	143 (14.3%)	38 (13.6%)	105 (14.5%)
16	188 (18.8%)	55 (19.7%)	133 (18.4%)
17	177 (17.7%)	50 (17.9%)	127 (17.6%)
18	217 (21.7%)	73 (26.2%)	144 (19.9%)
Medication
Liraglutide	417 (43.0%)	96 (35.2%)	321 (46.1%)	0.002
Semaglutide	580 (59.8%)	197 (72.2%)	383 (54.9%)	<.001

For race, ethnicity, multiracial, RUCA, sex, and age, the chi-squared test was performed for comparing weight loss to no weight loss.

For semaglutide and liraglutide, the chi-squared test was performed with on/off medication and weight loss/no weight loss.

Categories with values <10 were removed per Cosmos rules for protecting patient privacy.

Ages were combined such that patients remained above 10 per category.

Weight change outcomes

Cohort 1 (high quality data, n = 970)

In the high-quality data cohort, the median BMI before and after medication was 40.745 and 40.68, respectively, representing a minor percent difference of −0.16%. Among these patients, 273 (28.14%) lost significant weight. When stratified by medication type, 197 of 580 patients (33.96%) on semaglutide lost significant weight, with a median BMI change from 40.17 to 35.30 (−12.12%). For patients on liraglutide (n = 417), 96 (23.02%) experienced significant weight loss, with median BMI values changing from 40.9 to 36.79 (−10.04%). Significant weight loss was determined using the Wilcoxon signed-rank test (P < 0.05), identifying patients who experienced a statistically significant reduction in BMI after initiating medication compared with their baseline values.

Cohort 2 (high quality data and ≥2 years on medication, n = 198)

Among patients with high-quality data who remained on medication for at least 2 years, 55 (27.78%) achieved significant weight loss. The overall median BMI changed from 40.2 to 35.33, representing a −12.11% difference. For patients on semaglutide (n = 148), 37 (25%) lost significant weight, with median BMI values changing from 39.835 to 34 (−14.65%). Among those on Liraglutide (n = 151), 34 patients (22.5%) experienced significant weight loss, with median BMI values changing from 40.865 to 36.527 (−10.52%).

Demographic associations

Statistical testing showed no significant demographic differences between patients who lost significant weight and those who did not. State of residence was not included as a variable given the number of potential categories. Medication type was significantly associated with weight loss, with patients on semaglutide more likely to lose weight compared with those who did not receive semaglutide.

Complications

For Cohort 1, nausea (ICD 10 code R11.0) was the most common side effect, more so in semaglutide (n = 448/580, 77.24%) compared with liraglutide (n = 189/417, 45.32%). Nausea onset was earlier in semaglutide, on average, 1.19 months after starting the medication, compared with 2.19 months for liraglutide. Conversely, gastroparesis (ICD 10 code K31.84) was more common in liraglutide (31/417, 7.43%) compared with semaglutide (12/580, 2.07%). The average onset of gastroparesis from starting the medication was 2.94 months for liraglutide and 4.17 months for semaglutide. Acute pancreatitis (ICD 10 code K85*) was seen in 3.97% of patients, on average 4.57 months after starting the medication, who received semaglutide compared with none for liraglutide. See Table 2.

Table 2.

Comparison of Adverse Effect Rates Between Liraglutide and Semaglutide

Medication	Complication	n	Rate	Months after medication start
Liraglutide	Acute pancreatitis	0	0.00%
	Gastroparesis	31	7.43%	2.94 ± 0.14
	Nausea	189	45.32%	2.19 ± 0.38
Total patients		417
Semaglutide	Acute pancreatitis	23	3.97%	4.57 ± 0.33
	Gastroparesis	12	2.07%	4.17 ± 0.41
	Nausea	448	77.24%	1.19 ± 0.16
Total patients		580

Discussion

The recent clinical practice guidelines from the American Academy of Pediatrics emphasize the need for a comprehensive and chronic disease model in the management of childhood and adolescent obesity. These guidelines advocate for the integration of pharmacotherapy with lifestyle interventions in eligible individuals, particularly those with severe obesity and associated comorbidities.⁴ Given the recent FDA approvals of GLP-1 RAs for adolescent weight management, understanding real-world utilization patterns is critical for informing clinical practice and health policy.

Our study examined the prescription trends of semaglutide and liraglutide for the indication of weight management in youth, excluding individuals with diabetes mellitus. As expected, prescription rates increased following FDA approval, with semaglutide outpacing liraglutide (Fig. 2), likely due to its higher efficacy and more convenient weekly dosing schedule.¹⁶ These findings align with broader trends, as a recent publication reported that between 2020 and 2023, the total number of GLP-1RA dispensing for all indications (adolescents and adults) increased by nearly 600%.¹⁷

FIG. 2.

Monthly new outpatient prescriptions for liraglutide and semaglutide in the United States, July 2020–July 2024.

Interestingly, our data showed that majority of prescriptions were for White females in urban settings with the greatest response to medication for patients who initiated treatment between 16 to 18 years old. While GLP-1RA utilization was found to be highest in Texas, Florida, and North Carolina, interestingly, states with the highest prevalence of pediatric obesity (e.g., West Virginia, Kentucky, and New Mexico¹⁸) did not exhibit high utilization. This discrepancy may be attributable to insurance-related barriers, differences in prescribing practices, disparities in health care access, or the potential underrepresentation of these states in the Cosmos™ database. Further research is needed to examine whether policy-level interventions, such as Medicaid coverage and state-level health care initiatives, impact access to these medications.

Another key finding was that BMI reduction varied depending on follow-up consistency. We focused our analysis on individuals with consistent follow-up, defined as having ≥5 BMI measurements before and after medication initiation (“High Quality Data”). In this group, the median BMI decreased slightly from 40.75 to 40.68 (–0.16%). However, this modest median change masks important heterogeneity in treatment response. A notable subset of patients experienced clinically meaningful reductions in BMI, suggesting that average effects may obscure significant individual variation. Among those with substantial weight loss, individuals treated with semaglutide experienced greater reductions than those on liraglutide (–12.12% vs. –10.04%), echoing efficacy trends reported in clinical trials.^8,11

We also examined a subgroup of patients who had been on medication for more than 2 years, accounting for 27.78% of the high-quality dataset. In this longer-term cohort, BMI reductions were even more pronounced, with semaglutide users achieving greater losses than those on liraglutide (–14.65% vs. –10.52%). These findings highlight the potential of long-term pharmacotherapy as a component of obesity treatment in pediatrics, particularly for patients who demonstrate sustained engagement and tolerance. They also reinforce the growing recognition of semaglutide as a more potent agent within the GLP-1 receptor agonist class.

These observations, however, should be interpreted with caution. Patients with longer follow-up or significant weight loss may differ systematically from others in ways that affect outcomes, such as higher adherence, motivation, or engagement in concurrent lifestyle changes. In addition, differences in baseline characteristics or provider prescribing patterns could influence the apparent efficacy between medications. Further prospective research is needed to better characterize predictors of response and to clarify the long-term benefits and risks of these agents in pediatric populations.

Nausea was the most frequently reported adverse effect, occurring more often and with earlier onset for semaglutide (77.24%; mean 1.19 ± 0.16 months) compared with liraglutide (45.32%; mean 2.19 ± 0.38 months). More serious adverse effects such as acute pancreatitis and gastroparesis were reported at a higher prevalence compared to clinical trials. This discrepancy may be attributed to several key factors. Real-world populations are more heterogeneous and may include patients with a broader range of comorbidities, many of which are excluded from clinical trials, such as pre-existing gastrointestinal disorders. In addition, medication adherence and titration protocols are often less consistent in routine practice than in trial settings, potentially increasing the risk of adverse events due to more rapid uptitration. Missed doses, whether due to adherence issues or medication shortages, may result in patients restarting treatment at higher doses than the gradual titration typically used for medication-naïve individuals and thus possibly increasing the risk of adverse effects. Clinical trials also involve more intensive monitoring and earlier intervention when side effects emerge, which may help prevent progression to more serious complications. Together, these factors likely contribute to the higher observed rates of serious adverse effects in our analysis, underscoring the importance of close clinical monitoring and patient education during GLP-1 RA treatment, particularly in the early phase of therapy.

Limitations

Several limitations must be acknowledged. First, our analysis is subject to inherent biases in the Cosmos™ database, including ICD-10 coding misclassification, selection bias (as most data contributors are academic institutions), and underreporting of adverse effects. Although extreme data entry errors (e.g., BMI >100) were screened, other inaccuracies may persist. In addition, as the dataset relies on a single electronic health record system, findings may not be generalizable to the entire U.S. pediatric population. We explored additional contextual variables, including social determinants of health, socioeconomic indicators, household composition, and housing type. However, due to inconsistent or incomplete documentation, the quality of these data was insufficient for meaningful analysis or reporting. Medication adherence and dosing information were not available, limiting our ability to assess the intensity or consistency of treatment. To partially address this, we restricted our analysis to patients with ≥5 BMI measurements following initiation of GLP-1RA therapy, using this as a proxy for ongoing engagement with care. Nonetheless, follow-up duration still varied across patients, which may affect the comparability of weight-related outcomes over time. Furthermore, the role of lifestyle interventions in conjunction with GLP-1RA therapy was not assessed, limiting our ability to determine whether concurrent intensive behavioral interventions influenced weight outcomes. A small proportion of patients (<3%) with hypothalamic or syndromic obesity were included, which may have skewed certain findings, though their overall impact was likely minimal. Finally, this study examined trends and outcomes at the population level, not individual patient trajectories. As such, our results reflect broad patterns rather than personalized responses to treatment.

Conclusions and Future Directions

Despite these limitations, this large, retrospective, multi-center cohort study provides novel insights into the real-world utilization, effectiveness, and safety of GLP-1RAs in pediatric weight management. These findings highlight important temporal trends in prescribing practices and geographic disparities in access, contributing to a broader understanding of how these medications are being integrated into clinical care.

Future research should focus on identifying predictive factors that stratify individual responses to GLP-1RA therapy, assessing long-term adherence and safety beyond initial weight loss, and evaluating the role of concurrent behavioral interventions in optimizing outcomes. In addition, policy-level investigations should explore how insurance coverage, provider education, and health care access impact prescribing trends and treatment equity.

Footnotes

Authors’ Contributions

D.M., D.L., P.P., and S.B.: Conceptualization; D.M., D.L., P.P., and S.B.: Methodology; D.L.: Software; D.L.: Formal analysis; D.M. and D.L.: Investigation; D.L.: Data curation; D.M. and D.L.: Writing—original draft; D.M., D.L., and S.B.: Writing—review and editing; D.M. and D.L.: Visualization; P.P. and S.B.: Supervision; D.M.: Project administration.

Author Disclosure Statement

The authors have no relevant financial or nonfinancial interested to disclose.

Funding Information

The authors declare that no funds, grants, or other support were received during the preparation of this article.

References

Anonymous. Childhood Obesity Facts | Overweight & Obesity. CDC; n.d. Available from: https://www.cdc.gov/obesity/data/childhood.html [Last accessed: October 25, 2023].

Anonymous. 2022 CDC Extended BMI-for-Age Growth Charts for Children and Adolescents With Very High BMIs. n.d. Available from: https://www.cdc.gov/nccdphp/dnpao/growthcharts/extended-growth-charts.html [Last accessed: July 18, 2024].

Ogden

, Fryar

, Martin

, et al. Trends in obesity prevalence by race and hispanic Origin-1999-2000 to 2017-2018. JAMA, 2020; 324(12):1208–1210; doi: 10.1001/JAMA.2020.14590

Hampl

, Hassink

, Skinner

, et al. Clinical practice guideline for the evaluation and treatment of children and adolescents with obesity. Pediatrics, 2023; 151(2); doi: 10.1542/peds.2022-060640

Rankin

, Matthews

, Cobley

, et al. Psychological consequences of childhood obesity: Psychiatric comorbidity and prevention. Adolesc Health Med Ther, 2016; 7:125–146; doi: 10.2147/AHMT.S101631

Barlow

, Expert Committee. Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: Summary report. Pediatrics, 2007; 120 Suppl 4:S164–S192; doi: 10.1542/PEDS.2007-2329C

Raman

, Gupta

, Ashraf

, et al. Pharmacologic weight management in the era of adolescent obesity. J Clin Endocrinol Metab, 2022; 107(10):2716–2728; doi: 10.1210/CLINEM/DGAC418

Kelly

, Auerbach

, Barrientos-Perez

, et al.; NN8022-4180 Trial Investigators. A randomized, controlled trial of liraglutide for adolescents with obesity. N Engl J Med, 2020; 382(22):2117–2128; doi: 10.1056/NEJMOA1916038

Drucker

. Mechanisms of action and therapeutic application of Glucagon-like Peptide-1. Cell Metab, 2018; 27(4):740–756; doi: 10.1016/J.CMET.2018.03.001

10.

Anonymous. FDA Approves Weight Management Drug for Patients Aged 12 and Older. FDA. n.d. Available from: https://www.fda.gov/drugs/news-events-human-drugs/fda-approves-weight-management-drug-patients-aged-12-and-older [Last accessed: February 3, 2025].

11.

Weghuber

, Barrett

, Barrientos-Pérez

, et al.; STEP TEENS Investigators. Once-weekly semaglutide in adolescents with obesity. N Engl J Med, 2022; 387(24):2245–2257; doi: 10.1056/NEJMOA2208601

12.

Waldrop

, Johnson

, Stanford

. Inequalities in the provision of GLP-1 receptor agonists for the treatment of obesity. Nat Med, 2024; 30(1):22–25; doi: 10.1038/S41591-023-02669-X

13.

Ryder

, Fox

, Kelly

. Treatment options for severe obesity in the pediatric population: Current limitations and future opportunities. Obesity (Silver Spring), 2018; 26(6):951–960; doi: 10.1002/OBY.22196

14.

Virtanen

, Gommers

, Oliphant

, et al.; SciPy 1.0 Contributors. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat Methods, 2020; 17(3):261–272; doi: 10.1038/S41592-019-0686-2

15.

Anonymous. JASP—A Fresh Way to Do Statistics. n.d. Available from: https://jasp-stats.org/ [Last accessed: March 22, 2025].

16.

Rubino

, Greenway

, Khalid

, et al.; STEP 8 Investigators. Effect of weekly subcutaneous semaglutide vs Daily liraglutide on body weight in adults with overweight or obesity without diabetes: The STEP 8 randomized clinical trial. JAMA, 2022; 327(2):138–150; doi: 10.1001/JAMA.2021.23619

17.

Lee

, Sharifi

, Oshman

, et al. Dispensing of glucagon-Like Peptide-1 receptor agonists to adolescents and young adults, 2020-2023. JAMA, 2024; 331(23):2041–2043; doi: 10.1001/JAMA.2024.7112

18.

Anonymous. Ages 10-17—State of Childhood Obesity. n.d. Available from: https://stateofchildhoodobesity.org/demographic-data/ages-10-17/ [Last accessed: December 5, 2023].