Abstract
Background
Obesity in children, adolescents, and young adults is associated with the early development of several obesity-related complications (ORCs)1. This study aimed to assess the real-world clinical characteristics, treatment patterns, and healthcare costs, and resource utilization among children, adolescents, and young adults with obesity in the United States between 2019 and 2024.
Methods
A retrospective, observational analysis was conducted using Optum's de-identified Market Clarity data (Optum® Market Clarity) between January 2019 and December 2024. Eligible individuals included 6 to 25-year-olds with an obesity diagnosis or BMI ≥30 kg/m2, or BMI ≥95th percentile for age and gender (6–17-year-olds), who had ≥12 months of continuous medical and pharmacy enrolment. Most individuals included in this study had either Commercial or Medicaid insurance. The population was further stratified by age: children (6–11 years), adolescents (12–17 years), and young adults (18–25 years). All analyses conducted were descriptive in nature.
Results
The mean ages for children, adolescents, and young adults were 9, 15, and 22 years, respectively. Across all years, a higher proportion of young adults were female compared to adolescents and children. Asthma was reported as one of the most prevalent ORCs across age groups (2024 data: children: 18.2%; adolescents: 14.2%; young adults: 12.9%). Overall, 303 (2.9%) adolescents (mean BMI 38.0 kg/m2) and 1926 (8.5%) young adults (mean BMI 38.4 kg/m2) had a prescription for an obesity management medication (OMM) in 2024, predominantly glucagon‐like peptide‐1 receptor agonists (not approved for use in children). Compared to OMM non-users, adolescents and young adults prescribed OMMs had a higher prevalence of ORCs and incurred greater healthcare costs.
Conclusion
The study findings reveal limited use of OMMs among children, adolescents, and young adults in the US, underscoring the need for a prevention-focused, chronic treatment model tailored to this population.
Keywords: Childhood obesity, Obesity management, Pediatric obesity, Treatment patterns
Graphical abstract
1. Introduction
Obesity is a common chronic condition among children, adolescents, and young adults and is a significant public health concern [1,2]. Between 1999 and 2023, the prevalence of obesity in the United States (US) increased substantially, rising from 13.9% to 21.1% among individuals aged 2–19 years [3]. This increase in obesity prevalence is also associated with an increased risk of developing various cardiometabolic and respiratory complications, such as type 2 diabetes (T2D), dyslipidemia, hypertension, metabolic dysfunction-associated steatotic liver disease (MASLD), and asthma [1,4,5]. Furthermore, obesity during childhood and young adulthood is linked to poor psychological and psychosocial health outcomes, including anxiety, depression, and low self-esteem [6,7]. Childhood obesity often persists into adulthood, increasing the risk of long-term morbidity and mortality, making early intervention and effective treatment critical [4,[8], [9], [10], [11]].
Beyond direct health consequences, obesity in young individuals imposes a high economic burden on healthcare systems and society. The lifetime direct medical costs for a 10-year-old child with obesity in the US are estimated to be approximately $19,000 higher than those for a child of normal weight [12]. Several studies have also reported a strong association between childhood obesity and increased healthcare resource utilization (HCRU), including higher rates of outpatient visits, hospitalizations, and prescriptions [[13], [14], [15], [16]].
The clinical management of pediatric obesity has advanced in recent years, with updated guidelines from the American Academy of Pediatrics (AAP) in 2023 highlighting the importance of early, evidence-based interventions for evaluating and treating obesity in children and adolescents [1,17]. Multicomponent lifestyle interventions, including dietary improvements, increased physical activity, and behavioral modifications are recommended as the first line of treatment for this population [1,18]. Pharmacological and surgical interventions are recommended for children over 12 years old as an adjunct to lifestyle modifications, especially those with severe obesity or obesity-related complications (ORCs) [1,19]. In recent years, the US Food and Drug Administration (FDA) has approved several obesity management medications (OMMs), including glucagon-like peptide-1 receptor agonists (GLP-1 RAs), for children aged 12 and older [1,20,21]. These include phentermine [22], orlistat [23], phentermine-topiramate [24], liraglutide [25], and semaglutide [26]. Although OMMs have shown considerable success in terms of weight loss efficacy over the past few years, their use remains limited [27,28].
As FDA-approved pharmacological treatments for obesity become increasingly available for young individuals, understanding the trends in their usage in a real-world setting becomes essential. This study aims to address this gap by examining the real-world clinical characteristics, treatment patterns, and healthcare utilization among children (6–11 years), adolescents (12–17 years), and young adults (18–25 years) with obesity in the US from 2019 to 2024.
2. Methods
2.1. Study design and data source
This study was a retrospective, cross-sectional, observational analysis conducted using Optum's de-identified Market Clarity data (Optum® Market Clarity) between January 2019 and December 2024. Optum® Market Clarity is an integrated, multi-source medical claims, pharmacy claims, and electronic health records dataset. Optum® Market Clarity links electronic health record data, including lab results, vital signs and measurements, diagnoses, procedures and information derived from unstructured clinical notes using natural language processing, with historical, linked administrative claim data, including pharmacy claims, physician claims, clinical information facility claims and medications prescribed and administered. Diagnoses and procedures were identified using the International Classification of Diseases, ninth and tenth Revision, Clinical Modification (ICD-10-CM) for diagnoses, the ICD-10 Procedure Coding System (ICD-10-PCS) for procedures, Current Procedural Terminology, and the Healthcare Financing Administration Common Procedure Coding System. Outpatient pharmacy claims were recorded using National Drug Codes (NDCs).
This observational study utilized de-identified data from the Optum® Market Clarity database, and therefore, a formal Institutional Review Board approval was not required. Optum® Market Clarity is statistically de-identified under the HIPAA Privacy Rule's Expert Determination method and managed according to Optum® customer data use agreements. Additionally, an independent third party evaluated and certified that the data met the HIPAA statistical de-identification standard. This study was conducted in accordance with the ethical principles that have their origin in the Declaration of Helsinki and that are consistent with Good Pharmacoepidemiology Practices and applicable laws and regulations in the US.
2.2. Study population
The study population consisted of individuals aged 6–25 years who had a BMI (defined below) or diagnosis indicating obesity from ICD-10 codes during the index period (i.e., each calendar year). For children and adolescents aged 6–17 years, obesity was defined as BMI ≥95th percentile for their age and gender or BMI ≥30 kg/m2, or an obesity diagnosis from ICD-10 codes. For young adults aged 18–25 years, the criterion for obesity was BMI ≥30 kg/m2 or an obesity diagnosis from ICD-10 codes. Additionally, all included individuals were required to have ≥12 months of continuous medical and pharmacy enrollment with no more than a 30-day gap during the calendar year. The study period was from January 1, 2019 to December 31, 2024. The date of the first observed BMI or diagnosis indicating obesity was defined as the index date. The population was stratified into three subgroups: children (6–11 years), adolescents (12–17 years), and young adults (18–25 years).
2.3. Study outcomes
Demographic characteristics were assessed at the index date for all three age groups and comprised the following: age, sex, race, ethnicity, US census region, and payer type. Clinical characteristics were evaluated during the index period (calendar year) for 2019–2024 and included the prevalence and number of common ORCs. Treatment patterns, costs, and HCRU were assessed during the same periods as clinical characteristics. Treatment patterns included the use of prescription medications such as metformin, antihypertensive medications, lipid-lowering medications, antidepressants, anti-anxiety medications, and OMMs, as well as lifestyle interventions and bariatric surgery. Cost outcomes included all-cause medical, pharmacy, and total costs. HCRU outcomes included outpatient, inpatient, and emergency department (ED) visits. An individual was included in a given calendar year only if they had both medical and pharmacy benefit coverage during that year. Additional analyses were conducted to describe study outcomes among those receiving OMMs and those not receiving OMMs in 2024, stratified by age group.
2.4. Statistical analysis
All data were analyzed using descriptive statistics in SAS software version 9.4, with results reported separately for each age group. Continuous variables were summarized as means (with standard deviations), while categorical variables were presented as frequencies and percentages.
3. Results
3.1. Demographics and clinical characteristics
The final study population was categorized into three age-based groups – children, adolescents, and young adults, for each year from 2019 to 2024 (not mutually exclusive; individuals may be included in multiple annual subgroups). Details on year-wise group sizes and attrition are presented in the Supplement (Fig. S1).
The mean ages for children, adolescents, and young adults were 9, 15, and 22 years, respectively. Across years, a larger proportion of young adults were female than that of adolescents and children. In 2024, 63.7% of young adults were female, compared to 50.2% of adolescents and 47.4% of children. Most of the study population was Caucasian (61.5%–69.5%) and non-Hispanic (63.4%–80.1%). Additionally, more than 97% of individuals across age groups had either Commercial or Medicaid insurance. Yearly demographics by age group are displayed in Table 1. From 2019 to 2024, mean (SD) BMI ranged from 32.5 (9.0) to 33.5 (9.1) kg/m2 for children, 34.7 (6.0) to 35.1 (6.5) kg/m2 for adolescents, and 35.8 (6.3) to 36.2 (6.6) kg/m2 for young adults (Table 1).
Table 1.
Demographics and clinical characteristics among children, adolescents, and young adults.
| Characteristics | Children (6–11 years) |
Adolescents (12–17 years) |
Young adults (18–25 years) |
|||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2019 (N = 31,672) | 2020 (N = 25,855) | 2021 (N = 28,109) | 2022 (N = 21,610) | 2023 (N = 12,596) | 2024 (N = 3993) | 2019 (N = 78,502) | 2020 (N = 63,670) | 2021 (N = 68,143) | 2022 (N = 49,808) | 2023 (N = 32,223) | 2024 (N = 10,530) | 2019 (N = 148,700) | 2020 (N = 129,148) | 2021 (N = 128,526) | 2022 (N = 92,185) | 2023 (N = 73,461) | 2024 (N = 22,611) | |
| Age at index, (years), mean (SD) | 9.1 (1.7) | 9.1 (1.7) | 9.1 (1.7) | 9.1 (1.6) | 9.1 (1.6) | 9.2 (1.6) | 14.8 (1.7) | 14.8 (1.7) | 14.7 (1.7) | 14.7 (1.7) | 14.8 (1.7) | 14.8 (1.7) | 21.8 (2.3) | 21.8 (2.3) | 21.7 (2.3) | 21.8 (2.2) | 21.9 (2.2) | 21.9 (2.2) |
| Sex, n (%) | ||||||||||||||||||
| Female | 15,094 (47.7) | 12,342 (47.7) | 13,253 (47.2) | 10,055 (46.5) | 5990 (47.6) | 1893 (47.4) | 41,359 (52.7) | 34,108 (53.6) | 35,679 (52.4) | 25,643 (51.5) | 16,507 (51.3) | 5289 (50.2) | 100,093 (67.3) | 89,762 (69.5) | 89,143 (69.4) | 63,097 (68.5) | 48,092 (65.5) | 14,395 (63.7) |
| Male | 16,575 (52.3) | 13,513 (52.3) | 14,855 (52.9) | 11,554 (53.5) | 6602 (52.4) | 2100 (52.6) | 37,132 (47.3) | 29,551 (46.4) | 32,462 (47.6) | 24,161 (48.5) | 15,690 (48.7) | 5236 (49.8) | 48,590 (32.7) | 39,375 (30.5) | 39,373 (30.6) | 29,079 (31.6) | 25,310 (34.5) | 8200 (36.3) |
| Race, n (%) | ||||||||||||||||||
| Caucasian | 19,607 (61.9) | 16,040 (62.0) | 17,406 (61.9) | 13,280 (61.5) | 7866 (62.5) | 2669 (66.8) | 53,228 (67.8) | 43,323 (68.0) | 45,331 (66.5) | 32,445 (65.1) | 20,335 (63.1) | 7145 (67.9) | 103,315 (69.5) | 87,893 (68.1) | 86,447 (67.3) | 60,139 (65.2) | 45,668 (62.2) | 15,038 (66.5) |
| African American | 5934 (18.7) | 4707 (18.2) | 5420 (19.3) | 4015 (18.6) | 1960 (15.6) | 492 (12.3) | 12,815 (16.3) | 10,354 (16.3) | 11,776 (17.3) | 8344 (16.8) | 4687 (14.6) | 1307 (12.4) | 26,057 (17.5) | 24,587 (19.0) | 24,891 (19.4) | 17,327 (18.8) | 11,335 (15.4) | 3123 (13.8) |
| Asian | 537 (1.7) | 465 (1.8) | 577 (2.1) | 530 (2.5) | 359 (2.9) | 182 (4.6) | 1196 (1.5) | 913 (1.4) | 1188 (1.7) | 1136 (2.3) | 787 (2.4) | 35 (3.4) | 2397 (1.6) | 1969 (1.5) | 2246 (1.8) | 2080 (2.3) | 1705 (2.3) | 756 (3.3) |
| Missing/not reported | 5594 (17.7) | 4643 (18.0) | 4706 (16.7) | 3785 (17.5) | 2411 (19.1) | 650 (16.3) | 11,263 (14.4) | 9080 (14.3) | 9848 (14.5) | 7883 (15.8) | 6414 (19.9) | 1723 (16.4) | 16,931 (11.4) | 14,699 (11.4) | 14,942 (11.6) | 12,639 (13.7) | 14,753 (20.1) | 3694 (16.3) |
| Ethnicity, n (%) | ||||||||||||||||||
| Hispanic | 7134 (22.5) | 5704 (22.1) | 6552 (23.3) | 6009 (27.8) | 2653 (21.1) | 699 (17.5) | 12,254 (15.6) | 10,209 (16.0) | 12,148 (17.8) | 10,830 (21.7) | 5376 (16.7) | 1683 (16.0) | 16,314 (11.0) | 14,317 (11.1) | 15,989 (12.4) | 14,295 (15.5) | 9636 (13.1) | 3466 (15.3) |
| Non-Hispanic | 21,862 (69.0) | 17,997 (69.6) | 19,500 (69.4) | 13,690 (63.4) | 8387 (66.6) | 2708 (67.8) | 58,930 (75.1) | 48,042 (75.5) | 50,595 (74.3) | 33,923 (68.1) | 21,514 (66.8) | 7104 (67.5) | 118,433 (79.7) | 102,979 (79.7) | 102,943 (80.1) | 68,745 (74.6) | 50,696 (69.0) | 15,395 (68.1) |
| Missing/not reported | 2676 (8.5) | 2154 (8.3) | 2057 (7.3) | 1911 (8.8) | 1556 (12.4) | 586 (14.7) | 7318 (9.3) | 5419 (8.5) | 5400 (7.9) | 5055 (10.2) | 5333 (16.6) | 1743 (16.6) | 13,953 (9.4) | 11,852 (9.2) | 9594 (7.5) | 9145 (9.9) | 13,129 (17.9) | 3750 (16.6) |
| Region, n (%) | ||||||||||||||||||
| Midwest | 15,148 (47.8) | 12,404 (48.0) | 13,442 (47.8) | 6868 (31.8) | 2659 (21.1) | 842 (21.1) | 42,593 (54.3) | 33,163 (52.1) | 34,561 (50.7) | 17,183 (34.5) | 7002 (21.7) | 2168 (20.6) | 76,656 (51.6) | 64,499 (49.9) | 63,231 (49.2) | 31,460 (34.1) | 15,424 (21.0) | 4460 (19.7) |
| Northeast | 7627 (24.1) | 6969 (27.0) | 7353 (26.2) | 7712 (35.7) | 6545 (52.0) | 1779 (44.6) | 13,404 (17.1) | 12,699 (20.0) | 14,003 (20.6) | 15,665 (31.5) | 14,839 (46.1) | 4425 (42.0) | 21,853 (14.7) | 20,747 (16.1) | 21,417 (16.7) | 24,708 (26.8) | 27,206 (37.0) | 7969 (35.2) |
| South | 4925 (15.6) | 3425 (13.3) | 4282 (15.2) | 4520 (20.9) | 2015 (16.0) | 719 (18.0) | 11,757 (15.0) | 9294 (14.6) | 11,035 (16.2) | 10,790 (21.7) | 6512 (20.2) | 2166 (20.6) | 26,085 (17.5) | 22,662 (17.6) | 24,367 (19.0) | 23,039 (25.0) | 21,771 (29.6) | 6156 (27.2) |
| West | 2555 (8.1) | 1936 (7.5) | 1842 (6.6) | 1599 (7.4) | 958 (7.6) | 531 (13.3) | 7125 (9.1) | 5700 (9.0) | 5559 (8.2) | 4356 (8.8) | 2926 (9.1) | 1468 (13.9) | 15,146 (10.2) | 13,720 (10.6) | 12,300 (9.6) | 8348 (9.1) | 6288 (8.6) | 3136 (13.9) |
| Missing/not reported | 1417 (4.5) | 1121 (4.3) | 1190 (4.2) | 911 (4.2) | 419 (3.3) | 122 (3.1) | 3623 (4.6) | 2814 (4.4) | 2985 (4.4) | 1814 (3.6) | 944 (2.9) | 303 (2.9) | 8960 (6.0) | 7520 (5.8) | 7211 (5.6) | 4630 (5.0) | 2772 (3.8) | 890 (3.9) |
| Payer type, n (%) | ||||||||||||||||||
| Commercial | 12,284 (38.8) | 8452 (32.7) | 8421 (30.0) | 7019 (32.5) | 7245 (57.5) | 3719 (93.1) | 39,253 (50.0) | 26,965 (42.4) | 26,846 (39.4) | 20,995 (42.2) | 21,609 (67.1) | 9873 (93.8) | 101,169 (68.0) | 74,897 (58.0) | 68,834 (53.6) | 52,160 (56.6) | 56,036 (76.3) | 21,480 (95.0) |
| Medicaid | 18,730 (59.1) | 17,172 (66.4) | 19,447 (69.2) | 14,445 (66.8) | 5288 (42.0) | 261 (6.5) | 37,522 (47.8) | 35,873 (56.3) | 40,463 (59.4) | 28,350 (56.9) | 10,351 (32.1) | 614 (5.8) | 42,837 (28.8) | 51,046 (39.5) | 57,180 (44.5) | 38,660 (41.9) | 16,383 (22.3) | 856 (3.8) |
| Medicare | 298 (0.9) | 46 (0.2) | 43 (0.2) | 86 (0.4) | 49 (0.4) | 13 (0.3) | 653 (0.8) | 194 (0.3) | 208 (0.3) | 242 (0.5) | 216 (0.7) | 41 (0.4) | 1568 (1.1) | 1101 (0.9) | 1216 (1.0) | 1088 (1.2) | 979 (1.3) | 267 (1.2) |
| Missing/not reported | 360 (1.1) | 185 (0.7) | 198 (0.7) | 60 (0.3) | 14 (0.1) | 0 (0.0) | 1074 (1.4) | 638 (1.0) | 626 (0.9) | 221 (0.4) | 47 (0.2) | 2 (0.0) | 3126 (2.1) | 2104 (1.6) | 1296 (1.0) | 277 (0.3) | 63 (0.1) | 8 (0.0) |
| BMI (kg/m2), mean (SD) | 32.5 (9.0) | 32.6 (9.2) | 32.8 (8.9) | 33.0 (9.5) | 32.8 (8.8) | 33.5 (9.1) | 34.7 (6.0) | 34.9 (6.3) | 35.1 (6.5) | 35.0 (6.6) | 34.8 (6.3) | 34.8 (6.4) | 35.9 (6.3) | 36.2 (6.5) | 36.2 (6.6) | 36.2 (6.6) | 36.0 (6.4) | 35.8 (6.3) |
Abbreviations: BMI: body mass index; SD: standard deviation.
Table S1 in the Supplement presents demographics and clinical characteristics of 2024 OMM users and non-users. Overall, 303 (2.9%) adolescents and 1926 (8.5%) young adults had a prescription for an OMM in 2024. Mean (SD) BMI was higher among OMM users than non-users (adolescents: 38.0 (7.5) vs. 34.7 (6.3) kg/m2; young adults: 38.4 (8.0) vs. 35.5 (6.1) kg/m2, respectively). Since there were low rates of OMM use in individuals under 12 years old, the comparison between OMM users and non-users did not apply to this age group.
3.2. Prevalence of ORCs
Between 2019 and 2024, among those with obesity, asthma was reported as one of the most prevalent ORCs across age groups. In 2024, the prevalence of asthma was as follows: 18.2% among children, 14.2% among adolescents, and 12.9% among young adults. Additionally, a substantial proportion of adolescents and young adults reported experiencing anxiety, with a prevalence of 11.9% among adolescents and 16.3% among young adults in 2024. Fig. 1a presents the year-wise prevalence of ORCs by age group.
Fig. 1.
Prevalence of cooccurring obesity-related complications among children, adolescents, and young adults.
aData not shown for children aged 6–11 years, as <10 individuals in this age group reported using OMMs.
Abbreviations: GERD: gastroesophageal reflux disease; MASLD/MASH: metabolic dysfunction-associated steatotic liver disease/metabolic dysfunction-associated steatohepatitis; OMM: obesity management medication; ORC: obesity-related complication; OSA: obstructive sleep apnea; PCOS: polycystic ovarian syndrome; SD: standard deviation; T2D: type 2 diabetes.
Relative to OMM non-users, adolescents and young adults using OMMs had a higher prevalence of ORCs. The prevalence of dyslipidemia, prediabetes, metabolic syndrome, polycystic ovarian syndrome (PCOS), obstructive sleep apnea (OSA), and MASLD/metabolic dysfunction-associated steatohepatitis (MASH) were at least two-fold higher in OMM users than non-users (Fig. 1b). Mean (SD) number of ORCs among OMM users compared to non-users were as follows: 1.4 (1.3) vs. 0.6 (0.9) for adolescents and 1.6 (1.5) vs. 0.9 (1.2) for young adults.
3.3. Treatment patterns
Between 2019 and 2024, medication use among children with obesity was low. The most commonly used medications in this age group were anti-anxiety, antidepressant, and antihypertensive medications, each used by less than 10% of the population. Lifestyle interventions were coded for 4.4%–10.3% of children (Fig. 2a). Among adolescents and young adults, approximately 20–40% of the population used antidepressants and anti-anxiety medications. However, less than 8.1% of adolescents and 3.6% of young adults had documented lifestyle interventions (Fig. 2a). Metformin was used by less than 6.0% of adolescents and young adults, and less than 1.5% of children. Furthermore, less than 0.5% of children, adolescents, and young adults included in this study underwent bariatric surgery between 2019 and 2024.
Fig. 2.
Medication use and lifestyle intervention among children, adolescents, and young adults.a.
aData not shown for children aged 6–11 years, as <10 individuals in this age group reported using OMMs.
Abbreviations: OMM: obesity management medication; SD: standard deviation.
Among OMM users and non-users, a higher proportion of adolescents and young adults using OMMs had documented lifestyle interventions relative to OMM non-users (adolescents: 19.5% vs. 3.9%; young adults: 9.9% vs. 3.0%, respectively). Medication use was also substantially higher among OMM users compared to non-users. However, the rates of bariatric surgery were low for both groups (adolescents: 0.0% vs. 0.02%; young adults: 0.6% vs. 0.3%). The mean (SD) overall medication burden for OMM users vs. non-users was as follows: 1.2 (1.2) vs. 0.5 (0.9) for adolescents and 1.3 (1.3) vs. 0.8 (1.1) for young adults (Fig. 2b).
Between 2019 and 2024, the prescription rates of OMMs increased among adolescents and young adults, rising from 0.2% to 2.9% for adolescents and from 2.0% to 8.5% for young adults (Table 2). Most individuals who were prescribed OMMs were given incretin-based therapies, such as semaglutide (2024 data: adolescents: 78.6% [n = 238]; young adults: 51.6% [n = 993]), tirzepatide (2024 data: adolescents: 11.9% [n = 36]; young adults: 42.7% [n = 823]), and liraglutide (2024 data: adolescents: 6.6% [n = 20]; young adults: 2.9% [n = 56]). Prescription rates of semaglutide and tirzepatide showed the largest increase between 2023 and 2024.
Table 2.
Medication use among adolescents and young adults.
| OMM use | Adolescents (12–17 years) |
Young adults (18–25 years) |
||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2019 (N = 78,502) | 2020 (N = 63,670) | 2021 (N = 68,143) | 2022 (N = 49,808) | 2023 (N = 32,223) | 2024 (N = 10,530) | 2019 (N = 148,700) | 2020 (N = 129,148) | 2021 (N = 128,526) | 2022 (N = 92,185) | 2023 (N = 73,461) | 2024 (N = 22,611) | |
| Semaglutide (Wegovy) | 0 (0.0) | 0 (0.0) | 4 (0.0) | 22 (0.0) | 310 (1.0) | 238 (2.3) | 0 (0.0) | 1 (0.0) | 201 (0.2) | 471 (0.5) | 2059 (2.8) | 993 (4.4) |
| Tirzepatide (Zepbound) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 7 (0.0) | 36 (0.3) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 1 (0.0) | 135 (0.2) | 823 (3.6) |
| Liraglutide (Saxenda) | 8 (0.0) | 13 (0.0) | 66 (0.1) | 118 (0.2) | 114 (0.4) | 20 (0.2) | 279 (0.2) | 232 (0.2) | 270 (0.2) | 420 (0.5) | 450 (0.6) | 56 (0.3) |
| Phentermine-topiramate (Qysmia) | 3 (0.0) | 6 (0.0) | 1 (0.0) | 8 (0.0) | 32 (0.1) | 16 (0.2) | 97 (0.1) | 84 (0.1) | 85 (0.1) | 76 (0.1) | 93 (0.1) | 47 (0.2) |
| Naltrexone-bupropion (Contrave) | 9 (0.0) | 8 (0.0) | 5 (0.0) | 0 (0.0) | 2 (0.0) | 0 (0.0) | 183 (0.1) | 138 (0.1) | 165 (0.1) | 116 (0.1) | 123 (0.2) | 49 (0.2) |
| Orlistat (Xenical or Alli) | 6 (0.0) | 12 (0.0) | 9 (0.0) | 4 (0.0) | 7 (0.0) | 2 (0.0) | 32 (0.0) | 40 (0.0) | 35 (0.0) | 29 (0.0) | 12 (0.0) | 4 (0.0) |
| Phentermine | 166 (0.2) | 115 (0.2) | 117 (0.2) | 94 (0.2) | 90 (0.3) | 38 (0.4) | 2622 (1.8) | 2344 (1.8) | 2418 (1.9) | 1673 (1.8) | 1426 (1.9) | 468 (2.1) |
| Any OMM | 189 (0.2) | 148 (0.2) | 189 (0.3) | 231 (0.5) | 460 (1.4) | 303 (2.9) | 3040 (2.0) | 2697 (2.1) | 2950 (2.3) | 2444 (2.7) | 3547 (4.8) | 1926 (8.5) |
| Non-OMM GLP-1 RA use (anti-diabetes medication) | 2019 (N = 78,502) | 2020 (N = 63,670) | 2021 (N = 68,143) | 2022 (N = 49,808) | 2023 (N = 32,223) | 2024 (N = 10,530) | 2019 (N = 148,700) | 2020 (N = 129,148) | 2021 (N = 128,526) | 2022 (N = 92,185) | 2023 (N = 73,461) | 2024 (N = 22,611) |
| Dulaglutide | 34 (0.0) | 24 (0.0) | 36 (0.1) | 39 (0.1) | 69 (0.2) | 19 (0.2) | 210 (0.1) | 267 (0.2) | 373 (0.3) | 439 (0.5) | 389 (0.5) | 49 (0.2) |
| Exenatide | 5 (0.0) | 5 (0.0) | 10 (0.0) | 27 (0.1) | 8 (0.0) | 0 (0.0) | 54 (0.0) | 58 (0.0) | 63 (0.1) | 31 (0.0) | 8 (0.0) | 0 (0.0) |
| Liraglutide | 60 (0.1) | 82 (0.1) | 147 (0.2) | 153 (0.3) | 75 (0.2) | 7 (0.1) | 269 (0.2) | 246 (0.2) | 255 (0.2) | 177 (0.2) | 134 (0.2) | 14 (0.1) |
| Liraglutide insulin degludec | 1 (0.0) | 1 (0.0) | 1 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 4 (0.0) | 5 (0.0) | 1 (0.0) | 1 (0.0) | 0 (0.0) | 0 (0.0) |
| Lixisenatide | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) |
| Lixisenatide insulin glargine | 1 (0.0) | 0 (0.0) | 2 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 10 (0.0) | 7 (0.0) | 7 (0.0) | 5 (0.0) | 3 (0.0) | 1 (0.0) |
| Semaglutide (Ozempic) | 18 (0.0) | 21 (0.0) | 61 (0.1) | 100 (0.2) | 176 (0.6) | 57 (0.5) | 201 (0.1) | 335 (0.3) | 691 (0.5) | 1180 (1.3) | 1462 (2.0) | 432 (1.9) |
| Semaglutide (Rybelsus) | 0 (0.0) | 5 (0.0) | 6 (0.0) | 9 (0.0) | 8 (0.0) | 2 (0.0) | 7 (0.0) | 70 (0.1) | 95 (0.1) | 107 (0.1) | 117 (0.2) | 31 (0.1) |
| Tirzepatide (Mounjaro) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 24 (0.1) | 27 (0.1) | 30 (0.3) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 543 (0.6) | 796 (1.1) | 348 (1.5) |
| Any obesity or antidiabetic GLP-1 RA medication except insulin combos | 117 (0.2) | 135 (0.2) | 281 (0.4) | 386 (0.8) | 558 (1.7) | 323 (3.1) | 855 (0.6) | 1013 (0.8) | 1562 (1.2) | 2527 (2.7) | 3977 (5.4) | 2070 (9.2) |
∗Tirzepatide is not currently approved for use in individuals under 18 years of age. Tirzepatide received approval for chronic weight management in adults (≥18 years) in November 2023. Therefore, no usage is reported here, as relevant data were unavailable at the time of this study.
Abbreviations: GLP-1 RA: glucagon-like peptide 1 receptor agonist; OMM: obesity management medication.
3.4. Costs and healthcare resource utilization
Mean all-cause medical costs for children exhibited a downward trend between 2019 and 2024. Among adolescents and young adults, these costs marginally increased from 2019 to 2021, followed by a decline from 2022 to 2024. For young adults, mean all-cause pharmacy costs increased between 2019 and 2024, whereas for adolescents and children, these costs peaked in 2022 (Fig. 3a). Mean total healthcare costs for all three age groups peaked between 2021 and 2022, followed by a decline in 2023 and 2024. In 2024, adolescents and young adults who used OMMs had higher all-cause medical, pharmacy, and total costs compared to those who did not use OMMs, with the most substantial difference observed in pharmacy costs. (Fig. 3b).
Fig. 3.
Mean healthcare costs among children, adolescents, and young adults.
aData not shown for children aged 6–11 years, as <10 individuals in this age group reported using OMMs. All costs reported in USD.
Abbreviations: OMM: obesity management medication; USD: US dollars ($).
Between 2019 and 2024, over 90% of individuals across age groups had outpatient visits, while ED visits were limited to roughly 20–45% of the population. Inpatient visits were the lowest among children (<5%), followed by adolescents (<10%) and young adults (<20%) (Fig. 4a). The mean number of outpatient, inpatient, and ED visits per person per year remained largely stable within age groups from 2019 to 2024 (Fig. 4a). In 2024, minimal differences in HCRU were observed among adolescents and young adults who used OMMs versus those who did not (Fig. 4b).
Fig. 4.
Mean healthcare resource utilization among children, adolescents, and young adults.
aOMM data not shown for children aged 6–11 years, as <10 individuals in this age group reported using OMMs.
Abbreviations: ED: emergency department; OMM: obesity management medication.
4. Discussion
This retrospective study provides a real-world assessment of patient characteristics, treatment patterns, healthcare costs, and resource utilization among children, adolescents, and young adults with obesity in the US over a five-year period. Our findings indicate that obesity treatment, including lifestyle interventions, pharmacotherapy, and bariatric surgery, was recorded for only a minority of individuals across all age groups. ORCs were prevalent across all age groups, with a notably higher prevalence in those using OMMs compared to non-users. Additionally, OMM users also incurred greater healthcare costs than those who did not use OMMs. Although overall OMM utilization remained low among adolescents and young adults, there was a notable increase in utilization levels, particularly for incretin-based therapies from 2022 to 2024. This coincides with the approval of semaglutide and tirzpetide and warrant continued monitoring.
More than 13% of the study population had asthma, with a higher prevalence observed among children compared to adolescents and young adults. This is consistent with existing literature, which indicates that obesity is often associated with impaired respiratory functioning [29,30]. Obesity has also consistently been identified as a significant risk factor for anxiety and depression in young individuals [31,32], further evidenced by the high prevalence of anxiety and depression observed among adolescents and young adults in this study. Previous meta-analyses have reported that individuals with obesity have a 55% higher risk of developing depression compared to those with a normal BMI. Among children and adolescents, those with obesity are 34% more likely to suffer from depression during their lifetime than individuals who are of normal weight [33,34].
Adolescents and young adults using OMMs were found to have a higher BMI and a greater ORC burden compared to those not using OMMs. The prevalence of various ORCs was also at least two times higher in OMM users than non-users. Individuals with a higher burden of comorbidities may be more likely to be treated with an OMM, as OMMs are typically prescribed to individuals with severe obesity and additional comorbidities in accordance with existing clinical guidelines [1]. Congruent with these findings, real-world evidence from adult populations has also shown that multimorbidity is common among OMM users [[35], [36], [37]]. Prior literature indicate that early-onset of these ORCs may be predictive of adulthood disease progression [38]. Our findings suggest a potential need to implement early interventions to prevent and manage obesity, thereby mitigating its long-term consequences, which is consistent with observations from other longitudinal cohort studies [[8], [9], [10], [11]].
In line with the high prevalence of anxiety and depression observed among adolescents and young adults, there was high usage of anti-anxiety medications and antidepressants in these age groups. Less than 10% of the population across age groups engaged in lifestyle interventions for obesity, with the lowest engagement observed among young adults. This may be due to under-coding of lifestyle interventions in EMR and claims data, although previous studies have indicated that young adults with obesity often face significant barriers to adopting lifestyle interventions due to their busy lifestyles and multiple priorities [39].
GLP-1 RAs, especially liraglutide and semaglutide, were the most frequently prescribed OMMs across age groups. Their usage increased between 2019 and 2024, coinciding with their approvals for obesity management in individuals aged 12 and older in 2020 and 2022, respectively [25,26]. This aligns with the results of a recent longitudinal study conducted by Lee et al., which examined the dispensing of GLP-1 RAs for T2D and/or weight management in adolescents and young adults. The study found that dispensing rates increased by nearly 600% from 2020 to 2023. Notably, the dispensing of semaglutide for weight management witnessed a sharp surge in 2023 [40]. Although no OMMs are approved for children under 12 years of age, some off-label prescriptions were observed in this study. Recent phase 3 results show that treatment with liraglutide 3.0 mg as an adjunct to lifestyle intervention in children aged 6–11 years resulted in a mean BMI change of −5.8%, compared to +1.6% for those receiving placebo plus lifestyle interventions [41]. While some adverse effects were noted, the study's findings are promising and pave the way for more effective treatment options for this population in the near future. Additionally, pediatric clinical trials are currently underway to investigate the safety and efficacy of tirzepatide, a GLP-1 RA approved for chronic weight management in adults [42,43].
Despite the increase in incretin OMM prescription rates between 2019 and 2024, overall OMM utilization remained low, with only 2.9% of adolescents and 8.5% of young adults being prescribed OMMs in 2024. Similarly, a recent study by Rodriguez et al. found that only 0.4% of children and adolescents aged 8 to 17 had evidence of pharmacotherapy prescribing between 2021 and 2024. Although overall pharmacotherapy treatment levels showed an upward trend, the absolute likelihood of treatment remained low [17]. This observation in the pediatric population also aligns with previous analyses conducted among adults with obesity, which indicate that OMMs are frequently underutilized and/or under-prescribed, even among those who are eligible [[44], [45], [46], [47]]. Between 2018 and 2022, only 1.0% of US adults with BMI ≥27 kg/m2 used an OMM [48]. Estimates from a recent survey indicate that only 11.8% of US adults reported using GLP-1 RAs for weight management or treatment of a chronic condition in 2025 [49]. Several factors may contribute to this low utilization, including limited access and awareness, concerns regarding the long-term effectiveness and safety of these medications in young populations, and other factors related to cost, insurance coverage, and reimbursement. Prescriber preference for lifestyle interventions in young individuals may have contributed to the relatively lower utilization observed in this study. Although current clinical guidelines from the American Academy of Pediatrics recommend pharmacological interventions as an adjunct to lifestyle modifications for individuals aged 12 years and older, healthcare providers generally prefer to limit medication exposure in younger populations. Consequently, they often consider lifestyle interventions as the first-line weight management strategy for these individuals [1,[50], [51], [52]]. Moreover, limited OMM utilization may also stem from the bias and stigma associated with obesity, including the notion that children can manage obesity simply by eliminating unhealthy foods and increasing physical activity [53].
The rise in healthcare costs from 2019 to 2022, followed by stabilization/decline between 2023 and 2024, may have been influenced by the COVID-19 pandemic, which may have increased the overall demand for healthcare resources between 2020 and 2022, with COVID-19 disproportionately impacting individuals with obesity. The shift in trend between 2022 and 2023 may also be influenced by changes in the payer mix, with all three cage groups seeing a shift toward a predominantly commercial-insured population during this time. Historically, individuals covered by Medicaid insurance tend to have poorer health and higher healthcare utilization compared to those with commercial insurance [54]. Adolescent and young adult OMM users incurred higher healthcare costs, particularly pharmacy costs, and outpatient visits compared to non-users. This can be directly correlated to the higher ORC prevalence observed among OMM users, which could drive the medical costs and resource utilization associated with obesity [55].
Although lifestyle modification therapy remains the cornerstone of pediatric obesity management, their uptake and impact are generally modest, even when applied intensively and continuously [28,56], highlighting the need for additional therapeutic options. OMMs and bariatric surgery offer important adjuncts to lifestyle interventions [1], potentially contributing to sustained weight reduction and reducing the associated risks of ORCs. In order to optimize the management of obesity in young individuals, further research and follow-up studies are necessary to help refine clinical guidelines, expand access, and raise awareness about the benefits of OMMs in achieving safe and effective long-term health outcomes.
5. Limitations
As with all real-world, retrospective database analyses, this study is subject to a number of limitations. Healthcare claims used for this analysis were primarily intended for administrative purposes related to reimbursement. As a result, there may be coding inaccuracies or data omissions that could have led to potential errors in the estimation of certain diagnoses, procedures, or visits. The study findings may not be generalizable to the broader population, as the dataset is skewed toward commercially insured individuals, who may have different characteristics than those with public or no health insurance. Additionally, it is to be noted that the lower population size observed in 2024 was due to a decrease in the number of members with enrollment in the Optum® Market Clarity database. Lifestyle interventions are often underreported/undercoded in claims and electronic health records, which may have impacted the accuracy of the results. As this was a descriptive study, potential confounding factors between age groups and across time were not accounted for in the analyses. Lastly, due to limitations inherent to the cross-sectional design of the study, causal inferences between OMM usage, ORC burden, and healthcare costs cannot be established.
6. Conclusion
Despite the high prevalence of obesity and the growing burden of obesity-related comorbidities among children, adolescents, and young adults, findings from this descriptive analysis indicate that OMM utilization remains low in these populations. These results emphasize the need for an integrated, prevention-focused, chronic care model to effectively address obesity in younger populations.
Key takeaways:
-
•
Individuals prescribed OMMs may have a higher burden of obesity-related comorbidities and incur greater healthcare costs compared to OMM non-users.
-
•
Between 2019 and 2024, OMM prescription rates among adolescents and young adults showed a notable increase, particularly between 2022 and 2024.
-
•
In 2024, 2.9% of adolescents and 8.5% of young adults included in this study were prescribed OMMs, predominantly incretin-based therapies such as semaglutide, tirzepatide, and liraglutide.
Authorship
All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.
CRediT author statement
The concept and design of the submission were by THG and JX. Statistical analysis and data acquisition were performed by AH and CH. THG, AH, CH, MA, CL, JR and JX were involved in the interpretation of the data. MA wrote the first draft. THG, AH, CH, MA, CL, JR and JX critically reviewed, edited, and approved the final version for submission.
Ethics review
This retrospective, observational study utilized de-identified data from the Optum® Market Clarity database, and therefore, a formal Institutional Review Board approval was not required.
Data statement
Data are available from the corresponding author upon reasonable request.
Declaration of artificial intelligence (AI) and AI-assisted technologies utilized in the writing process
AI was not used in the writing process.
Source of funding
The study and all support for the manuscript was funded by Eli Lilly and Company, Indianapolis, United States.
Declaration of competing interests
THG, AH, CH, MA, CL, and JX, Employment and stockholder, Eli Lilly and Company; AH is a full-time employee of Tigermed-BDM Inc., a paid consultant to Eli Lilly and Company; JR, conducts multi-center trials for Eli Lilly, and RECORDATI, receives research support in the form of donation of drug and placebo from Boehringer Ingelheim, and serves as an advisor for Calorify.
Acknowledgment
The authors thank Ernesto Ulloa-Pérez for providing statistical peer review support, funded by Eli Lilly and Company.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.obpill.2026.100252.
Contributor Information
Theresa Hunter Gibble, Email: hunter_theresa_marie@lilly.com.
Ahong Huang, Email: Ahong.Huang@tigermedgrp.com.
Callie Higgins, Email: callie.higgins@lilly.com.
Mythili Ananth, Email: ananth_mythili@lilly.com.
Clare Lee, Email: clare.lee@lilly.com.
Justin R. Ryder, Email: jryder@luriechildrens.org.
Jiayin Xue, Email: jiayin.xue@lilly.com.
Appendix A. Supplementary data
The following is the Supplementary data to this article:
References
- 1.Hampl S.E., Hassink S.G., Skinner A.C., Armstrong S.C., Barlow S.E., Bolling C.F., 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. [DOI] [PubMed] [Google Scholar]
- 2.Lee K.X., Quek K.F., Ramadas A. Dietary and lifestyle risk factors of obesity among young adults: a scoping review of observational studies. Curr Nutrition Rep. 2023;12(4):733–743. doi: 10.1007/s13668-023-00513-9. [DOI] [PubMed] [Google Scholar]
- 3.MMWR Morb Mortal Wkly Rep . 2024. QuickStats: prevalence of obesity and severe obesity among persons aged 2–19 years — united States, 1999–2000 through 2021–2023. 2024:73:936. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Li X., Keown-Stoneman C.D.G., Lebovic G., Omand J.A., Adeli K., Hamilton J.K., et al. The association between body mass index trajectories and cardiometabolic risk in young children. Pediatr Obes. 2020;15(8) doi: 10.1111/ijpo.12633. [DOI] [PubMed] [Google Scholar]
- 5.Reyes-Angel J., Kaviany P., Rastogi D., Forno E. Obesity-related asthma in children and adolescents. Lancet Child Adolesc Health. 2022;6(10):713–724. doi: 10.1016/S2352-4642(22)00185-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Sethy G., Samal P., Panda M. In: Obesity - current science and clinical approaches. Srivastava G., editor. IntechOpen; Rijeka: 2025. Obesity in children and adolescents and its impact on mental health. [Google Scholar]
- 7.Rankin J., Matthews L., Cobley S., Han A., Sanders R., Wiltshire H.D., Baker J.S. Psychological consequences of childhood obesity: psychiatric comorbidity and prevention. Adolesc Health Med Therapeut. 2016;7:125–146. doi: 10.2147/AHMT.S101631. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Simmonds M., Llewellyn A., Owen C.G., Woolacott N. Predicting adult obesity from childhood obesity: a systematic review and meta-analysis. Obes Rev. 2016;17(2):95–107. doi: 10.1111/obr.12334. [DOI] [PubMed] [Google Scholar]
- 9.Llewellyn A., Simmonds M., Owen C.G., Woolacott N. Childhood obesity as a predictor of morbidity in adulthood: a systematic review and meta-analysis. Obes Rev. 2016;17(1):56–67. doi: 10.1111/obr.12316. [DOI] [PubMed] [Google Scholar]
- 10.Whitaker R.C., Wright J.A., Pepe M.S., Seidel K.D., Dietz W.H. Predicting obesity in young adulthood from childhood and parental obesity. N Engl J Med. 1997;337(13):869–873. doi: 10.1056/NEJM199709253371301. [DOI] [PubMed] [Google Scholar]
- 11.Fitzgerald E., Walsh O., Fitzsimons J. Childhood obesity: tackling the social divide. Pediatr Res. 2025 doi: 10.1038/s41390-025-04122-w. [DOI] [PubMed] [Google Scholar]
- 12.Finkelstein E.A., Graham W.C., Malhotra R. Lifetime direct medical costs of childhood obesity. Pediatrics. 2014;133(5):854–862. doi: 10.1542/peds.2014-0063. [DOI] [PubMed] [Google Scholar]
- 13.Hasan T., Ainscough T.S., West J., Fraser L.K. Healthcare utilisation in overweight and obese children: a systematic review and meta-analysis. BMJ Open. 2020;10(10) doi: 10.1136/bmjopen-2019-035676. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Hagman E., Halsteinli V., Putri R.R., Hansen Edwards C., Waaler Bjørnelv G., Marcus C., Ødegård R.A. Association between adolescent obesity and early adulthood healthcare utilization—a two-cohort prospective study. BMC Med. 2025;23(1):33. doi: 10.1186/s12916-025-03866-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Wijga A.H., Mohnen S.M., Vonk J.M., Uiters E. Healthcare utilisation and expenditure of overweight and non-overweight children. J Epidemiol Community Health. 2018;72(10):940–943. doi: 10.1136/jech-2017-210222. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Hampl S.E., Carroll C.A., Simon S.D., Sharma V. Resource utilization and expenditures for overweight and Obese children. Arch Pediatr Adolesc Med. 2007;161(1):11–14. doi: 10.1001/archpedi.161.1.11. [DOI] [PubMed] [Google Scholar]
- 17.Rodriguez P.J., Do D., Gratzl S., Cartwright B.M.G., Stucky N.L., Wright R.D. Treatment of obesity in US children and adolescents before and after the AAP guidelines. medRxiv. 2024:2024;10.25 [Google Scholar]
- 18.Gaskin C.J., Cooper K., Stephens L.D., Peeters A., Salmon J., Porter J. Clinical practice guidelines for the management of overweight and obesity published internationally: a scoping review. Obes Rev. 2024;25(5) doi: 10.1111/obr.13700. [DOI] [PubMed] [Google Scholar]
- 19.Tully L., Arthurs N., Wyse C., Browne S., Case L., McCrea L., et al. Guidelines for treating child and adolescent obesity: a systematic review. Front Nutr. 2022;9 doi: 10.3389/fnut.2022.902865. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Singhal V., Sella A.C., Malhotra S. Pharmacotherapy in pediatric obesity: current evidence and landscape. Curr Opin Endocrinol Diabetes Obes. 2021;28(1):55–63. doi: 10.1097/MED.0000000000000587. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Vajravelu M.E., Tas E., Arslanian S. Pediatric obesity: Complications and current day management. Life (Basel) 2023;13(7) doi: 10.3390/life13071591. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Ryder J.R., Kaizer A., Rudser K.D., Gross A., Kelly A.S., Fox C.K. Effect of phentermine on weight reduction in a pediatric weight management clinic. Int J Obes (Lond) 2017;41(1):90–93. doi: 10.1038/ijo.2016.185. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Chanoine J.P., Hampl S., Jensen C., Boldrin M., Hauptman J. Effect of orlistat on weight and body composition in obese adolescents: a randomized controlled trial. JAMA. 2005;293(23):2873–2883. doi: 10.1001/jama.293.23.2873. [DOI] [PubMed] [Google Scholar]
- 24.U.S. Food and Drug Administration . 2022. FDA approves treatment for chronic weight management in pediatric patients aged 12 years and older. 2022. [Google Scholar]
- 25.U.S. Food & Drug Administration . 2020. FDA approves weight management drug for patients aged 12 and older. 2020. [Google Scholar]
- 26.Novo Nordisk USA . 2022. FDA approves once-weekly wegovy® injection for the treatment of obesity in teens aged 12 years and older. Novo Nordisk USA: Plainsboro, NJ, USA. [Google Scholar]
- 27.Ryder J.R., Lim F., Hur C. The economic value of treating pediatric obesity: costs are not keeping up with efficacy. JAMA Netw Open. 2023;6(10) doi: 10.1001/jamanetworkopen.2023.36337. [DOI] [PubMed] [Google Scholar]
- 28.Czepiel K.S., Perez N.P., Campoverde Reyes K.J., Sabharwal S., Stanford F.C. Pharmacotherapy for the treatment of overweight and obesity in children, adolescents, and young adults in a large health system in the US. Front Endocrinol (Lausanne) 2020;11:290. doi: 10.3389/fendo.2020.00290. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Fang C., Jiang Z., Su X., Fan W. The association between body mass index and asthma in children: a cross-sectional study from NHANES 1999 to 2020. Sci Rep. 2025;15(1):9448. doi: 10.1038/s41598-025-92619-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Yang W., Yang Y., Guo Y., Guo J., Ma M., Han B. Obesity and risk for respiratory diseases: a Mendelian randomization study. Front Endocrinol. 2023;14 doi: 10.3389/fendo.2023.1197730. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Lindberg L., Hagman E., Danielsson P., Marcus C., Persson M. Anxiety and depression in children and adolescents with obesity: a nationwide study in Sweden. BMC Med. 2020;18(1):30. doi: 10.1186/s12916-020-1498-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Xiang A.H., Martinez M.P., Chow T., Carter S.A., Negriff S., Velasquez B., et al. Depression and anxiety among US children and young adults. JAMA Netw Open. 2024;7(10) doi: 10.1001/jamanetworkopen.2024.36906. e2436906-e. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Quek Y.H., Tam W.W.S., Zhang M.W.B., Ho R.C.M. Exploring the association between childhood and adolescent obesity and depression: a meta-analysis. Obes Rev. 2017;18(7):742–754. doi: 10.1111/obr.12535. [DOI] [PubMed] [Google Scholar]
- 34.Luppino F.S., de Wit L.M., Bouvy P.F., Stijnen T., Cuijpers P., Penninx B.W., Zitman F.G. Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Arch Gen Psychiatry. 2010;67(3):220–229. doi: 10.1001/archgenpsychiatry.2010.2. [DOI] [PubMed] [Google Scholar]
- 35.Hankosky E.R., Chinthammit C., Meeks A., Huang A., Ward J.M., Mojdami D., Gibble T.H. Real-world use and effectiveness of tirzepatide among individuals without type 2 diabetes: results from the optum market clarity database. Diabetes Obes Metabol. 2025;27(5):2810–2821. doi: 10.1111/dom.16290. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Hankosky E.R., Desai K., Chinthammit C., Grabner M., Stockbower G., He X., et al. Real-world use and effectiveness of tirzepatide among people without evidence of type 2 diabetes in the United States. Diabetes Metab. 2025;51(3) doi: 10.1016/j.diabet.2025.101636. [DOI] [PubMed] [Google Scholar]
- 37.Hunter Gibble T., Chinthammit C., Ward J.M., Cappell K., Sedgley R., Bonafede M., et al. Real-world use of tirzepatide among individuals without evidence of type 2 diabetes: results from the veradigm® database. Diabetes Obes Metabol. 2025 doi: 10.1111/dom.16330. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Bae J.P., Nelson D.R., Boye K.S., Mather K.J. Prevalence of complications and comorbidities associated with obesity: a health insurance claims analysis. BMC Public Health. 2025;25(1):273. doi: 10.1186/s12889-024-21061-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Lanoye A., Gorin A.A., LaRose J.G. Young adults' attitudes and perceptions of obesity and weight management: implications for treatment development. Curr Obes Rep. 2016;5(1):14–22. doi: 10.1007/s13679-016-0188-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Lee J.M., Sharifi M., Oshman L., Griauzde D.H., Chua K.P. 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Fox C.K., Barrientos-Pérez M., Bomberg E.M., Dcruz J., Gies I., Harder-Lauridsen N.M., et al. Liraglutide for children 6 to <12 years of age with obesity - A randomized trial. N Engl J Med. 2025;392(6):555–565. doi: 10.1056/NEJMoa2407379. [DOI] [PubMed] [Google Scholar]
- 42.US Clinical Trials Registry. Clinical trial NCT06439277: a study of tirzepatide in adolescents with obesity and weight-related comorbidities (SURMOUNT-ADOLESCENTS-2).
- 43.US Clinical Trials Registry. Clinical trial NCT05696847: a study of tirzepatide (LY3298176) in pediatric participants with obesity.
- 44.Xia Y., Kelton C.M., Guo J.J., Bian B., Heaton P.C. Treatment of obesity: pharmacotherapy trends in the United States from 1999 to 2010. Obesity. Silver Spring, Md) 2015;23(8):1721–1728. doi: 10.1002/oby.21136. [DOI] [PubMed] [Google Scholar]
- 45.MacEwan J., Kan H., Chiu K., Poon J.L., Shinde S., Ahmad N.N. Antiobesity medication use among overweight and Obese adults in the United States: 2015-2018. Endocr Pract. 2021;27(11):1139–1148. doi: 10.1016/j.eprac.2021.07.004. [DOI] [PubMed] [Google Scholar]
- 46.Saxon D.R., Iwamoto S.J., Mettenbrink C.J., McCormick E., Arterburn D., Daley M.F., et al. Antiobesity medication use in 2.2 million adults across eight large health care organizations: 2009-2015. Obesity. 2019;27(12):1975–1981. doi: 10.1002/oby.22581. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Suissa K., Schneeweiss S., Kim D.W., Patorno E. Prescribing trends and clinical characteristics of patients starting antiobesity drugs in the United States. Diabetes Obes Metabol. 2021;23(7):1542–1551. doi: 10.1111/dom.14367. [DOI] [PubMed] [Google Scholar]
- 48.Almazan E., Schwartz J.L., Gudzune K.A. Use of medications associated with weight change among participants in the all of us research programme. Clin Obes. 2023;13(5) doi: 10.1111/cob.12609. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Bozick R., Donofry S.D., Rancaño K.M. 2025. New weight loss drugs: GLP-1 agonist use and side effects in the United States. [PMC free article] [PubMed] [Google Scholar]
- 50.Johnson V.R., Cao M., Czepiel K.S., Mushannen T., Nolen L., Stanford F.C. Strategies in the management of adolescent obesity. Curr Pediatr Rep. 2020;8(2):56–65. doi: 10.1007/s40124-020-00214-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Fortenberry M.W. Pediatric obesity and treatment controversies: is the use of medications too good to be true? Am J Health Syst Pharm. 2024;82(8):385–386. doi: 10.1093/ajhp/zxae340. [DOI] [PubMed] [Google Scholar]
- 52.Hooper L., Framson C., Donaldson A., Cifra N., Dave S., Weiss A.L., et al. The American academy of pediatrics guideline for obesity: an adolescent health perspective. J Adolesc Health. 2024;75(4):535–537. doi: 10.1016/j.jadohealth.2024.07.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Fox C.K., Kelly A.S., Reilly J.L., Theis-Mahon N., Raatz S.J. Current and future state of pharmacological management of pediatric obesity. Int J Obes (Lond) 2024 doi: 10.1038/s41366-024-01465-y. [DOI] [PubMed] [Google Scholar]
- 54.Kaiser Commision . March 2004. Medicaid: a lower-cost approach to serving a high-cost population. 2004. [Google Scholar]
- 55.Pearson-Stuttard J., Banerji T., Capucci S., de Laguiche E., Faurby M.D., Haase C.L., et al. Real-world costs of obesity-related complications over eight years: a US retrospective cohort study in 28,500 individuals. Int J Obes. 2023;47(12):1239–1246. doi: 10.1038/s41366-023-01376-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Herouvi D., Paltoglou G., Soldatou A., Kalpia C., Karanasios S., Karavanaki K. Lifestyle and pharmacological interventions and treatment indications for the management of obesity in children and adolescents. Children (Basel) 2023;10(7) doi: 10.3390/children10071230. [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.