ABSTRACT
Aim
To explore changes in body composition in adolescents with obesity treated with glucagon‐like peptide‐1 receptor agonist (GLP‐1 RA) and their association with metabolic syndrome (MetS) components.
Methods
This real‐world retrospective study included adolescents (12–18 years) with obesity who received multidisciplinary lifestyle‐based obesity care between 2020 and 2024. GLP‐1 RA therapy (liraglutide or semaglutide) was prescribed and dosed on an individualised basis. BMI and muscle‐to‐fat ratio (MFR) z‐scores (by bioimpedance), and MetS components (glucose intolerance, hypertension, and dyslipidemia) were evaluated and compared with patients not receiving GLP‐1 RA at corresponding time points. Multivariable regression analyses adjusted for sex, age, physical activity, and length of follow‐up evaluated contributors for improvement in body composition.
Results
Of 67 eligible adolescents, 41 (61.2%) received GLP‐1 RA therapy. The GLP‐1 RA‐treated group experienced greater improvements in BMI z‐scores (−0.34 [−0.57, −0.07] vs. −0.07 [−0.17, 0.05], p < 0.001), and MFR z‐scores (0.41 [0.12, 0.56] vs. 0.13 [0.01, 0.25], p = 0.007) compared to those without GLP‐1 RA therapy. GLP‐1 RA treatment duration was identified as the sole contributor to BMI and MFR z‐score improvements (R 2 = 0.515, p < 0.001 and R 2 = 0.231, p = 0.012, respectively). A 0.25‐unit decrease in BMI z‐score and a 0.25‐unit increase in MFR z‐score were associated with 2.8‐fold and 1.8‐fold higher odds of MetS component improvement, respectively.
Conclusion
Adolescents with obesity under GLP‐1 RA therapy combined with lifestyle intervention demonstrated improvements in body composition and MetS components.
Keywords: adolescent obesity, bioimpedance analysis (BIA), body composition, GLP‐1 receptor agonists (GLP‐1 RA), metabolic syndrome, muscle‐to‐fat ratio (MFR)
Summary
This real‐world study evaluates the impact of GLP‐1 RA on body composition in adolescents receiving lifestyle‐based obesity care.
Compared with non‐treated controls, longer GLP‐1 RA therapy predicted improvements in BMI and muscle‐to‐fat ratio z‐scores, with sex‐specific differences, as boys showed greater reductions in fat percentage and larger gains in muscle mass.
These changes were linked to improvement in metabolic syndrome components, suggesting metabolic benefits of GLP‐1 RA‐related body composition changes.
Intensive lifestyle modification remains the cornerstone of adolescent obesity management, yet its impact on body mass index (BMI) reduction is modest [1]. The American Academy of Pediatrics now recommends early introduction of pharmacotherapy alongside lifestyle intervention for appropriate candidates [2]. Glucagon‐like peptide‐1 receptor agonists (GLP‐1 RAs) are pharmacologic agents that enhance insulin secretion, reduce appetite through central and gastrointestinal pathways, and promote lipolysis [3]. In December 2020, the Food and Drug Administration (FDA) granted approval for liraglutide for the management of obesity among adolescents aged 12 years and older. This once‐daily injectable medication established a new therapeutic paradigm for paediatric weight management. When combined with lifestyle intervention, liraglutide demonstrated a substantial decrease in BMI z‐scores compared to placebo in this population [4]. Semaglutide, a once‐weekly GLP‐1 RA, was approved by the FDA for adolescent use in December 2022, and owing to its superior efficacy and convenient dosing schedule, has become a preferred therapeutic option [5, 6].
While GLP‐1 RA therapy exerts pleiotropic, multi‐organ effects with metabolic benefits extending beyond weight loss [7], including improvements in glycemic control, insulin resistance [8], and cardiovascular outcomes in adults [9], paediatric research has primarily focused on weight status assessed by BMI z‐scores [1, 2]. Although BMI is widely used as a marker of adiposity, it cannot distinguish between muscle and fat mass or provide information on distribution. Previous studies have highlighted the muscle‐to‐fat ratio (MFR) as a more informative indicator, demonstrating its strong ability to predict the early development of metabolic syndrome (MetS) components, such as glucose intolerance, hypertension, and dyslipidemia, in paediatric populations [10, 11, 12, 13, 14]. Given that muscle mass is an independent risk factor for all‐cause and cardiovascular disease‐driven mortality [15], and adolescence represents a critical period for muscle mass accrual, preservation and promotion of muscle tissue during weight management is particularly important in this age group.
This real‐world retrospective study examined changes in weight status (BMI z‐score), body composition parameters (fat percentage, appendicular skeletal muscle mass z‐score, and MFR z‐score), and MetS components in adolescents with obesity receiving multidisciplinary, lifestyle‐based care, comparing those treated with GLP‐1 RA to controls who did not receive pharmacologic intervention. The secondary objective was to identify factors contributing to improvement in MetS components.
1. Patients and Methods
1.1. Study Protocol and Eligibility Criteria
During the study period (January 2020 to December 2024), more than 500 adolescents with obesity were referred to the Institute of Paediatric Endocrinology at Dana‐Dwek Children's Hospital, and their medical records were reviewed. The institutional bioelectrical impedance analysis (BIA) database was cross‐referenced with the electronic medical records to identify individuals with documented obesity. Adolescents with consistent follow‐up who fulfilled all inclusion and exclusion criteria were included.
Eligible participants were 12–18 years old, had a BMI ≥ 95th percentile for sex and age and had complete assessments at all three study time points: anthropometrics, BIA, blood pressure measurements and laboratory data (fasting glucose and lipid profile), with at least one year of follow‐up. Exclusion criteria included type 1 or 2 diabetes, gender dysphoria, hypothalamic obesity, metabolic bone disease, prolonged treatment with steroids, growth hormone, or gonadotropin‐releasing hormone agonists, malignancy, or genetic syndromes known to affect body composition.
The study protocol was approved by the Tel Aviv Sourasky Medical Center Ethics Committee, which waived the requirement for informed parental consent (approval number TLV‐0057‐24). All procedures followed GCP guidelines, and data were handled in accordance with institutional ethical standards.
1.2. Sample Size Calculation
The target effect size of a 0.25‐z‐score change was selected as a small‐to‐medium effect size that, while not established as a minimal clinically important difference for MFR in adolescents, is consistent with effect sizes linked to metabolic improvements in BMI z‐score [16]. At least 35 adolescents with delta MFR z‐scores values before and during GLP‐1 RA treatment were required for a paired t‐test comparison to achieve 80% power at a significance level of 0.05, as calculated using WinPepi software (version 11.65).
1.3. Study Groups and Timeline
The analysis included a total of 67 adolescents receiving multidisciplinary, lifestyle‐based obesity care, who were classified into two groups. The study group included 41 adolescents treated with GLP‐1 RA, with data collected at three time points: 3–6 months prior to treatment initiation (baseline), at treatment initiation, and during treatment, after at least 3 months of therapy. The control group consisted of 26 adolescents who received nutrition and lifestyle management, without pharmacologic therapy, and had complete assessments at three time points.
1.4. Clinical Management
The obesity clinic team was comprised of paediatric endocrinologists, dietitians and psychosocial specialists, and they conducted a thorough medical, dietary, and lifestyle anamnesis for each patient. That assessment included evaluation of the patient's sociodemographic characteristics, medical history, comorbidities, growth patterns, physical activity, and family history of cardiometabolic diseases, all intended to guide individualised management plans. Psychosocial professionals were involved throughout the process, with particular attention to identifying and addressing barriers and early signs of eating disorders.
1.4.1. Nutrition and Lifestyle Management
Dietitian consultations provided evidence‐based nutritional counselling. Core strategies included reducing the intake of sugar‐sweetened beverages and adopting the United States Department of Agriculture MyPlate framework in combination with principles of the Mediterranean diet. Counselling emphasised dietary patterns low in added sugars and saturated fats, with a focus on nutrient‐dense, minimally processed foods. Recommendations were individualised to promote sustainable, family‐centred changes that support healthy energy balance, as well as appropriate growth and development.
Lifestyle counselling included personalised guidance on physical activity, highlighting safe, enjoyable, and developmentally appropriate forms of exercise. The goal was to attain 60 min of daily physical activity gradually, while promoting a reduction in screen time and overall sedentary behaviour.
1.4.2. GLP‐1 Receptor Agonist Use in Clinical Practice
Initiation of GLP‐1 RA therapy in adolescents with obesity who had not achieved sustainable improvement with lifestyle modification was determined through shared decision‐making with the adolescent, parents, and the case manager paediatric endocrinologist. The discussion addressed expected benefits, potential adverse effects, and the need for ongoing nutritional support, while dosing and titration were individualised according to tolerance and clinical response. Liraglutide or semaglutide, approved by the Israeli ministry of health, were initiated at standard low starting doses and gradually titrated to individualised target doses as tolerated, with the aim of maintaining the lowest effective dose that achieved clinical benefit without significant adverse effects. Following the approval of semaglutide, liraglutide‐treated patients were offered transition to semaglutide, whereas new patients commenced semaglutide therapy. A registered nurse provided education on proper medication administration, consistent with routine clinical practice. Adverse effects and psychosocial well‐being were documented at each clinic visit based on patient and family reports.
1.5. Data Collection
Sociodemographic characteristics, medical conditions, medications, family history of cardiometabolic risk factors, relevant clinical findings, anthropometric measurements, vital signs, pubertal status, body composition, and laboratory data were obtained from the hospital's electronic medical records. Socioeconomic position (SEP) was determined from the patient's home address according to the Israel Central Bureau of Statistics' Characterization and Classification, SEP cluster, determined by the locality of residence is scored on a scale of 1–10 and categorised into low (1–4), medium (5–7), and high (8–10) groups, while the SEP index integrates 14 variables across demographics, education, standard of living, and employment (from −2.797 to 2.590) [17]. Height was measured by a Harpenden stadiometer (Holtain Ltd., Crosswell, United Kingdom), weight in light clothing was measured by BIA. BMI and anthropometric measurements z‐scores were calculated with PediTools Electronic Growth Chart Calculators based upon CDC growth charts [18]. Pubertal staging was assessed by paediatric endocrinologists according to Tanner staging, with full puberty defined as stage 5 [19, 20]. Evidence of comorbid conditions, including obstructive sleep apnea (diagnosed by polysomnography), metabolic dysfunction‐associated steatotic liver disease (MASLD), orthopaedic complications, idiopathic intracranial hypertension, and psychiatric disorders (attention‐deficit/hyperactivity disorder [ADHD], autism spectrum disorder [ASD], anxiety and schizophrenia) were obtained from medical records.
1.5.1. Body Composition Assessment
Body composition assessment by BIA has been implemented in our Institute of Pediatric Endocrinology since 2018 [21] as part of the routine evaluation of children older than five years. BIA was chosen over the gold‐standard DXA method due to its greater accessibility, absence of radiation exposure, reliable accuracy when performed in the fasting state, and its suitability as a longitudinal tool for assessing body composition [22].
BIA (Tanita MC‐780 MA, GMON Professional Software) measured whole‐body and segmental fat and muscle mass [21]. Calculated variables included appendicular skeletal muscle mass (ASMM; sum of limb muscle mass), ASMM% (ASMM divided by body weight × 100), and muscle‐to‐fat ratio (MFR; ASMM/fat mass). Body composition z‐scores were determined using paediatric reference curves [23].
1.5.2. MetS Components
Metabolic syndrome components were defined as follows: glucose intolerance (fasting glucose ≥ 100 mg/dL), hypertension (systolic blood pressure ≥ 130 or diastolic blood pressure ≥ 80 mmHg) [24], and dyslipidemia: triglycerides [TG] ≥ 150 mg/dL (1.7 mmol/L) and/or high‐density lipoprotein cholesterol [HDL‐c] < 40 mg/dL (1.03 mmol/L), and/or TG/HDL‐c ≥ 2 [25, 26].
1.6. Statistical Analysis
All analyses were performed using SPSS Statistics version 29 (SPSS Inc., Chicago, IL, USA). Continuous variables were assessed for normality using the Shapiro–Wilk test and are presented as mean ± standard deviation (SD) for normally distributed data or median [Q1–Q3] for skewed data. Categorical variables are presented as frequencies and percentages.
Changes in body composition parameters were compared across treatment periods in adolescents receiving GLP‐1 RA versus those managed with lifestyle interventions alone. The Wilcoxon signed‐rank test, a nonparametric test used to compare skewed data from the same individuals across time points, was employed. Multiple linear regression analyses were conducted to identify predictors of changes in body composition (the dependent variables: ΔBMI z‐score, ΔMFR z‐score, Δfat percentage, and ΔASMM) during GLP‐1 RA therapy, adjusting for the independent variables: sex, age, SEP, physical activity (hours per week), follow‐up duration, and GLP‐1 RA treatment duration. Improvement in MetS was defined as a reduction in the total number of MetS components, and predictors [sex, age, SEP, GLP‐1 RA treatment duration and body composition changes (ΔBMI z‐score or ΔMFR z‐score)] were evaluated using logistic regression. A two‐tailed p‐value ≤ 0.05 was considered statistically significant.
2. Results
2.1. Baseline Characteristics
The cohort comprised 67 adolescents with a median age of 14.9 years (interquartile range [IQR]: 13.5, 16.2) and above‐average SEP (SEP cluster 8 [IQR: 7, 9]; SEP index 1.255 [IQR: 0.549, 1.591]). Adolescents treated with GLP‐1 RA (n = 41; 25 girls, 61%) did not differ in sex distribution, age, pubertal status, or SEP from those receiving lifestyle intervention alone (Table 1). Sixteen of the 27 patients (59.3%) who had initiated therapy with liraglutide transitioned to semaglutide during the study period, whereas semaglutide was the initial therapy in 14 patients.
TABLE 1.
Characteristics of adolescents with obesity at baseline, stratified according to GLP‐1 RA therapy.
| All | GLP‐1 RA treated | Non‐GLP‐1 RA treated | p | |
|---|---|---|---|---|
| Number | 67 | 41 | 26 | |
| Female sex, n (%) | 39 (58.2) | 25 (61) | 14 (53.8) | 0.564 |
| Age, years | 14.9 [13.5, 16.2] | 15.27 [13.9, 16.3] | 14.08 [12.8, 15.95] | 0.068 |
| Pubertal status, n (%) | ||||
| Prepubertal | 2 (3) | 1 (0.4) | 1 (3.8) | 0.231 |
| In puberty | 15 (22) | 6 (14.6) | 9 (34.6) | |
| Fully pubertal | 50 (74.6) | 34 (82.9) | 16 (61.5) | |
| Anthropometric measures | ||||
| Height, z‐score | 0.45 ± 1.01 | 0.56 ± 1.15 | 0.29 ± 0.72 | 0.236 |
| Weight, z‐score | 2.56 ± 0.65 | 2.70 ± 0.54 | 2.33 ± 0.74 | 0.034 |
| Body mass index, kg/m2 | 35.5 [31.7, 42.1] | 38.2 [34.2, 42.6] | 31.5 [29.7, 38.8] | 0.002 |
| Body mass index, z‐score | 2.40 [1.98, 3.23] | 2.69 [2.13, 3.27] | 2.11 [1.80, 2.66] | 0.024 |
| Body composition assessment | ||||
| Fat percentage (%) | 43.0 [39.2, 48.4] | 44.2 [41.6, 49.0] | 40.0 [36.3, 47.6] | 0.033 |
| Appendicular skeletal muscle (%), z score | 2.69 ± 1.52 | 2.92 ± 1.42 | 2.27 ± 1.64 | 0.107 |
| Muscle‐to‐fat ratio, z‐score | −1.92 ± 0.44 | −1.96 ± 0.42 | −1.86 ± 0.47 | 0.353 |
| Blood pressure (mmHg) | ||||
| Systolic | 121.9 ± 9.0 | 122.5 ± 7.9 | 120.9 ± 10.5 | 0.499 |
| Diastolic | 71.8 ± 7.4 | 71.3 ± 7.6 | 72.7 ± 7.2 | 0.472 |
| Hypertension, n (%) | 12 (17.9) | 5 (12.2) | 7 (26.9) | 0.191 |
| Comorbid conditions | ||||
| Metabolic associated steatotic liver disease (MASLD) | 32 (47.8) | 20 (48.8) | 12 (46.2) | 1.000 |
| Obstructive sleep apnea | 7 (10.4) | 5 (12.2) | 2 (7.7) | 1.000 |
| Orthopaedic complications | 6 (9.0) | 4 (9.8) | 2 (7.7) | 1.000 |
| Pseudotumor cerebri | 2 (3) | 1 (2.4) | 1 (3.8) | 1.000 |
| Dyslipidemia | 45 (67.2) | 28 (68.3) | 17 (65.4) | 0.805 |
| Glucose intolerance | 12 (17.9) | 6 (14.6) | 6 (23.1) | 0.315 |
| Psychiatric morbid conditions | ||||
| ADHD | 18 (26.9) | 11 (26.8) | 7 (26.9) | 0.993 |
| Autistic spectrum disorder | 3 (4.5) | 2 (4.9) | 1 (3.8) | 1.000 |
| Anxiety‐depression | 9 (13.4) | 6 (14.6) | 3 (11.5) | 0.641 |
| Schizophrenia | 2 (3.0) | 2 (4.9) | 0 | 0.520 |
| Family history | ||||
| Obesity | 43 (64.2) | 30 (73.2) | 13 (50) | 0.050 |
| Hypertension | 22 (32.8) | 14 (34.1) | 8 (30.8) | 0.797 |
| Type 2 diabetes | 38 (56.7) | 24 (58.5) | 14 (53.8) | 0.802 |
| Dyslipidemia | 20 (29.9) | 16 (39) | 4 (15.4) | 0.039 |
| Cardiovascular disease | 20 (29.9) | 10 (24.4) | 10 (38.6) | 0.277 |
Note: Data are expressed as number and (percent), mean ± standard deviation or median [interquartile range]. Pubertal status was assessed according to Tanner staging and categorised: prepubertal (1), in puberty (2–4) and full puberty (5). Chi squared tests were performed to compare categorical variables between groups, T‐test were performed to compare linear variables with normal distribution and Mann–Whitney were performed to compare linear variables with skewed distribution. A p‐value of ≤ 0.05 was considered significant. Bold indicates significant.
At baseline, the GLP‐1 RA group had higher median BMI z‐score (2.69 vs. 2.11, p = 0.024) and fat percentage (44.2 vs. 40.0, p = 0.033), while the MFR and ASMM % z‐scores were similar. The entire cohort reported a median of 0 h per week of structured physical activity (range: 0–12 h), with no significant difference between the groups. Their mean systolic and diastolic blood pressure were 121.9 ± 9.0 mmHg and 71.8 ± 7.4 mmHg, respectively. Dyslipidemia was the most common MetS component (67.2%), followed by hypertension (17.9%), and glucose intolerance (17.9%). Obesity‐related comorbidities included MASLD (47.8%), obstructive sleep apnea (10.4%), orthopaedic complications (9%), and idiopathic intracranial hypertension (3%). Psychiatric comorbidities were reported in 34.3%, most commonly ADHD (26.9%) and anxiety (13.4%), followed by ASD (4.5%) and schizophrenia (3%). The prevalence of obesity‐related and psychiatric comorbidities did not differ significantly between treatment groups.
Family history analysis revealed higher rates of obesity (p = 0.05) and dyslipidemia (p = 0.039) in the GLP‐1 RA group, while other metabolic complications did not differ between groups.
2.2. Anthropometric and Body Composition Changes
During the initial lifestyle‐only phase, weight status and body composition remained stable in both groups, with no significant within‐group changes or between‐group differences. During the second study period, the treatment groups diverged significantly. The GLP‐1 RA‐treated group experienced substantial weight loss, while the changes in the lifestyle alone group showed progressive weight gain (p < 0.001). The GLP‐1 RA group demonstrated marked improvement in BMI z‐score (ΔBMI z‐score: −0.34, IQR: −0.57 to −0.07) compared to changes in the lifestyle alone group (ΔBMI z‐score: −0.07, IQR: −0.17 to 0.05; p < 0.001).
Fat mass reduction was significantly greater in the GLP‐1 RA‐treated group (Δfat%: −3.40, IQR: −7.55 to −1.45) compared to the changes in lifestyle alone group (Δfat%: −1.45, IQR: −3.93 to 0.90; p = 0.004). Changes in ASMM% z‐score did not differ significantly between groups. Detailed comparisons of these changes across both study periods are presented in Table 2.
TABLE 2.
Delta‐anthropometric and body composition parameters across study periods, stratified by GLP‐1 RA therapy.
| Delta (Δ) variable | GLP‐1 RA treated (n = 41) | Non‐ GLP‐1 RA treated (n = 26) | p between 1st study periods of treated and non‐treated | p between 2nd study periods of treated and non treated | ||||
|---|---|---|---|---|---|---|---|---|
| 1st study period | 2nd study period | p | 1st study period | 2nd study period | p | |||
| ΔWeight (kg) | 0.00 [−4.30, 4.90] | −6.00 [−14.15, −2.90] | < 0.001 | 0.30 [−2.30, 3.88] | 1.75 [−0.30, 4.78] | 0.191 | 0.807 | < 0.001 |
| ΔBMI (kg/m2) | −0.25 [−1.54, 1.28] | −2.62 [−5.18, −1.03] | < 0.001 | −0.07 [−1.29, 0.69] | 0.10 [−0.66, 1.30] | 0.304 | 0.990 | < 0.001 |
| ΔBMI z‐score | −0.04 [−0.22, 0.09] | −0.34 [−0.57, −0.07] | < 0.001 | −0.04 [−0.24, 0.02] | −0.07 [−0.17, 0.05] | 0.675 | 0.620 | < 0.001 |
| ΔFat percentage | 0.40 [−1.45, 2.00] | −3.40 [−7.55, −1.45] | < 0.001 | −0.35 [−3.20, 1.03] | −1.45 [−3.93, 0.90] | 0.957 | 0.142 | 0.004 |
| ΔASMM% z‐score | −0.03 [−0.19, 0.19] | 0.32 [0.07, 0.94] | < 0.001 | 0.04 [−0.21, 0.46] | 0.31 [−0.10, 0.52] | 0.809 | 0.187 | 0.217 |
| ΔMFR z‐score | −0.01 [−0.07, 0.06] | 0.16 [0.05, 0.41] | < 0.001 | 0.07 [−0.02, 0.19] | 0.10 [−0.17, 0.33] | 0.919 | 0.230 | 0.148 |
Note: Delta (Δ) calculated for each variable in each patient between 2 adjacent study time points. Data are expressed as median [interquartile range]. The Wilcoxon signed‐rank test, a nonparametric test used to compare skewed data from the same individuals across time points. A p‐value of ≤ 0.05 was considered significant. Bold indicates significant.
Abbreviations: ASMM, appendicular skeletal muscle mass; BMI, body mass index; GLP‐1 RA, Glucagon‐Like Peptide‐1 Receptor Agonist; MFR, muscle to fat ratio.
Among participants treated with GLP‐1 RA (median treatment duration: 8.6 months, range: 3.9–24.2 months), boys achieved greater absolute weight loss (median: 9.8 kg vs. 5.2 kg) and a larger reduction in BMI z‐score (ΔBMI z‐score: −0.52, IQR: −0.78 to −0.40 vs. ΔBMI z‐score: −0.22, IQR: −0.40 to −0.07; p = 0.004) compared with girls. Boys also exhibited significantly greater fat percentage reduction (p < 0.001) and improvements in both ASMM% z‐score (p = 0.001) and MFR z‐score (p = 0.007) compared to girls (Table 3). The individualised changes in BMI and MFR z‐scores during the second study period, stratified by GLP‐1 RA treatment group and sex, are shown for the entire cohort in Figure 1.
TABLE 3.
Delta‐anthropometric and body composition parameters across study periods in GLP‐1 RA treated adolescents, stratified by sex.
| Delta (Δ) variable | Boys (n = 16) | Girls (n = 25) | p between 1st study periods of boys and girls | p between 2nd study periods of boys and girls | ||||
|---|---|---|---|---|---|---|---|---|
| 1st study period | 2nd study period | p | 1st study period | 2nd study period | p | |||
| ΔWeight (kg) | 1.80 [−3.43, 6.03] | −9.80 [−14.30, −5.15] | < 0.001 | −1.30 [−4.50, 3.90] | −5.20 [−10.30, −0.30] | 0.018 | 0.259 | 0.059 |
| ΔBMI z‐score | −0.00 [−0.19, 0.14] | −0.52 [−0.78, −0.40] | 0.002 | −0.57 [−0.23, 0.08] | −0.22 [−0.40, −0.07] | 0.009 | 0.451 | 0.004 |
| Δ Fat percentage | −1.25 [−2.80, 1.98] | −7.05 [−2.23, −3.38] | 0.001 | 0.40 [0.00, 2.00] | −2.20 [−3.70, −0.50] | < 0.001 | 0.059 | < 0.001 |
| Δ ASMM% z‐score | −0.04 [− 0.33, 0.38] | 0.92 [0.19, 1.30] | 0.001 | −0.02 [−0.15, 0.16] | 0.17 [−0.01, 0.43] | 0.002 | 0.741 | 0.001 |
| Δ MFR z‐score | 0.02 [−0.10, 0.08] | 0.41 [0.12, 0.56] | 0.002 | −0.01 [−0.06, 0.03] | 0.13 [0.01, 0.25] | 0.004 | 0.702 | 0.007 |
Note: Delta (Δ) calculated for each variable in each patient between 2 adjacent study time points. Data are expressed as median [interquartile range]. The Wilcoxon signed‐rank test, a nonparametric test used to compare skewed data from the same individuals across time points. A p‐value of ≤ 0.05 was considered significant. Bold indicates significant.
Abbreviations: ASMM, appendicular skeletal muscle mass; BMI, body mass index; GLP‐1 RA, Glucagon‐Like Peptide‐1 Receptor Agonist; MFR, muscle to fat ratio.
FIGURE 1.
The individualised changes in BMI and MFR z‐scores during the second study period, stratified by GLP‐1 RA treatment group and sex.
Reported adverse effects were primarily gastrointestinal, including abdominal pain, nausea, diarrhoea, and constipation, and occurred mainly during the initial days of treatment or following a dose increase. Additional reported effects comprised weakness, headache, and mild pain at the injection site. If symptoms were persistent or negatively affected well‐being, the GLP‐1 RA dose was reduced. No new psychiatric diagnoses were recorded during follow‐up, and no deterioration of pre‐existing psychiatric conditions was reported.
2.3. Predictors of Body Composition Changes
Multivariable regression analyses identified factors contributing to changes in body composition parameters during GLP‐1RA treatment (Table 4). The change in the BMI z‐score was highly significant (R 2 = 0.515, p < 0.001), with GLP‐1 RA treatment duration (months) emerging as the sole significant predictor [β = −0.041, standard error (SE) = 0.007, 95% confidence interval (−0.054, −0.028), p < 0.001]. The model for delta‐fat percentage was also significant (R 2 = 0.426, p < 0.001), with female sex [β = 0.248, SE = 1.083, 95% CI (0.382, 4.715), p = 0.022] and GLP‐1 RA treatment duration [β = −0.514, SE = 0.105, 95% CI (−0.723, −0.304), p < 0.001] emerging as significant contributors. The overall model reached a level of significance for the delta‐ASMM% z‐score (R 2 = 0.197, p = 0.035), although no single variable achieved a level of significance. Reported physical activity (hours per week) showed a trend toward significance [β = 0.072, SE = 0.037, 95% CI (−0.002, 0.146), p = 0.058]. The delta‐MFR z‐score was significant (R 2 = 0.231, p = 0.012), with longer GLP‐1 RA treatment duration [β = 0.017, SE = 0.007, 95% CI (0.003, 0.032), p = 0.021] emerging as the only significant contributor.
TABLE 4.
Linear regression models evaluating the association between delta‐body composition parameters in the second study period and patients' characteristics.
| Variable | Beta | Standard error | 95% confidence interval | p value |
|---|---|---|---|---|
| Delta BMI z‐score | < 0.001 for overall model | |||
| Sex | 0.118 | 0.068 | −0.017, 0.254 | 0.086 |
| Age | 0.015 | 0.017 | −0.019, 0.048 | 0.388 |
| SEP index | 0.016 | 0.037 | −0.058, 0.091 | 0.663 |
| Physical activity (h/week) | −0.007 | 0.016 | −0.038, 0.025 | 0.665 |
| Follow‐up duration | 0.002 | 0.005 | −0.007, 0.011 | 0.682 |
| GLP‐1 RA treatment duration | −0.041 | 0.007 | −0.054, −0.028 | < 0.001 |
| Delta fat percentage | < 0.001 for overall model | |||
| Sex | 0.248 | 1.083 | 0.382, 4.715 | 0.022 |
| Age | 0.305 | 0.267 | −0.228, 0.839 | 0.257 |
| SEP index | −0.070 | 0.597 | −1.263, 1.124 | 0.907 |
| Physical activity (h/week) | −0.044 | 0.250 | −0.644, 0.356 | 0.566 |
| Follow‐up duration | 0.047 | 0.073 | −0.099, 0.192 | 0.522 |
| GLP‐1 RA treatment duration | −0.514 | 0.105 | −0.723, −0.304 | < 0.001 |
| Delta ASMM% z‐score | 0.035 for overall model | |||
| Sex | −0.76 | 0.161 | −0.398, 0.245 | 0.638 |
| Age | −0.058 | 0.040 | −0.138, 0.021 | 0.146 |
| SEP index | 0.039 | 0.089 | −0.139, 0.216 | 0.665 |
| Physical activity (h/week) | 0.072 | 0.037 | −0.002, 0.146 | 0.058 |
| Follow‐up duration | 0.001 | 0.011 | −0.021, 0.023 | 0.928 |
| GLP‐1 RA treatment duration | 0.025 | 0.016 | −0.006, 0.056 | 0.112 |
| Delta MFR z‐score | 0.012 for overall model | |||
| Sex | −0.004 | 0.076 | −0.156, 0.149 | 0.963 |
| Age | −0.012 | 0.019 | −0.049, 0.026 | 0.540 |
| SEP index | 0.021 | 0.042 | −0.063, 0.105 | 0.624 |
| Physical activity (h/week) | 0.033 | 0.018 | −0.002, 0.068 | 0.066 |
| Follow‐up duration | 0.001 | 0.005 | −0.10, 0.011 | 0.888 |
| GLP‐1 RA treatment duration | 0.017 | 0.007 | 0.003, 0.032 | 0.021 |
Note: Linear regression models evaluating the association between delta body composition parameters during the second study period and patients' characteristics. For patients who were not treated with GLP‐1 RA, the treatment duration was recorded as 0. A p value of ≤ 0.05 was considered significant. Bold indicates significance.
Abbreviations: ASMM, appendicular skeletal muscle mass; BMI, body mass index; GLP‐1 RA, Glucagon‐Like Peptide‐1 Receptor Agonist; MFR, muscle to fat ratio; SEP, socioeconomic position.
2.4. Change in Metabolic Syndrome Components
At baseline, 49 participants (73.1%) exhibited at least one MetS component, and 3 (4.5%) met the criteria for complete MetS (≥ 3 components). The distribution of MetS component counts did not differ between groups at GLP‐1 RA initiation (p = 0.455), as presented in Figure 2. Following GLP‐1 RA treatment, 18 participants (43.9%) in the treated group no longer met any MetS criteria, whereas the prevalence remained unchanged in the lifestyle‐alone group, resulting in a significant between‐group difference (p = 0.003; Figure 2). Overall improvement in MetS components count (reduction of ≥ 1 component) was observed in 29.3% of the GLP‐1 RA‐treated group versus 19.2% of the lifestyle‐alone group (p = 0.186), though this difference did not reach statistical significance.
FIGURE 2.
The categorisation of GLP‐1 RA–treated and non‐treated participants based on the number of metabolic syndrome components at each of the three study time points. Before and at GLP‐1 RA initiation, 73.1% of the entire cohort had ≥ 1 metabolic syndrome component, with no differences between groups and no change during the first study period. Improvement was observed only in the GLP‐1 RA–treated group, in whom 44% had no MetS components during treatment, vs. 16% in controls (p = 0.003).
Finally, logistic regression models, adjusted for sex, age, and treatment duration, assessed the relationship between body composition changes and MetS improvement (Table 5). Each 0.25‐unit reduction in BMI z‐score was associated with 2.8 higher odds of improvement in MetS components count (OR 0.358, 95% CI 0.171–0.753; p = 0.007), and each 0.25‐unit increase in MFR z‐score was associated with a 1.8‐fold higher odds of improvement in MetS components count (OR 1.763, 95% CI 1.036–3.00; p = 0.036).
TABLE 5.
Logistic regression models evaluating factors contributing to metabolic syndrome (MetS) components improvement.
| Odds ratio | 95% Confidence interval | p | ||
|---|---|---|---|---|
| Lower | Higher | |||
| Sex | 0.574 | 0.156 | 2.112 | 0.403 |
| Age (years) | 0.807 | 0.551 | 1.182 | 0.271 |
| GLP‐1 RA treatment duration (months) | 0.935 | 0.798 | 1.096 | 0.406 |
| Δ BMI z‐score (for each 0.25 SD) | 0.358 | 0.171 | 0.753 | 0.007 |
| Sex | 0.479 | 0.138 | 1.657 | 0.245 |
| Age (years) | 0.819 | 0.570 | 1.176 | 0.279 |
| GLP‐1 RA treatment duration (months) | 1.046 | 0.938 | 1.165 | 0.420 |
| Δ MFR z‐score (for each 0.25 SD) | 1.763 | 1.036 | 3.00 | 0.036 |
Note: Logistic regression models assessing the association between improvement in MetS components count during the second study period, patients' characteristics (sex, age, GLP‐1 RA treatment duration) and change in BMI or MFR z‐scores. Non‐GLP‐1 RA patients were assigned a duration of 0. A p ≤ 0.05 was considered significant; Bold indicates significance.
Abbreviations: BMI, body mass index; GLP‐1 RA, Glucagon‐Like Peptide‐1 Receptor Agonist; MFR, muscle to fat ratio.
3. Discussion
In this real‐life study, adolescents with obesity treated with GLP‐1 RAs demonstrated significant improvements in weight status (BMI z‐scores), body composition, and metabolic syndrome components compared with untreated peers. GLP‐1 RA treatment emerged as the primary predictor of improvements in BMI and MFR z‐scores. Notably, the magnitude of change differed by sex, with boys showing greater reductions in fat percentage and larger gains in muscle mass. Overall, improvements in weight status and body composition were associated with a corresponding decrease in the number of metabolic syndrome components, highlighting the clinical benefits of GLP‐1 RA therapy in this population.
The adolescents with obesity enrolled in this study received comprehensive care from a multidisciplinary team at our academic medical center. Their management included nutritional counselling, guidance on physical activity, and comprehensive metabolic screening at each clinic visit. GLP‐1 RA therapy was initiated when clinicians, patients, and parents perceived the patient to be actively engaged and motivated in behavioural therapy. The initial medications were either liraglutide or semaglutide, with most patients eventually transitioning to semaglutide. The choice of drug and dosing was individually tailored based upon medical insurance coverage, medication availability, treatment efficacy, and patient tolerance. Access to the medication has improved, with all four major Israeli health funds offering a 50% discount through supplemental insurance programs to eligible patients. However, it remains excluded from the national health basket, a drawback cited by many families as a reason for discontinuation. Social stigma surrounding the use of weight loss medications, along with concerns about drug dependence and long‐term complications, further impede the initiation of GLP‐1 RA [27].
The baseline BMI z‐scores and fat percentages of patients treated with GLP‐1 RA indicated that their obesity was more severe than those who were not considered candidates for the medication, while the muscle mass components of both groups were similar. MASLD affected nearly one‐half of the cohort and almost three‐quarters had at least one metabolic syndrome component, with no differences between groups. Following treatment, the GLP‐1 RA group underwent reductions in weight and fat percentage while preserving muscle mass at levels comparable to the untreated group, leading to a more favourable body composition. This contrasts with previous observations in adults, in whom GLP‐1 RA treatment has been associated with muscle loss [28].
Unlike adults, weight reduction in adolescents with obesity was characterised by muscle gain rather than loss, and followed a sex‐specific pattern in its effects on body composition. Under lifestyle intervention and sibutramine therapy, earlier body composition analyses demonstrated that absolute lean body mass increased in boys and decreased in girls, whereas the proportion of lean mass relative to total weight increased in both sexes [29]. Sex‐specific changes in muscle mass likely reflect behavioural, hormonal, and metabolic differences during adolescence, with rising testosterone promoting muscle accretion in boys, whereas oestrogen secretion from the ovaries stimulates both hyperplasia and hypertrophy of adipose tissue [30]. In another study, the reduction in fat percentage was linked to a sex‐specific pattern of body composition change during the weight loss process, underscoring the need for careful monitoring and sex‐specific interpretation of results [31]. Treatment with GLP‐1 RA in our study elicited sex‐specific differences in body composition response. Boys demonstrated a marked reduction in fat mass together with significant improvements in muscle indices, whereas girls showed smaller decreases in fat mass while gains in lean mass indices were lower. Collectively, these findings suggest a more favourable body composition adaptation in boys under GLP‐1 RA therapy.
Early metabolic complications are common in adolescents with obesity [26], often requiring medical intervention when lifestyle modification fails. Noteworthy, nearly three‐quarters of our cohort presented with at least one metabolic syndrome component at study initiation. The prevalence remained stable until the introduction of GLP‐1 RA therapy, which marked a turning point, with 44% of treated patients becoming free of any component. The reduction in metabolic syndrome components was closely linked to improvements in BMI and MFR z‐scores, suggesting that GLP‐1 RA might exert its main therapeutic effect through favourable changes in body composition components of fat and muscle. Notably, 5% of patients treated with GLP‐1 RA did not improve and progressed to meet three MetS components. Plausible explanations include the natural progression of metabolic derangements with age and pubertal development, an incomplete individualised response to treatment, and non‐adherence to therapy.
Together, these multidimensional burdens emphasise the importance of patient‐centred counselling prior to treatment initiation, addressing misconceptions, providing education about side effects and injection techniques, and ensuring access to psychological support [32]. Observing the success of other family members may encourage treatment initiation and help alleviate anxiety. Notably, more patients in the GLP‐1 RA treated group reported a family history of obesity and dyslipidemia, suggesting greater awareness of these medical issues and possibly influencing their decision to seek treatment following successful parental GLP‐1 RA therapy.
This study has several limitations. The main limitation is potential information bias inherent to the real‐world, retrospective design, which may affect data completeness and limit precise evaluation of the effects of GLP‐1 RA type, dose, and treatment duration on outcomes. The limited cohort size did not allow for extensive subgroup analyses, constraining evaluation of the impact of GLP‐1 RA type transitions. Waist circumference was not available because it is not routinely assessed in our clinical practice and therefore could not be included in the cardiometabolic risk evaluation. Additionally, the study did not include questionnaires for documenting nutritional, behavioural, or psychological factors. Rather, dietary practices were self‐reported and could not be verified. Physical activity was also self‐reported as hours per week, without accounting for type or intensity or provided by objective measurements. As a central tertiary referral center, our findings may be influenced by selection bias and may not generalise to the wider paediatric obesity population. A major strength of the study is the comparison between patients treated with GLP‐1 RA and controls who were not, in a real‐world setting where all participants received medical follow‐up, including nutritional therapy. Another notable strength is the uniformity of care and assessment provided by a single multidisciplinary team, including body composition and anthropometric measurements, blood pressure monitoring, and laboratory evaluations, all performed by the same qualified staff. The use of sex‐ and age‐adjusted z‐scores and percentiles further enhanced the comparability and interpretation of the data.
In conclusion, adolescents with obesity exhibit marked improvements in weight status and body composition during GLP‐1 RA therapy, beyond those observed during the untreated period or among patients who do not receive GLP‐1 RA. These favourable changes may contribute to the measurable metabolic benefit. Body composition changes reflected individualised responses, characterised by a sex‐specific pattern. It should be borne in mind that our study captures only a snapshot within the long and complex process of obesity treatment, given that many young patients continue to experience obesity‐related comorbidities requiring ongoing medical care. Further research is warranted to elucidate the long‐term outcomes of adolescents with obesity treated with GLP‐1 RA.
Author Contributions
Hadar Moran‐Lev: investigation, writing – review and editing, data curation. Erez Azoulay: investigation, formal analysis, writing – review and editing. Yael Issan: data curation, investigation, writing – review and editing. Hagar Interator: investigation, writing – review and editing, data curation. Ronit Lubetzky: investigation, writing – review and editing, data curation. Avivit Brener: conceptualization, investigation, funding acquisition, writing – original draft, writing – review and editing, methodology, formal analysis, project administration, data curation, supervision. Adar Lopez: conceptualization, investigation, methodology, formal analysis, data curation, writing – original draft, writing – review and editing. Ophir Borger: investigation, data curation, writing – review and editing. Eyal Cohen‐Sela: investigation, formal analysis, methodology, writing – review and editing. Yael Lebenthal: conceptualization, investigation, writing – review and editing, data curation, methodology, supervision. Shira Zelber‐Sagi: investigation, methodology, supervision, writing – review and editing. Liat Perl: investigation, software, formal analysis, writing – review and editing, methodology.
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgements
The authors are grateful to the patients and families and to the multidisciplinary team of dedicated nurses, dieticians, psychosocial workers, and physicians at the Diabetes Center at Dana‐Dwek Children's Hospital.
Lopez A., Perl L., Cohen‐Sela E., et al., “Dynamics of Body Composition and Metabolic Risk in Adolescents With Obesity Under GLP‐1 Receptor Agonist Therapy,” Acta Paediatrica 115, no. 8 (2026): 1726–1736, 10.1111/apa.70554.
Portions of this research were presented at ECO 2025 in Malaga, Spain, at ENDO 2025 in San‐Francisco, USA and at Nutrition & Growth 2026, Prague, Czech Republic.
Contributor Information
Yael Lebenthal, Email: yaelleb@tlvmc.gov.il.
Avivit Brener, Email: avivitbrener@tauex.tau.ac.il.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.