Glycemic Variability in Youth Prescribed Obesity Pharmacotherapy: A Prospective Case Series with Continuous Glucose Monitoring

Abstract

Background:

The effects of obesity pharmacotherapy on glycemic variability in youth with obesity remain insufficiently explored.

Objective:

This prospective case series aimed to explore glycemic variability patterns in youth with obesity prescribed obesity pharmacothreapy as part of standard clinical care.

Methods:

This 12-week case series enrolled youth, ages 7–17 years, with obesity from a comprehensive obesity program at a tertiary children’s hospital. Participants, prescribed an obesity pharmacotherapy, wore an unmasked continuous glucose monitor (CGM) for 14 days at baseline and at 4-week intervals for a total of three sessions. The primary outcome was glycemic variability, measured by standard deviation, coefficient of variation, and modified percent time in range (70–140 mg/dL) from CGM data. Demographic and medical data were also collected. Data were analyzed using EasyGV and R with repeated measures to assess changes in glycemic variability over time and across obesity pharmacotherapy.

Results:

Sixteen youth consented to participate (56% female, 69% Hispanic, mean age 14.1 ± 1.2 years, 62% publicly insured), with 6 completing the 12-week CGM data collection (44% retention). Medications included topiramate (n = 1), phentermine/topiramate (n = 1), liraglutide (n = 2), semaglutide (n = 1), and tirzepatide (n = 1). Participants wore the CGM for an average of 6.3 ± 1.2 days per 14-day period. Medication-specific changes in glycemic variability were observed: topiramate monotherapy showed a 2.8% increase in modified percent time in range (%mTIR), a 6.4% reduction in standard deviation (SD), and a 0.06 decrease in the coefficient of variation (CV). The phentermine/topiramate combination had a 0.8% decrease in %mTIR, a 4.1% increase in SD, and a 0.02 increase in CV. Liraglutide Case 1 saw a 12.5% decrease in %mTIR, a 4.1% increase in SD, and a 0.07 increase in CV, while Liraglutide Case 2 exhibited a 9.2% decrease in %mTIR, a 25.6% reduction in SD, and a 0.25 decrease in CV. Semaglutide resulted in a 1.1% increase in %mTIR, a 6.6% reduction in SD, and a 0.05 decrease in CV. Tirzepatide showed a 0.5% increase in %mTIR, a 4.7% reduction in SD, and a 0.05 decrease in CV.

Conclusion:

This exploratory case series suggests that obesity pharmacotherapy may have varying effects on glycemic variability in youth with obesity. While most medications demonstrated improvements in standard deviation and modified percent time in range, the magnitude of changes varied across agents.

Impact:

These findings highlight the potential for obesity optimizing medications to influence glycemic control in this population, warranting further investigation in larger, controlled studies to confirm these effects and understand their long-term implications.

Introduction

Youth-onset obesity is increasingly associated with comorbidities traditionally observed only in adults, including type 2 diabetes, hypertension, dyslipidemia, nonalcoholic fatty liver disease, and obstructive sleep apnea.¹ Compared with adults, the obesity-related comorbidities that manifest in childhood tend to be more aggressive and progress more rapidly, leading to earlier onset of complications.^1,2 In response to this growing concern, the American Academy of Pediatrics has recently updated its clinical practice guidelines, emphasizing the importance of early identification and management of youth-onset obesity.² These guidelines advocate for a comprehensive approach that includes behavior and lifestyle interventions, obesity pharmacotherapy, and, in severe cases, bariatric surgery.²

Currently, five medications are approved by the Food and Drug Administration (FDA) for use in children and adolescents with obesity: orlistat (ages 12+), phentermine (12-week course for ages 16+), liraglutide 3 mg daily (ages 12+), combination phentermine/topiramate (ages 12+), and semaglutide 2.4 mg weekly (ages 12+).^2–7 Several additional agents are used off-label, with more in the clinical pipeline.^1,7 Although these obesity pharmacotherapies have diverse mechanisms of action, most improve peripheral insulin sensitivity directly or indirectly, reduce hunger, enhance satiety, and decrease adiposity over time.¹

There is growing interest in the application of obesity medications among pediatric populations for achieving effective and sustained weight reduction, which is often difficult to achieve through lifestyle modifications alone. Despite this interest, the use of obesity pharmacotherapies in youth remains limited, and relatively little is known about their metabolic effects, particularly their impact on glycemic profiles.^{1, 7–9} Continuous glucose monitoring (CGM) offers a method to measure interstitial glucose levels through a wearable sensor, providing real-time feedback on glycemic excursions in relation to dietary intake and behavior.^10,11 While CGM systems are approved for use in youth with diabetes, their utility in managing obesity without diabetes is less established.¹² CGM is increasingly utilized in adult obesity research for its capacity to provide robust outcome measures, enhancing the quality of glycemic variability assessment and treatment efficacy evaluation.¹⁰

Although CGM is frequently employed in obesity research due to its ability to measure glycemic variability, no studies have assessed glycemic profiles in youth with obesity (without diabetes) who are beginning obesity pharmacotherapies.^13,14 This prospective case series aimed to explore glycemic variability patterns in youth with obesity prescribed obesity pharmacotherapies as part of standard clinical care.^13,14 The objective of this exploratory study is to evaluate changes in glycemic variability associated with obesity pharmacotherapy use in this population, with secondary aims including the assessment of changes in dietary intake, eating behaviors, and body anthropometrics. Based on previous findings in adults, we hypothesize that obesity pharmacotherapies use will result in a reduction in glycemic variability, illustrated by decreased standard deviation and coefficient of variation, and an increase in modified percent time in range (TIR < 140 mg/dL) as captured by CGM.¹⁴

Methods

Study design and setting

This prospective case series was conducted from January 2023 to June 2024 at a tertiary care safety net children’s hospital’s comprehensive obesity program. Consecutive eligible youth who had completed their clinical visit and were prescribed obesity pharmacotherapy were recruited for a 12-week trial. Participants were instructed to wear an unmasked CGM daily for 14 days at baseline and again for 14 days at 4-week intervals following obesity pharmacotherapy initiation. Data were prospectively collected on demographics, medical history, and anthropometric measurements through patient and parent interviews, entered into a REDCap database, and verified via electronic medical records during routine clinical encounters. All youth had point of care hemoglobin A1c (HbA1c) measures collected at baseline, weeks 4, 8, and 12. CGM satisfaction was assessed through a modified version of the CGM Satisfaction Scale^15,16 (CGM-SAT, Supplementary Data) and semi-structured interviews throughout the study.

Participants

Youth ages 7–17 who were part of the Comprehensive Obesity Program at Children’s Hospital Los Angeles and were prescribed an obesity pharmacotherapy by their clinician were approached for enrollment in the study. To meet the inclusion criteria, participants had to have a body mass index (BMI) greater than the 95th percentile for their age and sex and be willing and able to wear a CGM for the required duration, as outlined in the study protocol. Youth with Prader-Willi syndrome, Type 1 or Type 2 diabetes, a history of bariatric surgery, or those who were unwilling or unable to wear the CGM or attend research visits alongside their clinical visits were excluded from participation. The rationale for these eligibility criteria was to utilize a convenience sample, focusing on the addition of CGM to standard clinical care for pediatric obesity in youth prescribed obesity pharmacotherapies, while ensuring the safety and feasibility of the study. Eligibility was screened via electronic medical records, and eligible participants were approached either in-person or via telephone. The study protocol was approved by the Children’s Hospital Los Angeles Institutional Review Board (approval number CHLA-23-0014), and informed consent was obtained from participants and their caregivers. A HIPAA waiver was obtained for screening participants through electronic medical records.

Study assessments

Participants attended two in-person study visits (weeks 0 and 6), with a virtual visit at week 3 and weekly phone check-ins with study staff. During in-person visits, anthropometric measurements (body weight, height) were taken, and dietary assessments were conducted.

Intervention

All participants received standardized nutritional counseling from a registered dietitian. At the initial visit, baseline data, including demographics, anthropometrics, and dietary assessments, were collected. Due to its availability and suitability for this project, the FreeStyle Libre 2 CGM was selected as the CGM device. Participants were educated on how to apply the CGM, but they were not provided specific instructions on interpreting the CGM data as part of the intervention. While participants were reminded to wear their CGM for up to 14 days each month, they were not advised to specifically monitor glucose changes in response to different foods or to modify the timing or type of intake based on the data. The CGM was used as a tool to support the intervention but was not the primary intervention itself. Instead, the intervention primarily focused on dietary modifications, and the CGM was a complementary component that allowed for objective glucose monitoring during the study.

Adherence monitoring

Weekly phone encounters assessed adherence and addressed barriers. CGM downloads were reviewed to determine if treatment escalation was necessary, and adverse events were monitored. Protocols were in place for unhealthy compensatory behaviors, with a withdrawal process for participants requiring further medical evaluation.

Measurements

CGM satisfaction survey

Administered at baseline and the final visit, this survey included 27 items using a 1–5 Likert scale and open-ended questions about the CGM experience. Correlations were explored between satisfaction scores and adherence to CGM usage and medication, assessing how satisfaction influenced continued engagement in the study.

Glycemic variability

CGM data were analyzed for mean, median, maximum, and minimum glucose levels, percent CGM wear, glucose management indicator, standard deviation (SD), coefficient of variation (CV), and percent time in range (%TIR). Relationships between glycemic variability metrics and changes in body weight, dietary intake, and adherence to obesity pharmacotherarpy were examined using Pearson or Spearman correlation coefficients, depending on data distribution.

Twenty four-hour dietary recall

Conducted at each study visit (∼week 0, 3, and 6) using the Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24), which captured intake over the previous day. Dietary data were analyzed for correlations with changes in glycemic variability and weight, using paired analyses to examine how shifts in nutrient intake related to metabolic outcomes.

Adult eating behavior questionnaire

Assessed appetitive traits at baseline and monthly intervals through a 35-item questionnaire using a 1–5 Likert scale. Scores were calculated for eight subscales related to eating behavior, and correlations were analyzed to understand how appetitive traits related to glycemic variability and changes in dietary intake.

Body anthropometrics

Measured at baseline and the final assessment using calibrated equipment. BMI and zBMI were calculated using center for disease control growth charts. Changes in anthropometrics were correlated with glycemic metrics to evaluate the relationship between weight loss and improvements in glycemic control.

Statistical analysis

Continuous variables were summarized using medians and interquartile ranges, while categorical variables were summarized as frequencies and percentages. Glycemic data were analyzed with R package CGM analysis.¹⁷ Intention-to-treat analyses included all baseline participants. Changes in glycemic variability metrics were assessed using a univariate median quantile regression model, while dietary intake and eating behavior changes were evaluated with paired Wilcoxon rank tests. Changes in anthropometrics were assessed using Wilcoxon signed rank tests with continuity correction.

Results

Participant characteristics and feasibility

A total of 16 eligible youth were recruited, with a median age of 14 years. The cohort was 56% female (9/16), 69% Hispanic (11/16), and 62% publicly insured (10/16, Table 1). All participants had a family history of obesity, a median BMI z-score of 2.58 SD, and a median BMI percentage of 158% of the 95^th percentile. The majority were classified with class 3 obesity (75%). Six out of 16 participants prescribed Semaglutide. Despite a high attrition rate, with 9 out of 16 participants withdrawing by week 8, 6 participants completed the study with full CGM data at week 12. Of the 9 withdrawals, 5 participants dropped out in week 1 due to reluctance to wear the CGM, and 4 withdrew for various reasons related to the obesity pharmacotherapy, including side effects, insurance issues, or a family decision to trial lifestyle modification before starting the obesity pharmacotherapies. One participant did not formally withdraw but had an incomplete data set at week 12. The data analysis was based on the 6 participants who completed the full CGM data at week 12. No significant demographic or baseline differences were observed between completers and noncompleters.

Table 1.

Demographic Characteristics and Baseline Anthropometrics for All Consented Participants

Characteristic	Overall, N = 16
Age (years), median (IQR)	14 (10.75, 16)
Sex, n (%)
Male	7 (44)
Female	9 (56)
Ethnicity, n (%)
Hispanic	11 (69)
White	4 (25)
Black	1 (6.2)
BMI (kg/m²), median (IQR)	38.68 (34.3, 45.07)
BMI z-score (SD), median (IQR)	2.58 (2.42, 2.76)
%BMI_p95 (%), median (IQR)	158 (142.25, 169.75)
Class of obesity, n (%)
2	4 (25)
3	12 (75)
Hemoglobin A1c (%), median (IQR)	5.5 (5.18, 5.73)
ALT (U/L), median (IQR)	21.5 (17, 34)
Triglyceride (mg/dL), median (IQR)	134 (105.5, 177)
Missing	1
HDL cholesterol (mg/dL), median (IQR)	42 (37, 45)
Missing	1
Family history of obesity, n (%)	16 (100)
Insurance, n (%)
Private	6 (38)
Public	10 (62)

CGM adherence and satisfaction

All 6 participants who completed the study provided responses to the CGM satisfaction survey, with the majority reporting that the device taught them about how dietary intake and activity affected their blood glucose levels. Specifically, 6 out of 6 participants agreed or strongly agreed that the CGM educated them about the impact of eating on blood sugar, and 6 out of 6 participants felt that watching their glucose levels fluctuate throughout their day influenced their daily habits and food choices. Based on survey responses, the CGM was deemed user-friendly, with no participants finding it more complicated than expected or experiencing difficulty in understanding its feedback. All 6 survey completers indicated that they would recommend the CGM to other children focused on weight control.

Of the 9 withdrawals, 5 participants dropped out in week 1 due to reluctance to wear the CGM. Exit interviews conducted at the time of withdrawal revealed the following reasons for reluctance to wear the CGM — 1) fear of having something on their body throughout the day, 2) adhesion irritation, 3) burning sensation during initial application, and 4) challenges with sensor placement. For the six study completers, no significant safety issues were reported, however, common concerns mirrored those of the participants who withdrew due to CGM issues and included 1) irritation from frequent alarms for glucose values less than 70 mg/dL secondary to compression which resolved with repositioning and did not require treatment (56%), 2) burning sensation during initial application, and 3) discomfort from seeing the needle.

Case series

This case series includes six pediatric patients treated for class III obesity with various obesity pharmacotherapies, examining changes in BMI, percent BMI-for-age percentile (BMI_p95), caloric intake (Table 2), and eating behaviors (Table 3) over 12 weeks. In addition, medication-specific changes in glycemic variability as captured by CGM were assessed (Table 4).

Table 2.

Change in Food Responsiveness, Satiety Responsiveness, Hunger, and Slowness in Eating as Captured on the Childhood Eating Behavior Questionnaire (Completed by Caregiver) for Each Participant Case by Antiobesity Medication

Variable	Case
	Oral		Injectable
	Topiramate 100 mg monotherapy	Phentermine 15 mg/topiramate 100 mg combination	Liraglutide 3 mg daily		Semaglutide 1 mg weekly	Tirzepatide 10 mg weekly
	1	2	3	4	5	6
Food responsiveness
Baseline	3	2.5	2.75	3.75	2	4.75
M1	4	2.25	2	2.75	2.25	4.75
Δ	+1	−0.25	−0.75	−1	+0.25	0
M2	3.5	2	2.75	3.25	1.75	4
Δ	+0.5	−0.5	0	−0.5	−0.25	−0.75
M3	2.75	1.75	2.25	2.75	1.5	3.5
Δ	−0.25	−0.75	−0.5	−1	−0.5	−1.25
Satiety responsiveness
Baseline	2.5	2.75	2	3.5	4.25	2.25
M1	2.5	3.5	3.75	4	3.5	1.5
Δ	0	+0.75	+1.75	+0.5	−0.75	−0.75
M2	2.25	3	4	3.5	4.75	2.75
Δ	−0.25	+0.25	+2	0	+0.5	+0.5
M3	4	3.25	4.25	3.75	4.75	3.25
Δ	+1.5	+0.5	+2.25	+0.25	+0.5	+1
Hunger
Baseline	3	2	3.8	3.8	3	4.2
M1	3.4	1.8	2.6	2.6	2.6	4.6
Δ	+0.4	−0.2	−1.2	−1.2	−0.4	+0.4
M2	2.6	2	3.4	3.2	2.6	3
Δ	−0.4	0	−0.4	−0.6	−0.4	−1.2
M3	1.4	1.8	3.2	2.4	2	3
Δ	−1.6	−0.2	−0.6	−1.4	−1	−1.2
Slowness
Baseline	1.25	2.75	2	2.75	3.5	1
M1	2.25	3.5	2.25	3	2.75	1
Δ	+1	+0.75	+0.25	+0.25	−0.75	0
M2	2.25	3.75	3.75	3	4	2.5
Δ	+1	+1	+1.75	+0.25	+0.5	+1.5
M3	4.25	3.75	3.75	3.25	4.25	2.5
Δ	+3	+1	+1.75	+0.5	+0.75	+1.5

Δ, change from baseline.

Table 3.

Changes in Hemoglobin A1c, Percent Time in Range (70–140 mg/dL), Standard Deviation, and Coefficient of Variation from Continuous Glucose Monitoring at 12 Weeks by obesity pharmacotherapies in Participants with Complete CGM Data

	Case
	Oral		Injectable
Variable	Topiramate 100 mg monotherapy	Phentermine 15 mg/topiramate 100 mg combination	Liraglutide 3 mg daily		Semaglutide 1 mg weekly	Tirzepatide 10 mg weekly
Case	1	2	3	4	5	6
Hemoglobin A1c (%)
Baseline	5.2	4.8	5.2	5	5.1	5.2
M1	4.6	4.8	4.9	4.6	4.9	5
Δ	−0.6	0	−0.3	−0.4	−0.2	−0.2
M2	4.9	5	4.9	4.8	4.6	5.1
Δ	−0.3	+0.2	−0.3	−0.2	−0.5	−0.1
M3	5.3	5.5	4.6	4.4	4.9	5.1
Δ	+0.1	+0.7	−0.6	−0.6	−0.2	−0.1
% Modified time in range [70–140 mg/dL] (%)
Baseline	97.2	98.4	99.7	97.1	98.2	93.7
M1	83.9	89.2	98.5	99.7	99.7	98.1
Δ	−13.3	−9.2	−1.2	+2.6	+1.5	+4.4
M2	100	100	98.8	98.9	96.2	100
Δ	+2.8	+1.6	−0.9	+1.8	−2	+6.3
M3	100	97.6	87.2	87.9	99.3	94.2
Δ	+2.8	−0.8	−12.5	−9.2	+1.1	+0.5
Standard deviation
Baseline	16.8	10.3	11.2	35.4	16.5	19.5
M1	13.3	14.8	10.8	5.4	7.3	13.5
Δ	−3.5	+4.5	−0.4	−30	−9.2	−6
M2	14	4.8	10.7	9	8	8.9
Δ	−2.8	−5.5	−0.5	−26.4	−8.5	−10.6
M3	10.4	14.4	15.3	9.8	9.9	14.8
Δ	−6.4	+4.1	+4.1	−25.6	−6.6	−4.7
Coefficient of variation
Baseline	0.16	0.11	0.11	0.37	0.16	0.19
M1	0.16	0.16	0.12	0.06	0.08	0.14
Δ	0	+0.05	+0.01	−0.31	−0.08	−0.05
M2	0.15	0.05	0.11	0.1	0.09	0.09
Δ	−0.01	−0.06	0	−0.27	−0.07	−0.1
M3	0.1	0.13	0.18	0.12	0.11	0.15
Δ	−0.06	+0.02	+0.07	−0.25	−0.05	−0.04

Δ, change from baseline.

Table 4.

Change in Total Calories and Percent Calories from Carbohydrates, Protein, and Sugar as Captured by 24-Hour Dietary Recall (Automated Self-Administered Dietary Assessment Tool) for Each Participant Case by Obesity Pharmacotherapies

Variable	Case
	Oral		Injectable
	Topiramate 100 mg monotherapy	Phentermine 15 mg/topiramate 100 mg combination	Liraglutide 3 mg daily		Semaglutide 1 mg weekly	Tirzepatide 10 mg weekly
	1	2	3	4	5	6
Total calories (kcal/day)
Baseline	2389.5	887.9	2930.1	1039.5	1080.9	2146.4
M1	1587.3	1175.9	1977.5	541	1279.9	3691.1
Δ	−802.2	+288	−952.6	−498.5	+199	+1545
M2	2254.9	821.7	2132.9	235	511.8	1919.6
Δ	−134.6	−66.2	−797.2	−804.5	−569.1	−226.8
M3	978.9	1454.1	1499.7	1396.6	513	2420.9
Δ	−1410.6	+566.2	−1430.4	+357.1	−567.9	+274.5
Percent carbohydrate (%)
Baseline	51	29	44.1	43.5	41.6	42.9
M1	49.4	54.2	55.5	31.3	42.7	52.9
Δ	−1.6	+25.2	+11.4	−12.2	+1.1	+10
M2	38.7	57.5	47.5	72.6	34	40.3
Δ	−12.3	+28.5	+3.4	+29.1	−7.6	−2.6
M3	58.7	45.7	41.6	42.5	57.5	47.2
Δ	+7.7	+16.7	−2.5	−1	+15.9	+4.3
Percent protein (%)
Baseline	18.4	25.8	14.2	17.9	18.7	19.8
M1	12.2	23.6	9.6	29.2	23.7	13.5
Δ	−6.2	−2.2	−4.6	+11.3	+5	−6.3
M2	26.6	15.3	10.7	17.5	21.5	17.3
Δ	+8.2	−10.5	−3.5	−0.4	+2.8	−2.5
M3	20.8	13	12.4	13.5	18.2	16.3
Δ	+2.4	−12.8	−1.8	−4.4	−0.5	−3.5
Percent sugar (%)
Baseline	13.9	10.1	20.7	5.8	9.7	20.7
M1	25	12.5	22.9	14.7	19.2	18.8
Δ	+11.1	+2.4	+2.2	+8.9	+9.5	−1.9
M2	8.6	10.3	19.1	24.3	4.7	23.4
Δ	−5.3	+0.2	−1.6	+18.5	−5	+2.7
M3	12	15.7	20.5	18.3	28.3	24.5
Δ	−1.9	+5.6	−0.2	+12.5	+18.6	+3.8

Δ, change from baseline.

Topiramate monotherapy

An 8-year-old male with a BMI of 37.77 kg/m² and HbA1c of 5.7% was prescribed 100 mg of topiramate nightly and experienced no side effects. After 12 weeks, he showed a reduction in BMI (−0.75 kg/m²), %BMI^p95 (−7%), and total caloric intake (−1410.6 kcal/day). CGM data revealed a 2.8% increase in %mTIR, a 6.4% reduction in SD, and a 0.06 decrease in CV, indicating improved glycemic control and reduced variability (Fig. 1).

FIG. 1.

Aggregated continuous glucose monitoring (CGM) data for an 8-year-old male with class 3 obesity on topiramate 100 mg nightly for 12 weeks. (a) Daily CGM data overlay with LOESS smoothing (mean ± SD) at baseline, Month 1 (M1), Month 2 (M2), and Month 3 (M3). Baseline data were collected before starting topiramate, with M1, M2, and M3 representing 14-day CGM wear periods. CGM data showed a 2.8% increase in modified percent time in range (%mTIR), a 6.4% reduction in standard deviation (SD), and a 0.06 decrease in coefficient of variation (CV), indicating improved glycemic control and reduced variability. The X-axis represents time (24 h), and the Y-axis represents sensor glucose in mg/dL.

Phentermine/topiramate combination

A 10-year-old male with a BMI of 33.91 kg/m² was prescribed a combination of 100 mg topiramate nightly and 15 mg phentermine daily. After 12 weeks, he showed a minor reduction in BMI (−0.18 kg/m²), %BMI^p95 (−8%), but an increase in caloric intake (+566.2 kcal/day). CGM data showed a 0.8% decrease in %mTIR, a 4.1% increase in SD, and a 0.02 increase in CV, indicating slightly worsened glycemic control and increased variability.

Liraglutide (case 1)

A 14-year-old transgender male with a BMI of 39.44 kg/m² experienced nausea but remained compliant with 3 mg of liraglutide daily. After 12 weeks, he achieved a significant reduction in BMI (−2.68 kg/m²), %BMI^p95 (−15%), and total caloric intake (−1430.4 kcal/day). CGM data showed a 12.5% decrease in %mTIR, a 4.1% increase in SD, and a 0.07 increase in CV, suggesting some reduction in time spent in the target glucose range and increased glycemic variability despite improvements in weight and caloric intake.

Liraglutide (case 2)

A 16-year-old female with a BMI of 47.15 kg/m² experienced mild nausea initially, which resolved by 8 weeks while taking 3 mg of liraglutide daily. She showed a reduction in BMI (−1.51 kg/m²), %BMI^p95 (−7%), and an increase in caloric intake (+357 kcal/day). CGM data showed a 9.2% decrease in %mTIR, a 25.6% reduction in SD, and a 0.25 decrease in CV, indicating a significant reduction in glycemic variability with the medication despite increased caloric intake.

Semaglutide

A 17-year-old female with a BMI of 47.34 kg/m² initially experienced nausea on a 1 mg weekly dose of semaglutide but adjusted to a 0.5 mg weekly dose. After 12 weeks, her BMI decreased (−2.5 kg/m²), %BMI_p95 (−10%), and total caloric intake reduced (−568 kcal/day). CGM data showed a 1.1% increase in %mTIR, a 6.6% reduction in SD, and a 0.05 decrease in CV, suggesting slight improvement in glycemic control and reduced variability with decreased caloric intake.

Tirzepatide

A 12-year-old male with a BMI of 37.92 kg/m² experienced mild gastrointestinal symptoms but tolerated a titration to a 10 mg weekly dose of tirzepatide. After 12 weeks, he experienced a reduction in BMI (−0.75 kg/m²), %BMI^p95 (−11%), and an increase in caloric intake (+274 kcal/day). CGM data showed a 0.5% increase in %mTIR, a 4.7% reduction in SD, and a 0.05 decrease in CV, indicating slightly improved glycemic control and reduced variability despite an increase in caloric intake.

Discussion

This prospective case series aimed to explore glycemic variability patterns in youth with obesity prescribed obesity pharmacotherapies as part of standard clinical care. While the overall outcomes were mixed, several findings emerged regarding youth adherence to CGM, youth satisfaction with the device, and the varying effects of different obesity pharmacotherapies on glycemic variability. The results underscore the complexities of treating pediatric obesity and suggest the need for more personalized approaches to treatment.

The high attrition rate observed in this study (9 out of 16 participants withdrew by week 8) is a significant limitation that highlights the challenges of conducting CGM interventions in youth with obesity, without diabetes. However, the six participants who completed the study provided valuable insights into the feasibility of using CGM in a pediatric obesity cohort. Despite early dropouts, all six youth who completed the study reported high satisfaction with the CGM device. However, the study also revealed some challenges with CGM use in this cohort, particularly with sensor placement and adhesive issues. These issues were most prominent among participants who withdrew early, suggesting that device-related discomfort may be a barrier to continued participation.

One of the key objectives of this study was to examine the effects of different obesity pharmacotherapies on glycemic variability, as measured by CGM. The results were highly variable, with medications showing differing impacts on glycemic variability. For example, the participant on topiramate monotherapy demonstrated a reduction in glycemic variability, including improvements in modified percent time in range, as well as decreases in both standard deviation and coefficient of variation of glucose levels. In contrast, two participants on liraglutide exhibited divergent outcomes, with one showing improvements in weight and caloric intake but a worsening of glycemic variability, characterized by a decreased time in range and increased variability. The participants on semaglutide and tirzepatide showed more consistent results, with both medications leading to slight improvements in glycemic variability and reductions in coefficient of variation. Semaglutide was also associated with reductions in BMI and caloric intake, while tirzepatide resulted in improvements in time in range and reductions in standard deviation and coefficient of variation. These findings highlight the individualized responses to obesity pharmacotherapies, emphasizing the importance of personalized treatment approaches for managing glycemic variability in pediatric obesity.

Glycemic variability provides valuable insights into metabolic health, even in the absence of overt hyperglycemia, as indicated by normal HbA1c levels. In this cohort, all participants had normal baseline HbA1c values, yet variations in coefficient of variation and standard deviation were observed, suggesting underlying differences in glucose regulation. Although some of these variations may reflect expected inter-individual variability, several participants experienced more stable glucose regulation, potentially indicating improvements in metabolic control over the course of the study. These findings align with adult studies showing that reductions in glycemic variability, even in individuals with normal HbA1c, are associated with better metabolic outcomes, such as improved insulin sensitivity and reduced long-term risk of type 2 diabetes. While research on glycemic variability as a predictive marker for the progression to type 2 diabetes in youth with obesity is limited, these results suggest that targeting glycemic variability could help reduce the risk of obesity-related comorbidities and underscores its clinical value in pediatric obesity management.

Study limitations and future directions

There are several limitations to this study that must be considered. First, the small sample size and high attrition rate limit the generalizability of the findings. The reasons for participant withdrawal, including device-related discomfort and issues with obesity pharmacotherapy side effects, suggest that future studies should focus on strategies to improve participant retention, such as providing more comprehensive support during the intervention and addressing potential side effects early in the treatment process. Second, the relatively short duration of the study (12 weeks) limits our ability to assess the long-term effects of obesity pharmacotherapies on glycemic variability and obesity management. Future research should include longer follow-up periods to better understand the sustained impact of these medications.

Conclusions

In conclusion, this study suggests that obesity pharmacotherapies may have varying effects on glycemic variability and obesity management in a pediatric cohort. CGM proved to be a valuable tool for tracking glycemic variability. However, the individualized nature of treatment responses highlights the need for personalized approaches in managing pediatric obesity. Future research should explore ways to optimize the use of pharmacological interventions, improve adherence to CGM, and evaluate the long-term effects of these treatments on both glycemic variability and obesity management.

Footnotes

Availability of Data and Material

The datasets from this study will be available from the corresponding author on written request. De-identified core data are available in the supplemental file with the publication along with the main analysis codes.

Authors’ Contributions

J.N., J.E., M.B., M.H.V., D.S.B., and A.P.V. made a significant contribution to the work reported. J.N., J.E., M.B., M.H.V., D.S.B., and A.P.V. participated in the conception, study design, execution, acquisition of data, analysis and interpretation; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work. M.H.V. participated in the acquisition of data, analysis and interpretation; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; has agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work. All authors approved the final article as submitted and agree to be accountable for all aspects of the work.

Consent for Publication

Not applicable.

Author Disclosure Statement

The authors have no financial relationships or conflict of interest relevant to this article to disclose. J.E. receives grant funding from NIH, FDA, and the Helmsley Charitable Trust, and is a consultant for Sanofi.

Funding Information

This work was supported by grants 1) K23DK134801 NIH NIDDK, 2) Sacchi Foundation Research Scientist, 3) Supported by American Diabetes Association grant #11-22-ICTSN-32, and 4) The Southern California Center for Latino Health Pilot Award 2022. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Supplementary Material

Abbreviations Used

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Supplementary Material

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