Time Limited Eating in Adolescents (Time LEAd): a Pilot Study (TimeLEAd)

The investigators propose a randomized controlled trial in 90 children (age 13-21y) with obesity recruited from clinical programs at the Children's Hospital Los Angeles (CHLA). Patients will be randomized to one of three treatment groups for a 12-week intervention: Group 1) Low sugar and carbohydrate diet (LSC, <90 gm carbohydrate (CHO)/day, <25 gm added sugar/day) + blinded CGM (used to monitor adherence and glycemic outcomes without real time feedback). Group 2) LSC+TLE (16-hour fast/8-hour feed for 3 days per week) + blinded CGM, Group 3) LSC+TLE+ real time feedback via CGM (to evaluate effect of providing CGM data on intervention efficacy).

The majority of adolescents with obesity demonstrate declining beta cell (β-cell) function and progressive insulin resistance over their lifetime.1 In our population of lower income minority teens, 1 in 3 have obesity or severe obesity and of those 30-50% go on to develop PD or T2D during adolescence or as young adults.1 Although diet and increased adiposity play a significant role in the pathogenesis of these conditions, the standard treatment model of intensive lifestyle modifications often result in modest decrease in BMI z-score of -0.1-0.2 SD.2, 3 There is a paucity of trials that have examined the effect of time limited eating (TLE) interventions in the treatment of youth with obesity.4 Novel dietary approaches like time limited eating have been shown to be effective for weight loss and improved glycemic control in adults with obesity but have not been examined in children.5, 6 A TLE approach involves interspersing normal daily caloric intake with 16-hour periods of calorie restriction/fasting several times a week.7-9 TLE may actually be more feasible, non-stigmatizing, flexible and effective for adolescents than alternatives like severe caloric restriction because it removes the need for intensive counting of daily caloric intake or macronutrient content and focuses on a straightforward task of consuming food during a pre-specified time period.4, 10, 11 One major limitation to implementing any dietary intervention in pediatric populations is concern for poor adherence and difficulty in reliably assessing compliance. We aim to overcome these issues with the use of continuous glucose monitoring (CGM) to monitor and promote adherence to the intervention and thus improve overall efficacy. In addition, the use of CGM will provide important outcome data related to overall glycemic response. Finally, we will evaluate whether providing individual feedback based on CGM data to subjects as real time biofeedback as part of the intervention, enhances efficacy. We propose a randomized controlled trial in 60 children (age 14-18) with obesity (BMI% > 95th percentile) recruited from clinical programs at the Children's Hospital Los Angeles (CHLA). Patients will be randomized to one of three treatment groups for a 12-week intervention: Group 1) Low sugar and carbohydrate diet (LSC, <90 gm carbohydrate (CHO)/day, <25 gm added sugar/day) + blinded CGM (used to monitor adherence and glycemic outcomes without real time feedback). Group 2) LSC+TLE (16-hour fast/8-hour feed for 5 days per week) + blinded CGM, Group 3) LSC+TLE+ real time feedback via CGM (to evaluate effect of providing CGM data on intervention efficacy). We have 3 Specific Aims: Aim 1. Test the efficacy of adding a TLE approach to a LSC intervention on body fat and weight loss (Group 2 vs. Group 1). Hypothesis 1: LSC+TLE will result in greater decrease in body fat and zBMI than LSC alone. Aim 2. Test the efficacy of LSC+TLE compared to LSC alone on reduction on glycemic response (CGM) and psychosocial parameters (Group 2 vs. Group 1). Hypothesis 2: TLE+LSC will result in a greater improvement in glucose control (FBG) and psychosocial parameters. Aim 3. Evaluate if CGM use is a feasible tool to determine dietary compliance to TLE type interventions and determine the impact of unblinded CGM on dietary intervention adherence and efficacy (Group 3 vs. Group 2). Hypothesis 3a: CGM will be a feasible tool to determine dietary compliance. Hypothesis 3b: Unblinded CGM data will result in 1) improved adherence to the dietary intervention as assessed by percent time in range when compared to those wearing a blinded CGM and 2) improve intervention effects. Overall Impact: This research will generate new knowledge that can readily be integrated into clinical weight management programs to optimize their impact and accelerate healthy changes for youth with obesity. This dietary intervention could lead to global improvement and result in slowed disease progression, decreased complications and reduced prevalence of secondary comorbidities that arise from a lifetime of obesity. Virtual Adaptation: To respond to the COVID-19 research restriction the study protocol was adapted for a 100% virtual model in which all study procedures, consent and outcome measures were collected virtually. For this cohort the aim was to recruit 10-12 completer per study arm with a maximum anticipated recruitment of 20-30 adolescents per group. For the virtual adaptation there is no DEXA scan or blood testing that is collected due to the in-person restriction.

Group 1) Low sugar and carbohydrate diet (LSC, <90 gm carbohydrate (CHO)/day, <25 gm added sugar/day) + blinded CGM (used to monitor adherence and glycemic outcomes without real time feedback)

Group 3) LSC+TLE+ real time feedback via CGM (to evaluate effect of providing CGM data on intervention efficacy).

Percent Change in BMI in excess of the 95th percentile (%BMIp95) as calculated by the CDC extended SAS equations at week 12 minus %BMIp95 at baseline. For example If the BMI is greater than the 95th percentile: BMI percentile equals 90 plus 10 times the cumulative distribution function (CDF) of the standard normal distribution. Sigma is the value from the data table corresponding to the sex of the child and the age in months. and are the cumulative distribution function (CDF) of the standard normal distribution and its inverse function. Standard normal distribution tables can be found in statistics textbooks, online sources, and statistical computer programs. Example: A boy aged 4 years and 2 months (50.5 months) with BMI = 22.6. For this boy, P95 (95th percentile) is 17.8219 so his BMI is above the 95th percentile and sigma = 2.3983.

Inclusion Criteria: age 14-18 BMI> 85th percentile parent, guardian or family member ages 18 years and older willing to participate Exclusion Criteria: Insulin requirement previous diagnosis of Prader Willi Syndrome, brain tumor or hypothalamic obesity serious mental conditions (e.g. developmental or intellectual disability or previously diagnosed eating disorder or positive screen at consent visit) physical, mental of other inability to participate in the assessments (e.g. inability to wear CGM, inability to be in the imaging modality without sedation, or inability to eat by mouth) previous or planned bariatric surgery current use of medication that impacts weight or executive functioning (e.g., antipsychotics, sedatives, hypnotics, off-label obesity medication) current psychotherapy regarding weight or eating behavior current participation in other interventional studies. In our experience, children younger than 13 years of age and older than 21 years would require different intervention/counseling strategies.

Naguib MN, Hegedus E, Raymond JK, Goran MI, Salvy SJ, Wee CP, Durazo-Arvizu R, Moss L, Vidmar AP. Continuous Glucose Monitoring in Adolescents With Obesity: Monitoring of Glucose Profiles, Glycemic Excursions, and Adherence to Time Restricted Eating Programs. Front Endocrinol (Lausanne). 2022 Feb 25;13:841838. doi: 10.3389/fendo.2022.841838. eCollection 2022.

Vidmar AP, Goran MI, Naguib M, Fink C, Wee CP, Hegedus E, Lopez K, Gonzalez J, Raymond JK. Time limited eating in adolescents with obesity (time LEAd): Study protocol. Contemp Clin Trials. 2020 Aug;95:106082. doi: 10.1016/j.cct.2020.106082. Epub 2020 Jul 16.