Effects of Processed Foods on Brain Reward Circuitry and Food Cue Learning

NCT06165952 | Recruiting

• 1 other identifier: 71541
• Study Type: observational
• Target: 162
• Locations: 1 country
• Sites: 1

Brief Summary

Examine if ultra-processed (UP) foods are more effective in activating reward, attention, and memory brain regions and in promoting food cue learning than minimally-processed foods. Assess individual differences in neurobehavioral responses to UP foods.

Conditions

Ultra-processed Foods (UP)

Outcome Measures

Primary Outcomes (5)

• Body Fat

  Investigators will use air displacement plethysmography (ADP) via the Bod Pod and bioelectrical impedance (BIA) via the SECA medical body composition analyzer (SECA mBCA) to assess % body fat. The investigators will average the values of both measures. Body density is calculated as body mass divided by body volume; body density is used to calculate % body fat. ADP % body fat shows high test-retest reliability (r = .92-.99) and correlates with dual energy x-ray absorptiometry (DEXA) and hydrostatic weighing estimates (r = .98-.99), with ADP estimate of % body fat falling an average of only 1.7% different relative to DEXA estimates. Height will be measured using a direct reading stadiometer. Weight will be assessed using digital scales with participants wearing light clothing without shoes or coats. Body mass index (BMI)(Kg/M2) will be used to confirm participants are initially at a healthy weight (BMI scores between 25th to 75th age- and sex-adjusted BMI percentile).

  Baseline, 1-year, 2-year, 3-year, 4-year follow-up

• Baseline functional magnetic resonance imaging (fMRI) Ultra-Processed Foods Taste, Anticipated Taste, and Picture Paradigm

  Participants will complete four rounds of an adapted version of a milkshake paradigm to assess activation in response to receipt (taste) and anticipated receipt of 1) high-calorie, ultra-processed food taste, 2) low-calorie, ultra-processed food taste, 3) high-calorie minimally-processed food taste, 4) low-calorie, minimally-processed food taste, and 5) a tasteless odorless solution containing the main ionic components of saliva. Participants will be cued with a picture of the specific food and label (high-calorie milkshake, low-calorie milkshake, high-calorie smoothie, low-calorie smoothie) before receipt of the tastes.

  Baseline

• Baseline fMRI Ultra-Processed Food Picture Paradigm

  Participants will complete two rounds of a block version of the food picture paradigm to examine activation in response to 40 high-calorie ultra-processed food pictures, 40 low-calorie ultra-processed food pictures, 40 high-calorie minimally-processed food pictures, 40 low-calorie minimally-processed food pictures, and 40 bottles of water. Fifty percent of each category will be branded foods/bottles to increase ecological validity. Pictures will be matched for complexity, valence and intensity across categories. After the scan, participants will be shown 40 food pictures, including pictures that were and were not used in the paradigm, and will be asked whether they had seen the foods to assess aided recall. Participants will also be asked to rate palatability of 20 food pictures per food category.

  Baseline

• Baseline fMRI Ultra-Processed Food Cue Learning Paradigm

  Participants will complete two rounds of an adapted version of the food reward learning paradigm wherein four arbitrary fractal cues will signal the delivery of either 0.7 ml of an ultra-processed milkshake, a minimally-processed smoothie, a tasteless solution, and no taste. To make learning more challenging, the paradigm includes both paired cue trials in which the taste is delivered as cued and unpaired cue trials in which the taste is not delivered at an 80:20 ratio. There will be one fractal cue that is not consistently paired with a taste. Participants will be asked to respond with a button press as soon as possible to indicate on which side of the fixation cross the stimuli appeared (providing a behavioral measure of reaction time to each cue).

  Baseline

• Baseline Post-fMRI Ad Lib Food Intake

  Participants will be presented with a 20-item buffet spread that includes ultra-processed foods (high- and low-calorie), and minimally processed foods (high- and low-calorie). Participants first perform a taste test of 1g of each of the foods. They will then complete perceptual hedonic ratings of the pleasantness, sweetness, savoriness, and desire to consume on generalized labeled magnitude scales. After the taste test, participants will be told that they are free to eat as much as they like because the investigators have to discard the food after each participant. Participants will be alone during the 15-min tasting to minimize demand characteristics. Food will be pre- and post-weighed to determine ad lib intake. Total caloric intake will be calculated and translated to % of calorie needs.

  Baseline

Study Arms (1)

Adolescent youth age 13-15

age- and sex- adjusted body mass index (zBMI) scores between the 25th and 75th percentile

Eligibility Criteria

• Age: 13 Years - 15 Years
• Sex: all
• Age Groups: Child (0-17)
• Sampling Method: Non-Probability Sample

Study Population

Community sample

You may qualify if:

• female and male adolescents 13-15 years of age
• age- and sex- adjusted zBMI scores between the 25th and 75th percentile
• participant and their guardian must be able to read and speak English to gather valid consent

You may not qualify if:

• current eating disorders or other major psychiatric disorders (e.g., depression, bipolar, schizophrenia, substance use disorder)
• fMRI contra-indicators (e.g., metal implants, braces, claustrophobia, pregnancy)
• serious medical problems (e.g., Type 2 diabetes, cancer)
• history of food allergies or restrictive dietary requirements (e.g., lactose intolerance, vegan)
• use of psychoactive drugs more than once weekly
• medications that impact appetite or reward functioning (e.g., metformin, anti-psychotic medication, insulin)

Sponsors & Collaborators

• Stanford University: lead
• University of North Carolina, Chapel Hill: collaborator
• Oregon Research Institute: collaborator

Study Sites (1)

Stanford University

Stanford, California, 94305, United States

RECRUITING

Related Publications (8)

• Hall KD, Ayuketah A, Brychta R, Cai H, Cassimatis T, Chen KY, Chung ST, Costa E, Courville A, Darcey V, Fletcher LA, Forde CG, Gharib AM, Guo J, Howard R, Joseph PV, McGehee S, Ouwerkerk R, Raisinger K, Rozga I, Stagliano M, Walter M, Walter PJ, Yang S, Zhou M. Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of Ad Libitum Food Intake. Cell Metab. 2019 Jul 2;30(1):67-77.e3. doi: 10.1016/j.cmet.2019.05.008. Epub 2019 May 16.

  PMID: 31105044 BACKGROUND

• Demos KE, Heatherton TF, Kelley WM. Individual differences in nucleus accumbens activity to food and sexual images predict weight gain and sexual behavior. J Neurosci. 2012 Apr 18;32(16):5549-52. doi: 10.1523/JNEUROSCI.5958-11.2012.

  PMID: 22514316 BACKGROUND

• Stice E, Burger KS, Yokum S. Reward Region Responsivity Predicts Future Weight Gain and Moderating Effects of the TaqIA Allele. J Neurosci. 2015 Jul 15;35(28):10316-24. doi: 10.1523/JNEUROSCI.3607-14.2015.

  PMID: 26180206 BACKGROUND

• Yokum S, Gearhardt AN, Harris JL, Brownell KD, Stice E. Individual differences in striatum activity to food commercials predict weight gain in adolescents. Obesity (Silver Spring). 2014 Dec;22(12):2544-51. doi: 10.1002/oby.20882. Epub 2014 Aug 25.

  PMID: 25155745 BACKGROUND

• Kuczmarski RJ, Ogden CL, Grummer-Strawn LM, Flegal KM, Guo SS, Wei R, Mei Z, Curtin LR, Roche AF, Johnson CL. CDC growth charts: United States. Adv Data. 2000 Jun 8;(314):1-27.

  PMID: 11183293 BACKGROUND

• Joyner MA, Gearhardt AN, Flagel SB. A Translational Model to Assess Sign-Tracking and Goal-Tracking Behavior in Children. Neuropsychopharmacology. 2018 Jan;43(1):228-229. doi: 10.1038/npp.2017.196. No abstract available.

  PMID: 29192653 BACKGROUND

• Stice E, Yokum S, Rohde P, Cloud K, Desjardins CD. Comparing healthy adolescent females with and without parental history of eating pathology on neural responsivity to food and thin models and other potential risk factors. J Abnorm Psychol. 2021 Aug;130(6):608-619. doi: 10.1037/abn0000686.

  PMID: 34553956 BACKGROUND

• O'Doherty JP, Buchanan TW, Seymour B, Dolan RJ. Predictive neural coding of reward preference involves dissociable responses in human ventral midbrain and ventral striatum. Neuron. 2006 Jan 5;49(1):157-66. doi: 10.1016/j.neuron.2005.11.014.

  PMID: 16387647 BACKGROUND

Study Officials

• Eric Stice, PhD

  Stanford University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Eric Stice, PhD

CONTACT

541-222-0615; estice@stanford.edu

Teena Ambrose, BS

CONTACT

310-658-6193; tambrose@stanford.edu

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor of Psychiatry and Behavioral Sciences

Study Record Dates

• First Submitted: December 1, 2023
• First Posted: December 12, 2023
• Study Start: October 4, 2024
• Primary Completion (Estimated): February 1, 2029
• Study Completion (Estimated): February 28, 2029
• Last Updated: April 24, 2026

Record last verified: 2026-04

Locations

US (1)