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Recruiting NCT06165952

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

Observational Ultra-processed Foods (UP)

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
This is an observational study: the protocol does not assign a study treatment.
Who it may be relevant to
Registry conditions: Ultra-processed Foods (UP). Basic parameters: 13 years — 15 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
United States
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

Overview

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.

Detailed description

Obesity is the second leading cause of premature death. Consumption of ultra-processed foods is theorized to be a key cause of obesity. Ultra-processed foods are formulations of cheap industrial sources of dietary energy and nutrients plus additives such as fat, sugar, and flavors that enhance acceptability of the foods.

A cross-over experiment with overweight adults found that ad lib access to an ultra-processed diet for 2-weeks resulted in increased caloric intake (508 kcal/day) and more weight gain versus ad lib access to a minimally-processed diet matched for presented calories, energy density, macronutrients, sugar, sodium, and fiber. The fact that ad lib access to ultra-processed foods resulted in a large increase in caloric intake and weight gain implies that ultra-processed foods may more effectively activate brain regions implicated in reward processing, attention/salience, and memory that influence eating behavior.

However, no brain imaging study has experimentally tested whether ultra-processed foods are more effective in activating brain regions implicated in reward, attention, and memory than minimally-processed foods or experimentally investigated the relative role of the elevated caloric density versus the flavor enhancers of ultra-processed foods in driving greater activation of these brain regions. Preliminary data showed that tastes of ultra-processed high-calorie chocolate milkshake produced greater activation in regions implicated in reward valuation (caudate, nucleus accumbens), attention/salience (precuneus), and memory retrieval (medial temporal gyrus, dorsomedial prefrontal cortex) than tastes of ultra-processed low-calorie chocolate milkshake.

The investigators propose to evaluate the efficacy of ultra-processed foods to activate reward, attention, and memory regions compared to minimally-processed foods, investigate the relative role of the higher caloric content versus the flavor additives/enhancers of ultra-processed foods to engage this circuitry using a 2 x 2 experimental design, test whether ultra-processed foods are more effective in increasing the incentive salience of food cues than minimally-processed foods, which is important because elevated reward region response to food cues/images increases risk for excess weight gain, and test whether individuals who show the greatest responsivity of reward, attention, and memory regions to ultra-processed foods and stronger food reward cue learning are at risk for greater ad lib intake of ultra-processed foods and future body fat gain.

Aim 1: Test the hypothesis that tastes, anticipated tastes, and images of ultra-processed foods activate reward, attention, and memory brain regions more than tastes, anticipated tastes, and images of minimally-processed foods, and evaluate the relative role of the higher caloric content versus flavor additives/enhancers in activating these regions using a 2 x 2 experimental design.

Aim 2: Test the hypothesis that ultra-processed foods foster stronger learning of cues that predict impending tastes of ultra-processed foods than minimally-processed foods, reflected by greater increases in striatal response over the course of cue exposure and quicker responses to cues for tastes of ultra-processed foods.

Aim 3: Test the hypothesis that participants who show greater activation in reward/attention/memory regions in response to tastes, anticipated tastes, and images of ultra-processed foods will consume more ultra-processed foods ad libitum (Aim 3a) and show greater future body fat gain (Aim 3b). Exploratory analyses will establish neural fingerprints that predict ad lib intake of ultra-processed foods and body fat gain (Aim 3c).

Aim 4: Test the hypothesis that participants who show the most pronounced reward cue learning in response to ultra-processed foods will consume more ultra-processed foods ad libitum (Aim 4a) and show greater future body fat gain (Aim 4b).

Primary outcome measures

  • Body Fat [Time frame: 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 [Time frame: Baseline]
  • Baseline fMRI Ultra-Processed Food Picture Paradigm [Time frame: Baseline]
  • Baseline fMRI Ultra-Processed Food Cue Learning Paradigm [Time frame: Baseline]
  • Baseline Post-fMRI Ad Lib Food Intake [Time frame: Baseline]

Eligibility criteria

Inclusion criteria

  • 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

Exclusion criteria

  • 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)

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Study design

Observational model
Case-only

Study locations

United States · 1 center
  • Stanford University — Stanford

Publications

  • 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. Cel PMID 31105044
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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

Identifiers

NCT: NCT06165952 · 71541

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗