Obesity, Childhood
Conditions
Keywords
Obesity, food-cue reactivity, functional magnetic resonance imaging, eating behaviors, food marketing, food advertisement
Brief summary
Strong empirical evidence shows food marketing promotes excess energy intake and obesity. Yet, not all children are susceptible to its effects and this variability is poorly understood. Identifying sources of this variability is a public health priority not only because it may elucidate characteristics of children who are most susceptible, but also because it may highlight novel sources of resiliency to overconsumption. The proposed research will use state-of-the art, data-driven approaches to identify neural, cognitive and behavioral phenotypes associated with resiliency to food-cue (i.e. food advertisement) induced overeating and determine whether these phenotypes protect children from weight gain during the critical pre-adolescent period.
Detailed description
The investigator's central hypothesis is that children who are resistant to food-cue induced overeating will exhibit a distinct cluster of neural, behavioral, and cognitive traits that protect them from weight gain, even in the context of high-familial obesity risk. Identifying these traits is critical to the development of successful, individually tailored obesity prevention programs. This hypothesis is informed by compelling preliminary research showing that children who are less susceptible to food-cue induced eating in the laboratory show decreased neural activation in somatosensory (i.e., post-central gyrus) and reward (i.e., striatum) regions and increased activation in cognitive control regions (i.e., dorsolateral prefrontal cortex-dlPFC) following food commercial exposure. These results provide a strong foundation for characterizing neural responses that are associated with resiliency to food-cue induced eating, but highlight major gaps in the literature that must be addressed to advance the field. This proposal will make three novel contributions to the understanding of the etiology of obesity. Using a prospective, family-risk design, the investigators will follow 100, 7-9 year-old children who have healthy weight but vary by risk for obesity (based on maternal weight status) for 1 year to characterize neural and behavioral responses to food commercials and identify common neural networks associated with resiliency to food-cue induced overconsumption. Second, the investigators will use sophisticated behavioral coding to characterize children's eating following food commercial exposure at both homeostatic (i.e., meal consumed when hungry) and non-homeostatic (i.e., eating in the absence of hunger - EAH snack buffet) events and relate individual differences in eating behavior to neural phenotypes. Finally, the investigators will follow children over 1 year to determine whether the neural and behavioral responses at baseline are protective against adiposity gains, during a critical period where children are cognizant of the purpose of advertising, but cannot fully defend against its effects. Baseline data will be collected over 4 weekly initial visits, followed by a 5th visit one year later.
Interventions
None listed
Sponsors
Penn State University
University of Kansas Medical Center
University of Missouri, Kansas City
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Study design
Observational model
CASE_CROSSOVER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
7 Years to 9 Years
Healthy volunteers
Yes
Inclusion criteria
Child Inclusion Criteria: 1. In order to be enrolled, children must be of good health based on parental self-report. 2. Have no learning disabilities (e.g., ADHD). 3. Have no allergies to the foods or ingredients used in the study. 4. Not be claustrophobic. 5. Not be taking any medications known to influence body weight, taste, food intake, behavior, or blood flow. 6. Be 7-9 years-old at enrollment. 7. The child must speak English. Parent Inclusion Criteria: 1. The parent who has the most knowledge of the child's eating behavior, media access, sleep and behavior must be available to attend the visits with their child. This would be decided among the parents. 2. The biological mother must have a body mass index either between 18.5 - 25 kg/m2 (low-risk group) or greater than or equal to 30 kg/m2 (high-risk group). One parent can report on both parents' BW and height.
Exclusion criteria
Children would be excluded if: 1. They are not within the age requirements (\< than 7 years old or \> than 9 years-old at baseline). 2. If they are taking cold or allergy medication, or other medications known to influence cognitive function, taste, appetite, or blood flow. 3. If they don't speak English. 4. If they are colorblind 5. If they report being claustrophobic. 6. if they have a learning disability, ADD/ADHD, language delays, autism or other neurological or psychological conditions. 7. if they have a pre-existing medical condition such as type I or type II diabetes, rheumatoid arthritis, Cushing's syndrome, Down's syndrome, food allergies, severe lactose intolerance, Prader-Willi syndrome, HIV, cancer, renal failure, or cerebral palsy. 8. if they are allergic to foods or ingredients used in the study. 9. if they have tattoos, permanent makeup, dental ware, pacemakers, or metal implants that would preclude safe completion of the MRI. 10. if the child has had an X-ray in the month prior to Visits 1 and 6. If so, they will be scheduled at a later date. Parent
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| fMRI blood oxygen level dependent (BOLD) response to food commercials | baseline | Whole-brain response to food commercials, followed by images of high and low energy density foods in a fMRI scan |
| fMRI blood oxygen level dependent (BOLD) response to toy commercials | baseline | Whole-brain response to toy commercials, followed by images of high and low energy density foods in a fMRI scan |
| Food intake in grams after no commercial viewing | baseline | Intake in grams from an eating in the absence of hunger paradigm consumed following advertisement exposure when children are not hungry (i.e., non-homeostatic intake) |
| Food intake in kcals after no commercial viewing | baseline | Intake in kcals from an eating in the absence of hunger paradigm consumed following advertisement exposure when children are not hungry (i.e., non-homeostatic intake) |
| Food intake in grams after viewing food commercials | baseline | Intake in grams from an eating in the absence of hunger paradigm consumed following advertisement exposure when children are not hungry (i.e., non-homeostatic intake) |
| Food intake in kcals after viewing food commercials | baseline | Intake in kcals from an eating in the absence of hunger paradigm consumed following advertisement exposure when children are not hungry (i.e., non-homeostatic intake) |
| Food intake in grams after food commercial viewing | 1 year | Intake in grams from an eating in the absence of hunger paradigm consumed following advertisement exposure when children are not hungry (i.e., non-homeostatic intake) |
| Food intake in kcals after food commercial viewing | 1 year | Intake in kcals from an eating in the absence of hunger paradigm consumed following advertisement exposure when children are not hungry (i.e., non-homeostatic intake) |
| Food intake in grams after viewing toy commercials | baseline | Intake in grams from an eating in the absence of hunger paradigm consumed following advertisement exposure when children are not hungry (i.e., non-homeostatic intake) |
| Food intake in kcals after viewing toy commercials | baseline | Intake in kcals from an eating in the absence of hunger paradigm consumed following advertisement exposure when children are not hungry (i.e., non-homeostatic intake) |
| Video recording of meal and EAH snack buffet | baseline | A digital recording of the Child eating the Test Meal and the EAH snack buffet will be saved. We have developed a behavior coding technique to count specific behaviors while the child eats. We will also be transcribing the audio. |
| Change from baseline DXA analysis for total body fat mass of child at 1 year | baseline | Examine change in children's fat mass index = total fat mass(kg) / height (m2) |
| fMRI Region of Interest (ROI) response to toy commercials and subsequent views of high and low energy density food pictures. | baseline | We will examine the strength of the neural connections between reward/somatosensory and cognitive control regions. |
| fMRI Region of Interest (ROI) response to food commercials and subsequent views of high and low energy density food pictures. | baseline | We will examine the strength of the neural connections between reward/somatosensory and cognitive control regions. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Change in Android fat mass as measured by DXA analysis | Baseline and 1 year | Android fat mass (%) = (Android fat mass (kg) / total fat mass (kg))\*100 |
| Change in Gynoid fat mass as measured by DXA analysis | Baseline and 1 year | Gynoid fat mass (%) = (Gynoid fat mass (kg) / total fat mass (kg))\*100 |
| Child screen time | Baseline and 1 year | Child questionnaire quantifying the number of hours per day a child is exposed to different types of media. |
| Child's brand awareness | Baseline and 1 year | Child's brand awareness assessed by a child computerized task. Children are asked to match pictures of products and brand logos. No scaling is used. |
| Change in scores from the NIH Toolbox: Child Cognitive battery-Flanker test | Baseline and 1 year | The Flanker is a measure of executive function, specifically tapping inhibitory control and attention. Scoring is based on a combination of accuracy and reaction time. A 2-vector scoring method is employed that uses accuracy and reaction time, where each of these "vectors" ranges in value between 0 and 5, and the computed score, combining each vector score, ranges in value from 0-10. For any given individual, accuracy is considered first. If accuracy levels for the participant are less than or equal to 80%, the final "total" computed score is equal to the accuracy score. If accuracy levels for the participant reach more than 80%, the reaction time score and accuracy score are combined. Higher scores indicate higher levels of ability to attend to relevant stimuli and inhibit attention from irrelevant stimuli. |
| Change in scores from the NIH Toolbox: Child Cognitive battery-List Sorting Working memory test | Baseline and 1 year | The List Sorting Working memory test assesses working memory. The List Sorting test requires immediate recall and sequencing of different visually and orally presented stimuli (i.e., "working memory"). Pictures of different foods and animals are displayed with accompanying audio recording and written text (e.g., "elephant"), and the participant is asked to say the items back in size order from smallest to largest, first within a single dimension (either animals or foods, called 1-List) and then on two dimensions (foods, then animals, called 2-List). The test takes approximately seven minutes to administer. List Sorting is scored by summing the total number of items correctly recalled and sequenced on 1-List and 2-List, which can range from 0-26.Higher scores on each of these indicate higher levels of working memory within the normative standard being applied. |
| Change in scores from the NIH Toolbox: Child Cognitive battery-Dimensional Change Card Sort Test (DCCS) | Baseline and 1 year | The Dimensional Change Card Sort Test is used to measure cognitive flexibility. Two target pictures are presented that vary along two dimensions (e.g., shape and color). Scoring is based on a combination of accuracy and reaction time. A 2-vector scoring method is employed that uses accuracy and reaction time, scores ranging from 0-10. For any given individual, accuracy is considered first. If accuracy levels for the participant are less than or equal to 80%, the final "total" computed score is equal to the accuracy score. If accuracy levels for the participant reach more than 80%, the reaction time score and accuracy score are combined. Higher scores indicate higher levels of cognitive flexibility. |
Countries
United States
Contacts
PRINCIPAL_INVESTIGATORKathleen L Keller, Ph.D.
Penn State University
Outcome results
None listed