The Impact of the Physiological Response to Sugar on Brain Activity and Behavior

The goal of this pilot study is to test the feasibility of assessing how biological factors and chemical properties of sugars may influence metabolism and food reward in humans. The main questions it aims to answer are:

* Can differences in appetitive responses and neural activations to sucrose (table sugar) and its chemical components (glucose and fructose) be measured and quantified?
* Are there detectable differences in how combinations of sugars and non-nutritive sweeteners commonly found in our food supply influence appetitive responses and neural activation?

This study is a crossover design, meaning every participant will complete every condition. Participants will consume beverages containing sucrose, glucose, or fructose, which are each novelly flavored, 6 times within a week. During one of the consumption times, energy expenditure, carbohydrate oxidation, and blood glucose will be measured in the lab before and for 2 hours after consumption. After participants have consumed each condition, they will undergo a tasting task in the MRI scanner, neural responses to receipt of the beverages are measured. Another group of participants will undergo the same study design but with sucrose, high fructose corn syrup, or sucrose + non-nutritive sweetener as the conditions.

Prior studies in humans indicate that while energy expenditure response is similar after consumption of equal amounts of fructose, glucose, and sucrose (a dimer of glucose + fructose), carbohydrate oxidation and blood glucose responses differ. Elevated carbohydrate oxidation responses appear to be driven by the presence of fructose, and elevated blood glucose responses appear to be driven by the presence of glucose. Prior work also suggests that post-ingestive signals of glucose availability, measure specifically as blood glucose levels, intestinal glucose transporter activity, and carbohydrate oxidation rate, are all associated with elevated brain response to calorie-predictive flavor cues and reward learning of these flavor cues. However, in animal models, glucose has been shown to repeatedly and reliably condition these calorie-predictive learning responses, but fructose does not. Human work has indicated that oxidation of glucose is critical for these responses. Thus, it is unclear what roles fructose and glucose each play in conditioning reward responses and flavor-calorie learning. We hypothesize that fructose plays a synergistic role in enhancing flavor-calorie learning without itself conditioning the reward response.

Eligibility Criteria:
Inclusion Criteria:

1. BMI between 18.5-25 kg/m2
2. Not pregnant or planning to become pregnant during study participation
3. Residing in the Roanoke area and/or willing/able to attend sessions at the Fralin Biomedical Research Institute (FBRI) at Virginia Tech Carilion
4. Weigh at least 110 lbs

Exclusion Criteria:

1. Current inhaled nicotine use
2. History of alcohol dependence.
3. Current or past diagnosis of cardiometabolic disease or problems, including diabetes, endocrine, heart, or thyroid problems, that may influence study outcomes
4. Hemoglobin A1C \>5.7%
5. Taking medications known to influence study measures (including attention-deficit/hyperactivity disorder (ADHD), allergy, antidepressant, antipsychotic, anxiolytic, birth control, blood pressure, blood glucose, cholesterol, thyroid, sleep, or weight loss medications)
6. Active medical or neurologic disorder, including cardiometabolic conditions or gastrointestinal conditions that may influence study outcomes
7. Recent change in body weight (gain or loss of \> 5 lbs within the past 3 months)
8. Current shift work (typical pattern of work/activity overnight)
9. Previous weight loss surgery
10. Adherence to a special diet within the past 3 months (e.g., low-carb or ketogenic diet, exclusion of food groups/specific macronutrients, intermittent fasting, etc.)
11. Allergy to any food or ingredient included in the study diets, meals, or beverages
12. Currently pregnant or planning to become pregnant during study participation
13. Claustrophobia
14. Contraindications for MRI, including pacemaker, aneurysm clips, neurostimulators, cochlear or other implants, metal in eyes, regular work with steel, etc. (Note: This is an fMRI-specific exclusion criterion. Participants may be allowed to participate in all other study sessions and measures that do not involve fMRI.)
15. Contraindications for bioelectrical impedance analysis, specifically implanted devices
16. Use of substances (or combinations of substances) in doses and frequencies that could influence neural outcomes of study.