Ultra-processed food reward: neural and metabolic factors
- Institution
- VIRGINIA POLYTECHNIC INST AND ST UNIV, VA
- Principal investigator
- Alexandra Gold Difeliceantonio
- Funding
- $416K
- Duration
- 2023–2028
Moonbase score
Still being scored
Abstract
PROJECT SUMMARY The food environment has changed dramatically and rapidly in the last 50 years. A glance around any grocery store will demonstrate that ultra-processed foods, defined as industrial formulations of sources of energy, nutrients, and additives have come to dominate the food supply. In fact, ultra-processed foods now make up around 58% of total calories consumed in the US. Ultra-processed food consumption has been linked with various forms of cancer, increased cardiovascular disease, and increased all-cause mortality. Therefore, basic understanding of the mechanisms that drive consumption of ultra-processed foods must keep pace with their increasing availability and expanding percentage of the US food supply. The long-term goals of this research are to identify specific properties of ultra-processed foods that support their consumption, the physiological and neurobiological mechanisms they exploit to affect activity in brain reward systems, and the individual factors that drive susceptibility to overconsumption so they can be later targeted for intervention. Our central hypothesis is that ultra-processed foods are enriched in rapidly useable calories, which enhances reward related brain activity to those specific foods (and associated cues and flavors) and renders them more reinforcing. To this end, in Aim 1, we will parametrically vary the speed of nutrient availability and assess brain response to calorie predictive flavors and measure the rewarding properties of those flavors. In Aim 2, by varying the level of processing in foods, while controlling for caloric density and macronutrient profiles, we will directly assess the specific role of food processing on the time course of the metabolic response to consumption and its relation to brain activity and food value. We will use data collected in both aims to create predictive models that will allow us to pinpoint key factors, both within the individual and within the food, that drive food reward. To our knowledge, this proposal is one of the few to assess processing as a key factor, while controlling for other confounding variables such as energy density and macronutrient content. By combining this with an innovative analysis approach we will have a unique opportunity for generating a data driven assessment of key factors in ultra-processed food reward. The data generated in support of this proposal have high potential significance in that they could provide key evidence needed for changing dietary guidelines with the ultimate goal of reducing diet-related mortality and disease.
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