||5R01CA211224-03 Interpret this number
||University Of Oregon
||Devaluing Energy-Dense Foods for Cancer Control: Translational Neuroscience
Obesity and intake of certain foods increases cancer risk, but the most common treatment (behavioral weight
loss programs) rarely produces lasting weight loss and eating behavior change, apparently because caloric
restriction increases the reward value of food and prompts energy-sparing adaptations. Interventions that
reduce the implicit valuation of cancer-risk foods (e.g., red meats, refined sugar) may be more effective.
Emerging data suggest that behavioral response training and cognitive reappraisal training reduce valuation of
such foods, which leads to decrease intake of these foods and weight loss. Internalized incentive value is
reflected in a ventromedial prefrontal/orbitofrontal cortex valuation system (“vmPFC”), which encodes the
implicit reward value of food and is central to a reinforcement cycle that perpetuates unhealthy eating. Thus,
the vmPFC valuation system is a promising target for intervention because changes to the system might
disrupt the unhealthy reinforcement cycle. Interestingly, various interventions influence the vmPFC through
distinct pathways. Behavioral training alters motor input to valuation regions, whereas cognitive training relies
on lateral prefrontal “top-down” regions. The proposed translational neuroscience experiment will compare the
efficacy with which two novel treatments cause lasting change in food valuation, and whether a composite of
theory-based baseline individual differences in relevant processes (such as response tendencies and cognitive
styles) moderate treatment effects. We will randomize 300 overweight/obese adults who are at risk for eating-
and obesity-related cancers to behavioral response training toward healthy foods and away from cancer-risk
foods, a cognitive reappraisal intervention focused on cancer-risk foods, or non–food inhibitory control training.
Aim 1 compares the efficacy and mechanisms of action of these two interventions to reduce valuation of
cancer-risk foods relative to the active control condition, using neural, behavioral, self-report, and physiological
measures of the process and outcomes. Aim 2 is to establish the temporal pattern and durability of the effects
across time ; food intake and habits , body fat, BMI, and waist-to-hip ratio will be measured pre, post, and at 3-,
6-, and 12-month follow-up. Aim 3 uses machine learning to build and validate a low-cost, easy-to-administer
composite that predicts whether and for how long an individual is likely to respond to intervention, and to which
treatment. We hypothesize that self-report measures specifically related to valuation (e.g., willingness-to-pay)
and to intervention-specific pathways to valuation (e.g., behavioral response tendencies, cognitive style) will
predict differential response. Discovering these individual differences will provide a practical, low-cost tool to
help interventionists “match” a given person to an effective treatment for that person . This project is very
innovative because no study has directly compared the distinct and common effects of these treatments on
valuation, used brain imaging to study the mechanism of effects, tested whether these interventions produce a
lasting change in food valuation and body fat, or built and validated a composite that moderates response .
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