Neural correlates of appetite in adolescents.

Appetitive characteristics are associated with child adiposity, but their biological underpinnings are unclear. We sought to investigate the neural correlates of psychometric and behavioral measures of appetitive characteristics in youth. Adolescents (14-18y; 39F, 37M) varying in familial obesity risk and body weight (20% with overweight, 24% with obesity) viewed pictures of high energy-density (ED) foods, low-ED foods and non-foods during fMRI scanning on two separate days. On one day participants consumed a 474 ml preload of water (0 kcal, fasted) and on another (counter-balanced) 474 ml milkshake (480 kcal, fed), before scanning. A multi-item ad libitum meal (ALM) followed scanning. Parents completed Child Eating Behavior Questionnaire (CEBQ) sub-scales assessing food approach and food self-regulation. Caloric compensation was calculated as the percentage of preload intake compensated for by down-regulation of ALM intake in the fed vs. fasted condition. Analyses correcting for multiple comparisons demonstrated that, for the fasted condition, higher CEBQ Food Responsiveness scores were associated with greater activation to high-ED (vs. low-ED) foods in regions implicated in food reward (insula, rolandic operculum, putamen). In addition, higher caloric compensation was associated with greater fed vs. fasted activations in response to foods (vs. non-foods) in thalamus and supramarginal gyrus. Uncorrected analyses provided further support for associations of different measures of appetitive characteristics with brain responses to food cues in each condition. Measures of appetitive characteristics demonstrated overlapping and distinct associations with patterns of brain activation elicited by food cues in fasted and fed states. Understanding the neural basis of appetitive characteristics could aid development of biobehaviorally-informed obesity interventions.
Competing Interests: Declaration of competing interest The authors have no conflicts of interest to declare. This project was funded by NIH grants R00DK088360 with additional support for SC and team from R01DK113286 and UH3OD023313, and additional support for neuroimaging from P41 EB015909-19 to the Kirby Center for Functional Brain Imaging.
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