Samantha R Winter1, Sonja Yokum2, Eric Stice2, Karol Osipowicz3, Michael R Lowe3. 1. Department of Psychology, Drexel University, Philadelphia, PA; and srwinter21@gmail.com. 2. Oregon Research Institute, Eugene, OR. 3. Department of Psychology, Drexel University, Philadelphia, PA; and.
Abstract
Background: Both an elevated brain-reward-region response to palatable food and elevated weight variability have been shown to predict future weight gain.Objective: We examined whether the brain-reward response to food is related to future weight variability.Design: A total of 162 healthy-weight adolescents, who were aged 14-18 y at baseline, were enrolled in the study and were assessed annually over a 3-y follow-up period with 127 participants completing the final 3-y follow-up assessment. With the use of functional magnetic resonance imaging, we tested whether the neural responses to a cue that signaled an impending milkshake receipt and the receipt of the milkshake predicted weight variability over the follow-up period. Weight variability was modeled with a root mean squared error method to reflect fluctuations in weight independent of the net weight change. Results: Elevated activation in the medial prefrontal cortex and supplementary motor area, cingulate gyrus, cuneus and occipital gyrus, and insula in response to milkshake receipt predicted greater weight variability. Greater activation in the precuneus and middle temporal gyrus predicted lower weight variability.Conclusions: From our study data, we suggest that the elevated activation of reward and emotional-regulation brain regions (medial prefrontal cortex, cingulate cortex, and insula) and lower activation in self-reference regions (precuneus) in response to milkshake receipt predict weight variability over 3 y of follow-up. The fact that the reward response in the current study emerged in response to high-calorie palatable food receipt suggests that weight variability may be a measure of propensity periods of a positive energy balance and should be examined in addition to measures of the net weight change. With our collective results, we suggest that weight variability and its brain correlates should be added to other variables that are predictive of weight gain to inform the design of obesity-preventive programs in adolescents. This trial was registered at clinicaltrials.gov as NCT01807572.
Background: Both an elevated brain-reward-region response to palatable food and elevated weight variability have been shown to predict future weight gain.Objective: We examined whether the brain-reward response to food is related to future weight variability.Design: A total of 162 healthy-weight adolescents, who were aged 14-18 y at baseline, were enrolled in the study and were assessed annually over a 3-y follow-up period with 127 participants completing the final 3-y follow-up assessment. With the use of functional magnetic resonance imaging, we tested whether the neural responses to a cue that signaled an impending milkshake receipt and the receipt of the milkshake predicted weight variability over the follow-up period. Weight variability was modeled with a root mean squared error method to reflect fluctuations in weight independent of the net weight change. Results: Elevated activation in the medial prefrontal cortex and supplementary motor area, cingulate gyrus, cuneus and occipital gyrus, and insula in response to milkshake receipt predicted greater weight variability. Greater activation in the precuneus and middle temporal gyrus predicted lower weight variability.Conclusions: From our study data, we suggest that the elevated activation of reward and emotional-regulation brain regions (medial prefrontal cortex, cingulate cortex, and insula) and lower activation in self-reference regions (precuneus) in response to milkshake receipt predict weight variability over 3 y of follow-up. The fact that the reward response in the current study emerged in response to high-calorie palatable food receipt suggests that weight variability may be a measure of propensity periods of a positive energy balance and should be examined in addition to measures of the net weight change. With our collective results, we suggest that weight variability and its brain correlates should be added to other variables that are predictive of weight gain to inform the design of obesity-preventive programs in adolescents. This trial was registered at clinicaltrials.gov as NCT01807572.
Authors: Nils B Kroemer; Lena Krebs; Andrea Kobiella; Oliver Grimm; Sabine Vollstädt-Klein; Uta Wolfensteller; Ricarda Kling; Martin Bidlingmaier; Ulrich S Zimmermann; Michael N Smolka Journal: Hum Brain Mapp Date: 2012-03-28 Impact factor: 5.038
Authors: Sonja Yokum; Ashley N Gearhardt; Jennifer L Harris; Kelly D Brownell; Eric Stice Journal: Obesity (Silver Spring) Date: 2014-08-25 Impact factor: 5.002
Authors: Jetro J Tuulari; Henry K Karlsson; Jussi Hirvonen; Paulina Salminen; Pirjo Nuutila; Lauri Nummenmaa Journal: PLoS One Date: 2015-02-06 Impact factor: 3.240
Authors: Elena Andreeva; Maria Neumann; Mariel Nöhre; Elmar Brähler; Anja Hilbert; Martina de Zwaan Journal: Obes Facts Date: 2019-07-02 Impact factor: 3.942
Authors: Paul A M Smeets; Alain Dagher; Todd A Hare; Stephanie Kullmann; Laura N van der Laan; Russell A Poldrack; Hubert Preissl; Dana Small; Eric Stice; Maria G Veldhuizen Journal: Am J Clin Nutr Date: 2019-03-01 Impact factor: 7.045
Authors: Malgorzata Maciukiewicz; Ilona Gorbovskaya; Arun K Tiwari; Clement C Zai; Natalie Freeman; Herbert Y Meltzer; James L Kennedy; Daniel J Müller Journal: J Neural Transm (Vienna) Date: 2018-09-18 Impact factor: 3.575
Authors: Diego Gómez-Carmona; Francisco Muñoz-Leiva; Alberto Paramio; Francisco Liébana-Cabanillas; Serafín Cruces-Montes Journal: Foods Date: 2021-04-22