Shift of Circadian Feeding Pattern by High-Fat Diets Is Coincident with Reward Deficits in Obese Mice


Recent studies provide evidence that high-fat diets (HF) trigger both i) a deficit of reward responses linked to a decrease of mesolimbic dopaminergic activity, and ii) a disorganization of circadian feeding behavior that switch from a structured meal-based schedule to a continuous snacking, even during periods normally devoted to rest. This feeding pattern has been shown to be a cause of HF-induced overweight and obesity. Our hypothesis deals with the eventual link between the rewarding properties of food and the circadian distribution of meals. We have investigated the effect of circadian feeding pattern on reward circuits by means of the conditioned-place preference (CPP) paradigm and we have characterized the rewarding properties of natural (food) and artificial (cocaine) reinforcers both in free-feeding ad libitum HF mice and in HF animals submitted to a re-organized feeding schedule based on the standard feeding behavior displayed by mice feeding normal chow ("forced synchronization"). We demonstrate that i) ad libitum HF diet attenuates cocaine and food reward in the CPP protocol, and ii) forced synchronization of feeding prevents this reward deficit. Our study provides further evidence that the rewarding impact of food with low palatability is diminished in mice exposed to a high-fat diet and strongly suggest that the decreased sensitivity to chow as a positive reinforcer triggers a disorganized feeding pattern which might account for metabolic disorders leading to obesity.

DOI: 10.1371/journal.pone.0036139

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@inproceedings{Morales2012ShiftOC, title={Shift of Circadian Feeding Pattern by High-Fat Diets Is Coincident with Reward Deficits in Obese Mice}, author={Lidia Morales and Nuria Del Olmo and Ismael Valladolid-Acebes and Alberto Fole and Victoria Cano and Beatriz Merino and Paula Stucchi and Daniela Ruggieri and Laura L{\'o}pez and Luis Fernando Alguacil and Mariano Ruiz-Gayo}, booktitle={PloS one}, year={2012} }