Wei Li1
Qinghua He1,2*- 1Ministry of Education (MOE) Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, China
- 2Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, China
Editorial on the Research Topic
Community series in overeating and decision making vulnerabilities
This Research Topic is the second volume of the “Overeating and decision making vulnerabilities” (He et al., 2019) (http://www.frontiersin.org/research-topics/4946/). We aimed to organize this Research Topic to gather new findings from various perspectives to deal with overeating and decision making vulnerabilities.
The prevalence of overweight and obesity has been growing globally. According to WHO, the adult population with obesity had exceeded those with normal weight in the world (World Health Organization, 2020). The COVID-19 and associated lockdown changed the way we live and communicate (Xiao et al., 2022), which may also promote obesity and associated metabolic diseases (Clemmensen et al., 2020). Anyhow, overweight and obesity was one of the top five factors affecting the disease risk (Kelley and Berridge, 2002; Rolls, 2007; Trinko et al., 2007; Volkow et al., 2008; Chen et al., 2018), which can impose a heavy burden the health system (He et al., 2014a,b, 2015; Gao et al., 2020; Liu et al., 2022).
In this series, we have collected five original articles (Duszka et al.; Guerrini Usubini et al.; Han et al.; Isham et al.; Kardas et al.) dealing with different aspects of food choice and obesity in community, such as food preference (Kardas et al.), portion estimation and choice (Duszka et al.), time perception and food comsumption (Isham et al.), cognitive inflexibility (Guerrini Usubini et al.), as well as brain and impulsivity (Han et al.).
Kardas et al. investigated the preferences and consumption of yogurt in terms of sensitivity to recognize sweetness and obesity in 160 children aged 7–9. They didn't find significant correlation between body weight and the frequency of fermented milk product consumption. However, they found that children who were normal- or under-weight was better recognize sweetness, and they preferred more plain unsweetened yogurt. They also call future studies to consider the influence of other food types and sensory attributes to shape food preferences in children (Kardas et al.).
Isham et al. surveyed 137 volunteers about their eating behavior and time perception (i.e., time estimation and passage of time judgement) during COVID-19 lockdown. Their results found that participants who judged time to pass slowly would tend to snack and overeat during lockdown. They discussed their findings though implications for eating disorders and potential time-based behavioral intervention strategies (Isham et al.).
Duszka et al. used two studies (N = 725 and N = 436, respectively) to understand the impacting factors of portion size preferences. In the first study, participants were asked to estimate the weight of the pictured foods, in which they found modest impacts of gender, BMI, age, and hunger on the estimation. In the second study, they asked the participants to do a portion choice task, and they found that males generally choose larger ones. Gender, external- and restrictive-eating behavior, age, hunger, but not BMI could also influence the portion size selection (Duszka et al.).
Guerrini Usubini et al. investigated and confirmed the mediation role of cognitive (in)flexibility in the relationship between anxiety and depression and emotional eating in 123 individuals with obesity. They reported the indirect mediated effect accounted for 19 and 12% of the variance in different models. Their study may help to guide new behavioral intervention method designed for obesity treatment (Guerrini Usubini et al.).
Han et al. investigated the impulsivity and resting state fMRI in 35 individuals with obesity and 31 controls. Direct comparison showed that individuals with obesity had a lower fractional amplitude of low-frequency fluctuations (fALFF) in the bilateral dorsolateral prefrontal cortex (dlPFC) than controls. They also found that the right dlPFC completely mediated the relationship between non-planning impulsiveness and BMI. Their results may provide a target for brain-based stimulation treating obesity (Han et al.).
All five articles covered in this Research Topic are important to understand the mechanism of overeating and decision making vulnerabilities from different perspectives. We hope they could be translated into effective ways to treat overweight and obesity as well as to guide a better life style.
Author contributions
WL wrote the first draft. QH made critical revision. WL and QH approved the final version of the manuscript.
Funding
QH was supported by research grants from the National Natural Science Foundation of China (31972906), Natural Science Foundation of Chongqing (cstc2020jcyj-msxmX0215), Fundamental Research Funds for the Central Universities (SWU2209235), the Innovation Research 2035 Pilot Plan of Southwest University (SWUPilotPlan006), and the High-end Foreign Expert Introduction Program (G2022168001L).
Acknowledgments
We would like to thank all authors and editors who contributed to this Research Topic.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
Chen, R., Li, D. P., Turel, O., Sørensen, T. A., Bechara, A., Li, Y., et al. (2018). Decision making deficits in relation to food cues influence obesity: a triadic neural model of problematic eating [hypothesis and theory]. Front. Psychiatry 9, 264. doi: 10.3389/fpsyt.2018.00264
Clemmensen, C., Petersen, M. B., and Sorensen, T. I. A. (2020). Will the COVID-19 pandemic worsen the obesity epidemic? Nat. Rev. Endocrinol. 16, 469–470. doi: 10.1038/s41574-020-0387-z
Gao, M., Lv, J., Yu, C., Guo, Y., Bian, Z., Yang, R., et al. (2020). Metabolically healthy obesity, transition to unhealthy metabolic status, and vascular disease in Chinese adults: a cohort study. PLoS Med. 17, e1003351. doi: 10.1371/journal.pmed.1003351
He, Q., Chen, C., Dong, Q., Xue, G., Chen, C., Lu, Z., et al. (2015). Gray and white matter structures in the midcingulate cortex region contribute to body mass index in chinese young adults. Brain Struct. Funct. 220, 319–329. doi: 10.1007/s00429-013-0657-9
He, Q., Gao, X., Li, Y., and Chen, H. (2019). Editorial: overeating and decision making vulnerabilities. Front. Psychol. 10, 587. doi: 10.3389/fpsyg.2019.00587
He, Q., Xiao, L., Xue, G., wong, s., Ames, S. L., and Bechara, A. (2014a). Altered dynamics between neural systems sub-serving decisions for unhealthy food [original research]. Front. Neurosci. 8, 350. doi: 10.3389/fnins.2014.00350
He, Q., Xiao, L., Xue, G., Wong, S., Ames, S. L., Schembre, S. M., et al. (2014b). Poor ability to resist tempting calorie rich food is linked to altered balance between neural systems involved in urge and self-control. Nutr. J. 13, 92. doi: 10.1186/1475-2891-13-92
Kelley, A. E., and Berridge, K. C. (2002). The neuroscience of natural rewards: relevance to addictive drugs. J. Neurosc. 22, 3306–3311. doi: 10.1523/JNEUROSCI.22-09-03306.2002
Liu, X., Turel, O., Xiao, Z., He, J., and He, Q. (2022). Impulsivity and neural mechanisms that mediate preference for immediate food rewards in people with vs without excess weight. Appetite 169, 105798. doi: 10.1016/j.appet.2021.105798
Rolls, E. (2007). Understanding the mechanisms of food intake and obesity. Obes. Rev. 8, 67–72. doi: 10.1111/j.1467-789X.2007.00321.x
Trinko, R., Sears, R. M., Guarnieri, D. J., and DiLeone, R. J. (2007). Neural mechanisms underlying obesity and drug addiction. Physiol. Behav. 91, 499–505. doi: 10.1016/j.physbeh.2007.01.001
Volkow, N. D., Wang, G. J., Fowler, J. S., and Telang, F. (2008). Overlapping neuronal circuits in addiction and obesity: evidence of systems pathology. Philos. Trans. R. Soc. B Biol. Sci. 363, 3191–3200. doi: 10.1098/rstb.2008.0107
World Health Organization. (2020). Overweight and Obesity. Available online at: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight
Keywords: eating, decision making, obesity, overweight, fMRI
Citation: Li W and He Q (2023) Editorial: Community series in overeating and decision making vulnerabilities. Front. Psychol. 13:1101004. doi: 10.3389/fpsyg.2022.1101004
Received: 17 November 2022; Accepted: 15 December 2022;
Published: 20 January 2023.
Edited and reviewed by: Xavier Noel, Université Libre de Bruxelles, Belgium
Copyright © 2023 Li and He. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Qinghua He, 
heqinghua@gmail.com