Skip to main content

Overweight adolescents’ brain response to sweetened beverages mirrors addiction pathways

Abstract

Many adolescents struggle with overweight/obesity, which exponentially increases in the transition to adulthood. Overweight/obesity places youth at risk for serious health conditions, including type 2 diabetes. In adults, neural substrates implicated in addiction (e.g., orbitofrontal cortex (OFC), striatum, amygdala, and ventral tegmental area) have been found to be relevant to risk for overweight/obesity. In this study, we examined three hypotheses to disentangle the potential overlap between addiction and overweight/obesity processing by examining (1) brain response to high vs. low calorie beverages, (2) the strength of correspondence between biometrics, including body mass index (BMI) and insulin resistance, and brain response and (3) the relationship between a measure of food addiction and brain response using an established fMRI gustatory cue exposure task with a sample of overweight/obese youth (M age = 16.46; M BMI = 33.1). Greater BOLD response was observed across the OFC, inferior frontal gyrus (IFG), nucleus accumbens, right amygdala, and additional frontoparietal and temporal regions in neural processing of high vs. low calorie beverages. Further, BMI scores positively correlated with BOLD activation in the high calorie > low calorie contrast in the right postcentral gyrus and central operculum. Insulin resistance positively correlated with BOLD activation across the bilateral middle/superior temporal gyrus, left OFC, and superior parietal lobe. No relationships were observed between measures of food addiction and brain response. These findings support the activation of parallel addiction-related neural pathways in adolescents’ high calorie processing, while also suggesting the importance of refining conceptual and neurocognitive models to fit this developmental period.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

References

  • Batterink, L., Yokum, S., & Stice, E. (2010). Body mass correlates inversely with inhibitory control in response to food among adolescent girls: an fMRI study. NeuroImage, 52(4), 1696–1703. doi:10.1016/j.neuroimage.2010.05.059.

    Article  PubMed  PubMed Central  Google Scholar 

  • Bodo, M. J., Jimenez, E. Y., Conn, C., Dye, A., Pomo, P., Kolkmeyer, D., et al. (2015). Association between circulating CCL2 levels and modifiable behaviors in overweight and obese adolescents: a cross-sectional pilot study. Journal of Pediatric Endocrinology & Metabolism. doi:10.1515/jpem-2015-0260.

    Google Scholar 

  • Burger, K. S., & Stice, E. (2011). Variability in reward response and obesity: evidence from brain imaging studies. Current Drug Abuse Reviews, 4, 182–189.

    Article  PubMed  PubMed Central  Google Scholar 

  • Burger, K. S., & Stice, E. (2014). Neural responsivity during soft drink intake, anticipation, and advertisement exposure in habitually consuming youth. Obesity (Silver Spring), 22, 441–450.

    CAS  Article  Google Scholar 

  • Centers for Disease Control and Prevention. (2009). Percentile data files with LMS values. Retrieved from http://www.cdc.gov/growthcharts/percentile_data_files.htm

  • Chen, G., Tang, Z., Guo, G., Liu, X., & Xiao, S. (2015). The Chinese version of the Yale food addiction scale: an examination of its validation in a sample of female adolescents. Eating Behaviors, 18, 97–102. doi:10.1016/j.eatbeh.2015.05.002.

    Article  PubMed  Google Scholar 

  • Claus, E. D., Ewing, S. W., Filbey, F. M., Sabbineni, A., & Hutchison, K. E. (2011). Identifying neurobiological phenotypes associated with alcohol use disorder severity. Neuropsychopharmacology, 36(10), 2086–2096. doi:10.1038/npp.2011.99.

    Article  PubMed  PubMed Central  Google Scholar 

  • Claus, E. D., Feldstein Ewing, S. W., Filbey, F. M., & Hutchison, K. E. (2013). Behavioral control in alcohol use disorders: relationships with severity. Journal of Studies on Alcohol and Drugs, 74(1), 141–151. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3517257/pdf/jsad141.pdf

  • Cook, S., Auinger, P., Li, C., & Ford, E. S. (2008). Metabolic syndrome rates in United States adolescents, from the National Health and nutrition examination survey, 1999-2002. The Journal of Pediatrics, 152(2), 165–170. doi:10.1016/j.jpeds.2007.06.004.

    Article  PubMed  Google Scholar 

  • DeBoer, M. D., Gurka, M. J., Woo, J. G., & Morrison, J. A. (2015). Severity of the metabolic syndrome as a predictor of type 2 diabetes between childhood and adulthood: the Princeton lipid research cohort study. Diabetologia, 58(12), 2745–2752. doi:10.1007/s00125-015-3759-5.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Deichmann, R., Gottfried, J. A., Hutton, C., & Turner, R. (2003). Optimized EPI for fMRI studies of the orbitofrontal cortex. NeuroImage, 19, 430–441.

    CAS  Article  PubMed  Google Scholar 

  • Feldstein Ewing, S. W., Filbey, F. M., Chandler, L. D., & Hutchison, K. E. (2010). Exploring the relationship between depressive and anxiety symptoms and neuronal response to alcohol cues. Alcoholism, Clinical and Experimental Research, 34(3), 396–403. doi:10.1111/j.1530-0277.2009.01104.x.

    Article  PubMed  Google Scholar 

  • Feldstein Ewing, S. W., McEachern, A. D., Yezhuvath, U., Bryan, A. D., Hutchison, K. E., & Filbey, F. M. (2013a). Integrating brain and behavior: evaluating adolescents' response to a cannabis intervention. Psychology of Addictive Behaviors, 27, 510–525.

    Article  PubMed  Google Scholar 

  • Feldstein Ewing, S. W., McEachern, A. D., Yezhuvath, U., Bryan, A. D., Hutchison, K. E., & Filbey, F. M. (2013b). Integrating brain and behavior: evaluating adolescents' response to a cannabis intervention. Psychology of Addictive Behaviors, 27(2), 510–525. doi:10.1037/a0029767.

    Article  PubMed  Google Scholar 

  • Feldstein Ewing, S. W., Sakhardande, A., & Blakemore, S. J. (2014). The effect of alcohol consumption on the adolescent brain: a systematic review of MRI and fMRI studies of alcohol-using youth. NeuroImage: Clinical, 5, 420–437. doi:10.1016/j.nicl.2014.06.011.

    Article  Google Scholar 

  • Feldstein Ewing, S. W., Apodaca, T. R., & Gaume, J. (2016a). Ambivalence: prerequisite for success in motivational interviewing with adolescents? Addiction. doi:10.1111/add.13286.

    PubMed  PubMed Central  Google Scholar 

  • Feldstein Ewing, S. W., Ryman, S. G., Gillman, A. S., Weiland, B. J., Thayer, R. E., & Bryan, A. D. (2016b). Developmental cognitive neuroscience of adolescent sexual risk and alcohol use. AIDS and Behavior, 20(Suppl 1), 97–108. doi:10.1007/s10461-015-1155-2.

    Article  Google Scholar 

  • Feldstein Ewing, S. W., Tapert, S. F., & Molina, B. S. (2016c). Uniting adolescent neuroimaging and treatment research: recommendations in pursuit of improved integration. Neuroscience and Biobehavioral Reviews, 62, 109–114. doi:10.1016/j.neubiorev.2015.12.011.

    Article  PubMed  Google Scholar 

  • Filbey, F. M., & Dunlop, J. (2014). Differential reward network functional connectivity in cannabis dependent and non-dependent users. Drug and Alcohol Dependence, 140, 101–111. doi:10.1016/j.drugalcdep.2014.04.002.

    Article  PubMed  PubMed Central  Google Scholar 

  • Filbey, F. M., Claus, E., Audette, A. R., Niculescu, M., Banich, M. T., Tanabe, J., et al. (2008). Exposure to the taste of alcohol elicits activation of the mesocorticolimbic neurocircuitry. Neuropsychopharmacology, 33(6), 1391–1401. doi:10.1038/sj.npp.1301513.

    CAS  Article  PubMed  Google Scholar 

  • Filbey, F. M., Myers, U. S., & DeWitt, S. J. (2012). Reward circuit function in high BMI indivduals iwth compulsive overeating: similarities with addiction. NeuroImage, 63, 1800–1806.

    Article  PubMed  Google Scholar 

  • Filbey, F. M., Aslan, S., Calhoun, V. D., Spence, J. S., Damaraju, E., Caprihan, A., et al. (2014). Long-term effects of marijuana use on the brain. Proceedings of the National Academy of Sciences of the United States of America, 25, 16913–16918.

    Article  Google Scholar 

  • Gearhardt, A. N., Grilo, C. M., DiLeone, R. J., Brownell, K. D., & Potenza, M. N. (2011a). Can food be addictive? Public health and policy implications. Addiction, 106, 1208–1212.

    Article  PubMed  PubMed Central  Google Scholar 

  • Gearhardt, A. N., Yokum, A. N., Orr, P. T., Stice, E., Corbin, W. R., & Brownell, K. D. (2011b). Neural correlates of food addiction. Archives of General Psychiatry, 68, 808–816.

    Article  PubMed  PubMed Central  Google Scholar 

  • Gearhardt, A. N., Roberto, C. A., Seamans, M. J., Corbin, W. R., & Brownell, K. D. (2013). Preliminary validation of the Yale food addiction scale for children. Eating Behaviors, 14(4), 508–512. doi:10.1016/j.eatbeh.2013.07.002.

    Article  PubMed  Google Scholar 

  • Goldstein, R. Z., & Volkow, N. D. (2002). Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex. The American Journal of Psychiatry, 159(10), 1642–1652. doi:10.1176/appi.ajp.159.10.1642.

    Article  PubMed  PubMed Central  Google Scholar 

  • Harris, K. M., Gordon-Larsen, P., Chantala, K., & Udry, J. R. (2006). Longitudinal trends in race/ethnic disparities in leading health indicators from adolescence to young adulthood. Archives of Pediatrics & Adolescent Medicine, 160(1), 74–81. doi:10.1001/archpedi.160.1.74.

    Article  Google Scholar 

  • Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17, 825–841.

    Article  PubMed  Google Scholar 

  • Karoly, H. C., Weiland, B. J., Sabbineni, A., & Hutchison, K. E. (2014). Preliminary functional MRI results from a combined stop-signal alcohol-cue task. Journal of Studies on Alcohol and Drugs, 75(4), 664–673. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4108606/pdf/jsad664.pdf

  • Katz, A., Nambi, S. S., & Mather, K. (2000). Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. The Journal of Clinical Endocrinology and Metabolism, 85, 2402–2410.

    CAS  Article  PubMed  Google Scholar 

  • Kong, A. S., Dalen, J., Negrete, S., Sanders, S. G., Keane, P. C., & Davis, S. M. (2012). Interventions for treating overweight and obesity in adolescents. 23, 544–570.

  • Kong, A. S., Sussman, A. L., Yahne, C., Skipper, B. J., Burge, M. R., & Davis, S. M. (2013). School-based health center intervention improves body mass index in overweight and obese adolescents. Journal of Obesity, 2013, 575016. doi:10.1155/2013/575016.

    Article  PubMed  PubMed Central  Google Scholar 

  • Lanza, H. I., Grella, C. E., & Chung, P. J. (2015). Adolescent obesity and future substance use: incorporating the psychosocial context. Journal of Adolescence, 45, 20–30. doi:10.1016/j.adolescence.2015.08.014.

    Article  PubMed  PubMed Central  Google Scholar 

  • Lee, H., Lee, D., Guo, G., & Harris, K. M. (2011). Trends in body mass index in adolescence and young adulthood in the United States: 1959-2002. The Journal of Adolescent Health, 49(6), 601–608. doi:10.1016/j.jadohealth.2011.04.019.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Magnussen, C. G., Koskinen, J., Chen, W., Thomson, R., Schmidt, M. D., Srinivasan, S. R., et al. (2010). Pediatric metabolic syndrome predicts adulthood metabolic syndrome, subclinical atherosclerosis, and type 2 diabetes mellitus but is no better than body mass index alone: the Bogalusa heart study and the cardiovascular risk in young Finns study. Circulation, 122(16), 1604–1611. doi:10.1161/circulationaha.110.940809.

    Article  PubMed  PubMed Central  Google Scholar 

  • Meule, A., Hermann, T., & Kubler, A. (2015). Food addiction in overweight and obese adolescents seeking weight-loss treatment. European Eating Disorders Review, 23(3), 193–198. doi:10.1002/erv.2355.

    Article  PubMed  Google Scholar 

  • Miller, W. R., & Heather, N. (Eds.). (1986). Treating Addictive Behaviors: Processes of Change (1 ed.): Springer US.

  • Morrison, J. A., Friedman, L. A., Wang, P., & Glueck, C. J. (2008). Metabolic syndrome in childhood predicts adult metabolic syndrome and type 2 diabetes mellitus 25 to 30 years later. The Journal of Pediatrics, 152(2), 201–206. doi:10.1016/j.jpeds.2007.09.010.

    CAS  Article  PubMed  Google Scholar 

  • National Institute of Diabetes and Digestive and Kidney Diseases. (2014). Insulin resistance and prediabetes. In N. I. o. Health (Ed.).

  • Ogden, C. L., Carroll, M. D., Kit, B. K., & Flegal, K. M. (2014). Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA, 311(8), 806–814. doi:10.1001/jama.2014.732.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Poobalan, A. S., Aucott, L. S., Clarke, A., & Smith, W. C. (2012). Physical activity attitudes, intentions and behaviour among 18-25 year olds: a mixed method study. BMC Public Health, 12, 640. doi:10.1186/1471-2458-12-640.

    Article  PubMed  PubMed Central  Google Scholar 

  • Poobalan, A. S., Aucott, L. S., Clarke, A., & Smith, W. C. (2014). Diet behaviour among young people in transition to adulthood (18-25 year olds): a mixed method study. Health Psychol Behav Med, 2(1), 909–928. doi:10.1080/21642850.2014.931232.

    Article  PubMed  PubMed Central  Google Scholar 

  • Pursey, K. M., Stanwell, P., Gearhardt, A. N., Collins, C. E., & Burrows, T. L. (2014). The prevalence of food addiction as assessed by the Yale food addiction scale: a systematic review. Nutrients, 6(10), 4552–4590. doi:10.3390/nu6104552.

    Article  PubMed  PubMed Central  Google Scholar 

  • Rhee, K. E., Jelalian, E., Boutelle, K., Dickstein, S., Seifer, R., & Wing, R. (2016). Warm parenting associated with decreasing or stable child BMI during treatment. Child Obes, 12(2), 94–102. doi:10.1089/chi.2015.0127.

    Article  PubMed  PubMed Central  Google Scholar 

  • Schacht, J. P., Hutchison, K. E., & Filbey, F. M. (2012). Associations between cannabinoid receptor-1 (CNR1) variation and hippocampus and amygdala volumes in heavy cannabis users. Neuropsychopharmacology, 37(11), 2368–2376. doi:10.1038/npp.2012.92.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Schmiege, S. J., Broaddus, M. R., Levin, M., & Bryan, A. D. (2009). Randomized trial of group interventions to reduce HIV/STD risk and change theoretical mediators among detained adolescents. Journal of Consulting and Clinical Psychology, 77(1), 38–50. doi:10.1037/a0014513.

    Article  PubMed  Google Scholar 

  • Simon, J. J., Skunde, M., Hamze Sinno, M., Brockmeyer, T., Herpertz, S. C., Bendszus, M., et al. (2014). Impaired cross-talk between mesolimbic food reward processing and metabolic signaling predicts body mass index. Frontiers in Behavioral Neuroscience, 8, 359. doi:10.3389/fnbeh.2014.00359.

    PubMed  PubMed Central  Google Scholar 

  • Sinaiko, A. R., & Caprio, S. (2012). Insulin resistance. The Journal of Pediatrics, 161(1), 11–15. doi:10.1016/j.jpeds.2012.01.012.

    Article  PubMed  PubMed Central  Google Scholar 

  • Smith, S. M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17(3), 143–155. doi:10.1002/hbm.10062.

    Article  PubMed  Google Scholar 

  • Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., Johansen-Berg, H., et al. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(Suppl 1), S208–S219. doi:10.1016/j.neuroimage.2004.07.051.

    Article  PubMed  Google Scholar 

  • Stice, E., & Yokum, S. (2014). Brain reward region responsivity of adolescents with and without parental substance use disorders. Psychology of Addictive Behaviors, 28, 805–815.

    Article  PubMed  Google Scholar 

  • Stice, E., Yokum, S., Bohon, C., Marti, N., & Smolen, A. (2010). Reward circuitry responsivity to food predicts future increases in body mass: moderating effects of DRD2 and DRD4. NeuroImage, 50(4), 1618–1625. doi:10.1016/j.neuroimage.2010.01.081.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Stice, E., Yokum, S., & Burger, K. S. (2013). Elevated reward region responsivity predicts future substance use onset but not overweight/obesity onset. Biological Psychiatry, 73, 896–876.

    Article  Google Scholar 

  • Stice, E., Burger, K. S., & Yokum, S. (2015). Reward region responsivity predicts future weight gain and moderating effects of the TaqIA allele. The Journal of Neuroscience, 35(28), 10316–10324. doi:10.1523/jneurosci.3607-14.2015.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Volkow, N. D., & Baler, R. D. (2015). NOW vs LATER brain circuits: implications for obesity and addiction. Trends in Neurosciences, 38(6), 345–352. doi:10.1016/j.tins.2015.04.002.

    CAS  Article  PubMed  Google Scholar 

  • Volkow, N. D., Wang, G. J., Tomasi, D., & Baler, R. D. (2013a). The addictive dimension of obesity. Biological Psychiatry, 73, 811–818.

    Article  PubMed  PubMed Central  Google Scholar 

  • Volkow, N. D., Wang, G. J., Tomasi, D., & Baler, R. D. (2013b). Obesity and addiction: neurobiological overlaps. Obesity Reviews, 14(1), 2–18. doi:10.1111/j.1467-789X.2012.01031.x.

    CAS  Article  PubMed  Google Scholar 

  • Weiss, R., Bremer, A. A., & Lustig, R. H. (2013). What is metabolic syndrome, and why are children getting it? Annals of the New York Academy of Sciences, 1281, 123–140. doi:10.1111/nyas.12030.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Yip, S. W., Lacadie, C. M., Sinha, R., Mayes, L. C., & Potenza, M. N. (2016). Prenatal cocaine exposure, illicit-substance use and stress and craving processes during adolescence. Drug and Alcohol Dependence, 158, 76–85. doi:10.1016/j.drugalcdep.2015.11.012.

    CAS  Article  PubMed  Google Scholar 

  • Yokum, S., Gearhardt, A. N., Harris, J. L., Brownell, K. D., & Stice, E. (2014). Individual differences in striatum activity to food commercials predict weight gain in adolescents. Obesity (Silver Spring), 22, 2544–2551.

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank Dustin Truitt for his contribution to this project.

Sources of support

This research was supported by the University of New Mexico Pediatric Research Allocations Committee, the La Tierra Sagrada Society, and the Mind Research Network.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sarah W. Feldstein Ewing.

Ethics declarations

Financial disclosures

The authors declare that they have no competing financial or other conflicts of interest relating to the data included in the manuscript.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Feldstein Ewing, S.W., Claus, E.D., Hudson, K.A. et al. Overweight adolescents’ brain response to sweetened beverages mirrors addiction pathways. Brain Imaging and Behavior 11, 925–935 (2017). https://doi.org/10.1007/s11682-016-9564-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11682-016-9564-z

Keywords

  • Adolescents
  • Overweight/obesity
  • Cue exposure
  • fMRI
  • Addiction