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Brain–gut–microbiome interactions in obesity and food addiction

Abstract

Normal eating behaviour is coordinated by the tightly regulated balance between intestinal and extra-intestinal homeostatic and hedonic mechanisms. By contrast, food addiction is a complex, maladaptive eating behaviour that reflects alterations in brain–gut–microbiome (BGM) interactions and a shift of this balance towards hedonic mechanisms. Each component of the BGM axis has been implicated in the development of food addiction, with both brain to gut and gut to brain signalling playing a role. Early-life influences can prime the infant gut microbiome and brain for food addiction, which might be further reinforced by increased antibiotic usage and dietary patterns throughout adulthood. The ubiquitous availability and marketing of inexpensive, highly palatable and calorie-dense food can further shift this balance towards hedonic eating through both central (disruptions in dopaminergic signalling) and intestinal (vagal afferent function, metabolic endotoxaemia, systemic immune activation, changes to gut microbiome and metabolome) mechanisms. In this Review, we propose a systems biology model of BGM interactions, which incorporates published reports on food addiction, and provides novel insights into treatment targets aimed at each level of the BGM axis.

Key points

  • Food addiction refers to maladaptive ingestive behaviours resulting from a shift from primarily homeostatic to hedonic regulatory mechanisms of food intake; this shift reflects alterations at all levels of the brain–gut–microbiome (BGM) axis.

  • Normal ingestive behaviour is the result of the tightly regulated interplay between orexigenic and anorexigenic gut hormones, leptin signalling from adipose tissue, hypothalamic nuclei, the dopaminergic reward system and prefrontal inhibitory influences.

  • In food addiction, a disinhibition of reward and anorexigenic mechanisms at all levels of the BGM axis results in unrestrained craving for food.

  • Several adverse early-life events, including nutrition, stress and antibiotic intake, can influence the development of BGM interactions and of ingestive behaviour.

  • Lifelong dietary choices can modulate BGM interactions and eating behaviours; for example, chronic ingestion of a Western diet can result in systemic low-grade immune system activation, reducing feedback inhibitory mechanisms restraining food intake.

  • Pharmacological treatment options for food addition are limited and bariatric surgery is the only therapy providing long-term benefits; however, novel treatment approaches, including time-restricted eating and cognitive behavioural interventions, are being evaluated.

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Fig. 1: Model of brain–gut–microbiome interactions in ingestive behaviour.
Fig. 2: Model of altered brain network interactions in food addiction.
Fig. 3: Mechanisms in the homeostatic and hedonic systems leading to food addiction.
Fig. 4: Interactions between food, gut microbiota and intestinal permeability in the regulation of ingestive behaviour.
Fig. 5: Circular model of brain–gut–microbiome interactions in obesity and targets for intervention.

References

  1. 1.

    Centers for Disease Control and Prevention Overweight & Obesity http://www.cdc.gov/obesity/data/adult.html (2014).

  2. 2.

    World Health Organization Obesity and Overweight http://www.who.int/mediacentre/factsheets/fs311/en/ (2016).

  3. 3.

    State of Childhood Obesity Obesity Rates & Trend Data http://stateofobesity.org/rates/ (2016).

  4. 4.

    Biener, A., Cawley, J. & Meyerhoefer, C. The high and rising costs of obesity to the US health care system. J. Gen. Intern. Med. 32, S6–S8 (2017).

    Google Scholar 

  5. 5.

    Mancini, M. C. & de Melo, M. E. The burden of obesity in the current world and the new treatments available: focus on liraglutide 3.0 mg. Diabetol. Metab. Syndr. 9, 44 (2017).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Zhang, Y. et al. Obesity: pathophysiology and intervention. Nutrients 6, 5153–5183 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Heymsfield, S. B. & Wadden, T. A. Mechanisms, pathophysiology, and management of obesity. N. Engl. J. Med. 376, 254–266 (2017).

    CAS  PubMed  Google Scholar 

  8. 8.

    Osadchiy, V., Martin, C. R. & Mayer, E. A. The gut-brain axis and the microbiome: mechanisms and clinical implications. Clin. Gastroenterol. Hepatol. 17, 322–332 (2019).

    CAS  PubMed  Google Scholar 

  9. 9.

    Mayer, E. A. et al. Functional GI disorders: from animal models to drug development. Gut 57, 384–404 (2008).

    CAS  PubMed  Google Scholar 

  10. 10.

    Keita, A. V. & Soderholm, J. D. The intestinal barrier and its regulation by neuroimmune factors. Neurogastroenterol. Motil. 22, 718–733 (2010).

    CAS  PubMed  Google Scholar 

  11. 11.

    Yu, M. et al. Variations in gut microbiota and fecal metabolic phenotype associated with depression by 16S rRNA gene sequencing and LC/MS-based metabolomics. J. Pharm. Biomed. Anal. 138, 231–239 (2017).

    CAS  PubMed  Google Scholar 

  12. 12.

    Moreira, C. G. et al. Bacterial adrenergic sensors regulate virulence of enteric pathogens in the gut. mBio 7, e00826–16 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Houlden, A. et al. Brain injury induces specific changes in the caecal microbiota of mice via altered autonomic activity and mucoprotein production. Brain Behav. Immun. 57, 10–20 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Sovran, B. et al. Age-associated impairment of the mucus barrier function is associated with profound changes in microbiota and immunity. Sci. Rep. 9, 1437 (2019).

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Barrett, E., Ross, R. P., O’Toole, P. W., Fitzgerald, G. F. & Stanton, C. γ-Aminobutyric acid production by culturable bacteria from the human intestine. J. Appl. Microbiol. 113, 411–417 (2012).

    CAS  PubMed  Google Scholar 

  16. 16.

    Shishov, V. A., Kirovskaia, T. A., Kudrin, V. S. & Oleskin, A. V. Amine neuromediators, their precursors, and oxidation products in the culture of Escherichia coli K-12. Prikl. Biokhim Mikrobiol. 45, 550–554 (2009).

    CAS  PubMed  Google Scholar 

  17. 17.

    Asano, Y. et al. Critical role of gut microbiota in the production of biologically active, free catecholamines in the gut lumen of mice. Am. J. Physiol. Gastrointest. Liver Physiol 303, G1288–G1295 (2012).

    CAS  PubMed  Google Scholar 

  18. 18.

    Maslowski, K. M. et al. Regulation of inflammatory responses by gut microbiota and chemoattractant receptor GPR43. Nature 461, 1282–1286 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Dalile, B., Van Oudenhove, L., Vervliet, B. & Verbeke, K. The role of short-chain fatty acids in microbiota-gut-brain communication. Nat. Rev. Gastroenterol. Hepatol. 16, 461–478 (2019).

    PubMed  Google Scholar 

  20. 20.

    McLoughlin, R. F., Berthon, B. S., Jensen, M. E., Baines, K. J. & Wood, L. G. Short-chain fatty acids, prebiotics, synbiotics, and systemic inflammation: a systematic review and meta-analysis. Am. J. Clin. Nutr. 106, 930–945 (2017).

    CAS  PubMed  Google Scholar 

  21. 21.

    Byrne, C. S. et al. Increased colonic propionate reduces anticipatory reward responses in the human striatum to high-energy foods. Am. J. Clin. Nutr. 104, 5–14 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Lal, S., Kirkup, A. J., Brunsden, A. M., Thompson, D. G. & Grundy, D. Vagal afferent responses to fatty acids of different chain length in the rat. Am. J. Physiol. Gastrointest. Liver Physiol 281, G907–G915 (2001).

    CAS  PubMed  Google Scholar 

  23. 23.

    Diaz Heijtz, R. Fetal, neonatal, and infant microbiome: perturbations and subsequent effects on brain development and behavior. Semin. Fetal Neonatal Med. 21, 410–417 (2016).

    PubMed  Google Scholar 

  24. 24.

    Bliss, E. S. & Whiteside, E. The gut-brain axis, the human gut microbiota and their integration in the development of obesity. Front. Physiol. 9, 900 (2018).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Torres-Fuentes, C., Schellekens, H., Dinan, T. G. & Cryan, J. F. The microbiota-gut-brain axis in obesity. Lancet Gastroenterol. Hepatol. 2, 747–756 (2017).

    PubMed  Google Scholar 

  26. 26.

    Ochoa-Reparaz, J. & Kasper, L. H. The second brain: is the gut microbiota a link between obesity and central nervous system disorders? Curr. Obes. Rep. 5, 51–64 (2016).

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Buhmann, H., le Roux, C. W. & Bueter, M. The gut-brain axis in obesity. Best. Pract. Res. Clin. Gastroenterol. 28, 559–571 (2014).

    CAS  PubMed  Google Scholar 

  28. 28.

    Myers, M. G. Jr., Leibel, R. L., Seeley, R. J. & Schwartz, M. W. Obesity and leptin resistance: distinguishing cause from effect. Trends Endocrinol. Metab. 21, 643–651 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Guyenet, S. J. & Schwartz, M. W. Clinical review: regulation of food intake, energy balance, and body fat mass: implications for the pathogenesis and treatment of obesity. J. Clin. Endocrinol. Metab. 97, 745–755 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Rossi, M. A. & Stuber, G. D. Overlapping brain circuits for homeostatic and hedonic feeding. Cell Metab. 27, 42–56 (2018).

    CAS  PubMed  Google Scholar 

  31. 31.

    Volkow, N. D., Wang, G. J., Fowler, J. S., Tomasi, D. & Baler, R. Food and drug reward: overlapping circuits in human obesity and addiction. Curr. Top. Behav. Neurosci. 11, 1–24 (2012).

    CAS  PubMed  Google Scholar 

  32. 32.

    Volkow, N. D., Wang, G. J., Tomasi, D. & Baler, R. D. Obesity and addiction: neurobiological overlaps. Obes. Rev. 14, 2–18 (2013).

    CAS  PubMed  Google Scholar 

  33. 33.

    Lindgren, E. et al. Food addiction: a common neurobiological mechanism with drug abuse. Front. Biosci. 23, 811–836 (2018).

    CAS  Google Scholar 

  34. 34.

    Gearhardt, A. N., Corbin, W. R. & Brownell, K. D. Food addiction: an examination of the diagnostic criteria for dependence. J. Addict. Med. 3, 1–7 (2009).

    PubMed  Google Scholar 

  35. 35.

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

    PubMed  PubMed Central  Google Scholar 

  36. 36.

    Schulte, E. M. & Gearhardt, A. N. Associations of food addiction in a sample recruited to be nationally representative of the United States. Eur. Eat. Disord. Rev. 26, 112–119 (2018).

    PubMed  Google Scholar 

  37. 37.

    Schulte, E. M., Potenza, M. N. & Gearhardt, A. N. A commentary on the “eating addiction” versus “food addiction” perspectives on addictive-like food consumption. Appetite 115, 9–15 (2017).

    PubMed  Google Scholar 

  38. 38.

    Gearhardt, A. N., Davis, C., Kuschner, R. & Brownell, K. D. The addiction potential of hyperpalatable foods. Curr. Drug Abuse Rev. 4, 140–145 (2011).

    PubMed  Google Scholar 

  39. 39.

    Randolph, T. G. The descriptive features of food addiction; addictive eating and drinking. Q. J. Stud. Alcohol. 17, 198–224 (1956).

    CAS  PubMed  Google Scholar 

  40. 40.

    Meule, A. & Gearhardt, A. N. Food addiction in the light of DSM-5. Nutrients 6, 3653–3671 (2014).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Corsica, J. A. & Pelchat, M. L. Food addiction: true or false? Curr. Opin. Gastroenterol. 26, 165–169 (2010).

    PubMed  Google Scholar 

  42. 42.

    Hone-Blanchet, A. & Fecteau, S. Overlap of food addiction and substance use disorders definitions: analysis of animal and human studies. Neuropharmacology 85, 81–90 (2014).

    CAS  PubMed  Google Scholar 

  43. 43.

    Gearhardt, A. N., Corbin, W. R. & Brownell, K. D. Development of the Yale Food Addiction Scale version 2.0. Psychol. Addict. Behav. 30, 113–121 (2016).

    PubMed  Google Scholar 

  44. 44.

    American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM-5 5th edn (American Psychiatric Association, 2013).

  45. 45.

    Sengor, G. & Gezer, C. Food addiction and its relationship with disordered eating behaviours and obesity. Eat. Weight Disord. 24, 1031–1039 (2019).

    PubMed  Google Scholar 

  46. 46.

    Penzenstadler, L., Soares, C., Karila, L. & Khazaal, Y. Systematic review of food addiction as measured with the Yale Food Addiction Scale: implications for the food addiction construct. Curr. Neuropharmacol. 17, 526–538 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Burrows, T., Kay-Lambkin, F., Pursey, K., Skinner, J. & Dayas, C. Food addiction and associations with mental health symptoms: a systematic review with meta-analysis. J. Hum. Nutr. Diet. 31, 544–572 (2018).

    CAS  PubMed  Google Scholar 

  48. 48.

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

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Chen, M., Sun, Y., Lu, L. & Shi, J. Similarities and differences in neurobiology. Adv. Exp. Med. Biol. 1010, 45–58 (2017).

    PubMed  Google Scholar 

  50. 50.

    Kalon, E., Hong, J. Y., Tobin, C. & Schulte, T. Psychological and neurobiological correlates of food addiction. Int. Rev. Neurobiol. 129, 85–110 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    DiLeone, R. J., Taylor, J. R. & Picciotto, M. R. The drive to eat: comparisons and distinctions between mechanisms of food reward and drug addiction. Nat. Neurosci. 15, 1330–1335 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Rogers, P. J. Food and drug addictions: similarities and differences. Pharmacol. Biochem. Behav. 153, 182–190 (2017).

    CAS  PubMed  Google Scholar 

  53. 53.

    Ouellette, A. S. et al. Establishing a food addiction diagnosis using the Yale Food Addiction Scale: a closer look at the clinically significant distress/functional impairment criterion. Appetite 129, 55–61 (2018).

    PubMed  Google Scholar 

  54. 54.

    Davis, C. et al. Evidence that ‘food addiction’ is a valid phenotype of obesity. Appetite 57, 711–717 (2011).

    PubMed  Google Scholar 

  55. 55.

    Meule, A. How prevalent is “food addiction”? Front. Psychiatry 2, 61 (2011).

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Avena, N. M., Gearhardt, A. N., Gold, M. S., Wang, G. J. & Potenza, M. N. Tossing the baby out with the bathwater after a brief rinse? The potential downside of dismissing food addiction based on limited data. Nat. Rev. Neurosci. 13, 514 (2012).

    CAS  PubMed  Google Scholar 

  57. 57.

    Ziauddeen, H. & Fletcher, P. C. Is food addiction a valid and useful concept? Obes. Rev 14, 19–28 (2013).

    CAS  PubMed  Google Scholar 

  58. 58.

    Ziauddeen, H., Farooqi, I. S. & Fletcher, P. C. Obesity and the brain: how convincing is the addiction model? Nat. Rev. Neurosci. 13, 279–286 (2012).

    CAS  PubMed  Google Scholar 

  59. 59.

    Muller, A. et al. Food addiction and other addictive behaviours in bariatric surgery candidates. Eur. Eat. Disord. Rev. 26, 585–596 (2018).

    PubMed  Google Scholar 

  60. 60.

    Sevincer, G. M., Konuk, N., Bozkurt, S. & Coskun, H. Food addiction and the outcome of bariatric surgery at 1-year: prospective observational study. Psychiatry Res. 244, 159–164 (2016).

    PubMed  Google Scholar 

  61. 61.

    Gearhardt, A. N., Boswell, R. G. & White, M. A. The association of “food addiction” with disordered eating and body mass index. Eat. Behav. 15, 427–433 (2014).

    PubMed  PubMed Central  Google Scholar 

  62. 62.

    Gearhardt, A. N., White, M. A. & Potenza, M. N. Binge eating disorder and food addiction. Curr. Drug Abuse Rev. 4, 201–207 (2011).

    PubMed  PubMed Central  Google Scholar 

  63. 63.

    Nakazato, M. et al. A role for ghrelin in the central regulation of feeding. Nature 409, 194–198 (2001).

    CAS  PubMed  Google Scholar 

  64. 64.

    Wren, A. M. et al. Ghrelin causes hyperphagia and obesity in rats. Diabetes 50, 2540–2547 (2001).

    CAS  PubMed  Google Scholar 

  65. 65.

    Jiang, H., Betancourt, L. & Smith, R. G. Ghrelin amplifies dopamine signaling by cross talk involving formation of growth hormone secretagogue receptor/dopamine receptor subtype 1 heterodimers. Mol. Endocrinol. 20, 1772–1785 (2006).

    CAS  PubMed  Google Scholar 

  66. 66.

    Shah, M. & Vella, A. Effects of GLP-1 on appetite and weight. Rev. Endocr. Metab. Disord. 15, 181–187 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Karra, E., Chandarana, K. & Batterham, R. L. The role of peptide YY in appetite regulation and obesity. J. Physiol. 587, 19–25 (2009).

    CAS  PubMed  Google Scholar 

  68. 68.

    Tolhurst, G. et al. Short-chain fatty acids stimulate glucagon-like peptide-1 secretion via the G-protein-coupled receptor FFAR2. Diabetes 61, 364–371 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Cani, P. D. et al. Gut microbiota fermentation of prebiotics increases satietogenic and incretin gut peptide production with consequences for appetite sensation and glucose response after a meal. Am. J. Clin. Nutr. 90, 1236–1243 (2009).

    CAS  PubMed  Google Scholar 

  70. 70.

    Parnell, J. A. & Reimer, R. A. Weight loss during oligofructose supplementation is associated with decreased ghrelin and increased peptide YY in overweight and obese adults. Am. J. Clin. Nutr. 89, 1751–1759 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Rodin, J., Wack, J., Ferrannini, E. & DeFronzo, R. A. Effect of insulin and glucose on feeding behavior. Metabolism 34, 826–831 (1985).

    CAS  PubMed  Google Scholar 

  72. 72.

    Jiao, N. et al. Gut microbiome may contribute to insulin resistance and systemic inflammation in obese rodents: a meta-analysis. Physiol. Genomics 50, 244–254 (2018).

    CAS  PubMed  Google Scholar 

  73. 73.

    Perry, R. J. et al. Acetate mediates a microbiome-brain-β-cell axis to promote metabolic syndrome. Nature 534, 213–217 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Fang, S. et al. Intestinal FXR agonism promotes adipose tissue browning and reduces obesity and insulin resistance. Nat. Med. 21, 159–165 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Pathak, P. et al. Intestine farnesoid X receptor agonist and the gut microbiota activate G-protein bile acid receptor-1 signaling to improve metabolism. Hepatology 68, 1574–1588 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Vrieze, A. et al. Impact of oral vancomycin on gut microbiota, bile acid metabolism, and insulin sensitivity. J. Hepatol. 60, 824–831 (2014).

    CAS  PubMed  Google Scholar 

  77. 77.

    Leitao-Goncalves, R. et al. Commensal bacteria and essential amino acids control food choice behavior and reproduction. PLoS Biol. 15, e2000862 (2017).

    PubMed  PubMed Central  Google Scholar 

  78. 78.

    Ribeiro, C. & Dickson, B. J. Sex peptide receptor and neuronal TOR/S6K signaling modulate nutrient balancing in Drosophila. Curr. Biol. 20, 1000–1005 (2010).

    CAS  PubMed  Google Scholar 

  79. 79.

    Storelli, G. et al. Lactobacillus plantarum promotes Drosophila systemic growth by modulating hormonal signals through TOR-dependent nutrient sensing. Cell Metab. 14, 403–414 (2011).

    CAS  PubMed  Google Scholar 

  80. 80.

    Kahathuduwa, C. N., Boyd, L. A., Davis, T., O’Boyle, M. & Binks, M. Brain regions involved in ingestive behavior and related psychological constructs in people undergoing calorie restriction. Appetite 107, 348–361 (2016).

    PubMed  Google Scholar 

  81. 81.

    Berthoud, H. R. Metabolic and hedonic drives in the neural control of appetite: who is the boss? Curr. Opin. Neurobiol. 21, 888–896 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Simon, J. J. et al. Integration of homeostatic signaling and food reward processing in the human brain. JCI Insight 2, e92970 (2017).

    PubMed Central  Google Scholar 

  83. 83.

    Abizaid, A., Gao, Q. & Horvath, T. L. Thoughts for food: brain mechanisms and peripheral energy balance. Neuron 51, 691–702 (2006).

    CAS  PubMed  Google Scholar 

  84. 84.

    Gao, Q. & Horvath, T. L. Neurobiology of feeding and energy expenditure. Annu. Rev. Neurosci. 30, 367–398 (2007).

    CAS  PubMed  Google Scholar 

  85. 85.

    Berthoud, H. R., Munzberg, H. & Morrison, C. D. Blaming the brain for obesity: integration of hedonic and homeostatic mechanisms. Gastroenterology 152, 1728–1738 (2017).

    PubMed  PubMed Central  Google Scholar 

  86. 86.

    Berthoud, H. R. & Morrison, C. The brain, appetite, and obesity. Annu. Rev. Psychol. 59, 55–92 (2008).

    PubMed  Google Scholar 

  87. 87.

    Harding, I. H. et al. Brain substrates of unhealthy versus healthy food choices: influence of homeostatic status and body mass index. Int. J. Obes. 42, 448–454 (2018).

    CAS  Google Scholar 

  88. 88.

    Gupta, A. et al. Patterns of brain structural connectivity differentiate normal weight from overweight subjects. NeuroImage. Clin. 7, 506–517 (2015).

    PubMed  PubMed Central  Google Scholar 

  89. 89.

    Gupta, A. et al. Sex differences in the influence of body mass index on anatomical architecture of brain networks. Int. J. Obes. 41, 1185–1195 (2017).

    CAS  Google Scholar 

  90. 90.

    Kenny, P. J. Reward mechanisms in obesity: new insights and future directions. Neuron 69, 664–679 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Volkow, N. D., Wang, G. J. & Baler, R. D. Reward, dopamine and the control of food intake: implications for obesity. Trends Cogn. Sci. 15, 37–46 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. 92.

    Bartholdy, S., Dalton, B., O’Daly, O. G., Campbell, I. C. & Schmidt, U. A systematic review of the relationship between eating, weight and inhibitory control using the stop signal task. Neurosci. Biobehav. Rev. 64, 35–62 (2016).

    PubMed  Google Scholar 

  93. 93.

    Gearhardt, A. N., Yokum, S., Stice, E., Harris, J. L. & Brownell, K. D. Relation of obesity to neural activation in response to food commercials. Soc. Cogn. Affect. Neurosci. 9, 932–938 (2014).

    PubMed  Google Scholar 

  94. 94.

    Steward, T. et al. Food addiction and impaired executive functions in women with obesity. Eur. Eat. Disord. Rev. 26, 574–584 (2018).

    PubMed  Google Scholar 

  95. 95.

    Stice, E., Yokum, S., Burger, K. S., Epstein, L. H. & Small, D. M. Youth at risk for obesity show greater activation of striatal and somatosensory regions to food. J. Neurosci. 31, 4360–4366 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. 96.

    Olivo, G. et al. Resting-state brain and the FTO obesity risk allele: default mode, sensorimotor, and salience network connectivity underlying different somatosensory integration and reward processing between genotypes. Front. Hum. Neurosci. 10, 52 (2016).

    PubMed  PubMed Central  Google Scholar 

  97. 97.

    Morrow, J. D., Maren, S. & Robinson, T. E. Individual variation in the propensity to attribute incentive salience to an appetitive cue predicts the propensity to attribute motivational salience to an aversive cue. Behav. Brain Res. 220, 238–243 (2011).

    PubMed  PubMed Central  Google Scholar 

  98. 98.

    Garcia-Garcia, I. et al. Alterations of the salience network in obesity: a resting-state fMRI study. Hum. Brain Mapp. 34, 2786–2797 (2013).

    PubMed  Google Scholar 

  99. 99.

    Volkow, N. D. & Baler, R. D. NOW vs LATER brain circuits: implications for obesity and addiction. Trends Neurosci. 38, 345–352 (2015).

    CAS  PubMed  Google Scholar 

  100. 100.

    Wijngaarden, M. A. et al. Obesity is marked by distinct functional connectivity in brain networks involved in food reward and salience. Behav. Brain Res. 287, 127–134 (2015).

    CAS  PubMed  Google Scholar 

  101. 101.

    Seeley, W. W. et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J. Neurosci. 27, 2349–2356 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. 102.

    Menon, V. & Uddin, L. Q. Saliency, switching, attention and control: a network model of insula function. Brain Struct. Funct. 214, 655–667 (2010).

    PubMed  PubMed Central  Google Scholar 

  103. 103.

    Zald, D. H. The human amygdala and the emotional evaluation of sensory stimuli. Brain Res. Brain Res. Rev. 41, 88–123 (2003).

    PubMed  Google Scholar 

  104. 104.

    Kilpatrick, L. A. et al. Influence of sucrose ingestion on brainstem and hypothalamic intrinsic oscillations in lean and obese women. Gastroenterology 146, 1212–1221 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  105. 105.

    Park, B. Y., Chung, C. S., Lee, M. J. & Park, H. Accurate neuroimaging biomarkers to predict body mass index in adolescents: a longitudinal study. Brain Imaging Behav. https://doi.org/10.1007/s11682-019-00101-y (2019).

    Article  PubMed  Google Scholar 

  106. 106.

    Meng, Q. et al. Disrupted topological organization of the frontal-mesolimbic network in obese patients. Brain Imaging Behav. 12, 1544–1555 (2018).

    PubMed  Google Scholar 

  107. 107.

    Baek, K., Morris, L. S., Kundu, P. & Voon, V. Disrupted resting-state brain network properties in obesity: decreased global and putaminal cortico-striatal network efficiency. Psychol. Med. 47, 585–596 (2017).

    CAS  PubMed  Google Scholar 

  108. 108.

    Harrold, J. A. & Halford, J. C. The hypothalamus and obesity. Recent. Pat. CNS Drug Discov. 1, 305–314 (2006).

    CAS  PubMed  Google Scholar 

  109. 109.

    Munzberg, H., Qualls-Creekmore, E., Berthoud, H. R., Morrison, C. D. & Yu, S. Neural control of energy expenditure. Handb. Exp. Pharmacol. 233, 173–194 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  110. 110.

    King, B. M. The rise, fall, and resurrection of the ventromedial hypothalamus in the regulation of feeding behavior and body weight. Physiol. Behav. 87, 221–244 (2006).

    CAS  PubMed  Google Scholar 

  111. 111.

    Purnell, J. Q., Lahna, D. L., Samuels, M. H., Rooney, W. D. & Hoffman, W. F. Loss of pons-to-hypothalamic white matter tracks in brainstem obesity. Int. J. Obes. 38, 1573–1577 (2014).

    CAS  Google Scholar 

  112. 112.

    Carmo-Silva, S. & Cavadas, C. Hypothalamic dysfunction in obesity and metabolic disorders. Adv. Neurobiol. 19, 73–116 (2017).

    PubMed  Google Scholar 

  113. 113.

    Fu, O. et al. Hypothalamic neuronal circuits regulating hunger-induced taste modification. Nat. Commun. 10, 4560 (2019).

    PubMed  PubMed Central  Google Scholar 

  114. 114.

    Zagmutt, S., Mera, P., Soler-Vazquez, M. C., Herrero, L. & Serra, D. Targeting AgRP neurons to maintain energy balance: lessons from animal models. Biochem. Pharmacol. 155, 224–232 (2018).

    CAS  PubMed  Google Scholar 

  115. 115.

    Morrison, C. D. & Berthoud, H. R. Neurobiology of nutrition and obesity. Nutr. Rev. 65, 517–534 (2007).

    PubMed  Google Scholar 

  116. 116.

    Pedram, P. et al. Food addiction: its prevalence and significant association with obesity in the general population. PLoS ONE 8, e74832 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  117. 117.

    Chao, A. M. et al. Prevalence and psychosocial correlates of food addiction in persons with obesity seeking weight reduction. Compr. Psychiatry 73, 97–104 (2017).

    PubMed  Google Scholar 

  118. 118.

    Pursey, K. M., Stanwell, P., Gearhardt, A. N., Collins, C. E. & Burrows, T. L. The prevalence of food addiction as assessed by the Yale Food Addiction Scale: a systematic review. Nutrients 6, 4552–4590 (2014).

    PubMed  PubMed Central  Google Scholar 

  119. 119.

    Eichen, D. M., Lent, M. R., Goldbacher, E. & Foster, G. D. Exploration of “food addiction” in overweight and obese treatment-seeking adults. Appetite 67, 22–24 (2013).

    PubMed  PubMed Central  Google Scholar 

  120. 120.

    Michaelides, M., Thanos, P. K., Volkow, N. D. & Wang, G. J. Translational neuroimaging in drug addiction and obesity. ILAR J. 53, 59–68 (2012).

    CAS  PubMed  Google Scholar 

  121. 121.

    Hommel, J. D. et al. Leptin receptor signaling in midbrain dopamine neurons regulates feeding. Neuron 51, 801–810 (2006).

    CAS  PubMed  Google Scholar 

  122. 122.

    Lutter, M. & Nestler, E. J. Homeostatic and hedonic signals interact in the regulation of food intake. J. Nutr. 139, 629–632 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  123. 123.

    Morton, G. J., Cummings, D. E., Baskin, D. G., Barsh, G. S. & Schwartz, M. W. Central nervous system control of food intake and body weight. Nature 443, 289–295 (2006).

    CAS  PubMed  Google Scholar 

  124. 124.

    Sinha, R. & Jastreboff, A. M. Stress as a common risk factor for obesity and addiction. Biol. Psychiatry 73, 827–835 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. 125.

    Sinha, R. Role of addiction and stress neurobiology on food intake and obesity. Biol. Psychol. 131, 5–13 (2018).

    PubMed  Google Scholar 

  126. 126.

    Onaolapo, A. Y. & Onaolapo, O. J. Food additives, food and the concept of ‘food addiction’: is stimulation of the brain reward circuit by food sufficient to trigger addiction? Pathophysiology 25, 263–276 (2018).

    CAS  PubMed  Google Scholar 

  127. 127.

    Stice, E., Spoor, S., Bohon, C., Veldhuizen, M. G. & Small, D. M. Relation of reward from food intake and anticipated food intake to obesity: a functional magnetic resonance imaging study. J. Abnorm. Psychol. 117, 924–935 (2008).

    PubMed  PubMed Central  Google Scholar 

  128. 128.

    Stoeckel, L. E. et al. Effective connectivity of a reward network in obese women. Brain Res. Bull. 79, 388–395 (2009).

    PubMed  PubMed Central  Google Scholar 

  129. 129.

    Stoeckel, L. E. et al. Widespread reward-system activation in obese women in response to pictures of high-calorie foods. NeuroImage 41, 636–647 (2008).

    PubMed  Google Scholar 

  130. 130.

    Volkow, N. D. et al. Cocaine cues and dopamine in dorsal striatum: mechanism of craving in cocaine addiction. J. Neurosci. 26, 6583–6588 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  131. 131.

    Blum, K., Thanos, P. K. & Gold, M. S. Dopamine and glucose, obesity, and reward deficiency syndrome. Front. Psychol. 5, 919 (2014).

    PubMed  PubMed Central  Google Scholar 

  132. 132.

    Jastreboff, A. M. et al. Neural correlates of stress- and food cue-induced food craving in obesity: association with insulin levels. Diabetes Care 36, 394–402 (2013).

    PubMed  PubMed Central  Google Scholar 

  133. 133.

    Loeber, S. et al. Impairment of inhibitory control in response to food-associated cues and attentional bias of obese participants and normal-weight controls. Int. J. Obes. 36, 1334–1339 (2012).

    CAS  Google Scholar 

  134. 134.

    Martin, L. E. et al. Neural mechanisms associated with food motivation in obese and healthy weight adults. Obesity 18, 254–260 (2010).

    PubMed  Google Scholar 

  135. 135.

    Blum, K., Oscar-Berman, M., Barh, D., Giordano, J. & Gold, M. Dopamine genetics and function in food and substance abuse. J. Genet. Syndr. Gene. Ther. 4, 1000121 (2013).

    PubMed  PubMed Central  Google Scholar 

  136. 136.

    Hardman, C. A., Herbert, V. M., Brunstrom, J. M., Munafo, M. R. & Rogers, P. J. Dopamine and food reward: effects of acute tyrosine/phenylalanine depletion on appetite. Physiol. Behav. 105, 1202–1207 (2012).

    CAS  PubMed  Google Scholar 

  137. 137.

    Volkow, N. D. et al. Low dopamine striatal D2 receptors are associated with prefrontal metabolism in obese subjects: possible contributing factors. NeuroImage 42, 1537–1543 (2008).

    PubMed  PubMed Central  Google Scholar 

  138. 138.

    Gaiser, E. C. et al. Elevated dopamine D2/3 receptor availability in obese individuals: a PET imaging study with [(11)C](+)PHNO. Neuropsychopharmacology 41, 3042–3050 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  139. 139.

    Volkow, N. D., Wang, G. J., Fowler, J. S. & Telang, F. Overlapping neuronal circuits in addiction and obesity: evidence of systems pathology. Philos. Trans. R. Soc. B Biol. Sci. 363, 3191–3200 (2008).

    Google Scholar 

  140. 140.

    Sinha, R., Gu, P., Hart, R. & Guarnaccia, J. B. Food craving, cortisol and ghrelin responses in modeling highly palatable snack intake in the laboratory. Physiol. Behav. 208, 112563 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  141. 141.

    Johnson, P. M. & Kenny, P. J. Dopamine D2 receptors in addiction-like reward dysfunction and compulsive eating in obese rats. Nat. Neurosci. 13, 635–641 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  142. 142.

    Pepino, M. Y. et al. Sweet dopamine: sucrose preferences relate differentially to striatal D2 receptor binding and age in obesity. Diabetes 65, 2618–2623 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  143. 143.

    Molteni, R., Barnard, R. J., Ying, Z., Roberts, C. K. & Gomez-Pinilla, F. A high-fat, refined sugar diet reduces hippocampal brain-derived neurotrophic factor, neuronal plasticity, and learning. Neuroscience 112, 803–814 (2002).

    CAS  Google Scholar 

  144. 144.

    Roessmann, U. & Gambetti, P. Astrocytes in the developing human brain. An immunohistochemical study. Acta Neuropathol. 70, 308–313 (1986).

    CAS  PubMed  Google Scholar 

  145. 145.

    Zhang, S. C. Defining glial cells during CNS development. Nat. Rev. Neurosci. 2, 840–843 (2001).

    CAS  PubMed  Google Scholar 

  146. 146.

    Hoban, A. E. et al. Regulation of prefrontal cortex myelination by the microbiota. Transl. Psychiatry 6, e774 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  147. 147.

    Diaz Heijtz, R. et al. Normal gut microbiota modulates brain development and behavior. Proc. Natl Acad. Sci. USA 108, 3047–3052 (2011).

    PubMed  Google Scholar 

  148. 148.

    Sudo, N. et al. Postnatal microbial colonization programs the hypothalamic-pituitary-adrenal system for stress response in mice. J. Physiol. 558, 263–275 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  149. 149.

    Zellner, D. A. et al. Food selection changes under stress. Physiol. Behav. 87, 789–793 (2006).

    CAS  PubMed  Google Scholar 

  150. 150.

    Oliver, G., Wardle, J. & Gibson, E. L. Stress and food choice: a laboratory study. Psychosom. Med. 62, 853–865 (2000).

    CAS  PubMed  Google Scholar 

  151. 151.

    Epel, E., Lapidus, R., McEwen, B. & Brownell, K. Stress may add bite to appetite in women: a laboratory study of stress-induced cortisol and eating behavior. Psychoneuroendocrinology 26, 37–49 (2001).

    CAS  PubMed  Google Scholar 

  152. 152.

    Bose, M., Olivan, B. & Laferrere, B. Stress and obesity: the role of the hypothalamic-pituitary-adrenal axis in metabolic disease. Curr. Opin. Endocrinol. Diabetes Obes. 16, 340–346 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  153. 153.

    Lee, M. J., Pramyothin, P., Karastergiou, K. & Fried, S. K. Deconstructing the roles of glucocorticoids in adipose tissue biology and the development of central obesity. Biochim. Biophys. Acta 1842, 473–481 (2014).

    CAS  PubMed  Google Scholar 

  154. 154.

    Cong, X., Henderson, W. A., Graf, J. & McGrath, J. M. Early life experience and gut microbiome: the brain-gut-microbiota signaling system. Adv. Neonatal Care 15, 314–323 (2015).

    PubMed  PubMed Central  Google Scholar 

  155. 155.

    Neuman, H., Forsythe, P., Uzan, A., Avni, O. & Koren, O. Antibiotics in early life: dysbiosis and the damage done. FEMS Microbiol. Rev. 42, 489–499 (2018).

    CAS  PubMed  Google Scholar 

  156. 156.

    Lundgren, S. N. et al. Maternal diet during pregnancy is related with the infant stool microbiome in a delivery mode-dependent manner. Microbiome 6, 109 (2018).

    PubMed  PubMed Central  Google Scholar 

  157. 157.

    Chu, D. M. et al. The early infant gut microbiome varies in association with a maternal high-fat diet. Genome Med. 8, 77 (2016).

    PubMed  PubMed Central  Google Scholar 

  158. 158.

    Bhagavata Srinivasan, S. P., Raipuria, M., Bahari, H., Kaakoush, N. O. & Morris, M. J. Impacts of diet and exercise on maternal gut microbiota are transferred to offspring. Front. Endocrinol. 9, 716 (2018).

    Google Scholar 

  159. 159.

    Hohwu, L., Li, J., Olsen, J., Sorensen, T. I. & Obel, C. Severe maternal stress exposure due to bereavement before, during and after pregnancy and risk of overweight and obesity in young adult men: a Danish National Cohort Study. PLoS ONE 9, e97490 (2014).

    PubMed  PubMed Central  Google Scholar 

  160. 160.

    Jasarevic, E. et al. The maternal vaginal microbiome partially mediates the effects of prenatal stress on offspring gut and hypothalamus. Nat. Neurosci. 21, 1061–1071 (2018).

    CAS  PubMed  Google Scholar 

  161. 161.

    Mueller, N. T. et al. Prenatal exposure to antibiotics, cesarean section and risk of childhood obesity. Int. J. Obes. 39, 665–670 (2015).

    CAS  Google Scholar 

  162. 162.

    Dominguez-Bello, M. G. et al. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc. Natl Acad. Sci. USA 107, 11971–11975 (2010).

    PubMed  Google Scholar 

  163. 163.

    Marcobal, A. & Sonnenburg, J. L. Human milk oligosaccharide consumption by intestinal microbiota. Clin. Microbiol. Infect. 18, 12–15 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  164. 164.

    Roger, L. C., Costabile, A., Holland, D. T., Hoyles, L. & McCartney, A. L. Examination of faecal Bifidobacterium populations in breast- and formula-fed infants during the first 18 months of life. Microbiology 156, 3329–3341 (2010).

    CAS  PubMed  Google Scholar 

  165. 165.

    Tamburini, S., Shen, N., Wu, H. C. & Clemente, J. C. The microbiome in early life: implications for health outcomes. Nat. Med. 22, 713–722 (2016).

    CAS  PubMed  Google Scholar 

  166. 166.

    Hart, A. L. et al. Modulation of human dendritic cell phenotype and function by probiotic bacteria. Gut 53, 1602–1609 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  167. 167.

    O’Sullivan, A., Farver, M. & Smilowitz, J. T. The influence of early infant-feeding practices on the intestinal microbiome and body composition in infants. Nutr. Metab. Insights 8, 1–9 (2015).

    PubMed  PubMed Central  Google Scholar 

  168. 168.

    Bogen, D. L., Hanusa, B. H. & Whitaker, R. C. The effect of breast-feeding with and without formula use on the risk of obesity at 4 years of age. Obes. Res. 12, 1527–1535 (2004).

    PubMed  Google Scholar 

  169. 169.

    Koenig, J. E. et al. Succession of microbial consortia in the developing infant gut microbiome. Proc. Natl Acad. Sci. USA 108, 4578–4585 (2011).

    CAS  PubMed  Google Scholar 

  170. 170.

    Forbes, J. D. et al. Association of exposure to formula in the hospital and subsequent infant feeding practices with gut microbiota and risk of overweight in the first year of life. JAMA Pediatr. 172, e181161 (2018).

    PubMed  PubMed Central  Google Scholar 

  171. 171.

    Yan, J., Liu, L., Zhu, Y., Huang, G. & Wang, P. P. The association between breastfeeding and childhood obesity: a meta-analysis. BMC Public. Health 14, 1267 (2014).

    PubMed  PubMed Central  Google Scholar 

  172. 172.

    Monteiro, C. A. et al. Household availability of ultra-processed foods and obesity in nineteen European countries. Public. Health Nutr. 21, 18–26 (2018).

    PubMed  Google Scholar 

  173. 173.

    Martinez Steele, E. et al. Ultra-processed foods and added sugars in the US diet: evidence from a nationally representative cross-sectional study. BMJ Open 6, e009892 (2016).

    PubMed  PubMed Central  Google Scholar 

  174. 174.

    Hall, K. D. Did the food environment cause the obesity epidemic? Obesity 26, 11–13 (2018).

    PubMed  Google Scholar 

  175. 175.

    Sadeghirad, B., Duhaney, T., Motaghipisheh, S., Campbell, N. R. & Johnston, B. C. Influence of unhealthy food and beverage marketing on children’s dietary intake and preference: a systematic review and meta-analysis of randomized trials. Obes. Rev. 17, 945–959 (2016).

    CAS  PubMed  Google Scholar 

  176. 176.

    Uribe, R. & Fuentes-Garcia, A. The effects of TV unhealthy food brand placement on children. Its separate and joint effect with advertising. Appetite 91, 165–172 (2015).

    PubMed  Google Scholar 

  177. 177.

    Hicks, L. A., Taylor, T. H. Jr. & Hunkler, R. J. U.S. outpatient antibiotic prescribing, 2010. N. Engl. J. Med. 368, 1461–1462 (2013).

    CAS  PubMed  Google Scholar 

  178. 178.

    Stark, C. M., Susi, A., Emerick, J. & Nylund, C. M. Antibiotic and acid-suppression medications during early childhood are associated with obesity. Gut 68, 62–69 (2019).

    CAS  PubMed  Google Scholar 

  179. 179.

    Yassour, M. et al. Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability. Sci. Transl Med. 8, 343ra381 (2016).

    Google Scholar 

  180. 180.

    Ajslev, T. A., Andersen, C. S., Gamborg, M., Sorensen, T. I. & Jess, T. Childhood overweight after establishment of the gut microbiota: the role of delivery mode, pre-pregnancy weight and early administration of antibiotics. Int. J. Obes. 35, 522–529 (2011).

    CAS  Google Scholar 

  181. 181.

    Cox, L. M. et al. Altering the intestinal microbiota during a critical developmental window has lasting metabolic consequences. Cell 158, 705–721 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  182. 182.

    Candon, S. et al. Antibiotics in early life alter the gut microbiome and increase disease incidence in a spontaneous mouse model of autoimmune insulin-dependent diabetes. PLoS ONE 10, e0125448 (2015).

    PubMed  PubMed Central  Google Scholar 

  183. 183.

    Gaskins, H. R., Collier, C. T. & Anderson, D. B. Antibiotics as growth promotants: mode of action. Anim. Biotechnol. 13, 29–42 (2002).

    CAS  PubMed  Google Scholar 

  184. 184.

    Cho, I. et al. Antibiotics in early life alter the murine colonic microbiome and adiposity. Nature 488, 621–626 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  185. 185.

    Ruiz, V. E. et al. A single early-in-life macrolide course has lasting effects on murine microbial network topology and immunity. Nat. Commun. 8, 518 (2017).

    PubMed  PubMed Central  Google Scholar 

  186. 186.

    Hemmingsson, E. Early childhood obesity risk factors: socioeconomic adversity, family dysfunction, offspring distress, and junk food self-medication. Curr. Obes. Rep. 7, 204–209 (2018).

    PubMed  PubMed Central  Google Scholar 

  187. 187.

    Farr, O. M. et al. Posttraumatic stress disorder, alone or additively with early life adversity, is associated with obesity and cardiometabolic risk. Nutr. Metab. Cardiovasc. Dis. 25, 479–488 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  188. 188.

    Hemmingsson, E., Johansson, K. & Reynisdottir, S. Effects of childhood abuse on adult obesity: a systematic review and meta-analysis. Obes. Rev. 15, 882–893 (2014).

    CAS  PubMed  Google Scholar 

  189. 189.

    Forster, G. L., Anderson, E. M., Scholl, J. L., Lukkes, J. L. & Watt, M. J. Negative consequences of early-life adversity on substance use as mediated by corticotropin-releasing factor modulation of serotonin activity. Neurobiol. Stress. 9, 29–39 (2018).

    PubMed  PubMed Central  Google Scholar 

  190. 190.

    Whitesell, N. R. et al. Childhood exposure to adversity and risk of substance-use disorder in two American Indian populations: the meditational role of early substance-use initiation. J. Stud. Alcohol. Drugs 70, 971–981 (2009).

    PubMed  PubMed Central  Google Scholar 

  191. 191.

    Moffett, M. C. et al. Maternal separation alters drug intake patterns in adulthood in rats. Biochem. Pharmacol. 73, 321–330 (2007).

    CAS  PubMed  Google Scholar 

  192. 192.

    Moussaoui, N. et al. Chronic early-life stress in rat pups alters basal corticosterone, intestinal permeability, and fecal microbiota at weaning: influence of sex. J. Neurogastroenterol. Motil. 23, 135–143 (2017).

    PubMed  PubMed Central  Google Scholar 

  193. 193.

    Isohookana, R., Marttunen, M., Hakko, H., Riipinen, P. & Riala, K. The impact of adverse childhood experiences on obesity and unhealthy weight control behaviors among adolescents. Compr. Psychiatry 71, 17–24 (2016).

    PubMed  Google Scholar 

  194. 194.

    Windle, M. et al. A multivariate analysis of adverse childhood experiences and health behaviors and outcomes among college students. J. Am. Coll. Health 66, 246–251 (2018).

    PubMed  PubMed Central  Google Scholar 

  195. 195.

    Campbell, J. A., Farmer, G. C., Nguyen-Rodriguez, S., Walker, R. J. & Egede, L. E. Using path analysis to examine the relationship between sexual abuse in childhood and diabetes in adulthood in a sample of US adults. Prev. Med. 108, 1–7 (2018).

    PubMed  Google Scholar 

  196. 196.

    Van Niel, C., Pachter, L. M., Wade, R. Jr., Felitti, V. J. & Stein, M. T. Adverse events in children: predictors of adult physical and mental conditions. J. Dev. Behav. Pediatr. 35, 549–551 (2014).

    PubMed  Google Scholar 

  197. 197.

    Osadchiy, V. et al. History of early life adversity is associated with increased food addiction and sex-specific alterations in reward network connectivity in obesity. Obes. Sci. Pract. 5, 416–436 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  198. 198.

    Martin, B. et al. Sex-dependent metabolic, neuroendocrine, and cognitive responses to dietary energy restriction and excess. Endocrinology 148, 4318–4333 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  199. 199.

    Inam, Q. U., Ikram, H., Shireen, E. & Haleem, D. J. Effects of sugar rich diet on brain serotonin, hyperphagia and anxiety in animal model of both genders. Pak. J. Pharm. Sci. 29, 757–763 (2016).

    CAS  PubMed  Google Scholar 

  200. 200.

    Osadchiy, V. et al. Correlation of tryptophan metabolites with connectivity of extended central reward network in healthy subjects. PLoS ONE 13, e0201772 (2018).

    PubMed  PubMed Central  Google Scholar 

  201. 201.

    Leigh, S. J., Lee, F. & Morris, M. J. Hyperpalatability and the generation of obesity: roles of environment, stress exposure and individual difference. Curr. Obes. Rep. 7, 6–18 (2018).

    PubMed  Google Scholar 

  202. 202.

    Chao, A., Grilo, C. M., White, M. A. & Sinha, R. Food cravings, food intake, and weight status in a community-based sample. Eat. Behav. 15, 478–482 (2014).

    PubMed  PubMed Central  Google Scholar 

  203. 203.

    Mozaffarian, D., Hao, T., Rimm, E. B., Willett, W. C. & Hu, F. B. Changes in diet and lifestyle and long-term weight gain in women and men. N. Engl. J. Med. 364, 2392–2404 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  204. 204.

    Schulte, E. M., Joyner, M. A., Potenza, M. N., Grilo, C. M. & Gearhardt, A. N. Current considerations regarding food addiction. Curr. Psychiatry Rep. 17, 563 (2015).

    PubMed  Google Scholar 

  205. 205.

    Nunes-Neto, P. R. et al. Food addiction: prevalence, psychopathological correlates and associations with quality of life in a large sample. J. Psychiatr. Res. 96, 145–152 (2018).

    PubMed  Google Scholar 

  206. 206.

    Wu, G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  207. 207.

    Xu, Z. & Knight, R. Dietary effects on human gut microbiome diversity. Br. J. Nutr. 113, S1–S5 (2015).

    CAS  PubMed  Google Scholar 

  208. 208.

    English, L., Lasschuijt, M. & Keller, K. L. Mechanisms of the portion size effect. What is known where do we go here? Appetite 88, 39–49 (2015).

    PubMed  Google Scholar 

  209. 209.

    Kerr, M. A., McCann, M. T. & Livingstone, M. B. Food and the consumer: could labelling be the answer? Proc. Nutr. Soc. 74, 158–163 (2015).

    PubMed  Google Scholar 

  210. 210.

    Lucan, S. C., Maroko, A. R., Sanon, O. C. & Schechter, C. B. Unhealthful food-and-beverage advertising in subway stations: targeted marketing, vulnerable groups, dietary intake, and poor health. J. Urban. Health 94, 220–232 (2017).

    PubMed  PubMed Central  Google Scholar 

  211. 211.

    Coates, A. E., Hardman, C. A., Halford, J. C. G., Christiansen, P. & Boyland, E. J. Social media influencer marketing and children’s food intake: a randomized trial. Pediatrics 143, e20182554 (2019).

    PubMed  Google Scholar 

  212. 212.

    Norman, J. et al. Sustained impact of energy-dense TV and online food advertising on children’s dietary intake: a within-subject, randomised, crossover, counter-balanced trial. Int. J. Behav. Nutr. Phys. Act. 15, 37 (2018).

    PubMed  PubMed Central  Google Scholar 

  213. 213.

    Folkvord, F., Anschutz, D. J., Wiers, R. W. & Buijzen, M. The role of attentional bias in the effect of food advertising on actual food intake among children. Appetite 84, 251–258 (2015).

    PubMed  Google Scholar 

  214. 214.

    Deglaire, A. et al. Associations between weight status and liking scores for sweet, salt and fat according to the gender in adults (the Nutrinet-Sante study). Eur. J. Clin. Nutr. 69, 40–46 (2015).

    CAS  PubMed  Google Scholar 

  215. 215.

    Geiker, N. R. W. et al. Does stress influence sleep patterns, food intake, weight gain, abdominal obesity and weight loss interventions and vice versa? Obes. Rev. 19, 81–97 (2018).

    CAS  PubMed  Google Scholar 

  216. 216.

    Chao, A., Grilo, C. M., White, M. A. & Sinha, R. Food cravings mediate the relationship between chronic stress and body mass index. J. Health Psychol. 20, 721–729 (2015).

    PubMed  PubMed Central  Google Scholar 

  217. 217.

    Sinha, R. Chronic stress, drug use, and vulnerability to addiction. Ann. NY Acad. Sci. 1141, 105–130 (2008).

    CAS  PubMed  Google Scholar 

  218. 218.

    Chao, A. M., Jastreboff, A. M., White, M. A., Grilo, C. M. & Sinha, R. Stress, cortisol, and other appetite-related hormones: prospective prediction of 6-month changes in food cravings and weight. Obesity 25, 713–720 (2017).

    CAS  PubMed  Google Scholar 

  219. 219.

    Dallman, M. F. Stress-induced obesity and the emotional nervous system. Trends Endocrinol. Metab. 21, 159–165 (2010).

    CAS  PubMed  Google Scholar 

  220. 220.

    Christiansen, A. M., Dekloet, A. D., Ulrich-Lai, Y. M. & Herman, J. P. “Snacking” causes long term attenuation of HPA axis stress responses and enhancement of brain FosB/deltaFosB expression in rats. Physiol. Behav. 103, 111–116 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  221. 221.

    Ulrich-Lai, Y. M. et al. Pleasurable behaviors reduce stress via brain reward pathways. Proc. Natl Acad. Sci. USA 107, 20529–20534 (2010).

    CAS  PubMed  Google Scholar 

  222. 222.

    Bharwani, A. et al. Structural & functional consequences of chronic psychosocial stress on the microbiome & host. Psychoneuroendocrinology 63, 217–227 (2016).

    CAS  PubMed  Google Scholar 

  223. 223.

    Depommier, C. et al. Supplementation with Akkermansia muciniphila in overweight and obese human volunteers: a proof-of-concept exploratory study. Nat. Med. 25, 1096–1103 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  224. 224.

    Gheorghe, C. E. et al. Focus on the essentials: tryptophan metabolism and the microbiome-gut-brain axis. Curr. Opin. Pharmacol. 48, 137–145 (2019).

    CAS  PubMed  Google Scholar 

  225. 225.

    Osadchiy, V., Martin, C. R. & Mayer, E. A. Gut microbiome and modulation of CNS function. Compr. Physiol. 10, 57–72 (2019).

    PubMed  Google Scholar 

  226. 226.

    O’Mahony, S. M., Clarke, G., Borre, Y. E., Dinan, T. G. & Cryan, J. F. Serotonin, tryptophan metabolism and the brain-gut-microbiome axis. Behav. Brain Res. 277, 32–48 (2015).

    PubMed  Google Scholar 

  227. 227.

    Wikoff, W. R. et al. Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc. Natl Acad. Sci. USA 106, 3698–3703 (2009).

    CAS  PubMed  Google Scholar 

  228. 228.

    Yano, J. M. et al. Indigenous bacteria from the gut microbiota regulate host serotonin biosynthesis. Cell 161, 264–276 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  229. 229.

    Kim, D. Y. & Camilleri, M. Serotonin: a mediator of the brain-gut connection. Am. J. Gastroenterol. 95, 2698–2709 (2000).

    CAS  PubMed  Google Scholar 

  230. 230.

    Hood, S. D., Bell, C. J. & Nutt, D. J. Acute tryptophan depletion. Part I: rationale and methodology. Aust. N. Z. J. Psychiatry 39, 558–564 (2005).

    PubMed  Google Scholar 

  231. 231.

    Pagoto, S. L. et al. Acute tryptophan depletion and sweet food consumption by overweight adults. Eat. Behav. 10, 36–41 (2009).

    PubMed  Google Scholar 

  232. 232.

    Roager, H. M. & Licht, T. R. Microbial tryptophan catabolites in health and disease. Nat. Commun. 9, 3294 (2018).

    PubMed  PubMed Central  Google Scholar 

  233. 233.

    Bender, D. A. Biochemistry of tryptophan in health and disease. Mol. Asp. Med. 6, 101–197 (1983).

    CAS  Google Scholar 

  234. 234.

    Schwarcz, R., Bruno, J. P., Muchowski, P. J. & Wu, H. Q. Kynurenines in the mammalian brain: when physiology meets pathology. Nat. Rev. Neurosci. 13, 465–477 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  235. 235.

    Marin, I. A. et al. Microbiota alteration is associated with the development of stress-induced despair behavior. Sci. Rep. 7, 43859 (2017).

    PubMed  PubMed Central  Google Scholar 

  236. 236.

    Stavrum, A. K., Heiland, I., Schuster, S., Puntervoll, P. & Ziegler, M. Model of tryptophan metabolism, readily scalable using tissue-specific gene expression data. J. Biol. Chem. 288, 34555–34566 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  237. 237.

    Favennec, M. et al. The kynurenine pathway is activated in human obesity and shifted toward kynurenine monooxygenase activation. Obesity 23, 2066–2074 (2015).

    CAS  PubMed  Google Scholar 

  238. 238.

    Kennedy, P. J., Cryan, J. F., Dinan, T. G. & Clarke, G. Kynurenine pathway metabolism and the microbiota-gut-brain axis. Neuropharmacology 112, 399–412 (2017).

    CAS  PubMed  Google Scholar 

  239. 239.

    Chimerel, C. et al. Bacterial metabolite indole modulates incretin secretion from intestinal enteroendocrine L cells. Cell Rep. 9, 1202–1208 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  240. 240.

    Mangge, H. et al. Obesity-related dysregulation of the tryptophan-kynurenine metabolism: role of age and parameters of the metabolic syndrome. Obesity 22, 195–201 (2014).

    CAS  PubMed  Google Scholar 

  241. 241.

    Buckman, L. B. et al. Obesity induced by a high-fat diet is associated with increased immune cell entry into the central nervous system. Brain Behav. Immun. 35, 33–42 (2014).

    CAS  PubMed  Google Scholar 

  242. 242.

    Teixeira, T. F., Collado, M. C., Ferreira, C. L., Bressan, J. & Peluzio, M. do C. Potential mechanisms for the emerging link between obesity and increased intestinal permeability. Nutr. Res. 32, 637–647 (2012).

    CAS  PubMed  Google Scholar 

  243. 243.

    Sonnenburg, E. D. et al. Diet-induced extinctions in the gut microbiota compound over generations. Nature 529, 212–215 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  244. 244.

    Rasoamanana, R., Even, P. C., Darcel, N., Tome, D. & Fromentin, G. Dietary fibers reduce food intake by satiation without conditioned taste aversion in mice. Physiol. Behav. 110-111, 13–19 (2013).

    CAS  PubMed  Google Scholar 

  245. 245.

    Tailford, L. E., Crost, E. H., Kavanaugh, D. & Juge, N. Mucin glycan foraging in the human gut microbiome. Front. Genet. 6, 81 (2015).

    PubMed  PubMed Central  Google Scholar 

  246. 246.

    Shen, W. et al. Intestinal and systemic inflammatory responses are positively associated with sulfidogenic bacteria abundance in high-fat-fed male C57BL/6J mice. J. Nutr. 144, 1181–1187 (2014).

    CAS  PubMed  Google Scholar 

  247. 247.

    Ding, S. et al. High-fat diet: bacteria interactions promote intestinal inflammation which precedes and correlates with obesity and insulin resistance in mouse. PLoS ONE 5, e12191 (2010).

    PubMed  PubMed Central  Google Scholar 

  248. 248.

    Cani, P. D. et al. Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes 56, 1761–1772 (2007).

    CAS  PubMed  Google Scholar 

  249. 249.

    Chang, M., Alsaigh, T., Kistler, E. B. & Schmid-Schonbein, G. W. Breakdown of mucin as barrier to digestive enzymes in the ischemic rat small intestine. PLoS ONE 7, e40087 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  250. 250.

    Hotamisligil, G. S. Inflammation and metabolic disorders. Nature 444, 860–867 (2006).

    CAS  PubMed  Google Scholar 

  251. 251.

    Wang, Q., Liu, D., Song, P. & Zou, M. H. Tryptophan-kynurenine pathway is dysregulated in inflammation, and immune activation. Front. Biosci. 20, 1116–1143 (2015).

    CAS  Google Scholar 

  252. 252.

    Larraufie, P., Dore, J., Lapaque, N. & Blottiere, H. M. TLR ligands and butyrate increase Pyy expression through two distinct but inter-regulated pathways. Cell Microbiol. 19, e12648 (2017).

    Google Scholar 

  253. 253.

    Palazzo, M. et al. Activation of enteroendocrine cells via TLRs induces hormone, chemokine, and defensin secretion. J. Immunol. 178, 4296–4303 (2007).

    CAS  PubMed  Google Scholar 

  254. 254.

    Kidd, M., Gustafsson, B. I., Drozdov, I. & Modlin, I. M. IL1beta- and LPS-induced serotonin secretion is increased in EC cells derived from Crohn’s disease. Neurogastroenterol. Motil. 21, 439–450 (2009).

    CAS  PubMed  Google Scholar 

  255. 255.

    de Lartigue, G., Ronveaux, C. C. & Raybould, H. E. Vagal plasticity the key to obesity. Mol. Metab. 3, 855–856 (2014).

    PubMed  PubMed Central  Google Scholar 

  256. 256.

    de Lartigue, G., Barbier de la Serre, C., Espero, E., Lee, J. & Raybould, H. E. Diet-induced obesity leads to the development of leptin resistance in vagal afferent neurons. Am. J. Physiol. Endocrinol. Metab. 301, E187–E195 (2011).

    PubMed  PubMed Central  Google Scholar 

  257. 257.

    de Lartigue, G., de La Serre, C. B. & Raybould, H. E. Vagal afferent neurons in high fat diet-induced obesity; intestinal microflora, gut inflammation and cholecystokinin. Physiol. Behav. 105, 100–105 (2011).

    PubMed  PubMed Central  Google Scholar 

  258. 258.

    Qin, Y. et al. An obesity-associated gut microbiome reprograms the intestinal epigenome and leads to altered colonic gene expression. Genome Biol. 19, 7 (2018).

    PubMed  PubMed Central  Google Scholar 

  259. 259.

    Cani, P. D. et al. Selective increases of bifidobacteria in gut microflora improve high-fat-diet-induced diabetes in mice through a mechanism associated with endotoxaemia. Diabetologia 50, 2374–2383 (2007).

    CAS  PubMed  Google Scholar 

  260. 260.

    Peterli, R. et al. Effect of laparoscopic sleeve gastrectomy vs laparoscopic Roux-en-Y gastric bypass on weight loss in patients with morbid obesity: the SM-BOSS randomized clinical trial. JAMA 319, 255–265 (2018).

    PubMed  PubMed Central  Google Scholar 

  261. 261.

    Chang, S. H. et al. The effectiveness and risks of bariatric surgery: an updated systematic review and meta-analysis, 2003-2012. JAMA Surg. 149, 275–287 (2014).

    PubMed  PubMed Central  Google Scholar 

  262. 262.

    Scholtz, S. et al. Obese patients after gastric bypass surgery have lower brain-hedonic responses to food than after gastric banding. Gut 63, 891–902 (2014).

    PubMed  Google Scholar 

  263. 263.

    Pepino, M. Y. et al. Changes in taste perception and eating behavior after bariatric surgery-induced weight loss in women. Obesity 22, E13–E20 (2014).

    PubMed  Google Scholar 

  264. 264.

    Sanmiguel, C. et al. Bariatric surgery is associated with changes in the brain’s reward system architecture and eating behaviors. Gastroenterology 150, S824 (2016).

    Google Scholar 

  265. 265.

    Kanerva, N., Larsson, I., Peltonen, M., Lindroos, A. K. & Carlsson, L. M. Changes in total energy intake and macronutrient composition after bariatric surgery predict long-term weight outcome: findings from the Swedish Obese Subjects (SOS) study. Am. J. Clin. Nutr. 106, 136–145 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  266. 266.

    Konttinen, H., Peltonen, M., Sjostrom, L., Carlsson, L. & Karlsson, J. Psychological aspects of eating behavior as predictors of 10-y weight changes after surgical and conventional treatment of severe obesity: results from the Swedish Obese Subjects intervention study. Am. J. Clin. Nutr. 101, 16–24 (2015).

    CAS  PubMed  Google Scholar 

  267. 267.

    Makaronidis, J. M. et al. Reported appetite, taste and smell changes following Roux-en-Y gastric bypass and sleeve gastrectomy: effect of gender, type 2 diabetes and relationship to post-operative weight loss. Appetite 107, 93–105 (2016).

    PubMed  Google Scholar 

  268. 268.

    Pepino, M. Y., Stein, R. I., Eagon, J. C. & Klein, S. Bariatric surgery-induced weight loss causes remission of food addiction in extreme obesity. Obesity 22, 1792–1798 (2014).

    PubMed  Google Scholar 

  269. 269.

    Basso, N. et al. First-phase insulin secretion, insulin sensitivity, ghrelin, GLP-1, and PYY changes 72 h after sleeve gastrectomy in obese diabetic patients: the gastric hypothesis. Surg. Endosc. 25, 3540–3550 (2011).

    CAS  PubMed  Google Scholar 

  270. 270.

    le Roux, C. W. et al. Gut hormones as mediators of appetite and weight loss after Roux-en-Y gastric bypass. Ann. Surg. 246, 780–785 (2007).

    PubMed  Google Scholar 

  271. 271.

    Faulconbridge, L. F. et al. Changes in neural responsivity to highly palatable foods following Roux-en-Y gastric bypass, sleeve gastrectomy, or weight stability: an fMRI study. Obesity 24, 1054–1060 (2016).

    PubMed  Google Scholar 

  272. 272.

    Li, J. V. et al. Metabolic surgery profoundly influences gut microbial-host metabolic cross-talk. Gut 60, 1214–1223 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  273. 273.

    Li, M. et al. Symbiotic gut microbes modulate human metabolic phenotypes. Proc. Natl Acad. Sci. USA 105, 2117–2122 (2008).

    CAS  PubMed  Google Scholar 

  274. 274.

    Arora, T. et al. Roux-en-Y gastric bypass surgery induces early plasma metabolomic and lipidomic alterations in humans associated with diabetes remission. PLoS ONE 10, e0126401 (2015).

    PubMed  PubMed Central  Google Scholar 

  275. 275.

    Gralka, E. et al. Metabolomic fingerprint of severe obesity is dynamically affected by bariatric surgery in a procedure-dependent manner. Am. J. Clin. Nutr. 102, 1313–1322 (2015).

    CAS  PubMed  Google Scholar 

  276. 276.

    Ryan, K. K. et al. FXR is a molecular target for the effects of vertical sleeve gastrectomy. Nature 509, 183–188 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  277. 277.

    Coveleskie, K. et al. The effect of the GLP-1 analogue exenatide on functional connectivity within an NTS-based network in women with and without obesity. Obes. Sci. Pract. 3, 434–445 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  278. 278.

    Braas, D. et al. Dynamic changes in gut microbial derived indole and phenol products after bariatric surgery and its relationship to weight loss. Gastroenterology 154, S158 (2018).

    Google Scholar 

  279. 279.

    Jacobs, J. et al. Glutamate and hedonic eating: role of the brain-gut-microbiome axis on changes on hedonic eating after bariatric surgery. Gastroenterology 154, s201 (2018).

    Google Scholar 

  280. 280.

    Lee, C. J. et al. Changes in gut microbiome after bariatric surgery versus medical weight loss in a pilot randomized trial. Obes. Surg. 29, 3239–3245 (2019).

    PubMed  Google Scholar 

  281. 281.

    Guo, Y. et al. Modulation of the gut microbiome: a systematic review of the effect of bariatric surgery. Eur. J. Endocrinol. 178, 43–56 (2018).

    CAS  PubMed  Google Scholar 

  282. 282.

    Luijten, J., Vugts, G., Nieuwenhuijzen, G. A. P. & Luyer, M. D. P. The importance of the microbiome in bariatric surgery: a systematic review. Obes. Surg. 29, 2338–2349 (2019).

    PubMed  Google Scholar 

  283. 283.

    Monte, S. V. et al. Reduction in endotoxemia, oxidative and inflammatory stress, and insulin resistance after Roux-en-Y gastric bypass surgery in patients with morbid obesity and type 2 diabetes mellitus. Surgery 151, 587–593 (2012).

    PubMed  Google Scholar 

  284. 284.

    Iannelli, A., Anty, R., Schneck, A. S., Tran, A. & Gugenheim, J. Inflammation, insulin resistance, lipid disturbances, anthropometrics, and metabolic syndrome in morbidly obese patients: a case control study comparing laparoscopic Roux-en-Y gastric bypass and laparoscopic sleeve gastrectomy. Surgery 149, 364–370 (2011).

    PubMed  Google Scholar 

  285. 285.

    Iannelli, A. et al. Body composition, anthropometrics, energy expenditure, systemic inflammation, in premenopausal women 1 year after laparoscopic Roux-en-Y gastric bypass. Surg. Endosc. 28, 500–507 (2014).

    PubMed  Google Scholar 

  286. 286.

    Yadav, R. et al. Effect of Roux-en-Y bariatric surgery on lipoproteins, insulin resistance, and systemic and vascular inflammation in obesity and diabetes. Front. Immunol. 8, 1512 (2017).

    PubMed  PubMed Central  Google Scholar 

  287. 287.

    Peng, Y., Li, J. Z., You, M. & Murr, M. M. Roux-en-Y gastric bypass improves glucose homeostasis, reduces oxidative stress and inflammation in livers of obese rats and in Kupffer cells via an AMPK-dependent pathway. Surgery 162, 59–67 (2017).

    PubMed  Google Scholar 

  288. 288.

    Lindegaard, K. K., Jorgensen, N. B., Just, R., Heegaard, P. M. & Madsbad, S. Effects of Roux-en-Y gastric bypass on fasting and postprandial inflammation-related parameters in obese subjects with normal glucose tolerance and in obese subjects with type 2 diabetes. Diabetol. Metab. Syndr. 7, 12 (2015).

    PubMed  PubMed Central  Google Scholar 

  289. 289.

    van de Sande-Lee, S. et al. Partial reversibility of hypothalamic dysfunction and changes in brain activity after body mass reduction in obese subjects. Diabetes 60, 1699–1704 (2011).

    PubMed  PubMed Central  Google Scholar 

  290. 290.

    Blackburn, A. N., Hajnal, A. & Leggio, L. The gut in the brain: the effects of bariatric surgery on alcohol consumption. Addict. Biol. 22, 1540–1553 (2017).

    PubMed  Google Scholar 

  291. 291.

    Vrieze, A. et al. Transfer of intestinal microbiota from lean donors increases insulin sensitivity in individuals with metabolic syndrome. Gastroenterology 143, 913–916 (2012).

    CAS  PubMed  Google Scholar 

  292. 292.

    Kootte, R. S. et al. Improvement of insulin sensitivity after lean donor feces in metabolic syndrome is driven by baseline intestinal microbiota composition. Cell Metab. 26, 611–619 (2017).

    CAS  PubMed  Google Scholar 

  293. 293.

    Dailey, F. E., Turse, E. P., Daglilar, E. & Tahan, V. The dirty aspects of fecal microbiota transplantation: a review of its adverse effects and complications. Curr. Opin. Pharmacol. 49, 29–33 (2019).

    CAS  PubMed  Google Scholar 

  294. 294.

    Longo, V. D. & Panda, S. Fasting, circadian rhythms, and time-restricted feeding in healthy lifespan. Cell Metab. 23, 1048–1059 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  295. 295.

    Melkani, G. C. & Panda, S. Time-restricted feeding for prevention and treatment of cardiometabolic disorders. J. Physiol. 595, 3691–3700 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  296. 296.

    Di Francesco, A., Di Germanio, C., Bernier, M. & de Cabo, R. A time to fast. Science 362, 770–775 (2018).

    PubMed  Google Scholar 

  297. 297.

    Kohsaka, A. et al. High-fat diet disrupts behavioral and molecular circadian rhythms in mice. Cell Metab. 6, 414–421 (2007).

    CAS  Google Scholar 

  298. 298.

    Hatori, M. et al. Time-restricted feeding without reducing caloric intake prevents metabolic diseases in mice fed a high-fat diet. Cell Metab. 15, 848–860 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  299. 299.

    Gill, S. & Panda, S. A smartphone app reveals erratic diurnal eating patterns in humans that can be modulated for health benefits. Cell Metab. 22, 789–798 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  300. 300.

    Thaiss, C. A. et al. Transkingdom control of microbiota diurnal oscillations promotes metabolic homeostasis. Cell 159, 514–529 (2014).

    CAS  PubMed  Google Scholar 

  301. 301.

    Racz, B., Duskova, M., Starka, L., Hainer, V. & Kunesova, M. Links between the circadian rhythm, obesity and the microbiome. Physiol. Res. 67, S409–S420 (2018).

    CAS  PubMed  Google Scholar 

  302. 302.

    Ara, R. et al. What is the clinical effectiveness and cost-effectiveness of using drugs in treating obese patients in primary care? A systematic review. Health Technol. Assess. 16, 1–195 (2012).

    Google Scholar 

  303. 303.

    Shin, J. H. & Gadde, K. M. Clinical utility of phentermine/topiramate (Qsymia) combination for the treatment of obesity. Diabetes Metab. Syndr. Obes. 6, 131–139 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  304. 304.

    Hainer, V. & Aldhoon-Hainerova, I. Tolerability and safety of the new anti-obesity medications. Drug. Saf. 37, 693–702 (2014).

    CAS  PubMed  Google Scholar 

  305. 305.

    Billes, S. K., Sinnayah, P. & Cowley, M. A. Naltrexone/bupropion for obesity: an investigational combination pharmacotherapy for weight loss. Pharmacol. Res. 84, 1–11 (2014).

    CAS  PubMed  Google Scholar 

  306. 306.

    Wellman, P. J. & Maher, T. J. Synergistic interactions between fenfluramine and phentermine. Int. J. Obes. Relat. Metab. Disord. 23, 723–732 (1999).

    CAS  PubMed  Google Scholar 

  307. 307.

    Lam, D. D. et al. Serotonin 5-HT2C receptor agonist promotes hypophagia via downstream activation of melanocortin 4 receptors. Endocrinology 149, 1323–1328 (2008).

    CAS  PubMed  Google Scholar 

  308. 308.

    McElroy, S. L. et al. Topiramate for the treatment of binge eating disorder associated with obesity: a placebo-controlled study. Biol. Psychiatry 61, 1039–1048 (2007).

    CAS  PubMed  Google Scholar 

  309. 309.

    Anderberg, R. H. et al. Glucagon-like peptide 1 and its analogs act in the dorsal raphe and modulate central serotonin to reduce appetite and body weight. Diabetes 66, 1062–1073 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  310. 310.

    Wang, Z. et al. Gut microbiome differences between metformin- and liraglutide-treated T2DM subjects. Endocrinol. Diabetes Metab. 1, e00009 (2018).

    PubMed  Google Scholar 

  311. 311.

    Foster, D., Sanchez-Collins, S. & Cheskin, L. J. Multidisciplinary team-based obesity treatment in patients with diabetes: current practices and the state of the science. Diabetes Spectr. 30, 244–249 (2017).

    PubMed  PubMed Central  Google Scholar 

  312. 312.

    Cooper, Z. & Fairburn, C. G. A new cognitive behavioural approach to the treatment of obesity. Behav. Res. Ther. 39, 499–511 (2001).

    CAS  PubMed  Google Scholar 

  313. 313.

    Klumpp, H., Fitzgerald, D. A., Angstadt, M., Post, D. & Phan, K. L. Neural response during attentional control and emotion processing predicts improvement after cognitive behavioral therapy in generalized social anxiety disorder. Psychol. Med. 44, 3109–3121 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  314. 314.

    Jensen, K. B. et al. Cognitive behavioral therapy increases pain-evoked activation of the prefrontal cortex in patients with fibromyalgia. Pain 153, 1495–1503 (2012).

    PubMed  Google Scholar 

  315. 315.

    Seminowicz, D. A. et al. Cognitive-behavioral therapy increases prefrontal cortex gray matter in patients with chronic pain. J. Pain. 14, 1573–1584 (2013).

    PubMed  Google Scholar 

  316. 316.

    Brunoni, A. R. et al. Cognitive control therapy and transcranial direct current stimulation for depression: a randomized, double-blinded, controlled trial. J. Affect. Disord. 162, 43–49 (2014).

    CAS  PubMed  Google Scholar 

  317. 317.

    Clay, S. W., Allen, J. & Parran, T. A review of addiction. Postgrad. Med. 120, E01–E07 (2008).

    PubMed  Google Scholar 

  318. 318.

    Labus, J. et al. Randomised clinical trial: symptoms of the irritable bowel syndrome are improved by a psycho-education group intervention. Aliment. Pharmacol. Ther. 37, 304–315 (2013).

    CAS  PubMed  Google Scholar 

  319. 319.

    An, H., He, R. H., Zheng, Y. R. & Tao, R. Cognitive-behavioral therapy. Adv. Exp. Med. Biol. 1010, 321–329 (2017).

    PubMed  Google Scholar 

  320. 320.

    Sawamoto, R. et al. Predictors of successful long-term weight loss maintenance: a two-year follow-up. Biopsychosoc. Med. 11, 14 (2017).

    PubMed  PubMed Central  Google Scholar 

  321. 321.

    O’reilly, G. A., Cook, L., Spruijt-Metz, D. & Black, D. S. Mindfulness-based interventions for obesity-related eating behaviours: a literature review. Obes. Rev. 15, 453–461 (2014).

    PubMed  PubMed Central  Google Scholar 

  322. 322.

    Lappalainen, R. et al. The effectiveness and applicability of different lifestyle interventions for enhancing wellbeing: the study design for a randomized controlled trial for persons with metabolic syndrome risk factors and psychological distress. BMC Public Health 14, 310 (2014).

    PubMed  PubMed Central  Google Scholar 

  323. 323.

    Cani, P. D. & Everard, A. Talking microbes: when gut bacteria interact with diet and host organs. Mol. Nutr. Food Res. 60, 58–66 (2016).

    CAS  PubMed  Google Scholar 

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Acknowledgements

We acknowledge C. P. Sanmiguel for her contributions in making editorial suggestions to the gut-directed therapies section of this review and C. Liu for invaluable editorial services. E.A.M. has been supported by grants from the National Institute of Diabetes and Digestive and Kidney Diseases (DK048351, DK064539 and DK096606). A.G. has been supported by grants from the National Institute of Diabetes and Digestive and Kidney Diseases (DK106528) and CURE at the University of California, Los Angeles/Clinical and Translational Science Institute (ULTR001881/DK041301).

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Correspondence to Emeran A. Mayer.

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E.A.M. serves on the scientific advisory boards of Amare, APC Microbiome Ireland, Axial Biotherapeutics, Bloom Science, Danone, Mahana Therapeutics, Pendulum and Viome. A.G. and V.O. declare no competing interests.

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Nature Reviews Gastroenterology & Hepatology thanks R. Brown, E. Jerlhag, P. Kenny and L. Leggio for their contribution to the peer review of this work.

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Glossary

Systems biology

An interdisciplinary field of study that focuses on complex interactions within multiple biological systems, rather than focusing on individual mechanisms.

Hedonic-driven eating behaviour

The continued consumption of highly palatable foods even after energy requirements have been met (also known as ‘food addiction’).

Dopaminergic reward system

The extensive network of neurons in the extended reward network that depend on dopamine as the primary neurotransmitter for reward-related processing.

Extended reward network

A network comprising interconnecting brain networks such as reward and salience networks, associated with processing of reward stimuli and modulation of food-seeking behaviours (used interchangeably with ‘greater reward system’).

Neural substrates

A brain region or network associated with a specific behaviour.

Cortical performance monitoring

Processes associated with reward sensitivity, motivation, interoceptive awareness, stress reactivity and self-control.

Nucleus accumbens

Region of the basal ganglia and a key hub for the core reward system, responsible for many dopaminergic processes, especially those related to pleasure, motivation and aversion.

Ventral tegmental area

Key region of the midbrain that houses the dopaminergic cell bodies that project to all regions of the core and extended reward network.

Salience network

The brain network responsible for monitoring the homeostatic state of the body to make adaptive adjustments to real or expected disturbances in homeostasis through the autonomic nervous system and behavioural responses.

Corticostriatal communication

The extensive communication network between the cortex, which houses the extended reward network (including the frontal cortex and insula) and the striatum, which houses the core reward network (nucleus accumbens, basal ganglia).

Prebiotic

Dietary fibre or other substrates that can only be digested by commensal gut microorganisms, thereby promoting gut microbiota diversity and health.

Maladaptive coping

Behaviours used to cope with stressful situations to alleviate the stress or symptoms, but are not necessarily healthy and do not address the core cause of the stress.

Psychosocial stress

Stress originating from the environment that is sufficient to cause dysregulation of homeostatic responses and physical or psychological symptoms.

Perceived stress

Stress from events in an individual’s life perceived as stressful. The most widely used scale for perceived stress is the Perceived Stress Scale.

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Gupta, A., Osadchiy, V. & Mayer, E.A. Brain–gut–microbiome interactions in obesity and food addiction. Nat Rev Gastroenterol Hepatol 17, 655–672 (2020). https://doi.org/10.1038/s41575-020-0341-5

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