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Physiology

Neuroanatomical differences in obesity: meta-analytic findings and their validation in an independent dataset

Abstract

Background

Obesity has been linked with subtle differences in brain structure. These differences tend to be especially relevant in prefrontal cortex regions, areas which play an important role in executive control. However, results in this field are often contradictory: although studies tend to report lower gray matter volume in relation to obesity, some have also observed null or positive associations. To overcome this issue, we conducted a meta-analysis on voxel-based morphometry (VBM) differences associated with obesity-related variables and validated the findings with an independent dataset.

Methods

The literature search included combinations of the following key words: (i) neuroimaging terms: MRI, gray matter, brain, magnetic resonance; (ii) obesity-related terms: obesity, obese, body mass, waist circumference, adiposity. We conducted the meta-analysis using Anisotropic Effect-Size Seed-Based d Mapping (AES-SDM) software. Twenty-one studies on obesity and VBM fulfilled our inclusion criteria, representing 5882 participants (54% females) aged 18–92 years. To examine the validity of our meta-analytic results, we additionally tested on an independent dataset (Human Connectome Project, n = 378 participants) whether mean VBM values obtained for each cluster showed correlations with body mass index (BMI).

Results

We found that obesity-related variables were consistently associated with lower gray matter volume in areas including the medial prefrontal cortex, bilateral cerebellum, and left temporal pole. The clusters showed negative associations between gray matter volume and BMI in the independent dataset, with the exception of one cluster in the cerebellum.

Conclusions

Our findings provide robust evidence that obesity and body mass are related to significantly lower gray matter volume in brain areas with a key role in executive control. These findings might suggest a neurobiological link between obesity and self-regulatory deficits.

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References

  1. 1.

    Kurth F, Levitt JG, Phillips OR, Luders E, Woods RP, Mazziotta JC, et al. Relationships between gray matter, body mass index, and waist circumference in healthy adults. Hum Brain Mapp. 2013;34:1737–46.

    Article  Google Scholar 

  2. 2.

    Walther K, Birdsill AC, Glisky EL, Ryan L. Structural brain differences and cognitive functioning related to body mass index in older females. Hum Brain Mapp. 2010;31:1052–64.

    Article  Google Scholar 

  3. 3.

    Taki Y, Kinomura S, Sato K, Inoue K, Goto R, Okada K, et al. Relationship between body mass index and gray matter volume in 1,428 healthy individuals. Obesity. 2007;16:119–24.

    Article  Google Scholar 

  4. 4.

    He Q, Chen C, Dong Q, Xue G, Chen C, Lu ZL, et al. Gray and white matter structures in the midcingulate cortex region contribute to body mass index in Chinese young adults. Brain Struct Funct. 2015;220:319–29.

    Article  Google Scholar 

  5. 5.

    Kharabian Masouleh S, Arélin K, Horstmann A, Lampe L, Kipping JA, Luck T, et al. Higher body mass index in older adults is associated with lower gray matter volume: implications for memory performance. Neurobiol Aging. 2016;40:1–10.

    Article  Google Scholar 

  6. 6.

    Raji CA, Ho AJ, Parikshak NN, Becker JT, Lopez OL, Kuller LH, et al. Brain structure and obesity. Hum Brain Mapp. 2010;31:353–64.

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Cieslik EC, Mueller VI, Eickhoff CR, Langner R, Eickhoff SB. Three key regions for supervisory attentional control: evidence from neuroimaging meta-analyses. Neurosci Biobehav Rev. 2014;48C:22–34.

    Google Scholar 

  8. 8.

    Zhang B, Tian X, Tian D, Wang J, Wang Q, Yu C, et al. Altered regional gray matter volume in obese men: a structural MRI study. Front Psychol. 2017;8:1–7.

    Google Scholar 

  9. 9.

    Kullmann S, Heni M, Veit R, Ketterer C, Schick F, Haring HU, et al. The obese brain: association of body mass index and insulin sensitivity with resting state network functional connectivity. Hum Brain Mapp. 2011;33:1052–61.

    Article  Google Scholar 

  10. 10.

    Horstmann A, Busse FP, Mathar D, Muller K, Lepsien J, Schlogl H, et al. Obesity-related differences between women and men in brain structure and goal-directed behavior. Front Hum Neurosci. 2011;5:58.

    Article  Google Scholar 

  11. 11.

    Weise CM, Piaggi P, Reinhardt M, Chen K, Savage CR, Krakoff J, et al. The obese brain as a heritable phenotype: a combined morphometry and twin study. Int J Obes. 2017;41:458–66.

    Article  CAS  Google Scholar 

  12. 12.

    Willette AA, Kapogiannis D, Does the brain shrink as the waist expands?. Ageing Res Rev. 2014;20:86–97.

    Article  Google Scholar 

  13. 13.

    Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International. Circulation. 2009;120:1640–5.

    Article  CAS  Google Scholar 

  14. 14.

    Tchernof A, Després J-P. Pathophysiology of human visceral obesity: an update. Physiol Rev. 2013;93:359–404.

    Article  CAS  Google Scholar 

  15. 15.

    Amato MC, Guarnotta V, Giordano C. Body composition assessment for the definition of cardiometabolic risk. J Endocrinol Invest. 2013;36:537–43.

    PubMed  CAS  Google Scholar 

  16. 16.

    Radua J, Mataix-Cols D, Phillips ML, El-Hage W, Kronhaus DM, Cardoner N, et al. A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps. Eur Psychiatry. 2012;27:605–11.

    Article  CAS  Google Scholar 

  17. 17.

    Radua J, Rubia K, Canales-Rodríguez EJ, Pomarol-Clotet E, Fusar-Poli P, Mataix-Cols D. Anisotropic kernels for coordinate-based meta-analyses of neuroimaging studies. Front Psychiatry. 2014;5:1–8.

    Article  Google Scholar 

  18. 18.

    Van Essen DC, Ugurbil K, Auerbach E, Barch D, Behrens TEJ, Bucholz R, et al. The human connectome project: a data acquisition perspective. Neuroimage. 2012;62:2222–31.

    Article  Google Scholar 

  19. 19.

    Coupe P, Yger P, Prima S, Hellier P, Kervrann C, Barillot C. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans Med Imaging. 2008;27:425–41.

    Article  CAS  Google Scholar 

  20. 20.

    Sled JG, Zijdenbos a P, Evans a C. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging. 1998;17:87–97.

    Article  CAS  Google Scholar 

  21. 21.

    Collins DL, Neelin P, Peters TM, Evans AC. Automatic 3D intersuject registration for MR volumetric data in standardized Talairach space. J Comput Assist Tomogr. 1994;18:192–205.

    Article  CAS  Google Scholar 

  22. 22.

    Marqués-Iturria I, Pueyo R, Garolera M, Segura B, Junqué C, García-García I, et al. Frontal cortical thinning and subcortical volume reductions in early adulthood obesity. Psychiatry Res. 2013;214:109–15.

    Article  Google Scholar 

  23. 23.

    Medic N, Ziauddeen H, Ersche KD, Farooqi IS, Bullmore ET, Nathan PJ, et al. Increased body mass index is associated with specific regional alterations in brain structure. Int J Obes. 2016;40:1177–82.

    Article  CAS  Google Scholar 

  24. 24.

    Veit R, Kullmann S, Heni M, Machann J, Häring H-U, Fritsche A, et al. Reduced cortical thickness associated with visceral fat and BMI. Neuroimage Clin. 2014;6:307–11.

    Article  Google Scholar 

  25. 25.

    Sharkey RJ, Karama S, Dagher A. Overweight is not associated with cortical thickness alterations in children. Front Neurosci. 2015;9:1–7.

    Article  Google Scholar 

  26. 26.

    Karlsson HK, Tuulari JJ, Hirvonen J, Lepomäki V, Parkkola R, Hiltunen J, et al. Obesity is associated with white matter atrophy: a combined diffusion tensor imaging and voxel-based morphometric study. Obesity. 2013;21:2530–7.

    Article  Google Scholar 

  27. 27.

    Papageorgiou I, Astrakas LG, Xydis V, Alexiou GA, Bargiotas P, Tzarouchi L, et al. Abnormalities of brain neural circuits related to obesity: a Diffusion Tensor Imaging study. Magn Reson Imaging. 2017;37:116–21.

    Article  Google Scholar 

  28. 28.

    Mueller K, Anwander A, Möller HE, Horstmann A, Lepsien J, Busse F, et al. Sex-dependent influences of obesity on cerebral white matter investigated by diffusion-tensor imaging. PLOS ONE. 2011;6:e18544.

    Article  CAS  Google Scholar 

  29. 29.

    Kullmann S, Callaghan MF, Heni M, Weiskopf N, Scheffler K, Häring H-U, et al. Specific white matter tissue microstructure changes associated with obesity. Neuroimage. 2016;125:36–44.

    Article  Google Scholar 

  30. 30.

    Verstynen TD, Weinstein A, Erickson KI, Sheu LK, Marsland AL, Gianaros PJ. Competing physiological pathways link individual differences in weight and abdominal adiposity to white matter microstructure. Neuroimage. 2013;79:129–37.

    Article  Google Scholar 

  31. 31.

    Haber SN, Behrens TEJ. The neural network underlying incentive-based learning: implications for interpreting circuit disruptions in psychiatric disorders. Neuron. 2014;83:1019–39.

    Article  CAS  Google Scholar 

  32. 32.

    Stoodley CJ, Schmahmann JD. Evidence for topographic organization in the cerebellum of motor control versus cognitive and affective processing. Cortex. 2010;46:831–44.

    Article  Google Scholar 

  33. 33.

    Buckner RL. The cerebellum and cognitive function: 25 years of insight from anatomy and neuroimaging. Neuron. 2013;80:807–15.

    Article  CAS  Google Scholar 

  34. 34.

    Habas C, Kamdar N, Nguyen D, Prater K, Beckmann CF, Menon V, et al. Distinct cerebellar contributions to intrinsic connectivity networks. J Neurosci. 2009;29:8586–94.

    Article  CAS  Google Scholar 

  35. 35.

    Tsapkini K, Frangakis CE, Hillis AE. The function of the left anterior temporal pole: evidence from acute stroke and infarct volume. Brain. 2011;134:3094–105.

    Article  Google Scholar 

  36. 36.

    Storsve AB, Fjell AM, Tamnes CK, Westlye LT, Overbye K, Aasland HW, et al. Differential longitudinal changes in cortical thickness, surface area and volume across the adult life span: regions of accelerating and decelerating change. J Neurosci. 2014;34:8488–98.

    Article  CAS  Google Scholar 

  37. 37.

    Fjell AM, Westlye LT, Grydeland H, Amlien I, Espeseth T, Reinvang I, et al. Accelerating cortical thinning: unique to dementia or universal in aging? Cereb Cortex. 2014;24:919–34.

    Article  Google Scholar 

  38. 38.

    Bruce-Keller AJ, Keller JN, Morrison CD. Obesity and vulnerability of the CNS. Biochim Biophys Acta. 2009;1792:395–400.

    Article  CAS  Google Scholar 

  39. 39.

    Strike LT, Couvy-Duchesne B, Hansell NK, Cuellar-Partida G, Medland SE, Wright MJ. Genetics and brain morphology. Neuropsychol Rev. 2015. https://doi.org/10.1007/s11065-015-9281-1

    Article  Google Scholar 

  40. 40.

    Chouinard-Decorte F, McKay DR, Reid A, Khundrakpam B, Zhao L, Karama S, et al. Heritable changes in regional cortical thickness with age. Brain Imaging Behav. 2014;8:208–16.

    Article  Google Scholar 

  41. 41.

    Van Gaal LF, Mertens IL, De Block CE. Mechanisms linking obesity with cardiovascular disease. Nature. 2006;444:875–80.

    Article  CAS  Google Scholar 

  42. 42.

    Guillemot-Legris O, Muccioli GG. Obesity-induced neuroinflammation: beyond the hypothalamus. Trends Neurosci. 2017;40:237–53.

    Article  CAS  Google Scholar 

  43. 43.

    Ronan L, Alexander-Bloch AF, Wagstyl K, Farooqi S, Brayne C, Tyler LK, et al. Obesity associated with increased brain age from midlife. Neurobiol Aging. 2016;47:63–70.

    Article  Google Scholar 

  44. 44.

    Bobb JF, Schwartz BS, Davatzikos C, Caffo B. Cross-sectional and longitudinal association of body mass index and brain volume. Hum Brain Mapp. 2014;35:75–88.

    Article  Google Scholar 

  45. 45.

    Brooks SJ, Benedict C, Burgos J, Kempton MJ, Kullberg J, Nordenskjold R, et al. Late-life obesity is associated with smaller global and regional gray matter volumes: a voxel-based morphometric study. Int J Obes (Lond). 2013;37:230–6.

    Article  CAS  Google Scholar 

  46. 46.

    Figley CR, Asem JSA, Levenbaum EL, Courtney SM. Effects of body mass index and body fat percent on default mode, executive control, and salience network structure and function. Front Neurosci. 2016;10:1–23.

    Article  Google Scholar 

  47. 47.

    Janowitz D, Wittfeld K, Terock J, Freyberger HJ, Hegenscheid K, Völzke H, et al. Association between waist circumference and gray matter volume in 2344 individuals from two adult community-based samples. Neuroimage. 2015;122:149–57.

    Article  Google Scholar 

  48. 48.

    Mathar D, Horstmann A, Pleger B, Villringer A, Neumann J. Is it worth the effort? Novel insights into obesity- associated alterations in cost-benefit decision-making. Front Behav Neurosci. 2016;9:1–13.

    Article  Google Scholar 

  49. 49.

    Opel N, Redlich R, Kaehler C, Grotegerd D, Dohm K, Heindel W, et al. Prefrontal gray matter volume mediates genetic risks for obesity. Mol Psychiatry. 2017;22:703–10.

    Article  CAS  Google Scholar 

  50. 50.

    Pannacciulli N, Del Parigi A, Chen K, DSNT Le, Reiman EM, Tataranni PA. Brain abnormalities in human obesity: a voxel-based morphometric study. Neuroimage. 2006;31:1419–25.

    Article  Google Scholar 

  51. 51.

    Shott ME, Cornier M-A, Mittal VA, Pryor TL, Orr JM, Brown MS, et al. Orbitofrontal cortex volume and brain reward response in obesity. Int J Obes (Lond). 2015;39:214–21.

    Article  CAS  Google Scholar 

  52. 52.

    Tuulari JJ, Karlsson HK, Antikainen O, Hirvonen J, Pham T, Salminen P, et al. Bariatric surgery induces white and grey matter density recovery in the morbidly obese: a voxel-based morphometric study. Hum Brain Mapp. 2016;37:3745–56.

    Article  Google Scholar 

  53. 53.

    Weise CM, Thiyyagura P, Reiman EM, Chen K, Krakoff J. Fat-free body mass but not fat mass is associated with reduced gray matter volume of cortical brain regions implicated in autonomic and homeostatic regulation. Neuroimage. 2013;64:712–21.

    Article  Google Scholar 

  54. 54.

    Yao L, Li W, Dai Z, Dong C. Eating behavior associated with gray matter volume alternations: a voxel based morphometry study. Appetite. 2016;96:572–9.

    Article  Google Scholar 

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Acknowledgements

This work was supported by a Foundation Scheme award to AD from the Canadian Institutes of Health Research. IGG and AM are recipients of post-doctoral fellowships from the Canadian Institutes of Health Research.

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Correspondence to Isabel García-García.

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García-García, I., Michaud, A., Dadar, M. et al. Neuroanatomical differences in obesity: meta-analytic findings and their validation in an independent dataset. Int J Obes 43, 943–951 (2019). https://doi.org/10.1038/s41366-018-0164-4

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