Association between regional brain volumes and BMI z-score change over one year in children

English version

Autoři: Travis D. Masterson ^aff001; Carly Bobak ^aff002; Kristina M. Rapuano ^aff003; Grace E. Shearrer ^aff004; Diane Gilbert-Diamond ^aff001
Působiště autorů: Department of Epidemiology, Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth College, Hanover, New Hampshire, United States of America ^aff001; Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth College, Hanover, New Hampshire, United States of America ^aff002; Department of Psychology, Yale University, New Haven, Connecticut, United States of America ^aff003; Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America ^aff004
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0221995

Souhrn

Purpose

Associations between brain region volume and weight status have been observed in children cross-sectionally. However, it is unclear if differences in brain region volume precede weight gain.

Methods

Two high-quality structural brain images were obtained approximately one year apart in 53 children aged 9–12 years old. Children’s height and weight were also measured at each scan. Structural images were processed using the FreeSurfer software-package providing volume measures for regions of interest including the entorhinal cortex, nucleus accumbens, and hippocampus. Age- and sex-adjusted BMI z-scores (BMIz) were calculated at both timepoints. The association between brain region volume and BMIz was examined cross-sectionally using linear regression and longitudinally using structural equation modeling. All models were adjusted by estimated cranial volume to account for individual variation in head size and were corrected for multiple comparisons (pFDR<0.05).

Results

The sample of children was primarily healthy weight at baseline (79.78%). Cross-sectionally at the one-year follow-up, a positive relationship was observed between right hippocampal volume and BMIz (β = 0.43, 95% CI = (0.10, 0.77)). Longitudinally a negative relationship was observed between right entorhinal volume at baseline and BMIz at the one-year follow-up (β = −0.25, 95% CI = (−0.44, −0.07)).

Conclusion

These results suggest that measured volumes from certain regions of the brain that have been associated with BMI in adults are associated with both concurrent BMIz and BMIz change over one-year in a primarily healthy weight sample of children. As the entorhinal cortex integrates signals from both reward and control regions, this region may be particularly important to weight management during child development.

Klíčová slova:

Body Mass Index – Biology and life sciences – Research and analysis methods – Neuroscience – Anatomy – Medicine and health sciences – Physiology – Physiological parameters – Diagnostic medicine – Body weight – Imaging techniques – Brain – Obesity – Neuroimaging – Diagnostic radiology – Magnetic resonance imaging – Radiology and imaging – Childhood obesity – Cerebral cortex – Hippocampus – Entorhinal cortex – Nucleus accumbens – Weight gain

Zdroje

1. Skinner A, Ravanbakht SN, Skelton JA, Perrin EM, Armstrong SC. Prevalence of Obesity and Severe Obesity in US Children, 1999–2016. Pediatrics. 2017;141: e20173459. doi: 10.1542/peds.2017-3459 29483202

2. Schwimmer JB, Burwinkle TM, Varni JW. Health-Related Quality of Life of Severely Obese Children and Adolescents. Jama. 2003;289: 1813–1819. doi: 10.1001/jama.289.14.1813 12684360

3. Perlaki G, Molnar D, Smeets PA, Ahrens W, Wolters M, Eiben G, et al. Volumetric gray matter measures of amygdala and accumbens in childhood overweight/obesity. Plos One. 2018;13: e0205331. doi: 10.1371/journal.pone.0205331 30335775

4. Vainik U, Baker TB, Dadar M, Zeighami Y, Michaud A, Zhang Y, et al. Neurobehavioural Correlates of Obesity are Largely Heritable. Biorxiv. 2018; 204917. doi: 10.1101/204917

5. Willette AA, Kapogiannis D. Does the brain shrink as the waist expands? Ageing Res Rev. 2015;20: 86–97. doi: 10.1016/j.arr.2014.03.007 24768742

6. Moreno-López L, Soriano-Mas C, Delgado-Rico E, Rio-Valle JS, Verdejo-García A. Brain Structural Correlates of Reward Sensitivity and Impulsivity in Adolescents with Normal and Excess Weight. Plos One. 2012;7: e49185. doi: 10.1371/journal.pone.0049185 23185306

7. Lenroot RK, Giedd JN. Brain development in children and adolescents: Insights from anatomical magnetic resonance imaging. Neurosci Biobehav Rev. 2006;30: 718–729. doi: 10.1016/j.neubiorev.2006.06.001 16887188

8. Deshmukh-Taskar P, Nicklas T, Morales M, Yang S-J, Zakeri I, Berenson G. Tracking of overweight status from childhood to young adulthood: the Bogalusa Heart Study. Eur J Clin Nutr. 2006;60: 48. doi: 10.1038/sj.ejcn.1602266 16132057

9. Kelley AE, Domesick VB. The distribution of the projection from the hippocampal formation to the nucleus accumbens in the rat: An anterograde and retrograde-horseradish peroxidase study. Neuroscience. 1982;7: 2321–2335. doi: 10.1016/0306-4522(82)90198-1 6817161

10. Rapuano KM, Huckins JF, Sargent JD, Heatherton TF, Kelley WM. Individual differences in reward and somatosensory-motor brain regions correlate with adiposity in adolescents. Cerebral cortex. 2015;26.

11. Rapuano KM, Zieselman AL, Kelley WM, Sargent JD, Heatherton TF, Gilbert-Diamond D. Genetic risk for obesity predicts nucleus accumbens size and responsivity to real-world food cues. Proc National Acad Sci. 2017;114: 160–165. doi: 10.1073/pnas.1605548113 27994159

12. Demos KE, Heatherton TF, Kelley WM. Individual Differences in Nucleus Accumbens Activity to Food and Sexual Images Predict Weight Gain and Sexual Behavior. J Neurosci. 2012;32: 5549–5552. doi: 10.1523/JNEUROSCI.5958-11.2012 22514316

13. Coveleskie K, Gupta A, Kilpatrick L, Mayer E, Ashe-McNalley C, Stains J, et al. Altered functional connectivity within the central reward network in overweight and obese women. Nutr Diabetes. 2015;5: e148. doi: 10.1038/nutd.2014.45 25599560

14. Horstmann A, Busse FP, Mathar D, Müller K, Lepsien J, Schlögl H, et al. Obesity-Related Differences between Women and Men in Brain Structure and Goal-Directed Behavior. Front Hum Neurosci. 2011;5: 58. doi: 10.3389/fnhum.2011.00058 21713067

15. Vainik U, Baker TE, Dadar M, Zeighami Y, Michaud A, Zhang Y, et al. Neurobehavioral correlates of obesity are largely heritable. Proc National Acad Sci. 2018;115: 201718206. doi: 10.1073/pnas.1718206115 30154161

16. Mestre Z, Bischoff-Grethe A, Eichen D, Wierenga C, Strong D, Boutelle K. Hippocampal atrophy and altered brain responses to pleasant tastes among obese compared with healthy weight children. International Journal of Obesity. 2017;41: 1496–1502. doi: 10.1038/ijo.2017.130 28572588

17. Schmajuk NA, Cox L, Gray JA. Nucleus accumbens, entorhinal cortex and latent inhibition: A neural network model. Behav Brain Res. 2001;118: 123–141. doi: 10.1016/s0166-4328(00)00319-3 11164510

18. Burger KS, Stice E. Greater striatopallidal adaptive coding during cue–reward learning and food reward habituation predict future weight gain. Neuroimage. 2014;99: 122–128. doi: 10.1016/j.neuroimage.2014.05.066 24893320

19. Canto CB, Wouterlood FG, Witter MP. What Does the Anatomical Organization of the Entorhinal Cortex Tell Us? Neural Plast. 2008;2008: 381243. doi: 10.1155/2008/381243 18769556

20. Davidson TL, Kanoski SE, Schier LA, Clegg DJ, Benoit SC. A potential role for the hippocampus in energy intake and body weight regulation. Curr Opin Pharmacol. 2007;7: 613–616. doi: 10.1016/j.coph.2007.10.008 18032108

21. Wang G-J, Yang J, Volkow ND, Telang F, Ma Y, Zhu W, et al. Gastric stimulation in obese subjects activates the hippocampus and other regions involved in brain reward circuitry. Proc National Acad Sci. 2006;103: 15641–15645. doi: 10.1073/pnas.0601977103 17023542

22. Berns GS, McClure SM, Pagnoni G, Montague RP. Predictability Modulates Human Brain Response to Reward. J Neurosci. 2001;21: 2793–2798. doi: 10.1523/jneurosci.21-08-02793.2001 11306631

23. Davidson TL, Jarrard LE. A role for hippocampus in the utilization of hunger signals. Behav Neural Biol. 1993;59: 167–171. doi: 10.1016/0163-1047(93)90925-8 8476385

24. Wallner-Liebmann S, Koschutnig K, Reishofer G, Sorantin E, Blaschitz B, Kruschitz R, et al. Insulin and Hippocampus Activation in Response to Images of High‐Calorie Food in Normal Weight and Obese Adolescents. Obesity. 2010;18: 1552–1557. doi: 10.1038/oby.2010.26 20168310

25. Bauer C, Moreno B, González‐Santos L, Concha L, Barquera S, Barrios F. Child overweight and obesity are associated with reduced executive cognitive performance and brain alterations: a magnetic resonance imaging study in Mexican children. Pediatric Obes. 2015;10: 196–204. doi: 10.1111/ijpo.241 24989945

26. Hannah B, Victoria S, Aziz T, Bina S, Antonio C. Obese Adolescents with Type 2 Diabetes Mellitus Have Hippocampal and Frontal Lobe Volume Reductions. Neurosci Medicine. 2011;2011: 34–42. doi: 10.4236/nm.2011.21005 21691448

27. Inokuchi M, Matsuo N, Takayama JI, Hasegawa T. BMI z-score is the optimal measure of annual adiposity change in elementary school children. Ann Hum Biol. 2011;38: 747–751. doi: 10.3109/03014460.2011.620625 22014004

28. Ogden CL, Kuczmarski RJ, Flegal KM, Mei Z, Guo S, Wei R, et al. Centers for Disease Control and Prevention 2000 Growth Charts for the United States: Improvements to the 1977 National Center for Health Statistics Version. Pediatrics. 2002;109: 45–60. doi: 10.1542/peds.109.1.45 11773541

29. Carskadon MA, Acebo C. A self-administered rating scale for pubertal development. J Adolescent Health. 1993;14: 190–195. doi: 10.1016/1054-139x(93)90004-9

30. Crockett, L. J. Pubertal development scale: Pubertal categories. Unpublished. 1988;

31. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. Whole Brain Segmentation Automated Labeling of Neuroanatomical Structures in the Human Brain. Neuron. 2002;33: 341–355. doi: 10.1016/s0896-6273(02)00569-x 11832223

32. Aanes S, Bjuland K, Skranes J, Løhaugen G. Memory function and hippocampal volumes in preterm born very-low-birth-weight (VLBW) young adults. Neuroimage. 2015;105: 76–83. doi: 10.1016/j.neuroimage.2014.10.023 25451477

33. Valk SL, Martino A, Milham MP, Bernhardt BC. Multicenter mapping of structural network alterations in autism. Hum Brain Mapp. 2015;36: 2364–2373. doi: 10.1002/hbm.22776 25727858

34. Gilmore A, Buser NJ, Hanson JL. Variations in Structural MRI Quality Impact Measures of Brain Anatomy: Relations with Age. Biorxiv. 2019; 581876. doi: 10.1101/581876

35. Yerys BE, Jankowski KF, Shook D, Rosenberger LR, Barnes K, Berl MM, et al. The fMRI success rate of children and adolescents: Typical development, epilepsy, attention deficit/hyperactivity disorder, and autism spectrum disorders. Hum Brain Mapp. 2009;30: 3426–3435. doi: 10.1002/hbm.20767 19384887

36. Team CR. R: A language and environment for statistical computing. 2016;

37. Rosseel Y. Lavaan: An R package for structural equation modeling and more. Version 0.5–12 (BETA). Journal of statistical software. 2012;48.

38. Kenny DA, Harackiewicz JM. Cross-lagged panel correlation: Practice and promise. Journal of Applied Psychology. 1979;64.

39. Selig JP, Little TD. Autoregressive and cross-lagged panel analysis for longitudinal data. 2012;

40. Hedley AA, Ogden CL, Johnson CL, Carroll MD, Curtin LR, Flegal KM. Prevalence of Overweight and Obesity Among US Children, Adolescents, and Adults, 1999–2002. Jama. 2004;291: 2847–2850. doi: 10.1001/jama.291.23.2847 15199035

41. Widya RL, de Roos A, Trompet S, de Craen AJ, Westendorp RG, Smit JW, et al. Increased amygdalar and hippocampal volumes in elderly obese individuals with or at risk of cardiovascular disease. Am J Clin Nutrition. 2011;93: 1190–1195. doi: 10.3945/ajcn.110.006304 21450935

42. Raji CA, Ho AJ, Parikshak NN, Becker JT, Lopez OL, Kuller LH, et al. Brain structure and obesity. Hum Brain Mapp. 2010;31: 353–364. doi: 10.1002/hbm.20870 19662657

43. Nouwen A, Chambers A, Chechlacz M, Higgs S, Blissett J, Barrett TG, et al. Microstructural abnormalities in white and gray matter in obese adolescents with and without type 2 diabetes. Neuroimage Clin. 2017;16: 43–51. doi: 10.1016/j.nicl.2017.07.004 28752059