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KIBRA polymorphism modulates gray matter volume to influence cognitive ability in the elderly

  • Rui Li
  • Wenyu Wan
  • Juan LiEmail author
ORIGINAL RESEARCH
  • 8 Downloads

Abstract

Genetic variation in the kidney and brain expressed protein (KIBRA) rs17070145 gene has been linked to episodic memory and cognitive aging; yet, the neural mechanism underlying this association remains to be fully understood. Using the magnetic resonance imaging (MRI) technique, this study investigated the effect of KIBRA polymorphism on gray matter volume in 37 healthy, Chinese adults from the older population. Voxel-based morphometry (VBM) analysis revealed that KIBRA gene selectivity influences the prefrontal cortex and the parahippocampal cortex. The gray matter volume (GMV) in these structures is significantly lower in KIBRA C-allele carriers than in TT carriers. Moreover, multi-voxel pattern correlation analysis revealed that decreased prefrontal GMV could in turn affect individual cognitive function in C-allele carriers; whereas, TT individuals utilized more integrated gray matter volume in whole-brain voxels to achieve relatively better cognitive function. Overall, the findings suggest that the KIBRA rs17070145 polymorphism modulates gray matter volume, which in turn further influences cognitive function in the elderly.

Keywords

KIBRA Gray matter volume VBM Aging Prefrontal 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (31671157, 31470998, 61673374, 31711530157, 31861133011), the Beijing Municipal Science and Technology Commission (Z171100000117006, Z171100008217006), and the National Key Research and Development Program of China (2018YFC2001701, 2017YFB1401203, 2018YFC2000303,  2016YFC1305904). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Compliance with ethical standards

Declaration of interest

None.

Ethical approval

The study was approved by the institutional review board of Institute of Psychology of Chinese Academy of Sciences. All participants provided written informed consent according to the institutional guidelines prior to their participation in our experiments. The study was conducted in accordance with the Declaration of Helsinki.

References

  1. Andrews-Hanna, J. R., Snyder, A. Z., Vincent, J. L., Lustig, C., Head, D., Raichle, M. E., & Buckner, R. L. (2007). Disruption of large-scale brain systems in advanced aging. Neuron, 56(5), 924–935.  https://doi.org/10.1016/j.neuron.2007.10.038.CrossRefGoogle Scholar
  2. Bray, M. S. (2008). Implications of gene-behavior interactions: Prevention and intervention for obesity. Obesity, 16, S72–S78.  https://doi.org/10.1038/oby.2008.522. CrossRefGoogle Scholar
  3. Burke, S. N., & Barnes, C. A. (2006). Neural plasticity in the ageing brain. Nature Reviews Neuroscience, 7(1), 30–40.  https://doi.org/10.1038/nrn1809.CrossRefGoogle Scholar
  4. Cabeza, R., & Dennis, N. A. (2012). Frontal lobes and aging: Deterioration and compensation. In D. T. Stuss & R. T. Knight (Eds.), Principles of frontal lobe function (2nd ed.). New York: Oxford University Press.Google Scholar
  5. Chang, Y. M., Rosene, D. L., Killiany, R. J., Mangiamele, L. A., & Luebke, J. I. (2005). Increased action potential firing rates of layer 2/3 pyramidal cells in the prefrontal cortex are significantly related to cognitive performance in aged monkeys. Cerebral Cortex, 15(4), 409–418.  https://doi.org/10.1093/cercor/bhh144. CrossRefGoogle Scholar
  6. de Brabander, J. M., Kramers, R. J. K., & Uylings, H. B. M. (1998). Layer-specific dendritic regression of pyramidal cells with ageing in the human prefrontal cortex. European Journal of Neuroscience, 10(4), 1261–1269.CrossRefGoogle Scholar
  7. Dresler, M., Shirer, W. R., Konrad, B. N., Mueller, N. C. J., Wagner, I. C., Fernandez, G., et al. (2017). Mnemonic training reshapes brain networks to support superior memory. Neuron, 93(5), 1227–1235.  https://doi.org/10.1016/j.neuron.2017.02.003.CrossRefGoogle Scholar
  8. Fjell, A. M., McEvoy, L., Holland, D., Dale, A. M., Walhovd, K. B., & Alzheimer Dis Neuroimaging, I. (2013). Brain changes in older adults at very low risk for Alzheimer's disease. Journal of Neuroscience, 33(19), 8237–U8511.  https://doi.org/10.1523/jneurosci.5506-12.2013.CrossRefGoogle Scholar
  9. Grady, C. L., Springer, M. V., Hongwanishkul, D., McIntosh, A. R., & Winocur, G. (2006). Age-related changes in brain activity across the adult lifespan. Journal of Cognitive Neuroscience, 18(2), 227–241.  https://doi.org/10.1162/089892906775783705.CrossRefGoogle Scholar
  10. Greicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004). Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI. Proceedings of the National Academy of Sciences of the United States of America, 101(13), 4637–4642.  https://doi.org/10.1073/pnas.0308627101.CrossRefGoogle Scholar
  11. Hayashi, N., Kazui, H., Kamino, K., Tokunaga, H., Takaya, M., Yokokoji, M., Kimura, R., Kito, Y., Wada, T., Nomura, K., Sugiyama, H., Yamamoto, D., Yoshida, T., Currais, A., Soriano, S., Hamasaki, T., Yamamoto, M., Yasuda, Y., Hashimoto, R., Tanimukai, H., Tagami, S., Okochi, M., Tanaka, T., Kudo, T., Morihara, T., & Takeda, M. (2010). KIBRA genetic polymorphism influences episodic memory in Alzheimer's disease, but does not show association with disease in a Japanese cohort. Dementia and Geriatric Cognitive Disorders, 30(4), 302–308.  https://doi.org/10.1159/000320482. CrossRefGoogle Scholar
  12. Kauppi, K., Nilsson, L.-G., Adolfsson, R., Eriksson, E., & Nyberg, L. (2011). KIBRA polymorphism is related to enhanced memory and elevated hippocampal processing. Journal of Neuroscience, 31(40), 14218–14222.  https://doi.org/10.1523/jneurosci.3292-11.2011.CrossRefGoogle Scholar
  13. Lawton, M. P., & Brody, E. M. (1969). Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontologist, 9(3P1), 179–186.CrossRefGoogle Scholar
  14. Li, R., Zhu, X., Yin, S., Niu, Y., Zheng, Z., Huang, X., et al. (2014). Multimodal intervention in older adults improves resting-state functional connectivity between the medial prefrontal cortex and medial temporal lobe. Frontiers in Aging Neuroscience, 6, 39.  https://doi.org/10.3389/fnagi.2014.00039.CrossRefGoogle Scholar
  15. Lindenberger, U., Nagel, I. E., Chicherio, C., Li, S.-C., Heekeren, H. R., & Backman, L. (2008). Age-related decline in brain resources modulates genetic effects on cognitive functioning. Frontiers in Neuroscience, 2(2), 234–244.  https://doi.org/10.3389/neuro.01.039.2008.CrossRefGoogle Scholar
  16. Ling, J., Huang, Y., Zhang, L., Wei, D., & Cheng, W. (2018). Association of KIBRA polymorphism with risk of Alzheimer's disease: Evidence based on 20 case-control studies. Neuroscience Letters, 662, 77–83.  https://doi.org/10.1016/j.neulet.2017.08.057.CrossRefGoogle Scholar
  17. Milnik, A., Heck, A., Vogler, C., Heinze, H.-J., de Quervain, D. J. F., & Papassotiropoulos, A. (2012). Association of KIBRA with episodic and working memory: A meta-analysis. American Journal of Medical Genetics Part B-Neuropsychiatric Genetics, 159B(8), 958–969.  https://doi.org/10.1002/ajmg.b.32101.CrossRefGoogle Scholar
  18. Muse, J., Emery, M., Sambataro, F., Lemaitre, H., Tan, H.-Y., Chen, Q., Kolachana, B. S., Das, S., Callicott, J. H., Weinberger, D. R., & Mattay, V. S. (2014). WWC1 genotype modulates age-related decline in episodic memory function across the adult life span. Biological Psychiatry, 75(9), 693–700.  https://doi.org/10.1016/j.biopsych.2013.09.036.CrossRefGoogle Scholar
  19. Nacmias, B., Bessi, V., Bagnoli, S., Tedde, A., Cellini, E., Piccini, C., Sorbi, S., & Bracco, L. (2008). KIBRA gene variants are associated with episodic memory performance in subjective memory complaints. Neuroscience Letters, 436(2), 145–147.  https://doi.org/10.1016/j.neulet.2008.03.008.CrossRefGoogle Scholar
  20. Nyberg, L., Salami, A., Andersson, M., Eriksson, J., Kalpouzos, G., Kauppi, K., Lind, J., Pudas, S., Persson, J., & Nilsson, L. G. (2010). Longitudinal evidence for diminished frontal cortex function in aging. Proceedings of the National Academy of Sciences of the United States of America, 107(52), 22682–22686.  https://doi.org/10.1073/pnas.1012651108. CrossRefGoogle Scholar
  21. Palombo, D. J., Amaral, R. S. C., Olsen, R. K., Mueller, D. J., Todd, R. M., Anderson, A. K., et al. (2013). KIBRA polymorphism is associated with individual differences in hippocampal subregions: Evidence from anatomical segmentation using high-resolution MRI. Journal of Neuroscience, 33(32), 13088–13093.  https://doi.org/10.1523/jneurosci.1406-13.2013.CrossRefGoogle Scholar
  22. Papassotiropoulos, A., Stephan, D. A., Huentelman, M. J., Hoerndli, F. J., Craig, D. W., Pearson, J. V., Huynh, K. D., Brunner, F., Corneveaux, J., Osborne, D., Wollmer, M. A., Aerni, A., Coluccia, D., Hanggi, J., Mondadori, C. R. A., Buchmann, A., Reiman, E. M., Caselli, R. J., Henke, K., & de Quervain, D. J. F. (2006). Common KIBRA alleles are associated with human memory performance. Science, 314(5798), 475–478.  https://doi.org/10.1126/science.1129837.CrossRefGoogle Scholar
  23. Papenberg, G., Lindenberger, U., & Backman, L. (2015). Aging-related magnification of genetic effects on cognitive and brain integrity. Trends in Cognitive Sciences, 19(9), 506–514.  https://doi.org/10.1016/j.tics.2015.06.008.CrossRefGoogle Scholar
  24. Pardo, J. V., Lee, J. T., Sheikh, S. A., Surerus-Johnson, C., Shah, H., Munch, K. R., Carlis, J. V., Lewis, S. M., Kuskowski, M. A., & Dysken, M. W. (2007). Where the brain grows old: Decline in anterior cingulate and medial prefrontal function with normal aging. Neuroimage, 35(3), 1231–1237.  https://doi.org/10.1016/j.neuroimage.2006.12.044.CrossRefGoogle Scholar
  25. Porter, T., Burnham, S. C., Dore, V., Savage, G., Bourgeat, P., Begemann, K., et al. (2018). KIBRA is associated with accelerated cognitive decline and hippocampal atrophy in APOE epsilon 4-positive cognitively normal adults with high a beta-amyloid burden. Scientific Reports, 8.  https://doi.org/10.1038/s41598-018-20513-y.
  26. Roberts, R. E., & Vernon, S. W. (1983). The Center for Epidemiologic Studies Depression Scale: Its use in a community sample. American Journal of Psychiatry, 140(1), 41–46.CrossRefGoogle Scholar
  27. Wang, D., Liu, B., Qin, W., Wang, J., Zhang, Y., Jiang, T., & Yu, C. (2013). KIBRA gene variants are associated with synchronization within the default-mode and executive control networks. Neuroimage, 69, 213–222.  https://doi.org/10.1016/j.neuroimage.2012.12.022.CrossRefGoogle Scholar
  28. Wersching, H., Guske, K., Hasenkamp, S., Hagedorn, C., Schiwek, S., Jansen, S., Witte, V., Wellmann, J., Lohmann, H., Duning, K., Kremerskothen, J., Knecht, S., Brand, E., & Floel, A. (2011). Impact of common KIBRA allele on human cognitive functions. Neuropsychopharmacology, 36(6), 1296–1304.  https://doi.org/10.1038/npp.2011.16.CrossRefGoogle Scholar
  29. Witte, A. V., Koebe, T., Kerti, L., Rujescu, D., & Floeel, A. (2016). Impact of KIBRA polymorphism on memory function and the Hippocampus in older adults. Neuropsychopharmacology, 41(3), 781–790.  https://doi.org/10.1038/npp.2015.203.CrossRefGoogle Scholar
  30. Yu, J., Li, J., & Huang, X. (2012). The Beijing version of the Montreal cognitive assessment as a brief screening tool for mild cognitive impairment: A community-based study. BMC Psychiatry, 12, 156.  https://doi.org/10.1186/1471-244x-12-156.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Center on Aging Psychology, CAS Key Laboratory of Mental Health, Institute of PsychologyChinese Academy of SciencesBeijingChina
  2. 2.Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
  3. 3.Magnetic Resonance Imaging Research Center, Institute of PsychologyChinese Academy of SciencesBeijingChina
  4. 4.State Key Laboratory of Brain and Cognitive Science, Institute of BiophysicsChinese Academy of SciencesBeijingChina

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