Hostname: page-component-8448b6f56d-tj2md Total loading time: 0 Render date: 2024-04-24T14:52:09.895Z Has data issue: false hasContentIssue false
  • English
  • Français

Global cognitive dysfunction and β-amyloid neuropathology in late-life and treatment-resistant major depression

Published online by Cambridge University Press:  26 October 2021

Cheng-Ta Li ,
Jong-Ling Fuh ,
Bang-Hung Yang ,
Chen-Ji Hong ,
Chi-Wei Chang ,
Pei-Chi Tu ,
Jia-Shyun Jeng ,
Mu-Hong Chen ,
Shih-Jen Tsai  and
Ya-Mei Bai
...Show all authors
Cheng-Ta Li*
Affiliation:
Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan Division of Psychiatry, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan Institute of Brain Science and Brain Research Center, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan Institute of Cognitive Neuroscience, National Central University, Jhongli, Taiwan
Jong-Ling Fuh
Affiliation:
Department of Neurology, Taipei Veterans General Hospital, Taipei, Taiwan
Bang-Hung Yang
Affiliation:
Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
Chen-Ji Hong
Affiliation:
Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan Division of Psychiatry, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
Chi-Wei Chang
Affiliation:
Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
Pei-Chi Tu
Affiliation:
Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan Division of Psychiatry, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
Jia-Shyun Jeng
Affiliation:
Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
Mu-Hong Chen
Affiliation:
Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan Division of Psychiatry, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan Institute of Brain Science and Brain Research Center, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
Shih-Jen Tsai
Affiliation:
Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan Division of Psychiatry, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
Ya-Mei Bai
Affiliation:
Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan Division of Psychiatry, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
Tung-Ping Su
Affiliation:
Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan Division of Psychiatry, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan Institute of Brain Science and Brain Research Center, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan Department of Psychiatry, Cheng Hsin General Hospital, Taipei, Taiwan
Hsuan Lee
Affiliation:
Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
Wen-Sheng Huang*
Affiliation:
Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
*
Author for correspondence: Cheng-Ta Li, E-mail: ctil2@vghtpe.gov.tw; Wen-Sheng Huang, E-mail: wshuang01@gmail.com
Author for correspondence: Cheng-Ta Li, E-mail: ctil2@vghtpe.gov.tw; Wen-Sheng Huang, E-mail: wshuang01@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Cognitive impairment is common in late-life depression, which may increase Alzheimer disease (AD) risk. Therefore, we aimed to investigate whether late-life major depressive disorder (MDD) has worse cognition and increases the characteristic AD neuropathology. Furthermore, we carried out a comparison between treatment-resistant depression (TRD) and non-TRD. We hypothesized that patients with late-life depression and TRD may have increased β-amyloid (Aβ) deposits in brain regions responsible for global cognition.

Methods

We recruited 81 subjects, including 54 MDD patients (27 TRD and 27 non-TRD) and 27 matched healthy controls (HCs). Neurocognitive tasks were examined, including Mini-Mental State Examination and Montreal Cognitive Assessment to detect global cognitive functions. PET with Pittsburgh compound-B and fluorodeoxyglucose were used to capture brain Aβ pathology and glucose use, respectively, in some patients.

Results

MDD patients performed worse in Montreal Cognitive Assessment (p = 0.003) and had more Aβ deposits than HCs across the brain (family-wise error-corrected p < 0.001), with the most significant finding in the left middle frontal gyrus. Significant negative correlations between global cognition and prefrontal Aβ deposits existed in MDD patients, whereas positive correlations were noted in HCs. TRD patients had significantly more deposits in the left-sided brain regions (corrected p < 0.001). The findings were not explained by APOE genotypes. No between-group fluorodeoxyglucose difference was detected.

Conclusions

Late-life depression, particularly TRD, had increased brain Aβ deposits and showed vulnerability to Aβ deposits. A detrimental role of Aβ deposits in global cognition in patients with late-onset or non-late-onset MDD supported the theory that late-life MDD could be a risk factor for AD.

Keywords

β-Amyloidcognitiondementiamajor depressiontreatment-resistant depression
Type
Original Article
Information
Psychological Medicine , Volume 52 , Issue 16 , December 2022 , pp. 4116 - 4126
Check for updates
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Acosta-Cabronero, J., & Nestor, P. J. (2014). Diffusion tensor imaging in Alzheimer's disease: Insights into the limbic-diencephalic network and methodological considerations. Frontiers in Aging Neuroscience, 6, 266. doi:10.3389/fnagi.2014.00266CrossRefGoogle ScholarPubMed
Akamatsu, G., Ikari, Y., Ohnishi, A., Nishida, H., Aita, K., Sasaki, M. … Senda, M. (2016). Automated PET-only quantification of amyloid deposition with adaptive template and empirically pre-defined ROI. Physics in Medicine & Biology, 61(15), 5768–5780. doi:10.1088/0031-9155/61/15/5768CrossRefGoogle ScholarPubMed
Bazin, N., & Bratu, L. (2014). Depression in the elderly: Prodroma or risk factor for dementia? A critical review of the literature. Geriatrie et Psychologie Neuropsychiatrie du Vieillissement, 12(3), 289–297. doi:10.1684/pnv.2014.0490Google ScholarPubMed
Becker, J. T., Chang, Y. F., Lopez, O. L., Dew, M. A., Sweet, R. A., Barnes, D. … Reynolds, C. F., 3rd. (2009). Depressed mood is not a risk factor for incident dementia in a community-based cohort. The American Journal of Geriatric Psychiatry, 17(8), 653–663. doi:10.1097/jgp.0b013e3181aad1fe.CrossRefGoogle ScholarPubMed
Brommelhoff, J. A., Gatz, M., Johansson, B., McArdle, J. J., Fratiglioni, L., & Pedersen, N. L. (2009). Depression as a risk factor or prodromal feature for dementia? Findings in a population-based sample of Swedish twins. Psychology and Aging, 24(2), 373–384. doi:10.1037/a0015713CrossRefGoogle ScholarPubMed
Butters, M. A., Klunk, W. E., Mathis, C. A., Price, J. C., Ziolko, S. K., Hoge, J. A. … Meltzer, C. C. (2008). Imaging Alzheimer pathology in late-life depression with PET and Pittsburgh Compound-B. Alzheimer Disease & Associated Disorders, 22(3), 261–268. doi:10.1097/WAD.0b013e31816c92bfCrossRefGoogle ScholarPubMed
Butters, M. A., Whyte, E. M., Nebes, R. D., Begley, A. E., Dew, M. A., Mulsant, B. H. … Becker, J. T. (2004). The nature and determinants of neuropsychological functioning in late-life depression. Archives of General Psychiatry, 61(6), 587–595. doi:10.1001/archpsyc.61.6.587CrossRefGoogle ScholarPubMed
Byers, A. L., & Yaffe, K. (2011). Depression and risk of developing dementia. Nature Reviews Neurology, 7(6), 323–331. doi:10.1038/nrneurol.2011.60CrossRefGoogle ScholarPubMed
Chan, Y. L., Chen, M. H., Tsai, S. J., Bai, Y. M., Tsai, C. F., Cheng, C. M. … Li, C. T. (2020). Treatment-resistant depression enhances risks of dementia and Alzheimer's disease: A nationwide longitudinal study. Journal of Affective Disorders, 274, 806–812.CrossRefGoogle ScholarPubMed
Coon, K. D., Myers, A. J., Craig, D. W., Webster, J. A., Pearson, J. V., Lince, D. H. … Stephan, D. A. (2007). A high-density whole-genome association study reveals that APOE is the major susceptibility gene for sporadic late-onset Alzheimer's disease. The Journal of Clinical Psychiatry, 68(4), 613–618. doi:10.4088/jcp.v68n0419CrossRefGoogle ScholarPubMed
da Silva, J., Goncalves-Pereira, M., Xavier, M., & Mukaetova-Ladinska, E. B. (2013). Affective disorders and risk of developing dementia: Systematic review. The British Journal of Psychiatry, 202(3), 177–186. doi:10.1192/bjp.bp.111.101931CrossRefGoogle ScholarPubMed
Devanand, D. P., Mikhno, A., Pelton, G. H., Cuasay, K., Pradhaban, G., Dileep Kumar, J. S. … Parsey, R. V. (2010). Pittsburgh Compound B (11C-PIB) and fluorodeoxyglucose (18 F-FDG) PET in patients with Alzheimer disease, mild cognitive impairment, and healthy controls. Journal of Geriatric Psychiatry and Neurology, 23(3), 185–198. doi:10.1177/0891988710363715CrossRefGoogle ScholarPubMed
Dubois, B., Feldman, H. H., Jacova, C., Hampel, H., Molinuevo, J. L., Blennow, K. … Cummings, J. L. (2014). Advancing research diagnostic criteria for Alzheimer's disease: The IWG-2 criteria. Lancet Neurology, 13(6), 614–629. doi:10.1016/S1474-4422(14)70090-0CrossRefGoogle ScholarPubMed
Dupont, W. D., & Plummer, W. D. Jr. (1990). Power and sample size calculations. A review and computer program. Controlled Clinical Trials, 11(2), 116–128.CrossRefGoogle ScholarPubMed
Eyre, H. A., Siddarth, P., van Dyk, K., St Cyr, N., Baune, B. T., Barrio, J. R. … Lavretsky, H. (2017). Neural correlates of apathy in late-life depression: A pilot [(18) F]FDDNP positron emission tomography study. Psychogeriatrics, 17(3), 186–193. doi:10.1111/psyg.12213CrossRefGoogle ScholarPubMed
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). ‘Mini-mental state’. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198.CrossRefGoogle Scholar
Gao, Y., Huang, C., Zhao, K., Ma, L., Qiu, X., Zhang, L. … Xiao, Q. (2013). Depression as a risk factor for dementia and mild cognitive impairment: A meta-analysis of longitudinal studies. International Journal of Geriatric Psychiatry, 28(5), 441–449. doi:10.1002/gps.3845CrossRefGoogle ScholarPubMed
Gispert, J. D., Pascau, J., Reig, S., Martinez-Lazaro, R., Molina, V., Garcia-Barreno, P., & Desco, M. (2003). Influence of the normalization template on the outcome of statistical parametric mapping of PET scans. Neuroimage, 19(3), 601–612. doi:S1053811903000727.CrossRefGoogle ScholarPubMed
Gorwood, P., Corruble, E., Falissard, B., & Goodwin, G. M. (2008). Toxic effects of depression on brain function: Impairment of delayed recall and the cumulative length of depressive disorder in a large sample of depressed outpatients. American Journal of Psychiatry, 165(6), 731–739. doi:10.1176/appi.ajp.2008.07040574CrossRefGoogle Scholar
Hameed, S., Fuh, J. L., Senanarong, V., Ebenezer, E. G. M., Looi, I., Dominguez, J. C. … Simon, O. (2020). Role of fluid biomarkers and PET imaging in early diagnosis and its clinical implication in the management of Alzheimer's disease. Journal of Alzheimer's Disease Reports, 4(1), 21–37. doi:10.3233/ADR-190143CrossRefGoogle ScholarPubMed
Hamilton, M. (1967). Development of a rating scale for primary depressive illness. The British Journal of Social and Clinical Psychology, 6(4), 278–296.CrossRefGoogle ScholarPubMed
Han, S., Weaver, J. A., Murray, S. O., Kang, X., Yund, E. W., & Woods, D. L. (2002). Hemispheric asymmetry in global/local processing: Effects of stimulus position and spatial frequency. NeuroImage, 17(3), 1290–1299. doi:10.1006/nimg.2002.1255CrossRefGoogle ScholarPubMed
Heaton, R. K., Chelune, G. J., Talley, J. L., Kay, G.G., & Gurtiss, G. (1993). Wisconsin Card Sorting Test Manual: psychological assessment resources.Google Scholar
Herrmann, L. L., Goodwin, G. M., & Ebmeier, K. P. (2007). The cognitive neuropsychology of depression in the elderly. Psychological Medicine, 37(12), 1693–1702. doi:10.1017/S0033291707001134CrossRefGoogle ScholarPubMed
Hoeffer, C. A., & Klann, E. (2010). mTOR signaling: At the crossroads of plasticity, memory and disease. Trends in Neurosciences, 33(2), 67–75. doi:10.1016/j.tins.2009.11.003CrossRefGoogle ScholarPubMed
Hopper, M. W., & Vogel, F. S. (1976). The limbic system in Alzheimer's disease. A neuropathologic investigation. The American Journal of Pathology, 85(1), 1–20.Google ScholarPubMed
Li, C. T., Chen, M. H., Juan, C. H., Huang, H. H., Chen, L. F., Hsieh, J. C. … Su, T. P. (2014). Efficacy of prefrontal theta-burst stimulation in refractory depression: A randomized sham-controlled study. Brain, 137(Pt 7), 2088–2098. doi:10.1093/brain/awu109CrossRefGoogle ScholarPubMed
Li, C. T., Chen, M. H., Lin, W. C., Hong, C. J., Yang, B. H., Liu, R. S. … Su, T. P. (2016a). The effects of low-dose ketamine on the prefrontal cortex and amygdala in treatment-resistant depression: A randomized controlled study. Human Brain Mapping, 37(3), 1080–1090. doi:10.1002/hbm.23085CrossRefGoogle ScholarPubMed
Li, C. T., Cheng, C. M., Chen, M. H., Juan, C. H., Tu, P. C., Bai, Y. M. … Su, T. P. (2020). Antidepressant efficacy of prolonged intermittent theta burst stimulation monotherapy for recurrent depression and comparison of methods for coil positioning: A randomized, double-blind, sham-controlled study. Biological Psychiatry, 87(5), 443–450. doi:10.1016/j.biopsych.2019.07.031CrossRefGoogle ScholarPubMed
Li, C. T., Hsieh, J. C., Wang, S. J., Yang, B. H., Bai, Y. M., Lin, W. C. … Su, T. P. (2012). Differential relations between fronto-limbic metabolism and executive function in patients with remitted bipolar I and bipolar II disorder. Bipolar Disorders, 14(8), 831–842. doi:10.1111/bdi.12017CrossRefGoogle ScholarPubMed
Li, C. T., Lin, C. P., Chou, K. H., Chen, I. Y., Hsieh, J. C., Wu, C. L. … Su, T. P. (2010). Structural and cognitive deficits in remitting and non-remitting recurrent depression: A voxel-based morphometric study. NeuroImage, 50(1), 347–356. doi:10.1016/j.neuroimage.2009.11.021CrossRefGoogle ScholarPubMed
Li, C. T., Su, T. P., Wang, S. J., Tu, P. C., & Hsieh, J. C. (2015). Prefrontal glucose metabolism in medication-resistant major depression. British Journal of Psychiatry, 206(4), 316–323. doi:10.1192/bjp.bp.113.140434CrossRefGoogle ScholarPubMed
Li, C. T., Yang, K. C., & Lin, W. C. (2018). Glutamatergic dysfunction and glutamatergic compounds for major psychiatric disorders: Evidence from clinical neuroimaging studies. Frontiers in Psychiatry, 9, 767. doi:10.3389/fpsyt.2018.00767CrossRefGoogle ScholarPubMed
Li, X., Wang, H., Tian, Y., Zhou, S., Li, X., Wang, K., & Yu, Y. (2016b). Impaired white matter connections of the limbic system networks associated with impaired emotional memory in Alzheimer's disease. Frontiers in Aging Neuroscience, 8, 250. doi:10.3389/fnagi.2016.00250CrossRefGoogle ScholarPubMed
Lindsay, J., Laurin, D., Verreault, R., Hebert, R., Helliwell, B., Hill, G. B., & McDowell, I. (2002). Risk factors for Alzheimer's disease: A prospective analysis from the Canadian Study of Health and Aging. American Journal of Epidemiology, 156(5), 445–453. doi:10.1093/aje/kwf074CrossRefGoogle ScholarPubMed
Lowe, V. J., Kemp, B. J., Jack, C. R. Jr., Senjem, M., Weigand, S., Shiung, M. … Petersen, R. C. (2009). Comparison of 18F-FDG and PiB PET in cognitive impairment. Journal of Nuclear Medicine, 50(6), 878–886. doi:10.2967/jnumed.108.058529CrossRefGoogle ScholarPubMed
Lozupone, M., La Montagna, M., D'Urso, F., Piccininni, C., Sardone, R., Dibello, V. … Panza, F. (2018). Pharmacotherapy for the treatment of depression in patients with Alzheimer's disease: A treatment-resistant depressive disorder. Expert Opinion on Pharmacotherapy, 19(8), 823–842. doi:10.1080/14656566.2018.1471136CrossRefGoogle ScholarPubMed
Mirza, S. S., Wolters, F. J., Swanson, S. A., Koudstaal, P. J., Hofman, A., Tiemeier, H., & Ikram, M. A. (2016). 10-year Trajectories of depressive symptoms and risk of dementia: A population-based study. Lancet Psychiatry, 3(7), 628–635. doi:10.1016/S2215-0366(16)00097-3CrossRefGoogle ScholarPubMed
Morimoto, S. S., Kanellopoulos, D., Manning, K. J., & Alexopoulos, G. S. (2015). Diagnosis and treatment of depression and cognitive impairment in late life. Annals of the New York Academy of Sciences, 1345, 36–46. doi:10.1111/nyas.12669CrossRefGoogle ScholarPubMed
Muhlau, M., Hermsdorfer, J., Goldenberg, G., Wohlschlager, A. M., Castrop, F., Stahl, R. … Boecker, H. (2005). Left inferior parietal dominance in gesture imitation: An fMRI study. Neuropsychologia, 43(7), 1086–1098. doi:10.1016/j.neuropsychologia.2004.10.004CrossRefGoogle ScholarPubMed
Nasreddine, Z. S., Phillips, N. A., Bedirian, V., Charbonneau, S., Whitehead, V., Collin, I. … Chertkow, H. (2005). The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society, 53(4), 695–699. doi:10.1111/j.1532-5415.2005.53221.xCrossRefGoogle Scholar
Rao, D., Xu, G., Lu, Z., Liang, H., Lin, K., & Tang, M. (2019). Comparative study of cognitive function between treatment-resistant depressive patients and first-episode depressive patients. Neuropsychiatric Disease and Treatment, 15, 3411–3417. doi:10.2147/NDT.S226405CrossRefGoogle ScholarPubMed
Roiser, J. P., & Sahakian, B. J. (2013). Hot and cold cognition in depression. CNS Spectrums, 18(3), 139–149. doi:10.1017/S1092852913000072CrossRefGoogle ScholarPubMed
Signorini, M., Paulesu, E., Friston, K., Perani, D., Colleluori, A., Lucignani, G. … Fazio, F. (1999). Rapid assessment of regional cerebral metabolic abnormalities in single subjects with quantitative and nonquantitative [18F]FDG PET: A clinical validation of statistical parametric mapping. Neuroimage, 9(1), 63–80. doi:S1053-8119(98)90381-0.CrossRefGoogle ScholarPubMed
Tolboom, N., Yaqub, M., Boellaard, R., Luurtsema, G., Windhorst, A. D., Scheltens, P. … van Berckel, B. N. (2009). Test-retest variability of quantitative [11C]PIB studies in Alzheimer's disease. European Journal of Nuclear Medicine and Molecular Imaging, 36(10), 1629–1638. doi:10.1007/s00259-009-1129-6CrossRefGoogle ScholarPubMed
Treusch, S., Cyr, D. M., & Lindquist, S. (2009). Amyloid deposits: Protection against toxic protein species? Cell Cycle, 8(11), 1668–1674. doi:10.4161/cc.8.11.8503CrossRefGoogle ScholarPubMed
Wechsler, D. (1997). Wechsler Memory Scale – third edition. Administration and scoring manual. USA: The Psychological Corporation.Google Scholar
Wolfe, K. J., & Cyr, D. M. (2011). Amyloid in neurodegenerative diseases: Friend or foe? Seminars in Cell and Developmental Biology, 22(5), 476–481. doi:10.1016/j.semcdb.2011.03.011CrossRefGoogle ScholarPubMed
Wu, K. Y., Liu, C. Y., Chen, C. S., Chen, C. H., Hsiao, I. T., Hsieh, C. J. … Lin, K. J. (2016). Beta-amyloid deposition and cognitive function in patients with major depressive disorder with different subtypes of mild cognitive impairment: (18)F-florbetapir (AV-45/Amyvid) PET study. European Journal of Nuclear Medicine and Molecular Imaging, 43(6), 1067–1076. doi:10.1007/s00259-015-3291-3CrossRefGoogle Scholar
Yamane, T., Ishii, K., Sakata, M., Ikari, Y., Nishio, T., Ishii, K. … Group, J. A. S. (2017). Inter-rater variability of visual interpretation and comparison with quantitative evaluation of (11)C-PiB PET amyloid images of the Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) multicenter study. European Journal of Nuclear Medicine and Molecular Imaging, 44(5), 850–857. doi:10.1007/s00259-016-3591-2CrossRefGoogle Scholar
Yang, A. C., Huang, C. C., Liu, M. E., Liou, Y. J., Hong, C. J., Lo, M. T. … Tsai, S. J. (2014). The APOE varepsilon4 allele affects complexity and functional connectivity of resting brain activity in healthy adults. Human Brain Mapping, 35(7), 3238–3248. doi:10.1002/hbm.22398CrossRefGoogle Scholar
Yasuno, F., Kazui, H., Morita, N., Kajimoto, K., Ihara, M., Taguchi, A. … Nagatsuka, K. (2016). High amyloid-beta deposition related to depressive symptoms in older individuals with normal cognition: A pilot study. International Journal of Geriatric Psychiatry, 31(8), 920–928. doi:10.1002/gps.4409CrossRefGoogle ScholarPubMed
Supplementary material: File

Li et al. supplementary material

Li et al. supplementary material

Download Li et al. supplementary material(File)
File 3.7 MB