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Tau pathology is associated with synaptic density and longitudinal synaptic loss in Alzheimer’s disease

Molecular Psychiatry (2024)Cite this article

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Abstract

The associations of synaptic loss with amyloid-β (Aβ) and tau pathology measured by positron emission tomography (PET) and plasma analysis in Alzheimer’s disease (AD) patients are unknown. Seventy-five participants, including 26 AD patients, 19 mild cognitive impairment (MCI) patients, and 30 normal controls (NCs), underwent [18F]SynVesT-1 PET/MR scans to assess synaptic density and [18F]florbetapir and [18F]MK6240 PET/CT scans to evaluate Aβ plaques and tau tangles. Among them, 19 AD patients, 12 MCI patients, and 29 NCs had plasma Aβ42/40 and p-tau181 levels measured by the Simoa platform. Twenty-three individuals, 6 AD patients, 4 MCI patients, and 13 NCs, underwent [18F]SynVesT-1 PET/MRI and [18F]MK6240 PET/CT scans during a one-year follow-up assessment. The associations of Aβ and tau pathology with cross-sectional and longitudinal synaptic loss were investigated using Pearson correlation analyses, generalized linear models and mediation analyses. AD patients exhibited lower synaptic density than NCs and MCI patients. In the whole cohort, global Aβ deposition was associated with synaptic loss in the medial (r = −0.431, p < 0.001) and lateral (r = −0.406, p < 0.001) temporal lobes. Synaptic density in almost all regions was related to the corresponding regional tau tangles independent of global Aβ deposition in the whole cohort and stratified groups. Synaptic density in the medial and lateral temporal lobes was correlated with plasma Aβ42/40 (r = 0.300, p = 0.020/r = 0.289, p = 0.025) and plasma p-tau 181 (r = −0.412, p = 0.001/r = −0.529, p < 0.001) levels in the whole cohort. Mediation analyses revealed that tau tangles mediated the relationship between Aβ plaques and synaptic density in the whole cohort. Baseline tau pathology was positively associated with longitudinal synaptic loss. This study suggested that tau burden is strongly linked to synaptic density independent of Aβ plaques, and also can predict longitudinal synaptic loss.

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Fig. 1: Group differences in synaptic density among the AD, MCI and NC groups.
Fig. 2: Associations of synaptic density in the medial temporal lobe with amyloid deposition and tau burden according to VOI analysis.
Fig. 3: Associations of regional synaptic density with regional tau burden according to VOI analysis.
Fig. 4: Associations of plasma Aβ42/40 and p-tau 181 levels with synaptic density, amyloid deposition and tau burden based on voxelwise analysis in the whole cohort.
Fig. 5: Tau pathology mediates the relationship between Aβ pathology and synaptic density.
Fig. 6: Associations of tau and Aβ pathology detected by PET imaging and plasma analysis with longitudinal synaptic loss.

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Data availability

The data supporting the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

References

  1. Turab Naqvi AA, Hasan GM, Hassan MI. Targeting tau hyperphosphorylation via kinase inhibition: strategy to address Alzheimer’s disease. Curr Top Med Chem. 2020;20:1059–73.

    Article  PubMed  Google Scholar 

  2. Lee A, Kondapalli C, Virga D, Lewis T, Koo S, Ashok A, et al. Aβ42 oligomers trigger synaptic loss through CAMKK2-AMPK-dependent effectors coordinating mitochondrial fission and mitophagy. Nat Commun. 2022;13:4444.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Lauterborn J, Cox C, Chan S, Vanderklish P, Lynch G, Gall C. Synaptic actin stabilization protein loss in down syndrome and Alzheimer disease. Brain Pathol. 2020;30:319–31.

    Article  CAS  PubMed  Google Scholar 

  4. Coleman PD, Yao PJ. Synaptic slaughter in Alzheimer’s disease. Neurobiol Aging. 2003;24:1023–7.

    Article  CAS  PubMed  Google Scholar 

  5. Mecca A, O’Dell R, Sharp E, Banks E, Bartlett H, Zhao W, et al. Synaptic density and cognitive performance in Alzheimer’s disease: a PET imaging study with [C]UCB-J. Alzheimer’s Dement. 2022;18:2527–36.

    Article  CAS  Google Scholar 

  6. Selkoe D, Hardy J. The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol Med. 2016;8:595–608.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Koffie R, Meyer-Luehmann M, Hashimoto T, Adams K, Mielke M, Garcia-Alloza M, et al. Oligomeric amyloid beta associates with postsynaptic densities and correlates with excitatory synapse loss near senile plaques. Proc Natl Acad Sci USA. 2009;106:4012–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Forner S, Baglietto-Vargas D, Martini A, Trujillo-Estrada L, LaFerla F. Synaptic impairment in Alzheimer’s disease: a dysregulated symphony. Trends Neurosci. 2017;40:347–57.

    Article  CAS  PubMed  Google Scholar 

  9. Lan G, Cai Y, Li A, Liu Z, Ma S, Guo T. Association of presynaptic loss with Alzheimer’s disease and cognitive decline. Ann Neurol. 2022;92:1001–15.

    Article  CAS  PubMed  Google Scholar 

  10. Stout K, Dunn A, Hoffman C, Miller G. The synaptic vesicle glycoprotein 2: structure, function, and disease relevance. ACS Chem Neurosci. 2019;10:3927–38.

    Article  CAS  PubMed  Google Scholar 

  11. Finnema S, Nabulsi N, Eid T, Detyniecki K, Lin S, Chen M, et al. Imaging synaptic density in the living human brain. Sci Transl Med. 2016;8:348ra396.

    Article  Google Scholar 

  12. Chen M, Mecca A, Naganawa M, Finnema S, Toyonaga T, Lin S, et al. Assessing synaptic density in Alzheimer disease with synaptic vesicle glycoprotein 2A positron emission tomographic imaging. JAMA Neurol. 2018;75:1215–24.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Mecca A, Chen M, O’Dell R, Naganawa M, Toyonaga T, Godek T, et al. In vivo measurement of widespread synaptic loss in Alzheimer’s disease with SV2A PET. Alzheimer’s Dement. 2020;16:974–82.

    Article  Google Scholar 

  14. Vanhaute H, Ceccarini J, Michiels L, Koole M, Sunaert S, Lemmens R, et al. In vivo synaptic density loss is related to tau deposition in amnestic mild cognitive impairment. Neurology. 2020;95:e545–e553.

    Article  CAS  PubMed  Google Scholar 

  15. O’Dell R, Mecca A, Chen M, Naganawa M, Toyonaga T, Lu Y, et al. Association of Aβ deposition and regional synaptic density in early Alzheimer’s disease: a PET imaging study with [C]UCB-J. Alzheimer’s Res Ther. 2021;13:11.

    Article  Google Scholar 

  16. Zhou L, McInnes J, Wierda K, Holt M, Herrmann A, Jackson R, et al. Tau association with synaptic vesicles causes presynaptic dysfunction. Nat Commun. 2017;8:15295.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Hoover B, Reed M, Su J, Penrod R, Kotilinek L, Grant M, et al. Tau mislocalization to dendritic spines mediates synaptic dysfunction independently of neurodegeneration. Neuron. 2010;68:1067–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Gao F, Shang S, Chen C, Dang L, Gao L, Wei S, et al. Non-linear relationship between plasma amyloid-β 40 level and cognitive decline in a cognitively normal population. Front Aging Neurosci. 2020;12:557005.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Guo Q, Zhao Q, Chen M, Ding D, Hong Z. A comparison study of mild cognitive impairment with 3 memory tests among Chinese individuals. Alzheimer Dis Assoc Disord. 2009;23:253–59.

  20. Ding D, Zhao Q, Guo Q, Liang X, Luo J, Yu L, et al. Progression and predictors of mild cognitive impairment in Chinese elderly: a prospective follow-up in the Shanghai aging study. Alzheimer’s Dement. (Amsterdam, Netherlands) 2016;4:28–36.

  21. Zhao Q, Guo Q, Hong Z. Clustering and switching during a semantic verbal fluency test contribute to differential diagnosis of cognitive impairment. Neurosci Bull. 2013;29:75–82.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Zhao Q, Guo Q, Liang X, Chen M, Zhou Y, Ding D, et al. Auditory verbal learning test is superior to rey-osterrieth complex figure memory for predicting mild cognitive impairment to Alzheimer’s disease. Curr Alzheimer Res. 2015;12:520–6.

    Article  CAS  PubMed  Google Scholar 

  23. McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7:263–9.

    Article  PubMed  Google Scholar 

  24. Jack C, Lowe V, Senjem M, Weigand S, Kemp B, Shiung M, et al. 11C PiB and structural MRI provide complementary information in imaging of Alzheimer’s disease and amnestic mild cognitive impairment. Brain. 2008;131:665–80.

    Article  PubMed  Google Scholar 

  25. Bondi M, Edmonds E, Jak A, Clark L, Delano-Wood L, McDonald C, et al. Neuropsychological criteria for mild cognitive impairment improves diagnostic precision, biomarker associations, and progression rates. J Alzheimer’s Dis. 2014;42:275–89.

    Article  Google Scholar 

  26. Su H, Sun X, Li F, Guo Q. Handgrip strength could be an early predictor of cognitive impairment in the Chinese population. 2021. Preprint https://doi.org/10.21203/rs.3.rs-400381/v1.

  27. Lin L A. Conceptual framework for research on cognitive impairment with no dementia in memory clinic. Curr Alzheimer Res. 2020;17:517–25.

  28. Sattlecker M, Khondoker M, Proitsi P, Williams S, Soininen H, Kłoszewska I, et al. Longitudinal protein changes in blood plasma associated with the rate of cognitive decline in Alzheimer’s disease. J Alzheimer’s Dis. 2016;49:1105–14.

    Article  CAS  Google Scholar 

  29. Wilson D, Rissin D, Kan C, Fournier D, Piech T, Campbell T, et al. The Simoa HD-1 analyzer: a novel fully automated digital immunoassay analyzer with single-molecule sensitivity and multiplexing. J Lab Autom. 2016;21:533–47.

    Article  CAS  PubMed  Google Scholar 

  30. Pan F, Huang Q, Wang Y, Wang Y, Guan Y, Xie F, et al. Non-linear character of plasma amyloid beta over the course of cognitive decline in Alzheimer’s continuum. Front Aging Neurosci. 2022;14:832700.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhang J, Wang J, Xu X, You Z, Huang Q, Huang Y, et al. In vivo synaptic density loss correlates with impaired functional and related structural connectivity in Alzheimer’s disease. J Cereb Blood Flow Metab. 2023;43:977–988.

  32. Qi H, Ren S, Jiang D, Hua F. Changes in brain glucose metabolism and connectivity in somatoform disorders: an 18F-FDG PET study. Eur Arch Psychiatry Clin Neurosci. 2020;270:881–91.

    Article  Google Scholar 

  33. Su J, Huang Q, Ren S, Xie F, Zhai Y, Guan Y, et al. Altered brain glucose metabolism assessed by F-FDG PET imaging is associated with the cognitive impairment of CADASIL. Neuroscience. 2019;417:35–44.

    Article  CAS  PubMed  Google Scholar 

  34. Gonzalez-Escamilla G, Lange C, Teipel S, Buchert R, Grothe MJ. PETPVE12: an SPM toolbox for Partial Volume Effects correction in brain PET - application to amyloid imaging with AV45-PET. Neuroimage. 2017;147:669–77.

    Article  PubMed  Google Scholar 

  35. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15:273–89.

    Article  CAS  PubMed  Google Scholar 

  36. Lundeen T, Seibyl J, Covington M, Eshghi N, Kuo P. Signs and artifacts in amyloid PET. Radiographics. 2018;38:2123–33.

    Article  PubMed  Google Scholar 

  37. Minoshima S, Drzezga A, Barthel H, Bohnen N, Djekidel M, Lewis D, et al. SNMMI procedure standard/EANM practice guideline for amyloid PET imaging of the brain 1.0. J Nucl Med. 2016;57:1316–22.

    Article  CAS  PubMed  Google Scholar 

  38. Krishnadas N, Huang K, Schultz S, Doré V, Bourgeat P, Goh A, et al. Visually identified tau 18F-MK6240 PET patterns in symptomatic Alzheimer’s disease. J Alzheimer’s Dis. 2022;88:1627–37.

    Article  CAS  Google Scholar 

  39. Seibyl JP, DuBois JM, Racine A, Collins J, Guo Q, Wooten D, et al. A visual interpretation algorithm for assessing brain tauopathy with (18)F-MK-6240 PET. J Nucl Med. 2023;64:444–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Shuping J, Matthews D, Adamczuk K, Scott D, Rowe C, Kreisl W, et al. Development, initial validation, and application of a visual read method for [F]MK-6240 tau PET. Alzheimer’s Dement. 2023;9:e12372.

    Article  Google Scholar 

  41. Soleimani-Meigooni D, Rabinovici G. Tau PET visual reads: research and clinical applications and future directions. J Nucl Med. 2023;64:822–4.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Coomans E, Schoonhoven D, Tuncel H, Verfaillie S, Wolters E, Boellaard R, et al. In vivo tau pathology is associated with synaptic loss and altered synaptic function. Alzheimer’s Res Ther. 2021;13:35.

    Article  CAS  Google Scholar 

  43. Mielke MM, Frank RD, Dage JL, Jeromin A, Ashton NJ, Blennow K, et al. Comparison of plasma phosphorylated tau species with amyloid and tau positron emission tomography, neurodegeneration, vascular pathology, and cognitive outcomes. JAMA Neurol. 2021;78:1108–17.

    Article  PubMed  Google Scholar 

  44. Bilgel M, An Y, Walker K, Moghekar A, Ashton N, Kac P, et al. Longitudinal changes in Alzheimer’s-related plasma biomarkers and brain amyloid. Alzheimer’s Dement. 2023;19:4335–45.

    Article  CAS  Google Scholar 

  45. Hyman B, Marzloff K, Arriagada P. The lack of accumulation of senile plaques or amyloid burden in Alzheimer’s disease suggests a dynamic balance between amyloid deposition and resolution. J Neuropathol Exp Neurol. 1993;52:594–600.

    Article  CAS  PubMed  Google Scholar 

  46. Jack C, Wiste H, Lesnick T, Weigand S, Knopman D, Vemuri P, et al. Brain β-amyloid load approaches a plateau. Neurology. 2013;80:890–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Ingelsson M, Fukumoto H, Newell K, Growdon J, Hedley-Whyte E, Frosch M, et al. Early Abeta accumulation and progressive synaptic loss, gliosis, and tangle formation in AD brain. Neurology. 2004;62:925–31.

    Article  CAS  PubMed  Google Scholar 

  48. Vanderlinden G, Ceccarini J, Vande Casteele T, Michiels L, Lemmens R, Triau E, et al. Spatial decrease of synaptic density in amnestic mild cognitive impairment follows the tau build-up pattern. Mol Psychiatry. 2022;27:4244–51.

    Article  CAS  PubMed  Google Scholar 

  49. Spires-Jones T, Hyman B. The intersection of amyloid beta and tau at synapses in Alzheimer’s disease. Neuron. 2014;82:756–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Pooler A, Noble W, Hanger D. A role for tau at the synapse in Alzheimer’s disease pathogenesis. Neuropharmacology. 2014;76:1–8.

  51. Das S, Goossens J, Jacobs D, Dewit N, Pijnenburg Y, In ‘t Veld S, et al. Synaptic biomarkers in the cerebrospinal fluid associate differentially with classical neuronal biomarkers in patients with Alzheimer’s disease and frontotemporal dementia. Alzheimer’s Res Ther. 2023;15:62.

    Article  CAS  Google Scholar 

  52. Mielke M, Przybelski S, Lesnick T, Kern S, Zetterberg H, Blennow K, et al. Comparison of CSF neurofilament light chain, neurogranin, and tau to MRI markers. Alzheimer’s Dement. 2021;17:801–12.

    Article  CAS  Google Scholar 

  53. Portelius E, Zetterberg H, Skillbäck T, Törnqvist U, Andreasson U, Trojanowski J, et al. Cerebrospinal fluid neurogranin: relation to cognition and neurodegeneration in Alzheimer’s disease. Brain. 2015;138:3373–85.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Galasko D, Xiao M, Xu D, Smirnov D, Salmon D, Dewit N, et al. Synaptic biomarkers in CSF aid in diagnosis, correlate with cognition and predict progression in MCI and Alzheimer’s disease. Alzheimer’s Dement. 2019;5:871–82.

    Article  Google Scholar 

  55. Wu JW, Hussaini SA, Bastille IM, Rodriguez GA, Mrejeru A, Rilett K, et al. Neuronal activity enhances tau propagation and tau pathology in vivo. Nat Neurosci. 2016;19:1085–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Fein JA, Sokolow S, Miller CA, Vinters HV, Yang F, Cole GM, et al. Co-localization of amyloid beta and tau pathology in Alzheimer’s disease synaptosomes. Am J Pathol. 2008;172:1683–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Robbins M, Clayton E, Kaminski Schierle G. Synaptic tau: a pathological or physiological phenomenon? Acta Neuropathol Commun. 2021;9:149.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Crimins J, Pooler A, Polydoro M, Luebke J, Spires-Jones T. The intersection of amyloid β and tau in glutamatergic synaptic dysfunction and collapse in Alzheimer’s disease. Ageing Res Rev. 2013;12:757–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors thank Jianfei Xiao for generous assistance with tracer production and Xiangqing Xie, Yue Qian, and Dan Zhou for assistance with patient recruitment.

Funding

This research was sponsored by the National Key R&D Program of China (2016YFC1306305 and 2018YFE0203600); the National Science Foundation of China (81801752, 81571345, 8217052097 and 82201583); the Shanghai Sailing Program (18YF1403200 and 19YF1405300); the startup fund of Huashan Hospital, Fudan University (2017QD081); the Shanghai Municipal Key Clinical Specialty (3030247006); the Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01); and ZJLab Clinical Research Plan of SHDC (No. SHDC2020CR2056B).

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  1. These authors contributed equally: Jie Wang, Qi Huang.

Authors and Affiliations

  1. Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, 200040, China

    Jie Wang, Qi Huang, Kun He, Yihui Guan & Fang Xie

  2. Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, 310000, China

    Xing Chen, Zhiwen You & Jun Zhao

  3. Department of Gerontology, Shanghai Jiaotong University Affiliated Sixth People’s Hospital, Shanghai, 200233, China

    Qihao Guo

  4. PET Center, Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, CT, 06520-8048, USA

    Yiyun Huang

  5. Beijing United Imaging Research Institute of Intelligent Imaging, Beijing, 100089, China

    Yang Yang

  6. Central Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, 201807, China

    Zengping Lin

  7. Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, 518000, China

    Tengfei Guo

  8. Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China

    Binyin Li

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Contributions

Qihao Guo, Yiyun Huang, Tengfei Guo, Jun Zhao, Yihui Guan, Binyin Li and Fang Xie contributed to the study’s conception and design. Material preparation and data collection and analysis were performed by Jie Wang, Xing Chen, Kun He, Zengping Lin, ZhiWen You and Yang Yang. Data analysis were performed by Jie Wang and Fang Xie. The first draft of the manuscript was written by Jie Wang and Qi Huang. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yihui Guan, Binyin Li or Fang Xie.

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Wang, J., Huang, Q., Chen, X. et al. Tau pathology is associated with synaptic density and longitudinal synaptic loss in Alzheimer’s disease. Mol Psychiatry (2024). https://doi.org/10.1038/s41380-024-02501-z

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