Skip to main content

Advertisement

SpringerLink
Log in

Canine Cognitive Dysfunction (CCD) scores correlate with amyloid beta 42 levels in dog brain tissue

  • Original Article
  • Published:
GeroScience Aims and scope Submit manuscript

Abstract

Alzheimer’s disease (AD) is a significant burden for human health that is increasing in prevalence as the global population ages. There is growing recognition that current preclinical models of AD are insufficient to recapitulate key aspects of the disease. Laboratory models for AD include mice, which do not naturally develop AD-like pathology during aging, and laboratory Beagle dogs, which do not share the human environment. In contrast, the companion dog shares the human environment and presents a genetically heterogeneous population of animals that might spontaneously develop age-associated AD-like pathology and cognitive dysfunction. Here, we quantitatively measured amyloid beta (Aβ42 or Abeta-42) levels in three areas of the companion dog brain (prefrontal cortex, temporal cortex, hippocampus/entorhinal cortex) and cerebrospinal fluid (CSF) using a newly developed Luminex assay. We found significant positive correlations between Aβ42 and age in all three brain regions. Brain Aβ42 abundance in all three brain regions was also correlated with Canine Cognitive Dysfunction Scale score in a multivariate analysis. This latter effect remained significant when correcting for age, except in the temporal cortex. There was no correlation between Aβ42 in CSF and cognitive scores; however, we found a significant positive correlation between Aβ42 in CSF and body weight, as well as a significant negative correlation between Aβ42 in CSF and age. Our results support the suitability of the companion dog as a model for AD and illustrate the utility of veterinary biobanking to make biospecimens available to researchers for analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

The full data used for this analysis are provided in Supplemental Table 1.

References

  1. Khachaturian ZS, Khachaturian AS, Thies W. The draft “National Plan” to address Alzheimer’s disease - National Alzheimer’s Project Act (NAPA). Alzheimers Dement. 2012;8(3):234–6.

    Article  Google Scholar 

  2. Hebert LE, et al. Alzheimer disease in the United States (2010–2050) estimated using the 2010 census. Neurology. 2013;80(19):1778–83.

    Article  Google Scholar 

  3. Lopez-Otin C, et al. The hallmarks of aging. Cell. 2013;153(6):1194–217.

    Article  CAS  Google Scholar 

  4. Kaeberlein M, Galvan V. Rapamycin and Alzheimer’s disease: time for a clinical trial? Sci Transl Med. 2019;11(476):eaar4289.

    Article  CAS  Google Scholar 

  5. Kaeberlein M. Time for a new strategy in the war on Alzheimer’s disease. Public Policy Aging Rep. 2019;29(4):119–22.

    Article  Google Scholar 

  6. Drummond E, Wisniewski T. Alzheimer’s disease: experimental models and reality. Acta Neuropathol. 2017;133(2):155–75.

    Article  CAS  Google Scholar 

  7. Jankowsky JL, Zheng H. Practical considerations for choosing a mouse model of Alzheimer’s disease. Mol Neurodegener. 2017;12(1):89.

    Article  Google Scholar 

  8. Cotman CW, Head E. The canine (dog) model of human aging and disease: dietary, environmental and immunotherapy approaches. J Alzheimers Dis. 2008;15(4):685–707.

    Article  CAS  Google Scholar 

  9. Plassais J, et al. Whole genome sequencing of canids reveals genomic regions under selection and variants influencing morphology. Nat Commun. 2019;10(1):1489.

    Article  Google Scholar 

  10. Schutt T, et al. Dogs with cognitive dysfunction as a spontaneous model for early Alzheimer’s disease: a translational study of neuropathological and inflammatory markers. J Alzheimers Dis. 2016;52(2):433–49.

    Article  Google Scholar 

  11. Colle MA, et al. Vascular and parenchymal Abeta deposition in the aging dog: correlation with behavior. Neurobiol Aging. 2000;21(5):695–704.

    Article  CAS  Google Scholar 

  12. Torp R, Head E, Cotman CW. Ultrastructural analyses of beta-amyloid in the aged dog brain: neuronal beta-amyloid is localized to the plasma membrane. Prog Neuropsychopharmacol Biol Psychiatry. 2000;24(5):801–10.

    Article  CAS  Google Scholar 

  13. Dewey CW, et al. Canine Cognitive Dysfunction: pathophysiology, diagnosis, and treatment. Vet Clin North Am Small Anim Pract. 2019;49(3):477–99.

    Article  Google Scholar 

  14. Salvin HE, et al. The canine cognitive dysfunction rating scale (CCDR): a data-driven and ecologically relevant assessment tool. Vet J. 2011;188(3):331–6.

    Article  Google Scholar 

  15. Head E. A canine model of human aging and Alzheimer’s disease. Biochim Biophys Acta. 2013;1832(9):1384–9.

    Article  CAS  Google Scholar 

  16. Schmidt F, et al. Detection and quantification of beta-amyloid, pyroglutamyl Abeta, and Tau in aged canines. J Neuropathol Exp Neurol. 2015;74(9):912–23.

    Article  CAS  Google Scholar 

  17. Johnstone EM, et al. Conservation of the sequence of the Alzheimer’s disease amyloid peptide in dog, polar bear and five other mammals by cross-species polymerase chain reaction analysis. Brain Res Mol Brain Res. 1991;10(4):299–305.

    Article  CAS  Google Scholar 

  18. Selkoe DJ, et al. Conservation of brain amyloid proteins in aged mammals and humans with Alzheimer’s disease. Science. 1987;235(4791):873–7.

    Article  CAS  Google Scholar 

  19. Satou T, et al. The progression of beta-amyloid deposition in the frontal cortex of the aged canine. Brain Res. 1997;774(1–2):35–43.

    Article  CAS  Google Scholar 

  20. Pugliese M, et al. Canine cognitive deficit correlates with diffuse plaque maturation and S100beta (-) astrocytosis but not with insulin cerebrospinal fluid level. Acta Neuropathol. 2006;111(6):519–28.

    Article  Google Scholar 

  21. Yu CH, et al. Histopathological and immunohistochemical comparison of the brain of human patients with Alzheimer’s disease and the brain of aged dogs with cognitive dysfunction. J Comp Pathol. 2011;145(1):45–58.

    Article  CAS  Google Scholar 

  22. Head E, et al. A two-year study with fibrillar beta-amyloid (Abeta) immunization in aged canines: effects on cognitive function and brain Abeta. J Neurosci. 2008;28(14):3555–66.

    Article  CAS  Google Scholar 

  23. Urfer SR, et al. Cross species application of quantitative neuropathology assays developed for clinical Alzheimer’s disease samples. Pathobiol Aging Age Relat Dis. 2019;9(1):1657768.

    Article  CAS  Google Scholar 

  24. Keene CD, et al. Luminex-based quantification of Alzheimer’s disease neuropathologic change in formalin-fixed post-mortem human brain tissue. Lab Invest. 2019;99(7):1056–67.

    Article  Google Scholar 

  25. Sandor S, et al. Man's best friend in life and death: scientific perspectives and challenges of dog brain banking. GeroScience. 2021. https://doi.org/10.1007/s11357-021-00373-7

  26. Team RC. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2020. https://www.R-project.org/. Accessed 8/5/2021.

  27. Madari A, et al. Assessment of severity and progression of canine cognitive dysfunction syndrome using the CAnine DEmentia Scale (CADES). Appl Anim Behav Sci. 2015;171(10):138–45.

    Article  Google Scholar 

  28. Jansen WJ, et al. Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis. JAMA. 2015;313(19):1924–38.

    Article  Google Scholar 

  29. Head E, et al. Region-specific age at onset of beta-amyloid in dogs. Neurobiol Aging. 2000;21(1):89–96.

    Article  CAS  Google Scholar 

  30. Urfer SR, et al. Lifespan of companion dogs seen in three independent primary care veterinary clinics in the United States. Canine Med Genet. 2020;7:7.

    Article  Google Scholar 

  31. Galis F, et al. Do large dogs die young? J Exp Zool B Mol Dev Evol. 2007;308(2):119–26.

    Article  Google Scholar 

  32. Kraus C, Pavard S, Promislow DE. The size-life span trade-off decomposed: why large dogs die young. Am Nat. 2013;181(4):492–505.

    Article  Google Scholar 

  33. Watowich MM, et al. Age influences domestic dog cognitive performance independent of average breed lifespan. Anim Cogn. 2020;23(4):795–805.

    Article  Google Scholar 

  34. Montine TJ, et al. Recommendations of the Alzheimer’s disease-related dementias conference. Neurology. 2014;83(9):851–60.

    Article  Google Scholar 

  35. Montine TJ, et al. National Institute on Aging-Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer’s disease: a practical approach. Acta Neuropathol. 2012;123(1):1–11.

    Article  CAS  Google Scholar 

  36. Hyman BT, et al. National Institute on Aging-Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer’s disease. Alzheimers Dement. 2012;8(1):1–13.

    Article  Google Scholar 

  37. Head E. Neurobiology of the aging dog. Age (Dordr). 2011;33(3):485–96.

    Article  CAS  Google Scholar 

  38. Torp R, et al. Ultrastructural evidence of fibrillar beta-amyloid associated with neuronal membranes in behaviorally characterized aged dog brains. Neuroscience. 2000;96(3):495–506.

    Article  CAS  Google Scholar 

  39. Kaeberlein M, Creevy KE, Promislow DE. The dog aging project: translational geroscience in companion animals. Mamm Genome. 2016;27(7–8):279–88.

    Article  CAS  Google Scholar 

  40. Martin SB, Dowling AL, Head E. Therapeutic interventions targeting Beta amyloid pathogenesis in an aging dog model. Curr Neuropharmacol. 2011;9(4):651–61.

    Article  CAS  Google Scholar 

  41. Barone E, et al. Biliverdin reductase-A: a novel drug target for atorvastatin in a dog pre-clinical model of Alzheimer disease. J Neurochem. 2012;120(1):135–46.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors thank study participants, their dogs, and community veterinarians for their important contributions.

Funding

This work was supported by NIH grant 3U19AG057377-02S3 to DP. The Dog Aging Project is supported by U19 grant AG057377 from the National Institute on Aging, a part of the National Institutes of Health, and by private donations. The Senior Family Dog Project receives funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant Agreement No. 680040).

Author information

Authors and Affiliations

  1. Dog Aging Project, University of Washington, Seattle, WA, USA

    Silvan R. Urfer, Daniel E. L. Promislow, Kate E. Creevy & Matt Kaeberlein

  2. University of Washington Department of Laboratory Medicine and Pathology, Seattle, WA, USA

    Silvan R. Urfer, Martin Darvas, Daniel E. L. Promislow & Matt Kaeberlein

  3. Senior Family Dog Project, Department of Ethology, ELTE Eötvös Loránd University, Budapest, Hungary

    Kálmán Czeibert, Sára Sándor & Enikő Kubinyi

  4. Department of Biology, University of Washington, Seattle, WA, USA

    Daniel E. L. Promislow

  5. Texas A&M Veterinary Medicine & Biomedical Sciences, College Station, TX, USA

    Kate E. Creevy

Authors
  1. Silvan R. Urfer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Martin Darvas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kálmán Czeibert
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sára Sándor
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Daniel E. L. Promislow
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kate E. Creevy
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Enikő Kubinyi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Matt Kaeberlein
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Silvan R. Urfer.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Disclaimer

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps (including brain maps) and institutional affiliations.

Enikő Kubinyi and Matt Kaeberlein jointly supervised this work.

Supplementary Information

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Urfer, S.R., Darvas, M., Czeibert, K. et al. Canine Cognitive Dysfunction (CCD) scores correlate with amyloid beta 42 levels in dog brain tissue. GeroScience 43, 2379–2386 (2021). https://doi.org/10.1007/s11357-021-00422-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11357-021-00422-1

Keywords

Search

Navigation