Skip to main content

Advertisement

Log in

Polygenic hazard score: an enrichment marker for Alzheimer’s associated amyloid and tau deposition

  • Original Paper
  • Published:
Acta Neuropathologica Aims and scope Submit manuscript

Abstract

There is an urgent need for identifying nondemented individuals at the highest risk of progressing to Alzheimer’s disease (AD) dementia. Here, we evaluated whether a recently validated polygenic hazard score (PHS) can be integrated with known in vivo cerebrospinal fluid (CSF) or positron emission tomography (PET) biomarkers of amyloid, and CSF tau pathology to prospectively predict cognitive and clinical decline in 347 cognitive normal (CN; baseline age range = 59.7–90.1, 98.85% white) and 599 mild cognitively impaired (MCI; baseline age range = 54.4–91.4, 98.83% white) individuals from the Alzheimer’s Disease Neuroimaging Initiative 1, GO, and 2. We further investigated the association of PHS with post-mortem amyloid load and neurofibrillary tangles in the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort (N = 485, age at death range = 71.3–108.3). In CN and MCI individuals, we found that amyloid and total tau positivity systematically varies as a function of PHS. For individuals in greater than the 50th percentile PHS, the positive predictive value for amyloid approached 100%; for individuals in less than the 25th percentile PHS, the negative predictive value for total tau approached 85%. High PHS individuals with amyloid and tau pathology showed the steepest longitudinal cognitive and clinical decline, even among APOE ε4 noncarriers. Among the CN subgroup, we similarly found that PHS was strongly associated with amyloid positivity and the combination of PHS and biomarker status significantly predicted longitudinal clinical progression. In the ROSMAP cohort, higher PHS was associated with higher post-mortem amyloid load and neurofibrillary tangles, even in APOE ε4 noncarriers. Together, our results show that even after accounting for APOE ε4 effects, PHS may be useful in MCI and preclinical AD therapeutic trials to enrich for biomarker-positive individuals at highest risk for short-term clinical progression.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Bennett DA, Schneider JA, Arvanitakis Z, Wilson RS (2012) Overview and findings from the religious orders study. Curr Alzheimer Res 9:628–645

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Bennett DA, Schneider JA, Buchman AS et al (2012) Overview and findings from the rush memory and aging project. Curr Alzheimer Res 9:646–663

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Bonham LW, Desikan RS, Yokoyama JS (2016) The relationship between complement factor C3, APOE e4, amyloid and tau in Alzheimer’s disease. Acta Neuropathol Commun 4:65. https://doi.org/10.1186/s40478-016-0339-y

    Article  PubMed  PubMed Central  Google Scholar 

  4. Brookmeyer R, Gray S, Kawas C (1998) Projections of alzheimer’s disease in the United States and the public health impact of delaying disease onset. Am J Public Health 88:1337–1342 (PMID: 9736873)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Cohen J, Cohen P, West SG, Aiken LS (2003) Applied multiple regression/correlation analysis for the behavioral sciences, 3rd edn. Erlbaum, Hillsdale

    Google Scholar 

  6. Darst BF, Koscik RL, Racine AM et al (2017) Pathway-specific polygenic risk scores as predictors of amyloid-β deposition and cognitive function in a sample at increased risk for Alzheimer’s disease. J Alzheimers Dis 55:473–484. https://doi.org/10.3233/JAD-160195

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Desikan RS, Rafii MS, Brewer JB et al (2013) An expanded role for neuroimaging in the evaluation of memory impairment. Am J Neuroradiol 34:2075–2082. https://doi.org/10.3174/ajnr.A3644

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Desikan RS, Fan CC, Wang Y et al (2017) Personalized genetic assessment of age-associated Alzheimer’s disease risk. PLoS Med. 14:e1002258. https://doi.org/10.1371/journal.pmed.1002258

    Article  PubMed  PubMed Central  Google Scholar 

  9. Fagan AM, Mintun MA, Mach RH et al (2006) Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Abeta42 in humans. Ann Neurol 59:512–519. https://doi.org/10.1002/ana.20730

    Article  CAS  PubMed  Google Scholar 

  10. Gibbons LE, Carle AC, Mackin RS et al (2012) A composite score for executive functioning, validated in Alzheimer’s disease neuroimaging initiative (ADNI) participants with baseline mild cognitive impairment. Brain Imaging Behav 6:517–527. https://doi.org/10.1007/s11682-012-9176-1

    Article  PubMed  PubMed Central  Google Scholar 

  11. Goldberg TE, Harvey PD, Wesnes KA et al (2015) Practice effects due to serial cognitive assessment: implications for preclinical Alzheimer’s disease randomized controlled trials. Alzheimers Dement (Amst) 1:103–111. https://doi.org/10.1016/j.dadm.2014.11.003

    Google Scholar 

  12. Harrisson TM, Mahmood Z, Lau EP et al (2016) An Alzheimer’s disease genetic risk score predicts longitudinal thinning of hippocampal complex subregions in healthy older adults. eNeuro. https://doi.org/10.1523/eneuro.0098-16.2016

    Google Scholar 

  13. Jack CR, Knopman DS, Jagust WJ et al (2010) Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol 9:119–128. https://doi.org/10.1007/s12035-011-8228-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Jansen WJ, Rik Ossenkoppele, Knol DL et al (2015) Prevalence of cerebral amyloid pathology in persons without dementia. JAMA 313:1924–1938. https://doi.org/10.1001/jama.2015.4668

    Article  PubMed  PubMed Central  Google Scholar 

  15. Karch CM, Goate AM (2015) Alzheimer’s disease risk genes and mechanisms of disease pathogenesis. Biol Psychiatry 77:43–51. https://doi.org/10.1016/j.biopsych.2014.05.006

    Article  CAS  PubMed  Google Scholar 

  16. Lambert JC, Ibrahim-Verbaas CA, Harold D et al (2013) Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet 45:1452–1458. https://doi.org/10.1038/ng.2802

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Landau SM, Mintun MA, Joshi AD et al (2012) Amyloid deposition, hypometabolism, and longitudinal cognitive decline. Ann Neurol 72:578–586. https://doi.org/10.1002/ana.23650

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Marion RE, Campbell A, Hagenaars SP et al (2017) Genetic stratification to identify risk groups for Alzheimer’s disease. J Alzheimers Dis 57:275–283. https://doi.org/10.3233/JAD-161070

    Article  Google Scholar 

  19. McEvoy LK, Brewer JB (2012) Biomarkers for the clinical evaluation of the cognitively impaired elderly: amyloid is not enough. Imaging Med 4:343–357. https://doi.org/10.2217/iim.12.27

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Nelcon PT, Braak H, Markesbery WR (2009) Neuropathlogy and cognitive impairment in Alzheimer disease: a complex but coherent relationship. J Neuropathol Exp Neurol 68:1–14. https://doi.org/10.1097/NEN.0b013e3181919a48

    Article  Google Scholar 

  21. Roe CM, Fagan AM, Grant EA et al (2013) Amyloid imaging and CSF biomarkers in predicting impairment u to 7.5 years later. Neurology 80:1784–1791. https://doi.org/10.1212/WNL.0b013e3182918ca6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Sabuncu MR, Buckner RL, Smoller JW et al (2012) The association between a polygenic Alzheimer score and cortical thickness in clinically normal subjects. Cereb Cortex 22:2653–2661. https://doi.org/10.1093/cercor/bhr348

    Article  PubMed  Google Scholar 

  23. Shaw LM, Vanderstichele H, Knapik-Czajka M et al (2009) Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Ann Neurol 65:403–413. https://doi.org/10.1002/ana.21610

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Shulman JM, Chen K, Keenan BT et al (2003) Genetic susceptibility for Alzheimer disease neuritic plaque pathology. JAMA Neurol 70:1150–1157

    Article  Google Scholar 

  25. Sperling RA, Aisen PS, Beckett LA et al (2011) Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7:280–292. https://doi.org/10.1016/j.jalz.2011.03.003

    Article  PubMed  PubMed Central  Google Scholar 

  26. Steele NZ, Carr JS, Bonham LW et al (2017) Fine-mapping of the human leukocyte antigen locus as a risk factor for Alzheimer’s disease: a case–control study. PLoS Med 14:e1002272. https://doi.org/10.1371/journal.pmed.1002272

    Article  PubMed  PubMed Central  Google Scholar 

  27. Tan CH, Hyman BT, Tan JJX et al (2017) Polygenic hazard score in preclinical Alzheimer’s disease. Ann Neurol 82:484–488. https://doi.org/10.1002/ana.25029

    Article  PubMed  Google Scholar 

  28. Voyle N, Patel H, Folarin A et al (2017) Genetic risk as a marker of amyloid-β and tau burden in cerebrospinal fluid. J Alzheimers Dis 55:1417–1427. https://doi.org/10.3233/JAD-160707

    Article  CAS  PubMed  Google Scholar 

  29. Zhu X, Lee HG, Perry G, Smith MA (2007) Alzheimer disease, the two-hit hypothesis: an update. Biochim Biophys Acta 1772:494–502

    Article  CAS  PubMed  Google Scholar 

  30. Zlokovic B (2011) Neurovascular pathways to neurodegeneration in Alzheimer’s disease and other disorders. Nat Rev Neurosci 12:723–738. https://doi.org/10.1038/nrn3114

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the Shiley-Marcos Alzheimer’s Disease Research Center at UCSD, UCSF Memory and Aging Center and UCSF Center for Precision Neuroimaging for continued support. This work was supported by the Radiological Society of North America Resident/Fellow Award, American Society of Neuroradiology Foundation Alzheimer’s Disease Imaging Award, National Alzheimer’s Coordinating Center Junior Investigator award, National Institutes of Health Grants (NIH-AG046374, K01AG049152, P20AG10161, R01AG15819, R01AG17917), the Research Council of Norway (#213837, #225989, #223273, #237250/European Union Joint Programme–Neurodegenerative Disease Research), the South East Norway Health Authority (2013–123), Norwegian Health Association, the Kristian Gerhard Jebsen Foundation. Please see Supplemental Acknowledgements for Alzheimer’s Disease Neuroimaging Initiative, National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site and Alzheimer’s Disease Genetics Consortium funding sources.

Author information

Authors and Affiliations

Authors

Consortia

Corresponding authors

Correspondence to Chin Hong Tan or Rahul S. Desikan.

Ethics declarations

Conflict of interest

JBB served on advisory boards for Elan, Bristol-Myers Squibb, Avanir, Novartis, Genentech, and Eli Lilly and holds stock options in CorTechs Labs, Inc. and Human Longevity, Inc. AMD is a founder of and holds equity in CorTechs Labs, Inc., and serves on its Scientific Advisory Board. He is also a member of the Scientific Advisory Board of Human Longevity, Inc. (HLI), and receives research funding from General Electric Healthcare (GEHC). The terms of these arrangements have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies.

Additional information

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 3112 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tan, C.H., Fan, C.C., Mormino, E.C. et al. Polygenic hazard score: an enrichment marker for Alzheimer’s associated amyloid and tau deposition. Acta Neuropathol 135, 85–93 (2018). https://doi.org/10.1007/s00401-017-1789-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00401-017-1789-4

Keywords

Navigation