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ADNC-RS, a clinical-genetic risk score, predicts Alzheimer’s pathology in autopsy-confirmed Parkinson’s disease and Dementia with Lewy bodies

Acta Neuropathologica volume 140, pages 449–461 (2020)Cite this article

Abstract

Growing evidence suggests overlap between Alzheimer’s disease (AD) and Parkinson’s disease (PD) pathophysiology in a subset of patients. Indeed, 50–80% of autopsy cases with a primary clinicopathological diagnosis of Lewy body disease (LBD)—most commonly manifesting during life as PD—have concomitant amyloid-beta and tau pathology, the defining pathologies of AD. Here we evaluated common genetic variants in genome-wide association with AD as predictors of concomitant AD pathology in the brains of people with a primary clinicopathological diagnosis of PD or Dementia with Lewy Bodies (DLB), diseases both characterized by neuronal Lewy bodies. In the first stage of our study, 127 consecutive autopsy-confirmed cases of PD or DLB from a single center were assessed for AD neuropathological change (ADNC), and these same cases were genotyped at 20 single nucleotide polymorphisms (SNPs) found by genome-wide association study to associate with risk for AD. In these 127 training set individuals, we developed a logistic regression model predicting the presence of ADNC, using backward stepwise regression for model selection and tenfold cross-validation to estimate performance. The best-fit model generated a risk score for ADNC (ADNC-RS) based on age at disease onset and genotype at three SNPs (APOE, BIN1, and SORL1 loci), with an area under the receiver operating curve (AUC) of 0.751 in our training set. In the replication stage of our study, we assessed model performance in a separate test set of the next 81 individuals genotyped in our center. In the test set, the AUC was 0.781, and individuals with ADNC-RS in the top quintile had four-fold increased likelihood of having AD pathology at autopsy compared with those in each of the lowest two quintiles. Finally, in the validation stage of our study, we applied our ADNC-RS model to 70 LBD individuals from 20 Alzheimer’s Disease Research Centers (ADRC) whose autopsy and genetic data were available in the National Alzheimer’s Coordinating Center (NACC) database. In this validation set, the AUC was 0.754. Thus, in patients with autopsy-confirmed PD or DLB, a simple model incorporating three AD-risk SNPs and age at disease onset substantially enriches for concomitant AD pathology at autopsy, with implications for identifying LBD patients in which targeting amyloid-beta or tau is a therapeutic strategy.

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References

  1. Arnold SE, Toledo JB, Appleby DH, Xie SX, Wang L-S, Baek Y et al (2013) Comparative survey of the topographical distribution of signature molecular lesions in major neurodegenerative diseases. J Comp Neurol 521:4339–4355. https://doi.org/10.1002/cne.23430

    Article  PubMed  Google Scholar 

  2. Barnes J, Dickerson B, Frost C, Dipstat MA, Jiskoot LC, Wolk D et al (2015) Alzheimer’s disease first symptoms are age dependent: evidence from the NACC dataset. Alzheimers Dement 11:1349–1357. https://doi.org/10.1016/j.jalz.2014.12.007

    Article  PubMed  PubMed Central  Google Scholar 

  3. Beekly DL, Ramos EM, Van BG, Deitrich W, Clark AD, Jacka ME et al (2004) The National Alzheimer’ s Coordinating Center (NACC) Database: an Alzheimer disease database. Alzheimer Dis Assoc Disord 18:270–277

    PubMed  Google Scholar 

  4. Bras J, Guerreiro R, Darwent L, Parkkinen L, Ansorge O, Escott-Price V et al (2014) Genetic analysis implicates APOE, SNCA and suggests lysosomal dysfunction in the etiology of dementia with Lewy bodies. Hum Mol Genet. 23:6139–6146. https://doi.org/10.1093/hmg/ddu334

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. Clinton LK, Blurton-Jones M, Myczek K, Trojanowski JQ, Laferla FM (2010) Neurobiology of disease synergistic interactions between a, Tau, and -synuclein: acceleration of neuropathology and cognitive decline. J Neurosci 30:7281–7289. https://doi.org/10.1523/JNEUROSCI.0490-10.2010

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. Congdon EE, Sigurdsson EM (2018) Tau-targeting therapies for Alzheimer disease. Nat Rev Neurol 14:399–415

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. Van Deerlin VM, Sleiman PMA, Martinez-Lage M, Chen-Plotkin A, Wang L-S, Graff-Radford NR et al (2010) Common variants at 7p21 are associated with frontotemporal lobar degeneration with TDP-43 inclusions. Nat Genet 42:234–239. https://doi.org/10.1038/ng.536

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. Dobbin KK, Simon RM (2011) Optimally splitting cases for training and testing high dimensional classifiers. BMC Med Genomics. https://doi.org/10.1186/1755-8794-4-31

    Article  PubMed  PubMed Central  Google Scholar 

  9. Escott-Price V, Sims R, Bannister C, Harold D, Vronskaya M, Majounie E et al (2015) Common polygenic variation enhances risk prediction for Alzheimer’s disease. Brain. https://doi.org/10.1093/brain/awv268

    Article  PubMed  PubMed Central  Google Scholar 

  10. Guttmacher AE, Collins FS, Nussbaum RL, Ellis CE (2003) Alzheimer’s disease and Parkinson’s disease. N Engl J Med 348:1356–1364. https://doi.org/10.1056/NEJM2003ra020003

    Article  Google Scholar 

  11. Harris SE, Davies G, Luciano M, Payton A, Fox HC, Haggarty P et al (2014) Polygenic risk for Alzheimer’s disease is not associated with cognitive ability or cognitive aging in non-demented older people. J Alzheimer’s Dis. https://doi.org/10.3233/JAD-131058

    Article  Google Scholar 

  12. Hosmer DW, Lemeshow S (1980) Goodness of fit tests for the multiple logistic regression model. Commun Stat Theory Methods. https://doi.org/10.1080/03610928008827941

    Article  Google Scholar 

  13. Huang YWA, Zhou B, Wernig M, Südhof TC (2017) ApoE2, ApoE3, and ApoE4 differentially stimulate APP transcription and Aβ secretion. Cell. https://doi.org/10.1016/j.cell.2016.12.044

    Article  PubMed  PubMed Central  Google Scholar 

  14. Irwin DJ, Hurtig HI (2018) The contribution of tau, amyloid-beta and alpha-synuclein pathology to dementia in Lewy body disorders. J Alzheimers Dis Park 8:444. https://doi.org/10.4172/2161-0460.1000444

    Article  Google Scholar 

  15. Janelidze S, Mattsson N, Palmqvist S, Smith R, Beach TG, Serrano GE et al (2020) Plasma P-tau181 in Alzheimer’s disease: relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer’s dementia. Nat Med. https://doi.org/10.1038/s41591-020-0755-1

    Article  PubMed  Google Scholar 

  16. Jansen IE, Savage JE, Watanabe K, Bryois J, Williams DM, Steinberg S et al (2019) Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat Genet 51:404–413. https://doi.org/10.1038/s41588-018-0311-9

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. Jellinger KA (2014) Neuropathology of multiple system atrophy: new thoughts about pathogenesis. Mov Disord. https://doi.org/10.1002/mds.26052

    Article  PubMed  Google Scholar 

  18. Karikari TK, Pascoal TA, Ashton NJ, Janelidze S, Benedet AL, Rodriguez JL et al (2020) Blood phosphorylated tau 181 as a biomarker for Alzheimer’s disease: a diagnostic performance and prediction modelling study using data from four prospective cohorts. Lancet Neurol. https://doi.org/10.1016/S1474-4422(20)30071-5

    Article  PubMed  Google Scholar 

  19. Kövari E, Horvath J, Bouras C (2009) Neuropathology of Lewy body disorders. Brain Res Bull 80:203–210

    Article  PubMed  Google Scholar 

  20. Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28:1–26. https://doi.org/10.18637/jss.v028.i05

    Article  Google Scholar 

  21. Kukull W, Bigio E, Cairns N, Kofler J, Montine T, Nelson PSJ (2015) Neuropathology data set coding guidebook. In: Natl. Alzheimer’s Coord. Cent. https://www.alz.washington.edu/NONMEMBER/NP/npguide10.pdf

  22. Kukull WA (2015) NACC Uniform Dataset Researchers Data Dictionary. In: Natl. Alzheimer’s Coord. Cent. https://www.alz.washington.edu/WEB/rdds_uds.pdf

  23. Kukull WA (2015) researchers data dictionary genetic data. In: Natl. Alzheimer’s Coord. Cent. https://www.alz.washington.edu/WEB/rdd_gen.pdf

  24. Kunkle BW, Grenier-Boley B, Sims R, Bis JC, Damotte V, Naj AC et al (2019) Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat Genet 51:414–430. https://doi.org/10.1038/s41588-019-0358-2

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. Lambert J-C, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C 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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. Lasorsa A, Malki I, Cantrelle FX, Merzougui H, Boll E, Lambert JC et al (2018) Structural basis of tau interaction with BIN1 and regulation by tau phosphorylation. Front Mol Neurosci. https://doi.org/10.3389/fnmol.2018.00421

    Article  PubMed  PubMed Central  Google Scholar 

  27. Marras C, Beck JC, Bower JH, Roberts E, Ritz B, Ross GW et al (2018) Prevalence of Parkinson’s disease across North America. NPJ Park Dis. https://doi.org/10.1038/s41531-018-0058-0

    Article  Google Scholar 

  28. Mata IF, Samii A, Schneer SH, Roberts JW, Griffith A, Leis BC et al (2008) Glucocerebrosidase gene mutations: a risk factor for Lewy body disorders. Arch Neurol. https://doi.org/10.1001/archneurol.2007.68

    Article  PubMed  PubMed Central  Google Scholar 

  29. Mattsson N, Andreasson U, Zetterberg H, Blennow K, Weiner MW, Aisen P et al (2017) Association of plasma neurofilament light with neurodegeneration in patients with Alzheimer disease. JAMA Neurol. https://doi.org/10.1001/jamaneurol.2016.6117

    Article  PubMed  PubMed Central  Google Scholar 

  30. Max Kuhn Contributions from Jed Wing A, Weston S, Williams A, Keefer C, Engelhardt A, Cooper T et al (2019) Package “caret”: classification and regression training. https://cran.r-project.org/web/packages/caret/caret.pdf

  31. McKeith IG, Boeve BF, Dickson DW, Halliday G, Taylor J-P, Weintraub D et al (2017) Diagnosis and management of dementia with Lewy bodies. Neurology 89:88–100. https://doi.org/10.1212/WNL.0000000000004058

    Article  PubMed  PubMed Central  Google Scholar 

  32. Montine TJ, Phelps CH, Beach TG, Bigio EH, Cairns NJ, Dickson DW et al (2012) National Institute on aging-Alzheimer’s association guidelines for the neuropathologic assessment of Alzheimer’s disease: a practical approach NIH Public Access. Acta Neuropathol 123:1–11. https://doi.org/10.1007/s00401-011-0910-3

    CAS  Article  PubMed  Google Scholar 

  33. Naj AC, Jun G, Beecham GW, Wang L, Vardarajan BN, Buros J et al (2011) Common variants at MS4A4/MS4A6E, CD2AP, CD33, and EPHA1 are associated with late-onset Alzheimer’s disease. Nat Genet 43:436–441. https://doi.org/10.1038/ng.801

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. Nakamura A, Kaneko N, Villemagne VL, Kato T, Doecke J, Doré V et al (2018) High performance plasma amyloid-β biomarkers for Alzheimer’s disease. Nature. https://doi.org/10.1038/nature25456

    Article  PubMed  Google Scholar 

  35. Panza F, Lozupone M, Logroscino G, Imbimbo BP (2019) A critical appraisal of amyloid-β-targeting therapies for Alzheimer disease. Nat Rev Neurol 15:73–78. https://doi.org/10.1038/s41582-018-0116-6

    Article  PubMed  Google Scholar 

  36. Postuma RB, Berg D, Stern M, Poewe W, Olanow CW, Oertel W et al (2015) MDS clinical diagnostic criteria for Parkinson’s disease. Mov Disord 30:1591–1601. https://doi.org/10.1002/mds.26424

    Article  PubMed  Google Scholar 

  37. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC et al (2011) pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform 12:77. https://doi.org/10.1186/1471-2105-12-77

    Article  Google Scholar 

  38. Robinson JL, Lee EB, Xie SX, Rennert L, Suh E, Bredenberg C et al (2018) Neurodegenerative disease concomitant proteinopathies are prevalent, age-related and APOE4-associated. Brain 141:2181–2193. https://doi.org/10.1093/brain/awy146

    Article  PubMed  PubMed Central  Google Scholar 

  39. Shi Y, Yamada K, Liddelow SA, Smith ST, Zhao L, Luo W et al (2017) ApoE4 markedly exacerbates tau-mediated neurodegeneration in a mouse model of tauopathy. Nature. https://doi.org/10.1038/nature24016

    Article  PubMed  PubMed Central  Google Scholar 

  40. Sing T, Sander O, Beerenwinkel N, Lengauer T (2015) Package “ROCR”: visualizing the Performance of Scoring Classifiers

  41. Slowikowski K (2019) Proxysnps: Get proxy SNPs for a SNP in the 1000 Genomes Project. In: Github. https://github.com/slowkow/proxysnps

  42. Smith C, Malek N, Grosset K, Cullen B, Gentleman S, Grosset DG (2019) Neuropathology of dementia in patients with Parkinson’s disease: a systematic review of autopsy studies. J Neurol Neurosurg Psychiatry. https://doi.org/10.1136/jnnp-2019-321111

    Article  PubMed  Google Scholar 

  43. Sperling R, Mormino E, Johnson K (2014) The evolution of preclinical Alzheimer’s disease: implications for prevention trials. Neuron. https://doi.org/10.1016/j.neuron.2014.10.038

    Article  PubMed  PubMed Central  Google Scholar 

  44. Spires-Jones TL, Attems J, Dietmar RT (2017) Interactions of pathological proteins in neurodegenerative diseases. Acta Neuropathol 134:187–205. https://doi.org/10.1007/s00401-017-1709-7

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  45. Toledo JB, Van Deerlin VM, Lee EB, Suh E, Baek Y, Robinson JL et al (2014) A platform for discovery: the University of Pennsylvania integrated neurodegenerative disease Biobank. Alzheimer’s Dement 10:477–484.e1. https://doi.org/10.1016/j.jalz.2013.06.003

    Article  Google Scholar 

  46. Yamazaki Y, Zhao N, Caulfield TR, Liu CC, Bu G (2019) Apolipoprotein E and Alzheimer disease: pathobiology and targeting strategies. Nat Rev Neurol 15:501–518. https://doi.org/10.1038/s41582-019-0228-7

    CAS  Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

We would like to acknowledge Nick Cullen and Marijan Posavi for their assistance with formulating analyses in this paper. We thank Travis Unger for technical assistance. We additionally thank our patients and their families for their generosity in contributing to this research.

Funding

This research was supported by the NIH (RO1 NS115139, P30 AG010124, U19 AG062418) and a Biomarkers Across Neurodegenerative Diseases (BAND) grant from the Michael J. Fox Foundation/Alzheimer’s Association/Weston Institute. Alice Chen-Plotkin is additionally supported by the Parker Family Chair. The NACC database is funded by NIA/NIH Grant U01 AG016976. NACC data are contributed by the NIA-funded ADCs: P30 AG019610 (PI Eric Reiman, MD), P30 AG013846 (PI Neil Kowall, MD), P30 AG062428-01 (PI James Leverenz, MD) P50 AG008702 (PI Scott Small, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P50 AG047266 (PI Todd Golde, MD, PhD), P30 AG010133 (PI Andrew Saykin, PsyD), P50 AG005146 (PI Marilyn Albert, PhD), P30 AG062421-01 (PI Bradley Hyman, MD, PhD), P30 AG062422-01 (PI Ronald Petersen, MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Thomas Wisniewski, MD), P30 AG013854 (PI Robert Vassar, PhD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG010161 (PI David Bennett, MD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank LaFerla, PhD), P30 AG062429-01(PI James Brewer, MD, PhD), P50 AG023501 (PI Bruce Miller, MD), P30 AG035982 (PI Russell Swerdlow, MD), P30 AG028383 (PI Linda Van Eldik, PhD), P30 AG053760 (PI Henry Paulson, MD, PhD), P30 AG010124 (PI John Trojanowski, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg, MD), P30 AG049638 (PI Suzanne Craft, PhD), P50 AG005136 (PI Thomas Grabowski, MD), P30 AG062715-01 (PI Sanjay Asthana, MD, FRCP), P50 AG005681 (PI John Morris, MD), P50 AG047270 (PI Stephen Strittmatter, MD, PhD). The Alzheimer’s Disease Genetic Consortium (ADGC) supported collection and genotyping of samples used in this study through National Institute on Aging (NIA) grants U01AG032984 and RC2AG036528. Samples from the National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD), which receives government support under a cooperative agreement grant (U24 AG21886) awarded by the National Institute on Aging (NIA), were used in this study. We thank contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible.

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  1. David L. Dai and Thomas F. Tropea have contributed equally.

Authors and Affiliations

  1. Departments of Neurology, University of Pennsylvania, 3 West Gates, 3400 Spruce Street, Philadelphia, PA, 19104, USA

    David L. Dai, Thomas F. Tropea, Howard Hurtig & Alice S. Chen-Plotkin

  2. Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA

    John L. Robinson, Eunran Suh, Vivianna Van Deerlin, Edward B. Lee & John Q. Trojanowski

  3. Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Daniel Weintraub

  4. Parkinson’s Disease and Mental Illness Research, Education and Clinical Centers (PADRECC and MIRECC), Philadelphia Veterans Affairs Medical Center, Philadelphia, PA, USA

    Daniel Weintraub

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  1. David L. Dai
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Correspondence to Alice S. Chen-Plotkin.

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Dai, D.L., Tropea, T.F., Robinson, J.L. et al. ADNC-RS, a clinical-genetic risk score, predicts Alzheimer’s pathology in autopsy-confirmed Parkinson’s disease and Dementia with Lewy bodies. Acta Neuropathol 140, 449–461 (2020). https://doi.org/10.1007/s00401-020-02199-7

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Keywords

  • Parkinson’s disease
  • Alzheimer’s disease
  • Genetics
  • Dementia