Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Developing the ATX(N) classification for use across the Alzheimer disease continuum

Abstract

Breakthroughs in the development of highly accurate fluid and neuroimaging biomarkers have catalysed the conceptual transformation of Alzheimer disease (AD) from the traditional clinical symptom-based definition to a clinical–biological construct along a temporal continuum. The AT(N) system is a symptom-agnostic classification scheme that categorizes individuals using biomarkers that chart core AD pathophysiological features, namely the amyloid-β (Aβ) pathway (A), tau-mediated pathophysiology (T) and neurodegeneration (N). This biomarker matrix is now expanding towards an ATX(N) system, where X represents novel candidate biomarkers for additional pathophysiological mechanisms such as neuroimmune dysregulation, synaptic dysfunction and blood–brain barrier alterations. In this Perspective, we describe the conceptual framework and clinical importance of the existing AT(N) system and the evolving ATX(N) system. We provide a state-of-the-art summary of the potential contexts of use of these systems in AD clinical trials and future clinical practice. We also discuss current challenges related to the validation, standardization and qualification process and provide an outlook on the real-world application of the AT(N) system.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Temporal evolution of AD pathophysiology and biomarkers.
Fig. 2: The evolving ATX(N) system.

Similar content being viewed by others

References

  1. GBD 2016 Dementia Collaborators. Global, regional, and national burden of Alzheimer’s disease and other dementias, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 18, 88–106 (2019).

    Article  Google Scholar 

  2. McKhann, G. et al. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34, 939–944 (1984).

    Article  CAS  PubMed  Google Scholar 

  3. Kovacs, G. G. et al. Non-Alzheimer neurodegenerative pathologies and their combinations are more frequent than commonly believed in the elderly brain: a community-based autopsy series. Acta Neuropathol. 126, 365–384 (2013).

    Article  CAS  PubMed  Google Scholar 

  4. Jack, C. R. Jr et al. NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 14, 535–562 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Jack, C. R. Jr et al. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology 87, 539–547 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. O’Connor, J. P. et al. Imaging biomarker roadmap for cancer studies. Nat. Rev. Clin. Oncol. 14, 169–186 (2017).

    Article  PubMed  CAS  Google Scholar 

  7. Siravegna, G., Marsoni, S., Siena, S. & Bardelli, A. Integrating liquid biopsies into the management of cancer. Nat. Rev. Clin. Oncol. 14, 531–548 (2017).

    Article  CAS  PubMed  Google Scholar 

  8. Mattsson-Carlgren, N. et al. The implications of different approaches to define AT(N) in Alzheimer disease. Neurology 94, e2233–e2244 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Cummings, J. The National Institute on aging-Alzheimer’s association framework on Alzheimer’s disease: application to clinical trials. Alzheimers Dement. 15, 172–178 (2019).

    Article  PubMed  Google Scholar 

  10. Sevigny, J. et al. Amyloid PET screening for enrichment of early-stage Alzheimer disease clinical trials: experience in a phase 1b clinical trial. Alzheimer Dis. Assoc. Disord. 30, 1–7 (2016).

    Article  CAS  PubMed  Google Scholar 

  11. Jack, C. R. Jr et al. Associations of amyloid, tau, and neurodegeneration biomarker profiles with rates of memory decline among individuals without dementia. JAMA 321, 2316–2325 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Mattsson, N., Andreasson, U., Zetterberg, H. & Blennow, K. Alzheimer’s disease neuroimaging, I. Association of plasma neurofilament light with neurodegeneration in patients with Alzheimer disease. JAMA Neurol. 74, 557–566 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Tolar, M., Abushakra, S., Hey, J. A., Porsteinsson, A. & Sabbagh, M. Aducanumab, gantenerumab, BAN2401, and ALZ-801-the first wave of amyloid-targeting drugs for Alzheimer’s disease with potential for near term approval. Alzheimers Res. Ther. 12, 95 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Sejbaek, T. et al. Dimethyl fumarate decreases neurofilament light chain in CSF and blood of treatment naive relapsing MS patients. J. Neurol. Neurosurg. Psychiatry 90, 1324–1330 (2019).

    PubMed  Google Scholar 

  15. Olsson, B. et al. NFL is a marker of treatment response in children with SMA treated with nusinersen. J. Neurol. 266, 2129–2136 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Connolly, A., Gaehl, E., Martin, H., Morris, J. & Purandare, N. Underdiagnosis of dementia in primary care: variations in the observed prevalence and comparisons to the expected prevalence. Aging Ment. Health 15, 978–984 (2011).

    Article  PubMed  Google Scholar 

  17. Savva, G. M. & Arthur, A. Who has undiagnosed dementia? A cross-sectional analysis of participants of the Aging, Demographics and Memory Study. Age Ageing 44, 642–647 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Hampel, H. et al. Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic. Nat. Rev. Neurol. 14, 639–652 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Soldan, A. et al. ATN profiles among cognitively normal individuals and longitudinal cognitive outcomes. Neurology 92, e1567–e1579 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Ebenau, J. L. et al. ATN classification and clinical progression in subjective cognitive decline: the SCIENCe project. Neurology 95, e46–e58 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Tan, M. S. et al. Longitudinal trajectories of Alzheimer’s ATN biomarkers in elderly persons without dementia. Alzheimers Res. Ther. 12, 55 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Allegri, R. F. et al. Prognostic value of ATN Alzheimer biomarkers: 60-month follow-up results from the Argentine Alzheimer’s disease neuroimaging initiative. Alzheimers Dement. 12, e12026 (2020).

    Google Scholar 

  23. Altomare, D. et al. Applying the ATN scheme in a memory clinic population: the ABIDE project. Neurology 93, e1635–e1646 (2019).

    Article  PubMed  Google Scholar 

  24. Mattsson-Carlgren, N. et al. Longitudinal plasma p-tau217 is increased in early stages of Alzheimer’s disease. Brain 143, 3234–3241 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Palmqvist, S. et al. Discriminative accuracy of plasma phospho-tau217 for Alzheimer disease vs other neurodegenerative disorders. JAMA 324, 772–781 (2020).

    Article  CAS  PubMed  Google Scholar 

  26. Morgan, A. R. et al. Inflammatory biomarkers in Alzheimer’s disease plasma. Alzheimers Dement. 15, 776–787 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Ottoy, J. et al. Association of short-term cognitive decline and MCI-to-AD dementia conversion with CSF, MRI, amyloid- and (18)F-FDG-PET imaging. Neuroimage Clin. 22, 101771 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Cedarbaum, J. M. et al. Rationale for use of the clinical dementia rating sum of boxes as a primary outcome measure for Alzheimer’s disease clinical trials. Alzheimers Dement. 9 (Suppl. 1), S45–S55 (2013).

    Google Scholar 

  29. Donohue, M. C. et al. The preclinical Alzheimer cognitive composite: measuring amyloid-related decline. JAMA Neurol. 71, 961–970 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Evans, S. et al. The importance of endpoint selection: How effective does a drug need to be for success in a clinical trial of a possible Alzheimer’s disease treatment? Eur. J. Epidemiol. 33, 635–644 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Papp, K. V., Rentz, D. M., Orlovsky, I., Sperling, R. A. & Mormino, E. C. Optimizing the preclinical Alzheimer’s cognitive composite with semantic processing: the PACC5. Alzheimers Dement. 3, 668–677 (2017).

    Article  Google Scholar 

  32. Aschenbrenner, A. J. et al. Alzheimer disease cerebrospinal fluid biomarkers moderate baseline differences and predict longitudinal change in attentional control and episodic memory composites in the Adult Children Study. J. Int. Neuropsychol. Soc. 21, 573–583 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Devanarayan, P., Devanarayan, V. & Llano, D. A., Alzheimer’s Disease Neuroimaging Initiative. Identification of a simple and novel cut-point based cerebrospinal fluid and MRI signature for predicting Alzheimer’s disease progression that reinforces the 2018 NIA-AA research framework. J. Alzheimers Dis. 68, 537–550 (2019).

    Article  CAS  PubMed  Google Scholar 

  34. Jack, C. R. Jr et al. Defining imaging biomarker cut points for brain aging and Alzheimer’s disease. Alzheimers Dement. 13, 205–216 (2017).

    Article  PubMed  Google Scholar 

  35. Salvado, G. et al. Centiloid cut-off values for optimal agreement between PET and CSF core AD biomarkers. Alzheimers Res. Ther. 11, 27 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Lowe, V. J. et al. Tau-positron emission tomography correlates with neuropathology findings. Alzheimers Dement. 16, 561–571 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Lowe, V. J. et al. Neuroimaging correlates with neuropathologic schemes in neurodegenerative disease. Alzheimers Dement. 15, 927–939 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Janelidze, S., Stomrud, E., Brix, B. & Hansson, O. Towards a unified protocol for handling of CSF before beta-amyloid measurements. Alzheimers Res. Ther. 11, 63 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Hansson, O. et al. Pre-analytical protocol for measuring Alzheimer’s disease biomarkers in fresh CSF. Alzheimers Dement. 12, e12137 (2020).

    Google Scholar 

  40. Kuhlmann, J. et al. CSF Abeta1-42 - an excellent but complicated Alzheimer’s biomarker — a route to standardisation. Clin. Chim. Acta 467, 27–33 (2017).

    Article  CAS  PubMed  Google Scholar 

  41. Carlyle, B. C., Trombetta, B. A. & Arnold, S. E. Proteomic approaches for the discovery of biofluid biomarkers of neurodegenerative dementias. Proteomes 6, 32 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Wesenhagen, K. E. J., Teunissen, C. E., Visser, P. J. & Tijms, B. M. Cerebrospinal fluid proteomics and biological heterogeneity in Alzheimer’s disease: a literature review. Crit. Rev. Clin. Lab. Sci. 57, 86–98 (2020).

    Article  CAS  PubMed  Google Scholar 

  43. Bittner, T. et al. Technical performance of a novel, fully automated electrochemiluminescence immunoassay for the quantitation of beta-amyloid (1-42) in human cerebrospinal fluid. Alzheimers Dement. 12, 517–526 (2016).

    Article  PubMed  Google Scholar 

  44. Lifke, V. et al. Elecsys((R)) total-tau and phospho-tau (181P) CSF assays: analytical performance of the novel, fully automated immunoassays for quantification of tau proteins in human cerebrospinal fluid. Clin. Biochem. 72, 30–38 (2019).

    Article  CAS  PubMed  Google Scholar 

  45. Boulo, S. et al. First amyloid beta1-42 certified reference material for re-calibrating commercial immunoassays. Alzheimers Dement. 16, 1493–1503 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Andreasson, U. et al. A practical guide to immunoassay method validation. Front. Neurol. 6, 179 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Roe, C. M. et al. Amyloid imaging and CSF biomarkers in predicting cognitive impairment up to 7.5 years later. Neurology 80, 1784–1791 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Palmqvist, S. et al. Detailed comparison of amyloid PET and CSF biomarkers for identifying early Alzheimer disease. Neurology 85, 1240–1249 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Hansson, O. et al. CSF biomarkers of Alzheimer’s disease concord with amyloid-beta PET and predict clinical progression: A study of fully automated immunoassays in BioFINDER and ADNI cohorts. Alzheimers Dement. 14, 1470–1481 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Muller, E. G. et al. Amyloid-beta PET-Correlation with cerebrospinal fluid biomarkers and prediction of Alzheimer s disease diagnosis in a memory clinic. PLoS One 14, e0221365 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Klunk, W. E. et al. The Centiloid Project: standardizing quantitative amyloid plaque estimation by PET. Alzheimers Dement. 11, 1–15.e4 (2015).

    Article  PubMed  Google Scholar 

  52. Mattsson-Carlgren, N. et al. Abeta deposition is associated with increases in soluble and phosphorylated tau that precede a positive Tau PET in Alzheimer’s disease. Sci. Adv. 6, eaaz2387 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Xia, C. F. et al. [18F]T807, a novel tau positron emission tomography imaging agent for Alzheimer’s disease. Alzheimers Dement. 9, 666–676 (2013).

    Article  PubMed  Google Scholar 

  54. Mattsson, N. et al. 18F-AV-1451 and CSF T-tau and P-tau as biomarkers in Alzheimer’s disease. EMBO Mol. Med. 9, 1212–1223 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Johnson, K. A. et al. Tau positron emission tomographic imaging in aging and early Alzheimer disease. Ann. Neurol. 79, 110–119 (2016).

    Article  PubMed  Google Scholar 

  56. Ossenkoppele, R. et al. Discriminative accuracy of [18F]flortaucipir positron emission tomography for Alzheimer disease vs other neurodegenerative disorders. JAMA 320, 1151–1162 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Chien, D. T. et al. Early clinical PET imaging results with the novel PHF-tau radioligand [F-18]-T807. J. Alzheimers Dis. 34, 457–468 (2013).

    Article  CAS  PubMed  Google Scholar 

  58. Ashton, N. J. et al. Plasma p-tau231: a new biomarker for incipient Alzheimer’s disease pathology. Acta Neuropathol. 141, 709–724 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Scholl, M. et al. PET imaging of tau deposition in the aging human brain. Neuron 89, 971–982 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Ost, M. et al. Initial CSF total tau correlates with 1-year outcome in patients with traumatic brain injury. Neurology 67, 1600–1604 (2006).

    Article  CAS  PubMed  Google Scholar 

  61. Kovacs, G. G. et al. Plasma and cerebrospinal fluid tau and neurofilament concentrations in rapidly progressive neurological syndromes: a neuropathology-based cohort. Eur. J. Neurol. 24, 1326–e77 (2017).

    Article  CAS  PubMed  Google Scholar 

  62. Antonell, A. et al. Synaptic, axonal damage and inflammatory cerebrospinal fluid biomarkers in neurodegenerative dementias. Alzheimers Dement. 16, 262–272 (2020).

    Article  PubMed  Google Scholar 

  63. Wan, J. C. M. et al. Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat. Rev. Cancer 17, 223–238 (2017).

    Article  CAS  PubMed  Google Scholar 

  64. Hampel, H., Goetzl, E. J., Kapogiannis, D., Lista, S. & Vergallo, A. Biomarker-drug and liquid biopsy co-development for disease staging and targeted therapy: cornerstones for Alzheimer’s precision medicine and pharmacology. Front. Pharmacol. 10, 310 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Liu, J. L. H., Hlavka, J. P., Hillestad, R. & Mattke, S. Assessing the Preparedness of the U.S. Health Care System Infrastructure for an Alzheimer’s Treatment (RAND Corporation, 2017).

  66. Mattke, S., Cho, S. K., Bittner, T., Hlavka, J. & Hanson, M. Blood-based biomarkers for Alzheimer’s pathology and the diagnostic process for a disease-modifying treatment: Projecting the impact on the cost and wait times. Alzheimers Dement. 12, e12081 (2020).

    Google Scholar 

  67. Ovod, V. et al. Amyloid beta concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis. Alzheimers Dement. 13, 841–849 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Nakamura, A. et al. High performance plasma amyloid-beta biomarkers for Alzheimer’s disease. Nature 554, 249–254 (2018).

    Article  CAS  PubMed  Google Scholar 

  69. Schindler, S. E. et al. High-precision plasma beta-amyloid 42/40 predicts current and future brain amyloidosis. Neurology 93, e1647–e1659 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Mielke, M. M. et al. Plasma phospho-tau181 increases with Alzheimer’s disease clinical severity and is associated with tau- and amyloid-positron emission tomography. Alzheimers Dement. 14, 989–997 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Barthelemy, N. R., Horie, K., Sato, C. & Bateman, R. J. Blood plasma phosphorylated-tau isoforms track CNS change in Alzheimer’s disease. J. Exp. Med. 217, e20200861 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Janelidze, S. et al. Plasma P-tau181 in Alzheimer’s disease: relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer’s dementia. Nat. Med. 26, 379–386 (2020).

    Article  CAS  PubMed  Google Scholar 

  73. Karikari, T. K. et al. 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. 19, 422–433 (2020).

    Article  CAS  PubMed  Google Scholar 

  74. Thijssen, E. H. et al. Diagnostic value of plasma phosphorylated tau181 in Alzheimer’s disease and frontotemporal lobar degeneration. Nat. Med. 26, 387–397 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Mattsson, N., Cullen, N. C., Andreasson, U., Zetterberg, H. & Blennow, K. Association between longitudinal plasma neurofilament light and neurodegeneration in patients with Alzheimer disease. JAMA Neurol. 76, 791–799 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Preische, O. et al. Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer’s disease. Nat. Med. 25, 277–283 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Pannee, J. et al. Round robin test on quantification of amyloid-beta 1-42 in cerebrospinal fluid by mass spectrometry. Alzheimers Dement. 12, 55–59 (2016).

    Article  PubMed  Google Scholar 

  78. Busche, M. A. & Hyman, B. T. Synergy between amyloid-beta and tau in Alzheimer’s disease. Nat. Neurosci. 23, 1183–1193 (2020).

    Article  CAS  PubMed  Google Scholar 

  79. Hood, L., Heath, J. R., Phelps, M. E. & Lin, B. Systems biology and new technologies enable predictive and preventative medicine. Science 306, 640–643 (2004).

    Article  CAS  PubMed  Google Scholar 

  80. Johnson, E. C. B. et al. Large-scale proteomic analysis of Alzheimer’s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation. Nat. Med. 26, 769–780 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Lancour, D. et al. Analysis of brain region-specific co-expression networks reveals clustering of established and novel genes associated with Alzheimer disease. Alzheimers Res. Ther. 12, 103 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Toschi, N. et al. Biomarker-guided clustering of Alzheimer’s disease clinical syndromes. Neurobiol. Aging 83, 42–53 (2019).

    Article  CAS  PubMed  Google Scholar 

  83. Beltran, J. F. et al. Inexpensive, non-invasive biomarkers predict Alzheimer transition using machine learning analysis of the Alzheimer’s disease neuroimaging (ADNI) database. PLoS One 15, e0235663 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Wang, Z. et al. AD risk score for the early phases of disease based on unsupervised machine learning. Alzheimers Dement. 16, 1524–1533 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Refolo, L. M. et al. Common Alzheimer’s disease research ontology: National Institute on Aging and Alzheimer’s Association collaborative project. Alzheimers Dement. 8, 372–375 (2012).

    Article  PubMed  Google Scholar 

  86. Yamazaki, Y., Zhao, N., Caulfield, T. R., Liu, C. C. & Bu, G. Apolipoprotein E and Alzheimer disease: pathobiology and targeting strategies. Nat. Rev. Neurol. 15, 501–518 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Sarnowski, C. et al. Whole genome sequence analyses of brain imaging measures in the Framingham Study. Neurology 90, e188–e196 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Poewe, W. et al. Parkinson disease. Nat. Rev. Dis. Prim. 3, 17013 (2017).

    Article  PubMed  Google Scholar 

  89. Barber, T. R., Klein, J. C., Mackay, C. E. & Hu, M. T. M. Neuroimaging in pre-motor Parkinson’s disease. Neuroimage Clin. 15, 215–227 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  90. McKeith, I. G. et al. Research criteria for the diagnosis of prodromal dementia with Lewy bodies. Neurology 94, 743–755 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Shahnawaz, M. et al. Development of a biochemical diagnosis of Parkinson disease by detection of alpha-synuclein misfolded aggregates in cerebrospinal fluid. JAMA Neurol. 74, 163–172 (2017).

    Article  PubMed  Google Scholar 

  92. Parnetti, L. et al. CSF and blood biomarkers for Parkinson’s disease. Lancet Neurol. 18, 573–586 (2019).

    Article  CAS  PubMed  Google Scholar 

  93. Hampel, H. et al. Revolution of Alzheimer precision neurology. passageway of systems biology and neurophysiology. J. Alzheimers Dis. 64 (Suppl. 1), S47–S105 (2018).

    Article  Google Scholar 

  94. Hampel, H., Lista, S., Neri, C. & Vergallo, A. Time for the systems-level integration of aging: resilience enhancing strategies to prevent Alzheimer’s disease. Prog. Neurobiol. 181, 101662 (2019).

    Article  PubMed  Google Scholar 

  95. Hohman, T. J., Koran, M. E. & Thornton-Wells, T. A., Alzheimer’s Disease Neuroimaging Initiative. Genetic modification of the relationship between phosphorylated tau and neurodegeneration. Alzheimers Dement. 10, 637–645.e1 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  96. Smith, A. R., Mill, J. & Lunnon, K. The molecular etiology of Alzheimer’s disease. Brain Pathol. 30, 964–965 (2020).

    PubMed  PubMed Central  Google Scholar 

  97. Teutsch, S. M. et al. The evaluation of genomic applications in practice and prevention (EGAPP) initiative: methods of the EGAPP Working Group. Genet. Med. 11, 3–14 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  98. Jack, C. R. Jr et al. Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 12, 207–216 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Marquez, F. & Yassa, M. A. Neuroimaging biomarkers for Alzheimer’s disease. Mol. Neurodegener. 14, 21 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Young, P. N. E. et al. Imaging biomarkers in neurodegeneration: current and future practices. Alzheimers Res. Ther. 12, 49 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  101. Blennow, K. A review of fluid biomarkers for Alzheimer’s disease: moving from CSF to blood. Neurol. Ther. 6, 15–24 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  102. Blennow, K. & Zetterberg, H. Biomarkers for Alzheimer’s disease: current status and prospects for the future. J. Intern. Med. 284, 643–663 (2018).

    Article  CAS  PubMed  Google Scholar 

  103. Albert, M. S. et al. The diagnosis of mild cognitive impairment 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. 7, 270–279 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  104. Dubois, B. et al. Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurol. 13, 614–629 (2014).

    Article  PubMed  Google Scholar 

  105. Jack, C. R. Jr. et al. Introduction to the recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 257–262 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  106. McKhann, G. M. 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. 7, 263–269 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  107. Sperling, R. A. et al. 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 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  108. Jack, C. R. Jr et al. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 9, 119–128 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Grontvedt, G. R. et al. The amyloid, tau, and neurodegeneration (A/T/N) classification applied to a clinical research cohort with long-term follow-up. J. Alzheimers Dis. 74, 829–837 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

J. L. C. is supported by KMA, NIGMS grant P20GM109025, NINDS grant U01NS093334 and NIA grant R01AG053798. K. B. is supported by the Swedish Research Council (#2017-00915); the Alzheimer Drug Discovery Foundation (ADDF), USA (#RDAPB-201809-2016615); the Swedish Alzheimer Foundation (#AF-742881), Hjärnfonden, Sweden (#FO2017-0243); the Swedish state under the agreement between the Swedish government and the County Councils, ALF-agreement (#ALFGBG-715986); and the European Union Joint Program for Neurodegenerative Disorders (JPND2019-466-236). C. R. J. receives research support from NIH (R37AG11378, P30AG62677, U01AG06786, U19AG32438, U19AG24904, R01AG41851, R01AG43392, W81XWH-13-1-0259, R01AG49704, R01NS92625, RF1AG50745, R01AG51406, R01AG54787, R01NS97495, R01AG53267, R01AG40282, R01AG54029, R01AG54449, R01AG54491, RF1AG55151, R01AG55469, R01AG55444, R01AG56366, U01AG57195, U24AG57437, ZEN-18-533411, RF1AG58729, R01AG58676, 18-PAF01312, R01AG59798, R01AG60502, JHS, R01AG61848, R01AG62689, R01AG63689, GC-201711-2014043, GC-201711-2014042, U01HL96812, U19NS115388 and U24AG67418) and the Alexander Family Alzheimer’s Disease Research Professorship of the Mayo Clinic. The authors thank Dr Min Cho for his kind support in the critical revision of the article.

Author information

Authors and Affiliations

Authors

Contributions

H. J. H. and A. V. developed the initial concept and theoretical framework for this article. All authors contributed to researching the literature and data for the article, discussing its content, and writing, reviewing and/or editing of the manuscript.

Corresponding author

Correspondence to Harald Hampel.

Ethics declarations

Competing interests

H. H. is an employee of Eisai and serves as Senior Associate Editor for the journal Alzheimer’s & Dementia. He has not received any fees or honoraria since May 2019; before May 2019, he was a consultant and received lecture fees from Servier, Biogen and Roche, research grants from Pfizer, Avid and MSD Avenir (paid to the institution), travel funding from Eisai, Functional Neuromodulation, Axovant, Eli Lilly and Company, Takeda and Zinfandel, GE Healthcare and Oryzon Genomics, consultancy fees from Qynapse, Jung Diagnostics, Cytox, Axovant, Anavex, Takeda and Zinfandel, GE Healthcare, Oryzon Genomics and Functional Neuromodulation, and participated in the scientific advisory boards of Functional Neuromodulation, Axovant, Eisai, Eli Lilly and Company, Cytox, GE Healthcare, Takeda and Zinfandel, Oryzon Genomics and Roche Diagnostics. He is co-inventor in the following patents as a scientific expert and has received no royalties: In Vitro Multiparameter Determination Method for the Diagnosis and Early Diagnosis of Neurodegenerative Disorders (patent no. 8916388); In Vitro Procedure for Diagnosis and Early Diagnosis of Neurodegenerative Diseases (patent no. 8298784); Neurodegenerative Markers for Psychiatric Conditions (publication no. 20120196300); In Vitro Multiparameter Determination Method for the Diagnosis and Early Diagnosis of Neurodegenerative Disorders (publication no. 20100062463); In Vitro Method for the Diagnosis and Early Diagnosis of Neurodegenerative Disorders (publication no. 20100035286); In Vitro Procedure for Diagnosis and Early Diagnosis of Neurodegenerative Diseases (publication no. 20090263822); In Vitro Method for the Diagnosis of Neurodegenerative Diseases (patent no. 7547553); CSF Diagnostic in Vitro Method for Diagnosis of Dementias and Neuroinflammatory Diseases (publication no. 20080206797); In Vitro Method for the Diagnosis of Neurodegenerative Diseases (publication no. 20080199966); Neurodegenerative Markers for Psychiatric Conditions (publication no. 20080131921); and Method for Diagnosis of Dementias and Neuroinflammatory Diseases Based on an Increased Level of Procalcitonin in Cerebrospinal Fluid (publication no. US Patent 10921330). J. C. has provided consultation to Acadia, Actinogen, Alkahest, Alzheon, AnnovisBio, Avanir, Axsome, BiOasis, Biogen, Cassava, Cerecin, Cortexyme, Cytox, Diadem, EIP Pharma, Eisai, Foresight, GemVax, Genentech, Green Valley, Grifols, Maplight, Merck, Otsuka, Resverlogix, Roche, Samumed, Samus, Signant, Third Rock, and United Neuroscience pharmaceutical and assessment companies. He has stock options in ADAMAS, MedAvante, QR pharma/AnnovisBio, and BiOasis. K. B. has served as a consultant, on advisory boards or on data monitoring committees for Abcam, Axon, Biogen, Julius Clinical, Lilly, MagQu, Novartis, Roche Diagnostics and Siemens Healthineers, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is part of the GU Ventures Incubator Program. C. R. J. serves on an independent data monitoring board for Roche and is a speaker for Eisai but receives no personal compensation from any commercial entity. P. G. is an employee of Eisai. A. V. is an employee of Eisai. He has not received any fees or honoraria since November 2019. Before November 2019, he received lecture honoraria from Roche, MagQu LLC and Servier.

Additional information

Peer review information

Nature Reviews Neurology thanks T. Benzinger, who co-reviewed with C. Chen, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hampel, H., Cummings, J., Blennow, K. et al. Developing the ATX(N) classification for use across the Alzheimer disease continuum. Nat Rev Neurol 17, 580–589 (2021). https://doi.org/10.1038/s41582-021-00520-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41582-021-00520-w

This article is cited by

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research