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Suspected non-Alzheimer disease pathophysiology — concept and controversy

Nature Reviews Neurology volume 12, pages 117124 (2016) | Download Citation


Suspected non-Alzheimer disease pathophysiology (SNAP) is a biomarker-based concept that applies to individuals with normal levels of amyloid-β biomarkers in the brain, but in whom biomarkers of neurodegeneration are abnormal. The term SNAP has been applied to clinically normal individuals (who do not meet criteria for either mild cognitive impairment or dementia) and to individuals with mild cognitive impairment, but is applicable to any amyloid-negative, neurodegeneration-positive individual regardless of clinical status, except when the pathology underlying neurodegeneration can be reliably inferred from the clinical presentation. SNAP is present in 23% of clinically normal individuals aged >65 years and in 25% of mildly cognitively impaired individuals. APOE*ε4 is underrepresented in individuals with SNAP compared with amyloid-positive individuals. Clinically normal and mildly impaired individuals with SNAP have worse clinical and/or cognitive outcomes than individuals with normal levels of neurodegeneration and amyloid-β biomarkers. In this Perspectives article, we describe the available data on SNAP and address topical controversies in the field.

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  1. 1.

    et al. An operational approach to NIA-AA crtiteria for preclinical Alzheimer's disease. Ann. Neurol. 71, 765–775 (2012).

  2. 2.

    et al. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Assocation workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 7, 280–292 (2011).

  3. 3.

    et al. The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging and Alzheimer's Association Workgroup. Alzheimers Dement. 7, 270–279 (2011).

  4. 4.

    et al. The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging and the Alzheimer's Assocation Workgroup. Alzheimers Dement. 7, 263–269 (2011).

  5. 5.

    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).

  6. 6.

    et al. Comparison of different methodological implementations of voxel-based morphometry in neurodegenerative disease. NeuroImage 26, 600–608 (2005).

  7. 7.

    et al. PART is part of Alzheimer disease. Acta Neuropathol. 129, 749–756 (2012).

  8. 8.

    et al. Short-term clinical outcomes for stages of NIA-AA preclinical Alzheimer disease. Neurology 78, 1576–1582 (2012).

  9. 9.

    et al. Synergistic effect of β-amyloid and neurodegeneration on cognitive decline in clinically normal individuals. JAMA Neurol. 71, 1379–1385 (2014).

  10. 10.

    et al. Classification of non-demented patients attending a memory clinic using the new diagnostic criteria for Alzheimer's disease with disease-related biomarkers. J. Alzheimers Dis. 43, 835–847 (2015).

  11. 11.

    et al. Associations between Alzheimer disease biomarkers, neurodegeneration, and cognition in cognitively normal older people. JAMA Neurol. 70, 1512–1519 (2013).

  12. 12.

    et al. Preclinical Alzheimer's disease and its outcome: a longitudinal cohort study. Lancet Neurol. 12, 957–965 (2013).

  13. 13.

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

  14. 14.

    et al. Preclinical AD predicts decline in memory and executive functions in subjective complaints. Neurology 81, 1409–1416 (2013).

  15. 15.

    et al. Neuronal injury biomarkers and prognosis in ADNI subjects with normal cognition. Acta Neuropathol. Commun. 2, 26 (2014).

  16. 16.

    et al. APOE predicts amyloid-beta but not tau Alzheimer pathology in cognitively normal aging. Ann. Neurol. 67, 122–131 (2010).

  17. 17.

    et al. Effect of apolipoprotein E on biomarkers of amyloid load and neuronal pathology in Alzheimer disease. Ann. Neurol. 67, 308–316 (2010).

  18. 18.

    et al. The 18F-FDG PET cingulate island sign and comparison to 123I-β-CIT SPECT for diagnosis of dementia with Lewy bodies. J. Nucl. Med. 50, 1638–1645 (2009).

  19. 19.

    et al. Brain injury biomarkers are not dependent on β-amyloid in normal elderly. Ann. Neurol. 73, 472–480 (2013).

  20. 20.

    et al. Age-specific population frequencies of cerebral β-amyloidosis and neurodegeneration among people with normal cognitive function aged 50–89 years: a cross-sectional study. Lancet Neurol. 13, 997–1005 (2014).

  21. 21.

    et al. Amyloid-first and neurodegeneration-first profiles characterize incident amyloid PET positivity. Neurology 81, 1732–1740 (2013).

  22. 22.

    et al. Regional dynamics of amyloid-β deposition in healthy elderly, mild cognitive impairment and Alzheimer's disease: a voxelwise PiB-PET longitudinal study. Brain 135, 2126–2139 (2012).

  23. 23.

    et al. Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer's disease: a prospective cohort study. Lancet Neurol. 12, 357–367 (2013).

  24. 24.

    et al. Brain β-amyloid load approaches a plateau. Neurology 80, 890–896 (2013).

  25. 25.

    et al. Criteria for mild cognitive impairment due to alzheimer's disease in the community. Ann. Neurol. 74, 199–208 (2013).

  26. 26.

    et al. Mild cognitive impairment with suspected nonamyloid pathology (SNAP): prediction of progression. Neurology 84, 508–515 (2015).

  27. 27.

    et al. Prediction of dementia in MCI patients based on core diagnostic markers for Alzheimer disease. Neurology 80, 1048–1056 (2013).

  28. 28.

    et al. Prevalence and prognosis of Alzheimer's disease at the mild cognitive impairment stage. Brain 138, 1327–1338 (2015).

  29. 29.

    et al. Amyloid positron emission tomography with 18F-flutemetamol and structural magnetic resonance imaging in the classification of mild cognitive impairment and Alzheimer's disease. Alzheimers Dement. 9, 295–301 (2013).

  30. 30.

    et al. Application of the National Institute on Aging-Alzheimer's Association AD criteria to ADNI. Neurology 80, 2130–2137 (2013).

  31. 31.

    et al. Revising the definition of Alzheimer's disease: a new lexicon. Lancet Neurol. 9, 1118–1127 (2010).

  32. 32.

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

  33. 33.

    et al. Two phase 3 trials of bapineuzumab in mild-to-moderate Alzheimer's disease. N. Engl. J. Med. 370, 322–333 (2014).

  34. 34.

    et al. Existing Pittsburgh Compound-B positron emission tomography thresholds are too high: statistical and pathological evaluation. Brain 138, 2020–2033 (2015).

  35. 35.

    et al. Reference measurement procedures for Alzheimer's disease cerebrospinal fluid biomarkers: definitions and approaches with focus on amyloid β42. Biomark Med. 6, 409–417 (2012).

  36. 36.

    et al. Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects. Ann. Neurol. 65, 403–413 (2009).

  37. 37.

    et al. The EADC-ADNI Harmonized Protocol for manual hippocampal segmentation on magnetic resonance: evidence of validity. Alzheimers Dement. 11, 111–125 (2015).

  38. 38.

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

  39. 39.

    et al. The Alzheimer's Association external quality control program for cerebrospinal fluid biomarkers. Alzheimers Dement. 7, 386–395 (2011).

  40. 40.

    et al. Concordance between cerebrospinal fluid biomarkers and [11C]PIB PET in a memory clinic cohort. J. Alzheimers Dis. 41, 801–807 (2014).

  41. 41.

    et al. Age, neuropathology, and dementia. N. Engl. J. Med. 360, 2302–2309 (2009).

  42. 42.

    et al. Alzheimer's disease is not 'brain aging': neuropathological, genetic, and epidemiological human studies. Acta Neuropathol. 121, 571–587 (2011).

  43. 43.

    , , , & The neuropathology of older persons with and without dementia from community versus clinic cohorts. J. Alzheimers Dis. 18, 691–701 (2009).

  44. 44.

    et al. Ecology of the aging human brain. Arch. Neurol. 68, 1049–1056 (2011).

  45. 45.

    et al. Neuropathology of older persons without cognitive impairment from two community-based studies. Neurology 66, 1837–1844 (2006).

  46. 46.

    et al. Neuropathologic outcome of mild cognitive impairment following progression to clinical dementia. Arch. Neurol. 63, 674–681 (2006).

  47. 47.

    Vulnerable neural systems and the borderland of brain aging and neurodegeneration. Neuron 77, 219–234 (2013).

  48. 48.

    , , , & Brain changes in older adults at very low risk for Alzheimer's disease. J. Neurosci. 33, 8237–8242 (2013).

  49. 49.

    , , & Stages of the pathologic process in Alzheimer disease: age categories from 1 to 100 years. J. Neuropathol. Exp. Neurol. 70, 960–969 (2011).

  50. 50.

    et al. The biochemical pathway of neurofibrillary degeneration in aging and Alzheimer's disease. Neurology 52, 1158–1165 (1999).

  51. 51.

    et al. Primary age-related tauopathy (PART): a common pathology associated with human aging. Acta Neuropathol. 128, 755–766 (2014).

  52. 52.

    PART and SNAP. Acta Neuropathol. 128, 773–776 (2014).

  53. 53.

    et al. PART, a distinct tauopathy, different from classical sporadic Alzheimer disease. Acta Neuropathol. 129, 757–762 (2015).

  54. 54.

    & Prevalence, incidence and duration of Braak's stages in the general population: can we know? Neurobiol. Aging 18, 362–369; discussion 89–92 (1997).

  55. 55.

    , , , & Dissociation of Alzheimer type pathology in a disconnected piece of cortex. Acta Neuropathol. 93, 501–507 (1997).

  56. 56.

    et al. Tau aggregation in the hippocampal formation: an ageing or a pathological process? Exp. Gerontol. 37, 1291–1296 (2002).

  57. 57.

    & Tangles and plaques in nondemented aging and 'preclinical' Alzheimer's disease. Ann. Neurol. 45, 358–368 (1999).

  58. 58.

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

  59. 59.

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

  60. 60.

    et al. The relationship of primary age-related tauopathy (PART) to SNAP: controversy or parallel? Presented at the Alzheimer's Association International Conference (2015).

  61. 61.

    et al. Cerebrospinal fluid β-amyloid 42 and tau proteins as biomarkers of Alzheimer-type pathologic changes in the brain. Arch. Neurol. 66, 382–389 (2009).

  62. 62.

    et al. Automated 3D mapping of hippocampal atrophy and its clinical correlates in 400 subjects with Alzheimer's disease, mild cognitive impairment, and elderly controls. Hum. Brain Mapp. 30, 2766–2788 (2009).

  63. 63.

    et al. Antemortem MRI findings correlate with hippocampal neuropathology in typical aging and dementia. Neurology 58, 750–757 (2002).

  64. 64.

    et al. Hippocampal sclerosis of aging, a prevalent and high-morbidity brain disease. Acta Neuropathol. 126, 161–177 (2013).

  65. 65.

    , & Hippocampal sclerosis dementia: a reappraisal. Acta Neuropathol. 114, 335–345 (2007).

  66. 66.

    et al. Does TDP-43 type confer a distinct pattern of atrophy in frontotemporal lobar degeneration? Neurology 75, 2212–2220 (2010).

  67. 67.

    et al. Hippocampal atrophy is the critical brain change in patients with hypoxic amnesia. Hippocampus 18, 719–728 (2008).

  68. 68.

    et al. Fluorodeoxyglucose F18 positron emission tomography in progressive apraxia of speech and primary progressive aphasia variants. Arch. Neurol. 67, 596–605 (2010).

  69. 69.

    et al. Relationships between hippocampal atrophy, white matter disruption, and gray matter hypometabolism in Alzheimer's disease. J. Neurosci. 28, 6174–6181 (2008).

  70. 70.

    et al. Sequential relationships between grey matter and white matter atrophy and brain metabolic abnormalities in early Alzheimer's disease. Brain 133, 3301–3314 (2010).

  71. 71.

    et al. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. J. Neurosci. 29, 1860–1873 (2009).

  72. 72.

    et al. Amyloid deposition is associated with impaired default network function in older persons without dementia. Neuron 63, 178–188 (2009).

  73. 73.

    & Impact of aging on hippocampal function: plasticity, network dynamics, and cognition. Prog. Neurobiol. 69, 143–179 (2003).

  74. 74.

    , & Insights into CNS ageing from animal models of senescence. Nat. Rev. Neurosci. 13, 435–445 (2012).

  75. 75.

    & Amyloid-β peptides and tau protein as biomarkers in cerebrospinal and interstitial fluid following traumatic brain injury: a review of experimental and clinical studies. Front. Neurol. 4, 79 (2013).

  76. 76.

    The amyloid hypothesis for Alzheimer's disease: a critical reappraisal. J. Neurochem. 110, 1129–1134 (2009).

  77. 77.

    Alzheimer disease: a β-independent processes-rethinking preclinical AD. Nat. Rev. Neurol. 9, 123–124 (2013).

  78. 78.

    Reimagining Alzheimer's disease — an age-based hypothesis. J. Neurosci. 30, 16755–16762 (2010).

  79. 79.

    Preclinical biomarkers in Alzheimer disease: a sum greater than the parts. JAMA Neurol. 71, 1357–1358 (2014).

  80. 80.

    et al. Multiple pathologies are common and related to dementia in the oldest-old: the 90+ Study. Neurology 85, 535–542 (2015).

  81. 81.

    et al. Serial PIB and MRI in normal, mild cognitive impairment and Alzheimer's disease: implications for sequence of pathological events in Alzheimer's disease. Brain 132, 1355–1365 (2009).

  82. 82.

    et al. Episodic memory loss is related to hippocampal-mediated β-amyloid deposition in elderly subjects. Brain 132, 1310–1323 (2009).

  83. 83.

    , & Multimodal techniques for diagnosis and prognosis of Alzheimer's disease. Nature 461, 916–922 (2009).

  84. 84.

    et al. Segregation of a missense mutation in the amyloid precursor protein gene with familial Alzheimer's disease. Nature 349, 704–706 (1991).

  85. 85.

    et al. A mutation in APP protects against Alzheimer's disease and age-related cognitive decline. Nature 488, 96–99 (2012).

  86. 86.

    et al. Association of missense and 5′-splice-site mutations in tau with the inherited dementia FTDP-17. Nature 393, 702–705 (1998).

  87. 87.

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

  88. 88.

    et al. Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol. 9, 119–128 (2010).

  89. 89.

    et al. Evidence for ordering of Alzheimer disease biomarkers. Arch. Neurol. 68, 1526–1535 (2011).

  90. 90.

    , Biomarker modeling of Alzheimer's disease. Neuron 80, 1347–1358 (2013).

  91. 91.

    & Origins of Alzheimer's disease: reconciling cerebrospinal fluid biomarker and neuropathology data regarding the temporal sequence of amyloid-beta and tau involvement. Curr. Opin. Neurol. 25, 715–720 (2012).

  92. 92.

    et al. Nonoverlapping but synergetic tau and APP pathologies in sporadic Alzheimer's disease. Neurology 59, 398–407 (2002).

  93. 93.

    et al. Rates of β-amyloid accumulation are independent of hippocampal neurodegeneration. Neurology 82, 1605–1612 (2014).

  94. 94.

    et al. Selective worsening of brain injury biomarker abnormalities in cognitively normal elderly persons with β-amyloidosis. JAMA Neurol. 70, 1030–1038 (2013).

  95. 95.

    et al. Imaging of tau pathology in a tauopathy mouse model and in Alzheimer patients compared to normal controls. Neuron 79, 1094–1108 (2013).

  96. 96.

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

  97. 97.

    et al. In vivo evaluation of a novel tau imaging tracer for Alzheimer's disease. Eur. J. Nucl. Med. Mol. Imaging 41, 816–826 (2014).

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C.R.J.Jr receives research funding from the NIH and the Alexander Family Alzheimer disease Disease Research Professorship of the Mayo Foundation. D.D. receives research support from the NIH (P50-AG016574; P50-NS072187; P01-AG003949) and CurePSP: Foundation for PSP/CBD and Related Disorders. A.M.F. receives research funding from the the DIAN Pharma Consortium and the Alzheimer disease Association. R.A.S. received research support from the BrightFocus Foundation. W.M.v.d.F. receives research funding from the Netherlands Organization for Scientific Research (NWO), ZonMw, Cardiovasculair Onderzoek Nederland, and European Union (EU) 7th Framework Programme (FP7); all funds are paid to her institution. P.J.V. receives research funding from EU Joint Programme–Neurodegenerative Disease Research (JPND) and ZonMw, and from EU FP7 and Innovative Medicines Initiative joint resources, which are composed of financial contributions from EU FP7 (FP7/2007-2013) and in-kind contributions from the European Federation of Pharmaceutical Industries and Associations (EFPIA). S.J.B.V. receives research support from the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement n°115372, resources that are composed of financial contributions from EU FP7 (FP7/2007-2013) and in-kind contributions from EFPIA.

Author information


  1. Department of Radiology, Mayo Clinic and Foundation, 200 First Street SW, Rochester, Minnesota 55905, USA.

    • Clifford R. Jack Jr
  2. Department of Neurology, Mayo Clinic and Foundation, 200 First Street SW, Rochester, Minnesota 55905, USA.

    • David S. Knopman
    •  & Ronald C. Petersen
  3. INSERM, Université de Caen, EPHE, CHU de Caen, U1077, Caen, France.

    • Gaël Chételat
  4. Department of Pathology, Mayo Clinic and Foundation, 4500 San Pablo Road South, Jacksonville, Florida 32224, USA.

    • Dennis Dickson
  5. Department of Neurology, Knight Alzheimer's Disease Research Center, Washington University School of Medicine, 4488 Forest Park Avenue, Suite 101, St Louis, Missouri 63108, USA.

    • Anne M. Fagan
  6. University Hospitals and University of Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Genève, Switzerland.

    • Giovanni B. Frisoni
  7. Helen Wills Neuroscience Institute, University of California Berkeley, 175 Li Ka Shing Center, Berkeley, California 94720, USA.

    • William Jagust
  8. Department of Neurology, Massachusetts General Hospital, Harvard Medical School, 221 Longwood Avenue, Boston, Massachusetts 02115, USA.

    • Elizabeth C. Mormino
    •  & Reisa A. Sperling
  9. Alzheimer Center, Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, PO Box 7057, 1007 MB Amsterdam, Netherlands.

    • Wiesje M. van der Flier
  10. Department of Molecular Imaging & Therapy, Centre for PET, Austin Health, 145 Studley Road, PO Box 5555 Melbourne, Victoria, Australia 3084.

    • Victor L. Villemagne
  11. Department of Psychiatry and Neuropsychology, Institute of Mental Health and Neuroscience, Maastricht University, PO Box 616 MD Maastricht, Netherlands.

    • Pieter J. Visser
    •  & Stephanie J. B. Vos


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C.R.J.Jr researched data for the article and wrote the article. All authors provided substantial contribution to discussion of content and reviewing/editing of manuscript before submission.

Competing interests

C.R.J.Jr has provided consulting services for Eli Lilly. D.S.K. is on a data safety monitoring board for Lundbeck Pharmaceuticals and is participating in clinical trials sponsored by Lilly Pharmaceuticals and TauRx Pharmaceuticals. A.M.F. has provided consulting services for Eli Lilly, Roche, AbbVie, IBL International and Novartis. W.J. is a consultant to Synarc–Bioclinica and to Banner Alzheimer disease Institute–Genentech. R.C.P. is on a data monitoring committee for Pfizer and Janssen Alzheimer Immunotherapy; is a consultant for Merck, Roche, and Genentech; receives royalties from publishing Mild Cognitive Impairment (Oxford University Press, 2003). R.A.S. has been a consultant for Janssen Eisai, Lundbeck, Isis, Boehringer Ingelheim, Roche and Genentech; and receives research support from the Fidelity Biosciences, and Janssen. W.M.v.d.F. has provided consulting services for Boehringer Ingelheim and received research funding from Boehringer Ingelheim; all funds are paid to her institution. V.L.V. has provided consulting services for Bayer Healthcare and Novartis, and has received speaker's honouraria from AstraZeneca, GE Healthcare and Piramal Imaging. P.J.V. has provided consulting services for Bristol–Myers Squibb, Élan–Wyeth, Ipsen, and Roche Diagnostics. G.C. and G.B.F. declare no competing interests.

Corresponding author

Correspondence to Clifford R. Jack Jr.

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    Supplementary Table 1

    Characteristics of individuals with SNAP in different study cohorts

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