Which methods are used to diagnose the preclinical stage of Alzheimers disease?

Currently there is no single test that can accurately diagnose dementia. 

A detailed medical history, memory and thinking tests (called neuropsychological or cognitive tests), laboratory tests and brain scans are typically used in the diagnosis process.

Current research into the diagnosis of Alzheimer's disease and other types of dementia aims to develop better methods for accurate and earlier diagnosis. Early diagnosis of dementia is currently important to allow time for planning and to maximise the potential for treatment.

In the future, identification of individuals in the preclinical phase of dementia, before symptoms of cognitive decline are evident, will be possible. So we will be able to predict who is going to develop dementia, rather than wait to diagnose dementia after it emerges. This could lead to lifestyle prevention strategies in order to delay dementia onset, and to earlier use of therapies that slow or halt the disease process.

Biomarker analysis

The search is on for "biomarkers" - biological markers which can indicate the presence of Alzheimer's disease, even before symptoms become evident.

Researchers have already identified several possible biomarkers for Alzheimer's disease in the cerebrospinal fluid (CSF), the liquid which surrounds the brain and spinal cord. Levels of beta-amyloid and tau (two proteins involved in the pathology of Alzheimer’s disease) in the CSF are already being used to aid diagnosis in some parts of the world.

Markers of inflammation and other brain changes associated with dementia can also be detected in the CSF and might be used alone or in combination with beta-amyloid levels to help clarify diagnosis.

Obtaining CSF requires a lumbar puncture, also known as a spinal tap, which involves inserting a needle into the spinal column. While this is a safe procedure, a simple blood test would be less invasive, so researchers are also investigating similarbiomarkers of dementia in the blood. Unfortunately, to date blood biomarkers have not proven to be as stable or accurate as those measured in the CSF, but research is continuing.

Neuroimaging: Visualising the brain

Neuroimaging describes a range of tools which are used to visualise the living brain, including computerised tomography (CT) scans, magnetic resonance imaging (MRI), single photon emission computerised tomography (SPECT) and positron emission tomography (PET).

Researchers are working on new ways of using neuroimaging tools to diagnose Alzheimer's disease and other types of dementia. 

Positron Emission Tomography

In 2004, researchers successfully viewed beta-amyloid plaque deposits in the living human brain. The study used Pittsburgh Compound-B (PiB), a substance which binds to amyloid and can be visualised with PET scanning. The results demonstrated that people with Alzheimer's disease displayed more amyloid deposits in certain brain areas compared to people without the condition. More recent research has shown that PiB-PET can also detect the early brain changes of Alzheimer's disease before symptoms become apparent.

While PiB has proved quite effective, its widespread clinical use may be limited by the need for specialised equipment to produce PiB at the site of the PET scanner. Researchers are currently developing and testing other compounds that bind to beta-amyloid and may overcome the limitations of PiB.

Glucose metabolism in the brain is altered in dementia and these changes can be visualised using another form of PET imaging called FDG-PET. Different patterns of reduced glucose metabolism can be suggestive of different types of dementia and so FDG-PET is sometimes used as an aid to diagnosis. Recent research also suggests that FDG-PET can detect early brain changes before the emergence of dementia symptoms and predict progression to dementia. Research is continuing to further refine this procedure for dementia diagnosis.

Another type of PET scan uses compounds that bind to acetylcholine to detect brain changes due to Alzheimer’s disease. Acetylcholine is a neurotransmitter, or chemical messenger in the brain, that is involved in memory function. Detecting reduced acetylcholine activity in the memory areas of the brain may aid diagnosis of Alzheimer’s. 

Magnetic Resonance Imaging

MRI is able to image the structure of the brain, which changes in dementia, to a very high resolution.

For example, a characteristic sign of Alzheimer’s disease is atrophy (shrinking) of a brain region called the hippocampus. This can easily be seen on an MRI scan and is currently used to aid diagnosis. International teams of researchers are working on standardising the scanning and analysis techniques used in MRI and establishing databases of scans of people with dementia.

It is hoped this research will eventually enable an individual’s scan taken anywhere in the world to be compared with those in the database to determine whether it is normal or suggests the presence of Alzheimer’s or another type of dementia.

New MRI methods are being used to image the white matter (nerve fibres) of the brain. The use of these in dementia research is in early stages, but may lead to the identification of characteristic patterns of white matter change that indicate different types of dementia and can be used in diagnosis. 

One large Australian research study currently using PiB-PET and MRI is the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL), a longitudinal study of ageing comprised of patients with Alzheimer's disease (AD), Mild Cognitive Impairment (MCI) and healthy volunteers. For more information visit the AIBL website.

  1. Hughes J. This is one of the biggest global health crises of the 21st century. World Econ Forum 2017. https://www.weforum.org/agenda/2017/09/dementia-trillion-dollar-global-crisis/#:~:text=This%20World%20Alzheimer’s%20Day%2C%20the,crises%20of%20the%2021st%20century (accessed May 18, 2020).

  2. Alzheimer’s Disease International. World Alzheimer Report 2019. Attitudes to dementia 2019. https://www.alz.co.uk/research/WorldAlzheimerReport2019.pdf (accessed February 13, 2020).

  3. Alzheimer’s Association. 2020 Alzheimer’s disease facts and figures. Alzheimers Dement 2020;16:391–460.

    Article  Google Scholar 

  4. Deb A, Thornton JD, Sambamoorthi U, Innes K. Direct and indirect cost of managing alzheimer’s disease and related dementias in the United States. Expert Rev Pharmacoecon Outcomes Res 2017;17:189–202.

    PubMed  PubMed Central  Article  Google Scholar 

  5. Serrano-Pozo A, Frosch MP, Masliah E, Hyman BT. Neuropathological alterations in Alzheimer disease. Cold Spring Harb Perspect Med 2011;1:a006189.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  6. Bateman RJ, Xiong C, Benzinger TL, et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med 2012;367:795–804.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. Jack CR, Bennett DA, Blennow K, et al. NIA-AA Research Framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement 2018;14:535–62.

    PubMed  PubMed Central  Article  Google Scholar 

  8. Dubois B, Feldman HH, Jacova C, et al. Revising the definition of Alzheimer’s disease: a new lexicon. Lancet Neurol 2010;9:1118–27.

    PubMed  Article  Google Scholar 

  9. U.S. Food and Drug Administration (FDA). Early Alzheimer’s disease: developing drugs for treatment guidance for industry 2018. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/alzheimers-disease-developing-drugs-treatment-guidance-industy (accessed August 11, 2020).

  10. Insel PS, Weiner M, Mackin RS, et al. Determining clinically meaningful decline in preclinical Alzheimer disease. Neurology 2019;93:e322–33.

    PubMed  PubMed Central  Article  Google Scholar 

  11. Vermunt L, Sikkes SA, van den Hout A, et al. Duration of preclinical, prodromal, and dementia stages of Alzheimer’s disease in relation to age, sex, and APOE genotype. Alzheimers Dement 2019;15:888–98.

    PubMed  PubMed Central  Article  Google Scholar 

  12. Cho SH, Woo S, Kim C, et al. Disease progression modelling from preclinical Alzheimer’s disease (AD) to AD dementia. Sci Rep 2021;11:4168.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. Knopman DS, Parisi JE, Salviati A, et al. Neuropathology of cognitively normal elderly. J Neuropathol Exp Neurol 2003;62:1087–95.

    CAS  PubMed  Article  Google Scholar 

  14. Bennett DA, Schneider JA, Arvanitakis Z, et al. Neuropathology of older persons without cognitive impairment from two community-based studies. Neurology 2006;66:1837–44.

    CAS  PubMed  Article  Google Scholar 

  15. Kazim SF, Iqbal K. Neurotrophic factor small-molecule mimetics mediated neuroregeneration and synaptic repair: emerging therapeutic modality for Alzheimer’s disease. Mol Neurodegener 2016;11:50.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  16. Tolbert S, Liu Y, Hellegers C, et al. Financial management skills in aging, MCI and dementia: cross sectional relationship to 18F-florbetapir PET cortical β-amyloid deposition. J Prev Alzheimers Dis 2019;6:274–82.

    CAS  PubMed  Google Scholar 

  17. Ye BS, Kim HJ, Kim YJ, et al. Longitudinal outcomes of amyloid positive versus negative amnestic mild cognitive impairments: a three-year longitudinal study. Sci Rep 2018;8:5557.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  18. Roberts RO, Aakre JA, Kremers WK, et al. Prevalence and outcomes of amyloid positivity among persons without dementia in a longitudinal, population-based setting. JAMA Neurol 2018;75:970–9.

    PubMed  PubMed Central  Article  Google Scholar 

  19. Dubois B, Padovani A, Scheltens P, Rossi A, Dell’Agnello G. Timely diagnosis for Alzheimer’s disease: a literature review on benefits and challenges. J Alzheimers Dis 2016;49:617–31.

    Article  PubMed  Google Scholar 

  20. Isaacson RS, Ganzer CA, Hristov H, et al. The clinical practice of risk reduction for Alzheimer’s disease: a precision medicine approach. Alzheimers Dement 2018;14:1663–73.

    PubMed  PubMed Central  Article  Google Scholar 

  21. Isaacson RS, Hristov H, Saif N, et al. Individualized clinical management of patients at risk for Alzheimer’s dementia. Alzheimers Dement 2019;15:1588–602.

    PubMed  PubMed Central  Article  Google Scholar 

  22. Gauthier SG. Alzheimer’s disease: the benefits of early treatment. Eur J Neurol 2005;12:11–6.

    PubMed  Article  Google Scholar 

  23. Cummings J, Lee G, Ritter A, Sabbagh M, Zhong K. Alzheimer’s disease drug development pipeline: 2020. Alzheimers Dement NY 2020;6:e12050.

    Google Scholar 

  24. Galvin JE, Aisen P, Langbaum JB, et al. Early stages of Alzheimer’s disease: evolving the care team for optimal patient management. Front Neurol 2021;11:592302.

    PubMed  PubMed Central  Article  Google Scholar 

  25. Liu JL, Hlavka JP, Hillestad R, Mattke S. Assessing the preparedness of the U.S. Health Care System infrastructure for an Alzheimer’s treatment 2017. https://www.rand.org/pubs/research_reports/RR2272.html (accessed May 5, 2018).

  26. Sabbagh MN, Lue L-F, Fayard D, Shi J. Increasing precision of clinical diagnosis of Alzheimer’s disease using a combined algorithm incorporating clinical and novel biomarker data. Neurol Ther 2017;6:83–95.

    PubMed  PubMed Central  Article  Google Scholar 

  27. Balasa M, Gelpi E, Antonell A, et al. Clinical features and APOE genotype of pathologically proven early-onset Alzheimer disease. Neurology 2011;76:1720–5.

    CAS  PubMed  Article  Google Scholar 

  28. Galvin JE. Using informant and performance screening methods to detect mild cognitive impairment and dementia. Curr Geriatr Rep 2018;7:19–25.

    PubMed  PubMed Central  Article  Google Scholar 

  29. Sabbagh MN, Boada M, Borson S, et al. Early detection of mild cognitive impairment (MCI) in primary care. J Prev Alzheimers Dis 2020;7:165–70.

    CAS  PubMed  Google Scholar 

  30. Galvin JE, Sadowsky CH, NINCDS-ADRDA. Practical guidelines for the recognition and diagnosis of dementia. J Am Board Fam Med 2012;25:367–82.

    PubMed  Article  Google Scholar 

  31. Aisen PS, Cummings J, Jack CR, et al. On the path to 2025: understanding the Alzheimer’s disease continuum. Alzheimers Res Ther 2017;9:60.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  32. Blennow K, Zetterberg H. Biomarkers for Alzheimer disease - current status and prospects for the future. J Intern Med 2018;284:643–63.

    CAS  PubMed  Article  Google Scholar 

  33. Karikari TK, Pascoal TA, Ashton NJ, 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 2020;19:422–33.

    CAS  PubMed  Article  Google Scholar 

  34. Janelidze S, Mattsson N, Palmqvist 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 2020;26:379–86.

    CAS  PubMed  Article  Google Scholar 

  35. Iliffe S, Robinson L, Brayne C, et al. Primary care and dementia: 1. diagnosis, screening and disclosure. Int J Geriatr Psychiatry 2009;24:895–901.

    PubMed  Article  Google Scholar 

  36. Arvanitakis Z, Shah RC, Bennett DA. Diagnosis and management of dementia: review. JAMA 2019;322:1589–99.

    PubMed  PubMed Central  Article  Google Scholar 

  37. Robinson L, Tang E, Taylor J-P. Dementia: timely diagnosis and early intervention. BMJ 2015;350:h3029.

    PubMed  PubMed Central  Article  Google Scholar 

  38. Zucchella C, Bartolo M, Pasotti C, Chiapella L, Sinforiani E. Caregiver burden and coping in early-stage Alzheimer disease. Alzheimer Dis Assoc Disord 2012;26:55–60.

    PubMed  Article  Google Scholar 

  39. Pfistermeister B, Tümena T, Gaßmann K-G, Maas R, Fromm MF. Anticholinergic burden and cognitive function in a large German cohort of hospitalized geriatric patients. PLoS One 2017;12:e0171353.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  40. Liu C-C, Liu C-C, Kanekiyo T, Xu H, Bu G. Apolipoprotein E and Alzheimer disease: risk, mechanisms, and therapy. Nat Rev Neurol 2013;9:106–18.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. Sadigh-Eteghad S, Sabermarouf B, Majdi A, Talebi M, Farhoudi M, Mahmoudi J. Amyloid-beta: a crucial factor in Alzheimer’s disease. Med Princ Pract 2015;24:1–10.

    PubMed  Article  Google Scholar 

  42. Karch CM, Goate AM. Alzheimer’s disease risk genes and mechanisms of disease pathogenesis. Biol Psychiatry 2015;77:43–51.

    CAS  PubMed  Article  Google Scholar 

  43. Ungar L, Altmann A, Greicius MD. Apolipoprotein E, gender, and Alzheimer’s disease: an overlooked, but potent and promising interaction. Brain Imaging Behav 2014;8:262–73.

    PubMed  PubMed Central  Article  Google Scholar 

  44. 23andMe. What to know about health report test result - 23andMe. 23&Me® 2020. https://www.23andme.com/test-info/ (accessed October 15, 2020).

  45. Hendry K, Green C, McShane R, et al. AD-8 for detection of dementia across a variety of healthcare settings. Cochrane Database Syst Rev 2019;3:CD011121.

    PubMed  Google Scholar 

  46. Patnode CD, Perdue LA, Rossom RC, et al. Screening for cognitive impairment in older adults: an evidence update for the U.S. Preventive Services Task Force. Rockville (MD): Agency for Healthcare Research and Quality (US); Report No.: 19-05257-EF-1, 2020.

  47. Teng E, Becker BW, Woo E, Cummings JL, Lu PH. Subtle deficits in instrumental activities of daily living in subtypes of mild cognitive impairment. Dement Geriatr Cogn Disord 2010;30:189–97.

    PubMed  PubMed Central  Article  Google Scholar 

  48. Marshall GA, Amariglio RE, Sperling RA, Rentz DM. Activities of daily living: where do they fit in the diagnosis of Alzheimer’s disease? Neurodegener Dis Manag 2012;2:483–91.

    PubMed  Article  Google Scholar 

  49. Rosenberg PB, Mielke MM, Appleby BS, Oh ES, Geda YE, Lyketsos CG. The association of neuropsychiatric symptoms in MCI with incident dementia and Alzheimer disease. Am J Geriatr Psychiatry 2013;21:685–95.

    PubMed  Article  Google Scholar 

  50. Ismail Z, Emeremni CA, Houck PR, et al. A comparison of the E-BEHAVE-AD, NBRS and NPI in quantifying clinical improvement in the treatment of agitation and psychosis associated with dementia. Am J Geriatr Psychiatry 2013;21:78–87.

    PubMed  PubMed Central  Article  Google Scholar 

  51. Bowden VM, Bowden CL. The Journal of Neuropsychiatry and Clinical Neurosciences. JAMA 1992;268:1473–4.

    Article  Google Scholar 

  52. Galvin JE. The quick dementia rating system (QDRS): a rapid dementia staging tool. Alzheimers Dement Amst 2015;1:249–59.

    PubMed  PubMed Central  Article  Google Scholar 

  53. Koster N, Knol DL, Uitdehaag BM, Scheltens P, Sikkes SA. The sensitivity to change over time of the Amsterdam IADL Questionnaire(©). Alzheimers Dement 2015;11:1231–40.

    PubMed  Article  Google Scholar 

  54. Sikkes SA, Pijnenburg YA, Knol DL, de Lange-de Klerk ES, Scheltens P, Uitdehaag BM. Assessment of instrumental activities of daily living in dementia: diagnostic value of the Amsterdam Instrumental Activities of Daily Living Questionnaire. J Geriatr Psychiatry Neurol 2013;26:244–50.

    PubMed  Article  Google Scholar 

  55. LaRue RH. Functional Assessment Screening Tool (FAST). In: Volkmar FR, editor. Encycl. Autism Spectr. Disord., New York, NY: Springer New York, 2018:1–2.

    Google Scholar 

  56. Lindgren N, Rinne JO, Palviainen T, Kaprio J, Vuoksimaa E. Prevalence and correlates of dementia and mild cognitive impairment classified with different versions of the modified Telephone Interview for Cognitive Status (TICS-m). Int J Geriatr Psychiatry 2019;34:1883–91.

    PubMed  Article  Google Scholar 

  57. Wang H, Fan Z, Shi C, et al. Consensus statement on the neurocognitive outcomes for early detection of mild cognitive impairment and Alzheimer dementia from the Chinese Neuropsychological Normative (CN-NORM) Project. J Glob Health 2019;9:020320.

    PubMed  PubMed Central  Article  Google Scholar 

  58. Franzen S, van den Berg E, Goudsmit M, et al. A systematic review of neuropsychological tests for the assessment of dementia in non-western, low-educated or illiterate populations. J Int Neuropsychol Soc 2020;26:331–51.

    PubMed  Article  Google Scholar 

  59. Costa A, Bak T, Caffarra P, et al. The need for harmonisation and innovation of neuropsychological assessment in neurodegenerative dementias in Europe: consensus document of the Joint Program for Neurodegenerative Diseases Working Group. Alzheimers Res Ther 2017;9:27.

    PubMed  PubMed Central  Article  Google Scholar 

  60. MoCA Test Inc. Upcoming mandatory training for MoCA testing. MoCA Montr - Cogn Assess 2021. https://www.mocatest.org/mandatory-moca-test-training/ (accessed February 3, 2021).

  61. Tabira T, Hotta M, Murata M, et al. Age-related changes in instrumental and basic activities of daily living impairment in older adults with very mild Alzheimer’s disease. Dement Geriatr Cogn Disord Extra 2020;10:27–37.

    Article  Google Scholar 

  62. Martyr A, Nelis SM, Quinn C, et al. The relationship between perceived functional difficulties and the ability to live well with mild-to-moderate dementia: findings from the IDEAL programme. Int J Geriatr Psychiatry 2019;34:1251–61.

    PubMed  PubMed Central  Article  Google Scholar 

  63. Ismail Z, Smith EE, Geda Y, et al. Neuropsychiatric symptoms as early manifestations of emergent dementia: provisional diagnostic criteria for mild behavioral impairment. Alzheimers Dement 2016;12:195–202.

    PubMed  Article  Google Scholar 

  64. McAllister-Williams RH, Bones K, Goodwin GM, et al. Analysing UK clinicians’ understanding of cognitive symptoms in major depression: a survey of primary care physicians and psychiatrists. J Affect Disord 2017;207:346–52.

    PubMed  Article  Google Scholar 

  65. Richard E, Schmand B, Eikelenboom P, Yang SC, Ligthart SA. Symptoms of apathy are associated with progression from mild cognitive impairment to Alzheimers disease in non-depressed subjects. Dement Geriatr Cogn Disord 2012;33:204–9.

    CAS  PubMed  Article  Google Scholar 

  66. Weintraub S, Besser L, Dodge HH, et al. Version 3 of the Alzheimer Disease Centers’ Neuropsychological Test Battery in the Uniform Data Set (UDS). Alzheimer Dis Assoc Disord 2018;32:10–7.

    PubMed  PubMed Central  Article  Google Scholar 

  67. Harrison JE, Hendrix S. Chapter 21 - The assessment of cognition in translational medicine: a contrast between the approaches used in Alzheimer’s disease and major depressive disorder. In: Nomikos GG, Feltner DE, editors. Handb. Behav. Neurosci., vol. 29, Elsevier, 2019:297–308.

  68. Roberts R, Knopman DS. Classification and epidemiology of MCI. Clin Geriatr Med 2013;29:753–72.

    PubMed  Article  Google Scholar 

  69. Harper L, Barkhof F, Scheltens P, Schott JM, Fox NC. An algorithmic approach to structural imaging in dementia. J Neurol Neurosurg Psychiatry 2014;85:692–8.

    PubMed  Article  Google Scholar 

  70. Frisoni GB, Boccardi M, Barkhof F, et al. Strategic roadmap for an early diagnosis of Alzheimer’s disease based on biomarkers. Lancet Neurol 2017;16:661–76.

    PubMed  Article  Google Scholar 

  71. Dubois B, Feldman HH, Jacova C, et al. Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurol 2014;13:614–29.

    PubMed  Article  Google Scholar 

  72. Johnson KA, Fox NC, Sperling RA, Klunk WE. Brain imaging in Alzheimer disease. Cold Spring Harb Perspect Med 2012;2:a006213.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  73. Dubois B, Hampel H, Feldman HH, et al. Preclinical Alzheimer’s disease: definition, natural history, and diagnostic criteria. Alzheimers Dement 2016;12:292–323.

    PubMed  PubMed Central  Article  Google Scholar 

  74. Villemagne VL, Doré V, Burnham SC, Masters CL, Rowe CC. Imaging tau and amyloid-β proteinopathies in Alzheimer disease and other conditions. Nat Rev Neurol 2018;14:225–36.

    CAS  PubMed  Article  Google Scholar 

  75. Clark CM, Pontecorvo MJ, Beach TG, et al. Cerebral PET with florbetapir compared with neuropathology at autopsy for detection of neuritic amyloid-β plaques: a prospective cohort study. Lancet Neurol 2012;11:669–78.

    CAS  PubMed  Article  Google Scholar 

  76. Wong DF, Rosenberg PB, Zhou Y, et al. In vivo imaging of amyloid deposition in Alzheimer disease using the radioligand 18F-AV-45 (florbetapir F 18). J Nucl Med 2010;51:913–20.

    CAS  PubMed  Article  Google Scholar 

  77. Duits FH, Martinez-Lage P, Paquet C, et al. Performance and complications of lumbar puncture in memory clinics: results of the multicenter lumbar puncture feasibility study. Alzheimers Dement 2016;12:154–63.

    PubMed  Article  Google Scholar 

  78. Shaw LM, Arias J, Blennow K, et al. Appropriate use criteria for lumbar puncture and cerebrospinal fluid testing in the diagnosis of Alzheimer’s disease. Alzheimers Dement 2018;14:1505–21.

    PubMed  Article  Google Scholar 

  79. Hansson O, Seibyl J, Stomrud E, et al. CSF biomarkers of Alzheimer’s disease concord with amyloid-β PET and predict clinical progression: a study of fully automated immunoassays in BioFINDER and ADNI cohorts. Alzheimers Dement 2018;14:1470–81.

    PubMed  PubMed Central  Article  Google Scholar 

  80. Blennow K, Dubois B, Fagan AM, Lewczuk P, de Leon MJ, Hampel H. Clinical utility of cerebrospinal fluid biomarkers in the diagnosis of early Alzheimer’s disease. Alzheimers Dement 2015;11:58–69.

    PubMed  Article  Google Scholar 

  81. Hansson O, Lehmann S, Otto M, Zetterberg H, Lewczuk P. Advantages and disadvantages of the use of the CSF Amyloid β (Aβ) 42/40 ratio in the diagnosis of Alzheimer’s Disease. Alzheimers Res Ther 2019;11:34.

    PubMed  PubMed Central  Article  Google Scholar 

  82. Lee SA, Sposato LA, Hachinski V, Cipriano LE. Cost-effectiveness of cerebrospinal biomarkers for the diagnosis of Alzheimer’s disease. Alzheimers Res Ther 2017;9:18.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  83. Dolgin E. Alzheimer’s disease is getting easier to spot. Nature 2018;559:S10–2.

    CAS  PubMed  Article  Google Scholar 

  84. Morris E, Chalkidou A, Hammers A, Peacock J, Summers J, Keevil S. Diagnostic accuracy of 18F amyloid PET tracers for the diagnosis of Alzheimer’s disease: a systematic review and meta-analysis. Eur J Nucl Med Mol Imaging 2016;43:374–85.

    CAS  PubMed  Article  Google Scholar 

  85. Engelborghs S, Niemantsverdriet E, Struyfs H, et al. Consensus guidelines for lumbar puncture in patients with neurological diseases. Alzheimers Dement Amst 2017;8:111–26.

    PubMed  PubMed Central  Article  Google Scholar 

  86. Barthélemy NR, Bateman RJ, Hirtz C, et al. Cerebrospinal fluid phosphotau T217 outperforms T181 as a biomarker for the differential diagnosis of Alzheimer’s disease and PET amyloid-positive patient identification. Alzheimers Res Ther 2020;12:26.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  87. O’Bryant SE, Mielke MM, Rissman RA, et al. Blood-based biomarkers in Alzheimer disease: current state of the science and a novel collaborative paradigm for advancing from discovery to clinic. Alzheimers Dement 2017;13:45–58.

    PubMed  Article  Google Scholar 

  88. C2N Diagnostics. Press release. Alzheimer’s breakthrough: C2N first to offer a widely accessible blood test. C2N Diagn 2021. https://www.c2ndiagnostics.com/press/press/2020/10/28/alzheimers-breakthrough-cn-first-to-offer-a-widely-accessible-blood-test (accessed January 25, 2021).

  89. Barthélemy NR, Horie K, Sato C, Bateman RJ. Blood plasma phosphorylatedtau isoforms track CNS change in Alzheimer’s disease. J Exp Med 2020;217:e20200861.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  90. Janelidze S, Stomrud E, Smith R, et al. Cerebrospinal fluid p-tau217 performs better than p-tau181 as a biomarker of Alzheimer’s disease. Nat Commun 2020;11:1683.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  91. Chen J, Duan Y, Li H, Lu L, Liu J, Tang C. Different durations of cognitive stimulation therapy for Alzheimer’s disease: a systematic review and meta-analysis. Clin Interv Aging 2019;14:1243–54.

    PubMed  PubMed Central  Article  Google Scholar 

  92. Cummings J, Fox N. Defining disease modifying therapy for Alzheimer’s disease. J Prev Alzheimers Dis 2017;4:109–15.

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Birks JS, Harvey RJ. Donepezil for dementia due to Alzheimer’s disease. Cochrane Database Syst Rev 2018;6:CD001190.

    PubMed  Google Scholar 

  94. Birks JS, Chong LY, Grimley Evans J. Rivastigmine for Alzheimer’s disease. Cochrane Database Syst Rev 2015;9:CD001191.

    PubMed  Google Scholar 

  95. Tariot PN, Farlow MR, Grossberg GT, et al. Memantine treatment in patients with moderate to severe Alzheimer disease already receiving donepezil: a randomized controlled trial. JAMA 2004;291:317–24.

    CAS  PubMed  Article  Google Scholar 

  96. Cummings J. New approaches to symptomatic treatments for Alzheimer’s disease. Mol Neurodegener 2021;16:2.

    PubMed  PubMed Central  Article  Google Scholar 

  97. Loy C, Schneider L. Galantamine for Alzheimer’s disease and mild cognitive impairment. Cochrane Database Syst Rev 2006;1:CD001747.

    Google Scholar 

  98. Atri A. The Alzheimer’s Disease Clinical Spectrum. Med Clin N Am 2019;103:263–93.

    PubMed  Article  Google Scholar 

  99. Berlowitz DR, Foy CG, Kazis LE, et al. Effect of intensive blood-pressure treatment on patient-reported outcomes. N Engl J Med 2017;377:733–44.

    PubMed  PubMed Central  Article  Google Scholar 

  100. Porsteinsson AP, Drye LT, Pollock BG, et al. Effect of citalopram on agitation in Alzheimer’s disease - the citAD randomized controlled trial. JAMA 2014;311:682–91.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  101. Sheline YI, Snider BJ, Beer JC, et al. Effect of escitalopram dose and treatment duration on CSF Aβ levels in healthy older adults: a controlled clinical trial. Neurology 2020;95:e2658–65.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  102. Sheehan B. Assessment scales in dementia. Ther Adv Neurol Disord 2012;5:349–58.

    PubMed  PubMed Central  Article  Google Scholar 

  103. Haubois G, Annweiler C, Launay C, et al. Development of a short form of Mini-Mental State Examination for the screening of dementia in older adults with a memory complaint: a case control study. BMC Geriatr 2011;11:59.

    PubMed  PubMed Central  Article  Google Scholar 

  104. Horton DK, Hynan LS, Lacritz LH, Rossetti HC, Weiner MF, Cullum CM. An Abbreviated Montreal Cognitive Assessment (MoCA) for dementia screening. Clin Neuropsychol 2015;29:413–25.

    PubMed  PubMed Central  Article  Google Scholar 

  105. Mini-Cog©. Mini-Cog© In other languages. Mini-Cog© Lang 2021. https://mini-cog.com/mini-cog-in-other-languages/ (accessed April 21, 2021).

  106. Carnero Pardo C, de la Vega Cotarelo R, López Alcalde S, et al. Assessing the diagnostic accuracy (DA) of the Spanish version of the informant-based AD8 questionnaire. Neurologia 2013;28:88–94.

    Article  Google Scholar 

  107. Harrison JK, Fearon P, Noel-Storr AH, McShane R, Stott DJ, Quinn TJ. Informant questionnaire on cognitive decline in the elderly (IQCODE) for the diagnosis of dementia within a secondary care setting. Cochrane Database Syst Rev 2015;3:CD010772.

    Google Scholar 

  108. Sanchez MA, Correa PC, Lourenço RA. Cross-cultural adaptation of the “Functional Activities Questionnaire - FAQ” for use in Brazil. Dement Neuropsychol 2011;5:322–7.

    PubMed  PubMed Central  Article  Google Scholar 

  109. Kim G, DeCoster J, Huang C-H, Bryant AN. A meta-analysis of the factor structure of the Geriatric Depression Scale (GDS): the effects of language. Int Psychogeriatr 2013;25:71–81.

    PubMed  Article  Google Scholar 

  110. Mapi Research Trust. NPI - Officially distributed by Mapi Research Trust. Neuropsychiatr Inventory Quest NPI-Q 2021. https://eprovide.mapi-trust.org/instruments/neuropsychiatric-inventory-questionnaire (accessed April 21, 2021).

  111. Mapi Research Trust. A-IADL-Q-SV - Amsterdam Instrumental Activity of Daily Living Questionnaire - Short version. Amst Instrum Act Dly Living Quest - Short Version -IADL-Q-SV n.d. https://eprovide.mapi-trust.org/instruments/amsterdam-instrumental-activity-of-daily-living-questionnaire-short-version (accessed April 21, 2021).

  112. Ismail Z, Agüera-Ortiz L, Brodaty H, et al. The Mild Behavioral Impairment Checklist (MBI-C): a rating scale for neuropsychiatric symptoms in predementia populations. J Alzheimers Dis 2017;56:929–38.

    PubMed  PubMed Central  Article  Google Scholar 


Page 2

From: Diagnosis of Early Alzheimer’s Disease: Clinical Practice in 2021

A - Presentation
• A 63-year-old Caucasian male patient (J.K) visited the memory clinic accompanied by his wife, having been referred by his PCP for evaluation of memory loss
• He presents with a history of an insidious onset of cognitive difficulties that have been progressive over the past 2 years. He considers his memory similar to his peers, and his deficits are not observable to people who know him casually
• At work, he has uncharacteristically confused orders and misplaced items, but has no difficulty keeping track of time, and his math, reading, and writing are intact. His wife says that people at work have started to notice him struggling to keep up and gently voiced their concerns to her
• The patient’s basic activities of daily living are intact, but more complex instrumental activities of daily living are showing erosion. He still drives, but no longer wants to drive to areas he is not familiar with
• He presents with no gait difficulty or balance problems. In terms of neuropsychiatric symptoms, his mood is more labile. He chokes up easily and is overall a little more down but attributes this to the fear and frustration over what is happening to him. He does have some mixed neuropsychiatric symptoms with intermittent depressive symptoms and anxiety as well as irritability
• Past medical history significant for hypertension, dyslipidemia, mild obesity, and glucose intolerance
• No history of neurotoxic exposure, head injuries with post-concussion syndrome, strokes, or seizures
• A positive family history of dementia with his father and paternal grandmother, where onset occurred in the late 60s
C - Assess/Differentiate
Blood tests: All normal, except for serum glucose of 115 and HgbA1c of 6.5%
Neurologic examination: Non-focal with faint bilateral palmomental reflex
Genotyping: Homozygous for ApoE ε4; no autosomal dominant genes
Cognitive assessments: MoCA score of 21/30
Structural imaging: MRI showed mild small vessel disease and mild generalized atrophy Hippocampal volume and ratio were reduced by 25% based on volumetric software
D - Diagnose
CSF biomarkers: Increased p-tau and t-tau Reduced Aβ42 Aβ42/40 ratio of 0.23
Diagnosis: The most likely etiology is Alzheimer’s disease, especially in view of a positive family history with similar age of onset, ApoE ε4 status, and biomarker verification
E - Treat
• Advised patient to make lifestyle modifications, including controlling vascular risk factors and optimizing the management of other medical problems
• No treatment intervention required for neuropsychiatric symptoms at the time of diagnosis
• Provided information on local social worker to help support him and his family
• Encouraged regular follow-ups and monitoring
• Patient was referred for possible participation in a clinical trial

  1. Abbreviations: Aβ, amyloid beta. ApoE, apolipoprotein E. HgbA1c, hemoglobin A1c. MoCA, Montreal Cognitive Assessment. MRI, magnetic resonance imaging. PCP, primary care physician. p-tau, phosphorylated tau. t-tau, total tau