CoChat
Sign Up
Cited by 143Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsCancer-related cognitive impairment studies

New landscape of the diagnosis of Alzheimer’s disease

Open access
Frisoni, Giovanni B, Hansson, Oskar, Nichols, Emma, Garibotto, Valentina, Schindler, Suzanne E, van der Flier, Wiesje M, Jessen, Frank, Villain, Nicolas +10 more·2025·The Lancet
DOI 10.1016/s0140-6736(25)01294-2
Open this paper in CoChat — free account

Abstract

Alzheimer's disease involves a drastic departure from the cognitive, functional, and behavioural trajectory of normal ageing, and is both a dreaded and highly prevalent cause of disability to individuals, and a leading source of health and social care expenditure for society. Before the advent of biomarkers, post-mortem examination was the only method available to establish a definitive diagnosis. In this first paper of the Series, we review state-of-the-art diagnostic practices and the typical patient journey in specialist settings, where clinicians engage in a differential diagnosis to establish whether Alzheimer's pathology (cerebral deposition of β-amyloid and hyperphosphorylated tau) is a contributor to cognitive impairment. Biomarkers indicating dysregulation of β-amyloid and tau homeostasis, measured with PET and cerebrospinal fluid analysis, allow a molecular-level diagnosis-a mandatory step in defining eligibility for the recently approved anti-amyloid treatments. We anticipate that easily accessible blood biomarkers, already available in some countries, will lead to a new diagnostic revolution and bring about major changes in health-care systems worldwide.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/s0140-6736(25)01294-2

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • AD is increasingly viewed from a geroscience perspective as a multifactorial condition shaped by systemic aging processes, including immune dysregulation, vascular disease, and metabolic dysfunction.

    “AD is increasingly viewed from a geroscience perspective as a multifactorial condition shaped by systemic aging processes, including immune dysregulation, vascular disease, and metabolic dysfunction [9].”

    From Clonal Hematopoiesis of Indeterminate Potential as an Emerging Interdisciplinary Risk Factor in Alzheimer’s Disease: Current Evidence and Future Directions (2026) · cites this paper as [9]

    The risk-factors section (p. 4) identifies older age as the strongest risk factor for sporadic Alzheimer’s disease and lists hypertension, obesity, and diabetes among relevant factors. The trajectories section (p. 5) also says age, frailty, and other factors modulate disease trajectories. The paper does not frame Alzheimer’s disease explicitly from a geroscience perspective or identify immune dysregulation as one of the systemic aging processes shaping it. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Large-scale epidemiological studies have identified substantial overlap between AD and vascular risk factors, and mixed dementia phenotypes are the rule rather than the exception in older populations.

    “Large-scale epidemiological studies have identified substantial overlap between AD and vascular risk factors, and mixed dementia phenotypes are now understood to be the rule rather than the exception in older populations [9].”

    From Clonal Hematopoiesis of Indeterminate Potential as an Emerging Interdisciplinary Risk Factor in Alzheimer’s Disease: Current Evidence and Future Directions (2026) · cites this paper as [9]

    The Risk and protective factors section (p. 4) identifies several cardiovascular and metabolic factors associated with dementia risk, including hypertension, obesity, diabetes, and high LDL cholesterol. The Incidence and prevalence section (p. 4) says mixed neurodegenerative and vascular dementia becomes increasingly frequent with older age. It does not establish that mixed phenotypes are the rule rather than the exception in older populations, and its risk-factor discussion concerns dementia broadly rather than demonstrating substantial overlap specifically with Alzheimer’s disease. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Current consensus criteria emphasize that "pure" AD is relatively uncommon in advanced age, with most individuals exhibiting overlapping vascular, inflammatory, and neurodegenerative processes.

    “Current consensus criteria emphasize that “pure” AD is relatively uncommon in advanced age, with most individuals exhibiting overlapping vascular, inflammatory, and neurodegenerative processes [9].”

    From Clonal Hematopoiesis of Indeterminate Potential as an Emerging Interdisciplinary Risk Factor in Alzheimer’s Disease: Current Evidence and Future Directions (2026) · cites this paper as [9]

    In “Biomarker use in the clinic” (p. 11), the paper says that multiple pathologies co-occurring with Alzheimer’s disease are the rule, particularly in the oldest-old. “Incidence and prevalence of Alzheimer’s disease” (p. 4) also says mixed neurodegenerative and vascular forms become more frequent with age. These passages support the point that ‘pure’ AD is uncommon at advanced ages, but do not establish that most individuals have overlapping inflammatory processes. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Vascular brain injury lowers cognitive reserve and amplifies the clinical expression of neurodegenerative pathology, complicating the distinction between AD and VCID.

    “vascular brain injury lowers cognitive reserve and amplifies the clinical expression of neurodegenerative pathology, complicating the distinction between AD and VCID [9].”

    From Clonal Hematopoiesis of Indeterminate Potential as an Emerging Interdisciplinary Risk Factor in Alzheimer’s Disease: Current Evidence and Future Directions (2026) · cites this paper as [9]

    The section on excluding non-neurodegenerative causes of cognitive impairment (p. 8) identifies small-vessel cerebrovascular disease as a possible cause of cognitive impairment and says MRI is key to the differential diagnosis. It does not say that vascular brain injury lowers cognitive reserve or amplifies the clinical expression of neurodegenerative pathology, so those mechanisms are unsupported. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • The diagnosis of clinical Alzheimer's disease often encompasses a spectrum of mixed pathologies, particularly in older adults, where VCID frequently coexists with amyloid and tau pathology.

    “The diagnosis of clinical Alzheimer’s disease often encompasses a spectrum of mixed pathologies, particularly in older adults, where VCID frequently coexists with amyloid and tau pathology [9].”

    From Clonal Hematopoiesis of Indeterminate Potential as an Emerging Interdisciplinary Risk Factor in Alzheimer’s Disease: Current Evidence and Future Directions (2026) · cites this paper as [9]

    The Incidence and prevalence section (p. 4) describes mixed Alzheimer’s disease with other pathologies and says mixed neurodegenerative and vascular dementia becomes increasingly frequent with older age. The Alzheimer’s pathology section (p. 2) identifies amyloid and tau as Alzheimer’s pathology. The paper does not specifically say that VCID frequently coexists with both pathologies, so the claim is more specific than the evidence. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 195 · Cited by 143

Research works printed but not linked: 159

  1. 1 Scheltens P, De Strooper B, Kivipelto M, et al. Alzheimer's disease. Lancet 2021; 397: 1577-90.
  2. 2 Frisoni GB, Aho E, Brayne C, et al. Alzheimer's disease outlook: controversies and future directions. Lancet 2025; published online Sept 22. https://doi.org/10.1016/S0140-6736(25)01389-3. · DOI
  3. 4 Palmqvist S, Tideman P, Mattsson-Carlgren N, et al. Blood biomarkers to detect Alzheimer disease in primary care and secondary care. JAMA 2024; 332: 1245-57 .
  4. 8 van Dyck CH, Swanson CJ, Aisen P, et al. Lecanemab in early Alzheimer's disease. N Engl J Med 2023; 388: 9-21.
  5. 9 Sims JR, Zimmer JA, Evans CD, et al, and the TRAILBLAZER-ALZ 2 Investigators. Donanemab in early symptomatic Alzheimer disease: the TRAILBLAZER-ALZ 2 randomized clinical trial. JAMA 2023; 330: 512-27 .
  6. 10 Hansson O. Biomarkers for neurodegenerative diseases. Nat Med 2021; 27: 954-63.
  7. 12 Schindler SE, Gray JD, Gordon BA, et al. Cerebrospinal fluid biomarkers measured by Elecsys assays compared to amyloid imaging. Alzheimers Dement 2018; 14: 1460-69.
  8. 13 Leitão MJ, Silva-Spínola A, Santana I, et al. Clinical validation of the Lumipulse G cerebrospinal fluid assays for routine diagnosis of Alzheimer's disease. Alzheimers Res Ther 2019; 11: 91.
  9. 14 Fox NC, Belder C, Ballard C, et al. Treatment for Alzheimer's disease. Lancet 2025; published online Sept 22. https://doi. org/10.1016/S0140-6736(25)01329-7 . · DOI
  10. 15 Petersen RC, Weintraub S, Sabbagh M, et al, and the Dementia Nomenclature Initiative. A new framework for dementia nomenclature. JAMA Neurol 2023; 80: 1364-70.
  11. 16 Niu H, Álvarez-Álvarez I, Guillén-Grima F, Aguinaga-Ontoso I. Prevalence and incidence of Alzheimer's disease in Europe: a metaanalysis. Neurologia 2017; 32: 523-32.
  12. 19 Hayes-Larson E, Ackley SF, Turney IC, La Joie R, Mayeda ER, Glymour MM, and the Alzheimer's Disease Neuroimaging Initiative. Considerations for use of blood-based biomarkers in epidemiologic dementia research. Am J Epidemiol 2024; 193: 527-35.
  13. 20 Savva GM, Wharton SB, Ince PG, Forster G, Matthews FE, Brayne C, and the Medical Research Council Cognitive Function and Ageing Study. Age, neuropathology, and dementia. N Engl J Med 2009; 360: 2302-09.
  14. 21 Mielke MM, Vemuri P, Rocca WA. Clinical epidemiology of Alzheimer's disease: assessing sex and gender differences. Clin Epidemiol 2014; 6: 37-48.
  15. 22 Mielke MM, Aggarwal NT, Vila-Castelar C, et al, and the Diversity and Disparity Professional Interest Area Sex and Gender Special Interest Group. Consideration of sex and gender in Alzheimer's disease and related disorders from a global perspective. Alzheimers Dement 2022; 18: 2707-24.
  16. 24 Weiss J, Beydoun MA, Beydoun HA, et al. Pathways explaining racial/ethnic disparities in incident all-cause dementia among middle-aged US adults. Alzheimers Dement 2023; 19: 4299-310.
  17. 25 Shiekh SI, Cadogan SL, Lin LY, Mathur R, Smeeth L, Warren-Gash C. Ethnic differences in dementia risk: a systematic review and meta-analysis. J Alzheimers Dis 2021; 80: 337-55.
  18. 27 Gan J, Zeng Y, Huang G, et al. The updated prevalence and risk factors of dementia in old adults in China: a cross-sectional study. J Alzheimers Dis 2024; 102: 1209-23.
  19. 28 Ng TP, Leong T, Chiam PC, Kua E-H. Ethnic variations in dementia: the contributions of cardiovascular, psychosocial and neuropsychological factors. Dement Geriatr Cogn Disord 2010; 29: 131-38.
  20. 29 Suemoto CK, Leite REP, Paes VR, et al. Neuropathological lesions and cognitive abilities in black and white older adults in Brazil. JAMA Netw Open 2024; 7: e2423377 .
  21. 30 Arenson M, Bahorik A, Xia F, Peltz C, Cohen B, Yaffe K. Understanding racial disparities in dementia prevalence among veterans. J Alzheimers Dis 2024; 100: 1075-82.
  22. 31 Luppa M, Luck T, Brähler E, König HH, Riedel-Heller SG. Prediction of institutionalisation in dementia. A systematic review. Dement Geriatr Cogn Disord 2008; 26: 65-78.
  23. 32 Wu YT, Beiser AS, Breteler MMB, et al. The changing prevalence and incidence of dementia over time-current evidence. Nat Rev Neurol 2017; 13: 327-39.
  24. 33 Langa KM, Larson EB, Crimmins EM, et al. A comparison of the prevalence of dementia in the United States in 2000 and 2012. JAMA Intern Med 2017; 177: 51-58.
  25. 34 Grasset L, Brayne C, Joly P, et al. Trends in dementia incidence: evolution over a 10-year period in France. Alzheimers Dement 2016; 12: 272-80.
  26. 38 Bieger A, Brum WS, Borelli WV, et al, and the Alzheimer's Disease Neuroimaging Initiative. Influence of different diagnostic criteria on Alzheimer disease clinical research. Neurology 2024; 103: e209753.
  27. 39 Jack CR Jr, Andrews JS, Beach TG, et al. Revised criteria for diagnosis and staging of Alzheimer's disease: Alzheimer's Association Workgroup. Alzheimers Dement 2024; 20: 5143-69.
  28. 40 Dubois B, Villain N, Schneider L, et al. Alzheimer disease as a clinical-biological construct-an International Working Group recommendation. JAMA Neurol 2024; 81: 1304-11.
  29. 41 Mattke S, Hanson M. Expected wait times for access to a disease-modifying Alzheimer's treatment in the United States. Alzheimers Dement 2022; 18: 1071-74.
  30. 42 Mattke S, Tang Y, Hanson M. Expected wait times for access to a disease-modifying Alzheimer's treatment in England: a modelling study. J Health Serv Res Policy 2024; 29: 69-75.
  31. 43 Mattke S, Tang Y, Hanson M, et al. Current capacity for diagnosing Alzheimer's disease in Germany and implications for wait times. J Alzheimers Dis 2024; 101: 1249-59.
  32. 44 Mattke S, Gustavsson A, Jacobs L, et al. Estimates of current capacity for diagnosing Alzheimer's disease in Sweden and the need to expand specialist numbers. J Prev Alzheimers Dis 2024; 11: 155-61.
  33. 45 Mattke S, Cho SK, Bittner T, Hlávka 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 (Amst) 2020; 12: e12081.
  34. 47 Eyting M, Xie M, Michalik F, Heß S, Chung S, Geldsetzer P. A natural experiment on the effect of herpes zoster vaccination on dementia. Nature 2025; 641: 438-46.
  35. 48 Howard C, Mukadam N, Hui EK, Livingston G. The effects of sleep duration on the risk of dementia incidence in short and long followup studies: a systematic review and meta-analysis. Sleep Med 2024; 124: 522-30.
  36. 49 Pomirchy M, Bommer C, Pradella F, Michalik F, Peters R, Geldsetzer P. Herpes zoster vaccination and dementia occurrence. JAMA 2025; 333: 2083-92.
  37. 50 Chen S, Parmigiani G. Meta-analysis of BRCA1 and BRCA2 penetrance. J Clin Oncol 2007; 25: 1329-33.
  38. 51 Fortea J, Pegueroles J, Alcolea D, et al. Publisher Correction: APOE4 homozygosity represents a distinct genetic form of Alzheimer's disease. Nat Med 2024; 30: 2093.
  39. 52 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: 970-79.
  40. 53 Ossenkoppele R, Pichet Binette A, Groot C, et al. Amyloid and tau PET-positive cognitively unimpaired individuals are at high risk for future cognitive decline. Nat Med 2022; 28: 2381-87 .
  41. 54 Frisoni GB, Altomare D, Ribaldi F, et al. Dementia prevention in memory clinics: recommendations from the European task force for brain health services. Lancet Reg Health Eur 2023; 26: 100576.
  42. 55 Hardy JA, Higgins GA. Alzheimer's disease: the amyloid cascade hypothesis. Science 1992; 256: 184-85.
  43. 56 Beyreuther K, Masters CL. Amyloid precursor protein (APP) and beta A4 amyloid in the etiology of Alzheimer's disease: precursorproduct relationships in the derangement of neuronal function. Brain Pathol 1991; 1: 241-51.
  44. 57 Musiek ES, Holtzman DM. Three dimensions of the amyloid hypothesis: time, space and 'wingmen'. Nat Neurosci 2015; 18: 800-06.
  45. 58 Frisoni GB, Altomare D, Thal DR, et al. The probabilistic model of Alzheimer disease: the amyloid hypothesis revised. Nat Rev Neurosci 2022; 23: 53-66.
  46. 60 Jagust WJ, Teunissen CE, DeCarli C. The complex pathway between amyloid β and cognition: implications for therapy. Lancet Neurol 2023; 22: 847-57 .
  47. 61 Korczyn AD, Grinberg LT. Is Alzheimer disease a disease? Nat Rev Neurol 2024; 20: 245-51.
  48. 62 Arenaza-Urquijo EM, Boyle R, Casaletto K, et al, and the Reserve, Resilience and Protective Factors Professional Interest Area, Sex and Gender Professional Interest area and the ADDRESS! Special Interest Group. Sex and gender differences in cognitive resilience to aging and Alzheimer's disease. Alzheimers Dement 2024; 20: 5695-719.
  49. 63 Soldan A, Pettigrew C, Cai Q, et al, and the BIOCARD Research Team. Cognitive reserve and long-term change in cognition in aging and preclinical Alzheimer's disease. Neurobiol Aging 2017; 60: 164-72.
  50. 65 Canevelli M, Jackson-Tarlton C, Rockwood K. Frailty for neurologists: perspectives on how frailty influences care planning. Lancet Neurol 2024; 23: 1147-57 .
  51. 66 Arenaza-Urquijo EM, Vemuri P. Resistance vs resilience to Alzheimer disease: clarifying terminology for preclinical studies. Neurology 2018; 90: 695-703.
  52. 67 Jack CR Jr, Knopman DS, Jagust WJ, et al. Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol 2010; 9: 119-28.
  53. 68 Lewis A, Gupta A, Oh I, et al. Association between socioeconomic factors, race, and use of a specialty memory clinic. Neurology 2023; 101: e1424-33.
  54. 69 Patnode CD, Perdue LA, Rossom RC, et al. Screening for cognitive impairment in older adults: updated evidence report and systematic review for the US Preventive Services Task Force. JAMA 2020; 323: 764-85.
  55. 70 Tang-Wai DF, Smith EE, Bruneau MA, et al. CCCDTD5 recommendations on early and timely assessment of neurocognitive disorders using cognitive, behavioral, and functional scales. Alzheimers Dement (N Y) 2020; 6: e12057 .
  56. 71 Verghese J, Chalmer R, Stimmel M, et al. Non-literacy biased, culturally fair cognitive detection tool in primary care patients with cognitive concerns: a randomized controlled trial. Nat Med 2024; 30: 2356-61.
  57. 72 Atri A, Dickerson BC, Clevenger C, et al. Alzheimer's Association clinical practice guideline for the Diagnostic Evaluation, Testing, Counseling, and Disclosure of Suspected Alzheimer's Disease and Related Disorders (DETeCD-ADRD): executive summary of recommendations for primary care. Alzheimers Dement 2024; published online Dec 23. https://doi.org/10.1002/alz.14333. · DOI
  58. 73 Petrazzuoli F, Vinker S, Koskela TH, et al. Exploring dementia management attitudes in primary care: a key informant survey to primary care physicians in 25 European countries. Int Psychogeriatr 2017; 29: 1413-23.
  59. 75 Lopera F. Clinical history in the study of a patient with dementia. Rev Neurol 2001; 32: 1187-91.
  60. 76 Rogers RL, Meyer JS. Computerized history and self-assessment questionnaire for diagnostic screening among patients with dementia. J Am Geriatr Soc 1988; 36: 13-21.
  61. 77 Dewey ME, Copeland JR. Diagnosis of dementia from the history and aetiology schedule. Int J Geriatr Psychiatry 2001; 16: 912-17 .
  62. 78 Zhang J, Wang L, Deng X, et al. Five-minute cognitive test as a new quick screening of cognitive impairment in the elderly. Aging Dis 2019; 10: 1258-69.
  63. 79 Brodaty H, Pond D, Kemp NM, et al. The GPCOG: a new screening test for dementia designed for general practice. J Am Geriatr Soc 2002; 50: 530-34.
  64. 80 Folstein MF, Folstein SE, McHugh PR. 'Mini-mental state'. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12: 189-98.
  65. 82 Creavin ST, Fish M, Lawton M, et al. A diagnostic test accuracy study investigating general practitioner clinical impression and brief cognitive assessments for dementia in primary care, compared to specialized assessment. J Alzheimers Dis 2023; 95: 1189-200.
  66. 84 Sabbagh MN, Boada M, Borson S, et al. Rationale for early diagnosis of mild cognitive impairment (MCI) supported by emerging digital technologies. J Prev Alzheimers Dis 2020; 7: 158-64.
  67. 85 Montero-Odasso M, Pieruccini-Faria F, Ismail Z, et al. CCCDTD5 recommendations on early non cognitive markers of dementia: a Canadian consensus. Alzheimers Dement (N Y) 2020; 6: e12068.
  68. 86 Frisoni GB, Festari C, Massa F, et al. European intersocietal recommendations for the biomarker-based diagnosis of neurocognitive disorders. Lancet Neurol 2024; 23: 302-12.
  69. 87 de Wilde A, van Maurik IS, Kunneman M, et al. Alzheimer's biomarkers in daily practice (ABIDE) project: rationale and design. Alzheimers Dement (Amst) 2017; 6: 143-51.
  70. 88 Vernooij MW, Pizzini FB, Schmidt R, et al. Dementia imaging in clinical practice: a European-wide survey of 193 centres and conclusions by the ESNR working group. Neuroradiology 2019; 61: 633-42.
  71. 89 Schindler SE, Galasko D, Pereira AC, et al. Acceptable performance of blood biomarker tests of amyloid pathology-recommendations from the Global CEO Initiative on Alzheimer's Disease. Nat Rev Neurol 2024; 20: 426-39.
  72. 90 Jessen F, Amariglio RE, Buckley RF, et al. The characterisation of subjective cognitive decline. Lancet Neurol 2020; 19: 271-78.
  73. 91 Fong TG, Davis D, Growdon ME, Albuquerque A, Inouye SK. The interface between delirium and dementia in elderly adults. Lancet Neurol 2015; 14: 823-32.
  74. 92 Inouye SK. Delirium in older persons. N Engl J Med 2006; 354: 1157-65.
  75. 93 Morandi A, Davis D, Bellelli G, et al. The diagnosis of delirium superimposed on dementia: an emerging challenge. J Am Med Dir Assoc 2017; 18: 12-18.
  76. 94 Inouye SK, Westendorp RG, Saczynski JS. Delirium in elderly people. Lancet 2014; 383: 911-22.
  77. 95 Leighton SP, Herron JW, Jackson E, Sheridan M, Deligianni F, Cavanagh J. Delirium and the risk of developing dementia: a cohort study of 12 949 patients. J Neurol Neurosurg Psychiatry 2022; 93: 822-27 .
  78. 96 Persico I, Cesari M, Morandi A, et al. Frailty and delirium in older adults: a systematic review and meta-analysis of the literature. J Am Geriatr Soc 2018; 66: 2022-30.
  79. 97 Postema MC, Dubbelman MA, Claesen J, et al. Facilitating clinical use of the Amsterdam Instrumental Activities of Daily Living Questionnaire: normative data and a diagnostic cutoff value. J Int Neuropsychol Soc 2024; 30: 615-20.
  80. 98 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.
  81. 99 Sikkes SA, Knol DL, Pijnenburg YA, de Lange-de Klerk ES, Uitdehaag BM, Scheltens P. Validation of the Amsterdam IADL Questionnaire, a new tool to measure instrumental activities of daily living in dementia. Neuroepidemiology 2013; 41: 35-41.
  82. 100 Hermann P, Zerr I. Rapidly progressive dementias-aetiologies, diagnosis and management. Nat Rev Neurol 2022; 18: 363-76.
  83. 101 Moorhouse P, Rockwood K. Vascular cognitive impairment: current concepts and clinical developments. Lancet Neurol 2008; 7: 246-55.
  84. 102 Charidimou A, Boulouis G, Frosch MP, et al. The Boston criteria version 2.0 for cerebral amyloid angiopathy: a multicentre, retrospective, MRI-neuropathology diagnostic accuracy study. Lancet Neurol 2022; 21: 714-25.
  85. 103 Lezak MD, Howieson DB, Bigler ED, Tranel D. Neuropsychological assessment, 5th edn. Oxford: Oxford University Press, 2012.
  86. 104 Dubois B, Albert ML. Amnestic MCI or prodromal Alzheimer's disease? Lancet Neurol 2004; 3: 246-48.
  87. 105 Wagner M, Wolf S, Reischies FM, et al. Biomarker validation of a cued recall memory deficit in prodromal Alzheimer disease. Neurology 2012; 78: 379-86.
  88. 107 Perry DC, Brown JA, Possin KL, et al. Clinicopathological correlations in behavioural variant frontotemporal dementia. Brain 2017; 140: 3329-45.
  89. 108 Frisoni GB, Geroldi C, Beltramello A, et al. Radial width of the temporal horn: a sensitive measure in Alzheimer disease. AJNR Am J Neuroradiol 2002; 23: 35-47 .
  90. 109 Frisoni GB, Fox NC, Jack CR Jr, Scheltens P, Thompson PM. The clinical use of structural MRI in Alzheimer disease. Nat Rev Neurol 2010; 6: 67-77 .
  91. 110 Wolk DA, Nelson PT, Apostolova L, et al. Clinical criteria for limbic-predominant age-related TDP-43 encephalopathy. Alzheimers Dement 2025; 21: e14202.
  92. 111 Lesman-Segev OH, La Joie R, Iaccarino L, et al. Diagnostic accuracy of amyloid versus 18 F-fluorodeoxyglucose positron emission tomography in autopsy-confirmed dementia. Ann Neurol 2021; 89: 389-401.
  93. 112 Hyman BT, Trojanowski JQ. Consensus recommendations for the postmortem diagnosis of Alzheimer disease from the National Institute on Aging and the Reagan Institute Working Group on diagnostic criteria for the neuropathological assessment of Alzheimer disease. J Neuropathol Exp Neurol 1997; 56: 1095-97 .
  94. 116 Schindler SE, Bollinger JG, Ovod V, et al. High-precision plasma β-amyloid 42/40 predicts current and future brain amyloidosis. Neurology 2019; 93: e1647-59.
  95. 117 Janelidze S, Barthélemy NR, Salvadó G, et al. Plasma phosphorylated tau 217 and Aβ42/40 to predict early brain Aβ accumulation in people without cognitive impairment. JAMA Neurol 2024; 81: 947-57 .
  96. 118 Ossenkoppele R, Schonhaut DR, Schöll M, et al. Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer's disease. Brain 2016; 139: 1551-67 .
  97. 119 Minoshima S, Drzezga AE, Barthel H, et al. SNMMI procedure standard/EANM practice guideline for amyloid PET imaging of the brain 1.0. J Nucl Med 2016; 57: 1316-22.
  98. 120 Klunk WE, Koeppe RA, Price JC, et al. The Centiloid Project: standardizing quantitative amyloid plaque estimation by PET. Alzheimers Dement 2015; 11 : 1-15.e4.
  99. 122 Therriault J, Vermeiren M, Servaes S, et al. Association of phosphorylated tau biomarkers with amyloid positron emission tomography vs tau positron emission tomography. JAMA Neurol 2023; 80: 188-99.
  100. 123 Barthélemy NR, Li Y, Joseph-Mathurin N, et al, and the Dominantly Inherited Alzheimer Network. A soluble phosphorylated tau signature links tau, amyloid and the evolution of stages of dominantly inherited Alzheimer's disease. Nat Med 2020; 26: 398-407 .
  101. 125 Horie K, Salvadó G, Barthélemy NR, et al. CSF MTBR-tau243 is a specific biomarker of tau tangle pathology in Alzheimer's disease. Nat Med 2023; 29: 1954-63.
  102. 126 Vromen EM, de Boer SCM, Teunissen CE, et al, and the Alzheimer's Disease Neuroimaging Initiative. Biomarker A+T-: is this Alzheimer's disease or not? A combined CSF and pathology study. Brain 2023; 146: 1166-74.
  103. 127 de Wilde A, Reimand J, Teunissen CE, et al. Discordant amyloid-β PET and CSF biomarkers and its clinical consequences. Alzheimers Res Ther 2019; 11: 78.
  104. 128 Sala A, Nordberg A, Rodriguez-Vieitez E, and the Alzheimer's Disease Neuroimaging Initiative. Longitudinal pathways of cerebrospinal fluid and positron emission tomography biomarkers of amyloid-β positivity. Mol Psychiatry 2021; 26: 5864-74.
  105. 129 Altomare D, Barkhof F, Caprioglio C, et al, and the Amyloid Imaging to Prevent Alzheimer's Disease (AMYPAD) Consortium. Clinical effect of early vs late amyloid positron emission tomography in memory clinic patients: the AMYPAD-DPMS randomized clinical trial. JAMA Neurol 2023; 80: 548-57 .
  106. 130 Rabinovici GD, Gatsonis C, Apgar C, et al. Association of amyloid positron emission tomography with subsequent change in clinical management among medicare beneficiaries with mild cognitive impairment or dementia. JAMA 2019; 321: 1286-94.
  107. 131 Hazan J, Wing M, Liu KY, Reeves S, Howard R. Clinical utility of cerebrospinal fluid biomarkers in the evaluation of cognitive impairment: a systematic review and meta-analysis. J Neurol Neurosurg Psychiatry 2023; 94: 113-20.
  108. 132 Siontis KC, Siontis GC, Contopoulos-Ioannidis DG, Ioannidis JP. Diagnostic tests often fail to lead to changes in patient outcomes. J Clin Epidemiol 2014; 67: 612-21.
  109. 133 van Maurik IS, Broulikova HM, Mank A, et al. A more precise diagnosis by means of amyloid PET contributes to delayed institutionalization, lower mortality, and reduced care costs in a tertiary memory clinic setting. Alzheimers Dement 2023; 19: 2006-13.
  110. 134 Rabinovici GD, Carrillo MC, Apgar C, et al. Amyloid positron emission tomography and subsequent health care use among medicare beneficiaries with mild cognitive impairment or dementia. JAMA Neurol 2023; 80: 1166-73.
  111. 135 Goedert M, Crowther RA, Scheres SHW, Spillantini MG. Tau and neurodegeneration. Cytoskeleton 2024; 81: 95-102.
  112. 137 Moon H, Chen X, and the Alzheimer's Disease Neuroimaging Initiative. Plasma p-tau217 predicting brain-wide tau accumulation in preclinical AD. J Prev Alzheimers Dis 2025; 12: 100252.
  113. 138 Ossenkoppele R, Rabinovici GD, Smith R, et al. Discriminative accuracy of [18F]flortaucipir positron emission tomography for Alzheimer disease vs other neurodegenerative disorders. JAMA 2018; 320: 1151-62.
  114. 139 Smith R, Cullen NC, Pichet Binette A, et al, and the Alzheimer's Disease Neuroimaging Initiative. Tau-PET is superior to phosphotau when predicting cognitive decline in symptomatic AD patients. Alzheimers Dement 2023; 19: 2497-507 .
  115. 140 Groot C, Smith R, Collij LE, et al. Tau positron emission tomography for predicting dementia in individuals with mild cognitive impairment. JAMA Neurol 2024; 81: 845-56.
  116. 142 Pontecorvo MJ, Devous MD, Kennedy I, et al. A multicentre longitudinal study of flortaucipir (18F) in normal ageing, mild cognitive impairment and Alzheimer's disease dementia. Brain 2019; 142: 1723-35.
  117. 143 Soleimani-Meigooni DN, Rabinovici GD. Tau PET visual reads: research and clinical applications and future directions. J Nucl Med 2023; 64: 822-24.
  118. 144 Warmenhoven N, Salvadó G, Janelidze S, et al. A comprehensive head-to-head comparison of key plasma phosphorylated tau 217 biomarker tests. Brain 2025; 148: 416-31.
  119. 145 Mielke MM, Dage JL, Frank RD, et al. Performance of plasma phosphorylated tau 181 and 217 in the community. Nat Med 2022; 28: 1398-405.
  120. 147 Bornhorst JA, Lundgreen CS, Weigand SD, et al. Quantitative assessment of the effect of chronic kidney disease on plasma p-tau217 concentrations. Neurology 2025; 104: e210287 .
  121. 148 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.
  122. 149 Devanarayan V, Doherty T, Charil A, et al. Plasma pTau217 predicts continuous brain amyloid levels in preclinical and early Alzheimer's disease. Alzheimers Dement 2024; 20: 5617-28.
  123. 150 Salvadó G, Horie K, Barthélemy NR, et al. Disease staging of Alzheimer's disease using a CSF-based biomarker model. Nat Aging 2024; 4: 694-708.
  124. 151 Li Y, Yen D, Hendrix RD, et al. Timing of biomarker changes in sporadic Alzheimer's disease in estimated years from symptom onset. Ann Neurol 2024; 95: 951-65.
  125. 154 Palmqvist S, Warmenhoven N, Anastasi F, et al. Plasma phosphotau217 for Alzheimer's disease diagnosis in primary and secondary care using a fully automated platform. Nat Med 2025; 31: 2036-43.
  126. 155 Wang J, Huang S, Lan G, et al, and the Translational Biomarker Research of AgIng and Neurodegeneration (TBRAIN). Diagnostic accuracy of plasma p-tau217/Aβ42 for Alzheimer's disease in clinical and community cohorts. Alzheimers Dement 2025; 21: e70038.
  127. 156 Oh HS-H, Urey DY, Karlsson L, et al. A cerebrospinal fluid synaptic protein biomarker for prediction of cognitive resilience versus decline in Alzheimer's disease. Nature Medicine 2025; 31: 1592-1603.
  128. 157 Taddei RN, Duff KE. Synapse vulnerability and resilience across the clinical spectrum of dementias. Nat Rev Neurol , 2025; 21: 353-69.
  129. 158 DeCarli C, Frisoni GB, Clark CM, et al, and the Alzheimer's Disease Cooperative Study Group. Qualitative estimates of medial temporal atrophy as a predictor of progression from mild cognitive impairment to dementia. Arch Neurol 2007; 64: 108-15.
  130. 159 Molinder A, Ziegelitz D, Maier SE, Eckerström. Validity and reliability of the medial temporal lobe atrophy scale in a memory clinic population. BMC Neurol 2021; 21: 289.
  131. 160 Pizzini FB, Ribaldi F, Natale V, et al. A visual scale to rate amygdalar atrophy on MRI. Eur Radiol 2024; 35 : 4246-56.
  132. 161 Ferreira D, Nordberg A, Westman E. Biological subtypes of Alzheimer disease: a systematic review and meta-analysis. Neurology 2020; 94: 436-48.
  133. 163 McKeith IG, Boeve BF, Dickson DW, et al. Diagnosis and management of dementia with Lewy bodies: fourth consensus report of the DLB Consortium. Neurology 2017; 89: 88-100.
  134. 166 Höglinger GU, Respondek G, Stamelou M, et al, and the Movement Disorder Society-endorsed PSP Study Group. Clinical diagnosis of progressive supranuclear palsy: the movement disorder society criteria. Mov Disord 2017; 32: 853-64.
  135. 167 Armstrong MJ, Litvan I, Lang AE, et al. Criteria for the diagnosis of corticobasal degeneration. Neurology 2013; 80: 496-503.
  136. 168 Khalil M, Teunissen CE, Lehmann S, et al. Neurofilaments as biomarkers in neurological disorders-towards clinical application. Nat Rev Neurol 2024; 20: 269-87 .
  137. 169 VandeVrede L, Cho H, Sanderson-Cimino M, et al. Detection of Alzheimer neuropathology in Alzheimer and non-Alzheimer clinical syndromes with blood-based biomarkers. JAMA Neurol 2025; 82: 344-54.
  138. 170 Rajbanshi B, Prufer Q C Araujo I, VandeVrede L, et al, and the ALLFTD Consortium. Clinical and neuropathological associations of plasma Aβ 42 /Aβ40 , p-tau217 and neurofilament light in sporadic frontotemporal dementia spectrum disorders. Alzheimers Dement (Amst) 2025; 17: e70078.
  139. 171 Rabinovici GD, Knopman DS, Arbizu J, et al. Updated appropriate use criteria for amyloid and tau PET: a report from the Alzheimer's Association and Society for Nuclear Medicine and Molecular Imaging Workgroup. J Nucl Med 2025; 21: e14338.
  140. 172 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.
  141. 175 Bergeron D, Ossenkoppele R, Jr Laforce R. Evidence-based interpretation of amyloid-β PET results: a clinician's tool. Alzheimer Dis Assoc Disord 2018; 32: 28-34.
  142. 178 McKeith I, Fairbairn A, Perry R, Thompson P, Perry E. Neuroleptic sensitivity in patients with senile dementia of Lewy body type. BMJ 1992; 305: 673-78.
  143. 179 Nielsen KD, Boenink M. Ambivalent anticipation: how people with Alzheimer's disease value diagnosis in current and envisioned future practices. Sociol Health Illn 2021; 43: 510-27 .
  144. 181 Pittock RR, Aakre JA, Castillo AM, et al. Eligibility for anti-amyloid treatment in a population-based study of cognitive aging. Neurology 2023; 101: e1837-49.
  145. 182 Rosenberg A, Öhlund-Wistbacka U, Hall A, et al. β-amyloid, tau, neurodegeneration classification and eligibility for anti-amyloid treatment in a memory clinic population. Neurology 2022; 99: e2102-13.
  146. 184 Mielke MM, Anderson M, Ashford JW, et al. Recommendations for clinical implementation of blood-based biomarkers for Alzheimer's disease. Alzheimers Dement 2024; 20: 8216-24.
  147. 187 Mielke MM, Anderson M, Ashford JW, et al. Considerations for widespread implementation of blood-based biomarkers of Alzheimer's disease. Alzheimers Dement 2024; 20: 8209-15.
  148. 188 Karikari TK. Blood tests for Alzheimer's disease: increasing efforts to expand and diversify research participation is critical for widespread validation and acceptance. J Alzheimers Dis 2022; 90: 967-74.
  149. 189 Wilkins CH, Windon CC, Dilworth-Anderson P, et al. Racial and ethnic differences in amyloid PET positivity in individuals with mild cognitive impairment or dementia: a secondary analysis of the Imaging Dementia-Evidence for Amyloid Scanning (IDEAS) cohort study. JAMA Neurol 2022; 79: 1139-47 .
  150. 190 Morris JC, Schindler SE, McCue LM, et al. Assessment of racial disparities in biomarkers for Alzheimer disease. JAMA Neurol 2019; 76: 264-73.
  151. 191 Ramanan VK, Graff-Radford J, Syrjanen J, et al. Association of plasma biomarkers of Alzheimer disease with cognition and medical comorbidities in a biracial cohort. Neurology 2023; 101: e1402-11.
  152. 192 Molina-Henry DP, Raman R, Liu A, et al. Racial and ethnic differences in plasma biomarker eligibility for a preclinical Alzheimer's disease trial. Alzheimers Dement 2024; 20: 3827-38.
  153. 193 Xiong C, Luo J, Wolk DA, et al. Baseline levels and longitudinal changes in plasma Aβ42/40 among Black and white individuals. Nat Commun 2024; 15: 5539.
  154. 194 Ashton NJ, Pascoal TA, Karikari TK, et al. Plasma p-tau231: a new biomarker for incipient Alzheimer's disease pathology. Acta Neuropathol 2021; 141: 709-24.
  155. 195 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.
  156. 196 La Joie R, Mundada NS, Blazhenetz G, et al. Quantitative amyloidPET in real-world practice: lessons from the imaging dementiaevidence for amyloid scanning (IDEAS) study. Alzheimers Dement 2023; 19: e082874.
  157. 197 Zetterberg H, Blennow K. Moving fluid biomarkers for Alzheimer's disease from research tools to routine clinical diagnostics. Mol Neurodegener 2021; 16: 10.
  158. 198 Yun J, Shin D, Lee EH, et al. Temporal dynamics and biological variability of Alzheimer biomarkers. JAMA Neurol 2025; 82: 384-96.
  159. 199 Leuzy A, Mattsson-Carlgren N, Cullen NC, et al. Robustness of CSF Aβ42/40 and Aβ42/P-tau181 measured using fully automated immunoassays to detect AD-related outcomes. Alzheimers Dement 2023; 19: 2994-3004.

Authors
Frisoni, G. B., Hansson, O., Nichols, E., Garibotto, V., Schindler, S. E., van der Flier, W. M., Jessen, F., Villain, N., Arenaza-Urquijo, E. M., Crivelli, L., Fortea, J., Grinberg, L. T., Ismail, Z., Minoshima, S., Ossenkoppele, R., Zetterberg, H., Petersen, R. C., & Dubois, B.
Affiliations
  • University of Geneva (CH)
  • Lund University (SE)
  • University of Southern California (US)
  • Washington University in St. Louis (US)
  • University of Cologne (DE)
  • Centre National de la Recherche Scientifique (FR)
  • Hospital de Sant Pau (ES)
  • Universidade de São Paulo (BR)
  • University of Calgary (CA)
  • University of Utah (US)
  • University of Wisconsin–Madison (US)
  • Mayo Clinic (US)
  • Inserm (FR)
Corresponding author
Frisoni, Giovanni B
ORCID
Venue
The Lancet
Publisher
Elsevier BV
Published
2025-09
Volume
406
Issue
10510
Pages
1389-1407
ISSN
  • 0099-5355 (ISSN-L)
  • 0140-6736
DOI
10.1016/s0140-6736(25)01294-2
OpenAlex
W4414441409
Cited by
143
References
195
Citations by year
2026: 128 · 2025: 15
FWCI
25.57
Type
journal article
Language
en
Open access
Licence
Locations
  • The Lancet (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Pure Amsterdam UMC (publishedVersion, cc-by)
  • DZNE Pub (submittedVersion, cc-by)
  • Lund University Publications (Lund University) (submittedVersion)
Funders
  • Università degli Studi di Genova
  • Université de Genève
  • University of California
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Cancer-related cognitive impairment studies
Keywords
Alzheimer's disease, diagnostic biomarkers, amyloid-beta, tau protein, positron emission tomography, cerebrospinal fluid analysis, blood biomarkers, differential diagnosis, cognitive impairment, postmortem diagnosis, patient journey
MeSH terms
Alzheimer Disease, Diagnosis, Differential, Humans, Biomarkers, Amyloid beta-Peptides, tau Proteins, Positron-Emission Tomography

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review