Skip to main content

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

  • Expert Review
  • Published:

Biomarkers for Alzheimer’s disease—preparing for a new era of disease-modifying therapies

Molecular Psychiatry volume 26, pages 296–308 (2021)Cite this article

Subjects

Abstract

Clinical trial results presented in 2019 suggest that antibody-based removal of cerebral amyloid β (Aβ) plaques may possibly clear tau tangles and modestly slow cognitive decline in symptomatic Alzheimer’s disease (AD). Although regulatory approval of this approach is still pending, preparing the healthcare system for the advent of disease-modifying therapies against AD is imperative. In particular, it will be necessary to identify the most suitable biomarkers to facilitate appropriate treatment of AD. Here, we give an update on recent developments in fluid and imaging biomarkers for AD-related pathologies and discuss potential approaches that could be adopted to screen for and clarify the underlying pathology in people seeking medical advice because of cognitive symptoms. We succinctly review recent data regarding biomarkers for Aβ and tau pathology, neurodegeneration, synaptic dysfunction, and inflammation, highlight the need for further research into common copathologies, and suggest how different biomarkers could be used (most likely in combination) to facilitate the development and clinical implementation of novel drug candidates against AD.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Biomarkers for Alzheimer’s disease.
Fig. 2: Representative neuroimaging scans for assessing primary and secondary Alzheimer-related pathologies.
Fig. 3: A model of the temporal pattern of biomarker abnormalities for AD-related pathophysiological processes.

Similar content being viewed by others

References

  1. DeTure MA, Dickson DW. The neuropathological diagnosis of Alzheimer’s disease. Mol Neurodegener. 2019;14:32.

    PubMed  PubMed Central  Google Scholar 

  2. Palmqvist S, Insel PS, Stomrud E, Janelidze S, Zetterberg H, Brix B, et al. Cerebrospinal fluid and plasma biomarker trajectories with increasing amyloid deposition in Alzheimer’s disease. EMBO Mol Med. 2019;11:e11170.

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Jack CR Jr., Holtzman DM. Biomarker modeling of Alzheimer’s disease. Neuron. 2013;80:1347–58.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Hardy JA, Higgins GA. Alzheimer’s disease: the amyloid cascade hypothesis. Science. 1992;256:184–5.

    CAS  PubMed  Google Scholar 

  5. De Strooper B, Karran E. The cellular phase of Alzheimer’s disease. Cell. 2016;164:603–15.

    PubMed  Google Scholar 

  6. Sala Frigerio C, Wolfs L, Fattorelli N, Thrupp N, Voytyuk I, Schmidt I, et al. The major risk factors for Alzheimer’s disease: age, sex, and genes modulate the microglia response to abeta plaques. Cell Rep. 2019;27:1293–306. e6.

    CAS  PubMed  Google Scholar 

  7. Kivipelto M, Mangialasche F, Ngandu T. Lifestyle interventions to prevent cognitive impairment, dementia and Alzheimer disease. Nat Rev Neurol. 2018;14:653–66.

    PubMed  Google Scholar 

  8. Loera-Valencia R, Cedazo-Minguez A, Kenigsberg PA, Page G, Duarte AI, Giusti P, et al. Current and emerging avenues for Alzheimer’s disease drug targets. J Intern Med. 2019;286:398–437.

    CAS  PubMed  Google Scholar 

  9. Walsh DM, Teplow DB. Alzheimer’s disease and the amyloid beta-protein. Prog Mol Biol Transl Sci. 2012;107:101–24.

    CAS  PubMed  Google Scholar 

  10. Huang LK, Chao SP, Hu CJ. Clinical trials of new drugs for Alzheimer disease. J Biomed Sci. 2020;27:18.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Suidan GL, Ramaswamy G. Targeting apolipoprotein E for Alzheimer’s disease: an industry perspective. Int J Mol Sci. 2019;20:2161. https://doi.org/10.3390/ijms20092161.

  12. Cummings J, Lee G, Ritter A, Sabbagh M, Zhong K. Alzheimer’s disease drug development pipeline: 2019. Alzheimers Dement. 2019;5:272–93.

    Google Scholar 

  13. Bittar A, Bhatt N, Kayed R. Advances and considerations in AD tau-targeted immunotherapy. Neurobiol Dis. 2020;134:104707.

    CAS  PubMed  Google Scholar 

  14. Selkoe DJ, Hardy J. The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol Med. 2016;8:595–608.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Hardy J, Allsop D. Amyloid deposition as the central event in the aetiology of Alzheimer’s disease. Trends Pharm Sci. 1991;12:383–8.

    CAS  PubMed  Google Scholar 

  16. Cohen AD, Landau SM, Snitz BE, Klunk WE, Blennow K, Zetterberg H. Fluid and PET biomarkers for amyloid pathology in Alzheimer’s disease. Mol Cell Neurosci. 2019;97:3–17.

    CAS  PubMed  Google Scholar 

  17. Olsson B, Lautner R, Andreasson U, Ohrfelt A, Portelius E, Bjerke M, et al. CSF and blood biomarkers for the diagnosis of Alzheimer’s disease: a systematic review and meta-analysis. Lancet Neurol. 2016;15:673–84.

    CAS  PubMed  Google Scholar 

  18. Rasmussen MK, Mestre H, Nedergaard M. The glymphatic pathway in neurological disorders. Lancet Neurol. 2018;17:1016–24.

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  20. Lewczuk P, Matzen A, Blennow K, Parnetti L, Molinuevo JL, Eusebi P, et al. Cerebrospinal fluid abeta42/40 corresponds better than abeta42 to amyloid PET in Alzheimer’s disease. J Alzheimers Dis. 2017;55:813–22.

    CAS  PubMed  Google Scholar 

  21. Mattsson N, Palmqvist S, Stomrud E, Vogel J, Hansson O. Staging beta-amyloid pathology with amyloid positron emission tomography. JAMA Neurol. 2019;76. https://doi.org/10.1001/jamaneurol.2019.2214 [Online ahead of print].

  22. Klunk WE, Koeppe RA, Price JC, Benzinger TL, Devous MD Sr., Jagust WJ, et al. The centiloid project: standardizing quantitative amyloid plaque estimation by PET. Alzheimers Dement. 2015;11:1–15. e1–4.

    PubMed  Google Scholar 

  23. Johnson KA, Minoshima S, Bohnen NI, Donohoe KJ, Foster NL, Herscovitch P, et al. Update on appropriate use criteria for amyloid PET imaging: dementia experts, mild cognitive impairment, and education. Amyloid imaging task force of the Alzheimer’s association and society for nuclear medicine and molecular imaging. Alzheimers Dement. 2013;9:e106–9.

    PubMed  Google Scholar 

  24. Liu JL, Hlavka JP, Hillestad R, Mattke S. Assessing the preparedness of the U.S. health care system infrastructure for an Alzheimer’s treatment. Santa Monica, CA: RAND Corporation; 2017.

    Google Scholar 

  25. Liu JL, Hlavka JP, Coulter DT, Baxi SM, Mattke S, Gidengil CA. Assessing the preparedness of the Canadian health care system infrastructure for an Alzheimer’s treatment. Santa Monica, CA: RAND Corporation; 2019.

    Google Scholar 

  26. Hlavka JP, Mattke S, Liu JL. Assessing the preparedness of the health care system infrastructure in six European countries for an Alzheimer’s treatment. Santa Monica, CA: RAND Corporation; 2018.

    Google Scholar 

  27. Mattke S, Hlávka JP, Yoong J, Wang M, Goto R. Assessing the preparedness of the Japanese health care system infrastructure for an Alzheimer’s treatment. Santa Monica, CA: RAND Corporation; 2019.

    Google Scholar 

  28. Arneric SP, Batrla-Utermann R, Beckett L, Bittner T, Blennow K, Carter L, et al. Cerebrospinal fluid biomarkers for Alzheimer’s disease: a view of the regulatory science qualification landscape from the coalition against major diseases CSF biomarker team. J Alzheimers Dis. 2017;55:19–35.

    PubMed  Google Scholar 

  29. Kuhlmann J, Andreasson U, Pannee J, Bjerke M, Portelius E, Leinenbach A, et al. CSF abeta1-42—an excellent but complicated Alzheimer’s biomarker—a route to standardisation. Clin Chim Acta. 2017;467:27–33.

    CAS  PubMed  Google Scholar 

  30. Janelidze S, Stomrud E, Palmqvist S, Zetterberg H, van Westen D, Jeromin A, et al. Plasma beta-amyloid in Alzheimer’s disease and vascular disease. Sci Rep. 2016;6:26801.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Nakamura A, Kaneko N, Villemagne VL, Kato T, Doecke J, Dore V, et al. High performance plasma amyloid-beta biomarkers for Alzheimer’s disease. Nature. 2018;554:249–54.

    CAS  PubMed  Google Scholar 

  32. Ovod V, Ramsey KN, Mawuenyega KG, Bollinger JG, Hicks T, Schneider T, et al. Amyloid beta concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis. Alzheimers Dement. 2017;13:841–9.

    PubMed  PubMed Central  Google Scholar 

  33. Schindler SE, Bollinger JG, Ovod V, Mawuenyega KG, Li Y, Gordon BA, et al. High-precision plasma beta-amyloid 42/40 predicts current and future brain amyloidosis. Neurology. 2019;93:e1647–59.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Palmqvist S, Janelidze S, Stomrud E, Zetterberg H, Karl J, Zink K, et al. Performance of fully automated plasma assays as screening tests for Alzheimer disease-related beta-amyloid status. JAMA Neurol. 2019;76:1060–9.

    PubMed Central  PubMed  Google Scholar 

  35. Irwin DJ. Tauopathies as clinicopathological entities. Parkinsonism Relat Disord 2016;22 Suppl 1:S29–33.

    PubMed  Google Scholar 

  36. Jack CR Jr., Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 2018;14:535–62.

    PubMed  PubMed Central  Google Scholar 

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

    PubMed  Google Scholar 

  38. Itoh N, Arai H, Urakami K, Ishiguro K, Ohno H, Hampel H, et al. Large-scale, multicenter study of cerebrospinal fluid tau protein phosphorylated at serine 199 for the antemortem diagnosis of Alzheimer’s disease. Ann Neurol. 2001;50:150–6.

    CAS  PubMed  Google Scholar 

  39. Skillback T, Farahmand BY, Rosen C, Mattsson N, Nagga K, Kilander L, et al. Cerebrospinal fluid tau and amyloid-beta(1-42) in patients with dementia. Brain. 2015;138:2716–31.

    PubMed  Google Scholar 

  40. Maia LF, Kaeser SA, Reichwald J, Hruscha M, Martus P, Staufenbiel M, et al. Changes in amyloid-beta and tau in the cerebrospinal fluid of transgenic mice overexpressing amyloid precursor protein. Sci Transl Med. 2013;5:194re2.

    PubMed  Google Scholar 

  41. Sato C, Barthelemy NR, Mawuenyega KG, Patterson BW, Gordon BA, Jockel-Balsarotti J, et al. Tau kinetics in neurons and the human central nervous system. Neuron. 2018;98:861–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Leitao MJ, Silva-Spinola A, Santana I, Olmedo V, Nadal A, Le Bastard N, et al. Clinical validation of the lumipulse G cerebrospinal fluid assays for routine diagnosis of Alzheimer’s disease. Alzheimers Res Ther. 2019;11:91.

    PubMed  PubMed Central  Google Scholar 

  43. Blennow K, Shaw LM, Stomrud E, Mattsson N, Toledo JB, Buck K, et al. Predicting clinical decline and conversion to Alzheimer’s disease or dementia using novel Elecsys Abeta(1-42), pTau and tTau CSF immunoassays. Sci Rep. 2019;9:19024.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. De Vos A, Bjerke M, Brouns R, De Roeck N, Jacobs D, Van den Abbeele L, et al. Neurogranin and tau in cerebrospinal fluid and plasma of patients with acute ischemic stroke. BMC Neurol. 2017;17:170.

    PubMed  PubMed Central  Google Scholar 

  45. Bogoslovsky T, Wilson D, Chen Y, Hanlon D, Gill J, Jeromin A, et al. Increases of plasma levels of glial fibrillary acidic protein, tau, and amyloid beta up to 90 days after traumatic brain injury. J Neurotrauma. 2017;34:66–73.

    PubMed  PubMed Central  Google Scholar 

  46. Zetterberg H. Review: tau in biofluids—relation to pathology, imaging and clinical features. Neuropathol Appl Neurobiol. 2017;43:194–9.

    CAS  PubMed  Google Scholar 

  47. Pereira JB, Westman E, Hansson O, Alzheimer’s Disease Neuroimaging I. Association between cerebrospinal fluid and plasma neurodegeneration biomarkers with brain atrophy in Alzheimer’s disease. Neurobiol Aging. 2017;58:14–29.

    CAS  PubMed  Google Scholar 

  48. Randall J, Mortberg E, Provuncher GK, Fournier DR, Duffy DC, Rubertsson S, et al. Tau proteins in serum predict neurological outcome after hypoxic brain injury from cardiac arrest: results of a pilot study. Resuscitation. 2013;84:351–6.

    CAS  PubMed  Google Scholar 

  49. Mielke MM, Hagen CE, Xu J, Chai X, Vemuri P, Lowe VJ, et al. Plasma phospho-tau181 increases with Alzheimer’s disease clinical severity and is associated with tau- and amyloid-positron emission tomography. Alzheimers Dement. 2018;14:989–97.

    PubMed  PubMed Central  Google Scholar 

  50. Thijssen EH, La Joie R, Wolf A, Strom A, Wang P, Iaccarino L, et al. Diagnostic value of plasma phosphorylated tau181 in Alzheimer’s disease and frontotemporal lobar degeneration. Nat Med. 2020;26:387–97.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Janelidze S, Mattsson N, Palmqvist S, Insel P, Smith R, Eichenlaub U, 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  Google Scholar 

  52. Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991;82:239–59.

    CAS  PubMed  Google Scholar 

  53. Saint-Aubert L, Lemoine L, Chiotis K, Leuzy A, Rodriguez-Vieitez E, Nordberg A, et al. imaging: present and future directions. Mol Neurodegener. 2017;12:19.

    PubMed  PubMed Central  Google Scholar 

  54. Ng KP, Pascoal TA, Mathotaarachchi S, Therriault J, Kang MS, Shin M, et al. Monoamine oxidase B inhibitor, selegiline, reduces (18)F-THK5351 uptake in the human brain. Alzheimers Res Ther. 2017;9:25.

    PubMed  PubMed Central  Google Scholar 

  55. Betthauser TJ. AD molecular: imaging tau aggregates with positron emissions tomography. Prog Mol Biol Transl Sci. 2019;165:107–38.

    CAS  PubMed  Google Scholar 

  56. Marquie M, Siao Tick Chong M, Anton-Fernandez A, Verwer EE, Saez-Calveras N, Meltzer AC. et al. [F-18]-AV-1451 binding correlates with postmortem neurofibrillary tangle Braak staging. Acta Neuropathol.2017;134:619–28.

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Marquie M, Normandin MD, Meltzer AC, Siao Tick Chong M, Andrea NV, Anton-Fernandez A. et al. Pathological correlations of [F-18]-AV-1451 imaging in non-alzheimer tauopathies. Ann Neurol.2017;81:117–28.

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Betthauser TJ, Cody KA, Zammit MD, Murali D, Converse AK, Barnhart TE, et al. In vivo characterization and quantification of neurofibrillary tau PET radioligand (18)F-MK-6240 in humans from Alzheimer disease dementia to young controls. J Nucl Med. 2019;60:93–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Kuwabara H, Comley RA, Borroni E, Honer M, Kitmiller K, Roberts J, et al. Evaluation of (18)F-RO-948 PET for quantitative assessment of tau accumulation in the human brain. J Nucl Med. 2018;59:1877–84.

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Mueller A, Bullich S, Barret O, Madonia J, Berndt M, Papin C, et al. Tau PET imaging with (18)F-PI-2620 in patients with Alzheimer’s disease and healthy controls: a first-in-human study. J Nucl Med. 2019. https://doi.org/10.2967/jnumed.119.236224. [Online ahead of print].

  61. Sanabria Bohorquez S, Marik J, Ogasawara A, Tinianow JN, Gill HS, Barret O, et al. [(18)F]GTP1 (Genentech Tau Probe 1), a radioligand for detecting neurofibrillary tangle tau pathology in Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2019;46:2077–89.

    CAS  PubMed  Google Scholar 

  62. Scholl M, Maass A, Mattsson N, Ashton NJ, Blennow K, Zetterberg H, et al. Biomarkers for tau pathology. Mol Cell Neurosci. 2019;97:18–33.

    PubMed  PubMed Central  Google Scholar 

  63. 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.

    PubMed  PubMed Central  Google Scholar 

  64. Fox NC, Warrington EK, Rossor MN. Serial magnetic resonance imaging of cerebral atrophy in preclinical Alzheimer’s disease. Lancet. 1999;353:2125.

    CAS  PubMed  Google Scholar 

  65. Jack CR Jr., Petersen RC, O’Brien PC, Tangalos EG. MR-based hippocampal volumetry in the diagnosis of Alzheimer’s disease. Neurology. 1992;42:183–8.

    PubMed  Google Scholar 

  66. Dickerson BC, Stoub TR, Shah RC, Sperling RA, Killiany RJ, Albert MS, et al. Alzheimer-signature MRI biomarker predicts AD dementia in cognitively normal adults. Neurology. 2011;76:1395–402.

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Vogt NM, Hunt JF, Adluru N, Dean DC, Johnson SC, Asthana S, et al. Cortical microstructural alterations in mild cognitive impairment and Alzheimer’s disease dementia. Cereb Cortex. 2019. https://doi.org/10.1093/cercor/bhz286. [Online ahead of print].

  68. Khalil M, Teunissen CE, Otto M, Piehl F, Sormani MP, Gattringer T, et al. Neurofilaments as biomarkers in neurological disorders. Nat Rev Neurol. 2018;14:577–89.

    CAS  PubMed  Google Scholar 

  69. Gaetani L, Blennow K, Calabresi P, Di Filippo M, Parnetti L, Zetterberg H. Neurofilament light chain as a biomarker in neurological disorders. J Neurol Neurosurg Psychiatry. 2019;90:870–81.

    PubMed  Google Scholar 

  70. Bridel C, van Wieringen WN, Zetterberg H, Tijms BM, Teunissen CE, and the NFLG, et al. Diagnostic value of cerebrospinal fluid neurofilament light protein in neurology: a systematic review and meta-analysis. JAMA Neurol. 2019;76:1035–48.

    PubMed Central  PubMed  Google Scholar 

  71. Weston PSJ, Poole T, O’Connor A, Heslegrave A, Ryan NS, Liang Y, et al. Longitudinal measurement of serum neurofilament light in presymptomatic familial Alzheimer’s disease. Alzheimers Res Ther. 2019;11:19.

    PubMed  PubMed Central  Google Scholar 

  72. Preische O, Schultz SA, Apel A, Kuhle J, Kaeser SA, Barro C, et al. Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer’s disease. Nat Med. 2019;25:277–83.

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Mattsson N, Cullen NC, Andreasson U, Zetterberg H, Blennow K. Association between longitudinal plasma neurofilament light and neurodegeneration in patients with Alzheimer disease. JAMA Neurol. 2019;76:791–9.

    PubMed  PubMed Central  Google Scholar 

  74. Terry RD, Masliah E, Salmon DP, Butters N, DeTeresa R, Hill R, et al. Physical basis of cognitive alterations in Alzheimer’s disease: synapse loss is the major correlate of cognitive impairment. Ann Neurol. 1991;30:572–80.

    CAS  PubMed  Google Scholar 

  75. Alexander GE, Chen K, Pietrini P, Rapoport SI, Reiman EM. Longitudinal PET evaluation of cerebral metabolic decline in dementia: a potential outcome measure in Alzheimer’s disease treatment studies. Am J Psychiatry. 2002;159:738–45.

    PubMed  Google Scholar 

  76. Reiman EM. Fluorodeoxyglucose positron emission tomography: emerging roles in the evaluation of putative Alzheimer’s disease-modifying treatments. Neurobiol Aging. 2011;32 Suppl 1:S44–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Zimmer ER, Parent MJ, Souza DG, Leuzy A, Lecrux C, Kim HI, et al. [(18)F]FDG PET signal is driven by astroglial glutamate transport. Nat Neurosci. 2017;20:393–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Chen MK, Mecca AP, Naganawa M, Finnema SJ, Toyonaga T, Lin SF, et al. Assessing synaptic density in Alzheimer disease with synaptic vesicle glycoprotein 2A positron emission tomographic imaging. JAMA Neurol. 2018;75:1215–24.

    PubMed  PubMed Central  Google Scholar 

  79. Constantinescu CC, Tresse C, Zheng M, Gouasmat A, Carroll VM, Mistico L, et al. Development and in vivo preclinical imaging of fluorine-18-labeled synaptic vesicle protein 2A (SV2A) PET tracers. Mol Imaging Biol. 2019;21:509–18.

    CAS  PubMed  Google Scholar 

  80. Li S, Cai Z, Zhang W, Holden D, Lin SF, Finnema SJ, et al. Synthesis and in vivo evaluation of [(18)F]UCB-J for PET imaging of synaptic vesicle glycoprotein 2A (SV2A). Eur J Nucl Med Mol Imaging. 2019;46:1952–65.

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Kvartsberg H, Duits FH, Ingelsson M, Andreasen N, Ohrfelt A, Andersson K, et al. Cerebrospinal fluid levels of the synaptic protein neurogranin correlates with cognitive decline in prodromal Alzheimer’s disease. Alzheimers Dement. 2015;11:1180–90.

    PubMed  Google Scholar 

  82. Wellington H, Paterson RW, Portelius E, Tornqvist U, Magdalinou N, Fox NC, et al. Increased CSF neurogranin concentration is specific to Alzheimer disease. Neurology. 2016;86:829–35.

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Portelius E, Olsson B, Hoglund K, Cullen NC, Kvartsberg H, Andreasson U, et al. Cerebrospinal fluid neurogranin concentration in neurodegeneration: relation to clinical phenotypes and neuropathology. Acta Neuropathol. 2018;136:363–76.

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Casaletto KB, Elahi FM, Bettcher BM, Neuhaus J, Bendlin BB, Asthana S, et al. Neurogranin, a synaptic protein, is associated with memory independent of Alzheimer biomarkers. Neurology. 2017;89:1782–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  85. De Vos A, Jacobs D, Struyfs H, Fransen E, Andersson K, Portelius E, et al. C-terminal neurogranin is increased in cerebrospinal fluid but unchanged in plasma in Alzheimer’s disease. Alzheimers Dement. 2015;11:1461–9.

    PubMed  Google Scholar 

  86. Gratuze M, Leyns CEG, Holtzman DM. New insights into the role of TREM2 in Alzheimer’s disease. Mol Neurodegener. 2018;13:66.

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Molinuevo JL, Ayton S, Batrla R, Bednar MM, Bittner T, Cummings J, et al. Current state of Alzheimer’s fluid biomarkers. Acta Neuropathol. 2018;136:821–53.

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Suarez-Calvet M, Kleinberger G, Araque Caballero MA, Brendel M, Rominger A, Alcolea D, et al. sTREM2 cerebrospinal fluid levels are a potential biomarker for microglia activity in early-stage Alzheimer’s disease and associate with neuronal injury markers. EMBO Mol Med. 2016;8:466–76.

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Elahi FM, Casaletto KB, La Joie R, Walters SM, Harvey D, Wolf A, et al. Plasma biomarkers of astrocytic and neuronal dysfunction in early- and late-onset Alzheimer’s disease. Alzheimers Dement. 2019. https://doi.org/10.1016/j.jalz.2019.09.004. [Online ahead of print].

  90. Scarf AM, Kassiou M. The translocator protein. J Nucl Med. 2011;52:677–80.

    CAS  PubMed  Google Scholar 

  91. Parbo P, Ismail R, Hansen KV, Amidi A, Marup FH, Gottrup H, et al. Brain inflammation accompanies amyloid in the majority of mild cognitive impairment cases due to Alzheimer’s disease. Brain. 2017;140:2002–11.

    PubMed  Google Scholar 

  92. Edison P, Archer HA, Hinz R, Hammers A, Pavese N, Tai YF, et al. Amyloid, hypometabolism, and cognition in Alzheimer disease: an [11C]PIB and [18F]FDG PET study. Neurology. 2007;68:501–8.

    CAS  PubMed  Google Scholar 

  93. Owen DR, Yeo AJ, Gunn RN, Song K, Wadsworth G, Lewis A, et al. An 18-kDa translocator protein (TSPO) polymorphism explains differences in binding affinity of the PET radioligand PBR28. J Cereb Blood Flow Metab. 2012;32:1–5.

    CAS  PubMed  Google Scholar 

  94. Shen XN, Niu LD, Wang YJ, Cao XP, Liu Q, Tan L, et al. Inflammatory markers in Alzheimer’s disease and mild cognitive impairment: a meta-analysis and systematic review of 170 studies. J Neurol Neurosurg Psychiatry. 2019;90:590–8.

    PubMed  Google Scholar 

  95. Robinson JL, Lee EB, Xie SX, Rennert L, Suh E, Bredenberg C, et al. Neurodegenerative disease concomitant proteinopathies are prevalent, age-related and APOE4-associated. Brain. 2018;141:2181–93.

    PubMed  PubMed Central  Google Scholar 

  96. James BD, Wilson RS, Boyle PA, Trojanowski JQ, Bennett DA, Schneider JA. TDP-43 stage, mixed pathologies, and clinical Alzheimer’s-type dementia. Brain. 2016;139:2983–93.

    PubMed  PubMed Central  Google Scholar 

  97. Mollenhauer B. Quantification of alpha-synuclein in cerebrospinal fluid: how ideal is this biomarker for Parkinson’s disease? Parkinsonism Relat Disord. 2014;20(Suppl 1):S76–9.

    PubMed  Google Scholar 

  98. Paciotti S, Bellomo G, Gatticchi L, Parnetti L. Are we ready for detecting alpha-synuclein prone to aggregation in patients? the case of “protein-misfolding cyclic amplification” and “real-time quaking-induced conversion” as diagnostic tools. Front Neurol. 2018;9:415.

    PubMed  PubMed Central  Google Scholar 

  99. Shahnawaz M, Tokuda T, Waragai M, Mendez N, Ishii R, Trenkwalder C, et al. Development of a biochemical diagnosis of parkinson disease by detection of alpha-synuclein misfolded aggregates in cerebrospinal fluid. JAMA Neurol. 2017;74:163–72.

    PubMed  Google Scholar 

  100. Fairfoul G, McGuire LI, Pal S, Ironside JW, Neumann J, Christie S, et al. Alpha-synuclein RT-QuIC in the CSF of patients with alpha-synucleinopathies. Ann Clin Transl Neurol. 2016;3:812–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  101. Groveman BR, Orru CD, Hughson AG, Raymond LD, Zanusso G, Ghetti B, et al. Rapid and ultra-sensitive quantitation of disease-associated alpha-synuclein seeds in brain and cerebrospinal fluid by alphaSyn RT-QuIC. Acta Neuropathol Commun. 2018;6:7.

    PubMed  PubMed Central  Google Scholar 

  102. Jack CR Jr., Knopman DS, Jagust WJ, Petersen RC, Weiner MW, Aisen PS, et al. Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 2013;12:207–16.

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Suarez-Calvet M, Araque Caballero MA, Kleinberger G, Bateman RJ, Fagan AM, Morris JC, et al. Early changes in CSF sTREM2 in dominantly inherited Alzheimer’s disease occur after amyloid deposition and neuronal injury. Sci Transl Med. 2016;8:369ra178.

    PubMed  PubMed Central  Google Scholar 

  104. Brookmeyer R, Abdalla N. Estimation of lifetime risks of Alzheimer’s disease dementia using biomarkers for preclinical disease. Alzheimers Dement. 2018;14:981–8.

    PubMed  PubMed Central  Google Scholar 

  105. Elobeid A, Libard S, Leino M, Popova SN, Alafuzoff I. Altered proteins in the aging brain. J Neuropathol Exp Neurol. 2016;75:316–25.

    PubMed  PubMed Central  Google Scholar 

  106. Mattsson N, Rosen E, Hansson O, Andreasen N, Parnetti L, Jonsson M, et al. Age and diagnostic performance of Alzheimer disease CSF biomarkers. Neurology. 2012;78:468–76.

    CAS  PubMed  PubMed Central  Google Scholar 

  107. Tang Y, Whitman GT, Lopez I, Baloh RW. Brain volume changes on longitudinal magnetic resonance imaging in normal older people. J Neuroimaging. 2001;11:393–400.

    CAS  PubMed  Google Scholar 

  108. Zhang Y, Qiu C, Lindberg O, Bronge L, Aspelin P, Backman L, et al. Acceleration of hippocampal atrophy in a non-demented elderly population: the SNAC-K study. Int Psychogeriatr. 2010;22:14–25.

    PubMed  Google Scholar 

  109. Haeberlein SB, von Hehn C, Tian Y, Chalkias S, Muralidharan KK, Chen T, et al. Topline results from phase 3 aducanumab studies clinical trials on Alzheimer’s disease. Abstract and presentation at CTAD2019, San Diego; 2019.

Download references

Acknowledgements

HZ is a Wallenberg Scholar supported by grants from the Swedish Research Council (#2018-02532), the European Research Council (#681712), Swedish State Support for Clinical Research (#ALFGBG-720931) and the UK Dementia Research Institute at UCL. BBB is supported by grants from the National Institute on Aging (P30AG062715, R01AG037639, RF1AG059312, R01AG062285, RF1AG057784, UF1AG051216).

Author information

Authors and Affiliations

  1. Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden

    Henrik Zetterberg

  2. Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden

    Henrik Zetterberg

  3. Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK

    Henrik Zetterberg

  4. UK Dementia Research Institute at UCL, London, UK

    Henrik Zetterberg

  5. Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA

    Barbara B. Bendlin

Authors
  1. Henrik Zetterberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Barbara B. Bendlin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Henrik Zetterberg or Barbara B. Bendlin.

Ethics declarations

Conflict of interest

HZ has served at scientific advisory boards for Denali, Roche Diagnostics, Wave, Samumed and CogRx, has given lectures in symposia sponsored by Fujirebio, Alzecure and Biogen, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB, a GU Ventures-based platform company at the University of Gothenburg. BBB has received precursors and imaging agents from Avid Radiopharmaceuticals.

Additional information

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zetterberg, H., Bendlin, B.B. Biomarkers for Alzheimer’s disease—preparing for a new era of disease-modifying therapies. Mol Psychiatry 26, 296–308 (2021). https://doi.org/10.1038/s41380-020-0721-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41380-020-0721-9

This article is cited by

Search

Advanced search

Quick links