An update on blood-based biomarkers for non-Alzheimer neurodegenerative disorders

Abstract

Cerebrospinal fluid analyses and neuroimaging can identify the underlying pathophysiology at the earliest stage of some neurodegenerative disorders, but do not have the scalability needed for population screening. Therefore, a blood-based marker for such pathophysiology would have greater utility in a primary care setting and in eligibility screening for clinical trials. Rapid advances in ultra-sensitive assays have enabled the levels of pathological proteins to be measured in blood samples, but research has been predominantly focused on Alzheimer disease (AD). Nonetheless, proteins that were identified as potential blood-based biomarkers for AD, for example, amyloid-β, tau, phosphorylated tau and neurofilament light chain, are likely to be relevant to other neurodegenerative disorders that involve similar pathological processes and could also be useful for the differential diagnosis of clinical symptoms. This Review outlines the neuropathological, clinical, molecular imaging and cerebrospinal fluid features of the most common neurodegenerative disorders outside the AD continuum and gives an overview of the current status of blood-based biomarkers for these disorders.

Key points

  • Neurodegenerative disorders are characterized by protein aggregation and other pathological processes, which can affect the composition of biofluids such as blood and cerebrospinal fluid (CSF).

  • Analysis of CSF and molecular imaging of the brain enable the stratification of patient populations on the basis of underlying pathology, but are limited as population screening tools.

  • Advances in ultra-sensitive immunoassays for the measurement of amyloid-β, neurofilament light chain, total tau and phosphorylated tau, as well as mass spectrometry-based methods for the measurement of amyloid-β, have demonstrated that a blood-based screening tool for Alzheimer disease is a realistic and plausible possibility.

  • Evidence now suggests that blood-based biomarkers could also be important for other common neurodegenerative disorders: for example, Lewy body dementia, atypical parkinsonian disorders and frontotemporal dementia.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Examples of PET and MRI scans from individuals with AD, bvFTD, CBS or PSP.
Fig. 2: Current strategies for blood-based biomarker discovery in neurodegenerative disorder research.

References

  1. 1.

    Cummings, J. Lessons learned from Alzheimer disease: clinical trials with negative outcomes. Clin. Transl Sci. 11, 147–152 (2018).

    Article  Google Scholar 

  2. 2.

    Kovacs, G. G. Molecular pathological classification of neurodegenerative diseases: turning towards precision medicine. Int. J. Mol. Sci. 17, 189 (2016).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  3. 3.

    Neary, D. et al. Frontotemporal lobar degeneration: a consensus on clinical diagnostic criteria. Neurology 51, 1546–1554 (1998). Consensus criteria for the clinical diagnosis of the three syndromes associated with frontotemporal lobar degeneration: frontotemporal dementia, progressive nonfluent aphasia and semantic dementia.

    CAS  Article  Google Scholar 

  4. 4.

    McKeith, I. G. et al. Diagnosis and management of dementia with Lewy bodies: fourth consensus report of the DLB consortium. Neurology 89, 88–100 (2017). Consensus criteria for the clinical diagnosis of dementia with Lewy bodies and recommendations on the clinical management of the disease.

    PubMed Central  Article  PubMed  Google Scholar 

  5. 5.

    Hoglinger, G. U. et al. Clinical diagnosis of progressive supranuclear palsy: the Movement Disorder Society criteria. Mov. Disord. 32, 853–864 (2017). Clinical diagnostic criteria for progressive supranuclear palsy.

    PubMed Central  Article  PubMed  Google Scholar 

  6. 6.

    Harper, L. et al. Patterns of atrophy in pathologically confirmed dementias: a voxelwise analysis. J. Neurol. Neurosurg. Psychiatry 88, 908–916 (2017).

    PubMed Central  Article  PubMed  Google Scholar 

  7. 7.

    Zimmer, E. R. et al. [18F]FDG PET signal is driven by astroglial glutamate transport. Nat. Neurosci. 20, 393–395 (2017).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  8. 8.

    Ashton, N. J. et al. Update on biomarkers for amyloid pathology in Alzheimer’s disease. Biomark. Med. 12, 799–812 (2018).

    CAS  Article  Google Scholar 

  9. 9.

    Leuzy, A. et al. Tau PET imaging in neurodegenerative tauopathies–still a challenge. Mol. Psychiatry 24, 1112–1134 (2019).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  10. 10.

    Scholl, M. et al. Biomarkers for tau pathology. Mol. Cell Neurosci. 97, 18–33 (2019).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  11. 11.

    Heurling, K. et al. Synaptic vesicle protein 2A as a potential biomarker in synaptopathies. Mol. Cell Neurosci. 97, 34–42 (2019).

    CAS  Article  Google Scholar 

  12. 12.

    Tarasoff-Conway, J. M. et al. Clearance systems in the brain–implications for Alzheimer disease. Nat. Rev. Neurol. 11, 457–470 (2015).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  13. 13.

    Leuzy, A., Heurling, K., Ashton, N. J., Scholl, M. & Zimmer, E. R. In vivo detection of Alzheimer’s disease. Yale J. Biol. Med. 91, 291–300 (2018).

    CAS  PubMed Central  PubMed  Google Scholar 

  14. 14.

    Molinuevo, J. L. et al. Current state of Alzheimer’s fluid biomarkers. Acta Neuropathol. 136, 821–853 (2018).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  15. 15.

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

    PubMed Central  Article  PubMed  Google Scholar 

  16. 16.

    Albert, M. S. et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 270–279 (2011).

    PubMed Central  Article  PubMed  Google Scholar 

  17. 17.

    McKhann, G. M. et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 263–269 (2011).

    PubMed Central  Article  PubMed  Google Scholar 

  18. 18.

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

    Article  Google Scholar 

  19. 19.

    Jack, C. R. Jr. et al. NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 14, 535–562 (2018). A research framework that defines Alzheimer disease as a biological entity characterized by biomarker or neuropathological evidence of amyloid and tau pathology.

    PubMed Central  Article  PubMed  Google Scholar 

  20. 20.

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

    PubMed Central  Article  PubMed  Google Scholar 

  21. 21.

    Aarsland, D. et al. Cognitive decline in Parkinson disease. Nat. Rev. Neurol. 13, 217–231 (2017).

    PubMed Central  Article  PubMed  Google Scholar 

  22. 22.

    Collins, L. M. & Williams-Gray, C. H. The genetic basis of cognitive impairment and dementia in Parkinson’s disease. Front. Psychiatry 7, 89 (2016).

    PubMed Central  Article  PubMed  Google Scholar 

  23. 23.

    Postuma, R. B. et al. MDS clinical diagnostic criteria for Parkinson’s disease. Mov. Disord. 30, 1591–1601 (2015).

    Article  Google Scholar 

  24. 24.

    Emre, M. et al. Clinical diagnostic criteria for dementia associated with Parkinson’s disease. Mov. Disord. 22, 1689–1707 (2007).

    Article  Google Scholar 

  25. 25.

    Goetz, C. G., Emre, M. & Dubois, B. Parkinson’s disease dementia: definitions, guidelines, and research perspectives in diagnosis. Ann. Neurol. 64, S81–S92 (2008).

    Article  Google Scholar 

  26. 26.

    Irwin, D. J. et al. Neuropathologic substrates of Parkinson disease dementia. Ann. Neurol. 72, 587–598 (2012). This study highlights the importance of cortical Lewy bodies and Lewy neurites, APOE*ε4, and Alzheimer co-pathology as pathological substrates of cognitive impairment and dementia in Parkinson disease.

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  27. 27.

    Saredakis, D., Collins-Praino, L. E., Gutteridge, D. S., Stephan, B. C. M. & Keage, H. A. D. Conversion to MCI and dementia in Parkinson’s disease: a systematic review and meta-analysis. Parkinsonism Relat. Disord. 65, 20–31 (2019).

    Article  Google Scholar 

  28. 28.

    McKeith, I. G. et al. Diagnosis and management of dementia with Lewy bodies: third report of the DLB consortium. Neurology 65, 1863–1872 (2005).

    CAS  Article  Google Scholar 

  29. 29.

    Palma, J. A. & Kaufmann, H. Autonomic disorders predicting Parkinson’s disease. Parkinsonism Relat. Disord. 20, S94–S98 (2014).

    PubMed Central  Article  PubMed  Google Scholar 

  30. 30.

    Gomperts, S. N. Lewy body dementias: dementia with Lewy bodies and Parkinson disease dementia. Continuum 22, 435–463 (2016).

    Google Scholar 

  31. 31.

    Elobeid, A., Libard, S., Leino, M., Popova, S. N. & Alafuzoff, I. Altered proteins in the aging brain. J. Neuropathol. Exp. Neurol. 75, 316–325 (2016).

    PubMed Central  Article  PubMed  Google Scholar 

  32. 32.

    Hotter, A., Esterhammer, R., Schocke, M. F. & Seppi, K. Potential of advanced MR imaging techniques in the differential diagnosis of parkinsonism. Mov. Disord. 24, S711–S720 (2009).

    Article  Google Scholar 

  33. 33.

    Mahlknecht, P. et al. Significance of MRI in diagnosis and differential diagnosis of Parkinson’s disease. Neurodegener. Dis. 7, 300–318 (2010).

    Article  Google Scholar 

  34. 34.

    Heim, B., Krismer, F., De Marzi, R. & Seppi, K. Magnetic resonance imaging for the diagnosis of Parkinson’s disease. J. Neural Transm. 124, 915–964 (2017).

    Article  Google Scholar 

  35. 35.

    Spetsieris, P. G., Ma, Y., Dhawan, V. & Eidelberg, D. Differential diagnosis of parkinsonian syndromes using PCA-based functional imaging features. Neuroimage 45, 1241–1252 (2009).

    Article  Google Scholar 

  36. 36.

    Ishii, K. Clinical application of positron emission tomography for diagnosis of dementia. Ann. Nucl. Med. 16, 515–525 (2002).

    Article  Google Scholar 

  37. 37.

    Higuchi, M. et al. Glucose hypometabolism and neuropathological correlates in brains of dementia with Lewy bodies. Exp. Neurol. 162, 247–256 (2000). A key study that used 18F-FDG PET to show the neuropathological correlates of occipital glucose hypometabolism in dementia with Lewy bodies.

    CAS  Article  Google Scholar 

  38. 38.

    Ishii, K., Hosaka, K., Mori, T. & Mori, E. Comparison of FDG-PET and IMP-SPECT in patients with dementia with Lewy bodies. Ann. Nucl. Med. 18, 447–451 (2004).

    Article  Google Scholar 

  39. 39.

    Meyer, P. T., Frings, L., Rucker, G. & Hellwig, S. 18F-FDG PET in parkinsonism: differential diagnosis and evaluation of cognitive impairment. J. Nucl. Med. 58, 1888–1898 (2017).

    CAS  Article  Google Scholar 

  40. 40.

    Brooks, D. J. & Tambasco, N. Imaging synucleinopathies. Mov. Disord. 31, 814–829 (2016).

    Article  Google Scholar 

  41. 41.

    McCleery, J. et al. Dopamine transporter imaging for the diagnosis of dementia with Lewy bodies. Cochrane Database Syst. Rev. 1, CD010633 (2015). A review of findings from dopaminergic PET and SPECT in dementia with Lewy bodies and Parkinson disease.

    Google Scholar 

  42. 42.

    Petrou, M. et al. Amyloid deposition in Parkinson’s disease and cognitive impairment: a systematic review. Mov. Disord. 30, 928–935 (2015).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  43. 43.

    Gomperts, S. N. et al. Imaging amyloid deposition in Lewy body diseases. Neurology 71, 903–910 (2008). A study that used PET to compare the patterns of amyloid-β deposition in dementia with Lewy bodies and Parkinson disease.

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  44. 44.

    Gomperts, S. N. et al. Brain amyloid and cognition in Lewy body diseases. Mov. Disord. 27, 965–973 (2012).

    PubMed Central  Article  PubMed  Google Scholar 

  45. 45.

    Edison, P. et al. Amyloid load in Parkinson’s disease dementia and Lewy body dementia measured with [11C]PIB positron emission tomography. J. Neurol. Neurosurg. Psychiatry 79, 1331–1338 (2008).

    CAS  Article  Google Scholar 

  46. 46.

    Gomperts, S. N. et al. PET radioligands reveal the basis of dementia in Parkinson’s disease and dementia with Lewy bodies. Neurodegener. Dis. 16, 118–124 (2016).

    CAS  Article  Google Scholar 

  47. 47.

    Guo, J. L. et al. Distinct α-synuclein strains differentially promote tau inclusions in neurons. Cell 154, 103–117 (2013).

    CAS  Article  Google Scholar 

  48. 48.

    Gearing, M., Lynn, M. & Mirra, S. S. Neurofibrillary pathology in Alzheimer disease with Lewy bodies: two subgroups. Arch. Neurol. 56, 203–208 (1999).

    CAS  Article  Google Scholar 

  49. 49.

    Schonhaut, D. R. et al. 18F-flortaucipir tau positron emission tomography distinguishes established progressive supranuclear palsy from controls and Parkinson disease: a multicenter study. Ann. Neurol. 82, 622–634 (2017).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  50. 50.

    Smith, R. et al. Increased basal ganglia binding of 18F-AV-1451 in patients with progressive supranuclear palsy. Mov. Disord. 32, 108–114 (2017).

    CAS  Article  Google Scholar 

  51. 51.

    Coakeley, S. et al. Positron emission tomography imaging of tau pathology in progressive supranuclear palsy. J. Cereb. Blood Flow. Metab. 37, 3150–3160 (2017).

    CAS  Article  Google Scholar 

  52. 52.

    Smith, R., Scholl, M., Londos, E., Ohlsson, T. & Hansson, O. 18F-AV-1451 in Parkinson’s disease with and without dementia and in dementia with Lewy bodies. Sci. Rep. 8, 4717 (2018). A tau PET study showing that 18F-flortaucipir might bind to neuromelanin in the substantia nigra and therefore could be useful in the study of Parkinson disease.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  53. 53.

    Hansen, A. K. et al. In vivo imaging of neuromelanin in Parkinson’s disease using 18F-AV-1451 PET. Brain 139, 2039–2049 (2016).

    Article  Google Scholar 

  54. 54.

    Wong, D. F. et al. Characterization of 3 novel tau radiopharmaceuticals, 11C-RO-963, 11C-RO-643, and 18F-RO-948, in healthy controls and in Alzheimer subjects. J. Nucl. Med. 59, 1869–1876 (2018).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  55. 55.

    Woollacott, I. O. & Rohrer, J. D. The clinical spectrum of sporadic and familial forms of frontotemporal dementia. J. Neurochem. 138, 6–31 (2016).

    CAS  Article  Google Scholar 

  56. 56.

    Bang, J., Spina, S. & Miller, B. L. Frontotemporal dementia. Lancet 386, 1672–1682 (2015).

    PubMed Central  Article  PubMed  Google Scholar 

  57. 57.

    Rascovsky, K. et al. Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain 134, 2456–2477 (2011). Guidelines for the diagnosis of behavioural variant frontotemporal dementia developed by an international consortium.

    PubMed Central  Article  PubMed  Google Scholar 

  58. 58.

    Mackenzie, I. R. & Neumann, M. Molecular neuropathology of frontotemporal dementia: insights into disease mechanisms from postmortem studies. J. Neurochem. 138, 54–70 (2016).

    CAS  Article  Google Scholar 

  59. 59.

    Kwiatkowski, T. J. Jr. et al. Mutations in the FUS/TLS gene on chromosome 16 cause familial amyotrophic lateral sclerosis. Science 323, 1205–1208 (2009).

    CAS  Article  Google Scholar 

  60. 60.

    Vance, C. et al. Mutations in FUS, an RNA processing protein, cause familial amyotrophic lateral sclerosis type 6. Science 323, 1208–1211 (2009). Together with Kwiatkowski et al. (2009), this study identified ALS-causing mutations in the gene encoding FUS.

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  61. 61.

    Dormann, D. et al. Arginine methylation next to the PY-NLS modulates transportin binding and nuclear import of FUS. EMBO J. 31, 4258–4275 (2012).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  62. 62.

    Suarez-Calvet, M. et al. Monomethylated and unmethylated FUS exhibit increased binding to transportin and distinguish FTLD-FUS from ALS-FUS. Acta Neuropathol. 131, 587–604 (2016). Together with Dormann et al. (2012), this study showed that the methylation pattern of FUS aggregates differs between ALS-FUS and FTLD-FUS.

    CAS  Article  Google Scholar 

  63. 63.

    Choo, I. H. et al. Topographic patterns of brain functional impairment progression according to clinical severity staging in 116 Alzheimer disease patients: FDG-PET study. Alzheimer Dis. Assoc. Disord. 21, 77–84 (2007).

    Article  Google Scholar 

  64. 64.

    Del Sole, A. et al. Individual cerebral metabolic deficits in Alzheimer’s disease and amnestic mild cognitive impairment: an FDG PET study. Eur. J. Nucl. Med. Mol. Imaging 35, 1357–1366 (2008).

    Article  Google Scholar 

  65. 65.

    Hirono, N., Hashimoto, M., Ishii, K., Kazui, H. & Mori, E. One-year change in cerebral glucose metabolism in patients with Alzheimer’s disease. J. Neuropsychiatry Clin. Neurosci. 16, 488–492 (2004).

    CAS  Article  Google Scholar 

  66. 66.

    Langbaum, J. B. et al. Categorical and correlational analyses of baseline fluorodeoxyglucose positron emission tomography images from the Alzheimer’s disease neuroimaging initiative (ADNI). Neuroimage 45, 1107–1116 (2009).

    PubMed Central  Article  PubMed  Google Scholar 

  67. 67.

    McMurtray, A. M. et al. Positron emission tomography facilitates diagnosis of early-onset Alzheimer’s disease. Eur. Neurol. 59, 31–37 (2008).

    Article  Google Scholar 

  68. 68.

    Matias-Guiu, J. A. et al. Visual and statistical analysis of 18F-FDG PET in primary progressive aphasia. Eur. J. Nucl. Med. Mol. Imaging 42, 916–927 (2015).

    CAS  Article  Google Scholar 

  69. 69.

    Matias-Guiu, J. A. et al. Clustering analysis of FDG-PET imaging in primary progressive aphasia. Front. Aging Neurosci. 10, 230 (2018).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  70. 70.

    Cerami, C. et al. Different FDG-PET metabolic patterns at single-subject level in the behavioral variant of fronto-temporal dementia. Cortex 83, 101–112 (2016). Togther with McMurtray et al. (2008) and Matias-Guiu et al. (2015), these three papers outline the main patterns of glucose hypometabolism detected with 18F-FDG PET in frontotemporal dementia.

    Article  Google Scholar 

  71. 71.

    Villemagne, V. L. et al. Amyloid imaging with 18F-florbetaben in Alzheimer disease and other dementias. J. Nucl. Med. 52, 1210–1217 (2011).

    Article  Google Scholar 

  72. 72.

    Rowe, C. C. et al. Imaging β-amyloid burden in aging and dementia. Neurology 68, 1718–1725 (2007).

    CAS  Article  Google Scholar 

  73. 73.

    Rabinovici, G. D. et al. 11C-PIB PET imaging in Alzheimer disease and frontotemporal lobar degeneration. Neurology 68, 1205–1212 (2007). A case-series showing that the likelihood of amyloid-β positivity, as measured with amyloid PET, is low in individuals with frontotemporal dementia.

    CAS  Article  Google Scholar 

  74. 74.

    Tsai, R. M. et al. 18F-flortaucipir (AV-1451) tau PET in frontotemporal dementia syndromes. Alzheimers Res. Ther. 11, 13 (2019).

    PubMed Central  Article  PubMed  Google Scholar 

  75. 75.

    Smith, R. et al. 18F-Flortaucipir in TDP-43 associated frontotemporal dementia. Sci. Rep. 9, 6082 (2019). Together with Tsai et al. (2019), this study shows slightly elevated tau PET signal with 18F-AV-1451 in disease-typical regions across various FTD syndromes.

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  76. 76.

    Marquie, M. et al. Pathological correlations of [F-18]-AV-1451 imaging in non-alzheimer tauopathies. Ann. Neurol. 81, 117–128 (2017).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  77. 77.

    Lowe, V. J. et al. An autoradiographic evaluation of AV-1451 tau PET in dementia. Acta Neuropathol. Commun. 4, 58 (2016).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  78. 78.

    Aguero, C. et al. Autoradiography validation of novel tau PET tracer [F-18]-MK-6240 on human postmortem brain tissue. Acta Neuropathol. Commun. 7, 37 (2019).

    PubMed Central  Article  PubMed  Google Scholar 

  79. 79.

    Karageorgiou, E. & Miller, B. L. Frontotemporal lobar degeneration: a clinical approach. Semin. Neurol. 34, 189–201 (2014).

    Article  Google Scholar 

  80. 80.

    Olsson, B. et al. CSF and blood biomarkers for the diagnosis of Alzheimer’s disease: a systematic review and meta-analysis. Lancet Neurol. 15, 673–684 (2016). A key systematic review and meta-analysis of the diagnsotic capabilities of CSF and blood-based biomarkers for AD, which demonstrated that immunoassays for blood amyloid are of little value as diagnostic tools.

    CAS  Article  Google Scholar 

  81. 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. 11, 34 (2019).

    PubMed Central  Article  PubMed  Google Scholar 

  82. 82.

    Oeckl, P., Steinacker, P., Feneberg, E. & Otto, M. Neurochemical biomarkers in the diagnosis of frontotemporal lobar degeneration: an update. J. Neurochem. 138, 184–192 (2016).

    CAS  Article  Google Scholar 

  83. 83.

    Sjögren, M. et al. CSF levels of tau, β-amyloid1–42 and GAP-43 in frontotemporal dementia, other types of dementia and normal aging. J. Neural Transm. 107, 563–579 (2000).

    Article  Google Scholar 

  84. 84.

    van Steenoven, I. et al. Cerebrospinal fluid Alzheimer’s disease biomarkers across the spectrum of Lewy body diseases: results from a large multicenter cohort. J. Alzheimers Dis. 54, 287–295 (2016).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  85. 85.

    Andersson, M., Zetterberg, H., Minthon, L., Blennow, K. & Londos, E. The cognitive profile and CSF biomarkers in dementia with Lewy bodies and Parkinson’s disease dementia. Int. J. Geriatr. Psychiatry 26, 100–105 (2011).

    CAS  Article  Google Scholar 

  86. 86.

    Otto, M. et al. Elevated levels of tau-protein in cerebrospinal fluid of patients with Creutzfeldt-Jakob disease. Neurosci. Lett. 225, 210–212 (1997).

    CAS  Article  Google Scholar 

  87. 87.

    Riemenschneider, M. et al. Phospho-tau/total tau ratio in cerebrospinal fluid discriminates Creutzfeldt-Jakob disease from other dementias. Mol. Psychiatry 8, 343–347 (2003).

    CAS  Article  Google Scholar 

  88. 88.

    Buerger, K. et al. CSF tau protein phosphorylated at threonine 231 correlates with cognitive decline in MCI subjects. Neurology 59, 627–629 (2002).

    CAS  Article  Google Scholar 

  89. 89.

    Hampel, H. et al. Measurement of phosphorylated tau epitopes in the differential diagnosis of Alzheimer disease: a comparative cerebrospinal fluid study. Arch. Gen. Psychiatry 61, 95–102 (2004).

    CAS  Article  Google Scholar 

  90. 90.

    Cicognola, C. et al. Novel tau fragments in cerebrospinal fluid: relation to tangle pathology and cognitive decline in Alzheimer’s disease. Acta Neuropathol. 137, 279–296 (2019).

    CAS  Article  Google Scholar 

  91. 91.

    Hall, S. et al. Accuracy of a panel of 5 cerebrospinal fluid biomarkers in the differential diagnosis of patients with dementia and/or parkinsonian disorders. Arch. Neurol. 69, 1445–1452 (2012).

    Article  Google Scholar 

  92. 92.

    Wagshal, D. et al. Divergent CSF tau alterations in two common tauopathies: Alzheimer’s disease and progressive supranuclear palsy. J. Neurol. Neurosurg. Psychiatry 86, 244–250 (2015).

    Article  Google Scholar 

  93. 93.

    Sjögren, M. et al. Neurofilament protein in cerebrospinal fluid: a marker of white matter changes. J. Neurosci. Res. 66, 510–516 (2001).

    Article  Google Scholar 

  94. 94.

    Agren-Wilsson, A. et al. CSF biomarkers in the evaluation of idiopathic normal pressure hydrocephalus. Acta Neurol. Scand. 116, 333–339 (2007).

    CAS  Article  Google Scholar 

  95. 95.

    Sjögren, M. et al. Cytoskeleton proteins in CSF distinguish frontotemporal dementia from AD. Neurology 54, 1960–1964 (2000).

    Article  Google Scholar 

  96. 96.

    Steinacker, P. et al. Neurofilaments in blood and CSF for diagnosis and prediction of onset in Creutzfeldt-Jakob disease. Sci. Rep. 6, 38737 (2016).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  97. 97.

    Bech, S. et al. Amyloid-related biomarkers and axonal damage proteins in parkinsonian syndromes. Parkinsonism Relat. Disord. 18, 69–72 (2012).

    Article  Google Scholar 

  98. 98.

    Hansson, O. et al. Blood-based NfL: a biomarker for differential diagnosis of parkinsonian disorder. Neurology 88, 930–937 (2017). This was the first study to demonstrate that blood NfL can differentiate between PD and aytpical PD.

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  99. 99.

    Petzold, A. Neurofilament phosphoforms: surrogate markers for axonal injury, degeneration and loss. J. Neurol. Sci. 233, 183–198 (2005).

    CAS  Article  Google Scholar 

  100. 100.

    Kusnierova, P., Zeman, D., Hradilek, P., Cabal, M. & Zapletalova, O. Neurofilament levels in patients with neurological diseases: a comparison of neurofilament light and heavy chain levels. J. Clin. Lab. Anal. 33, e22948 (2019).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  101. 101.

    Benatar, M. et al. Neurofilaments in pre-symptomatic ALS and the impact of genotype. Amyotroph. Lateral Scler. Frontotemporal Degener. 20, 538–548 (2019).

    CAS  Article  Google Scholar 

  102. 102.

    Kuhle, J. et al. A comparative study of CSF neurofilament light and heavy chain protein in MS. Mult. Scler. 19, 1597–1603 (2013).

    Article  CAS  Google Scholar 

  103. 103.

    Eusebi, P. et al. Diagnostic utility of cerebrospinal fluid α-synuclein in Parkinson’s disease: a systematic review and meta-analysis. Mov. Disord. 32, 1389–1400 (2017).

    CAS  Article  Google Scholar 

  104. 104.

    Kasuga, K. et al. Differential levels of α-synuclein, β-amyloid42 and tau in CSF between patients with dementia with Lewy bodies and Alzheimer’s disease. J. Neurol. Neurosurg. Psychiatry 81, 608–610 (2010).

    Article  Google Scholar 

  105. 105.

    Gao, L. et al. Cerebrospinal fluid alpha-synuclein as a biomarker for Parkinson’s disease diagnosis: a systematic review and meta-analysis. Int. J. Neurosci. 125, 645–654 (2015).

    CAS  Article  Google Scholar 

  106. 106.

    Hansson, O. et al. Levels of cerebrospinal fluid α-synuclein oligomers are increased in Parkinson’s disease with dementia and dementia with Lewy bodies compared to Alzheimer’s disease. Alzheimers Res. Ther. 6, 25 (2014).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  107. 107.

    Groveman, B. R. et al. Rapid and ultra-sensitive quantitation of disease-associated α-synuclein seeds in brain and cerebrospinal fluid by αSyn RT-QuIC. Acta Neuropathologica Commun. 6, 7 (2018).

    Article  CAS  Google Scholar 

  108. 108.

    Fairfoul, G. et al. Alpha-synuclein RT-QuIC in the CSF of patients with alpha-synucleinopathies. Ann. Clin. Transl Neurol. 3, 812–818 (2016).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  109. 109.

    van Rumund, A. et al. α-Synuclein real-time quaking-induced conversion in the cerebrospinal fluid of uncertain cases of parkinsonism. Ann. Neurol. 85, 777–781 (2019).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  110. 110.

    Garrido, A. et al. α-synuclein RT-QuIC in cerebrospinal fluid of LRRK2-linked Parkinson’s disease. Ann. Clin. Transl Neurol. 6, 1024–1032 (2019).

    CAS  PubMed Central  PubMed  Google Scholar 

  111. 111.

    Bongianni, M. et al. α-Synuclein RT-QuIC assay in cerebrospinal fluid of patients with dementia with Lewy bodies. Ann. Clin. Transl Neurol. 6, 2120–2126 (2019).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  112. 112.

    Franceschini, A. et al. High diagnostic value of second generation CSF RT-QuIC across the wide spectrum of CJD prions. Sci. Rep. 7, 10655 (2017).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  113. 113.

    Kester, M. I. et al. Neurogranin as a cerebrospinal fluid biomarker for synaptic loss in symptomatic Alzheimer disease. JAMA Neurol. 72, 1275–1280 (2015).

    PubMed Central  Article  PubMed  Google Scholar 

  114. 114.

    Kvartsberg, H. et al. Cerebrospinal fluid levels of the synaptic protein neurogranin correlates with cognitive decline in prodromal Alzheimer’s disease. Alzheimers Dement. 11, 1180–1190 (2014).

    Article  Google Scholar 

  115. 115.

    Thorsell, A. et al. Neurogranin in cerebrospinal fluid as a marker of synaptic degeneration in Alzheimer’s disease. Brain Res. 1362, 13–22 (2010).

    CAS  Article  Google Scholar 

  116. 116.

    De Vos, A. et al. C-terminal neurogranin is increased in cerebrospinal fluid but unchanged in plasma in Alzheimer’s disease. Alzheimers Dement. 11, 1461–1469 (2015).

    Article  Google Scholar 

  117. 117.

    Portelius, E. et al. Cerebrospinal fluid neurogranin concentration in neurodegeneration: relation to clinical phenotypes and neuropathology. Acta Neuropathol. 136, 363–376 (2018).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  118. 118.

    Palmqvist, S. et al. Cerebrospinal fluid and plasma biomarker trajectories with increasing amyloid deposition in Alzheimer’s disease. EMBO Mol. Med. 11, e11170 (2019). This study was the first to use different platforms to investigate the changes in established plasma and CSF biomarkers during the development of AD, as defined by amyloid PET positivity, and provided evidence that some markers, including neurogranin, change early in the process of amyloid accumulation.

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  119. 119.

    Bereczki, E. et al. Synaptic proteins in CSF relate to Parkinson’s disease stage markers. NPJ Parkinsons Dis. 3, 7 (2017).

    PubMed Central  Article  PubMed  Google Scholar 

  120. 120.

    Hall, S. et al. Cerebrospinal fluid levels of neurogranin in Parkinsonian disorders. Mov. Disord. 35, 513–518 (2019).

    Article  CAS  Google Scholar 

  121. 121.

    Sandelius, A. et al. Elevated CSF GAP-43 is Alzheimer’s disease specific and associated with tau and amyloid pathology. Alzheimers Dement. 15, 55–64 (2019).

    Article  Google Scholar 

  122. 122.

    Ohrfelt, A. et al. The pre-synaptic vesicle protein synaptotagmin is a novel biomarker for Alzheimer’s disease. Alzheimers Res. Ther. 8, 41 (2016).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  123. 123.

    Brinkmalm, A. et al. SNAP-25 is a promising novel cerebrospinal fluid biomarker for synapse degeneration in Alzheimer’s disease. Mol. Neurodegener. 9, 53 (2014).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  124. 124.

    Heywood, W. E. et al. Identification of novel CSF biomarkers for neurodegeneration and their validation by a high-throughput multiplexed targeted proteomic assay. Mol. Neurodegener. 10, 64 (2015).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  125. 125.

    Shi, M. et al. Cerebrospinal fluid peptides as potential Parkinson disease biomarkers: a staged pipeline for discovery and validation. Mol. Cell Proteom. 14, 544–555 (2015).

    CAS  Article  Google Scholar 

  126. 126.

    Sjodin, S. et al. Mass spectrometric analysis of cerebrospinal fluid ubiquitin in Alzheimer’s disease and parkinsonian disorders. Proteom. Clin. Appl. 11, 1700100 (2017).

    Article  CAS  Google Scholar 

  127. 127.

    Abdi, F. et al. Detection of biomarkers with a multiplex quantitative proteomic platform in cerebrospinal fluid of patients with neurodegenerative disorders. J. Alzheimers Dis. 9, 293–348 (2006).

    CAS  Article  Google Scholar 

  128. 128.

    Zhang, J. et al. CSF multianalyte profile distinguishes Alzheimer and Parkinson diseases. Am. J. Clin. Pathol. 129, 526–529 (2008).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  129. 129.

    Plog, B. A. & Nedergaard, M. The glymphatic system in central nervous system health and disease: past, present, and future. Annu. Rev. Pathol. 13, 379–394 (2018).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  130. 130.

    Anderson, N. L. & Anderson, N. G. The humanplasma proteome: history, character, and diagnostic prospects. Mol. Cell Proteom. 1, 845–867 (2002).

    CAS  Article  Google Scholar 

  131. 131.

    Apweiler, R. et al. Approaching clinical proteomics: current state and future fields of application in fluid proteomics. Clin. Chem. Lab. Med. 47, 724–744 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  132. 132.

    Yoshimura, T. et al. Stability of pro-gastrin-releasing peptide in serum versus plasma. Tumour Biol. 29, 224–230 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  133. 133.

    Zetterberg, H. Review: Tau in biofluids – relation to pathology, imaging and clinical features. Neuropathol. Appl. Neurobiol. 43, 194–199 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  134. 134.

    Bolstad, N., Warren, D. J. & Nustad, K. Heterophilic antibody interference in immunometric assays. Best. Pract. Res. Clin. Endocrinol. Metab. 27, 647–661 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  135. 135.

    O’Bryant, S. E. et al. Comparing biological markers of Alzheimer’s disease across blood fraction and platforms: comparing apples to oranges. Alzheimers Dement. 3, 27–34 (2016).

    Google Scholar 

  136. 136.

    Hendricks, R. et al. Establishment of neurofilament light chain Simoa assay in cerebrospinal fluid and blood. Bioanalysis 11, 1405–1418 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  137. 137.

    Karikari, T. K., et al. Blood phosphorylated tau 181 as a biomarker for Alzheimer’s disease: a diagnostic performance and prediction modelling study using data from four prospective cohorts. Lancet Neurol. https://doi.org/10.1016/S1474-4422(20)30071-5 (in the press). The first study to use a Simoa for the detection of P-tau 181 in blood (serum and plasma), showing that blood P-tau 181 levels can be used to predict tau and Aβ pathology, differentiate AD from other neurodegenerative disorders, and identify AD throughout the clinical continuum in both primary care and specialist settings.

  138. 138.

    Li, D. & Mielke, M. M. An update on blood-based markers of Alzheimer’s disease using the SiMoA platform. Neurol. Ther. 8, 73–82 (2019).

    PubMed Central  Article  PubMed  Google Scholar 

  139. 139.

    Ashton, N. J. et al. A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer’s disease. Sci. Adv. 5, eaau7220 (2019).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  140. 140.

    Pannee, J. et al. A selected reaction monitoring (SRM)-based method for absolute quantification of Aβ38, Aβ40, and Aβ42 in cerebrospinal fluid of Alzheimer’s disease patients and healthy controls. J. Alzheimers Dis. 33, 1021–1032 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  141. 141.

    Hye, A. et al. Proteome-based plasma biomarkers for Alzheimer’s disease. Brain 129, 3042–3050 (2006). The first study to demonstrate that the proteomic profile of blood differs between individuals with dementia and aged-matched healthy controls.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  142. 142.

    Ray, S. et al. Classification and prediction of clinical Alzheimer’s diagnosis based on plasma signaling proteins. Nat. Med. 13, 1359–1362 (2007).

    CAS  Article  Google Scholar 

  143. 143.

    Hye, A. et al. Plasma proteins predict conversion to dementia from prodromal disease. Alzheimers Dement. 10, 799–807 (2014).

    PubMed Central  Article  PubMed  Google Scholar 

  144. 144.

    Thambisetty, M. et al. Plasma biomarkers of brain atrophy in Alzheimer’s disease. PLoS One 6, e28527 (2011).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  145. 145.

    Burnham, S. C. et al. A blood-based predictor for neocortical Aβ burden in Alzheimer’s disease: results from the AIBL study. Mol. Psychiatry 19, 519–526 (2014).

    CAS  Article  Google Scholar 

  146. 146.

    Ashton, N. J. et al. Blood protein predictors of brain amyloid for enrichment in clinical trials? Alzheimers Dement. 1, 48–60 (2015).

    Google Scholar 

  147. 147.

    Westwood, S. et al. Blood-based biomarker candidates of cerebral amyloid using PiB PET in non-demented elderly. J. Alzheimers Dis. 52, 561–572 (2016).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  148. 148.

    Westwood, S. et al. Plasma protein biomarkers for the prediction of CSF amyloid and Tau and [18F]-flutemetamol PET scan result. Front. Aging Neurosci. 10, 409 (2018).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  149. 149.

    Kiddle, S. J. et al. Plasma based markers of [11C] PiB-PET brain amyloid burden. PLoS One 7, e44260 (2012).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  150. 150.

    Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51, 404–413 (2019).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  151. 151.

    Zlokovic, B. V. et al. Glycoprotein 330/megalin: probable role in receptor-mediated transport of apolipoprotein J alone and in a complex with Alzheimer disease amyloid beta at the blood-brain and blood-cerebrospinal fluid barriers. Proc. Natl Acad. Sci. USA 93, 4229–4234 (1996).

    CAS  Article  Google Scholar 

  152. 152.

    Hansson, O. et al. Evaluation of plasma Aβ40 and Aβ42 as predictors of conversion to Alzheimer’s disease in patients with mild cognitive impairment. Neurobiol. Aging 31, 357–367 (2010).

    CAS  Article  Google Scholar 

  153. 153.

    Lachno, D. R. et al. Validation of a multiplex assay for simultaneous quantification of amyloid-β peptide species in human plasma with utility for measurements in studies of Alzheimer’s disease therapeutics. J. Alzheimers Dis. 32, 905–918 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  154. 154.

    Okereke, O. I. et al. Performance characteristics of plasma amyloid-β 40 and 42 assays. J. Alzheimers Dis. 16, 277–285 (2009).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  155. 155.

    Nakamura, A. et al. High performance plasma amyloid-β biomarkers for Alzheimer’s disease. Nature 554, 249–254 (2018). A key study that used a validated IP-MS method to measure the blood APP 699–711:Aβ 42 ratio and predict amyloid-β burden in individuals with AD.

    CAS  Article  Google Scholar 

  156. 156.

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

    PubMed Central  Article  PubMed  Google Scholar 

  157. 157.

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

    CAS  Google Scholar 

  158. 158.

    Janelidze, S. et al. Plasma β-amyloid in Alzheimer’s disease and vascular disease. Sci. Rep. 6, 26801 (2016). The first study to use the ultra-sensitive digital ELISA (Simoa) to show lower plasma Aβ 42 and Aβ 40 concentrations in patients with AD dementia than in those in other diagnostic groups.

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  159. 159.

    Palmqvist, S. et al. Performance of fully automated plasma assays as screening tests for Alzheimer disease-related β-amyloid status. JAMA Neurol. 76, 1060–1069 (2019). The first study to use a fully automated assay to succesfully measure plasma amyloid and predict amyloid status as defined by PET and CSF biomarkers.

    PubMed Central  Article  PubMed  Google Scholar 

  160. 160.

    Mattsson, N. et al. Plasma tau in Alzheimer disease. Neurology 87, 1827–1835 (2016). The largest study to examine plasma total tau in AD, demonstrating that blood total tau is not a reliable biomarker for dementia.

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  161. 161.

    Pase, M. P. et al. Assessment of plasma total tau level as a predictive biomarker for dementia and related endophenotypes. JAMA Neurol. 76, 598–606 (2019). The findings of this study provide evidence that plasma total tau levels can predict future dementia and could be used as a risk stratification tool in prevention trials.

    PubMed Central  Article  PubMed  Google Scholar 

  162. 162.

    Zetterberg, H. et al. Plasma tau levels in Alzheimer’s disease. Alzheimers Res. Ther. 5, 9 (2013).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  163. 163.

    Mielke, M. M. et al. Association of plasma total tau level with cognitive decline and risk of mild cognitive impairment or dementia in the Mayo Clinic study on aging. JAMA Neurol. 74, 1073–1080 (2017).

    PubMed Central  Article  PubMed  Google Scholar 

  164. 164.

    Mielke, M. M. et al. Plasma phospho-tau181 increases with Alzheimer’s disease clinical severity and is associated with tau- and amyloid-positron emission tomography. Alzheimers Dement. 14, 989–997 (2018). This study shows that plasma P-tau 181 levels, detected with an ECL method, are higher in individuals with AD dementia than in cognitively unimpaired individuals, and that plasma P-tau 181 is associated with positive tau PET and Aβ PET.

    PubMed Central  Article  PubMed  Google Scholar 

  165. 165.

    Thijssen, E. H. et al. Diagnostic value of plasma phosphorylated tau181 in Alzheimer’s disease and frontotemporal lobar degeneration. Nat. Med. 26, 387–397 (2020). Together with Janelidze et al. (2020), these two studies replicated the findings of Mielke et al. (2018) and showed that plasma P-tau 181 levels can be used to accurately distinguish between AD and non-AD neurodegenerative disorders.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  166. 166.

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  167. 167.

    Yang, C. C. et al. Assay of plasma phosphorylated tau protein (threonine 181) and total tau protein in early-stage Alzheimer’s disease. J. Alzheimers Dis. 61, 1323–1332 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  168. 168.

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

    PubMed Central  Article  PubMed  Google Scholar 

  169. 169.

    Lewczuk, P. et al. Plasma neurofilament light as a potential biomarker of neurodegeneration in Alzheimer’s disease. Alzheimers Res. Ther. 10, 71 (2018).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  170. 170.

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

    PubMed Central  Article  PubMed  Google Scholar 

  171. 171.

    Weston, P. S. J. et al. Serum neurofilament light in familial Alzheimer disease: a marker of early neurodegeneration. Neurology 89, 2167–2175 (2017).

    PubMed Central  Article  PubMed  Google Scholar 

  172. 172.

    Preische, O. et al. Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer’s disease. Nat. Med. 25, 277–283 (2019). Together with Weston et al. (2017), this study showed an increase in plasma NfL in individuals with familial AD many years before the onset of clinical symptoms.

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  173. 173.

    Ashton, N. J. et al. Increased plasma neurofilament light chain concentration correlates with severity of post-mortem neurofibrillary tangle pathology and neurodegeneration. Acta Neuropathol. Commun. 7, 5 (2019).

    PubMed Central  Article  PubMed  Google Scholar 

  174. 174.

    Benedet, A. L. et al. Plasma neurofilament light associates with Alzheimer’s disease metabolic decline in amyloid-positive individuals. Alzheimers Dement. 11, 679–689 (2019).

    Google Scholar 

  175. 175.

    Benedet, A. L. et al. Associations between plasma NFL and brain PET in the Alzheimer’s disease [abstract IC-P-070]. Alzheimers Dement. 15, P64–P65 (2019).

    Article  Google Scholar 

  176. 176.

    Strydom, A. et al. Neurofilament light as a blood biomarker for neurodegeneration in Down syndrome. Alzheimers Res. Ther. 10, 39 (2018).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  177. 177.

    Fortea, J. et al. Plasma and CSF biomarkers for the diagnosis of Alzheimer’s disease in adults with down syndrome: a cross-sectional study. Lancet Neurol. 17, 860–869 (2018).

    CAS  Article  Google Scholar 

  178. 178.

    Kvartsberg, H. et al. Characterization of the postsynaptic protein neurogranin in paired cerebrospinal fluid and plasma samples from Alzheimer’s disease patients and healthy controls. Alzheimers Res. Ther. 7, 40 (2015).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  179. 179.

    Lin, C. H. et al. Plasma biomarkers differentiate Parkinson’s disease from atypical Parkinsonism syndromes. Front. Aging Neurosci. 10, 123 (2018). The results of this study suggest that using a combination of plasma biomarkers (α-synuclein, T-tau, P-tau 181, and Aβ 42) improves the differential diagnosis of PD from aytpical PD, Lewy body dementias and frontotemporal dementias.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  180. 180.

    Uhlén, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

    Article  CAS  Google Scholar 

  181. 181.

    Ashton, N. J. et al. No association of salivary total tau concentration with Alzheimer’s disease. Neurobiol. Aging 70, 125–127 (2018).

    CAS  Article  Google Scholar 

  182. 182.

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

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  183. 183.

    Noguchi-Shinohara, M., Hamaguchi, T., Nozaki, I., Sakai, K. & Yamada, M. Serum tau protein as a marker for the diagnosis of Creutzfeldt-Jakob disease. J. Neurol. 258, 1464–1468 (2011).

    CAS  Article  Google Scholar 

  184. 184.

    Thompson, A. G. B. et al. Neurofilament light chain and tau concentrations are markedly increased in the serum of patients with sporadic Creutzfeldt-Jakob disease, and tau correlates with rate of disease progression. J. Neurol. Neurosurg. Psychiatry 89, 955–961 (2018).

    PubMed Central  Article  PubMed  Google Scholar 

  185. 185.

    Yang, S. Y. et al. Analytical performance of reagent for assaying tau protein in human plasma and feasibility study screening neurodegenerative diseases. Sci. Rep. 7, 9304 (2017).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  186. 186.

    Foiani, M. S. et al. Plasma tau is increased in frontotemporal dementia. J. Neurol. Neurosurg. Psychiatry 89, 804–807 (2018). This study showed that plasma tau levels are increased in individuals with FTD of all clinical groups; of the genetic subtypes of the disease only individuals with MAPT mutations showed increased levels of plasma tau.

    PubMed Central  Article  PubMed  Google Scholar 

  187. 187.

    Randall, J. et al. Tau proteins in serum predict neurological outcome after hypoxic brain injury from cardiac arrest: results of a pilot study. Resuscitation 84, 351–356 (2013).

    CAS  Article  Google Scholar 

  188. 188.

    Shahim, P. et al. Blood biomarkers for brain injury in concussed professional ice hockey players. JAMA Neurol. 71, 684–692 (2014).

    Article  Google Scholar 

  189. 189.

    Evered, L., Silbert, B., Scott, D. A., Zetterberg, H. & Blennow, K. Association of changes in plasma neurofilament light and tau levels with anesthesia and surgery: results from the CAPACITY and ARCADIAN studies. JAMA Neurol. 75, 542–547 (2018).

    PubMed Central  Article  PubMed  Google Scholar 

  190. 190.

    Chen, Z. et al. Learnings about the complexity of extracellular tau aid development of a blood-based screen for Alzheimer’s disease. Alzheimers Dement. 15, 487–496 (2018).

    PubMed Central  Article  PubMed  Google Scholar 

  191. 191.

    Tatebe, H. et al. Quantification of plasma phosphorylated tau to use as a biomarker for brain Alzheimer pathology: pilot case-control studies including patients with Alzheimer’s disease and Down syndrome. Mol. Neurodegener. 12, 63 (2017).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  192. 192.

    Disanto, G. et al. Serum neurofilament light: a biomarker of neuronal damage in multiple sclerosis. Ann. Neurol. 81, 857–870 (2017).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  193. 193.

    Piehl, F. et al. Plasma neurofilament light chain levels in patients with MS switching from injectable therapies to fingolimod. Mult. Scler. 24, 1046–1054 (2018).

    CAS  Article  Google Scholar 

  194. 194.

    Wilke, C. et al. Neurofilament light chain in FTD is elevated not only in cerebrospinal fluid, but also in serum. J. Neurol. Neurosurg. Psychiatry 87, 1270–1272 (2016).

    Article  Google Scholar 

  195. 195.

    Novakova, L. et al. Monitoring disease activity in multiple sclerosis using serum neurofilament light protein. Neurology 89, 2230–2237 (2017).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  196. 196.

    Gisslen, M. et al. Plasma concentration of the neurofilament light protein (NFL) is a biomarker of CNS injury in HIV infection: a cross-sectional study. EBioMedicine 3, 135–140 (2016).

    Article  Google Scholar 

  197. 197.

    Gaetani, L. et al. Neurofilament light chain as a biomarker in neurological disorders. J. Neurol. Neurosurg. Psychiatry 90, 870–881 (2019).

    Article  Google Scholar 

  198. 198.

    Rojas, J. C. et al. CSF neurofilament light chain and phosphorylated tau 181 predict disease progression in PSP. Neurology 90, e273–e281 (2018).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  199. 199.

    Bischof, A. et al. Serum neurofilament light chain: a biomarker of neuronal injury in vasculitic neuropathy. Ann. Rheum. Dis. 77, 1093–1094 (2018).

    CAS  Article  Google Scholar 

  200. 200.

    Sandelius, A. et al. Plasma neurofilament light chain concentration in the inherited peripheral neuropathies. Neurology 90, e518–e524 (2018).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  201. 201.

    Bjornevik, K. et al. Serum neurofilament light chain levels in patients with presymptomatic multiple sclerosis. JAMA Neurol. 77, 58–64 (2019).

    Article  Google Scholar 

  202. 202.

    Shahim, P., Zetterberg, H., Tegner, Y. & Blennow, K. Serum neurofilament light as a biomarker for mild traumatic brain injury in contact sports. Neurology 88, 1788–1794 (2017).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  203. 203.

    Gattringer, T. et al. Serum neurofilament light is sensitive to active cerebral small vessel disease. Neurology 89, 2108–2114 (2017).

    PubMed Central  Article  PubMed  Google Scholar 

  204. 204.

    Verde, F. et al. Neurofilament light chain in serum for the diagnosis of amyotrophic lateral sclerosis. J. Neurol. Neurosurg. Psychiatry 90, 157–164 (2019).

    Article  Google Scholar 

  205. 205.

    Meeter, L. H. et al. Neurofilament light chain: a biomarker for genetic frontotemporal dementia. Ann. Clin. Transl Neurol. 3, 623–636 (2016).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  206. 206.

    Rohrer, J. D. et al. Serum neurofilament light chain protein is a measure of disease intensity in frontotemporal dementia. Neurology 87, 1329–1336 (2016).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  207. 207.

    Rojas, J. C. et al. Plasma neurofilament light chain predicts progression in progressive supranuclear palsy. Ann. Clin. Transl Neurol. 3, 216–225 (2016). Together with Rohrer et al. (2016), this study demonstrates that high blood NfL levels are predictive of more severe outcomes in FTD and PSP.

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  208. 208.

    Donker Kaat, L. et al. Serum neurofilament light chain in progressive supranuclear palsy. Parkinsonism Relat. Disord. 56, 98–101 (2018).

    Article  Google Scholar 

  209. 209.

    Wilke, C. et al. Correlations between serum and CSF pNfH levels in ALS, FTD and controls: a comparison of three analytical approaches. Clin. Chem. Lab. Med. 57, 1556–1564 (2019).

    CAS  Article  Google Scholar 

  210. 210.

    De Schaepdryver, M. et al. Comparison of elevated phosphorylated neurofilament heavy chains in serum and cerebrospinal fluid of patients with amyotrophic lateral sclerosis. J. Neurol. Neurosurg. Psychiatry 89, 367–373 (2018).

    Article  Google Scholar 

  211. 211.

    Feneberg, E. et al. Multicenter evaluation of neurofilaments in early symptom onset amyotrophic lateral sclerosis. Neurology 90, e22–e30 (2018).

    CAS  Article  Google Scholar 

  212. 212.

    Li, D., Shen, D., Tai, H. & Cui, L. Neurofilaments in CSF as diagnostic biomarkers in motor neuron disease: a meta-analysis. Front. Aging Neurosci. 8, 290 (2016).

    PubMed Central  PubMed  Google Scholar 

  213. 213.

    Koel-Simmelink, M. J. et al. The impact of pre-analytical variables on the stability of neurofilament proteins in CSF, determined by a novel validated SinglePlex Luminex assay and ELISA. J. Immunol. Methods 402, 43–49 (2014).

    CAS  Article  Google Scholar 

  214. 214.

    Schaap, F. G., Binas, B., Danneberg, H., van der Vusse, G. J. & Glatz, J. F. Impaired long-chain fatty acid utilization by cardiac myocytes isolated from mice lacking the heart-type fatty acid binding protein gene. Circ. Res. 85, 329–337 (1999).

    CAS  Article  Google Scholar 

  215. 215.

    Kurtz, A. et al. The expression pattern of a novel gene encoding brain-fatty acid binding protein correlates with neuronal and glial cell development. Development 120, 2637–2649 (1994).

    CAS  Google Scholar 

  216. 216.

    Olsson, B. et al. Cerebrospinal fluid levels of heart fatty acid binding protein are elevated prodromally in Alzheimer’s disease and vascular dementia. J. Alzheimers Dis. 34, 673–679 (2013).

    CAS  Article  Google Scholar 

  217. 217.

    Desikan, R. S. et al. Heart fatty acid binding protein and Aβ-associated Alzheimer’s neurodegeneration. Mol. Neurodegener. 8, 39 (2013).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  218. 218.

    Cheon, M. S., Kim, S. H., Fountoulakis, M. & Lubec, G. Heart type fatty acid binding protein (H-FABP) is decreased in brains of patients with Down syndrome and Alzheimer’s disease. J. Neural Transm. Suppl. 67, 225–234 (2003).

    CAS  Article  Google Scholar 

  219. 219.

    Teunissen, C. E. et al. Brain-specific fatty acid-binding protein is elevated in serum of patients with dementia-related diseases. Eur. J. Neurol. 18, 865–871 (2011).

    CAS  Article  Google Scholar 

  220. 220.

    Guillaume, E., Zimmermann, C., Burkhard, P. R., Hochstrasser, D. F. & Sanchez, J. C. A potential cerebrospinal fluid and plasmatic marker for the diagnosis of Creutzfeldt-Jakob disease. Proteomics 3, 1495–1499 (2003).

    CAS  Article  Google Scholar 

  221. 221.

    Steinacker, P. et al. Heart fatty acid binding protein as a potential diagnostic marker for neurodegenerative diseases. Neurosci. Lett. 370, 36–39 (2004).

    CAS  Article  Google Scholar 

  222. 222.

    Mollenhauer, B. et al. Serum heart-type fatty acid-binding protein and cerebrospinal fluid tau: marker candidates for dementia with Lewy bodies. Neurodegener. Dis. 4, 366–375 (2007).

    CAS  Article  Google Scholar 

  223. 223.

    Wada-Isoe, K., Imamura, K., Kitamaya, M., Kowa, H. & Nakashima, K. Serum heart-fatty acid binding protein levels in patients with Lewy body disease. J. Neurol. Sci. 266, 20–24 (2008).

    CAS  Article  Google Scholar 

  224. 224.

    Malek, N. et al. Alpha-synuclein in peripheral tissues and body fluids as a biomarker for Parkinson’s disease - a systematic review. Acta Neurol. Scand. 130, 59–72 (2014).

    CAS  Article  Google Scholar 

  225. 225.

    Foulds, P. G. et al. A longitudinal study on α-synuclein in blood plasma as a biomarker for Parkinson’s disease. Sci. Rep. 3, 2540 (2013).

    PubMed Central  Article  PubMed  Google Scholar 

  226. 226.

    Ishii, R. et al. Decrease in plasma levels of α-synuclein is evident in patients with Parkinson’s disease after elimination of heterophilic antibody interference. PLoS One 10, e0123162 (2015).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  227. 227.

    Malec-Litwinowicz, M. et al. The relation between plasma α-synuclein level and clinical symptoms or signs of Parkinson’s disease. Neurol. Neurochir. Pol. 52, 243–251 (2018).

    Article  Google Scholar 

  228. 228.

    Williams, S. M., Schulz, P. & Sierks, M. R. Oligomeric α-synuclein and β-amyloid variants as potential biomarkers for Parkinson’s and Alzheimer’s diseases. Eur. J. Neurosci. 43, 3–16 (2016).

    Article  Google Scholar 

  229. 229.

    Daniele, S. et al. α-Synuclein heterocomplexes with β-amyloid are increased in red blood cells of Parkinson’s disease patients and correlate with disease severity. Front. Mol. Neurosci. 11, 53 (2018).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  230. 230.

    Wang, X., Yu, S., Li, F. & Feng, T. Detection of α-synuclein oligomers in red blood cells as a potential biomarker of Parkinson’s disease. Neurosci. Lett. 599, 115–119 (2015).

    CAS  Article  Google Scholar 

  231. 231.

    Zhao, H. Q., Li, F. F., Wang, Z., Wang, X. M. & Feng, T. A comparative study of the amount of α-synuclein in ischemic stroke and Parkinson’s disease. Neurol. Sci. 37, 749–754 (2016).

    Article  Google Scholar 

  232. 232.

    Vicente Miranda, H. et al. Posttranslational modifications of blood-derived alpha-synuclein as biochemical markers for Parkinson’s disease. Sci. Rep. 7, 13713 (2017).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  233. 233.

    Lin, C. H. et al. Plasma α-synuclein predicts cognitive decline in Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 88, 818–824 (2017). The results of this study suggest that plasma α-synuclein concentrations correlate with cognitive decline but not with severity of motor symptoms in patients with PD.

    PubMed Central  Article  PubMed  Google Scholar 

  234. 234.

    Muramori, F., Kobayashi, K. & Nakamura, I. A quantitative study of neurofibrillary tangles, senile plaques and astrocytes in the hippocampal subdivisions and entorhinal cortex in Alzheimer’s disease, normal controls and non-Alzheimer neuropsychiatric diseases. Psychiatry Clin. Neurosci. 52, 593–599 (1998).

    CAS  Article  Google Scholar 

  235. 235.

    Umoh, M. E. et al. A proteomic network approach across the ALS-FTD disease spectrum resolves clinical phenotypes and genetic vulnerability in human brain. EMBO Mol. Med. 10, 48–62 (2018).

    CAS  Article  Google Scholar 

  236. 236.

    Foerch, C. et al. Diagnostic accuracy of plasma glial fibrillary acidic protein for differentiating intracerebral hemorrhage and cerebral ischemia in patients with symptoms of acute stroke. Clin. Chem. 58, 237–245 (2012).

    CAS  Article  Google Scholar 

  237. 237.

    Yue, J. K. et al. Association between plasma GFAP concentrations and MRI abnormalities in patients with CT-negative traumatic brain injury in the TRACK-TBI cohort: a prospective multicentre study. Lancet Neurol. 18, 953–961 (2019).

    CAS  Article  Google Scholar 

  238. 238.

    Heller, C. et al. Plasma glial fibrillary acidic protein is raised in progranulin-associated frontotemporal dementia. J. Neurol. Neurosurg. Psychiatry 91, 263–270 (2020). This study showed that increased GFAP concentrations seem to be specific to GRN-related FTD and increase before symptom onset.

    Article  Google Scholar 

  239. 239.

    Vagberg, M. et al. Levels and age dependency of neurofilament light and glial fibrillary acidic protein in healthy individuals and their relation to the brain parenchymal fraction. PLoS One 10, e0135886 (2015).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  240. 240.

    Oeckl, P. et al. Glial fibrillary acidic protein in serum is increased in Alzheimer’s disease and correlates with cognitive impairment. J. Alzheimers Dis. 67, 481–488 (2019).

    CAS  Article  Google Scholar 

  241. 241.

    Zetterberg, H., van Swieten, J. C., Boxer, A. L. & Rohrer, J. D. Review: fluid biomarkers for frontotemporal dementias. Neuropathol. Appl. Neurobiol. 45, 81–87 (2019).

    CAS  Article  Google Scholar 

  242. 242.

    Feneberg, E. et al. Limited role of free TDP-43 as a diagnostic tool in neurodegenerative diseases. Amyotroph. Lateral Scler. Frontotemporal Degener. 15, 351–356 (2014).

    CAS  Article  Google Scholar 

  243. 243.

    Steinacker, P. et al. TDP-43 in cerebrospinal fluid of patients with frontotemporal lobar degeneration and amyotrophic lateral sclerosis. Arch. Neurol. 65, 1481–1487 (2008).

    PubMed Central  Article  PubMed  Google Scholar 

  244. 244.

    Foulds, P. et al. TDP-43 protein in plasma may index TDP-43 brain pathology in Alzheimer’s disease and frontotemporal lobar degeneration. Acta Neuropathol. 116, 141–146 (2008).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  245. 245.

    Foulds, P. G. et al. Plasma phosphorylated-TDP-43 protein levels correlate with brain pathology in frontotemporal lobar degeneration. Acta Neuropathol. 118, 647–658 (2009).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  246. 246.

    Suarez-Calvet, M. et al. Plasma phosphorylated TDP-43 levels are elevated in patients with frontotemporal dementia carrying a C9orf72 repeat expansion or a GRN mutation. J. Neurol. Neurosurg. Psychiatry 85, 684–691 (2014).

    Article  Google Scholar 

  247. 247.

    Verstraete, E. et al. TDP-43 plasma levels are higher in amyotrophic lateral sclerosis. Amyotroph. Lateral Scler. 13, 446–451 (2012).

    CAS  Article  Google Scholar 

  248. 248.

    Lee, E. B., Lee, V. M. & Trojanowski, J. Q. Gains or losses: molecular mechanisms of TDP43-mediated neurodegeneration. Nat. Rev. Neurosci. 13, 38–50 (2011).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  249. 249.

    Sheta, E. A., Appel, S. H. & Goldknopf, I. L. 2D gel blood serum biomarkers reveal differential clinical proteomics of the neurodegenerative diseases. Expert. Rev. Proteom. 3, 45–62 (2006).

    CAS  Article  Google Scholar 

  250. 250.

    Zhang, X. et al. Quantitative proteomic analysis of serum proteins in patients with Parkinson’s disease using an isobaric tag for relative and absolute quantification labeling, two-dimensional liquid chromatography, and tandem mass spectrometry. Analyst 137, 490–495 (2012).

    CAS  Article  Google Scholar 

  251. 251.

    Chen, H. M., Lin, C. Y. & Wang, V. Amyloid P component as a plasma marker for Parkinson’s disease identified by a proteomic approach. Clin. Biochem. 44, 377–385 (2011).

    CAS  Article  Google Scholar 

  252. 252.

    O’Bryant, S. E. et al. A proteomic signature for dementia with Lewy bodies. Alzheimers Dement. 11, 270–276 (2019). This study provided evidence of the potential utility of a multi-tiered blood-based proteomic screening method for detecting DLB and distinguishing DLB from PD.

    Google Scholar 

  253. 253.

    King, E. et al. Peripheral inflammation in prodromal Alzheimer’s and Lewy body dementias. J. Neurol. Neurosurg. Psychiatry 89, 339–345 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  254. 254.

    Simpson, R. J., Kalra, H. & Mathivanan, S. ExoCarta as a resource for exosomal research. J. Extracell. Vesicles 1, 18374 (2012).

    CAS  Article  Google Scholar 

  255. 255.

    Jan, A. T. et al. Perspective insights of exosomes in neurodegenerative diseases: a critical appraisal. Front. Aging Neurosci. 9, 317 (2017).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  256. 256.

    Candelario, K. M. & Steindler, D. A. The role of extracellular vesicles in the progression of neurodegenerative disease and cancer. Trends Mol. Med. 20, 368–374 (2014).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  257. 257.

    Thompson, A. G. et al. Extracellular vesicles in neurodegenerative disease – pathogenesis to biomarkers. Nat. Rev. Neurol. 12, 346–357 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  258. 258.

    Winston, C. N. et al. Prediction of conversion from mild cognitive impairment to dementia with neuronally derived blood exosome protein profile. Alzheimers Dement. 3, 63–72 (2016).

    Google Scholar 

  259. 259.

    Abner, E. L., Jicha, G. A., Shaw, L. M., Trojanowski, J. Q. & Goetzl, E. J. Plasma neuronal exosomal levels of Alzheimer’s disease biomarkers in normal aging. Ann. Clin. Transl Neurol. 3, 399–403 (2016).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  260. 260.

    Goetzl, E. J. et al. Low neural exosomal levels of cellular survival factors in Alzheimer’s disease. Ann. Clin. Transl Neurol. 2, 769–773 (2015).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  261. 261.

    Kapogiannis, D. et al. Dysfunctionally phosphorylated type 1 insulin receptor substrate in neural-derived blood exosomes of preclinical Alzheimer’s disease. FASEB J. 29, 589–596 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  262. 262.

    Fiandaca, M. S. et al. Identification of preclinical Alzheimer’s disease by a profile of pathogenic proteins in neurally derived blood exosomes: a case-control study. Alzheimers Dement. 11, 600–607 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  263. 263.

    Goetzl, E. J. et al. Cargo proteins of plasma astrocyte-derived exosomes in Alzheimer’s disease. FASEB J. 30, 3853–3859 (2016).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  264. 264.

    Zhao, Z. H. et al. Increased DJ-1 and α-synuclein in plasma neural-derived exosomes as potential markers for Parkinson’s disease. Front. Aging Neurosci. 10, 438 (2018).

    CAS  Article  Google Scholar 

  265. 265.

    Shi, M. et al. Plasma exosomal α-synuclein is likely CNS-derived and increased in Parkinson’s disease. Acta Neuropathol. 128, 639–650 (2014).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  266. 266.

    Goetzl, E. J. et al. Decreased synaptic proteins in neuronal exosomes of frontotemporal dementia and Alzheimer’s disease. FASEB J. 30, 4141–4148 (2016).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  267. 267.

    Athauda, D. et al. Utility of neuronal-derived exosomes to examine molecular mechanisms that affect motor function in patients with Parkinson disease: a secondary analysis of the exenatide-PD trial. JAMA Neurol. 76, 420–429 (2019).

    PubMed Central  Article  PubMed  Google Scholar 

  268. 268.

    Zarovni, N. et al. Integrated isolation and quantitative analysis of exosome shuttled proteins and nucleic acids using immunocapture approaches. Methods 87, 46–58 (2015).

    CAS  Article  Google Scholar 

  269. 269.

    Tauro, B. J. et al. Comparison of ultracentrifugation, density gradient separation, and immunoaffinity capture methods for isolating human colon cancer cell line LIM1863-derived exosomes. Methods 56, 293–304 (2012).

    CAS  Article  Google Scholar 

  270. 270.

    Zhang, J. et al. Exosome and exosomal microRNA: trafficking, sorting, and function. Genomics Proteom. Bioinforma. 13, 17–24 (2015).

    CAS  Article  Google Scholar 

  271. 271.

    Sheinerman, K. S. et al. Circulating brain-enriched microRNAs as novel biomarkers for detection and differentiation of neurodegenerative diseases. Alzheimers Res. Ther. 9, 89 (2017).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  272. 272.

    Viswambharan, V., Thanseem, I., Vasu, M. M., Poovathinal, S. A. & Anitha, A. miRNAs as biomarkers of neurodegenerative disorders. Biomark. Med. 11, 151–167 (2017).

    CAS  Article  Google Scholar 

  273. 273.

    Bhatnagar, S. et al. Increased microRNA-34c abundance in Alzheimer’s disease circulating blood plasma. Front. Mol. Neurosci. 7, 2 (2014).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  274. 274.

    Tan, L. et al. Circulating miR-125b as a biomarker of Alzheimer’s disease. J. Neurol. Sci. 336, 52–56 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  275. 275.

    Kumar, P. et al. Circulating miRNA biomarkers for Alzheimer’s disease. PLoS One 8, e69807 (2013).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  276. 276.

    Leidinger, P. et al. A blood based 12-miRNA signature of Alzheimer disease patients. Genome Biol. 14, R78 (2013).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  277. 277.

    Swarbrick, S., Wragg, N., Ghosh, S. & Stolzing, A. Systematic review of miRNA as biomarkers in Alzheimer’s disease. Mol. Neurobiol. 56, 6156–6167 (2019).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  278. 278.

    Funahashi, Y. et al. DNA methylation changes at SNCA intron 1 in patients with dementia with Lewy bodies. Psychiatry Clin. Neurosci. 71, 28–35 (2017).

    CAS  Article  Google Scholar 

  279. 279.

    Salemi, M. et al. Reduced mitochondrial mRNA expression in dementia with Lewy bodies. J. Neurol. Sci. 380, 122–123 (2017).

    CAS  Article  Google Scholar 

  280. 280.

    Fernandez-Santiago, R. et al. MicroRNA association with synucleinopathy conversion in rapid eye movement behavior disorder. Ann. Neurol. 77, 895–901 (2015).

    CAS  Article  Google Scholar 

  281. 281.

    Shamir, R. et al. Analysis of blood-based gene expression in idiopathic Parkinson disease. Neurology 89, 1676–1683 (2017).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  282. 282.

    Sørensen, S. S., Nygaard, A. B. & Christensen, T. miRNA expression profiles in cerebrospinal fluid and blood of patients with Alzheimer’s disease and other types of dementia – an exploratory study. Transl Neurodegener. 5, 6 (2016).

    PubMed Central  Article  PubMed  Google Scholar 

  283. 283.

    Snowden, S., Dahlen, S. E. & Wheelock, C. E. Application of metabolomics approaches to the study of respiratory diseases. Bioanalysis 4, 2265–2290 (2012).

    CAS  Article  Google Scholar 

  284. 284.

    Han, X. et al. Metabolomics in early Alzheimer’s disease: identification of altered plasma sphingolipidome using shotgun lipidomics. PLoS One 6, e21643 (2011).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  285. 285.

    Oresic, M. et al. Metabolome in progression to Alzheimer’s disease. Transl Psychiatry 1, e57 (2011).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  286. 286.

    Trushina, E., Dutta, T., Persson, X. M., Mielke, M. M. & Petersen, R. C. Identification of altered metabolic pathways in plasma and CSF in mild cognitive impairment and Alzheimer’s disease using metabolomics. PLoS One 8, e63644 (2013).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  287. 287.

    Kaddurah-Daouk, R. et al. Metabolomic changes in autopsy-confirmed Alzheimer’s disease. Alzheimers Dement. 7, 309–317 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  288. 288.

    Mapstone, M. et al. Plasma phospholipids identify antecedent memory impairment in older adults. Nat. Med. 20, 415–418 (2014).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  289. 289.

    Fiandaca, M. S. et al. Potential metabolomic linkage in blood between Parkinson’s disease and traumatic brain injury. Metabolites 8, 50 (2018).

    PubMed Central  Article  CAS  Google Scholar 

  290. 290.

    Han, W., Sapkota, S., Camicioli, R., Dixon, R. A. & Li, L. Profiling novel metabolic biomarkers for Parkinson’s disease using in-depth metabolomic analysis. Mov. Disord. 32, 1720–1728 (2017).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  291. 291.

    Hatano, T., Saiki, S., Okuzumi, A., Mohney, R. P. & Hattori, N. Identification of novel biomarkers for Parkinson’s disease by metabolomic technologies. J. Neurol. Neurosurg. Psychiatry 87, 295–301 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  292. 292.

    Trupp, M. et al. Metabolite and peptide levels in plasma and CSF differentiating healthy controls from patients with newly diagnosed Parkinson’s disease. J. Parkinsons Dis. 4, 549–560 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  293. 293.

    Wuolikainen, A. et al. Multi-platform mass spectrometry analysis of the CSF and plasma metabolomes of rigorously matched amyotrophic lateral sclerosis, Parkinson’s disease and control subjects. Mol. Biosyst. 12, 1287–1298 (2016). This study identified increased CSF leucine, isoleucine and ketoleucine as markers for PD and ALS, and identified CSF glucose, creatine, creatinine and α-hydroxybutyrate as specific markers for ALS.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  294. 294.

    Nagesh Babu, G. et al. Serum metabolomics study in a group of Parkinson’s disease patients from northern India. Clin. Chim. Acta 480, 214–219 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  295. 295.

    Bogdanov, M. et al. Metabolomic profiling to develop blood biomarkers for Parkinson’s disease. Brain 131, 389–396 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  296. 296.

    Chang, K. H. et al. Alternations of metabolic profile and kynurenine metabolism in the plasma of Parkinson’s disease. Mol. Neurobiol. 55, 6319–6328 (2018).

    CAS  Article  Google Scholar 

  297. 297.

    Zhao, H. et al. Potential biomarkers of Parkinson’s disease revealed by plasma metabolic profiling. J. Chromatogr. B 1081–1082, 101–108 (2018).

    Article  CAS  Google Scholar 

  298. 298.

    Stoessel, D. et al. Promising metabolite profiles in the plasma and CSF of early clinical Parkinson’s disease. Front. Aging Neurosci. 10, 51 (2018). The first study to provide evidence that metabolomics approaches could be used for early diagnosis of PD.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  299. 299.

    Weisskopf, M. G., O’Reilly, E., Chen, H., Schwarzschild, M. A. & Ascherio, A. Plasma urate and risk of Parkinson’s disease. Am. J. Epidemiol. 166, 561–567 (2007).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  300. 300.

    Annanmaki, T., Pessala-Driver, A., Hokkanen, L. & Murros, K. Uric acid associates with cognition in Parkinson’s disease. Parkinsonism Relat. Disord. 14, 576–578 (2008).

    Article  Google Scholar 

  301. 301.

    Okuda, S., Nishiyama, N., Saito, H. & Katsuki, H. 3-Hydroxykynurenine, an endogenous oxidative stress generator, causes neuronal cell death with apoptotic features and region selectivity. J. Neurochem. 70, 299–307 (1998).

    CAS  Article  Google Scholar 

  302. 302.

    Pearson, S. J. & Reynolds, G. P. Increased brain concentrations of a neurotoxin, 3-hydroxykynurenine, in Huntington’s disease. Neurosci. Lett. 144, 199–201 (1992).

    CAS  Article  Google Scholar 

  303. 303.

    Perez-De La Cruz, V., Carrillo-Mora, P. & Santamaria, A. Quinolinic acid, an endogenous molecule combining excitotoxicity, oxidative stress and other toxic mechanisms. Int. J. Tryptophan Res. 5, 1–8 (2012).

    CAS  PubMed Central  PubMed  Google Scholar 

  304. 304.

    Braidy, N., Grant, R., Adams, S. & Guillemin, G. J. Neuroprotective effects of naturally occurring polyphenols on quinolinic acid-induced excitotoxicity in human neurons. FEBS J. 277, 368–382 (2010).

    CAS  Article  Google Scholar 

  305. 305.

    Lewitt, P. A. et al. 3-hydroxykynurenine and other Parkinson’s disease biomarkers discovered by metabolomic analysis. Mov. Disord. 28, 1653–1660 (2013).

    CAS  Article  Google Scholar 

  306. 306.

    Schwarz, M. J., Guillemin, G. J., Teipel, S. J., Buerger, K. & Hampel, H. Increased 3-hydroxykynurenine serum concentrations differentiate Alzheimer’s disease patients from controls. Eur. Arch. Psychiatry Clin. Neurosci. 263, 345–352 (2013).

    Article  Google Scholar 

  307. 307.

    Gulaj, E., Pawlak, K., Bien, B. & Pawlak, D. Kynurenine and its metabolites in Alzheimer’s disease patients. Adv. Med. Sci. 55, 204–211 (2010).

    CAS  Article  Google Scholar 

  308. 308.

    Sleeman, I. et al. Urate and homocysteine: predicting motor and cognitive changes in newly diagnosed Parkinson’s disease. J. Parkinsons Dis. 9, 351–359 (2019).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  309. 309.

    Gunnarsson, M. et al. Axonal damage in relapsing multiple sclerosis is markedly reduced by natalizumab. Ann. Neurol. 69, 83–89 (2011).

    CAS  Article  Google Scholar 

  310. 310.

    Khalil, M. et al. Neurofilaments as biomarkers in neurological disorders. Nat. Rev. Neurol. 14, 577–589 (2018).

    CAS  Article  Google Scholar 

  311. 311.

    Andreasson, U. et al. Commutability of the certified reference materials for the standardization of β-amyloid 1-42 assay in human cerebrospinal fluid: lessons for tau and β-amyloid 1-40 measurements. Clin. Chem. Lab. Med. 56, 2058–2066 (2018).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

The authors’ research is partly funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. H.Z. is a Wallenberg Academy Fellow 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. K.B. is supported by the Torsten Söderberg Foundation, Stockholm, Sweden. P.S. is a Wallenberg Clinical Scholar and is supported by the Swedish Foundation for Strategic Research and the Van Geest Foundation. T.H. is supported by the Hungarian Brain Research Program (2017-1.2.1-NKP-2017-00002). R.L.J. is supported by an Alzheimer’s Association Research Fellowship (AARF-16-443577). M.S. is supported by the Wallenberg Centre for Molecular and Translational Medicine, the Swedish Research Council, the Swedish Alzheimer’s Foundation, and AFTD UK. M.S.-C. received funding from the European Union’s Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie action grant agreement no. 752310. N.J.A is supported by the Wallenberg Centre for Molecular and Translational Medicine.

Author information

Affiliations

Authors

Contributions

N.J.A. and D.A. provided the initial idea and outline of content for the manuscript. G.D.R. and R.L.J. provided imaging data for creation of Fig. 1. All authors contributed to the content of the article, and critically reviewed and edited the manuscript.

Corresponding author

Correspondence to Dag Aarsland.

Ethics declarations

Competing interests

D.A. has received research support and/or honoraria from Astra-Zeneca, GE Health, H. Lundbeck and Novartis Pharmaceuticals, and served as paid consultant for Eisai, H. Lundbeck, Heptares, Mentis Cura and Sanofi. K.B. has served as a consultant or at advisory boards for Alector, Alzheon, Biogen, CogRx, Lilly, Novartis and Roche Diagnostics, all unrelated to the work presented in this paper. H.Z. has participated in scientific advisory boards for CogRx, Roche Diagnostics, Samumed and Wave, has given lectures in symposia sponsored by 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. M.S. has served on an advisory board for Servier. All other authors declare no competing interests.

Additional information

Peer review information

Nature Reviews Neurology thanks M. Mielke and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note

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

Glossary

Mass spectrometry

A sensitive technique used to detect, identify and quantify molecules on the basis of their mass-to-charge ratio.

Negative predictive value

The probability that individuals with a negative test result do not have the disease of interest.

Positive predictive value

The probability that individuals with a positive test result have the disease of interest.

Colorimetric enzyme-linked immunosorbent assay

A common protein analysis technique, usually conducted in a 96-well plate format, in which the antigen is stabilized on a solid surface and probed with a specific enzyme-conjugated antibody; the resulting enzymatic reaction is then measured with a chromogenic reporter.

Selection reaction monitoring

(SRM). A targeted mass spectrometry technique for the detection and quantification of specific predetermined analytes with known fragmentation properties.

Endophenotype

Any characteristic that is normally associated with a condition but is not a direct symptom of that condition.

Immunomagnetic reduction

(IMR). An immunoassay in which magnetic particles are coated with antibody and the reduction in the spin of the particles correlates with the amount of ligand bound.

Receiver operating characteristic

(ROC). The ROC curve is a plot of the true-positive rate against the false-positive rate for a diagnostic test. The area under the ROC curve indicates the accuracy of the test; values close to 1 mean that the test reliably distinguishes between the two conditions, whereas a value of 0.50 means that the test is no better than chance.

MicroRNA

(miRNA). Non-coding RNA molecules, generally 21 to 24 nucleotides in length, which are usually cleaved from a larger hairpin-containing RNA.

Random forest model

A machine learning algorithm that uses a collection of decision tree data structures to perform regression or classification.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ashton, N.J., Hye, A., Rajkumar, A.P. et al. An update on blood-based biomarkers for non-Alzheimer neurodegenerative disorders. Nat Rev Neurol 16, 265–284 (2020). https://doi.org/10.1038/s41582-020-0348-0

Download citation

Further reading

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing