Skip to main content
SpringerLink
Log in
Book cover

Neurodegenerative Diseases pp 491–528Cite as

Biomarkers in Neurodegenerative Diseases

  • Chapter
  • First Online:

Part of the book series: Advances in Neurobiology ((NEUROBIOL,volume 15))

Abstract

The past decade has seen tremendous efforts in biomarker discovery and validation for neurodegenerative diseases. The source and type of biomarkers has continued to grow for central nervous system diseases, from biofluid-based biomarkers (blood or cerebrospinal fluid (CSF)), to nucleic acids, tissue, and imaging. While DNA remains a predominant biomarker used to identify familial forms of neurodegenerative diseases, various types of RNA have more recently been linked to familial and sporadic forms of neurodegenerative diseases during the past few years. Imaging approaches continue to evolve and are making major contributions to target engagement and early diagnostic biomarkers. Incorporation of biomarkers into drug development and clinical trials for neurodegenerative diseases promises to aid in the development and demonstration of target engagement and drug efficacy for neurologic disorders. This review will focus on recent advancements in developing biomarkers for clinical utility in Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS).

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Abbreviations

AD:

Alzheimer’s disease

ADNI:

AD neuroimaging initiative

ALS:

Amyotrophic lateral sclerosis

APOE :

Apolipoprotein E gene

APP:

Amyloid precursor protein

AT1R:

Angiotensin-2 type 1 receptor

Aβ42 :

Amyloid-β1-42 peptide

BBB:

Blood–brain barrier

BDNF:

Brain-derived neurotrophic factor

FDG-PET:

18F-fluorodeoxyglucose

CLIA:

Clinical Laboratory Improvement Amendments

CNS:

Central nervous system

CReATE:

Clinical research in ALS and related disorders for therapeutic development

CSF:

Cerebrospinal fluid

DAT:

Dopamine transporter

DATscan:

Dopamine transporter imaging

DRPs:

Dipeptide repeat proteins

DTI:

Diffusion tensor imaging

EIM:

Electrical impedance myography

ENCALS:

European Network for the Cure of ALS

EOAD:

Early onset Alzheimer’s disease

FDA:

Food and Drug Administration

FTD:

Frontotemporal dementia

FTLD:

Frontotemporal lobar degeneration

GBSC:

Global Biomarkers Standardization Committee

GWAS:

Genome-wide association studies

HD:

Huntington’s disease

IGF-1:

Insulin-like growth factor-1

IVD:

In-vitro diagnostics

LBD:

Lewy body disease

LDT:

Laboratory-developed test

LOAD:

Late onset Alzheimer’s disease

MCI:

Mild cognitive impairment

MRI:

Magnetic resonance imaging

MTL:

Mesial temporal lobe

MUNE:

Motor Unit Number Estimation

NCI:

No cognitive impairment

NEALS:

Northeast ALS Consortium

NFL:

Neurofilament light chain

NFTs:

Neurofibrillary tangles

NIA:

National Institute of Aging

NINDS:

Neurological Disorders and Stroke

p-Tau:

Phosphorylated tau

PD:

Parkinson’s disease

PD:

Pharmacodynamic

PDD:

Parkinson’s disease with dementia

PDBP:

Parkinson’s Disease Biomarkers Program

PET:

Positron emission tomography

PiB:

Pittsburgh compound-B

PPMI:

Parkinson’s Progression Markers Initiative

pNFH:

Phosphorylated heavy chain

RUO:

Research-use only

SBM:

Surface-based morphometry

SN:

Substantia nigra

SOD1:

Superoxide dismutase-1

SOPHIA:

Sampling and Biomarker Optimization and Harmonization in ALS and other Motor Neuron Diseases

SOPs:

Standardized operating procedures

SPECT:

Single photon emission computerized tomography

TCS:

Transcranial sonography

UPDRS:

Unified Parkinson’s Disease Rating Scale

VaD:

Vascular dementia

VBM:

Voxel-based morphometry

wrCRP:

Wide-range C-reactive protein

References

  1. McGoldrick P et al (2013) Rodent models of amyotrophic lateral sclerosis. Biochim Biophys Acta 1832:1421–1436

    Article  CAS  PubMed  Google Scholar 

  2. Fleming TR, Powers JH (2012) Biomarkers and surrogate endpoints in clinical trials. Stat Med 31(25):2973–2984

    Article  PubMed  PubMed Central  Google Scholar 

  3. Group, B.D.W (2001) Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clin Pharm Therapeutics 69(3):89–95

    Article  Google Scholar 

  4. Vucicevic D, Schrewe H, Orom UA (2014) Molecular mechanisms of long ncRNAs in neurological disorders. Front Genetics 5:48. doi:10.3389/fgene.2014.00048

    Google Scholar 

  5. Wan P, Su W, Zhou Y (2016) The role of long noncoding RNAs in neurodegenerative diseases. Mol Neurobiol. doi:10.1007/s12035-016-9793-6

    Google Scholar 

  6. Quinn JF et al (2015) Extracellular RNAs: Development as biomarkers of human disease. J Extracellular Vesicles 4:27495

    Article  Google Scholar 

  7. Smith R et al (2015) Amyotrophic lateral sclerosis: Is the spinal fluid pathway involved in seeding and spread? Med Hypotheses 85(5):576–583

    Article  PubMed  Google Scholar 

  8. Quinn C et al (2013) Post-lumbar puncture headache is reduced with use of atraumatic needles in ALS. Amyotroph Lateral Scler 14(7-8):632–634

    Article  Google Scholar 

  9. Anderson NL, Anderson NG (2002) The human plasma proteome. Mol Cell Proteomics 1:845–867

    Article  CAS  PubMed  Google Scholar 

  10. Schneider A, Simons M (2013) Exosomes: vesicular carriers for intercellular communication in neurodegenerative disorders. Cell Tissue Res 352(1):33–47

    Article  CAS  PubMed  Google Scholar 

  11. Weiner MW et al (2015) 2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception. Alzheimers Dement 11(6):e1–120

    Article  PubMed  PubMed Central  Google Scholar 

  12. Sperling RA et al (2011) 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(3):280–292

    Article  PubMed  PubMed Central  Google Scholar 

  13. Jack CR Jr et al (2010) Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol 9(1):119–128

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Fagan AM et al (2007) Cerebrospinal fluid tau/beta-amyloid(42) ratio as a prediction of cognitive decline in nondemented older adults. Arch Neurol 64(3):343–349

    Article  PubMed  Google Scholar 

  15. Mattsson N et al (2009) CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. Jama 302(4):385–393

    Article  CAS  PubMed  Google Scholar 

  16. Snider BJ et al (2009) Cerebrospinal fluid biomarkers and rate of cognitive decline in very mild dementia of the Alzheimer type. Arch Neurol 66(5):638–645

    Article  PubMed  PubMed Central  Google Scholar 

  17. Trojanowski JQ et al (2010) Update on the biomarker core of the Alzheimer's Disease Neuroimaging Initiative subjects. Alzheimers Dement 6(3):230–238

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Goate A et al (1991) Segregation of a missense mutation in the amyloid precursor protein gene with familial Alzheimer's disease. Nature 349(6311):704–706

    Article  CAS  PubMed  Google Scholar 

  19. Cruts M, Theuns J, Van Broeckhoven C (2012) Locus-specific mutation databases for neurodegenerative brain diseases. Hum Mutation 33(9):1340–1344

    Article  CAS  Google Scholar 

  20. Levy-Lahad E et al (1995) Candidate gene for the chromosome 1 familial Alzheimer's disease locus. Science 269(August 18):973–977

    Article  CAS  PubMed  Google Scholar 

  21. Sherrington R et al (1995) Cloning of a gene bearing missense mutations in early-onset familial Alzheimer's disease. Nature 375(June 29):754–760

    Article  CAS  PubMed  Google Scholar 

  22. Cohn-Hokke PE et al (2012) Genetics of dementia: Update and guidelines for the clinician. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 159B(6):628–643

    Article  CAS  Google Scholar 

  23. Strittmatter WJ et al (1993) Apolipoprotein E: high avidity binding to B-amyloid and increased frequency of type 4 allele in late-onset familial Alzheimer disease. Proc. Natl. Acad. Sci. USA 90:1977–1981

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Corder EH et al (1994) Protective effect of apolipoprotein E type 2 allele for late onset Alzheimer disease. Nat Genet 7:180–184

    Article  CAS  PubMed  Google Scholar 

  25. Zou Z et al (2014) Clinical Genetics of Alzheimer’s Disease. BioMed Research International 2014:291862. doi:10.1155/2014/291862

    PubMed  PubMed Central  Google Scholar 

  26. Medway C, Morgan K (2014) Review: The genetics of Alzheimer's disease; putting flesh on the bones. Neuropathol Appl Neurobiol 40(2):97–105

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Olsson B et al (2016) CSF and blood biomarkers for the diagnosis of Alzheimer's disease: a systematic review and meta-analysis. Lancet Neurol 15(7):673–684

    Article  CAS  PubMed  Google Scholar 

  28. Blennow K (2004) Cerebrospinal fluid protein biomarkers for Alzheimer's disease. NeuroRx 1(2):213–225

    Article  PubMed  PubMed Central  Google Scholar 

  29. Maddalena A et al (2003) Biochemical diagnosis of Alzheimer disease by measuring the cerebrospinal fluid ratio of phosphorylated tau protein to beta-amyloid peptide42. Arch Neurol 60(9):1202–1206

    Article  PubMed  Google Scholar 

  30. Kang JH et al (2013) Clinical utility and analytical challenges in measurement of cerebrospinal fluid amyloid-beta(1-42) and tau proteins as Alzheimer disease biomarkers. Clin Chem 59(6):903–916

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Riemenschneider M et al (2002) Cerebrospinal fluid tau and beta-amyloid 42 proteins identify Alzheimer disease in subjects with mild cognitive impairment. Arch Neurol 59(11):1729–1734

    Article  CAS  PubMed  Google Scholar 

  32. Hansson O et al (2006) Association between CSF biomarkers and incipient Alzheimer's disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurol 5(3):228–234

    Article  CAS  PubMed  Google Scholar 

  33. Verwey NA et al (2009) A worldwide multicentre comparison of assays for cerebrospinal fluid biomarkers in Alzheimer's disease. Ann Clin Biochem 46(Pt 3):235–240

    Article  CAS  PubMed  Google Scholar 

  34. Shaw LM et al (2011) Qualification of the analytical and clinical performance of CSF biomarker analyses in ADNI. Acta Neuropathol 121(5):597–609

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Toledo JB et al (2013) Longitudinal change in CSF Tau and Abeta biomarkers for up to 48 months in ADNI. Acta Neuropathol 126(5):659–670

    Article  CAS  PubMed  Google Scholar 

  36. Zetterberg H et al (2016) Association of Cerebrospinal Fluid Neurofilament Light Concentration With Alzheimer Disease Progression. JAMA Neurol 73(1):60–67

    Article  PubMed  Google Scholar 

  37. Ewers M et al (2015) CSF biomarkers for the differential diagnosis of Alzheimer's disease: A large-scale international multicenter study. Alzheimers Dement 11(11):1306–1315

    Article  PubMed  Google Scholar 

  38. Leinenbach A et al (2014) Mass spectrometry-based candidate reference measurement procedure for quantification of amyloid-b in cerebrospinal fluid. Clin Chem 60(7):987–994

    Article  CAS  PubMed  Google Scholar 

  39. Irizarry MC (2004) Biomarkers of Alzheimer disease in plasma. NeuroRx 1(2):226–234

    Article  PubMed  PubMed Central  Google Scholar 

  40. Kiddle SJ et al (2014) Candidate blood proteome markers of Alzheimer's disease onset and progression: a systematic review and replication study. J Alzheimers Dis 38(3):515–531

    CAS  PubMed  Google Scholar 

  41. Fiandaca MS et al (2015) Identification of preclinical Alzheimer's disease by a profile of pathogenic proteins in neurally derived blood exosomes: A case-control study. Alzheimers Dement 11(6):600–607. e1

    Article  PubMed  Google Scholar 

  42. Schwartz M, Baruch K (2014) The resolution of neuroinflammation in neurodegeneration: leukocyte recruitment via the choroid plexus. EMBO J 33(1):7–22

    Article  CAS  PubMed  Google Scholar 

  43. DiFrancesco JC, Longoni M, Piazza F (2015) Anti-Abeta Autoantibodies in Amyloid Related Imaging Abnormalities (ARIA): Candidate Biomarker for Immunotherapy in Alzheimer's Disease and Cerebral Amyloid Angiopathy. Front Neurol 6:207

    Article  PubMed  PubMed Central  Google Scholar 

  44. Hyman BT et al (2001) Autoantibodies to amyloid-beta and Alzheimer's disease. Ann Neurol 49(6):808–810

    Article  CAS  PubMed  Google Scholar 

  45. Schneider P, Hampel H, Buerger K (2009) Biological marker candidates of Alzheimer's disease in blood, plasma, and serum. CNS Neurosci Ther 15(4):358–374

    Article  CAS  PubMed  Google Scholar 

  46. Hock C et al (2003) Antibodies against beta-amyloid slow cognitive decline in Alzheimer's disease. Neuron 38(4):547–554

    Article  CAS  PubMed  Google Scholar 

  47. Dodel RC et al (2004) Intravenous immunoglobulins containing antibodies against beta-amyloid for the treatment of Alzheimer's disease. J Neurol Neurosurg Psychiatry 75(10):1472–1474

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Holmes C et al (2008) Long-term effects of Abeta42 immunisation in Alzheimer's disease: follow-up of a randomised, placebo-controlled phase I trial. Lancet 372(9634):216–223

    Article  CAS  PubMed  Google Scholar 

  49. Fu HJ et al (2010) Amyloid-beta immunotherapy for Alzheimer's disease. CNS Neurol Disord Drug Targets 9(2):197–206

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Reardon S (2015) Antibody drugs for Alzheimer's show glimmers of promise. Nature 523(7562):509–510

    Article  CAS  PubMed  Google Scholar 

  51. Davydova TV et al (2007) Induction of autoantibodies to glutamate in patients with Alzheimer's disease. Bull Exp Biol Med 143(2):182–183

    Article  CAS  PubMed  Google Scholar 

  52. Gruden MA et al (2007) Differential neuroimmune markers to the onset of Alzheimer's disease neurodegeneration and dementia: autoantibodies to Abeta((25-35)) oligomers, S100b and neurotransmitters. J Neuroimmunol 186(1-2):181–192

    Article  CAS  PubMed  Google Scholar 

  53. Koval L et al (2011) The presence and origin of autoantibodies against alpha4 and alpha7 nicotinic acetylcholine receptors in the human blood: possible relevance to Alzheimer's pathology. J Alzheimers Dis 25(4):747–761

    CAS  PubMed  Google Scholar 

  54. Giil LM et al (2015) Autoantibodies Toward the Angiotensin 2 Type 1 Receptor: A Novel Autoantibody in Alzheimer's Disease. J Alzheimers Dis 47(2):523–529

    Article  CAS  PubMed  Google Scholar 

  55. Fleegal-DeMotta MA, Doghu S, Banks WA (2009) Angiotensin II modulates BBB permeability via activation of the AT(1) receptor in brain endothelial cells. J Cereb Blood Flow Metab 29(3):640–647

    Article  CAS  PubMed  Google Scholar 

  56. Mogi M, Iwanami J, Horiuchi M (2012) Roles of Brain Angiotensin II in Cognitive Function and Dementia. Int J Hypertens 2012:169649

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Palmqvist S et al (2015) Detailed comparison of amyloid PET and CSF biomarkers for identifying early Alzheimer disease. Neurology 85(14):1240–1249

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Klunk WE et al (2004) Imaging brain amyloid in Alzheimer's disease with Pittsburgh Compound-B. Ann Neurol 55(3):306–319

    Article  CAS  PubMed  Google Scholar 

  59. Rowe CC et al (2010) Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging. Neurobiol Aging 31(8):1275–1283

    Article  PubMed  Google Scholar 

  60. Levine H 3rd (1995) Soluble multimeric Alzheimer beta(1-40) pre-amyloid complexes in dilute solution. Neurobiol Aging 16(5):755–764

    Article  CAS  PubMed  Google Scholar 

  61. Mathis CA et al (2003) Synthesis and evaluation of 11C-labeled 6-substituted 2-arylbenzothiazoles as amyloid imaging agents. J Med Chem 46(13):2740–2754

    Article  CAS  PubMed  Google Scholar 

  62. Thal DR et al (2002) Phases of A beta-deposition in the human brain and its relevance for the development of AD. Neurology 58(12):1791–1800

    Article  PubMed  Google Scholar 

  63. Fagan AM et al (2006) Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Abeta42 in humans. Ann Neurol 59(3):512–519

    Article  CAS  PubMed  Google Scholar 

  64. Grimmer T et al (2009) Beta amyloid in Alzheimer's disease: increased deposition in brain is reflected in reduced concentration in cerebrospinal fluid. Biol Psychiatry 65(11):927–934

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Zwan M et al (2014) Concordance between cerebrospinal fluid biomarkers and [11C]PIB PET in a memory clinic cohort. J Alzheimers Dis 41(3):801–807

    CAS  PubMed  Google Scholar 

  66. Forsberg A et al (2008) PET imaging of amyloid deposition in patients with mild cognitive impairment. Neurobiol Aging 29(10):1456–1465

    Article  CAS  PubMed  Google Scholar 

  67. Koivunen J et al (2011) Amyloid PET imaging in patients with mild cognitive impairment: a 2-year follow-up study. Neurology 76(12):1085–1090

    Article  CAS  PubMed  Google Scholar 

  68. Cairns NJ et al (2009) Absence of Pittsburgh compound B detection of cerebral amyloid beta in a patient with clinical, cognitive, and cerebrospinal fluid markers of Alzheimer disease: a case report. Arch Neurol 66(12):1557–1562

    Article  PubMed  PubMed Central  Google Scholar 

  69. Bacskai BJ et al (2007) Molecular imaging with Pittsburgh Compound B confirmed at autopsy: a case report. Arch Neurol 64(3):431–434

    Article  PubMed  Google Scholar 

  70. Ikonomovic MD et al (2008) Post-mortem correlates of in vivo PiB-PET amyloid imaging in a typical case of Alzheimer's disease. Brain 131(Pt 6):1630–1645

    Article  PubMed  PubMed Central  Google Scholar 

  71. Kadir A et al (2011) Positron emission tomography imaging and clinical progression in relation to molecular pathology in the first Pittsburgh Compound B positron emission tomography patient with Alzheimer's disease. Brain 134(Pt 1):301–317

    Article  PubMed  Google Scholar 

  72. Lockhart A et al (2007) PIB is a non-specific imaging marker of amyloid-beta (Abeta) peptide-related cerebral amyloidosis. Brain 130(Pt 10):2607–2615

    Article  CAS  PubMed  Google Scholar 

  73. Burack MA et al (2010) In vivo amyloid imaging in autopsy-confirmed Parkinson disease with dementia. Neurology 74(1):77–84

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Fodero-Tavoletti MT et al (2007) In vitro characterization of Pittsburgh compound-B binding to Lewy bodies. J Neurosci 27(39):10365–10371

    Article  CAS  PubMed  Google Scholar 

  75. Ikonomovic MD et al (2012) Early AD pathology in a [C-11]PiB-negative case: a PiB-amyloid imaging, biochemical, and immunohistochemical study. Acta Neuropathol 123(3):433–447

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Kantarci K et al (2012) Antemortem amyloid imaging and beta-amyloid pathology in a case with dementia with Lewy bodies. Neurobiol Aging 33(5):878–885

    Article  CAS  PubMed  Google Scholar 

  77. Sojkova J et al (2011) In vivo fibrillar beta-amyloid detected using [11C]PiB positron emission tomography and neuropathologic assessment in older adults. Arch Neurol 68(2):232–240

    Article  PubMed  PubMed Central  Google Scholar 

  78. Price JL, Morris JC (1999) Tangles and plaques in nondemented aging and “preclinical” Alzheimer's disease. Ann Neurol 45(3):358–368

    Article  CAS  PubMed  Google Scholar 

  79. Landau SM et al (2014) Amyloid PET imaging in Alzheimer's disease: a comparison of three radiotracers. Eur J Nucl Med Mol Imaging 41(7):1398–1407

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Mason NS, Mathis CA, Klunk WE (2013) Positron emission tomography radioligands for in vivo imaging of Abeta plaques. J Labelled Comp Radiopharm 56(3-4):89–95

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Heurling K et al (2016) Imaging beta-amyloid using [(18)F] flutemetamol positron emission tomography: from dosimetry to clinical diagnosis. Eur J Nucl Med Mol Imaging 43(2):362–373

    Article  CAS  PubMed  Google Scholar 

  82. Vandenberghe R et al (2010) 18F-flutemetamol amyloid imaging in Alzheimer disease and mild cognitive impairment: a phase 2 trial. Ann Neurol 68(3):319–329

    Article  PubMed  Google Scholar 

  83. Trembath L, Newell M, Devous MD Sr (2015) Technical Considerations in Brain Amyloid PET Imaging with 18F-Florbetapir. J Nucl Med Technol 43(3):175–184

    Article  PubMed  Google Scholar 

  84. Lister-James J et al (2011) Florbetapir f-18: a histopathologically validated Beta-amyloid positron emission tomography imaging agent. Semin Nucl Med 41(4):300–304

    Article  PubMed  Google Scholar 

  85. Wong DF et al (2010) In vivo imaging of amyloid deposition in Alzheimer disease using the radioligand 18F-AV-45 (florbetapir [corrected] F 18). J Nucl Med 51(6):913–920

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Fleisher AS et al (2011) Using positron emission tomography and florbetapir F18 to image cortical amyloid in patients with mild cognitive impairment or dementia due to Alzheimer disease. Arch Neurol 68(11):1404–1411

    Article  PubMed  Google Scholar 

  87. Clark CM et al (2011) Use of florbetapir-PET for imaging beta-amyloid pathology. Jama 305(3):275–283

    Article  CAS  PubMed  Google Scholar 

  88. Cohen AD, Klunk WE (2014) Early detection of Alzheimer's disease using PiB and FDG PET. Neurobiol Dis 72 Pt A:117–122

    Article  PubMed  CAS  Google Scholar 

  89. Foster NL et al (2007) FDG-PET improves accuracy in distinguishing frontotemporal dementia and Alzheimer's disease. Brain 130(Pt 10):2616–2635

    Article  PubMed  Google Scholar 

  90. Friedland RP et al (1983) Regional cerebral metabolic alterations in dementia of the Alzheimer type: positron emission tomography with [18F]fluorodeoxyglucose. J Comput Assist Tomogr 7(4):590–598

    Article  CAS  PubMed  Google Scholar 

  91. Jagust W et al (2007) What does fluorodeoxyglucose PET imaging add to a clinical diagnosis of dementia? Neurology 69(9):871–877

    Article  CAS  PubMed  Google Scholar 

  92. Anchisi D et al (2005) Heterogeneity of brain glucose metabolism in mild cognitive impairment and clinical progression to Alzheimer disease. Arch Neurol 62(11):1728–1733

    Article  PubMed  Google Scholar 

  93. Drzezga A et al (2005) Prediction of individual clinical outcome in MCI by means of genetic assessment and (18)F-FDG PET. J Nucl Med 46(10):1625–1632

    CAS  PubMed  Google Scholar 

  94. Mosconi L et al (2004) MCI conversion to dementia and the APOE genotype: a prediction study with FDG-PET. Neurology 63(12):2332–2340

    Article  CAS  PubMed  Google Scholar 

  95. Villemagne VL Amyloid imaging: Past, present and future perspectives. Ageing Res Rev, 2016.

    Google Scholar 

  96. Chiotis K et al., Imaging in-vivo tau pathology in Alzheimer's disease with THK5317 PET in a multimodal paradigm. Eur J Nucl Med Mol Imaging, 2016.

    Google Scholar 

  97. Harada R et al (2013) Comparison of the binding characteristics of [18F]THK-523 and other amyloid imaging tracers to Alzheimer's disease pathology. Eur J Nucl Med Mol Imaging 40(1):125–132

    Article  CAS  PubMed  Google Scholar 

  98. Lemoine L et al (2015) Visualization of regional tau deposits using (3)H-THK5117 in Alzheimer brain tissue. Acta Neuropathol Commun 3:40

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  99. Okamura N et al (2014) Non-invasive assessment of Alzheimer's disease neurofibrillary pathology using 18F-THK5105 PET. Brain 137(Pt 6):1762–1771

    Article  PubMed  Google Scholar 

  100. Villemagne VL et al (2014) In vivo evaluation of a novel tau imaging tracer for Alzheimer's disease. Eur J Nucl Med Mol Imaging 41(5):816–826

    Article  CAS  PubMed  Google Scholar 

  101. Harada R et al (2015) [(18)F]THK-5117 PET for assessing neurofibrillary pathology in Alzheimer's disease. Eur J Nucl Med Mol Imaging 42(7):1052–1061

    Article  CAS  PubMed  Google Scholar 

  102. Harada R et al (2016) 18F-THK5351: A Novel PET Radiotracer for Imaging Neurofibrillary Pathology in Alzheimer Disease. J Nucl Med 57(2):208–214

    Article  CAS  PubMed  Google Scholar 

  103. Maruyama M et al (2013) Imaging of tau pathology in a tauopathy mouse model and in Alzheimer patients compared to normal controls. Neuron 79(6):1094–1108

    Article  CAS  PubMed  Google Scholar 

  104. Kimura Y et al (2015) PET Quantification of Tau Pathology in Human Brain with 11C-PBB3. J Nucl Med 56(9):1359–1365

    Article  CAS  PubMed  Google Scholar 

  105. Chien DT et al (2013) Early clinical PET imaging results with the novel PHF-tau radioligand [F-18]-T807. J Alzheimers Dis 34(2):457–468

    CAS  PubMed  Google Scholar 

  106. Chien DT et al (2014) Early clinical PET imaging results with the novel PHF-tau radioligand [F18]-T808. J Alzheimers Dis 38(1):171–184

    PubMed  Google Scholar 

  107. Johnson KA et al (2016) Tau positron emission tomographic imaging in aging and early Alzheimer disease. Ann Neurol 79(1):110–119

    Article  PubMed  Google Scholar 

  108. Scholl M et al (2016) PET Imaging of Tau Deposition in the Aging Human Brain. Neuron 89(5):971–982

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Declercq L et al (2016) Comparison of New Tau PET-Tracer Candidates With [18F]T808 and [18F]T807. Mol Imaging 15

    Google Scholar 

  110. Apostolova LG et al (2006) Conversion of mild cognitive impairment to Alzheimer disease predicted by hippocampal atrophy maps. Arch Neurol 63(5):693–699

    Article  PubMed  Google Scholar 

  111. Becker JT et al (2006) Three-dimensional patterns of hippocampal atrophy in mild cognitive impairment. Arch Neurol 63(1):97–101

    Article  PubMed  Google Scholar 

  112. Grundman M et al (2002) Brain MRI hippocampal volume and prediction of clinical status in a mild cognitive impairment trial. J Mol Neurosci 19(1-2):23–27

    Article  CAS  PubMed  Google Scholar 

  113. Moretti DV et al (2007) Hippocampal atrophy and EEG markers in subjects with mild cognitive impairment. Clin Neurophysiol 118(12):2716–2729

    Article  CAS  PubMed  Google Scholar 

  114. Morra JH et al (2009) Automated mapping of hippocampal atrophy in 1-year repeat MRI data from 490 subjects with Alzheimer's disease, mild cognitive impairment, and elderly controls. Neuroimage 45(1 Suppl):S3–15

    Article  PubMed  Google Scholar 

  115. Chetelat G et al (2008) Three-dimensional surface mapping of hippocampal atrophy progression from MCI to AD and over normal aging as assessed using voxel-based morphometry. Neuropsychologia 46(6):1721–1731

    Article  CAS  PubMed  Google Scholar 

  116. Chetelat G et al (2010) Relationship between atrophy and beta-amyloid deposition in Alzheimer disease. Ann Neurol 67(3):317–324

    CAS  PubMed  Google Scholar 

  117. Devanand DP et al (2007) Hippocampal and entorhinal atrophy in mild cognitive impairment: prediction of Alzheimer disease. Neurology 68(11):828–836

    Article  CAS  PubMed  Google Scholar 

  118. Wang H et al (2009) Alterations in regional brain volume and individual MRI-guided perfusion in normal control, stable mild cognitive impairment, and MCI-AD converter. J Geriatr Psychiatry Neurol 22(1):35–45

    Article  CAS  PubMed  Google Scholar 

  119. Archer HA et al (2006) Amyloid load and cerebral atrophy in Alzheimer's disease: an 11C-PIB positron emission tomography study. Ann Neurol 60(1):145–147

    Article  PubMed  Google Scholar 

  120. Fotenos AF et al (2008) Brain volume decline in aging: evidence for a relation between socioeconomic status, preclinical Alzheimer disease, and reserve. Arch Neurol 65(1):113–120

    Article  PubMed  Google Scholar 

  121. Frisoni GB et al (2009) In vivo mapping of amyloid toxicity in Alzheimer disease. Neurology 72(17):1504–1511

    Article  CAS  PubMed  Google Scholar 

  122. de Lau LM (2006) Epidemiology of Parkinson's disease. Lancet Neurol 5:525–535

    Article  PubMed  Google Scholar 

  123. Twelves D, Perkins KS, Consell C (2003) Systematic review of incidence studies of Parkinson's disease. Mov Disord 18:19–31

    Article  PubMed  Google Scholar 

  124. Jankovic J (2007) Parkinon's disease: clinical features and diagnosis. J Neurol Neurosurg Psychiatry 79:368–376

    Article  Google Scholar 

  125. Siderowf A et al (2002) Test-retest reliability of the unified Parkinson's disease rating scale in patients with early Parkinson's disease: results from a multicenter clinical trial. Mov Disord 17(4):758–763

    Article  PubMed  Google Scholar 

  126. Rosenthal LS et al (2016) The NINDS Parkinson's disease biomarkers program. Mov Disord 31(6):915–923

    Article  CAS  PubMed  Google Scholar 

  127. Klein C, Westenberger A (2012) Genetics of Parkinson's disease. Cold Spring Harb Perspect Med. doi:10.1101/cshperspect.a008888

    PubMed  Google Scholar 

  128. Deng H-X et al (2016) Identification of TMEM230 mutations in familial Parkinson's disease. Nat Genet 48(7):733–739

    Article  CAS  PubMed  Google Scholar 

  129. Nalls MA et al (2014) Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson's disease. Nat Genet 46(9):989–993

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Khoo SK et al (2012) Plasma-based circulating microRNA biomarkers for Parkinson's disease. J Parkinsons Dis 2(4):321–331

    CAS  PubMed  Google Scholar 

  131. Mouradian MM (2012) MicroRNAs in Parkinson's disease. Neurobiol Dis 46(2):279–284

    Article  CAS  PubMed  Google Scholar 

  132. Seifert KD, Wiener JI (2013) The impact of DaTscan on the diagnosis and management of movement disorders: A retrospective study. Am J Neurodegener Dis 2(1):29–34

    PubMed  PubMed Central  Google Scholar 

  133. Schwingenschuh P et al (2010) Distinguishing SWEDDs patients with asymmetric resting tremor from Parkinson's disease: a clinical and electrophysiological study. Mov Disord 25:560–569

    Article  PubMed  PubMed Central  Google Scholar 

  134. Vogt T et al (2011) Estimation of further disease progression of Parkinson's disease by dopamine transporter scan vs clinical rating. Parkinsonism Relat Disord 17:459–463

    Article  PubMed  Google Scholar 

  135. Ravina B et al (2012) Dopamine transporter imaging is associated with long-term outcomes in Parkinson's disease. Mov Disord 27:1392–1397

    Article  PubMed  PubMed Central  Google Scholar 

  136. Eckert T et al (2005) FDG PET in the differential diagnosis of parkinsonian disorders. Neuroimage 26:912–921

    Article  PubMed  Google Scholar 

  137. Pyatigorskaya N et al (2014) A review of the use of magnetic resonance imaging in Parkinson's disease. Ther Adv Neurol Disord 7(4):206–220

    Article  PubMed  PubMed Central  Google Scholar 

  138. Summerfield C et al (2005) Structural brain changes in Parkinson disease with dementia: a voxel-based morphometry study. Arch Neurol 62:281–285

    Article  PubMed  Google Scholar 

  139. Ramirez-Ruiz B et al (2005) Longitudinal evaluation of cerebral morphological changes in Parkinson's disease with and without dementia using serial magnetic resonance imaging. J Neurol 252:1345–1352

    Article  PubMed  Google Scholar 

  140. Berg D et al (2011) Enlarged substantia nigra hyperechogenicity and risk for Parkinson disease: a 37-month 3-center study of 1847 older persons. Arch Neurol 68(7):932–937

    Article  PubMed  Google Scholar 

  141. Iranzo A et al (2014) Five-year follow-up of substantia nigra echogenicity in idiopathic REM sleep behavior disorder. Mov Disord 29(14):1774–1780

    Article  PubMed  Google Scholar 

  142. Helmich RC et al (2015) Reorganization of corticostriatal circuits in healthy G2019S LRRK2 carriers. Neurology 84(4):399–406

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Vilas D et al (2015) Clinical and imaging markers in premotor LRRK2 G2019S mutation carriers. Parkinsonism Relat Disord 21(10):1170–1176

    Article  PubMed  Google Scholar 

  144. Hong Z et al (2010) DJ-1 and alpha-synuclein in human cerebrospinal fluid as biomarkers of Parkinson's disease. Brain 133:713–726

    Article  PubMed  PubMed Central  Google Scholar 

  145. Mollenhauer B et al (2011) α-synuclein and tau concentrations in cerebrospinal fluid of patients presenting with parkinsonism: a cohort study. Lancet Neurol 10:230–240

    Article  CAS  PubMed  Google Scholar 

  146. Hall S et al (2015) CSF biomarkers and clinical progression of Parkinson disease. Neurology 84:57–63

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. van Dijk KD et al (2014) Reduced α-synuclein levels in cerebrospinal fluid in Parkinson's disease are unrelated to clinical and imaging measures of disease severity. Eur J Neurol 21(3):388–394

    Article  PubMed  Google Scholar 

  148. Lee PH et al (2006) The plasma alpha-synuclein levels in patients with Parkinson's disease and multiple system atrophy. J Neural Transm 113(10):1435–1439

    Article  CAS  PubMed  Google Scholar 

  149. Li QX et al (2007) Plasma alpha-synuclein is decreased in subjects with Parkinson's disease. Exp Neurol 204:583–588

    Article  CAS  PubMed  Google Scholar 

  150. El-Agnaf OM et al (2006) Detection of oligomeric forms of alpha-synuclein protein in human plasma as a potential biomarker for Parkinson's disease. FASEB J 20:419–425

    Article  CAS  PubMed  Google Scholar 

  151. Yanamandra K et al (2011) α-synuclein reactive antibodies as diagnostic biomarkers in blood sera of Parkinson's disease patients. PLoS One 6:e18513

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. Tokuda R et al (2010) Detection of elevated levels of alpha-synuclein oligomers in CSF from patients with Parkinson disease. Neurology 75:1766–1772

    Article  CAS  PubMed  Google Scholar 

  153. Park MJ et al (2011) Elevated levels of alpha-synuclein oligomer in the cerebrospinal fluid of drug-naive patients with Parkinson's disease. J Clin Neurol 7(4):215–222

    Article  PubMed  PubMed Central  Google Scholar 

  154. Waragai M et al (2006) Increased DJ-1 in the cerebrospinal fluids of sporadic Parkinson's disease. Biochim Biophys Res Commun 345:967–972

    Article  CAS  Google Scholar 

  155. Shi M et al (2012) DJ-1 and alphaSYN in LRRK2 CSF do not correlate with striatal dopaminergic function. Neurobiol Aging 33:836–837

    PubMed  Google Scholar 

  156. de Lau LM et al (2005) Serum uric acid levels and the risk of Parkinson disease. Ann Neurol 58:797–800

    Article  PubMed  CAS  Google Scholar 

  157. Shen C et al (2012) Serum urate and the risk of Parkinson's disease: results from a meta-analysis. Can J Neurol Sci 88:73–79

    Google Scholar 

  158. Weisskopf MG et al (2007) Plasma urate and risk of Parkinson's disease. Am J Epidemiol 166:561–567

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. Schwarzschild MA et al (2008) Serum urate as a predictor of clinical and radiographic progression in Parkinson disease. Arch Neurol 65:716–723

    Article  PubMed  PubMed Central  Google Scholar 

  160. Costa A et al (2015) Brain-derived neurotrophic factor serum levels correlate with cognitive performance in Parkinson's disease patients with mild cognitive impairment. Front Behav Neurosci 9:253

    PubMed  PubMed Central  Google Scholar 

  161. Scalzo P et al (2010) Serum levels of brain-derived neurotrophic factor correlate with motor impairment in Parkinson's disease. J Neurol 257(4):540–545

    Article  CAS  PubMed  Google Scholar 

  162. Ziebell M et al (2012) Striatal dopamine transporter binding correlates with serum BDNF levels in patients with striatal dopamineric neurodegeneration. Neurobiol Aging 33(2):428.e1-5

    Article  PubMed  CAS  Google Scholar 

  163. Picillo M et al (2013) Insulin-like growth factor-1 and progression of motor symptoms in early, drug-naive Parkinson's disease. J Neurol 260(7):1724–1730

    Article  CAS  PubMed  Google Scholar 

  164. Pellecchia MT et al (2014) Insulin-like growth factor-1 predicts cognitive functions at 2-year follow-up in early, drug-naive Parkinson's disease. Eur J Neurol 21(5):802–807

    Article  CAS  PubMed  Google Scholar 

  165. Godau J et al (2010) Increased serum insulin-like growth factor 1 in early idiopathic Parkinson's disease. J Neurol Neurosurg Psychiatry 81(5):536–538

    Article  PubMed  Google Scholar 

  166. Liu C et al (2015) CSF tau and tau/Ab42 predict cognitive decline in Parkinson's disease. Parkinsonism Relat Disord 21(3):271–276

    Article  PubMed  PubMed Central  Google Scholar 

  167. Leblond, C.S., et al., Dissection of genetic factors associated with amyotrophic lateral sclerosis. Exp Neurol, 2014.

    Google Scholar 

  168. Renton AE, Chio A, Traynor BJ (2014) State of play in amyotrophic lateral sclerosis genetics. Nat Neurosci 17(1):17–23

    Article  CAS  PubMed  Google Scholar 

  169. Keller, M.F., et al., Genome-Wide Analysis of the Heritability of Amyotrophic Lateral Sclerosis. JAMA Neurol, 2014.

    Google Scholar 

  170. Rosen DR et al (1993) Mutations in Cu/Zn superoxide dismutase gene are associated with familial amyotrophic lateral sclerosis. Nature 362(March 4):59–62

    Article  CAS  PubMed  Google Scholar 

  171. Winer L et al (2013) SOD1 in Cerebral Spinal Fluid as a Pharmacodynamic Marker for Antisense Oligonucleotide Therapy. JAMA Neurol 70(2):201–207

    Article  PubMed  Google Scholar 

  172. Reaume AG et al (1996) Motor neurons in Cu/Zn superoxide dismutase-deficient mice develop normally but exhibit enhanced cell death after axonal injury. Nature Genetics 13(May):43–47

    Article  CAS  PubMed  Google Scholar 

  173. Ralph GS et al (2005) Silencing mutant SOD1 using RNAi protects against neurodegeneration and extends survival in an ALS model. Nat Medicine 11(4):429–433

    Article  CAS  Google Scholar 

  174. Miller TM et al (2005) Virus-delivered small RNA silencing sustains strength in amyotrophic lateral sclerosis. Ann Neurol 57(5):773–776

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  175. Raoul C et al (2005) Lentiviral-mediated silencing of SOD1 through RNA interference retards disease onset and progression in a mouse model of ALS. Nat Medicine 11(4):423–428

    Article  CAS  Google Scholar 

  176. Haidet-Phillips AM et al (2011) Astrocytes from familial and sporadic ALS patients are toxic to motor neurons. Nat Biotechnol 29(9):824–828

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  177. Renton AE et al (2011) A hexanucleotide repeat expansion in C9ORF72 is the cause of chromosome 9p21-linked ALS-FTD. Neuron 72(2):257–268

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  178. Majounie E et al (2012) Frequency of the C9orf72 hexanucleotide repeat expansion in patients with amyotrophic lateral sclerosis and frontotemporal dementia: a cross-sectional study. Lancet Neurol 11(4):323–330

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  179. Byrne S et al (2012) Cognitive and clinical characteristics of patients with amyotrophic lateral sclerosis carrying a C9orf72 repeat expansion: a population-based cohort study. Lancet Neurol 11(3):232–240

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  180. Akimoto C et al (2014) A blinded international study on the reliability of genetic testing for GGGGCC-repeat expansions in C9orf72 reveals marked differences in results among 14 laboratories. J Med Genet 51(6):419–424

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  181. Van Hoecke A et al (2012) EPHA4 is a disease modifier of amyotrophic lateral sclerosis in animal models and in humans. Nat Medicine 18:1418–1422

    Article  CAS  Google Scholar 

  182. Ryberg H, Bowser R (2008) Protein biomarkers for amyotrophic lateral sclerosis. Expert Rev Proteomics 5(2):249–262

    Article  CAS  PubMed  Google Scholar 

  183. Turner MR et al (2009) Biomarkers in amyotrophic lateral sclerosis. Lancet Neurol 8:94–109

    Article  CAS  PubMed  Google Scholar 

  184. Tortelli R et al (2012) Elevated cerebrospinal fluid neurofilament light levels in patients with amyotrophic lateral sclerosis: a possible marker of disease severity and progression. Eur J Neurol 19(12):1561–1567

    Article  CAS  PubMed  Google Scholar 

  185. Ganesalingam J et al (2011) Combination of neurofilament heavy chain and complement C3 as CSF biomarkers for ALS. J Neurochem 117:528–537

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  186. Ganesalingam J et al (2013) pNfH is a promising biomarker for ALS. Amyotroph Lateral Scler Frontotemporal Degener 14(2):146–149

    Article  CAS  PubMed  Google Scholar 

  187. Boylan KB et al (2013) Phosphorylated neurofilament heavy subunit (pNF-H) in peripheral blood and CSF as a potential prognostic biomarker in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry 84(4):467–472

    Article  PubMed  Google Scholar 

  188. Lehnert S et al (2014) Multicentre quality control evaluation of different biomarker candidates for amyotrophic lateral sclerosis. Amyotroph Lateral Scler Frontotemporal Degener 15:344–350

    Article  PubMed  Google Scholar 

  189. Boylan K et al (2009) Immunoreactivity of the phosphorylated axonal neurofilament H subunit (pNF-H) in blood of ALS model rodents and ALS patients: evaluation of blood pNF-H as a potential ALS biomarker. J Neurochem 111:1182–1191

    Article  CAS  PubMed  Google Scholar 

  190. Lu CH et al (2012) Plasma neurofilament heavy chain levels correlate to markers of late stage disease progression and treatment response in SOD1G93A mice that model ALS. PLoS ONE 7(7):e40998. doi:10.1471/journal.pone.0040998

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  191. Chen H et al (2014) Modeling ALS with iPSCs Reveals that Mutant SOD1 Misregulates Neurofilament Balance in Motor Neurons. Cell Stem Cell 14(6):796–809

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  192. Oeckl P et al (2016) Multicenter validation of CSF neurofilaments as diagnostic biomarkers for ALS. Amyotroph Lateral Scler Frontotemporal Degener. doi:10.3109/21678421.2016.1167913

    PubMed  Google Scholar 

  193. Noto Y et al (2011) Elevated CSF TDP-43 levels in amyotrophic lateral sclerosis: Specificity, sensitivity and a possible prognostic value. Amyotroph Lateral Scler 12(2):140–143

    Google Scholar 

  194. Verstraete E et al (2012) TDP-43 plasma levels are higher in amyotrophic lateral sclerosis. Amyotroph Lateral Scler 13(5):446–451

    Google Scholar 

  195. Feneberg E et al (2014) Limited role of free TDP-43 as a diagnostic tool in neurodegenerative diseases. Amyotroph Lateral Scler 15(5-6):351–356

    Google Scholar 

  196. Ash PE et al (2013) Unconventional translation of C9ORF72 GGGGCC expansion generates insoluble polypeptides specific to c9FTD/ALS. Neuron 77(4):639–646

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  197. Mori K et al (2013) Bidirectional transcripts of the expanded C9orf72 hexanucleotide repeat are translated into aggregating dipeptide repeat proteins. Acta Neuropathol 126(6):881–893

    Article  CAS  PubMed  Google Scholar 

  198. Donnelly CJ et al (2013) RNA toxicity from the ALS/FTD C9ORF72 expansion is mitigated by antisense intervention. Neuron 80(2):415–428

    Google Scholar 

  199. Sareen D et al (2013) Targeting RNA foci in iPSC-derived motor neurons from ALS patients with a C9ORF72 repeat expansion. Sci Transl Med 5:208ra149

    Google Scholar 

  200. Su Z et al (2014) Discovery of a Biomarker and Lead Small Molecules to Target r(GGGGCC)-Associated Defects in c9FTD/ALS. Neuron 83(5):1043–1050

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  201. Gendron TF et al (2015) Cerebellar c9RAN proteins associate with clinical and neuropathological characteristics of C9ORF72 repeat expansion carriers. Acta Neuropathol 130:559–573

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  202. Mackenzie IRA et al (2015) Quantitative analysis and clinico-pathological correlations of different dipeptide repeat protein pathologies in C9ORF72 mutation carriers. Acta Neuropathol 130:845–861

    Article  CAS  PubMed  Google Scholar 

  203. Robelin L, Gonzalez De Aguilar JL (2014) Blood Biomarkers for Amyotrophic Lateral Sclerosis: Myth or Reality? Biomed Res Int 2014:525097

    Article  PubMed  PubMed Central  Google Scholar 

  204. Mitchell RM et al (2009) A CSF biomarker panel for identification of patients with amyotrophic lateral sclerosis. Neurology 72:14–19

    Article  CAS  PubMed  Google Scholar 

  205. Keizman D et al (2009) Low-grade systemic inflammation in patients with amyotrophic lateral sclerosis. Acta Neurol Scand 119(6):383–389

    Article  CAS  PubMed  Google Scholar 

  206. Mitchell RM et al (2010) Plasma biomarkers associated with ALS and their relationship to iron homeostasis. Muscle & Nerve 42:95–103

    Article  CAS  Google Scholar 

  207. Kuhle J et al (2009) Increased levels of inflammatory chemokines in amyotrophic lateral sclerosis. Eur J Neurol 16:771–774

    Article  CAS  PubMed  Google Scholar 

  208. Sussmuth SD et al (2003) Amyotrophic lateral sclerosis: disease stage related changes of tau protein and S100 beta in cerebrospinal fluid and creatine kinase in serum. Neurosci Lett 353:57–60

    Article  CAS  PubMed  Google Scholar 

  209. Sussmuth SD et al (2010) CSF glial markers correlate with survival in amyotrophic lateral sclerosis. Neurology 74:982–987

    Article  CAS  PubMed  Google Scholar 

  210. Su XW et al (2013) Biomarker-based predictive models for prognosis in amyotrophic lateral sclerosis. JAMA Neurol 70(12):1505–1511. doi:10.1001/jamaneurol. 2013.4646

    Google Scholar 

  211. Blasco H et al (2013) Metabolomics in cerebrospinal fluid of patients with amyotrophic lateral sclerosis: An untargeted approach via high-resolution mass spectrometry. J Proteome Res 12(8):3746–3754

    Article  CAS  PubMed  Google Scholar 

  212. Kumar A et al (2010) Metabolomic analysis of serum by (1) H NMR spectroscopy in amyotrophic lateral sclerosis. Clinica Chimica Acta 411:563–567

    Article  CAS  Google Scholar 

  213. Lawton KA et al (2014) Plasma metabolomic biomarker panel to distinguish patients with amyotrophic lateral sclerosis from disease mimics. Amyotroph Lateral Scler Frontotemporal Degener 15(5-6):362–370

    Article  CAS  PubMed  Google Scholar 

  214. Lin Y-W, Lin T-S, Lai M-L (2011) The correlation between uric acid levels and amyotrophic lateral sclerosis. Am J Clin Med Res 1(3):35–39

    Article  CAS  Google Scholar 

  215. Paganoni S et al (2012) Uric acid levels predict survival in men with amyotrophic lateral sclerosis. J Neurol 259(9):1923–1928

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  216. Butovsky O et al (2012) Modulating inflammatory monocytes with a unique microRNA gene signature ameliorates murine ALS. J Clin Invest 122:3063–3087

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  217. Beers DR et al (2011) Endogenous regulatory T lymphocytes ameliorate amyotrophic lateral sclerosis in mice and correlate with disease progression in patients with amyotrophic lateral sclerosis. Brain 134(5):1293–1314

    Article  PubMed  PubMed Central  Google Scholar 

  218. Saresella M et al (2013) T helper-17 activation dominates the immunologic milieu of both amyotrophic lateral sclerosis and progressive multiple sclerosis. Clin Immunol 148(1):79–88

    Article  CAS  PubMed  Google Scholar 

  219. Chiu IM et al (2008) T lymphocytes potentiate endogenous neuroprotective inflammation in a mouse model of ALS. Proc Natl Acad Sci U S A 105(46):17913–17918

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  220. Chen, X., et al., An exploratory study of serum creatinine levels in patients with amyotrophic lateral sclerosis. Neurol Sci, 2014.

    Google Scholar 

  221. Chio A et al (2014) Amyotrophic lateral sclerosis outcome measures and the role of albumin and creatinine: a population-based study. JAMA Neurol 71(9):1134–1142

    Article  PubMed  Google Scholar 

  222. Ikeda K et al (2012) Relationships between disease progression and serum levels of lipid, urate, creatinine and ferritin in Japanese patients with amyotrophic lateral sclerosis: a cross-sectional study. Intern Med 51(12):1501–1508

    Article  CAS  PubMed  Google Scholar 

  223. Tetsuka S et al (2013) Creatinine/cystatin C ratio as a surrogate marker of residual muscle mass in amyotrophic lateral sclerosis. Neurol Clin Neurosci 1:32–37

    Article  CAS  Google Scholar 

  224. Bozik ME et al (2014) A post hoc analysis of subgroup outcomes and creatinine in the phase III clinical trial (EMPOWER) of dexpramipexole in ALS. Amyotroph Lateral Scler Frontotemporal Degener 15(5-6):406–413

    Article  CAS  PubMed  Google Scholar 

  225. Zurcher NR et al (2015) Increased in vivo glial activation in patients with amyotrophic lateral sclerosis: Assessed with [11C]-PBR28. Neuroimage Clin 7:409–414

    Article  PubMed  PubMed Central  Google Scholar 

  226. Canosa A et al (2015) 18F-FDG-PET correlates to cognitive impairment in ALS. Neurology 86:44–49

    Article  PubMed  CAS  Google Scholar 

  227. Pagani M et al (2014) Functional pattern of brain FDG-PET in amyotrophic lateral sclerosis. Neurology 83:1067–1074

    Article  CAS  PubMed  Google Scholar 

  228. Bede P et al (2013) Grey matter correlates of clinical variables in amyotrophic lateral sclerosis (ALS): a neuroimaging study of ALS motor phenotype heterogeneity and cortical focality. J Neurol Neurosurg. Psychiatry 84(7):766–773

    Article  PubMed  Google Scholar 

  229. Turner MR et al (2007) Volumetric cortical loss in sporadic and familial amyotrophic lateral sclerosis. Amyotroph Lateral Scler Frontotemporal Degener 8(6):343–347

    Article  Google Scholar 

  230. Chen Z, Ma L (2010) Grey matter volume changes over the whole brain in amyotrophic lateral sclerosis: A voxel-wise meta-analysis of voxel based morphometry studies. Amyotroph Lateral Scler 11(6):549–554

    Article  PubMed  Google Scholar 

  231. Rajagopalan V, Pioro EP (2014) Distinct patterns of cortical atrophy in ALS patients with or without dementia: an MRI VBM study. Amyotroph Lateral Scler Frontotemporal Degener 15:216–225

    Article  PubMed  Google Scholar 

  232. Schuster C et al (2013) Focal thinning of the motor cortex mirros clinical features of amyotrophic lateral sclerosis and their phenotypes: a neuroimaging study. J Neurol 260(11):2856–2864

    Article  PubMed  Google Scholar 

  233. Verstraete E et al (2012) Structural MRI reveals cortical thinning in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry 83:383–388

    Article  PubMed  Google Scholar 

  234. Walhout R et al (2015) Cortical thickness in ALS: towards a marker for upper motor neuron involvement. J Neurol Neurosurg. Psychiatry 86(3):288–294

    Article  PubMed  Google Scholar 

  235. Filippini N et al (2010) Corpus callosum involvement is a consistent feature of amyotrophic lateral sclerosis. Neurology 75:1645–1652

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  236. Metwalli NS et al (2010) Utility of axial and radial diffusivity from diffusion tensor MRI as markers of neurodegeneration in amyotrophic lateral sclerosis. Brain Res 1348:156–164

    Article  CAS  PubMed  Google Scholar 

  237. Rajagopalan V (2013) G.H. Yue, and E.P. Pioro, Brain white matter diffusion tensor metrics from clinical 1.5T MRI distinguish between ALS phenotypes. J Neurol 260:2532–2540

    Article  PubMed  Google Scholar 

  238. Verstraete E et al (2011) Impaired structural motor connectome in amyotrophic lateral sclerosis. PLoS One 6:e24239

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  239. Verstraete E et al (2014) Structural brain network imaging shows expanding disconnection of the motor system in amyotrophic lateral sclerosis. Hum Brain Mapp 35:1351–1361

    Article  PubMed  Google Scholar 

  240. Foerster BR et al (2014) Multimodal MRI as a diagnostic biomarker for amyotrophic lateral sclerosis. Ann Clin Transl Neurol 1(2):107–114

    Article  PubMed  PubMed Central  Google Scholar 

  241. Rutkove S (2009) Electrical impedance myography as a biomarker for ALS. Lancet Neurol 8(3):226. author reply 227

    Article  PubMed  PubMed Central  Google Scholar 

  242. Wang LL et al (2011) Electrical impedance myography for monitoring motor neuron loss in the SOD1 G93A amyotrophic lateral sclerosis rat. Clin Neurophysiol 122(12):2505–2511

    Article  PubMed  PubMed Central  Google Scholar 

  243. Li J, Sung M, Rutkove SB (2013) Electrophysiologic biomarkers for assessing disease progression and the effect of riluzole in SOD1 G93A ALS mice. PLoS One 8(6):e65976

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  244. Rutkove SB et al (2012) Electrical impedance myography as a biomarker to assess ALS progression. Amyotroph Lateral Scler 13(5):439–445

    Article  PubMed  PubMed Central  Google Scholar 

  245. Shefner JM, Cudkowicz M, Brown RH Jr (2006) Motor unit number estimation predicts disease onset and survival in a transgenic mouse model of amyotrophic lateral sclerosis. Muscle Nerve 34(5):603–607

    Article  PubMed  Google Scholar 

  246. Ngo ST et al (2012) The relationship between Bayesian motor unit number estimation and histological measurements of motor neurons in wild-type and SOD1(G93A) mice. Clin Neurophysiol 123(10):2080–2091

    Article  CAS  PubMed  Google Scholar 

  247. Ahn SW et al (2010) Motor unit number estimation in evaluating disease progression in patients with amyotrophic lateral sclerosis. J Korean Med Sci 25(9):1359–1363

    Article  PubMed  PubMed Central  Google Scholar 

  248. Shefner JM et al (2011) Multipoint incremental motor unit number estimation as an outcome measure in ALS. Neurology 77(3):235–241

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  249. Armon C, Brandstater ME (1999) Motor unit number estimate-based rates of progression of ALS predict patient survival. Muscle & Nerve 22:1571–1575

    Article  CAS  Google Scholar 

  250. Ravits JM, La Spada AR (2009) ALS motor phenotype heterogeneity, focality, and spread: deconstructing motor neuron degeneration. Neurology 73:805–811

    Article  PubMed  PubMed Central  Google Scholar 

  251. Sekiguchi T et al (2013) Spreading of amyotrophic lateral sclerosis lesions-multifocal hits and local propagation? J Neurol Neurosurg. Psychiatry 85:85–91

    Article  PubMed  Google Scholar 

  252. Basso M et al (2013) Mutant copper-zinc superoxide dismutase (SOD1) induces protein secretion pathway alterations and exosome release in astrocytes: Implications for disease spreading and motor neuron pathology in amyotrophic lateral sclerosis. J Biol Chem 288:15699–15711

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  253. Rani S et al (2011) Isolation of exosomes for subsequent mRNA, microRNA, and protein profiling. Methods Mol Biol 784:181–195

    Article  CAS  PubMed  Google Scholar 

  254. Brettschneider J et al (2006) Axonal damage markers in cerebrospinal fluid are increased in ALS. Neurology 66(6):852–856

    Article  CAS  PubMed  Google Scholar 

  255. Lu CH et al (2014) Plasma neurofilament heavy chain levels and disease progression in amyotrophic lateral sclerosis: insights from a longitudinal study. J Neurol Neurosurg Psychiatry 86(5):565–573. doi:10.1136/jnnp-2014-307672

    Article  PubMed  PubMed Central  Google Scholar 

  256. Gaiottino J et al (2013) Increased neurofilament light chain blood levels in neurodegenerative neurological diseases. PLoS One 8(9):e75091. doi:10.1371/journal.pone.0075091

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  257. Lu CH et al (2015) Neurofilament light chain: A prognostic biomarker in amyotrophic lateral sclerosis. Neurology 84(22):2247–2257

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  258. Siepel FJ et al (2013) (123I)FP-CIT-SPECT in suspected dementia with Lewy bodies: a longitudinal case study. BMJ Open 3(4):e002642

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

R.B. was supported by National Institutes of Health/National Institutes of Neurological Disorders and Stroke grants NS061867 and NS068179.

Conflict of Interest

R.B. is a founder of Iron Horse Diagnostics, Inc., a biotechnology company focused on diagnostic and prognostic biomarkers for ALS and other neurologic disorders. A.J. is an employee of Iron Horse Diagnostics, Inc.

Author information

Authors and Affiliations

  1. Iron Horse Diagnostics, Inc., Scottsdale, AZ, 85255, USA

    Andreas Jeromin M.D., Ph.D. & Robert Bowser Ph.D.

  2. Divisions of Neurology and Neurobiology, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, 350 W Thomas Rd, Phoenix, AZ, 85013, USA

    Robert Bowser Ph.D.

Authors
  1. Andreas Jeromin M.D., Ph.D.
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Robert Bowser Ph.D.
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Robert Bowser Ph.D. .

Editor information

Editors and Affiliations

  1. University of Melbourne Florey Institute of Neuroscience and Mental Health, Parkville, Australia

    Philip Beart

  2. Pediatrics and Pharmacology Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Michael Robinson

  3. School of Life Sciences Bradford School of Pharmacy, University of Bradford, Bradford, United Kingdom

    Marcus Rattray

  4. Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA

    Nicholas J. Maragakis

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Jeromin, A., Bowser, R. (2017). Biomarkers in Neurodegenerative Diseases. In: Beart, P., Robinson, M., Rattray, M., Maragakis, N. (eds) Neurodegenerative Diseases. Advances in Neurobiology, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-57193-5_20

Download citation

Publish with us

Policies and ethics

Search

Navigation