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Parkinson's Disease

Parkinson’s disease – early detection and risk stratification

In the 200 years since James Parkinson first published his essay that gave name to the disease,1 there have been many advances in the treatment of Parkinson’s disease. However, there still remain serious challenges to be addressed, with early detection one of them. An interesting workshop at the European Academy of Neurology (EAN) congress held in Budapest featuring Professors Christine Klein and Irena Rektorova, and Dr Wassilios Meissner explored methods available to aid earlier diagnosis and better understanding of the course of Parkinson’s disease, including how genetics, imaging and fluid biomarkers could help with earlier detection and better risk stratification of the disease.2 

Insights from genetics 

Parkinson’s disease (PD) is one of the synucleinopathies, characterized by aggregation of α-synuclein in neurons and glia.3 

Mutations in GBA1, the gene encoding the lysosomal enzyme glucocerebrosidase, are among the most common known genetic risk factors for the development of PD.4 However, at EAN 2023 Professor Christine Klein from the Institute of Neurogenetics at the University of Lűbeck discussed the other monogenic PD genes, both dominant (SNCA, LRRK2 and VP35) and recessive types (Parkin, PINK1 and DJ-1).5 Studies have shown that only about 14% of all people with PD carry these genes.6 

Only about 14% of people with Parkinson’s disease carry monogenic Parkinson’s disease genes 

During the session, Professor Klein highlighted a number of important points: 

  • Studies have looked at the cumulative burden of rare variants, by comparing healthy people and people living with PD. Although the effect size is small, it would be possible to use these to stratify patients and try and identify them in the prodromal phase.7,8 

  • Testing for genes themselves is not enough without understanding the genetic risk factors, especially since not all people with the genes will go on to develop PD.9 

  • Genetic penetrance is important. The higher the penetrance the more likely it is for a person to get the disease.9,10 It has been shown that different variants have different penetrance and also different ages of onset.9 Knowing this informs the time points at which to test for the mutations. 

  • Research into mutational carriers (especially mothers of people with X-linked genetic mutations) can help understand compensatory alleles in place, and potentially help understand the prodromal phases better.5 

Therefore, genetic biomarkers are useful indicators for the familial form of PD, which accounts for a small subset of people living with the disease, usually presenting as early-onset cases with a positive family history. However, for cases of late-onset sporadic PD, imaging-based or fluid biomarkers are a better approach.11 

 

Insights from imaging 

Imaging is already a core part of PD diagnosis, with normal functional neuroimaging of the presynaptic dopaminergic system an absolute exclusion criteria for PD.12 During the workshop, Professor Irena Rektorova from the International Clinical Research Center in Brno presented insights about imaging options for use during the prodromal phase and some highlights are shared here.2 

DAT SPECT can detect dopaminergic dysfunction in pre-symptomatic individuals at risk for Parkinson’s disease 

  • DAT imaging 
    Dopamine transporter (DAT) imaging, e.g. DAT SPECT (single-photon emission computed topography), allows early detection of PD. While it can be used to confirm or exclude a diagnosis of dopamine-deficient parkinsonism in cases where the diagnosis is unclear, it can also detect the dopaminergic dysfunction in pre-symptomatic individuals at risk for PD as the reduced radiotracer binding to DATs in striatum is already present in the prodromal stage of PD.13 Studies have shown that DAT imaging is associated with long-term outcomes in PD14 and is able to predict conversion to symptomatic PD within 3–5 years.15,16 

  • FDG PET 
    18-F-FDG PET (18-Fluorodeoxyglucose positron emission tomography) scan is showing promise as a diagnostic tool.12 Spatial covariance analysis consistently reveals a characteristic PD-related pattern and PD-cognitive pattern, and it is a good method for differential diagnosis of PD from other parkinsonian disorders or risk assessment of cognitive impairment in PD.12,17,18 

  • MRI: QSM and NMS 
    Magnetic resonance imaging (MRI) can help track disease progression, especially using quantitative susceptibility mapping (QSM) and neuromelanin-sensitivity (NMS). QSM shows iron deposition in the substantia nigra, which starts to happen several years before the onset of PD, and can also monitor longitudinal changes over time.19,20 Neuromelanin is associated with dopamine metabolism so NMS MRI evaluates the dopaminergic neurones in the substantia nigra and can be an early marker 5 years before onset of disease.21,22 

 

Insights from fluid biomarkers 

Widespread aggregation of α-synuclein in the form of Lewy bodies and Lewy neurites are neuropathological hallmarks of PD.23 α-synuclein exists though in several species, including unfolded monomer, folded dimer, toxic oligomer, protofibrils and amyloid fibrils and Dr Wassilios Meissner from University Hospital Bordeaux, presented the latest findings on the use of fluid biomarkers to detect these different forms.2,23

  • α-Synuclein plasma levels 
    Cerebral spinal fluid (CSF) levels of α-synuclein are decreased in PD because the α-synuclein accumulates in the cells, however, results do not correlate with progression so offer limited diagnostic value.24 

  • Brain-derived blood exosomes 
    Brain-derived blood exosomes are an interesting window to detect and monitor abnormal brain function in PD already in the prodromal phase.25,26 

  • Seeding aggregation assays (SAAs) 
    SAAs are able to produce a fluorescence output from a very small amount of CSF. Studies have distinguised PD in the prodromal phase with a sensitivity of over 90% and demonstrated that different α-synuclein strains have distinct properties.27–29 However, while SAAs have a strong potential for diagnosis of PD, there is no correlation with disease severity or progression.29

While seeding aggregation assays have a strong potential for diagnosis of Parkinson’s disease, there is no correlation with disease severity or progression 

  • Neurofilament light chain (Nfl) 
    Nfl is a neuron-specific protein which presents as a main component of axons and also a byproduct of nerve cell degeneration. Blood Nfl has been suggested as a potentially reliable diagnostic biomarker of PD. Studies have shown that CSF and blood Nfl are slightly increased in PD and during the prodromal stage, but overall have probably limited usefulness for risk stratification of progression of PD.29,30

While multiple studies have reported the usefulness of several biochemical or imaging biomarkers in the differential diagnosis of PD, currently no single biomarker is specific enough for routine use in the diagnostic or prognostic evaluation in clinical cases of PD.12 

 

AN : European Academy of Neurology
PD : Parkinson's disease
GBA1 : Glucosylceramidase Beta 1
SNCA : Synuclein Alpha
LRRK2 : leucine-rich repeat kinase 2
VP35 : vesicle protein 35
PINK1 : PTEN-induced putative kinase 1
DAT : dopamine transporter
SPECT : single-photon emission computed tomography
18-F-FDG PET : 18-Fluorodeoxyglucose positron emission tomography 
MRI : magnetic resonance imaging
QSM : quantitative susceptibility mapping
NMS : neuromelanin-sensitivity 
CSF : cerebral spinal fluid
SAAs : seeding aggregation assays
Nfl : neurofilament light chain

BE-NOTPR-0344 approval date 08/2023

Our correspondent’s highlights from the symposium are meant as a fair representation of the scientific content presented. The views and opinions expressed on this page do not necessarily reflect those of Lundbeck.

References
  1. Parkinson J. J Neuropsychiatry Clin Neurosci 2002;14(2):223-36. 
  2. EAN/MDS-ES: Early/prodromal detection and risk stratification in Parkinson's disease and dementia with Lewy bodies. Online session at: 9th EAN congress; 2023 July 2; Budapest and virtual. 
  3. Koga S et al. Mol Neurodegeneration 2021;16:83.https://doi.org/10.1186/s13024-021-00501-z 
  4. Do J et al. Mol Neurodegeneration 2019;14:36. 
  5. Jia F et al. Genes 2022;13: 471. 
  6. Skrahina V et al. Mov Disord. 2021;36(4):1005-10. 
  7. Makarious M et al Med Rxiv 2022;11(8):22280168. 
  8. Vollstedt E-J et al. Mov Disord. 2023;38:286-303. 
  9. Blauwendraat C et al. Lancet Neurol. 2020;19(2):170–8. 
  10. Day JO and Mullin S. Genes (Basel). 2021;12(7):1006. 
  11. Ganguly U et al. Frontiers in Aging Neuroscience 2021;13:702639. 
  12. MDS Clinical Diagnostic Criteria for Parkinson’s Disease. 2018. Available at https://medicalcriteria.com/web/mds-parkinson-disease/.  
  13. Akdemir UO et al. Turk J Med Sci. 2021;51:400–10. 
  14. Ravina B et al. Mov Disord 2012;27(11):1392-7. 
  15. Jennings D et al. JAMA Neurol. 2017;74(8):933-40. 
  16. Sierra M et al. Neurology 2017;89 (5):439-44. 
  17. Meles S et al. Eur J Nuclear Medicine and Molecular Imaging. 2020;47:437–50. 
  18. Huang C et al. NeuroImage. 2007;34(2):714–23. 
  19. Biondetti E et al. Brain 2021;144:3114–25. 
  20. Martin-Bastida A et al. Eur J Neurol. 2017;24(2):357-65. 
  21. Guarav R et al. Mov Disord. 2021;36(7):1592-602. 
  22. Biondetti E et al. Brain. 2020;143(9):2757–70. 
  23. Dehay B et al. Lancet Neurol. 2015;14(8):855–66. 
  24. Mollenhauer B et al. Mov Disord. 2019;34(9):1354-64. 
  25. Tofaris G Journal of Parkinson’s Disease 2017;7:569–76. 
  26. Jiang C et al. Journal of Neurology, Neurosurgery & Psychiatry 2020;91:720-29. 
  27. Rossi M et al. Acta Neuropathologica 2020;140:49–62. 
  28. Shahnawaz M et al. Nature. 2020;578(7794):273–7. 
  29. Kang U et al. Mov Disord. 2019;34(4):536-44. 
  30. Liu Y et al. Front Neurosci. 2022 Sep 1;16:959261.

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