Concluding remarks
Despite the multiple biomarker candidates for PD and the ongoing intensive research efforts, there is no single definitive biomarker with sufficient accuracy or reproducibility that could be used in clinical practice to diagnose PD, predict the onset of the disease or indicate response to therapeutic interventions in clinical trials [64]. The use of a combination of biomarkers, however, could detect multiple pathological aspects of the disease and result in improved diagnostic accuracy. Background risk factors (genetic, demographic and environmental), combined with typical prodromal symptoms and different biochemical and imaging biomarkers can be used in tandem to improve the predictive diagnosis of PD [9, 58]. AI-algorithms may be of great value to define the diagnosis early on if these markers can be properly validated [11, 65, 66].
More accurate disease subtyping would contribute to the development of translational disease models and design of successful clinical trials with stratified inclusion criteria. The failed attempts to find neuroprotective strategies for PD may stem from the reductionist approach in the conducted clinical trials which have paid little attention to the variability of the disease at the individual level [67]. Development of digital biomarkers may help to address many of the current diagnostic shortcomings in an economical fashion. They would allow an objective approach to continuously track fluctuations in motor and non-motor symptoms during patients’ daily life. The resulting rich real-world datasets may prove to be highly predictive in assessing clinical improvement in PD studies and permit personalized therapeutic adaptations [68].
Ideally, preclinical drug development would already be accompanied with a reliable and accessible biomarker that could be followed through the whole process from animal models to clinical trials and regular patient monitoring. This kind of biomarker would help to predict the effects of an intervention in a patient population based on preclinical tests in animal models, and thus, increase the likelihood of successful clinical translation.
Figure:  Summary of biomarker candidates for Parkinson’s disease and matrices from which they are being analyzed. Aβ, beta-amyloid; a-syn, alpha-synuclein; GFAP, glial fibrillary acidic protein; ER, endoplasmic reticulum; NTFs, neurotrophic factors; miRNA, microRNA; MRI, magnetic resonance imaging; PET, positron emission tomography; SPECT, single-photon emission computed tomography. Figure created with BioRender.com.
References
1. Chmielarz P, Saarma M. Neurotrophic factors for disease-modifying treatments of Parkinson’s disease: gaps between basic science and clinical studies. Pharmacol Rep . 2020;72:1195-217. doi 10.1007/s43440-020-00120-3
2. Bondarenko O, Saarma M. Neurotrophic factors in Parkinson’s disease: Clinical trials, open challenges and nanoparticle-mediated delivery to the brain. Front Cell Neurosci . 2021;15. doi 10.3389/fncel.2021.682597
3. Yilmaz R, Hopfner F, van Eimeren T, Berg D. Biomarkers of Parkinson’s disease: 20 years later. J NeurTrans . 2019;126:803-13. doi 10.1007/s00702-019-02001-3
4. Heinzel S, Berg D, Gasser T, Chen H, Yao C, Postuma RB, et al. Update of the MDS research criteria for prodromal Parkinson’s disease.Mov Disord . 2019;34:1464-70. doi 10.1002/mds.27802
5. Youssef P, Hughes L, Kim WS, Halliday GM, Lewis SJG, Cooper A, et al. Evaluation of plasma levels of NFL, GFAP, UCHL1 and tau as Parkinson’s disease biomarkers using multiplexed single molecule counting. Sci Rep . 2023;13:5217. doi 10.1038/s41598-023-32480-0
6. Schulz I, Kruse N, Gera RG, Kremer T, Cedarbaum J, Barbour R, et al. Systematic assessment of 10 biomarker candidates focusing on alpha-synuclein-related disorders. Mov Disord . 2021;36:2874-87. doi 10.1002/mds.28738
7. Mahlknecht P, Marini K, Werkmann M, Poewe W, Seppi K. Prodromal Parkinson’s disease: hype or hope for disease-modification trials?Transl Neurodegener . 2022;11:11. doi 10.1186/s40035-022-00286-1
8. Marek K, Jennings D, Lasch S, Siderowf A, Tanner C, Simuni T, et al. The Parkinson Progression Marker Initiative (PPMI). Progr Neurobiol . 2011;95:629-35. doi https://doi.org/10.1016/j.pneurobio.2011.09.005
9. Parnetti L, Gaetani L, Eusebi P, Paciotti S, Hansson O, El-Agnaf O, et al. CSF and blood biomarkers for Parkinson’s disease. Lancet Neurol . 2019;18:573-86. doi https://doi.org/10.1016/S1474-4422(19)30024-9
10. Brumm MC, Siderowf A, Simuni T, Burghardt E, Choi SH, Caspell-Garcia C, et al. Parkinson’s Progression Markers Initiative: A milestone-based strategy to monitor Parkinson’s disease progression. J Parkinsons Dis . 2023. doi 10.3233/JPD-223433
11. Hansson O. Biomarkers for neurodegenerative diseases. Nat Med . 2021;27:954-63. doi 10.1038/s41591-021-01382-x
12. Chahine LM, Stern MB. Parkinson’s disease biomarkers: Where are we and where do we go next? Mov Dis Clin Pract . 2017;4:796-805. doi 10.1002/mdc3.12545
13. Angius F, Mocci I, Ercoli T, Loy F, Fadda L, Palmas MF, et al. Combined measure of salivary alpha-synuclein species as diagnostic biomarker for Parkinson’s disease. J Neurol . 2023. doi 10.1007/s00415-023-11893-x
14. Jimenez-Jimenez FJ, Alonso-Navarro H, Garcia-Martin E, Santos-Garcia D, Martinez-Valbuena I, Agundez JAG. Alpha-synuclein in peripheral tissues as a possible marker for neurological diseases and other medical conditions. Biomolecules . 2023;13. doi 10.3390/biom13081263
15. Sidorova Y, Domanskyi A. Detecting oxidative stress biomarkers in neurodegenerative disease models and patients. Methods Protoc . 2020;3. doi 10.3390/mps3040066
16. Kang UJ, Boehme AK, Fairfoul G, Shahnawaz M, Ma TC, Hutten SJ, et al. Comparative study of cerebrospinal fluid alpha-synuclein seeding aggregation assays for diagnosis of Parkinson’s disease. Mov Disord . 2019;34:536-44. doi 10.1002/mds.27646
17. Constantinescu R, Mondello S. Cerebrospinal fluid biomarker candidates for parkinsonian disorders. Front Neurol . 2013;3. doi 10.3389/fneur.2012.00187
18. Palmqvist S, Rossi M, Hall S, Quadalti C, Mattsson-Carlgren N, Dellavalle S, et al. Cognitive effects of Lewy body pathology in clinically unimpaired individuals. Nat Med . 2023;29:1971-8. doi 10.1038/s41591-023-02450-0
19. den Heijer JM, Cullen VC, Pereira DR, Yavuz Y, de Kam ML, Grievink HW, et al. A biomarker study in patients with GBA1-Parkinson’s disease and healthy controls. Mov Disord . 2023;38:783-95. doi 10.1002/mds.29360
20. Parnetti L, Paciotti S, Eusebi P, Dardis A, Zampieri S, Chiasserini D, et al. Cerebrospinal fluid β-glucocerebrosidase activity is reduced in parkinson’s disease patients. Mov Disord . 2017;32:1423-31. doi 10.1002/mds.27136
21. van Dijk KD, Persichetti E, Chiasserini D, Eusebi P, Beccari T, Calabresi P, et al. Changes in endolysosomal enzyme activities in cerebrospinal fluid of patients with Parkinson’s disease. Mov Disord . 2013;28:747-54. doi 10.1002/mds.25495
22. Berg D, Borghammer P, Fereshtehnejad SM, Heinzel S, Horsager J, Schaeffer E, et al. Prodromal Parkinson disease subtypes - key to understanding heterogeneity. Nat Rev Neurol . 2021;17:349-61. doi 10.1038/s41582-021-00486-9
23. Lowes H, Pyle A, Santibanez-Koref M, Hudson G. Circulating cell-free mitochondrial DNA levels in Parkinson’s disease are influenced by treatment. Mol Neurodeg . 2020;15:10-. doi 10.1186/s13024-020-00362-y
24. Pyle A, Brennan R, Kurzawa-Akanbi M, Yarnall A, Thouin A, Mollenhauer B, et al. Reduced cerebrospinal fluid mitochondrial DNA is a biomarker for early-stage Parkinson’s disease. Ann Neurol . 2015;78:1000-4. doi 10.1002/ana.24515
25. Zubelzu M, Morera-Herreras T, Irastorza G, Gomez-Esteban JC, Murueta-Goyena A. Plasma and serum alpha-synuclein as a biomarker in Parkinson’s disease: A meta-analysis. Parkinsonism Relat Disord . 2022;99:107-15. doi 10.1016/j.parkreldis.2022.06.001
26. Xylaki M, Chopra A, Weber S, Bartl M, Outeiro TF, Mollenhauer B. Extracellular vesicles for the diagnosis of Parkinson’s disease: Systematic review and meta-analysis. Mov Disord . 2023. doi 10.1002/mds.29497
27. Barba L, Paolini Paoletti F, Bellomo G, Gaetani L, Halbgebauer S, Oeckl P, et al. Alpha and beta synucleins: From pathophysiology to clinical application as biomarkers. Mov Disord . 2022;37:669-83. doi 10.1002/mds.28941
28. Devic I, Hwang H, Edgar JS, Izutsu K, Presland R, Pan C, et al. Salivary α-synuclein and DJ-1: potential biomarkers for Parkinson’s disease. Brain . 2011;134:e178-e. doi 10.1093/brain/awr015
29. Khalil M, Teunissen CE, Otto M, Piehl F, Sormani MP, Gattringer T, et al. Neurofilaments as biomarkers in neurological disorders. Nat Rev Neurol . 2018;14:577-89. doi 10.1038/s41582-018-0058-z
30. Koros C, Simitsi AM, Papagiannakis N, Bougea A, Prentakis A, Papadimitriou D, et al. Serum uric acid as a putative biomarker in prodromal Parkinson’s disease: Longitudinal data from the PPMI study.J Parkinsons Dis . 2023. doi 10.3233/JPD-230007
31. Tansey MG, Wallings RL, Houser MC, Herrick MK, Keating CE, Joers V. Inflammation and immune dysfunction in Parkinson disease. Nat Rev Immunol . 2022;22:657-73. doi 10.1038/s41577-022-00684-6
32. Mnich K, Moghaddam S, Browne P, Counihan T, Fitzgerald SP, Martin K, et al. Endoplasmic reticulum stress-regulated chaperones as a serum biomarker panel for Parkinson’s disease. Mol Neurobiol . 2023;60:1476-85. doi 10.1007/s12035-022-03139-0
33. Rahmani F, Saghazadeh A, Rahmani M, Teixeira AL, Rezaei N, Aghamollaii V, et al. Plasma levels of brain-derived neurotrophic factor in patients with Parkinson disease: A systematic review and meta-analysis. Brain Res . 2019;1704:127-36. doi https://doi.org/10.1016/j.brainres.2018.10.006
34. Konovalova J, Gerasymchuk D, Arroyo SN, Kluske S, Mastroianni F, Pereyra AV, et al. Human-specific regulation of neurotrophic factors MANF and CDNF by microRNAs. Int J Mol Sci . 2021;22. doi 10.3390/ijms22189691
35. Kuzkina A, Panzer C, Seger A, Schmitt D, Rossle J, Schreglmann SR, et al. Dermal real-time quaking-induced conversion Is a sensitive marker to confirm isolated rapid eye movement sleep behavior disorder as an early alpha-synucleinopathy. Mov Disord . 2023;38:1077-82. doi 10.1002/mds.29340
36. Vilas D, Iranzo A, Tolosa E, Aldecoa I, Berenguer J, Vilaseca I, et al. Assessment of α-synuclein in submandibular glands of patients with idiopathic rapid-eye-movement sleep behaviour disorder: a case-control study. Lancet Neurol . 2016;15:708-18. doi https://doi.org/10.1016/S1474-4422(16)00080-6
37. Corbillé AG, Clairembault T, Coron E, Leclair-Visonneau L, Preterre C, Neunlist M, et al. What a gastrointestinal biopsy can tell us about Parkinson’s disease? Neurogastroenterol Motil . 2016;28:966-74. doi 10.1111/nmo.12797
38. Schliesser P, Struebing FL, Northoff BH, Kurz A, Remi J, Holdt L, et al. Detection of a Parkinson’s disease-specific microRNA signature in nasal and oral swabs. Mov Disord . 2023. doi 10.1002/mds.29515
39. Ryman SG, Poston KL. MRI biomarkers of motor and non-motor symptoms in Parkinson’s disease. Parkinsonism Relat Disord . 2020;73:85-93. doi 10.1016/j.parkreldis.2019.10.002
40. Diez-Cirarda M, Cabrera-Zubizarreta A, Murueta-Goyena A, Strafella AP, Del Pino R, Acera M, et al. Multimodal visual system analysis as a biomarker of visual hallucinations in Parkinson’s disease. J Neurol . 2023;270:519-29. doi 10.1007/s00415-022-11427-x
41. Bidesi NSR, Vang Andersen I, Windhorst AD, Shalgunov V, Herth MM. The role of neuroimaging in Parkinson’s disease. J Neurochem . 2021;159:660-89. doi 10.1111/jnc.15516
42. De Marzi R, Seppi K, Högl B, Müller C, Scherfler C, Stefani A, et al. Loss of dorsolateral nigral hyperintensity on 3.0 tesla susceptibility-weighted imaging in idiopathic rapid eye movement sleep behavior disorder. Ann Neurol . 2016;79:1026-30. doi 10.1002/ana.24646
43. Saeed U, Compagnone J, Aviv RI, Strafella AP, Black SE, Lang AE, et al. Imaging biomarkers in Parkinson’s disease and Parkinsonian syndromes: current and emerging concepts. Transl Neurodeg . 2017;6:1-25. doi 10.1186/s40035-017-0076-6
44. Gaurav R, Yahia-Cherif L, Pyatigorskaya N, Mangone G, Biondetti E, Valabregue R, et al. Longitudinal changes in neuromelanin MRI signal in Parkinson’s disease: A progression marker. Mov Disord . 2021;36:1592-602. doi 10.1002/mds.28531
45. Castellanos G, Fernández-Seara MA, Lorenzo-Betancor O, Ortega-Cubero S, Puigvert M, Uranga J, et al. Automated neuromelanin imaging as a diagnostic biomarker for Parkinson’s disease. Mov Disord . 2015;30:945-52. doi 10.1002/mds.26201
46. Saeed U, Lang AE, Masellis M. Neuroimaging advances in Parkinson’s disease and atypical parkinsonian syndromes. Front Neurol . 2020;11:572976. doi 10.3389/fneur.2020.572976
47. Rolinski M, Griffanti L, Szewczyk-Krolikowski K, Menke RAL, Wilcock GK, Filippini N, et al. Aberrant functional connectivity within the basal ganglia of patients with Parkinson’s disease. NeuroImage: Clinical . 2015;8:126-32. doi https://doi.org/10.1016/j.nicl.2015.04.003
48. Szewczyk-Krolikowski K, Menke RAL, Rolinski M, Duff E, Salimi-Khorshidi G, Filippini N, et al. Functional connectivity in the basal ganglia network differentiates PD patients from controls.Neurology . 2014;83:208-14. doi 10.1212/WNL.0000000000000592
49. Vlaar AMM, van Kroonenburgh MJPG, Kessels AGH, Weber WEJ. Meta-analysis of the literature on diagnostic accuracy of SPECT in parkinsonian syndromes. BMC Neurology . 2007;7:27-. doi 10.1186/1471-2377-7-27
50. Bäck S, Raki M, Tuominen RK, Raasmaja A, Bergström K, Männistö PT. High correlation between in vivo [123I]β-CIT SPECT/CT imaging and post-mortem immunohistochemical findings in the evaluation of lesions induced by 6-OHDA in rats. EJNMMI Research . 2013;3:46 (2013). . doi https://doi.org/10.1186/2191-219X-3-46
51. Benamer HTS, Patterson J, Wyper DJ, Hadley DM, Macphee GJA, Grosset DG. Correlation of Parkinson’s disease severity and duration with 123I-FP-CIT SPECT striatal uptake. Mov Disord . 2000;15:692-8. doi 10.1002/1531-8257(200007)15:4<692::AID-MDS1014>3.0.CO;2-V
52. Molinet-Dronda F, Gago B, Quiroga-Varela A, Juri C, Collantes M, Delgado M, et al. Monoaminergic PET imaging and histopathological correlation in unilateral and bilateral 6-hydroxydopamine lesioned rat models of Parkinson’s disease: a longitudinal in-vivo study.Neurobiol Dis . 2015;77:165-72. doi 10.1016/j.nbd.2015.01.007
53. Ray Chaudhuri K, Leta V, Bannister K, Brooks DJ, Svenningsson P. The noradrenergic subtype of Parkinson disease: from animal models to clinical practice. Nat Rev Neurol . 2023;19:333-45. doi 10.1038/s41582-023-00802-5
54. Eidelberg D, Moeller JR, Dhawan V, Spetsieris P, Takikawa S, Ishikawa T, et al. The metabolic topography of parkinsonism. J Cerebr Blood Flow & Metab . 1994;14:783-801. doi 10.1038/jcbfm.1994.99
55. Villemagne VL, Fodero-Tavoletti MT, Masters CL, Rowe CC. Tau imaging: early progress and future directions. Lancet Neurol . 2015;14:114-24. doi https://doi.org/10.1016/S1474-4422(14)70252-2
56. Alzghool OM, van Dongen G, van de Giessen E, Schoonmade L, Beaino W. Alpha-synuclein radiotracer development and in vivo imaging: Recent advancements and new perspectives. Mov Disord . 2022;37:936-48. doi 10.1002/mds.28984
57. Knudsen K, Borghammer P. Imaging the autonomic nervous system in Parkinson’s disease. Curr Neurol Neurosci Rep . 2018;18:1-13. doi 10.1007/s11910-018-0889-4
58. Kalia LV. Biomarkers for cognitive dysfunction in Parkinson’s disease. Parkinsonism Rel Disord . 2018;46:S19-S23. doi 10.1016/J.PARKRELDIS.2017.07.023
59. Karekal A, Miocinovic S, Swann NC. Novel approaches for quantifying beta synchrony in Parkinson’s disease. Exp Brain Res . 2022;240:991-1004. doi 10.1007/s00221-022-06308-8
60. Klassen BT, Hentz JG, Shill HA, Driver-Dunckley E, Evidente VGH, Sabbagh MN, et al. Quantitative EEG as a predictive biomarker for Parkinson disease dementia. Neurology . 2011;77:118-24. doi 10.1212/WNL.0b013e318224af8d
61. Caviness JN, Hentz JG, Belden CM, Shill HA, Driver-Dunckley ED, Sabbagh MN, et al. Longitudinal EEG changes correlate with cognitive measure deterioration in Parkinson’s disease. J Parkinson’s Dis . 2015;5:117-24. doi 10.3233/JPD-140480
62. Shaban M. Deep learning for Parkinson’s disease diagnosis: A short survey. Computers . 2023;12. doi 10.3390/computers12030058
63. Latreille V, Carrier J, Gaudet-Fex B, Rodrigues-Brazète J, Panisset M, Chouinard S, et al. Electroencephalographic prodromal markers of dementia across conscious states in Parkinson’s disease. Brain . 2016;139:1189-99. doi 10.1093/brain/aww018
64. Dorsey ER, Papapetropoulos S, Xiong M, Kieburtz K. The first frontier: Digital biomarkers for neurodegenerative disorders.Digital Biomarkers . 2017;1:6-13. doi 10.1159/000477383
65. Alfalahi H, Dias SB, Khandoker AH, Chaudhuri KR, Hadjileontiadis LJ. A scoping review of neurodegenerative manifestations in explainable digital phenotyping. NPJ Parkinsons Dis . 2023;9:49. doi 10.1038/s41531-023-00494-0
66. Coelho BFO, Massaranduba ABR, Souza CAdS, Viana GG, Brys I, Ramos RP. Parkinson’s disease effective biomarkers based on Hjorth features improved by machine learning. Expert Syst Applicat . 2023;212. doi 10.1016/j.eswa.2022.118772
67. Espay AJ, Brundin P, Lang AE. Precision medicine for disease modification in Parkinson disease. Nat Rev Neurol . 2017;13:119-26. doi 10.1038/nrneurol.2016.196
68. Lipsmeier F, Taylor KI, Postuma RB, Volkova-Volkmar E, Kilchenmann T, Mollenhauer B, et al. Reliability and validity of the Roche PD Mobile Application for remote monitoring of early Parkinson’s disease.Sci Rep . 2022;12:12081. doi 10.1038/s41598-022-15874-4