Objective: Analyze current efforts and quality of fluid and tissue biomarkers in Parkinson’s Disease (PD) to offer insight into their utility for diagnosis vs healthy controls (HC).

Background: Alpha-synuclein is emerging as the leading biomarker in PD. However, other biomarkers also offer diagnostic and prognostic information but often lack replication [1, 2, 3]. Our primary aim is to assess the diagnostic quality of the diversity of biomarker data and offer insight into the disease pathophysiology.

Method: Inclusion criteria included original English studies investigating biomarkers from CSF, blood, and stool samples, for the diagnosis of PD vs HC, from June 2005 – June 2025. Excluded meta-analysis and in saliva [4], reviews, and studies publicly unavailable or through institution access; as well as those missing: measures and strengths of associations, and outcomes. Studies were identified via PubMed search (last search: February 6th, 2026), Elicit search (last search: August 14th, 2025), and manual search on PubMed (last search: June 18th, 2025). The review was structured using PRISMA 2020 [5] and the Newcastle-Ottawa Scale to assess risk of bias [6]. Studies with a high risk of bias were excluded. Results were synthesized and presented using R. Departmental funding was used for this review, and it was registered on INPLASY (INPLASY202630044). Many studies did not provide 95% confidence interval (CI), they were estimated using the Wald method and values greater than 1, were truncated to 1 to reflect the logical constraints of AUC [7,8].

Results: From the sources, 114 records (excluding duplicates) were identified, 18 met our inclusion criteria (Figure.1). The total number of participants included was 4722 (2555 PD, 2167 HC). A threshold of an AUC = 0.8 (Area Under the ROC Curve) for a biomarker was considered effective (Figure.2, Table.1).

Conclusion: Batteries of protein-based blood biomarkers performed the best in our analyses of sensitivity and specificity for differentiation of PD and HC, with comparable AUC versus alpha-synuclein. Multiple candidates could be better assessed by larger studies and replication. There is a significant representation of blood-based biomarkers versus others biofluids (CSF, skin, and fecal).

Figure 1

Figure 2

Table 1

References: 1. Wang, Z., Gilliland, T., Kim, H. J., Gerasimenko, M., Sajewski, K., Camacho, M. V., Bebek, G., Chen, S. G., Gunzler, S. A., & Kong, Q. (2024). A minimally invasive biomarker for sensitive and accurate diagnosis of Parkinson’s disease. Acta Neuropathologica Communications, 12(1), 167. https://doi.org/10.1186/s40478-024-01873-1

2. Qu, Y., Li, J., Qin, Q., Wang, D., Zhao, J., An, K., Mao, Z., Min, Z., Xiong, Y., Li, J., & Xue, Z. (2023). A systematic review and meta-analysis of inflammatory biomarkers in Parkinson’s disease. NPJ Parkinson’s Disease, 9, 18. https://doi.org/10.1038/s41531-023-00449-5

3. Xiang, C., Cong, S., Tan, X., Ma, S., Liu, Y., Wang, H., & Cong, S. (2022). A meta-analysis of the diagnostic utility of biomarkers in cerebrospinal fluid in Parkinson’s disease. NPJ Parkinson’s Disease, 8(1), 165. https://doi.org/10.1038/s41531-022-00431-7

4. Nijakowski, K., Owecki, W., Jankowski, J., & Surdacka, A. (2024). Salivary Biomarkers for Parkinson’s Disease: A Systematic Review with Meta-Analysis. Cells, 13(4), 340. https://doi.org/10.3390/cells13040340

5. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71

6. Wells, G., Shea, B., O’Connell, D., et al. (2011) The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Non-Randomised Studies in Meta-Analyses. Ottawa Hospital Research Institute, Ottawa.

7. Altman, D. G., & Bland, J. M. (2011). How to obtain the P value from a confidence interval. BMJ, 343, d2304. https://doi.org/10.1136/bmj.d2304

8. Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29–36. https://doi.org/10.1148/radiology.143.1.7063747

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