Analysis of biomarkers in speculative CNS-enriched extracellular vesicles for parkinsonian disorders: a comprehensive systematic review and diagnostic meta-analysis

Abstract

Background and objective: Parkinsonian disorders, including Parkinson's disease (PD), multiple system atrophy (MSA), dementia with Lewy bodies (DLB), progressive supranuclear palsy (PSP), and corticobasal syndrome (CBS), exhibit overlapping early-stage symptoms, complicating definitive diagnosis despite heterogeneous cellular and regional pathophysiology. Additionally, the progression and the eventual conversion of prodromal conditions such as REM behavior disorder (RBD) to PD, MSA, or DLB remain challenging to predict. Extracellular vesicles (EVs) are small, membrane-enclosed structures released by cells, playing a vital role in communicating cell-state-specific messages. Due to their ability to cross the blood-brain barrier into the peripheral circulation, measuring biomarkers in blood-isolated speculative CNS enriched EVs has become a popular diagnostic approach. However, replication and independent validation remain challenging in this field. Here, we aimed to evaluate the diagnostic accuracy of speculative CNS-enriched EVs for parkinsonian disorders.

Methods: We conducted a PRISMA-guided systematic review and meta-analysis, covering 18 studies with a total of 1695 patients with PD, 253 with MSA, 21 with DLB, 172 with PSP, 152 with CBS, 189 with RBD, and 1288 HCs, employing either hierarchical bivariate models or univariate models based on study size.

Results: Diagnostic accuracy was moderate for differentiating patients with PD from HCs, but revealed high heterogeneity and significant publication bias, suggesting an inflation of the perceived diagnostic effectiveness. The bias observed indicates that studies with non-significant or lower effect sizes were less likely to be published. Although results for differentiating patients with PD from those with MSA or PSP and CBS appeared promising, their validity is limited due to the small number of involved studies coming from the same research group. Despite initial reports, our analyses suggest that using speculative CNS-enriched EV biomarkers may not reliably differentiate patients with MSA from HCs or patients with RBD from HCs, due to their lesser accuracy and substantial variability among the studies, further complicated by substantial publication bias.

Conclusion: Our findings underscore the moderate, yet unreliable diagnostic accuracy of biomarkers in speculative CNS-enriched EVs in differentiating parkinsonian disorders, highlighting the presence of substantial heterogeneity and significant publication bias. These observations reinforce the need for larger, more standardized, and unbiased studies to validate the utility of these biomarkers but also call for the development of better biomarkers for parkinsonian disorders.

Keywords: Alpha-synuclein; Diagnosis; Exosome; L1CAM; Movement disorders; Parkinson’s disease.

Fig. 1

PRISMA flow diagram for inclusion of selected studies

Fig. 2

Diagnostic accuracy of biomarkers in speculative CNS-enriched EVs for the differential diagnosis of Parkinson’s disease (PD) from healthy controls (HCs). A-E Univariate Forest plots for sensitivity, specificity, diagnostic odds ratio (DOR), positive (posLR) and negative (negLR) likelihood ratios, respectively. F Summary receiver operating characteristics (SROC). The dotted circle shows the mean summary estimate of sensitivities and specificities using a bivariate model. The summary line is obtained from a hierarchical SROC model. CNS central nervous system; EVs extracellular vesicles

Fig. 3

Publication bias was assessed using A Begg’s correlation, B Egger’s regression, C Deek’s regression, D Deek’s funnel plot, E A bagplot and F Funnel plot after application of the trim-and-fill method for biomarkers in speculative CNS-enriched EVs for the differential diagnosis of Parkinson’s disease from healthy controls. Collectively, they suggested a substantial presence of publication bias. The trim-and-fill method estimated five missing studies (white circles) on the left side of the figure with either small or null diagnostic accuracy. CNS central nervous system; EVs extracellular vesicles

Fig. 4

Summary receiver operating characteristics (SROC) comparing isolation of speculative CNS-enriched EVs using A plasma vs serum, B the anti-L1CAM antibody clone UJ127 vs 5G3 and C quantification methodology. CNS central nervous system; ECLIA electrochemilumiscence ELISA; ELISA enzyme-linked immunosorbent assay; EVs extracellular vesicles; L1CAM L1 cell adhesion molecule

Fig. 5

Diagnostic accuracy of biomarkers in speculative CNS-enriched EVs for the differential diagnosis of Parkinson’s disease (PD) from multiple system atrophy (MSA). A–E Univariate Forest plots for sensitivity, specificity, diagnostic odds ratio (DOR), positive (posLR) and negative (negLR) likelihood ratios, respectively. F Summary receiver operating characteristics (SROC). The dotted circle shows the mean summary estimate of sensitivities and specificities using a bivariate model. The summary line is obtained from a hierarchical SROC model. CNS central nervous system; EVs extracellular vesicles

Fig. 6

Diagnostic accuracy of biomarkers in speculative CNS-enriched EVs for the differential diagnosis of Parkinson’s disease (PD) from progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS). A–E Univariate Forest plots for sensitivity, specificity, diagnostic odds ratio (DOR), positive (posLR) and negative (negLR) likelihood ratios, respectively. F Summary receiver operating characteristics (SROC) using a univariate model. CNS central nervous system; EVs extracellular vesicles

Fig. 7

Diagnostic accuracy of biomarkers in speculative CNS-enriched EVs for the differential diagnosis of multiple system atrophy (MSA) from healthy controls (HCs). A–E Univariate Forest plots for sensitivity, specificity, diagnostic odds ratio (DOR), positive (posLR) and negative (negLR) likelihood ratios, respectively. F Summary receiver operating characteristics (SROC) using a univariate model. CNS central nervous system; EVs extracellular vesicles

Fig. 8

Diagnostic accuracy of biomarkers in speculative CNS-enriched EVs for the differential diagnosis of REM behavior disorder (RBD) from healthy controls (HCs). A–E Univariate Forest plots for sensitivity, specificity, diagnostic odds ratio (DOR), positive (posLR) and negative (negLR) likelihood ratios, respectively. F Summary receiver operating characteristics (SROC) using a univariate model

Fig. 9

Summary receiver operating characteristic (SROC) comparisons for patients with Parkinson’s disease (PD), multiple system atrophy (MSA), progressive supranuclear palsy (PSP), corticobasal syndrome (CBS) and REM behavior disorder (RBD)